Heart Health.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart Health Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Data form heart.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Thal</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>typical</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
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       "      <td>2.3</td>\n",
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       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>reversable</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex     ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  \\\n",
       "0   63    1       typical     145   233    1        2    150      0      2.3   \n",
       "1   67    1  asymptomatic     160   286    0        2    108      1      1.5   \n",
       "2   67    1  asymptomatic     120   229    0        2    129      1      2.6   \n",
       "3   37    1    nonanginal     130   250    0        0    187      0      3.5   \n",
       "4   41    0    nontypical     130   204    0        2    172      0      1.4   \n",
       "\n",
       "   Slope  Ca        Thal  Target  \n",
       "0      3   0       fixed       0  \n",
       "1      2   3      normal       1  \n",
       "2      2   2  reversable       1  \n",
       "3      3   0      normal       0  \n",
       "4      1   0      normal       0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import warnings\n",
    "df=pd.read_csv('C:/Users/Ammar/heart.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleansing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing Null Values form data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age          0\n",
      "Sex          0\n",
      "ChestPain    0\n",
      "RestBP       0\n",
      "Chol         0\n",
      "Fbs          0\n",
      "RestECG      0\n",
      "MaxHR        0\n",
      "ExAng        0\n",
      "Oldpeak      0\n",
      "Slope        0\n",
      "Ca           0\n",
      "Thal         2\n",
      "Target       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Droping Null values because Null values are very less as compared to data size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Give the info of Data Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 301 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      "Age          301 non-null int64\n",
      "Sex          301 non-null int64\n",
      "ChestPain    301 non-null object\n",
      "RestBP       301 non-null int64\n",
      "Chol         301 non-null int64\n",
      "Fbs          301 non-null int64\n",
      "RestECG      301 non-null int64\n",
      "MaxHR        301 non-null int64\n",
      "ExAng        301 non-null int64\n",
      "Oldpeak      301 non-null float64\n",
      "Slope        301 non-null int64\n",
      "Ca           301 non-null int64\n",
      "Thal         301 non-null object\n",
      "Target       301 non-null int64\n",
      "dtypes: float64(1), int64(11), object(2)\n",
      "memory usage: 35.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Describing the wohle Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.00000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.451827</td>\n",
       "      <td>0.681063</td>\n",
       "      <td>131.714286</td>\n",
       "      <td>246.936877</td>\n",
       "      <td>0.146179</td>\n",
       "      <td>0.990033</td>\n",
       "      <td>149.700997</td>\n",
       "      <td>0.325581</td>\n",
       "      <td>1.043189</td>\n",
       "      <td>1.601329</td>\n",
       "      <td>0.69103</td>\n",
       "      <td>0.458472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.067258</td>\n",
       "      <td>0.466841</td>\n",
       "      <td>17.655729</td>\n",
       "      <td>51.859869</td>\n",
       "      <td>0.353874</td>\n",
       "      <td>0.994937</td>\n",
       "      <td>22.860817</td>\n",
       "      <td>0.469372</td>\n",
       "      <td>1.163384</td>\n",
       "      <td>0.616872</td>\n",
       "      <td>0.94915</td>\n",
       "      <td>0.499102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              Age         Sex      RestBP        Chol         Fbs     RestECG  \\\n",
       "count  301.000000  301.000000  301.000000  301.000000  301.000000  301.000000   \n",
       "mean    54.451827    0.681063  131.714286  246.936877    0.146179    0.990033   \n",
       "std      9.067258    0.466841   17.655729   51.859869    0.353874    0.994937   \n",
       "min     29.000000    0.000000   94.000000  126.000000    0.000000    0.000000   \n",
       "25%     48.000000    0.000000  120.000000  211.000000    0.000000    0.000000   \n",
       "50%     56.000000    1.000000  130.000000  242.000000    0.000000    1.000000   \n",
       "75%     61.000000    1.000000  140.000000  275.000000    0.000000    2.000000   \n",
       "max     77.000000    1.000000  200.000000  564.000000    1.000000    2.000000   \n",
       "\n",
       "            MaxHR       ExAng     Oldpeak       Slope         Ca      Target  \n",
       "count  301.000000  301.000000  301.000000  301.000000  301.00000  301.000000  \n",
       "mean   149.700997    0.325581    1.043189    1.601329    0.69103    0.458472  \n",
       "std     22.860817    0.469372    1.163384    0.616872    0.94915    0.499102  \n",
       "min     71.000000    0.000000    0.000000    1.000000    0.00000    0.000000  \n",
       "25%    134.000000    0.000000    0.000000    1.000000    0.00000    0.000000  \n",
       "50%    153.000000    0.000000    0.800000    2.000000    0.00000    0.000000  \n",
       "75%    166.000000    1.000000    1.600000    2.000000    1.00000    1.000000  \n",
       "max    202.000000    1.000000    6.200000    3.000000    3.00000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pandas Profiling Provide the Report of data set including Corelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
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       "            }\n",
       "\n",
       "        /* Freq mini */\n",
       "            .freq.mini td{\n",
       "                width: 50%;\n",
       "                padding: 1px;\n",
       "                font-size: 12px;\n",
       "\n",
       "            }\n",
       "            table.freq.mini {\n",
       "                 width:100%;\n",
       "            }\n",
       "            .freq.mini th {\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "                white-space: nowrap;\n",
       "                max-width: 5em;\n",
       "                font-weight: 400;\n",
       "                text-align:right;\n",
       "                padding-right: 0.5em;\n",
       "            }\n",
       "\n",
       "            .missing {\n",
       "                color: #a94442;\n",
       "            }\n",
       "            .alert, .alert > th, .alert > td {\n",
       "                color: #a94442;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Bars in tables */\n",
       "            .freq .bar{\n",
       "                float: left;\n",
       "                width: 0;\n",
       "                height: 100%;\n",
       "                line-height: 20px;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                background-color: #337ab7;\n",
       "                border-radius: 3px;\n",
       "                margin-right: 4px;\n",
       "            }\n",
       "            .other .bar {\n",
       "                background-color: #999;\n",
       "            }\n",
       "            .missing .bar{\n",
       "                background-color: #a94442;\n",
       "            }\n",
       "            .tooltip-inner {\n",
       "                width: 100%;\n",
       "                white-space: nowrap;\n",
       "                text-align:left;\n",
       "            }\n",
       "\n",
       "            .extrapadding{\n",
       "                padding: 2em;\n",
       "            }\n",
       "\n",
       "            .pp-anchor{\n",
       "\n",
       "            }\n",
       "\n",
       "</style>\n",
       "\n",
       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>24 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>301 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>0.0% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>29.8 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>101.3 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>7 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>17 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_Ca\"><code>Ca</code></a> has 175 / 58.1% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_Oldpeak\"><code>Oldpeak</code></a> has 98 / 32.6% zeros <span class=\"label label-info\">Zeros</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Age\">Age<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>41</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>13.6%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>54.452</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>29</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>77</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram3662009047252403552\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAdhJREFUeJzt3DHu0nAYx%2BG3xLVlJ/QW7q7eyc3E0fuYeAVvAeEA7U4dFB30/42otBSeZwSSX0n64aWlpZmmaaqZHY/H6vt%2B7mVZucPhUPv9ftY1X8262ndt21bVtzfcdd0Sm8CKDMNQfd//2G/mtEggTdNUVVXXdQL5D16/%2B3TV6798eHujLbmty34zp83sK8KKCAQCgUCwyDEIL7v2eILbMkEgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoHAtVhP6G%2Bu91rrPST/ygSBQCAQ%2BIp1hWe5tZWfTBAITJAbcvPT%2BpkgEDztBPHpzp8wQSAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEgqe9mpfrPOsfPZggEDzEBHFvB7digkAgEAgEAoFAIBAIBA9xFov79Ai/nSwSyDRNVVU1DMMvz715/3nuzeGO/G6fuDx22W/mtEgg4zhWVVXf90sszx3bfnz5uXEca7vdzrcxVdVMC2R5Pp/rdDpV27bVNM3cy7My0zTVOI612%2B1qs5n3sHmRQGAtnMWCQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIPgKZEVQrljauO8AAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives3662009047252403552,#minihistogram3662009047252403552\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives3662009047252403552\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles3662009047252403552\"\n",
       "                                                  aria-controls=\"quantiles3662009047252403552\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram3662009047252403552\" aria-controls=\"histogram3662009047252403552\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common3662009047252403552\" aria-controls=\"common3662009047252403552\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme3662009047252403552\" aria-controls=\"extreme3662009047252403552\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles3662009047252403552\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>29</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>40</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>48</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>56</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>61</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>68</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>77</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>48</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>13</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>9.0673</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.16652</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-0.53703</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>54.452</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>7.4532</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.21269</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>16390</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>82.215</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>2.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram3662009047252403552\">\n",
       "            <img 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AAQAzXvkR4xx13qL6%2BXj179lRycrISEhJittfU1Dg0GQAAiHeuDawFCxY4PQKAz8hNnzoPAJ%2BFawNr6tSpTo8AAABwTq69BkuS/v3vf6uwsFB33HFH9CXBTZs26Z133nF4MgAAEM9cG1hbt25V3759VVZWpueff17Hjx%2BXJL355pv6wQ9%2B4PB0AAAgnrk2sB588EEVFhbqz3/%2Bc8wnt19//fXatm2bg5MBAIB459rAevvtt3XzzTc3We/evbuOHDniwEQAAABnuDawLrvsMoVCoSbrO3fu1Be%2B8AUHJgIAADjDtYF122236aGHHtKRI0ein%2Bj%2Bt7/9TXPnztWkSZMcng4AAMQz1wZWUVGRgsGgUlNTdfz4cfXp00e5ubkaPHiwvv/97zs9HgAAiGOu/RysxMREPffcc3r33Xf15ptvqrGxUddcc42uvvpqp0cDAABxzrWBddaVV16pK6%2B80ukxAAAAolwbWN/5znc%2Bcfvy5ctbaBIAAIBYrg2s6urqmPunTp3S7t27VV9fr%2Buuu86hqQAAAFwcWK%2B88kqTtQ8//FDf/e531bt3bwcmAgAAOMO1gXUuXq9Xc%2BfO1YgRIzR79mynx4HLZc/f4PQIAACXcu3HNJzPvn37dOrUKafHAAAAccy1Z7DmzZsXc98Yo%2Brqaq1fv1633367Q1MBAAC4OLD%2B%2Bte/xty/5JJL1K1bNy1YsED33HOPQ1MBAAC4OLC2bt3q9AgAAADn1OauwQIAAHCaa89gDR48OPqXPH%2BaN954o5mnAQAA%2BP9cG1hf%2BcpX9Otf/1pXXnmlcnJyJEnbtm3Tnj17NG3aNPl8PocnBAAA8cq1gXXs2DHNmDFDRUVFMevz58/X%2B%2B%2B/r9/85jcOTQYAAOKda6/Bev7553XnnXc2WZ8yZYpeeOEFByYCAAA4w7WB5fP5VF5e3mS9vLz8ol4eXLZsmfr376/k5GQlJycrJydHr732WnR7JBLRzJkz1bVrV3Xq1Enjxo3TwYMHrXwPAACgbXLtS4T333%2B/pk%2BfrrfeektDhw6VdOYarBUrVujhhx%2B%2B4P306NFDCxYs0BVXXCFJWr16tb7%2B9a/rrbfeUt%2B%2BfZWfn69XXnlFJSUlSklJ0Zw5czR27FhVVFSoXbt2zfK9AQAAd/MYY4zTQ3xWa9eu1RNPPKF33nlHktS7d2/NmjVLEydO/Fz77dKli37605/q5ptvVrdu3fTMM8/o1ltvlSQdPnxY6enpevXVV3XDDTdc0P7C4bD8fr/q6uqUnJz8uWZDy%2BHvIgTwcTseu9HpEXARnPz969ozWJI0ceLEzx1TH3X69Gm98MILOnHihHJyclRRUaFTp04pLy8v%2Bpi0tDRlZWWpvLz8vIEViUQUiUSi98PhsLUZAQBA6%2Bfaa7CkM%2BGyatUq/eAHP1Btba0k6e9//7uqq6svaj%2BVlZW69NJL5fP5NH36dK1bt059%2BvRRKBRSYmKiOnfuHPP4QCCgUCh03v0VFxfL7/dHb%2Bnp6Rf/zQEAANdy7RmsXbt2adSoUerYsaOqqqo0ZcoUde7cWc8//7wOHjyo1atXX/C%2BrrrqKu3cuVPHjh3Tiy%2B%2BqMmTJ6usrOy8jzfGfOKHnBYUFGj27NnR%2B%2BFwmMgCACCOuPYM1gMPPKCJEyfqX//6l9q3bx9dv%2Bmmm7Rly5aL2ldiYqKuuOIKZWdnq7i4WAMGDNATTzyhYDCohoaG6Nmxs2pqahQIBM67P5/PF31X4tkbAACIH64NrO3bt%2Bvee%2B9tcibpC1/4wkW/RPhxxhhFIhENGjRICQkJKi0tjW6rrq7Wrl27lJub%2B7m%2BBgAAaLtc%2BxJhYmKijh8/3mR979696tq16wXv5%2BGHH9aYMWOUnp6u%2Bvp6lZSU6C9/%2BYs2bNggv9%2BvqVOnas6cOUpJSVGXLl00d%2B5c9evXT6NGjbL57QAAgDbEtYE1btw4/ehHP9Jzzz0nSfJ4PDp06JAeeughjR8//oL38/777%2BuOO%2B5QdXW1/H6/%2Bvfvrw0bNmj06NGSpMcff1xer1cTJkzQyZMnNXLkSK1atYrPwAIAAOfl2s/Bqqur04033qi9e/fq2LFjSk9P1%2BHDhzV48GBt2LBBl156qdMjRvE5WO7E52AB%2BDg%2BB8td%2BBysz8Dv96u8vFylpaV688031djYqGuuuUY33HDDJ77DDwAAoLm5MrBOnTqlr371q/rlL3%2BpvLy8mA8CBQAAcJor30WYkJCgt956izNVAACgVXJlYEnSpEmTtHLlSqfHAAAAaMKVLxGetXTpUm3evFnZ2dnq1KlTzLaFCxc6NBUAAIh3rg2siooK9e/fX5L09ttvx2zjpUMAAOAk1wXWvn37lJGRoa1btzo9CgAAwDm57hqszMxMHTlyJHr/1ltv1fvvv%2B/gRAAAALFcF1gf/1zUV199VSdOnHBoGgAAgKZcF1gAAACtnesCy%2BPxNLmInYvaAQBAa%2BK6i9yNMZoyZYp8Pp8k6YMPPtD06dObfEzDSy%2B95MR4AAAA7gusyZMnx9yfNGmSQ5MAAACcm%2BsCi09vBwAArZ3rrsECAABo7QgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAy%2BI%2BsIqLizV48GAlJSWpe/fu%2BsY3vqE9e/bEPCYSiWjmzJnq2rWrOnXqpHHjxungwYMOTQwAAFq7uA%2BssrIyzZgxQ9u2bVNpaak%2B/PBD5eXl6cSJE9HH5Ofna926dSopKdHrr7%2Bu48ePa%2BzYsTp9%2BrSDkwMAgNbK6/QATtuwYUPM/ZUrV6p79%2B6qqKjQddddp7q6Oj311FN65plnNGrUKEnSmjVrlJ6ers2bN%2BuGG25wYmwAANCKxf0ZrI%2Brq6uTJHXp0kWSVFFRoVOnTikvLy/6mLS0NGVlZam8vNyRGQEAQOsW92ewPsoYo9mzZ%2Bvaa69VVlaWJCkUCikxMVGdO3eOeWwgEFAoFDrnfiKRiCKRSPR%2BOBxuvqEBAECrwxmsj7jvvvv09ttv63e/%2B92nPtYYI4/Hc85txcXF8vv90Vt6errtUQEAQCtGYP0/M2fO1Pr16/XnP/9ZPXr0iK4Hg0E1NDSotrY25vE1NTUKBALn3FdBQYHq6uqit6qqqmadHQAAtC5xH1jGGN1333166aWX9Kc//UkZGRkx2wcNGqSEhASVlpZG16qrq7Vr1y7l5uaec58%2Bn0/JyckxNwAAED/i/hqsGTNmaO3atfr973%2BvpKSk6HVVfr9fHTp0kN/v19SpUzVnzhylpKSoS5cumjt3rvr16xd9VyEAAMBHxX1gLVu2TJI0YsSImPWVK1dqypQpkqTHH39cXq9XEyZM0MmTJzVy5EitWrVK7dq1a%2BFpAQCAG8R9YBljPvUx7du3189//nP9/Oc/b4GJAACA28X9NVgAAAC2EVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWeZ0eAAAAt8iev8HpES7KjsdudHqEuMUZLAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMviPrC2bNmir33ta0pLS5PH49HLL78cs90Yo8LCQqWlpalDhw4aMWKEdu/e7dC0AADADeI%2BsE6cOKEBAwZo6dKl59y%2BcOFCLV68WEuXLtX27dsVDAY1evRo1dfXt/CkAADALbxOD%2BC0MWPGaMyYMefcZozRkiVLNH/%2BfI0fP16StHr1agUCAa1du1bTpk1ryVEBAIBLxP0ZrE%2Byf/9%2BhUIh5eXlRdd8Pp%2BGDx%2Bu8vLy8z4vEokoHA7H3AAAQPyI%2BzNYnyQUCkmSAoFAzHogENB777133ucVFxfr0UcfbdbZ3Ch7/ganRwAAoEVwBusCeDyemPvGmCZrH1VQUKC6urroraqqqrlHBAAArQhnsD5BMBiUdOZMVmpqanS9pqamyVmtj/L5fPL5fM0%2BHwAAaJ04g/UJMjIyFAwGVVpaGl1raGhQWVmZcnNzHZwMAAC0ZnF/Buv48eP65z//Gb2/f/9%2B7dy5U126dNHll1%2Bu/Px8FRUVKTMzU5mZmSoqKlLHjh01ceJEB6cGAACtWdwH1o4dO/SVr3wlen/27NmSpMmTJ2vVqlWaN2%2BeTp48qXvvvVe1tbUaMmSINm3apKSkJKdGBgAArZzHGGOcHqKtC4fD8vv9qqurU3JystPjOIZ3EQJAy9rx2I1Oj%2BAoJ3//cg0WAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZQQWAACAZV6nBwAAAM0je/4Gp0e4YDseu9HpEaziDBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlfNCoy7npQ%2BQAAIgXnMG6QL/85S%2BVkZGh9u3ba9CgQdq6davTIwEAgFaKwLoAzz33nPLz8zV//ny99dZb%2BvKXv6wxY8bowIEDTo8GAABaIQLrAixevFhTp07V3Xffrd69e2vJkiVKT0/XsmXLnB4NAAC0QlyD9SkaGhpUUVGhhx56KGY9Ly9P5eXl53xOJBJRJBKJ3q%2Brq5MkhcNh6/Odjpywvk8AAFpac/yOPLtPY4z1fX8aAutTHD16VKdPn1YgEIhZDwQCCoVC53xOcXGxHn300Sbr6enpzTIjAABu51/UfPuur6%2BX3%2B9vvi9wDgTWBfJ4PDH3jTFN1s4qKCjQ7Nmzo/cbGxv13//%2BVykpKed9TnMJh8NKT09XVVWVkpOTW/RrxxuOdcvhWLccjnXL4VjbZ4xRfX290tLSWvxrE1ifomvXrmrXrl2Ts1U1NTVNzmqd5fP55PP5YtYuu%2ByyZpvxQiQnJ/MfbAvhWLccjnXL4Vi3HI61XS195uosLnL/FImJiRo0aJBKS0tj1ktLS5Wbm%2BvQVAAAoDXjDNYFmD17tu644w5lZ2crJydHy5cv14EDBzR9%2BnSnRwMAAK1Qu8LCwkKnh2jtsrKylJKSoqKiIv3sZz/TyZMn9cwzz2jAgAFOj3ZB2rVrpxEjRsjrpaebG8e65XCsWw7HuuVwrNsOj3HivYsAAABtGNdgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgtQHLli1T//79ox9Ol5OTo9deey26PRKJaObMmeratas6deqkcePG6eDBgw5O3HYUFxfL4/EoPz8/usbxtqOwsFAejyfmFgwGo9uNMSosLFRaWpo6dOigESNGaPfu3Q5O7G6HDh3SpEmTlJKSoo4dO%2BpLX/qSKioqots53nZ88YtfbPLvtcfj0YwZMyTx86MtIbDagB49emjBggXasWOHduzYoeuvv15f//rXoz/88vPztW7dOpWUlOj111/X8ePHNXbsWJ0%2Bfdrhyd1t%2B/btWr58ufr37x%2BzzvG2p2/fvqquro7eKisro9sWLlyoxYsXa%2BnSpdq%2BfbuCwaBGjx6t%2Bvp6Byd2p9raWg0bNkwJCQl67bXX9I9//EOLFi2K%2BRsoON52bN%2B%2BPebf6bMfYn3LLbdI4udHm2LQJnXu3Nn85je/MceOHTMJCQmmpKQkuu3QoUPmkksuMRs2bHBwQnerr683mZmZprS01AwfPtzMmjXLGGM43hY98sgjZsCAAefc1tjYaILBoFmwYEF07YMPPjB%2Bv9/86le/aqkR24wHH3zQXHvttefdzvFuPrNmzTK9evUyjY2N/PxoYziD1cacPn1aJSUlOnHihHJyclRRUaFTp04pLy8v%2Bpi0tDRlZWWpvLzcwUndbcaMGbrppps0atSomHWOt1179%2B5VWlqaMjIydNttt2nfvn2SpP379ysUCsUcZ5/Pp%2BHDh3OcP4P169crOztbt9xyi7p3766BAwdqxYoV0e0c7%2BbR0NCgNWvW6K5dxw6PAAAEB0lEQVS77pLH4%2BHnRxtDYLURlZWVuvTSS%2BXz%2BTR9%2BnStW7dOffr0USgUUmJiojp37hzz%2BEAg0OQvsMaFKSkpUUVFhYqLi5ts43jbM2TIED399NPauHGjVqxYoVAopNzcXP3nP/%2BJHsuP/4XrHOfPZt%2B%2BfVq2bJkyMzO1ceNGTZ8%2BXffff7%2BefvppSeJ4N5OXX35Zx44d05QpUyTx86Ot4bP424irrrpKO3fu1LFjx/Tiiy9q8uTJKisrO%2B/jjTHyeDwtOGHbUFVVpVmzZmnTpk1q3779BT%2BP433xxowZE/1zv379lJOTo169emn16tUaOnSoJDU5phznz6axsVHZ2dkqKiqSJA0cOFC7d%2B/WsmXL9O1vfzv6OI63XU899ZTGjBmjtLS0T3wcx9mdOIPVRiQmJuqKK65Qdna2iouLNWDAAD3xxBMKBoNqaGhQbW1tzONramqa/N8oPl1FRYVqamo0aNAgeb1eeb1elZWV6cknn5TX61UgEOB4N5NOnTqpX79%2B2rt3b/TdhB//v3qO82eTmpqqPn36xKz17t1bBw4ckCSOdzN47733tHnzZt19993RNX5ety0EVhtljFEkEtGgQYOUkJAQfaeKJFVXV2vXrl3Kzc11cEJ3GjlypCorK7Vz587oLTs7W7fffnv0zxzv5hGJRPTOO%2B8oNTVVGRkZCgaDMce5oaFBZWVlHOfPYNiwYdqzZ0/M2rvvvquePXtKEse7GaxcuVLdu3fXTTfdFF3j53Ub4%2BQV9rCjoKDAbNmyxezfv9%2B8/fbb5uGHHzaXXHKJ2bRpkzHGmOnTp5sePXqYzZs3mzfffNNcf/31ZsCAAebDDz90ePK24aPvIjSG423LnDlzzF/%2B8hezb98%2Bs23bNjN27FiTlJRk/v3vfxtjjFmwYIHx%2B/3mpZdeMpWVleZb3/qWSU1NNeFw2OHJ3eeNN94wXq/XPPbYY2bv3r3m2WefNR07djRr1qyJPobjbc/p06fN5Zdfbh588MEm2/j50XYQWG3AXXfdZXr27GkSExNNt27dzMiRI6NxZYwxJ0%2BeNPfdd5/p0qWL6dChgxk7dqw5cOCAgxO3LR8PLI63HbfeeqtJTU01CQkJJi0tzYwfP97s3r07ur2xsdE88sgjJhgMGp/PZ6677jpTWVnp4MTu9sorr5isrCzj8/nM1VdfbZYvXx6zneNtz8aNG40ks2fPnibb%2BPnRdniMMcbps2gAAABtCddgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWPZ/3a9hvQBv%2BMgAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common3662009047252403552\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">58</td>\n",
       "        <td class=\"number\">19</td>\n",
       "        <td class=\"number\">6.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">57</td>\n",
       "        <td class=\"number\">17</td>\n",
       "        <td class=\"number\">5.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">54</td>\n",
       "        <td class=\"number\">16</td>\n",
       "        <td class=\"number\">5.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">59</td>\n",
       "        <td class=\"number\">14</td>\n",
       "        <td class=\"number\">4.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">51</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">4.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">52</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">4.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">60</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">4.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">62</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">44</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">56</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (31)</td>\n",
       "        <td class=\"number\">166</td>\n",
       "        <td class=\"number\">55.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme3662009047252403552\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">29</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">34</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">35</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">37</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">38</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">70</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">71</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">1.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:75%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">74</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">76</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">77</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Ca\">Ca<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>4</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>1.3%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.69103</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>3</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>58.1%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-6186899339666851958\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAbhJREFUeJzt3DGOm1AUhtGLlRYWYMEusojsKXX2lEVkFyAvAGuKFDEpEqeZ6JcsjR/D6JzSlrjPMp/QewXdtm1bNbYsS03T1HosBzfPc43j2HTmp6bT/ur7vqr%2B/OBhGPZYAgeyrmtN0/Tvvmlpl0C6rquqqmEYXgXy%2Bev3h6/349uXN1kX79v9vmnp1HwiHIhAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQ7PJ296N69M3z3jp/fJ4gEAgEAoFAIBAIBAKBU6wPwOna83iCQCAQCAQCgUAgsEnnaR49PKh6fwcIniAQCAQCgUAgEAh22aRv21ZVVeu6vvru18%2BXh6/3v%2Bs8w6Nrs663%2BS/vn93vm5a6bYepy7LUNE2tx3Jw8zzXOI5NZ%2B4SyO12q8vlUn3fV9d1rcdzMNu21fV6rfP5XKdT213BLoHAUdikQyAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBD8BjGzXeEgdwDRAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-6186899339666851958,#minihistogram-6186899339666851958\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-6186899339666851958\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-6186899339666851958\"\n",
       "                                                  aria-controls=\"quantiles-6186899339666851958\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-6186899339666851958\" aria-controls=\"histogram-6186899339666851958\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-6186899339666851958\" aria-controls=\"common-6186899339666851958\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-6186899339666851958\" aria-controls=\"extreme-6186899339666851958\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-6186899339666851958\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.94915</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.3735</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>0.13252</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.69103</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.80352</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.1474</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>208</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.90089</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>2.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6186899339666851958\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xt8FPW9//H3SsgimKxcs0kJGDlcVBQJQQOo3GogKhxLvVCUBsUUWsRC5NAEjjV4KkHrvbQcr0ArHtCDUFotJVhI8IQIgSCCHgSMgJIQaCFLEJYA8/vDw/5ckpBAv5uZZF/Px2MeD%2BY735n97Nfp5N3vzO66LMuyBAAAAGMusbsAAACApoaABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGRdhdQDg4c%2BaM9u/fr6ioKLlcLrvLAQAgLFiWpaNHjyouLk6XXNKwc0oErAawf/9%2BxcfH210GAABhad%2B%2BferYsWODviYBqwFERUVJ%2BvY/cHR0tM3VAAAQHnw%2Bn%2BLj4wN/hxsSAasBnL0tGB0dTcACAKCB2fF4Dg%2B5AwAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAxr0gErPz9fI0aMUFxcnFwul5YvXx603eVy1bj8%2Bte/DvS54oorqm3PzMxs6LcCAAAakSb9Te7Hjh1Tr1699MADD%2BiHP/xhte2lpaVB63/5y180fvz4an2feOIJpaenB9Yvu%2Byy0BQMAACahCYdsFJTU5Wamlrrdq/XG7T%2Bxz/%2BUYMHD9aVV14Z1B4VFVWtLwAAQG2a9C3CC3HgwAG99957Gj9%2BfLVtTz31lNq2bavrr79eTz75pE6ePHneY/n9fvl8vqAFAACEjyY9g3UhFi5cqKioKI0aNSqo/ec//7kSExPVunVrbdiwQVlZWSopKdFrr71W67FycnI0a9asUJcsSUqaubJBXseEoieH210CAAANgoD1f9544w3dd999atGiRVD71KlTA/%2B%2B7rrr1Lp1a911112BWa2aZGVlKSMjI7Du8/kUHx8fmsIBAIDjELAkrVu3Tjt27NCSJUvq7JucnCxJ2rVrV60By%2B12y%2B12G60RAAA0HjyDJen1119Xnz591KtXrzr7FhcXS5JiY2NDXRYAAGikmvQMVmVlpXbt2hVYLykp0ZYtW9SmTRt16tRJ0re379555x09%2B%2Byz1fZfv369CgsLNXjwYHk8Hm3cuFFTp07VyJEjA/sDAACcq0kHrKKiIg0ePDiwfva5qLS0NC1YsECStHjxYlmWpR/96EfV9ne73VqyZIlmzZolv9%2Bvzp07Kz09XdOnT2%2BQ%2BgEAQOPksizLsruIps7n88nj8aiiokLR0dFGj82nCAEAqFko//7WhWewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIY16YCVn5%2BvESNGKC4uTi6XS8uXLw/aPm7cOLlcrqAlOTk5qI/f79fkyZPVrl07tWrVSiNHjtRXX33VkG8DAAA0Mk06YB07dky9evXS3Llza%2B0zfPhwlZaWBpb3338/aPuUKVO0bNkyLV68WB9%2B%2BKEqKyt1xx136PTp06EuHwAANFIRdhcQSqmpqUpNTT1vH7fbLa/XW%2BO2iooKvf766/rDH/6g73//%2B5KkN998U/Hx8Vq9erWGDRtmvGYAAND4NekZrPpYu3atOnTooG7duik9PV3l5eWBbZs2bVJVVZVSUlICbXFxcerZs6cKCgrsKBcAADQCTXoGqy6pqam6%2B%2B671blzZ5WUlOixxx7TkCFDtGnTJrndbpWVlSkyMlKtW7cO2i8mJkZlZWW1Htfv98vv9wfWfT5fyN4DAABwnrAOWPfee2/g3z179lRSUpI6d%2B6s9957T6NGjap1P8uy5HK5at2ek5OjWbNmGa0VAAA0HmF/i/C7YmNj1blzZ%2B3cuVOS5PV6dfLkSR0%2BfDioX3l5uWJiYmo9TlZWlioqKgLLvn37Qlo3AABwFgLWd/z973/Xvn37FBsbK0nq06ePmjdvrtzc3ECf0tJSbdu2Tf3796/1OG63W9HR0UELAAAIH036FmFlZaV27doVWC8pKdGWLVvUpk0btWnTRtnZ2frhD3%2Bo2NhYffnll5oxY4batWunH/zgB5Ikj8ej8ePH69FHH1Xbtm3Vpk0bTZs2Tddee23gU4UAAADnatIBq6ioSIMHDw6sZ2RkSJLS0tI0b948ffLJJ/r973%2BvI0eOKDY2VoMHD9aSJUsUFRUV2Of5559XRESE7rnnHh0/flxDhw7VggUL1KxZswZ/PwAAoHFwWZZl2V1EU%2Bfz%2BeTxeFRRUWH8dmHSzJVGjxdKRU8Ot7sEAEAYCeXf37rwDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYU06YOXn52vEiBGKi4uTy%2BXS8uXLA9uqqqr0i1/8Qtdee61atWqluLg4/fjHP9b%2B/fuDjnHFFVfI5XIFLZmZmQ39VgAAQCPSpAPWsWPH1KtXL82dO7fatm%2B%2B%2BUabN2/WY489ps2bN%2Bvdd9/V559/rpEjR1br%2B8QTT6i0tDSw/Pu//3tDlA8AABqpCLsLCKXU1FSlpqbWuM3j8Sg3Nzeo7Te/%2BY1uuOEG7d27V506dQq0R0VFyev1hrRWAADQdDTpGawLVVFRIZfLpcsvvzyo/amnnlLbtm11/fXX68knn9TJkyfPexy/3y%2Bfzxe0AACA8NGkZ7AuxIkTJ5SZmakxY8YoOjo60P7zn/9ciYmJat26tTZs2KCsrCyVlJTotddeq/VYOTk5mjVrVkOUDQAAHMhlWZZldxENweVyadmyZbrzzjurbauqqtLdd9%2BtvXv3au3atUEB61xLly7VXXfdpUOHDqlt27Y19vH7/fL7/YF1n8%2Bn%2BPh4VVRUnPfYFyNp5kqjxwuloieH210CACCM%2BHw%2BeTyekPz9rUvYz2BVVVXpnnvuUUlJif72t7/V%2BR8gOTlZkrRr165aA5bb7Zbb7TZeKwAAaBzCOmCdDVc7d%2B7UmjVrag1M31VcXCxJio2NDXV5AACgkWrSAauyslK7du0KrJeUlGjLli1q06aN4uLidNddd2nz5s3685//rNOnT6usrEyS1KZNG0VGRmr9%2BvUqLCzU4MGD5fF4tHHjRk2dOlUjR44M%2BpQhAADAdzXpgFVUVKTBgwcH1jMyMiRJaWlpys7O1ooVKyRJ119/fdB%2Ba9as0aBBg%2BR2u7VkyRLNmjVLfr9fnTt3Vnp6uqZPn95wbwIAADQ6TTpgDRo0SOd7hr%2Bu5/sTExNVWFhouiwAANDE8T1YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGOTZgvfnmmzpx4oTdZQAAAFwwxwasjIwMeb1eTZgwQRs2bLC7HAAAgHpzbMDav3%2B/3njjDZWWluqmm27SNddco2effVYHDx60uzQAAIDzcmzAioiI0KhRo7RixQrt3btXaWlpeuONN9SxY0eNGjVK7733nizLsrtMAACAahwbsL7L6/Vq6NChGjRokFwul4qKijRmzBh17dpV69ats7s8AACAII4OWIcOHdILL7ygXr16acCAASovL9fy5cu1Z88eff3117rjjjv04x//2O4yAQAAgkTYXUBtfvCDH%2Bj9999XQkKCHnroIaWlpal9%2B/aB7ZdddpmmT5%2Bul156ycYqAQAAqnNswIqOjtbq1at1880319onNjZWO3fubMCqAAAA6ubYgLVw4cI6%2B7hcLnXp0qUBqgEAAKg/xz6DNXXqVM2dO7da%2B29/%2B1s9%2BuijNlQEAABQP44NWO%2B8846Sk5Ortffr109LliyxoSIAAID6cWzAOnTokFq3bl2tPTo6WocOHbKhIgAAgPpxbMDq0qWL/vrXv1Zr/%2Btf/6qEhAQbKgIAAKgfxz7kPmXKFE2ZMkV///vfNWTIEEnSBx98oKefflrPPPOMzdUBAADUzrEBKz09XSdOnNDs2bP1%2BOOPS5I6duyol156SQ8%2B%2BKDN1QEAANTOsbcIJWny5MkqLS3V119/rX/84x/au3fvBYWr/Px8jRgxQnFxcXK5XFq%2BfHnQdsuylJ2drbi4OF166aUaNGiQtm/fHtTn8OHDGjt2rDwejzwej8aOHasjR44YeX8AAKBpcnTAOis2NlaXX375Be937Ngx9erVq8ave5Ckp59%2BWs8995zmzp2rjRs3yuv16tZbb9XRo0cDfcaMGaMtW7Zo5cqVWrlypbZs2aKxY8de9HsBAABNn2NvER48eFDTp0/XBx98oPLycp05cyZo%2B8mTJ%2Bs8RmpqqlJTU2vcZlmWXnjhBc2cOVOjRo2S9O2Xm8bExOitt97ShAkT9Nlnn2nlypUqLCzUjTfeKEl69dVX1a9fP%2B3YsUPdu3f/J98lAABoihwbsMaNG6fdu3fr3/7t3xQbGyuXy2X0%2BCUlJSorK1NKSkqgze12a%2BDAgSooKNCECRO0fv16eTyeQLiSpOTkZHk8HhUUFBCwAABAjRwbsPLz85Wfn6/evXuH5PhlZWWSpJiYmKD2mJgY7dmzJ9CnQ4cO1fbt0KFDYP%2Ba%2BP1%2B%2Bf3%2BwLrP5zNRMgAAaCQc%2BwxWx44djc9a1eTc17AsK6itphrO7XOunJycwEPxHo9H8fHx5goGAACO59iA9fzzzysrK0tfffVVSI7v9XolqdpMVHl5eWBWy%2Bv16sCBA9X2PXjwYLWZr%2B/KyspSRUVFYNm3b5/BygEAgNM59hbh2LFjdfToUXXu3FnR0dFq3rx50Pby8vJ/6vgJCQnyer3Kzc0N3IY8efKk8vLy9NRTT0n69ncPKyoqtGHDBt1www2SpI8%2B%2BkgVFRXq379/rcd2u91yu93/VH0AAKDxcmzAmjNnzj99jMrKSu3atSuwXlJSoi1btqhNmzbq1KmTpkyZotmzZ6tr167q2rWrZs%2BerZYtW2rMmDGSpKuuukrDhw9Xenq6Xn75ZUnST37yE91xxx084A4AAGrl2IA1fvz4f/oYRUVFGjx4cGA9IyNDkpSWlqYFCxZo%2BvTpOn78uH72s5/p8OHDuvHGG7Vq1SpFRUUF9lm0aJEeeeSRwKcNR44cWev3agEAAEiSy7Isy%2B4iavPll19qwYIF2r17t5599ll16NBBq1atUnx8vK666iq7y6s3n88nj8ejiooKRUdHGz120syVRo8XSkVPDre7BABAGAnl39%2B6OPYh93Xr1umaa65RXl6e3n77bVVWVkqSNm/erF/%2B8pc2VwcAAFA7xwasX/ziF8rOztaaNWsUGRkZaB8yZIgKCwttrAwAAOD8HBuwtm7dqrvuuqtae4cOHXTw4EEbKgIAAKgfxwasyy%2B/vMZvS9%2ByZYu%2B973v2VARAABA/Tg2YI0ePVqZmZk6ePBg4FvTP/roI02bNk3333%2B/zdUBAADUzrEBa/bs2fJ6vYqNjVVlZaWuvvpq9e/fX3379tVjjz1md3kAAAC1cuz3YEVGRmrJkiX6/PPPtXnzZp05c0aJiYnq0aOH3aUBAACcl2MD1lndunVTt27d7C4DAACg3hwbsH7yk5%2Bcd/srr7zSQJUAAABcGMcGrNLS0qD1qqoqbd%2B%2BXUePHtUtt9xiU1UAAAB1c2zA%2BtOf/lSt7dSpU/rpT3/aqH4mBwAAhB/HfoqwJhEREZo2bZp%2B/etf210KAABArRpVwJKkL774QlVVVXaXAQAAUCvH3iKcPn160LplWSotLdWKFSt033332VQVAABA3RwbsNavXx%2B0fskll6h9%2B/aaM2eO0tPTbaoKAACgbo4NWOvWrbO7BAAAgIvS6J7BAgAAcDrHzmD17ds38CPPddmwYUOIqwEAAKg/xwaswYMH6%2BWXX1a3bt3Ur18/SVJhYaF27NihCRMmyO1221whAABAzRwbsI4cOaJJkyZp9uzZQe0zZ87UgQMH9Nprr9lUGQAAwPk59hmst99%2BWw888EC19nHjxumdd96xoSIAAID6cWzAcrvdKigoqNZeUFDA7UEAAOBojr1F%2BMgjj2jixIkqLi5WcnKypG%2BfwXr11Vc1Y8YMm6sDAAConWMD1syZM5WQkKAXX3xRb7zxhiTpqquu0quvvqoxY8bYXB0AAEDtHBuwJGnMmDGEKQAA0Og49hksSfL5fFqwYIF%2B%2Bctf6vDhw5Kkjz/%2BWKWlpTZXBgAAUDvHzmBt27ZN3//%2B99WyZUvt27dP48aNU%2BvWrfX222/rq6%2B%2B0sKFC%2B0uEQAAoEaOncGaOnWqxowZo927d6tFixaB9ttvv135%2Bfk2VgYAAHB%2Bjp3B2rhxo%2BbNm1ft53K%2B973vcYsQAAA4mmNnsCIjI1VZWVmtfefOnWrXrp0NFQEAANSPYwPWyJEj9R//8R86deqUJMnlcunrr79WZmamRo0aZXN1AAAAtXNswHr22We1f/9%2Beb1eHT9%2BXEOGDNGVV16pFi1aVPt9QgAAACdx7DNYHo9HBQUFys3N1ebNm3XmzBklJiZq2LBh1Z7LAgAAcBJHzmBVVVXp1ltv1a5du5SSkqLMzEzNmDFDw4cPNx6urrjiCrlcrmrLpEmTJEmDBg2qtm306NFGawAAAE2LI2ewmjdvruLi4gaZqdq4caNOnz4dWN%2B2bZtuvfVW3X333YG29PR0PfHEE4H1Sy%2B9NOR1AQCAxsuRM1iSdP/992v%2B/Pkhf5327dvL6/UGlj//%2Bc/q0qWLBg4cGOjTsmXLoD4ejyfkdQEAgMbLkTNYZ82dO1erV69WUlKSWrVqFbTt6aefNv56J0%2Be1JtvvqmMjIyg2bNFixbpzTffVExMjFJTU/X4448rKiqq1uP4/X75/f7Aus/nM14rAABwLscGrE2bNum6666TJG3dujVoW6huHS5fvlxHjhzRuHHjAm333XefEhIS5PV6tW3bNmVlZenjjz9Wbm5urcfJycnRrFmzQlIjAABwPpdlWZbdRXzXF198oYSEBFs%2BKThs2DBFRkbqT3/6U619Nm3apKSkJG3atEmJiYk19qlpBis%2BPl4VFRWKjo42WnPSzJVGjxdKRU8Ot7sEAEAY8fl88ng8Ifn7WxfHPYPVtWtXHTx4MLB%2B77336sCBAyF/3T179mj16tV66KGHztsvMTFRzZs3186dO2vt43a7FR0dHbQAAIDw4biAde6E2vvvv69jx46F/HXnz5%2BvDh066Pbbbz9vv%2B3bt6uqqkqxsbEhrwkAADROjn0GqyGdOXNG8%2BfPV1pamiIi/v%2BQ7N69W4sWLdJtt92mdu3a6dNPP9Wjjz6q3r17a8CAATZWDAAAnMxxAevsl3me2xZKq1ev1t69e/Xggw8GtUdGRuqDDz7Qiy%2B%2BqMrKSsXHx%2Bv222/X448/rmbNmoW0JgAA0Hg5LmBZlqVx48bJ7XZLkk6cOKGJEydW%2B5qGd99919hrpqSkVLs1KUnx8fHKy8sz9joAACA8OC5gpaWlBa3ff//9NlUCAABwcRwXsBri29sBAABCyXGfIgQAAGjsCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwxz3Te4ALk7SzJV2l1BvRU8Ot7sEAAgpZrAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBY2Aes7OxsuVyuoMXr9Qa2W5al7OxsxcXF6dJLL9WgQYO0fft2GysGAABOF/YBS5KuueYalZaWBpZPPvkksO3pp5/Wc889p7lz52rjxo3yer269dZbdfToURsrBgAATkbAkhQRESGv1xtY2rdvL%2Bnb2asXXnhBM2fO1KhRo9SzZ08tXLhQ33zzjd566y2bqwYAAE5FwJK0c%2BdOxcXFKSEhQaNHj9YXX3whSSopKVFZWZlSUlICfd1utwYOHKiCggK7ygUAAA4XYXcBdrvxxhv1%2B9//Xt26ddOBAwf0q1/9Sv3799f27dtVVlYmSYqJiQnaJyYmRnv27Kn1mH6/X36/P7Du8/lCUzwAAHCksA9YqampgX9fe%2B216tevn7p06aKFCxcqOTlZkuRyuYL2sSyrWtt35eTkaNasWaEpGAAAOB63CM/RqlUrXXvttdq5c2fg04RnZ7LOKi8vrzar9V1ZWVmqqKgILPv27QtpzQAAwFkIWOfw%2B/367LPPFBsbq4SEBHm9XuXm5ga2nzx5Unl5eerfv3%2Btx3C73YqOjg5aAABA%2BAj7W4TTpk3TiBEj1KlTJ5WXl%2BtXv/qVfD6f0tLS5HK5NGXKFM2ePVtdu3ZV165dNXv2bLVs2VJjxoyxu3QAAOBQYR%2BwvvrqK/3oRz/SoUOH1L59eyUnJ6uwsFCdO3eWJE2fPl3Hjx/Xz372Mx0%2BfFg33nijVq1apaioKJsrBwAAThX2AWvx4sXn3e5yuZSdna3s7OyGKQgAADR6PIMFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGFh/1uEANDUJM1caXcJ9Vb05HC7SwBCghksAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwLOwDVk5Ojvr27auoqCh16NBBd955p3bs2BHUZ9CgQXK5XEHL6NGjbaoYAAA4XdgHrLy8PE2aNEmFhYXKzc3VqVOnlJKSomPHjgX1S09PV2lpaWB5%2BeWXbaoYAAA4XYTdBdht5cqVQevz589Xhw4dtGnTJt1yyy2B9pYtW8rr9TZ0eQAAoBEK%2Bxmsc1VUVEiS2rRpE9S%2BaNEitWvXTtdcc42mTZumo0eP1noMv98vn88XtAAAgPAR9jNY32VZljIyMnTTTTepZ8%2Begfb77rtPCQkJ8nq92rZtm7KysvTxxx8rNze3xuPk5ORo1qxZDVU2AABwGALWdzz88MPaunWrPvzww6D29PT0wL979uyprl27KikpSZs3b1ZiYmK142RlZSkjIyOw7vP5FB8fH7rCAQCAoxCw/s/kyZO1YsUK5efnq2PHjuftm5iYqObNm2vnzp01Biy32y232x2qUgEAgMOFfcCyLEuTJ0/WsmXLtHbtWiUkJNS5z/bt21VVVaXY2NgGqBAAADQ2YR%2BwJk2apLfeekt//OMfFRUVpbKyMkmSx%2BPRpZdeqt27d2vRokW67bbb1K5dO3366ad69NFH1bt3bw0YMMDm6gEAgBOF/acI582bp4qKCg0aNEixsbGBZcmSJZKkyMhIffDBBxo2bJi6d%2B%2BuRx55RCkpKVq9erWaNWtmc/UAAMCJwn4Gy7Ks826Pj49XXl5eA1UDAACagrAPWAAANFVJM1fW3ckhip4cbncJRoX9LUIAAADTCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSsevrd736nhIQEtWjRQn369NG6devsLgkAADgUAaselixZoilTpmjmzJkqLi7WzTffrNTUVO3du9fu0gAAgAMRsOrhueee0/jx4/XQQw/pqquu0gsvvKD4%2BHjNmzfP7tIAAIADRdhdgNOdPHlSmzZtUmZmZlB7SkqKCgoKatzH7/fL7/cH1isqKiRJPp/PeH2n/ceMHzNUQvH%2B8f9xLuAszgWcFe7nwtljWpZl/Nh1IWDV4dChQzp9%2BrRiYmKC2mNiYlRWVlbjPjk5OZo1a1a19vj4%2BJDU2Fh4nrW7AjgF5wLO4lzAWaE8F44ePSqPxxO6F6gBAaueXC5X0LplWdXazsrKylJGRkZg/cyZM/rHP/6htm3b1rrPxfD5fIqPj9e%2BffsUHR1t7LhNCWN0foxP3RijujFG58f41C1UY2RZlo4ePaq4uDhjx6wvAlYd2rVrp2bNmlWbrSovL682q3WW2%2B2W2%2B0Oarv88stDVmN0dDT/o60DY3TS%2BdRPAAAH7ElEQVR%2BjE/dGKO6MUbnx/jULRRj1NAzV2fxkHsdIiMj1adPH%2BXm5ga15%2Bbmqn///jZVBQAAnIwZrHrIyMjQ2LFjlZSUpH79%2BumVV17R3r17NXHiRLtLAwAADtQsOzs72%2B4inK5nz55q27atZs%2BerWeeeUbHjx/XH/7wB/Xq1cvu0tSsWTMNGjRIERFk5dowRufH%2BNSNMaobY3R%2BjE/dmtoYuSw7PrsIAADQhPEMFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYDnc7373OyUkJKhFixbq06eP1q1bd97%2BS5cu1dVXXy23262rr75ay5Yta6BK7XEh47NgwQK5XK5qy4kTJxqw4oaVn5%2BvESNGKC4uTi6XS8uXL69zn7y8PPXp00ctWrTQlVdeqf/8z/9sgErtc6FjtHbt2hrPo//93/9toIobVk5Ojvr27auoqCh16NBBd955p3bs2FHnfuFyLbqY8Qm3a9G8efN03XXXBb5EtF%2B/fvrLX/5y3n2awvlDwHKwJUuWaMqUKZo5c6aKi4t18803KzU1VXv37q2x//r163Xvvfdq7Nix%2BvjjjzV27Fjdc889%2Buijjxq48oZxoeMjffstwaWlpUFLixYtGrDqhnXs2DH16tVLc%2BfOrVf/kpIS3Xbbbbr55ptVXFysGTNm6JFHHtHSpUtDXKl9LnSMztqxY0fQedS1a9cQVWivvLw8TZo0SYWFhcrNzdWpU6eUkpKiY8dq/xHhcLoWXcz4SOF1LerYsaPmzJmjoqIiFRUVaciQIfrXf/1Xbd%2B%2Bvcb%2BTeb8seBYN9xwgzVx4sSgth49eliZmZk19r/nnnus4cOHB7UNGzbMGj16dMhqtNOFjs/8%2BfMtj8fTEKU5kiRr2bJl5%2B0zffp0q0ePHkFtEyZMsJKTk0NZmmPUZ4zWrFljSbIOHz7cQFU5S3l5uSXJysvLq7VPuF2Lvqs%2B4xPu1yLLsqzWrVtbr732Wo3bmsr5wwyWQ508eVKbNm1SSkpKUHtKSooKCgpq3Gf9%2BvXV%2Bg8bNqzW/o3ZxYyPJFVWVqpz587q2LGj7rjjDhUXF4e61EaltnOoqKhIVVVVNlXlTL1791ZsbKyGDh2qNWvW2F1Og6moqJAktWnTptY%2B4XQtOld9xkcK32vR6dOntXjxYh07dkz9%2BvWrsU9TOX8IWA516NAhnT59utoPSsfExFT74emzysrKLqh/Y3Yx49OjRw8tWLBAK1as0H/913%2BpRYsWGjBggHbu3NkQJTcKtZ1Dp06d0qFDh2yqylliY2P1yiuvaOnSpXr33XfVvXt3DR06VPn5%2BXaXFnKWZSkjI0M33XSTevbsWWu/cLoWfVd9xyccr0WffPKJLrvsMrndbk2cOFHLli3T1VdfXWPfpnL%2BNI3vo2/CXC5X0LplWdXa/pn%2Bjd2FvN/k5GQlJycH1gcMGKDExET95je/0UsvvRTSOhuTmsa0pvZw1b17d3Xv3j2w3q9fP%2B3bt0/PPPOMbrnlFhsrC72HH35YW7du1Ycfflhn33C7Fkn1H59wvBZ1795dW7Zs0ZEjR7R06VKlpaUpLy%2Bv1pDVFM4fZrAcql27dmrWrFm1xF5eXl4t2Z/l9XovqH9jdjHjc65LLrlEffv2bdL/r/FC1XYORUREqG3btjZV5XzJyclN/jyaPHmyVqxYoTVr1qhjx47n7RtO16KzLmR8zhUO16LIyEj9y7/8i5KSkpSTk6NevXrpxRdfrLFvUzl/CFgOFRkZqT59%2Big3NzeoPTc3V/37969xn379%2BlXrv2rVqlr7N2YXMz7nsixLW7ZsUWxsbChKbJRqO4eSkpLUvHlzm6pyvuLi4iZ7HlmWpYcffljvvvuu/va3vykhIaHOfcLpWnQx41PTMcLtWmRZlvx%2Bf43bmsz5Y8uj9aiXxYsXW82bN7def/1169NPP7WmTJlitWrVyvryyy8ty7KssWPHBn1i7n/%2B53%2BsZs2aWXPmzLE%2B%2B%2Bwza86cOVZERIRVWFho11sIqQsdn%2BzsbGvlypXW7t27reLiYuuBBx6wIiIirI8%2B%2BsiutxByR48etYqLi63i4mJLkvXcc89ZxcXF1p49eyzLsqzMzExr7Nixgf5ffPGF1bJlS2vq1KnWp59%2Bar3%2B%2ButW8%2BbNrf/%2B7/%2B26y2E3IWO0fPPP28tW7bM%2Bvzzz61t27ZZmZmZliRr6dKldr2FkPrpT39qeTwea%2B3atVZpaWlg%2BeabbwJ9wvladDHjE27XoqysLCs/P98qKSmxtm7das2YMcO65JJLrFWrVlmW1XTPHwKWw/32t7%2B1OnfubEVGRlqJiYlBH/0dOHCglZaWFtT/nXfesbp37241b97c6tGjR5O96J91IeMzZcoUq1OnTlZkZKTVvn17KyUlxSooKLCh6oZz9isFzl3OjktaWpo1cODAoH3Wrl1r9e7d24qMjLSuuOIKa968eQ1feAO60DF66qmnrC5dulgtWrSwWrdubd10003We%2B%2B9Z0/xDaCmsZFkzZ8/P9AnnK9FFzM%2B4XYtevDBBwPX6fbt21tDhw4NhCvLarrnj8uy/u8JVgAAABjBM1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAz7f%2Bne5hIKUDs8AAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6186899339666851958\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">175</td>\n",
       "        <td class=\"number\">58.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">65</td>\n",
       "        <td class=\"number\">21.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:37%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">40</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">21</td>\n",
       "        <td class=\"number\">7.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6186899339666851958\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">175</td>\n",
       "        <td class=\"number\">58.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">65</td>\n",
       "        <td class=\"number\">21.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:37%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">40</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">21</td>\n",
       "        <td class=\"number\">7.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">175</td>\n",
       "        <td class=\"number\">58.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">65</td>\n",
       "        <td class=\"number\">21.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:37%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">40</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">21</td>\n",
       "        <td class=\"number\">7.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Chol\">Chol<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>152</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>50.5%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>246.94</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>126</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>564</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-2011080880862546749\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAdBJREFUeJzt3bHO0mAYhuG3xJUeAGnPwt3JxHMycTDxnEyc3D0LCAdQ4uAgdVBcfvMEjHyl/tc1Qpq3kN4phQ/o5nmeq7HD4VDjOLYey8rt9/sahqHpzBdNp/2y3W6r6ucD7vt%2BiV1gRaZpqnEcfx83LS0SSNd1VVXV9/1igbx8%2B/Hmbb58eHOHPeFal%2BOmpU3zibAiAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCBZZ7v6v/c3SdbjGfxFIK7eG6Psj6%2BclFgQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAILHe/I3%2BxsH7OIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBA83CfpfiWRR%2BIMAoFAIBAIBA93DfLctbgGs2L4eosEMs9zVVVN0/Tkvu/fvrbenWfnT8/7Pbx6/%2BnmbT6/e/3ktsv%2BXo6blrp5gamHw6HGcWw9lpXb7/c1DEPTmYsEcj6f63g81na7ra7rWo9nZeZ5rtPpVLvdrjabtpfNiwQCa%2BFdLAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCASCHyG9UnBVPV79AAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-2011080880862546749,#minihistogram-2011080880862546749\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-2011080880862546749\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-2011080880862546749\"\n",
       "                                                  aria-controls=\"quantiles-2011080880862546749\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-2011080880862546749\" aria-controls=\"histogram-2011080880862546749\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-2011080880862546749\" aria-controls=\"common-2011080880862546749\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-2011080880862546749\" aria-controls=\"extreme-2011080880862546749\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-2011080880862546749\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>126</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>175</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>211</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>242</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>275</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>327</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>564</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>438</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>64</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>51.86</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.21001</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>4.4708</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>246.94</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>39.353</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.126</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>74328</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>2689.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>2.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-2011080880862546749\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl0VPXdx/HPQMiwNBlZzAwx0UYbFQ1SCxwIWkHAiAXRcqxLMIIrlkUjWDRSlS4kSAVRqVRcgGop6lEsPbUpwSXACQgJRhYtWqEQMCFYwyQgTID8nj8st44BiT6/5N6Q9%2BucOYe5987Md3LFvLlzZ8ZnjDECAACANa3cHgAAAOBkQ2ABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYRmABAABYFuP2AC1BXV2dPv30U8XFxcnn87k9DgAALYIxRjU1NUpMTFSrVk17TInAagKffvqpkpOT3R4DAIAWqaysTElJSU36mARWE4iLi5P05Q6Oj493eRoAAFqG6upqJScnO7%2BHmxKB1QSOviwYHx9PYAEA0MTcOD2Hk9wBAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAs48uegePoNSXf7RG%2BleJpQ9weAQDwXxzBAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsOykDqwVK1boyiuvVGJionw%2Bn15//fWo9cYYTZ06VYmJiWrXrp0GDBigzZs3R21TVVWlrKwsBQIBBQIBZWVlae/evU35NAAAQDNzUgfW/v371aNHD82ZM%2BeY62fMmKFZs2Zpzpw5WrdunUKhkC677DLV1NQ422RmZqq0tFT5%2BfnKz89XaWmpsrKymuopAACAZuik/i7CK664QldcccUx1xljNHv2bE2ZMkUjRoyQJC1cuFDBYFCLFi3SmDFj9OGHHyo/P19r1qxRnz59JEnPPPOM0tPTtWXLFp1zzjlN9lwAAEDzcVIfwfom27ZtU0VFhTIyMpxlfr9f/fv3V1FRkSRp9erVCgQCTlxJUt%2B%2BfRUIBJxtAAAAvu6kPoL1TSoqKiRJwWAwankwGNT27dudbRISEurdNiEhwbn9sUQiEUUiEed6dXW1jZEBAEAz0WKPYB3l8/mirhtjopZ9ff2xtvm6vLw856T4QCCg5ORkewMDAADPa7GBFQqFJKnekajKykrnqFYoFNLu3bvr3XbPnj31jnx9VU5OjsLhsHMpKyuzODkAAPC6FhtYKSkpCoVCKigocJbV1taqsLBQ/fr1kySlp6crHA5r7dq1zjbvvvuuwuGws82x%2BP1%2BxcfHR10AAEDLcVKfg7Vv3z7961//cq5v27ZNpaWl6tSpk04//XRlZ2crNzdXqampSk1NVW5urtq3b6/MzExJUrdu3TRkyBDdfvvtevrppyVJd9xxh4YNG8Y7CAEAwHGd1IFVXFysSy%2B91Lk%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%2BuXv/ylUlJS1K5dO5155pn69a9/rbq6OmcbY4ymTp2qxMREtWvXTgMGDNDmzZtdnBoAAHhZiw%2BsRx55RH/4wx80Z84cffjhh5oxY4Z%2B97vf6cknn3S2mTFjhmbNmqU5c%2BZo3bp1CoVCuuyyy1RTU%2BPi5AAAwKtafGCtXr1aV111lYYOHarvf//7uuaaa5SRkaHi4mJJXx69mj17tqZMmaIRI0YoLS1NCxcu1BdffKFFixa5PD0AAPCiFh9YF198sd5880199NFHkqT3339fq1at0k9%2B8hNJ0rZt21RRUaGMjAznNn6/X/3791dRUZErMwMAAG%2BLcXsAt913330Kh8M699xz1bp1ax05ckTTpk3TDTfcIEmqqKiQJAWDwajbBYNBbd%2B%2B/Zj3GYlEFIlEnOvV1dWNND0AAPCiFn8E66WXXtKLL76oRYsWaf369Vq4cKEeffRRLVy4MGo7n88Xdd0YU2/ZUXl5eQoEAs4lOTm50eYHAADe0%2BID6xe/%2BIXuv/9%2BXX/99erevbuysrJ0zz33KC8vT5IUCoUk/e9I1lGVlZX1jmodlZOTo3A47FzKysoa90kAAABPafGB9cUXX6hVq%2BgfQ%2BvWrZ2PaUhJSVEoFFJBQYGzvra2VoWFherXr98x79Pv9ys%2BPj7qAgAAWo4Wfw7WlVdeqWnTpun000/X%2Beefr/fee0%2BzZs3SLbfcIunLlwazs7OVm5ur1NRUpaamKjc3V%2B3bt1dmZqbL0wMAAC9q8YH15JNP6sEHH9TYsWNVWVmpxMREjRkzRg899JCzzeTJk3XgwAGNHTtWVVVV6tOnj5YtW6a4uDgXJwcAAF7lM8YYt4c42VVXVysQCCgcDvNyYTPSa0q%2B2yN8K8XThrg9AgB4ipu/f1v8OVgAAAC2EVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWEVgAAACWxbg9AFqOXlPy3R4BAIAmwREsAAAAywgsAAAAywgsAAAAywgsAAAAywgsAAAAy3gXIXCSaE7v0iyeNsTtEQCgUXEECwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDLPBtaLL76ogwcPuj0GAADAt%2BbZwJo4caJCoZDGjBmjtWvXuj0OAABAg3k2sD799FM9//zzKi8v18UXX6zzzz9fM2fO1J49e9weDQAA4Bt5NrBiYmI0YsQILV26VDt27NCoUaP0/PPPKykpSSNGjNDf/vY3GWPcHhMAAKAezwbWV4VCIQ0aNEgDBgyQz%2BdTcXGxMjMzlZqaqpUrV7o9HgAAQBRPB9Znn32m2bNnq0ePHrroootUWVmp119/Xdu3b9euXbs0bNgw3XTTTW6PCQAAEMWz30X405/%2BVG%2B88YZSUlJ02223adSoUTr11FOd9d/73vc0efJkPfHEEy5OCQAAUJ9nAys%2BPl7Lly/Xj3/84%2BNu07VrV3388cdNOBUAAMCJeTawFi5ceMJtfD6fzjrrrCaYBgAAoOE8ew7WPffcozlz5tRb/vvf/16TJk1yYSIAAICG8WxgvfLKK%2Brbt2%2B95enp6XrppZdcmAgAAKBhPBtYn332mTp27FhveXx8vD777DMXJgIAAGgYzwbWWWedpX/84x/1lv/jH/9QSkqKCxMBAAA0jGdPcs/OzlZ2drb%2B85//aODAgZKkN998UzNmzNCjjz7q8nQAAADH59nAuv3223Xw4EHl5ubq4YcfliQlJSXpiSee0C233OLydAAAAMfn2ZcIJWnChAkqLy/Xrl279Pnnn2vHjh2NEle7du3SjTfeqM6dO6t9%2B/b64Q9/qJKSEme9MUZTp05VYmKi2rVrpwEDBmjz5s3W5wAAACcHTwfWUV27dtUpp5zSKPddVVWliy66SG3atNHf//53ffDBB5o5c2bU482YMUOzZs3SnDlztG7dOoVCIV122WWqqalplJkAAEDz5tmXCPfs2aPJkyfrzTffVGVlperq6qLW19bWWnmcRx55RMnJyZo/f76z7Pvf/77zZ2OMZs%2BerSlTpmjEiBGSvvwQ1GAwqEWLFmnMmDFW5gAAACcPzwbW6NGj9cknn%2BgXv/iFunbtKp/P1yiPs3TpUl1%2B%2BeX62c9%2BpsLCQp122mkaO3asbr/9dknStm3bVFFRoYyMDOc2fr9f/fv3V1FREYEFAADq8WxgrVixQitWrNCFF17YqI%2BzdetWzZ07VxMnTtQDDzygtWvX6q677pLf79dNN92kiooKSVIwGIy6XTAY1Pbt2495n5FIRJFIxLleXV3deE8AAAB4jmcDKykpqdGOWn1VXV2devXqpdzcXEnShRdeqM2bN2vu3Lm66aabnO2%2BPosx5rjz5eXl6Ve/%2BlXjDQ0AADzNsye5P/bYY8rJydHOnTsb9XG6du2q8847L2pZt27dtGPHDklSKBSSJOdI1lGVlZX1jmodlZOTo3A47FzKysoaYXIAAOBVnj2ClZWVpZqaGp1xxhmKj49XmzZtotZXVlZaeZyLLrpIW7ZsiVr20Ucf6YwzzpAkpaSkKBQKqaCgwHm5sra2VoWFhXrkkUeOeZ9%2Bv19%2Bv9/KfAAAoPnxbGBNnz69SR7nnnvuUb9%2B/ZSbm6trr71Wa9eu1bx58zRv3jxJX740mJ2drdzcXKWmpio1NVW5ublq3769MjMzm2RGAADQvHg2sG699dYmeZzevXtryZIlysnJ0a9//WulpKRo9uzZGjlypLPN5MmTdeDAAY0dO1ZVVVXq06ePli1bpri4uCaZEQAANC8%2BY4xxe4jj%2Bfe//60FCxbok08%2B0cyZM5WQkKBly5YpOTlZ3bp1c3u8BquurlYgEFA4HFZ8fLzb47im15R8t0eARxRPG%2BL2CABaADd//3r2JPeVK1fq/PPPV2FhoV5%2B%2BWXt27dPkrR%2B/Xo99NBDLk8HAABwfJ4NrPvuu09Tp07V22%2B/rdjYWGf5wIEDtWbNGhcnAwAA%2BGaeDawNGzbommuuqbc8ISFBe/bscWEiAACAhvFsYJ1yyin1PntKkkpLS3Xaaae5MBEAAEDDeDawrr/%2Bet1///3as2eP84np7777ru69917deOONLk8HAABwfJ4NrNzcXIVCIXXt2lX79u3Teeedp379%2Bql379568MEH3R4PAADguDz7OVixsbF66aWX9NFHH2n9%2BvWqq6vTj370I5177rlujwYAAPCNPBtYR5199tk6%2B%2Byz3R4DAACgwTwbWHfcccc3rj/6VTYAAABe49nAKi8vj7p%2B6NAhbd68WTU1NbrkkktcmgoAAODEPBtYf/3rX%2BstO3z4sH7%2B8583q6/JAQAALY9n30V4LDExMbr33nv1u9/9zu1RAAAAjqtZBZYkbd26VYcOHXJ7DAAAgOPy7EuEkydPjrpujFF5ebmWLl2qkSNHujQVAADAiXk2sFavXh11vVWrVjr11FM1ffp03X777S5NBQAAcGKeDayVK1e6PQIAAMB30uzOwQIAAPA6zx7B6t27t/Mlzyeydu3aRp4GAACg4TwbWJdeeqmefvppnX322UpPT5ckrVmzRlu2bNGYMWPk9/tdnhAAAODYPBtYe/fu1bhx45Sbmxu1fMqUKdq9e7eeffZZlyYDAAD4Zp49B%2Bvll1/WzTffXG/56NGj9corr7gwEQAAQMN4NrD8fr%2BKiorqLS8qKuLlQQAA4GmefYnwrrvu0p133qn33ntPffv2lfTlOVjPPPOMHnjgAZenAwAAOD7PBtaUKVOUkpKixx9/XM8//7wkqVu3bnrmmWeUmZnp8nQAAADH59nAkqTMzExiCgAANDuePQdLkqqrq7VgwQI99NBDqqqqkiS9//77Ki8vd3kyAACA4/PsEaxNmzZp8ODBat%2B%2BvcrKyjR69Gh17NhRL7/8snbu3KmFCxe6PSIAAMAxefYI1j333KPMzEx98sknatu2rbN86NChWrFihYuTAQAAfDPPHsFat26d5s6dW%2B/rck477TReIgQAAJ7m2SNYsbGx2rdvX73lH3/8sbp06eLCRAAAAA3j2cAaPny4fvOb3%2Bjw4cOSJJ/Pp127dun%2B%2B%2B/XiBEjXJ4OAADg%2BDwbWDNnztSnn36qUCikAwcOaODAgTrzzDPVtm3bet9PCAAA4CWePQcrEAioqKhIBQUFWr9%2Bverq6vSjH/1Il19%2Beb3zsgAAALzEk4F16NAh/eQnP9FTTz2ljIwMZWRkuD0SAABAg3nyJcI2bdrovffe40gVAABoljwZWJJ04403av78%2BW6PAQAA8K158iXCo%2BbMmaPly5erV69e6tChQ9S6GTNmuDQVAADAN/NsYJWUlOiCCy6QJG3YsCFqHS8dAgAAL/NcYG3dulUpKSlauXKl26MAAAB8J547Bys1NVV79uxxrl933XXavXu3ixMBAAB8O54LLGNM1PU33nhD%2B/fvd2kaAACAb89zgQUAANDceS6wfD5fvZPYOakdAAA0J547yd0Yo9GjR8vv90uSDh48qDvvvLPexzS89tprbowHAABwQp4LrFGjRkVdv/HGG12aBAAA4LvxXGDx6e0AAKC589w5WAAAAM0dgfUVeXl58vl8ys7OdpZFIhFNmDBBXbp0UYcOHTR8%2BHDt3LnTxSkBAIDXEVj/tW7dOs2bN8/5ep6jsrOztWTJEi1evFirVq3Svn37NGzYMB05csSlSQEAgNcRWJL27dunkSNH6plnnlHHjh2d5eFwWM8995xmzpypwYMH68ILL9SLL76ojRs3avny5S5ODAAAvIzAkjRu3DgNHTpUgwcPjlpeUlKiQ4cOKSMjw1mWmJiotLQ0FRUVNfWYAACgmfDcuwib2uLFi1VSUqLi4uJ66yoqKhQbGxt1VEuSgsGgKioqjnufkUhEkUjEuV5dXW1vYAAA4Hkt%2BghWWVmZ7r77bv3pT39S27ZtG3w7Y8w3frp8Xl6eAoGAc0lOTrYxLgAAaCZadGCVlJSosrJSPXv2VExMjGJiYlRYWKgnnnhCMTExCgaDqq2tVVVVVdTtKisrFQwGj3u/OTk5CofDzqWsrKyxnwoAAPCQFv0S4aBBg7Rx48aoZTfffLPOPfdc3XfffUpOTlabNm1UUFCga6%2B9VpJUXl6uTZs2acaMGce9X7/f73zVDwAAaHladGDFxcUpLS0talmHDh3UuXNnZ/mtt96qSZMmqXPnzurUqZPuvfdede/evd4J8QAAAEe16MBqiMcee0wxMTG69tprdeDAAQ0aNEgLFixQ69at3R4NAAB4lM8YY9we4mRXXV2tQCCgcDis%2BPh4t8dxTa8p%2BW6PAI8onjbE7REAtABu/v5t0Se5AwAANAYCCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDI%2BBwtAk2tuH9nBx0oA%2BLY4ggUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGBZiw%2BsvLw89e7dW3FxcUpISNDVV1%2BtLVu2RG0TiUQ0YcIEdenSRR06dNDw4cO1c%2BdOlyYGAABe1%2BIDq7CwUOPGjdOaNWtUUFCgw4cPKyMjQ/v373e2yc7O1pIlS7R48WKtWrVK%2B/bt07Bhw3TkyBEXJwcAAF4V4/YAbsvPz4%2B6Pn/%2BfCUkJKikpESXXHKJwuGwnnvuOb3wwgsaPHiwJOnFF19UcnKyli9frssvv9yNsQEAgIe1%2BCNYXxcOhyVJnTp1kiSVlJTo0KFDysjIcLZJTExUWlqaioqKXJkRAAB4W4s/gvVVxhhNnDhRF198sdLS0iRJFRUVio2NVceOHaO2DQaDqqioOOb9RCIRRSIR53p1dXXjDQ0AADyHI1hfMX78eG3YsEF//vOfT7itMUY%2Bn%2B%2BY6/Ly8hQIBJxLcnKy7VEBAICHEVj/NWHCBC1dulRvv/22kpKSnOWhUEi1tbWqqqqK2r6yslLBYPCY95WTk6NwOOxcysrKGnV2AADgLS0%2BsIwxGj9%2BvF577TW99dZbSklJiVrfs2dPtWnTRgUFBc6y8vJybdq0Sf369Tvmffr9fsXHx0ddAABAy9Hiz8EaN26cFi1apL/85S%2BKi4tzzqsKBAJq166dAoGAbr31Vk2aNEmdO3dWp06ddO%2B996p79%2B7OuwoBAAC%2BqsUH1ty5cyVJAwYMiFo%2Bf/58jR49WpL02GOPKSYmRtdee60OHDigQYMGacGCBWrdunUTTwsAAJqDFh9YxpgTbtO2bVs9%2BeSTevLJJ5tgIgAA0Ny1%2BHOwAAAAbCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALItxewD8//Saku/2CAAA4Gs4ggUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGBZjNsDAADs6jUl3%2B0RGqx42hC3RwAaBUewAAAALCOwAAAALOMlQgA4geb0khsAb%2BAIFgAAgGUEFgAAgGUEFgAAgGUEFgAAgGUEVgM99dRTSklJUdu2bdWzZ0%2BtXLnS7ZEAAIBH8S7CBnjppZeUnZ2tp556ShdddJGefvppXXHFFfrggw90%2Bumnuz0eAADH1JzeAXuyfegsR7AaYNasWbr11lt12223qVu3bpo9e7aSk5M1d%2B5ct0cDAAAexBGsE6itrVVJSYnuv//%2BqOUZGRkqKio65m0ikYgikYhzPRwOS5Kqq6utz3ckst/6fQJAU2mM/y/if5rT74jG%2BG/h6H0aY6zf94kQWCfw2Wef6ciRIwoGg1HLg8GgKioqjnmbvLw8/epXv6q3PDk5uVFmBIDmKjDT7QngFY3530JNTY0CgUDjPcAxEFgN5PP5oq4bY%2BotOyonJ0cTJ050rtfV1enzzz9X586dj3sbNJ7q6molJyerrKxM8fHxbo/TorEvvIX94R3si8ZhjFFNTY0SExOb/LEJrBPo0qWLWrduXe9oVWVlZb2jWkf5/X75/f6oZaecckqjzYiGiY%2BP539cHsG%2B8Bb2h3ewL%2Bxr6iNXR3GS%2BwnExsaqZ8%2BeKigoiFpeUFCgfv36uTQVAADwMo5gNcDEiROVlZWlXr16KT09XfPmzdOOHTt05513uj0aAADwoNZTp06d6vYQXpeWlqbOnTsrNzdXjz76qA4cOKAXXnhBPXr0cHs0NFDr1q01YMAAxcTwbwq3sS%2B8hf3hHeyLk4vPuPHeRQAAgJMY52ABAABYRmABAABYRmABAABYRmABAABYRmCh2cnLy1Pv3r0VFxenhIQEXX311dqyZUvUNpFIRBMmTFCXLl3UoUMHDR8%2BXDt37ozaZseOHbryyivVoUMHdenSRXfddZdqa2ub8qmcFObOnasLLrjA%2BYDE9PR0/f3vf3fWsy/ck5eXJ5/Pp%2BzsbGcZ%2B6PpTJ06VT6fL%2BoSCoWc9cYYTZ06VYmJiWrXrp0GDBigzZs3R91HVVWVsrKyFAgEFAgElJWVpb179zb1U8F3QGCh2SksLNS4ceO0Zs0aFRQU6PDhw8rIyND%2B/f/7UtPs7GwtWbJEixcv1qpVq7Rv3z4NGzZMR44ckSQdOXJEQ4cO1f79%2B7Vq1SotXrxYr776qiZNmuTW02q2kpKSNH36dBUXF6u4uFgDBw7UVVdd5fyiYF%2B4Y926dZo3b54uuOCCqOXsj6Z1/vnnq7y83Lls3LjRWTdjxgzNmjVLc%2BbM0bp16xQKhXTZZZeppqbG2SYzM1OlpaXKz89Xfn6%2BSktLlZWV5cZTwbdlgGausrLSSDKFhYXGGGP27t1r2rRpYxYvXuxss2vXLtOqVSuTn59vjDHmjTfeMK1atTK7du1ytvnzn/9s/H6/CYfDTfsETkIdO3Y0zz77LPvCJTU1NSY1NdUUFBSY/v37m7vvvtsYw9%2BNpvbwww%2BbHj16HHNdXV2dCYVCZvr06c6ygwcPmkAgYP7whz8YY4z54IMPjCSzZs0aZ5vVq1cbSeaf//xn4w6P/zeOYKHZC4fDkqROnTpJkkpKSnTo0CFlZGQ42yQmJiotLU1FRUWSpNWrVystLS3qC0Avv/xyRSIRlZSUNOH0J5cjR45o8eLF2r9/v9LT09kXLhk3bpyGDh2qwYMHRy1nfzS9jz/%2BWImJiUpJSdH111%2BvrVu3SpK2bdumioqKqH3h9/vVv3//qH0RCATUp08fZ5u%2BffsqEAg428C7%2BLhYNGvGGE2cOFEXX3yx0tLSJEkVFRWKjY1Vx44do7YNBoPOl3ZXVFTU%2B7Lujh07KjY2tt4Xe%2BPENm7cqPT0dB08eFDf%2B973tGTJEp133nkqLS1lXzSxxYsXq6SkRMXFxfXW8XejafXp00cLfRHzAAAD5UlEQVR//OMfdfbZZ2v37t367W9/q379%2Bmnz5s3Oz/LrP%2BtgMKjt27dL%2BnJfJCQk1LvfhIQE9kUzQGChWRs/frw2bNigVatWnXBbY4x8Pp9z/at/Pt42aJhzzjlHpaWl2rt3r1599VWNGjVKhYWFx92efdE4ysrKdPfdd2vZsmVq27Ztg2/H/mgcV1xxhfPn7t27Kz09XWeddZYWLlyovn37Sqr/s2ZfnDx4iRDN1oQJE7R06VK9/fbbSkpKcpaHQiHV1taqqqoqavvKykrnX4uhUKjevwCrqqp06NChev%2BixInFxsbqBz/4gXr16qW8vDz16NFDjz/%2BOPuiiZWUlKiyslI9e/ZUTEyMYmJiVFhYqCeeeEIxMTEKBoPsDxd16NBB3bt318cff%2By8m/DrP%2Buv74vdu3fXu589e/awL5oBAgvNjjFG48eP12uvvaa33npLKSkpUet79uypNm3aqKCgwFlWXl6uTZs2qV%2B/fpKk9PR0bdq0SeXl5c42y5Ytk9/vV8%2BePZvmiZzEjDGKRCLsiyY2aNAgbdy4UaWlpc6lV69eGjlypPNn9od7IpGIPvzwQ3Xt2lUpKSkKhUJR%2B6K2tlaFhYVR%2ByIcDmvt2rXONu%2B%2B%2B67C4bCzDTzMpZPrge/s5z//uQkEAuadd94x5eXlzuWLL75wtrnzzjtNUlKSWb58uVm/fr0ZOHCg6dGjhzl8%2BLAxxpjDhw%2BbtLQ0M2jQILN%2B/XqzfPlyk5SUZMaPH%2B/W02q2cnJyzIoVK8y2bdvMhg0bzAMPPGBatWplli1bZoxhX7jtq%2B8iNIb90ZQmTZpk3nnnHbN161azZs0aM2zYMBMXF2f%2B/e9/G2OMmT59ugkEAua1114zGzduNDfccIPp2rWrqa6udu5jyJAh5oILLjCrV682q1evNt27dzfDhg1z6ynhWyCw0OxIOuZl/vz5zjYHDhww48ePN506dTLt2rUzw4YNMzt27Ii6n%2B3bt5uhQ4eadu3amU6dOpnx48ebgwcPNvGzaf5uueUWc8YZZ5jY2Fhz6qmnmkGDBjlxZQz7wm1fDyz2R9O57rrrTNeuXU2bNm1MYmKiGTFihNm8ebOzvq6uzjz88MMmFAoZv99vLrnkErNx48ao%2B/jPf/5jRo4caeLi4kxcXJwZOXKkqaqqauqngu/AZ4wxbh5BAwAAONlwDhYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBl/wfmhTIxSmc9tgAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-2011080880862546749\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">234</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">197</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">212</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">269</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">254</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">204</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">282</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">177</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">239</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">240</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (142)</td>\n",
       "        <td class=\"number\">253</td>\n",
       "        <td class=\"number\">84.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-2011080880862546749\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">126</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">131</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">141</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">149</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">157</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">394</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">407</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">409</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">417</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">564</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_ExAng\">ExAng<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.32558</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable5239163408308709856\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 67.4%\">\n",
       "            203\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:48%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 32.6%\">\n",
       "            98\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable5239163408308709856, #minifreqtable5239163408308709856\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable5239163408308709856\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">203</td>\n",
       "        <td class=\"number\">67.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">98</td>\n",
       "        <td class=\"number\">32.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:48%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Fbs\">Fbs<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.14618</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable672870025621362828\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 85.4%\">\n",
       "            257\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:17%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 14.6%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        44\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable672870025621362828, #minifreqtable672870025621362828\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable672870025621362828\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">257</td>\n",
       "        <td class=\"number\">85.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">44</td>\n",
       "        <td class=\"number\">14.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_MaxHR\">MaxHR<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>91</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>30.2%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>149.7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>71</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>202</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram3898380760254524388\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAddJREFUeJzt3EGu0lAYhuG/xGnLnNBdOHfqnpyZOHQ/Jm7BXUBYQDunDpQ7Ub9cVFoKzzOkJKck581pexqaaZqmmtnxeKy%2B7%2BcelpU7HA613%2B9nHfPNrKP91LZtVf34wV3XLXEKrMgwDNX3/cu8mdMigTRNU1VVXdcJ5D94%2B%2BHLVd//9un9jc7kti7zZk6b2UeEFREIBAKBQCAQCAQCgUAgEAgEAsEiG4Us69qNxar1bi7%2BKysIBAKBQCAQCAQCgUDgKdad%2BZsnTNyOFQQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUDgVZMb8trI%2BllBIBAIBAKBQCAQuEnnVZ71n1AEcgVPpZ6PSywIBAKBQCAQCAQCgUAgEAgEgqfdB7GnwWtYQSAQCAQPcYnlcolbeYhAuE%2BP8IKjSywIFllBpmmqqqphGH459u7j17lPhzvyuzlx%2Bewyb%2Ba0SCDjOFZVVd/3SwzPHdt%2B/vOxcRxru93OdzJV1UwLZHk%2Bn%2Bt0OlXbttU0zdzDszLTNNU4jrXb7WqzmfeuYJFAYC3cpEMgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQfAex2E8VEoIzuQAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives3898380760254524388,#minihistogram3898380760254524388\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives3898380760254524388\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles3898380760254524388\"\n",
       "                                                  aria-controls=\"quantiles3898380760254524388\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram3898380760254524388\" aria-controls=\"histogram3898380760254524388\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common3898380760254524388\" aria-controls=\"common3898380760254524388\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme3898380760254524388\" aria-controls=\"extreme3898380760254524388\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles3898380760254524388\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>71</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>108</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>134</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>153</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>166</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>182</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>202</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>131</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>32</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>22.861</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.15271</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-0.035017</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>149.7</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>18.424</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.54288</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>45060</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>522.62</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>2.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram3898380760254524388\">\n",
       "            <img 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vlks/n43osIAR1n51jdwloInY9MdDuEvAvsPPzN2RXsCIiIjRs2DBt3LhRR44cUXp6uv7whz8oISFBw4YN0xtvvKEQzY4AACDEhWzA%2Bj6Px6N%2B/fqpb9%2B%2Bcjgc2rVrl0aOHKlOnTrpnXfesbs8AAAQZkI6YH355ZdatGiRunbtqhtvvFElJSV6/fXXdfjwYR07dkxDhgzRvffea3eZAAAgzETYXcAP9ctf/lJvvvmmkpKSdP/99ys9PT3o2qsLL7xQM2bM0HPPPWdjlQAAIByFbMCKiYnR5s2bddNNN521T1xcnA4dOtSIVQEAAIRwwPr%2BT9mcjcPh0BVXXNEI1QAAAHwnZK/BmjJlihYvXlyr/YUXXtC0adNsqAgAAOC0kA1Yr732mm644YZa7b169dIrr7xiQ0UAAACnhWzA%2BvLLL9W2bdta7TExMfryyy9tqAgAAOC0kA1YV1xxhd56661a7W%2B99ZaSkpJsqAgAAOC0kL3IPSMjQxkZGfrqq690yy23SJL%2B9re/af78%2BXr66adtrg4AAISzkA1Yv/nNb3TixAllZWXpd7/7nSQpISFBzz33nMaMGWNzdQAAIJyFbMCSpMmTJ2vy5MkqLi5Wq1atdNFFF9ldEgAAQGgHrG/FxcXZXQIAAEBAyF7k/sUXX%2Bi%2B%2B%2B7TpZdeqpYtWyoqKipoAwAAsEvIrmCNHj1an3zyiR566CHFxcXJ4XDYXRIAAICkEA5YW7du1datW3XdddfZXQoAAECQkD1FmJCQwKoVAABokkI2YC1cuFCZmZk6evSo3aUAAAAECdlThKNGjVJ5ebk6duyomJgYRUZGBu0vKSmxqTIAABDuQjZgzZs3z%2B4SAAAA6hSyAWvs2LF2lwAAAFCnkL0GS5L%2B8Y9/aM6cORo1alTglOCmTZv00Ucf2VwZAAAIZyEbsN555x0lJycrLy9Pr776qioqKiRJu3fv1mOPPWZzdQAAIJyFbMCaOXOm5syZoy1btgTduf2WW27Rjh07bKwMAACEu5ANWB988IHuuOOOWu0dOnTQF198YUNFAAAAp4VswLrooovk9Xprte/du1eXXHKJDRUBAACcFrIB684779SsWbP0xRdfBO7o/u6772r69Om65557bK4OAACEs5ANWFlZWfJ4PIqLi1NFRYWuueYapaamqkePHnr00Ud/0DGzs7PlcDiUkZERaPP7/Zo8ebLat2%2BvNm3aaOjQodw9HgAAnFPI3gcrKipKr7zyig4ePKjdu3erpqZG119/va6%2B%2BuofdLydO3dq2bJluvbaa4PaMzIy9Je//EXr1q1TbGyspk2bpiFDhqigoEAtWrSwYigAAKCZCdmA9a0rr7xSV1555Y86RkVFhe6%2B%2B2699NJL%2Bv3vfx9o9/l8Wr58uVavXq3%2B/ftLktasWaPExERt3rxZAwYM%2BFGvCwAAmqeQDVj/9m//ds79y5Ytq/exJk6cqMGDB6t///5BAaugoEDV1dVKS0sLtMXHxyslJUX5%2BfkELAAAUKeQDVjFxcVBj6urq3XgwAGVl5fr5ptvrvdx1q1bp4KCAu3atavWPq/Xq6ioKLVt2zao3e121/kNxm/5/X75/f7A47KysnrXAwAAQl/IBqy//OUvtdpOnjypBx54QJ07d67XMYqKivTb3/5WmzZtUsuWLev92saYwDcX65Kdna25c%2BfW%2B3gAAKB5CdlvEdYlIiJC06dP11NPPVWv/gUFBSopKVG3bt0UERGhiIgI5eXl6bnnnlNERITcbreqqqpUWloa9LySkhK53e6zHjczM1M%2Bny%2BwFRUV/ahxAQCA0BKyK1hn8%2Bmnn6q6urpeffv166d9%2B/YFtd133326%2BuqrNXPmTCUmJioyMlK5ubkaPny4pNOnJvfv36/58%2Bef9bhOp1NOp/OHDwIAAIS0kA1YM2bMCHpsjFFxcbE2btyou%2B%2B%2Bu17HiI6OVkpKSlBbmzZtFBsbG2gfO3aspk2bptjYWLVr107Tp09Xly5dAt8qBAAAOFPIBqzt27cHPb7gggt08cUXa968efrNb35j2essXLhQERERGj58uCorK9WvXz%2BtXLmSe2ABAICzchhjjN1FNHdlZWVyuVzy%2BXyKiYmxuxwA/6Lus3PsLgFNxK4nBtpdAv4Fdn7%2BNquL3AEAAJqCkD1F2KNHj3PeKuH73nvvvQauBgAA4DshG7B%2B9rOf6T/%2B4z905ZVXqlevXpKkHTt26OOPP9a4ceP4Fh8AALBNyAasr7/%2BWhMnTlRWVlZQ%2B%2BzZs/X555/rP//zP22qDAAAhLuQvQbr1Vdf1X333VerffTo0XrttddsqAgAAOC0kA1YTqdT%2Bfn5tdrz8/M5PQgAAGwVsqcIH3zwQY0fP1579uzRDTfcIOn0NVgvvfSSHn74YZurAwAA4SxkA9bs2bOVlJSkZ599Vn/4wx8kSZ07d9ZLL72kkSNH2lwdAAAIZyEbsCRp5MiRhCkAANDkhOw1WNLpO7SuXLlSjz32mEpLSyVJ77//voqLi22uDAAAhLOQXcHav3%2B/%2Bvfvr9atW6uoqEijR49W27Zt9eqrr%2Bro0aNatWqV3SUCAIAwFbIrWFOmTNHIkSP1ySefqGXLloH2wYMHa%2BvWrTZWBgAAwl3IrmDt3LlTS5curfVzOZdccgmnCAEAgK1CNmBFRUWpoqKiVvuhQ4fUvn17GyoCUF/dZ%2BfYXQIANKiQPUU4dOhQ/fu//7tOnjwpSXI4HDp27JhmzZqlYcOG2VwdAAAIZyEbsJ555hl99tln8ng8qqys1C233KLLL79cLVu2rPX7hAAAAI0pZE8Rulwu5efnKzc3V7t371ZNTY2uv/56DRgwoNZ1WQAAAI0pJANWdXW1fv7zn2vJkiVKS0tTWlqa3SUBAAAEhOQpwsjISO3Zs4eVKgAA0CSFZMCSpHvuuUcrVqywuwwAAIBaQvIU4bcWL16szZs3q3v37mrTpk3Qvvnz59tUFQAACHchG7AKCgp07bXXSpI%2B%2BOCDoH2cOgQAAHYKuYD16aefKikpSe%2B8847dpQAAANQp5K7B6tSpk7744ovA4xEjRujzzz%2B3sSIAAIBgIRewjDFBj998800dP37cpmoAAABqC7mABQAA0NSFXMByOBy1LmLnonYAANCUhNxF7sYYjR49Wk6nU5J04sQJjR8/vtZtGtavX29HeQAAAKEXsNLT04Me33PPPTZVAgAAULeQC1hW37196dKlWrp0qf7xj39IkpKTk/XYY49p0KBBkiS/36/p06fr5ZdfVmVlpfr166clS5YoISHB0joAAEDzEXLXYFktISFB8%2BbN065du7Rr1y7dcsst%2BsUvfqEDBw5IkjIyMrRhwwatW7dO27ZtU0VFhYYMGaJTp07ZXDkAAGiqHObM%2Bx5A7dq101NPPaU77rhDF198sVavXq0RI0ZIkj777DMlJibqzTff1IABA%2Bp1vLKyMrlcLvl8PsXExDRk6UBI6D47x%2B4SgB9k1xMD7S4B/wI7P3/DfgXr%2B06dOqV169bp%2BPHj6tWrlwoKClRdXa20tLRAn/j4eKWkpCg/P/%2Bsx/H7/SorKwvaAABA%2BCBgSdq3b58uvPBCOZ1OjR8/Xhs2bNA111wjr9erqKgotW3bNqi/2%2B2W1%2Bs96/Gys7PlcrkCW2JiYkMPAQAANCEELElXXXWV9u7dqx07duiBBx5Qenq6Pvzww7P2N8ac895bmZmZ8vl8ga2oqKghygYAAE1UyH2LsCFERUXpJz/5iSSpe/fu2rlzp5599lmNGDFCVVVVKi0tDVrFKikpUWpq6lmP53Q6A/fpAgAA4YcVrDoYY%2BT3%2B9WtWzdFRkYqNzc3sK%2B4uFj79%2B8/Z8ACAADhLexXsB5%2B%2BGENGjRIiYmJKi8v17p16/T2228rJydHLpdLY8eO1bRp0xQbG6t27dpp%2BvTp6tKli/r372936QAAoIkK%2B4D1%2Beefa9SoUSouLpbL5dK1116rnJwc3XrrrZKkhQsXKiIiQsOHDw/caHTlypVq0aKFzZUDAICmivtgNQLugwUE4z5YCFXcByu0cB8sAACAZoSABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYLOx/KgcAgPoKtV8h4M7z9mEFCwAAwGIELAAAAIsRsAAAACzGNVhAMxFq14YAQHPGChYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWC/uAlZ2drR49eig6OlodOnTQ7bffro8//jioj9/v1%2BTJk9W%2BfXu1adNGQ4cO1dGjR22qGAAANHVhH7Dy8vI0ceJE7dixQ7m5uTp58qTS0tJ0/PjxQJ%2BMjAxt2LBB69at07Zt21RRUaEhQ4bo1KlTNlYOAACaqgi7C7BbTk5O0OMVK1aoQ4cOKigo0M033yyfz6fly5dr9erV6t%2B/vyRpzZo1SkxM1ObNmzVgwAA7ygYAAE1Y2K9gncnn80mS2rVrJ0kqKChQdXW10tLSAn3i4%2BOVkpKi/Px8W2oEAABNW9ivYH2fMUZTp05V7969lZKSIknyer2KiopS27Ztg/q63W55vd46j%2BP3%2B%2BX3%2BwOPy8rKGq5oAADQ5LCC9T2TJk3SBx98oJdffvm8fY0xcjgcde7Lzs6Wy%2BUKbImJiVaXCgAAmjAC1j9NnjxZGzdu1JYtW5SQkBBo93g8qqqqUmlpaVD/kpISud3uOo%2BVmZkpn88X2IqKihq0dgAA0LSEfcAyxmjSpElav369/v73vyspKSlof7du3RQZGanc3NxAW3Fxsfbv36/U1NQ6j%2Bl0OhUTExO0AQCA8BH212BNnDhRa9eu1Z///GdFR0cHrqtyuVxq1aqVXC6Xxo4dq2nTpik2Nlbt2rXT9OnT1aVLl8C3CgEAAL4v7APW0qVLJUl9%2B/YNal%2BxYoVGjx4tSVq4cKEiIiI0fPhwVVZWql%2B/flq5cqVatGjRyNUCAIBQEPYByxhz3j4tW7bU888/r%2Beff74RKgIAAKEu7K/BAgAAsBoBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALBYhN0F2G3r1q166qmnVFBQoOLiYm3YsEG33357YL8xRnPnztWyZctUWlqqnj176oUXXlBycrKNVaMxdJ%2BdY3cJAIAQFfYrWMePH1fXrl21ePHiOvfPnz9fCxYs0OLFi7Vz5055PB7deuutKi8vb%2BRKAQBAqAj7FaxBgwZp0KBBde4zxmjRokWaPXu2hg0bJklatWqV3G631q5dq3HjxjVmqQAAIESE/QrWuRQWFsrr9SotLS3Q5nQ61adPH%2BXn55/1eX6/X2VlZUEbAAAIHwSsc/B6vZIkt9sd1O52uwP76pKdnS2XyxXYEhMTG7ROAADQtBCw6sHhcAQ9NsbUavu%2BzMxM%2BXy%2BwFZUVNTQJQIAgCYk7K/BOhePxyPp9EpWXFxcoL2kpKTWqtb3OZ1OOZ3OBq8PAAA0TaxgnUNSUpI8Ho9yc3MDbVVVVcrLy1NqaqqNlQEAgKYs7FewKioq9L//%2B7%2BBx4WFhdq7d6/atWunSy%2B9VBkZGcrKylKnTp3UqVMnZWVlqXXr1ho5cqSNVQMAgKYs7APWrl279LOf/SzweOrUqZKk9PR0rVy5UjNmzFBlZaUmTJgQuNHopk2bFB0dbVfJAACgiXMYY4zdRTR3ZWVlcrlc8vl8iomJsbsc1BN3cgcQ6nY9MdDuEmxl5%2Bcv12ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFgs7H8qB42HO6MDAMIFK1gAAAAWI2ABAABYjIAFAABgMa7BAgCgmQqla193PTHQ7hIsxQoWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWCzC7gJCxZIlS/TUU0%2BpuLhYycnJWrRokW666Sa7y1L32Tl2lwAAAM7AClY9vPLKK8rmJeuwAAAMJ0lEQVTIyNDs2bO1Z88e3XTTTRo0aJCOHDlid2kAAKAJImDVw4IFCzR27Fjdf//96ty5sxYtWqTExEQtXbrU7tIAAEATxCnC86iqqlJBQYFmzZoV1J6Wlqb8/Pw6n%2BP3%2B%2BX3%2BwOPfT6fJKmsrMzy%2Bk75j1t%2BTAAAGltDfEZ%2Be0xjjOXHPh8C1nl8%2BeWXOnXqlNxud1C72%2B2W1%2But8znZ2dmaO3durfbExMQGqREAgFDneqbhjl1eXi6Xy9VwL1AHAlY9ORyOoMfGmFpt38rMzNTUqVMDj2tqavR///d/io2NPetzQklZWZkSExNVVFSkmJgYu8uxDfPwHebiO8zFaczDd5iL7zT2XBhjVF5ervj4%2BAZ/rTMRsM6jffv2atGiRa3VqpKSklqrWt9yOp1yOp1BbRdddFGD1WiXmJiYsH%2BzkJiH72MuvsNcnMY8fIe5%2BE5jzkVjr1x9i4vczyMqKkrdunVTbm5uUHtubq5SU1NtqgoAADRlrGDVw9SpUzVq1Ch1795dvXr10rJly3TkyBGNHz/e7tIAAEAT1GLOnDlz7C6iqUtJSVFsbKyysrL09NNPq7KyUqtXr1bXrl3tLs02LVq0UN%2B%2BfRUREd4ZnXn4DnPxHebiNObhO8zFd8JlLhzGju8uAgAANGNcgwUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjICFOp08eVKPPPKIkpKS1KpVK11%2B%2BeV6/PHHVVNTE%2BhjjNGcOXMUHx%2BvVq1aqW/fvjpw4ICNVVtj69atuu222xQfHy%2BHw6HXX389aH99xl1aWqpRo0bJ5XLJ5XJp1KhR%2BvrrrxtzGD/aueahurpaM2fOVJcuXdSmTRvFx8fr3nvv1WeffRZ0jOYwD9L5/ya%2Bb9y4cXI4HFq0aFFQezjNxUcffaShQ4fK5XIpOjpaN9xwg44cORLY7/f7NXnyZLVv315t2rTR0KFDdfTo0cYcxo92vnmoqKjQpEmTlJCQoFatWqlz585aunRpUJ/mMA/Z2dnq0aOHoqOj1aFDB91%2B%2B%2B36%2BOOPg/rUZ5xHjhzRbbfdpjZt2qh9%2B/Z68MEHVVVV1ZhDsRwBC3V68skn9eKLL2rx4sX66KOPNH/%2BfD311FN6/vnnA33mz5%2BvBQsWaPHixdq5c6c8Ho9uvfVWlZeX21j5j3f8%2BHF17dpVixcvrnN/fcY9cuRI7d27Vzk5OcrJydHevXs1atSoxhqCJc41D9988412796tRx99VLt379b69et18OBBDR06NKhfc5gH6fx/E996/fXX9e6779b5sxzhMheffPKJevfurauvvlpvv/223n//fT366KNq2bJloE9GRoY2bNigdevWadu2baqoqNCQIUN06tSpxhrGj3a%2BeZgyZYpycnK0Zs0affTRR5oyZYomT56sP//5z4E%2BzWEe8vLyNHHiRO3YsUO5ubk6efKk0tLSdPz48UCf843z1KlTGjx4sI4fP65t27Zp3bp1%2BtOf/qRp06bZNSxrGKAOgwcPNmPGjAlqGzZsmLnnnnuMMcbU1NQYj8dj5s2bF9h/4sQJ43K5zIsvvtiotTYkSWbDhg2Bx/UZ94cffmgkmR07dgT6bN%2B%2B3Ugy//M//9N4xVvozHmoy3vvvWckmcOHDxtjmuc8GHP2uTh69Ki55JJLzP79%2B03Hjh3NwoULA/vCaS5GjBgReJ%2Boy9dff20iIyPNunXrAm3Hjh0zF1xwgcnJyWmwWhtSXfOQnJxsHn/88aC266%2B/3jzyyCPGmOY5D8YYU1JSYiSZvLw8Y0z9xvnmm2%2BaCy64wBw7dizQ5%2BWXXzZOp9P4fL7GHYCFWMFCnXr37q2//e1vOnjwoCTp/fff17Zt2/Tzn/9cklRYWCiv16u0tLTAc5xOp/r06aP8/Hxbam4M9Rn39u3b5XK51LNnz0CfG264QS6Xq1nPjc/nk8PhCPzuZjjNQ01NjUaNGqWHHnpIycnJtfaHy1zU1NTojTfe0JVXXqkBAwaoQ4cO6tmzZ9Dps4KCAlVXVwf9PxQfH6%2BUlJRmNRe9e/fWxo0bdezYMRljtGXLFh08eFADBgyQ1HznwefzSZLatWsnqX7j3L59u1JSUoJWfgcMGCC/36%2BCgoJGrN5aBCzUaebMmbrrrrt09dVXKzIyUtddd50yMjJ01113SVLgx6/P/MFrt9td64exm5P6jNvr9apDhw61ntuhQ4dmOzcnTpzQrFmzNHLkyMAPuIbTPDz55JOKiIjQgw8%2BWOf%2BcJmLkpISVVRUaN68eRo4cKA2bdqkX/7ylxo2bJjy8vIknZ6LqKgotW3bNui5ze2947nnntM111yjhIQERUVFaeDAgVqyZIl69%2B4tqXnOgzFGU6dOVe/evZWSkiKpfuP0er213lPbtm2rqKiokJ0Lid8ixFm88sorWrNmjdauXavk5GTt3btXGRkZio%2BPV3p6eqCfw%2BEIep4xplZbc3S%2Bcdc1B811bqqrq3XnnXeqpqZGS5YsCdoXDvNQUFCgZ599Vrt37z7nuMJhLr79EswvfvELTZkyRZL005/%2BVPn5%2BXrxxRfVp0%2Bfsz63uc3Fc889px07dmjjxo3q2LGjtm7dqgkTJiguLk79%2B/c/6/NCeR4mTZqkDz74QNu2bTtv33B4z2QFC3V66KGHNGvWLN15553q0qWLRo0apSlTpig7O1uS5PF4JKnWvy5KSkpq/UukOanPuD0ejz7//PNaz/3iiy%2Ba3dxUV1dr%2BPDhKiwsVG5ubmD1SgqfeXjnnXdUUlKiSy%2B9VBEREYqIiNDhw4c1bdo0XXbZZZLCZy7at2%2BviIgIXXPNNUHtnTt3DnyL0OPxqKqqSqWlpUF9mtN7R2VlpR5%2B%2BGEtWLBAt912m6699lpNmjRJI0aM0NNPPy2p%2Bc3D5MmTtXHjRm3ZskUJCQmB9vqM0%2BPx1HpPLS0tVXV1dUjOxbcIWKjTN998owsuCP7zaNGiReBfqElJSfJ4PMrNzQ3sr6qqUl5enlJTUxu11sZUn3H36tVLPp9P7733XqDPu%2B%2B%2BK5/P16zm5ttwdejQIW3evFmxsbFB%2B8NlHkaNGqUPPvhAe/fuDWzx8fF66KGH9NZbb0kKn7mIiopSjx49an1N/%2BDBg%2BrYsaMkqVu3boqMjAz6f6i4uFj79%2B9vNnNRXV2t6urqc76HNpd5MMZo0qRJWr9%2Bvf7%2B978rKSkpaH99xtmrVy/t379fxcXFgT6bNm2S0%2BlUt27dGmcgDcGea%2BvR1KWnp5tLLrnE/PWvfzWFhYVm/fr1pn379mbGjBmBPvPmzTMul8usX7/e7Nu3z9x1110mLi7OlJWV2Vj5j1deXm727Nlj9uzZYySZBQsWmD179gS%2BHVefcQ8cONBce%2B21Zvv27Wb79u2mS5cuZsiQIXYN6Qc51zxUV1eboUOHmoSEBLN3715TXFwc2Px%2Bf%2BAYzWEejDn/38SZzvwWoTHhMxfr1683kZGRZtmyZebQoUPm%2BeefNy1atDDvvPNO4Bjjx483CQkJZvPmzWb37t3mlltuMV27djUnT560a1j/svPNQ58%2BfUxycrLZsmWL%2BfTTT82KFStMy5YtzZIlSwLHaA7z8MADDxiXy2XefvvtoPeBb775JtDnfOM8efKkSUlJMf369TO7d%2B82mzdvNgkJCWbSpEl2DcsSBCzUqayszPz2t781l156qWnZsqW5/PLLzezZs4M%2BPGtqaszvfvc74/F4jNPpNDfffLPZt2%2BfjVVbY8uWLUZSrS09Pd0YU79xf/XVV%2Bbuu%2B820dHRJjo62tx9992mtLTUhtH8cOeah8LCwjr3STJbtmwJHKM5zIMx5/%2BbOFNdASuc5mL58uXmJz/5iWnZsqXp2rWref3114OOUVlZaSZNmmTatWtnWrVqZYYMGWKOHDnSyCP5cc43D8XFxWb06NEmPj7etGzZ0lx11VXmmWeeMTU1NYFjNId5ONv7wIoVKwJ96jPOw4cPm8GDB5tWrVqZdu3amUmTJpkTJ0408mis5TDGmIZaHQMAAAhHXIMFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMX%2BHyaZBfJAn6CFAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common3898380760254524388\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">162</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">160</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">163</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">152</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">2.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">172</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">144</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">125</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">173</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">132</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">150</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (81)</td>\n",
       "        <td class=\"number\">222</td>\n",
       "        <td class=\"number\">73.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme3898380760254524388\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">71</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">88</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">90</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">95</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">96</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">190</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">192</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">194</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">195</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">202</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Normal_restECG\">Normal_restECG<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.49834</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable1066379811270018043\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 50.2%\">\n",
       "            151\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:99%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 49.8%\">\n",
       "            150\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable1066379811270018043, #minifreqtable1066379811270018043\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable1066379811270018043\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">151</td>\n",
       "        <td class=\"number\">50.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">150</td>\n",
       "        <td class=\"number\">49.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:99%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Oldpeak\">Oldpeak<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>40</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>13.3%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>1.0432</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>6.2</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>32.6%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-6901792406946509941\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAdZJREFUeJzt3TGqE1EYhuF/gm1mASGzCMHeSnBBdoKF4HLsBSt7dzEhC0iwsDBjobG58GEuyZnMvc/ThuSfwHk5Z6aZbpqmqRrb7XY1DEPrsSzcOI613W6bznzRdNpf6/W6qv784b7v57gEFuRwONQwDP/WTUuzBNJ1XVVV9X3/IJBX779c/HvfP729ynVx387rpqVV84mwIAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAhmef3BtXllArdiB4FAIBA8iSPWYziW8T/sIBAIBAKBQCAQCJ7tTfpjXHpj76Z%2B%2BQRyQ56ULZ8jFgQCgcAR6844lt0XOwgEdpAnwNO125klkGmaqqrqcDg8%2BOzXzx%2BtL%2BfZefnu88Xf%2BfbhzcXfef3x61XmnNfJed201E0zTN3tdjUMQ%2BuxLNw4jrXdbpvOnCWQ0%2BlU%2B/2%2B1ut1dV3XejwLM01THY/H2mw2tVq1vW2eJRBYCk%2BxIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAh%2BAys3WKTxz1AfAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-6901792406946509941,#minihistogram-6901792406946509941\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-6901792406946509941\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-6901792406946509941\"\n",
       "                                                  aria-controls=\"quantiles-6901792406946509941\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-6901792406946509941\" aria-controls=\"histogram-6901792406946509941\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-6901792406946509941\" aria-controls=\"common-6901792406946509941\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-6901792406946509941\" aria-controls=\"extreme-6901792406946509941\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-6901792406946509941\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0.8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>1.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>3.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>6.2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>6.2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>1.6</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>1.1634</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.1152</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>1.5522</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>1.0432</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.93281</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.2637</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>314</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>1.3535</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>2.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6901792406946509941\">\n",
       "            <img 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zekAAACa5tiCNWXKFH355ZfKycnRP//zP0uSevTooTfffFNPPvmkzekAAACa5tiCJUmzZs3SrFmzdPLkSXXu3FndunWzOxIAAMBVObpgXREdHW13BAAAgGvm2EXup0%2Bf1hNPPKGePXuqU6dOCg4ODngBAAA4lWNnsCZNmqQ///nPeu655xQdHS2Xy2V3JAAAgGvi2IK1ZcsWbdmyRQMGDLA7CgAAwHfi2FuEPXr0YNYKAAC0So4tWK%2B//royMzP1%2Beef2x0FAADgO3HsLcK0tDTV1tbqpptuUmhoqDp27Biwv6qqyqZkAAAA386xBevll1%2B2OwIAAMDfxLEFa/LkyXZHAAAA%2BJs4dg2WJP3lL39Rdna20tLS/LcEN27cqAMHDhj7jEuXLumf/umfFBcXp86dO%2Bv73/%2B%2Bfv7zn6u%2Bvt5/jGVZys7OVkxMjDp37qxhw4Zp//79xjIAAIC2xbEFa%2BvWrerdu7eKi4v1wQcfqK6uTpK0Z88evfjii8Y%2B55VXXtG//uu/Ki8vTwcOHNCCBQv06quvasmSJf5jFixYoEWLFikvL0%2B7d%2B9WVFSUHnzwQdXW1hrLAQAA2g7HFqznn39e2dnZ2rx5c8CT2x944AHt2LHD2Ods375dP/rRjzRq1CjdfPPNeuSRR5ScnKzS0lJJX81eLV68WFlZWRo3bpz69OmjFStW6IsvvtCqVauM5QAAAG2HYwvWxx9/rEceeaTBeEREhE6fPm3sc%2B6%2B%2B279/ve/16FDhyRJf/zjH7Vt2zb98Ic/lCQdPXpUlZWVSk5O9r/H7XbrvvvuU0lJSaPn9Pl8qqmpCXgBAID2w7GL3Lt166bKykrFxcUFjJeXl%2BvGG2809jnPP/%2B8vF6vbr/9dnXo0EGXL1/W/Pnz9Q//8A%2BSpMrKSklSZGRkwPsiIyP12WefNXrO3NxcvfTSS8YyAgCA1sWxM1iPPvqo5s2bp9OnT/uf6L5z507NmTNHEyZMMPY5a9as0cqVK7Vq1Srt2bNHK1as0GuvvaYVK1YEHPfNp8pbltXkk%2BYzMzPl9Xr9r4qKCmN5AQCA8zl2BisnJ0dpaWmKjo5WfX297rjjDl28eFHjx4/XCy%2B8YOxznnvuOc2bN0%2BPPvqoJKlv37767LPPlJubq4kTJyoqKkrSVzNZ0dHR/vdVVVU1mNW6wu12y%2B12G8sIAABaF8cWrODgYK1Zs0aHDh3Snj17VF9fr4EDB%2Br22283%2BjlffPGFrrsucCKvQ4cO/sc0xMXFKSoqSkVFRf4vnr5w4YKKi4v1yiuvGM0CAADaBscWrCtuvfVW3Xrrrc12/jFjxmj%2B/Pnq2bOnevfurb1792rRokV68sknJX11azA9PV05OTmKj49XfHy8cnJy1KVLF6WmpjZbLgAA0Ho5tmA99dRT37q/oKDAyOcsWbJEL7zwgqZPn66qqirFxMRo6tSpAc/amjt3rs6fP6/p06erurpagwcP1saNGxUSEmIkAwAAaFtclmVZdodozJgxYwK2L168qP3796u2tlb33nuv1q9fb1Oy766mpkYej0der1ehoaFGz52QVWj0fM2pdP5IuyMAANqR5vz9ezWOncH67W9/22Ds0qVL%2Bsd//Ef16tXLhkQAAADXxrGPaWhMUFCQ5syZo1dffdXuKAAAAE1qVQVLko4cOaKLFy/aHQMAAKBJjr1FOHfu3IBty7J08uRJrV%2B/Xo899phNqQAAAK7OsQVr%2B/btAdvXXXedbrjhBr388suaMmWKTakAAACuzrEFa%2BvWrXZHAAAA%2BJu0ujVYAAAATufYGay77rqryS9T/qZdu3Y1cxoAAIBr59iCdf/99%2Butt97SrbfeqsTEREnSjh07dPDgQU2dOpUvUwYAAI7l2IJ19uxZzZgxQzk5OQHjWVlZOnXqlP7t3/7NpmQAAADfzrFrsD744AM98cQTDcYnTZqkX//61zYkAgAAuDaOLVhut1slJSUNxktKSrg9CAAAHM2xtwhnz56tadOmae/evRoyZIikr9ZgLVu2TD/72c9sTgcAANA0xxasrKwsxcXF6Y033tDbb78tSerVq5eWLVum1NRUm9MBAAA0zbEFS5JSU1MpUwAAoNVx7BosSaqpqdHy5cv14osvqrq6WpL0xz/%2BUSdPnrQ5GQAAQNMcO4P1ySefaMSIEerSpYsqKio0adIkXX/99frggw/0%2Beefa8WKFXZHBAAAaJRjZ7CeeeYZpaam6s9//rM6derkHx81apS2bNliYzIAAIBv59gZrN27dys/P7/B1%2BXceOON3CIEAACO5tgZrODgYNXV1TUYP3z4sLp3725DIgAAgGvj2II1duxY/cu//IsuXbokSXK5XDp%2B/LjmzZuncePG2ZwOAACgaY4tWAsXLtSJEycUFRWl8%2BfP64EHHtD3v/99derUqcH3EwIAADiJY9dgeTwelZSUqKioSHv27FF9fb0GDhyohx56qMG6LAAAACdxZMG6ePGifvjDH2rp0qVKTk5WcnKy3ZEAAACumSNvEXbs2FF79%2B5lpgoAALRKjixYkjRhwgS98847dscAAAD4zhx5i/CKvLw8bdq0SQkJCeratWvAvgULFtiUCgAA4Ns5tmCVlZWpX79%2BkqSPP/44YB%2B3DgEAgJM5rmAdOXJEcXFx2rp1q91RAAAA/iaOW4MVHx%2Bv06dP%2B7d/8pOf6NSpUzYmAgAA%2BG4cV7AsywrY/t3vfqdz584162ceP35cEyZMUHh4uLp06aI777xTZWVlAZmys7MVExOjzp07a9iwYdq/f3%2BzZgIAAK2X4wpWS6uurtbQoUPVsWNHffTRR/rTn/6khQsXqlu3bv5jFixYoEWLFikvL0%2B7d%2B9WVFSUHnzwQdXW1tqYHAAAOJXj1mC5XK4Gi9ibc1H7K6%2B8otjY2IBHQtx8883%2Bf7YsS4sXL1ZWVpb/OxBXrFihyMhIrVq1SlOnTm22bAAAoHVyXMGyLEuTJk2S2%2B2WJH355ZeaNm1ag8c0fPjhh0Y%2Bb/369XrooYf04x//WMXFxbrxxhs1ffp0TZkyRZJ09OhRVVZWBjxN3u1267777lNJSUmjBcvn88nn8/m3a2pqjGQFAACtg%2BNuEU6cOFERERHyeDzyeDyaMGGCYmJi/NtXXqYcOXJE%2Bfn5io%2BP14YNGzRt2jTNnj1b7777riSpsrJSkhQZGRnwvsjISP%2B%2Bb8rNzQ3IGhsbaywvAABwPsfNYLX009vr6%2BuVkJCgnJwcSdKAAQO0f/9%2B5efn6/HHH/cf983blJZlNXnrMjMzUxkZGf7tmpoaShYAAO2I42awWlp0dLTuuOOOgLFevXrp2LFjkqSoqChJajBbVVVV1WBW6wq3263Q0NCAFwAAaD/afcEaOnSoDh48GDB26NAh3XTTTZKkuLg4RUVFqaioyL//woULKi4uVlJSUotmBQAArYPjbhG2tGeeeUZJSUnKycnR%2BPHjtWvXLhUUFKigoEDSV7cG09PTlZOTo/j4eMXHxysnJ0ddunRRamqqzekBAIATtfuCddddd2nt2rXKzMzUz3/%2Bc8XFxWnx4sV67LHH/MfMnTtX58%2Bf1/Tp01VdXa3Bgwdr48aNCgkJsTE5AABwKpf1zUenw7iamhp5PB55vV7j67ESsgqNnq85lc4faXcEAEA70py/f6%2Bm3a/BAgAAMI2CBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYFmR3ALQfCVmFdkf4Tkrnj7Q7AgCglWIGCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2B9TW5urlwul9LT0/1jPp9Ps2bNUvfu3dW1a1eNHTtWn3/%2BuY0pAQCA01Gw/tfu3btVUFCgfv36BYynp6dr7dq1Wr16tbZt26a6ujqNHj1aly9ftikpAABwOgqWpLq6Oj322GNatmyZrr/%2Bev%2B41%2BvVr371Ky1cuFAjRozQgAEDtHLlSu3bt0%2BbNm2yMTEAAHAyCpakGTNmaNSoURoxYkTAeFlZmS5evKjk5GT/WExMjPr06aOSkpKWjgkAAFqJILsD2G316tUqKytTaWlpg32VlZUKDg4OmNWSpMjISFVWVjZ5Tp/PJ5/P59%2BuqakxFxgAADheuy5YFRUVevrpp7Vx40Z16tTpmt9nWZZcLleT%2B3Nzc/XSSy%2BZiAgbJWQV2h3hOymdP9LuCACA/9WubxGWlZWpqqpKgwYNUlBQkIKCglRcXKw333xTQUFBioyM1IULF1RdXR3wvqqqKkVGRjZ53szMTHm9Xv%2BroqKiuX8UAADgIO16Bmv48OHat29fwNgTTzyh22%2B/Xc8//7xiY2PVsWNHFRUVafz48ZKkkydP6pNPPtGCBQuaPK/b7Zbb7W7W7AAAwLnadcEKCQlRnz59Asa6du2q8PBw//jkyZP17LPPKjw8XGFhYZozZ4769u3bYEE8AADAFe26YF2L119/XUFBQRo/frzOnz%2Bv4cOHa/ny5erQoYPd0QAAgEO5LMuy7A7R1tXU1Mjj8cjr9So0NNTouVvbQmw0Hxa5A0Cg5vz9ezXtepE7AABAc6BgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMCzI7gAAzEjIKrQ7wjUrnT/S7ggA0KyYwQIAADCMggUAAGBYuy9Yubm5uuuuuxQSEqKIiAg9/PDDOnjwYMAxPp9Ps2bNUvfu3dW1a1eNHTtWn3/%2BuU2JAQCA07X7glVcXKwZM2Zox44dKioq0qVLl5ScnKxz5875j0lPT9fatWu1evVqbdu2TXV1dRo9erQuX75sY3IAAOBU7X6Re2Fh4MLgd955RxERESorK9O9994rr9erX/3qV3rvvfc0YsQISdLKlSsVGxurTZs26aGHHrIjNgAAcLB2P4P1TV6vV5IUFhYmSSorK9PFixeVnJzsPyYmJkZ9%2BvRRSUmJLRkBAICztfsZrK%2BzLEsZGRm6%2B%2B671adPH0lSZWWlgoODdf311wccGxkZqcrKykbP4/P55PP5/Ns1NTXNFxoAADgOBetrZs6cqY8//ljbtm276rGWZcnlcjW6Lzc3Vy%2B99JLpeECb0Zqe2SXx3C4A3x23CP/XrFmztH79em3evFk9evTwj0dFRenChQuqrq4OOL6qqkqRkZGNniszM1Ner9f/qqioaNbsAADAWdp9wbIsSzNnztSHH36oP/zhD4qLiwvYP2jQIHXs2FFFRUX%2BsZMnT%2BqTTz5RUlJSo%2Bd0u90KDQ0NeAEAgPaj3d8inDFjhlatWqXf/OY3CgkJ8a%2Br8ng86ty5szwejyZPnqxnn31W4eHhCgsL05w5c9S3b1//XxUCAAB8XbsvWPn5%2BZKkYcOGBYy/8847mjRpkiTp9ddfV1BQkMaPH6/z589r%2BPDhWr58uTp06NDCaQEAQGvQ7guWZVlXPaZTp05asmSJlixZ0gKJAABAa9fu12ABAACYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLB2/2XPAHA1CVmFdkf4Tkrnj7Q7AtDuMYMFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDC%2BKgcA2pjW9NU%2BfK0P2ipmsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDeA7WNVq6dKleffVVnTx5Ur1799bixYt1zz332B0LAIAm8Uw0%2BzCDdQ3WrFmj9PR0ZWVlae/evbrnnnuUkpKiY8eO2R0NAAA4EDNY12DRokWaPHmyfvrTn0qSFi9erA0bNig/P1%2B5ubk2pwOA1qs1zbBIbW%2BWBc2HgnUVFy5cUFlZmebNmxcwnpycrJKSkkbf4/P55PP5/Nter1eSVFNTYzzfZd854%2BcEADSuOf53vDm1pt8RzXFtr5zTsiy395CyAAAHhUlEQVTj574aCtZVnDlzRpcvX1ZkZGTAeGRkpCorKxt9T25url566aUG47Gxsc2SEQDQMjwL7U7QdjXnta2trZXH42m%2BD2gEBesauVyugG3LshqMXZGZmamMjAz/dn19vf7nf/5H4eHhTb7nb1FTU6PY2FhVVFQoNDTU2HlbO65L07g2jeO6NI7r0jSuTeOcdl0sy1Jtba1iYmJa/LMpWFfRvXt3dejQocFsVVVVVYNZrSvcbrfcbnfAWLdu3ZotY2hoqCP%2Bi%2Bw0XJemcW0ax3VpHNelaVybxjnpurT0zNUV/BXhVQQHB2vQoEEqKioKGC8qKlJSUpJNqQAAgJMxg3UNMjIylJaWpoSEBCUmJqqgoEDHjh3TtGnT7I4GAAAcqEN2dna23SGcrk%2BfPgoPD1dOTo5ee%2B01nT9/Xu%2B995769%2B9vdzR16NBBw4YNU1AQXfnruC5N49o0juvSOK5L07g2jeO6fMVl2fG3iwAAAG0Ya7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAWrlVq6dKni4uLUqVMnDRo0SFu3brU7ku22bNmiMWPGKCYmRi6XS%2BvWrbM7kiPk5ubqrrvuUkhIiCIiIvTwww/r4MGDdsdyhPz8fPXr18//UMTExER99NFHdsdynNzcXLlcLqWnp9sdxVbZ2dlyuVwBr6ioKLtjOcbx48c1YcIEhYeHq0uXLrrzzjtVVlZmdyzbULBaoTVr1ig9PV1ZWVnau3ev7rnnHqWkpOjYsWN2R7PVuXPn1L9/f%2BXl5dkdxVGKi4s1Y8YM7dixQ0VFRbp06ZKSk5N17lzr%2BRLY5tKjRw%2B9/PLLKi0tVWlpqR544AH96Ec/0v79%2B%2B2O5hi7d%2B9WQUGB%2BvXrZ3cUR%2Bjdu7dOnjzpf%2B3bt8/uSI5QXV2toUOHqmPHjvroo4/0pz/9SQsXLmzWbzFxOh7T0AoNHjxYAwcOVH5%2Bvn%2BsV69eevjhh5Wbm2tjMudwuVxau3atHn74YbujOM7p06cVERGh4uJi3XvvvXbHcZywsDC9%2Buqrmjx5st1RbFdXV6eBAwdq6dKl%2BsUvfqE777xTixcvtjuWbbKzs7Vu3TqVl5fbHcVx5s2bp//%2B7//mbsrXMIPVyly4cEFlZWVKTk4OGE9OTlZJSYlNqdCaeL1eSV8VCfyfy5cva/Xq1Tp37pwSExPtjuMIM2bM0KhRozRixAi7ozjG4cOHFRMTo7i4OD366KM6cuSI3ZEcYf369UpISNCPf/xjRUREaMCAAVq2bJndsWxFwWplzpw5o8uXLzf4ounIyMgGX0gNfJNlWcrIyNDdd9%2BtPn362B3HEfbt26fvfe97crvdmjZtmtauXas77rjD7li2W716tcrKypgV/5rBgwfr3Xff1YYNG7Rs2TJVVlYqKSlJf/3rX%2B2OZrsjR44oPz9f8fHx2rBhg6ZNm6bZs2fr3XfftTuabdr3c%2BxbMZfLFbBtWVaDMeCbZs6cqY8//ljbtm2zO4pj3HbbbSovL9fZs2f17//%2B75o4caKKi4vbdcmqqKjQ008/rY0bN6pTp052x3GMlJQU/z/37dtXiYmJuuWWW7RixQplZGTYmMx%2B9fX1SkhIUE5OjiRpwIAB2r9/v/Lz8/X444/bnM4ezGC1Mt27d1eHDh0azFZVVVU1mNUCvm7WrFlav369Nm/erB49etgdxzGCg4P1d3/3d0pISFBubq769%2B%2BvN954w%2B5YtiorK1NVVZUGDRqkoKAgBQUFqbi4WG%2B%2B%2BaaCgoJ0%2BfJluyM6QteuXdW3b18dPnzY7ii2i46ObvB/Snr16tWu//iKgtXKBAcHa9CgQSoqKgoYLyoqUlJSkk2p4GSWZWnmzJn68MMP9Yc//EFxcXF2R3I0y7Lk8/nsjmGr4cOHa9%2B%2BfSovL/e/EhIS9Nhjj6m8vFwdOnSwO6Ij%2BHw%2BHThwQNHR0XZHsd3QoUMbPP7l0KFDuummm2xKZD9uEbZCGRkZSktLU0JCghITE1VQUKBjx45p2rRpdkezVV1dnT799FP/9tGjR1VeXq6wsDD17NnTxmT2mjFjhlatWqXf/OY3CgkJ8c9%2Bejwede7c2eZ09vrZz36mlJQUxcbGqra2VqtXr9Z//dd/qbCw0O5otgoJCWmwRq9r164KDw9v12v35syZozFjxqhnz56qqqrSL37xC9XU1GjixIl2R7PdM888o6SkJOXk5Gj8%2BPHatWuXCgoKVFBQYHc0%2B1holX75y19aN910kxUcHGwNHDjQKi4utjuS7TZv3mxJavCaOHGi3dFs1dg1kWS98847dkez3ZNPPun/9%2BiGG26whg8fbm3cuNHuWI503333WU8//bTdMWz1k5/8xIqOjrY6duxoxcTEWOPGjbP2799vdyzH%2BO1vf2v16dPHcrvd1u23324VFBTYHclWPAcLAADAMNZgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAw7P8BFe9K2eek%2B2UAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6901792406946509941\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">98</td>\n",
       "        <td class=\"number\">32.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.2</td>\n",
       "        <td class=\"number\">17</td>\n",
       "        <td class=\"number\">5.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.6</td>\n",
       "        <td class=\"number\">14</td>\n",
       "        <td class=\"number\">4.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.0</td>\n",
       "        <td class=\"number\">13</td>\n",
       "        <td class=\"number\">4.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.8</td>\n",
       "        <td class=\"number\">13</td>\n",
       "        <td class=\"number\">4.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.4</td>\n",
       "        <td class=\"number\">13</td>\n",
       "        <td class=\"number\">4.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.2</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">4.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.6</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.8</td>\n",
       "        <td class=\"number\">10</td>\n",
       "        <td class=\"number\">3.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2.0</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (30)</td>\n",
       "        <td class=\"number\">91</td>\n",
       "        <td class=\"number\">30.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:92%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6901792406946509941\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">98</td>\n",
       "        <td class=\"number\">32.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.1</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.2</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">4.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.3</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">1.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.4</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.0</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">1.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.2</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:67%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.6</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6.2</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_RestBP\">RestBP<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>50</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>16.6%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>131.71</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>94</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>200</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-4691353226284018339\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAdtJREFUeJzt3U2u0mAYhuG3xCllTuguXIR7cuzQ/bgIdwFhATQOHEgdKGeiPjnHhK8tXNcQSL6S9M7bHxq6aZqmaux0OtUwDK2XZeWOx2MdDoema75rutpv2%2B22qn594b7v59gEVuRyudQwDC/7TUuzBNJ1XVVV9X2/qkDef/zyps9//fThTlvynG77TUub5ivCiggEAoFAMMs5yBK89XyC52SCQCAQCAQCgUAgEAgEAoHgaS/ztvA/l5L9PGVZTBAIBAKBQCAQCAQCgUAgEAgEAoFA8BA3Cj3bwb2YIBAIBAKBQCAQCAQCgUAgEAgEAoHgIW4UPhJPIS6LCQKBQCAQCAQCgUAgEAgEAoFAIBAI3Ch8AP59935MEAgWN0E8X86SmCAQLG6CcH9%2BEPl6JggEAoHAIRav0uriydIO5WYJZJqmqqq6XC5/vPfj%2B7fWm8OC/G2fuL12229amiWQcRyrqmoYhjmWZ8F2n//93jiOtdvt2m1MVXXTDFler9c6n8%2B13W6r67rWy7My0zTVOI613%2B9rs2l72jxLILAWrmJBIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEPwEWKZTtkOsGAAAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4691353226284018339,#minihistogram-4691353226284018339\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-4691353226284018339\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-4691353226284018339\"\n",
       "                                                  aria-controls=\"quantiles-4691353226284018339\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-4691353226284018339\" aria-controls=\"histogram-4691353226284018339\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-4691353226284018339\" aria-controls=\"common-4691353226284018339\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-4691353226284018339\" aria-controls=\"extreme-4691353226284018339\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-4691353226284018339\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>94</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>108</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>120</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>130</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>140</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>160</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>200</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>106</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>20</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>17.656</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.13405</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>0.8528</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>131.71</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>13.666</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.6999</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>39646</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>311.72</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>2.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-4691353226284018339\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xt41NWdx/HPSJIJxGTklgwxEbMWWiBAFXiASIUKBCiXtuxTtFwKFV1QiBsuUiNrS7s1UFYuAgtdWAQqtWh3gdJHCyRb5PKECAQoF11hhYUAiQE3ThIMk0DO/kGdMoRLag/5ZZj363l%2Bz%2BPv/M6cfH9Hwnw4c2bGZYwxAgAAgDX3OF0AAADA3YaABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWRThdQDioqanRuXPnFBsbK5fL5XQ5AACEBWOMysvLlZiYqHvuqd81JQJWPTh37pySk5OdLgMAgLBUWFiopKSkev2ZBKx6EBsbK%2Bnq/%2BC4uDiHqwEAIDyUlZUpOTk58DxcnwhY9eCLlwXj4uIIWAAA1DMntuewyR0AAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlvFlz8BdouvMzU6XUGf7XhnodAkAcEexggUAAGAZAQsAAMAyAhYAAIBlYR%2BwHnzwQblcrlrHpEmTJEl%2Bv18ZGRlq0aKFYmJiNGzYMJ05c8bhqgEAQEMW9gFr7969KioqChw5OTmSpO9973uSpMzMTG3YsEHr1q3Trl27VFFRoSFDhujKlStOlg0AABqwsH8XYcuWLYPO58yZo4ceeki9e/eWz%2BfTypUr9cYbb6hfv36SpLVr1yo5OVm5ubkaMGCAEyUDAIAGLuxXsK5VVVWltWvX6qmnnpLL5VJBQYGqq6uVnp4e6JOYmKjU1FTl5eU5WCkAAGjIwn4F61obN27UZ599pnHjxkmSiouLFRUVpaZNmwb1S0hIUHFx8U3H8fv98vv9gfOysrI7Ui8AAGiYWMG6xsqVKzVo0CAlJibesp8xRi6X66bXZ8%2BeLY/HEziSk5NtlwoAABowAtafnTp1Srm5uXr66acDbV6vV1VVVSotLQ3qW1JSooSEhJuOlZWVJZ/PFzgKCwvvWN0AAKDhIWD92apVqxQfH6/BgwcH2rp06aLIyMjAOwslqaioSEeOHFFaWtpNx3K73YqLiws6AABA%2BGAPlqSamhqtWrVKY8eOVUTEX6bE4/Fo/PjxmjZtmpo3b65mzZpp%2BvTp6tixY%2BBdhQAAANcjYEnKzc3V6dOn9dRTT9W6tmDBAkVERGjEiBGqrKxU3759tXr1ajVq1MiBSgEAQChwGWOM00Xc7crKyuTxeOTz%2BXi5EHdM15mbnS6hzva9MtDpEgCEASeff9mDBQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACzjc7CAmwiljz0AADQsrGABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsCSdPbsWY0ePVrNmzdXkyZN9PWvf10FBQWB68YYzZo1S4mJiWrcuLH69Omjo0ePOlgxAABoyMI%2BYJWWlurRRx9VZGSk/vCHP%2BiDDz7QvHnzdN999wX6zJ07V/Pnz9eSJUu0d%2B9eeb1e9e/fX%2BXl5Q5WDgAAGqoIpwtw2i9%2B8QslJydr1apVgbYHH3ww8N/GGC1cuFAzZ87U8OHDJUlr1qxRQkKC3nzzTU2YMKG%2BSwYAAA1c2K9gbdq0SV27dtX3vvc9xcfH6%2BGHH9aKFSsC10%2BePKni4mKlp6cH2txut3r37q28vLwbjun3%2B1VWVhZ0AACA8BH2AevEiRNatmyZ2rRpoy1btmjixIl6/vnn9atf/UqSVFxcLElKSEgIelxCQkLg2vVmz54tj8cTOJKTk%2B/sTQAAgAYl7ANWTU2NHnnkEWVnZ%2Bvhhx/WhAkT9Mwzz2jZsmVB/VwuV9C5MaZW2xeysrLk8/kCR2Fh4R2rHwAANDxhH7BatWql9u3bB7W1a9dOp0%2BfliR5vV5JqrVaVVJSUmtV6wtut1txcXFBBwAACB9hH7AeffRRffTRR0Ftx44dU%2BvWrSVJKSkp8nq9ysnJCVyvqqrS9u3blZaWVq%2B1AgCA0BD27yKcMmWK0tLSlJ2drREjRmjPnj1avny5li9fLunqS4OZmZnKzs5WmzZt1KZNG2VnZ6tJkyYaOXKkw9UDAICGKOwDVrdu3bRhwwZlZWXpZz/7mVJSUrRw4UKNGjUq0GfGjBmqrKzUc889p9LSUnXv3l1bt25VbGysg5UDAICGymWMMU4XcbcrKyuTx%2BORz%2BdjP1YI6Tpzs9Ml3LX2vTLQ6RIAhAEnn3/Dfg8WAACAbQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMvC/rsIAdS/UPsaIr7aB8BfixUsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYFvYBa9asWXK5XEGH1%2BsNXDfGaNasWUpMTFTjxo3Vp08fHT161MGKAQBAQxf2AUuSOnTooKKiosBx%2BPDhwLW5c%2Bdq/vz5WrJkifbu3Suv16v%2B/furvLzcwYoBAEBDRsCSFBERIa/XGzhatmwp6erq1cKFCzVz5kwNHz5cqampWrNmjT7//HO9%2BeabDlcNAAAaKgKWpOPHjysxMVEpKSl68skndeLECUnSyZMnVVxcrPT09EBft9ut3r17Ky8v76bj%2Bf1%2BlZWVBR0AACB8hH3A6t69u371q19py5YtWrFihYqLi5WWlqZPP/1UxcXFkqSEhISgxyQkJASu3cjs2bPl8XgCR3Jy8h29BwAA0LCEfcAaNGiQ/v7v/14dO3ZUv3799M4770iS1qxZE%2BjjcrmCHmOMqdV2raysLPl8vsBRWFh4Z4oHAAANUtgHrOvFxMSoY8eOOn78eODdhNevVpWUlNRa1bqW2%2B1WXFxc0AEAAMIHAes6fr9fH374oVq1aqWUlBR5vV7l5OQErldVVWn79u1KS0tzsEoAANCQRThdgNOmT5%2BuoUOH6oEHHlBJSYl%2B/vOfq6ysTGPHjpXL5VJmZqays7PVpk0btWnTRtnZ2WrSpIlGjhzpdOkAAKCBCvuAdebMGX3/%2B9/XhQsX1LJlS/Xo0UP5%2Bflq3bq1JGnGjBmqrKzUc889p9LSUnXv3l1bt25VbGysw5UDAICGymWMMU4XcbcrKyuTx%2BORz%2BdjP1YI6Tpzs9MloIHY98pAp0sA8CU4%2BfzLHiwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALAvZgLV27VpdunTJ6TIAAABqCdmANXXqVHm9Xk2YMEF79uxxuhwAAICAkA1Y586d0%2Buvv66ioiL16tVLHTp00Lx583T%2B/HmnSwMAAGEuZANWRESEhg8frk2bNun06dMaO3asXn/9dSUlJWn48OF65513ZIxxukwAABCGQjZgXcvr9apv377q06ePXC6X9u3bp5EjR6pNmzbauXOn0%2BUBAIAwE9IB68KFC1q4cKE6d%2B6sRx99VCUlJdq4caNOnTqls2fPasiQIfrBD37gdJkAACDMRDhdwJf13e9%2BV%2B%2B%2B%2B65SUlL09NNPa%2BzYsWrZsmXg%2Br333qsZM2Zo0aJFDlYJAADCUcgGrLi4OOXm5uob3/jGTfu0atVKx48fr8eqAAAAQjhgrVmz5rZ9XC6XHnrooXqoBgAA4C9Cdg/WlClTtGTJklrt//qv/6pp06Y5UBEAAMBVIRuwfvvb36pHjx612nv27Km33nrLgYoAAACuCtmAdeHCBTVt2rRWe1xcnC5cuOBARQAAAFeFbMB66KGHtGXLllrtW7ZsUUpKigMVAQAAXBWym9wzMzOVmZmpTz/9VI8//rgk6b/%2B6780d%2B5cvfrqqw5XBwAAwlnIBqxnnnlGly5dUnZ2tn7yk59IkpKSkrRo0SI99dRTDlcHAADCWcgGLEnKyMhQRkaGioqK1LhxY913331OlwQAABDaAesLrVq1croE1EHXmZudLgEAgHoRspvcz58/rx/%2B8Id64IEHFB0draioqKADAADAKSG7gjVu3Dh9/PHHeuGFF9SqVSu5XC6nSwIAAJAUwgFrx44d2rFjhx5%2B%2BGGnSwEAAAgSsi8RJiUlWV%2B1mj17tlwulzIzMwNtfr9fGRkZatGihWJiYjRs2DCdOXPG6s8FAAB3l5ANWAsWLFBWVpa1sLN3714tX75cnTp1CmrPzMzUhg0btG7dOu3atUsVFRUaMmSIrly5YuXnAgCAu0/IvkQ4ZswYlZeXq3Xr1oqLi1NkZGTQ9ZKSkjqPVVFRoVGjRmnFihX6%2Bc9/Hmj3%2BXxauXKl3njjDfXr10%2BStHbtWiUnJys3N1cDBgywczMAAOCuErIBa86cOdbGmjRpkgYPHqx%2B/foFBayCggJVV1crPT090JaYmKjU1FTl5eURsAAAwA2FbMAaP368lXHWrVungoIC7du3r9a14uJiRUVF1fpS6YSEBBUXF990TL/fL7/fHzgvKyuzUisAAAgNIbsHS5L%2B93//V7NmzdKYMWMCLwlu3bpVH374YZ0eX1hYqH/8x3/Ur3/9a0VHR9f55xpjbrnBfvbs2fJ4PIEjOTm5zmMDAIDQF7IBa%2BfOnerQoYO2b9%2But99%2BWxUVFZKk/fv368c//nGdxigoKFBJSYm6dOmiiIgIRUREaPv27Vq0aJEiIiKUkJCgqqoqlZaWBj2upKRECQkJNx03KytLPp8vcBQWFn75GwUAACEnZAPWj370I82aNUvbtm0L%2BuT2xx9/XPn5%2BXUao2/fvjp8%2BLAOHjwYOLp27apRo0YF/jsyMlI5OTmBxxQVFenIkSNKS0u76bhut1txcXFBBwAACB8huwfr0KFD%2BvWvf12rPT4%2BXufPn6/TGLGxsUpNTQ1qi4mJUfPmzQPt48eP17Rp09S8eXM1a9ZM06dPV8eOHQPvKgQAALheyAas%2B%2B67T8XFxUpJSQlqP3jwoO6//35rP2fBggWKiIjQiBEjVFlZqb59%2B2r16tVq1KiRtZ8BAADuLiEbsJ588km9%2BOKL%2Bo//%2BI/AhvP3339f06dP1%2BjRo7/0uO%2B9917QeXR0tBYvXqzFixf/LeUCAIAwErJ7sLKzs%2BX1etWqVStVVFSoffv2SktLU7du3fTyyy87XR4AAAhjIbuCFRUVpbfeekvHjh3T/v37VVNTo0ceeURf%2B9rXnC4NAACEuZANWF9o27at2rZt63QZAAAAASEbsP7hH/7hlteXL19eT5UAAAAEC9mAVVRUFHReXV2to0ePqry8XI899phDVQEAAIRwwPr9739fq%2B3y5ct69tln1a5dOwcqAgAAuCpk30V4IxEREZo%2Bfbr%2B5V/%2BxelSAABAGLurApYknThxQtXV1U6XAQAAwljIvkQ4Y8aMoHNjjIqKirRp0yaNGjXKoaoAAABCOGDt3r076Pyee%2B5Ry5YtNWfOHD3zzDMOVQUAABDCAWvnzp1OlwAAAHBDd90eLAAAAKeF7ApWt27dAl/yfDt79uy5w9UAAAD8RcgGrG9%2B85v6t3/7N7Vt21Y9e/aUJOXn5%2Bujjz7ShAkT5Ha7Ha4QAACEq5ANWJ999pkmTZqk7OzsoPaZM2fqk08%2B0b//%2B787VBkAAAh3IbsH6%2B2339YPf/jDWu3jxo3Tb3/7WwcqAgAAuCpkA5bb7VZeXl6t9ry8PF4eBAAAjgrZlwiff/55TZw4UQcOHFCPHj0kXd2DtWLFCr300ksOVwcAAMJZyAasmTNnKiUlRa%2B99ppef/11SVK7du20YsUKjRw50uHqAABAOAvZgCVJI0eOJEwBAIAGJ2T3YElSWVmZVq9erR//%2BMcqLS2VJP3pT39SUVGRw5UBAIBwFrIrWEeOHFG/fv3UpEkTFRYWaty4cWratKnefvttnTlzRmvWrHG6RAAAEKZCdgVrypQpGjlypD7%2B%2BGNFR0cH2gcPHqwdO3Y4WBkAAAh3IbuCtXfvXi1btqzW1%2BXcf//9vEQIAAAcFbIrWFFRUaqoqKjVfvz4cbVo0cKBigAAAK4K2YA1bNgw/fM//7MuX74sSXK5XDp79qxefPFFDR8%2B3OHqAABAOAvZgDVv3jydO3dOXq9XlZWVevzxx/V3f/d3io6OrvX9hAAAAPUpZPdgeTwe5eXlKScnR/v371dNTY0eeeQRDRgwoNa%2BLAAAgPoUkgGrurpa3/rWt7R06VKlp6crPT3d6ZIAAAACQvIlwsjISB04cICVKgAA0CCFZMCSpNGjR2vVqlVOlwEAAFBLSL5E%2BIUlS5YoNzdXXbt2VUxMTNC1uXPnOlQVAAAIdyG7glVQUKBOnTopKipKhw4d0u7duwNHfn5%2BncdZtmyZOnXqpLi4OMXFxalnz576wx/%2BELju9/uVkZGhFi1aKCYmRsOGDdOZM2fuxC0BAIC7RMitYJ04cUIpKSnauXOnlfGSkpI0Z84cfeUrX5EkrVmzRt/%2B9rd14MABdejQQZmZmfr973%2BvdevWqXnz5po2bZqGDBmigoICNWrUyEoNAADg7hJyK1ht2rTR%2BfPnA%2BdPPPGEPvnkky893tChQ/Wtb31Lbdu2Vdu2bfXKK6/o3nvvVX5%2Bvnw%2Bn1auXKl58%2BapX79%2Bevjhh7V27VodPnxYubm5Nm4HAADchUIuYBljgs7fffddXbx40crYV65c0bp163Tx4kX17NlTBQUFqq6uDvoYiMTERKWmpiovL%2B%2Bm4/j9fpWVlQUdAAAgfIRcwLoTDh8%2BrHvvvVdut1sTJ07Uhg0b1L59exUXFysqKkpNmzYN6p%2BQkKDi4uKbjjd79mx5PJ7AkZycfKdvAQAANCAhF7BcLletz7/6Wz8P66tf/aoOHjyo/Px8Pfvssxo7dqw%2B%2BOCDm/Y3xtzyZ2ZlZcnn8wWOwsLCv6k%2BAAAQWkJuk7sxRuPGjZPb7ZYkXbp0SRMnTqz1MQ3r16%2Bv85hRUVGBTe5du3bV3r179dprr%2BmJJ55QVVWVSktLg1axSkpKlJaWdtPx3G53oD4AABB%2BQm4Fa%2BzYsYqPjw%2B8/DZ69GglJiYGvSTn8Xj%2Bpp9hjJHf71eXLl0UGRmpnJycwLWioiIdOXLklgELAACEt5BbwbL96e0vvfSSBg0apOTkZJWXl2vdunV67733tHnzZnk8Ho0fP17Tpk1T8%2BbN1axZM02fPl0dO3ZUv379rNYBAADuHiEXsGz75JNPNGbMGBUVFcnj8ahTp07avHmz%2BvfvL0lasGCBIiIiNGLECFVWVqpv375avXo1n4EFAABuymWu/9wDWFdWViaPxyOfz6e4uDiny3FM15mbnS4B%2BFL2vTLQ6RIAfAlOPv%2BG3B4sAACAho6ABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJaF/edgAcDthNpHjPCxEoDzWMECAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYFvYBa/bs2erWrZtiY2MVHx%2Bv73znO/roo4%2BC%2Bvj9fmVkZKhFixaKiYnRsGHDdObMGYcqBgAADV3YB6zt27dr0qRJys/PV05Oji5fvqz09HRdvHgx0CczM1MbNmzQunXrtGvXLlVUVGjIkCG6cuWKg5UDAICGKsLpApy2efPmoPNVq1YpPj5eBQUFeuyxx%2BTz%2BbRy5Uq98cYb6tevnyRp7dq1Sk5OVm5urgYMGOBE2QAAoAEL%2BxWs6/l8PklSs2bNJEkFBQWqrq5Wenp6oE9iYqJSU1OVl5d3wzH8fr/KysqCDgAAED4IWNcwxmjq1Knq1auXUlNTJUnFxcWKiopS06ZNg/omJCSouLj4huPMnj1bHo8ncCQnJ9/x2gEAQMNBwLrG5MmTdejQIf3mN7%2B5bV9jjFwu1w2vZWVlyefzBY7CwkLbpQIAgAaMgPVnGRkZ2rRpk7Zt26akpKRAu9frVVVVlUpLS4P6l5SUKCEh4YZjud1uxcXFBR0AACB8hH3AMsZo8uTJWr9%2Bvf74xz8qJSUl6HqXLl0UGRmpnJycQFtRUZGOHDmitLS0%2Bi4XAACEgLB/F%2BGkSZP05ptv6ne/%2B51iY2MD%2B6o8Ho8aN24sj8ej8ePHa9q0aWrevLmaNWum6dOnq2PHjoF3FQIAAFwr7APWsmXLJEl9%2BvQJal%2B1apXGjRsnSVqwYIEiIiI0YsQIVVZWqm/fvlq9erUaNWpUz9UCAIBQEPYByxhz2z7R0dFavHixFi9eXA8VAQCAUBf2e7AAAABsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALAswukCAAB2dZ252ekS6mzfKwOdLgG4I1jBAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMCysA9YO3bs0NChQ5WYmCiXy6WNGzcGXTfGaNasWUpMTFTjxo3Vp08fHT161KFqAQBAKAj7gHXx4kV17txZS5YsueH1uXPnav78%2BVqyZIn27t0rr9er/v37q7y8vJ4rBQAAoSLC6QKcNmjQIA0aNOiG14wxWrhwoWbOnKnhw4dLktasWaOEhAS9%2BeabmjBhQn2WCgAAQkTYr2DdysmTJ1VcXKz09PRAm9vtVu/evZWXl%2BdgZQAAoCEL%2BxWsWykuLpYkJSQkBLUnJCTo1KlTN32c3%2B%2BX3%2B8PnJeVld2ZAgEAQINEwKoDl8sVdG6MqdV2rdmzZ%2BunP/3pnS5LktR15uZ6%2BTkAAKDueInwFrxer6S/rGR9oaSkpNaq1rWysrLk8/kCR2Fh4R2tEwAANCwErFtISUmR1%2BtVTk5OoK2qqkrbt29XWlraTR/ndrsVFxcXdAAAgPAR9i8RVlRU6H/%2B538C5ydPntTBgwfVrFkzPfDAA8rMzFR2drbatGmjNm3aKDs7W02aNNHIkSMdrBoAADRkYR%2Bw9u3bp29%2B85uB86lTp0qSxo4dq9WrV2vGjBmqrKzUc889p9LSUnXv3l1bt25VbGysUyUDAIAGzmWMMU4XcbcrKyuTx%2BORz%2Bez/nIhm9wBhLJ9rwx0ugTcxe7k8%2B/tsAcLAADAMgIWAACAZWG/BwsA4JxQ2%2BbAS5qoK1awAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgWYTTBQAAECq6ztzsdAl/lX2vDHS6hLDFChYAAIBlBCwAAADLCFgAAACWEbAAAAAsY5M7AAB3qVDalH%2B3bchnBQsAAMAyAhYAAIBlBCwAAADLCFh1tHTpUqWkpCg6OlpdunTRzp07nS4JAAA0UASsOnjrrbeUmZmpmTNn6sCBA/rGN76hQYMG6fTp006XBgAAGiACVh3Mnz9f48eP19NPP6127dpp4cKFSk5O1rJly5wuDQAANEB8TMNtVFVVqaCgQC%2B%2B%2BGJQe3p6uvLy8m74GL/fL7/fHzj3%2BXySpLKyMuv1XfFftD4mAAD17U48R34xpjHG%2Bti3Q8C6jQsXLujKlStKSEgIak9ISFBxcfENHzN79mz99Kc/rdWenJx8R2oEACDUeebdubHLy8vl8Xju3A%2B4AQJWHblcrqBzY0ytti9kZWVp6tSpgfOamhr93//9n5o3b37TxzRUZWVlSk5OVmFhoeLi4pwuJ2Qxj3Ywj/Ywl3Ywj3bcqXk0xqi8vFyJiYnWxqwrAtZttGjRQo0aNaq1WlVSUlJrVesLbrdbbrc7qO2%2B%2B%2B67YzXWh7i4OP7ysIB5tIN5tIe5tIN5tONOzGN9r1x9gU3utxEVFaUuXbooJycnqD0nJ0dpaWkOVQUAABoyVrDqYOrUqRozZoy6du2qnj17avny5Tp9%2BrQmTpzodGkAAKABajRr1qxZThfR0KWmpqp58%2BbKzs7Wq6%2B%2BqsrKSr3xxhvq3Lmz06XVi0aNGqlPnz6KiCCP/y2YRzuYR3uYSzuYRzvutnl0GSfeuwgAAHAXYw8WAACAZQQsAAAAywhYAAAAlhGwAAAALCNghaEdO3Zo6NChSkxMlMvl0saNG4OuG2M0a9YsJSYmqnHjxurTp4%2BOHj0a1Ke0tFRjxoyRx%2BORx%2BPRmDFj9Nlnn9XnbTjuVvNYXV2tH/3oR%2BrYsaONBHzfAAAIqklEQVRiYmKUmJioH/zgBzp37lzQGMzjVbf7M3mtCRMmyOVyaeHChUHtzGXd5vHDDz/UsGHD5PF4FBsbqx49euj06dOB636/XxkZGWrRooViYmI0bNgwnTlzpj5vw3G3m8eKigpNnjxZSUlJaty4sdq1a6dly5YF9WEer35tXLdu3RQbG6v4%2BHh95zvf0UcffRTUpy7zdPr0aQ0dOlQxMTFq0aKFnn/%2BeVVVVdXnrXwpBKwwdPHiRXXu3FlLliy54fW5c%2Bdq/vz5WrJkifbu3Suv16v%2B/furvLw80GfkyJE6ePCgNm/erM2bN%2BvgwYMaM2ZMfd1Cg3Crefz888%2B1f/9%2Bvfzyy9q/f7/Wr1%2BvY8eOadiwYUH9mMerbvdn8gsbN27U%2B%2B%2B/f8OvvWAubz%2BPH3/8sXr16qWvfe1reu%2B99/SnP/1JL7/8sqKjowN9MjMztWHDBq1bt067du1SRUWFhgwZoitXrtTXbTjudvM4ZcoUbd68WWvXrtWHH36oKVOmKCMjQ7/73e8CfZhHafv27Zo0aZLy8/OVk5Ojy5cvKz09XRcvXgz0ud08XblyRYMHD9bFixe1a9curVu3Tv/5n/%2BpadOmOXVbdWcQ1iSZDRs2BM5ramqM1%2Bs1c%2BbMCbRdunTJeDwe88tf/tIYY8wHH3xgJJn8/PxAn927dxtJ5r//%2B7/rr/gG5Pp5vJE9e/YYSebUqVPGGObxZm42l2fOnDH333%2B/OXLkiGndurVZsGBB4BpzWduN5vGJJ54wo0ePvuljPvvsMxMZGWnWrVsXaDt79qy55557zObNm%2B9YrQ3ZjeaxQ4cO5mc/%2B1lQ2yOPPGL%2B6Z/%2ByRjDPN5MSUmJkWS2b99ujKnbPL377rvmnnvuMWfPng30%2Bc1vfmPcbrfx%2BXz1ewN/JVawEOTkyZMqLi5Wenp6oM3tdqt3797Ky8uTJO3evVsej0fdu3cP9OnRo4c8Hk%2BgD2rz%2BXxyuVyB76VkHuuupqZGY8aM0QsvvKAOHTrUus5c3l5NTY3eeecdtW3bVgMGDFB8fLy6d%2B8e9PJXQUGBqqurg37/ExMTlZqayjxeo1evXtq0aZPOnj0rY4y2bdumY8eOacCAAZKYx5vx%2BXySpGbNmkmq2zzt3r1bqampQavWAwYMkN/vV0FBQT1W/9cjYCHIF19qff0XWSckJASuFRcXKz4%2BvtZj4%2BPja30pNq66dOmSXnzxRY0cOTLwRabMY9394he/UEREhJ5//vkbXmcub6%2BkpEQVFRWaM2eOBg4cqK1bt%2Bq73/2uhg8fru3bt0u6Oo9RUVFq2rRp0GOv/f2HtGjRIrVv315JSUmKiorSwIEDtXTpUvXq1UsS83gjxhhNnTpVvXr1UmpqqqS6zVNxcXGt56OmTZsqKiqqwc/l3fF59LDO5XIFnRtjgtquv36jPriqurpaTz75pGpqarR06dKga8zj7RUUFOi1117T/v37bzkvzOWt1dTUSJK%2B/e1va8qUKZKkr3/968rLy9Mvf/lL9e7d%2B6aPZR6DLVq0SPn5%2Bdq0aZNat26tHTt26LnnnlOrVq3Ur1%2B/mz4unOdx8uTJOnTokHbt2nXbvnfL8w0rWAji9Xolqda/DEpKSgL/ivB6vfrkk09qPfb8%2BfO1/qUR7qqrqzVixAidPHlSOTk5gdUriXmsq507d6qkpEQPPPCAIiIiFBERoVOnTmnatGl68MEHJTGXddGiRQtFRESoffv2Qe3t2rULvIvQ6/WqqqpKpaWlQX2u/f0Pd5WVlXrppZc0f/58DR06VJ06ddLkyZP1xBNP6NVXX5XEPF4vIyNDmzZt0rZt25SUlBRor8s8eb3eWs9HpaWlqq6ubvBzScBCkJSUFHm9XuXk5ATaqqqqtH37dqWlpUmSevbsKZ/Ppz179gT6vP/%2B%2B/L5fIE%2B%2BEu4On78uHJzc9W8efOg68xj3YwZM0aHDh3SwYMHA0diYqJeeOEFbdmyRRJzWRdRUVHq1q1brbfJHzt2TK1bt5YkdenSRZGRkUG//0VFRTpy5Ajz%2BGfV1dWqrq7WPfcEP302atQosErIPF5ljNHkyZO1fv16/fGPf1RKSkrQ9brMU8%2BePXXkyBEVFRUF%2BmzdulVut1tdunSpnxv5shzaXA8HlZeXmwMHDpgDBw4YSWb%2B/PnmwIEDgXe3zZkzx3g8HrN%2B/Xpz%2BPBh8/3vf9%2B0atXKlJWVBcYYOHCg6dSpk9m9e7fZvXu36dixoxkyZIhTt%2BSIW81jdXW1GTZsmElKSjIHDx40RUVFgcPv9wfGYB6vut2fyetd/y5CY5hLY24/j%2BvXrzeRkZFm%2BfLl5vjx42bx4sWmUaNGZufOnYExJk6caJKSkkxubq7Zv3%2B/efzxx03nzp3N5cuXnbqtene7eezdu7fp0KGD2bZtmzlx4oRZtWqViY6ONkuXLg2MwTwa8%2ByzzxqPx2Pee%2B%2B9oL8DP//880Cf283T5cuXTWpqqunbt6/Zv3%2B/yc3NNUlJSWby5MlO3VadEbDC0LZt24ykWsfYsWONMVc/quEnP/mJ8Xq9xu12m8cee8wcPnw4aIxPP/3UjBo1ysTGxprY2FgzatQoU1pa6sDdOOdW83jy5MkbXpNktm3bFhiDebzqdn8mr3ejgMVc1m0eV65cab7yla%2BY6Oho07lzZ7Nx48agMSorK83kyZNNs2bNTOPGjc2QIUPM6dOn6/lOnHW7eSwqKjLjxo0ziYmJJjo62nz1q1818%2BbNMzU1NYExmEdz078DV61aFehTl3k6deqUGTx4sGncuLFp1qyZmTx5srl06VI9381fz2WMMXdqdQwAACAcsQcLAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBl/w%2BaoDdXv9MHYgAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-4691353226284018339\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">120</td>\n",
       "        <td class=\"number\">37</td>\n",
       "        <td class=\"number\">12.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:35%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">130</td>\n",
       "        <td class=\"number\">36</td>\n",
       "        <td class=\"number\">12.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">140</td>\n",
       "        <td class=\"number\">32</td>\n",
       "        <td class=\"number\">10.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">110</td>\n",
       "        <td class=\"number\">19</td>\n",
       "        <td class=\"number\">6.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">150</td>\n",
       "        <td class=\"number\">17</td>\n",
       "        <td class=\"number\">5.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">138</td>\n",
       "        <td class=\"number\">12</td>\n",
       "        <td class=\"number\">4.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">125</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">160</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">128</td>\n",
       "        <td class=\"number\">10</td>\n",
       "        <td class=\"number\">3.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">112</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (40)</td>\n",
       "        <td class=\"number\">107</td>\n",
       "        <td class=\"number\">35.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-4691353226284018339\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">94</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">100</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">101</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">102</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">104</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">174</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">178</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:67%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">180</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">1.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">192</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">200</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Sex\">Sex<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.68106</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable-1059014089758175430\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 68.1%\">\n",
       "            205\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:47%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 31.9%\">\n",
       "            96\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-1059014089758175430, #minifreqtable-1059014089758175430\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-1059014089758175430\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">205</td>\n",
       "        <td class=\"number\">68.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">96</td>\n",
       "        <td class=\"number\">31.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Slope_downsloping\">Slope_downsloping<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.069767</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable5950512337155342449\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 93.0%\">\n",
       "            280\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:8%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 7.0%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        21\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable5950512337155342449, #minifreqtable5950512337155342449\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable5950512337155342449\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">280</td>\n",
       "        <td class=\"number\">93.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">21</td>\n",
       "        <td class=\"number\">7.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Slope_flat\">Slope_flat<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.46179</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable-2967472347057307146\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 53.8%\">\n",
       "            162\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:85%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 46.2%\">\n",
       "            139\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-2967472347057307146, #minifreqtable-2967472347057307146\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-2967472347057307146\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">162</td>\n",
       "        <td class=\"number\">53.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">139</td>\n",
       "        <td class=\"number\">46.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:85%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Slope_upsloping\">Slope_upsloping<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.46844</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable2681615606037810463\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 53.2%\">\n",
       "            160\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:88%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 46.8%\">\n",
       "            141\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable2681615606037810463, #minifreqtable2681615606037810463\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable2681615606037810463\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">160</td>\n",
       "        <td class=\"number\">53.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">141</td>\n",
       "        <td class=\"number\">46.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:88%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Target\">Target<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.45847</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable6302209489638695673\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 54.2%\">\n",
       "            163\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:84%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 45.8%\">\n",
       "            138\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable6302209489638695673, #minifreqtable6302209489638695673\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable6302209489638695673\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">163</td>\n",
       "        <td class=\"number\">54.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">138</td>\n",
       "        <td class=\"number\">45.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:84%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Ventricular_ht_restECG\">Ventricular_ht_restECG<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.48837</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable6309549707251003460\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 51.2%\">\n",
       "            154\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:95%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 48.8%\">\n",
       "            147\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable6309549707251003460, #minifreqtable6309549707251003460\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable6309549707251003460\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">154</td>\n",
       "        <td class=\"number\">51.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">147</td>\n",
       "        <td class=\"number\">48.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:95%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Wave_abnormal_restECG\">Wave_abnormal_restECG<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.013289</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable6289567373451383345\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 98.7%\">\n",
       "            297\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:2%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 1.3%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        4\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable6289567373451383345, #minifreqtable6289567373451383345\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable6289567373451383345\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">297</td>\n",
       "        <td class=\"number\">98.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_asymptomatic\">asymptomatic<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.47508</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable4527744680959144566\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 52.5%\">\n",
       "            158\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:90%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 47.5%\">\n",
       "            143\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable4527744680959144566, #minifreqtable4527744680959144566\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable4527744680959144566\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">158</td>\n",
       "        <td class=\"number\">52.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">143</td>\n",
       "        <td class=\"number\">47.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:90%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_fixed\">fixed<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.059801</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable1309038731292748072\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 94.0%\">\n",
       "            283\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:7%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 6.0%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        18\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable1309038731292748072, #minifreqtable1309038731292748072\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable1309038731292748072\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">283</td>\n",
       "        <td class=\"number\">94.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">18</td>\n",
       "        <td class=\"number\">6.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_index\">index<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>301</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>150.83</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>302</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.3%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram1258250131597923700\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAcBJREFUeJzt1zGO01AUhtHraFo7fRTvgkWwJ2pK9sMi2EWiLMAWBQUxBWSagV9iFJ6d4ZwylnLfs94Xx92yLEs1dj6faxzH1mN5cKfTqY7HY9OZT02n/dL3fVX93PAwDGssgQcyTVON4/h8blpaJZCu66qqahiGF4G8%2B/D5r7/vy8f3d1nXFryl/d97L7dz09Ku%2BUR4IKs8Qe6t1a/ua%2Ba00GJdW31K/WtvIpDX2Oph36r/9X75iwWBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCB4WmPosixVVTVN04tr3799bb0cNuR3Z%2BL22e3ctLRKIPM8V1XVOI5rjGfD9p/%2BfG2e59rv9%2B0WU1XdskKW1%2Bu1LpdL9X1fXde1Hs%2BDWZal5nmuw%2BFQu13bt4JVAoFH4SUdAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgeAHfJFQqgx%2Br%2BAAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives1258250131597923700,#minihistogram1258250131597923700\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives1258250131597923700\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles1258250131597923700\"\n",
       "                                                  aria-controls=\"quantiles1258250131597923700\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram1258250131597923700\" aria-controls=\"histogram1258250131597923700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common1258250131597923700\" aria-controls=\"common1258250131597923700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme1258250131597923700\" aria-controls=\"extreme1258250131597923700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles1258250131597923700\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>15</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>75</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>151</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>226</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>287</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>302</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>302</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>151</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>87.575</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.58062</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.1957</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>150.83</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>75.658</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.00044569</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>45400</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>7669.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>2.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram1258250131597923700\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3X90VOWdx/HPAMmAmIzEkExiYk5qwR8EOEJYCSpG1EAU1GpbKEhDS1lxIW4E1jVyXOFsJRxbf7VUaltL8VeDPQVrDxYNFgKcEIVABKLrRokQNCHAwkxIYQjw7B%2BuU8f87PokN5N5v8655%2BTe%2B8zN9369Qz4%2Bc2fGZYwxAgAAgDV9nC4AAACgtyFgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBl/ZwuIBKcP39en332mWJiYuRyuZwuBwCAiGCMUWNjo5KTk9WnT/fOKRGwusFnn32m1NRUp8sAACAi1dbWKiUlpVt/JwGrG8TExEj6/D9wbGysw9UAABAZ/H6/UlNTg3%2BHuxMBqxt88bJgbGwsAQsAgG7mxO053OQOAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMv4sucwl7l4g9MldNrOxyY5XQLw/xJOzzOJ5xr%2BLpyu3d523TKDBQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJZFfMBauXKlRowYodjYWMXGxiorK0t/%2BctfgvsDgYDy8/MVHx%2BvgQMH6vbbb9ehQ4ccrBgAAPR0ER%2BwUlJStHz5cu3cuVM7d%2B7UhAkTdMcdd6iqqkqSVFBQoHXr1qm4uFjbtm3TyZMnNXnyZJ07d87hygEAQE8V8Z%2BDNWXKlJD1xx57TCtXrlR5eblSUlL0/PPP68UXX9TNN98sSXrppZeUmpqqjRs3auLEiU6UDAAAeriIn8H6snPnzqm4uFhNTU3KyspSRUWFmpublZOTExyTnJysjIwMlZWVtXmcQCAgv98fsgAAgMgR8TNYkrR3715lZWXp9OnTuvDCC7Vu3TpdddVVqqysVHR0tAYNGhQyPjExUfX19W0er6ioSEuXLu3qssNOOH2isBR%2Bnyocbv1F1%2BFa6Drh9u8CnMMMlqTLL79clZWVKi8v13333ae8vDy9//77bY43xsjlcrW5v7CwUD6fL7jU1tZ2RdkAAKCHYgZLUnR0tL75zW9KkjIzM7Vjxw4988wzmjp1qs6cOaPjx4%2BHzGI1NDRo3LhxbR7P7XbL7XZ3ed0AAKBnYgarFcYYBQIBjR49WlFRUSopKQnuq6ur0759%2B9oNWAAAILJF/AzWww8/rNzcXKWmpqqxsVHFxcXavHmzNmzYII/Ho9mzZ2vhwoW6%2BOKLFRcXp0WLFmn48OHBdxUCAAB8VcQHrMOHD2vmzJmqq6uTx%2BPRiBEjtGHDBt1yyy2SpKeeekr9%2BvXTd7/7XZ06dUo33XSTfve736lv374OVw4AAHqqiA9Yzz//fLv7%2B/fvr5///Of6%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%2BD9VU%2Bn0%2BSFBcXF7L95ZdfVnx8vIYNG6ZFixapsbGxzWMEAgH5/f6QBQAARI6In8H6MmOMFixYoOuuu04ZGRnB7TNmzFB6erq8Xq/27dunwsJCvffeeyopKWn1OEVFRVq6dGl3lQ0AAHoYlzHGOF1ETzFv3jytX79e27ZtU0pKSpvjKioqlJmZqYqKCo0aNarF/kAgoEAgEFz3%2B/1KTU2Vz%2BdTbGys1ZozF2/oeBAAAD3czscmWT%2Bm3%2B%2BXx%2BPpkr%2B/HWEG6//k5%2Bfr9ddf15YtW9oNV5I0atQoRUVFqbq6utWA5Xa75Xa7u6pUAADQw0V8wDLGKD8/X%2BvWrdPmzZuVnp7e4WOqqqrU3NyspKSkbqgQAACEm4gPWPPmzdMrr7yiP/3pT4qJiVF9fb0kyePxaMCAAfr444/18ssv69Zbb1V8fLzef/99LVy4UFdffbWuvfZah6sHAAA9UcS/i3DlypXy%2BXzKzs5WUlJScFmzZo0kKTo6Wm%2B//bYmTpyoyy%2B/XPfff79ycnK0ceNG9e3b1%2BHqAQBATxTxM1gd3eOfmpqq0tLSbqoGAAD0BhE/gwUAAGAbAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALIv4gFVUVKQxY8YoJiZGCQkJuvPOO/Xhhx%2BGjAkEAsrPz1d8fLwGDhyo22%2B/XYcOHXKoYgAA0NNFfMAqLS3VvHnzVF5erpKSEp09e1Y5OTlqamoKjikoKNC6detUXFysbdu26eTJk5o8ebLOnTvnYOUAAKCn6ud0AU7bsGFDyPqqVauUkJCgiooKjR8/Xj6fT88//7xefPFF3XzzzZKkl156Sampqdq4caMmTpzoRNkAAKAHi/gZrK/y%2BXySpLi4OElSRUWFmpublZOTExyTnJysjIwMlZWVtXqMQCAgv98fsgAAgMhBwPoSY4wWLFig6667ThkZGZKk%2Bvp6RUdHa9CgQSFjExMTVV9f3%2BpxioqK5PF4gktqamqX1w4AAHoOAtaXzJ8/X3v27NHvf//7DscaY%2BRyuVrdV1hYKJ/PF1xqa2ttlwoAAHowAtb/yc/P1%2Buvv65NmzYpJSUluN3r9erMmTM6fvx4yPiGhgYlJia2eiy3263Y2NiQBQAARI6wDVgvvfSSTp8%2B/bWPY4zR/PnztXbtWv31r39Venp6yP7Ro0crKipKJSUlwW11dXXat2%2Bfxo0b97V/PwAA6H3CNmAtWLBAXq9X9957r959993/93HmzZunl156Sa%2B88opiYmJUX1%2Bv%2Bvp6nTp1SpLk8Xg0e/ZsLVy4UG%2B//bZ2796te%2B65R8OHDw%2B%2BqxAAAODLwjZgffbZZ/rtb3%2Bruro6XXfddRo2bJieeOIJHTly5B86zsqVK%2BXz%2BZSdna2kpKTgsmbNmuCYp556Snfeeae%2B%2B93v6tprr9UFF1ygP//5z%2Brbt6/t0wIAAL2AyxhjnC7i66qvr9cLL7yg1atX66OPPtJtt92m2bNn69Zbb23zRvTu5Pf75fF45PP5rN%2BPlbl4Q8eDAADo4XY%2BNsn6Mbvy729HwnYG68u8Xq9uuukmZWdny%2BVyaefOnZo%2BfbqGDBmirVu3Ol0eAACIMGEdsI4ePaqnn35aI0eO1LXXXquGhga99tprOnDggD799FNNnjxZ3//%2B950uEwAARJiw/aqcb33rW3rjjTeUnp6uH/3oR8rLy9PgwYOD%2By%2B88EI9%2BOCD%2BtnPfuZglQAAIBKFbcCKjY3Vxo0bdf3117c5JikpSdXV1d1YFQAAQBgHrNWrV3c4xuVy6bLLLuuGagAAAP4ubO/BeuCBB7RixYoW23/xi19o4cKFDlQEAADwubANWH/4wx80duzYFtuzsrJCPsMKAACgu4VtwDp69KgGDRrUYntsbKyOHj3qQEUAAACfC9uAddlll%2BnNN99ssf3NN99s8X2CAAAA3Slsb3IvKChQQUGBjh07pgkTJkiS3n77bT3%2B%2BOP66U9/6nB1AAAgkoVtwJozZ45Onz6tZcuW6dFHH5UkpaSk6Gc/%2B5l%2B%2BMMfOlwdAACIZGEbsCQpPz9f%2Bfn5qqur04ABA3TRRRc5XRIAAEB4B6wvJCUlOV0CAABAUNje5H7kyBH94Ac/0KWXXqr%2B/fsrOjo6ZAEAAHBK2M5gzZo1Sx9//LH%2B7d/%2BTUlJSXK5XE6XBAAAICmMA9aWLVu0ZcsWXX311U6XAgAAECJsXyJMSUlh1goAAPRIYRuwnnrqKRUWFurQoUNOlwIAABAibF8inDlzphobG5WWlqbY2FhFRUWF7G9oaHCoMgAAEOnCNmAtX77c6RIAAABaFbYBa/bs2U6XAAAA0KqwvQdLkj755BMtWbJEM2fODL4k%2BNZbb%2BmDDz5wuDIAABDJwjZgbd26VcOGDVNpaaleffVVnTx5UpK0a9cu/cd//IfD1QEAgEgWtgHr3//937VkyRJt2rQp5JPbJ0yYoPLycgcrAwAAkS5sA9aePXv07W9/u8X2hIQEHTlyxIGKAAAAPhe2Aeuiiy5SfX19i%2B2VlZW65JJLHKgIAADgc2EbsKZNm6aHHnpIR44cCX6i%2BzvvvKNFixbpnnvucbg6AAAQycI2YC1btkxer1dJSUk6efKkrrrqKo0bN05jxozRI4884nR5AAAggoXt52BFR0drzZo1%2Bu///m/t2rVL58%2Bf16hRo3TFFVc4XRoAAIhwYRuwvjB06FANHTrU6TIAAACCwjZg/fM//3O7%2B3/1q191UyUAAAChwjZg1dXVhaw3NzerqqpKjY2NGj9%2BvENVAQAAhHHA%2BvOf/9xi29mzZ3XffffpyiuvdKAiAACAz4Xtuwhb069fPy1atEg/%2BclPnC4FAABEsF4VsCRp//79am5udroMAAAQwcL2JcIHH3wwZN0Yo7q6Or3%2B%2BuuaMWOGQ1UBAACEccDavn17yHqfPn00ePBgLV%2B%2BXHPmzHGoKgAAgDAOWFu3bnW6BAAAgFb1unuw/lFbtmzRlClTlJycLJfLpddeey1k/6xZs%2BRyuUKWsWPHOlQtAAAIB2E7gzVmzJjglzx35N13321zX1NTk0aOHKkf/OAHuvvuu1sdM2nSJK1atSq4Hh0d/Y8VCwAAIkrYBqwbb7xRzz33nIYOHaqsrCxJUnl5uT788EPde%2B%2B9crvdnTpObm6ucnNz2x3jdrvl9Xq/ds0AACAyhG3AOnHihObNm6dly5aFbF%2B8eLEOHz6s3/zmN9Z%2B1%2BbNm5WQkKCLLrpIN9xwgx577DElJCRYOz4AAOhdwjZgvfrqq9qxY0eL7bNmzVJmZqa1gJWbm6vvfOc7SktLU01NjR555BFNmDBBFRUVbc6SBQIBBQKB4Lrf77dSCwAACA9hG7DcbrfKyso0ZMiQkO1lZWWdfnmwM6ZOnRr8OSMjQ5mZmUpLS9P69et11113tfqYoqIiLV261FoNAAAgvIRtwLr//vs1d%2B5c7d69O/iuvvLycv3617/Www8/3GW/NykpSWlpaaqurm5zTGFhoRYsWBBc9/v9Sk1N7bKaAABAzxK2AWvx4sVKT0/XM888o9/%2B9reSpCuvvFK//vWvNX369C77vceOHVNtba2SkpLaHON2u63OogEAgPAStgFLkqZPn/61w9TJkyf10UcfBddrampUWVmpuLg4xcXFacmSJbr77ruVlJSkTz75RA8//LDi4%2BP1rW996%2BuWDwAAeqmwDlh%2Bv19r167V/v379cADD2jQoEF67733lJCQ0O4M05ft3LlTN954Y3D9i5f28vLytHLlSu3du1cvvPCCTpw4oaSkJN14441as2aNYmJiuuScAABA%2BAvbgLVv3z7dfPPNuuCCC1RbW6tZs2Zp0KBBevXVV3Xo0CGtXr26U8fJzs6WMabN/W%2B%2B%2BaatkgEAQIQI26/KeeCBBzR9%2BnR9/PHH6t%2B/f3D7bbfdpi1btjhYGQAAiHRhO4O1Y8cOrVy5ssXX5VxyySWqq6tzqCoAAIAwnsGKjo7WyZMnW2yvrq5WfHy8AxUBAAB8LmwD1u23367//M//1NmzZyVJLpdLn376qR566KE2PwAUAACgO4RtwHriiSf02Wefyev16tSpU5owYYK%2B8Y1vqH///i2%2BnxAAAKA7he09WB6PR2VlZSopKdGuXbt0/vx5jRo1ShMnTmxxXxYAAEB3CsuA1dzcrFtvvVXPPvuscnJylJOT43RJAAAAQWH5EmFUVJR2797NTBUAAOiRwjJgSdI999yjVatWOV0GAABAC2H5EuEXVqxYoY0bNyozM1MDBw4M2ff44487VBUAAIh0YRuwKioqNGLECEnSnj17Qvbx0iEAAHBS2AWs/fv3Kz09XVu3bnW6FAAAgFaF3T1YQ4YM0ZEjR4LrU6dO1eHDhx2sCAAAIFTYBSxjTMj6G2%2B8oaamJoeqAQAAaCnsAhYAAEBPF3YBy%2BVytbiJnZvaAQBATxJ2N7kbYzRr1iy53W5J0unTpzV37twWH9Owdu1aJ8oDAAAIv4CVl5cXsn7PPfc4VAkAAEDrwi5g8entAACgpwu7e7AAAAB6OgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLIj5gbdmyRVOmTFFycrJcLpdee%2B21kP3GGC1ZskTJyckaMGCAsrOzVVVV5VC1AAAgHER8wGpqatLIkSO1YsWKVvc//vjjevLJJ7VixQrt2LFDXq9Xt9xyixobG7u5UgAAEC76OV2A03Jzc5Wbm9vqPmOMnn76aS1evFh33XWXJGn16tVKTEzUK6%2B8onvvvbc7SwUAAGEi4mew2lNTU6P6%2Bnrl5OQEt7ndbt1www0qKytzsDIAANCTRfwMVnvq6%2BslSYmJiSHbExMTdeDAgTYfFwgEFAgEgut%2Bv79rCgQAAD0SM1id4HK5QtaNMS22fVlRUZE8Hk9wSU1N7eoSAQBAD0LAaofX65X095msLzQ0NLSY1fqywsJC%2BXy%2B4FJbW9uldQIAgJ6FgNWO9PR0eb1elZSUBLedOXNGpaWlGjduXJuPc7vdio2NDVkAAEDkiPh7sE6ePKmPPvoouF5TU6PKykrFxcXp0ksvVUFBgZYtW6YhQ4ZoyJAhWrZsmS644AJNnz7dwaoBAEBPFvEBa%2BfOnbrxxhuD6wsWLJAk5eXl6Xe/%2B50efPBBnTp1Sv/yL/%2Bi48eP65prrtFbb72lmJgYp0oGAAA9nMsYY5wuorfz%2B/3yeDzy%2BXzWXy7MXLzB6vEAAHDCzscmWT9mV/797Qj3YAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYHViyZIlcLlfI4vV6nS4LAAD0YP2cLiAcDBs2TBs3bgyu9%2B3b18FqAABAT0fA6oR%2B/foxawUAADqNlwg7obq6WsnJyUpPT9e0adO0f/9%2Bp0sCAAA9GDNYHbjmmmv0wgsvaOjQoTp8%2BLB%2B/OMfa9y4caqqqtLFF1/c6mMCgYACgUBw3e/3d1e5AACgB2AGqwO5ubm6%2B%2B67NXz4cN18881av369JGn16tVtPqaoqEgejye4pKamdle5AACgByBg/YMGDhyo4cOHq7q6us0xhYWF8vl8waW2trYbKwQAAE7jJcJ/UCAQ0AcffKDrr7%2B%2BzTFut1tut7sbqwIAAD0JM1gdWLRokUpLS1VTU6N33nlH3/72t%2BX3%2B5WXl%2Bd0aQAAoIdiBqsDhw4d0ve%2B9z0dPXpUgwcP1tixY1VeXq60tDSnSwMAAD0UAasDxcXFTpcAAADCDC8RAgAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbA66dlnn1V6err69%2B%2Bv0aNHa%2BvWrU6XBAAAeigCViesWbNGBQUFWrx4sXbv3q3rr79eubm5OnjwoNOlAQCAHoiA1QlPPvmkZs%2BerR/96Ee68sor9fTTTys1NVUrV650ujQAANAD9XO6gJ7uzJkzqqio0EMPPRSyPScnR2VlZa0%2BJhAIKBAIBNd9Pp8kye/3W6/vXKDJ%2BjEBAOhuXfE38otjGmOsH7sjBKwOHD16VOfOnVNiYmLI9sTERNXX17f6mKKiIi1durTF9tTU1C6pEQCAcOd5ouuO3djYKI/H03W/oBUErE5yuVwh68aYFtu%2BUFhYqAULFgTXz58/r//5n//RxRdf3OZj/j/8fr9SU1NVW1ur2NhYa8ftLehP%2B%2BhPx%2BhR%2B%2BhP%2B%2BhP%2B7qjP8YYNTY2Kjk5uUuO3x4CVgfi4%2BPVt2/fFrNVDQ0NLWa1vuB2u%2BV2u0O2XXTRRV1WY2xsLE/edtCf9tGfjtGj9tGf9tGf9nV1f7p75uoL3OTegejoaI0ePVolJSUh20tKSjRu3DiHqgIAAD0ZM1idsGDBAs2cOVOZmZnKysrSr371Kx08eFBz5851ujQAANAD9V2yZMkSp4vo6TIyMnTxxRdr2bJl%2BulPf6pTp07pxRdf1MiRI50uTX379lV2drb69SMrt4b%2BtI/%2BdIwetY/%2BtI/%2BtK8398dlnHjvIgAAQC/GPVgAAACWEbAAAAAsI2ABAABYRsACAACwjIAVpp599lmlp6erf//%2BGj16tLZu3ep0SY5YsmSJXC5XyOL1eoP7jTFasmSJkpOTNWDAAGVnZ6uqqsrBirveli1bNGXKFCUnJ8vlcum1114L2d%2BZnhw/flwzZ86Ux%2BORx%2BPRzJkzdeLEie48jS7TUX9mzZrV4poaO3ZsyJhAIKD8/HzFx8dr4MCBuv3223Xo0KHuPI0uU1RUpDFjxigmJkYJCQm688479eGHH4aM6cz5Hzx4UFOmTNHAgQMVHx%2Bv%2B%2B%2B/X2fOnOnOU%2BkSnelPdnZ2i2to2rRpIWN663Ns5cqVGjFiRPDDQ7OysvSXv/wluD%2BSrh0CVhhas2aNCgoKtHjxYu3evVvXX3%2B9cnNzdfDgQadLc8SwYcNUV1cXXPbu3Rvc9/jjj%2BvJJ5/UihUrtGPHDnm9Xt1yyy1qbGx0sOKu1dTUpJEjR2rFihWt7u9MT6ZPn67Kykpt2LBBGzZsUGVlpWbOnNldp9ClOuqPJE2aNCnkmnrjjTdC9hcUFGjdunUqLi7Wtm3bdPLkSU2ePFnnzp3r6vK7XGlpqebNm6fy8nKVlJTo7NmzysnJUVPT379YvqPzP3funG677TY1NTVp27ZtKi4u1h//%2BEctXLjQqdOypjP9kaQ5c%2BaEXEPPPfdcyP7e%2BhxLSUnR8uXLtXPnTu3cuVMTJkzQHXfcEfyfuIi6dgzCzj/90z%2BZuXPnhmy74oorzEMPPeRQRc559NFHzciRI1vdd/78eeP1es3y5cuD206fPm08Ho/55S9/2V0lOkqSWbduXXC9Mz15//33jSRTXl4eHLN9%2B3YjyfzXf/1X9xXfDb7aH2OMycvLM3fccUebjzlx4oSJiooyxcXFwW2ffvqp6dOnj9mwYUOX1eqUhoYGI8mUlpYaYzp3/m%2B88Ybp06eP%2BfTTT4Njfv/73xu32218Pl/3nkAX%2B2p/jDHmhhtuMP/6r//a5mMi6TlmjDGDBg0yv/nNbyLu2mEGK8ycOXNGFRUVysnJCdmek5OjsrIyh6pyVnV1tZKTk5Wenq5p06Zp//79kqSamhrV19eH9MrtduuGG26I2F51pifbt2%2BXx%2BPRNddcExwzduxYeTyeiOnb5s2blZCQoKFDh2rOnDlqaGgI7quoqFBzc3NID5OTk5WRkdEr%2B%2BPz%2BSRJcXFxkjp3/tu3b1dGRkbIF%2BxOnDhRgUBAFRUV3Vh91/tqf77w8ssvKz4%2BXsOGDdOiRYtCZogj5Tl27tw5FRcXq6mpSVlZWRF37fS%2Bj07t5Y4ePapz5861%2BKLpxMTEFl9IHQmuueYavfDCCxo6dKgOHz6sH//4xxo3bpyqqqqC/WitVwcOHHCiXMd1pif19fVKSEho8diEhISIuMZyc3P1ne98R2n2rY2LAAAFS0lEQVRpaaqpqdEjjzyiCRMmqKKiQm63W/X19YqOjtagQYNCHtcbn4PGGC1YsEDXXXedMjIyJKlT519fX9/iGhs0aJCio6N7VY9a648kzZgxQ%2Bnp6fJ6vdq3b58KCwv13nvvBb/Ttrc/x/bu3ausrCydPn1aF154odatW6errrpKlZWVEXXtELDClMvlClk3xrTYFglyc3ODPw8fPlxZWVm67LLLtHr16uCNyfSqpY560lp/IqVvU6dODf6ckZGhzMxMpaWlaf369brrrrvafFxv7M/8%2BfO1Z88ebdu2rcOxkXgNtdWfOXPmBH/OyMjQkCFDlJmZqV27dmnUqFGSend/Lr/8clVWVurEiRP64x//qLy8PJWWlrY5vrdeO7xEGGbi4%2BPVt2/fFkm%2BoaGhReqPRAMHDtTw4cNVXV0dfDchvfq7zvTE6/Xq8OHDLR575MiRiOxbUlKS0tLSVF1dLenz/pw5c0bHjx8PGdfbrqv8/Hy9/vrr2rRpk1JSUoLbO3P%2BXq%2B3xTV2/PhxNTc395oetdWf1owaNUpRUVEh11Bvfo5FR0frm9/8pjIzM1VUVKSRI0fqmWeeibhrh4AVZqKjozV69OjgVPMXSkpKNG7cOIeq6jkCgYA%2B%2BOADJSUlBafov9yrM2fOqLS0NGJ71ZmeZGVlyefz6d133w2Oeeedd%2BTz%2BSKyb8eOHVNtba2SkpIkSaNHj1ZUVFRID%2Bvq6rRv375e0R9jjObPn6%2B1a9fqr3/9q9LT00P2d%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%2BPHjDpyNfe31529/%2B5vJyckxgwcPNlFRUebSSy81eXl55uDBgyHHOHXqlJk/f76Ji4szAwYMMJMnT24xJly11htJZtWqVcExnTn/AwcOmNtuu80MGDDAxMXFmfnz55vTp09389nY11F/Dh48aMaPH2/i4uJMdHS0ueyyy8z9999vjh07FnKc3voc%2B%2BEPfxj82zR48GBz0003BcOVMZF17biMMab75ssAAAB6P%2B7BAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABg2f8CX6X4yMeWIFkAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common1258250131597923700\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">302</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">94</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">96</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">97</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">98</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">99</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">100</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">101</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">102</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">103</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (291)</td>\n",
       "        <td class=\"number\">291</td>\n",
       "        <td class=\"number\">96.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme1258250131597923700\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">298</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">299</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">300</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">301</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">302</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_nonanginal\">nonanginal<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.28239</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable4043057727019243636\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 71.8%\">\n",
       "            216\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:39%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 28.2%\">\n",
       "            85\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable4043057727019243636, #minifreqtable4043057727019243636\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable4043057727019243636\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">216</td>\n",
       "        <td class=\"number\">71.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">85</td>\n",
       "        <td class=\"number\">28.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:39%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_nontypical\">nontypical<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.16611</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable1605250751178176191\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 83.4%\">\n",
       "            251\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:20%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 16.6%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        50\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable1605250751178176191, #minifreqtable1605250751178176191\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable1605250751178176191\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">251</td>\n",
       "        <td class=\"number\">83.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">50</td>\n",
       "        <td class=\"number\">16.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_normal\">normal<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.5515</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable6774721891027361794\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 55.1%\">\n",
       "            166\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:81%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 44.9%\">\n",
       "            135\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable6774721891027361794, #minifreqtable6774721891027361794\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable6774721891027361794\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">166</td>\n",
       "        <td class=\"number\">55.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">135</td>\n",
       "        <td class=\"number\">44.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:81%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_reversable\">reversable<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.3887</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable916471878613045486\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 61.1%\">\n",
       "            184\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:63%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 38.9%\">\n",
       "            117\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable916471878613045486, #minifreqtable916471878613045486\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable916471878613045486\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">184</td>\n",
       "        <td class=\"number\">61.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">117</td>\n",
       "        <td class=\"number\">38.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:63%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_typical\">typical<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.7%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.076412</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable-3720483975804193691\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 92.4%\">\n",
       "            278\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:9%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 7.6%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        23\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-3720483975804193691, #minifreqtable-3720483975804193691\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-3720483975804193691\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">278</td>\n",
       "        <td class=\"number\">92.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">23</td>\n",
       "        <td class=\"number\">7.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAtIAAAKGCAYAAABndXxLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8jNf%2BwPHPLEkmeyISscQuFG3F2lsaBJfW0jTXpaVKd7dIUW21t6q6KLfbbflVvXC1pbqhitJNXVpV2tLW0qhdCBKRyCayze%2BPMSMjec7zyEyKm%2B/79fIamXPmPGfOs%2BTMyTPfr8lut9sRQgghhBBCXBLz5e6AEEIIIYQQVyOZSAshhBBCCFEFMpEWQgghhBCiCmQiLYQQQgghRBXIRFoIIYQQQogqkIm0EEIIIYQQVSATaSGEEEIIIapAJtJCCCGEEEJUgUykhRBCCCGEqALr5e6AEELUdEePHqVXr16a5T4%2BPgQFBdG4cWN69OjBnXfeSVBQ0J/YQyGEEJUxSYpwIYS4vMpPpGNjYytMkouLizl9%2BjTHjh0DoF69erz99ts0atToT%2B%2BrEEKIC2QiLYQQl1n5ifS7775Lly5dKq23ZcsWHnroIfLy8oiLi%2BODDz74M7sphBDiInKPtBBCXCW6dOnCxIkTAdi%2BfTs7d%2B68zD0SQoiaTSbSQghxFenTp4/r/7/%2B%2Butl7IkQQgj5sqEQQlxFgoODXf/Pz893K/vxxx9ZtGgR27ZtIzs7m5CQENq1a8eIESP4y1/%2BUml7OTk5fPDBB2zYsIF9%2B/aRl5eHv78/DRs2pGfPntx1112Ehoa6vaZly5YAbNq0iRkzZrBu3TrMZjNt2rThP//5D1arlV9//ZW3336b3bt3c/z4cfz8/GjSpAm9e/dm2LBhlX5ZsrCwkA8%2B%2BIA1a9awb98%2BiouLqVOnDjfeeCP33HMPjRs3dqu/ZcsW7rrrLq6//nree%2B89Fi1axIoVKzh8%2BDA%2BPj60adOGESNG0Lt376oMtRBC6JKJtBBCXEUOHz7s%2Bn90dLTr/y%2B//DLz5s0DIDQ0lNjYWNLT01m3bh3r1q3jvvvu49FHH3Vr69ChQ4waNYrjx49jtVpp2LAh9evX59ixY%2BzatYtdu3bx2WefsWzZMgIDAyv0Zdy4cWzfvp3Y2FhOnz5NZGQkVquVL7/8kgkTJlBSUkJ4eDjNmzcnPz%2Bf3377jV9//ZWVK1fywQcfuE2mT5w4wd13382BAwcAaNy4MYGBgezfv58PP/yQFStWMGPGDG655ZYK/SguLub%2B%2B%2B9n8%2BbNhIeH06xZMw4ePMgPP/zADz/8wDPPPMMdd9zh2cALIURl7EIIIS6r1NRUe2xsrD02Ntb%2Bww8/KOs%2B9thj9tjYWHubNm3sGRkZdrvdbn///fftsbGx9o4dO9o//fRTV92ysjL7Z599Zm/Xrp09NjbW/tFHH7m1deedd9pjY2PtQ4YMsZ88edLtdZ988om9VatW9tjYWPvixYvdXufsa9u2be1bt2612%2B12e2lpqT0rK8teWlpq79q1qz02NtY%2Bb948e0lJiet1O3futN9www322NhY%2B9y5c13Pl5SU2G%2B99VZ7bGysvW/fvvbff//dVZabm2v/5z//6XrPv/zyi6vshx9%2BcPWlXbt29pUrV7rKcnJy7CNHjrTHxsbaO3fubC8uLlaOqxBCVIXcIy2EEFe4wsJCdu/ezdSpU1mxYgUAo0aNonbt2hQVFTFr1iwApk%2BfzqBBg1yvM5lM3HLLLa6V6FmzZlFSUgJAZmYme/fuBeC5554jKirK7XWJiYl07twZgD179lTar5tvvplOnToBYDabCQsL4/Tp02RkZAAwZMgQLBaLq36bNm2YMGECvXv3JiwszPX8559/zu%2B//46fnx/z5s2jVatWrrKgoCCef/55brrpJoqLi3nttdcq7UtycjIDBw50/RwcHOx639nZ2Rw8eFBjdIUQourk1g4hhLiC3HXXXbp1/v73v/Pwww8Djugdp06dIjAwUDOpy6BBg3juuec4efIku3fv5rrrriMiIoIffviBwsJCbDZbhdeUlpa6br0oLCystN0OHTpUeC48PJzQ0FDOnDnDpEmT%2BMc//sH111%2BP2exYtxkyZAhDhgxxe80333wDQEJCAjExMZVu6%2B677%2Bbbb79l69at5Obmut0rDtCzZ88Kr2nWrJnr/zk5OZW2K4QQnpCJtBBCXEEuTshiMpnw8/MjLCyMli1b0rt3b5o3b%2B4qd64qFxcXM3z4cM12LRYLZWVlHDhwgOuuu871vM1m4/jx4/z6668cOXKE1NRU9u/fz%2B%2B//05BQQEAZWVllbYZGRlZ6XYmTZrElClT2LBhAxs2bCA0NJQuXbrQtWtXevTo4XZvN%2BBaLW7Tpo1m/51lpaWlHD58mLZt27qV16lTp8Jryn9AKC0t1WxbCCGqSibSQghxBXnqqac0E7JUJjc3F4CioiK2bdumW7/8yuyBAwf417/%2BxYYNG9wmy0FBQXTs2JH09HRSUlI026psJRscq86NGjVi4cKFfP/995w5c4Yvv/ySL7/8EpPJRI8ePXjmmWdcE%2Bq8vDyACqvM5ZX/cHFxtBJwpFFXsUvuMSFENZCJtBBCXMX8/f0Bx4rt8uXLDb8uMzOTO%2B%2B8k8zMTOrVq8eQIUNo3bo1TZs2pUGDBphMJh555BHlRFqlS5cudOnShcLCQn766Sd%2B/PFHvv32W3bt2sX69es5fvw4K1aswGQyuSKCOD8UVKb8B4DKIogIIcTlIBNpIYS4ijVp0gRwhLIrKSnBaq14Wbfb7WzZsoXo6Gjq1auHr68vy5YtIzMzk7CwMJYtW0atWrUqvO7kyZOX3J%2BioiJSU1PJy8vj%2Buuvx2az0a1bN7p168aECRP47LPPmDhxIikpKezZs4dWrVrRtGlTdu/eza5duzTb3bFjB%2BC41aVhw4aX3C8hhKgOErVDCCGuYp06dSI4OJj8/HzNFelVq1YxcuRIbr75Zk6cOAHA0aNHAahXr16lk%2Bh9%2B/bxyy%2B/AJd2f/HGjRu55ZZbeOCBBygqKqpQfuONN7r%2B72zX%2BUXBb775htTU1ErbfffddwFo164dISEhhvsjhBDVSSbSQghxFQsICOCBBx4A4IUXXmDZsmVu9zt//fXXTJ06FXCEq3Ou5jZt2hSAlJQUvvjiC1d9u93Oxo0bue%2B%2B%2ByguLgbg7NmzhvsTHx9PeHg42dnZPP7442RnZ7vK8vPzmTlzJgB169alRYsWAPTr14%2BWLVty7tw57r//frfbSfLy8pgyZQrfffcdVquVSZMmGR8cIYSoZnJrhxBCXOXuv/9%2BUlNT%2Beijj3jyySd56aWXaNCgASdPniQ9PR2A9u3b8/zzz7teM3jwYJYsWcLhw4dJTk6mfv36hIeHc/z4cTIzM/Hx8aFz585s3br1km7x8PX15fXXX%2Bfee%2B9lzZo1rFu3joYNG2I2m0lNTaWgoAB/f39mzJiBr68vAFarlTfffJP777%2BfAwcOcOutt7plNnSG6Js2bRodO3b07uAJIYQHZCIthBBXOZPJxHPPPUffvn354IMP%2BOWXX1wJTtq1a8eAAQMYOnSoa%2BIKjigYS5cuZd68eaxfv56jR49y6tQpoqOj6dGjByNHjiQgIIDevXuTkpJCWloa9erVM9SfLl268PHHH7Nw4UJ%2B/vlnDh06hNVqJTo6mm7dunHPPfdUaKtBgwYsW7aM999/n88//5z9%2B/dz4sQJ6taty0033cTw4cNp3LixN4dNCCE8ZrJLTCAhhBBCCCEumdwjLYQQQgghRBXIRFoIIYQQQogqkIm0EEIIIYQQVSATaSGEEEIIccU5ffo0ffr0YcuWLZp1NmzYwMCBA2nXrh0333wz69evdyufN28e8fHxtGvXjhEjRnDgwAGv9lEm0kIIIYQQ4ory888/M3ToUI4cOaJZ59ChQ4wbN46HH36Yn376iXHjxjF%2B/HhXyM5PPvmERYsWsWDBArZs2UKbNm1ITk7Gm3E2ZCIthBBCCCGuGJ988gmTJk1iwoQJuvU6duxI7969sVqt3HLLLXTq1IkPP/wQgI8%2B%2Bohhw4bRokUL/Pz8eOSRR0hLS1OucF8qmUgLIYQQQgivSU9PZ9euXW7/nMmhjOjWrRtfffUVt9xyi7Levn37iI2NdXuuefPmruyoF5f7%2BPjQuHFjt%2BypnpKELKLmMJnU5U2awN690KIFHDyoWc1e5vmfhE6fVpebzRAWBtnZUC7bcwUhIfrbslqhpERd59Qp7TKLBSIjISMDSku16wUFqbdhMkFgIOTng%2BqvauUySmv2p25dOH5c3R%2B93CFms3psAY4eVZdbrY7tpKWpx7hR2Bl1QyYTBAdDbq56cKyKS7bJBP7%2BcPasug2zzvqJyQR%2BfnDunLKdkzn%2BymYsFoiIgMxM9X6qY81U99XAiVAaFqHsi7Mpvf1tOaJ93mO1QoMGjoNC74QqKtIu8/FxXGsOHoTzKdgr83tZS%2BUmfHygWTPYv1/ZDM2bq7tq5PoA4JPj%2BX5KLVDvJ6PndkxQlrovISGQk6O/wwMC1OW%2Bvup9CZwp9FOWGz21Q0PVXak2er8Xq%2BjDN95g9uzZbs%2BNHTuWcePGGXp9ZGSkoXr5%2Bfn4%2B7tfi2w2GwUFBYbKvUEm0kI4hYU5ruRhYR41YzKpL5hG23D%2B85SnbXirL%2BXb8WR8zGZHG2az%2Bpetkf54qnxfPOKNwfHWADvb8pBXjhsvnggeN%2BGtne2ldiwWRzMWi3oirccr8ygv7SevnNtX2AXLm6fl1WTo0KEkJCS4PWd0cnwp/P39KSwsdHuusLCQwMBAQ%2BXeIBNpIYQQQoiayONVgMpFRUURFRVVLW2XFxsby65du9ye27dvH23btgWgRYsW7N27l549ewJQXFzMoUOHKtwO4gm5R1oIIYQQoiYym6vn359k0KBBbN26lTVr1lBSUsKaNWvYunUrt956KwB/%2B9vfWLx4MSkpKZw7d45XXnmF2rVr07FjR6/1QSbSQgghhBDiqhAXF8fKlSsBaNasGf/3f//H3Llz6dSpE2%2B%2B%2BSazZs2iSZMmAAwePJhRo0YxZswYbrjhBnbv3s3cuXPx8fHxWn/k1g5xSdLS0ujfvz%2BfffYZ9fS%2BzXWRyZMnAzBjxozq6JoQQgghLsWfuHpcVXv27HH7efv27W4/33TTTdx0002VvtZkMnHPPfdwzz33VFv/ZCItLkm9evUqHMRCCCGEEDWRTKTFJTl69Ci9evVi3bp19OrVi6eeeorFixeTnp5Oy5YtmTZtGi1bOsI2rVu3jldffZVjx47RpUsXAMLDw11tffbZZ7z11lukpaXRqFEjJk6cSLdu3cjKyuLWW29lwIABPPbYY5SUlHDnnXdSr149Xn31VUP9TE9PJyMjw%2B25yCZNiFJF5GjVyv2xGlksxsr16nmLKqqas0xVB/QXNpzlevX0/uJmtD/e4Ovrpb54a3BU5c4oBXoRIfSiGZRvR0HvPRs%2BhlUV/uwTQbXDnQemkT8Jq8bOuQ2dg8umE%2BHBYDPe44X95LVzW7Udo%2BcSqPeTwfPAG6e2XpS%2BanUVrEhf6Ux2b%2BZJFP/zLp5Ix8XFMWvWLGw2G8nJyZjNZhYsWMCBAwcYNGgQ06dP55ZbbuG///0vycnJDBo0iBkzZrBhwwYeeeQR5syZQ/v27dm4cSMTJ07ko48%2BokWLFmzZsoV7772XRYsWsX79er744guWLVtGkF6w4vNmzZpVMYblQw8x7uGHq2NYhBBCiCo5c%2BYyxpH2Yhg4N/n51dPuFUhWpIVHRowY4YoNefPNNzN37lwA1qxZQ9u2bRk0aBAAvXv3doWfAVi8eDF33HEHnTp1AqBnz54kJCTwwQcfMGXKFLp06cK9997L%2BPHjycnJ4f333zc8iQaNGJYDB8I772i/qFUrWLIEhg0DRdYj%2B8/blNs2Ei/0jE5%2BDovlQhB/VTxVI9dAHx/9OLOqJChWK4SHQ1aWOnGDXm4Ds/lCvhDVCkxurrodqxVq13YkkVH1Ry9kqcWiH6tWLxGX1XohWY2qL3WDdN6U2XwhW41qcPQSsthsUFioPgCNrEgbSMiSmW9TNuMMyZ6drR7nCIvi4DN4IpQG68d%2BN7K/LSeOaRf6%2BEBUlOOg0DuhVOW%2Bvhey%2BCiSfRy0N1ZuwtcX6teHY8fUOUMaNFB31cj1AcAn3/P9dKJQvZ%2BMntvRATnahWazIztUXp7%2BUq9NcQybTBcGR3Ee5J5T/0nA6Kktrl4ykRYeqV27tuv/VqsV5x84Tp48WeHLiA0bNiQry5GR6tixY2zdupX333/fVV5aWsoNN9zg%2BnnYsGHMnz%2BfuLg4Wl3i7RaVxrBUZCt0k5IC1XwfuNFkA6WlniUdMcpIZrOSEnU9o78kysrUdY0mlygp8SwRhRE6Sc3c%2BqKs663BUZU5/0Rrtxurp8duV04gjBwz4Dh%2B1XUNHOB/1olgZIcXF%2BvXM9JOUZHjw4qGQoOHTFGR47NTtTMy/jr7yWvntpG%2BlJXp1zPyB3md88Bbp/ZlI7d2eEwm0qJaREdH89///tftuRMnTuDn5%2BcqT0xM5IEHHnCVp6WlYSu3QjBlyhRuuukmduzYwZIlSxg2bNif0nchhBBCCCPko4ioFoMGDeKPP/7go48%2BoqSkhO%2B%2B%2B46vvvrKVT5kyBDeffddfvvtNwB27NhBUlISq1evBuCdd95h9%2B7dvPjiizz77LPMnDmTvXv3Xpb3IoQQQvxPusoTslwJZEVaVIuYmBjeeustZsyYwQsvvECbNm3o06ePq7xfv34UFBTw5JNPkpaWRlhYGKNGjWLEiBGkpKTw8ssv88YbbxAeHk6vXr245ZZbmDhxIkuXLnWtagshhBDCAzVs0lsdZCItLkmDBg1cwdEvDpKelJREUlKS6%2Be//OUvfPrpp5ptXVzfqVWrVuzYscPtuRdffNGTbgshhBBCeJ1MpIUQQgghaiJZkfaYjKAQQgghhBBVICvSosawl%2BmHOjJhIE60WRGHNy4Otm3D1KG9MoReUZq6L85wwcXF6tBhPl%2BvVbZDSAh07YrP1k2Qox17tW65MIYVBARAZBsi03dBQYF2vYvDDV7M1xcC6xKYc1wZHuyDLxspm4mIgKQk%2BP57yMzUrnf/oJPahVYrRERgyc5UDnCj9CPKvhAQAHXbUPe0zthsPaRuJywM%2BvSBH35QB/VWBQUODITrroO9e9XJEIzsp5gYR3BsxX4KiGqmbMa50GWzqcN%2B7UmL0Czz84PGYXAoO0wVKY6WNp3kD%2BeDmFuK1EHMfy9sollmA5oAB4vq64abu6Z2hnah8%2BQODVUee9fk7FdvxOoLxNDEmgq%2B2vvp193a%2B8nfH2JjHZFBz55Vb%2B56y3HtQpvNcQyfOqWMxRfTQicXgMkE%2BBJdq0gdmq7QCxk%2BgYNp2t%2B3ccXpzvBVRjNs4quIPQ6OWNTBUQSf1Yk/Hlpf3U51kRVpj8lEWgghhBCiJpKJtMdkBIUQQgghhKgCWZEWQgghhKiJZEXaYzKCQgghhBBCVIFMpEW1eu%2B992jZsiVvv/325e6KEEIIIcqTzIYeq1nvVvzp3nvvPe644w7effddSlThJ4QQQgjx55KJtMdq1rsVf6rNmzeTmZnJ5MmTKSsr44svvnCVZWVlMWHCBDp06ECvXr1YtGgRrVu35ujRowAcOXKE0aNH06VLF3r27Mlrr71GkSoGkRBCCCHEn0y%2BbCiqzaJFixgyZAg2m41hw4bxn//8h/79%2BwMwadIkTCYT69ato6ysjEmTJlFaWgpAQUEBo0aNon///rz%2B%2BuucPn2a5ORkysrKeOSRRwxtOz09nYwM91iutWtHEqUXQ9eIuDjtslat3B81WHXOPGe5Xj1CQtTlgYHuj1oCArTLbDb3Ry2%2Bvupyg28qQjusMOAIV1v%2BUXd7lbFY3B%2B1qMYFjI%2BNXmeDg90ftaj2o7%2B/%2B6MWvf3k4%2BP%2BqEFv0clZrlfPTzuUr6urel3W3YjB2MKq3Wi4L%2BCdY89L%2B0l1ODjHXrUPXMyKwTHakEkRg798uV491f42euChHmKDw6tfwch1TxVfurrVsNXj6mCy21VRz4WommPHjtGvXz%2B%2B%2BuoroqOjyc7Opnv37sybN49GjRoRHx/P2rVradq0KQD79u2jf//%2BrFu3jt9%2B%2B43p06fz7bffYjp/Qf3uu%2B9ITk5m2zZ1shSnWbNmMXv2bLfnxowZS3LyOO%2B%2BUSGEEMITx445sr9cDo3UCbCq7PDh6mn3CiQr0qJaLFmyhJKSEm699VbXcyUlJfznP/9h9OjRADQol6UtJibG9f9jx45x%2BvRpOnXq5HrObrdTXFxMZmYmEXpLlsDQoUNJSEhwe6527UhlsixwLITo1unQXruwVStYsgSGDYOUFM1qGV%2BoPxBYrRAeDllZ6syGkX9sUnc2MBDatYNfflFnu1OtmNps0KwZ7N%2BvzFpGrVrqvlitEBnpyJineFPLN9dVNhMWBgkJ8M036iSASd0VaQ8tFkdD2dlw/i8hlTpxQtkXw2OTlqZuJzgYbrjBkdkwN1e7Xp062mX%2B/tCihSOzoSpNXXi4ui8%2BPhAd7XjvipWy/FoxmmXgSibIWXUyQTIUSQB9faFePcfwqe7salxHJy2fyeTYV4WFyhP84Ant5VtXprtj6r4ANAnL0i60WBx/ScrJUR97eXnqjRjcT3%2Bc1d5Pfn6OedThwygzRwLEmvdpF/r5ObJhpqaqG2rYUL0Rk8nxvoqL1Rdi1TbMZsd1Lz9ffeABx3K0/wLk4%2BNIApquk5Cwvk%2B6chtYrY5r4%2BnT6ov55SIr0h6TibTwunPnzrF06VJeeOEFbrzxRtfzf/zxBw888AAPPvgg4JgwN2nSxPV/p%2BjoaBo2bMjnn3/uei4vL4/MzExq6U3WzouKiqpwG4fX/vaiSP3tkpKirGf0elpSolNXkfbbTX6%2Buq6Rv1cXFqrTYAfppP91KilRzkRUab/Ly87WqWtkkEtL1fVU77c8vbFRzfjLy81V1zUyxmfPqj806d3m41RcrNxPOnMUt3qqunoTOHB0Q1lPrzPOyYLdrqyrl/rb2Rfdet449ox%2BJ0RnP%2Bml/gbH2OrWsxgYnHPn1INj9CJst6vrGjn49A48jA2xzvACBm/LKCm5vLdwaJGJtMdkBIXXrVq1CpPJxMCBA4mOjnb9i4%2BPJzY2luXLl9OzZ09eeuklzpw5w5kzZ/jXv/7len3Pnj3Jz89n/vz5FBUVkZOTw%2BOPP86ECRNct3oIIYQQQlxuMpEWXrdkyRIGDhyITyVfwhg6dCiffvopL7zwAiaTiR49enDbbbfRunVrAHx8fAgKCuLtt99my5YtxMfH07t3b8xmM3PmzPmz34oQQgjxv0vC33lMbu0QXrd8%2BXLNsuHDhzN8%2BHA2bdrEK6%2B8gu381%2BT37NnDnDlzCDt/r26zZs2YN2/en9JfIYQQokaqYZPe6iAjKC6LmTNnMmfOHEpKSsjLy2POnDnceOON%2BBmKwySEEEIIcfnJRFpcFq%2B88gq//PILN9xwAwkJCVgsFrf7pIUQQghRzeTWDo9JHGlRY%2BhFgzAaEU31DW6DEd6oW0/nS5NxcbBtG7Rvr4z%2BkXVaffoajbR14IB2mb8/tG4Nu3erv9nfoZUiUgQYjod2pkQdVcJsdkSLy81Vfyk/NH2vdqGfnyMU15Ej6nAQelEyjO5wPUbb2blTu8xoCL127fT7YiD24tFzkcpmfHwc0fpOnlQHK1BFXjSbHTlxCgrU%2Bzoo/6SyL1itjkw/mZnK91RcSxFekAuR2fScVHTH6Lg0CPfO%2BVRqU59PFov62mCUkXYsBYrjEoyHrlOFpTR6bgMLNrbQLIuIgMREWLFC/bvj3rBlym0QFga9esG6deqIPH/7m7qd6nLNNdXT7u%2B/V0%2B7VyC5R1oIIYQQoiaqYavH1UEm0kIIIYQQNZFMpD0mIyiEEEIIIUQVyIq0EEIIIURNJCvSHpMRFEIIIYQQogpkRVpUizNnzvDaa6%2Bxfv16zpw5Q1BQEF27dmXChAlER0df7u4JIYQQQlakPSYjKKrFhAkTyMrKYunSpfzyyy%2BsWLGCoqIi7r77bko8CRMmhBBCCO%2BQONIeq1nvVvxpfv75Z/r06UNkpCPWbO3atXnyySe5/vrrycnJIS8vj2effZbu3bvzl7/8hQkTJnDq1CkAPvvsM9q2bUtKSgoAu3fv5rrrrmPjxo2X7f0IIYQQQlxMbu0Q1aJ///5MnTqVn376ic6dO3P99ddTv359ZsyYAUBycjL5%2BfksX74cm83GjBkzGDt2LO%2B//z79%2B/fnu%2B%2B%2B47HHHmPRokVMmDCBUaNGER8fb3j76enpZGRkuD3n6xtJZGSU5mssFvdHLVbFWeMsU9UBHAlXVFq1cn/UoNdX58KA3gKBv792mc3m/qi7MS0m04VHRV29Zoy%2BJ1Tp5n183B%2B16O1Iwztch9F2goO1ywIC3B/1tqXF4Ingo8iXUX4zeptT7UeDh4zX3pO3qA4rw4eMl86nK4q3Tm5vnNs4kq5oCQ11f9QUrMgoBBfOWdW5q0rUUt2ulmPnCiaZDUW1KCsrY9WqVaxZs4aff/6Z3NxcGjZsyLhx4%2BjatSs33ngja9eupWnTpgCcPXuWjh078uGHH9K2bVsKCgpISkqiqKiIevXq8c4772C5hF%2BCs2bNYvbs2W7PjRkzluTkcV59n0IIIYRHli27fJkNO3WqnnZ//LF62r0CyYq0qBZms5lbb72VW2%2B9Fbvdzv79%2B/n000957LHHmDhxIgBDhgxxe43FYuHo0aO0bduWgIAA/va3v/Hyyy8zZsyYS5pEAwwdOpSEhAS353x9I5Uf/C2WC6mnValuVWl9DWZXJrJve%2B1CcKxEL1kCw4bB%2BVtcKpPz323KZsxmR5brvDx1xt2jR7XLbDZo2tSRRrywULte6yaK/OHgWDmz2RyNKD6/55YolscxnkU4OOuIdqGPD9StC8ePq3eoaqkejO9wPUbb2b9fuywgAK67Dn77zZFTW0vLluq%2BGMwrf7IoXNmMwazcyoU6k%2BlCFmzVkk/AWUUOZ3C8p7Awx8qf4j0VhyiWKDGeIvz0ae0yo%2BNSJ8Q751Opr/oY/lNThBd6J%2B25Mme30XMbWLGtoWZZaChj/nteAAAgAElEQVT07Anr18OZM9ptJAavU26D4GDo3Bm2bnX8crnSyIq0x2QiLbzu22%2B/JTk5mfXr1xMWFobJZKJ58%2BY88sgjbNq0iaKiIgDWrl3ruocaYN%2B%2BfcTExABw5MgR5syZw9///nf%2B9a9/0bVr10uK9hEVFUVUlPttHJmZxn5hlJaq6xmZL5WU6NTbvl2/EXBMohV1jf4CLCtT1z2r8zsbHL%2BvlfVUv/jgwgXbblfW1WumfD1l3XPn9BspLlbXM/DnYcDADjdIrx0jv4gLCtT1jPaztFRZ18iE0rk5VV3VPjR4yHjtPXmLkbHRGxdvnU9XFG%2Bd3N44t1HPx53OnNGpV2rwtozc3Mt7C4cWmUh7TEZQeF2nTp2IiIjgiSeeYM%2BePRQXF5OXl8fKlSs5dOgQN998Mz169OCFF14gKyuL4uJi5syZw%2BDBg8nJyaG4uJiJEyfSv39/nn/%2BeTp16sSjjz5K2dXyy0IIIYQQNYJMpIXX2Ww2lixZQmRkJP/4xz/o2LEjPXr0YOXKlSxcuJBmzZrxr3/9i5CQEBITE7nhhhvYsGED8%2BfPJzIyktdff52srCwmT54MwLPPPsu%2BffuYO3fuZX5nQgghxP8QCX/nMbm1Q1SLqKgonn32Wc3y0NBQzfJJkyYxadIk18%2B1atVi8%2BbNXu%2BjEEIIIa48mZmZTJkyha1bt2KxWBg0aBCPP/441ovC3dx33338/PPPbs8VFBQwdOhQnn32WU6dOkXXrl0JKBfNKDw8nG%2B%2B%2BcZrfZWJtBBCCCFETXSFrh6PHz%2BeOnXq8O2333Lq1Cn%2B8Y9/8Pbbb3Pfffe51Zs/f77bz0uXLmX27NmMHTsWgB07dlC/fn2vTpwvJhNpIYQQQoiaqJom0pXlcoiMjKwQBKAyhw8fZuvWrWzcuBF/f39iYmJ46KGHeOmllypMpMs7cOAAzz33HAsWLHBtZ8eOHbRt29azN6NDJtKixggJMVYvMFBd7vP1WvVGIrsS%2BccmR/gwDVmn1eHbLRYIwRHeThVtI7yWSd3ZuDjYto2QHu2V0T86HDig3YavL1Cf1qHHwL9Iu96BPHVfbDZo0cIRa08RR8/S5FplM87rvsVyISdFpWrX1i5zhlMMC1OHMzl0SNkX/P0hMtLxtX5VSJNLiDij1FA7XJcrSUV0tCOUnha9E8E5qIGByrBqoYpDAS7sp6AgdQAG1bBZrY6ofufOqYNtBOlFQ/Dzc8Scy81VRnI4kF1H2UTjxnDsmH7QiJbRinhpZjMQTJ2AXPXAnDil3oivL8TEwKlTUKS9M06HNNMsc0ZdzMnRD2YSGaYTisTig6VMp44qLiA43lNgoKNDivdErVqKfpw/t0NCdMMa9eih7gpAhw7qrmC7QbkNV%2BSfa681Hurmf8CHH35YIZfD2LFjGTdOP5fD3r17CQsLo06dC%2Bdjs2bNSEtLIycnhxCNa9i0adNITEykY8eOrud27NjBmTNnGDBgAKdOneLaa6/l8ccfp3nz5lV8ZxXJRFoIIYQQoiaqphXpynI5lA93q5Kfn4//RTH8nT8XFBRUOpH%2B6aef%2BPXXX3n55Zfdng8JCaF58%2Bbcf//9%2BPr68vrrr3P33XezZs0aglVB7C%2BBTKSFEEIIIYTXVJbLwaiAgADOXvSnKufPgRp/Mv7www%2B5%2BeabK0zWX3nlFbefn3jiCZYtW8ZPP/1Ez549q9S/i12Zd5kLIYQQQojqdQWGv2vRogXZ2dmcOnXh9qb9%2B/cTHR1d6SpySUkJ69atY9CgQW7P5%2BXlMXPmTI4dO%2BZ6rrS0lJKSEmw2m0d9LE8m0kIIIYQQNdEVOJFu3LgxHTp0YPr06eTl5ZGamsqbb77J4MGDK62/Z88ezp07R/v27d2eDwoK4vvvv2fmzJnk5uaSn5/Pc889R4MGDdzuo/aUTKRFtUhPT6egoOByd0MIIYQQV5k33niDkpISevXqxZAhQ7jpppt46KGHAIiLi2PlypWuuqmpqYSGhuLn/MJ1OW%2B%2B%2BSZlZWX07t2bm266iYyMDObNm4eP80ugXiD3SNdQCQkJZGRkuIKb2%2B12zGYz11xzDf/85z9p3bp1lds%2BdeoUffv2ZdWqVQQEBLB8%2BXKefPJJ15cFysrKqFWrFrfddhvjxo3DdD5KQMuWLfHz88Ny/lvXdrudWrVqMWzYMGXIGyGEEEJUwRUaR7p27dq88cYblZZtvygCVb9%2B/ejXr1%2BldevXr18heoi3yUS6Bps2bRpJSUmun0%2BdOsVTTz3F2LFj%2BfrrrzFX8QQrLCyssBpdr149t4DoO3fu5N5776V%2B/fr87W9/cz0/b948unTp4vp569at3HvvvYSHh7vVE0IIIYS43GQiLVxq167N0KFDGT16NNnZ2ZSVlTFjxgw2b96MyWQiISGBxx57jKCgIEpKSnj%2B%2Bef56quvKCkpoVmzZjzyyCO0a9eOAQMGADBgwACmT59e6bbatm1L586d2blzp3KC3LlzZ1q0aMHu3bsvaSJdWTD48PBIIiOr9i1iN6o4vM5vFOsEo3aGOtXi/Ayj%2B1kmLk5d3qqV%2B6MWZ9DUyjj/BKb3pzC9L284/%2BxWyZ/fytN7z84wxyaTTl3VIBsd4ItCMFVg8D1h1bnUOsv16qm249yHqn0JOsG3cR9gBb2hMzrEqrfs3IV654vu%2BBscG1UrRocXUL9powOjtyGD56VXxtdb9N6T0fNA1eFLeFPeuOzpVjDyni5nfOkrdEX6aiITaeFy/PhxFi9ezLXXXktYWBi33347jRs35osvvqC4uJgnnniCp59%2BmldffZVPP/2U7du3s3btWgIDA3njjTeYNm0aK1euZPXq1fTq1YvVq1fToEEDli9fXmFbKSkp/PLLL8ycOVOzP0VFRaxfv54//vjDle7TqMqCwY8ZM5bkZP1g8LoXzq5d9TvQrp2y2GBuGIKCdCps22asoSVLDG5RoYqhjCpQJRUBAgw2ozfHNTTKegNsNItPkybG6ulRJVIBR/IXPfXqeacvOidCoJEJJfr7SS8BEhjYDeGNjXVGZ2yMtGJseA3Ep9V740Zj3Ook%2B9E5ogCjh7mBe0r1Lp516xrZkLHjXI%2BB8YsJ029GP5eSweuiKolMuagSfzqZSHtMJtI12LRp05g%2BfTolJSUUFxcTHR1Nnz59ePDBB9m5cye7du1i4cKFrriNjz/%2BOP369WPKlCnYbDaOHj3K0qVLiY%2BP5%2BGHH2bChAma20pLS3N9S/bcuXMUFRXRtWvXCqk7R48e7bpHGqBhw4ZMmTKlQmB3PZUFgw8Pj9T94O/jo7844LN1k3ZhYKBjEv3LL5Cfr1kt51r1ZNxsdszx8vLUyc9CerTXLgTHSvSSJTBsGKSkaNdbtUq7zMfHMYlOT1cPjt6XS/38HJPoI0eUqeEK6rdQNmMyOSZnZ88qE%2B8RUKKdWdLwAKelKfuCn59jEn3woDrdXUSEuh1nirmsLHWKOcUxha%2BvY5aXlqZOxaY3EzSZLpwIigHOL1bPpM3mC/tJNcSqrlosjkleTo46SV34mUPKvhgdm0OKqbTR4QVoHJGrXWg2O64T%2BfnqgdHL1ujj45jlnTihPC%2BzgmI0y4yOL0B4kBcunqd0sjVarY5JdEaG%2BjxQfTqzWByT6Nxc3TeVmqs9kzY4vMT4pSu3gdXqmESfPq2fPlJclWQiXYNNnTqVpKQkioqKePfdd3nrrbfo3r074eHhbN68mdLSUrp37%2B72Gl9fX1JTU%2Bnfvz/FxcV8/PHHvPrqq0RERDB69GjuuOOOSrd18T3Sx44d44knnuDuu%2B9m2bJlruffeustt3ukq6qyYPBe%2B%2BuZIvW3S36%2Bsp7eLy2nsjKduoq0325SUtR19WYG4BhAVT1F2m83584p66rmFnBhAcVu16lrZJD1BliVv7q8c%2BfUdY3%2BAi0pUdfVy00Njn2kqqf69HFxPUVdvf1Uvp6qrpGhKS3VqWdkXEB3bIy0oje8gLHB0RsYI%2Bck6J6XXhlfbzH6nkpK1HWN3F9TWqp7DfDGZQ%2BzwV8sJSVXZopwWZH2mEykBb6%2Bvtx3332cOXOGhx56iPfff5/o6GhsNhtbtmxxrRAXFRWRmppKo0aNOHjwIG3atCExMZHCwkI%2B//xzHn/8cTp27FghtWdl6tevz6hRo/jHP/5BZmYmEXordkIIIYQQVxj5KCJcxo8fT8uWLZk4cSKxsbE0atSIGTNmkJ%2BfT2FhIdOnT2fUqFGUlpayfv16xo4dy9GjR7HZbISFhWG1WgkODnbFcszLy9Pc1unTp/noo49o1qwZ4Xr3hQohhBDC%2B67AhCxXG1mRFi4Wi4WXXnqJxMREXnnlFebOncvMmTP561//yrlz57juuutYuHAhfn5%2B3HXXXZw8eZLbb7%2BdvLw86tevz2uvvUZ0dDR2u50%2BffowdOhQJk%2BejJ%2BfH2lpacSVizDh6%2BtLly5dmDt3bpXD7AkhhBDCA/L712Myka6hyt%2BvXF5MTAw///yz6%2BfXXnut0npWq5UnnniCJ554okKZyWSqEDGjfLxqLXv27NGtI4QQQghxpZCJtBBCCCFETSQr0h6TERRCCCGEEKIKZEVa1BhGQ5hmZ6tDQdWtXVu7MOB8OpGwMGWIpgMH1H3x94fWreHoUXVUtQ56DTn7sGqVOoZT06baZXFxjsQvAwd6HkIPoHFjZXGgwauSbnAYnTC8hsTGqsudqzmNGnkWE9gZZk4vpl%2BYIoOEM3NacLB6cBRfAgYccXh9fR1xwRXhw6wB6myCzsSIFot60Uv1lpz08ubk2loqy81mCATyIxurI84dUrcBBsKhgTo8m3NgfHzUoQj1Qi86X6sTTjJMnf8IMJD4CdRv2mx2vJ%2BSEuXxm1urkXITrv0UUle5n1SHsNUKkUBGcZhuSD/VZc%2BpQQOdCnk62WycB05goPGYkX8mWZH2mEykhRBCCCFqIplIe0xGUAghhBBCiCqQFWkhhBBCiJpIVqQ9JhNpIYQQQoiaSCbSHpMRFF5x8OBBHn/8ceLj44mLi6N37968/PLL5OfnA9CyZUu2bNlSpbZnzZrFiBEjvNldIYQQQgiPyURaeGzbtm3cdttt1K9fnxUrVrB9%2B3bmzZvHr7/%2Byj333EOp4pv/QgghhLhMJEW4x2rWuxXV4umnnyYxMZHk5GRq1aoFQJMmTXjttdeIiIggNTUVgE2bNnHrrbcSFxfH4MGD%2BeOPP1xt/PTTTwwfPpyOHTuSkJDAv//9b4qMhlMTQgghhLgM5B5p4ZEjR46wd%2B9ennnmmQpltWvX5s0333T9vHXrVhYsWEBQUBDjxo1j5syZLFiwgAMHDnD33XczadIkFi5cyPHjxxk3bhx5eXk89dRTVepXeno6GRkZbs%2BZTJFERkZpvsYZhteqd1Y4Y0VXxmZzf9Tgb1JvwmAz6ni14IjtWv5RS1ycdlmrVu6PVwuLRbvMuWKit3KiV%2B6MCWwyqevqjb/Rg0/1npxlqjqXUq5Tz6RzDJcfmupmdDfq1VOdb35%2B7o9KqjdtdGD0Tv5L6pAXqAbP4Hmgt3JndD%2BpThPD13Fv8cbBdznjS9ew1ePqIBNp4ZHTp08DjkmznrvvvttVr3fv3syfPx%2BAVatW0bJlS0aOHAlAo0aNeOSRR0hOTubJJ5%2BsUr8%2B/PBDZs%2Be7fbcmDFjSU4ep/va8HCdCpFt9DvQrJmyuLV%2BC4CRhAH1jTUUpf0BAnAkXNGzZImxbenRmVQanXfpTtBCdBIlgMFMFAboTXp0s8ecd/4vOh4xkuHEiOBgZbHeZzwnb8zz9D6H6JU76e2GFi3022hoIMGJodHRG5jmzY1sCGJilMVGhsbQ%2BPkYOIZ1zoNAA5sB/f0UaKAh3eu4QbrXGSOdAfWbys013B%2Bvk4m0x2QiLTwSGRkJQEZGBo0ryVh36tQp1%2BQ5rNwveB8fH9e905mZmcRc9MugQYMGFBYWkpmZWaV%2BDR06lISEBLfnTKZILlqkdmO1Oi6%2BWVnqzIaR6bu0C202xyR6/35ltrHdJvVk3GZzTKIPHFA2Q%2BvQY8p28PFxTKLT0x0p2bQMHKhd1qqVYxI9bBikpGjXM/JlUh8fdT8Au1X/t7rJpE4KB2DKzdEuNJsdk%2Bi8PPVqkN4Mw2Ry7KzCQnWH9H5RWq2OSfTp0%2BqDT29FOizMkUVR9b0EIyvSwcGOPivaKbSpJ%2Bwmk2OueO6cemj0umPgkNHNNGg2O%2BYxZ8%2Bqd3damnaZn59jEn3kiOM9qbSIUZy0Rgfm6FH1Rvz8HJPo1FRlh4obqSfkRsYXwKdEkWnR4HmQX6aeIRvdTwUF2mVGr%2BMAeus/hq4zBfnqCkbflLhqyURaeKR%2B/frExsayZs0aOnXq5FaWmZlJz549efHFF3Xb%2BPLLL92eO3LkCL6%2BvoSGhlapX1FRUURdtAp7/Lj%2BhRUcdZT1VFdxp8JCZb2zBhcBCgt1MgX7G7yPXC%2BvsSr1t1NKirF6VwojX3ItK/Ns0ulczdFL7W1kpgKOA09VV%2B%2B3Ojjej5ED3Ug7irEx0hVnPaN1q8ro/KSsTF1X9aHVSScjt4ORN6w3MEY6Y7hDXqAaOIPngbf2k1eu497irTd1uciKtMdkBIXHpkyZwrJly5g9ezZZWVnY7XZ%2B//13Ro8eTZs2bejbt6/y9f3792f//v288847FBUVceTIEV599VUGDhyIr949wEIIIYQQl4msSAuPde7cmcWLF/PWW2/Rv39/zp49S%2B3atenXrx8PPvggPjp/Hm/QoAHz58/n1VdfZdasWdhsNgYMGMD48eP/pHcghBBC1ECyIu0xmUgLr7juuuvcInRcbM%2BePW4/JyUlkZSU5Pq5Y8eOLNH4Mtu4cfpfEBRCCCHEJZKJtMdkBIUQQgghhKgCWZEWQgghhKiJZEXaYzKRFjWGXshg5/UkIEDny9WqmMzOL0fWqqXcYIfaBkIm4U/rJjohkw7kqdtxxnUtKFB/s99IFkm98HZ6XwyNi3PEq%2B7SRRn9o6xEP%2BKBxaL/BXiLkTjSejFgjf6S0YsjffCg/uujoiAnR72frrlGvy86caQzs9WRSCxAGJBNGKq4JxHZx9X9sFrBFoktN0MZPuFMQF3NMrPZEZ6tsFC9v0M5o%2B4LZiCYwLJcZUP16mlHCXIGcImM1A8Ik1uiHebNbHbEU84vtSnfU3CdOuqNODOOREQox9fIIWykTp5d%2B1wx2yEAKLD7U6Y4fYNzDITrDIwiMF8drjNY2WErEEkkGYA6bEfGKe1jzxlGLztbJxxqkM40yhmI2mKRSev/KJlICyGEEELURDK595hMpIUQQgghaiKZSHtMRlAIIYQQQogqkBVpIYQQQoiaSFakPSYjKIQQQgghRBXIirSoFgkJCWRkZGC1uh9icXFxFBcX07lzZ0m0IoQQQlxOsiLtMZlIi2ozbdo0t%2ByFTiNGjLgMvRFCCCGEG5lIe0xGUFwWR44cYcSIEXTq1Inbb7%2Bd3377zVW2ZMkSevfuTceOHRk4cCAff/zxZeypEEIIIUTlZEVaXBbr1q1j7ty5tGvXjvnz53P//ffz1VdfcebMGV588UU%2B/fRTmjZtyrfffsuYMWPo3r07UapEKBdJT08nIyPD7bmAgEhlG84P5rof0FWJR5y3slh1Ti29jTiD%2BJtM6rp6iUD8/Nwfq1NcnLq8VSv3x5rkCtpPFnU%2BFle5Xj3dY9zguaA6vA2fk3prQgYbUr1nw%2BOisxnD70lvfC%2BlQ16g6q/RyxU%2BPuqNGL1%2BOjfoSRs6VQwPr6ov5ctV9ez6SaiqjaxIe8xkt1/OPSj%2BVyUkJJCZmYnPRRfOjRs38uCDD3LNNdfw5JNPAmC32%2BnevTuPPvooHTp04K9//SvDhg2jb9%2B%2BXH/99ZjNZsyXeLLPmjWL2bNnuz03ZsxYkpPlvmwhhBBXkLNnwV87G2a1evTR6mn3pZeqp90rkKxIi2ozderUSu%2BRBmjQoIHr/yaTiejoaE6ePEm9evVYtGgR8%2BfPZ/To0ZSWlpKUlMSjjz6K3yWs1g0dOpSEhAS35wICIslXZOY2mx3XsrM6WbkDcxSpka1WRw7hDHVaZL00zphMjlXMwkL1asXRo%2Bp2/PygYUM4cgTOndOu17ixuh0fH2XKXsCR%2BlulVStYsgSGDYOUFM1qpT9uU7eDY5VIL02zRZng2mAjRj7AmUz6K0r796vL/fwgJgZSU9X7qUkTdTsG3lN2rk6KcAsEB0NurrqpsOIM7UK4kGM5K0t5LuTaIjXLzGZHFvf8fPU5GUyuui8GG8ouDdYsMzouoF54NXydKcpSb8RigZAQR1p5RYdKQ8J1m9F7P6A%2BLE2mC%2B9JdSoE5KWrN2K1Qq1acPq0%2BvqptyJt4LgDyLJqH3sGh5dw/0LlNjCZHOf3uXOXd%2BVZi6xIe0wm0uKySE%2B/cEEtKysjLS2N%2BvXrk5mZSWlpKf/3f/9HWVkZ27ZtIzk5mSZNmjB8%2BHDD7UdFRVW4jSM3V/2L60J/dOoVFek3UlKirqfXEefFzW5X1y3UuYg7nTtnvG5Vbd9urF5KivG6/yuuoP1kZNLkrKesqzNJcaunqOuVcxIDjRhoyMjY6I4Lxu620H1PRse3tNR4XQ%2Bo%2Bmr0cqX7YdyppERd18jkT%2Be4AzAyarrDa3RybLfLRPp/lIyguCyWLl3Kr7/%2BSlFREbNmzcJqtdK9e3fS0tK455572Lx5M2azmTp16gAQHq5eVRFCCCGE%2BLPJirS4LP76178ydepUjhw5Qtu2bVmwYAEBAQFce%2B21PP300zzzzDOkp6cTHBzMsGHDuPnmmy93l4UQQoj/LbIi7TGZSItq8c0332iWLVq0SPnawYMHM3jwYG93SQghhBBXgczMTKZMmcLWrVuxWCwMGjSIxx9/vEKSN4D77ruPLVu2uJW9/vrrxMfHU1payssvv8ynn37K2bNnueGGG5g2bdolRQHTIx9FhBBCCCFqIrO5ev55aPz48QQEBPDtt9%2BydOlSNm/ezNtvv11p3Z07d7JgwQK2b9/u%2BhcfHw/AnDlz2LRpE8uWLePbb7/FZrPx1FNPedy/8mRFWgghhBCiJqqmWzsqy%2BUQGanO5eB0%2BPBhtm7dysaNG/H39ycmJoaHHnqIl156ifvuu8%2BtbmpqKmfOnKF169aVtvXxxx8zadIk6tatC8A///lPunXrRmpqKjExMVV8d%2B5kIi1qjOxsdbmPjyNCVm6u%2BgvjH3zZSLMsIgKSkmD55rpkZmq3MWSIui9mMwQDuSX%2Bym/BW5pcq9tOAFBQv4U61JZe/gPAblUnUygr0f9GugX98HYWq06Cg7g42LYNS6f2yugfC%2BZr9yciAhITYcUqi3I/BQSouxIeDv36wedfmMhSRCuLjm6rbCcoCDoBP2Y1Jy9Pu97O9dplkZFw%2B%2B3wwccWMhSR6YKClF0hIgIGDYKNG1GOzd2dFIXgCN8YGQlnzigjkYT%2B8Ye6sx06EPzHzygHRuOXqIvzT746kRwiyk6qGgEiCCvN1I0GcTivjmaZr6/jOpOTow7sUxKiHZoNzodnA3Is4cpAj%2BGvKuL5RkXByJFYFr8D6erQdAtt2vGGncfeypUoj7277qqv3IbruucfRZki2mlamnaZzQZNIuFgXqRuAJxr9q/WLgwJgfh4wndsdOwsDZsjBii3ERgI110Hv/1hU4Zf/ctf1H292nz44YcVcjmMHTuWceP0czns3buXsLAwV7ABgGbNmpGWlkZOTg4hISGu53fs2EFgYCATJkxgx44d1K5dm1GjRjF48GByc3M5ceIEsbGxrvq1a9cmNDSUPXv2yERaCCGEEEJ4oJpWpCvL5RAZqf5w6JSfn4//RQlqnD8XFBS4TaSLiopo164dEyZMoEWLFmzZsoVx48YRGBhI3PlsuwEXrYjYbDbyVZ9qLpFMpIUQQgghhNdUlsvBqICAAM6ePev2nPPnwMBAt%2BcTExNJTEx0/dytWzcSExNZu3YtN954o9trnQoLCyu04wn5sqEQQgghRE10BX7ZsEWLFmRnZ3Pq1CnXc/v37yc6OprgYPfso0uXLmXt2rVuzxUVFeHn50doaCh16tRh3759rrKMjAyys7PdbvfwlEykhRBCCCFqoitwIt24cWM6dOjA9OnTycvLIzU1lTfffLPSsLh5eXk899xz7N69m7KyMv773/%2ByevVqhg4dCkBSUhJz5swhNTWVvLw8pk%2BfTufOnWnYsKFHfSxPbu0QQgghhBBXjDfeeINnn32WXr16YTabSUxM5KGHHgIgLi6OadOmMWjQIEaOHElBQQFjx44lMzOTmJgYZs6cSceOHQEYM2YMJSUlDB8%2BnPz8fLp06cK///1vr/ZVJtI1WMuWLQFYu3YtTZs2dStbuHAhM2bMMPwtW5XJkycDMGPGDLfnjx49Sq9evVi3bh0NGjRg8uTJrFq1Cl9fX1edsrIyYmJiGD16NAMGqL8dLYQQQohLcIVmNqxduzZvvPFGpWXby0VrMplMPPTQQ65J9sV8fHyYNGkSkyZNqpZ%2BgtzaUeOFh4fzySefVHh%2B%2BfLlBOnFyaoGAwcOdAuqvmXLFvr27cujjz7KwYMH//T%2BCCGEEEJokRXpGm7gwIF8%2BumnTJgwAfP5T6a//fYbRUVFbgHO8/LymDFjBlu3biU9PZ3g4GCGDx/O6NGjOXz4MImJiUyaNInhw4eTl5fHbbfdRr9%2B/XjkkUc86p/NZuOee%2B5h9uzZ7NmzhyZNmhh6XWXB4MvKIomM1P4WsTPUbCUZSN1ERGiXhYW5P2rRWwRwlhutp8VkuvB4hS48VHQ%2BZJGmVq3cHzWo9lNoqPujFptNXe6MwlQuGlOl9D6TOqMz6cWtVkWPCg93f9TblhajY6M7OH5%2B7o9aVIPjDIF1USisCvROWovF/VGLMmi7wTZwxIrWYvQ6o7cZo9cIVJETatVyf1SIVOxGo8eet657qkPPOfaqfeCiOnGdx6XOyasX/MHIIezFSGyX7qr5xXDlkol0DdejRw9Wr2wPVwEAACAASURBVF7N999/T7du3QDHt2AHDx7Mxo0bXfVefvlljh49ytKlSwkODubLL78kOTmZm2%2B%2BmUaNGjF16lSmTZtGfHw8//73v4mKimL8%2BPGu169evZqvv/7abdtlql9a5%2BXl5fHWW28RHBxM%2B/btDb%2BvyoLBjxkzluRk/dtUatdWlycl6W//ovCZVeatCD168xAjTDp5UgzML4zV26ZO2OKyZImyOFFZ6tCzp7FN6TkfZcljbdqoyzt10m%2Bjb1/v9KV7d70azY015I2kB3oJV4zS%2B8RjhN6nZKCugWYMhtTVpfuHw5Ej9RsZOFC3yu0G%2BuKtY0/vundR4IZK1VfnfnFoEq9fR%2Bf3znUGNgPQooV22ebNBhupDjKR9phMpGs4q9XKwIED%2BeSTT%2BjWrRuFhYV88cUXrF692m0iPW7cOCwWC0FBQZw4cQK/86tM6enpNGrUiMTERDZt2sTIkSM5e/YsK1aswFJuxjRgwADNe6TLc064S0tLKS4uJiAggPj4eN5///1LiklZWTD4srJITpxQjYVjEn3qlDpx2fffa5eFhTkm0d98o86k2KePdhk4rm2BgY6VCiOLZFpMJsck%2BuxZsCsSD%2BpNtE0m9etB3U8niwVKVWnYwJGxUKVVK8cketgwSEnRrLbiae0JeWioYxK9fr0j8Z4WIyvSN97oOCYUyc%2BUq%2BPgWCVu0wZ27YKCAu16%2B/drl4WHOyYyX3yBMsuikRXp7t1hwwb12AxqvU%2B7EBwr0TExkJoK585p11NtxN/fMYnevdtxEGu56DseFVgsjp2Vk6M%2BAPVOtrAwx4mtcxAfL9Le4VarYxKdkaG%2BzuhNJs1mxyQ6L0/d7ZBP3tEurFXLMYletQpOn1Zu7wM/7Qm50WOvf3/lJgxf98pFRavA19cxiT52TJ05EqBJ6kbtwqAgxyR62zZlVs3fwtSTcX9/xyR67171ISyuXjKRFiQlJTF06FDy8vL4%2Buuvad%2B%2BfYUMRJmZmbzwwgvs3r2bBg0a0LatI%2BVx%2BVXlESNGsHLlShITE91Se16K8hPuDRs28OijjxIbG0sL1cf5SlQWDD41VZ3626mkRF1PlTbZKTtbXc/IpNNZT1VXb5XYudhgtxvf5mWnSPvtJiVFWdfIfjpzRl1Pb9LplJOjnkDo3d3gVFCgzoStSr/slJWlrmf0qw96Y6Obf9np3Dl1XdUbdjp7Vl1PJ2W3S2mpuq6Rk0SvDfQncOBoQlVP70OcU1mZzrxeJ/U34JhE69TLMNAfvWPPW9c9I4deUZGBeqpPv055ecp6%2BT76TYDjEL6st3BokRVpj8kIClq1akXTpk1Zu3Yty5cvrzRW48MPP0zbtm3ZvHkzn3zyCRMnTnQrLyoq4umnn2bAgAF88cUXbNiwweN%2Bde/enZdeeonXXnuNpUuXetyeEEIIIcq5AuNIX21q1rsVmpKSknj77bc5ePAg3Su5OTI3NxebzYbFYuH06dM8//zzABSfX7p9%2BeWXKS0t5cUXX2TixIlMnjy5wpf9qqJ79%2B7cc889PPfcc%2BxX/V1bCCGEEOJPJhNpAThuqTh8%2BDCDBg3CWsnXyV988UXWrFlD%2B/btSUpKok6dOrRu3Zo//viDjRs3smTJEmbOnImvry8jRoygRYsWTJ48GbvejbUGjB8/nmbNmjFp0iSKjPzNVAghhBD6ZEXaY3KPdA22Z88e1//Dw8PZuXOnW/miRYtc/7/pppsq5LMvr/xrTSYT7777ruvni79k6NSgQQO3PmjV8/HxYfny5ZrbFkIIIYS4HGQiLYQQQghRE9Ww1ePqIBNpIYQQQoiaSCbSHpOJtKgx6tUzVk8vUcL9g05qF1qtQARJ3TPVIbLSdcIu%2BflBcEOCs46oY/DqZY%2BxWIAQAkp04ucqYl47Y/CactVtWAwlu7BgQR2Dd8F89X31ERGOZCsrnt6mDM92732K2IBxcZC4jcRn26vD7a1Zo%2BwLwSFAV/oFbwK7Yp/W04lz7OcHNKZT5CEI0d7fPXd%2BqWgkErid2/kAUHzR95ROTDBzFDCSQVnvwCntkGjrTj6qbCY4GDoDW083JzdXu54qNGCgGeKA7eYO5CtiprfQCatmLYMIILMsnBJFXVXINWuZY4QzyiKUbQA0qq2Ic2Y2A/7UDTur3qBeWMDz15qQYvW1Zm1b7f0UEgJdgU3NR5KjE6Z/nG29dmFQENCJ25v9CHVU/dZLqmUGggkmF9Aem9BoveD6ITSJ0LnmAR/vHKBZFuYHfYCvzsWTrYj//PdTq5TboDgUiOe67I3qmOnoJ8URVyaZSAshhBBC1ESyIu0xGUEhhBBCCCGqQFakhRBCCCFqIlmR9phMpIUQQgghaiKZSHtMJtLCTUJCAhkZGZUmZZk3bx4dO3bUbWPv3r0MHDiQnj17MmfOnOrophBCCCHEZScTaVHBtGnTSEpKqvLrFy9eTFJSEqtXr%2BbgwYM0adLEi70TQgghhFfIirTHZASFYYcPHyYuLo733nsPgLy8PPr06cMrr7ziqpObm8vKlSsZPnw4ffr0YeHChW5tTJ48maeffprRo0cTFxdHr1693LIgZmVlMWHCBDp06ECvXr1YtGgRrVu35ujRo3/OmxRCCCGEMEhWpIVhjRo1YurUqUybNo34%2BHj%2B/e9/ExUVxfjx4111li1bRmxsLG3atGHEiBGMHDmS8ePHU6tWLVed5cuXM3fuXGbPns3SpUt59tln6du3L3Xq1GHSpEmYTCbWrVtHWVkZkyZNolQnFmhl0tPTychwj6UbERFJZKROsFQjKrntxcVicX/UogqeC%2BDj4/6otz0tztUGT1YdvNHGJYiIUJeHhro/aoqL0y5r1cr9UYtebOzAQPdHLXr729fX/VGLKsh5eLj7oxZVXHIA57la7pytTHCwupmAAPdHLapD3N/f/VGL6pQE46elMo601di2APW5YjJdeFTV89KbUh3CRg9fAHyDtMuM7my9a4jRa41JESP%2BEq5XYWHaZc7jW%2B84x0/nQhQU5P5YGWV86WomK9IeM9ntdnX2A1GjJCQkkJmZic9Fv93q1q3LqlWOwPOPPvooP//8M2fPnmXFihXUqVMHALvdTr9%2B/Xj44Ye55ZZbABgyZAjx8fGMHTsWcKxIZ2RksGDBAgCKi4tp27YtixcvpmHDhsTHx7N27VqaNnUksNi3bx/9%2B/dn3bp1NGjQwPD7mDVrFrNnz3Z7bsyYsSQnj6vCqAghhBDVZNUqGHiZErK88071tDtyZPW0ewWSFWlRwdSpU5X3SI8YMYKVK1eSmJjomkQDbNy4kUOHDvHMM88wbdo0AAoLC0lNTeX%2B%2B%2B/H7/yqXGS5VTXnhL2srIzjx48DuE2YY2JiqvQehg4dSkJCgttzERGReomusFh0k2FhyVak07NYHMsc2dnqhvIVmc/AsUxXty4cPw7Fxdr1VEsq4FhtCApyZElTLbd5ow0jy1oGBnjFKvUKW2go9OwJ69erF3ISn1VkUWvVCpYsgWHDICVFu96sWcq%2BEBgI7drBL7%2Bo92n9%2Bup2fH0dqTfT0qCoSLveDz9ol4WHQ9%2B%2B8MUXkJWlXc/IivTAgY5f7qdPa1bbeo36F2VAALRtCzt3QkGBdj29FelWrRy76Kwiu1zDhsquGD4t9Vakw8MdQ6tKWgoQGaTorMkENhsUFoJqHUs1aGD4TW1K0f4Tj9HDF6Cr74/ahQEB0KYN7Nql7rfeX4DMZken8vPVO0M1bpdwzftqy/%2Bzd%2BZxUVXvH/8MMMOwKfuOYii4I4i5RSqkmCYZqCiWlksaCtii8vVVUGru/kzFLa2s1FzRNLdKcylNK7dMccsFRdll35nfH5c7MsA95%2BqASzzv18vXyJxnzn3uueeceebccz%2BP9HK9hQXQpYsw5FiZOXsbH2EeA%2BbmgK8vcOoUP1sl8UxCgTTxUJSUlCA2NhavvPIK9u/fj379%2BqFHjx4AgPXr1yMsLAwRERFa%2B9LSUoSEhGDHjh0ICwtj1u1cmcP7zp072gcU79y580h%2B2tvbw95edxvHI%2BwQqR3et6h4MJYdL5gRKS1l28o9qYoK/RugLuqQASvtd1Wyszm2rNTfIomJbLscTip3kfx8ti0vlbtISQn7eqcxUn%2BLZGWx7Yo4KcJFMjOBVOkU4bkybxAVFLADEd6uF0AIolmBnpwhCfCHpZzfmmVlMo7Hqki8la7RsO3q6KTkdGFe9wUAqGUEgQUF7GBR7o/5igq2rZx6ZMxX9%2B/zq8nN5dipZW7LyMt7sls4pKCtHXpDLUg8FAsWLEB5eTlmz56N9957T7tV49atWzhy5AiGDRsGR0dH7T83Nze8%2Buqr%2BOqrr8DbRWRvb49evXph/vz5yM7ORnZ2NubNm/eYzowgCIIgCOLhoECaqEFcXBx8fHxq/IuPj8eGDRswd%2B5cqFQqvPHGG2jRogViYmKwbt06eHl5oVWrVjXqCwsLw/Xr13Hw4EHusT/99FMoFAr07NkTr732Glq3bg0ANfZsEwRBEAShJwYG9fOvAUFbOwgdeMGu%2BNAgACgUCh3pOilatGiBS5cuAQACAwNrlItlAJCYmIiFCxdCrVZry1asWAFL3l5ggiAIgiAejgYW9NYH1ILEU8XcuXOxYsUKlJWVIS8vDytWrEC3bt20DyoSBEEQBEE8LVAgTTxVLFy4EGfOnEGXLl0QEBAAQ0ND2idNEARBEPUBbe3QG9raQTxVtGjRAl/Xk64lLzmiSiUozqWmshXImqbeki40NRUyity7x5aB4mlii8kYTEzY%2BmA3brDrMTERMjIkJ7P1wzw9pcvESVGpZCd/kDt5cux4OR0qd/1ArebY7tkjXSZmqVi6lC1XUKmHLomPjyBrFRnJVv/Yt49dj4UF4O4uXCeWxEVd0KED3xcA8PISJPkkaN6cXY2YW8bNjT2eWOqDYlfhqXY4IIXtDIwA2MAGGQCkFS6uFThIlonnU1TEPh8AQMZ16TK1Wmi8O3fYCiq8zESicgVH4eLYMekqnJyA7t2Bs2cFpU0WL/sxxomYIIUn/7F5M/sgNjZASAjw009sSR7WuFQqhfFdVMSWDgVQViadvEgU/OApvRAEBdIEQRAEQRANkQa2elwfUCBNEARBEATREKFAWm%2BoBQmCIAiCIAjiEaAVaYIgCIIgiIYIrUjrDbUgQRAEQRAEQTwCFEg3IP755x9ERUWhS5cu8PHxQe/evTF37lzcv38fAJCQkICAgADJz8fExCAmJqbe/eT5QRAEQRBEHUDyd3rTsM62AfPLL78gPDwczZo1w/fff49Tp05h5cqVSEpKwsCBA5GSwpOPIgiCIAjiPwUF0nrTsM62gVJSUoIPP/wQ48aNw7vvvgsHBwcoFAp4eHhgyZIlcHR0xOzZs2t87sCBA%2Bjfvz86dOiAcePGISsrS1u2dOlSREREIDIyEh06dEBAQAA2bdqkLc/Ly8P06dPRo0cPdO3aFe%2B%2B%2By7S09O15QcPHsTQoUPRtWtXeHt74/XXX8eNWjSRS0pKMHbsWAwfPhx5eXl12zAEQRAEQRB6QA8bNgBOnz6N9PR0DBw4sEaZgYEBBg0ahI8//hjdu3fXvv/vv/8iOjoas2bNQr9%2B/XDo0CFERUUhODhYa3PgwAHExMTg//7v/3DixAmMHz8eTZo0QdeuXTFt2jTk5%2BcjISEBarUac%2BbMwcSJE/Hdd98hJSUF0dHRWLx4MQICApCVlYWJEydi2bJlmD9/vrb%2BoqIiTJgwAQqFAl988QXUYiYOGaSmpiItLU3nvfJyO9jZ2Ut%2BRsyBYsQbFawsIFWzhbDgHUSuMyYm7HIxtTovxTprBUFMtqBQPJaVBivpHAkAHuRSEV8lsWAYmJnpvkrh48Mub9lS91XSFwt2uVx/7Oyky8SG4zUgzxexf3My44gJSqSQ24VZ7shtFu5BxERCrIRCYJ%2BTmBeJlR9Ji4Yx/uWOyTqaI5ycpMtsbXVfmTRuLF1mbq77KoWY5UQKS0vdVylYF0H2RM4eKrLnGVNGuwDy2iY7m3OQeqSBrR7XBxRINwBSU1MBALYSs6W9vT1KS0t1Vpz37NmDtm3bagPnl156Cb169dL5nJeXF9566y0AwAsvvICgoCB8//338PT0xP79%2B7F3717YVGbnmjZtGvz8/PDPP//A09MTu3fvRpMmTZCXl4d79%2B7ByspKZ3tJSUkJxo8fj6ysLGzZsgUq3rd2NTZt2oT4%2BHid9yZMmIioqEjuZ1mxCgDAqQ3fAQ8Pvo0ceEER19lKmjXT35eH%2BCHDRAzMJejbV1413brxLLrzDPhZ/k6dkufMhg3y7Hh4e7PL%2BScNBAXVjS/t2zOLGfGZDrwuygr0RHjNAnCyAIpwAjQ3GdU4Oso5ECftIyCkfKwLOHNERAS/iiFD5ByoJ9/Ez09ORXzq4hkZa2uuiZy5hj/kXpTlDnx9pct27ZJXB/FUQoF0A8Cu8pssOTkZ7u7uNcpv374NpVIJqyoTckpKCpyrpQdu0qSJTrBdvS4nJydcvHgRd%2B7cAQAMqTY7Gxoa4vbt22jTpg1%2B%2BOEHbNy4EQqFAp6ensjLy4NRlRWEtLQ0tGzZEteuXcP58%2Bfhy5qEaiEsLKzGA4vl5XbMNLhGRsKXfloaOyWsU%2BY/0oVqtRBEX7vGTv9rL70yrnXGygrIymI7w0qjCwirXs2aAdevA8XF0nZNm0qXKRTCeRUVARqNtJ2cQFuhYNcBYN9%2BdqDdqJHw5XbsGDsbcV%2BL36QLzcyEIPrMGXbu6UjOD6%2BWLYUgOjycneu62o%2B6Wv3x9hZyNbP8ucVIT29lJQTR%2B/cL/UaKWuYAHUxNhSD63Dlmmvu7Tbswq5E7nq4zsmnLbZZuXpxxYGgoBNH37zNXRZMKpCNppVIIou/d42aehlvxVelCY2MhiE5KYo9J1gowIHuOWL5F%2BpeMra0QRG/eDFTZeVcrEa0PSReamwtB9J9/AqwteJmZ7INYWgpB9MGDwrWS4oUXpMuMjIQgOjOTm9t73ynpeVj2PGN6hHkMmJsLQfSpU%2By2eVLQirTeUCDdAOjYsSPs7OywdetWfPDBBzpl5eXlWpUMwyq3PR0dHXHo0CEd23v37sG4yu3I6g8o3r59G05OTnBwcAAA7N27VxvEA8DVq1fh5uaGvXv3Yt26dfjuu%2B/QtDKAmzFjBi5fvqy1tbe3x%2BrVqzFv3jzExMRgx44dMOXcaq6Kvb097KsFqzdvAiUl/M%2BWlXHsGMGFlqIith1ngtexY9kWFsqrp7iYbVtRIV0mTrQaDduujmDFgFXJyeHYahjffiL5%2BexvydOn5TmTmMi2zc2VV09%2BPtu22nalWsnKYtvZyFy9LShg%2BiJnLAH88SSnaXjNIns8lZczbeWcU2mpDDvWj2iR4mK2HXc/SyWcOYK1eCCSni7DzkXG9oO8PPY2Bd4Pf5H799m2vF8ygNAmHDs5cw13nimVuS2D1zZPCgqk9YZasAGgVCoxe/ZsrFu3DosWLUJKSgoqKipw9epVTJw4Effu3cP//vc/nc8EBwfj8uXL2Lx5M8rKyvDrr7/ip59%2B0rE5c%2BYMvv/%2Be5SXl%2BPw4cM4cOAAQkND4eDggJ49e%2BLTTz9FVlYWSktLsWLFCgwaNAg5OTnIzc2FgYEB1Go1NBoNjhw5gh07dqC0yqSnVCqhUCgwadIkGBgYYO7cuY%2BlrQiCIAiCIORCgXQDwd/fHxs3bsStW7cQGhoKX19f7cOBO3fuhFO1jYpubm5YuXIl1q9fj44dO2L58uXo3bu3jk2rVq1w4MABdOnSBXPmzMH8%2BfPhU/lw1rx589CoUSMMHDgQXbp0weHDh7FmzRrY2dnhtddeQ7du3dC/f3906dIFK1aswMiRI3H9%2BnWUVFvqMTY2xuzZs7FlyxYcOcK5hUYQBEEQhHxI/k5vaGtHA6Jly5ZYtGiRZHlISAhCQkK0f3ft2hXff/%2B9pL2FhQWWLFlSa1njxo0xffr0WstUKhXmzZtX4/2oqKha/fDx8cGFCxck/SAIgiAI4r9DRkYGPvroI5w8eRKGhoYIDg7G1KlTdZ6lEvnuu%2B%2Bwdu1apKamwt7eHiNGjMDw4cMBABUVFejYsSM0Gg0UVR50/%2B233x5quygLCqQJgiAIgiAaIk/p6vGkSZPg4OCAo0ePIj09He%2B88w7Wrl2LMWPG6Nj9/PPP%2BL//%2Bz%2BsXr0a3t7eOHPmDN5%2B%2B23Y2toiKCgIV69eRWlpKU6dOvXQ6l9yoUCaIAiCIAiiIVJPgXRtuRzs7OxqiADUxs2bN3Hy5EkcOXIEJiYmcHNzQ0REBObPn18jkE5JScHYsWPRoVLK1MfHB507d8Yff/yBoKAg/P333/Dy8qq3IBqgQJp4RCJ5smBPIU0tOU9MGxgAsICTeS5bneLkDekyS0ugTRsgOZkt3%2BTqyvZFLjxRW/E2mI0NW9mA5atSKSR%2Byc1lPwXP0jEDBHm85s250oCOjm2Z1Yh5DWxsODktnJ%2BTLhM/6OLCzkaxbx/TF202kfh4tqwET7DWx0eQx5o4ka3%2BUcuWKC2ilBpPDeIpw8VFukxUVLS3ZyfGyFI5MI9haAg0ApCjtEE5I25gxRRimawtoCwdefH2sqsrWwqyUv%2BfC0cWZcQI6Y%2BKw%2BCVV9hKfMJxPKXLxAvVtCmz7xX2eZV5CIUCUAMo6hfCbBqTPIYqjTjnKZVczXrW9Cl7njHlBIbi9gErK34Snv8QteVymDhxoqzY4cqVK7C0tNQqgAGAh4cHkpOTkZOTg0ZVJgNxC4dIRkYG/vjjD62Awt9//43i4mKEhobizp078PDwwPvvv//QkrosKJAmCIIgCIJoiNTTinRtuRzsZCYQy8/Ph0m1rL3i3wUFBTqBdFXS0tIwbtw4tG3bFq%2B88goAQK1Wo3379oiOjkbjxo2xfv16jB49Gjt37oRbHSVFokCaIAiCIAiCqDNqy%2BUgF1NTUxRWy3sg/m0moa9%2B5swZREdHw8/PD7Nnz9Y%2BlBgTE6NjN3r0aCQkJODw4cN4/fXXH8m/6jydu8wJgiAIgiCI%2BuUplL9r0aIF7t%2B/j/Qq6TavXbsGR0dHWIjb6aqwdetWvPnmmxg5ciQWLlyosx960aJFNVS/SkpKdJLL6QsF0gRBEARBEA2RpzCQdnd3R8eOHTFr1izk5eUhKSkJy5cvx6BBg2rY7t%2B/Hx9//DGWLl2KUaNG1Si/fPkyPv30U6SlpaGkpATx8fHIy8urkRdDHyiQJh47169fx9SpU/Hiiy/Cx8cHL730EhYsWID8/Pwn7RpBEARBEE%2BYJUuWoKysDIGBgRgyZAj8/f0REREBQFDm2LlzJwAgPj4e5eXliIqKgo%2BPj/ZfbGwsAGD27Nlo0qQJXn31VXTu3BknT57EV199BUtLyzrzlfZIE4%2BVU6dOYdSoURg1ahR27NgBa2trXL9%2BHbGxsRg1ahQ2bNgAQ0PDJ%2B0mQRAEQfz3eUp1pG1tbSUTvp2uomy0a9cuZj2WlpaYPXt2nfpWnaezBYn/LLGxsRg4cCCioqJgbW0NAGjWrBkWLVoEGxsbJCUl4dSpUxgxYgReeOEFtGvXDiEhIThz5swT9pwgCIIg/mM8hVs7njVoRZp4bNy6dQtXrlzBxx9/XKPM1tYWy5cvR1FREcLCwhAVFYVhw4ahqKgI06ZNw7x587BhwwbZx6pVDN7EBPYs%2BZ2qQrEsWLeExAchankgQoda0pzWWs6z4yG3HpZoq9w6RC1ZKcSHOzgPeYj6rVKIsqzc7K6s44gPo/BE%2BnnXUXyCXOJJci0%2BPuzyli11X6VgPQVf%2BcNU%2ByoF75xkNjCv6eqi28jsMuDdxJI7tFnnpFTqvjJh6ReLZRyN47pqYGPGYR7qnIwYF0rmeOKdstymYZ6z2Blk3NlkzTWy5xk1x0Ds4KyOXlDAOQjxNKPQaFjfoARRd5w5cwZhYWHYv38/3N3da7UpLy9HUlISmjZtiuLiYiQlJWH9%2BvU4evQoDhw4IPtYS5curSkGP2ECIqOi9DkFgiAIgqhbTpwAOnd%2BMsf%2B44/6qbdTp/qp9ymEVqSJx4Yoxp6WllZrIJ2eng5bW1ucOHECY8eORUFBAZo3bw4jIyM87O%2B9WsXgxex8UhgYCCuL%2BfnszIa//y5dZmEBdOki2LCOVZnOVBIjIyETVlYWOyMhD7n18Fakra2BzEx2HTk5bF%2BMjQE3NyApiZlG7Y%2Bs5sxqTE2F5JH//MNeyOlkd0O6UKUCnJ2FDJSMrHBITmb6AjMzwNsbOHtW6DdSTJzIrqdlS2DDBiA8HEhMlLaLjpYus7YGBgwAdu0SrpUUXl5sX0xNgfbtgXPnmA18t2kXZjVGRoCdHZCWxu42rGaT2WWYC/WAMLTNzYG8PPbQZg1ZpVI4TmoqO8EnALjYMDJLKhTCiRUXs8ddVhb7IDIb%2BFapk2SZUgk4OQF37/LPqUk5I3OpSiWkqLxzhzmeipyaMY8ht2nUhYy2MTQU0mDm5ADl5czj/XHVSrJM9jyj/pt5DKjVQIsWwJUrz1TGUUI%2BFEgTjw0XFxd4enpiz5496FTt12pGRgZ69eqFcePGYeXKldi4cSPathVSRX/55Ze4zks/XY1axeCzs9nfoiIVFWw7Vjptkdxctp3c4LisTL9AWm49ctqlrIz9bSv3S4KTwjovT141BQUc20a8nMcQvvRZERorsqpKfj7blpX2uyqJiWxbOWmjMzPZds7O8nwpKGCeE%2Bv3R1U4GaxldRte1nNOvKSlooJtK%2BecSktl2Mn54a/RsO3qqIGLZZ4TP0W4jAtVUsK8UHLXQ3hNI2tOLC/n2smZa7jzTIXMbRlFRU/nFo4Gtp%2B5PqAWJB4rH330EbZt24b4%2BHhkZWVBo9Hg4sWLGD9%2BPNq0aQNvb28YGBhAXbmf7MyZM/jmm29QIvdLhSAIgiAIedDDhnpDK9LEY%2BX555/HunXrsHLlSvTv3x%2BFhYWwtbVF3759MW7cOJiZmSE8PBzDhw9HRUUFXF1dBfAocAAAIABJREFU8cYbb2DhwoXarR8EQRAEQRBPAxRIE4%2Bd9u3bY/ny5ZLlMTExiImJ0Xlv9OjR9e0WQRAEQTQsGtjqcX1ALUgQBEEQBEEQjwCtSBMEQRAEQTREaEVabyiQJgiCIAiCaIhQIK03FEgTDQdeijVxQjEyYsvBubpKl4kZ7hwc2Gmzzp9n%2B2JhIWjEXrvGllVr0oRdj7GxUE9%2BPlvfipWtsWqmMJYmVatWbF9EmrG1ZM//wv64nZ2g9X/tmiChK0Wv8z%2ByK3F3F/S%2BWZXwsLMDunUDbt1i1zNvHrseUaoxOpotXTdlinSZjw8wciSweDFbQu/DD9m%2BODkJWuinTgkCwxKcKejNrKZxY6GqS5cE5UkpWF1PHELZ2WwJMlbCUuDB0C8tfXQpdBMT4TU/HygsZB8PlgypOENDQVu4pIStxceTXhQz5eXnMyXnfmXk27CxEaaQM2eAjAz24Vqoz0gXWlkJ4/rSJab%2BtQlPetHAAFBbQF2ay56DWSpO4hxVWsoVx/6RMUU4OwvzzO%2B/s%2BXke5n9xDwGHB2Bdu2EpCv37knbPamELITeUCBNEARBEATREKEVab2hFiQIgiAIgiCIR4BWpAmCIAiCIBoitCKtN9SCxDPBjRs3nrQLBEEQBPHfgjIb6g2tSBM1iI2Nxa5duwAAZWVlKC0thYn4pA2A1atXw8/P77H58/XXX%2BPQoUP46quvHtsxCYIgCIIgeFAgTdRg%2BvTpmD59OgAgISEB8fHxOHjw4BPzJzMzExqWWgRBEARBEA9PA1s9rg8okCYemtzcXMyePRt//PEH0tLS0KhRI7z%2B%2But4%2B%2B23AQAvvvgievXqhZ9//hkODg7Ytm0bfvvtN8yfPx9JSUlo2bIlvL29cfHiRaxduxYAsHPnTqxevRrJyclo2rQpPvjgA3Tr1g1btmzBF198gYqKCnTu3BknTpx4gmdOEARBEATxAAqkiYdm3rx5SElJQUJCAszNzbF3716899576NevH1wrNZb//vtv7Nu3DxqNBklJSXjnnXcQFxeHgQMH4q%2B//sI777yD9u3bAwAOHDiAGTNmYOXKlejQoQMOHTqECRMmYOvWrRg8eDBu376Ns2fPaoNuOaSmpiKtmq6vnbk57EW93tpQKB68sn6li1rRtSFugamyFYZ5LClMTXVfpTA2ZperVLqvUrA0tqvqSD8GeJrAVla6r4ya6qKSunGGpeENANbWuq9S%2BPhIl7VsqfsqhZMTu9zWVvdVgsaN2dWIGtAsOXVeudzhxJOIl9uFWccRhxpvyHEPJM4tvJVAUSdaT4dsbKTLxGvIu5YAABWjjzdqpPsqBe%2Bc5baNUildJnYGXqeAoBUthTgP8eYjmDiyy8ULwLoQLH3p%2BoZWpPVGoaF75gSD2rZ2pKenw8jICI0aNcK9e/dw4cIFTJgwAd999x18fX3x4osv6qxQx8fH49ixY9iwYYO2jtmzZ%2BPSpUtYu3YtRo0ahfbt22PSpEna8ujoaDg4OGDatGlYtGjRQwfSS5cuRXx8vM57EydMQGRU1CO2BEEQBEHUAzNn8hMl1Rc3b9ZPvU2b1k%2B9TyG0Ik08NOnp6Zg1axYuXrwINzc3tGnTBgBQUSUTVdWV37t378LFxUWnDjc3N1y6dAkAcOfOHfz1119Yt26dtry8vBwvvPDCI/sYFhaGgIAAnffszM3ZKckUCmEVqKiIncHvyhXpMhMToEULwYZ1rIIC6TJAWIlu3x44d45t68hZDVGphGWX5GR2NjALC%2BkyQ0Mh/dz9%2B%2BxMbKwUdVXrYtUBYOMW9rKhlRUQFATs389Mooah2Kh/JTzk1iNnRXrAAGDXLiAzU9pu8WLpspYtgQ0bgPBwIDFR2m7MGLYvtrbAkCHA5s1Aerqk2aHWEcxqzM0BPz/gzz/ZWQlZXc/EBGjdGrhwgT2c3N2ZrsjuwqzsfsbGQmxw8yb/cno6MlIkGhgIjZOXx87ex8pwKTrk5gYkJTEd2nmhuWRZ48ZAjx7A4cPs7JMAEKzaJ13YqJGQ4fPYMXZ6yO7d2QcxMBDu%2BOXns9uG1RmMjITxlJnJTmMJYOkm6TuUdnbA0KHAxo3spKWRJmuYx4CNDfDaa8D27fz0kcQzCQXSxEMTFRWFl19%2BGV9%2B%2BSWMjIyQnp6OzZs369goqmxdcHZ2xrFjx3TK79y5o/2/g4MDhgwZgtGjR%2BuUm/Du5zKwt7evuY2DNzmLt7g0GrZdfj7fgcJCth0rsqhKQQE7VbDcrQklJexvfzltXV7O/WKqC%2BRm7M7K4tnKqIhfSd04w0jhrENmJjuAYqX%2BFklMZNsx0n7rkJ7OtM12kSzSIS%2BPHaTxdjkBwnBiDRm53ZLXhbmpvyEMI64d58ciAGGOYdnJ7TPFxUxbObFbdrYMO7WMH5w5OewflKx5tbody5aT%2BhuAcKE5dqzU3yJpaRw7M5nbMjIynuwWDiloa4feUAsSD01ubi6MjY1haGiIjIwMzJw5EwBQKjFpDRw4EH///Td27tyJ8vJynD59Glu3btWWh4WF4euvv8b58%2BcBAOfOnUNISAj27t0LADA2Nkae3MCTIAiCIAjiMUGBNPHQzJkzBzt37oSvry8GDRoEV1dXeHl54fLly7Xau7i4YMmSJVi5ciX8/PywcOFC%2BPv7Q1n5wEj//v0RGRmJqVOnwsfHB5MmTcLo0aMxfPhwAEBgYCDS09PRsWNHCqgJgiAIoq6ghCx6Q1s7CCYhISEICQnRea9Hjx7o0aOHznsffPCB9v9HjhzRKUtOToaDgwP27NmjfW/mzJnaQBoABg8ejMGDB9fqg5eXFw4dOvSop0AQBEEQRG00sKC3PqAWJOqd9PR0hIeH48KFCwCAixcvYvfu3ejZs%2BeTdYwgCIIgCEIPaEWaqHfat2%2BPqVOnIjo6Gunp6bC1tUVERARefvnlJ%2B0aQRAEQTRcaEVabyiQJh4LQ4cOxdChQ5%2BsE7wJQ25CFlZSFzHxiZUVO3FLc2lJKgAPkgl4ebFlBngJEMRzcnZmS/qx9p7LzGaRcZ9drpUgyzVkihXwEnhUzVXDtE1nqB6ICiYcxQN06MB2RtRuc3dnJ1zgIdbj5cXOEsHSmhUTrYwZw1bmqHw4WBIfHyAiAlizhqn%2BseftGcxq3NyAnj2BX38VFNqkaNtWuszODujYUVCT1EcUxcJCUGe7dIktgsMaluLQNjF5TLmJWLqAwAOHzMyYCUpY5yROUa6uMgSAjNz5FTk7s7O78JLMiPOVsTF7vmJJgsqdxyEojEohtkeLFpzcRKbtmMfQtkfz5jKyuxDPIhRIEwRBEARBNERoRVpvKJAmCIIgCIJoiFAgrTfUggRBEARBEATxCNCKNEEQBEEQREOEVqT1hlqwgZOamooC1oMb9ciNGzeeyHEJgiAIgiDqAgqkGzDp6ekICgpCZmbmYz/2hQsX8Morr2j/jo2NRWxs7GP3gyAIgiAaLJTZUG9oa0cDpqio6ImtRufm5qK0tFT79/Tp05%2BIHwRBEATRYGlgQW99QIF0PXPw4EF8/vnnuHnzJgoKCtCuXTvMnDkTtra2%2BOijj3Ds2DEYGRmhZcuWmDZtGlxdXfHCCy8gNjYWAwYMAACUlJTA398fn332Gf78809cvXoVarUaP/30E8zNzfG///0P//77L9avX4%2BysjK8%2BeabiIiIACCk1548eTLWrVuH7OxsPP/885g%2BfTpsbW21K8KvvPIKZs2ahX79%2BmHLli1Yu3Yt7t69CxcXF4wdOxbBwcEAgDfeeAOdOnXC8ePHcfHiRTRp0gQzZ87E119/jV9%2B%2BQWWlpaIjY3VZizcunUrNmzYgDt37qCkpATPP/88Zs%2Bejfz8fIwdOxYA4OPjgy%2B//BKbNm0CAMyZMwcA8PXXX2PdunVIT09Hs2bNMHnyZHTt2lV2u6empiKtmvCsXaNGsGfpeFbVH2Uh6rfWhqjnytB1BfBAJ1oKmdrNXF/lnhPrODJ94Unryj0lnhyzKMvKkqsFABgw9L6trXVfpeBp%2BVYVtdYHufWIWtG1IYrdMkVvIehEs2jZUvdVAjc3djWOjrqvUrCGpKjly9M45l0mUeaYJe0O1M3QBsDu5GLgwgtgWM4AD%2BYQzlzCOmcTE91X9vHqoKK6mq9Y5yyzXQB2vxIl%2BnlS/VBzJiJR7J4lep%2BdzTkI8VSjIeqNu3fvatq2bas5cOCARqPRaDIzMzXh4eGaDz74QLN48WLNmDFjNIWFhZri4mLN1KlTNePHj9doNBpNXFycZvTo0dp69u3bp%2BnVq5emoqJCs2TJEo2Xl5dm7969moqKCs38%2BfM1rVq10syZM0dTUlKi%2BemnnzSenp6a27dvazQajcbT01PTv39/za1btzQ5OTma8ePHa4YNG6bRaDSapKQkjaenpyYpKUmj0Wg027Zt0/j6%2BmqOHTumKSsr0xw7dkzj6%2Bur%2BfHHHzUajUbz%2Buuva7p166a5cuWKpri4WDN8%2BHBNmzZtND/99JOmpKREM2fOHE1AQIBGo9Fozp49q/H29tacPXtW2xZ9%2BvTRLFq0SKPRaDS///67xtPTU3uOU6dO1UydOlXrx/PPP685deqUpry8XLN582aNt7e3JisrS3bbL1myROPp6anzb8nixQ95BQmCIAiintmx48kdOy%2Bvfv41IGhFuh6xtrbG7t270aRJE%2BTl5eHevXuwsrJCSkoKWrRogcTEROzYsQPdu3fHrFmzYFC5QhEaGoqwsDCkpaXBzs4O27dvR0hICBSVv9KbN2%2BOvn37AgC6d%2B%2BO1atXY/z48VAqlQgICAAAJCcnw8XFBQAQHR0Nt8rloylTpqBv3764fft2DX%2B3bduGsLAw7cpv165dERYWho0bN6J3794AgKCgIDSvTJXl5%2BeHnJwcvPTSSwCAF198EV999RUAwNPTEz/88ANcXV2RnZ2N1NRUWFtbIyUlhdtu27dvR1hYGHwqV84GDx4MDw8PqHlZsaoQFhambQsRu0aN2CnQFAoho1ZxMTurFivFmlIpLMHduwdU2bpSA176PkNDYSkkJwfMNIC8JTaFQvCptPTRM4UZGgpLfrm5TF/uw5LpisxqcOQIsxo0bgz06AEcPsxeyAnO%2Blq60NoaGDAA2LULYD0j4OXFdsbUVEiPdu4cuw15yK3n1CnpMltbYMgQYPNmID1d2m7NGrYvLVsCGzYA4eFAYqKk2cwQhi8QhsGYMcLh7t2TtmNl3rOyAoKCgP37gawsabsmTZiuwMwM8PYGzp4F8vOl7Vir7HKHNgC4Nc6RLjQwEMZ/Xh5QUSFtx3IUEFZc7eyE%2BYiR/fTUXem7GCYmQKtWwMWLQGEh%2B3C%2BRuekC01MhBSAV66wK%2BLc5ZA9X7EGvpGR0HGysthZYQHs%2B0v6dkijRkI2zGPHhGlYir7qQ8xjwNwc8PMD/vyTnUGWeGahQLoeUSqV%2BOGHH7Bx40YoFAp4enoiLy8PRkZGGDt2LFQqFbZu3Yrp06fDzc0N77//Pvr06YN27drBw8MDu3fvxoABA/Drr7/iwyqpgS0tHwQsYvDduPI%2Bt/h3RZUJumnTptr/O1emHxaD9Kqkp6drA24RV1dXHDx4sNZjGxoaao8rHltTOfkZGBjgm2%2B%2Bwa5du2BqagovLy/k5eVpy1mkpaVp/RTx9fXlfq4q9vb2sK%2BeyruwkD05i2g0bLuSEn4dpaVsO84Er6W8nG0r53xEO5YtK7KtasOwk1GDnGqQkSGvnuxsjm16Kr%2BSzEwglWHHStddlYICdu5pufDqYaX%2BFklPZ9sx0n7rkJjItE3qJK%2Bae/fYKcK5W3QgxESs36/c9NaV5Oezm7cuhjYAeeOpooJtJ8cZQJgfGLa8eBwQpkaunVEdVFRX85Wc%2BbOsjGvH%2BnEmkpPDsTOVuS0jL%2B/p3MJBe6T1hgLpemTv3r1Yt24dvvvuO20wO2PGDFy%2BfBmXLl1CQEAA3nzzTeTm5mLDhg1499138fvvv8PCwgKhoaHYvXs3lEol/Pz84Orqqq1Xwds/Vo2UlBR4enoCgHYl2tnZWedhP0AImm/duqXzXlJSkk7ALffYa9euxW%2B//YZdu3bBtnLP5vjx42V91snJCXerBQKLFi1CcHAwPDw8ZNVBEARBEAQHCqT1hlqwHsnNzYWBgQHUajU0Gg2OHDmCHTt2oLS0FFu2bMGUKVOQkZEBc3NzmJubw9TUFKrKB0yCg4ORmJiILVu2ICQkRC8/li1bhrS0NOTk5GDu3Lnw9/eHg4MDjI2NAQB5lbebBg0ahE2bNuH48eMoLy/H77//jk2bNiE0NPShjymuvCuVSpSVleH777/H0aNHtcG7eOzcWpaHQkJCsGnTJpw7dw4VFRXYtm0b1q9fDyu5y04EQRAEQRCPAVqRrkdee%2B01/PXXX%2Bjfvz8MDQ3x3HPPYeTIkVi/fj3WrFmDGTNmoH///iguLsZzzz2H5cuXawNMa2tr9OjRA7///jv69Omjlx9t2rRBeHg4srKy0LNnT%2B02EVtbW/Tu3RthYWGIiYnBsGHDkJeXh5kzZyI5ORkODg6YMmUKBg4c%2BNDHHDVqFC5fvoxevXrB2NgYrVu3Rnh4OH7//XcAwh7qjh07wt/fH4sXL9b57IABA5CTk4PJkycjLS0NzZs3x%2BrVq2HNU1ggCIIgCEI%2BtCKtNxRI1yMqlQrz5s2r8X5UVBQAYO7cuczPu7i4oH///joP2UVGRurYdO7cGZcuXdJ5r/rfffr0wUcffVSjfoVCgfj4eJ33Bg8ejMGDB9fqz7fffqvzN8sXS0tLrFy5stZ6AMDU1BQbNmzQ/t2jRw%2Bd8uHDh2P48OGSnycIgiAIQk%2Be0kA6IyMDH330EU6ePAlDQ0MEBwdj6tSpMKpF1vDw4cNYsGABkpKS4OTkhClTpqBXr17a8tWrV%2BPbb79FTk4O2rVrh08%2B%2BQTPPfdcnfn6dLZgA%2Bfu3bs4fvw4duzYgaFDhz5pdwiCIAiCIB4bkyZNgqmpKY4ePYqtW7fi%2BPHjWLt2bQ27GzduIDIyEtHR0fjzzz8RGRmJSZMmaRXCtm/fjm%2B//RZffPEFTpw4gTZt2iAqKkqW8IFcKJB%2BCtm8eTMiIiIwYsQItGrV6km7QxAEQRDEf5F6ShGempqKf/75R%2BdfKkshqQo3b97EyZMnMXnyZJiYmMDNzQ0RERFYv359Ddvt27fDz88PL730EoyMjNCvXz906tRJm%2BRt8%2BbNCA8PR4sWLWBsbIz3338fycnJOHHiRJ01IW3teAqJjo5GdHR0ndRVfZtHQyYlh511y8gIsFEDGflqpmqSqb20coiBAWAGIN/ajSkRy1NBUlYADgBSSqyYmrWNOQpZBgaAmQrIL1Ux/TEyNZYsUygANYAitSVTkcrmPkeazcgIgB0sS9m6t2914ujfqdUAmiO49VWmLviBlMmSZRYWwPMATrYaiVxXSTOmxjEgJJ9zAnC3aRfZamX61HOmoLdkWePGQE8Ah1pHINtFuo49b89g%2BuLmBnwIQSeaJXG36nOOgo%2BPD/DhKXyY4MuU0du/T7pTiRkLmzRhS9zxsieKCfdsbdnS6x6mjD5c2X/d1GmAEVtWrbwRIwMlhCyg5WbslHmpReyHq42MADsAaUZOYHnj/9wd6UKlEoA9fF1TueLYKUbSmWWNjAAbABku7ZlzJ1tpHlAAUAEogQqs9cJMSLevcJWANNgx2wUAhjX/Q7rQ1BRAG/R1%2BwewkdZ2P2/yKvMYajXQHMBV154oYiQdbct29Zlj06ZNNbaOTpw4scaW0Nq4cuUKLC0t4eDgoH3Pw8MDycnJyMnJQaMq6SavXr2qVSUTad68ORIrNfCvXr2qzaQMCLLE7u7uSExMRJcuXR7p3KpDgTRBEARBEEQDRIOHk9OVS61J0eykE%2BBUJT8/HybV0s2LfxcUFOgE0rXZqtVqFFQmtuKV1wUUSBMEQRAEQTRAWHcq9aHWpGgyMTU1RWG1DJni32bVbimZmJigqNqdyaKiIq0dr7wuoD3SBEEQBEEQxFNBixYtcP/%2BfaSnp2vfu3btGhwdHWEh7veqxNPTE1euXNF57%2BrVq2jRooW2rqrlpaWluHHjRo3tIPpAgTRBEARBEEQDpKKifv7pg7u7Ozp27IhZs2YhLy8PSUlJWL58OQYNGlTDNjg4GCdPnsSePXtQVlaGPXv24OTJk3j1VWHvemhoKNatW4fExEQUFxdj4cKFsLW1hZ%2Bfn35OVoECaaLeiI2NRWxsbJ3U5eXlVadP2RIEQRAE8XSyZMkSlJWVITAwEEOGDIG/vz8iIiIAAD4%2BPti5cycA4SHEZcuWYdWqVejUqROWL1%2BOpUuXolmzZgCEjM1vvvkmJkyYgC5duuDChQtYtWoVlEplnflKe6SJemP69OlP2gWCIAiCICSorz3S%2BmJra4slS5bUWna6mgqQv78//P39a7VVKBQYNWoURo0aVec%2BilAg/Qxw%2B/ZtBAYGYubMmVixYgWys7PRvn17zJ49G46Ojvj555%2BxfPly3LhxA3Z2dhg2bBhGjBgBAwMDxMTEQKVSITU1FSdOnIC1tTVGjhyJESNGABD2Hc2bNw%2BXLl1CZmYmXF1dMXnyZPTq1Yt7XADYvXs3lixZgoyMDHh7e8PZ2RmlpaWYM2cOYmJiAABz5szB0qVLceXKFahUKhw6dAimpqZ49dVX8f777wMAUlJSMHv2bJw7dw4ZGRmwtbXFO%2B%2B8U%2ButHIIgCIIg9OdpDaSfJSiQfoY4dOgQduzYgZKSErz11ltYvnw5%2BvXrh0mTJmHevHno06cPLl26pL398eabbwIAEhISsGrVKsTHx2Pr1q2YPn06goKC4ODggMjISAQGBiI%2BPh4ajQYLFizAxx9/rJNes7bjTp8%2BHadPn8bUqVOxZMkSvPjii/jll18wadIkDBgwoFb/f/zxR8yZMwdz587Fr7/%2BinHjxiEwMBAdOnTAhx9%2BCEtLS%2BzevRsqlQrffPMNZsyYgZdffvmRnq5NTU1FWlqaznuGhnaws5N%2BitjQUPdVClZGVbGMl3WVd1dJzIJaSzZU2b48jD8KhgKSWMayAcB3Vu5JqdXscmNj3VcJqj2TooOpqe6rFCoVu1zuKfGQW0/jxtJl5ua6r1K4ubHLRU1mnjYzfHzY5S1b6r5KwLpO4tDnTQEmbIl4uV2GfQHq6mLLpK6GEzSMyeYhzsmIMS/KnTt5c4jcuabOLhNrAhDnIc58pOb0KTl9jyGHTzwDUCD9DDF27FitfmJAQABOnz6NhIQEBAYGol%2B/fgCANm3a4O2338a3336rDaQ7d%2B6M7t27AxA23sfFxeHWrVtwcHDAqlWr4ODgAI1Ggzt37qBRo0ba1Jqs4wLAtm3b0KdPH61WZO/evfHSSy9J%2Bu/u7o6BAwcCAHr06AE7OzvcuHEDHTp0wMyZM2FmZgalUonk5GSYmZmhqKgI2dnZjxRI1yYGP2HCRERF8cXgLXlZA2TA%2B2KXe0o2Nvr7AvD9kQM3CFHL0whlZtYAAJlao7yI8HkZVbStoywIcl3Wtx4ndp4PAADvGZqePeX5MmYMx%2BDDU/Iq2rCBWdxNRhXe3vIOxaNpU56FjAvJ678QEq5wbThGcvsU3x0ZEmTW1lwTOVNRXcydAH%2BhQU7byLhMgF0bvo2HdAIuQEi2IgfWdHX%2BvMxK6gFakdYfCqSfIWxtH6RFMjIygkajQUZGRo004q6urrhz50E2q6oi6OIG%2B4rK0ZOYmIiIiAikpaXBw8MD1tbWNXLQ13ZcALh79y5at26tY%2Bvm5qYjWVOV6mLsSqVS60dSUhLmzZuHGzduwN3dHU0rv/EqHnGU1yYGb2hohwxG0jxDQ%2BGL4P59oLxc2o61QGFgIASthYXsCSovT7oMqMwUZgNkZDCTAHJXH%2BX6w/pSVyiEILq4GMzMhurcNOlCQDgpKysgK4t9Ury0j8bGwrdSUpLglAQnM6W/4kxNhSD6/HmApcvPW701MhK%2B1NPYyRq5yK2HlajU3FwIov/8k92/fv2V7YujoxBEr1kD3Lsnbfdhgi%2B7opYthSA6PByozDJWG8fipQNyMzMhiD57FsjPlz6ULSNjHCB0maZNgZs3mV0GnlaMPiy3/wIot2ZHeoaG7DkGADIz2eVy3bHTMNIyGxkJQXRmJvecMgzZd/PkzJ2suw%2BAMNcolUKSRdZcw5oiHuIywS71H%2BlCtVoIoq9dYy4ZXzVmB%2BMypyviGYYC6WccFxcX3Lp1S%2Be9pKQkWRmEUlJSEB0djfj4eG3QuX//fvz444%2Byj52cnKzzXnJyMlS8%2B%2BHVKC0txbhx4/Dee%2B8hPDwcCoUC58%2Bf1z6V%2ByjUJgafkiIv2CkvZ9vJie15EkCcbLxaysrYtnJ/Z/D84W39AIQvNtaXm%2BxIsqyMbSv3PmdxMdM2N5dfRUEB205u2u%2ByMvm2%2BtTD%2B40BCEE0yy4pSZ4v9%2B5xbBlpv3VITGTayrlO%2BflsO7l3eIqLhR%2BVkljI6MO8/ltH1NVwQoWMyYY30QAoY439SnhzJ3P%2BqGbHspXTNrIuk5zsdkVFTLsimefEma6eGLQirT8kf/eMExoaioMHD2Lv3r0oLy/HhQsXsHr1aoSGhnI/m5%2Bfj/Lycm36zKtXr2LZsmUAgBIZUcHgwYPx008/4ejRoygvL8fhw4dlB%2BFVKS0tRVFREdRqNRQKBZKTkzF//nxtGUEQBEEQdc/TqCP9rEEr0s843t7eWLx4MZYtW4Zp06bBysoKw4YNw9ixY7mffe655zBlyhRMnjwZhYWFcHR0xJAhQzB//nxcvnwZlpwNb%2B3atcMnn3yCjz/%2BGFlZWfDz80PXrl0fWp/R1NQUs2bNwuLFizFz5kzY2NhgyJAhuHr1Ki5fvqzVgyQIgiAIgniaoED6GcDV1RWXqm2OjIx88NBcYGAgAgMDa/3snDlzarxXta7Ro0dj9OjROuUjR46s1bb6ca9fv4727dvjwIEDOuXWlQ%2BuVD121c%2BJHDx4UPv/V1/gLqhSAAAgAElEQVR9VZuJSOTtt9%2BW9IMgCIIgCP1oaKvH9QFt7SAematXr2LkyJHaPdonTpzA0aNH0aNHjyfsGUEQBEEQRP1DK9LEI9O7d29cvXoVI0aMQHZ2NlxcXDBjxgz4%2BnKe5icIgiAI4olDK9L6Q4E0oRfvvPMO3nnnnSftBkEQBEEQDwkF0vpDgTTRYHAwYohIA5ViypawMbwPQFoM9VKydGoCY2NBiistja0Z6uLCdkWUo7OwYE90TCkvCJqqZmaCpBpLCkpOIgVeAolsU3a2EAMDwAJArtqOeU6NL19mH0gUz87OZgomsxLIiM/DKpVsO4b8MQDh%2Bjg5Adevs%2BXZeNdb1CbPz2dLZLGuk9gsFhbszHC8JDSicmbz5uxMivv3sXW/LCyEZCvH4k8x2yaoL8NZHx/g1Cl0m%2BjLlNC7/i/bF1GR08yMk%2ByD1TFFPTaNhht9sFTVDAwEP4qKHlMQU00eVQczM8DeXtA6ZAl1A7itku7EJiaC7n1KCntO4mUSNTAQrlVpKbttWONA7PsWFjLk9m5zymXAE5YSMyzKUBgknlEokCYIgiAIgmiA0Iq0/tDDhgRBEARBEATxCNCKNEEQBEEQRAOEVqT1h1akCUlu3rz5pF0gCIIgCKKeoMyG%2BkOBNFErc%2BfOxYoVK/SuZ%2BfOnejfv7/e9cTExCAmJkbvegiCIAiCIOoK2tpB1EpWVlad1BMcHIzg4OA6qYsgCIIgiLqjoa0e1we0Iv0Mc/v2bXh5eWHLli0ICAhAx44d8dZbb%2BHevXsAgJ9//hkhISHw9fVFUFAQ1q5di4rKURMTE4PY2FiMHz8ePj4%2BCAwMxDfffAMAWLZsGXbt2oVdu3YhODgYn3/%2BOYKCgnSO/cUXX2D48OEAAC8vL6xZswY9e/aEj48Pxo0bh5SUFABAQkICAgICtJ/77bffMGjQIPj4%2BCAgIADr1q0DAGg0Gnz%2B%2BecYMGAA/Pz80KlTJ7z//vsoYmmBEQRBEARBPEFoRfo/wKFDh7Bjxw6UlJTgrbfewvLly9GvXz9MmjQJ8%2BbNQ58%2BfXDp0iVEREQAAN58800AQpC7atUqxMfHY%2BvWrZg%2BfTqCgoIwYcIEJCUlAQDmzJmD1NRUfPbZZzh79iy8vb0BADt27NDWI/797bffwtLSElOmTMG7776LDRs26Ph5/fp1jB8/HnFxcRg4cCASExMxYsQING3aFLm5ufjmm2%2Bwbt06uLu749q1awgPD8euXbswePDgh26T1NRUpKWl6bxnp1LBXhTJrQ1RKJkjmMzSHRb1asVXKQw4P2FFLVSFgm1rxBnBMk%2BpTuCdk1jOs9MKIkthYqL7KoEZ4zgyq%2BD6amam%2ByqFqBMthdinWH0LYDeN3HNiDQEAsLLSfZXCwoJdLrdt4OMjXdaype6rBLzxVlU3XJZhbYiDjTfowO43cscB7zCy3WFdALmdBoAJ4zhi/%2Bb187qaI1g66VXnTi4sYWuZJ2XCmVvljG1ePoD6hFak9YcC6f8AY8eORaNGjQAAAQEBOH36NBISEhAYGIh%2B/foBANq0aYO3334b3377rTYA7ty5M7p37w4ACA0NRVxcHG7dugUHBwed%2Bu3t7eHv74/vv/8e3t7e%2BOeff3D79m307dtXaxMdHQ03NzcAwJQpU9C3b1/cvq2rdr979260adMGgwYNAgC0bdsWGzZsgL29PVQqFXx9feHo6IjMzExkZWXB0tJSu7L9sGzatAnx8fE6702cMAGRUVH8D3MiBHcZyUucnfk2cuB9v/ESHIhUdg%2B94AUh3CClEm5g1bGjvIpat2YWM8IzLZz4TDaVvy/1pnII6QWnWWQ3b7WbUI8Mt21OneJXUu1HeXU4%2BW602NtzLfiVWFtzTXhdHOCPbe44qYT3gwd27fmVtGjBNeF0KwDAc8/JMJKBjLieC%2B9HKQCgTRu%2BjYcHs1juFNKsmXQZI9dQvUOBtP5QIP0fwNbWVvt/IyMjaDQaZGRkoFWrVjp2rq6uuHPnjvZvuypLU8rKKKhCYlSFhIQgLi4O//vf/7B9%2B3b07dsXZlVm%2BqZNm2r/71wZRVZfEU5NTdWWibSsjGRyc3OxaNEi/PLLL7C2tkarVq1QWloKDTc1Ve2EhYXpbCkBhBVp3L8v/SFDQyGIzs0FyqUzG964Lx1Jq1RCEJ2cLGQTlIL3ha5QCF8mhYXs7Fys7ImAcEqNGgE5OcxT4i4CK5X8rFy8XThiRrf8fPbkbXH5L3ZFJiZCtHjhAnMp57SBdMRoYiIE0YmJ7NUg3kqRmZkQKJ49y04Mx7vexsZCEJ2UxL6m2dnSZTKbBVeusH2xshKC6P37AdajEk2asOuR2zbdJvpKF7ZsKQTR4eHMNJN3drGDcaVSuAapqex%2B7KJMlS40MhKC6MxMdppQAPlm0hfcwODB2GaNA1Z2RNEdKyvhGrHcsbt7TrrQxEQIoq9c4Xb2C0bSAblaLQTR//7LngeqfE3Uity2Yd1hUyiE8VRczM9sqL72D6NQLQTR164xTyrRkB2MGxsLQfT16/z5mng2oUD6P4qLiwtuVUsNm5SUpBM8PwwBAQGIi4vDb7/9hr1792Lx4sU65SkpKfD09AQA7Uq0s7Mzrl%2B/rrVxcnLC4cOHdT63bds22NjY4JdffkFycjIOHjwI88qobsCAAY/kKyCsottXj14yMtjRpEh5OdNOzmRYUsK2460CiLc2edmIOd/nWsrL5ds%2BKnJXNrjySIy03zoUFjJt82VsZyksZAd5nKzJOnasNNhy7wgUF7MDETlNw2kWVPt9K0lWFtuWuxJaCa9tZC3HJSYy7Vg/WqtSWsqzlZHDWUauZzljgTcO5I7XsjKOrZxOzBsIAAo522cAoe%2By4vG6miO428MgzJ3cdRjerxVAOCmGXaHMKKq4%2BMlu4ZCCVqT1hx42/I8SGhqKgwcPYu/evSgvL8eFCxewevVqhIaGyvq8SqVCbpVvP6VSieDgYCxevBjm5ubw8/PTsV%2B2bBnS0tKQk5ODuXPnwt/fv8YWkf79%2B%2BPChQvYsWMHysvLcf78ecyZMwdGRkbIy8uDsbExDA0NUVxcjC%2B//BKXL19GKW8ZlCAIgiAI4glBgfR/FG9vbyxevBirV6%2BGn58fJk6ciGHDhmH8%2BPGyPt%2BvXz%2BcOnUKPXv21L4XEhKCCxcuICQkpIZ9mzZtEB4ejoCAADRq1AgLFiyoYdOkSRN8/vnnWL9%2BPZ5//nm89957iImJwQsvvIBJkyahqKgI3bp1Q0BAAM6cOYNXX30Vly9ffuQ2IAiCIAhCGkrIoj%2B0teMZxtXVFZcuXdJ5LzIyUvv/wMBABAYG1vrZOXPm1Hival1dunTB8ePHdcodHR2hUqkwcODAGp/t06cPPvrooxrvh4SE6ATeXbp0wZYtW2rYubm5aaXw5PpLEARBEMSj09CC3vqAAmmCS0lJCW7evIlvvvkGPXr0qLFlgyAIgiAIoiFCgTTBpaSkBEOHDoWTkxNWrlz5pN0hCIIgCKIOoBVp/VFoHlVfjCCeMeQIdhga8u0MixhPt8vVb%2BLJLxgZATY2gtII65F8lpwfIGgvubsDN24wZURynb0ky2TL1lUwtNnEikR5QVZFPAkGmbpfKRXSCjVym9cBHB1zmRVlqdh3ceTKFLKaRu45nTnDdAUWFkC3bsCxY2y1DUdHdj0mJoCnJ3D5MlutgKVoolIBLi7AnTvsc2/2HCf7ho%2BPoFft68tU/8i%2BL/11KLf7in5LoVAIympFRWxVCZMihvYgILvT5Cml5VUMDAQt%2BoIC/jmZm/AmRhmTZ04Ovw45A4GVJEVuAwP8TD4yzqm4jC0PpFAIhykpYbsjS/e6Hrh4sX7qraa%2B%2B5%2BGVqQJgiAIgiAaILQirT8USBMEQRAEQTRAKJDWH5K/IwiCIAiCIIhHgFakCYIgCIIgGiC0Iq0/FEgTtVJcXIysrCw48p4oIgiCIAjimYQCaf2hrR1ErYSHh%2BPYsWN61REbG4vY2Fi9fQkICEBCQoLe9RAEQRAEQdQltCJN1EpWFkd6SQbTp0%2BvA08IgiAIgqgPaEVafyiQJmowatQoJCcnIy4uDjt37oSbmxtmzJihLR83bhxat26NLl26YPLkyQgNDcWGDRsAAMHBwZg8eTJUKhViYmIAPEjv/fXXX2PdunVIT09Hs2bNMHnyZHTt2hV5eXmYM2cOTp48idTUVFhYWGD48OEYP378I59Damoq0tLSdN6zsbGDnZ39I9epxYBxI0ehePDKsjPiDD1DQ91XKXjio6JOKkcvleWqWMayqbTgFMusqI7axojxBSG3eblTpMyKeMepi6aRe04WFuxyMzPdVylMTNjlYteU20VrQ6nUfZXEx4dd3rKl7qsEdTMOHkwDrDKWDYA66zR1MV3VGXU1EOqkgesG3mHkuEPZPJ5tKJAmavDll18iICAAEydOhFqtRlxcHD766COoVCqkp6fjt99%2Bw4cffojk5GSkpKTg%2BvXrOHDgANLT0zF27FiYmZlh0qRJOnUmJCRg%2BfLlWLlyJby9vbFt2za88847OHToED777DPcvn0bW7duhYWFBX788UdERUXh5ZdfRtOmTR/pHDZt2oT4%2BHid9yZMmIioqEjuZ7nBFS%2BCANgJA%2BTWAQCWluxyGxt59Tg7M4s5MRMAOS5zIjTtweQcTQasTB4A5LQMr3nl1cKviO3pA8zNZRo%2Buivo1k1ePd7e%2BvsCAI84hHWw5/3%2BPXVKXkWVP/ilkNOD66r7chNwqGX2Gk6nMZVRhbzpiPurkz95csaslroYCHWV4YRzTioZzQKwfwwycmXVO7QirT8USBNMXnrpJXzyySc4ePAg%2Bvbti127dsHHxwdubm5ITk6GQqFAXFwczM3NYW5ujjFjxmDVqlU1Aunt27cjLCwMPpUrR4MHD4aHhwfUajUiIyNhaGgIc3Nz3Lt3D8aVE2BqauojB9JhYWEICAjQec/Gxo6ftVBOZsMSRpo2uVm1Cgr4jlhaCpkLWQ6xUs8BwnKfszOQnMxMDZdv5y5ZJjdZo1kFxxe5KRJZafkA2dnPMiqkM7rJbV4bZPB9kVFRjpIdkBsYCLFDXh67aUpL9XYFly4xXYGZmRBEnz0rXCopbG3Z9RgbC0H0zZvsQIEVmCqVQhCdmso%2Bd5cBvmxnWrYUgujwcCAxUdIs97B0QC63%2B4p%2BS6FQCG1TXMyeItQlnCyAMjtNgZF08KpQPBjbvFVRU%2BM6mDxZHQqQPxB4qSPlNDDAv9Uh45xKyvmZDZVKof8%2BjSvPFEjrDwXSBBOVSoVXXnkF33//Pfr27Yvt27dj1KhR2vLGjRvDyupBwOLk5ITU1NQa9aSlpcG52qqor6/w5Xfr1i18%2BumnuHDhAlxdXdG2bVsAQIUeI9ze3h721Zax5KQIlwXLL/GWpEajX7AoUl7OtpW7lFFSwrSV09QVFRw7udeLV1EdtU2ZDHd4zQvUjS/lMm%2BbV1Sw%2B6mcpuGdE%2B%2B3l0h%2BPttW7spscTE7RTh32waEIISZOZ6R9luHxESmbZ2MA8gLmDQajp3cCYvTaSpkbFfhTVd1Rh2dU900cN0g9xCPyR3iCUCqHQSX0NBQHD16FKdPn8bt27cRFBSkLcvNzUVhlW/J27dv1wiYASHAvnv3rs57ixYtwrVr1xAdHY22bdvi%2BPHj2L59O9577736OxmCIAiCIAA8%2BGFY1/8aEhRIE7WiUqmQW7kc1bp1azRv3hzTp09Hv379YFJlQ115eTnmzp2L4uJi/Pvvv/jiiy8waNCgGvWFhIRg06ZNOHfuHCoqKrBt2zasX78eVlZWyM3NhVqthqGhITIzMzFz5kwAQCnrXi5BEARBEMQThgJpolYGDRqERYsW4YMPPgAgBMIXLlxAaGhoDdvGjRsjMDAQI0aMwGuvvYYxY8bUsBkwYAAiIyMxefJk%2BPn5YdOmTVi9ejWsra0xe/Zs7NmzB76%2BvggJCYGDgwNat26Ny5cv1/t5EgRBEERDhVak9Yf2SBO1MmbMGJ2A2MXFBc8995z2YcGqvPvuu3j33XdrvC/K3okMHz4cw4cPr2Hn7%2B%2BPvXv3Svpy8ODBh3GdIAiCIAgZPItBb0FBAWbMmIGDBw%2BirKwMgYGBiIuLg5nEgxv79%2B/H8uXLkZSUBEtLS4SEhCAiIgIGlQ8JvPzyy0hOTtb%2BDQBbt26Fh4eHLH8okCaYZGVl4d69e1ixYgWGDRv2pN0hCIIgCKIBM2PGDNy9exf79%2B9HeXk5Jk2ahAULFiAuLq6G7fnz5zFlyhR89tln6NGjB65fv46xY8fC1NQUo0aNQl5enlbC18XF5ZH8oa0dBJPz589j6NChsLOzw9ChQ5%2B0OwRBEARB1BH1tbUjNTUV//zzj86/2hS9HpbCwkLs2rULUVFRsLS0hI2NDT744AMkJCToCB%2BI3LlzB0OHDkWvXr1gYGAADw8P9O7dG3/88QcAIcaxtLR85CAaoBVpgoO/vz/Onj1ba1nnzp1xiSdK%2BxRheOs620ClAlxcYHjvDlNr62JRM8kytRpo1gy4fs8ERUXSh2renK8dpgRQ2oitP/zvfQdmuTEAdwA34A6WUF7JDekytRpo0UKQomadk7NzY6YvhoaAJYD75RZs7eaKFGY92nuRnM14rFuWMqvAtQJ2%2B6pUgJsNkFRgw5Rn4yVqU6kEaezcXLbMWw5DWtjERMjPk5HBlptr3pzvCwC4ubF98TC9K10IVKZhtIOnVRpgwdDjY10ApRKAPVyUqQCkHz7Ovs/WFTMwEJKt5B4%2BxTxcY0tG%2BjkfH%2BDUKVj08OXK7SXdkvZHqQQcHQW9b9bz1M2bS%2BugA0CHDsCJE0Dn3o1w5oy0XfE5xhxtbAy4u8M09QZXSvOm2kuyTKUCnJyAu6mGzD7TqBH7nAwhJC/KQSOwhPIMOGPNQg3klqq52xYaGzDOWaF4oCPN0K1TyczIIkfm8b9EbUnRJk6ciMhIflK0oqIipKTU/j1QWFiI0tJSeHp6at/z8PBAUVERbty4gVatWunYBwUF6SiNFRUV4dChQxgwYAAA4O%2B//4aJiQlef/11XLlyBS4uLoiMjESvXr1knysF0gRBEARBEA2Q%2BtojXVtSNDs7O1mfPXv2LEaMGFFrWXR0NADA1PRBzk5RSSyfk/AnLy8P0dHRUKvVePPNNwEACoUC7dq1w3vvvQdnZ2fs27cPkZGRWLduHTp06CDLXwqkCYIgCIIgGiD1FUjXlhRNLqy73RcuXMDixYtRWFiofbhQ3NJhzkgt/%2B%2B//yIqKgo2Njb45ptvtLbVVcaCg4Pxww8/YP/%2B/bIDadojTRAEQRAEQTz1NGvWDEqlElevXtW%2Bd%2B3aNSiVSri7u9f6mcOHD2Pw4MHw9/fHF198gcaNH2xD/OKLL3D8%2BHEd%2B5KSEhgbG8v2iQJpgiAIgiCIBsizpiNtYmKCl19%2BGQsWLEBmZiYyMzOxYMECvPLKK1Cr1TXsz5w5gwkTJuB///sfpk6dCiMj3Y0Yd%2B/exSeffIKkpCSUlZVh69atOH36NF577TXZPlEgTTwUqampKCgoeNJuEARBEATRAImLi4O7uzsGDBiAvn37wtXVFbGxsdry/v37Y%2BXKlQCAlStXoqysDJ9%2B%2Bil8fHy0/8QtHVOmTMGLL76I8PBw%2BPn5YePGjfj888/RtGlT2f7QHulqeHl5oUePHli1ahUUigdPcCckJCA%2BPv6pSA6ydOlSnDx5Et9%2B%2B%2B1jPW56ejqCgoKwa9cumJqa4sSJExgxYoTOpn8RZ2dn7N69W/v3uXPnsGbNGvz5558oKiqCg4MDBg4ciNGjR%2Bv8QkxOTsaqVatw9OhRZGZmQqVSoV27dhg1ahS6d%2B/%2BWM6TIAiCIBoCz2JCFnNzc8yYMQMzZsyotbxq7CEG1FKoVCpMmzYN06ZNe2R/aEW6Fg4fPow1a9Y8aTeeOoqKimpdjT59%2BnSNf1U78r59%2BzBixAh06tQJP/74I/766y8sXLgQu3btwvvvv6%2B1u3z5MoKDg1FSUoLVq1fjr7/%2Bwo8//ojg4GBMmDABhw8ffiznSRAEQRANgWdta8fTCK1I18Ibb7yBxYsXo2PHjvD19a3V5tKlS1iwYAHOnj0LtVqNgIAAvP/%2B%2B7CwsEBCQgLWrVsHS0tLnDt3DnFxcdi8eTM6deqE48eP4%2BLF/2fvzOOqqrYH/uUyXZwCBcRZQ8WhFAhQMyfI6aFpzlFo%2BcocMYdwKBVnUkOfQz5/DpUpidNzQMt65utpKj5zqFRMTRNEARURVAaB3x8Xblzh7LMRrMz9/Xz8XLl733XW3meffdbZZ%2B21zlC7dm1mzZrFp59%2Byr59%2B3B0dGTq1Km0b98eMKWnjIyM5MqVK2RlZeHn58fcuXOpXLlyidoyceJE7t69y7lz50hJSWHjxo2UK1eO8PBwDh06hJWVFf7%2B/oSGhlKhQgXu37/PrFmz%2BPrrr7l//z7u7u6MGzcOT09PunXrBkC3bt2YM2cOVaqIYxwDZGZmMm3aNEaMGEFwcLD5%2ByZNmrBgwQI%2B%2Bugjbt26ZW5/69atmTt3rrmeo6MjPXr0IDc3l2xR0NUHSEpKIjk52eI7l%2BxsXEXhdwoCfeoE/CzqhfUbBTF4Cz4fNXr7IWT1EcU5LjiG3rGsdcKpFpTr1dOdliQF2Qgm84KXIDY6h9LrN8khoxtHWlZOfpSnYpE9T2XVJt3Ok%2B1kQYxeWRl6/VtQrlcPLy/tskaNLD8FiPpOtlv0AgZ4eFh%2BaiIaECWYsERVZNukd%2B3LnicrQbhv6XOtJ6igTFRHoUAZ0sXSsWNH8vLyGDt2LNu2bcPR0dGiPCUlhYEDB9KrVy%2BWLFlCWloa48ePJzQ0lOXLlwNw6tQpwsPD%2Bec//0lubi4bN24kKiqKTz/9lNq1azN48GCCgoJYtGgR4eHhREREMHPmTNq3b88PP/zArFmzWLt2Lc2aNePatWsMGjSItWvX8s4775S4Pfv37ycqKgo3NzcqVKjAgAEDqFu3Lnv27CE7O5tJkyYxdepUIiIi2L59O8ePH%2BeLL76gfPnyLF68mOnTp7Njxw6io6MJCAggOjqamjVrEhMTo3vsY8eOcevWLbMRXphGjRqxePFiAK5du8bx48f55JNPipVTEsd/0AgGP2IEo0JC9H%2BsE7JHOx3Lb5QiSZIFesaMxiblIlSvXmpVqF279DIAKlbUq6H/gAbAA9flg8hELHUS54eQxs2tbOQ8ZLQoC0rg2idEv01yMWHLpJN1FhB0h1Q%2B5fXyIB07pi8kMlK3isxwcHYWl0tMrwCsXatXo66%2BEIkJopqELpJhgnURRDGTRvdcAyCx4qEzCcua2SJ7XPQs%2Bah50laPHwXKkNZgwoQJHD9%2BnIkTJ5qN4wL27t2Lra0t48ePx9raGqPRyJQpUwgMDDSvgtra2tKjRw8MhR6LO3fuTP38tGI%2BPj7cvn2bF198EYC2bdvy8ccfA9CwYUOzsZqamkpSUhKVK1fWzPSjh6enpzkL0A8//MCpU6f4%2BOOPzTEYJ0yYQJcuXZgyZQpGo5H4%2BHg2b95M27ZtGT16NGPGjBHK9/HxKfLdkCFDGDJkCDdv3gTAWeeuce3aNQDcCt29Dx06ZM6ClJOTg6urK3v27JFqc7HB4LOz4coV7R/Z2posmaQkYcqxi1naVnJ%2BckSuiJMjUrOmdllhdfQW4UXNKdCnenVTVkKRPqLj2NubjOjLl8XJz/RuotbWJiM6LQ1hZkPHnBv6ghwdTanhBIKSc7UNchsbk32XkgL3BUn3RJkc4bcsddeuiftQZkVaYughyjdgb28yon/9VXyeRKvaBbrItKmWMVm7EOQ7WW9FunJluHlTKCPNQfwEYjCYDKs7d8SGQ8V2xb%2BBBEwr0ZGREBQEsbHC413brW2Q29iYjOjr18Xd0qOH8BB4eJiM6IEDQZRgNibqknah7AQBXLWvq1lmY2O6/pOTxW3SM24NBpMRnZ4uPk96K9Iy5xqgor2gzVZWv03CgjGaZ6tvjFtZ/bHGsuLRogxpDezs7Fi0aBEvv/wya9aswanQqsqNGzeoXr061oXeU9XMt4yu5Fs2Li4uFkY0YLGybW1tbRHL0GAwkJd/pRkMBtauXWve1Ofh4UF6erq5vKQUDooeHx9PTk4O7dq1K9LeuLg4AgMDyc7OZtOmTURERFClShWGDh3KK6%2B8oin/6NGjmmUFmYySk5OpXsyqR3JyMi4uLuZ6iYmJ1KtnWvNt1aqVWXbBZk9Zig0Gf/Gi7s0CME2cgnp6xhWYfi5Tr7ToZPU1k5UlrivTLZmZ4jaJjOMH6wnriu7EDwoS1L0vsdJy/774cDL9ArpDRu41s4QcUervAjIzxfX0XWvkdMFG8jzpdbLMktj9%2B0KrPlcy5Kuu/6ZO6m/AZETr1JPxQtNpkjDtd2HOntWpKzNJ6E0QQJbE0uv9%2B%2BIxU0x0smLJzRXPETLXk5Svrsw9NS/vL20FqxXp0qMMaQG1a9dm5syZhIaG0qtXL/P3NWrUICEhgZycHLMxffnyZcBkOP7yyy8WET8KKO674vjkk0/47rvv2Llzp3kld%2BjQoQ/djsLHdXNzw2g0EhMTY9Y9KyuLuLg46tSpw8WLF2natCk9e/YkIyODL7/8kgkTJuDj42NOw1kSPD09cXR0ZPfu3UUyCMXGxtKjRw82bNiAl5cXzz77LJs2baJly5YP3VaFQqFQKBRyKEO69KioHTr87W9/o3fv3kRFRZm/K1jNXbBgARkZGSQnJzN79mxatmxJjTJwjk1PT8fGxgZbW1vu37/P9u3b2b9/f4k222nRrFkz6tSpQ3h4OHfu3CEjI4M5c%2Bbw%2Buuvk5OTw759%2Bxg5ciTx8fEYjUYcHR2xsbGhYsWK5kw/6enp0sezs7Pj/fffZ%2BnSpaxfv547d%2B6Qk5PD0aNHGT16NJ07d8Yrf4PPnDlz2L9/P1OmTOHixYvk5eWRnp7Otm3bWLJkyUOnG1UoFAqFQqF4FKgVabpz2y8AACAASURBVAkmT57MyZMnuX37NgAVK1bk448/Jjw83GxUBwQEEBoaWibHGzx4MD///DMdOnTA3t6eJk2aEBQUxOHDh0st28bGhhUrVvDBBx/QqVMnMjMzadasGR9//DH29vYMHDiQxMREBgwYQHp6OjVq1GDhwoW4ubmRl5dHx44d6d%2B/PxMnTuTpp58GMBvCD/Lll19StWpVunfvjpOTE2vWrGHJkiVkZmZSrVo1evfuzRtvvGGuX%2BAbvnLlSoYOHUpycjJWVlZ4eHjw5ptv0rdv31K3X6FQKBQKhQm1Il16rPIe1vFWoXjcuHhRXC65U/BMhnbcDqMR6tUzHUrkT5y/51SIzGbDX34Rl9vbmyJ7XLr08D7SRiM0aADnzonbpLfxX3KPIFXu62yqtbGBKlXgxg2h7%2B3V3KpCETKbo/SSeNrZQa1aEBdXOh9p2U2q%2Bc/yxeLgAA0bws8/i32k9SIiyLbJvdxVsSDZThbdySV3YaZWEL8JNBh%2B2%2BgqOtxTjgL3Oy8vU1QPb29dH%2Bm4y9q3VdnNnHpzhKenKbJHixZiH%2BnMHwQ7EWUnCOBXo3acPTs7qFYNrl4Vj5lKlYSHwNraVOf27Yf3kZY91wBPGQVttrIyNSwrS7zZ0E7fQV9ms%2BEfFWXvX/96NHJLGGjrsUatSCsUCoVCoVA8gagV6dKjDOnHmI8//tgch7k4unfvzowZM35HjRQKhUKhUDwuKEO69CjXDsWTgyjYKsi/5hQlh5CMnRufKQ66bGsLVatCYqL49W/NiqlCOdLvOUVpy6ysTP4dGRnC95Np98VRXWTju%2BaHHheqKvMauY6zIOiywWDyhbh3T6yMnjuQ0Wh6B3/%2BvNjvxd1dLEeyj4XHkH0vroeknJxK%2BolWrK31VRG5z8iOGb2MerLde/26dpmsSwZArdqldxERuYeURB%2BRy4DsPANQs3yKdqHs2NMLbSkbe1xvMEi4fgHkOGu7f4Hc%2BNULkSk79h4iKFaZsGnTo5H7JG1pUivSCoVCoVAoFE8gakW69KjwdwqFQqFQKBQKxUOgVqQVCoVCoVAonkDUinTpUSvSCmkuXbr0R6ugUCgUCoWijChIpV7W/54kpA3pESNGMH78eIvvtm/fjoeHBx9%2B%2BKHF94sWLbJIqf1nw8PDg5iYmD9ajYfC39%2BfrVu3/u7HXb9%2BPVOmTDH/PXHiRJo2bYqXl1eRf//85z/N9XJzc4mMjKRPnz74%2BPjQokULBg0axKFDh4ocY9%2B%2Bffz973%2BnZcuWNG/eHH9/f8LCwkhKSvpd2qhQKBQKhUJREqQN6fbt2xcxPvfu3YuXlxdff/21xfeHDh3C39%2B/bDRU/Cm4WUwohe7du3P8%2BPEi/4YOHQpAXl4eo0aN4vPPP2fixIkcPnyY/fv3061bN4YOHcrevXvNspYtW8aECRPo0qUL0dHRHD9%2BnE8%2B%2BYSMjAyCgoK4J8owoVAoFAqFosSoFenSI21It2vXjuTkZC5cuABAVlYW%2B/fvZ9KkScTHx5u/T0tL48cff6RDhw4cO3aMgQMH8sILL/Dss8/Sq1cvTuSnYHr11VeJiIiwOEbfvn1ZtWoVAKdOnSI4OBhfX186derEJ598gmykvsTERN555x38/f1p3rw5AQEBbN682aLOgQMH6Nq1Ky1atCAkJITk5GQAYmJi8Pf3Z/ny5bRp0wY/Pz9GjRpFenq6%2BbebNm0iMDAQb29vunfvzo4dO8xlwcHBTJw4kQ4dOtC%2BfXvOnj2Lh4cH27Zto0OHDnh6ejJp0iSOHj3KSy%2B9hJeXF4MGDTIbqunp6bz//vt06tQJT09P2rRpY7HCWxI8PDyYNWsWLVq0MBu3Bw8eNK8OBwYGWuh%2B7tw5Xn31VXx9fenQoQMTJkwgPT2df/3rX6xYsYKjR4/i4%2BMjffwvv/yS//73v6xYsQIfHx9sbGyws7Ojb9%2B%2BjBo1yjxmfvnlF5YuXcr8%2BfPp27cvzs7OGAwGateuzYwZM%2BjcuXOxhrxCoVAoFArFH4n0ZkNXV1eaNGnC4cOHcXd357vvvsPV1ZXmzZvj6%2BvL3r17cXd3JyYmBmdnZ9zd3WnXrh0hISG88sorZGRkMHnyZObNm0dkZCR9%2B/Zl0aJFvPPOOxgMBi5cuMCZM2dYvnw5iYmJDBo0iDFjxrBmzRp%2B/fVXhg8fjtFoZMCAAbq6vv/%2B%2Bzg6OrJr1y7s7OxYu3YtM2fOpGvXrpQvXx6Ab7/9llWrVvHUU0/x7rvvMn78eD799FMArly5QmJiIl9//TWJiYm8%2BuqrREZGMmTIELZu3Up4eDhLly7Fz8%2BPI0eOMHLkSBwcHOjYsSNgMlY3bdqEg4MDt/Nz%2Bn777bfs3r2buLg4evbsyenTp1m5ciW2trYMGDCAyMhIRo4cyYIFC4iPj2fz5s1UrFiRr776ipCQELp27UqdOnVKfIIvX77Mf/7zH7Kzs4mNjWXYsGHMnz%2BfgIAATp48yfDhw3FycqJNmzZMnz6dVq1asW7dOlJSUhg0aBCbNm3ijTfeID4%2BniNHjvDZZ59JH/ubb77B29ub6sXkjn7zzTfN///yyy%2BpWrUq7dq1K1LPzs6Od999t8TtTkpKMj8cFeBy7x6uzs7aPyqIpSyKqQzioLXW1pafGtjqPLEXHEIvPq5u7umCcr16omCzBWU6OWzLShXZ7i9V3xRuk6ie0Sg%2Bhr295afe8WT0ESEaV7IdrEdZySnh4UqjSll1r62tdpn0uANTrGgtGjWy/HwIXUqij6jNJWrT7zH2JOdP4ZKnrIwyoizG3h%2BZzeNJWz1%2BFJQoake7du2IiYnh1Vdf5d///jcBAQGAyW83OjqaIUOG8N1339GhQwdsbW2JioqiTp06ZGZmcuXKFRwdHfnxxx8B6NKlC7NnzyYmJoZWrVqxdetW2rVrh7OzMytXrsTd3Z1XX30VgPr16/P3v/%2BddevWSRnSs2bNonz58tja2pKQkED58uXJyMggNTXVbEiHhIRQo0YNAEJDQ%2BnSpQuJiYlmGSNGjMBoNFKnTh1atGjBxfzEDFu2bKF///60atUKgFatWtG/f382bNhgNqTbtm1L1aqmQO8FhvTgwYNxcHCgYcOGuLi48PLLL5vreHp6cuXKFQBGjRqFtbU1FSpU4Nq1a9jn36CTkpIeypDu1q0bDg4OODg4EBERQUBAAJ06dQLA29ubfv36sX79etq0aYO9vT379%2B/H3d2dVq1asX37dgyCiTE6Opp///vfRb7fsWMH1atX5%2BbNmziLDNd8rl27hpubm8V3kydP5quvvgIgOzubt99%2Bm%2BHDh0u3OyoqiqVLl1p8N3LECEaFhOj/uBjDv8RUqiQsFqcB%2BI0qVfRqVJQTlD/uS4WOsSh7BL3EA7Kquohz2gASGQ70DOX69eWUqVVLrp4eega5nr4AFSqUjS46cmTNFD17RuZ8l1WyCr3ufWAaKhaJKc2UcEWPyEixLhKHAUl9dNCfZwDEcxpQdmNPZ/6UwtFRt4rMGNYbv7L2umjs/ZGei8qQLj0lMqTbt29PZGQk9%2B/fZ9%2B%2BfSxZsgQwGdJz584lJSWFgwcPMnnyZKytrYmJieGtt97i7t271K9fHxsbG7N7htFopHv37mzbtg0/Pz927NjBzJkzAdOK8KlTpyzcCHJzc7GWHLFxcXHMmzePS5cuUbduXbMBmltoxNSsWdP8/4IV08KGtEuhu7Stra1Z7%2BvXr1PrgZtmzZo1%2Beabb8x/u7q6FtHJsdBFbW1tTaVCE4XBYDDLv3HjBrNnz%2Bb06dPUrFmTZ555pojuJaGwLleuXOHw4cMW/ZqTk0Pt2rUB0ybRJUuWsHDhQsaOHYu3tzdhYWE0aNCgWNndunUjPDxceOyCB4QHSU9Px9raGgcHB1xdXTlw4IBF%2BZw5c5gzZw5gcpfJKWGmtv79%2Bxfx03e5d8%2BUtVALOzuTEZ2QIE5X9dRT2mWSGb4Ss8RZ4WSTc1UtlyaUI50aTrT8ZWVlugtkZgqXTu7kiI082WSC%2Bc%2BemtjYmIzo5GRx31RzFNydZNONaYxfM/b2JiM6Lk6cDbPQfKOpj0QfC8elwWAyZNLTS3d3lJSTU17f2JHJDCdK1ig7ZvRuDbLde%2BuWdpmNjclovX5dP0Gf29%2B8tQsbNTIZ0UFBEBurWe3abrExLquP3oq0ZBJAqjoILkzZsac3GGQzJOoNBkdH08nUOV6Oo/gJQmb86mWElB17iseXEhnSzz77LAaDgW3btpGXl4dX/uurGjVq0KBBA7Zv305SUhItW7bk5MmTzJw5kw0bNpiNwTVr1phXdgH69evHK6%2B8QseOHbGysqJNmzYAuLm50aJFC1avXm2um5KSwp07gpS/%2BRSsXo4dO5agoCCsrKz46aefLHyBwbTC2yj/1VpcXBxgMojPnTsnlF%2BzZk0uX75s8V1cXJyF4W1VzMxV3HfFMXr0aPz9/Vm9ejU2NjakpKSwceNGqd8WR%2BHjurm58fLLLzNjxgzzd0lJSeTl5ZGbm8vp06cZNWoUkydP5urVq8ydO5eJEyeyZcuWhzp2hw4dGDduXLErzkuWLOHbb7/liy%2B%2BoFOnTixdupTvvvuO1q1bP1xDH8DV1bXoA83Zs2Jjp4CsLHE9vTsOmGZfQT29ybfwoYR1ZQ0mvR0gMjN8Xp6wXlmpopdyt4D793Xqig5S8KYlL09cT2TlFSYzU1xX9g6q08dSqb9zc0uXIrys5UgcRqaOzOnUQ697Za5L3WsShKm/zcTGCuuV1Rwhc%2BuRapNdGYw9mbkTdOdPqUGjJ6OMKKtL%2B49CrUiXnhI5NBkMBtq2bcs///lPOnToYPHa39/fn08//ZTnn38ee3t70tLSMBgMGPNfRZ44cYK1a9eSVejO16hRI55%2B%2BmnmzJnDyy%2B/bF5x7t69OydOnGDHjh3cv3%2BfpKQkhg4dKlz9LCA7O5uMjAyMRiNWVlYkJCQwf/58c1kBS5YsITExkdTUVMLDw%2BnUqROVK1fWld%2BnTx%2BioqI4dOgQOTk5HD58mKioKHr37i3XiTqkpaVhNBqxtrbm5s2bzJo1q4juD0ufPn2Ijo7mwIED5ObmcunSJV577TXWrFmDwWBg1qxZLFq0iMzMTCpXroy9vT1OTqaVU3t7e9LT06U3fAJ07NiRFi1aMGTIEI4dO0Zubi7p6el88sknrF%2B/nvHjx2NlZUXDhg0ZPXo0Y8eOZdOmTaSlmVZZ4%2BLi%2BOCDD/j%2B%2B%2B8tHlQUCoVCoVAo/gyUeGdAu3btiIuLK/LaPCAggISEBDp06ABA69atCQoKMkeBmD59OsHBwdy8eZPr16%2Bbf9evXz8SEhLo06eP%2BbsaNWqwatUqoqKieP755%2BnRowdPP/20lCFdrlw55syZw7Jly/Dy8mLgwIG0bt0aZ2dnfv75Z3O9Nm3a0K9fPzp16oSzszOzZ8%2BWan/Xrl2ZNGkSs2bNwsfHh7CwMEJDQ%2BnZs6fU7/WYO3cuu3fvxtvbm169elG1alWaNGliofvD0rx5cyIiIoiIiMDX15fXXnsNf39/xo0bB5hcOy5cuMALL7zA888/T1pamtndpkOHDty6dYvnnnvO7Pe9c%2BfOYuNIF0QIsbKy4qOPPqJLly5MnToVX19fAgIC%2BPbbb1m5ciUvvviiWbdhw4axcOFCvvnmG7p06YKnpydBQUEkJiaydu1aKd94hUKhUCgU8qjwd6XHKq8kS4wKxePM2bPicnt7qFvX5Ectcu0QvbmwsQEnJ0hJEb5WjM8Ur7Db2kLVqpCYKH7lWrNiqlAOBgNUrAhpaeLZTRQqQ9KfOO2%2BeEeYrLu2XqRDOzuoVg2uXhW7dtRxFriCyTrfFnJFKxaj0bQh8fx5sWuHu7tYjqzPtugYsv6lekjKyakk9vMvEKWnyt272mWyY0YmcoVM9xZa4ymCra1pM%2BK1a/puELVqC/wpvLxMmxG9vYWuHXGXxbdmWX1Erh2y8wxAzfIp2oWyY0/P1UJy/tQdDJKO3znO4m3fMuNXzxVNduyV1YbakrJixaOR%2B/bbj0bunxGVIlyhUCgUCoVCoXgISrTZ8M/A7NmziyRXKczbb79tdi34K9OrVy%2BLjZsPsnLlyhIlT1EoFAqFQvFk8aS5YTwKlGuH4onhzBlxudEI9eqZ3uaL3qA3trugXWhn91s4NNE7P72AtbKuB9euieXI6iMKZCrrvlBVJzq25GvbFBux24vsW2SnrETtQtnXv3p3GdlYfHrvf2X9VdIE4Q5lz1NFndjjkrpctRPHtZftmrKQUc0ocDsA6UFj76btruLpCTEx0KIF5Cfo1eT8ee0yWZcMoXsISLuIXPxF%2BxZvZwc1apiiPOoN0Xp2glCQtrbg6gpJSeJG6cV2lp339JItyfhSAJkGbX8KKytT/2RlicXY20i4Ucn4iPxOCWQeZNmyRyN3xIhHI/fPiHLtUCgUCoVCoVAoHoLHzrVDoVAoFAqFQlF6lGtH6VEr0gqFQqFQKBQKxUOgVqQVCoVCoVAonkDUinTp%2BUutSCclJXFXFJT0L0BmZibX9DaYPSLS0tK4qRfkV6FQKBQKxWOBSshSen43Q3rw4MGMHDmy2LKNGzfy/PPPW6QPLynXr1%2Bnc%2BfOQkMvMDCQHTt2PPQxAOLj4/Hw8CA%2BPr5UcrTw9/dn69atmuVBQUEcPHjwkRxbj44dO3Lu3Dngt37w9PQsNrthYS5evMiECRNo27YtXl5evPjiiyxYsIA7dywTZty6dYsPPviAzp074%2BXlxXPPPcdrr73G7t27f7c2KhQKhUKhUMjyu7l2BAcHM3LkSJKTk3FxsQxv9fnnnzNgwADsRNnVdMjIyNBdjd61a9dDy/%2BzkJKiE%2Bbpdz52dHQ0NWvW1PzNsWPHGDx4MIMHD2bbtm1UrlyZixcvMnXqVAYPHkxkZCTW1tYkJSXRt29fGjduzKJFi3B3dycrK4sjR44wefJkUlNTeeWVVx5l8xQKhUKheKJ40laPHwW/myHdrl07qlevzr/%2B9S%2BGDBli/v7EiROcO3eOFStWkJ6eTkREBHv37iUrK4uWLVvy3nvv4ezsTHx8PAEBAcyaNYvly5eTmppKs2bNmDt3Li4uLnTr1g2Abt26MWfOHC5cuMDx48dJTU0lLi6OZcuWMWHCBEaOHEmvXr24e/cuCxYs4IsvviA7OxsvLy/CwsKoUaMG/v7%2B5noAMTExDBw4kLPFpJg%2BduwYixYt4pdffiE1NZUGDRowdepUPD09iYmJITQ0FB8fH7799luGDBli0XYtTp06xcaNG/n555%2BpVq0a06ZNw8/Pj8GDB5OQkMC0adP46aefmDp1qlDOkiVLivRB48aNNfu44DebN2/m3r171KpVi%2BHDhxMQEEDnzp0BeOuttxg1ahRdu3aVOu9Tp06lZ8%2BehISEmL%2BrV68eCxcuZOrUqcTFxVG3bl0%2B%2BOAD3NzcWLZsGdb58TTt7Ozw9/dnzpw5JXYpSUpKIjk52eK7O3dccHFx1fxNwXOc7vOcjaCCra3lpxaiOKjwW15fKytxXT1lZfURBkq1t/zUQi9Pc0GcVJ14qXrhVAu6Q68LhfpI6iIVR1rvWDLIyjEatctkz5PemJHURU/VsugaaRllNGg8PbXLPDwsP0WILjfpNj3wZq8IjRpZfmogOt2y04NuJdlGldW8J8p7XliGDmUkRvGE87sZ0gaDgaCgICIjI3nrrbewyh%2Bdn3/%2BOV26dMHV1ZWQkBDu3LnD1q1bMRqNhIeHM3LkSD7//HOznP/85z9s27aNrKws3njjDT766CNmzJhBdHQ0AQEB5hXSJUuWcOjQIdasWUOzZs2wf%2BDmMmPGDC5cuMDWrVupUqUK06ZNY%2BzYsURFRUm3KSMjg2HDhhESEsIrr7xCRkYGkydPZt68eURGRgJw7do1nn76acLDw8nMzJSSe%2BDAAVauXEn16tUJCwtjypQp7NmzhzVr1hQx8vV4sA/Gjh2r2ccxMTFERUWxdetWXFxciIqK4r333qNt27bs2bMHDw8PVq5cSYsWLaRcWy5fvsy5c%2BcICwsrUubs7MxHH30EQE5ODl999RXTpk0zG9GF8ff3l2prYaKioli6dKnFdyNGjCQkZJTub2vU0KtRS18BvYQrsogMJzAlW5GhLPSRPZYelSqJiyXFVKigV6OKvhC9BBGyOGkn8igRLuJkNFKU1XnS0UVW07LoGn0ZkqNGZ9DExOiLWLtW7lB65K9daHPsmJyg/HuNFrrTGaZcKhK19KtUriwjSB%2B9eU8GvQdKQOYduP5DhmQiFdHDnl6ylkeIWpEuPb9r1I4%2BffqwePFiDh8%2BTKtWrbh16xZffPEF69at48aNG%2BzZs4cvvviCKlVMN8DJkyfj4%2BPDqVOncMy/4b311ltUyr8R%2B/v7c1yQ0alWrVq0atWqyPdZWVns2rWL5cuXU61aNQAmTZrEr7/%2BWqL22NraEhUVRZ06dcjMzOTKlSs4Ojry448/Fmm3ra0ttlKP/dC/f39q164NQJcuXYQ%2B03oU7gO9Pra3tyc1NZWNGzfSoUMH%2BvbtS//%2B/c0PPcXx0ksvYXhg5aBbt26EhYWZV5Gdde4YN2/eJCsrC7dCxt6lS5fo06cPALm5uWRnZxfpVxH9%2B/cvYoDfueOCIKu6dJavejZx2oWyacv07qKy2bmuXxfLkdVH9JBnb/9bdkRRvSo6hqtkdrnb1mKryWAw2UPp6eKbQKXsG2JdHB3h1i3xTUxmRVoiW6Nuaj/ZFH4P7CuwQPY8lS9fJrok21TTFSPTNWUhw8X%2BtliQ5KBp0VHbIPfwMBnRAwdCMS8nLdi%2BXbvMxsZ0%2BV%2B/Lm6T29%2B8xQdp1MhkRAcFQWysZrUrO7UNctmEhAA1bJO0C21sTEb0zZviRull1ZSd9/SWku3tTdeATmbDLIO2wW5lZeqf7GyxGDvrMsps%2BAehDOnS87sa0hUrVuSll15i06ZNtGrVii1bttCkSROaNWvGDz/8AEC/fv0sfmNtbU18fLzZkC5slNnY2CDKcO6q8ZidmppKVlYW1atXN39XqVIlnn322RK1x9rampiYGN566y3u3r1L/fr1i9VJSw8tHAutktna2pJTiguw8LGvXDGleNXq4y5durBkyRI%2B%2B%2BwzVq1ahdFoJDg4mGHDhhUxlgvYsWOHpo90gS98cnIydevWLVJ%2B/fp1nJ2dcXJywtbWlsTE31I6161bl6NHjwK/udaUBFdX1yL9fuaMOHNyAVlZOvXsJDbFZmeLrXG92augv/PyxHVlN%2Bjq6SPTMZmZ4nqy1lJOjrCu7GjPzdW5N8noo6OL9F3m/v3SpQgvLOdRnyfJB3o9XWRtY72uKRMZMmmaQXfQ6KX%2BBpMRrVdPzygFU3uE9QSLRBbExgrrygw9vekhv5a%2BIL1GldW8p%2BvXlS9Dx5DWKZYVo3jC%2Bd3jSAcHB/Pyyy%2BTkpLCxo0bzb6zVatWBeCLL76w2Ix4/vx5atWqVcTfVQatldQqVapgZ2fH1atXefrppwHTau3KlSt55513MBgMZBeaDLQ2%2BJ08eZKZM2eyYcMGnnnmGQDWrFnDxQeWPUUruo%2BawsfW6%2BOEhASqVKnC6tWrycrK4tChQ4wcOZKmTZvSvn37Eh%2B7Ro0aNGzYkN27d%2BPr62tRduPGDTp06MDcuXPp1q0b/v7%2BbNmyhZdfflnTaFcoFAqFQlF2qBXp0vO7Wyz169fnueeeIzw8nHv37tGpUyfAZOS1b9%2Be2bNnk5KSQnZ2NsuXL6dPnz7cvq3z2g7MPtDp6em6dQ0GAz179mTJkiUkJiaSmZnJokWLOHHiBEajEXd3d/bu3UtGRgbJycms1XCKS0tLw2AwYMz35zpx4gRr164tVRg/Pezs7EhLS3uo3%2Br18Y8//sibb75JbGwsdnZ2ZvcPp3wHxYc59pQpU9iyZQtLly4lJSWFvLw8zpw5w9ChQ2natKl5E%2BN7773H1atXGTlyJLGxseTm5pKZmcm///1vZs6cWSTSi0KhUCgUCsUfzR%2By9Pfaa6%2Bxbds2XnnlFQu/4Xnz5lGpUiV69uxJy5Yt%2Bfbbb1m1apWUEeXs7EzHjh3p37%2B/xeZELSZOnMgzzzxD3759adOmDSkpKfzjH/8AYPz48dy5c4fWrVszcOBAXnrppWJltG7dmqCgIF599VV8fX2ZPn06wcHB3Lx5k%2Bt6vqsPSZ8%2BfVi4cCHjx49/qN%2BL%2Brhz584MHjyYYcOG4enpyejRo5k8eTLNmzcHTH7H48aNY%2BHChWZ53bp1KzaOdIHvup%2BfH%2BvWreP06dMEBgbi7e1NSEgILVu2ZNWqVebzX7VqVXbs2EHDhg0ZP348vr6%2BtGzZko8%2B%2BoiXXnqJL7/8spQ9p1AoFAqFojCPY0KWu3fvMmnSJFq0aMFzzz1HaGhokbwUhZk2bRrPPPOMhY1SOLDEypUradu2LZ6engQHB/PLL7%2BUSB%2BrPJGTsULxF%2BLMGXG50Qj16sHFi2IX08Z2F7QL7ex%2B2/AlejOhF0XDYAAHB7h3Tzwr6WW5lNXn3j3tMqMR6teH8%2BfFHZPvOqSJ5M6xFBvxg7PknkWcshK1C21sTJsjb9wonY%2B07CZBvbdUdnZQrRpcvSquK3ojJHue9DZ8Sepy1a6OUIxs15SFjGpGnfj6koPG3k17o6unpymqR4sW%2Bj7S589rl8nu/61VW8cl0MvLFNnD21voI33xF%2B1bvOwGa4B6dle0C2V3LepFyZGd9/RC48lsWAQyDQ5CMXZ2pn4RRgeV8c%2BX2WyoF8LxETFz5qORO2XKo5ELpuAQV69eZdGiReTk5PDOO%2B9Qv359pk2bVmz9Xr16md2KH%2BRf//oXCxcuZPXq1dSuXZuFCxdy4MABdu7cKe2Wq5xRFQqFQqFQKBR/eu7du8fOnTsJCQnB0dGRKlWqMH78eLZu3cq9YhaEsrKy%2BPnnn8372B5k48aNBAUF0aBBA%2Bzt7Rk3bhwJCQnEyMTCzOd332z4JDNixAhheu/p06drupE8yJ49e5g4caJm7GyaFgAAIABJREFU%2BXPPPceqVatKrKNCoVAoFIong0flhlFcUjQXFxepKGYZGRkWUbwKc%2B/ePbKzs2nYsKH5O3d3dzIyMrh06RKNGze2qB8bG8v9%2B/dZvHgx33//PRUrVqR37968%2BeabGAwGzp8/z1tvvWWub2trS926dYmNjaVly5ZSbVWG9O/IsmXLykxW586dzRv1FHLUry9XT5DxHICTp901yxwcoCHw871aQm%2BJZyTyDVgDOXbarx4BblbS1gXyvSmAlAq1hK/GHWuLdbEFsuuIO1Am2Io1kFNJHCfaKWK%2BWIirKwwaRKV/fWp6lazBF8%2B8q1lWqRK0bg3fxVZBtJdZ8NwLmDwghg%2BHjza5cPWqdj296I32VlAbuJxdjUzBK/YD/9Muq1IFXqoPO07X54YghLbedVC%2BPHhXg2NXqwnDVrd5WvCqHyDPFnDFJS8JcgWv%2By9fFivj0gyXqz8IY2inP1s0X0BhDAYoB9y1qUSuYJxm/iAIEG1vD9QlJuqSOE43EG%2Blnf6wcMY80ZtjkUsG5LtlYIoTLYx7/7TgIPnuITW6i91DAOrV1danaVOIjoZug105dUpbxpo1wkNQoQL4%2BsL/fnJAFDdAlPfFwQEaNoSfLxuFczBA8/NbtAsdHSEgALv9e03x5jX41rm38BgVKsBzz8H3J6yFbWrXTqzr40ZxSdFGjhzJqFH6SdFOnjypGfJ29OjRAJQrV878nYOD6T5ZnJ90Wloafn5%2BBAcHExERwZkzZxgxYgQGg4E333yTO3fumH9fgNFo5O7du7p6FqAMaYVCoVAoFIonkEe1Il1cUjTZ6FstWrTgrEbWo9OnT/OPf/yDe/fuUT4/uVSBS0eFYjKXtm7dmtatW5v/btasGYMGDWL37t28%2BeabODg4kPHAnpKMjAyzbBmUIa1QKBQKhULxBPKoDOnikqKVBfXq1cPW1pbz58%2BbI4pduHDB7JLxIP/%2B97%2B5fv06AwYMMH%2BXlZVlDlvcoEEDzp07R4cOHQDIzs7m0qVLFq4jeqjNhgqFQqFQKBSKPz0ODg507dqVBQsWcPPmTW7evMmCBQvo1q2b2TguTF5eHnPnzuXQoUPk5eVx/Phx1q5dS//%2B/QHo3bs369atIzY2lszMTD788EOcnZ3x8fGR1kmtSCsUCoVCoVA8gTyOmQ2nTZvGBx98QPfu3cnOziYgIIApheLtBQYG0r17d4YOHUrHjh2ZNGkSYWFhJCYm4uzszKhRo%2BjRowdgys2RlpbGiBEjuHnzJs8%2B%2BywrVqywyHGihzKkBaSmprJw4UL27dtHamoqFSpUoHXr1owZMwY3Nzf8/f0ZOXIkvXr1%2BqNVfSQURAUJDw8vlZyEhAQCAwPZtWsX1atXLwvVFAqFQqFQPIFUqFCBmTNnMlMjCPauXbss/h4wYICFa0dhrKysGDx4MIMHD35ofZRrh4AxY8aQkpLC5s2bOXHiBNu2bSMrK4s33niD%2Bw%2BbYeAJpHr16hw/flwZ0QqFQqFQ/Il4HDMb/tlQhrSA77//no4dO5p3mjo7O5tTZt9%2BIGZWRkYG8%2BbNo127dvj6%2BhIcHMwPP/xgLvfw8GDVqlW0b98eLy8v3n77bYs4iQcPHqRPnz74%2BPgQGBjIjh07pPX09/dn69at5r9jYmLw8DCFX4qPj8fDw4PPPvuM1q1b89xzz/Huu%2B%2BSnh%2BHJzExkTfffBM/Pz/atm3LyJEjSdIIKbZp0yZzmu/u3btb6BgcHEx4eDi9evXC09OTXr16cfToUQsd4uPjzX3x2Wef0blzZ7y8vBgwYIDFDt2DBw/Ss2dPvL29GTBgAPPnzyc4OFi6PxQKhUKhUOijDOnSo1w7BAQGBjJt2jSOHj2Kn58fzZs3p0aNGsW6OoSFhXH69GnWrl1LtWrV%2BPzzz3n99deJjo42r8Ru27aNzz77DEdHR0JDQxkzZgyRkZHExsYybNgw5s%2BfT0BAACdPnmT48OE4OTnRpk2bMmnLV199xc6dO8nJyWHEiBFMnz6d%2BfPnExERgZubG8uXLyczM5OQkBD%2B7//%2Bj/fff9/i91u3biU8PJylS5fi5%2BfHkSNHGDlyJA4ODnTs2BEwxY1cvnw53t7erF69mmHDhvHVV18Vq8%2BuXbtYt24dRqORkJAQ5s2bx%2BrVq4mPj2fo0KG899579O7dmxMnTjB06NAiQdb1KC4YvJOTCy4upd9F7CAI7Wxvb/n5qLHRuYILss7%2BQdlnHw69nd4FQWRFwWQxxYrWoiCykV6Eo2rVxOXOzpafWuiNhwJ3PD23vCpVtMueesryUwu9NheMb9E4B/SVLRiceoNUpJCkMnoxzAvHbhbWFZ0oOzvLTwGirpHtFr3sxLJjBi8v7bJGjSw/BTQVvFB0d7f81KKY6GQWFIQGLhQiuFjKbA4WpSyvWNHyUwO9NskMYVF8acWfH2VIC5g1axYtWrRg9%2B7dTJ06lbS0NGrXrs2oUaMsMhBmZmYSHR3NsmXLqFOnDgCDBg1i586dREdHM2TIEMAUSLxWrVoAhIaG0qVLF%2BLj49mwYQMBAQF06tQJAG9vb/r168f69evLzJCeNGkSlfMNj5CQEIYNG8bs2bOxt7fnf//7H7t27aJVq1asWrUKQzF3mi1bttC/f39atTIlPmjVqhX9%2B/dnw4YNZkO6d%2B/e5kxAQ4cO5fPPP2ffvn34%2BfkVkRccHGxe6e/atSsrVqwAYOfOnTRu3Ni8o9bHx4d%2B/frx448/lqi9xQWDHzFiJCEh%2BsHg9W5MMlFx8odBqdEzgJ3EuU3MiIxKWUqw90KIrlE/aJCcoO7dhcWthaUmPD3F5a1lhAD9%2BsnV00PPcK%2BtkzgHyi6xg/6zq%2BRDqc4Dj%2B6DE0CDBsJiHbvLjO7DQTHhs4og4aJWVUIX0UNRSdDtvmPH9IVERupWiZbQ5R//kKgkQdOmpZchNQc3DNCvU8z9qzDPyalDkybaZd9%2BKynkEfCkrR4/CpQhLcBgMNCjRw969OhBXl4eFy5cYPv27YSGhloEFk9NTSU7O5uaD6TEq1mzptmdATAb2YB5lTo5OZkrV65w%2BPBhi3ArOTk51Ja5a0pS%2BNjVqlUjKyuLW7du8f7777NixQpWr17NxIkTadSoEe%2B//36R0C/Xr183PwQUbt8333xj/rtwDEcrKyvc3NyKrAoX4FxoCc/Gxoa8PFPWrKtXr1KjRg2LurVq1SqxIV1cMHgnJxeyBQnWwGQs6tW5eFG7zN7eNIH/%2Bqs4%2BZneyg2YDM6cHHEdUVa%2BAhmVKpnqiWTprarI9ItUZkOJNlmv%2B1RcoXJlkxG9cyfcvKlZ7bv62gZ5%2BfImI/rECWHCPE6eFKvi7GwyojduhOvXtet16yaWY2trMqKvXhX384kT2mVPPWUyor/9FlJTtevpZe50cDAZ0WfOIMwM511TO6skYFpyrVzZdI5Ee0quXRMr06ABnDsnVOZu/WZCVaysTKLu3YM8QcLAckmXtAvt7ExGdEICwlSCQKJDXc0yGxuTEX3jhrhb9Lbh2NqajOikJPGYqdHdW7uwUSOTER0UBLGxwuN1q65tkLu7m4zo0aPhwgVtGdOmCQ9BuXImI/rUKRAllhO9dZGdgwEaxu3VLqxY0WREHzkCaWma1b53FBvjDg4mI/r0afH1pHh8UYa0Bvv37yckJIR9%2B/bh6OiIlZUV9evXZ9y4cXz33XecPn3aXNfZ2Rl7e3vi4uJwL2QhXb582cKYS0xMNAf5LjCwq1evjpubGy%2B//DIzZsww101KSjIbl3oYDAayC82kKSkpReokJiby9NNPm4/t4OCAk5MTp06don///owaNYqbN2%2BybNkyRo4cyeHDhy1%2BX7NmTS4/kMo3Li7O4oGisM93bm4uCQkJVNNbYnuAGjVqsG/fPovvEhISSiQDig8Gr2cIyiIzGWZm/j6Tpuye15wc%2Bbp/OIK03xbcvCmse1tiofPOHfHDiCjtd2GuXxfX1buhF5CdLa4rSv1dQGqquJ7sW4x798QPGdIX1P374rrCg8gpo7eqVvCQl5enU1fmRGVl6dbLlriz6nWLbPdmZ%2BvY9TqpvwGTEa1T71TR20oRLlxAmCJc1oXh7l1xXQnvGrk5WJD620xamrBeuqQVde/en9OFQ61Ilx612VADX19fqlSpwqRJkzh79izZ2dmkp6ezY8cOLl26RPv27c11DQYDvXv3JiIigl9//ZWsrCw%2B/fRTzp8/T2BgoLnesmXLSE5O5vbt23zwwQe0adOGqlWr0qdPH6Kjozlw4AC5ublcunSJ1157jTVr1kjp6u7uzt69e8nIyCA5OZm1a9cWqfPhhx%2BSnp5OYmIiixcvpkePHtja2vLPf/6TmTNnkp6eTqVKlcwG9oP06dOHqKgoDh06RE5ODocPHyYqKorevXub62zatImffvqJrKwsli1bRl5enjlbkCw9evTgzJkzbNu2jZycHE6ePMnGjRtLJEOhUCgUCoU%2BarNh6VEr0hoYjUYiIyNZunQpw4YN48aNG9ja2uLp6cnHH39ssfIMJp/nJUuW8Prrr3Pr1i08PDxYvXo19erVM9dp2rQpQUFBpKSk0L59e/OGvubNmxMREUFERASjR4/GwcGBbt26MXbsWCldx48fT1hYGK1bt8bV1ZVBgwbx/fffW9SpXbs23bp14969e3Tv3p13330XgBkzZjB9%2BnQCAgLIysrimWee4R/FOLp17dqV9PR0Zs2aRUJCAlWrViU0NJSePXua6/j5%2BTFjxgzOnz9PkyZNWLNmDRUrViRV9J75Adzc3Fi8eDHz5s1j%2BvTpNG7cmBdeeKHYVXaFQqFQKBSKPxJlSAtwdXW1cLd4kML%2BwQ4ODoSGhhIaGqpZv1OnThbZdwrTvn17i1XuktCgQQPWr19v8d2DwcdfffVVJkyYUOS3rq6uLFu2rFi5D0Yn6du3L3379hXqsXjx4iLf16xZ0yK8XeH/A/Tq1cuc1Obq1au4uLhYBFQvbUIYhUKhUCgURXnSVo8fBcq1Q/GnIiUlhaCgIH766ScAYmNj2bFjR4ldRBQKhUKhUCgeNWpF%2Bk/OiBEjOHjwoGb59OnTLULxPe40adKE9957j7Fjx5KcnIyzszNDhgyhm17YA4VCoVAoFCVCrUiXHmVI/0486M4gi5bbhSwPulU8Kj777LMyk6XnQqJQKBQKhaL0KEO69ChDWvHEYHtbJ3aYtTU4OmJ755Yw2HFza0GcM4MRqE9Dw3mwztCslsMzOtrK4eIoEyPLFqcKOvVE8bMMBrB1wPb%2BPeGsm54nTplnMJjixGZmiifvj43vCuW42MMAYIP9IJKN2vVGGfdpF9pVAHxpbfc/MGrHpOrqoxOo%2B6mngPYMb/IfqCHYVHtfJ4uPjRGoR%2B2ci3Bfe9w0MAoCSds5AV14ye5LMAo259rU1dGlPNAMb5sfwEY75FyiTSuxGGuoAtywduW%2BIJJnvF0NzTIHG2gCnLZpxj1ByLPnHHSCkwNgTTl7cb1fjR6aZXZ2UA24al%2BXLJ2sg3XKC/rf2hqoRFWH22An0EcUSBnyMyS5UsM2CdC%2BvuvV1e78ptVNiVa6VT%2BmG97u4iVBo528gGNEJ3jDJe0wevuQC%2BmqR/NKgmD%2BdnZADRqWvwK24njfeyr01iyr6ADPAwcdAkgThA7tbIwRK2tXDniW5%2Bx%2BBKPonLYQy1H8aVGGtEKhUCgUCsUTiFqRLj1qs6FCoVAoFAqFQvEQqBVphUKhUCgUiicQtSJdepQhrfjTk5OTQ0JCArVq1fqjVVEoFAqF4i%2BDMqRLj3LteMxJTU0lLCyMdu3a4enpyQsvvMCECRO4du0aAP7%2B/mzduvUP1hLOnz9Ply5d8PLy4sMPPyyRXmPGjGHbtm2PWEOFQqFQKBSKkqEM6cecMWPGkJKSwubNmzlx4gTbtm0jKyuLN954g/v3BVuNf2e%2B/vprjEYjR48eZdy4cSX6rUoPrlAoFApF2ZOb%2B2j%2BPUkoQ/ox5/vvv6djx464uLgA4OzszOTJk2nevDm3b1uG7srIyGDevHm0a9cOX19fgoOD%2BeGHH8zlHh4erFq1ivbt2%2BPl5cXbb79NYmKiufzgwYP06dMHHx8fAgMD2bFjh5SO4eHhLFu2jLNnz%2BLj48PFi5ahixITE3nnnXfw9/enefPmBAQEsHnzZgDee%2B89jh49yooVKxg6dOhD9ZFCoVAoFIqiKEO69Cgf6cecwMBApk2bxtGjR/Hz86N58%2BbUqFGD8PDwInXDwsI4ffo0a9eupVq1anz%2B%2Bee8/vrrREdHU716dQC2bdvGZ599hqOjI6GhoYwZM4bIyEhiY2MZNmwY8%2BfPJyAggJMnTzJ8%2BHCcnJxo06aNUMeJEydSvnx5jhw5Umzilvfffx9HR0d27dqFnZ0da9euZebMmXTt2pXZs2dz%2BfJl/Pz8GDVqlHS/JCUlkZycbPGdi50drvkPHMVibW35qYVRELzY3t7y83HBIHimtrL67VNQz6ATIlZSDKJTBODkZPmpSYUK2mXlyll%2BamGlEzC44BiiY4F4zEB%2B7NtCn1qIGl2pkuWnFuXF8b5xcLD81MBG5%2B4hezmJDlPQbXrdV1aIur%2BgvXrtBsSNLhj8oosA8uNEC5BUqGlT7TJ3d8tPIU5e2mWNGll%2BaqB3mchelsITVdBvev0HVKyoXVZwmehdLrrKygxivZjhij81ypB%2BzJk1axYtWrRg9%2B7dTJ06lbS0NGrXrs2oUaMsUodnZmYSHR3NsmXLqFOnDgCDBg1i586dREdHM2TIEABGjx5t3tQXGhpKly5diI%2BPZ8OGDQQEBNCpUycAvL296devH%2BvXr9c1pGXaUL58eWxtbUlISKB8%2BfJkZGSQmppKed1ZrHiioqJYunSpxXcjR4xgVEiI/o9FsyuAo6O%2BDJ2NkTq2xW/19Cpa698sAP2bisRNR8%2Ba0bv3FaBjnzFggJyczp31avjqCxFZGSXBx6ds5NTQTk4CQL16%2BjKef75sdGnQQFhcRVKM3uVSRULQ00/r1ZC8onQuqGrV9EXoPeiZ0HmYAX2rUu%2BBqIDKlYXF0dH6Iv7xD5kDHdOvEhkpLJa4IgGZy1LnOgFwddWt8ryEmObN9Wo8qy8ExNdTjE5Sl0fIk7Z6/ChQhvRjjsFgoEePHvTo0YO8vDwuXLjA9u3bCQ0NNbt7gGlTYnZ2NjVr1rT4fc2aNYmPjzf/XWBkA%2BZV6uTkZK5cucLhw4fxKWQw5OTkULt27VK3IS4ujnnz5nHp0iXq1q1r1iG3FFd4//798ff3t/jOxc4Obt3S/pG1tcmITksTZjbk%2BnXtMnt7kxEdF2dK4adBTr362jIKqSNSA8A6VyKzoa0tZOvUE/nTW1mZjOiMDMjTXna%2Bmye2kK2sTEb0vXtCMeh5DDk5mYzoPXtA5D4/wP1/2oXlypnu1qdOiVeD7mhn9gNMxpCPDxw9CunaGRIpdF0Vi52dyYi%2BckWcZfLsWe2ySpVMRvTBg3BbkJEx/7rWxMHBdNM/d850sjS4UaOZUEx%2BolBuiROFUsh7rAhGo8mI/uUX0/DToomHRGZDiQvqapK2oW1jYzKik5PFlwtAtfKC/jcYTOMmPV1sxYgaXKBQ5cpw86ZQoW6DtQ1Kd3eTET16NFy4ID5cdIK3dmGjRiYjOigIYmM1q/1vhdgYl70sfatf0S60tTUZ0UlJuvPewV%2B1Leny5U1G9MmT4mng%2BYo/Co%2BB0fjb9aR3ThWPJcqQfozZv38/ISEh7Nu3D0dHR6ysrKhfvz7jxo3ju%2B%2B%2B4/Tp0%2Ba6zs7O2NvbExcXh3uh93iXL1%2B2MDgTExNp2NCUzrjAwK5evTpubm68/PLLzJgxw1w3KSmJPJFFJEF2djZvv/02Y8eOJSgoCCsrK3766Sdp/2stXF1dcX1wReLGDX3LFEx1RPVkJsPMzMdr0hTd0AteQeflCevl6gwFSTE84JGjSUqKTt2qAsO2gLt3xQawyCAtTHo6pApShMuOhawscV2Zjbe3b4vrPfWUnC737gktCNm9zDk54roCW91MRoZcvdIieoYp4P59iXpGiXkmN1c8z%2Bg9/BZWSFD31Cl9ERcuSNQTpP42ExsLx7XriS61wuhdllInKjtbt15amr6YO3d06llLumVkZPwpXTjUinTpUZsNH2N8fX2pUqUKkyZN4uzZs2RnZ5Oens6OHTu4dOkS7du3N9c1GAz07t2biIgIfv31V7Kysvj00085f/48gYGB5nrLli0jOTmZ27dv88EHH9CmTRuqVq1Knz59iI6O5sCBA%2BTm5nLp0iVee%2B011qxZU6o2ZGdnk5GRgdFoxMrKioSEBObPn28uA7CzsyNNZsZTKBQKhUIhjdpsWHqUIf0YYzQaiYyMxMXFhWHDhuHj40P79u3ZsWMHH3/8scXKM5h8nl944QVef/11WrRowRdffMHq1aupV8jvsmnTpgQFBeHv70%2BlSpVYsGABAM2bNyciIoKIiAh8fX157bXX8Pf3L3EouwcpV64cc%2BbMYdmyZXh5eTFw4EBat26Ns7MzP//8MwA9e/Zky5YtBAUFlepYCoVCoVAoFGWJcu14zHF1dbVwt3iQb775xvx/BwcHQkNDCQ0N1azfqVMnpkyZUmxZ%2B/btLVa5S8KDETcK61Xg412Ygs2PAN27d6d79%2B4PdVyFQqFQKBTF86StHj8K1Iq0QqFQKBQKhULxEKgVaUWpGTFiBAcPHtQsnz59ukUoPoVCoVAoFH88akW69FjllTbsgkLxmBAXJy63tQU3N7h2Tbxhvpardlg7rKxMocyyssQx3vR2nRsMpvhLd%2B6IZ7qbN8Vy7OxMwXGvXhUeM62ydng2WVUq3haEpALpsFSpFcTBXQ2G36IUivR5CkEUDVkhGzcKdaFKFejVC7ZuNUWF0eDea28JxUhGGMQhqwzapJfdRHIMZyJOOlQWl4L02LuvE83E2toUHvD2bWGkjBS0E95IigDA6b4gnIyNjSmGY0qKOJyJXqIPg%2BG3eJKCztl3RDsWf4UK4OsL//uffESN0sjp4K%2BT4MjLC44dA29vYfQPEhK0y0oQp/Aq2oHDZcVUq6CzEV56EOvkL3hE9O37aORu2vRo5P4ZUa4dCoVCoVAoFArFQ6BcOxQKhUKhUCieQJRrR%2BlRK9IKhUKhUCgUCsVDoFakFQqFQqFQKJ5A1Ip06VGGtEKhUCgUCsUTiDKkS89f0rUjNTWVsLAw2rVrh6enJy%2B88AITJkzg2rVrAPj7%2B7N169Y/WMuixMTE4OHh8UerYSY4OJglS5aUWs7Ro0fx8vIqA40UCoVCoVAo/jz8JQ3pMWPGkJKSwubNmzlx4gTbtm0jKyuLN954g/s64XAUZY%2BPjw/HRaGMFAqFQqFQ/O7k5j6af08Sf0nXju%2B//57Zs2fj4uICgLOzM5MnT%2BbDDz/k9u3bFnUzMjJYvHgxu3bt4u7duzRq1Ih3332XZs2aAeDh4cG7777LunXrSE1Nxc/PjxkzZlC1alUADh48SEREBJcuXaJq1aq8/fbb0slHkpKSmDp1KkeOHMHJyYnAwECL8rNnz7JgwQJOnjyJ0WjE39%2BfcePGkZuby/PPP8/mzZtp3LgxmZmZ%2BPr6EhwczLvvvgtASEgI7u7u1KpVi02bNtG0aVOio6OxsrLC39%2BfsLAwbG1tOXfuHGFhYfz8889UqFABPz8/pkyZQoUKFSx0yc3NZdWqVWzcuJGUlBTq1avH6NGjadOmDWBa5e/Vqxc7d%2B4kKSmJJk2aMH36dOrXr09MTAwDBw7k7NmzxMfHExAQwKxZs1i%2BfDmpqak0a9aMuXPn4ubmBsCuXbtYvHgxN27coHnz5lSvXp3s7GzCw8Olx0BSUhLJyZaxXHNzXXBxcdX8jY2N5acmVoJYqAVlojpgii0qU65Xz85OXC7ZKNFhZFXB1vaR61IifUTrBLJCqlQRlzs6Wn5qoDccZIdNmZyoMlJGT9WyuBSkz7W1tdxBdASJpMiPOxDeWgt0ldVZi8IdLKj7wFRuQUGoar2Q1XpIy9F7M9mokeWnFqI5RHoiFxtA0mLKYsJ60izPvxh/SUM6MDCQadOmcfToUfz8/GjevDk1atQo1hALCwvj9OnTrF27lmrVqvH555/z%2BuuvEx0dTfXq1QHYtm0bn332GY6OjoSGhjJmzBgiIyOJjY1l2LBhzJ8/n4CAAE6ePMnw4cNxcnIyG5gixowZg5OTE//9739JS0tj2LBh5rKUlBQGDhxIr169WLJkCWlpaYwfP57Q0FCWL1%2BOn58f//3vf2ncuDFHjhzBysrKnF0wKyuLAwcOMGzYMM6cOcOxY8do27Yt%2B/fv58yZMwwaNIjnn3%2BewMBApk%2BfTqtWrVi3bh0pKSkMGjSITZs28cYbb1joumzZMjZv3sxHH32Eh4cHX331FcOHD2f9%2BvXmh46oqCj%2B7//%2Bj3r16jFnzhyGDh3KF198UWzb//Of/1i8Kfjoo4%2BYMWMGx48fZ8KECSxevJi2bduyb98%2B3nnnHbp37y538vOJiopi6dKlFt%2BNGDGSkJBRur91dtaroWO8gr5RqWcAF%2BDgIC4vr51swYL8h0pNMWWgCuW1H1IsqFxZWCyblkC/6RKS9IT06iWnjL%2B/sFgnBYoZe3GOEzCWQZtk0RnDkiO4TC4F3bFHJTllRFalpBQdEfloJ3b57WCSOuuhk2DH11dfRNOmZaOKrpxjx%2BQERUaWWhec9M%2BBeFaUFSN5vYkGcZpOUpdHiLLhS89f0pCeNWsWLVq0YPfu3UydOpW0tDRq167NqFGjLFaLMzMziY6OZtmyZdSpY8rsNmjQIHbu3El0dDRDhgwBYPTo0dSqVQuA0NBQunTpQnx8PBs2bCAgIIBOnToB4O3tTb9%2B/Vi/fr2uIX3lyhWOHj3Knj17qFChAhUqVGDkyJGMGDECgL1792Jra8v48eOxtrbGaDQyZcoUAgMDSU5O5sUXX2T37t28/fbbHDhwgP79%2B7Nu3Tpu3rzJTz%2B0gTdSAAAgAElEQVT9hJOTE40bN%2BbMmTMYjUaGDh2KlZUVzZo1w8PDg4sXLwJgb2/P/v37cXd3p1WrVmzfvh1DMU/OW7ZsYciQITTNnyn/9re/sWfPHjZv3mw2pP/%2B97/TuHFjACZNmoSPjw/HNCbOt956i0r5NxJ/f3%2Bz68eWLVvo1KkT/vmGSceOHXnxxReFfVkc/fv3N8soIDfXhXw3%2BWKxsTEZ0devizNZuVUWpGKzsjJZDtnZ4nRuotSJIJ21jAfesBRBMj3XnUraGb5kVSl/J0lfl8qVTdkYBbqkOYgNculEYQhuTrJCvv5aqAuOjiYj%2Bptv4NYtzWoZfxMb5FZWJiM6M1M8bIzZZdAmPWtdcgxn6ZjSZXEpSI%2B9HJ3rwGAwWcDp6UJBtwWmtKQIACrlCDItyqZIlMlAKZEO838/aRtw5cqZjN9Tp%2BDuXfHhRMjK8X3bWyyoUSOTER0UBLGx2vX27NEuk80cCSQLTGlZMS7l7giPIT2I/yD%2BhCo9dvwlDWmDwUCPHj3o0aMHeXl5XLhwge3btxMaGmp29wDTpsTs7Gxq1qxp8fuaNWsSHx9v/rvAyAbMq9TJyclcuXKFw4cP4%2BPjYy7Pycmhdu3aujomJiZayAMsfnfjxg2qV6%2BOdaHXfwV6XrlyhRdffJG5c%2BeSlpbG/v37mTt3LkeOHOHw4cPExMSYjXuAKlWqYFXo/aqtrS0FmeEXLVrEkiVLWLhwIWPHjsXb25uwsDAaNGhgoe/169fNDxOF9YktNNkV7icHBwccHR1JTk626PMC/p%2B98w6L6uji8I8OgoIiTSQaUcGGFMWGIiBYUKNgw94Fu6iJBUUlWCMWRLFix4IoIBo1FoJG1Ngb2AVBioB0WGDn%2B2Pd/VjYvfcurImGeZ%2BH57J3Zs49t587c%2BachhW6fZWVlUX6fPz4Ea1btxara2Jigk%2BfPlWRwYS%2Bvj709cUNsqQkdvsVEDw0GesxWQUV6zDV4/r0YnM4Y0s1LqSsjLEuF3VYfd%2B4HFyhLgx1%2BWy9slz1gRx2iiHttxifPzPW5XLJCOsx1pXHiZKTMhylyOVWYD3XbDm7KwpiqMtFCosIAVzm4pSXM9djOzDCDg9CGOtySf1dWFizFOGc5XCdKxMfz1yXy/EtK2Otx2XGFKsYeT3LKd8t/7nJhrGxsbCyssLnL71DCgoKaN68OebPn4/WrVvj2bNnoroNGzaEmpoakpKSxGQkJiaKGWFCoxeAyMBu1KgRDA0NMXjwYPz999%2BivwsXLmDXrl2segr9gStuO7VCd6mxsTFSUlJQXuGJnZiYCADQ09ODgYEBzM3NER4ejk%2BfPqFt27aws7PDX3/9hWvXrokZ0tLg8/l49uwZZs2ahYsXL%2BLKlSvQ1dXFokWLqtQ1NjaucpySkpKkHqeCggJkZ2fDyEh6T6ckhPtdkcq/KRQKhUKh1Bw62bDm/OcM6Y4dO0JXVxeLFy9GQkICSktLkZ%2Bfj8jISLx79w49e/YU1VVUVIS7uzsCAgLw/v178Hg8HDhwAK9evRKb%2BBcUFISMjAzk5uZi3bp16N69OwwMDDBkyBCcPXsW169fB5/Px7t37zB69Gjs27ePVc9GjRrBzs4Oa9asQU5ODjIyMsR8eu3t7QEAv/32G4qLi5GRkQF/f3907twZxsbGAARuDzt27EDnzp2hpKSEbt264ezZs%2BDz%2BbC0tGTVQVFREb/%2B%2Bis2b96MkpISNGjQAGpqaqgvwSls6NCh2LVrF54%2BfYry8nKcP38eV65cweDBg0V1QkJC8P79exQVFWHNmjVo1qyZzGHvhg4dikuXLiE2Nhbl5eWIiYnBxYsXZZJBoVAoFAqF8k/wn3PtUFdXx9GjR7Ft2zZ4eXkhMzMTKioqsLS0REhICExNTcXq//zzzwgMDMT48ePx%2BfNnmJmZYe/evfjxxx9Fddq0aYORI0ciOzsbPXv2hI%2BPDwCgffv2CAgIQEBAAObMmQMNDQ30798f3t7enHTduHEjVq5cCQcHB2hpacHNzQ0PHz4EANStWxchISFYu3atyKh2cnLCzz//LGrfq1cvBAQEoFu3bgAAGxsbKCgooFevXmKuHExs3rwZfn5%2BsLOzA5/PR8eOHeHn51el3oQJE8Dn8zFv3jxkZGSgSZMmCAgIgK2traiOjY0NZsyYgZSUFHTs2BG7du2S6G/NRLt27bBy5UqsWLEC2dnZ6NChA7p06QIVthlLFAqFQqFQZOJ77D0uLCyEn58frly5grKyMjg5OcHX1xeaEiZaL1%2B%2BHFFRUWLriouL0bVrV%2Bzduxd8Ph82NjYghIjZTTdu3EAdjuFsFAjh6jBXOzEzM8PBgwfRqVOnf1uVbxpHR0fMnDkTblyjHEjh7du34PP5Yh88s2bNQrNmzTBv3rwaya7kmVIFFRXA0BBITWV29zXRL5FeqKAgCEPA4zE7hrL5NnOdOJaVxSxHVRUwMgI%2BfmTcZl6DJlLLOE/uy01m1kVFBdDXB9LTGQ9wjpYxoxhFRaBuXcFEdyZ9tJFTcyEnTjDqAl1dQWSP8HBGH%2Bmi0VMYxXCcNwYNnhz2icskNg7XcAmYndnlcStwvvbKGCb3AZwn%2BGUzRNvgOkcQAOqXZUgv5DqLje0lznES29Xb0qNKaGkJonrcuVMzH2muchwcWTp4rKwEkT2srZl9pJnc/ThOsAaAj5DueshVjJEWS8QNzhcx13hF8sXJ6evIvXz568gFBMEMPn78iM2bN6O8vBxz585F8%2BbN4evry9r2%2BvXrmD9/Pg4fPowWLVrgxYsXcHNzw71796DKNZpWJf5zrh2U75tXr15h3LhxIn/wW7duITY2VtQrT6FQKBQKpXZSVFSEqKgozJ49Gzo6OtDV1cWCBQsQHh6OoqIixrZZWVlYsGABli5dKgqo8PjxY5iZmVXbiAb%2Bg64d3wozZswQxXWWxMqVKzknbqlNODs749WrVxg7dixycnJgbGwMPz8/WFuzhE2iUCgUCoUiE1/LtUNSUjQ9Pb0q0bQkUVxcLBa8oCJFRUUoLS1Fy5YtRetMTU1RXFyMd%2B/eiULwSuK3335D27ZtxWyvx48fo6SkBO7u7khOToapqSnmz58vk81BDWkWEhISqtUuKChIzpp821y5ckVusry8vMSS08gLEy0Ow7%2BoB8M6LGO3xSyp2FRVBQGBmZ5Q6Swxl9XUBMOBmZkCWdJgSW4iyp6mocGY%2BYJpOFZZWaBKYSHzEGddOWViYwvSoq4uGAX99EngCiENbUOG4y/UhSV0GPr1Y1ZG6LtvZ8forqKRzzDUDwgOsnp9qBexDPcz%2BUGoqAgOTFERs28SW8Bg4Zh2Tg6jLlkMw%2BIyiGFMCik8TUpKLAnklDm4qwCCe4DBz0SRJUS8cMk69YPpmhKWsYU3kNP9xPSIEOYI0dZmT4zTvt5b6YWqqgCM0bFRMvM1ynZzC9MIXrjAfNFUCBtbBaF7SO/erOH2Hv0u/VqoW1dw/b58yZwvJaUhs0uGhgbQujXw7L0mmDpMbWwYxXx3SEqKNnPmTMyaxZ4U7eHDhxg7dqzEsjlz5gCAmP%2ByxpcLuaBAekzvpKQkREZG4uTJk2Lr1dXVYWFhgTlz5kBbWxtHjhzBpEmTEBkZWSXkrzSoIU2hUCgUCoVSC/laPdKSkqJJyikhiU6dOkntxHz27Bm2bNmCoqIi0eRCoUuHFkPa0VOnTsHKyqpKj3XlcL%2BTJk1CeHg4YmJiMHr0aE76UkOaQqFQKBQKpRbytQxpSUnR5MGPP/4IFRUVvHr1Cu3btwcAvH79GioqKmjatKnUdhcvXsTEiROrrN%2B0aRN69%2B4tlgiOx%2BNBjS0DbAXoZEMKhUKhUCgUyjePhoYG%2Bvbti99%2B%2Bw1ZWVnIysrCb7/9hv79%2B0NdSkSi7OxsvH79Gh07dqxS9uLFC/j7%2ByMjIwM8Hg/btm1Dfn4%2BnJ2dOetEDWkKhUKhUCiUWsj3mNnQ19cXTZs2xYABA9CnTx80btwYy5cvF5W7uroiODhY9FuYkdrAwKCKrDVr1uCHH37ATz/9hE6dOuH27dsICQmBDtPkjUpQ1w5KjTh37hz8/PyQlZUFJSUl/PHHH2jENBGkGgh9mNauXStXuRQKhUKhUL4vtLS04OfnJzF5HABER0eL/W7Xrp1Un2sdHR2sWbOmRvpQQ5pSI06ePAlXV1dRtkcKhUKhUCjfB99jZsNvDeraQak2Q4YMQVxcHI4dO4ZWrVrBzMwMHz58wN27d9GmTRvExMQAAFJTU9GpUyccO3YMAPDp0ycsWLAA3bp1g52dHZYvX478CvHXLl%2B%2BDFdXV1haWmLatGnIzmYJW0ehUCgUCkVmvkfXjm8N2iNNqTZhYWEYM2YMbG1tMXjwYDh9yTVqY2MDLy8v%2BPj4ICIiAt7e3rCzs8OIESPA5/Mxffp0NG3aFBcuXEBpaSkWL16M5cuXIyAgAG/evMGcOXOwevVq9OvXD9euXcPs2bNlTl4jMRi8ujr0mcLvCGOxco3fWhMZbDOChfGJhUtpCONEs5Wz1FNmeBIIy5jqfKnBUsxNEFsGa2G8W9ZEVEz7zPU8sR1/rgeHrZzjeWLMtc1VF6brVwY5bJeDPNSpGCqZRQq3jbAIYrocuF4yAJh3muu5ZttpjvskjBUtCeFjiFOAAqYbjuvziut9wlbPykp6mbm5%2BJIBpqzcXyKriZbSYDq%2BwP%2BfZ0zPNZaEfJRvHAVCmJ7MFAozlQ3py5cvo3HjxuDz%2BRg3bhySk5OhoqKC8PBwaGpq4tGjR/Dw8MDt27dFMSDfvn2LPn36IC4uDkeOHMH169dFvdeAIEtk3bp1ZfKRDgwMrBoMfsYMzJo9Wz47TqFQKBSKHLh7999LyCIhkIVcuHPn68j9FqE90pSvgqKiIjw8PDBv3jzMmDFDZDR/%2BPAB5eXlsLe3F6uvqqqKpKQkpKWlVZms%2BMMPP8js3iExGLy6OpCby6Q0oKUlSPPHNDbF1iOtqQkUFDDLYNsfFRXAyAj4%2BJE5S129esxylJQE3S55eYzZGjNKpc9QVlYG6tcXqMyUbEwPHLL3cRD0Np85aL%2BqKmBsDCSzJFH7UVcO55opdSIg2KcGDYCsLOaDw2VkoV49wfXJlFWT6VrgqguXHmkO5ykDzOeJ63XD1CuooCDoLS0pYe6MVwfLeeIoKK9Uerch11sbAOryMqUXKikJ0jl%2B/sx8rtm6Qjnu04tE6fukpgY0aQK8f8%2BcQBUAWmomSy9UUQH09QUZW5muUbZhJK4XTe/e0svMzYGjR4GRI4H4eMbN/bXtntQyTU2gfXvg4UPBOZcGW3AHdXWgWTPgzRv2xwnl%2B4Qa0pSvwufPn7Fu3Tq4u7tjz549cHZ2RqtWrWBoaAh1dXXcunULSl%2BGNnk8HpKSktCkSRMYGhri2rVrYrJSU1NlCo4OSAkGn53N/OISwucz1%2BMytsvmKMb21hJSWspcl8v%2BCOsx1GV6Z1Wsw1yPgxAOgri%2BbHg8lrryONdMRkFFysqY67L7JggoL2c%2ByFz0YdOFk28CWM8Tx7PNet1wGRMlhK0ex4FVFkFcfDs5%2BYByuaHYzjXXwWKWfeLiNlBSwqGeCsNXq5DSUuavWzlde2ypvwEIjGiWekypv4UUFDDX4/pqKi7%2BNl04aps/89eATjakfBV8fHzQvHlzrF69Gh4eHvD29kZRUREsLCzQpEkTrF27FgUFBSguLsbq1asxfvx4lJeXY%2BDAgXjx4gVOnDiBsrIyXL9%2BHZcuXfq3d4dCoVAolP8cdLJhzaGGNEXuhIaG4tatW/D39wcAeHt7Q0FBAf7%2B/lBWVsbOnTvx6dMnuLi4wM7ODomJiQgJCYGamhpMTEwQHByMI0eOwMbGBtu3b5cpwxCFQqFQKBTKPwV17aDUiEOHDon%2BFwY89/DwgIeHh2i9mpoazp07J/ptaGiITZs2SZXZpUsXREREfAVtKRQKhUKhCKltvcdfA2pIUygUCoVCodRCqCFdc6hrB4VCoVAoFAqFUg1ojzSFQqFQKBRKLYT2SNccmpCFUntgCzmnoCCIc8rjMYaTepsiPd4R1xjHV64wq6KrCwwaBJw5A2QyhKTt2ZNZjqoqYGICJCUx69OsGbMcBQX2aFyfPjGXc45Hfesss6B69YAePYA//2SMC36yqL/UMh0dwNkZuHRJEM5XGmxRzOrXB/r0AX7/nTk0uKEhsxwtLUFihDt3BKGtpXHxovSyRo2AWbOAwEAgJUV6PQsLZl247pNHc5aMC3XqAG3aAE%2BfAoWFzHVrKsPaml2WkhJ7SES2%2BNscng8AUK7MHBOtpqrIoo7a2VPSC3V0ACcn4PJl5hsBwAUtd6lldesCXbsCf/3FHCqO7dpTVgb09ICMDOb9f/RIehlXXQCgdx%2BGsJRWVsC9e4JriyGMnklj5muhbVvg/Hmgb1/gyRPp9ZKSmHX9WrRp83XkPn36deR%2Bi9AeaQqFQqFQKJRaCO2RrjnUkKZQKBQKhUKphVBDuubQyYYUCoVCoVAoFEo1oD3SFAqFQqFQKLUQ2iNdc2iPNOW7YMyYMQgMDPy31aBQKBQKhUIRQXukKRQKhUKhUGohtEe65tAeaYqIDx8%2BwMzMDCdPnoSjoyNsbGwwYcIEpKamAgD%2B%2BOMPuLm5wdraGr1798b%2B/fvB/3IXLlq0CLNnz0bfvn3RuXNnJCYmwszMDMePH0fv3r3Rvn17eHp64smTJxgxYgSsrKzg7u6O9%2B/fAwB4PB7WrVuHvn37wsrKCl26dIGfnx9odEYKhUKhUL4OfP7X%2BatN0B5pShWuXbuGM2fOgMfjYcKECdi%2BfTv69euHuXPnYv369XBxcUFCQgKmT58OABg/fjwAIDY2FsePH4ehoSHq1asHAIiKisLx48fB4/Hg6uqK6dOnIyQkBEZGRpg0aRKCg4OxZs0aHDhwALGxsThw4AD09fVx//59jB49Gr169UKXLl1k3of09HRkZGSIrdPT1oa%2Bvr70RgoK4kspqKpKL1NREV9KQ1eXuVxbW3xZHV1k0UceKLM8TZSUxJdS%2BXLtSEVLS3wpBR2GUL5164ovpcEW61eoKleVpVGnjvhSGo0aSS/T0xNfSqN%2BfeZyrvvEqqy6uviyOshDhiww3fscnw//hCoVy1nV0dGRXsb1RgBQV0N6maam%2BFIabM8IYTlbPSZ1ueoCQBArWhrm5uJLKbQ1YN6Eqan4UhJM8aUp3z7UkKZUYcqUKSJD2NHREffv30d4eDicnJzQr18/AECbNm0wdepUHDp0SGRIW1paomXLlmKyRo8eDZ0vD/IWLVqgdevWMP3yROncuTPu3r0LABg2bBgGDx4MXV1dpKeno7i4GJqamkhLS6vWPhw/fhzbtm0TWzdzxgzMmj2bvTGL1WlszC6CyV7nKgMAHBy41WODLSEIF9he2GwGmhBWA61HD26CWJJwOHMQ0bkzt02x0bWrfOSwJUfo2JFdxogR8tGFfZ84ZnJgsiC4Ig8ZAPtXHOtXHjh9lXKQIhdVOKnj5MQuxNaWtQqXS7x9ew6VOMD2LGH7WOSsy7177HWOHmUsPs9hMwBQ6XUkhokJRyFfgdrWe/w1oIY0pQoNGzYU/a%2BsrAxCCDIzM9GqVSuxeo0bN0ZycrLot6TeXp0KvSFKSkrQrtDFqqioKHLdKCoqwqpVq3Dnzh0YGhqidevWIISIXEdkZfjw4XB0dBRbp6etzZzeT0FB8FYqLWVMFZacIb0bWEVFYESnpwvESOMOS1I4bW2BEX31KpCTI72ejQ2zHBUVgRGdmsqsT%2BPGzHK4ZDZkSYwGJSWBEZ2by9zTW//xn8yCtLQERvS9e4xpAC%2BVSDfI69YVGNFxcczZz7j0SAuzqDEkWWQdgeCawC8uTnqZnp7AiD52TJAZThotWjDrwnWf%2BpiwpC5TVxcYwK9fA8XFzHVrKoOl1xAAt3SCTOUcnw8AUK7EPFRUU1VkUUc19rL0wrp1BUb07dusaQD/0pBukGtqCgzXhw%2BBggLpMtiuPa7ZT1%2B%2BlF7GVRcA6DqT4WPc3FxgRI8cCcTHS63W14DZGDc1FRjRM2cKLmPKfw9qSFM4YWxsjMTERLF1SUlJ0KvQNaAgoctS0jpJ%2BPj4QFtbG9evX4eamhr4fD46cul%2Bk4K%2Bvn5Vw76khN0aBAR1GOox2eJCSkuZ6zGl/a5ITg5zXS66cNFHHrClNBZSXs5Sl8l6q0h%2BPmPdz0XsIvLyapYiXEhuLnM6bTXmjNEiCguZU4Qzpf4WkpHBXK/CdzIjbPsEXY5pv4uLq58iXJ4yuCCH58M/qYqwHmNdti9cgP1GAJDH4V4oKGC2x7neT2VlzHXZUn9z0QUAY%2BpvEfHxjPWesHRCCHn9%2Btt04aA90jWHTjakcMLd3R1XrlzB%2BfPnUV5ejmfPnmH37t1wd3eXi/z8/HyoqalBUVER%2Bfn5WL9%2BPfLz81HK1I1KoVAoFAql2tDJhjWHGtIUTrRv3x5btmzB7t270aFDB8ycORMeHh7w9PSUi3wfHx/Ex8fD1tYWffr0QX5%2BPrp3744XL17IRT6FQqFQKBSKvKGuHRQRjRs3RkJCgti6WbNmif53cnKCk5SJK2vXrq2yrrKsQ4cOSZXdtm1bhIeHS9WtclsKhUKhUCg1o7b1Hn8NaI80hUKhUCgUCoVSDWiPNIVCoVAoFEothPZI1xxqSFNqDTnFzKETFBWBuqpAXokq48PlR9Vk6YUqKgD0YaySDkD6RMlJOgxxzACgrg4AJwyqexkoZ5hNr84SCPmLPiZq6YAiw8TNfIbgzoqKgKYmFAoLGJ%2B6elosjxMFBQDqqK9RzBhm4KZuf0YxmpqABYBHOj1QwBBDd%2BinKOmFatoAesBZ7U9AnSG%2BIBt1BHL61PkTKGWQU4clsLh6HQDt0FH9McCXHp3CQfOSdBkahgAmY5bGHkAzlUGXdiy6aAPoiT7q14A60vfpicZPzGLUgOYAXqm1QTFDVAmm%2BcQaSoA5gHilNihiuLxas0SDUFAAVJUAXrkSc6g4VebgzQoAiApLFiQAPIZIfQoKgvB3bGHrNFRZ4t8BAJSgqsRcL6ah9AnhWlqADYC7Ok7IZ7l9e6vfkl5YR3D9dq37GFBiiK6i1Zp5I4qKADShV4f5WZPSUHpGFo0viWN0dNij5Zg0ln4C2hoIYkT3NbjHGJkj6QNLZCo9KwD3cD7NGvjAFCXk38niSw3pmkNdOygUCoVCoVAolGpAe6QpFAqFQqFQaiG0R7rm0B5pCoVCoVAoFAqlGtAeaQqFQqFQKJRaCO2RrjnUkKZQKBQKhUKphVBDuuZQ1w7KP8KYMWMQGBgotdzMzAy3bjHMCqdQKBQKhUL5xqA90hQKhUKhUCi1ENojXXOoIf2d8eHDBzg5OWHChAk4deoU%2Bvfvjw4dOiA4OBgpKSlo0qQJvL29YWdnh5s3b2LatGm4ceMG6tYVxN2MiYnBwoULcf36dQDAjh07EBkZiby8PLRv3x4%2BPj5o0qQJAEEv8ZgxYxAVFQUrKyv89ttvWLZsGf766y8oKyvD3NwcS5YsgampKXg8HjZt2oRr164hNTUV6urq6NevH3x8fKCgIIizmZiYiDFjxiA%2BPh6mpqZYsmQJLCwsquxjfn4%2BAgICcPnyZfB4PHTu3BlLly5Fw4YNOR%2Bn9PR0ZGRkiK3T0NCDnp70eL6KiuJLqagwBC9WVhZfSkNHh7n8y/kSLaujiyz6MO001wOjwBJPVVjOUk9Tk1mMME6scCmVUm3pZVpa4svqwlVOnTrM5erq4ktpGBpKL9PVFV9KQ5vhuACc94lNVWEMX7ZYvkyXJlcZcrr05AbTdv5pXZhOI%2Bd7CQBUGa5hrtcv2zOE47OGSV%2BuqgBA27bSy0xNxZdS0bNiLjc3F19K4j5TfGnKNw%2BhfFckJSWRli1bEh8fH1JSUkIiIyOJjY0NuX37NikrKyNXrlwhlpaW5MWLF4TP55NevXqR48ePi9rPmTOH%2BPn5EUIIWbt2LRk0aBBJTEwkxcXFJDAwkDg6OpLi4mJCCCEtW7YkU6ZMIYWFhSQnJ4ds2bKFTJ48mRQVFZGSkhLyyy%2B/EE9PT0IIIbt27SKurq4kLS2NEELIvXv3SOvWrclff/1FCCFk9OjRxMrKity%2BfZvweDyyfft2YmtrS3JyckTbiouLI4QQMmvWLDJx4kTy6dMnkp%2BfT3x8fMjw4cMJn8/nfJy2bt1KWrZsKfa3ZcvWGh59CoVCoVDkzL9oitWp83X%2BahO0R/o7ZdCgQVBVVUVkZCQ8PDzQsWNHAICDgwMcHR1x7NgxLFu2DEOGDMGZM2cwbNgw5Obm4sqVKzhx4gQIITh27Bi2bt0KExMTAMCMGTNw4sQJXLt2Db179wYA9O/fHxoaGtDQ0IC6ujri4%2BNx5swZdOvWDatXr4bil56DYcOGYfDgwdDV1UV6ejqKi4uhqamJtLQ0kc5DhgwR6enp6YnQ0FDExMRgwIABojqZmZm4cOECzp8/D90vPWtLlixBhw4d8PTpU7Rl6kKowPDhw%2BHo6Ci2TkNDD3l50tt8SeCHAuakWqhblC69UFkZaNAAyMoCyhjSrT1%2BLL0MEPRE29oCt2%2BDUel2LFnquOrD1A2sqCjoAioqYj4wSsxZ4aCgIOhaLClhTOn26AVzV5KGBtCiBfDypUAlaVh8/lN6oZYWYG0N3LsH5Ocz680EVzn16zPLUVf//04VM6TFY5pHoKsLDB4MnD4NZGZKr9e8ObMuWlpAhw7A338z7tOrxj0ZxaipASYmQFKS4JRLg%2BmyVFMDfvwRePuWWUazZoyqQEFBMHjDlk2QbYBHQYG5vRAmXTneBlBX4ZDZUEkJKGeud/eB9PtSQwNo3Rp49oz5XgIAG1WGZxbX65ftRHF81jx7L/15pa4u2MybN8yqAMD8%2BdLLTE2BbduAmTOB16%2Bl1zufZs28EXNz4OhRYORIID6eue6/AHXtqDnUkP5O0dcXuCgkJyfj9u3bCP6csicAACAASURBVA0NFZWVl5ejc2dB6mg3NzcEBgYiKSkJsbGxaNGiBczNzZGZmYnCwkLMmTNHZAwDQGlpKZKTk6tsBwCmTJkCVVVVhIWFYdWqVTAxMcH8%2BfPh4uKCoqIirFq1Cnfu3IGhoSFat24NQgj4Fe7Sxo3/n2dVQUEBhoaGYoa2cH8AgWFeESUlJXz48IGzIa2vry%2BmOwDk5HB7aPD5LPWYchoLKStjrveZIe13RfLymOty0YWLPvI4MKw%2BMV8Q9L9ILS4o4CamqIilbg6H1N/5%2Bdzq1VQOm2%2BCkOJioJAhxXIqQ%2BpvIZmZzPX09LjpwrJPxRw9rUpKmA0aLpdwSQmzocfFuBXW41q3JnDZxj%2BlC5fvxKIiDvXUGa5LIWzXL1erjeVZw2b0C1Vhq/fkCbuc169Z6jGm/a5AfDx14fiPQg3p7xSh37GhoSEGDRqEqVOnispSUlKg/sVBTE9PDz169MDZs2cRExODIUOGAADq168PNTU17Nu3D5aWlqK2b968gYGBQZXtAEBCQgIcHR0xfvx45OXl4ejRo5g3bx7i4uLg4%2BMDbW1tXL9%2BHWpqauDz%2BaLeZyHp6f/vyeXz%2BUhJSYGxsbFYHeG2z58/D70KL/xXr16Jes4pFAqFQqHUHNojXXNo%2BLvvnGHDhuHgwYN49OgRAODx48dwc3PD2bNnxeqcOHECCQkJIjcKRUVFDBkyBBs3bkRqair4fD5Onz6N/v374/379xK3dfLkSfz888/IzMyElpYWtLS0UKdOHaiqqiI/Px9qampQVFREfn4%2B1q9fj/z8fJRW6G4KCwvDw4cPwePxEBgYCGVlZdjb24ttw8DAAD179oS/vz%2Bys7NRWlqKHTt2YMiQIcjNzZX34aNQKBQKhUKpNtSQ/s7p06cPvL29sWTJElhbW2POnDkYP348xowZI6rTvXt38Pl8uLi4QKvCFO5ffvkF7du3x8iRI9GhQwfs378fW7duRevWrSVuy9vbG02aNIGrqyusra0RHh6O7du3Q01NDT4%2BPoiPj4etrS369OmD/Px8dO/eHS9evBC1d3Fxga%2BvLzp37oy7d%2B9i7969qCMhmsH69etRr149DBo0CJ07d0ZMTAz27Nkj1kNNoVAoFAqlZgi9aOT9909QVFSE4cOHIzw8nLHew4cPMXToUFhZWcHR0REnT54UKz99%2BjScnZ1haWkJNzc33JfRBYe6dnxnNG7cGAkJCWLr3Nzc4ObmJrWNkpISYmJiqqxXU1PDggULsGDBAontKm9HU1MT69atk1i3bdu2jBfzoUOHpJZV3pa2tjZWrVrFWJ9CoVAoFErN%2BF5dO16%2BfIlffvkFT58%2BxfDhw6XWy8nJwdSpUzF79mwMHz4cd%2B7cwYwZM2BmZgYLCwvcunULfn5%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%2Bzfvx%2BlpaWIi4tDVFSUyC96yJAhiIqKQlxcHEpLS7F//35kZmbC2dmZ8zaoIU2hUCgUCoVC%2BU/g6uqK4OBgAIIszvv27cPvv/%2BOTp06wcfHBz4%2BPujcuTMAoEuXLvD19cWKFStga2uL6Oho7N69Gzo6Opy3p7RixYoVX2NHKJTvEU1NTdja2srkH/U1ZHxrcr4lXeQl51vSRV5yviVd5CWH6vJ15XxLushLzrekizzlUKoybtw4tGrVSmzdqFGj0KFDB9FvAwMDDBkyBNOmTcPYsWOr1Dc3N8fo0aPh6emJYcOGwdDQUCYdFAghpPq7QKFQKBQKhUKh1E6oaweFQqFQKBQKhVINqCFNoVAoFAqFQqFUA2pIUygUCoVCoVAo1YAa0hQKhUKhUCgUSjWghjSFQqFQKBQKhVINqCFNoVAoFAqFQqFUA2pIUygUCoVCoVAo1YAa0hQKhUKhUCgUSjWghjSFQqFQKBQKhVINqCFNociJrKysf1sFCoVCoVAo/yDUkKZQJMDj8TjVKysrw6ZNm2BjYwNHR0ckJSXB3d0d6enpMm/z%2BvXrEtfv3LlTZlm1hbKysn98mwUFBRLXSzt/siCvj7HCwkKZ2%2BTm5oqu%2Bzdv3iA7O1suulD%2Bm3h5eUlcP3r06H9Yk2%2BXZ8%2Be4eLFi%2BDxeMjMzPy31aF8JZT/bQUolH%2BT%2BfPn49dff4WGhoZo3evXr%2BHt7Y2IiAjW9oGBgYiLi8OWLVswb9486OrqwtDQEP7%2B/tiyZYtMusyYMQMTJkzAnDlzoKCggLS0NCxcuBCvX7/GtGnTZN636jBmzBgoKCgw1jl48KBMMj99%2BoSGDRuCx%2BMhLCwM9evXR9%2B%2BfTm3f//%2BPRISEmBtbY2GDRuK1r958wYLFixAeHg4JzkpKSkS16uoqEBbWxuqqqqc5Hh6emLv3r2i%2BsXFxVi7di3CwsLw5MkTTjIePXqE9evXIy0tDXw%2BHwBQWlqKrKwszjIAoEuXLrh582aV9fb29rhz5w5nOXFxcfDy8kJISAgsLS0RGRmJo0ePYs%2BePbCwsGBsy2U7HTt2ZK2zePFi1jpr1qxhLD9z5gyrjEGDBrHWERIbGwsejwcnJyf07dtX9KFhYGCAAwcOQEVFhbF9SUkJsrKyYGRkhODgYNGHn76%2BPoYNG8ZZj%2BPHj0NFRQVubm6wsLBAaWkpAEBPTw/nz5%2BHpqYmZ1nS7nEVFRU0aNAADg4O6Nevn8S2Hz58EB3j69evY9u2bWLl%2Bfn5SEhI4KyLtHMu1KVnz56wtLSUuX1F2K4ZAFX2QxIzZ85krSMkMzMTM2bMwJMnT6CiooKwsDAMGTIE%2B/btg5WVFWc5lO8DakhTajXJyclwc3PD1q1b0aJFC4SGhmLdunVSXySViYqKQmhoKAwMDKCgoIA6depgzZo1cHZ2llmX0NBQzJs3D/fu3cPgwYOxdu1a2NraIioqirOM06dP4%2BnTp%2BjevTvs7e1l1qFTp04yt2Hi5MmT8Pf3x4MHD7BhwwacO3cOCgoKePv2LaZPn87aPjo6GgsXLgSfz0e9evUQGhoKU1NThIeHY9WqVTA1NeWsi7Ozs8hoJYSIGROKioro2rUr1q1bhwYNGjDKUVdXx6xZsxAUFIQnT57gl19%2BgZKSEg4fPsxZl1WrVsHExAQtWrRAUlISunXrhoMHD2L%2B/Pmsbd%2B/f49Vq1aBEILc3FxMnDhRrDw/Px9aWlqcdQGADRs2YMmSJSKjZe7cuTAxMcHq1atx7NgxxrZjxoxhLFdQUMDz589l0qe6bN26lVUXrob0X3/9hdmzZ8PX1xcAkJaWhmXLloEQgm3btuHUqVMYMWKE1PYZGRkYNWoUevfujfnz5yM4OBjt2rUDANy/fx8tWrTgZFSdO3cOmzdvFhmEKioq2LNnDwgh%2BPXXX3Hs2DFMmjSJ0z4BQPv27XH8%2BHEMGzYMJiYmSElJwfHjx9GjRw80bNgQ/v7%2ByMzMlHheGzVqhJcvXyIrKwvl5eW4deuWWLmamproeHFBRUUF4eHh6NWrl0iXixcvomvXrvj8%2BTMOHDgAf39/1udxdnY2YmNj4eDgABMTE6SlpeHSpUtwcXHhpEfl/agMW%2BdCZVavXo2WLVsiJCQEPXr0gKmpKaZOnYr169cjNDRUJlmU7wBCodRiysrKyMaNG4mVlRUZPXo06datG7l8%2BTLn9p06dSI8Ho8QQkiHDh0IIYSUlJSQzp07V0ufnJwc4uDgQMzNzcmyZctkartnzx7Srl074ubmRtq1a0dOnDhRLR0q8%2BnTJ1JaWlqttgMHDiTXr18nZWVlxNramty9e5ckJiYSe3t7Tu379%2B9PAgMDSUFBAdmwYQNZuHAh2b17N2nVqhXZsGGDTHodOnSITJw4kbx69YqUlJSQ169fk2nTppHAwECSkJBA5s6dSxYsWMAqp6SkhEycOJEMGjSItG3blqxdu5aUlJRw1oMQQiwsLEhRURF5%2BfIlGT9%2BPCGEkPv375PBgwdzan/gwAGyadMm0qZNG7Jp0yaxv%2B3bt5OEhASZ9LG2tq6yjs/nExsbG5nk/JeYPHkyiYiIEP3u2LGj6P/Tp0%2BTUaNGMbZfsWIFWbRokegaFT4fCCFk7dq1xNvbm5Meo0ePJleuXJGox6VLl4i7uzsnOUI8PDzInTt3xNY9ePBAtD/Pnz8nLi4urHKWLl0q03YlMXnyZHLp0iWxddeuXSPTpk0jhBASFxdH%2Bvfvzypn2rRp5I8//hBbFxsby3qOvhZdu3YlhYWFhJD/ny8ejyd2DVD%2BO1BDmlLruX//PrG3tydt2rQhXl5e5PPnz5zbTps2jQQEBBBC/v/A3LNnD5kyZYrMesTHx5OffvqJ9OnTh%2BzevZtYW1uT5cuXix7IbDg6OpLbt28TQgj5/fffiZubm8w6COHxeMTf359YWloSc3NzYmFhQXx8fGQ2GIXH5O7du8TW1la03srKilN7S0tL0Tbz8vKIra0tsbW1JTExMTLpQQghvXr1ItnZ2WLrPn/%2BTJycnMTkc6G4uJiMHz%2B%2BWueZEELs7OxEcrp16yZaz3X7Qk6ePFmt7VfG2dmZPHz4UGzdo0ePSO/evWWSU1hYSD5%2B/EiSk5NJcnIyeffuHbl48aJMMkpKSkhYWBgJCgoigYGBJDAwkAQEBBBPT0%2BZ5CQmJpI7d%2B6Q27dvk9u3b5MbN26QkJAQzu1tbW1JTk6O6HdFIyg/P5/VKHJ0dCSJiYkS2ycmJpIePXpw0qNDhw6koKBAopySkhKJH0FM2NjYkPLycrF15eXlYvck1/uzpue7Y8eOEnWp%2BAHHRRdLS8sqcsrKyoilpSVnXYTcvHmTnDlzhpw%2BfZqcPn2anDhxgvj5%2Bckkw97enmRlZRFC/n%2B%2BsrOzOXcgUL4vqGsHpVazYcMGHDx4EJMnT4aHhwd8fHzQv39//Prrr5xcI5YuXYpx48bh9OnTKCgoQL9%2B/VBQUICQkBCZdXF3d8dPP/0EHx8faGhowMXFBd7e3hg4cCAuXbrE2j4rK0vki%2Brg4IBly5bJrIOQ7du349atW9i8eTMaN26MxMREbNq0CZs3b8bPP//MWY62tjbev3%2BPCxcuwNbWFoDAH1dPT49TewUFBZEvspaWFgoLCxEUFIQePXrIvE/Z2dlQUlKqIl84CUhDQ0Pk%2BiEJR0dHsSFeHo%2BHjIwM2NvbQ1lZ8Ci9fPkyJ12aNWuG0NBQeHh4oE6dOnj%2B/DlUVVVlHkIeMmQI3r59i%2BPHj%2BPjx49YuXIlzp8/Dw8PD5nkjBo1ClOnTsXw4cNhbGyMlJQUnDhxQia/0FOnTsHPzw8lJSVi63V1dWVydVqyZAliY2NRv359lJaWok6dOnj58qVMvs07d%2B7Epk2bRMeTfHHladWqFcaPH89JBo/HQ926dUW/K7qNaGpqMl4rgOB%2BNDExEf2u6GZgYmKC3NxcTnqUl5eL%2Be%2BHhYWJ/ldRUZH5mjExMcGpU6cwdOhQ0bqoqCg0atQIAPD06VNO96fQvaom57tBgwaIjY0Ve9bevHkTOjo6AICkpCRoa2uzyjE2Nsb58%2Bfh6uoqpl%2BTJk046SFE6Coj9DkvLy9HQUEBunfvLpMcR0dHLFy4ED4%2BPqJnDNd3CuX7gxrSlFrN2bNnsXfvXpGRt2vXLhw4cACzZ8/Gw4cPWdubmJggOjoaV69eRUpKCgwNDdGzZ0%2BZfVQBYP369WK%2BgD/88ANCQ0NZ/T6FKCr%2BPwgP14lz0oiKikJISIjIEDA1NYWpqSlGjRolkyE9YcIEDBgwAABw6NAh3L17F9OmTZPJj7Iiqqqq1TKiAaB79%2B6YP38%2Bli5dikaNGiElJQUbNmyAnZ0deDwegoKC0KZNG6ntZ82aVa3tSmLOnDnw8vJCt27dMGnSJAwbNgxKSkoyG8A3b97EjBkz0KNHD8TGxqKwsBCbN29GYWGhTH6z48aNQ926dXHmzBlcvHgRRkZGWLJkCfr3789ZRnBwMObOnQtNTU3cuXMH48aNw4YNG9CtWzeZ9ik2NhahoaHIyspCaGgoNm7ciH379uHRo0ecZRw9ehRbt26Fqqoqrly5Am9vb/j5%2BcHIyIizjAYNGuDdu3f48ccfAQgmdgp59%2B6d2MRXSairqyMrK0vkc%2B/v7y8qy8/P5zxBUF9fH69fv4aZmRkAiBmHCQkJMu0TACxcuBBeXl44deqU6KMpPj4eW7duxfPnzzF69GgsXbqUVc6OHTtqfL5nzZqFmTNnwsXFBY0bN0ZycjL%2B%2BOMPrFixAm/evMG4ceM4RQGZN28e5syZgyNHjsDIyAgfPnzAixcvEBwczFkXADh//jwOHz6MoqIiREZGYvXq1Vi3bp3MUXDmz5%2BPxYsXo0%2BfPgAAOzs72NvbY9WqVTLJoXwn/Ntd4hTKv0nloX4hz58/59ReOKRZ%2BS89PV1mNwghT58%2BJRcuXCAlJSXk06dPnNtVHgKt6EspKx06dKjif1xdH7/ExESSkpJCCCEkMzOTPH78mHNbee5TdnY2mThxIjEzMyPm5ubE3NyceHp6kszMTHLjxg0ycOBA8urVq2rL5%2Bqvzefzyfv370lxcbFoODowMJBcv35d5m26u7uL/GeF5%2Bbhw4cid5V/kvbt2xM%2Bn0%2BSkpLI8OHDCSGC%2B8PZ2VkmOcL9yMzMJH379iWEVHWBYUM4pP/x40eR33lmZiZxcHDgLMPX11eqH/CyZcvI6tWrGdtPmjSJhIWFSSwLCwsjXl5enPRYu3Yt8fT0JHw%2Bv0rZjBkzyJYtWzjJqUhSUhLZvn07Wb58Odm5cydJTU0lhAiO17NnzzjJkNf5vnfvHvHx8SGTJk0iy5YtEz0f3r59W8V/monXr1%2BTwMBAsmzZMhIUFCTmVsMV4fMmPT2dDBgwgBAicPnq3r27zLIIEVxzDx8%2BJGlpadVqT/k%2BoD3SlFqNjo4OXr9%2BjdDQUKSmpsLPzw/R0dGcY6FWjARRGVkiQQA1D5nE5/Px999/gxACQBBjueJvgFsYMgAwMzPDsWPHxI7DsWPH0LJlS07tK1K/fn3ExMQgLS0NxsbGMg1v8ng8sRBXhYWFVUJecQlvBQjO9d69e5GWlobU1FQ0atRINITdtWtXTuEOASAxMRFBQUFVQte9ffsWcXFxjG0LCwsxceJENGzYUBRyKzMzE7t27ULbtm1hZWWFOnXqcNIDEPSM9uzZE8D/IwtYWFhwjgG9YsUKrFixgjGMGNfjq6uri9LSUhgZGeHt27cABFEeZI2fa2hoiKSkJJiYmCAzMxOFhYVQVFSUGr9bEvr6%2BsjPz4eBgQE%2BfPgAQggaNGiAnJwczjKmTZuGgQMHorCwECNGjICBgQHS0tJw8uRJ/Pnnn4iOjmZsP2rUKCxatAgGBgaws7MTrb958yY2bNiATZs2cdLD09MTP/30Ezw8PDB8%2BHCRHqdOnUJKSgrWrl3LeZ%2BENG7cWGIcaENDQxgaGnKSIa/zbWVlJfH51rRpUzRt2pSznGbNmmHMmDFISkpCq1atUF5eLpMegGD/MzMzoaenh9TUVJSWlkJdXR35%2Bfmc2ksLBfn%2B/Xu8f/8eAPdnMOX7gRrSlFrNjRs3MGvWLDg4OOCvv/5CcXExgoKCUFhYiKlTp7K2X7x4Ma5evYolS5bAxMQEHz58wPr169G2bVu4uLhgx44dWLNmDTZs2MAqq6Yhk4qLi6t8AFT8LUsYsrlz52LixImIjIyEiYkJEhMT8erVK%2Bzdu5dTeyGPHz/G5MmToa6uDkNDQyQnJ2PdunXYs2cPmjVrxtq%2BsmuB0E2kuqSnpyMpKQmEELx79w7v3r0DINvLbenSpSCEoH79%2BsjMzETr1q1x5swZTr63O3bsgIqKClauXClap6uri6tXr8LLyws7d%2B7EvHnzOOtiZGSEhw8fisXaffr0Kefh/oofWTXFwsICy5cvx7Jly9C0aVOEhoZCXV1d5O/KlQEDBmDkyJEICwtDz5494eXlBTU1NbRt25azjI4dO2L27NnYvHkzWrdujYCAAKipqcHAwICzDCMjIxw5cgTLly/H2LFjoaCgAEII2rVrh8OHD7O6djg4OGDs2LGYOnUqDA0NYWBggPT0dHz8%2BBGzZ88WcxVhQltbGydPnsTatWvh6%2BsLHo8HZWVlODk5ISAgQGY3spcvX2L9%2BvV49%2B5dlU4Arj7%2BgHzOd1paGnbs2CFRF1ni1RcUFGD58uWIjo6Guro6wsPDMWHCBISEhHB6zgixt7fH%2BPHjceDAAXTs2BFLliyBmpoaZ4P%2BWwoFSfkH%2BRd7wymUfx03Nzdy7do1Qsj/h5QfPXpEHB0dObWXZySIby1k0uvXr8mWLVvIsmXLyM6dO8mHDx9kluHh4UF27Ngh%2Bs3n88nmzZvJuHHj5KgpNw4ePEhat25NzMzMxP7Mzc1lkmNpaUk%2Bf/5Mnj17RqZOnUoIISQmJoaMHDmSta2zszN59%2B6dxLJnz55xCjtWkYiICGJra0u2bNlCLC0tyb59%2B4i9vb1UlwJpPHjwQOJ6WaKjpKWlkSlTppC0tDRy584dYmNjQ9q0aUMiIyNl0oUQQs6dO0cKCgpIXl4e8fX1Jd7e3jIN1efl5ZEVK1aQzMxM8uLFC9K3b19iZ2dXLfcZQghJTU0lDx48IMnJyTK3ff36tciNIjAwkLx48aJaOhAiiGiRkZFBysrKqi1jxIgRZMKECeTEiRMkPDxc7E8W5HG%2BJ0yYQAYMGEACAgJEEVqEf7KwfPly4unpSd68eUM6dOhAeDwe8fX1JRMnTpRJDo/HI7t37ya5ubkkNTWVTJo0iQwfPpw8efJEJjmU2gU1pCm1GhsbG5HvYUX/W67xc21sbEhubq7YupycHJGPZllZGWdD%2BGuFTMrMzKxR%2B5rQsWPHKi99Ho/H%2BfhK80Gv%2BMcVJycncuzYsWrHxBbSpUsXQoggBFrPnj1F67nEDmcLVcY17FhF/vjjDzJx4kTi4uJCxo4dS6Kjo2WWIWm7eXl51QofJqS0tJRz6MbKXL9%2BXXQvXLt2jdy4cUNmGTk5OaJ5Cq9evRLJq46cikj76PiaCP3qKxIdHV0tg9rKyork5eXJSzUR1TnfHTp0kMvzqXv37qKwpcLneFFRUY3mVNSUvLw8cvbsWbJ3715y4cIFUlRU9K/pQvm6UNcOSq2mUaNGuHfvHmxsbETrHj9%2BzHloXFIkiPXr16Nbt26cIkFURB4hk06fPo0nT57A2dkZbdu2xfjx4/H06VM0b94cwcHBMDY25iTn1atXWLt2LZKSkkRpjYXIMvxrZmaGBw8eiB3f58%2Bfi4UFY6JyyLmKkC8hzbgOlWZlZWHo0KFi0U2qww8//ICYmBjY29uDz%2BcjKSkJqqqqVY6TJLS0tJCdnY369etXKfv8%2BbNYqnquODk5wcnJSeZ279%2B/h6urK8rLy0EIQatWrarUsba2lknmo0eP8Pbt2youI7KErjty5Ag2bdqEo0ePitxn1q1bh8WLF3OWUznleVRUFOeU50LKy8uxYMEC8Pl8bNmyBYAg3f3w4cPRp08fbNy4sUo4xcpcvXoVr169wpQpUwAI3K/c3NywYMECODo6ctJDml/9okWLcPjwYezZs0cmv3p9fX1RqvPqIM807HXr1q1xhCFAMD9EKEd47VVcx5WkpCQEBwcjOTm5Rq4mklzaVFVVObu0Ub4vFEjlJx6FUouIjo7GypUr4eHhgYMHD2L69Ok4dOgQvL29Ob0MPn/%2BjPnz5%2BPGjRsig69nz57w9/dHfHw81q1bh4CAAE6prAsKCrB48WJcvHgRgMCfzt7eHhs2bBCLZyuNwMBAHD58GJ06dcKDBw/QokULlJWVYfz48Thx4gTU1NSwefNmVjkAMHToUNSvXx%2B9evWCioqKWNngwYNZ2wtf%2BImJibhy5QqGDBmCxo0bIz09HWFhYXBxccGKFStY5SQnJwMQvBx/%2BuknREZGVqnD9ePA09MT48aN4%2BybKo0rV65g3rx5OHv2LKKionDkyBEoKSmha9eurBO/Fi1ahMaNG0uMz7x9%2B3Y8ffoUQUFBnHWZMGGCxA8NFRUVNGjQAA4ODoxpkp8/f47c3FxMnToVu3fvFitTU1NDy5YtORv3AQEB2L17N/T09ERxtQHBdSzLx1evXr2wZcsWsQ/QJ0%2BeYP78%2Bbhw4QInGe7u7hgxYoRYrORTp07h5MmTrCnPhezatQtRUVHYuHGj2CTb%2BPh4zJkzB8OGDWMMMRgXFwdPT0/Mnj1blMa9oKAAW7duRWhoKPbs2SMKu8nExo0b8eDBA2zevBm6urqi9ZmZmfDy8kKXLl1k8qs/fPgwoqOjMXbs2Cp%2B3lzmCrB9AMhyvsPCwhATE4MpU6ZU0UUY15oLCxYsgIqKCpYvXw57e3vcvn0bq1evxqdPnxAQEMBZztChQ6GiooLOnTtX%2BeCWJab6yJEj0aNHD3h6egIQPL%2B2bt2KVeAFbQAAIABJREFU%2B/fvY//%2B/ZzlUL4PqCFNqfXExMTgyJEjSE5OhqGhIYYNG4bevXvLJEMYCYIQgvDwcERGRuLBgwec2/P5fOTk5KB%2B/frIysrCqVOnUFpaij59%2BnDuwXB0dERgYCDatGmD%2BPh4DB48GFeuXIGRkRGysrLQr18/1qgSQqysrHDr1q1q9xZxmXQjSw8PANja2uL27dvV0gcAfH19cfr0aXTq1KnKS5trZAohaWlp0NXVhbKyMs6dO4f8/HwMGjSI9Xi9ffsWbm5ucHNzQ79%2B/aCnp4f09HScP38ep06dwuHDh2WaVLd%2B/XocP34cQ4cOhYmJCZKTkxEWFobu3bujQYMGiI6OxowZMzBq1ChGOcIoGTWhZ8%2BeWLlyZY2TTlhZWeHu3btihgyfz4etrS3%2B/vtvTjJsbGxw9%2B5dsXWEEHTs2JGzDFdXV2zcuBHm5uZVyv7%2B%2B2%2BsWLECZ8%2Beldp%2BwoQJ6Nevn5gxL2T//v2IjY3lNHnXxcUFu3fvlphc5Pnz55g7dy7nDwwAEvcH4D4RbtOmTZg3bx7evXsnU1QNNl0qJ8%2BRZVKe8KPi2bNnKC8vh7q6Opo2bYrg4GCZJphaWVnh5s2bUFdX574TErC1tcXNmzfFRixKS0vRpUsXztcf5fuBunZQaj329vY1fvknJSVh7969iImJQYsWLbBw4ULObdPS0jBx4kRYWFhgzZo1uHHjBjZt2gRzc3Ps27cPISEhaNeuHauc7OxsUS%2Beubk5VFRURC4qDRo0QGlpKWedmjRpgvz8fE5h%2ByShqqoqZiQUFxfX%2BOVUU3g8nljms5pgYGCAZ8%2Be4cOHD%2BjVqxfy8vI4fXT8%2BOOP2Lt3L3x9fXHkyBFRJIiWLVti9%2B7dMhnRgMCVYseOHWI9m87OzggICMDGjRvh5uaGuXPnshrSWlpa2Lp1a5WQfi9evJA4CiCJgoKCaifLqUjz5s0REREhNvIRFRUl05C4rq4uHj16JObG8eTJE9ZIGxVJS0uTanRaW1vj48ePjO3j4%2BOxa9cuiWVDhw7lnCwkMzNTaoa%2BVq1aISMjg5OcinrVhEOHDmHu3Llwc3PDvXv3aiRLlpEKJnR1dXH8%2BHE8fvxY1CFiYWHB6npTGXNzc6Smptb4A6GmLm2U7wtqSFNqJUxxc4Ww9VLy%2BXz8/vvvCAkJwcuXL1FWVoadO3fKnE5206ZNMDMzw4IFCwAIXDSmTJmCefPmITIyEoGBgVJfyBWpPMRf2XDlMvgkjINqb2%2BPmTNnYuLEiVVS9HIZ/r1//77Y7x49etSoN1keSDufV69elUlOTeN9W1tbIyoqCklJScjKyoKenp5Mw9gViY%2BPR4cOHcTWtW/fHk%2BfPgXA3dBavHgx3r17hwYNGqCgoABGRka4fv06qwFekZ49eyIqKgoDBw6UbScqMXfuXHh5eeHEiRNo1KgRPn78iGfPnnG6B4TII%2BW5mpoaCgoKJGYgLC4uZv1wKisrk2rI1alTh3OcY3n51aempsLQ0BApKSlS63C5DvX19eHu7o7i4mKMHTtWYh2uo01c3bLYOHHiBIYNGwYLCwvRx1NJSQlWrlwpUzZBHx8fjB8/Hi4uLqhXr55YGZdrR%2BjSZmRkhGnTpkl0aaP896CGNKVWk52djdjYWDg4OMDExARpaWm4dOkS6wPvwIEDOHjwIPh8Pjw8PLB792706dOnWglLbty4gYiICDRo0AApKSlITEwUGSNOTk749ddfq7Vv1aGyS0blHieuQ66VjfZvzYOMx%2BMhIiICISEhePv2rUzDyDWN9y3ExMSkxj1UjRs3rtJ7e%2B7cOdFIxPPnzzn1wt65cwfnzp1DWloadu3ahW3btiEiIoLRdaEyJSUlWLRoEYKDg6tsUxY3nm7duiEiIgLR0dHIyMhAjx49sG7dOpmOlTxSntvY2ODMmTMSPyYiIiLQunVrxvZNmzbFw4cPJX5cPXjwgHPiky5duuDIkSMSDbmjR4%2BKxRBnol%2B/frh3755oAm/le5LrvR0UFISLFy/ixYsXnHy8JWFtbY179%2B7B3Nxc6mRiWe7JjRs34s6dO1i1ahU0NDQQHx8Pb29vmScWBwYGorCwEE%2BfPhVrK03Hyty6dUv0f6tWrfD06VPRR62pqSnevHkjkz6U7wNqSFNqJcLeSU9PT2zdulUs6sH169dZh13XrFmDkSNHYtGiRTWedV7RheLhw4eoV6%2BeaHKimpoaZ5eMoqIisf3Iy8sT%2B11cXMwqIz4%2BXhSJouJwcnR0NHr37i02iYyJyi8eri%2BiyrBlNQRk82/Ozs7G0aNHcfToUZSWlmLgwIHYunWrTDrFxcXhjz/%2BgIaGhmi/Jk%2BejH379skkRx4sWLAA06dPx6lTp0Q9r48fP8aWLVsQHx%2BPkSNHchp9UVZWhoGBATQ0NJCQkABA4CO8fv16zrq0bNmyWh%2BSkvjxxx9l6j2WhNAXvbpMnjwZ48aNQ0lJCVxdXdGwYUNkZGTg3LlzCAwMZJ0UOnjwYPj6%2BmL37t1ifrppaWlYuXIlZzejadOmwc3NDdnZ2VL96rkgzMRYU3cKU1NTeHl5ITc3l/UcnT17VuLHi3B04cCBA9V%2BNlQkIiIC3t7ecHNzg6urK/bs2YMRI0bA29tbJjm3bt3CpUuXZHIBqsihQ4eq1Y7yfUMNaUqt5tatW9i%2BfbvYui5dumDWrFmM7ZYtW4ajR4/C3t4ew4YNw8iRI6v9QtDW1kZWVhYaNGiA27dvi4Uce/PmjcQhXUmsXr26WtuviLRQW4sXL8aRI0dkDrUlT2qS1fD9%2B/fYt28fIiMj0bJlSxQWFuLs2bPVGlpWUVFBcXExNDQ0RL160lwAvjZ2dnaIjo5GREQEUlNT0aVLF6xbt07kEnHw4EFO/vXGxsZ48uQJ2rZti4KCAmRlZUFZWZnTx5eQmhq%2BAwYMQFRUFGPIQzYjUJ4pz4VzFlasWCGWmVRHRwd%2Bfn7o2rUrY3sPDw/cuHEDLi4usLa2Fhni9%2B/fR6dOnTB58mROesjLr144SmFsbIycnBxcvXoV6enpMDY2hr29vcwZEn/55RfWOsuXL5doSAvdkTp16oTy8nLcu3cPGRkZMDIy4uQeVRlDQ0MEBQXB3d1dtFy0aJHMcvT19aGmpiZzO0nExcUhLS1N9IwoLS1FQkICfHx85CKf8u1ADWlKrcbY2Bjnz58X6x0KDw%2BXOrlHyKhRozBq1CjcvHkThw8fhrOzM8rLy3Hz5k0MGDBApkkuDg4O8PPzg7OzM6KiouDr6wsAyM3NxZYtWzj7XAuH98%2BfP4%2B%2BfftWKT9%2B/DirDHmlsC4rKxOLN1taWlol/iyX8IKyRtOQxIwZM3Djxg306tUL%2B/fvR/v27dG5c2fOveuVkUe8b3kxZMgQ7N%2B/X6IRa2RkxDke%2BsiRIzFmzBhER0ejf//%2BGDduHJSVlTn5w8vLeJ06dSoAsH7EMiFvF6I%2BffrA0dERd%2B/eRXZ2NvT09GBpaVklJKQkFBQUEBQUhPPnz%2BPq1avIysqCkZERRo0aBWdnZ5n0kKdf/d27d%2BHl5QUNDQ2Rz/SaNWsQEhKCFi1aVEumNNjOx%2BvXr%2BHp6YmPHz9CR0cH2dnZMDU1xa5duzi7vgDAn3/%2BiWXLlqFJkyZYuHAh1q1bhylTpsDf3x/6%2Bvqc5UyaNAnTp0/H2LFjoa2tLfZBx%2BVeEPLrr7/i2LFjoo/r8vJyFBQUyDx/hvJ9QMPfUWo1ly9fxpw5c2BhYQEjIyN8%2BPABL168QHBwMDp16sRZTnJyMo4ePYpTp05BUVERAwcO5Nwjkpubi7lz5%2BLevXtwdXWFv78/AEEoJj09PRw9epR1qLGoqAjZ2dkABEPy586dE3uJ5eXlYcSIEVUmAVZGXqG25BlrFhBMyJw7d67Yiy0zMxO//PIL9uzZw9i2VatWcHd3x6hRo0RJRzp37oyIiAiZQmMJqWm8b3liZ2eHCxcuyKU3/NGjRzAzM4OioiJCQkJQUFAgcbJpZXx9fbFy5UosWrRIak9ydT6IUlJSRL2UshhD/xQZGRnQ09P7t9XAw4cP0b59e8713d3d4ezsLBbjeNu2bbh9%2B7bcXROEvtDSGDNmDJo1a4bFixdDXV0dBQUFWLNmDT59%2BsQ5qgkAtG3bFl5eXpg%2BfToUFBTw%2BfNn%2BPj44M6dO2J%2By2zUNDSgkG7duiEoKAhFRUWIjIzE6tWrsW7dOvyPvTuPqzn//gD%2BKirLCKWkxIws2UsqJKnUWIpoMUOyfe0ky0SGpIQp2YdsmTB20WpLCYUiO2GI9ts2SllKfX5/%2BHXHLdXn073dWzrPx8PjMfdz7%2BfdKYxz3/e8z3n//j2nw4%2BknhDTBEVC6qyXL18y27dvZ1atWsX8%2BeefTFJSUo3X%2BvTpE3Py5Elm7NixQsd17do15uPHj6xem5mZyWhpaTGampoVfnXr1o3R1NRk5s%2BfX%2B06tTHCWhTMzMwYe3t7JjMzk2EYhomKimIGDhzITJ48udp7X7x4waxevZrR1tZmxo8fz4SFhTF6enpMRkaGUDHl5OQw9%2B/fZ3g8nlDrCGP16tWMtbU1s3fvXiYwMJAJCgri/6qvMjMzmYkTJwr82Z05c2aFUd1VKS4uZrZu3cqYmJgwvXv3ZszNzZl9%2B/ZxisPCwkLg8Z49ewQeV/d3Yffu3QKP09PTBR7Pnj2bVRzlv86aNWs4xVGelpYWU1xcLHCtqKio2r/7NVFdbNra2vwx7mXev3/P6OjocPo6d%2B7c%2Beb1o0ePclpHmP/3f63s%2B87MzGQsLS0ZhvkyMtzQ0FAk65O6hUo7SIPXqVMnoes7y8jKysLGxgY2NjZCrzV48GDWr1VSUkJ4eDg%2BfPgAS0vLCt0W5OTkWB2gqY0R1qIQEBCA1atXw8rKCkOGDMH58%2BexePHiage/AF/6Eru5uWHp0qU4deoUtmzZgry8POzcuRMTJkxAt27dOMdz8%2BZNBAYGIisrC6qqqrCxsWE9elqUylr3ld9JlJKS4lRTHhUVhbVr1yI1NbXCx/Fsd%2BIqK%2B0om7I4dOhQVh0mVq1aBXl5eZw7dw6qqqpITk6Gl5cXPD098ccff7CKxcvLC1euXMGsWbPQrl07JCcnw8/PD58%2BfcLcuXNZrZGSkiLweO/evfxR30D1ZQu%2Bvr78chUAGD16tEALSLY7peW/TkhICFxdXVnHUd5PP/2Eu3fvCpQqvHjxAp07d%2Ba0jigoKysjMTFR4O9g2acQbJS19KusrR/Xvubjx4/HxYsXOdeLl6eiooKcnBwoKSkhIyMDxcXFaNKkCQoKCoRal9RNlEiTBu3Fixfw8vLC69ev%2BYMoyohqWIC4lI0PLj8V7uXLl6zrgUXVakvUfvjhBzg5OWHy5Mk4c%2BYMLC0t8csvv3BeY8qUKZgyZQqioqJw5MgRWFlZoXv37ggICGC9zokTJ%2BDh4QFzc3N0794dKSkpmDRpEjZu3Mi59lVYUVFRIlnH3d0d5ubmMDIy4twyrIyMjAwCAgIwbNgwqKurIy0tDRcvXsSgQYPw9u1b%2BPv7w9PTEyNHjqxynbi4OERFRfGTGQ0NDXh7e2PYsGGsYwkKCsKJEyfQoUMH/rUBAwZg8uTJrBPp8mUq5RPW6g4Xl38914RXVHGUp6%2Bvj9mzZ8Pa2hodO3ZEZmYmTp48CT09Pf4BY0D4w6NsWFhYYObMmZg%2BfTo6duwIHo8HPz8/9O/fX%2BBMRWXnKYYNG4ZHjx4J3dKvTKtWrcDj8YROpI2MjDBlyhT4%2B/tDV1cXK1asgJycnNCDXkjdRIk0adBcXV3RtGlTzJw5s8aHz%2Bqae/fuwd3dHWfPnsWxY8fg5uaGxo0bY8uWLdUmI6JqtSVqR48ehbe3N8zMzDBx4kSsXr0a1tbW8PLyqrSusSpl0yyTkpLw999/c7p3z5498PX1hYGBAf9aVFQUvLy8xJ5IA19qiTMzMytMJGSzW1/m7du3WLp0KedJcF9LT0%2Bv8GcsKioKR48exbZt23Dr1i2sXbu22kRaXl4e%2Bfn5AslMUVERWrVqxToWhmEq1C%2B3b99eqMOIXBNWUbWAFDaO8h49eoQePXrg6dOn/CRTQ0MDOTk5yMnJEcnXKFPdzzsgIACNGjXCX3/9JXA9JiYGMTEx/FgqS6TLhtpIS0sjPDxc6MOmXbp0gZ2dHbS0tCrU5XOp81%2B8eDEUFRUhIyMDV1dX/P777ygoKICHh4dQ8ZG66fvIHAipoWfPnuHq1atC70DUJT4%2BPhg6dCgYhsHu3buxYcMGtGrVCj4%2BPtUm0qIeYS0q3t7ecHV15f%2BDeuLECXh7e8POzg4PHjxgtca3Diw2b94cL1%2B%2B5BRLTk4OBgwYIHDN0NCQ01h4Udm7dy82bdrEf8wwDKSkpNCtWzdOibSxsTGioqKqPSRalfv372P37t0C1wwNDbFkyRIAX3ZCU1NTq13H3t4es2bNgpOTE3%2BXcseOHRgyZAh/8iZQdReFiRMnYuXKlVi9ejXk5eXx6dMn/PHHHyIpuarvxNnr%2BOs3m98SEREh1PqtW7eGp6cnGjVqhDNnznwzkeays96sWTORTB%2BUkZHhtzds0aIFtmzZAllZWaFnDpC6iRJp0qApKyujqKhI0mGI1KtXr3D48GG8evUK2dnZGDlyJGRlZVm1rQNE22pLVM6ePSvwMb2MjAxWrFjBqZ3UuXPnEB8fj02bNkFJSQlXr17F8uXLOQ8RMTQ0xOHDhzF58mT%2BtdDQ0Gr7CteGv//%2BGz4%2BPpCVlcXVq1excOFCeHh4CPys2HBwcMCECRPQuXPnCqOR2U4lVFBQwLVr1wTaAN64cYO/k5ycnFxtBxAA/J7N8%2BbNE7h%2B9%2B5d/ici1X1kf/r0afB4PJw7dw4tW7bEu3fv8PnzZwAQSPa5fOwvKQzDID09nZ8klpaWCjyuyS5seHg4jh8/jtTUVCgpKcHGxqZGfdqjo6Nx6NAhZGZmYvfu3fDz88OSJUv4n%2B59XSpSmUePHuHUqVP8WMaNG1dh7H1lVq1ahZMnT6K0tBQ3b96s8DzXnXVRtNsEvpTTbdq0CX/%2B%2BScuXbqERYsWoXnz5ti5cyd0dHRE8jVI3UGJNGnQ7O3tMW/ePDg4OFQ4jMelb2hd0qhRIxQWFuLq1avQ0tKCrKwsUlNTOe%2B6i2KEtah06NABycnJFQYcvHz5knUyLcyBxa%2BVlJRgw4YNOHPmDH/H9P79%2B%2BjevTscHBz4r%2BMyFrum8vLyMHLkSKSnp2PXrl1QVFTEqlWrYGtry2mqm6urK7S1tdG/f/8al3csWLAA8%2BfPh7m5Odq3b4/U1FSEh4fDzc0Nr169wuTJk2Fvb1/tOgkJCTX6%2Bl/7eoBKTb1//57fLhH4krB%2B/bg6RUVFVU7mZPsG/sOHDzAxMeF/2sAwDP%2BTg7JrXAQHB2PNmjUYP348TExMkJSUBDc3N3z8%2BBG2trac1lm/fj1sbW35nxRERERASkoKzs7OrNa4fv065s6dCxMTE3Tr1g1JSUmYOnUqNm/ezKomfsSIERgxYgRsbW1FttPu7%2B9f4U3GrFmzOP2c161bB2VlZTAMg02bNsHR0RHNmzfHhg0bcPLkSZHESeoO6iNNGjRR9Q2tS9zc3HDv3j2kpqZi5cqV6NmzJ%2BbNmwcTExNW08jqot27d2Pz5s38f8zKEgiuBwWTk5MxefJkpKWlwdLSEuvWrWM1XONrbHbZAPEc1jI3N8eZM2fQrFkz6Ovr49atW5CSkoKOjg7u3LnDeh1tbW3ExsZy/lmUd/fuXQQEBCA9PR2qqqqws7NDr1698Pr1a/zzzz%2BskqNJkybB2toaw4cPR5MmTWoUx/bt2zFu3LgaTa4s83WHjcro6elV%2Bhyb0exsdkDZlMNw%2BT5Hjx6NFStWCJQn3bx5E%2B7u7ggLC2O9jqWlJTw8PKClpQVdXV3ExcXh9evXcHBwwNWrV1mtYWdnh6lTpwoMkDp37hz27t3L6e%2B1qPj7%2B%2BPAgQOYOXMm2rdvj6SkJOzbtw8TJkwQ6MBSncGDByMyMhI8Hg8///wzbt26hebNm0NHR6fKvtqknhJHjz1C6pqynq4pKSlMamoqk5KSIvArNTVVwhHW3OfPn5nTp08zoaGhDMMwTGJiIuPn58d8/vxZwpHV3JAhQ5gLFy4wkZGRzKpVq5h///2XWbx4MePt7c16jSNHjjDa2tqMs7Mzc//%2BfcbKyoqxtLRknj59WouR1y4XFxdm%2BvTpTF5eHuPg4MBs3ryZ2bVrFzN8%2BHBO6/z666/My5cvaylKbvbt28dYWFgw/fr1Y1asWFFpj%2BCqzJo1i%2BnVqxfj4ODABAYGsu7H/i1hYWHfvH7s2LEar8kwX/oKc1FZH%2BzNmzdzWkdHR4cpLS0VuFZSUsK5H3X//v356%2Bjq6jIMwzClpaWcekD379%2BfKSkpEToWURk%2BfDjz%2BPFjgWuPHz9mTExMOK2jr6/PfPr0iTl27Bhja2vLMMyXvvMDBgwQWayk7qDSDtIgjRw5EvHx8TA1Na30I7v6uiPdqFEjjBs3Dnl5efwT%2BhMnThSqI4Ok5efnw9zcHBkZGdi2bRtatWqF33//HTY2Nli6dCmrNYQ9sCiqHUZRuHPnDnR0dLBixQp4eXnh8%2BfPWLFiBRwdHVFQUIANGzZwWm/gwIFwcHDA8OHDK3THYLuzzuPxsGvXrm%2B2kuRS5jJ9%2BnRMnz4djx8/xpkzZ%2BDo6IgffvgB1tbWGDNmDKsph76%2BvsjJyUFQUBD2798Pd3d3jBw5knW/768nha5YsQJaWloVJoVu2LAB48ePr3YtPT29b%2B5uDx06FLdv367y3tzcXP5h2O3bt6Nv374V4vD394eTk1O1cZRRUVFBXFycwG56XFwc53MQP/74Iy5fvizwKUNMTMw3p6JWplWrVnj%2B/LnAJ4MJCQkSmxiZmZlZ4VNKTU1NvH37ltM6AwcOxIIFC5CQkIDp06cjOTkZzs7OGDp0qAijJXUFJdKkQQoNDQVQ/3pFs1FYWAhXV1eEhoaiSZMmCAgIwNSpU3HgwAF06tRJ0uHViLKyMgoKCtC2bVukpKSAYRgoKCggLy%2Bv2nvLks7KDiyyOQD3tX///RfXrl2DsbEx1NXVwePxcOnSJZGc9mdrxowZiI%2BPxw8//MAfOaygoMBqfPu3xMbG4qeffsKzZ88ErnOpC3VxcUF2djaMjY2FLhEBgJ49e0JTUxOGhobYunUrfHx8sGPHDhgZGWH58uXVJn6KioqYOnUqpk6dym8JefLkSXTq1AkTJkzA%2BPHjK215WVBQgFGjRuHjx48ABEfeM/9fVlRVmcqbN2/g6uoKhmFQUFAgUDtftn75Q53fIisrC0dHR35SX77GXFZWllUy/7XJkydj3rx5GD9%2BPNTV1ZGUlITjx4%2BzeqP4tUWLFmHu3LkwNTXFp0%2Bf4ObmhpCQEPj4%2BLBew9bWFnPmzMGsWbP4pRR79%2B7FhAkTOMUiKh07dsSlS5fw888/869dunSJ05sDAFi7di38/Pygo6MDBwcHJCQkoGfPnvwONuT7QjXShHxnVq9ejczMTDg7O8POzg4xMTHw9PREcnIy9u/fL%2BnwamTlypVIS0vDli1b4OjoiN69e0NOTg5hYWHV1nX269dPoC5xzJgxCAwMrPT56syePRu2trYwNTXlX7t%2B/Tp8fX3F1mdbW1sbd%2B/eFcvXYktXVxcXLlyAgoKC0Gs9ePAAQUFB/N9bS0tLjBs3Dm3btoWPjw/u37%2BPoKCgKtcoLi5GZGQkAgMDcfXqVXTu3JlfN71r1y60bdu2ynr3nJwcoSaF/v333/j333/h6%2BuL2bNnCzwnKysLExMTTtMEhw8fjvPnz7N%2BfVUCAgIQEBCA7OxsqKmpwdbWFsOHD%2Be8TkJCAv9gnoqKCucJnwzDYMeOHRVimTp1ao0HAwkjPDwcTk5OMDMzg7q6Ot68eYOIiAhs27YNxsbGrNeZM2cOvL29v6u2qqQKkqsqIYTUBkNDQ%2Bbt27cMw/xXu/jhwwf%2Bf9dH7969Y9zc3JicnBzm2bNnzIgRI5jBgwcz165dq/ZeLS0tgcf9%2B/ev8nk265Wv6/z8%2BTPndYRRGzWkly5dYv73v/8xI0aMYBwcHJigoCBO9xsbG3Ou%2B/2Wn3/%2BmenZsycza9Ys5sKFC0xxcbHA88%2BfP6%2B2DnfVqlWMnp4eo6enx3h4eDBPnjwReP7JkydM3759WcVT/vf6n3/%2BYTIyMljdyzAMc%2BbMGdavrc7X5xyuXLnCPHjwQGRr11Rqaipz7949hsfjSToUkbh58ybj7OzMTJ8%2BnXF1dWXu37/PeY2yGmnSMFBpByHfmdLSUn7jf%2Bar3rP1eRjADz/8gNWrVwP4UsLApbtAdRPmuLYPU1NTw7lz5zBq1Cj%2BtYCAAM4f/wrjw4cPAjvi38KlbEkULdHmzp0LFxcXzJgxo8JuLZf623HjxmHs2LGV1sl27NgRV65cqXKN5ORkuLm5wdTUVODPvbOzM7y8vNCuXTvW3VeEmRQKAGZmZvjrr78wZcoU/PPPP3BxcYGCggLc3d3Rtm1bVjEAX1rLrVy5EjExMdi5cyd8fX0hJSWF33//HXZ2dtXeL%2Boa/8zMTCxatAjx8fH8cpchQ4bA29u72rIVNj97cXS9Ka%2Bs93hGRgZKS0uRmJiIjRs3AuBW529hYQFHR0dYWlpCSUlJ4P8x9bWtKqkcJdKEfGcGDBgAd3d3uLq68v8HvmXLlipbddVVdfEf3EWLFmHhwoX4%2B%2B%2B/0a5dO6SkpOD58%2Bfw9fUVWwwyMjIi/b737t2LHTt2CLREMzIygru7O%2BtEeuXKlQC%2B1JSWb1PI5eBuWFjYN1uNmZiYICIiotIJcTweDzdu3AAAxMfHY8yYMQJvuN69e4dLly4B%2BHLIbfDgwaziEWZSKAB4eHjg6dOnmDJlCtzc3KCqqgo5OTm4ublh165drGIAgJ07d8LJyQmlpaU4fPgwtm/fDkVFRSxatIhVIi1qrq6uaNmyJc6dOwdVVVUkJyejIPLoAAAgAElEQVTDy8sLnp6e%2BOOPP6q899atW1U%2BL6oR5VyJqs6/rMSr/Bu%2B%2BtxWlVSOEmlCvjMuLi6YM2cOdHV1UVJSAm1tbfz4449iTfREZceOHWjRogW6d%2B/%2BzQlukvgH19TUlF%2B/m5mZCSMjI2zcuFGsw2saN26MsWPHimy9tLQ06OvrC1zT09NDRkYG6zWEObiblJTETyrLdm2/VlBQwD/4V5nWrVvj8OHDyM3NRVFREbZt2ybwvJycXI3efAg7KTQ2NhYBAQHIy8tDfHw8IiMjOSXyZZKTk2FnZ4cnT57gw4cPMDAwQOPGjZGdnc3qflF3lImLi0NUVBS/DlhDQwPe3t6s3lyIc0w5Fw8fPhRJnb8ohgqR%2BoMSaUK%2BM40aNcLx48fx8OFD/iGgrl27YtOmTVi1apWkw%2BNk2bJlCAgIQGZmJmxtbWFlZQVFRUVOa3z%2B/Blnz57lPy4uLhZ4XFJSwjmuTp06SeSj5zLfelMhDFG0RPvWUJDPnz/j%2BfPn1Q4M6dChA1q3bs3vTlGegoICNm/eXOUasrKyOHXqFIAvLfREdbBW2EmhhYWFaNWqFc6fPw91dXW0bdsWRUVFnN8ENm3aFDk5OYiIiICOjg4aN26MhIQEtG7dmtM62dnZ2LNnD1asWIHbt2/D0dERCgoK2Lp1KzQ0NFivIy8vj/z8fIGfQ1FRUYX2iVV5//49Tpw4gSlTpuDly5dYvnx5jcpeRKVFixYiK4ErKipCVFQUUlNTMX78eLx586bSAWCkfqOuHYR8J54%2BfYr58%2BcjLS0Nffr0wZ49e9CyZUs8e/YMS5YsAY/H44/yrW8ePHiA06dP4%2BLFi%2BjXrx9sbW0xZMgQVif7v25dVpmIiAhW61SX/IirneLq1auxZs0aka138uRJeHl5fbMlmrW1Nas1rly5gjVr1giMcQe%2B7J4/fPiQdSw7d%2B7E3LlzOX8PtUXYSaH29vYYNGgQPxGfP38%2BtmzZgpcvX%2BLAgQOs49i%2BfTtOnDiB/Px8bNu2DYqKivjf//6HadOmcZq6t2DBArx//x779u3DuHHjoKOjg6ZNm%2BLBgwfw9/dnvc7%2B/ftx9uxZODk5oWPHjuDxeNixYwd69uwp0D6uqprg5cuX4%2BnTpwgMDIS9vT0UFRUhJyeHd%2B/ecSp7EZVTp04hKipK6Dr/pKQkTJs2DcXFxcjPz0dAQAAsLCywY8cOTt0/SP1AiTQh3wl7e3u0aNEC48ePx6FDh9C1a1cYGRlh7ty56NatG7y9vdG%2BfXtJhymUjx8/4vz58zhz5gxev36NMWPGYPHixWL52mfOnOH/9%2BfPn/Hvv/9CQUFBYNCNKMstxE3YlmgWFhYwMDCAvLw8nj17BgsLC/z555%2BwsbHBpEmTOMWSmZmJpKSkCjvvkjioVVJSgsDAQDRp0gQjR47E69evERkZCQcHB1ZDjv755x%2BsWbMGcnJy2LJlC548eQIPDw9s27YNP/30E6dYbt26BTk5OWhpaSE9PR0PHz7k3L986NChCAsLQ0FBAYyMjBATE4MWLVpAX1%2Bf01h5Nrur1dUEm5iYICAgAFJSUhg4cKBA2Ysk3vR//T0JU%2Bc/a9Ys9O3bF3PmzIGenh7i4uJw5swZHDx4UOD/I%2BQ7IZFeIYQQkevXrx%2BTk5PDMMyXEeimpqaMrq4u4%2BPjU6GFV31WUFDAnDhxghk1ahTTu3dvsX7tzMxMxsnJienVqxejqanJ9OrVi3F0dOTUDq0uEsUY7D59%2BjDFxcVMYmIiY29vzzAMw7x48YKxsLDgFMvBgweZHj16MN26dRP4pampyWkdUXv79i3z8OFDpqSkRKjWZuXHc3P1%2BPFj5sKFC8ynT5%2BY7Oxszvfr6ekxJSUlzNmzZ5nRo0czDPOlvaSenp5QcdVE2dc8d%2B4cY25uzjAMw3z69ElirTpTUlIq/cWFnp4e/89I2fdSUlLCaXw6qT%2BoRpqQ70RpaSn/kIyKigoyMjKwePFiTJs2TcKRiUZMTAxOnz6NiIgI/PTTT/jll19gYWEhtq%2Bfl5cHOzs7qKiowM3NDcrKykhOTsaZM2dga2uLoKAgTvWhkibKMdjAlzpmaWlpqKqq8sdad%2B7cmdOBRQDw9/eHq6srrK2tK508KE6imBQaHR2Nw4cPg8fjYffu3fDz88OSJUs4fX85OTmYN28eHj16BBkZGZw6dQo2Njbw8/ODtrY263X69OkDNzc33LlzByNGjEB2djbc3d1r1NUnIyMDwcHBSE1NhbKyMiwsLASmh1anS5cu2LlzJ65evQpjY2MUFBRgy5Yt6NmzJ%2BdYRKG6Wn62WrRogezsbIFykKysLM5TVEn9IP7RQYSQWlG%2BfldGRobzR%2Bp1zevXr7F582YMHToUv/32G9q0aYNjx44hICAA9vb2Yk1cfX190atXLxw5cgTW1tYwNDTEhAkTcPz4cfTq1Qu7d%2B8WWyyiUDYG29TUFB8/foSJiYnALysrK06dJbp164atW7cC%2BDKeOyoqil%2BKwEVubi5sbW3rRBINAF5eXnj//j3OnTsHGRkZqKurw9jYGJ6enqzuDw4OxtKlS9G1a1e8efMGwJea/E2bNnGKY926dejatSvi4uLQuHFjaGhoYObMmfDy8uK0jqenJ4qKitC/f3/MmjULqampKCoq4vdpZ%2Bvhw4cYNWoULl68iLy8PFy%2BfBmjR4/mVB7i5uaGGzdu4IcffsD8%2BfPx5MkT3Lp1C66urpxiqWssLS0xf/58REdHo7S0FA8ePMDSpUsFes%2BT74ikt8QJIaJRftpdfZ5kWEZTU5MZOHAgs27dOiYmJoaJjY2t8EtczM3NmVevXn3zuRcvXjBmZmZii0VUsrOzmeTkZEZLS6vCR9lZWVmc1vrnn3%2BYkSNHMikpKczFixeZHj16MJqamoyfnx%2BndWbNmsXExMRwuqc2CTsp1MLCgrl79y7DMP9N1UxMTGQMDQ05xTFo0CDm/fv3AnEUFRVVmNTJVlFREZOVlSUwLZGLSZMmMf7%2B/gLX/vrrL%2BaXX36p0Xrfk6KiImbDhg2MlpYW061bN6Zv376Mh4cHTTv8TtWNt/yEEKFV1%2BYNAKysrMQdllAYhkFubi78/f2/2VFAnAMOsrKyKj0cpqGhgaysLLHEIUplrQTv3LkDaWlppKWlISsrC%2B3atavQtaA6GhoaCA0NBfDlI/LIyEgUFhZyPlDXtm1bzJo1C/r6%2BhViEHUvZDaEnRSakZGBvn37AvjvU6OOHTvi/fv3nOKQkZHBx48f0bRpU34chYWFaN68Oad1CgoK4OHhgfPnz6OoqAhNmjTB2LFjsXz5ck6t3549ewY/Pz%2BBaxMmTKjQv7sqJSUluHDhAl6/fo3S0lKB5yTZXlIUli1bhmXLliE3NxetW7eW2JAZUvsokSbkO9GmTRuBf8Rat24t8FhKSqreJdJ1abCBnJwcsrKyvjm6Oisri3NCU5fk5OTAycmJ/7E8l3HPX/tWt43s7GxO3TaKiorq1Efgwk4K/fHHH3H58mWBQSUxMTGcR8qbmJjgt99%2Bw8qVKyElJYWcnBysXbsWRkZGnNZxd3fHmzdvsHPnTrRr1w7JycnYvn07Nm7ciBUrVrBep2nTpkhPTxcYRJSens6pDnj16tUIDQ2FpqamQClPfU86Bw8ejDFjxsDGxgZdu3aVdDikllH7O0IIYcHR0REaGhpYuHBhhee2bt2KN2/ecK57rStmz54NaWlpODs7C4x7btWqVbXjnsscOnQIGzZsqDDgpr6PRc7JycGcOXPw5MkTlJSUoEmTJvxJoWyGhsTExGDu3LkwNTVFeHg4xo4di5CQEPj4%2BHBKggsLC%2BHi4oKLFy8C%2BPJzNTIygre3N1q0aMF6HV1dXZw/f15gsBGPx8OYMWNw8%2BZN1ut4eXkhJiYGS5YsQfv27ZGUlITNmzdj8ODBWLp0Kas1DAwM4Ovri969e7P%2BuvVBVFQUzp49i4iICHTt2hU2NjawsLCo12%2B2SeUokSaEEBYeP36MiRMnYs6cORgzZgzatGmDtLQ0nDp1CocOHcKxY8fQrVs3SYdZIzo6OgLjngEgPz8fw4YNQ2xsLKs1hg0bhhkzZoik24a/vz%2BOHz%2BO1NRUKCkpwcbGBrNmzZLITmVycjLat28vMCm0T58%2BrHpIl0lISOB/PyoqKrCxsUGfPn04xXH79m1oa2sjLy8PKSkpUFFRgbKyMtdvB4MHD0ZISIjAQd2CggJYWloiMjKS9TqfPn3i7ygXFxdDTk4O1tbWcHZ2RpMmTVitMXDgQFy/fp3Tz7I%2Byc/PR0hICIKCgvD8%2BXP8/PPPEilPIrWLEmlCCGEpMjISq1atQk5ODv9amzZtsH79ek4dLuoaY2Nj/P333wLturKzszFhwgT%2BDmh1%2BvXrh9u3b7OaNlkVf39/HDhwADNnzuTvdO7btw8TJkzgNMFPVAYNGoSLFy%2ByHgle3pw5c%2BDt7V3j%2B8vo6%2BvjypUraNq0qVDrHD58GBcvXsSKFSv4Ewl9fHzQoUMHTJw4kf86tpP8ioqKkJeXhzZt2nB%2Bo%2BPp6QklJSWJ/L6KS0pKCkJCQnDixAkwDMPpzQqpHyiRJoQQDoqKinDv3j1kZmZCSUkJOjo6daZVW02JYtzz7NmzMXnyZAwcOFCoWEaMGAEfHx/06NGDf%2B3JkydYsGCB2Eawf23kyJHYvn07NDQ0anT/gAEDcPXqVU4H%2Bb7FxsYGzs7ONer3/LXy0/u%2BTgHKHldVjlP%2BAPO3sD2LMWHCBMTHx6Np06b8HvhlJPF7LSoFBQU4d%2B4czp49iwcPHsDIyAi2trYwNDQU%2Bo0mqXsokSaEkAZOFOOeV69ejTNnzgjdbUNHRwdxcXECCUdpaSl0dXU59SgWlYULF%2BL69evQ0tKqUErB5vtau3YtUlJSYGlpCSUlJYFdWy6HMKdPn46bN2%2Biffv2UFZWFljn4MGDrNdJTU1l9brKhpOYmJhUeZ%2BUlBTrJLiqcdljx45ltUZd1LdvX6ioqMDa2hrjxo3j3AGH1C/1exuFEEKI0ETRHUVU3TY6duyIS5cuCeyEX7p0iXOXC1Fp1qwZzM3Na3z/4cOHAQBXrlwRuM71EKa2tjanCYaVEXZ6X0REBEpKSpCXl8ffRb5x4wYSEhJgZGTEetoj8KXswczMjNUbufpk9%2B7dGDBggKTDIGJCO9KEEEKQnp6O1NTUCh/19%2B/fX6xxhIeHw8nJCWZmZlBXV8ebN28QERGBbdu2wdjYWKyxsOXm5gY3N7ca33/nzh3o6OgIHcfMmTOxZ8%2BeKl%2BjqalZaS0zm8Sex%2BNh2rRp6NOnD9avX4/g4GAsW7YMmpqaSEpKwoEDB1h34XB0dER0dDQUFBRgbm4Oc3Nzfs/t%2Bu7ly5c4evQoMjIy4OHhgdDQUNjb20s6LFILKJEmhJAGbteuXfzx3l/jumsqqm4bt27dQkBAAHJycqCmpgZra2vOXS7EqV%2B/foiPj5fY/VzWKd%2BFJTc3F4cOHcKYMWNgZ2dX7ddYvnw5ioqK8Pvvv0NRURHm5uYYMWIEFi1ahKCgIISEhFSbzH%2BtuLgYN27cQEREBKKiolBaWgozMzOsXLmS9Rp1TXR0NBYsWABjY2NERkYiNDQU48aNw9SpU7/rg5UNFSXShBDSwOnr62PdunUwMTGpcYs5UXXb4PF42LVrFxITE1FaWlrjWmBx0tbWxt27dyV2f5maJuRZWVmYMmUKfzJlVQwNDREYGAgFBQWkpaXBxMQEoaGh0NDQQGFhIYyNjVm3TCzz9u1b3LhxAzExMQgJCYGUlJRI3lhIirW1NRwdHWFkZARdXV3ExcXh4cOHcHJyqteHKMm3UY00IYQ0cI0bN8bQoUOF6tN87Ngx7Ny5U6DbRr9%2B/bBgwQJOibSLiwuys7NhbGwMGRmZGscjTsL2t5b0JD95eXnweDxWry0oKODXRt%2B/fx/y8vL8jiZycnIoLi5m/XV9fHwQHR2N58%2Bfo2vXrjAwMMCuXbtEUuYiSW/evMGQIUMA/Pd727t3b%2BTl5UkyLFJLKJEmhJAGbuLEidi8eTNmz55d437HmZmZFQ6NaWpq4u3bt5zWefjwIS5cuFChHRoRjfLt64qLi3H58mV0796d1f0tW7ZEbm4uFBQUEBsbi379%2BvGfe/XqFVq3bs06Fn9/f0hLS8Pe3h4jR45E7969Jf6mQhRUVVURHx8v8Ibg4cOHaNeunQSjIrWFEmlCCGngOnXqhCVLlmD//v0VnmNbIy2qbhstWrQQuucyqdy2bdsEHjdq1AgaGhpYvXo1q/uNjY3h4eEBMzMzBAcH8%2B/Lz8/H1q1bYWhoyDqW27dvIzY2FtevX%2Bd/EjFgwAAMHjwYtra27L%2BpOmb27NmYM2cOfv31VxQXF2Pv3r04dOgQFi9eLOnQSC2gGmlCCGnghg4dCktLSwwaNKjCuGa2A0BE1W3j1KlTiIqKwowZMyr032U7bU/c6tNhQzZCQkJgYWHxzefy8/Ph5OSE%2BPh4jBo1Cp6engC%2B1HkrKSnhyJEjNeqbXFBQgICAAOzatQtv377ldMi1rvHw8IC2tjaCgoL4Y%2BHt7OwE3mSS7wcl0oQQ0sDp6OiIZNiJKLptlJ%2B8B6DaaXuSJuxhQVElwJI8tHj9%2BnXo6upCTk6O9T0JCQm4evUqrl27hnv37qF79%2B4YNmwYhg0bxqkfdV2jp6eHmJiYej/xlLBDv8uEENLAmZmZ4dKlSzAzM6vxGjweD%2BfOnUNGRgZKS0uRmJiIjRs3AuDWbaM%2BdjVYuHChUPf/%2BOOP1b5m7dq132wJ5%2BzsDC8vLwDAH3/8IVQcZWqyvzZ48GDO99jY2EBPTw/Dhw/Hxo0b0bZtW85r1EXW1tbw8PDA2LFjK0yzrKufqpCao0SaEEIauI8fP2LhwoXQ0NBAq1atatRyTlTdNoSdvCcqkyZNqvbgW9nPZsqUKRWeK3%2Bo71usrKwAAAEBAd98nsfj4caNGwCAkydPolevXgLPv3v3DpcuXeI/FmYC49fEdeAvJiYG8vLyyMnJ4Q8DUlFREcvXrk0HDhwAABw/fhzAl59nXf9UhdQcJdKEENLAde7cGZ07dxZqje%2Bt24a%2Bvr5Q95c/1FeelJQUP5GuTOvWrXH48GHk5uaiqKiowppycnKYP3%2B%2BUHFKkrS0NObNm4eIiAh%2Bojlw4EBs2bIF8vLykg6vxurjpyqk5qhGmhBCGrhbt24JnTiamJggKCioxu3zSNWmT5/%2Bza4qtUFUNdvVWbNmDRITE7Fq1Sq0b98eb968wbp166Curg4PD49a//qEiAIl0oQQ0sDp6%2BujRYsWGDduHMaOHVujfrf1sdsGG//%2B%2By8OHToEHo%2BH0tJSAF96Lz9//hxBQUGs1khOTgaPx%2BPXHpfd/62SkLpAXIn00KFDcfr0aSgqKvKvZWVlYfTo0fySFkLqOirtIISQBu769euIiIjA2bNnsWvXLujq6mLcuHEwNzdn3dO57CDcpUuX6k23DTZcXFzw%2BvVrKCgooKCgAKqqqrh%2B/TomTpzI6v7du3dj8%2BbNFX4m3bt355RI3759G6tWrUJycjJKSkoEnhP1z1dc%2B2sfPnxAixYtBK7Jy8vz37AQUh9QIk0IIQ2cjIwMfv75Z/z888/Izc3F%2BfPn4efnB3d3d4waNQrjx4%2BvMLWwvO%2B1LjQuLg5hYWHg8XjYs2cPduzYgcDAQISEhLC6/8iRI9i2bRtkZWURERGBxYsXw8PDg/Ouv5ubG/r374%2BVK1fW%2Buh0AwODWl2/TN%2B%2BfbF161YsXbqUfyBv69at6N27t1i%2BPiGiQKUdhBBCAAA5OTkICQlBaGgoEhISYGRkBDU1NZw9exZTpkzB7NmzJR2i2Onr6%2BPWrVvIz8%2BHtbU1Ll26hM%2BfP8PIyAjR0dHV3l/W2zkjIwNz585FQEAAcnNzYWNjg4iICNZx6OjoIC4uDtLS0sJ8OwCA6OhoHDp0CJmZmdi9ezf8/PywZMkSsfc9fv78OSZNmgRZWVmoqakhNTUVUlJSOHDgADQ0NMQaCyE1RTvShBDSwIWGhiIwMBAxMTHo1KkTxo0bB19fX34HDiMjI8ybN69BJtJqamp49OgRevXqhcLCQuTm5qJx48b4%2BPEjq/uVlZVRUFCAtm3bIiUlBQzDQEFBAXl5eZzi6NKlC9LS0tC%2BffuafBt8wcHBWL9%2BPWxtbREXFwcAiIiIgJSUFJydnYVam6uuXbviwoULCA8PR25uLtTU1GBkZEQHVkm9QjvShBDSwOnp6WHUqFGwtrau0KsYADIyMnDixAk4OjpKIDrJOnXqFDw9PREaGoq//voLN27cQOPGjdG2bVv4%2BvpWe//KlSuRlpaGLVu2wNHREb1794acnBzCwsIQFhZW7f1l/agfPXqE6OhojB8/Hq1atRJ4TXVt9L5maWkJDw8PaGlpQVdXF3FxcXj9%2BjUcHBxw9epV1usQQr6gRJoQQhq4oqIigUOFBQUFkJWVZX3Q8Hv34MEDdOvWDdLS0jhw4AAKCwsxbdo0tGzZstp7CwoK4OPjgwULFiA7OxsLFy5EQUEB1q9fz2oaoImJSZXPS0lJcapP19XVRWxsLKSkpKCnp4fY2FgwDANdXV3cvn2b9TrC0NTUrHboS30%2BoEoaFkqkCSGkgXv16hV8fHzw559/4tKlS1i0aBGaN2%2BOnTt3QkdHR9LhEZZCQkJgYWFR5WtsbW0xa9YsDBs2jJ9IR0dHY9OmTTh9%2BrRY4oyNjQXwpVb76tWrmD9/Pjp06ID09HT8%2BeefMDAwaJCffpD6iRJpQghp4KZPnw5lZWWsW7cOI0eOxNixY9G8eXOcPXsWJ0%2BelHR4EhUVFYW1a9fyR1h/jc2u6du3b3HkyBGkpqZWaOu2fv16kcbKpv9zTEwM5s6dC1NTU4SHh2Ps2LEICQmBj48PjIyMRBpPdczMzHD48GG0bduWfy0rKwu2tra4cuWKWGMhpKbosCEhhDRwz549g6%2BvL1JTU5GUlIQJEyagefPm8PHxkXRoEufu7g5zc3MYGRnVqGOGk5MT0tPToaWlJZKOG1Vhsy82aNAgHDt2DMePH4e%2Bvj5KS0vh5%2BeHPn361Gps35Kbm1thFLicnBzevXsn9lgIqSlKpAkhpIH7/PkzGIZBdHQ0evbsiR9%2B%2BAG5ubmQk5OTdGgS9/btWyxduhSNGjWq0f33799HZGRkhQOCtaG6uuMympqamDFjBrKystCuXTsoKyvXcmTfpquri2XLluG3336DiooKkpOTsWHDBrHvjBMiDEqkCSGkgRs0aBAWLFiAhIQETJ8%2BHcnJyXB2dqaEBoCxsTGioqKqPfRXmQ4dOqC4uFjEUdVcZmYmFi1ahPj4eP6UxSFDhsDb27vC7nBt8/DwgJOTE8zMzPhvAgwMDODm5ibWOAgRBtVIE0JIA1dYWAg/Pz80adIEM2bMQEJCAk6dOsU/dNiQPXjwABMmTEDnzp0rJJoHDx6s9v47d%2B5g7dq1sLKyqtDlg0vbOjbY1EjPnj0b0tLScHZ2hqqqKpKTk%2BHl5YVWrVrhjz/%2BEGk8bKWlpYHH40FFRaXCxEc2BygJkSRKpAkhpIFLSkrCjh07kJmZyT8QV1xcjMTERNy8eVPC0UmWlZUVWrRogf79%2B1co75g/f36197u4uCAoKAhKSkoCNdJc29axwSaR1tHRQVRUlMDQk/z8fAwbNozfTaMuYfM9ESJJVNpBCCEN3O%2B//w6GYdC6dWvk5OSgR48e/LHgDd2bN28QGxsLGRmZGt1//vx5BAYGonPnziKOrCI2%2B2Ly8vLIz88XSKSLiorEUsNdE7TXR%2Bq62j1CTAghpM579OgR/vzzT8ydOxctWrTAypUrsWnTJty4cUPSoUlc9%2B7dkZycXOP7W7dujQ4dOggdx759%2B/D%2B/fsqX2NgYFDtOvb29pg1axYuX76Mf/75B9HR0ViwYAGGDBmCuLg4/q%2B6gu0BSkIkhUo7CCGkgRs0aBBiYmJQWFgICwsLREZGAgAGDhzY4JPp7du34/jx4xg%2BfHiFXVs2pR1nz57FtWvXMH36dLRs2VIgMVRVVWUdh56eHm7cuFHj7iFlNDU1q32NlJRUnZksSKUdpK6j0g5CCGngOnTogKioKBgZGaG0tBTJycmQlZXF58%2BfJR2axMXGxuKnn37Cs2fPBK6z3Sldvnw5ACA0NJR/T1m3DC7JqqGhIfbu3Ytx48YJ1a4uISGhxvcSQiqiHWlCCGngIiIisGjRIoSEhCA4OBh///03GjVqhEGDBmHDhg2SDk%2BiQkNDMWzYsBr31H7%2B/HmlnU/U1NRYrzN06FBkZGR8M4HnunuckZGB4OBgpKamQllZGRYWFiIpP6kNtCNN6jpKpAkhhIDH40FRURGNGzdGWFgYCgoKYGVlBVlZWUmHJlF6enqIjo6u8WFDExMTBAUFCRzuq4mqOmro6emxXufhw4eYMmUKOnXqhPbt2yMpKQkvX77E/v37oaOjI1SMtUFbWxt3796VdBiEVIpKOwghhKBt27b8/x45cqQEI6lbevfujbCwMIwZM6bGa3z48EHoRLqyZDk3N5fTOt7e3li4cCEcHBz41/z9/bFx40YcPXpUqBhrA5sDlIRIEu1IE0IIIZWwtrbG48ePISsrizZt2giUVrDpA%2B3i4oIbN25gyJAhFWqb2RxWLPPgwQN4eXmBx%2BMJ9PrOzc3Fo0ePWK%2Bjr6%2BP6OhoNG783z5acXExBgwYgDt37rBeR1Sio6Nx6NAhZGZmYvfu3fDz88OSJUsE4iOkLqM/qYQQQkgl7O3thbo/JSUF6urqSExMRGJiIv8617Zu7u7uUFdXR5cuXZCcnAwDAwMcPHgQS5Ys4bRO06ZNkZ6eDnV1df619PT0ClMXxSE4OBjr16%2BHra0tv%2BVeREQEpKSk4OzsLPZ4CKkJ2pEmhBBCqpGTk4PU1FQoKSlVGGMtDn379sWtW7eQkpICT09PHDhwAPfu3YO7uzsCAgJYr%2BPl5YWYmBgsWTSexYUAABM6SURBVLKEXyO9efNmDB48GEuXLq3F76AiS0tLeHh4QEtLC7q6uoiLi8Pr16/h4OCAq1evijUWQmqKdqQJIYSQShQUFGDZsmWIiIjgt60bOHAgtmzZAnl5eVZrhIeH4/jx4/xE3MbGBpaWlpzikJeXR5MmTaCuro4XL14AALS0tJCamsppnYULFyI3Nxdz585FcXEx5OTkYG1tzanMRFQyMjLQt29fAP/t0Hfs2LHawTOE1CU02ZAQQgiphI%2BPDwoLCxESEoL79%2B8jMDAQpaWl8Pb2ZnV/cHAwli9fjq5du2LSpEno0aMH3NzccPLkSU5xdOrUCUePHoWcnByaNWuGp0%2Bf4uXLl5xLROTk5LBhwwbcuXMH165dw7179%2BDq6oomTZpwWkcUfvzxxwp15jExMejYsaPYYyGkpqi0gxBCCKnE0KFDcfr0aSgqKvKvZWVlYfTo0aymPo4ePRorVqzAgAED%2BNdu3rwJd3d3hIWFsY4jPj4ec%2BbMwcmTJ3Hjxg2sXbsWjRo1wq%2B//oply5ZVe//Zs2erfY2VlRXreEQhJiYGc%2BfOhampKcLDwzF27FiEhITAx8cHRkZGYo2FkJqiRJoQQgiphL6%2BPq5duybQT/vTp08YMmQIbt26Ve39/fv3R1xcnMDOcWlpKfr378950MinT58gIyMDaWlpPHjwAO/evWPdHs7ExKTK56WkpFh1IRG1hIQEftmLiooKbGxs0KdPH7HHQUhNUY00IYQQUom%2Bffti69atWLp0KaSkpMAwDLZu3YrevXuzul9FRQVxcXECfaDj4uKgqqrK6v60tLRvXm/Tpg3atGmDtLQ0VmtFRESgpKQEeXl5UFBQAADcuHEDCQkJMDIyQqdOnVjFI2qampqYMWMGsrKy0K5dO6HGnxMiCbQjTQghhFTi%2BfPnmDRpEmRlZaGmpobU1FRISUnhwIED0NDQqPb%2BkydPwsvLC%2BPHj4e6ujqSkpJw/PhxuLi4wNrautr7NTU1%2BbvZZYcdy5Q9ZjMinMfjYdq0aejTpw/Wr1%2BP4OBgLFu2DJqamkhKSsKBAwdYvzkQlczMTCxatAjx8fH872XIkCHw9vZmfZCTEEmjRJoQQgipwtu3bxEeHo7c3FyoqanByMiI06TCgIAABAQEIDs7G2pqarC1tcXw4cNZ3cumK4eamlq1r1m%2BfDmKiorw%2B%2B%2B/Q1FREebm5hgxYgQWLVqEoKAghISEYM%2BePaxiEpXZs2dDWloazs7OUFVVRXJyMry8vNCqVSv88ccfYo2FkJqiRJoQQgj5zhkaGiIwMBAKCgpIS0uDiYkJQkNDoaGhgcLCQhgbGyM2NlasMeno6CAqKkrgTUl%2Bfj6GDRsm9lgIqSmqkSaEEELKMTExqbK1nJSUFMLDw6tdp7CwEEeOHMHr16/5o73LrF%2B/nnU8X5d4lMemtKOgoIBfG33//n3Iy8vzS1Pk5ORQXFzMOhZRkZeXR35%2BvkAiXVRUhFatWok9FkJqihJpQgghpJwFCxZ88/q9e/dw/Phx9OjRg9U6Li4uuHv3LvT19SEjI1PjeA4ePCjwODc3F4cOHcKYMWNY3d%2ByZUvk5uZCQUEBsbGx6NevH/%2B5V69eoXXr1jWOrabs7e0xa9YsODk5oWPHjuDxeNixYweGDBnCHxkOALq6umKPjRC2qLSDEEIIYcHPzw%2BbNm2Cra0tXFxcBFriVUZfXx%2BnTp2Curq6yOPJysrClClTEBoaWu1r16xZg7dv38LMzAyurq5YvXo1LC0tkZ%2BfDxcXF7Rp0wZr1qwReYxV0dTUrPY1bA9TEiIptCNNCCGEVCE/Px/Lli3D7du34e3tjREjRrC%2BV05ODm3btq2VuOTl5cHj8Vi9dtGiRXBycsKKFSswatQo/ohyIyMjKCkpiT2JBr70kCakvqMdaUIIIaQS9%2B7dw6JFi9C6dWts3bqV886yr68vMjMzMX/%2BfH6Nck2Un0xYXFyMy5cvo7CwEIcOHarxutevX4euri7k5ORqvIYwMjIyEBwcjNTUVCgrK8PCwgIdOnSQSCyE1AQl0oQQQsg37Nu3D1u3bsX48ePh7OzMqpSjPBMTE6SlpX3zoCCXkoXykwkbNWoEDQ0NLF26FJ07d%2BYcV13w8OFDTJkyBZ06dUL79u2RlJSEly9fYv/%2B/dDR0ZF0eISwQok0IYQQUs7s2bMRFRUFe3t7mJubf/M1bA7BVdXG7etph1x9%2BvRJYrvIouLg4IBhw4bBwcGBf83f3x/nz5/H0aNHJRgZIexRIk0IIYSUU91BOK6H4LKyssDj8aCmplajDhlpaWlYvHgxVq1ahZ49e%2BKPP/7AvXv3sG3bNigpKXFery7Q19dHdHQ0Gjf%2B77hWcXExBgwYgDt37kgwMkLYo8OGhBBCSDmiOgiXnZ2NpUuX4tatW/wx2Obm5vD09OQ0HXHNmjXo1KkTOnbsCACYMWMGNm/eDA8PD2zbtk0ksYpb06ZNkZ6eLlB3np6ejpYtW0owKkK4kZZ0AIQQQsj3ysPDA82aNcO5c%2Bfw4MEDBAcH4/3791i3bh2nde7evYs1a9bwk28FBQWsXLkSt27dqo2wxWLkyJFYsGABrl27hsTERERFRcHR0REjR46UdGiEsEY70oQQQkgtiY2NRXh4OJo3bw4A6Ny5MzZu3Ijhw4dzWqdx48bIzc0VaKWXl5eHJk2aiDRecVq4cCFyc3Mxd%2B5cFBcXQ05ODtbW1pg/f76kQyOENUqkCSGEkFrSunVrvHv3jp9IAzU7KDh8%2BHA4OjrCyckJ7dq1Q3p6OrZt24aff/5Z1CGLjZycHDZs2AB3d3fk5eWhTZs2VY5lJ6QuosOGhBBCiIiVjbi%2Bdu0arl69ioULF0JNTQ2ZmZnYvn07DA0NOe28fvjwAWvWrEFYWBiKioogKysLKysrLF%2B%2BHM2aNautb6NWlO%2BJ/S1WVlZiiIQQ4VEiTQghhIiYqLt%2BlCkuLkZeXh4UFRXr7e5t%2BZ7Y5UlJSeHy5ctiioYQ4VAiTQghhNRRISEhsLCwqHIXtz7u3paUlCAvL48/7fHGjRtISEiAkZEROnXqJOHoCGGPEmlCCCGkFn348AF5eXkoLS0F8GVX%2Bfnz5zAzM6v2XgsLC4SEhFS6i1sfd295PB6mTZuGPn36YP369QgODsayZcugqamJpKQkHDhwAL1795Z0mISwQok0IYQQUktOnz4NDw8PfPr0SeC6oqIirl%2B/znqd27dvQ1tbG40aNRJ1iGK3fPlyFBUV4ffff4eioiLMzc0xYsQILFq0CEFBQQgJCcGePXskHSYhrFDXDkIIIaSW%2BPr6wsnJCc2bN0dcXBwmT54Mb29vGBgYcFpn3rx5uHLlCpo2bVpLkYpPdHQ0AgMDoaCggLS0NCQlJWH06NEAAFNTU6xdu1bCERLCHg1kIYQQQmpJVlYWJk%2BejIEDByIpKQk9e/bEunXrcPLkSU7rqKur4%2BHDh7UUpXgVFBTwa6Pv378PeXl5aGhoAPjSEq%2B4uFiS4RHCCe1IE0IIIbVEUVERxcXFaNeuHRITEwEAqqqqyMnJ4bROy5YtMXXqVLRv3x7KysoCHTsOHjwo0phrW8uWLZGbmwsFBQXExsaiX79%2B/OdevXqF1q1bSzA6QrihRJoQQgipJX369IGrqytWrVqFH3/8EUePHkWTJk3QqlUrTutoa2tDW1u7lqIUL2NjY3h4eMDMzAzBwcFYvXo1ACA/Px9bt26FoaGhhCMkhD06bEgIIYTUkszMTKxcuRJr165FUlISZs%2BejY8fP2L9%2BvWwtLRkvc65c%2BcwYsSICtePHz%2BO8ePHizLkWpefnw8nJyfEx8dj1KhR8PT0BPDlzYKSkhKOHDmCNm3aSDhKQtihRJoQQggRk8%2BfP6O4uFjg0OCePXswc%2BbMCq/98OED/v33XwDAqFGjEBYWhq//yX737h1%2B%2BeUX3L17t/YDF4Pr169DV1eX8/h0QiSJEmlCCCFEgvr164f4%2BPgK17OysmBubo6PHz9WeI5hGEhJSWHYsGHYvn27OMIkhHwD1UgTQgghElTZfpaSkhLCw8Px4cMHWFpaIiQkROB5OTk5KoEgRMIokSaEEEIk6OsOHOUpKioCAO7cuQNpaepYS0hdQ4k0IYQQUsdlZWVh165deP36NX/UeJn61v6OkO8JJdKEEEJIHefi4oLs7GwYGxtDRkZG0uEQQv4fJdKEEEJIHffw4UNcuHCBPxGQEFI3UMEVIYQQIkFsmme1aNECsrKyYoiGEMJFIzc3NzdJB0EIIYQ0VLm5udVO82vevDn279%2BP9u3bo6SkBO/eveP/atGihZgiJYSUR32kCSGEkFp04sQJHDp0CJmZmThz5gw2bNiA9evXo3nz5qzX0NTUFHgsJSXF7yX99OlTUYdMCGGJaqQJIYSQWvLXX3/h6NGjmD59Ory8vNC8eXNkZmZi/fr1WLt2Let1Ll%2B%2BXItREkJqimqkCSGEkFpy9OhR7Ny5E3Z2dpCWlkbLli2xbds2REZGclpHTU0NampqyMvLw%2BPHj6GkpIQmTZpATU2tliInhLBBiTQhhBBSS/7991/89NNPAP47VKioqIjPnz9zWicnJwe//PIL7OzssGzZMiQnJ2PYsGG4e/euyGMmhLBHiTQhhBBSSzQ1NXH8%2BHEA/00wDAsLQ5cuXTits27dOnTt2hVxcXFo3LgxNDQ0MHPmTHh5eYk8ZkIIe3TYkBBCCKkljx8/xpQpU6ChoYFHjx5h4MCBuHfvHvbt24e%2BffuyXsfAwADh4eFo2rQp9PT0EBsbi%2BLiYgwaNAhxcXG1%2BB0QQqpChw0JIYSQWtKzZ0%2BEhoYiKCgI3bt3h4qKCtasWQNVVVVO68jIyODjx49o2rQpv0SksLCQU%2BcPQojoUSJNCCGE1BI/Pz9YWVnhf//7n1DrmJiY4LfffsPKlSshJSWFnJwcrF27FkZGRiKKlBBSE1QjTQghhNSS8%2BfPw8jICAsWLMDVq1dZTTH8WnFxMQBgyZIlaNasGYYPH478/HwMHjwYHz58wNKlS2sjbEIIS1QjTQghhNSily9fIiAgAMHBwZCWlsa4ceMwbtw4tG/fvtp7DQwM8Ouvv%2BLXX3%2BFoqIicnNzkZKSAhUVFSgrK4shekJIVSiRJoQQQsSgtLQUkZGR8PT0REZGBp48eVLtPSdOnMCxY8fw4sULjBw5Eg4ODujZs6cYoiWEsEGJNCGEEFLLbt68icDAQFy4cAGdOnWCnZ0d7OzsWN9/7949HDt2DOfOnUOPHj3g4OAAc3NzNGrUqBajJoRUhxJpQgghpJZs3rwZwcHBKCwshIWFBWxtbaGpqVnj9fLy8nDq1CmcOHECnz59woQJEzBz5kwRRkwI4YISaUIIIaSW2Nvbw9bWFsOHD4ecnJzI1n379i02bNiAwMBAPH36VGTrEkK4oUSaEEIIEbPc3FwoKChwvu/WrVs4ceIEwsPDoaOjg4kTJ8LU1LQWIiSEsEF9pAkhhJBa8uDBA3h5eYHH46G0tBTAl5Z2ubm5ePToEas1cnJyEBAQgFOnTiErKwujR49GQEAANDQ0ajN0QggLlEgTQgghtcTd3R3q6uro0qULkpOTYWBggIMHD2LJkiWs7l%2BwYAEiIyOhqqqKCRMmwMbGBj/88EMtR00IYYsSaUIIIaSWvHjxAocPH0ZKSgo8PT0xdepUaGtrw93dHVOnTq32/vfv32PHjh0wMjKClJSUGCImhHBBiTQhhBBSS%2BTl5dGkSROoq6vjxYsXAAAtLS2kpqayun///v21GR4hREg0IpwQQgipJZ06dcLRo0chJyeHZs2a4enTp3j58iXtLhPynaAdaUIIIaSWLFy4EHPmzIGBgQGmT58OOzs7NGrUCL/%2B%2BqukQyOEiAC1vyOEEEJEzMbGBiYmJjA2NoaGhgZkZGQgJSWF%2B/fvo6CgAAYGBpIOkRAiApRIE0IIISK2ZcsW3Lx5Ew8fPoSSkhKMjY1hbGyMAQMGQFZWVtLhEUJEhBJpQgghpJYUFBTg5s2biImJQUxMDDIzM2FgYAATExOMHTtW0uERQoREiTQhhBAiBm/fvkVgYCD8/f2Rnp5Oo70J%2BQ7QYUNCCCGkliQmJiI8PByXL1/Go0eP0KVLF1hZWdFYb0K%2BE7QjTQghhIjY5s2bcenSJSQnJ0NXVxcmJiYwMTGBqqqqpEMjhIgQJdKEEEKIiGlqaqJfv35Yvnw5%2BvTpI%2BlwCCG1hBJpQgghRMQCAwNx%2BfJlXL9%2BHW3btoWpqSlMTU2hra0t6dAIISJEiTQhhBBSS4qKihAdHY3Lly8jMjISAGBsbAxTU1MYGxtLODpCiLAokSaEEELEoKSkBGfPnoWvry9SUlKoawch3wHq2kEIIYTUksTERNy8eRM3b95EbGwspKWlYWhoiMWLF0s6NEKICNCONCGEECJiv/32G2JjY5GVlQVNTU0MHToUQ4cORe/evSElJSXp8AghIkI70oQQQoiIffjwAQsWLICRkRGUlJQkHQ4hpJbQjjQhhBBCCCE1IC3pAAgh/9duHdMAAAAwDPLveh76LQEVAACPRBoAAAKRBgCAQKQBACAQaQAACEQaAAACkQYAgGAmFGM3gyt%2BrQAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n",
       "    <img 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pnuC%2BUDKSFEEIIIYTXpKen8%2Buvv7r9cyaFMqJ79%2B588cUXNcbLr%2BrgwYPExcW5PXfVVVeRkpJSY7nNZqN58%2Baucm%2BQhCyi/tDLcNaiBRw4AK1aweHDmtXslZ5/JXTmjLrcbIbQUMjOhipZnqvx9VW3YzJBQAAUFoLqm6yCAu0yiwXCwyErCyoqtOsFBOj3JTDQsSxVX/Lz1e1YLBAZCRkZ6v5ERanbMZvV2xZg7151uc0GcXGwfz%2Bcy6Jdo3YxZ/U7ExICubnqTlVJpFKNyQT%2B/lBUpN7AZp35E5PJcWCVlCjbScv1VzZj9LhpZM1S99XAiVARGq7si7Mpvf1tOaZ93mO1QmwsnDgB5eXqhkpLtctsNse15vBh5UGzt7K1chE2G7RsCYcOqY%2B9q65Sd9Vq1V8dAFuu5/vpeKF6P1kscMUVcOqU%2BphpEqQ4n4yeS6B/0fLxUe9LIKdYfRE2mSA4GPLy1KdlgwbqrtSaWsr8uXL%2BfBYuXOj23Pjx45kwYYKh10dGRhqqV1BQgL%2B/%2B7XIz8%2BPwsJCQ%2BXeIANpIZxCQx1X8tBQj5oxmdQXTKNtOP95q52L7dPl1Bdv98dTFoujHYtFPZgx1BlPV8pbG9jZloe8sp%2B8tbPxQhNms6MRvQ8il6gdbx17XhlHeWk/Vd00qoH0peiLt84nb56WfyTDhw8nMTHR7Tmjg%2BML4e/vT3FxsdtzxcXFBAYGGir3BhlICyGEEELUR55%2BMNQQFRVFlN7Xgl4QFxfHr7/%2B6vbcwYMHadeuHQCtWrXiwIED9O7dG4CysjKOHDlS7XYQT8g90kIIIYQQ9ZHZXDv/LpEhQ4awY8cONmzYQHl5ORs2bGDHjh0MHToUgFtvvZV33nmHlJQUSkpKeOWVV4iIiKBTp05e64MMpIUQQgghxB9CQkICH330EQAtW7bkH//4B4sXL6Zz58689tprLFiwgBYtWgAwbNgwxowZw8MPP8z111/Pnj17WLx4MTabzWv9kVs7xAVJTU1l4MCBfPLJJzRu3PiCXjtt2jTAESNXCCGEEHXsEs4eX6x9%2B/a5/b1r1y63v2%2B88UZuvPHGGl9rMpkYO3YsY8eOrbX%2ByUBaXJDGjRtXO4iFEEIIIeojGUiLC3LixAn69OnDpk2b6NOnD08%2B%2BSTvvPMO6enptG7dmpkzZ9K6tSNs06ZNm3j11Vc5efIkXbt2BSAsLMzV1ieffMLrr79OamoqzZo149FHH6V79%2B6cPXuWoUOHMmjQIB5//HHKy8u58847ady4Ma%2B%2B%2Bqqhfqanp5ORkeH2XGSLFkSpInLEx7s/1iKLxVi5Xj0jkcycj6q6VsWVwFt9cZbr1VP1pWq5Xj1v8PNTlzvDD%2BqFITS88YxuxJoY3dl60QyqtqOgt/2NHjfKCoYb8RJVeEHnV8FGvhJWbTvnMlTLAvx0IjwYbMZ7vLCf9Dad4XNbtRyj5xKo95PB88Ab1z29KH216g8wI325M9m9mSdR/M87fyCdkJDAggUL8PPzY%2BLEiZjNZpYtW8Zvv/3GkCFDmDVrFjfffDNfffUVEydOZMiQIcyePZstW7YwZcoUFi1aRIcOHdi6dSuPPvoo77//Pq1atWL79u3cc889LF%2B%2BnM2bN/PZZ5%2BxevVqgoKCDPVzwYIF1WNYPvQQEx55pDY2ixBCCHFRcnLqMI60F8PAuVElJ/gfIzPSwiOjRo1yxYYcMGAAixcvBmDDhg20a9eOIUOGANC3b19X%2BBmAd955hzvuuIPOnTsD0Lt3bxITE/nXv/7FU089RdeuXbnnnnuYNGkSubm5vPfee4YH0aARw3LwYHjrLe0XxcfDihUwYgQosh7Zf9ipXLaReKE5Oepyi%2BX3IP6qeKp6s1FGc3QUFan74sy1oOqL3uyt2fx7X1QzMHpx8q1WCAuDs2fViSQaNlS3Y7Hox6pV5OUBHDPRTZrA8eOO/CVarorKVTdkNkNQkCMbjWrjqKb0TCbHTiguVu9sIzPSBhKyZBWod7jR4ybckq1uxMCJUBGsH/vdyP62nD6pXWizObL8pKfrB25Wlfv4QOPGkJqqTPZx2N5cuQgfH4iJgZMn1TlDYmPVXbXZjMWhthV4vp9OF6v3k9UKERGQmak%2Bt6MDFOeT0XMJ1Bctk%2Bn3jaM4D/JK1Bdhs/n3RFR1OvMsao0MpIVHIiIiXP%2B3Wq04v%2BBIS0ur9mPEpk2bcvasIyPVyZMn2bFjB%2B%2B9956rvKKiguuvv97194gRI1i6dCkJCQnEX%2BDtFjXGsNQbFTmlpEAt3wduNNlARYW6rt6F2fmtnd2urmsks1lFhbqe0TeJykrP%2B%2BKsZ7TuxTovjr%2BmkhKdukZ3eGWluq6Rr7T1drbRr3LtduUAwui21ztuwMC20TsRvEUnix3gGFjp1TPSTmmp8tNXscHzqbTU%2BHHqESPbX2c/GU0cU16uU9dIX/TOJTCWIUXnPPDWda/OyK0dHpOBtKgV0dHRfPXVV27PnT59Gt9zN5NGR0eTlJTE/fff7ypPTU3Fr8oMwVNPPcWNN97Izz//zIoVKxgxYsQl6bsQQgghhBHyUUTUiiFDhrB//37ef/99ysvL%2Beabb/jiiy9c5bfddhtvv/02P/30EwA///wzycnJrF%2B/HoC33nqLPXv28OKLL/Lss88yZ84cDhw4UCfrIoQQQvxP%2BoMnZLkcyIy0qBVNmjTh9ddfZ/bs2bzwwgu0bduWfv36ucpvuukmCgsLmTFjBqmpqYSGhjJmzBhGjRpFSkoKc%2BfOZf78%2BYSFhdGnTx9uvvlmHn30UVatWuWa1RZCCCGEB%2BrZoLc2yEBaXJDY2FhXcPTzg6QnJyeTnJzs%2BvtPf/oT69at02zr/PpO8fHx/Pzzz27Pvfjii550WwghhBDC62QgLYQQQghRH8mMtMdkCwohhBBCCHERZEZa1Bv2Sv1QRyYMxIk2K%2BLwJiTAzp2YOnZQhtArTVX3xZnZq6xMHTosfNt6ZTuEhECPHgR8vxVytWOvBsXEaLfh7w/h8YRnpKgDTlcJhVgjmw0CownMO62MbfWvT5somwkPh%2BRk%2BPpryMrSrndfUoZ24blg1JZcdTDqdmk/KftCcDDQhavO7HDE0NWy%2B5S6nYYN4eab4Ztv4MwZ7XrNmmmXBQZChw6wd686GUJ0tLovvr7QtKkjXrIiPJtPRCtlM85IfTabetJr7%2BlwzTI/P2gRCofPhipDvF1tPqvsCxYLhIRgKchVhkTbV9pCs8zXBM2BI2UxlOhEt2sdrQgU79wY4eHKeGhXZx5SL8TqAzShhfU4%2BGh3aNcvLTXL/P0d4fMPHVKf2gAJ1lTtQj8/R9DwjAxlLL4mcTq5AEwmwIfohqXq0HQ6seaNOnRC%2B/c2Pj7nYsSn%2BSijGbb0U8QeB8cJEBxFcJFO/PEGimtwbZIZaY/JQFoIIYQQoj6SgbTHZAsKIYQQQghxEWRGWgghhBCiPpIZaY/JFhRCCCGEEOIiyEBa1Kp3332X1q1b8%2Babb9Z1V4QQQghRlWQ29Fj9Wltxyb377rvccccdvP3225Srwk8IIYQQ4tKSgbTH6tfaiktq27ZtZGVlMW3aNCorK/nss89cZWfPnmXy5Ml07NiRPn36sHz5ctq0acOJEycAOHbsGOPGjaNr16707t2befPmUaqKQSSEEEIIcYnJjw1FrVm%2BfDm33XYbfn5%2BjBgxgn/%2B858MHDgQgKlTp2Iymdi0aROVlZVMnTqVinPxXQsLCxkzZgwDBw7k73//O2fOnGHixIlUVlYyZcoUQ8tOT08nI8M9jnBERCRRUVGer1hCgnZZfLz7owarzpnnLNerR0iIujwoyP1Ri7%2B/dpmvr/ujFptNXW5wpcK1wwoDjnC1VR91l1cTZ5Bj56OW4GB1eUCA%2B6MWRTxm4Pf9qLc/AwO1y5z7ULUvwfh%2B1NmfepvOOSmlNznl56dd5uPj/ljbnVFtGsN90VuO0Q2jtyCD%2B8kbpzYAFsWOMtqQSRGDv2q5Xj3V/ja6fVFvYoOb1zvXPVV86dpWz2aPa4PJbldFPRfi4pw8eZKbbrqJL774gujoaLKzs%2BnZsydLliyhWbNm9OjRg40bN3LllVcCcPDgQQYOHMimTZv46aefmDVrFl9//TWmcxfUb775hokTJ7JzpzpZitOCBQtYuHCh23MPPzyeiRMneHdFhRBCCE%2BcPAmqpFi1SZXkyRNHj9ZOu5chmZEWtWLFihWUl5czdOhQ13Pl5eX885//ZNy4cQDExsa6ypo0%2BT2b3cmTJzlz5gydO3d2PWe32ykrKyMrK4twvSlLYPjw4SQmJro9FxERqUyWBY6JEN06HTtoF8bHw4oVMGIEpKRoVsv4TP2B4FziPc6qE%2B8RuXerurNBQY5sdzt3Qn6%2Bdj3VTL2vL7RoAYcPq2dW9aaIrVZH9sPMTOVKrflOnXkvNBQSE%2BHf/4bsbO16yb0V2e7OZbojV53pjgMHlH0hIADatYNffoFCRbq1zEx1OyEh0L27I7OhIgOlMiuhvz9cfbUjs6GnGSivuAJOnVLOlOWGNlU2YzY7Dr/8fGUCP2V2Sh8fx/ji5EmU2eVahCu22QV05sgZ7W8EfHygcWNITVX3BaB5uCLLpdns%2BGahoEC9YVQHNzj2U3Q0nFZnCk0p0M4UavTUBoi3KM4FZzbMY8fUDekN2kwmx3qVlakvxKo0l0YPPOB4jvb%2BNrh5aeKbrlwGVqsjc%2BmZM%2BqLeV2RGWmPyUBaeF1JSQmrVq3ihRde4IYbbnA9v3//fu6//34eeOABwDFgbtGihev/TtHR0TRt2pRPP/3U9Vx%2Bfj5ZWVk0bNjQUB%2BioqKq3cbhte9eFKm/XVJSlPWMXk/Ly3XqqgZdVeXnq%2Bvq3cIAjjdI1QBNddtBVeXlyncm1cCqquxsnbpGNnJFhbqeKu13VYWF6rqqtN9V5eaq6xrZT0VF6hThRtoAxz5SDIpUnz%2BqqqxU11WNiZxKS3XqeakzeoNJZ1906%2BkM4Fx1VPWM/iakrExZVy/1N%2Bif2gBYDeyokhL1jjJ6Ebbb1XWN7G%2B9Aw9jm1hn84LZ4G0ZOte9OiMDaY/JFhRe9/HHH2MymRg8eDDR0dGufz169CAuLo41a9bQu3dvXn75ZXJycsjJyeGll15yvb53794UFBSwdOlSSktLyc3N5YknnmDy5MmuWz2EEEIIIeqaDKSF161YsYLBgwdjq%2BFHGMOHD2fdunW88MILmEwmevXqxS233EKbNm0AsNlsBAUF8eabb7J9%2B3Z69OhB3759MZvNLFq06FKvihBCCPG/S8LfeUxu7RBet2bNGs2ykSNHMnLkSL799lteeeUV/M79ZH/fvn0sWrSI0HP32bZs2ZIlS5Zckv4KIYQQ9VI9G/TWBtmCok7MmTOHRYsWUV5eTn5%2BPosWLeKGG27A11AcJiGEEEKIuicDaVEnXnnlFXbv3s31119PYmIiFovF7T5pIYQQQtQyubXDYxJHWtQbetEgLBZHaLXsbPWPvVW/4LZaITISMjLUwSCuaKzzo8mEBEfIug4dlNE/crLVp6/Z7AjSkJenDhCQmqpd5uf3e4gs1Q/yr26qiBTh7Iy/vyM8gKIzeZXq6B9Go4cFn/ZCuC69RCs2myN0YHq6%2Bhf5elEcjMbaUoRUJDgYrr8e/vMfdQSR9u3VfTEYe/FESaSyGZsNGjWCtDT1KqmiJprNjl1QWKjehEEFacq%2BYLU6Mv1kZSnXqaxhI2UzzshsetIU3TG6XWLDvHM%2BVfipzyeLxXjQE0/bsRTqRMExenKfPq1dZvTcBpZtbaVZFh4OSUnw4Yfq9457Qlcrl0FoKPTpA5s2qUMa3nqrup3acvXVtdPu3r210%2B5lSO6RFkIIIYSoj%2BrZ7HFtkIG0EEIIIUR9JANpj8kWFEIIIYQQ4iLIjLQQQgghRH0kM9Ieky0ohBBCCCHERZAZaVErcnJymDdvHps3byYnJ4egoCC6devG5MmTiY6OruvuCSGEEEJmpD0mW1DUismTJ3P27FlWrVrF7t27%2BfDDDyktLeXuu%2B%2BmXBUXTgghhBCXhsSR9lj9Wltxyfzwww/069ePyEhHrNmIiAhmzJjBddddR25uLvn5%2BTz77LP07NmTP/3pT0yePJnMzEwAPvnkE9q1a0fKuZi5e/bs4dprr2Xr1q11tj5CCCGEEOeTWztErRg4cCDPPPMM33//PV26dOG6664jJiaG2bNnAzBx4kQKCgpYs2YNfn5%2BzJ49m/Hjx/Pee%2B8xcOBAvvnmGx5//HGWL1/O5MmTGTNmDD169DC8/PT0dDIyMtye8/GJJDIySvM1Fov7oxar4qxxlqnqAI6EKyrx8e6PGvQ%2B%2BDvL9er5%2BWl1hHszAAAgAElEQVSX%2Bfi4P150Z0ym3x8VdfU%2B3RtdJ1Tp5m0290e9elqM7nC9hCxG2wkO1i5zJo/RSyKjtwyDJ4LNS6uk2o8GDxmvrZO3qA4bw9cIL51PlxVvXbC8cW7jSLqipUED90dNwYqMQvD7Oas6d1WJWmrbH%2BXYuYxJZkNRKyorK/n444/ZsGEDP/zwA3l5eTRt2pQJEybQrVs3brjhBjZu3MiVV14JQFFREZ06dWLlypW0a9eOwsJCkpOTKS0tpXHjxrz11ltYLuBNcMGCBSxcuNDtuYcfHs/EiRO8up5CCCGER1avrrvMhp071067//1v7bR7GZIZaVErzGYzQ4cOZejQodjtdg4dOsS6det4/PHHefTRRwG47bbb3F5jsVg4ceIE7dq1IyAggFtvvZW5c%2Bfy8MMPX9AgGmD48OEkJia6PefjE6n84G%2Bx/J5OW5XqVpXW12B2ZSL7d9AuBMdM9IoVMGKEMi103padymaMZtw9d1dNjXx8ICYGTp5Up0dvEV2k7Asmk2Pqu7gYFJ/fCyr9lc0YzIxMYNYx7UKbDa64Ak6dUu9Q1VQ9OHZ4w4Zw5ox6hxuZkY6IcOwIVTtHjmiXBQTAtdfCTz85cmprad1a3ReLBUJCIDdXeSKklYYpmzGYlVs5UWcy/b6vVVM%2BAUWKHM7gWKfQUMfMn2KdykIUU5QYTxF%2B5ox2mdHt0ijEO%2BdThY/6fLqkKcKLvZP2XJmz2%2Bi5DXy4s6lmWYMG0Ls3bN4MOTnabSQFb1Iug%2BBg6NIFduxwvLlcbmRG2mMykBZe9/XXXzNx4kQ2b95MaGgoJpOJq666iilTpvDtt99Sem40tnHjRtc91AAHDx6kSZMmABw7doxFixbxl7/8hZdeeolu3bpdULSPqKgooqLcb%2BPIyjL2hlFRoa5n5LeS5eU69Xbt0m8EHINoRV298VnVeqq6xcX6bZSW6tTT64zzgm23K%2Bt6a50oKdFvpKxMXc/oB7jycvWbttGV0mvHyBtxYaG6ntEf%2B1ZUKOsaGVA6F3exm8bgIeO1dfIWI9tGb7t463y6rHjr5PbGuY16PO6Uk6NTr8LgbRl5eXV7C4cWGUh7TLag8LrOnTsTHh7O9OnT2bdvH2VlZeTn5/PRRx9x5MgRBgwYQK9evXjhhRc4e/YsZWVlLFq0iGHDhpGbm0tZWRmPPvooAwcO5Pnnn6dz58489thjVP5R3iyEEEIIUS/IQFp4nZ%2BfHytWrCAyMpIHH3yQTp060atXLz766CPeeOMNWrZsyUsvvURISAhJSUlcf/31bNmyhaVLlxIZGcnf//53zp49y7Rp0wB49tlnOXjwIIsXL67jNRNCCCH%2Bh0j4O4/JrR2iVkRFRfHss89qljdo0ECzfOrUqUydOtX1d8OGDdm2bZvX%2ByiEEEKIy09WVhZPPfUUO3bswGKxMGTIEJ544gms54W7uffee/nhhx/cnissLGT48OE8%2B%2ByzZGZm0q1bNwKqRDMKCwvj3//%2Bt9f6KgNpIYQQQoj66DKdPZ40aRKNGjXi66%2B/JjMzkwcffJA333yTe%2B%2B9163e0qVL3f5etWoVCxcuZPz48QD8/PPPxMTEeHXgfD4ZSAshhBBC1Ee1NJCuKZdDZGRktSAANTl69Cg7duxg69at%2BPv706RJEx566CFefvnlagPpqn777Teee%2B45li1b5lrOzz//TLt27TxbGR0ykBb1hiqGP/x%2BPfHxUf9gPHzbeu3CkBCI7EHk3q2O8GEacrLV4dvNZgjGEd5O1ZcGoSZlOyQkwM6dBPfsoIz%2B0eDgQe02rD5AE1pYj4OPIv7dCZ1oCL6%2B0Lw5pKUpf01vilGHZ6uah8KkWn1VlBfnzg4PV%2B/s1FRlX1wHVWGhOkJAw4bqdpxfV/r6qiOFXHWVdpkzY06TJuo4hSEh6r44N2pgoDKsWqRmiXszYWHq0HV6oeICAhzhG1XBNoIK89Wd8fFx7OvCQuW2OZLbSLPM1xeaNnVEVdMLGtGqqaKCyQT40CisVL1hTpxWL8THx7GvMzOV65TbsKVmmTPSYUGBfkSjsCCdUCQWG5ZKnTqqsIzg2OGBgY7wQKodrjqfnOdPSIjuSvXtq13mPJ26dlWfTgToJApzntsdOlySiDGXi5UrV1bL5TB%2B/HgmTNDP5XDgwAFCQ0Np1Oj387Fly5akpqaSm5tLiMY1bObMmSQlJdGpUyfXcz///DM5OTkMGjSIzMxMrrnmGp544gmuUl1LL5AMpIUQQggh6qNampGuKZdD1XC3KgUFBfj7u8c/d/5dWFhY40D6%2B%2B%2B/58cff2Tu3Lluz4eEhHDVVVdx33334ePjw9///nfuvvtuNmzYQLAqiP0FkIG0EEIIIYTwmppyORgVEBBAUZF7QiLn34GBgTW%2BZuXKlQwYMKDaYP2VV15x%2B3v69OmsXr2a77//nt69e19U/853ed5lLoQQQgghatdlGP6uVatWZGdnk1kl5e6hQ4eIjo6ucRa5vLycTZs2MWTIELfn8/PzmTNnDidPnnQ9V1FRQXl5OX56WWsvgAykhRBCCCHqo8twIN28eXM6duzIrFmzyM/P5/jx47z22msMGzasxvr79u2jpKSEDh06uD0fFBTEd999x5w5c8jLy6OgoIDnnnuO2NhYt/uoPSUDaVEr0tPTKdT7YYkQQgghxHnmz59PeXk5ffr04bbbbuPGG2/koYceAiAhIYGPPvrIVff48eM0aNAA3xoiCrz22mtUVlbSt29fbrzxRjIyMliyZAk2m81rfZV7pOupxMREMjIyXMHN7XY7ZrOZq6%2B%2Bmv/7v/%2BjTZs2F912ZmYm/fv35%2BOPPyYgIIA1a9YwY8YM148FKisradiwIbfccgsTJkzAdO7n/a1bt8bX1xfLuV9d2%2B12GjZsyIgRI5Qhb4QQQghxES7TONIRERHMnz%2B/xrJd50Wguummm7jppptqrBsTE1Mteoi3yUC6Hps5cybJycmuvzMzM3nyyScZP348X375JeaLPMGKi4urzUY3btzYLSD6L7/8wj333ENMTAy33nqr6/klS5bQtWtX1987duzgnnvuISwszK2eEEIIIURdk4G0cImIiGD48OGMGzeO7OxsKisrmT17Ntu2bcNkMpGYmMjjjz9OUFAQ5eXlPP/883zxxReUl5fTsmVLpkyZQvv27Rk0aBAAgwYNYtasWTUuq127dnTp0oVffvlFOUDu0qULrVq1Ys%2BePRc0kK4pGHxgoDoYfNX4xMrPEKo4vEFB7o8a9D6jOMt1P8skJKjL4%2BPdH7U4g6bWxPkVmN5XYar4x1WXoVoW%2ButseD/ZFYVGN7Be8HGD64RV51Lr3HZGt2FNjO4nZfBt3Dewqpq6FaPNKDeNs0xv8%2Bluf4PbxlcR1tno5gXUK210w3hpnVSHlOHrjLd46zxQlRttA/UmNnzseWOd6jK%2B9GU6I/1HIgNp4XLq1CneeecdrrnmGkJDQ7n99ttp3rw5n332GWVlZUyfPp2nn36aV199lXXr1rFr1y42btxIYGAg8%2BfPZ%2BbMmXz00UesX7%2BePn36sH79emJjY1mzZk21ZaWkpLB7927mzJmj2Z/S0lI2b97M/v37Xek%2BjaopGPzDD49n4kT9YPDnha%2BsrodOAH5wBN9XMBq9UiPSz%2B927jTW0IoVBpeooEpwciEaN1YWBxhsRnc/obfxDDSiuwPO0Vknw/SSpYSF6bdxkSGnqtEZoOkM84w2g5HQsvqr3cRYZ3SO4aYGmrjiCiMLMrB19DZME%2B%2Bsk84RBeh%2B7j/HwCcIvXUKDzeyIAgNNVZPxUCM4CsMLEb/%2BDRwToL63D5v0ueSkoG0x2QgXY/NnDmTWbNmUV5eTllZGdHR0fTr148HHniAX375hV9//ZU33njDFbfxiSee4KabbuKpp57Cz8%2BPEydOsGrVKnr06MEjjzzC5MmTNZeVmprq%2BpVsSUkJpaWldOvWrVrqznHjxrnukQZo2rQpTz31VLXA7npqCgYfGBipTKxlMjnGVUVF6oRjAd9v1S4MCnIMonfuhHztbGt5CerBuNnsGMMVFKgT7wX3VA/YiY93DKJHjICUFO1669Zpl9lsjjfr06ehTJG5TC81mo%2BPY8CZmqpMFVYY1VzZjOH9ZC/QLjSbf29EtYH13uAMrhMNGqjbcaaYy81Vb0fVAWyzOQbR6enq/aQ3MjCZHG2VlSk3cKnOYNFgM%2BTkaJdZrY5B9Nmz6km7yOLjyr4YPYaP2bUHrzabYxB96pR68wI0jVYcC0Y3TFqaeiEG1ym3gfY6mc2OS1Z%2Bvvo0AAjx11lp5zqpKLK9Ao7zIDQUsrPV54HejHRwMOTl6V6TThVpj6StVsepkpGhPvau8DurXIbhc1v8YclAuh575plnSE5OprS0lLfffpvXX3%2Bdnj17EhYWxrZt26ioqKBnz55ur/Hx8eH48eMMHDiQsrIyPvjgA1599VXCw8MZN24cd9xxR43LOv8e6ZMnTzJ9%2BnTuvvtuVq9e7Xr%2B9ddfd7tH%2BmLVFAxe783C%2BcHcbtd5U9F7M3AuTFFP702raj1lXUXabzcpKeq6yhy455SVqesZ/XqytFSZY1lv2xjeT3YDG1lvA%2BvlgnbSWSfD26aiQl3XG/tJNXg7v56irsFW9JoxtGnKy3XqGdkuoLttSgwcMmVlBg4LI9tYb8N4aZ2MjN0qKy/RGM9b54HRNnRWysgmLi/XqWe9hOtUG2RG2mMykBb4%2BPhw7733kpOTw0MPPcR7771HdHQ0fn5%2BbN%2B%2B3TVDXFpayvHjx2nWrBmHDx%2Bmbdu2JCUlUVxczKeffsoTTzxBp06dqqX2rElMTAxjxozhwQcfJCsri3CjX/kJIYQQQlwm5KOIcJk0aRKtW7fm0UcfJS4ujmbNmjF79mwKCgooLi5m1qxZjBkzhoqKCjZv3sz48eM5ceIEfn5%2BhIaGYrVaCQ4OdsVyzFfc2nDmzBnef/99WrZsSZiR%2Bz6FEEII4V2XYUKWPxqZkRYuFouFl19%2BmaSkJF555RUWL17MnDlz%2BPOf/0xJSQnXXnstb7zxBr6%2Bvtx1112kpaVx%2B%2B23k5%2BfT0xMDPPmzSM6Ohq73U6/fv0YPnw406ZNw9fXl9TUVBKqRJjw8fGha9euLF68%2BKLD7AkhhBDCA/L%2B6zEZSNdTVe9XrqpJkyb88MMPrr/nzZtXYz2r1cr06dOZPn16tTKTyVQtYkbVeNVa9u3bp1tHCCGEEOJyIQNpIYQQQoj6SGakPSZbUAghhBBCiIsgM9Ki3ihQhBUGR9zQgABHaGFVlKKgmBjtQmfEkqgoZUKA1FR1X/z8HC/PzITiYu16DQ4eVDfkTN21bp06htNVV2mXJSQ44mIPHep5CD0A1fYDMBhxTpcq5rLV6gjUXVys3tlNdVJ0OLPTRUerQ5mdPq1ux8fHETC5sFC9HVWzR1XT1Knq6YVvtFod/SkoUG6bYh91PGqz2dFMSYk6wqCRPD8REeryvPyWun0JBAoaNlH2pfiIfl9KStTnpGuBekwmdXbDoiL1653Hm06H9HL8gMG8Q6WK88QZG7uiQnkeFIU0Ui7CZAI/oDgwXHk6ZWZql9lsEA2cLg7VDWvdLFYv5p%2BFK6J06hTrpJByHgt%2Bfsbjnl5KMiPtMRlICyGEEELURzKQ9phsQSGEEEIIIS6CzEgLIYQQQtRHMiPtMRlICyGEEELURzKQ9phsQeEVhw8f5oknnqBHjx4kJCTQt29f5s6dS8G5X/i1bt2a7du3X1TbCxYsYNSoUd7srhBCCCGEx2QgLTy2c%2BdObrnlFmJiYvjwww/ZtWsXS5Ys4ccff2Ts2LFUVOj9MloIIYQQl5ykCPdY/VpbUSuefvppkpKSmDhxIg0bNgSgRYsWzJs3j/DwcI4fPw7At99%2By9ChQ0lISGDYsGHs37/f1cb333/PyJEj6dSpE4mJifztb3%2Bj1Gg4NSGEEEKIOiD3SAuPHDt2jAMHDvDXv/61WllERASvvfaa6%2B8dO3awbNkygoKCmDBhAnPmzGHZsmX89ttv3H333UydOpU33niDU6dOMWHCBPLz83nyyScvql/p6elkZGS4PWexRBIZGaX5GovF/VGTM1Z0TXx93R81%2BKmLXeGfnY%2BarDoVbDb3Ry0JCdpl8fHuj7VMbzLDGXrXZNKpa1Vc3ozubFWc3/M7o6K3I43uJ9UKO9dXtd5Gyg1uG739VDWsdW3zVl/8/LTLDJ7a3qPqDFz6DqmOcYPngc5ZYvh0Up0mRk8Dr/HGBasu40vXs9nj2iADaeGRM2fOAI5Bs567777bVa9v374sXboUgI8//pjWrVszevRoAJo1a8aUKVOYOHEiM2bMuKh%2BrVy5koULF7o99/DD45k4cYLua0NDdSqEGxhQtmihLtZvAdDPXQJNjDWkl/Vi5079NlasMLYsPTqDxQCdsaST6vOMo6Fw/UZ0d7ZBeoMZ/R3pEKX9Qc%2BwSHWiFMN0MnlopxtyZyjZhw69gZXRZegdM61a6behl6PHwcBBrPehSZUkqaom6muA3rwAGJg8ALDoDOxBfwLBwGIMNOOVJD4OBlZcb%2BPoXojOUX0w0ssWVptkIO0xGUgLj0See9POyMigefPm1cozMzNdg%2BfQKoMWm83munc6KyuLJue9GcTGxlJcXExWVtZF9Wv48OEkJia6PWexRKJqzmJxjKuysx0JurSEZ6RoF/r6OgbRhw87Mo5pOOyrHoz7%2BDjGXidPqhPdtbAeV7bjSPMV7cisp0rzNXSodll8vGMQPWIEpCjW3ciPSW02dT%2BAwjL1AMNkcrx3FRWpkwkGFHlhZ%2BuN0Ewmxz4vKVF3Ru84ttkcg%2Bj0dPX20ZuRjoyEjAx1tka9mU6LxTGIzs1Vbps8a5iyGbPZsfkKCtQTbkFB6u6YTOpNC%2Bokls6%2BOI8ZVV9UGUd9fR2D6GPHlKc2AK2a66TUM3AecPSoutzX1zGIPn5c2aGKFuoBucWiPgVc9coU6RwNngfFOkNpo6dTdrZ2mdXqGERnZqpPA4DoSJ0VN7Jx9G5BNJkc51xxsf6BLP6QZCAtPBITE0NcXBwbNmygc%2BfObmVZWVn07t2bF198UbeNzz//3O25Y8eO4ePjQ4MGDS6qX1FRUUSdN7uXlqZ/YQXHdVNZTy91LzjeCRT1ig1eT0tLddIR%2Bxi8j7ysTH3BV6X%2BdkpJMVbPQ3rfcjrHkna7Tl1v7Gyjb3x2u7qu0fv99faTkdmj8nJ1G0a/89bZNpUGJ7IqK2v/m2uj7ev1RTf1NwZThHuD0YVcqg4ZORd0zgOjw0i900nvMwg4Dl0j9TzmtQtWHZEZaY/JFhQee%2Bqpp1i9ejULFy7k7Nmz2O129u7dy7hx42jbti39%2B/dXvn7gwIEcOnSIt956i9LSUo4dO8arr77K4MGD8dG9SVgIIYQQom7IjLTwWJcuXXjnnXd4/fXXGThwIEVFRURERHDTTTfxwAMPYNO5FzA2NpalS5fy6quvsmDBAvz8/Bg0aBCTJk26RGsghBBC1EMyI%2B0xGUgLr7j22mvdInScb9%2B%2BfW5/Jycnk5yc7Pq7U6dOrND4MduECfo/EBRCCCHEBZKBtMdkCwohhBBCCHERZEZaCCGEEKI%2Bkhlpj8lAWtQbAQHqcuf1xM9P58fVqgClzvvBQ0OVodOubqgTN9RsBvxpEa0Tr%2BuETmQKZwxUvegURqJK6IW30/thaEKCI151167K6B%2B%2BpcZ%2B268Xhhd/A4Fk9eJI60X%2BcAY4Nps9i9rhbEcvaoeR2MI6saizstVxcS0mCAWyTWFUKN5jwwtPqfthtUJwJMHF6nB8OVyhWWY2Q3Aw5OerT4MG5Kj7ghkIJrAyT9lQ48baUYKcp1JkpH5EtLxi7YPTbIZAGxSU2pTrFNyokXohzugr4eHGItR4KL9CO16y2QwBQGGlnzrUYc5J9UJsNvCLwi9XHQayiVVxYFqsQCTRlgywq7dLxhntY89qhbAwOJtrUW7eyCBvZZASf1QykBZCCCGEqI9kcO8xGUgLIYQQQtRHMpD2mGxBIYQQQgghLoLMSAshhBBC1EcyI%2B0x2YJCCCGEEEJcBJmRFrUiMTGRjIwMrFb3QywhIYGysjK6dOkiiVaEEEKIuiQz0h6TgbSoNTNnznTLXug0atSoOuiNEEIIIdzIQNpjsgVFnTh27BijRo2ic%2BfO3H777fz000%2BushUrVtC3b186derE4MGD%2BeCDD%2Bqwp0IIIYQQNZMZaVEnNm3axOLFi2nfvj1Lly7lvvvu44svviAnJ4cXX3yRdevWceWVV/L111/z8MMP07NnT6J0Ek1UlZ6eTkZGhttzAQGRyjacH8x1P6CrMoE4b2Wx6pxaegsxGsTf11fdjjNJil6yFG9ISFCXx8e7P/4ROPeDXrlevctoP1nU%2BVhc5Xr1dI9xg%2BeC6vA2fE7qzQkZbEi1zoa3i85iDK%2BT3va9kA55gaq/hnOO6GVRMnr9VJ1vRtvQqWJ483rjGqFK5lTbZEbaYya7vS73oPhflZiYSFZWFrbzLpxbt27lgQce4Oqrr2bGjBkA2O12evbsyWOPPUbHjh3585//zIgRI%2Bjfvz/XXXcdZrMZ8wWe7AsWLGDhwoVuzz388HgmTpT7soUQQlxGiorAXztzZK167LHaaffll2un3cuQzEiLWvPMM8/UeI80QGxsrOv/JpOJ6Oho0tLSaNy4McuXL2fp0qWMGzeOiooKkpOTeeyxx/DVm9WrYvjw4SQmJro9FxAQSYEiM7fZ7LiWFelk5Q7MO61daLU6UohnZqrT9jbQTkUMOGYv/PyguFg9W5GWpm7HxwcaN4bUVHXq6ZgYdTs2mzJlL%2BBI/a0SHw8rVsCIEZCSolmtbPtOdTsGu2Mz6%2BRxtlj0cz3rlZtMv3dGtZ9SU9XtGN1PTZqo2zGwTtl5OinCLY603Hl56qZCyzK0C6FKjuWzynMhzy9Ss8xshsBAKChQn5PB5Kn7YrCh7IpgzTKj2wXUE6%2BGrzOlZ9ULsVggJARyc5UdqggJ021Gb30ASkq0y0ym39dJdRoE5KerF2K1QsOGcOaM%2BvqpNyNt4LgDOGvVPvYMbl7C/IuVy8BkcnwjVVJStzPPWmRG2mMykBZ1Ij399wtqZWUlqampxMTEkJWVRUVFBf/4xz%2BorKxk586dTJw4kRYtWjBy5EjD7UdFRVW7jSMvT/3G9Xt/dOrpjeDAcQFX1dPriPPiZrer66re3aoqLTVe92Lt2mWsXkqK8bp1zegbn92urnsZ7ScjgyZnPWVdnUGKWz1FXa%2BckxhoxEBDRraN7nbB2N0WuutkdPtWVBiv6wFVX41ergxdO0H/%2Bmlk8Kdz3AEY2Wq6m9db14i6IgNpj8kWFHVi1apV/Pjjj5SWlrJgwQKsVis9e/YkNTWVsWPHsm3bNsxmM40aNQIgLEw9qyKEEEIIcanJjLSoE3/%2B85955plnOHbsGO3atWPZsmUEBARwzTXX8PTTT/PXv/6V9PR0goODGTFiBAMGDKjrLgshhBD/W2RG2mMykBa14t///rdm2fLly5WvHTZsGMOGDfN2l4QQQgjxB5CVlcVTTz3Fjh07sFgsDBkyhCeeeKJakjeAe%2B%2B9l%2B3bt7uV/f3vf6dHjx5UVFQwd%2B5c1q1bR1FREddffz0zZ868oChgeuSjiBBCCCFEfWQ2184/D02aNImAgAC%2B/vprVq1axbZt23jzzTdrrPvLL7%2BwbNkydu3a5frXo0cPABYtWsS3337L6tWr%2Bfrrr/Hz8%2BPJJ5/0uH9VyYy0EEIIIUR9VEu3dtSUyyEyUp3Lweno0aPs2LGDrVu34u/vT5MmTXjooYd4%2BeWXuffee93qHj9%2BnJycHNq0aVNjWx988AFTp07liiuuAOD//u//6N69O8ePH6eJXhQkg2QgLeqN/Hx1udXqiJBVWKj%2Blfa/PtU%2B%2BcLDITkZ1nwXTVaWdhu3367uixkIBAoq/ZW/gjfFtFa3Y4YAoDCqufrX9IpAEWYzBNigsMymbMO3VP8X6Tb0w9vZfHQSHCQkwM6d2Lp2UEb/WPSadn8iIuAvf4EP1ljIzNReVHGxOvxCVBSMHAnvfuBDuiKyV5s26v0UHAw3NIfvUpuTp4jklvKpdllkpCOy4IqVFjIUkemCgpRdITwckpLgq69QHsP3dFFsOHCEb4yMhOxsRxhHDQ0ytUMhEhQEnTsTnPJf9Ql87bXqvjjDaOiE3AjPPazdho8PhMYQWnBSHaIQOEwLZTOBgY7NomomXxEWEBxv3pFARnmY8np1xUsvahc2agRjx2J565%2B6oTSX%2BE3XLHOeB2vXojwPxoxRh9m0WCAEyPWLokIRQvDECe0yPz9oFQkHsiNVhx0A1%2BxfrV0YGgp9%2BhC2c5NjZ2nYEnGrchlBQdCxI/zwq5/yEO7ZU93XP5qVK1dWy%2BUwfvx4JkzQz%2BVw4MABQkNDXcEGAFq2bElqaiq5ubmEhIS4nv/5558JDAxk8uTJ/Pzzz0RERDBmzBiGDRtGXl4ep0%2BfJi4uzlU/IiKCBg0asG/fPhlICyGEEEIID9TSjHRNuRwiI9UfDp0KCgrwPy9BjfPvwsJCt4F0aWkp7du3Z/LkybRq1Yrt27czYcIEAgMDSTiXbTcgIMCtLT8/PwpUSSUukAykhRBCCCGE19SUy8GogIAAioqK3J5z/h0YGOj2fFJSEklJSa6/u3fvTlJSEhs3buSGG25we61TcXFxtXY8IT82FEIIIYSojy7DHxu2atWK7OxsMqvcd3fo0CGio6MJDnbPPrpq1So2btzo9lxpaSm%2Bvr40aNCARo0acfDgQVdZRkYG2dnZbrd7eEoG0kIIIYQQ9dFlOJBu3rw5HTt2ZNasWeTn53P8%2BHFee%2B21GsPi5ufn89xzz7Fnzx4qKyv56quvWL9%2BPcOHDwcgOTmZRYsWcfz4cfLz85k1axZdunShadOmHvWxKrm1QwghhBBCXDbmz5/Ps88%2BS58%2BfTCbzSQlJfHQQ4I1hG4AACAASURBVA8BkJCQwMyZMxkyZAijR4%2BmsLCQ8ePHk5WVRZMmTZgzZw6dOnUC4OGHH6a8vJyRI0dSUFBA165d%2Bdvf/ubVvspAuh5r3doRSWDjxo1ceeWVbmVvvPEGs2fPNvwrW5Vp06YBMHv2bLfnT5w4QZ8%2Bfdi0aROxsbFMmzaNjz/%2BGB8fH1edyspKmjRpwrhx4xg0aJBH/RBCCCFEFZdpZsOIiAjmz59fY9muKtGaTCYTDz30kGuQfT6bzcbUqVOZOnVqrfQT5NaOei8sLIy1a9dWe37NmjUE6cXJqgWDBw92C6q%2Bfft2%2Bvfvz2OPPcbhw4rQVEIIIYQQl5jMSNdzgwcPZt26dUyePBnzuU%2BmP/30E6WlpW4BzvPz85k9ezY7duwgPT2d4OBgRo4cybhx4zh69ChJSUlMnTqVkSNHkp%2Bfzy233MJNN93ElClTPOqfn58fY8eOZeHChezbt48WLbTjs1ZVUzB4kymSyEjtXxE7s4vWkIHUTXi4dlloqPujFr1JAGe5Xj2TTshlZ7nJdPETD95o44KcC1mkKT7e/VFDRIR2mdH9pBMumLAw90ct5/0%2BphrnD8j1fkiuih5ltC/nRYKqpkED90dNfn7qcl9f90ctqg/szs7qddqijvftKterV%2BXbsGpsNvdHVTOKMqPN6F2HjF6vqBKLtxrnxUx1UTsnSrEbjR57epvf6HVPdegZPewA9QXAedLqnLx6803OKG7nRXNzo5fjoFZdpjPSfyQykK7nevXqxfr16/nuu%2B/o3r074PgV7LBhw9i6daur3ty5czlx4gSrVq0iODiYzz//nIkTJzJgwACaNWvGM888w8yZM%2BnRowd/%2B9vfiIqKYtKkSa7Xr1%2B/ni%2B//NJt2ZXKDCEO%2Bfn5vP766wQHB9OhQwfD61VTMPiHHx7PxIn6t6novRkkJ%2Bsv/7zwmRdNdfG91O14qy%2B645Cd6oQtLitWKIv/YqCJfv2MLUrPzTd7p53rrlOXn4vmpDRggHf60ru3Xo1Wxhryxo962rb1vA3Q/0Sj98kKHNlHdKjTjhhuxhC96xVjx%2Bo3MnSobpWRBvrirfNAb3BaJYywJkOHXas%2B%2BnW6dFEWdzSwGACNxHsAbNlisJHaIANpj8lAup6zWq0MHjyYtWvX0r17d4qLi/nss89Yv36920B6woQJWCwWgoKCOH36NL7nPu6np6fTrFkzkpKS%2BPbbbxk9ejRFRUV8%2BOGHWKpMPwwaNEjzHumqnAPuiooKysrKCAgIoEePHrz33nsXFJOypmDwJlOkMuOb1ep4Uzp7Vp3Z8OuvtctCQx2D6H//W5kMi/79tcvAcW3z94eiItSZDQ3MSDvbsesnHvSoDQMTddhsUFamU6erzgem%2BHjHIHrECEjRzor3wXTtAXloqGMQ/cUX6v1kZEb65pthwwbHcaNF74uUwEDHIPrHH0GVJ%2BDIEXVfBgyAjRvVfTEyI927N2zeDDk52vWS2h5QN%2BTr6xjNHDsGJYrUmaodEBDgGET/%2Bqsj5aiWVjqDeovFMYjOy1NmNlRufJvNMfpNT9c9iE8qhtJGm1FNjoPx61Xkx//ULgwPdwyi161Tp7EE3vXVHpAbPQ8GD1YuArPZMYjOz1df91RJGI0edgCtjm3SLgwOdgyid%2BxAlW70h1D1YNzf3zGI3rPHcQ0V/3tkIC1ITk5m%2BPDh5Ofn8%2BWXX9KhQ4dqGYiysrJ44YUX2LNnD7GxsbRr1w5wn1UeNWoUH330EUlJSW6pPS9E1QH3li1beOyxx4iLi6OV3hvleWoKBn/qlPoNx6m8XF1P5/0GcIwNVPUMTMa76nkykHZONtjtxpdZG21cEEXabzcpKcq6qtTfTtnZ6np6KYadzp5Vp0ZW3WZSVUGB8j1b%2BUGwal88SRHulJOjc6wb3TglJeq6Rr7XLixU11MNjs%2Bvp6qr98kJHKNfnXoGWtFtxuhEod71Si/1N%2BDY0Tr10nXu5AH988DobqqsVNc1cujpHXaA%2BkOcU16esl6%2BwVFUUVEd38KhRWakPSZbUBAfH8%2BVV17Jxo0bWbNmTY2xGh955BHatWvHtm3bWLt2LY8%2B%2BqhbeWlpKU8//TSDBg3is88%2BY4sXvqvq2bMnL7/8MvPmzWPVqlUetyeEEEKIKi7DONJ/NPVrbYWm5ORk3nzzTQ4fPkzPnj2rlefl5eHn54fFYuHMmTM8//zzAJSd%2B25y7ty5VFRU8OKLL/Loo48ybdq0aj/2uxg9e/Zk7NixPPfccxw6dMjj9oQQQgghvEUG0gJw3FJx9OhRhgwZgrWGn4G/%2BOKLbNiwgQ4dOpCcnEyjRo1o06YN%2B/fvZ%2BvWraxYsYI5c%2Bbg4%2BPDqFGjaNWqFdOmTcN%2BsTfmVjFp0iRatmzJ1KlTKTXy1asQQggh9MmMtMfkHul6bN%2B%2Bfa7/h4WF8csvv7iVL1%2B%2B3PX/G2%2B8sVo%2B%2B6qqvtZkMvH222%2B7/j7/R4ZOsbGxbn3Qqmez2VizZo3msoUQQggh6oIMpIUQQggh6qN6NntcG2QgLYQQQghRH8lA2mMykBb1htEw1A0bqsvvS9IJRk0Yyb11grue1gm75OsLgU0JzNIJhhodrW7HbgYCCbAXgF0Ru04Vo9dqhYBwAoqy1OvkbyTGmwWbWR0Da9Fr6vvqIyIcyVY%2BmL5TGbruwYcUsQETEuAvO/nLix3U4fY%2B/ljZF0dmiB6MbLIVGuRq14tuom7H3x%2BI44aI/RCoHWy2//6tmmWYI4HbGWH%2BF5gVx%2BgZnZhg1ihgNEk5b8EZ7Vhmm9IfUzYTHAxdgB1nWylD%2Bqky0AVaIQHYZe1MgSJOeSudsJZWIBzIqghVHsKVftoZ/qxWiAQyfGIo1xl7tAhRxKM2mwF/YhrqBInXi5VmdqxVpDkLzNortbH9dM2ykBDoBnzbeiy5V6gXN9lvs3ZhUBDQmZFx/4XGin6bdWLEm8xAMCGmPDBrb5uwWMV2s1iAEFo1ytWNt/dByq2aZaGV0A/4orIP2YrF/SVX5xphagD0oGPBVshVBGZHJ8i2uGzJQFoIIYQQoj6SGWmPyRYUQgghhBDiIsiMtBBCCCFEfSQz0h6TgbQQQgghRH0kA2mPyUBauElMTCQjI6PGpCxLliyhU6dOum0cOHCAwYMH07t3bxYtWlQb3RRCCCGEqHMykBbVzJw5k%2BTk5It%2B/TvvvENycjLr16/n8OHDtGjRwou9E0IIIYRXyIy0x2QLCsOOHj1KQkIC7777LgD5%2Bfn069ePV155xVUnLy%2BPjz76iJEjR9KvXz/eeOMNtzamTZvG008/zbhx40hISKBPnz5uWRDPnj3L5MmT6dixI3369GH58uW0adOGEydOXJqVFEIIIYQwSGakhWHNmjXjmWeeYebMmfTo0YO//e1vREVFMWnSJFed1atXExcXR9u2bRk1ahSjR49m0qRJNKwSnHnNmjUsXryYhQsXsmrVKp599ln69%2B9Po0aNmDp1KiaTiU2bNlFZWcnUqVOp0IkFWpP09HQyMtxj6YaHRxIZaTCYtEoNt724WCzuj1pUwXMBbDb3Ry16swnOcr163lgnL4nQCUcdGur%2BqCkhQbssPt79UUtIiLo8KMj9UYu/v7rceTzoHReRkdplYWHuj1pUccnh90DqOgHVg4PVzQQEuD9qUR3izs2mt/lUhy8YP4RVYZ2dy9BbFv%2BfvTOPs7n6//hz9tUYzIYZSxhrNEhUwkhEpLGM6Ku%2BSmRvEd8eReGbpfqJJKX6qlCyxrfkW6RNUYmyjKUswzCbYRZmMXN/f3zmXnNn5nPOh5kJzfv5eHhcc8/7ns/5LOd83vfc83m9QN3fXFwuvariymmnVJewn5/zqxJPxTVu9WSX13jlotCIt1oH6jHEfn3rrnO8qqrLrYwR51T60hWMzEiXGRebzaZ2PxAqFdHR0aSmpuJR7O5Ws2ZNNhSaU0ycOJFffvmFCxcusG7dOkJDQwGw2Wz06NGD8ePH07NnTwAGDhzIHXfcwZgxYwBjRjo5OZl33nkHgLy8PFq0aMHSpUupU6cOd9xxBxs3buSGG24A4PDhw/Tq1YvNmzcTHh5ueT9ee%2B01FixY4PTe6NFjGDdu7BUcFUEQBEGoIDZsgN5XyZDlvfcqpt4HH6yYeq9BZEZaKMHUqVOVa6T/8Y9/sH79evr27etIogG%2B%2BeYbjh49yvPPP88LL7wAQHZ2NvHx8QwfPhyvwtm24CKzavaEvaCggFOnTgE4JcwRERpHOBNiY2OJjo52eq9GjWCd0RVublozLNzS09QVBARAusZVS2X1BsY0Xc2acOoU5OWZx9Uwd2IDjNkGHx%2B4oHFRy1a43bm5GVM3Z8%2Bq90k7RYylA7xyjXqGLTAQunWDL74wmmTGgJkKF7UmTWD5chg8GOLizONefVXZFvz9oXVr2LlT7USnc6D08oK6deHYMfWM8c6d5mXVqkH37rBpE6QprlErM9K9exs39zNnTMN2NFXfKH19oUUL2LNHbZypm5Fu0sQ4RRfMDR%2BpU0fZFMuXsG5Gulo149Cq3BEBgv0VjXVxAW9vo8%2Bp5rFUBw0s79T3ceZjhJ8f3HQT7NoFWQozRoDbPH8yL/T1hebNYe9edbt1vwC5uhqNyspSnwzVcXN1NfplZqa6DuCL7ebT9VWqQPv28OOP6uG6m5fCbRSsjxHCdYsk0sJlkZuby5QpU7jnnnvYtGkTPXv2pFOnTgAsW7aM2NhYRo0a5YjPy8sjJiaGdevWERsbq6y7Vq1aAJw8edLxgOLJkyevqJ0hISGEFPMEv4IVIqWju4vaN6aK0yUzdvLy1LGaG4VTnCq2PPapnFDZfhfl7FlNrMr6205cnDouXWH7XZTMTHVsVc3Pv3ZyctQZY7LC%2BttOWpo6TvWlqShnzkCSuUV4hsUfiM6fVyciutUsYBwSVaJn9bLUXcJWutPFixa2p6rI/lO6zVb2PgnanbJyCWdlWYjztpAEnj%2BvThbLa7yyUk9BgXbQV30Rt5ORoYnztrgsIzPz6i7hMEOWdpQZOYLCZfHyyy%2BTn5/PzJkzeeKJJxxLNY4fP84333zD/fffT1hYmONfREQE9957L//5z3/QrSIKCQmhS5cuvPTSS5w7d45z584xZ86cv2jPBEEQBEEQLg9JpIUSTJ06laioqBL/FixYwPLly5k9ezaenp784x//oFGjRkyePJmlS5fSuHFjmjZtWqK%2B2NhYjhw5wpYtW7Tb/ve//42LiwudO3fmvvvuo1mzZgAl1mwLgiAIglBGXF0r5l8lQpZ2CE7okl37Q4MALi4uTtJ1ZjRq1IgDBw4A0LVr1xLl9jKAuLg4XnnlFby9vR1lb7zxBoFW1t8KgiAIgmCdSpb0VgRyBIVritmzZ/PGG29w8eJFMjMzeeONN7j11lsdDyoKgiAIgiBcK0giLVxTvPLKK%2BzatYv27dsTHR2Nm5ubrJMWBEEQhIpAlnaUGVnaIVxTNGrUiPcqSNdy/351ubc3NGwIR46ohQ1aJP5mXlilCrRrB4cOqaUKdDJQ9jXh3t5qw4WEBHU9Xl6GnFRyslr9Q6UfZjc/8PNTy07pVAZcXC7J3ynqyc5Wy9/l5l56VQpQFOqel4rdpeLVV9VyBTpt16goQ9ZqwgS1%2BsfWrep67McuI0OtemDVOEcV11ohCwiXHCiaNgWFdnvh4wum2JtQv7760tixw7zM/rlz59SCB7fXjVc3xuYBhFEj77RSTvJYgV5u8%2BLFS9egKadPm5d5ekJEhCE5o6pIZ/Jjkc8%2BMy8LD4fbboPvvgOdeezdkXvMC4OD4eab4Y8/1IoxOo29gAC44w6jL6n6ZatW5mUeHkY958%2BrpUOB7Gxz8yLL44y7ZtyzX8SW5F6E6xFJpAVBEARBECojlWz2uCKQRFoQBEEQBKEyIol0mZEjKAiCIAiCIAhXgMxIC4IgCIIgVEZkRrrMyBEUBEEQBEEQhCtAEulKxN69exk3bhzt27cnKiqKbt26MXv2bM6ePQvAmjVriI6ONv385MmTmTx5coW3U9cOQRAEQRDKAZG/KzOVa28rMV999RWDBw%2Bmfv36fPLJJ%2BzcuZNFixYRHx9P3759SUxMvNpNFARBEAThr0QS6TJTufa2kpKbm8uzzz7LiBEjePzxxwkNDcXFxYUGDRowf/58wsLCmDlzZonPbd68mV69enHTTTcxYsQI0tLSHGWvvfYao0aNYuzYsdx0001ER0ezYsUKR3lmZibTpk2jU6dOdOjQgccff5yUlBRH%2BZYtWxg0aBAdOnSgVatWPPDAAxw9erTUtg8fPpwhQ4aQqdLXFQRBEARB%2BIuRhw0rAb/%2B%2BispKSn07du3RJmrqyv9%2B/fn%2Beef57bbbnO8/%2BeffzJ%2B/HhefPFFevbsydatWxk3bhx9%2BvRxxGzevJnJkyfzf//3f2zfvp2RI0dSp04dOnTowDPPPENWVhZr1qzB29ubWbNmMWbMGD788EMSExMZP3488%2BbNIzo6mrS0NMaMGcPrr7/OSy%2B95Kg/Ozub0aNH4%2BLiwjvvvIO3t7flfU5KSiK5mDFARkYwwcEhpp%2Bxu5Br3cjthhWl4evr/GqG3XDFDLubhcpYA/SN9fR0fjXDbrqiKlPFXE65Ji7E/BQBUK2a86spdtOV0rCbXehML6Ki1OV2Yx2dwY5uOz4%2Bzq9mBAWZlwUGOr%2Baobp%2BwfI1rLs0rV7CVaual1k9TeXVnzwVfkNW9wcAV0V/s7fVapvNKGrAo0DhqUNoqPOrkuBg8zKrnVLVJ8H6CVcdu8s4UdWrm5fZm6prMj6KCxis7ZPKbaiiqWSzxxWBJNKVgKSkJACCTG7CISEh5OXlOc04f/bZZ7Ro0cKRON9555106dLF6XONGzfmn//8JwC333473bt355NPPiEyMpJNmzaxceNGatSoAcAzzzxD27Zt2bt3L5GRkXz66afUqVOHzMxMTp8%2BTbVq1ZyWl%2BTm5jJy5EjS0tJYuXIlnrpEsBgrVqxgwYIFTu%2BNHj2GcePGaj8boTU3a6dvQIsW%2BhgrqEb6y6FWrbLXof2GYRFNAjFkiLVqevbURdyhr0Tn8rdzp7XGLF9uLU6Hzi6wTRt9Hd26lU9bNNewIq1yQpdbde6sr6NtW11EmLXGqL6IADUtVKHKJy%2Bhd0gkzGKbdWi%2BOE2apK/ioYesbGiQPqR7dysV6dH1SytozjVAr176ajp21EVYfJ6nneK%2BsXattTqEaxJJpCsBwYUjf0JCAvXq1StRfuLECTw8PKhW5I6XmJhIrWLJV506dZyS7eJ11axZk/3793Py5EkABg4c6FTu5ubGiRMnaN68Of/973/56KOPcHFxITIykszMTNyLzCAkJyfTpEkT/vjjD/bs2UPryxxYY2NjSzywmJ4ezOHD5p/x8jKS6Ph4tZt2wzMKT2NfXyMB2bPHsKg1o5Tz4IS7u5FEnzmjtpVVbQOMmehatQwrcZUdseqm7uJiHJycHLVFuG5mw8XFSKLz8pT1LFup/tJUrZqRRH/2GRS5HEswJOIb80J/f%2BNmvXOn2pJ7wgRlW2jSxEiiBw%2BGuDjzuMWL1fX4%2BBhJ9L59cOGCedyff5qXBQYaSfQXX0DhA8SlUreuui0Wr%2BHk%2BuovlO7uxrlKS1Nfwnv3mpf5%2BxtJ9M8/q09T5yYKS257Y4KCDFtuRWNO2cz7gbu7kUQnJ%2BudnmteVFiWe3gY/e202q5c%2B6uWm5txzs%2Behfx807DZb9cwLQsNNZLoJUtA95jMpLofmRdWq2Yk0Zs2qTul7gu91X4ZGWleZvFcA3z6i/n5Dggwkuhvv1W7lffy2aLcBv7%2BRhK9Y4d6n64WMiNdZiSRrgS0adOG4OBgVq1axVNPPeVUlp%2Bf71DJcCvyE2FYWBhbt251ij19%2BjReRWYliz%2BgeOLECWrWrElo4e%2BEGzdudCTxAIcPHyYiIoKNGzeydOlSPvzwQ%2BoW3tSnT5/OwYMHHbEhISEsXryYOXPmMHnyZNatW4ev7sZShJCQEEKKrRHYsweys/WfzcnRxGVk6Cs5f14dp7qBFuXiRXWsKuMvSm6uOlaVIBeNUcVZqcNCPYU/oGhJS9PEVlXc/exkZqrvkr/%2Baq0xcXHqWKs30AsX1LFFnjMw5exZdZzVXzk017AumSwap4q18qt2ZqYmrpz6U26BtSpU30mNIF0ARjtUFVn9FS4/X3mAT5zQV5GYaCHON1kTgNEpkxVx2jU6hej6pZXzrRs7MeYpdKSna%2BL8LS7L0F7EVwlJpMuMHMFKgIeHBzNnzmTp0qXMnTuXxMRECgoKOHz4MGPGjOH06dP861//cvpMnz59OHjwIB9//DEXL17ku%2B%2B%2B44svvnCK2bVrF5988gn5%2Bfl8/fXXbN68mX79%2BhEaGkrnzp3597//TVpaGnl5ebzxxhv079%2Bf9PR0MjIycHV1xdvbG5vNxjfffMO6devIKzLoeXh44OLiwoQJE3B1dWX27Nl/ybESBEEQBEGwiiTSlYSOHTvy0Ucfcfz4cfr160fr1q0dDweuX7%2BemjWdVwdGRESwaNEili1bRps2bVi4cCHdiq29bNq0KZs3b6Z9%2B/bMmjWLl156iajCh7PmzJlDQEAAffv2pX379nz99de8/fbbBAcHc99993HrrbfSq1cv2rdvzxtvvMGDDz7IkSNHyC02Q%2BPl5cXMmTNZuXIl33yj%2BKleEARBEITLQ%2BTvyows7ahENGnShLlz55qWx8TEEBMT4/i7Q4cOfPLJJ6bxVapUYf78%2BaWWVa1alWnTppVa5unpyZw5c0q8P27cuFLbERUVxb59%2B0zbIQiCIAjC34fU1FSee%2B45duzYgZubG3369GHSpElOz1LZ%2BfDDD1myZAlJSUmEhIQwdOhQhhQ%2BtV5QUECbNm2w2Wy4FFGM%2Bv777y9ruagKSaQFQRAEQRAqI9fo7PGECRMIDQ3l22%2B/JSUlhccee4wlS5bwyCOPOMV9%2BeWX/N///R%2BLFy%2BmVatW7Nq1i0cffZSgoCC6d%2B/O4cOHycvLY%2BfOnZet/mUVSaQFQRAEQRAqIxWUSJfm5RAcHFxCBKA0jh07xo4dO/jmm2/w8fEhIiKCUaNG8dJLL5VIpBMTExk%2BfDg33XQTYPyCfcstt/DTTz/RvXt3fv/9dxo3blxhSTRIIi1cIWPH6vWYrzVa1FbIMkGhsUEADUPSlXJS7DplXmZXxkhJUT/qrZJvAigouPRaoJAS0Ckw2H8Gq1pVLZ1wWiEf5ukJtWtDaqpaZUAnZeDlZcj%2BJSQoFUSaNWusrMbuJ1K/vkYqNkyh5Ws3PgkLUzuCFFOuKYFdhWDxYo0%2BW2d1PVFRhuTX8OFq9Y/p083L7Prc584Z58oM1fVUtFxz7WmVKwrRqVw0aGBeZvdgCg9Xn%2Bs0f7Vus5sbBADpvmHKrl2gUA202iUBtQuK/efl0FC10o2qT8IlVY/cXOUBVik42qsYPNjC%2BbzQxbzMfqLatlVKHp2LUGuTu7pCFSAj6g7lMa6aq1AGsY95Xl5asxqVGp99nAkOvrR7peKr0QP38zNeg4L0hkt/I0rzchgzZoyl3OHQoUMEBgY6FMAAGjRoQEJCAunp6QQUcckZUsx4IDU1lZ9%2B%2BskhoPD777%2BTk5NDv379OHnyJA0aNODJJ5%2B8bEldFZJIC4IgCIIgVEYqaEa6NC%2BHYGtuRmRlZeFT7EuH/e/z5887JdJFSU5OZsSIEbRo0YJ77rkHAG9vb1q2bMn48eOpWrUqy5Yt4%2BGHH2b9%2BvVE6N3XLCGJtCAIgiAIglBulOblYBVfX18uFDOmsv/tZ5/hL8auXbsYP348bdu2ZebMmY6HEidPnuwU9/DDD7NmzRq%2B/vprHnjggStqX3GuzVXmgiAIgiAIQsVyDcrfNWrUiLNnz5JSxFjqjz/%2BICwsjCr2NTdFWLVqFQ899BAPPvggr7zyitN66Llz55ZQ/crNzXUylysrkkgLgiAIgiBURq7BRLpevXq0adOGF198kczMTOLj41m4cCH9%2B/cvEbtp0yaef/55XnvtNYYNG1ai/ODBg/z73/8mOTmZ3NxcFixYQGZmZglfjLIgibTwl3PkyBEmTZrEHXfcQVRUFHfeeScvv/wyWVlZV7tpgiAIgiBcZebPn8/Fixfp2rUrAwcOpGPHjowaNQowlDnWr18PwIIFC8jPz2fcuHFERUU5/k2ZMgWAmTNnUqdOHe69915uueUWduzYwX/%2B8x8CAwPLra2yRlr4S9m5cyfDhg1j2LBhrFu3jurVq3PkyBGmTJnCsGHDWL58OW6aJ60FQRAEQSgHrlEd6aCgIFPDt1%2BLKBtt2LBBWU9gYCAzZ84s17YV59o8gsLflilTptC3b1/GjRtH9ULptvr16zN37lxq1KhBfHw8O3fuZOjQodx%2B%2B%2B3ceOONxMTEsGvXrqvcckEQBEH4m3ENLu243pAZaeEv4/jx4xw6dIjnn3%2B%2BRFlQUBALFy4kOzub2NhYxo0bx/333092djbPPPMMc%2BbMYfny5Za3VaoYvLc3ISr5HXvn1w0CKu1muyyPiTyPA7vmrxl2LdRS7FBLjTPDPruvm%2BVXidXb26prcxH7VeU2NML4pTxL4oT9oW2Th7cvodJstT9oonvgRKW9XXQbOn3YqCh1eZMmzq9mhCk0a2vUcH41Q3eA7ba5Gvtcnb%2BB1UtYpdFr9TTpLm%2BrXbs8ugGg7gv2Mqv9xQyLB9hTsc%2BXtU%2B2sp8o3fG3ep6U%2B2x1zEPdFSx2A/DWDERWxghZ1nhd42KzqRThBaH82LVrF7GxsWzatIl69eqVGpOfn098fDx169YlJyeH%2BPh4li1bxrfffsvmzZstb%2Bu1114rKQY/ejRjx40ryy4IgiAIQvnyww/QocPV2fZPP1VMvTffXDH1XoPIjLTwl2EXY09OTi41kU5JSSEoKIjt27czfPhwzp8/T8OGDXF3d%2Bdyv%2B%2BVKgbv7Q3p6eYfcnU1nOoyM9XWZd99Z14WEAC3327EqLalc1Vyfb45TQAAIABJREFUdzecsFJS1LOiVqbqAgKMtqgs3c6fNy/z8ICQEEhKgrw88zhVGRgzbLVqGc6GChu1bQn1lNX4%2BUGrVrB7t3oi59agg%2BaFXl5Qty4cO6Z0WSQjQ9kWfHygWTPYtw%2BK6Z46MXy4up4mTWD5csNiLi7OPK7wYZtSqVED7rsP1q5VOxu2ULvL4esLLVvCb78pr4tTddsrq3F3N1zhkpPVl7DqHHp5QUQExMerT5NOrtZq11adbqvdAKB2sMIm0MXFqCwvT%2B1sqDqHYPkAx1%2BsaVrm4WH8yHH6tH6fInIOmxdaPFEZoQ2V23B1Nfp3Vpb6PFW5qHCptTrmATsOVTMt8/U1usqePerhsZ33b8pt4OMDjRrBoUPqMUK4bpFEWvjLqF27NpGRkXz22WfcXOzbampqKl26dGHEiBEsWrSIjz76iBaFN/x3332XI0eOXNa2ShWDT0vTDqyAMYKr4lTW33bS09VxuruWnYsX1bFWH8zMz1dnM1b8nvPyymYRXjROdbPV5K52srI0sX4Wblo5Oeqbm8r2uygXLqhjVbbfRYmLU8fqbKPBSMBUcXXrWmvL%2BfPKA1xeFuEKR2kHOTnqOCvdGvRduzy6AaBOkIvGqOLK6QDnalYngcV9KocTpbVWLxKnjNUtuQL9mIe1sUbTDSDf4rKMCxeuzSUclWw9c0UgR1D4S3nuuedYvXo1CxYsIC0tDZvNxv79%2Bxk5ciTNmzenVatWuLq64l24cHLXrl28//775Fq9qQiCIAiCYA152LDMyIy08JfSrl07li5dyqJFi%2BjVqxcXLlwgKCiIHj16MGLECPz8/Bg8eDBDhgyhoKCA8PBw/vGPf/DKK684ln4IgiAIgiBcC0giLfzltGzZkoULF5qWT548mcmTJzu99/DDD1d0swRBEAShclHJZo8rAjmCgiAIgiAIgnAFyIy0IAiCIAhCZURmpMuMJNKCIAiCIAiVEUmky4wk0kLlQecUZh9QPDzUsnIq%2BTC73V5YmNo2S6UVDMZnw8Lg6FG19lJDtS4rnp5QrZqh4aRSPlENpkXtxlRxurbYiYhQFsd9rv54cDDceqtxaIqZVzrR/eA36koiI2HnTnUlOlu%2BoCBo0wb%2B/NPQ/DZj%2BnR1PXbHwlGj1NJ1zz1nXhYVBY88AgsXqiX0nn1W3ZaaNaF9e%2BPYnDplGrbrfDdlNVWrGlUdOADnzpnHBQaal/n7G6/nzqnVBVWGpXDpNOblqRXRVNLvdmO6rCwLcsCBCuFhNzejX2Znq7X4dNpsdkvIrCyl5Nw3Cr%2BNGjWM7vjrr3rZ6gbev5sXVqtm9P/Dhw2ZUROqavq%2Bsdq0ClXIABT6d6qxzC4pmJenlRn98kvzslq1oF072L7dkL43o6ufxigsLMzQZd%2BxQ923r5Yhi1BmJJEWBEEQBEGojMiMdJmRIygIgiAIgiAIV4DMSAuCIAiCIFRGZEa6zMgRFK4Ljh49erWbIAiCIAh/L8TZsMzIjLRQgilTprBhwwYALl68SF5eHj72J22AxYsX07Zt27%2BsPe%2B99x5bt27lP//5z1%2B2TUEQBEEQBB2SSAslmDZtGtOmTQNgzZo1LFiwgC1btly19pw5cwab/UlsQRAEQRDKh0o2e1wRSCItXDYZGRnMnDmTn376ieTkZAICAnjggQd49NFHAbjjjjvo0qULX375JaGhoaxevZrvv/%2Bel156ifj4eJo0aUKrVq3Yv38/S5YsAWD9%2BvUsXryYhIQE6taty1NPPcWtt97KypUreeeddygoKOCWW25h%2B/btV3HPBUEQBEEQLiGJtHDZzJkzh8TERNasWYO/vz8bN27kiSeeoGfPnoSHhwPw%2B%2B%2B/8/nnn2Oz2YiPj%2Bexxx5j6tSp9O3bl19%2B%2BYXHHnuMli1bArB582amT5/OokWLuOmmm9i6dSujR49m1apVDBgwgBMnTrB7925H0m2FpKQkkovpAwf7%2BxMSEmL%2BIReXS6%2Bqb%2Bl2rejSsC%2BBKbIUplQ8PNTlvr7Or2botLHt29FtT7W/dhFenaZyOaHTBK5WzfnVFFdFRVYrUemJwyURZJUYMuiPf40azq9mREWZlzVp4vxqRs2a6vKgIOdXE6pWVVdj14C2v%2BriSsNqd9JdmvbTqDudqu14eTm/WtpgaRTVZVdh14kuY4NUl5T9HOrOJQCeir4SEOD8aoZun60eG1V/uozxqlYt8zL7OKQbj/AJU5db6dsqfemKRmaky4yLTX4zFxSUtrQjJSUFd3d3AgICOH36NPv27WP06NF8%2BOGHtG7dmjvuuMNphnrBggVs27aN5cuXO%2BqYOXMmBw4cYMmSJQwbNoyWLVsyYcIER/n48eMJDQ3lmWeeYe7cuZedSL/22mssWLDA6b0xo0czdty4KzwSgiAIglABzJihN0qqKI4dq5h6VcZlfzNkRlq4bFJSUnjxxRfZv38/ERERNG/eHICCgktOVEVnfk%2BdOkXt2rWd6oiIiODAgQMAnDx5kl9%2B%2BYWlS5c6yvPz87n99tuvuI2xsbFER0c7vRfs76%2B2JHNxMWaBsrMvuWOVxv795mU%2BPtC0qRGj2pbKmQuMmeiWLeG33wxXQjN0TmEeHhASAklJapcv3Yx0cLDhAKiyhVPN9ttxc1O7uQHLV6inDatVg7vvho0blSZqDHb9SF1J9%2B6waZO6Eisz0t26wRdfwNmz5nEqaz8wZqvuuw/WrlVbzC1caF7WpAksXw6DB6udMx95RN2WoCAYOBA%2B/ljp1ri12ShlNf7%2B0LYt/Pyz2pVQZQDq4wPNmsG%2BferuVK%2Besim4uRmn6uxZ9eWnOvReXkZucOwY5OSotxcZprBIdHU1Dk5mJhQo3PuSktQb8fIy%2Bn98vLJB6/eZO45WrQqdOsHXX%2Bsv0T6eCsvRgADDbnTbNrU95G23qTfi6mr84peVpT42qovB3R2qV4czZ9TjFfDaCvMxKzgYBg2Cjz5Sm5%2BO9XlbuQ3LfVu4bpFEWrhsxo0bx9133827776Lu7s7KSkpfPzxx04xLvZlEkCtWrXYtm2bU/nJkycd/w8NDWXgwIE8/PDDTuU%2But9zFYSEhJRcxqEbnO3JpM2mjsvK0jfgwgV1nMLS14nz59VWwbqE3E5e3pVbhNu5eNH69sqA6qZVlLQ0TayrhYp0lVhdznL2rNoi3OoNNDVV/TOvyvrbTlycOk5h%2B%2B1ESooy9lxt0yInMjPVSVqRocKUCxfUybgmX3KQn6%2BO1Vp/Y%2BSs2jjNl0XAGGNUcVbHiJwcZayVS%2B/cOQtx3oovnHbS09VfTFXjavE4VazG%2BhswTrQmTmX9bSc5WRPnZ3FZhq5vXy1kaUeZkSMoXDYZGRl4eXnh5uZGamoqM2bMACDPZNDq27cvv//%2BO%2BvXryc/P59ff/2VVatWOcpjY2N577332LNnDwC//fYbMTExbNy4EQAvLy8yVXdRQRAEQRCEq4Ak0sJlM2vWLNavX0/r1q3p378/4eHhNG7cmIMHD5YaX7t2bebPn8%2BiRYto27Ytr7zyCh07dsSj8IGRXr16MXbsWCZNmkRUVBQTJkzg4YcfZsiQIQB07dqVlJQU2rRpIwm1IAiCIJQXYshSZmRph6AkJiaGmJgYp/c6depEp06dnN576qmnHP//5ptvnMoSEhIIDQ3ls88%2Bc7w3Y8YMRyINMGDAAAYMGFBqGxo3bszWrVuvdBcEQRAEQSiNSpb0VgRyBIUKJyUlhcGDB7Nv3z4A9u/fz6effkrnzp2vbsMEQRAEQRDKgMxICxVOy5YtmTRpEuPHjyclJYWgoCBGjRrF3XfffbWbJgiCIAiVF5mRLjOSSAt/CYMGDWLQoEFXtxG6AcOqIUuYQoDfbo4QFKTW9dIZeNjVIho3VssM6AwQ7PsUHKyW9FNJVtnb4u2tVLFIPauWinNIkGW4KcUKdAYeRb1qlLFnFKoHdrkwjeIBrVurG2M/x3XrGpJbZujUCuz1tGih1l9Vac3ajVYeeUStzFH4cLApUVEwahS8/bZS/eOzR6crq4mIgM6d4bvvDIU2M1q0MC8LDoY2beDQIbW4ik7gokoVQ53twAG1CE5Dc6U4h/eRj49eFbFcUI0fcKlBfn5KgxLVPtm9pcLDLRgcudfTV1SrltrdRWcyYx%2BvvLzU45VKEtTqOI6hMGqG/Xg0aqTxJvK9UbkNx/Fo2NCCu4twPSKJtCAIgiAIQmVEZqTLjCTSgiAIgiAIlRFJpMuMHEFBEARBEARBuAJkRloQBEEQBKEyIjPSZUaOYCUnKSmJ86oHNyqQo0ePXpXtCoIgCIIglAeSSFdiUlJS6N69O2fOnPnLt71v3z7uuecex99TpkxhypQpf3k7BEEQBKHSIs6GZUaWdlRisrOzr9psdEZGBnl5eY6/p02bdlXaIQiCIAiVlkqW9FYEkkhXMFu2bOGtt97i2LFjnD9/nhtvvJEZM2YQFBTEc889x7Zt23B3d6dJkyY888wzhIeHc/vttzNlyhR69%2B4NQG5uLh07duTVV1/l559/5vDhw3h7e/PFF1/g7%2B/Pv/71L/7880%2BWLVvGxYsXeeihhxg1ahRg2GtPnDiRpUuXcu7cOdq1a8e0adMICgpyzAjfc889vPjii/Ts2ZOVK1eyZMkSTp06Re3atRk%2BfDh9%2BvQB4B//%2BAc333wzP/zwA/v376dOnTrMmDGD9957j6%2B%2B%2BorAwECmTJnicCxctWoVy5cv5%2BTJk%2BTm5tKuXTtmzpxJVlYWw4cPByAqKop3332XFStWADBr1iwA3nvvPZYuXUpKSgr169dn4sSJdOjQwfJxT0pKIrmY8GxwQAAhKh3PovqjKuxa0aVh13NV6LoCSj1m4JJQrU6wVtdWq/ukao/FtrhpNmF1l2rUUJfbZVlVcrUAuIeYl9k1n1Xaz6DX8i0qaq1CpyNttR67VnRp2MVulaK3GDrRKpo0cX41ISJCXY1dbl0luw5qaV27lq9O41h3muwyx/ZXM%2BzSzKVhtWsD6ovcnrjoEhhVY%2BBSn9WMJap99vFxflVvrxwq%2BivGK4vHBdTXlV2iXyfVj7dmILKL3atE78%2Bd02xEuKaxCRXGqVOnbC1atLBt3rzZZrPZbGfOnLENHjzY9tRTT9nmzZtne%2BSRR2wXLlyw5eTk2CZNmmQbOXKkzWaz2aZOnWp7%2BOGHHfV8/vnnti5dutgKCgps8%2BfPtzVu3Ni2ceNGW0FBge2ll16yNW3a1DZr1ixbbm6u7YsvvrBFRkbaTpw4YbPZbLbIyEhbr169bMePH7elp6fbRo4cabv//vttNpvNFh8fb4uMjLTFx8fbbDabbfXq1bbWrVvbtm3bZrt48aJt27ZtttatW9v%2B97//2Ww2m%2B2BBx6w3XrrrbZDhw7ZcnJybEOGDLE1b97c9sUXX9hyc3Nts2bNskVHR9tsNptt9%2B7dtlatWtl2797tOBZ33XWXbe7cuTabzWb78ccfbZGRkY59nDRpkm3SpEmOdrRr1862c%2BdOW35%2Bvu3jjz%2B2tWrVypaWlmb52M%2BfP98WGRnp9G/%2BvHmXeQYFQRAEoYJZt%2B7qbTszs2L%2BVSJkRroCqV69Op9%2B%2Bil16tQhMzOT06dPU61aNRITE2nUqBFxcXGsW7eO2267jRdffBHXwhmKfv36ERsbS3JyMsHBwaxdu5aYmBhcCr%2BlN2zYkB49egBw2223sXjxYkaOHImHhwfR0dEAJCQkULt2bQDGjx9PROH00dNPP02PHj04ceJEifauXr2a2NhYx8xvhw4diI2N5aOPPqJbt24AdO/enYaFVllt27YlPT2dO%2B%2B8E4A77riD//znPwBERkby3//%2Bl/DwcM6dO0dSUhLVq1cnMTFRe9zWrl1LbGwsUYUzZwMGDKBBgwZ461yxihAbG%2Bs4FnaCAwLUFmguLsZsc06O2lUrKcm8zMPDmDU8dQqKLF0pgW4Kzc3NmApJT0dpA6ibYnNxMdqUl6fep6ysMrflrIt62tDNzdjtjAz1Lm3dqqyGqlWhSxf46iv1RE7fc%2B%2BZF1avDr17w4YNoHpGoGlTdWN8fQ1rvj171G5rVmakW7aE335T17Nzp3lZUBAMHAgffwwpKeZxb7%2BtbkuTJrB8OQweDHFxpmEzYhRtwZiJfuQRY3OnT5vHqZz3qlWD7t1h0yZISzOPq1NH2RT8/KBVK9i9W32pq2bZPTyMfTp9Wt21ASKqKpxCXV2N2cnMTPV1oWooGDOuwcGG5aPC/XTnKfNfMXx8jEt8/364cEG9udbuv5kX%2BvgYFoCHDqkr0vzKYXm8UnV8d3fjwklLU7vCAp//Yv5zSECA4Ya5bZva%2BLWH91blNvD3h7Zt4eefjXMu/O2QRLoC8fDw4L///S8fffQRLi4uREZGkpmZibu7O8OHD8fT05NVq1Yxbdo0IiIiePLJJ7nrrru48cYbadCgAZ9%2B%2Bim9e/fmu%2B%2B%2B49ki1sCBReyl7cl31cLfue1/FxQZoOsWsRyuVasWgCNJL0pKSooj4bYTHh7Oli1bSt22m5ubY7v2bdsKBz9XV1fef/99NmzYgK%2BvL40bNyYzM9NRriI5OdnRTjutdVbNxQgJCSEkpNhP%2BxcuqAdnOzabOs5uL60iL08dZ%2Bm3VIyMU3UzsLI/9jhVrOaGY6Ut%2BRaX2uXnqxPp1FRr9Zw7p4k9o/jC44g5o/5iFB5urTHnz6u9p3WJtNV6VNbfdlJS1HEK228n4uKUsfE3W6vm9Gm1Rbh2iQ5GTqSyCNfaWxeSlaU%2BvLm5%2Bjry8izEqS5wOwUF6jgrjQGjTypidfk4GEOjNs69HCr6K8erixe1caovZ3bS0zVxvhaXZWRmXptLOGSNdJmRRLoC2bhxI0uXLuXDDz90JLPTp0/n4MGDHDhwgOjoaB566CEyMjJYvnw5jz/%2BOD/%2B%2BCNVqlShX79%2BfPrpp3h4eNC2bVvCi9zQXXTrx4qRmJhIZGQkgGMmulatWk4P%2B4GRNB8/ftzpvfj4eKeE2%2Bq2lyxZwvfff8%2BGDRsIKlyzOXLkSEufrVmzJqeKJQJz586lT58%2BNGjQwFIdgiAIgiBokES6zMgRrEAyMjJwdXXF29sbm83GN998w7p168jLy2PlypU8/fTTpKam4u/vj7%2B/P76%2BvngWPmDSp08f4uLiWLlyJTExMWVqx%2Buvv05ycjLp6enMnj2bjh07EhoailfhQ3OZhT839e/fnxUrVvDDDz%2BQn5/Pjz/%2ByIoVK%2BjXr99lb9M%2B8%2B7h4cHFixf55JNP%2BPbbbx3Ju33bGaVMD8XExLBixQp%2B%2B%2B03CgoKWL16NcuWLaOa1WknQRAEQRCEvwCZka5A7rvvPn755Rd69eqFm5sbN9xwAw8%2B%2BCDLli3j7bffZvr06fTq1YucnBxuuOEGFi5c6Egwq1evTqdOnfjxxx%2B56667ytSO5s2bM3jwYNLS0ujcubNjmUhQUBDdunUjNjaWyZMnc//995OZmcmMGTNISEggNDSUp59%2Bmr59%2B172NocNG8bBgwfp0qULXl5eNGvWjMGDB/Pjjz8CxhrqNm3a0LFjR%2BbNm%2Bf02d69e5Oens7EiRNJTk6mYcOGLF68mOo6hQVBEARBEKwjM9JlRhLpCsTT05M5c%2BaUeH/cuHEAzJ49W/n52rVr06tXL6eH7MaOHesUc8stt3DgwAGn94r/fdddd/Hcc8%2BVqN/FxYUFCxY4vTdgwAAGDBhQans%2B%2BOADp79VbQkMDGTRokWl1gPg6%2BvL8uXLHX936tTJqXzIkCEMGTLE9POCIAiCIJSRazSRTk1N5bnnnmPHjh24ubnRp08fJk2ahHspsoZff/01L7/8MvHx8dSsWZOnn36aLl26OMoXL17MBx98QHp6OjfeeCMvvPACN9xwQ7m19do8gpWcU6dO8cMPP7Bu3ToGDRp0tZsjCIIgCILwlzFhwgR8fX359ttvWbVqFT/88ANLliwpEXf06FHGjh3L%2BPHj%2Bfnnnxk7diwTJkxwKIStXbuWDz74gHfeeYft27fTvHlzxo0bZ0n4wCqSSF%2BDfPzxx4waNYqhQ4fSVCe/JQiCIAiCcCVUkEV4UlISe/fudfqXpFJIKsKxY8fYsWMHEydOxMfHh4iICEaNGsWyZctKxK5du5a2bdty55134u7uTs%2BePbn55psdJm8ff/wxgwcPplGjRnh5efHkk0%2BSkJDA9u3by%2B0QytKOa5Dx48czfvz4cqmr%2BDKPykxiulpyzt0danhDapa3UjXJM6iRaZmbGwQA6YF1lMpWOqkpjwIIBRJzqyk1axWmcAC4AJ5ALp6ovn9ne5rX5OoKVYAM92oUKL561zivkWZzdweCCcxT694%2B3E6hgQzg7Q00om/zQ0pd8M1JE03LqlSBdsCOpg%2BSoVC4a9ZM3RR3d%2BMcJNdvp7xmdEpmnp5QEzhVt70ydtf5bqZlVatCZ2Brs1Gcq21ex2ePTle2JSICnsXQiVZJ3L35lkbBJyoKnt3Js2taK2X0Nn1ufmXa5dbr1FFL3OncE%2B1qk0FBaun1Br6Ka7jw%2Bo3wTgZ3taxafoDCgRJwA/L91JZ5Sdnqh6sd1557TVSt6XjDSfNCDw8ghNbhSVpx7ER3c2dZd3eoAaTWbqnsB4HmRYD18eoM5sfXOEuQTLDyuADc3/An80JfX6A5PSL2Qg1zbfc9Pvcqt%2BHtDQ2Bw%2BGdyVaYjrZQN/W6Y8WKFSWWjo4ZM6bEktDSOHToEIGBgYSGhjrea9CgAQkJCaSnpxNQxG7y8OHDDlUyOw0bNiSuUAP/8OHDDidlMGSJ69WrR1xcHO3bt7%2BifSuOJNKCIAiCIAiVEBuXJ6drlVJN0YJ1Uz8GWVlZ%2BBTzWrD/ff78eadEurRYb29vzhcaW%2BnKywNJpAVBEARBECohVr2iLpdSTdEs4uvry4ViDpn2v/2K/aTk4%2BNDdrFfJrOzsx1xuvLyQNZIC4IgCIIgCNcEjRo14uzZs6SkXFrm98cffxAWFkYV%2B3qvQiIjIzl06JDTe4cPH6ZRo0aOuoqW5%2BXlcfTo0RLLQcqCJNKCIAiCIAiVkIKCivlXFurVq0ebNm148cUXyczMJD4%2BnoULF9K/f/8SsX369GHHjh189tlnXLx4kc8%2B%2B4wdO3Zw773G2vV%2B/fqxdOlS4uLiyMnJ4ZVXXiEoKIi2bduWrZFFkERaqDCmTJnClClTyqWuxo0bl%2BtTtoIgCIIgXJvMnz%2Bfixcv0rVrVwYOHEjHjh0ZNWoUAFFRUaxfvx4wHkJ8/fXXefPNN7n55ptZuHAhr732GvXr1wcMx%2BaHHnqI0aNH0759e/bt28ebb76Jh4dHubVV1kgLFca0adOudhMEQRAEQTChotZIl5WgoCDmz59fatmvxVSAOnbsSMeOHUuNdXFxYdiwYQwbNqzc22hHEunrgBMnTtC1a1dmzJjBG2%2B8wblz52jZsiUzZ84kLCyML7/8koULF3L06FGCg4O5//77GTp0KK6urkyePBlPT0%2BSkpLYvn071atX58EHH2To0KGAse5ozpw5HDhwgDNnzhAeHs7EiRPp0qWLdrsAn376KfPnzyc1NZVWrVpRq1Yt8vLymDVrFpMnTwZg1qxZvPbaaxw6dAhPT0%2B2bt2Kr68v9957L08%2B%2BSQAiYmJzJw5k99%2B%2B43U1FSCgoJ47LHHSv0pRxAEQRCEsnOtJtLXE5JIX0ds3bqVdevWkZubyz//%2BU8WLlxIz549mTBhAnPmzOGuu%2B7iwIEDjp8/HnroIQDWrFnDm2%2B%2ByYIFC1i1ahXTpk2je/fuhIaGMnbsWLp27cqCBQuw2Wy8/PLLPP/88072mqVtd9q0afz6669MmjSJ%2BfPnc8cdd/DVV18xYcIEevfuXWr7//e//zFr1ixmz57Nd999x4gRI%2BjatSs33XQTzz77LIGBgXz66ad4enry/vvvM336dO6%2B%2B%2B4rero2KSmJ5ORkp/fc3IIJDjZ/itjNzflVF1cadrdVneuq7lcluwtqKW6oTrholIvs5bo4VXut7pO2sVZ3yttbXe7l5fxqQrFnUpzw9XV%2BNaO8dkmH1XqqVjUv8/d3fjUjIkJdbtdk1mkzExWlLm/SxPnVBNV5snd93RDgo5aIt3rJqE9AeZ1si5TbtWdTDDaXsU/uinHP6thZXuNVuZ0m1QBgH4c045G35pqycu0p5PCF6wBJpK8jhg8f7tBPjI6O5tdff2XNmjV07dqVnj17AtC8eXMeffRRPvjgA0cifcstt3DbbbcBxsL7qVOncvz4cUJDQ3nzzTcJDQ3FZrNx8uRJAgICHNaaqu0CrF69mrvuusuhFdmtWzfuvPNO0/bXq1ePvn37AtCpUyeCg4M5evQoN910EzNmzMDPzw8PDw8SEhLw8/MjOzubc%2BfOXVEiXZoY/OjRYxg3Ti8GH6hzDbCALpkJUHsxOKhRo%2BxtAX3i7umpr0N7GqpY0whVOmsAWNQapU4dZXE7C1W0KCcXBN0uWUW36zXVPh8A6J6h6dzZWlseeUQT8OxOaxUtX64svtVCFa1aWduUjrp1dREWrj0LJ1uTTxoxmiCr3UDfHAsSZNWra0OsDEXlMXaCfryycmws9cng5vqYBg2UxQ0tbAbUX2D37LFYSQUgM9JlRxLp64igoEu2SO7u7thsNlJTU0vYiIeHh3Py5CU3q6Ii6PYF9gWFvScuLo5Ro0aRnJxMgwYNqF69egkP%2BtK2C3Dq1CmaFbN%2Bi4iIcJKsKUpxMXYPDw9HO%2BLj45kzZw5Hjx6lXr161C284xVcYS8vTQzezS2Y1FTzz7i5GTeCs2dRuhKqBnlXVyOJzsxUD1DFJDJL4O5uJNGpqUoTQO3NwsXFaG9eHtgUVmE5OeZlrq5GEp2Vpd6nKtnJ5oVg7FS1apCWpt6ps2fV9Xh5GUn08ePKhu9IM3eg9PU1kug9e0Cly1/4vIopVndJVWavJzgYktWmj6iMSv39jST655%2BN68%2BM775TtyUszEii334bTp82j3t2TWt1RU2aGEn04MFQ6DJWGtsWmCfkfn5GEr1JOKooAAAgAElEQVR7t9oNNEjhGAfGJVO3Lhw7pr7WI6sprmGrJxvIr67O9Nzc1GMMwJkz6nKrzQm2KWyZ3d2NJPrMGe0%2Bpbqpf82zMnaqfn0A6%2BPVuXPmZZdxmghO2mte6O1tJNF//KGcMj7spU7GvbyMJDo%2BXn3tCdcvkkhf59SuXZvjx487vRcfH2/JQSgxMZHx48ezYMECR9K5adMm/ve//1nedkJCgtN7CQkJeFqZ3ixCXl4eI0aM4IknnmDw4MG4uLiwZ88ex1O5V0JpYvCJifqBFYwbgSpOu8QBI%2BFU3VA0brwOLl5Ux6puNsXjVLFWvq9oZY2sHFx7nCrW6u%2BcOTnK2IwMfRXnz6vjymuXdBbhRetRxaoSCDuZmeq4%2BHhrbTl9WhOrsP12Ii5OGWvlPGVlqeOs/miVk6P5ElvFwgnXnexyoryuPQosDDa6gQa4aGGs0Y2d5TVeWTk2lk6TFXe77GxlXLbFfdIMV1cNmZEuOyJ/d53Tr18/tmzZwsaNG8nPz2ffvn0sXryYfv36aT%2BblZVFfn6%2Bwz7z8OHDvP766wDkWrjzDxgwgC%2B%2B%2BIJvv/2W/Px8vv76a8tJeFHy8vLIzs7G29sbFxcXEhISeOmllxxlgiAIgiCUP9eijvT1hsxIX%2Be0atWKefPm8frrr/PMM89QrVo17r//foYPH6797A033MDTTz/NxIkTuXDhAmFhYQwcOJCXXnqJgwcPEqhZ8HbjjTfywgsv8Pzzz5OWlkbbtm3p0KHDZesz%2Bvr68uKLLzJv3jxmzJhBjRo1GDhwIIcPH%2BbgwYMOPUhBEARBEIRrCUmkrwPCw8M5UGxx5Nixlx6a69q1K127di31s7NmzSrxXtG6Hn74YR5%2B%2BGGn8gcffLDU2OLbPXLkCC1btmTz5s1O5dULH1wpuu2in7OzZcsWx//vvfdehxORnUcffdS0HYIgCIIglI3KNntcEcjSDuGKOXz4MA8%2B%2BKBjjfb27dv59ttv6dSp01VumSAIgiAIQsUjM9LCFdOtWzcOHz7M0KFDOXfuHLVr12b69Om0bq15ml8QBEEQhKuOzEiXHUmkhTLx2GOP8dhjj13tZgiCIAiCcJlIIl12JJEWKg2h7goRaSh0SAikhttZwFy7bv9pc2sCb2/DbCU1VS11pHOXs0vsVamiHuisaM0GBxtyaCopKK2LHXqTmXOo3UJcXaEKkOEdrNynqinmmsNODTl7VimYrHISsz8P6%2BGhjtuxQ92UqlUNg5O9e9WScxpPB4d5WlaW%2BrpRPf9rPyxVqqid4XQmNHblzIYN1U6Kmz5X635VqWKYrWxbsFMpXde9h6KxUVGwcye3jmmtlNA78qe6LXZFTj8/jdmH6sK067HZbNrsQ6WqZtdlz87%2Bi5KYYvKoTvj5QUiIoXWoEuoGTnjWNi3z8TF07xMT1fKCOidRV1fjXOXlqY%2BNqh/Yr/0qVSzI7Z3QlFtAJyxld1i0oDAoXKdIIi0IgiAIglAJkRnpsiMPGwqCIAiCIAjCFSAz0oIgCIIgCJUQmZEuOzIjLZhy7Nixq90EQRAEQRAqCHE2LDuSSAulMnv2bN54440y17N%2B/Xp69epV5nomT57M5MmTy1yPIAiCIAhCeSFLO4RSSUtLK5d6%2BvTpQ58%2BfcqlLkEQBEEQyo/KNntcEciM9HXMiRMnaNy4MStXriQ6Opo2bdrwz3/%2Bk9OnTwPw5ZdfEhMTQ%2BvWrenevTtLliyhoLDXTJ48mSlTpjBy5EiioqLo2rUr77//PgCvv/46GzZsYMOGDfTp04e33nqL7t27O237nXfeYciQIQA0btyYt99%2Bm86dOxMVFcWIESNITEwEYM2aNURHRzs%2B9/3339O/f3%2BioqKIjo5m6dKlANhsNt566y169%2B5N27Ztufnmm3nyySfJVmmBCYIgCIIgXEVkRvpvwNatW1m3bh25ubn885//ZOHChfTs2ZMJEyYwZ84c7rrrLg4cOMCoUaMAeOihhwAjyX3zzTdZsGABq1atYtq0aXTv3p3Ro0cTHx8PwKxZs0hKSuLVV19l9%2B7dtGrVCoB169Y56rH//cEHHxAYGMjTTz/N448/zvLly53aeeTIEUaOHMnUqVPp27cvcXFxDB06lLp165KRkcH777/P0qVLqVevHn/88QeDBw9mw4YNDBgw4LKPSVJSEsnJyU7vBXt6EmIXyS0NNzfnVxPsmr%2BlYdertb%2Ba4ar5CmvXQnVxUce6a3qwvVwXVx7o9slerovTClbbxWg1orR%2Bin328XF%2BNUOlvQ2XmqprsuqagUta1ipNa912rO6TqgsAVKvm/GpGlSrqcj8/51dToqLMy5o0cX41QdffiuqGWwosjcvoTKpr3Go/KLe%2BrToBVi8awEexHfv1rbvOy2uMUOmkFx07tajGEIs75aO%2BXVjq2yrt7YpGZqTLjiTSfwOGDx9OQEAAANHR0fz666%2BsWbOGrl270rNnTwCaN2/Oo48%2BygcffOBIgG%2B55RZuu%2B02APr168fUqVM5fvw4oaGhTvWHhITQsWNHPvnkE1q1asXevXs5ceIEPXr0cMSMHz%2BeiEKXkaeffpoePXpw4oSz2v2nn35K8%2BbN6d%2B/PwAtWrRg%2BfLlhISE4OnpSevWrQkLC%2BPMmTOkpaURGBjomNm%2BXFasWMGCBQuc3hszejRjx43Tf1iTIdRXmAHYqW3uXXBZ6O5vOoMDO7qkyAq6G5MusbKjTaxuvtlaRc2bK4sV6ZkDTX5mmbZty6cenVGPFZo1U5e3aWOtnmI/Ql0xhd%2B9zdm5U19JsS/lxbHa3UJCtBH6SqpX14boLnHQ921tPylE27eDW%2BoradRIG6K5rAC44QYLQRawkNdr0X0pBbRjCKB1U7I6hNSvb16m8BqqcCSRLjuSSP8NCAoKcvzf3d0dm81GamoqTZs2dYoLDw/n5MmTjr%2BDi0xNeRTOxBSY9KqYmBimTp3Kv/71L9auXUuPHj3wKzLS161b1/H/WrVqAZSYEU5KSnKU2WlSmMlkZGQwd%2B5cvvrqK6pXr07Tpk3Jy8vDprWmKp3Y2FinJSVgzEhz9qz5h9zcjGwwIwPyzZ0Nj6SZZ9KenkYSffIk5Oaab6rYd5USuLgYN5MLF9TuXBozMtzdjRttWpp6drXIJWTaHt2pUJgMApcc3bKy1IN3lbif1BX5%2Bho3wL17lRZyv7qbJ%2BQ%2BPkYSHRenng1SuRWCMUPcti38/LN6/8PD1fV4eRlJdHw85ORcWXt8fIwket8%2B9T4dOqRuS7VqRhK9aZNx3ZhRp466Hj8/I4nevVt9nd46prV5YZMmRhI9eLBxskw4uUGdjHt4GEl0UpLaXa62R5J5obu7kUSfOaP9qSLLzzwhd3W91LdV/UDljmhvjpW%2BHXzqN/NCHx8jiT50SDstus/dPCH39jaS6D//VDtzFrlNlIrVY6P60dDFxehPOTn6Mcv7j72KQm8jif7jD%2BVOxbmpk3EvLyOJPnJE3beF6xdJpP%2Bm1K5dm%2BPFrGHj4%2BOdkufLITo6mqlTp/L999%2BzceNG5s2b51SemJhIZGQkgGMmulatWhw5csQRU7NmTb7%2B%2Bmunz61evZoaNWrw1VdfkZCQwJYtW/Av/P26d%2B/eV9RWMGbRQ4pPP6WmKhNkB/n5yjgry7Zzc9VxulkA%2B0%2BbOjdi3dKDonFWY68UqzMbWnkkXUZu5/x5ZWyW7md8jBu2KsnTJdJ2MjPVsbovKnZyctTXjZVDc%2BGCOq7Y91tT0tLUsVZ/5cjKQmkRbmk6Li5OGaf60lqUvDxdrAUPZwtez1b6gq4flFvf1n3bBn1HAC5ols%2BAce2q8vHyGiO0y8Mwxk7tPIzu2woYO6WIu2Axi8rJubpLOMyQGemyIw8b/k3p168fW7ZsYePGjeTn57Nv3z4WL15Mv379LH3e09OTjCJ3Pw8PD/r06cO8efPw9/enbbHfsl9//XWSk5NJT09n9uzZdOzYscQSkV69erFv3z7WrVtHfn4%2Be/bsYdasWbi7u5OZmYmXlxdubm7k5OTw7rvvcvDgQfI0NyxBEARBEISrhSTSf1NatWrFvHnzWLx4MW3btmXMmDHcf//9jBw50tLne/bsyc6dO%2BncubPjvZiYGPbt20dMTEyJ%2BObNmzN48GCio6MJCAjg5ZdfLhFTp04d3nrrLZYtW0a7du144oknmDx5MrfffjsTJkwgOzubW2%2B9lejoaHbt2sW9997LwYMHr/gYCIIgCIJgjhiylB1Z2nEdEx4ezoEDB5zeGzt2rOP/Xbt2pWvXrqV%2BdtasWSXeK1pX%2B/bt%2BeGHH5zKw8LC8PT0pG/fviU%2Be9ddd/Hcc8%2BVeD8mJsYp8W7fvj0rV64sERcREeGQwrPaXkEQBEEQrpzKlvRWBJJIC1pyc3M5duwY77//Pp06dSqxZEMQBEEQBKEyIom0oCU3N5dBgwZRs2ZNFi1adLWbIwiCIAhCOSAz0mXHxXal%2BmKCcJ1hRbDDzU0f55au0ARzc4OAAEhPV1ekkxlwd4caNQylEdUj%2BToZB0/PS7pqim1mhJhrpVqWrSvQSFy4ul6SFyyLXIGbGwQGGlKGimOceLGGaZnVwxuaG69ui4cHhIXB6dNKJYc0f7VAdHlcNlb3adcuZVOoUgVuvRW2bVOrbYSFqevx8YHISDh4UK1WUCiBXypW5STr36AROY%2BKMvSqW7dWqn%2BcO2t%2BO7R6%2BdrbbYaLi6Gslp2tVpXwyVaMM2D5osn0MJdXcXU11CTPn9fvk7%2BPbmC0MHimp%2BvrsNIRVCYpVg8w6J18LOxTzkW1I4uLi7GZ3Fx1cyzpXlcA%2B/dXTL3F1Hf/1siMtCAIgiAIQiVEZqTLjiTSgiAIgiAIlRBJpMuOyN8JgiAIgiAIwhUgM9KCIAiCIAiVEJmRLjuSSAulkpOTQ1paGmG6J4oEQRAEQbgukUS67MjSDqFUBg8ezLZt28pUx5QpU5gyZUqZ2xIdHc2aNWvKXI8gCIIgCEJ5IjPSQqmkpWmklywwbdq0cmiJIAiCIAgVgcxIlx1JpIUSDBs2jISEBKZOncr69euJiIhg%2BvTpjvIRI0bQrFkz2rdvz8SJE%2BnXrx/Lly8HoE%2BfPkycOBFPT08mT54MXLL3fu%2B991i6dCkpKSnUr1%2BfiRMn0qFDBzIzM5k1axY7duwgKSmJKlWqMGTIEEaOHHnF%2B5CUlERycrLTezVqBBMcHHLFdTpwU%2BiGuro6v5rhrul69m2otgV6HVQPD%2BdXE1TNtbpL2h%2B4rFak22eLx0Z1hK0eXmzq4%2BY4j5rzqdtOeVw2VvepShV1uZ%2Bf86sZPj7qcrsurk4fV3UJW7x8DZ1oFU2aOL%2BaUD79wNAO1pWpYoByu2hUxUXbYmW/ykx5dYRyOcDlg24zVpojbh7XN5JICyV49913iY6OZsyYMXh7ezN16lSee%2B45PD09SUlJ4fvvv%2BfZZ58lISGBxMREjhw5wubNm0lJSWH48OH4%2BfkxYcIEpzrXrFnDwoULWbRoEa1atWL16tU89thjbN26lVdffZUTJ06watUqqlSpwv/%2B9z/GjRvH3XffTd26da9oH1asWMGCBQuc3hs9egzjxo3VflabXKkcJOz4%2B%2BtjrBAYqC6vYW464oRmrbsmZwL0iRNoMjTHxqxszQKajNDKkdEdXrD4jEBQkLLYwhUDlM9lo9unW2%2B1Vk%2BrVmVvC8AVdmEnQnTff3futFZR4Rd%2BM6xcweV1%2BWoNOLwtXjWai8bXQhX6vg2gGxjRD55Wxk4on45QXg4nmn3ytHBYQP1lMCfnMtpTzsiMdNmRRFpQcuedd/LCCy%2BwZcsWevTowYYNG4iKiiIiIoKEhARcXFyYOnUq/v7%2B%2BPv788gjj/Dmm2%2BWSKTXrl1LbGwsUYUzRwMGDKBBgwZ4e3szduxY3Nzc8Pf35/Tp03gVDoBJSUlXnEjHxsYSHR3t9F6NGsF610IrzoZZCncuV1fjJpCZqR6hFC54joZYcO/j/Hl1PRad97KqmzvvuboaN9oLF9S75FegsMGzV2TFItHKSbJbzCliU/PNM0qrh7dG3ml1W9zdjSQ6JUVpJ5juq07Iy%2BOysbpPBw4om4Kfn5FE795tnCozNN8d8PIykuhjx9SJgiox9fAwkuikJPW%2B1%2B7dWt2YJk2MJHrwYIiLMw3L%2BNo8Ibd6%2BdrbbYaLi3FscnLUM5HeuRoXQIsXzXl38%2BTVxeVS39bNivp6lcPgqbqgwHpH0FlHWjnAoP%2Bpw8I%2B5ebrnQ09PIzr91qceZZEuuxIIi0o8fT05J577uGTTz6hR48erF27lmHDhjnKq1atSrVqlyxoa9asSVJSUol6kpOTqVWrltN7rVsbN7/jx4/z73//m3379hEeHk6LFi0AKChDDw8JCSGk2DSWFYtwS1ipqKBAHaezwS66LVWszmrcTl6eMtbKoS4o0MRZPV%2B6iqyeqPx8ZayVQ6w7vNovPEU3poi1ukvlcdno9kll%2B12UrCx1rNWZ2ZwctUW4dtkG2stXafvtRFycMrZc%2BgHWEiabTRNXThdNgYXlKjbbX5RQlVdHKJcDXD5Y3cRf1BzhKiCqHYKWfv368e233/Lrr79y4sQJunfv7ijLyMjgQpG75IkTJ0okzGAk2KdOnXJ6b%2B7cufzxxx%2BMHz%2BeFi1a8MMPP7B27VqeeOKJitsZQRAEQRCAS18My/tfZUISaaFUPD09ySicjmrWrBkNGzZk2rRp9OzZE58iC%2Bry8/OZPXs2OTk5/Pnnn7zzzjv079%2B/RH0xMTGsWLGC3377jYKCAlavXs2yZcuoVq0aGRkZeHt74%2BbmxpkzZ5gxYwYAeVZnAwVBEARBEK4CkkgLpdK/f3/mzp3LU089BRiJ8L59%2B%2BjXr1%2BJ2KpVq9K1a1eGDh3KfffdxyOPPFIipnfv3owdO5aJEyfStm1bVqxYweLFi6levTozZ87ks88%2Bo3Xr1sTExBAaGkqzZs04ePBghe%2BnIAiCIFRWZEa67MgaaaFUHnnkEaeEuHbt2txwww2OhwWL8vjjj/P444%2BXeN8ue2dnyJAhDBkypERcx44d2bhxo2lbtmzZcjlNFwRBEATBAtdj0nv%2B/HmmT5/Oli1buHjxIl27dmXq1Kn4mTy4sWnTJhYuXEh8fDyBgYHExMQwatQoXAsfErj77rtJSEhw/A2watUqGjRoYKk9kkgLStLS0jh9%2BjRvvPEG999//9VujiAIgiAIlZjp06dz6tQpNm3aRH5%2BPhMmTODll19m6tSpJWL37NnD008/zauvvkqnTp04cuQIw4cPx9fXl2HDhpGZmemQ8K1du/YVtUeWdghK9uzZw6BBgwgODmbQoEFXuzmCIAiCIJQTFbW0Iykpib179zr9K03R63K5cOECGzZsYNy4cQQGBlKjRg2eeuop1qxZ4yR8YOfkyZMMGjSILl264OrqSoMGDejWrRs//fQTYOQ4gYGBV5xEg8xICxo6duzI7t27Sy275ZZbOKATpb2GcDt%2BRB3g6Qm1a%2BN2%2BqRSa%2BtAbn3TMi8vqBcAR88EKLVzb7hB11rwAPIC1LYiR9NDleVeNqgDHLdFkKP4CS/7qHmZtzc0agQJCZCdbR5Xq1ZVZVvc3CAQOJtfRa3dnG7hPAUGGvpsKkk/b/NjZ/85U7ee71iBub42gKcNagKnbGHkKuopOKusBk9Pw6siI0Mt85aukBb28TH8eVJT1XJzDRvq2wIQEaFuSwPfU%2BaFUGjDGExktWSootDjU50ADw8ghNoeSYD5w8fnzqp1xVxdDbOVjK93KjdXNVBhPxcVBTt3UqVTa63cXvxx8/bYpd3PnlWrKzZsWM28ELjpJti%2BHW7pFsCuXeZxOb8pxmgvL6hXD9%2Bko1pXkGPejU3LPD2hZk04leSmvGYCAtT75IZhXpROACqhPFfFNlxdoYo3ZOR5a5ctVHVV7LOLyyUdaYVunadFRxYrMo9/J0ozRRszZgxjx%2BpN0bKzs0lMTCy17MKFC%2BTl5REZGel4r0GDBmRnZ3P06FGaNm3qFN%2B9e3cnpbHs7Gy2bt1K7969Afj999/x8fHhgQce4NChQ9SuXZuxY8fSpUsXy/sqibQgCIIgCEIlpKLWSJdmihYcHGzps7t372bo0KGllo0fPx4AX99Lnp12JbEsjeFPZmYm48ePx9vbm4ceeggAFxcXbrzxRp544glq1arF559/ztixY1m6dCk33XSTpfb%2BP3tnHlZV1TXwHxe4XJwCBURxzAHTUkBAzZzgdfrAeQxDy7fMEXMIh1JxJjU0lczXoXJAUTMH1LTB1zQVX%2BdSMTVNEAUURFCZ%2Bf64cOMKd5%2BDoGnu3/P4XLl73b3X3mefc9bZZ%2B21pCEtkUgkEolE8gLypAzpopKiqUX0tvv8%2BfN89tlnPHz40LC5MN%2Blo5wgtfwff/xBQEAAlSpVYs2aNQbZR6OMde3alYiICPbu3avakJY%2B0hKJRCKRSCSSZ57atWtjaWnJ5cuXDd9duXIFS0tLatWqVeRvDhw4QJ8%2BfWjVqhWrVq3ipZf%2BckNctWoVR44cMZLPyMjAyspKtU7SkJZIJBKJRCJ5AXne4khbW1vTuXNnFixYQGJiIomJiSxYsABfX190Ol0h%2BdOnTzNixAgmTZrEhAkTsLAwdsS4efMm06dPJzo6mqysLLZs2cKpU6fo0aOHap2kIS0pFvHx8Tx48ODvVkMikUgkEskLyLRp06hVqxZdunShU6dOVKtWjalTpxrKfXx8%2BOKLLwD44osvyMrKYvbs2bi6uhr%2B5bt0BAYG0rp1a/z8/HB3d2fjxo385z//oWbNmqr1kT7Sj%2BDs7EybNm1Yvnw5ZmZ/7eDeunUrS5cufSaSgyxZsoRjx46xdu3ap9ru7du36dixIzt37qRMmTJERkYycOBAI6f/fKpWrcquXbsMf589e5aVK1dy/Phx0tLSqFy5Mt27d%2Bff//630RNibGwsy5cv5%2BDBgyQmJqLVannttdcYPHgwLVu2fCr9lEgkEonkReB5TMhSrlw5Zs6cycyZM4ssL2h75BvUptBqtUyePJnJkyc/tj5yRboIDhw4wMqVK/9uNZ450tLSilyNPnXqVKF/BSfyd999x8CBA/Hw8GDfvn2cOHGCTz/9lJ07dzJu3DiD3O%2B//07Xrl3JyMhgxYoVnDhxgn379tG1a1dGjBjBgQMHnko/JRKJRCJ5EXjeXDueReSKdBH4%2B/vz2Wef0bRpU9zc3IqUuXjxIgsWLODMmTPodDq8vLwYN24c5cuXZ%2BvWraxbtw4bGxvOnj3LtGnT2LRpEx4eHhw5coQLFy5Qo0YNZs2axddff83%2B/fuxsbFh6tSptG3bFtCnpwwLC%2BPGjRtkZGTg6enJ3LlzqVixYrH6MnHiRB48eMClS5dISkpi06ZNlClThuDgYI4cOYKZmRleXl4EBgZSrlw5srKymDVrFt9//z1ZWVnUqVOHcePG4eLigq%2BvLwC%2Bvr7MmTOHSpXEMY4B0tPTmTZtGiNGjMDf39/wfcOGDVmwYAGff/45d%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%2B%2BuqrIuspjuM/mAgGP2IEowIClH%2BsELKnlor2q1ZVIaQCJWOmRg119VSpUnJd1LalRPnyCgI2KjNMKRwnNRFLbcX5IVSjMjyqIo8ZLcqIYrj2CXF0VJJQ2enSGGSFBQSlKZVPXrQs05w8qVxJWJiiiOLQAXZ24nIVl1cA1qxRkqilXImKC5aaS0hpnQeCKGaqUTzWAKhY8VC4CKs1s0X2uOhZ8knzoq0ePwmkIW2CCRMmcOrUKSZOnGgwjvP58ccfsbS0ZPz48Zibm6PT6ZgyZQo%2BPj6GVVBLS0u6deuGpsBjcceOHambl1bM3d2de/fu8a9//QuA1q1b8%2BWXXwJQv359g7GanJxMfHw8FStWNJnpRwkXFxdDFqCzZ89y7tw5vvzyS0MMxgkTJtCpUyemTJmCTqcjJiaGLVu20Lp1a0aPHs2YMWOE9bu7uxf6bsiQIQwZMoTExEQA7BTuGrdu3QLAscDd%2B8iRI4YsSNnZ2Tg4OLB3715VfS4yGHxmJty4YfpHlpZ6SyY%2BXphy7FqmaUNPq9Xfk2JjxVnh1GQjtbQUZz4DuKmQXM7SMi/j2E1xXaKkZlZWeiP6%2BnWxnNJN1Nxcb0SnpCDMbGhzX3CMQPVxStCaHmQLC719l5QEWYKke6Ky/Hrs7SEhQSyrZkVaRZcQ5RuwstIb0X/%2BKT5OolXtfF0cHeHWLbEu1XUJpgtB/SArrUhXrAiJicI6UqzFTyAajd6wun9ffCzKtyn6DSSgX4kOCwM/P4iKErZ3a7dpg9zCQm9E374tHpZu3YRN4OysN6IHDgRRgtnI8GumC9VesICbVrVMlqk9D5SMW41Gb0SnpoqPk9KKtJpjDVDeStBnM7O/LsKCOZprqWyMm5n9vcay5MkiDWkTaLVaFi1aRH/mS4sAACAASURBVI8ePVi9ejW2BVZV7ty5Q9WqVTEv8J6qWrVqgD6vO%2Bgz%2BGgeebdUcGXb3NzcKJahRqMhN%2B9M02g0rFmzxrCpz9nZmdTUVEN5cSkYFD0mJobs7GzatGlTqL/R0dH4%2BPiQmZnJ5s2bCQkJoVKlSgwdOpQ333zTZP3Hjx83WZafySghIYGqRax6JCQkYG9vb5CLi4ujdm19Cu4WLVoY6s7f7KmWIoPBX72qeLMA9BdOgVy6iioyMhQz7pYKatvIzBTLilJ/F2xLJCcyjh%2BVE8qqOUageJyyVLzazcoS3/jVqpKVJZZVu%2Bqj0CVh6u980tPFcoquNSp1wULhKSMfpUFWMzhZWUKrPkdlyFdF/02F1N%2BA3ohWkFPjhabQJWHa74JcvKggq%2BYioeKClaFi6VXpPCgiOlmR5OSIrxFq3DZU%2Beqquafm5v6jrWC5Il1ypCEtoEaNGsycOZPAwEB69uxp%2BN7JyYnY2Fiys7MNxvT169cBveH4xx9/GEX8yKeo74riq6%2B%2B4pdffmHnzp2GldyhQ4c%2Bdj8Ktuvo6IhOpyMyMtKge0ZGBtHR0dSsWZOrV6/SqFEjunfvTlpaGt999x0TJkzA3d3dkIazOLi4uGBjY8Pu3bsLZRCKioqiW7dubNy4EVdXV1577TU2b95M8%2BbNH7uvEolEIpFI1CEN6ZIjo3Yo8H//93/06tWL8PBww3f5q7kLFiwgLS2NhIQEZs%2BeTfPmzXFS885egdTUVCwsLLC0tCQrK4vt27dz8ODBYm22M0Xjxo2pWbMmwcHB3L9/n7S0NObMmcPbb79NdnY2%2B/fvZ%2BTIkcTExKDT6bCxscHCwoLy5csbMv2kpqaqbk%2Br1fLxxx%2BzdOlS1q9fz/3798nOzub48eOMHj2ajh074pq3wWfOnDkcPHiQKVOmcPXqVXJzc0lNTWXbtm0sWbLksdONSiQSiUQikTwJ5Iq0CiZPnsyZM2e4d%2B8eAOXLl%2BfLL78kODjYYFR7e3sTGBhYKu0NHjyY33//nXbt2mFlZUXDhg3x8/Pj6NGjJa7bwsKC5cuX88knn9ChQwfS09Np3LgxX375JVZWVgwcOJC4uDj69%2B9PamoqTk5OLFy4EEdHR3Jzc2nfvj39%2BvVj4sSJvPzyywAGQ/hRvvvuOypXrkyXLl2wtbVl9erVLFmyhPT0dKpUqUKvXr145513DPL5vuErVqxg6NChJCQkYGZmhrOzM%2B%2B%2B%2By59%2BvQpcf8lEolEIpHokSvSJccs93EdbyWS542rV8XlWq1%2BF%2BCNG0JHv4sZtU2WWVlBrVpw7ZrY5TDvGUSIms2G166Jy9VuFBT5Put0UK8eXLokllPa%2BG9uDjY2cPeu2P%2Bx0r3SOU43daaPk9rNUUo%2B0lrtX5s5S%2BIjrbJL5D3LF4m1NdSvD7//LvaRVoqIoNVC9eoQHS3WpU4ZhZ2upbETU%2BUuzORy4jeBGs1fG11Fzb1kI3C/c3XVR/Vwc1P0kY6%2Bbvq2qnYzZ96%2BdJO4uOgjezRrJvaRTj8r2Imo9oIF/KkzHWdP7XlQoYKwCczN9TL37j2%2Bj7TaYw3wkk7QZzMzfccyMsSbDbXKDvpqNhv%2BXVH2vv32ydRbzEBbzzVyRVoikUgkEonkBUSuSJccaUg/x3z55ZeGOMxF0aVLF2bMmPEUNZJIJBKJRPK8IA3pkiNdOyQvDqJgq6D%2BNacoU4XK94oxKS%2BZLAP969/KlSEuTvz6t5q9Qmgrla8nFeNJqfAzSUkTJy5QG9/19m2xKmrdIGo7CIIuazR6X4iHD8XK5MU3Fyqjxg8iLzymSdQepwcPTJepfS%2BuhMp6sisoJ1oxN1dWRdQltXNGKaOemZneRSktTTy8ormn1iUDoHqNkruIiNxDiqOPyGVA7XUGoFrZJNOFaueemsDsamKPK02GSpXgzh3F9rLtKgvL1cxfJfcvtXPvMYJilQqbNz%2BZel%2BkLU1yRVoikUgkEonkBUSuSJccGf5OIpFIJBKJRCJ5DOSKtEQikUgkEskLiFyRLjlyRVqimmtKsdYkEolEIpE8N%2BSnUi/tfy8Sqg3pESNGMH78eKPvtm/fjrOzM59%2B%2BqnR94sWLTJKqf2s4ezsTGRk5N%2BtxmPh5eXF1q1bn3q769evZ8qUKYa/J06cSKNGjXB1dS3074svvjDI5eTkEBYWRu/evXF3d6dZs2YMGjSII0eOFGpj//79/Pvf/6Z58%2BY0adIELy8vgoKCiI%2BPfyp9lEgkEolEIikOqg3ptm3bFjI%2Bf/zxR1xdXfn%2B%2B%2B%2BNvj9y5AheXl6lo6HkmSAxMbHQd126dOHUqVOF/g0dOhSA3NxcRo0axYYNG5g4cSJHjx7l4MGD%2BPr6MnToUH788UdDXaGhoUyYMIFOnToRERHBqVOn%2BOqrr0hLS8PPz4%2BHogwTEolEIpFIio1ckS45qg3pNm3akJCQwJUrVwDIyMjg4MGDTJo0iZiYGMP3KSkp/Prrr7Rr146TJ08ycOBA3njjDV577TV69uzJ6bwUTAMGDCAkJMSojT59%2BrBy5UoAzp07h7%2B/Px4eHnTo0IGvvvoKtZH64uLi%2BOCDD/Dy8qJJkyZ4e3uzZcsWI5lDhw7RuXNnmjVrRkBAAAkJCQBERkbi5eXFsmXLaNWqFZ6enowaNYrU1FTDbzdv3oyPjw9ubm506dKFHTt2GMr8/f2ZOHEi7dq1o23btly8eBFnZ2e2bdtGu3btcHFxYdKkSRw/fpyuXbvi6urKoEGDDIZqamoqH3/8MR06dMDFxYVWrVoZrfAWB2dnZ2bNmkWzZs0Mxu3hw4cNq8M%2BPj5Gul%2B6dIkBAwbg4eFBu3btmDBhAqmpqXz77bcsX76c48eP4%2B7urrr97777jp9//pnly5fj7u6OhYUFWq2WPn36MGrUKMOc%2BeOPP1i6dCnz58%2BnT58%2B2NnZodFoqFGjBjNmzKBjx45FGvISiUQikUgkfyeqNxs6ODjQsGFDjh49Sp06dfjll19wcHCgSZMmeHh48OOPP1KnTh0iIyOxs7OjTp06tGnThoCAAN58803S0tKYPHky8%2BbNIywsjD59%2BrBo0SI%2B%2BOADNBoNV65c4cKFCyxbtoy4uDgGDRrEmDFjWL16NX/%2B%2BSfDhw9Hp9PRv39/RV0//vhjbGxs2LVrF1qtljVr1jBz5kw6d%2B5M2bJlAThw4AArV67kpZde4sMPP2T8%2BPF8/fXXANy4cYO4uDi%2B//574uLiGDBgAGFhYQwZMoStW7cSHBzM0qVL8fT05NixY4wcORJra2vat28P6I3VzZs3Y21tzb28nL4HDhxg9%2B7dREdH0717d86fP8%2BKFSuwtLSkf//%2BhIWFMXLkSBYsWEBMTAxbtmyhfPny7Nu3j4CAADp37kzNmjWLfYCvX7/Of//7XzIzM4mKimLYsGHMnz8fb29vzpw5w/Dhw7G1taVVq1ZMnz6dFi1asG7dOpKSkhg0aBCbN2/mnXfeISYmhmPHjrF27VrVbf/000%2B4ublRtYjc0e%2B%2B%2B67h/9999x2VK1emTZs2heS0Wi0ffvhhsfsdHx9veDjKx/7hQxzs7Ez/SKs1/jSFUn5aJRn08VtF5MfFVYqPq5hXNr/8KeSfVQpFrXJoFIc/f%2ByUxlDYUMFxEcmVljKldZzMzU2XqR1gJUqrnmI2VxJVSmt4RYdR9TkJ%2BljRpmjQwPjzMXQpjj6iPherT09j7uW3IWoLxEueausoJUpj7v2d2TxetNXjJ0Gxona0adOGyMhIBgwYwA8//IC3tzeg99uNiIhgyJAh/PLLL7Rr1w5LS0vCw8OpWbMm6enp3LhxAxsbG3799VcAOnXqxOzZs4mMjKRFixZs3bqVNm3aYGdnx4oVK6hTpw4DBgwAoG7duvz73/9m3bp1qgzpWbNmUbZsWSwtLYmNjaVs2bKkpaWRnJxsMKQDAgJwcnICIDAwkE6dOhEXF2eoY8SIEeh0OmrWrEmzZs24evUqAN988w39%2BvWjRYsWALRo0YJ%2B/fqxceNGgyHdunVrKlfWB3rPN6QHDx6MtbU19evXx97enh49ehhkXFxcuHHjBgCjRo3C3NyccuXKcevWLaysrAC9Yfg4hrSvry/W1tZYW1sTEhKCt7c3HTp0AMDNzY2%2Bffuyfv16WrVqhZWVFQcPHqROnTq0aNGC7du3oxFcGCMiIvjhhx8Kfb9jxw6qVq1KYmIidiLDNY9bt27h%2BEiSk8mTJ7Nv3z4AMjMzef/99xk%2BfLjqfoeHh7N06VKj70aOGMGogADlHxdh%2BBebvHlmisrl1VVTqZKShIKhl4%2Bi1VnyOsqqbEIp8YDC0BlwcFBsSbkSnU5cXr26OmVESXqKg9JxUjLsAcqVKx1dFOpRa6Yo2TNqjndpJavIu5yaRM1hVHFJ0ydcUSIsTKyLimZApT4KKF9nACooi5TW3Kugoi0lbGwURdTMYaX5q9ZeF829v9NzURrSJadYhnTbtm0JCwsjKyuL/fv3s2TJEkBvSM%2BdO5ekpCQOHz7M5MmTMTc3JzIykvfee48HDx5Qt25dLCwsDO4ZOp2OLl26sG3bNjw9PdmxYwczZ84E9CvC586dM3IjyMnJwVzljI2OjmbevHlcu3aNWrVqGQzQnAIzplqBTGP5K6YFDWl7e3vD/y0tLQ163759m%2BqP3FyrVavGTz/9ZPjboYg7vE2Bk9rc3JwKBS4UGo3GUP%2BdO3eYPXs258%2Bfp1q1arz66quFdC8OBXW5ceMGR48eNRrX7OxsatSoAeg3iS5ZsoSFCxcyduxY3NzcCAoKol69ekXW7evrS3BwsLDt/AeER0lNTcXc3Bxra2scHBw4dOiQUfmcOXOYM2cOoHeXyS5mprZ%2B/foV8tO3f/hQn7XQFFqt3oiOjRWnqxLddVSmYot7ILak1SbnqmyrIq1WflZC0bKHmqU6hdRn9zOUMxuqSSZ4966yKg4OEB8vVsmpouDuVBqp7vKVUZNerrI4g5rq45SWZrpMo9EbMqmpJbs7qqwnu6yysaMmM5xSl9TMGaVbg5mZ3pBJTxcPr2juWVjojdbbt5UT9Dn%2Bn5vpwgYN9Ea0nx9ERZkUu7VbbIyr1UdpRVplEkAqW98zXah27ilNBrUZEpUmg42N/mAqtJdtI36CUDN/lTJCqp17kueXYhnSr732GhqNhm3btpGbm4tr3usrJycn6tWrx/bt24mPj6d58%2BacOXOGmTNnsnHjRoMxuHr1asPKLkDfvn158803ad%2B%2BPWZmZrRq1QoAR0dHmjVrxqpVqwyySUlJ3L8vSPmbR/7q5dixY/Hz88PMzIzffvvNyBcY9Cu8DfJerUVHRwN6g/jSpUvC%2BqtVq8b169eNvouOjjYyvM2KuHIV9V1RjB49Gi8vL1atWoWFhQVJSUls2rRJ1W%2BLomC7jo6O9OjRgxkzZhi%2Bi4%2BPJzc3l5ycHM6fP8%2BoUaOYPHkyN2/eZO7cuUycOJFvvvnmsdpu164d48aNK3LFecmSJRw4cIA9e/bQoUMHli5dyi%2B//ELLli0fr6OP4ODgUPiB5uJFcervfDIyxHJqjBSFHRdKF998srIUZNVemXNzS2ZIq0Ct7aa0GUUp5W4%2BmZkKsqJG8t%2B05OY%2BHWVK6zipeaDMySlZivDSrkdFM2pk1BxOJZSGV815qXhOgjD1t4GoKKFcaV0j1JzaqvqkLYW5p2St55Od/fgpwtXWUUqU1qn9dyFXpEtOsRyaNBoNrVu35osvvqBdu3ZGr/29vLz4%2Buuvef3117GysiIlJQWNRoMu79Xp6dOnWbNmDRkFbjYNGjTg5ZdfZs6cOfTo0cOw4tylSxdOnz7Njh07yMrKIj4%2BnqFDhwpXP/PJzMwkLS0NnU6HmZkZsbGxzJ8/31CWz5IlS4iLiyM5OZng4GA6dOhAxYoVFevv3bs34eHhHDlyhOzsbI4ePUp4eDi9evVSN4gKpKSkoNPpMDc3JzExkVmzZhXS/XHp3bs3ERERHDp0iJycHK5du8Zbb73F6tWr0Wg0zJo1i0WLFpGenk7FihWxsrLC1tYWACsrK1JTU1Vv%2BARo3749zZo1Y8iQIZw8eZKcnBxSU1P56quvWL9%2BPePHj8fMzIz69eszevRoxo4dy%2BbNm0lJSQH0DyiffPIJJ06cMHpQkUgkEolEInkWKPbOgDZt2hAdHV3otbm3tzexsbG0a9cOgJYtW%2BLn52eIAjF9%2BnT8/f1JTEzkdoHXpX379iU2NpbevXsbvnNycmLlypWEh4fz%2Buuv061bN15%2B%2BWVVhnSZMmWYM2cOoaGhuLq6MnDgQFq2bImdnR2///67Qa5Vq1b07duXDh06YGdnx%2BzZs1X1v3PnzkyaNIlZs2bh7u5OUFAQgYGBdO/eXdXvlZg7dy67d%2B/Gzc2Nnj17UrlyZRo2bGik%2B%2BPSpEkTQkJCCAkJwcPDg7feegsvLy/GjRsH6F07rly5whtvvMHrr79OSkqKwd2mXbt23L17l6ZNmxr8vnfu3FlkHOn8CCFmZmZ8/vnndOrUialTp%2BLh4YG3tzcHDhxgxYoV/Otf/zLoNmzYMBYuXMhPP/1Ep06dcHFxwc/Pj7i4ONasWaPKN14ikUgkEol6ZPi7kmOWW5wlRonkeebiRXG5lRXUqqX3oxa5doh2JGk0UL48pKQIryYxKS8JVbG01LvVxsWJX7lWs1dwVTEz0/t%2BZ2SI3ysqvRtX4SOdkqbsI63CfVzRLVmrBScnuHFD7E1R20HgCqbW%2BfbWLWVlqleH6GixMgX2ZBSJ2uP04IHpMrX%2BpUqorCe7gq2qqpRUEXVJ7ZxRE7mipC7xat3hAarXEPhTuLrqNyO6uQldO6Kvi2/NavURuXaovc4AVCubZLpQ7dxTcrWwsABbW0hKenzXjmI4fmfbifcuqJm/St5faudeaW2oLS7Llz%2BZet9//8nU%2BywiU4RLJBKJRCKRSCSPQbE2Gz4LzJ49u1BylYK8//77BteCfzI9e/Y02rj5KCtWrChW8hSJRCKRSCQvFi%2BaG8aTQLp2SF4YLlwQl%2Bt0ULs2XL0qDsn1ivaK6UK1r/qVAtY%2BbdcDUSBTnQ7q1oXLl8UDoxTiTeVr25tZ4o2lFhZgbw8JCeI3t1U0caYLixP3S0kZNfWIxg30x6lKFbh5U3yc8jbiFona41ReIYi5Sl1uasVx7dUep9Koo4pO4HYAql0PrBxNu6u4uEBkJDRrBnkJek1y%2BbLpMrUuGUL3EFDtInL1D9O3eLVuUgC1tUWHMgXUx6RUiu2s9rqnlGxJjS8FkK4x7U%2Bh1tvKykKFG5UaH5GnlEDmUUJDn0y9I0Y8mXqfRaRrh0QikUgkEolE8hg8d64dEolEIpFIJJKSI107So5ckZZIJBKJRCKRSB4DuSItkUgkEolE8gIiV6RLzj9qRTo%2BPp4HoqCk/wDS09O5pbTB7AmRkpJCYmLi39K2RCKRSCSS0kUmZCk5T82QHjx4MCNHjiyybNOmTbz%2B%2ButG6cOLy%2B3bt%2BnYsaPQ0PPx8WHHjh2P3QZATEwMzs7OxMTElKgeU3h5ebF161aT5X5%2Bfhw%2BfPiJtK1E%2B/btuXTpEvDXOLi4uBSZ3bAgV69eZcKECbRu3RpXV1f%2B9a9/sWDBAu7fN06YcffuXT755BM6duyIq6srTZs25a233mL37t1PrY8SiUQikUgkanlqrh3%2B/v6MHDmShIQE7O2Nw1tt2LCB/v37o9VqH7v%2BtLQ0xdXoXbt2PXb9zwpJSQphnp5y2xEREVQTZG07efIkgwcPZvDgwWzbto2KFSty9epVpk6dyuDBgwkLC8Pc3Jz4%2BHj69OnDK6%2B8wqJFi6hTpw4ZGRkcO3aMyZMnk5yczJtvvvkkuyeRSCQSyQvFi7Z6/CR4aoZ0mzZtqFq1Kt9%2B%2By1DhgwxfH/69GkuXbrE8uXLSU1NJSQkhB9//JGMjAyaN2/ORx99hJ2dHTExMXh7ezNr1iyWLVtGcnIyjRs3Zu7cudjb2%2BPr6wuAr68vc%2BbM4cqVK5w6dYrk5GSio6MJDQ1lwoQJjBw5kp49e/LgwQMWLFjAnj17yMzMxNXVlaCgIJycnPDy8jLIAURGRjJw4EAuFpFi%2BuTJkyxatIg//viD5ORk6tWrx9SpU3FxcSEyMpLAwEDc3d05cOAAQ4YMMeq7Kc6dO8emTZv4/fffqVKlCtOmTcPT05PBgwcTGxvLtGnT%2BO2335g6daqwniVLlhQag1deecXkGOf/ZsuWLTx8%2BJDq1aszfPhwvL296dixIwDvvfceo0aNonPnzqqO%2B9SpU%2BnevTsBAQGG72rXrs3ChQuZOnUq0dHR1KpVi08%2B%2BQRHR0dCQ0Mxz4unqdVq8fLyYs6cOcV2KYmPjychIcHou/v37bG3dzD5m/znOMXnOQuBgKWl8acplFJy5%2Bf1NTMTyyopq1YfYaBUK%2BNPUyjlac6Pk6oQL1XpopTfjFJzaAQCKnVRRG09SsdJbad0OtNlao9TKemipKrq41QadSiNf/45pHDeubiYLnN2Nv4UITrdVPfpkTd7hWjQwPjTBKLDrfbyoCiktlOldd0T5T0vWIcCpVSN5AXnqRnSGo0GPz8/wsLCeO%2B99zDLm50bNmygU6dOODg4EBAQwP3799m6dSs6nY7g4GBGjhzJhg0bDPX897//Zdu2bWRkZPDOO%2B/w%2BeefM2PGDCIiIvD29jaskC5ZsoQjR46wevVqGjdujNUjN5cZM2Zw5coVtm7dSqVKlZg2bRpjx44lPDxcdZ/S0tIYNmwYAQEBvPnmm6SlpTF58mTmzZtHWFgYALdu3eLll18mODiY9PR0VfUeOnSIFStWULVqVYKCgpgyZQp79%2B5l9erVhYx8JR4dg7Fjx5oc48jISMLDw9m6dSv29vaEh4fz0Ucf0bp1a/bu3YuzszMrVqygWbNmqlxbrl%2B/zqVLlwgKCipUZmdnx%2Beffw5AdnY2%2B/btY9q0aQYjuiBeXl6q%2BlqQ8PBwli5davTdiBEjCQgYpfhbJyclierKCiglXFGLyHACfbIVNZSGPmrbUqJCBWGxOB3LX9iazp2RRyXlSpQSRKiltOqxV9t7AaV1nBR0Kb3jVBp1iOeUgXLlhMWRkcpVrFmjrikl8tYuTHPypLqK8u41plC8nKHPpaJCSlmkYkU1FSmjdN1Tg9IDJaDmHbjyQ4bKh3HRw55SspYniFyRLjlPNWpH7969Wbx4MUePHqVFixbcvXuXPXv2sG7dOu7cucPevXvZs2cPlSrpb4CTJ0/G3d2dc%2BfOYZN3o3rvvfeokHcj9vLy4pQgo1P16tVp0aJFoe8zMjLYtWsXy5Yto0qVKgBMmjSJP//8s1j9sbS0JDw8nJo1a5Kens6NGzewsbHh119/LdRvS0tLLFU99kO/fv2oUaMGAJ06dRL6TCtRcAyUxtjKyork5GQ2bdpEu3bt6NOnD/369TM89BRF165d0TyycuDr60tQUJBhFdlO4Y6RmJhIRkYGjgWMvWvXrtG7d28AcnJyyMzMLDSuIvr161fIAL9/3x5BVnXVWb5qW0SbLlSbtkzpLqo2O9ft2%2BJ61OojesizsvorO6JIrpKC4aoyu1xClthqUpkgEXvNHbEuNjZw927JbmJq61Ha/6E2hd8j%2BwqMUHucypYtFV0SLKooVqPmOJVGHfZW98QVaTR6Izo1VWg5NGtv2iB3dtYb0QMHQhEvJ43Yvt10mYWF/vS/fVvcJ8f/cxM30qCB3oj284OoKJNiN3aaNsjVJiQEcLKMN11oYaE3ohMTxZ1Syqqp9rqntJRsZaU/BxQyG2ZoTBvsZmb68cnMFFejNS%2BlzIZ/E9KQLjlP1ZAuX748Xbt2ZfPmzbRo0YJvvvmGhg0b0rhxY86ePQtA3759jX5jbm5OTEyMwZAuaJRZWFggynDuYOIxOzk5mYyMDKpWrWr4rkKFCrz22mvF6o%2B5uTmRkZG89957PHjwgLp16xapkyk9TGFTYHXL0tKS7BKcgAXbvnFDn%2BLV1Bh36tSJJUuWsHbtWlauXIlOp8Pf359hw4YVMpbz2bFjh0kf6Xxf%2BISEBGrVqlWo/Pbt29jZ2WFra4ulpSVxcX%2BldK5VqxbHjx8H/nKtKQ4ODg6Fxv3CBeVMzaC3eYRyWhWbYjMzxcaT0tUrf7xzc8WyajfoKumjZmDS08Vyaq2l7GyhrNpqsrIUZDUqKlLQRTVK9ag9TllZT/44qXygV9JF7agpHqfSqENNmmbQn0uC66lS6m/QG9FKckpGKej7I5QTLBIZERUllFUz9ZQuD3lSyhUpdaq0rntKLiL5dSgY0grFaquRvOA89TjS/v7%2B9OjRg6SkJDZt2mTwna1cuTIAe/bsMdqMePnyZapXr17I31UNplZSK1WqhFar5ebNm7z88suAfrV2xYoVfPDBB2g0GjILXAxMbfA7c%2BYMM2fOZOPGjbz66qsArF69mquPLHuKVnSfNAXbVhrj2NhYKlWqxKpVq8jIyODIkSOMHDmSRo0a0bZt22K37eTkRP369dm9ezceHh5GZXfu3KFdu3bMnTsXX19fvLy8%2BOabb%2BjRo4dJo10ikUgkEknpIVekS85Tt1jq1q1L06ZNCQ4O5uHDh3To0AHQG3lt27Zl9uzZJCUlkZmZybJly%2Bjduzf37im8tgODD3RqaqqirEajoXv37ixZsoS4uDjS09NZtGgRp0%2BfRqfTUadOHX788UfS0tJISEhgjQmnuJSUFDQaDbo8f67Tp0%2BzZs2aEoXxU0Kr1ZKSkvJYv1Ua419//ZV3332XqKgotFqtwf3DNs9B8XHanjJlCt988w1Lly4lKSmJ3NxcLly4wNChQ2nUqJFhE%2BNHH33EzZs3GTlyJFFRUeTk5JCens4PP/zAzJkzC0V6kUgkEolEIvm7%2BVuW/t566y22bdvGm2%2B%2BaeQ3PG/ePCpUqED37t1p3rw5Bw4cYOXKlaqMKDs7O9q3b0%2B/fv2MNieaYuLEibz66qv06dOHVq1akZSUxGeffQbA%2BPHjuX//qRqeEQAAIABJREFUPi1btmTgwIF07dq1yDpatmyJn58fAwYMwMPDg%2BnTp%2BPv709iYiK3lXxXH5PevXuzcOFCxo8f/1i/F41xx44dGTx4MMOGDcPFxYXRo0czefJkmjRpAuj9jseNG8fChQsN9fn6%2BhYZRzrfd93T05N169Zx/vx5fHx8cHNzIyAggObNm7Ny5UrD8a9cuTI7duygfv36jB8/Hg8PD5o3b87nn39O165d%2Be6770o4chKJRCKRSAryPCZkefDgAZMmTaJZs2Y0bdqUwMDAQnkpCjJt2jReffVVIxulYGCJFStW0Lp1a1xcXPD39%2BePP/4olj5muSInY4nkH8SFC%2BJynQ5q14arV8Uupq9or5gu1Gr/2vAlejOhFEVDowFra3j4UHxVUspyqVafhw9Nl%2Bl0ULcuXL4sHpg81yGTqNw5djNL/OCsdl9eFU2c6UILC/3myDt3SubAq7YeJd9mrRaqVIGbN8XHSfRGSO1xUtrwpVKXm9qawmrUHqfSqKOKTiG%2BvsqNrlaOpje6urjoo3o0a6bsI335sukytft/q9dQcAl0ddVH9nBzE/pIX/3D9C1e7QZrgNraG6YL1e5aVIpuo/a6pxQaT82GRSBdYy2sRqvVj4swOqga/3w1mw1LGorzMZk588nUO2XKk6kX9MEhbt68yaJFi8jOzuaDDz6gbt26TJs2rUj5nj17GtyKH%2BXbb79l4cKFrFq1iho1arBw4UIOHTrEzp07VbvlSmdUiUQikUgkEskzz8OHD9m5cycBAQHY2NhQqVIlxo8fz9atW3lYxIJQRkYGv//%2Bu2Ef26Ns2rQJPz8/6tWrh5WVFePGjSM2NpZINbEw83jqmw1fZEaMGCFM7z19%2BnSTbiSPsnfvXiZOnGiyvGnTpqxcubLYOkokEolEInkxeFJuGEUlRbO3t1cVxSwtLc0oildBHj58SGZmJvXr1zd8V6dOHdLS0rh27RqvvPKKkXxUVBRZWVksXryYEydOUL58eXr16sW7776LRqPh8uXLvPfeewZ5S0tLatWqRVRUFM2bN1fVV2lIP0VCQ0NLra6OHTsaNupJ1FG3rjo5QcZzAE79VsdkmbU1NACi7lcXeks0rqWshzmQrTX96hHgXkXTukDeG23g3kvVhW8WFXKk6HWprXIAleqpII4TXWXeXHEllSvD4MHY71wNJi62AHtcJpksq1ABWraEX6IqIdrLvHu3WJVq1WDCBPhkZSVEOYo%2B%2BEBcj1ajT/MTnVWFDIELw8//M11WqRJ0rQs7ztfljiCEttJ5ULYsuFWBkzerCMNWt3pZ8KofINcScMA%2BNx5yBK/7r18XK2PfGPubZ4UxtFNfK5wvoCAaDZQBHlhUIEfwHjb9rCBAtJUVUIvI8GviON1AjJnp9IcFM%2BaJ3hyLXDIgzy0DfZxoYdz7lwWN5LmHOHURu4cA1K5lWp9GjSAiAnwHO3DunOk6Vq8WNkG5cuDhAf/7zRpR3ABR3hdra6hfH36/rhNegwGaXP7GdKGNDXh7oz34oz5OvAkO2PUStlGuHDRtCidOmwv71KaNWNfnjaKSoo0cOZJRo5STop05c8ZkyNvRo0cDUKZMGcN31tb6%2B2RRftIpKSl4enri7%2B9PSEgIFy5cYMSIEWg0Gt59913u379v%2BH0%2BOp2OBw8eKOqZjzSkJRKJRCKRSF5AntSKdFFJ0dRG32rWrBkXTWQ9On/%2BPJ999hkPHz6kbF5yqXyXjnJFZC5t2bIlLVu2NPzduHFjBg0axO7du3n33XextrYm7ZE9JWlpaYa61SANaYlEIpFIJJIXkCdlSBeVFK00qF27NpaWlly%2BfNkQUezKlSsGl4xH%2BeGHH7h9%2Bzb9%2B/c3fJeRkWEIW1yvXj0uXbpEu3btAMjMzOTatWtGriNKyM2GEolEIpFIJJJnHmtrazp37syCBQtITEwkMTGRBQsW4OvrazCOC5Kbm8vcuXM5cuQIubm5nDp1ijVr1tCvXz8AevXqxbp164iKiiI9PZ1PP/0UOzs73N3dVeskV6QlEolEIpFIXkCex8yG06ZN45NPPqFLly5kZmbi7e3NlALx9nx8fOjSpQtDhw6lffv2TJo0iaCgIOLi4rCzs2PUqFF069YN0OfmSElJYcSIESQmJvLaa6%2BxfPlyoxwnSkhDWkBycjILFy5k//79JCcnU65cOVq2bMmYMWNwdHTEy8uLkSNH0rNnz79b1SdCflSQ4ODgEtUTGxuLj48Pu3btomrVqqWhmkQikUgkkheQcuXKMXPmTGaaCIK9a9cuo7/79%2B9v5NpREDMzMwYPHszgwYMfWx/p2iFgzJgxJCUlsWXLFk6fPs22bdvIyMjgnXfeIaskSRxeMKpWrcqpU6ekES2RSCQSyTPE85jZ8FlDGtICTpw4Qfv27Q07Te3s7Awps%2B89EjMrLS2NefPm0aZNGzw8PPD39%2Bfs2bOGcmdnZ1auXEnbtm1xdXXl/fffN4qTePjwYXr37o27uzs%2BPj7s2LFDtZ5eXl5s3brV8HdkZCTOzvrwSzExMTg7O7N27VpatmxJ06ZN%2BfDDD0nNi8MTFxfHu%2B%2B%2Bi6enJ61bt2bkyJHEx8cX2c7mzZsNab67dOlipKO/vz/BwcH07NkTFxcXevbsyfHjx410iMmLD5avT8eOHXF1daV///5GO3QPHz5M9%2B7dcXNzo3///syfPx9/f3/V4yGRSCQSiUQZaUiXHOnaIcDHx4dp06Zx/PhxPD09adKkCU5OTkW6OgQFBXH%2B/HnWrFlDlSpV2LBhA2%2B//TYRERGGldht27axdu1abGxsCAwMZMyYMYSFhREVFcWwYcOYP38%2B3t7enDlzhuHDh2Nra0urVq1KpS/79u1j586dZGdnM2LECKZPn878%2BfMJCQnB0dGRZcuWkZ6eTkBAAP/5z3/4%2BOOPjX6/detWgoODWbp0KZ6enhw7doyRI0dibW1N%2B/btAX3cyGXLluHm5saqVasYNmwY%2B/btK1KfXbt2sW7dOnQ6HQEBAcybN49Vq1YRExPD0KFD%2Beijj%2BjVqxenT59m6NChhYKsK1FUMHhbW3vs7Uu%2Bi9haENrZysr480mjlFU2P5OuKKPuM4dSqvFKlYw/TSCKjZ0f2UgpwpFSTPF8VZVU1mrF5fnueEpueaIuv/SS8acplPqcP79F8xxQVtbCwvjzcRRSqYzS/C4Yu1koKzpx8w%2Bi0sFEPDRqh0UpO7HaOYOrq%2BmyBg2MPwU0ErxQrFPH%2BNMURUQnMyI/NHCBEMFFUmrXYFHK8vLljT9NoNQnNVNYFF9a8uwjDWkBs2bNolmzZuzevZupU6eSkpJCjRo1GDVqlFEGwvT0dCIiIggNDaVmzZoADBo0iJ07dxIREcGQIUMAfSDx6tWrAxAYGEinTp2IiYlh48aNeHt706FDBwDc3Nzo27cv69evLzVDetKkSVTMi2IfEBDAsGHDmD17NlZWVvzvf/9j165dtGjRgpUrV6Ip4k7zzTff0K9fP1q00Cc%2BaNGiBf369WPjxo0GQ7pXr16GTEBDhw5lw4YN7N%2B/H09Pz0L1%2Bfv7G1b6O3fuzPLlywHYuXMnr7zyimFHrbu7O3379uXXX38tVn%2BLCgY/YsRIAgKUg8Er3ZhU3HOoXVtZRg1KhrJSIpV8lC72paFLqdWj1lctb7OIKVoKS/W4uIjLW6qpBHj7bXVySjg6isvzLh9CSiuxg/Kzq8qHUlH2DAA1IbLq1RMWK9hdBhQfDooIn1UIFS5qCs9VgOJzoGoUh%2B/kSeVKwsIURSJU6PLZZyqEVNCoUcnryLsVi6nvrSxTxP2rIE3VqUPDhqbLDhxQWckT4EVbPX4SSENagEajoVu3bnTr1o3c3FyuXLnC9u3bCQwMNAosnpycTGZmJtUeWb6qVq2awZ0BMBjZgGGVOiEhgRs3bnD06FGjcCvZ2dnUqFGj1PpSsO0qVaqQkZHB3bt3%2Bfjjj1m%2BfDmrVq1i4sSJNGjQgI8//rhQ6Jfbt28bHgIK9u%2Bnn34y/F0whqOZmRmOjo6FVoXzsbOzM/zfwsKC3Fx91qybN2/i5ORkJFu9evViG9JFBYO3tbUnU5BgDfRGtJLMlSumy6ys9Eb01avi5GcKdgGgNzhF2QhBmOwN0K%2B%2BlSunX/EQXTCVVinV6KIGNfWYf62Q/qxSJb0RvX07ohR%2BvzibNsjLltUb0adPi8fw0CGxKpUr643or74SJlnEz09cj6Wl3oi%2BdUs8/0TJ5156SW9EHzgAycmm5ZRW2a2t9Ub0hQsIM8O5VSvaBcyAhYXeiE5MBNGeklu3xMrUqweXLgmVeVC3sVAVMzN9VQ8fQq4gYWCZ%2BGumC7VavREdG4swlSAQZ13LZJmFhX4K37kjHhalbTiWlnojOj5ePGecuriZLmzQQG9E%2B/lBVJSwPd%2Bqpg3yOnX0RvTo0eLr47RpwiYoU0ZvRJ87B6LEcqK3LlZWeiP6zz8VE1BSP/pH04Xly%2BuN6GPHICXFpNgJG7Exbm2tN6LPnxefT5LnF2lIm%2BDgwYMEBASwf/9%2BbGxsMDMzo27duowbN45ffvmF8%2BfPG2Tt7OywsrIiOjqaOgXebV2/ft3ImIuLizME%2Bc43sKtWrYqjoyM9evRgxowZBtn4%2BHiDcamERqMhs8CVNCkpqZBMXFwcL7/8sqFta2trbG1tOXfuHP369WPUqFEkJiYSGhrKyJEjOXr0qNHvq1WrxvVHUvlGR0cbPVAU9PnOyckhNjaWKlWqqOpDPk5OTuzfv9/ou9jY2GLVAUUHg1cykNWi5mKYnv50LppqjducnNIxhJ8KIou0IHfuCGXvqZh69%2B8jTBEuSvtdkLg4sayC3WUgM1MsK0r9nU9ysljOVpyh3cDDhwoPampPqKwssazS06AKZZRW1fJfsuXmKsgqWV6gP0AKcpkq7qxKw6J2eJXmjFLqb0BvRCvInSt8WynElSsIU4SrdWF48EAsq8K7Rt01WJD620BKilAuVaUV9fDhs%2BnCIVekS87z5Dn5VPHw8KBSpUpMmjSJixcvkpmZSWpqKjt27ODatWu0bdvWIKvRaOjVqxchISH8%2BeefZGRk8PXXX3P58mV8fHwMcqGhoSQkJHDv3j0%2B%2BeQTWrVqReXKlenduzcREREcOnSInJwcrl27xltvvcXq1Qorc3nUqVOHH3/8kbS0NBISElizZk0hmU8//ZTU1FTi4uJYvHgx3bp1w9LSki%2B%2B%2BIKZM2eSmppKhQoVDAb2o/Tu3Zvw8HCOHDlCdnY2R48eJTw8nF69ehlkNm/ezG%2B//UZGRgahoaHk5uYasgWppVu3bly4cIFt27aRnZ3NmTNn2LRpU7HqkEgkEolEoozcbFhy5Iq0CXQ6HWFhYSxdupRhw4Zx584dLC0tcXFx4csvvzRaeQa9z/OSJUt4%2B%2B23uXv3Ls7OzqxatYraBZxlGzVqhJ%2BfH0lJSbRt29awoa9JkyaEhIQQEhLC6NGjsba2xtfXl7Fjx6rSdfz48QQFBdGyZUscHBwYNGgQJ06cMJKpUaMGvr6%2BPHz4kC5duvDhhx8CMGPGDKZPn463tzcZGRm8%2BuqrfFaEo1vnzp1JTU1l1qxZxMbGUrlyZQIDA%2BnevbtBxtPTkxkzZnD58mUaNmzI6tWrKV%2B%2BPMmi98yP4OjoyOLFi5k3bx7Tp0/nlVde4Y033ihylV0ikUgkEonk70Qa0gIcHByM3C0epaB/sLW1NYGBgQQGBpqU79Chg1H2nYK0bdvWaJW7ONSrV4/169cbffdo8PEBAwYwYcKEQr91cHAgNDS0yHofjU7Sp08f%2BvTpI9Rj8eLFhb6vVq2aUXi7gv8H6NmzpyGpzc2bN7G3tzcKqF7ShDASiUQikUgK86KtHj8JpGuH5JkiKSkJPz8/fvvtNwCioqLYsWNHsV1EJBKJRCKRSJ40ckX6GWfEiBEcPnzYZPn06dONQvE97zRs2JCPPvqIsWPHkpCQgJ2dHUOGDMHX1/fvVk0ikUgkkn8UckW65EhD%2BinxqDuDWky5XajlUbeKJ8XatWtLrS4lFxKJRCKRSCQlRxrSJUca0pIXBst7CrHDzM3BxgbL%2B3eFseJcLQTh%2BMx1QD0amF8CizSTYtm8pqCtOmzLqYmRZUkFawW5DEHQWjMzMNdhnpkmDMKbmq2cfa5MGX1YKtHFe4VukrAeBysYAKy3Gky8zrTcGN1%2B04XacoAHLbX/A53pmFSd6/8m1AV7e6A/E2puhDJFx0wH4KGCa1KuDqhL9fTLkGZ63tTRCeKpa22BTnTVfgc6weZci1piXSzKAo1xszgLFqZDzsVZtBBXYw6VgDvmDmQJInnGaJ1MlllbQEPgvEVjHgpCnjW1VhPb0ZwyVmK5P3XOJsu0WqgC3LSqRYZC1sGaZQXjb24OVKCy9T3QCvQRBVKGvMxRDjhZxgOmz%2B/atUwPfqOq%2BkQrvlVPKoa3u3pN0GlbV%2BAkEbFucM10GL39qAvpqkSTCldNF2q1gBP1y94AS3Hcyb3lepksK28NrwOHrb1JEVweO%2BoixcpqywCv0VT7K%2BhEx7SZuB7JM4s0pCUSiUQikUheQOSKdMmRmw0lEolEIpFIJJLHQK5ISyQSiUQikbyAyBXpkiMNackzT3Z2NrGxsVSvXv3vVkUikUgkkn8M0pAuOdK14zknOTmZoKAg2rRpg4uLC2%2B88QYTJkzg1q1bAHh5ebF169a/WUu4fPkynTp1wtXVlU8//bRYeo0ZM4Zt27Y9YQ0lEolEIpFIioc0pJ9zxowZQ1JSElu2bOH06dNs27aNjIwM3nnnHbKyBFuNnzLff/89Op2O48ePM27cuGL9VqYHl0gkEomk9MnJeTL/XiSkIf2cc%2BLECdq3b4%2B9vT0AdnZ2TJ48mSZNmnDv3j0j2bS0NObNm0ebNm3w8PDA39%2Bfs2fPGsqdnZ1ZuXIlbdu2xdXVlffff5%2B4uDhD%2BeHDh%2Bnduzfu7u74%2BPiwY8cOVToGBwcTGhrKxYsXcXd35%2BpV49BFcXFxfPDBB3h5edGkSRO8vb3ZsmULAB999BHHjx9n%2BfLlDB069LHGSCKRSCQSSWGkIV1ypI/0c46Pjw/Tpk3j%2BPHjeHp60qRJE5ycnAgODi4kGxQUxPnz51mzZg1VqlRhw4YNvP3220RERFC1alUAtm3bxtq1a7GxsSEwMJAxY8YQFhZGVFQUw4YNY/78%2BXh7e3PmzBmGDx%2BOra0trVq1Euo4ceJEypYty7Fjx4pM3PLxxx9jY2PDrl270Gq1rFmzhpkzZ9K5c2dmz57N9evX8fT0ZNSoUarHJT4%2BnoQE47i%2B9lotDnkPHEVibm78aQqdIHixlZXx5/OCmSBGbH6ZSAZ9nGg1TZiZiWUdHMT12Noaf5qkXDnTZWXKGH%2BaQjRfiqOMaM6A%2BnkjaqdCBeNPU5QtKy63tjb%2BNIGFwt1D7ekkaiZ/2JSGr7TQCmJV5/dXqd%2BAuNP5k1/phLG0FJerVKhRI9NldeoYfwqxdTVd1qCB8acJRKckqD8thQcqf9yUxg8oX950Wf5ponS6KCqrZhIrxQyXPNNIQ/o5Z9asWTRr1ozdu3czdepUUlJSqFGjBqNGjTJKHZ6enk5ERAShoaHUrFkTgEGDBrFz504iIiIYMmQIAKNHjzZs6gsMDKRTp07ExMSwceNGvL296dChAwBubm707duX9evXKxrSavpQtmxZLC0tiY2NpWzZsqSlpZGcnExZxatY0YSHh7N06VKj70aOGMGogADlH4uurgA2Nsp11KghLFawLf6SUxI0V75ZAMo3FRU3HSUjT%2Bnel4%2BCfcaAAerq%2Bb//U5LwUK5EZGUAeKioA6BjR3VySihtqK1bV7mO118vHV3q1RMWV1JZjdLpUklFRS%2B/rCSh8oxSOKGqVFGuQunZSo/CwwwoW5VKD0T5VKwoLI6IUK7is8/UNHRSWSQsTFis8mxSPC3BdBIfA0pP5MDrKqpp0kRJQmVyLdH5FKmQ1OUJ8qKtHj8JpCH9nKPRaOjWrRvdunUjNzeXK1eusH37dgIDAw3uHqDflJiZmUm1atWMfl%2BtWjViYmIMf%2Bcb2YBhlTohIYEbN25w9OhR3N3dDeXZ2dnUUDAY1RAdHc28efO4du0atWrVMuiQU4IzvF%2B/fnh5eRl9Z6/Vwt27pn9kbq43olNShJkNSRBksLOy0hvR16/rU/iZIPtlsZGSr45IDQDzHBWZDS0tIVNBTtSQmZm%2BX%2BnpwsyGD3LEy4ZmZnoj%2BuFDYTV8%2B61YVVtbvRG9ezeI3OcH1P%2Bf6cIyZfR363PnxKtBV64oK9OxI%2BzdK1amwHlTJFZWeiM6Olo4b7h82XRZhQp6I/rwYXjErcuIvPPaJNbW%2Bpv%2BpUv6g2WCO06NhdXkJQrlrjhRKAW8xwqh0%2BmN6D/%2BECZ8pKGzisyGKk6om/GmDW0LC70RnZAASttPqpQVjL9GozeiU1PFVoyow/kKVawIiYlChXwHmzYo69TRG9GjRytP9YhYN9OFDRrojWg/P4iKMin2v%2BViY1ztaelR9YbpQktLvREdH6943Tv8p2lLumxZvRF95gzcN53gk9fLC7KNgn4S559PSsdU8lwiDennmIMHDxIQEMD%2B/fuxsbHBzMyMunXrMm7cOH755RfOnz9vkLWzs8PKyoro6GjqFHiPd/36dSODMy4ujvr16wMYDOyqVavi6OhIjx49mDFjhkE2Pj6eXJFFpILMzEzef/99xo4di5%2BfH2ZmZvz222%2Bq/a9N4eDggMOjKxJ37ihbpqCXEcmpuRimpz9fF001xzE3Vyin9NyT/yY7N1csGx%2BvrAro7VahbFXTqb8NPHigN2hMIXpoelQZkazauaA0b9RsvL13Tyz30kvqdHn4UGhBqN3LnJ0tlhXY6gbS0tTJlZQMcUZpQN8XRTmdiutMTo74OqP08FtQIYHsuXPKVVy5okJOkPrbQFQUnDItJzrVCqJ0Wqo6UJmZinIpKcrV3L%2BvIGeu0i0jLe2ZdOGQK9IlR242fI7x8PCgUqVKTJo0iYsXL5KZmUlqaio7duzg2rVrtG3b1iCr0Wjo1asXISEh/Pnnn2RkZPD1119z%2BfJlfHx8DHKhoaEkJCRw7949PvnkE1q1akXlypXp3bs3ERERHDp0iJycHK5du8Zbb73F6tWrS9SHzMxM0tLS0Ol0mJmZERsby/z58w1lAFqtlhQ1VzyJRCKRSCSqkZsNS440pJ9jdDodYWFh2NvbM2zYMNzd3Wnbti07duzgyy%2B/NFp5Br3P8xtvvMHbb79Ns2bN2LNnD6tWraJ27doGmUaNGuHn54eXlxcVKlRgwYIFADRp0oSQkBBCQkLw8PDgrbfewsvLq9ih7B6lTJkyzJkzh9DQUFxdXRk4cCAtW7bEzs6O33//HYDu3bvzzTff4OfnV6K2JBKJRCKRSEoT6drxnOPg4GDkbvEoP/30k%2BH/1tbWBAYGEhgYaFK%2BQ4cOTJkypciytm3bGq1yF4dHI24U1Cvfx7sg%2BZsfAbp06UKXLl0eq12JRCKRSCRF86KtHj8J5Iq0RCKRSCQSiUTyGMgVaUmJGTFiBIcPHzZZPn36dKNQfBKJRCKRSP5%2B5Ip0yTHLLWnYBYnkOSE6WlxuaQmOjnDrlnjDfHUHQXgyMzN9soCMDHEkDKVd5xqNPv7S/fviK53SLnALC32g3jt3hKETHlaobLLMzEwfwSktTdwl60RBSCpQHZYqqYw4uKu5uT7S27174qAHtppk04UazV%2BhDkXje/CgUBcqVIDWreHnn4Uh55Jb%2BQqrUavOS5RCn5Sym6icw%2BmI44qXxqmg9jQon6UQzUTlpEnCdMIbtfMOwDZLEMHFwkIfNjEpSRzORCnRh0bzVzxJweDsP2Y6Fn%2B5cvpQ6f/7n/qIGiWpp52XOKkTrq5w8iS4uQmjfxAba7qsGHEKb2I6cLjaaqqUU9gIr3oSK%2BQveEL06fNk6t28%2BcnU%2BywiXTskEolEIpFIJJLHQLp2SCQSiUQikbyASNeOkiNXpCUSiUQikUgkksdArkhLJBKJRCKRvIDIFemSIw1piUQikUgkkhcQaUiXnH%2Bka0dycjJBQUG0adMGFxcX3njjDSZMmMCtW7cA8PLyYuvWrX%2BzloWJjIzE2dn571bDgL%2B/P0uWLClxPcePH8fV1bUUNJJIJBKJRCJ5dvhHGtJjxowhKSmJLVu2cPr0abZt20ZGRgbvvPMOWQrhcCSlj7u7O6dEoYwkEolEIpE8dXJynsy/F4l/pGvHiRMnmD17Nvb29gDY2dkxefJkPv30U%2B49Eus1LS2NxYsXs2vXLh48eECDBg348MMPady4MQDOzs58%2BOGHrFu3juTkZDw9PZkxYwaVK%2Bvj7h4%2BfJiQkBCuXbtG5cqVef/991UnH4mPj2fq1KkcO3YMW1tbfHx8jMovXrzIggULOHPmDDqdDi8vL8aNG0dOTg6vv/46W7Zs4ZVXXiE9PR0PDw/8/f358MMPAQgICKBOnTpUr16dzZs306hRIyIiIjAzM8PLy4ugoCAsLS25dOkSQUFB/P7775QrVw5PT0%2BmTJlCuXLljHTJyclh5cqVbNq0iaSkJGrXrs3o0aNp1aoVoF/l79mzJzt37iQ%2BPp6GDRsyffp06tatS2RkJAMHDuTixYvExMTg7e3NrFmzWLZsGcnJyTRu3Ji5c%2Bfi6OgIwK5du1i8eDF37tyhSZMmVK1alczMTIKDg1XPgfj4eBISjGO55uTYY2/vYPI3FhbGnyYxE8RCzS8TyYA%2BtqiaciU5JWXNzY0/TVAaXcLSUlyucoAVVFU9NJgJBNRWUqGCuDz/PHnkfDHVXEnVEa59qK1E6UCqPOBK06E0TgXV41JKk0ZUi/pjBMJbq8pzUvVxNDMTyoqmZn6oaqWQ1UqorkfpzWSDBsafphBdQ1RfyMUGkOpqSuPkftEsz38Y/0hD2sfHh2nTpnH8%2BHE8PT1p0qQJTk5ORRpiQUFBnD9/njVr1lClShU2bNhTmvxVAAAgAElEQVTA22%2B/TUREBFWrVgVg27ZtrF27FhsbGwIDAxkzZgxhYWFERUUxbNgw5s%2Bfj7e3N2fOnGH48OHY2toaDEwRY8aMwdbWlp9//pmUlBSGDRtmKEtKSmLgwIH07NmTJUuWkJKSwvjx4wkMDGTZsmV4enry888/88orr3Ds2DHMzMwM2QUzMjI4dOgQw4YN48KFC5w8eZLWrVtz8OBBLly4wKBBg3j99dfx8fFh%2BvTptGjRgnXr1pGUlMSgQYPYvHkz77zzjpGuoaGhbNmyhc8//xxnZ2f27dvH8OHDWb9%2BveGhIzw8nP/85z/Url2bOXPmMHToUPbs2VNk3//73/8avSn4/PPPmTFjBqdOnWLChAksXryY1q1bs3//fj744AO6dOmi7uDnER4eztKlS42%2BGzFiJAEBoxR/a2enJKFVVkDJqNSqqAP0CRdElDWdbMEIGxthsUKKDgCsxPk3QGf6IcWIihWFxQqmqwEF2xVQkeBAafxat1anjJtbSTVRpU6p9EktCnNY5QwulVNB6TRQPWsUJo2aWpTnHSBI7PJXY2pnugIKCXY8PJSraNSodFRRrOfkSXUVhYWVWBdslY%2BBfalUo/J8E03iFIWkLk8QacOXnH%2BkIT1r1iyaNWvG7t27mTp1KikpKdSoUYNRo0YZrRanp6cTERFBaGgoNWvWBGDQoEHs3LmTiIgIhgwZAsDo0aOpXr06AIGB/8/eeYdFdXRx%2BEdZFhRFRJpANEEFG9LELkqzYAUb9i7EjppYsBJ7xIK9ESsWQBHRqLHwgRExdlGwC4IUAemwwM73x7obFnbvvQuYaJj3eXgue2fm3HP7uTNnzvkJvXv3xvv373HixAk4OjrCxcUFAGBtbY1hw4bh2LFjrIZ0UlIS/vrrL1y6dAmamprQ1NTEjBkzMH36dADA1atXwePxMH/%2BfKioqEBdXR1Lly6Fq6sr0tPT4eTkhAsXLmDatGmIiorC8OHDcfToUWRmZuLJkyfQ1tZGy5Yt8ezZM6irq8PT0xNKSkqwsLCAmZkZ3rx5AwDg8/mIjIyEqakpOnXqhNDQUCjL%2BHIODg7G1KlT0frzk7Jv3764dOkSgoKCJIb0pEmT0LJlSwDAokWLYGtri3tyHpxTpkxB/c8vEgcHB4nrR3BwMFxcXODg4AAAcHZ2hpOTE%2BOxlMXw4cMlMsQIhbr47CYvE1VVkRH98SNzJiuDhgyp2JSURJZDSQlzOjem1IkA56xlKCpilqOiIjKiP31iTMdWVFdHbpmSksiILi5m3iX1nDRmXVRVRUZ0ZibjAc5hMciVlUXGTF4e86Gpr8TwcuKabYzNJUlTU2RE37vHmNIt14rZIOec/Aw1sE9sX0Qcr2EBiyldE7cC19ugbpn8rJISQRwumhwGU5rrdQcA9csYMi1yTZHIJQMlh5Sjd57IN%2BDq1BEZv7Gx7ElSmeAqp/005g9OmJuLjOiRI4G4OPn1Ll2SX8Y1cySAdAZTmqsY3Tr5jNvgfBH/S3yFKn1z/CcNaWVlZQwcOBADBw4EIQSvXr1CaGgofvrpJ4m7ByCalFhSUgJjY2Op9sbGxnj//r3kt9jIBiDppU5PT0dSUhKio6Nha2srKS8rK8N3333HqmNqaqqUPABS7TIyMtC4cWOolBv%2BE%2BuZlJQEJycnrF27Frm5uYiMjMTatWsRExOD6Oho3L59W2LcA4COjg6Uyo2v8ng8iDPDb9myBf7%2B/ti8eTO8vb1hbW2NFStWoHnz5lL6fvz4UfIxUV6fuHIPu/LHSUNDAw0aNEB6errUMRfTqFy3r6qqqkSfDx8%2BoFWrVlJ1TUxM8PHjx0oymNDT04OenrRBlpjIbr8CoocmYz0mq6B8HaZ6XJ9ebA5nXH3%2By8oY69bELnE6uADrAS5j6cEUIxSypGpW5nCM2Y4vQ9pvKfLyGOvW1OkGamCfuJxscT2Guhyl1MitwHpc2HJ2lxfEUJeLFNbrDuB2X7Lck6wHRtzhQQhjXS6pvwsKqpcinLMcrnNl4uKY63I5vqWlrPW4PD1ZxdTczU35RvnPTTaMjIyElZUVPn36BABQUlJCs2bNMG/ePLRq1QpPnz6V1G3UqBH4fD4SExOlZCQkJEgZYWKjF4DEwG7cuDEMDAwwePBg/PXXX5K/S5cuYe/evax6iv2By287pVx3qZGREZKTk1FW7omdkJAAANDV1YW%2Bvj7Mzc0REhKCjx8/ok2bNujatSv%2B/PNP3LhxQ8qQlodQKMTTp08xc%2BZMXL58GdeuXYOOjg4WLlxYqa6RkVGl45SYmCj3OOXn5yMrKwuGhoaselTcTnJystS6ir8pFAqFQqFUHzrZsPr85wzp9u3bQ0dHB4sWLUJ8fDxKSkqQl5eHc%2BfO4e3bt%2BjRo4ekrrKyMtzd3eHn54d3795BIBDg0KFDePnypdTEvx07diA9PR05OTlYv349unXrBn19fQwZMgTnz59HVFQUhEIh3r59i9GjR%2BPgwYOsejZu3Bhdu3bF2rVrkZ2djfT0dCmfXnt7ewDAr7/%2BiqKiIqSnp2P16tXo2LEjjIyMAIjcHnbt2oWOHTtCRUUFXbp0wfnz5yEUCmFpacmqg7KyMn755Rds2bIFxcXFaNiwIfh8PrRlOIUNHToUe/fuRWxsLMrKynDx4kVcu3YNgwcPltQJCAjAu3fvUFhYiLVr1%2BKHH35QOOzd0KFDceXKFURGRqKsrAwRERG4fPmyQjIoFAqFQqFQ/gn%2Bc64d6urqOH78OLZv3w4vLy9kZGSAx%2BPB0tISAQEBMDU1lar/008/wd/fH%2BPHj8enT59gZmaGAwcO4Pvvv5fUad26NUaOHImsrCz06NEDPj4%2BAIB27drBz88Pfn5%2BmD17NjQ0NNCvXz94e3tz0nXTpk1YuXIlevbsCU1NTbi5ueHhw4cAgHr16iEgIADr1q2TGNWOjo746aefJO2dnJzg5%2BeHLl26AABsbGygpKQEJycnKVcOJrZs2QJfX1907doVQqEQ7du3h6%2Bvb6V6EyZMgFAoxNy5c5Geno4mTZrAz88PdnZ2kjo2NjaYPn06kpOT0b59e%2Bzdu1emvzUTbdu2xcqVK7FixQpkZWXB1tYWnTp1Ao9txhKFQqFQKBSF%2BBZ7jwsKCuDr64tr166htLQUjo6OWL58OerKmGi9bNkyhIWFSa0rKipC586dceDAAQiFQtjY2IAQImU33bx5E3U4hrNRIoSrw1ztxMzMDIcPH0aHDh3%2BbVW%2BahwcHDBjxgy4ublVS86bN28gFAqlPnhmzpyJH374AXPnzq2W7AqeKZXg8QADAyAlhdnd10SvWH6hkpIoDIFAwOwYKmCYsAhwnzjGNkNIVRXQ0QEyMhgd/Qrr68st4zinCRqZScy68HiAnh6QlsZ4gLPqGDGK4TpXS1s5W36hsjJQr55otjzT8Y2MZNQF9euLInv873%2BMPtLZ3foxiuGqjhZqYJ%2B4TGLjcA0Xg3nSYk3cCpwnYZYyTO4DOF80WQzRNrhedwCgXZouv5DrLDa2lzjHSWzXY%2BRHldDUFEX1uHOnej7SXOX0dGDp4LGyEk3ctbZm9pFmcvdTVQV0dYH0dFYf6Q%2BQ73rIVYyhJkvEDc4XMdfYPjWLo%2BOXkXv16peRC4iCGXz48AFbtmxBWVkZ5syZg2bNmmH58uWsbaOiojBv3jwcPXoUzZs3x/Pnz%2BHm5oZ79%2B5BjWs0rQr851w7KN82L1%2B%2BxLhx4yT%2B4Ldv30ZkZKSkV55CoVAoFErtpLCwEGFhYZg1axYaNGgAHR0dzJ8/HyEhISgsLGRsm5mZifnz52PJkiWSgAqPHz%2BGmZlZlY1o4D/o2vG1MH36dElcZ1msXLmSc%2BKW2oSzszNevnyJsWPHIjs7G0ZGRvD19YU1S5xeCoVCoVAoivGlXDtkJUXT1dWtFE1LFkVFRVLBC8pTWFiIkpIStGjRQrLO1NQURUVFePv2rSQEryx%2B/fVXtGnTRsr2evz4MYqLi%2BHu7o6kpCSYmppi3rx5Ctkc1JBmIT4%2BvkrtduzYUcOafN1cu3atxmR5eXlJJaepKUw0OQz/oj4M6rCM3TJ5U6ioiMazi4qYZbCF8%2BPzRcOBGRmiAM7yYEluwjWLGpM6YpeXT59YXF5UayYTW7nIkzJRVxcNsaemMofR1jZmeEOIdWEJHYZ27ZiVEfvut2jBeHC0BAxD/cDn9GnaIhcFpnFkJj8IHk80PFxYyHyiuLgD6eoC2dmMumQyDIsrIIYxV5D4NKmosCSQU%2BXgrgKI7k0GPxNllhDx4iXr1A%2Bma0pcxhbeoIYyGzI9IsQ5QrS02BPjtKv/Rn6hmhoAI7RvnMR8jbJFYBKnEbx0ifmiKRc2thJi95BevVjD7T36Xf61UK%2Be6Pp98YI5X0pyI2aXDA0NoFUr4Om7umDqMLWxYRTzzSErKdqMGTMwcyZ7UrSHDx9i7NixMstmz54NAFL%2ByxqfL%2BT8fPkxvRMTE3Hu3DmcPn1aar26ujosLCwwe/ZsaGlp4dixY5g0aRLOnTtXKeSvPKghTaFQKBQKhVIL%2BVI90rKSosnKKSGLDh06yO3EfPr0KbZu3YrCwkLJ5EKxS4cmQ9rR4OBgWFlZVeqxrhjud9KkSQgJCUFERARGjx7NSV9qSFMoFAqFQqHUQr6UIS0rKVpN8P3334PH4%2BHly5do93nE8NWrV%2BDxeGjatKncdpcvX8bEiRMrrd%2B8eTN69eollQhOIBCAz5YBthx0siGFQqFQKBQK5atHQ0MDffr0wa%2B//orMzExkZmbi119/Rb9%2B/aAuJyJRVlYWXr16hfbt21cqe/78OVavXo309HQIBAJs374deXl5cHZ25qwTNaQpFAqFQqFQaiHfYmbD5cuXo2nTpujfvz969%2B4NY2NjLFu2TFLu6uqK3bt3S36LM1Lr61cO87p27Vp89913GDhwIDp06ICYmBgEBASgAdPkjQpQ1w5Ktbhw4QJ8fX2RmZkJFRUV/PHHH2jMNBGkCoh9mNatW1ejcikUCoVCoXxbaGpqwtfXV2byOAAIDw%2BX%2Bt22bVu5PtcNGjTA2rVrq6UPNaQp1eL06dNwdXWVZHukUCgUCoXybfAtZjb82qCuHZQqM2TIEERHR%2BPEiRNo2bIlzMzM8P79e9y9exetW7dGREQEACAlJQUdOnTAiRMnAAAfP37E/Pnz0aVLF3Tt2hXLli1DXrl0WFevXoWrqyssLS0xbdo0ZGWxhK2jUCgUCoWiMN%2Bia8fXBu2RplSZoKAgjBkzBnZ2dhg8eDAcP%2BcatbGxgZeXF3x8fBAaGgpvb2907doVI0aMgFAoxI8//oimTZvi0qVLKCkpwaJFi7Bs2TL4%2Bfnh9evXmD17NtasWYO%2Bffvixo0bmDVrlsLJa2QGg1dXhx5T%2BB1xLFbWILEMcJXBNiNYHJ9YvJQHS3xornGkmTYjDu2qyva0UGGpwFEQWwZr8aFjnVTNtM9czxPb8ed6cNjKOZ4nxlzbXHVRYknTzFEO2%2BVQE%2BqUD5XMIoXbRlgEMV0OCj0emHaa67lm22mO%2BySOFS0LzvcSwBxomuvziut9wlbPykp%2Bmbm59JIBpqzcnyOrSZbyYDq%2BwN/PM6bnGktCPspXjhIhTE9mCoWZiob01atXYWxsDKFQiHHjxiEpKQk8Hg8hISGoW7cuHj16BA8PD8TExEhiQL558wa9e/dGdHQ0jh07hqioKEnvNSDKElmvXj2FfKT9/f0rB4OfPh0zZ82qmR2nUCgUCqUGuHv330vIIiOQRY1w586Xkfs1QnukKV8EZWVleHh4YO7cuZg%2BfbrEaH7//j3Kyspgb28vVV9NTQ2JiYlITU2tNFnxu%2B%2B%2BU9i9Q2YweHV1ICeHSWlAUxPIy6v62BRXGZ8%2BMcvh8QBDQ%2BDDB%2BYsdfXrM8tRURF1u%2BTmMmZaTCmSP0NZVRVo1EiU/ZAp2ZiBCofsfdraQBZz9r4Xn5iD9vP5wHffAQkJzEkfm%2BvXwLnmkgWQy8Fh6/JTURGdyxyWrJpM14KqqiiNXWYmsy5ceqQ5nKd0MJ8njmIYewWVlESHrriYuTNeHQwpLhUQlFsiv9tQWVnUO5mfz/54qCfIkF%2BooiJK5/jpE/O5ZusK5bhPzxPk7xOfDzRpArx7x3wvAUCLuknyC3k8QE8PSEtjvkbZ0idyvWh69ZJfZm4OHD8OjBwJxMUxbu7P7ffkltWtK0ps%2BvCh6JzLgy24g7o68MMPwOvXzJlYKd8u1JCmfBE%2BffqE9evXw93dHfv374ezszNatmwJAwMDqKur4/bt21D5PLQpEAiQmJiIJk2awMDAADdu3JCSlZKSolBwdEBOMPisLOYXlxihkFu96shge2uJKSlhrstVz7IyxrpM7z4xpaUs9QjDi6%2BiIIaXJNeXTXExS92aONdcDgzAfnDYhvHFlJUxGxA1caK4ui6xnCeOZ5tNDKOBXL4Ocz2OA6ssgrh8P3PyAWXaYTFs55rrYDHLPnFxGygu5lCPx5D6W0xJCXOK8Bq69thSfwMQGdEs9ZhSf4vJz2eux/XVVFT0dbpw1DZ/5i8BnWxI%2BSL4%2BPigWbNmWLNmDTw8PODt7Y3CwkJYWFigSZMmWLduHfLz81FUVIQ1a9Zg/PjxKCsrw4ABA/D8%2BXOcOnUKpaWliIqKwpUrV/7t3aFQKBQK5T8HnWxYfaghTalxAgMDcfv2baxevRoA4O3tDSUlJaxevRqqqqrYs2cPPn78CBcXF3Tt2hUJCQkICAgAn8%2BHiYkJdu/ejWPHjsHGxgY7d%2B5UKMMQhUKhUCgUyj8Fde2gVIsjR45I/hcHPPfw8ICHh4dkPZ/Px4ULFyS/DQwMsHnzZrkyO3XqhNDQ0C%2BgLYVCoVAoFDG1rff4S0ANaQqFQqFQKJRaCDWkqw917aBQKBQKhUKhUKoA7ZGmUCgUCoVCqYXQHunqQxOyUGoPbCHnlJREcU4FAsZwUq/ey493pKYGmJgAiYnMUaAqRPirhI4OMGgQcPYskMEQktbJiVmOmtrf4aiZ9GlizBIqTkWFNZxceiZziDeuIWJ1/xfMrEuDBoCjI3D1KmM87tNCd0YRzs7AlSvMIb3ZQvE1bAi4ugLh4aLwzfKoEBq9EvXqAXZ2QEwMc6itP/5g3sbMmYC/P5CcLL%2BehQWzLtraQO/ewO%2B/i86VPDyasWRcqFMHaN0aiI1lj8ddXRnW1uyyOFzDrPG3OTwfAKBMlTkmWnVVUUQd/nmG%2B4njvQQAlzTl30/16gGdOwN//sl8/bJde6qqgK4ukJ7OvP%2BPHskv46oLAPTqzRBT3coKuHdPdG0xhNEzMWa%2BFtq0AS5eBPr0AZ48kV8vMZFZ1y9F69ZfRm5s7JeR%2BzVCe6QpFAqFQqFQaiG0R7r6UEOaQqFQKBQKpRZCDenqQycbUigUCoVCoVAoVYD2SFMoFAqFQqHUQmiPdPWhPdKUb4IxY8bA39//31aDQqFQKBQKRQLtkaZQKBQKhUKphdAe6epDe6QpEt6/fw8zMzOcPn0aDg4OsLGxwYQJE5CSkgIA%2BOOPP%2BDm5gZra2v06tULv/32G4Sf78KFCxdi1qxZ6NOnDzp27IiEhASYmZnh5MmT6NWrF9q1awdPT088efIEI0aMgJWVFdzd3fHu3TsAgEAgwPr169GnTx9YWVmhU6dO8PX1BY3OSKFQKBTKl0Eo/DJ/tQnaI02pxI0bN3D27FkIBAJMmDABO3fuRN%2B%2BfTFnzhxs2LABLi4uiI%2BPx48//ggAGD9%2BPAAgMjISJ0%2BehIGBAerXrw8ACAsLw8mTJyEQCODq6ooff/wRAQEBMDQ0xKRJk7B7926sXbsWhw4dQmRkJA4dOgQ9PT3cv38fo0ePhpOTEzp16qTwPqSlpSE9PV1qna6WFvT09OQ3UlKSXspBTU1%2BGY8nvZSHjg5zuZaW9LIqugCiuKzll18Stm2oqEgv5dKgAXN5vXrSS3liGB7mHEUwxt4GgM%2BXuWTJtj151KkjvZQHUzxqXV3ppTy0tZnLue4Tq7Lq6tLLqlATMhSB6d7n%2BHz4J1QpX86qDtP9xPVGAFBPQ35Z3brSS3mwPSO4Pq%2BY1OWqCwBRrGh5mJtLL%2BXQRp95E6am0ktZMMWXpnz9UEOaUokpU6ZIDGEHBwfcv38fISEhcHR0RN%2B%2BfQEArVu3xtSpU3HkyBGJIW1paYkWLVpIyRo9ejQafH6QN2/eHK1atYLp5ydKx44dcffuXQDAsGHDMHjwYOjo6CAtLQ1FRUWoW7cuUlNTq7QPJ0%2BexPbt26XWzZg%2BHTNnzWJvzGIFm5iwizAwYC7nIgMAevbkVo8NNuMKYLNuwWoBsxloYlgNNEdHboLs7BiLnTmI6NiR26bY6NatZuS0acNczrLLAIARI2pGl86d2WpwzOTAZEFwpSZkAOxfcaxfeWD/Sganu6lGVOGkDpf7icOFxXo5AGjXjkMlDrA9S9ifZxx1uXePvc7x44zFFzlsBgAqvI6k4Po%2B%2BBLUtt7jLwE1pCmVaNSokeR/VVVVEEKQkZGBli1bStUzNjZGUlKS5Les3t4G5XpDVFRUoFWui1VZWVniulFYWIhVq1bhzp07MDAwQKtWrUAIkbiOKMrw4cPh4OAgtU5XS4u5i1FJSfRWKilhTBWWmCq/G5jHExnRKSkiMfL4/P0gFy0tkRF9/TqQnS2/XocOzHK4Zgoz1Kt%2BZsOsHOY3v4qKyIjOyWEWpX3vKrMuHNMAXhHKNyDq1RMZ0dHRzNnPuPRId%2BsGREaK9ksebC/%2BOnVERvSTJ8wJ/G7fZt7GiBHAiROi8y2P5s2Zdalf/%2B/McEz71NuEJXWZurrIAH71ij1FZHVlsPQaAuCWTpCpnOPzAQDKVJiHiqqriiLqqEUy3E9cU2oC%2BFND/v1Ut67IcH34EMjPly%2BD7drjmv30xQv5ZVx1AYDOMxgyYpqbi4zokSOBuDi51froMxvjpqYiI3rGDNFlTPnvQQ1pCieMjIyQkJAgtS4xMRG65SwEJRljjLLWycLHxwdaWlqIiooCn8%2BHUChE%2B/btq6yvnp5eZcO%2BuJj1BQhAVIehHptxBYhebkz1mNJ%2Blyc7m7kuF10A0UuJa92qwpbSWExZGUtdllTFEnJzGet%2B4vANxiKCs/2Xk8OcIpyrZ0JBAbM9w5T6W0x6OnO9ct/JjOTkMKcIhw7HtN9FRVVPEV6TMrhQA8%2BHf1IVcT3GulzuJ7YbAUAuh/s7P5/5%2BuX6jCgtZa7Llvqbiy4AGFN/S4iLY6z3xJhdBCAyor9GFw7aI1196GRDCifc3d1x7do1XLx4EWVlZXj69Cn27dsHd3f3GpGfl5cHPp8PZWVl5OXlYcOGDcjLy0MJU7cuhUKhUCiUKkMnG1YfakhTONGuXTts3boV%2B/btg62tLWbMmAEPDw94enrWiHwfHx/ExcXBzs4OvXv3Rl5eHrp164bnz5/XiHwKhUKhUCiUmoa6dlAkGBsbIz4%2BXmrdzJkzJf87OjrCUc7ElXXr1lVaV1HWkSNH5Mpu06YNQkJC5OpWsS2FQqFQKJTqUdt6j78EtEeaQqFQKBQKhUKpArRHmkKhUCgUCqUWQnukqw81pCm1huwiPmO5sjJQTw3ILVZjfLiYqifJL%2BTxAOjBhJ8GKMufKGnaIJpZ2XoNADhiUL2rQBnDbPo63ZnlqKoC0IahehagyjANvoghuYayMqChIQr7wXBgdDVZBriUlACoQ1ujiDHMQEQj5gmsmpqADYC7DRyRx/AEG5oTJr%2BQrwWgO5z5/wPUGeILMh0zANDQAuAAV41rgCaDnDosgcXV6wKwgJ36I6BMfswux7oMocw0DABMxkyN/UDdFAZd2rLoogWgB3qr3wDqyN%2BnJxoDmcXwgWYAXvJbo4ghqgTTfGINFcAcQJxKaxQynOtWLKdJSQlQUwEEZSrMoeLUmEM4KgEgPJYsSAAEDNFelJRE4e/YwtZpqLHEvwMAqEBNhbke0/3E9V4CgF7qDLEX69QB0Bad6z0GVBiiq2i2Yt6IsjKAutCtk8/4rEluJD8ji8bnxDENGgB85kc%2BTIzln4A2%2BqIY0X307zFG5kh8zxKZStcKwD1cTLUG3jNFCfl3svhSQ7r6UNcOCoVCoVAoFAqlCtAeaQqFQqFQKJRaCO2Rrj60R5pCoVAoFAqFQqkCtEeaQqFQKBQKpRZCe6SrDzWkKRQKhUKhUGoh1JCuPtS1g/KPMGbMGPj7%2B8stNzMzw%2B3bDLPCKRQKhUKhUL4yaI80hUKhUCgUSi2E9khXH2pIf2O8f/8ejo6OmDBhAoKDg9GvXz/Y2tpi9%2B7dSE5ORpMmTeDt7Y2uXbvi1q1bmDZtGm7evIl69URxNyMiIrBgwQJERUUBAHbt2oVz584hNzcX7dq1g4%2BPD5o0aQJA1Es8ZswYhIWFwcrKCr/%2B%2BiuWLl2KP//8E6qqqjA3N8fixYthamoKgUCAzZs348aNG0hJSYG6ujr69u0LHx8fKCmJ4mwmJCRgzJgxiIuLg6mpKRYvXgwLC4tK%2B5iXlwc/Pz9cvXoVAoEAHTt2xJIlS9CoUSPOxyktLQ3p6elS6zQ0dKGrqye3jbKy9FIuPJ78MlVV6aU8GjRgLv98viRLtu3JQ0VFeikPpp3%2BfP6gpMStHhc5DGhqMosRx4kVL%2BVvT4t9I2wbK2UJUMxVTt26zOVcd8qAIR61jo70Uh5aDMcF4LxP6urMYsQxfNli%2BTJdwlxl1NClV2Mwbeef1oXpNHK%2BlwBAjSHWvPhiYLso2B6uHB/CTPpyVQUA2rSRX2ZqKr2Ui64Vc7m5ufRSFveZ4ktTvnoI5ZsiMTGRtGjRgvj4%2BJDi4mJy7tw5YmNjQ2JiYkhpaSm5du0asbS0JM%2BfPydCoZA4OTmRkydPStrPnj2b%2BPr6EkIIWbduHRk0aBBJSEggRUVFxN/fnzg4OJCioiJCCCEtWrQgU6ZMIQUFBSQ7O5ts3bqVTJ48mRQWFpLi4mLy888/E09PT0IIISaIESkAACAASURBVHv37iWurq4kNTWVEELIvXv3SKtWrciff/5JCCFk9OjRxMrKisTExBCBQEB27txJ7OzsSHZ2tmRb0dHRhBBCZs6cSSZOnEg%2BfvxI8vLyiI%2BPDxk%2BfDgRCoWcj9O2bdtIixYtpP62bt1WzaNPoVAoFEoN8y%2BaYnXqfJm/2gTtkf5GGTRoENTU1HDu3Dl4eHigffv2AICePXvCwcEBJ06cwNKlSzFkyBCcPXsWw4YNQ05ODq5du4ZTp06BEIITJ05g27ZtMDExAQBMnz4dp06dwo0bN9CrVy8AQL9%2B/aChoQENDQ2oq6sjLi4OZ8%2BeRZcuXbBmzRoof%2B45GDZsGAYPHgwdHR2kpaWhqKgIdevWRWpqqkTnIUOGSPT09PREYGAgIiIi0L9/f0mdjIwMXLp0CRcvXoTO5561xYsXw9bWFrGxsWjD1IVQjuHDh8PBwUFqnYaGLnJz5bdRVhZ1HOYzJ9VCvcI0%2BYWqqkDDhkBmJnNv5uPH8ssAUU%2B0nR0QEwNGpa2tmeWoqAD16wM5OUAZQwY0pu4bJSVReRFzRkJO3YJ8PlBczCjnbixzV5KGBtCqFfD0KVBYKL%2BeTf7/5BdqaoqO3b17QF6e/HpceqTF54lJDttoioYG0Lw58OIF807FxMgv09EBBg8GzpwBMjLk12vWjFkXTU3A1hb46y/GfXpp3INRDJ8PmJgAiYmiUy4PpkPM5wPffw%2B8ecMs44cfGFWBkpJoIIktmyDTYJNYDlN7MUy6crwNoM7jkNlQRYX5vgZw94H80Siu9xIA2KgxPLPU1f%2B%2BfosY0jqynShxFtXCQsaH8NN38kd41NVFm3n9mlkVAJg3T36ZqSmwfTswYwbw6pX8ehdTWZ7B5ubA8ePAyJFAXBxz3X8B6tpRfagh/Y2ipydyUUhKSkJMTAwCAwMlZWVlZejYsSMAwM3NDf7%2B/khMTERkZCSaN28Oc3NzZGRkoKCgALNnz5YYwwBQUlKCpKSkStsBgClTpkBNTQ1BQUFYtWoVTExMMG/ePLi4uKCwsBCrVq3CnTt3YGBggFatWoEQAmG5u9TY%2BO88q0pKSjAwMJAytMX7A4gM8/KoqKjg/fv3nA1pPT09Kd0BIDub20NDKGSpx5TTWExpKXO9Twxpv8uTm8tcl83QE1NWxlyXaYfF1wch3OqxIep/kVvMZI%2BWp7CQpW4OQ8ru8hvLZqjH9fiyyeE0dg7RTuXLTxGOFIbU32IyMpjr6epy04Vln4o4eloVFzMbNFxup%2BJiZkOPi3Errse1bnXgso1/Shcu9xPrvQQA6gypv8UUFQEFDPW4Wm0sD2E2o1%2BsClu9J0/Y5bx6xVKPMe13OeLiqAvHfxRqSH%2BjiP2ODQwMMGjQIEydOlVSlpycDPXPPYy6urro3r07zp8/j4iICAwZMgQAoK2tDT6fj4MHD8LS0lLS9vXr19DX16%2B0HQCIj4%2BHg4MDxo8fj9zcXBw/fhxz585FdHQ0fHx8oKWlhaioKPD5fAiFQknvs5i0tL97coVCIZKTk2FkZCRVR7ztixcvQrfcC//ly5eSnnMKhUKhUCjVh/ZIVx8a/u4bZ9iwYTh8%2BDAePXoEAHj8%2BDHc3Nxw/vx5qTqnTp1CfHy8xI1CWVkZQ4YMwaZNm5CSkgKhUIgzZ86gX79%2BePfuncxtnT59Gj/99BMyMjKgqakJTU1N1KlTB2pqasjLywOfz4eysjLy8vKwYcMG5OXloaRcd1NQUBAePnwIgUAAf39/qKqqwt7eXmob%2Bvr66NGjB1avXo2srCyUlJRg165dGDJkCHJycmr68FEoFAqFQqFUGWpIf%2BP07t0b3t7eWLx4MaytrTF79myMHz8eY8aMkdTp1q0bhEIhXFxcoFluCvfPP/%2BMdu3aYeTIkbC1tcVvv/2Gbdu2oVWrVjK35e3tjSZNmsDV1RXW1tYICQnBzp07wefz4ePjg7i4ONjZ2aF3797Iy8tDt27d8Pz5c0l7FxcXLF%2B%2BHB07dsTdu3dx4MAB1KlTeSb4hg0bUL9%2BfQwaNAgdO3ZEREQE9u/fL9VDTaFQKBQKpXqIvWhq%2Bu%2BfoLCwEMOHD0dISAhjvYcPH2Lo0KGwsrKCg4MDTp8%2BLVV%2B5swZODs7w9LSEm5ubrivoAsOde34xjA2NkZ8fLzUOjc3N7i5uclto6KigoiIiErr%2BXw%2B5s%2Bfj/nz58tsV3E7devWxfr162XWbdOmDePFfOTIEbllFbelpaWFVatWMdanUCgUCoVSPb5V144XL17g559/RmxsLIYPHy63XnZ2NqZOnYpZs2Zh%2BPDhuHPnDqZPnw4zMzNYWFjg9u3b8PX1xb59%2B2BhYYFjx47By8sL169fhwbHuS20R5pCoVAoFAqF8k1w69YtjBs3DoMHD0bjxo0Z616%2BfBkNGjTAqFGjoKqqik6dOqF///44duwYAJHLqqurK2xsbMDj8TB%2B/Hhoa2vjwoULnPWhPdIUCoVCoVAotZAv1SMtKymarq5upWhasigqKqoU0au8DHNzc1y/fh18Ph8BAQGMsl68eIEWLVpIrWvWrBmCgoIAiAIZuLu7VyqPUyRU4b8dyJpC%2BVpITU0l27ZtkySV%2BbdkfG1yviZdakrO16RLTcn5mnSpKTlUly8r52vSpabkfE261KScbw1ZSdG2beOWFC06OrpSW/HflStXpOr27NmTBAcHy5W1ePFismDBAql1p06dIk5OToQQQpycnMjp06elyufPn08WL17MSVdCCKGuHRTKZ9LT07F9%2B/ZKX9H/tIyvTc7XpEtNyfmadKkpOV%2BTLjUlh%2BryZeV8TbrUlJyvSZealPOtIZ4EWP6PyZe5PB06dEB8fLzMPycnJ4X00NDQQFGFQPbihHFcyrlAXTsoFAqFQqFQKDWGrKRo/wYtWrTAzZs3pda9fPkSzZs3BwA0b94cL168qFTevXt3ztugPdIUCoVCoVAolP8czs7O%2BPjxI3777TeUlJQgOjoaYWFhEr/oIUOGICwsDNHR0SgpKcFvv/2GjIwMODs7c94GNaQpFAqFQqFQKP8JXF1dsXv3bgCiLM4HDx7E77//jg4dOsDHxwc%2BPj7o2LEjAKBTp05Yvnw5VqxYATs7O4SHh2Pfvn1o0KAB5%2B2prFixYsWX2BEK5Vukbt26sLOzU8g/6kvI%2BNrkfE261JScr0mXmpLzNelSU3KoLl9WztekS03J%2BZp0qUk5lMqMGzcOLVu2lFo3atQo2NraSn7r6%2BtjyJAhmDZtGsaOHVupvrm5OUaPHg1PT08MGzYMBgYGCumgRAghVd8FCoVCoVAoFAqldkJdOygUCoVCoVAolCpADWkKhUKhUCgUCqUKUEOaQqFQKBQKhUKpAtSQplAoFAqFQqFQqgA1pCkUCoVCoVAolCpADWkKhUKhUCgUCqUKUEOaQqFQKBQKhUKpAtSQplAoFAqFQqFQqgA1pCkUCoVCoVAolCpADWkKpYbIzMz8t1WgUCgUCoXyD0INaQpFBgKBgFO90tJSbN68GTY2NnBwcEBiYiLc3d2Rlpam8DajoqJkrt%2BzZ4/CsmoLpaWl//g28/PzZa6Xd/4UoaY%2BxgoKChRuk5OTI7nuX79%2BjaysrBrRhfLfxMvLS%2Bb60aNH/8OafL08ffoUly9fhkAgQEZGxr%2BtDuULofpvK0Ch/JvMmzcPv/zyCzQ0NCTrXr16BW9vb4SGhrK29/f3R3R0NLZu3Yq5c%2BdCR0cHBgYGWL16NbZu3aqQLtOnT8eECRMwe/ZsKCkpITU1FQsWLMCrV68wbdo0hfetKowZMwZKSkqMdQ4fPqyQzI8fP6JRo0YQCAQICgqCtrY2%2BvTpw7n9u3fvEB8fD2trazRq1Eiy/vXr15g/fz5CQkI4yUlOTpa5nsfjQUtLC2pqapzkeHp64sCBA5L6RUVFWLduHYKCgvDkyRNOMh49eoQNGzYgNTUVQqEQAFBSUoLMzEzOMgCgU6dOuHXrVqX19vb2uHPnDmc50dHR8PLyQkBAACwtLXHu3DkcP34c%2B/fvh4WFBWNbLttp3749a51Fixax1lm7di1j%2BdmzZ1llDBo0iLWOmMjISAgEAjg6OqJPnz6SDw19fX0cOnQIPB6PsX1xcTEyMzNhaGiI3bt3Sz789PT0MGzYMM56nDx5EjweD25ubrCwsEBJSQkAQFdXFxcvXkTdunU5y5J3j/N4PDRs2BA9e/ZE3759ZbZ9//695BhHRUVh%2B/btUuV5eXmIj4/nrIu8cy7WpUePHrC0tFS4fXnYrhkAlfZDFjNmzGCtIyYjIwPTp0/HkydPwOPxEBQUhCFDhuDgwYOwsrLiLIfybUANaUqtJikpCW5ubti2bRuaN2%2BOwMBArF%2B/Xu6LpCJhYWEIDAyEvr4%2BlJSUUKdOHaxduxbOzs4K6xIYGIi5c%2Bfi3r17GDx4MNatWwc7OzuEhYVxlnHmzBnExsaiW7dusLe3V1iHDh06KNyGidOnT2P16tV48OABNm7ciAsXLkBJSQlv3rzBjz/%2ByNo%2BPDwcCxYsgFAoRP369REYGAhTU1OEhIRg1apVMDU15ayLs7OzxGglhEgZE8rKyujcuTPWr1%2BPhg0bMspRV1fHzJkzsWPHDjx58gQ///wzVFRUcPToUc66rFq1CiYmJmjevDkSExPRpUsXHD58GPPmzWNt%2B%2B7dO6xatQqEEOTk5GDixIlS5Xl5edDU1OSsCwBs3LgRixcvlhgtc%2BbMgYmJCdasWYMTJ04wth0zZgxjuZKSEp49e6aQPlVl27ZtrLpwNaT//PNPzJo1C8uXLwcApKamYunSpSCEYPv27QgODsaIESPktk9PT8eoUaPQq1cvzJs3D7t370bbtm0BAPfv30fz5s05GVUXLlzAli1bJAYhj8fD/v37QQjBL7/8ghMnTmDSpEmc9gkA2rVrh5MnT2LYsGEwMTFBcnIyTp48ie7du6NRo0ZYvXo1MjIyZJ7Xxo0b48WLF8jMzERZWRlu374tVc7n8yXHiws8Hg8hISFwcnKS6HL58mV07twZnz59wqFDh7B69WrW53FWVhYiIyPRs2dPmJiYIDU1FVeuXIGLiwsnPSruR0XYOhcqsmbNGrRo0QIBAQHo3r07TE1NMXXqVGzYsAGBgYEKyaJ8AxAKpRZTWlpKNm3aRKysrMjo0aNJly5dyNWrVzm379ChAxEIBIQQQmxtbQkhhBQXF5OOHTtWSZ/s7GzSs2dPYm5uTpYuXapQ2/3795O2bdsSNzc30rZtW3Lq1Kkq6VCRjx8/kpKSkiq1HTBgAImKiiKlpaXE2tqa3L17lyQkJBB7e3tO7fv160f8/f1Jfn4%2B2bhxI1mwYAHZt28fadmyJdm4caNCeh05coRMnDiRvHz5khQXF5NXr16RadOmEX9/fxIfH0/mzJlD5s%2BfzyqnuLiYTJw4kQwaNIi0adOGrFu3jhQXF3PWgxBCLCwsSGFhIXnx4gUZP348IYSQ%2B/fvk8GDB3Nqf%2BjQIbJ582bSunVrsnnzZqm/nTt3kvj4eIX0sba2rrROKBQSGxsbheT8l5g8eTIJDQ2V/G7fvr3k/zNnzpBRo0Yxtl%2BxYgVZuHCh5BoVPx8IIWTdunXE29ubkx6jR48m165dk6nHlStXiLu7Oyc5Yjw8PMidO3ek1j148ECyP8%2BePSMuLi6scpYsWaLQdmUxefJkcuXKFal1N27cINOmTSOEEBIdHU369evHKmfatGnkjz/%2BkFoXGRnJeo6%2BFJ07dyYFBQWEkL/Pl0AgkLoGKP8dqCFNqfXcv3%2Bf2Nvbk9atWxMvLy/y6dMnzm2nTZtG/Pz8CCF/PzD3799PpkyZorAecXFxZODAgaR3795k3759xNramixbtkzyQGbDwcGBxMTEEEII%2Bf3334mbm5vCOogRCARk9erVxNLSkpibmxMLCwvi4%2BOjsMEoPiZ3794ldnZ2kvVWVlac2ltaWkq2mZubS%2Bzs7IidnR2JiIhQSA9CCHFyciJZWVlS6z59%2BkQcHR2l5HOhqKiIjB8/vkrnmRBCunbtKpHTpUsXyXqu2xdz%2BvTpKm2/Is7OzuThw4dS6x49ekR69eqlkJyCggLy4cMHkpSURJKSksjbt2/J5cuXFZJRXFxMgoKCyI4dO4i/vz/x9/cnfn5%2BxNPTUyE5CQkJ5M6dOyQmJobExMSQmzdvkoCAAM7t7ezsSHZ2tuR3eSMoLy%2BP1ShycHAgCQkJMtsnJCSQ7t27c9LD1taW5Ofny5RTXFws8yOICRsbG1JWVia1rqysTOqe5Hp/Vvd8t2/fXqYu5T/guOhiaWlZSU5paSmxtLTkrIuYW7dukbNnz5IzZ86QM2fOkFOnThFfX1%2BFZNjb25PMzExCyN/nKysri3MHAuXbgrp2UGo1GzduxOHDhzF58mR4eHjAx8cH/fr1wy%2B//MLJNWLJkiUYN24czpw5g/z8fPTt2xf5%2BfkICAhQWBd3d3cMHDgQPj4%2B0NDQgIuLC7y9vTFgwABcuXKFtX1mZqbEF7Vnz55YunSpwjqI2blzJ27fvo0tW7bA2NgYCQkJ2Lx5M7Zs2YKffvqJsxwtLS28e/cOly5dgp2dHQCRP66uri6n9kpKShJfZE1NTRQUFGDHjh3o3r27wvuUlZUFFRWVSvLFk4A0NDQkrh%2BycHBwkBriFQgESE9Ph729PVRVRY/Sq1evctLlhx9%2BQGBgIDw8PFCnTh08e/YMampqCg8hDxkyBG/evMHJkyfx4cMHrFy5EhcvXoSHh4dCckaNGoWpU6di%2BPDhMDIyQnJyMk6dOqWQX2hwcDB8fX1RXFwstV5HR0chV6fFixcjMjIS2traKCkpQZ06dfDixQuFfJv37NmDzZs3S44n%2BezK07JlS4wfP56TDIFAgHr16kl%2Bl3cbqVu3LuO1AojuRxMTE8nv8m4GJiYmyMnJ4aRHWVmZlP9%2BUFCQ5H8ej6fwNWNiYoLg4GAMHTpUsi4sLAyNGzcGAMTGxnK6P8XuVdU53w0bNkRkZKTUs/bWrVto0KABACAxMRFaWlqscoyMjHDx4kW4urpK6dekSRNOeogRu8qIfc7LysqQn5%2BPbt26KSTHwcEBCxYsgI%2BPj%2BQZw/WdQvn2oIY0pVZz/vx5HDhwQGLk7d27F4cOHcKsWbPw8OFD1vYmJiYIDw/H9evXkZycDAMDA/To0UNhH1UA2LBhg5Qv4HfffYfAwEBWv08xysp/B%2BHhOnFOHmFhYQgICJAYAqampjA1NcWoUaMUMqQnTJiA/v37AwCOHDmCu3fvYtq0aQr5UZZHTU2tSkY0AHTr1g3z5s3DkiVL0LhxYyQnJ2Pjxo3o2rUrBAIBduzYgdatW8ttP3PmzCptVxazZ8%2BGl5cXunTpgkmTJmHYsGFQUVFR2AC%2BdesWpk%2Bfju7duyMyMhIFBQXYsmULCgoKFPKbHTduHOrVq4ezZ8/i8uXLMDQ0xOLFi9GvXz/OMnbv3o05c%2Bagbt26uHPnDsaNG4eNGzeiS5cuCu1TZGQkAgMDkZmZicDAQGzatAkHDx7Eo0ePOMs4fvw4tm3bBjU1NVy7dg3e3t7w9fWFoaEhZxkNGzbE27dv8f333wMQTewU8/btW6mJr7JQV1dHZmamxOd%2B9erVkrK8vDzOEwT19PTw6tUrmJmZAYCUcRgfH6/QPgHAggUL4OXlheDgYMlHU1xcHLZt24Znz55h9OjRWLJkCaucXbt2Vft8z5w5EzNmzICLiwuMjY2RlJSEP/74AytWrMDr168xbtw4TlFA5s6di9mzZ%2BPYsWMwNDTE%2B/fv8fz5c%2BzevZuzLgBw8eJFHD16FIWFhTh37hzWrFmD9evXKxwFZ968eVi0aBF69%2B4NAOjatSvs7e2xatUqheRQvhH%2B7S5xCuXfpOJQv5hnz55xai8e0qz4l5aWprAbhJjY2Fhy6dIlUlxcTD5%2B/Mi5XcUh0PK%2BlIpia2tbyf%2B4qj5%2BCQkJJDk5mRBCSEZGBnn8%2BDHntjW5T1lZWWTixInEzMyMmJubE3Nzc%2BLp6UkyMjLIzZs3yYABA8jLly%2BrLJ%2Brv7ZQKCTv3r0jRUVFkuFof39/EhUVpfA23d3dJf6z4nPz8OFDibvKP0m7du2IUCgkiYmJZPjw4YQQ0f3h7OyskBzxfmRkZJA%2BffoQQiq7wLAhHtL/8OGDxO88IyOD9OzZk7OM5cuXy/UDXrp0KVmzZg1j%2B0mTJpGgoCCZZUFBQcTLy4uTHuvWrSOenp5EKBRWKps%2BfTrZunUrJznlSUxMJDt37iTLli0je/bsISkpKYQQ0fF6%2BvQpJxk1db7v3btHfHx8yKRJk8jSpUslz4c3b95U8p9m4tWrV8Tf358sXbqU7NixQ8qthivi501aWhrp378/IUTk8tWtWzeFZREiuuYePnxIUlNTq9Se8m1Ae6QptZoGDRrg1atXCAwMREpKCnx9fREeHs45Fmr5SBAVUSQSBFD9kElCoRB//fUXCCEARDGWy/8GuIUhAwAzMzOcOHFC6jicOHECLVq04NS%2BPNra2oiIiEBqaiqMjIwUGt4UCARSIa4KCgoqhbziEt4KEJ3rAwcOIDU1FSkpKWjcuLFkCLtz586cwh0CQEJCAnbs2FEpdN2bN28QHR3N2LagoAATJ05Eo0aNJCG3MjIysHfvXrRp0wZWVlaoU6cOJz0AUc9ojx49APwdWcDCwoJzDOgVK1ZgxYoVjGHEuB5fHR0dlJSUwNDQEG/evAEgivKgaPxcAwMDJCYmwsTEBBkZGSgoKICysrLc%2BN2y0NPTQ15eHvT19fH%2B/XsQQtCwYUNkZ2dzljFt2jQMGDAABQUFGDFiBPT19ZGamorTp0/jf//7H8LDwxnbjxo1CgsXLoS%2Bvj66du0qWX/r1i1s3LgRmzdv5qSHp6cnBg4cCA8PDwwfPlyiR3BwMJKTk7Fu3TrO%2ByTG2NhYZhxoAwMDGBgYcJJRU%2BfbyspK5vOtadOmaNq0KWc5P/zwA8aMGYPExES0bNkSZWVlCukBiPY/IyMDurq6SElJQUlJCdTV1ZGXl8epvbxQkO/evcO7d%2B8AcH8GU74dqCFNqdXcvHkTM2fORM%2BePfHnn3%2BiqKgIO3bsQEFBAaZOncraftGiRbh%2B/ToWL14MExMTvH//Hhs2bECbNm3g4uKCXbt2Ye3atdi4cSOrrOqGTCoqKqr0AVD%2BtyJhyObMmYOJEyfi3LlzMDExQUJCAl6%2BfIkDBw5wai/m8ePHmDx5MtTV1WFgYICkpCSsX78e%2B/fvxw8//MDavqJrgdhNpKqkpaUhMTERhBC8ffsWb9%2B%2BBaDYy23JkiUghEBbWxsZGRlo1aoVzp49y8n3dteuXeDxeFi5cqVknY6ODq5fvw4vLy/s2bMHc%2BfO5ayLoaEhHj58KBVrNzY2lvNwf/mPrOpiYWGBZcuWYenSpWjatCkCAwOhrq4u8XflSv/%2B/TFy5EgEBQWhR48e8PLyAp/PR5s2bTjLaN%2B%2BPWbNmoUtW7agVatW8PPzA5/Ph76%2BPmcZhoaGOHbsGJYtW4axY8dCSUkJhBC0bdsWR48eZXXt6NmzJ8aOHYupU6fCwMAA%2Bvr6SEtLw4cPHzBr1iwpVxEmtLS0cPr0aaxbtw7Lly%2BHQCCAqqoqHB0d4efnp7Ab2YsXL7Bhwwa8ffu2UicAVx9/oGbOd2pqKnbt2iVTF0Xi1efn52PZsmUIDw%2BHuro6QkJCMGHCBAQEBHB6zoixt7fH%2BPHjcejQIbRv3x6LFy8Gn8/nbNB/TaEgKf8g/2JvOIXyr%2BPm5kZu3LhBCPl7SPnRo0fEwcGBU/uajATxtYVMevXqFdm6dStZunQp2bNnD3n//r3CMjw8PMiuXbskv4VCIdmyZQsZN25cDWrKjcOHD5NWrVoRMzMzqT9zc3OF5FhaWpJPnz6Rp0%2BfkqlTpxJCCImIiCAjR45kbevs7Ezevn0rs%2Bzp06ecwo6VJzQ0lNjZ2ZGtW7cSS0tLcvDgQWJvby/XpUAeDx48kLlekegoqampZMqUKSQ1NZXcuXOH2NjYkNatW5Nz584ppAshhFy4cIHk5%2BeT3Nxcsnz5cuLt7a3QUH1ubi5ZsWIFycjIIM%2BfPyd9%2BvQhXbt2rZL7DCGEpKSkkAcPHpCkpCSF27569UriRuHv70%2BeP39eJR0IEUW0SE9PJ6WlpVWWMWLECDJhwgRy6tQpEhISIvWnCDVxvidMmED69%2B9P/Pz8JBFaxH%2BKsGzZMuLp6Ulev35NbG1tiUAgIMuXLycTJ05USI5AICD79u0jOTk5JCUlhUyaNIkMHz6cPHnyRCE5lNoFNaQptRobGxuJ72F5/1uu8XNtbGxITk6O1Lrs7GyJj2ZpaSlnQ/hLhUzKyMioVvvq0L59%2B0ovfYFAwPn4yvNBL//HFUdHR3LixIkqx8QW06lTJ0KIKARajx49JOu5xA5nC1XGNexYef744w8yceJE4uLiQsaOHUvCw8MVliFru7m5uVUKHyampKSEc%2BjGikRFRUnuhRs3bpCbN28qLCM7O1syT%2BHly5cSeVWRUx55Hx1fErFffXnCw8OrZFBbWVmR3NzcmlJNQlXOt62tbY08n7p16yYJWyp%2BjhcWFlZrTkV1yc3NJefPnycHDhwgly5dIoWFhf%2BaLpQvC3XtoNRqGjdujHv37sHGxkay7vHjx5yHxmVFgtiwYQO6dOnCKRJEeWoiZNKZM2fw5MkTODs7o02bNhg/fjxiY2PRrFkz7N69G0ZGRpzkvHz5EuvWrUNiYqIkrbEYRYZ/zczM8ODBA6nj%2B%2BzZM6mwYExUDDlXHvI5pBnXodLMzEwMHTpUKrpJVfjuu%2B8QEREBe3t7CIVCJCYmQk1NrdJxkoWmpiaysrKgra1dqezTp09Sqeq54ujoCEdHR4XbvXv3Dq6urigrKwMhBC1btqxUx9raWiGZjx49wps3byq5jCgSuu7YsWPYvHkzjh8/LnGfWb9%2BPRYtWsRZTsWU52FhYZxTnospKyvD/PnzIRQKEjID7AAAIABJREFUsXXrVgCidPfDhw9H7969sWnTpkrhFCty/fp1vHz5ElOmTAEgcr9yc3PD/Pnz4eDgwEkPeX71CxcuxNGjR7F//36F/Or19PQkqc6rQk2mYa9Xr161IwwBovkhYjnia6/8Oq4kJiZi9%2B7dSEpKqpariSyXNjU1Nc4ubZRvCyVS8YlHodQiwsPDsXLlSnh4eODw4cP48ccfceTIEXh7e3N6GXz69Anz5s3DzZs3JQZfjx49sHr1asTFxWH9%2BvXw8/PjlMo6Pz8fixYtwuXLlwGI/Ons7e2xceNGqXi28vD398fRo0fRoUMHPHjwAM2bN0dpaSnGjx%2BPU6dOgc/nY8uWLaxyAGDo0KHQ1taGk5MTeDyeVNngwYNZ24tf%2BAkJCbh27RqGDBkCY2NjpKWlISgoCC4uLlixYgWrnKSkJACil%2BPAgQNx7ty5SnW4fhx4enpi3LhxnH1T5XHt2jXMnTsX58%2BfR1hYGI4dOwYVFRV07tyZdeLXwoULYWxsLDM%2B886dOxEbG4sdO3Zw1mXChAkyPzR4PB4aNmyInj17MqZJfvbsGXJycjB16lTs27dPqozP56NFixacjXs/Pz/s27cPurq6krjagOg6VuTjy8nJCVu3bpX6AH3y5AnmzZuHS5cucZLh7u6OESNGSMVKDg4OxunTp1lTnovZu3cvwsLCsGnTJqlJtnFxcZg9ezaGDRvGGGIwOjoanp6emDVrliSNe35%2BPrZt24bAwEDs379fEnaTiU2bNuHBgwfYsmULdHR0JOszMjLg5eWFTp06KeRXf/ToUYSHh2Ps2LGV/Ly5zBVg%2BwBQ5HwHBQUhIiICU6ZMqaSLOK41F%2BbPnw8ej4dly5bB3t4eMTExWLNmDT5%2B/Ag/Pz/OcoYOHQoej4eOHTtW%2BuBWJKb6yJEj0b17d3h6egIQPb%2B2bduG%2B/fv47fffuMsh/JtQA1pSq0nIiICx44dQ1JSEgwMDDBs2DD06tVLIRniSBCEEISEhODcuXN48OAB5/ZCoRDZ2dnQ1tZGZmYmgoODUVJSgt69e3PuwXBwcIC/vz9at26NuLg4DB48GNeuXYOhoSEyMzPRt29f1qgSYqysrHD79u0q9xZxmXSjSA8PANjZ2SEmJqZK%2BgDA8uXLcebMGXTo0KHSS5trZAoxqamp0NHRgaqqKi5cuIC8vDwMGjSI9Xi9efMGbm5ucHNzQ9%2B%2BfaGrq4u0tDRcvHgRwcHBOHr0qEKT6jZs2ICTJ09i6NChMDExQVJSEoKCgtCtWzc0bNgQ4eHhmD59OkaNGsUoRxwlozr06NEDK1eurHbSCSsrK9y9e1fKkBEKhbCzs8Nff/3FSYaNjQ3u3r0rtY4Qgvbt23OW4erqik2bNsHc3LxS2V9//YUVK1bg/PnzcttPmDABffv2lTLmxfz222%2BIjIzkNHnXxcUF%2B/btk5lc5NmzZ5gzZw7nDwwAMvcH4D4RbvPmzZg7dy7evn2rUFQNNl0qJs9RZFKe%2BKPi6dOnKCsrg7q6Opo2bYrdu3crNMHUysoKt27dgrq6OvedkIGdnR1u3bolNWJRUlKCTp06cb7%2BKN8O1LWDUuuxt7ev9ss/MTERBw4cQEREBJo3b44FCxZwbpuamoqJEyfCwsICa9euxc2bN7F582aYm5vj4MGDCAgIQNu2bVnlZGVlSXrxzM3NwePxJC4qDRs2RElJCWedmjRpgry8PE5h%2B2ShpqYmZSQUFRVV%2B%2BVUXQQCgVTms%2Bqgr6%2BPp0%2Bf4v3793ByckJubi6nj47vv/8eBw4cwPLly3Hs2DFJJIgWLVpg3759ChnRgMiVYteuXVI9m87OzvDz88OmTZvg5uaGOXPmsBrSmpqa2LZtW6WQfs%2BfP5c5CiCL/Pz8KifLKU%2BzZs0QGhoqNfIRFham0JC4jo4OHj16JOXG8eTJE9ZIG%2BVJTU2Va3RaW1vjw4cPjO3j4uKwd%2B9emWVDhw7lnCwkIyNDboa%2Bli1bIj09nZOc8npVhyNHjmDOnDlwc3PDvXv3qiVLkZEKJnR0dHDy5Ek8fvxY0iFiYWHB6npTEXNzc6SkpFT7A6G6Lm2UbwtqSFNqJUxxc8Ww9VIKhUL8/vvvCAgIwIsXL1BaWoo9e/YonE528%2BbNMDMzw/z58wGIXDSmTJmCuXPn4ty5c/D395f7Qi5PxSH%2BioYrl8EncRxUe3t7zJgxAxMnTqyUopfL8O/9%2B/elfnfv3r1avck1gbzzef36dYXkVDfet7W1NcLCwpCYmIjMzEzo6uoqNIxdnri4ONja2kqta9euHWJjYwFwN7QWLVqEt2/fomHDhsjPz4ehoSGioqJYDfDy9OjRA2FhYRgwYIBiO1GBOXPmwMvLC6dOnULjxo3x4cMHPH36lNM9IKYmUp7z%2BXzk5%2BfLzEBYVFTE%2BuFUWloq15CrU6cO5zjHNeVXn5KSAgMDAyQnJ8utw%2BU61NPTg7u7O4qKijB27FiZdbiONnF1y2Lj1KlTGDZsGCwsLCQfT8XFxVi5cqVC2QR9fHwwfvx4uLi4oH79%2BlJlXK4dsUuboaEhpk2bJtOljfLfgxrSlFpNVlYWIiMj0bNnT5iYmCA1NRVXrlxhfeAdOnQIhw8fhlAohIeHB/bt24fevXtXKWHJzZs3ERoaioYNGyI5ORkJCQkSY8TR0RG//PJLlfatKlR0yajY48R1yLWi0f61eZAJBAKEhoYiICAAb968UWgYubrxvsWYmJhUu4fK2Ni4Uu/thQsXJCMRz54949QLe%2BfOHVy4cAGpqanYu3cvtm/fjtDQUEbXhYoUFxdj4cKF2L17d6VtKuLG06VLF4SGhiI8PBzp6eno3r071q9fr9CxqomU5zY2Njh79qzMj4nQ0FC0atWKsX3Tpk3x8OFDmR9XDx484Jz4pFOnTjh27JhMQ%2B748eNSMcSZ6Nu3L%2B7duyeZwFvxnuR6b%2B/YsQOXL1/G8%2BfPOfl4y8La2hr37t2Dubm53MnEityTmzZtwp07d7Bq1SpoaGggLi4O3t7eCk8s9vf3R0FBAWJjY6XaytOxIrdv35b837JlS8TGxko%2Bak1NTfH69WuF9KF8G1BDmlIrEfdOenp6Ytu2bVJRD6KioliHXdeuXYuRI0di4cKF1Z51Xt6F4uHDh6hfv75kciKfz%2BfsklFYWCi1H7m5uVK/i4qKWGXExcVJIlGUH04ODw9Hr169pCaRMVHxxcP1RVQRtqyGgGL%2BzVlZWTh%2B/DiOHz%2BOkpISDBgwANu2bVNIp%2BjoaPzxxx/Q0NCQ7NfkyZNx8OBBheTUBPPnz8ePP/6I4OBgSc/r48ePsXXrVsTFxWHkyJGcRl9UVVWhr68PDQ0NxMfHAxD5CG/YsIGzLi1atKjSh6Qsvv/%2Be4V6j2Uh9kWvKpMnT8a4ceNQXFwMV1dXNGrUCOnp6bhw4QL8/f1ZJ4UOHjwYy5cvx759%2B6T8dFNTU7Fy5UrObkbTpk2Dm5sbsrKy5PrVc0GcibG67hSmpqbw8vJCTk4O6zk6f/68zI8X8ejCoUOHqvxsKE9oaCi8vb3h5uYGV1dX7N%2B/HyNGjIC3t7dCcm7fvo0rV64o5AJUniNHjlSpHeXbhhrSlFrN7du3sXPnTql1nTp1wsyZMxnbLV26FMePH4e9vT2GDRuGkSNHVvmFoKWlhczMTDRs2BAxMTFSIcdev34tc0hXFmvWrKnS9ssjL9TWokWLcOzYMYVDbdUk1clq%2BO7dOxw8eBDnzp1DixYtUFBQgPPnz1dpaJnH46GoqAgaGhqSXj15LgBfmq5duyI8PByhoaFISUlBp06dsH79eolLxOHDhzn51xsZGeHJkydo06YN8vPzkZmZCVVVVU4fX2Kqa/j2798fYWFhjCEP2YzAmkx5Lp6zsGLFCqnMpA0aNICvry86d%2B7M2N7DwwM3b96Ei4sLrK2tJYb4/fv30aFDB0yePJmTHjXlVy8epTAyMkJ2djauX7%2BOtLQ0GBkZwd7eXuEMiT///DNrnWXLlsk0pMXuSB06dEBZWRnu3buH9PR0GBoacnKPqoiBgQF27NgBd3d3yXLhwoUKy9HT0wOfz1e4nSyio6ORmpoqeUaUlJQgPj4ePj4%2BNSKf8vVADWlKrcbIyAgXL16U6h0KCfk/e3ceV3P%2B/QH8VVSMEUpJiRkZspdUSFIqW9kqZpIwvraQLBMZKiVRsi9ZJmMZu2i3pBQVRXbC2NpvG6UspT6/P/y647Z%2BPnXrls7z8fB4zP3c%2B3l3CuPc9z3vc3wrPdxTaurUqZg6dSpiYmJw9OhRGBkZobi4GDExMTA1NeV0yEVfXx%2Burq4wMjJCQEAAnJycAAB5eXnYtm0b65rr0o/3Q0JCMHr06HLPnzx5sto1hDXC%2BsuXLwL9ZouKisr1n2XTXpBrN42KLFiwAFFRUTA0NMTff/%2BN/v37Y9CgQax318sSRr9vYTE3N8fff/9dYRLbsWNH1v3QLS0tMW3aNAQFBcHExATTp09H8%2BbNWdXDCyt5nTNnDgBU%2Bya2KsIuIRo1ahQMDAxw%2B/ZtvH37FnJyclBTUyvXErIiYmJi2LVrF0JCQhAeHo6cnBx07NgRU6dOhZGREac4hFlXf/v2bcyfPx8tW7bk10y7u7vj4MGD%2BOWXX2q0ZmWq%2B/148eIF5s2bh7S0NLRt2xZv376FiooK9u3bx7r0BQAiIyOxZs0adOnSBX/88Qc2btyI2bNnw83NDfLy8qzXmTVrFmxsbGBtbY02bdoIvKFj83eh1Lp163DixAn%2Bm%2Bvi4mIUFBRwPj9DGgdqf0eatCtXrmDx4sXo168fOnbsiOTkZDx79gze3t7Q1tZmvU5KSgqOHTuGs2fPQlxcHOPGjWO9I5KXlwc7OzvEx8dj7NixcHNzA/C1FZOcnByOHTtW7UeNHz9%2BxNu3bwF8/Ug%2BODhY4B%2Bx9%2B/f49dffy13CLAsYbXaEmavWeDrgUw7OzuBf9iys7OxYsUKHDhwoMp7e/bsCTMzM0ydOpU/dGTQoEHw8/Pj1BqrVG37fQvT0KFDcfHiRaHsht%2B/fx89evSAuLg4Dh48iIKCggoPm5bl5OSEtWvXYuXKlZXuJNfkDVFqaip/l5JLMlRfMjMzIScnJ%2BowcO/ePfTv35/1683MzGBkZCTQ43jnzp2IjY0VemlCaS10ZaZNm4auXbvCwcEBLVq0QEFBAdzd3ZGVlcW6qwkA9OnTB/Pnz4eNjQ3ExMTw7t07rF69GnFxcQJ1y9WpbWvAUjo6Oti1axc%2BfvwIf39/rF%2B/Hhs3bsSHDx84HX4kjUQ9TVAkpMF68eIFs2PHDmbNmjXMrl27mMTExBqv9fnzZ%2Bb06dPMxIkTax3XtWvXmE%2BfPrF6bUZGBqOmpsaoqqqW%2B9WjRw9GVVWVWbhwYbXr1MUIa2EwMjJirKysmIyMDIZhGCYiIoIZPHgwM3369Grvff78OePk5MSoq6szU6ZMYYKDgxktLS0mPT29VjFlZ2cz9%2B7dY3g8Xq3WqQ0nJyfGzMyM2b9/P%2BPn58f4%2B/vzfzVWGRkZzNSpUwX%2B7M6ZM6fcqO6qFBUVMdu2bWMMDAyYvn37MsbGxsyBAwc4xWFiYiLweN%2B%2BfQKPq/u7sHfvXoHHaWlpAo/nzZvHKo6yX2ft2rWc4ihLTU2NKSoqErhWWFhY7d/9mqguNnV1df4Y91IfPnxgNDQ0OH2d27dvV3j9%2BPHjnNapzf/7v1X6fWdkZDCmpqYMw3wdGa6rqyuU9UnDQqUdpMnr2rVrres7S0lKSsLc3Bzm5ua1Xmvo0KGsXysnJ4fQ0FB8/PgRpqam5botSElJsTpAUxcjrIXB19cXTk5OmDBhAoYNG4YLFy5g6dKl1Q5%2BAb72JXZ2dsby5ctx5swZbN26Fbm5udi9ezcsLS3Ro0cPzvHcuHEDfn5%2ByMzMhKKiIszNzVmPnham0tZ9ZXcSxcTEONWUR0REYN26dUhJSSn3cTzbnbjKSjtKpywOHz6cVYeJNWvWQFpaGiEhIVBUVERSUhI8PDzg5uaGjRs3sorFw8MDV69exdy5c9GxY0ckJSXBx8cHnz9/ho2NDas1kpOTBR7v37%2BfP%2BobqL5swdvbm1%2BuAgDjxo0TaAHJdqe07NcJDAyEo6Mj6zjK%2Bvnnn3Hnzh2BUoXnz5%2BjW7dunNYRBnl5ebx69Urg72DppxBslLb0q6ytH9e%2B5lOmTMGlS5c414uXpaCggOzsbMjJySE9PR1FRUVo0aIF8vPza7UuaZgokSZN2vPnz%2BHh4YHXr1/zB1GUEtawgPpSOj647FS4Fy9esK4HFlarLWH78ccfYWdnh%2BnTp%2BPcuXMwNTXFr7/%2BynmNGTNmYMaMGYiIiMCxY8cwYcIE9OzZE76%2BvqzXOXXqFFxdXWFsbIyePXsiOTkZ06ZNw6ZNmzjXvtZWRESEUNZxcXGBsbEx9PT0OLcMKyUhIQFfX18YGhpCWVkZqampuHTpEoYMGYJ3797h0KFDcHNzw5gxY6pcJy4uDhEREfxkRkVFBZ6enjA0NGQdi7%2B/P06dOoXOnTvzrw0aNAjTp09nnUiXLVMpm7BWd7i47Ou5JrzCiqMsbW1tzJs3D2ZmZujSpQsyMjJw%2BvRpaGlp8Q8YA7U/PMqGiYkJ5syZg1mzZqFLly7g8Xjw8fHBwIEDBc5UVHaewtDQEA8fPqx1S79Sbdu2BY/Hq3UiraenhxkzZuDQoUPQ1NTEqlWrICUlVetBL6RhokSaNGmOjo5o2bIl5syZU%2BPDZw3N3bt34eLigvPnz%2BPEiRNwdnZG8%2BbNsXXr1mqTEWG12hK248ePw9PTE0ZGRpg6dSqcnJxgZmYGDw%2BPSusaq1I6zTIxMRH//PMPp3v37dsHb29v6Ojo8K9FRETAw8Oj3hNp4GstcUZGRrmJhGx260u9e/cOy5cv5zwJ7ltpaWnl/oxFRETg%2BPHj2L59O27evIl169ZVm0hLS0sjLy9PIJkpLCxE27ZtWcfCMEy5%2BuVOnTrV6jAi14RVWC0gaxtHWQ8fPkSvXr3w5MkTfpKpoqKC7OxsZGdnC%2BVrlKru5%2B3r64tmzZrh77//FrgeHR2N6OhofiyVJdKlQ23ExcURGhpa68Omv/zyCyZPngw1NbVydflc6vyXLl0KWVlZSEhIwNHREX/%2B%2BSfy8/Ph6upaq/hIw/R9ZA6E1NDTp08RGRlZ6x2IhsTLywvDhw8HwzDYu3cvNmzYgLZt28LLy6vaRFrYI6yFxdPTE46Ojvx/UE%2BdOgVPT09MnjwZ9%2B/fZ7VGRQcWW7VqhRcvXnCKJTs7G4MGDRK4pqury2ksvLDs378fmzdv5j9mGAZiYmLo0aMHp0RaX18fERER1R4Srcq9e/ewd%2B9egWu6urpYtmwZgK87oSkpKdWuY2Vlhblz58LOzo6/S7lz504MGzaMP3kTqLqLwtSpU7F69Wo4OTlBWloanz9/xsaNG4VSctXY1Wev42/fbFYkLCysVuu3a9cObm5uaNasGc6dO1dhIs1lZ/2HH34QyvRBCQkJfnvD1q1bY%2BvWrZCUlKz1zAHSMFEiTZo0eXl5FBYWijoMoXr58iWOHj2Kly9fIisrC2PGjIGkpCSrtnWAcFttCcv58%2BcFPqaXkJDAqlWrOLWTCgkJQXx8PDZv3gw5OTlERkZi5cqVnIeI6Orq4ujRo5g%2BfTr/WlBQULV9hevCP//8Ay8vL0hKSiIyMhKLFy%2BGq6urwM%2BKDWtra1haWqJbt27lRiOznUooIyODa9euCbQBjImJ4e8kJyUlVdsBBAC/Z/OCBQsErt%2B5c4f/iUh1H9mfPXsWPB4PISEhaNOmDd6/f48vX74AgECyz%2BVjf1FhGAZpaWn8JLGkpETgcU12YUNDQ3Hy5EmkpKRATk4O5ubmNerTHhUVhSNHjiAjIwN79%2B6Fj48Pli1bxv9079tSkco8fPgQZ86c4ccyadKkcmPvK7NmzRqcPn0aJSUluHHjRrnnue6sC6PdJvC1nG7z5s3YtWsXLl%2B%2BjCVLlqBVq1bYvXs3NDQ0hPI1SMNBiTRp0qysrLBgwQJYW1uXO4zHpW9oQ9KsWTMUFBQgMjISampqkJSUREpKCuddd2GMsBaWzp07IykpqdyAgxcvXrBOpmtzYPFbxcXF2LBhA86dO8ffMb137x569uwJa2tr/uu4jMWuqdzcXIwZMwZpaWnYs2cPZGVlsWbNGlhYWHCa6ubo6Ah1dXUMHDiwxuUdixYtwsKFC2FsbIxOnTohJSUFoaGhcHZ2xsuXLzF9%2BnRYWVlVu05CQkKNvv63vh2gUlMfPnzgt0sEvias3z6uTmFhYZWTOdm%2Bgf/48SMMDAz4nzYwDMP/5KD0GhcBAQFYu3YtpkyZAgMDAyQmJsLZ2RmfPn2ChYUFp3Xc3d1hYWHB/6QgLCwMYmJisLe3Z7XG9evXYWNjAwMDA/To0QOJiYmYOXMmtmzZwqomfvTo0Rg9ejQsLCyEttN%2B6NChcm8y5s6dy%2BnnvH79esjLy4NhGGzevBm2trZo1aoVNmzYgNOnTwslTtJwUB9p0qQJq29oQ%2BLs7Iy7d%2B8iJSUFq1evRu/evbFgwQIYGBiwmkbWEO3duxdbtmzh/2NWmkBwPSiYlJSE6dOnIzU1Faampli/fj2r4RrfYrPLBtTPYS1jY2OcO3cOP/zwA7S1tXHz5k2IiYlBQ0MDt2/fZr2Ouro6YmNjOf8syrpz5w58fX2RlpYGRUVFTJ48GX369MHr16/x77//skqOpk2bBjMzM4waNQotWrSoURw7duzApEmTajS5stS3HTYqo6WlVelzbEazs9kBZVMOw%2BX7HDduHFatWiVQnnTjxg24uLggODiY9TqmpqZwdXWFmpoaNDU1ERcXh9evX8Pa2hqRkZGs1pg8eTJmzpwpMEAqJCQE%2B/fv5/T3WlgOHTqEgwcPYs6cOejUqRMSExNx4MABWFpaCnRgqc7QoUMRHh4OHo%2BHkSNH4ubNm2jVqhU0NDSq7KtNGqn66LFHSENT2tM1OTmZSUlJYZKTkwV%2BpaSkiDjCmvvy5Qtz9uxZJigoiGEYhnn16hXj4%2BPDfPnyRcSR1dywYcOYixcvMuHh4cyaNWuYt2/fMkuXLmU8PT1Zr3Hs2DFGXV2dsbe3Z%2B7du8dMmDCBMTU1ZZ48eVKHkdctBwcHZtasWUxubi5jbW3NbNmyhdmzZw8zatQoTuv89ttvzIsXL%2BooSm4OHDjAmJiYMAMGDGBWrVpVaY/gqsydO5fp06cPY21tzfj5%2BbHux16R4ODgCq%2BfOHGixmsyzNe%2BwlxU1gd7y5YtnNbR0NBgSkpKBK4VFxdz7kc9cOBA/jqampoMwzBMSUkJpx7QAwcOZIqLi2sdi7CMGjWKefTokcC1R48eMQYGBpzW0dbWZj5//sycOHGCsbCwYBjma9/5QYMGCS1W0nBQaQdpksaMGYP4%2BHiMGDGi0o/sGuuOdLNmzTBp0iTk5ubyT%2BhPnTq1Vh0ZRC0vLw/GxsZIT0/H9u3b0bZtW/z5558wNzfH8uXLWa1R2wOLwtphFIbbt29DQ0MDq1atgoeHB758%2BYJVq1bB1tYW%2Bfn52LBhA6f1Bg8eDGtra4waNapcdwy2O%2Bs8Hg979uypsJUklzKXWbNmYdasWXj06BHOnTsHW1tb/PjjjzAzM8P48eNZTTn09vZGdnY2/P398ddff8HFxQVjxoxh3e/720mhq1atgpqaWrlJoRs2bMCUKVOqXUtLS6vC3e3hw4fj1q1bVd6bk5PDPwy7Y8cO9O/fv1wchw4dgp2dXbVxlFJQUEBcXJzAbnpcXBzncxA//fQTrly5IvApQ3R0dIVTUSvTtm1bPHv2TOCTwYSEBJFNjMzIyCj3KaWqqirevXvHaZ3Bgwdj0aJFSEhIwKxZs5CUlAR7e3sMHz5ciNGShoISadIkBQUFAWh8vaLZKCgogKOjI4KCgtCiRQv4%2Bvpi5syZOHjwILp27Srq8GpEXl4e%2Bfn56NChA5KTk8EwDGRkZJCbm1vtvaVJZ2UHFtkcgPvW27dvce3aNejr60NZWRk8Hg%2BXL18Wyml/tmbPno34%2BHj8%2BOOP/JHDMjIyrMa3VyQ2NhY///wznj59KnCdS12og4MDsrKyoK%2BvX%2BsSEQDo3bs3VFVVoauri23btsHLyws7d%2B6Enp4eVq5cWW3iJysri5kzZ2LmzJn8lpCnT59G165dYWlpiSlTplTa8jI/Px9jx47Fp0%2BfAAiOvGf%2Bv6yoqjKVN2/ewNHREQzDID8/X6B2vnT9soc6KyIpKQlbW1t%2BUl%2B2xlxSUpJVMv%2Bt6dOnY8GCBZgyZQqUlZWRmJiIkydPsnqj%2BK0lS5bAxsYGI0aMwOfPn%2BHs7IzAwEB4eXmxXsPCwgLz58/H3Llz%2BaUU%2B/fvh6WlJadYhKVLly64fPkyRo4cyb92%2BfJlTm8OAGDdunXw8fGBhoYGrK2tkZCQgN69e/M72JDvC9VIE/KdcXJyQkZGBuzt7TF58mRER0fDzc0NSUlJ%2BOuvv0QdXo2sXr0aqamp2Lp1K2xtbdG3b19ISUkhODi42rrOAQMGCNQljh8/Hn5%2BfpU%2BX5158%2BbBwsICI0aM4F%2B7fv06vL29663Ptrq6Ou7cuVMvX4stTU1NXLx4ETIyMrVe6/79%2B/D39%2Bf/3pqammLSpEno0KEDvLy8cO/ePfj7%2B1e5RlFREcLDw%2BHn54fIyEh069aNXze9Z88edOjQocp69%2Bzs7FpNCv3nn3/w9u1beHt7Y968eQLPSUpKwsDAgNM0wVGjRuHChQusX18VX19f%2BPr6IisrC0pKSrCwsMCoUaM4r5OQkMA/mKegoMB5wifDMNi5c2e5WGbOnFnjwUC1ERoaCjs7OxgZGUFZWRlv3rxBWFgYtm/fDn19fdbrzJ8/H56ent9VW1VSBdFVlRBC6oKuri7z7t07hmH%2Bq138%2BPEj/78bo/fv3zPOzs5MdnY28/TpU2b06NHM0KFDmWvXrlV7r5qamsDjgQMHVvk8m/XK1nUmsYOpAAAgAElEQVR%2B%2BfKF8zq1URc1pJcvX2b%2B97//MaNHj2asra0Zf39/Tvfr6%2BtzrvutyMiRI5nevXszc%2BfOZS5evMgUFRUJPP/s2bNq63DXrFnDaGlpMVpaWoyrqyvz%2BPFjgecfP37M9O/fn1U8ZX%2Bv//33XyY9PZ3VvQzDMOfOnWP92up8e87h6tWrzP3794W2dk2lpKQwd%2B/eZXg8nqhDEYobN24w9vb2zKxZsxhHR0fm3r17nNcorZEmTQOVdhDynSkpKeE3/me%2B6T3bmIcB/Pjjj3BycgLwtYSBS3eB6ibMcW0fpqSkhJCQEIwdO5Z/zdfXl/PHv7Xx8eNHgR3xinApWxJGSzQbGxs4ODhg9uzZ5XZrudTfTpo0CRMnTqy0TrZLly64evVqlWskJSXB2dkZI0aMEPhzb29vDw8PD3Ts2JF195XaTAoFACMjI/z999%2BYMWMG/v33Xzg4OEBGRgYuLi7o0KEDqxiAr63lVq9ejejoaOzevRve3t4QExPDn3/%2BicmTJ1d7v7Br/DMyMrBkyRLEx8fzy12GDRsGT0/PastW2Pzs66PrTVmlvcfT09NRUlKCV69eYdOmTQC41fmbmJjA1tYWpqamkJOTE/h/TGNtq0oqR4k0Id%2BZQYMGwcXFBY6Ojvz/gW/durXKVl0NVUP8B3fJkiVYvHgx/vnnH3Ts2BHJycl49uwZvL296y0GCQkJoX7f%2B/fvx86dOwVaounp6cHFxYV1Ir169WoAX2tKy7Yp5HJwNzg4uMJWYwYGBggLC6t0QhyPx0NMTAwAID4%2BHuPHjxd4w/X%2B/XtcvnwZwNdDbkOHDmUVT20mhQKAq6srnjx5ghkzZsDZ2RmKioqQkpKCs7Mz9uzZwyoGANi9ezfs7OxQUlKCo0ePYseOHZCVlcWSJUtYJdLC5ujoiDZt2iAkJASKiopISkqCh4cH3NzcsHHjxirvvXnzZpXPC2tEOVfCqvMvLfEq%2B4avMbdVJZWjRJqQ74yDgwPmz58PTU1NFBcXQ11dHT/99FO9JnrCsnPnTrRu3Ro9e/ascIKbKP7BHTFiBL9%2BNyMjA3p6eti0aVO9Dq9p3rw5Jk6cKLT1UlNToa2tLXBNS0sL6enprNeozcHdxMREflJZumv7rfz8fP7Bv8q0a9cOR48eRU5ODgoLC7F9%2B3aB56WkpGr05qO2k0JjY2Ph6%2BuL3NxcxMfHIzw8nFMiXyopKQmTJ0/G48eP8fHjR%2Bjo6KB58%2BbIyspidb%2BwO8rExcUhIiKCXwesoqICT09PVm8u6nNMORcPHjwQSp2/MIYKkcaDEmlCvjPNmjXDyZMn8eDBA/4hoO7du2Pz5s1Ys2aNqMPjZMWKFfD19UVGRgYsLCwwYcIEyMrKclrjy5cvOH/%2BPP9xUVGRwOPi4mLOcXXt2lUkHz2XquhNRW0IoyVaRUNBvnz5gmfPnlU7MKRz585o164dvztFWTIyMtiyZUuVa0hKSuLMmTMAvrbQE9bB2tpOCi0oKEDbtm1x4cIFKCsro0OHDigsLOT8JrBly5bIzs5GWFgYNDQ00Lx5cyQkJKBdu3ac1snKysK%2BffuwatUq3Lp1C7a2tpCRkcG2bdugoqLCeh1paWnk5eUJ/BwKCwvLtU%2BsyocPH3Dq1CnMmDEDL168wMqVK2tU9iIsrVu3FloJXGFhISIiIpCSkoIpU6bgzZs3lQ4AI40bde0g5Dvx5MkTLFy4EKmpqejXrx/27duHNm3a4OnTp1i2bBl4PB5/lG9jc//%2BfZw9exaXLl3CgAEDYGFhgWHDhrE62f9t67LKhIWFsVqnuuSnvtopOjk5Ye3atUJb7/Tp0/Dw8KiwJZqZmRmrNa5evYq1a9cKjHEHvu6eP3jwgHUsu3fvho2NDefvoa7UdlKolZUVhgwZwk/EFy5ciK1bt%2BLFixc4ePAg6zh27NiBU6dOIS8vD9u3b4esrCz%2B97//4ffff%2Bc0dW/RokX48OEDDhw4gEmTJkFDQwMtW7bE/fv3cejQIdbr/PXXXzh//jzs7OzQpUsX8Hg87Ny5E7179xZoH1dVTfDKlSvx5MkT%2BPn5wcrKCrKyspCSksL79%2B85lb0Iy5kzZxAREVHrOv/ExET8/vvvKCoqQl5eHnx9fWFiYoKdO3dy6v5BGgdKpAn5TlhZWaF169aYMmUKjhw5gu7du0NPTw82Njbo0aMHPD090alTJ1GHWSufPn3ChQsXcO7cObx%2B/Rrjx4/H0qVL6%2BVrnzt3jv/fX758wdu3byEjIyMw6EaY5Rb1rbYt0UxMTKCjowNpaWk8ffoUJiYm2LVrF8zNzTFt2jROsWRkZCAxMbHczrsoDmoVFxfDz88PLVq0wJgxY/D69WuEh4fD2tqa1ZCjf//9F2vXroWUlBS2bt2Kx48fw9XVFdu3b8fPP//MKZabN29CSkoKampqSEtLw4MHDzj3Lx8%2BfDiCg4ORn58PPT09REdHo3Xr1tDW1uY0Vp7N7mp1NcEGBgbw9fWFmJgYBg8eLFD2Ioo3/d9%2BT7Wp8587dy769%2B%2BP%2BfPnQ0tLC3FxcTh37hwOHz4s8P8R8p0QSa8QQojQDRgwgMnOzmYY5usI9BEjRjCampqMl5dXuRZejVl%2Bfj5z6tQpZuzYsUzfvn3r9WtnZGQwdnZ2TJ8%2BfRhVVVWmT58%2BjK2tLad2aA2RMMZg9%2BvXjykqKmJevXrFWFlZMQzDMM%2BfP2dMTEw4xXL48GGmV69eTI8ePQR%2BqaqqclpH2N69e8c8ePCAKS4urlVrs7Ljubl69OgRc/HiRebz589MVlYW5/u1tLSY4uJi5vz588y4ceMYhvnaXlJLS6tWcdVE6dcMCQlhjI2NGYZhmM%2BfP4usVWdycnKlv7jQ0tLi/xkp/V6Ki4s5jU8njQfVSBPynSgpKeEfklFQUEB6ejqWLl2K33//XcSRCUd0dDTOnj2LsLAw/Pzzz/j1119hYmJSb18/NzcXkydPhoKCApydnSEvL4%2BkpCScO3cOFhYW8Pf351QfKmrCHIMNfK1jFhcXh6KiIn%2Bsdbdu3TgdWASAQ4cOwdHREWZmZpVOHqxPwpgUGhUVhaNHj4LH42Hv3r3w8fHBsmXLOH1/2dnZWLBgAR4%2BfAgJCQmcOXMG5ubm8PHxgbq6Out1%2BvXrB2dnZ9y%2BfRujR49GVlYWXFxcatTVJz09HQEBAUhJSYG8vDxMTEwEpodW55dffsHu3bsRGRkJfX195OfnY%2BvWrejduzfnWIShulp%2Btlq3bo2srCyBcpDMzEzOU1RJ41D/o4MIIXWibP2uhIQE54/UG5rXr19jy5YtGD58OP744w%2B0b98eJ06cgK%2BvL6ysrOo1cfX29kafPn1w7NgxmJmZQVdXF5aWljh58iT69OmDvXv31lsswlA6BnvEiBH49OkTDAwMBH5NmDCBU2eJHj16YNu2bQC%2BjueOiIjglyJwkZOTAwsLiwaRRAOAh4cHPnz4gJCQEEhISEBZWRn6%2Bvpwc3NjdX9AQACWL1%2BO7t27482bNwC%2B1uRv3ryZUxzr169H9%2B7dERcXh%2BbNm0NFRQVz5syBh4cHp3Xc3NxQWFiIgQMHYu7cuUhJSUFhYSG/TztbDx48wNixY3Hp0iXk5ubiypUrGDduHKfyEGdnZ8TExODHH3/EwoUL8fjxY9y8eROOjo6cYmloTE1NsXDhQkRFRaGkpAT379/H8uXLBXrPk%2B%2BIqLfECSHCUXbaXWOeZFhKVVWVGTx4MLN%2B/XomOjqaiY2NLfervhgbGzMvX76s8Lnnz58zRkZG9RaLsGRlZTFJSUmMmppauY%2ByMzMzOa3177//MmPGjGGSk5OZS5cuMb169WJUVVUZHx8fTuvMnTuXiY6O5nRPXartpFATExPmzp07DMP8N1Xz1atXjK6uLqc4hgwZwnz48EEgjsLCwnKTOtkqLCxkMjMzBaYlcjFt2jTm0KFDAtf%2B/vtv5tdff63Ret%2BTwsJCZsOGDYyamhrTo0cPpn///oyrqytNO/xONYy3/ISQWquuzRsATJgwob7DqhWGYZCTk4NDhw5V2FGgPgccZGZmVno4TEVFBZmZmfUShzCVthK8ffs2xMXFkZqaiszMTHTs2LFc14LqqKioICgoCMDXj8jDw8NRUFDA%2BUBdhw4dMHfuXGhra5eLQdi9kNmo7aTQ9PR09O/fH8B/nxp16dIFHz584BSHhIQEPn36hJYtW/LjKCgoQKtWrTitk5%2BfD1dXV1y4cAGFhYVo0aIFJk6ciJUrV3Jq/fb06VP4%2BPgIXLO0tCzXv7sqxcXFuHjxIl6/fo2SkhKB50TZXlIYVqxYgRUrViAnJwft2rUT2ZAZUvcokSbkO9G%2BfXuBf8TatWsn8FhMTKzRJdINabCBlJQUMjMzKxxdnZmZyTmhaUiys7NhZ2fH/1iey7jnb1XUbSMrK4tTt43CwsIG9RF4bSeF/vTTT7hy5YrAoJLo6GjOI%2BUNDAzwxx9/YPXq1RATE0N2djbWrVsHPT09Tuu4uLjgzZs32L17Nzp27IikpCTs2LEDmzZtwqpVq1iv07JlS6SlpQkMIkpLS%2BNUB%2Bzk5ISgoCCoqqoKlPI09qRz6NChGD9%2BPMzNzdG9e3dRh0PqGLW/I4QQFmxtbaGiooLFixeXe27btm148%2BYN57rXhmLevHkQFxeHvb29wLjntm3bVjvuudSRI0ewYcOGcgNuGvtY5OzsbMyfPx%2BPHz9GcXExWrRowZ8UymZoSHR0NGxsbDBixAiEhoZi4sSJCAwMhJeXF6ckuKCgAA4ODrh06RKArz9XPT09eHp6onXr1qzX0dTUxIULFwQGG/F4PIwfPx43btxgvY6Hhweio6OxbNkydOrUCYmJidiyZQuGDh2K5cuXs1pDR0cH3t7e6Nu3L%2Buv2xhERETg/PnzCAsLQ/fu3WFubg4TE5NG/WabVI4SaUIIYeHRo0eYOnUq5s%2Bfj/Hjx6N9%2B/ZITU3FmTNncOTIEZw4cQI9evQQdZg1oqGhITDuGQDy8vJgaGiI2NhYVmsYGhpi9uzZQum2cejQIZw8eRIpKSmQk5ODubk55s6dK5KdyqSkJHTq1ElgUmi/fv1Y9ZAulZCQwP9%2BFBQUYG5ujn79%2BnGK49atW1BXV0dubi6Sk5OhoKAAeXl5rt8Ohg4disDAQIGDuvn5%2BTA1NUV4eDjrdT5//szfUS4qKoKUlBTMzMxgb2%2BPFi1asFpj8ODBuH79OqefZWOSl5eHwMBA%2BPv749mzZxg5cqRIypNI3aJEmhBCWAoPD8eaNWuQnZ3Nv9a%2BfXu4u7tz6nDR0Ojr6%2BOff/4RaNeVlZUFS0tL/g5odQYMGIBbt26xmjZZlUOHDuHgwYOYM2cOf6fzwIEDsLS05DTBT1iGDBmCS5cusR4JXtb8%2BfPh6elZ4/tLaWtr4%2BrVq2jZsmWt1jl69CguXbqEVatW8ScSenl5oXPnzpg6dSr/dWwn%2BRUWFiI3Nxft27fn/EbHzc0NcnJyIvl9rS/JyckIDAzEqVOnwDAMpzcrpHGgRJoQQjgoLCzE3bt3kZGRATk5OWhoaDSYVm01JYxxz/PmzcP06dMxePDgWsUyevRoeHl5oVevXvxrjx8/xqJFi%2BptBPu3xowZgx07dkBFRaVG9w8aNAiRkZGcDvJVxNzcHPb29jXq9/ytstP7vk0BSh9XVY5T9gBzRdiexbC0tER8fDxatmzJ74FfShS/18KSn5%2BPkJAQnD9/Hvfv34eenh4sLCygq6tb6zeapOGhRJoQQpo4YYx7dnJywrlz52rdbUNDQwNxcXECCUdJSQk0NTU59SgWlsWLF%2BP69etQU1MrV0rB5vtat24dkpOTYWpqCjk5OYFdWy6HMGfNmoUbN26gU6dOkJeXF1jn8OHDrNdJSUlh9brKhpMYGBhUeZ%2BYmBjrJLiqcdkTJ05ktUZD1L9/fygoKMDMzAyTJk3i3AGHNC6NexuFEEJIrQmjO4qwum106dIFly9fFtgJv3z5MucuF8Lyww8/wNjYuMb3Hz16FABw9epVgetcD2Gqq6tzmmBYmdpO7wsLC0NxcTFyc3P5u8gxMTFISEiAnp4e62mPwNeyByMjI1Zv5BqTvXv3YtCgQaIOg9QT2pEmhBCCtLQ0pKSklPuof%2BDAgfUaR2hoKOzs7GBkZARlZWW8efMGYWFh2L59O/T19es1FracnZ3h7Oxc4/tv374NDQ2NWscxZ84c7Nu3r8rXqKqqVlrLzCax5/F4%2BP3339GvXz%2B4u7sjICAAK1asgKqqKhITE3Hw4EHWXThsbW0RFRUFGRkZGBsbw9jYmN9zu7F78eIFjh8/jvT0dLi6uiIoKAhWVlaiDovUAUqkCSGkiduzZw9/vPe3uO6aCqvbxs2bN%2BHr64vs7GwoKSnBzMyMc5eL%2BjRgwADEx8eL7H4u65TtwpKTk4MjR45g/PjxmDx5crVfY%2BXKlSgsLMSff/4JWVlZGBsbY/To0ViyZAn8/f0RGBhYbTL/raKiIsTExCAsLAwREREoKSmBkZERVq9ezXqNhiYqKgqLFi2Cvr4%2BwsPDERQUhEmTJmHmzJnf9cHKpooSaUIIaeK0tbWxfv16GBgY1LjFnLC6bfB4POzZswevXr1CSUlJjWuB65O6ujru3LkjsvtL1TQhz8zMxIwZM/iTKauiq6sLPz8/yMjIIDU1FQYGBggKCoKKigoKCgqgr6/PumViqXfv3iEmJgbR0dEIDAyEmJiYUN5YiIqZmRlsbW2hp6cHTU1NxMXF4cGDB7Czs2vUhyhJxahGmhBCmrjmzZtj%2BPDhterTfOLECezevVug28aAAQOwaNEiTom0g4MDsrKyoK%2BvDwkJiRrHU59q299a1JP8pKWlwePxWL02Pz%2BfXxt97949SEtL8zuaSElJoaioiPXX9fLyQlRUFJ49e4bu3btDR0cHe/bsEUqZiyi9efMGw4YNA/Df723fvn2Rm5sryrBIHaFEmhBCmripU6diy5YtmDdvXo37HWdkZJQ7NKaqqop3795xWufBgwe4ePFiuXZoRDjKtq8rKirClStX0LNnT1b3t2nTBjk5OZCRkUFsbCwGDBjAf%2B7ly5do164d61gOHToEcXFxWFlZYcyYMejbt6/I31QIg6KiIuLj4wXeEDx48AAdO3YUYVSkrlAiTQghTVzXrl2xbNky/PXXX%2BWeY1sjLaxuG61bt651z2VSue3btws8btasGVRUVODk5MTqfn19fbi6usLIyAgBAQH8%2B/Ly8rBt2zbo6uqyjuXWrVuIjY3F9evX%2BZ9EDBo0CEOHDoWFhQX7b6qBmTdvHubPn4/ffvsNRUVF2L9/P44cOYKlS5eKOjRSB6hGmhBCmrjhw4fD1NQUQ4YMKTeume0AEGF12zhz5gwiIiIwe/bscv132U7bq2%2BN6bAhG4GBgTAxManwuby8PNjZ2SE%2BPh5jx46Fm5sbgK913nJycjh27FiN%2Bibn5%2BfD19cXe/bswbt37zgdcm1oXF1doa6uDn9/f/5Y%2BMmTJwu8ySTfD0qkCSGkidPQ0BDKsBNhdNsoO3kPQLXT9kSttocFhZUAi/LQ4vXr16GpqQkpKSnW9yQkJCAyMhLXrl3D3bt30bNnTxgaGsLQ0JBTP%2BqGRktLC9HR0Y1%2B4ilhh36XCSGkiTMyMsLly5dhZGRU4zV4PB5CQkKQnp6OkpISvHr1Cps2bQLArdtGY%2BxqsHjx4lrd/9NPP1X7mnXr1lXYEs7e3h4eHh4AgI0bN9YqjlI12V8bOnQo53vMzc2hpaWFUaNGYdOmTejQoQPnNRoiMzMzuLq6YuLEieWmWTbUT1VIzVEiTQghTdynT5%2BwePFiqKiooG3btjVqOSesbhu1nbwnLNOmTav24Fvpz2bGjBnlnit7qK8iEyZMAAD4%2BvpW%2BDyPx0NMTAwA4PTp0%2BjTp4/A8%2B/fv8fly5f5j2szgfFb9XXgLzo6GtLS0sjOzuYPA1JQUKiXr12XDh48CAA4efIkgK8/z4b%2BqQqpOUqkCSGkievWrRu6detWqzW%2Bt24b2tratbq/7KG%2BssTExPiJdGXatWuHo0ePIicnB4WFheXWlJKSwsKFC2sVpyiJi4tjwYIFCAsL4yeagwcPxtatWyEtLS3q8GqsMX6qQmqOaqQJIaSJu3nzZq0TRwMDA/j7%2B9e4fR6p2qxZsyrsqlIXhFWzXZ21a9fi1atXWLNmDTp16oQ3b95g/fr1UFZWhqura51/fUKEgRJpQghp4rS1tdG6dWtMmjQJEydOrFG/28bYbYONt2/f4siRI%2BDxeCgpKQHwtffys2fP4O/vz2qNpKQk8Hg8fu1x6f0VlYQ0BPWVSA8fPhxnz56FrKws/1pmZibGjRvHL2khpKGj0g5CCGnirl%2B/jrCwMJw/fx579uyBpqYmJk2aBGNjY9Y9nUsPwl2%2BfLnRdNtgw8HBAa9fv4aMjAzy8/OhqKiI69evY%2BrUqazu37t3L7Zs2VLuZ9KzZ09OifStW7ewZs0aJCUlobi4WOA5Yf9862t/7ePHj2jdurXANWlpaf4bFkIaA0qkCSGkiZOQkMDIkSMxcuRI5OTk4MKFC/Dx8YGLiwvGjh2LKVOmlJtaWNb3WhcaFxeH4OBg8Hg87Nu3Dzt37oSfnx8CAwNZ3X/s2DFs374dkpKSCAsLw9KlS%2BHq6sp519/Z2RkDBw7E6tWr63x0uo6OTp2uX6p///7Ytm0bli9fzj%2BQt23bNvTt27devj4hwkClHYQQQgAA2dnZCAwMRFBQEBISEqCnpwclJSWcP38eM2bMwLx580QdYr3T1tbGzZs3kZeXBzMzM1y%2BfBlfvnyBnp4eoqKiqr2/tLdzeno6bGxs4Ovri5ycHJibmyMsLIx1HBoaGoiLi4O4uHhtvh0AQFRUFI4cOYKMjAzs3bsXPj4%2BWLZsWb33PX727BmmTZsGSUlJKCkpISUlBWJiYjh48CBUVFTqNRZCaop2pAkhpIkLCgqCn58foqOj0bVrV0yaNAne3t78Dhx6enpYsGBBk0yklZSU8PDhQ/Tp0wcFBQXIyclB8%2BbN8enTJ1b3y8vLIz8/Hx06dEBycjIYhoGMjAxyc3M5xfHLL78gNTUVnTp1qsm3wRcQEAB3d3dYWFggLi4OABAWFgYxMTHY29vXam2uunfvjosXLyI0NBQ5OTlQUlKCnp4eHVgljQrtSBNCSBOnpaWFsWPHwszMrFyvYgBIT0/HqVOnYGtrK4LoROvMmTNwc3NDUFAQ/v77b8TExKB58%2Bbo0KEDvL29q71/9erVSE1NxdatW2Fra4u%2BfftCSkoKwcHBCA4Orvb%2B0n7UDx8%2BRFRUFKZMmYK2bdsKvKa6NnrfMjU1haurK9TU1KCpqYm4uDi8fv0a1tbWiIyMZL0OIeQrSqQJIaSJKywsFDhUmJ%2BfD0lJSdYHDb939%2B/fR48ePSAuLo6DBw%2BioKAAv//%2BO9q0aVPtvfn5%2BfDy8sKiRYuQlZWFxYsXIz8/H%2B7u7qymARoYGFT5vJiYGKf6dE1NTcTGxkJMTAxaWlqIjY0FwzDQ1NTErVu3WK9TG6qqqtUOfWnMB1RJ00KJNCGENHEvX76El5cXdu3ahcuXL2PJkiVo1aoVdu/eDQ0NDVGHR1gKDAyEiYlJla%2BxsLDA3LlzYWhoyE%2Bko6KisHnzZpw9e7Ze4oyNjQXwtVY7MjISCxcuROfOnZGWloZdu3ZBR0enSX76QRonSqQJIaSJmzVrFuTl5bF%2B/XqMGTMGEydORKtWrXD%2B/HmcPn1a1OGJVEREBNatW8cfYf0tNrum7969w7Fjx5CSklKurZu7u7tQY2XT/zk6Oho2NjYYMWIEQkNDMXHiRAQGBsLLywt6enpCjac6RkZGOHr0KDp06MC/lpmZCQsLC1y9erVeYyGkpuiwISGENHFPnz6Ft7c3UlJSkJiYCEtLS7Rq1QpeXl6iDk3kXFxcYGxsDD09vRp1zLCzs0NaWhrU1NSE0nGjKmz2xYYMGYITJ07g5MmT0NbWRklJCXx8fNCvX786ja0iOTk55UaBS0lJ4f379/UeCyE1RYk0IYQ0cV%2B%2BfAHDMIiKikLv3r3x448/IicnB1JSUqIOTeTevXuH5cuXo1mzZjW6/969ewgPDy93QLAuVFd3XEpVVRWzZ89GZmYmOnbsCHl5%2BTqOrGKamppYsWIF/vjjDygoKCApKQkbNmyo951xQmqDEmlCCGnihgwZgkWLFiEhIQGzZs1CUlIS7O3tKaEBoK%2Bvj4iIiGoP/VWmc%2BfOKCoqEnJUNZeRkYElS5YgPj6eP2Vx2LBh8PT0LLc7XNdcXV1hZ2cHIyMj/psAHR0dODs712schNQG1UgTQkgTV1BQAB8fH7Ro0QKzZ89GQkICzpw5wz902JTdv38flpaW6NatW7lE8/Dhw9Xef/v2baxbtw4TJkwo1%2BWDS9s6NtjUSM%2BbNw/i4uKwt7eHoqIikpKS4OHhgbZt22Ljxo1CjYet1NRU8Hg8KCgolJv4yOYAJSGiRIk0IYQ0cYmJidi5cycyMjL4B%2BKKiorw6tUr3LhxQ8TRidaECRPQunVrDBw4sFx5x8KFC6u938HBAf7%2B/pCTkxOokebato4NNom0hoYGIiIiBIae5OXlwdDQkN9NoyFh8z0RIkpU2kEIIU3cn3/%2BCYZh0K5dO2RnZ6NXr178seBN3Zs3bxAbGwsJCYka3X/hwgX4%2BfmhW7duQo6sPDb7YtLS0sjLyxNIpAsLC%2BulhrsmaK%2BPNHR1e4SYEEJIg/fw4UPs2rULNjY2aN26NVavXo3NmzcjJiZG1KGJXM%2BePZGUlFTj%2B9u1a4fOnTvXOo4DBw7gw4cPVb5GR0en2nWsrKwwd%2B5cXLlyBf/%2B%2By%2BioqKwaNEiDBs2DHFxcfxfDQXbA5SEiAqVdhBCSBM3ZMgQREdHo6CgACYmJggPDwcADB48uMkn0zt27MDJkycxatSocru2bEo7zp8/j2vXrmHWrFlo06aNQGKoqKjIOpX0EewAABXbSURBVA4tLS3ExMTUuHtIKVVV1WpfIyYm1mAmC1JpB2noqLSDEEKauM6dOyMiIgJ6enooKSlBUlISJCUl8eXLF1GHJnKxsbH4%2Beef8fTpU4HrbHdKV65cCQAICgri31PaLYNLsqqrq4v9%2B/dj0qRJtWpXl5CQUON7CSHl0Y40IYQ0cWFhYViyZAkCAwMREBCAf/75B82aNcOQIUOwYcMGUYcnUkFBQTA0NKxxT%2B1nz55V2vlESUmJ9TrDhw9Henp6hQk8193j9PR0BAQEICUlBfLy8jAxMRFK%2BUldoB1p0tBRIk0IIQQ8Hg%2BysrJo3rw5goODkZ%2BfjwkTJkBSUlLUoYmUlpYWoqKianzY0MDAAP7%2B/gKH%2B2qiqo4aWlparNd58OABZsyYga5du6JTp05ITEzEixcv8Ndff0FDQ6NWMdYFdXV13LlzR9RhEFIpKu0ghBCCDh068P97zJgxIoykYenbty%2BCg4Mxfvz4Gq/x8ePHWifSlSXLOTk5nNbx9PTE4sWLYW1tzb926NAhbNq0CcePH69VjHWBzQFKQkSJdqQJIYSQSpiZmeHRo0eQlJRE%2B/btBUor2PSBdnBwQExMDIYNG1autpnNYcVS9%2B/fh4eHB3g8nkCv75ycHDx8%2BJD1Otra2oiKikLz5v/toxUVFWHQoEG4ffs263WEJSoqCkeOHEFGRgb27t0LHx8fLFu2TCA%2BQhoy%2BpNKCCGEVMLKyqpW9ycnJ0NZWRmvXr3Cq1ev%2BNe5tnVzcXGBsrIyfvnlFyQlJUFHRweHDx/GsmXLOK3TsmVLpKWlQVlZmX8tLS2t3NTF%2BhAQEAB3d3dYWFjwW%2B6FhYVBTEwM9vb29R4PITVBO9KEEEJINbKzs5GSkgI5OblyY6zrQ//%2B/XHz5k0kJyfDzc0NBw8exN27d%2BHi4gJfX1/W63h4eCA6OhrLli3j10hv2bIFQ4cOxfLly%2BvwOyjP1NQUrq6uUFNTg6amJuLi4vD69WtYW1sjMjKyXmMhpKZoR5oQQgipRH5%2BPlasWIGwsDB%2B27rBgwdj69atkJaWZrVGaGgoTp48yU/Ezc3NYWpqyikOaWlptGjRAsrKynj%2B/DkAQE1NDSkpKZzWWbx4MXJycmBjY4OioiJISUnBzMyMU5mJsKSnp6N///4A/tuh79KlS7WDZwhpSGiyISGEEFIJLy8vFBQUIDAwEPfu3YOfnx9KSkrg6enJ6v6AgACsXLkS3bt3x7Rp09CrVy84Ozvj9OnTnOLo2rUrjh8/DikpKfzwww948uQJXrx4wblEREpKChs2bMDt27dx7do13L17F46OjmjRogWndYThp59%2BKldnHh0djS5dutR7LITUFJV2EEIIIZUYPnw4zp49C1lZWf61zMxMjBs3jtXUx3HjxmHVqlUYNGgQ/9qNGzfg4uKC4OBg1nHEx8dj/vz5OH36NGJiYrBu3To0a9YMv/32G1asWFHt/efPn6/2NRMmTGAdjzBER0fDxsYGI0aMQGhoKCZOnIjAwEB4eXlBT0%2BvXmMhpKYokSaEEEIqoa2tjWvXrgn00/78%2BTOGDRuGmzdvVnv/wIEDERcXJ7BzXFJSgoEDB3IeNPL582dISEhAXFwc9%2B/fx/v371m3hzMwMKjyeTExMVZdSIQtISGBX/aioKAAc3Nz9OvXr97jIKSmqEaaEEIIqUT//v2xbds2LF%2B%2BHGJiYmAYBtu2bUPfvn1Z3a%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%2Bvj9jY2HqNSUNDAxEREQJvSvLy8mBoaFjvsRBSU1QjTQghhJRhYGBQZWs5MTExhIaGVrtOQUEBjh07htevX/NHe5dyd3dnHc%2B3JR5lsSntyM/P59dG37t3D9LS0vzSFCkpKRQVFbGORVikpaWRl5cnkEgXFhaibdu29R4LITVFiTQhhBBSxqJFiyq8fvfuXZw8eRK9evVitY6DgwPu3LkDbW1tSEhI1Diew4cPCzzOycnBkSNHMH78eFb3t2nTBjk5OZCRkUFsbCwGDBjAf%2B7ly5do165djWOrKSsrK8ydOxd2dnbo0qULeDwedu7ciWHDhvFHhgOApqZmvcdGCFtU2kEIIYSw4OPjg82bN8PCwgIODg4CLfEqo62tjTNnzkBZWVno8WRmZmLGjBkICgqq9rVr167Fu3fvYGRkBEdHRzg5OcHU1BR5eXlwcHBA%2B/btsXbtWqHHWBVVVdVqX8P2MCUhokI70oQQQkgV8vLysGLFCty6dQuenp4YPXo063ulpKTQoUOHOolLWloaPB6P1WuXLFkCOzs7rFq1CmPHjuWPKNfT04OcnFy9J9HA1x7ShDR2tCNNCCGEVOLu3btYsmQJ2rVrh23btnHeWfb29kZGRgYWLlzIr1GuibKTCYuKinDlyhUUFBTgyJEjNV73%2BvXr0NTUhJSUVI3XqI309HQEBAQgJSUF8vLyMDExQefOnUUSCyE1QYk0IYQQUoEDBw5g27ZtmDJlCuzt7VmVcpRlYGCA1NTUCg8KcilZKDuZsFmzZlBRUcHy5cvRrVs3znE1BA8ePMCMGTPQtWtXdOrUCYmJiXjx4gX%2B%2BusvaGhoiDo8QlihRJoQQggpY968eYiIiICVlRWMjY0rfA2bQ3BVtXH7dtohV58/fxbZLrKwWFtbw9DQENbW1vxrhw4dwoULF3D8%2BHERRkYIe5RIE0IIIWVUdxCO6yG4zMxM8Hg8KCkp1ahDRmpqKpYuXYo1a9agd%2B/e2LhxI%2B7evYvt27dDTk6O83oNgba2NqKiotC8%2BX/HtYqKijBo0CDcvn1bhJERwh4dNiSEEELKENZBuKysLCxfvhw3b97kj8E2NjaGm5sbp%2BmIa9euRdeuXdGlSxcAwOzZs7Flyxa4urpi%2B/btQom1vrVs2RJpaWkCdedpaWlo06aNCKMihBtxUQdACCGEfK9cXV3xww8/ICQkBPfv30dAQAA%2BfPiA9evXc1rnzp07WLt2LT/5lpGRwerVq3Hz5s26CLtejBkzBosWLcK1a9fw6tUrREREwNbWFmPGjBF1aISwRjvShBBCSB2JjY1FaGgoWrVqBQDo1q0bNm3ahFGjRnFap3nz5sjJyRFopZebm4sWLVoINd76tHjxYuTk5MDGxgZFRUWQkpKCmZkZFi5cKOrQCGGNEmlCCCGkjrRr1w7v37/nJ9JAzQ4Kjho1Cra2trCzs0PHjh2RlpaG7du3Y%2BTIkcIOud5ISUlhw4YNcHFxQW5uLtq3b1/lWHZCGiI6bEgIIYQIWemI62vXriEyMhKLFy%2BGkpISMjIysGPHDujq6nLaef348SPWrl2L4OBgFBYWQlJSEhMmTMDKlSvxww8/1NW3USfK9sSuyIQJE%2BohEkJqjxJpQgghRMiE3fWjVFFREXJzcyErK9tod2/L9sQuS0xMDFeuXKmnaAipHUqkCSGEkAYqMDAQJiYmVe7iNsbd2%2BLiYuTm5vKnPcbExCAhIQF6enro2rWriKMjhD1KpAkhhJA69PHjR%2BTm5qKkpATA113lZ8%2BewcjIqNp7TUxMEBgYWOkubmPcveXxePj999/Rr18/uLu7IyAgACtWrICqqioSExNx8OBB9O3bV9RhEsIKJdKEEEJIHTl79ixcXV3x%2BfNngeuysrK4fv0663Vu3boFdXV1NGvWTNgh1ruVK1eisLAQf/75J2RlZWFsbIzRo0djyZIl8Pf3R2BgIPbt2yfqMAlhhbp2EEIIIXXE29sbdnZ2aNWqFeLi4jB9%2BnR4enpCR0eH0zoLFizA1atX0bJlyzqKtP5ERUXBz88PMjIySE1NRWJiIsaNGwcAGDFiBNatWyfiCAlhjwayEEIIIXUkMzMT06dPx%2BDBg5GYmIjevXtj/fr1OH36NKd1lJWV8eDBgzqKsn7l5%2Bfza6Pv3bsHaWlpqKioAPjaEq%2BoqEiU4RHCCe1IE0IIIXVEVlYWRUVF6NixI169egUAUFRURHZ2Nqd12rRpg5kzZ6JTp06Ql5cX6Nhx%2BPBhocZc19q0aYOcnBzIyMggNjYWAwYM4D/38uVLtGvXToTREcINJdKEEEJIHenXrx8cHR2xZs0a/PTTTzh%2B/DhatGiBtm3bclpHXV0d6urqdRRl/dLX14erqyuMjIwQEBAAJycnAEBeXh62bdsGXV1dEUdICHt02JAQQgipIxkZGVi9ejXWrVuHxMREzJs3D58%2BfYK7uztMTU1ZrxMSEoLRo0eXu37y5ElMmTJFmCHXuby8PNjZ2SE%2BPh5jx46Fm5sbgK9vFuTk5HDs2DG0b99exFESwg4l0oQQQkg9%2BfLlC4qKigQODe7btw9z5swp99qPHz/i7du3AICxY8ciODgY3/6T/f79e/z666%2B4c%2BdO3QdeD65fvw5NTU3O49MJESVKpAkhhBARGjBgAOLj48tdz8zMhLGxMT59%2BlTuOYZhICYmBkNDQ%2BzYsaM%2BwiSEVIBqpAkhhBARqmw/S05ODqGhofj48SNMTU0RGBgo8LyUlBSVQBAiYpRIE0IIISL0bQeOsmRlZQEAt2/fhrg4dawlpKGhRJoQQghp4DIzM7Fnzx68fv2aP2q8VGNrf0fI94QSaUIIIaSBc3BwQFZWFvT19SEhISHqcAgh/48SaUIIIaSBe/DgAS5evMifCEgIaRio4IoQQggRITbNs1q3bg1JScl6iIYQwkUzZ2dnZ1EHQQghhDRVOTk51U7za9WqFf766y906tQJxcXFeP/%2BPf9X69at6ylSQkhZ1EeaEEIIqUOnTp3CkSNHkJGRgXPnzmHDhg1wd3dHq1atWK%2Bhqqoq8FhMTIzfS/rJkyfCDpkQwhLVSBNCCCF15O%2B//8bx48cxa9YseHh4oFWrVsjIyIC7uzvWrVvHep0rV67UYZSEkJqiGmlCCCGkjhw/fhy7d%2B/G5MmTIS4ujjZt2mD79u0IDw/ntI6SkhKUlJSQm5uLR48eQU5ODi1atICSklIdRU4IYYMSaUIIIaSOvH37Fj///DOA/w4VysrK4suXL5zWyc7Oxq%2B//orJkydjxYoVSEpKgqGhIe7cuSP0mAkh7FEiTQghhNQRVVVVnDx5EsB/EwyDg4Pxyy%2B/cFpn/fr16N69O%2BLi4tC8eXOoqKhgzpw58PDwEHrMhBD26LAhIYQQUkcePXqEGTNmQEVFBQ8fPsTgwYNx9%2B5dHDhwAP3792e9jo6ODkJDQ9GyZUtoaWkhNjYWRUVFGDJkCOLi4urwOyCEVIUOGxJCCCF1pHfv3ggKCoK/vz969uwJBQUFrF27FoqKipzWkZCQwKdPn9CyZUt%2BiUhBQQGnzh%2BEEOGjRJoQQgipIz4%2BPpgwYQL%2B97//1WodAwMD/PHHH1i9ejXExMSQnZ2NdevWQU9PT0iREkJqgmqkCSGEkDpy4cIF6OnpYdGiRYiMjGQ1xfBbRUVFAIBly5bhhx9%2BwKhRo5CXl4ehQ4fi48ePWL58eV2ETQhhiWqkCSGEkDr04sUL%2BPr6IiAgAOLi4pg0aRImTZqETp06VXuvjo4OfvvtN/z222%2BQlZVFTk4OkpOToaCgAHl5%2BXqInhBSFUqkCSGEkHpQUlKC8PBwuLm5IT09HY8fP672nlOnTuHEiRN4/vw5xowZA2tra/Tu3bseoiWEsEGJNCGEEFLHbty4AT8/P1y8eBFdu3bF5MmTMXnyZNb33717FydOnEBISAh69eoFa2trGBsbo1mzZnUYNSGkOpRIE0IIIXVky5YtCAgIQEFBAUxMTGBhYQFVVdUar5ebm4szZ87g1KlT%2BPz5MywtLTFnzhwhRkwI4YISaUIIIaSOWFlZwcLCAqNGjYKUlJTQ1n337h02bNgAPz8/PHnyRGjrEkK4oUSaEEIIqWc5OTmQkZHhfN/Nmzdx6tQphIaGQkNDA1OnTsWIESPqIEJCCBvUR5oQQgipI/fv34eHhwd4PB5KSkoAfG1pl5OTg4cPH7JaIzs7G76%2Bvjhz5gwyMzMxbtw4%2BPr6QkVFpS5DJ4SwQIk0IYQQUkdcXFygrKyMX375BUlJSdDR0cHhw4exbNkyVvcvWrQI4eHhUFRUhKWlJczNzfHjjz/WcdSEELYokSaEEELqyPPnz/%2BvvTtWaSWKojC8JiEaLOyCINgoQhqDEQRhkOTMvEAECVgGK4sgCIHUgrUWFjYWgmBrah1sNAzpRMEiSAoTBG0sgoIg%2BAj33vGMww3/1x9Y5WLD2Vunp6fq9/va29tTrVZTsVjU7u6uarXaH9%2B/v7/r8PBQpVJJjuP8QmIA/4IiDQBATCYnJ5XNZjUzM6NutytJWlxc1GAw%2BKv3x8fHccYD8EOcCAcAICazs7M6OzvT%2BPi4JiYm9PDwoMfHR6bLwIhgIg0AQEy2t7e1tbUl13W1ubmparWqdDqtjY2NpKMBsID1dwAAWLa%2Bvi7P82SM0dzcnDKZjBzH0e3trYbDoVzXTToiAAso0gAAWHZwcKAwDHV3d6dcLidjjIwxWllZ0djYWNLxAFhCkQYAICbD4VBhGKrdbqvdbuvl5UWu68rzPK2trSUdD8APUaQBAPgFb29varVaOjk50fPzM6e9gRHAZ0MAAGLS6/V0eXmpIAh0f3%2Bv%2Bfl5VSoVznoDI4KJNAAAlu3v7%2Bvi4kJPT09aXl6W53nyPE/T09NJRwNgEUUaAADL8vm8lpaW1Gw2VSgUko4DICYUaQAALGu1WgqCQNfX15qampLv%2B/J9X8ViMeloACyiSAMAEJPPz0/d3NwoCAJdXV1Jkowx8n1fxpiE0wH4KYo0AAC/4OvrS%2Bfn5zo6OlK/32drBzAC2NoBAEBMer2ewjBUGIbqdDpKpVJaXV3Vzs5O0tEAWMBEGgAAyxqNhjqdjl5fX5XP51Uul1Uul7WwsCDHcZKOB8ASJtIAAFj28fGher2uUqmkXC6XdBwAMWEiDQAAAESQSjoAAAAA8D%2BiSAMAAAARUKQBAACACCjSAAAAQAQUaQAAACACijQAAAAQAUUaAAAAiOAbhAKV/jJieC8AAAAASUVORK5CYII%3D\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Target</th>\n",
       "      <th>asymptomatic</th>\n",
       "      <th>nonanginal</th>\n",
       "      <th>nontypical</th>\n",
       "      <th>typical</th>\n",
       "      <th>Normal_restECG</th>\n",
       "      <th>Wave_abnormal_restECG</th>\n",
       "      <th>Ventricular_ht_restECG</th>\n",
       "      <th>Slope_upsloping</th>\n",
       "      <th>Slope_flat</th>\n",
       "      <th>Slope_downsloping</th>\n",
       "      <th>fixed</th>\n",
       "      <th>normal</th>\n",
       "      <th>reversable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x2a3c8f22e10>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_profiling\n",
    "pandas_profiling.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    163\n",
      "1    138\n",
      "Name: Target, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "d=df['Target'].value_counts()\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Heart Diseases Ratio in Dataset\n",
    "###### Blue Graph indicate no heart desease and Orange Graph show Heart desease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "def plotTarget():\n",
    "    sns.countplot(x='Target', data=df, ax=ax)\n",
    "    for i, p in enumerate(ax.patches):\n",
    "        count=df['Target'].value_counts().values[i]\n",
    "        x=p.get_x()+ p.get_width() /2.\n",
    "        y=p.get_height() + 3\n",
    "        label='{:1.2f}'.format(count / float(df.shape[0]))\n",
    "        ax.text(x, y,label, ha='center')\n",
    "        \n",
    "fig_target,ax=plt.subplots(nrows=1, ncols=1, figsize=(5, 2))\n",
    "plotTarget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking the corelation of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098138</td>\n",
       "      <td>0.284734</td>\n",
       "      <td>0.208287</td>\n",
       "      <td>0.121670</td>\n",
       "      <td>0.149037</td>\n",
       "      <td>-0.395982</td>\n",
       "      <td>0.092985</td>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.162228</td>\n",
       "      <td>0.331939</td>\n",
       "      <td>0.224394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>-0.098138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065284</td>\n",
       "      <td>-0.202126</td>\n",
       "      <td>0.041025</td>\n",
       "      <td>0.029016</td>\n",
       "      <td>-0.057065</td>\n",
       "      <td>0.140802</td>\n",
       "      <td>0.098482</td>\n",
       "      <td>0.031571</td>\n",
       "      <td>0.100345</td>\n",
       "      <td>0.272006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RestBP</th>\n",
       "      <td>0.284734</td>\n",
       "      <td>-0.065284</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129371</td>\n",
       "      <td>0.178498</td>\n",
       "      <td>0.147089</td>\n",
       "      <td>-0.046402</td>\n",
       "      <td>0.065564</td>\n",
       "      <td>0.188801</td>\n",
       "      <td>0.117437</td>\n",
       "      <td>0.100535</td>\n",
       "      <td>0.151471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chol</th>\n",
       "      <td>0.208287</td>\n",
       "      <td>-0.202126</td>\n",
       "      <td>0.129371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015762</td>\n",
       "      <td>0.171185</td>\n",
       "      <td>-0.005690</td>\n",
       "      <td>0.064250</td>\n",
       "      <td>0.044836</td>\n",
       "      <td>-0.004228</td>\n",
       "      <td>0.106125</td>\n",
       "      <td>0.086762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fbs</th>\n",
       "      <td>0.121670</td>\n",
       "      <td>0.041025</td>\n",
       "      <td>0.178498</td>\n",
       "      <td>0.015762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.079892</td>\n",
       "      <td>-0.012297</td>\n",
       "      <td>0.013534</td>\n",
       "      <td>0.004855</td>\n",
       "      <td>0.054079</td>\n",
       "      <td>0.164689</td>\n",
       "      <td>0.015613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RestECG</th>\n",
       "      <td>0.149037</td>\n",
       "      <td>0.029016</td>\n",
       "      <td>0.147089</td>\n",
       "      <td>0.171185</td>\n",
       "      <td>0.079892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077950</td>\n",
       "      <td>0.092626</td>\n",
       "      <td>0.117580</td>\n",
       "      <td>0.140144</td>\n",
       "      <td>0.127330</td>\n",
       "      <td>0.177049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxHR</th>\n",
       "      <td>-0.395982</td>\n",
       "      <td>-0.057065</td>\n",
       "      <td>-0.046402</td>\n",
       "      <td>-0.005690</td>\n",
       "      <td>-0.012297</td>\n",
       "      <td>-0.077950</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.386043</td>\n",
       "      <td>-0.349391</td>\n",
       "      <td>-0.393527</td>\n",
       "      <td>-0.256365</td>\n",
       "      <td>-0.425870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExAng</th>\n",
       "      <td>0.092985</td>\n",
       "      <td>0.140802</td>\n",
       "      <td>0.065564</td>\n",
       "      <td>0.064250</td>\n",
       "      <td>0.013534</td>\n",
       "      <td>0.092626</td>\n",
       "      <td>-0.386043</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.287926</td>\n",
       "      <td>0.254076</td>\n",
       "      <td>0.151731</td>\n",
       "      <td>0.427860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oldpeak</th>\n",
       "      <td>0.203604</td>\n",
       "      <td>0.098482</td>\n",
       "      <td>0.188801</td>\n",
       "      <td>0.044836</td>\n",
       "      <td>0.004855</td>\n",
       "      <td>0.117580</td>\n",
       "      <td>-0.349391</td>\n",
       "      <td>0.287926</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.576795</td>\n",
       "      <td>0.274451</td>\n",
       "      <td>0.423894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>0.162228</td>\n",
       "      <td>0.031571</td>\n",
       "      <td>0.117437</td>\n",
       "      <td>-0.004228</td>\n",
       "      <td>0.054079</td>\n",
       "      <td>0.140144</td>\n",
       "      <td>-0.393527</td>\n",
       "      <td>0.254076</td>\n",
       "      <td>0.576795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.335806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ca</th>\n",
       "      <td>0.331939</td>\n",
       "      <td>0.100345</td>\n",
       "      <td>0.100535</td>\n",
       "      <td>0.106125</td>\n",
       "      <td>0.164689</td>\n",
       "      <td>0.127330</td>\n",
       "      <td>-0.256365</td>\n",
       "      <td>0.151731</td>\n",
       "      <td>0.274451</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.454822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <td>0.224394</td>\n",
       "      <td>0.272006</td>\n",
       "      <td>0.151471</td>\n",
       "      <td>0.086762</td>\n",
       "      <td>0.015613</td>\n",
       "      <td>0.177049</td>\n",
       "      <td>-0.425870</td>\n",
       "      <td>0.427860</td>\n",
       "      <td>0.423894</td>\n",
       "      <td>0.335806</td>\n",
       "      <td>0.454822</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Sex    RestBP      Chol       Fbs   RestECG     MaxHR  \\\n",
       "Age      1.000000 -0.098138  0.284734  0.208287  0.121670  0.149037 -0.395982   \n",
       "Sex     -0.098138  1.000000 -0.065284 -0.202126  0.041025  0.029016 -0.057065   \n",
       "RestBP   0.284734 -0.065284  1.000000  0.129371  0.178498  0.147089 -0.046402   \n",
       "Chol     0.208287 -0.202126  0.129371  1.000000  0.015762  0.171185 -0.005690   \n",
       "Fbs      0.121670  0.041025  0.178498  0.015762  1.000000  0.079892 -0.012297   \n",
       "RestECG  0.149037  0.029016  0.147089  0.171185  0.079892  1.000000 -0.077950   \n",
       "MaxHR   -0.395982 -0.057065 -0.046402 -0.005690 -0.012297 -0.077950  1.000000   \n",
       "ExAng    0.092985  0.140802  0.065564  0.064250  0.013534  0.092626 -0.386043   \n",
       "Oldpeak  0.203604  0.098482  0.188801  0.044836  0.004855  0.117580 -0.349391   \n",
       "Slope    0.162228  0.031571  0.117437 -0.004228  0.054079  0.140144 -0.393527   \n",
       "Ca       0.331939  0.100345  0.100535  0.106125  0.164689  0.127330 -0.256365   \n",
       "Target   0.224394  0.272006  0.151471  0.086762  0.015613  0.177049 -0.425870   \n",
       "\n",
       "            ExAng   Oldpeak     Slope        Ca    Target  \n",
       "Age      0.092985  0.203604  0.162228  0.331939  0.224394  \n",
       "Sex      0.140802  0.098482  0.031571  0.100345  0.272006  \n",
       "RestBP   0.065564  0.188801  0.117437  0.100535  0.151471  \n",
       "Chol     0.064250  0.044836 -0.004228  0.106125  0.086762  \n",
       "Fbs      0.013534  0.004855  0.054079  0.164689  0.015613  \n",
       "RestECG  0.092626  0.117580  0.140144  0.127330  0.177049  \n",
       "MaxHR   -0.386043 -0.349391 -0.393527 -0.256365 -0.425870  \n",
       "ExAng    1.000000  0.287926  0.254076  0.151731  0.427860  \n",
       "Oldpeak  0.287926  1.000000  0.576795  0.274451  0.423894  \n",
       "Slope    0.254076  0.576795  1.000000  0.102041  0.335806  \n",
       "Ca       0.151731  0.274451  0.102041  1.000000  0.454822  \n",
       "Target   0.427860  0.423894  0.335806  0.454822  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Select Age as most dependent data on label \n",
    "###### Disease Probability Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotAge():\n",
    "    facet_grid = sns.FacetGrid(df, hue='Target')\n",
    "    facet_grid.map(sns.kdeplot, \"Age\", shade=True, ax=axes[0])\n",
    "    legend_labels = ['Disease false', 'Disease true']\n",
    "    for t, l in zip(axes[0].get_legend().texts, legend_labels):\n",
    "        t.set_text(l)\n",
    "        axes[0].set(xlabel='Age', ylabel='Density')\n",
    "\n",
    "    avg = df[[\"Age\", \"Target\"]].groupby(['Age'], as_index=False).mean()\n",
    "    sns.barplot(x='Age', y='Target', data=avg, ax=axes[1])\n",
    "    axes[1].set(xlabel='Age', ylabel='disease probability')\n",
    "\n",
    "    plt.clf()\n",
    "\n",
    "fig_age, axes = plt.subplots(nrows=2, ncols=1, figsize=(15, 8))\n",
    "plotAge()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Checking For Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "asymptomatic    143\n",
       "nonanginal       85\n",
       "nontypical       50\n",
       "typical          23\n",
       "Name: ChestPain, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df['ChestPain']\n",
    "x.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal        166\n",
       "reversable    117\n",
       "fixed          18\n",
       "Name: Thal, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df['Thal']\n",
    "x.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ploting Function For Categorical Data \" Chest Pain\" && \"Thalassemia\"\n",
    "##### Ploting Function For Continoius Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "category=[('ChestPain', ['typical','nontypical','nonanginal','asymptomatic']),('Thal',['fixed','normal','reversable',])]\n",
    "continuous = [('Age', 'Age in year'),\n",
    "              ('Sex','1 for Male 0 for Female'),\n",
    "              ('RestBP','BP in Rest State'),\n",
    "              ('Fbs','Fasting blood glucose'),\n",
    "              ('RestECG','ECG at rest'),\n",
    "              ('Chol', 'serum cholestoral in mg/d'),\n",
    "              ('MaxHR','Max Heart Rate'),\n",
    "              ('ExAng','Exchange Rate'),\n",
    "              ('Slope','Slope of Curve'),\n",
    "              ('Oldpeak', 'ST depression by exercise relative to rest'), \n",
    "              ('Ca', '# major vessels: (0-3) colored by flourosopy')]\n",
    "\n",
    "        \n",
    "def plotCategorial(attribute, labels, ax_index):\n",
    "    sns.countplot(x=attribute, data=df, ax=axes[ax_index][0])\n",
    "    sns.countplot(x='Target', hue=attribute, data=df, ax=axes[ax_index][1])\n",
    "    avg = df[[attribute, 'Target']].groupby([attribute], as_index=False).mean()\n",
    "    sns.barplot(x=attribute, y='Target', hue=attribute, data=avg, ax=axes[ax_index][2])\n",
    "    \n",
    "    for t, l in zip(axes[ax_index][1].get_legend().texts, labels):\n",
    "        t.set_text(l)\n",
    "    for t, l in zip(axes[ax_index][2].get_legend().texts, labels):\n",
    "        t.set_text(l)\n",
    "\n",
    "\n",
    "def plotContinuous(attribute, xlabel, ax_index):\n",
    "    sns.distplot(df[[attribute]], ax=axes[ax_index][0])\n",
    "    axes[ax_index][0].set(xlabel=xlabel, ylabel='density')\n",
    "    sns.violinplot(x='Target', y=attribute, data=df, ax=axes[ax_index][1])\n",
    "    \n",
    "    \n",
    "def plotGrid(isCategorial):\n",
    "    if isCategorial:\n",
    "        [plotCategorial(x[0], x[1], i) for i, x in enumerate(category)] \n",
    "    else:\n",
    "        [plotContinuous(x[0], x[1], i) for i, x in enumerate(continuous)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Categorical Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_categorial,axes=plt.subplots(nrows=len(category), ncols=3, figsize=(10, 10))\n",
    "plotGrid(isCategorial=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Continuous Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YLyiJE5HFPT+zBjYcTTu3/VX+5O2dfTWE2S0syItnXm4sQVYzr++sDHR4nzIuLpSXvjKTZTsr+dnK/dzw+Druu3wcDyzMHRbJpnZOnUqpGHrGRCulZgGtgQ0pMDo6Oli3bh0frFrFzh07EBF8IbG4M+fgickG08iY7SfWYFyZl+FOnoq17gCbdxayefNmgh0Orpg/n4ULFzJt2jTM5pH37/PnP/sZu3bvxmoSbh3XzcQoD6mhBvYR9Fa6DUVQUBCLFy9mxYoVdBsDksIPOIdFenXPfsInUNtloqTNwgeVHjZu3MjmTZtY9vrrREVFDXgc/fpXp5SaA+wWkU6l1N3AdOC3IlI+INFpWg+34eO9/TW8tKWcjSWNhAVZeGBhLvfOyWLlnupznyCAlFLcOiOVK/Pj+dnKA/xhdQkrCqv52c2TmJcbF+jwtLP7DrAc/ySuDUAc/vqYY0ZhYSFvvvkma9euwzA8EBSOK3EyRkw2vuCB/1AaKmILwZ1agDtlOua2KjyNpbzz/ir++c9/EhkVzfXXXcuSJUtISEgIdKh9dv0NN7Bv3z7cHg+vlTpY5xBSHB5/65vDS3ywl/jg4d1V2mUoFi9ezP3334+IsHblq4EO6YxEoNNQ1HX7u1vruv3j6qq6LFR0mPH0zAVZeNVVREREDEoM/f3q9EdgqlJqKvB9/ONEngfm9zcwTfP5hF3Hmnlnbw2v76yksdNNalQw37smj7tnZRARPLKKOEaH2PjVv0zllhkp/OiNvXz+6a185uJk/vvGiUSHjOxunNFKRHYqpeYDefh7pg6KiKcvxyqlyoB2wAsYIlKglIrGP6YuE3+duH853dqpw0FDQwMPPfQLduzYjrLYcUXn4InJxhcSx6jqg1MmvBGpeCNScfoMLC0VGI3FvPTyy7zyyivcfvvt3HvvvSOiZW7RokVcfvnl7NmzhwMHDnD48GGOlh2hsLIKw/tJi5FZ+Yvtxtg8xAZ5iQnyERvkO/F7TJAPS4BqJTsswooVKxARVq5cScIglSfpCxFodSsanGYanCYanaZPfndZaHSa6TY+HV9cTDTpuVlckp1NXl4e06ZNIzo6etBi7G8SZ4iI9Cwt85iIPK2UumcgAtNGvpe2nL1BVkTwimB4BbfXR7vToLnTTW27k8rmbo42dvkXoleQlxjO4inJ5CaEYlKKlYXDu/XtbC7LjuWf35zHH1aX8IePillf3MBPPzOZayclBjo07SRKqc+etGm8UqoV2CMidX04xQIRaej1/AfAKhH5hVLqBz3Ph2Vdzf/+8Y85UHQIZ9qleOLywDyEX5q87k91qXV43ec+ZiCYLBjRmRjRmShXB/aqXbz00kuEh4dzxx13DE0M/WS32ykoKKCg4JMVmwzDoK6ujqqqKqqqqqitraW6upramhr211TTVNtC7yU4lYLoIIi1e4gP8rfeJfS05iU5vIPaPRtsEZwdTpYtW+Z/Hjm4SZzXB/VOEzVd/rFt/hY1E/UuKw1OheekEbChIQ4SEhLISEpmZmIiiT2PlJQUkpKSCA4OHtR4T9bfJK69Z8Dv3cDlSikzMLKaR7QBJSKUNnSyt7KVd/fV0NbtodNt0OEy6HZ7MbyCx+fD6/Mnb6f756mAmFAbE5PDyY4LZXxCGMG24f8t+HwEWc18Z9F4rp2YyPde+5h/fWEHN05N5n9u0q1yw8y9wGzgo57nVwCb8Sdz/ysiz5/n+Zb0nAPgWWA1wzSJa2lpQcw2vOFJQ5vAAcpws/imT7rUXv3Hu0N6ffB3txphiVgbDtPcPCwbS/vMYrGQnJxMcnLyaV/3eDzU1dVRW1tLbW0tNTU11NTUUFVZyf6qStbVfPr9xzkgzeEmM8xgXLhBboRBcABbzPrK64PyDjOHW62UtZsp77RR3Wk60e0J4AgOIjklhdzkFC5PSiIhIeFEopaQkEBISEjg3sBp9DeJux34HHCviNQopdKBR/ofljaS+HzC5iON/OPjKj4qqqemzV/B26wUYUEWQuwWwuxW4sOCsJoVFpMJS89P/3OFxWwiPMhCRLCNuDA7tkC15Q+gc7VEHnfHJemsOVTH24XVbCpp4KefmcS1k0Zn2YMRyAdMEJFaOFE37o/ATGAt/uEjZyLAe0opAf4sIk8CCcfXThWRaqXUsK0G/Z8/+AE/+tF/wf7luGNycKdMR2xD8wEmFtunutTEMrRV9M3tNQRVbMfUUcdFEydy1113Den1h5rVaj1R9+x0nE4nVVVVJ1ZsOHLkCMWHD7GrrAoRwawgJ8JgZryTyxLdg7ZCw4XwCexptLKh1k5hk52unsEQkRHhjJ+Qz+ysLDIzM0lP9xf3jYiIGFHFoFXvJtTRqqCgQLZv3x7oMEa8k5MSl+Fle1kzm0obaep0YzObGJ8QSk58GOnRDuLC7H1aXF7zq27t5qODdeytbOOmqck8uGQSEQ7dsH0hlFI7RKTg3Hue8zx7RGRyr+cKf1fqJKXULhGZdpZjk0WkqidRex/4BrBcRCJ77dMsIqfMDlBK3QfcB5Cenj7j6NGj/X0rF6StrY3nnnuON958E59Ad9osPHHjB/26wUVvY2mvOfHcCEukO//6Qb8uPoOgo5uwNhwmOiaGLy1dynXXXTcixsMFQldXF/v372fXrl1s3LCeI2VHcVjh5sxOFqU6L2jyxAuHHKyr9hfOzQjznveKDb0dbrXw14NhHOswER4Wypy58ygoKGDy5MnExcUN62Str/ewfiVxPeNFHgbi8feCKfyFgANYpvtUOokbGMeTOMPrY3NpI2sO1dPp9pIe7WDWuBguSgofFS1ogXRbQSp/XF3C46sOExtq55HbpugZrBdgAJO4PwDpwN97Nt0CVADfA1aIyII+nucnQAfwFeCKnla4JGC1iOSd7djhcP+qrq7m0UcfZceOHXTlXYs3/PTdcgMlUEmcvXwLttp93H333dx9990EBemVVs5HUVER//d/T7N16zZmxbu476KOC5og8fOd/hTih9PbLjiWDdU2nioKIzYujq/c91WuuOKKEbXofV/vYf19R78EbhSRA/08jzZClDZ08NauKuo7XOTEh7IwP56MmOE1RmAk+/v2CmJD7Xz18mxe3X6Mzz+9lVnjYrh2YuIZE+TPzUwf4ijHlK8DnwXm9jzfCiSJSCdwxgROKRUCmESkvef3q4H/xV+u5B7gFz0/3xrE2AdMUlISP//5z7nmmmswt9cMehIXKNaOGgoKCvjyl78c6FBGpPz8fB5++Je89NJL/OUvf6HDMPFvE9sJG8Rluk7mE3i7PIhXS0KYNu1iHnzwp4SGhg7Z9Ydaf5O4Wp3AjQ1NnW6W7ahgR3kzUQ4r98zO1CsPDKKUqGDuvzKH9/bVsKGkkeK6dm6bkUZa9NCODRrrembfl+AfA/cvwBFgWR8OTQDe6OmusQAvicg7SqltwKtKqXuBcuC2wYl84Cml/A93p7/2wjDuirogXjcYbkwDsOLEWKaU4q677iIqKopf/+pX/HCrlVuzOpib6GKwV0IsabXwckkIh1osLFiwgB/84AfDctH6gdTfJG67UuoV4E3AdXyjiLzez/Nqw4SInFhtoLXbw/zxcSzIi9fdpkPAajZxw5Rk8pPCeW1HBX9eW8IVefEsyIvXYw0HmVJqPHAHcCfQiL+2m+pr96mIlAJTT7O9EVg4gKEOGZvNxvXXX8/KlSuxdjXiisnBiExD7OEjN6ETH+aOOizNR7E3lYDh4sYbbwx0VKPC9ddfT25uLr/+1aM8XXSQ5UdDuCa1i3lJToIHsFfTJ7Cnyco7x4LZ12QlMjyM//iPr3HttdcO6zFvA6W/f5ThQBf+roLjBNBJ3ChQXNfBf725h82lTczIiGJOTiyJ4XqMyFDLjgvlgStzWVFYxYdFdRysaee2GanE67+LwVQErMM/XKQYQCn17cCGFHjf/e53mTJlCn9/7TWKD2+FY1vBHoonJB5vaDzekDh8jmgw9W8igM8RjXT51/70OmL85xwIhgtzZwPmznrMHbVYO+sRw43ZbGb27Mu4++67yM/PH5hraeTm5vKHP/6JjRs38tKLL/LC/v0sOxLCvMRuFqU6SXD4zn2SM3AasK4miPcrHdR0KmKiIvnqV29nyZIlOBxjp8eiX0mciCwdqEC04cPp8fKH1SX8aXUJQVYTD312MrcXpPG3bccCHdqYFWwzc1tBGhOSwnlzdyVPfFTMNRMTmZ0dE+jQRqtb8LfEfaSUegf4G/6JW2OayWTimmuu4ZprrqG6upotW7awe/dudn9cSEt5qX8nZcLniMZwxOALicUbEutfnkv1vfXelT4LU1cTwIVPaPB6MHc2YOpswNzVgLWrEZz+gfJKKdLS05l2xXVMmzaNGTNmEBamh4cMBqUUc+bMYc6cORw4cIBly5bx4Ucf8X5FMJfEu1iS2U1aqPfcJ+rR6VG8c8yfvHV5YEJ+HvfecitXXHEFVuvYm83f39mp4/HXTEromXI/BbhJRH46UAEOhOEwu2ukWHOonp8s38eRhk4+c3EyP7rhIuLC/GMK+lr3TBtc7U4Pb+yqpKimnXFxITx/70xSIoe2SvhwN4CzU0OAz+DvVr0Sf4HeN0Tkvf6eu69Gwv1LRKitraWoqIiDBw9SVFREUdFBurv9pSGU2YLhiMUIjccbmoA3LAHMZy9qHVz0NtD3JE65OjC315xoYVPdzf6xe0BsXBwT8vPJz88nLy+P/Pz8UT3YfbhrbGxk2bJlvPnG63R3O5mb5OT27C7Cbf6/rxcO+VvSepcW8Ql8WGln2ZFQOj0wd+4c7rzzc0ycODEg72GwDVWJkTX4p9r/+Xi9JKXUXhGZdMEnHQQj4SYYaMeaunhwxX7e219LZoyDBz9z6uLsOokbPkSE7UebWVlYjd1i4n+WTOTmaSljYgxIXwxUEnfSOaPxT0S4XUSuHMhzn81IvX/5fD4qKyt7Eroi9u7dx+Hiw/i8XlAKX2g8nvBUPFGZSPCpi4OfM4kTH+b2WizNR7G1V0J3q/84h4NJEydy0UUXkd+TuEVFnVKKTxsG2tvbeeGFF1j22ms4LF6+nNfGxbGnLk3c6DTxx/1hHGqxMH36NL72ta+Tk5MTgIiHzlAlcdtE5JLeRS+VUrtF5OILPukgGKk3waHQ5vTw1Loj/HlNCSal+MbCHO6dm4XdcuqYFp3EDT9NnW5WH6xj+9FmrpuUyM9unqyX7WJwkrhAGU33L6fTyf79+9m5cyebt2yh+PBhAHwhsbji8jFisk+MpztTEqc8XVhrDxDUVIy4OrFarUyfPp2CggKmTZvGuHHj9AzTEaa0tJSf/fRBSkqPcOu4Lm7M6D4xV+ZQi4XH9kbgNQfxzW99m6uvvnpMfFkdqjpxDUqpbPyTGVBK3QqM3JXJx5AOl8GzG8t4cm0prd0eFk9J4ofXTyBZd8uNKNEhNl756myeXFvKr98/yNYjTXx70XjuuCQNy2DP59e08xQUFMT06dOZPn06X/7yl6mrq2PNmjX8Y8UKysvWQ/VuulMKMKKzTj3Ya2Cr3k1Q7X4QL5fOnMl1117LzJkzh3zRcW1gjRs3jj/88U888sgjvPbBB3R4FHfmdHGg2cKv9kQQn5DEQ794mPR0XRPzZP1tiRsHPAlcBjTjr6F0l4gEZo2YMxhN32T7q7bNyUtbyvnLulK63F7yE8NYmJ9ASpS+CY5Ux4v9Hqhu48dv7WNrWRPjE0L50Q0XMX/82FztQbfEjSwiwrZt23jyyb9QXHwYT0wOytUOStGdfz3K1U5I8QeormYWLVrEPffcQ2pqaqDD1gaYiPD444/zxhtv8NmsLt6pcBCfnM5vH3ucyMjIc59gFBnU7lSl1HdO2hQMmIBOABH59XmfdBCNhZvg2fh8wpYjTbyw+Sjv7qvB8Al5CWFcmR+vi8eOAr1XbBAR3t1Xw8/fLqK8qYuZWdH86/xsrsgb3usEDrRAJ3FKqTTgOSAR8AFPishjPctvfQWo79n1hyLy9tnONZbuX16vl+eee45nn30WnyUIX1AkzpwFhBatIMQs/OQnP+aSSy4JdJjaIPL5fHzrW9+ksHAPJpOJF154geTk0blCyNkMdnfq8bnYecAl+JeOUcDngbUXeE5tAIkIH1e0suLjKlbUPGNTAAAgAElEQVTuqaa61UlEsJWlczK5e1YGG4obAx2iNgiUUlw7KYkF+fG8uNnf4rr0r9sYnxDKV+aN44YpSThsI2f9wBHMAL4rIjuVUmHADqXU+z2v/UZEHg1gbMOW2Wxm6dKlWK1WnnrqKXwmM/byLZiNbn712yd0DbcxwGQysXTpl/jud7/LzTffPCYTuPPR3+7U94BbRKS953kY8HcRuXaA4hsQY+WbbJvTw8biBtYcqmfNwXqqWp1YzYr54+NYPCWZayYmEmzzDxrWkxTGBsPnY09FK+sON1DT5sRmNjEhKYwHFuYyLzdu1K68EeiWuJMppd4CngDmAB3nk8SNlftXb16vl3vv/TJHjlWgvB4+97nPcd999wU6LG0IGYYxohasH2hDNbEhHXD3eu4GMvt5Tq0PRISaNic7j7bw4pajHG3sorq1G5+A3WIiJz6U2dkxXJQUQbDNTJfbyxu7KgMdtjbELCYT09KjuDgtkiONnRQea2VPZSv3PrudEJuZgsxoZmfHMGtcDBOSwk47K1nrH6VUJjAN2II/ibtfKfUFYDv+1rrm0xxzH3AfMCYHc5vNZpYsuYnHHnsMgCVLlgQ4Im2ojeUE7nz090/peWCrUuoN/DNUb8ZfDPOslFLXAo8BZuApEfnFSa/b8Y8nmYF/3cLbRaSs57X/BO4FvMADIvJuP9/DsObzCbXtTo42dnG0sZPDtR0cqGnjQHU7TZ3+/NlqVqRGOZg/Po7c+DDSoh16bU3tU5RSjIsNZVxsKIunJpESGczqg/VsLm3kF/8sAsBiUmTFhpCXGEZufBipUcEkRwaTHBlEYkSQTvAugFIqFFgGfEtE2pRSfwQexH+/fBD4FfClk48TkSfxTxqjoKDgwrtLRrDp06ef+D0hISGAkWja8NXfZbd+ppT6JzCvZ9NSEdl1tmOUUmbg98AioALYppRaLiL7e+12L9AsIjlKqTuAh4HblVIX4V8KZyKQDHyglBovIn1fsyMAvD7BbfhwGz463QadLoMOl0Gny9vz0/+8uctNQ4eL+vaeR4eLujYXLuOT9eXsFhN5iWEsmpDAhKQwLk6PYk9Fq07atD6zmEzUtrmYkBTOhKRw2p0eynpacmtbnWwobmBF4amVguLC7MSG2okOsRLlsBEdYiPSYSPaYSUqxEZEsJUQu4UQm4UQuxlHz89gq3lMTao4TillxZ/AvSgirwOISG2v1/8CrAhQeMNeSkoKAJmZmYENRNOGsX63V4rITmDneRxyKVAsIqUASqm/AUuA3kncEuAnPb+/Bjyh/J8CS4C/iYgLOKKUKu4536Z+vYkev1t1mPXFDQj+7koR8Ikg+Jf8+NS2np/g/+kT8Hh9eAwfbq8Pl+HD4/Unbr7z+B7tsJkJtVsIC7IQE2InIzqE6BAbMaE2YkLsRARbP5Ww7a9q0wmc1i9hQVYmp0QwOeWTqvker4/Wbg+t3R5aujy0dLuJDbHT2OmiqdNNVUsbzV1uWrpOra5+MqXAYTVjMZuwmhUWkwmLWWE1mzCbFBaT/3eLWaHwtxqaFCgUPf+hFNwzO5PrJicN3h/EAOq5Xz0NHOg9W18plSQixzPkm4G9gYhvJLBYLPzud78jLm5slsnRtL4IRKdzCtB7JfUKYOaZ9hERQynVCsT0bN980rEpp7tI7zElQIdS6uB5xBgLNJzH/oNpuMQyXOIAHcvpDJc4YJBieeX8ds8Y6Oufpzn4Z+vvUUrt7tn2Q+BOpdTF+LtTy4CvnutEO3bsaFBKDavam0NoOP1/rQ2tsf5336d7WCCSuNM1G53cVnWmffpyrH9jrzEl50sptX24zGwbLrEMlzhAxzKc44DhFUugiMh6Tn+/OmtNuDOca8w2Ren/l8Yu/XffN4GoL1ABpPV6ngpUnWkfpZQFiACa+nispmmapmnaqBeIJG4bkKuUylJK2fBPVFh+0j7LgXt6fr8V+FD8Be2WA3copexKqSwgF9g6RHFrmqZpmqYNG0Pendozxu1+4F38JUb+T0T2KaX+F9guIsvxDwh+vmfiQhP+RI+e/V7FPwnCAL4+SDNTL6gbdpAMl1iGSxygYzmd4RIHDK9YtJFN/780dum/+z7o14oNmqZpmqZpWmCMzjV3NE3TNE3TRjmdxGmapmmapo1AY2JxstjYWNFVvzVt7NixY0fDaCnNoe9fmjb29PUeNiaSuMzMTLZv3x7oMDRNGyKjqTiuvn9p2tjT13vYmEjiNG0ovbSlPCDX/dzM9IBcV9P6yzAM1q9fz8t/+xvFxcVn3M9kMnH5vHncdttt5OXljck1eTWtN53EaZqmaUPOMAwKCwtZu3Ytqz78iPa2VggKxx07ATntYhegvC4+XL2WVatWkZmZxVVXLWTu3LlkZGTohG4E8fl8VFRUcPDgQbq6us77eLPZTFZWFjk5Odjt9kGIcOTQSZymaZo26ESEiooKdu3axfbt29m2fTvdXV0oswV3eCqe3EvxRqSAOvt8O1fqJVibSiltLOapp57iqaeeIj4hkVkzL2XGjBlMnTqVyMjIIXpX2tkYhkFNTQ2VlZWUl5dTXl5OaWkJpSUldDtd/T6/yWQiLTWF7JxcMjMzSU9PJyUlhZSUFBwOxwC8g+EvoEmcUmox8LaI+AIZh6ZpmjawvF4vR44cobCwkMLCQnZ/XEhLc5P/RXso7rBUvMmpGOEpYLb2/cQWG574fDzx+Sh3J5aWY1S2VvCPlf9k+XL/4j8ZmZlcPHUqU6ZMYfLkycTHxw/CO9RcLhf19fXU19dTW1tLXV0dNTU1VFdXU11VSX1DA17vJx/voTZFssPDnBiDjDCDceEG4bbz//h3exVHOyyUtZk52lHCns3H+PDDT+8TGRFOUlIyScnJJCQkkJiYSHx8/IlHaGjoqGi9DXRL3B3AY0qpZcAzInIgwPFomqZpF8DlclFUVORP2vbsYe+evXR3+7vKlD0Ed0gC3ox8jPAkxB4OA/ABKraQEwmd0+fD3FmPub2GkuYayle8zVtvvQVAbFwcF0+dyuTJk5kyZQqZmZmj4gN8sIgIra2tNDQ0nPKor6+nob6O+vp62to7Tjk2wq6IDfKQGeTlklQvCcE+EhxeEh1ewq0yEH/tgBAX7Kag19xNlxequ8zUdZup7TJR1+2kvraBveVFrHGC96Rc0W63ERcbS1x8PLGxccTGxhIXF0dMTMyJ36Ojo7FYAp0mnV1AoxORu5VS4cCdwDNKKQGeAV4WkfZAxqZpmjYYlFL/BywG6kRkUqDjuVAiQklJCVu2bGHb9u3s3bMXw/D4X3NE4QlNw5uYgDcsEbGFDEjSdlYmE96wBLxhCcBUusWHqasJc3st1R21NK7dyAcffABAWHgEM6ZP45JLLmHWrFnExMQMbmzDiIjQ1NR0ouXseEua/1FHQ10djU1NeIxTV7QMtyuibF6ibAbpYT5i4nxE2X3E2L3EBPmIDvJhDVD1WbsZMsO8ZIadGrdPoNWtaHSaaXKZaHSaaHJ10+xqp7m0nGNFFppdpyZ6SikiI8KIjY0nPiHhRHIXHx9PXFwcCQkJxMXFYbWeR0vyAAt4iikibT0tccHAt4Cbge8ppR4Xkd8FNjpN07QB91fgCeC5AMdxQZqamnjrrbd45913qa2pAUAc0XhixuMNS8QISwBLUL+uYS/fjKm9BmW4EYsNX1girvRZ53cSZcIXEosvJBYPE3GKoFztmNtr8LRXs2bTNlavXo1SiosmTmTxDTdw1VVXBfQDeSC1tbVRUlJCWVkZFRUVVFRUUFV5jNraetwez6f2tZkhOgiirB7G2X3MSPYnZ5F2/88ou49Imw/LECVoh1stFDVbyI8yyI0w+n0+k4IouxBlP/O5fAIdHkWzy3TSo5vm5gbKaw/xsctMh/vTS5UqpYiNjiIpJYXU1DRSUlLIzMwkKyuLpKSkQW/xDfSYuJuApUA28DxwqYjUKaUcwAHglCROKZWG/+aXCPiAJ0XksaGLWtM07cKJyFqlVGag47gQH330EQ/94he4XS684cl4MudgRKQhtoEdRG5qryHU18XimxazYsUKOtpr+n9SpZCgcIygcIy48ThFMHU3Y2kpZ19JKfsefpjnX3iBXz78MKmpqf2/XgC0tLTwyiuvsGH9OsqPVZzYHmRRJAR7SQzyMDnJR2yQv+Usxu5vPQuxDFQ3Z/8dbrXweFEC11x/A4+/vZIH8msHJJE7F5OCcJsQbvOScZrWvONcXmhymWhymmhymanvNtHg7Ka+vI6Nh/bS4vwkyYsMD2N6wSXccsstTJw4cVDiDnRL3K3Ab0Rkbe+NItKllPrSGY4xgO+KyE6lVBiwQyn1vojsH+xgNU3ThoJS6j7gPoD09OFT/+9Xv/41TrHSNXkxEhQxaNdRhpvFNy3m/vvvR0R49R/vDsJFFD5HNG5HNO6kqVhayqkqXsVLL73E97///YG/3hD45S9/ycaNG1EIN2V0kxdpkBLqJcrmGzZJ2rkUNVu45vob+NrXv4EIFO18bkiSuL6ymyHJ4SPJ4cOfjnxat6Go7DRztN3Mh1UGH374IevWreXNN98iJCRkwOMJdBJXfXICp5R6WET+Q0RWne4AEakGqnt+b1dKHQBSAJ3EaZo2KojIk8CTAAUFBXKO3YfMpEmT2LxpE/Zj2/DE5+MNSwbTwPexicXGihUrEBFWrlyJWAaxXIThwtpchr3O/xFy0UUXDd61BtmVV17Jtm1b8XgMVpQ72NHoI83hIdHhJcHhJT7YR1yQlwjb8Gl5O1l+lMHjb69EBN7750oeyB8+CdzpdBuK+m4T9U4TNV1marvNVHRaqOi04jT8/3QXLFgwaCVPlEjg7g9KqZ0iMv2kbYUiMqWPx2cCa4FJItJ2pv0KCgpEL1ujDRW9YkPgKaV2iEhBoOM4k55714q+TGwYTvcvl8vFyy+/zGvLltHR3o6yBuEOS8aISMEbnuyfwDAAgovextKrC9UIS6Q7//oBOTcimLqbMLdVYW2txNxRAz4fGZmZfPnee5k3b97AXCdAuru72bNnD3v27KGkpITS4sPU1jfQ+7PeaoLoYIixeYi2e4nu6VaN6jUGLswqmAKU6A30mLgLIQJOr6LppDFyJyZGuC00ucx0njRGLiIslIzMLLJzcpgwYQIXX3zxBZW46es9LCAtcUqpfwO+BmQrpQp7vRQGbOjjOUKBZcC3TpfADdfuCE3TtJHKbrfzxS9+kTvvvJOtW7eyfv16Nm7aTPuRUv8OQeF4QhMwQv2zRAeqlEi/+LyYuhoxt9di7qjF1lmHeJwApKWnM/eG25k/f/6oWcYrODiYSy+9lEsvvfTENrfb7a/dVl19oo5bXV0ddbW1FNVW01Tbgu+kBh2zgsggiLQZ/hmpvRK86F4/beaBfw+5EYObvPkEWtwmmp2mU5K0ZpeJZo+FZpcJl3FqI1dYaAjx8fGkJSYxIz6exMTEE4+UlBTCwsIGLe7TCVR36kvAP4GHgB/02t4uIk3nOlgpZcWfwL0oIq+fbp/h2h2hadrYppR6GbgCiFVKVQA/FpGnAxvV+bHb7cybN4958+bh8/koKSlh9+7d7N69m48LC+loOAyAsgXjdsT3lP5IxOeIPueKDP3m9WDuqMPcXoOloxZLZwPi8ycESckpTLvsSqZOncq0adPGTBFgm81GRkYGGRkZp33d6/XS3NzsrwHXUwuusbHxRG24urpa9jc00NXtPOXYUJsi2u4lxm4QY/cRE+QlLtg/eSI+2Eeodeg/ft1eqHeaaejp5mxwmml0mmh0mWlyW2hx+hO53qwWMzHR0cSmxpPfUzeu9yMuzr9tuC3zFagkTkSkTCn19ZNfUEpFny2RU/6vSk8DB0Tk14MZpKZp2pkopR4E/kdEjJ7n4cBjIrL0bMeJyJ1DEd9QMZlM5Obmkpuby2233YbP5+PYsWMUFhayd+9edu3+mLpjWwFQFhuekASM8GS8ESn4giL631Ln82HuqMXcVom1vRpTZ4O/y9RkIjs7m6lT5zJp0iQmT548purBnQ+z2XwiWTmbrq6uTwr+9vysq6vradWr4XBdHR2dn14LNcSmSAgySAw2SA7xkhJikBbqJS6o/5Mt2j2KYx1mKjosVHWZqe40U+ey0tQNvXM0q8VMfFwc8eOSyO6p8XbyIyIiYkS2xAayJW4xsAP/n3XvPzkBxp3l2DnA54E9SqndPdt+KCJvD0agmjYceX1CXbuT6lYnHU4Dl+GluctNTIiN3IQwLkoKJ3gw+jm03izAFqXUUvwlj37HacoijTUmk+lEq8+NN94IQH19vX/prd272b5jJ9XHtsAx/AveR6Thic7CFxLX94TOZ2BpqcDSfARbWyViuDGZTOTl5zNj+jVMnTqViRMnjpn1M4eKw+EgPT39rEOUOjo6Pll6q7qaqqoqKiqOcbisjI2ljSf2C7EqskLdjI/0MDHaQ3a4cc4xePXdJgobrRxssVLSYae+V74Y4ggmIyOTgvR0kpOTSU5OJikpiaSkJKKiojANwgSc4SCgExuGynAaGKyNfoM1scEnwqGadj6uaKGoph2XceY1B20WE5dlx3DjlGRumJJEkHVsJXRDNbFBKXUV8A+gGbhcRIoH+hqj8f5VU1PDli1b2LhxI9t37MBrGEhwJK74CXhicwk+9N5pJzYodye2mn3Ymw4jHhdh4RHMmzuH2bNnM3369EEp4aANnK6uLsrKyigpKeHgwYPs37eXI2VHERHC7TArrptr0pzEBX9yb3N5YW11EGurgzna7k/EYqKjmDR5Cnl5eeTm5jJu3Diio6NHZEvamfT1Hhbo2alzgN0i0qmUuhuYDvxWRAb0U3A03gS14WugkzivT9h+tIl1hxto6nTjsJmZkBROdlwoyZFBRARZsVlM3DIjlbo2Fwdr29lU0sgHB2opb+oi0mFl6WVZLJ2bSXjQ6KhGfy5DkcQppS4H/gi8AEwGooEviUjVQF5ntN+/Ojo6WLNmDW8tX86hgwfBHorXZMXc3XxiHyM0AW94MvaaQkwIl19+OTfccAPTpk0b9mtbamfX1tbG9u3bWbNmDRvWr0fEy2czO1mc4eRQq4U/HYigsRvyxudy1aKrmTVrFqmpqaMqYTudkZLEFQJTgSn4V2x4GvisiMwfyOuM9pugNrwMZBJ3sKadFYVVNHa6SYsKZk5OLBOTIzCfpt/h5BIjIsKmkkb+b0MZHxyoJdJh5btX5/G5S9NPe/xoMkRJ3Fbgi8cLjSulPgv8XETyB/I6Y+X+JSLs3LmTx3/3O46WlX36NbMN5XWzYMGV3HffV0hKSgpMkNqgqq+v5/e//z2rV69mTqKTbfVBxCUk873vf5+LL7440OENqWFdYqQXQ0REKbUE/4Dgp5VS9wQ4Jk0LuC6Xwco91ew61kJcmJ0vzMogLzHsvL59KqW4LCeWy3Ji2VPRys/fPsD/e3Mvf99+jF//y1Ry4od2KvwoNFtETqzPIyKvK6XWBDKgkUwpxYwZM3jyz3/mttv+hba21k9e87r59re/zZIlSwIYoTbY4uLi+PGPf4zH42HDhg1YrRZ+98QTREdHBzq0YSvQI/3alVL/CdwNrFRKmYGx0d+jaWewt7KV36w6zMcVLSzIi+cbC3LITwrvV/fB5NQIXvrKTB6/cxoVzd3c8Ph6nttUxlgYEzuIYpVSTyul3gFQSl0EfCbAMY14drud9PS0T20LDQ3VCdwYoZTihhtuACA+PkEncOcQ6CTudsAF3CsiNfiXz3oksCFpWmAYPh/LP67ipa3lRARb+PqCHBZdlIDFPDD/TJVS3DQ1mXe+NY/Z2TH891v7+OIz26hrP7X2k9YnfwXeBY737R0CvhWwaDRtlDieuOnxjucW0CRORGpE5Ncisq7nebmIPBfImDQtENq6PTy17gibSxuZmxPLv83PISkieFCuFR8WxDNfvIQHl0xky5FGbnh8PdvKzlljWztVrIi8CvgAeurFec9+iHYuhmFQUVHxqW0dHR28++67AYpIG2rt7e0A+HxnnoGv+QU0ze0ZCPwwEI+/VpzCXwg4PJBxjWZ6Xc/h50hDJy9vLcdt+LjjkjSmpEYO+jWVUnx+diYzx8Xw1ed3cOeTm/l/iy/iC7MzRv2srwHUqZSKoaeuqFJqFtB69kO0sykrK+ORRx+lpaXlU9vFbOOhhx7i0KFDLF26lNDQ0ABFqA2FwkL/apwd7W2IiL4nnUWgu1N/CdwkIhEiEi4iYTqB08YKEWF9cQNPry8lyGri367IHpIErrfxCWG8df8crsiL48fL9/HdVz+m260bk/roO8By/GtAbwCeA74R2JBGpoMHD/LTn/6UpUuXsr/oMN6giE+97g2Owh0/gWXLlnH7HXfyzDPP0NjYeIazaSNZbW0tb7y+DIDmllbeeeedAEc0vAW6w7lWRA4EOAZNG3Iuw8vrOyvZU9nKRUnh3DojNWAFecODrDz5+QKe+KiY33xwiKKadv78+RmkRetq96ejlLoEOCYiO5VS84GvArcA7wEVZz1YO6Guro7Vq1fz7rvvUVJSjDJbccVPxJ00maCSj8DZq1FTKVwZs/HE5mJU7eLZZ5/l+eefZ+bMWVx99SJmzZpFcPDgDD/Qhk5paSk/+uF/4nV38YuZzTx7KIxHH30En8/H9ddfr1vkTiPQSdx2pdQrwJv4JzgA/qn6gQtJ0wZXQ7uLF7Ycpb7dxTUTE7k8NzbgNyeTSfHAwlwmp0Twzb/t4sYn1vPb2y/miryxsUD4efozcFXP75cBP8LfAncx8CRwa4DiGtZEhPLycjZs2MDadesoOuD//u4LicWdPgtPTA5YbGc9hy8klu7cRShnK7b6Q2ze+TGbNm3EarMxa+ZM5s6dy6xZs4iIiDjrebThpbOzk5dffplX/vYyDrOX709pJTnExzcnt/G7vWE88sgjrF2zhq99/etkZGQEOtxhJdBJXDjQBVzda5sAOonTRqX9Va38fUcFZpNi6ZwscuKH19ieBfnxLL9/Lv/6wg6W/nUb37gyl28uzB31xYHPk1lEjs8EuR14UkSWAct6reesAV6vl71797JhwwbWrd9AdVUlABISiztlOp7oLCTo/BMuCYrAlXYJrtQZmNtrsTSXsW7rTtatW4dSiomTJjF3zhzmzJlDWlrauU+oBUR7eztvvvkmr77yN9o7Opmd4OKu3E7Cbf7SRw6L8L2pbbx3LIg3dm5l6Re3ceXChdx1111kZWUFOPrhIaBJnIgsDeT1xwKfT2jodFHX5sInQmVLNwqwmU1EOKxYB6h8hXZ2Xp/w3r4a1hU3kBoVzOcuTSfScfZWh0DJjA3hja/N4b/e3Mvjqw6zq7yZx+6YRnTI8Iw3AMxKKUvPbNSFwH29XjvnPVUpdS3wGGAGnhKRXwxOmIHh8/koLCxk1apVrF6zhva2NjCZMcISMTJmY0SkIfYB+vKiTHjDk/CGJ+GSWZi6GrE0H2VPaQV79/yJP/3pT6RnZLDwyiu56qqrSElJGZjrav3S0tLCK6+8wltvvkFXt5OpMW5uzu9iXPip43FNCq5Nd3JZoouV5cF8uHoVH3zwAZdddhlf+MIXyM8f0AVSRpxAz04dj3/twQQRmaSUmoJ/osNPAxnXSNXY4WL3sZYTjyMNndS2OfF4z1zQNcRuIcphJTE8iIwYB+nRIcSG2gLevTeatHS5eXlrOceau5k1LprrJyUNWO23wRJsM/PobVMoyIzix8v3sfjxdfz+rulMS48KdGjDwcvAGqVUA9ANrANQSuVwjtmpPQXNfw8swj9+bptSavnxpbtGMsMwWLlyJX975VWqqypRZivuiFSM7BkYEalgHuQ67kr5u2ZDYnEzA+XqwNJSzpGmMp555hmeeeYZpk+fwRe+8Pkxt4TTcGEYBn//+9957tm/4nS5uDTOxeJJ3WSEnXsyVbhNuDOni8UZ3bx/LIj3t23kXzduZP78+TzwwAPExMQMwTsYfgLdnfoX4Hv4x5ggIoVKqZcAncT1gc8nfFzRwqoDdawqquNAdRsAZpMiLyGMgowokiKDSYoIIj7MjsVkYs2hekQEl+GjpdtDc6eb5i43+6ra2H7Uv+C0w2ZmfEIYE5LCGR8fij1AA+5HgwPVbby2owKfyJCVDxkoSinuvDSd/8/eeYfHUV2N+z3btKvee7fk3m1ssI0hdIyp4aOFhITkC+SDJKRByBdK8gsJBEJoSb4ECDElFTCdAMGAsbENknu3miWrWLasvtp+f3/sSpaNi+ruSrrv8+yzM3funTmzM3vnzLn3nDM1M45vvVDKVX9cwx0XTOTGhQUYxvDwqlLqPhF5H3+Q33fV4bQXBk7unToPKFNKVQCIyN+BS4ERrcS1tbXxwx/+iN27d+GLTsVZsBhPQj4YQ/eIURHRuNMm406bjLg6MR/cw4ZtO1l/221ceeWV3HLLLfplNYj4fD7uveceVq1ezaxkF1fP6CQzqv9x4GLMiisKu7gwt4u3q228ueojtm7exBO//8OYzKkbaiUuUin16VF/JE+ohBkJKKXYVtfG8g21vLapjgPtTowGYU5eArdfMIG5eYlMzYol0nLsS9vY7jxmuU8pDrY7qT5kp+JgJ7sa2tlY04LRIBSlRDMzN55J6bFYTOFtQQoXulxe3txSx/rqFjLirFw7L5fk6IhQizUgpmXH8ca3F/HDf23mF2/u4N3t+3noyhnkJo1d71Wl1NpjlO3uQ9MsoKbX+j5g/tGVROSbBIZpc3PDP8biyy+/zO7du+gadyaehAIIM+VIWaJwZc7ElT4V6941vPjii5x33nmMHz8+1KKNGbZu3cqq1au5stDOJfldg96fzQRXFHYxN9XFvSXw4osv8u1vj70IP6FW4g6KyDgOB8u8EqgPrUjhSV1LF69srGX5+lr2NHZgNgpnTUxlybQMzhifMuj5VQYRUmOtpMZamZufiNenqD5kZ0d9G1tqW/nHZ1iUbjMAACAASURBVDVEmAxMyYxjVm48BclRGMKsow4Xdja08cqGWjqcHr4wIYUvTEzFZBjZym98pIUnvzKHF0v38fPXt3PBoyv5yZJJXDcvd0xb5QbAsX6sz813UEr9Cb+nK3Pnzg37BLfJyckAmFpq8EanoSxRIZbo2Bg7D2KyN2E0mYiPHzlW8dFAfHw8IsKeVhPtbiHGPPjb2qdg2yEzHh9jNsdqqJW4W/B3VBNFpBaoBL4UWpHCh3aHm7e3NrB8fS1rK5tQCubmJXDf5VO5aFrGsE6MNxqEguQoCpKjuGBqOpUHO9lY3cLWulbWVzcTZzMzIzuOmbkJpMdah02OkcTepk7uf3snb29tIC02gi+fmk9WwuiJXSUi/NfcHBYWJXP7i5v56Stb+VdJDXdfPJk5eWOzAx0A+4De7pLZQF2IZBkylixZwoEDB3ju+ecxH6rEHZ+LO7kIb2wWGPo/HcMXmXjEkIwvcuD3l7gdmJqrsDTtwdBxgPiERO685z5SU3X4nGCSm5vL9773PR599BHuWGfh4txOzspyEDGA2TpKweZDZl6ujKKyzciihQu58sqxGdlHDk/nCOJBRb5/VJEN/3ySTgCl1MNDeby5c+eqkpKSodzlsOH2+vh4zwFeXl/Le9v34/T4yEuK5PJZWVw+K4u8pMG94Q427Zbb62NHfRsbqlvY09iOT0FGnJVZOfHMyIknxnrsycujOe1Wq93N4yv2sGxNFWajgQXjklhcnBJ054Vg/sZKKZZvqOWBf+9kf5uTS2ZkcseFE8mKDw+lVURKlVJzQy3H0YiICdiN36u1FvgMuE4pte14bUZS/1VfX8/y5ct5++1/097ehpgicMVm40nIDY5zQ4BupwZzSzXG9npQirz8fC695BKWLFmC1apfPENFRUUFv//dE5SUrifaAudl2Tk320FUHyxzPgUlByy8UR1FVZuBtNQUvvHf3+Scc84ZdfMb+9qHhUqJuyewOAE4BXgV/zDDxcBKpdQ3hvJ44d4J+nyKDTUtvL6pjtc31dHU6SIh0szS6ZlcNiuL2bnxQ3aDDmXu1A6nh837WthQ3dITuqQoNZpZufFMyoglwnT4FWs0KnENrQ7+8kkVf123l3anh6vm5PCD88bznx2NIZEnFL9xp9PDHz8q548rK/ApxSUzsvjm4kImpMcEXZbehKsSByAiS4BH8IcY+bNS6r4T1Q/3/utYuN1uSkpK+Oijj/h41Wo6O9rBYMATnYEnPgdPQt7QDrkq1RNexNJag9j9Yfyyc3I4Y/FizjzzTIqKikbdg34ks3XrVl54/nnWrF2LzQTnZdtZktuF7Rjjg0rBZwcsvFwZTV2nkJ2VyXVfup5zzz0Xszk4LwbBJqyVuJ6Di7wLfFEp1R5YjwH+pZS6YCiPE46doMvj49PKQ7yzrYF3tjXQ2O7EYjJw7qQ0LpuVxRnjU4bFiWAolbjeNLY7ekKbtNjdmAzCuJRoJmfGMjE9hpvOGDcsxw02SvkV7ufX7OW1TXX4lOKCqenc+oViJmf60/4O1298MkKpKNe2dPHkygr+8VkNXW4vX5iQwpfm57F4mO7jkxHOSlx/Ccf+qz94PB62bdv2uYC/vugUXPH5eBILBhY3TikMnQcwH6rE0rIXnB09gX5PX7SIBQsW6EC/I4Dy8nKefXYZH320kngrfG18G7OS3T3bD3QZeGpnDDuaTeTn5XLDV7/G4sWLMRpHd9SEkaLE7QRmKKWcgfUIYJNSakij94VDJ+j1KXY1tPNJ+UFWlR1kXcUhutxebGYjZ4xP4fypaZw1MY042/C+VQy3guFTir1NdrbXtbK9vo1mu//PODkjloVFSSwoSmZefiJREaGejtl3fD6/R/AbW+p4Y1M9tS1dRFqMXDU3hxsXFnzOS3MsKnHdNHe6eH7tXpatqeJgh4s4m5kl09K5eHomc/ITjrDODidaiQtf9u7dy6pVq/jwo4/Ys9vv0OuNzcSVMhFPQi7ISZR+jxPzwd1YD+6GrlaMJhPzTpnH4sWnc9ppp2mHhRHK9u3b+c1DD1JeUclpaU6SrV5cPmFVgw1lsnLzt/6Hiy66aNQrb92MFCXuf4GrgOX4PbQuB/6hlPrVSdr1K+J5sDtBh9tL5cFOyho72FrXysbqFrbUtmJ3+QMaFqZEcXpRMouKU1hUlIzNErybMpgKhlKKhjYHOxva6XB4KN3bjMvrwyBQnBrDjJw4pmfHMzE9hqLU6LDJYOBwe9mzv4OSvYdYW9HEp5WHaA5YF08vTmbp9EzOnZJG7HHm/41lJa4bt9fHqj0HeXVjLe9u34/d5cVqNjAnL4EF45KZnZvAxPQYEoYpC4RW4kYGtbW1vPfee7zx5pscPHAAbHF0ZczCk3iMMCVeN5aGLVgbt6M8LqZMmcpFFy1h8eLFREeHV/o6zcBwuVw8/PDD/Oe991ABp+3cnBz+3y/uIzs7O8TSBZcRocQBiMhs4PTA6kql1IaT1DfinxjcE/EcuPZEEc+HohP0+hQdTg8dTg+dTg/tDg+HOl00tHbR0OagvtXB/jYHtc1dVB+y4wv8rGajMDkjlpk58czMjWd+QRKZIZz8HUoFo8vlpWTvIUqqmtm0r4VNNS09ljqA5GgLBclRpMfZSI+NIC3WSnqclfRYK/GRFmKsJqIjTERajAOa29Id5LjN4aaty0Obw83+Vgd1rQ4aWrvY22RnT2MHVU2ddP8tshNszC9I4tTCRM6ZlNYnpUMrcUdid3lYtecgayqaWFPexM6G9p5tqTERTEiPIScxksw4K5nxNtJircRYTcRYzURHmIixmrD2M+C0VuJGFl6vl1WrVvHMX5ZRVVkBBiNylEVOeT2A4owzzuD666+nuLg4NMJqNEGgr31YyMe0lFLrgfX9aDJsEc9/snwLb2+px6f8w4Iq8O31+R/+x8NoENJiIkiPszIlM45LZmRSlBZDcWo0BclR/X4AjVZsFiOnF6dwenEK4Feq9jV3saexnbLGDsobO6ls6mTzvhbebXUc9zc3iD9dWKTFiMlgwGQUTAbBZDBgNEjPNfP6FJ7At8Ptpd3hweU99j4jTAayE2xMyojh0pmZjE+LYUZOfNh4W45kIi0mzpuSznlT0gF/eritdW3samhjZ0M7e/Z3sK2ugUOdruPuw2I08NOlk/jKaflBkloTTIxGI2eccQaLFi3i3Xffpaqq6nN1RISFCxcybdq04Auo0YQpIbfE9ZdAQOALuj1YReTLwHyl1K1H1euJeI7fC3bXCXabDBwcBnHDEX2uo5exdL4nO9c8pVRKsIQZTkTkALA31HKEiLF0T2uOZKxf+z71YSG3xA2Afkc8P+kORUpGy9DLydDnOnoZS+c7ls51tCijA2EsXWfNkehr3zdGYi6gURnxXKPRaDQajaY/jEQl7jOgWEQKRMQCXAO8FmKZNBqNRqPRaILKiBtOVUp5RORW4B0ORzw/bsqaPtKnYddRgj7X0ctYOt+xdK5jGX2dxy762veBEefYoNFoNBqNRqMZmcOpGo1Go9FoNGMercRpNBqNRqPRjEDGpBInIoki8p6I7Al8JxynnldENgY+I8p5QkQuEJFdIlImIj8+xvYIEflHYPs6EckPvpRDQx/O9asicqDXtfxGKOQcCkTkzyLSKCJbj7NdROSxwG+xOZARZUTSh3M9U0Rae13Xu4Mto2b4ONn/WjM6Odn/XnMkY2JOXHJyssrPzw+1GBqNJkiUlpYeHC3x1XT/pdGMPfrah40479SBkJ+fz2jPPajRaA4jIqMmw4HuvzRjjba2Nn72s3u56qqrmT9/fqjFCQl97cPGhBKnCS19TQgfrgncNZrjISI5wLNAOuAD/qSUelREEoF/APlAFXCVUqpZRAR4FFgC2IGvBvJHazSaAJs2baK0dD1tbe1jVonrK6N2TpyI/EdEtupxdY1GM4x4gB8opSYBpwK3iMhk4MfA+0qpYuD9wDrAhUBx4PNN4A/BF1mjCW8+++wzAOpqa/F6vSGWJrwZlBInIktFJCwVQaXUOUqpqUqpqaGWRaPRjE6UUvXdljSlVDuwA8gCLgWWBaotAy4LLF8KPKv8rAXiRSQjyGJrNGGL0+nkgxXvYxRFp92upxKchMEqYNcAe0Tk1yIyaSgE0mg0mmAiIlYRuU1EnhCRm0RkQNNMAh7es4B1QJpSqh78ih6QGqiWBdT0arYvUKbRaIC1a9fS3tHJd6e1E2OBf//736EWKawZ1Jw4pdT1IhILXAs8IyIKeAb4W+CtVDNC6cs8Nj2HTTNKWAa4gY/xD3dOBr7bnx2ISDTwEnCbUqrNP/Xt2FWPUfa5EAEi8k38w63k5ur/mWbssG7dOqIsMD3JzewkB5+uW4vX68VoNIZatLBk0I4NgQ7rJcAG3AZcDvxIRB5TSj0+2P1rxg7aAUITIiYrpaYBiMjTwKf9aSwiZvwK3AtKqZcDxftFJEMpVR8YLm0MlO8Dcno1zwbqjt6nUupPBHJHzp07d/THgToOpaWlpKSkaEV2DLFt6xaKYlwYBMbHe/iovouamhp0mJ1jM9g5cZeIyHJgBWAG5imlLgRmAD8cAvk0Go1muHF3LyilPP1pGPA2fRrYoZR6uNem14AbAss3AK/2Kv9KICjzqUBr97Cr5kiqq6v5wQ9+wC233spYiGeqgYaGBvZW11Ac5/8bFsf5/5qfftqv96oxxWDnxF0J/FYpNV0p9aBSqhFAKWUHbhy0dBqNRjP8zBCRtsCnHZjevSwibSdpuxD4MnBWr8wRS4D7gXNFZA9wbmAd4C2gAigDngT+Z1jOaISjlOIvf/kLAO1tbaxYsSK0AmmGHa/Xy2OPPYbJAAvSnQCkR/qYEO/hheefo7Gx8SR7GJsMdji1Xim1sneBiDyglLpDKfX+IPetGSEopdjbZGdLbSvlBzqoPmTnUKeLTqcHgwgtdjdxNjNJ0Ray4m1kJ0RiMYWlU7NmDKKUGvBkG6XUKo49zw3g7GPUV8AtAz3eWOG1115jxYoVODNnYm6r48GHHqKoqIi8vLxQi6YZBjweDw888ACffPIJ1xd38u9qKwDXj7fz1Qkd/Hy9ie/d9l0e+s3DZGRoZ+7eDFaJOxe446iyC49RphllONxe3t5Sz4e7DvDh7kb2t/nfnEQgPdZKSkwEURYTPqVod7ipabZjd/nj/RgNQmFyFBPSY5iYHktilCWUp6LRHIGIROJ3bqhSSh0MtTxjjYqKCh577DE8cdm4MmfiTh6Pcefr3HX33Tz91FOYzeZQi6gZQjo6Orjn7rspXb+eLxbYOS/HwS/Xx/Zsz4ry8sPprTy8Gb5180386v4HmDRJB8PoZqCu9N/CPwwwTkQ299oUA6zuQ/sL8EctNwJPKaXuP2p7BP4o6HOAJuBqpVSViHQPS1gAF/AjpZS2swcJpRTlBzr5rOoQ2+va8CpFjNXE4uIUFhYlMz07juK0aCJMRxo2uh0WOp0e9jV3UXGgg50N7byxuZ43NteTGWdlRk4807PjibPpDloTXETkEuAx4BDwU+B3wH4gX0TuUEotO1F7zdCybNkyfAYTXYWLQQyoiGjsuQuoLnufDz74gPPOOy/UImqGiIaGBn58x+3UVFfzjYkdLM50HrNecZyHn85u4eEtitu++x3+96d3sXjx4iBLG54M1BL3V+Bt4FccjkQO0K6UOnSihiJixN9JnovfU+szEXlNKbW9V7WvA81KqSIRuQZ4ALgaOAhcrJSqE5GpwDvoGEvDjlKKHfXtfLCrkdqWLmxmI/MLE/nu2cXMzkvAbOzb0GhUhIkJ6TFMSI/hwmkZNHU42VHfxubaVt7e2sC/tzZQkBzFjJx4pmbGYbNol3JNUPh/wHlAHPABMF0pVSEiqfizLWglLki43W7WrluHM76AiLqNADhzT8UTn4tERLF69WqtxI0SSkpK+MXPf4arq50fzmhlSuKJfYqyorzcM7uZR7bEcffdd3PDDTfw5S9/GZNpbGcPHejZq4Bl7HNzO0Qk8SSK3DygTClVEaj/d/xRzHsrcZcC9waWXwSeEBFRSm3oVWcbYBWRCKXUsdV3zaCpb+3ijc31VB7sJCnKwhWzspiRE4/ZaGB+YdKg9p0UHcGi4hQWFadwsN3Jpn0tbKxpYfmGWt7YXMeM7HgWjEsmPc46RGej0RwTn1JqN4CIVHb3TUqpRhHpl7eqZnC89NJLOB0OPDk5WBq2HN4ggjM2m08++YSysjKKiopCJ6RmULS0tPD000/z+uuvkxXt487ZrWRE+frUNtai+PHMFpbtimLZsmWsXbuG733v+0ycOHGYpQ5fBmOJWwqU4g9U2XtirwIKT9D2WBHLj85w21NHKeURkVYgCb8lrpsvAhu0Ajc8eH2Kj3Y3smJnI1azkUtmZHJKfiJGw3GDmA6K5JgIzp6UxlkTU6lt6eKzqmY21jRTsreZSRmxnDspTStzmuHCICIJ+L31fYHl7htde+AEgd27d7Ns2TJWr16NJz4Xb1w29FbiAFfmTCxt+/jWt77F1VdfzRVXXEFiYmKIJNb0l6amJl5++WWWv/wSDoeD83O6uLLQTkQ/B1wsRvjvyZ1MS3LzQtlubr75ZhYtXMi1113H5MmTOUGg7VHJgJQ4pdTSwHfBAJr3JWL5CeuIyBT8Q6zHtavriOcDZ8/+dv7vo3JqW7qYnh3HJdMziYwIjslaRMhOiCQ7IZILpqTzSflBVpcf5PEVbcwrSOS8yelBkUMzpojD/0La3e+sD6EsY4bGxkZWrlzJO+++y57duxGTBWfWHFwZ0/weUkehLFF0TLyYiJp1PP/88/z1b39jwWkLOOecs5k/fz42my0EZ6E5ER6Ph5KSEt5++y1WrVqFz+vjlFQnl0/vIitqcIntT01zMSOpiX/X2Hjn09WsWr2a4qIiLlq6lC984QvExcUN0VmEN4N6MovIQmCjUqpTRK4HZgOPKKVOFHq/LxHLu+vsC+QxjMM/6RgRyQaWA19RSpUf7yA64nn/8foUT31cwW/e243JIFw7L5dpWaH7I9gsRs6elMZp45L4z45GPq1sYldDO1OyYjl1kEO5Gk03Sqn8UMswFvD5fJSVlbF27VpWrV7N7l27AFBRSThz5+NOKgJTxAn3oSyROMZ9AWfWbCyNO1n9aSmrVn2M2Wxm7txTWLDgNObPn09qauoJ96MZPux2O6WlpaxevZrVqz6mvaOTaAucm9nF2VkO0iL7NnTaF2wmuLygiwtyuljdEMEH9bt55JFHePzxx5g1axaLFp3O/PnzR3VYksGaV/6AP1DmDOB2/JHLnwPOOEGbz4BiESkAaoFrgOuOqtMd7XwN/oDCK5RSSkTigTeBO5VSJ/WC1fSdssYO7nhpM6V7mzl/ShqzcxOIsYaHp2ikxcQlMzKZlRPPP0tquPbJtdx8xji+d854HW9OM2SIyPtKqbNPVqbpOwcOHGD9+vWUlpay7tNPaW1pAcAXnYI7aw7uhHyUrf8visoahzN3Ps6cUzC278fUvJc1G7awZs0nAOTm5TF/3jzmzJnD9OnTiYyMHNLz0hzG4XCwY8cONm7cyIb1pWzbvh2v10ekGWYmOplX4GR6kpvh7KptJjgn28nZWU6qO4ys3R9ByfYSHikpBSAnO4s5c09h5syZTJs2jaSk0WMEkMGkMxGR9Uqp2SJyN1CrlHq6u+wk7ZYAj+APMfJnpdR9IvJzoEQp9ZqIWPErg7PwW+CuCXiL/RS4E9jTa3fndWeKOB5z585VJSUlAz7P0YzL4+P/PirniRVl2CxGfnbJFC6dmcnfPq05eeMQ4PR42b2/nb99WsPUrFieuHY2+clRoRZLE2aISKlSam4f61qBKPzpA8/k8LBqLPC2UiqkQalGSv/l8Xiorq5mx44dbN26lY0bN1Ff7x9kEbMNV0wGnrgsvHHZKPPJhz5tO98CoGvikr4JoBQGRwvGln2Y22oxduwHnxeDwcD48eOZMWMGU6ZMYeLEiaSkpIy5uVNDgVKKhoYGduzYwfbt29m2dSt79uzB4/UiQF6sjynxTqYnuSiO8wxIcXt+dyQf1/stsnkxXnKjPVw/3j4AWaHebmBzk4VtzWZ2tlpwBtyUMjPSmTptOlOmTGHSpEkUFhaGnZdrX/uwwSpxHwH/Br4GLAYO4B9enTbgnQ4DI6UTDDYlVYf4yfIt7N7fwcUzMrl76WRSYvx/nr4mow8F183P5Z1tDdz+4mZ8PsWvr5zOhdNGr7lc03/6qcR9F7gNyMQ/OtD9dG8DnlRKPTE8UvaNcOu/PB4PDQ0N7Nu3j+rqaqqqqigvr6CisgK3ywWAmK24olLxxqTjjc3AZ0s85jy3E9FvJe5ofB6M7fsxttdj6tiPsfMg+PzzsGJi4yguLqKwoIC8vDxyc3PJysoiKSlJK3e96OjoYOfOnezYscP/2b6N5pZWAMxGKIzxUBTrZkK8m+I4D1Hmwc9c+uX6WHa2HB4Fmhjv5iezT5b97uR4fLC33cTuVhO7W8yUtVtoDbhFRljMFBePZ3JAyZ80aRLp6ekhvRf62ocNVvW8Gv9Q6NeVUg0ikgs8OMh9aoaZbXWt/Obd3azY2UhGnJWnb5jL2ZPSQi1Wvzh/SjpTMmO59a8b+NYL6/nqgnzuXDLxc4GGNZqToZR6FHhURL6tlHo81PKEGp/PR1NTE/v376ehoaHnU19fz77aWg40NuLzHZ7XJBYbHms83oRivJFJeKNSUNbYfittvYmoXovR3gT4lTlfZCLO3FP7txODCW9cFt64LFzgt8rZmzB2HsRtb6Jk5142bNyE8h6OImOJiCAjPYPs7CzS09M/94mOjh61Sp5SiurqarZu3cq2bdvYtnUL1TX76Db0ZEYpJsc4GTfew7hYDznR3mEdIh1qTAYYF+dhXJyHC3MdKAUHHQbKWk1UtJso37eJ5Tu34Q74WyTExzF5ylSmTp3aY8G1WMIvu9CglDilVAPwcK/1avyZFjRhSFljB7/9z27e3FxPnM3M7RdM4KsL8om0hJcZua9kJ0Tyz5tO44F/7+TpVZWsr27md9fNJidRz3/RDIgGEYlRSrUHpm7MBn6hlBp13qqdnZ3U1tZSW1tLXV0d9fX11NfXU1tXz4EDjXg9R4bHE4sNryUaryUaX9pUfNY4VESs/9s89KF/DPZDiNcNgKm9gSEJ1mcw4otOxRediru7TCnE1YHB0YrB0YbL2UZZawd7D2xHnJ+ivO4jdmG1RZKenk5WZgbp6elkZmaSkZFBdnY2GRkZIyolmFKKmpoaSktL2bhxI5s2rKelrR2AaItQFONkbr5f6SmIGRorWzghAik2Hyk2F6el+y3IHh/UdBgpbzNR3uagfGMLq1f7p9+bzSYmTZrErFmzmT17NpMnTw6L6z1Y79Qr8If6SMU/BCH4AwHHnrChZsg50fBnc6eL93c2sqG6mUiLkW+fVcQ3Ti8cFSmuLCYDdy2dzLyCRH74r00seexjfnHZVC6ZkTlq35g1w8ZdSql/icgi4HzgIfzOW0fHsRwxKKWora1l+/bt7Nmzh/Lycioqq2hpPjIeu1hseM3ReCOi8SVPQkVE47NE93xjHPl9xTERQUXE4I2IwdvLv8IB/klVXicGZwcGZwfiasfl7KSjuZ3K/TswHKXkGQwG0jMyKCwooKioiPHjxzN58mTi4+ODflrHw+12s379ej755BPWrvmE/Y0HAEiywZQ4JxMy3YyPc5MR6RuMIXXEYjJAQayXglgv5+AEOmlzCXtaTexqMbOraiPPbtnCsmXLsFkjmDP3FE477TQWLlwYsus8WBPMr/GnwdoxFMJohha7y8N/djTyWeUhRGBhUTKPXjOTpOgTu/GPRM6fks7kjFi+8/cNfPfvG3ltYx0zc+L7dK7XzddxBDUAdAeuugj4g1LqVRG5N4TyDJi9e/fyyiuv8NHKjznU5I+RLkYTXmsCXmsivuxCfNZYfBGx+CJiRq+SNhhEwGTFZ7Lii0r+/HalEI8DcbZjcLRhcLSy195KXelWVq1a1VNtXFEx55x9FkuXLiUmJiaIJ3CY3bt389prr/HhByvo6LQTYRKmxDu5cIKLKQluUm1jU2nrC7EWxZwUN3NS3ICdTrewo8XMliYHm0tXsWrVKn7z0EPMnjObJUsuYvHixUF1khjskfZrBS788ClFSVUz725vwOH2MicvkbMmphJnM49KBa6bnMRIXrx5Ac+sruQ37+7mg12NzC9I4gsTU4kOUrBizYimVkT+CJwDPCAiEYzAjA1r1qzhf3/6U5QSXHFZePPG441OxWeLBxkhp+N1YbVaWbp0KW+88QYdXleoJfo8IiizDWW24Ys+HJfOAeB1Y7Q3YWxvYE/DPsr/+EdefPElnn76qaBabBwOB7/65S/5aOVKIkzC3GQH88Y5mZLgJlxTU3d55Ihr3+UJr8x3UWbF3BQXc1NcKNXJ3g4jJY0W1mwr4eclpWRlZnDvz35OcXFxUOQZ7JOtRET+AbwC9KS/Ukq9PMj9agZIbXMXr2yspbali/ykKC6ekUFG3NiJZG40CN84vZBLZmRy6183sK6yifXVzSwqTua0gqSgZZ7QjEiuAi4AHlJKtYhIBvCjEMvUb0pKSvB5vXSNOxNP4okyIIYv4nGx9JKl3HrrrSil+Ofr74RapP5hNPs9c2PScWVMx1rxEU1NlVRUVDB79gkjcA0pb7/9Nh+tXMniDAfXFduJNIX/vDa7R1i69PC1X/nmP0Mt0nERgfwYL/kxXVxR2MVnjRZ+tw2eePxxHn3ssaDIMNgnWixg58j0VwrQSlyQ8SnFR7sP8P6O/URFmLhqbg4zsuPG7Lyw1Fgrl83KYsG4JN7Zvp/3dzSycvcBZucmsKgoeVRbJDUDQyllF5FGYBH+WJQejoxJOSK48sor+eSTNdSXf4j3wC5cScV44nNOmg0hnFAmC2+88QZKKd58802UaeQ5K4mjDXNzsn0H/AAAIABJREFUFREHd4OjjTPPPJMZM2YEVYbJkycTYTGzugFaXQZmJrmYlBDec94iTeqIa58W5opnm0vY1WJm6yEzJQf9Tj6z58wJ2vEH6536taESRDNwag7ZefLjCvY22ZmWFcdlM7OwhautPMikxlr58ql5NLQ5WL3nICV7m/m08hCTMmI5vTiZXO3JqgkgIvcAc4EJwDOAGXgeWBhKufpLRkYGzzzzZ5YvX87Ly5dzoHIliOCLTMITnYo3KgVfZCI+a1z4Dq8aLTjsh3jppZf86zHh4xxwTLxuDF3NGDsPYuw8iLmzERz+2GZTpk7l2muuYeHChUF/qZ4wYQLPPvc8y5cv58MV77Nst9+RIdoiFEY7KYj1kBfjJS/aQ7I1PBQ7m0nh6HD0XHtbfPgocZ1uobrDSFW7iap2ExXtFvbb/T9apM3K/IWncfHFFwfV2jpY79Tx+L230pRSU0VkOnCJUuoXQyKd5qS8sqGWu17Zisvr47/mZDMzJ37MWt9ORHqslS/Oyea8KWmsqWhiXcUhtte3kRVvw2wycMmMTKxmrfiOcS7HnyVmPYBSqk5EQjMTfZBYrVauvfZarr76anbs2MG6devYsHEjO3fuxL1/u7+SwYiyxuGxxuGzJeCzxuGzxuOzxoJB/xeOicfpD0fS1YIx8G12tqIch4PRxsbFMeOUGcyaNYvTTjst5Hk709LSuPnmm7npppvYt28fmzdvZtu2bezcvo3X91bjC8SBizILOVFucqLd5ER7yYn2kh3lIWIM3go+BfvtBmo6TVR3GKnpMFHTaeFg1+E6yUkJTJw9hUsnT2batGlMmjQpJFkfBnvEJ/HPGfkjgFJqs4j8FdBK3DDTandz16tbeW1THXPzEjhzQiqJUeEXiDDciLGaOW9yOmeOT2V9dTNrK5q4/cXN/PKtHVw9N4frT83TcebGLq5AjmYFICIjPp+bwWBgypQpTJkyBTicGqusrIyKigoqKyupqKziQG3l4UYiYI3FHRGHzxbvV/ACSt6YUe48LoxdzRi6mjF0tWBwNGN2tqGcnT1VjCYT2VnZFBbOoaCggHHjxlFUVERqampYvkiLCDk5OeTk5HDRRRcBfseH8vJyysrKKCsro7ysjI8rKnDs809xFyA1CrJtLrKj/QF+s6K8pNlGVqDf46EUNDsN7Os0UttppKbDyD67hdpOQ0/QX4MI2dlZTJtWTFFREUVFRRQXF5OYmBha4QMMVomLVEp9etQNG16uJKOQj3Yf4PYXN9HU4eL7547nf84cxz9L9vWpbTin0womFpOBUwuTmF+QSGFKNM+uqeKpVZX86eMKzhifwuWzsjhvcroelh5b/DPgnRovIv8N3Ag8daIGIvJnYCnQqJSaGihLBP4B5ANVwFVKqWbxd5SPAkvwzyX+arADCZtMJgoLCyksPNLhweFwUFNT05NGa+/evVRUVlFXtw2fN/A0EwMqMh6PNQGvLdE/JBuZ2Kc8qP3FF5mICmRs8EYm4YscpgemUoizHaP9EIauQxjsh7A4mlGO9p4qEVYr+fn5FOTPIC8vrydNV3p6etjl2+wvVqv1CCUf/Bk76uvrqaioOPwp38OGvfU9VjujAdIjfWRFusmM9JIZ5Vfu0iO9mAep3OVGe9jb7u93u3OnDhafgiaHgTq7kbpO/6fWbqLObsbuPjxcm5gQT+H4ccwrHNejmOfl5REREb7zSQd7Bx4UkXH4nRkQkSuB+kFLpTkmnU4Pv3xrBy+sq6Y4NZqnvnIK07LjTt5Qc1xEhNPGJXHauCTqW7v467pqXirdx3f/vpHoCBMXTk1nybQMThuXpIdbRzlKqYdE5Fz8OVMnAHcrpd47SbO/AE9wZKaaHwPvK6XuF5EfB9bvAC4EigOf+YRRIGGr1UpxcfHnwiK43W5qamp6Hubl5eXs3lNG877ynjoSEYk7IgFfZLdyl4DPGj8oq50z91QMdn9A4gHnTj0aj9NvXbMfwtDVjKmrGWNXc0/AXhEhOyeH4tmnMG6c/yFeWFhIWlpaWFrWhguDwUBWVhZZWVmcfvrpPeVOp5Pq6moqKyupqqryfyrLKdnb2KPciUBqJGTaXGRGesmK8pAV5Vfy+jose/14O9UdftWkvzlTfQoauwzUdhqp7TT5FbYuM/WdBpzew/XiY2PILyzk3Lx8CgoK/Ep6QQFxcSPveSrdedEG1FikEPgTsABoBiqBLyml9g6NeENDuCWQ7g9/XVeNUopN+1p5Z1sDbV1uFhUlc87kNMzGUWDPDgOODvbr8ynWVjaxfH0tb29toMPpwWY2UpAcxaSMGCakx54w7pwOHhx6+po8+iT7MALXKKVeOEm9fOCNXpa4XcCZSqn6QJiSD5VSEwJWvg+VUn87ut6J9h+O/VdLSwvl5eU9n7Kycqr2Vh1O1yUCtnjcEfF+5S4yEZ8tEWWJ6nNOVdvOt4ABKHHKh6GrtceyZuw6hNnRcsRQaFRUNOOKxlFcVERBIMNCQUFBWFtcwhWn08m+ffuoqqo6bMmtqmTfvlo8ASuuAGlRipxIF7nRXvJjPRTGeog5TiqvX673J306kRLn9kF1u4nKdiN7201Ud5qp7TTi6qWspSQnkZfvV9K6rah5eXkjQlnrax82IEuciHy/1+pbwAf4g2J2Al+kVz5VzeCoOWTnjc111DR3kRlv5ZpTcshLGvFTdcIag0FYMC6ZBeOS+cXlU1lbcYj/bN/Pa5vq2F7fhlBLdoKNotRoilJjyEm0YTJohXqkIiKxwC1AFvAa8F5g/UfARuCEStwxSOtWzAKKXHck2Cygple9fYGyzylxIvJN4JsAubnh91IQHx/PnDlzmNMrlILH46G2tpby8nL/XLuKCsrKy9nfa76dmK24bYl4o5LxRaXgjU4d3HCsUoizDWNHI8aOA5jsTRi6DoHP/yQ3mkzk5uZSNG5BzzByYWEhycnJY8q6NpxEREQwbtw4xo0bd0R59/2wd+/eHktu2Z7dfFbZ0FMnI0oxMc7JtCQ30xJdPda6Yw2h+hRUtJnY1GRmR4uFijYTHp9/W2xMNEXF4zkloIx3K22RkaN/fvNAh1O7PbYmAKcAr+JXtr8MrBwCucY0SilWlzXx1KoKPtx1gJgIE1+cncWs3AQMuuMJKhEmI2eMT+GM8SlMTI+hvtXBjvo29jR28NHuA3yw6wAWo4GC5KiAUheNUko/IEYWz+EfSVgDfAO/8mYBLlVKbRzC4xzrpjimKUIp9Sf8oxzMnTs3fGIsnACTydRj6eiN3W73P8DLytizZw87d+6isurwXDsVmYA7JhNPfA7emPSThz3xuDC11mBqqcHS0YBy2QF/cvqJE8YzfvwXKC72T0LPyckZ8fPWRiq974fFixf3lHd2drJ79262b9/Oli1bWLdpIx/UOYgwwtwUBxflOrh+vP1wfbfwn31WPmyIpKnL72gwfsJ4vnj+TKZMmcLEiRNJSUkZs33ugO5updTPAETkXWC2Uqo9sH4v8K8hk26M0Wp38/bWev7ySRU7G9pJjo7gnElpLByXRISejxVyRITMeBuZ8TbOnpSGw+2l4kAHexo7KGvsYNd+/2ToF9ZVMyM7jmnZcUzLimN8WgwZcVZMevg7XClUSk0DEJGngINAbne/NgD2i0hGr+HUxkD5PiCnV71soG6gQo8UIiMjmTp1KlOnTu0pczqd7Nq1iy1btrB+wwY2bdyEZ/82xGLDkViEO23K55wZDB2NWBq2YW6tBp+XmNg45i9ewMyZ/od5Xl4eBm0RD3uioqKYNWsWs2bNAvwWuy1btrBixQree/ddPtlvZWmunS8WdrHtkJk/7oylzQlz58zmpvMv4NRTTyU2NjbEZxE+DPYVJRfondTOhd8jS9NHWuwuVuxs5I3N9Xy85wBur2JiegwPXjmdS2Zm8lJpbahF1BwHq9nI5Mw4Jmf651c0212UN3aggC21razY1Uj3lFOTwa8A5iTaSIyKIM5mIs5mJt5mIc5mJsZqItpqIirCRHRE4NtiIirCqJW/4cfdvaCU8opI5SAUOPAPyd4A3B/4frVX+a0i8nf8Dg2tJ5sPN1qJiIhg+vTpTJ8+nS996Ut0dXXx6aef8u677/HJJ6uxNu7AkTkTV/o08DixVq3G3FxFVFQ0F1x+GWeddRaTJk3SStsowGQy9Sh13/jGN/i///s/Xn/7bfZ1mthyyEJuXj4P3fkTxo8fH2pRw5LBKnHPAZ+KyHL8wwKXA8tO1khELsDvam8EnlJK3X/U9gj83l5zgCbgaqVUVWDbncDXAS/wHaXUiEmqp5Sisd3J5n2trK1oYk15Ezsa2lAKsuJtfG1hARdNy2D6GE6XFQqGKuxKQqSFufl+68Ep+Yk4PV7qWxwc6HByqNNFs91FdZOdnfXtdLm9OD0+vL6Tj5SZjYLFZMRmNhJjNRFjNRFrNQeWzX5lMNLMTYsLtcI3MGaISPcMagFsgXUBlFLquK/9IvI34EwgWUT2AffgV97+KSJfB6qB/wpUfwt/eJEy/CFGdMabADabjTPOOIMzzjiDuro6fv/7P7Bq1cf+1FX2g5icrdxw441ceeWVY2Ke01glLi6O22+/HafTyYoVK4iLjeGRRx/TlrcTMCjvVAARmQ10+yGvVEptOEl9I7AbOBf/8MJnwLVKqe296vwPMF0pdbOIXANcrpS6WkQmA38D5gGZwH+A8Uop79HH6U2wvbscbi8NrQ72HrJT3dTJ3iY7u/a3s72ujaZOv+HSYjIwJzeBBeOSWFScfNxMCzqu2+jl2nk5dDg9tHa5aevy0Ony0OH00Bn4dDi9dDg8lFQdwunxYXd5aHd4aHd6aHe4cXuP/O8axJ+ZonvINysh8B1vJT7SQkyEX+mLsZqItBhH9YvCUHinhgvh6J063CilePzxx3n5ZX8a7gcffJBTTjklxFJpgkV9fT1//vOfOf3004+YTzeW6GsfNmglrr+IyGnAvUqp8wPrdwIopX7Vq847gTprRMQENAAp+OMt9dTtXe9Ex+xrJ/hJ+UGqDtrxKoXPp/D6FD7l//b4AmVK4fb66HR66XR6sLu8/oevw0NTp4uD7U7anUd61kSYDIxLiWZKZqz/kxXH9ro2HSJEM2CUUjg9Ptq63LR2uWnpcpOdYKO2pYu6li5qW7qob3HgOY6lz2gQrCYDBoNgEMEg/jLpXpbAsoHAdn9597IElo0Gf7kElk0GwWw0YDIKJoMBs1EwGQ2YAttMxkBZz7bj1zMapMcTQARm5SYwPq1vWbC0Ejfy6ezs5MEHHyQ/P5+vfvWroRZHowkqwxpiZJAcy83+6ICXPXWUUh4RaQWSAuVrj2qbdayD9HbRBzoCMZn6QzL+Cc5Dwm7g7YE1HVI5BoGWI7xkAC3H0fSWI+9EFUcSpaWlB0UkrGJvBpFk4ODXvqZHnscg4dKvhIo+9WGhUOL64mZ/vDoDctEfCCJSEg5v8lqO8JMjHGTQcoSvHEONUiol1DKEitF6TTUnR1/7vhGK8by+uNn31AkMp8YBh/rYVqPRaDQajWbUEwol7jOgWEQKRMQCXIPf9b433S76AFcCK5R/8t5rwDUiEiEiBfhzEH4aJLk1Go1Go9FowoagD6cG5rjdCryDP8TIn5VS20Tk50CJUuo14GngOREpw2+BuybQdpuI/BPYDniAW07mmToIBjwUO8RoOY4kHOQIBxlAy3E04SKHZujQ13Tsoq99Hwi6d6pGo9FoNBqNZvDoGBcajUaj0Wg0IxCtxGk0Go1Go9GMQLQSB4jI90Rkm4hsFZG/iYg14HixTkT2iMg/Ak4YQ33cP4tIo4hs7VWWKCLvBY77nogkBMpFRB4TkTIR2RzIlDGccjwoIjsDx1ouIvG9tt0ZkGOXiJw/nHL02vZDEVEikhxYD+rvESj/duCct4nIr3uVB+33EJGZIrJWRDaKSImIzAuUD+fvkSMiH4jIjsC5fzdQHrR79QQyBP0+1QQHEbkgcO3KROTHoZZHExxO9BzQfJ4xMScuOTlZ5efnh1oMjUYzQJTyZ23oK6Wlpc34A4b3K0VfOKL7L41m7FFaWnqwLzEiQxHsN+jk5+czFtPWaDSjgTaHm+n3vstPL5rEN04v7FMbEbEDf1dKOYHKgKf7POCEKfrCkbHafzU1NdHU1ARAbm4uVqs1xBJpgkF7ezvPPfccHR0diAgXX3wxEydODLVYQUf6mKVlTChxGo1m5GJ3+o1nURH96q4sfD693zFT9GnCjw0bNvCjH/0Ij8efhzo7J5cnHn+M+Pj4k7TUjGQcDgf33HM369dvIMEKzQ5Y+dGHPPrY4xQW9u0Fbqyh58RpNJqwptPlf5BHWoyD3dXonzsyCnjvvfe4/Y478Jgi6So8E0fOPPbV1nLLrbdSWVkZavE0w0RdXR3f/c632bB+A9+c1M6jC5p46LRmTJ5Obr3lf/j4449DLWJYopU4jUYT1nS5/Ja4SEu/LHEudIq+EUVTUxP33HMP9913H86IRDomXoQnqRB3+lTs48+nrrGJ//7vb/LCCy/0WOg0Ix+fz8drr73G12/8GjWVe/jutDYWZbgASLX5uGt2M2nmTu666y7uv/9+2tvbQyxxeDHsSpyILBURrSxqNJoB0en0P7Cj+meJayFMU/Rp77sj8fl8vPrqq1x//ZdZ+fEqnFlz6JxwAcps66njjUmnffJlOGIyefLJJ7nx619n61b98410Ghoa+MH3v8/DDz9MQWQnv5jbzOwU9xF1kq0+7prdwiV5dt5959989StfZs2aETe1ddgIhnJ1DbBHRH4tIpOCcDyNRjOKsAcscbb+KXEOoDtF378Z3hR9/eUvwAWhFiIcaG9v50e3385vf/tbOszxtE+5DFfmDDjGe78y2+gqOht78TlUNzTx7W9/m2XLljEWIiyMRjZs2MA3vn4jO7du4msTOrhjRivJNt8x65oMcOW4Lu6d20Kk+xB33nknTz31lL72BMGxQSl1vYjEAtcCz4iIAp4B/qaU0nZRjUZzQrqVuH46NqCUug+4bxhEGhRKqZUikh9qOUKNz+fjjh//mO07duDIW4A7ZUKf4sh443Npj8nAuvcTnnnmGcxmM9ddd10QJNYMFfX19dz54x+TZO7itrmtpEUeW3k7mvwYL/fObebZXVE8//zzpKSkcOmllw6ztOFNUIY5lVJtwEvA34EM4HJgvYh8OxjH12g0I5chdGwYMYjINwPBnEsOHDgQanGGhbKyMrZv24Yjex7u1In9CwRoNOMoWIwnNosXX3p5+ITUDAsrVqzA4XTyg+l9V+C6MRvgxomdFMZ6eOP114dJwpFDMObEXSIiy4EVgBmYp5S6EJgB/HC4j6/RaEY2dme3Ejd2IiIppf6klJqrlJqbknLSeJ8jksTERAxGI6a2OvD1f6RbnO2YnC2kpo7O32c0031Pbz9kHlD7gw4DjQ4To/W/0R+CYYm7EvitUmq6UupBpVQjgFLKDtwYhONrNJoRjN3d7Z06dixxY4Hk5GRuveUWTC3VRO98A4P9UN8aKoWpqZyYHa8TZRJ+8P3vD6+gmiHnrLPOYtasmTy9M5qXKmx4+mGM29xk5mfrExBLFDfdfPPwCTlCCIYSV6+UWtm7QEQeAFBKvR+E42s0mhGM3enFaBAiTNrJfbRxxRVXcN999xFn9BC1/VUiaj4Dr/u49cXRRuTud7BVfMSE4kL+9Kc/UlxcHESJNUOByWTi/vsf4Lzzz+fVqkh+VprA3vYTv6TZPcKfd0bx0KZYEtJyeOJ3vycvLy9IEocvwegVzz1G2YUna9TfpNsajWZ00unyEGk2Iv2ZMxXGiMjf8Kf/miAi+0Tk66GWKZQsXLiQ5557liUXXoilYQsx21/F0NF4ZCWlMO/fTsy2V4h2t/Cd73yH3z3xBFlZOgnHSCUiIoI777yTn//857QZE7i3JJ5XK234juFwur3ZxE8+TWRlvY1rr72WPz35lFbgAgybEici3xKRLcBEEdnc61MJbO7DLjzAD5RSk4BTgVtEZDLwY+B9pVQx8H5gXaPRjFK6XF4iI0bPUKpS6lqlVIZSyqyUylZKPR1qmUJNXFwct99+O4888gipcZFE7XwL08Ey/0blw1r5Mdbqtcw7ZS7PPfcsV1xxBUbj6LknxjKLFy9m2bPPccYXzuKlykge3hyLo1cs53dqrDywMY6o5Cx+/4c/cNNNNxERERE6gcOM4bTE/RW4GHg18N39maOUuv5kjZVS9Uqp9YHldmAH/tyHlwLLAtWWAZcNvegajSZc6HR5iRpDTg1jmZkzZ/L0U08yc+YMbFUfY2ytJaLmM8xNZXzta1/j/vt/RXJycqjF1AwxsbGx3HXXXXzve99j6yELj22Nxafg/X0RvLAnikWLFvHHPz3JxIkTQy1q2DGcSpxSSlUBtwDtvT6ISGJ/dhSIqTQLWAekKaXqAweoB1KHTGKNRhN22J2e/gb6DRoi8l8iEhNY/qmIvCwis0Mt10gmJiaGX/3yl2RnZxO5+x0s+7dx2WWXccMNN4yaIXXN5xERLr30Un7wwx+y9ZCZv+6J5IWyaObPn8c999xLZGRkqEUMS4bbEgdQCpQEvkt7rfcJEYnGH2PutkC8ub62G/VxljSasYA9vC1xdyml2kVkEXA+/tGBP4RYphGPzWbjqzfc0LN+4406kMFYYcmSJcyaOZN399lAjPzoR7djMoXt/z/kDJsSp5RaGvguUEoVBr67P4V92YeImPErcC8opbojOu4XkYzA9gyg8Vhtx0KcJY1mLGB3ecJ5Tlx3gLOLgD8opV4FLCGUZ9QwZ86cnuXY2NgQSqIJJiLCOef6/SGTU1L08PlJCEaw34UiEhVYvl5EHhaR3D60E+BpYIdS6uFem14Dul/RbsA/506j0YxSOl3ecI4RVysifwSuAt4SkQiClAlntBMfHx9qETQhIj8/HwCzeWDBgMcSwehs/gDYRWQGcDuwF3iuD+0WAl8GzhKRjYHPEuB+4FwR2YM/fMn9wyS3RqMJA+xOTzhna7gKeAe4QCnVAiQCPwqtSKMDPf9t7GKz2QAwGPT70MkIRs/oUUopEbkUeFQp9bSI3HCyRkqpVcDx/sVnD6mEGo0mbLG7vUSFqSVOKWUXkSrgQhG5AFitlHo3xGJpNKMCrcifnGCoue0icidwPfCmiBjx51DVaDSak2J3erGFqSVORO7G78yQBCQDz4jIT0MrlUYzsvH5/Hm4lDpG5F/NEQSjZ7wauA74ulKqITAf7sEgHFej0Yxw3F4fLq8vbC1xwLXALKWUA0BE7gfWA78IqVSjAJfLFWoRNCGivr4e0PdAXxh2JU4p1QA83Gu9Gnh2uI87UP66rnpA7a6bf1JfDY1G00/sLr/zZ7jGiQOqACvgCKxHAOUhk2YUsXnz4cQ+TqdTR+kfI/h8Pl595RUADjQ20tjYSGqqDgd7PILhnXpFIM9pq4i0iUi7iPQ53ptGoxm72F3+/DtREeE1nCoij4vIY4AT2CYifxGRZ4CtQEdopRv51NTU8NBvftOz/qtf/Yqurq4QSqQJBgcPHuSee+6mpLSUC3K6MIuX7377VjZu3Bhq0cKWYPSMvwYuVkrtCMKxNBrNKKLT6bfEhWGIke6A5aXA8l7lHwZflNGBUoqdO3fy1ltv8dbbb+MTE52TlmJs38+HH37Ips1buPKLV3Duuedqy8wowuVysWHDBv7zn//w4QcrUD4v1xZ1ckGOg/lpLn63DW677TamTpnCBRdeyIIFC0hM7FfSp1FNMJS4/VqB02g0A6HL1a3EhZclDvggMDVEM0DcbjcVFRXs2rWLLVu28FlJKS3Nh8BgxJU0DlfmbJQlEl90Kr7oVLx163nyySd58skn+f/svXeYXGd59/95zvS6fXZne1/tqq6aOwhsbNkWdqgGQwgmBN7QQ94E8nvjKyEB0kgCJCRgTCcGEwvb4N5lhCVZbb0rbdH23vvs9HOe3x8zWq26ZO3uzK7O57rONXPanHvmzJzznfu5S3FJKVs217Ju3ToqKyvJzc3VMxlXCBMTE7S2ttLY2MixYw0ca2ggFI5gN8FN2UHuKAzgscUSG8rcUf5h+zgv91t5qesY3/jGcQBKS4rZuKmWmpoaqqqqyMvLw2BIuj96y8JyXBkPCSEeBh4jNvQAwIIODDo6OjrnZO7kcGryeeIeAzYDCCF2Synfk2B7khIpJVNTUwwMDNDf309fXx+9vb20d3TQ19eHpsZEujDbCTuyiZasJZpaCMbT499UVzb+qtsRwRlMk120j/fT/djj7N69GwCr1UZJSTElJSXk5+dTUFBAXl4eOTk5es/NBKCqKqOjo/T19dHX10dPTw9dXV10drQzOTUNxOqH5bs03uoJsT49QnVahHP9zC0G2FkY5LaCID0+A2+Mm2mcbOWp33bz6KMxJ7jFYqa4uJji4hKKioooKCggPz+f3NzcVR9LuRwizg34gVsXLJOALuJ0dHQuyMmYOHuSxcRxeg3LS2ojuBoJBoOMjY0xOjo6Pw0PDzM8PMzg0BDDw8OEgsFTOwgBFhdRawpq1lo0RzqqPRNpccXWXQRpdRP2biDs3QCaihKYxDA3RjgwybG+SZrbu5Fh/2n7uNwp5ORk483JIScnh6ysrNOm9PR0vTfnZaJpGpOTk/PnemhoiMHBQYaGhhjo72N4eJhIVJ3f3moU5NqjrLNHKKhQKXJGKXZFsV3Gxy4EFLlUilwB7ioOoGrQP2eg22eke9ZA/8hxDnSf4Nng6ftlpKeRl5ePNzeXnPh3IDs7m+zsbDwez4rvCrEc2an3LfUxdHR0Vif+cNLGxMnzPL8o8aLA3wIMwINSyqTrOhONRpmYmGBsbIzx8fHTHsfGxhgZHWVsdAy/f+6sfYXJimZ2EDU5kCmlaB4XmsWFtLjRLC5QFulcKgY0Ryaa44zemtEwSmgmPs0SDs0yOTRHa189IvQaUo2ebq/Hjb58AAAgAElEQVQQuFNS8WRl4vF4yMjIIDMz86xHt9t91XQQCIVCDA8PMzIyctrj8PAwI0ODjIyNEYmc/jk6zYJMq4rXEmFjrkaWTSXHruK1q6Sa5aVo9MvCoEChS6XQpXKT99Ryf1Qw5FcY8hsYCRgYCQQZ7R3lYFsDk4HTf6xCCNLTUsnOziE7JwePxzMv7k4+ut3upB6qX3IRJ4SoJNZ6K1tKuU4IsQG4S0qp11HS0dG5IP7kTWzYGM+yF4BtQca9AKSU8pwd2+PFzr9DrGVgH3BQCPEbKWXjchgNMYE2OjrK0NAQI/ESDiMjIzFxNjLK6NgoszMzZxdaFQJhcaAabahGG9JZiEyzo5ntSLMDzexAmhxgSLBXy2hGM55D3AFICWoYJTyHCM/FHiN+QmE/4yNznBhoQokEkOGzM2ENBgNp6el4FnjxPB4PWVlZ856d9PT0pL7hn0TTtPnhzv7+/nkv2uDgAEODg0xNn15AQghItUCmJUqBRWWjVyPTqpJh1ciwamRaNezG5CjMazdKSt0qpW71rHVRDcaDCuNBhbGggbGgwngowMTQME09TewNQEQ7fR+b1UJ2djY53ly8Xi85OTnk5eWRn5+P1+tN+HDtcvzavk+sl+D3AKSU9UKIh9CLYero6FyEUzFxyTXcJaV8s6pyO9AmpewAEEL8ErgbWHQRFwwGOXHiBG1tbXR1dcVjk3oZHx87S6AJsw3NZEc12tDM2UhvSUyYmexIkx1ptiGNVhDL54lSfCMYZwaJur1ozkXKRhUCjBY0owXs6Zx9m4+jqYiIHxEJoIT9iMgcIuwnGPEz1DuJsXMAEfad5dUzmUxk53gpLIjF5ZWWllJRUUFxcXHCvHiaptHW1saxY8doaWmho72dnp5uQuHI/DYGBTJtkGUOs9GukZl+ukhLs2gYl9H81mkjzZNG1qRFqUiJXnyHS8SoQLZdI9uuAWe/rpQwGxExcRc0xMVegLG5WQaOd/HGYQOByKnfjiIE3pxsikpKqayspLq6mo0bN2K1WhfN5ou+p2U4hl1K+foZ/04W76zo6OisWlZAsd/LJQ/oXTDfB1yzmAfo6+vjO//1Xxx8/SDRaOxGLUwWVLMb1eJG8+ahmZ1IizP2aLaDklwiWfGNkNHzCnfefjtPPv0044U7Fk/IXZIBBqTFhbS40M63zWlePR9KyEc45KNzdoa+uibYfwC02PfX5U7h9p238cd//MfL6rl57rnn+N53/5vxiUkAUixQ4AizIzs2zJljU/HYNdItGkqSOBBbp418uzmb2+64k28/9SSfWzO8qELuQggBbrPEbT63J09K8EUFIwEDw/Eh2/65brreGGTfa68hAZPJyG237eTzn//8ssTbLccvd0wIUUZ8KFoI8V5gcBmO+6bxh6MYFIHFuGpuHDo6KwpfKIqqygW/xVUTi3SuW+VZ41BCiE8AnwAoLLy8bjC7d+9m32uvARDOqiSSWYnmyLqkxIFkwTgzyJ23385nP/NpAB7a10F4OUXcpXABr14QQItinOrFNNLM7Mwgv/rVr9i+fTtbt25dNhP/9RvfIBQOszYtzLtLA5S7o0n/NWieNHLbHXfyqU9/Fimh+chPl03EXQwhwGWSuExRys4ImJgIKTzbY+XpXhtPPPEEN9xwA9ddd92S27QcIu7TwAPAGiFEP9AJfGgZjvumefB3nXjcFj6wTW+lpaOTCP7s4TpGZkPUFqRiNxtWRJzRJdIHFCyYzwcGztxISvkAsesmW7duvaxgo3vuuYfh4WFef/0gjJ7APHoi5omzuFHNLjSrG2l2opkdaBZncsSxnUHU7eXJp58G4MmnnyZauCOxBp2LeU+cDxGaQwn7UEKziNAspvAsBGdOeeJcbu6443Zqa2uX1cQ/++IX+cGDD3B8bILjh82kWCDfHiHfGU1aT9yatCjffupJpITnnn6Sz61JDgEHp3viTiZPDMwZ6PWbGfYLpASL2cQ7br2NzZs3L4tNS/bLFUJ8ccHsU8DLxNp8zQHvYUE/1WRiOhBhaCbIVCCMJiXK6rl56OisCMJRjb2tYwQiKllOczImNVwJB4EKIUQJ0A98ALh3MQ+Qk5PD17/+dUKhUCwGqqODzs7OWExcbx9jg51nx8SZrGhmO6rRHouDM8di4TTzyZg4+7LGxGlOD+OFO3hoXwfR5R5KhVMxcWE/SsR/+vOwH2M0gAjPIdXIabuZTGa8uV4K8msoLCykpKSEiooKioqKEhITt3PnTm699Vba29tpaGigtbWV9rZWXuk+d0xcpjlMZjxRIVExcRUpUT63ZpjmIz/lc2sWNybuYmjxmLjxeEzcWDwJYjRoYDxkZDR47pi4is0V3F5eztq1a1m7di02m23ZbF7Kv1+u+GMVsA14nNhQwh8Cry7hca+IjtFY28NgRGNwKkhe2vKdDB0dHXijb4pAJObB+H3bON6U5QsSXmqklFEhxGeAZ4mVGPmhlPL4UhzLYrGwYcMGNmzYcNryk9mpJ0tGnKzvNjIywujoGKNjo8yMT507OzUu9FSTbV7cxbJST2WoYlicOCDN6Vn8IVQpEdFQPFFhLpa0EJ47Jc7UACLsR0aCZ+1qMBpJT88gqzCT7HhW6snpZHZqWlpa0nmNFUWhoqKCioqK+WWapjE2NkZvby+Dg4MMDAwwODjI4OAA9UODTA6ekZ0KpFoh3RIlw6LOZ6RmWFQybYufnVqRsjTiLaLBRFBhLJ6dOh5/Ph40MBExMXGO7FS7zUp2dja55V625sYyVL1eLwUFBXi9Xsxm86LbeTksmYiTUn4FQAjxHLBZSjkbn/9b4H+X6rhXSvuoD4tRIRTVaB/14XFbONg1QW1B2moKrtbRSSom58I8VtfPvdcU8vu2MYQAm8mAP6xit6yu352U8ilioxMJwWg0zt+IzsfJOnFn1og7+TgyMsrYeB9z8T+9CxEmC5rZGasTZ3aiWVxo1niSgMW9eHXizmt8CCUYqxF3cnhTCfswRvznzCgVQpCalkaWN4uszOJz1ojLyMggJSUl6QTam0VRFDwez3l70IZCodOE/sI6cf2DAxwdPLtOnMMUqxPnsUbIsmlk29TYFB+uXa6Pbi4iGIonHgwHDIwEFEYDRkZDRqaCZ9eJy0hLxePNoWZBMeiceM04j8eDy+VK6vO+HIEQhUB4wXwYKL6SF1yqYplSStpH56jIdjEyE6R91IcqJc83DjPmC3HXxjyimkYwouFMvgryOjorgocOxFqO+kJRbCYDBkWw+0gfh7sneb1zgqbBWXJTbLisRpqHZvEFozx0oId7r9FjVJcLo9F4wZv8SU7e7GPCbmTeozc8PMzg4BBDwx1nd2ywuoiaU1BtaWj2dFRHJtLs4Nw5HxdCQwlMYZgbQwlMYAhMYQxNn1XjzZ2SQo43B29OBdnZ2afVd8vKyiItLU3v2HAGFouF/Px88vPzz7leSnlWx4aTXRsG+/uoGxw+TeRZjJBrV8mzRyhwqhS5opS4VGxX4L072bGhc9ZIr89A35yRgYCJqdO+boLM9DS8Rflsj/9xOdmxIScnh8zMTL1jwyXwM+B1IcSjxETwu4CfvNkXW8pimd3jfqYDEXZkOXBajBzunqBnwo9REbzeOcH2kgwer+tncCrIx28qITfVxqGuyfnhHiklvlAUl3Vlfyl0dBaL2WBk/vdwqGuC9lEfmpT0Tvj5wd5OCtPt3LHey5HuSYyK4JWWUcJRjevLM3BZTTQPzWJePZmpq45LudlPT0+f1ju1p6eHjs4uenuPz/dOvVKsNhulpaUUF22msLCQvLw88vLy8Hq9yxqfdLUghCA9PZ309HSqq6vPWr+wmPDC3qmN7W3sHVrQO9WpUewMY7iMn7iUMBI00DFjIl4LHIvFTElJCdee0Ts1GYrxLjXL0Xbra0KIp4Gb4ovuk1IevYKXXLJima+1jwNQlunEaTGyv2McgeRjN5bw031dfPeVdiKqhsNi5MevdeFxWekan0MRoCE52j3F610T3FLt4ebqbH77xgD+sMp7NuehSdjbNkZRup1rSzNoHfHRM+FnU0EKWS4Lx/tnUBTB2lw3cyGVrvE5ctxWclNtDEwFmA1GKMywYzYY6J/yYzEa8KZY8YWijM+FyXSacVlNjM2GiGiSLKcFKSXjc2FsJgNpDhOzwSj+sEqKzYTFqDDljyCBNLuJqCaZCUSwmQ04LEbmQlHCUQ2X1YRBgZlgFEUInBYjEVXDH45iMxmxmhT8YZWIquGyGpES5sIqJkVgMxsIRTVCUQ2byYDJIAiEVTQJdosBTZP4wyomg4LVFBvCDkc17GYDihLbVhCrERbVJMGIitmgYDYqBCMaqiaxmRVAEIyoKEJgNSlENUkoomExKRgVQTCiIaXEajYgpSQQ1jAosW3DUY2wqmE1GTDGjynjx1Q1SSCsYjYqWOLHjGqx9yJEbFtFxLaNqJJARMVqjNnnD6uomsRhMcQymkJRTAYFu9lAIKISjKjYzUZMBgVfKIomJW6rkagqmQ1GsZoM2C0G5kJRAmEVt82EURFM+iOx+BS7iWBEYzoQwWk14jAbmPRHCEZUMpyxGI3R2RAWo0KG08JMIDL/PXGYjQxMBwhHNfLT7AQjKj0TflJsJrwpNvqnAgzPBCnJdGAzGzjeH7vo1uS6GZ4JUd83TXGGnbIsJ6+1j9M36eemikxUTfLo0X5cVhN3bvDyzLEh9pwY5brSDNblufnB3k40CaVZDgangthMBjrG5njg1Q7MRoUPXVPED3/fGfsNZjlxWWOXJ7Ne6mfFIoQgNTWV1NRUampqTlsXiUTo6uqiubmZqampN/XaeXl5VFVV4fV6k3rI62pDUZT5OMEtW7actm5ycpITJ07Q3NxMQ309jZ0dZ8dfXoT0jAzufNtGampqqKqqIi8v76ppiXYmy+JDllIeAY4s0sstWbHM19rHcFuNZDjNOCxGDIqgtiCVsiwnN5Zn8XLLCLs2eKn0uPjuq+30T/m5e1MunWNzfG9PB1kuCx+5rojHjvbzQtMIhel2HBYj9z8ei1suSLexp2WUB/fGblQui5FfvB4bWhIi9g9DRyfZcVmMzIZiQyVmo4LHZeG5xmEA1uW56Ryb469+3UCq3cRHriviqYZB9nWM8+7NedQWpPKV3zZiMxn45FvLOD4wzdPHhnhHTTblHicb81NoHJyhKMOOyaDgthpxrrKYOJ0YJpPprIB7ndVPWloa11xzDddcs6g1rq9axOUq4EQjhHgfcJuU8uPx+T8EtkspP3vGdvPFMollyLZc4iEygbFFMnc50e1eXlaq3bBybb8cu4uklFlLacxyIYQYBboTbUeCWKnfVZ0r52o/95d0DVuJ0ZyXXSzzchBCHJJSLl9J7UVCt3t5Wal2w8q1faXafaWsFjH6Zrhaz7mOfu4vlZU4iDxfLFMIYSZWLPM3CbZJR0dHR0dHR2dZWXGeuOUslqmjo6Ojo6Ojk6ysOBEHS14s87KHYJME3e7lZaXaDSvX9pVqt86bRz/nVy/6ub8EVlxig46Ojo6Ojo6OzgqMiRNC/FAIMSKEOJZoW3R0dHR0dHR0EsWKE3HAj4GdV/oiQoidQogWIUSbEOLL51hvEUI8HF9/QAhRfKXHXAwuwe4vCiEahRD1QogXhRBFibDzTC5m94Lt3iuEkEKIpMhKuhS7hRDvj3/mx4UQDy23jefiEr4nhUKIl4UQR+PflTsSYeeZXOxPmojx7fj7qhdCbF5uG3WWh0u9ZuisLnRHzeWxIodT44LqCSnlukvZPjMzUxYXFy+lSTo6OknE4cOHx1ZLaQ79+qWjc/VxqdewFZnYcLkUFxdz6NChRJuho6OzTAghVk1x3Kvl+jUyMsLAwFklP89CURTKy8ux2+3LYJXOUjE7O8uRI0cYHR297H2tViubNm0iLy9v1bZbu9Rr2KoVcQs7NhQWFibYmuTjoQM9y37Me6/Rz4PO6kQIkUes5BHAgJQymkh7VgoDAwPs37+fV17ZQ31D/SX3HrRYrNxww/XcdNNNbN26FZfLtcSW6lwJmqbR399PS0sLTU1NvPFGHR3tHWhXOBKYlZnBxk211NTUUF1dTWlp6apveH8mq1bELezYsHXr1pU3ZqyzItHF8dWBEOKvAJOU8u/ii/YBU4AZ+AnwD4myLVnRNI3e3l4aGxtpaGjg0OEjjAwPxVba0wh5N6G6soGLeFa0KOGpbl7+3Wu89NJLCCGorKxi06aNrF+/npqaGtLT05f8/eicjZSSiYkJuru76e7uprOzk/b2Njo7OvAHggBYDFDqinBXUYR16RFyHerFzvhZzEQExydMNE+FOLR3nBdeeAGIeWkLC/IpK6+gpKSE4uJiioqK8Hq9GI2rU+6sznelo6Ojs7S8D7hpwfy4lLJWCGEA9nCVi7iTnpfW1lZaW1tpbmmhpaUF/9wcAMJkIezIRi28lmhKPtLqvqzXV1MLCEkNg28Uw0w/jQODtLQ+wsMPPwxAVpaH6uo1VFVVUV5eTkVFhS7sFgkpJdPT0/T39zMwMEBfXx/9/f309nTT29s7L9YAHCZBviPC9RkRipwqJa4oeQ4VwxWmVDpMEq89xC35IaT0MR5S6Jo10jVjoHeujTde6+HFF09tbzAo5Hq9FBQWkZ+fT15e3vzk8XgwGAznP1iSk1ARJ4TYBTwlpdQuY59fADuATCFEH/A3UsofLJGJOjo6OudESjm3YPZb8WWqEMKWIJMSgs/no6Ojg/b2djo6Omhra6O9o4NwKBTbQChIezoRez5qVhaaMwvNmgpXGsskFFRXNqormzCAFsUwN4biG2XAP8bo63W8+uqr85unpKRSUVFOWVnZ/FRYWIjJZLoyO1Yh0Wh0PkbxtKm/j4GBgdOEmgAy7eCxhLk+Q8Vr18i1x8Raille8Wm+GEJAplUj0xpm64I0gEAUBv0G+ueMDPkVBvwd9NT3cvCAQkQ9tZ3RYCA720NuXkzceb1ecnNzyc3Nxev1Jn3sZaI9cR8AviWE2A38SErZdLEdpJQfXHqzdHR0dC6IUwhhklJGAKSUP4ZYaSLgvG4lIUQB8FMgB9CAB6SU31p6c68cKSWjo6OcOHGC1tZW2traONHaxujI8Pw2wmQhak1DTSlDtaej2TPQbKmgLIOnQzGiunJQXTlEgCBANITBP4HinyDiH2eqqZNDR46CFruLGwwGCgqLqKyIeevKy8uprKzE4XAsvb0JJhqNMjg4SG9v77w3rb+/j/6+XoZHxtC0U74VowJZNonHGuH6DJVsm4bHpuKJP5qSsFiZzQilbpVSt3rack3CZEhhJKAwHDAwEjAwEvAz2tLH8Toj/sjp0Vdpqe64wIuJvPz8fPLz8ykoKEgKgZdQESel/LAQwg18EPiREEICPwJ+IaWcTaRtOjo6OhfgEeB7QojPSCn9AEIIB/Cf8XXnIwr8uZTyiBDCBRwWQjwvpWxcepMvD1VVaW1tpa6ujoaGBo4dP8701NSpDWypRGxpaHlbYoLNloY0O67cw7aYGC2obi+q20vk5DJNQwlNo/gnUPyTtE1M0L3n9zz33HPzu+XnF7Bu3Vo2bNhAbW0tXq83IeYvBifFd0tLy7y3tKuzg/6BAVT1lFBzmAXZ1ihF1ijbCk4JNI9NI9WioSTRab0SFAEZVo0Mq0Z12tn5R3MRwUhAiYs7hZFAkJH+cQ53NPJc4PRtMzPSKCoupbQ0NlVUVFBcXLys8XeJ9sQhpZyJe+JswBeAdwF/IYT4tpTyPxJrnY6Ojs45uR/4GtCzoBRAIfCD+LpzIqUcBAbjz2eFEE1AHpA0Iq6rq4vdu3fzyp5XmZ2Zji20pRCxZ6EWrkF1ZKLZ0sCwdMOQlp79KP6J+XnNnk6o8NrFeXFFQbOlxd5DRmxRABCRAMrcGAb/OF2+Ufpf3MMzzzwDQEFhIbfdeit33333isiEDYfDvPbaa+zdu5ejRw4zPjEJxIY+sx2SPFuEjflRvHaVHLtGtl3FZUqO/L+fn7DTMmXCHxXYjZKq1AgfrvQv2/EdJkmJSaXkDA8eQFiFkYCBoYCBwTmFAX+QgdYxGuoOE45vbrGYWb9uPddcey1vf/vbycjIWFJ7Ex0TdxdwH1AG/AzYLqUcEULYgSZAF3E6OjpJh5RSBb4shPgKUB5f3CalDFxgt9OIFy2vBQ4suoFvkldeeYWvfOUroBgIpxQSLd2E6s5FmpY3zE/xT2CcHZqfX456LdJkQ00tQE0tACAgJUpwCsP0AF2T3Tz44IM8svvXPPC97+LxeJbBojfH+Pg4n/vsZ+gfGMRtgZqUEHdURil1Rcl3RrEkeQx/y5SJ4aiTXbt28cQTT8CUL9EmzWM2QL5TJd+pwoL4O03CsF+hc9ZI+4yRxpaDfOfwYb7/wAP89f3385a3vGXJbEq0J+69wL9LKV9duFBK6RdCfCxBNuno6OhcECHEh4l1vPkZ0LBg+Z8Ac1LKC7ZfE0I4gd3AF6SUM+dYn5A6l/v27UNKSdBbSyS7GpRE3yISiBDzHrtoejHWjj1MTQ7R3Nyc1CKupaWF/oFBLAbJ/6meZU1qBGMSxqydD39UsGvXLj7zmc8gpeTVJ3+VaJMuiiLA69DwOsJclx1mwB/it102XhuG3//+96taxA2eKeCEEP8kpfySlPLF8+2ko6Ojk2D+HDjXlflh4GXgvCJOCGEiJuD+R0r563Ntk6g6lx/5yEdoa2unvf0gtsE6wq5YPJnqzEGzp4FYQWrgSlAjGOZGMcwOYZoZQPGNALBz506uv/76BBt3YbZv38773/9+Hn/8Mf65TmA2QKkrSok7QpEzSoFTxWtXk1bY2Y2SJ554AiklTz75JNnG5BjmPRdSwnhIoc9noMdnpHPWQPushal48u7WrVv46Ec/uqQ2JFrEvQP40hnLbj/HMh0dHZ1kwnCu5Kt4jO95g8VErEfQD4AmKeW/LaWBb4a8vDwefPD71NXVsWfPHvbt389wT2y0VxiMRO0ZqPbMU5mn1lRQklQNXCpqeD6D1eAfx+gfRwQmQcr5QsI33LCLm2++mby8vERbe1GMRiOf+tSnuO+++zh06BB1dXUcP36MF9raiERjgVuKAI9dkmuLkGNXY5NNJduukroMZUEuhM0oCfqC7N69OzafmngR548Khv2xbNYhf2waDBgZ9BsJRk/Zl5frZeuNa9m0aRPbtm0jOzt7yW1LiIgTQvwp8CmgTAhRv2CVC/h9ImzS0dHRuQxMQgjHGbXiiGecmi+w3w3AHwINQoi6+LL/T0r51BLZedkIIaitraW2tpYvAMPDwxw7dozGxkaampppbWslMhyObawYkLbUWFkRezqaPR3Nlo40WRP6Hs6JlIjQLIbAxHxmqik4CcFTo9nulBSq169hzZo1rF27lpqaGpxOZwKNfvPYbDZuuukmbropVpM6EonQ29sby07t6qKnp4euzg4aBgbnxR3E4r48No1sazSenRrLUM22q2RYtCsu1JuMSBnrAjESMDDiP1V6ZDhoYCRgYjZ8upDMysygsKKY2qIiioqKKC0tpaSkJCHflUR54h4CniZW1fzLC5bPSiknzr2Ljo6OTtLwA+ARIcSfSim7YD5R4TvxdedESrmXi/aVSi6ys7PJzs7m5ptvBmKlR/r6+mhtbaW9vX2+Xtx0b9v8PsLiIGJNjwk7R8x7t6zlR6SGEphC8Y9jmBvHEBjHGJhERmPiUwiBNzeXyk2b5wv/VlZWkpGRsWobqptMpvlSGAtRVZWRkZH5WnF9fbGCvv19vTQMDhGOzBdnwSBihX2zLWGy7bF6cTn22PBshjX5y5DMhgVD8940hSG/gZGgieGA4TSPmhCCrMwM8koLqF7Q3eHkZLUmz5+URIk4KaXsEkJ8+swVQoh0Xcjp6OgkM1LKbwghfMCeeJKCBOaAf5RS/ndirVtaDAYDRXEPxC233DK/fHJycl7Utbe3c+JEK729DfNFY4XZTtieieb0EHXloNkzF28oNhrG4BvGMDuEcW4Uo38MqcZyWi0WK2XlZVRWXEd5eaxjQ0lJSVLdiBOJwWDA6/Xi9XrZtm3baes0TWN8fDwm6uJttvr7++nr7WFvXx+BYGh+W5OBeLeGCPkOlXxHlEJnTNwtty6eiwh6fQZ654z0+QwM+I30+034FnjUDAaFnOxs8ksL2RpvxXWyU0NOTg5m84Uc6slDIj1xu4DDxC5+C0+xBErPtZOOjo5OsiCl/C7w3biIE1d7gfK0tDS2bt3K1q1b55eFQiHa29tpbm6mubmZ+oZjDPUdwgIIo5mwMwc1tYBoatFlD8EqgUmMk92YpvtQ5kZBSgwGA+XlFaxbdwNVVVVUVVWRn5+/ontjJhJFUcjKyiIrK4uNGzeetu5ks/u+vj56e3vp6emJN71vZ3/H2Px2TrOg2Bmm1B2hMiVKRUoE2yIqD01C96yBlmkTHdNGOnwWRhaUlXM67BSXlLCjuITCwkIKCgooKCggJydnWYvyLhUJeQdSyl3xx5JEHP9qZMwXonXYR++kn7lQlINdk9hMCm6biRy3FZdV7x+oo3OpCCG+KaX8Qnz2jxe2zhJC/FhK+dHEWJZcWCwWampqqKmpmV82MTFBfX09hw8fZt/+/Yx1/R7EPiIpBURy1qK6cs7/gpqGcbwN62gzYi4mFCqrqrj2mp3U1tZSXV2te9iWCSEEGRkZZGRknCXw5ubm6Orqig21nzhBS3MTT3R2oWkaBgHlKVFqM0Ns94TJtF5y6/R5QiocHTNzaNTMsUkL/viIrycrkzVba7i7qmre67qah8gh8cV+bwDqpJRz8bpLm4FvSil7EmnXakBKSX3fNI/XDfBq6yhtIxcumJjuMFPhcbIhP5XiDPuq/tKvRDQpGZoO0jrio3/Sz/BsCF8wSiiq8tUnG3FbTRRl2FmT4+La0gyuL8skxa4L8yVkYXmRPwIW9j/dsMy2rCjS09PZsWMHO1deuwwAACAASURBVHbsQEpJW1sbL7zwAk89/TSzzU8RTSsCefaNXfGN4uh6FQLTFBeX8M6PfZAdO3YseUV8ncvH4XCwdu1a1q5dO78sEAjQ2NjIkSNHOLB/H79s6+DhNgcbM8P8QbH/rB6n52I2LHii28YrQzYCEUhPS2XHO65ny5YtbNiwgaysrIu+xmoj0b7E/wY2CiE2An9JLCD4Z8BbE2rVCiYc1Xi8rp8Hf9dJy/AsZqPCtaUZvHdLPmtz3RSlO3BZjfz6SD+BiMqkP8zQdJD2UR9HeiY50DlBhsPMDeWZbClKw7QaU5FWENOBCAe7JjjaM8lk/O9mhsNMtttKWZYDi9FAhcfJpD9C1/gc/3u4j5/s68ZkEOyo8vCBbQW8rcqDkuwRxysPcZ7nOpeBEIKKigoqKiq477772L17Nz/80Y+InqHhhBrBeeJpMjPS+bP7v851112n/9FcYdhsNrZs2cKWLVv4kz/5EwYGBnjmmWd4/LFH+cohM+8q8XN38fkbnrRMGfmP4yn4woIdb3sbd911Fxs2bEBZ6SVurpBEi7iolFIKIe4GviWl/IEQ4o8SbNOKRErJs8eH+PpTzfRM+FmT4+If3r2eO9Z7SbGd7ZGxmQ3YzAbSHWbKspzcUJ5JOKpxbGCaAx3j/OaNAV5qHuHWmmw2F6Wh6BfMZWXcF+Kl5hHe6JtCSijzOHn7Gg8V2S7cZwx933vNqYr+EVXjjd4pnj42xG/eGOD5xmFKsxx8/uYK3rkhVxdzi4cihEgDlAXPT364egDWm8BqtfKhD32IwsJC7r//9PazIjyHw27je9/9Lunp6QmyUGcxyc3N5WMf+xj33HMP3/zmN/n188+TYz+3N242LPj3hhQysvP45t/9/VkZtlcziRZxs0KIvwI+DLxFCGEA9DGgy+RY/zR//0QjBzonqMx28qOPbmNHVdZl/1M1GxU2F6ZRW5BK5/gczx0f5tdH+9nfOc6u9bkUZzqW6B3onMQfjvJ84zAHuyYwKIJrSjO4sSyTNMelZUqZDApbi9PZWpzOl29fw1MNg/z3K+18/pd1/HBvJ//vzhq2l+g3wUUghVhi1skf2ZEE2rKqMJnOfQtQFIOeoLAKcTgcfPnLX6bu6GEOj4bOuU3TlAl/BP75y3+lC7gzSLSIuwe4l1hg8JAQohD4lwTbtGIIRzX+7fkTfO/VdtLsZr76B+v4wLYCjFc4BCqEoDTTySff4qC+b5pnjg/xwO86qC1IZdeGXGxm/UK62Egpqeud4qmGQQIRlW3F6bxtjecsr9vlYDIo3L0pj10bcnn0aD/feLaF939vHzvX5vCVu9eS7dYDwN8sUsriRNuw2vD7/Tz66KP8+Mc/jvVs1U61vZdmBzOzU3zik5/kM5/+NDfccMNVP4y2mqivr2d8YpJrC1Smw2efV48t5qF7+eWXWbt2rT6UvoCEijgp5RDwbwvme4CfJs6ilUPbyCyf/2Udxwdm+OD2Ar58e/U5h02vBCEEGwtSqfa62XNihD0nRmkb9fHu2jyqctyLeqyrmTFfiMfq+ukYnaMgzcYf1ObhTbEt2usbFMF7t+Rz53ovD/6ug++80sY7/m2M+3fV8N4t+foF8QoQQrwopbz5Yst0zk0oFJpv8fXyy68QCPiJphUiwkEMcyPz20mDibnKnQx37+X+++8nx+vltltv5cYbb6SsrEwXdCuUmZkZHn74YX75y1+QY9O4ozDItxrOvo8Vu1RuyQ+we/duenq6+eQn/w/l5eUJsDj5SHR26ruBfwI8xIYlBLFCwLpCOA9SSn6+v5uvPtmEw2LkgT/cwq1rL5CSvwiYjQrvqMmhJjeFRw738pN93WwpSuPO9V6sJt0r92aRUnKgc4Knjw1iUAR3b8plW3H6ksUf2swGPntzBbs25vKlR+r5i0fqebJhkK+/az25qYsnGq8GhBBWwAFknhEP5wZyE2ZYkjM5OUlLSwtNTU3U19dz7NgxIpEIwmAinFpEuLgazZmFrfnsLmSaK5vZmndhnOykf+wEP/nJT/jJT36COyWF2k2bWLduHdXV1ZSXl+tlRpKYaDRKXV0dL7zwAi+/9CKhcIQbckJ8uGIOh+n8fVL/sMKP167xSN0hPv7xj7Nx4wZ27rydG264Abf76pUMiR5O/WfgnVLKpgTbsSIYnQ3xl4+8wcsto7y1Mot/ed8GPK7lu1jlpdr49I5yXmwe4dUTo3SM+nj/1gKKMvRYuctlJhDh10f7ODHso8Lj5D2b83Evsif1fJRkOvjlJ67lZ/u7+cenm9n5zVf56rvWc9dGXXtcBp8EvkBMsC2MjZsh1nrrqsbn89Hb20t3dzddXV10dHTS2tbK5ES8GY8QSHsGkfRKou5cVLc3NoR6MRSFaEYZ0YwyRCSAYbqPyMwAew4cYc+ePfGXFuTm5VNZUU5JSQnFxcUUFhaSl5d33ng7naVDSkl/fz91dXUcPHiQw4cO4pvzYzPCtVlBbi0IUuC8eHkRIeAd+UGuzw7xUr+FPa1v8E9v1KMoCuvW1rBt+zXU1tZSVVV1VZ3nRIu4YV3AXRovNg3zl4/U4wtF+cpda/nIdUUJGQYzGhRuW5vDmhwXvzrUywOvdrCjKou3r8nGoGc+XhIN/dM8drSfqKZx18ZcrilJX/ZzqSiCP7q+mB1VWfzZw3V87hdHebFpmL+7e92iD8uvRuLFfb8lhPislPI/Em3PciOlZHZ2loGBgfmpv7+f3t5Y9f7p6alTGysGpDWFqC0NtaAczZGJas8Aw5V9z6TJRjSzgmhmBUFAhP0oc2MY/GN0+Sbo33eYl19++ZQZikKWJ5uiwoL5Hpi5ubnk5eWRk5ODxWK5Int0YgQCAVpbW2lsbOT48eMcP1bPxOQ0AGlW2JwWpLY0zPr0CG8mvNphkryzOMiuoiCdswYOj5qp76rnBw3HALCYTayprmbt2phntqamZlXXEky0iDskhHgYeAyYT0uRUv46cSYlF4FwrJjr/xzoodrr5hcf2ERltivRZlGU4eBzb6/gifpBXm4Z5cSwj3u2FpDp0i+E5yMQVvlt/QB1vVPkp9l435YCshL8eRVlOPjVJ6/jv15p51svtnKwc4J/ff8mritbvRe9RWZICOGSUs4KIf6aWMHyr0opV3y2ajAYZGhoiMHBQQYHB+ef9w8MMDg4SMDvP217YbETNbtQLVnI/HI0awqqNRVpdYFY+pg1abajmgtR02Ild4IAagQlOD0/9QWnGTregXKkDhkNn7Z/alo6ublecuN9RE9OOTk5ZGVlrYoWTYtNKBSio6ODlpYWWlpaaG5qpLu7B03GhkWz7FDlClFVFWFNagSvffH6qAoBpW6VUneA95UFmA0LmqdMtEwZae08ysP19ajx0dnMjDTWVK+db8VWWVlJamrq4hiSYBL9rXQDfuDWBcskoIs4oKFvms8/fJTOsTk+8ZZS/vzWSizG5IlBs5gMvGdLPlU5Lh492s9/vNzKHeu9bC9efs9SstM6PMvuI334QlHevsbD26o8SeO5NBoUPndzBW+pjHnl7n1wP5+4qZQvJtn3LUm5X0r5v0KIG4HbgG8QK2J+zfl2EELsJNbhwQA8KKX8x2Wx9AxO9r7s7++fb24+71UbGGRmoTcNEAYj0uIianKgOYvQMlxIiwstPl2pZ21JMJjQHJlojszTl0uJiAYRoVmU+DQammWsZ5ym9h5kyAfyVHyWoihkZmWRF2+QfnI66dFzOFZ/SImmafT09NDY2EhTUxNNTY10dnaiqrHKzC4zlDjDvLMoSqk7NqWYzx/jtti4zJJtnjDbPGHAT1iFbp+R9hkjnTMh2o5OsHfv3vntczxZVFXXzHvrqqqqVqQ3NtHZqfcl8vjJiqpJvrunnX9//gSZTgv/88fXcH155sV3TBDr8lIoTLfzyJE+Hq8boGVolndvzsdpSfR/hMQTiqo8fWyI1zsnyHJZ+PC1ReSn2RNt1jnZVJDKk5+7ka892cT3Xu3g1dYxvnnPJqpyEu/5TWJOBvPcCfy3lPJxIcTfnm/jeC3M7wDvAPqAg0KI30gpG5fKQCklw8PDtLe309HREWtS3tVFX18foWBwoXEIi5OIyYlm8SDzytAsTjSLC2lxIo02Fs2NkmiEQJpsSJMNzek5bVUAQNMQYR9K2IcS8iFCs/SHZhlq7afueDMyfHpnAXdKCkVFRRQXFVFcXExpaSnl5eW4XCv7t9PX18f+/fs5cuQIDfVvMOubA8BhijW1vyM/QrFLpcQVJcO6eF62xcBsgIqUKBUpp0rV+KOCrlkDnTNGOmdDNB4cnY+lNBoMVFVVUrt5C9u2bWPt2rUrwvua6OzUSmL/WrOllOuEEBuAu6SUX02kXYmkfdTH//3fNzjaM8Wd67187V3rSLVfWqHXROK2mfjo9cXsax/n2eNDfOvFVt5Tm8ca79WbNdQ5Nscjh3uZ8ke4qTyTW2qyk76Nmd1s5GvvWs/b13j40u563vmfe/nSzjXcd32x3u3h3PQLIb4H3AL8kxDCQqyLw/nYDrRJKTsAhBC/BO4GFlXEzc7OsmfPHg4cOEB9QwPTU6e8asLqImJ2o7lL0TxuNKsbzeJGmp2gl+qIoShIqxvV6uacIfdqBCU0gxKcRQnNMBacZrJjiGNNLcjIqYK1+fkF1NZu4vrrr2f79u0rolhxNBrl2Wef5dFf76atvQMAj12yOSVERX6UipQI2XaNlXg5sBslNWlRatJOCjsfM2FB27SR1mkTLf3HeKipiZ///Oekul3svONO7rnnHtLS0hJq94VItMz8PvAXwPcApJT1QoiHgKtOxKma5Ee/7+Rfnm3BajLwrQ9s4q6NuStqWFIRghvKMynzOPnVwV5+ur+b2oJUbl/vvaq8coGwyvNNwxzoGCfNYeZPbipdcd0ubq7O5pkvvIUv727g759o5IXGYb72rnWUZjkTbVqy8X5gJ/ANKeWUEMJL7Jp2PvKA3gXzfVxg6PXNUFdXx5e+/OWYl83iJOLMQS2qRrVnoNnSknPY80zUMFarlV27dvHEE0/gU8MX32c5MZjQ7Blo9jNiR6VERAIogQkMc2N0zY7S/9Qz/Pa3v6WwqIjv/Od/Jr137qtf/XteeWUPRS6NeysCbM4M47FpF99xheI2SzZnRdicFQH8BKKChgkT+4dD/OrhX/Lcs8/w/Qd/kLTJEYm+s9qllK+fIVSi59t4tXKsf5r7Hz/G0Z4pbqn28PV3rcezgqvp57itfGpHGS+1jPC7E2M0Dc1w29oc7tlWkDRxYEuBpkmOdE/y9PEh/KEo15RmcNva7BUbV5bptPD9j2zh4YO9fO2pJnZ+83d86m1l/OmOshX7nhYbKaVfCDEC3Ai0Ert+tV5gl3P9AM4KHBJCfAL4BEBhYeFZO1yI1157jVAwiOr0EMrbjOr0XFr5jiRCRMPsumsXn/nMZ5BS8qvfPptoky4NIeIJFnbUlHxEJEhkug9b56v0dHfT3t7Opk2bEm3lBTl86BB2o+S+qhlKXGpSDZEuBzajZLsnTGVKhJAqaJiYoru7Wxdx52FMCFFG/CImhHgvMJhYk5aPybkw33iuhYde7yHdbuZf37eRd2/OW1Het/NhNCjcWpPDpoJUflM3wON1A3SOzfGXt63hhvKMVfEeF3Kwa4J/erqZQ92TFKbbue/64lVRQFcIwQe2F/L2ag9ffaKJb77Qym/qBvjrXdW8rcqz6s7j5SKE+BtgK1AF/IhY7+efAzecZ5c+oGDBfD4wcOZGUsoHgAcAtm7delnR4ffeey+Tk5O89PLLGFqeAaGg2TOI2tPRbGnxKSWpY9yk0cwTTzyBlJInn3wSaUzOONJ5pBZLkghOY/BPogQmMAUmIBArrZGSmsr73/c+Nm7cmGBDL86f/9+/4Bv/8s/87SFBrkNSmxFkbXqEipQIllX8302T0Ocz0Dhpom7cTPOUCQncddddSX3ehJTLlz1y1sGFKCV2oboemAQ6gQ9JKbsX8zhbt26Vhw4dWsyXvCL84Sj/s7+H77zSxmwwykeuK+ILt1Qua32uhw70LNuxpJS80TfN3tZRBqaDXFuazl/cVsWWopXfiP1IzyT//vwJftc6RqbTwlsrM6ktTFuyrgvn4t5rLs9TcyXsOTHK3zx+jK5xP9uL0/nS7WvYUpR88SJCiMNSyq3LcJw6oBY4IqWsjS+rl1JuOM/2RuAEcDPQDxwE7pVSHj/fMd7s9Wtubo433niDhoYGGhubaG1rwz/nO2WL0YxqcaNaXGgWdzyBIZZpKs32ZSkLcj4sPftRZocQ0TDSaEZz5RAqvDZh9gCgReeTHE5mtCrBGYyRWQjOgnYqei4zy0P1mirWrFnDpk2bqKqqWhFB8ifx+Xy8+OKLvPLKy9TX16OqGgYBRS6VUneYMleUEneUnCWIjfv5CTs9vlOfVaEzyocr/RfY480xHRZ0zhjpmIllsLbPWvBHYnqosCCfG296Czt37rxsT/hicanXsISIOCHEF89YZCMWDDwHIKX8t7N2ugKSRcT5w1F+vr+bB17tYMwX5sbyTP56VzVrEtCHdDlF3EnesyWPhw708J2X2xnzhbi+LIOPXFfMLdUejEke8L+QqKrxUvMIP9vfze9ax0h3mPnTt5bx4WuLePRo/7Lbs5wiDiCiavzyYC/feqGVMV+IW6o9fOItZWwrTksaz9wyirjXpZTbhRBHpJSbhRAOYN/5RFx8nzuAbxIrMfJDKeXXLnSMxbp+SSkZGxuju7ubnp4eenp66Ovro7evj5HhYU67FwgFrPFyIhYn0uxavZmqJzlHRqoSis0bIz5kaO60zU1mM3m5eRQU5JOfn09hYSGFhYUUFRXhdK6e2FG/309DQwMNDQ0ca2igubmZYCiWvGE1CgqdEYqdEYpdUYpdKl67SjJdzqWEybBCdzwrtdtnpMtnZjKemK0IQXFREWvXr2fdunXU1tbi8Xgu/KLLQLKLuL+JP60CtgGPE4sVeSfwqpTy44t5vESLuPZRH798vYdHDvcx6Y9wU0Umn7+5gq3FifNEJULEnRQb/nCUn+3r5qf7uumfCpCbYuWD2wu5a1NuUrfw6p3w89jRfn7xeg8D00Fy3Fb+6PpiPnJdEY544kYiP9flxh+O8sO9nTy4t5Mpf4T1eSl87MZi7ljvTXjM3DKKuP8LVBArGfIPwMeAX0gpv71Yx1iO61c0GmVkZISBeCHfk9PA4CADA4PMzkyftv2pmnExYadZT9aLS0FanKAk6bibGkYJzsxnlorQLEp4FlPYd1ZtOCEEGZlZ5OXGiv7m5uaSk5NDbm4uXq+X9PSrsx6mqqp0d3fT0tLCiRMnONHSTFt7O6FQLPnEbIACZ5QSV4QSl0qpO4rXri5bNut0WNAR97B1zsYE20w8YVgIQUF+LpVV1VRWVrJmzRrKy8ux25NvuD6pRdz8wYV4DniPlHI2Pu8C/ldKuXMxj5MIETc6G+Kl5mF+faSfA50TGBXBO2qy+fhNJUkxjJgMYuOkR+un+7rZ2zYGQLXXzW1rs7mlOptqrzuhiRCqJmkanOH5xmGeaxymaXAGgJsqMvnQNUXn9CAmw+e63ATCKr8+2scP93bSPjqH22rk9nVedm30cl1pRkK8rMsl4uLHegexguUCeFZK+fxivn6i/4RCrJXS0NDQfNeGoaEhBgYG6OsfYHBw4Kx6c1hdRM1uNGsKmi0V1Zoay4w1LkO5JCkRET9KYBIlMIUSmMIQmsYYmkWGTx+Wc6ekkp+Xe5pAy8nJwev16l0aLgNVVenp6aG1tTUu7FpobT1BIBhTT3aToMwVojIlSlVqhPKUKMZFuCxICYN+Jd6pwUTbrIXR+ClWhKCoqJDKqjVUVVVRUVFBWVlZUgq2c7FSRFwzsFFKGYrPW4A3pJRrLrLfZVU8X46LYDCicqx/mgOdE7zQNExd7xRSQmG6nQ9sL0iKFksLSTax0Tvh59njQzx7fIhD3ZNICU6LkdrCVLYWpbM2102Zx0lBmm1JREFU1eidDNA+4qOhf5rD3ZPU9U7hC0URArYVpXPr2mxuW5tDQfr5LwLJ9rkuJ5om+V3bGI8d7ef5xmF8oSgZDjM3lGdyXVkG15dlUJhuXxbvxXKKuDOOawA+IKX8n8V6zWQQcRdCSsn09PR854e+vlj/1K7ubvr6+oiEF5QHsbqIWNNj/VOdHlRH5hWXPBFhPwbfCMrcKEb/GMbAJDJySlQ6XS6KCosoKiokPz829JmXl4fX610xN/SViKqq9Pb20tTUFOuj2lBPR1cs3N1igDWpYWozw2zODJNquXQdElbh+KSJI6Nm6iet88OiGWmprF2/gZqaGmpqaqioqMBmW7nJZStFxP0/YnWWHiWWofou4GEp5T9cYB8DscDg+YrnwAcvVPF8MS+CUVVjcDpI+6iPzrE52kd9NPRN0zg4QyTeqG1Dfgq3VGdzc7WHGq87KV3uySw2RmaDvNY2zqHuCQ51TdIyPDs/ymE2KBRm2MlxW/G4LXhcVtIdJhwWI06LEYfZiMFw9ucdVSX+cBRfKMpcKMr4XJjRmRAjsyEGpwP0TPjnz58iYE2Omy1FaWwpSuPGikwynZcmwJP5c11OghGVV1pGeObYEK+1jzMyG/tH7nFZqPa6WeN1UZ3jpjjTQW6qlSynZVF/J0st4oQQbuDTxOq+/QZ4Pj7/F0CdlPLuxTpWsou4C6FpGsPDw3R2dtLZ2UlbWxtNzS0MDcYTcoWC6sxCtbg5d/WV8yOkhsk/BoFYIWODwUBpaSlVVVWUlZVRUlJCcXHxqumRuRqYmZmhvr6eQ4cOcWD/PgaHhhEC1qdHWJ8evuiQa/+cgf0jVvwRsNusbNt+DVu3bqW2tpa8vNVR2eEkK0LEAQghNgM3xWdflVIevcj21wF/K6W8LT7/VwAXEn6XehF8qmGQY/3TBCIqwYhKIKwSiKj4wyqjsyFGZ0NM+MMLwyZwWYzU5LrZXJRGbUEqtYVpSeVxOx8rSWzMBCO0jfhoH/HRNuqja2yO4ZnY+RiZDc6Lr8vBbFDIclnIclnIdlv+f/beOzyu6s7/f32maka992rL3ZaLsE21gRBKHFoglFBCyLIbwqb8smzKk2w2dQP73WSBJdmFAEsJbYGAY1NCMAYDtrHlJtuSbKv3ZvUyo5l7fn/ckSwb2RpZZUbSfT3Pfe7ccu58rmZ07nvO+RRy4sOYEx9GTnwo8xLDzzo58XT6u04VSilKm3vYXtbK3qo2iuu7ONbUjdt7IoGozWIiOTKEKKeNSIeVKIcuzO0WE3aLCZvFxMULEliZ4V8k7BSIuDfQI+q3o0eaRgM24NtKqX0T+V7TWcSdjs7OTg4fPsyBAwcoKNhDU3PzmK8hIuTOncOKFStYunQpubm52GzBX93GQEcpRXl5OVu2bOHtt96kpfX4qG2sVgvr1q3nsssuY+XKlVit0yBx9Vnibx8W8Al/pdQeYM8YmkxaxvO/Hmpgc2E9IVYzDqsZh+3EOi3ayYqMaBLC7SRFhpATF0pOfBhxYbYZpf6DkYgQKyszokd8gGuaosftocflHRpl03wq+6+HGofOEwG7xTwkCOwW02c+N7dHo7i+i+L6rsm9oVmGiDA3IYy5CWHcvjYT0CNcy1t6qGrtpa6jj9q2Puo7+mnvG6Cj101Vaw/dLg8uj4bbo+H2asSG2vwWcVNAjlJqKYCI/BFoATIG/XsNzkxERARr165l7doApw0xCBgiQk5ODjk5Odx11110dY3+r+NwOKZlkfrJJOAi7iwYc8ZzoFtESsbwHnHonfJMZsrv8StT+WY6s+Fz5Cuz4z7j7voNLXf5f37m5JkCwMDgC6WUV0TKJ0vAFRQUtIjIhObOnEbMhu+2wcjM9s/erz5sOoq4MWc8HysisjsQTtFTiXGPM4fZcJ9BeI95ItLpey2Aw7ctgFJKTVjyR6VU/ERda7oRhJ+7wRRhfPb+MR1F3C4gV0Sy0TOe3wzcGliTDAwMZhNKqSBNhGZgYDCbmHYiTinlEZH7gHc4kfH8tCVrDAwMDAwMDAxmItNOxAEopd4E3pzEtziradhphnGPM4fZcJ+z4R4NPovxuc9ejM/eDwKeYsTAwMDAwMDAwGDsBFGZWgMDAwMDAwMDA38xRNwIiMiNInJIRDQRmXHRMSJyhYiUiMgxEflBoO2ZaETkSRFpEpGDgbZlshCRdBF5X0SKfN/VbwfapslAREJE5FMR2e+7z58F2iaDqWGm91MGIzMb+u+JZFZMp8bFxamsrKxAm2FgYDBFFBQUtMyU1BxG/2VgMPvwtw+bloENYyUrK4uZVrbGwMDg9Myk5Lizuf+qqqoiMjKSyMjIQJtiYDCl+NuHzQoRZzA6o9X8DPb6nAYGBjMLl8vFHXfcwdKlS3nkkUcCbY6BQVBi+MQZGBgYGAQdBw/qLlGFhYV4PJ4AW2NgEJwYIs5gRJRSeLWZ7y9pYGAQnOzatWvo9eHDhwNoiYFB8OLXdKqIbADeVEppk2yPQYDodnkorGmnrKWH2rY+ulwevJrCbjERE2qjuKGTi+cncGFuHBazof0NDAwml7a2thFfGxgYnMBfn7ibgYdE5FXgKaVU0STaZDCFHGvq5j//doQ3C+vRFEQ7rWTEOolyWLGaTfS6vTR3u3i1oIZntleSGGHnq+dlc8e5mYTaDZdKAwMRqQC6AC/gUUrli0gM8BKQBVQAX1ZKtYmIAA8BVwG9wFeVUnsCYXewk5mZOfTaiM41MBgZv57CSqnbRCQCuAV4SkQU8BTwglKqazINNJgYTg1ccHm8vHOogZ1lx7FaTJybE0t+VgyJESEjtr9hVRpbipv4085KHni7mD9uK+P+y+fz5fx0TCaZilswMAhmLlZKtQzb/gHwnlLqN74cZz8Avg9cCeT6ljXAH3xrg1NYu3Ytjz2mV17KyDACqwwMRsLvoRSlVKdvJM4BfAe4DrhfRB5W7xfYvQAAIABJREFUShmhQ9OIqtYeXtpdTXvvAGtyYrlkQQJho4yqvVJQA8CVS5JZnBzBW4ca+MFrhfxhayk35qdz3yVzp8J0A4PpwjXAet/rp4Gt6CLuGuAZpSfo3CEiUSKSrJSqD4iVQczw0Td9ANPAwOBU/HJuEpGrReTPwBbACqxWSl0J5AH/NIn2GUwwe6raePyjckSEv7swh6vzUkYVcKeSERvKPRfm8KWVaTR29fPIlqNsOlA3SRYbGAQ9CviriBSIyD2+fYmDwsy3TvDtTwWqh7Wt8e0zOAWTyfC9NTAYDX+f3jcAv1NKfTh8p1KqV0S+NvFmGUwGH5Q08c7hRnLiQ7l1dQZO29n7tIkIqzKjyYkL5cVdVdz3/F4+PtbKv2xYhMNmnkCrDQyCnvOVUnUikgC8KyLFZzh3pCGlz4SB+8TgPWBMJRoYGJwef3/q1J8q4ETkAQCl1HsTbpXBhPO+T8DlpUVy13nZ4xJww4kOtXHPRXP4h3VzeOHTKq7/wyfUtPVOyLUNDKYDSqk637oJ+DOwGmgUkWQA37rJd3oNkD6seRrwmWFspdRjSql8pVR+fPyMqB5mYGAwCfgr4i4bYd+VE2mIweTx9CcVvHu4kRXpUdyYn455ggMRzCYhI8bJnedmUd7SzeW/+5Bfby7i+Z1VJy0GBjMNEQkVkfDB18DngYPARuBO32l3Am/4Xm8E7hCdtUCH4Q9nYGBwtpxxOEZEvgHcC8wRkQPDDoUDH0+mYQYTw9sHG/jXvxxiYXIEX1qVhmkSHYTnJ4XzjXVzeWZ7BU98VM7Vy1M4Jytm0t7PwGC8iMjKMx33I/1HIvBnn+O9BXheKfW2iOwCXhaRu4Eq4Ebf+W+ipxc5hp5i5K5xmD+j6eoyEh/MVpqamvj973/PpZdeyoUXXhhoc4Ka0ebUngfeAv4NPUR+kC6l1PFJs8pgQihu6OQ7L+0lLy2Ka5enTqqAGyQ+3M696+fywq4q/ry3lobOfq5akjzho38GBhPEf5zhmAIuOVNjpVQZeoDXqftbgUtH2K+Ab47RxlnJtm3bhl63trYSGxsbQGsMpgqlFP/93//N1q1bKSjYzdKlS4mKigq0WUHLaNOpSilVgd7pdA1b8CWzNAhSuvoHuPe5PYSHWHnsjlXYLFMX6eWwmbnz3CzOnxPL9tJWnt5eQZ/bO2Xvb2DgL0qpi8+wnFHAGUweSin+75VXUCZ9nOGNN94YpYXBTGHTpk1s2bKF/HgX/T3d/PIXP0f/7WMwEqM92Z/3rQuA3b51wbDtMyIiV4hIiYgc8yW8PPW4XURe8h3fKSJZw4790Le/REQuH7a/QkQKRWSfiIxqw2xEKcUPXiukorWHR25ZQUL4yAl8JxOzSfjCshSuX5FKeXMPv996jGNN3VNuh4GBP4iIVUS+JSKv+Jb7RMQaaLtmK/X19ZSXleFKy8cbkcLWDz4ItEkGU8Sfnn2G+VEe7lvSzU1zethdsIeSkpJAmxW0nFHEKaU2+NbZSqkc33pwyTlTWxExA4+iB0AsAm4RkUWnnHY30KaUmgv8DnjA13YReqmvxcAVwO991xvkYqXUcqVUvt93Oot4+pMKNh+o5/7LF7A2J7BTEPlZMdx9QTb9A16u+/3HbC1pGr2RgcHU8wdgFfB737LKt88gANTW1gKgOWPwOmOoq6szRmNmCc7QUHo9ujTp9ehuOKGhoYE0KajxN9nv+b7IK0TkNhH5rYiMlrxoNXBMKVWmlHIDL6JnKx/ONejZzAFeAS711Ra8BnhRKeVSSpWjOwGv9u+WZjd7q9r41ZtFXLoggb+/6Iw6e8rIigvl3ovnkhbt5Gv/u4s/bC1F04wO2SCoOEcpdadSaotvuQs4J9BGzVYOHjwIgGaPQLNH4BkY4MiRIwG2ymAq+OLV11DdbaKq28xHjQ7y8vJIT08fveEsxV9HqT8AvSKSB/wzUAk8O0obfzKTD52jlPIAHUDsKG1Hyo5uALT1uLnv+b0kRoTwH1/OC6qaptFOG6/8w7lcsSSJB94u5ubHd1DVauSTMwgavCIyZ3BDRHLQC9obTCEej4cXX3yRZ599Fs0ejq3hAAMxWYjNwQ9++CP27t0baBMNJgFN0zh69CiPPfYYT/zxcSwmeL82hMzQAfbv389Pf/pTtm/fTn9/f6BNDTr8zfjqUUopEbkGeEgp9YSI3DlKG38yk5/unDO1/Ux29FMTEcPsy3j+3I5KntleQUNnP39/UQ5vFjYE2qTPEGq38OitK3l1Ty0/23iIKx76kB9/YRG3rE43aiMaBJr7gfdFpAy9/8nESP8xZbS3t7NlyxZeeeVV6upq8URlgMeFqfc4WELozr0cVbqF7373u+Sfcw7XXXstq1evxmo13BanI8ePH6e0tJSSkhKKioo4WLifjs5uTALLY920mYT6XjPfy+skqcLBlk8+4IMPPsBqMTN//gIWLV7MggULyM3NJSUlBbN59lYJ8lfEdYnID4HbgIt8/mmj/ff4k5l88JwaEbEAkcDxM7Udnh3dV891NfAZEaeUegx4DCA/P3/Gz919eKSZI43dXJ2XQlq0M9DmnBYR4YZVaZw7J5Z/fmU/P/pzIZsL6/jVtUvJijP8HgwCg1LqPRHJBeaji7hipZQrwGbNWPr7+ykuLmb//v3s2rWLQ4cOoZRCC43DNfdSPFEZOEreGjpfc8bQtehabE2H2L3/ELt37SLE4eCc/HxWrlxJXl4emZmZs/phHoy0t7dTVVVFZWUlFRUVlJeXU1Z6jPaOzqFzkkIVS8NdLEz1kBfrJsKm+PWeCADsZrhxTh/XZvdR1GblUJuVIzUHeK3oEB5Nb2+3WcnKyiI7Zw5ZWVlkZmaSlZVFYmLirKi/66+Iuwm4FbhbKdXg84f791Ha7AJyRSQbqEUPVLj1lHMGs5pvR6/PusU34rcReF5EfgukALnApz6/PJNSqmtYdvSf+3kPM5ZPSlt493Ajy9IiWZM9PTK/pEY5ePZra3j+0yoeeKuYy//zQ9bNi+fC3PjT5pS7dc3MH1E1CCirgCz0fjFPRFBKPRNYk6Y/mqZRXV1NUVERRUVFHDp0mLKyUjRNfwqr0FjcScvwxGShOWLgdKPyZgvu5DzciUsxd9bibq9k26d7h/LJhTgcLFywgEWLFrFw4UIWLVpETMz06A+nMx6Ph/r6eqqrq6mqqtLXlZVUVVXQ0XkiI4HdDKmhXpY6B0iP95IR7iEzzEuodfQxFqsJlsUOsCx2AOjFo0FNj5mqLgvVPWaqmw+zo+oobw+bbbVZraSnp5GRmUVGRgbp6emkp6eTlpY2owIl/BJxSqkG4LfDtquAM3ZuSimPiNwHvAOYgSeVUodE5OfAbqXURuAJ4FkROYY+Anezr+0hEXkZOAx4gG8qpbwiMmJ29DHd8QyjsbOfb72wj7gwO9etSJ1W05Imk3Db2kwuW5TIv248xFsHGzhQ08G1K1LJiAne0USDmYeIPAvMAfZxwhdOMUo/Z/BZNE2jtLSUPXv2sHfvPgoLC+np0R/mYrHhccbhSVyCNywBb1giWOxjewOTCW9UOt6odFxKIa4uzN1NuLub2HOkmr379oPSBWJiUhLL8/JYvnw5q1atIiEhYaJvd1bg8XhoaGigtraW2tpaampq9HVVJfWNTUOCHCDcJiQ5Blge6iElwUtKqJcUp5fYEI2JctO2mCAr3EtW+Mluq90DQl2Pmbpes77uKOHQznK2vn+yL1d0VARpaRmk+URdamrq0OJ0Tq9nj/gTti0i16On/0hAn2oQ9ETAEZNr3sSQn5+vdu+eeSnl+ge83PQ/2zna1M3fXZhDYsTU54MbC6ONpP34zwfZuL+Wrn4Pa3Ji+fyiREKsJ6ZHjJE4A38RkYKxpCASkSJgkQrCPBbB3n+5XC7Ky8s5dOgQBw4coGDPHroHS2Y5InGHJuINS0ALi0cLiTr9SNspOIrfBKBvwVVjM0jzYO5pxdTThLmrCVtPI2pAH6JJSU0lf9UqlixZwqJFi0hJSZkVU27+0tbWRkVFBVVVVVRVVVFTU0NNVSUNTU14vSeEmt0CSQ6NRMcASQ4vSU4viU6NZKeXMD9G1s7Ec0ecbKvXhX1muJeMMA+3zRtfEJzbC019Zup7zTT0mmjsM9PYZ6Gx30L7KbESMVGRpKXrAi89PZ2MjAwyMzNJTk6e0ul6f/swf6dTHwS+qJQqGp9ZBhOFUorvv3qAA7Ud/M9tq2jpdgfapFF5fmfVGY8vSokgJz6Udw83sqOslcN1HVy7PJUFydPit4LB9OYgkASMqRi9iKSjj9YlARrwmFLqIRH5V+DvgGbfqT9SSr3pa/ND9ByZXuBbSql3JuQOJhGlFMePH6euro76+npqamqoqqqirLycmurqoZEYsYfhDkvCk70Cb0QKynZ2oxr2qh2Ye1sBXcxpzhhcGWv9a2yy4A1PxBueyEAS9CuFqa8Nc2cdVZ311L/5Nhs3btTfJySEnOxssrKySE9PJzU1lZSUFFJSUmbUlNtIdHV1UVhYyKFDhygpKeHYkRLaO0/Uq7WbIcmpkeoYYGWalySnRoLDS6LDS6RN+avFx0xVt4U+ry6si9snRmDbzJAW5iUt7LMB530eXeA19plp7DXR0NdPY1UrHx05SKfrhCC126xkZWczf/4CFi5cSF5eHikpKRNi33jwV8Q1GgIuuPjDB6W8sa+O+y+fz+cXJ40qkKYLIVYzX8xLYXl6FH/eW8szOyo5f04sly9JCrRpBjMQEfkL+kxLOHBYRD4FhgIalFJXj3IJD/A9pdQeEQkHCkTkXd+x3yml/t8p7zc8kXkK8DcRmaeUCmg6k4GBAZqammhqaqKxsXFo3djYSF19Pc1NTQwMDJxoIAIhEXhsEXgTl6A5Y/GGxqPsYRNij6n3OOLV38/S1YBnPBcTQXPGoDljGEhaQp/SdFHX04q7t5WDNe0Ul32Acp082uN0hpKYlEhKcjKJiYkkJCQMrZOSkoiOjp52o3hdXV28++67vL/lPQ4dOoymFGaBtDCNpWFu0hO9pDr1KdAYuzZpQi2YcFj0Eb/M8M/+C/YM6FGytT1m3Qev6RDvlh0Z+hGQkpzEhRet48orryQrK2uKLdfxV8TtFpGXgNc5uYN7bVKsMjgjfzvcyL+/U8LVeSncu37O6A2mIekxTu5dP4e3DjbwcWkrFa29rJsXT2bszP51bDDlbAQSgW2n7F+HHpB1RpRS9fhG73wBV0V8Nh/mcIYSmQPlPn/g1ejBXZNKR0cH1dXVQ/5M9fX11NXXU19fT3tb22cqIojdidcSitcWiopdgGYLRQuJQLOHo2xhYJqmkaBiQnPGojlPqWbjcWNydWFydSGuLtzubjpbuylrKMLk3oXynDzbYTabiYtPIC01heTkZJKTk0lNTSUtLY309HTs9jH6+k0yb7/9Ng8/9J/09vWTHq6xIbOfxdED5ER4sAfRR9nnEUJCQtiwYQObNm2izzMuCT8uQq2KuZEe5kaesEFTUNdjprjdwr7WKl75v5d46aWXuPzyy/ne976HzWabUhv9FXERQC96NOggCjBE3BSzp6qNb724l6WpkTx4w7JpFcgwVixmE1/MSyEnPpRX99Sw4eGPeOCGZVy1NDnQphnMHK5Bn+o8MHyniPQAP0UPvvILX+3nFcBO4HzgPhG5A73O9PeUUm3oAm/HsGYjJUEfd55Lt9vNvn372L9/P0VFRRwrLaWzo2P4GyD2MAasoSh7LFpyJpo9DGULRbPp64CKNK/7pAd5t3cK3EUsNjRLLFroaUoVetyY3N2Iu0dfu7qpcXdTX1KFufAwyt03dKqIkJCYxPx5uSxatIhVq1Yxd+7cgPXXLpeLBx98EDNefryyk3lRgRNGo9HrETZs2MB9992HUooPN78caJNOwiQnpmY/l+ai0y387kA477zzDqtXr+bSSy+dUnv8jU41kl4GkMGp0obOfh7/sAyHzcwXlibz2p5RBwpmBItTIkmJdPBuUSP3/mkPd52fxQ+vXIjNMr2mMgyCkqxTBRyAUmq3T5T5hYiEAa8C31FKdYrIH4BfoP/Y/QXwH8DX8C8J+rjyXBYUFPCTf/kXent6QEwoZwwDjgS09PloIZFoIRFBP5ImHjcbrj7xIH/5L0HgNmixoVliwBkzcikP7wAmVyem/g5MfR3U9LXR+Ok+PvxQT2Oam5vLgw8+SHR09JSaDWCz2bjggvP58MNtPLg/knmRA+RGDpAd7iEjzEt0EE2dOi2KTZs2oZRi8+bNJFqCK9aozyNUd5up6LJQ2mmhqMNOez/Ex8WyePHiKbfHLxEnIvPQS28lKqWWiMgy4Gql1C8n1TqDIRo6+3nio3IsZuFr52cTHjK7MpVHh9p4+e/P5ddvFvHUxxXsq27n0VtXkhLlCLRpBtObM4V0+/XlEhEruoD706CLiVKqcdjxx4FNvk1/kqCPi4KCAl3AAe6EBXhDE4bEG+bp0W8oi+2kB7myTIO0D2arPk3riEHC+zH1d+Dt78DcXo21vYqjR49SV1cXEBEnIvzsZz+nsLCQrVu3sndPAa9XVA1NoYfahBTHAClODymherRpitNLXIiGeYp/Kzssiv7ufl599VV9O2rqRZxS0Dkg1PfoEa31vb60Jb02Wk4MuBITHcnytSs477zzWLduXUCm0P2dTn0cvSzN/wAopQ6IyPOAIeKmgLr2Pp78uByzSfj6BTnEhE7tnHuwYLOY+NerF5OfFc33XznAFx7exn/evIJ18+IDbZrB9GWXiPydUurx4TtF5G6gYLTGos+PPQEUKaV+O2x/ss9fDuA69OhX0H3wPpPIfPy3cYLbb78dh8PBRx9/zNGjJWiNh0/Ya7Wj2cLwWpxoNgfK6vQtDjSrA2UJQVkdYLL4nQpkUjDb6O89PvQgJzwqcLYMohTicSGePmSgHxno8y29mAb6kIEeLAN9iKsbpZ2YrnQ4nSxdvYarrroyICM1g4gIy5YtY9myZQD09PRQWlpKaWkpZWVlVFZUsL+ygg/qT0Somk2Q6FQkhwyQHOol2SfuUkK9OIJshOxs8GjQ2KfnlBsUa/W9Fhr6LPQOnByZmp6eTt7ybLKzs8nJyWHu3LnEx8cH3KXJXxHnVEp9eoqxwTupPoPYdrSZx7eVEWI1c/cF2cSFBZez7FQyPAL3novm8PynlXz1yU9ZNy+eSxYmcMe5WYEzzmC68h30BOJf4YRoywds6OJrNM4HbgcKRWSfb9+PgFtEZDn6VGkF8Pdw+kTmE3QvADgcDm6//XZuv/12XC4XtbW1VFVVUVtbS3NzM42NjTQ0NtLa2khXS+dnAhoAxGQBWwiaOQSv2Y6yhugCzyfyNEvIiX3BIPrOhlNFmce3DAxf92HxuhCPCzXQpw/RnILZbCYqOob4lDiSkuYPRbGmpaWRkZERtOWfQkNDTxJ1gwwGwAxWYKiqqqK6qoJ9NfUn5YqLcUCKw01qqPekxTlOcZcR5qGyS5/qH8wTN148GjT4okwHl7o+Gw09gneYuXGx0WTMzWbZsAoP6enpQfsZgv8irkVE5uDz3RCRGxhjPiWDsaGU4rkdlfzsL4eJC7Nz53lZRDqmx1TIVBAfbucb6+aycX8dW480U9zQxcqMaJakRgbaNINphG/a8zwRuRhY4tu9WSm1xc/2HzGyn9ubZ2jzK+BXY7X1bLDb7eTk5JCTkzPicY/HQ1tbG8ePH6e9vZ22traT1h0dHRxva6OtrZ3O9lpcrv4RryNmC1gdeC0heC0OlE0f4dOsTj1Ywj4YLOHfI0dzxqB8eeK8zlg05xjKZykF3uFBCD2Iu3doxMzs6cPk6dcDEZQ24iVCw8KIjIwiOjqO6KgoonxLdHT0SevY2FgiIiICPhozkURGRhIZGcmSJUtO2u/xeKirqzupFmplRTlbKytxuU+kn4kOgVTnyeIuxelfeS2A2+b1UtWtf09+tLJzlLNPZkCDxlPEWm2fjcZhYs0kQnJSIpnLclifnU1mZiYZGRlkZGRMu2oN4L+I+ya6k+0CEakFyoGvTJpVs5yu/gF+8vpBXt9Xx/r58VyUG39S5QIDHZvFxA2r0licEsHre2u59tGPuXf9HL55yVzsFuPvZeA/Sqn3gfcDbcdUY7FYiI+PJz7eP5cEl8s1JPI6OjpOEn+DYrC5pYXW1hp6urs/017soXhs4XjtEWiOaDRnDN7QuM/46rky1mLqPQ6cuWKDuHsw97Rg6mvTc7+5ujC7ulAe18nniRAZFU1cQixxsWnExMQQHR09tAwKs0EBY7H4+2icPVgsliGxc8EFFwzt1zSNhoaGoQL3lZWVlJeVsbW6CpfrRFRxVAikOdykhnnJCPOSGab7351NfJpS0OoyUdllprrbQnW3mdpeGw29gjZcrCUnkZWbzcW+hM5ZWXod1WBL/zIezvhNFZH/b9jmm+idnAnoAb7EsHqqBhPD1pImfvRaIQ2d/Xz3c/P4x0vm8uKu6kCbFdQsTI4gM9bJobpOHt5yjNf21nL/5fP54rIUTBNVrM/AwAC73U5iYiKJiYmjnutyuWhtbR1KItzQ0EBdXR01NTVUVFbR3XJEP1EE5YzBHZGKJzr79Ck+ADQNc2cNlrZKbF314PLVZBUhPiGBzDnZpKamkuxL0JuYmEh8fDzR0dFTWjJpNmEymYaqXJx33nlD+zVNo7GxkfLycn3UrrKSstJS3q+sxO1LHG01Q3a4hwWRbvLiBpgT4TltfdWWPhN7WmwUtVk52mWj06fTRYSUpERyVszlEp9QGxxdm0li7XSM9nMj3LeeD5wDvIE+dXA78OEk2jXrKG7o5IG3inm/pJm5CWG8+o3zWJEx9VFM0xWnzcLvblrO9StT+bc3i/n2i/t47MMy/vmKBVyUGzejpjsMDKYDdrt96OE+EsePH+fYsWMcPnyYPXv2cvDgQbT6A2hhCfSnrjz5ZE3D2lxESONBcPXgDA3lnLX5LFu2jAULFpCdnT0tp8JmMiaTaSgJ8nBx5/V6qa2t5ejRoxQXF1NYeIBNJUfYWKmIdyguT+tFqRMulsVtFl6vCOVwmy5XkhMTOHf9ChYuXEhubi45OTk4HLM3S4H4U+9ZRP4KfEkp1eXbDgf+Tyl1xSTbNyEEawFpj1dj29EWnt5ewdaSZsJDLPzjJXO549ysk6ZPZ0pJrcnm1jV6UlRNU2zcX8f/+2sJNW19zE0I4/a1mVy/MnXWpWaZrfhbPHo6EKz910TT2dnJ3/72N1548UWam5rwOqLxhifhTl6G89gWTD3N5OXlcdNNN7F69WpjynMG0dXVxfbt2/nLxo0UHjxIlE1jRZybCJvGGxVO4mJjuOba67j44otJS0sLtLlTgr99mL8irhjI85WKQUTswH6l1IJR2l0BPASYgT8qpX5zynE7evHoVUArcJNSqsJ3bMQi0aNdcySCqRNs63HzacVxtpY08e7hRlq63YTZLZw7J5Y12TE4bUbHNFF4vBoHajrYUd5KTVsfNrOJDXnJXLYwkQvnxRNmN/7WMxVDxE1fXC4XTzzxBC+//DIDMdlY+44TgpsffP/7rFu3zhhVn8EopdiyZQu/+bdfM+DRg7avvPJKvv3tbxMScqaUjjMPf/swf59izwKfisif0SNUrwOeHsUAM/AocBl6gstdIrJRKXV42Gl3A21KqbkicjPwAHDT6YpE+9qMds2gQClFU5eL0qZujjV3s7+6g71VbZS16Ek4w+wW1s2L59oVqdR39GEJ0vDl6YzFbGJlZjQrM6OpaetlZ9lx3itq4rU9tdjMJtbkxLA8PYpFyREsSokgPdrptw+dpin6Brz0uD30ub2YRLBZTNjMJhw2sxGIYmBwltjtdu69917MZjMvvPACAA8+/PBnUmEYzDxEhEsvvZSGhgYef/xxEhMTuP/++4M2vUcw4G/ZrV+JyFvAhb5ddyml9o7SbDVwTClVBiAiL6LXKRwuuK4B/tX3+hXgv3zJM09XJBo/rjmpeDVFj9tDj8tDd7+Hzn4PLd0umjr7aejsp669n7Lmbkqbe+h2nchv47SZyYhxcvmiRNJjnWTEOLGYTDR3uQwBNwWkRTtJW+Xky/lp7K5s472iRj440syj7x8bimaymoUop40oh5Vopw2TCQa8Co9Xw+1V9Lk9tHS7cXs03N6RUxMMEmozExduJy7MTlyYjbgwO7G+17GhdmJCbcSF2Yhy2nTRZzFhmerU6OPAqyk8mobHq/Bo+t/IqykGNIXXqxjQ9G2vpjCbBLNJsJpMWMyiL77XVpNJP2YWY4TF4CRuvfVWXnjhBebPn28IuFnGhg0bOHDgANdff70h4EbB7/kkpdQeYM8Yrp0KDA+rrAHWnO4cpZRHRDqAWM5cJHq0a541v9x0mHeLGvFqCk1TeJXCq4Gm9IfRgFej1336vJxmk5AYbicnPowvrUxlTkIYc+LDOFDTQUSIxXhIBQEv764BIDsujOy4MAa8Go2d/dR39NPa7SI12kF77wBtvW40BSFWExa7BavZRKjdTF17P3aLaWjUzW41YTWbQDEkXNwejW6Xh26Xh7YeN9XHe+l26SN2Z3JesJgEh9WM3WrGbAJBENFD5UF39BXR95tE/9Uq6EPjg24R+mtQKH3te8PTHmd4/tLh+9SJc5W+3zMo3DQ1Us7TcXNC7AkWswmrWd+2mPTX912Syw2rZoc/jAGEh4fz9NNPExlp5H6cbURGRvLAAw8E2oxpwWQ6BflT6Pl055xu/0iSfMTHiYjcA9zj2+wWkZLT2Hm2xAEtp+4sA7ZP8BtNACPaGqQYtk4O097WD8Z2jcwJsiXgFBQUtIhIZaDtCBDT6XtrMLHM9s/erz5sMkWcP4WeB8+pERELEAkcH6WtX8WjlVKPoSconhREZPd0cZw2bJ0cDFsnh+lk61SglJq1xYGN78Lsxfjs/WMyJ5t3Abkiki0iNvRAhY2nnLMRuNP3+gbxrcfAAAAgAElEQVRgi9LnfTYCN4uIXUSyOVEk2p9rGhgYGBgYGBjMeCZtJM7n43Yf8A56OpAnfcWffw7sVkptBJ4AnvUFLhxHF2VnLBI90jUn6x4MDAwMDAwMDIKVSU2UpZR6k1MKQSul/mXY637gxtO0HbFI9EjXDBCTNlU7CRi2Tg6GrZPDdLLVYHIxvguzF+Oz9wO/kv0aGBgYGBgYGBgEF0YCFgMDAwMDAwODaUhARJyIXCEiJSJyTER+MMJxu4i85Du+U0SyfPsvE5ECESn0rS+ZJPueFJEmETk4bF+MiLwrIkd962jffhGRh322HhCRlae/8qTYmi4i74tIkYgcEpFvB6u9IhIiIp+KyH6frT/z7c/2fc5HfZ+7zbd/xO/BVCIiZhHZKyKbpoGtFb7/jX0istu3L+i+B773jxKRV0Sk2PfdPTdYbTUIDKM9JwxmJiM9fw1Oz5RPp4pejusIw0pnAbcML50lIvcCy5RS/yB6Oa7rlFI3icgKoFEpVSciS4B3lFKpI7zNScTFxamsrKzJuB0DA4MgpKCgoGWmpOYw+i8Dg9mHv31YICqAn3U5rlNKfR0CQkTE7ivPdVqysrKYTQWkDQxmOzKDkuPO5v7L5XJhtVqN0ksGsw5/+7BAiLjxlOManr35S8De0QScgcFk8vzOqjGdf+uajEmyxCAQiEg68AyQBGjAY0qph0QkBngJyAIqgC8rpdpERICHgKuAXuCrvpKGBqfgdru5/vovsXr1Ofz0pz8NtDkGU0xDQwNxcXFYLIGQKdOHQPy8GU85Lv2gyGLgAeDvT/smIveIyG4R2d3c3HxWhhoYGBiMggf4nlJqIbAW+KaILAJ+ALynlMoF3vNtA1yJnrw8F70s4B+m3uTpQUFBAT093bz//vv09vYG2hyDKaSyspKbb76ZJ554ItCmBD2BEHFjKceFnFyOCxFJA/4M3KGUKj3dmyilHlNK5Sul8uPjZ4RrjIGBQZChlKofHElTSnUBRegzCdcAT/tOexq41vf6GuAZpbMDiBKR5Ck2O+jp7Ozkl786kSb00UcfxUiHNXsoKioCYOvWrYE1ZBowLhEnIhtEZKzXOOtyXCISBWwGfqiU+ng8thsYGBhMJL6I5BXATiBRKVUPutADEnynjeROMmpw1mxBKcWOHTv4u3vuoae7G68jBlfyMjZv3swPf/hDampqAm2iwRTwySefANDa0mKMwo7CeCebbwYeEpFXgaeUUkWjNRhPOS7gPmAu8BMR+Ylv3+eVUk3jvA8Dg3GhKUV3vwe3R0NTihCrGafdjMVwyJ62+H40ftNXPWa0c8OAV4HvKKU6dde3kU8dYd9nhphE5B706VYyMma2H6XH4+HIkSPs3LmTv733HrU1NRASgdcRg7LYcKeuQlkc7Ny1mx23387KFStZt+4i8vPzSUlJ4Qx/a4NpSH19Pdu2fUiUzUu7GzZv3syNN45Y2MmAcYo4pdRtIhIB3AI8JSIKeAp4wTe1cLp2Z1WOSyn1S+CX47HZwGC8KKWoaO3lo6PNvLqnlpq2Ptp63HhPme4xCcSG2UmPdpCbGM78xPAAWWxwJnzBCT8BUoDXgeeBXwC3Ay/40d6KLuD+pJR6zbe7UUSSlVL1vunSwR+a/riToJR6DF/Zofz8/Bkzj9jf309VVRXl5eWUlpZSUnKE4uJiXK5+ALTwJFzZF+KJycFx5B29kQgDSYvxxOZgbSpmz+Gj7NlTAEBUdAxLFi8iNzeXnJwcsrOzSU5Oxmw2B+oWDcZBX18fv/7VL0FBbIhGWpjGU08+wfLly8nNzQ20eUHJuMM+fL86XwUcwHeA64D7ReRhpdQj472+gUGwUNHSw8b9dbyxr5bS5h4AIkIspEY7WZwSQaTDit1iwiRCv8dLR98AjR39FNV3saeqHatZOFTXyd0XZLMoJSLAd2MwjGeAD9CF2BXADvQURsuUUg1nauiLNn0CKFJK/XbYoUGXkN/41m8M23+fL7XSGqBjcNp1JqCUoq2tjYaGhqGlrq6O2tpaqqpraG05EWQmJgteRzSeyGy84Yl4w5NRVsfpr2114E5dgTtlOab+Dsxd9TR3NfFRQSEfffTR0Hlmi4Xk5BQy0tNISUkhJSWFpKQkkpKSSExMJDQ0dFL/BgZjRylFQUEBjzz8EFXV1SQ7PVhNcPeCbn6xx8o/3vdNvnb317nmmmuw2+2BNjeoGFeyXxG5GrgLmAM8CzytlGoSESd6p5Y5MWaOj/z8fDVb8ywZjI/+AS8b99fxp51V7K9uB2B1dgxfWJrMunnxfFLaOuo1NKWoPt7Lnqp2Dtd10OP28rmFCXz/igXkGqNzk4KIFCil8v08d79SKm/YdiOQ4U/6IhG5ANgGFKKnGAH4Ebpf3MtABlAF3KiUOu4Tff+FLhZ7gbuUUmfsnIKl/1JK0d3dTXNzM01NTUPrwaWhsZHm5mY8AwMntRObA68tDK89Ai0kEi0kCs0RhRYSAWdwqXYU65M1fQuuGt047wCmvnZMfW2Y+jsw9XdgcXdjcnWivJ6TTnU6Q0lITCQpMYGEhBNLfHz80GIIhamhtraWbdu28de336asooI4B9w1r5O/VOpi/kcrOznuMvFkcRgHWq2Eh4Vy2ecvZ/369SxevHhGj7j624eNV8Q9A/xRKfXhCMcuVUq9d9YXn0CCpRM0mD40dPTz3I5Knv+0iuM9buYlhnHDqjQ2LEshJerEaMFY88R9YWkyT2+v4PFtZfS6vdy+NpP7L59PqN3IhTSRjFXEAes54a/2/vBtpdTxSTDRb6aq/3K73TQ2Ng4twwVafUMjrS0tQ9OeQ4ggNideayheqxNlD0OzhaHZw1C2MDR7OJitY7bFXrUDa8tRALzOWDRnDK6MtWO/KaUQTz/i6sLk6sbk7kbcPZhc3Zg9vZjcPaiB/s80CwuPICEhnqTExCGRl+h7nZiYSFxcnJGAeIwopWhoaODQoUPs37+fPQW7qa3TB6FzIrysT+nj/CQXVhP8eo8+U/GjlZ1D7YvbLPytNoS9LXYGNAgLdbJi5SqWL1/OkiVLmDNnzozKKedvHzbeO64/VcCJyANKqe8Hi4AzMBgLv3//GB8caWZ3ZRuapliQHMF1K1LJiQtFRNhaMr6cg5FOK9+6NJfb1mby23dLeHp7Be+XNPHbL+exKjNmYm7CYKxEAgWcHHQwmIBXATlTbtEk4fF4qK2tpaKigurqaqqrq6mrq6Omtpa245/VqmLXxZnXGoqKmoNmC0XZnGi2MJTVibI5zziadraYeo8jXn1Ez9LVgGeU80+LCMrqQFkdaGEJI5/j9SADPZjcPbrAc/fidvfQ3tJDaUMxJncBauDkQVmz2UxCYiJpqamkpqaSnp5ORkYGWVlZxMXFzfpgC6UUzc3NHD16lCNHjnDkyBGKiw7T1t4BgMMC8yLdrM8dYGWcm3iHNsoVYUG0hwXR3fR5ejjQaqXweD+Hd29j27ZtANhtVubm5rJgwUJyc3OZN28eGRkZM0rYjcR47+4y4Pun7LtyhH0GBkFNY2c//7XlGM9/WgUKVmVGc9G8eGJCbRP6PsNH7hYlR3L3Bdm8UlDDDX/YzkXz4rl0YcJJEa1GhYfJRymVFWgbJou2tjYKCgooLCzkcFERZWVleD0nJJHYQ/HYwtDssWgpmWj2cJQtVB9Js4bCbBhtMltQ5ki8IZGnP8c74BN53Zhc3Yirm6q+LmoPl2PeewDlOSHynKFhzJ8/j8WLFpGXl8eyZctm9PTswMAAlZWVHDt2jNLSUo4dPcqxY0fp6tb9hgVIDlMsCnMxZ56HuZEeMsK8mM5S5zosijWJbtYkuoEeWvpNHOuwUNppobz2AJuKD+Py6udaLWaysrKZm5vLnDlzmDt3LnPmzCE8fOa4sZyViBORbwD3AnNE5MCwQ+GAkb/NYNrg8nh58qMKHtlyFLdHY2VGNOvnxxPtnFjxdjpy4sL41iW5vFlYzwdHmilr7uYrazKJcIx9Csrg7BCRlWc6Ph3LYn3yySe8+OJLFBYeQCmFWGx4nLF44xbgdcT4fNIiz2qqc1ZitqI5osARhffUY74p20GfPHdfG3tKqti7dy/PPfccdnsI69ZdxG233Tbt08V4PB7KysooKiqipKSEI0dKqCivwOPV/ypWM6SHelkZPkBmii7WMsM92CfRdS0uRCMuxM3aRDcAmoL6XjOVXWaqui1UthbxcfUx3nrrRJukxARy581nwYIFzJ+vr8PCwibPyEnkrHziRCQSiAb+jRPlZAC6Au0/MhKGT5zBSLxf3MTPNx2mvKWHzy1M4CcbFvHxsdEDFSaLg7UdvFJQg91q4rY1maTHOI2RuLNkjD5x7w/bXIU+tTqIUkpdMqHGjZGx9l+bN2/m3//93yEkAlfMHDxR6WjOmEmZ9pwsHIdeJ0zrZcOGDWzatIluk5O+xdeO3jCY8A5g7mrA0l6FrfUYNquFp//3f0lOnj4FOjRNo6ioiN27d7Nv314OHzqEy61Pc4fZhKxQN5nhHjLCPGSEe0lyeDGP82v23BEn2+r1kcvMcC8ZYR5umze+hL9KQYdbdFHXZaay20JFt42mXn04UETIzsxk2fLl5Ofns3LlSpxO5/huZJxMtk+cUkpViMg3R3jjmGAUcgazj9MFHbR2u9hcWE9xQxdxYTbuPDeL+UnhARVwAEtSI4kLs/PsDj3w4doVqYaImwKUUhcPvhaRvcO3pyOtrfr32Gt1oKwhYLIwco7h4EU8bjZcvYH77rsPpRQv/+WdQJs0dkTAbNXTppjMuF0uuru7A23VqCilKCkp4e233+aDre/T1t6BABnhGusSXMyN9DAnwkNciMZkuP5VdVvo8+pKsLh9Yn54iECUXRFlH2BZ7Ino6Z4BobzLwrEOC0c6jvLWpkpef/11rBYz+fnncNnnP88FF1yAzTY1MzNnw9mKuOeBDei/WBUn9xAzyhHYYObg8njZWtLMR8daMJuEKxYncd7c2KCqqpAUGcK96+fywqdVvFJQQ0pkCN+9bN6sd5SeQqZ9Yt1bb70Vm83GG29spL5yO4A+neqIweuI9k2lRqE5IlEWB5PyJB4nymJj06ZNKKXYvHkzyhLYUZEzojTE1Y2pvx1TXwfmvjYs/W1Ib5t+TISlS5fxla/cGtQJazVN48MPP+SF5/9EyZGj2MzC8th+Vi1ysyR2gHDrtP/X+AyhVsWSmAGWxAwAfXg0ONphYU+LjV37drB9xw6iIiO47vovcf311welL91ZiTil1AbfOntizTEwmHiUUhyo6eCtg/V09ntYkR7F5YuTgtbvLNRu4a7zs3l9Xy0PbzlGc7ebX1yzGMt45ykMZgUWi4Wbb76Zm266idraWgoLCykuLubo0aOUlpXhajqRUkMsNjR7OB5bOFpIBMoegWYP9wU4TE7kqV+YbfT3HufVV1/Vt8OjAmPHIJrHF9DQhcnVialfX1vcXdDfBepEdGV0TAxzF80Z8rVaunQpkZFnCJoIAg4dOsRv/+P/UVpWTlKo4o55PZyX5MZpmXnC7UxYTLAw2sPCaA+3zO3l4HEr79YM8NRTT/F/L7/E1+7+Otdee21QpZcZV3SqiJwP7FNK9YjIbcBK4D+VUmNLnmVgMElUH+/lzYP1VLb2khIVwi2rM8iMDf6M7WaTcP2KVNbmxPDo+6W0drt4+JYVhFhnbnLLQCEij3BiBC5NRB4eflwp9a2pt2r8iAhpaWmkpaVx5ZVXAvpoS0tLC5WVlUMpRgarKTQ1HULzDnPbN5nBHo7HFnpC2A0TeTMqKOKkfHInLxZ3N8p18jSo3R5CSmoK6Wl5pKWlDaUYycjICMrRmjOxe/duvv/9fybKpvEPi7pYm+g+68jRmYRJYFmsPv1a1WXmxdIBHn74YSorK/nud78baPOGGG+KkT8AeSKSB/wzvsL1wLrxGmZgMB6qj/fy4q4qDtR0EGq3cN3yVFZlRWMKwqmj0yEi3H/5AuLD7Pxs02HuePJT/nhnPhEhM+jhGRwMjxooOO1ZMwCTyTSUvPacc8456ZjH46G5uZna2lrq6+upq6ujvr6emtpa6uqq6W3qOel8PT1J+LBKDJFojiiUPSx4gyg0D6a+Dn3qc7Cyg290TXncJ50aHRNDWlYqKSl5Q+W7kpOTSUlJITo6esa4OLz99ttoXo0fr2gjNmT0fG2TTZ9HCAkJGQpq6fOcdZbACSMj3Mv9eZ38ck8Eb27ePKNEnEcppUTkGuAhpdQTInLnRBhmYHA2VLT08Ni2Ml4pqEEpxcXz47koNx77NB7B+ur52cSG2fnuS/u49fEdPH3XamLDZm7eqalGKfU0gIiEKKVOSt8vInGBsWrqsVgsJCcnnzZ6squra0jg1dbWUltbS3V1DVXVVXS2HBk6T8wWvCFReJwxaKHxeMKSUCERY/K905wxqF5fgIavYsOY0byYelowdzdh7mnB2n8c+jpO2ClCQmIimXNzSPUl7U1JSSE1NZWkpKQZndttOJ/73OfYsmULv9gTzbVZ3UNVEwJFr0fYsOFEUMuHm18OnDHoka1HOiz8uTyUox0WvvCFzwfUnlMZr4jrEpEfArcBF4mIGTCGCQymFKUU+6rb+eNH5bxVWI/FZOJLq9LIiHESGaR+b2Pli3kphIVY+MZzBXz5f7bz3NfXkBx5+mLhBmfFpyJyj1JqB4CIfAk9jdK8wJoVHISHh7NgwQIWLFjwmWNdXV1UVVVRVVVFWVkZpaWlFJeU0NvsE3f2MNxRmbgTFqDOlFTXhytjLaZePcmBX7VTB1EKc2cd1uYSbJ21KF/Vh/iERBYsWUZOTg7Z2dlkZmaSmpoa1FGHU8XatWt55JFHePjhh3iy+AivlIexPrmX9cku4vyopDDROC3qpKCWxAD55bm8sLPRznt1Dso7zURGhPPtb3+Na665JiD2nI7x1k5NAm4FdimltolIBrBeKfXMRBk4ERh54mYeSikO13ey+UA9mwt1n7cwu4Xb1mbytfOzSIgIGXNd02Dk1BQjn5Yf5+7/3UWEw8pzX19Ddlzw+/cFgrHkiRvWZinwJLAVSAFiga8rpWom3kL/ma79l1KK6upq9u/fz/bt29m5cydeTcOVsgJ3ct6oI3OO4jeBMYg4jxtn+VbM7TWER0Syft1FnHPOOSxdupTo6Ojx3s6MRylFQUEBr77yCjt27gAFi2MGuCCpn1Xx7klN2DucX++JoLj9xI/vBVEDJ9VQnUyU0qNTP2qws7M5hL4ByExP47ov3cAVV1xBSEjIlNgBU1Q7VSnVAPx22HYVEFQCzmBmUdLQxeYDdWw6UE9ZSw9mk3DenFjuXT+HK5cmzzh/sZGE6B3nZfHUx+V88ZGPuOv8rJNG5Iy8cmePUqpQRH6F7tfbBVwUaAE3nRGRIWf/L37xi7S2tvLoo4+yZcsW0Ly4U1dOXHoTr5vQY3/F2tvKN+67j2uuuQardWb1BZONiJCfn09+fj4NDQ289dZbvPXmZv77cAt2C6yMdbE20cWSmIGATrdONEpBVbeZnU12djaF0Nwn2O02Llq/jg0bNrBs2bKg9n8cb3Tq9cADQAJ6rjhBTwQcMQG2GRgAcKypm80H6tl0oI6jTd2YBNZkx3L3hdlcsThp1vmHpUY5uOeiHJ76WE8K/NVzs8iYBhG3wY6IPAHMAZahT6H+RUT+Syn1aGAtmxnExsby4x//GIfDwebNmzH3Hac/fY3uL3e2KIW5sxZn9Q5Mrh5+8i8/Yf369RNm82wlKSmJu+66izvvvJMDBw7wt7/9jQ+2vs/2Az04rZAf18+5iS4WRnumbSRrQ6+J7Y12djQ5qO8RTCYT+fmr+Pqln+PCCy8MeMUGfxmvT9yDwBeVUkUTYYzB7OJM052t3S4O1HZQWNNBQ2c/ApyTHcPPr1nMFUuSSAifumHtYCQhPIR7LsrhyY/KeeLjcm5bm0luwvRKbRCEHESfPlVAuYisZdhMg8H4MZlM/NM//RNZWVk8/vgfsRx8jYGYHNzJS9EcY5jyVApzRzUhDQcwdTWRlJzMD3/wC/Ly8ibP+FmIyWRi+fLlLF++nO985zvs3r2bLVu28NG2D/mwvp/oELggsZcLkl0kOwMf2ToavR5hR6ONbQ0OSjvMiEDesjxuufRSLrroIqKiApyP8CwYr4hrNAScwURxvMdNYW0HhbXt1LXrQYIZMU42LEtmcUok31g/J8AWBhfRTtvQiNwz2yu5+Zz0QJs0rVFK/e6U7Q7g7jO1EZEn0avXNCmllvj2xQAvAVlABfBlpVSb6HMyDwFXAb3AV5VSeyb4NoIeEeHGG2/k4osv5oUXXmDjX/6CtfUYnuhMXCkrhiJRR4xIVQpLWwUh9fuR3uPExcdz293f4aqrrjKCFCYZi8XC2rVrWbt2LS7X9/jkk094++232fzpp/yl0sm8KA8XJvWzOsGNYxzBCBlhHiq7dAe8wdqp40FTUNRm4aOGEHY123F7ITMjnX+45QtceumlxMfHj+v6gWa8gQ0PAUnA64BrcL9S6rXxmzZxTFfH4JnO8zuraO8dFG4d1LT1AZAW7WBZaiRLUiOJchod82j0ub08vb2C6uO9PHjDMm7MN8TcWQY25KJHoy4ChoZ6lVKnLSMoIhcB3cAzw0Tcg8BxpdRvROQHQLRS6vsichXwj+gibg16WqY1o9k10/uv9vZ2XnvtNf7vlVfo6+3FnbAAV9pqMJ88xiD9HTgrtmHqaiI9PYPbb7+NSy65BItlvGMRBuOhpaWFd955h7fe3ExNbR1WMyyPcbEm0U1e7NkFRPx6jz7FfrYBDZqC0k4LnzbZ+LTZ8f+zd97hcVXX3n7XVM1oVEddliz33o0L2HRjigMhmBq4KRACwaRAyg25+ZJAwg3JDS3hJiF0fCkJptnGgA0YbCDuvRfZ6r2X6fv7Y8a2bMu2bEs6I2m/zzPPzDmz9znrzJH2/GbvVaj1QKzTwcWXXMqVV17J8OHDo9rPDTo+hp2tiHu+nd1KKfXtMz5oF9DbB8GeRnmDh8WbS3nhiwMU1LQAYT+vMdkJjMlOIClWC7fTxRcIMX/VQfZWNPH/5ozk2zP6dkW8MxRxK4FfAY8BXwG+RXiM/NUp+uUBi9qIuF2Eo/RLRSQTWK6UGiYif4+8fvXYdic7fl8ZvxobG3nhhRdY8OabhJwpNA+dDZbwWGBqqsC150OcMXbu+d7dzJ49G7O55+Z+7I0opdi+fTtLly5l+ScfU1ffgM0Mo5N8TEr1MSHFh6uD9VfPRMQFQrCzzsq6Shvrq2Oo9YDVYuacKVO59NJLOe+883pU7r/uik791tn01/QdKho9vL+1jEWbSllzsAalIDMhhstGpjMmO6HPBSd0NjaLif+Y1p8v9lXz4KLt1Lb4+NGlQzH1VK9jY3AopT4SEVFKHQR+LSIrCAu70yH9kDCLCLm0yP5soLBNu6LIvuNEnIjcCdwJkJvbNyKO4+LiuPfee5k4cSK//OX/I+bgF3gGXQgBH659H5Oe6uaJxx8nPT3daFM17SAijBo1ilGjRnHvvfeyefNmVqxYwYrPlrN+Rw0mCacLmZLmZXKqj3jb2ed/C4RgS42V1RU2NlbH0OwHu83KlKlTmTnzfKZPn97jyqCdLmcbnTqUcOmtdKXUaBEZC1ytlPptp1in6dGUN3hYsqWU97aWseZAWLgNTXfxo0uHctXYTFbtrzHaxF6FxWziL7dM4BdvbeXPH+9lR2kjj944rtelXelCPCJiAvaIyDygmHDkfWfRnqJu95tMKfU08DSEZ+I60Yao57zzzuO2227lxRdfxJcxGktdAcrXwq9/9agWcD0Es9nMhAkTmDBhAvfeey+7du1ixYoVfLr8E17YVcJLu2FMso+ZmV4mpviwnGbKkvwGM5+WxrCqIizcXLFOZlw8kxkzZnDOOed0az43ozlbZ4J/AD8B/g6glNosIq8AWsT1QQ75uG0raWBrcT0FNS0oIC3OzsXD0hidnUB6fPifSwu4rsFiNvH768YwIjOOhxbv4Kt/+Zyn/2MSg3Xkakf4IeAEvg88BFwMnEkZwXIRyWyznFoR2V8EtHVY7AeUnIW9vZYbb7yRN95YQKBoLdaWas6bMbPdShGa6EdEDlf6uOOOO9i/fz8fffQRSz98n79srSHRDpdmt3BpPw/OkwREhBSsq7TxXqGTffVmbFYrM2bOZNasWUyePLnP5gU8WxHnVEqtPsZB0PhqtZpupai2hfe3lvHiFwcojAQnZMTHcMmIdEZnxZMW33d+FUUDIsI3zxvA8Mx45r2ynmv+8jkPXjOar03MjnpnXiNRSq2JvGwi7A93prxLWPz9PvL8Tpv980TkNcKBDfWn8ofrqzidTq699qvMnz8fBdxyy81Gm6TpBESEQYMGMWjQIG6//XbWrFnDggVv8MaatSwpdHLdgCYuzvYeF5G6r97CC7vjONhoIjsrk+9/43pmzZrV65dKO8LZirgqERlEZElARObSjn+HpvdRWNPCe5Gl0k2FdcARH7fRWQmkxGkfN6OZNtDNwntncO8rG7j/X5t4b0spD39tzOHZUE0YEXn3ZO8rpa4+Sd9XgQuBFBEpIuw/93vgnyJyO1AAXB9p/h7hyNS9hFOMaJ/ikzBr1izmz58PwIgRIwy2RtPZmM3mwylL9uzZw1//+r+8tH4DW2ps3DOqEVskbuWjIjsv73HhdifzwL13cckll+igljacrYi7h7DfxnARKQbyga+ftVWaqEMpxZ6KJpZuL+f9rWVsKa4HYEx2Aj+7fDhXjM7gi33VBlupOZbMBAevf3c6z3+ezx8/2MWsRz/ll3NGct3Efjro4QjTCQccvAqson3ftXZRSp1oiuiSdtoqwmOmpgPk5ORwwYUXMi7Kyx5pzp4hQ4bwpz89yptvvsmf//xn5u+J5dvDm9lSbeXF3S6mT5vGL/7rv3C5XEabGnWcUYoREbnvmF0OwEC4vXkAACAASURBVAQ0AyiloirLeV8J0e9s/MEQq/NrWLajnGU7yimsCS+VjuuXwJVjMrlyTCY5yUdKk/SGgvM9nZPVTs2vauanb2xizYFaRmTG85PZQ7loWFqv/II8nRQjImIGZgE3Ey65tRh4VSm1rQtN7DB6/NL0Jf7yl7+w4I03eOzcWp7aHkezox/Pv/Bij0oP0hl0dYqRQwvRw4BzCPt8CHAb8NkZHlNjMEop9lY08cW+ar7YV8Wnuyvx+ENYTMKgVBfXjM9ieEY8CY6wA+mKPVUGW6w5llMJ6WvGZzMgJZZlOyr49gtrmdw/iXsvGcLMwSl9dmZOKRUE3gfeFxE7YTG3XEQeVEr92VjrNJq+xVVXXcUbb7zBqgobe+ss/Mc1s/ucgDsdzkjEKaV+AyAiHwITlVKNke1fA//qNOs0XUogGGJ3eRMbCmtZtb+GL/ZVU9UULryRk+xgdFYCwzPiGZzmwna6MeCaqMQkwvicJEZnJ7DuYC1f7qvmG8+tJifZwU3n5HL9pH59MhAlIt6uIizg8oAngaiqPKPR9AXy8vJIiI/j3QMhFDB+/HijTYpqztYnLhfwtdn2ER4ANVFGMKR46pO9lNZ7KKppobC2heK6VvzB8HJ6XIyFQakuzh+SwsBUF8m6akKvxmIyMXWAmz/MHcuH28p5dXUBf/xgF48u3c2k3CQuGp7GxcPTGJru6pXLrW0RkReB0cAS4DdKqa0Gm6TR9FlEhPETJvLpp59is1oZOXKk0SZFNWcr4l4GVovIW4QjVK8FXjxVJxG5nHAhaDPwjFLq98e8bwdeAiYB1cCNSqkDkfd+TrgodRD4vlLqg7O8hl5Fiy9AcW0rRbWt7K1oYld5I7vKGtlT0YjHHwLAbBKyEmKYnJdMTpKTnCQHybG2Xv9lrTkeu8XMV8Zl8ZVxWeRXNfPm+iI+2lHBI+/v5JH3d5IWZ2d8TiLjchIZ2y+BYRlxpLrsve1v5TbC/rxDge+3uTYhHI8Qb5RhGk1fZOLEsIhzp7j1UuopONuyW78TkSXAzMiubymlNpysT8SJ+CnCjsRFwBoReVcptb1Ns9uBWqXUYBG5CXgEuFFERgI3AaOALGCZiAyN+LT0KEIhhT8Uwh9UBIKR51CIQFDhD4YIhBS+QPi57fstviD1LX7qWn3Utfipa/VT0+SjpD4s3GqafUedJy3OzrCMOL4+tT91LT7S42PIiI/BYtbLo5rjfegyExzcOq0/9a1+dpc3sr+yiXUHa/lwe/nhNi67hbwUJ/3dsWTEx5AaZyfVZQ8/x9lJcFiJtVtw2S2Ye4CfnVJK/zNoNFHEkCFDABg2TCd4PhVnOxOHUmo9sP40ukwB9iql9gNEEl9eA7QVcdcAv468fgP4i4R/Hl8DvKaU8gL5IrI3crwvz+oiIjy7Mp81+TUoFCEVdvRXCkIqvB2KRPKGlCIU4qh2IQWBkMIfCB0RY4dFWUSYBUP4I6Is1AmFdEwCDpsFp81MosPK4FQXSf2tJMbaSHLacMfaiLWf9S3W9EESHFbOyUvmnLxkAFp9QYrrWqls9OB22cmvamZbcT3Ld1bQ7Dvxb6gYqwmX3YLTZsFiFiwmwWIyYTULZpNgMYdfW0wmLCYhPAkWfj4k/8KvhRvO6cfFw3XZJY2mtzN8+HAeeOABJk2aZLQpUY8R3/DtFYGeeqI2SqmAiNQD7sj+fx/TN7u9k7QtIA00iciuM7A1BTAyBNPo80eDDUafPxpsMPr80WBDyt9P7/z9u8ySbmbdunVVInLQaDsMwui/O41x9PV736ExzAgR15Ei0Cdqc0YFpM8UEVnb0VxTXYHR548GG4w+fzTYYPT5o8EGo89vJEqpVKNtMIq+fN/7OvredwwjfEE6UgT6cBsRsQAJQE0H+2o0Go1Go9H0eowQcWuAISIyQERshAMVjq1deKiANMBc4ONIyZp3gZtExC4iA4AhwOpusluj0Wg0Go0mauj25dSIj9s84APCKUaeU0ptE5EHgbVKqXeBZ4GXI4ELNYSFHpF2/yQcBBEA7uniyNSzWo7tBecH420w+vxgvA1Gnx+Mt8Ho82uMQd/3vou+9x3gjGqnajQajUaj0WiMRedH0mg0Go1Go+mBaBGn0Wg0Go1G0wPpE5lgU1JSVF5entFmaDSabmLdunVVvSU1hx6/NJq+R0fHsD4h4vLy8li7dq3RZmg0mm6iNyXH1eOXRtP36OgY1idEnOYIx9bK7C5umZpryHk1Go1GE/2sXr2ap//+N2pqao57z2KxcPU1X+WGG27AZrMZYF30okWcRqPRaDSabicYDLJ69Wpef+01Nm7aRJpTMSbBG6mhfISKVjPPPPMM77z9FjfceBNXXHEFLpfLGKOjDC3iNBqNRhN1HDx4kMrKSgCGDBlCQkKCwRZpOouDBw+ybNkyPvzgfcorKkmKga8PaebibA/WE4Rbbqux8PaBAE899RTPPPMPzj//Ai677DImTJiAxdJ3pYyhVy4ic4D3lFIhI+3QaDQaTfTw/vvv88gjj3Aoj2my282f/ud/GDBggMGWac4EpRR79+5l5cqVfPbpcvIPHEQERiX5mTvKw6RUH5ZT5MoYlRxgVHI9+Q1mlpfEsHL5UpYuXUpCfBznzZjJzJkzmThxIna7vXsuKkowWr7eBDwhIguA55VSOwy2R6PRaDQGUVtby1//+lc+/PBDgnEZeLPGI34PtUWr+e537+I737mDa6+9tk/PvPQUAoEAmzdvZuXKlXy+cgXlFZWIwNCEALcO8TIlzUui/fSLDQyIDzIgvpmvD2lmc42N1RVePvnwPd577z1i7HamTJ3KjBkzmD59OnFxcV1wZdGFof8JSqlbRSQeuBl4XkQU8DzwqlKq0UjbNBqNpisQkeeAOUCFUmq00fZEA16vlzfeeIP58/+PVo8Hb+Y4fFkTwBSenmmMy8BxYCVPPfUU77z7LnffdRfnnnsucqzzlMZQQqEQmzZtYtmyZaz47FMaGpuwmmF0ko85w31MSPERb+ucKlE2M0xO9TE51Yc/1MT2WivrK21sWP0Zn332GWaziUmTJnHxxZdw/vnn43Q6O+W80UZUlN0SkRTgVuCHwA5gMPCkUurPnXH8yZMnKx2iH0ZHp2r6AiKyTik12Wg72kNEzgeagJc6IuJ6+/i1YsUKHn/8Caqrqwgk5uLtN5mQI/H4hkphqSvAUbwWWusZN3489993H7m5emwxGr/fz8KFC3njX/+kpLSMGIsw3u3hnFQfY90+7OaOH2v+bie76qy0BASnRTEs0c+tQ1s63D+kIL/BwtpKG6urYqhsERwxdmZffgW33HILaWlpZ3CF3U9HxzCjfeKuBr4FDAJeBqYopSpExElYzB0n4kQkB3gJyABCwNNKqSe6z2qNRqMJIyJbgGN/CdcDa4HfKqWqj+2jlPpMRPK63rro59lnn+Xll19GxbppHXYFwfjMEzcWIZDUn8aEHKxVu9i8bT3f+c6d/P73/82ECRO6z2jNUZSVlfGzn/6EgwWFDEkIctfIFialnp5wa8uuOivlARdz5sxh0aJFUNd0Wv1NAoMSAgxKCHDDoBb21FtYXmJn4btvs2TJe/z85w9w4YUXnplxUYjRjgVzgceUUp+13amUahGRb5+gTwC4Xym1XkTigHUislQptb2rjdVoNJpjWAIEgVci2zdFnhuAF4CvnMlBReRO4E6g1840FRUV8fLLL+N3D8aTN+Pw0ukpMZnwp40gkJhL7O4P+NOjjzH/5Ze61ljNCXnqqacoLynkvrENjE/xn/XxWgLCnDlzmDdvHkopPlv8zzM+lggMTQwwNDHAtQNaeWpbHA//7ndMmTKl1yyvGi3iSo8VcCLyiFLqZ0qpj9rroJQqBUojrxtFZAeQDWgRp9FoupvzlFLntdneIiKfK6XOE5Fbz/SgSqmngachvJx6tkZGI6ZDoi0UJLyocpqlvENBRIUwm89wykfTKZjNZkIKWgOCUhyX4+10cVoUixYtQinF4sWLSbd0zp+/NygElAmz2dSrfClP87+m05nVzr4rOto5siQxAVjVSfZoNBrN6eASkamHNkRkCnAoC2nAGJN6BllZWdx5551Ya/OJ2/4O5roC6IiPdiiArXQTcdvfwWkO8tOf/LjrjdWckHnz5jFg8FD+uj2OX6xJ4qMiO83+MxdJDovC4/GwYMECPB4PjrMQccEQbKq28sQWF79YnUhtKI5f/fo3OByOMz5mtGHITJyI3A18DxgkIpvbvBUHfN7BY7iABcAPlVIN7bzf65cjNBqN4dwBPBcZj4TwMuodIhIL/LehlvUAbrnlFoYOHcqjjz1GyZ5lBOMy8ORMIRSbcnxjpbBU78NRsh68TUw791x++IMfkJ6e3v2Gaw6TkpLC//7vX1m2bBn/+ufrvLh7P/P3wpgkH+ek+Rif4iPO2n2TyYEQ7KyzsqbCxrrqGBq8kBAfxy1fv5rrr7+exMR2gmZ6MIZEp4pIApBEeJD7zzZvNSqlji+cdnx/K7AI+EAp9eip2vf26K7TQUenavoC3R2dGhnTRClV14G2rwIXAilAOfArpdSzJ2rfF8avQCDAwoULee7552lsaMCXNhJvzmQwhecZxNOA88AKTI3lDBkyhO9973s6mCEKUUqxZ88eli1bxvJPPqaisgpTJDfcxBQvk1J9pDpOntv/4fXx7KyzHt4enujngYnHzdMcRWtA2FRtZX2Vjc01dlr8EGO3MW36uVxyySVMmzYNq9V60mNEG9EenaqUUgdE5J5j3xCR5JMJOQkvZj8L7OiIgNNoNJquQkTswHVAHmA55GujlHrwRH2UUjd3i3E9CIvFwrXXXsusWbN49tlneeutt7C0VNE89DJMrfW49n5IjNXMvT/9KZdffvkRfzpNVCEiDB06lKFDh3L33Xeze/duVqxYwecrV/DK3oO8sjeW3LgQ56R4mJbuJd155sWaWgMSTiNSYWNbrY1AKDzjdsGlM5g5cyaTJk3qE9UbjBJxrxBOdrmOcHh+2wV0BQw8Sd/zgNsIOxBvjOx7QCn1XlcY2hto9gbYV9lEbYuf7SX12K1mXHYLKS47ZlPvcfDUaAzgHcIpRdYBXoNt6fG4XC5+8IMfMH78eH7zmwdx7PsES2stqclJPP7Yo2RlZRltoqaDiAjDhg1j2LBh3HHHHRQXF/P555/z6fLlLNi+nQX5TgYnBLkgs5Vp6d4OpSRRCnbVWfi4JIZ1VXb8QUhPS+Vrcy9ixowZjBo1qs8Fuhgi4pRScyLPp10ITym1kqNFn+YYlFJsLqrnva2lfLyjgr2VTe36C1tMQk6ykxGZ8YzJTiDB0bOmmzWaKKCfUupyo43obVxwwQXcdtutvPjiiwA89OBjWsD1cLKzs7nhhhu44YYbqKio4OOPP2bJ4kU8u7OI1/e7uKJfM5fleNrtq1Q4QOHNAy4ONJhwxTq56iuXMWvWLEaOHNmrok1PF0MrNojIecBGpVRzJBx/IvC4UqpTHbf6gk8JQDCkWLS5hGdW5LOluB6LSZg20M05eckMy4gjNc7Gxzsr8fqD1LX6Ka1rZW9lE+UNXkwCI7MSuGBoKtmJnR+5o33iNN1Jd/nEicjTwJ+VUlu66hx9Zfw6ltLSUm6+ObzyvHz5cmON0XQJSik2bdrEq6++wqpVq0l1KpymIAebjswvDUnwE2dVrK+ykZWZwS1fv5VZs2b1+qXSaPeJO8RfgXEiMg74KWFft5eBCwy1qgeyck8VDy7axu7yJganuXjomlFcPT77uNm1XWVtsl/nJgFQ1eRlTX4Naw7WsLW4nlFZ8Vw5JpMkp607L0Gj6YnMAL4pIvmEl1OFsM/vWGPN6vlkZGQYbYKmixERxo8fz/jx49mwYQN/eOT3HCwrP6rNwUYLymThrru+w9y5c7FYjJYt0YXRn0ZAKaVE5BrgCaXUsyLyDYNt6lHUNvt4aPF23lxfTJ7byZ9vnsBVYzIxnYavW4rLzhVjMrloeBor91axck8VT5Tv4bJR6Uwb6MbUh6eqNZpT0OG8lprToy8vkfVFJkyYwN/+/jQ33HADXu8R91JfSHjsT/+jo5FPgNEirlFEfg7cCpwvImZAO2Z1kHc2FvPgwu3Ut/qZd9Fg5l08mBjrmTt1xljNXDoinUn9k3hnYzGLNpeyqbCOr03sR3p8TCdartH0DpRSBw+9juSG+ypwC3CVYUZpND2UhIQEsrKyyM/PP7wvKSlJC7iTYHSc9o2ElyBuV0qVES6f9UdjTYp+PP4gP31jEz94bSP9kp0s+v4Mfjx72FkJuLYkOW18Y3oeN0zuR3Wzj6c+2cua/BqM9J/UaKIREbGJyFdF5J+EywFeCvzNYLN6BXq86ZscOwOrZ2RPjqEzcRHh9mib7QJAVzI+CQXVLdz9f+vYVtLAvRcP5oeXDu2SNCEiwvicJAanxfGvtYW8tbGY/VVNfHV8NvZOEou9FSMSKuvAke5FRGYBNwOzgU8I+/JOUUp9y1DDehEVFRVGm6DpZlpaWigpKTlqX21NDdu3b2fkyJEGWRXdGDoTJyJfE5E9IlIvIg0i0igiJ0/N3If5eGc5c/68gsKaFp775mTuv2xYl+d5c9ktfOPcPGaNTGdzUT1PLd9LWX37YeAaTR/iA2AQMEMpdatSaiHhKu6aTsDj8fDkk08e3n7zzTf1zFwvJz8/n3vn3YPHc/T3i8WkuP++H/Hhhx/qv4F2MNon7g/AV5RSOwy2I6oJhhRPLNvNkx/vZWRmPH+7dRK5bme3nd8kwkXD0ujvdvL6mkL++ulevjahH+NyelcNOo3mNJgE3AQsE5H9wGuAnqI+S4qLi1m+fDlvvvkW1dVVeHKmYG0s4cknn+Sjjz7mmmuuZvr06cTFxRltqqaTqKys5NVXX+Wdd97GaQ7RLzZAUfMRadLfFUBMzTz88MMsXrSI2++4g7FjdfD3IYwWceVawJ2cmmYfP3htAyv2VHH9pH489NXRneb7droMTHEx76LBvLK6gNfXFlJY28IVozN11QdNn0MptQHYAPwsku/yZsAmIkuAt5RSTxtqYA+gvr6e/Px89u/fz86dO9m0eTPlZWUABOMz8Q6/kmBcBv70kVgrd7Ft3za2PfwwIsKQoUMZM3o0Q4cOZeDAgeTm5vb6vGG9CaUU27dv5+233+aTjz8mFApyQaaH6wa28JetRwt0iwn+c0I9nxTbeWvXZr7//e8zcsRwrv3adZx//vl9/r4bLeLWisjrwNu0KVmjlHrTOJOih42FdXxv/jqqmn38/mtjuGmK8X5PcTFW7pgxkCVbS/liXzUlda3cPCWXuBgdVKzpmyilPgc+F5HvA7MIB2z1eRHX2tpKZWUlFRUVVFRUUFZWRklJCSUlJRQUFtLU2Hi4rdic+JwpBHOnEkjMRdnbfJGLCX/aCPypwzE1V2KpL2JHSRl79r6DCgbCTURwp6TSPzeHrKwsMjIyyMjIIDU1lfT0dNxut84vFgVUVVWxbNkylry3mIMFhTgscGFmK1fkeEh1nNgbwSRwST8vMzK9fFoSw9KDO/jd737Hk088ziWXzmL27NkMHz68TwZBGP1XHQ+0AJe12aeAPi3ilFLMX1XAgwu3kR4fw4K7zmVMvwSjzTqM2STMGZtFvyQnb20o4qlP9nLzlFz6u2ONNk2j6VZE5Hal1LMASqmQiCwDphlsVpehlKKxsZHa2lpqamqOelRXV1NdXU1FZSVVVVW0trQc3VkEsbvwW2MJxWQRSown5Egi5EhCWZ1wqi9gEUKuNHyuNHwAKoTJ04CptQZTaz2lnnoqdhVg3rId5Ws9pqsQF59AWmoqqakpuN1ukpOTDz8nJyeTlJREUlISDkfnV6zpy3i9XlauXMn77y9h3dp1hJRicEKQbw0L10x1nIYKsZvhshwPl/bzsL3WymelXhYvfJu3336b3Jx+zL78CmbPnk1KSkrXXVCUYXR0qo7kOoYmb4AH3tzCu5tKuGhYKo/dOJ7EKK2cMD4nkfR4O/+3qoBnVuRz5dhMpg1I7pO/hroTjz9IXYufuhYf9R4/wZDCFwgCYLeayUyIITvRQVaig1i70b/Tej2XiMh1wO1ACvAc8OnJOojI5cAThH3onlFK/b7LrTwJh4RZdXX1UeKstrb28KO6uobqmhoa6usIBoPHH8RkRmxOgpYYghYnytUflewkZI1F2V2EbLFhoWbqRFcQMRFyJBJytOObG/Rj8jUhvmZMvmbE14zX10JNZTN7Sndj8rccJ/QOYbfHkJiYiNt9tLg79PqQ6EtOTtaC7yRUVFSwYMEC3lu8iMamZtwOuCq3hRkZXjJjzy4GyCQwOtnP6GQ/zf5mVlfYWFl+gH/84x88++yznHvudObOvZ5x48b1+u8jQ0d4ERlKuPRWulJqtIiMBa5WSv3WSLuMYkdpA/f833oOVDfzk9nDuPuCQadVecEIMhMc3HPhYP65tpCFm0ooqG7mmvHZhvnt9TaCIUVxXSsHq5spqGmhoLqFRm/guHaLNpe2279fkoNJ/ZOYmJvEpP5JjMyMj/q/qZ6EUuoWEbkR2EJ4VeHmyPJqu0QSmj9FeNm1CFgjIu8qpbZ3lY0tLS0UFxdTVlZGWVkZFRUVR5Y5K6uoq6sl4Pe3Y6zpsDALWWJQ1mRCqdkoqwNljUFZnShrDCGrA8z2U8+knSampgosDaUE4jMJudJOr7PZSsiRBI4k2pGcYUIhJOBB/K2IvwUJeDD5W/H5W2nyt1JcVIvlQBkSaD2h4IuJcZDsTiYtNY20tFRSU1NJS0sjMzPz8KOvLeN6PB6ee+453lywgFAoyORULxcO9jIyyU9XDD2xVsVF2V4uyvZS1mLi05IYPlv9OStXfs6E8eP40X33k5trvCtSV2H0X9c/gJ8AfwdQSm0WkVeAPiXilFK8srqABxduJ8Fh5ZXvTGPaQLfRZnUYh83MbdP7s3xXJR/tKKegpoUbJufo5dUzxBsIsqe8iR2lDewsa6TVH/4aSo61MSjNRUZ8DIlOK0lOGwkOKxaTcN2kfgC0+oOU1LVSXNdKUW0r20rq+ff+at7ZGM69lBZn55IR6Vw2Mp3pg9xabJ8lIjIE+AGwABgB3CYiG5RSLSfoMgXYq5TaH+n/GnAN0Gkizuv1snTpUlavXs32HTupqjw635qYrSh7LAGzA2WLI+ROjwgyx+FHWJjZOl2YdRRTUwXuguVcdcUVLF6yhOrcC09fyJ3yJCaUzYmyOYFTjLeqreALP0z+Fny+VhpbWijcW4xl516UtxnUkVkms9lMTk4uY8aM5oILLmDy5FPWM+/RBAIB7rvvR2zfvoMLMj1ck9dKykl83TqbDGeIGwe3cO2AFpaXxPD29k3c9d07+ctT/8vAgQO7zY7uxGgR51RKrT5muvP4aYZeTEldKz9bsJkVe6qYOSSFx24cT4qr50XbmES4eHgag1Jj+efaQv6xYj8XDUvjwmFpOnq1A4SUYl9lExsK6thWUo8/qHBYzQzPiGN4Zjx5budJg0eSYsNL7klAVqKDtl8VSilK6j38e181y3aU887GYl5dXUBcjIU5YzO5bmI/JvVP6vXLDl3EQuAepdRHEv4A7wPWAKNO0D4bKGyzXQRMPbaRiNwJ3Amc9izC888/z2uvvQZA0JFEIGt82PfMHkfIHmeoOOsoloZSrrriCu6ddw8Ar3y5H19ni7jTQUwRoXuK1E4qFBZ43kbE24i5qZL8gr0cOJDPwoULefLJJ3t1eoyNGzeyffsOvjG0iUv6eU/doYuwRXznJqX6+OmqJN566y3uv/9+w+zpSowWcVUiMohwMAMiMpdw6Zpej1KKf60t4qFF2wkqxUNfHc3Xp+T2+KWu/u5Y7r14CAs3lfDRzgq2lzZwzfhso82KWiobvawvqGVjYR31rX5irCYm5iYxJjuB/u7YThHAIkJ2ooPrJvXjukn98PiDfLm/moUbS3h7Qwmvri6kv9vJtROy+dqEft2ag7AXMEUp1QCgwplI/yQi756kfXs39LgMppEUJU8DTJ48+bQynM6YMYNFixfT1NiIubUWs6cOYuIJWF2E7C6UzUXI5kRF/NRCVieYrVEl7ALxmSxesgSAxUuWEMi90FiD2kOpyOxcC+JrCfve+VsweRsx+Zqx+BrDM3MRRo0a3Wtngw6RnZ2NxWxmRZmDkcl+Mp3G5b/2BuH9whj8wdP/IdSTECMzIIvIQMID1blALZAPfL1tUenOYPLkyWrt2rWdecizYktRPQ8t2s7qAzVMG5jMH+eOIye5e744u7Mk1LaSehZuKqHRE+Dmqbn8bPZwEpy9PxXJqT7jVl+QzcV1rD9YS2FtKyaBIWlxTOyfxPCMOKzm0y+kcqZlt5q9Ad7fWsaC9UV8ub8apWDKgGTmTuzHlWMzcfXQwAgRWaeU6rK1KxH5qVLqD5HX1yul/tXmvYeVUg+coN904NdKqdmR7Z8DKKX++0TnOpPxSylFUVERO3bsoKCggMLCQkpKSyktLaOp8fiiOGK2gM1JwBxDyOJo4/fmQFmOLLEqqwNMlm4RfGflE3emKIUEvEigtc3SaXgZ1RTZZw54MAVaUb4WOOb7U0Rwu1PIzMwgKyuL/v37k5eXx4gRI0hKSuqeazCYFStW8N8PP4zX08r0dA+zczz0jzuhZ+JxzN/tpKDpyLiT6wpw69ATeSccT4NP+LTEzofFsdR74dprr2XevHmYzT3LdaSjY5ghIk5E7jtml4NwCbBmAKXUo8d1OguiRcSV1Xv4wwc7eXN9MSkuG/fNGsZN5+R06+xbd9f19PqDLNtRzpf7q0l02rjrgoHcNi0Ph61n/UOdDu19xsGQYk95I+sL69hZ2kAgpEiPtzMxN4nxOYlnnWevM2qnFte18vaGYhasK2J/VTMOq5nLR2cwd1I/pg9096hZ4m4QceuVUhOPfd3e9jH9LMBu4BKgmPDS6y1KqW0nOldnj18eh8/DlwAAIABJREFUj4eqqioqI6lADqUHORSVWlVdTU1NbbtiDyKCz+ogaHEQtMS08aULz+opm/Nw0ANiaGXHI4SC4UhVf0tYmPla2gizlogw87QrzABMZjMJCYm4k5Nxu49OTeJ2u0lNTSUlJYXk5OQ+F8jQHtXV1bzyyissWrQQr9fHwPggMzNamZbuI9ba+ZojGIKttVZWlNpZX2UnEILJkyfxzW9+i9GjR3f6+bqDaBdxv4q8HAacA7xDeJnhK8BnSqk7OvN8Rou4/KpmnlmxnzfWFaEUfHvGAO65aJAhCXKNKM4OMLZfAo+8v5MVe6pIcdm5+8JBfH1qbq90rD/0GSsVjizdWFjHpsI6mn1BYm1mxuUkMiEniazEmE7zQ+sMEXcIpRTrC+pYsL7o8ExqVkIMX5vYj6vHZzEkzRX1/nPdIOI2KKUmHPu6ve12+l4JPE44xchzSqnfnexcRo1fgUCA+vr649KNtN2urAqLv8aG+uMPIILYYwlYnIRskTQjEb+8UEwCyhbbeTN6oSAmbwPiacTkbcDkbUJ8TVj8zSdMJ2Iym0lMTAqLsjZpQw6lFGmbVsTlcmEyRYkg7UE0NjbywQcf8N6iRew/cACLCSa4w0l7xyb7OYNFh6MoajKzotTOlxUO6rwQH+di1mWz+cpXvkJeXl6nXINRRLWIO3xykQ+B65RSjZHtOOBfSqnLO/M8RgyCwZDi3/ureenLA3y4vRyr2cR1E7P53oWDu23ptD2MEnGHRMaaAzU8tnQ3X+yrJsFh5doJ2dw8JZdhGb2jFmIwpHhkyU62ldSzraSBulY/ZpMwIiOOCblJDE2P65JAj84UcW3x+IMs3V7OG+uKWLGnkpCCgamxXDE6g8tHZTIqKzpTlkTrTNyZYPSP0I4QCASOzORVVVFdXU1lZSWVlZWUl5dTWlpGVVXlUTnmxGwhFJNAwJEcjoY9XZTC5GnA6q0DT8NRM2j2mBjS09PJzMggLS2N1NRw+g+32334ER8fr4VZN6GUYs+ePXzwwQd8tPRD6hoaSbTDzIwWLs724o7puO+cLwhfltv5pMTB/gYzZrOJ6dOnM3v25UybNg2rtXe47PQUEbcTGKeU8ka27cAmpdTwzjxPdw2CwZBiY2EdCzeVsHhLKZWNXhKdVm6b1p//mJ5HapzxUadGi7hDrM6v4eV/H+SDrWX4giEm5iZyxehMLh6RxsCU2Kif6TmEUoqCmhZW7q1i5Z4qPt9bRYMngMUkDE5zMSorgRGZcThtXbvE0lUiri0VDR4+2FbGkq1lrMqvIRhSpLhszBicwswhqZw72E1mQnQkP+0GERck7P4hhN1BDjntCBCjlOq0b5KeIOI6QjAYpKqqipKSEoqLiykoKGD//v3s2buPxhMs3Z4MAdIzMhgyeDB5eXnk5OSQnZ1NVlYW8fHxPWYM6WsEAgG+/PJL3lu8mFWrVgGKaWlerslrOWkS4NYALC1y8EGRk0Yf9M/NYc5XrmbWrFkkJraT8LmH01NE3C+AG4C3CEdoXQu8fjIn30i/08p43lWDoMcfZHd5I2sO1PLlvmpW51fT4Algs5i4eFgaV4/P4uLhaVG1ZBgtIu4QNc0+3lxfxBvrithZFq6lmOd2MnNIKhNyExmXk8gAd2xUzPYcStWxu7yRbcX1bCysY0NBHdXNPgCyEmKYMSQFkwjD0uOwd+N97w4R15aaZh+f7KxgxZ5KVuypOvwZZMTHMLF/eLl4RGY8Q9JdpMXZu/0LtatFXHfSW0ScRnMsZWVlvPnmm7z7ztv4fD4u69fK3IEtHOsyvarcxv/tjaPOC1OnTuGmm25m/PjxvVqo9wgRByAiE4GZkc3PlFIbTtHeTNgx+HDGc8JZ0k+YLPNMBkFvIEiTJ0CTN0B9q5+yeg+l9R5K6ls5WNXCrvJGDlY3E4p8fP3dTqYNcHPuYDcXDU8jPkoLwkebiGtLUW0Ln+ys4KOdFazOr6HFF156iYuxMCTNRZ47lryUWPq7naS67KTE2Ulx2Ul0WM9a5PmDIepa/NS3+qht8VPb7KOswUNxbStFda0U1bSwt6KJZt+R5aBBqbGMz0lifG4i0we6GZQankE04jPubhHXllBIsb20gTUHathQUMeGwloKa474IMXHWBiU5qJfkpPsRAfZSQ7S4sL3LdFpI9FpJcFh7dQfO1rEaTQ9h9raWp555hkWL15MjAWc1iPjeSCkaPDCsKFD+P4PfsioUSdKwdi76DEi7nTpyhD9X72zlUWbS2n0BvAF2p/WtZlN9EtyMDQ9jmEZ4cf4nESyEqNjGelURLOIa0swFE5+u7Gwjs1FdeyraOZAdTOl9Z5228dYTThtFpw2M06bGYfVjIggEl52ERFMEh4QvP4Q3kAQbyCENxDC4wu2W8oKwvc7KzGGfklOBqe5GJzmCt/79LgTpkvpayKuPaqavOwub2RPeRO7yxvZX9lMSX0rJXWt+IPtjzk2iwm7xYTNbMJiFqzm8GsEUPC9iwYzN1KZ4lRoEafR9DzWrl3Lxx9/fNz+3Nxc5s6d26cif3uziJsLXH4oglVEbgOmKqXmHdPucMZzwlGwu05y2BSgqgvMjUb0tfZO9LUeTX+lVGp3GNPViEgl0Km5M3sQfenvWnM0ff3ed2gM64my9rQznp/ygCJre8uv9lOhr7V3oq+199JbxOiZ0NfuteYI+t53jJ4YX10E5LTZ7geUGGSLRqPRaDQajSH0RBG3BhgiIgNExAbcBJysVqFGo9FoNBpNr6PHLacqpQIiMg/4gCMZz09YsqaDdGjZtZegr7V3oq9V0xvR97rvou99B+hxgQ0ajUaj0Wg0mp65nKrRaDQajUbT59EiTqPRaDQajaYH0idFnIgki8hSEdkTeU46QbugiGyMPHpU8ISIXC4iu0Rkr4j8Zzvv20Xk9cj7q0Qkr/ut7Bw6cK3fFJHKNvfyDiPs7AxE5DkRqRCRrSd4X0TkychnsTlSEaVH0oFrvVBE6tvc1//X3TZquo5T/V9reien+r/XHE2f8IlLSUlReXl5Rpuh0Wi6iXXr1lX1lvxqevzSaPoeHR3Delx06pmQl5eHLluj0fQdRKTXVDjoq+PXtm3bePHFF4mNjeXHP/4xsbGxRpuk6QZCoRCvvvoqRUVFxMTEcMstt5Ca2it+j50WHR3D+oSI6810RZ3OaKvDeSx98Zo1mr5EMBjk8ccfZ8+ePUC4dua3vvUtg63SdDVKKf7+97/z+uuv47BAawC2bNnMo48+Rnx8vNHmRSW91idORJaJyFa9rq7RaDQ9i+eff549e/bQOvBC/MkDefnl+WzevNloszRdSFNTE7/5zW94/fXXuTjbw99mVnP/uAYO7N/H3Xd9l927dxttYlRiqIgTkTki0iU2KKUuVUqNVkqN7orjazQazekiIjEislpENonINhH5jdE2RRtLlixh/vz5+FKGEkgegKf/dEJ2Fw/84hcUFHT+LLzGeNavX8+3v/VNPvt0OTcMauYbQ5sRgXFuPz+fUE9LTSnfu/tuXn75ZQKBgNHmRhVGz8TdBOwRkT+IyAiDbdFoNJquxgtcrJQaB4wHLheRaQbbFDVs2bKFP/7xfwgmZOPtfy6IgMVO0+BZNHsD/Oxn/0lzc7PRZmo6CaUUL730Evfffx+mlkp+ObGeOf09iBxpMyQhwG/PqWGiu4Vnn32Wn/z4fhobG40zOsowVMQppW4FJgD7gOdF5EsRuVNE4oy0S6PRaLoCFaYpsmmNPHp/ioAOoJTif/70J0I2Jy0DL8JetBp7wb/D78XE0zzwIkpLS3j11VcNtlTTWbz99ts899xzTE/z8tDkGgYlhGfZ5u92Mn+383C7OKvinlFNfGdEE1s2b+K/fvEL+kJmjY5g9EwcSqkGYAHwGpAJXAusF5F7DTVMo9FougARMYvIRqACWKqUWtVOmztFZK2IrK2srOx+Iw2gpqaGgwcO4EkdCRYbppYaTC01h98PxmUQiMtk1erVBlqp6UwWvvsOg+ID3DmyCbv5yP6CJgsFTUfHXYrAzEwv1w9sZtPmzRQVFXWztdGJ0T5xV4vIW8DHhH+RTlFKXQGMA35spG0ajUbTFSilgkqp8UA/YIqIHOe3q5R6Wik1WSk1ua+kV4iNjcVitWJqrWm/QSiAxdeIO9ndvYZpuoysrGxKWi0UNplP3RjwBGBDlR273UZSUrs5+vscRs/EzQUeU0qNVUr9USlVAaCUagG+baxpGo1G03UopeqA5cDlBpsSFcTExPC1a6/FVrUHS+0xKbKUwn7wS/A2ceONNxhjoKbTuWfePFwJbn67PpHPSuycbIW0sMnMb9Ynsbveyk9+8lNcLlf3GRrFGC3iSpVSn7XdISKPACilPjLGJI1Gozk1IjJURD46lMZIRMaKyH+dok+qiCRGXjuAS4GdXW9tz+D2229n2LDhOPM/Q4L+w/ut5VuxVe3htttuY8KECQZaqOlMMjMz+dvfn2bE6HE8s9PFX7e78AaPb7e8xM6v1ybSbE7kD3/8I5deemn3GxulGC3iZrWz74put0Kj0WhOn38APwf8AEqpzYQj7k9GJvCJiGwG1hD2iVvUpVb2IOx2Ow8//DuSEuMxe+pAKUwtNcQUreX888/XCX97IW63mz89+ii33347qyti+NPmhKNm5D4ojOG5nS7GTZzE8y+8yOTJk40zNgoxRMSJyN0isgUYHinSfeiRD+iMjhqNpifgVEod62V/0iRWSqnNSqkJEReS0UqpB7vQvh6J2+3mRz/8IYSCSNCHtWIndrudn/zkJ5hMRs87aLoCs9nMbbfdxn3338/OWgtNgXCOkUAI/rU/lqlTp/L73z+i/eDawaiyW68AS4D/Bv6zzf5GpdQJvFo1Go0mqqgSkUFEUoSIyFyg1FiTegdmc8TRXSkkFMBiseqUEr2cgwcP8u9/h1PKmCJ54gSIMUPBwQOsXr2aqVOnHvnb0ADGiTillDogIvcc+4aIJJ9MyInIc8AcoEJXY9BoNAZyD/A04RWFYiAf+LqxJvVsGhsbeeutt3j55ZfBZEZZ7PjSRtC8cz/fufNO7r7rLmbMmIHFost+92SUUpSVlbFr1y62bdvG2jWryT9wEKsZbhzUzKZqGwBmE/xgTD1/26F44IEHSIyP45yp0xgzZgwjRoygf//+2Gw2g6/GWIyciZsDrCP8K7ZNfmYUMPAkfV8A/gK81FXGaTQazalQSu0HLhWRWMCklNJp5M+Auro61q5dy4oVK/jiiy/w+/0EkvojvlYQIeRKpWXY5ZQf/IJf//rXJCUnc/FFFzF9+nTGjBmD3W43+hI07RAKhaitraWsrIzS0lJKS0spKiqi4OBBDh48QEurBwCrCQYn+LlliI9z073E29RhEQfhig2PTKlhQ5WNtZVeVn22lKVLlwJgNpvol51Nbv88cnJyyMrKIiMjg8zMTNLS0rBarYZce3diiIhTSs2JPA84g76fiUheZ9uk0Wg0p4OIuIFfATMAJSIrgQeVUtXGWha9KKUoKSlhx44dbN26lY0bN3HgQD4AYnPgTRqMP3UoIacbx873DvcLxmXQOOqrWOoK8VftYcFbb7NgwQKsVisjR45i7NgxjB49muHDh5OQkGDU5fUJlFK0trZSXV1NTU0N1dXVVFdXU1VVRXV1NZWVlZSXlVJVVU0geHSoabIDMmL8TE8OkusKkBsXpL8rgOUUro4WE5yT5uOcNB9KNVHRaiK/MZxfrqh5H/vWF/DF50IwdHS/5MQEUtPTSUtLx+12k5KSgtvtPuoRHx/fo30tDZ2TFpHzgI1KqWYRuRWYCDyulDrrKscicidwJ0Bubu7ZHk6j0WiO5TXgM+C6yPbXgdcJpw1pFxHJIbyKkAGEgKeVUk90sZ2GUV1dzc6dOw8/duzcSVOk7qWYrQRiUwlkTyQQn00o1g1yki9TMRFI6k8gqT+eoB9zYymWhlI27i1k0+ZNHAppzMjIZMSI4QwfHn4MGTIEp9N54uNqAPB6vdTU1Bz1qK2tpba2NrJdTU11FTW1dXi9vuP6W82QHAOJ1gAD7EEmZYdwxwRJiQmR6giREhM8qirDmSIC6c4Q6U4f09KP7A+GoNZrotJjorLVTI3XRLXHQ3VlJfnFu1jvM9HsO96v0mw2kZiQQHJyMsnuFJKTk0lKSiIpKSm8r83D5XIhbQu7RgFGOxb8FRgnIuOAnwLPAi8DF5ztgZVSTxP2V2Hy5MnaI1aj0XQ2yUqph9ps/1ZEvnqKPgHgfqXU+kiN6HUislQptb3rzOwelFIUFBSwbt06tmzZwuYtW6muipQME0E5kvA7MwkljyHoSiXkSDq5aDsZZivBxFyCibl4AYJ+zM1VmJsrKWyuovzLtXzyySeRUwv98wYwbuwYxo4dy6RJk0hMTOyUa+4JKKWoq6ujvLyciooKysvLD8+aVVVVUlVZSU1NzeHlzWNx2YREW4h4q588m2JsWohEW4hEe4gEmyLRFiLJHsJpURipb8wmSHGESHGEGJHUfpC4LxgWenU+E/U+E3VeE/U+od7XQn1tORUVZnb7zDR4IdiOarBazCQlJeJ2p5KSmkpKSgopKSmkpqaSnp5OWloaqamp3eqzabSICyillIhcAzyhlHpWRL5hsE1RTX5VM+9vLWNLcR37K5upavJiMZlIibPTL9HB6OwEkmP7pqOnUorSeg8lda1UN/tQSmExm0iNs5OT5Oyzn4umy/hERG4C/hnZngssPlkHpVQpkQhWpVSjiOwAsoEeK+I8Hg8LFixg0eLFlJaUACB2Fz5nKsGcAYRiUwk63WDu+NeNveDfmFvCq9KOne8RcibjzZ124g5mK8H4TILxmWGbAPG3YooIu73VFRxcvIR33nkHEWHMmLFcf/1cZsyYEXUzK2fL3r172bBhA7t27eLggQMUFxcdJ9CsJkiMgSRrgCx7kJHuEAm2sChLsIVIsIe346zqlEudnc383U72N5gJKfjl6gSGJfq5dWhLpxzbZj40ixc6abuQguaA0OAzUe8V6iOir95nos7XTF15KfuLrKz3mmj2H632zGYTmRkZ5OT2Z8iQIYwcOZJJkyZ1mX+e0SKuUUR+DtwKnC8iZsI1VDVtUEqxfHclT328l7UHawHIczsZnObCajbhD4YoqWtla3E9728rI88dy8XD0xiUGtvrBqj2aPIG+GJfFZsK66htCWd5NwmYRAiGFIf+xbISY5iUm8TkvGSs5p7rA6GJGr4L3AfMj2ybgGYRuY9wBH78yTpHfHsnAKu60MYu58GHHuKLzz8nGJeJv/90AvHZqJiTXvopMbXUHK7YYGksO3nyvROgrA6CiTkEE3MAaFUhTM3VWOoL2bx7P5t/+Ut+9rOfccUVvSe//B/+8AjvvbcECPuf9XP4OM8dJN0RXtp0x4Rwx4RwGTxrdjJ21Vkx2ZxcPWcOixYtgrqmbrfBJBBnVcRZg2THnrytNwg1HhPVXjNVHhOVrSbKWvIp3FLEl19+CUCKO5kXXnypS0qFGS3ibgRuAW5XSpWJSC7wx5N1EJFXgQuBFBEpAn6llHq2yy01iL0Vjfzira2syq8hO9HBA1cOZ87YLLISHQC8suqI+2Bdi49NRfX8e381z32ez6DUWK4Zn02Kq3dGb/kCIT7ZVcEX+6oIBBVD0l1cPDyd/u7wrJtJhEAwRGWTl30VTWwqqmfh5lI+2VXJJSPSOCcvGVO0jmSaqEcpFXemfUXEBSwAfqiUamjn/R7j0+v1egEIWeyoyCM6EZTFhrLYCZmtmDhie2+hpeXIjFWsOYTTqoi1KGItIWItCpdF4TBHr4ADaAkIc+bMYd68eSil+GzxP0/dyUBsJoi1KlqDIWItQotVEWtVxPpDCCYU4dnqrspzKH0hgeLkyZPV2rVrjTbjtAiFFE+v2M+fPtyF02bhx7OHcePkHGzHzG23FXGHCARDrD5Qw9Lt5QRDitmjMjh3kLvDs3K3TI3uL41XVhWwu7yRtzcUU9fqZ3xOIhcNSyM17tRfHvurmli2vYID1c3kJDm4bmI/0uJjov6aNaeHiKxTSnV5fR4RuRo4P7K5vCMltETECiwCPlBKPXqq9tE+fjU2NjJ//nzeW/I+jQ31AChnMv7YNIKuVIKxaeGZudNQDo6d72FpLDu8HYjLoHX4ladnWCiAqaUGc1Ml5uYKbM2VKG94VicvbwDXXz+XK6+8sletVoRCITZu3MjGjRvZuXMnBQfyKa+sOkpACJAQIyTZAiTagiTawkunifZjllRtIWwG5NX95eoEygMurrrqKhYvXky6pYmHptR3ux3BEDT6j1lKjSyt1vlM1HrN1Pkt/H/2zjw+rqru/+/v7DOZ7HuTJmm673ShLQUsighiAS3wAAL+VBZBUAQFlEcfWfRREHxcKwKWXRSk7K0IhUJpWdp0S7pvaZO0afZ1Mvv5/TGTNN2StM1kZpLzfr3u6869c8+9n5u5OfOdc75Lkxt8R8zOJic5yc8vYPSYMUyYMIGzzjrrhINr+tqHRTs6dQHwIJBF6NkS+jANMdhpaPdy2z/WsWJHHRdOzuH+Syad0GiayWhg7sgMJuUl8+q6Kt4qPcDeBheXTsvDao7vbNcef4C3Nu5n5a56shKt3Hh2MUUZvYx3d6M4w8kNZyewvqKJt0oP8OflO5k/eRhXzRo+qDpzTeQRkV8DpwPPh3fdJiJnKaV+3EMbIRTAtaUvBlw8kJiYyM0338wNN9zA1q1bKSkpYePGjZRt2oSndisAYrLgd2TgT8gI+cg5s1Bme/+JUApxt2Bsr8HYVovZVYe4GkCFvl3T0tOZOvd0pk6dysyZM8nPz++/a8cQBoOB6dOnM3369K59Ho+H6upqamtrqa6upqam5lBgQ20Ne+rqaGo5dopDu0lItiqSzT6SzCFfuZRuRl6KNRTkkGRRXVUWThW7SeFuC/lZAthT+m+gSanQSF9T2NetK8DBa6A5HOTQ4jfR7DXS6jnkitMdh91GamoqmcOyKMzIJD09naysLLKzs7uWpKSBM2GiPZ36EHCRUmpLlHXEDLtq2/j2U6s50OzmVwsmc+XpJ29cJNnMXDOnkBU76vjPpmqqm91cPbuA7CRbP6seGGpbPdz0XAklexuZU5zOlyflnJRvm4gwrSCVkZlO/lVSySvrq7CYDNz/1YlYTfFt5GoGlAuB05QKWQoi8jSwjsNLCR7JmcC1QKmIrA/vu0cptaSHNnGByWRi0qRJTJoUKqQTCATYu3dvV3qRzVu2sHv3JoKducPsyXidOQSS8vAn54HxxNyhxduOqakCY0sVlrYalK8jdFq7g3HjxjJ+/JcYN24c48ePJzMzs1/vNZ6wWq0UFhZSWFh43GP8fj9NTU1dud8OTy3SQGNjAwfr6tjS2Ehb+9FBBiKQYoVUS4BUS4A0W4A0a8j/LtMeJNMWINEc+WlcTwBqO4zUug3Uuw3Uu43UewzhiFQzjZ5QhOqRWMxm0lJTSMvJoKBbSpHONCOpqamkp6eTmpqK3d6PPz76gWgbcQe1AXeIVbvquOnZEsxGAy/cMIcZhade7NcgwrwxmQxPtfOP1RUsXL6TK2YWMGFYfA12llY2c+Oza2hy+bhqVgGT8049oWeS3cw3zyxi2ZYa/rmmgl21bfzlmhl9mpbVaMKkAJ1lAnt9KJVSH3F4hZpBi9FopLi4mOLiYi68MDQV6vF42L59O5s2bWL9+g2s37Aed+02xGjCk1qMN28aytLzyLqxtRrr/nUYW0JlajMyM5l57jwmT57MxIkTKSgoiOvkrdHAZDJ1pcvoDY/HQ2Nj41HJfjsT/dbWHGRLTc1REbE2k5Dr8JNr95OX4KcwMcCIJD+J5hMfafMFYW+rib2tRiraTRxoN3LAbabpiCwpZpORjIx0MgtymJSZeVjC37S0tK51QkL8BgFG24hbIyL/BF4FujxMlVKLoycpOry4uoJ7XillREYCi755OsPT+jc5ZXGmk1s/P4rnPt3L85/u5cuTcjhzVEZcPLivb9jPnS9tIMNp5V83n8GGiv7zjzCIcN6EbC6dkcePXtrAJX/6iMe+MZNJ/WAkagY9vwLWicj7hAyzzwE/ia6k2MZqtTJ58mQmT57MlVdeid/vp7S0lGXLlrFk6VIsrVW0TrrsuO2NrQdxbF1Calo6X/v2t5k3bx4FBQVx0Y8NFqxWKzk5OeTk5PR4XFtbGzU1NVRXV7N//37279/Pvn372Lm3nFW767qOy3cGmZ7h5vPDPKTbjp/6wxeEldVWPquxsq3ZjC88ouZMcFBYWMScggLy8/PJzc0lNzeX7OxsUlNTB71BH20jLglwAV/qtk8BQ8aICwYVD729jUc/2MXZozP489XTSbJFJstKkt3M9WcV81JJBUvKqqlr83LR1GEY+8uZoZ8JBhW/e3c7f3hvJ6cXpfKXa2aQ4bT2qxHXyfwpwyhKT+DGZ9Zw+aMf87srT+P8iT13UpqhjVLqBRFZTsgvToC7lVLVPbfSdMdgMJCUlEROTg5mkwmPzwPBY8x3hZHwlGlqSjKZmZk4HA5twMUoTqcTp9NJcfHRpdBbW1vZuXMnmzZtomTNGt7csIGlFQ7umnrsvt0TgHtLUqlqM5CfN4xLvjCXKVOmMHbsWLKysob0MxBVI04p9a1oXj/adHgD3P7P9fx7UzVfn13AfRdPjHj+MovJwFWzCnhn80E+2F5Lo8vLVbMKsMVYwIPbF+CHL23grY0HuHxGPr/82uSjInP7m0l5ybx665nc+EwJNz1Xwo8vGMeNnyse0h2E5mhEZPoRuyrD62EiMkwptXagNcU6nVUDKioqqKiooLy8nJ07d7J12zY6wmkxgok5dIyYAabjJ+X2pxbiLpzL7v2lPPjggwBkZmYxduwYRo4cSWFhIcOHDycvL0+X2ophEhMTmTZtGtOmTePKK6/khRde4G9/+9thhe+7c8BlpKrNwKhRo3ifcMs6AAAgAElEQVT44YeHVMWN3oh2dOoYQqW3spVSk0RkCnCxUuoX0dQ1ENS0uLn+mTWUVjXz06+M57qzRgyYsWAQ4fyJOaQnWHh1fRWPfrCL/3dGEakxUtGgpsXNDc+WsLGyiR9/eRzfGUBDKivRxj9unMMPX9rAr5ZuZXdtOw98dVLEDUhNXPEIoRmDzofySKeeLwysnOjj8Xiora09VAD94MGuddX+/RysPojHc8hhSYwmArZU/AnDCWRnEUga1qsvXKih4Msahy9zbCh9SGs1+9tqqC0p46OPPjrsUGdiEsOG5TIsN5esrKyuCMLuPlEDWR5JcwiXy8X69ev59NNP+WD5+zQ1t1CUFOS8/A52Nh+dfrHQGeC8/A7e2bmTyy+7lNNnzWbu3LnMmjWLrKysKNxB7BDtJ/hx4E7grwBKqY0i8ndgUBtxGyqauPHZNbS6/Tx27UzOm5Dde6MIMLMojdQEC89/upeFH+ziG3MK+90X70TZtL+Z658OBTA8es2MqExp2sxG/njlNIozEvjjezvZ1+Di0WtmkOzQxUQ0ANwNVIRLaBEuFXgpUA7c21tjEVkEzAdqlFKTIifz1FBK4XK5upzWOyMVD3Nkr6ujvr4BV/vRWfXFbCVoScBvdqJSRhK0Ognakglak1BW58nXTQUQIZiQTjAhHR8TcUMoL5y7BYO7GYOnBa+njaYDrWzfV4142lFB/xGnEJKSU8jMyCAjI5309ENL96Ln6enpWCyx8QM3XmlqamLTpk2UlZWxYf06tm3fTiAQxGoSpqS6OWuKh6npvuOmKRGBa8e4+EKeh+X7raxZt4pVq1YBMCw3h6mnTevytczPzx9SsyfRNuIcSqnPjviDn0yFlbjhlXWV3P1yKVmJVl6+eS7jc6MbJToy08lN80byzMd7eXzFbr56Wl7UEt++sWE/d7+8kWS7mZduOiOqwQUGg/DDL41lREYCP365lK8tXMmj185gTPZJJ+nXDB4eBb4IICKfIxTg8D3gNOAxQjVUe+Ip4E/AM5GT2DN+v5+6ujpqamq68oZ15g6rra2jtq6OxoYGvN5jVDQwGBGLg4DJTsBkRyXko1ISCFocKLMDZUkgaEk44ZQhp4zBRNCRRtCRdvR7SiF+D+JtR3ztGLwuxOfC43VRX+vCeGAHBt9GlPfYNTodCU7S09PIChc974xyzMzM7Cp+npKSMqSMh56or69n3bp1bNiwgQ3r17GvIuRxYDRAcaKfC/O9TEj1MTrZf0IJhfMSAlw92sXXR7mobDeyqcHM1qZ9rFhWzdKloXJjKUmJTJ56GlOmTGHGjBmMGDFws1zRINpGXJ2IjCQ8HSEilxEuDj3Y8AWC/ObtbTz24W5mj0jjL9fMiJmC7FmJNm6aN5IXPtvHv9ZWElSK+786Cad1YB4Pl9fPfa9v5p9rKphWkMJfr5lBVozkslswPZ/8VAfffb6Ei//0EfdfMonLZwytX3qaozAqpTrTilwBPKaUehl4uVvut+OilPowXDc14ng8HrZs2cKuXbsoLy+nsrKSisoq6utqjyoDJEYTypJAwGQnaHagUkeF1hYHymxHmUP7MVpOqPpCTCCCMttQZhuQznFDJ1QQ8bkRXwfic2EIr70+F80tHextqMC4aRvK4+pKJNyJ2WwhJzeH/Lw8hg8fzogRIxgzZgzFxUPDr7a9vZ0lS5bw7rvvsG3bdgAcZmF0kofZxSGDrTjpxIy24yECw50BhjsDXFDgJqhCfnPbm0xsb3azrWQFK1asAEJ1S+ed83kuvvjiHnPlxSvRNuJuIfTLdZyIVAF7gKujK6n/2VnTyu3/3EBpVTPXzCng5xdFPoDhRHFaTVx31gje31rDq+urWFfRxB+vmhbx0bAtB1q49e9r2V3XznfPGcnt542Jub/NrBFpLPn+2fzgn+u5618b+XhXPb/46iQSBsjI1cQcRhExKaX8wLmEa5yGiYmHIhgM8vvf/57XX3+9y1gTsw2/NYmgJZFgTi7K6iRoSTg0cmYwx45xFvBis9mYHy6C3hbwDsx1xRAyWi0Oejb2FOJ3I952DN52xNuO19PK7pY29tVtw/Dp6q7pW6czkdtv/wHnnnvuwNxDFKipqeHmm75DfUMjI5ICXFbsYXKal8LEQL9VcugJg4RG6fISAnw+zwO0U+c2sKnBzPo6L6+98jKvvPIK99xzD1/84hcjL2gAiUqHIyJ3dNtcArwPGIB2Qr4lg6IcTTCoeObjcn61dCsOi5FHr5nOBZNyoy3ruBhEOHd8Njd8rpjb/rGOry1cyXc+N5JbvzCq36NX3b4AC9/fyaMf7CbFYea562Zz5qjeE01Gi6wkG89eN5s/vbeT3y/bztp9jfxqwWTmjoxdzZqI8QLwgYjUAR3ACgARGQX0S/4bEbmRsHFYUHDi7g2NjY288cYbKKVQRgu+zDH4k/II2pJCAQSn4o82AIjfy/yLDxVBf/GNt6Mt6XBEDo1OJnTrAwJ+DJ5WDO4mTI3lmBv20NbWysuLFw9qI27Lli3UNzRSlOjn5gmt5DiOn+9toMiwBZk3zMPpWV7eqbDx8h4HK1eu1EZcP9HpWDSWUI6l1whFel0LfBglTf3K5v0t3PvGJj7b08Dnx2by4GVTyEqMjSnC3phTnM7S2z7HL97czJ/e38kr66q447wxfHVa3innlAsEFa+uq+L/3t1OZWMHXz1tGD+bP4H0E6gNGy2MBuG2L45mdnEad7+8ka8//ikLpudx1/njyEmOj89Wc+oopX4pIsuAXOA/6tC8pIGQb1x/XOMxQrMUzJw584RT2qenp/PII4/wxhtvsG79Bhqry7BUl4XeFAPYnPhNh0bhlMVB0JwQnjp1hKYdo2joKZOFN998E6UUb731FsoUA+lCgn7E2znF2h567XUh3naMvtCiPO1dh4sI+QUFnDFnDpdeemkUhUeeuXPnctFFF/HWW29x1yep5DmDTEr1MCHVx5hkPwknUZXhZPEHobzVxJZGE5saLWxvNuMPwtixY7juuusGTMdAIUf6RQzoxUX+A1yqlGoNbycCLymlLujP68ycOVOtWbOmP095XPbWt/PH93ayeG0lyXYzd18wjitOof5pb/z90339fs7ugQ0f76rnf5dsobSqmcJ0B9+cW8SCafknHKnZ7PKxeF0lT68qp7zexaS8JO758njmnsToW6TvuS90eAP88b0dPLFiD0aD8I25hVx/VnHESnb19z1HK3hloBCREqXUzGjrOB5hn7g3+xKd2h/9V2NjI3v37qWqqorKykoOHjxI9cGDVFcfpLGh/ij/OEQQs52g2R72kbOHR55CRl7XtsUBhv4fC7Du+wRDazXi96JMFoKJOXgK5vT7dVAKAt4u37cuXzjvoW1ToAPxdqD8Rwd5mMxmMjMzyc3JISsri2HDhpGXl0deXh5FRUXYbEPrx11tbS3Lly/n449XUVpais8XmlLOcwYZneRldLKfMck+suzB487cP7fdwb62Q89UgdPPNWOOHXAC0OYTdjSb2NlsYnuzhd2tpq5qDiOKCpk1ew7z5s1j/PjxceWb2Nc+LNpG3FZgqlLKE962AhuUUuP68zqRNuKUUqwub+Tpj8v5d1k1JoNwzZxCvv+F0RFPSzEQBo1Sin+XVfP4it2s3deE2SicPTqTs0ZlMKMwlZFZzqOCINo8fnbVtLFmbyMf7ajlo511+AKK6QUp3HB2MRdMyjnpf6hYMOI6qWhw8fB/tvHGhv2YjQYumjqMq2YVML2gfyPVTvSefYEgLR0+mt0+Wjr8ePwBfP4g3oAiEAwyOT8FowhGQyilSpLdTJLNTJLdRFaijZxk24AFtkSCWDbiROQF4BwgAzgI/Fwp9bfjHR/p/quz+HldXR21tbVHpRGpq6unvqGe5qYmgsGjp8nEbEWZHfhNISMvaHGgLM5ua2ePCXwjhlIho8zb1uW3Flq7MPhdmPwd4HEdlXoEQkEKqWmpoXqi4ZQjnVGpnZGpGRkZJCYmxpVhMJB0BtWUlpZSVlbKprIy2tpDxliKFcYmh0bqJqf7yOih3NaRuP2wqdHM5kYzW5utVLSGRoyNRgOjR49m0qRQqpEpU6aQmnrq9cejRbwYcf8N/BfwCqEI1a8B/1RK/aqXdhcAvweMwBNKqV/3dHwkOsFAULG+opFlW2p4Y+N+Kho6SLKZuHJWAdefNWLAoisH2qAprWzmjY37+XdZNfsaDv06Srabu770W90+WtyHOsaCNAcXTMrh4qnD+iVQIpaMuE721LXz+IrdvLauinZvgLwUO+dNyGb2iDRmFKWe8lR693v2BYI0d/ho7vCFDLVuS0uHj6YOHy5vD6WLODo77bFItJrITraRk2QjN9nGsBQ7eal28lPsDEuxk5tiw2qKrUofncSyEXeiDORMQk8EAgGam5u7jLxjFT+vqa2lsaHhKGNPTFaCVid+SyJBa2IoX5w9haA99dRSkYQNNUNHIwZ3UyhPnKcFk7cdPK1HlfAyWyxkZGSQlZVFZjejrHtuuPT09LguiB6rBINBysvLKS0tpbS0lHVrS6hvaASgIDHI7MwOzs71kGI9unfyBWFNjYVVB61sarTgD4LVYmby5ClMmTqVKVOmMG7cuEE18hkXRhx0lbA5O7z5oVJqXS/HG4HtwHmEyt2sBq5SSm0+XptT7QSbO3xUNrrYU9fOxspmNlQ0UVbVTLs3gNEgzB2ZziWn5XHh5BwcloEdvYimQbO/qYMNFU3srmvnYIubdk8AhcJpNZGTbGNEegKnFaSQm2zvV32xaMR10ubxs7T0AEtKD7BqVz0ef+jLrDDdweisRPJT7eSl2MlJtuGwGLGbjVjNRgwCXn8QT3hpcnmpb/dS3+ahvs1L2f5mWt1+mo9joNnNRlIcodG0ZLuZJLuZlPA6yW7CZjZiMRowGw0YDcJVs4YTVKEfIx2+AC0dPlrdfpo6vNS2ejjQ7Ka62c3BFjcHmt0caO6gptXDkd1FZqKVvJTQPeWl2hmWbCMv1UFeip0Mp4UUhyUq1S60ERc9AoEAjY2NXTnoqqurOXjwIAcOHKCispLqAwcIBLo9w/Zk/BYnhwpg9JFgALOnCeXt6NplszvIy8tjeH5eV5H27OxssrOzyczMxOl0auMsRlBKsW/fPj755BM+/OADNm3ejNEAwxKCRz0JjR4DrV7IyszgnM9/gTPOOINJkyZhNg/eBOx97cOiPl8SrjN4IrUGZwE7lVK7AUTkH8AlwHGNuL7y4uoKPitvoMnVOaLh5UCzm9Zuo0oWo4EJw5K4bEY+M4vS+NzozCGbyX9YeERGcwin1cTlM4dz+czheP1BNu1vZk15IyV7Gymvb+fT3fW0evqez9pmNpCeYEUkNNpZkOboMtKSw0uSzXzChpKIYJRQsIbFZCDZ3vsz7PUHqW52U9nkYn+Tm6rGDqrCrzcfaOGdLQfx+o+eFkmwGElxWEhNCGl1WIzYzMYuI9ZmMWIUwSCCSEibEMp2YRAhEFT4AkHOHZ/FjMJjJHLVxBRGo7FrunHChAlHvR8IBDhw4AB79+5l165d7Nixg+qDB0/4OiajkaKi0xg9ejQjRoygoKCAtLQ0baTFCSJCYWEhhYWFXHHFFVRWVvLaa6+xf//+o44dY7dz/vnnM2PGDAyG2I6sHmiiPhJ3ooQTAl+glLo+vH0tMFspdesRx3WF6BOKgt3WjzIygLp+PN+pEEtaILb0xJIWiC09saQF+l9PoVIqsx/PFzVEpBbYG20dUSLWnlPNwDHUP/s+9WFRH4k7CY71M+soS7R7iH6/CxBZEytTNbGkBWJLTyxpgdjSE0taIPb0xBKDxRg9GfRzMXTRn33fiMdxyUpgeLftfODo8VeNRqPRaDSaQUw8GnGrgdEiMkJELMCVwOtR1qTRaDQajUYzoMTddKpSyi8itwJvE0oxskgptWmAZURkmvYkiSUtEFt6YkkLxJaeWNICsadHExvo52Looj/7PhB3gQ0ajUaj0Wg0mvicTtVoNBqNRqMZ8mgjTqPRaDQajSYO0UZcD4jIBSKyTUR2isiPj/H+N0WkVkTWh5frI6hlkYjUiEjZcd4XEflDWOvGcCWMaGk5R0Sau/1d/ieCWoaLyPsiskVENonIbcc4ZiD/Nn3RMyB/HxGxichnIrIhrOW+YxxjFZF/hv82n4YLs0eEPuoZsP8pTWzTW/+rGZz09v2iOZwh4ROXkZGhioqKoi1Do9EMECUlJXWDJb+a7r80mqFHX/uwuItOPRmKioqIp9qDGo3m1BCRQVPhQPdfmqFGMBjkX//6F/PmzSM7OzvacqJCX/uwIWHExRMnU9y9v4q3azSaU0NErEopT7R1aDTxTEVFBQsXLqSsrIz7778/2nJiGu0Tp9FoNCeBiCw6YtsJLImSHI1m0NDU1ARAeXl5dIXEAdqI02g0mpOjSkT+AiAiqcB/gOeiK0mjiX8OHjwIwFDw2T9VImLEich8EdEGokajGbQopX4GtIjIo4QMuEeUUk9GWZZGE/dUVVUBEPD7oqwk9omUoXUlsENEHhKR8RG6hkaj0Qw4IrKgcwE+A+YA6wAV3qfRaE6B6upqAOobGqKsJPaJSGCDUuoaEUkCrgKeFBEFPAm8oJRqjcQ1NRqNZoC46IjtdYA5vF8BiwdckUYziGhubgbA4/Hi8XiwWq1RVhS7RCw6VSnVIiIvA3bgB8DXgDtF5A9KqT9G6roajUYTSZRS3zqV9iJSDrQCAcCvlJrZH7o0msFCIBA45mvN0UTEiBORi4FvASOBZ4FZSqkaEXEAWwBtxGk0mrhGRGzAdcBEwNa5Xyn17T40/7xSqi5S2jSaeKb7yJvFYomiktgnUj5xlwH/p5SaopT6jVKqBkAp5QL60sFpNBpNrPMskAOcD3wA5BMaYdNoNKdAQkICAFaLGZNJp7PtiUgZcQeUUh923yEiDwIopZZF6JoajUYzkIwKR6i2K6WeBr4CTO5DOwX8R0RKROTGiCrUaOKQzinUoFI6zUgvRMqIO+8Y+74coWtpNBpNNOjMf9AkIpOAZKCoD+3OVEpNJ9Qn3iIinzvyABG5UUTWiMia2trafhMcb+zatYsGHaE4pFBKsW3rFgB8Pj979uyJsqLYpl+NOBG5WURKgXEisrHbsgfY2J/X0mg0mijzWDjJ78+A14HNwEO9NVJK7Q+va4BXgFnHOOYxpdRMpdTMzMxea2APSvx+P9dddx0/+9nPoi1FM4C89957VFRWccXIdsxGePLJRXo0rgf6eyTu74TC7F8LrzuXGUqpa/r5WhqNRhM1lFJPKKUalVIfKKWKlVJZSqlHe2ojIgkiktj5GvgSUDYQeuONnTt3ArBp0yb9JT5E2Lp1Kw//5iGSLUEa3Aa+VtTOihUf8dxzuhDK8ehvI04ppcqBWwg5+HYuiEhaP19Lo9FoooaIZIvI30RkaXh7gohc10uzbOAjEdlAKFHwW0qpf0daazyyefPmrtc1NTVRVKIZCEpLS/nhHbfjFDcZtgAV7SYuLHAzN9vD3/72N5566iltzB+DSIzEAZQAa8Lrkm7bPSIiF4jINhHZKSI/Psb73xSRWhFZH16u70/xGo1GcwI8BbwNDAtvbyeUE/O4KKV2K6WmhpeJSqlfRlhj3NLW1nbM15rBx/Lly/nhHXeQKC5+cloT5rBlYhC4YXwbZ+e4eeqpp3j44Yfx+/3RFRtj9GvsrlJqfng94kTbiogR+DOhoIhKYLWIvK6U2nzEof9USt16ymI1Go3m1MhQSr0oIj8BUEr5RURnJu0n7HZ712ubzdbDkZp4RSnFs88+y6JFixidHOAHk5tJtBw+2mY0wPXj20m1BXn9rbfYX1XJvffdT3JycpRUxxYRiU4VkTPD/h6IyDUi8lsRKeil2SxgZ/iXqhf4B3BJJPRpNBpNP9AuIumEUoYgInOA5uhKGjzokbjBjcvl4t5772XRokXMzfZw92lNRxlwnYjAZcUd3Di+ldKNG7jpOzeye/fuAVYcm0QqxchfAJeITAXuAvYSSozZE3lARbftyvC+I7k0HPH6LxEZfryT6RB9jUYTYe4gFJU6UkRWAs8A34uupMFDZxH0I19r4p+Kigq+e/NNfPjhB1wxsp3vTGjDYuy93Vm5Xu6Z1kxH00Fuvvkmli3TaWcjZcT5VcgD8RLg90qp3wOJvbSRY+w70ix/AyhSSk0B3gWePt7J4jVE3+MLsLu2jY931bFyZx2ryxuobHThDwSjLU2j0XRDKbUWmAfMBb4DTFRK6VRK/URDQwPKEsrc39jYGGU1mv7i448/5js33kD9gX3cObWFrxS6kWN9+x+HUcl+7p/RQJHdxQMPPMCf//znIe0nF6l6Fq1hP5FrgM+F/d3MvbSpBLqPrOUD+7sfoJSq77b5OPBgP2iNCbYfbOWJFbtZvLYKf/DoIWWLycDE3CROL0qjKCMhCgo1Gk13wrVTvwucRegH5woReVQp5Y6usvhHKcWu3XvwJ2RiCXh1wtdBwuLFi/njH/9AYWKQ709rJsN2coMTKVbF3ac188JOBy+99BIVFfv4+c/vPcyPcqgQKSPuCuDrwHVKqeqwP9xvemmzGhgtIiOAKuDK8Dm6EJFcpdSB8ObFwJb+lT3wdHgD/G7Zdp5YsQeL0cD0glTG5yaRm2zDbDTQ4Quwv6mD7QdbKa1qZl1FEyMyEjh3XBbFmc5oy9dohjLPEEqh9Mfw9lWE3EYuj5qiQcK7775LQ30dkmTFm5TPf/7zDldffTVZWVnRlqY5SZ5//nkef/xxpmd4uXliK9bjTJ8+t93B3tbQm/+7NokCp59rxriOOs5kgGvHuMhLCPD0p59y91138usHH8LhcETyNmKOiBhxSqlq4LfdtvcR6vB6auMXkVsJhewbgUVKqU0icj+wRin1OvB9EbkY8AMNwDcjoX+gqGhwccMza9ha3coVM4fz4y+PY2nZ4b4fdouRtAQLk/KSmT9lGKvLG/hwey1PfLSHERkJnDc+O0rqNZohz1il1NRu2++H879pToGSkhIe+s1vUAYzBP14Cufg3vw6d951F7995BHS09OjLVFzgjz33HM88cQTzM32cMP4Now9OHLtazPREQgdsLWpd4+vL+R5cJgUj5aV8eO77xpyhlykolMXiMgOEWkWkRYRaRWRlt7aKaWWKKXGKKVGduZPUkr9T9iAQyn1k3BupalKqc8rpbZGQv9AULK3gYv/9BH7mzp48lun8+BlU0hNsPTYxmIycOaoDH50/ljmT8mlrtXDYyt2860nP2Pz/l7/vBqNpn9ZF45IBUBEZgMro6gnrnG5XDz55JPceeedeE1OgvZUEEHZkmkfdS77Kiq57vobWL58OcGg9hGOB5RSPPHEE10G3I0TejbgTpY52V5untBKWVkZP7zjdpqbh06QeKSmUx8CLlJKxf10ZyRYubOO659eQ06yjUXfPJ0RJ+jjZjYamDsyg5mFaXy8u56Pd9Vx4R9WcNHUYdx4djGT83X+HI1mAJgNfENE9oW3C4At4frRKhyApekBn89HaWkpy5cv551336XD5cKXVoy7cC72ne92HRdIGkbbuK8Q3LOCe++9l2F5eVxw/vmcffbZFBUVISfiGa8ZEPx+P4888ghLly7lnGFuvjm2HUMEP6bZ2V5MhhYWbtrG9269hYd+8zA5OTmRu2CMECkj7qA24I7Nih21XPf0GkakJ/Ds9bPISjz5JJYWk4F5YzJ56LIp/PWDXTy9qpw3NuxnZmEq3zpzBOdPzMYUiZ89Go0G4IJoC4g3lFLs2bOHtWvXsmZNCevWr8PjdiNGE97kAryFEwk6j51NIOhIp23CxZga9lBZu41FixaxaNEi0jMymXX6TKZPn8706dP1dGsM4PV6ue++e1m5chWXFLlYMKLjhCJQT5YZmT7unNrM78rg1lu+y2//73cUFPSWoja+iZQRt0ZE/gm8Cng6dyqlFkfoenHBZ3sauOGZNRRnJPDCDXN6nT7tK8l2M3ddMI6bzhnJS2sqeWrVHm75+1rSEyx8aWIO88ZkMrMolQyntavN3z/d18MZj+brswf3P4JG01e61YFuPdb7SqmGAZQTF5SVlfH222/z0cqVNDaE/zy2JLyJhQTy8/En5YGxD19HYsCfPhJ/+kjE246pqYLqlir+/e57LF26FIDikSM5Z948LrjgAh0IEQWUUjz44IOsXLmKa8e0c17+wAZrj0v189/TmnhwA/zwjtt59K+PDWrDPlJGXBLgAr7UbZ8ChqwRt7GyiW8/tZphyXaevW52vxlw3UmymbnurBF8c24R722t4bX1Vby2vooXPgsZbMUZCUwrSGVkVgIVDR2kJ1hIS7BgM/chy6JGo+mkhFB/JoSmUBvDr1OAfcAJlx0crPh8Pu69915WrlyJGM14k/IIFE3AnzQMZT1+dL113ycYXaGMUvatSwg60vAUzDnsGGVJwJc1Dl/WONwqiMHVgKmlih01lexetIinn36aW265hQULFkT0HjWH89FHH7Fs2TIuHeE6KQOuwy/YbDbmz5/Pm2++ScdJ5IAb7gxw55Qm7l8LCxcu5Gc/+9kJnyNeiFR06rcicd54ZVt1K99Y9BkpDjPP3zCbzERr741OAaNBOG9CNudNyMbjD1BW1cKa8gZWlzfy4Y5aXl5bedjxVpOBZLuZFIeZZHtoSbFbSO62rdFoQnTWhhaRR4HXlVJLwttfBr4YTW2xxqpVq1i5ciWeYdPw5kwCY9/6EoOrAQn4ADC1VtPr17gYCCZk4E3IwJs7FfG0YitfxZ/+/GcuvPBCXXt1AHn//fdJtcFFRR0n1d7lF+bPn8+tt96KUooP33rxpM5TmBhgXm4HH3ywnMA992A0Ds7BiogYcSIyhlDprWyl1CQRmQJcrJT6RSSuF8vsrGnl6ic+xWI08Pz1s8lNHthkhFaTkRmFqcwoTOU780L72jx+/vrBLuravCrvRjUAACAASURBVDS5vDR1+Gh2+Wju8FHV5Kbdc3SX+YdlOxiRkcD43KTwksi4nCTsfamVotEMTk5XSt3UuaGUWioiD0RTUCxRWVnJkiVLQARv9oQ+G3D9gbIm4ssYhamlihdffJEFCxbgdOq8mgOBiCBy7BJMfcFhUrz55psopXjrrbfINh27nmpfMA6BeJdITac+DtwJ/BVAKbVRRP4ODCkjbmdNG1c+9ikAf79hNoXpkam0cKL+bQC5yfbjGpS+QJCWDl/IuOvw0eTykZloYWdNG6+uq+LZT/YCoaLExRkJzChMZWZRWqiaRLpDR4pphgp1IvJT4DlC06vXAPU9Nxl8KKVoamqioqKCPXv2sGPHDtZv2EhlxT4QA+7808EU2dmHY+FPLcSfUsCi8NTqhAkTmDhxIqNGjaKwsJC8vLwhlU9soJg+fTrLli2jvNXIiKTACbe3mxTuNjcvv/xyaDvl5Iw4pWBtvY3Tpk0btKNwEDkjzqGU+uyIL/MhVdxsV20bVz3+CaB44YY5jMrqrXRs7GA2Gkh3Wkl3Ht7xTs5LQZ2maHT5qG7uYH+zm/1NHbyx4QAvrglN0SZYTRSmOShMd3Dj54qZOCwZi0lHyGoGJVcBPwdeCW9/GN7XIyJyAfB7QknNn1BK/TpiCk8Bv99Pc3MzLS0tNDc309TURGNjIw0NDdTX11NXV8eB6mpqamrwuA/5PonJii8hA//wWfjTRnTVPx1wDCY6Rn8RQ1stpsZyNuyuprRsE6hDOeYSk5LJyckmOyuLjIwM0tLSSEtLIyUlhdTUVJKTk0lKSsLpdGIw6H6sL8yZE/Jd3NZsPikjrr9o9BqodQlXnTE3ahoGgkgZcXUiMpJwAXsRuQw40HOTwcOu2jaueuwTgkHFCzfOYXR2/BhwvSEipIUDIiYMC+WjCypFbauHffUuyuvb2dvgYvOBFpaWVWM1GZg6PIWZhanMLEplekEqKY7+D+rQaAaacBTqbSfSJlxH+s/AeYTqRa8WkdeVUpsjIBEIjZS1tbXR2NhIU1MTra2ttLS00NLSQmtra9d2a2srTc3NNDeHXrs7ji51FL4JxOIgaLLjNztQScUEsxIJWpMI2lNDRtupjsYHvIc5t7cFvCd9qqAzE68zEy9AMIDB3YLB3YTB3YLX20pjdTs7Kjchvg6U79iO+CJCgjORpKREkpOSSE5OJjExkaSkpKPWSUlJXUbgUPTFS0tLw2Aw0OKNrtHb7Ak9g5mZx05ZM1iIlBF3C/AYME5EqoA9wNURulZMsaa8geufWYNRhL/fMIcxg8iAOx4GEbKTbGQn2Th9RCj7QovbR0GagzXljZTsbeCxD3ezcHloWHx0lpOZRalMyU9hbE4iY7MTSbBG6lHUaPoXEXmD8A/UY6GUuriH5rOAnUqp3eFz/QO4BOg3I27fvn08+eSTVFRU0NzSSkN9HYHAcUZERBCzDWW0EjSaCRqtKJMTlZiOSrWiwu8psw1lsofXVpDIfkGL38v8iw85t7/4xtv9c2KDkaAjlaAj9djvB/2Iz4343YivA/F7wosbj99DY4cHaW3GuK8GQ8ALfi/K7zn2uQCrzUZGRiZWi5m5c+dy9dVXD/oi7SLC2DGj+ah8G6elexmTMvCTcE0eYfEeBwYRRo0aNeDXH0j69ZtTRO7otrkEeJ9Qaa924FK61VMdbCileKmkkp++WkZeip0nv3k6RSdYiWEwkWQzc+HkXC6cnAtAhzfAhsomSvY2sqa8gbc2HuCFzyq6ji9MdzA2O5GxOYkMT3WQn2onP9VBbooNs05YrIktHg6vHcAoIAjsAvoSjpcHVHTbriRU+eEwRORG4EbghJOVLlu2jPfff/+Y7wXNdoLWJJQ1kaDViTI7UEYLymgGoxllMIVeG8woowkMpogbbMdCmSyHObcr0wD5rhlMKKvz8PQnwUDIuAv4kKAfAj4k6AtFzwZ8SMCLeNsxeFrDS0tXZK3H7aaqMvRx7969m7PPPpuxY8cOzL1EkTvvupu777qTX6yF0Sl+5ma5mZ7pJdXau39bgdPPQVfomct2BClw9s0I9ARgc6OZTw9aWV1rJSgGbvvBbeTm5p7SvcQ6/T380TnsNBY4HXiNUJDKtYT8RQYlzR0+7nt9E4vXVTF3ZDp/+vp00iKQBy6esVuMzClOZ05xKOliMKioaupga3UrWw+0sPVgaP3O5oOHDXEIkGQ3k2QzkWgz47SZSLSawmsziTYTV88pIMNp1fnuNAPFKuCXwLcJ5YUTIB94Crinl7bHmmc86ptNKfUYodkMZs6ceUKe3ddeey1jxoxh7969HDx4kKampvDSTFt7G+3trbjrD/b5fGIwgckcMnCMZoJiImgwhgrUG83htQnVafgZLeHX4feNltBisoDB3LepVqMFt6uhy7mdxJS+/wFUEAJexO8NGVjdja3uxtdhBpkfgj4MQT8G5e96T/l9h/nQ9YTBYMCRkEBCRjpOp5PU1JSuadXc3FzGjRs3JAw4gOLiYp559jneeOMN3nzjdZ7eXsnT26EgMcikVA8TU32MSfFhPUaXfc0YF9eMOc5UfjeCCirbjGxqNFPaYGFbsxlfABITErjwoi9y+eWXk5+fH4G7iy1EqZMP3z3uSUX+A1yqlGoNbycCLymlolKmZubMmWrNmjX9ft5gUPHGxv088OYWGto9fO8Lo/n+uaMxnkKBuJOJNB1MBIKK5g4fja5Q+pNGl4/Gdi+tbj+tHh+tbj8u77GnhhJtJjITrWQ6rWQmWslwWklPsISDNCyHvU60mnQU7SBGREqUUjMjdO7/A5zAHd36uCRCI3QupdQPemh7BnCvUur88PZPAJRSvzpem0j0X4FAgI6ODlwuV9e6c3G73XR0dBy2dO7rXLd3Hd+B2x06xuftg9+aCGKyoEw2gkZraDHbUZbQiGAnpsa9GHyHvsiDZgf+1MJD51EKg9+N+FyIrwNjwIsh4AG/O2R49SpDsFpt2Ox27A47DpudhAQHDocDm82G3W7HZrNhs9lwOBxd23a7HbvdjsPh6Fo6t202m+5TjoFSivLyclatWsXqzz6jrKwMfyCAyQBjkn1MTvMyLcPLsITejeU2n7Ch3szGegubmqy0hGeyC4bnc/qs2ZxxxhlMnToVszn+c5v2tQ+LlBG3FZiqlPKEt63ABqXUuF7a9Ri1FT7PM8AMQqH8VyilynvT09+doNsXYGnZAf6yfBfbD7YxOS+ZXy2YzKS8Uy88P9SNuL4QCCraPX5aPX7a3D4mDkumts1DbWu3pc1DfZuHFvexh+ItRgPpzlCARrrTSkY4WKMz6XGS3UyKw0JKZ/Jjh5lEm/mUDHTNwBFhI24HMEYd0XmGgxa2KqVG99DWBGwHzgWqgNXA15VSm47XJlI/Qvub7oZh96WtrY329nba2tpoa2vrCqRobm6mobGR+voGWluaOdHvIpPJREpqGunpaaSmpHQFGyQmJuJ0OnE6nSQkJJCQkHCY0aUNrujS0dHBxo0bKSkpYc1nn7K7PJSyKs8Z5OycDublekgwH3oWggrW1llYvt9GWYOZoILUlGRmnj6LGTNmMGPGjEEZvNDXPixS3uTPAp+JyCuEpgq+BjzdU4M+Rm1dBzQqpUaJyJXAg8AVkbiBI6lpcfNZeQPLt9Xyn03VtLj9jMpy8oerpvGVybn6y30AMRokNMVqNwN2/EFFqsNCqsNyVCCJPxCk3Rug3eOnzeOnPby0eQ7t213bxsbK0H5f4PhfJCKQaDWR4jjC2OtW7SLFbgkbgIfvs5kN+ktj8KCONODCOwMi0qMlopTyi8itwNuEfqwu6smAiyeMRmOX8XSi+P1+3O6+l2gSERwOnZMyHrHb7cyePZvZs2fDd79LTU1NuFTXu/xj02Ze25vAmVlubCaFUrChwUplm4HMjHSuuPJLzJs3jzFjxuiUL2EiVXbrlyKyFDg7vOtbSql1vTTrS9TWJcC94df/Av4kInKsDvVkKKtqZldtG3VtXhraPdS3ednX4GJXbRsHw+O2yXYz547P5vIZ+cwpTsegjbeYxmQ0kGw39Ll0mD8YpMMbCC2+0NrlO3y7c11e137YvmAPT6HFaOgqY+a0mnBaTSRYjSRYQz5+CeHFZjZiMQpmoyG0mAxYjILFZMAQ/sI67DKq+8tDG53/EV3rrv3qqHMc+u9Rx2zTidDpznQoI7uIdO03hHceuV+6HU/4uK5j5PjnNYTb+oNBfAHF8LTjJ6geYDaLyDeUUs903yki1wBbe2scLtO1JFLi4hGTyaQrKgxRsrKyWLBgAQsWLGDnzp08/vhjfFBS0vV+ZmYmP7v9Bs4555xBnbT3ZIlYXgel1Fpg7Qk06UvUVtcx4V+0zUA6UHcKUrt4fMVuXlu/HwiN9qQ6zOSnOjhzZAYT85I5bXgKU/OTMeloyUGLyWAg0WYg0XZiPhVKKbz+4GEGn8sbwH2YERjy52v3+Glo9+L2BfD6g3j8QTz+no1ADfzP/Al8+6yYqC1/C7BYRL4NlBCyd08H7IRmHTQazUkwatQoHnzwoWjLiCtiKTlXX6K2+hTZBYeH6ANtIrLtZESV9H5If5NBPxmlMYa+r/gi5u7rugdD/hR9pLD3Q04OpVQVMFtEvgBMJNQvLVVKLYvE9UpKSupEZG8kzh0HxNxzqBkwhvpn36c+LJaMuEpgeLftfGD/cY6pDDsIJwMNxzpZ9xD9eEJE1kTKITua6PuKLwbrffUnSqn3gPcG4DqDz2u7j+jncOiiP/u+EUvzgquB0SIyQkQswJXA60cc8zrw/8KvLwPe6y9/OI1Go9FoNJp4ImZG4o4XtSUi9wNrlFKvA38DnhWRnYRG4K6MnmKNRqPRaDSa6BEzRhwcO2pLKfU/3V67gcsHWtcAE3dTwH1E31d8MVjvSxNf6Odw6KI/+z4QkWS/Go1Go9FoNJrIEks+cRqNRqPRaDSaPqKNuBhBRC4QkW0islNEfhxtPf2BiAwXkfdFZIuIbBKR26KtqT8REaOIrBORN6Otpb8QkRQR+ZeIbA1/bmdEW5NmaDIY+0RN74jIIhGpEZGyaGuJB4bEdGpGRoYqKiqKtgyNRjNAlJSU1A2W1By6/9Johh597cNiKrChL4jIcOAZIAcIAo8ppX7fU5uioiLioYC0RqPpHwZTclzdf2mGGuXl5Wzfvp3k5ORQjdUhSF/7sLgz4gA/8EOl1FoRSQRKROQdpdTm3hrGO3//dN9Jtfv67IJ+VqLRaDQaTf/jdrv54R23U9/QCMAjjzzCjBkzoqwqdok7nzil1IFwXVaUUq3AFkI1VTUajUaj0cQxCxcupL6hkW+NbSPZqvjtIw/T2toabVkxS9wZcd0RkSJgGvDpMd67UUTWiMia2tragZam0Wg0mlPA6/Vy3fXXs3DhwmhL0QwQL774Iq+//jpfLujg83kebp3YQvWBA/zspz/F4/FEW15MElUjTkTmi8hJaRARJ/Ay8AOlVMuR7yulHlNKzVRKzczMHBT+zRqNRjNk2L59O7t27uTFF18kGAxGW44mwrz66qssXLiQWVkerhjpAmBsip8bxreyYcMGfvrT/8br9UZZZewR7ZG4K4EdIvKQiIzvayMRMRMy4J5XSi2OmDqNRqPRRIUVK1Z0vS4tLY2iEk2kWb58Ob/73e+YluHlpgltGOTQe3NzvHx7XBurV6/h17/+NUMho8aJEFUjTil1DaHp0F3AkyLycXgaNPF4bURECNVQ3aKU+u0ASdVoNBrNAPLe+8vxJ+YgBhMffvhhtOVoIkRTUxMPPfggI5MD3DKxFZMBntvu4Lntjq5j5g3zcFmxi/fee49ly5ZFUW3sEe2ROMJToS8D/wByga8Ba0Xke8dpciZwLfAFEVkfXi4cGLUajUajiTQul4vamoMEkvPxO9LYtWtXtCVpIsQ777yDq6OD68e1YjGG9u1rM7Gv7fDkGfMLO8h3Blm8+OUoqIxdoppiREQuBr4FjASeBWYppWpExEEo6vSPR7ZRSn0EyJH7NRqNJpr0JYeliJwDvAbsCe9arJS6fyB1xgMmkwmDwQABL4agD5vNFm1Jmgixb98+kqxCXkKgx+MMAmOSvazed3KptgYr0c4Tdxnwf0qpw8bKlVIuEfl2lDRpNBrNydDXHJYrlFLzo6AvbrBYLIwbN55N27chfg9TpkyJtiRNhLBYLPj7GLcSCILZbI6soDgj2tOpB4404ETkQQCllJ741mg0cYPOYdm/nHXWmYg/lFbizDPPjLIaTSQJ9jFWIagg5Bav6STaRtx5x9j35QFXodFoNP1ITzksgTNEZIOILBWRiQMqLI7onhoqIyMjiko0kcLtdrPqoxUUOn19Or4oKUB9QyMbN26MsLL4ISpGnIjcLCKlwDgR2dht2QPoT0ej0cQtveSwXAsUKqWmEvL5ffU45xjSycoDgQBvLVnStb2k22vN4MDn8/HAA/dTfbCGrxW196nN2TluMu2K+37+P1RWVkZYYXwQrZG4vwMXEXLwvajbMiOcdkSj0Wjijt5yWCqlWpRSbeHXSwCziBw1zDSUk5UHAgEeeugh1q9bh7voTPwpBfzlL3/RqSUGER6Ph/++5x5WrlzFtWPamJDm71M7mwlun9yMt72J7916C7t3746w0tgnWkacUkqVA7cArd0WRCQtSpo0Go3mpOlLDksRyQkfh4jMItQH1w+cythGKcVDDz3E22+/jWfYNHyZY+konocvIYsHfvELli9fHm2JmlNEKcX999/H6tWr+fa4Nr6Yf3g5ree2O9jbamRvq5H/XZt0WL44gHxngP+e1gjuZu64/QfU1NQMpPyYI5ojcQAlwJrwuqTbtkaj0cQbx8xhKSI3ichN4WMuA8pEZAPwB+BKpVPQd7Fu3bqwAXca3rxpoZ1GM67RXyKYkMlvfvMwPl/f/Kc0scmqVatYuXIVV41q55xhR9dD3ddmoiNgoCNgYGuT+ah8cQDDEoLcPbWRjrYWHn/88YGQHbNEJcVIZ3i9UmpENK6v0Wg0vSEiWUBXgjKlVI8JqvqSw1Ip9SfgT/0icBBSV1cHQCBp2OFvGE34E7JoP1iG2+3WaSbimLKyMowC5+W7T+k8wxKCTE33UFY6tN3ooxqdKiJnikhC+PU1IvJbESmIpiaNRjO0EZGLRWQHoYS8HwDlwNKoihoizJ07l/SMTBJ2L0fczV37TXU7sRws47zzziMx8bhVGTVxQE5ODgEF1R3GUzqPUlDlMpOTk9tPyuKTaKcY+QvgEpGpwF3AXkKVGzQajSZaPADMAbaHZwvOBVZGV9LQwOl08vBvHsJpM+Pc8R/wezG2HMBevoLTpk3jRz/6UbQlak6Rs846CxEoqbWc0nmqOwxUtRn43Lx5/aQsPom2EecP+4NcAvw+XKJG/8zSaDTRxKeUqgcMImJQSr0PnBZtUUOFESNG8MtfPADuVswNu7FUl5Gens7//vKXWK3WaMvTnCLJycnYbTYaPadmfjSF2+fm6pG4aNIqIj8BrgHeEhEjoJ0dNBpNNGkK53r7EHheRH5PqKSWZgBwu91dUajKZEOZrLQ0N/PJJ5+gY0Dim2AwyMKFC3F1uJmS7j2lc41I9OO0wGOPPkpjY2M/KYw/om3EXQF4gOuUUtWEStT8JrqSNBrNUERERonImYRmBlzA7cC/CaUA+V40tQ0F6uvreeaZZ7jiyitZvHgx3qwJ+FML8Qw/HY81hfvvv58bv/Md3nnnHbzeUzMANANPc3Mz99zzExYvXsz5wzs4Lf3UooxtJvjuhBYqK8q58YbrKSsr6yel8UVUjTilVLVS6rdKqRXh7X1KqWeiqSkW8QWCNLR78QWC+peoRhM5fge0KqXalVJBpZRfKfU0sAS4N7rSBi/bt2/nvvvu4/L/+i8WLVpEg0rANe5CPIVzQARlttM+7iu4i85kR8VBfvnLX7Lg0kt5/PHHh/QITDxRXl7ODddfx5rPPuUbY9r4+igX/VECdVKaj59Ob0Jcddx22/d58803T/2kcUZUUox0IiILgAeBLEKh+UIoEXBSNHVFm0BQ8cH2Gv5dVs3Hu+upauzoKhCcYDVRmOZgfG4Sk/OSsZiiPZiq0QwaipRSR+UrUEqtCddC1fQzixYt4plnnkFMVjwZ4/BmjkPZk48+UAz4MsfiyxiDsWU/vpotPP/88yx+5RV+8cADzJgxY+DFa/pEe3s7P/rhHfjbG/jp9CaKkwI9Ht/hF2w2G/Pnz+fNN9+kw9+zJ0NRYoD7ZjTyl82JPPzww2RmZjJ79uz+vIWYJqpGHPAQcJFSakuUdcQEgaDi5ZJKFi7fSXm9i0SribPHZPC10/JITbDw8a56als97KlvZ/OBFt4q3c+ZIzM4a3TG/2/v3MPjqsr9/3nnmkwm9wtNQ9MmTUuxBVqoBdojIFAsUi2IgqCoFbxwLIooytGjB1HU81M5erygiB4EW0AuBaRWWyml2JbeaQstpaX30uZ+m0wyk5l5f3/snWSaJuk0TTK5rM/z7Gf23rP23u/al7W/+11rvQuv6/S6axsMho6YcF2QOmBWjBCCwSCPPvoo0ZQsgmfPBVcCvRVFiGYWEc0sItxUBTteYOHChUbEDWI2b95MVXUN35jacFIBBxCMCHPnzmXBggWoKquW/OWk26S5la+c08BX1+aybNkyI+IGkHIj4Cy2Ha7j24vfYPuRes4pyuTXN5/P7PeccZynrU2oqSr7qptYs6eal96qYN2+GuaeW8g5RZlIX/ioDYaRyQYR+ZyqHhcCXkRuxRpNxtCHpKamMmvWLFavXk3a7mW0FJ5LNHMMJ61ni7birtxFSvkbIMIVV1wxMAYbekVb79Gt1W6m5Jy8HZzPpbz44ouoKkuWLOEMV2JNiPY2uGgMw+jRo0+eeBiRbBG3UUSeBJ7D6uAAQFcDRw9XVJVH1uzn/iU7yUnz8IuPT+XD543uUYyJCKV5fkrz/BysCfLXre/yxIZDbD9Sz3XTivB5kndZF63rMah9t9x8oYnxbEg6dwKLReQTdIi26YAHuC5pVg1TRIT77ruPpUuX8uijj1Gx+5/E/AUESy9Dvf4ut3HWHSLtwL/QcDNTp07ltttuY8qUKQNruOGUKCsr47rrrmPx4sXkemPMKe55pIZUl9ISaOGZZ56xlrNOLuIOBZz8z/ZMikaP5sYbb+wTu4cKyRZxGVi9wK6KW6fAiBBxgVCEbz6zjSXbjnLl2QX87GNTyfSdWoSV4hwfX7x0PP/aU8U/d5Tzq7o93PTeYsbk+E6+scFgaEdVy4GZIvJ+oE0ZLFHVFUk0a1jjdDqZO3cul19+OT/96U9ZsWIFnqPbCI2b2WV638E1aLiZu+66i7lz5+JwmDbBQ4EFCxZQXV3N46tWMTGrNaFq1USJxuCXb2aSmp7NT3/2AH5/1x8Aw5WkijhVnZ/M4yeTXccauX3hJvZXNfHNOZP4wiWlOBy9qwp1OoRLJ+ZTmpfG4xsO8tCqvVx9ziguLs011asGwyliB/d9Odl2DCdUlcbGRmpqaqioqKCiooLy8nIOHz7M/gMHOHDgALFoFHF7ieSM63Y/odwJeI9u5YEHHuA3D/6WsvGlFBcXU1RUREFBAQUFBeTn55OdnU1qqmnGOFhwOp3cc889rFm9mg0VXkozgn227wMBJ8eahG995XZGjRrVZ/sdKiS7d+pErKG3zlDVKSJyLvBhVf1BMu3qbxZvOcy3nn2DNK+LhbddxMXjc/tkv2NyfCx4fxlPbzrMi9uOsr+qiY+cf2af7NtgMJwcEZkD/AJwAg+r6o87/e8FHgUuwIo/d6Oq7h9oO0+HWCxGU1MTjY2N7VNDQ0P7b319PfX19dTV1VFbV0dtbS11dXVEO/cyFIGUdCKeDKIFk4n6C6yB7x3dv5bCRefTWjAJZ/0RwoFKth6o5M2396LhE0WBNyWF7OwccrKzyM7OJjMzs33y+/1kZGSQnp7e/uv3+0lNTTUfvv3E+vXriUSj5Kf2nRcOIMcbwymwZs0aZs+ePeKuX7KrU38P3A38DkBVt4nIImBYiriW1ij3vbiDResOMqMkh1/dNI2CjJ46xJ06Po+LWy4ay6u7q1i24xjHVu5hZlkuk0aN6KgtBkO/Y48482tgNnAYq6PEC6q6Iy7ZrUCtqpaJyMexQiwNeCMeVaW5ubldcAUCgXYhFj8FAgFLnLUtNwZobg72GK9SnG7wpBJ1eIm5vKgri1heIepOtSZPGjGPH3X7oIfqUEegAlfDUSIZhcT8BR22u31E8iYQyZvQkTjaiiPchNiTI9JMuLWFQCjI4cO1OA+U44i0oK0tEOteRDicTtLS/PjT/WSmZ5Ce7m8XeOnp6e1TRkZGuxDMyMggMzMTj+f0xgIdzmzcuJH77/8BZZlRLikMnXyDUyDLq3ykpImnVq4kJyeHO+64Y0QJuWSLOJ+qru90wofl8DaHaoL8+8LNbD9SzxcuLeXuq87C5eyf9hwiwiUT8xmT4+OJ9Qe59teruf/ac7j+AuOVMxj6kRnAHlXdCyAiT2CN/hAv4ubRETj4aeBXIiLax1G8m5qaeOeddzh48CDHjh2jqqqK6upqqqqrqauto7GxgUgP8bfE6QKXF3V6iDo8qNODuvyoPxfN8qBOL+qyfnF5UJfXmpxecJx+uCNHoILcgyu55uqrWbJ0KdXFlx0n5E7A6SaWmgWpWT3vWBViESQSQqIhJBJGIi1INIxEQhAJ0xINURMMI40NOKJVOGJhJBpGW0OgsW537U1JISMjk+zsLPLz8sjNzSUvL4+ioiKKi4spLS3F6Rx5oaBqa2v5r+9+h1HeMHedW09/hDadO7aFhlYHzz77LGeffTazZ8/u+4MMUpIt4qpEZDxWZwZE5KPA0eSa1Pf8c0c5X3tqKzFVHrrlAq6aPDD19iV5aSy4vIyXd1Xwtae2snpPFffOm0xGihmetreY3reGHigCDsUtHwY69nAmEAAAGBRJREFUB6xqT6OqERGpB3KBqvhEIvJ54PMAxcWndu8sW7aMH/7wh8etE28aUZePmDuFmCsPzStqH5dUXSkdQswWZz1VaQ4EroajXHP11dyx4EsALFq7l3BPIi5RRMDpRp1ulFNsAN+dAIyEkEiIcKSFQKSFo0cDuA5XIuGg5fmLY9GiRSMuBMamTZtoCjZz5/mN+N39M+KQCNxUFmR9RQqvvPKKEXEDyJeAh4BJInIE2Ad8Irkm9R2NLa384MWdPLnxEO8pzODBT57P2Ny0AbUhPcXNn2+9kP9dsYdfv7yHdftq+MG1U3j/pD4oEBNEVWloiVAXDNPSGiMaiyEieFwOfB4nOWmmGsIwLOiqDqfzWyuRNKjqQ1hlI9OnTz+lN1+XvfMiYRwONxp1IOKw3nrtv4IKSJxp6iKpQi6SUciSpUsBWLJ0KZHiy5JmC9CNgAtZHrz4KRK2PXetaOT48V0dDgderzdJGUgeEydOxOl0sGiPn8+f3UhRWt+2iQNojsDTe33UhoTJkyf3+f4HM0l5SkXkrrjFv2H1BHMATcD1wAPJsKsvWbOniruf3sbR+ma+eOl4vjp7QtJGVXA5Hdw1eyKXnZXPN57exvxHNjBn8ii+MecsSvP7tjt2NKYcrW/mQHWQQ7VBKhtDVAVCtEZ7fg89/Oo+ygr8TBqVzoySHN5bkmM8hoahxmFgTNzymcC73aQ5LCIuIBOo6UsjZs6cyfLly62en/v3U1FRQVVVFZWVldTW1lJTV0d93TEaqxp6btvmcIG7rUrVHVeF2ua16/DeHe/JO/0q1Zi/gOriy1i0di+Rk1WlJkq8EIsXY9FQe1WqREMdYixqC7JICI2ErO27weV2W1WpeVlkZ5/RXpWal5dHYWEhJSUlFBQUjKi2Wm0UFxdz333f58c/+iHfWu/kooIQc8Y0U9JNmJFif6TH5XgawsKKIyksO+IjEIbrr7+eG264oU/tH+wk61Mr3f49C3gv8DzWF+otwKok2dQnVDS08JN/7OKpTYcpyUvjqS/O5IKx2ck2C4Dzi7P525ffx0Or3uE3K99h+c5y5k0dzWdnlTClqIvxChOgpTXK1kN1rNtXw7p91WzYV0s4arUbyUp1c0ZGCqV5aeT6vWT7PKS6HbicDlQhFI3SFIpSHQiR6nGyuzzA2neq+d2qvTgEphRlcvH4XC6ZkM/0cdlmaDHDYGcDMEFESoAjwMeBmzuleQH4NLAW+Ciwoq/bwwG43W5KSkooKSnpNk18L9P43qVtnRniOzZYnRsCNDY2JNa5weUBl5eo00vMlYK6U9t/1eND3T5injTUndZt54aYvyCxKtRoK45wAAkHkdYgjtYg0tqCtDYjkRac0RYckVCvOzXEd2zw+/34/X4yMzOP6+BgerX2zKxZs3jszwt5/PHHeeH551hb7mV8ZpQrRgeZURDGE1e0f3Jiz+FHVGF3vYuXjqSwodJLJAYXXXQhn/nMfCZNmtTPORl8SD+UH4kfXGQZcL2qNtrL6cBTqjqnL48zffp03bhxY1/u8gQqG0M8/K+9PLb2AK3RGPNnlfDVKyeS6uk74dGX7bEqG0P8ZuUentxwiGA4yqRR6Xxg8iguKs3l3DMzSfOeqO9DkSj7q4Lsrmhk17FG1u2r4fVDdYQjMURg0qgMslLdjM31MTY3jczUxD1pbTa2tEbZfLCW1/bW8No71Ww+WEskpqS6nVxYmsMlE/K5ZGI+4/PTBqTQrAuG2VvVxN7KJvZVBVi9p5rmcJTmVmuKxRSnQ3CI4HIKaR4X/hQX6V4XmT43+X4v+elePn9J6YAW8iO97Z6IbFLV6Uk47geBn2OFGPmjqt4vIvcBG1X1BRFJAR4DpmF54D7e1hGiOwai/DpVegoz0tbjtaGhgbq6OmpqrTAj9fVdhxkRr5+Y00PXNc09oThag2i4+YR/UlJSyczKIjcnuz28SFtP0rbepPG9TY0QGzgCgQB///vfef65xRw6fAS/By4vDHLVmBYyPN3rkWgMXqvw8PdDaRxodOBLTWHO1R9k3rx5jB07dgBzMDAkWoYlW8S9BZynqiF72QtsVdUe5fTJYjF1pr8KwWhM2bC/hic3HGLJ9qNEojGuOXc0X5s9kXF5fd/2rT9ezPXNrSzefJgl24+y8UBte41BTpqHfL8Xt0uIRJWG5lbKG0NEY1YCh8Dk0ZlcWJLDhaW5zBiXQ6bP3ec2BkIRXnunmld3V7JqdxX7qpoAGJ2ZwsyyPM4bk8V5Z2Zy1qj0XnvqQpEoB6qD7K1sYm9VgH2VTeytamJfVRM1TR3tWlwOIT3Fhc/jIsXtIMXtxOkQYjElphCJxWgKRWlsaSUQihCLe7QyUlyML/BTlu9nwhl+JhSkU1bgpygrtddBnnuit9fhphljhsWLLFkirj8YjCKuN7QF/G3rKVtRUcGxY8c4evQowWDvgr/m5ORQWFjIqFGjyIvrEZqS0rehmwx9j6qyZcsWnnnmadasWYPHAR84M8iHxjXjdcang42VHp7c66ciKIwtHsNHP3YDV1555bAO6DxURNy3gRuAxViNe68DnlTVH/WwjRN4m7hYTMBNnWIxHUdfFYKxmPJOZYBNB2rZeKCWlbsqqQqESPe6uHZaEfNnjevzNmbx9Ld3pbYpzOuH6njz3XqO1LVQHbBEm8MhZKS4GZ2VQlmBn7ICP+Pz/aS4TxRN/W3joZogq3ZX8urbVazbV01t0BpQ2eN0UJzroygrlaLsVEZnpuDzuPC4HHhdDhQItERoCkVoDEU4Vt/CkbpmjtQ2U97Yclxzl4J0LyV5aZTm+ynNS6M0P42SvDTG5Ph4auPhhOyMqSV8KwMhKhtDZPnc7KkIsKeiiapAR5ykVLeTsgI/Ewr8lJ3hZ2xOGoVZKRRmppDv955yGJqW1ii1wTCL1h0kGI7aU4TmuPlgp/nWaIxYzLJZsUYAcTsFj9OBz+Miy+cmM9VNls9NVqqHLJ+bnDQPuX4veX4PeX4vuX4POWmeQVPlbUScwTB0OHjwII888ggrVqxgVJpy9ZlNeJ1Wobyp0sOGSi+l48Zx6+c+x8yZM4fFh+bJGBIiDkBEzgfeZy+uUtUtJ0l/MXCvqn7AXv4PgJ6EX6KFYHlDC+UNLdQ3t9LQHKGuOcy7dc0cqmnmUG2QdyoCNLRY1QHZPjcXj8/l6imFXHF2wYAMOj8UqsgG0kZV5XBtM9sO17PtSB37q5rahVmbuOsKr8tBQYaXoqxUzsy2hF9pfhqleX7G5flI76FDRV/kry4YZk9FgN0VAd4ub7TmywMcazg+HIEI+L0uMlLctgfQiUPE6lCIEIpEaQpHCYYi1m840mMHErdT8Hms/ViTNe9xOXCIVSV83phMIlElHI0RjsQIhiPUN7dSG2ylPthKXXOY2mAr4UjX8bLSU1yWqEvzkNsu8Lzk+Nz4PC5S7WOnxh0/1fZoinBc/hz2clSVSFTxp7jwd1HN3xVGxBkMQ48tW7Zw3/fupbauvn2diDB//nxuvvlmXK5kB9QYOBItw5J+RlR1M7D5FDZJJBZTr/j+izt4cdvxYeqcDmF0Vgpjsn1cc+5ophVnccHYbErzBqZNlqF7RIQxOT7G5Pi45tzC4/5raY0Sao0RikQJ2YLD73WR5rW8c8kky+dh+rgcpo/LOW59Q0srR2qbOVrfzNH6FsrrW2hoidDQYn1UtLRGUZRYDBQly+ehKNsSQ2keJz6vJXKyfR7eOFLfLpjaxJI7Aa9eImJaVWkKWx1SqgJhqgIhqgNhqgMhqps6lvdVNbFhfy21wXBPHfsS5r8+9B7mz+q+ob7BYBjaTJs2jUWPP0FVVUfYRJ/PR25u3wxNORxJuifuVBGRjwEfUNXb7OVbgBmqekendO3BMrF6we4aUEP7hzw6BQUdwpi8DE6GS17Gqmp+so3oC0SkEjiQbDuSxHC5Hw2nzki/9gmVYUn3xPWCRGIxHRcsc7ggIhuHSxWRycvgZDjlZbgwXMRobzD348jFXPvESG69Uu9oj8UkIh6sWEwvJNkmg8FgMBgMhgFlyHni7PEGFwD/oCMW05tJNstgMBgMBoNhQBlyIg5AVf+GNVzXSGM4VQ+bvAxOhlNeDEMfcz+OXMy1T4Ah17HBYDAYDAaDwTA028QZDAaDwWAwjHiMiBsCiMgcEdklIntE5J5k23M6iMgfRaRCRN5Iti2ni4iMEZGXRWSniLwpIl9Jtk29RURSRGS9iGy18/K9ZNtkGNkMp3LPkDjD6R0xEJjq1EFOb4YZG8yIyCVAAHhUVack257TQUQKgUJV3Swi6cAm4NqheG3EilydpqoBEXED/wK+oqqvJdk0wwhkuJV7hsQZTu+IgcB44gY/M4A9qrpXVcPAE8C8JNvUa1R1FVCTbDv6AlU9ao84gqo2AjuxRhQZcqhFwF5025P5wjMki2FV7hkSZzi9IwYCI+IGP10NMzYkhcJwRkTGAdOAdcm1pPeIiFNEXgcqgOWqOmTzYhjymHLPYEgAI+IGP10N0Go8JIMIEfEDzwB3qmpDsu3pLaoaVdWpWKOgzBARU5VhSBam3DMYEsCIuMFPQsOMGZKD3X7sGWChqj6bbHv6AlWtA1YCc5JsimHkYso9gyEBjIgb/JhhxgYpdmeAPwA7VfWBZNtzOohIvohk2fOpwJXAW8m1yjCCMeWewZAARsQNclQ1ArQNM7YT+MtQHmZMRB4H1gJnichhEbk12TadBrOAW4DLReR1e/pgso3qJYXAyyKyDesFulxVX0yyTYYRynAr9wyJM8zeEf2OCTFiMBgMBoPBMAQxnjiDwWAwGAyGIYgRcQaDwWAwGAxDECPiDAaDwWAwGIYgRsQZDAaDwWAwDEGMiDMYDAaDwWAYghgRd4qIyI9E5DIRuVZE7umnYzwsIu/pj333FyISOHmqbre9U0Q+Zc/niMhyEdlt/2Z3s80fRGSriGwTkaftURMQkQUiMr+3tnQ6Rq/zdJL9fkZEftXF+ntF5Ounsd+fiMib9u9p7ctgMJweIpIbF3romIgciVv29NMxzxcRE6R7BGFE3KlzIdb4mJcCr/bHAVT1NlXdkWh6EXH2hx0DgYi4gM8Ci+xV9wAvqeoE4CV7uSu+qqrnqeq5wEGsmFIAfwS+3I8md8kguQZfAM5X1bv7aof29TEYDKeIqlar6lR7KLvfAv/Ttqyq4ZNt38sy5XzMSCsjCiPiEsT2bmwD3osViPA24EER+W4XaR8RkQdF5GUR2Ssil4rIH0Vkp4g8EpfuQRHZaHtPvhe3fqWITLfnbxKR7SLyhoj8d1yagIjcJyLrgIvj1p8tIuvjlsfZdiMiF4jIKyKySUT+ISKF9vovi8gO26v1hL3u0rivxi0ikm6vv1tENthp222OO16hiKyyt3tDRN53klN7ObDZDu4JMA/4kz3/J+DarjZqG6PUHjUhFXtcRVUNAvtFZEYXtp0hIottD95WEZlpr7/LtvUNEbmzi+3Evv5v2NfiRnv9ZfY1XgRst9d9UkTW2/n/XVtBLCLzReRtEXkFK0hwd5wnIitsT+Tn7G0fE5F5cfYsFJEPd7LxBSANWNdmX9x/U0XkNfuaLRbbu9npPssTkf32/GdE5CkR+SuwrIf8d3mtT3LP/kxENovIS2KNEjFeRDbHpZkgIpt6OD8Gw5BHRP5ql8Nvisht9jqXiNSJyA/sMnyGiHxYRHaJyKsi8ksRec5O6xfrPbPeLp8/JNZIK98FPmE/kx9NYhYNA4WqminBCZgB/BJwA6t7SPcI8ATWIM7zgAbgHCzRvAmYaqfLsX+dWGNVnmsvrwSmA6OxvEz5gAtYAVxrp1Hghm6O/zpQas9/E/hP2+Y1QL69/kbgj/b8u4DXns+yf/8KzLLn/fbxrwIesvPlAF4ELrHTBOzfrwHfjstXuj3/MDC9C1u/B9wRt1zX6f/aHs7z/wHlwMuAL279t4GvdZH+SaxB6ttsywQuwBJgaXY+3wSmdcrT9cBye5sz7GtSCFwGNAEldrqz7fPmtpd/A3zKTtt2HT3AauBXXdh3L7AVS5TmAYfse+BS4Dk7TSawD3B1sX2g076+bs9vAy615+8Dfh5/n9nzecB+e/4zWGNX5pwk/ydca05+z37Cnv9u2zmwr1/bM/HD+PvBTGYaDlP882gvtz1bPmAHkG0/Lwp8JO6/w8BYrDL3qbhy4P8BH7fns4G3gRQs58LPk51fMw3cZDxxp8Y0LIE0CevB64m/qvWEbQfKVXW7qsawRMI4O80NthdiCzAZ6NwO7r3ASlWtVMtTtRC4xP4vijXwelf8BbjBnr8RS7ycBUwBlovI61jC7kw7zTZgoYh8EmjziK0GHhCRL2MJuwiWiLvKtnezfR4mdDr2BmC+iNwLnKOqjdBeRbyxC1sLgcpu8tEjqjofSzTstPPZRoW9vjOXAw/a20ZVtR74N2CxqjapagB4FujsPfw34HF7m3LgFaxrA7BeVffZ81dgicIN9jm+AijFqoJvu45hrOvRHc+rarOqVmGJmxmq+gpQJiIFwE3AM9rhuewREcnEun6v2Kv+RMc91BPLVbXGnu8u/11d657u2Vhc3v9s7xcsgT/f9lreSEfVusEwXPmqiGzFqtU5Exhvrw8Di+359wC7VPWA/S55PG77q4Bv2+XMy1gCrnhALDcMKoyISwC7Oup14H7gbmAJMMd2Wad2s1nI/o3Fzbctu0SkBPg6cIVa7bqWYD2Ixx26B7NaVDXazX9PYgnEiYCq6m57X29qR5uMc1T1Kjv9NcCvsQTIJhFxqeqPsb7qUoHXRGSSvY8fxe2jTFX/EH9gVV2F9dI+AjwmdoeFHmjulO9y6ajmLcQSZIhV/fu6iDzc6XhRO7/Xx61OsfebCD2d40TSNHVK96e483OWqt7bZmqC9nRO17b8GPAJYD6WB7IviNBRBnS+9zrn6wS6udaJnM/2Xdi/zwBXA3OBTapafQr7MBiGFCJyJdZzc5Gqnof1Ed32/DXbgg16fpYEy8PdVtYUq+rb/We1YbBiRFwCqOrrajVOfRvr62gF8AH74UlULHQmA+tFWS8iZ2C9xDqzDrjUbq/kxPLCvNJFus72voPlqfsOHZ6PXUC+iFwMICJuEZksIg5gjKq+DHwDyAL8IjLe9h7+N7ARy+v2D+Cz0tETtMj2DrUjImOBClX9PfAHrIa2PbETKItbfgH4tD3/aeB5O09t5/s2u41WmX08AT4EvBW3j4nAG10c6yXgdns7p4hkAKuAa0XEJyJpwHWc2GFlFXCjvU0+VgG8nhN5Cfho2zkRq6ftWKzreJlYvdXcwMd6OB/zRCRFRHKxqms32OsfAe60z0XCA4Hb3sZa6WibeAsd99B+LOEO0FP7mS7z38217umedcQd52bgX7aNLVj31oP0nUA1GAYrmUCNqjaLyGQ6vPqdeRNrEPgxdjkXX9vwD+I6cInINHu2EatZg2GEYHqeJYj98qpV1ZiITNJT6D3aFaq6VUS2YD2oe7GqLzsl0aMi8h9Y7nIB/qaqzyd4iCeBnwAl9s7CdkPX/7Wr2FzAz7GE6Z/tdYLVg6pORL4vIu/HEoM7gKWqGhKRs4G1VplCAPgktrfM5jLgbhFptf9vCx3yMPDbLqpUl2J5mdr4MfAXEbkVq21VV4JHgD/ZIkyw2pHdHvf/LKy2dp35CvCQve8ocLuqrhWrs0mbKHtYVbd02m4xVueRrVjeo2+o6jHbO9mOqu4Qkf/E6gzgAFqBL6nqa3aV41rgKFZVdHc9z9ZjeWWLge+r6rv2vstFZCfwXDfb9cSngd+KiA/rXmsLwfJTrHN9C9aHSXd0l/9P0+lan+SebQIm2x0X6jn+pbQQ+AiwrBf5MxiGEkuAz9vVqW9hfficgKoGRWQB8E+sJicbgBz77+8BPxeR7VgfR3uw2l+vwHomtwD3q+rT/ZoTQ9KRDs+tYbBgP5gfjmtrNawRkcVYwmB3H+xrGnCXqt5y+pYNHmwBth0rhEh9su3pDSISUFV/N/99HchU1e8MsFkGw6BFRPyqGrA9cb8DtqvqL5Ntl2HwYKpTBxkishzrQR0RAs7mHqwODn1BHlY18rDBbkPzFvDLoSrgesIW8Z8CfpFsWwyGQcbtdnvsHVjtk3+fZHsMgwzjiTMYDAaDwWAYghhPnMFgMBgMBsMQxIg4g8FgMBgMhiGIEXEGg8FgMBgMQxAj4gwGg8FgMBiGIEbEGQwGg8FgMAxBjIgzGAwGg8FgGIL8fzmdq5Yq6wDEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_continuous, axes = plt.subplots(nrows=len(continuous), ncols=2, figsize=(10,10))\n",
    "plotGrid(isCategorial=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Creating Dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dummy for chest Pain\n",
    "chestpain_dummy = pd.get_dummies(df.loc[:,'ChestPain'])\n",
    "chestpain_dummy.rename(columns={1: 'Typical', 2: 'Asymptomatic',3: 'Nonanginal', 4: 'Nontypical'}, inplace=True)\n",
    "#dummy for RestECG\n",
    "restecg_dummy = pd.get_dummies(df.loc[:,'RestECG'])\n",
    "restecg_dummy.rename(columns={0: 'Normal_restECG', 1: 'Wave_abnormal_restECG',2:'Ventricular_ht_restECG'},inplace=True)\n",
    "#dummy for Slope\n",
    "slope_dummy = pd.get_dummies(df['Slope'])\n",
    "slope_dummy.rename(columns={1: 'Slope_upsloping', 2:'Slope_flat',3: 'Slope_downsloping'},inplace=True)\n",
    "#dummy for Thal\n",
    "thal_dummy = pd.get_dummies(df['Thal'])\n",
    "thal_dummy.rename(columns={3: 'Thal_Normal', 6: 'Thal_fixed',7: 'Thal_reversible'}, inplace=True)\n",
    "#concatination in data frame\n",
    "df = pd.concat([df,chestpain_dummy, restecg_dummy, slope_dummy, thal_dummy], axis=1)\n",
    "#droping Column because their dummies are created\n",
    "df.drop(['ChestPain','RestECG', 'Slope', 'Thal'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking the No object will left "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 301 entries, 0 to 302\n",
      "Data columns (total 23 columns):\n",
      "Age                       301 non-null int64\n",
      "Sex                       301 non-null int64\n",
      "RestBP                    301 non-null int64\n",
      "Chol                      301 non-null int64\n",
      "Fbs                       301 non-null int64\n",
      "MaxHR                     301 non-null int64\n",
      "ExAng                     301 non-null int64\n",
      "Oldpeak                   301 non-null float64\n",
      "Ca                        301 non-null int64\n",
      "Target                    301 non-null int64\n",
      "asymptomatic              301 non-null uint8\n",
      "nonanginal                301 non-null uint8\n",
      "nontypical                301 non-null uint8\n",
      "typical                   301 non-null uint8\n",
      "Normal_restECG            301 non-null uint8\n",
      "Wave_abnormal_restECG     301 non-null uint8\n",
      "Ventricular_ht_restECG    301 non-null uint8\n",
      "Slope_upsloping           301 non-null uint8\n",
      "Slope_flat                301 non-null uint8\n",
      "Slope_downsloping         301 non-null uint8\n",
      "fixed                     301 non-null uint8\n",
      "normal                    301 non-null uint8\n",
      "reversable                301 non-null uint8\n",
      "dtypes: float64(1), int64(9), uint8(13)\n",
      "memory usage: 39.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Target</th>\n",
       "      <th>...</th>\n",
       "      <th>typical</th>\n",
       "      <th>Normal_restECG</th>\n",
       "      <th>Wave_abnormal_restECG</th>\n",
       "      <th>Ventricular_ht_restECG</th>\n",
       "      <th>Slope_upsloping</th>\n",
       "      <th>Slope_flat</th>\n",
       "      <th>Slope_downsloping</th>\n",
       "      <th>fixed</th>\n",
       "      <th>normal</th>\n",
       "      <th>reversable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
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       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
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       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  RestBP  Chol  Fbs  MaxHR  ExAng  Oldpeak  Ca  Target  ...  \\\n",
       "0   63    1     145   233    1    150      0      2.3   0       0  ...   \n",
       "1   67    1     160   286    0    108      1      1.5   3       1  ...   \n",
       "2   67    1     120   229    0    129      1      2.6   2       1  ...   \n",
       "3   37    1     130   250    0    187      0      3.5   0       0  ...   \n",
       "4   41    0     130   204    0    172      0      1.4   0       0  ...   \n",
       "\n",
       "   typical  Normal_restECG  Wave_abnormal_restECG  Ventricular_ht_restECG  \\\n",
       "0        1               0                      0                       1   \n",
       "1        0               0                      0                       1   \n",
       "2        0               0                      0                       1   \n",
       "3        0               1                      0                       0   \n",
       "4        0               0                      0                       1   \n",
       "\n",
       "   Slope_upsloping  Slope_flat  Slope_downsloping  fixed  normal  reversable  \n",
       "0                0           0                  1      1       0           0  \n",
       "1                0           1                  0      0       1           0  \n",
       "2                0           1                  0      0       0           1  \n",
       "3                0           0                  1      0       1           0  \n",
       "4                1           0                  0      0       1           0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Selecting label means selected Column to predict in df_X and input column in df_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_X= df.loc[:, df.columns != 'Target']\n",
    "df_y= df.loc[:, df.columns == 'Target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True  True False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True]\n",
      "[2 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.325026\n",
      "         Iterations 10\n",
      "                           Results: Logit\n",
      "=====================================================================\n",
      "Model:                Logit             Pseudo R-squared:  0.529     \n",
      "Dependent Variable:   Target            AIC:               229.6658  \n",
      "Date:                 2019-05-06 12:38  BIC:               292.6867  \n",
      "No. Observations:     301               Log-Likelihood:    -97.833   \n",
      "Df Model:             16                LL-Null:           -207.60   \n",
      "Df Residuals:         284               LLR p-value:       8.6861e-38\n",
      "Converged:            1.0000            Scale:             1.0000    \n",
      "No. Iterations:       10.0000                                        \n",
      "---------------------------------------------------------------------\n",
      "                        Coef.  Std.Err.    z    P>|z|   [0.025 0.975]\n",
      "---------------------------------------------------------------------\n",
      "Sex                     1.3366   0.4946  2.7022 0.0069  0.3671 2.3060\n",
      "RestBP                  0.0225   0.0108  2.0888 0.0367  0.0014 0.0436\n",
      "Fbs                    -0.4942   0.5960 -0.8293 0.4070 -1.6623 0.6739\n",
      "MaxHR                  -0.0144   0.0101 -1.4242 0.1544 -0.0342 0.0054\n",
      "ExAng                   0.7277   0.4334  1.6792 0.0931 -0.1217 1.5771\n",
      "Oldpeak                 0.4169   0.2294  1.8175 0.0691 -0.0327 0.8664\n",
      "Ca                      1.2215   0.2589  4.7184 0.0000  0.7141 1.7289\n",
      "asymptomatic            0.3413      nan     nan    nan     nan    nan\n",
      "nonanginal             -1.5666      nan     nan    nan     nan    nan\n",
      "nontypical             -0.5997      nan     nan    nan     nan    nan\n",
      "typical                -1.7778      nan     nan    nan     nan    nan\n",
      "Normal_restECG         -1.2178      nan     nan    nan     nan    nan\n",
      "Wave_abnormal_restECG  -0.5247      nan     nan    nan     nan    nan\n",
      "Ventricular_ht_restECG -0.6054      nan     nan    nan     nan    nan\n",
      "Slope_upsloping        -1.4520      nan     nan    nan     nan    nan\n",
      "Slope_flat             -0.3749      nan     nan    nan     nan    nan\n",
      "Slope_downsloping      -1.0587      nan     nan    nan     nan    nan\n",
      "fixed                  -1.4437      nan     nan    nan     nan    nan\n",
      "normal                 -1.4342      nan     nan    nan     nan    nan\n",
      "reversable             -0.0078      nan     nan    nan     nan    nan\n",
      "=====================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "selected_features=[]\n",
    "lr=LogisticRegression()\n",
    "rfe=RFE(lr,20)\n",
    "\n",
    "warnings.simplefilter('ignore')\n",
    "rfe.fit(df_X.values,df_y.values)\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)\n",
    "\n",
    "for i, feature in enumerate(df_X.columns.values):\n",
    "    if rfe.support_[i]:\n",
    "        selected_features.append(feature)\n",
    "\n",
    "df_selected_X = df_X[selected_features]\n",
    "df_selected_y=df_y\n",
    "\n",
    "lm=sm.Logit(df_selected_y,df_selected_X)\n",
    "result = lm.fit()\n",
    "\n",
    "print(result.summary2())\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Spliting Values into test and traning Dataset in the ratio 0.75:0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test, y_train, y_test=train_test_split(df_selected_X,df_selected_y, test_size = 0.25, random_state =0)\n",
    "columns = X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculating Accuracy Function of the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "def cal_accuracy(y_test, y_predict): \n",
    "    \n",
    "    print(\"\\nConfusion Matrix: \\n\", \n",
    "    confusion_matrix(y_test, y_predict)) \n",
    "      \n",
    "    print (f\"\\nAccuracy : {accuracy_score(y_test,y_predict)*100:0.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Test Dataset: 0.855\n",
      "Accuracy of Train Dataset: 0.853\n"
     ]
    }
   ],
   "source": [
    "lr=LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "y_predict=lr.predict(X_test)\n",
    "print(f\"Accuracy of Test Dataset: {lr.score(X_test,y_test):0.3f}\")\n",
    "print(f\"Accuracy of Train Dataset: {lr.score(X_train,y_train):0.3f}\")\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Vale Prediction for Test dataset for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:\n",
      "[1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 1 1 1 0 0 0 1 0 0 1\n",
      " 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 0 0 1\n",
      " 1 0]\n",
      "\n",
      "Confusion Matrix: \n",
      " [[36  4]\n",
      " [ 7 29]]\n",
      "\n",
      "Accuracy : 85.526\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values:\") \n",
    "print(y_predict)\n",
    "cal_accuracy(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Test Dataset: 0.855\n",
      "Accuracy of Train Dataset: 0.880\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "svm_linear = svm.SVC(kernel='linear')\n",
    "svm_linear.fit(X_train,y_train)\n",
    "warnings.simplefilter('ignore')\n",
    "print(f\"Accuracy of Test Dataset: {svm_linear.score(X_test,y_test):0.3f}\")\n",
    "print(f\"Accuracy of Train Dataset: {svm_linear.score(X_train,y_train):0.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Vale Prediction for Test dataset for SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:\n",
      "[1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 1 1 1 0 0 0 1 0 0 1\n",
      " 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 0 0 1\n",
      " 1 0]\n",
      "\n",
      "Confusion Matrix: \n",
      " [[36  4]\n",
      " [ 7 29]]\n",
      "\n",
      "Accuracy : 85.526\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values:\") \n",
    "print(y_predict)\n",
    "cal_accuracy(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Test Dataset: 0.855\n",
      "Accuracy of Train Dataset: 0.849\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "gini = DecisionTreeClassifier(criterion = \"gini\", random_state =100,max_depth=3, min_samples_leaf=5)\n",
    "gini.fit(X_train, y_train)\n",
    "warnings.simplefilter('ignore')\n",
    "print(f\"Accuracy of Test Dataset: {gini.score(X_test,y_test):0.3f}\")\n",
    "print(f\"Accuracy of Train Dataset: {gini.score(X_train,y_train):0.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Vale Prediction for Test dataset for Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:\n",
      "\n",
      "[1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 0 1\n",
      " 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 1 0\n",
      " 0 0]\n",
      "\n",
      "Confusion Matrix: \n",
      " [[36  4]\n",
      " [ 7 29]]\n",
      "\n",
      "Accuracy : 85.526\n"
     ]
    }
   ],
   "source": [
    "y_predict=gini.predict(X_test) \n",
    "print(\"Predicted values:\\n\")\n",
    "print(y_predict) \n",
    "cal_accuracy(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Test Dataset: 0.882\n",
      "Accuracy of Train Dataset: 1.000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest=RandomForestClassifier(n_estimators=100)\n",
    "forest.fit(X_train,y_train)\n",
    "\n",
    "warnings.simplefilter('ignore')\n",
    "print(f\"Accuracy of Test Dataset: {forest.score(X_test,y_test):0.3f}\")\n",
    "print(f\"Accuracy of Train Dataset: {forest.score(X_train,y_train):0.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Over Fitting Issue\n",
    "##### Vale Prediction for Test dataset for Rondom Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:\n",
      "\n",
      "[1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1\n",
      " 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 1\n",
      " 1 0]\n",
      "\n",
      "Confusion Matrix: \n",
      " [[35  5]\n",
      " [ 4 32]]\n",
      "\n",
      "Accuracy : 88.158\n"
     ]
    }
   ],
   "source": [
    "y_predict=forest.predict(X_test)\n",
    "print(\"Predicted values:\\n\")\n",
    "print(y_predict)\n",
    "cal_accuracy(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation For Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated Accuracy of  Linear Regression:: 0.823\n",
      "Cross validated Accuracy of  Support Vector Machine:: 0.809\n",
      "Cross validated Accuracy of  Decision Tree:: 0.773\n",
      "Cross validated Accuracy of  Random Forest:: 0.804\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "kfold=model_selection.KFold(n_splits=10, random_state=7)\n",
    "models=[('Linear Regression', lr), ('Support Vector Machine', svm_linear),('Decision Tree',gini),\n",
    "        ('Random Forest',forest)]\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "for model in models:\n",
    "    results=model_selection.cross_val_score(model[1],X_train,y_train,cv=kfold,scoring='accuracy')\n",
    "    print(f\"Cross validated Accuracy of  {model[0]}:: {results.mean():.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Traning Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.881579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistics Regression</td>\n",
       "      <td>0.853333</td>\n",
       "      <td>0.855263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.855263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.848889</td>\n",
       "      <td>0.855263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model  Traning Accuracy  Test Accuracy\n",
       "3         Random Forest          1.000000       0.881579\n",
       "0  Logistics Regression          0.853333       0.855263\n",
       "1                   SVM          0.880000       0.855263\n",
       "2         Decision Tree          0.848889       0.855263"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models=pd.DataFrame({'Model':['Logistics Regression','SVM','Decision Tree','Random Forest'],\n",
    "                     'Traning Accuracy':[(lr.score(X_train,y_train)),svm_linear.score(X_train,y_train),gini.score(X_train,y_train),forest.score(X_train,y_train)],\n",
    "                     'Test Accuracy':[(lr.score(X_test,y_test)),svm_linear.score(X_test,y_test),gini.score(X_test,y_test),forest.score(X_test,y_test)]})\n",
    "models.sort_values(by='Test Accuracy', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model for Dataset is Linear Regression"
   ]
  }
 ],
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