Eva Akurut.final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACE_COMPETITION\n",
    "#### Eva Akurut\n",
    "#### REG Number:2019/HD07/24850U\n",
    "\n",
    "#### Exploration Data Analysis assignment\n",
    "* Import the datasets into the notebook\n",
    "* Find out the Dimensions of the data imported\n",
    "* Descibe the data using statistics(Descriptive statistics)\n",
    "* Looking at how the variables correlate with each other\n",
    "* Data Visualisation\n",
    "* Preparation of data for Machine Learning\n",
    "* Evaluate the Performance of Machine Learning Algorithms with Resampling\n",
    "* Spot-Checking Classiffication Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# Importing the tools to use for the assignment\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data structure analysis\n",
    "import matplotlib.pyplot as plt # for visualisation\n",
    "import seaborn as sns#\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1)Loading the data to be used into my notebook\n",
    "The data to be analyzed was read in using the pandas method of pd.read_csv(),\n",
    "* Two data sets were loaded in AMP_Trainset.csv \n",
    "* Test.csv\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Importing the data sets into the notebook using read.csv\n",
    "Train=pd.read_csv(\"Data/AMP_TrainSet.csv\")\n",
    "Test=pd.read_csv(\"Data/Test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2)Checking the dimensions of my data\n",
    "* Dimensions give a deep insight into the data to be analysed\n",
    "* It helps to give sumarry of all the data types present in our data using data.info()function\n",
    "* Determines the Number of columns and rows using data.shape()function\n",
    "* Finds out if there are missing values in the data using is.null()function\n",
    "* The data.head() Funstion gives mre the top 5 rows with all the columns present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FULL_Charge</th>\n",
       "      <th>FULL_AcidicMolPerc</th>\n",
       "      <th>FULL_AURR980107</th>\n",
       "      <th>FULL_DAYM780201</th>\n",
       "      <th>FULL_GEOR030101</th>\n",
       "      <th>FULL_OOBM850104</th>\n",
       "      <th>NT_EFC195</th>\n",
       "      <th>AS_MeanAmphiMoment</th>\n",
       "      <th>AS_DAYM780201</th>\n",
       "      <th>AS_FUKS010112</th>\n",
       "      <th>CT_RACS820104</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.951</td>\n",
       "      <td>74.842</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-3.663</td>\n",
       "      <td>0</td>\n",
       "      <td>0.282</td>\n",
       "      <td>73.444</td>\n",
       "      <td>5.661</td>\n",
       "      <td>1.041</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.405</td>\n",
       "      <td>0.931</td>\n",
       "      <td>71.595</td>\n",
       "      <td>0.957</td>\n",
       "      <td>-4.011</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600</td>\n",
       "      <td>68.222</td>\n",
       "      <td>6.537</td>\n",
       "      <td>1.453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.5</td>\n",
       "      <td>5.405</td>\n",
       "      <td>0.873</td>\n",
       "      <td>73.595</td>\n",
       "      <td>0.961</td>\n",
       "      <td>-2.512</td>\n",
       "      <td>0</td>\n",
       "      <td>0.593</td>\n",
       "      <td>69.444</td>\n",
       "      <td>4.934</td>\n",
       "      <td>1.722</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.167</td>\n",
       "      <td>0.895</td>\n",
       "      <td>66.250</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-1.362</td>\n",
       "      <td>0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>67.222</td>\n",
       "      <td>4.316</td>\n",
       "      <td>1.382</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.5</td>\n",
       "      <td>8.537</td>\n",
       "      <td>0.932</td>\n",
       "      <td>64.720</td>\n",
       "      <td>0.979</td>\n",
       "      <td>-2.091</td>\n",
       "      <td>0</td>\n",
       "      <td>0.616</td>\n",
       "      <td>72.944</td>\n",
       "      <td>4.540</td>\n",
       "      <td>1.539</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
       "0          5.0               0.000            0.951           74.842   \n",
       "1          4.0               5.405            0.931           71.595   \n",
       "2          5.5               5.405            0.873           73.595   \n",
       "3          5.0               4.167            0.895           66.250   \n",
       "4          7.5               8.537            0.932           64.720   \n",
       "\n",
       "   FULL_GEOR030101  FULL_OOBM850104  NT_EFC195  AS_MeanAmphiMoment  \\\n",
       "0            0.975           -3.663          0               0.282   \n",
       "1            0.957           -4.011          1               0.600   \n",
       "2            0.961           -2.512          0               0.593   \n",
       "3            0.999           -1.362          0               0.614   \n",
       "4            0.979           -2.091          0               0.616   \n",
       "\n",
       "   AS_DAYM780201  AS_FUKS010112  CT_RACS820104  CLASS  \n",
       "0         73.444          5.661          1.041      1  \n",
       "1         68.222          6.537          1.453      1  \n",
       "2         69.444          4.934          1.722      1  \n",
       "3         67.222          4.316          1.382      1  \n",
       "4         72.944          4.540          1.539      1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head(5)\n",
    "# To check the first 5 rows on my dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(FULL_Charge           float64\n",
       " FULL_AcidicMolPerc    float64\n",
       " FULL_AURR980107       float64\n",
       " FULL_DAYM780201       float64\n",
       " FULL_GEOR030101       float64\n",
       " FULL_OOBM850104       float64\n",
       " NT_EFC195               int64\n",
       " AS_MeanAmphiMoment    float64\n",
       " AS_DAYM780201         float64\n",
       " AS_FUKS010112         float64\n",
       " CT_RACS820104         float64\n",
       " CLASS                   int64\n",
       " dtype: object, FULL_Charge           float64\n",
       " FULL_AcidicMolPerc    float64\n",
       " FULL_AURR980107       float64\n",
       " FULL_DAYM780201       float64\n",
       " FULL_GEOR030101       float64\n",
       " FULL_OOBM850104       float64\n",
       " NT_EFC195               int64\n",
       " AS_MeanAmphiMoment    float64\n",
       " AS_DAYM780201         float64\n",
       " AS_FUKS010112         float64\n",
       " CT_RACS820104         float64\n",
       " dtype: object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the datatype of of my data to determine the class of my data\n",
    "Train.dtypes ,Test.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3038, 12), (758, 11))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out the rows and columns present in my data set \n",
    "Train.shape , Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3038 entries, 0 to 3037\n",
      "Data columns (total 12 columns):\n",
      "FULL_Charge           3038 non-null float64\n",
      "FULL_AcidicMolPerc    3038 non-null float64\n",
      "FULL_AURR980107       3038 non-null float64\n",
      "FULL_DAYM780201       3038 non-null float64\n",
      "FULL_GEOR030101       3038 non-null float64\n",
      "FULL_OOBM850104       3038 non-null float64\n",
      "NT_EFC195             3038 non-null int64\n",
      "AS_MeanAmphiMoment    3038 non-null float64\n",
      "AS_DAYM780201         3038 non-null float64\n",
      "AS_FUKS010112         3038 non-null float64\n",
      "CT_RACS820104         3038 non-null float64\n",
      "CLASS                 3038 non-null int64\n",
      "dtypes: float64(10), int64(2)\n",
      "memory usage: 284.9 KB\n"
     ]
    }
   ],
   "source": [
    "# Printing the summary of the data frame\n",
    "# Summary includes list of all columns with their data types and the number of non-null values in each column. \n",
    "#we also have the value of rangeindex provided for the index axis.\n",
    "Train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(      FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
       " 0           False               False            False            False   \n",
       " 1           False               False            False            False   \n",
       " 2           False               False            False            False   \n",
       " 3           False               False            False            False   \n",
       " 4           False               False            False            False   \n",
       " 5           False               False            False            False   \n",
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       " 8           False               False            False            False   \n",
       " 9           False               False            False            False   \n",
       " 10          False               False            False            False   \n",
       " 11          False               False            False            False   \n",
       " 12          False               False            False            False   \n",
       " 13          False               False            False            False   \n",
       " 14          False               False            False            False   \n",
       " 15          False               False            False            False   \n",
       " 16          False               False            False            False   \n",
       " 17          False               False            False            False   \n",
       " 18          False               False            False            False   \n",
       " 19          False               False            False            False   \n",
       " 20          False               False            False            False   \n",
       " 21          False               False            False            False   \n",
       " 22          False               False            False            False   \n",
       " 23          False               False            False            False   \n",
       " 24          False               False            False            False   \n",
       " 25          False               False            False            False   \n",
       " 26          False               False            False            False   \n",
       " 27          False               False            False            False   \n",
       " 28          False               False            False            False   \n",
       " 29          False               False            False            False   \n",
       " ...           ...                 ...              ...              ...   \n",
       " 3008        False               False            False            False   \n",
       " 3009        False               False            False            False   \n",
       " 3010        False               False            False            False   \n",
       " 3011        False               False            False            False   \n",
       " 3012        False               False            False            False   \n",
       " 3013        False               False            False            False   \n",
       " 3014        False               False            False            False   \n",
       " 3015        False               False            False            False   \n",
       " 3016        False               False            False            False   \n",
       " 3017        False               False            False            False   \n",
       " 3018        False               False            False            False   \n",
       " 3019        False               False            False            False   \n",
       " 3020        False               False            False            False   \n",
       " 3021        False               False            False            False   \n",
       " 3022        False               False            False            False   \n",
       " 3023        False               False            False            False   \n",
       " 3024        False               False            False            False   \n",
       " 3025        False               False            False            False   \n",
       " 3026        False               False            False            False   \n",
       " 3027        False               False            False            False   \n",
       " 3028        False               False            False            False   \n",
       " 3029        False               False            False            False   \n",
       " 3030        False               False            False            False   \n",
       " 3031        False               False            False            False   \n",
       " 3032        False               False            False            False   \n",
       " 3033        False               False            False            False   \n",
       " 3034        False               False            False            False   \n",
       " 3035        False               False            False            False   \n",
       " 3036        False               False            False            False   \n",
       " 3037        False               False            False            False   \n",
       " \n",
       "       FULL_GEOR030101  FULL_OOBM850104  NT_EFC195  AS_MeanAmphiMoment  \\\n",
       " 0               False            False      False               False   \n",
       " 1               False            False      False               False   \n",
       " 2               False            False      False               False   \n",
       " 3               False            False      False               False   \n",
       " 4               False            False      False               False   \n",
       " 5               False            False      False               False   \n",
       " 6               False            False      False               False   \n",
       " 7               False            False      False               False   \n",
       " 8               False            False      False               False   \n",
       " 9               False            False      False               False   \n",
       " 10              False            False      False               False   \n",
       " 11              False            False      False               False   \n",
       " 12              False            False      False               False   \n",
       " 13              False            False      False               False   \n",
       " 14              False            False      False               False   \n",
       " 15              False            False      False               False   \n",
       " 16              False            False      False               False   \n",
       " 17              False            False      False               False   \n",
       " 18              False            False      False               False   \n",
       " 19              False            False      False               False   \n",
       " 20              False            False      False               False   \n",
       " 21              False            False      False               False   \n",
       " 22              False            False      False               False   \n",
       " 23              False            False      False               False   \n",
       " 24              False            False      False               False   \n",
       " 25              False            False      False               False   \n",
       " 26              False            False      False               False   \n",
       " 27              False            False      False               False   \n",
       " 28              False            False      False               False   \n",
       " 29              False            False      False               False   \n",
       " ...               ...              ...        ...                 ...   \n",
       " 3008            False            False      False               False   \n",
       " 3009            False            False      False               False   \n",
       " 3010            False            False      False               False   \n",
       " 3011            False            False      False               False   \n",
       " 3012            False            False      False               False   \n",
       " 3013            False            False      False               False   \n",
       " 3014            False            False      False               False   \n",
       " 3015            False            False      False               False   \n",
       " 3016            False            False      False               False   \n",
       " 3017            False            False      False               False   \n",
       " 3018            False            False      False               False   \n",
       " 3019            False            False      False               False   \n",
       " 3020            False            False      False               False   \n",
       " 3021            False            False      False               False   \n",
       " 3022            False            False      False               False   \n",
       " 3023            False            False      False               False   \n",
       " 3024            False            False      False               False   \n",
       " 3025            False            False      False               False   \n",
       " 3026            False            False      False               False   \n",
       " 3027            False            False      False               False   \n",
       " 3028            False            False      False               False   \n",
       " 3029            False            False      False               False   \n",
       " 3030            False            False      False               False   \n",
       " 3031            False            False      False               False   \n",
       " 3032            False            False      False               False   \n",
       " 3033            False            False      False               False   \n",
       " 3034            False            False      False               False   \n",
       " 3035            False            False      False               False   \n",
       " 3036            False            False      False               False   \n",
       " 3037            False            False      False               False   \n",
       " \n",
       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104  CLASS  \n",
       " 0             False          False          False  False  \n",
       " 1             False          False          False  False  \n",
       " 2             False          False          False  False  \n",
       " 3             False          False          False  False  \n",
       " 4             False          False          False  False  \n",
       " 5             False          False          False  False  \n",
       " 6             False          False          False  False  \n",
       " 7             False          False          False  False  \n",
       " 8             False          False          False  False  \n",
       " 9             False          False          False  False  \n",
       " 10            False          False          False  False  \n",
       " 11            False          False          False  False  \n",
       " 12            False          False          False  False  \n",
       " 13            False          False          False  False  \n",
       " 14            False          False          False  False  \n",
       " 15            False          False          False  False  \n",
       " 16            False          False          False  False  \n",
       " 17            False          False          False  False  \n",
       " 18            False          False          False  False  \n",
       " 19            False          False          False  False  \n",
       " 20            False          False          False  False  \n",
       " 21            False          False          False  False  \n",
       " 22            False          False          False  False  \n",
       " 23            False          False          False  False  \n",
       " 24            False          False          False  False  \n",
       " 25            False          False          False  False  \n",
       " 26            False          False          False  False  \n",
       " 27            False          False          False  False  \n",
       " 28            False          False          False  False  \n",
       " 29            False          False          False  False  \n",
       " ...             ...            ...            ...    ...  \n",
       " 3008          False          False          False  False  \n",
       " 3009          False          False          False  False  \n",
       " 3010          False          False          False  False  \n",
       " 3011          False          False          False  False  \n",
       " 3012          False          False          False  False  \n",
       " 3013          False          False          False  False  \n",
       " 3014          False          False          False  False  \n",
       " 3015          False          False          False  False  \n",
       " 3016          False          False          False  False  \n",
       " 3017          False          False          False  False  \n",
       " 3018          False          False          False  False  \n",
       " 3019          False          False          False  False  \n",
       " 3020          False          False          False  False  \n",
       " 3021          False          False          False  False  \n",
       " 3022          False          False          False  False  \n",
       " 3023          False          False          False  False  \n",
       " 3024          False          False          False  False  \n",
       " 3025          False          False          False  False  \n",
       " 3026          False          False          False  False  \n",
       " 3027          False          False          False  False  \n",
       " 3028          False          False          False  False  \n",
       " 3029          False          False          False  False  \n",
       " 3030          False          False          False  False  \n",
       " 3031          False          False          False  False  \n",
       " 3032          False          False          False  False  \n",
       " 3033          False          False          False  False  \n",
       " 3034          False          False          False  False  \n",
       " 3035          False          False          False  False  \n",
       " 3036          False          False          False  False  \n",
       " 3037          False          False          False  False  \n",
       " \n",
       " [3038 rows x 12 columns],\n",
       "      FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
       " 0          False               False            False            False   \n",
       " 1          False               False            False            False   \n",
       " 2          False               False            False            False   \n",
       " 3          False               False            False            False   \n",
       " 4          False               False            False            False   \n",
       " 5          False               False            False            False   \n",
       " 6          False               False            False            False   \n",
       " 7          False               False            False            False   \n",
       " 8          False               False            False            False   \n",
       " 9          False               False            False            False   \n",
       " 10         False               False            False            False   \n",
       " 11         False               False            False            False   \n",
       " 12         False               False            False            False   \n",
       " 13         False               False            False            False   \n",
       " 14         False               False            False            False   \n",
       " 15         False               False            False            False   \n",
       " 16         False               False            False            False   \n",
       " 17         False               False            False            False   \n",
       " 18         False               False            False            False   \n",
       " 19         False               False            False            False   \n",
       " 20         False               False            False            False   \n",
       " 21         False               False            False            False   \n",
       " 22         False               False            False            False   \n",
       " 23         False               False            False            False   \n",
       " 24         False               False            False            False   \n",
       " 25         False               False            False            False   \n",
       " 26         False               False            False            False   \n",
       " 27         False               False            False            False   \n",
       " 28         False               False            False            False   \n",
       " 29         False               False            False            False   \n",
       " ..           ...                 ...              ...              ...   \n",
       " 728        False               False            False            False   \n",
       " 729        False               False            False            False   \n",
       " 730        False               False            False            False   \n",
       " 731        False               False            False            False   \n",
       " 732        False               False            False            False   \n",
       " 733        False               False            False            False   \n",
       " 734        False               False            False            False   \n",
       " 735        False               False            False            False   \n",
       " 736        False               False            False            False   \n",
       " 737        False               False            False            False   \n",
       " 738        False               False            False            False   \n",
       " 739        False               False            False            False   \n",
       " 740        False               False            False            False   \n",
       " 741        False               False            False            False   \n",
       " 742        False               False            False            False   \n",
       " 743        False               False            False            False   \n",
       " 744        False               False            False            False   \n",
       " 745        False               False            False            False   \n",
       " 746        False               False            False            False   \n",
       " 747        False               False            False            False   \n",
       " 748        False               False            False            False   \n",
       " 749        False               False            False            False   \n",
       " 750        False               False            False            False   \n",
       " 751        False               False            False            False   \n",
       " 752        False               False            False            False   \n",
       " 753        False               False            False            False   \n",
       " 754        False               False            False            False   \n",
       " 755        False               False            False            False   \n",
       " 756        False               False            False            False   \n",
       " 757        False               False            False            False   \n",
       " \n",
       "      FULL_GEOR030101  FULL_OOBM850104  NT_EFC195  AS_MeanAmphiMoment  \\\n",
       " 0              False            False      False               False   \n",
       " 1              False            False      False               False   \n",
       " 2              False            False      False               False   \n",
       " 3              False            False      False               False   \n",
       " 4              False            False      False               False   \n",
       " 5              False            False      False               False   \n",
       " 6              False            False      False               False   \n",
       " 7              False            False      False               False   \n",
       " 8              False            False      False               False   \n",
       " 9              False            False      False               False   \n",
       " 10             False            False      False               False   \n",
       " 11             False            False      False               False   \n",
       " 12             False            False      False               False   \n",
       " 13             False            False      False               False   \n",
       " 14             False            False      False               False   \n",
       " 15             False            False      False               False   \n",
       " 16             False            False      False               False   \n",
       " 17             False            False      False               False   \n",
       " 18             False            False      False               False   \n",
       " 19             False            False      False               False   \n",
       " 20             False            False      False               False   \n",
       " 21             False            False      False               False   \n",
       " 22             False            False      False               False   \n",
       " 23             False            False      False               False   \n",
       " 24             False            False      False               False   \n",
       " 25             False            False      False               False   \n",
       " 26             False            False      False               False   \n",
       " 27             False            False      False               False   \n",
       " 28             False            False      False               False   \n",
       " 29             False            False      False               False   \n",
       " ..               ...              ...        ...                 ...   \n",
       " 728            False            False      False               False   \n",
       " 729            False            False      False               False   \n",
       " 730            False            False      False               False   \n",
       " 731            False            False      False               False   \n",
       " 732            False            False      False               False   \n",
       " 733            False            False      False               False   \n",
       " 734            False            False      False               False   \n",
       " 735            False            False      False               False   \n",
       " 736            False            False      False               False   \n",
       " 737            False            False      False               False   \n",
       " 738            False            False      False               False   \n",
       " 739            False            False      False               False   \n",
       " 740            False            False      False               False   \n",
       " 741            False            False      False               False   \n",
       " 742            False            False      False               False   \n",
       " 743            False            False      False               False   \n",
       " 744            False            False      False               False   \n",
       " 745            False            False      False               False   \n",
       " 746            False            False      False               False   \n",
       " 747            False            False      False               False   \n",
       " 748            False            False      False               False   \n",
       " 749            False            False      False               False   \n",
       " 750            False            False      False               False   \n",
       " 751            False            False      False               False   \n",
       " 752            False            False      False               False   \n",
       " 753            False            False      False               False   \n",
       " 754            False            False      False               False   \n",
       " 755            False            False      False               False   \n",
       " 756            False            False      False               False   \n",
       " 757            False            False      False               False   \n",
       " \n",
       "      AS_DAYM780201  AS_FUKS010112  CT_RACS820104  \n",
       " 0            False          False          False  \n",
       " 1            False          False          False  \n",
       " 2            False          False          False  \n",
       " 3            False          False          False  \n",
       " 4            False          False          False  \n",
       " 5            False          False          False  \n",
       " 6            False          False          False  \n",
       " 7            False          False          False  \n",
       " 8            False          False          False  \n",
       " 9            False          False          False  \n",
       " 10           False          False          False  \n",
       " 11           False          False          False  \n",
       " 12           False          False          False  \n",
       " 13           False          False          False  \n",
       " 14           False          False          False  \n",
       " 15           False          False          False  \n",
       " 16           False          False          False  \n",
       " 17           False          False          False  \n",
       " 18           False          False          False  \n",
       " 19           False          False          False  \n",
       " 20           False          False          False  \n",
       " 21           False          False          False  \n",
       " 22           False          False          False  \n",
       " 23           False          False          False  \n",
       " 24           False          False          False  \n",
       " 25           False          False          False  \n",
       " 26           False          False          False  \n",
       " 27           False          False          False  \n",
       " 28           False          False          False  \n",
       " 29           False          False          False  \n",
       " ..             ...            ...            ...  \n",
       " 728          False          False          False  \n",
       " 729          False          False          False  \n",
       " 730          False          False          False  \n",
       " 731          False          False          False  \n",
       " 732          False          False          False  \n",
       " 733          False          False          False  \n",
       " 734          False          False          False  \n",
       " 735          False          False          False  \n",
       " 736          False          False          False  \n",
       " 737          False          False          False  \n",
       " 738          False          False          False  \n",
       " 739          False          False          False  \n",
       " 740          False          False          False  \n",
       " 741          False          False          False  \n",
       " 742          False          False          False  \n",
       " 743          False          False          False  \n",
       " 744          False          False          False  \n",
       " 745          False          False          False  \n",
       " 746          False          False          False  \n",
       " 747          False          False          False  \n",
       " 748          False          False          False  \n",
       " 749          False          False          False  \n",
       " 750          False          False          False  \n",
       " 751          False          False          False  \n",
       " 752          False          False          False  \n",
       " 753          False          False          False  \n",
       " 754          False          False          False  \n",
       " 755          False          False          False  \n",
       " 756          False          False          False  \n",
       " 757          False          False          False  \n",
       " \n",
       " [758 rows x 11 columns])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying if there are no missing values in the data\n",
    "Train.isnull(), Test.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111d1aa50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization to check for missing data\n",
    "import missingno as msno\n",
    "msno.bar(Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n",
       "       'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n",
       "       'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n",
       "       'CLASS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the column names to get to know the names of the columns\n",
    "Train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  In general, both the train and Test datasets have float and intenger data types\n",
    "* The Train Data set has 3038 rows and 12 columns\n",
    "* The Test Data set has 758 rows plus 11 columns\n",
    "* The data has no missing values since the data.info() as well as the isnull() show none missing\n",
    "*  Having obtained the above results, It shows that i should classify my data using the column not preset in the Test data set  \n",
    "* There is no need for further data cleaning since we have no missing values, therefore i proceed to the next stage of descriptive stattistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3)Descriptive statistics\n",
    "* Descriptive statistics can give you deeper insight into the spread of the data and shape of each attribute/Variable making the data easily readible and creating summaries of our data.\n",
    "* This is done using the  describe() function on the Pandas DataFrame lists 8 statistical properties of each attribute:-\n",
    " * Count-Indicating the number of times the variable was carried out,\n",
    " * Mean(or average) which is the sum of all of the data values divided by the number of data values.\n",
    " * Standard Deviation which is a measure of the average distance between the values of the data in the set and the mean. If the data values are all similar, then the standard deviation will be low (closer to zero).\n",
    " * Minimum Value which the smallest mathematical value in hich the data \n",
    " * Maximum Value which is the largest mathematical value in the data \n",
    " * 25th Percentile which are the values which are in the 25% range (First quatile)\n",
    " * 50th Percentile (Median) which shows the number of values which are either 50% higher or lower\n",
    " * 75th Percentile also known as Q3 are the number of values in the 75% range\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>FULL_Charge</th>\n",
       "      <th>FULL_AcidicMolPerc</th>\n",
       "      <th>FULL_AURR980107</th>\n",
       "      <th>FULL_DAYM780201</th>\n",
       "      <th>FULL_GEOR030101</th>\n",
       "      <th>FULL_OOBM850104</th>\n",
       "      <th>NT_EFC195</th>\n",
       "      <th>AS_MeanAmphiMoment</th>\n",
       "      <th>AS_DAYM780201</th>\n",
       "      <th>AS_FUKS010112</th>\n",
       "      <th>CT_RACS820104</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "      <td>3038.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.060237</td>\n",
       "      <td>8.521520</td>\n",
       "      <td>0.971410</td>\n",
       "      <td>73.668760</td>\n",
       "      <td>0.994007</td>\n",
       "      <td>-2.432927</td>\n",
       "      <td>0.088545</td>\n",
       "      <td>15.683233</td>\n",
       "      <td>73.650828</td>\n",
       "      <td>5.911361</td>\n",
       "      <td>1.235255</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.819929</td>\n",
       "      <td>7.586652</td>\n",
       "      <td>0.107413</td>\n",
       "      <td>8.527489</td>\n",
       "      <td>0.031333</td>\n",
       "      <td>1.707223</td>\n",
       "      <td>0.284133</td>\n",
       "      <td>11.575665</td>\n",
       "      <td>9.166092</td>\n",
       "      <td>0.693689</td>\n",
       "      <td>0.210012</td>\n",
       "      <td>0.500082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.684000</td>\n",
       "      <td>42.750000</td>\n",
       "      <td>0.866000</td>\n",
       "      <td>-10.432000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>42.778000</td>\n",
       "      <td>3.533000</td>\n",
       "      <td>0.785000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.516000</td>\n",
       "      <td>0.895000</td>\n",
       "      <td>68.294000</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>-3.606000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.587500</td>\n",
       "      <td>67.556000</td>\n",
       "      <td>5.459250</td>\n",
       "      <td>1.082000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.143000</td>\n",
       "      <td>0.963000</td>\n",
       "      <td>74.059500</td>\n",
       "      <td>0.994000</td>\n",
       "      <td>-2.296500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.988500</td>\n",
       "      <td>73.697000</td>\n",
       "      <td>5.925500</td>\n",
       "      <td>1.184000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.158000</td>\n",
       "      <td>1.041000</td>\n",
       "      <td>79.343750</td>\n",
       "      <td>1.011000</td>\n",
       "      <td>-1.283250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.807750</td>\n",
       "      <td>79.778000</td>\n",
       "      <td>6.382000</td>\n",
       "      <td>1.351000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>46.667000</td>\n",
       "      <td>1.451000</td>\n",
       "      <td>101.682000</td>\n",
       "      <td>1.196000</td>\n",
       "      <td>3.576000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.280000</td>\n",
       "      <td>103.167000</td>\n",
       "      <td>8.662000</td>\n",
       "      <td>2.192000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
       "mean      2.060237            8.521520         0.971410        73.668760   \n",
       "std       3.819929            7.586652         0.107413         8.527489   \n",
       "min     -16.000000            0.000000         0.684000        42.750000   \n",
       "25%       0.000000            2.516000         0.895000        68.294000   \n",
       "50%       2.000000            7.143000         0.963000        74.059500   \n",
       "75%       4.000000           13.158000         1.041000        79.343750   \n",
       "max      30.000000           46.667000         1.451000       101.682000   \n",
       "\n",
       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
       "std           0.031333         1.707223     0.284133           11.575665   \n",
       "min           0.866000       -10.432000     0.000000            0.041000   \n",
       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
       "max           1.196000         3.576000     1.000000           51.280000   \n",
       "\n",
       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
       "mean       73.650828       5.911361       1.235255     0.500000  \n",
       "std         9.166092       0.693689       0.210012     0.500082  \n",
       "min        42.778000       3.533000       0.785000     0.000000  \n",
       "25%        67.556000       5.459250       1.082000     0.000000  \n",
       "50%        73.697000       5.925500       1.184000     0.500000  \n",
       "75%        79.778000       6.382000       1.351000     1.000000  \n",
       "max       103.167000       8.662000       2.192000     1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the spread of my data\n",
    "Train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The count values in all the columns are 3038 which corellates with the number of rows\n",
    "* The columns are 12\n",
    "* The highest mean is in the FULL_DAYM780201 which is 73.668760\n",
    "* The column with the lowest min value is FULL_Charge with -16.0\n",
    "* The highest deviation is in the AS_MeanAmphiMoment and lowest in FULL_AURR980107"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting my data into by classfication to find out how balanced the through classification\n",
    "* A groupby() operation  will be used\n",
    "* It involves a combination of splitting the object,then anapplication of a function, and combining the results.\n",
    "* It entails categorizing identical data into groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a12b38310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Train.groupby(\"CLASS\").size().plot(kind=\"bar\", color=(\"green\",\"skyblue\"))                            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* According to the results above, the data got split  into two equal groups with values that are balancing out\n",
    "* There was therefor no need to manipulate the data to become balanced out\n",
    "* There will therefore be no alogarithm balance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  A count plot \n",
    "* It can be thought of as a histogram across a categorical, instead of quantitative, variable. \n",
    "* The basic API and options are identical to those for barplot(), so you can compare counts across nested variables.\n",
    "* I intended to see the spread of the data across a variable FULL_Charge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1051bb810>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'FULL_Charge', data = Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The data in the FULL_Charge variable is normally distributed attaining a bell shaped so it gives me an insight in the data contained in the variable FULL_Charge "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4)Checking how my data correlates with each other \n",
    "* Correlation helps us determine how the different attributes relate with one another.\n",
    "* It also helps us when deciding which attributes to select especially in the presence of highly correlated attributes in which case one of the two attributes would be sufficient.\n",
    "* The corr function is used with Pearson's correlation method since most of the attributes are continous with the exception of NT_EFC195 and CLASS.In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data.\n",
    "* Here the intension was to find out if there is a relationship between the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>FULL_Charge</th>\n",
       "      <th>FULL_AcidicMolPerc</th>\n",
       "      <th>FULL_AURR980107</th>\n",
       "      <th>FULL_DAYM780201</th>\n",
       "      <th>FULL_GEOR030101</th>\n",
       "      <th>FULL_OOBM850104</th>\n",
       "      <th>NT_EFC195</th>\n",
       "      <th>AS_MeanAmphiMoment</th>\n",
       "      <th>AS_DAYM780201</th>\n",
       "      <th>AS_FUKS010112</th>\n",
       "      <th>CT_RACS820104</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FULL_Charge</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.612996</td>\n",
       "      <td>-0.490977</td>\n",
       "      <td>-0.434603</td>\n",
       "      <td>-0.058725</td>\n",
       "      <td>-0.283758</td>\n",
       "      <td>0.088068</td>\n",
       "      <td>0.355477</td>\n",
       "      <td>-0.365374</td>\n",
       "      <td>-0.090570</td>\n",
       "      <td>0.232929</td>\n",
       "      <td>0.534602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULL_AcidicMolPerc</th>\n",
       "      <td>-0.612996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.794796</td>\n",
       "      <td>0.541481</td>\n",
       "      <td>0.115201</td>\n",
       "      <td>0.513344</td>\n",
       "      <td>-0.143168</td>\n",
       "      <td>-0.431590</td>\n",
       "      <td>0.449621</td>\n",
       "      <td>0.002334</td>\n",
       "      <td>-0.213543</td>\n",
       "      <td>-0.598816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULL_AURR980107</th>\n",
       "      <td>-0.490977</td>\n",
       "      <td>0.794796</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548253</td>\n",
       "      <td>0.346139</td>\n",
       "      <td>0.462712</td>\n",
       "      <td>-0.169540</td>\n",
       "      <td>-0.426097</td>\n",
       "      <td>0.456260</td>\n",
       "      <td>0.032958</td>\n",
       "      <td>-0.403599</td>\n",
       "      <td>-0.584111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULL_DAYM780201</th>\n",
       "      <td>-0.434603</td>\n",
       "      <td>0.541481</td>\n",
       "      <td>0.548253</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010118</td>\n",
       "      <td>0.334778</td>\n",
       "      <td>-0.090058</td>\n",
       "      <td>-0.408793</td>\n",
       "      <td>0.894191</td>\n",
       "      <td>0.055915</td>\n",
       "      <td>-0.326792</td>\n",
       "      <td>-0.554838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULL_GEOR030101</th>\n",
       "      <td>-0.058725</td>\n",
       "      <td>0.115201</td>\n",
       "      <td>0.346139</td>\n",
       "      <td>0.010118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.319157</td>\n",
       "      <td>-0.230417</td>\n",
       "      <td>-0.160269</td>\n",
       "      <td>-0.029085</td>\n",
       "      <td>0.040480</td>\n",
       "      <td>-0.151935</td>\n",
       "      <td>-0.260470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULL_OOBM850104</th>\n",
       "      <td>-0.283758</td>\n",
       "      <td>0.513344</td>\n",
       "      <td>0.462712</td>\n",
       "      <td>0.334778</td>\n",
       "      <td>0.319157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.230561</td>\n",
       "      <td>-0.336297</td>\n",
       "      <td>0.275640</td>\n",
       "      <td>-0.452769</td>\n",
       "      <td>0.155304</td>\n",
       "      <td>-0.453287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NT_EFC195</th>\n",
       "      <td>0.088068</td>\n",
       "      <td>-0.143168</td>\n",
       "      <td>-0.169540</td>\n",
       "      <td>-0.090058</td>\n",
       "      <td>-0.230417</td>\n",
       "      <td>-0.230561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.178683</td>\n",
       "      <td>-0.036844</td>\n",
       "      <td>0.145924</td>\n",
       "      <td>0.080898</td>\n",
       "      <td>0.260702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS_MeanAmphiMoment</th>\n",
       "      <td>0.355477</td>\n",
       "      <td>-0.431590</td>\n",
       "      <td>-0.426097</td>\n",
       "      <td>-0.408793</td>\n",
       "      <td>-0.160269</td>\n",
       "      <td>-0.336297</td>\n",
       "      <td>0.178683</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.322378</td>\n",
       "      <td>0.025580</td>\n",
       "      <td>0.171524</td>\n",
       "      <td>0.693552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS_DAYM780201</th>\n",
       "      <td>-0.365374</td>\n",
       "      <td>0.449621</td>\n",
       "      <td>0.456260</td>\n",
       "      <td>0.894191</td>\n",
       "      <td>-0.029085</td>\n",
       "      <td>0.275640</td>\n",
       "      <td>-0.036844</td>\n",
       "      <td>-0.322378</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>-0.256060</td>\n",
       "      <td>-0.437168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS_FUKS010112</th>\n",
       "      <td>-0.090570</td>\n",
       "      <td>0.002334</td>\n",
       "      <td>0.032958</td>\n",
       "      <td>0.055915</td>\n",
       "      <td>0.040480</td>\n",
       "      <td>-0.452769</td>\n",
       "      <td>0.145924</td>\n",
       "      <td>0.025580</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.445284</td>\n",
       "      <td>0.033432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_RACS820104</th>\n",
       "      <td>0.232929</td>\n",
       "      <td>-0.213543</td>\n",
       "      <td>-0.403599</td>\n",
       "      <td>-0.326792</td>\n",
       "      <td>-0.151935</td>\n",
       "      <td>0.155304</td>\n",
       "      <td>0.080898</td>\n",
       "      <td>0.171524</td>\n",
       "      <td>-0.256060</td>\n",
       "      <td>-0.445284</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.267652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLASS</th>\n",
       "      <td>0.534602</td>\n",
       "      <td>-0.598816</td>\n",
       "      <td>-0.584111</td>\n",
       "      <td>-0.554838</td>\n",
       "      <td>-0.260470</td>\n",
       "      <td>-0.453287</td>\n",
       "      <td>0.260702</td>\n",
       "      <td>0.693552</td>\n",
       "      <td>-0.437168</td>\n",
       "      <td>0.033432</td>\n",
       "      <td>0.267652</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  \\\n",
       "FULL_Charge            1.000000           -0.612996        -0.490977   \n",
       "FULL_AcidicMolPerc    -0.612996            1.000000         0.794796   \n",
       "FULL_AURR980107       -0.490977            0.794796         1.000000   \n",
       "FULL_DAYM780201       -0.434603            0.541481         0.548253   \n",
       "FULL_GEOR030101       -0.058725            0.115201         0.346139   \n",
       "FULL_OOBM850104       -0.283758            0.513344         0.462712   \n",
       "NT_EFC195              0.088068           -0.143168        -0.169540   \n",
       "AS_MeanAmphiMoment     0.355477           -0.431590        -0.426097   \n",
       "AS_DAYM780201         -0.365374            0.449621         0.456260   \n",
       "AS_FUKS010112         -0.090570            0.002334         0.032958   \n",
       "CT_RACS820104          0.232929           -0.213543        -0.403599   \n",
       "CLASS                  0.534602           -0.598816        -0.584111   \n",
       "\n",
       "                    FULL_DAYM780201  FULL_GEOR030101  FULL_OOBM850104  \\\n",
       "FULL_Charge               -0.434603        -0.058725        -0.283758   \n",
       "FULL_AcidicMolPerc         0.541481         0.115201         0.513344   \n",
       "FULL_AURR980107            0.548253         0.346139         0.462712   \n",
       "FULL_DAYM780201            1.000000         0.010118         0.334778   \n",
       "FULL_GEOR030101            0.010118         1.000000         0.319157   \n",
       "FULL_OOBM850104            0.334778         0.319157         1.000000   \n",
       "NT_EFC195                 -0.090058        -0.230417        -0.230561   \n",
       "AS_MeanAmphiMoment        -0.408793        -0.160269        -0.336297   \n",
       "AS_DAYM780201              0.894191        -0.029085         0.275640   \n",
       "AS_FUKS010112              0.055915         0.040480        -0.452769   \n",
       "CT_RACS820104             -0.326792        -0.151935         0.155304   \n",
       "CLASS                     -0.554838        -0.260470        -0.453287   \n",
       "\n",
       "                    NT_EFC195  AS_MeanAmphiMoment  AS_DAYM780201  \\\n",
       "FULL_Charge          0.088068            0.355477      -0.365374   \n",
       "FULL_AcidicMolPerc  -0.143168           -0.431590       0.449621   \n",
       "FULL_AURR980107     -0.169540           -0.426097       0.456260   \n",
       "FULL_DAYM780201     -0.090058           -0.408793       0.894191   \n",
       "FULL_GEOR030101     -0.230417           -0.160269      -0.029085   \n",
       "FULL_OOBM850104     -0.230561           -0.336297       0.275640   \n",
       "NT_EFC195            1.000000            0.178683      -0.036844   \n",
       "AS_MeanAmphiMoment   0.178683            1.000000      -0.322378   \n",
       "AS_DAYM780201       -0.036844           -0.322378       1.000000   \n",
       "AS_FUKS010112        0.145924            0.025580       0.045562   \n",
       "CT_RACS820104        0.080898            0.171524      -0.256060   \n",
       "CLASS                0.260702            0.693552      -0.437168   \n",
       "\n",
       "                    AS_FUKS010112  CT_RACS820104     CLASS  \n",
       "FULL_Charge             -0.090570       0.232929  0.534602  \n",
       "FULL_AcidicMolPerc       0.002334      -0.213543 -0.598816  \n",
       "FULL_AURR980107          0.032958      -0.403599 -0.584111  \n",
       "FULL_DAYM780201          0.055915      -0.326792 -0.554838  \n",
       "FULL_GEOR030101          0.040480      -0.151935 -0.260470  \n",
       "FULL_OOBM850104         -0.452769       0.155304 -0.453287  \n",
       "NT_EFC195                0.145924       0.080898  0.260702  \n",
       "AS_MeanAmphiMoment       0.025580       0.171524  0.693552  \n",
       "AS_DAYM780201            0.045562      -0.256060 -0.437168  \n",
       "AS_FUKS010112            1.000000      -0.445284  0.033432  \n",
       "CT_RACS820104           -0.445284       1.000000  0.267652  \n",
       "CLASS                    0.033432       0.267652  1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=Train.corr(method='pearson')\n",
    "corr\n",
    "# The correllation 1 is a positive relationship, -1 a negative relationship and 0 no relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Heatmap is a way of representing the data in a 2-dimensional form. \n",
    "* The data values are represented as colors in the graph. \n",
    "* It's goal is to provide a colored visual summary of information so I could easily visualise the summary of the relationships between the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a12c77710>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rYHvm/TKOtz9+gk8mfI/JZKqwHK+Vdv48GAy0/2gabd+fzKXfN5AVG0teejoJh0No9/5k2n80DVN2NjG791RorouPXaH3kv18uPccz7Qxz++xU4q2fjV4ZdMJHl4dQt+6Nel8i216Mv6uD2c+xQ/rx5Gbk8cf+89Yrdv022F692tVypYVb8DoYRxau52vX5xGTmY2Bvub7+vtjtreNPZw5bvThRXBmMwc/rPpD4asP0j/QB88nGzTk3GjNh69wu0Tf2fAB5vZcSKWDx8t/C65kpTJgA820/PdDdzXIZCabk5/sifxb3RzXBWUP2el1GHL4/Na63ttfLxg4LDWOr+U9Q2BoVrrJ5VSy4D7gUXAj1rrLwGUUpOAx4EZlm0CgNu01vmWYUubtNZTlVL9LHEopZoAQ4AuWutcpdRMYBiwsOjBlVJPAU8B2Hu0w961+OTaymDx4jUsW7YOgObNGxIVFVewLioqHl9f6y5fD4/qODs7cccdnQHo168LK1b8Xm75Lv1uMytXbAMguFldq1b9mOhEvH2tLyyOhJzj+LFwBvZ9nfz8fBLiU3lyxId8+c2r1K3nz8wvXwIgIjyKHVv/fLjL37X8++2s+mE3AE2bBRIdVdgqFhOdVGxScGhIOGHHLjD4zonk5ZlITEjj/0bOYNbXz1vF9RvYjpeemcNTz/YvkzyPrN3GccsYf58GgaTFF+aZFp9k1UIJ4OpZw6pHwNyrYC7/au6Fra7Bd9zGL5PmAGCws6Pbf+8vWLfi9em41/L5x7mvXb6D9av2AtCgaW3iowvzio9JxtO75InXM6cup1btmtw99PaCZRtX72Xcp08C0Lh5ELk5uaQkpePuWXpr541y9HAnJ7GwfHMSE3HycC8Wk52YgJOnBzo/n7zMTOxdXYjdtw+PZsEY7O1wrF6d6g3qkxYeAUrhVLMmDm7mPL3atCb17Dl8Oncqs7yvisrIxt+18OLHz8WJ6PTSW3R/ORPLxK4NeQ1zb8L+K8kkZpk7cbdGJhBc05Xdl8q+JXvl0p2s+dF8XjQOrk1MVOEx4qKTqfknE/IdnRzo0iOYnVuO0q6TeZhbfl4+2zeFMue7F8s0z4NrthGyzvwZ4d8w0KpVPzU+CTcv6zzdvGpYx8QVxnjV9uXh954FIOFSDGf3HyvTXEsTm5WDT5E5Tz7OTsSW0MrfzrsG/2lUm2e3hRYMEyoqLiuHc6kZtPSqzpbL8TbNOSopE3/3whtJ+LsbiU7OtIpJysgteLx0dzivDwoutp+YlCxOXUmhfX0vfj182Wb5ivJXVXsIMrXWrSx/VysDpY3sLIcRn5zXWl+toBwEgiyPmymltiulQjFfyBd9dy4vUsHoCiwB0Fr/Blz99u0NtAX2WypAvYF61x5caz1Xa91Oa92uslYGAIYNG8iqVZ+xatVn9OnTiZ9+2oTWmsOHT+DmVs1quBCYh5H17NmBvXvNF8+7d4dQv35gueU75JGeLPlxPEt+HE+P3q34ZfUetNYcCTmLq6tzseFCDz7cg9+3fMSa9e8z/9vXqBPky5ffvApAQrx5/LDJZGLenDXcP6S7TXJ+cGg3Fq0Yy6IVY7m9V3N+Xb0frTWhIeG4ujoXGy50/5CurNn0Lj+tG8/chaMJDPIuqAxERsQWxG3bFEqdumU32KbFgNt5+OPXefjj16nXsQUnNu9Da03UyfM4VjNaDRcCcPGsgaOzkaiT59Fac2LzPup2aA5gNd/g3J4QvOr4A5CbnUNulnlYQeThExjsDHjW9v/HuQ94sCsfL3qFjxe9Qsfbm7H514NorTkZGkE1V2OJw4UWz/6VjLQs/vvSIKvlNf08OLLffKeRC+ejycnJo4aH6z/OsSi3oCAyo2PIio3DlJdH7L4DeLZsaRXj2bIFMbvMLfxxBw9Ro3FjlFI4eXqSHHYCMM8lSD13Hmc/P5w8PUk9d4787By01iSFncDZ//rGQ9+o0JhUgmo4E+BmxMGgGNjAm40R1hdvdaoXznfpWceT8BTzhdb2C4k08qyG0d6AnYL2/jU4k5iBLdw7pAvzlr7MvKUv06VnML//cgCtNcePRODiaiw2XCgzI7tgXkF+Xj57doQRGFRYYT249zS1g3yKNTz8U20H3s5/P3uN/372Gg07teDoJvN779KJ8zhVM5ZYGXeqZuTSCfN77+imfTTsZHnvWW7yoE0mdi5dR6v+tr3b21UnElMJcHXGv5oT9krRO8CbHVesh2E2rOHC2FYNeG33cZJyCi+0vY2OOFpuLODmYEcLr+pEpllfmNvCkcgkgrxdCfCshoOd4q42AWwItb67kXf1wkpOn+b+nIk2l6+fuxEnB3PO1Z0daFfPi3PRN9/cwOuhlKHC/25WVbWHoCTxwLUD6jyBuBJib9QxoKVSyq6UXoLsIo/zgavV+G+AwVrrEKXUCKBHkbj06ziuAhZord+44YzLyIIZz9OtcxNqerhxZu/nvDd9BQuWbqmodAp0796OrVsP0LfvUzg7OzFlSuEdOAYNGs2qVZ8BMGbMCMaOnc6UKfPw9KzO1KnmuCNHTvHcc1NISUlj8+b9zJixmDVrZtos3663N2fHtlAG9X8Lo9GRCZNGFKx7+L6JLPlx/J9u/9vafSz7fjMAvfq0YdC9tv/i7NKtKbu2hXH/gEkYjY68M2lowbpHH5jGohV/fhvaLz75mcjwGAxK4VfLk9feKftbBwLUaRtMxMHjfPt/72Lv5EDv5x8tWLfkpfd5+GPzbVC7jxrCxs8WkZeTS502TajTxjxmfdfCVcSev4hSCjcfT3o+/TAAmcmprJ44E6UULl416PPCY2Wee9suTTi4K4z/u38qTkYHnn/n4YJ1Lz36Pz5e9Apx0Ums+HoDtwT58MpjHwMw4MEu9B3UiZGj72bm1OX8/P02UIrR7zxc5vOplJ0d9R55mGOffAomEz5dulDtllpE/LQa16A6eLVqiW+3rpyaN5+Db7yNvYsLjUY9AYB/zx6c/noBh8ZNAA0+XTrjUtt8+8yabdsQ8t4klMEOl8Da+N3erUzzvipfw8QdZ5g/oBl2SrHiZBRnEjN4oV0dQmNT2RSRwPBmt3DbLe7kmTTJ2XmM3XwSgJScPOaHXuLHe1ujMfcQbIm0/fydTl2bsHfHCR69532cjA68NmFIwbonhkxn3tKXyczM4a0X55Obm4/JZKJ1uwbc80DngrhN62w/XKh+u6acO3CMOU+9i4OTIwNeGFawbv7oD/jvZ68BcMf/PcSaTxaTl5NDvbZNqdfW/N47vu0gh9aYhxI26tySFn0Ke4hmPj6BnIws8vPyOL3nCEPefYaagf+8Qg7mc+LjkLNM79IMO+CXiGjOp2bwRJNATiSmsSMqgWeb1cXZ3o5JHRoDEJ2ZzWt7wghyq8ZzzeuiMX9Bf3/6IudSbFNJtMrZpJmw4ggLnrkNg0GxfE8Ep6NSeXFAY0Ijk9h4NIoR3evTu5kf+SZNUkYOry4y33yiga8bbw5uVpDzl5tOc/JK8Unq4t9NXb2TQ1WilErTWrtes8wX2At00lpHWe4utBhoYpkEHA6001rHFdlmhGXZX852tAwFOgW8o7XWSqkgzC3+x4BftNbNLHFjAFet9QSlVBzmiceJwFrgktZ6hFLqG8s2KyzbfAFEaq0/UErdAawDvAEfYBXmIUMxSilPwE1rHVFans6BQ/9VJ0Rm5MSKTuGGpeeVzz2ny1Ku6XrqnzePb8/cfGOJ/0rvWrafWFjWPgot294EW9t+/OZtnSvN1uHF78x1M1t30fGvg24y8465VHQKN+zK5rJoqyxf5z4bXOH3J/K6dXSFX+PEn/qswsuhJNJDYKG1jlZKvQCsVeY+nTTM4/qLzrA7opS6+nwZcAQYoZQqekP3Tlrrkm4j8QTwP+CMUioTc8/Dq3+R1juYKymxln9LG9g7EfheKTUc2A1EAala6zil1NvA75bXlAs8C5RaIRBCCCGEqIxu5iE7Fa1KVgiu7R0osnwV5hb1ktYFlbK7b67zmCnAk6WsblYk7qMij2cBs0rY14hrFiUDd2qt85RSnYH2WutsS+xSYOn15CiEEEIIIaqeKlkhqIQCgWWWXoAcSq94CCGEEEJUSYqbcrTOTUEqBGXM8tsA196gd7jW2jb3eQS01qcB2/yUpBBCCCGEqNSkQlDGtNYdKzoHIYQQQgghrpdUCIQQQgghRKUnk4pLJyUjhBBCCCHETUIp1U8pdVIpdUYp9XoJ6+sopTYqpY4opbYopQL+6TGlh0AIIYQQQlR6/4YeAqWUHfAF0Be4COxXSq3WWh8vEvYRsFBrvUAp1QuYCgz/J8e9+UtGCCGEEEKIqqEDcEZrfU5rnQMsAQZdE9MU2GR5vLmE9TdMKgRCCCGEEEKUA6XUU0qpA0X+nrom5BbgQpHnFy3LigoB7rM8vhdwU0p5/ZO8ZMiQEEIIIYSo9G6GIUNa67nA3H+4mzHA50qpEcA24BKQ/092KBUCIYQQQgghbg6XgNpFngdYlhXQWl/G0kOglHIF7tdaJ/2Tg0qFQAghhBBCVAEV30NwHfYDDZVSdTFXBB4GHikaoJSqCSRorU3AG8D8f3rQf0XJCCGEEEIIUdlprfOA54B1QBiwTGt9TCn1rlLqHktYD+CkUuoU4AtM/qfHlR4CIYQQQgghbhJa67XA2muWjSvyeAWwoiyPKRUCIYQQQghR6d0Mk4pvVlIyQgghhBBCVGHSQyCEEEIIISo96SEonZSMEEIIIYQQVZhUCIQQQgghhKjCZMiQEEIIIYSo9JS0g5dKSkYIIYQQQogqTHoIhJXMyIkVncINcQ4cX9Ep3LDavl0rOoUbltexVkWncEM6PeBZ0SncsOnzsys6hRv2yfOZFZ3CDZnesaIzuHEtpxkrOoUb8lh/VdEp3LDpXZIrOoUb1uHeRhWdwr+STCounZSMEEIIIYQQVZhUCIQQQgghhKjCZMiQEEIIIYSo9JT69w1pKy/SQyCEEEIIIUQVJhUCIYQQQgghqjAZMiSEEEIIISo9uctQ6aRkhBBCCCGEqMKkh0AIIYQQQlR68kvFpZOSEUIIIYQQogqTCoEQQgghhBBVmAwZEkIIIYQQlZ5MKi6dlIwQQgghhBBVmPQQCCGEEEKISk96CEonJSOEEEIIIUQVJhUCIYQQQgghqjAZMiSEEEIIISo9+R2C0knJCCGEEEIIUYVJD4EQQgghhKj8ZFJxqaRkhBBCCCGEqMKkQiCEEEIIIUQVJkOGhBBCCCFEpSe/Q1A6KRkhhBBCCCGqMOkhEEIIIYQQlZ5SqqJTuGlJhUD8Y1prJk+ey9atBzEanXj//RcIDm5QLC4nJ5f33pvDvn2hKKV46aXh3HlnF/bvP8qUKV9y8mQ406ePpV+/LhXwKgrN/nAU/Xu3JjY+hXZ9x1ZoLld16xzIW690xc5gYPmq48xdcMhq/RsvdaFTuwAAjE72eHk6067XPADGPNeZHl3rADDzqwOsXX+mXHK+vYkP4x5ogcGgWLYrgtnrT1mtv79jIK8PbkZ0ciYAC7eeY9nuiIL1rkZ71r3Vh/VHLjNh+ZFyyTn56FEuLFsKJhM1u3bFr19/q/Wpp05xYdlSMi9dot4TT+LRti0AGRcuELl4MflZmSiDAb/+A/Bs397m+d7eyJvxg5phMCiW7o1k9mbr/9v72wXwxl1NiU7OAmDhznCW7osE4JsnOtK6jgf7zyfwxPx9Ns/1Kq01q2f+yIn9YTg4OfDQmEcIaFi7WNxvX6/h4Pr9ZKZlMGn1tGLrQ7eH8O17X/P85y9T+9ZAm+c8/f0f2bU9DKPRgXcmPULjpsVzvmrM819y6WI83698HYDZM9ayfXMoyqDw8HRj3KRH8PapYbN8u9fzYvydjbBTiiWHLzFrV3iJcf0b+zD7gZbc9dVeQq+kEFDDyManb+NsfAYAf1xK5q1fw8o0tyshxzi8cAXaZKJuzy40uecOq/X5ubnsm7WQxPOROLq60Hn047h4ewEQtmod57fsQhkMtH7sQfxaNiUjPpG9sxaQnZwKQL1eXbm1f08AQpf9zOWDR1AGhVN1Nzo8PRxnD/cyeR1aaxZ9upKQPWE4OTny5JtDCWoUYBWTnZXD5+8sIOZyPAaDolWXYIY8fRcAJ9iGuOMAACAASURBVA6fZfFnP3Hh3BWeGT+cDj1blkleN5L/5Mnz2Gb53p76/miCg+sXizN/b89l375jGJTixZeGceedt5VrrqJ8SIVA/GPbth0kPPwyv/8+h5CQk0yYMIvly/9XLG727GV4etZg3bo5mEwmkpLMH+D+/t5Mnfoi8+evLO/US/Tt8q3MXrCOeR8/U9GpAGAwKMaPvZ2Rz60mKjqNHxY8yMZt5zl7PrEgZurHOwseD3+oOU0aeQPQo0sdght7M2jYUhwd7Fg0ZzBbd0WQnp5r25wVTHyoJY99vpOopEx+erUnG0KvcCYq1SpuzaGLpV7svzSwCfvPxtk0z6K0yUTk999x64sv4eDhwYmpU6jRoiXOtWoVxDh6ehI0YiTR63+32tbg6EjQyJEYfX3JSUoibPIkqgcHY1+tms3yNSh4997mDJ+7h6jkTFa90I0Nx6M4E51mFbcm5DLjVx4ttv3cLWdxdrRjaKc6NsuxJCf2hxF3KZaxX79F5IkIVn62nOdnvFwsrkmnYG67pyvTRk4uti4rI4sdK7cS2Lh8ct+1PYwLEbGsWPMWR49EMG3ScuZ/VzxngM0bQnB2drJa9ujIXjz9/AAAli7eylez1/H6uIdskqtBwXv9GzNs8SGiUrJY/XhHNpyK5XRculWci6MdIzsEcuhiktXyiMRMBszbY5PcTCYTh75eRvc3nsfZy50Nb0+jVpvm1AjwL4g5v2U3Di7VGPDxRCJ3HeDI9z/RefTjJF+8QuTug9w57W0yE5PZOmUG/aePRxkMtBp2Hx51A8nNzGL9Wx/g27wxNQL8aXxXH5o/dDcAp37bzLEff6Xd40PL5LUc2RNG9MU4Pvz+Tc4ej+Cb/61gwtwXi8X1H9qDpm0akpebx/svziJkTxgtOzXBy9eDJ98cyq9LtpRJPjdq27aDRIRfYd3vswgJOcXECbNZtvzDYnGzZ6/Ay9OddetmYjKZSE5KK2FvojIo9zkESql8pdThIn9BSqkRSqnPr4nbopRqZ3kcrpSqec36Ytv8xXFbKaW0UqrfdcQ+rZR6rITlQUqpo5bH7ZRSn/3FfsKVUtuvWXb46j7+ZLseSqlfLI9HKKViLdsdV0o9+Vf5l7eNG/cweHAvlFK0atWYlJR0YmISisX98MMGRo16EACDwYCnp7mFLCDAl8aN62Iw3BxdeTv3nSDhJvrQaxHsQ8SFZC5cSiE3z8Sa9afp071uqfED72zIL+vMrfH163qy/4/L5OdrMrPyOHE6nts72/4iqmWQJxFx6VyIzyA3X/PLoYv0beH/1xtaNKvtTs3qRraHxdgwS2vp589j9PHBydsbg709Hu3akxQSYhXjVLMm1QICinU7G319Mfr6AuDo7o5D9erkpVpXfspay0APIuLTuZBgLuOfD1+mb7DfdW+/60wcadl5NsywZMd3hdKmb3uUUtRpEkRmeiYp8cnF4uo0CaK6V8mt6L8vWEuPIb2xdyyfNq1tm0Ppf4855+Ytg0hNzSQutnjOGRnZfLdwCyNHWbd6u7oaCx5nZuZgy1ELrWrVIDwhgwtJmeSaND8fi6Lvrd7F4l7pXp/Zu8LJzjfZLplrJJwJx9XXG1ffmtjZ2xPYuS2XD1o3CFw6cISgbh0BCOjYmuijJ9Fac/ngEQI7t8XOwQFXn5q4+nqTcCYcZ48aeNQ19xA5OBupfosvmYnmSo5DNeeC/eZn51CWxX5ox1G69GuHUooGwUFkpGWSFJdiFeNkdKRpm4YA2DvYE3RrAAkx5ty8/T0JbFCrwoawbNy4j0GDe1i+txuV+r394w8beGrU/YD5e9vDs3p5p1qmFIYK/7tZVURmmVrrVkX+wsvpuEOBHZZ//5TWerbWeuFfxBzQWo++juO6KaVqAyilmlxXpsUt1Vq3AnoAU5RSvtezkVKqXL4to6Pj8fMrrK/5+XkRHR1vFZOSYr7A/vTTRdx77wuMHv0+cXGJiL/m6+1KVJFW36joNHy9XUqMreXnRkCt6uw5cAmAE6fj6NY5EKOTPR41jHRqdwv+vq42z9mvhpEriZkFz68kZuJbw1gsrl+rW1j7Ri++eLwD/u7mL2+l4M37mjF1ZajN8ywqNykJBw/PgueOHu7kJt34OZp+/jw6Lw8n7+IXYWXJr4aRK0mFZRyVlIVfSWXc3J9fX+7OzMfa4l/C+vKWHJ+Mu7dHwXP3mu4kl1AhKM3F0xdIik2iScdgW6RXotiYZHz9CnP28XUnNqZ4znNmrGXYf3piNDoUWzfrszXc3WcC69Yc5KlnB9gsVz83J66kZBc8v5KajZ+bdY9FMz83alU3sulM8R642u7OrH2iI0uHt6N97bIZXnNVZmIS1bwKy9HZ053MhKRSYwx2djhUcyYnNZ3MBOttq3m5F1z4X5UeG09S+EW86gcVLAtdupqfn3uLiJ37CX7wrjJ7LQmxKXj6FJaPp7c7CXGln8fpqZn8sfMYwe1uLbMc/ono6AT8i31vW1cICr+3v+O+e1/mhdHTiIuzLnNRedy8VZUypMxV8AeBEUBfpZSxyLrHlFJHlFIhSqlvLcsmKKXGWB63tawLAZ4tsl3RVnxXpdTXSqlQy77uL3L4ZcAQy+OhwPdF9mEsst0fSqmef/Y6tNYxwFmgjlLKRSk1Xym1z7LtIMs+RyilViulNgEbLctesxwjRCn1fgnl85RS6oBS6sDcuUuvr1BvUF5ePlFRcbRu3YSVKz+ldevGfPDBfJscqyobeEcD1m08i8mkAdi59wJbd0awdP79TJ98B3+ERpNvKr8WwT+z8WgUt49fx4Cpm9hxIoYPh5vH4z/arR5bjkUTlZRVwRneuNzkJM5/PZ86/xmBMlT8x+vG49F0m7yR/tO3sv1UHB8NbV3RKf0jJpOJX+b8xF1PDaroVIo5deIily7G0aN3ixLX/9/ogfy8YQJ3DmzL8u+3lxhTHhTwdt9bmbThVLF1MWnZdJ6xnQHz9vLe+pN8dm9zXB3tyj/JvyE3K4tdH39Jq+EPWPUMNB9yD3d/Ppk6Xdpz5vetFZJbfl4+syZ+S98HuuFTy6tCcvg78vNMREXF07p1Y35cOZ1WrRsx7YOvKzotYSMVMYfAWSl12PL4vNb63nI45m2WY51VSm0BBgI/KKWCgbeB27TWcUopzxK2/Rp4Tmu9TSlVfICd2TtAsta6OYBSyqPIuh8s+/gIuBsYBgy3rHsW0Frr5kqpxsDvSqlSmw+UUvWAesAZ4C1gk9b6v0opd2CfUmqDJbQN0EJrnaCU6g8MAjpqrTNKeo1a67nAXPOzU7q04xe1ePEali1bB0Dz5g2JiipsaYqKisfX1/pDz8OjOs7OTtxxR2cA+vXrwooV1uOwRcmiY9PwK9Kq7+frSnRseomxA+9oyMRp26yWzf76ILO/PgjA/97rS3jE9bfG/l1RyVn4exR+Kft7OBdMbL0qKT2n4PHSXeG8PrgZAG3qetK+vhePdqtLNSd7HOwMZGTnM231MZvm7ODuTm5iYQtZTmISDu4ef7KFtfzMTE7PmMEtgwbjWq+eLVK0EpWcVdCrAuDnbiTq2jLOKJwrsnRvBK8P/LudlP/MrtXb2bt2NwC1GwWSFFvY85IUl0SNUoYGXSs7M5uo8CjmvGoeLZqakMo34+Yx4t0nynxi8fLvt7PqB3POTZsFEh1VmHNMdFKxScGhIeGEHbvA4DsnkpdnIjEhjf8bOYNZXz9vFddvYDteemYOTz1rPWG9rESlZuNfvbBHwN/NiajUwh4DVyd7Gnm7smR4OwC8XR356qFWPL7sMKFXUsjJNJ8zR6NSiUjMoK6XC6FXrIfC/F3OHu5kxBeWY2ZCEs6e7iXGVPPywJSfT25GJo5uLjh7Wm+bEZ9UMEHYlJfPro/nEdilPQEdWpV47MAu7dk+bSbNHvj7vQQbftzBlp/N8yvqNq5dMPwHICE2Cc+aJZ/H8z9cjm9ATfo91P1vH7ssLF68luXLzN+7zZs35Eqx723rywN3DzfL93YnAPr1u40fVmzg30x+h6B0FVEhyLQMfymqtIvQ67o4vQ5DgSWWx0uAxzBfqPcClmut4wC01lb9ZZYLbXet9dUrrG+Bkj7F+wAPFyStddFxBvFAolLqYSAMyCiyrisww7LNCaVUBFBShWCIUqorkA2Mslzo3wHcc7UnAzACV78R1xd5LX2Ar7XWGSW9xr9r2LCBDBs2EIAtW/azaNEvDBx4OyEhJ3Fzq4aPj/UHi1KKnj07sHdvKJ07t2T37hDq17ftnUEqi9DjMQQF1iCglhvRMekM7NuQl99ZXyyuXh13qrs58ceRqIJlBoOiupsjScnZNGrgRaOGXuyYEGnznI9EJBLk7UqAVzWikzK5q00AL36z3yrGu7oTsZahDX2a+xdMOH5pwYGCmPs7BtI80N3mlQEAl6AgsmJiyI6Lw8HdncQD+6n7+BPXta0pL4+zs2bh1alzwZ2HbO3IhSSCaroQ4GmubN3dqhYvLLa++5S3mxOxlovBPsF+nI2pmLkxt93Tjdvu6QZA2N5j7Fq1nVY92hB5IgJnF+dS5wpcy9nFmQkrCicZzx4zg4FPDbLJXYYeHNqNB4eac96x7RgrvtvOHf3bcPRIBK6uztT0ts75/iFduX9IVwAuX4rnlee+LKgMREbEEljHPIRs26ZQ6tS9rlGff0vI5RTqelajtruRqJRs7g72Y3SR4Xep2Xm0nl7YUr5keFsmbzhN6JUUPKs5kJSZi0mbhw7V9ahGZGJGSYf5Wzzr1yEtKoa0mDicPd2J3H2QTs+NsIqp1bY54dv3UvPWelzc+wc+wbeilKJW2+bs+fwbbh3Qi8zEZNKiYvBsEITWmv1zF1H9Fj8aDextta/UKzG4+fsAcPngEarX+mfl3ue+rvS5z/x/fHjXcTb8uINOvVtz9ngE1VyNuNcsPr5+xZdryUzP5PHXbDOJ/EYMGzaAYcPMw9W2bDnA4kVrGTiwGyEhp3Bzcynle7s9+/YepVPnFuzefYT69Uu/u5b4d7tZ7jIUD1zbFOcJ/ONbjCil7ID7gUFKqbcw95h6KaXc/um+b8BS4AvMQ5b+1vZa6+euWaaA+7XWJ60WKtURKLn52Ea6d2/H1q0H6Nv3KZydnZgy5YWCdYMGjWbVKvPc6zFjRjB27HSmTJmHp2d1pk41xx05cornnptCSkoamzfvZ8aMxaxZM7M8X4KVBTOep1vnJtT0cOPM3s95b/oKFizdUmH55Odr3p22na8+uwc7O8WK1WGcOZfA6FEdOBoWw6Zt4YC5d2Dt+tNW29rbG/hu7n0ApKXn8Oq4DeTnl1U9+09yNmkmLAthwbNdMChYvieC01GpvDiwCaGRiWwMjWJEj/r0bu5Pfr4mKSOHVxcdtHlef0bZ2RH48FBOf/oJ2mSiZpcuONeqxeXVq6hWpw7uLVuRHh7O2Vkzyc/IIOnIES7/vJrgCRNJPHCA1NOnyEtPI373LgCCRoykWm3bfXnmmzTjVx5l4ZOdMCjF8v0XOB2dxkt3NiL0QhIbjkczomtd+gT7kW8ykZSRy5glhwu2X/bMbdTzccXFyZ5db/fh9WUhbDsVa7N8r2rcoSkn9oXxwYhJODo58uCYwmldHz89jZdmm2/1u+bL1RzefJDc7FwmPzKe9v06ccdjtmlV/ytdujVl17Yw7h8wCaPRkXcmFeb86APTWLTiz29P/MUnPxMZHoNBKfxqefLaOw/aLNd8rRn320kWDm2DnUGx7PBlTsel83L3+hy5nMKG06X/H3cM9ODl7vXJzddorXnz1zCSs8pu4rnBzo42Ix5i2/tfmG872qMzNQJqcXT5L3jUC+SWti2o1+M29s5cwNqXxuPo4kKn5/8LQI2AWtTu1IbfXp2Ewc5Am5FDMBgMxJ44Q8SOfdSoXYvf35gCQPOH7sG/dTOOLFlF6pVolFJUq+lJ2zK6wxBAy85NCNkTxqsPT8HR6MATbxTu++2RHzHp6zEkxCSxeuEG/Ov4MO7x6YC5UtHj7k6cC4vk07e+Ns8t2HWMlfN/Y+q3r5VZfn+le/e2bNt6kDv6Po3R2YkpUwqnRA4e9CI/rfoEgFfGPMZrYz9hypSv8PSszpSp1zN18iYmv0NQKqW17S8OrA6oVJrW2vWaZb7AXqCT1jrKcnehxUATrbVJKRUOtLvakm/ZZoRl2bUXytce7w7gFa31nUWWLcA8vv4gsBLorLWOV0p5WlrfJwBpWuuPlFJHgGe01juUUh8AA7XWzZRSPYAxWuu7LOPyjVrrFy3799BaJ17NG3PL/jPAx0At4BfLPl4GgrXWj1uGCq3H3EPQuci+S3ydSqkpQHXgea21Vkq11lr/cW285a5K44A+V4cM/XkvwfUNGbpZOAeOr+gUblht364VncINy+tY66+DbiKdHihp9N/NbfcvZTMsozx98vzN0qZ0fbpf/42ubhotp/27yvix/v++C667amf/ddBNpoOP7YclljVFkwo/OW7tMLPCr3FO7XumwsuhJDfFYCqtdTTwArDWMr/gE2Co1rro7McjSqmLlr/plmUjiiy7qJQKuHbfmIcLXXuD+x8s+z8GTAa2WiYNT792Y2Ak8IUlr9L+EycBHkqpo5b9WE0O1lqnaq0/0FrnXLPdTMCglArF3IswQmt9vZ9M7wEOmMvlmOV5MVrr34DVwAHLaxhTUpwQQgghhKiayr2HQNzspIfA1qSHwPakh6B8SA+B7UkPge1JD0H5uCl6CDrdBD0Ee6SHQAghhBBCCHGT+Xc1PfwFpdRewOmaxcO11uX7C0dCCCGEEOLmIpOKS1WpKgRa644VnYMQQgghhBD/JjJkSAghhBBCiCqsUvUQCCGEEEIIUSIZMlQq6SEQQgghhBCiCpMeAiGEEEIIUflJM3ippGiEEEIIIYSowqRCIIQQQgghRBUmQ4aEEEIIIUSlp2VScamkh0AIIYQQQogqTHoIhBBCCCFE5ScdBKWSHgIhhBBCCCGqMKkQCCGEEEIIUYXJkCEhhBBCCFH5GWTMUGmkh0AIIYQQQogqTHoIhBBCCCFE5Se3HS2V9BAIIYQQQghRhUmFQAghhBBCiCpMhgwJIYQQQojKT0YMlUoqBMJKel5URadwQ2r7dq3oFG7YhegdFZ3CDfPXD1Z0CjfkwOl/36e+dnOs6BRumLN9fkWnUOkprSs6hRuSmWdX0SncsAbV/33nsdamik7hhsnw/ZubDBkSQgghhBCiCpMeAiGEEEIIUfnJ7xCUSnoIhBBCCCGEqMKkh0AIIYQQQlR+MpGhVNJDIIQQQgghRBUmFQIhhBBCCCGqMBkyJIQQQgghKj8ZMVQq6SEQQgghhBCiCpMeAiGEEEIIUfnJbUdLJT0EQgghhBBCVGFSIRBCCCGEEKIKkyFDQgghhBCi8pMRQ6WSHgIhhBBCCCGqMOkhEEIIIYQQlZ6WXyoulfQQCCGEEEIIUYVJhUAIIYQQQogqTIYMCSGEEEKIyk9+h6BU0kMghBBCCCFEFSY9BEIIIYQQovKTDoJSSQ+BEEIIIYQQVZhUCIQQQgghhKjCZMiQEEIIIYSo/OR3CEolFQLxj2mt+XDqEnZsC8Xo7MjEySNp0rROqfEvPvs5ly7GsnzVRABOnbjA5HcXkZmRjX8tLyZPewJXV2eb5tytcyBvvdIVO4OB5auOM3fBIav1b7zUhU7tAgAwOtnj5elMu17zABjzXGd6dDW/vplfHWDt+jM2zfV6zP5wFP17tyY2PoV2fcdWdDoA3N7Ul3EPtMBgUCzbGc7s9aes1t/fKZDXBzcnOjkTgIVbz7FsVzgAp2fcy8nLyQBcTsjkqTm7yyXnbgEevNW5PnZKsfxkFHNDLlitf7iJP8Oa1sKkNRm5+by9/TRnkzK4xdWJXx9sx3nLazkck8L4HbY/L7rXr8m4fo2xMyiWHrrIrJ3nS4zr18SX2Q+14u65uwm9kgJAYx9XptwVjKuTPSatGfTlHrLzTTbPWWvND5+v5NjeMByNDjw6dii1b61dLO7nr9aw7/cDZKRm8L+1HxQs/+GLlZw+bC7bnOxc0hJTmfbzVJvnPP39lezaHobR6MA7k4bSuGnxnK8a8/w8Ll2M5/uVrwEwe8Zatm8+ijIoPDxdGTfpEbx9atgs3+71vRh3Z2PslGLpHxeZZXlfXatfYx9mP9iKu+ftsT4vBjYtPC/m7bXZeRFz5BhHFy1DmzSB3bvQ8O47rdbn5+ZyeM4CksIjcXR1oe2zT1DN24vEs+Ec+XqxOUhrbr33LvzbtSI/J5ddU/6HKTcPk8lErfataXTf3TbJ/Vpaaz7+YBW7d5zAaHTg7feG0KhJQKnxY0d/zaWL8Sz+cUy55Hc1xymTv2LbtkMYjU5MmfocwcH1i8U9NvwdYmMTMRodAZj31Ti8vNy5fDmWN16fQWpqOvn5Jl5+5VG6d29bbvkL25IKgfjHdm4/SmREDKt+nUzokXNMfXcxC5e8WWLsxvWHqFbNyWrZu+MW8NKrD9K2fSN++nEHC+ev45nRg22Wr8GgGD/2dkY+t5qo6DR+WPAgG7ed5+z5xIKYqR/vLHg8/KHmNGnkDUCPLnUIbuzNoGFLcXSwY9GcwWzdFUF6eq7N8r0e3y7fyuwF65j38TMVmsdVBgUTH2rJYzN2EJWUyU9je7Ih9ApnolKt4tYcusiEZSHFts/KzeeuqZvKK13AnPP4Lg0YuTaUqPRsfhjcmo0R8ZxNyiiI+flMDEvCrgDQK9CTNzrV44nfjgIQmZLFoB8PlbhvW+X77oAmPPrtAaJSslj9ZGfWn4zhTFy6VZyLox0jOwbyx8WkgmV2SvHxfS14eWUoYdGpuDs7kGuyfWUA4PjeMGIuxTLu2zcJD4tg6ScrGDPzpWJxzToHc/vgrrw7fIrV8vufvbfg8dYft3HxzCWb57xrexgXImJZseZNjh6JYNqkFcz/rnjOAJs3HMHZ2foz7tGRvXj6+QEALF28ja9mr+P1cQ/ZJFeDgnf7NeHRxQfN58UTnVh/Krbk86JDneLnxeDmvLwqlLDoNJueF9pkInThEjqNHY2zpwfbx7+PX5sWuN3iXxBzYesuHFyq0fujd7m0Zz9hS1fS9rkncAuoRbeJr2OwsyMrKZmtb03Ct3VzDA72dH79ReyNRkx5+eyc9BE+LYLxaFDPJq+hqN07TnAxMo5lP7/GsdBIPpz0I/MWjy4xdsuGUJyrOdo8p2tt23aIiIgr/LbuC0JCTvHuxLksXfZBibEffvgizZo3sFo2e9YK+vW/jaFD+3HmzAVGPTWJjZvmlEfqZUduO1qqCp1DoJTKV0odLvIXpJQaoZT6/Jq4LUqpdpbH4UqpmtesL7bNXxy3lVJKK6X6FVkWpJQ6ek3cBKXUGMvjb5RS5y15hiilel+T30nL8v1KqVZF1g1RSh1RSh1TSn1QZHmgUmqzUuoPy/oBRda9oZQ6Y9nnnUWWz1dKxZSQp6dSar1S6rTlXw/L8leLlO1RS3l7Xm85Xa8tmw5z1z2dUErRomV9UlMziI1NKhaXkZ7F4gXreWLUQKvlkRExtGl3KwCdOjdl43rbXlS1CPYh4kIyFy6lkJtnYs360/TpXrfU+IF3NuSXdebW7fp1Pdn/x2Xy8zWZWXmcOB3P7Z1L7w0pLzv3nSAhKa2i0yjQMsiTiNh0LsRnkJuv+eXgRfq28P/rDStQC283IlIyuZCaRa5Js+ZsLH3qeFnFpOfmFzx2drAr7xSttLqlBhEJGVxIyiTXpPn52BXuaOxTLO6Vng2ZvfM82XmFF3bd6ntxIjqVsGhzBS0pMxeTLp+8Q3cdpUPf9iilqNs0iMy0TJLjk4vF1W0aRA2vP29FP7jpD9r2amOrVAts23yU/veYc27eMojU1EziYovnnJGRzXcLtzByVF+r5a6uxoLHmZk5KBsOW2hVqwYRiUXPiyjuaFTCedGjAbN3lXBexKQSFm3+LLHleZF4NhwXH29cfLwx2NtTq1M7og5ZNw5EHQohoGsnAPzbtyH2+Am01tg7OWKwM7//TLm5BcNAlFLYG81lbcrPx5SfX25DRLZvPka/u9uilKJZizqkpWYRF5tSLC4jI5sl325jxJN9yiWvojZt3MegQT1QStGqVSNSUtKJiUm47u2VgrQ0cwNJamoGPj5lfjkhKlBFTyrO1Fq3KvIXXk7HHQrssPx7I17VWrcCXgRmX7NumNa6JTAT+BBAKeVledxbax0M+BWpSLwNLNNatwYetmyHUqqp5Xkw0A+YqZS6euXxjWXZtV4HNmqtGwIbLc/RWn94tWyBN4CtWuvrf/dfp5iYRHz9Cj8YfHw9iI0uXiGYOWMVj47oi9HZumWkXoNabNl0GIAN6w4QHVXmKVrx9XYlKrrw4jkqOg1fb5cSY2v5uRFQqzp7DphbIU+cjqNb50CMTvZ41DDSqd0t+Pu62jTffyM/dyNXEjMLnl9JysTXvfgwsH6tbmHtm7354omO+BdZ72RvYNXYnvwwpke5VSR8XZyISssueB6Vno2vS/FWvGFN/dkwpD1jO9TjvV2Fw4IC3Iz8dG8bFt3VgnZ+1W2fr5uRyylZBc+vpGTh62a0ign2c8O/upHNp+OsltfzckFrWDisLb881ZlRtwXZPN+rkuKS8fBxL3ju7u1Oclzxi+u/khCVQHxUPLe2bliW6ZUoNiYZX7/CnH183YmNKZ7znBlrGfafHgVDLYqa9dka/p+9+46OqtgDOP6dDUk2jfRCTUKRjvQOgo1mQenY8Ild1OezYMGCIlaez4ZiRUU6CChSpfdOSEIJkAak91523h+7JNkUimYTyu9zzp6zO3fu7m83s3fvzPzm5vab32LVH3t55MnBNovVv25l7cJ6xqKkXUSUaxdezuZ2Ma4Tv0/owaM9g2wWZ15qGk7eniWPjV6e5KWmVVnHYGeHvbMTBVnmmY7UE6dY//IUNrzyDu3HjyvpIGiTiY2vTWX1Uy/iZ9soWQAAIABJREFU27YVnk2rHuypTokJGfj7l7YRX3/3StvIN1+sYuz9/TAa7WskrrLi41MIqFc6nhoQ4E1CfOW/t6+88jl3DXuOL7+cj9bmXuGTT41m+bJN9L9hAo89+g6vvTahRuIWNaO2OwQ1TpmHZkYC44FblFLG8+9Rqe1Ag4vY1gQ4rrVOtDxeCwy33NfAubMGd+CM5f6dwFytdb7W+hQQAXQD0FpvAir79t4JzLLcnwVUlm8zFphTWcBKqUeUUnuUUnu+/2ZZFW/rnzkaHk1sTCI33lxxNO+Ntx9gwdwNjBv5Ntk5edjbXz6ZbENvbcaqdScwWYbJtu6MYePWKOZ9P5zpU29lf0g8xTWUanG1WRcSR7/XVzLk3XVsOZLAh/eX5qL2nbySOz9Yz7M/7GLyiPY09qm8w1YbZoed5eZ5u/lw10me6GieHUrIKaD/nJ0MW7KPaTtO8vGAVrjU8gyCAiYPbMnU1UcqbLMzKLo29uCZxYcY8f1OBrb0p1fwlTXat3f9fjr0ux6D3eXxM3bsyGlOxybT/6b2lW5//OmhLF/7BgOHdmbBnM01HF0pBUy+pQVT1xytsM3OoOjayJNnloQw4sddDGzpR6+gy7NdeDYNZsC01+n75ktE/L6S4gJz2qYyGLjhnVe55ZN3STsZSUas7VPKLtaxI6c5HZPMDTe1q+1QzuvDj55l2fJP+OWXqezdE87SpRsAWPHHFu66awAbNn7LV1+/xksv/Q/Tlfb7py6D22Wqts+8nJRSByz3T2mt7zpv7erRy/JaJ5RSG4ChwKJLfI5BwG8XsS0CaKGUCgJiMZ+onxs6ehNYrZSaCLgA5+YPGwA7yjxfLFV3Ps7x11qftdyPA/zLblRKOVvieqqynbXWM4GZANlFmy5qgnjer+tZsnATAG3aBluN6ifEp+JbZqQE4NDBk4SFRjL0lkkUFxeTkpzJw+M/5JsfXyC4ST2+/MacjxsVGceWjSEXE8LfFp+YRUCZUf0Af1fiE7MrrTv01ua89cEmq7KvftjLVz/sBeDjt28hMurSRzevdnFpedTzLB3xr+fhRHxarlWdtOyCkvvztp5i0rC2JY/j080jnDHJOew4nkSbRu5EJ1X+N6ou8dn5BLiWjqQGuDgSXybG8v44kchbfZrDRig0adLyiwAITcoiOiOXYHcnDifZLo0rPjOP+nVLxzPq1TUSn1k6MuzqWIfr/FyZO74bAL6uDnw7tiMT5uwnLiOPXVGppOaaT6LWRyTStl5dtp2yzezcpt+2sO0P88Lwxi0ak5pQOhKclpiGu8+lL7Ddt34/I58efuGKf9OCOVtYusgcc+u2jYmPK405IT6twqLgkIORhIfGMGzgFIqKTKSmZPH4g58z4wfrw+6goZ359xMzbTZLEJ9RWbsonfkqaRf3dwUs7WJ0BybMO0BcZj67osu2iyRzu4is/nZh9PQgN7l03VZeSipGT49K6zh5eWIqLqYwJxcHV+vBAbcG9bBzdCQz9gweTUrTN+1dnPFpdR2Jh8Ko2/BCP6F/z6K5W1m2eCcALds0Ir7MzHhifHqFNnL4UBRHwmK5e/C7FFvayJMPzeCL7x63SXwAs2f/ycIFawBo264ZcWdLZ4Xi4pLx86/Y4fP3N6dKurg6cdttfQk5FMGwYQNYuGgd33wzGYCOHVuQn19IamoG3t4eFZ5DXHlqu0OQa0lnKauqE9LqymQcC8y13J8L3I+5Q3Axr/uhUupdoCHQs1y92UopB8AV6ACgtU5VSj0OzANMwDbg3JL+scCPWuuPlVI9gZ+VUm35h7TWWilV/r3cDmytznSh0eMGMHrcAAA2bzzEvF/XM3BIN0IOncTV1QlfX+sDxMgx/Rk5pj8AZ04n8cwTn/HNjy8AkJKcgZd3XUwmE99+/QfDR99QXWFWKiQsgaDG7jSs70Z8QjZDb2nOc5PXVKjXJNCDum6O7D8UV1JmMCjqujmQlp5Pi2betGjuzZY3o20a75XoUFQqQX6uNPR2Jj4tl9s6N+TZH3db1fGtayTRktpwc/v6JQuO6zrZk1dYTEGRCU8XB7o08WZmuSsU2UJIYiZBdZ1o6GYkPjufoU19eW699eh6YF0jUZaY+zf2ItJyVSFPoz3p+eZ860ZuRoLcnYgpc3JuCwdPZxDk7UxDDyfiM/K4vU09nl5cmoOdmV9Epw/Xlzye+0BXpq4+SsjZDKJSc3i0dzDGOgYKizXdA734bkekzWLtN6wP/Yb1AeDwjlA2/baFzjd2JDI8CqOL0wXXCpQXFx1PTmYOwW2CbBCt2cixfRg51hzzlk2hLPx1C7cO7sjhQ1G4ujrh42sd8/DRvRk+ujcAZ06n8J+nvinpDERHJdI40Hxhgk1/hRAYXDGnv7ocPJNBkFfZdhHA00sOlWzPzC+i08cbSh7Pva8LU9ceK20XPYNK20VjT77bGWWTOD2aBJIdn0BOYhJGTw/O7NhDp8f/ZVXHv1N7YrfswKt5E87u3odP6xYopcz7eHlisLMjJymZrLPxOPl6k5+RaU4tcnGmuKCAxMPhNBs6sIoI/rnhY3ozfIz5b751UziL5m7llkEdCA2JxsXViI+vderg3aN6cfeoXgCcPZ3C8xO/t2lnAOCeewZzzz3mzueGDXv4dfafDBnah4MHj+Hm5lxhHUBRUTGZmdl4etalsLCIDRv20LPX9QDUr+fDju2HuOvuGzlxIpb8/AK8vGx3tSxRs2q7Q1CZZMCzXJkXkFRJ3UtiycUfDtyplHoV8+SNt1LK7TyvW/Y6fi9orRdaRvW/B8peb+seYC/mNQOfAXcDaK2XA8str/8IcG5V4kNY1gNorbdbUpd8gNNA2evZNbSUnU+8Uqqe1vqsUqoekFBu+xiqSBeqDn36tWPLphDuHPwqRqMDb74zvvSF736LuYvfOO/+K1fsYv4c84nLjTd34s67etsqVACKizVTPtjMd5/egZ2dYuGycCJOpvD0o904HJ7AX5siAfPswIo1x632rVPHwK8z7wYgK7uAF15fS3FxDa3GPI9Zn02kb89W+Hi6EbHzc96evpBZ8zbUWjzFJs2b8w8w68neGAyKBdujOH42k2eHtiIkOo11IWcZ378pN7WvR3GxibScQl74eQ8AzQLcmDq2IyatMSjFV6uPVrg6kU1i1jBlWwTfDW6LnVIsPBpHRGoOT3cO5HBiJn9Fp3Bvmwb0auBBkUmTnl/ESxvNaRddA9x5pksgRSaNSWte33KcdMuMge3i1by+Ipyf7u2MnVLMP3Ca44nZ/Lt/M0LOpLP2WGKV+2bkFfHt9kiWPdwTjWb98aQK6wxspU331oTtDGfKvVOxNzpw74tjSra99/CHTPrGPFDw29fL2LtuH4X5hUwe9SY9h/RgyHjzEqp9f+2n04CONl2cW1bvvq3Ztimc4UOmYjQ6MPmd0pjvHfEhvyx84bz7f/HJ70RHJmBQioD6nrw0eaTNYi3WmtdXHuGncZ3M7eKgpV3c0JSQsxkXbhc7o1g2oQdam2eOyq8zqC4GOzva3j+GHR98htYmGvXrhVvD+hxZtByP4MYEdLqexv16s//rH1n3/Os4uDrT6YmHAEg+doKI31eZ1w0oRbsHxuDo5kpGdCz7Z84y57ybTNTv3hn/jjWTntOrb0u2bwln5G3vYTQ68OqU0qtIPTBqOrPmP1cjcZzPDTd0ZtOmfQy89QnzZUffLZ29umvYcyz5bToFBYVMeGgKRUXFFJtM9OrZnpEjzQkML740ntcnf8msWctRSjFt2sQa+w5Wmyst3hqkzi0WqZUXVypLa+1arswf2An00FrHWa4uNBtopbU2KaUigS5a66Qy+4y3lFWaElOm3q3Af7TWZa/cMwvzgtyflFJ7gBe11n9ZrsazAxhsSS/6Efjd0iFQwD5gktZ6lSX16Hmt9R6llBNwArhRa31EKeWntU6wXPlnPTBKa31MKfUnME9r/aNSqhXmxcANgNbAr5jXDdS3lDfXWhdb4g2yxFEym6CU+hBI1lq/p5SaBHhprV+0bHPH3KlppLW+YM7FxaYMXS469rRtepEtxMRvqe0QLlm92213AmMLdTr4XLjSZabgTO6FK11mZk4ovnCly0g339qO4NJ1eP9yHLer2ohbr6x4AV6+/vK5QtvF8nSs+P8DLncG1abWz8ab3f1zrZ/jRCy+r9Y/h8pcHquxytBaxwPPACss6ws+AcZqrcuuXDmklIq13KZbysaXKYtVSlX2H0HGAkvKlS2i9GpD9wOTLa/7F/CW1vpEJTFq4B2gwn+A0lrnAh8D54aM/qeUCgO2Au9prc/lPvwHeFgpdRDz6P14bRYKzAfCgJXAk2U6A3MwL1puYXmPD1me6z3MC6SPY16L8F6ZkO4CVl9MZ0AIIYQQ4qqlVO3fLlO12pUvPztQpnwpsLSKbUFVPN2PF/F6D1ZStgxYZrkfBgyoYt/x5R4vwrIYWWvdv9y2j8vcr/TSppbXqjQ3Rms9FZhaSXlVz5UM3FTFth+5iM9GCCGEEEJcmy67GQIhhBBCCCFEzbnykv0uklJqJ+BYrvg+rfWVl3QuhBBCCCH+GRkGr9JV2yHQWnev7RiEEEIIIYS43F21HQIhhBBCCCFKXMaLemubTJ4IIYQQQghxDZMOgRBCCCGEENcwSRkSQgghhBBXP8kYqpLMEAghhBBCCHENkxkCIYQQQghx1dMGmSKoiswQCCGEEEIIcQ2TDoEQQgghhBDXMEkZEkIIIYQQVz/5PwRVkhkCIYQQQgghrmEyQyCEEEIIIa5+MkFQJZkhEEIIIYQQ4homHQIhhBBCCCGuYdIhEEIIIYQQVz+Dqv3bRVBKDVJKHVVKRSilJlVRZ5RSKkwpFaqU+vWffjSyhkAIIYQQQojLgFLKDvgCuAWIBXYrpZZprcPK1GkOvAz01lqnKqX8/unrygyBEEIIIYQQl4duQITW+qTWugCYC9xZrs7DwBda61QArXXCP31R6RAIIYQQQoirn1K1flNKPaKU2lPm9ki5KBsAMWUex1rKyroOuE4ptVUptUMpNeiffjSSMiSsFJqyazuES1LUvX5th3DJ6umRtR3CJTu7fEFth3BJnntwQm2HcMkWG5xqO4RLdjo7t7ZDuCQmn/zaDuGS5c3ZWNshXJK8m//xeUmNSy+88q5FWdehoLZDuGQXmT5/1dNazwRm/sOnqQM0B/oDDYFNSql2Wuu0f/KEQgghhBBCXN2ujE7JaaBRmccNLWVlxQI7tdaFwCml1DHMHYTdf/dFJWVICCGEEEKIy8NuoLlSKlgp5QCMAZaVq/Mb5tkBlFI+mFOITv6TF5UOgRBCCCGEEJcBrXUR8BSwCggH5mutQ5VSU5RSd1iqrQKSlVJhwHrgBa118j95XUkZEkIIIYQQV78rZCGD1noFsKJc2etl7mvgOcutWsgMgRBCCCGEENcwmSEQQgghhBBXvytkhqA2yAyBEEIIIYQQ1zDpEAghhBBCCHENk5QhIYQQQghx1dOSMVQlmSEQQgghhBDiGiYzBEIIIYQQ4uoni4qrJDMEQgghhBBCXMOkQyCEEEIIIcQ1TFKGhBBCCCHE1U9JylBVZIZACCGEEEKIa5jMEAghhBBCiKufLCqukswQCCGEEEIIcQ2TDoEQQgghhBDXMEkZEkIIIYQQVz8ZBq+SfDRCCCGEEEJcw2SGQAghhBBCXP3ksqNVkhkCIYQQQgghrmEyQyD+Ma01099bzLbN4RiN9kx+ZxwtWzeqsv7zE7/hdGwyc5ZMAuCrz1aweX0IyqDw9HLj9XfG4evnbtOY+7Xy4/UR7TEYFPO3RfHVmmNW24d3b8ykYW2JT88F4KeNJ5m/Papku6uxDqtevZk1h87w5oJDNo0VoF9r/9J4t0ZWjLdHYyYNa2cd77ZIAI5/dhdHz6QDcCYll0e+3m7zeC/kqw8fZfBNHUlMzqDLLS/Wdjgl4g6GcuDnBWiTJrh/L1reMdBqe3FhIbtnzCI1MgYHVxd6THwIF19v8jOz2PG/b0g5GU1Qvx50HD+6RuLt08CTV3o0xWBQLDwax7eHYqy2j25Zj3Gt6lOsNTmFxbyx9Tgn0nJo5+PGW32aA6CAL/ZHsTYquUZi1lqzduYiTuwNw97RgaHP3ENAs4rHi7iIaP74ZDaFBYU07dyamx8ZjlKK+FOnWfXFPArz8qnr58Udz9+Po7OTzWP+7/tL2bb5iPkY9/ZoWrRuWGX9Fyb+wJnYZGYveR6AdasP8t2MNUSeTOC7XyfSqk3Vx8fqMKDPdbz98h3Y2SlmL9zN599usNreoJ4Hn747irp1jdgZDEz975+s23QUe3s7Pnzzbq5v0wCTSTN52nK27T5psziTDoVy9Nf5aJOJBv16E3zbIKvtpsJCDn/zIxmR0di7utD+8Qk4+fqQm5jEtlfewjnAHwD3psG0Hn+PeZ+iIo78PJfUI8dAKZoNvxP/rp1sEr/Wmq8+WsrureE4Gh34z5ujadayYrt4beI3pCRlUFxsom2HYJ546W7s7Az8NGMl2zeGYjAo3D1d+c+bo/H2td1vn9aaae/OYvOm/RiNjkx993FatwmuUG/8/W+RlJiGo9EBgJnfvoK3tzu/LdnAxx/Oxs/fC4Cx4wYyYuSNNotX1CzpEIh/bNvmcGKiEln4x6scPhTFB+8s4Ptfn6u07vq1B3FycrQqu/fBG3ls4hAA5s3eyHdfrWLS66NsFq9BwVujruf+z7cSl5bLby8MYG3IWSLiMq3q/bEvtsqT/X8PbcXuE0k2i7Gskng/22KO98XzxDv/YIX98wqLuW3aXzUS68X6ecFGvpq1im//+0Rth1JCm0zs/3EefV9+GmcvD9ZNfp/6ndpTt2G9kjqRG7bh4OLM4OlvEbN9DyFzltDj6QnY2dvTZuTtpMecISP2bI3Ea1AwuVczHloZQnx2PvPv6Mj66GROpOWU1Pn9RALzjpjjGdDYi5e6N+GRVYc5nprNyKX7KNbg6+TAkrs6sT46mWJt+7hP7g0j9Uwij349mTNHI1k1Yz4PfPyfCvVWfTmfQU+NoX6LIBa8+RUn94bTtEtr/vx0Djf+604at2vOwTXb2bn4L/rdO9SmMW/fcoSYqCQW/P4SoYei+eCdxXz369OV1t2wNgQnZwersqbNApg2/X7ef3uRTeMEMBgU014bxqgJ33I2Pp2V855i9fowjp1IKKnz7KM3smzlIWbN28F1Tf2Y/dWDdL3lfe4d0Q2AAcM+wcfLhdlf/4tBoz5H6+pvGNpk4sjPc+j0wjMYvTzZ+dY0fDu2x7VB/ZI6pzdtpY6zM30+eJu4Hbs5vmAJ7Z94GAAnP196vv1ahec9tfxPHOq60fv9KWiTicLsnAp1qsvurUc4E5PId0smceRwNJ9PW8Qns56pUO/laffh4mpEa83UF39i89qD9B/YkeH39ef+x82doKVzN/PrN2uY+MoIm8W7edMBoqPOsmLlJxw6GMHbU75lzrypldZ978OnaNu2aYXyQYN78urkf9ksRpuT/0NQpVpLGVJKFSulDpS5BSmlxiulPi9Xb4NSqovlfqRSyqfc9gr7nOc1I5VSIZZbmFLqHaWUsVydZ5VSeUopd8tjP8t+AWXqfKGUelkp1V8ppZVSE8ps62Ape97yeF6Z9xiplDpgKbdXSs2yxBKulHq5zHMMUkodVUpFKKUmlSmfbSk/rJT6XillbylXSqlPLfUPKaU6ldlnpVIqTSn1+8V8Rn/HpvUhDL6jK0op2l0fRGZmLkmJ6RXq5eTk8+tPG3jw0Vutyl1dS/8EubkFNk/xuz7Ii6ikbGKScygs1vy+L5Zb2te78I4WbRt54FPXyObwhAtXrgbXB3kRlVgm3r2XFu/laOuuI6SkZdV2GFZSTkTi6u+Lq58Phjp1aNSjM2f2Wnewzuw9RGC/HgA06NaRhNCjaK2pY3TEp0Uz7Oztayze9r5uRGfkEpuZR6FJs+JkIjc29raqk11YXHLfqY4d587r8opNJSf/DnYGaqAfUOL4jhDa3tgNpRQNWgaTn51LVor18SIrJZ38nDwatAxGKUXbG7txfIe5c556JoFGbZsBENyhJUe3HbB5zJvWhzL49s7mWK4PJCszj6TEjAr1cnLymfPzJh585Gar8qAm/gQG+9k8ToCO7RpxKjqZ6NgUCguL+e3Pgwy8sbVVHQ24uZoHZtxcjcQlmAcXrmvqx5YdEQAkpWSTkZlHh7YNbBJn+slInP39cPbzxVCnDgHdu5K433oAJnH/Ier36QmAX9dOpIQduWDn5PTmbSUzDcpgwMHN1SbxA+zYGMpNQ7qglKJVO3O7SEmq2C5cLL9xxcUmCouKUJYfOZcyv315uQU2z29f/9ce7rizH0opru/QnMyMHBITUm36muLKUZszBLla6w5lC1TNLPYYoLVOUkq5AjOBr4EHymwfC+wG7gZ+0FonKKXeAz4C7rWcbPcFOgO9gcPAKODbMvuXnEVorUtyB5RSHwPnfvlGAo5a63ZKKWcgTCk1B4gBvgBuAWKB3UqpZVrrMGA2cK9l/1+BCcAMYDDQ3HLrbinrbqn3IeAMPPr3Pq4LS0xIxz/As+Sxn78HiQnp+JSb+vz6sxXc88AAjMaKJ00zPv2DFct24+pm5MvvnrJVqAAEuBs5m5pb8vhsai4dgjwr1BvUoQHdmvlwKiGLdxaFcDYtF6Xglbvb8tysPfRuUTM/8AEe5eJNy6VDkNf54114iLNp5n0c6xhY+uIAikyar1YfZc2hmhnBvtLkpqTh5F3aDpy8PEk5EWldJzUNJy9zHYOdHfbOThRkZeNow5OOqvg5OxKXnV/yOD4nn/a+bhXqjWtVjwfaNsTeYODBP0s7OO193Zja9zrquRqZtPFIjcwOAGQmp+Pm41Hy2M3bg8zkdFy93Kuu42OuA+DTOIDjO0K4rmd7jmzdT2ZSms1jTkzIwD+gNB5ff3fLMa6uVb2Zn69i7P39Kj3G1ZR6/u6ciSv9TM7GpdOpfWOrOh99voZ53z7Ev+7pjbOTPaMeMv98hR49y8AbW7NkxUEaBLjTvnUD6gd4sD8kttrjzE9NxdGr9Pvm6OlBxslTVnXyUtMwlvm+1XFyojArG4DcxCR2vD4VOycjze6+A88WzUtmAyIWLyP1yDGcfH1ped8YHN2t/07VJTkxHZ8y7cLH352khHS8fCq+3qtPzeRYaAxderWkz03tS8p//OJP1q3Yg4uLkfe+ftwmcZ4TH59CQEDpoIF/gBfxCSn4+lX8/Zv8ylcY7Azccks3Hn387pLzszWrd7FnzxGCggJ4cdL91KvnU2FfcWW6ZhcVa62zgMeAYUopLwClVFPAFXgN84n9OTOBpkqpAZhP1p/SWhdatkUBRqWUvzJ/YwYBf5Z/Pcu2UcCccyEALkqpOoATUABkAN2ACK31Sa11ATAXuNMS8wptAewCziUr3gn8ZNm0A/BQStWz7LMOsM4tqQXHjsRyOjaJ/mUOhGU9/vRQlq99k4FDO7NgzuYajq6idYfj6PfGKoZM+4stRxL48L7OANzbtwkbQuOJS8ur5QitrQuJo9/rKxny7jpzvPd3LtnWd/JK7vxgPc/+sIvJI9rT2MelFiMVNe3X8LMMXLCbj3ef5LEOgSXlhxIzuX3xXkYt3cfD1zfCwe7KmEof8vQ97FuxmR+e/YCC3HwMdexqOyQAjh05zemYZPrf1K62Q7mgu4Z2YN5ve+l047vc89gPfP7+aJRSzFm8hzNx6axaMJEpL9/OngNRFJtMtR1uBY4e7vSd/i49prxKi7EjCPn6e4pyc9EmE/kpqXg0a0KPt17Fo1kTjs+1fZrWxZj6+SPMXvk6hQVFHNwdUVI+/snB/PzHZAYM7sTy+VtrMcJS7384kSXLPuSnX95k794jLFtq/k3u378zq9d9xpKlH9CzV3tefXlG7Qb6N2ilav12uarNGQKnc+kzwCmt9V01HYDWOkMpdQrzyPpOYAzmE/DNQAullL/WOl5rbVJKPQ78BSzTWm8q91QLMY/47wf2AflU1BeI11ofL7PPncBZzCP4/9ZapyilGmCeJTgnltLRfsCcbgTcB5xLVqxsnwaW574gpdQjwCMA//1iIuMnDL7gPgvmbGbpIvPi1NZtGxMfVzrtmBCfVmFRcMjBSMJDYxg28C2KikykpmTx+IOfMeOHiVb1Bg3twr+f+JpHnrxwDH9XXHoe9TxLFyHW83QiPt36BD8tu6Dk/rxtkUwa1haATsFedG3qzb19g3F2rIO9nYGc/GI+WBZqu3jTysXr4UR8Wq5VHat4t54qiRcoeW8xyTnsOJ5Em0buRCdl2yzeK5WTlwe5yaXtODclFSdP63bs5OlBbkoqzt6emIqLKczJxcG1djpYCTn5BLiUrsfxd3Ykvkw7KG/FyUTe6N28QvnJ9FxyCk0093QhNMk2aVx7/9jEwVXm40W95o2tRvUzk9Nw87b+nN283a3rJJXW8W7kz5i3nwQg5XQCJ3bb5ru3cO5Wli3aCUCrNo2ILzPqnhifXuEYd/hgFEfCYrlr0LsUW45xT/xrBl9+b9tR3/LOxqdTv8yodb0Ad84mWKdkjRvelbGPfAfA3oPRODrUwdvTmaSUbN54vzS7dPnsJzgZaZu1Uo6enuSnlH7f8lPTcPS0Hqk2enqQl5KK0cv8fSvKzcXe1QWlFA6W9Ly6QYE4+/qQHZdA3aDGGBwc8OvcEQD/rp04val6T7KXz9/Kyt/M7eK61o1IKtMukuLT8TnPBTEcHO3pcUMbdmw8TKce11ltGzC4E68//S33PTqwir3/njmzV7FwoXkNWdu2TYmLK714QHxcCv5+FWeb/S2Lhl1cnBh6W28Oh0Rw57B+eHiWzkAOH3Ej0z+aXa2xitp1WaUMQZWprLaczC7bXRsL3GXpACzCfJL/OYDW+oBS6jDwZSXPMR+YB7TEPAPQq5I6YymdHQDzTEAxUB/wBDYo4JmSAAAgAElEQVQrpdZeZMxfApu01tUylK61nol5FoS0gj8v6rMeObYvI8f2BWDLplAW/rqZWwd34vChKFxdnSqkCw0f3Yfho/sAcOZ0Mv956puSzkB0VCKNA30B2PRXCIHB/tXxtqp0KCqVIF9XGno7E5+Wy22dGvLsj7ut6vjWdSQxw9yvu7ldvZIFvP+etaf0PXVvTLvGHjbtDJTE61cm3s6VxWskMcN84n9z+/ol8dZ1sievsJiCIhOeLg50aeLNzHJXKBJmnk0CyYpLIDshCScvD2J27KXbkw9a1anXqT1Rm3bg3bwJp3ftx69Ni5pKdawgJDGTwLpONHA1kpCTz5Amvryw4YhVncC6RqIs7eKGRl5EWa5C1cDVSFx2HsUa6rs60sTdidOZtpv16jy0H52H9gMgYnco+37fRKt+nThzNBJHZ6NVuhCAq5c7js5GTh85Rf0WQRz+axedbzfvn52WiYuHG9pkYuu8VXQY3NsmMY8Y05sRY8zPvXVTOAvnbOWWwR0IPRSNi5uxQrrQ3aN7cfdo86H/7OkUnn/q+xrvDAAcOBxLk0BvGjfw5GxCBsMGX88TL861qnP6bBp9ezRj3m97ad7ED0dHe5JSsnEy2qMU5OQW0q9nc4qKi60WI1enusGB5MQnkJuYhKOnB3E7d9PusYes6vh2aM+ZLdvxaNaEhN378Gpl/r4VZGSaOwYGAzkJieTEJ+Ds64NSCt8O7Uk9cgyv1i1JCTuCS/3qXW91+6je3D7K3C52bQlj+fyt3DCwA0cOR+PiaqyQLpSbk09uTj5ePnUpLipm99Zw2nQwX9nndHQiDRqbf/u2bwilYVD1p6GOvWcgY+8xdzI2btjHnF9XMXhILw4djMDVzblCulBRUTGZmdl4etalsLCIjRv20aOnedYrMSG1pP76v/bQpIlt1pfY1DWbF3Nhl9tVhpIxnxyX5QXYZIhCKeUGBAHHlFLtMM8UrLH8wDsAp7B0CCxMlpsVrXWcUqoQc97/M5TrEFjSgu7GvO7gnHHASkvqUYJSaivQBfNIf9lr0jUETpd5rjcAX6zXBJw+3z621rtva7ZtCmf4kHcwGh2Y/E5pttW9Iz7gl4Xnv6zkF58sJzoyAYNSBNT34qXJI20ab7FJ8+b8g8x6sjcGBQt2RHE8LpNnh7YiJDqVdSFxjO/flJva1aO4WJOWU8ALv+y1aUwXjveAOV6DYsH2KI6fPRdvGutCzprjbV+P4mITaTmFvPCzuePSLMCNqWM7YtIag1J8tfpohasT1YZZn02kb89W+Hi6EbHzc96evpBZ8zbUakwGOzs6jB/N5vc/R5tMBN3QE/eG9QlduBzP4EDqd25PcP9e7JrxI38+9wYOLs50n1h6ArPimdcozM3DVFTMmT0H6TtpotUViqpbsYZ3tkfw7aC2GJRi8bE4ItJymNgpkMNJmayPTmFc6wb0qu9BoUmTkV/Ey5uOAtA5oC4Pt29DoUmjtWbK9gjS8otsFmtZTbu05uSeUL5+ZAr2jg4Meeaekm3fP/0+//r0JQBufXwUf3wym6KCApp0bk2TzuaFsWGb9rLvD/NYSIue19P+5h42j7lX35Zs2xzOyKHv4Wh04LW3S6+Cdv/I6fy0oPKrqp2zYV0I06ctJS01i/88+T3XtazPJ189bJNYi4tNvDJ1KXO+eQg7g4E5S3ZzNCKeF5+6hQOhsaxeH86bH/zOR28N55H7+6CBZ16ZD4CPlytzvnkIk0kTl5DOxEnzbBIjmL9vLe4dzb6PPkWbTNTv2wvXBvWJWLyMusGB+HW8nvr9enN45g9seXEy9i7OtHvcfP2O1KPHObFkOcrODmVQtHrgHuwtM3XNR93F4Zk/cPTXBTi4udJ6wgPnC+Mf6dq7Fbu3HuFfw97DaLTn32+UXm74yXHT+eLX58jLLeDN576nsKAYbTLRvkszhg43L5T+4bMVxEYloAwG/Op5MPFl211hCKDfDR3ZvOkAgwc+g5PRkbfffaxk2/C7XmLRkvcpKCjk0QnTKCwqxlRsokevtowYeRMAv/yykg1/7cWujgF3d1femVbzHV5hO8oWlxO7qBdWKktr7VquzB9z6k4Py0l2F8wLaVtZRu0jgS5a66Qy+4y3lF1wJWrZ/S2LimcAJq31A0qpd4FMrfW0MvVPAf211lGWxxuA57XWeyyP+1se36aU6gX4aa1/U0q9CWRprT+y1BsEvKy1vqHMc78EtNRaP6iUcsG8kHkMEAYcA27CfFK/GxintQ61XM3oX8BNWuvcMs81FHgKGII5vehTrXW3MttL4rzQZ3SxMwSXi07PXV65/Bellr5z/8TZ5QtqO4RL8tziCReudJlZfLD2FqL+XS/2zb1wpcvInYGVZXNe3lp3vDzyyi/WiO8GXbjSZea5tpfXFc8uRiOXqv8PxuXK3tCx1hPog/+9tNZ/gE/9985a/xwqc1nNEGit45VSzwArlFIGIAsYq7UuOyp/SCl17vF84BAwXik1rEydHlrrqi6LsN6ywNcALAHetpSPwXxCXdYSS/n7FxH7tvNsHoN1uhCYFyf/oJQKxZy29IPW+hCAUuopYBVgB3yvtT6Xk/IV5kXM2y2zGIu11lOAFZbYI4AcoCTPQSm1GXMqk6tSKhZ4SGu96kLvRwghhBDiqiL/h6BKtdYhKD87UKZ8KbC0im1BVTzdjxf5mlXtj9a6SSVlz5V73L/c4w3Ahkr2e7Pc4/GV1MnCvEahslhWYD7JL19e6d/LctWhJ6vY1reyciGEEEIIIeAymyEQQgghhBDCJi7jy37WtquyQ6CU2gk4liu+T2sdUhvxCCGEEEIIcbm6KjsEWuvuF64lhBBCCCGEuCo7BEIIIYQQQliRRcVVkn/RIIQQQgghxDVMZgiEEEIIIcTVTyYIqiQzBEIIIYQQQlzDpEMghBBCCCHENUxShoQQQgghxFVPy6LiKskMgRBCCCGEENcwmSEQQgghhBBXP5khqJLMEAghhBBCCHENkw6BEEIIIYQQ1zBJGRJCCCGEEFc/JSlDVZEZAiGEEEIIIa5hMkMghBBCCCGufjIMXiX5aIQQQgghhLiGSYdACCGEEEKIa5ikDAkhhBBCiKufLCqukswQCCGEEEIIcQ2TGQJh5ecIu9oO4ZL0GOFV2yFcsj3Hr7wRiucenFDbIVyS6Xd/W9shXLKmU56o7RAuWTuvotoO4ZLsSrSv7RAu2bNzbq7tEC5REQl5V9bvSGjqlXcqlFV4urZDuGTtvTrWdgjiPK68b4EQQgghLktXWmdAXGMMV96AXE2RlCEhhBBCCCGuYTJDIIQQQgghrn4yQ1AlmSEQQgghhBDiGiYdAiGEEEIIIa5hkjIkhBBCCCGuelr+D0GVZIZACCGEEEKIa5jMEAghhBBCiKufDINXST4aIYQQQgghrmHSIRBCCCGEEOIaJilDQgghhBDi6ieLiqskMwRCCCGEEEJcw2SGQAghhBBCXP3kPxVXSWYIhBBCCCGEuIZJh0AIIYQQQohrmKQMCSGEEEKIq5+kDFVJZgiEEEIIIYS4hskMgRBCCCGEuPrJBEGVZIZACCGEEEKIa5h0CIQQQgghhLiGScqQEEIIIYS46mlZVFwlmSEQQgghhBDiGiYdAiGEEEIIIa5hkjIk/hatNZu/W0TU3lDqODpw08R78WvaqEK9hBPRrP30F4oLCgns3Ia+Dw1HKcXOuSsIW7MNp7quAPS493aCOrehuLCI9V/NJSEiGmVQ9H1oBA3bNq/2+NMPHyZm/jwwmfDp04eAQYOttmceO0bM/Hnknj5NkwkP49m5MwA5MTFEz55NcV4uymAgYPAQvLp2rfb4yuvb0JNXezbFTikWHI1j5sEYq+1jWtXjntb1MWlNTmExr20+zom0HBq4OvLnyC6cSs8F4EBCBm9sibB5vABxB0M58PMCtEkT3L8XLe8YaLW9uLCQ3TNmkRoZg4OrCz0mPoSLrzf5mVns+N83pJyMJqhfDzqOH10j8V7IVx8+yuCbOpKYnEGXW16s7XAA6N3Ak0ndmmCnFIuOx/FdSKzV9lEtAhjTsrRdvLktgpPpOfSs58GznYOwtzNQWGzi4z2n2BWXXiMxa6356ZMlHNwejoPRgUdfHUtwi4ZWdfLzCvj0tVnEn07GYFB06tOGMY/fBsDaJdtYs3gLBoMBo7MjD704kobBATaPefEXiwnbGY69oz33vDiORtdVPN79/t0f7F6zm5zMHD784wOrbfs37OfPWStRSlG/aX0eePX+ao9x5w8Lid1vPib3eeI+fJpUjDHpZDSbv/iZ4oJCGnZsQ/cHR6CUYvfPS4jZexhDHTvc/H3o88S9OLo4U1xUxLaZc0g6EY0yGOg+fjj12lxXrbEDJBwKJeyX+WiTptENvWl2e8XjxcGvZ5EeGY2Dqwsdn5yAs693yfbcpBQ2vjyF5ncNpemQW6o9vvK01iz9cjHhu8JxcLRn9AvjaNi84uf95/d/sGftbnIzc3h3eWmb2L1qJ79/swx3b3cAet/Zl+5Deto85h/++xv7toXjaHTgycljaFLJd+/jV38iPjYJg52Bzn1ac+8Tt5Vs37b2APO/W41SENisPs9OudemMVc7JSlDVZEOgfhbovaFkXYmgXu/fJ34Y5Fs/HoeIz94vkK9DV/N48YnxuJ/XRDL355B9L4wAju3AeD62wfQadhNVvVD12wDYNz/XiEnLZPlb89g1IfPowzVN5mlTSai5/zKdc/+G3tPT45Mexf39tfjVL9+SR0HLy+Cxj9I/JrVVvsaHBwIevBBjP7+FKSlET71Heq2aUMdZ+dqi688g4I3ejfjwRUhxGXns2hYR9ZFJXMiLaekzvKIBOaGnwXgxsZevNyjCRNWHgYgOiOPOxfvs1l8ldEmE/t/nEffl5/G2cuDdZPfp36n9tRtWK+kTuSGbTi4ODN4+lvEbN9DyJwl9Hh6Anb29rQZeTvpMWfIiD1bo3Gfz88LNvLVrFV8+98najsUwNwuXuvelIdXHyYuJ595t3VgfXQKJ9NL28UfJxOZfzQOgP6NvHixWzCPrQklNb+Qp9aFkZhbQDMPZ76+pS03LdhVI3Ef3B5OXGwSH897hYjQKH74aCFTvnm2Qr0hY/vTpnNzigqLePfpGRzYHk6Hnq3odWsnbr6rFwB7Nx9m9mdLeWn6ozaNOWxXOImxibz206tEhUex4H8LeO6L5yrUa9uzDX2H9eGd+6dalSfEJrJmzlqe/fQZnN2cyUzNrPYYY/eHkRGXyPBP3yDxeCTbv53L7e++UKHe9m/m0fvRcfg2D2LNtBmcPhBGw45tqN++JZ3H3YHBzo7dv/zGoSWr6XrvMI6t3QrAXR+/Sm56Jmve/ZLbp71Q7cfk0J/m0v3FpzF6ebLljffw79Qetwalx4uYjduwd3FmwEdTOLNjN0fmLaHTUxNKtofNWYhv+zbVFtOFHNkVTuLpRCb9+CrR4VEs+nQBz3xWsU207tGG3nf24b3xUytsu/6Gjtw9cURNhAvA/u1HOBuTxGcLXuZ4aDTffLCIad89U6HeHeP607ZzMwoLi5gy8Sv2bw+nY89WnI1JZMlP63jn66dwretMekr1t2NRe2o1ZUgpVayUOlDmFqSUGq+U+rxcvQ1KqS6W+5FKKZ9y2yvsc57XdFVKzVBKnVBK7VNK7VVKPWzZFqSUyi0X0/2Wbe5KqZ+UUhGWfX9SSrlXsl+YZZt9mdd82bLfUaXUQEuZUSm1Syl1UCkVqpR6q0z9YKXUTss+85RSDpbyfpaYi5RSI8q9rweUUscttwfKlE9VSsUopbIu5vO5WKd2hdByQDeUUgS0CCY/O5fsFOsRxuyUdApy8whoEYxSipYDunFyV8h5nzc1Jo6G7cyjT84ebji6OJEQEV2doZN96hRGPz8cfX0x1KmDZ5eupB08aFXH0ccH54YNUeVGE4z+/hj9/QFw8PDAvm5dijJte1Bs7+tGVEYuMZl5FJo0f5xI5OZAb6s62YXFJfed7O1sGs/FSDkRiau/L65+Phjq1KFRj86c2Wv9GZ/Ze4jAfj0AaNCtIwmhR9FaU8foiE+LZtjZ21f21LVm664jpKRV69foH2nn40Z0Zh6xWXkUmTR/nkrkxsZeVnWs2kUdO7Q23z+Skk1ibgEAEWk5GOsYsK+hxXZ7txym76AuKKVo3jaInMxcUpMyrOo4Gh1o09k8M1jHvg5BLRqSkpgGgLOLsaRefl5BjYz4Hd4aQtdbu6KUIqh1ELlZuaQnV5xRCWodVDLiW9b2P7bT944+OLuZBw7cPN2qPcboPYdo1s98TPa7LpiC7FxyUq1jzElNpzA3D7/rzMfkZv26EbX7EAANrm+Fwc587PC7LpicFPPnnRYbR722LQBwcnfDwcWJpJPVe0xOOxGJs58vzn7mY3L9Hl2I32d9vIjfd5CGfczHi4CunUgKO4K2NOi4vQdw9vG26kDYWuj2ELrcbG4Tga2DyMvKJaOSNhHYOoi6lbSJ2rB702FuGNwZpRTXtQ0kO6vy717bzs0AsLevQ3CLhiQnmN/X2qU7GDSiN651ze3Y3av627HNGVTt3y5Ttb2GIFdr3aHMLbIGXvNbIBVorrXuBAwCyv6KnigX00+W8u+Ak1rrZlrrpsApy3NZ7Qe0AxoCowCUUq2BMUAby2t9qZSyA/KBG7XW1wMdgEFKqR6W53of+K/Wupkl1ocs5dHAeODXsm9IKeUFvAF0B7oBbyilPC2bl1vKqlVWchqu3p4lj129Pcgq1yHISknH1dvDuk5yWsnjkBWbmPPsNNZ9Npu8LPOopndwA07tCsFUXExGfBIJJ2LILLNPdShMS8Pes/RP7uDpQWFa6iU/T/apU+iiIhx9faszvAr8XRyJy8oveRyXnY+/i0OFeve0rsfa0V15sVsT3t5WmhbU0M3Ib3d14pfb2tMloK5NYz0nNyUNpzLtw8nLk9xyJye5qWk4eZnrGOzssHd2oiAru0biuxr4OTsSl13aLuKzC/BzdqxQb0zLevx5dxf+0yWYaTtPVNh+S6APYclZFJq0TeM9JyUxA2+/0uOCl58HqYlVpytlZ+ayb2sobTuXpqmsXrSFf4+cypwvf+eBZ++yabwAaUnpePiWtmd3Xw/Sky4+xSoxNoGE2EQ+efp/TH/qv4TvCq/2GHNS0nDxKY3Rxduj5KS+bB3nMsdk50rqABz/azsNO7QGwCuoATF7zMfkzIQkkk/GkJ106cfL88lLtT5eGL08yUtNq1DH6G19vCjMyqYoL48Tv6+m+V1DqzWmC0lPSsfDr0yb8Lm0NgEQsuUQHz/yPrOm/EBaQvV+ppVJSUzH27/07+/t607KBb57e7eE0q6LuXN+NiaRM9GJvPbIZ7wy4X/s337E5jGLmlPbHYIapZRqivnk+DWttQlAa52otX7/Avs1AzoDb5cpngJ0sTxnCa11MbALaGApuhOYq7XO11qfAiKAbtrs3HCjveWmlXlI+kZgoWXbLGCY5bkjtdaHAFO5EAcCa7TWKVrrVGAN5s4HWusdWuvz5l0opR5RSu1RSu3ZOn/F+apWm3aD+nDfjDcYM/0lnD3rsvWHJQC0vqkHrj4ezH/+QzZ/t5h6LYMxXIY96sL0NE798D2BD4yv1qnzf2J22FlunrebD3ed5ImOgQAk5BTQf85Ohi3Zx7QdJ/l4QCtcLoMZBFFz5h45y+DFe5i+5xSPXt/YaltTD2ee6xzElO01s67kUhUXFfP5mz8zcERf/BqUzordOrwP/13wKmMeH8pvP66pxQgvTnGxicTTiUyc/hQPvHo/c6fPIycr58I71oKDi1ei7Aw06WteG9V8QE+cvTxYPukDdv64CN8WwZfNMQ/g2JI/CB50E3WMxgtXvoy07tmWV39+nf/MfInrOl3HnA9/vfBONai4qJhPXv+FISP74m/57hUXmTgbk8SbXz7BM1Pu5ev35pOdmVvLkYrqUttrCJyUUgcs909prW091NMGOHiuM1CFpmViApgIeAIHLCf7gPnE31KvDXDoXLlSyoh5pP5cYl4DYEeZ54u1lGGZKdgLNAO+0FrvtKRDpWmti8rXP48GQNlVphezTwmt9UxgJsBnYaurHCY8tGITYZYcf79mjclKLh3RyEpOw9XLelrU1cvdakbAPKtgHp1w9igdqW5zay9+f+drwDzy0/dfw0u2LZw0HY/6fhf7Vi6KvYcHhakpJY8LUtOw9/A8zx7WinNzOf7ZZzS4cxiuTZpUa2yVic/OJ8C1dOQ3wMWR+OyCKuv/cSKRt/o0h41QaNKk5ZubUmhSFtEZuQS7O3E4ybapL05eHuSWaR+5Kak4eVq3DydPD3JTUnH29sRUXExhTi4Ori42jetqkpCTT4BLabvwd3EgISe/yvp/nkpkcs9mpfWdHfjfgFa8suUYMZl5No119aItrF9mPgw2adWI5ITS40JKQhqevpWnVHz3wQICGvowePQNlW7veXNHfvhoUfUHDGz+bTPbV2wHoHGLxqQllrbn9MQ03H0uPg3Ew9eDwFaB2NWxw7ueN74NfUmMTSKwZeML73we4Ss3cmyd+Zjs0zTQauQ+OzkNZy8Pq/rOXh7klDkm55Src3zDDmL2HmbQ60+XpEsa7OzoPr70mPz7ax/jXs3HZKOn9fEiLyUVo6dHhTp5yak4eZUeL+xdXUg7cYq43fs4Mm8xhTm5KKWws7cn6Jb+1RojwNalm9lpaRONWjS2GtVPT7q0NuFSt/RY131wT/74Znn1BVrGyoVbWLtsJwDNWjUiOb7075+cmI5XFd+9r99bQL1GPgwd06+kzNvPg+ZtGlOnjh3+9b2p18iXszGJNGv9z9pxjbr8xhcvG7XdIci1pNmUVdUJabXPZyulXgVGAn5a63MrSk+Uj0kpdcdFPN25jkQw8IdlJP+8LB2MDkopD2CJUqotEHdJb6IGtR/Sj/ZDzAeHyD2HObRiE837dCb+WCQOzkZcynUIXLzccXAyEnf0FP7XBXFk/S7aDzX/sGenpJfUP7njIN6B5tzPwvwC0Bp7oyPRB45gsDPg1ah680JdgoLIS0ggPykJew8PUvfsJvihCRfeETAVFXFixgy8e/QsufKQrYUkZhJU14mGbkbis/MZ2tSX59ZbT9UG1jUSlWE+qevf2ItIy1WFPI32pOcXYtLQyM1IkLuTzU/+ADybBJIVl0B2QhJOXh7E7NhLtycftKpTr1N7ojbtwLt5E07v2o9fmxYV1myIqh1OyqRxXSMNXB2JzylgcLAvL246alWnsZuRaMvfu19DL6IzzO3CzcGOL29uwyd7I9mfkFHhuavbrcP7cOvwPgDs3xbG6kVb6HlzRyJCo3ByNeLpUzGVbf7MFeRk5TJh0iir8riYRAIamdP0DmwLJ6ChT4V9q0PfYX3pO6wvAKE7Qtn822Y6DehEVHgURhenStcKVKV973bs/WsfPQZ1Jys9i8TYRHzqeV94xwtoNegGWg0yH1Nj9h0mfOUmgnt3JvF4JA7OTjiX64Q7e7pj72Qk4dgpfJsHEbFpV8n+sQfCCFm6liFvPUMdx9KUxKL8ArTlmHz6UDgGOwMeDav3mOzeJJDs+ARyEpMwenpwZsceOj7+L6s6/p3aE7tlB57NmxC3ex8+rc3Hi16vlV7M4tji37EzOtqkMwDmKwH1vtPcJsJ2hrJ16WY6DOhEtKVNXMpagYzk9JL6odsP49fY3yYxDxrRh0EjzN+9vVvDWLlwK71v6cjx0GicXSr/7s35+k9ysvN47BXr717Xfm3ZumY/A27rRkZaFmdjEktmD8SVr7Y7BJVJxjwiX5YXkFQNzx0GXK+UMmitTVrrqcDUi1hwG4b5xN1wbnZBKWXAnPsfZqlzQmvdwTLCv1UpdYfWehlwGih7LbKGlrISWus0pdR6zGk+HwMeSqk6llmCCvUrcRroX+41Nlxgn38ksHMbovaG8fPjU6jjaM9NE0svPTb33+8x5r+TALjh0dGs+/QXigoKCezUisBO5rzUbT8tJfFULEop3Py8GPDYGABy0zNZ9taXKKVw8Xbn5meq99J8AMrOjsZjxnL8f5+gTSZ8evfGqX59zixbinNgIB7XdyA7MpITM76kOCeHtEOHOLN8GW3efIvUPXvIPH6MouwskrebR+aCxj+Ic6OKl5urLsUapmyL4LvBbbFTioVH44hIzeHpzoEcTszkr+gU7m3TgF4NPCgyadLzi3hpo/nEsGuAO890CaTIpDFpzetbjpOeX3SBV/znDHZ2dBg/ms3vf442mQi6oSfuDesTunA5nsGB1O/cnuD+vdg140f+fO4NHFyc6T7xoZL9VzzzGoW5eZiKijmz5yB9J020ukJRbZj12UT69myFj6cbETs/5+3pC5k1b0OtxVOs4d0dJ/j6FnO7WBIRz4m0HJ7sEEhociYbYlIY16o+Pep5UKQ1GflFvLLlGABjW9ankZsTj3VozGMdzKN7j6w+TEpeoc3j7tCzFQe2h/PcqHdxMNrz6CtjS7a9/MBHTJv1PMkJaSydtZb6gX68+uB0wNypGHBHD1Yv2sLh3cewq2OHi5sTj702zuYxt+7emrCd4bx93zs4GB34P3v3HR5F1bdx/Hs2vZEeOoROCD30JnawN6r9VYFHRAV5UEQERUWx1wdRUUBFmhRBBVGkS28JCTWUAEk2vdc97x+7JJuyQCRLIPl9risXuzNndu4MszNz5pwzGf7f4swzRsxgwizzY2iXf7mCXX/tIj83n1eHTKHnbT0Y+OhAWndtTdTOKN56fDoGBwN3j7gLD+/KbQ1r0CmUmN0RLHn2NRycnej7dPExefl/p3P3uxMB6PnkYDZ+YX4UdP2ObWjQyXxM/uebhRQWFLB6mvn5HIEtguk1YhjZqemsefNzlEHh7udDv2ceLbvyy2RwcKDtI0PZPuNTtDbRoF8vvBrU49CSX/Bp0ojanTvQsF9v9n75HUoRWwoAACAASURBVOvGv4qTpzudn37i4h9sRyHd2hC1LZK3H30DJxdnhowv3ic+GDmDcV+a94mVX61gj2WfmDZsCt0G9uDWRwayadkGIrZGYHAw4O7lztD/2n8/7twrhD1bIhkzaDrOLk6MfmVo0bzxj7zPe3NfIDE+hZ+/W0v9xkFMeOxDAAY+0Jsb7+pBxx6t2Lf9EM8Pm4HBoHj4mTvxquT92N6uot5uVx11fpR+laxcqQyttWepabWBbUAPrXWs5elCPwAhWmuTUuoE0EVrnWC1zGOWac9cwjoXYu7HP9nS7ccVSNRaeyilgoGVWuu25Sz3M+ZuQ69b3r8KdNBa3196OaXUvcAErXVPpVQo5kHA3YB6wJ9AC8yVnHxLZcANWAO8o7VeqZRaBCzRWv+klJoJ7Ndaf2GV5TvL+hZb3vth7nrU2VJkNxCmtU6yWqbMti7PhboMXY22xpcdRHm123nk2rsLPqiT/S8UK9MH93198UJXmWavXx2PM62I7+6wf+tCZUrIufauBvYmXo337WyLz7n2xihdX9d2V7urVWPPwosXusq097ujyk9+wZ+tr/JrnBPPXFfl26E8V93RUWsdh7n//a+WLjgfAcNK9fvfr5SKsfx8YJn2mNW0GKVUg9KfbfEk4A8cVUrtxDwA1/qvDDUr9djRZy3TnwBaWh45egxoSfHTf0pbBrgrpfpqrSOAhZhbEn4HRlu6CtUF1iml9gM7MA8KXmlZ/kVgnFLqqCXrNwBKqa5KqRjM3Zy+VEpFWLZZEuYBzzssP6+frwwopWZYlnG3bJepNjILIYQQQogaqEpvPdi6Y621Xg4stzEv2MbHfXeJ60wDyv0rNpbHnrrZmJcMlPsn+SzLtbV6r4EOVu/fBN4stcx+oJONzztOOY8K1VrvwNwdqLxlZgOzy5k+gZIVHiGEEEKIGkeGqdl21bUQCCGEEEIIIa6ca6tzYgUopbYBpTuYP6y1vvCfyhVCCCGEENWOtBDYVm0rBFrr7lWdQQghhBBCiKuddBkSQgghhBCiBqu2LQRCCCGEEEKcJ3/80jZpIRBCCCGEEKIGkxYCIYQQQghR7UkDgW3SQiCEEEIIIUQNJhUCIYQQQgghajDpMiSEEEIIIao96TJkm7QQCCGEEEIIUYNJC4EQQgghhKj2lNwGt0k2jRBCCCGEEDWYVAiEEEIIIYSowaTLkBBCCCGEqPZkULFt0kIghBBCCCFEDSYVAiGEEEIIIWow6TIkhBBCCCGqPYN0GbJJWgiEEEIIIYSowaSFQAghhBBCVHsyqNg2qRCIEm6sl1fVESrkg9m5VR2hwrSXc1VHqLCfDW5VHaFCmr3+dFVHqLBjr35R1REq7o6HqjpBhYT4FFR1hAqbtNGzqiNUyLoHcqo6QoU9vP7a2sYA86679razuLpJlyEhhBBCCCFqMGkhEEIIIYQQ1Z50GbJNWgiEEEIIIYSowaSFQAghhBBCVHtKmghskhYCIYQQQgghajCpEAghhBBCCFGDSZchIYQQQghR7Sm5DW6TbBohhBBCCCGuEkqpAUqpQ0qpo0qpl8qZP0opdUAptVcptUkp1eZy1ykVAiGEEEIIUe0pVfU/F8+oHIDPgYFAG2BYORf8P2qt22mtOwIzgA8ud9tIhUAIIYQQQoirQzfgqNb6uNY6D/gJuNu6gNY6zeqtB6Avd6UyhkAIIYQQQoirQ33gtNX7GKB76UJKqdHAOMAZuOFyVyotBEIIIYQQotqr6u5C5h81Qim10+pnxL/5XbTWn2utmwEvAq9c7raRFgIhhBBCCCGuAK31LGDWBYqcARpavW9gmWbLT8D/LjeXtBAIIYQQQohqr+pbBy4p5g6ghVKqiVLKGRgKrCj5e6gWVm9vB45c7raRFgIhhBBCCCGuAlrrAqXUM8BqwAGYrbWOUEq9DuzUWq8AnlFK3QTkA8nAo5e7XqkQCCGEEEIIcZXQWv8K/Fpq2qtWr5+r7HVKhUAIIYQQQlR7hkvrslMjyRgCIYQQQgghajBpIRBCCCGEENXeJQ7qrZGkhUAIIYQQQogaTCoEQgghhBBC1GDSZUgIIYQQQlR70mXINqkQiMumteabD5axa0skLq7OjJk8lGatG5Qok5uTx7sT5xJ7JgGDwUCXvm14ZPQdABhjk/nktflkZmRjMmkefvp2wnqH2DVzv1aBTLm7LQaDYsG2U8xcd7TE/Pu7NGDiHW2IS80BYO7mEyzYfgqA757sTqfGvuyITuLJ2dvtmvO865oF8OqA1jgYFAt2x/C/zdHllhsQUpuZgzty56ytHDiXBkDrIE/euiMUTxdHTFpz91f/kFtosnvmPvV9eblHMwwGxeJDsXy9/3SJ+UNa12V4SD0KtSYrv5Apm49wLCWLdgFevNbH/DdXFPD5npOsPZlo97wAvev78lK3pjgoxZIjsXxzIKbE/MGt6jC0dT1MlsxTtxzleGoWPev68HxYME4OBvILTby/M5rtsalXJPOFzHx3JANv7IQxMY0uN0+o6jiA+Xgx96Ol7NsaibOrMyMnDaNJq7LHi09emUPcmUQMBkXnPqEM/Y/5eLF26Rb++HkTBoMBV3cXnpgwiAZN6tg98xfvLmfHZvMxbvzUIbQIaVCm3MvPfEVSQhqFhSbadmrCMy/eh4NDcUP84nl/M+ujlSxa+xrevh52y9uzri/jO5v342XHYvkusuR+/GCr+tzTrA6FWpOck89r2w4Tm5ULwLMdg+lTzw8Dim2xyby7+7jdclrTWvP+9MVs3hiBq6szU958mNZtGtosP+6ZmZyJSWTBskkAzPp8FcuWbMHH1xOA0c/dRe9+oXbLmxERTvzi+WiTCZ/effG/5bYS87OOHCZuyU/knomh3uMjqNW5S9G8/KREzv0wh4LkJFCKBk8/h7N/gN2ynnetbWNxZUmFQFy23VuiOHs6gS8WT+Rw+Cm+nLGEGbPLPiL37gf7065Lc/LzC5gyeia7tkQS1iuERbPX0vumjgy4vxenj8cybdzXzOr9it3yGhS8fm87Hp71D7Gp2Sx/ri9rD8ZyNC6jRLlV+84yZWl4meVn/X0MN2cHhvVobLeMZfLeFsJD83YSm5bDiqd68seheI4mZJYo5+HswOPdG7EnJqVomoNSfHhfe8YtPUBkXDo+bk7km+xfGTAomNyrOU/8foC4zFwW3tWJdacSOZaSVVRm5bF4FkSdA+D6Rn682L0pI1aHcyQ5k0HLd1OoIdDNmaX3dmbdqUQKtf0zv9K9GU+tCSc2K5cFd3Rk3akkjqcWZ1513MjCQ7EA9G/ox4RuTRj1RwTJufk88+dBjNl5NPdx58ub23LjoitTWbyQeYvWM3POar7+8OmqjlJk39ZIYmMSeH/ByxyNOMm37y3m9a+eL1PutmH9CQ1rQUF+AW89+z/2bo2kY88Qet3SmZvu7QXAro3h/PDpcl78YKRdM+/YHMWZ00a+XfYSUeGn+GT6Ej6dW/YYN+nth/HwdEVrzbQJc9mwdh/X39oJgPjYFHb9c5igOj52zWpQ8FJYM55eF05cdi7zbunI+jNJRKcV78eHkjN4ePUecgpNPNC8Ls91bMLELVG0D/CiQ0Athv62G4BvbupAWJA3u+LtX7ndsvEgp04Z+fnXKYTvP8Hb037iu/n/LbfsX3/sxd3dpcz0YQ9fz8OP32TvqGiTibiFP9BwzDicfHw5MeMNPNt1xKVuvaIyjn5+1H34cZLWrimz/Nm53xBw6+14hIRiysm5Ys/CvJa2sbjyrvgYAqVUoVJqr9VPsFLqMaXUZ6XK/a2U6mJ5fUIpFVBqfpllLrBOb6XUXKXUUaXUMctrb6v5oUqpv5RSh5RSR5RSk5UyNyxZ1mO0ZI1QSi1WSrlb5k1VSmmlVHOrz3reMu189mFKqQNKqf1Kqd/P/x6WZc9YbYfbrD5joiXrIaXUrVbTZyul4pVSJa5SlVJ+Sqk/LNn/UEr5lprfVSlVoJR64FK2V0Vt3xDO9QPDUErRql1jMtOzSUpIK1HGxdWZdl3Mm8nJyZGmrRqQaDnJKAVZmeY78ZmZOfgF1LJHzCIdGvlyMjGT00lZ5Bdqftl7lptDL/0O45ajCWTkFtgxYUkd63tzMimL0ynZ5Js0v0Sc45bWQWXKvXB9C2Zujia3oPiCv28zf6Li0omMSwcgJTsfk50vrAHaB3pxKi2bmPQc8k2aX48buaGRf4kymfmFRa/dHB3Qllw5haaii39nBwNXIC4A7QK8OJWeQ0xGDgUmzW/RRm5o5HdJmaOSMjFm5wFwNCULV0cDTlfBA683b48iKSXj4gWvoF2bwuk7oAtKKVq0DSYrPZvkco4XoWHmViJHJ0eCWzUgyWiu6Lp7uBaVy83JuyJ9ALasj+Dm282ZQ9o1JjMjh0RjWplyHp7mbIUFJvLzC1BW2WZ+sJwnn7ujxDR7CPXz4nRGDmcyzfvxmlNG+jcouR/vjE8lx9JKeCAxjSB3ZwC0BhcHA04GA84GA45KkZiTZ9e8561ft5/b7+qGUop2HZqQnp5NgrFsRSQrK5cf5/7F/40ccEVylSfnRDTOgUE4BwSiHB2pFdaNjP17S5Rx9g/AtX7DMvtn7rmzUGjCI8R8Z93g6orBueyFtz1cS9vYXpRBVfnP1aoqBhVna607Wv2cuALr/AY4rrVurrVuBkQDXwMopdyAFcDbWutWQAegF2B9S22BJWsokAcMsZp3ABhq9X4QEGH5bEfgY+B6rXV7YD/wjFXZD622w6+WZdpYPi8UGAB8oZRysJT/zjKttJeAP7XWLYA/Le+xfJ4D8A5Q9jZFJUk0puJfu/iul3+QN0nlHGTOy0zPZuemCNp3NZ/whzx1K+t/38WTd7zOG2O/5qkX7rVXVADqeLtyLiW76H1sSg51vF3LlBvQri6/jbuOLx4Jo24586+U2l6unE3LKXp/Li2H2l4l84TW8aJuLVfWHUkoMb2pvwdaw9wHw1g5oicjewVficgEubsQm5lb9D4uK5faHs5lyg0PqcvqQV0Z37Upb/1T3G2rfaAXv9wXxvL7wnht8xG7tw6Umzkzj6By7pANbV2X3+7rwgtdmjB927Ey829uHMDBxAzyr0TN6xqUZEzDP6j4eOEX5EPyRY4XuzdH0DasZdG0NUs2MXbQm8z/YiWPPm/f4wVAYnwqgVbHuIAgbxJtZJ44ehaDb56Ku7srfW9sD8CWv8MJCPSmWct65S5TmYLcXYjLsv7u5RHoZvuC8+6mddhyLhmAA4np7IxLZfU93Vl9T3e2xiZzIi3b5rKVyRiXQu06xfeygmr7EB+XUqbczE9X8uCjN+LqWvZ4smj+Bobd+xavv/I9aVYte5UtPyUZR9/irI4+vuSnJF/SsnnxcRjc3YmZ9TnR018j/udF6CvQagvX1jYWV161f8qQ5e59GDDNavLrQBelVDNgOLBZa70GQGudhfmi/aVyPssR8ACsv/nLgLst85sBqcD5qzJl+fGwtDjUAs5eJPLdwE9a61ytdTRwFOhmybYBSLKxzBzL6znAPVbzxgBLgHhbK1RKjVBK7VRK7Vz43e8XiXd5CgsKeX/y99w+uC916pvvGG9cs4cbbu/K1ytf5ZUPn+SjqfMxXaEDpC1/Hoyj75t/MvCD9Ww8nMB7wzpVaZ4LUcDkW1vz5pqoMvMcDIqujXx47uf9PDB7G7e2rk2vJn5lP6SK/Bh5jlsX7eD9HccZ1bG4C9Z+Yzp3/ryLwct381SHhjg7XD13VX6KOsfAn3fywc5oRnZoVGJeMx93xoUF8/rWozaWFhVRWFDIZ1PncesDfQmqX9zCdMv9ffhw0SSG/ud2ln33RxUmLGv65yP4afWr5OcXsHfHUXKy85g/+08eHXXrxRe+wgYGB9LGz5O5ljEGDTxdaVLLnYHLtzFg+Ta61vahY6B9W2wr4lBUDDGnjVx/U4cy8+4f0pelv03lhyUvERBYi4/e/bkKEl6cLiwk++gRgu4bTPCEV8hLNJL6z+aqjlWkOmzjC1Gq6n+uVlUxhsBNKXW+bS1aa23v2zttgL1a66K2fq11oSVDqOVnl/UCWutjSilPpdT5I+EQpVQfoC5wGPjFqngacFop1RbzhfkC4HHL5+Qrpf6DuRUhEzgCjLZa9hml1CPATuAFrXUyUB/4x6pMjGXahdTWWp+zvI4FagMopeoD9wLXA11tLay1ngXMAjiYsvKSbmv+umgTfyzfBkDzNg1JtLrLkBifil+gd7nLfTF9EfUaBnDnsH5F0/5csY1XP34KgNbtgsnPyyctJRMfP69LiVJhsak51PVxK3pfx8eV2NScEmVSsvKLXi/YdpKXbrfvIOcLiUvPoV6t4haBurVciUsvzuvp4kjLIE9+eqwbAIGeznw9rBNPzt9DbFoO208mk5xt/n3WHTXStm4ttkSXV6+sPPFZudTxKL4rWdvdhbhM210Pfj1uZErvFmWmH0/NJivfRAtfDyIS7Nv1pUxmD2fire60lvZbtJHJPZsXl3d35uPrQ3h502FOp+fYXK4mWrNkE+tWmA9rTUMakhhffLxIik/B18bx4psZi6jTIICBQ64rd37Pmzrx7XtLKj8wsGLhZn5daj7GtWrTEKPVMS4hPhV/G5kBnF2c6HldKFvXh+Pn70Xs2SRGDfsAAGN8Kk8/+CGfzn3WLt0j47Nyqe1u/d1zxphddj/uVtuHJ9o04qk/9xe1Zl3fwJ8DiWlkW7odbjmbTPuAWuwtp3tUZVg4fz3LFm8BoE3bxsTFFt9ri49LIah2yfEWB/ZGExlxirtueZXCQhNJiemMfOwjvvzuefyttuU9D/Rm7OiZdskM4OTjS0FycdaClGScfHwvsITVsr6+uDRoiHNAIABe7TuRfcJ+A7ev1W0srryq7jJ0vjJg6yL0amlzX6C17gjUwXxxX3oUzk+Yu/ncAyw9P1Ep5QT8B+gE1MPcZWiiZfb/gGZAR+Ac8H5lBNVaa4q320fAi1rrSr/dftugPnz4/Qt8+P0LdO/XlnW/7UJrzaEDJ3H3dC33RPfDzN/Iysjh/8beXWJ6QB1f9u84AsDp6Djy8grwtjzFwB72n04hOMCDBn5uODko7uxYj7URsSXKBHoVn1BvCq3Dsfiq64e970wawf7uNPBxw8mguDO0Ln8cKm7wSc8toPO76+jz8Qb6fLyBPTGpPDl/DwfOpbH+WAKtanvh6mjAQSm6N/bjiNH+v8sBYzqNa7lR39MVJ4PitqaBrDtV8klBja0qOdc19ONkqrlrQn1PV843CNTzdKGptxtnrsAFdnhCOo1quVLf0wVHg2Jgk0DWnS5ZcWpk1VWrXwM/Tlm6U3g5O/DFTaF8tOsEe+Ltc/F0Lbvl/j5MnzOe6XPG06VfOzb+vhOtNUfCT+Dm6YpvOceLhbN+JSsjm4efu6fE9NjTxqLXe7dEUqeBfZ7Octfg3sycP46Z88fRq38of6wyZ448cBIPT1f8S905z87KLRpXUFhQyPZNkTQMDqJJi7osWvsa81ZOYt7KSQQGefPFD2PtNlbqYFI6Db1cqedh3o9vaRTI+piS+3ErXw8mdW3O2A3mAfHnxWbl0jnIGwcFjkrROcibaDt2Cxk87Dp+XDKRH5dMpP8N7Vm1Yjtaaw7si8bT042AUpWuB4b25bd1b7Fizet8NXcsjYKD+PI784B0677wf/+5j2bN69ott2vjYPLi48hLMKILCkjbtR3PdmXvqJe/bBNM2VkUpJvHdWUdjsSljv2yXqvbWFx5V8tThhKB0tVrP4q73lyOg0BHpZTh/IWxUsqA+UL8IBAE9LNeQCnVFMjQWqdZDwDTWmul1C+Yu+G8bbXISuBdYGepZTpaljtm+dyFWLoiaa3jrNb3leUzAM4A1s8Ba2CZdiFxSqm6WutzSqm6FHcP6gL8ZMkTANymlCrQWi+7yOdVSFjvEHZtieQ/90/HxdWJMZOLh1SMfeh9Pvz+BRLiUlj87VrqBwfxwiMfAnDboN7cfHcPHn/2Tr6Yvohf5m8ApXh28lC7DrwrNGmmLA1n7lM9MCjFoh2nORKXwdhbW3HgdAprD8bxWJ8m3BRah0KTiZSsfMb/VDxgbOHTvWga5ImHiyNbXrmJlxbuY8Nh4wXWeJl5tebVXyOZ+1AYDkqxcO8ZjhgzGdu/OQfOprL2AutOyyng660nWPFUTzSadUcSyowzsE9meGPrUb4e0BaDUvx8OJajKVmM6dyY8IR01p1KYnib+vSq50O+SZOWW8DEDYcACKtTi6fah5Jv0miteX3rUVKuwCDuQg1v/XOML29ui4NSLD0ax7GULEZ3bExEYjp/n05ieEg9etT1oUCbM7+86TAAw1rXo6GXG6M6NmJUR3M3ohFrwknKyb/QKu1uzqdj6NszhABfL45u+4xpHyxmzoK/qzRTx54h7N0aybjBb+Hs6sTIl4cVzZv46HtMnzOexPgUls9ZS73GQUx63Hxn/Zb7+3D9XT1Ys2QT4TsO4+DogIeXG6NeGW73zN36hLB9cxSP3f02Lq5OjJ9aPIxs1LAPmDl/HDnZeUwZN5v8vEJM2kTHLs254/6eds9WWqGGGTuP8Vl/8368/Hgcx9OyGNWuMQeT0tlwJonnOjbBzcmBd/qYWz5jM3MZt/Egf55OoGttHxYMDEMDW84lsfGsfVsTz+vdL5TNGyO4d+BruLo58eq0h4rmDb9/Oj8umXiBpeGT95dx+FAMCkXd+n68PGXYBctfDuXgQO3Bwzn9+UdgMuHdszcu9epjXLkM10bBeLXvSPbJaM7M+oLCrEwywveRsGoFTSe/jjIYCLp3EKc/eQ8Al4aN8end7yJrrBzX0ja2l6u5y05VU1pf2ZvwSqkMrbVnqWm1gW1AD611rOUJPT8AIVprk1LqBNBFa51gtcxjlmnWg3RtrfNnzN2GXre8fxXooLW+3zKoOAIYobVea3m/CFittf609HqUUm8CtbTWY5RSUzFXHN5TSg0FDmutdyul/gbGYx4vsAtor7U2KqWmAe5a6xfOX8BbPnMs0F1rPVQpFQr8iHncQD3Mg4RbnO/ypJQKBlZqrdta/X7vAola67eVUi8BflrrEg8dV0p9Z1lu8YW21aV2Gbpa3P7GNRUXAO1VdqDW1c6tgdvFC11FHK6iMQeX6tirX1R1hArbuPuhixe6igS6Vu3YpH/jvl/s+5jSyrbugWuvu9zD66vuoRH/1rzrrr3tXMvp5io/MHdfvKnKLxq2PdCnyrdDea6KFgKtdZxS6jngV8vd+wxgWKmuLvuVUuffL8Tc/eYxpZR1e3IPrXXJv8Bi9gTwqVLq/CNBtlqmobXOVkrdbZn/OeAAzAOsH2l6fgyBAXOf/sfK+R1+KmfaWaXUa8AGpVQ+cNJq2RlKqY6Yu/ecAEZalomwtCQcBAqA0VaVgflAfyBAKRUDTNFaf4O5tWKhUuoJyzoGl7MNhBBCCCFqLGkhsO2KVwhKtw5YTV8OLLcxL9jGx313ietMBmzeytJaH8B8oV3evO9srUdrPdXG9P5Wr2cCZUbeaK0fvkCeN4E3y5lebvuc1joRuNHW51nKPHah+UIIIYQQomaq9o8dFUIIIYQQQth2VXQZqixKqW1A6b/A8rClBUAIIYQQQtRQV/EfCq5y1apCoLXuXtUZhBBCCCGEuJZUqwqBEEIIIYQQ5ZFBxbbJGAIhhBBCCCFqMKkQCCGEEEIIUYNJlyEhhBBCCFHtKbkNbpNsGiGEEEIIIWowaSEQQgghhBDVngwqtk1aCIQQQgghhKjBpEIghBBCCCFEDSZdhoQQQgghRLWnpM+QTdJCIIQQQgghRA0mLQRCCCGEEKLakwYC26SFQAghhBBCiBpMKgRCCCGEEELUYNJlSAghhBBCVHvSZcg2aSEQQgghhBCiBpMKgRBCCCGEEDWYdBkSQgghhBDVnnQZsk0qBKKE9w54VnWECvloTHZVR6gwN8fCqo5QYWcyr63t3M6voKojVNwdD1V1ggrr2/n7qo5QIf2+Gl3VESpsSOi19d17dL1rVUeosEDXa++YfNMyt6qOUGHbB1V1AnEhUiEQQgghhBDVnkFaCGySMQRCCCGEEELUYFIhEEIIIYQQogaTLkNCCCGEEKLaky5DtkkLgRBCCCGEEDWYtBAIIYQQQohqz6B0VUe4akkLgRBCCCGEEDWYVAiEEEIIIYSowaTLkBBCCCGEqPZkULFt0kIghBBCCCFEDSYtBEIIIYQQotqTu+C2ybYRQgghhBCiBpMKgRBCCCGEEDWYdBkSQgghhBDVnvwdAtukhUAIIYQQQogaTFoIhBBCCCFEtSePHbVNWgiEEEIIIYSowaRCIIQQQgghRA0mXYaEEEIIIUS1J3fBbZNtI4QQQgghRA0mFQIhhBBCCCFqMOkyJIQQQgghqj15ypBt0kIghBBCCCFEDSYtBOJfSQ4P5/j8hWAyUbtvHxrcNqDEfFN+Poe/+ZbMk6dw9PSg1cincA0IwFRQyNE5c8k8dQpdaCKoVw8a3DYQgDNr1hK3aRMKhXuD+rR4/FEMTk52ya+1ZsUXPxO1IxInFycGjx9OgxYNy5T7/dtV7PpjB9kZWbyxYkaZ+Qc27mPetG8Z89k4GrZsZJes5/Mu+WwpEdsicXZ14qEJw2jYsmzeX75ZxfY1O8lKz+L9X98pmr7k86Uc2XsUgLzcfDKS05nxy3S75T2fee2sJRzbdRAnF2duf+5B6jQvmzn26ClWffQD+Xn5NAtrw00j7kcpRVz0GVZ/voD8nFxqBflx1/hHcHF3s3vmuR8tZd/WSJxdnRk5aRhNWjUoUSY3J49PXplD3JlEDAZF5z6hDP3PHQCsXbqFP37ehMFgwNXdhScmDKJBkzqS9xLNfHckA2/shDExjS43T6iyHKV1CfBhVOumOCjFbzFxLIyOKTG/rW8tRrVuSlNPD97aH8WmuMSieU+0DKZ7oC8Kxe7EFP4XddwuGbXW/PPtHMBZNgAAIABJREFUYk7vjsDRxZl+ox8moGnZ71vCsVNs+HweBXn5NOwcSo/HH0Apxa6fVnJyx36UUrh6e9Fv9EN4+Pmwf/lajm3cAYDJZCI1JpYHv3kbFy+PSs2fERFO7OL5aJMJ3959CbjlthLzM48cJm7JT+SciaHB4yOo1blL0byDzzyFSz3zfu/k50ejUWMqNdt5SQfM5z2tTdTp24eG5Zz3Dn3zLRknT+Hk4UHrUcXnvSNz5pJx8hTaZKJ2zx40vN183js8ew5J+w/g5OVF2LQpdsl9Xo/aPrzQqSkGpVh+PI65h0rux8Nb1OOupnUoNGlScvOZtvMIsVm5ANR2c2FSl+bUdndBA2M3RnDOMu9aouQvFdskLQQ2KKW0Uup9q/fjlVJTlVKTlFJ7LT+FVq+ftfE5U5VSZ6zK7VVK+Sil+iulUq2mrbVa5hGlVLhS6oBSao9Sarxl+iClVIRSyqSU6mJV3lkp9a2l/D6lVH+reX8rpQ5ZrSfocreNNpk4/sN8Qp8fQ6dpUzFu30HW2bMlysRt2oyjhwdh09+g3s03cWLxzwAk7tqFLiig02tT6DB5ErHrN5KTkEBucjLn/vqLDq+8TKfXp6BNJozbd1xuVJuidkSScMbIhG8ncf/zQ1j6yaJyy4X0CGXMp2PLnZeTlcOmpetp1Lqx3XKed3BbJPFnjLw672WGjhvMgo8Wl1uubc9Qxn/xfJnp94++l5e++i8vffVfrrunDx36trd3ZI7vOkjyWSMjv5zMgNFDWP2/heWWW/3FQgY8M5SRX04m+ayR47siAfjtk/n0f/ROnvhsIi17tmfbz3/ZPfO+rZHExiTw/oKXeWLCIL59r/ztfNuw/rw3/yXe+u4FDu+PZu9Wc+Zet3TmnXkTmD5nPHcMv54fPl0ueStg3qL13P3I21WaoTQDMDqkGa/siuCpTbu5vm4gjTxKVkyN2bm8f+Aw684ZS0xv4+NFqE8tRm3ew8jNu2np7Ul7X2+75IzZc5C0c0YGfTqFPiOHseWrn8ott/mrBfQZNZxBn04h7ZyRmL0HAWh3143c9/7L3PveRBqFtWXv4t8AaH/3Tdz73kTufW8iXYffRZ02LSq9MqBNJs4t/IFGo5+n+eRppO7cTu65kucUJz8/6j38ON5dupdZXjk50+zlKTR7eYrdKgPaZOLYD/MJHTuGsGlTMW7bQWap817sxs04unvQ1XLei7ac9xJ27sKUX0DY61PoNHkS5yznPYDavXvSdmy5lw+VygBM6NyM5zZGMOT33dzaKJAmXiX340MpmTy6di8P/rGHv2ISGNM+uGje1G4t+f7QGYas3s3ja/eSlJtv98ziypIKgW25wH1KqQDriVrrN7XWHbXWHYHs86+11p9c4LM+tCrXUWudYpm+0WraTQBKqYHA88AtWut2QA8g1VI+HLgP2FDq85+yZGsH3Ay8r5Sy/r990Go98RXeEqWkR0fjGhSEa2AgBkdHArt1IWnvvhJlkvbuI6hXDwACwjqTGhWF1uaaeWFuLrqwEFN+HsrRAQdX80FJF5ow5eWb5+Xl4ezjc7lRbTq45QCdb+6KUorGIcFkZ2aTlphaplzjkGBq+Zd/Al8z51f6D7kRR2f7N7Qd2BJON0veJm2Cyc7IJrWcvE3aBONtI+95u/7aQ9gNne0VtciRfw7Q9oZuKKWo37oJuZnZZCSVzJyRlEpuVg71WzdBKUXbG7px5J/9ACSfjadh2+YANOnYmkNb9to9865N4fQd0AWlFC3aBpOVnk1yQlqJMi6uzoSGtQDA0cmR4FYNSDKav9LuHq5F5XJz8kDZt8PqtZb3YjZvjyIpJaNKM5TWytuLs1k5xGbnUqA1f58z0jPIv0SZuJxcojOyMFHy7qPW4Gww4Ggw4GQw4KgUyXl5dsl5csd+ml9n/r4FtWxCXmY2Wcklv29ZyankZ+cQ1NL8fWt+XTdObjd/35ytWt8KcnOBsvvCsU07adonrNKzZ5+IxjkwCOeAQJSjI95h3UjfX/L77uwfgGv9hlW2j6YfN5/33KzPe3tKnvcS9+6jtuW8F9ilMymRlvOeAlNe8XnPYHXe827VEkcPd7vnD/XzIiYjh7OZ5v14zWkj/eqX3I93GVPJLTQBcCApnSA3FwCaeLnhYIDt8ebjRnahqaicqD6ky5BtBcAsYCww6QqudyIwXmt9FkBrnQt8ZXkdCaDKHhDbAH9ZysQrpVKALsB2ewTMS07B2de36L2zry/px6PLlHHx9QNAOTjg6OZGQUYm/mFhJO3dx/YXJmDKy6PJkEE4eXoAHtS/9WZ2vjgRg5MTPqFt8A1tY4/4AKQmpuITWPw7+AT4kJqYavPiv7SYI6dJMaYQ0j2U9Yvsf+c6JSEV36DiCpJPoA+pCakXvfgvLSk2icTYRFp2alHZEctIT0zFK6A4s5e/D+mJqXj6edsuE2AuAxDQqA5H/jlAy57tidq8h/SEFOwtyZiGv9V29gvyIdmYim9ArXLLZ6Zns3tzBAMG9SuatmbJJn77aT0FBYVM+uQ/kvca5+/qjDGnuGtEQk4urX28LmnZyNR09iWlML9/NxSw4tQ5Tmdm2yVnVlIKHv7FxzR3fx8yk1Jwt2qRyExKwcO/eH/x8PchK6n4e7XzxxUc3bAdJ3c3bptS8q51QW4eMXsj6fXE4ErPXpCSjJPVOcXRx5fsE5fetUoX5HP8nWkogwP+twykVodOlZ4xNyUFF79S573ocs57fmXPewFhYSTu2cc/48znvaZDz5/3rpxAN2firLr4xGflEupvez++q0lttsYmA9DIy42MvELe6dmaeh6ubI9P4fP9J7gWqwQyqNg2aSG4sM+BB5VSl9vGO9aqy846q+l9raafr3S0BXZV8PP3AXcppRyVUk2AMMC68+i3lnVMVuXUJpRSI5RSO5VSOw+t+KWCq66YjOhoMBjo+t4Mwt5+kzNr1pJjNFKQmUnS3n10eftNur43A1NuLvFb/7Frln/LZDKx8stl3DHi7qqOUmG71u2hY78OGByu/q/+bc8+yO5fN/Lt8zPIy87F4OhQ1ZFKKCwo5LOp87j1gb4EWd1pu+X+Pny4aBJD/3M7y777owoTlnSt5a0O6rm70tDTnQfXb2f4+u108PemrU/5lbWrQZfhdzF05hs079uFyN9LNkSf2nmA2q2bVnp3ocrQYto7NH1xMvUff4q4xT+RZ7zshvBKlR4djTIY6P7+DLq+8yZnVq8l22i8+IJVZECjQEJ8PZlnGWPgoBQdA2vx8f5oHvtzL/U9XLkjuHYVpxSVTVoILkBrnaaUmgs8C1zObZ0PtdbvlTN9o9b6jsv43PNmAyHATuAksAUotMx7UGt9RinlBSwBHgbmWi+stZ6FuTWE/9v490VH3Dj7+pCXnFz0Pi85GRdfnzJlcpOTcPHzRRcWUpCdjaOnB8bt2/FtG4rB0QHnWrWo1bwZGSdOglK4BATg5GW+Y+HfuRPpx44T1LPHv9og5dmyYiPbft0KQMNWjUgxFv8OKQkpl3y3PTc7l9gTsXz5388ASE9K57tXv+ax15+s1IHFG5ZtYssqc95GrRqRHF98Jy/FmIJ3QMXrqbvX7WHQs/dXWsbSdq3awL7V5sx1WzQqcVc/PTEFr1Lb2Mvfu2SZhOIy/g1rM3TaaACSzsRzbEeEXTKvWbKJdSvMlc+mIQ1JtNrOSfEp+AaWv52/mbGIOg0CGDjkunLn97ypE9++t6TG573WJebkEejqUvQ+wNWFhJxL6/bTK8ifqJR0cizdK3YmJBPi40V4StpFlrw0B39fz6G1W8y5mjcmM7H4mJaVmIKHX8njsoefD5mJxftLZmIK7n5lu2Y269OV1dP/R+chtxdNO755F816V353ITC3CORbnVMKUpJx8vG9wBIlnS/rHBCIe4tW5Jw+hXPgZQ+XK8HFx4fcpFLnPZ9yzntJ5Zz3tpV/3nMLDKzUjBdizM6jtnvxfhzk7oIxu+x+3DXIm8dDGjLq7wPkm8yXA/HZeRxOyeRsprmFYf2ZRNr6e8GJKxK9Ul39t8Kqjmybi/sIeAK4UrdFIjDf4b9kWusCrfVYyxiBuwEf4LBl3hnLv+nAj0C3yw3oFRxMdlw8OcYETAUFGLfvxK9DhxJl/Dq0J36L+aIlYdduvFu3RimFi58fqZFRgHksQfrxaNzq1MHFz4/048cpzM1Da01KZBRudSv3aSe97urL2JkTGDtzAqG92rH7jx1orTkZeQI3D7dL7i7k5uHG1MVvMnHeFCbOm0KjkMaVXhkA6HdPn6KBwO37tGW7JW/0wRO4erhVuLtQ7Kk4stKzaBIaXKk5rYXd3o//++RF/u+TF2nRoz3hf21Ha82ZqGhc3F1LdBcC8PTzxsXdlTNR0WitCf9rOy16tAMgMyUdMA/m27xgNR0H9rZL5lvu78P0OeOZPmc8Xfq1Y+PvO9FacyT8BG6eruV2v1k461eyMrJ5+Ll7SkyPPV1812/vlkjqNAgovWiNy3utO5SWTn13N2q7ueCoFP3rBvJPfNIlLWvMyaW9nzcGZb7L2s7Xm1OV2GWozYDrigb8Nu7anqPrzd+3+MPROLm7leguBODu642Tmyvxh83ft6Prt9O4q/kBA6nniu+qn9y5H596xXeA8zKzOXfwKI262udhBG6Ng8mLjyMvwYguKCB113Y823W4+IJAYVYmpnzzANeCjHSyjx/FpW69Ss/o1SSYnNLnvY4lM/p3bE+c5bxn3LkbH+vzXlTxeS/teDTuda7s07wOJqfT0NONeu7m/fiWhoFsPFtyP27p48HEsOaM33yQZKtBwweT0vFycsTHMl6uS5AP0Wn26fomqo60EFyE1jpJKbUQc6Vg9hVY5XTgXaXU7VrrWKWUM/CI1vprWwsopdwBpbXOVErdDBRorQ8qpRwBH611glLKCbgDWGvrcy6VcnCg6fChRHz0MZhMBPXujXv9epxctgLP4Mb4d+xA7b59OPz1bHZNfAVHDw9ajXwSgLrX9+fIt3PY/epU0BDUuyceDc2PiwsI68y+aW+gDA54NGpInX59LzeqTa27tSFqeyTvPPYGzi7ODBo/rGjeh6NmMHam+ZGHq75awd51u8jPzefN4VPoOqAHtzwy0G65bAnt3oaD2yJ5/aE3cXJ15qEJQ4vmvf3Uu7z01X8BWPblCnb9uZv83HwmD55Kz9t6cNtj5kfj7f5rD52v71TeGBS7aNalDcd3RvDliNdxcnHmtuceLJo3+9l3+L9PXgTglv8MZtVHP1CQl0fTsDY0DTOPHTm4YRe7V20EoFXPDrS/qfJai2zp2DOEvVsjGTf4LZxdnRj5cvF+MfHR95g+ZzyJ8Sksn7OWeo2DmPT4B+bf4f4+XH9XD9Ys2UT4jsM4ODrg4eXGqFeGS94KmPPpGPr2DCHA14uj2z5j2geLmbPg7yrNZNLweeQx3gpri0HBmjNxnMzM4pHmjTicmsE/xiRa1vLk1U4heDk60iPQj0eaN2LE5j1sjE2gg583X/bqjMbcQrDNeGmViYpq2DmUmD0RLBrzGo7OTvQd/VDRvKXjp3PvexMB6PXUYDZ8/j2Fefk06NiGBp3M37edPywn5Ww8Sik8A/3o/VTxMebE9n3U79AaJ6uWksqkHByoM3g4pz7/CG0y4dOzN6716hO/chlujYLxat+R7JPRnJ71BYVZmWSE78O4agXNJr9Obuw5zs2fZx5srDX+twy0S4VAOTjQ7MGhhH/4sfnRoX1641G/HieWrcDLct6r07cPh76azQ7Lea+15bxX74b+HJ49h12Tp6I11OlTfN6L+vJrUg4doiAjg23jX6Tx3XdSp2+fSs9fqOHdPcf4pJ95P/4lOo7jaVmMCG1EZFIGG88l8Wz7Jrg5OjC9Z2sAYrNyGb85EhPw8b5oPr+uHUpBVHIGy47HVnpGUbXU+Se/iJKUUhlaa0/L69pANDBDaz21vDIX+JypmJ8CZN1h8B4gGPPg4TJdhpRSjwMvYH7MgwZma60/UErdC3wKBAIpwF6t9a1KqWBgNWACzgBPaK1PKqU8MD+RyAlwwFwZGKe1LsSGS+kydDW5u9G1d5fCzfGa2sQAnMm8uvrvX0w7v4KqjlAj9O38fVVHqJB+X42u6ggVdmPda+sYtzXe9eKFrjL+rjZPiVet/eeuvfu52wf1qfIhvcP/Xl/lJ+Af+19X5duhPNfeHnWFWF/oa63jgDLPBbtYZcBSZiowtZxZJ4C/bSzzLfBtOdOXAkvLmX4CaFXO9Ewq2P1ICCGEEELULFIhEEIIIYQQ1Z48dtQ2qRBUEstjQweVmrxIa/1mVeQRQgghhBDiUkiFoJJYLvzl4l8IIYQQQlxTpEIghBBCCCGqPXnWvm2ybYQQQgghhKjBpIVACCGEEEJUezKo2DZpIRBCCCGEEKIGkwqBEEIIIYQQNZh0GRJCCCGEENWeQVX5Hyq+akkLgRBCCCGEEDWYtBAIIYQQQohqTwYV2yYtBEIIIYQQQtRgUiEQQgghhBCiBpMuQ0IIIYQQotqTu+C2ybYRQgghhBCiBpMKgRBCCCGEEDWYdBkSQgghhBDVnvwdAtukhUAIIYQQQogaTFoIhBBCCCFEtSd/h8A2aSEQQgghhBCiBpMKgRBCCCGEEDWYdBkSJWw8eG3VET/oXtUJagZTQG5VR6iQ7Uanqo5QYSE+BVUdocL6fTW6qiNUyIanPq/qCBX2Rfjwqo5QIQk5DlUdocIOpDhXdYQKS/r2SFVHqLhBfao6gXQZuoBr6+pPCCGEEEIIUamkhUAIIYQQQlR7chfcNtk2QgghhBBC1GBSIRBCCCGEEKIGky5DQgghhBCi2pO/VGybtBAIIYQQQghRg0kLgRBCCCGEqPbksaO2SQuBEEIIIYQQNZhUCIQQQgghhKjBpMuQEEIIIYSo9uQuuG2ybYQQQgghhKjBpIVACCGEEEJUezKo2DZpIRBCCCGEEKIGkwqBEEIIIYQQNZh0GRJCCCGEENWekr9UbJO0EAghhBBCCFGDSQuBEEIIIYSo9mRQsW3SQiCEEEIIIUQNJhUCIYQQQgghajDpMiSEEEIIIao9uQtum2wbIYQQQgghrhJKqQFKqUNKqaNKqZfKme+ilFpgmb9NKRV8ueuUCoEQQgghhBBXAaWUA/A5MBBoAwxTSrUpVewJIFlr3Rz4EHjnctcrXYbEZevb0JdXejXDQSkWRsUya+/pEvOHhdTlwdB6mLQmM7+QyRuOcDQlC4BWfh5M69cCTycHTBruW7qbvEL7PydYa80Hb//Mlo2RuLo6MfmN4bRu09Bm+fFjvuJMTCLzl5or6jM//ZWN6w6gDApfPy9efWM4gUHeds671CrvsIvk/dqS90WrvOGWvJ52z3s+84fvLGfLxihz5mlDaNWmgc3y/x3zLWdjEvlh6XgA/lyzj2/+9wcnjsfzzY9jCAm1/ftWZuafP/+Zg9sicXJx4sEJw2nYsux6V36zih1/7CArPYt3V80oMW/P33v4bc7vKKWo16wej056xK55v3h3OTs2R+Li6sz4qUNoEVJ2G7/8zFckJaRRWGiibacmPPPifTg4FN8PWjzvb2Z9tJJFa1/D29fDbnkBugT4MKp1UxyU4reYOBZGx5SY39a3FqNaN6Wppwdv7Y9iU1xi0bwnWgbTPdAXhWJ3Ygr/izpu16yXYua7Ixl4YyeMiWl0uXlCVccBzPvFl+8X7xfjpgyheeuy+8XkMV+RlJhGYYGJ0E5NeHqCeb/45uNf2LbxII5OjtRt4M/YV4fg6eVW6Tnj9kdwYN5CMGka9e9NyztvLTG/MD+f3V/OITX6FE6eHnR95kncA/2JPxDJwYVLMRUUYnB0IHTofQSGtgbAVFDA/jkLSIg6jFKKkEF3Ua9r50rPHuZv3o8NSvF7TByLTpTdj0e2akoTTw/ePlC8H7f39WZEqyZF5Rp6uPP2/ii2GpMqPWNp/cLq88rI7jgYFAtXH+bLRQfKlLmtbzDPPtgJrTWR0UmMm7EBgP8+3oXru5r3oc9+2sevG6LtntceDNfG3yHoBhzVWh8HUEr9BNwNHLQqczcw1fJ6MfCZUkpprf/1LygVAnFZDAqm9m7OY6sOEJuZy5L7OvHXicSiC36AX47GMz/yHAA3NPZjYq+mPPFrOA4K3ruhFf/96xBRSZn4uDhSYLoyX9YtGyM5fdLI4lWTCN9/khlvLGL2j+PKLbtu7T7c3FxKTHvo8RsYNeY2ABb8sJ5vZq7mpVcHX4G8L1vyLmb2j2Nt5N1/kbwb7J4XYOumKE6fTGDRyheJ2H+KGW/8zDc/Pltu2b/XHsDN3bnEtGbN6zD9g0d4Z9oSu+a0dnB7JMYYI6/MncTJyJMs+ngR4z4vu1+07RlK33v68MYjb5aYHh9j5I/5a3n+k+dw93InPTndrnl3bI7izGkj3y57iajwU3wyfQmfzn2uTLlJbz+Mh6crWmumTZjLhrX7uP7WTubMsSns+ucwQXV87JoVzE3So0OaMXFnOAk5eXzasyP/xCdyKjO7qIwxO5f3DxzmgeCSF7BtfLwI9anFqM17AHi/e3va+3qzPznV7rkvZN6i9cycs5qvP3y6SnNY27klijOnjHz980scCj/FZ28v4aPvyu4XE6c/jLtlv3jzxbls+nMf193SiU7dW/LY6NtwcHRg9qcrWfjdn/zfmDsqNaM2mdg/5yd6vfgsbn6+rH/1bep0bk+t+nWLypxavwVnD3duev91YrbuIGLBUro+8yTOXp50H/c0br4+pJ0+w9Z3P+XWT94G4PDy33Cp5clN776GNpnIy8yyFeFfO78fv7zLvB9/3KMj24wl9+P47FzeDz/M/aX24/3JqTzzz14APB0dmd03jN2JKZWesUxmg2Lq0z14dNJqYhOy+PmjO/nzn1McPV38/WlcrxajBrdn8PhVpGXk4eftCkD/rg0Ibe7Hnc8sx9nJgR/eGcCGHTFkZOfbPXd1pJQaAYywmjRLaz3L6n19wPrOagzQvdTHFJXRWhcopVIBfyDh3+a6pC5DSql7lFJaKdXa8t6glPpEKRWulDqglNqhlGpygeVPKKU2lpq2VykV/m+DXySvo1LKqJR6u5I/N8PG9FFKqUcsr79TSmUppbys5n9k2X4BlZnnUimlXrbXZ7cP8uJkWjan03PIN2lWHTVyY7B/iTIZ+YVFr90dHThff+3TwJdDSZlEJWUCkJJbwBWqD7Bh3QEG3tUVpRTtOgSTnp5NgrHshUVWVi4/zv2bx0feUmK6p6dr0evs7DyUnZ9tvGFdeAXz3nyRvPZ/GPOGdREMvDMMpRRtOzQmIz2HBGNauZnnz9vA4yNuKjE9uGltGjcJsntOa+GbD9D1FvN2Dm4TTHZGNqmJZbdzcJtgvP3LtrBsXbWVvnf1wd3LHQAvX68yZSrTlvUR3Hx7F/Od0HaNyczIIbGcbexh+f8vLDCRn19Q4v9/5gfLefK5O67IPtHK24uzWTnEZudSoDV/nzPSM6jk8SIuJ5fojCxMlDwYaA3OBgOOBgNOBgOOSpGcl2f3zBezeXsUSSnlnhqqzD/rI7jRsl+0bteYzPQckhLK7hfu5/eLQhMF+QWAeR/o3KMVDo4OALRu25iEuMqvdCUfO4FH7UA8ggIxODpSv0cXYnftK1Hm3O59NOzTA4B63TqTEBGF1hqf4Ia4+ZorsF4N6lGYl09hvvni9OSGrbS4cwAAymDAxcuz0rO3LLUfr4810qPUfhyfk8uJjCwudMO2b21/diYkk2syVXrG0jq0DODk2XROx2aQX2Bi1Ybj3NSzUYkyQwa05PuVkaRlmL9XSak5ADRv5MOO8DgKTZrs3AIORSfTr0t9u2e2B4Oq+h+t9SytdRern1kXT25/l9pCMAzYZPl3CjAEqAe011qblFINgMyLfIaXUqqh1vq0UirkXye+NDcDh4FBSqmJl9OEcim01jNLTTqKuTnne6WUAbgBOGPPDBfxMvCWPT64jrsL5zJyi97HZubSIajsRdCDoXX5v3YNcHIw8PAv5oN+Ex93tIbZt7XFz9WJVceMfLUvpsyy9mCMT6V2Hd+i90G1fTDGpxIQWPIi78tPf+XBR6/H1dWpzGf875NV/LpiB55ernzxzTNXIG/xHdwL5+2Pq6tz6Y+w5N1pyTvarnnNmdNKZA6s7W3JXKtEuVmfrWbYI/3K3cZXWkpCKj6BxfuFd6APqQmp5V78l8cYEw/AR89+jMlkYuAjAwjpZr/DXWJ8KoG1i7dxQJA3icZU/EttY4CJo2dxKOI0XXu1pu+N7QHY8nc4AYHeNGtZz24Zrfm7OmPMKT5eJOTk0trn0ipNkanp7EtKYX7/bihgxalznLa6IyuKJRjL7hcJ8an4BZTdL14ZM4vDEacJ69WaPpb9wtqaFdvpd3PHSs+Yk5yCm1/xd83Nz5fkYyW7oeQkpeDmby5jcHDA0d2NvIzMEhf553bswTu4IQ5OTuRbWgOilvzC/7N33+FRVOsDx78nm7JppCck9F6DJHTp2MBeUEDFLvhDUC+2y8UuNlC81wpYQZEuiiiCID30lgChCSEkpHfSkz2/P2aTbBpFEwLk/TxPHrIzZ2bfPZyZnTPnPZPkyCO4+vsR/OAIzB6VP/c/4VtFO27nceGd/wGBfiyNujiXBgE+LsQll12mxSfncFU7v3JlWjQy6mnB+zdislN8NHcvG3bFcuh4KhPu68pXP+7H2cmeXl0CORZd+6Ma9VgsYJur2pjK15AlZWKUUvaAB5DCP3DOEQKllBvQD2MCw0jr4kAgTmttAdBax2it086xq4UYHQkwOhbzbN7DpJSaZh1pCFdKjS15b6XUGqXUbutIxG3W5c2VUpFKqS+UUgeUUquUUrYJjqOA/wHRQB+b94lSSr1jHZ3YqZQKVUqtVEr9pZR6wlpmkFJqg1LqV+sM7xnWi/qSfbyllNqnlNqqlAqwLntNKfVzPQzIAAAgAElEQVSczfvPt/msg4DNQJHNPiZaR1f2K6WesflMh6wjDEeUUnOVUtcqpTYrpY4qpXpay7kqpb5WSm1XSu2xqZOHlFI/KqV+t5afal3+LuBs/cxzq/qPUUqNsdbHzoyNy87x3/j3zD0QxzXzdzBt23HGhTYDwKQU3Rp68Oyfhxi5bB/XtfClT6PaT1s4X0cOxRAbk8ygKr4kAf7vqZv4ZfVr3HBTNxbN21hlmYvpyKFYYmNSzhHvq5dMvGCN+VQKg64JrutQakRxsYWk2CQmTB/Pg5MfYP70BeScqfmUhb/jnU/HMH/lKxQWFrF3xzHycguY9/UaHnzihnNvfAkIcjHTxM2F+9Zv597127nKx4POnjV7oVcfTfl4DN+veIXCgiL27TxWbt38r1djsjcxeFjN5+DXhMyY0xxYsJSuD98HgMViIS81De82LRk05T94tWnBgXkXL+XwQng5OtDCzZVdFyFd6HyZTHY0D2rAfS+u4Jn31vPWU31xd3Vk057TrNsRw8L3b+LDFwey51AixRdrOL9+2gG0UUq1UEo5Ylx7V7w4WwY8aP19OPDnP735fT4pQ7cBv2utjwApSqluGBf3t1gvMj9QSoWcx36WAHdaf78F+MVm3aNAhta6B9ADeNyagpQH3KG1DgUGAx+osnHtNsCnWutOQDpwF4BSygxca93/PIzOga1orXVXYCPwLUZF9gZetynTE5iAMbu7lU3crsBWrfVVwAbg8Wo+6xHATynlZX3/+SUrrPX3MEY+WG/rZy2pv9bAB0B768+9GJ2x5zDu8gNMxviP72mtk2lKqZJZgF0xOiLBwAjriMy/gVytdVet9X1VBWs7fOXR/9ZqPlLV4nPyCXQry1dv6OpEQnb1w/jLjyVxnTWlKD47nx1xGaTlFZFXZGF9dCqdfGt+eLfEonkbuX/4VO4fPhVfvwYkxJf1YRMT0itNso3YF0XkgVPcfsPrjHngI6Kjkvi/hz+utN+hN3Vn7ep9lZb/83g3cf/wadw/fJo13rIvjrPH+4ZNvJ9UEW831q4Or/F4ARbP38wDd0/ngbun4+vrXi7mpISMSjHv33eSQwdjuGPo24x98DOiTyYz7pHPayW26mz8aSNTx0xl6pipePg0ID2prF1kJKXj4Xv+k689/TzpfHVnTPYmfAJ98GvsR1LM307prNKyhZt5YtR0nhg1HW/fBiQllNVxcmIGPn7Vx+vo5ECfgZ3Ysn4/cTEpxJ9O5YlR0xl981skJWYw7r4Pq0wtqSkpeQX4mcvOF75mJ5Lzzi/t52p/Hw6lZ5FXbCGv2MLO5DQ6nOfoQn3wy8LNjL93OuPvnY63T+V24XuWhwiUtIut68uyeP/4ZQfbN0Xy/Jv31ko6mdnLk9zUsmMtNzUNs1f5G0Jmb09yU4wyluJiinJycXRzLS2//X8zCR37EK4Bxp1uRzdXTI6OBHY3RjQa9QwlI6r8Qy5qQnIV7Tgl/8LS1wY09CMsMYXi2k1gKJWQkkOgb9kDAxr6upCQUj6xIz45mzXboikq1sQknOFEbAbNg4xO9+cLwrl1wjIemrwKpeBEbO2dJ2pTXacL2Z3HoaS1LgLGAyuBSGCh1vqAUuoNpVTJRdpXgI9S6hgwEaj0aNILrpvzKGN7QTsfGKW1jgHaAZMAC7BGKXXNOfaTAqQppUZifEDb22bXAw8opfYC2zAmRrTBSGh8WykVDqzGmEQRYN3mhNZ6r/X3XUBz6+83A2u11rkYnZDblfEIpxIlvawIYJvWOktrnQTkK6VKzkbbtdbHtdbFGJ2KftblBcDyKt6zKj9i9Op6YXQ+SvQDlmqts7XWZ6zl+tt8pgjryMsBYI21xxdh817XA/+21tU6wAyUJAKu0VpnaK3zMGajNztLfDUiIjGL5h7ONHY342CnuKm1H2tOlh+1atagLH99cDNvojKNYf6Np9Jo5+2C2d4Ok4IegR4cS6u9u6l3j+rP94tf4PvFLzBgSDArlu1Aa03Evijc3Jwrpd/cNaIfv/75Bj+tfJVZc56iaXM/Pv9mAgDRJ5NKy234M4JmLQKoaXeP6sf3i5/n+8XPM2BI5/OIty+//vk6P618xSbe8dXEWzu5+cNH9mXOoonMWTTRiPmXXWit2b/vJK7u5krpQneOuJpf1rzM0t//w8zZ42jazJfPvv6/WomtOv1v788Ls17ghVkvENw3mB2rjHqOOhiF2dX5vNOFALr0DebYXuMu65mMMyTFJOEb6HOOrS7Mrff0Zca8icyYN5GrB3Xij193Gk8EiTiJq5u5UrpQbk5+6byC4qJitm+KpElzf1q0CWTR6tf5bvlkvls+GT9/Dz6b+68q00pqyuHMLBq5OBPg7IS9UgwK9GNr4vk9XSUpL58u3h7YKWN0MdjLo9wkzvrulnv68skPE/nkh4n0GdSJNdZ2ccjaLir+v+bm5Jd2/mzbBRiTkhd/t5ZXP3i4yvTDmuDZshnZ8YlkJyZjKSoidutOGoaWH91sGNKFU5u2AnB6+258O7ZDKUVhdg5b3/+Ujvfcjk/bVqXllVI0DAkmOfIIAEkHDuMeFEhNO5KZRZBNOx7Y8PzbcYlBDX1ZF5d07oI1JPxIMs2CGtA4wA0HeztuGtCSNVvLd5ZWb4mmV7BRX14NnGjRyINT8VnY2Sk83Y0OULvmXrRv7s2m3XWZBX3l01r/prVuq7VupbV+y7rsFa31MuvveVrru7XWrbXWPUueSPRPnHUOgVLKGyP/PVgppQEToJVSz2ut84EVwAqlVAJwO7DmHO+3AOPZqg9VfCtggtZ6ZYX3fwjwA7pprQuVUlEYF8AA+TZFi4GSlKFRQD9rWTA6F0OAPypsZ6mwDwtl9VGxy17yutBmSKaYs9ffAoxOw2zrPIuzFC1VMR7bWEveSwF3aa0P226olOpF5Tqp9adIFWt4fdMxvr6xMyalWHw4nmNpOTzdvRkRSVn8eTKV0Z0bcXUjT4osmoz8Il5Ya4SeWVDE1xGx/HhHCBpYH53Kuujaf/QaQN/+HQnbEMldN07BbHbk5SllA0n3D5/K94vP/vjAT//7C9FRidgpRcMgb158+e6LFO9b1nhHlq67f/g0vl/8/DniXW4Tr1etxwtwdf/2hG2M5O6b3sXJ7MhLb5Y91eiBu6czZ1HVT3UqsW5NBNPf+Zn0tDM8++TXtG0fxH9nVDcoVzM69urIwW2RvDl6Co5mR+59vqxdTB0zlRdmGe3i55nL2PXnLgrzC3llxKv0ubE3wx4cRvse7Tm08xBvP/wOdiY7bhtzK64etfcYz579OrB98yEeuu1dnMwOPPfaiNJ1T4yazox5E8nLLeDViV9TWFCMRVvo2r01N9/V5yx7rT0WDZ9G/sXb3Tpjp2BVbAIns3N4oHVTjmScYWtSKm0buPFKSAfc7e3p7efNA62bMmbzHjbGJ3OVtwczrw5FAzuT09h2ER7VeC6zP55A/z4d8PVy59i2T3hz+mJmL1hXpzH16NuBHZsP8egdRrv41ytl7WL8vdP55AejXbw+8WsKC4vRFgtdurfmxjuNdvH5tKUUFhQx+UljrmO74KZMmDS8RmO0M5no8sBItkz7GG2x0HTA1TRoHETkkl/wbNGUwNCraDawL7tnfMvqZ1/Bwc2F7k8+CsDxP9aRnZDE4Z9+4/BPvwFw9QsTcPJoQMeRd7B7xrfsn7sYR3c3Qh6v+cf+WjR8fugvpoR2xmRtx9HZOYxu1ZQjmWfYZm3HL3ftgJuDPb38vLm/VVOeCDOekOVvdsLX7ETERXxCVrFF8/rnW/lmyvWY7BSLVh3laHQ6T98fwv6jyazZdooNu2LpFxrE7zPuoNiieferHaRn5ePoYGL+NOMpdWdyCnj2/Q2XbcqQ6dxF6i11tpQjZTwaqZvWeqzNsvXAyxjPSD1tza//FgjXWr9fzX6igO4YF6zjMP6IQhCwXGvd2fo+NwJ3Wy/822JMmHgMaK21nqCUGgz8CZQ8zWi51rqzdf/PAW7AdIwJvU2sHRaUUg8D/bXWj5TEobVOtnY2umutx1eIsTNGR6cjcNL6+yyt9RKl1BmttZu1/HDgZq31Q0qp14AzWuv3lVLfWmNbbJ0LsVpr/ZfN/pta66s3xsX9NmA0kFbhM9nup7lNXb0NNMDoQGmlVIjWek8Vn2c58L7Wep1SKg3w11qf8xlhbWZuuKyO8h0Pn2su+6XosqpiACy66NyFLiHbk+p+gvKF6uB5edUxwJjNXucudAnZ8PindR3CBdu//966DuGCzDxUu3/HojZEpNfOKEhtOvrfo3UdwgU79tvDtf8os3OYsmd1nX8BvxRybZ3XQ1XOdQd5FJX/+tkSYDaQqpQqSaLbDlROVq5Aa51Vsr8Kd8y/xEiJ2W2dI5CEMeIwF/hFKRUB7AQOneMt7sDIr7e9U/4zMNUm1vOxA+PztAbWAksvYNtSWuuZVSzbbb3Y325d9KX1gr75ee72TeC/QLi1M3YCI03qbGZZy++ubh6BEEIIIYSon846QlAfKaUGAc9prWv2r7BcJmSE4GK4rKoYkBGCi0FGCGqfjBDUPhkhuDhkhODveXvvH3X+BfyfrtfVeT1U5bz+MJkQQgghhBDiylSjk06VUtuAiqk5o7XWETX5PrVJa70O4+k9QgghhBDiCnE+j/2sr2q0Q6C17lWT+xNCCCGEEELULkkZEkIIIYQQoh6r9efUCyGEEEIIUdckZah6MkIghBBCCCFEPSYjBEIIIYQQ4opnkhGCaskIgRBCCCGEEPWYdAiEEEIIIYSoxyRlSAghhBBCXPFkUnH1ZIRACCGEEEKIekw6BEIIIYQQQtRjkjIkhBBCCCGueHZK13UIlywZIRBCCCGEEKIekxECIYQQQghxxZNJxdWTEQIhhBBCCCHqMekQCCGEEEIIUY9JypAQQgghhLjimeo6gEuYjBAIIYQQQghRj8kIgRBCCCGEuOLJpOLqSYdAlLN+dEZdh3BBrppqrusQLpjSl99zkPPmra/rEC7IM/OuresQLtjkjW51HcIFG9Ept65DuCCf7b+3rkO4YJ07/1DXIVyQsQsfr+sQLti73dPrOoQL1vynwLoOQVxhJGVICCGEEEKIekxGCIQQQgghxBVP/lJx9WSEQAghhBBCiHpMRgiEEEIIIcQVzySTiqslIwRCCCGEEELUY9IhEEIIIYQQoh6TlCEhhBBCCHHFk79DUD0ZIRBCCCGEEKIekxECIYQQQghxxZMRgurJCIEQQgghhBD1mHQIhBBCCCGEqMckZUgIIYQQQlzxJGWoejJCIIQQQgghRD0mIwRCCCGEEOKKZ1K6rkO4ZMkIgRBCCCGEEPWYdAiEEEIIIYSoxyRlSAghhBBCXPHkLnj1pG6EEEIIIYSox6RDIIQQQgghRD0mKUNCCCGEEOKKJ3+HoHoyQiCEEEIIIUQ9JiMEQgghhBDiiicjBNWTDoH4x7TWfDz1Z7ZtjsRsduTF10fQtkPjSuVeePILUpIyKS620CWkBU9PuhOTyY7XX/yOU1FJAJzJysXN3ZkvF0ys1ZgHtvTh1RvaYVKK+Xtj+Twsqspyw9r7M2P4Vdz81TYi4jJp7GFmzRNX81dKDgB7YjOYvCKyVmMFGNjKh1duaI9JKRbsiak23qHt/Zlxd1du+XIrEXGZALT3d+Ptmzri5mSPRWtu+3Ib+cWWWo95cL+2vDnpVkwmxdzFO/jky3Xl1jcK9OSjt++hQQMzJjs73vpwBWs2HMbBwcS01+7kqk6NsFg0L7/zC2E7jtdanFprtn2zmJg9B7B3cqTfuNH4tmxSqVzy8Wg2fvodxQWFNA7pRK+Hh6OUYsd3Szm1az929ibcA3zpN+5+nFxdKC4qImzWPJL/ikbZ2dHrobsI7NS2RmPvE+jFc6EtMSnFT3/F821kTLn197VrxO2tGlKsNWl5hby+7QjxOfkAPNW1Of2CvLFDsS0+jWm7a7eOt36zmFO7jToe8GQ1dfxXNBs+/Y6igkKahHait7WOd81fzskd4SilMHu4M+DJ+3H19iT859X8tXEHABaLhYyYeO776l2c3F1rPP6ZH/zMjs2ROJkdmfjqCFq3r3yOe3nCF6SmZFJcZKFTSAvGvWCc47763y9s23gQewd7Ahv78K9XRuDm7lyjMV6IGdPGMuyaEJJSMul+3Qt1FkdS+AEi5y5EWzSNB/al1c03lFtfXFhI+KzZZEZF4+DmStdxj+Hi51O6PjcllY2T3qD17TfR8sbrACjMziHi6+85E3saUAQ/Nhqv1i1rPHatNd98+BN7wow2Me7lkbRsV75N5OcVMH3yHBJikrEz2dGtX0fuG3dz6fqw1XtZ9NUqlIJmrYN4+o37azzOijF/8O6PhG08iNnswCtT7qN9x8rHYYlnJ3xBbEwy85dOKrd87uw/+d/7P7Nqw1t4ernVaszi4pEOgfjHtm06RGx0Et///G8iI6L58O0lfP7d05XKvfreaFzdzGitefW5Oaz/Yx9Dhobw6nujS8t89sEyXN3MtRqvnYI3h7Xnvrm7ic/MY9mjvVh9JImjydnlyrk6mni4Z1N2x6SXW34yLZcbv9xaqzFWjPeNoR24f+4uI97HevPHkSSOVRlvM/bYxGtSig9vD2bizxFEJpzB09mBQkvtdwbs7BTvvHQ79zz2JXEJGfy+YDyr1h7kyF+JpWWeGTuEZb+HM3vBVtq28mfujIfpcd173D+8JwCDb/8vvt6uzJ35CEPv+QSta+cvTMbsOUhmfBJ3ffQqSUej2PLlfG55+/lK5bZ8sYC+Y+/Fr01z/njnc2L3HqRxSCeCurSn2723YmcyseP7nwhfuooe99/OkdWbAbjjg8nkZmTxx9ufccs7z6PsaiZT007Bv7u1Ytza/STk5vPd9V1ZH5vKicyc0jKH084weuUe8ootDG8dyNNdWzAp7BBdfN25yrcBI1fsBuCra6+im78HuxIzaiS2imL2HCQzLom7PzbqOOyL+dz6TuU63vzFAvo9YdTxqrc/J2bvQZqEdCL41mvoNtK4kDrw2zr2Ll5B3zGj6HLbtXS57VoAondGsH/52hrvDADsDDPOcV/++G8O74/mk3eX8N9vK5/jJr0zGhfrOe6tF+ewac0+Bl4fQkivtjz05I2Y7E18/fFyFn67hkcm3FzFO10c3y1az4zZK/nyw3F1FoO2WDgwZz49X3gKs7cXYa+9i39IF9wbBZaWidkQhoOrCwOnvcHprTs4vHApIU8+Vro+8ofF+HXpVG6/kXMX4hfckdAJY7AUFVGcX1Ar8e/Zcoj4U8l8tGgSRw9E8+XUJbz9VeU2ccu9g+jcrTVFhUW8MWEGe7ZEEtKnA3GnkvhpzhrenDketwYuZKRm1UqctsI2HuTUySSW/PoS+8NP8t6URXzzQ9U339au3oezs2Ol5QnxaWwNO0zDQK/aDldcZJfNHAKl1O1KKa2Uam99baeU+kgptV8pFaGU2qGUanGW7aOs5SKUUgeVUlOUUuYKZZ5RSuUppTysr/2t2zW0KfOpUmqSUmqQNZ7HbNZ1tS57zvp6gVJqr/UnSim117rcQSk12xpLpFJqks0+hiqlDiuljiml/m2zfK51+X6l1NdKKQfrcmWth2NKqXClVKjNNr8rpdKVUsv/fs2f2+b1B7j+5u4opejYpRnZWXmkJGVWKldyoV9cZKGoqAhU+bE7rTXr/tjHNUNDajNcugZ5EJWaw6n0XAotml8OxHNdW79K5Z4d2IoZYVEX5W762XQN8uBkWvl4r2/nX6ncs4NaMyPsBPlFZfH2b+XDocQsIhPOAJCeW4jlIvzl9pDgJpyITiE6JpXCwmJ+WrGPG4Z0LFdGA+5uTgC4u5mJTzS+ENu28mfT1mMAJKdmk5mVR9fOjWot1uid4bQe0BOlFP5tW1CQnUtOWvkL45y0DApz8/Bv2wKlFK0H9OTkjnAAGl3VATuTCQD/ti3ISTU6ZOkx8QR2bgeAs4c7jq7OJB+PrrG4O3m7c+pMHrHZeRRZNKuikxjU2LtcmZ2JGeRZ229ESib+LsYXvNbgZLLDwc4ORzs77JUiJa92LpwATu4Ip/XAC6zjgT05ud2oY0eXsrvpRfn5QOVx/7827aRlv261Ev/W9Qe45ibjHNc+2DjHpSZXPse5lJzjii0UFRaVxhnaux0me6ONtO/cjOSE2ul4na/N2w+Rmn6mTmNIPx6Fa4AfLv5+2NnbE9irO4m795Urk7h7H4369QagYY9QUg4eKr0xkLBrLy5+PrjZdCAKc3JJPXyMxgP7AmBnb4+Dq0utxL9zw34GDOuGUoq2nZuRfSaXtAptwsnsSOdurQGwd7CnRbvGpFg73Wt+3soNw/vi1sCIz8PbvVbitLVh7X5uvLUHSimCr2pOVlYuyUmV22JOTj4/zFnLI2NvqLTuw6lLmTDxVpS6PHNvTErX+c+l6rLpEACjgE3WfwFGAEFAF611MHAHkF7NtiUGW8v2BFoCM6t4jx3AnQBa60TgXeB9AOvFdv+S18B+4J4K25ee0bTWI7TWXbXWXYElwI/WVXcDTtZYugFjlVLNlVIm4FNgGNARGKWUKrmKmgu0B4IBZ6CkIzIMaGP9GQN8bhPPNGA0tSw5MQP/hp6lr30DPEiu5k7j8+Nmccc1r+HsYmbgtV3KrQvffRwvb3caN6t8cV6TGro7EZeZX/o6Liufhu5O5cp0buhOUAMzfx5LrrR9E09nfnusFwtGd6dHE89K62taQAMzpzPzyuLNzCOgQrydGroT2MDM2grxtvR2QWuYc28oyx/rzdg+zWs9XoDAAA9Ox5cdjnHxGQT6e5Qr8/4nf3DXLSHs/vM/zJ3xMJPf+hmAA4fjuGFIR0wmO5o28qJLx0YENay9es5JTcfVt+xul6uPZ+lFvW0ZF5+yGFyqKANw9M8tNO5qHLLezRtxamcEluJishKTSTl+iuzktBqL29/FiYScsnackFOAn7NTteVva9mQsDjj/SNSstiZkMHK23ux8vZebIlPIyozt8ZiqygnNR1Xn7I6dvHxJLtC/WWnpuNqU8cV/x92/rCM+U+8xLGNOwkdcVO5bYvyC4jZG0mLXl1rJf7kpAz8AmzOcf7Vn+NemjCLe69/DWdXM/2u6VJp/apl2+l+dftaifNykpeWjtm7rE2Yvb3IS0uvtoydyYS9szOFZ7Ipysvj+K+raH17+XaQm5SMo7sbEV/OYdPLbxHx1XfWDmTNS03KwNemTfj4eZBaxcV1ieysXHZtOkBw9zYAnD6VRFx0Ei+P+ZjJj/2PvVsO1UqcthIT0wmwOZf6B3iQWEU7nvHxr9z74GDMZodyy9f/GYGfvwdt29XeDRpRdy6LDoFSyg3oBzwKjLQuDgTitNYWAK11jNb6vL5ttdZngCeA25VS3tb3aAW4AS9R1ukAmAW0UkoNxrhYH6+1LrSuOwmYlVIByuguDwVWVBG/wug4zCsJAXBVStljXNwXAJkYHZVjWuvjWusCYD5wmzXm37QVsB0oSVa8DZhjXbUV8FRKBVq3WQPU/jjkBZj22RiW/PEKhQVF7NlxrNy6P3/fyzVDa+cL/UIo4KXr2jJl9ZFK6xLP5NPn443c+OU23vzjMB/dEYybo+niB2lDAS9f1463/jhcaZ3JTtGjiRdPL41g+LfbuaG9P1c39668kzpwx01dWfDTLkKHvM19T3zDJ++NQCnFvB93cjo+g5WLJvDGpFvYufckxRchzemf2vfj7yiTHS379wCgzeA+uHh78su/p7Lt2yX4tWtRY+lCF2pYcz86ersxxzrHoLGbmRYNXBj28zaG/ryNHgGedPVrUCexna/u997KyBlTaN2/O5G/byi3LnpnBAHtW9ZKutCFmvLxGL5fYZzj9u0sf46b//VqTPYmBg8LrWZrcT6OLf2V5jdcg725fHqptljIPHmKpkMG0O/NyZicnDi+fGUdRVmmuKiY/73yPcPu7k9AI2MOhKXIQtypZF79bBxPv3E/M99dSHZW7XXKz9eRQzHExqQw+Jqryi3Pyy3g2y//YOyTN9ZRZDXDTtX9z6XqcplDcBvwu9b6iFIqRSnVDVgIbFJK9QfWAN9rrfec7w611plKqRMYd9a3YXQ05gMbgXZKqQCtdYLW2qKU+j/gT2CZ1npDhV0txrjjvwfYDVR1O6I/kKC1PmqzzW1AHOAC/EtrnaqUagScstkuBuhluyNrqtBooCRZsaptGln3fV6UUmMwRhd47+Nx3P/I0HNus3TBZn79cRsA7Ts1IdHmbnByQga+Fe4G23J0cqDvoE5sXref7r2NSZbFRcVs/DOCmT88c75h/23xWfkENii7kxro7kR8Vtl/m5uTPe383Jg/ujsAfm6OfHVPVx5duJeIuEwKco3+4P74LE6m5dDCx7V0Am9tSMjMI6hB2RdfYAMzCRXibevvxvwHepTG++WIrjy2YC/xWflsj04jzRrz2mPJdA5sQFhUaq3FCxCXkFHurn5gQw/iKtyJuveuHowa8xUAu/ZF4+Roj4+XC8mp2bz6XlmW2y9zx3E8qvJIzT8R+ft6jqwJA8C3VbNyd+6zU9Jx8S4/IuHi7UlOSlkbz6lQ5ui6rZzatZ+hrzxVOpRuZzLR66G7Ssssf+kDPIIqp3r9XYk5+QS4lLXjABdHknIrn356BnjyaMemPL4mnEJrvtjgxj5EpGSSa00vCzudRhffBuytItXv7zr4+3oOr7bWcetmZKeU1XFOSjquFerY1duTbJs6rur/AaBVvx6sfOfzcqMExzfvolXfmk0X+mXhZlb+ZJzj2nRsQlKCzTku8dznuD4DO7F1/X5CexnnuD9+2cH2TZG8/dnYyzbdoiaZvTzJSy1rE3mpaZi9PKss4+zthaW4mKLcXBzcXEk/foL4nbs5vPBHCnNyUUphcnCgYY8QzN6eeLYysocb9gjh+K+raizm3xdvYs0yo0206tCEZJs2kZKUgRQYYjMAACAASURBVLdf1W1i5ruLaNjEl5tGDihd5u3vSZtOTbG3N+Ef5ENgEz/iTiXRumPTGosXYNG8jfy0ZAsAHTs3JcHmuzoxIQP/Cu04fF8UkQeiue2G1ykuKiY19QxPPPwxz026i9OxKdw3fKp123RG3zONb+Y9i6/vpX0zQZyfy6VDMAr4n/X3+cAorfVzSql2wBDrzxql1N3Wu+Lny/asPAq4w9oBWIJxkf8JgNZ6r1JqP/BZFftYCCzASOeZB1xdTfzzbF73BIoxUp68gI1KqdXnGfNnwAat9cbzLH9OWutZGCMhnM755bwS3O4Y0Zc7Rhh5mls2HuSn+ZsZMrQrkRHRuLqZ8alwtzE3J5+c7Hx8/BpQXFTM1k2RdAkpm/Kxa9tRmjT3LzcsX1v2nc6khbcLTTzNxGfmc0unhjy1NKJ0fVZ+ESHT15e+nj+6G2+tPkpEXCbeLg6lefhNPJ1p4eVCdFpOVW9To/E293ahsaczCZl51njDy8Ub+sE6m3i789bqI0TEZXIyLYexfZpjtrejsFjTq6kXX207WavxAuzdH0PLZj40beRFXGImtw+7inEvzC9XJjYunf69W7Pgp120aemPk5MDyanZOJsdUApycgsZ0KcNRcXF5SYj14QOQwfSYehAAE7t3k/k7xto0bcbSUejcHRxxsWr/Jeki5cHDs5mEo+cwK9Nc45t2F66fczeg0T8vJobX38ae6eySXhF+QVorXEwOxEbHomdyQ7PxoHUlIOpWTRxNxPk6kRibgHXN/Vjclj5UaJ2Xq5M7tGa8ev2k5ZfWLo8PiefO1o1xHTwFApFqL8HPxyOrbHYADoOHUhHax1F7zLquKW1jh3Op47Xb6fjMGP7jLhEPAKNztTJneF4BgWUbleQnUvcwWMMfOrBGo3/lnv6css9xjlu+6aD/LJwMwOv78rh/cY5ztu38jkuNycfb1/jHLd9UySdree4nWGHWPzdWqbOHIfZXHmiZn3k0aIZ2QmJ5CQlY/byJG7bTq564pFyZfxDuhC7aSterVsSv2M3Ph3aoZSi9+TnSsscXbock5MTza4bBBipR2fi4nELbEjKwcO4BTWkpgwd3o+hw/sBsHvzQX5fvJm+14Vw9EA0Lq5mvKq4MJ4/cwU52Xk88Z97yi3vOaAzm/7Yw+Cbe5KZfoa4U0mlowc16e5R/bl7VH8ANm04wKIfNnL9sFD2h5/Ezc2Mb4VOzPAR/Rg+wviMp2NTmDh+FjO+mQDAyvVvlZa77YbXmT3/WXnK0BXkku8QWFN6hgDBSikNmACtlHpea52PkaKzQimVANyOMVpwPvt1B5oDR5RSwRgjBX9Y79w4AiewdgisLNafcrTW8UqpQuA6jLv25ToE1rSgOzHmCpS4F2PEoxBIVEptBrpj3Om3fQZYYyDWZl+vAn7AWJsysWfb5mLo3a8D2zYd4v5b38XJ7MCLr40oXffYiOl8uWAiubkFTH7mawoLi7FYLIR0b82tw/uUlvtz5cVLFyrWmld+P8ycUaGY7BQL957maHI2Ewe2Ivx0JquPJlW7ba+mXkwc2IrCYo3Wmv+siCQjr+gixHuIOfeGYlKKhftiOZqUzb8GtiIiLpPVR6qPNzOviC+3nWTZY73RGtYeS6o0z6BWYi628J+3fmbeF49isrNj3tIdHD6WwAvjr2PvgRhWrY3ktanLef/1uxjzQD808PR/FgLg6+3GvC8exWLRxCdmMOHfC2o11sYhnYjZfYAlT72OydGB/uPKHv338/PvcNs0Y85/n8fuYeNn31NcUEijrh1pHGLMFdj61UKKi4pY+aZxuvBr05yrx4wiNyOLVW99irJTuHh7MmB8zV6wFmuYuvMvPhnUGZNS/Hw8geOZOTwR3IyDqVlsiE3l6a4tcHYw8V6/DgDEZ+czceNB1pxKpkeAJwuGdUMDYXGpbDxde6NGTUI7EbPnAIsmvI69owP9nyyr46XPvcMd7xt1fPXj97DhU6OOG9vU8c65P5N+OhGlFG5+3vR9fGTp9lHb99HoqvY4mKufP/FP9ejbgR2bD/HoHcY57l+vlJ3jxt87nU9+mEhebgGvTzTOcdpioUv31tx4p3GO+3zaUgoLipj85CwA2gU3ZcKk4bUW77nM/ngC/ft0wNfLnWPbPuHN6YuZvWDdRY3BzmSi4+iR7Jj2MdpiofGAq3FvHMSRH3/Bo3lTAkKvovGAvoTP+pb1z7+Cg6sLXcc9es79drx/BPtmfIMuKsbZ35cuj9XONLqQqzuwOyySp+5+B0cnB8a9VNYmn3/gA6bNeZaUxHR+/HY1jZr58+JDHwIwdHhfrrm1N1f1bse+7Yf516ip2Nkp7h9/C+4etZvy1rd/R8I2HOTOG9/EbHbk5Sn3lq67b/hU5i6uu0fQXiyXcspOXVO19Si/mmJNZ+mmtR5rs2w98DJGvv1ppZQd8C0QrrV+v5r9RAHdtdbJ1jkJnwMWrfWDSqm3gSyt9Ts25U8Ag7TWJ62v1wHPaa13Wl8Psr6+WSl1NeCvtf5JKfUacKYkDqXUUGCS1nqgzb5fBNprrR9WSrliTGQeCRwEjgDXYFzU7wDu1VofsD7N6BHgGq11rs2+bgLGAzdipBd9pLXuabO+NM7zqe/zHSG4VPSZXruPKK0N6hI/5qqSN+9CBt7q3jPzrq3rEC7YooOXX1se0Snv3IUuIXc1v7ziBejc+Ye6DuGCjF34eF2HcMEeblu7o7y1obnbJX8/txIPx6F1fjn+S/SKOv8CvqXpsDqvh6pcDi1qFPBehWVLgNlAqlKq5LbQdsrf0a/KWusEXztgKfCmdflIjAtqW0utyyu+dyVa67CzrB5J+XQhMCYnf6OUOoCRtvSN1jocQCk1HliJMRLytdb6gHWbGRiTmLdYRzF+1Fq/Afxmjf0YkAM8XPImSqmNGKlMbkqpGOBRrXXdz7ASQgghhLjIZISgepd8h0BrPbiKZR8BH13gfpqfZV2lP2OotZ5Y4fWgCq/XAeuq2O61Cq8fqqLMGYw5ClXF8hvGRX7F5VX+X1mfOvRkNev6V7VcCCGEEEKIEpfFY0eFEEIIIYQQteOSHyG4UEqpbUDF2WWjtdYRVZUXQgghhBBXPpOkDFXriusQaK17nbuUEEIIIYQQAq7ADoEQQgghhBAV2ak6f8jQJUvmEAghhBBCCFGPSYdACCGEEEKIekxShoQQQgghxBVP7oJXT+pGCCGEEEKIekxGCIQQQgghxBVP/lJx9WSEQAghhBBCiHpMOgRCCCGEEELUY5IyJIQQQgghrnjyl4qrJyMEQgghhBBC1GPSIRBCCCGEEKIek5QhIYQQQghxxbNTuq5DuGTJCIEQQgghhBD1mIwQCCGEEEKIK578HYLqyQiBEEIIIYQQ9Zh0CIQQQgghhKjHJGVICCGEEEJc8SRlqHpKa5lxLcp8c2TlZdUgjmeZ6jqEC5ZbdPkNzOVZLq+zqKPdZdWMAXgttLiuQ7hgD64313UIF6RNg8K6DuGCFVjqOoILM/OeL+o6hAsWf+yhug7hgr25x6GuQ7hg7/caUudfJFsSf63zL4c+/jfVeT1URUYIhBBCCCHEFe/yux138UjdCCGEEEIIUY9Jh0AIIYQQQoh6TFKGhBBCCCHEFU9dktn7lwYZIRBCCCGEEKIekxECIYQQQghxxZMBgurJCIEQQgghhBD1mHQIhBBCCCGEqMckZUgIIYQQQlzxZFJx9WSEQAghhBBCiHpMRgiEEEIIIcQVT+6CV0/qRgghhBBCiHpMOgRCCCGEEELUY5IyJIQQQgghrnhK6boO4ZIlIwRCCCGEEELUY9IhEEIIIYQQoh6TlCEhhBBCCHHFkz9DUD0ZIRBCCCGEEKIekxECIYQQQghxxZO/VFw9GSEQQgghhBCiHpMOgRBCCCGEEPWYpAwJIYQQQogrnmQMVU9GCIQQQgghhKjHZIRA/C1aa1bPWsJfuw7i4OTITU/fR8PWTSqViz8Wza//nUthQSGtunXk2jF3oZQi4UQsKz9dQGFePg38vbn1uQdwcnEmNzObpe9+RdzRaIKv6cX1T9z9t2OM23eAvXMWoy0WWgzuS4dbry+3vriwkO2fzyHtRDSObq70eepRXP18AIj8eSUn1oWh7OwIeeBuGl7VkZyUNLZ9Ppv8jCwAWg7pR9thgwGIWPgLp3eFo+wUTg3c6fnEaJy9PP927ACJ4QfY//1CtEXTdGBf2txyQ6X4986cTXqUEX+3Jx/Dxc+HtL+iCP9mrlFIa9recTOB3btSXFBI2NsfYCkswmKxENQjhHZ33vKPYqwoOfwAh39YiLZYaDSgLy1uHlpuvaWwkP1ffEtmVDQObq50+b/HcPbzJTcpmbD/vI5LwwAAPFq1oOND9xnbFBVx6Lv5pB06AkrR+q7bCOgRWqNxl0gMP8BBa503GdiX1lXU+b6Zs8mw1nmItc5L5Cansn7SG7S54yZa3XhdrcRoS2vNB+8sZvPGA5jNjrz61mjad6x8HJaYOH4GsTEpLPhpMgCzPv2Vn5aE4enlBsCTT99K3wGdajXmMwf2E794Htpiwatvf3yvv7Hc+uyjR0hYMp+82BgaPzyGBqHdS9cdHP84TkGNAXDw9qbpExNqJcaE8ANEfLcQLJqmg/rStop2sHvmbDJOGO24x3ijHSRGRHJw4VIsRcXY2ZvoNPJO/Dq1B4x2HD57AcmHjqCUosPdtxJUg+04KfwAkXONttt4YF9a3Vw55vBZs0uPva7jKrTdlFQ2TnqD1rffREtr2y3MziHi6+85E3saUAQ/Nhqv1i1rLObzNWPaWIZdE0JSSibdr3vhor9/dbTWfPDuEsI2HsRsduSVKfed9fh7dsIsYmNSmL90EgCzPvuNn5dsKT3+xj11c40ff5fj90htspMhgmpJh0D8Lcd3HSTtdBJjZ77M6cNRrPx8IQ9+8Gylcis/W8jQ8SMJatecRa/N4PiuSFp178iKj+Yx5JHbaBrchn1/bGHbj38y4P6bMDna0/++m0iOjiPpZNzfjs9isbD7m4UMnDQBZx9PVr80laDQYDwaB5aWObFuCw6uLtz44etEh+0kfN5P9HnqUTJi4ojesosbpr5EbloG69/+mGHTX0XZ2dH1vjvxatGUwtw8/pj8HgHB7fFoHEj7m68l+B7jpHjk97Uc+HEF3R8d9bfj1xYLEXPm0/uFp3D29mLjq+/SMLQL7o3K4j+1PgwHVxeuef8NYrfuIHLBUrqNfwz3xkH0f/3f2JlM5KVnsH7yFAJCgrFzsKfPv5/B3mzGUlTM5inv49+lU419wWuLhUPfzSP0+acxe3ux7fV38AvpglujoNIysRs2Y+/iQr+pbxK/dQdHFy2ly7jHAXD296PPmy9V2u+JX1bg2MCdvu+9gbZYKMzOqZF4q4r/wJz59HrhKczeXmx69V0Cqqnzwe+/wemtOzi0YCmh4x8rXX9w3mL8utTuBbWtsI0HiY5O4sffXmV/eBTvvjmfb+c9X2XZP//Yi4uLU6Xlo0YPZvTD19Z2qIBRx3EL59JswkQcPL04PnUK7sFdcQosayMO3t4EjX6YlNWrKm2vHBxp9Z9Xaz3G8NnzufpF49hb/4px7DWwaQfR68NwdHXh2g/eIGbLDg4sWEqP8Y/h6O5Gr4njcPbyJPNULFumfcwNH70LwJGfV+DUwI1rp72OtlgoqMF2XNJ2e1rbbthr7+IfUr7txmww2u7AaUbbPbxwKSFPlrXdyB8qt93IuQvxC+5I6IQxWIqKKM4vqLGYL8R3i9YzY/ZKvvxwXJ28f3XCNh7k1Mkklvz6MvvDo3hvykK++aHy9yDA2tX7cHau6vgbxP0PXVMr8V2O3yOi7lyWKUNKqduVUlop1d762k4p9ZFSar9SKkIptUMp1eIs20dZy+21/lytlBqklFpeody3Sqnh1t/XKaW6W39voZQ6qpS6QSnlopSaa93ffqXUJqWUm7XcUKXUYaXUMaXUv232O966TCulfG2WK+vnOKaUCldKhdqs+10plV5FjNXt6z7rPiKUUmFKqav+bn1X5ejWCDoP6YlSikbtW5CfncuZ1IxyZc6kZpCfk0ej9i1QStF5SE+Obg0HIO10Ik06twagRdf2HA7bC4Cj2YkmnVphcnD4R/GlHovCLcAPtwBfTPb2NO3TjdO7wsuVid0ZTvP+vQBo3CuEhP2H0Vpzelc4Tft0w+TggJu/L24BfqQei8LZywOvFk0BcHA206BRALlp6cZrF+fS/RbnF/zjPMW0v6Jw9ffD1d8PO3t7gnp3J373vnJl4nfvo3G/3gAE9ggl6eAhtNbYOzliZzIBxh35kuesKaWwN5uN5cXFWIqLa/QZbBnHo3AJ8MfFGnPDXj1I2lO+zpP2hBPUrw8A/j1CSbXGfDaxG8NKRxqUnR2O7m41FrOt9L+icPH3K40/qHd3EirUeYJNnTfsEUqyTfzxu/bi4utT7su2tq1fG85NtxrHYfBVLcjKyiU5KaNSuZycfH6Y8yePjB1axV4untyoEzj6+ePo64eyt8ejW0+ywveWK+Po44u5UZM6ez5g2l9RuAaUHXuNencnflf5dhC3ex9NrO0gqGcoyQeMduDZvEnpyKB74yCKCwopLiwE4OSGLbS5pawdO9VgO04/bsRc0nYDe3UnsULbTdy9j0Y2bTfFpu0m7NqLi58PbjZttzAnl9TDx2g8sC8Advb2OLi61FjMF2Lz9kOkpp+pk/c+mw1rI7jxvI+/tTwy9voq9lJ7LsfvEVF3LtcRglHAJuu/rwIjgCCgi9baopRqDGSfYx+DtdbJJS+UUoPO542t+/4deFZrvVIpNQlI0FoHW9e3AwqVUibgU+A6IAbYoZRaprU+CGwGlgPrKux+GNDG+tML+Nz6L8A0wAUYW2Gb6vZ1AhiotU5TSg0DZtns6x/LSsnA3bcsJcbdx5OslAzcvD2qL+NrlAHwbdqQo1sjaNunC4c27yErOb2mQgMgNy0dFx+v0tfO3p6kHouqtoydyYSDizMFWdnkpqbj06asP+ni41l64V8iOymF9KgYfFo1L10WsWAZURu34eDizKCXnv5H8eelpeNsE7/Z24v0v05UW6Y0/jPZOLm7kfbXCfZ++R25yamEjH2o9MSuLRY2vPIO2QlJNL92IF6tqu03X7D8tDScvMtidvLyJPN45ZjN3mUx2zs7U3jGOFRzk5LZ+spbmJzNtL7zVrzatSkdDTj24zLSDh3B2c+P9qNH4uTRoMbito3tfOrcXKHOC89kY+dgz1/LV9Hrxac4/tvqGo+tOkkJ6QQ0LIvZP8CTxIR0fP08ypWb8fFy7nvwGsxmx0r7WDRvA78t206HTk155vk7aeBRexd9RelpOHiVxWvv6UVu1PHz3l4XFXL8vTdRdiZ8rh9Gg6tCajzGvLR0nL1tzx1epFVsB6nljz17m2OvRNyOPXg0b4LJwaG0HR9a8gvJkUdw9fcj+MERmGuoHdseV3CWtlvFsWfnYM/xX1fR44WnOLGirO3mJiXj6O5GxJdzyIyOwaN5Uzrcfw/2TpXvctdXiYkZBDQs+47zD/AkMTGjiuPvV+59cHA1x99Gflu2gw6dmvD0c3fU6PF3OX6P1DbpulTvshshsN597wc8Coy0Lg4E4rTWFgCtdYzWOq0W3j4QWAVM1lovs1kWW1JAa31Ya50P9ASOaa2Pa60LgPnAbdYye7TWUVXs/zZgjjZsBTyVUoHWbdYAWRU3qG5fWuswmzrYCjSu7kMppcYopXYqpXauW/Db2Wughtz41H3s/m0j3zwzlYLcfOzsTRflfWtCYV4eYR9+QdfRw8uNDASPuJVbPnmLZn17cGzV+jqMELxatWDwO6/Q/7UXObb8d4oLjLuUys6OgVMmc91/3yb9eBSZMbHn2NPF4eTpQf/pb9P7jcm0GzWciJlfU5Sbi7ZYyE9Nw7N1S3q/PhnP1i05On9JXYdbyZGlv9Ji6DWld84uJYcPxRBzKonB11YeJLxrRH+WrniNuUv+ja9fA/477cc6iPD8tXnzPVq++DKNHn6chMXzKUhKrOuQqpQZc5oDC5bS9WHrPBiLhbzUNLzbtGTQlP/g1aYFB+ZdGu342NJfaX5D5barLRYyT56i6ZAB9HtzMiYnJ44vX1lHUV6+jhyKITYmmcHXVHH83dOPH397he8Xv4CPnwf/e39pHURYvcvte0T8M5fjCMFtwO9a6yNKqRSlVDdgIbBJKdUfWAN8r7Xec479rFVKFQP5WuvzvXM+G3hJa73YZtnXwCpratEaYLbW+ijQCDhlUy6Gc9+hr2qbRsDfT6Y3PAqsqG6l1noWxggC3xxZWW3+xq5fN7Bv5RYAAts0LXdXPyslHXef8ndF3H08ypdJLivj0ySAkW8+CUBqbCJ/7ThwoZ/prJy9PMlJKesT5qam4+ztWWUZFx8vLMXFFObk4ujuirN3+W1zUtJL0wAsRcWEffglTfv2oHHPrlW+d9O+Pdg49TM6D7/5b8dv9vIk1yaGvNQ0zBUmKZeUcfa2id/NtVwZ90aBmJycyIo5jWfLZqXLHVxd8O3QlqTwgzRo3Ohvx2nLycuL/NSymPPT0nGyuRtcEnNeahpma8xFubk4uLmilMLRmibWoHkzXPx8yY5PpEHzptg5OuLfzbgTHNAjlNgNm2sk3orOt87zKtS5g5sr6X+dIH7Hbg4t+JHCnFyUUpgcHGh+3aAaj3PhvPX8tDgMgI6dm5EQXxZzYkI6/gHlY47Ye4LIA9Hcev0rFBdbSE3JYuxD/2Xmt8/g41t2h/r24X3515MzajxeW/aeXhSmlcVblJ6Gg6fXWbYor6Sso68fLm3akXcqGkc//xqN0ezlSW6q7bmjinbgXf7YK7I59nJT09j+v5mEjn0I1wA/I143V0yOjgR2N84ZjXqGEr0+rEZjzks9j7abahOz9dhLP36C+J27ObywfNtt2CMEs7cnnta7vw17hHD818rzOuqbRfM28NMS43uwY+emJMSXfcclJqTj71/+ezB8n3H83XbDaxQXFZOaeoYnHv6IGd88Vf74u6sPE8fPqtFYL8fvkdom2U3Vu+xGCDDShOZbf58PjNJaxwDtgEmABVijlDrXLJ3BWuuuNp2B6i6EbZevBu5XSpWO6Wmt9wItMVJ6vDFSgzpcyAeqTUqpwRgdghf/6b663TSARz56kUc+epE2vbuw/8/taK2JPXQCJxdzuXQhADdvD5xczMQeOoHWmv1/bqdN72AAstONwQ5tsbB5wUq6Duv7T8Mrx7tVM87EJ3ImMZnioiKit+wiqFtwuTJB3YKJ2rgNgJhte/Dv1BalFEHdgonesoviwkLOJCZzJj4R79bN0VqzY9b3NGjUkHY3lW9eWXFldypP7wqnQVDAP4rfs2UzshMSyUlKxlJUxOmtO2kY0qVcmYDQLsRs2gpA3I7d+HZsh1LK2Ka4GICc5BTOxCXg7OdDfmZWaepCcUEBSfsjcQts+I/itNWgRTNyEhLJtcYcv20HfhVi9uvahdObjC/TxB278e5gxFyQmYW2WIyYE5PISUjExc8XpRR+XbsYTxgCUg8ewjWodnL0Paqo84Cz1Hm8TZ1f/dJzDJn+FkOmv0WL64fQ6pahtdIZALhn1EB+WDKJH5ZMYtCQLvy6zDgOI/adwM3NuVK6wvCR/Vmx9m2WrXqDL+b8i6bN/Zn57TMA5fKd163ZR6vWtTv/wblZcwoSEyhITkIXFZGxaztuwec3vak4J9vIZQaKzmSRe/xYucnINcWzZTOy4xPJTjTaQezWnTQMLd8OGoZ04ZS1HZzeXtYOCrNz2Pr+p3S853Z82rYqLa+UomFIMMmRRjtOOnAY9xpsxx4tyrfduG078a/Qdv1DuhBr03Z9rMde78nPMeiDtxj0wVs0v34ILW8eSrPrBuHk6YHZ24szcfEApBw8jFtQzZ0vLld3jxrA3MUvMnfxiwwc0oXfyh1/5srH34j+/PbnFH5e+Rqz5jxD0+b+zPjmKaDi8Rde48ff5fg9IurOZTVCoJTyBoYAwUopDZgArZR63pqmswJYoZRKAG7HuGN/vlKAireqvIFkm9dTgdHAIqXUbVrrIgCt9RngR+BHpZQFuBEIA2yfP9YYm9SiasT+jW2qpZTqAnwJDNNap/zd/VSlVfeOHN95gJlj3sDByZEbn76vdN3XT73HIx8Z/Y/r/+8efv3vXIoKCmjZrSMtu3UE4OCGXez+dSMA7fpcRZdre5du/9mjr1GQk0dxURFHt4Yz4o1x+Da9sBOlnclE6EP3sOHdT43Hjg7qg0fjIPYvWo5Xy6Y06taFloOuZttns/ntX6/i6OpK7wmPAODROIgmvUP5/fkp2JnsCH14BHZ2diQdOsbJTdvxaBLEqklvAxB8z60EhnQmfP7PZMUloJTCxdebbv/gCUMl8Xd+YCRbp36M1haaDLga9/9n777jo6rSP45/nvQG6ZUuCNKrCCJtsYANuyDWXRd2rfwAQVSwgIKCXVfAgqCA0kFFUVA6SC8JoXdID4GEhLQ5vz9mElKBaCaTkOe9L147c++Zm2/GMzf33FOmdgR75v2AX4O6hLVrTd1uXdg2+WuWDxuNm48X7Z76FwBJ+w5y4Mel1vGeIrR8rB/uNXw4e+wE26ZMs04ktFiIuK49oW1bXiJJ2TI3efhBtk78CGOxENH1enxqRXBg/mJqNqhHSNvWRHTrQuSUqawZPgpXby9a/te6ysnpvfs5uOAHxNkZcRKaPjYAV9tdqqsfuJvIKVPZO3MObjV8aPbkY+WWuWj+Fo/2Y6PtPa9te8/32t7z0HatqdOtC9snf80fw0bjWuA9d5Qu3ZqzdnUUd/d5HQ9PV0aPeTh/30P3jmPmvJEXff1H7y5k394TCEJ4rQBeevXv1dtLEWdnwh54iGOffoCxWPDr3AWPiFrE/7gQz7r1qdGqDRlHD3N8yv/ITT9HWuQOEn5aNS9GRAAAIABJREFUTMNRb5AZG0PMrG+st/iMIfDmPnZpEDg5O9Pq0X6sn/AxxmKhbrfrqVk7gmhbPQhv15p63buwddLXLBtqrQcdnrbWg0O/reBcXAJ7Fy5h70Lr8Mvrhz+Lu29NmvW7m62TviZyxlzcavjQ9t+PlmvmZo/0Y5Mtc17d3Tf/B3zrW+tu7W5d2Dnla1a+MBpXby/aXEbdbfbwg+yYNBWTk4tnSBCtnnyk3DKXxbSPn6Vr56YE+dfgwJ+fMOa9uUz7foVDshTUpWsz1q2K4p5b38DDw41RYy/8HRxw39vMmHvx+3Afv7eIfXtOImL9/I0c/WC55quKf0eU48ilVvioTERkINDeGDOowLaVwCis4/VPiYgT8DWw0xgzsZTjHAE6FJlU7A7sAW41xkSLSD1gFdaJymdEZAUwDNgCzASygMeB64Hdtsm7blgnHP8PWAjsA3phvajfBDxkjIkq8DML5RCR24BnsDYorgM+MsZ0LFC+BzDMGFNsLEoJx6oL/A48aoy57L7piw0ZqowOpVaduQd5MnKqXsfceUvV6md1c6pS1RiA19rlOjpCmT22svLNmbiYq2tmOzpCmWVZHJ2gbCY/8LmjI5RZ7IHHHR2hzMZs+3sr8TnCxOv+4fA/JNEpPzr8j0NTv9sd/j6UpKpdmfQHis66mYd1bP8PIhIJ7ARygE/KcmBbD8PDwFQR2Q7MBZ40xpwpUs4Aj2GdTPwO0BBYKSK7gG3AZmCerffgGWApEA3MzmsMiMhzInICaw/AThH5wnb4JcAh4ADwOZC/6LKIrAbmAL1E5ISI3HKJY40GAoH/2ZZW3VyW90MppZRSSlUPVWrIkDGmZwnbPgI+KuNx6peyfS3QqZR9PQo8zgIKLig8vZTXLMF6kV90e4mZbY2Np0s5VtdStpd2rCeBJ4u/QimllFKq+qmUt+bLwDZ0/nugPnAEeKDoqpq2ES4LsN70dwU+NsZccsWIqtZDoJRSSimlVHX0IrDcGHM11nmyL5ZQJgbobIxpg3X4+YsicskJV1Wqh6CsRORPoOi3qDxijNnliDxKKaWUUkr9RX2BHrbH07B+KW2h2eu2USx53LnMm/9XdIOgDN8voJRSSimlrmBOlWDMkG2BnIEFNk2xfR/U5Qg1xuR9N1UsUOIa5yJSB/gJaAS8YIw5dakDX9ENAqWUUkoppSqLgl8GWxIRWQaU9OUOLxc5jrEtwV/SzzgOtLINFVooInONMXEXy6UNAqWUUkopdcWrBB0El2SMubG0fSISJyLhxpgYEQkH4ksrazvWKdsKnF2xrp5ZKp1UrJRSSimlVOW3GOvS99j+f1HRAiJSW0Q8bY/9gRuAvZc6sDYIlFJKKaWUqvzGAzeJyH7gRttzRKRDge+hagr8KSI7gJXAxMtZTEeHDCmllFJKqSteKUPuqwxjTBLQq4Ttm7F995Qx5jegVVmPrT0ESimllFJKVWPaIFBKKaWUUqoa0yFDSimllFLqilcVVhlyFO0hUEoppZRSqhrTHgKllFJKKXXFE+0iKJX2ECillFJKKVWNaYNAKaWUUkqpakyHDCmllFJKqSue3gUvnb43SimllFJKVWPaQ6CUUkoppa54Oqm4dNpDoJRSSimlVDWmDQKllFJKKaWqMR0ypAr5Isrb0RHK5L0uZxwdocwa1cx1dIQyO5NdtfpZo05XvVPbIyt9HB2hzII9qlZd3pXi5ugIZTa+Q4qjI5TJ6wced3SEMgtr9LWjI5RZi4lPOzpC2V3n6AD6TcUXoz0ESimllFJKVWNV7zaaUkoppZRSZaSTikunPQRKKaWUUkpVY9ogUEoppZRSqhrTIUNKKaWUUuqKpyOGSqc9BEoppZRSSlVj2kOglFJKKaWueE7aRVAq7SFQSimllFKqGtMGgVJKKaWUUtWYDhlSSimllFJXPB0xVDrtIVBKKaWUUqoa0x4CpZRSSil1xRMxjo5QaWkPgVJKKaWUUtWYNgiUUkoppZSqxnTIkFJKKaWUuuLppOLSaQ+BUkoppZRS1Zg2CJRSSimllKrGdMiQUkoppZS64omOGSqV9hAopZRSSilVjWkPgVJKKaWUuuJpB0HptIdAKaWUUkqpakwbBEoppZRSSlVjOmRI/W3XhfgxuNVVOInww9E4vt13otD+BxtFcEe9MHKNISUzm7e27icuI5NQT3fGdWqKE+DiJMw9GMPCI7EVktkYw7cfLmDHhmjc3d3490v9qd+kdqEymeez+GTUNOJPJeHkJLTp0pwH/3M7AHu2H2TGRws5fiiGp159hI49W1dI7oL53397EevX7MHDw5VXxjxIk6a1Sy0//LmpnDyRxIz5wyo046SJi9i0Nhp3DzeGvvYgja4pnvGVZz8nOfEsubkWWrRpwFMj7sHZ2Ynpn/3C+pVRODkJvv4+DH3tQQKDfe2eedH/5hO9MRo3d1cefOEhal9dp1i5n7/6ic3LNpGRms5bP7yTv33T0j/58fPF+AZac3bp25Xrbu1st7xpUZHEz52FsVjw69KVwJtvLbQ/ff8+4uZ9R+bJE0Q8MZCa7Trk78tOTiJmxjRyTieDCLWfeh63wCC75EzeFcmhWbMxxkJY1xuoc2vvQvst2dns/XIqaUeP4ertzTX/+TceQUFYcnLZP206aUePYSwWQjt3os5tfQDY99U0knfuwrVGDdqPedUuuQHaB/rxn2us57dfTsQx50jh81sL/5oManIVDXy8Gb9rD2vikgBo5e/LwCYN8svV8fZi/M49rE9ItlvWPMYYpr6/kG3rrJ+9p0b146oSzm/vvTyduBOJODk70f6GZgx46vb8/euWbWfOl78iAvUaRfD8Gw/bPfO74+exbvVuPDzcGD12ANc0K/7ZyzP02SmcPJHEdwtGAjDlf0tYNG89fv4+ADz13O106dbcrpkvZtKEQfTp1ZaEpLN0uGm4w3IU1DnMj6FtrHV50eE4pu0pXJcfahxB3wYX/la/sWk/semZtA/2ZUibC3W5Xk0vXl6/h5Wn7F+Xy5veBS+dNgjU3+IEDG3dkMFrI4nPyOKLnm1YE5PEkdSM/DL7U87xr8Pbycy1cFeDMJ5uUZ/Rm/aSdD6LQSt3kG0xeDo78U2vdqyJTSbxfJbdc+/cEE3ciUQmzHqJg7uP8vW7c3ltyuBi5fr070GzdleTk53D+MGfsWNDNK07NSUw1J9/v9Sfn79bYfesJVm/Zg8njiUy+4cRRO06xoSx8/lixnMlll2xbBeeXm4VnBA2rd3DqeMJfLngRfZEHuOTcfP4YNrzxcqNHPcI3j4eGGN4c/h0Vi/bQY9b2nLvIz149L/WC8dF361m5ue/8exL99k1856N0SScTODFr1/mWPRR5n00h+c/HlKsXLNOzenS9wbGP/5msX2tu7flnmftmxPAWCzEzZ5BnWeH4Ornz5F3xuLTsg3u4RH5ZVwCAgh/5AmSl/1a7PWnpn9J0C234d20OZbz58HJPqNrjcXCwRmzaDF0MO7+/mwfM46ANq3wjriQM3b1Wly8vLl23Fji/9zE4bnzafqfgSRu3oIlO4f2b7xKbmYWW0a9RvB11+IRFERol85E9OrJ3i+m2iU3WM9vTzdtyEtbIkk8n8WHndrwZ0ISx85dOL/FZ2TybuQ+7q1f+IJ75+kzPLNhOwA+Li581bU9W5NS7Ja1oG3r9xB7PJGP5oxkf9QxvnhnHm99Wfyzd8dDPWjRvhE52Tm88ewktq2Ppm3npsQcT2Dh9OWMmfwMPjW9OJOcavfM61bv5vjRBOb9NIrInUd4e+xsps4cWmLZP5btwNPTvdj2/o/04OHHe9k76mX5Zs5KJk1byhfvP+XoKID14z28XUOeWRlJXEYW025sw6pTSRw+e6Eu7z19jkcPWv9W39swjOda1eelDXvZknCGAb9Z63JNNxfm92nPhriKqcuq4lS5xpKIhInIdyJyUES2iMgfIpIuIttFJFlEDtseLyvl9fVFJMNWZreITBcR1yJlPhCRkyLiVGR7HxHZbHvdNhF517a9iYissB0zWkSm2La7isg0Edll2z7Str2OLfduEYkSkecL/IwAEflNRPbb/t/ftv0aEVkvIpkiMqxIrt4isldEDojIiyX8zh+JSNpfe8cvrmlADU6cO8+p9ExyjGH5iQS6hgcWKrM18QyZuRYAopJTCbadyHOMIdtiAHB1dqrQ5cC2romkS+8OiAiNmtcnPS2DlMSzhcq4e7jRrN3VALi4ulC/cW2S460nweDwAOo2ikActIbZ6j+i6H1He0SEFq3qkZZ6nsSEs8XKpadn8t03q3j83zdWeMYNK6Podav1PW7a0poxObF4Rm8fDwBycy1k5+Tkv6d52wHOZ2RVyHpxUet30eHGaxER6jWrz/m0DM4mnSlWrl6z+tQMtG9vxaWcP3IYt+AQ3IKCERcXarbvSNrO7YXKuAUG4VGrTrH3LjPmFORa8G5qvYPq5OGBk1vxC6zykHroMB4hIXgGB+Pk4kJwxw4kb9tRqEzS9h2EXt8JgOAO7UiJ3oMxBgQsWZmY3Fws2Vk4uTjj7OEJgG+Txrh4e9klc57GvjU4lX6e2Azr+W1lbAKdQgqf3+LPZ3IkLd2atxRdQwPZnHiaTIvFrnnzbF4VSbc+1vND4xb1OJeWwekSzm8t2jcCrOe3Bk1qkxRvrevLF23glvu64FPT+v76BtSwe+ZVf+zi1js7IiK0bN2A1NQMEhOKf/bS0zOZOf0P/jnoZrtn+jvWbtxDcopd/uz+Jc0DanA87Twnz2WSYzH8diyB7hGF6/KWhAt/q3clpRLiVfyc0Kt2IOtjT+eXq2pEHP+vsqpSPQRivVJYAEwzxvSzbWsN1DTGrBaRr4EfjTFzL3Gog8aYNiLiDPwGPADMsB3PCbgbOA50B/6wbW8BfALcZozZY3vtQNvxPgLeN8YsspVtadt+P+BujGkpIl7AbhGZBWQCQ40xW0WkBrBFRH4zxuwGXgSWG2PG2y7uXwRGAMnAc8BdRd4TZ+BT4CbgBLBJRBbbjoWIdAD8L+8dLrtgDzfiMzLzn8dnZNLcv/Q/HnfUC2VD3On85yGebkzo3Jza3h58GnmkQnoHAJITzhIQ4pf/PCDYj+TEM/gF1Syx/LnUDLatjeLm+7tVSL5LSYg/S2johfzBob4kxJ8hKLhw/s8/XUr/R7vh4eFa9BB2l5RwhqCwCxmDQn1JjD9DQAnv8cvPTGFf1HE6XH8NN/Rqlb/9609/ZvmSzXh7ezB+8n/tnvlM4hn8Qi58XHyD/DiTeKZMF/+71uzk8K6DBNUOoe9/7ip0vPKUnXIaF/8Lx3bx8yfjyKHLem1WfBxOXl6cmPIp2UmJeDdpRvBd9yJO5X+PKDMlBfeACznd/P1JPXy4cJ7TKbgHBAAgzs64eHqSk3aOoPbtSdq2gw1DhmPJyuKqfvfj6uNd7hlLE+ThRsL5C+e3xPOZNPEt+8Vxt/BgFhw5WZ7RLio54QxBBc4PgcG+JCecwf8i57cta6K49YGuAJw6ngDAqIEfY7FYuP9ft9Cm8zV2zRwff4bQAueLkFA/4uPPEFRkmOCkj3/iocd64uFRvNdzzqzVLFm8iabN6/D8sLup6WvfBmNVEuzpRlz6hbocl5FJi4s09Po2CGVdzOli22+qE8zMfRVXl1XFqWo9BD2BbGPMpLwNxpgdxpjVf+VgxphcYCNQq8DmHkAU8BnQv8D24cCbxpg9ea81xnxm2xeO9WI877i78h4C3iLiAngCWcBZY0yMMWarrWwqEF0gQ19gmu3xNGwNAGNMvDFmE5Bd5NfoCBwwxhwyxmQB39mOkddYmGDLXioRGWjr+dgc++viixX9W26uE8w1/j7M3H9h3GJ8RhaP/b6NB3/bQp+6Ifi7V/yF66Xk5uTy2evfcNN9XQkpckelMtu35yQnjyfRvVfLSxd2sDc/GciMX0aTnZXDjk0H8rc//nQfvvlpFD37tOOH2WsdmPDyNOvcgpe/Gc3QKSNo3K4xsybMdHSkEpncXDIO7CfkngeoP/wVspISOLOh8r2/qYcPI05OXPfuO1z79pucXLqMjIQER8cqE383Vxr4eLOlgoYLlVVuTi4fjv6WPvd3JbSW9fxmybEQczyRV//3FM+/8TCTx8/mXIFhoI6yb88JTp5IpGev4nO27n3gBuYvGc23c4cTGOzLhxMXOCDhlaFP3WCaBvjwzd7CcwwCPVxp5OvN+tjKWZfV31OlegiAFsCW8jqYiHgA1wEFB1f2B2YBi4C3RMTVGJNt+9nvlnKo94HfRWQd8Csw1RiTAszFenEeA3gB/2eMKTQLR0TqA22BP22bQo0xMbbHsUDoJX6NWlh7M/KcsP1OAM8Ai40xMRcb2mKMmQJMAeiyYE3p/d4lSDifRUiBsZwhnu4klHCXv0OwL481qcPTq3blDxMqKPF8FodS02kdWJMVp5LKEuGyLZu/hhU/bACgwTV18of/ACQnpBAQVPJd4K8mzCG0dhC9H+hul1yXa953a1k831pNrmleh7gCYzgT4s4QHFI4f+TOo+zZfYJ7+rxFbo6F08lpPP2vz/j0S/vdaf9h9lp+WWjN2LhZHRIL/OFIjDtDUEjpd9rd3F3p1L05G1ZG0q5T40L7evZpx+jnvuCRQbeUe+a1i1bz55L1ANRpUpeU+At3xc4kpuBbSr0oiXfNC3evr+vTmZ8+/6H8ghbh6udPzukLWXNSTuPqd3m9Ea7+/rjXroNbUDAANVq1vezehbJy9/MjM/lCzqzTp3H38ytUxs3fj8zkZNwD/DG5ueRkZODi403Cnxvxb9EcJxdn3GrWpGajhqQdOYpncLBdshaVeD6LYI8L57cgD3eSMsvWi9ktLJh18UnkXmRIUXn4Ze4ali+2fvYaNq1DYoHzQ1LCGQJKmZA/efwcwuoEcVu/C72fASF+XN28Li4uzoREBBJeJ5iY4wk0ala3XDPPmbWKhfOsn71mLeoSV+B8ER+XQkiR88XOHYeJjjpG31teIzcnl+TkNP7zxEdMmvocgQV6P+66tzNDnplSrlmruoSMLEILDAEK9XQnIaN4Xe4Y4ssTzeow6I/if6tvqhPMipP2r8v2VYnH7DhYVeshKC8NRWQ7EAfEGGN2AoiIG3ArsNAYcxbrRfolr0CMMVOBpsAcrD0MG0TEHevd+1wgAmgADBWRq/JeJyI+wDxgsO3nFT2uwdrLUGYiEoF1yNLHf+X1l2vP6VRq+3gS7uWOiwi9agezJqbwygNX+3ozvE0jRqzfTUrWhQ6OYA833GxDFGq4OtMqsCbH0ux3F+rGe25g7NRhjJ06jPZdW7L2l80YYzgQdQQvH48ShwvN/XwJGecyGPDcXSUcsWLd268L02YPYdrsIXTr2YJfftiCMYbInUfx9vEoNlzongeuZ/GyUcz/+SUmff0UdeoF2bUxAHDHA134dOYQPp05hM49mrN8ifU9jt5lzVh0uFBGemb+vILcnFw2rY2mdv0QAE4eu3AneP2KqPzt5a1L364MmTycIZOH07xLSzYv24QxhqO7j+Dh7Vmm4UIF5xtErY8kpO6l2vN/nUe9+mTFx5GVmIDJyeHslo34tLy81a486jXAkpFOTqp1smj6vmjcw8LtkrNGg/qcj4vnfEIilpwcEjZuJqBN4ZyBbVoRt87aWE/YvBW/a65BRHAPCODMnj0A5GZmcvbQYbzCwuySsyT7zqYS4eVJqKf1/NY9LJgN8WVbWaVHWBArYuzfq9H7vhuYMH0oE6YPpWO3Fqz62Xp+2Bd5FC9vjxKHC303+WfSz53n8cF9C23v2K0FUVsPAnA2JY2Y4wn5vQfl6f7+3ZgxdwQz5o6g+z9asWTxRowx7NpxGB8fj2LDhe57sCtLfh/LoqWvMWX6YOrWD2HSVOtiCgXnG6xYvpOGjexTn6uq3cmp1PXxJMLbHRcn4aa6wawqskpQYz9vRnZoxNA1uzmdWXQwAtxcN4ilx6pWD526fFWthyAKKI/lO/LmEAQBa0XkTmPMYqwX/37ALtsddS8gA/jR9rPbAztKOqAx5hTwFfCViERi7VF4CPjF1sMQLyJrgQ7AIdtE5nnADGPM/AKHihORcNtd/XAg/hK/y0mg4NpstW3b2gKNgAN5v4uIHDDGNLqcN+hy5Rp4f8dB3uvSAmfgx6NxHE5N58mmddlzOo01sck83aIBni7OjO1oHYMal5HJiA3R1K/hxTMtG2Cwttln7T/BobPp5RmvVK07N2XHhmhe6PcWbh6uPDnywuiwV56YyNipw0iOT2Hx9GWE1wth9L/eA6yNih53dOJQ9DE+fHmqdW7BuigWfPUL474ZUSHZAa7veg3r10Rz/+3j8fBw4+U3Hsjf99gD7zFtdvGVcSratV2asmntHv5513g8PFz5v1cfzN/39EPv8enMIZzPyOK1IV+RnZWLsVho1aERt91rXaZz6sdLOHE0HnFyIiTcj2dH2n/lnqYdm7Hnz2jGPzYWV3c3Hhx2oV68N+gdhky2jr778fPFbPt9C9mZ2Yzp/yod+3Tilkf7sGbhKqLWR+Hk7IRXDS/6vfCQ3bKKszOhDzzE8U8/AIsF385dcI+oRcKPC/GoW58ardqQcfQwJ6f8j9z0c6RF7iDxp8VcNeoN63t69/0c/2giAO516uHXxT7zY8TZmYYD+hH5/ofWpUNv6IJ3rQiOLFxMjfr1CGzTmrCuN7D386/YNPIVXLy9uWbQkwBE/KMH+76axpZRr2EMhN3QGe861tV89kz+gpS9e8lJS+PPYSOo1/cOwrreUK7ZLQY+23OQse1a4Czw68k4jp1L55GGddl3No0/E5JpXNOHUW2a4uPqwnXBATzcsC7/WbcNgBAPd4I83Nl1uvjkWHtqe31Ttq6L5rn7x+Hm7spTr/TL3/fCo+8yYfpQkuJTmP/1MmrVC2HE4+8D0Pu+LvS6sxOtOzVhx8a9/F//d3ByEh5+5g5q+Np37kaXrs1YtyqKe259Aw8PN0aNHZC/b8B9bzNj7sXPrx+/t4h9e04iIoTXCmDk6AcvWt7epn38LF07NyXIvwYH/vyEMe/NZdr3KxyWJ9fAO1sP8lE3a11efDiOQ2fTGdS8LtGn01h1KpnnW1v/Vo+3zReJTc9k6NpoAMK93An1dGdrCRO9qxLRHoJSycVWRqhsbJOKNwBf2oa5ICKtAN/LnVRsG6LzozGmhe353cBwY0xnEZkJ/GCMmWXb5w0cBupjvbieD9xqjNlnm3w80BgzSUR6Y50InC0iYcA2rBfkjwHXGGOesB1rE9AP2IV1fkCyMabQWpciMgFIKjCpOMAYM7zA/teANGPMRNtzF2Af0AtrQ2AT8JAxJqrIcdOMMT6Xeo/LOmTI0d7rUvVOTo1q5jo6Qpmdya5aJ9Go01XtXgd8se+SH89KJ9ijatXlk+lVr16M71C1xmvX96l888AuJazR146OUGYtJj7t6AhltumBGxz+h+R05o8Ov8bxd7/d4e9DSarUkCHbEJq7gRtty45GAeOwjrX/qxZivXveHegN/FTg550D1gB32IYVDQZmiUg0EAnkDf+5GYgUkR3AUuAFY0ws1tV/fGw5N2GdW7AT6AI8AvzDtlTpdhHJ+0ah8cBNIrIfuNH2PG+51RPAEOAVETkhIjWNMTlY5wosxTo5eXbRxoBSSimllFKlqXK3S2xDcx4oZd/jl/H6I1iH8+Q9N0DegNaAEsrfU+Dxj1iHDxUtMwTrhXrR7WlYx/EX3b6GUma2GGOSsN7tL7o9FutwoJJeswRYUtK+AmWq3u1HpZRSSqlyUuTrpVQB+s4opZRSSilVjVW5HoLLZftysG+KbM40xlxXUnmllFJKKXUlq5TD9yuFK7ZBYPtysDaOzqGUUkoppVRlpkOGlFJKKaWUqsau2B4CpZRSSiml8uj3EJROewiUUkoppZSqxrSHQCmllFJKVQPaQ1Aa7SFQSimllFKqGtMGgVJKKaWUUtWYDhlSSimllFJXPP2m4tLpO6OUUkoppVQ1pg0CpZRSSimlqjEdMqSUUkoppaoBXWWoNNpDoJRSSimlVDWmPQRKKaWUUuqKp99UXDrtIVBKKaWUUqoa0waBUkoppZRS1ZgOGVJKKaWUUlc8HTJUOu0hUEoppZRSqhrTHgKllFJKKVUN6H3w0miDQBUS80eioyOUSce7mzg6QpkZY3F0hDKr6Zbl6AhlkpZ90tERyuyb7ucdHaHMblzo6egIZZI8db+jI5RZ/YXhjo5QJmO2uTo6Qpm1mPi0oyOUWeSwTx0doeweuMHRCdRFaFNJKaWUUkqpakx7CJRSSiml1BVPRCcVl0Z7CJRSSimllKrGtIdAKaWUUkpVA9pDUBrtIVBKKaWUUqoa0waBUkoppZRS1ZgOGVJKKaWUUlc8/abi0mkPgVJKKaWUUtWY9hAopZRSSqlqQO+Dl0bfGaWUUkoppaoxbRAopZRSSilVjemQIaWUUkopdcXTScWl0x4CpZRSSimlqjFtECillFJKKVWN6ZAhpZRSSil1xRPRIUOl0R4CpZRSSimlqjHtIVBKKaWUUtWA9hCURnsIlFJKKaWUqsa0QaCUUkoppVQ1pkOGlFJKKaXUFU/0Pnip9J1RSimllFKqGtMeAqWUUkopVQ3opOLSaINA/W3dmoYw+p6WODkJs9cfZdKy/YX239uxLi/e1Zy4lPMATF99iNnrjxLh78mkJ6/DSQQXZ2H6qkPMXHukwvMbY3jzzS9YtXILHh7ujBv/HM2bNyxWLisrmzFjprBxYxROIgz+vwHccsv1FZbxrTe/ZNWqrXh4uPPWuGdKzPjoI6NISDiNh4cbAF98OZrAQD9OnUpg5Isfk5p6jtxcC0OGPkz37u3tnnncW9NYvWobHh7uvPnWf2nWvEGxco8/+jqJCSm42zJP+eIlAgN9WbhgBe9OmEFIaAAA/R+6hfvu/4fdM099fyFb10Xj7uEgo0WJAAAgAElEQVTG06P6cVWT2oXKZJ7P4t2XpxN3IhEnZyfa39CMh5+6PX//umXbmf3lr4hAvUYRDH7jYbvmfXfcXNaujsLDw41X33yEa5rVKbX8kGcmcfJEEt8vfBmAKZ/+xMJ56/Dz9wHg6efvpEu35nbLC9Ap1I+hba/CSYRFh+KYvvdEof0PXR3BnVeFkWsxpGRmM2bzfmLTMwEI9XTn5Q6NCPVyxwD/tzqKGNs+e+nWvhavDLoOZydh9tJ9TJ6zq1iZW7vW57kBbTHGEH04mSHvrALghSc60PNaa/355LsdLFl12K5Z8xhjeHf8fNat3o2Hhyujxw64aL0Y+uznnDyRyHcLRhbaPmPa73w4cRG/rnozv46Up/idUUR+OxtjMdTt3oWr77il0P7c7Gy2T55GypFjuPl40/7pJ/EKDuT0wSPsnDoj75el8d23E96hDblZ2ax7610s2TlYLBYirm1Lk3vuKPfcAJ3D/BjaxlaPD8cxbU+Retw4gr4Nwsg11nr8xiZrPW4f7MuQNhfOg/VqevHy+j2sPJVsl5yXa9KEQfTp1ZaEpLN0uGm4Q7Mox9AGgQOJSBjwAXAtkALEAYOB+caYFiWUdwFigC+NMS8W2H47MAbrEDBX4ENjzGQRaQJMBvwAd2C1MWZgef4OTgKv39+aRz9dS2xKBguH9WBZZCwHYlMLlftp60lem7uz0LaEs+e57/1VZOVY8HJz5peRvVi2K5b4s+fLM+IlrVq1haNHYlj662fs2LGP11+bxOw5E4qVmzRpLoEBfixd+j8sFgtnUtIqMONWjh6N4Zeln7Jjxz7eeH0K389+u8SyEyYMpkXLRoW2TfpsLr37XE///r05cOA4gwaOZfnvk+2aefWq7Rw7GsOSXz5g544DjHnjC2Z9/2aJZcdPeIYWLYo3cHr36czLo/5p15wFbVu/h5jjiXw8ZyT7o47x+TvzGPfl88XK3flQD1q0b0R2dg5vPDuJbeujadu5KTHHE1gwfTljJz+DT00vziSnlvBTys+61bs5diyB+UteJXLnEcaP+Y6vZ71QYtnff9uOl5d7se39H+nJI0/caNeceZyA4e0a8syqSOLTs5h2YxtWn0ricGpGfpm9Ked4bNl2MnMt3HtVGM+2qs/LG/YC8FrHxkyNPs7G+BQ8nZ2w2Duvk/DaU5147OWlxCamM/+DO1i+4RgHjp/JL1Mvoib/eaAVDwz7ibNpWQT4egDQ49raNG8UwB3PLMLN1ZkZb/dm1aYTpGVk2zm1tV4cP5rAvJ9eIXLnUd4eO4epM4eUWPaPZTvw9HQrtj0u9jQb1u0lLNzfLhmNxcKu6d/RafhzeAb4s/rV8YS1a0WNWuH5ZY6vXIertxe9Jr7ByQ2biP5+Ae2feZIatSPo+vqLODk7cz7lDCtfHkto25Y4ubrQ+cXBuHh4YMnJZe3YiYS0ao5/o6vKNbuT2OrxykjiMqz1eNWpJA6fLVCPT5/j0YO2etwwjOda1eelDXvZknCGAb9tB6Cmmwvz+7RnQ1xKueb7K76Zs5JJ05byxftPOTqKchCdQ+AgYv26vAXACmNMQ2NMe2AkEHqRl90E7APut70eEXEFpgB3GGNaA22BFbbyHwHvG2PaGGOaAh+X9+/Rup4/RxPSOJ6UTnau4cetJ7ipZdhlvTY715CVY/2T7ubihJODevKWL99I37t6ICK0adOEs2fPER9f/G7N/HnLGDjoXgCcnJzwD6hZYRl/X76Rvn0vnbE0IpCWlg5Aamo6ISEB9oqa74/fN3Nn326ICK3bXE3q2XQS4k/b/ef+HZtWRdK9T3tEhMYt6nEuLYPTiWcLlXH3cKNFe2uDy9XVhQZNapMUb71AXLZoA73v64JPTS8AfANq2DXvyj92ctudHRERWrZuQGpqBokJZ4qVS0/PZOb03/nnoN52zXMpzQNqcCLtPKfOZZJjDL8eT6BbrcBCZbYknCEz13pe2JWcSointRHToIYnzk6wMd568ZSRa8kvZy+tGwdx9FQqx2PTyM6x8NOqQ9zYuW6hMg/2bsy3P0ZzNi0LgOQz1hsajer6sSkyjlyLISMzh72HT9OtQy275s2z6o9Ibr3zWlu9qH+JevEH/xx0S7F977+zgGeH3Gm3b3Y9ffAI3iHBeIcE4+TiQkSnDsRu3VGoTOzWHdS+oRMA4de2I2H3HowxuLi74eTsDIAlO9t6gsP6LbQuHtYGmSU3F0tubv6+8tQ8oAbH085z8lwmORbDb8cS6B5xkXqclEpICY3xXrUDWR972u71+HKs3biH5Aq8yeUoIuLwf5WV9hA4Tk8g2xgzKW+DMWaHiNS/yGv6Ax8C/wU6A+uAGlj/OybZjpEJ7LWVDwfy+zGNMcX7uv+mMD9PYlIu3BWJSTlPm3rF7yj1bh1Bx4aBHE5IY+z8yPzXhPt58uWgTtQL9mb8oqgK7x0AiItLJjwsKP95WFggcXHJhS6az561nig//HAmmzZGUqdOGKNGDyQoyK/CMoaFF84YXyRjnpde+gRnZyduurkT//3v/YgITz/zIE/+6w1mfLuEjIxMvvrqtYrJHHbhj2RoWABx8ckEhxSvH6NemoSTsxM33dSRQf+9J/+k+duvG9m8eQ/164cx/MVHCS/wHthDcsIZAkMv/DcNDPYlOeEM/kElN/7OpWawZU0Utz3QFYCY4wkAvDLwYywWC/f/6xbadr7GbnkT4lIIDbvwfoaE+hEfl0JQsG+hcpM+/pEBj/XKH0pW0JxZq1iyeCNNm9dl8Av3UNPXy255gz3diCswxCc+PZPmgaU3mu5sEMr6WGsjsm4NT9Kycnm78zVEeHuwMT6FT3cesWsvQWigFzGJ5/Kfxyam07pJcKEyDWpZ68b3E2/F2Un4aMZ2Vm05yZ5DyTw7oA1fzo/E092F61qFc+BYxdwJjo9PITTsQj0OCfUlPv5MCfXiJx56rCceHq6Ftq/8fRfBIb40bmK/Bsz50yl4Bl6oux4B/qQcPFxqGSdnZ1y9PMlKO4d7DR9OHzzM9i++ISMxmbaDHs9vIBiLhVWjx3EuLoH6N3bHv2HxYYp/V9F6HJeRSYuLNP77NghlXUzxmyE31Qlm5r6T5Z5Pqb9CewgcpwWw5XILi4gHcCPwAzALa+MAY0wysBg4KiKzRGSAiOT9d30f+F1EfhaR/xORirl6LWJ5ZAzdXv+VW9/+gzV7EpjwcLv8fTEpGdz69h/0fGMZ93SsS1CN4ndRKoPcHAuxsUm0bXsN8xe8R5u2TXjn7amOjlXMhImDWfzDB3z77Zts2RzNokUrAFjy0xruvrsnK1Z+waTJrzBixIdYLI6/KwXw9oRnWbB4AtO/fY0tW/aweNFqAHr0aM+vyz9mwaJ36Hx9K14e+ZljgxaRm5PLB6O/5db7uxJqu8udm2Mh5ngir/3vKZ5/42Emj5/NuQLDYRxh754TnDieQM8bWxfbd++DXVnw82vMmPciQcE1+WDCfAckLFnvusE09ffhG9scA2cR2gTX5MOdh3l8+XZqeXtwe/2LdahWDGdnJ+pH1GTAiJ8Z/PZK3nyuCzW83Viz7RQrNp1g9sTbeH9Ed7btiSfXYhwdN9++PSc4eSKJnr0K14vzGVl8/cVvDHr6Vgcluzz+DRvQc9xour42ggM//kJulnUoljg50X3sy9z0wVukHDrC2ROOveDuUzeYpgEX6nGeQA9XGvl6sz7W8cOFqhepBP8qJ20QVB23A38YYzKAecBdIuIMYIx5EugFbASGAV/Ztk8FmgJzgB7ABhEpdsUtIgNFZLOIbD4b+WuZQsWmZBDu55n/PNzPg7gzhS+AUtKz84cGfb/+CC3rFG+XxJ89z76Ys1zbMLDYPnuYMWMJd/UdzF19BxMS7E9MbGL+vtjYJEJDC9959/OvgaenOzffbO2+7t37enbvPmTnjD9z911DuPuuIQSH+BMbUzhjSGjx3oHQUOv75+3jye23d2XXzgMAzJ23nN59ugDQtm0TMjOzOX36bLHX/12zZizl3rtHcO/dIwgO9ic2Nil/X1xsMqEl9Gjkvdfe3p7cdnsXIndZM/v518DNzXrn8t77/sHuKPu837/MXcOwR99l2KPv4h9Uk6QC43mTEs4QUOSuap7J4+cQXieI2/p1y98WGOLHtV2b4+LiTGhEIOF1gvN7DcrL7FkreejecTx07zgCg32Ji71w5zE+LoWQ0MKfr13bDxMddYw7bx7Nvx99n2NH4hn0+AfWvEE1cXZ2wsnJibvu60JU5NFyzVpUQkYWoQWGToR4uZOQkVWs3LUhvjzRtA7D1kaTbbuIjs/IYl/KOU6dyyTXwMqTSTTx97Zr3rikdMKDLvyMsCAv4pLOFSoTm3iO5X8eIyfXcCIujcMnz1A/wtpr8Nn3O7nz2cU8/rJ1kvnhk+X/mcszZ9ZqBtz3DgPue4egYF/iClxoxsedISSkcD3eueMI0VHH6HvL6wx89EOOHUngP098zInjiZw6mcSA+96h7y2vEx+XwiMPTCAxsXyze/j7kZF0oe6eTz6Nh79fqWUsublkp2fg5lP4v3mNWuE4u7uTeuJUoe2u3l4ENW1Mws7d5ZobitfjUM+S63HHEF+eaFaHoWsu1OM8N9UJZsXJJHJN5WkkqupNGwSOEwWUZZmX/sCNInIEa89CIJC/5IoxZpcx5n2s8wzuLbD9lDHmK2NMXyAHa89EIcaYKcaYDsaYDjVb3FymX2LnsRTqB/tQO8ALV2fh9na1WbYrtlCZ4JoXTpw3tgznQJx1omWYnwfurtYqWNPTlQ5XBXIormLGMA4YcCsLF33AwkUf0OvG61i0cAXGGLZv30uNGt7FhuKICD17XsvGPyMBWL9+Jw0blr5qR/lk7MOChe+xYOF79OrVkUWLCmb0KpYxJyc3/yI/OzuHFSs2c3Vj63jniPAgNqy3Tuo+ePAEmZlZBASUfKH7d/QfcAvzFrzNvAVv849eHVi8aBXGGHZs349PDa9iw4WKZl65YiuNrra+rwXnG/zx+2auuso+wxd633cDE6cPZeL0oVzbrQUrf96CMYZ9kUfx8vYocbjQrMk/k37uPI8P7lto+7XdWhC19SAAZ1PSiDmekN97UF4e6N+dmfNGMnPeSHr8oxU/Ld6IMYZdOw7j4+NZbFjIff268vMfb7H41zf4fPr/Ubd+CJO/HgxQaFz5iuU7aNgoHHvafTqVOj6eRHi54yLCzXWCWV1kdZXGft6MbN+IYWt3czrzwgTc3cmp1HB1wc/NOtK1Q4hfoUmc9rBzXyL1ImpSO9QHVxcnbut2Fcs3HC9UZtn6Y1zX0vq++dd0p0EtX47HpuLkJPjZejyb1PfnmvoBrNlqv7vV9/fvyoy5w5kxdzjd/9GSJYs32erFEXx8PIrXiwdvYMnvY1i09FWmTH+euvWDmTT1WRo1jmDpyjdZtPRVFi19lZBQP76Z/QJBpQyb+6v8rqrHubh40hMSseTkcGrDZsLatipUJrRdK06s2QBAzKatBDVrgohYX5ObC0B6YhJpMXF4BgeSeTaV7HPWuVK5WVkkREbjE355c9rKYndyKnV9PInwdsfFSbipbjCrSqrHHRoxdE3hepzn5rpBLD1WvjcLlPo7dA6B4/wOvCUiA40xUwBEpBVQ7CpNRGoCXYE6tjkCiMgTQH8RWQ90MMassBVvAxy1lekNLDfGZNtWNAoEyvUvUq7F8NrcnUx76nqcnIQ5G46yPzaVwbdew65jKSyPjOXx7g3p1cK2jGB6Fi98uxWARqE1eOmuFhisnWif/76fvTH2u4NWmu7d27Nq5RZuvuk/eHi689Zbz+Xvu6vvYBYust5NHTrsUUYM/4C33vqSgICavDXuudIOaZ+Mq7Zyy81PWZcdfeuZ/H133zWEBQvfIysrmyf/9QY5ObnkWixc37kV999vXT1m+IjHGT3qf0yb9gMiwrhxz9p9clO37m1ZvWo7fW55Hk8Pd8a89Z/8fffePYJ5C94mKyubQU+OIzsnF0uuhU7Xt+C++3sB8O23v7Di9y04uzjh6+vD2HH/tWtegHbXN2XbumievX8cbu6uPP1Kv/x9wx59l4nTh5IUn8L8r5dRq14Iwx9/H4A+93Wh152daNOpCTs27mVw/3dwchIeeeYOavja7y52l27NWbs6irv7vI6Hpyujx1xY4vShe8cxc97Ii7waPnp3Ifv2nkAQwmsF8NKr/e2WFSDXwIRtB/moWwucBH44HMehs+kMbF6X6OQ0Vsck81yrBni6ODPONvciNj2TYWujsQAf7jjMp91bIgJ7Tqex8FDsxX/g381rMbz+2Qamjr0ZZydhzq/72X8shecfbkvk/kSW/3mcVVtOckO7CH6ZdDe5FsP4LzeRkpqJm6sz302wDrtJS89i6MRVFTZkqEvXZqxbtZt7bh2Dh4cbo8Y+lL9vwH3vMGOu45eVdHJ2psWj/djwzscYY6FOt+upUTuCPfN+wK9BXcLataZuty5sm/w1y4eNxs3Hi3ZP/QuApH0HOfDjUuu8ARFaPtYP9xo+nD12gm1TpmGMAYuFiOvaE9q2ZblnzzXwzlZrPXYWWGyrx4Oa1yX6dBqrTiXzfGtrPR5foB4PXRsNQLiXO6Ge7mwtYaK3o0z7+Fm6dm5KkH8NDvz5CWPem8u071c4Ola5028qLp0Y7a5yGBGJwLrsaHvgPHAE67Kju7EuQZrnZaCPMaZfgdcGYJ083AjrnIKGQAZwDnjeGLNZRN4DbrMdG2CCMebbi2W66rmFVapCHPyoiaMjlJkxlWPsflnkmuLd4ZVZdErVm6hXv5LOn7mYGxd6XrpQJZI8df+lC1UyWxbat9emvI3ZVnziemW38mjVyxw57FNHRyizjGOzHD6APtuyzeHXOK5ObR3+PpREewgcyBhzCnighF2uJWybVuS1yUDechclzv4yxgwBSl58WimllFKqWqmU1+KVgvadKKWUUkopVY1pg0AppZRSSqlqTIcMKaWUUkqpK57okKFSaQ+BUkoppZRS1Zj2ECillFJKqSuevZfbrsq0h0AppZRSSqlqTBsESimllFJKVWM6ZEgppZRSSlUDeh+8NPrOKKWUUkopVY1pg0AppZRSSqlqTIcMKaWUUkqpK55+D0HptIdAKaWUUkqpakx7CJRSSimlVDWgPQSl0R4CpZRSSimlKjkRCRCR30Rkv+3//UspV1dEfhWRaBHZLSL1L3VsbRAopZRSSilV+b0ILDfGXA0stz0vyXRggjGmKdARiL/UgXXIkFJKKaWUuuKJVPkhQ32BHrbH04AVwIiCBUSkGeBijPkNwBiTdjkH1h4CpZRSSimlKr9QY0yM7XEsEFpCmcZAiojMF5FtIjJBRJwvdWDtIVBKKaWUUtWA4++Di8hAYGCBTVOMMVMK7F8GhJXw0pcLPjHGGBExJZRzAboCbYFjwPfA48CXF8ulDQKllFJKKaUqgO3if8pF9t9Y2j4RiRORcGNMjIiEU/LcgBPAdmPMIdtrFgKduESDwPFNJaWUUkoppdSlLAYesz1+DFhUQplNgJ+IBNue/wPYfakDa4NAKaWUUkpd8aQS/O9vGg/cJCL7gRttzxGRDiLyBYAxJhcYBiwXkV1Yv3zh80sdWIcMKaWUUkopVckZY5KAXiVs3ww8WeD5b0CrshxbjClpPoJS5U9EBhacOFPZVbW8UPUyV7W8oJkrQlXLC5q5IlS1vKCZVdWhQ4ZURRp46SKVSlXLC1Uvc1XLC5q5IlS1vKCZK0JVywuaWVUR2iBQSimllFKqGtMGgVJKKaWUUtWYNghURapqYxKrWl6oepmrWl7QzBWhquUFzVwRqlpe0MyqitBJxUoppZRSSlVj2kOglFJKKaVUNaYNAqWUUkoppaoxbRAopZRSSilVjWmDQCmlrnAicqejMyilKjcRcRWRtiIS4ugsquJpg0DZlVg9LCKjbc/rikhHR+e6GBF5S0T8Cjz3F5GxjsxUkoIZrwQicpOjM5RVXr2uTETkniL/7gWm5D13dL6SiEhQkecPi8hHIjJQRMRRuS5FRL65nG2VWVX83FUlIrLP0RlKIyKTRKS57bEvsAOYDmwTkf4ODacqnDYIlL39D+gM5J1cUoFPHRfnsvQxxqTkPTHGnAZudWCe0iSKyDIR+dcV0jj40tEB/oInHR2gBN8D/wRuB+6w/b93gceV0a95D0TkFeARYAtwE/Ceo0JdhuYFn4iIM9DeQVn+qkr5uRORliKyQUSOi8gUEfEvsG+jI7OVRkRSReSs7V+qiKQCDfO2OzpfCboaY6Jsj58A9hljWmKtw8MdF0s5goujA6gr3nXGmHYisg2sF9ci4uboUJfgLCLuxphMABHxBNwdnKkk0cAHWBtb74jIGmAWsMgYk+HQZKUQkcWl7QICKzLL5brIH3IBPCsyy2W6HhgPbDLGfAYgIj2MMU84NtZFFewFuAfrhco5EZkJbHVQplKJyEjgJcCzQP0QIItKuIZ7VfzcAZ8BrwEbsDa814jIncaYg4CrI4NdxFTAD3jBGBMHICKHjTENHBurVFkFHt8EzAEwxsRW4o45ZSfaIFD2lm27a2YARCQYsDg20iXNAJaLyFTb8yeAaQ7MU5psY8yPwI+2RssdQD/gUxFZaox5yLHxStQVeBhIK7JdgMo6lCwFuDbvD3xBInLcAXkuyhizyTYM5FkR+QMYge3zV4l5ikhbrL3WzsaYcwDGmGwRyXVstOKMMeOAcSIyzhgz0tF5LkNV/NzVMMb8Yns8UUS2AL+IyCNU0vpsjHlORNoDs0RkIfAJlTSrTYqI3A6cBLoA/wIQERcq580OZUfaIFD29hGwAAgRkTeB+4BXHBvp4owxb4vIDuBG26YxxpiljsxUivxbOLYegdnAbNtY0LscluriNgDpxpiVRXeIyF4H5Lkc04F6QLEGATCzgrNcFmOMBfhQROYC7zs6z2WI4cLQoGQRCTfGxIhIIJDjwFwXZYwZKSK1sNYPlwLbVzkuVYmq4ucOEfE1xpwBMMb8YZsPMw8IcGyy0hljtojIjcAzwErAw8GRLmYQ1r/RYcBgY0ysbXsv4CeHpVIOod9UrOxORK7BeoIRYLkxJtrBkUpl681YZozp6egslyIiw4wxEx2dQyl7sX0e3Y0x6Y7OUhIRGY+1V243kNeTYYwxuqrT3yQiDwGHjDEbimyvC4wyxvzbMckun4iEA22NMUscnUWpS9EGgbIrESnpTk6qMSa7wsNcJhFZDtyTd2dKlT8RCQVq2Z6eLGk4TmVi63XpTYHMwNKCk88rC1vWkVh7iUKwDlmIBxYB4ytj5jwi0gGog/Xiep8xZo+DI12U7e56q7z5RpVdVfvcVUW2G2B9KXyuWFwZb4SJyL+BFcaY/bbVvL4C7gWOAI8ZY7Y5Mp+qWLrKkLK3rUACsA/Yb3t8RES22sZaVkZpwC4R+dK29OFHIvKRo0MVJSK+IjJeRPaISLKIJIlItG1bpVx1SKxrXG8AVgDv2P6ttK0m0s6h4UohIo9ircc9AC/bv57AFtu+ymY2cBroYYwJMMYEYs172rav0hGR7iKyGetk6K+AgcCXIrJCROo4Nt1FHaLyTnDNVxU/dxcjIpVu4jaAiIwAvsPaG77R9k+wzil40ZHZSvE81ot/sC5O0QpoAAzBOpRIVSPaQ6DsSkQ+B+bmjcEXkZux3oGYCnxojLnOkflKIiKPlbTdGFOpJhaLyFLgd2Ba3thPEQkDHgN6GWNudmS+kojIdmCQMebPIts7AZONMa0dk6x0trvA1xW9s25bBvFPY0xjxyQrmYjsNcY0Kes+R7KtQnazMSZBRBoA7xlj7rZNjn6hMtZlABGZB7QGlgP5vQTGmOccFqoEVfRzV9o8AQF2GGNqV2SeyyHW7xxoXrQH3LayXpQx5mrHJCuZiGw3xrSxPZ6J9Xz2oe35VmNMlWssqr9OJxUre+tUcKynMeZXEZlojBkkIpVxKU+MMdNsq/bUNcZU2gl3QH1jzNsFN9gaBm+LyD8dlOlSvItelAAYYzaIiLcjAl0GoeSVQiwUXi6zsjgqIsOxNhTzlj4MBR4HKt2qSDbOxpgE2+NjWCfpYoz5TUQ+cFysS1ps+1fZVcXPXQJwlMKfMWN7Xlm/SdcCRGDNXVA4lXN1PYttnsNprPP83iywT1cZqma0QaDsLaZANyrAg/x/e/cef+lY73/89R6HDDnNFpFxJiJGW+0JuzJiszeFyiClol07KezaGx12Gyk72ol0QCUVjaS2RAo/kRxGjcNQxlmEyCHkOO/fH9e1ZtasWWt9D3zXdd3f+Twfj3nMWvc9X4+P+/G917o+9/W5PhfcnxcL1vgBiaSdgWOAJYG1JU0BDq9woWATB37nSTqX1LmnFeNk4N3A+T1/qqzPAr+VdAHzY16D1Lf7iGJR9TYdOIRUEtIaON1PGrjuXiyq/mZKOoU04/UWUmkLkpYGFisYV18NenjQxPvuNtJM512dJ1Rhu9/sQFLL6jks+FmxHqnrUG0+Dcwk3WP/19qkTNIbSdc/LEKiZCiMKUkrAf8FbE16uvNr4HDgUdKX6C0Fw+sq97ueRlpstXk+doPtTcpGtqBcsnIIaQFb58DvaNt/KRVbP5J2pPuiu2o7ceRr/U8svKj44XJRjR+SlgDeD7wKuBb4pu3n82B7ZdudT1yr0P7wwHbNDw8ad99J2h+4zPa1Xc4dYPv4AmENSdIE0t4O7df5atvV7acB8/YcWLb9s6yViNv+a7nIwqBFQhDGTJ4FONr2x0rHMhKSrrA9VdLv2hKC62xvWjq2EF4ISe+1/a2h/2UYjqY8PAjlSXqp7c6N4aqSOw1NA/YCdrK9SuGQwgBFl6EwZvITka1LxzEKs5V6YC8maX1JxwOXlw5qJCS9t3QM3UhaTNIHJB0hacuOc1VvWNeNpOtLxzBC/106gJGSdF7pGPp4tkt74upKIcfhfbdd6RhG4ZNI7BYAACAASURBVMbSAfQiaapSJ707Se2JfwVsWDaqMGgxQxDGlKSvkqZOzwSeaB23/aNiQQ0hT5d+Amh1Nvk5cKTtp8pFNTKS7rK9Ruk4Okk6mdS28yrgXcAltg/O56rsaiFpt16ngK/Zftkg4xmKpOt6nQI2sF3dYv4+rS8F/NT2qoOMZ7jyuocLSaV7bwM+Aixh+4NFA+vQxPuun4o/3w7udQr4hO2qdliWdBTwDtJC/tOBs4GZttcuGlgoIhKCMKYkdStPsO0qu+BIehmpw8ktNW/gBI0d+M0rvcq1qycCK5F6YF/RKruoiaRnge/RvdPQ220vO+CQ+pJ0P2m9Q+f6BgGX215t8FH1J+l54BK6d22aarvKjicdDw9EenhwRG0PDxp63/Xq3iRgmu3quiNJegr4AvBcl9MH2a5qfxhJD5D2CPoScI7tpyXdZnudwqGFAiIhCCGTtB9wFHAraXOWf7VdbUvBhg78fm97w45jnyb9f6xcW59umFcnvo/tG7qcu9t2VRtn5afW37J9WZdz37e9V4Gw+pJ0A7Cr7TldzlV3jZumoffdw8DepI0iFzgF/KDG+nZJlwMH2L6my7nqfo/zOr/tSInhtsDFwJuByba7JTVhHIu2o2FMSVoK2BfYGFiqdbzSGYIDSZvK/FnSOqSnwtUmBMBPgZfantV5QtL/G3w4wzJT0g6257U6tH24pHuBrxaMq58Dgcd6nNt1kIEMh+19+5yrLhnIPkPvNW0HDDCOEZG0BXAYsBZt36cVNiBo4n13BfCk7Us6TyhtFlij9wK9urttMchAhiOv8zsfOF9pX6CdSPsP3CPpwoo/L8IYiBmCMKYknQn8ntS14HDgncBNtj9aNLAuOmtpm1hbGwLM6xbS2frwKscH/osqD0w/DlxP22LiWtukhjAckpYD3mr7tNKxhMGJhCCMqVbrzlYNa+43fqntqaVj65TrKc9oO7RH+3vbHxl4UENo4sBP0oZ074d+U7moess11/uSZgNaZVj3kLpxnGL72VKxdSNpe1KN+BxSnACrkzZH+pDtC0rF1ktejPmo7VM6ju9L6pFe5W7Fki6z3YhOak2771qUNlucF3NrE8YaSVoeOBTYhbQ3jIEHSJ8Vn699XVq7Whduh7ETCUEYU5Kusv06Sb8CPgTcRxqwVrdoSdI+/c7bPnVQsQxHQwd+/0mqVz0D+GM+vDo5+bL9+VKx9SLpdOAR4FQWjHkfYJLt6aVi60bSTcCOtu/oOL428DPbGxUJrI+8TmNqZ3IlaUlS15PaSnAAkLQt6ff5QuDp1vHauqg19L7bnFTOtDwLfr49Qvp8+22p2HqR9HPSbtun2r4vH3s56bNiW9vb9/v5mtS45iGMrUgIwpjKC3XPAjYFvgW8FPi07a8VDWwcaOjA72bSOo1uA7/ZlS5uvNn2BiM9V4qkOcBGnYsC8zW+0fZ6ZSLrTdK1tjfrce56268edEzDIem7pH7ts5lfMlRdF7WG3nezgA/YvrLj+FTg671+X0qS9AfbrxzpuRrFDMGiJxYVhzFl++T88hKgulmBdpLOoXtrSQBsv2WA4QzH4sx/2tfuHmCJAccyXHNJZTedNdarUuGGTtlfJL0DOMv2XABJE0j9uzs7PNXgm8DVks4A7s7HJpOeBp/S86fKmiBplc5ykFwuUrPXNmSQ18T7bpnOZADA9hWSqms5mt0p6T9IMwT3w7zf4fcw/16shtLGit2+8wTUfu+FF1kkBGFM5c4Fb2PhLhyHl4qpj2NKBzBC3QZ+awDTqXfgdyBwYX6K3R7zesCHi0XV3x7A0cCJuRWigBVIpQF7lAysG9ufk/RjUr346/Phe4B32q51t9QvAOdK+negVQry9/l4zffl5ZJeVfF1bWnifXeepHOB77BgYvtuUmecGk0nbVJ3iaSV87H7Sd3qdi8WVW+7kQb+ncnKZFJ5b1iERMlQGFOSzgceBa4Bnm8dt31ssaCGIU+lt0pB/lDbwtEWSa8C3sLCCwWrHaDkp+udC6Gvzi3wqibp7wBsP1Q6lvFG0o6kwdQmpKeWs0kLMc8rGlgfuWxvXeB20hoCkUqGqlvz0MT7Lv9OdFsI/bNyUY0fkn4KHGr7+o7jrwaOsr1zmchCCZEQhDEl6Qbbm5SOYyQkvYm0gPQO0hf8ZNLGVL8qGNawSFrJ9oOl4+hH0hrAY7YfkbQWqT/3TbZnFw2sD0mvIw30rs5J2A6kmKsbrLb3m89dT44lDQRvIO2WWm2XlqaRtGa34zW2HW3ifddEuZvTK0g7QD/RdnyBfSBqIOlq26/tca7atTthbPTaCCaEF8vl+WlDkxwLbG/7jbbfQNrN838Lx7QQSTtKul3SZZI2lzQbuFLSH3P3k+pIOoS0nuSKvOD8fGBHYEZuPVkdSf8FfBn4qqTPAScAywCHSvpE0eC6O6rt9bGkqf+dgauBrxeJaAiSLmh7fWjJWEYiD/xXIF3fnYEVKk0GGnff9SPpG6Vj6EbSR0gtRg8AZkt6a9vpo7r/VFEr9Dk3cWBRhCrEDEEYE22LlRYH1gduo/Ip9ZbWnglDHSstd+HYk/Sh/lPgX/KCu42A79W4qVpOWrYAlibNwKzjtDP0MsCVNc4m5d/lKcBLSIPr1W0/JmkiKebafi/mbagnaZbtKW3nFnhfC+X9SvLrxmwIKOmjwPuBVpvRXYFv2D6+XFQLa+h9N6nXKeBa26sPMp7hyJ8Vr7f9eJ6F+SFwmu3j2n/Ha5FbKl9k+6SO4/sB29XWUjmMrVhUHMbKTqUDeAFmSjoZ+G5+/05gZsF4epnb2lRI0pO2rwCwfVOuF67R87b/JukZ4G/AQwC2n5BUNrLenst11k9KutX2YwD5/6PGDi0r56e+ApaTJM9/8lPr70VTn0ztC/xDqzRE0tHAb4CqEgKaed/9mdQVqT1A5/crd/2J8ibYfhzA9h25/PSHubSsxgt9IHC2pHeS1vlBShyXJCW3YRESCUEYKysDK3XWWOdFYg+wcPu7mvwbsD/Q2pn4UtIGYLV5RNIHgOWAhyUdBMwA3gw8XjSy3n4r6fukkpsLgVPzwvNpQK0LoZ+RtLTtJ0mdb4B59fk1JgQnAcvm16cCKwF/VtogaVaxqPpbR9L/kQZNrdfzVNjyt0W0NUvIr2sc+DXxvruNtJnXXZ0nJFXXwjO7X9IU27MA8kzBTqSOcNWVzub1RFtK2oa0mB/gXNsXFQwrFBIlQ2FMSLoIeG9nPW1+UvIt29PKRDZ+SJoMfJL01OwzpPKhfUnJ1sdaswc1kbQ4qX+/SdPprwP2Au4CvtK+CK8Wkl5i++kux1cCVu3s0BFGTtIb+523fcmgYhmJPBOzD3B2PrQL8G3bXyoX1cIaet/tD1xm+9ou5w6orSwLQNLqpBnFhVp2StrK9q8LhBXCsERCEMbEEN0LqqvHhxRXv/M1xhwGQ6muorNl41Wu8AM0115/GLiXtB/FYaT9CG4itRKsbjM1Scu1SrG6nFuj21PiWkh6DbB1fnup7d+VjCeU1aOb0+9t31A0sBCGEAlBGBOSbrG93kjPlZQX6Rr4PnAOqdZ2nkq7h2xD2vhtMqlc4WbgJNu3Fg2sB0m/JS3APL3WGDtJ2p5UMjaHlAgArE7a1OlDti/o9bMlSPoZcD2plGyj/HoGsB2wme239vnxIjoWQl9oe9tu52rRZ8ErALb/MqhYhqOJ9x3Ma+HZbR+C6mY/YV43pw+QGmgcA3wM+DUwFTjF9hcLhhdCX7GGIIyVX0r6LPDJ1lPU/JT1v0k7vFbH9pT8BbQnKSm4Mf99ge3nigbXRW6B+XJSTfDLSZsj3UpaxHaU7TNLxtfDiqSuSBdLug84HfiB7XvLhtXXccCbbd/RflDS2sDPSIPumqxm+5/z/fZH22/Kxy/NSW+N2uvuOwfbNdbkPwj8EWh9LnQufF1n4BH117j7TtJ/kj6LzwCuyodXB06XdIbtzxcLrrd3Aa+iRzcnIBKCUK2YIQhjIn8Ankwqs2gNQjYjdevZr9WJoWaSpgNfAY62/YXS8XRS28YxuUb4EttbSVqRVLpQYyvB9ifB/0j6wt+NVM5yuu3q+otLmgNs1JkUKu1mfWNts1259O2NpIXF15NmBe5Q2mX5UtuvKhpgFx2/FwvMCFQ6Q/AlYBvS09/TSbXu1X6ZNvS+uxnY2B27xOf7brbt9ctE1lurHFbSYsCfgJfbnpvPNW6TzrBoiRmCMCbyIrU9Ja0DbJwPz7Z9W/u/k7SxK9opU9IrgD1ILdceBg5i/oLB2syVNCmXJ6wGLAZg+2FV3EuwxfalpKfWB5DKWaYD1Q1MSB1CrpZ0BtDqbjKZ9HtySrGoevsc8Pv8+n3AyZJMenL538Wi6q+9VWrrNfn9y8qF1Z3tA/M99ibSU+HjlTZX+6rt24sGN4QG3XdzSZ9rnaWaq1Jndy/o3c1pW+rt5hQCEDMEobCanv5JuoT0VHUGcBa5V3dLhXXB04H/Ia0beCXwb7bPlfQy4DjbexUNsIs81b9H6ThGSmmzt261zFV+yecnlLL9XJ49mgLcY/tPhUPrSmk36J5s15rIIGkFUnJ4BHBY5yZPNWjifSdpB9Ku4HOYn4ivQVq782Hb55eKrZcu3Zz+gTQbU203pxBaIiEIRami3Rsl3cH8DZLab4zW7sq11QW3FjeuA9xi+5HS8SxqamwlmLve9GT7t4OK5cUm6VDbn6sgjmVICeJ00gzGj4AZNXdDaiKlDRY7u3tdnTcKrJ6kJUj9/e+x/UDpeELoJxKCUFRNMwTjhaQNgI/bfn/pWDq1lYJ0VWMXjvy0fXfSoOR82zfkzYYOAybWktC2KO2efANp4St0LHht8h4gtXxeSHqC9OT6jPz3Al+ktn9UIq5eGnrfLQ0821pDIOmVwD8Dd9iusoxT0teA423PVtq48Dek7m+TSHvDnF40wBD6iDUEIXSQtCtwke1H8/sVgDfZ/nHZyBYkaVNSa7vVgB+TFkCfQJqmPrZgaP0cQ1pkfh6pNV/1ax1I6wQmkzqdfFnSvaTe4ofU9juRHQy8ndQ29wzg7CYs4h+mWn5fziQlAa/Mf9qZNGNQkybed+eTNlqcI2k90uD6e8BOkl5n+9Ci0XX3j7Y/mF+/F7jZ9i5Ku4SfR1qAHkKVYoYgDJyk1Vrt7iRdYXtq6ZjaSZple0rHsWpKm1okXQl8lfRFuQPpifWpwKdtP1Uytl4kbUaqqd0BuIb0BXlh5R1abgA2tT1X0lLAfcC6th8a4keLygv69yCVttxJ2pSs1rajw1LLDEHTNPS+a++idgQwyfb+ucvQNa1zNWn/npB0LnCm7W93nguhRhNKBxAWSVe0XtSWDGTd7osaZ9NeYvvbtv9g+zjgCdv/UWsyAGD7WtuH5ITrFNJg9UZJbykcWj/PtFoH5mt7W+3JAEDu6PUT4AJSHfYGZSN6UVTxZFvS3vnvg7v9KR1fp4bed+3JyjTgFwC2n6HeLkOPSNpJ0ubAVqRZjtZi44lFIwthCDUOcsL4V8WXeh8zJX2RVIIDsD/pqVptlspfPK3r+XT7+5oXj+ZOSJsDryZt8FTzgrsNc29/SNd23fy+tdh803KhLaxjZuBuUtnQUbb/1vcHC5L0YdsnDOOf1rLZ3jL572WLRjFCDbvvrpN0DGkh8XqkxLZVwlmrDwBfJm0UeaDt+/LxbYFzi0UVwjBEyVAYOEl32V6jdBy95A4inwLenA/9AjiytpZxki7uc7rKxaOS3kdaoLsUqS3fjNq7b0has99525190ovKi4qvI80OPMbCC15rXEAapUBjqKH33UTgo6R9B75p+9p8fEtSyd5pJeMLYbyJhCCMCUnH0zEQaZ0C9rG93IBDChVo64DTGkR3DlarLWGQtDbzN9m7sXOTvVpI+gzd7z2gzp7+TU0I8hP39wNr0Tbjbvt9pWLqpon3naTtbP+ix7mjbf/noGMaiqQvkFpAf73j+AeAtW0fUiayEIYWCUEYE5L26Xfe9qmDimW4JH0p70B6Dl0GVDV+aXYjaTvgP2xvVzqWTpLe2O+87UsGFctwSVoOOJnUWai1KHcKqYxsX9uPlYptvJD0HPBkt1Ok2a4qHyBIuhy4lPS7MK83vu2zigXVRUPvu5uBg2yf23ZsAmnn8Jfb3qFYcD1IugbYonOxdo77OtublIkshKHFGoIwJvoN+HNdaI1aU9C1xrcASdOArzG/7ejRwLdIg6jPFgytn6nAMU3ZWCj7MnAjsEdrcbEkkcrKTgDeXTC2heROSNOBh4FzgI8DbwBuBY6w/WCfHy/l+oZ2YFm6xifVXTTxvvsn4DxJS9o+O/9e/5BUBrdz2dB6ekm3zk25Q1nta+fCIi5mCMLA1b6GoCkk/Q44iNR2dEfgu6Te+MNZnFmEpBOArYH9a9vhtxdJc2yvP9JzpUiaATxLWvi6IqlU5BzSdZ9ie6eC4XXV1JaMko4ELrf9s9Kx9NPE+w5A0urAz4Hjgb1JuxQfVDaq3iRdDexle07H8fWB021vUSayEIYWCUEYOEl3255cOo5Okq6nf+11bd1kFqi7lvQH252bJFVH0mtIT9ZvIu2jMK+FYI2dkYZICG6xvd6gY+pH0g22N8mtDv9o++Vt5661vVnB8LqSdJjto0rHMVyS/kr6rBAp8XqGlIRBpSVODbzvWp9tq5H2V/kF8D+t85XGvCMpeTmS+Z3ptgAOJXUdqjpxDIu2KBkKY0LSpF6nqLftaOvJ6f7571YJ0d70SRQKWkHSbm3vF29/b7u23VKB9EUu6TDgLGBd5l9bk/qN1+ZySZ8mldvM+z2Q9CnS7ExtngGw/VzeVbldrSUjS+Rr3I1tHzHQaIZgu1HtRqGR9137buvXAau0HasyZtvnSdqFVKZ3QD48G3ib7evLRRbC0GKGIIwJSbcz/wlaJ9teZ8AhDVu38oUau6BI+laf066t0wmApJVJX+rrAB9qtRKsWV5UfArwGhZcVPw7YD/bj5SKrRtJD5D2HhBpLcEZrVPA7rZXKRVbL5L+vcvhpYH9gL+z/dIBhzRsOQnfmvR5d6ntHxcOaSFNvO/Gk7z+YWfbteyjEcJCIiEIoYOkWbTV2ua+1yfmXT7DC5ATxc8BJ3VbfFczSesCr8pvb7R9a8l4emlih692kpYl9Z/fF5gBHFtrz3xJJ5I2zTo9H5oO3Gp7/94/NXhNvO86Zj8hJVwPArNs/7VASCMiaTHSwug9ge1JyeLby0YVQm+REIQx0Vb/2WLgQdt3l4hnJCT9Pam13fKkp6oPA++rrWZV0sEdh1pfmJfZvr1ASEOS9DLbf+5yfDKpi88XCoTVl6S9bX83v96qfVHmCHbYLULSSwFsP146lqHkMsODgXeSasaPs/1w2aj6k/R7YKPWIDu3l5xte6OykS2oofddtxnQScCmpHa/Fw04pGHJLV73Av4ZuArYCljHdre2uiFUI9YQhLFybJdjkyQtCexpe1aX81WwfQ2wmaTl8/tHC4fUS7c65rWAT0j6jO0zupwvqn1Qkjd1egfpCdpqwNml4hrCwaQOTpAWDLYnu+8jLdSsiqR/Iy1kXCa/fxw42vaJRQPrIW/otBvwDeDVTUhgsluANZi/4dfkfKwqTbzvbL+32/G8c/gM4B8GG9HQJP0RuIu0aPtjtv8q6fZIBkITREIQxoTtbbodl7QFqa/7GwYb0dBaT4I7n7y32kfb/mKRwHpwjx1n85PWXzK/drwauRxkN9ITtA2AH5F28Fy9aGD9qcfrbu+Lk/RJYEvgTc67KUtaBzhO0iTbRxYNsLt/B54GPklKaFvHq96YjJSU3yTpqvz+tcBMSf8H9Wxm2ND7rivbd0paonQcPfwQ2IVUOva8pJ9QZ0OKEBYSCUEYKNszW2UMFVom/924DiLtbP+l4k1wHiBNo3+SVNpkSbsWjmko7vG62/savAvYzPZTrQO2b5O0O3AtqSViVWxPKB3DKPXqjFSbJt53XUl6JSl5rI7TTvcHAW8izcD8D7B8vvd+1qCZr7AIioQgDJSkVahzEIXtr+e/uz55bwpJ25DWPdToUGAP4ETgdEk/KBzPcGwo6TrS0+p182vy+xq7Zbk9GWg7+DdJc7v9QBgd25fAvE5Ui7cd/0uxoLpr3H0n6RwW/q6YBKxKagVdpbye5GLg4jyT0VpYfCKwUsnYQugnFhWHMSHpeLp/mG8JfNT2OYOPangknUqK8ZH8fkVSp5Oq2nj22EhtEnAvsI/tmwYf1fDkEpY9SF+U6wP/BZxt++aigXWRa5Z7sn1nv/ODJulC4CjbF3YcnwZ8qlc5Xxg5Sf8KHA48Rdroq1XiVGOi2LT77o0dhww8BMyx/UyBkF4QSW+w/avScYTQSyQEYUx0aX3Y+jC/utYWgi099iFY6FhpXQaqBh6y/USJeEZL0iak2ubdXdmuv00kaWPgJ8BlLLhb6lbAW23PLhXbeCNpDvB62w+WjmWkar/vJE21fUXpOEYitxrdHXgFcL7tGyTtBBwGTKztOySEdpEQhDEhaQ3bd5WOYzQkXUtakPlwfj8JuMT2q8tGNjRJywC7kjo5/UvpeHqRtALpCSXAzRV3ckLSvsCkVmtGSfeQ1pkI+Ljtr5WMr5u8EdJewMb50I3A97qVEoXRk3Q+sFtTusg07L6btxmkpN/Yfn3pmIYi6dukTlNXkbog3UtKxg+pccO6ENrFGoIwVn5Mbs8o6Szbbyscz0gcC/xG0pmkQd/bgaPKhtRbbuX6L6QB4D8BZwHVDVIBJL0E+DqpE8ftpOu7pqSzgQ9WWgrwQWCHtvcP2H5FHnT/nAqvte2nJF1MWkwKaSO1SAZefIcCl0u6kraFrrY/Ui6khTX0vmtvjLBUsShGZgtgU9tz8+fDfcC6th8qHFcIQ4qEIIyV9g/zKutpe7H9HUkzgWn50G62bywZUzeStmf+LpgXA98BXturf3clPgEsAUxu7TaaWyJ+BfhU/lMbdXyhnwnzBt0TC8XUU17gejLw98As0r04RdI1pA2dHisZ3zjzdeAi4HrSGoJaNfG+m5DXb01oez3ve6XChdsAz9ieC/M+H26LZCA0RZQMhTHRMd0773XT5BKc3Ui7eVZVgpM7xlwKvKe1M3H+Aqo2AZN0A/C6zhKL3Ir2CtublImsN0m3dKuxzrvS3lLb9c5lC3cAh7cGJ7kN7aeA9Wy/u1x040uNa4u6aeh9dwfzF2p3qnLhtqQnmb8xnYB18/vWYvNNS8UWwlBihiCMlc0kPUb6IJyYX0P9Gw01qQTnNaSOIb+UdBtpI7LFyoY0pLnd6q1tPy6p1qcTF0g60vYnO44fDlxQIqAhbGX7Pe0HcivEw/Mi2PDiOS93GjqHBUuGant63bj7zvZaw/l3kjauaKH8RqUDCGG0IiEIY8J27QPThTStBMf2LFJJyCGStiTFvoSk80itBL9RNMDu3Dn136bWkouPAydLuoW0sRfAZsBMYL9iUY1OrRvWNdWe+e9D246Z+sokm3jfDddp5PVqpQ23BXFTFkmHRUuUDIWQNbEEp1MuY3kzqcTpfflYNU/QmlgG0JJ7uM/r2mP71o7zVVznvI/GrcARbvuAl/QpYAPb7yoW3CJA0pK1LdJt8n03lKaUbbVrYsxh/IuEIIRM0hRSCc47gFYJzqdt992YqnZNXMNRy+B6JGq5znlR8Smkp6az8uEpwO9Ii4qrbTXZVHmNxjRSmeFOtlcpHNKoxH03GE2MOYx/E0oHEEItbM+yfYjtdUk7eE4hl+DkOuGmamKZyGmlAxiFKq6z7cdsv4NU+vbt/Gd7229vTwbyBmbhBZA0VdKXgTtJm8H9CtiwbFQvSBPvuxDCiyASghC6sH257QOA1YH/Baa2zjVwINXEacAqBtcjVNV1tn2r7XPyn1u7/JMY/I2SpKPyAu3PAtcBmwN/tn1qa0PDhmrifVdVedYwNfE6h3EuEoIQ+rA91/YFrXr8LAZSY6+qwfU4FYOS0dsPuB/4KnBa7jU/Hn5nq/l/kLSmpOXb3m8j6ThJB+dOcADYntr9vzB4kobbdSzW8YTqREIQwshVP5CStFrb2yY+QWuEhl/nagZ/DbQqcCSwM3CrpNNI7ZWjc9+LZwawDMxb33UmcBepw9eJBePq52XD+Ue2bxjrQEIYqfjwCmHkmjCQugJYA+p6gtaPpNVs35vfNmVw3bjrHF44288D5wPnS3oJsBMwEbhH0oW29yoa4OjVdN9NbPs82Bv4pu1jcye1WX1+rqTlJe3W66TtHw0ymBBGIhKCEMan6mcxumji4LpR17mhSVfVbD9N2rzwLEnLArsWDmkhktYEHmktKpe0DbALaTH0Ca02qZXdd+331jTyXg+25+auTjVanpQcdm3vCkRCEKoVCUEIw9DAgVQTZjE61fol30/TrnMTk65q5dmBtwFrUff36QxSovJoW/nN55hfflPjBnsXSZoB/AlYEbgIQNKqwFMlA+vjzo71ZiE0Rs0fYCHUpLqBlKTj6T4gFbDCgMN5MVQ5uB5n17mJSVfNfgI8ClwDPF04ln6aWH5zIDCdtF5ja9vP5uPrAZOKRdXfKyVtZfvX7QclbQXc16PbVwhViIQghOGpcSA1c5Tnimno4Lpx17mPKpOuBlvd9g6lgxiGxpXf5F22zwCQtLmkA0mbRt4OfKlkbH1cCTzW5fhjpJh3Hmw4IQxfJAQhDE91Aynbp/Y6J+mYQcYyAo0bXDftOjc06WqqyyW92vb1pQMZQuPKbyRtAOyZ/zwI/ACQ7W2KBtbfst1+F2xfL2mtwYcTwvBFQhBCNs4GUrsDHysdRKemDa6Hocbr3Likq8G2Bt4j6XZSyZBID7c3LRvWQppYfvN74FJgJ9u3AEg6qGxIQ1qxz7mJA4sihFGIhCCE+cbTQKrKMoAh1Di4Hkp113kcJl0127F0AMPR0PKb3YA9gIslnU+Kv7r7rcPVkt5v+6T2g5L2I60zCaFasAlEIwAABfRJREFUSp8TIYR+JB1ju6rBqqReT/YEXGt79UHG80JJutv25NJxdBpP11nSXbbXKB3HeCNpZWCp1nvbdxUMZyE9ym8+ZnvNooENg6RlgLeSYp8GfAc42/ZwdwUeGEmrAGeTOtG1EoAtgCWBXW3fVyq2EIYSCUEIw1DjQCqXKZgePa9trzPgkIbUxMF1E69zL7UmXU0l6S3AscBqwAPAmsBNtjcuGlgHSXNJ5Tf7tpXf3Nak310ASSuSZjam2962dDy95H0eNslvZ9u+qGQ8IQxHJAQhDEMMpF4c42lwXasmJl1NJela0lPrX9rePA8E97a9b+HQFiBpF1L5zVakHZbPAE62vXbRwEII1YiEIISsaQMpSa/pOGTgQdt3l4hnvGradY6ka3AkzbS9RU4MNs9tPK+1vVnp2LppUvlNCGGwIiEIIWvaQErSxV0OTyLVq+5pu7oNh5o2uIZmXucwGJJ+CewCfB74O1LZ0Gttb1k0sGFoSvlNCGEwIiEIYZyRtAXwRdtvKB1Lp/E0uK71Ojcx6Wqq/MT9b8AE4J3A8sD3bD9UNLAQQhihSAhCyMbTQErSb213/v9Uq9bB9VBqvM7jKelqAklrAuvb/qWkpYHFbP+1dFwhhDASsQ9BCPMd2+XYJEmNGkjl1neNyvRtz5T00tJxjESt17nXTq456foy0Kikq2aS3g/8KynhWhd4BfA1IEpwQgiNEglBCFnTBlI9dlaeBGwJfHTwEY1erYNrGD/XuYlJVwPsD7wOuBLA9py8J0EIITRKJAQhDKHigVTn7skGHgIOtv1AgXiG1NDBdeOuczc1J10N9rTtZ6TUh0DS4sQ1DiE0UCQEIQyh4oHUxbXtiDoMTRxcN+o6NzTpaqpLJB0GTJS0HfAh4JzCMYUQwojFouIQsqEGUrar+qJvX9Aq6Szbbysd01AkrdGkwTU07zpL2qfjUCvpurripKuRJE0A9gW2J7Ur/jlpw6/4Yg0hNErMEIQwX9OeXrfvl1DVHgl9/BhozOA6a9p1btSMRpPZnguclP+EEEJjRUIQwnxNG0i5x+uaNW1wDc27zk1MuhpF0nX9ztvedFCxhBDCiyESghDma9pAajNJj5EG2RPza/J7216uXGg9NW1wDc27zk1MuppmLun39/ukNQN/KxtOCCG8MJEQhDBfowZSthcrHcMoNG1w3cTr3MSkq1FsT5G0IbAnKSm4Mf99ge3nigYXQgijEIuKQ8g6Fo9WtwNtCMMh6XngCXLSBTzZOkWlSVfTSZoOfAU42vYXSscTQggjFQlBCFkMpEIIwyXpFcAewK7Aw8AM4GzbjxcNLIQQRiESghBCCGEEJF0CLEtKAs4idSObx/ZfSsQVQgijFQlBCCGEMAKS7mD++oz2L9HWbGL1a5BCCKFdJAQhhBDCGJC0se3ZpeMIIYShTCgdQAghhDBOnVY6gBBCGI5ICEIIIYSxoaH/SQghlBcJQQghhDA2oiY3hNAIkRCEEEIIIYSwCIuEIIQQQhgbz5QOIIQQhiO6DIUQQggjIGlN4BHbj+b32wC7AHcCJ9iORCCE0CgxQxBCCCGMzAxgGQBJU4AzgbuAzYATC8YVQgijsnjpAEIIIYSGmWj73vx6b+Cbto+VNAGYVTCuEEIYlZghCCGEEEamvZ3oNOBCANtziVajIYQGihmCEEIIYWQukjQD+BOwInARgKRVgadKBhZCCKMRCUEIIYQwMgcC04FVga1tP5uPrwdMKhZVCCGMUnQZCiGEEEZJ0ubAXsA7gNuBH9k+vmxUIYQwMjFDEEIIIYyApA2APfOfB4EfkB6wbVM0sBBCGKWYIQghhBBGQNJc4FJgX9u35GO32V6nbGQhhDA60WUohBBCGJndSAuKL5Z0kqRtie5CIYQGixmCEEIIYRQkLQO8lVQ6NA34DnC27QuKBhZCCCMUCUEIIYTwAklakbSweLrtbUvHE0IIIxEJQQghhBBCCIuwWEMQQgghhBDCIiwSghBCCCGEEBZhkRCEEEIIIYSwCIuEIIQQQgghhEXY/wezuWvZgSuLiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(Train.corr(method='pearson'),annot=True,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the graphs above since correlation  is informative regarding which choice of features to retain for analysis, those variables/features which highly correlate with the class Attribute are to be consindered in feature selection and those that do not correlate or correlate weakly should be eliminated\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FULL_Charge           0.534602\n",
       "FULL_AcidicMolPerc   -0.598816\n",
       "FULL_AURR980107      -0.584111\n",
       "FULL_DAYM780201      -0.554838\n",
       "FULL_GEOR030101      -0.260470\n",
       "FULL_OOBM850104      -0.453287\n",
       "NT_EFC195             0.260702\n",
       "AS_MeanAmphiMoment    0.693552\n",
       "AS_DAYM780201        -0.437168\n",
       "AS_FUKS010112         0.033432\n",
       "CT_RACS820104         0.267652\n",
       "CLASS                 1.000000\n",
       "Name: CLASS, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Showing how each variable correlates with the CLASS variable\n",
    "Train.corr(method='pearson')['CLASS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For more depth of understanding the data the clustermap method is used \n",
    "* THe clustered heatmap of a pandas DataFrame shows  hierarchicay of data\n",
    "* The .clustermap() method uses a hierarchical clusters to order data by similarity.\n",
    "* This reorganizes the data for the rows and columns and displays similar content next to one another "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1a12d7b250>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(Train.corr(method='pearson'),annot=True, cmap=\"Paired_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The heatmap went further to show me the similarity matrix between variables so i could compare with the output from the heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5)Data Visualisation\n",
    "* Data visualizations in python can be done via many packages.\n",
    "* Using matplot enables one to have a visual figures for the data\n",
    "* Every Axes has an x-axis and y-axis for plotting. \n",
    "* Contained within the axes are titles, ticks, labels associated with each axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1)To check for skewness in my dataset\n",
    "* Skew refers to a distribution that is assumed Gaussian (normal or bell curve) that is shifted in one direction or another\n",
    "* Positive Skew is when the curve shifts to the right \n",
    "* Negative skew is when the curve shifts to the left\n",
    "* Skewed data can affect the performance of a machine learning algorithm\n",
    "* Skewed data should be normalized using different transformation methods which include the square transformation for negatively skewed data and log transfomations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e47ff10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Train.skew().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the graph above most of the attributes are positively shwed with the exceptio of 4,6,9, and 10 but since the skew is to to a small extent then the data will not be normalised since it will have little effect on our logarithm model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Use of the box plot\n",
    "* Boxplots summarize the distribution of each attribute\n",
    "* A line for the median (middle value) is drawn while and a box put around the 25th and 75th percentiles.\n",
    "* The whiskers give an idea of the spread of the data and dots outside of the whiskers show candidate outlier values \n",
    "* The data.plot(kind='box') function is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Train.plot(kind='box', subplots=True, layout=(4,3), sharex=True,sharey=True)\n",
    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* AS_MeanAmphiMoment * From the above graphs, the class with no outliers is AS_MeanAmphiMoment and has a normal distribution in its variables\n",
    "* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Scatter plot: \n",
    "* This is plot the relationship between two variables as dots in two dimension.\n",
    "* This is done so we could spot if there is a structured relationship within the two variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a1b91b390>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kIDp2c5zJZ1GafUGWTy1Nep+lS4Ko3BzbjpFQRdMVkuZUcZvrtLFmdsU5fcapkl6BTIjR5VNLsVtEt5LJKhRdHZtF8N1ri1m2o4pb1u5i2Y4qvnttMTZL2xPAjpgBfAJ4CSgQQvwcmAb8awecR6FQdCLJiiDnOm0cqG1m3jO7WTq55Ix1zQpznXxa04zbF0wq1zSO/+I943H7Qxw64ebHL39IbbOP1bMqyI0kkvEj2wsnDDWLHEPXl2YpvnzOJtFsr6zY2O9Yo7dVsy026+maTg2eQNJ9app8bK+sZvPdV2DRhDmLkk4JYlctaN6VMZLm+BjpzKQ5VdwW57v4R30LwZDOsimXkmm30OIP4WijaUSqWURvQOfptw6ydHKJOSvx9FsHeejGS7uVTFah6OoEQpJFm/bE3FOLNu1hy/yxbT5W2hNAKeUmIUQlMJGw/ei3pJQfpfs8CoWi84l3mqtt8plf+Kvf+JTlU0vNZCx6DWBhrpOnZpTz0G/DCV2qDoGmCYQQzP71X2M6HQs3Vpr7xHfE8rLs3UqapfjyOVvy1N7kytgv3+VIiP345EDXJUhYMa2UB7btY/Ubn5p/m663syvon2Wn/MLchIQs3RJE5ZjbNtqTNLdXDtna/VLF7dYF4/i8roWlL/8tIXlrSzKWLOldMa2UdX/5jLlXDo6J3XW3j0JKqdpihSKN6CnuKb0dJf3SngAKIfoBNcBzUc/ZpJSBdJ9LoVB0LaI7pXurG3j09/t59s4x1DT5CITCSvAl119CgyeAJsLbAHj8QWqbMDs20R0eTSNmZHn1G5+yt7ohphPhcljYMHcMmgCbRUs6Si2E4Eh9i5rdUJw1eWpPcqXrEn8wxGM3j6TBE+DlvUfMuC3MdTIgO8OMaZtVo9kb5KTbz0t7jrB+zmgsmsCiCVbOLKev04ZFCJwOjb4OO/WeAEcbPTGxe7Y1XWrtVcdjJM3Gex3/GUVzrrPKrdkvVdwGQjqZdgvjh+Qx76ohWDRBSJes+8tnbUrGTFXGovF4AzqagJAumT3uIpp9QR7+9mVm+zuwr5M6t1+tLVUo0ogQImX/pq10hAR0D1AE1BOeAcwBjgkhjgPzpJSVHXBOhULRBYjvlO6tbuBQipHnpZNLzL8/OtbEsh1VplzJkJHmuxz825QRLNtRFeOM+PRbB5GEO0cNHr9pRpDvcvDQjSUJMymrZ1Xw0G//xmtVNcolVHHW5KmthhnJOunLp5by6O/Djre//e6VZkxHz5xk2i3cVH4Bcze8az6/cmY5P3+lyozV1bMqeGLnJwmxm+u0sXpWRUI9qFynTTnjfom09r0+11nl1uyXMm4tGrlZNmaNuygh1jLtbbeCqGv2J431vdUN5qyiUR+wq8lkFYrujNOmsXJmuSkDNe5jp63t93FHmMD8AfiGlLK/lDIPuB7YASwiXCJCoVD0UJKZSVyUl5nw3Ipppax+41Oz87D6jU/Njk1N82kZ6cIJQxP07ou372PJ9cNp9ASoc/vx+ENmsrdwwlC+s3kvj/xuP0snl7Bl/liWTbkUjz/Ea1U15jFUjarezdlMT9pqipKsk754+z7unVjMuttHEdRlUvdEV4Ytwflz0aY9TK0oMh8v3FgZ89iI3XpPgCd2fmLG+dLJJTyx8xPqI/eFqsv25dDa97q9kt227Jcqbu02QZbdmnTtUIu/bR59qWJ94YShCfdJtEz2zcXX8NKiK9UghEJxDvgCOk++fiCm3X/y9QP4Al2jDMRYKeU844GU8jUhxKNSygVCCLXAQKHowWiaoDjfxdYF4xBIIsuc6Jtp5YUF4/CHdGwWjUy7xi9vvZyPjjXx6O/3A7BmdgU5Thu6lOS7HKZjaLLOT5M3yLFTXnKcNkJRmnhj+8P1HhY8e1psEL9AWq1D6b0Ycr0+GVa2LhiHRYCmaTGyvbas7zqT6+fQAheFOU6ONnrM/5cV5bBwwlBynDbsFs2M9ejnC7IdlBXlsLe6IcY51ziuPxgiJCWvVdWYAxsGP/lmyNwu/npUzKef1iRoui5TSrfOJodsq9S3ON+VELdHGz0E9eRrh4QIr91ujVQ4XuZsyPHzXQ5GnN+HzfOuIMNmidleyZAVivQR0CWXnd+X4QP7oEvJwBwnl53fl0A7nHU7IgE8KoRYDDwfeXwLcDxSGkKVg1AoejC6LjlQ28zjf9jPHeMHs3j7vqQFrtfdPooBfRws21FFvsvB/V8fltQsJpUzYnaGlaf+VE35haVIpLlNIKQn3T6+aVTrUHonbZFGtsYU5Wyun06bJWa9XqpYf2nPEaaUXRDzfLSE1HDONY4b0iVfpDinzaohaF+yoWg7rUnQ9h9v4vE/7D+rMVAyziSjbG08260W9EAo4TonlRRQ7w6wYOOus94PqWTOL+89wk3lF3Dr2thjREv5lQxZoUgPfTIsTBg+gBnrTt9vq2ZV0Cej7W27kO1wjjnjAYXoD/wE+Crhwf83gZ8CjcCFUsq/p/WEbWTUqFFy9+7dnXkJbaathd3biioEn5JO/5bqbvFa2+TjppVvsnRyiblub83sCvNvg8JcJy8uGk9ds59GT4D7X3g/4f/LplzKEzsPJCSPT80o57/3HeFb5UUMG5ANYHZMVkwrBYjZfsW0UgblZXLzmrN3cnoAnf6CunLMGvF5Lk6IyY6XLLFLVlbiWKM36XrYDXPHMGf9O0nvgf7ZDn4VtQZwzewKfvnHT5h75WAgMdaHnZdNH4eNLxo91DT5qHP72V5Zzff/aVhXjPlOv5hzjdezJWHRMWfM8uZl2Tk/x8l5fTLOyQW0tcfWdcnxJg8nmgPcE7VmdNPdVzDzP//aqvuhpsnLt1e+1erY3bpgHNPXvJ22e60L0e1jVtF9OVLfwi2RwRaDwlwnW+aP5YLczGS7pIzXtM4ARmb5lkgpv5dik05N/hQKRdtpi4zHkENFSzdTyTi9/hC5mTYy7Zak/x+Sn8Uvb70cV4aVFxeNJxDUEUJgEbBgwlcI6tJ03ftK/yy2LhgHSD6rdbNh7mgyrBr+kKTRE0CX8NvvXonHr6RIvZlzLZ0Qfy/oum5Kjh/9/f4Y18+BfZ0xktLifBc5TmtSR1ubRSSViF6Q68QTCLHk+uH87FuXEghJQrpkyfXD8QdD/MuLfzOPp8vw/5q9QZq9QX4WZSSzZnYFxfkuFfPt5Ext4NnkwvHOyIY0/c3F18R8HsGgTk1z2C3ZZtEocDmwRur0JZuNjpYelxXlpByAgPC6PQ1BYY6DLfPHEtQlVk2ktJSPvx90XdLiDyWNUZtFkO9yJMR1IKQrGXIncKY4UnR/Ukm5g50tAZVShoQQX03nMRUKRefRVjdBQw4VLcVMJeP8tNaN3aphT1G24bNaN8POy6ZflsO8lgZP2PTF6w1ytNHL8lc/NgvD/9d7h/lG6QUsefGDlLLT1s6AqLUrPZO2untGk+xeWDO7gkklBbxWVWN27qNdEKP3PXrKQ6MnGONo+9SMcqwWgVUTTCopoLbJn9CRXz61lL/sP84NIy9g0aY95Lsc3DuxmEH9M1l8/SUsf/VjAO7/+jCWvPhBzH61TX7zunrAzEun0Jo28Exy4dbEXDCo8/HxpgRH10sGZCftvMdLjxdOGGrGDJw2otnxvSs50uBlwbPh404qKeDeiReb51k/Z3Sr7ocGjx9dl0llzC8sGGe2tUZsPjp9JFZNyZC/bNoaR4ruR6oyVzZL13AB3SuE+K0QYrYQ4tvGTwecR6FQdDBtdRM01qs4rBZWTCsNfwFFClxHO9Mtn1rKEzsPsHj7PnzBUML/V80s54mdBzAk6rouOVTnZv+xJm5Zu4urV7zB/S+8z5LrLyHf5WDhxkqmjbqQ72wOu9wtnDA0wV2xtS6IRufqppVvcuXyP3HTyjfZf7wpXLhb0a1pq7tnNMnuhQXPVvKvN5Sc9Xh1bj++oDQ7Zsb+RrzesnYX3722mMXXX5LQkV+8fR/TRl1oJn/3f30YS1/+G9c8+mfuf+F9HrxuGA9eNyzpfgsnDDUfq5mX9nHC7UvaBp5w+1q1f2tirqbZlxAbCzdWUtOc/BxGLD6x8wDLp5aSl2VPmBXIdzlo9ATN5A9gakVRzHme2Hkgoe1NFr8ef4iHX/2I5VNLuXdicUys+YIhM/kzYnPiY3/m3/7rQ1bNqmjXvaZoH22NI0X3w2EJ14qNvq9WzizHYekadQAzgDrg2qjnJPBiB5xLoVB0IG2VzBlyqCMNLdz73Hsx8rSNd13B8VNeGjwBs2YUQF+njUZPgE13X4GuS040++njtJGfbTdHi+vcfj6PqidoSJAsmuBXM8qQUiI57XyYSnbqCYTQdXnG2bz21utSdH3a4u4ZT6p7AQGb512BVQgcNg1fQKe6vgWbJrBaNAKhsPeZRUvuzJnjtJHvclDX7GdIflbSbaxaWCK6dHJJQqL3wLZ9PD9/bMpjg5p5ORe8geSfuzfOdj2VaiA65nRdJyRByvC2xjap5JLBUHLfPCMWDenxI5EkLvoY904sprbJF/Pc+X0zWDGtlPP6ZBCSkmONXl7ac8R0SU51P4T0sONsbZOfX9wyMqYNPj/HmTQ2X6uqYXBeZozkNMuhYrAjSRVHgRRxpOh+eII6ew7VsXneWHQp0YTg9aqj9BsxsM3HSnsCKKWcm+5jKhSKzqG9kjkpobbZF1OK4fn5Y7n/hffDM3aR9SOaCHc0oqVrK2eWs3nXIe6deDG5kQ6srusU9XOmXO+yalYF/TJtZ5WdftHgwe0LnlEKeq7rxBRdm9a4e8aj6+H1dUmlzDVu5m54l0klBXzv2mLuiSrQa7jZ1jb72HT3FUn3D4R0M56XTi5Juo01IvtJNbAR1CXbFo6jzu031xUW5jrN+0DNvLQfS4ryDdED7tFOn1MrisjLsuPxBzm/rxOrVTOLoqeSkqaSdVlTyLps1tPb761u4MFt+1gxrTRG8j64fxb7jzeZ25UV5WC3auCD2b95x9zu8ekjcdotptQ+nmBk7XVhrpPiAheaCMuV7xg/mKffOsiS64ebsRm9FlCXEptFMw0rjPthQJ8MBuVlKUl9B5BKdmtV73WPwW4RVAzKi3UBnVmOvR0zgGmXgAohMoQQ3xFCrBRC/Mb4Sfd5FApFx9MeyVyd28/PXqli+dRYadGF/Zw8NaOMB68bxrIdVdyydhfNvqDZYYbTxYnLB+WxcGMl9Z6AOStYfdKTcr3LPRsr+aLBy6qZFSllpyumlSKlPKsU1Eh6o1EzKL2bVDG9MiJVhrC8Lj6WH9gWlmEervfw81eqWBMniVs+tRSLJsx4Xv3GpwnneGpGORvfPsjyqaW0+ENJY/NgrZtpq99m2Y4q7v/6MCaVFLBmdgWXF/ZVxbfPEafdkrQtcdpPtwd1br9Z+mbZjiqmrX6bGf/5V/bXnJaOn0lOX+BysDouNlbPqqDAlTwps2oi5ppqm31k2i28sGCcWXA902Fhe2W1GU8LJwzliwZvgjT++1vfP6OBRE2zj2U7PuSxm0cy/+qh/PyVKpZcP5yn3zrIHeMH0+wL8vj0kWhCxLTt3oDOP295L+F++LyupVVSfEXbyXJYksoD1cxrzyEQkgnfM/ds2kMg1DXqAD4LfAx8nXD5h5nAR+dywEgCORmokVJeGnmuH7AFGAQcAqZLKevP5TwKhSKWeMmc4cJZ5/aTk2Gl3hPAH9IJ6RKnzUK/TDtBPcTUiiIy7RbWzxmNNxDii0YvwZBElxJvQDcLCfd3Ja5dOVzvYdiAbH51Wxn+YIgGj58FGyvJdzlYNbMcX1BP6jqnS8mvXv+E9XNGc9LtR5eSTXdfwbHGsOz0kd/tZ8n1l5x1Nu9MdbcUvRN/MGRK4KLjToApZU41O1dc4KKsKIfaJj/9XXaWTbmUnEwbfZ02Hn71I753bXHMMV/ee4Slk0u45LxsLJrAokH5oDwy7RYG9s1g/ZxRzN2wO2ZW5ZHf7TfPt3j7PrYuGJeyxEC0VNFm1bBqIsEdV5kgnaaPw0ZhbiYb5o7BIuBEs588l50c5+n2wBcMt3nxA1PR5jtnUhZYrQ4uGZDN1gXjCIZ0rGdwb9R1SbMvyCO/2x8TNw/9toonZ5SZVvC6Lvn+Pw3j8T+EtysZmI0/lNxBMBBMLREMhKe1LJwAACAASURBVHQz9v/j1st5raqGeyZ8xXy9m+ddwZH6IBf2c8bY0xvuztGOoUabr9QUHUOfDDv52SGei5IHWi3h5xU9g6Auk/Z/Ot0FNMJXpJQ3CyGmSCmfFkJsBv7nHI+5AXgSeCbquSXATinlw0KIJZHHi8/xPAqFIo5k8qVJJQU8cN0lnGjyxciONswdjZQSu0XDH9Q53OIhN8vG9spqlk25FKummev4jNkNw0XRYFJJAUFd4g/qfHK8maJ+Yec5gCyHlT5OwWPTR7Llnc8pH5RHXpadx6aPpMkb4LWqGr53bTENngB5WXb8QZ3tlYeZWDKAH90wnL5OG5NKCs44m3e2dWKqc9z7MGaFo238jTp9BtHOtwbGSPxPp4xAl5Jjp3zM3fAuANMrCvnxN0cQCOn0C9jxBcOze9+bWBxOCjRBSJeEdNheWW2WdPjNnFHmuiqLEDyx84CZhMLptWPJ1romc7SMlqmqAt6x6LpMeC/WzK7gwtxM870IBsMDYIYRS3zCo+vhz0IIkVSma7RFVqvG+TnOM15LvceH168jJTxxWxkCSXW9h51Vx7l3YjGaBl80eMJtsNVCcb6Ln99Uiq7rNPmCWFJIBEO6TLk22pCn3ja6EJtFsH7OaHKcNkKRjugpT5AHtu3jsZtHxhzXop2WikbL9VfOLI+ZPVWkF7tVC8ecBE2EHyt6Dk6rluBwvmJaKc52fM4dkQAGIr8bhBCXAseAgnM5oJTyL0KIQXFPTwEmRP5+GngDlQAqFB1CvHxpakURh096YopaH673UO8OENT1mCRvxbRSlk4u4Wijl+8+tzfBBfGZO8dQdbTJTCy/e22xWVjY2P9n37qUBk+A26PWrqycWc6Trx8wO8YrZ5az4P8bhC4xrfaN4y2KWpe1elaFubYwFanWibW1LIaiZ5B0Vnj2KGxWYVrjZ2dYWTmzPCbWlk8t5eFXP+K2MRfhtFsoyHaY208pu4Bb1+4yS5Y88/Yh7hg/mF/+8ZOETrNR0gGgrtnPndtiE7gDNc1mEliYGy6xcsobTLB/TyZDfGBbeO3hgmcrmffMbrYuGKdMkCKkcn6Nfi9qm32mLDJZwrNh7mhOtsS2GcunlvL0Wwf5/j8Na5WywHBBrmv28f2t78d89n0yrNx91WAe/f3+hHMbbVOd28+JZj+/+d/PWD61NHb99MxyfvZKFT+/qTTp52vIU/u77FSfDBtx5bscPDZ9JPdOLDZdJ6PXXZcV5ZCdYWXJ9cPNNtt4/xZt2sOLi8an8VNSGLj9fr5o8HFPVBmIVbMqsFsE2Rm9697tqYQkCTLuB7bt44UF49p8rI5IANcKIXKBpcBvARfw4w44zwAp5dHI38eAAak2FELMB+YDXHjhhR1wKQpF+uiK8RovXzLcBePlRP1ddtNgAMJW5FZNQwhBTqadpZNLzNFvY38h4OFvX4bNEh4Bv23droTGbcv8sSyIs7detGkPSyeXUNvkZ+GEofiDOrePH8wzbx00ne6EJjhY6ybf5TBd8xZurGTrgnFIKVNK4FKhHEKT0xVjtjWcaTY3/n/F+S5zVthm1SBidLTp7iuwCMFPd3zIj24oYcPc0ZGYD983D3z9kkitP42Dtad4ft5YJJhxvnRyiZmEGUYwyUo6PHvnGIK6ZMXvP064P5ZNuZS5G941O3x9Mqw0tAQ43uTFabeQ4wy/rlQyxBynzZy5CoT0pPdpT5LttTZezyTbNOMjIpHMcdr54TdKmPXrv8Z8PtUnPTz3zucxkq2n3zrIQzdemlKmG1/M26qJGBdkCLet3oBOfrYVTcCPbiihtslnfnYAzd4gXzR6zNm6HKedwlwnz80bS0iXnGj20TfTxmtVNfzkm+HPNz7uc502CrId+EO6mXwa61XvuWaomfD1ybCyamY592zaw8IJQ7nr6d385+0VCVK1vdUNZ5ScKpLTmpg95Qnxq52fxLznv9r5CT/55giyM77Mq1V0FP5Q8iUw/nY4vXaEC+h/Rv78MzAk3cdPcU4phEgpgJVSrgXWAowaNUoV81J0aTorXpN98dd7Aubav2jpUCCyTiX6ubKiHIQQMY9/cmMJHn+IWyNrQyaVFPDY9JE0egLUNPnYXlnN8VM+Zv36HQD++IOrkna4fMHk9tYF2Y4ER9DfzBlFY0sgxulu+dRSs/TE4XoPtU0+jp3ykpdlp1+WnVfe/4JLC3MY3D+LTIeF/lmOpB0z5RCanO7Yxp5pNhfgUJ2bz+tayLRbaPGHuCgvk0F5WQDmfvkuBw/dWEKey873ri1m5n/+NUreXIY3oHPfC6dnbFbPqqDRG8Bh1cw4MtYOxv+O5nC9h5omH/e98H5MgXfjf0X9nGxbOI5+WXZOeQMx1xHtvBjtHmlQmOtElzJpAXrjnulpJkitjVebVWNSSQFTK4rMztb2ympsFs10/Vw6eQSFuU62Vh5m9vhBSQfFks3oCmJLQRjEF/OeVFLAv3xjODmZtpi2Nf7zWjmznK3vVvPWZ3U8OaMMixA0+4LcunYXK6aVMuw8F7ePHxQTGytnlpNh08zPN/6eMArHP7HzE350Q0nMa9taeZi5Xx1sznoatQAfn345BX0c5LvCbag94mRqt2j85MYSVv7p7+EBFEWbaE3MCkHSWFPilJ5DhlXjoRtLOOkOiy3tlvDjjK4gARVCOICphM1ZzONLKX+a5lMdF0IMlFIeFUIMBGrOuodC0cvRdUmDx4/HHyIkJRm2cLIDJHSGV8+q4Imdn/BaVQ2TSgrMx3OvHEx/l52+TpspeTNkbMdOec0O5sIJQ6l3B2Jq990xfnCMjHPVrAoGZNv5nwevQSAJSVKuUUn2vMth5XtxstKT7gD3RzrdxnPGzMqCZyspzHWSnWHlO5tPy0S/d+3F3LOpMiERiE8C21sWQ9H1SDab+/gf9vOzb12GLiUt/lCMlHnVrApOun14g+FZmf+45XIG9AnfO1IK7tm0h/FD8ph31RAsmsBm0RJmsxdurGTp5BLsUYMnhnQu/nd8jDV4AgmxbPzvRLMfKSVN3gD17oBpsrT6jU/NGcLsDJvpHhm/fiSkS7MUi3GtxnmW7ahizeyzy6Z7InaLSJCQr5pVgSYwSz4ca/Ty6zsqyM2y4/EnrgN12q0Jrn2Lt+9j/ZzRzN2wK2bQwRiAWxgxvQo7fWby95pmivplxrSt8bPEizaF5fQHapqpj3QOjZnHi/IyafKePu7SySWc3zcDp92K16+z+e4ryHXaEoreT60o4omdn3DXV4dgs8QOAk6vCK8JjJZ55rvCM4Wf1br54TeGU9fsN++hSSUFLLl+OD+6oYRQKPWawy+bnrSmW0qSqgeMOo+K7k+qyGxPxHbEMMzLhNfnBQF31E+6+S1wR+TvOyLnVSgUKTDWkew/1sQta3dx1SNv8O2Vb7H/eBMNnsTO8MKNlUytKAKgtsmPzQLfvbYYb0Bn2+5qapr8PPn6AZZOLuEXt4zkgW37WP7qxzx280izLpThBAekLN+wt7qR29btorregzcQTLBDXz61lHV/+SzBHn/VzAr8cYVvy4pyyM92pJS5GYntw69+FNPJMZI/Y9tUpSLaUxZD0TWJn80tK8ph0TVf4dPaZj46enoGBsIx8audn/BFo5db1+7ia7/4M/+85T1Ouv180eDl+Ckv44fkMWvcRczd8C7XPvZnjp/ypozDJ3YeMG38jZIlhmV/tHU/nL4HDFnf4XqPGW9GAjc4L5PdB0+iCcHSl//GLWt3mSUh8l0OMu0W/MEQHn/IdI/cMn8sD3/7svBx+mUmvdbiAhdLJ5fwyz9+woHaZrOkQW/B7QuZyR+cbrM+OHKKO8YP5vy+GRysbSLDbsXj19m861BC+2XREqXyh+s9NPuCZlvT4PGz/3gTN618k0BE4vWTG0sAOH7KyxM7D+CwCjMuUs0SN3mDYQOaTJs587hsRxX+oKS2yUe+K6yY2F5ZzSlvkDnr3+GqFW8w4z//yj/qW2jxJ0r9bx83iPteeD+h9MQ91wxlzvp38QX0hDb+1Q+Ocl7fDHOgIXrw7+oVb3DzmrfZf7yp0+PJmPG8aeWbXLn8T9y08s0ucV3tJaQnd3kNddPXo0hEB3Nw8pa1u1j68t9o8Ydoj6i6I9YAFkopr0vnAYUQzxE2fOkvhDgM/AR4GNgqhLgL+ByYns5zKhQ9jTq3P2EdidEB2TJ/bMrOKhCpIeVj6ct/Y9XMcmaOG0y9288DX78EbyBkfvEcrvfw8Ksfs3RyCQXZDg7VtZijxqk6LcbzxkyFy2Fl87yx1JzyUuf2mzK0Bo+f9XNGR1zOJKe8AXIzHDGj0gsnDOUfUec0KMx1UpDtYNmUS8l32ZlaUcRdXx2CLiUF2RmtlnWezSFU0TVozah+/Gxu9Ix1vKMhhAcK4pOBk5Htl04uYf7VQ03zIgjfb6lm8vZWN/DI7/bz7F1jsAhBkzfIj785AiklSyePQAhpWrl/Vus27wHjGH2dNrYtHEdfp40Ht+3jP269nG+VFybMOC7evo9n7xqDVdPCa8ksgtpmnzl7aBxv/ZzRSa/1QE2zuW3V0aZet9Y1JJN3qDPtFu574X3WzxnNuK/kEwjpnHT7mXDJAPJcdp69awwhXXKs0YtMoWqoafKZx/P4Q2a5BiEEK24eGW5ft+3j4W9fRm2zj8/rWnjm7UNm25rsmJl2CzlOG9kZ4XY7euaxzu3n3onFKdeZfl7XwkV5p2cZy4py6Jdlx6KFZf2egM4jv9vPUzPK6JflQI+8N1kZ1oQ2fmLJAAKhxMQw/nuns+Opp63pTuXyalHfTz2GoC6TmsA8345Z3o6YAXxLCHFZOg8opbxNSjlQSmmTUhZKKX8tpayTUk6UUhZLKb8mpTyZznMqFN0VXZecdPs4Ut/CP066qXN7qWny0uIPUtTPmbRDY0gvozHWBq2ZXUFxgYtBeZmMH5KHLmHGul1MeepN5m54l1PeICea/eb+hlX+v7/6EYPzM3nmzjHm+qRJJQUJ52jwBMzryLRbwvI7XwC7VWPZjir2VjcwqaSAH36jhEBIj8jrBLlZdrzBECumlZqFr0sGZpNh01gVVwx3+dRS/v3Vj8iwaTT5gvTLtGO3agzok0GTN5D0taeSdRoOoRfkZpKfnXytoOLLQ9fDsxtH6luobfIRDOrmqP53N+/lb0ca+cfJFmqavDEj+7lOG5vvvoJtC8exZnYFg/IyzXVWDZ6AGVNb5o9lzewKiuLWu66ZXcFFeZksnVzCzqrjWC0i5t5KVtR99awKLjkvm+kVhdQ2+3BYNbIzLNhtGgeON/NFg5e/1zTj9gX5vM5NQ0uAIflZ5GfbKSvKYf2c0Tx95xiOn/Ly81c+4qTbz5hBOeGSEVKyfs5oplcUmtdwuN5DzSkft63bxfFTPnyBEM/NuyLmmlZMSzXDXk6fDCtlRTnmsXrbWteMyCBBNOG2QbB0cglWS7guaoZNIxDScTms3Lz6ba559M/MWf8urgwrLoeFNUlUDTurjrNmdgWv3PtVc+3W9spqPj7WhC8YYmBOButmj6IwN5OnZpTxP5+ES9ws21HFD7a+n1CcfvWsisj5rDisImbmUYhwKZEL88IzvQVJVBID+zqwWQSP3TySSSUF3P/1YWzb/Q/skXWQFk0wZlAOmXYLf69pxmHVeH7+WKwaZuwY8uUcp40vGsKS2bKiHIbmZ7V6kO3LpKet6bZZRcJ336qZ5dis6juqp5Bqlrc9s9ZpmwEUQnwAyMgx5wohPgN8hKWpUkpZmq5zKRSK5Bgyz+OnvOai/OiaMalG+jNsWoLNvWGmYpRUKMx18sydYxJsvRdv38eKaaUJFvg/mHQxTd5QgiU1YJZuMIwmjOuQgMcf4ry+GTikzqa7r6DZFyQYkqa7ntFptVk0/nnLe4wfksd3rinmqT8d4Lxris3XvWzKpVyUl4kQ4LBq3D5ukFnvLNo18TdzRvH49JEx9upK1tk9SGbksmZ2Bb/84yem3C2ZLT6QWN8tYnVfmOtkZ9XxhLVfm+4OJ07Jjrt8ailSxq5T3VvdwNNvHWTL/LH4Q5JDJ9ws/b9/o7bZx8qZ5Xx/UjGnPEFCuoUTTb6Y9YYrZ5bzzNuHzPtkw9zReAJ6zL20YlopeS47ky8vNAtwG/tC2KQjeu3gPZsqWTblUgr6OPjtd6/E4w+7mTZ7g7z1WR0Happ59OaRDOiTwaETbn788ofUNvvMe7S22dfr1rpaLCS0DU/NKEMTIqZdXD2rAqfNEjPjlu9yIIBPa92sf/MgSyeXkJdlJz/bwZ8+OsaUsgt4+q2D3DF+MA6rxfzb+L3g2dOf9WM3j2TG2EE0egI8P38sxxq9BEI6/3HL5eRm2flHXYsZW6tmVWCzaBxpOK2E8AaC3DvxYmqbfEwqKSA30x4Tq2VFOWQ5bBxr9JJh03jwuuHMWf8OK6aFDUSWTh6BVRPc+dUhfHbCzecnmijo4+D+F95n87yxPP3WQXNd4VMzyjnp9vPcO5+zamY5zb4g1Sc9XXLtdI9b0y0FmoBlUy41zas0EX5e0TOwW5IbedksbZ/PE1KmRxsshLjoTP+XUn6elhOdI6NGjZK7d+/u7MtoE4OWvNKhxz/08A0devxuTKe3mm2N19omH3870mh2JtfMrjA7KhD+ojccpJK5G9a5/Xj8QT461kRelp21f/k0xgHvvD4ZTHnqzYTzbpk/lnc+q+OmikLTuhww3T8NCnOdbJk/Fl/EBvzhVz8yO7nGiHad28+r+75g9vjBHGv0kp/t4LNaN5l2i2lsYdjvX73iDfM1GoYV8ed7bt5YNr59kPJBeaacbcv8sdyydpe5zYpppZzyBslx2mjxhxhZ1Jd+We2XAHWisUC3i9lzobbJx00r30z4zJdODq+fShYPLy26EiDpfs/cOQabRQCC4xEJsmFdbzgi1kYla9H7PjWjDCFErGHIzHL6Zzu4efXbCds/P38sRxvC8R1dOsD4/3PzxvJ5nZvHXvuEeycWJz2nEcfxz4cNRt6NcfKEcNzf98L7MRK36FgVQjB9TeK1LptyKef1zeiIepddOl7/cdLNkXoPVk2jv8tOSIYLut8RNQhWVpTDvROLGXaeiw+OnDLbyiy7hUBInvFzM9qsX91WRk2TL6Ydy3c5zILyLf4QFw9wEZISZFgG9nldS9iEJcq8xzj+5nlX4A/q1Db5eGDbPp6OxPXxRh8FfRzUnPIR1HXWv3mQqRVFXDzAxSlPkCZvgCUvfsBjN4/klrW7+NN9V/Pvr37Ej24oIdykC25du4vN88YyIyI3/t3/+Sp17gCLt4cH3n74jeEU9nNy0u2nyRvk/hfeTzkYU5zvMp2mO0NO3866rl02Zr+ob2F6kvZg6/yxnJ+b+WVeoqKDqGn08OkJd4KR19D+WRT0dSbbJWW8pnMNYAHQX0r5asyZhbiesENnl0gAFYqejD8YMmVsZUU5XFzginEEBGI6JcaMSW2Tl36Z4dHp2qZwx3nVzHIWXfMV01Gur9NGQR8H2xaOi+kYG1LRUYP78dP/+pD5Vw0lz2XHqmlm/T2Dw/UegrpEAM3+EP86eQT/NmUEgRA0egKAIMOqMWvcoMj6PEe40wNsrzzM9ZcN5LHpI8m0Wwjpkj/+4GqsmojUuEq+xvD4KS9XDRtAn4xwc1eY6+T8HCdv3D8BiyY46fahCcFt6/5q7vfm4msgq32JnCoWn5ro99NptxDUJYGg3u7OXzIJV77LwcUDXAgEm+eNRRMSXQpafAE+P+lB13WCKWQ0AMdP+fjnLe/FJHFZjnCtNZtFo1+WzZR9TiwZQI7TRiCkM6BPBhLJ8/PHEtRluL5kIETNKV/ScwVDkovynHgCyUucfNHgMTvj0WZKMcdI8TrsVo1n7hzDfVvfj1k7aMwGxkvcJJKQlGiR9y/+nh1a4KIwx5myRmJPXQdrEYKCbAf1UWVlti0cF5P83f/1YTz91kG+G5Fnjh+Sxz3XDEUTImYdnMHh+tNlbYw2q6bJR16W3XwuWcJkJHXVJ8NSeYDC3OTmPVKG14MN7p9priUNhiT//39/xC9uGYkuJS/tOcL3ri3mnk17WDWznOwMK4GQzvgheQzMcTKppAAhBK9V1fDjb47AG9CxWSJys8j6v/u+Vkx2ho1f/OETVkwrxeWwmm64868eQlaf8PrqfJcDTcCzd44hJCVOm4Ush4X9x5vM+q7Gd1H/LDuapp0xplK5WRvbx9dRLHA5sCaxye9pa7pTtQfBNE30KDofT1DnpT1HWD9nNBZNENIl6/7yGd+99ittPlY6E8DlwNwkz1cB64Fr03guhUKRBKfdQl+nzazNFF8Lz2HVzM4thL8cFjxbyZO3lVHT7OeSApfpdOlyhDud0cniimmlpoxy+dRSU64khOA3//sZd311SEzn2dg+uhMKmNc1qaQgQWr3+PSRIKDeHYgZ5Vo5s5wnXz9AbZM/RtZqnCcQSrRgN2YUl+2oMuWvq2ZVsGzHh+bM48qZ5bgia52MhNYWMZppTyLX04wF0kX0+xkvTW5vkhwv4SoryuHB64Yx+9excW/E6fbK6rCMM4WJhi6JuT/yXQ6afUFT2mccb8+hOmaNuygmbqPvjVWzKvD6Q3x/63spZddHG8OPvYHkcWska/e98D4b5o5Juo0mkps+aEJgs4UNX4znjNnAwlwnQpxO5OJjPNk967RZYpK/3jLAkeWwoAnBHevfNd/jaHMfw9zk4W9fxqJI4jN73EVm/KX67I2yNsaaudVvfMpj00eazxlmLfFy0to4qfAzdyaPi4+PNbFsR5UZ+z/55ggsGjwUcRZt8YeYccWF3BMp4aNLaPYFuSA3g1njLmLT2+GEtqEl/FqtmiDDJtAQ5uOHJl9CxeD+NHkDzL1yMN6AzgPb9pgzl3/Zf5xrhp8XUycwWm79RYOX729N/C4yZkBTxVT8Mof4GNR1GVNH0Vx7OyA7ZRLYU9rl+Nq8QOTzUnUXewpOu4Wbyi9g7oZ3Y9rsDHvbZcvpjIrsZDLPyHP903gehUKRgqAuefjVj/jhNxJd3hZv30d/V4oSCZl27tlYSU2zzxwVFUIkdZtaOGGoebylk0dwUZ6T8/pmMLWiyCx6Hb39vROLAcy1TEcbvTx280jWzK7g9nGDEpwVv7/1fayaJeHcizbtYe6Vg3kkqo5Z9HkcVktK+/zD9R6sFsEzd45BA/7l+uH88QdX8fC3L+PJ1w8Q0sNOdUZjatVEQiKX73JwrNHL4Yaw2UiqRdc9zVggXUS/nwsnDE34DFOV3jgT8WU57p1YnHDcxdv3MbWiyPy94NlKrJpIKOexfGopHn8w5rNLdp2Lt+9j2qgLE+I2+t64Z2MlA/uG663ZLBob77rCNEAy1nQN6BO2yX9i54Gzln1w2jSejZgprZldYdbl1ASsnxP7OlbNrOD/7jmML6jz6M0j+dP9V7NsyqXmOj6jCHltky+h7luyezZ+PWyqAY62fnbdAX9IJpSaiTb3MWbwzusbnumad9WQmHWARrkPw1Bo28JxbLr7CoSAJ2eUsb2ymidnlHHvxGJCus7qWRVsr6xmUP/MhDgM6tJcS7hl/tjw73c+T2owY7R5i7fvY8n1wxECBIKT7gD//t8fkZtlIzcy47hwwlC+s3lPpFSIZNGmPZQPymPRpj1IJL+89XL8IR0QSOCxm0fS7A3wtREDuWdjJRk2K4/8br9pMHPfpIs56fZzbclAfv5KFUuuH57wXbRgYyX9Xfbk30WR9zRVTBlu1qnaj5pmX0IJl4WR77aejibCa1aj4+Hx6SNVIfgeRDCoJ+3/BINtLwSRzhnA3DP8T4mPFYo0k0yGFQjq1Db5sWgiRvq5t7rBTIKSjhBawjJKIcKjzP5gKKXblFEa4nC9h5NuPw/99kMemz7SlDDFbz8kP4u/PDABh1XjRLPfLNJemOvk2bvGJOyT73KQYQ07eWbYLDT7gtQ0+dhZdRyXw0pjZGYk/jwFfRzYrRpb54/li8bYEhLGKKhE4rRbqHP7qXP72V5ZzV1fHYLDKhh2Xja/uu1y3L4QTd4gloi09HC9x5R6JTMUUcXiW0d0YpxKrusPhtokLzQGK15cNB5vQEemsO03zmf89vhDDOjj4Pn5YzlSH3b9fHnvEeZdNYRtC8fhDYSwaIIBfZKXCDGs8ZOdB8Ix3OAJsr2ymtvHDeK8vk6WTh7BQzeOQEpo8QfRZXifw/UeHv19uARAjtPG+TlOfrXzgDkDN6mkgAZPIMYUZM3sCiwCPjneTFE/J1sXjCWkhwuXb323mi2Vh7nusoHcunYXf/zBVeGkc/pIQrpk2+5/0OzLY9mOKjbedUXS11HUz8nr912Nw6oxsK8z5v0/0wBHT5OGSl2a1vrRa/I0Idg6f6zpnmyLzLwki4vcTBsPXncJ1SfD9dg+q3XTL8uGzaLxfyZeDGDO6j00+RIeunEEgVCsoVCO04ZFE9wxfnBMG/TUjHKyndZwUgl8dKwpZs3n4XoPQoAvqGMRgky7hdeqaqht8vOrGWUU5jpNR9AMq0YoEpMF2Q7yXQ7ysx2caPYjCLsPBnXJ6x8d47axgwjpMtxW2zR++I3hWDXBpJICBuY4+XtNM/2zHbxWVcM9E76SNF5Eitnrfll2tswfS4MngK4ndmr9wVBKSbQ/GDJlkGVFOebnFXaaDg96dPeYPBMyIoeNNoHJsFlIl9eHovMJpOiXBdrhAprOGcA/CiF+LgxtCSDC/BR4PY3nUSh6PakK2GY5LDx43TBuW7crphh0WVFOuKMSV8zXmPHSpeShG0uobwmYx9QjrobRGBIl4++cTBv52Xbu2/o+/bLsSbc/2uilut5D1dHT6z0g3GgdOtESs48h4fvpjg855Q0yd8O73LTyLZbtqGLWuIv41esHTAlW/Hk+PtbEzavfps7tx2YRZgkJ4zVaNGhoCXD7b95h2uq3WbajijvGD+bX//sZIKg95cOirHG7ggAAIABJREFUaSx58QOuXvEGt67dxYPXDTM7EsnqWKli8a3HHmWpb0jfoinMdeK0W9pVmLmu2c+Mdbv4+FhTypiN/h3SJTc++SYffnGK+154n9VvfMqUsrCsZtrqt1ny4gcAppV9/PEMCV+y80B4JvKJnZ9w11eHsOTFD/jaL/7Mbet28XldCy3+IC3+ENUnT8e+UTrlvhfe5+81zVx/2UDzmEsnjzCTPzgtlWv2hYsB1zaFa8Tdtm4Xtc1+xg7tz8qZ5az7y2dMKinAG9DN4vRzN7zLDSMvYGfVcQ7Xezh4wp30dVSfDNeFs1stKQc40vXZdWXsVoFVEzw1o4wHrxvGsh1V3LJ2F9/f+h71ngB2i2DD3NH4I+Vo9KhyOsagUZM3ZEo3ows3t/hC9HHazDaxrCiHq4cNYP+xZjbvOsTKKDv/Fn9YPRDfBn1n8x6khJ+/UkVISrPNMyjMdXLoRAsWTWDRwjN4xjEFsHJmOdkZNgpzww7MRhLX12njh98YjpSCV/d9gcOmYbOEy+988/JCDta6sVs1fvatEZxo9tPsC5qzff+oa2F7ZTW2SOLs9gWT3yst/pjXaMjxV/z+Y/O7q7bZnzCzYbeGE5tkx7RbLdgsmlnGwvi8lu2o4qQ7wI9e2tftY/JMSAT3bNrD3A3vcsvaXczd8C73bNqD7HzfGkWaMO6raIy+XVtJZwJ4HzAE+LsQYrsQYjtwALgY+EEaz6NQ9HpSybBa/InygMXbw5KulTPLCeg6/SMF0bfMH8uyKZeS57Lz5M6/c9IdiOlo7njvC1bHyYtWTAvLiwyp0c9fqWLp5BH86IbhCMI1A+O/0AtzMli8fV/SUdsndh5g/ZxRrJ8zmi3zx7Li5pGsf/Mgc68cjD+om1LRfJeDRZv2MLWiKGl9tWjZ0z2b9tDkDcZIpR753X5COgmyPUMWGNIlvmAoqazv3onFZ5yxiifaWODNxdewdcE4+mXaqHP7e2zHozVEJ8ar3/g0YSBi3e2jCOqyzfLC6HshVWxsr6w2f6+ZVcHPXqmK2T5+zZXx2Vs0kfR423b/I0F6F31vXJiXmVISfbjey0l3gCd2Hkhar/KJnQco6uc07089blazrCiHpZNLyM8OS0zXv3mQgX3DM1S1TT4G9HHQL9PO9yZ+hR9/cwRP7PwkQUo9sWQAEL7/4u/ZFdNKuSA3g4sHuJDIhJhNNcDRns+uq9Pi13n34AnyXY6EdnXBs5W0BEJk2izM3bCbR363H7cvYLaZxqBRf5c9qWSrv8tufrZlRTk8Mq2UOrefTLuF8kF5PPn6AbMNy7BpIEnaBtU2+ZhaUYQu4akZiTXgXv3gKBYhCOqSolwn6+eO5t6Jxfzbf31IbqYNR6R2XEgPmwEtuX44D7/6Eef1zeCk289tVwzCH5SEdIkmBAs3VvLqB0fRdUlNk5+FGyvNmcVGTziu7xg/mEy7xqpZFTislqSDjp5AiCdfP8CGuWN44/4JPD9/LE++foDXqmrM15ZMupmXZeeivMyk7Udelp0Cl4Mf3ZC4BGLhxkqmVhR1+5g8E4GQTr7LEVO/NN/lIBhquzxQ0TWxWrWk91Oy9a1nPVa6LkpK6QZuE0IMAUZEnv5QSvlZ9HZCiBFSyg/TdV6FojeSSoaVynVuaH4WVosACYdqmxl+ft9IUXWNFl+AiSUD6O+yk+8KdyrPzwmvb5FINs8bi65L7FZBICRNKdm6v3zGa1U1ke0z8AV1Wnwhnp8/FouAkAR/UEfTBA9/+zIuyMlg/ZzR9HfZTWlnMCTxBvQYY4MnZ5RhEYIHtsUabzz6+/3kZdnZW91gSubysuz0ddp4cNs+c+Q73+WgMDeT46e8NHgC7Kw6zr0TixM60sZ7c16fDGwWQVG/5I56w85zmSP78dbszriF19ESOJtVwxfUzbqJZ5KN9gbiHfecdgsvLhof4wJ6tNHT6kTbIPpeiI6Niwe4CIYkIV3nga9fgjcQ4offKMFhFWYn09h+xc2lSc87oE8GjR4/z9w5BqsWng3SNEFR7iAc1vDavhPNPryBEFZNY+WsMiDszFZc4GLp5BJTgg3h2ByUl4mmCR6dPhIppSnXavAEzHV6FiFOx+7Xik2pXDIpctil1MKPbhgeY8AUbX6T47RzU/kFnNcng5CU2CwaZUU55GeH7/mtEedSLfIa1//vZ6z5n0NmzMbb9RfnuxKcE9vz2XV1Mmwag/r34YtGb8xrm15RyLyrhqBFEitDyjvlqbeYXlHI8/PH4o84feoSs1015Iir3/iUDLtGSIf/eXACp7xBGj0BvIEQgZAkL8vOa1U1ZpyWFeWYks34NigQ0snLsuOwajiz7eHYL3Bhs2rUNfu4qfyCmDqRa2ZVMOw8V0SeOZScTDuaEAgBwZCk0RMgx2mPyAZlZBAgPBvqC4Zln7PHXURQl6bjtDGz7vYFqW328fLeI1yQ8xUGZNvwBKz88/Pv8dSMMvplOQjpYcfZTHu4NqvLEXZ0lkBtU2xidrjeQzCkm8sSjFgblJdFTqaNLREZboZNo3+kbE+9J4AQyZNlYyCvO8fkmXBYNX74jUti6lY+Pn0k9nYkB4quiccfYvfBk+F+mQwPyry85zAX5CQtAXFG0rkGEIBIwvfZGTZ5FihP93kVit5EqnVmthQuYJ/Wus3C52tmVWC3CE66gzEuhr+ZMyrmy2NSSQFLrh9OkzdAbpaNZl+QOzecdv577OaR3DF+EEIIWvw6xxo96HoIb8Bm1p8y3B6fefsQd311CM+98zl3jB9sGiWsnzOa7289XSfrcL2HencgpnaWMVO3bMqlMTJTu0Ujz2Xn+KnTI8RGncODJ8J1A/s6bcy7eghz17/L0sklSd+bnEybWZMr/v+TSgqobfLz1J8O8OSMMjz+UKzz3OxR5DjDa0pSOSoa6wiNGZHe7AZ6Nse99qyfjN9nb3UDy3ZUsWzKpczd8G7McTbedQUWTUvYvtkbTHreAzXNLNtRxYpppby05wg3lV+Q4D77/9g78/ioyrP9f59zZp+EbCQgJMhiWAIGk8iqVZAWxY3XEhZJUBFZtbxvS1Fai7Xy2qJIF5S9FhRQRKw/WyzqWxRtBRQDBTWAyKKAQBbIMpl9zvn9ceYcZjIzihYLxLk+Hz5k5qxzzn2e8zzPfd3XpStmzvh+PiMycql1+Zm65ky93lNji3B5g6TZzahoCrjZKVamD8mnY2sHnVo7+fXfKg1V2t+N6s3izQcYdvklTB3cBVkShhl5PCry1DU7WDm+b4y6rybSVMAzWw4xY2g3TjX5o1SBF5eXkOEwcbCmCZtZ4r7ndhrnNabfpYzt35Ev6jy8d6AaRVGj5PrjTWS0tNrXYFDBE1CYuqYiqm0YVZJL+YBLDRW+N2dcy9CCHMZf1Ym8DDsIbQIAtPbDYZViFG/XTupHjSvA1NUV/H70FfzPC/9i7g8vx2qSaZ9hRhJnYrQoL51f3lqA3SSxcnyfqLZ1+pB82rSyYTVJqKrKh19o6p+jS3K5rSQXTyCWETJ5dYWhThpSYO17n3HLFbmAZnNikSWmfz+foKKS6bSiohr1gSYJfjW8Jy5fkBSbyaCPbqo8ycKxxaQ5TIYCrixBVWMAly/I8N5tkYTE7cu3RcXfPz6pwmqS+PXf9pKdamH+qN7UewJUNfoMv1eVM56dkbGX6bSC88z9imx/E7X1+kD1Yo3Jr4KqYry/4Yyo2ouTB5znM0viXCHFKjOoe47hwxk5Cfh1cT6mBb57U99JJHGOkYiGlZNijfl+XqlGK4MzHYB6TzCG7njstNd4eRTlpXPnwE7c8af3Gb7wXcYuf49al5/sFKux/tP/PEhIVRmzbBvf/+3bzPrzh1zaOpUjpzxGp0NXUdTpcLoao37ceLTQRAX+HbIcBnXwkeE9mf3KRwx+4m1++uIuo1bv5zf2wO0PGfU2P31xFzWNPrJTrHHpgUvLS3i0GR0wcvmsYT2497kdvFFZhcsbjFWeW3WGThSPlqsrQ0b+jpY6+3wu8E3qJ+Nts7ismEynOeY5qG7UJguar98mzRZDd46kFc9cv5uJ13ROqIpblJfO4B5a/ZY++AMt8+Pxh5j15w/5ot7LtLDs/k+v72bEb/nT73Hv4Hz+et9VzBneC4tJYnhRe2P5yCVbSXdaeGJkb/JzUuI+G9KXZDxGlOTxRZ035tynrq7g4y8a+emLu/D4QwzsnBV1XmV/fA+HRWZwj7YxtbvxaHQtrfZV95Fr3jZMvKaz0XYW5aXjsEjMvL4bDovMkdMexizbxrXzNjM3bKDubTYIG9g5CxBMDV/T1ima4EqKzUTbNCuogue2HTbonFMGdSEQpmCaZckY/On36rr5bzN62TbqPUE2VZ7kqbFF3NS7Hd6AJnQULy5cviCPjSgk3WHhe11zWLDpk7DPpQABY5ZtY9C8zdy+fBuNniC+oEIwpCIEZDotrHj3EMGQgs0seGpsEbcVt+dvu49hkiSe3PQJbdNsHKvzMmV1BXazpCmGrqmIib/SKzvw43W7ePCmHsb7Rq/5vv+Gbqwc38don/Xtvkwd9Gyo4BdzTH4VEjGAAnHEdJK4OOENKFFKw/okoDdwflVAzxbf3SKYJJI4R4hnYJthN3PaEyDVZuKFSf0RAkIKUYqCuqCJ2STF0NMcFpmBnbOYeE1nbGbZmK2FM53d5yb256lN+1lXcZTxV3XCF1AMc98T9V4CIYWOWQ5DgbRdWB5dNzfuku2MekHVeQJM/l5HSq/sgEkSyLKEgLizt9WNPtZVHGVUn7yYBnDm+t2sndTfUNmLzLrNXK9lQiavquCVnZqBqtMqo6oQUlVGlOQZ1CNVVVk5vg82s0ytSzOI13+L02qK+3L1BkIcO+0G4pto68qQ+u9oqbPP5wLfxJi5+Tag0UKP1XlZOb4PshCcaPDy+Gv7+MPtVxAIKTgtMmsn9cdmklBU8IcUMp1mnp/YD03uXiUQUnnolgIjG5FI+bNdmo3fju7NuKffZ/7I3sY6el2XninS6Wezb46tT7r3uR2sndifdIcFiywYsWRr1PLxK7bzwqT+hohH82dD/z87xcqMoV1pm2ZDlgQ2s0y7kGIYjzc/d/2cZq7fzYq7+hjnqi8/1RQgEIpPnW4+kdHSTLV1inxuht2gCi8cW4TFJBnXY8qgLgRCUNsUwBOeeMpOsTKvtJC8DE0V1BfEGCzef0M3OmQ6ou6HLAmmD8nnvud2suaeftQ2+Xn/cB2Durdh1d19Dc9GFS3DM39kbzKdlph7NXl1Bc9P1Np9byCEzSzhD6px4yUnVaNiypIw6lU/rWqia5uUWMGh8H4VVUWoWnZzREkeR097sVtkbCZtUPr70VcQVFTeqKzi/hu6I9Daw0yn1aDDRuLo6TNqutmpVsr++F5Mm/7CpP6apdGwHizdfIB1FUc5etqDQOWLOk+U0Xs8KvjcH15Oh0yNcm2WBHNHFBqMjZYIXbG2+f2WRcv8vd9F6JTzSBw97SH4DfQFzscAMIkkkjgHiKTTxaMf6jVA46/qxP4qF0BM/ZBeW7fzSB0pVpNBbYrsyOo4etpDVYOX8gGXkpdhp3WKlRqXL4ZW9uSmTww625LyEl6dfjUpVhM/v1GTQo98QX1yvIGbr8iNMjX9011X8ocxV/Dfa8/UMy0dV0KG08zbMwcZ59L83PxBhf1VLl6qOMLDt/ZEEpq5cUhRaZduZ/WEvqTYTMx7fW+MnPryO0oIhNQoSuyisuIoal4i0+VASGHGul1Uu3xxTbR1Bb+LPSPyn8I3MWaO3KbO7eO0O8CsP38YFed9O6ZT5w4Y93hoQQ4zr+9GjctvZFV+fmN3lv/jYEx8aCqysXYAKmCzyNS6/Bw9faYWSs/QRFqW6Mvapdli6sF2HqnDGwxR5w6QnWplYOcs1lUcNX5fdooVFaj3BFhUVhxjQO8JhHj6zhIaPMGo+p95pYXYLTKBBAMBXbU0sjMeCUfYMuVsqZ0tyVTbLEsEFYXF5SVMXV1Bfk4KaQ5zVCc7J9WKLEF2qpUGT4DsFCuPDO+Joqog4FSTNiE3tCCHaYMvQ6DJuOsD+ewUKyZZG4QN7JyFoqoEQgr339CNFe8eYkRJHpe3b4VJElQ3+vnDpk8YUZJHdtimoXkcfVHnYcaLu1hcXoLFJDF34x4eG1EYXTNaXsKv/vqx0a7pYka/ePkjFtx+Rdy2NaSqqKqK0yTjCypcmuWg3h3gpYqjTB3chYGds2ibZkWEjeJP1Htpm2bj/hu6EQgphmpu8xjSv080uXK83kvpkq3GeZf178DOz05xvMFnZFD190xuhi3mGLIkGBseWOrtb7q95ba/kqRNEMyIsFqaP7I3SR/4lgNTgkG+6TyrgCaEEKJdxMeWKb+URBLnEfHoh7rCpa5kGa9+6IGXdhsG6Gl2s9GxTCTTX9vkZ9qaHfxXca4xS9uc1jOiJM/4rBvynm7y8+N1u1iwaX+UUt2wwnbGi1zf5u6VH9A6RVMq3fSTa3l+Yn+yUy3UNvop++N7+INK3HPT67XuHNiJhW/tx+ULAjDrzx9y3XyNourxhxh/VaeY63Ci3hdDidVVR/XPczfuiZEtf2xEIXM37jEMwGNMtMddSe+8NN59YDAvT7vqOysA85+E26/EGEE/8NJuygd0irrHehYjkq7843WxNGX9vlY3+mLsAHSKsTegydI3VxWNtCxZsvkAT40tQoUoefqfXt+NoQU5HDnloXTJVsr++B7lAy6lKC8dOGONMmbZNm5a8E+eenM/z97dl5enDTQUbievqsBpMcfU/8xcv5vTTQF8YZuCyNhdVFZsmM3nZtijLAzOXMuQoaAaRZ0eV9LiJzJyUqxYZIk2qRZWju/LT4Z2RVFg1ZZDLCorZmhBDq1TLKgqfF7rJtVm5oFh3alx+fEHVSQEf9j0CXaLzIM3FXC6KcCppgCygGAoxKKyYqYPyWflPw9iliXuve4yDte4EUKw4l1NvGfOhkq8AQV/SOUPmz4xvjvZ4I2KQ50yqQtdTV1dgT+oZeN0UaT1UwbwwqT+xgQdnMnwpdnNhvhQvLbVFLaRCIRUnt1yiFZWEyow7PJLOFzjZtK1XfAGNAGseaWFPLv1MA6zzMz1u1GB9R98HtN2Li4vYf0Hn0dNrjQ/rk711H+T02o2DOgj43zK6grcfiWKgjx9SH5Cs/iWCgmBzSxFqXzbzBJSsvKqxcBqkuLap1jPpwroV2Ab0AFAVdX+/6FjJpHEdwaJVEF1ilfnbCcq8TNn3dumMmd4r6j6Ab0jGzlzPH9kb+Zu3MvR0x4UVU1YqxdJedQ/p9hMBiVTErDq7r5UNfowJZj5VVSV8Su38/K0gWQ6LSiKoMblZ3FZMWkOM2vu6YfLF8QiS7j9IVJtJl7d9QWzby7AapKYeX13LCYpLq3o+Yn9DVqnnn35qt9SlJfOiJI80uxm1k7sj8sX4LNTHiN7OuHqzga9tlNrJ2/NuJYal5+cNEuMWEES3y4S1cEEm32v31v9u+Zm8c23zwwPeO59bqexPDvFijeg0CHLwbN392Xuxj1hVVEtg76p8iTP3t2XU01+apv8SEIYdYB69sbtDzH75gKe3PQpS8eVkG43U+vy8/MbezBy6VZ+fmMPgCiq9dyNewxLFD0bGUqgcusIK9XO3bjXoElXN/qQJcHOI3UMLcjhwZsKAFhzTz8effVM1jsn1cJ/D+nKHzZ9YqjuZqdaaZ+mdT6aqzO2pMkNXVbdG1Ro8gWwmSVOhemZNxa246FbelLvDuCwymz88DjTv38ZbdNsnKz30jrFQiBMlfQGFOrcAeM+hBSY8EwFAztnce91l7Fg034mfE/gCags2LSfJ0b1NiYhslOsSOIM7VJvj82yxLJ3DmgKzGk2bGbNGy/FZqIoL52dR+owy4LVE/oy/41PmLyqAoC3Zw4yBn86jp720OAJsHBsMVKE4FBku28zSQQVLTu59B+HGX91Z9qn2wgqKj95YRe/G3MFp5v8+IMKj7+2j4dvLTAMqwPBENd0a8OruzT6vSwJzLKE1Swo7pjF46/t45H/6hX3faOoqmEKv6nyJDazRCiREXZIISvFwl/uuwqPP5TweWjJNdhBReVvu7+g9MoOyJIWN+s/+Jw7BnY636eWxDmCP6RgMQlWju+LJEBRIaiE8H8Dq4//1ACw5bwVkkjiAkQiBT49k+cPKhwMmz7How6MX7md//vxNcZyvYZizvBedGrt5FBNE1azZGwjCYEkBCvu6mNI2OuqbTqtDGDy9zpySbodVVX5+0+uZdnbB/ii3otFlpj9ykcJaZWSELw6/Wpa2cw4LBLH6308//5nTBt8GQeqXMYMeWSHYVFZseEjlZthZ9WEvnE7ACcbvIxetu1M58YskeW0xq+dkERc6f1FZcW0bWVl1rDuuP1avU08eq3Xr6DYNW6+bg/REjvLFxISKeEGm9HQ6jwBLBHrNjeLb779Z7XuqImCeHGxcGwxNrOEzayZUQ8vah9lA7KkvISBnbMYXtQ+hpY3tn8H7gsPLvV13555LWZZotYVreC5qKyYNLuJWcO6G3QvXdmx+XnrEyTVLh8ff9HAnA2V/G5Ub1KsJv5631WoQNkf3zNUJX92Yw8euqUnZklQ79WsKUaU5JHltJDptJBmNyFJIoZy3tIsToJBBZcviEkWCCEIhs7QM+99bicr7roSh1XGZpa5rbg9NY1+slKsuP0hQqpm2JzltGiD9gatzQOMLN3+KheSgPtv6Mbnpzy0TbNR7fJxusmvDbTDdFJZ0iwaspwWjp7Wagnbplm5c2Anw+Zj6ppoy5xnthziyCk3s/78IYvKihFAjctvmEg3j5Ej4cmKe6+7jBc/OGrU/MmSoMkX4Ocvf8jDt/ZEhDOE/qBCvTdIllPLHIYUlTq31u737ZhOmsNsmMpbTDLPbNGoq6ea/NR5ArxUcYQRJXlMXlVBboadercfWRKsursvigp2i0x1o9eYbBlakMN91+UzZtm2hHEuCcEvXv6QH/+gG93apH4t6nJLgVkW3NS7fVRJxaKyYk3cJ4mWARV++4b2POn075cqjvDwLT2/ettm+E8xg5PCL0kk8S0ingKfrnqmU20SmW/bLdrgcfk7B6OoBdUuH1kpFha99SnjV27nvud2GobyQqjYLbKhtqlTkFaM78NLFUcAbfB38xW5jF2uqeLdteJ9ygdcyifHG8h0mlk4tgiPPxiXVqkrv92+fBsn6n0sCNe+nG4KGKqizWl6zSmbh2vcX0krmvHiLlJtZh7Z8HEMzW1eaSE5raxxqbPT1uwg1WZm7sa9zH7lI1rZzHHptZ6AQk2Tj30nG7lt0btc9dhb3LboXfadbPxOm8J/m7CaRExMzSstZPk7B6Pi/6WKI7TPsBnfLdl8gN+N6h2X8rhgTBHtM2y4/SHj+3hxce9zO4zasZ/d2COuGfWka+NYOayu4HRTIGZdVRVhZdFYerIsSVFG8ws27ed3o3rH/O7cTDutUy2smtDXUDX98bpdHDnlpsblj6tMOmbZNmqa/Dzx+j7eqKxi8qoKSpds5Y4/vY8/pMalnLc0el2Vy8eRUx6CIZi2Zgc1Lh9CCINWaDWbqGn0oygqK949RJrDhEUWZDq1wY/NJJGdakUWgpcqjpDhNNMu3YrFpE06TBnUhWNhddbaJj8WSbC0vJhUm0x2qtWwW9hUeRyzJAwLnCmDuqCqwqD4x2t3Zg3rQYrNRHaKlWlrdvBFvZfZr3xEUFVZ3EztdnF5CTsO1zKkoA12k8Rtxe25Pdxmj1m2jVNNAaYO6oKiaoPXeaWFCCFIs5tw+zVq8br3P6N9ho28TBvjBnYiGAKLSeLBmwqYu3GPQV3V3xU/GtKVJZsPGBMdqTYTM9fvZvD8t7lzxfvUNvlZ+NanUXRtnb7d/D2lD3JWbz0UZfTe0lRpzwaRdexwpq0IhJLvmpYCs0li/FXRz9P4qzphPp8UUCHEk8Qf6Akg/VwdJ4kkkohFPCVEScDDt/bCaZW4Y2AnygaA0yrFmG+DJos/8dkPAFg5vi9mWeAPKix/56AhRqFTSevcfg7XuA2RDX3ZzPW7eer2IkaU5DHh6s5ckm43vGr0daat2cHzE/tzot5Lu3Qbo5dtY3FZcRSVyeULMqIkz1AQnby6gmfv7ouqQo3L96U0vUj66YJN+1lSXmLUg+kdBQEsHVdi0D9PNfl5o7KK6kZ/jLn878dcQfe2qXGPdarJz+OlhZoJuCzF0EqPnvbQ6A1gMUlxO8vfZT/AbwuKouLyhVi99TODbgbgCYS497rLsJkk1k7qjyQ0RcVTbi3b98Kk/gQUjdb8y1t6YpIFL4TN0YOKytLNB/hBQQ4926WxakJfDte4SXeY4wpxmCSBwyLjDsSnZZvk+LTndIfZoIDq+6px+RLSk5urc+48UseLHxxlbfi8ZSE41eRj3mt7mTWsB7IkmDKoixGfeZkOY1/xlEknr9L87yIpgxqlTzH+bn5OLYleFwgpOCyyYbHx6Kt7mD+qt5GFU1UVXzCEL6gwoiSPkCLwBRUe/kslD99agFm28taeE1x/eTsmX9uFRW99ys9vLODvHx9ncVkJvmAIq1kmO8VKK5sJBGQ4Lew74cLlbaDo0iymrdnB7JsL8AYVlmw+wMKxmoVyUNHozDmp8W0e6j0BfvT8TkP9uOCSVJ69uy8CwZNhOq8eZ09u+oQHbyqgzh0goKhxrU7WTtSowyFF5fHX9rGwrAhFFdy1YrshipRilUEV+EIKkoATDT5SraaotlU/ZmunmUf+qxcZ4UxhaTPV26mro2Mvsr1fV3GUvAy7kaU0SVrd2w96XkKm08LsmwtQVYUTDV5SbSbWTR6ALCCkgizgRIMXWYAkSS2OiXEuFSKTuDDh9od4eccZOnVIUVn+zkHuve6yr72vc0kB/eAbLksiiSTOAb5MgS/NHvdrA93apPLCpP4cr/dyoNpFltMSZSwNYapMZXJwAAAgAElEQVTQKTflT7/PC5P6x33R+IKKUW/y5oxr466jqCqn3H5DTv2Lei8vVRyJoTItHFts1LMAfH7KbZxHIppeJP202uUj02lmzvBeWofcH0JVVX75l0qqXT6eGluEN6AYHe8lmw8Y5/7CpP5Uh20gPq1yxT2WNxDCbpFp8AZjKFhPvL6PapePDIcFqyyY+8PLMctS1ACxJXWWLwToSrgn6r1sOVhrTFzoIip/+PsnUbQ5vcZKN+nW/45HL14wpohUu4lRy86Y766b3D/G4HteaSEqsPydA4zt3zFu3CgJFBHT7GZ+9PzOqH0FQgoNCUzqzbLglXuvwu3XlG6dVhNpdjOPRCg8PjaikOpGP6ea/Iaa4pLyEgRQ1aBl+HMz7AknVPJzUqImSyIpdC2dXmeSBCoghPbb9Mmiyd/ryJh+lxo2GyoaA0MSWhtc7fIxfOEW3p45iIc37OX6yy9BlgQTv9cFFZWHN+xlxvfzGdOvQ5Ti5/8O7xW2bbBhNTuRBcy+uYBubVIxSYItB2sBmDZY6+gNLcghzW6Oex+qGs9MlA0tyKHOHWDqmh3MH9mbNyqrYuoAfzasB+3TbTT5oyct9LrmUHigJQnClM8zg9DsFCsds+zUNgVRFJUUmwlV1TLxkddOb1tzw4M3lzfA1NUVCRWnIzN1ke19UV46fTtnGTZFOj1Uf1/lZthZXFbCk2+eUaNurk6t02R1umhLGQQmoviaW8jvSwLsZi1LH0nznVdaiN389TOA54wCqqrqM4n+AZefq+M0hxDiBiHEPiHEp0KIWd/WcZJI4mKCEhZ6OHbaTXWj76zohjazhNUsM2dDJYGQEkMXnVdaiDXcwYukwunQ64106BLfoHUklo4rYf2UAciSINNhISNs1L1k8wFmDYuly9373A5DofSzWjcLNu3nknQri8qK49L0dCqTfpw19/TjmXcPMX7ldkYv28b4ldu597mdPF5ayOKyYiQh+OmLu/j+b9/hpYoj/H7MFWyacW04QyRYXFbC6q2HWLBpf9SxhhbksOaefrTPsJNqM/PMlkMxFKwHhnVn1YS+eANBTjb6mPXnDw26xiPDe/Li5AHYLdGd5ch7VtXo5VTT17t/30VEXrMTDV5+93/7Yu6Xrgao0+UiaXNTBnWJUgFNRC+evnYnR095or77/FSsCu7M9bs5Xu/l9n4defTVypgYXVJegqLGPltLy0uYu3FPzL5a2cxsqjwZY1K/uLyER/76MQ//5WNAU7q9bdEW7vjT+9w5sBNFeelGLE4fkh9Fe56yugK7RSbdYaLBE2DVhL5kpVjjPs+6sq6uVKpT6L4L9DqbWeLSLAc28xnVvdapFsoGdEQg8AZCtM+wY5Y1GwirScIkCRaOLWLFXX2wyBKTv9cRCYE3oNAu3cbhGjdDC3Io7piJ2x/i81MeVrx7iJnXd0OFsLiWCYFmIbHjcC1Ws8AkCZaOK2Fsvw5s3nsCh0XiFzcXGDYPzWNsU+VJoz2eNayHMUGVSN25xuWnKdx2r7irD0V56UaN65wNlVw7bzOjl22jwRvkT3ddiYpmSj+0IIffjOiF02pm6uoK0h1mgqEQJhmcFlPcZ0AvIdCfnUTnpFNeAaOUQafARlKfI+mhoBtjR6tRN1en1tuBlkZbNslS3Pe2SU76QLQUBEPxs/TBb0Dz/U+JwIwCfnqudyqEkIGFwA+Ao8B2IcRfVFWtPNfHSiKJiwXxPAG/TKAhcv2BnbMMCuj/rP1XFG3n8df28cSo3rw9cxBf1Hli6JW6oIo+A7n+g8+Nmdd4vmoWWTCvtJCZ63dH+aXp0GeBF5cV89ArWke33h3kzT0nmDWsB55AiBV39TFELj4+dppbr8hlcsQ5PTaikPcP1xlZRJ0eVdvkZ86GSmNG+c6BnQzF0NwMTeY+zWZi6T8OAxhS6u3SbAghotbVsyyRx2jTysq4p99n9s0FxnH0ZVPX7GDO8F6crPcZpsTx7pnuKVjt8rU4gY1zgUTel0+8vs+4X+l2M23DVOJ4Kp/x/k6UDXM0G7AL4tMgBZpf3xuVVaTbLVEqoELAsrcPMX3IZayaoNGa5XBmJZ46o8Nq4s6Bl/KXfx019pNmNzPv9b28UVnF0nElMZ2BB17abVD/jp720CHLwU/X7Yra76kmP5lOC/c9/4GRRdH97ppfS32f6yYPoG0rmxGDLcn0PR5aWc3UNPnwBBQEsHBsEQ6z5oMnhOaRqKoqde4g2almUAXbD9XQKbsVLq8Ph6UVpX06UOPy86d/HuRnN/Zg44fHefCmAsr++B5r7umH02pi6qAuyJKEP6Qyd+MeHrypAFkIJq+uYMVdffAGFGxmiUynmZCiMqh7W6pdfuxmOS69Ugi4rbg9E6/pRI3LH9W2xlN3/tNdV3K6KRDVps0rLQSIia3Jqyr4/egrSLFqWb6Hbi7AH1KjjN4nPFPB6JJcbi1qH/f8Mp0WhOBLz+mxEYUs2aypnObnpLC/ysXmPSe1gXWYOaLjbMoBdLps8+UtjbbsDYZ4/LV9Me/tP9x+xfk+tSTOEQIJaL6BC9gI/tt6K/QFPlVV9SCAEGItMBxIDgCT+M4ikUBDopqzyPXXVRxlXcVRVtzVh2qXz6DtgDab+GmVi86tncxcv5s19/Rj5fi+1Lm1zu3cjXsBmDO8F12ynfhDKv/85CS/uLlnTC3gzPW7WTWhb9TLKhEtrsblo9rlY/bNBcx4cRezby4wlBUj131+Yn+DFqQfJ7IzrK9X2+SP6jTEE/OYvKqCtZP6G+ekU5hW3NWH2a989JXHOFzj/srBxMRVH7Bu8gBUVUUIEXPPZq7fzbzSQhq8QZp8QU40eKM64N91JPK+1O+FrjC4bvKAKNpwJJ0s3t+J6MWR2W04kwWPR0Pu7LQwtCCHidd0NgZ/umXDsMsvYcxyrbO9dFyJoQ4Zb1+Ha5oAKO6YxR1/ep/sFCvzRhbGrY3SEdn5zc2wU93oMyYn9O9qm/xkODSlyaOnPcb+9LqS/VUuw+JE36eqqt+p2KvzBlFU+KxWo57nZTrwh1R8QRWrSUJRNZZDhsNCk0/BLCt0vySd8Su3s3ZSf1w+haOnPMx+5SNm31zA4Ro3wy6/hDq3NiCTJRG+T2ZONnjJTrXyRmUVD93S07A6kCWBqoKigCdwRubdIkt8VuuOS6/UJ53m/vByyp9+n6XjSqLaMV3duXO2EyEEsoC7V8a2PWsnxqf5Awyc+xbrpwygdYrVmJTIzbAbFixdL2llGFY3P791k/rjDShxz6lDpoNPq8/E3paDtVFt6wsVR6PaZeCsygFyM+ykWE0xy1sabVkWIu57Wxbfnee2peOCNIIXQmQm+JfFtzcAbA8cifh8NPxd83ObJIT4QAjxQXV19bd0KkkkcW7w78ZrIk/ARDOd8dZfsGk/i+MoKS7ZfMDI7D237TCyBKVLtjJ5VQU7j2iZtvErtyNJIEvw8Ia9HK/zxD0fkySYN7I3+TkpqBBzvEVlxcx7fS8v7zjGs3f3JT8nxcjCxdufXpPS/Hudlqbvc8nmA1G0o0Sd6JCisnZSf1790dUU5aUztCCHTtnOrzzG4rJiFmzaD5CQ3lQXnpX/os7DVY+9xRdxrlF2ipUUq4k5GyopXbKVUUu3XrDqoeejjU0U55H3YvkdV5KTYmX5HVcatOFI+nCkMq7+dzx68ZLyEnIz7VHf5WbaY1Q354/szZLNBxACfjSkK+NXbqd0yVaDRrmp8iQdshxRWUeHRTaet6EFOQaF+dm7+7Lxw+N0bO2gW5tUQ6nzyCmPccwviy/9vO3hrLy+TL8Gp5r8zB/Vm5enDWT1hL6Mv6qTISrwUsWRmEFjSFGN2NOzr7ctepf7ntvJR8fq+fyURl2+EOOzOc4mXv3BEEFF8+bLcJoxy9q18QRCnGrSrA8kIVBUFUlomVw9OxUKiwe1Dou0pNvNLNi0nw5ZDk40eBlakIPNJCFLGu3TGwghhS0WZEkYtVwaLVQT95AlaPQGMMsCtz9kUJ0jY2bNPf3YVHmSo6c1r8CivHScFpln7u5rUDurXT5sZon/Wfsv1mw9lFA4JKSqcWNLp2au234EIbSJGN3ovcalWS+k280g4iuOWs0SM9btiqIqVofFjly+AHM2VBr1pjrdX99+SXkJFpkoSvRLFUfiqoJGbvfYiELDJy3yGXj27r6oqBcF1f5sYlYIeCpMQX5hUn9W3NWHp8YWkRz/tRy0sktxn6tW9vNrBF+BpgIaL9TOK8laVdVlwDKAK6+88sJ9wpNIgn8/XhN5Aiaa6Yy3frXLR06qlRfCxtFCCEyy4LejeyMAp1XmjoGdjCL/5sfyB1XsZjlhRmVoQQ71nmAUhXRJeQnrJvenutGPwyIjBPxqeC9qXf4oL7WFY7WOciRlLjfDHuXpFvl9mt3M+ikDyHRa8PhD7DxSF0U7SjSDvPdEI3M2VLKorJhF5UXUNPo5VB3fSzHTaeHNGddyqslPukPzxoLE9KYnXt9HboYmJAPE9ayaPiQ/Rv7/QlUPPR9tbKI4b5du590HBkfREru1SeXR2wpRFIVf3qLJ66+4qw8uXxCLSTIUP20miV/d2gsV1Yh9RQWrWWCRJMN8N6SovLb7OP27tDbM1Y/Xe5m7cW+4gy1z14rtcbOT1Y2+qKyjRZaodvlQVJX7rss36pmM+h1J8GlVE9OH5BvCNXpM6YPWSCGahWOLMcmCZ+/ui0kWhEIKqyf0o8blo7bJzzNbDjFt8GX4AkrUczWvtJAZ63ZR7fKxuLwEIEo0439freTR2wrJTrUa2Vd9UBoZ3xcDXfls4tVukfH6Q1S7fDy37XOmfz8fAdjNMo+/9gkP39ITlz+I06LRIS0miUBQMWbjTZJAdpzJjFW7fByv87DjcC0/vb4bLn8QsyQhBGQ4LJhlwYrxfQAVi0li5fg+BEOauTuqQFE1lVFJYPg6vrLzGPcOzufe56JFqOo8flSIuTdLykvw+EP8+m97yM9J4abe7TmYoE2rbvSxuKw4SuBKnwScfXMBXXNSAAwBr1d3HWNs/44sLS+hqtHHr/5SyeybCwy1TkkITDIcrHYza1h3FFVl7g8vJ9VmJtNpQZZACMHaSf0JBBUO17pZvfUzQ1Xa7Q/ROsXCDxdv5ek7S4xnVpYEJgmeur2IzBQLshA8u+WQsV2dJ8AzWw5x/w09eHnaQFJtZuxmid+MuJyqBj93LNpyUcTu2cSsLASBoGKwVHIztEmqZAaw5cAXgA4ZViP+TZIgxSrhC0Cq7evtS6jqxTseEkIMAB5WVfX68OefAaiq+ptE21x55ZXqBx9cXKKkHWe9+q3u//Dcm77V/V/EOO+t5jeJ13+nBjDe+l+2XFFU9p5sjBnIWc0S817by4SrO/P0Pw8y4erORuF+boadNff0M2pOdORm2JkzvBfjV243vmtOudTXi9xeP6aiqjT5gqx495BhXN06xUogFOJgjZslmw8wf1Rvo9M7tCCHWcN6YDdLVIf90PT9LS7T5NZ1hdLZN/fk9uXb4nZ4I+tlVo7vy+Ov7YmqeRxakMODNxWgqCqHazQxm2qXz9ju9uXvxTUVf+buvgyZ/3bM/Xr3gcG0z3Akuv0XZcx+E3zdOP+62ymKyuHaJk42eKNUQiMHW4vLS9jwr6OMG9CR/VVNpDvMpNnNCGBwnHu3fsoA0h1mal1+Zry4i+wUKw/fWoDbH0KERYmax/oTI3vz2Ma9PDGqtxEPo0pymXhNZ2RJYDVpg4gv6rwG1VTPoMwZ3ovcDDv3r9/Nb0f1pqrRR50ngNMiR9m46MeafXMBSzYfYPqQfDq1dqKGTcwff02j5Omxd+y0m6see4ul40qialz1/XyNSYoLNl6rGr00eAJUN/rwBhS2Hahm0rVd8ARCfFbrIRQK0iWnFc9uOcQ913QhEM4wHapx0/2SFPwBVVM8bvLjDShYzRJPvbmfWcN6GLTSnFQrmSlmgiFo8AbIdGhm723TbNS6fNz3/E4WlRWz43AtP+h5CUJoXm8L3/yUsv6X4gmE4sbMmnv6YTFJjIywV9CX6ZTK//vxNYxfuT1umzZ/ZG/mbtzL/Td0o22ajaoGH63sWhZUn9hYP2UA67Yf4Y6BHQ2f1iynhXbpNkyyoLrBzx8ivs9JtdI21con1U1R74t5pYU4LDJrtn3OyCtzWf6Pg0wbfBmBoMKP1+2KGsjYzDK3PPVu3PZSf4aBmOd76bgSsp0WvCEVm1mitVObxLht0bvfJHYv2Jj9os7NqKXbYn7Tusn9aZee8J2RxEWEE/UeHnrloxgj+EeG96JtfLn3hPF6Ln0Ai5t9pQI1qqoeibf+OcJ2IF8I0Qk4BowBxn6Lx0siiQsezT0Bv0qg4avW/7LlkiTo3iaVdZMHEAwpmGQJkwxPvPYJM6/vjs0s8dAtPZEExoyVPueUqDYuEok80OrcAcOzT1PgE3xW6+HlHfFnxJdsPkC1y0e9xx/ln7Nk8wHuve4yclKtPD+xP6qqElJVfvO3PVHZD1nSjnv0tCdKYCQn1cpP1u1i1rDuYdqViCt8IEuw8O8HuK24Pb8d1RtJEhw55SYtXKu180gdz2w5xIq7+nCqyU9ehh0h4nP9W1LNyr+DrxvnX3c7SRJ0zHKS7jAb2UCbWWbd5AEEQgomSZBq0zw2g4pqTFwU5aWz4PaihNlom0ni6X8eZO4PL6dtmg2bSSKnlcAfjE/FE2jxcbxOi+fsFCvDi6JlwJeUl/Doq3uiaJv682SSBDuP1HG41m1MprwwqX9cD8N2abaYjvVjIwqN89djT8++JqJPtwRhjUBQodbl5+Udx5g2+DI2fnicJn8If0DhnX0nGdPvUvwhhdPuIIGQgi+oYJYlntlyiAdvKgBAEhot1G6RCYY0v0AhIN1hxh9UwvRPBUWBYEjBGwyhqCqBkIIsS2SnWHFaTTy8YS+3XNGeBm8QwRlLiKmDuySs01PV+PGkU6RlScRt09ql25n+/E7yc1K4JM3OvpONxiB/xvfzjYx3pDXFrGE9tIxnOPMZb/DXLs2OySTRvU0qL4afIUkS2M0SQgj++/v52C0y/3vb5ahh1skLk/oTUtHosjJ4/FqGVa8b1N8B7dLtCQWKQorK/75aabTny++4ktZO69culbgYEAwl8AFMGsG3GPiDSlwrl1/cpCTYIjHOJQV0fpzvMoUQFuB2VVX/dQ6PBYCqqkEhxH3A64AM/ElV1Y/P9XGSSOJiw5d5An6T9b9suckk0S79zMxTdaMvxoft4Vu1DpHbH2Lm+t08c3ffuB3ksxXaONHgjRIWWDupP25/iGGXX2IM/uAM9W7O8F60z7BR7w7E+OcEQgoHq5sYv3I779w/mHERmUl9+3iCMPpserXLZ9BIAyHV6KBEnt+ae/oxdXBnqhr8jIug3S0qKzbqcu4c2In71++m2uXj5WlXGVL7zTNVLUlq/9/F143zr7udJAkynVZwxl9e3ejjV3/9mFnDejSLU5XfjeodlcGYV1rI/et3c/8N3bh38GW4/SHDSPv+G7pFCWPoiHwm5r/xiVbLFFRiRIumrK6IyZ7r28phanSm08z8kb2Z8eIuFFWN62HYym6OoR3rz0/bNJsRe3psnqj3tthJCrNJIs1u4rbi9lQ3+igfcCkAVrPE6L6XElLgyCk3PxpyGahwuMZNz3atuHfwZYYgg6Jq/ozH67xkpViYs0GjRXbJTqHG5cNuMXG4xo3dItMuzcaB6iZSrCacFhlZEkwfks/ntW4mf68j3oDC4Ro3KVYT80oLjc/xrv/B6iayU61xqfLZqVajpjNem/b8xP707ZjOTb3bc6imyaiJfWbLIYo7ZjJm2Rn/vUVlxUxbs4P9VS4jniKVj/VjR2bWTCaJS9LjZCoSPGORUOyq0SbuPFLHnA2VLL/jyhhxLP35rm70RWX5Imn0X7dU4mKAnEAgRL4AKa1JfDNckCIwqqoOjvOvNzAOWHCujhPnuH9TVbWrqqpdVFV99Ns6ThJJJHF2aO4RNn1IPvc+t5OH/6KJ8666uy8OS2wh85LyEvKaCW3kpFpiPND0jJ4OXXQhN9MeJbARubxDpgOHxWR0yPXvZ67fjcMskxn2JExkR1Hr8scIgywcWxwlLLK4rJhlbx9g4djimPN99NVKTJIc5V919LSHaWt2sOD2IuYM72WYx+uDvMhM1bsPDOblaVddsPUp31VkOS384ibNj02/71MGdWHOhkrSnRaeGNmbv//kGtbc00/rkLp8PP7aPtLslhj/web+hTrtTffL1LPEiYSILs1yRG07r7SQ9hk2bCbBqgl9WbPtcwIhhZXj+9Ah0xHXS0pV42fmL81y0KaVNYYV0DsvjaXNns+WMklhkgStbGZmrt+NLxhi2podqCqcbPBR7wkgCU0sSwhBICwWY5Ih02nFLAtsFsnwBsxOtRqefS9VHMFqErRPtyFL2j5sZgkV7e82aVaa/CEefbWSDlkOFmzaz7iBnYxjmE0Ch0UmL9MRN2Z0EaopqyuMiYnIZd5AkFUT+iIELC6LFZP4fzuOUjagk9Y2bdrPnQM78cyWQ8y8vnvUxMMblVU89eZ+1k7qz5Nji4x4+jazwl+3TfyyLF9L9LKUBDHx8NiIQpKvjBaEBPf4m5R5fus2EKqqfiCESPm2j5NEEklcGGhOsdNV5rJTrDR4g0hCy5gVd0gzPAclIVi99RDvH65jzvBeXJrlwG6ROXZa89TThQRkSfDIXz+OUSgUQrDz8Cmuys+OOztmM0sJ1e4CikpOKyvPT+yHEIL1UwbE1FKdaPAa4gc6ZS7LaWbm9d2xmyVu73spLl+QdRVHGTfgUuaVFtIu3W7QXTWFxfid60ZvgK5tUvj9mCswyxI5KdbkIO8igRSmvb1RWcWPrss36MVvVFYxY2hX2qdrAiD+oEKG02TQ52RJGBYMeoe5ORWvbZpW0R8pWFPV6KMujmCQPsu/akJfZCEQYXXK57YdZtzATtjMMrcVtzc66eunDIhPN00g6gQaJbL5b890Wkm3W1qkH6DHf6btMsuSQaULhBTSHTYUVRPL0u9ntUvzDAwpCr6gRNAfQhaCkKri8oUMavj9N3RDEoIal59GbyBKZKba5UNRtKKdNyqruGNAR7JTLYbiaLVLE1d5+NYCbGZNPCgyZtz+EC5f0GgfTZJGozx6WhMceugVre0sykvnt6N6YzOLKJEWswxHwrTj+SN7U+cJ8MrOY4woyTMoo5F4o7KKCVd3Bs60bYlEtc5VZu3rZP2/LMunv6f+PG0g3oCCLDThn4sZIRWe2XIo6j31zJZDPHRLz/N9akmcIyjn8B5/6wNAIUQbtHrAJJJI4juCyJd0daOPoQU5MWbwS8tLsFtkAiGVu1acoV2OX7mdoQU53HddPv/zwr+iRABap1r40XX5VB5vjKJRvrLjKFd2yuRXf/04RnVzXmkhKuqXUidcvhA1jb4oSpxOe7p3cD4L39qf0G9rzvBeZKVYeOpNzfrB5QtikkSMsbLbH4pLyXJaTYwO06q+SszgQlap+65C72TqgkE/v7GAoQU5+IIqE545c18Xl5fw5KZPjFqkeaWFPP7avqgOc3N68ZLNB6Jq8oYW5DDzhu4xyp/zR/bm0VcrjWyN/v/4qzpxqslvZLL02I+nOpubYed4vdeg9en7Xji2GE8gpHnfKbFegN+Uhnuhw2KSIahR0Os8Ac26waxl9B5/bQ8/GdqVxeUlmGUJScCK8X041RTA6w+Rl2nHIssEFRWrJGGSzlhxKCr86q8f87NhPVj4lpbB0ymf80oLkSVh1P/Of+MTFtx+BbIkUXG4xoghly/EwrfOtHV6zDw2Qosp0O7ngeomclKtPP3Pg1HtTrXLx+FaTZCqec3norJi5mz4OKoG+onX9zF9SH5CirI/dIa+HE/5+Hxl1s6GRl/r8reYNtZqkhh/VacYarfVdM7IfkmcZ9hMUoxa9KKyYmzf4B6fMxVQIcSTxA70MoGBwH+rqvrXc3KgfxNJFdBYJFVAE+K8vwEuxnhtDkVROXrazdg4qp9/njYQrz/ENfM2R22TSF1w7aT+2EwSvqBiSIDbTBLeoGLUphTlpTNlUBeynBbSHRYUVeGJ1/fx/e459GifztQIBbrF5SVkOk18cqIprtroirv6sPydgwwvah8jjPHMlkNMH9KVrBQziqpy9JSXGS/uYu4PL4+rsKhnNiOl95eWl/CH8KAgct2Xp10F8E1U6pIx+x+Grir6u//bx50DO7H/RD2De7SNq3IbaWqtx8SCTfvjqovqg0VdrdYXVEi1mRizTFOjnTKoi5H16domhY++aIjKWq8c3xdfMESa3cSJeh+lS7Ya5xJPRVHv6GenWph5fXfqPQHatLJFDQa+hc7xBRuviqJy2u3jWJ2Xp97cz89vLOBEg9dQ3SzKS2fpuGKsZkGTTyGkwO3LtzH3h5fTOsVCqs1MSFVp9AT5w6ZPuHNgJ/wREv2v3DsQIQRPvbmfB28qwOMP4g0qZDktWGSJgzVNzFy/m9UT+mE3a23cc9sOUzagE2OXR7d1bVvZSHeYeTRC7ES/n9UuH8/d089of/XJtHSnxTCqP5s4bZ9hIxBSmbyqImqwZDVJzN0Yq3z8i5sKwt6I5zcrrCgqtU3+uBnq5jWC+u+9WNvYk/UeTjR4OdUUwGGRcftDZDrNtG1lo018hcgkLjJckCqgQPNoVIFa4CeqqlbFWT+JJJL4DkCnycWlXwaVuIXrWU5L3PWrG334gwpt02xcGx40FuWl8/sxVxjrR2bqNs8cxK9f3ccblVWMKMmj4lANz4XVPoUQvFl5nCEFlyRUGz3V5GddxVHqwuqhLl+QFKsJIeChW3riDyp4Awo2s2z4WrVLt8fdl8MiU+/R1Eu7tknFImuKec3VvCLrZb6tWpokzh2a+wx2au0kqChx7116WPVV/9w528nvRl9BdaOPuT+8HLMsUecJsOFfR0DsHQUAACAASURBVLn/hh6Gj9mMdbuYPiSfJp/JoIvqMQ6awm7kZ422KNi4+yS3XNGeDIeFFXf1YcEmLZOt1xNG0gOfeH2fQR2ccHVnRi/bxvopA4z4jBTQaIkZv+aQwsyA1Vs/Y9awHgih9aQi25lDYRGWI6c95IRN351WEyZZo5z7gwqTwxNO1Y1+nhjV2xi4ZTgtfF7r5hc3FyAJwfF6H8+//xkzr++OosLjr2nUTiHAG6bfLv3HYa7r0TZuW7d11mB+eUtPZg3rQUhRWf7OQeN+CiGYM7wXDotMnSfAr/+2l+xUCw/eVHBWcdqptZPn3zvM1MGXRSk+63R1PfbXTR6AqqpYTDIZdjOnPQH8wRC1Tf7zNgj8sgx1S1MC9QYVHv5LJVMGdcGBjD+kff79mCvO96klcY7gCypUN0Zbq1c3+vEFz68K6Fuqqn5+DveXRBJJtBB8WS2GLBGjmJjptMRdv84dADR59UjaXH2CuhObSTIMgXNSrSzZfICHN+yNOreru+YkVBvV6XnTh3Rl/Qefs/Qfhw16qu6HpQvYmCSJnFYWVOLXUbn9Ieo8AeZsqGTNPf1wWmUCofjrmk0SgqQNxMWC5p3MSLN3HXo8RX6WJUF1o4+RS7fSHGP6dYzyzuyQ5eDTKlfc/WalaMbAup1DtctHVYOPa7vnUP50NBX58de0rND0IV35os4bJUwUeZ65GXZqm6I7Ghdz5/ibQJYEdR4/Dd4AWU5rTDtR5wnQNs3GYxv38uTtRQwtyCHDYUFF8wB0+6MHGAIYWpDDtMGXUecO8OzWw9w5sBMdMjWV1vuuy2fe63t56JaeZKeeoSmqKkZ9ZqIaO09AiWIXPDaikP1VLqpdPiRBlEKsjl/e0vOs4vRQTROj+17K8XpfXLpk8wHWN/Xo/E+jpSmBmsK1qJGTQXqZQxItA3aTFFfB2f4NKKDnkhj8//Q/hBAvncP9JpFEEhc5vkxxLd1uISvFyhMje7NpxrWsndQfjz8Uo6Y5r7RQo7OkWXFYJRaVnVnuCyjMK22u0llErcvPnA2VjF62jXF/ep/7b+hGUV66cV65GXZO1HvJcJpjttfVR2ffXMCCTZ9wyxW5/OP+Qdx/Qw+Dfw9nZPhdviBD5r/NYxv3xCiXzistJMNpNlRDH321ErdfiXtd5pUW4vIGybCbW5xK3XcF8e7r4vISXqo4EvV5y/5q6twBYz0duRl2LLLg2bv7sn7KAM1upNFnxE/z/T7+2h5GL9vGnA2V3H9DN54aW2QoV0bG6cywIfyc4b0QAn79tz0xca8rVS6NON+o87pIO8ffBGZJMGtYD043BdhUeZzcTHvU9Xqp4ojR6ZZlbd3qRh8mSeAPhkh3mI11pwzqwtyNe4z9TVuzgxEleTyz5RBN/iB2i4wsBA/eVIAswY+GdGXOhkoWvvkpQqiYZcGTtxfFj4GyYuZu3BN1rx94aTfTh+Sz/I4rsVvkuDFmt8gxcbqorDgqTpeWl9A7L40Um8kY0OnHmPjsBzGTBKDVmJ7tuucTLU0JNJ669uLyEhyWZA1gS0FQUeMqOAeVr1/Ody5rAHeqqlrU/O8LDRdjfUqyBvC84bxPm12M8ZoIX1aLEbnMbpE5XufF7Q8RUlWyU61YZAmTLDQDbiFQBfzh//Yz8ZrOyJKmIvrjF/5l1EXVeQI4LXLCWrzxK7dH1ckA3H9DN3IzHPiCIUyS4Pn3PqO4Y1YUz372zT35os7D6GXbYn7fy9MGUtXoI91uRhKC9hmaAqQsCVRVpc4d4It6r1Gn9c7MQXTIcnKqyceuI/UGPUvP4OhegImuWQIkY/YCQfN4T7PKVDf5CYZNtO0WiUZvCFnAsYhMnB6Xr+w8xu398midYkNRVTz+IEEFFkQYbLdNs/HIXz+OqSHV6aTx4vSFSf0ZvWwbL08byG2LtlCUl86T4ckSh0VGq97QnjWn1RRVV3Y2WZwve87j4IKO1y/q3PiCClUNPqMNGH9VJ9q2shEKZ/jap9v4ot5Hut1EUFGpavDxUsVRJnyvExlOM1UNfqasrmD+yN7GdfcHFUYv22ZkbedsqGR0SS6lffKwmgQev8Kv/voxI0ry6JqTwm827uE3P7wcX1Bh3wkXrVMs2MwyLl+QOneA/BwnVz++Oeb837l/ELnpDgCjTrW5ObskCWqafIYSptMq4w+pBIJK1P07dtrNVY+9FXOMdx8YTPsMh/FZUVSO1rm5Js75NF/3QsDXjFe4gGP26Gk3q7YcovTKDsiSIKSorP/gc8YN7ETuBXbdk/hm+Ky2ySh/icTbMwdxaVZcM83/SA2gmuDvJJJIIokvrcWIt+xUU4CZL+5ixtCuXJJu59MqTbWu2uVjaXkJdR4/96/fzZRBXbgsO4XpQ/KNGicgodR9hywHm2Zcy+e17qi6p5nrd/P70VfgDYTolO3kxsL2hqm83ikXqAnpoml2Mz96fucZIY+yYh565WOqXT6DeqcfKzfDjknWZmU9/lBcepY/GGqxCovfBcS7d+0tJqPD6faHkCXB8TovT//zIKvu7ktVo4/WqVYe27iHNyqrWFdxFNBi+dFX9/DI8J7c3vdSHBaZRm+Q1ilq3BpSsyyhqCor7uoTM7GgUwjT7WaK8tKpdvkIhBSGL3w3rjjM0nElzBneC0mSvrJzfLFQ/84WshAIBG6/5hv3RmWVcb31a7X7aAOf1TTyg56XcLLBi8cfYsvBWvZXuXi8tJDsFAtzhvciJ2zAXtXowyJLBtUyy2khO8XK97pl0+DxI4TAZpYNUZX5I3vzRmUVc4ar+IJK3Lbi7z+5Jm6bZDebjOuen53Cf3+/a4yIS7c2qeSk2r7yWpwNXVK//yfqvRcNtbIltbEhRWXpPw6z9B+Ho74v69/xvJxPEuce5gRq5ubzaQQP9BZCNAghGoHC8N8NQohGIUTDOTxOEkkk0cKRbrfgtGhc91l//pAh899m9isf8dPru5GdYmXy6goevrUn99/QjTkbKhnyW225TvGMrCOMRG6GnU+rXDy2cQ+tUyxUu3zG978b1ZtWdhOz/vwhHx1rMAZ/cIZSJYQgMw5ddFEcCtbUNTuYMqiLQdGYPiTfWH9JeQk5KVqnQ+9YNT/PC7GzlMS/B72DfNuid7n6sbcYs2wbdovMvYMv43Ctmxkv7uKn63Zx58BOUfGVnWpl+pB8pq7ZwfiV2xm9bBvjV27nRIM3buzIkiYwNPuVj2KooTqF8Dcb9zB9SD7zSguxmbQByZRBXaLMvnWxGUmSyE79an/Ki4X6d7ZQgV//rZIMpzmmPdGv1YJN+8lvm8azWw6R5TRzSZqVeaWFVLt8LH/nIApgM0v8JsII/pJ0q0G1zHRamD4kn9NNAewWE7UuP/6gatwH3YKi0RdEVYl7v0/Ue2PapOZUxtOegDH4g69/b86GLqnf/3gG9RcztfJigdUkJXiXJCmgLQVWsxTzrM8rLcRq/vr3+JxlAFVVTfZWkkgiiXMCTTlUiuG6P/DSbkOiXFGJy4VfdXdfPqlysWTzARaXlTB1zZkZ7yXlJWSlWMhJtaCoapQynsUkcey0l/kje9M2zca80kIkIYzsyc4jdYQUFUWFdul21tzTj0BIoc4dINNpjpuJ0dX0dMXHt2cOwiQJclKsmMIv5bPxqkqiZSDeAGnK6gqevbsvWU4LS8pLmLK6gide38ec4b3o2NqJWdboopflpBjm3Ho8qqoa4wk4r7SQ9hl2Ri7ZGvNsrLmnHz8b1oOfrNvFziN1/OKmAswmgc0s8dw9/Qzj80h8HeGXlqaqGFJUw8D94VsLjPtz9LTHUCo+etrDE6/vY8bQrqTYTJgliQyHhbWT+mOWBI2+EI+/to8pg7rQymbiwZsKMEmCX72hUTztFomOrR1UNfgQCGau382TtxcZ13HJ5gPMH9Wbz2rdPP/+Zzw1tojTETL/eZl2HBYZkxBxFTp1/Lv3Rle7fXnaVQnpkvox9Guim1XnZti5JEw3TeLbgwAWji2KsYFIXvWWA29AMRSC9fKUx1/bx1Njv37V3bduBJ9EEkkk8U0QCCWW0s/NsOMNRHdodE8snX9e5/GT5jDxwqT++EMqh2uamP3/PqLa5eOxEYWkO8wGnaooL51Hhvc0PLH0jvTcjXuN9Z/ZcogD1U0x9YPVLh/P3t33S9X0cjPsHIzYNpIWdzYdqyRaBhJ1wk81+SldspWivHRm31xAltNCptNCg8dPusNCgyfI4ZozFGg99syyxKOv7onpDPw2bDfQ/Dh17gAnGryGV6AKrN9+lCs7ZTJzvTa5Ei+OhdBqwL4qNlucqmKYqrnzSB3DF2r1knOG96JztjYw139rfk4K7dPtnGoKEAyp3PvcDgZ2zuLOgR2pavQZyow6bdRqkgw66dJxJaTZzXj8IULhAXhVhIrsziN1NPlDOCyyJvceOOMlOLQghwdvKiAYUgmi8r9fUq95Lu7NV9ElI4+hW1TkZmi+esn27NtHSFWRRPR1loQgdI60PpI4/7CY5LhKr9+kjU3mhZNIIokLEomokW5/iMdGFBp1JnCmHmfOhkqum/82czZU8qMhXQGVQEhl3NPvMX7ldnYeqTMyia1s0Qp9U+MoJuoUzgde2s2sYT1YsGm/sVxX2VtUVswL738WQ3maV1rIks0HjL8jt21OvdI7Vu0zHGdFtUvi4kSimNbphTuP1DFnQyWBkMKSzQdw+0OU/fE9Bj8RTYHWYy/TaTE6A6OXbWPyqoqwImV8Klim02LE5KKyYh59tZLhxblGBnHJ5gMxcbykvISH//IRVz32Frctepd9JxtREijOtTRVRcIZVv33VLt82MwSQmj1gYvKihlakEP5gEsZF7Zg0KnjUwZ1YfLqiig6pE4brW3yG/tcsvkANrNEptOMJIWvebP7YDdr2ZzpQ/INsaCivHTuHNiJsj++xzXzNjP2j+9x58BOFOWlx21j/hP3psXd/4sMshC4fEGD+j37lY9w+YLIIvk+aSk4l89YMgOYRBJJXJCIR41cUl5Co1fLcgAsLis2au2a1y5NXV3B2kn9kWQY2DmL24rb0y7djj4ZapYFf7rrSu5e+YFh4hyJ5hTOek/AEHHRv+uc7WThm58ypKANnVs7WDupP8dOewiEFBwWmd+O6o0kCY6ccsfs+2KlxSXxzREvppeWl2A3S7wwqT9mWeALqgRCCv/9/XxGL9uWkALdKdtJIBhiUVmxYfegD+xsZsl4NiJFjKwmwYLbi/AHFUyyoLrRj6KqZKdYjSyioqrM/eHldMh0YJIlHv7LR2dtBt/SstneoMLLO46x4q4+2MwSKoJ6TwCBwBtUEMDM67szfuV2o73Q75cs/X/2zjw+iird+79Tvac7pENI2BIEMaARg0mzBBh9UeYyqFFeTEBlR4UgKnccF7gzk9EZxntBZJxBhSAqyKYg6KuD4zaM6B0WkYAyGo0MAgaEJIQkpPelzvtHp4qudHVIoEOS7uf7+fQHUl1dfbrOU+ec5zwbC3OHzEyzKBTtBdsO4WBFHVZ88m/8/o5rodMIWDXNhr/8/XsIDFh/3zAIjEGvYeidbIRGEOTrq415ofLRdIy5HH0Ta/3f2fBFKBHwxpy8dm4ZES2i+YyRAkgQRIdEbaDTCMAdL5xPZGD3+LFo/CD0SzWrKnAna1145Z8/4LFfDES904cpLyuLYncxavHi5BxYEyIUng9x4ZSK0Ie+LzCGLaUnsPuHGrw9bxS0AB598yukWgx47BcD8eCmg4oFuJR1tDO7xREXT1OZDojn3fbGZqXh4TED8MCGUqRaDHg2ghun5AJ9tNGleGxWGjbePxx1Th8a3D6InOPOFbuRajFg0fhB6JOSgFN1LnxWXgmLQZnZdmlhNhL0GtXCwhajFi5vQDW2tbnNi1jKqmjUCpiS1wd1Th+MOkGhUG+8fzjO2L1INGrlfgot0i6w8y6ikjvkmplDZUuvpBSmmPXoZTWhexcjBIEhyajD/DED5FjD9GQTthTlwWLQgPPz1wxVNiVCN63UxpjL0Tex1P+dDTFCDG8kiz3ROYnWM0YuoARBdFiaukZaTUr3hzW7jiLFooc/wFVd3upcPhTYMnCy1o1HtnwVtjNa1eBFgl6LTXuPqRael9zlVk7JRVezLuz9M3a3wgVDsvDMH5Opujs/d3R/pCebsHbWUHBwnKx1orrBQxN0HCHJtF6rweSXP5cVrAJbBh5oXPTPHd0fP9Y4I7pAPzdpsOxS/FFZFaa8/DkSjVr4AhwPNW46HKyow6y1X2DGq/vgC3DcNeyKsMy2j289BJc3ELGwcLxnqNVogiUg7B5/mIv4pr3HkN416FY7NisNq6bZ0CvJiJLGQtzn3D4sLcyW39s6dwSuSjNj1TSbwt3XbNCiR6PyBwDVDq+s/Enf9eNZF9w+jt//9RvZNVRSNkMJPU6ul/GHtrFEQCjpySZoyQJLqEAWQIIgOg1NLSis0T2KMWDlVJu8gA61uC285WoAUN0ZTdBroNMIuHFgd7z4yWF5R76bxQCTTsDCW66GLyBC5BwJeo2cNdTpDaBbogEpZp1csF1awA3sngizQaP6fZlpFiwtzIbHL+LOFbsVWT87a6004uJomhAm1KJjNemw+P3vZDfBUBdolzcAnVYpJydqXWAM6NdN3RJ+ZaoZIle3DoT+G3rc5xfRM8kU1xlqPT4Rj28N1uJreo9y+6Zg6Qff4dGxA2TL7YlaFz785Q1Yf+8w6DQMWgF46OZMhYvu2llD8da8kWGF1iXUkl+Z9RoIApMzkhbnZ6FXkhEvTs5VWHRXTbOhm1kfNiYRcQIDlk0cLMeJpiebsGzi4A5Qup7oiJACSBBEpyKS+4NGYFg7K7jw+qHaIbtb1rl8cuHlpi6eTm8AWg2TF9mSNSY92YTNc/LkiRQIJpqZPyYTKRY96qodWPrBd3jqjkHoZQ0voqwVBGydOwI1Dq+csj892YTDVXboNQIe3xpejytSXBURmzTNyhjqPljn8qHa7lGk0nd6A2hw+3DP6s+RnmySY72AoLx+X2mX/99Uzn+odiA92aT6nlZgGJuVpnD1lKx88R7TJZXFCO0bCakwfIEtA4u2l8nvCYzhWI0TiUYt3L4AFr71L8WzPnPNF3h73ij0Tk5Q/U6dylhl0mtlLwfJnRQAxmalYc3Moah3+dDLalJYEi83oshR4/DGpZx0FDgHXvnnD4qswK/88wc8efu17d00IopE61kjF1CCIGIChyeAn//pU5xrrOknFXnfVlqB9ORgofemLpzJZh0QwTLCAWy8fzi2zh2BVdNsSE3UQ68V8Pibh1C0vhQflVXBHxAVn5MKfU9atQeFJXuwaHsZHvvFQIzNSpOLQEey0lBSmPiiaTa3baUVsvtgsIZlrpzh89E3v4JeK8jJj07UumQrXHqyCSun2oKf33kkrEjwkoJgBlpvQAzL8LmkIBu//+s3mD9mAMZmpcnHQ6188ZyhVtfoUqeWHTUt0YD0ZFNYLJ7bF0A3S7CMh1GnQarFgFXTbNg8Jy84jlgMzT7raRaDLAfSd2kFhpc+PSK7j0rHZ4zsh6UffgeTXtPuyl95ZQMmrNjVomyxRNuQoBfw8JgBWLS9DHe9tFfOhp2gp6V+rBDNZ40sgARBxASSReWneje2lVYodkF3Ha7CuOt6YeP9w+UsoKfqXVjxyb9RnH+tqmUkIHJF0pgVU3KxYc9xORNoerIJWo1yYlUr9L1g2yFsnpMHrcDw9IRscPBLrsdFdH7UrGvJJp38t04jYNH4QcjoakLFWZds0QaC8tKjixF/m/8z6LUaeHwB/DY/CwGRQyMwPDtxMBiCVkWpVmVygh7dzHpsnpOHU/Vu1Di88jXLTjVgS9EIPHk7J+tNCAkGjZxN9dkPy7Fo/CD07ZYAgQXdzldPHyKXowlNTJWg18DlDaCX1aiaYMekj/ysa7UCru6eqCjqrhUYdv9Qg4fHXIVnJw5GWqIBOo0ADo57f3YlUi3tq5irjXvk1XD5cXpFlB49g02z88A5B2MM/yg7ha7X9kSyub1bR0SDaD5rpAASBBETSBaV5z4ux4yR/RSxU6um2qDTMJyx+xXZ9ZYUZOP5xjpdTc9/+r0yxSA7b+MBLBo/CFtKT8jxWGkW5YAbqdA3AHRPCu7ciyKP67gq4jxq7szS36LIkZZowDMffIcZI/vJFm1Jbv+w/RssvOUaBEQOi1GLn+rceCwkA22oPK+eNkS2EJ2sdaKwZI/iO0/UusA5j+iWGK94AxzP/+OwYjPpf/72LaaP6IuFb/0L7z40Ct27BC18UukFxpic/Xfr3BGqCXbemjey2e/VagX0sp5P5iGNGWcdPtz90t6w83ctuCm6P7yVRBr3yKvh8hLgHE9t/w5Pbf9OcXxMVo92ahERbaL5rHV4BZAxNhHAUwCuATCMc74/5L3/AnAfgACA+ZzzD9ulkQRBtDuSReXpCdkQRRFbikaA8/MWjRqHF8t3fC/X4zpcZZctIIer7PJxgzZY6Fkt/X3/VDM+e3w0tBoBaRYDtFqlBbBpXBdw3roX6rffvYuh2UQQBCEIDD2tRtwz7Apc0dWENTOHwu7xo6rBI8vtb2/LwqNbvsLCW64GA8LqzkklI3ommWT5ak5GCSU+v4iPyqrksSAnw4q5o/ujlzUYg+nzi+ieZILVpMdb80bC7RPBQ1zKpVqAoUgJdlqDNLadPufukH1HMtUxCC09IpGebAKjQvAxQzSftc7gGPw1gDsBfBZ6kDGWBeBuANcCGAdgBWOMRhuCiGMki0r3JBN6WU2KuKUUsx6P/MdALNpehsNVdizaXia71Ekp2Y/XOKHXasCgnk5bpxHQJ8WMXlZTmPIHhMd1Sda9ZJNO4bd/xwu7UGP3omeSKe7iqoiWYzXpcUVKArwBjllrv8CEFbtRtL5UTiqk1QRjXetcPji9AVnupEQhwdhBjUK+kk26sBizkqk2JDfWjyPOE1oGIyfDisd+ERw/bl72KRZtL8MZhxeiyCEIDGmJRvTpmoAEvRbpySbkZFiRZNKpjiMXtVgTGHp0MaqOL+3tPRBp3GvvdsUbeo0QFgO8tDAbek1nWOoTLSGazxrjvHME6TLGdgJ4TLIANlr/wDn/n8a/PwTwFOd8T8SLABgyZAjfv39/c6d0OPoufK9Nr39s8W1tev1OTLuvyjujvHZkJCucKIo44/DKblvSRNm9ixF9U8yodXnw/Wl7WOzOgB4WpJjDs36qfUdohq4ahxcTVuwK27VrgxgZktkYo6rBjd++/a9wt+ZpNgxMS8Thajue+7gc8266SlHTL1J5keoGD37z9iEU2DJkt8ZtpRV4ekJ2e8RrdWh5lRIuzF63H8X5WYpsn4D6Myx95nS9G6/vO67ab9f06HLRmz4dNdtmR21XG9DuPyqSzFadc+NUvQtnHT65XFFXsw49k0xI69L8vEV0Hlr5rEV8o8O7gDZDbwChzvAnGo+FwRibA2AOAPTp06ftW0YQlwDJ66XR3OAYGnOVmmiU3bY0DDDpNbCague6vSKe+aBcEfvzzAfleGFyDnCBYHq1uK5Yj5EhmW07JDdEqf6b1aSDyDmSTTpUNriRYtFjcUE2fH4RjDFsnpOHAAeMOgHdzOHWZa8/oHBrlHjy9tiQxZbQUnkNTdTj9Ppb9AxLnzEbNGH9VufyodslKkaSN4M0xtU4vB1C2YpUnoeIDi2RWZcvgI17f8TsG6+ERmAIiByrP/sBD9181eVsKtHGROtZ6xAKIGPs7wDUolR/wzl/51Kvzzl/CcBLQHDn5FKvF2tcjIWRrIZtB8nrxRO6Y3+hIuuS25Yaeq1GTsEvcSkxLbEeI0My23ZIsiO5deZkWPHEuIGY9NLeC8p4c9eLVVlsCa2RV2mxVd2gXmNR7b4JAoNJpw2r2ydZDC+F1oxxROzQEpk16TSYkNsbs9Z+ofBcMeri59kmWk6HUAA55z+/iI+dBJAR8nd647EOT1u7dBJEvBKtFMmSn320MnVG+3pE/NBUduaPyQzLLNkaGSdZvDhae9/a6j5TyQWiOVSzzj7QfNZZIj7pEArgRfIugE2MsT8B6AUgE8C+9m0SQRDtSbRcLdVqtF2Km1W0r0fED01lJxCSZVKiNTJOsnhxtPa+tdV9jnV3cuLi8QVE9ayzgdZlnSXigw6vADLGJgB4HkAqgPcYY19yzn/BOf+GMbYFQBkAP4AHOec0AhJEHBPVFMlRjmmhGBniYgmVneoGzyXLOMnixdHa+9YW95lceIlIkGwQraHD54blnL/NOU/nnBs45905578Iee9pznl/zvlAzvn77dlOgiDaH0pHTsQ6JOPxDfU/EQmSDaI1dHgLIEEQREsh9zYi1iEZj2+o/4lIkGwQrYEUQIIgYgpybyNiHZLx+Ib6n4gEyQbRUjq8CyhBEARBEARBEAQRHUgBJAiCIAiCIAiCiBPIBZQgiLhCFDlqHF6KkSCIRuiZaB66P0RngWSVaCmkABIEETeIIkd5ZUNYceaB3ROjMknS5Et0NkSR41iNA8drnEjQa+D0BnBFSgL6pphJdtH2Y8bFtIfGGEINepaJ1kAKIEEQcUONwysv5IBgkdzZ6/bj7XmjLjlwvqMtFAmiJdS5vKg850bxO1/Lcru0MBvWBB26mimZRFuOGa2FxhiiOehZJloDxQASBBE3eP0BRZFcILig8/oDl3ztSAvFGof3kq9NEG2FyxvA41sPKeT28a2H4PJe+jMRC7TlmNFaaIwhmoOeZaI1kAJIEETcoNdq5CK5EunJJui1mmY/J4oc1Q0enKx1orrBA1HkYed0pIUiQYTSnPwGOFeV20C4iMclFztmtAU0xhDNQc8y0RpIASQIIm5IMeuxevoQeUEnuVClmPURPyO5XU1YsQujlnyCCSt2obyyIUwJ7EgLRYKQuJD8GnXqcmvU0fIAuLgxo62INMYERK66KUXEF8YI8mHU0rNMhENSQRBEvM8V6wAAIABJREFU3CAIDAO7J+LteaOwa8FNeHveqAvGz7TU7aojLRQJQuJC8tvNbFCV224UMwTg4saMtiLFrMeqaTZFXy0pyMYf3ysjN1ACWg3D0sJshXwsLcyGVkPxoUQ4lASGIIi4QhBYq5I3tNTtKnShSBn6iI7CheSX5PbCtHbMaMt2dDPrUZyfBatJhzqXD89+WI6DFXV48nZyA413XN4AnvmgXCEfz3xQjhcm5wDm9m4d0dEgBZAgCKIZJLer0EV0JNfOjrJQJAiJlsgvyW3nQRAELNpe1qLxiIgv9FoNqu0eFK0vlY+RbBCRIBdQgiCIZiDXTqIzQ/IbW1B/EpEg2SBaA1kACYIgmoFc5IjODMlvbEH9SUSCZINoDaQAEgRBXABykSM6MyS/sQX1JxEJkg2ipZALKEEQBEEQBEEQRJxACiBBEARBEARBEEScQC6gUaDvwvfauwmXndb+5mOLb2ujlhAEQRAEQRAE0VLIAkgQBEEQBEEQBBEnkAWQuCyQxZAgCIIgCIIg2h/GOW/vNlxWGGPVAI63dzuiRDcAZ9q7EVGmI/2mM5zzce3ZABV57Uj3JxLUxuhwMW3siDLbEjpDf0hQW6NHZ5HXjn4fL0Rnbn9HazvJbMck3n4v0LLfHFFe404BjCUYY/s550Paux3RJBZ/UzTpDPeH2hgdOkMbo0Vn+q3U1vijs9/Hztz+ztz29iTe7lu8/V7g0n8zxQASBEEQBEEQBEHECaQAEgRBEARBEARBxAmkAHZuXmrvBrQBsfiboklnuD/UxujQGdoYLTrTb6W2xh+d/T525vZ35ra3J/F23+Lt9wKX+JspBpAgCIIgCIIgCCJOIAsgQRAEQRAEQRBEnEAKIEEQBEEQBEEQRJxACiBBEARBEARBEEScQAogQRAEQRAEQRBEnEAKIEEQBEEQBEEQRJxACiBBEARBEARBEEScQAogQRAEQRAEQRBEnEAKIEEQBEEQBEEQRJxACiBBEARBEARBEEScQAogQRAEQRAEQRBEnEAKIEEQBEEQBEEQRJxACiBBEARBEARBEEScQAogQRAEQRAEQRBEnEAKIEEQBEEQBEEQRJxACiBBEARBEARBEEScEHcK4Lhx4zgAetGrJa92h+SVXq18tTsks/RqxavdIXmlVytf7Q7JLL1a8YpI3CmAZ86cae8mEESLIXklOhsks0RnguSV6GyQzBLRIO4UQIIgCIIgCIIgiHiFFECCIAiCIAiCIIg4gRRAgiAIgiAIgiCIOIEUQIIgCIIgCIIgiDiBFECCIAiCIAiCIIg4QdveDSDCEUWOGocXXn8Aeq0GKWY9BIFd8D2CiBf6LnyvVecfW3xbG7WEINqfC80LNG9cHtrzPlMfEwDg94uosnvgC4jQaQSkWQzQasnWQ4RDCmAHQxQ5yisbMHvdfpyodSE92YTV04dgYPdEAIj4Hg30BEEQ8Udzc4YgsAu+T0SH9rzP1McEEFT+vqtswNwNpbIclEy14eruiaQEEmGQRHQwahxeeRAHgBO1Lsxetx81Dm+z7xEEQRDxx4XmBZo3Lg/teZ+pjwkAqLJ7ZOUPCMrB3A2lqLJ72rllREeELIAdDK8/ID+8EidqXfD6A/L/I71HEARBxCaRXPwuNGdc6H2ieVrqWtme95n6mAAAX0BUlQN/QGynFhEdmQ5lAWSMvcoYq2KMfR1yrCtj7GPG2OHGf5MbjzPG2HLG2L8ZY4cYY7nt1/LooddqkJ5sUhxLTzZBr9VEfC8gcogiv5zNJAiCIC4TkovfhBW7MGrJJ5iwYhfKKxsgirzZOQNofk4hmqe5+96U9rzP1McEAOg0gqocaDUdaqlPdBA6mlSsBTCuybGFAHZwzjMB7Gj8GwBuAZDZ+JoDYOVlamObkmLWY/X0IfJDLPnyp5j1SDHrsWqaTfHekoJs/PG9MnL1IAiCiFGac/Frbs4Amp9TiOZpjWtle95n6mMCABL0AlZMyVXIwYopuUjQd7SlPtER6FAuoJzzzxhjfZscHg9gdOP/XwOwE8CCxuPrOOccwF7GmJUx1pNzfurytLZtEASGgd0T8fa8UaouJ93Meiy+8zr0SDJCwxhOn3OjusFLrh4EQRAdjGhlZmzOxe9Cc8aF3ici0xrXSkFgyEy1YEvRCEUGxstxn6mPCQBweAL49LsqbJqdB845GGN458AJ3GlLhzWhvVtHdDQ6lAIYge4hSt1pAN0b/98bQEXIeScaj4UpgIyxOQhaCdGnT5+2a+lForZISE00qJ6r0wrQCAwz13whZ3laWpgNk55cPWKFji6vBNEUktlwopmZUXLxC1VGQl38BIFFnDNa8r7U3ngpI9BSeW1633MyrJg/JhMBzlHd4Akr0XS42t5umThb0sdE56UlMmvSa5DXPwWTV++VZfC5SYNpfUio0qnswo3WvlYHu3HOX+KcD+GcD0lNTW2Dll08ajEGx2ocqGpw42StE9UNHoiNMX7VDR64vQE8vvWQwiXl8a2H4KcYwJihI8srQahBMhtONDMzNufiJ80N0nzh94uKv1sSH96aWLdYoKXyGnrfczKseGLcQBS/8zVufGZn2D064/BQJk6izWiJzPoDHI9s+Uohg49s+Qr+QGw+x8Sl0RksgJWSaydjrCeAqsbjJwFkhJyX3nisU9F0kZBqMaDynBvTXz2k2EU0aAVMf3Uflk0crOqS4vNTlieCIIiOQjQzM0Zy8QOUtWHHZqVh/pgBijpgLbFCRVJW3543Ku6tSgatgEXjB6FvSgKmvbpP9R6lmPVweigTJ9G+uCOMOR6SQUKFzmABfBfAjMb/zwDwTsjx6Y3ZQPMA1HfG+L+mi4S5o/uHWfhmr9uP4zXOoKIXECnbF0EQRAcn2pkZJRe/3skJSE0MxpY1VdwKbBlhdcDUrFBNrYZURkCdGocXi9//Ft6ACI7IZZhqHF4cPeOguZloVzSMqcqgwGLTlZu4NDqUBZAx9jqCCV+6McZOAHgSwGIAWxhj9wE4DmBS4+l/A3ArgH8DcAKYddkb3AIuFFfRNMbAatKpTjIJeg1yMqywGLVYWpgtK4mU7YsgCKLjIbkPNo0Ji+ZY3VRxk+aPnAwr5o7uD6tJhzqXD6J43kNELTZx0/3Dm40xjFdEUcSMkf2wYNshFOdnRbxHXn8Ay3ccxpKCbCzYdn5uXjXVhmSTTlayYz22kmhfGANemJyDWocPCXoNnN4Aks06kLgRanQoBZBzfk+Et8aonMsBPNi2Lbo0WpIEoOkiwekNqE4yTm8Ac0f3x0ObDiLVYkBxfhasJh2c3gC6d7k8mcYIgiCIlnE5MjM23UCsc/kwNitNVlpkRWSaDamJRlWr4YlaF/74XhlWTbOhaH0pbSyGEOCQ72PJziNhCp50j2ocXlTbPXj2w3LF3Nw72diuiWGI+EIrCPD5RRS/87UiCYxG6AzOfsTlhgX1qPhhyJAhfP/+/Zflu6obPJiwYleYMtc0riLUSmjSa1B5zqOcMKYNgU7LUOf0obBkT9j37FpwE3onU47fNqDdZ+jLKa+dib4L32vV+ccW39ZGLelwkMx2Ui4mC2fTTcaxWWn4zW1ZmPLy5xHnnZO1Toxa8knYtT7/r5shCMLltlR1aHlteq8ky+o1PRJh0mvlexRpszfFosedK3ZfcA1AdCo6rMz+VOvEpJf2hsnbljl56EVrxHglorx2KAtgrNHSuIqm6ZutJr28a2zUC3B6RDS4feiRZMR783+Gcy4fAiKHTiPA6Q1Qil+CIIhOQlNFL9mkw1mXF05PAEfPOLB8x2FU2z0tshSFWhlFUUSAA76AiOL8LJTsPIKDFXUAlPNOpJISgiCQUtKEpvfqYEUdFm0vw6b7h7eo1uKpelerYisvpRRHPJXxINTxc67wEKtz+VCy8wj8cWboIVoGKYBRpOkArNMKqhOtTiuExQQACBu8AeDb0+cUbjnLJg5Ggl6DBzcdVFgIAcDlpYGfIAiio6JmKSqZasPyHd/jo7IqeYwXOYfD48fpc2706GK84HiuEYCqc14UhWT/XFKQjWc/LMfBijo5Vk0UOTg4Ns/Jg8sX3Fisc/pwRUpC3Lt7qpFi1oe5xi6bOBh/fK8MT0/IVijMgsBkd1ApMYxOo74GCDSWdgrt10upGxnNmpNE58Wk0+CJcQMVOSKWFmbDpCMjAREOOQZHCbU6Sna3X7V2k93tD6u3dLLOia9P1uNErQtfn6zHD2fsOF3vkiceILhz+OibX+Gsw4dUiwGrptmwbOJgnD7nxpEqR1zUbyIIguisqMXfzd1QigJbhvz3o29+BbdPRGHJHkxatQfllQ046/Cgst6Fn+pcYfVhyysb8FVFvaz8SddZsO0Q5o7uL887ySYdyisb8MpnR+Dxi6hzelHV4MHr+47DQ2WEIiKVgfjrQ6OwdtYwpHUxYPqIvorEOkD4GuDlz/6NercPSwuz5TXA2Kw0rL9vGGqdPpw+51bM0y2pG9k0e6v0+WjWnCQ6L74Ax5pdR1Gcn4XNc/JQnJ+FNbuOwkd1AAkVyAIYJdQG4Omv7sO7D42S3UJ0WgHgwJ0rdyvOe+7jcjx0c6YcuDs2Kw0Lb7kGPpGruo90s+jx2C8GKoLRV07JRU6GFQcr6qh+E0EQRAckUliA1aST48vSEg3oZjHgrw+Nwk/1bjz3cTlm39Af3oAYloCkZ5IBp+vd6NstQfW61/QIuiUmm3Sosnvw3MflePCmTExvrGcnWQqf+7g8zKJFBOf1N7/4EZOGXQGNwOAPcKz85Ah2/1CjSKwjnRu6Bigc0gez1nwhu+T1SjKCA5j2yj5VK50kG5EyuDZn5aMyHkQQHpYAaklBNhhIASTCIQtglIg0ALu8AaQmGtAzyYQauxc/nnWGnVdgy8C8jQfkwX/GyH6Y/uo+fHe6QbWmi0mvlR9w6Xse2HgAc0f3l/8OHfgj7RoSBEEQl49ItQFFzvHYLwZi0fYyTFixG/es3otzbj+2lVZgxsh+SOtiCBvzZ6/bj3qXH6/vOw7OEXGuSDHrcbjajp/qXCiwZeDBTQfCLIUFtgxSFlRg4CgYkoEfa5yorHej4qwTs2+8EnfZ0lG0vlRhYWu6BtAKDCdqXThYUYei9aX4qd4tz/NAuJVOr9VgbFaaLAd3vbQXi7aX4YzDK4eXRLLyRbvmJNE54SFZa4Hzzzct+Qg1SAGMEhcagGscXjz3cTmSTDpsnTsCq6bZkJNhBRCMM5Ae2Lmj++O13UETflqiAevuHYaxWWny9f581/XQCOoFaa0mXdj3qrmmkosoQRDE5Ucq+xMaFrBiSi5MOo3qwq3AloEF2w6Bc/Uxv7rBgwJbBha//y2WFGQrrrtqmk2OSXvu43Ikm/XITLOgOD9Lnnuk66Qnm8AYo03CJgREjhq7F8XvfI27XtqL4ne+xhm7B4VDM5BqMSiUZtakCLemMf5PIlKNX+kaKWY9fntbVpgcSIpmc1a+ULnKybBizcyh2HDfcHBw6ss4IhDBayxAMkCoQC6gUeJCRX+lgrKz1n6hMM1/Vl6J7l2M2Dp3BGocXlzZLQHzbroKtQ4fvH4Rx2uceGLc1XjqjmsREAG/GECD268aWF7n8oV9b6Rdw3cfGoWACMoYRhAE0QKilaGxexcD3po3Er7GuLu3Sk8g//peqgu3tMSg+6BGYKpjvtsXgNWkQ3WDFwatgA33DYdGYAA4TDoNfqp3QWDAwzdnYkYTt08pQczYrDRwDkxatYcSiDTBL3I8vvVQWO1dAJg/JlNhYdMwKOoEev0BlEy1YW5jbGakGr86bXAfXhAYNI1WQ+B8yYleSUaIogi/yFU/DwTn+cxUC959aBRO1bnxlx3fo8CWgRSzHm5vAL2STNBq1ff74yF7aDz8RgDQatTHCa0m9n5rPBMteSYFMEqopYFONunkTgKA13YfDdvh3Xj/cNyzeq888W6ek4czjTuOoVmcjDoBdU4/MrubkaAHlhZmKzI9lUy1oUcXA96eN0ohDGq7hqkWA07VuRUZ42jCJwiCUKetMjTWOLwYlG7FsTNO1YVbkkmHh18/iFSLAc9NGoxHtnylmBcSGksA/frWq/HLzV/K762YkouAiWPqy/tQnJ+FRdvLwuaeNTOHot4VLC/0h79+E7ZJGO9x5KLI4Q2ISLUYFDH3Uq3FK1PN4Px8Nk+dVoBeI+D12Xnw+kWAASLnWDtrGOqcXvgCIv581/WKflpamA27249u5uA1JE8i6Ttf230UM0b2wxm7F6/vOx5WiL5kqg1/+fth1Lm8+O1twY2Cv+z4PiwObNU0G67p0SVMVuMhe2g8/EYJo07Ayqk2PBCytls51Qajjpz9YoVoyjMpgBG4GA07tJ6fWictKchGdYNXUZupusGjmHi9AVFW7KRjj289hNdn5+Hh1/fKk3nTHUmXNyBncqtxeOX2mvQarJk5FAl6jVwTZv6YzLCMcTThEwRBqBPJk0IaM5ubLyQXzNDaXFLSlRSzHv26mbHik3/jxcm5cnyetLhf/P63OFEbrCXnFzkWjR8kj+XPfFCOarsH6+4dJid1kdo2b+MBrLt3GFIthoiuh/UuHwpL9iA92YQXJudg+oi+0GkEeZ6I95jAGocXx844MX9MpqxMSTH6U17+XLH4yky1oPKcB49s+RKpFgPmj8nEVWlmBHQCDFqGGocXVpMOqYkG1T6U5EjyJDpd78aCbYdQnB90CV02cTA+KquC1aTHhvuGIyByCAw4Y/fi3hv6ot7px+SXP8eyiYNlt+GmbqRq8/uF5DoWiIffKOHyitj+5QmsmTkUGoEhIHJs3f8jpo/sB5jbu3VENIimPJMCqEI0NGy1TpIG9KL1pQCCO7xN0zRH8uH2+kU5zk9aEEjXASBbDse/uCtsUmpqTezTVT1jXLxP+ARBEGo0F391oflCcv9vmplPFEXUOLxI0AmYkNsbL35yGMX5WUgx69HVrIdRJ+Cjsir5+wTGMGvtF2FtC3UbDG3bWYcXc0f3l0MDmloXpbkn1WKAyxvAwrf+pZgnTPr4TiDi9QewfMdhPDtpsCJGXy0Zz5aiEZi9bn+YtbDohr644/p02QK7de4I1T6U5l7Jk8hs0Cjme6kPJ+T2xql6l8L7Z8WUXLzyzx/k80JzCkhEmt/jIXtoPPxGCcaAGwd2Dws1YrFl6IxroinPl9UuzBj7j8v5fRdLNGrqROokKTYvPdmEFyfn4sCxGmy4bxj+/qsb8cmj/0cuHBtKerIJp+qD15ImgqbvcwDJZj3enjcSa2YOhcg5KhvcYb/j8a2HwoLVpWtQxjCCIIhwmkvydaH5IhAhM59P5Hj5s3/D2xhn9lFZFYrWl6KwZA+mv7oPLp8oJ/V4ffZwpCebVNugFdTH8xqHF5lpFnQxarFs4mBFgpglBdko2XkEQFCpUfM6cXkDcZ0QRq/VoNruwak6l3zvIllTfYHgBq2kIEp1eu8edoXC26bG4b3g3CsIDCadVhHXX7LzCJZNHIyeSaawvpq38YBcR7Jk5xF0NesjfkfTjOA6rfp6I5bWAvGUITVSFlAen49wTBJNeb7cjsGvXObvuyiioWFH6qSeScGEL8X5WfisvAr5g3tj4Vv/ws//9BmmvboPdU4vSqbaFJP1yim5WLfnGIDgAB+a7W1sVho23j8cPboYcaTKjj/8tQyz1n6BOqcPdS6f6u/gnKsWqJeUU4IgCOI8atk7peLqXn8AyyYOVmR2PlHrgssXwMlaJ/yiqBiHczKsKM7Pgj/AMTmvL+ojjNNOjx9rZw3FU3dkIUGvwRm7R1FUXIrtMhsErGoyZywpyMa20gocrrLj8a2HYNQFY9PeeXAU1swcitd2H5VDESIpNSdqXXGdNTrFrMeqaTb87/dVWDklV6GQhZKebJI3bq0mnWwFXLS9DFWNIR45GVasmmZTzeytNvdK8rattAJLCrKRmqhHUoIWjKlng70q1SLXAS7ZeQQrGtsrfceqqTYkm3RhGcHtbn/MrwUiPbux9BslRK7uQSaSBhgzRFOeo+4Cyhh7N9JbAFIu4br/CWB243VWc87/zBjrCmAzgL4AjgGYxDmvvdjvkJCUt6YuM63RsCNlBe2eaMQ5tx+/3PwlivOz8ECTukAPbjqI5yZdjzUzh4I1+viLnOPhmzNRdqoBByvq8Nruo1h37zB4/QF4/FwRjyBld5OC/CP9joHdTYqENbGaFYsgCOJSiZTk63C1PSzO+9kPg3FdR6rsmLX2C8U4nJNhVbgISp4gY7PSFO6e6ckm2D1+JJv1qDgbHL+L3/k6LPbb6Qngm5/sOGt3YeP9w1Hd4EGNwysnD3n2w3J5XnnrgZHQawUs/fA7zBjZD2WnGoKKZoTslNIGYqzGS10IQWDo0cWA6SP74fd//UYu5t40VnP19CFIsxiwevoQ1Lt8ipjBOpcPY7PSwlyAS6ba8J8/H4DkBD16dDGGzb2SvD09Iegq/OTt1+L7SjtMevUM4D+edeKJcQPxzAfl2P1DDSbn9cHiO6+DUaeBNUGPrmYdal2+MEv19Ff34d2HRsX0WkDt2Y213yihEQRV+dAIlAQmVoimPLdFDOANAKYCsDc5zgAMu5gLMsYGIaj8DQPgBfABY2w7gDkAdnDOFzPGFgJYCGDBxTZc4kIlHVpCc50kHXd6/RF3a+weP7x+EXe/tBfA+V3jzDQLDlfZ8eiWrzB3dH/V7G5SnKFWYFh37zAsfv9bfFRWpfgdoQlrCIIgiOZpOmZWN3hU47wXjR8Eo07AMx+UAwCW7zgsZ21WiyF7cFMwYYukkKUnm/DqzCHw+jlO17vlTJ+RYr+lMX/T3mOYMqIfUhMNKLBlyGUepM+6/QFkplrw5O3X4qzDizUzh8Lu8cPtC1owH33zqzBFVvpsLMZLtQS3T4TLJ+KjsipZQZfm4qt7JMIX4Ohq1kEQGFIseiQn6ODwBmRFvVeSEb++NQtTX/lc0edzN5Ri85w8cM4VSdtCCZW3k7VOJOg1WPz+dxH7qtruwfp7h+H7Kjt+/26Z3Pc7HxsNq0mPU/Uu1fWGyxtA7+SEtr6V7Uq8rHcEFp4hfmlhNmJQ141roiXPbaEA7gXg5Jx/2vQNxlj5RV7zGgCfc86djdf5FMCdAMYDGN14zmsAdiIKCmC0NOxInSQdP1kbUOwMzx3dHylmPawJehi0DMdrwlODixzQa4K7OZFcd6wmHdKTTfi+yo5F28uwaqoNi8YPgiAIMbvzRRAEcTmJFCrQP9WM/3zjS3kBfrCiDs98UI7XZ+fJsWLSeC9lBfX6A1hamI0eSUYIjEErMDz74TcosGXI473azn73LkYsvvM69E42ITPNgj9uD35G2hgMnVe8fo4fa52wJujwwEsHFNcam5WGLUUj4Bc5jlTZFcpjrMZLtQS/yKFhynt/sKIOi7aX4c25IyCKAbh9AXxbfw5F60uxbOJg6LUMT4wbiDW7jqLAloEBRq2qnHj8Ih7d8hWq7R6smmZDN7NeMUeHZpZljMHpDSA1UQ+Rc7w+Ow8/1QUtjFJf5WRYARZcF8wd3R8lO4+g2u5BgkGjKDFxKZ5NRMfG4xfx9oGTiiygqz/7AQ/dfFV7N42IIh22DiDn/JZm3rvxIi/7NYCnGWMpAFwAbgWwH0B3zvmpxnNOA+iu9mHG2BwErYXo06dPi76wLXeMpM7TCgzPTRqM1f/7Q5iLyMqpNlyZasZf7r4eH319Crdm91a4nSwtzIYvIKoO6E5vQN4VPFHrQtEG9RTQRMfkYuSVINqTeJTZSAtqrUZAtd2jOLfa7oHIOQIix9isNNz3sysVVpyVU3KRoNdg2ivKYu3vHDyJyXl9wBC+s//cpKCysfCtf8mlBxbccg3OubxYMSUXL/zjcNi8srQwG2a9Bm8W5WHtrqPI7ZuCFLMeaYkGpFkMEAQGh8cvt1+KHxNFEdUNnpjZQGypvGoFhp/OecLu/SszbPD6g1lcu5r1KFpfKrt8Jpl0WLPrqHzvi/OzVOXkeI0Tc0f3R9H6UhStL5VLPEkZvEPdi8dmpeG3+Vl4+OZMPLDxQFhtx5wMK54YN1AhPyum5CI5QYdu5uC8Hw3PJqL9aInMJug1mJDbW5EFlDL6xhbRrAPIeBsFhzLGugPo3fjnSc555SVe7z4A8wA4AHwDwANgJufcGnJOLec8ubnrDBkyhO/fv/9SmnJJhHZeqsWAxQWDYDHocNdLe8MmiEXjB6F/mhmcA0+/V4YCWwasJh1EzsEYQ68kIziC70kuniun5KJbogFV59xw+0R50ZHRNQEJem2LJ/Bo7TB0ctr9B7e3vHZU+i58r1XnH1t8Wxu1pMNBMnuZiDQR90wy4GStW87+KMV86bUM2/ZXYNqIfjh9zo0ahxclO4/gYEWdPN6HlghITzahOD8LO8oq8eDNV8Gk18DnF+EXOfwix6qdR/DwmKtwxu6BVhDkePL0ZBPWzBwCk16Lu1Xmlddn58EvihAYU8wd0iJCFDl+qneh1ulDolEbFkLQdKFxiXNFh5bXGocbZ+1eOL0BnHX4kKDXQGDB+rpzN5Qi1WLAn+4ajJueDTo85WRY8eykwThV55LLakyypWPGyL4oajx//phMXJGSgJ/qXEgy6fC7d77B3NH95fCObaUVeOqOQZi0ao+i796Yk4fH3vwKqRYDHh07AD2tJvxY48TyHYfx61uvgd3jV9T7rbZ78MacPPRIDMYY1ji8EMWg/AREDq1GQJrFAK2W4sNaSYeV2Z9qnXht91EUDumjqAM4Y2Q/9IpxN994obrBg9+8fUjWB+pcPmwrrcDTE7IjGXkiymtbJIHJAbASQBKAk42H0xljdQDmcc4PXMx1OeevoDGLKGPsvwGcAFDJGOvJOT/FGOsJoKq5a3QEQlOGp1oMqHP64fapZ266IiUBdU4fkhN08m5iqsWAJ8YNxGMhu8cvTs7Fwzdn4qd6N373zjf4zW3XoMHtx/Idh/HgzKLrAAAgAElEQVTEuIGK+k4t2SmI5g4DQRBELNI0VECnFWB3+5H//C6MvDIFa2cNg07DoNMIMOkZFmz9F+bc2B93r94bFr91sKJOjvWTkM6ZnNcHVec8MOqUSt6KKblgDEg06jFzjbIQ/Ky1+7Hx/uERslmLGPOnT+Xvr27w4mBFnZzsBQAmv/w5ivOz8OAmZYx504QwsT5XeHwiApwj2axHV7MBAc6hFZisWBfnZyEgKl1EjVqGntbzf9/7s75IMGixafZwnHP5MTdkY+CVGUOw8Jarw2L6/AExrO8YEFZnMD3ZhLWzhsLjE/HIli/D5Op0vRv1Lh9MOg2mv6q0Lr+2+yge+Y+BMdNXBKDVMNw2WGkBXDElF1oN9W+s0Fxd2dbSFjGAawAUcc4/Dz3IGMtrfG/wxVyUMZbGOa9ijPVBMP4vD0A/ADMALG78951LaXhb4PeLqLJ74AuI0GkEGHUMi++8Dj2SjDBoNbhn9d6I2ToZCxb/9fg5Xtt9FMX5WRiQZsG0V5WT/YObDmDNzKGwmnSYPyYTvoCIBL1Gtb7T7HX7sXlOXrM7tZHqWpEbKdFWtNaiRxAtoa09GUJDBaobPJj+6j6kWgwYn9NbVsokN8qHbs7EvCZZnyUXwUXby+D0BhSxgcGYLwOOVNnBGAvLGD1v4wGsv3cYNEJQMQidP07UuiA01nttOq/UOs8XgPf6RSydmI0j1Q6U7DwiJ3uRYsnV4hVDFxqxPldwDvzpo+8Vu+09uhjl32s16eDy+vHC5BwIjEFgDAEO/NgYvz/yyhQERGDy6s/lfg7N5KoRBFn5A87LxOY5eWF95/QGsOCWq+XNX+n8irMuFL/ztWoyotREQ9BjCMF1x7KPvsfBijpZ7qLRV+Qt1HHwBXjYGDNv4wFsnpPXzi0jokWAQ9YHpDHptd1H8dQdg1p9rbZQAM1NlT8A4JzvZYyZL+G62xpjAH0AHuSc1zHGFgPY0ugeehzApEu4ftQHMr9fxHeVDfKO39isNDw8ZgDW7TmGAlsGMtMsKM7PAsCxpCBbodEvmzgYRp0G//O3b/Hk7dfKvv/LJg5W3dXVaQV0Neth1AXQxaTFOZcPVkSu7/Tom19F3KmNRh1EgiCI9qSl1qmWjvsXOk8aN4vzs2RvDWmSrmrwoH+qWVWhCiqIuehi0uPZSYPxY40Ti9//DtV2D0qm2rDzu0pMGnaF6phc1eDBo29+haWF2XjmA2XiltP17rCMkSVTbUix6LHv1zfj9DmPvFgMjRWSLFqRShismmZDaqNbYazPFQKD4vePzUrD7/KvlZWzOpcvqPgJgDsg4tV//oD/uvUaLN9xGCun5MJs0MqWt9AagdL1ts4doXr/tAJTxOsV3dAXgzO6wOER8fw9ObAYtHD7Avip3g1rgvo836+bGVXn3Hhky/n+//Nd1+Pp977FwYo6WcH3+gMtegbUzgEQ0xbgzkZAVPcmC8RhHc9YRcOAOTf2xy83f6l4ri/GyNsWCuD7jLH3AKwDUNF4LAPAdAAfXOxFOec3qByrATDmYq8ZSlu4slTZPbLyBwAFtgw8v+P7sAn1xcm5+Ky8Ul4s+AIijDoBZx1ezBjZD99X2uUdPmnB0HRXFxwKs//KKbnokWS8qPpOlC2MIIjOTkusU61REiOdJ31XgHOsmTkU3RINqq56G+8frqpQrZ01FB6/iHtUXEPnbijFmplDZYtSpLH88a2H5BhCyeL42//3NZ4YNxDPThyMtEQDjtc4Ufz/vka13YN19w4LsxQ8vjVoedJpBbl80MJbrpEVGOm8ovXnk4rF+lwhcsh9lZNhxYyR/fCH7d/IG7Y7yiox/+eZWL/7KO4efgUKbBk4dsaJarsHZoMWZx1e+d7UNakRCATlRi0TeIADV3UzY0vRCBi0DLVOH07UuhUK+5KCbGwrrcDCW65R7QORc1n5A4J998vNX2Lxnddh4Vv/ktcSJr3mgs+Amvyvu3cYjDoNHB4/ivOz5HjWWLIAdzY0grrVX0PKeMwgAnjpsyMKC+BLnx3B78e33gIY9ehfzvl8AC8AuAnAfzW+bgLwIuf8oWh/X7SItFiocXgv+pq+Jn78VpMOBbYM1TpQdw27Aou2l+Gul/bCF+B4cNNB6DUCFmw7hAS9Rj6/ZOcRLCnIDip9gLyr+99/U8ZqPLDxAGodwWxwoecuKchGyc4j8nlqO7VStrDQz1G2MIIgOhMtsU61dNyPdN4ZhwfllQ2YsGIXbnxmJ17fdxxdjNqwhf6JWheefq8Mv7ktK+x4xVmXnEVSOrZgW7Bm4IlaFzQCw/Idh8PG/ZVTchVjeUZXEzbPycOi8YPQ1aJHtd0DgTHUu3yY/uo+zFr7BQ5W1OFErUuhmITemxO1Lty5YjdcvgCeKcyGVmDN3sNYnytEznGXLR2fPXETnp+cgwXbDuGjsiq8c/AkNt4/HNNH9oXD48ddw67AsTNOpJj1cl8xdl7BA4Jzd5+UBMX9lObzsVlpeOwXA7FoexkKS/bgqXe/RnmVHU+9+zUa3AFUnHWpug8X2DKw+P1vw+b5lVNy4fCo1xnumXReeVw1zQbOuWwtXjXNhlSLQfEMiCLH6XNuhfynWgyoPOfGpFV7UFiyB4u2l+GxXwxEToY1pizAnQ3GEDZOLCmgOoCxBOccM0b2k/WFRdvLMGNkv6C/eitpCwsgOOfvA3i/La7dVkTDlUVykZAybQHAmplDsXzHYRysqIMvICLFrFd8T06GFY+OHQCNwPDGnDxoBCYHgNsbB/BQq9/Bijo8+2E5Fo0fhD4pCWAAXL6AXKQ2tO1Wsx7V5zxyzGHFWVdYfSfGgvWGQne7o1UHkSAIor1oiXWqJeO+KHK4fOqLabdPlDM6F+dnoX+qGafq3ejXLSHs/I/KqvDrW68JOx66wRd6bameq14bLCvx7Ifl8q6v0xuA3eNXjOVHqh3YUVaJ2TdeCa9fxNa5I+Dxi0hNNCgsNIDS8hR6bySLYtH6Umy8fzj0GqHZexjrc4XFoMG47J6orHcjrUvQsru0MBsWgxY/VDvw+r7jKM6/FpXn3Fi+4zCWTRos99Xzk3OwrbQCf77revxyc7AuZHWDJ8zi172LAU/efq0iE3iBLQNzNwRLQ5yxe5qVkY/KqlCcn4XFd14HnUaALyAiLdGAslMNqn1n0AmwJgQ3o989eAK3X58uexiFlh/x+gM4WetEQORoaKJMquUXCI1nbYkFmGIHo48YIT7sd7df295NI6IEj9DHT15EH7dFFlANgPsBpAN4n3O+O+S933LO/xjt74wGl+rKIrlIPPdxuWrtpbcPnESiUQtrgh5js9JQYMtAWqIBVpMO/xOSZntpYTCVa9ENfWExaIMWvsZdQuma1XYPLAYNqs658fjWyHWGyk83YNH2MiwpyMZLn/6A8Tm9FfWdlhRk46l3v1bNBNaWdRAJgiDampbUPbvQuC+N66fr3equVUw9M+Prs8OTeKQnmyAI4QqV0xtQPZcjuJtv1ApYNdWGog3BenGS18dfvzwhn7tySi70WoZpI67ArLVfINViwK9vvVoR/7VySi7qXT7oNAIExrByqg0PhGSklNxOgeCCvrrBA86Bkqk2RebKpvcwlucKr5/D6fHDL4qoOufBE+MGygl5lk0cjAJbBhrcPtQ4vKi2e7D/aI3cVy5fAI+OHQi3L4BF4wfJJSRKptqwvDEU5LXdwXqBBq2gUAoz0yyyglfj8CLdaoqosAcT+/gw9ZV98vFNs4ejd7JRtXbkqTo3Jq7aAwBYNc2mCFORFLl19w6TFVIpTGVsVpq80SzFD4ZyotYlP3MXsgDHevbY9kInMMwa1U/R50sLs6GjexozCAJUs4AKF+HP2RYWwFUAEgDsA/A8Y+xTzvmvGt+7E0CHVAAvtUiq5CIkJQBQi62466W9eGWGDQ+PGRA28UqpuB/fegjPTboet1+fjqUffiena+acY+2sYTBoBdQ6vTDqBFgMWpyodYUpiKGTeejOnGQ57JdqRvnpBtkaWHaqoUU++7RjRxBEZ6El1qkLjfvSuJ5qMYSNsUsLs6HTCKrunufcPtUxWacBVk7JVZRz6GU14rlJgxXK2tLCbPRMMmLT3mO4IiUBBp2ADfcNxxm7BzUOL5bv+B4P3ZyJqSP64t9VDvzunW+wdOJgPP+Pb1Gcn4WrUi348axTzg6aajHA7vErSgKtnTUUm2YPR43dC2uCDr/a/BUOVtQhJ8OK+WMyYU3QQcMY3th3HFuKRoBz3qJxP5bmCZ/I4fKJeHzrISy+8zqs23MMxflZKM7PQo8kIzRCsMzHttIKLCnIRvcuRgAcW4ry4Atw2H1+PLjpoEJZGpuVht/dfi3ufmmvvF4ozs9C0Q19cWt2bzy46QAW33mdrOAdOFaD/kP74LlJg7H6f39AgS0DKWY9upr12LzvOJYWZsOaoANwfmP3j9vL8KuxA5CRbML6+4YhIHKcsXuRbNbj8TcPyW2JpMiFughLYSrr7h2GslMNOFHrirhp0ctqQo8uxgv298Vkj40luWortBqGXlYj1s4aBoEFLYI6DagMRAwhigibb6TMwa2lLRTAYZzzbABgjL0AYAVj7C0A96ADFNCMxKW6skiuRJEGVH9jdqbqBq88CUvvyRNAYxxIN4teLvVgNenx4E2ZeHDT+QVDyVQbdh+uxs+zeiI92SS7hRbnZ8nFZENdPaV2Vds96GbRQycwhTtQS1xdaceOIIjOxoWsUxca96Vx/UStS+GCmZZowMqdR/Do2AHoq+LueaLWhW2lFWFuOvcMuwLLdxzG67PzUNlYDN7rF/Hff/tOce4zH5TjqTuuRf7gdFlRWLS9TPE9Zaca8Nyk6+ENiFh4y9Uw6gT8auwAnKrz4Eyjp8eTd2Th9++WqbrszVzzhZwQZOP9w1Ft9yAnw4onxg1UWBCWFGRDw4Du1gsXko61eULDgNTEoBKt0wiYNaofap0+LNpehqWFQYXP5Qvgvp9diVf++QOeuuNaMMbg9gXwxufHcffw89lbJeterySjnB9AWi98f+ocJuf1xZSXP0eqxYAkkw5LC7OxZtdRORHPi5NzMH/MAIU1tmSqDZ98W4le1t7YOncEahxexcbu0sJsnHP7cU2PRPRPtUAjQPYCysmwoqtZr6rIhcbASu1mADbePxwAoNMwrJpmk9csUj+3RPkDWh9yE2ty1VZ4/CJq7D7Mf+OgfJ+W350DvSbq6T6IdiJiptcOEgMom8w4534AcxhjvwPwDwCWNvi+qHEpriySK1GkLJ3axuxMOo2gSA9e5/KhZOcRWE3nd/ACHPLnx2R1l5U/IHh87obG+Awtk3eOD1bUYdH2Mqy7dxi2lVYo0oxvK61AL6sJi8YPwu/e+QbVdo+iAHFLXF1jvd4TQRDxSXPjfqiL6MGKOtkFc/Gd12FCbm8UlOyRrTWhY/620gr855gBKApZrEuhAHNH9wfAodMIWLS9DMX5Wai2e1C0vlT+fHqyCclmPSY3ZgbtlWQMmzMAwKTXYNGWoGI4NisND92cidf3HZetRF2MOvzPnYNQ71KPYTTqNFg9fQh6dTFi0/3DEeAc015RZv1csO0QthSNaNG9jLV5Qgyp6ecLiOidbJLvj8AY9h+twc8GpMIfEDH7hv5wegPgAHSCgNy+KTh2JvjZVIsBT96RhVqHDxaDVj4urRfGXdcT1Q0enKh1YfGd1+GBjQeQajFg7uj+EFjwPiYn6DG5UUGUZKG6wYP/m9sbBo2AwpI9irafqHUhNdGANbuO4ukJwdASUeRYPX2IHKoieRk1LfPxl79/DyCo/EnuzakWg2JzYGxWGjbePxwCY0gwCAiIwKl6V4s2z1sbchNrctVmcKDk038rx4pP/42nKAYwZoiY6ZW1fiOkLRTA/YyxcZxzueQD5/wPjLGfAKxsg+/rEEiuRM99XB42oJZMtSHNYsC6e4dBq2FhO6xLC7Mhci776JsNGqyZORQJeg3SGlOKh3a2FJ/RvYsRXUw6/Pmu65Fk0sKg00JgPGzhsXKqDRv2HMWq/z0mXyM0YLslrq6xXu+JIAiiKWouoksKsqHXCHhkS7AO07KPvg8b82eM7Id3vzyBN+bkwesXwRiw8pMjGJ/TW1FTbt29w+D1B/Di5FyFl8eSgmw0uIMJWSbZ0sEYky2AY7PSsGzSYADA8Zrzbp4Ftgy899XJMI+RVVNtMOnVF9w9k4zgAOrcfvRKMuGncy7VcZ63cHc51uaJgMjlrJ76xthJ6ffptQxD+qWg6pwHD71+EMX5WTDoBDg8fvRIMiLFrMfT732LFybnoJtFjzqnH8XvfI1lEwdj+Y7DeGFyDnx+jpVTcqERmJyYp0eSUbY6l+w8gr/ccz3GZqUhwLkcb/ra7qOyki9yAAJD0Q19FXN8erIJFWddmD9mAHQa4GStE3qtBpmpFjx1xyBMWrVH9koqzs9CilmPXlYT0iwGPPIfA1F2qgFzR/eX5bU4P0thRf6orAplpxqwYkou6l0sLE60Oetca0NuYk2u2grGgHk3XYVahw8AoNcImHfTVbgI3YDooAiNmV47ZAwg53xqhOMvA3g52t/XUZBciZ6ekA1RFLF5Th4CIodRF9zRqrJ7oBUYztq9Ya44j289hI33D8ei8YOQ3tWEsw6vIitX0yK/Y7PSkGTSQeQcP9W5cW3vRFTWezBrbbir0IlaFx5ozCaGkMnhRK0LV/cIuj5Ju3XN+djHer0ngiAINQxaAYvGD4I1QQdrgg4mnQYev6jIrPnsh+VYM3MonN4AEvQauH0B/Me1PeEXg8rf95V2TBySAbvHj2UTB8MXEKERGM46vEgy6XDweBU2z8kLKlqMwR8IZmYuuqEv7hp2hVyLT6pFJ/0dGu/dK8mIzJBzgca6fRtK8cacPDk5ifS5FVNy8fu/fiMnIFs11RbRJbCl43wszROiyMEBOavn0onZACBb9JITDDhj98Cg08junMkJeszbeABLC7PRO9mE8YN7QCcIOFzpUNTyTU3Uwx/geGTLl0i1GLD8nhw5jlAXknn1iXEDwQD85rYs/FDtwPwxmXLiGIXVbqoNs352JY7WOOX+lOSi2u5R1IhcPX0Iuhi1ch8drKhDyc4jmDu6P1ITDah1+ZCZasHb80bB6T1vOY4U3pKcoMc9q/cqLJOn693o3sUAq0mvuqZobchNW8lVrMUVCoyFxVmxxuNEbCBy4KzdhTcadQyNwHDweA36plzYRb8pbVIGgjF2NYDxAHo3HjoJ4F3O+bdt8X0dhaauRGp+66/dO0x1ED1d78astV/gnwtuCqsJFVrkd2xWGh6+OVNR9H3VNBvcPhHLJg5GikWvev2mO2uSW6ovIOJUvQsmvQaV5zwRfewvNUkOQbQnfRe+16rzjy2+rY1aQnQmahxehfLVNNtnqCu9NyCCg6PO6UM3ix5mA6AVBGgFhgPHanD79emy1XBsVhoW3nIN6hvLLowakIZ1u4/i5mt64NE3v1KM7VrNeatTqEUGUMaQG3WaiPX9vH4RGgGypSetixF/3P6NnNVRUhSfm3T9BbN+NkcszRM1Di+efq9M3m0/Uu3ANT0TgzGRApMT8lzbq4vszilZ7575oByvzLThpmt6YMrLn2PZxMFyv+woq8Rv87MwefXnsqXv+R2H8fDNmXj+H4fxh/GD5OydPboYUXnOg9REA5bvOIxnJw1WrSVctKEUi8YPwvwxA/DwzZn4qd6tyAOQoNfI585etx9bikYoylE0lWtp7ufg8nlNw1ukZEEBkWNpYTYExhSyu3bWUFTWezB7vfqaojUhN20hV7EYV8gRzCrc1IDQ+ugwoqOSoBfQN7UL7g7J0rtyqg0J+tabAKMeGcoYWwDgDQQ3HvY1vhiA1xljC6P9fR0RUeSobvDgVL0rzG9diicIJTQWwB8hwPPKVDO2zh2B/7r1GjmDnPRe0fpSBDjHXS/tRcVZl+r1pZ1d6e+VU3JRbfdg0qo9GLXkE3xVUa/qY3/6nFuuEyjt2O1acBPenjeqUw+UBEEQFyLU9SyS8vXEuIFYM3MoUsx6MAB+UcS0V/fh5mWfypP0PcP7ykpVqBVvwordKH7na5xp8OCe4X3lBbR0/aL1pfAHuDx2R7LCSLUBQwuPS6QnmyBy4HhjwfnCkj3wB0TV2rEi51i+43u8WTQCnz1xEzbPyUP3Li2PsYqlecLrD9bXlZL/XNktAQwMn5VXonsXA2ocXmwrrYBBK6Bkqg2nah1yrP/Bijq4vKIc1yfN70Awrt8fUM7zW0pPwC+KuGfYFfD4RTzzQfA7BSGYUEYrMFTbPThV5wqrJQwE+y5Br8HcDaU4Y/eiaH2pokZkncunOFfDgHX3DsOamUPxp0mDw+RaKgSvFYJ5BtKTg+WoVjYWnM/JsOKpO7IAAA1uHzK6JoTJ7hm7V1b+Qq97xuFpdV+0hVxFiisMTYDT2fCLXNXDTKpLTXR+nF5RriIAnPfyc3rFVl+rLVID3QdgKOd8Med8Q+NrMYBhje/FNKLIcazGga9P1sPjF8MG6uU7DsuDKHA+bfOBYzVYf+8waBlTncAZA5JMOkUMgsSJWhfSGnfSlu84jKWF2WHKXsnOIyjOz8LWuSOweU4euiXq8VBIeupIhWZ/qnOhvLJBVgJTEw3onZyA1ERDp5zUCYIgWgoLGY9Dla+cDCtWTbPh+Xty0Mtqwuv7jsPlDeCswxe2APvl5i8h8vMLfjVF8vGth8AYVMdgIBjzEbpRGMrYrDT0sppgTdAhyaTDC5NzFON/yVQbdBrArNdg85w8rJn5/9k78zApynPt/97qfZl9A5mRdVhGHISR1Y0lQYic8CmLAoMCsglIYhD0xEOWQ8yHIscToiwSBWRREJJjgkE9QTGJiJphCxlAZHOGbYZh1t67q74/qqvonu4RRfiUoe/r4nLsrq6u7nr7fZ/nfe7nvnvqQgKR0DYKHx2YzwWXn7Erd3HbM+/zwxc+1NeAr4Lmsk5otENN/OfYeTfVbj/392rNifNudp+o4vG7O1HrCWA1CQZ0acH//usMy4qLGFyQTVBW8AZCevKk3cPsJAsC9ERqxfgiNk7tQ4rNzMTVn1JZ76OywcdnZ+owSAKL0YBBEiwaWcirH52I2szVoN27LKeFNpn2qPu/NLz+Rx5rMkoqjfnNA1SEk9RIaP11gaBMRli/YMmY7mQmmVkwvCu/HXMLbn+I1z45SUhR4sY6mU2wkbyBrx+oQuy4Aqis93Gq2k1lve8rj08NzbGvsCmFyK/73STw3UVTRaLLSfKvBgVUBm4ATjZ6vGX4uWaNGo+fc3Ve5r95IK5Be2WDjwZfUPfjk2WFkCxzT7dWjH/lExaNLIwxb100spBztT5GrfiIv80bEF8BSBJ0z0tlT1kNz759mNen9kGWFWQFaj1+NpWUs6lENQ7+YG5/5JDC4lHddEW5ptRLq1x+frxxb0JtK4EEErjuYDYIXp3Uiwsu1UNtcEE2lfX+uFRQWWl6I81kkBhckM2Iojzd5LvxMU2puwnQq1A3pFijKJqa8ueYlRfpQM+P7qZ7FGYlWUi2Gfj8nCvKA/A3D9zCC2O765uAWqKw6J1DjCjKi+kjvx4VF9NsJl0RU7tvczbt4/n7VRGXxaO7UVGvVrM8/hDpDjMt0xz8dvtnzL27M8Zw8qbdr+feOczzo28h3WGmos7Hi2O74/aH9LX+Lz+5i2l3tKFFsoVN03pT5Qryn3/6F08M7YI3XBWc3r89IVmOoekuHtWNRe8cYt6QThglwfrJvVEUVZUz02nmV/d2pbz6ojWJxx/Sq19Nrf0mo0RlvS/K6mHxqG6kOUwEwpWm+cNUZdNqV+w5mlYrvPR3L8sKNR4/Hn+IkKJqKWQ6Lm4mXAn6ZnPqV9VgMsT/zhM+gM0HpiZ+V6bL2Gi7GhXAHwPbhRDbhBAvhf+9DWwHfnQV3u87BU/EhB656wfqTXpxbA+cFiO5aTZ+/VYpAxd/wLHzbmaEaZ3Pvn1YlfYe3pWNU/uwYHhXfRFfNaEnJoNgaZwK4tNvlYblxdUkMxhSuHPRDopf/piQrO5Ya8cbJMGJ825AVYl6duTNtEi26FQP7bhlxUUkW41kOS3X9K5YAgkk0HyhUe4vtxLQ1Dkr6r3U+4KcrHLz9FsHeeiVT5g1MJ8nhnbWZfFXjC9i8ahu+IMyJoPQDbIjkZtmw2SARwfms2BrKUcqGuIeo/VSRc7Bi0YWcrbOq1ehHlm/m3pvgPnDCtg4tQ/zh92krx2gJmuPbdpHXrods1Hix6/vxeWTdZN57Zgfvb6XNLuZVyf1Ysfc/qye2IsX3jvCu6UVTdJMr7c1oNoTIDvJwqyI+1bZ4NOFYWo9AQRqpSvVbsJuNtAi2cq7pRVccPkxGQSZSWYynWrV7D+H30ROipWn3yolyWZQTdnDsUL3vFSSrQZ+eEsuD6z8mJAseGRdCe+WVnCmxoPLG9StQr7//N+Y/z8HWDC8K9t/chcbp/bBbjYwoiiPVR8e51SNl7vCa7/DYqTaFaDGrdpB/XL4TdR6gpxvuNgrGi9OWTG+CEVWYvQI5ryxjwZvEIHQmUd2s0FXSo08h8UoxcQUz4/uhs385QmWxqI6fLae+1/axZ3P7uC+pTujqtBXgr6p9RVGXt+12q+qwSBE3DnkciwCEvhuwijFv8fGy0gAr4YK6NtCiI6olM9IEZhPFUW57BVECPEYMBm1z/WfwETUquLrQAZQAoxXFOWKEbgvRyEqFEH1iTRo79wiidM1Huq9AUwGCafFSGW9eqmNF9wGb5C8dBsWowGzQRBSQFZk8tJVdblMpznK50Vr9n749nb6YDhTe3FinP36HhbedzOBkEKbTDsC6NwyiQZvALc/hNsfwh9USLGrlhIAqXYzDb4Aqz48zrwhnS45aSeQQAIJ/P/G1RByiHfOF8f2QBLg8oe4Md2my/FHVgFXT+xJTopF92bVHl9eXN0D2NQAACAASURBVERIRu/d1gLuxhXEF9/7nLF9bmTB8K7YzQbc/hCZSRYWvX0IuLjZ94fdpxhUkINBElHrjYbyag8hWSE7ycKv/k9X5CYoQ5X1PhwWIx0yHZxr8PHw7e0YUZRHICQ3u8rI5cAfVD39ZkTctxfGdsdkEDwzopAadwCbWaVnJllNgEKq3aRTdYMh1evRF5RZsv0Ij9/dCVmBVJsZf1DBFwjoyd/Pf1iANyjrKq1y2PJh0chC8tJsSFK08XpWkpnWGXZd3j/S39cRIfgyY/1u1k7qxW+2f8bE29riDypMX1fCqgk9dTXT6f3bk2w1snpiLwxCpT1X1vtwmeJTJB0Wo25b5bQYKXd7dKVULS5x+0PIskKqwxw1ntMcZpItprjftxZveQJBZAVWfXi8ySr0laBvfl0l0msBvtDF/lEtPnz27cMsGXPLt31pCVwheEMyf9h9Sqfyh2SFlX89xqxBHb72ua6KCiiQCxxSFGWXEKINcCtQD/zrck4mhGgFzAYKFEXxCCE2AQ8APwCeVxTldSHEctQewyviNXi5gYXVFEsrMBskTAZBhtPMqWovJgOcqHLzix8W8Is/luoUjMZBxeCCbOYN6cz5Bj9ZSRbKLrjZ9s8zTO/fPoqiA+oC3SrNxoYpvZFlWPr+5/pz5dUeWqXZGf/yx1G00gynmV1HK+nfOYe8NBsPrPyYBcO7kuE089IHR9l5rEr3/vn9jH5X4muN+z03JxnmBBJI4P8froZBdLxzztywW7fYWTquB08M7czjjUQvJqz6lJcfKiKtUdAblOWoHiltY3DhfTeTl27n0Nl6fRPvSEUD0/u3p12Kg2OVLlZ+cIwxvVrz8x/ehCRUq54H+10UlNEC+cZrwbFKF1aTRGaSBYTQ6adaULilpIy8sDhMea2HE+fdLNl+hMoGHy+M7c7iUd2iFB2v9crI5cBklPD6oxONDIeZQEhhzc7jTLytLTem25AVBbMBFCTW7jzO0nE9eGvfKXq1SUUGfH5Zt2+YP6yA6f3b8+Arn7DwvpvJTbMxvX97AkG1XUNLCC1GiV/8sAC3P8QDKz9mWXERLZPNbJ7eF4MkqKjzxViBvLnnFP6gTJLVxLqHe2GQhCr/L2DibW1pkWzVRWlCsswLY7tHMZY0vQBZUe0p4rWw5KbZSLObqaz3say4CH9IJs1h0ttWpq0t0dlDMjBx1acxr980rS8tkq1R63y8eOuZEYVU1vt1MZvIBM9klOJem8n49UhtX0eJ9FqASZLISor+nWYlmTFejklcAt9JWAwS9/ZoFeUEsGhkIRbDd0MF9EngA2CXEGIy8DYwFNgkhPjJNzi1EbAJIYyAHTgDDAQ2h59fA/yfb3D+KFwuxSDTYdFpBd3zUpk3pBPz3zzAySo3VQ2qv9/9L+1i/psHcPtD/PQHXVi+4yjPj+7G7EH5UeIAj/RvT2W9j8ff2MegxR8w/80DjOvTmo2fnIxLLf3PP/2Lsgsefv3nUoZ3bxVF+zxx3hUjOnCq2svIW29k7ub9KGFKR166jRfeO8KgghzKqz16dTIQvPLtm9qkf+/SD7ntmfe5d+nXExtIIIEErm9cDSGHps6pzYUz1u+mZVjuv/ExdrOJCas+ZeLqT7n/pV1MXP0pS9//HEs4YI2EQRIcq3SxYGupHuTuKathwdZSjlW6mLj6UzaVlLPtn2dw+YKcOO/is3MNevIH8UW/nhlRyJLtR1RhGQSyIuv00/tf2sWCraU8fncnyms8jFrxEQOeU9eWx+/uRJbTwqwNe5CE2kd2rSt5fhMYJaGrempWCbIC//fPB3moX1vmbt5PrTvIH0pOoSAIBGV6tMnghfeOMKZ3G/5+pJIGbwhJEtyYYWdEUR6nary6VYfDYuD50d3IcKg0UUVRGFyQzeLR3ajzBKIEhVJtRmo8QWo9ftz+kF4pBHXcrdl5nHF9WjP/zQP0f24HT/7+nwAs3HaI8S9/QpLVhM1soGWqlQ/m9sduNpJsNcUIFj2yfjcXXIEmW1iWFxdR6/Hz2Ka9/Ox/DpBiM+mbzWsn9eIvP7mT50Z1IxgKUVEXX1zmdI2Hg2frCEbEFPHirSe27NfbWrT316rQV5IG15xgMgidsqz91mcNzMeU6AFsNriSSq9XowI4HihATdJOAO0URakUQjiAj4H/+ronVBTllBDiOeALwAO8i0r5rFEUJRg+rJyLlNNvjMsNLDRawcapffAHVTnw8moPLVNsFIcrcNq55m7ez+tT+7BoVCEhWSErKTqoyHRa9eZ+7TXaTnQk3aLGE0AS8G5pBaVn6pk/rED3hlqwtZRl43rwszeji69qsKLSV7Kc6g7Y5ul9URSorPeTartIZfkq9J/LqeRdjd37BBJI4PrB1RByaOqcmpR+ebUHScRvxA/JSpQhtqyoNMBf/ulfUbTP2YPymbtZ7SNsTAddXlzE/P85AKi929P7t+dklZv5bx6I8pMDdNGv16b04XSNJ6olAKDGrbJHGlsHnar26l5h2mPamjFtbQlpDhNWo0ROSnTSej3B4w8hBCwe1Q23P8QTW/az7uHevFtaQWW9n0UjC7FbDPRsl4E3EMISHjcP9m2D2Sjo3jqDs7VespIsun1DKLxODi7IpkWKlXpvEElIKEC9J8CjgzpiNAgUJFLtF1tDDJKg2hUgw2nWq3iRGFGUx8wNu2PiC+1+Tl9XwnOjuhGSFX2sbZ7et4lNDPW307iF5VilizS7ifvD1ibl1R6MEswamM8L7x1hRFEeGQ4zrVKthGQQgiiaqUYNNUgwbW0Jr0/pg9kkkelomtKpVZ0bV6E9/lBcquMLY7uD44oPhWsG3qAc0xM8Y/1uXp/a51u+sgSuFIKN1hhNyDF0GQng1agLhxRF8QA1qMlaFYCiKK7LPaEQIg3VWL4tqsKoAxjyNV4/VQjxDyHEPyorK7/Sa7QgIBJfNbCQJIHZaKCqkSlvvAkuKCu4/SEWv3uY8upoj0C5if6ODIdZFwXQdnlO13r157Wd6vxsJ/OHFeANyFQ2RHvv5KbZcPvVZHbekE6MWbmLkcs/YuLqT/nFDwtolWbltSl9yM928tqUPqRajU0KLVxuJa85yjBfCVzOeE0ggW8T39aYvRpCDvHO+cyIQpbvOEr3vFRWTehJICSzfnJvBhdk68esmnArFpPEvCGd9B14b0Dmxxv3RvnJbZ7el3ZZDsqrPVFB9sapfXh9ah8ynGayksysGF/EolGFCIGeDMSzgahs8OENhJjzxr4Y/7cqlz9uwtCUWmn7sN8sCGRFabZsjK8yXs1GA4oCL//9GHnpF5N9zRqizhvk6bdKaZliRVHAEwiCAk/+/p/ICnqyZ5AEf/usgkynBbc/xJaSMn42rIBASOG5dw5TWe/FbJTwBFR/L4Hgiyo3SVaTfq8DIYVMp5kLLn9cr8emvAFTbSb970ynOYph1JRnpBYXwMWKtCQES7YfiZKg756XyvmGAG/tO8XMAflsKSkjEJKp9QQpfvljTtd4eHFs96jfw/w3D2CQJLKcFk7VeHRxF1OcCnlumo2WKVb+Om8Av5/RL6oKbTYadFGc+1/axbS1JVQ2+Jp1n+pXGbMJG4jmD5sxeo1ZsLWUeUM6Yf2a9Ge4OgngbiHEBuD3qMqfa4QQ44QQLwOll3nO7wHHFUWpVBQlED73bUBqmBIKat/hqXgvVhTlJUVRblUU5dasrKyv9IbfNLDIcJjJTrLor9d2wyKhSXy/8N4R5t7dmRS7OSqo0BqtG78m0gdocEE2r07qRXaSqkg37Y42pDvUXoGQrLClpAyTUbB4VLQal0qfsNLgC0aVk7VqYNkFD2NW7mLg4g8Ys3IXhysaeOoP++MmeJdLl21q0m/Ok/hXweWM1wQS+DbxbY3Zq2EQ3fic6yf3Zs3O4wA6pX/g4g8Y97uP+dGgjnz07wPZNK0PvqDCySp31HwamWhpm3ZPv3UQgxBsnt6XFeOLALUiMueNfZw47+Kdf57m0UEdWbC1lO/9118Z//InpIQZGfFoec+MKGTlX4/xzIhCBhdks2J8EZun9+XVSb3YXnoubqDflFpp2QUPI5d/xIRVn4QTzmvXFPvL8FXGa4bDjM0s8aNBHTnfoH6HZ2o9OvUwO8nCu6UVCNTv0xtQRVyynBZkWcEoCbaUlGGUYFi3Vuw4dJa8dBszB3RAQR2fGpUURSEn2RLeFFZFY6wmod9TgyRQQDeff2ZEIdPuaMP/PnYn7825i5Yp1rj3U6ta56bZMDTyEI6r/llcRJrDFDO+FBSeGNqZM7Ve/bnp/dszfV0JPdpk8OL7R3ioX1uSrSaWbP+M+cMKyEm2IoSIoavNWL+b2YPyqfEE9FjBKIm48VbLFBs3ptvJToruGWyOCp6XwlcZsyZD/JjKeBn9YQl8NxFoggIa+I5QQCcDo1DVOjejqoGOBQ4DL17mOb8A+ggh7KhVxUHAP4D3gZGoSqAPAW9+oyuPwDdViJIkwQ0p6oQ6bV0Jp2s8Mepwz4/uRo3bz0P92kY1dL44tgePDsynzhNgWXERj0T4/awoLsJuNvDalN6YDBLnG/x6M/jggmweHdQx5lzrd53kSEWDXjJulWbDIIEig8mgMH9YAct3HGVPWQ3T+7fngisQQw/SlMMevr0dsqJQ6wlQXuPGZjIiy7EmsJeq5MmyQoM3GON52Nwn8QQSSODK4moIOUSes6LOy5hercnPdnCkwhXln/qb7Z8xb0gXQLWiiKTtdc9L1Tfjqlx+3Yx73pBOPBDh2/fMiELW7DzOw7e3Y+G2Q0zv316f80GdSxduO8jScT144b0jKIrChim9kYTad3aqxsORigb+evgcPxrUUe8Py01TfdveO3iWpeN66NSw3DQbaQ5TzHq0aGQhz759WH/Pxzbt4/WpfQjJvutWnMvtl8lKNmPxqJYGK/92jBkDOuj2TJrwSLrDhKygC7ldcPlpkWJl4m1tcftlHlmvtm48+/YhfvqDAnxBGYtR0u1EFARKxIZvVpKZkAxrdh7XPQUxSGwpKePh29vhCwQYdkuuvtYPLsiOiRW0+6n9fbbOG0Vb3lNWw5qdx1k1oScXXCpV2GgQ/GpraRS9bM3O40y6vR0tUqw89vpeXSDohlSbXmUcUZTHmp3H+dm/FfBQv7Y8sWU/L47tTrIt+vegUUFvSLXx2+1HAHWsefyhrxVvXUkFz+YkRGc3i5hxsKy4CLv52vw8CcQipMSngMrKdyABDPfkvRbx0M7wv29yzo+FEJuB3UAQ2AO8BLwFvC6E+FX4sZe/yfs0xjcNLIxGiRYpFtY93BuXP4jJIKLU4awmA7WegG7QC9F9ftPWljC6KFftJwwpnKvzEpBlfvbmAR7q1xZ/UI5K1EYU5cUEDtq5NpWU6wpdG6f2oaLOr/cMRCaKqTbVz6jx4NpTVkOtJ8DCbarZ7OMRCnErxhcxuCCbd0sr9M8+uCAbIQSnqt1xJ9Uql5q4Rg5ktz9ETrLlmp18E0gggWsXTQWCIUVVOqx2B/X5NjfNxgtju+MLyHq1LDfNxquTekUpOmvG6hkOM4tHd6PBF4zp0Xliy35WTejJvM372VNWE9eH793SCn45/CZmD+rIku2f6UF2ZLCfm2aP6Rmf88Y+1j3cm6Xvf87C+27mhlQbJoOEJKCi3svC+27GZJBolWbj0Q17dPqo9vpT4XN8U2uNaxHnG3xIAjx+mWffPsSMAR2Yckd7Mh1mMhwWJAEriotQFAUFkITQhdz6tctg1sAO/GH3KWYO7KAnSpX1fkKKwtlaj55AzR9WQLXbT4sUC8uKizBIgl8Ov4mKOh8Tb2tLrSdAktWAEDBjQAdSbEZMBhv/+ad/Ra3TW/eWs2FKH87UeMgJK37+9wO3UO3yk+Y0U1HrY9m4Hno/aG6ajYm3tWXe5v0A/Ofwm/jte0dixpbmB2yUBJUNPhZuO8T8YQWkhy0vAiGZDIeZEUV5eAMXewydVhPn6nxxFc5z02z89/236N6KZqMBSRJkOMz6b7DK5b9kEvhNN36uho3MtwmXT2br3vIoi4DN//iCB/u1JdX+bV9dAlcCFoNKAY0snCwaWYj5Mqq8VzwBFELsRqVovqYoytErdV5FUX4O/LzRw8dQK4zfGr5s90iWFc7W+nQPnkxndGUrpCgkWeOb7mrc/RqPH0kSnDjXQF66StGZeJtKG2ksCNCUgW+HLCfd81KpbPDx/OhufHHBHVNCnrlhN69N6YMk1ORMs5mI3KGucvmZ3r99zGunrS1h49Q+TLq9HQJ1IbSZDYxe8VGTk6rW/6e9XsOHTwy4rpu4E0gggSuPS+3yNw4EBxdk89Q9BaqUPoACj2yI3lyrjsOUWLjtICuKi6io97Fm5/GYYHp5cRFZTktMb3itJ3BRuCXc5xcrMAPT15Uwf1iBbimgBf+rPjzOT+8piDv/CwFDb26JySBxsspNusPEL/5YGtUruGla37h94pE0vetNnMsbCGGQBDXuACOK8lj6/uc81K8tD6xUxdz+/kR/zEaBoqjevUlWI20y7WQ5LUy5sx0GSXBvj1YEQor+Xc4elM8XVW5e++QkT92j2iyk2ky4fEECQTNb95Yz+c72eAMyMzfsURPJQfko+jXJOC0Co6TwUL+2rNl5XN9guL9Xa0wGmPPGPhYM74rVJHG6xsOzbx/mV//nJhp8QYwGE6sn9sJiFJyr82E0qEnd/GEFemJYWe9n4X03c2O6HSEE5dVutu4tZ1TPG1k0spBVHx4n2WpECHhmhBp4ZoZbR+q9AX0Mmg2CVLuRpeN6UNXgj+o/LK/28OONe3lhTHcsJvX3qBnAn6xy65vkrTPstMlwXLVkrLkJ0QkBQwtv0MV83P4QQwtvIOED33wgK8SlgL4xre/XPtfVoICmAanA+0KIs6jVwI2Kopy+Cu/1reJSu0dVLr9OxxlRlBd10wC9GteU4tzggmwev7sT5+t9UTvPy4qLdB+/VRN6smT7EfaU1TQZOFQ2+PjP4TfhDciYjAJro14AUAfRuTov3kAopiL5xJb9rJ/cmxff+5wRRblxX+sLynpVcNWEnjy2ae+XTqpXQ70vgQQSSKAxvsouf5XLz/P/q4qxtE634bCYuODy4/YHyUoykxZHZCOekMq7pRX86HsdyUu3MaIoLybonb6uhAXDuzJx9af6a3LTVA/Xv/zkLuq9AQSwamJPFr19sXqYlWShLpyM3ZBijUksnxlRqFtNNJ5TJSGi149xPXSvMK2tINup2hc19mF77p2LlNDrTZxLCldQUu0mHBZjzP30BxUmrv4Hv3+kLw6LkaCsYDUZmDekE7WeABajxNzN+1k2rgcriov4zfbPeGJoFx7ftI8nh3bm/YNnWVZchMsXxGSQCMoKPdpk4A2ovpH92mUwvX976r0BMh1mvKEgOckWFAUUBH89fI6ZA/KjmDwriotYPbGnasSuKPzyj6pAhNsfihoDz4/uBgg27PqCVyepe+ja58rPdnJDqk1l8NR42HHoHOP7teWBl3aFK5v5VDX4cftl1uw8zi+H34QvKJOVZOFYpUsfg0IIztT4eO2Tk8wb0jlu3KDqGailqfMNPly+6Cr7opGFpNpNpDuuTjLW3IToDGFaeON7bUhkgM0G/lD8lqtA6OtbtV2NztBqRVEeVxTlRmAOkI8qDPO+EGLqVXi/bw2XEj+JnFyaqs6ZjIIVxUVRzczLi4sobJXMU/cUIAkpRsL7kXUleAMy3/uvvzL/zQPMG9KJ0UW5pNvNLG90rkUjC1EURfX3cfuZtWGPblbfPS+VFeOL2Di1j65sZzJIca8zJCtMuVPtA4jXZFwRoTTXlMJc5KTaVBN3ms3UpNpoAgkkkMDXxVcRqZJlmYf6tWVLSRlVrgBjVu5i+Isf4g3IVLuCuu1DJJoSUimv9nC00tWkMuONGfaoee/Fsd05Ve1hwqpPuHfpTma9tocGb5A5g1Wlt5HLP+Lpt0pJsprYPL0vKXZzTGL5xJb9yLIS1xvtbK03ev1Yv5v5w27i/Tl38fzoW2iRYqHaEyDZamTTtL589O8DWTC8a5SdxPW4OWeUBBajRJ03SNkFt34/tXVTCLXnzxuUefqtUoyS2sc3d/N+qlx+FNTvu8EXxGKSGNOrNUZJkJVkxmKSuL1jDlv3ltM6w066w4whTIEMyQqyIlPctzUPvvIJ9yz5OwFZQVEEbp+6hgaCIe7v1TrG+mHauhLsZiNVDX5+Ga7y3pBq03s9teMe27SPlqlWZn8vn4XbDnKkokGPCYr7tmbhtoNUuXxkOk0M69ZKH0ODCnKYsX43drOBlz44ylP3dMEfVDhe6WbDrhO0ybSzdFwPBhdkIysKdrOBd0srOBpODCORm2bTN2AOn6vnX6frYmKduZv34/FfvWTsm6i9fxcRlJW49/pyPOIS+G7CIMWuRZG/pa+Dq1EB1KEoyt+AvwkhHgW+D9yP2rvXLHCp3SMR4RWlVeciPXEUoM4T5I97y3l1Ui8uuPx4AyFCssyoFWovx6W8erRJcv3k3oz73cdkOS0sGN6VNpl2JCE4XePBaTHqtFJtQXphbHc8/lAUj3jxqG4EQnLcXWQB1HoCBEJyjHCLlmRqaKoSGTmpxmviTrOZOFLZcFX5+M2p4TuBBBK4NCLn6UghCn8wRCAQotLlJxCS8QdlnV6v9SZr82idJxAz76U7TKry8tqLgguRVbPFo7vFnQerXX5WT+yFySAIhBTc/iA/3rgnKmh79LU9qtCI08LC+26mZaqNzysaWLL9CE/d0yXumnDBHcBuNkT1mWclWZj7xv6YY0/XqL19qyf25GydL+ozrBx/K60z7Dol9HoV5zIZBV6/as2gUjE7MO2ONvygsBUzN+xm0chC5g3pRDCk8G5pBQ/2bUNeup3yatVE/YWx3clNs+GwGJmw6lM9eVw8uhsPvvIJi0d1o0ebDE5Ve9j0aRlzh3SibZYdr1/GaDAyafXFfk5JgMUkUecNkGE2EZSJSuw1aGyczi2dPHVPl6hEtPFxWn/n4lHdSLEZWVFcRH24R3X+sAJmbdjDwvtu5snf/5P5wwp05VMtnlEVYgWV9T7sZgMr/naCOzpm8+pHJ3hyaBcEQt8k0RRHG/etmg2SvkHTuKVFu85QnNzlSq3j2kZ045jjWh3rkTYdGrQN/ASaB0ySiBuDm74jCeBnjR9QFCUEvB3+12xwKRqjIcyRf2LLfpbvOMorE26lqsEfc+P6d87RlTxXjC+KomBqEt7xKKIaVN6+WoErr/YwcfWn5KbZdCP4F8eqO3JaYlZR7yPFZorhEc95Yx+LRhbGTNTPj+7G+QY/F9yqQbzWBB5pwPrUPV3061m+42jMAF0xvoi0cF+jhsZN3JX1vqvKx29uDd8JJJDApaHN042FKDTV5CjFvHE96Ncug+HdW0XNgYtHdSPNYYpKrmxmAx5/iNen9sEflAnJCoveOaRXzd7ad5rlxUVMb6TibDaKKOGY9ZN7xw3aMp3mGOGMZ0YU4g2E4q4Jp2vUxGP2oHwynGZqKl1UuwJf2ttXdsET08c4Ze0/+P0j/a6IwuK1jDSbhTKvW698eQMhxvZpo2+0tkyx8es/lzJ/2E3kptnYc7KatpkO9e+yGoSAZeN6kGwzRYmdCYGeRGmiJzuPVbH3i2puSLOT7jDhD0YH8oqi9v7M2rCHNZN6IQmaHAfn6rzkJFt4+q2DVDb4eH1K020m2rr/3KhuWE0SLZKtZDkttA/7VGqMoOU7jrJ4VDdS7SYGF2STbDXy1D0FVNb7qHL5MYftB0wGicp6Nek0GgTpDpMeCzz3zuHw5rQDTa8i3W6myqX2IGq2WY2v02qKJqpdyXX8SqqJfhdglETc79BwjX6eBGJhNgoykyxRa1FmkgWz8evf4ytOAVUU5YErfc7vKi7lRSNJkt6s/+TQztjNxrjNmy2SrU1SRZfvOBpDEV00slCXFNcea+y5V16tmvpmOS3M3LCbfx/ahS0lZSwvLuLmVsnckGKNG3RIQrBm53FendSLP8zox4LhXUmxm0iyGVi+4yg1nkBcA9ZIb0L1/02sn9ybzdP7Mn9YAb/5y2ccqWz4Ukrn1ebjX65fYQIJJHDtQpunNYVG7fcfTzX5kfW7mXpX+xiK5Zw39mE2GGiTYSfDaSY/x8nv/nqcmRv20OALYJAEm//xBTMH5OtUulvbpuueaJun92X95N6EFLVvLPLcTfm92szGuFRPgyTiegFqas0TV39KVYOfaWtL+PWfD7JsXI+4x0LTdH1vINQsAuJvAkkSGCXBtDva0CknCZMkUVnvI8tp4ec/LADUMXS21suK4h7cV5SrC6MMLshGUcDlD2GOMG5euO0QioJeFUt3mDlT7WJ5cREdWyQzbW2JWgFsRPMKygrBkEyW06Kr/VmMhriUX0VRqPMEmTO4I+XVHs7UemOOixwD2mbDrA17sBgF84Z0ouyCukGiret7ymqwmiSsJolHB3Vk7ub9uin9lpIy0sKJntsf0oVuvIEQLVOt3JhuZ/3k3vx2bHe6tEzi02PnGbvyYwxCcMHjp94XZMHWUn6yaV/Mda588FYyG/X/Xel1XNuIbpVmJyvp2lYhN4arQ43HhPEa/kwJRMPjlzl0uob8HCctUqzk5zg5dLoGj//r9wBeDRXQn3zZ84qi/NeVfs9vC5faPcpwmHns+530yeovP7kr7mIrJKGLuTSmT+4pqyHdaYqiiNrNhih6zvLiIpZsjy685qapiqGP392J5945jEESjOnVmvn/c4DKBh/rJ/eOu1PUKs3GmF6tmbNpX1T/x9qHe/Hk0M7IiqL7AGm7b8uLi6j3BnRJ8RpPgMp6P0/+viTq/KVn6mOqeZFUjkjKbOQ1XSk+fnNr+E4ggQQuDW2edliik52m+rKNhvgiWRdcfp5+6yA//UEXWqQYmDmwAzMHduCCy0eyzcTInjdS4wqwdlIvjAZJt2TQ7HFyPD+SHwAAIABJREFU02ysntgz5tx1nkCMT9+ycT0wSvGvQxKCVR8eY9WEnggBZRc8Mf16NZ4A3fNSmT0oH7vFyOqJvbCbJQ6fbYg6VqPoNZ5zQ4rCnc/suO5ZEg6LxLBbcjl+3kXrDDveQIif/qALHn+IVJu6xm/6tIzHh3Skos6Pw2Jgzc7jPDlUZcTkJFsIRRg3zx9WwMJtB3lhbHeqXQGcFonbOmajfbNqZdBPqzRblJ+bJAQGI+FEUrV/SHeaeXzTvrhsHLvZgMOirpu//vNBFo0qZMHwrrqSeOPxopnEK2GFQU3s5cMjFbqXcbLVhMsn69dUUe9jS0kZD/Vry9L3P2fibW3Jz3EQCCkse/8oswZ14GytL2ZcF96YqivhaqyjLKeFPWU1PPu2WiVsl+XAZjaQ6YhNyL7uOn49tX34gjLPvn04Zkz8Zswt3/alJXCFYDQIWmcm8cBLF71kl47rgfEyKoBXgwL6HLAX2Ab4gOb5Swvjy7xoGieIUhMJzuGz9SzYWsqikYX8YfepGPqkLygTkmXqvUHsZgP+oGoEXOsJcrrGQ4bTxKMD8xnTq7VeEk5zmPjlH0upbPCxYHhXZAVdea57XioVdb4YetKikYWgKFEKdaBOrnWeIPe/tIvcNBuvTLiVDVN6g6JOOJ5AiJkb9kR9rqZ6FyMn6XjS642v6Ury8RPKowkkcH1CkgQ2kzHq9994s01LmJqiUXkDIX7+wwI8/pCe3GkbYEYhOF/v0+ftpuY/rYE/shc8yWpi4ycno4K23753RLcJaHwdbn9I92/LSjLz6KCOURuC2jrS2Cvqv++/hTSHKerYdIeJxaO68fLfj0UpjpoMgu55qewpq7mmZfG/CWRZ0RMerQfQYjTQIsWqJ2FZSRaG3twSX0Bh+roSFo0s5Edh2wYhwChJ+IMXVfs0L0CDEDgtRmRF4PYFSbaZ9PjAG5Bx+2ROVNbx+tQ+hGRFpUwqQr+fqTYzswZ1oLLBx/IdR/WxNHtQPjnJFuq8QewmdV2rbPDhtBjpmONEVhSykixRY2DpuB7UedXfQiDcQ6aJvbw6qReeQIhXJ/VCCluiaJ9FQmH2oI78aW85c+/ujEESKIogFJIZenNL/EGFGet3k+W0sGhkIS2SrYQUsJokfvqDLlxw+3UWlOZ7rFWw/zq3P6KJ0NHUhNqtyRhLaLve2j6MBklX+NWQlWTGKF0NvccEvg0EQgpv7TsV1+vx6+JqJIDdgTHAPUAJqg3EdkW5DJv6ZoDIBFGWlSZFA7SJcMOU3hglVRnUGTZffXXnce4tyqVDtpNASMYgCSrqfIRkBbNB4kKD2g8YKf373/ffwrwhnZCE4IZUGxdc6oTfPS+Vx+/uxGOb9uqCMTdm2Kms92E1SZSFX994ck21m3hzZj+8AZlT1V7yc1SzvomrP2X1xJ68NL6IqRGfS6OOfFmy1ZjKoe2Ub5rWF0VR9N06UPsDEw3fCSSQwOUiw2GOmn+3lJTpxthZToueMGkBa2Ty9MyIQiRBXO+/6etKeH1qnyh6f1O92yZJ8OLY7viDsq7Wp/UYLtx2KMqI/dGB+THXsbxYVZ+s8wR4cmhn3P4QOUlmXp/ah2p3AJcvSOsMO48MaM/4lz+Jus4fb9zLwvtuZv6wAtpnOSi74OEXfywlP9vJrIH5UZWaRSMLeXbkzZxv8PPs24evS5ZElcuvi2oMKshh/UcnGNO7Df6gzIiiPBZsLeXpe1XBtVD4uD/sPsWsQR04WuGiXZaDijofOSkXe9tqPAF++oMuNPiCeAMygZCCJyATCPnJSbbw/OhuCCH4095T9O+cwwMv7SLLaWHJmO4oivoe3fNSGVSQg6IorJ7Yk8qIjYfcNBu/eeAWXaxFu5cCVT3wf/95hh8U3sD6yb31/r0X3jvClDva8cLY7vrmh1YdN4Sr0C2SrRgNghSbav4+e0B7OrZIxhcMcU+3Vkxc/an+fq9N6c2NGXYE6HRZjz/E+Fcu9r2uKC7ijX+cA6J9j0H9nRwMb4rHS9Y0mmNjLYV4NMfm5vN3KRgldd54JIZN8G1fWQJXCiaDiPnNLR3XA5Phu9EDuE9RlCcVRbkFeBkYDpQKIX54pd/rWoMkCXKSLKyd1Iv35tzF/GEFUVSM8moPsgyjV+zint/+nUNn6/GHZH5QeANGyUAwJGMzGzjf4OPHG/dy/0u7mP/mAexmQ4x88o837sUbkLn/pV2MWbkLWbmogKf1lWi7bQ+98gkZDjO//GMpz759mKVxekY0GfKF2w4x/80D1LiDGMJUqQmrPiUnxcKGiJ6/jZ+cjLGkaJxsxaNyvFtagaIoOh8fVInoe5d+yG3PvM+9Sz/k8Ln6y7KHiKzIfvjEAP4w47ZmuxOYQAIJREOSBJkOszo/Te3DiKI8ZEVhwfCu/NfobnpAGUlF2z7nLl6b0oc1O48jCdFkz1wwFC3asXzHUV18a8X4IjZP78urk3rxl9KzZDotMVLtc97Yx5zBHfXX56bZOF3r5Q+7T0X1Ui/Z/hluX5Bn3z7MnDf2ke4w0eAL4Q/KOMwGWqZYUBQFqQmvV6vJwLS1JdS4A0xc/Sl7ymr0ak/k9ajfhRdvQGbekE7YzNcfS8IfDEUlRD3aZCArCpUNPjIcZirr/XgCMkZJQgj1ng0qyKGizseS7UcAkBWFqgaf3pe1fMdRWqRYmbt5P3azgZCskOlUhWB8QZkUu4lUu5me7TJ4ZL26UTG9f3vqPGqf6bQ72vCLH97ElpIyqt3qY411BX70+l5EuMK4YHhXbGYD8988QIMvyK1tM/nX6XrG/e5jRi7/iGlrS3i3tILHNu0j06lWfpeO64HbH2JwQTYGSbClpIwMpxmnxUitJ8ALY7vTLz+Lynof3oAcNXaynBYCIYUzNWry+NMfdMFpiRWcm7auhEEFOcDFqrb2t9af2FRvn8cf0mmOG6f2Yf6wAp59+3Bcu4jrre0jJBNrG7Z+N5dhEZfAdxSBkBIzX89Yv5tAPLncS+Cq2UAIIbJQq4E3A+VAxdV6r2sJkiSo8wZwWEws2Foas0Os7bhBWADmwSIafCFdNW7VhJ4xO9B13mDcSS7VbtL/fvS1PTw3qhuZzvjeVG5/iD1lNTotKJKOpCWpU+9sz/T+7Zm2toTp60pYP7m3ThPy+mVy0+zYzEZapljpcWMaaTbTl6prfRVK5pXewfsyym4CCSTQvCFJEgu2luoUzCSribN1Pl0RUYO2OfaXn9zJuo+OM3NAPhfCgWhTPXONe7f3nKxi9qCOUZT2ZeN6NGnk2zIletPtuXcO88TQzoz73cdRx5eeqWf95N6crvHgDcjM3PBJ1PlNBsHJC/GZHCk2Exun9iEn2ao/31QvpGY1NOeNfbwxrS+yrETN3829t8psNGCQFJYVF3G+Xk36/MEQVpNEut3M7EH5VLsC5KVbqfeFWF5cREhWOFvn1SmWbn8Iq8nMs28f1NfUULiSV+MJ4LQYcViMbCkpY/6wmxizUjVbnzGgQxRttNrtp0WKRVchXTSyEI8/FLPxANpGsoIvKOMPybof4JherZn/5oEm7RbO1nrJTrbw1r5TPHRbW566p4ALLj9z7+6EJCAUPi7FZsIflAmEZDIi4onueam6OM7fPqtg8p3tsZkN1ISpno3fLzVcTXx+dDdaZzj469z+HDxbH7Mp3jhZMxsNuhCdhsZxgzY2Q4qi6ytcD56WgStoEp7AdxOhK2j1ccUrgEKISUKIt4E3UPv/RiuK8n1FUXZd6fe6FhGUFWZu2MPcN/bFKLktGlkYpQi3p6wGf1COUqqLtwNtN8c3M3VaLub35dUecpItlIUDg8bHpjvMvDfnLtZM6kV5tZctJWXUeAKk2kxM79+ewQXZVLn8OlWjvFq1npjev73Ov28cDBiN0peqa11KRRWuvx28BBJI4JtBlhUq632cqnZTWe+LYgtkOMy8OqmXrso4cPEHLNhaqlPbIpGbpsra39kphxffP0Kq3Uheui1GZW95cREOsxTDeBhY0FJP/uDibrwmmd/4vcxGSa8UvrnnFJUNPlo0odYcCMnUeYO6GFfk+Z1WEy1TrDHX88yIQuZt3s+cN/YBF03jtV7Ixtfj9of0xPhUjSeKeaH1Vl0JZsZ3FRkOMx6/zNa95bTLcnBDqhWz0cDekxfwh2RuzLBjNxvw+GXGrfyY9w+eI9NpZktJGc+MKMRsENyQaiHDaY5Szi6/4CY3zcb20nOkO1VT+JkDOhCU1eB9XJ8b9YoiqEF9usOMNyDrdk8tkq264Xy8eyeEoMrl15Vh4WLs0NT9TrGZqHEH+F5BCw6fbaDGHeDQ6VocZiMhWaVebikpIytJFbYRQlDrvniu6f3bU+0KsHDbQe7v1RpfQGb6uhLdrqLx+92QauO5Ud3IcFrIdFqwmY0s2FoaRYOOl6xdKm6IHJt3PruD+W8eYN6QTnTPS232bR9NmYQnbCCaD67kPb4aFcDfAQeAk8DdwGAhLl6YoijXNRU0EG4IL69Wlbg0k1+LQUJGQVGUKPWvxsae8UzW/SE5xrtP84vSkJtm48R5N0u2H4k5VuMP/3b7Uabe1Y7fl5TH+GMtHdeDt/adokebDP18VS4/LZKtrHzwVhq8QRZuO8jE29rSIsWK2x/EZjKQbjdT7Qk0uUuc4TSzYUpvDEJgMxtItX39KmECCSSQAMQXfVgxvohO2UkYjRKSJHBajTz4SjQlbeG2gzECVMuLi7AYJX2ufLe0gtFFucz+Xgc2Tu2DPyRzrs6HoiiMXL5L76lunWHHaFDFMOIlb0IQt4epvNrNmJUfk5tmY+2kXipFTolfcTQIQYYjPpvDH5R54b0jTLytLese7o0QcKbWyzPbDqmiIcVFfPT5ee7olM26h3sjSUStOdr1ZDjNuHxBNk/vS4rNxKJ3DvH0vYVkJVmui94qSRIEZYVPTtTQo009XVomsXDbQb1St/C+m0lzmDHLqldeWbUHScBD/dry18PnyEtrQ2W9H7vZGLXmvvrRCVZNuJXzDX5OVXvIcJiQJIkT591Mu6MNqXYzG3adYOm4Hrzw3hHaZzsIyermsZbwaVXEeAbrS8f1YPZre6hs8PHi2B5IAk7XelG4aD8RL15Y9M4hfvqDLjz25r9YNKqQkCzTpVUqdb4g6WE20Y++15HKeh/vHzzLA71bYzJc7MfTNoffLa3gkf4dSHeYyXJacFqNvDi2OxdcAV2kLi/dxrqPjjPhtnbkJFuRJHHJHv3IinNOsoXfz+hHICjHxBXxxubczfvZOLVPs6xUR0KzIWl8b5vpx70uYTIKXplwK6eqvfrvqVWaFdN3RAV0wFU4Z7NB44RGCMFDEc3Ry4uLuCHVwmtT+iArit6DoFGWspMsvDqpFwu3HeTd0gpy02zYTAZ+s/OzKNrmmp3HGdOrNYDedP0f/3OAPWU1PPfORZng7CQL/3fbQf7jngLu7dEKq1Fi6M0tY/yxNEWwOZv26ZPKmp3H+fm/3YTNbODJLfuZMaCD2uz9cvTnWbL9M/1ataZuIG6gFgjKSJKkT9IJ4ZYEEkjgqyJe8DdtbQkbJvcmN82OJAl9Ey4S75ZW8LN/u4nXpvThdI2HdIeZzf/4gtG9Wkcdu6mknCMVDSwZ053xL3/C/GEFPB6uwpVXe5i4+lNy02wsGN6V/Bxn3ORNUdRqTGNT+V/+sVS/5iqX6uP3xrS+McFzusOEyShokWKNe/46T4CH+rWNSjBXFBfxq3u7Ul7tYcn2z5g1MJ9at58hv/k7oNL31k/uTTCkYDZK1HkDePwhHn1tb1QgKcsqlex6YWZYwx5+czfv57djuvNuaQX/PrQL5dUe/rj3ND/6Xj4A0+5owz3dWhFSYM3O47oqptVkIBhSdD9gbX12+UK6+mWq3awrjc4c2IHKeh892mSw+0QV/zGsgJAMtWE2jlZdPN+gJoKR63mGw0yLFCuSgCeHdqbGE+DF94/ogjWrJvbURZA0U/YO2Q78IYU6T4BZA/MxGyUqG3wcrXRR0DKZxVv/xfxhN+ENypgMEpkOM25fiGHdWmE2CIIyej9edpKFE1VqdbOi3kdOsoXZg/JZ+v7nPHx7uyiRuhXFRfTvnIM/gprYWDVdCIFBqL/pNJuJI5UNX0nNs6mxCTSbzYmmIWLG2pqdx/nFD7t+2xeWwBWCIkOtOxD1e3p+dDdSrKZLv7gRroY2UB/g74qifBDv31V4v2sKkfSFeUM64Q/KLB7VjRXji9Qkb10JDV5VZvyuRTswGyVemXCrTlm6d+lOHnzlEx4dmM+fZt2mC67MHtSRBVtLuf+lXSzYWsrsQR3p3MLJX35yZ3i3OqT3Jewpq2Ha2hLmvLGPzyoaeLe0gqCssOrD4ygI2mU54k6gBknw1D1dmD+sgDU7j/Pw7e0wSgJPWJbcaTFhMkjMH1ZA97xUyqtVhbwRRXl0z0tl/rACXL4gZ+u8nHf54gZqe8tro+hECeGWBBJI4KuiqeCvIqx4qFEU41FoFAU+r2hgzhv78AZC3Nkphy/CAW33vFRWjC9i49Q+LB7djQsu/yX75wxxTJmfGVFIjTugH5vhVDeytD4t7TitV089j8T8Nw/ool8GScJqNGAzxdJOlxcXkZ1siTGQnxbe0NNEP2as340pgkVR2eDDIAm+uOBm9muqpU+ktU95tWpCr+kMaBuZjb/D5sbM0Dz8+rXLIM2uKlsHZYXBBdmM7XMjZ+u8SBIU923LjPW7MUowc0A+tZ6A3m9+vsHPlDva6W0VGQ4zqeHq7fIdR3W/x0EFOboyZ26ajaI2GYxd+bHe82M1ScwamM+ancd1ppCWBC7YWorNbKDBF2D0il16HPBQv7bcEKYRT1z1KWl2E+se7s1T93ShY46T0zVeHnrlE+5dupMZ63dzqtrDsnE92FJShqwojCjKwxcWw6lq8OMNyoxa8RFrPzoJCAIhRae3/mTTPt0QfktJGQYhaJvpYERRXgxVedq6ElqkWAnJCjUev07bPlPrQUHtX/zFHw+wt7yWk1UuztZ7ef5/D8dUnOOZv18vYzMeHGYRNxZ0mBPxUnNBQFZiRMQe27SPwHehBxDIA0qEELddqRMKIToJIfZG/KsTQvxYCJEuhPhfIcSR8H/TrtR7Xi1oCc0fZ91GktWkL+wLtpby+N2dwipaMqsm9OT9x+/CH5RxWowxKlqPrN/N6VovC7aWcmenHN4/eI5VE3ryXli17k97y9l/qo4Jqz7FG5R57eMyXp3Ui83T+7JifBGDC7JZNq4HyVYjgwuyOVnlZkRRHhdcfsrCPQqRyE2zEZIVMhxm2mc5eOqeAl7++zEkScJmNuC0GJmw6hNGLv9I/yxaEnhDipXH71YT2JHLP2L0io9w++IHalpAFTm5awtpU72ECSSQQALQdPCnJn8yJ6pcKCgsa6R0rHmhbfvnGV6d1Iskqyp0se2fZ/jdQ0UsHt2NDIeZGk8AWVFIshov2T9XfsGDzWxg9UR13l14381IQqXspzvM2MwGPP6Q3iOmvVbr1bv/pV2U13hi+ginryvB5Q8xeU0JAmJUQi+4AmQ5oysd2twa+f8i4npXFBchCXRl0KYSW83N6av0bzcHBGRFZ9/8+s+lPDOikM3/+IKn7img2hXgR6/vRZYhKMtkOS2cbwiw52QVuWk2/EGZP+45RZrDRLLNyKyB+foaeLzSpSdvkqR+f9lhau2WkjKSrSZdzTEkK5ypdlHtDmA3S8wb0oWcZCspNiOvT+3DB3P7q56ETjNnanxRG8pPbNlPit2sr8VufwiXP6j+HhRiErPHNu0jyWpiyh3tMYUZOIoCigLT15UgCcHggmym929PUFZ46YOjLB3XQ3/MaTHSLtPBz/7tJmQUhKBJqrIkBAu3HUSWFQ6erdP7Se9bupOqBh8zBnTQv68HXtrFzAHqpveK8UX654lXcY43NleMLyLN9vUrJNcaXH6ZJds/i1JIXbL9M1z+hAhMc0FTIjCX0399xSmgiqLMEkL0AF4QQhwElgFyxPO7L+Och4FbAIQQBuAU8AfgSVSPwYVCiCfD///EN/8UVxeSJAjJxCzsT2zZz9JxPfD4ZV0COjfNxtqHe8W94Z1bJLFgeFddNWvxX46Qm2Zj4X03s+JvJ9h8U0uWFRfxt8PnGN69FQ82opq+uvMEO49VsXRcD9Z9dJLRPfNIshpRwgIBkRSipeN6MGfTPl0pdMHwrjz2/U5kOMycd/lipIef2KLSWxZsLcVqirWpOH7eFZe+VOMJ6Mc0NzpRAgkkcHXR2Osvkq7ePS+Vc3Ve3etP80A1GVSDaxko7ts6ap58YWx3AkGFyWui584/7S3XDdQXj+qmB9Ja/5zdbOAXYUrnolGFPP3WQR6/u1OMp1+q3UiDN6gbfh+rdEWpIDYVPCuKSvOrqPfp1T0NpWfqWTC8KxNXf6o/Fjm3av9vNkr8adZtnK71kuk0I0kXDbbj9ZpHVlEa0/Waa2+VSRLMHqQqwL5bWkFlvZ/p/dvrNN4spwVPIITVZOCJoZ1Zsv0zZg7I52ytF5NBYmNJOWXVHmZ/L5+H1+zSv88l24/oHpQCtSc0yWpi2Y7PeahfW+q9F1Vpl+84yo++l8/9L6kKodP7t+eCy68ni7MG5rP7RBWtUm1RtDBNSbba5efxuzuxZudxzjf4yU6ysGBrKcuKi+KOrVpPAH9IRgjIdFqo9QSwmCSynBbMBsHcuzuhoIrC7DxWRV6ajdmDOrJk+2c81K8tz+48xIiiPLrekMzTb5Xy5NAuccdSZb2Pyno/Ln9I/71q1/DYpn0sGN416rGZG3brMYX2m45X1ZMkQX6Wkw2Te+uV/9/85TMe+36nZs8eCoYU3i2t0D2VNfzHPQXf0hUlcKVhCreFNf49xfPBvBSuig2Eoii7hRA/BbYA7QEtNVWAgd/w9IOAo4qinBRCDAf6hx9fA+zgGkgAoWmqUrrDzAMv7Yqa+E6cd8e94bKiYDVJUbvHi0YW6n+3TFEbQwd3bcnhsw0sHtWNGk+A5TuOMn1dCfOHFbCppJwZ63ezYHhX0h1mzAbBrA17yHJadB651neiJX/Li4tomWohzWZpsqemvNpDhsPM8uIidTe80fNLth9hRXGRHrxELlja9V8PlI0EEkjgyqJFioXXp6giLQrgDYT4j3sKCIZkPQGL7NdbP7k3D/zuYxbedzNP/v6fUXNvU8bv84cVsHDbIeYM7kjLVCsbwwmcrITNmF/bC8CcwR2xGA3MHpQfQ8ucvq5EpXlajCgKSIKYamBWkiVm7tf82QySoE2GnSynJer58moPbTLt+uty02wsKy7it9s/08+7dFwP1u48zsAuLdhSUkaPG9Oi+q2X7zgaswnYuMJ3PVjqGCVBm0w7n51r0Ct2y3cc5dmRhbj9IZ4Y2pln3z7EL4ffRMsUKyOK8pi5YbcubPb86G48tmkfI6pzo+7RnrIaGnxBnh99C0FZ4dm3D/ObMbfw8O3tePnvx5h7d2f9/m0qKWfmwA46TVTboNBQeqae16f2iYkbntiynwXDu+L2B/EHZZ66pwCDEPiCIV0cKF5cYTcbmLlhN6sn9qTeGyDFZsYg1ETYHQhRXu3FZjbQJkONBSrrffpvYs3O48wY0IFqV4CQrFBZ76feG9C1AEYU5ZHhMJOVZOHF9z5n9qB8Xdk0EhqNuvFjWmX6iS372TC5d5MV52pPgLFx7FOak0hRPBgktUI7oihP7wHcUlKWUAFtRhACVowvoqLOp/eFZydbEJfB57ziCaAQIhtYDLQDBiqKsu8Kv8UDwGvhv3MURTkT/vsskNPENU0FpgLceOONV/hyLg9NqVvGK+9G7hZGNn0aJcGzbx9m0chCbkhVe1gAjAZYPbEnQoA3IFPnCcbdGcwOT4RqwODgg0Nnub1jNuXVnigKkT8kYxCCj/99YJRAiwYh4u9ItEyxkpNkpaLBF/N8ZYOPlqlWfQc5JCv86q1SPclsjnSir4rv4nhNIIEvw3dhzEYqgGY5Lbp4R5ZTFaNoqre5st5HltNCy1RbzPNNGb9nOMzsKauh+OVPgIubbyFZoU2mg6wkMw/f3o45b+wjy2nhudHxvddCskK9N8hvwsFxi2Qrr0/tgyegUkhBiVInHVyQzayB+YxZuSuKnSFQlR6X7zhKZYOPU9UeXZFUCPjg0Dn+Y9hNPDm0CyFZYeVfj7GppJy3DpzjtSl99Dk9sqpnMxt0pUWTUcIoCc7UeppFte+rjtegoiAJoYuvrNl5nIf6tWXRO4f4yeCOWI1GRhTl8fM3/8XPht2kV2wFAk9A5td/PsSqCT0xGqLF3FJtJiShCvl8XtFAZYOPg2fqSbebGdOrNRaTFKXMKomLhvTxxlEgJOubttlJFpwWI95AiMwkC6eqPVGJvPY55g3prCeokWNJEuo5tV7V5TuO8uPv59Mm046sQG6aFQUIhFSj+zaZdj05m3hbWzz+EPPfPMDGqb2ZN6QTMzfsoV+7DGYNzNcNrLXfS26ajdIz9XHjB3cjY/fGDCGDJJocg7IsRwmhaHYY1zKr6KuMWZNB8OjA/KhYUfMGTaB5wGSQkOX/x963x0dR3us/78xes7u5soFAIpcQLhETkoUQ0KMgFaVSqQawkCAXBYIo1ipqT0trD3p+XKRW5BKkljvKrR4sFKQFqR4R0BDhaLikCJIgkBCSkM3eZ97fH7PzZmZ3FgVBINnn8/GD2WRnZ2fffed7eb7PQ1Ux/ZLCXOi5K88Ar8cM4H4AHwO461onf4QQA4CHIHkMqkCl4QRNEiyl9C1KaR9KaR+73X4tT+mqocVTn1Og9gGUUeP0ItFiwIoJedgzYyBWTMjDxs+r4PGLsNsM4AhB4Z/3Y+Bre1D09n5UXnTDJ4iYtrYMJ6qbNKmm0wdnMJ/A1AQzOAIM6tkOF5w+DMlMZjN78nxijVOiamjd+Pmg9HDoe9FxBDodx6wiQudF4s0GNtuXmhBDlRv+AAAgAElEQVSDVx/Oigq94OZcr1FEcTncDGtWqQBaPDCdJX/P398dM7d8iaPnGiPOBxYPTGeCL0q4fNoeZokWg2o/mzciC+8dPAOeIzhb78FvHsxktNCyynqcrdf2X+U5gjeC1LlZW8sxfNEn+MVb++D1i+jVIRZmPY8kqwGzhvfC+sn5mHF/DxZEA80KzfI8+AsPdMdfxvdBx6QYAIDZwMOo45CSYEFAEFFR7cQLmw5jQ2kVez4gyeufqXOhtsmHJIsBHRJikGgxItlmQkqcGbVOHx5a2HI8/77veuUJQb3Lhwl3dmbqni9uPoyd5dW40OhDIDgXX+BIw9kGD1sXUtFUune/sOkwGtx+LBqTw8TcHn1rH5Z9fAIBUcSCXRVYNEYSXkm0GrBgVwUEQRq8W/N4P/xrxkAYdRxj02itI44QlVDchBWf4ZIngG9qXfjl+i/C7v8FjjSMX/6ZJP42vi82FffH2if64ei3DdAFfSplo/e9X9cCAAw8BwNPYNDxmLjic/zPwSrExUjCb3Jy1i7OxJLN2iY/+//BmW3D1u2MTYfhEyhLrpXfpyWFuUiw6MNiipI9J9jPkRhCokhxocmnil+ev787hmQm39Ksou+zZgMiwsZtpq49iEB0BLDFwBsQNT9j71V8yNcjAcwLLlTV3YEQkkYImfEDjz0UwEFK6fngz+cJISnB46cAqI74zJsMoeqW707Ox22JZri8QphAwdKxDrj9AsYvP4CB8/Zg/PIDGJ7TAUKwyuXyCarB7xmbDsPA83htZDbSEsOr2lV1btyWFMMMWucUZIEQqZpn1BH85sHMMLrSk2sP4ptaF841SBXzQEBEbZMHVXUueAIiU7yTB49X7j0JLliR+D5KnlGhlyiiiOKHQEmrT7YZWSIo72Wy/5lyb5UD73iznjEtlL9PsOgxf2S26rH5I7Ox/sA3WDEhDx8+fw82F/dHx8QYTLu3K3Qch7ZxzcwKGfN3Hg977aVFDgBAgSMNL26WktWlYx2YPzIbNY1eeAMCKqqb8PstX8Kg4/DcxkNMfVQJJTVuxqbDcHkFlJ9txIQVn+Fikw8Xm/wq03tZoEs+D5HisobukTz/tBQYWxr8IsW0dWWYu+MYChxp4IOKnYBUiXf7Aki0GJBkMeC//34EPkHAX8b3AUeAC04f5o3IQo3Ti3X7TqNN8N4sP7/AkYZTF1yocXqxdt83mDnsdpj1ku3E2L8cwM8WfoKit/cjIFKcqHGijVWPzBSbSvl1SGYy1j7RD5RKbB+ZuSMnem2s2jOk8npJtpkkhc8mH76td6NfehvmQajnOSz58AQWjcmFSGnQlopjlM35/6yQ/IspxZwCSfmTEOn65KTFw2rUsdeO1Lm85Pbj8bu6MOuCTcX9sWpiHvQ6go+P1WD5+L7414yBeGdSPj46dl7FEEow61HT6MWZOhdqGr1szcoWKqFJ728fzGzxrCK5EywrFssxYUCIZoAtBaHe4IC0xgM3iQhMjfz/hBA7gJEARgNoD0m45YdgNJrpnwDwPoBxAGYH/93yA4//o0I5QyEG5ZAb3AGcutCItU/0Y5LQLm/4kPSLmw9j7RP9UO8KhLWCnd4AvAEBDy74Xywf3zfiAHa7WBNL1n79057QcRyqG33gCBeRk+/yCxj/1j6smNAXdU0+FX1k3ogszA6aDbfGeZEooojixkGm1dutRsSZ9WGUOaVnWkayFRXVTqzd9w3GDegMl0+A3WZAfIweKybkod4liWzI3nyzhvdCWqIZlRclOt7AHm3h9QdgM+vx9YUm1m38z5/2wJhl+zFzWKZq3y2rrMfKvSfx7uR8nGvwSMIUu47jN8Gg1G414qWhPVRiMkuLHFi59yQTH5k5LBPJseEzgaHUuLgYPdbtP43UBMlOQms2TBbTWFyYi1e3lYcld/KslChSuP0BzfuBKIqoafS2aBEYeSSjqk6y0Fg61qESynH5BGw9dAaj+3VCjdOLv3x8CuPu7ITXPjiCp+/NgEgJXh/VG/ExOnhCZuXjzXrM3n4UC8fkoK5JUpc9c9ETpvh9utYFm0kHjhDUOH1YsOs45o3IQlqiGfWuAAqDs27K8Y6yynqJJhlhPEMW+Tl90cVmYf/0aG8YdNLadvkEdIg3s+7f9J9k4JLHD5tRx2wqpg9KxwWnD05vACv3nkSBIw1ccK6weGA6vql1qa6V1nlUN3oRa9JhdF5HxBh41Db58Oq2I7DbDJg2KAMTVnymoqc+lJOKhBgDkq3GiL6AkfQVLkcZbSkwKnwrlXGZQXc9ej1R3AjI3uCh36WrEYG55quCEGIjhIwjhHwA4AAkEZjOlNJ0SunzP+C4FgD3Afir4uHZAO4jhFQA+Enw51sSHEeQaDGie7IVuR2TUPjn/RhR8immrC6FSLUzfgBMLVR+bOragyCEwGrU4cPn70EXuwVv/KK3qvL8+qhsJFr04DiJ1jFtUFeY9NJS6N7WhhiDtpR6ktXIfP5cPiHMi2TGpsN4c0xOq6dwRhFFFD8+ZFr99MEZmL39COYUZIVR5mTPNB1HMGtrOTaUVmHl3pPo3s6K3zwoJUVOrx9+QcSsrdJMco3TC4OOw4yNhzFhxWdIjjWiQ4IZsWY9AgJle+ILD3Rne2LJnhNhHn3TBmXgv/72FdvXd5ZX49Vt5Wgfb8IfH83W9Ep7rH8ndt5TVpfiV+sPhR1XSY0bkpkMHcfhyUFd8e7kfADa945uydag8Jc+TDGwqk5SYJZnKk9UN4XdD4ZkJuNCk++yncOWADnYkn0g28eZsDjYJS7ZcwKJFj0G9miLd/afQkmRA484Utln+7stX8HlE9AuzoSqOk8Yxbje7YfdZoDXL2Lmli/xy3e/gN1mDOvgfFlVD7vNCG+AYsrqUtQ0+sARgorz2uMdxQPTAUhr49wlT5gX5fyR2UiMMWDlxDzoecIsFX65/gtQEbAadXh+4yG8uasCiwtzMfSOFAQECgPPgeekecg/j3OgX3obLNh1HCa9FEMYeA4NLj+WFDmQZDFg+/+dVV2r0PNYUuhAyZ4T0PMcJqz4DI++tQ9TVpeirLKeiemEspBsRh3axZpQ5/ZH7Eq3Zh9AkSKsgDBj02G0sK9lq4YpOB+s+i4VOVgMfyW4Hiqg1ZASv99CMoSnhJCHf+hBKaVNAJJCHquFpAraYlDvCcAf0uKNVD0DoHlzt9uMqqrgX8b3wTuT8vFtveQDlGAxYONnp7H041PS5lzkQJNXQOVFqcvHEaISHpCrb3N3HMHO8mr2HC31OSG600QRRRQ3ADLV3GLkWdfshQe6hwloLXusD9rHmfHXJwfA5RVw8kITpq0tw8sP3Y6aRh+cngBuS4rB+sn5uOD04dwlD+uqpCaY8XVNExbsqgirtC8pzGV7YlllPeLMOpUQBUcQlmzVNPpQ5/LDqNMWm0mJC58Hjw0eN9kmdTpnbz+Cssp6TYGYkiIHpvxHJyz9+BQ7RmqCmRXnfAGqeW8x6HhG/bRbjZhTkMWotKkJZvz2wUyVymJo57ClQM8RLC3KxSVPgH3WMu2y3uVHgkUPu41g/F1dQADYTM20x7LKeoxetl9SejXwmL39qOo6bi6txG8ezGT36qo6Nzx+IWxdLS7MxYdHzmFQz3aoqnNj9iN34LmNhzB/pLawUHyw+z2nIAvLPzmJ54Z0xzuT8oMUThIcJ2nurM0fmY3Z24+irLIeAZFi6tqDsFuNGJN/G1w+AZ3bWKDnCRrcAixGHo/f1QUGnkdNoxcFjjQs/vDfePreDHYORh2BWW/A0DtSsHB3BfsOiJTitZHZSIkz4WyDB7FmHX7zYE+0izOFrcFI9idyFy+0y5eTFo/igelw+QIwG3ismpiH2duPMNXRZJuxVfgABgRtRfaAGKWAthR4/CJKT17Aukn5oJSCEILd5WeReHvKFR/reiSAv4ak1LkYwDuEkPXX4TVaLGRFTOWGWLLnBJYW5aK60cdkX1MTpE1U6+Z9utalujFPXPE51k3Kx6Nv7WN/M3NYJvDxKWlOZk0p3p2cD3NQdtnpDSDWzGPtE/3gF0QYdTxmbf2KBS/yc7S8po6ea8SsreWMjhHtAkYRRRQ/FjiOwKyXTNrlADwnLR6zhvdCut0CniMwG3hwHAEBQdHbzUmMyxfQDL43l1ay5G/eiCzM3XGMicyEsi+Ue6LHL3UR5b9R0gdlTB+cgSmrS7F6Yh6jr8oqkS6fgBhjs1q0fD6Ugh1XDnwn352OtrEmlvzJ51S8phRrn+iHk7UuVfFOzxN0amPGkg9PhCV3Mn3/bIObJSYydVZOLuTjKyF3DlsUOCDWbMCUNc3dqJ3l1Sg/24jZj9wBm0mH8w1eGPUcnlx7kH2Oymsjq1nWOL2q6+jyCSp2T05aPCwGXdi4x5NrJf87WSBO6ig20ypD10xqghnrJ+eDAnjhgZ7Y+Nk3GDugMwqX7ceqiXks+ZOP/9zGQ8wChQvOOM4bkQW3T2Br/NOXBiHRogelwO4j5zAmvxMTDJpwZ2c4vdIoikwttluNeP3RbOZJJ6/TeLMehAAd4k2YHrRKeXFoD6x+PA+nLriwYFcFapxeJGvYnyi7eEoV9Zy0eDx/f3f1Gh7bBy880EOV6LaGmCSiDQRpue+5tYHjgJ7t4zFGUeibPzIbVyECel1mAP8E4E+EkC6QEsH/AdCeEPIigPcopcev9Wu2JBh0PFbtPamqWud1igcFCZv1++zrWiwak8uoEvLjv9vyleqYVXVuCIohYLlKqPzZFxDxfIiZcVqCDvf88SOsn5yvSRMK85oKvnZLrQZH8f3R6aVtN/oUomilUPrZVdW5UeP0wm4z4pVt5SwJWvZYHyTG6FV+p21jTaqEUA6+JWGr2yFSioBI8V/Db4c5xB5CDnC72C1YPr4vFuyqwKbPT6uk/DeXVqp+Tk0w47YkSUL/3CUPFo3JgUsRdMtzgK+P6g2RUrh8AgiARbv/ze4PMqV1SWEuLji1/dRqGr2YcX8PPH5XF7h8Aho9fhSvKUVJkQMV1U6WlCRZDGgfb0a7WBM4jkCvazaGlymoqQlS55RAew5Fr+Na1lwgJRBE7a5K+3gzLAYepngTRgXnLGXKpfIzTLToYTHq2OPydfzTo71xJvg3VXWSYJHyM1QmTck2I7x+ASVFDvBBWmrJnhNYOCZHlajJ9+GUOBN+vngvU8EVg6wiQrQT95Q4WUpeOna7WBPGBv0GRzlS4RMonJ4AXD4BY/I7wR9U7/zPn2aCgmLs2wdgtxqRHrRasVuNzB5qQJckFPXvqLKAKClyICPZiuE5HVRxhyxa4hNErHuiX9h3VtYVUH7HlUJP8vuZtPrzMCP51hCT6HkSZrexOGoD0aJARYSNCzy38RA2TM6/4mNdFyN4AKCUfg3gvwH8NyGkF4AxAP4OoOv1es2WgCSLAT/PTcP/HKzEyol5kpUCR/BoyCD/1LUHsXx8X3j8AmY/cgdS4qXOn9MbYEbCMlITzCqFIKVogPzzNyFdwxmbDuOdSdKC8gui5s1e9pq6LSkGNY1eOL0BlFXWs2O0uGpwFFFEcdNDpoK+/9SdcPsEBESKsw0e1DRKqpVyMLhxSn+VV2Akrz5PQMS4YDAsU+uULA2tDsSSwlwkWQ0w6jmsmySpNPoFCr2OqGihsj3E3B3H8PqjvTFtnToBnRI02J6yuhSAtO++NjIbsWY9U3/kiCT+EMlPrbbJB0GkeG7jIcwpkDqYoSwOmbUhJ3+ANP8WmszMGyHZ+8Sb1Um2HKA7PQFmVN4Sui4+QcTJC00RBUxSEySLDPl3c3ccw38Nvx2vj+qNNlYDBCp9NrvKz6J7ShxWT8yDQCkuOH3wCyJWfXoKS4scmLKmFPFmPRNYkRM35ZpaOTEPsXoOAZGyz8XpCeClv/5fWGzw7qR8Vfd2wegcKUHnOM33YtRzmLNDomvOKcgCDR5LTkJf3VYuGclzklr49sPf4unB3RAQReh5jp3vBad0/s8N6YZXt5Vj4ZgcxJsNYYWV4jWlWDUxT2VqX1UnCe3Ia1JOCGcN7wWBSnZTcteR4wgy7FZsmNIf/gi0Ry0j+ZYek/gFqmkTs/4qkoMobk5cSxXQ6yYNRAiJJ4T0JYT0BVBJKf1PSmk0+fsOyMHLE3d3hUnHwcBz8Aa0NzieI/i2wYOX/vp/eH7DIfgEEVajjg1eA80Dops+P81+LilyYHNpper3C3ZVhB1fpBRDMpNhNek0BrhzEWfWwyeImLP9CJJtRszdcYwNy28q7g9CSIsTBYgiiihuDZy/5MWjb+3DPfP24PmNh1T2B1V1bvhFkSU3xQPTURMM6JXQotS/uPkwEiwGtidqdSCmrj2IY+ecCASkuStKAR1PIAiU+ZNNWV2K+TuPM6uAOldkiwflzylxJqzbJ9H3i97ej7vn7cHMLV8iNdEctvfL8vwdEsxYPr4vtpSdURXp0pOtEa153D4Bc3dI3UHZ3mfujmNw+wRNa5+2scawgP5Wt4sQRIoFuyo0feqMOgJBpCrRtLLKeqz+9BuY9BzG/uUAfvLHf2H0sn3o3TERgkhR3ejFiZomzNl+FG1jTfjtg5lIiTfinUn56JBgZp540wdnhK2pi04fREpxsUkaBZk1vBfax2vbPAkKP+GyynpccHrwxi96Q8+TsHv5vBFZAKXYWV6Nb2pdWLn3JAzB7m/xwHRcbPJhZ3k1jDoOFqMeFxp96NM5ERwAo45HXZMPLw7tgZV7T8Kk57BoTA5S4s3YWV6NgEAjBqxKSw3l43LiJieE3oCIUUs/Rb//txsPL/4Ep2qbUNvkwbHzjRi19NOI/p5aRvItXQhGiHCtBRqNw1oKuGCXXgnlXPeV4Jp3AAkhRgBLAfwcwEkABEBHQsh7AIoppbfu3eAGgOcJjFS7aidSMHUtJbVkSWEuVk3MA8cRBASpWpfbKQnre7aDyyfALwgYndcRv/5pJs5f8sCoI7DbDCrKicsnwGzgMHPY7Th/yQMhWHXkCIHLJ8DjFzFy6acsyDDpOdhtBowb0JnduIZkJuO3D2aC50jLoANFEUUUtwS0vOtk+wN5n+RJcwDaPs4EgVK8PipbZW0TkVIvUpYcZSRbI9DqjPAJFD5BxOlaab5peHY71vGR6amxJp3kfcZr7/OhbA1KgdxOSaoEQabov/LwHSoLoZV7T2LCnZ3x9Loy1Di9mFOQhYpqJ5tpNOv5iJQ4g45HjdPLuo/y68tBdKi1z5k6l+Z1uJW7LjqOaM7uOb0BJNtM4DkCnyBi/shmFddHHKkqo2a71Qi3T2CdOjnpOn/Jgz1Hz2NY71RMXVMKu9WIV35+O1y+cMYNAHgDAs7WeyFQCrvNiESLESRouxC6ZuqafCohN57jkBQjhXty8ijrCSRaDDjbIBU/3v/iWzx1bwYa3H7MH5kNk55Ho8fPWEQXm3zwBqT3Yrca8fuHMpFoNUAQKQocaXhqXRlmP3IHLjb5MeU/OiElzgSRap+jTGW93HqvqnPDL1AmrmS3GnH+kgff1IpsJEb29wydYzUqKMyhFNKWiogWAdEZwBaDyzEzrvhY1+H8fgNADyCNUtoISNYQABYBmBn8L4oIkKW3ldSaFRP6hqlylhQ5EGPgsGB0Dgw8wbpJ/VDX5EcbqwF/+Jsk2LJ+crPwixLvPTmAUSxmDsvE8xsP4d3J/VB50R02S/Dm7grGwZfoQ5Ja2PrJ+SxhNOo4UAq88vM78MiSvYw6Mm5AZ6YU1xLoQFFEEcWtgUheYPFmPStMiZSyeT2TnseEFZ9h3ogszBreC13sFnxd0xSRUi+IlCVHWuIuQzKTERARpshp1HGY98FRNnPXLs4Et0+iTdqtxrAb++LCXCzcXcFed96ILJxtcGsaa9c0Sp6xHAe0izPBbjNi4l1dMCeo8AhA5QH4XQGxPGf1+j+OfS81RaUwh/Ja3cpdF5OBYwm7XDiQ1TVnDrsdBp4g1qRDQBBZUtU21qS6BsUD04N/30z9Xf7JSUy+Ox2F/TszMYcBXZIgguDZDV+E+UgCQILFAKcnoJ6ZK8pVFRTkNRIXo4Mggp1TrEkPv0ABKmLtvtOYdHcX6HgCjhCs23cKQ7PaY9GYHAREioW7KzDj/h54+3+/xu+G3Q5fQMTSIgf8gohzlzxMobOqzo11+07j2fu64UR1I3tcz3N4/4sq/PK+DFxs8sPtEzQDVp4jYTRi2cc4Jy2eFSlOX3Th+fu747UPmsWXlAqoSn/Pnu1sMBt0bF2/9+SdLWce9XvAGLQIUM4ZLylywHgVFgFR3JzgEF7EiTHwV0XnvB4J4CMA8iilLvkBSmkjIeRJAPsQTQAvC63K9fjln+HN0b3x7qR8BCgFAfCqYjh6SZEDW7+oQm6nJOg4aQi4/GxjRPsIq1HHji0HEpQSTVW7mcMysbO8WlVBn7W1HCKlYTMKS4scGNAlCYMz26JbspUNkcvHaw1D2FFEEcWNR6RkpFNSDJ75STdVYWpJYS6jubeNNeHe+f/CjmfuQsekGLj9QpjQ1pyCLJXAS8meE2Gdw5eG9gyjQ8ozd7IyIgAsH9+XdTKq6tyYu+MYM50/1+CBQUcwOq8jE3Cx24yYu+MoChxpqveXkxaPFx7ozmat5CDbrDEH1bOdRN38roBYnrN65ifdmDLl5Qp5oeI7LaHrkmA2QhSBVRPzcLHJp+qqNnn9CIg8ap0+/HL9F+yz+PC5e1SfTfs4k4oZI6+htrFGBBQCM5Pu7sKMz7W6WrEmfZhC6JQ1B/H6qN6soGC3GeHxBwBKMH75AdU5xRg48CB4OLcDe51t0+9CbqckWAw8rEY9Ri79VOpMN/rw+4cyAUKRaDXAwBMAhAm/yOIuTw/uCp8gYsGuCswflc06eEPvSIFfoCgOdjZ//1CmKmC124z4/ZYvMbsgS2XH8rstX7FO9UfHzuPRvI5ocPtR7/LjhQe6gwt27UNjG1kMKTS+aG2xhjdA8eau46piw5u7juPlh3rd6FOL4lohUjf3Krq816MsICqTPxmUUieAKBH5OxCpcu0LULj9kjlv4Z/3qywZpq4pxYg+tyHerIfTG8CaT7/B8vF9kZli0zQN9vgF9rO8kUYym0+3W1RzM0nB2RdBpGEzClPWlKJ4YDpmbS1HdaO2It2tTAeKIooobg3IyYhy71v2WB8QjoQF0VPXHkSTV6qiflvvxpT/6ASfQPHYXw7gwQX/i0UfVuDdyfnYMu1OLB/fF7EmHXI7JWHrF1VYPr4v5o/KRkCk+NOjvfGvGQOZeNbl5ptkxISoiZZV1mPCis9Q6/Sh6O0D+PXmL+ETRLSLMwEAKKUYN6AzmxWT39/0wRmaBtB1TX5mDC5fB7NBB7vN+L26IXVuf9j1ijTXpzUXeKszPjiOQMdLwieNngDizXqMzuuIGAMPQaS45PYjPkbtWRdqvm426MLulS9uPgy/QKELirIAUM3EKbta/5oxEDOHZaLe7ddcUyKVDOJHlHyKwj/vh46XPBxDz4kDgV+kYevk4KlauHwivAGRmdC/NLQHYk16VJxvQqPbjzP1Hlzy+PHkoK5o8vqxaEwOnri7M6rq3Pi6pgk1Ti9K9pzAojGSbcptSTGgVDp+WWU9/vB+OXxBJfKuyVbEGCSvTrdPYHYsE1Z8hrLKelTVubFy70n8rHcqHvvLATy8eC9mbvkSVqMOHGlWQA2dy1z2WB8kmPWoafTiTJ0LNY3eVqdB4BdE7CyvxpTVpWzOWJrFjPoAthS4/QJeVnyffIKIl98vh9t/5bH19egAUkJIAqTZv1BEV+F3IFLlut7tl6gcGtSfqjo3DDoObWOlmYSEGB3ue/0jAGAeWGmJZpyoacLKvSdZ9Xj+yGxwhGDlxDwIoiT4orR7SE0wo/Kim9EvapzeIA3FD7+gnTBeDN546t1+TT+aW5kOFEUUUdwaUCYjSgqY7G2nRFWdG/UuSZXRbjNgTH4nZs4NSPN1k+9Ox4iST9lzZPo7zxE2D2i3GfDCAz3h9vlhM+k19/FQYQrZt02mzRcPTEeSxYA4s57R4GZtLcfaJ/qhcxsLAiKFgefwWP9OiDXpsHx8X/AcgUi1E86OSTEgRDrfGqf3ijtykQuS2sFG6FzgrQ5RpPi23os3/nkcBY402DgdutgtMOs5vPbBcRQ4UnGxyaX6rOfuOIaXFR0vLoL1AgCs+fQklhQ6MHVtaZj/r/zZr5uUj1lbyzVpoVozczqOMDVR+W/fO3gGjw3oFFZw2FV+HtPu7YrCP+/HxuL+Kh/MTcX9sf3/zuKJuzuDECDWpEet04spaw6yOb+ZW76E3WpktiQV1U5MH5wBo46DGHw/oT6FHAEobaYHa62xAkcaG3mR39fUtQexcUp/LB3rwJTVpXjtg2MqFfLkWAMqapxhHehbvQhxJYg0V9la3n9rAM9Jmh1K2G0G8DfJDGAcgFJoJ4CtqxxzFdCi0cwpyGL89wSLQfML/nVNE5vrW1LkAAAs/fgUapxeWI08Zmw8jBqnF4sLc5EQo8eqiXkQKVUZpS4uzAUA1cyfnPjNGt4LyTYjYgw8DDyBX6Ca5yFXhneVnw/zoykpckScHxFFitomX6vi60cRRRTXD3IyIu8tZxvczJssdF4vzqxHg9sPi1H6NzQgVQbUWrYPr4/KRnyMntHupvxHp7C57T892huxZp1KmCI1UZK6f+OfxzVpgiv3nsTjd3VBvcuHaevKVPROmS63tCgXMQad5n5cUe3ErK3lWFrkgN1mBKVUJaX/XWiJc31XgtomH+uA1jT6WDe1bawJ4wZ0QnWjF22shjBhHx1H0LmNBQ1uP3QRxH0Ike7RVqMe70zKh45H2PzW4sJcbDlYxdaCUmxGXgdzdxxTHVf26FNSSIfekYLiNaVY/bjaqH5wZlvUNHpht0rfE2V3sLbJh6F3pGDiis+lzuBjDsTF6Nmcn54Hoy6LlGLFhDwYdQQ6ngOlFDodp+1tOdaBtjYjK0ZcaPKGXYRf978AACAASURBVB95nlCJqjo3/IKIRIse80ZkoV2sJNx0tt6N+TuP443ROWHjM6FjJy09zjDqOM15S6MuOgPYUmDScXj63gwmNCWPMZiu4jO+Hkbwnb7P3xFCbqeUfvXdf9m6EFq5FkSKV7aVo6yyHptLK5F72x1YXJirSqyUNwGZErp+cj5G9e0IvY6g1unFS0N7oN7tx++3fIWXhvaAyyew2RP5ebJfzON3dUG924/XPjjGxAO62C0w6Tj4RYrRy/ZjQJeksPOQZxEB6cYS6kdTvKZUcwZQS/imtVXuoogiimuLQEBEjdMLT0DAqQuSCqfdZlAlZkMyk/HUvRlsJio1wYxFY3LD2BCbSyvZLKCW7cOzGw4x4+mctHjkdkoCIcD6yfnwBkR8U+vCq9uOIK9TPN6dnI+AQMFzBGs+PYk6VwAzh93OBGPkY764+TCWj++LC04vS/7k38k+rYQAT68rQ0ayNWw/lgt4Mj3/tZHZ+MVb+65of22Jc31XArk7FZr0D8lMxtODu7F76JDMZKx9oh9ECoBSnLvkwZQ1B9lzQ+f5lhQ5mGLj/H9WYPexGhQPTEfubXFYPzkfAVGyD/GLItaXVmH3sRpMH5yB25JiWGfRL4gwG3gmUiQXWbcf/hbTBmVg0YcVmDciC2mJknLsgC7SmlSukySLAX5BxAsPdEeD2w+71cjmx0RKmaCN1PUVcf6Sl3UdDcHE1m41wuMXsejDI3hyUFdmTL9xSj7izAbmbSl3uN0+Ad6AiHizDjVOLwgo6yDK16eN1aiZNJ++6IJRx0PHEaYxIBdgjHwzhVapaO4LCIwK2tLjDDFoSxIqENLaqLAtGT5BxJu7K9Rznrsr8PJDt1/xsa6bEfz3wGoAuTfw9W9aKGk0okjx8kO98OufSkFMg9vPZvx4jsDAc3j6nTKWqAFSgOANiBj8x38xpU+lkbBRxyE51qhZYQsEDYNDN16eI5i55Us8flcXVNW5MTizLRaGLsJdx/GbBzOx7cvzmip1kahDWsI3UcGYKKKI4moRCIg4er5R1YGTE6IFu45j/eR8nG3wIM6sZ8kfIO0909YdxJrH+6H8bCN77rgBnbF23zeXtX2IMfCa3cGFY3JgM+kwf1Q2RAr819++QoEjDbO2lqOqzo2lYx04f8kTkVLPEW2/NL8gwqDjkNcpHks/PoWKaidmP3IHUhNjcOxco6qAV1Xnht1mZPPc5xo8sBh5mPU6JJj1qHP7Nbsikai0LSVg/i4YdDyGZCZjxv09VOukwJHGOnWAxJopP9uI5eP7oqrODZtJp6JyyvN8GclWiBSoafRg6xdVrKgg0z3fmZSPi00+DF/0CXLS4rFgdG8sHJ2DuBg9Tl1w4VyDBxNWfMbOLyctnh3XoONg0HHo0zkRiz6swIQ7O6ONzYjKi26kJcZg8j3pKPrzAQzoksTiBz3P4Uy9G89vPIR5I7IYBdRuNWL64AzmB1hVJ1mfUEqxpNCBN3cfx4z7u+P1UdlwegU8t/EQZg7LRF2QFip1BcG66VrfC7mrOeWedKw/UKmicxp02lL3gKR18cv1h1Tf2Wc3HMLG4v4sIQ19rWWP9UGS1dDi4wxPQJoHKx6YjhjwbD7sT7/ofaNPLYprCC22yNXsyDcyAWwdd5AfCPlGO/Ztqdr10QuDsPfrWmwolTpt/3j2bk2Zch3PYf3kfCbyIj8+b0QW2saZwuYN5N9TSjU3Xkopq+6lJpgRb9ar1OxkTBuUgZnDMpFs067gaVGHrnTOJIoooojicqh2esNmiJQ+gLOG94LFqNOke1bVuUEBFiRXVDtZMrWhtErT9kGe79PqDj61rgwzh2WCUknKf2d5NSukAUC8WR82syUfU9lpiUTvLCly4GStCzWNPvgFCp4Qllwq//50rQsvPNAdIkVYN+rNXccZ9T+0K6I119fSqXQyEsx6TB/cLWydRCpwunwCEix6xIbMgMoJ3sxhmehqt6JLGwva53dCvcuP5eP7wieIsBl10PGAzSTReSUDdx8eXrwXoxypmHR3F5j0vOZxZw3vhU5tYnDqQhPzG3ysfydUXXRj5pYvsX5KPvTBDtmG0ioWP+SkxeOPj2azBE/295MTqAFdkhgtlSMEsWY9Yk06jM7rCJtJCh8TLFIxOT443iEnfG6/gPgY6TpofS/k7+Mz70q2F0prKo/fyHw25QLz3B3H8NLQHoiP0b72fkHEssf64FyDJ+y1Jq36HOsm9WvxcYbsWxnq3Xk1HnFR3Jygiv0baP4urZ+cf8XHupHE4CvqSRNC4gkhmwghRwkhRwgh/QkhiYSQfxBCKoL/Jlyvk72RoAqFTgKqUr+S5ciVSd6SIgfWfnoSj761D7O2loNS4G9P3YmZwzIxd8cxePwiCCRp6+Xj+yInLZ7xiBvcfthtRswa3gvrJ+dj1vBeMBt4zNpajromPwRRen1ZvECJ1ASzRH1ZXYpfbTikqdKlRR2S50xCj9Va5kyiiCKKawu/IGoGe/FmfVAUgUP3tja0jzdr7j06jqDB7WdJVlllPXLS4rF0rAPtYk1Y83g/DMlMZn8/f2Q2Ei36iLNLGclWtIsz4VyDW6W+DAD1bj8OnqrFojG5qv2ypMiB9nEmmILeXsrfzSnIQsmeE4xaP3NYJmb9vBdmbvkS098pU6lQyn+/YFcF2sWawoKHqWtKUeBIYz9HUvmUIVP2H178Ce6c8yEeXvwJjp1vbJE0s4suH4rXlLIEXYby85ORmmBGokUv3V8JsPYJ9RqZU5CFzaWVsJl0uOjyo/DP+zF80Sesoxdr1kEUgdnbj7B7Z3WjRLncUFqFFzYdRlWdK2wtSJ+1CecaPLCZmpOjdnEmJvrCEcIUNJVQ0kf1PCetJ0WyNjizLd7cdRzzRmTBpOcQZ9Zj1tZyGHQcTtW6Me+DY6CUsjXt8gkYkpmMuSOyMH75Z3i/7AxKiqTvTKTvY2jymGQxqHw2ZTVLu82ARIsBxgjxAk+kbnV6skXztXiN99/S4gyLMXyvWFLkgMUYnQFsKRBEbQFG4Sr23xvZAbxSvAFgB6V0BCHEACAGwH8C2EUpnU0IeQnASwBevJEneT2gHMT3+EWs3CuZymam2MBzBC5fACsm5IEjgJ7nsObTk1j68SkAUAUIspEtR4BH32o2KF5SmIs2NiMuNvkwdfVBLCnMhU8QGYXgD+9LAdDjd3UBAMzefhT/+dOeYSIHi8bkYtGHkmmxTHuZNbwX0pOtMOsjV4lb+5xJFFFEcW2hj9A1c/kEtrdwHEG7WFOYkfacgiy8uasC0+7tirc+OsGoaqG0m8WFufjdzzJBKcEljx+X3H4kx5ou261bNCYH80dm4+3//ZrNhe0qP4+i/h0ZpV72c3u/7Azm/7OCBfkzh2WiRzsbjmrQOwHC9uKqumY/wS52C/v7GqcXQgS7n3iFONd3dUVaE2Xf7ZfYKaG+fJtLK8NmLheOycH5S16VUXtJkQPP/KQbquoka4NnBndDQBDDrt+U1aVs9m9neTVqGn2YOSwT7eNM7L46bkBnRs9cODoHCRZJ+a+m0SMpdDb5kWRtFonjCWGFWn9AxO4j5zRFZrZ+8S0WjcmBxahn7B5ld3pneXXQ91AAIWDn98bo3hg3oDPmfXCUfUd+NaQbnro3g3VM5/9TigceyukQscMt/ys/lmgxoGTPCRUTSTmra7caNVlKsvppaJdUPq7ZwLf4OMPtp8yeRlYo3vT5aYy/qwvib/TJRXFNEElUSsffBCIwlwMhpD2l9Nvgj5FLjOHPiwNwN4DxAEAp9QHwEUKGAxgY/LOVAPagBSaAygRp2UdfY8b93VFV50G9O4A2FgPGL2++mayfnM+SPxnKyndJkQOvbitX3Xymrj2IdyblM7Wzbxs8mhQil0+AT5CEFWKMPN4vq1JRNP5++Aye+Uk3NjtT4/SiXZwJqfGXlyFu7XMmUUTRWvBjUQeTrcawAlVJkQMp8UYkmNUeeHabEasfl6xwzjV4sKXsDIbekQKDjuCZwd3wxq7jYTNgVXWSaNZrI7MREESkxJtRfcmLpSGBa6gYy7R1ZXh9VG/JHidGj3eDtJ1fBAtyMqVepsLJz5+7Q0rg3glaAoTuzd6AuuMp+wnu+tU9KNlzAtMHZ6BTm5iIdFKllcB3dUVaE2VfltVXzvElWQxIiTNhVbAQ2y3ZiuPVTjg9AUa/BJqLr8vH90WyzYiXhvbE7O1HVPRfGVV1bgiUMnuEssp6RuMbkpmM3/3sdrZGJDEXgsI/78fMYZnYXFqJ6YO74fmNh1TJ0blLHqaYGRApUhIsKpNwkVK4fAKG53ZAvcuPuTuOYP7IbJU1iZygtYs1weULIC4YR5RV1sMvNHsBywqpZr0OT6zcr7Ks6JYSi1e3lYcJ4ciJ7Ru/6I1Xth5hxWizgcMv78sApcC6Sf3AB7uXskm9ssCRlihZVSVZDZi/8zj2fl2LVRPzsGpiHr6pdTEhlI5JMYg3GxBvNrToOMMXELH041NhMWBhfqcbcj5RXHvoCLQLIFexjH/sDuA+ALcBAKX0SgirnQHUAFhOCMmGZDPxDIC2lNKzwb85B6Ct1pMJIZMBTAaA22677erO/AZCmSBRUFRf8rJB603F/VU3E3nDDr3Bt483491J+eA5hM3tVdW5EVBQpkr2nNCUm25jNcBq0uO9J+9EglmPn+emhVXTMuxWzQ32uwK/luYf9UNwq6/XKFofvs+a/THVfnU6Dj3a2rBhSn8EBBE6nkOy1QidQipb63zmj8xGYf5tWPThvzHhzs6w24yYeFcXRo9ToqpOElYZp1AjXFKYi3ZxRqyamAeeI/AFRCz76GtVty7RYkCS1YBv69347Xtf4nc/y9Q8do92Nswa3ot170qKHHhzV0VYIF1S5ABHELHr8crPe7EO55DM5LDO1eLCXCzcXcGeU1LkAMdRiCLV/FxagjXE991j9VyzGIk8b/f6qGwYdRwe6p2KKTL9dms55o/M1vwcLzb5UO/24+l3JCVX2Yc39PrpOQ4BiGGKmE/dm4E6V7OYSvHAdDwWXHMle05g/qhs9rMyOeqQYIbbJ8AXoDDoOCQF5093llc3i7JsOsTO3241wqjnwBGCJYW5eHN3BWJNOkli3sCB4wj0fPPvlP6GcsK6fnJ+83kFYwi5iyh3NeWCsd1mwFP3ZsDrFzF/VDYuNvkgUorfb/mKddtlMZoudjWtUy5w7H7uHvgEEa99cAwFjjRsKK3C7O1H8MxPmhVaUxPMWDa2D+rdPrh9UvyREnfreeN9nzUrK8uGrq2r8YiL4uaERxA152MXjL5yoZ8fOwG82lWog6QY+jSldD8h5A1IdE8GSiklhGiSYCmlbwF4CwD69OlzSw4qyAnSt/VulbhBqHhAyZ4TWDQmBxeb/Kz6lWwz4GKTF8mxJpxv8GhuEAJtDiDKKusxe/tRvDYyG+3iTKAixQWnD4QQ2C3NQVSkrl3U5uGHoSWs1yhaF77Pmv2xqYM6HYf28eaIv5fPRyl97/IJMBt4jBvQGR6/iLk7jmLcgM44eaFJc988XetSvZ83d1dg+uBuYeqjFdVOlFXWIzXBDJFSNLj8aBcXnAF0aRftzjZ40MVuwZtjeoNAonMNvSMFW8rOqM630ePHgl3HwxK7kiIHeAKW/AHNxb/l4/vC6ZU6OusPfIPH+nfCS0N74ptaF2b+z5fMNF5rj24JlP3vu8cmxhhwyRZgsvoUQLzFgJ8v3gu7VZqV79bWiqVjHai+FO5nJ3dXlbOhoXRSeY1wBOgQHwObyYf1kyWLDxpU0tTzHIZkJqPAkYaLTT5V4hUqUFNWWY8Fuyrwx1HZUucv3gSTjoNdIcymnPOTKZ8zh2XiqaDdyChHKqYNysC0dQcxoEsSnvlJBl7dVo6Xf3Y73txdgRn398CpC66w9yt3DyXbqiqsnpgHkPCuZmqCGe9Myscltx/zdx5HjdOL9ZPz8ehb+zBzWCZL/mQxGmVHUXltK6qd7JjyiEqBI42xmYDgPrP6c8wa3osJzdyK8cf3WbNmAx8W/yVa9DAbbp3iTBSXh45oC/3w5OYwgr8crjaYrQJQRSndH/x5E6QE8DwhJIVSepYQkgKgOuIRWghCxQ1K9pzAwjE5qAt+4SkAi1GnMg1eUpiLvx/+FvfdnoJXtx0Jm3mZNyILOh5hZraUUjz77hesep2aYMZfnxyAZJsJwPfv2rWmmZEooohCGz8mdTAQEFHt9MIviNBrdP/k89GSjC8pcmDl3pOYOrArChxpLBgN83IrzMXvtqitbAscaRHVR2XFzk2fn8bSj08hNcGM5eP7wGLUY/XjecyrsMbpxZyCLMzZfpR1SbR8X+W/m7ujeR5w+fi+TMhr0e5/Y1TftLBrvrO8GlMHdsXDi/ciJy0e80Zmg+ealaZlRNqjWxNlX6/n0SkhBjF6nnnzyfPzVXVullBsffpO2DVox0uLHEiw6Bm1Uy6wyvPxaYlmnKhpwsq9J/Hqw1ngOIL4GCNiTeFF0yXBTq/8s/xZyUIx8s85afF44YHuGPuXA6yD1tlugZHnsHxCX0xY/plqzk9mDSkfG5zZFtPWSWtucGZb+AQR8WYDvIKImkYfCAEWaHSjUxNMeH1UNp7dcAiDM9uycwj9u3kjsjD9nTLUOL2YPzIbs7cfRSAobqFMSOXnREqaX/tA8j9W0pgjCTHFBJOglhx/xBr1sBh1uNjUTOm2GHWINeov86wobiXodZwmi0N/MxjBE0LehHaiR4Crm0OllJ4jhFQSQrpTSo8BGAygPPjfOACzg/9uubqzvnWgJW7gD4gqusO8EVmwW43sJjV17UEsH98XhEiqX/ExOrw7OR9evwieI+A5YNbWctQ0+pgXD08Inl3/RZi/oMd/5cFaa5oZiSKKKLTxY1EHtTwAS4oc6NHWpkoCDToe0wdnhKliyqJZVqOOKa5V1bnx2gfHsHpiHqobvah3++H0BsIseC6nAjpzWCYW7DqOcQM648ApaV+94PRhwgpFkF+YC6c3wJK6pWMd7EYvH0s2gS8/e0klBrOzvBovDe2J2iYfjDoOG0qrMDizreY1t5mkgLDG6YWOIxF9CCPt0a2Jsq/X8+iQEAMAOFPnCrtOdqsRZ+ol9Wu5K9jFbgEB8Mq2cuwsr8aQzGRVcljj9MKk5zBj42HYbQb89sFM+AICahq9SLIYNIumU9dIFMs3/nlclQwdPFWrEneZPjiDicWEFjdeH5WNeSOyVDZNcnKlnP0LFYIJCBRTB6XjQqMPLzzQHZUXpfcgz0Wy7rmexwXRh9mP3IH28WbVd2fmsEyk2y2ovOhWFS2e23gIs4b3YvRFrYRUOYPZs50NCF5buasuF0VSE8yqTqeM0DnXlhp/XPL6UdPoDYsHEy0GJOpax/e1pcMbEMM8uBfursDvfnZzGMF/fpW/+y48DWBtUAH0awATINlYbCCEPA7gGwCjfsDxbwmEihtMH5yBZzccCgsQlObvVXVu8BwBIcCSIgfq3QGs/OQUivp3RK3Th3cOfCOJEgQX05ztRzDj/h6a/oLcVbSZW8LMSBRRRPHD8GNRB7U8AIvXlGLDlP4qSmiSxYDObbQl49vFmuDxC0i0NKsqllXW43hQzVOexwrtSij/XkYoVa38bCNmDssEADbIL7/u1LUHMXNYJguOIyWU3oCgKQYjq43OHJaJnLR4WAw8Vk7Mw+ladXfRYuTxyYuDYNDxoKCq4F95vOgerYbWvezFoT1Y8lc8MB0xBh5+gWL8cqn7tXSsA/FmPdw+ARun9IdIKTgSnKcrysUFpw9j/rxf9Z2IVRjJy5B/fva+7nj9H8cwb0QW2sWZoOc5/NffvmIBYZLVENZBk5//7IZDjP6rNKFfufckXn7odjbfp1zH9W4/dh85hzH5neANCMwrUF77srr44sJc1Ln8uOQJMOEh5XdnyupSbCrurzKyl8+rcxsLrCaezaMuKZSui/JayzOY7z15J5IsBrz6cBZ+/zMBeh0HHUewcEwOBJFi7b5Tmh3HuTuOsddsqWvb7RPC9pQZm4IecZYbfHJRXBMIQaXgUC2P3z6YecXHuuYJIKV0ZaTfEUJe+wHH/QJAH41fDb7aY96KUIkbiCIoheaNQinrLW120iZp4DlQAPVuH9Z8+g2e+UlGmLz5nIIsEAJ2g1BuovqroPm0hJmRKKKI4ofhelIHlSJTFGAMCBlVdZLQVej5GHXaqpiJFgP+Xe3E1kNnVPvg5tJK1m2RA+dVE/PQ4PbDGxBhDYpmKEU8lFQ1+VyUvmdKVNW52b54uW7GuQaPJiVu5d6TmDciC+8dPBPW/ZG7i8s/keiG9jgju3Zdky1htKLoHh0OrXvZbYnSvdFq1LHPfVNxf9itRrw0tIdKTG3pWAfMeh6ztx/BuAGd4VOwd4BmeuKGKf0jJuTd25oxuyALZ+s9GPv2Acwfma0KCJeOdYR10GRU1bnR4PZjRMmnGJKZjLVP9AMB8G2DB2fqPNj4eSWeujeDWTu8uPkwSvacwG+H9QTPEZj0fFhXr328GbEmHV4NdjvlWKFD8P3K83iXK5DEGHnYjAYkxATwwgM9YTHy8PjDBXGWjnVE1BqoEb14ePEnqKpz48CpeqbY2iHeDLdfUHkettS1HcnmRYgqCbQYRLpnGa6CAkoo/fFWBiHkNKX0hsoa9unTh37++Q9pRN48OFPnQk2jF08F1cVkpCaYVQPPS4oc2HPkPNaXVmFOQRY+OnYeY/I7od7lR4LFgFe2fqWqJsgD2m/uqsDQO1JwW1IMahq9sBl5pMSbkWi5cirBjyX/fo1xw0/wVl2vnV7adqNP4Qfh1OwHb/QpXC1a3ZrVEpmSK/7K+eXQDiAAnG9w4981TWGJVPs4Iygk1sSFRh+8AQEmPY9EiwHbDn2L/+hmR1yMAacuNLHO2uLCXAiiiCV7TjBGRaLFgHkfHA3bX2cOy4SB51TBv/y75eP74mKTjwk4+AIiY3koE8qMZCsm35MOHU+g4wg8fgHnGjywmnSoa/JrHnvW8F5oF2dSCWDI1+/1f0hKikkWA5JtRsSadWjyXvf9+pZbr/K9TBRFEEJQ0+hFtYJ2B0hJWJxZzzwBZaQmmPHayGw0uP1MOfTRt/aFvcb+X9+Liy5/ROG0mkYvfvPeYRQ40hitcsGuCpRV1rMZQI9f1FwDSnaQvCba2IxISzChySuwGUdZcTTdbpG0BYKdzcJgt1LGu5PzNd/nX6cOgEFHcMHpB0cAQgCDjuB0rVslYb90rAMpcSbEm6WETL62F5p8eOOfx1Vrsn2cOWyWV8aZOhfunPNh2OOfvDgIKXHmaxl/3LRrtrrRg0cW7w3/LBTaDVHc2qi+5MGZejdTFU5NMOPN0TnoEG9GcqzmZxxxvd4qKqBRKCDfgHiOwKDn2NC1cjG0jzPhXzMGwi9QvPWvE9hQWgUAWLn3JKYNymCbuBxQ1DT6VFLlFBRP3dsVhBA0ef1w+wS0sRquepi4Nc2MRBFFFD8etOalZmw6rCqClRQ5kGwN338IR7Ay6OcmU+A/OnYeP83qoGI/lBQ54PYJKNlzAoMz28Jq0uFCoxd6nuCloT3YHMaEOzurGBWygbXsjZqaIPmfcQRwegNhglyLC3Ox7KOvsffrWswpyMLL75cDABaOzkGixQAK4NVt0mMP53bA+OUHVM9d9ekp1DT6MC+CJUF6sjXMl1V5/ZQ+hLe6auL1AscRJFkMOHa+EecaPJi55UtmASEnTV3axMCg4zU/gzZWA0jw//2CqFnNJ9/RLRdFMYy5oxQHijHwMOm579WNjjHwmLqmFO89eSd7DGi2dvjHs3czA/bFRTlsBEUWmEmJM0XoOlGcbfCpktjXR2UjxsBj9cQ8CFRSF7/k9mPK6lK2xuQ4wW4z4dWHs7530na5UZPWEn+0sRg12VZtrqJoH8XNiYAoghAwZWKXTwAhgCCK3/3kEFwPEZjESL9CNAH8wVBWuzdMyYeOI2gfb2aLwS+IiI/RodErQMcThHZ4CxxpLLAB1Cp1yqpgQKBw+wU8uOB/2XNTE8yMf38LdvOiiCKKFohIIlPpdgs+mjFQ0wMQkPZSpyeAp+/thqlrm5OwVRPzmK+afKziNaVYNCYHxQPT0egJgFIgxsgjyWpAyZ7mAtvku9Px6rYjmDW8FzomxYDnCBbt/jdT56xt8uHl979i4hUbpuRj1cQ8XGzyobbJh4W7K/D0vRmYdm9X/FKhwPzzxXvxt6fuhNMbwK9/2hN6nmPG4PI5Prn2IN6ZlA+/IMIQgSZk1vNhe3Wk69caVBOvFnLSLCd+9W4/hmQmY9yAzli59yTGDeiMDvFmzc+A5wgTOuEVPoPKRE7HkcsmLQJF2HzfjE2HsfrxPJypc4MCePqdL5goTec2Fuh5gj/87SuVsJssjlJV52b389Bz5jmCqjo37FYjLrkDIAA2FvdHrdOH4jWlWD0xT/N9iiINK8w8u0HyHRy+aC/7W9k78IeuseioSetS6G2toBTMrkVGaoJZmvO8QlyPDmApJBVQrRXnuw6v16qgrNZecEoBwwsP9GCD1VumDUCjRwiTiAWADaVVSLIYVL5X9W4/SvacUM2dzCnIwuztR1DgSFO9dlWdG6IoRj39oogiipsGkSr/ZoPussFkbZMPj/3lAOaNyFIV0AgJn82zW43QcRxLDOV98o29xzFtUAYqqp2ocXqRYDHg5YduR6JF8l692OTH3q9rUVHtxPP3d2fCLfLzqy+FU/jLzzbitZHZqkB9SGYy4mIMECnAEwJ9MChXoqrOjfOXPGy+S6kMeblgONL1S7ZJAiYle06grLK+RaomXi3kpFlO5JSG7DOHZWLl3pP4zYOZmsmdScdhc2kl5hRkQc9zeHXbkTBT54VjciKKdgQCIgIhdlCA9PlXX/Ki6O0DyEmLZ8dMTTAjJc6M841ujBvQWdWNljuCcqdMK4mSiwnFA9Mxf+cxTBvUwoNlYgAAIABJREFUFYJImdDSJY8/rNO4uDAXAZFqxhqh+gSyOqechAJX5x0cTX4ktJZuZ2uFSLW/V+JVjPNdDxGYztf6mFE0Q1mtjTHwQU8egk3F/VHb5EOSxYhfLNunWiC1Th+m3dsVe7+uRdtYI154oHvYTalDghkfPn8PztS5sXLvSTx1bwYW7q5QvXZqgmQYH/X0iyKKKG4WXG3lX95L5+44hufv747nNh6C3WrEHx/NViVEsleeTLcE1MyJaesOSnNUVgOe33AIZZX12P/re5GWGIN2sSJWTOiLyotSR23FhDwYdQTf1Lqwcu9JvDi0p2Ygr5TpH5KZjKcHd8OYZftUc1NDMpPDZgtrm6Qaq/z4u5PzQYDLBsNa12/eiCz8asMhphq6cu/JFqmaeLWQk+aSPSdY8iMbsrePM2HcgM54dVs5nhzUVUXVahtrgt0qURtFUURApJqmzsprrZyfNxt4nG3wRDSdd/mkBEqmb8qsHY4j4DkOK/eexOxH7kBKvBmna1147YNjsNsMeOMXveELCKht8qFrGws2TOnPPDTbxOiZB+GvhnSDL0BxrqHZNqTRE8CqT0+FydL/dlimZqwhK4mHUlKV7zuSd/CGKf3RLtZ02SSwtccht6jeQhTfEwae0/xeGfibwwcwN+QhCuACpbTyWr9Wa4SyWhtj4PHCA90xTmH4KlDKNlml+lhJkQNrn+gHjhBNmeDXRmbD7RPQKSkGo/M6wqgjmD64m6pauOyxPqARVKai1eEooojiRkCr8p9g1n9nECTvpbLH2Juje0PP8/h/fz/CFBDtVqlgVu/yqRLC4oHpiDfrkWwzwm414rbEGOj45sCW4zgkWowQRYqLrkaVL9eSwlzclhSDX/+0J4wavq4yVXNjcX+4fQJEirDkc8rqUqx9op9mN0fGzvJqTL47HR2TLJcNipXXz+0XcKLaqRLQeXHzYax7ol+rotJ9F5RJs9MbwKzhvVi3zaTnWTesptGH4oHpsHE6dGtrRUqcWZWkiCK9bPEitBMmC65omavPH5kNk755PYUeq43FiGfv645Jqz5n8cLCMTmod/uZDcWQzGRMH9xN5aG57LE+uC3RhEaPAI9fxBNrpS6n/Dp6nguTpc9JiwcfIdbYOKU/PnlxEASRqrz8lOcaiZb8bb2kYhplHGnjajqnUdxaCIg0stXHFeJ6UEDnazyWGPTvGx20c4jiKqG88VAKTcPX5eP7hklLF68pxYoJeexnJarq3CCQOooUgE8Q8dLmL2G3GbBhSn9QSlkQVdvk0wxYotXhKFoSrlTF9BZWDW0RCA2qv08QpNxLyyrrUe8KYOaWL1jgPnNYJrq1tWLs280Br5a59rwRWTDoCGZtLccLD3RH21gTC2Q1Db3XHmRqnKlx4VL5yx7rg7axJpxtcOOxvxxgc2ZKyHv2zGGZyEi2QhAp5n1wNGy+q7bJh5S4yOp/od0CnkDTp40PzqRFIUGZNMuKlf9v+xFmqB4qpAJIapSh1/C7aIvK9ZOTFg+7TbI3qaqTbBiUs6Wztx8FIIlDpCdbYdarj6X1WhSUrT1A0ggI9dCctOpz/PXJARBECi5Ij5bN41/cfJjRYJUFkhce6I6zii6hjKo6N0RK0SEhBqJImZdf6PuOREuubfLhl+u/iDKOIiBS5zR6vVoOBDGC1Yd45RTQK+8ZfgcopYM0/ssGMBbAgmv9eq0Nyk08EFwIxQPTVQPhMQZt9bF6lw8cIUhNUEuhy9QRl09ghsVllfXYWV4NGtys7TYjUz9b9lgfdozWOGgdRRRR3LyIFATJ9EgZyr30kxcHIT3ZEha4V1/yqgLe6YMzNMU3zl/yYmd5NWZsOgyrSaqr1jR64fIFNPfi9GQrure1Qa/n0bNdLDuH9568kyWqsqeXHGArkZpgho7nMGtrOSqqnZj3wVGMG9BZtS8vGpOLzaWVEYtzcqL88OJPcOecD/Hw4k8giFTztaIFvnDIRYe2cWb0bBeLVx/OQle7BW2shiu6hvJxlPdZGcpOWPHAdJyudbFjl1XW42KTDyNKPmX37LLKekxY8Rl4Alawle2iRJGGvZY/oJ4ljOQd6A+IIABEClXXXC6MyN6DADB9cAZmbDrMisWRroN8LilxZvAccLbBjdMXm1Dd6EGCWR8WZ8wpyELJnhNRxtFlEKlzGr1eLQc8px3D81dRoLvmCWAkUEo/B2D9sV6vJUPeOOWFELppR1ogcWY9dpWfRUmRQ7WxzhuRhUSLHokWPUr2nFA9J/SmFRo0KQOWKKKIIoobDWUQlJMWj6VjHZg/Mhu+gAAxpEqqDIjNel3YvunyCaqANy3RfNnqqxwsy4lV5UWX9s2aAPVun2ZQLu+lJn3znNn8kdlhRbdkqyT5vrm0kqlPzhyWiU3F/bFqYh7+fvgMnr2ve8TinFai/Mq2clUwHy3wRYYoUtQ0enGmzhWcvzegbZwkuBKpSKp8jpyUXQ5yJwyQkrMFuyowpyCLPSavTyUkASQ+LLk/dr4x7PWUxwcQsdhACMEr28oREAUsLsxl34lZW8tBKdDNbsVfpw7Av2YMRBe7hRVNQtetbOSuvIanaptw7FwjHn1rH+6euwePLN6LY9WNSIkzYsOU/thU3B8zh2XitQ+OMbpotCChjdDPE4gWcFoaOIKw79X8kdm4mhD8R/MBJIS0hTQPGMU1glnPYd6ILHYTqKpzY5QjFbFmXZj62J8e7c2qxMmxBqyfnC9ROjgCniMICCI8fgHTB2ewgfWOSTGaN/7ooHUUUURxs0IOgrTompebh9ESQ+mYFKOiiVZedGtS02QlQzlYlo+hJfM/pyALf/jbV5hwZ2e0jTWhU5JF83xkT6/X/3EMRj2nEhPRB+cNu7e1MUGRlx/qBUopCCHgCfDE3V0vKwCh1S3YWV6NWcN7tXolxe/Cd9GMtWidAK54Pku5JuvdftQ4vazzFm/Ww2rUYe0T/VDT6EVtkw+bSyvx7H3dEdCwYNCiAoau+c2llcznT3mOPJHWRs3/Z+/cw6Oqrv7/3WfumQlJCAkoidxEMGIwBMLNtigtYkUpclNuAnITldYq4q++VFvq+4DoS6WIBKzcURD0xWJVLIq2ICIBpRqJvAiWIJAQkpDL3M/+/TE5J3Nmzklmhkkyk6zP8+SBTGbO2TOzzjp77b3Wd1W58Ow9WXh99iB4OYdBYOiY6BNlkRYUpF1BAEF2awpoxVJW48IPZbVBJStzNxfIrVSsJj1+s/3LsASe2iopFkPQ97dmSi5SLJH1byZiD50gwBxwXZkNAnRCbIjA/AXBgV57AEMA/Dra52uLSHUbDrcXme0t0An1DV9n/7S73LRVuknUurxISzRhb2EJCs9XYePMPDzw2mG5p1/RxSqs+LAID97aXSFWsG5a/5Z+qwRBEJqoKd5Jk9oLlY6gdM2G6mEamrhLvcpEzvHi+L4KgS2pAbf/ZFk6p8AYlr53QlGrJe1kFJ6vwpLRfZBoNjQ4nmfv6YMJ+Z8FBZ3bZg1ERt2uof/nYNAJIQVtWnVWgiDQAl8jNFZrpbZIWlrlDLs+K7DWUKoXnbu5QBZsmVwn4CLtsPVMs+FilXr9XWAqoJaAUuA1IKVzHjtbIffwkxRG9XpB8d6kdGmXR1TtV+b/fl0er2bJSoJRh2mvHcaeR4dix9zB8HhFuacnLUioU253Y+W+7xSKrCv3fYfnxmTTNd1K8HhFPKxyXe2IERGYIwG/cwBlAH7LOS9ReT4RBmorj2/MHoS/fHQSS++9GUa9IBeJ+0tLf/T4zwD4HOsVv8avWpMMKh4mCCKWaWgXplfHRFhN6hPLhuphtLIbjHqdHPRJPdZSrUakWI0oueLAU3f2RkaKL/3PXyhL2rW5XOPCxLWHgsaSYNQ1Oh4t5eWSKicsRr28iBeu8h81zo6cSGqtIq3P8rfJtESzHJwxxoLu2XM3F+Dt+UM1g3u1VEA1mw/8vTFb8X9vUrr08vHZjb5fo16nyGDyH2uF3Y00mwnnyh2YG7AjSWUn6rg83iBFVgB45m6qAWwtuDVEYNyxIAID4GPO+Ua/n02c83cp+IsOaiuPmz87jUdvvwFuL4fbq17EL9WoZKRYUFLlDCrGpvYOBEHEEw2JvQgCU63pi7Qexl/8Sqp9cnpEPLHjK9y/7nM8/uZXMOp1QUJZa/afUqTpB46l1uVtdDxadT3Sjl+oojeBUD135ERSaxWN+iz/etGG7tnRFmtrzFZYgLicf7p0Q+831WpEl9QELB+XrRjri+P7Ys3+U1gwvKcc/EnvLxTbbqtQDWDrR6+h8aGPERGY/5X+wxjb1QTHb9OorSLm//MMzAaGbh2sWPvJKblIG/AZxurJ/bDu0+/l+pNdBWeRPzUXbq8X58pr4XZ7yXEQBBFXNLajEs1JcOAEeNusgfi06CLmDeuBnfMGY9usgXKdjf9zV03KQa9OicjObBckrrJ8XLZmnTVQLzIiiiLyA4S7JD/OANjd6kqjoSzeNaRASWgTiW1F8ppA0RiPR8TlGicuVtrlY/gj3bObIrhvyFZ0DApxmowUC1KshkYFhQSBoWuqFb06JWL7nEH4ZOEwvDC+L5a+dwKl1U5062ClhekwIJX21o9Bx1Tn+FJdeDg0RQqo/yi6R+2gjJ0BUAXAC8DDOe/PGGsPYDuArgDOAJjAOS+P1jljEa3UjgSjHowxHPy+DACwfvoAX+8mxmAz6fDw7dfj0eE94XB7cH9eF18e8dZjKK124pUpueiVZqV0IIIg4obG0twa67EWLv6pch6PiHtyMoL690mTbLW0uhSLCW89NAQOty/13qgT0D6hfjyKOj69gGqHB9Ne8zWAn/uTrgqxj40HT2PG0G4oqXbC4RZDTvcjokNjtqVWmxquPQamOI/ISseTI3ujxulBrcuL9QdOBzWD979nR1usTes9+c4lyCq0Uu3Z6o//D0vHZjf6fgWBob3VBFh950gw6rFqUo7cp5BsO3Si7fOI2MPjBY6eKcO22YNk0a+PCs9jxE3Xhn2spggAucb/o8FtnPNLfr8/BWAf53wpY+yput8XRfmcMYVWLv41Sb7VAOlvOwqKMSIrHY8OvwFT/lpfJL5sbDa+Lq7AndnXYsXEW+D2ith55D9Iv7U7Um1GbJs9EDrGYDHqkGwhx0EQRGwSSg1bKJPghia2WpTb3XLwl5OZjHnDeqDG6cGFKw50amfWfL1/uqZ/0AgEK0QuH5eNNJuv8Xf+P8/gdFktnhx5I7wix9jcTDz/fhFKq514eVIOVk/uh/lbj9LiXTPib1v+NmQx6nDxilOzJjPUoCwwtXdsbibOXvb9XxJrK61yyfWonZMtMBkEnK+0R33i35jqaarViMd+0Svo7+HOIQI/H1Hkmtd4JNet1nuLxnFiBVJpb92YjQy3ZXWCyyNCYIDIOW7L6gSzMXybZZxHN0ZjjHkB1MC3E2gBUCv9CQDnnLeL8LhnAPT3DwAZY0UAhnHOzzPGrgGwn3Peq6Hj9O/fnx85EqhTE1805LD8/wYAE9ceUqyeScphK/d9h7G5mUi1GpHezgSXR8T09V9QobWSFn/z8WqvXZ96t6WH0KycWXpXSw9Bok3Z7NVO3hqb2Gqdr9blwYkLVdhXeBGjczqH1GqitMqJMasPqKoicnDcu/pg0N+Wj8vGFYdH3lW5NsmMu1cdkJ+Tk5mMP46+CX/56GS9P0804dokC/T68Co8WmgiHPf2GmhD66cPULQ1AILVL0PhXHkthi77WP59u5/KX6CgUE5mMv70qz4Ri6U09t03ZLtqQbCkJlpudzdJgAaE31JD69gRHCfubZaIXy5VOXC51oVz5Q65DUTnFDPaJxjRIdGs9hJNe436DiDnvKn25jmAvYwxDiCfc74WQEfO+fm6v18A0FHthYyxOQDmAMB1113XRMNrPhpa4REEhhSLAT9WeuARORaPysKa/adw7GwFAN8q4sp93+GBId0Ukxb/1WZSAG1ZWpu9Eq2flrLZq13tVhNQWfFhkdxTz3/iqjZZ3DQzT07TlF6v5Tsbqln0qgh6pNlMsJn0ih6Cqyf3w4TcDAzP6ohkiwGpNhOef/9bhfJfJMFGhBPhuCWa9hpoQ1ptDcKtWwtMca6wu5FkMcCuopqpJZbyziND4RXRYBAWyncfioJp4I5otOxJ7Rq/XOPEhUoHXhzfFxV2N9bsPxXRnKWxdh6xBM0LCADwihx2l9KX2F1eeM2xoQLaVNzKOe8H4E4ADzPGfur/R+7bylT9BDjnaznn/Tnn/dPS0pphqC2HKHIUlVRh0quf4/YXP8GSPYV44o5eyMlMBuBLmxqbmxnUH2vhzuOYN6yHfBwqtG452pK9Eq2DeLXZwIltTmYyHhjSDRPyP8PQZR9jzOoDKLpYJe9CBE4WL9e4Qp7sNyS0pWPBym4LhvfEQ3VpndJx5289innDemDJnkJMXHsI09cfxgNDusn+vaHzN0SkSqLxSjTtNdCGKuzuqAiqBQp67Co4i+vaW9DeaghSzezaIUF1AeF8hQNjVh8IsmV/QvnuwxWJa0p7EkWO8xUOLN79NSauPSTPcdJsprDtPtLWHC1BvPpYIrpwALUur2z/i3d/jVqXN6J6u7gJADnn5+r+LQHwNoA8ABfrUj9R92+bbzVRVuOSa1MAnzNbtMsX3GWkWJCWaEKq1ajq9JLrVOwAn3NnrPWt/BIEQUgETmznDeuh2jxeSkEL9JtSzz9/tCbGWgp9OsEn7R04qe+SGjypDww6/f17Y+dviHiaCMcKkkInoFTjlFp/XK0SY6CS53NjsiFy4Nl3CgEAm2fm4R+//SmW3nszTDpBdQEhlBYKoXz3arabPyUXOgFBAWWox4yUshpX0PtatOs4FgzvGbbdk/o5EW94RC5nhQD1GzieCPoANoUITNRhjFkBCJzzqrr/jwDwRwDvAHgAwNK6f3e33ChjAy3H2zPdhiWj+4AxX5NXNWWt2rptZUksJgJVWYIgiJihsdqmQCEZrcUxu9sLHUOQ3zx6pgyvTMnFQ361V2um5MotIQLH0c6sx465g6FjABMYqh0e3LPqAIZ0T8Wjw6/H5gfz4BU5LlW7YKib1Af66cAJvDRu6e+RBBvhNA4nlCmOaTYTlo/LxsKdx5FmM2HB8J7IbG/B9jmDIHLAZBDQwRpZi43A9MfSKidKq524f93n8mMZKRbseXQo8qfmKlRpG2qh4H9dSD38GvrupWD0rflDUOv04vSlGvzX/36N0mon1k3rj47tTLC76q+xprQnrTlOtw7WsO0+FCEpgoglRI1G8GoLMY0RFwEgfLV9b9ftSOkBbOOcv88Y+wLADsbYgwB+ADChBccYE2hNGk6WVGPu5oK6+pAhWDMlF/P8Ji0rJvRFpyQz3p4/BCVVTmw8eBrPjcluwXdCxBJtTdSFiH9CqUMSBIaeaTZsmzUQJVVOJFkMqv7zVEk1Vu47KU/0peNNHtQVf3q3UCF/v3Lfd3huTHaj9VAd25kw7bXDSLOZMDqnM+5fV6/WnD81F50STUGT0/ypuXjpH98p3mdGigXXJltwYNFtEYtt0EQ4PPxTHIvL7Xj+/SKsmHALEow6hRDL8nHZ6NjOjA7W6NSTqX1Pm2bm4XylEy/94ztZETQ90QSzQT0Isxh1Qe0lAucDat+9IDAwMFlVXGL2piNYMroPZmz4Qn5tzzRbk9mTZissky6i+kJqm0DEE3qNOb5eF35CZ9RVQGOd1q6eVHLFgVOl1YpJyrKx2XjhgyJZCObgU7dBYAx2txdekfuKqfd+h9JqJxaPysKSPYWtWgAgDFr8zceKvVIA2DCkAlpPqDbb1KqToSgXBj4vJzMZT9zRK0gg6/n3ff4zJzMZC4b3RI90GywGHVwer0KpUeLAotvQOSWhwXFsnzMIQ5d9jPypuViyp1B1nKlWIy7VOH29AxmDQWAQOccz73yDvYUlURVrIRXQ0AlU6ASg+T0uGd0HfTonRU1UJPB70lKQfeeRoYp2FCOy0vFfd2VBJzBVdXA14aNALlba8WVxpbzYIQnMbZ8zSFYm9bfdprCnGBAsikubJVoHP1bU4scKB36z/UvZ/v888RZ0TjbjmuQEtZc0nwoo0fQ0dKO2u714/v0iLB6Vhd6dEvF9aY0i+BuRlY7yGrdilXLZWN9OX3G5HTd2SpSddxsP/giCaCKaYxIXah2S//OOna3ACx/4/OeNnRKhExge2XZM9p/HzlZgxoYvcGDRbUhLNKG0ytloqpvWOASBYf30AeiSmhCk1uw/zrJqZe/A5eOy8eTI3lgyug8EQYiar6b+YaGjtgullT6cYNRFtZYy8Hs6V16rnrbs8sq7W6Io4lKNC5Ne/Rwvju8b9Py9hSV45m4uL1qoIYocl2pccpArzR02HjyNCrtbce6mrB2lXTuiLSMwhkSzDhtm5NX1AQS8ojcizQ4KAOOMxiZOBp2AvK7J6N7BCgDokpqAtESjvHLdLc2K06U1ipYPi3Ydl3f+9DqBJgEEQTQpDcmvR2vnINQ6JOl5aTYT5g3rgWSLAbUuL2xmPQQGLB/fFwLzyW+v+/R7HPy+TD5GKKmTauMYkZWOyzUuuV9cYKaGJMJ1vtIe9Dkt3Hk86rtKRHiofe/pDdTWN2UtpZZ9SfZj1OvABCbXB0oqpYHjNOgFlFY5Na87LYG5TTPz8PiOrwBAnmcIAvDthSuKmsRoLvCEs1jR2hq9E20bAUC104tfv1F/bb103y0I0DIKCQoA44zG+tZ0SDBg1C0ZmLGhvqn7K5P7wWwQMGPDEdXJhiQiQMIvBEE0B1q7YqIoRm1nMNS6tlSrEZtm5uHiFYcidf712QNRafcoaqNWT+6HR4dfLx8jlN0ItXH8111ZmPTq50GTaWkhbs2UXDz7ztd48NbuzbKrRISH2veeYjEEfc9SDWBT1lIG2teIrHQsGH4DJuR/Jo9jy4MDZTtas/8Ulo3NVqQ5r5vaH9UOj9zTMpxegIwBpdVO5GQm48mRvbBwZ70da81TmosYSBcliKji4Ry/fuNLxbX16ze+xI65g8I+FgWAcUZjaU2Xat34y77vFKIEf/noJO7P66I62ZCEYZIsBiz/4ASevadPs78ngiDaFlq7c16OqDRmVlPd1EqXFAQGm1mPaa8ppbWdHi4Hf9Jj87cexY65gxXHaGw3Qi1Y0PLjN3ZKxI65g/HsO19jb2EJxuZmtsiuEtE4at+7pJTpcIvQMcBi1CHZ0rQ7ToH2xRiTgz/AZ1enL9XIdiSlOS8Z3QeZ7S04e9mOVJsR975ysMHrTuuaPVdux5LRfdCzow331dUWJlsMIaVfNzUVdldUGsYTRKzg9qqrgHq8rbsRPIFQ+tZwPDCkm9woeMmeQjwwpBs62JQrkJKTlnYDl39wAg8M6UY7gARBNDlaPfE4V7+5hTNxlFb9pQbYE/I/w+Vad4OpX26PGHRegUHjRiuGPBb5WHXBQueUBKQlmjT9uMWoB+ccewt9LW3X7D+FF8f3VXxOy8dlo0tqAil0xiCCwJCeaMZ17RPQOSUB7SNs/xDJeSX7UruGVu47ifwpubIdlVY7YdQLWPjmcczY8AUc7sh6AS4bm41Nn51BpyQzGOqvFynN1J/mbisSzYbxBBEr6AWmem3pImkzE61BEc2D1sRJmgxwjqBGxot2HYfFqNzslaTDt80ehA42Ixbe0RsbD56GIJBJEATRtAQ2uX57/lD06pgYlcbMWmnygf3z/FE7r8ihOpZI5LYDaciP+4/l2NkKLH3vBF4Y3xefLhyG7XMGoVenRHRNtVIKG6GKmi2XVjtxTbIZO+YOxs55g7F4VJai3lSv0kheqxegdM3umDsY16dZ8dyY7KBrV0oz1ZqnNAfRbBhPELGCXmBYPi47aFFQH8H9gFJA44zGak441FetDbr6Zq+SwSx4/RhKq52yktdjv+hFq8oEQTQLail00ehHF6r6Z2PnNelZUH+0NVNykW67+tSxhvx44FhKq6X+hAkU9BGNonUNJVuMgAWotLsVEvLrpvVHui2456RWL0Ct1En/8x47W4GNB09j26yB0AmsRcRXotkwniBiBbeXy0r/UpnX8+8X4S/354R9LAoA45CGnLBJI0/fYtBhx9zB8HhF6HUCEowCVk3KAWMMOgY8Nyab1LEIgmhRoiHxHqr6ZyjnvaYdV/jNdJsJen10siS0/Lg0lnceGQq7ywsv5zAbaMeCCI3GriGtvzX0mlCUNGOtPUM0G8YTRKxgMuhQWu3E3M0F8mMZKRaYIrhHUADYytBa/at2NqzwRRBE5HR96t2wnh9DjeNjjqvtRxfpLqLaeQWB4drkCPS1o4B/E2/y2UQ4NHQNNbTwoPZ4OEqasdRLUssPdLDGxvgIIhJSLAbVzJQUiyHsY1EA2MpQW4XTCcA9qw60uCQzQRBEUxNrOxGR0Fi7H4JoLuLVFluDHyCIQMrtbqwMUPpfue87PDcmO+zrkQLAVkjgKty58tqYkGQmCIJoDmJpJyISIqljJIimIJ5tMd79AEEE4vJ4sbewRFaKlnjm7vCvR5J8bANEQ1mPIAiCaB7IZxOxAtkiQcQO0bweKQBsAzTWOoIgCIKIHchnE7EC2SJBxA7RvB7jKgWUMaYDcATAOc75KMZYNwBvAEgFUABgKudcu9lTG4Vy4QmCIOIH8tlErEC2SBCxQzSvx7gKAAH8GsC3ANrV/b4MwArO+RuMsTUAHgTwSksNLpahXHjCn3BVKwmCaF7IZxOxAtkiQcQO0boe4yYFlDGWAeAuAK/W/c4A3A5gZ91TNgL4VcuMjiAIgiAIgiAIIvaJmwAQwJ8BPAlArPs9FUAF59xT93sxgM5qL2SMzWGMHWGMHSktLW36kRLEVUD2SsQbZLNEPEH2SsQbZLNEtImLFFDG2CgAJZzzAsbYsHBfzzlfC2AtAPTv359HeXgEEVXCtVdK5yRaGvKxRDxB9krEG2SzRLT4FUYiAAAgAElEQVSJiwAQwFAA9zDGfgnADF8N4EsAkhlj+rpdwAwA51pwjARBECERbtB+ZuldTTQSgiAIgiDaGnGRAso5/3+c8wzOeVcA9wH4iHM+GcDHAMbVPe0BALtbaIgEQRAEQRAEQRAxD+M8vnaS61JAn6hrA9EdvjYQ7QEcAzCFc+5s5PWlAH5o8oE2Dx0AXGrpQUSZWHpPlzjnI1tyACr2GkufjxY0xugQyRhj0WZDIR6+Dwkaa/SIF3uN9c+xMeJ5/LE2drLZ2KStvV8gtPesaa9xFwAS9TDGjnDO+7f0OKJJa3xP0SQePh8aY3SIhzFGi3h6rzTWtke8f47xPP54HntL0tY+t7b2foGrf89xkQJKEARBEARBEARBXD0UABIEQRAEQRAEQbQRKACMb9a29ACagNb4nqJJPHw+NMboEA9jjBbx9F5prG2PeP8c43n88Tz2lqStfW5t7f0CV/meqQaQIAiCIAiCIAiijUA7gARBEARBEARBEG0ECgAJgiAIgiAIgiDaCBQAEgRBEARBEARBtBEoACQIgiAIgiAIgmgjUABIEARBEARBEATRRqAAkCAIgiAIgiAIoo1AASBBEARBEARBEEQbgQJAgiAIgiAIgiCINgIFgARBEARBEARBEG0ECgAJgiAIgiAIgiDaCBQAEgRBEARBEARBtBEoACQIgiAIgiAIgmgjUABIEARBEARBEATRRqAAkCAIgiAIgiAIoo1AASBBEARBEARBEEQboc0FgCNHjuQA6Id+Qvlpcche6SfMnxaHbJZ+wvhpcche6SfMnxaHbJZ+wvjRpM0FgJcuXWrpIRBEyJC9EvEG2SwRT5C9EvEG2SwRDdpcAEgQBEEQBEEQBNFWoQCQIAiCIAiCIAiijUABIEEQBEEQBEEQRBuBAkCCIAiCIAiCIIg2AgWABEEQBEEQBEEQbQR9Sw+grSOKHGU1Lrg8Xhj1OqRajRAE1tLDIog2DV2XREvS9al3w37NmaV3NcFIiOaCfA4RDciOiFChALAFEUWOootVmL3pCIrL7chIsWDdtP7o1TEx6hcsOQUilohle2zO65IgCIJ8jo9Yvi/EA2RHbYNoXSeUAtqClNW45AsVAIrL7Zi96QjKalxRPY/kFMasPoChyz7GmNUHUHSxCqLYYI9IgmgSYt0em+u6JAiCAMjnALF/X4gHyI5aP9G8TigAbEFcHq98oUoUl9vh8nijeh5yCkQsEev22FzXJUEQBEA+B4j9+0I8QHbU+onmdUIpoC2IUa9DRopFccFmpFhg1OuCnns1W77kFIhYItbtsbHrktKUCIKIJuHMBRojXv1TrN8X4gGjXocRWekYm5uJZIsBFXY3dhWcjciOiNgkmtcJ7QC2IKlWI9ZN64+MFAsAyPnaqVaj4nlXu+Ur3Vz8ifTmQhBXS6zbY0PXJaUpEQQRbUKdCzRGPPunWL8vxAMpFgMWDL8BS/YUYuLaQ1iypxALht+AFIuhpYdGRIloXieM89h3DNGkf//+/MiRIy09DJlQVutKq5wYs/pA0OrgjrmD0amdGYLAGjwOFQZHTIt/OLFmr9FAzR43zcyDzayH2yM2uGrdXKvbWufRuhbfnj8UaYmmqI8jAshmWwFtSAU0buy1qX1PNI7f0FyBcx7TO4JxNE9p8cFo2Wwc3J+IqySC60TTXikFtIURBNboham15ftjhR2Vdjd6ptlwsrRa0yAEgaFXx0S8PX8oXB4vDHoBeoHhfKU9pm8IRPwRyiQm0B4tRh0uXnFi2uqDDTq05pwgaF2XlKZEEG2P5vA9ocwFGqOhucK4NZ9d9bibMggOvC/Q3CR86P7UNrCZdNgwIw8CA0QOmPSRXSOUAhoHaG35SsWgJdXORotCpZvLNUkWlFW7cM+q+EsRIWKbcNKPJHvsnJIAr4iQippjQSSA0pQIou0RC74nFBqaKwBXN+7mSC/1vy+kJZoo+AsTxpjq988YfY6thQq7C8Xldkxffxi3v/gJpq8/jOJyOyrsJALTKpHqA/xXH5eNzcYLHxShuNwOj1cMedVH60ZGKQLE1RKpbYW6ahkLq5tq12IktTpE2yKSlE4idogF3xMKDc0VJCIdN80dYh8dA5aNzcaiXccV37+O4r9Wg93lxcKdxxXX4cKdx7F9ziDAGt6xYjIAZIxlAtgEoCMADmAt5/ylgOcMA7AbwOm6h97inP+xOcfZXEipETvmDsaPFXaU1bjwwgdFOHa2AhkpFuh1QsgKYvFyIyPij0htK1QFvGgq5UUKpSkRRNsjFnxPKAT6J8YYnn3naxw7WyE/J9Jx09wh9hEEARsPnsbiUVmyCujGg6fx3Jjslh4aESW8nKteh94INuJjMgAE4AHwOOf8KGMsEUABY+xDznlhwPP+yTkf1QLja3YEgaFTOzMq7W78ZvuXit2HdJtJc1ciMGffYoyPGxkRf0Q6SVJbtc6fmgtRFFFa5ZQDrFjZfYtGrQ5BEPFDrPieUPD3T6LI8dgveqHwfNVVjztS/x6vbSnikVSrEY/9oldc2CkRGWaDeqsPsyH8ir64UAFljO0GsIpz/qHfY8MAPBFuANjSCnVX6wy1Xq/2OADVwnWTXsC01w7HutJWLNDiH0hL22s4XI1Qgr/9ekWOP71biL2FJUHHaMnJRJxMZFp8QPFks81Fc6SAkgpoZDSlCmgs+IxojSES/x5Hyp7h0OIDb8hmPR4RJdVOuL0iDDoB6TYT9HqS+2gteDwiTlyswrwtBfI1tWZKLnp3TNT6njXtNeYDQMZYVwCfAujDOb/i9/gwALsAFAP4Eb5g8BuNY8wBMAcArrvuutwffvihaQetQXM5Q1HkqLC7YHd5MXHtoaAVu7fmDwEDi/WJbCzQIh9KrNhrJDTWjsT/bykWA8rtbsVzy2pcMSljHUcTGbLZGIQCQE1arb02p89o6fY4DT1PbQ7S0v78KolZm42j+xQRIRG0+tD84mN6WYAxZoMvyPuNf/BXx1EAXTjnfQH8BcD/ah2Hc76Wc96fc94/LS2t6QbcCKEoiYkiR2mVE+fKa1Fa5QxZYcv/dRev2HGqpBrnKx2qucJuj0hKWzFMrNhrJGipuKkpyJ24WIWn3z6Oocs+xtNvH0dxeS1qXZ6YrDPRunYvXHGQgi7i22aJtkdz2Gtz+YymVuf0n1uU1biQajU2OHfwH09xuT0m/Xk8EorNxotaLRE5zijW4sZsAMgYM8AX/G3lnL8V+HfO+RXOeXXd//8OwMAY69DMwwyLxoqoQ3HkagFi4OtOXKjGYzu+QlmNK2TJeq3jRhKMEq2Tq7EHtRvTvC0FGJubiZzMZDwwpBsmvfo5TlyoUrVZi1Enn/vHCjsuVtqb1SYb6q9FbVQIggjkanxGOL42nEl/uD48kuDSfzwVdje1zWlGSKin9cMA1WsqEmJSBIb5mpb8FcC3nPP/0XhOJwAXOeecMZYHXzBb1ozDDJvGiqi1HPn2OYOgFxgMegEXr/h6/qXZTFgwvCe6dbDCpBew4sMi+XUJRh2Ky+1Ys/9UkCSwWkGwWtrAppl5cHpESiUgAFx9aonWjSnZYsC8YT1kG1Wz2U0z82S7lx57cXxfCIyhxulBgkmHDlblanQkqUoNPU/r2i2rceE327/EW/OHID3RHM5HShBEK8Pfnwh1PdnC9Rnh+lpRFBWqj2v2n8KxsxVBk/5IfHgkrR/8fX2ocxAiOjDGVAVCqA9g64FptPqIZFoekwEggKEApgL4N2Psy7rHfgfgOgDgnK8BMA7AQ4wxDwA7gPt4jBc0qimJbZqZBw6Oc+W1AIA0m0lxw5BW0SwGHeDy4kKlA0O6p2J0TucgAyitcuHY2Qp51e3Y2Qq88EERFo/KQqrViGuSzLAY61fepJuV3e3BhUqHfO7icjt+KKvF4t1fU8+fNk5DNhKOPWgFUBV2N5ItBvlxf5u9sVMiLEY9ODimrT6osMXH3/wKS0b3wfj8z2TV0A5WIwRBQIrFgJOl1Y1OdLQmRD3TbHJtImMMOgYY9EKDvThrnV54LGJQTSMtlhBE66GxGmd/f/Lm3MFYPi5b7tml5jNEKw/yEQ0FXVKdtH8d9aUaF5bsKVScY+PB00G7bJdqnFjxYZEiWFzxYRH+NOZmzcUrtYW7NJsJLo8X58prVf2cv6+X/PmS0X3QI90Gi4H8YlOiF4Df3XUjPF5AYECqzYTf3XUjaMO19SByqLb6+P3dN4V9rJgMADnn/0Ijhbac81UAVjXPiKJDYI8ei1GHi1ec8uQ2I8WC5eOy8fz7RXLfnhFZ6fB4OaZtrlftfHlSP7z88UnFDWLRruNYPCoLczcXYM3+U1gxoS8e2/EVjp2twJI9hVgxoS/+c7kWC3celye5gZNk6eZ07GyFvIvoD6UStC3UAiR/GwnHHtQWP9ZMycXKfd9hbG6mIjiUbFYKLs+V16raYkLdYkZxuR1zNxdg8agsLNlTiG2zBoa0aq020VrxYRF+/fMbMHdzQdCE6qk7b9TsxVla5USNy6N4He2YE0TrobEdtEB/InKO598vwvrpA1Bpdwf5jNOXamA16YMW0LSyJURRDDp//tRcvPSP74LmAltnDUSKxaA4hlcU8cCQbkELx94G0jkDF+5yMpPx5MhesrCL2qKZxahT+PrSaic6JZmRkWwhX9jECIzhit2D+VuPyt/P6sn9YDPG5FSfiACTXsCjt/fEQ37f8SuT+8EUgdIrWUUz49+jp7TKGTQBXbjzOF4Y3xeVdjdSrUZ0bGfG/esOKZ7z8LajWDwqC3sLSwD4nPK8YT3QM92G/Km5OHqmDEkJBiwZ3QcJRh1qXV4Y9ALMjCnSSgPPLQWRa/afQqrNhJ3zBqOsxiWnlFDufusg1LRHtQDJf6EhHHtQa6CeYjHguTHZEEUR+VNzg4InKU3IoBeCdg9HZKWjQ6IJ2+cMktOepJ3EkiqnYsIyb1gPJFsMcHq8uFhphyAISLUaVSdaY3Mz5XEEvudprx3GO48MhcWgC1pxd3q8eGzHlxHvmMeCXDxBENqU1bhUd9CeG5ONtERTkD+psLtRWu3EkzuP45l7smDUCXjqzt6odXmRYjXgD+8UYtWknKDzaGVLeDmC/LG08CXNBaTHK2rdSDAqg0tRhBz8Sc9btOs4ts8ZpPmeAxfuFgzvKe9oSsdY8WERnhzZG2cv2+X5xvXpVrw1fwjcHpH8WTPi9Ihy8Af4vp/5W482+B0T8QUXOYx6hg0z8iAw346gV/SCR6BDQAGgBqLIcanGCYfbCx1jsBh1SLZcnRPzn+QxxuDxiqorfR3bmfHEm1+huNyOnfMGqz6nUztfykZOZjKeuKOXYlUvf0ouXvigSHFTyEixYNvsQcjJTMaxsxVwetTPfW2SGU+O7IXp6w8rJrgbD57GY7/oFXHuPk1wY4Nw6kACJzT+Cw3rpw9Al9QEpFgMKK1yhvS9qjVQl35PSzQrgkP/4+gFpkilGpGVjkdu74ll732LsbmZSLUa8eKEvqhyuAFAFj9Ks5mCro1lY7PxadFFTB/aDR6RB020Uq1G1euid6dEbJgxAA6XF51TzPLiSoXdjRc+KMLTd92I4nK7IuCssLvhEUXNVKlIvhOCIJofnyiaGLTyvmysbwEL8AVu/vVXIud46b5bkP/JKTjdolxSIdUwpyUaVRfQtJrOiyJX9U3dO1gVj2WkWHDhigMdbAG1/lz99Q3NGwMX7rwqx5gxtBtKq5yK97d6cj9c116P9MSERj9bInp4NWzEG9vVUUQYiACu2D14bEf9gvmKCX2RZAl/bk4BoApqE7Ll47LRsZ0ZXVOtUWuiumlmnupK35lLNfJj0mQ28DnJCQb5ZhO4qjd3SwGW3ntz0KpgyRUHnrijF05eqIRRL6ju8LW3GuUbnPS6RbuOY8fcwejUztxsDWSJpiGcon7/lWi1hYZNM/OC0ojzp+bihjQbKhyesIJ9teBQwu7y4vn361fe21uNWP7BiaB0ptWT+yEnMxm7Cs4if2ouSq44VVe8X589CDUuEeU1Lrw4vi8er1tsyUixIC3RpHq9fV9agxkbvvDZ7tT+6JKagGmv1S+SpCeaMCIrXXVMz+z+BqXVTk2bj0RogSCI5kG6f12odATVxUv3RgBIsRiwYPgNcoPmEVnpeHpUFhaPukmRxZNmM6HW5cXTd2XB4xXh8YiKBs5a2RIXqxwaInIC5v6kK/L/eUaxYNvvumzF+zAZ1HcWTQb11LHARdtrkixB85GczGRkpiTgvoAspflbj2LrrIFoZ6aF3uZErwvOlslIsUAvxKzgPxEmXpHjsR1fKa63x3Z8FdEub0RWwRi7J5LXxQtqE7KFO4/jh7LaiPupqB1z6Xvf4uVJ/WQJVymX971/n0f+1FxsnzMI7cx6rJ6sfM6ysdl47t1CLLyjN3qm29R38pItyMlMlh+T1Mc2HjyN/t064L61hzBuzWdYsqcQT9zRCyOy0rFmSi4uVbtUj8d5cLH61bx36k3TMoQqEy2KHDoByJ+Si4wUi0KpU3rND2W1Qd/rS//4DkUl1RH3pFKTKTfqdSitdmLu5gJMXHsI1U6P6sLH/K1HsfL+HDx9VxbSbCb0vkb92rh4xYGf/88neGzHlzAZBCy992ZsnzMIi0dlweXxYvm4bMX1tnxcNlbuOym/fvbmI7CZ9Xh7/lAcWHQb3p4/FNcmWfBfd2WpjmnesB4N2jxJdxNE7CLdv7Tq4iXtuct2lxz8Ab6dsR/r/I1/wPTEHb2wePfX+Nny/Rif/xlOXKyCxyMqjhvYT7Xc7oYgAGvq/DGgnAtMHdINO+cNxuJRWdh48DR+/fMbgrJ1OlhNWDetv+L166b1Rwdr8CKTWvuHM2U1EAQu3xOkesALV9T7DZdWOeke38zoGFTvXzqKwVsNmru8TZECyhi7N/AhAC8zxvQAoNajL97RmpAlGHURT8rUjrm3sASP3t4Ti0dloWe6DSdLqiFyjjH9OivS3X5/903YOCMPRr0ABo6z5XaUVrmgExi8KmlsGSkW/FBWiwXDe8q7FpJ4x7xhPRQ3Kf86gD/87ZsgQQ7peIwxiGJkQSBNcGOHxlqRAMod2zSbCUtG90H3NGvQd6g2IRqbmxlkX6HuZjWkyumfEpVoNmg6wYtXHBi3pl4ZdERWumInfERWOpIsBrl2cPXH/1dX93cYGSkWuQZ2yeg+6JFmhVfk+G2dmJJ/eqfbI+KaJKWogU5gqmNKrhNj0LL5UL4TgiBaBun+Jalrq90bAcDhVt7nOrUzY+prh7F4VJb8OrWFtHlbCrBj7mBcm6zdy0sURZRVu5Fk0StqECVRmafvykJaoglpiSY8ObI3Egy6utcpd/F6ptk0U+39CVy0TbOZcPGKA0vfO40ZQ7th84N5MOgE3Lf2kOL9+X8uZTUuXJNErXGaE4dHxJHTl7Ft9iCInENgDLuPFjdoW0R8oRPU28voIpibh5ICuh3ABwBKUK/MaQVwNwAOoNUFgFoTslqXN+JJmdYxf6x0YMmeQiwZ3QdzNxfgjTmD5OBPapB9n5/ilpTe8eTIXrh4xYH1B07jlSm5eGiLUrXwhQ+K8Of7bsFHj/8MXpFj3aff49jZCs0aJ4/IsbewBKVVLtUeI8++8zUe+0WviNI2aYIbO2jVl/ivFvvf/IvL7Zix4Qusnz4g6DusdXlDrqFrLNgXRY4LVxyaqZBSSpTH66ufNejUUzWlFeficp9AwrZZA1F4vkpeTHnkdt+iiL9ttzPrMSIrHU/deSMq7W7MG9YDK/edxEv35+BMWQ1Kq52atbbXJJuRXJd7zzT6flXY3fL/AwPtshpXoyI4BEG0HAadgPXTB6CDzYiXJ/XDw9vqawDXTMmFjvmuZZNOwMdP+O63FyodELnPD/n3wvNveSNRXG6Hxys2WEvt5cC8LQVYP32ALEAlIe32/FhhV7ScWDetP0x6QZGqHmrpReCi7bxhPbD+wGk8MKSbfA5Jn0Ct159WGipAegBNidWow896p2PSuvo54+rJ/WA10lyrtcAYsGpSDspr3LLoUorV0GR9AIcAWArgC875K74BsGGc8xnhny4+UJskSzWADU3KAh1bisUgSyMb9AI2zcxTOGPJSa6ZkotUmxE75w1GWmJ9H0C11UJJkXDhzuOKOr8NM/JQVu2UVwVLq52KuqVlY7NRYXehvdWokSPum7xKfXvUpKsLz1dFVJcUStBBNA9q9SWBN2C1HduV+04if0ou5votNHRJTQgKXLTsq6FgX9r5q3F6NINHKVC6XOPG3C0FeHlSDlZP7qeQu5YWPvxfqxOY/F4ByPLl0t8X7TqOHXMG4ZHbeyquzeXjsmE1CeiSmoCXJ+XAajLIwkjSa+duKcCS0X3QKckMk17A0ve+DZoIrZ7cD1s++0HekdQJwLnyWhj0AqodHvmcI7LSsW3WQOgERpMigogRPB4RpdX1AicjstKxaWYeBMZwqdqJJIseBr2AM2U1uHjFoQjAXp89UK7Tb2fWY/30ATBo1WjpGL4+VylP6LqkJij0Bnid+Mq6T78P8nurJ/dDaZUzSJ1z9qYjWDK6T0TZGIGLtskWQ1DavVQPGNhvOMliwPIPTqiKxnk8IopKqqhdThNBKqCtH70gwO1RikqtmNAXugjqPBsNADnnXzDGfgHgUcbYxwAWwbfz12qRJslvzR8Ch1uEjqFRFdDA9LURWemKgnDJ0b310BCcr3QgwaiDw+3F2NxMrNz3HWb/pAcu17rQsZ1Zdrxaq4XS4wad7wvfW1iCp+68USFmIfUTlF6zaNdxrJ8+ANsP/4A1U3IV41ozJRfpNpMcpAG+3Yzn3v0W84b1wFN39pal9iNJ2wwl6CCaj4YEVwD1HdvSaieuSQ5W6hRFjm2zBqKkyolUqxGv7D8VtDqV2d7SYLAv7ThqpRJ5RI6Jqw/Iff6Ky+1wuEUsfe9EkDCM1D9Tei1jDCkWAy7Xctjd6qnIHs6DbpoLdx7HWw8NwXUpCSgqqUJFrXptbGZ7C55//wTuz+uCvYUlSLYYseXBgbhU7at/WfXRSTx6e0/MvLUr2lkM+L60BmU1LuwqOIsZQ7shzeZb8NlbWBLxAgtBEE1DSV3tsXTtS9fpC+P7wu7yolOSGR6R44eyWoVAzJDuqTAZBDw6/AY8tKUAaTYTFgzviRs62vDGnEEoq3bhwhUHdhWcxcKRvXHeT2BGun8nJxjQvq4+T/LJOwqKAQDrpw+ATmDQ6wSs2ncSY3MzNMtWAh8L5R4euGhb6/IGZXf47/xJvVvzp+TCZtbjT2NulmsLpZ1Ng16A1ysGtdkhwavo4dEojfBEUB9GxCYeUcS6f36vSAVf98/v8ew9TdQInnMuAniJMbYTwIqwzxKHCAJDemLo+euBOfNqtVArPizCM3ffBEFgMBl0MBsEmA063HxtEswGAUv2FCLNZsLycdlYf+C05m6KVIvQ3mpETmYySqud0AsM66cPQLXTgw42Exa8fkwxGS4ut6PS7saw3h3x8bcXsWFGHgw6BoNOQLrNBL1eQK+OiXjnkaE4X+FAeY0LT47spVjRXD4uG5YIUwkaCzqI2EFrx1ZtAUQQGK5N8qUgeTlHhd0VJHmePzW3wfNJO47+EwppwtS1QwJEzjGke6piQUTqsTV3cwEAn7jC737ZW073lFI0y2tcuFzjE2f488RbVK8nAcG1e2k2E1weEeev2FFyxYlEs171tWcv2/HAkG5oZ/a50uFZHTHlr58rnld4vgpbZw3EhHxlKvf6A6cxb1gP+T2EOjkjCKJ5cHtFpNlMisnWmv2nwAB07WBFR5sJJdVORT10TmYyHvxJN1Q7vHLw98QdvbDx4OkgleCXJ/VDjcODX7+h7CG6cOdx7Jo3WA6epObqKz4swvCsjqi0u+XegydLqjXrE2tdSn+SkWKBQS802roncNHWYtSh2uFRnOPY2QpsPHgab8wZBAYEHUutpvuVyf3kRS8J8nvRQ69RH6anxfZWRaAfWTY2G5F8w2HtGXLOz3HOJ3DOu0dwrlZNYNpc4O6dVM83ce0h3P2Xf2HSukP4z2U7dh75D8bkZkCoC+B+f3cWEow6PHJ7Tyz/4ASWjVUqOi0bm41dBWexbGw2ln9wAk+O7IXXpvfHqo/+DzM2fAGH2wuRc5RWO+Xz5k/Nxc55g9GxnRlvHz2HF/9xEj//n08w+dXPYdAJsgS1IDB4RWDulgI4Pd6glJKFO4/TSlIbwP/mL6lcNpSiU253Y9Krn+O3279S7EQD9bV4ampwkuKnl3Osnz4APdNtEBiwY+4gLPlVHyze/TVue+ETTP3rYUwZ3AVGPZOvhTX7T+EVP0W8tEQjkhOMWDK6D7bPGYQlo/vAZBBQ6/Ji3pYCDOmeinYWfZBCWv6UXIjwnV9SzZXU7e5bdwg/eX4/Fu/+GkkWPV4JUON9cXxfrNx3Eot2HYfV5AsA0xNNQcFkcblPES8w9VTqFyZBdbEEEVtYDDo8ObIXluwpxMS1h7BkTyGeHNkLAmMw6Bh0OgFGvU6uhwZ8pRs/VjhwucaXNSCVcqgpFz+87ShsZn3QXGH5uGyUVrtkFc6ndh1HeqIRC4bfgCV7CjFuzWeY/OrnsLu8WDUpB13aW4LUwldM6IvM9pYg1c9qhycklWZ/JdL2VhOuTbLICqDS8R4Y0g1//Ns3MOgEpCWaFPcINfXvh7YexYLhPRXnaWm/p6Y8Ha+0swiK+2JGigWvTMlFOwu1gWgtcA7V0rBIzDYUFdAkAP8PwK8ApMOX/lkCYDeApZzzigZeHldcTXFyYNpc4IqcVj3f1lkD8ce/faOI6NdPHyDvoJRWubB4VBY6tTMj1WZEpd2NsbmZ2H3sHMbmZvoCOEHA1MFdMKZfZxh0AhgDXp89EFccHnhFrqgXeHF8X5wsqcaxsxWqK29SIGvQCaoTWXeAXHW0oMLw2CKcHVvJZqRdZjW7sbu9ChVZtdXh/Cm5+J/Ja+wAACAASURBVOL0JXSwmYJ2z9/96hymDumGLbMGQhQ5XB4vkuoCvq4dEqAXBEWvLcB389s8M8+XZvTT7lj+wQmfgt3MPIgAdIzhv/9eiL2FJYq06QXDewYtfszYcASvzx7oey33KXFV2l3yLrtOYBiRlQ6bqX6nUFINlepicjKT5ecXl9uRajWiyuGRx6pWF0vXBUG0HF6RB/mC9QdO44+j+8DhFnG2vBY2sw6Z7X3+Y+HO47g2yQyLUYdTpTXISLHIi8FaJR2S7xibm4ku7S3wiKir6SuQ/cgDQ7qh0u4J8osPbT2KJaP7YMaGLzAiKx2bZ+ah2uWF1aiDUccgCAw75g4G5752OoLAMXrVwYhSMPV6Ae1tBg0V0nr/LvmsWpdHdff0utQE2UdKGSJNoQcQiu9sbT2Kq+wi9nxZLKcIe0WOnUf+gweGdEMYCW1EDOMVuep11SRtIADsAPARgGGc8wsAwBjrBOCBur+NCPusMcjVOoLAtLldBWcVtXZa6oilVc6glUH/dJJjZyvkFLGPHv8Z7lr5L4UioZQqd11qAmxmPX67/SuUVjuxYkJfMMbwm+3K1JLH3/wKm2bmYc3+UxjfPxMcwH8u18Bs0KGD1SQHslopJU2xUtfanHBbQhQ5GGN4d8GtMOoETZGDUyXVqHF65O9UbXV47pYC7Jg7CKIIvDi+r+zYeqbbcFffzgo13Nem90dplRPXpfqCv8BeW1K7Br1OwIisdFgMgkLBTgr4SqvqVUMX7jyOzTPz4OVaLSaccouJ5eOy0SU1QX5/XpHjT7+6GR5RlMWd1NI0pAlTRoqv6Xz3NCsOLLpNdYJC1wVBtCxuUQzanZt/2/U4falG4UskkY03Zg9Epd2Ds5ftcqaOtDso3VPTbCbZP9W6vLCadHj09p74y0cnsfCO3pi16Qu8OL6vYvF448HT+N1dWQ3W+Un1iTvmDsIPZbWK8W2a6WvbUO3wyq1u/BejQk3BFMDQPsGIDjYjUm1GPD7iBmz67AxE7gu2Uq1G2WctH5etWkZS4/TIgjHpib6dxWj7s1B9p1aP4nitSfSIHIfPVKBf11Q5ODh8pgKTB8XvriahxFyXlRB4XZkN4c/NQwkAu3LOl/k/UBcILmOMzQz7jDHK1ToCNaGTFItBoUCoJVufbDEoIvr2VmNQ/zJpkpmRUt+UW6otUJtkPrbjK2ydNVD1hsE5MHVwF4icKybV+VNz0SnJhE0z81QVDZtKubO1OeG2gnSTXfFhkRzsSDWs/s5JssnSaqf8nQamTEtpl5dr3AqBIikFevKr9XV1aTYTyqpdCjlySZFO7Zp4ZXI/6HVC0A78wp0+RV3/GjyR+3YGta5V6XnrD5zG7+++CX9fcCsSzQbUujxwe0VYjDpsPHgaC+/oLbebkF4jKfgu2VOI1ZP7IcmiR3KCyddURwW6Lgii5RBFDs6V9+3HR9yA8hq3QvCluNyO7y5WY/Hur+Xre0j3VDx8W0+8/PFJzBjaDWum5GLlvu+walIO7C5feYW0eGt3iXC4RTx4a3c5g8J/AVZS4PR41Xv+Sm1mpIUvt5fD4RblWjuph980FZ8sLUapLeyqqZpXOz3wiCKm+ikmvzK5H67Y3TDqBIXP8oocT7317yCf++bcwUi1Gps0oyFU39naehSb9AJ+98veeGxHvSDgigl9YdJTCmhrweMVVcuzdkSg9BpKAPgDY+xJABs55xcBgDHWEcB0AGfDPmOMEg1HoJY2J/1+sdIeFFBtmDEA1Q4PUqxGvDihL5a+962cjrZ6cj8AUKSnrfv0e3lnr7jcjsWjslTTSpfeezNqXF7NhpECg5w64p+qZnd5UV7jgcCApWOz4faIivSRpnLWrc0Jt3akiYHd7cGFSgdmDK3fWSsut+P594uwZHQfdOtgBee+/n5AXYAlioq6v5X7TgIAnrijFxxuUU57kp6/aNdxvD57kMI+5g3roeiVmWQxwKDztVkpq3bhsR3KXe+Hth7FNo3FkMAavP9crsXKfSfxyuR+eMgvdfrlSf2w9dAPAKDZn/PFvUV47Oe98NSdN8r1P4Hn65luw5LRfZBg1KGdueHFFLouCKLlKKtx4bl3CxXCVNckW3DJr55X4uviCmydNRAi96Vnjc7pjJc/PimXaVyTZMLvR90EMOC+tYeCFqqkso/Fo7IwIisd7cx62QdV2N1ItRqx9pNTWD9jAIov22WF5fZWA559p1C1T6kU5Pn7SyB4MSp/ijIFUxR9Yl7nKxyKtj9vzBmE/1y2BwW/D209itWT+0FgDLWu+lY+WmUkIufonJLQlF+dpu8MLEVobT2KvZxHTSGSiE3cGkqv7iZKAZ0I4CkAnzDG0useuwjgHQATwj5jiDDGRgJ4CYAOwKuc86UBfzcB2AQgF0AZgImc8zORnq+pHIE0WXZ6RRjrJqlVDg+SEwyodnjwyOvHFA67tMpXVzR/61FsnJmHB2/tjk5JZvxYYcfUwV1g1AvQCYKitsCf4nI7MtsnYPKrn6vuxiwfl40LdelyCUad6o1j+bhs2Ex6eETtwK+hnodXWz8Zrc+eiD6hKLsdO1uBGRu+wM55g/Hcu99iwfCeeGFCX1TZ3ahyejB9/RcKW/OKHIt2HVekPUlIEwapRibZYkB6oglpNpM8ifJv7L5mSq66ypxXbFAlTxrL20fPYd6wHuiQaMTWWQNRWuVr5/DyxyfxwJBuOFlSrVnPu/Tem3HhigM90qy4JsmssfjiuyY62IyN1qfQdUEQLYfL48XewhK5Dv+GdBvOlNUCUO4KTsjNwD05nVHt9CDJYsCC4T1l/7C3sAQTcjMwb1gPXK5xITnBoLp4K5V97Cu8iEdu74n5W48izWbCktF90LOjDWKdwrIrQGF5zZRcpCUaVQVmpCBPa55wQ91iVHtr/SKY5N8v+LWlAHxZF4wBme0tQcdKs5mgYwzj8z9TtPLRKiPxilwRhDUFWr4zsBShNfYojpZCJBGbaJXZSG3hwqHRV3DOyznnizjnvTnn7et+bqx77HLYZwwBxpgOwMsA7gSQBeB+xlhWwNMeBFDOOb8evtYUy3AVSI4gUDHrahyBKHIUXajCmNUH8NPn9+OxHV/C4fFCYMD3pTXy6hpQ77DnDesh/36pyonH3/wKnAMLdx7Hj5UOPLjxCBa++RWWjc0GrxunP74JIsOfJ96Cp+7sDa/IsWLCLfjHb3+KJaP7wGLU4fn3i+TJr9pkduHO43B6fLUPX5+rxJmyGoUylnST8FcSO3GxCk+/fbxRZbHm+uyJpiEcZTe3V8QTd/TC4t1fY/iLn+CR14+htMqJNJtJfu3CnceR2T5BMWEIPI7d5cGjdep3E9cewtTXDuPJkb3w+Igbgmx33pYC1bFcqnYFKeq+MrkfbCY99v32Z3h99iAcOX0ZD9WJtlQ7vHjuXZ/a3tzNBdhbWCJfn1r1vNckW7B499f46fL9+MPfvglS5fvzxFvw338vRJfUBJyvdCqun28vXMHFSrtChY6uC4JoOaQgQqrDL6ly4r1/n0fnFLOsJjwhNwPThnTFc+8WoqLWjT/+7Rt07ZAg+4eczGRMHtQF0147jHFrPsPZy3bVxVvJ9w3P6qjoSeryiiirdsGsE/CH0TehpMqJF8f3Rf5U30LXvC0F+H+/vBG9OyVqZjj4K5RKZKRY4OVAitWAHy7bUVzhU8C8VOPEig+LggK9J0f2QnmNWx6/PwuG95TnMlIrn4wUC9bsPxXkA5eNzcaf3i1UVYWOJmq+c9nYbKzcdxKzNx2Rzx+u4nWswzmw8eBpLB6Vhe1zBmHxqCxsPHg6IoVIIjbRMwSpmS8flw19BCYbUh9ALRhjMzjn66/mGBrkAfg/zvn3ded5A8BoAIV+zxkN4Nm6/+8EsIoxxjjnEZl6UzQrv1TtxOzNRxSraEadDhVOl+pKmn9KWkaKBZ1TLNg6a6Cs6gkwbJgxABcqHdh97Bwevv16vDwpB3a3iA42I3QCg9WowxWHB1UODxKMOri9HDYzQ4JRh64dEnCh0oG0RCP+NKYPjDqGSrtHkQYqpQ1ccbgxce0h2bj8m9KqBQHzthRg8ags7C0s0cy3b87PnogOgTu9Wqk1gcpuy8Zma9aAbJ6Zh+9KqrFm/ykAgFEv4OMnfgZRBLY8OFChzLlmSi5MBh1m/fVw0HE2zMhrcCxpNhN+98sb0SnJDLHOLbwxeyCcHg69jmHboTO4+5YM/P34jxjUowOG3dhRUduyZkoufvuLG/DDZbssmtAz3SbX4gauwP2nrFZ+TKrfXT99AC7XuFDr8iIzxYLnxmSDg2PaaqUS39zNBXJKlr9YAV0XBNEypFqNyJ+SKwc3HMCdN1+DFz4owoyh3fDG7IHwcmDyq59j9eR+cuD2zN03yf7n+XHZilrglftOYvm4emEY6fF9hRexenI/uOoWXnMyk/HUnb3lljrvLrgVnAOLd38t1w6uvP8WuLwcAmOykmigbkDnFAsMAsOL4/vKx5r7k66YMrgbRM6RajVhy2enkf/PM7KYzfzbrgfA5NrqNftPoVM7M6a+dhhDuqfK71UaR7c0qyL744UPirB4VBayrkmEwHxzFh1juHDFgeff99UdPnN306axS75z+5xB8uKiVPMIQJFG35p6FAsMePquGyEwAV7OcW2yBb3vuhF0y2g92D0i3j56TqH0uu7T7/Hw7deHfayrCgAB/AFAUwSAnaGsLywGMFDrOZxzD2OsEkAqgEuBB2OMzQEwBwCuu+46zZNGyxF4PCJKqp1we+sVxKRUy+nrD8s5/1oF3VLh7qPbjmHm0Otw47XJKK9xKQp7/zzxFrg8Xri9HE+8Wf/4a9P7o7LWrUgTWT4uG4wxlFxxwuH27cq4PRxP7vw3lo/vixFZ6aoNaiXZ+oU7j2PH3MHyOEVRDJKgPXa2QlFPFW6tUmtywldLqPba1PiLvIzNzUSq1YhOGqmN5yvscvpPssWAS9UuWE061QCtpMqJJXsKsWpSDgwCw5kARb1XpuTi93f7ahb0AkNptXo9ndmgngqhFxi2zhoIl8eLS9UuuT1ERoqvDcpf//U9Fgy/ATNv7Y5atxf35HSGUSdgfP5nQYsam2bmYVfBWbmRs05gqHF68PKkfnh4W32N4CuT++H3u79RjHFvYQkevLU7Jq49BAD4ZOEwZKaYcb7SrlA5lVqySLsC/osn8XJdxIrNEkQohGKvgsCQYjVg26yB0OkY7C4vGGPYW1iCvYUlcvuCibkZSPTr5WfQMayfMQCXqpyodSkXzI6drcDz7xchf2o/vDIlV24UP75/BlZ9dBJP3XkjMlIseHzEDYp+qkadgBkbvmiwqfwrU3IB1OsGvDi+LzjnEDnDX//lqwvr3iEBDjdX+MTVk/uhvNaDHQXF8HIOu8uLR7YpS1P0OibXNq766KRcJvLQ1qOKtE/pPe4qOItr6lJZ/Y8DNF8auyAwGPU6xefYnOePNqHYrNkg4FKNGw9tOaywi4zkq53qE7GCWS9gTL/OitKX5eOyYY5A6KfRVzDGjmv8/BtAx0jeQHPDOV/LOe/POe+flpbWpOfyeEScuFiFCfmf4cSFKnmbNjDVUloJ9N/GXT25H65NMuOF8X3RKcmM/x5zM3K6pKLa4ZELe6Vt/bWfnoLFqA9q83Cu3CEHitJjC3ceh8PtxcS1h7B499coq/bVIviEXzz4LxUxmYe3HVWko7q8IjweEaLIcanGpWiM+8QdvTAiK11WI5PeTzw62VigOe21IcpqXPjfo2fx8G095ebDf/zbN0GNZn0CKN9hzf5TqKj17RyPfvmAIl0oJzMZ+VNzsXPeYLS3GpFmM6G8xo2SKleQQMFDWwpwqcqJW5d9jN/v/hrtzHrVFKbzFQ6sCRjL8nHZ+M0bX+L70hoUlzuCjv34m19h4R294XB7UeX0otLuxnPvFsLlVcq9S8/n3FdTsfHgaTx9VxZMegEPbT2KZ9/5BktG98HHTwzD5gfzkJJgQGm1U36vr88eiI+f+Bk6tjNjy4N5GJGVDp3AUHSxChPXHlJcOzmZyQo1P//Fk3hpUhwrNksQoRCqvZoNAiodHnx3oRrT13+B8xX1Pi3ZYkBZjQu/6peBi1ec8uM1Li88Xo71B04jxWoI8l15XZNhd/v6tW2amYc/33cLHtvxFfYWluDiFQeWj8tGp6T6gConMxmC4BN+a6ip/ENbCvDM3TfhH7/9KZbeezP++q/vca7CgRqXB7/+uS+FHmB4aKuy9GR+XQr/x4//DEadgPUHTgeVpgiMYdGdvbFo13GUVrnQwWaSRbLW7D+FDTMGYP30Adg+ZxDWTx+A3/0yS5HKKh1nwfCezZrG3prS6EOx2VqXiIcCe0VuKUCtq2n6NxPNj1hXEhY4x2+SRvDwBXl3ACgPeJwBOBj+KUPiHIBMv98z6h5Te04xY0wPIAk+MZgWpaTaKcvYS/nwi3YdD8r5l1YCNz+YB4ExfF9ag2fqdhCeuKMX7l/3uRzdb587SLWw160yafXvIShRXG6Xm0RKxvLGnEFon2BEO4vBl1qi0hvIPx3V4+UoqXbCoBMwd3Nw7eLWWQPx3LuF8vPj1cm2VdSa5oqiiIl5vvqVwNTGDTPyAHCcvWyXU2u2PJinusix/oB6T7wEo05OefKnuNyO5AQjcjKTMTY3E8+9WxikyrlsbDb+++/f4i+TbpGvoZIrTjg9Xjx1Z2+k2owo09g5rLS7Ff38nr4rC4ypt2kRGGQxhSsOD9qZ9Vh6783ompoADoYqhxsGnQCvyGWp9/m3XQ+7y4upf1WmkyYYBdy39kjQtbNkdB8Y9QJe+KBIPq9Rr6M+gATRgogih9vDcanKiS6pvrq+d778Easn98Oqj06ivdWIv/7re/S+Kwucc1lwzWzQgYHht7+4ASLn8vPH5maiUzszUm1GXKh0oF/XVEx77TA2zBgg+wSBMSx97wRW3p8jp5E+cUcv/KesVlE7qCXs4vCIeP79E5gxtBueHNkbOgZMfe0L7Hl0KLbPGaSpIHiuwrc4tnxcNubfdr0sRif9vdLuRsd29cJbUrsKCWeAOM2mmerp+d3TrOgcZt+/UBq6a9HW0ug9Gt+vJ0YXDonwUZv3F5fb4faGH+SHsme4B4CNc/5DwM8ZAPvDPmNofAGgJ2OsG2PMCOA++FRH/XkHvmb0ADAOwEeR1v9FE/8vxz8fvnOKJWglMC3RCL0gQFc3+fz93Vl4fpyvibT/JJFzBK32bTx4GgadgJ3zBiN/ai5yMpMBQLPg+0KlQ/69uNwOUeRIMAr4vrQGFyodMOoEPHNPlnwc/3TUVZNyYNAxuL0iXB6vLOLhfzy9wPDcmOxWUUjd2gncVfJ4xCBRn6KLVfByqLYz2FtYgrJqJxa+eRxGvYC0RCNenz0QnQMCKGmR4/d336SqUOfrn6durxwcT93ZG1nXJGJsbiYSTHpsmJGHvz0yFItHZcl9BV0e/v/Ze/PwqOrz/f/1PrMnk42QAEJkE5CAbAEMaJWlHwRF+SqLsilBWdxrFaSlVFtqPwj4s6IguAGyCli/WqyUitJWAcGwqWETAcOaELJNMvt5//44cw4zmRkFql81zn1dXoZZzjkzc85z3s/z3M99gwQhNMGEaX/9jNtf2kbxOXfcbesCAFlOGx6/FjTNQjB/VNeobqKumJuZbOVstRezIrg8M4lKd4CRL2/jxnkfUbBkB2eqvPxt93F+N7gD5TX+qArd5FAVNt6iaOmWI4Ynl148iedl9X0LKCSQQAJQ4fZR6vIx4+3POVTiYkBuNqPyL8dmFjzQrw1z/rGfqQOvxKwILCaF2RsOMGdYJ865fJRWe3HaLRwpreXdPSd4oJ/Gohgy/2NOV3oM0/Tj5W7Dd1Tbp5+sFCsCLaHUFUXnbTrEojF5Uaby4dDnkIfm5TBl3V7Ouny4fCq9W2VS4w0SUCX+gBrzfRWhhG7Kur2U1/gN9k/XnHQWj+tBktVMUIXf3tiex9/ca/iugjZzps9Jds1JZ86wToZSYd39fFVaQ3kYU+iboKqSkmoPX5+r5fMTlTywctdFC8zB+fGSphlJBq2+vsIcmgVdNDaPNybms2hsHgNyszHX48/8c4Nu7xaOZhkOTJfwG1+ICujdUsqP4jw36qL3eAGQUgaAB4B/APuANVLKL4QQfxRC3BJ62atAphDiS+DXaFYVPzjMdX6cXcUVzFxfhEUREZTPAbnZPNivDTPXf8Gxc24Kluzg1gVbKFiyg7t6tzQSMYBgnapOuA/ZsIVbI2iYzTLsLBobSYt7dkRnntl40Hi/frJUeQLMePtzgxrq9gWZOrCd0bG4LE1TO/MHVEa/8gnXz9nM7S9tY+rAdhHHp3VKBJJIpdCfAnXt54ZYCq4nK90xE42gqkbc6HU0y9AUZHcVV2hiRH3b4PGrHD1bG/XaUpc2dxor8bGZRYSinr7t5+7ogiIEdosJVcKbhcX0f+ZfjFu8HYDGqTYeH3Qly+7uSUm1l/V7TmISIqr72CDZErXtp4d2YuHmw8ZM7oy3P+f6OZsZ8dI2FEXh2RFdeGNifpRiboNkK80aOHh9yxH2naqOqeD7i7bZBIJq3C58ICQeU/e7VIRgZM/mxn51096ED2ACCfxwcPuCEWyeaYPaU17j52SFl/tW7GRjUQm1viB2q0JWio2sFCtN0hxMWl6IN6AlXElWE79om80LHxwyRjjSHBZ2Hi2jodOmFWdDtE9dOfO3N+byx/VFOKwmQyG5X7ssGjgtdGyayqIxebxZWBylajx/VDfmbTpEm2wnMwbnkmw1ce/yQh7+paaKbDMrrPv0a16MocypC3IdL9fsoTKTNQbG1IFajOwzdzPjFm/HYTWR5bQZ7KYBudk0TLEZyd8Tt2hi7TPXf2Ecn55ELh3fE4tJoKrf3qnQ71O3LdhCn7mbmfH25zx2QzuynLZEEewbYLcoEYrZM9cX8WD/ttitCSP4+gK7RWFxHcr14oIe2C0X/xtf0GSoEEKgKXM2DT10Atj+fXbcpJR/B/5e57Hfh/3tAYZ/X/u/VNjMSoQimC4Q4Q2ozN5wgJUT8imp8pDmsFCwZEdcM/cZg3OZtKxQ4/+LSEP3eD5kSwp6MmXtHrJCHma1viBefxCn3WzMJ+kJodsfjOL6T1mnmW7PHNKRVIeZh1ftZs7wzoZwTfjrZg7pSMGSHcaweUm1h/tDg+MDcrN5qH9b4+aZoK79eBCrq1QSw9j4eLkbRQhjoaEbIT/Uvw0tGiahCK3SeGu3ppyr8dE8M4kzVZ4ItTn93D9d6Y5Jrwyq2iKreWYSy+7uSVCVuDwBkmwmRr9yngId7o/5/AeHeKh/Wx5be16kYOGYPKq9gYjt7yqu4Ml3ijSK6PieBKXEYlJ46t0idhVXsGhsXswZGs3MGY6ereEP7xRR6vLy4pg80pPMvLPrBIv+c5Q32jeO+X1dlu7AZlaiFP7Of15pfJf6sS8am8fM9V9Eqfe9dd81CR/ABBL4ARGU0khsJvdpjRDaiAVgXJNCCHwBiSpVpg/ORQ0Va9McFsyKoNYX5Irs5AgK/JODr+TmLs1Y9clR5o/qxvwPD/HogHbMHNKRZhl2hMDwH5w9TEuyhvfIoczlJaBKMp1WfjOoPSaTYOn4nlS5/ZRUe1GEVnA7VOJi5voiFoT8WX1Blf/9+z4e/mVbbstrhtWssGpCPqqUfFVaE6GOqRf32jZy8uztXRjz6idRTIa5wztT6faTajfzu8EdCKpaV3Fyn9aU15wXoCut9vH8yC5YTKaItcCisXlkpdi/cS0Q6z4Vvi662CLYf0Mj/SnB4489A/jGxHxI/oEPLoHvBIGgpModKfT43B1dSLVdvNDPt75DCDEAWAAc4vwcXjPgCiHEfVLKjRe913qEuoHFpAje3XNeohXA7QtwuLSGrBQrUkoykq1IeX7OLtZiUp+fe6h/G6rcfl4f35NzNT6jIxPrPWUurxHIi05Vs3piPjazwhvbjzFjcC6ZyVayUmy8s+sEbxQeN2wbwrfhC2iWEqqUzPw/HamojT1H1Sormbfvv4bTVR5UKY3kD2BoXo4R8PXXX4wtRALfH2J1lWKdU80yHDisJh7+ZVuee/9ghOpbeHLnsJoijN1fGNWVWbddhcWkJUIub4BnNh6MSnzmDOvEI2/sptTl5dW7uuO0manw+WngtDEqpFAH0Tf+WOfW5OWFrJqQb3wGfcHWONWOKgUur59Kt5+mGQ4KrmlJ0anquNedBP7370UMzcth2qArqXD7eX7TQUb2bE67Jmma8p/TyuJxPZi36VDEwulYWS3pSWaahbqaEcqmo7uxaPNhbXE2pCM5DRw4LCbMioi4BvXj8AWCNElz1DuT4gQS+KnAbjFFKGTPGJyLNWS23CzDQe9WmaTazQSCkqNn3TTPTOJYaFbPbjFRWeunWQMHVpOCL6DyzPDOqFIalgpZThu/aJvN1IFXkmQ10SDZErKo0ZgCu4orePnfX/FgvzYEVEmtTyWgqtz/0idGMe6K7GRSHRYUIXBYzbwwqit/eKeI4+WauMvc4Z05eraWO3u1YNIyLRH4+lwtiz8+wn19r8BpM0UUh+cM60Sm04rFJOLOkzVKtRnK4+sm98IfVA3aZ122UoNkm1HM098/aVnht64F4rEf0h2Wiy6CfdMsNVCvEsOglGQ5bVEq7cEffjoqge8IAVXy8OpI8ceHV+/WkvyLxIWkjM8BvwzN/BkQQrRE69C1v+i91hPECyzhwhlrJ/XC5Q3QLMPOg/3aGsFQt4HQufzhwW5AbjaNUu188Oj1OKwmTpS7DcqZ3vGI5fkTPgR6vNzNiXJtsPuZ4Z2Z9d5+Y8Zo9cR8rm3bMCqINstw4LAoWEwWrGaF014vDcMWu4BhhO0PSmp9AeOmEn788RbXCeraDw+H1RSa6TAZN4c3C4ujutYv39mddIcVf0A1xAvGvhbZCb53xU5mDukY8dgDOqzvzwAAIABJREFUK3cZPlctGiZxpkpbXMz9xwFm3XYVOQ2S+Kq0xvCDArh76aesm9xLm/kIzdyFQ7/xA3FN2EGyaGwez71/MKbgzOtbj1JwTUs+PXKOxeN6YLfE7q6ZhODua1tR4fYb1wzAvX2uwKyIiKRuzrBOzN6gzSLqNhBzhnfivb2nuC2vGasn5hNUJSZFsGzLEdYUHqdZhoOGTiuzN+znqVvPy6LH6vL93AQMEkjgx4SGyTam35Rr3LMXbj7ME7fk4rBoLJ80h4Wn3i1i+k25NHRq1+W8TYd4Znhn/EEVlzdAuslszBFmOW38f7d3psodMMRU9Dj1/q+vJ9VhYeyr23l+ZBfmj+qKLyBpnGZn5MvbWHHP1TR0Wo3EcfpN7Xnp34ejYt0zwzsbx3+83E2TNDu/Wr2buSM6k+W0EVAlU9ZpyewDK3eR5bQx67araJxmx6RotPsn3v6c0mqfIURTNzadqfIyY3Aul6XZSUvS5hWtJgW/Kjl0xmW85/c3a/5zsSyjvm0tYDVryffQvBzjvW8WFlPrC150ESzeLPU7D1zDmSpvvRLZspsUpg5sF3WfspsSFND6gnhJvnoJSf6FJIBmNB++ujgBWGI8/rNBvMDy1/t689Z916CqKtXeAC5vALOicO+K8wvocIXE8O7IgNxsHuzf1vDpWTyuh9Hq1feh+5MVnaqOuMjDET7Y/epHXzF3RGfOVnupcGvdEI9fJcVuMRLJZhmaX8yTf/uC0mpfVBB5dkRnLGYlwh/oxdGaV2DdJDZWUpugrv3wUFWp3bzreEQ2dFqp9QWZO7wzl6VrnSk90VAUhZnri3hmeOeYiZdOiQKMmZEUu4Up684XLBaM7sbyrcd4fetRpt+US8GSHVHb8fjVCE+pLKeNyX1ak+7QhGJUqVXFs1JsMc8tIQQ2s8LUge2jKMt69X7Kur28Pr4nd4YWUXW7dAtGdzPomHriqIvNpNgtManQy+++moAqqfb4KHV5Oevy0a1FA557/xAP/7INo1/5xPgsb7RvTK0viMev8sj/tDMWMd/U5fup+AAmkEB9g6IIFHGe7rmruIKV275mTK/mvLvnBKPyW7CxqIRH/qcdDqvWCSx1eUlPMpNktXCmyovNrPDAKi3ReuKWXFSp0Uh1cRd9225fgNRQ4VQRIhRXg3hDKslmReAOat21v9zehV+9sTvm+Mija/cw67arGPPqdk1MSxJiHsGc4Z0xK5qfn16kPV7uZsyr243PvG5yL9IdVqYNao8QMipGLhqTh19Vee2jYu7q3ZL1e05wU+emhjH8k7fk8uyIzjyyZg+N0xyUhjxfw4txS7ccQQjBifLauEWtDIclaozkxTF55GTYSbVfXBEsXjfR7QvGXL/9lJlKASljWgSsmXTx3aEEfpywxUnyrZeQ5F9IAvgasEMIsZrz5uw5aMqcr170HusR4gUWf0ClaUYS52q8lJ7VFtzPj+wa8dpdxRW8tfME02/KxeUNsHhcD/xBlVSHhTteOk+BiycoUen2R1QAZm84wNzhnemak06py2t0/XTBmLteOy9JP39UNxZ/fITx17ZixuAOTBvUHptZoaLWz93XtqJBstUwmdT398iaPVHdHr0DpCez+gn5ZmExC8fkRc0AJqhrPyxiFSymrNvLrNuuYtpfP+OFUV0xC+281lXqdB+l05WemIlXrU+r5OqiKh6/aiR/+j7uW7GTFfdcTVCV+IMy5nb01y7cfJgXRnXF7QtGmcM/O6ILVrOI6lYuGN2NUxUeHlmzO26iqi94qj0BY+Eze8MBZg7pSKusZBQhImbx9MRx5pCONHRasZlFzO2eqfKQnWqjcZqdlROuxh+UfF1Wy129W6AIwbK7e3K22oc3EMSkCFplJZOeZCbFdn4RE6vLB1Ba7U10/hJI4AdE3fn7/rmNeOGDQ9zVu6Whxue0mQiokvW7T7JiwtUgocrtp3V2skGJnDE4l/IaP+U1fi5Lt3N5yFZiRF4zJlzXCrvFhBJSA0+1W6j2Bqj1BTlXoxVTFUUrcOlFsPCYFo7j5W6apDsYkJtNwTUtsVsED/VvaxSv9GJudqo9bhy+s3cLo0j25C25hs0OgNWsMHzhVmYMzmXpliP8bnAHRr28zShyJVlNJNvMzBzSkaAqDfEbfZ2i+6ku/fgrFv3naNyuW7nbH0X1v3e5Rh292DgYb5Zan/Gs+/39lJlKgWAcG4hgggJaXxBQNY/R8Otq8cdHeOLmDhe9rW9NAKWU/yuE+L/AEKBX6OETwGgpZdFF77EeIV5gEcDJCs2Xw6wozLrtKjKSoueGBl3VJIIfv2hsHpm+yKQyXjetotbPpGWFEY99fa6WPw7pQEayhXnvfxlX7OL+lTtZOr4nVpMwOo3hlLbpN7X/1m6P/lirrGRmD+/E2WpvRAcpw2FJUNd+QMQaeo9XsLCYFLKcNty+IE/+7QuG5uWQmWzF7QvQJORb5bSZDK9Ho2M8upvRmdOFieIlYBJNvrjG7Y+aB3x6aCfDh29XcQUuT4Bpf/0s6ua/bLyWTP1rfwmrJ+ZzulITU5rzj/3cfW0rjpe7418vocfDz+FdxRUULNnBusm9CKoy5ixeq6xk7GaFM9Veg5J0WZodu8VErS9Iss2M1x/kdOV503m9iz980VaynDamDmxnfB5dBKF94/PFkLpdvoT/XwIJ/PBQVYkQRBSccjIchgn72sn5vDgmD1VCtSdAfuuGqKqkyq35hSpCcLJKK5zpFPY3C49zf78rOFXpYdIvWjC0e04oziRhM5mYM6wTJpMgI8lqdNVeG9cdf1DV1P/GdTeUxuPFOinhd4NzKa/x8WVJTVQsfWTNHuYO7xxTkCrVbjY8iI+Xu3nynSLmjezK6Uqt0OUPdSQvS7NzX98rkCE62hO3aAmuWVEMmur80V1j0vEtJsF17Rqx/WgFu4orYnbdvksFZL2IWTeexhsD+CkzlfSiRN3PdCkWAQn8OCEEMa+rS/mJL6hnKKXcJ6WcJaV8MPTfrJ978gfnA0u4pPKzIzpT4vIyYtFWfrV6NwFV8ybTpYynDmzHiLxmLB7Xg1ZZycwYfN57L91hiZLdX7j5cJSU/TPDO5ORbIl47LVx3bGYBN6Aiscv+dX/tGHR2Dwap9pjBtLyGh8VtX7D00/vBk3u0xp/UDUkZnWPwWYZDurWkJplaL4+Hr/K2WrtpmAKnYR1vXeAhC3E/yPoCcT0t/by+ckqjpXVcLy8lmSbKeLcgvPJ0eQ+rQ3D9pnrixi2cCt/ereI/SUubluwhV/M3szoVz7h4V+25ZPf9OONifk8/8EhVmz7msXjetCucQozBucaCaEOXQI8EJQoQlDjC7J0yxFDEl2vJJ+scLMgJE9uMSkxz1lFETjtZvq1b4Q/qBJUJWZFUFrto8LtZ0BuNql2c0yZc13N1FfHLLVZhgOPPxjXV2v/6WqGLtwaom215c3CYqo8AQqW7GDI/I8Zt3g7/qCMUNQdmpdjKLFN7tM6ipIzaVkhZ2u8cX+/hP9fAgn88Dhb48UkBBlJFmYMzmXLtD447RZjDrm02sfzmw6SZDWR6jBzWbqdYEilU6LZNzksCi+O7katL0itL8igq5qwcttRGiRbGHdtK8pcPrYdLqXSHQiZuB9AQRjUz13FFfhDsVOqYLNoycnTQzux82gZ80dFx7rZG/ZR5Q6wYtvXcWOpzaww9x+ab+GHj13PinuupkGSlSpPIOr1qpRYzQKTIlBCCUaqw4K+3vztje1x+4LMePtzQ1F6cp/WqGq0f/Hjb+4lGHpc9xo8Xu5GVdWI9YHFHNtH8FKSs/BZ6nCf4obJtqj120+dqaQIouxBLjU5SOBHihi+4I+/uZdL0fm5EBXQgVLKDaG/04Bn0CwhPgcekVKeufjd1g+EBxa3P8jhEhcBVfJIaE5OnzsK/6EWf3wkituuzxrpg87hlblSl5fLMhwsu7snqgpfn6tl1nv7AQxlz+xUG5W1/oguw8Ixeew8WsbIq1vErAiV1fiYub7IUFfUjy87xYYvoEbNiSVZTSTZzBEzg+EzUivuuZpRYdL94R2LeB2NNllOyt3+RJfwO0ZZjY9n/3kgqkq0aGwer4/vyaz39hldvgbJVhZuPszQvGZGZTtWIgPnk5e/3tebQECltNrHXb1bGnRhvTjxwqiuhsBAXa764oIeTPhFKx5Zs8dQsvvNje2REtIc5m8UaNl/upqZ64t4cUweqz85RrcWmZgUwTMjOvPpkTIe6NfGqJrPHNKR5plJANR4AwzNy2HpliNMG9Te2LbexXR5A2wqOhNt3zImj837znC83E3xOTcz3v485tyNPruodxDDqVnxaFq13iBqsox5vif8/xJI4IeHxx/EJARfltSw82gZHS5L5UyoE6azCdIdVnwBldOVXi5voC26y1xeMkOF1QbJNpZvPcKYXi3w+IMoioIvmMncfxzg9zd3YMq6vSwe14OCJTtYPTGfUpcXb1AFeZ4q77SZQwIPgqAqqfD4+feBM9zYqSnzPzxkrAMaOm0s+PBLNhaVUHSqmhmDc+N2CdMcFtpkO1EljH01cjxkQG62oQPgtJl56t0i7ut7BZZQrJozrBNSwrkaP/M//JIZgzsYTCJ9f+kOC2ociqUMPZ4dKgwPyM3mbI2PScvOr4leH9/zO1VAjjdLXd9EtqTEKLCG026fvAR6YAI/TsRT5w1cQlPlQjqAfw77+xngNHAzsANYdNF7rGfQA4tJQMGSHShCfOPir+CalpRWazN6i8bmkeW0GdWwNwuLebBfG+MCXje5F6sn5nOqwsPYV7fz2No9NHRaKQ3ZPcxcX4Q3oPJ1Wa0hzw/nhWKGdb+cP/+9iIVj8qIqQgs3HzbmCHRo8wxmw8dN39aUdXup9gQoWLyDGYM7GJ0b3T9Iq4Z6I94T3rGI19E4WemOMCQ/cKY60Rm8CKiqjNlV9QWCUcmcnrylJZl5+JdtjS7fna9tZ2yv5jRJt0cpbMZLXty+IGZFRAkZHC/XRAgCQcmqCfnMG9k1qgBSsHgH2al21k7uxezhmnBRSZWXMpePU5VeCpbs4KFVu+KaFet00Nt7No/4DFe3bmjMm0wbdCW+oMqs9/ZxrKyWm1/4mJnriyi4piULNx82uo8zh3TE5Q1gt5iYcF2rCLPmGYNzeX7TQQZe1QQ4P4v7bbYtQEQ3MV5n8cjZmrgdPZ1aXvc9P2VqUgIJ/NRgEpoVwnufnWJs75b4Aipmk8AXCDJ/VDfMisLE61sjkWSl2FAlFJ9zM//DLwGJ06agSkm3FpmMfPkTpq77DIHGHNpYVGIs5EyKoHerTBQBL47JwyQ0psOzIzqHxQGBP6QoLKVk5NUtuH/lTkqrtRgSVCVHztZwazfNqlmPVbphe91YOuu9fUy4rlXM8ZDf3pjL1IHtEELw/AeHQirQNs1L1Sy4LMNheCJuLCrBF+pWAsb+FHG+WxgOfZ5RT0IH5Gbzu5tyjeRPP447X9tOo1RbVNfuu07O6jKVfsrJH2gU0Pv7XmEIglhNCvf3vSJBAa1H+Kbr6qK3dZGv7y6l/J2U8piU8lmgxUXvsZ5CX7SFL/j8QTXihxqR14wUu4UZb3/O7S9tY+b6Ih67oR1ZThttsp3c2asFDVOsjOzZnOwUG2kOC0FVYjMrBh3E5Q0wc0jHiCTMboktFGMKeYw1SLaweFwP1k3uFZG4hYt46PRVjz/+nNjxcjfeQJBH1+5h0rLCCA80jz8Y9R5fIIiqStz+aFrJ8fJIA/IEze3ioHdVYyXQeiUz1nfu8alRN9t7V+zkZIXHUNjUES95UYRACGgeEjKou49Mp42RL2/jZIU75vPnanz4Aipnq73GteDyBoyuePh5/sGj10ecs+HbCP8MLm/AoK/q19ZdvVvSrrGTDx+7nlm3XUWDZCtbvipj0rJCHl27B6tZYfHHR0hzWKh0+9lYVMKkZYXc/tI2Ji0rZGNRiXHj1M3d430n4d+dbqvRLMMRk8L99NBOzNt0KG5HLxa1/KdOTUoggZ8aHFYTFpNg0FVNDMp5it3C8XIPf997AotJYDEJVCk45/KhSkmS1cSdvVpw6EwNtT5p+P7pce3RNXtokGzVrm1JiPIumNynNV+fc9Mo1YrNouANqPz57/uZMTgXkyIIqtr+pYT5H36pJZ0hKwk95s14+3OcNrMxslHrC7KruIKlW46wbHxP3rqvtxFL9dgWr0O3+OMjNE23c1fvluw8WoZEoEqtw2QWglOVHiMmlteeH1vZVVzB3H8coGmGZitRN/bNGabREV+9qzuz3tvHn/7PVRqLI1QQ18dh9EJjrOQsXuEzAc0iwONXjfvqjLc/x+NXEz6A9QjWONeV9RISwAtRAc0WQvwaEECqEEJIaZxNCXOREPRF27P/PGBQOPUAOGXdXkMl687XoiXqZw7piMWkYLeY8Acl8zYd4rEb2vHi5i+ZNqg9KXaLQeOYveEAj93QzujSNctwGDeUujSPoCqNofB1n37Nde0aRUgyLxqTR60vyBsT86n1BclItpJsM2sL2jgy/KcrPcwf1ZVzNX6SrJoQRoNkC75AZIBpluHAYlY4cKY6roJk3WQvQXO7cMTrqr513zVkOCzUxrFLiKd8JoCV246yaEye4TkZyx/wxdF5zFz/BXf2aoHZpMTcx5kqDzMG55Id5xjSQl3n8O5gXbVb/Tw/VlZrnLPh26h77lhNSkxe/JKCnpgU8AclUmqdyXM1Pk5XeVi6RaNj65TYb7qGMpItLBqTx3Obok3tF4zuhsOq8Pr4npyr8VFW4+PdPSd4fXxPAM5UeZh121U0a5CEVCWnqzxkpVjjdvQS/n8JJPDDI91hpTTg4YrsZEDwr/2n+GWHxjR0Wrm9Z3Ne33KEsb1bUuX2YzZpyZEiBE0zHKzffZLWWcmhe1+SEVt2FVewcPNhXhvXHUXRhN8kcK7Gx46vymiSZsflCZCeZKHU5TVUkXWTdX9QMjQvh6Nna2MyMHRl7oZOKy5vwLi3n3X5GL5oq/HZ9FnrWDHPbFIYmpeDNyB5/M29rLjn6tCzErdfpcrt5+n39vOnWzvy2rjuuH1Bw/ohy2njtze2J6jC8q1fMbzH5cwc0tFYKzisJp585wt+f3MH7u1zBW5/MEJYLHysJFZ8TAhkfTOkJIrB9ejaPZdkEp7AjxdJVlPEdVVXoPFCcSEJ4MtASujvpUBDoFQI0RjYfUl7rYfQF21P3doJVVVZM6kX/qDKr1Zrfj1ts52UhXUtdBwvd9OiYbIhQf/R1L5GYJ8xOJdZ7+1jxuBcY9GpV/ReH98TIeDgGRdvbD/G/FHduH/l+YX6wjF5rPv0a54e2ok//O0LHuzflvW7jxvzAlkpNqxmga9SpaTax8LNhyl1aUqer43rTpnLF+UDuKSgB06biZJqX8SM4DPDO9M0w24YjNf6gjTPTMKsCCa8/ilZTltMxbHn3j8Y8V00y3DgsJoS8vcXgG+aEztU6uX/7iyOOie+Sfmswu1n0X+OMv7aVtpMqy/AvtPVLN96zJgnaJBsZc4/9rOxqITSah9/HNIhrk/UY2uLyHLaIhLIAbnZTBvUnurQAif8GOrOqujn+YzBuVGWIgvH5DFvU+S5U+uL/X1Ue/ykJ1kjztcFo7uR2ySF5F4tEALj89Q9R18cnQdIZgzOZcGHX/KHIR0jbFtqfUFS7Gb8wSBzNxxkVP7l2mdzWMhvnUWVx88f3ikyuu3L7u7JwRIXbxYW81D/tmQ44tuoJvz/Ekjgh4WiCHxBFV9Qsmb7Ucb2bonL48dpN6EIwS/aZvP+F6fol9sEh2riqXeLmDG4A96AyqBOl2FSBIs/PsIfh3Rg2fielIWKQzuPluENSE6U1+CwmnD7gviDKjd3aUpptZfGqTZ8QdW4D5dWe2maYSeoagqAmclWnnp3H3NHxFZcvrxBEiXVHka+/Inx+EeP9424Pzd0WjlX4+XF0d2M8RE9tp6u9JCZbKXao/kIK0IQDEqWbT3KhOtbARZ6tkjHrAiSrGbGL/nU8FV12szU+DT/wu1HKxjTqwW+oEoSJnxB1YiHE69rzbCFW425wwf7teFkpV6Ua0PjNG0koa6atUT+V959sdSx69P6IhhnPizRAaw/8ARUnnynSLNdCV1XT75TxHMju1z0ti7EBuIPcR4/Ddx50Xusx6i7aCup9lDq8jJpWSFvTMyPb5BuEoaAhIo0PILSHRY2FpVwZ68W/OdgCYvH9TAoaS5vgCSrCatJ4ZYuTVm65fxCXe/KdWuRaVDnik5Vs3JCPuU1PjKSLdR4A4x+pTAiiZv13n4E2nB33dmtR9bsYcU9V1PtCRoLev25R9fuYdWE/IhF9st3dkdVpXFM/qAaon9o/OVGKXamDWrPyJ7NjZvSFdnJnKnyJqp7F4C4FiRCGN/f9qMVRsJ/WbqDxql2INp4XK+6ajxyhawUG6XVGJ23NYXHAc0kWD9PdxVX8Pu3v+DJW3JZPTGfgCoJqpJkq4lhC7dyvNxtdBZnDM7lsjQ7qsTogK+emG/YKujnx/xRXbk/JKDULMNBwTUtMZsUik5UsGZSPv6gDIkhSB7q35aiU9XGa9OTLLET21o/FbX+iPP1vhU7DbPkRWPzDM+sy9IdLL9bq3Y7LAqvfXTeq2rhmDwCQRWTInDaNIl3h0UjQDy65nN2FVdwqMRlBOUrsp3MXP+FkfzNGdaJX7+xh1KXN0QBPchTt3ZKJHkJJPAjhiIE/mCQ7UcrGJWvLaJdniAev58m6aE531ofqaF79e8G5+L2BUhxWBAC7ut7BWUuv8GqaJbhYMU9VzP6lU/o3SqTe/u25uAZF41SbEgptVEKIRj76ie8Nq47ZkUh2WbCF5AIYMXWo4zKb0Gpy8upCndMto7dojB7wwG65qQzuU9rGqfakZIwMRmN3jp7w37SHVZWT8xHERBUtQTC4w9yWbqdA6ddBgtCCLix02WcqvBSeLSMUfkteOrdIn57U64R64Oq5PkPDvH4oPacqnDzUP82nKjwfCODQ587nDE4l5nri3h6aCdaZSXTKEW7V9Xt9i2/++q4hc9vw8+he2hSRMR9VRcWNIn68fkS0H5jPa/Q0SzDcUm/8YWogDYAHgBOohm//xbND3Af8GcpZflF7/VnAl1meMLrn8ZU+NSVBnUPtOPlbr4uqzWodXrC+M7uk4zp1TxCbXHOsE488fYXZKVYmX5TLuN/0RKT0CqWjdPs/OWfh4yFO4Rm7qo8WEwK51w+Hli1KyqJmzu8MxVuP9kho9lwaLMBWvUh1nP+oEqW02bcDJ795wEe7t82gnL69NBOLP74K566tROKokldhyeNelfwUqt7PyfE8zYyCSK6aHqQ+PjxvhHG43+9rze13iBHztYYlJvwObNY2w+nGnfNSWfqwHYoQnDHS9vi3qBPVnoMtdnwhcBfC4/zYL82EdXnRWPyWDu5F76AlmjNe/8QD/a/gm4tGnDW5TMKD4vH9WDb4dIIyuXKbUejOoVzhnWiodPK1HWfRXx3x8u1mVaAnUfLmDLwSs5Wew1PTj3hu7N3S27s1JQUu5k3th/junaNIq7dZ4Z3pnGanek3taesRuuiT1pWaCzyptxwJY8Pas85l2YEP23QlYYq29C8HNy+AKXV1LsqdAIJ1BfYLQpCmJk6sJ1W/LFbuOOlbcy67Sq+Lqslp0ESZS4vvoBqzEefdflItpnBpIDESP4Aspw2/EFtfm9U/uVIqc0MT78pF5MQ2Mwmgqqkd6tMfAFJUEoKlnzKX+/tRY0vyKL/HKW8NsCrd3Wnyh1gcUEPzlZ7o1gYPVukR8Ur3ee31OVlzrBO3NunNbU+FZfHT7UnwCNrzo+VPHdHF9o0SmbB6G5U1PrISrHRINnGzPVf8LvBuZRUeSm4piUCbe1ye14zmmZoHoml1V5e33qU39+ssSXqjhHoBUcderFbp+2vmdQLRRGUVnujun1HztbELqJfgEDWN41N1Jf1RZJV4cH+bQ31bn2NmWRLTGvVF+hWH9+FD+CFUECXA58BecCY0N9PA/8DLEEziE8gBsJneQKqSpM0u6E0qEvwO6wKT7z9hfGDvrP7JA/9sg3L7tZMrxeNzcNmNjFuceTs4JR1e5l121UIISIWrk8P7cRz7x+k4JqWHCpxRQi16NYPSwp6GIt4vXJY4fZzeQMHx8okaY7Y3RSzSfNEivXcsbJaHruhndFxHJqXE3Hj04P7ynuuJjPZGjMYT1pWGCGnrz+emAuMRrw5Md1HUv9eu+ak81D/NgSlNjivJ3gCgdNmom0jJ3+5owsWk0K28/ygfd3tCyGwWwQvjsnj+U0Huat3Szx+lSnrIrvBpdVeg2rkD6o0dFpZNSGfYKgbvHDzYXYVV9A/t1GUcu2k5YXMHNKRgiU7jIWMw2LiRIWHx8LmGpKsJrq1yIyYpwU4UlbL2km98IU6dSZFUFnro9QV6bkXXlwZld+Cr0prjEKEfiyTlxcyd3hnMpOtnKvxMSq/BacqPTwTKpIs3HyYR9fuiTjep4d2YumWI9zftw1Vbj+Vbj9XZCdTa1F4ZM3uiGs0PcnCvpCtRX2rQieQQH2Bx6/i8QeZsm6vUZw6Xu4m2Wbmj3/TTNJtZoX3PjvJwjF5mBXBZek2FAVMAjLCxLi65qTzxC25wHnvvOIyF9MH68mfQsMUKyZFcG/f1vzv3/fx+KD2oQKr5EyVl2YZDg6VuKio9fPBvtOM7tUi2md0ucY4OlXpiYi5U9btNWyfpqzby6oJ+Yx8eRurJuRzz+uR9+qHV+9m6fievPDBIQquaYlJEQQCKvf3vQIpNYE7p83Mym1HWX5PTwSCr0pryEy2YlI0tXOLouD1q5gVhbWTexEMsUTK68xv6/FY37cuMRFrzGHepkMRc+oXI5D1c7DXcfvUKOume5cXsmZiPhlJP/DBJfCdQEo459JYVMGQMvCuY2W0zLz4H/hCygKXSSk7RIHCAAAgAElEQVQfB+4D2oSM4P8jpfw90Pyi9/gzg04LVVXJE29/YbTmy2p8PLpmD96AZGNRCXP/cYDnR3bhzt4tuOOlbfSd+y9e/s9hkBrFJFbgapxmjyl8MTQvhynr9jJvZFdWTbiaAbnZvDgmz5DRNwmNJhCuIDZzfRHnavws/vgINrMSUzra6w/y4MpdcVUNw81d46lQ6may8YJx3UB+odW9nyNiSViHK0jqXboZb3/OdbM3G0qhR8tqmP7WXg6ccXH7S9u4fs5mRizayqFSV4SiWvj2G6fa8fglqir53eAOLN1yJEq4pWtOOqqUhgLZ61uP4gtKRr68jT5zNzNzfRFP3tKBvz1wDa2zkmP+/vows76QqfEFaZZhj1hENUi20ibbyYzBuXTNSadrTjqLxubx6/9pS1mNj9GvfMK1T3/I8IVb8QclSwp6RJyvi8bk0TUnzRiMr/s59P1np9goWLKDp97dx8kKN4+t3ROl3ht+vI+/uZff39yB+R8e4uYXPmbaXz+j1qdGJbqPv7mXVLvFuB4T6rcJJPDjg6pKkqwKJkVTwD5d6cYcothlJFnJSrFiswiyU23ccXVzjpRWYVYE/iD8+d19eIMq5jDJ9qkD2+H2BZm9YR+N0+xMWbeX7i0bYhIgpcQfUDlT5cWsCBQhGJqXw9dltQzIzUYIrRu5aEw35gzvzKsffcXtPZtTUuWNGbvOunwMW7jViFVdc9KNThtonUgh4JnhneP69Qm0+ejZGw5oFhAmQXqSFSkxLCKua9cIpNatm7fpEI1TbaQ6rMzecACJdsypDjNnq73cEbrXPLBqF7+98UpDrVS3+IHI+30sO5xSl5cm6fZLsof4Odjr+OPMAPoTSqn1Bkk2hZZZqcb1dMdL22iZlXpJXd4LeYcihMgAcgCnEKIFgBAiE/jOdcmFEHOEEPuFEHuFEG8JIdLjvO6oEOIzIcRuIcSn3/VxfNewmk0Gb1eXmS91eQkENZXBXcUVlFT7DAobYHTR9K5OOHTOb6yLXadTnKnyADB14JU0StF+qsXjemA2CX5/c4eo5HHy8kKNmuZXDQEO3W5i6ZYjRqCcveEAM4d05P1fXxflB9gm28nicT0Ms9y6x2wxK8b3Eet5QyKbhPz9pSC8c/fCqGgfvgmvf8qxstqYPoHxEhF9dmLEoq0Mmf8xo17exl29W2I1CxaP68EbE/NZNDaPqQPbRSiQDc3LiZoXvX/lTk5Weig+5475++uVYP31pdVepBRGQvvYDe0oWLKDfs/8i5nri3jillym39SemeuLOHbOHXH9HC/XVPEsJiXiXH5n93FKqn3c/tI29p+uNuTM6x7LsbJa7bro0zrqe3z8zb081L9N1PGervQYHezj5ZF2FeGvCzdtrW9V6AQSqA8oq/FR61MJhJSA//i3fVhMgidv6UCl28e0Qe05We6hvNaP1SRo3jAFT0Bl8nLNQkYgMCvC8DTNyXDg8as82K+NkXQFVUkgqM1b+1XJ0+/tB7Q5n8xkK/M2HWLaoPaoEt7bexI1VBAempdj0N9jxa66hanJfVob8VUvDN7x0jZuf2kbX5XWxNyGKjEKxf6A5Otzmt2OSYFGqTaG5uXw7wNnEEL7rrJSrKjA12W1BuvCG5B8WVITVQR7ZM0enrujCyvuuZqlW44Ys9KxxhDqrgfSHdZL8u77OdjrmOJ4xCV8AOsPar1q1Dpn8vJCar3qRW/rQhLA/wX2oxm/jwdeEUL8E9gL/OWi9/jt+CfQUUrZCTgI/OYbXttXStlFStn9eziO7xSxgs+iMXm89K/DhlF7XZNp/d+xzFznDOvE6SpP3EW0Tvmcsm4vxec0CskfhnRg1fZjfH1OW6jGSx7dvtiealUeP5P7tGZXcQUFS3ZQ4w1G+QEeKnEx4+3PUSCmV4k5FIgyk60simNQ/8bE/O/V/LW+I1yMKF6XLZ6heaxEJBZdd+mWI5gUJcLT0mkzk+U8P0sRbx/pDgvzNh2KeX7olWD9sbIaH+dqfCwckxdT9ry8xs+v3tgdUfiouz9VlUbhZVPRGUb3amkE0IWbD5ORbIk6lhfH5DFv06Fv/BzNM5NiHm/d7y7WNXr0bI1Rma9vVeifA1pMe/ei/kvgpwdV1ZI/ty/A00M7kZVixe3XHrt/5S58gSApDgtOmxmJ4L4VOw0Vxq456dgtArdfZfP+EtZN7kWFO8Cq7ccISs3Pb0BuNsFQIWhjUQnmkLjD2h3FWE1aDC91eal0+wmqkmHdL+feFTspq/EZdH9dVyA8ds0f1S3Cl1dn1ujx9aH+bSIKWrFi8bMjOlPj9TP9plwef3MvQamJ0FhNCjPXF2FRNJ2CGzs1xR+UvFlYzG8GtaekSusEvjCqKwFVct+KnXEZFqqEpmkOnrq1U8z7fXgx87tYD3zX2/sxwhLHI85Sjz7jzx3fZZf3QlRAVwkh1gBCShkQQrwNdAFOSClPXfQev31/G8P+uQ0Y9l3v44eCzaxEeHfYLAoVbh/pDjMzh3SM8k3TjeR1c9UZg3O5snEKpyo9SClx2syG1H+W08ZD/dtweWYSpdVeXhjVlT+8U2Qs+PVgPGNwrmExEVM10e2n1hdk1fbzqqLhwhU6hURXXhyQmx3l4XO83M3pKi+zNxyI2MbsDQd4YVRXSD6fpOjfR4Xbb4iR/Eq05bJ0R8zvMIELg6pKhBCsm9zLECjRq6y1viC+0LlV9/ePlYjEouvG6u7pHlQFS3YA0dYO+j4q3H52FVcwe8MBVk3I56zLS0aSlUr3+Xk9XWTl1Y++YmheDlc1TSXFbo6inLbITDLm8nSvyujZVa3ONSKvGWN6Naek6nzxY1dxBX94p4ipA9sZnPqgKnF5A8axxPscFpPCQ/3bGNfzZel2ntl4XtwANIGHunYc4V5XM4d0NCTPE0gggR8PghLMiibqsmr7MabccCVPvVvE9JDypcVs4uuyWlo2TMYX0ETQdMrn5D6t8QY0z8/81g3x+LWq/YzBuZTX+Fnw4ZdMvykXRQhUZCieCEMwpV9uI1LtZhaOyaO02kuDZKth3L5w82GeGdGZl/59mLt6t+TfB84YCuFWk8L7RadokpFsfI5mGQ4ykq2UVHmYM7wTFpMSEcv0WLx6Yj6+gEpJtZeAKlH9Wkehd6tMrCYlpDAaZGNRCVMGXkmaQxPE6d0qk8duaGd0AktdXlyeAKl2rXAWL34mWU2Yzco3CrD8N3Y48Swf6ovgSzxkpVhZUtATRYCWE1x8ZyiBHy/0Lm/d6+lSurwXogLaLexv/U8VaCKEaCKl3HnRe71wjAfeiPOcBDYKISSwSEr5UryNCCEmAhMBLr/88u/8IC8EZTW+KNGKAbnZzBjcAV9QpXlmEqu3H+OZ4Z0NGp3NbDJ81nYVVzBzfRGrJlyNlNKo4A3IzWbVhKupdAciFBD/cnsXpg5sh8WkkJ5kxWZWIrokelexrpLQ0i1HmDLwyiiFxhdHd2P9nhN0a5FpvPapd4t44uYOTL8pF19A5eV/f2V0A/UbQV2p2vAEQ0qJ1axEmNo/PbQTpp95seq/PV9jyV3rv+3d17bCZlFY8OGXUb9/PDpMLMuJeDOeLRomG6+NZSQfrgBX6vLi8Qd5cNUuspw2pg5sx7LxPVGl5nc167193NW7JUu3HKF9k1zDQuR4uZsRec0Ynd+csaFrSk8YXxjVlQfCrCQWjO6G2aSdexOua0XBkh0xix+a2IPK4VLNp2/KwCt5fmRXHly1i4WbD8f0O6xyR/phzrujK/f1vSLCnqLgmpas2HaMmUM60qJhEqqEWm+AyX1as3DzYVpnO2mW7vjJV6F/DDE2gQQuFBdyvsrQ3FvTDDsF17Sk0u1nY1EJD/Rrw4DcbBShdc/+ckcXhIQnbs5FCXVgdON2KSWN0x14Q+IxegF1Y1EJv7mxPUJoYqHPj+yKPyh5d88Jlo7viVkRPL/pS+65riVOmxmrWRBUhVEQXrj5MA/0a8O7e05wY6emEQrhL47JY/1uTQFcj7mPrdlDm2wnY3o1p8zljop/pS4v3oAmeCOAx8Ji3YLR3ahy+8lpoBW9BuRmY1EU1FAnYk3hcUb0yCE9ycrOo2UsGN0NKeFkyKYi1lpj0Zg8zN/jjb4+Wj5cyDlrUgQev2Ty8vP3xYVj8kh3/DQ/cwLRsJqUqPXInGGdsJoufgZQyG8xiBRCqMDnwFn9obCnpZSy30XvVIj3gcYxnpoupXw79JrpQHfgNhnjIIUQTaWUJ4QQ2Wi00QellP/+tn13795dfvrp//uRwRPltVzz9IfGv/V5pvCguGB0N7JTrOw75SI9yUJGkpVH3tgdodR58FQVAzs14fg5t9F5aJ3tZNTL26IqAksKejJlreY99uLobjz/wSGG5uUYcvy6CmhmspUmaXZc3gDJNjMmAU+880WUl8xvb8yl2uPH5Q0QVKWmHJlqi/A30+cBB+Rm80C/NhGL/7oBuLTay/S39kbt50fkj/aDR81LOV9Lq73cuuDjqPNh8bgeTF23F9AECVo2TNYqvVJit5homBx7nkJVJftOVUUor70+vmdUQaNZhsPoogGcqvTgsCik2i1INE+tP/+9yOgYvzi6G4VHy+jaPDOiQ7YwZI2iJ5EP/7It7+w6TsG1rThytobFHx9h2qD2Mfc/d3hnmqRpHlIBVfKfA2fon9sEVWpqWQ+u3AVgXHt64hkeTPVkefpNuXxVWkOS1YTdouC0WxCAxaRgNsHwhdHX3NLxPQ27lUynlSSLCbdfRVHgTKUnQmp9zrBOtGucQoPk7/Rc/0mesz81/BhpnUdn3fRDH8Kl4Ed7vpZWe6n1BVi5TfPes5gURizaypxhnWiUaqek2stja/ewemI+pyo8ZKfaCAQlj63dw+xhnXBYTZyu9BgicGNf285fbu9CQJW89tFX/G5wLkFVsvqTY9zZW1PaLHNp3bcUu4VjZbVGgemt+3rTKsvB1+e83Lu8kCynjVlDO5JktTAyzr0fJMXn3MzbdIhdxRX885HrKFiygyynLWrt8fTQTvz7wBnu7N2S21+K3t5fbu9CToYDs1ng9auaorTTZlgAvTExnySriWSbmVnv7eN3gzvwp/VfMPG61vzqjd0GQ6lFwyTMioLHHyDJaqbp9yRNGe8e+B1ZPvxoz9nTlW7Dh1dHswwH6yb3onFaglVVH1BW7aHc7ac4LAfIaeAgw2EhM+SfWQdxz9cLsYH4NRoN0w2sBt6SUrou6chDkFL+8pueF0KMAwYD/WMlf6FtnAj9v0QI8RbQE/jWBPCHQt0uyuQ+raPmme5bsZPVE/OZt+kQj93Qjoramqgu2uJxPZizYT9D83JIwoQvqPn4xOrGVNT6DGuGe1fsZNWEfJZvPWJU4/Su4otj8kBIztX4qPUFSE+ysrGoJMKOAeD+vm148p0veOyGdkz762dRXR2dWjpzfREP9muDIgRrJ/VClTKCgqEjM9nKI//TLqpKl6DD/XeIp7B6rsbHruIKuuZoukr6jUL/3hsm2wgEVEpcXvxB1bCGMJsVmqTbmTu8M41T7QgBK7cdjarqLhyTx7ItR7ix02WkOazc8dI2Y/8j8ppxz3UtGdmzOXdf24paX5AGTgt5LTJZv+dEhKff33YfZ1R+C9o3TmHKDVeSbDcxuldLvAGV2RsOMHtYp7jiKg2dVn61ejelLi/PjuhCXotMY4GkJ12zNxwwKNVtGzkZ+2qkxYp+HksJBUt2GMWacJP6hWPyjG3pXW9tJlGbAbSYBJVuP0kWE5c3SOJM1fnkT3/tlHV7+et9vb+fkyCBBBL4r5DhsOAPBvllbmNWbjvK0O45Rnev0u1HSsmzIzQVzfRkCyZFcKrSo3mq/vsrfj2gLVkpmtrmWZeP+aO60iB0b5s2qD1/Wl/E7wfn0ufKRri8AdIcFpJtFo6crWH+h1/ymxvbk+W0MWNwLg2SrVR7VAqPnGXVhHzMJsHRszVUe2LHepBUewI0z0wiKyQAp1NIj5e7jfiX7rDQNMNBpdvP7T2bE4gzX5SRbEVF8lVJLVkpdu5fuYslBT1YMLobL3xwiKwUG8fKalGl1t18sF8b7u/bhvkfHmLWbVfRJM2B1axQXuvD61dxeQN0bJp2Ub9HPEpnrNe4fYELnnGvT/DF8Wn2BRI00PoCvyoJqio5DZIMmm9QDV7SDOC39gyllH+RUl4LPIimBLpJCLFGCNHl4g/92yGEGAhMBW6RUtbGeU2yECJF/xsYgNal/NGirghMPAqdqkrmDO/E5Q2SaJWVzIp7NBsH0Co5l2cmsbGoJEJN1BIyjg+HLkihK4AdL9dUQW/u0oxWWUksHteDDx+7nmXje/L8poMEVchKsdHQacOsxN5emsMSU4gjfB9XNk5h8bgerN9zAkURNEq1x1Xr+jkMZf8QiKewWuvTbn51RQCOl2sKoFUeH/tDap+6NcT+M9UEAiqpNgstMpM4Velm5bajjOjZnDbZ2vm5bnIvZgzOZd6mg9zYqSlCCEM5T8eE61oxfsmnFCzZwe0vbaNgyQ4OntbU4XRPv2ELtzJpWSGL/nOU0a98gkkBq1nzkjpZoVmIlLq836h+d7i0xlCkbZJuj1Kfm7JOU+/Uix+qjC2U0zjVbnyGWMWaycsL8fhVQ8hF378uvJRit7Dgwy9RFAVFEXGl1v2JG3MCCfwoUe72Y7coNEqzM7Z3S8Yv+ZTZGw6Q5rDgDWgFsm2HyzAJwccHS7AoAodFYdHYPAZd1cRIOCwmBatZkOawcqbKy1mXz6CTvrXzBFkpNpKsZorPuZFIkqwmQxRm6sB2IYsmL0FVckWjVEa+vA2PT/MmjBcHi8+5uXXBFu58bTv3923Dlml9I9YJu4ormLSskEfX7sHjV7lp3kfc+dp2TCK2iqTDYiIYhEfW7CGgqqECs59395zg/r5tKHP5uCzdZqh4n6z0YDZpVhYWk8KXpS4eWrWLW174mEynleaZSRdV6NUpnbcu+Jhrnv7QsDQKty3SXzP9rb1R9x/9c9R3sa2ECmj9hzegMu3Nzzlc6qK02svhUhfT3vz8kpL8CyaNSim/At4GNqJ129pe9N4uDC8AKcA/QxYPCwGEEJcJIf4eek0j4CMhxB5gO/CulHLD93Q83wnqJjuXpTviJFkaZWzky5rHx+hXPuGh/m356PE+zB3eOWaA9gXVmL59us9YesjYvaxGs5nw+iUFS3Zwpkqjm9x9bStUFZw2MyNf/oSHVkV7/S0ck8eCD78kp4Ej5kJW38f+09UULNnBLV2b0SbL+a3JXCwvuwT+O8RUnB2bx5VNnHz8eF+ubOKM+RvWeIMx5YVLXF4Olbpw+4Ms/vgI17VrxF2vbWdXcSWjX/nESNw2FpVw/8qdpIaUZMPPSb36HA5dHS6WymaW00Z5bYDRr3xC37n/4rG1e3D7Aiwck0etLxhX/S7cTypeNbtVVrKRtJ6qiG1HkZ5k4b29J5kzrFPcYk2S1RQhsf7M8M7GNScE/P7mDqiqRpeymGMXVer7giSBBH6q8AWCmBWo9gTwB7WkZ1dxBVPX7aVpup1GqTauvzKbgCq5pk02KpK0JAs2s0KLhkl4/EGOl3tw+4Ks2Pa1IZJSUeunotbPgNxsurVowOwN+wmoKn/++z4EwrClUSVGoS4rxYZFETRO0+6/wVBBKZZC+Iuju9HQaWXR2DyynDbuX7kTVcLWL0t5sY7y9oLR3XjpX4fpmpMe6m4Knh3ROerebxLaOkNP/HRhmV+0zWb+h4dolGajojbAzPVf8MKorjRIsuK0mZm5vohZIWuLaYOuZPG4HjisCmaTiEjevg2xlKjr2hbprxmal8Os9/ZFfS+LxubVe3aREMRcC4rEsqrewGZWjK6+jqwUK1bzxc8AXogITCvgDmAIUIxGA/2zlNL9jW+8REgpr4jz+EngxtDfXwGdv4/9f58IV6BSVcnLd3aPoj+6vCr3xliELx3fk0q3n8xkC4vG5jFp2fl5LIfFxHNbDrJ4XA8q3X7KanzGLJ7e+QlX6BQCFo3phtWsRAyPLxqTR+9WmawpPG54/eU00LoqqQ4zawqP0z+3UUwForr7mLSs8Lvi2ydwkQgvNvgCQYKq5E/vnp+9WzQ2z1Bv1RErYdJnRP1BldOVHlpnJ3NnrxbG7FzbbKehwKmrjB4vdyOlNJTzdJqROVR9Dt++vtCJpRL3UP82UclowZJPeX5kF1pnJ/NQ/7bM23SQGYNzyUzWfKFWbjtqnPPzR3XjdKUn5rmqCMGwhVuNzxhLDOmpd4sYmpfD7A0HmDeya8ztVLj9Rtd71m1XMeu9/cb+D55xMXN9kTFPOG1Q+5jXe31fkCSQwE8VVrMJlyfIc+8fZMbgDkYM2FVcwVmXj6Aq+dUbu1k9MR+TInB5gtR4AzywahcrJ+RjM5tonGbGYVW4tVtTTlV6eLOwmPv6XoFFERFzzFNuuJJSl5cFH37JxOtbMWdYJ/zB8/FYIPAGVWyhQpIe28IVwvVZ/j/87YsoZe4qt59uLTL56OAZQ+3TYlJ4ftMhDpW4eOyGdnj8KkEpadZAmyE0KZrghMvrp8YXRAhNuM4cUitNspponpnE3de2wheQPLp2D1lOG16/yqsffcX0m9qzcsLVVNT6I7QAnh3RGRAgoVloBvDbqJ3xxhrCKZ36a9IdFjYWlVBa7YtQIW8YY7v1DhLDwzlcwf3Jmzv80EeWwHcEsyJ4qH/bCNHHhWPyDIu1i8GFpIxfAiOADcBW4HLgXiHEr4UQv77oPSYAxKc/xutaCGDSskKmrvsMVZXMHNKRNybmM3d4ZxxWhSk3tKOsxkdGstVQA9KrgXaLEpEQ+oMSm8XEnFCypu9j0vJCJlzXylj4J1lNmITgzcJiTlZopvK6smPdCmH4PvTt1Xe+/Y8ZerHBajYx6pVPIszJJy0r5Hc35Ub8hi/f2T2CIqTPvc1cX8T1czYz4+3Pqaz1azMlIRGBsa9tNzwAdSrkgNxspIQ2jZJ5oF8bw0vyg6JTUdXnZg20ZDRWN69Fw6Q4N3zJw6t24/IG+O2NueQ2SQ1RRQX5rbMMs/ckq0IgqLJ0fE8Wj+th+O3NGdaJ4+W1EVSouf/Qih3v//o6ZgzOZe4/DrCxqIR0h4VSl5fj5bWGmbN+fHqHXX9s2l8/M66v8O77429qIkd3vradRqm2b6Q7q6qktNrLifJaSqu9F1UhTyCBBL5bZCZb8auSoXk5LN96JCIGVNT6DTuFGq8fq1lh3adfk5ZkCd2vJQ2cVk5Xejnn8rP44yPYLQoF17Tkvb0nDWsaPcZ5/FoBdctXZbhDVH2H5Xw8FgKkBLNJUxl9fetRFo/rzvu/vp5nRnSmdZaT7FSbkfzp2378TY3ybjEpmon9+v0cPVsTSjxruS2vGc/e3gWTIrgiOxkp4Wy1j3GLt9N37r+4/aVtnKvxY1I0U/vpN+XywMpdLN96DFWVqBIeXbuH6pAOweQ+rXl07R6G5uVQUuVDSqLsgh5Zs4eGTisl1V7O1nhjUjsDATUiFl4Ig0IffdALijrNVb9HKcrFd0h+arCaFabf1J7WWU6yUmy0znIy/ab2l9QdSuDHCW8gthG89xIooBciAvNHNMsFAOdF7yGBuIjlSaP7CNXtNlhDAXDqwHacdfkM77yFmw+TlWLlgX7/P3tnHh9Vfe7/9zmzZw8hYUtEwLAEDJAIBGgVpUWpVK6CIJsCyiJavFZRu9DaUu+PRWq1yubCDoJQr1arckWpvSyiAeFqZJFFE7aEkIRMMvv5/v44c05mMjOQIFQI5/N68dLMcs6Zycnz/T7P83k+n2weD7FTWDgmH78SIBBiGaFtTue+/zUT+rfjwQEdwrpAJRUu7BY5QiFs4dh8LDJsmNqX1ikO/IoS5mfYLN4SdkNq1+ywmiir9pyzsmfg0iJW5dQkS3qHUPvdnHV7dduGaHNvD67exbIJvWPOgT43ogc2i6xXtQflZLD6AbUCHGc1seHz73S/qoAi2PDZd/wkpxUzh3QFBKsf6ENZtYfyGi+nznqi/h0oQvD4rZ3C7nXVK8tL27Q4vi2vZXPRKVr3bRsmVLRobD4ub4D/+sfXAGFdvzKnB7tFZsYbe/UChtbVnn93d2RJonmildcnF+ALKBw9Xav7+C0ck8fHX59kxcTeSMCBUmdEIUSjuLq8gZiqd01RttyAgSsZsixhkiXS4q3sPFrJkO5t9HVPliTSEtR5t2p3gAMnzzK81zVYg0U0t0+hvMaL3WLCZpEZlp/Fw2tUu5u5w3Mj7GiOB7uDM4fkkOSw8OBq1dtXs4VShGqLoxa3TDwysCNuv+DBELn/hWPzmXFrJ4blZ4UxMq5JU+mocnBvMX/TAV65Lx9fAJ1tpMXIZnGWqHPTfxnZg5bJdhB11g/Tf3Kd/prSajVea7EuxWGheYIVp8cf1o3SrksRatcvPdGmKqUm2HSBmuf+Zz+P/KRjGMtpxcTe52VQaKMPz/3P/gZbHDU1yDKcdQfCfq8Lx+aTaG/IVt/AlQC/InRxqNC/q8AFFIwbYgT/9IVcpIELQ0aCjaUTeoXZPGQ2c2AxwZpJfTjr8jNjQ90f95xhuQghophyq6azi7Yc0m+UjEQbv1y/h93FlRSdqGbtpAJ6ZqWEbXpBitz4rypk2YTepMTJmCQwm010bZ2E2xfAbJJJDwbe0OC8YmJvTp31GBvaHxjRPPy0ymn94kONJ8Cq7d+ydHwvLOZws2BQ74Vqt49r0qJ351om28MkyTcVlVJ0olpX+Nx5tJK8a9P0oLXzaCW3dGnJqbNuWiTZGfOK+t6eWSk8cVsnFo7JC/OinDMsl4Ai9MROO+/UVYXMGtqNO17cSmaqgzWT+jD65U8jXjP7ruv1e12jTWVnJFBa7SEt3hJmQr9wTB5+RUGWJF76+Bvu69dOp75OH5jNn0f2wGaSsFtlUuNbIYTArwjdYiX0u9Yq0uea94s142LQqA0Y+FfAHLsAACAASURBVGGgKAKrSaZZvJXpA7PDEiOAD/7zx8wZlktKnJXhi7azYWpftpaUsnBsPhs+/47hN1yDSZbUZDE4R1xS4dIVjEP98RZtOcQTt3Vi6VbVfkbz+Zr93j6WTehNQBFsKirlJ50zKLguHVlCt2CAunV66fheOvVcK1KVVXtolWzXC1XVbj/xVguz3vkqIkYum9A7amwPKAJZAimYRKYn2AAJRShkpjrYXHSKl0bncSYoSlPp8tE6xY7iqYuJ2pz0q/97GJOsMos0W6pQC6lh+Vl68qed/97XdvL2w/0jipahewmNVfXMnbkoisL6KX2DXsNXT/HZ5Y0cIXpwVSHrJhdA/A98cQYuCmxmmV//rHOYpdRzI7pfshlAOzASqAD+DswAbgQOAbOEEKfP8XYDjYTJJOP1K2EG04vG5jPz/X38anCXiNbvkxv3xgzaKQ6LToPITHUwc0hOPYqmwvSB2UxY9pk+AyiITkGVJXB5A8x4Y68qc10vmQudObOaTQgE9y7YZmxof2CkRUnOz2X4vu1wOesLS1g8Lj/63FutjxqPP2Z3TntMoxGnOCzIkkSLpEi/vXnDc5ElSa8Ea8nf47eqr0tPsOkG6rIk8cy7Rdz/o/ZR7884q0n/f68/+j3cKsQHSVMBXTmxNwDJcVbWTipAEYKKGi9Oj5+WyXbGvbqTmUNy9KKIOouo/r3MGtqNlsl2rmsez+kaLyZZsHhsfphfojYDeL4KdENmXAwYMPDvQ3mNF1lSaZfRKOlHy2uDXTt1PjCgCFqlxvPXzQcYlp9Fgt2EEKr/qaaOWVLhCqMoPvvBfuYNz6Vlsp04i9rZK6v2MPf9OpsGEOw76WRQTgadWiUz+uUdrJgYfc3XxLae3LiXWUO7YbfIpMZZqKr1kN+uuc7O0GJTWbU3bE9gkog5468V6uYMyw16Fqq2US+N7kmtN8BLHx9kQv92uufw9Xd00zuZM4fk0DrZjsNq5re35+D2BZhxq8pmSk+w6dY7U1YWxhTdCmVQxLKEiMaqupoQa4TIb4wTNCl8WHQynE31+XdM+FH7Rh+nISnjClSbhYnAFqAtqlJnNbCs0Wc0cE6U13gjql9TVxUyLD9LV/4KRUmFSx8MD4UWtLX/XzQ2nyS7OUyy/kSVqoj44S9vZMGYPFLj1M16tGMpAs66/TxxWyc9mTtd49FfowVebZPt8hob2ssBjbHaSHVYWBSc01u05VCEEuzL995AVjMHK7YfZf7dqlJcz6wUlo7vxfKJvTHJEoNyMsLmB0cu2cHYVz8loBBhPTFjw17SE21sLCzWqc+h1NPdxZVMWPYZ417diSzBjFs7k5Fki3p/Vrp8+s/aJqb+a6whfyeZqaoC3jt7jhNQBHct2MaP537MmFc+RZYl2qbFIUQ4hTMUWtI5acXnnDjr5u7F2+k3+2Oe33yANQ/0YeuTN7N+Sl+uS4/nmTtzz9v5jmXdYaiEGjDww8Drr/PWima1tLGwmOkDO3Kyys284bnUegOkxVt1m6a9JWd5+u2vgl0oKWZsBRj36k6+KKliyqpCymu8uv/vyCU7OFRWw8bCYn71sy48tGaX3pGLFi80GlhJhbq2t02L42+FxaTE2yM6Q5qCcej7T551R1XP7NomiRZJNpSgyEjLJDvVbnW2MdlhZcaGvWwqKmXu+/txevz86mdd8AUUfV58Y2ExASEoPlPLiSo3xyrduH0KS7ce4fFbO5GeYNOVxDMSo8d4LRY2xBLiaoW2joYiM9VxQQIhBi5PyBIMuyFLHV+p9lBS4WLYDVlcyK+4IQlgjhBiDKoZfCchxENCiPeFEL9F9QU0cBERqxOQFm/Vlb9CkZnq4LQzMmjPv7s7dovMuskFzBrajWq3jxkb9vL4rZ0YlJPB/Lu7s2L7UfadrGbu+/uQJIkRS3bwizWRFhAqzVTB7VNoneLQ5aXdvvCh09DAvO9ktbGhvUzQUKuNCpdPV9Z8anBnAJ69uzufPFGXOCbazIzq3ZY2KXaeG9GDPw7tysy3vmTg/H9yz5Id/GJgR54c3DmCRnza6Yl6X1e5fPxiYEcsZomFY/JiVn9PO708sWEvVS5fxP05b3iubv8AqulytNecdrqZNbQbHz12E7Pvup4XPzrI7d1bR1zrtNW7EAI9YdQq9qHQHtfmX0Jpr6Nf+RSr2UTrFActkh0NsjeJZt1xtcytGDBwOcJqNmGWwW5WffwW1xOxeuQnHfn8yGlaJdtIT7SRYDOTHpK8bC46xcO3ZFPl8nHqrEePrb+5vQtZzRy8PrmAv47uqRfGMoIsiPrWDhsLi5lxW2ck6kRjXv7kcIQY24Ixebz8yWH9530nqxmxeAcje7dFxCgea/GlTh9gP8u3HWHZhN6sm1zA2kkFpCda8foVar2CWq+fh2/JRiBIjbPyyMCOVAXjoFb4W7r1CAdOOTHJkj4v/uCADri8AWa+9SUjl+xg5ltf4vT4mdC/nS5Uk5nq4M1p/Wmd7DhnLDxd44lKlz9Z5brqxbMsQYXW+veFxWQkgE0FQkC50xv2t1TuVAWXGouGTIZ61ZMKvyRJx+s9Z7RzLjJizWylJ9p45t0ifSg8lEb3x7+rwhYzh+TQMslOSpyFZ0Jk/7XArlX9lo7vxbwP9jF9YEdSHGa6/ryrPk9QUuHSLSDap8fj9StsPVhKgi0tjJY6b3guNlO4guHJs25qgkPfm4tOXbWD2FcqvP4Am4pKw4SBALY+ebNOq7GYZewWmfIaH2dqvWEzb9q8war7+0RsNjTD4vr3dbN4K8cqXVTUgN0ikxJnjfq60moPu4srWfDxN8wcksPaSQX4Awo2i0yNNxA2vxdnlVGEHCZU5LCa+MPbRbpS58whOWwqKmXyjR1iUmYqa706nSmaVcSzH+wnM9UR5kWlvb+xne761h1X09yKAQOXI9LirZyocuHyBXB5A/z1o4NhtjOSBPntmuP2KwjA6fGTabazdPwNlFS4uTYtjnGv7WTFxN48sELtvmmxNTPVweoH+qAEKXs9s1JIDnbA6lNDJUnCKkvU+hQ9Nq4vLAFg2YTeWEyqWM3KbUdYX1iiF4Bnv7dPL7JpXbX6cbVVsp2PHx/A0dM1+szgE7d1YsYbe8jOSOCRn2Tj8ilIQmVWnKzysGL7UZ6+oys1ngApcWZcXkVnbyzfdkSfl944tS/XBFWjmyeEz4drDJA1D6hrRbvm8Tp7qLzGS7M4S8wZPtVfMTJm1/oCjF+y46rWGvAr8O6eYxH0wPv6N54eaODyhE8RUdlUr08uaPSxGpIAZkqS9AIghfw/wZ/bNPqMBs6JaDNb84bn8tJH3zCqd1uVghdUHLSYZdy+QFCq3sWsd4p4bkR3Emx2nhrchacGd+FgFFVCq1lmVO+2zPzvLylzelh5f/g8gUa9++ixm5iw7DMWjc3nhc0HIm649VP6AtEVDOcMy+Wt3ceYOSSHLi0TcVjNxob2MkGs+YlzCcZoSHFYdUqwZo4cCu3n+sfR7ENC/aDmDc9l+trdqv/VmDxe/OgAZdXeiGRr3nC1gDHlx9cyrl87fH4Fs0n1ERz32k59VvCatDhMksSj674AYOqADrRNjONgqVNP/rRrVGdrYiemihAoQhBnNTGqd1uS7GaWTeiNWZZUmtR7+yhzenhpdB4vfXxQf2/PrBSmD8wmIFRLh8bc81f7/IoBA5cTZFnCr6gJyPiln0UkcGsmFegCGyODBdS3HuqnzyMLCFszQ6H9LILHmjqgg25e/uTGvfrrxr2qzuz9Y/qPCCgiLDZuO1zOmIK2JNlNHK/0MK5fO0b0aktFrRdfQOGpwZ2p9QbwBwS1vkBE8XjBmDyOV7r5sOgEowuuZfaw64mzmpm+djcADw7oQLXbR0Wtj9Q4C/E2C3aLiU1Fpdzb91pWbD/KzCFdmffBPubf3R2TLDEsP0u/PpMsYTFJ/PpnXWKOrwhgyo+vJc6mfmfRlZAdYTHUJEVXSjdJ0gVpDcRaD69ESAh+2rVVmL/zX0b2QOLq7Yo2NQRizHleSOe7IQngjJD//7zec/V/NvA9EdoJcPv8CNT5g2H5mVS6fPzlfw4yuuAaVZAlRCjjuox4vAGBSQK3T2H2e1/rClv1A6UsSUxY9pn+2NHTtVEDqnajTV1VqHdMNJRUuPAHFN3moT4lQxvqnvVOkSH88gMjdIFzWE0x1VlDiw+a2mW75vEIBH6/QoXLh9cfwCzL/P7tL3lqcJeo940koavYaee4r187Vm3/Vu8sHy6rYe77dYWJaat36SIAmkJnisNCm+D8wp9H5OL0KHqneun4XnpHOlSYZen4XnpBZNGWQ8wdnhtTmRNiJ6Ynq9y0TLIzLiiaEPreZRN6M+/uXA6V1fCPvcd45CcdKTpRTXpCpNDN1VyNNmDgSodZlvDGKHQFgo+HCm+kJVgpPuNi5ltf6jYP2rxe/ThiliWsJok5w3KxmeUw8/KOGQl67BmRn0m8zczhshrW7vw2TP79pY8P6uv8uskFCAS+gBIWgxaNzaeyxkeyw8zrkws4VuHCF1BIjbNwvNLN4n8dZUDnFmQ1i+PgKSdlTg+z77qeWl+AJLuFB1aorI4vviunxzVpDMrJoGWyXfdH/PXtXfD6FWQp3Nuw1hvA5VPFtI5VuqJ+Byeq3Nzbrx2pdksYg0ij9J+schNvM2EPFiFdvgB2sxyxvswbnsvJs279/A1lYDQ1+x1FwJJPDoXdI0s+OcTvDSP4JgNLDKu4S2IEL4RYrv0DNgIb6z1m4CJD6wTE28xU1Hhx+wI89sYepqwsZH1hCU63P6wFbDFJeAOC78preXTdHkYu2cF9/drpNMz6s1A1Hh+Lx+WzbnIBi8fl897/ndAH1LXXhc4ThM4KaMhMdWCSJe5csFXfhIdCe49B+/xhUX9gfk9xVfT5ieDi2alFIm8/3J8//Uc3Zr71JQOe3cJdC7ax71Q1v3lzL89/eBC/Irj/R+2pdvsi5g0Wjc3HapaY+/5+Vk7srRuzP/vBftYXluiFhwnLPtOTP+06sjMSWDwuH4ApKwt57I09BBSB26cQEBIPrq4TMYizmqLec96AwsKx+QzKyeDxWzsx74N9Uf8GNBP36QM7kuwwM2toN31e1mE1sXTrEQIieuXeJEss+edhZr1TxH/kZdExPYH1U/ryl3t64PYpQYn0uu+2PkXUgAEDVwaax1l1b79QZKY6UKhbBzNTHYzIzwQkfW3WZvk2fP5d1LmsvxWW4PIpLN92JIz+OWVlYdhc8aQb2/PMu0W0SrHx8C3ZurjWxsJifnN7DlmpKitIllWD+vr0sKmrCmmToipwSqjG7WNf3cm35bWqzVSqg7nv7yegCF7YfJAXR/ckq1kcdosJj1+NZwJBj2uaYTVJ/OKWbE47vbo/ohAwcdnnzHhjT9gMpNkk4VcgIARz3tunC4dp38GisfnMeW8fihDsL3UyYvF2hi/azqx3inhqcGd+f0cOM9/6khvnbuGuhds4UeWi+EwtJ896aJMax3MjeoTF7Lnv79ePrXkQa0bysbojsex3rtSYbbfIzLitM1aTurW3mtSf7RbDCL7JQCLib2n+3d1VTmYj0SB3SEmSHgR+RdBJRJIkJzBHCLGg8ac00FC4vAGefrsowhPNbjHpcwNPDe4cRuvQZpO0DpzWTUmLt5ISZ8VsgrKznjBvnnnDc7FbZN3/zR8QLPnnIX3OQJvV0qoO2nvKg35GmkhG/YpE6xQHLZPsDaqkNSUaxuWE+gtcrMTpeKU6K9KpRSIBBd3KQHt+6qpC/jqqByZZ1mc5BuVk8IehXcMM3F/YfICHbs4mPVGd7Qv17IO6SlW0++VgqVP3hFq+7QgT+rfDJMMf/17Eb27PCXt9rHsuwWbG5fPz1OAuuuS5VlVPi7eSGm/ljNPLX+7pgdevsOHz77grP5P26fFqNzugsODjb5g+sCNxFjnqOU6ddfPIT7KZYe5MqsPCwTJnBP1Zo11fyDygAQMGLg+4AgFcvkBEx2nh2Hz8gQBLx9+Az6/+1ySra6cWL7RZvqkDOtAyyabbzPgVwT/2HOe261sx+72vuf9H7fVClUafrPUGGJSTwbD8LCzB7mAd7VK1VFAEjHnlU/2aFo/Np0WSLWp8r/b4ibeZkST087z9xXEeuLEd84bnsnTrEUyyRHqiFY9P9T+VEJyocvHk4M4cPV1LdkYC3oDgwdW7mDc8l/REO9MHZlMetHKYOqADNrOkMyoCikAItYtX5vQw+719emeq1hvAF1DHVwRShL3VY2/sYdbQbvpj6Qk2ar0BfT3RPm/zBCsev8Kf3q2b726MB3FTs9/xBwSVNd4wvYbnRnQnyWYYwTcVSEi8+r+Hw7q8r/7vYf5wR7dGH+u8ZQFJkn4L/BwYIIRIE0KkATcDg4PPGbhEsJpNlDk9jHr5U3731lfMHJLDJzMG6EIZjw3qqCd/EC7trHXgNL8zh8VEWbWb09Ve3UBSe8+MDXuxW0w8sWEvj77+BafOutl2uByo65hIEsy+63q9ozP3/f24gjYT9VXLtIDbmOTPkHW+NKi/wMVStNQSxdM1Hlw+f8SimJ5gIz3RrlMle2alcF+/duw74WTMK58yfNF2pqwsZFNRKQ+t2cVTg7tgM5t4aXRPlo7vxbrJBSwd34vXxt/A/3x1goX1Os5zhqldOe0ennFrZ+a+vx9FgWH5WRHy1tFsKhaMySPOauLPmw6EeRJqVfXhi7ZTUePFZpH5z9e/4KfPfcLifx3l/uWFyJIqLpDisPC7IV3VRHbNbhbWq9zPGZbLnPf2qd9Joo0Kly8q/VmTV68/Q2nAgIErBzWeAOOXfqb78mkdp2ZxFuZvOoDVbOKs24/DauZMjZfTTk9YnNpdXMnGwmJqvAGOV7oQAhZvOUTetc2ocvnYVFTK7Pf2cW/fa7k2LY51kwv46LGbaJVs4xfBbp/Pr4qsWEyybjFxvMqtW0KAGnemrCrEHRSKCYVWGLPIEiJo4zBveC7j+rZl4+fFtE5x8Lufd2XlNtWE/rE39iAhsJhk/nWglFbJdl7YfJBAcC66pMKFLEm8vfsY7ZrH4wsoPHGbavvz4KrdCCF4bkQPJElN7P5WWMKCMXm6tcVjb+wh2WFh4ZZDLBybj9sbud6UVNR5u4I6z12/s/n85gP4FYFJlnj6jm58+qtbeHNafxLs5gZ39Zqa/Y5PERH7u0fX79HtTAxc+ZAlmHJTB50JMOudIqbc1OGCbCAaUhYYB3QXQri1B4QQhyVJGgHsAf7U+NMaaAhCZ7K0RG79lAJdCj890R41cOp+Okl23nqoPylxFt7efYz5Hx5k3eSCqO85WeXmids66TLQK4IG2RaTjEmG5VuPsPhfR/X3ZKbW+Qxqlc5ZQ7vRISMBh6VxHbxYNAxtdtDoDl446gu7aIlTaDVb61iVVLio9QQ4crpGVZ4NVnVbpzhIcVgQQujzGZpf319H9Yx6P1W5fLRItnGi0h1RjbyuRRJ/DUqiZ2ckRBUqOhP0whLArHeK2DC1IGxWr8zpoVWKndcnF+D1KygCAkoARRH84pZsis9EnzlJdlhw+wIR9NNTZz1IklpZG9W7rT7v6vT4dTXRSpdPV8rTNgixKsja36BBgTZg4MqFNt9XUuFiyspC/fENU/syLF91wXpozW4Wjs2nvMbLxsJi/jKyB/+57gudJfGLW7J1MRetUPXiR+rsnkb7HPvqTtZNLqBlsp1b5v+TVff31rtdmuVDubNOsCqWN6ksS7w4uicPr9mtn2/+3d2xmCSsZgkpIDE9aDa/dOtB7uvXjjGvfMqyCb1Y/K+j3N3rGj2+ZiTagxYSUOb0IIFOd610+VhXWMKdeZlIksTjwUJ0SYWL1Tu+Y1zftgQUQXqCjaE92/BiPQXVOKvMzCFd2Vx0ggGdW0aN1bXeAD2zUpg6oAPZGQlhz2sFSE18J7TLd6Iq+khKtK5eNNG9KzlmX0yBEAOXJxShFj1CVc5NsoRyAT4QDUkARWjyF/KgS5IkJdobDFwcaIIwf5vWT9+YCwHv7DnG6IJr9WAcLXDOGZbLn975iukDO6IIhXVBOqfG+a//nvIaVdJ/5cTeHCh18tj6Pfx5ZHdKKmrZsu8Ut3dvw7tfntIXtd/enoMCLB3fixc2H6TM6aFlsp3MFEejk7Nz0TAu9pD21ZZM1l/gypwe0hNtrJtcgNuncKLKpSdfmakOjpyu0edANKGh0ERxY2ExT9/RFZtZIj3Bps+u1L+fSqs9JNotER3qR9fvYdX9fXS7icXj8qOKtNR6AywYk8cz76rPOT0BVm3/Vpe3NskSZdUefaOlJZcJNjMWs0SXVoksGpuvU4u065/3wT5G9W4b9h2F3v+rH+jDf77+hf7c3Pf38/itncJo1qEbhFjKqZmpqqdVU7+/DBhoyohFVy+vUWfghKij0G8sLOa+fu1Y8skhZt91Pa1TVLXk8Ut3hsVATfBq0ZZDvDi6JxU1PuKsJtISbNiC3qMtk+uKu9ooxkO3XKfHtFgU+O/Ka4mzmpg3PJfWKSrd/nilGySJYxUepqwqZGR+Jnf0bBOm2KkZ3VvNJgblZCBJEk6PD7vFjNvnZ/UDfUBCp8Mu3XqEF0f3RJJUJkToddzRozUPrt7F+il9dR/AkopwBdW1kwooOnGWVqnx/Nc/iiKUnxeMycNqlnRRLU1QRzuPVoCMVjRuiJq1hqZmv2MxxRAIMXwAmwwUgV7g0ZCZ6mDdJbKBOCZJ0kAhxObQByVJugU40egzngeSJD0NTALKgg/9Wgjxjyivuw14HjABrwghZl/sa7kcIMsSGYl2lHhBvM2M1SQY0iOTMa98yrzhuTw3orve8s9MdbBwTD5Oj09XWCw6Uc3rkwtUzyEhOOvyRbwn1DNIqyGkJ1o5VuFi7Ks7AZj4o/ZB2wdBRY2P0fVmD1ql2ElxXFjgPFfAPl93sDFoaopfDUH9BS6gCP4U4hE5b3gugP57/O1/f8nu4kqcbn/Y/J5GbZw5JIeH1uxi7aQCpg/MDpMu14oDTw3uQrXbj80sk54QvjkoqXCFFS40+nDo4r9obD4ub4C0eKu+aThZpdKStc1Q/cRRSy5fn1zAA8sLWTOpDxYTvD65AF9AwSTLeP0BfnN7Di6fP2yeNbQDKgHTB2bzwuaD7C6uZHdxJcu3HeFvD/bDF1AICIHdUreRiFVBbpFop8Ll40SV64rfVBgwcLXCZpajFpKWB+mS1mDCtmjLIR6/tRPLtx1hWH4WdosJq1kioKBT5qcO6KDP7Gi0Q49PCWNILB6Xz0uje0YUd9cXlrDtcDnPjejByvt7YzVFvy6NoTBraDdkScJukWmZbMflDVBa7SE9wUbHVkkcLqshLd6qX5s7mNhJCH1+Oj3BxoKxeVS5BPcv/1RX9e7XPo2ZQ3KodPlx+RROOz36vGKKw0KLJHtwZs9Pu+bxUT+/SVbZKL+5vUuY+qn2fLPgtdUX1NHWCe3aQ6EVjVsFjeTrx2STDMcqaiPicVOy3zFJEs/f04NHXq8rjD5/Tw9MkrH2NBX4g5310L+XRVsOEbhENhDTgbckSfpfQONA3AD0B4Y2+owNw3NCiGdjPSlJkgl4CfgpUAJ8JknS20KIokt0PT84tCB1rKKWB4NB//MjZ7gtt1VYKzgglDB5fY3eOXzR9mCCmEfbtDheHNUTj1+h1hvAFlSIChXiWDg2ny1fnwJgUE4Gp2u8TFlZqC8C9WcP3pzW/4I3uOeiYTSGznE+XMxk8kqCdu+UVXt01Vaom/9cN7kAq9mESVYT/8Xj8mmd4oj6vXdIjyc9wcaZGi/XNo8LW7zbNnPgV9DFV7QEM/R+zEx1cNrp1umcWoK1+oE+nHX7OV3twWaRsZjUSpe2CZq/6UCYj1WsDYCiCPq1T8PtC3Cmxsf9yyM3blNu6sAbU/tyrMJFeY03rAP69clqZr1TpF+3aozcWZ9fiVY4qF9BjiYM09QLDQYMNEXIsoTdIrFiYm/O1Hgpr/GyfNuRILNG8Pu3vtQTsWc/2M/0gdm0T4/nRJWb004vFTU+BuVk6ObooUWuX/+sC4+u/yJ8LV2pWi7YLFKEPc2isfmkJ1iZumoXALOHdWPtpAKOV7p0eroWZzukx2M2SZyo8kQkiUl2M7976yvmj1CVBKcO6MD9y1Xrn+dH9aTa7aOkwkV6gjp+oV2DRjsdmNOCY5VuHn9jj9pNzGvDw7dk66/b8vgApg/MZu77+/jtkJyYnz890UrzBJtOg9UotpmpDt6Y2pe2aY6wTa7mK9y5ZWLMzqwkSREx2WKWcbr93PHi1iYfjxVi0AMNH8AmA4fFFGE3pQo5Nn5u9bwJoBDiK0mSugGjAc1M5BNgSjRq6L8JvYFvhBCHASRJeh01GW2yCaCGUM+h265vxYSgQa2GzFSH7qem/awNP5dUuHhw9S4WjMnjVLWHFIclRPUwG6tZ1jshD64qVMU7Ckv47e05escv1uzB91HNOhcNozF0jvOhqSl+NQShlNdYZryg0nj8foXpAzvqvo/RvvfiMy6euK0TtV4/KXHh0uXRunIzNuxl1tBuuk/fnGG5uH0KS7eGexU9824Rw/KzmLKykEE5GUwf2JFV24+Ebbw++vokKyb2ptrtp3mCNer1KQIevLkDR0/X6pV17Vq0DuYjr3/Bmkl9aBZvZcknh/TkL7QTOGPDXl6fVMDBUicnq9wR3dDQwkH9CnJZteeqLDQYMNDU4FcE45d+rs9DpzgsjOrdlmSHmVEvq2vizCE5vDiqJ80Trbh96ka7yuXj8TeKSE+w8Zd7euhqnVCnqrx2UvR5fFC9fLW5OS1G/v2LEsb2bcdvbu+C2xcgzmrmm1JnWJwDNQ76FcGhE9VRY6DmlbpoyyFeGp2HL+hnWFLh4sXNB3noluv0pM0XomqqdSVbJ9uJs5nVzl7bVE4Ek0Gt02eS4Zq0OIblZ3GqyhOmyBz6+ZeO78XfgjerUwAAIABJREFUvzimJ9DpCTaeHNyZlsl2FEUQECLMzmB0wTUs+PgbnrkzF0VRIpgjc4blojEdQ2NyWbUn4vxNNR4rysWjBxq4POEP8fmEun3W+gv4HTfIHCSY6H0c+u8SJ38PS5K0V5Kk1yRJSo3yfBugOOTnkuBjUSFJ0mRJkj6XJOnzsrKyWC+7ImAJ8SQyyVLUBUSbTwpVV9SQnmBDgjAFofv6taNDRnyEEIfVLPPmtP5h54mlIvl9VbO0gN0mNU7fVENdd7C+wuiFDGlfKYpf3/d+9fsVjle6+La8huNVLl755Bv6z/mYQ6U15/z8FS6fXi1etOVQhNfMnGG5vLD5IDM27EWSJI5XusJeE6sr1z49ni0zBui2JLIkUVYdrshWVu0lxWEBVNXPFzYf4MZOLbj3tZ26N9QdPTKJs5pIT7BilqUIFdB5w3M5UeWi3OmNaXehFTBOVLq597WdPHRzNu9O/xHLJ/aOuP9PnnXzwuaDYfM4oceKVTi4GgsNTSnGGmj6aOj96vOryZFW5Bq5ZAcTln0Wlhi5fQEykmwUn3ExfulOis/U6rFwd3FlmCKxhpIKl24QH4rMVAeypFL5NMXPkUt2sGjLIW7s1IJRL+9g+KLtPPW3/6Oy1sd7/3ciQoF7wZg8Zr/3dcwY6PYrrJjYmxG9sqj1+nWLJ1CppgdOnuU3t+fw5Ma9SJJ6zJ5ZKbRJdfDXUT1xWM18V17LoJwM2qbF0TyhLu5PHdCBVduPYjFJpMVbUYSgyuWLeh1mk8SPO6aTaDezZlIfZv1HNx5/Yw8D5m1h5JIdVNb6WLvzW0Yu2cHMt75UbbHu6EqK3YwSTA5XTuzNh7+8kdl3Xc/ybUeQ5cgtbVOJxw25Z2OJwAQuQCDEwOUJX4zf8YUovTbEBiJJkqT1wIfABGAi8KEkSW9IkpTU6DOqx/xQkqQvo/wbCiwEOgA9UGcM51/IOUIhhFgihLhBCHFDenr69z3cDwqzjL7xjbWAtEy2s25yAcsn9mb5tiNhiofTB2brfoJQVxX0+kXY6zJT1QHy9ERbWOIUy/LhUqlmhXYHtz55M29O63/B1I2LmUxeSnyf+9XvV9h3qpoRi7dz07wt3LNkB7d3b8OI/Exe2HwwImkK/fz1F0qbRWbl/eFm7pq3XXqijTd3HSPBZtJN1DV7klBkpjrYd7KaI2U1zHqnSN8QabLhWhHiids6oQhBz6wUOrZI4FeDu0QM+U9ZVcjXJ6rZf8rJ1FW7SLJHGrjPfX8/5TVeXeyo/rVoBYzK4KbkoTW7cFhMON2+iNf6AgqP39pJVxSt/3yswsGVUmi4mGhKMdZA00dD79fYf8uh5vASHn9dVf7tL46HGaITpLLXP4bmyVe/yCbL4A2Er+3RRE8eXL2Lwde30r1+100uYOX9vZGATUWlMYu1SXazXlibsWEvihAsHqfa8ozIz6RFsoPK2rqkbdmEXjxxWydc3gB//HsRZlnihc1qd7Ki1qfHx55ZKXRplciNnVqwevtR0hNV7z6PP7o1hUmWeHjtbm6at4VDpTURXoDTVu/SlVa1LkdAgW8rajle5ebDohP4FYEsSbRNi+cPQ7tGXcubSjxuyD2rdWlDkZnqMGYAmxAsMX7HlgvYEzekA/gCKrUyWwgxTAhxF2qC9n/Ai40+IyCE+IkQoluUf28JIU4JIQJCCAV4GZXuWR/HgKyQnzODjzV5uHyK7kmU7DBH+KktHJuPLMNjb+yh9KybCf3bhT3fNi0uejUu5KbSOPp2i0xZtYdUh0VPnLSZrTUP9ImakCmKoKzaw7GKWsqqPRdFfjhWd/BCjnOxksnLFaVOT9SFdNKN7dldXMnc9/ezbnJB1M8fulBOHdCBh9fs5sApJ4+9sYcpKwvD5vhOVLq4Kz+TKat2MWHZZ4xcsoMZb+yJSDC1DnRo8hlQRFQKQ0qclSdu68S4V3dSWu2Jep/GWU3EWU3sLq7kt//9FW3T4jDJEt6Awh/eLtJ9t1qn2KNey8bC4rCueEmFalnx0JrdTB+Yrb/2pdF5SJLEkxv38sLmg40qelwphQYDBgycG2nxVl4eF/63PG94LooQ+uNuvxLWeRmY04I1O47y0ui8YLHJy3MjwtkUqsqlzPJtR/TkbeaQHJZvO4LXLxAIFoWs7bHYFW3T4sL89YSA00G7iGjF2lBlZVAZQRU1PtLiraydVMAjP1Fn+U6edZOZ6uCsy0ecxRRMvgRlTg/7T1VT5vQQUGDa6l26avQTt3XC6xc8uXEvO49WAoI2qXbaRInF84bncqqqLsafi7ER+rMiBMVnXGz68gS3d2/DhGWfccv8fzLq5R2cdvqi7jeupnhskqWoRQVTE9rjXO0wm6Kzny5E6bUhIjD9hRDjQx8QQgjgj5IkHWz0Gc8DSZJaCSE0ddE7gS+jvOwzIFuSpHaoid89qDOKTR5mWdID/vZf3UJqnJl1kwt0Q9RtB8vo1b4Zr08uUAN2tYfVD/QhoAgCiuBMTZ2XkAatGjlraDeuaRbHN2VO1Qj75mxe+vggj/60E9npCeeVSr4SVDabkuJXNGjzHKEoqXDpC0B6ohVJkhBRKCGhYjwaVTKWSmfzBCtef/i5tARz7SRVeTPU369nVgoAaycVxKRExVlNTFqhnscXUKLep7XeAN6Aop/vsfV7+OPQrmwsLGbqgA6kxVtpFm/l3T3HuaFdM1bd3wdZUmd5zCaJUb3b8tbuY/o8T603gNundj6vCRoxV7p8rN7xLVMGdNBnY7Qqu+bv1yo5tt1JU5MWN2DgaoUsS3RqmcjfHuyH2xdQ59JNMs3irJy1+XhuRA+S7GZOVLn1eJXisLD4X0fZebSSmUNySE+wqX6mIX6iv3/rK9ITrfrMdegc27qd3zKuXzu2HijV125rcPSjfjx0evwsn9gbh0UOeqEKOmTE89Lonjy0Zrfuz9s+PZ7DZTWkxFl0ZeWeWSn8/o4cXN4Awxdtp6TCxUeP3RQW990+BU9wTXn5k8MsHJPHXz9SC2KhHomaarTmCztzSA5jXlGVRP88ojtz39/PvOG5tEyyExCCk1VuEmx1/YdYthaVLl/YzwFFEGc1MbrgWg6X1TD/7u66CuKDqwpZP6UvrVPCuyNXUzw2yTIHT1axJrjOypLER0Un6Ngi8Ye+NAMXCS6fwpu7jumWWAFF8PInh3n4lusafayGJIDnwqX4C5orSVIPQABHgSkAkiS1RrV7+JkQwi9J0sPAB6g2EK8JIb66BNdy2cER9PiZsWEv/oDC8Up3mEfZwjF5OD0KD676TH8sVNHwpdE9WTgmT6eBalXBylovE5Z9xrrJBbqATNGJamYOyWnwwPTVqrJ5OcESY6MQUIQurjJi8faoCXr9hVLr+GrJT1q8lZQ4KzPe2EOZUy0s1D9XmdOjJ1SaIEzPrBQev7VOtWrp+F5Rr1GbNe2ZlUKC3RxhWP/ciO5YzTJPv10Udj5ZkqJupLR7/o2pBVQ7/Ty/+QDTbr6OO/PahB13/t3dGZSTwXfltUxY9pl+7LvyM/Xr1GaAMlMdDVK8beqFBgMGrhbIskRGkj3icVd1gDO1Xl7+1yGm3Xydbq+kJTNazFBVluWw2KJh5pCcCKXLoT3bUOXykd0ymTU7jjK64FoURMS6vXBMHgKY897XESqbi8fm64XhE1VuztR4aJ5g1cdGSipUMa8EmyWqaMju4kre2n2MsX3bcvR0LZmpDtYXljCqTxZ/GNoNCYGi1Kk0xwdFYUqrPWSmOvQCopb8pidaUQSMC1GIXjAmj55ZKewurmTRlkMR8V5LNrVrWjgmj0VbDnH3DVlUu/1h9hmagJc/EN2a+mqJxyl2M/ntmjP65R113+PYfFLs33erb+BygcNi4s48tfsduse/EBXQhlBAt0mS9DtJCicRS5I0E9je6DOeB0KIcUKI64UQuUKIO7RuoBDiuBDiZyGv+4cQoqMQooMQ4pmLfR2XK1IcVlok2XWfn/pG26edXt0mQntsxoa9TA12Mx5asxu/ojBraDc2//ImVcHso4O4fQqDcjLCKm6hohkNGZhuKsPWVzIyEmxh1CFtAUiNs/D0Hd0i6KGTVnyuq8RC3UKpeSlpm4FZ7xThCyjMeGOPPgf4zLtFEeeaMyyXDZ9/R1Yzh05TqD+/EkoH7ZmVwtLxvVg+UZ1dGZSTodNPtSrXR4/dxLIJvclq5lAFZJwe/XzzhucSbzNHfK4nN+5l+sBsFozJw+NXaJNq55k7c8lIsEXQTx97Yw+/uT2HrGaOsM+SkWi7aqhDBgwYiI5YYw1aJ2lTUSl/eLsIvyJ4fXKBTiHX4katN0BlbfR5PLdPYdY7Rcx+bx8Ak29qj9evcNblY/m2I+Rdm8bJKjfHKlSP3tcnF/DPoKCWx6/oc3LR5qVdPoUTVW4CimDhlkOkxFmwBOljg3IySLCZqaz1kp5gY/E4NWG0mWU9Ng/MaaFTPDVaoVmWOVPj5dRZD8cq3Swcm8+gnAySg8wIrXOozWBPHdCB2e99zVNRZrqnrd6l0+7LnB4cVhN/GdmDD395Iyvv703zRCu//lkX/jljAMsm9Gbl9m/Zdriclsn2iHi/fNsR5t3dHQEXbfTkSkRZTeT+78FVhZTVeM/zTgNXCmKpgMYqfpwLDSkL/AJ4FfhGkqQvgo/1AHYD9zf6jAa+F2RZ4tq0eBLtFlw+f0TCFYtL3zpFNZlNcVhIcVj549+/5qnBncO6fasf6MN/vv6F/j6NgtHQgemLadlg4MJgNst0ykjQq79mWSIjwYbFYuJYRW2jEnSbWdZpSxlJNn65bk+YUNCmolL+MLRrhEfWLwZ2JMlmpqLGx8qJvZHqqdVqVNHXJ/ehosYX0Y0WQp1NGdozvMq1aGw+K7d/y7N3d+eaZg6EAE+9+ZvQz9U+PZ7jlaq5u0WWaZMaF/M7MMsSWamRNGfgqqAOGTBgIBLnGmtIi7fi8vr1Itnc9/czf0R37BYTZW4P84bnYjHJtEiy4fErER2uecNzefmTw7w0uie13kAEK2Hazdfp3TmtuPbiRwf53ZCu7Dpazj192oYVaUNRUuHCYlKp/kl2MzNu7YxZliit9hBnNfHEbV0Yv3Qnfx3VI8xT7MNf3qRrDGRnJIRR4NdOUmXmT1a58SvqJjQ9wcYLo3oy652v9FGBZz/Yz69/1oVFY/Nx+wJsKirlwQHXRb3Gds3jeeuh/iTazcx+72s2FZXqsf7//UP9eVBOBk8N7sJDt1zH47d10pVZNfTMSuG+fu0Yv3RnxO/oaovVsUZALiQ5MHB5IpYKqP9SqIAKIc4KIe4GBgHLgv8GCSGGCyGqtNdJktQ1+hEMXGxoXRqTFKkGFEv9MMVh0VUXx722U1dd1FBS4UIIwrori8bms7GwuMFdj6tp2PpyhaIIvjldw8glO7gpKKf9zekaztTU/V5DEStBL6/xcu9rO3WBlwOnnPq9Efpej09Q7vSSFm/luvQEfv2zHFZuO4onoOCwmhj32k72n6yOOG+Z04OEFKFIO231LponWJk+MDuiYjx1VSGDr2+F3SJzrMLFyCU7uGX+P/nuTG3Uz7XvZDUzNuylxhvArwgURZxTES6a2NDFEiAyYMDAlYdYYw3lNV5kWaJ1skq3HJSTweO3duLe13YycP4/efyNPSTYzPgCCtPXfkGzeAtt0+JYO6mAzb+8iQVj8midonbI0mKwEpxuPzOH5LBhal+Wju/Fsx/sZ1NRKVUuH6MLrtWpmbHUPmVJQpYkjle5eWLDXk6d9fDQmt1BCr0gPcFGalz4uU0SusbAwVKnftzdxZX8dfNBAoqgeYJVf8/u4kpOOz1sKirVRwWeGtyZM7VeMpJstEq2k5nq0Kmh9a+xpKIWp8fP7Pe+Zlh+Fhum9mXFxN58/PUpJv6oPf+cMYDf/7wrKXEWslLjyEi0R8TwaAqp9ZktVwtCxfw0aOMVBpoGLubvuEE+gABCiENCiL8H/x2K8pKVjT67ge8FOYqMdGaqPap/21m3LyxAaqpeGjJTHShC6Ipks4Z2o3mClWfuzG1wJe1qUNm83BFrw7KnuIqH1+yOUI9aPC6f1BClNQ316bzRFOVeGp2H2+cnNd6KySTxTZmTR9d9wbbD5XxTWsOKbUdZO6mAji0SWHV/HwblZIS9N5bSZ0AIromhVts+PZ7UOCuPrq+jPkezt9AUP+cNz6VNip1n3i2ivMZ7wUWKS6Fua8CAgcsb5xtrMJtlmidYmTmka1SbhrNuf3AuWvD0219RUlFLrdfPtNW7uGneFu59bSf+QPSKvsUkM+udIjx+hSc27NXZFxW1XsqqPTo1U1M2rk/7X7X9SJjNTmqIN+GhshqmD8zmtDM8Bp8869ZjqTaXpx132+FyZBkCgrD3aMldqFfirHeK8PsVzEFVymjXuGhsPiu2H+XZD/YzqndbOrZIwK8IHlu/h/kfHuSeJTsY88qnSJJEiqOOeVE/hsdSSHX5AlddnLaYJRaMyQv7nheMycNibvBW38BlDlmObR/TWFzMyVBjl/9vhkDSZaS1IfLKWj+v/u/hsMeWbzui++loKKlw6UOjmakO/jKyBy5fQBe6mDc8F6tZpll84wanr5Zh68sVsTYsmnXCm7uOhVE2n//wAI/+tFNEol6fzqvZf6y8X3VlEQLW7fyWvGvTaJlkx2a20raZg+kDs2kWb2H1ju8Y2rMNo0KG0V8anccjP+lIgs3Mmh1Hybs2LSpl+LvyWjJT46I+V3ymNsKYXaeUTiqgzOkhwWbG7QswLD+Lue/v53c/z2FTUSm//3ngghThrgR1WwMGDFx8NGSsQZZlFH/kOEZJhYuWSXaWTeiFX1HYVFTKpqJSemalhCkKW8zRhbsyUx2sn9KXp9/+Mox6b7eYKK/xUub08OwH+4PqxxbWTipACFUNfMW2Iyz+11H9OmZs2KvPJ5ZUqCqfz47ozjfBLp927rnv7+fpO3J06r8sSayZ1AdFgf2nqnlx8zdMu/m6sPdEE3BRZell/IrQ9x9JdjNLx/fCF1BIdlgwyxJP39ENkwSSLBFQFO5ZsiPiOzxeqVr1hMbb0PEEzX+2/vd3qNRJjcd/VcVpn1/w7p5whcgNn3/Hvf3a/dCXZuBiQUTu+5dvO8If7ujW6ENdzLLA1VVquQxgkmBC/3ZhhtpWs8QjAzuGPTZ9YEc2FhaHvTcz1UGzeCsfBwU20hJU+fw3p/Vj1tBupCfa8PmVRnc7jE7JD4tYFEdN3GdgTgvdBHjKykI2FZVGpctE65RNH9iRX67bw7EKF7Pf+5obO7Vg1jtFDH1pK6Ne3kF5jY+1O7/F7VO4o0friIr4Q2t2qQGr1suPO2aQ0yoxolo5b3gu8zcdYO2nRyMEZhaMyWP+pgNRjdnLnB6QVBn0w6dr+N1bXzFlZSFlTo9eodY2bY2ldZ6LBmbAgIGmi4YwBtLirTENuFPjLXj8CodKa8LolJpvn9Vsonl8dLGpVskOWibZefSnnRiUk8HicflsmNqXlsl2vaOm0TUfWFHIWbePzNQ4AD3501BS4cIi1/mH7S6upKzaE9GZK3N6iLeZsQQ9xVLiLPwpKAA2650i1heWsODjb8LidpnTQ3qijTUP9OHDX97I7LuuZ+77+/EFFEwSTLv5OqwmWRfDCSiCkUt20Of/fcSIxds5U+ujebwNkyzHEMoJhMXb+uMJsfxnX9h88KqL0w6riQGdW+j+iBOWfcaAzi1wWA0dhqaCaPv+Cf3bcQE2gBe1A2jg3wRFEZTXeHH760zhtUrA028X8ac7u4U99vcvSnhkYEeKTlSHDZmfdfvChswXj8unZbKdtHgrf3q3SB/Ibmi3w+iU/PAI9fILrcbOfX8/QEzBgPpCMKGdMpcvwKFSJ75AgDKnB4tJjqo89+TGvcwcksNjb+xh2YReUc/j9iu89PE3TOjfjpNVHixmiWfv7k6LJBvVbj8BReHPI7tz9HQtK7YdVb0p0+I4UekiPdHKn0eqVhDRrEz++Pev9Ht2zrBclm87wn392rF825HvNYtqqNsaMHB1oiGMAVmWiLfKLBqbH2FFc6rKw6Prv6Bf+7Qw5sXGwmIe/Wkn/VjnOkd2egKP/KQjU1aqx9bsfF7YfEC358lItNE66E0aq2tpMcu6grjW3at/nPREm+7pV1LhYt3kAjYVlfKLW7J1kZf1hSVUurysmNgbOSgO/1//KAqLvemJVqxmEyYZfH5Ft2xYOr4Xj67/MqKY9ua0/piDCWr9TqL2Oi3e1o/HGgNk1f19UITgYKkzzOvV61epoFfDHiRUJT7OaqLWG6BFkp0Uh6HD0FTgirLvn/v+fp6/p0ejj/W9EkBJkloLIY4Hf7x6yiw/IEKTrHWTC/QKoIbMVAcVNd6wxwDG92+nG3R/W16LIkSY/09JhYspK1Uj1dGvfBo1QNendmqJqLZomWQMH8AfGPU3ExazjDM4hwJ1IkENUWrVOmXHKlR/vJ5ZKbrEd6y5Cy3B1Cri9c8jSzDj1s7M+2Afv/t5VypqvFS5fLz2v4cZlp/FlJWFvDmtLwDD8jOpdPl4fP2eCNPkQTkZrLy/N5W1PhJsZuZ9sE83ONaS0XWTCzDLEs/cmfu91DsNdVsDBq5eNGSswaeAJBG2KXv2g/08Nbizrmh8b4gH3uKx+WSnJ+gx6VznqHD59OQP0OPc03d0QwgRkTBGKwK+fO8NpDispDisJNot+pqd6rDwpzuvp9YT4MjpGtZ/VsyYgmv0REwTmDle5WZjYXHY55v93tfMuLWzrtQMdbF3zQN9SIu3UuHy4PQEdMP2lLhzFyCjbWyfGtw5LN5KUuTaUub04FcExWdqWbTlEI/f2inMF/FqKUSHqsQbytVNE2ZZirrvvxARmO/bAdwBXAMghCj4nscy0ACE0tEqXb7o3Pt606CZqQ48fsGAZ7foptwWkxxTLrgh3Y5o3b7FY/NJT7CFvd/olPz7UX8z0Txe6Amhw2qKujk4V3dMS4A0U/gnbutEeqItalKkbRjireaIivi84bn8cp1qIj9nWC4VNV5+/uJW/f33/6g9AP4A2C0yrwaTwt/c3oUWSXZ9nhDUTVDRiWpmDskhoAh9U6ShpMJFQBG0CVKivg9ibagMdVsDBgwAujXBrHeKwmJirTcQVdF4yqrCiMJo/YKqtnGPxkBQZ5qjx7fzdRTrJ5oSEmNfrSv6Hix18uufdeH1yQWYZInF4/J5/sMDUc3mbebo+wiTLFHp8nKi0hNm2L5iYu+o64YkSfgVhekDs3lh80F95jEz1UGtNxAWb01B8bvQa5kzLBeHRaZtWlzU7/tqKkQbOgxNG7E65eYfIAE0ygr/ZoQuBt4YreD5I7rrQVa7OWq8fgB9Ez83yJmvH4jNpugD6fW7HdHmoqasKmTW0G5MWPbZOd9r4N+L+gtCisPaKBGU0ARod3ElMzbsZe2kPiwel69XpkNply/fewPNE2yYTRKzhnYjq5mD4jMu5r6/X1/Yn9y4l6Xje+nnCE0e0xNtLPj4Gx66OZuH1qg0zw1T+0bdaGRnJOjvj6A8mS7OiPOFCMcYMGDg6oHVbNLn6UITk6xmqnl6rKJqWbVHL8ydOuuJOj5xIQyExiQB0SiVdy/eztYnb6ZVsoMWiXaeuTMXRVFYP6VvWNexvMYb9doCimBPcZWe/GmfefZ7X7NgTB7TQuj7i8bm8/TbX+oUUm1koczpYfHYfFql2MNUQGVZjiqC8ac7r6dVsgOzSYr5fRswcKXDc7lQQDGEX/7tCF0MEmzmqK1gWZLCOOBxVhOJ9rpf9e7iSuZ9sC9iA//yvTeQkWBrULcj1lxUu+bxYcmn0Sm5/NDYCmGsBKgNdSbpkiRhkgijW6Y4rLRMtlNZ6wsrCoB6r9R61QVZmz9tHq8mpgLB4Otb6ckfEHOjcbDUSZLdHLUiZrVcvATNqKoaMGAgFtLirTz600489z/7I+byzrhiJ0l3Ltiqz8bVT5a0rtWlZiCcL8E8V+yLdm2Lx+Xzp3eLuP9H7WN0Lrvyt2n98PkVJEnSkz/tc8/YoNL3YxXatO+6/vfRPF4V9HJYzAZl30CThenfSQGVJOmvRE/0JCCl0Wc08L0QGnDdvkBExXHhmDwWfPwNA3NaEIcJb0Dh6beL+Ms9PcISs0d/2ons9ISoXY2GdDtiLRpxNpPRKWmCiLUJOFdSpN1LJ8+6o94rGYk2tj55MwEhsFtM+gKuKIJ2zePDXq/5ENan/WhzNrPf2xe1IqYoHuMeNGDAwCWFFuueuTM3Yu1r5rBGFFu1JEmLcXFWU8yu1aVmIFxogqlRVpvFWcI6g0rQ8mJYflZMMZqMRDsAxypqo9L3Ifbacr7vw6DsG2jKMMkSz43ornshZ6Y6eG5E90s2A/j5BT5n4BIgNPj5/AH++tHBsI2v26ew7XA56wtL9PdodLjQOTC/IjhV7cZqNtEqqB4Weo7zdTtiBVltE2/AgLZBMElE7Ta7fIEwYYSX772B7PQEKlw+LKbwQX/Nh3DtpAI8/gAOi4k//P0rdhdXUunyRa2IfX2ymlnvFF01AgAGrm5c+9S7jXr90dm3X6IruToRbd1UFMHBMifPfxiu2Gkzy2GJj0Z/v5Au3MW47ovhjbpiYm8sZhlPQGHp+F68938nIop22h5BQ6xCMkBZdezi3bm+D4Oyb6ApQwKsIT6Ytd4AVrN8QfN4500AhRDLY16IJD17Aec08D2hBb8zNR4m9G8XRn179b58Fo7N58EQ8Y2FY/PR2A8tEu0cLHNGiLfU59k35BqMIHtlIJa4wKU+Z+gGYVBOBmse6IMpKFNukuGOF7eGUZ6e+5/9utx5eoItgtZ5X792TF+7mzKnh7cf7s+jP+1E0YnqqEbEWnfwYgoA/BDfowEDBq5chM7KawlfZqpq8B6a+ESLYZeyaxUtljUmPoZ+rp5ZKTxxWydqPP6wgt684bm8uesYs4Z2o13zeOJspojhqmYQAAAgAElEQVQCcSzboofXqHH+Qot3BmXfQFOF3SIRZzNzpsanPxZnM2O/gJGX7zsDOAJ4/Hsew8AFwuUNRAyDllV7WbH9qP6YL6Dg9gZw+xS8/gAeX0CfU9De8/zmA4zq3ZaWyfZGBVsjyF7+aKw348VKcipcHk5WuXX570VbDjH6lU/1mZYTVa6w53YXV+o2ECUVLkoqVNGYWUO70T49nsNlNTz7wX59U6BJmod2tf82rR9ub4CvT1bz7Ad1gjMXQwDA8Lg0YMBAYxFrVt4kEZb4lDk9tEiy67Nxl7LAdDFimfa5NFVxt09hxoZdYQW90Fm+VIeFCpePE1WusM8W7jfr51BpTZhY2KQVn/P2w/0JKDRqTTKKdQaaKpwehQ2ffcfwG67BJEsEFMGGz77j3n7tSG6k6LmhAnoFw2o2RVDfNkztS1m1N/i8THqijXU7v+XGTi14cuNeFo7Ji5BznjMsl7R4Cw+s+Jz1U/rSMsluBMsrENEWvWhqrefydYxm7RGrO+z3K5Q6PfgCChaTTEaCDbNZxu9XIuS/tY6coigR55gzLJe3dh+jQ3pChBrdhGWfseXxAXTPSubF0T3PK2leVu2JkGK/GAIAjfkeDRgwYABiUxxlWb4gBs3FSGwaG8uinVP7XFMHdODJjXuZf3f3qIkuQKrDwv7S6ogRAC3hlGWJtHgr353xR4iFpSfYOFHpZsqq6O+N9R1pa0x6go3pA7NjdiANGLjSIEswOLc1JRUunQI6OLc18gWInjdEBKZZrKe4BAmgJEnrgE7BH1OASiFEhL6pJElHgWogAPiFEDdc7Gu53BGNPtEq2c4Tt3UKo5K8NDqPlz4+SEmFC7vFFDE3uHzbEX47pCslFS6OV7qocvmMzsYVhlhV3WbnMd4NxbmsPep3h/1+hX2nqsN8/haNzadzi0RKnR79ce04T27cy6yh3QgIIs7x5Ma9rJjYm2/La6Nulo6crqFrmyQcVhMub4CSytow0ZhQXCoBgFiVfENa3ICBqxvnSsrOFY8ay6BpSOeuIQliY2JZrHNmpyfw8r03UOPxU1LhijnDqAg4XuUKM7KPlnCW13g5crom4hjTB2bryV+s99aHto6lJ9iuSkN4o/vZtCFLEi5vIKzAPm94LhcyBdiQnLEQVeylsN6/zwFvo894HgghRgohegSTvo3A387x8puDr73qkj9QKZjZ6Qmsm1zAhql9mTkkB19A0ZM/UAPmQ2t2MSw/C4CAonBfv3bMeqeIkUt2MOudIu7r1w5ZEmSmOvTgWV5z0X+1Bi4hYlV1A6JuqF5DrI5YrI1BnNUUcU9ES/KmrirUO4LRjnNt83iEEFGfO1Pj5YXNB1k4Jk+/Xq07+MLmg/j8CvtPVjNyyQ5unLuFuxZsY/+pahQlXKA4lFK09cmbeXNa/4uy4GsV71AY0uIGDFzd0BKkOxdspf+cj7lzwdawuKTFo7cfVuPRmkl9SEu4sGJUrBivxeXzXYuGxsSyWOesCBaJW6c41OJfUKW5fuw+WeWmtNpz3oTT6w/wwuaDEce4tnlcowtv2jqmdSdjfV9NEQ29BwxcufApImKPP2PDXnwX8Ds+bwIohGgnhGgf/G/9f+0bf/kNgyRJEuqM4dpLdY4rHZrC2IkqN8MXbWfKykIUQdSAqXVATLIcERSf3LgXISTmDMtl0ZZDRmfjCkSs5E0Iwcv33hC2qMbqiMXaGFS6fBH3RKwkzx+kg0Y7jt0sxzxHeY2X3cWVOD1+Zg3txrrJBcwckqPP/Xn8kYWNWIu5VllvkxpHeuLFofxolfyGfI8GDBi4OnC+pEzDqbOe8xavzofzde4aei2NiWXnOqcsS7RMsvPyvTdQ5vTw7AfqzPbmx25i9l3Xq5R/IXQP11DUTzi1cZZnP1D1CdZNLmDW0G76a8/13vrQ1pgUR8PZL00FDb0HDFy5CCjRi+gXkuSfNwGUJCmv3r+ekiRlNfpMjcePgVNCiIMxnhfAJkmSCiVJmnyuA0mSNFmSpM8lSfq8rKzsol/oD4XQbp0WJAOKiBowmyfYWDq+F2aTFH3jrii6cIbR2fhhcSH367mqug3tiP1/9t48PqryXvx/P2e2TBZICAEXUBERjQhCEFl6e2ntdakoV0GtAu4s0tbe1rXflqv3Un9XReutbVm0FhdcQNCrV29dSuW2V9wICNUoIoImCCSEBJLJZLbz/P6YnMNMZiYbySSTfN6v17ySOXPmnGfO+TzPeT7PZ0s2MbAWBaxjmaamqi4AwMrrzmbs0Py48zmbYgGXzy6JO87y2SUU5XqSnmPFnBLWlZYD8MDr23E7DW59YSvzny6lqj7Akpmj0SRf2EjXw7yrLIu9hd46xgq9k86S17a4U3bWpLw1y11bXTvbM5a1ds7YY/3mqrEA3LZmK7Mf/4At5bU0BCOsKy23LXtjh+az8rqzefrGCWi0PWm1ngtWToNbX9hKlsvAaah2L7xZx2oIRnqV10ZbZFZCFXo/7hQL7C5H+4MA25IE5qEk2wYopdzAVVrrj9p7UqXUn4Fjknz0C631y03/X0XL1r9vaa33KKUGAW8ppT7TWv812Y5a60eBRwHGjx/fa2zhVmePLZL92F+/ZOmscSx8ZnNcIo+wabLo5Y9ZNK04qa/+7gMNtvInlo3upSPy2hmxJtbD/MWFk2kIRNh1ID7zZoHXlRAPsmTmaB54PbrP8tkldiKY0wbnsWb+JMIR01YKnc7oANU8+UGB12WXdLDq/T11wwQO+UNU1gV44PXt/PrKMS3WyUoHkvU2Nb11jBV6J50lr6mSvMSOS501KW8tvrktbbFo61jWlphq61imqfEFwlTVB+xzn1iYzU//aSQPv7WdJTNHk+txcnPM3CQ2Js969jSGTBwKvG4H+d7oedqTLMc61uB+HlbMLklIIJOpc5u2yGx7ZEDITHKyFMtnlyTkX8jJav9itNK6Y2OfUmo88Gut9bc7dICWj+0E9gAlWuuKNux/D1CvtW61LuH48eP1pk29o359VV2AS5dGa6mNHZrPgqnDKcxxc3xBFhEz6iLqdERX0S5bttHer3lg9IrZJRTledBaYxiGBA0fodsvQnvktTODv1NlFLXkzWJIgZfn503EUCpOyevo+fyhCDsr63lk/Q47FfiQAi8vzJ/I7uqGhDpZHbHC9fIg+W7/IZk4xra3iHpvoIcUgs8IeU01ZrQlMUvsc9piSIG3QxmEWxq7uqpUTXvGy2T7Ava2Kx99r1OuQ1e0vR30WJmVckW9n29q/dzzysfMKBlqJ3JcV1rOPZeM4rh8b7KvpLzxHS4DobXepJTK7ej3W+F7wGeplD+lVA5gaK3rmv4/D/j3LmpLjyN2UHv2pnP41WtlvFlWyeJXy1gxuwSP0xGXtn9PTYM96G4pr+XBN7Zz32VncnxBNrsP+Pjlf318VEVXhZ7B0Vio2lIYONVKtoJUA0+baWkV+bFrxlOUm4XH5WD1vIlENGS5jA6l9JYHpCAI7aG1MaO1cg6dmZm4pTG+LW3pCG19rrSkbBXleeLmIRZd7Z7YF702PE6DxdNH2SUCPB1clBV6Jlpr3iyr5M2yyrjtd1/cfmNehxVApdRgonF4XcEPaOb+qZQ6DviD1vr7wGDgpWieGJzAs1rr17uoLT2KpLXa5pSwePqolNa75m4BW8prCUU0cx5/P25AlrpmfZO2KkXpcC9paRIzwOmBnKM7vtTzEwShPbQ2ZrSmZHSVYpbqXN0xjrXlGSLuiV1PtS/INX/8IK1WViG9uJxG0n7k6oCi35YkML9VSj3S7LUKeBe4p91nbANa6+u01subbfumSflDa/2l1npM0+sMrfW9XdGOnkjSWm1Pl2IYRsqMh8kSbwwbmCPBwgLQNdnjjoauyOJpIUHygiC0h84YM7pyTOsJtOUZIpmUux55vvV+nIZiycz4cilLZo7G2YExpS0WwOaOxhqoBn6mta5Msr/QhXSkgydbgdRoWY0TgI5lj8vU+DlZhRYEoT3ImNE6bXmG9IbnR09HZLX34w9GeOD1aLkUKwbwgde387urx7bbQ6otCuDbWuuvO9RSodPpaAdv7hpimrrT4hKEzKYrssf1VDozHkcQhN6PjBmt09ZnSKY/P3o6Iqu9H6tm5vynS+1tHVXy26IA/hcwDkAptU5rPaPdZxE6jc7q4LIaJ1j0pYeGyL0gCO1BxozW6UvPkJ6MyGrvpzP7WlsUwFjJObndZxA6lc7s4LIaJ0Dfe2iI3AuC0B5kzGiZvvYM6cmIrPZuOrOvtUUB1Cn+F7oJ6eBCZyMyJQiCIHQUeYYIQnrorL7WFgVwjFLqMFFLoLfpf5rea611v6NuhSAIgiAIgiAIgtDltKoAaq0lfZAgCIIgCIIgCEIvoP2VAwVBEARBEARBEISMRBRAQRAEQRAEQRCEPkJbYgCFXoBpaqp9QcnQ1QeQey0IgiAI8jwUeh+dJdOiAPYBTFOzfX9dQt2QkYPzZCDsZci9FgRBEAR5Hgq9j86UaXEB7QNU+4K2sABU1PiZ+9Qmqn3Bbm6Z0NnIvRYEQRAEeR4KvY/OlGlRAPsAwXDEFhaLiho/wXCkm1okdBVyrwVBEARBnodC76MzZVoUwD6A2+lgSIE3btuQAi9up1T46G3IvRYEQRAEeR4KvY/OlOluUwCVUpcrpT5RSplKqfHNPvu5UuoLpdR2pdT5Kb4/TCn1ftN+q5VS7vS0PPMozHHz2DXjbaGxfIYLc+SS9TbkXguCIAiCPA+F3kdnynR3JoH5GLgMWBG7USlVDPwAOAM4DvizUupUrXVz++b9wMNa6+eVUsuBG4FlXd/szMMwFCMH5/HSwikEwxFcTgOnodh7yC9ZsXoZze+1UgqHivqNy30WBEEQjpZMyazZ/HnYk9sqCG3BMBQjinJZM38S4YiJ02EwKNeTWVlAtdafAiiV0OjpwPNa6wCwSyn1BTABeNfaQUW/9F3g6qZNTwL3IApgSgxDUZTnkaxYfQDDUBTmuOU+C4IgCJ1Kps0hrLmPIPQGTFOzo6q+12YBPR4oj3lf0bQtlkKgVmsdbmEfIQmSFatvIPdZEARB6Gzk2SII3UfGZAFVSv1ZKfVxktf0rjxvknbMU0ptUkptqqqqSuepexySFavn0xnyKvdZSCcyxgqZhMhrx5FnS/cgMitABmUB1Vp/T2s9Ksnr5Ra+tgcYGvN+SNO2WKqBfKWUs4V9YtvxqNZ6vNZ6fFFRUUd+Sq9BsmL1fDpDXtN5n01TU1UXYE9NA1V1AUxTd/o5hJ6NjLFCJiHy2jqpxnWZQ3QPIrMC9JIsoC3wCvADpZRHKTUMGAF8ELuD1loDbwMzmzZdC7SkVApNFHhdLJ9dEpdBaPnsEgq8rm5umdCZpCv7mRUPcunSd5hy/9tcuvQdtu+vEyVQEAQhQ2lpXJfMmoLQfXTmHL7bksAopS4FfgsUAa8ppT7SWp+vtf5EKbUGKAPCwA+tDKBKqf8BbtJafwPcCTyvlPoVsAV4vFt+SIZR4w/xyPrPWTStmHyvi9qm9/deOloCpXsR6cp+lsof/aWFU0SeBKEHc9Jdr7Vr/933XdRFLRF6Gq2N65JZUxC6h86cw3dnFtCXgJdSfHYvcG+S7d+P+f9LotlBhXYQDEd4s6ySN8sq47bffbH47/c20pH9TOJBBEEQehetjeuSWVMQuofOnMP3RBdQoQsR/32hMxF5EgRB6F3IuC4IPZPeHgModCHivy90JiJPgiAIvQsZ1wWhZ9KZfbPbXECF7iFdsWFC30DkSRAEoXch47og9Ew6s2+KAtgHEf99oTMReRIEQehdyLguCD2TzuqbogAKgiAIvYr2ZrgUBEEQhL6ExAAKgiAIgiAIgiD0EUQBFARBEARBEARB6COIC6ggCIIgCC0iheMFQRB6D6IACoIgCGlFYvQEQRAEoftQWuvubkNaUUpVAV91dzs6iYHAge5uRCfTk37TAa31Bd3ZgCTy2pOuTyqkjZ1DR9rYE2W2LWTC/bCQtnYemSKvPf06tkYmt7+ntV1ktmfS134vtO03p5TXPqcA9iaUUpu01uO7ux2dSW/8TZ1JJlwfaWPnkAlt7Cwy6bdKW/semX4dM7n9mdz27qSvXbe+9nvh6H+zJIERBEEQBEEQBEHoI4gCKAiCIAiCIAiC0EcQBTCzebS7G9AF9Mbf1JlkwvWRNnYOmdDGziKTfqu0te+R6dcxk9ufyW3vTvradetrvxeO8jdLDKAgCIIgCIIgCEIfQSyAgiAIgiAIgiAIfQRRAAVBEARBEARBEPoIogAKgiAIgiAIgiD0EUQBFARBEARBEARB6COIAigIgiAIgiAIgtBHEAVQEARBEARBEAShjyAKoCAIgiAIgiAIQh9BFEBBEARBEARBEIQ+giiAgiAIgiAIgiAIfQRRAAVBEARBEARBEPoIogAKgiAIgiAIgiD0EUQBFARBEARBEARB6COIAigIgiAIgiAIgtBHEAVQEARBEARBEAShj9DnFMALLrhAA/KSV1te3Y7Iq7za+ep2RGbl1Y5XtyPyKq92vrodkVl5teOVkj6nAB44cKC7myAIbUbkVcg0RGaFTELkVcg0RGaFzqDPKYCCIAiCIAiCIAh9FVEABUEQBEEQBEEQ+giiAAqCIAiCIAiCIPQRRAEUBEEQBEEQBEHoI4gCKAiCIAiCIAiC0EdwdncDhERMU1PtCxIMR3A7HRTmuDEM1d3NEoSMQvqRIBxB+oMgCELm01ljuSiAPQzT1GzfX8fcpzZRUeNnSIGXx64Zz4iiXGr8oS57eMvkoO/QF+51qn40cnBe0t/aF66J0DV0h+y095zt7Q9CPCfd9Vq79t9930Vd1BJBEPoypqnZU9tAIKwxFPhDJo2hMMfnZ7d7LBcFsIdR7QvaD2mAiho/D7+1nZ9871TmP13aJQ9vmRz0HfrKvU7Wj+Y+tYmXFk6hKM8Tt29fuSZC59MdstORc7anPwiCIAg9k8ONQWoaQix8ZrM9/i+dNY68rCD52e0byzMmBlAptVsp9Xel1EdKqU1N2wYopd5SSu1o+lvQ3e08WoLhiP2QtphRMtRW/uDIw7vaF+yUc6aaHHTW8YWeQ1+518n6UUWNn2A4krBvX7kmQufTHbLTkXO2pz8IgiAIPRNfIGIrfxAdxxc+sxlfoP1jecYogE18R2t9ltZ6fNP7u4D1WusRwPqm9xmN2+lgSIE3blthjrtLH94yOeg79JV7nawfDSnw4nY6EvbtK9dE6Hy6Q3Y6cs729AdBEAShZxI2ddLxP2zqdh8r0xTA5kwHnmz6/0ngn7uxLZ1CYY6bx64Zbz+shxR4GZTn6dKHt0wO+g595V4n60ePXTOewhx3wr595ZoInU93yE5Hztme/iAIgiD0TFwOI+n473K0X53LJAVQA28qpUqVUvOatg3WWu9t+n8fMLh7mtZ5GIZi5OA8Xlo4hXfu/A4vLZzCcf29KR/epqmpqguwp6aBqroAZgdWAWRy0HdI573uDNnsKMn6UaoYKZF/oaN0hey01m86cs729AdBEAShZzIo18Py2SVx4//y2SUMym1/LLfSOn2TsqNBKXW81nqPUmoQ8BbwY+AVrXV+zD41WuuEOMAmhXEewAknnFDy1VdfpavZnUayrG9ApyUgkCyISemWC9DV8pqOe51piVV6kfz3SpntyXSm7LS134i8HuVJ2ymvkgVUiCEjZFbonTTPAmpq8DhVS1lAU8prxlgAtdZ7mv5WAi8BE4D9SqljAZr+Vqb47qNa6/Fa6/FFRUXpanKnYhiKojwPxxdkU5TnwTAU1b4gD7+1nUXTilk9byKLphXz8FvbO5SAINnxhe6hq+W1M+91KmtFpiVWEfk/OnrDGNtROlN22tpvDENRmOPG7XQQDEeo9gXTamHPdPqyvAqZicisANFnxOJXy9hZVU9VXYCdVfUsfrWsQ3OrjCgDoZTKAQytdV3T/+cB/w68AlwL3Nf09+Xua2XXkcr6FzYjXDt5GHeu22avFt8/YzSmabbpGDLJ7Z2k6163ZK1IV3KMnirXPbVdmUBPvXbpaFdb+01bLIU99ToKgiAIHcM0zTbP+1sjIxRAorF9LymlINrmZ7XWryulPgTWKKVuBL4CrujGNnYJyR70T90wgUDYxOUwbCGA6EThznXbWDN/UqvH6MnueELHSee9bqm2mJWoInYy29nJMXqqXPfUdmUCPfXapatdbe03rdX166nXURAEQeg4EU2b5v1tISNcQLXWX2qtxzS9ztBa39u0vVprfa7WeoTW+nta64Pd3dbOJtmD/qvqBuY+tYm6xlDS1WKHIs4tr9bfea6iQs/FNDX7DjfiC4RZNK2YsUPzu9T1siVrRVsTVbSW8KKlz3uqm2lPbVcm0FnXrrMTEHXFPU3Wxrb2m9Yshc3bW5TrYd+hRipq05+QSRAEQegcwhGTyScX8tZPv81fbv1H3vrpt5l8ciHhSO+1APZZkj3oj+3vYdG0YvKynLwwfxKBcASXw6DWH2Lz7moO+IJ24fghBV6euP5sbvzWydz6wlZ72++vHkcoEqGqLiCuQb2AZCv+988YzYNvbGdLeW2X1CWzrBVFuR4WTB1OvtdFQzCC1+2IyzqYygUtlZVicD8P/mAEl9OgvjHMNX/8gIoaP+cVD+KXFxXjMBRupwPTNHtk/T6pK9hxOuPadYV7ZKp2maZJVV2gzcexzmuaZsI4bbWxtX4DrVsKY9s7dmg+t50/Ms5lSKyBgiAImYfX7WDhd4djPRKVUiz87nC87vZ7V6XVAqiUerot24QjNK/5NHZoPmETFr9axu0vbCNsmtz14t+58tH3WPxqGRefNYTf/PnzuJXq8oN+W/mztv3w2c38fc9hLl36Dtv318mKcIaTzEJx57ptLJg6vMvqkhXmuHnqhgncccFIFr9axpWPvseilz9m/+EA4bDZ6gQ7lVVla/khptz/Npct3cj+w40U5XoYOzSfaycP4+o/vM+U+9/m0qXvcMAX5LziQXHH7An1+6SuYMfpjGuXSq4O+ALAEQXx0qXv2LLU2hiYrF3nFQ/igC/Y5uPEnvejikO28hfbxmpfsE1JZVqzFMa2d8HU4QkuQ2KRFgRByDwUcNgf5rqVH/Ddh/6X61Z+wGF/uEOpadPtAnpG7BullAMoSXMbMormD/pbzh3BglXRicOCqcO5fW38g33BqlJmlAyNO0a225F09Trf60qYHAmZSSoLhSU/XVHTzjAUuVnOBBmc+9Qmvjnkb3VinKrN2U0rWRU1fm5fG1Vik01i5z9dyi8vKu5x9fukrmDH6Yxrl0quGgIR2wLXXnfOZO365UXFKZW4ZMSe1xp7m7exrZbO1ur6xbb3aM8lCIIg9AwCYZOFz2yOe+4sfGYzgXAPdQFVSv0c+H+AVyl12NoMBIFH09GGTKW5K11Ea/vGp3qwN58sNQQjSd2Fav0h+zuNofYLj9BzSOUSdly+l2P6ZXVJnb9qXxB/MPlku7IukDAxXj1vYpw1MFWbLbm0vpvvddn/Nz+Pw1Ctusulm7a4vwrJ6Yxrl0qudh3wkeNxplQQ/cEwVXUkPV+ydrXXXTV2/1p/6KiTJFmWwlSfxba3qxMyCYIgCF1P2NRJnzvhDnjxpcUCqLX+D611HrBEa92v6ZWntS7UWv88HW3IZGJdgrwup70KbU0iYhlS4GVQnidupXpAjouHLh8Tt+3+GaNZvmGn/d4hc9OMJpXlpKuUP8uV7bN9dUllsLkVpKLGT0WNP84amKzNS2YekUtrW0MwklLW3U5Hj6zfJ3UFO87RXrvCHDcrZpckjHePrN9hK2/JZOnTfXUtunE2b1d73FVNUxMxtb3/8g07uX/G6C61ElvtPba/VyzSgiAIvQCnoZI+d5wdmGOkNQmM1vrnSqnjgRNjz621/ms625HJWJPmuU9tYvmGnfz+6rEc9IXIdjtoCEYYkOOin9fJSwun4A9F2FlZzz2vlAGwaFoxIwblAnDfnz5lS3mtPenuSACp0HNIp9Up1pXNmsjGJphYMaeE3/z587jvWJa95unqRxTlsmb+JEKRaFmTiGlSVR+wv2MlhQmFTVbMKUlImiGTWKE5hqE4Nj+LxdNHke12UOsP8eAb26mqD9j9whpDmydMsrJl5ngceF3OFvtQsuOkkslqX5BfvVZm95Ut5bU8uXEXz9x0Ds4ma3hX9VexSAuCIPQOvG6DldefTcVBvz3vHzLAi9fdfnteWhVApdR9wA+AMsDyk9GAKIBtJPZhHo5EqKoPsujlj+0JyLLZJQRCJkV5Wew95Of6Jz60vzv/6VLGDs1nyeWjuWrCidz4rZNpCEYY3C+LfK9MpDOdllzCOpNYV7Yt5bU8+Ea0xMjpx+ThdBi4HYqffO9UyvbWJUyw4YibnGlqdlTVJ0ygX/nRFPzBxIlqUV6WTGKFNpHvdXNM/6ykylnsGOoPhvl0X50tm+3JltlcsXI5DZyGYu8hf4J8BsMR3iyrpKouyKJpxeR7XdT6Q2Q5DQb39yYcu7NJ19ggCIIgdB3hiCYQMuPm/ctnlxCOtN8FNN1lIC4FRmqtJePIUWA9zL+p9ScEg968qpTF00dxTEOIwf08CbEfVfUBBuS46e91y0Ra6BDNY6y2lNeyrrScn3zvVNtCd17xIJ696RyUUnxRWW+Xo4AjbnItFbM+viA74bwyiRXaSmtWL0uWquqiGZUravysmFOSNFumZa1OdZ62FF63+syW8lrmP10KRPvBSwunpOeCCIIgCBlP2MROBAlHkj9mQiH4LwFXms/Z67AKCIciyeugDR3g5eG3thM2ddLYj3yvW+KThA7TloyIb5ZVcvUf3sfjMjimf1aCW2dhjruVZBxdV6za6j/7D/n5ptbfacXChZ5Fe8sppEqq5Q9FbBkJh82kslPrbzmzqGSGFQRBEI6WcIp5f9jsoVlAY2gAPlJKrQdsK6DW+suvk2cAACAASURBVJY0tyNjiV1pvu+yM5Nmd6tvDHPXhafTGIxQmOtOcKkzTc2+w4123NWgXA9OZ7rXAoTuwDQ1B3wBGkMRHErhdTvI97bPAtyWjIhjh+ZzxwUjCYZNctyOptUpDSgG5UatKUqppPL76b46Fr9a1mqx6vYW87a+s31/HQ+/tZ1rJw+T4th9HMNQnDIwh9XzJqIhqTxqrfl4bx3rSsu55dxT+e+PKvj2yMHxca+zSyjK9VBR42fs0HwWTB1OvtdluzpLHJ4gCIJwtLgcRtLnlMvo4TGAwCtNL6GDxLrNDRngZemscbYb6JACL8tmjcPrdnDNHz9IOrENh00+219nm5At/+HTBueJEtjLSeamtmTmaAb3y+Kkwpx2K4GxbnFVdQHOKx7EjJKhHNc/C7fT4KAvyA8efc8+10OXj+Hx//uSuy48nUDY5OG3tickkLFiBVtzv2v+W84rHsQvLyrG0UpCDav/LJpW3G53P6H3EQpF2F5Zz4JVpRTlenj4ijH8dM3WuP5R0xBkXWk5104exiPrP+fOC0/n2qbxFZpqUja53j+yfgd3XXgat76wNen4K7IlCIIgdJQsl2LZ7BJujpnDL5tdQpar52cBfVIp5QVO0FpvT+e5ewuxlhanYfC7v+yISyrQGDK5uVlc4NynNvHiwskMysuisj6Q0n/4uPyuT0YgdB/JYu5uX7uNxdNHkZflOqrJaYHXxS3nnsqCVaUsmlaM22HYQcrWuW59YSv3XXYmpsZuh5UUozDHTX+vizvWbrNjBVuqqRb7W8YOzefaycO4+g/vt2rNs/qPFMcWTFPzzeFGezy0ainFZg994PVo9lBrwWDRtGIcSiWVnWEDc7gzRvmztseOv+1tX3st3IIgCELvpSFo8tv1n8fN+3+7/nPuvvgMCnLad6x0ZwG9GHgQcAPDlFJnAf+utb4kne3IVGJrSVXU+ImYmjfLKnmzrNLeZ/W8iUknJ1ah91Rxg+GIFILv7aSKuct2O45a8anxh+yJdEuF24/N91LjC8YlkLGSYqxdMMlW/qDlYtWxv2XB1OFttuZZyTg6oxC3kNlU+4JU1QXiZMBQKi5zsoW1YFCY48ZhJHddzvY4OLZ/Vovjb1tpLamMIAiC0PcIJ5n3A/ziouJ2HyvdPn/3ABOAWgCt9UfAyWluQ8YSW0vKKvzYvCBkQzCStEikVejd8h9u/rnTIe6fvZ1UhasbmuJDj4ZgOEJRrocVc0oozHVTmOtJeq6vqxuo9gWTfjYo78h3mifJsBK3WEk3XM4jctwea56VjGNdaXmLhbibn08SxPQ+guFIgixaCwOxxC4YDMhxc9gfSpCd5bNLQEOWM/n4ao2/bZWrVBlyraQygiAIQt+jMwvBp3vWH9JaH2q2TUxPbcSqJWXVXVNKs3x2SdxE5PiCLB6+YkzctthC74NyPQnfWT67xE7MIfRekmUiXDJzNCcWZh91NkKv28EdF4xk8atlfO/Xf+WB1z9l6axxcedaNruER9bvsIvHN1e+jusfTYv/zp3f4aWFUxg5OA+AyrpGvj7YwMd7DvGjZ7dw6dJ3qG8M278l1aQ9mVJrJeO499LRnFKUw5r5k+LOZxjKtr5cuvQdptz/NpcufYft++tECexluJ2OhIWAdaXlLGs2Pt4/YzTrSstZOmscpjYZkOPilKIcXpg/ibdvm8ri6aNY9F8fc9myjRzwBfn91WOTjr/tkatU1npxURYEQei7eN1Gwtxq6axxHSoEr7RO36RGKfU4sB64C5gB3AK4tNYL0tWG8ePH602bNqXrdJ1KVV2AS5e+Ezcx+O0PRjPuxEL2Hmqk2hdkfdl+rp54AjW+ENluBw3BCEMHeMnzODEMw84CWlkfIBwxcUoW0Jbodl+rzpbXI1lATRwKOwsokBBvlGxbKvezyrpGLlu6MU42zysexL9OO4NAxMRQ4HU5mLn83bhMiYU5bo7L93JMv6yEYydzg7OSxFTVB3jlR1OImGCaJgd8QbsMxdG6yyXrZ1bNtgxI4tHrZLajtBZDF5sRdkbJUApzouVxjs3zcCgQIRiOoJRCKU3EjK68GkoxINuN02mwt9bP5SveTZCT5+ZO5IvKenv8PbEwm5MKc6j2BdssV+2VwQyOF+z2RrZFXk+667V2HXP3fRcdTZOEnk1GyKzQO9l/yE+tP4jDcGAoMDVEzAj5XjeD+yfN45FSXtOdBfTHwC+IloB4DngDWJzmNmQshTluVswuYX5M9p/Tju1PyNTMXP6uvd+OynoWTB3Oyf2jEaHPvrebFX/bHTcxloQvfRPDUAnJKJIpWk/dMIFA2Gw1BsmaePqDiRaLN8squfFbJ3Plo+8BUYVwxZwS5j9dypbyWha/WsaSmaNpDCVaNaxSJc3d4KxEHPOfLsUfjNgF44vysjotxb5YXzKftsTQxVqDm8tNkduZ8jhP3TCB3CwnwRTx1KGIychj8tBaxx2zrXJlmhqHQcJYn6puoMQLCoIg9A0awyZ3rfvYLjVU6w+xfMNO/vMHZ7X7WOnOAtpAVAH8RUe+r5RyAJuAPVrraUqpYcDzQCFQCszRWvfaIAnDUAzIdbFoWjHH9c9iYJ6HA3VBlDqSGAawJ9fPz5tIY8jk+6OPY8XfdkuqeyEpyeKNvqpusLN4WtY6XyDMvsONtrUuduK5aFpx0sQYA3LcrJ430R6kBua4k2ZZtKx5wXAEl9OgvjHMQV+QolxPXLar5Rt2ku91Jbh4tjfFfksWEytWUhLEZC6pYuiaj32tyc0BX4C5T22iKNfDfZedybH9vbgcBl8daCAQjiSVE4eh0FrbixMWbZGr2D5VlOth8fRRDBuYQ7bHwcAcj93vYmXX5YB9hxp56PIxdh+RcV4QBKH34TQUVfUBO3keZEgMoFJqvFLqRaXUZqXUNuvVjkP8BPg05v39wMNa61OAGuDGzmxvT8RhGGzeXU2Ox0ljKMLNz5Typ23fJMStLJ01jkf+vIPrVn6AYRhcUTIEEEuGkEgyy0S222Erf7edH43tm7n8Xa5Y8a4dt2RNjitq/Enj+pbOGseSNz7jvj99htth8OsrxxA2NQNz3fZEdUt5LUW5HvbWNtqxUZct3cj+w424HMqOK7zy0fdY/GoZd1wwEkOplNaQttBaLFayWMmjOZ+QfjrDimuamoZghMknF/Jv08/grhf/zrm//l+ueuw9wqbJwDw3D12eGG8dCEfsmOvYpC8Og1blKlZx3VJey/VPfMjsx99HoVLGp5bXNPLcB1/ZfeS280dSlOuRcV4QBKGXkeMxEub7y2aXkOPp+YXgnwFuB/5OO5O/KKWGABcB9wI/U0op4LvA1U27PEk0y+iyzmpsT6Qgy8XFZw3hmj9+wEOXj6Gixs+px/ZLqAvyu7/sYEbJUNaUVnDzqlKevGECa0orxJIhJJDMMmFlk22pxEJjKBJndbaSE512TB5uh8Hdr3xMVV2Q284fmVDsfV1pObedP5IH39jOLeeOsF3drHPcvnYbz82dyA+ffS9h++p5Exmclxgz2FZiFdfY32TVarNcAzvLpVRIP+2x4qayBtf4AzhUtMzINc0Kv1v1M7NcBoumFTNiUC47Kutti/aLCyendB99ceFkQmEzqVy1prgms2ze3FR7882ySttNevH0UTLOC4Ig9DJ8gdR1APOzW/9+LOlWAKu01q908Lv/CdwB5DW9LwRqtdbhpvcVwPHJvqiUmgfMAzjhhBM6ePqeQZUvaNdbs7If5ntdSeuC3PitaIWNihq/XbtqxewSHEZ00iMT2p5JuuXVsnjFTlSHDvCyYk5J0tg+a0LqUPH10CzX48XTR3Hq4FzeLKtkxZySBAXSiuOzJqrDBuYkPUdE66Tb9x5qtCfPHUl8Eau4xh43tlZbe11K+zo9bYxNJtPLZ5dgGDpu7AuHTb455KeyLkAoYqKUwhfIItvjIBg22VPbiNNIXvg9u8nKt/jVMjsu1SIUNpMqa9f88QNeWjglwT3UojXFNZWCaNXetN4PG5gjFusW6GnyKgitITIrQGbXAbxbKfUHpdRVSqnLrFdrX1JKTQMqtdalre2bDK31o1rr8Vrr8UVFRR05RI8htpD78g07+d3VY1PWXKv1h+z/3YbiiesnEIyYfL6/nv2H/VLjrIeSbnm1LF4vLpzMhqa09re/sI3f/PlzjumflbLEgtftYFmzdMT3zxjNI+t3YGptL04km7AOyovG9p1clIPbaXBe8aCEc4TCZtJzV/uCmKaZ0o3TNHVT6Qgfe2oaOOiLl3FLcW1+XIesh3SYnjbGGobilIE5rJ43kbdv+0e7VMP0322Mk5PtlXVc/Yf3ufe1aGTBbS9sZeqDG7hs6UYipkZByrqVDcEIDcEIS2aOZvmGnXGfRUzdITfU1tyPU9XytMZ66322x9EtC3yZUj+zp8mrILSGyKwAnVsHMN0WwOuB0wAXR1xANfBiK9+bAlyilPo+kAX0A34D5CulnE1WwCHAni5pdQ/CuvnWxCIQMln69qfcP2N0nJvd768eS11jmLULJlGU5yGsNdet/MD+fMnM0ba7kmSMEwxDoVDMfvz9hEmrlbmzeTZC09Q05kbikrpYJRoMpVgyc7TtStrcotHf6+LHz22Js85ANHOopUg+9tcvE+T6/hmjeXLjLs48vn9SN85XfjSF/YcDcZafJTNHM7hfFicVRrPiGkrx5A0T+Lq6gUfW76CqPhBXK1PIfExT83lVPZWHA3YyIwvLhRmw5XrRtGJuXxtvqd57qBF/MMLeGh/P3HQOVXUBqn1B1pWWc/2UYRTleWgIRvA4DarqA8CRRZBfvVbGPZeMancyodbcj1NZNh9Z/7l9/MeuGc/AnPRbryUbqSAIQteS7TZYef3ZVBz026WGhgzwkt2BOoDpVgDP1lqPbO+XtNY/B34OoJSaCtymtZ6llHoBmEk0E+i1wMud2NYeiccZLQK58JnNLJg6nFtf2EpRrgetNU9cfzYOQ9HP42Tf4QB3vfj3uElwUa6Hihq/HcNiuS1JxjgBou5lk08uZO63T8ZhKCKm5rG/fsnAHHfSCWm1L4hSMCDHzQ+f3RynpCkFL23eww+/ewrLZ5fYbsvWhHX1B1/FTbYXrCplzfxJ/PIik0DYpDEU4dziwby8ZQ9LZo5mSEE2kabSEHddeDo6hXtoYzCSoBha8Vr52a4E5XDZrHE0hkwKc912PUQh86luqgtpxUnHEmuFsz5LZql+sbSCOy8cSVGeh1l/eD9OfhXQz+vCH4zgMBQrrzs7rs+8WVbJ4ukkKGsr5pRQEOOumYxk7sexcYqD+3ni4ggLvC7uvXQ0d1/cvfGqbc28KgiCIHSMUERT3xi2FzaHFHj57VVj6Z/VfnUu3QrgRqVUsda6rJOOdyfwvFLqV8AW4PFOOm6PRaN4beseVl53Nm6nQVGuJyHJxjM3nZM0qcbK687mjrXb7MyLpw7KtVP0m2a7cvIIvZAcj4PZk07k+ic+tGVp6axxeFwG+dmJE7hgOOoC9/u3d8QFJD+5cRf/Nv0MFkwdTlVdAJdD8fSNE6iuD1LtC/LI+s+5dvIwahrCnFs82P6eQ0FNIGxbZc4rHsSiacXUNoS56rH34qwK+dku27pilak4pl8WITO5YpjtduBPohze/Mxm1syflLQQvZC5WO6XVpy0dc/HDs3nlnNHENEapxF1PX6zrDLpfrMmnsAhf9hevIAjixWLphWTm+WkIMfFoYZwXL2++2eMptYfxDAMRhTl8uxN51DZZD38zZ8/56f/NLJFq1issqeUwqEgEDb51WtltoW8uWWtJyhYUj9TEAShawlFtO09BdEx9sfPbWH1vIntPla6FcCJwEdKqV1Ei8ErQGutR7f1AFrrDcCGpv+/BCZ0fjN7LgXeaBbQ65/4kOfnTeSWc0ckJNmoqgskfRAf8oe47fyRvLxlD5eOO545f/wgbmW66CgyKwqZT0PQZOEzm+NkaWGTgpQsu5Tb6eCpjbv40XdH2N+z3I8P1AW5uWnbyuvO5ofPbkmQyR9+Z0Sc5XDF7BJ+s/5zW6m7dvIwvqj0xbnwxWbsfOya8Tz81naunTzM7gMrrzs7qdtdQzCSMqmM1pIQqbdhxcpZ5UnuXLeNolwPd1ww0nb1jHU9Xr5hJ0tmjrY/u+XcERz0hQhFkstMYY6bL6t8BCMmi18tS0hy9OxN59hJiq7+Q7xbddneupRWsWRulJbb87WTh1FVF2RLeW2PtKxJ/UxBEISuJZxikTvSgXjrdCeBuQAYAZwHXAxMa/ortJEaf4hH1n/OfZediaHgpIHZCcKQKmlBtS/Ineu2Me8fhyfEu8x/upRqXzBtv0PoecQmGLKoqPETjkStw80TPBR4XfzzuKG2Rfovt/4jz8+bSF1j2Fb+4EhNwVhmlAy1lT/rPPNXlTKjZCiAXX4i2XcravyEwiYjB+dxzyWj4hZAHlm/o8llNKZGzqxxnFiYTZYreQKNliaomZLUQojHipWrqg/w4BvbWTx9FI9cNTZh3FuwqpR7LhnF764ey8hj8lgzbyKr503khMJsst2OlGPpgBw3j6zfkTLJkcOI1u1rr1UsmRvlneu2MaNkKHeu28aCqcNbPUZ3IfUzBUEQuhZHiiQwHVnETqsFUGv9lVJqDPAPTZv+prXems42ZDrBcISquiC+YARDRW9481XXdaXl/P7qcXHWlWWzxvGvL39CRY0flyN5WvOeNqEQ0ovLYSRdwXc6jJQJHkYU5TJ/6in4myxshlLkZcVPipu710F0spjKsgJHYrKSfddS2gxDJcQCbimv5YHXt/P0DROorAvQEIwwuF8WA3OjlpLmMVktTVAlqUXmkiyZSiplTGttl2WoMgM8/n9fcteFp9MQjLB5d7Udc22PpbNLeG3rN2wpr21RPk1To5Ri7YJJVPuCLN+wky3ltS0uOrRU5sH6G3uOnoTUzxQEQehaXIaK81axcny4eroCqJT6CTCXI1k/VymlHtVa/zad7chkvG4H91xSzEFfiEDY5D/+JzED6G3nj6QxZLLqxnPwuAwipiYUMVkwdTjrSssJRbS46ggJDMr1JE3YMijXk9Qy8fBb27nnklEEwya7DvjsjJpr5k9k5XVn25lB15ftTxiwBuV5kspgUdN2a2Id68KXTGlzNZWQmFEy1I4lXFdazueV9Sx+tSyaEbFJ+av2BRmQ7WLN/ElorVudoEpSi8ymeTKVqrpAUpnzuh1U1QUIhiO4nAb/dskZ3P3KJyz8zilcOeFE7vvTpwlFd6+acCJAUvlcMaeE/CwnFTUNduzfutJybjt/JE9u3MVP/2lkykUHt9ORVJ6t/mD9Pdp6rrFxhp2pqEn9TEEQhK7l+IIsnps7kYjWOJRC07EcHumOAbwROEdr7QNQSt0PvAuIAthGtNY0BCMsevljHrp8DG+WVVJVF7QnKKbWhCKahc9sThrzsmx2CREzwkOXj+HWF7bGxV+Jq07fxuk0OG1wHmvmTyIcMXE6DAblenA6DdsyYSVcOa5/Fhq4YsW7tgw9dPkY1pVWsKfGH5ehavnsEgb1c/PizZMJRY5kLmxujbt/xmiefW83z8+dSE1D0LZiWy58Jw3MweNQZHuiCxWmqQmEIgkxiMtml3BsPw8vLZxiy3RyS17LbhOS1CJzaItCk6yEwlM3TEjIDLtidglVdUH+7ZUyHrxiTNKiuz//fjFDCrxsKa/lyY27eHbuOUTMaGIZl0NR5QvEJW2x4vjuuWRUiwmHCrwubjn31LhFmKWzxvHa1j1RxdLrYvH0Ufzyvz7ucAmfcNhke2VdQmkXsWwLgiD0bJxORWV9hJtXfRA35zkuv/3qnNI6fTEtSqm/Ey0F0dj0Pgv4UGt9ZrraMH78eL1p06Z0na7Tqahp4AePRjMirrpxgl3qwWLldWfbk+8Vc0rYvLuameNPsFOUr930NROHF1GQ46K+MYzLYdAQjDD2hP6EIojrTjzdfgF6irxW1QX4xUvb7IQr9112ZoLsDSnw8uQNE7j2jx9QlOthwdTh5HtdNAQjjDwml0G5WTidR8KOzaayDt/U+uNc5FZedzbPffAV10w6iWP7e3E7DfYdauSFTeVceOaxnFCYjdthkJflYMvXhxLqvA0p8PLizZMBCEZMIqamxhfk3/67jC3ltfY+rVnyquoCXLr0nYRj93ALYJ+T2fa46jZXFDWay5ZuTLjHD19xFgcbggwvyqH8oJ9H1u+Ik53HrhlPdpPlMLqoYfCT5z+iKNfDnReexrH9s3AoRVV9I//+359SlOfm9vNPw+00yHY7U46vlXWNSduzZv4kslwGl/zu6OTRNDUVNQ0JiWm6Ua4zQl5Puuu1dh1z930XHU2ThJ5NRsis0DvZU9PAW5/s5bvFx2I2hd38pWwv/3TGsXYoQzNSymu6LYArgfeVUi81vf9n+kDphs4kEpMByGEoHr5iDD9dc8SSd0JhNkW5HpbMHM3wQbkcl++NS+u/bHYJx/Rzs7OqgWP6Z1F+0M8pg3LYeyggsU5CSgpz3PzyomJ74nhM/6yk1rEsp8GSmaPJ9TjtRDBWDKrTMFAqmhjD645Ovk2tyc920Rg6YlX7uKI2wQryn1eexY/PPYX9hwN8UVnP5t3VXD3xJE4qzGbRtGLWl+2PKylhas2uA7446/d/XnkW9772KVvKa9tkyUtmMZKkFj2PVK66Ly6cjEIlLGrFKjl7ahoS5Lgo14PX7WDxmrI4K5zXZaCUgcOIxsv++39/QlVdkAdmjmbJG5+xZOZo8rJccXK7ZOZo/n36GUS0jhuHm4+vpqk54AvgD0ZYNK3YXgyxfk84YuLX2q7latFei3S1L0hliizRYtnuPNqrMIIojYIgtE6Wy6Bk2ECujimNtWx2CVmuHl4IXmv9a6XUBuBbTZuu11pvSWcbMh13TKIOQyncToMXFkwiYpporXAY8MDlozlQF+CwP8zNzWpY3byqlOfmTuS2GPfPZ246J+kEavW8ifbECeiSmBEhMzAMhcM4kjzIoVRCPNV5xYOobQjRGDK5fW18hs+bn9nM6nkTeeuTvby8dR+/+uczONwYTnBPNhT097psK7f1/X9Z/RGLp4/i+ic+tPe9N8bFbtmscfz2Lzvs98/OPSch4+O/rP6I5+ZOJBwxOVAfxOVoecCUpBaZQSpXXV8gwpzH329R6VJJ5PiWc0ck1P5b+MxmnrphAtfElM75zyvPwulQBCMm104e1iT3ifVXF08fhdft4D+vPIuBuR4chkKjqQsECYbBNE2qfUHmxbhkPnT5GO7702d2e0xAm5qls8dxoD7IN7V+lm/YSVV9IGXsdjK32GA4Ymc2lRhwQRCEzKIxZCad13ekDmBaykAopQZYL2A3sKrp9VXTNqGNFDUl6jiveBADctwMGeBFa02NL8RVj73H3/ccpuJgdOJhKJJOjGLT/VfUpK4bGIpoDvlDfHOogX2H/ew95Keixs/Hew6xu9pHOGymTJEv6fMzk+b3LfYeG0rxwvxJrJ43EaPJ+hyb8v0XFxUzf1VpytINYVNz0Zjj+d3VY6msCyYoaDevKiXP44yzcsd+P9vtiNvXKhlhKZh3X3wGz809h8knF6Y8xje1fub88QMMA8KmyZ6aBirrGqltiP7Grw/6qKxrtOXVshgdX5BNUZ5HlL8eiFV/LpYhBV52H/AlLGpV+4KYpqayrpGvDzawp8bPQ5fHy/EJhYmldSpq/Bz0BRMWFAbmesj3ulosWZLtdnBc/yze+mQvUx/cwFWPvceeGj+1/jA7q+oJm9pW/qzv3PrCVn5x0encccFIFr38MT99/iMqDwcIRzT9s5yccVw/ls4ay5p5E4mYJl9X+9hb62f/Ib/db7fvr+PSpe8w5f63+cVL26ioacDUmsF5Hp6bew5rF0xixZzos2TFnJZjwGU8FwRB6H5S1QEMd2BMTpcF8ABQAYSb3sfOojRwcpra0WNpa1Y2p9Pg1KIcfnLuqazd9DUXnzWEqrqAHQdlpQmvqIkWhky20tu8YGSqFWFTax54/VOunTyMJzfu4vopw3jg9e1U1Qd4+IoxmFpz3cpEtyZIlXRDXErTRUvylOqz5rFU5xUP4sfnnmqvNlkubZYMrLppAk/fOIHq+iDVviC1DSEqalKXbviyykdhrhutk9cGrKjxs/dQI5rE0iZWBsTYfS1Zt943hkxyPE5+dt4IAuHksl/rj7bxJ88fsSieVzwoIZGMyGvmkMxV1yp7E0tFjR/TNBPGpt9dPZb7LjuTLJeD/Gw3NSnGw+Z1Uitq/Oyp8duW8VRy3xCMEIxEFz9qGsKsKa3g9rXbeG7uRE4s9NIYSl5/c1Ceh6v/8D5FuR7uvqQYfzDCVTFuP1Zimdhx+aHLx/D4/33JLy8qtn/j2KH5XDt5mO2+3bwfL59dwqlFuSllXcqhCIIg9AycRqLXypACL84OjMXpKgT/CFADvA5cC5ystR7W9BLlr+kBa63WXrr0Hbbvr0u6ymqamn11AeavKuWKCSeyoJnFpdYfoiEYiT6k//olS2eNa1YUu4S1m76OO6ZVNzB2v/tnjOa+P31qFyCeUTKU29dGCxFX1Pj56ZqtlB/0J11hTxWTI4Xm00NL8tTSZ83v24ySoQmuBrEysKuqgTmPf8DM5e8y/+lS9h1ujCvd0FyeHlm/g4XPbCbb7bBlNBZLQdNaJxRzXzJzNMs37EzYN/a9oWDhM5sJm3Dva2VJ22AdI9aiOKNkqK38WZ+JvGYOsa6679z5HVbPm0h9IExVfSBuvyEFXiKahLHpR89uwReMMHP5u1TXR5O6NJe/3189jnWl5QnHq/WH7AW05Rt2JlgTl8wczXH5WfiDYRY+s5m53z7ZPu83tX58AZPdBxqS9gWjSbFcMHU4Nb5QgsU82bh86wtbuf380wibmkXTiu2svVaZCuu7sd9ZsKqU2sYwqZDxXBAEoWfgdRssm10S2W9WFAAAIABJREFUP6+fXYLX3UNjALXW/6KUUsBUYA7wW6XUm8AyrfWudLShJ9NavbFYi41Syg7id6jEleflG3Zy9yXFdt01gCeun4DLEc0C+trWb5h62mBe+3i/vZp77eRhPPPeVyyaVsyIQbnsqKznwTe2s6W8lhu/dbJtbWluYbQm0BaxiQQkyUD30ZI8QeIE2PqseSyVdc9jiZWB5lY8awJ86wtb7dINJxRms7fWb8sTQGMoQkGOK6E24NJZ47j75U+468LTuO9Pn9mlTUIRkwE5bntCb+37u7/ssN8vmzWOfYcbbVeI2PIoVibH2DbEKpCpfqfIa+YQm9ylqi7Aynd2Ja3PZ+rk7jP5Xpdtrct2O2z5G5Tnob/XxeoPvuLaycMo21sXZ4F78I3tAPa57vvTZzx4+RiO7Z+F1uAwYNW7uxl3UmF0zG5apbXkb1Ceh0fW70ho67JZ43A0rfTGjrnJ2t18XD7kDzFz+bt2G/tlOVvsx63JupRDEQRB6Bk4DCjMdfHc3Il2FlCHI7q9vaQtCYyO1pt4Wym1BfgBsBjYATyWrjb0RExT4w+FUz5gm7vfrF0wyV5xtiYIscWIt5TXsvTtL1g0rZhVN55DRGsMBW6nor7R5KIxx1FdH+SpGyZwsMlaZ02MN35ZzaJpxcx/uhQ4Mklp/tf6rCEYPwGITSQgSQa6j9YmbKk+s2KpYq3JLbliWlY86/Mt5bUYSrF4+iiy3Q4G5nm4/0+fxtVQG1Lg5ZtDjSzfsJM7LhjJ0zdOQCmFoeB3679gS3kttf4QVfUBWw4B5v/DSay68RwO1EcLa7+2dQ93XXg6N089hdqGEPnZLv715U+i/aIpsceW8lrmP13K2KH53HHByDgF0nKBa+l3irxmJoU5bn76TyN5+K3tLJpWTGGOm0F5Ho7tl8XeJit1MjfNh68Yg8tpUOOLl7+xQ/P59ZVjcBqKJ66fgKHgq+qGuAWFJzfuYuV1ZxOKmPTzujjU5Gq8rrScaycP48E3ttvu97Euqr+5aixV9QEefGO7veDREIyQn+2i8nAjy2ZFk75Y7UzWF5uPy5ZlzrISrrzu7Bb7cUuybpo6ZSiB9A9BEIT00hjUGIBWYGowVNSVszGo6e9t7dvxpCsJTI5S6mql1MvA/wC5QInWulcrf60FzlvK3c5KX1IXILfTwQFfIM5iU+0Lsq60nPtnjOawP8TSWePsCcTi6aPYcPtUfnFRMYtfLWPqgxu49o8fsO9QIwtXbeH6Jz4kojVet4P7/vQpgbDJ4lejtdEsq4rl5mStHlvnWldabrvhDSnw8uicEk4szI4zQ1sp8q2YnGSfCV2Py2mklKdUCTOsWMDY+7autDzBhThWBk4szGbFnHhXhMJcN9c/8SFXPvoet63ZyrxvD4/73JKxLeW13L52G75ABI/ToCEYZuOX1SnP++2Rg3nu/d0U5XkYeUweE4cXceuarfz4uS0U5rr57fovqKoP2P0i1oWvqj5AttvB0zdOYP2t/8iDl49hYJ7HVgiTnU/kNbPxOA2umnAi+V4XdY1hwqamLhjmV0lcg5fPLmH00H4MH5TLsf2yOOO4fqyIcbGpqg9wyB/CH4pQfrABXyBMYW68RfraycNY8sZnmE1Wv/5eF6cOzuWqCSfy4BvRWLtls0sAzeLpo6gPhNlSXksgFGZZ0xg+/+lSbn1hK4W5bhpDEX7/9hc0hkxOLsphSEFWgltqsnE51s0Zokqgx2mwvJnLUOx3WpL1al8w6TVrLWmMIAiC0PnkehR1gQif769n36FGPt9fT10gQq6n/TGAaSkEr5TyEbX2Pd/0N+6kWusXu7wRTaSrgGZbAuetQtNFuR5uO39knAvQY9eMZ0RRLhW1fqY+uME+7tih+dx2/kie3LiLn3zvVPKynIQjGoehCEU0j/7vTgqyncyZNIyI1rgMhdNQ+MMmhlL4AiGyPU4cSqEUaB3NKvRllY8//X0v5xYPZsSgXEwN4UgEh2HQGIrgC0Y4Pj+Lb2obaQhGyPU4KchxkeN2YmqdNNHIAV+AxpCJQ0GOx0EwogmFzUxKp9/tDWyvvJqmZne1j/2HG+PcK1tL0DOiKJcafwiFJhA2CZuaz/bVxdXXC0VMThmUS8Q8cr8jEZPK+oAtQy6HiisQP3ZoPndeeBrH9c/CYSg8ToPGcLQ4u9NQuByK1R+U85ftVdxy7ghOGJDNF1X19nmP6ZfFwFw3KPAHTfzBMIZS9M92ETGj8u0wFPsOB9h3uJF1peX88DunkO1xUnHQb8cbDshxke124HE52FnpY9Tx/agPRI9ltSsQNjFNTZbLwcDcjM342e2NTmeR4mQJjap9QS5d+k6cDN5y7ghOLspJkOlaf4izhvRncLOlU9PU7DvcyDe1fqp9QZZv2MmIQbksmDqcg74gDgMG5ERdTq1xde+hAMMH5eA0DJxGdLvVlwB+/5cv2PhlNctml/Db9Z/zZlklK687m/d2VnHVOSc2WcIVB+obWbZhJ9dPGYbX7WDp21/w4++OYECOm2DExHpkuxyK/YcDaA0D86LK2H/8T6LFffW8ifzbf3/CjJKhdj/2OB0M7p+F1xWfBKr5tdx7yM+U+9+2YwlbumYdJCPktSN1/dqL1AHMGDJCZoXeSV1jIwZQ648+W5yGIt9rYAJ5WVnJvtLtheBfIKr0jWx6xaKBtCmA6aK1uD444qpXUeOPcwEaUuDl2P5RV55dB3wJbnZPbtzF7eefxkFfEKcBeR4XJhCKaK7/1jByPU4MA5RWNISiFpb6QBi3wyAQ1njdsPnragpysji2v5dsj4NBeR42flnNmtKKpFkR758xmlue+8h2eVo9byLXrfyQ5+ZOxFCgSVxIqK6PXoOiXA93XDAyqUKSoZPsHku1L8g1f/yAolxPnEvZ4H5HFJrmte0KvC52VNXz8FvbuXbyMO5ct41F06JW5IoaP2tKK4DoZPKJ6yfgdirCEZNvDkUzzd77Whk3futkrn/iQ8YOzY+LZ6qqD+B1OZrKiTQy/qR8e1HAVIrH//YlK/6227Yemmj7vBu/rOb+GaO555VPeGDmmYCiv9dlu4yGtKaqPoihFAd9QfK9Lq6acCJet4M8j4NTB+cSNjVaw95Dfu55pYy7LjyN65/4kP+9fSrfefB/mf8PJ3HxWUPiinevmFOC1hrDMDJloaJPkmqRbUC2K075a764ZsXuWZ4PLy2cEqf8WPUhG4JhPE7Dlseq+gA3/sMwCnPdVNcH+WxfHZ/vPcwFZx6L2xmVt9/8eYfdXyw23vUdvqltZOF3TuHH547AYcC8bw/n598/HUMpBuZ5mP14tM/eeeFpHNs/i19OK8btMKj2BZlRMpTf/mUHd1xwGm6HQbhJA3QY0bjuWn+I/+9/PgXgtvNHxsUpLp01DlNHY2JjFUOAd+78jv0sCodNdh/0UR6zaDJ0gJd+Wa44d2rAvmaCIAhCejE1mM22hTtox0tXEpjr0nGenkRbAudjY66sB6z1cDUMRTAcSZog4JZzT2XJG5/ZD/QrSoZw07eHsaemkWy3g32HGjm+IAt/MMIPn91CUa6HB2aO5lBTxrpn3vuKc4sHk+VyYGrNwlWb+X/fP92O3ar1h1j17lcsnj6KYUU57KryJU2gUVHjZ//hRgJhkyc37uKn/zTSVupiFeBF04oTMtjNfWoTLy6cjEJJke1OJHZRITaG7q+3TyViYl9ja+IHUUv03Kc2sWhasS1nsXGlsRPnFz78iovGHJ+wOGDFo24pr7UXMwpz3PT3urhj7TZ7sv3CgklU1UWtk0W5Hm45dwRXTjiRb2r93P3yJ9xxwcg4ObTc57480MDiV8t4du5ErnrsvTiruXWcwf2zcBmKWn+IusZIXB+B+Jgpp6F4+YeTyc92M6spPT5gXzdLAY5dqGhrqRYhPaRaZFszf5I9rt563qkJGTBjFzgeu2Y8BV6XrUgW5Xr4f98/jfpAhEUvf8ySmaPj5PHxv+1i1sQTCEVMWzFcXVrBslnjcDpcthuzRTTuD/5l9UcJMXRWGZKxQ/NZed3ZNAQj5GU5ufe1Mt4sq2T+P5zEnEnDyB7s4O6Lz8BhwMzl79nHWTGnxG6DxZMbd9lJv5SCX71axoySoS3G8Jmmpqq+Ma6ckOUm2i/LmVBiQ9yjBUEQuoeISYK5JWx2zCydrhjA2U1/f5bslY42pJuWYq0sWouVczmNuAQBq+dN5Pl5ExmU5+b6KcPs7238shqtNScUZjOon4cRg3NZt6mce14pY8nM0VTVB7hj7TZ70rKmtILFr5YRipjcsXYbVfUB3E5FttvBrS9sZf7TpWz8shq302DpX74gy2XExbtYcSZWwgErHfncpzZxwBegqi5AQ/BIYptUWRYbApE2lb4Q2k4quft0X13Ka2wpjbH3yVLknps7kbULJrFoWjEPvrGdcScVJpRMuHPdNoryPHaM0pbyWha/WobbabDkjc9s5W/ZrHE88X9fMjDXzeLpo7jrwtPwuh2ETZO7Xvw7W8prWfnOLgbmeWw5tOL61pWWs2x2CX8p28v9M0bHxb3++soxeN0OAqEwDkNFYwDf+IxrJw+L61vWcZbPLsEfipCX5aKqKaNuLLHXIrZ4eFtLtQjpIdUim9aax64Zz3nFgzg235t0n9OPyeOVH02hMNfNN4f97DvUSFGuhwVTh/PTNVvtDLcPvL49YVwEOGFANqtuPIf1P/tHnrphAk+/+xX+YDhJ2Z1x/NfmioTtD10+hgE5R6xrS974DKWwS++sXTCJKyecyNPv7qI+EMFhwL++/ElcLJ7VJ2KPe/2UYdQHQtz7WhnBcNTyl6wsS+xzptoXpDFsJizS3b52G74m7wGrxMZLC6eI54YgCEI34XEQDVdpmnqYOvre04GcXOlyAc1p+pvXkS8rpbKAvwIeom1eq7W+Wyk1jGhcYSFQCszRWveI4kTJihM3XzmNrV+VzKrgNJSdKt+yDj4/byJfH4xOTJ66YQKq6TnsMhSBsEYphcOA2ZOGcfVETZbTYPW8iYRNjcdp8MxN53DQFyQQNhla4OXXV44BDT9bsxWAp2+YQGVdwLa+bCmvZUdlPc/cdA4RU9vZ76yJ+YNvbI+bMDcEIsx+/H0WTSu2V51TZVncdcDXoous0H6SyV3sfUp2jS2lsfl92lJeSyBsMnP5u/a+qZR5Q0XLQjxz0zlU1QUoyvNQ2xDk5xeezh0XnMa+Q40ArPjbbqaeNtj+7iF/iBdLK1g8fRRDB3jZWeUjy6l4bu5Eav2hqMsnmtvPP43X/76X75w+mEfWf25bGAfkuFn29k7WlFaw8rqzCZvgdTu4asKJ9MtysurGczAM7JjXf734DEKRCD9bvY2Hf3CWnVE3VYZEy2q/73AjvkCYRdOKWb5hJ1vKa0Veu5nmWWvhyCLbyMFe7rlkFNv31SXdJ8vlYP/hQEI/6dck31Zf2FJey31/+oz7LjuTY/t7cTsNKmoa+P/ZO/PwqMq7/X+ec2ZNJhshYY2CyBYQJBEI0CpKX6pCpcrSCkFBZRGXvq11aS1vF+pbK/L6q8qmtaAICG51oVpbFFsFFQFxARERMEEgIWSbzH7O8/vjzDnMZCZAEEKgc18XF8lMzplnznzPM9/1vm9dsSWuHX71pnJ2VnhZWDqAZ6L7raZLFq8zbLOs2m/dG1UNIZ545yvu/H4vHhzfH6dNIcNlx+NUmD26D75QBLfDhkAyeWhXXtxUzpDz28ZJm5izeC6bYNX0EvxhDVUIDtQF+O3LBrHXPVf0Boiryvdqn0Gaw9ZoXltHIJLe15ou8Yc0OuWkneqPM4UUUkghhWMgqEGWU6HGr1ssoFlOhaB2JNA6XrRUC+ji6P+/PcFTBIHLpJReIYQdeEcI8RrwM+AhKeUzQohFwI3AwpOy6G+JYwV3sX/XlAPpD2k88PqR2cCO2QaNeFVDiEpvkDtWb+XXVxVS3RCOm9sIB2Dq0o1xcyBrtu5jeK921AUiZEdpytMdKt/UBnCoRoWvvNrPFxXehLaiSm+Qzw/Us2jdLh6ZOIB7R/WOk4+IDR7MoG7ttoMsmFTErOWbWbRuV4Lm2+LSYn7110/j3m9KW+rbI9bu/KEI2w/Ux7XvJrvGZtD40D92JLR9OqOMokeThhhZmE+tP8LhhjC3rNhCebU/aXuaSUf/wOs7+Pn3e3LHs1ut1xkzoBN3PmtUo41gMC1OhB4Mx72kW1vGFhdYepX3rdluvTezavP8pjLGFhfgC2l8Uxtg854qxg88lyqvkdhYtG4Xld4gqhAWy22cXlxpMU+u32O9pqZLJv75vaQzZCl7PXF825baoyXZFEUgpWxSY0/T9YT20buf/5gV00oMZtBG0jr3vPAJ8ycWMf/1nUnbisHYJ7eW1wEk2P76r6q4uqgTmi45P99gB339k/2M6t+Rww0hdlV6eX5TGbMuPR9vIIJdDeMLaZyXl868f+5kcYfMpLN45r0yZcnGhHsltjhtVuWfnTGEUESjqiFkJSMPNYSoqAsmDZQPeUN0y/Mc92eSQgoppJDCqYOuQ01YQ5fRBJ6EmoCGU21+CbDFdAABhBB5wDSgS+xrSylvONpxUQ1Bb/RXe/SfBC4DJkYffxL4Da0kAISjB3fHA4dNjdOjevvO4Wi6jHNaf/vyNm4f0Z38TCeKEHxT42fH/lpWTishrBlsi899+DXDe7Xjgdfj5/jmjOnLonW7+P0P+1gBWrJgzXR4K71Byg77sCnCcnDM559cv5vFpYbQ8uLJxWS6bDz65k4reNWl5MHx/emY7cZtV1EVrLZSE41bZE2kZq+aB0UR5KY7KA9FEhzRZNfYDBrvu7ofuq6zesYQZJTZNcupsnhyMTOWbbKCq4WTirg5Zgbw3lGFTPrz+8wb3996rWQzhG09DispYLZvdmmbxsG6IH987XMqvUHmjuuHx2lD07UmkwZbymqSBpi+kGbprsW+7oJJRTQEw3EB59xx/TjcEGTqsK4seXd3XEXR7VAYUdiO9V9VsXhyMb9fsy0hUDBnyFJaaCeG42FJPhaOlWQz98/GGnveYIRgRE9a8XKoWPZt2ug5uWnsr/Gz/L1EMXhTTzL257wMBwsnFXPz8k0Jf7elrMZiJb2iXwcO1gXomO3GaVe5d1ShNf9n2u38N78EjkiVxM7ezh3XD7dD5bG3d7GotDiOyGhhaTEOm7CCOvP+Ka/2o0uJL6Rxbm4aHpeNGcs2kedxJtyvD03oT066g5yoYHwKKaSQQgqnFw47NIQhHNEtMjy7TZCRlAD06GgRGQjrxYRYD/wbo13TSp1LKZ8/jmPV6HHnA/OBucB7Usrzo88XAK9JKfsmOXY6MB3gnHPOKd67d++3fzMtgMZO0r/vupSnN+zmBxd25uG1XzC2uMByWld9sJeiLrksWreL347pw6Nv7rSeb+txUh8Ixzntpo5TIKwTCEdok+6M9hUbwvHVvjAep429VT4eXruTSm+QRaXFZLpt1ESfEwIimiSk6WS57fzulc8s52XhpCKWbdgbR7e+aN0uHp04gE45acftAJ4MR/Fb4LREmSfDXivrg9z74sdcP7QrT67fbdlCXoaTTllubLZjj/9GIjqfH6xHCMM5zs9w4nHaMHIvAodNYX9tgLwMJyPmvZ0QlDWm3zf1yUw6eaNKIanyhshJd6BLyYHaAEve3c2sS89HEYKcNOPx/bUBOmS5LMKWZOyOf5lyEbW+MI//+6u49+u2K+yt8hOMMjyGNZ0Ml435b33JrEvPj6ug56TbUYUg021HVYx26iF/eCvh2jw3cwjpTltrnIc6I2zWlMBpnJw4mS21yfYOM5k1c3i3hATCyMJ8fvK9Hvzpn19w3ZAudMhy47Qr1r4GR2z63Nw0bKpCjS9EutNGZX0QKSV2VSEvw8lb2w8wrHs+vpBGlttOrT9kVcfNGcD7o0kPkwxmQnFnpl18HjZVYFcVlq3fbTHkzh3Xjxc372Pm8G7IaNvPgboAD7xuJOaemzmE+oAxA2tXFf66uZw3d1Qyc3g3erTz4FAVdCnZVxOw1tkm3YFdFXz3gXXWezPvzY7ZbsqrfSx5N57c6xTgjLDX/0QZiOa+59a2/lOIM8JmUzg7cbghgAQCoSMyEC6HggDapDdPBqKlA8CPpJQXfstzZAMvArOBpccTAMaiteqnNFXlikQMnbWwpuOyKVR6Q3y4+xDDe7XnkDdoCcNPHdbVcgZWTBuMlIaTFQhr5KTZ+dPanXEBoy517nz2Eyq9QRZMKkJKyS0rtrB06iC+939vRyn/B2JXFaSE3YcarEDQrPiNLS6wqpNrf3YJP392q1VhTCYlMXdcP3q2z7D0s46nstcSjuJRcNo9+xO1133ROaV7R/Um1+Ngz6EjgXysJuDRrv83NX4mLN4QJwlhIpbF0Az88jxOfnNVH25ZEc8Q6lAVfro6kQXx6RsH0xCM4LSrgMSmKHE2ffuIHobGpCqI6PDKR+VxDKQjC/O5d1QhYGiu1fnDOGyC8igbri+k0bmNm+c2fs1lvdvHVQDnTxxAm3QnvqiuoDcYoaI+yPObyrh20LmA0Ua9ZMpAixkxdu2rZwyhfaartQV/cIbY7L5qH8P+mBhYv3v3pSd13iyZnt+Wshor2DMr251z3Dx1wyCu+8sHCUHh7SN6xFfXJhWRnWZnw5eHuKhrLi67sUce8oY4UBdg7baDXHVhR87NTaPOH+FPa79g6rCudMhyIwTsrw1YFe+HJvQnO93B1CUb4ypvGW4bgZBOTroDmyJoCEW4YWlySZ0Fk4pon+Wi3h/h68PGfR7b6bHshkFMjr4v8/z/+zfj9VdGWXUb2/fs0YUJsg+x++1J7Mo4I+w1FQAeG61t/acQZ4TNpnB2oiEQoDaoo2lECzYCVTXmANObqQPY0gHg74H1Usq/fcvz/A/gB+4G2kspI0KIIcBvpJTfP9qxrfHGaarK1T3Pw85Kb9zjz80cwvb99bT1OMhOc+APRfC47CjCYAJSFcGy9bv5YE+NIaydm0ZDMGKICEefd9oUQppORJOG4Luu8X9vfMFtI3rQPtOJL6RhU4SlaTWhuLMlgGw659cP7Ro3AzhnTF8cNsV6LFmLXuccNy/MGkp+M2rVLeUoNoFWvdEfzQk73BBkx4H6Jlt5X751WAIJRmPJg7JqH5fMXZe02rZwUhH/89JnVkub+fzQ83ITbOXO7/eixhfip6u3xh2/7vMKvtszD4HRvrnk3fhK5fu7DtGrYxazlm9m6Hm5TL+kG5kulbAmiegSVQgUxahWZ7psCGHMfYU0o4q955CPT8truKJfR9IcCkgIRHQERvXkxc37GFvcOaE1NM2h8psokcaAguwEh3vx5GJ65mccVxX1NKBV26yJlkzsHG1/rfaH8YeMllC7qjD8wXUJx79z13DKa4xKt0NVOFAb4H//tp1Kb5B54/vjtCu89vE3jO7fKa7LYmFpMe0yHdQHNIJhjUy3HYcqCGsGQYwQAiEky9bv4b/6dCAvwxlXBb/tsu4EwjqZbhVFHElsuB0qbdIdaFGimZCmEY5I/uelz/jdmD5xa1gwqYhH30ycW7z/mguY98YX/P6HfagLRJLuE2YQCfH77Unuyjgj7DUVAB4brW39pxBnhM2mcHaiIRBAB+pihOAz3QoKNDsAbJEZQCFEPWbfGPxSCBECwtGnpZQy8xjH5wFhKWWNEMIN/BfwR+AtYBwGE+j1wEun6C2cUhxNz6rx44GwztSlG4EjOlCrZ5REnV6BQxVcN7Qrk0okqmIwH9pVOy9uKqdv52zOzU1DEUbFxKYQ/RuDfS6ia3xZ4eXO5z5myZSBlqCxyXD3fxP6oyqCe67ozf2vbbeCv9jAwswc56Y7ks7YhCONJSyPjqMx/f0n41hOWESXCbTu5uzajGWb8Ie0pDb34qxh5KY72HGwHo/TZhFPxGr7dchyoUlpzXCaz88Z05eubdOprA8iBJyf7+Guy3vz7Ma9DO/VjmU3DEKTEm8ggtOusGqT0ab2yyt706VtOrNH90FKIykRq4VmsieaRBk/GdEDh01Q1RCmrcdBmsOwBW8wzC+e/9Sq8Nz5/V6kOVR2VXotG2qclKjxh1h24yAUYQS9dlVw28qPLOd3S1kND7xuyGEcrAtQ1RDiT//84lS3xZ31OB6W5JOFo80K5qY72F4X4E9rv+CeK3on3Wv21wbxOG1c/5cPLM3JByf0RxHwh79tp7I+xMzh3XDZFVZOK7Gysmu37adDTrrVdfHwP3cCUDrk3Lgq9q9GF6JpBkuzrks6ZruZPboPDcEwh7wRstPtPLfxa77bI592WS6QoOmSOn+Yb2oDFulRpTeIN8pUa2pwBsJaggB8ebWf9lku7hjZgxlPbybP47RmJHM9Th54fXtc8Nd4v23q+yrFiJtCCimkcGoRkWBr5HYonJgYfEuxgJ6Q/EMMOgBPRucAFWC1lPJVIcQ24JloZXEL8MS3fJ3Tgqb0rMJaIlGBKbhdXn1ErPvJd3dbrXFGG14hh6NzTU6bQocsF1cN6IQiBIe8Ae5/bRd3Xd6LkKZzsDYYN/+05N3dLJxUzHMffh33upXeIHuqfExdupGRhfncc0Vv7rmiNzsrvHHZYnO2Kz/DeVICt5Z0FM8kHMsJCzdBcmF+PpqUSZ83GQKnPfUh8ycOsIgnTBbBBZOKqA+E8bhscaQRhpakwoK3vmTMgE6WQPsvr+zNDy7sHNdCt6i0GAHMGdOX7DQ7GS57NPCTrHhvD6P7d+LaQedy43fOwxfSCEU0IlH2xBu+cx6eqFj29Iu7WS3KAP/3xhdWUuL6oV3jBOhN+YZ54/vHVfymDutKrT+MlJLbVn7E7NGFCeREld4g+2r8/Pix96zHtu2vTzm83wLHy5J8okhWHU/2WR1qCDLj6U3MHl3I/a9tT0q04rAJBDB7dCG922cQ0iS+YARvMMKN3znP0gg0K9tOu9EJcd2QLnQghRIeAAAgAElEQVTMdlNRH6QuEGb9V1WUV/vJSbPxzHSDpCuiSZZvMGz+D68ZweQvr+xN+ywXboeNnDSdUFjnuqFdiUQTFPuq/XHVdLMl3ySaMdv073ruY24f0T3pPqwKQfssF+XVfsqr/Va754CCbOb8sG8c0c3iycXouk5lfZDcdEeT31cpRtwUUkghhVMLm4BAo602oIGrFesAWhBCXAN8B6Mi+G8p5V+PdYyU8mNgQJLHvwIGnfRFtjCaqnLZVSXh8Tp/mIWlxTwSJYHxOG1cN7QrDpuRfY5oOqoqyPU4kVJiUxVUReCyKYR1SZt0J7+4sjcep4qmQ4bTYOhUFYEi4JdXFvLKR4ZkxJpPD8bNjbTPcvHirKHU+MKENZ15b+xIaC0y27hy3PaTEridakfxTMWxnLCmbMoX0nj8uotw2ZuurJrn/s3L2/jD2L4snToIRRDXLvzrH/ThyfUGe2Z+hpMst537X9vOG9sqqPGHWH7TYBQBQggCYY0lUwbiC2lkp9nRpc4f/vY5Y4sLUITAZVdoCGk4bQrXDe2KEETnVI02uVp/mFp/mL+885U1F2jOZZkVme7t0rnnit7cNqIH6Q41rkI9f2IR89/amaDplu5U8Yc1anxhGoIaCycV8cibibIBc8f1o3GrfMrh/fb4tizJTaE5LYqBsGYlRt7YVkG228HTNxqap4ow5vrSncbX5JxXtzH0vFwmD+1itVmOLMxn+U3GzHVEl0R0jec/LOPeUYU0BCPYVSNhpwhDLzCsS3ZXNvDwP3dyxQUd6NI2nasu7Iyqwg3fOY+2HgeqIgAjGTK8Vzs8Lhsuu0K1L4yqKBS0SeOZ6SXouoySvgh+/YM+aLrkkWsHsD+mRbVjtish6TF3XD8O1AVon+VK2AMqvUHaehy8MGso4YjBIv37GGbSx6+7iFyPI9WVkUIKrRQp1vSzHy4VAo1+PxG09AzgAgwWz5XRh34E7JJS3tJSa2iNvdNNOSwdspzsqwnEERXMnziA/Ewnh71hZsRUVRZPLqZtuoNAREdKcNgEmi6xqYKIBqoCRJ0URYjo4zoSYbUsffR1Fb06ZKMqgjSHipQQ1PSoiLbhbATCOnsO+Xjtk/1WpSd2Db3bZ8YIDJ/xG9FpX2xT9nqsGapkNrW4tJgO2S6y3UYQ3pSTXNUQss5tMgOarZ+HG0LU+sPkZTgIa1iVvVhClsZsnoGwTl6Gk3p/mPxMg23WoSpW/7rTpliafbHkGBFd0ilarTDtNqJpUdIYkNFWOEUR1PrCVPtCuOwqHbNd0WtkkHJs3lOVOJ81qYiQpvOTZz6KS3J0bpPGofogLrtqEcMsWreLe0f1ZtyiDUmvdStDq7VZaJk9oTnzhfuqffzosffiiI4mFHdm1qXnIwQWe233fA8zh3ejPhAhL8OBHrU9VQgcNsH+aCdFSNPxOG14XArVDZqVOHHYBK6o3YYjejRYlPx7x0Eu7tkOTUpcNpX6QBinXcWhCsBgoQ1pOuu2H+TK/p2IaDqhiEQIsEWDv8r6UNx3wfyJRdhUgduusmjdLnZWeLl9RHcK2rgpO+ynoI2bDKcxM7ur0hs3/zd3XD9sioLHZaNdppOrHk28jseaH24mWrW9mkjNAB4brW39pxCt1mZPM2t6Ci2AhoAR+tXEzABmuw0+gtZOAvM50Duq64cQQgE+k1L2bqk1tMYAEOIdI7tNwRuIsOqDvYy9qAC7qlBRZzCBelw2AmGdn0czuiY657hZOa0Ec9TykDdIfoaTg7U+2mWlGc6KIghrOgfqgpzTxh3NcgvSHArhiCQUJRUAWPHeHn48+Fx0aViPXVV4ZO1OdlZ4ueeKXtzx7Far+nJubhp2VaFDpqu1EmOcKE77jvltNvpjOdtNPd/UuXPTHVyzcL1ldxOKO3PLZecDENYkj729ixp/iHtHFSIEBkmLgLAOAkkgrLNl72GGnJ8HYLEPDijI5tdXFRII67T1OLCrikFUFNE53BBCCMhOcyAxZEf8oQiHvCE6ZjuprA+RF5WnMEkxTIIXU4KkU44bh03BF9TQpERgtFKbkhImzHsoGSviymklfFnhTWBSbYVfqqd9QafbOWkOcZRJlrTk3d2WfqQZDN5/zQXc88InCbImBW3c7KpssBhF//HT7wICmyKwqQp2VVB22JAdcdlV8jKcrHhvDx/sqeGOkT04JzcNgUFYpCrGcf6wjstuSDVENCOpYYvu14GwhhAKbrvRzRGJ2rnACC5Ln0hux3NePSLLs6i0mDbpdnQJ7TOMfVrXJXuqGthb5YsbA/jty9uo9AZZNb2kyevYIcudYgE9zTjVAVcqAGwSrdZmTzNregotgIO1fjxOkRAAeoOSdlnuZIecXhKYGHwJnAOYAiYF0cf+4xHbDnW4IcjeKh/XDu7C7kMNuOyKlaUdUJDNgzGC2ybMmUGAReu+ZP1XVTw3cwh2u50fPfaeFax1aZvOOW3S8LgEVd4IVVGJCZsi4vTTJpZ0Yf6bX7J6Uzmdc9wsv2mwNcPyxDtfsXJaCRIjA+52qGS7T04m/yyoGrYIjqc19lgtdk0939S5gbi23vVfVXHd0C68tf0gl1/QgZnDu2FTFZZvMPTLgKRssCML8/ntmD6WePWWshoWvPWlVUEUgDcYIcNpIzvNYRAZKUbVOqxIHKqd7DQHNlXgsttQFcGuijrOy89kYkxQZ9ruo9cOsILIQEjDoSqWtmEszHvooQn942as5o7rx+0rt1hamB2yneS4nSm7bCZaijykOcRR2W4H7TJdXDvoXDJdNpZOHYRdFZRX+5n3xhcJc64uu8Kdz34cJ7NQXh1g6tKNVqCl6Tp2VZCfmWbYrU3hBxd2Zs2nByl94gOrGt8+y0lDUKOiPmhJkIwtLmDzniquHdyFWn8Yh6oQ1qTB8iYMtly3Q2HZ+j1MGtIVaGKWV9O56/Le/M8P+oCUlFX7mf3XT6n0Bq3rrSiCLrnpOG2Kdcxvo+y3AJqkyet4qtp3U0ghhRNHaj737IfdJthXG2JfjNxVpxwXbT3N58Vo6QAwA9guhPgg+vtA4EMhxMsAUsqrWng9rQ66LtlfE7C0x0zR4EcnDuDWFVvYUlbD14d9Sb+Yd1Z4mfPqNuaO68etI84nokuyXDZWTTeY6QJhnWfe38Oo/p148t19XNGvIzZF0NbjRlHg3lGFRDSJ3SZYvmGP5UAvKi2mQ4aT1TOGENF0bKpCvsd50qt9qfaF5uFUOmEmQ6IZjFc1hMhNd9A9z8OKmwZTUW/o9flDGqs2lTMvynBoyiaY86PPbypjYWkxN8e0qN12WXcq6oLMf+tLi7EwN91hJRzevnM4ZYf9ZKcZ1e47nztCKNM+y4UQRvudObta6wujKCq1/jCLS4vj2uFMwhldSu5bsy2uypPsHnLaFLrle3hh1lACYZ1dFV4eeP0IydHMpzfx4qxhKXs8AbSUc9Ic4ihFEWSl2aDKCK68wQgds91J2W/zM52EIrpFEmS2DUd0yXMzh5Cf6aTGFyYQ1vGFNDwuG267iqZLctLsLL9psFXxk0j21wbjyJEWTCpizdZ9XNyzHQve+pJpF59HrT9s6Be++Kk103r/NRdQ0i0PgVEFTGbHX0cJu5LJOsReb0UROGyqNSMYew6XXUkRcKWQwhmEZLwRJp9ECmcHHCq47CoFbdLiRgwcJzAH2NItoJcc7Xkp5duneg2ttQXURFMl/PuvuYCGkEZuuoNO0S/42MH+2C/5ZCLsCyYVkZNmx5oriUhUBfbVBMhJs1FZH8KuKtT4w2zeU8W4i87hcEOIGn+Y5zeVcd/V/U55xrcVti+cdi//ROz1ZFRRmwrGG88FJdPJe3TiALyBiGVPa7cd5PbvdScc0dlTZcyPjr+ogPZZLsKazjc1RrXFdHAfHN+fth4naQ4FbzBiZbok0CnbRUSXHKwL0ibdzkd7qynqkotdFdZM1bDu+XiDEbLcdv614yAjCjtw7ePxc17JtA0bJxtOswbliaLV2mxLa/8d7z3w9eEGLn5gHWBUrNukOYjoeoLYejDappnutCEBuypwqAoH64LUB8LkpDnIdNuj5EXGue2qSJjXNoXkr308sXXzmekl/O4Vo3Uz2T5uMn6aOqyDumQnsOyabKCxVUpT/iXZ9T5a4g041R0ZrdZeY5FqAT02Ui2gLYembLbKG2BfTSDB9+uU7SLXc/z6yym0XtRFZwDrY1pAM6IzgJmtUQfQhBngCSEyY19bSnm4JdfRmtFUltyuKsx59RMWTCriuY1lDDg3hyenDsJpVyg77Iv7wh9bXGBtAObxs5Zv5ukbBxPRddzRWRMFgwjGH9atWRfTyTAp9E38+genvoUg1b7w7XGyqqhNteutml4S9xk11snLctuZ+/fPE9hhrzjYAZddIaTpTBhYEGX51KnyBuNsb+64ftE2u608OL6/RaefG50NNFhtJfkZTkIRjYu65rK3ymfN5i2YVGS9vilXokclL7LddmvtsdWd7vke0h0qQhHsr/Vbjm5TrYQiqhmYqgI2Dy2t/Xe8QaUqjkjrZEcDuAde38GyGwZRUR+kxh/m6Q17GTOgEz9d/VHcPrl5z2F+cGEnMt02pCRu7m7e+P6ENT1ujrC82s/NyzezclpJ0r1OSsldlxsSO3ZVIaJrcXIpTpuhw3rH6q1sKathS1kNVw3ozLIbj5Bh/2zV1ri923xfJlFX4+t9rHbyVKtnCimcGfCHddZs3ceSKQNRFYMI8LkPv2by0K6ne2kpnCQIINTIJQ5rnFAFsEUDQCHEdOB3GAymOsZ7kcB5LbmO1gy7LXkJv2O2mxXTSnhz236KurRJoKmPRVMi7BKD2vyp9bu5uGe7uIrhimklVNQFyM908ftXPzuqEPCpQkr0/dvjWHNWx1sZaSoYTzYXVOkN8mWFl6lLNzKgIJvfjekTpyNmVqfvuaKXpTdmtiXbFIVnppcQ0SVI2F/rt0goVEVYOmuxDId/+3gf4y46B29QI89ho0vbNO4d1ZuqhhBPb9jL2OICpl/czZKmuOtyQ+C7xh+OW7upbfjQhAvxO9U4tt3Hr7uI7nmehIDlj2P78ZuXP00JwZ8AWpOkSxzplqowd1w/7nzuY8KaTliTVHqDfBFtqTftZWeFlwfH96ddpos9hxp48O+G5t53e+YhpYwKqvfh3lGFlB32cf9rn3PvqN5NJLX0hPtoZGE+1b5wXPZ+4aQiPE4boYhO2WEfbdLt/CZmTq9zjhu3XeVnq7ayoLQIKUnQseyc4yY/08mSKQPJdNmSXu/UTN+Zh1SFLoXGUARc3LMdU5dujPvOUlNfU2cNQpEoq38MVGE83ly0dGPwnUBfKWUXKeV5UsquUspU8BcDmyKYP3EAS6YMZNX0EpZMGcijEwdw+8ot/GTlFi7u2c4K/sBwJu6MCv6C8WXfJt3QaYpF5xw3NkUhFNH40aBzeXL9bqvl7tbLurN8w26CEZ1H1+7k+qFdreOPlqXXdUllfZB91T4q64Po+rdrJzYrBMfz2ikkx9GqqGZ18OoF7zLsj29x9YJ32XGwPunnZiYiYtE5x43LpiR8RotKi3l4rTEDuKWsBm8wwpwxfVk1vYTZowstR7nGH7aOqfGHeeD1HYDkd698xr5qP6VPvM+1j79vVfIWvPUlT7zzFU/dMIg377iE2aML+dvH+7iyXyfm/v1zghGd+9Zso9Zv6FLOeXUbqzeVM+fVbQQjOnP//jlTh3UlomvMG9+f5zeV8cex/eLWPndcP9pnuazgz7xe0576kGp/mO55HlZNL+HNOy5hyZSBvLRlH29sq2DaUx9S1RBq8nM42ffG2QIz0OiUk2YRkbQ0Gt8Hv/rrJ7TNcEaJWVy0Sbczd1y/BHup9AbRdMmn5Yc5Ly+d31zVh6duGMSCt77k6gUb+PHj71PtC3Hfmm2UPvEBeRkO2me6kt5HigLzxvePs8V7ruid0Llx8/LN5HqM+cOcNDsS4mYQF5UWs2jdLkOaQpdous7ymwYzsjDf+pu54/pR6w8z9++foyipWaAUUjh7IRL8w7uf/zg6LZzC2QCXDeoCGv6wodPqD+vUBTRcJ1DOa+kZwNeBa6SUvhZ70UZo7TOAB2v97Gk037dgUhFZbhuKMFo3L5m7LuG4t+8cbhC4qAYr58G6eIKBxaXFtPE4CEV0wpqO265a2moOVdAQ0iwh75GF+fziyt7YFAWXPXmW/lQRtrQyFtDTvms2116PNmcFHPcMlkmP31gjrGf7DLLdR8hhhBDoUufHjx2ZZ0o2F2jOJVV6g3E/z584gLAm6ZBl9K6bciWKIghFdKobQrgdCpX1hsZfm3QH/9pxkEt7t8euCqQERQGBMQNoimMLYcy57q/1RwNNuH1Ed3p18KDrR/QwfaEwdlXlsnmJ48f/unM49cFIXGUwdta2qVnA00xmdMbZbEsj2T0y47tdmFjShUl/fp88j5O7Lu9J+0wXNlUghKDKa2hKPr+pjOuHdmXznsOMKeqMiMru6PKIfE5Rl1w657iREh5e+4VFPBS7F2e6bQZRTFS+RAhDFuLiJHv7Wz8fjqoYUiuV9UGklNhVhfZZTg7UBnFHNVsfXvuFxeKcl+HEbVdoCGmWJmcrrVqf9sWcqTOAzUVzK4CpCmOTaLU2W17t4ztJ5tbfuftSOrfeufUUmoGTqQPY0iygvwDWCyHeB6w+FSnl7S28jlYLTRLHyFZe7WfN1n2M7t+Zm5d/0CR7oV0RqFENN28wQrpTYc6YvrTNcJLtthOKaFaAqCoqDaEIVV6D+KVTjguXXeEXV/bmrst7ccgbQkrolO1u0lk4VZTuqVakb4ejzVntr/Uf94ylP6TxwOvGjFy2225V7B6dOIA26Uc+I7PCZbbQmXT5aQ6Vp24YRGW90crZIcvFg+P7Rx1qeOhHF1LjCyGB/15lzFU98uN+FJ2ba2xqQJpDwWVzUekNxc0JLiotJhCOMOnPm5gzpi/n5qZx3V8+SGinu/Wy7nFBaJt0O4fqQ3GC8AsmFVHtCya9p4IRPaEyGKsT11RrckvJHaRwYkhWJS/qkktlfZDyauMeufbx963nXrl1GAfqAnTP9zC2uMBKALy5o9IiEjJZakuHdDXmq4Xg2scN+R1FwLIbBqFLcNsVFEWwvzZAdpodf1ijIMvNgZiqXmM7lFIy8fEP4uy/U7aTb2qD/Peqj5g9utAKTGMDzQWTimgIGmRMv7mqL+0zXa0t+EshhRROImLnmU10znGjitR9f7ZAI7F1U2LM1DUXLR0ALgbeBD7hxNZ71kPKRF2ncRedY/V0L1q3K06byqyu+MIa9YEwihDcsmILeR4nd1/RC49T5UBtIE6U+K3tB+iQk062205VQ4jH/rWLad/tZtCYB8L4oxOmR6vGnSzCllZW8TvjkWzOKif6OUPTul6N4bCpVHqD1sye+bd2VeGbGkMvz64qeFwKDSEbijDIKkxdmnSnzaooQ7yIdtlhP93y0wlGdJ545ysrqSEl/Oix9+KqbRkuW0Jb3MynN3H/NReQ53HSJTeNukCE5TcNtqovuekOq1L41A2DUIRBuy8lcYGiSY40d1y/hHvqj2P7EQgnt3EzyG6qNTlFZtR6kGx/STZrbEqeJLs/vqkNMGPZpgRNyy1lNTy5fjcrp5UQihgMoYe8hrzJ3Vf0Js/j5Offj6+ELyot5qn1e1j/VZWVSFg9o4T9tQHOaeNOkDFZXFrM/a9tT7D/1TOGWMmJbLedscUFCa1fs5Zvttg/3737UpRogjC136aQwtkJRRCXjDX9w9QtfvYgFDa6nuIe00DXgWYSvbZ0AGiXUv6shV/zjEIy50RVRFL2wl7tM9B0yZqt39C3czYFbdKwKYIXZg0hGJYEIzpVXsPx75jtxhuM4LAJLuraNq49dOGkIgJhg47fzHx3znGz4qbBlrD2yMJ8fjWqEFUR2G0KShOZpuYQtqR0/049VAV2VnqZ9tSH5HmcCV8OTQUySSuJky+iLhBmypIjA+bLbxrMDUuNc88c3o00jM+/IRhh6rCuFhlMpTdIm3QHwYjOublpVHmDPPHOV0wd1pWCHDcIQSCsc/81F1iSEHc//zFLpw5MGkylO23cdXlPJkcDupGF+dw2okec3uCCSUUsWreLCQMLGLdoQwKDqXkuRQgeeP1zq9qZn+HkZ6u3MnN4tyYJmY5WTUmRGbUONLW/JCP3yc9w8ti/EpNrZssvwPObyhICtOuHduX2lYY+67/vGo4Erh10LjZFcPuI7glB2cynjar1mAGdePDvO8jzOKnyhvh5tOtjZGE+y24chJQgBDhtShyjrnmesKZb563xhy3irwEF2cwc3s2q2vfukMHIwnyEEEQiurUXpPbbFFI4+xCI6Ly4OZ4F9PF/fcUtl51/upeWwkmC3QY1fo1wxCB1DGuSiJRku5vvX7R0APhalAn0FeJbQP9jZSAaZ2Rz3PYE58TRSNzTZC9cMmUgc//+ObeP6GEFdE05wvWBMGFNEopIcj12nr5xMIe8hpj3I2/u5JZLu/O/L2+31mW+1qrpJThsggO1QSsYNDLTRcwb3z9uVnFxaTFhTWNftQ+HTQGElWFOlnluqlVu9YwhqXalE0Rjp3fJlIHMfulTq7Xtgdd3MGdMX7rle3A3Md8JySuJUkquWbg+ynZoBEthTVrnjq0W/vNnF/OvHZUsnToIh2q0J0sk5dUBwprOwnW7mHXp+QigLEpkZNrRvPH9uf+1z9lSVoOziWCqrcfJtY+/Zz0+trjAsnk4UgGZM6YvbdIdjCzMt8iRGp+rxh9mS1mNpZM2Z0xftpTVJK22PzShP7ZGchHm9TNtXNd1Fk8uTmAVTZEZtSyO1oqbrEr+0//qyUP/MJJr7TNdZKfZuW/NNoss65ZLu/PyR+WW5ElVQ8hqBzVahiUL3vqSscUF1PnDdGmbljTh0LVtOj9d9RHd8z3cNqJ7nB2/sa2CbfvrWXbDIPYc8tG1bXpSmw1r0np80bpdzJvQn5GF+UnbQH/+/Z688ek3fK9Ph+NqTY5Exe5Dmo6qCNwOhRz36SHsSSGFFI4fLpvC1UWd4lhA547rh8uWIn86WxAMSZyqwCYM3gOnYmh7B0MS3Mc+PhYtHQBeG/3/FzGP/cfKQDSVoe6Q5YzS5BstoTqSBZOKEsQ9H//XV4wtLrCCP2jaEX5megk7D3pZvbGMq4s6seTd3RZhwL2jClnx3p4E6YdgRGfq0o0sv2mwNTdlnnPG05t55NoLmT26kIIcN+lOG/et2WZpYC2YVMSarfv4YVEB3fM8STPPGS5bUgepsj5IrT+cykyfAEyn1wzSzs2Nd0K3lNUwdelG3r370mPOo8XOY+q6ZO/hBqutzXQyl0wZmNRBPeQNcUmvfKYsMSp0//zZJdbPZpWiTbqDXRUNVoAKxud/x7NbWTJlILX+MLqUPDShPz9dfSTRsLC0mLpAOO41YzX+TJRX+zkvL52Xt+zj1su6M/fvnycEdAtLi3lk7RfWuhdPLsZtN4JOs8XvqRsGUesPG/NUNoVrFq5PqKAAcffyyMJ8Vtw0GFURqVa704SjteI2njXWdYnHqTJ7dB8O1gU4UBdg+XtHJEXaZbp4ZO1OVm8qZ3eVj1sv6261g5pOlqrAjEu68ZNnjJnW13/yHZZMGUiaQ6XGH2bRul1UeoPWPGHpkHM5WBdIukahCEKazoK3vkxalXzs7V1WNX9LWQ2rPtjLvaMKmfTn9xP2/jlj+nJxz3YcqE3+WrGtyZGIzucH6xOE5dtlRuiSm56y4RRSaMXQJFYyFY6wxK+eMeQ0ryyFkwW3U3CgLkL5Yb81ctO5jZv2mfZmn6ulheAT1CiFEP+xafGmMtRzxvRl6tKNRovdtMHYhGDN1n2smFZCRNOtsv7qTeVMGFhwXI6wrktCms7M4d2o9Yf4nx/0QdMlYU3y8pZ9DO/VjjWfHkw6A2WSIzQ+Z4bLztULNvCPn16c1PFYMmUgU5duZPWMIU2KiicLHtIcKlOXbkyRZpwAQhEtLkhrijSoue2IVQ0h9hzyJbS1Pbx2Z0Jb6R/H9kNKGTe7p4gjVWWz2rZqeglpDjWpbdX6w4xbtIHOOW6WTh3Io9cOIBjR6ZjtZs6rnzG2uCDufTXW+DPfp6oIrujX0Qo+K+tDzB5dSG66g/xMFy6b4M7v9+Lm4edTUR+kbbqDvAwXL84ahj+ssavCa4luL55czD0vfBJXAT1QG6BdphNNJ87GzUpOyoZPH5rTilvVEOLLigZWfrCX64d2tYK79V9VMX9ikRX8gfHZ3nZZd+6/5gLaZ7lw2VXKDvvwBiL8/tXt3H/NBXRtm06NL2wlN8xAqm2Gk0P1Qavy19T9qQpBj3wPmS4bO/bX8sx0Y84wdu/fWeGNE6uvC0SS3ktpUZbQQFg75vWo8AbjEoqmAzlnTF8yXPaULaeQQitGbGu4CbNlPIWzA8EwtE23kxZl8rcpgjSHQjAMGc2cATwtdWFhYIQQ4gmg7HSsoTWgqQx1msP4Qs7zOPmm2s9vX/mMi3u24/evfsb+2oARVG0qN2ZXMp1xOlOmIxyLkYX5qIog221H0yUL1+3ithVbqPaFKDvsY9B5udgUhadvHMyLs4Yye3QhT67fzTe1Bt2sSY4Qi1hmqdgZxdj3YT7e1KYkBAm6bLGBZ4o0o/lw2NS4IM1sY4y9xifSjhiKaDy8difnJKkoPvD6DlZOK4nT/VNEvE1oukywIV9IM7JXSWwrrBltlPPG96fssJ+8DCd3PLuVYETjjW0VCe/r+U1lLCwtpnOOmwEF2SyZMpAnbxhERJOATAg+xy3aQHVDCG9Q478e+hdXL1jPnFe3oUXF6AE6Zrpon+WydNdy0x1WcD3n1W386LH3mP3Sp+yvCaSIX1ohmqMrGopopDlU3thWwYN/38GSKQN5buYQlkwZyPy3jgR/5nkOeUOUPvEBU5ZsBIys+ze1ASq9QUqf+I+iaj0AACAASURBVIDdhxqsWUE4Eki5bKolON/U/Tl3XD9uX7mFyX/5AIBhPfL53Sufcdm8t5m6dCMTS87h6RsHce+o3kR0yf2vfc6MZZs4VB9Mei/5QhoS6JTjZtkNg+I0Ahtfj6b26jSHmmDLKa3LFFJoXbApgpGF+SyeXMyq6SUsnlzMyMJ8bKnK/VkDmwp1wUY6gEGNE6EYaNEKoBCiBJgI/BBoA9wC/Lwl19Ca0FSG2hTMnjm8m1VZyXY7+NXoPmi6zoppJfiCYap9Yer94bgKjOkI39xoJjCWXdFse/vty9u4fUR38jOdKELEtXA2Jj9YVFqc0BZ0oC4QzSArSd+H6fTblOSEMSB4cv3uOKmBJ9fvtqo7KdKM5iM33UHXtulxAY9JGtS7fQYuh9rkDNvRYLKC7q/xW5+l2cqZm+5AAk+885VFWNG4Ivf4v75KaGPOSbdjV0RCBXHhpCJ8IS2uxW5RaTErpw1Gl1jtmeb7Mlk/M1w2HppwIS67Eif1sKjU+BKMJdPonOMm1+Ng2frd1u+LSov5zcufWveASRjy4qxh6LqRQX04WomMJauZEWVlTBG/tC4km2Ntyt4dNtVKRmwpq+Gu5z7m59/vydy/f871Qw0yozyPk9tHdOfcXENP65Vbh/FNbYAXN5WzeHIxf/rnF1a7pl1VkgZSNf4wT90wCHt0rruxHbfLdFmkMoBVfRtbXMAb2yrI8zjxh7Q4WZQ/ju3Hk+t30ybdnnSfzvU4eOD17ZZdz59YxE++14M8jxMEcXuBXU2+l/tCWpwtpwi8Ukih9SHdqSRwQCwsLSbdmZoBPFtgUww5IVVINF3iUAQOm+BExjxbRAheCPG/wHjga2Al8CLwYbKW0CaOLwCeAtphzAw+JqX8kxCiDbAK6ALsASZIKauPdq7WJFKc7EvUFMneUlbDqukl/Oix9xhQkB03d2V+6SvCcBBMBsZstx1fSKNvp0zCmiSs6dgUYQV/JjrnuC2NNl9IizriChX1QbLT7GS57RaFvznPt3lPFZf0aoeUsLfKR6ccF8GwziFviAvPyaS8OphAPLNmq9Fa2rtDBvtrg0nZ+BrPBprOTCsRLT7tnsyJ2GtFfYBrFqxP+MxfmDWUKm/oqE5bUzTxpq0+9I8dXD+0K0+u351AOGEmFt7YVpGUjOjpmwx2Q0UIbIpAUUCJXuKwJgnruuU4/zxGC9Nc/4Pj+yOlxOOyx513cWkxGW5bdI3eOKp+89inbhhkSUCYdta1bRo7Dnjp0jYNu6qwbP1uFv97T9xxL84aRm66gz1VDVR5g3GziP/vRxdy35rtbCmr4f1fXMZhX7g1OMRnpM2ebui6ZE9VAwfrAlYyYmRhPrNH96EuEMbjtFHnD8clFswkWaU3yAuzhkSrzSCl8SX14yT77pwxfXl47U5+/8O+6JDgqK3bfpAeHTKthNiidbu454pegCGR0liKwjyvMTMueXrDHr7bI5+CNmnoukRRjiT2Yv/+2Rkl1PgjCfZ6ftt0dlR4k8wAuuJmACvrg1y94N2EdZxAy/MZYa8pIfiTf/4zGK3WZr+p9jEhyb6zenoJHVNC8GcF6gMBdAnewBEheI9LQRGQ0UqF4G8CvgAWAq9IKYNCiOZEnhHgDinlZiFEBrBJCPEPYAqwVkp5vxDiHuAe4O6TvPZThsYZartNwRuIWC1nZkZ65vBuCXTidz//MctuGER5dSID45t3XIIQIDBYgpJlom2KoEO2C0UIQBqEM8DMpzdZme5fXFlosA2pggvPaUNY03HZjSzw3c99YmWpRxbm89sxfVg5zXBCVEUQjmiUdMujXaaLTJeDTJcjIRMP0C7zCOGNqghUAfdd3S9FmvEt0DbdmVQM3qaIo7IAHiur37NdBr+/+gI0XfLrH/SJSyyUV/u5+elNPDO9hOkXd6OqIcSm3Yd4ZnqJNWu64M1drP+qisWlxeRnOqgLaLjtCgfrgty2covldP/iyt5JbbZDlgtdl6iqYGV0HtZuU7ApwhBCFdKiw298bH0gwpIpAzncELIqzb/+QR+65KZxoDZgkSIREwCaLZxVDSEq6oNxQWl5tZ//XvUR919zAfe88AmKohx3tSmF1gdFEXTJTSc7zc6q6SVEdEl1QwinTSAlfFWZSFZ09/NGdS4/w2nYoA5hXRKK6NT6Q/zpxxdahDAWg6wqmDehP3urfHTIcrJ06iAUYbRI61Lnoq5tkmh4CQ77DDmfpma8Nd3Yd6d+5zyCkWji78/vM298/6QyErqkyb2gV7sMnp0xhLCmozTBAppqeU4hhdaHcBP+XjjVnn3WQCG5iPqJ1HhbKgDsAPwXBgvo/xNCvAW4hRA2KWXkWAdLKfcD+6M/1wshtgOdgDHA8OifPQms4wwKAIEENrq26dJyIt0OlccmF+MLJf+yFU20Vgqgyhti3KINTbI0qtEKTCDaR3zIG6JdppPlNw1G0yUSsKvQENIIaTptM5zctmIL91zRi6lLN8at5Y1tFcwZ0xeXXUHXdTRptFT17ZQV5wQ3Zt1LtRCdOpiBtSbBZVdom+5kf63/qE7b0Wjz8zIMBzA/OmVcXu1Lei4pJe2zXLT1OLmgYyaKIlCEEdzfOuJ8rinuTETX8QY0VCE45A3R1uNgxbTBaDrsOdRAlTe5IPf+2gA/fuy9pNXG31zVBzV6LyU7NjvNHicUf++oQp6KVvzMak52WjyLltnCGYpotPUkDyw7t0njqRsGWXYey5qaEtw+cxD7eQkhuG/NZ1TWh5g3oT+vfFTOtSVdkn7+5+Wl43aoBMJGNvalzfv4YVFnQhGj/d1MgNhVgSoEgYghrVDYIYNAROeSueus8z194yCrtdM8/53PfczKaSUADCjIRkJS+47okt+v2cbUYV3p2T6DTKedxZOLqagLJm/Pl8kdxVBEw2Zz0iH76HziseMLsa3gQgir8phCCim0LNQmfEI1dT+eNQjrYG8U7dmE8Xhz0SKNwVJKTUr5upTyeqAb8FfgXWCfEGJFc84lhOgCDADeB9pFg0OAAxgtosmOmS6E+FAI8WFlZeUJvouWgaIY2nkOm4o/pNEm3UHHbFfS4f4DtYEEAoGFk4pQVWGxPpksjY2JVm5dsYVfv/QZNkXBrir4Qxo/W7WVS+au47J5b3P9Xz7gs2/q+a+H/sWoh98BoNIbTEoy0znHjaIo5GU4yctwYVePbVZNBRtVDaFvdwHPAnwbezUD66sefZdhf3yLiY+/R5XXuKam0xaL2Dm1483qm2QPyc4lhKDssI/9tX4mPPYeJX94kwmL36PssA+7qvDjx95jzPz1XDrvbQ7WBbjq0Xe5feVHlB3284e/bSM7zUan6DxeY2IMl11hQEG2VW0cW1zAgIJsZl16PnurfIxbtIH5b37JgklFccfOn1jE+7sOMap/J+a8uo1xizYw6c/vM7p/J1ZOG0yex8ndz39MlttuDdA/N3MIK6eVoCjGezW/WBu/Xykl/rCGrkt0XVJRH2B/rY/t++u4eoHxGVy94F22H6gjEjl7mdjOpD22Mcx7xvy8JizewPVDu3LX5T25/7XtXNyzHREtkcSoc44bRQi+OFDP//vHTrbvr2fI+W0BuP+1z7l1xRb2Vvm4b802yg77GbtoA//9zEfsPOjFF9YswgYwgrtO2YYG5uLJxQwoyAaM+++QN8hPV3/E3PH9aOtxJOznC0uL8QYj3Duq0NrL64JheuZn0L8gi8WTE+8lQXJ7Pt6ZVZNgZ2RhvkWKNG7RBiYs3sCOg/WtnhDmTLbXFP4zcTw261BFwv4wd1w/HGoqADxboGngDemYO6zE+F07geaLFpkBbPLFjXbOq6WUTx3n33uAt4H7pJQvCCFqpJTZMc9XSylzjnaO1jCfcrTqQCSis6OiPk5EeunUgVQ3hOLmj8xZQYA7RvagY7abvVU+Hl67k0pvMG4+aUBBNo9MHEAoorO3ysdrn+znigs6WGQG/pBGXSCcQOUfK3L8ws1DqWoIWTNgsbNfTWmhHa2qt6/ax7A/vpVwbd69+1I6tZ5e9dO+azbXXo82m5Ob7jjq53OsuZ5Yu63xh6nxhRPmUrvlpfM/L33KrEvPp7ohbOnUFLRxU90Q5qerP7LOHzvPNKAgm7su74nHaePm5ZutNuRzctPYX+Pn5Y++4ZrizrTPdCGl5EBdAI/Txje1ARyqEteeN6Agm9tHdKegjZtdlQ0sWreLO0b2iKuumO9t2Y2DQEJdIExehpPqhjAzYtqgz8lNo84fIi/DSdlhf0J7HhhzuCtuGkxEl1z3lw+YPbow6ZzWipsG0zkn7VRXR844mz3ZaG71tSm7X3bDIH62eit3jOzBeXnp7D7ki7P3RaXFOO0Kc1//PKkAu8DooOic4+Z3r3zGdUO60CHbzdcx+7Q5K31xz3YJ95I5Xzh7dCEzlm1iyZSBzH7p04SZb5ddYcm7u7nnit7URu/LNul2ctId2BUFVcBn++vj9AjzMhz85Hs94r5nmtuBoevGfThh8YZvMwt4Rthragbw5J//DEartdlD9QGqfSHKqwNHNOJyXOSkOWjbXI2AFFolvIEAh30aoYhEEaBLcNgEbdJUPK10BtBYhRBOYCwGaUuzXlsIYQeeB5ZLKV+IPnxQCNFBSrlfCNEBqGj6DK0DTbU+ds/zcNgfwh/SqKgLkudxUl7tJ89jOJ7n5KaxZMpA3A4FKUUcU5yqCPZW+UhzqMwc3o212w5SH4jw4Pj+fFnp5flNZRyqD+IPa3Rvl05eRpe4If954/vTMdvFnDF9aZvhJNNliLqbwd+CSUUEIxrd8zzcd3U/dF1n9YwhaLphgO6obMWxWghj0RyNrhSOH01V8fxhjf21ftplOnnh5qFGK1rMZwdHsvqNbTM33RFnt/PG96fGH+b5TWUJDK6/Gl3I1GFdWfDWl4wtLiBDsXFubhpuh8Kdz34cJ2ody1i7payGQFjnzuc2Ww5umkPl6yofPdp5uHl4N/4QQ0w0d1w/HDaF/AwnoYgep81X4w/z8Nqd3DuqNzOWbWJAQTYFbdLi2tXMv/MGIty8fDMLJhWxt8pnkSo1Jl368/XF5HoczBnT19BVAzpluzjkNXQF64MRKuoMvcym5rQq6oO4HCoCcdzBSaqVtHk4kdbyZPdMnseJTVV4cEJ/vq7yEdZkAmPxw2u/4H+vvoDZo/tw7ePx87Czlm+2EgHPTB+cECCaAd6s5ZtZMa2EiY2ON+cLHTbFYmM2NTPNmW8zadI5J41fjio0OkJe+5xKb5D5EwdQ64/wyNovmD26T1zwZ35v/P6Hfa15R7uqkO9xNsu2FEUgj9JKmkIKKbQsAhGd/TU+erTLtAhCdlXU4Xa0qKufwimEALLTVOr9xtiBUxFkuJUTykq0tFW8BNQCm4Dg8R4khBDAE8B2KeX/xTz1MnA9cH/0/5dO3lJPDZIFSQ/9Y0dCNvaPY/vx0pZ9jBnQKSHr7LAJbh/RnTSHSljTaetxMHXphxaJxq2XdY+j219UWkxbj51gRCIQCUK/dzy7lVXTS8jPcOJx2Uh3qMwe3Yd7RxUihOClzeWs2lR+TLKQTJftuJ2BowUbKZw4mgqsd1V4mbp0IyML87l9RI+4BEBjopdkRCaV9UdYXM3g78bvnMcdzx6pSi8uLUYAD7y+wwqyqhpC3LdmO/83oT+V3qBFeW9WL9plOizyICBp8LVwUhGPvLmT64d2pbI+xJayGosev3OOm5Cq89sxfeJsfu64fnTMdjGyMJ9Zl54PGGRFjR3x+ROLyPM4mbV8MyunlVBe7Wf26MIE0qWbntzE3HH96NHOgz+sYVMM8pqqhhDPbyrjlku709Zj2G5Y05N+BlUNIdp6nJQ+8f5xBSepOdnmozlJKBON75kBBdn88speVlBn7qGzLj2fW1dsidujQ5qO3kQQ1DHLxezRhUQ0rCSFGcDd/fzHVmUv0oT23jlt0vj5s1utgE0RgiVTBh7Z9zMcVDeE49ZpVh7dDhvPbtzLjd85L+55M/DMy3BQUR9Kug+Y19GcQ4/oknBET5qASCXyUjCRqhiefqQ7FNp43HGyX4tKi0l3pGQgzhbU+PXoaMqRfbghqKHrgvRmFnlbOgDsLKW8/ASOGwZMBj4RQnwUfeyXGIHfaiHEjcBeYMLJWeapQ7Js89jiAiv4gyMZ4CVTBjJ16ca4ykUgrJGd5mLlB3vjqiFDz8tlRGE7uuWlG8LZMc7GzKc3sWJaCfet2cbNw8+Pc3RMR10C6S6V+9Zs47bLuifQnb+5o/KYZCHN0UJrjkZXCsePZIF1bLvw2OKChARAY6KXZEQmsaQRi9bt4tdXFSKAOWP6xkmHjC0uoNIbjGOl7Zzj5kBdwKr+zVi2ic45bv4y5SK+qvThtClkuOxkOONF7M313RytpsQ6zeXVftp6HKQ7VRw2hbAmWTipCG8wEiXdUIhokt+O6YM/pFPjCyet1NyyYrN1Tk0ac17JKnh5Hifts1zYbYJqn87MpzfG3R/z39rJXZf3ZkBBNh6XLUHz0JQ3uXbQuccdnJxIMPOfjhNhp2x8z9x9RS+r3d48fubTm3hwfH9mjy6kY5YLl91or/KFdCrrAwn73sjCfCTEaVnGttXneZz0yPfw3Mwh2FSFGd/tQlGX3LhzO+0Kg7oYe7Qxb4rVQm06dus+P9hk5TFW+sR83qwsnpublvCcuYdX+0L86Z9fJG1ZbZyASCXyUkih9cAf0nnlo3KWTBmIqgg0XfLch19z/dCu5KSf7tWlcDKQ7lQoayS7trC0mIKc5vsELR0ArhdCXCCl/KQ5B0kp36HpPtYR335ZLQe3Q7WyuGZLTlPU9aoiKK/2M6G4M5NKzuWWFfEOZWW9Qe5hUxSuGxrf1hnrbJRX+9F1ydjiArLT7HTOcTdZafnNVX34pibA7NGFVruQ6TQYhdimnSwpZbOcgcYMqCl8ezQOrAFuXXGkXbip9sRQRIsL+ExJEtNJjGWT3VJWgzcQsWbqFk8utmQcKutDcW2e5ub06kflfLdHPkunDsJpU7CpUH74iN6f6dD26pBhtZia9hfbVpntNpg6Z3y3C0IIJiw+kun8fz+6kM45buoCERyqQrUvbND6f7CXxf/ew3MzhyR9793zPTwzvQRVwLIbB6HpxAnHm612k59InO+LreaoilGZv3XFFoael8tTNwzicEOIqoYQT67fzU9G9OBXf/006bVPhhTVfvNxIhWpxvdMU9I57TKd/OWdr7h+aNe4BNm88f15dOKAuOrgL68stCq95vGmnSxat4tfXtmLyTG6lAtLi9m0+xDtM11W0s+sqmtS4rQpcYm9PI+Tyvog15Z04bs98pn3xhcAzBzeje75HmaPLsQfTm4/XfPSEdGfGz8Xiuj86Z9fNNmy2jgBkUrkpZBC64FNFYzu3yluD1k4qQhbigTmrIEA2mU6LIZpNSoEfya0gH4HmCKE2I3RAioAKaXs18LrOC3QdcnBuqBFWGFWZzrluJM6LU6bwsjCfGYO75Y0k7v8psHU+MIcbgjFkWuYz6+YVkKtL8QhbwibgjU3sqi0mMr6YNJKy8ppJYQ1nSy3nQcn9Gd/jZ95b3zBOblpCGGQtwiRnGr4i4Ne+hdkpZyB04zYwLqyPmjpSgIWi2vjz87tUBPaDeeO62c5nA+v3cn8iQM4HCV2yc90Ws/FBpVbymqsNs9e7TPYXxsg06Xygws7xyUolt80OGmVZc6YvkxdujGBCMNcd40/zMjCfCYN6cLEx+Md7P9e9RH/n70zj4+ivv//8zMze2VzhwSQhFOugFxRCGAVxC+VovK1gCjEKqhgPbCtovi1XqXtF8SjhyJorSiKcli/tliPXxXUihSNCGoEkctEhdzH3rsz8/tjdobd7AaRcgS6r8fDhyS7OzuTeX8+8z5frwemDkbV9IS26SlnFgHJ7fabRj/z//KJVSmt8QR5rKwEMCRO5o7rbZG/tBVA57ntKJLg9IJ08tMdrC6vYme1x6qw33PRAFx2Oe5emN/fVnCSaq/7/jjSipS5ZjRN55smf9K/uywJ7r5oQJzAe366A19IpUO6IzqjLROOHGwLbT1zelqWk3suKubGaMIEYjQ0ry3lslYV6jnRNXFxdE08NmMYmq4TCGtx7dcPTh2MwybFBaGPTh8Wl8gwr2NPjZee+e42r3FySVFS3VmzUt46AZFK5KWQQvtAWNWt5BQc9OtWzS49wWeWwtGCJ6ixq7qZXgWZgBFEbf+miV4FmWR9T/7E490YPAHoDYwHLgIujP7/pIWm6dS0BPm6wUdNS/CQ9NfJWrrmrd2GrulJqXttisSdE4up94aSOp1hVeOGlR9Z5ACtX69uDtAciPD85n00BSJsvnMcv798CBlOhZ757jaqjuB2KNy6ZivjHnyb+X/5hNsu6Is3GGH7ty2MXrSep9/bnUDTv2jyIP7w5k78IZX8DAddctKslsIUThxMh9i8Vy+WVybcu2VlJQTDWlLbvG5ML+tYgbDGXS9/yrTHN3HFk5u57YK+DC3KTpAG2VLZyIJ1FXzbFEDXdWRJSmg7rWkJJrW/tCgpjel0zh3Xm0WTB/FieSVLZgyjuHMGv73kDDQ9eQWjQ7o9wXm97tlyZEli7Ydf8VgriYjFUwaR7lTIT3dY12s65HdfNIBX5p4dt1bM+b5YFOa4yHXbmfv8Fi5/YpP1d9lS2cicFeXcsmYrNkUi2xV/L74rOGl971Ltdd+N2IrUe7eP5aXrRx/2zGQkovH5/maafOEEKZElM4aRZpPZ3xSwbGFoUTa3/rAvd738KeMeepuZyz+gyR/GaTfakscXF3DPxcXYo7I4dlnCZZfJTkve8RFqYxYwdk389LmPcDtsVvBn/v6WNVvJctlYPGUQz149nAenDqbeG+Leiwck3ad/80pFwj6wZMYwZIk2O1KyXbZUAiKFFNox2upeiLRzWZYUDh92RSLb7WTa45s4d/EGpj2+iWy3E7vy/cO541oB1HV9H4AQogBod5y035dx77tIGlofL9hGS1dY07n/tYPkGHZFkOWy4w+phqi7N7kwtiyMFtG2qjqBsMr8v3xiZW5fmF1KSyBCVYOfrOjDvPVnvEHVml0yz88UI372/X0MLcrmnL4d8YdUixGx0R+2KjUp56B9IVmLVrZTYc2ckQQiGntrvfzy/z7lzon923T6wGgta+10mkQsptZkrETC0ighjF0RBMKJjm1bNt3oD8d9f898N5KAey4agEORCERUDjQHqW5JLnBtronW11HvDTGsex5/fGsnq2aXEtZ0VE1n2YZdbNxdZ7Xm9SlI56XrR5HuUJCE0TIrBLwwu5S/lFfRIcOeMN+3ZMawOHZF8+9iVjIfvnQwnkCEDm7H92qXS7XXHRmOpCJlVv7mrChn4Y/P4Jn398Yxfj7y1k7uvmhAnN1eN6ZXQrJhzgqjYue0Sdx78QD21fkSOj46ZTmT2q6q6Ye1JiSRPPnR6AujSCJuHZqalvvqfNY+bdrpzef3SbjG2ef0ItdtT3oevpCaSkCkkEI7htKGELySemacMtA0HYciWD5zuCUDoWrqEWmvHm8ZiIuBB4HTMCQbugGfAwOO53kkQ+tgbnxxAb+cWBztr03ueB2KpKG15tqcH3RnxsgebS7OxVMH0xIIY1ckdJ242asXyysT5qoenT6M/c0G+cDSDbsSXjc1ymLb1vY3BQhGNF4sr+Sm83rz8KWD47QFF00ehCcYSepcaLrOuOKOjCs29KrMGcLYNqSUc9A+0ZrYZceBFvY3BeK089oKyHwho92rrapAUa6L+RP60SXHycIfn4HboZDlMubuppd255tGP4GwZs2dmu1wkhAsu6Ikjvk2lqzG/P7dNd64QCoY0Zj/l0/IT3cktXlzTbS+jjpviGyXjTcqqrn9gv6Me+htCnNc/OGyoVw+ooiOmU5+/d8D4+aylpaV4A+p5Lht9MhL42f/1ZtAWKPFH2Lhj8/AaZPJz3Dw6Ftfsrq8CjhIrNSjg5v1t5xLrSdEhkumuiWIIgtcNuV7BXGp9rrjgzpviOpoVdomS7xRUR3XOglw+4T+cXtxW+3A2Wk2QEfTsIIx87V5a7fxwrUjku7naz/8KqlNt14TbQWKuW47M/4U3xY9Z0U5q2aX4gupcUmKwhwXTpts/e7SkkLm/bAfNkXCJglemF3K/qaAxXJ787g+dM52ku1KJSBOVpwKWoYpHBoORUqaoHQcQXUohfYJSRIIIais91laj11ynEe0Lx/vGcAFQCnwD13XhwohxgJlx/kckiI2mBtalM2Vo3ow/U+Hpms/FElD6+Bweml3GrzBhKBr8ZRBeEMRHnh9B29UVPPC7FKLGAPgD2/u5L5JA3jkrZ3cdWExeW47uW47SzfsYme1x3IYHnh9BwsmDbSEs+9/bQfzJ/SzMsimE7xgXQUrZg1nb52P4s4ZPH9tKfXeEBlOhaUbdjGuuGNS5wKgZ4c06r1hqhoMdtFYSv/CHBeds1wp56Cdw7TLP14+NO4eJ0siLLuihA5uO2uvG9lmxXhXjZcF6yqsKjMcDILkaDUiP93BI9OH4g+pcdWJp646kxdmlxKKaHzT6I+bkTPnne5++TPAWFc/X72VldeOSGp/nbKchFWN+1/bntS5fnT9TiaXFFGY48I00aoGP3Nf2MKyshI0Hea0alM1mR8bvCEUWbCn5uCGm+O2cd9fK6jxBFkwaSCry6uslsDY735k+lBa/JE4spuUlMOxx/ft5jD37Nj9Mll1+Y4J/Xlh8z7uurC4zUpetstGrSdEpA15iGBEj7PdvHQHaz7Yxzl9O1pag3luOwWZDoIRLW5NLJo8iCfe2Z1QcV88ZRA2OXn1OxAxWrcfvnSwpfmX67azavM+5k/ox75aD/1Py2bm8g/IT3dw2wV9E6qIfQsyUFJOZAoptGtIEmSl2eKqQzZFIKWW7ikFXdcpyk2LqwAeCY63WYR1Xa8DJCGEi3+jeAAAIABJREFUpOv6euDM43wOSREbzCVr7bn2mQ+p84biPmOSNMTCnJFoHRxKQnDDyi1ENJ0FkwwB3rsuLOb+13Ywa/mHTC4pAqBDenylZUtlIwKDvr93QTqBsIqqaWzcXceWykae3riH564Zwb0XDyCkaty6eitlT26mxhPEF1Kt+akHpw5m6YZdVDUYgtR3vfwp1S1BFqz7DE8wwsJXP2dccUferDiQdL7vN69U4LQpcfNesTNOdkVOObQnAUy7THcoCXN7T2/cw6rZpbx3+1hWXjOC3//jC/bW+Ziy9H1uW2uIuLe2ixfLK3l0+jBeLK+MO9aCdRWo0XkEkzW0dTVk5vIP0XXYV+dj/l8+4b6/GoHkqtmlLJg0EE8wYlUsAEb1zEOKEhCZ32Pan6bpuB0y8yf0JzvNxlNXncXLN4zmmVnD+fu2r7lyVA9eLK+0qoQmqhr8pDsVQpHk81cd0u38fPVWNA1r/vGulz/FH1K57YK+VDX46ZqXRmGOK+m+0eANJ5DdJNtLUjh6MKvclyx5j9GL1nPJkvfYcaDlkC0ydkW2qnvm/1vPiv581cdc8efNTBzcheLOGTgVKWF2e8mMYfzvq5/TId3O/qZA0ueDIgtLKuWWNVvxBsNcNKSQpzfuYXJJkZXke+79vdS2BFn44zN485ZzWTBpIA+8voONu+tw2WUW/viMuOeIqpH8eSRL5Kc7+PnqrQTCGlOWvs9P/ryZc/p25K3P9zPq9Hx++ly51dbaep3OWVFOQ0wbagoppNA+EdFA0zQcioQsCRyKhKZpRLQTfWYpHC2k2Qzm/8p6g3ekst6HIkmk2b7/sY53BbBRCJEOvAs8J4SoBrzH+RySIpZx71BU+bE4FONc65Y60xmWhGDm8g8Svt+ctZKTMGzWekJWlWXBugoWTxkUp7+2ctNezuvfKU5zamlZCVkuhUZfmDt+1J/9TQG2VDZaGe6qhkR9tTy3nXHFHRGCuNkQc27kzonFDCrMSmjdM6/5+2bdUzj+MO08EFYTKmVXjuqBJAkUSeKSJe9R1eC3qmax7J6dMp10SDdYL++9eCBPv2dQ41d82xJXdWuKqaTYZCnpmgqrGrlum1XRMDUCl8wYxiNv7Yx7/+xze6HpOo9OHxYnifLo9GG8EG05rfOE6JBuR5YENtmQm5h6Vjf8oQiXD+9GfoaDeWu2Wcc0KoICSU4+O2HOFMYSMZmtfCtmDacwx8W3jYZ4fO+C9IRrbIugKSXlcOzwffQTzT1L0zRuPr8Pv//HF5ZcjsmIvK/Ox/2vHZydu/65j1j44zPISrPHzW43+sMIDObY2y7ox6JXt/Pg1MFxbfLLykp49K0vWfjjM+iU5cShyAih81L518yf0N+SDXn8nV3MHN3D+t6hRdksnjqYOyf2N8heXt+ewO6p6XrSyuD+pgDXjenFnBXlCSRLT111Vhz5zOE++1JIIYX2BwlQNdhb67G6VYpyXce90pPCsUWnTBtOm0xE01EkQbbryO7w8Q4AJwF+4GfADCAL+NVxPoekiA3m2moBak1wciiShjy3nWVlJVZbWaPv0O1F5qD//uZAwkO8IMPOYzOG8ce3dvLg1ME8GdWiuun5LeSnO5g7rjddsp08f20pmq4TiZJbrC6vojDHFdemaVLrQ/x8YJ7bTqcsJ/kZDrbvb4nTOjPPUZEEHTOcdMxwJlwzcEhCnBTaB0w7398U4PloK5vpvD69cQ/3XDSAkHaweh3bGmpW9pZdUULHDCeKIqFpOv89rIiH/9+OpC3Kv5s2hJ+t+rhNu/+m0ajAde/gjmrw6exvCvDs+/uYOTo+qFRkgarq/H3b13Eae4+u38kNY3vz6FtfMmloF67482ZG9cyjbGQ3Hnlrp1VV6ZnvRtO1hDZTgVEZSuo8R2cKW1fsjLlYrDbVLZWNcVqJJnwh9bD2khSOHg5XP7GtuW8hBJqus2DdZ1x9ds+EhF1Vgx+nTabJF7IqeSZMG9jfFKDGE2Thq9utNeYLqeSl29m4u86aGV01u5RGf5hV5VW8taPGmpGddXZPctJslq3WeIJ4gmFkSbD49e0JCZffTRvCyk17mXpWV4ucyxdScdll7vtrRdw4QOx1mGLRpo0e7rMvhRRSaH9w2sAWluLaA22KwHkE1aEU2ifCGrTuxtfhiKq8QtePLz2sEKIb0FvX9X8IIdIAWdf1luP1/Weeeab+4YcfJn0tNhtc6w0lVLm+bzDT6AtS7zV0+sKqhtMm89iGLxNEds1qxxsV1YwvLmDeBf2oqjfovx2KhNupsPaDr5hyZlecNglZCLB6f3VskkDVdfbV+cjPcHD10weDsEWTB/H0xj3cfdEAdh7w8Ic3d8YRAZhVxeeuGYE/FEGWJMvBaK2l1iXHQU5acvLWmpagVTUyUZjjSpp1P4lwwiPXQ9nrkULTdGo9QQ40B+IErRdPGUTfThmoGnH3cmhRNnPH9aYo10VlvZ/BRVnkuh1xx6vzhvCHI+yq9sbZ2PjiAm67oD9ORVDnDcdV7ky7r2kJ8ev/HogG/DRGK3D5zLOo9YQQGPqFpxekU+8JEdE0nnrvYLtcfoaDlZv2suzdvYwvLmD+hP60BCJ0znIQVo2EiCQEQuh82xikQ7odTQdZEgQjEZr9KhFNI80uWzqH5pzfkvVfctN5vfljdH2aKMxxseLq4dS2hJi67P1oJXJogj7bn686k2Z/hJ+t+vh4JUZOSZv9Pjjcvait95nMtnPH9aZ3x/Q43T/zPS/MLiUQVvGH1Lg19OSVZ9LsD/PEu7u5+uyecbawtKyEgkw71c0hSxblqavO4vnN+xL228fKSlj3cRU/6FNAYU4asiTwhSI4bTKyJJCj+38kKkP0+Du7uHJUD97ZcYCpZ3WjLqqduXTDLmtO1WmT4iqZ5nWs2LiHiYO7cP1zHyWdAUzZa4pA5Vhg78KJJ/oUjhTt1mY9gQAa0OLXrOpQhktCAtKd7Y54P4UjQL3XIHOMqKDphm+jyAYBUK476T1u016PawAohLgWmA3k6rreSwjRG1iq6/q443UOh+ucHI12Rk3T2VvnZV+dQR7hssvkuu2g6yAEEU0nourouoYQErJkOKVOm4Q/pNHoC5OdZkPTNVRNWOQVWS6FloDhtNZGW95CqsbsZ8oZ1TOP68b0sqojL5ZXctO4PnTNcVDVELQqkrHB4dxxfch0KZxz/wbGFxdYDm+sg21XBB0z2iZ5+brBx+hF6xN+/97tY+mS8z3VKdsP2u1G/+8iEtHYW++lMppoMFtFuue6kSSRUM01K8dbKhvj7mnsOpGEoLolwA0xYtRLy0q46/8+5ZbxfXjm/b1MLimiIMNBpstGml1C00DVdXQd1n++n9G9C6LMuxLpDomqhqDlLM/5QXemnNUVXzASF6j1KnAjC0FI1QHjofdlNBDNz7Bz36QBGMUfHSEEoYiGwGitTnfKKLLE4te2c/3Y02nwGmsuw2nDaZOIqDoRTSMY0eISQg9fOphst51Mh0IwomGLpuQiqgYIIpqGLAl+va6CmpYQc8f1pnsHN26HTAf3MdXHPGVt9nDxXfI8Jtras1bNLmXa45sAmPOD7lw0pNCyQXMt2GWJJ97dxZ0Ti9ld47XkcN6sOMCM0q7Ue8N0SLeT7lQAwd5aL69+8i1TzyyiY5bDsHtNx2mTaPSHrdZTs4IuhM6B5hAdM520BIzKX6bThqYbCT+7IiEk8ARUalqC1l4/d1wfJAlmP3PwfB+bUUKHDDsNvlDc75eWldAl28H+phD/t6WSKWd2RZYELrtMvSdEmkPBbZfpkJ6y11QAePSRCgCPHG3ZrDcQQCUxAJQBdyoAPCXQ4A3gj2ioMQGgLINLkchp5wHgx8Bw4F+6rg+N/u4TXdfPOF7ncLydk1gH2RYVBPaHVTRdp9kfxh/W6JjpQICl1RKIaDREHQhFFthlydItUySBLBl93uYCd9klQhGj0qFperQ3WCOsGq+nOyWa/RqSZMSe5nE0XScQ1lj74VdcPKQQuyIAwdoPv+IHfQrolOVEFoJaT4heBe64qk9rpCqAxwbHyl5rWoLc+dI2Y94p2gL6Ynklv7lkEPkZDjRNZ39zgG8a/dR5QxZdfOw9TeZoPzh1MJqu0zHTyc5qDwUZdm56/mNLlsRkBW1dZXhk+lA8gQidspzUekLouk5Omo3sNDuBsGZtdBlOmYiqE47auk2Woq0uRhAZ0XTs0dbUoKoTUQ37nnpWV9LsCmC8T9V1ZGEkVWyyRDCioaMDwlofiiRoCUao9YQ4LduJIgT+iIYkjLWqA79eV8EbFdUU5rh4ZtZw0p0KgbDKrmrD4R9X3NFq/xtUlEle8g36aOKUtdnvg8NJ4LW1Z5mMtiara698N56gSqMvZK2FX00aQHMgwtMb93DD2N5xle1HopXgzllOdtcYdnDpWUXkpTviAsHO2U40TSfNLhNSjf1algS26N6sI3DYBMGwYceyJBDC2PtlCQJhjUynkYAwnwUORaBqxD0vdHTsioyqagQiGqqmU+sJ4bJJZDhtdM1Jo94fIhg2bFsIAEG+205jIHKsZ7pPCntNBYBHH6kA8MhxqAAQoDEmADTnw1IB4KmBrxt8fLSvjqHd8lA145mxZV8dw7rltVVsadNej/cMYFDX9ZAwnjAIIQyP7BSGqeMViWhsP9DCdc+WW3N7PfPd+JsCNPnD3BhTNVk1ZwROm5NQRCMQ1nj2/b38aNBptAQiZDhtFslFJMpqF1GNual0p4IsCUCnwRvGE4ygA11zXdR6DPHspRt2cd2YXgkzfq98eoBnZg1n6YZdXDKsS5xz/lhZCRn2Q5vKoQhxUmh/CEXUpFpn91xkzElJkqBTppMmfzihfdG8p8nINm5Zs5XfTRuCIkssWFdBfrqDB6cORpYEv3nlc+66sJg+BemW3p75uRtXbmHFrOHsbw6gSIKfrzHs75+3j2FPrZfsNBt56XZ8IUMIvs4b4qO9dVw2vBuN/jDVLUEGF2YxPdqud2lJIdee0xNFFlwxqgcrNu5h895G5o7rbUmlPPjGF2ypbOSVuWeTGa3UhFUNHayWbZss0SXbRYMvhKppZDrtKLKEIguW/3O39feravDzkz9v5qXrR5PuUHDaJGvWy2yvjUSMdr0UOdKxx+HoJybbs2J190xW11WzS5EETFn6vvVZp022Wj+zXfa4mdQl679k5ugeaJpGhwyHZQf/+MU5dM1L4/rzTkcWgjpPkHuj83lpdplvmgIUZDjokG7n68YAxk4O3fJcRDSdA81BFBly0hxoutHys6fWx5oPK5lwRme6d3BT2eBn0avbqfEE46r2hTku1lw3kr21RjdKkz/MoleN9tCXrh9NQUa8c3i4VdQUUkih/SDZGFiKAPTUQpZLont+pjWaYProWUdABHO8A8C3hRD/A7iEEP8FXA/87TifwwlBtedgK5tBgf8Ba68byWWPb2JoUXYcGUe9xxjUT3co1HtDTBvejTpPCJsssb85wL1//Yzh3bOZMbJHNPsrrDmnLw54+GhvHVPP6obDJrGrxoumO7np+S3fyfRW7w0x4YzOuOwyD0wdTOcsJ9v3t/DHN7/g3osHclq2K+G6TByKECeF9odY1lsTrckevuuetkW2kZdu59frPrPYOhe+up2Hpg1mePdsenZwI6TkemXVLUHmrd3Gn686k0cuH0qGy0ZEwyLhGFqUzW0X9KVTpkFWNOC0TG5aucVycJ+7ZoR13NXlVRbRxru3jaG0Vz5Tz+pKZb2fW1dvjZuDMsmQFr66nT9OHxongH37hP7cuvpjbhnfh05ZTiQBVQ0+uuWlsezdvQnXYBKNtGaHNHU5b1mzlWVlJSlR7XaA1vZtUyQ8gQj5GXaWXVFisbpqOlS3BOPWiycYibO1ndUerhvTi94F6UwuKeL+13bwu8uG8Mc3DRKYXvluQHBlTOIDDhKA+UKqlZQzdTTz3HayXDZqW0L88a2d3HXhAL5p9LPjQAtvVhzg8hFFdMpycsN5p0fJXDQ6ZTp58NLB7Kz2WMEfmLapJWWgTsbw+X2YVFNIIYX2gSa/Ros/SKbr4BqtqveT4XKQkSoAnhKIaNAtz8Gq2aVxbb5HQgJzvNlh5wM1wCfAHODvwC+P8zmcEITVRJ0xUyrC1DOb9vgmFqyroNYT4pumAF9Ue7hlzVZuWb2VkGqQS8xZUU6NJ8iYfh25+fktnPfg25y7eAM/W/UxO6s9LFhXwfnFnfCHIsxbs40F6yqorPfHafuZzISxMJkOe+a7eXXbN8gCvmk0NKDeqKiOzjYZiESFu/fVefmm0U8kanlm1r1LThr5Gcd0biSFfxNm9SNWwyxZxfZQ97QtHcy9tT7eqKhGisqJ3H1RMVkuhQuHFDJz+Qfs2N+S9HOmPMms5R9yoCXIlX/ejBZlKARD9+/yJ/7FFX/ejBCCFRv3WMHf0rISZEkkPa6qGXIM97+2HbsiJQhrv1heSaM/TI0niMBYl9kuG5NLivi20U+NJ0jZk5s5/6F3GPvg28xbuw1I/l12RcauyBY75LTHN1lr1ry+Oc+Ws7Wy6Tu16VI49oi174IMJ11z0rj5/D4sWFfBzmoPhTku6r1BS6rEvOeNvnDc/TcZcndWe6z7rek6b1RUM2dFOXWeEPPWbGXJjGFxa860v8Icp7VHm8cKRjQWv76dWk+IK0f1YF+d13oGrC6v4pIl73P5E5tQNZ1f/e0ztu/3sONAC/vqfCxYVxGnoVmY4yKs6m3abGscLpNqCimk0H5glwSNfpVpj2/i3MUbmPb4Jhr9KvaUL3bKQJEgENat1kkd4+fWzKCHdayjeWLfBV3XNeCJ6H//UbDJUkLF5cXySpbMGMb1rZgYO2Q4WPzadmpaQhY1/QOv72DBpIF075BGOKIhJBHnyC4rKyHHbWPV7FL8YZV5a7ZZbUBPvbeHOycWW/Tgmq4n6FOZhDC3XdCfiYO7YFcEd7z4qXV8JTq/GNvKGksm0K9jBsqRWGAKJwRHo2KbrIXOJH0B+KYpYFU1/nn7WIvhM1ZaojXJDMTLk3iDER6bMSyOaXFpWQlbv6pj2vBuTC/tzu4aL6qmIUvwWFlJHJPokhnDWLL+Sxr9Ie6cWIyq6Tx/bSn13hD7mwM8vXEPV47qwdMb9/BYWQmeYDiuPXpoUXaCPMTSshLyv6PlufVrra8vzS6nKionCIeaD2zwhy2yH9NOH13/Jb8Y34cuOS6ev7YUVddxKhK/v2wIN7/wcdzeff9rO+L092LlFcwEwzOzhgNglyUkCWad3ZMn3tnDTeNO57lrRlikLiZBV166jXte/izueRBri6s277M0AwHuubg44X0PTh3M42/vSvp5WTL+JsmSO0ciB5HSg00hhRMDt0PQIcMRJwXTIcOB25Faf6cKQio4bYJwxAgBBcbPoSPIzR0XEhghxLZDva7r+qBjfhJRnCiCgmSB0+Ipg3jpo6+5eMhpBuGKJCyiC1/IGNZ3KBKqZhC8yJJA13UEAnuUHEDXow9uXScQ0clwSgTCehwz3MzRPXjpo6/jZvvGFxdw58RiGn1h9jcHeLG8kitH9SDTqfDT5z7igamDuezxTQkB3jeNfi5d9n6CY7B6zshDtoiepDjhu2Z7INQ4FFo7e5qmMXnp+1Yr260/7MvtL27j2atHMOaBDdbnzDa3fp0y2F3jbVOeZMWs4ciyIKLqFgGGP2QQs+S6bVHyI437X9vBeX3zmV7alUD44AC8jk4oYhDDCGH8u7YlRDCikp/hIN2hIKKU+v/47Fte3ro/gaDmsRmGqL1NlijIcPC/r37OvRcPRBag6qDreoKje1AaQ2VXdXL5lTkryo8FS27KZg+B75ptO9Dk5+OqJqt1982KA4wr7khx5wwCYc3S8mvyhclwKagxZCs1LSE0Xacgw8EvVm8FsOzfJD4KhLU42QdzHrx7B3d0xlQhpBrH1HSdx9bvIidN4YpRPaznQSii0WAyRGsGg3STP2Sx744vLuDuC4vREQQjKvubAta869CibB66dDDVLUF8IRWnTWLe2m0su6KEvgUHk3hHOgN4BJ87Kew1RQJz9JEigTlyHIoERsdoBTWfgVkuCUGKBOZUQW1LgKCqomnCIseTJB2HLNMheZ/vCSeB0TAqlSsxZv78h357PIQQfwYuBKp1XR8Y/V0usAroDuwFLtV1veHonfLRhaJI9OuYweo5I4moGjZZwhuKxBFFxGr2/epvnzF/Qn/8YYP0IjtN4dvGIGl2GUkICnONYEuRRZTV0CAFqG0xHJOIppPtsvGTkd0BuPSsIrrkuHhg6mAEkJduaKcN655ntbs9vdHQVqtq8NM5y8nb88YYTm+6w3IMkrWyVjX441pEUzg1EauT2Vbg0+gLWlW4LZWNPL1xD8/MGo4ii7iKgtnm9vClQ5K2ZT69cQ+LpwziF6u3Mndcb57fvM+SaUizG1UIt0NB08NkuuzUeII8+I+deIJhykb2sNgQVc1YFzZZ4AlqhCIqHdLtqLpBnBSKaFz0yHuWXuGYfp1w2SRWzS4lENb4tslvCb2bgdsbFdXMPsfPlKXvW9X3ztnxlRGztVDTdLzBSML1PfD6jpTA9gnAoWbb8tx2ar0hqwIca4v9ogLxmS4FTyCMXRE0eEP4Qirz1m7jwamDLemIZVeUUOMJUtXg54HXjVnQPLedLjku9jf5mTuuD3948wvr97luO4oEYx94h1vO783Us4pQoiRfN553OpJksNV6QhEkYSOs6WS5bFQ3G22mmm7ISay4ejiSEHzbFOCm5z9m7rje3PXyp3H7dY0nyBfRUQGzWl/VYLT6r7xmBIU5acb3HWGHQGp2MIUUThyCKjhaPVJE9PfuE3JGKRxtuGzgDUM4ykge1nVsiiD7COL74xIA6ro+RAjRD7gcIwisiP7/DV3XI4dxiOXAI8AzMb+bD7yp6/pCIcT86M+3H9UTP8pQFCmuSlbnCSS0/Nx0Xm9cdokbxvZm4auf84vxfSjIcOAPG3N7vpBKdpqN5f/cbZFQmELdXXPTSHcqBnucrnNrTBXj4UsH8/AbXzDhjM50y0vDZZf40aAucfTlsY7p7hovA7tkJTy0k7WyxraIpnBqwszsP/z/diSIVsdm+DOddjqkq7wQbZVTJAECGr3hhFbOx8pKQNd54t1d3HVhMZ0ynZYG2qyze9I118UDlw6m3hPi6rN7smT9l0wuKSJDUuiZ77bE3+f8oHvcOlqw7jOu/UFP7IqE26FQ7w0TjOjcGm15NmEGdIU5LuaO64PTJrhp5cfcObE/v3nlc279YV/m/+WTuErg3S9/Zs3LAtZM34JJA+mU5UyodJiO9F+uH4UvqLKn1ssDr++gxhNMseSeABxqtq3OG7LaP83f3/7iNlZcPZwGX4gbV26JY3B2O2zYZMELs0sRHCQUim1xNhMdy8pKqKr38/PVhiTKrLN7RuV/BN5gmOqgsb8/+I+dVDb4uW5ML/xhlXSHYlW/0502GrwhftqGYPvvLxtCl2yDuXf+hH5oup4wYvDo9GEosuCpq85C1bQ4kpjqliAuu2Lt+YfDpPp9/r4ppJDCsYUkoCmoYY6Wa7rxc5ot5Z+dKvCHITdNpjla5XVIgkyXhD8M31dl6rjNAOq6vh24B7hHCDENI5hbBCw+jM++I4To3urXk4Ax0X8/DWygnQeAsdA0HX9YY+WmvUw5syu5bjvzftgvqlnWjUfXG0Ls6AaBhT3aCmq0ssHFQwp55dMDVDUYJBX5GQ7cDhmiWmY9Orh5YXYpqqbjCUTITrMxZ0wvJAwBbLNt7rlrRqBqOvvqfJZjunjKIDpmOpM6pwXpDpaWlSTMABakp7K7pzLMzP5dFxZbwR8czPD/5fpRFGQ4kSRBxwwXjf4Q4YhGjSfE3z6uomxkDwLhCKtmlxKMapE98c5udlZ7uH/KIJr8YYvhFmDuuN6oGix69XMmlxTRv3MGt13Qn5aAIfmw+oNKrhzVneml3QGwK4JOWU4KMhzcc9EAQzMNw4m1yRJORUpqt7luG49cPhR/SCXLZeOhaUOMdj5P0KremDp+nmglL3aez/wbFOW6uP+17ZaOYiwkSVCQ4URz67gdCo9MH5qajToB0KLtmm3NtrUVvOg6VvBntnRayY8rziQ7TUHVdWu+Lrby3RSVKMlNtxFp1lg8xZjJvnJUD/7375/z0zG96JzlokM6LCsrYc6zBsGLObNqSkHIktF9kZduZ9XsUjRdxy4bleqwqtPoCyEE7Kn1xc2wXlpSGCdR8ej6ndx4Xm/WbvmKYd3z4v4Gdd4QnbP+vTaxf2d2MIUUUvj3IERiv5/A1PZM4VSAJEG9TyUc0Y0KoKoT0XUyWpd+DwPHLQAUQnQBLgMuARqAnwMv/RuH7Kjr+rfRf+8HOv57Z3h8UecNsWLjHi4c3IWZyz+Iq9TZZJg5ukdcdvepq86kzhvG7VDIdtnIcCmsvLYUVdNQJAmHTRCKGA6OTQJfWMVlk9EBl13mvr99ZglWG60/n1PjCbJ9fwtLN+zilvF9WDx1kKF9JglsbRC6tG5lVVq1iKZwasJ0jtuSEAmEVWpagnHtoUII/vDmF0wuKeLLag93vfwpK2YN5yetqPCb/OE4jTUwpB/+duNorj67J7es2Zq04lE2shvrP9/P6N4FqJrArhikGl/V+5i3dhujeuZZlRRQyEmzWdTJug7fNvn5w5tfMHN0D1x2mbnPb2H+hH68WF5lBYtzVpTHVU5WzS7lvr99lsCwWFnv58pRhvZbWziSikoKRw913hC/fqUigYBo2RUl5LntFitz6+BFYNh40uTHig8twoX7X9vBU1edRZM/TJ03xC1RuZHCHBcLJg3kD2/u5LYL+nLHhP44bBL3XDSAsGqk6vc3B3l0/U6rEp6XbscmCXxhjb21xoxsjSfIYzOGseL9fWzcXcfymWdhkyVqPUECYZUMp8JT7+2Ju76Nu+uYUdpRp5jpAAAgAElEQVSVQFi1Wv0feWsnd04s5jevVFjXaLa6Duv6743jp/RgU0jhxEERYFckNM2oAkrRn5VUAHjKIBzR0TQNh2IQOtqEIKKphCPtVAdQCPE2kAGsBmYCddGX7EKIXF3X6/+d4+u6rgsh2mSzEULMBmYDdO3a9d/5qqMCo/oXYdm7e2nwRSynVIvOJa3ctI+ykd15IVotscmC59435vVcNhlFNoI90FEkiVpPgF/97fM4Z8OuSDzw+g56F6Rz47jTufuiAfzPj4qp9QTJdClWpe/+1wytqLInN1OY42LF1cM5e9Hbhxzeb93KejLhZGCoa2/2Cgcz+43+cFInWUJw50vbEtpDF00eRKZT4e6XP2PR5EHUehKZDPPS7UmPWesJ8Yc3d0bZb90oEqyaXUqtJ0SWS8HtUDh/QGeLMMMTDPPQG19w87jeLPzxGdhkCVkStAQieAIRGv1hlm7YRX6GnbsvGkBBhpM7JvRnf3OA+/5aYUk1mPqBrSsnM0f3ID/Dwc3j+lDxbUtC63SNJ8jqOSOBk8POjibao822Riii8kZFNTUtoTiNxg7Re9OWMPz+ZoPNs63kR5pdtlg+b1u7jVt/2DdujtCctTNlTMCYEzSJt+zR9vk7JxZjcrJ92xigS46TF/5ldIg8NG0wdllCkQSzz+3JxUNO4/7XtnPfpIHUeUPkue0UZDr5ycjuZDoVnrrqLDzBCNkuG7+I0b008T8/KuauCwdw+wX9afCF0HW440f9iagakYh2xAm9k0UP9mSw1xRSiMXh2GxLUEeS4l1hVdNoiYjv3R6YQvuE0y5oDsLeWo/F9FqY68Jp//577PFiAd0LcbIV1ksY8VvPwzhGd2BdDAnMDmCMruvfCiE6Axt0Xe/7Xcc50Qx15izV/qaANaBvOgOTS4osp+TF8krm/bAfM5d/wKieeVw/9nRUzSj5GjTkMkIYelRzkrS13fvXgxU/00E1A0RzZsUTDHP10/GspIDlpBTmuKzh/cNxaNu703uqMtQdTbR1Dw81A7h4yiAUSaLeF4prPwOi1euzmLn8A6uK1yHdTlVDwNq8ehW4aQlErPkrk7Ze03VsskR2mp2algDz1m6La8PLT3dw+4R+dM5yIguBhk6LP4Jdkayq+vjiAm4a1ydOGmLR5EG8s+MAFw0pTGDlNRMiAOOLC5j3w37Ue0NW8GjOBxrMjWnoOizdsMsKGt+7fSyds1wJdvbMrOGkOxXCEe1Yr43/OJs9XNS0BLlkyXsJ9hlLUKJpOrXeIL6gik0W1HqCPLr+S64c1YNQREsgVYmt7sXapWkfkhBUNwf5+eqP4z43vriAG8/rzSNvGYmFdIeSIHVS7/FzWk46dZ4gHTOdhFSVWcsP2tSSGcPQdZ1Jj24E4B+/OIernvog7nueuuqspOe84urhVDcH0YG8dDszn/og7ruPo6zPSWGvKRbQo48UC+iRoy2bbQ4EqKwPxj1Ll11RQlGug8wUC+gpgSZ/gGBEtzr+ZElgVwQORZDl+n4soMclADxcCCEG6Lr+WRuvdSc+AFwM1MWQwOTqun7bd33HiXZOTCektSPbur3NlIiYemYhP1+91XIquual0eANYZMFN6zcwtyxvRjVO98yhI07a/jH9mrmT+iPMNQhWPjq51YwaAZ589Zu47EZwwiENTRdxxdSyXHbuO+v8QLCbTm0rQOnI6UNP544HAewFU74iR9Pe/2ue6hpOgeaA0Q0jWBEpyUQjgpiO7lt7SfcfVExlyzZmHDcl28YTSCsWrqT44sLLPs80BykW66L5e/tYdrwbggh4lreHr50MNluO4tf255And8tz3Cwf/v3Csu+H5sxjA3bq7ngjM7Yo5T5dZ4AnbPT4qRRbhrXh8+/bmRY9zwafSECYZV0p8KNUSr9ZAFhrHyD+fOCSQPJdduRhDFbO6gok2BYj5NKGVqUnbC+j+Ha+I+y2e+Dw7FvM/kx7fFNrLx2BF83+OmU6USWjVnS2pYQP30uPlHhsElxBDHdO7ixyYJ6b4hsl42bX/g4YXbQ3N+nj+hKjttukCVh2JApy3P12T1x2iRuiB574eSByJKMFJUtiWgqd7z4qZXYW/jjM3AoEj9ffVDf9dHpQ5GEiAsuzYTN1GXvt2nna+aMRJGl45HMOynsNRUAHn2kAsAjR5sBoD+AqoMveFAGIs0hIQvITB4cpHCSocEbQAMCoYP32GmXkICc5GXekyYA/EjX9WFJfv88BuFLB+AABpnM/2G0lHYF9mHIQHxnK+mJdk6+bvAxetF64KAWWq98Nw3eMMGIik2WCKtGf2+O20ZNS5DsNDtOmxznGC+feRZOm0xY1YioOu/trOb8AZ3Z3xSgzhti6YZdANx7cTH1Uep8X0ilKNdFml1mX52Pp97bY1U48tId3P+aESiaKMxx8ZfrRyEQSQMn87VQREUIkVQfsD3Rf8f+7WNxCC22drvRHwscToD8dYOPG1du4boxvaxqdWZ09mj+hP4J832FOS5WzS613icJg95eEoLmQJjmaEtpvTfMDSs/YvGUQQTCmtVWZ7ZszvthP0KqRpbThqbr7IpqBwJcN6YXeW472Wl2Grwhpi4z5gmXXVFiVSRvOb83/z2sEC3KTGqTBV83BgiEVRRJxCVZuuWlocgGzf8NMQHhg1MHs/DV7XEJklWzS7llzVZr9is/w0FY1Tj/oXes98SeR+zfxSTOOcr4j7LZ1viuLoS2pExyXDZ21ni49pkPrft836QBcQyaT88azrINu7j2nJ7YZImIpvP427vYWe2xbNBcJ6qm4w+pZLpsXP7EJvLTHdaaKch00BKIkOm0oaPT7A/TEojQMdOBXZHjEhXXjz0dTyCC0yaTn+Ggst5ntd/HJvbMGb55P+xLk98g/ZIlgcsm89AbXzCuuKO1Xpdu2MX8Cf0s2YrWiQ2At+eN4TevHEys/KcnLFIB4NFHKgA8crRls/VeQwewdXAggNxUD+gpga8bfHy0r46h3fKsws+WfXUM65b3vf3Y40YCc5hIeqK6rl/exvvHHcNzOWLEOiE2xZjb8IcMh8SmHJRR2FLZyJwV5YwvLuCGsb2tNiHzgT5vzTYru/vo9KEU5rj43WVDUDWdkKpy2eMfxL3/pfIqxvQriJs/MUlgCjKdNPlCeIIR7n9tOzNH94i2lWpMe3wTQ4uy+dWkAVR822K11nXKchKKaNgkQX66I86BrWrw4wuqlD35L6oa/Ky9bmS7p/9OMdQdGodD4W5XZGo8wThncXxxgUUqkYxgA+CXL33K/An9rCqgWXkwkxGm3IIkBDOXf5Bwblef3ZNpj2+iMMfF89eOsOZYqxr8LFhXweIpg/CHItiUg3qDL5ZXWjT4D/5jJ6vKq1g+8yyqm0NxVZJlZcNYee0IqpsNx3vhq59z/djTkYTg+WsNxkVZEvwqCflLoz9MVYPfIrIxW+jGFxdYyZS2Zsf8IZWvG3ztsl36ZMThdCGYs36t37esrITfv/kFVQ1+Gv1h5o7rbQV/YNyvek+IS4bFk3Y9NmMYnmCEp97bw/VjT2d/U4An/7mbn4zsTlFuGnCQHdQkFHryyjMJhlV++9bOhHbqP191Ji6bTJ+O6Vw+vJs1m/pYWYkVkI0vLuDWH/bl8uHd+OmY08lw2nAoglln96TRF7Eqew9fOpjTsl2W1qwJ025NVDX4KYhJ0pkyQFeO6kG2y8644o54gxH2NwfolOlM2WkKKbRT2GRoaSUDEYxoZDhSJH2nChyKRLcOGVz2+Ka4cQDHEbTst7cAsP2UI48AmqbT6A/xbWMgbi7PbLExK3fLriiJ69GeO64Pf/u4irsuLKYgw0GWy8bCVz+PCf6G8ej6nbxRUc3fbhxNcyDC0xv3WIxx2Wk2yznwBMO8MLsULZoZEAICYQ2XTTB2yUYr2ztv7TYWTBpIn47pvHT9KBp9YbQolXm60xY3M7V4yiDuvbiYe2PaQwtzXOyp9VoOUlsMeu0puEox1B0ahxMg57hsrJlTSiAq5VDrCVGQ4UCWRALBRljVyHPb8QRVFk8dzFVPbY5zqE0bLMo9+J1tkcyYDmtVg58DzUHCqsYDUwdTkOHAJkvsbwpw98ufkZ9h57lrRuALqTgUiVWb91mC2x3SHWzYvp8h3XIt5kZfSMVhk3Ha5DimRLMV+q1bzqWmJciT/9zN3DbIX1rrAl73bDnPzBpuvdcXUpNe0+4aLzOXf9Au26VPRnyXyLuZlBNCJLxvzrPl3HVhMW9UVLN0wy4emjY4IWgPRlQrUWF+7qfPfcSCSQO5YWxvhIAn/7k7Lqhbe91I7n/NkBPple+mst7Pk+/uYXppVy4f3o08t81KMkQ0nWXRedJX5p5NUW4av7tsCLoOz/9rLz8Z2Z07flTM3lovf3pnDxPO6EyWy8Y3jX6eeX8vs8/pha4bs4CV9X46ZTn5/T92JpAumc8jE4U5LjKcNuvfsaRGz149gt/+vcKQJAIiqsZpWa4U63MKKbRDqBq4bBK+GBZQl01CbZucOoWTDIoMWWk2ls8cbo0D2BTBkbjaqV38KMHMPm+tbLKCPzjo6F43phdVDX6ueuoDMp0KCyYN5K1bzuWuC4txKIJz+nZkwboKLlmykYWvfs78Cf1565ZzWTBpIM9t2seVo3pQmOPim6YAT2/cw+SSIgoyHGQ4FVZu2svkkiLWXjeS6aXd8QbDTP/Tvxi9aD2XLtvEgeYAe2p91vmYFYk0u8y3TQEuWbKRu17+lEhUJ+unSc6/3mtkxQErY2624AGW+HFhjst6T3sLrmIZ6t67fSwvXT865XTHwAyQ27qHmqbzVYOPvXU+rnhyM+c/9A63rtmKP6wiC6PyZla1F766HVkS3P3yp+yq8SAJ2mRQND8Lye1o0eRBVkuzGWxJQnDZ45vYWe1hwbrPqPeFmD+hH5NLivjNKxV8Ve/jltVbmV7andML0mkJRFiy/kuGdc9jyfovCakasiTolpfGmg++IhDWmP+XT5j2+CbmrCi3ki82WaLRH+aNimr8IZUHpg7mzei6NJ3kR6cPs87PvK4mf5i7Lixm7XUj6Znv5nfThsRd0+Ipg6z1YwYqZhCZwpHhUBXsHQdauGTJe4xetJ5vGv1J32fa+ZbKRnQd636ZcNrkNm34hpUfkRVNHsRKRdR5Q1bFfN6abdgViY2767jvr4YEg8Mm83Wjn5+98DHjHnyb1eVVFOa4cNlkPMEwv3mlgj21Xpa9u5eyJzfT7A/RLS+NS88qwiYL6jwhTst2cceP+hNWNaYue586T4iZyz+g1hNidXkV97+2gwWTBvLmL87lhdmluOxGFR8Orq80u6EpeNeFxRZhmGnHV47qwYJ1FUxZ+j7T//QvdlS3oGknda42hRROSagahCJaHONiKKKlAsBTDC6bhNNmsJw7bRIu25GFcu2tAnjSekBm9vnBqYmZYzPoMv99oDnIzOUfWLNBd11YzIvllayYNZzqFoOK/pbVWwG49Yd92bi7znB0Jw3k9AI3nc872J40vriA//lRMZIwSv0rN+1lwqDT4iocLrtsORyxVP6+kEooujNUNfi5+YWPeWbW8DadnM7ZLt67fSx2RUaWsJwIwBI/Xj1npDVX0x7b2lJabG3jUBTumqazvznAvjpfHKtgVYOfOSvKefjSIXFC63PH9bYEr29/cRt3XVictArmC6k0+kM8On0YN6z8KE5EOxBWcTsUfvNKhRWQLZkxjEfe2snkkiKLmv+Niuq42VUwWka3VDayu8bL85v3MbmkiMklhei6zh0/6k+dx5B3ePydXVw5qgfBcCShfXXR5EHIElZwp+k6l0Xbpa8b04v5E/rR6A+jyCKhNbS65WCbrCncba7JggxHAjV/e2uXPhnRVgW7dcWvrW6FXPdBOZJVm/fF2XPr12M/Z7YBg5FEiX196YZdh7TtlZv2ck7fjnEB2dKyEloCYasKffXZB0my7/1rBfdPOYOWQIQ0u4wnaLQ93/fXCuZP6Be3v1e3GMfcUtnIzOUfsGp2Kadlu1iy/ss4GYynN+7htgv6W+3ZsddmBret13t7mu1OIYUUDMgSeEMGQ6QpEq7pAkdq/O+UgY4xY27St+h69Gf5+/vax0sHsBvQqOt6U/TnscB/Y5C3PKLreghA1/XS43E+xwJm9vm7Wthi28XMasfTGw1HeW8r5xrg6Y17eP7aUlTNaBHSgWBYs3TOGv1hfvv3Cn4ysjvPvL+XueP6WOLbuTY7nbKccQ60+X2LpwyKCwzBeLjbZalNR90VJSIAoxrUup3y5//VNzUj0s5wKFmHZL9PFiBHIhrfNPmJaDpp9uRVEE3X+cObX/DC7FL2NwXITouvhizdsMuax2s9A6gDX9W18Nw1IywCjIWvfs61P+iJJBn6ZFef3ZMuOS68wTDX/qAnT7y7m0WTB7XZXmmuw6JcFzeP6xPXkv3I9KEAnF5gzFk98PoO5o7rzfPRdtFYx3j2Ob2sQM38LrPKaX7Xc9eMsM4htoUu9lzy3PY4DbjY5In5vvbULn0yoq0Wb7lV9dncd1sH+0s37GLBpIH0KnCjSBJuh+C5a0bQ6AuT4VRYtXkfD04dHDfHGtsG7FCkhCBxS2Ujf9/2dZu2Xdorn0ynwvKZw7HJgt01XkszEA7uvSa2VDbS6ItYP4dUzZoT9IVUa39/+NLB/Pbv2633mcexy4K54/rEBbaLJg/i8bd38diMYXFsoY9OH0ZI1dr9bHcKKaRgIBg2Aj+nTULVdOySAHSC4VTF/lRBW3HeEcR/x60CuBq4BGgSQgwB1gD/CwwGlgDXHKfzOGYws8/JnAtz5iK2ggHGw/yB13dw+4R+FOY40XQSHsI3j+uDQxGoGiiaQJLAaZfjCGOWXVFCTpqNuy8agNsucefEYmpaguxvDvD3bV9z58RifjmxGCEEsmSIAEsS/HpdRULlQpYFy8pKEmYYO2Y449o5TxbB3/9ktEWK0Ts/3WI8bIssI+4Y1S3MWWHMSLWVIDDbJG8+vw913hDZaTb6dEy3yIO2VDYigBdmlxIMa0gC9jcHuDfqvD5/bSn13iAtgQi9C9K5Y0J/fhGtgt8/ZRC3rNnKqJ55zD63JyC4Y0J/lCg1f+tKzbKyEnLcNpaVlTBvzTbyM+w8M2s4iizQdeLINO6cWMzvLx+CXZYSHOOlZSX84c0vrGssyLAnrM+lZSU4bYLfTRuCLAly3fa4hMvSshKy0xQCYd36uyXbI574yZnkuAzW39R6OjK0tSe1rvjFdiuEIhp7ar1WO+/M0T2IqDq/Xvcp1489HQHUe8Pkum1MObMrqqbx7NUj0NHZW+uzPvdYWQkHmoM8t2mfVfEz7+3FQwqxK4IO6Q6y02zceF5v8tx2hICMjraovqugyW8EmrHVwGVXlOCIIQ4rzHFhUwTpyHFERkvLSshPtxNSNWaf0wubLOKOYyZbwppGSyBsVaMb/WHrGi4Z1sWaly3IdPLc+4Y0S3uf7U4hhRQMpDsE3zRrVNX740TCczJT6/VUQSAMSjSJb855KpIgEIZM13d/PhbHSwh+m67rg6L/fgDQdF2/TQghAR+brx0PHCuK8lhnO1azzxuMkJNm40BzkEBYJT/DTr03HDeU/1hZCfnpNlQNJAk0zSjdK7IgEFa56qkPos5vL9LsEkIIVFUnqGrUtARx2SRCEZ2py97n3gv78aNBpxGM6Ki6jqYZxxHAl9UHZSQemT6UYFhLYGXs3sHN6598w5h+nSzh+VpPiBy3jQyHgiRJ/0mO6Qm/yH/HXmM1J00Kel9IpV+nDKYepmRHrDTE0KJs7rm4GH9IjbNfkzY/P8PODWN7xzm/sRpjy64ooXdBOuc9+HbCub75i3O5MkoSs+yKEuyyZFXDhxZlWwyi00oKuXhol7iqzJh+HS1qfFky9NdcNolYsfkct42Vm75iRmlX/GGNDul2ZEngUCSefm8Py97da+kTtgQMGv1tlfXYbTaLQMYbivDXLVVMObNrVHxVwi4LHnj9C2t26+FLh5CdZqPJHyY7zY5dEYQiGg5FotEXtoLH8cUF/HJicfQ48TIErYP1Bn/4+wSFJ7XNHgscKhHyVYOPfXU+0uwykhB0ynJS7w0ZwZEvbCXaWkt5DC3KZvHUwTT6QhRkOvn1us+YXFLEgnUVCest121DlqS45MKj04ei6TrpDoWqhgAd0u24HQoOm4Q/pFkamx3S7dgUQaMvYhEeyRIIIQiGVTQd7IqEpuv8ep1B1pLntnNatpMmfwS7LGFXJAQ6Nz3/sVXpbs0+GktUtmjyIN7ZcYCLhxTy14+r+NGgLnFr+igTFp0U9pqSgTj6SMlAHDnastmWQAAJaPQflIHIdkloQEZKCP6UQL03QL03TGVMkF+U6yLXbWtL6uPE6gAKIT7Rdf2M6L8/Au7Qdf311q8dDxxL58Sck/qm0W9p8W2pbGR8cQF3XzSA/U0BOmY6kIQgpB5kUeyQbmfeGoMo5sXySn4xvg8OxXBIXDaJsKpbi1kSENFgf1PAEnAvynWR6VDwRzSUqGNqCrwrkkA1/69BRNP44oDHmmuK1aZq8odZsv7LNvXc7rqwmAXrKlh2RQl9CzL+E5jg2u1GfzgwNftai1CvuHo4Yx9IDMKS6SG21k40Rc0Lc9LQdJ1vmwIsenU7NZ4gz10zghl/+leC3SyYNJCZyz+w1oFJXxz7nhWzhjM2GhgOLcpm0ZQzqPOErEBzfHEBv7ywGEyHV9MR4iDN9TeNft79opqrz+lJOGK8hg5BVU/Qz3TZZCJRllwjQNPjWHN1HZx2CW9QpcFrBJoLX/2cmpaQldhp8YdJc8jMWn4wqDDbAU2NNVMjcNXsUnRdBwG6LgirGjZZonOmEyV6LfubA0l1NFdeM4Lp0b/pYTrfJ7XNHiska3mu84bikhut18ljM4Zx98uG9IcpkxNb/X1k+lBkIXA7FM578O02jlHCH9/6gjNOy2LSsC5EVN0K2Oo8RqIiw2mz9ne3Q8IX1OLsNj/Dzp0TixGAIgtcdglvQCOs6dhkwbeNAbLTFJw2BU3XCauGPuHG3XVWS+jkkiLmrCi3ruOP0Tlao9pnBJZfN/gt/cGZo3uQ67ZR0xIix20ny2VDwLGoTJ8U9poKAI8+UgHgkaMtm/UGAoS0RCF4uwTuVAB4SuBAkx9fOIIiyWi6HtVWVkmzKXTMSloCPOE6gG8JIVYD3wI5wFsAQojOQOA4ncMxhyQJOmU6afKH+dmqgy2aM0f3oKYlQFjVONAc5DevfG4FXk3+MIXZTmo8QZZu2MW9Fw/goTe+sB7ORTku/GEVTyBCdpodRRakOyROy3YS0XRskkCWBEFVswSuQxEj+AtFNFZ++BUTB3fBZZNo8keo84biMtmmNtWq2aWs3PQVb1RUM/ucXm0S2ZgkACuvGUFhTprlCHyXAHMKxx92RWbuuN5xrIRVDX721voOu62rNbHGlspG5q3dxjOzhqNqGk3+MHdO7E/nLCc6yZk+u+amWWLwn3/TxGNlJXEyI0vLSqj1HGzT21LZyJ/e2cPN5/e2KPJN2/7l/31qiVMvnjKID/fUM3HwaThtMsO65/HLlz79/+zdeZwcdZ3/8de3qs85kkkmkxBIIAHCETSRJIQA/pRjVS7l5yYiEESicgq7uoiyup6su0B0WQ9IlF1BBISY6IqcrhzrrqhLAoI/AuGWDEcySSbHzPRZ9f39UV2V7p6eZBJmJjPp9/PxmMfMdFdVf7v6U9X1qW/V58ui46byiyde50Pv2pf9Wxs4eHwT3znrXXjWknCd6LIJ31o8P+g1BCj6lo5tOZY9vpYzjtwvOnCOuWm+8sEjKPqWomfpyRVZty3HQ39cFw1j8VLH9ssIw/sPw+Ig2YIf9W6Wr+9weII167bRnSvWXHfrt+UqPrtwWAMV4Ng1te5tLa8aevHxB/XaTsIhHhbd8jhPrt1MV65YcenkHX94jctPOhjL9gq433pwTXQZ5eh0HLDMnz2ZaeOb6OzO05SKY0tf2smYw4auPMYYPvnjyhMJk8emmTahies/+i5iZTd3xF1DT87Hs0HF5sdf3sCsKa3ES9O4BpyYw8J5B3DS9AnR/eXhPakdXTm6csWoTa5j6OzOc9/Tb/DRuQfg+UF7w97AL58+nXP//X/6vEJARIaPooWEAz1ljyWc4HHZO7iOoehZXt3Qtf0y3zEp3OQwLQIDfAb4KDAReLe1NhyF9mBg7BC1YUiU34eSyRfxbXAZXcxxaG2K4Rin10DaP7vomGisptv/8BeuOuVwNnVvr1L46RMOjoZocB1DtujzxubKHsDrHniOX69ezw8+NpsnXt0YXaa2YM7+LF/5GvMOauOQCU3s15Lqdc/U9WfO5I3NWU6aPoFlq9r7rJJXPhbb+m050okYbc3Jfg3AvDNKIAdea2OCqeMaeyUW333ohV73svU1ZEdrY6LXuJU3nDOLu/73L8ya0hrF8fKLj2HCqFQfVRjhip89RVtTks+ffCj3/Kmdm88/KrqH78m/bOTQiaMrxit77OWNXFA4kI1deQxBAZYp4xr44qmHc+F7DmJsY4Jt2QJ3rWrn4TUd/M1J05g2vokL33MQMddEg1+X98yFvfFXfuAwtmQKTBiV4qu/fIZfr17fq/dmcybPVacczpZMgfVbc0GPUanQRvnlrx87dkq07ZUXWSovDtLXEBj5ohdVD+6rSmr10BAqwNE//dmfmNLwI+Unt8q1d2aYMq4hmubm373C5ScdEp28uPn8o3hxfTd/eKkj2qc+uXYzV9+zmmvnz2Dxg89x9twDuOgnq7j5/KMqCnwdObmFr59xBFf9/M8ce2Art35ibrTPD6pyHhb1FL61JRf1zF383oODYjNNCVzHctTUVmKuw9X3PBPF4C2LjqKtOUEqHtwPXl0ELEzubj7/KK4oq0Z75twDuOb+5yruCy+vXq24ExneXAPZqs0060FStwDuNZvVvOIAACAASURBVNJx6I67TB7bUDEOYGlXvUuGJAG0wXWmdwIYY440xnwG+AjwCvCvQ9GGoRSebX5js19xWdeRk1v44qmH90rAxjTGKXp+dHZ5Y1eettLg2p9494G0NScpehZjDAnX4AP7jE7hWYtbqvj0t6VBqpc++hKfP/lQFt3yeMX9HWMbE3jWctkdf+K6Be+sGCYiHnOiMuIAK1at7dXG6sqGG7vzTBwdXFLQ3wGY+zoQG4gEUnpzHENDsndp/LAXIKx42ZP3gkuTa6xrxzHs15KqOEC94ZEXKnoVwniwll6DTl9/5ky2ZQv85BNzibkOZ98UXP75g/9+teK+qjNnT+LTJx7MnRfOo+hZwNKd88jkPRoSLnnP5+/ueoq25gR/e9IhXHP/s1x6wsFRHFuCy5s/c9efaGtKcvUZ7+CA1gZijuEfyw6AP37sVBY/+Bx/c9IhbOrOccH/OZDVb26LCoPc/qmj8W1Q4OOKZU/R0ZXjBx+bjWNgn1FBL+edF84jX/RZvy3H6HSML546nU+fMI2mVIyEa6LKouH9VOW9m6GwxzXshapVGOYHH5vNd37zfMXnoQIcO9ff/YlriNZ5X9WbE65TURn2J4+9ypdPn84h45twHMNn7vwTn/vAoax8ZQO3ffJoNnTloiTuovcexPjmJMsvPoaC5/O9s4/k8p8+SXtnho6uHMmYieZZtzWLMYZp45v4h9OP4OHVb3LMweMYnU6QiKUY15zkwvccxNL/epFFx00lHjOsWLmWv5q+Dz9b2c782ZP55LsPpCfvMSoZ46LbnuDi4w/iwHENfOWDR/D3px5eUbRm8YIZfH750xXVRl/b2MPFxx9UUeG2vHq14k5keDNA9Z05MWcYXLMqAyZTgHENbnSfZ7J0n2emALVvAezbUN0DeAhwdulnA3AX8Dlr7QGD/uJVhvL+lOr7p0K/uuw43tiSjQ4q5hzQwqbuPO2dQfn8loY4qbhLvuiXLk+zxByHYqkoi2+Dy9d8H97cEpyZ/tLp03EweL4lFXfY2JUnWRooMuYYbvv9q5w19wBO+PZ/8dMLjiZb8KNLmZY++lJ0uU949vq3a9ZxzrwpbM0WaU7G+Kf7Vvfq5fjmh2fQ1pzs833+7gsnsDVb3OmBWHmhkdAwueRoj+8332681joYLu8RC4X3/9XqOXlzS4bL7niS6xYEwy6E98SF8XDDObP42t3PcNUph3HN/c9Flze7jmHCqCTrtgYHxfuMSnHGDb+LXvOuC+fx0R/+oaK9YVLY1pSsWXDmxoWzGJ0OetJjbjDw7cauPNuyBcY0JBiVDk6mJGIOjhPcz9fZXaApFcMt3UNrLVEPumMME0YHJ1jirkMyZnhra47mZKx0Zs2hszvHqFScj/3of3sX1JnYzKWlg+2WdBzfWvYf28CbW7JRr82nTziYbFXBpXA7qL4P7eLjDyoV8UgzvinZ72qtZUZ8zL5d/d2fdGzL8aVfPM382ZPZd3RQhbm82Mn1Z85k4ugUZ93U+77Wm88/io3deT5X6tm++PiD2Hd0inQihmPgjc0ZprQ2YIFswSdb9BnflKBQGs7HAJt78tzwyIucd8wUJo5Ok4g5bM0WaEg4QDDIe8HzGdMQpzlVqhbqGOKle1U7tuVJxB0W3bz9ZN+ShbNobUpw5g8q77MN77/1/OC+3dbGOItq3L/6pdMOZ8HS3/cqDDOIJ+RGRLzqHsCBp3sAd9+O7gGE3kVgQPcA7i26slk29ngUiraiB7C1waWp9me8x+8BfA74b+B0a+2LAMaYzw7Ra+8xfQ1M/MaWbMVZ1p98ci7LV67lvGOnkiv6+Ba2ZPJ86serOPbAVj7zvmnR/MVSddAtPcXSoNIpvvLBI+jKFtiSKZYulUvTkHSjwa5XrFrLZSdO462twc7hugfW8PmTD604IL1x4SxaGxPcfP5R5D2fc+ZNIREzfPB7/8OZsydx1SnBpXfh2e3Pvu/Q6JLB/g7A3Nc9TOX34oR0ydHAcBzDtLYm7vjU0aUeqziLH3yu1/AfiZjbZ89Ja1OCjq4cm7rzfLRsIPRPvvtANmcKOCa4N7An70WXN4eXVJ590/YCJuXj5QE1e11WrFobDUPy9btX88VTD+fOC+dFB65f/eUzdHTluHHhLP7rufWc/M59aEzGGJWOEXMcjAl6yr/zmxd4YX0X/zz/HSTjLp4fFMfwfI9f/791nDZzX7ZlizSlYhgg7jqA5d//+xVmTWklUzo5M7YhwWeXBb2KYW9ReN/s4gUz6NiW6zUu4M8vPZYDWhuZODrFkfu/k5hjKBR9ll10DNbaip7w8rHrwssHbzpvTjSepoZa2XX93Z+0Nib47PsOjeL9/dPHc/unjsbzLcmYw2V3PMnnTz60V6/2tfNnkPd8rr3/Of71o+/iM3f9KYqJ8pMrN59/FEB06edjV53Apu4833/4BRYdN5X9xzZUJGVhMaUlC2dhgYmjg/u8IagObQniKA8kYw5NqRgNCYefXjCPoufjWfB8j3/9zxd6tfnjx04NKtOm40wekybmmJpDQUwcneJ3XziBeMwh5hi+f86RijsRkWFkTIPLtrIewOb07hVkHKoE8K+Bs4BHjDEPEFwOutd/m7Q2JnqNqbfk3Nl8r2xssW9/ZCZLHnmJM47cj/985k1mTx3HJbetoq0pybc+EpyB9nyIxQEfLMHBamMyqPhmAdexpBMxunIeec/nn+97rtTr4NHamOCqUw7HcYjGH+zoyjGuKcHtnzqagufz1pYs33/4Bb502nTynk867oKBraUD9GWr2tmcyfOl06YzcXSKWfvPqDgg6O8AzFD7QKyvBFKXHA2MzkwhqiQZJmar39zW6/6/vi7lvfuy4/jBx2azfmsuKnZRnvCE969NHpuO7hesVVTjm/eurtgeVqxa22tw+L856RBiLhWXnN703y/x9TOOYNKYNP/y0ZnEHIfbfh8M3fDnN7bwD6dPp+hZir5PKu6wpSfPYy9vpL0zw7/8+nm+dNp0sgWPdMJlc4/llBkTS+OyBT0y4fAobc0JLj9xWtVg2EdGg39/68E1XH3GO9i/tYGObTmaUzGKnl8xRttN581hXGPty2lr2VmSV6t4iexYf/cn1eveGIO1lpc7uqNx9K57YA1f/dB0rj7jHbSUeuJS8aBybEdXUNDrmr9+J/u2pHEdU3G/3eSxQUW2MBn71/98gUtPPIjPn3w4joFMwWdU2qXowYRRSb515kwcExR68Xz4z2fe5D2HTmBTd55swSMZc2ltSvDG5gy3/v5VLj9xGkXf5bWNQTnwZMxh/KgkZx41mYLn862PzGTCqBTbsoVo//6d37zAYy9v5NZPzGWf0ale++yJo9OVsds4FJ+YiAyU6mv6LHVwsF1Hij5BlfMy1u5eoZ8huQQ0ejFjGoEzCC4FPRG4FfiFtfbXQ9WGwR4Govryuc2ZPE+t3RKdaZ3Smibuumzqzke9MWERiouPP4jDJzbjGhN13zelHHpylrwXXA5qreVPr21i1gGtpUtCDZ09edqaE/g+UWVQ45joEtKYE4wDmCkEQ080JFy680XOL7t0KLys86sfPIKYY/jfVzYwrjnNPqNTOCYo1jG+Kdnn0A87K7MeqnUp1jC+B3CP7zcHIl5rDeVw8fEHcfg+zaQTsSjh2NGlvDHH8PKGbpqSsYoEKRx0/bVNGa69/zn+8cPvoL0zwyHjm6JhHcr96rLj2NCV54DWBuKuQ67oYW1Q2Wr9thz7taS4+p7VXFq6bLKtOclrG3uiBO0rHzwCtzRWZlgUKe4asgUfC9zxh1c5/rAJUdxaC7liEddxaUw6wVAoXjBfU9IhU/DJFbcPybJvS4qYY8jkfbrzwRieW7PBeGoFz6c5FSdf9EjGXbZkCmzNFDDGML45iTEwOh2nJb3z+14H0V4Rs2/H29mfhPuxoh+MsXrp7U9UjOva2Z0n7gZJ+abuQq+x/bZli6TiLuNHJdncU2BsQ5w3t+TYZ3SKfNHHYknG3KiyrcWSjrtkCj4Fz8d1HPLF4L7X8MjNWij4ls3deVqbEviWqCpurujTlQumdx1DKuaQLwZDRDjG8B9PtHPXqnZuXDgLay1n3PAYEOyHf3nZsfi+iZJf17AnxnkdEfGqS0AHni4B3X19xezWbBaX3peAesAoXQK6V9iwLYtvffLe9mOghAuOcRjXPDwvAQXAWtsN3AHcYYwZQ1AI5gvAkCWAg2VHgw2Xn2l9//TxfP7kw9iWLQaXGZ04LSpCcfU9QQ/J3X9qZ9aUVlrScQ5sa2BDd57vPbR9aIiZ+7file5f8q2ltTFB3rMsvOmP0cHK1HHBqVtrgx6Ob//6+eiyv4eueC8GKoobhJctfem06aztzDCuOY1jDM+v62LFqrV888MzdjjuX62eir56BqurTepSt8FVayiHq+9Z3SsR31nPyZXLn6atKVlRPKatOclfL3msoqfv6ntW91nVckNXnrGNCTIFj6ZUjILngwnG9huVjvHWlhyLjpvKjY+8yPzZk2lOxZg2oYlvfvgdrO3M8I1fPcPlJ04jWwh6+1KlMf1aGuJ05YrMO6gNxxhe3dDD2MY4X7t7ddCzd9IhnF8aiiFMXP/jiXWc/M59ontvxzcn+cd7Vkfj/R3Q2sDWbJHGRCwq7rFi1fP8zUmH8IP/epmTpk+Itp9r73+Oq045jFTM4dktWyuqpvYn+VAV3IHzdvYn4X7M9y2ZvBddJtnalOTa0n2vofdPH89PL5gXnczrzhXZryWNFwz5SCru8I17VvPpE6Zx9T3P8Ml3H1hx2f0Pzp1FprC9cFGYZHZlizQmXbJFn1c3BCc/5k5p4WPHTqXg+RQ9yyMvrI+uFgm/V7502nTe3JrD2mB7eHF9F3MPbOUdk1rwreXrd6+O2t7emaE75zOpJR19b0XfHW2NJFxnTyWEIrKbXCDvVz6W94OhIGTvkIhDZ4+N7gEseBbPGqqGcO6XIe0BHA4G6+x0X4UHll10DOObknRmCtGZ1kTpzG1QsMUlV/RZtzUoGnH4xGbec92j0TKqh3WwQEPCkC0ECWDQO2coej4WE51Z7skXSSdifONXz1QctEwak+bOC+fhAGfWGJA7LARTqwAABBU/fT+436T6fqZaRviB7R5v6EDEa397RHY0HVDzubENcY7+54ejZYSXmP52zTpOm7lfxeWdS86dje/7PPmXTZw6cz9ypV6PdCK4/9Dzg0uaP3Pnn6KiKgXPZ1xzgjc356KqtRNbkmzpKVYcTH//nCNxjGFMQwLfBuP57duSoifvkYoHl8flij7ZgsdbW7K4juHK5U+zeMEMsgWfyWPTdHYXyBU94qXevmTMZUxjnI5tOTzfsm9LmhfWd3HEvs2c9cPaA95Pm9BUc6D7uy6c12cvywD3gO8VMTsclO+70gmXdVtyXPCT7Z/Rv398Dk3JGMXSWVjHwNfu3j4cw/VnziQRc5gwKkUm7wVj9ZntxV0mjEr26l3OFj1cY7jm/mejExHlPY+j0nEcExSByRY80qXB313H8ItV7Xz7Ny8waUya5Rcfw1PtW2hJx9m3JR0NExEKvwfSCYe3NucY25RgY1e+V+Xn8H7vWrE4QPv2ERGv6gEceOoB3H19xezmnixxp3cPYMGHlgb1AO4NurNZ8j705LZ/xg1Jh4TTZ6GfvivGKQHs2658wfV1+dwvP30c45sTOMbQUfUF+4NzZzOmIV6RiD1yxXv52I+2Dxp95OQWPn/yoRU39P/o/Dn05IIelGTMJe4aMgWfouezfOVrzJrSytX3rOb2Tx3NG5szFfMuXjCDmOPQ0hAjX7QV9yf+60ffRcHzcYyhJ+/xzv1Gkfcs1lriMYeubJFr7n+Wjx87taJc/TC5XHMw7PE3NFAH0/2N5R1N19/LfN8/fTxf/eARfP1XzzB/9uSol2zFqrV85YNH4Biiy+fCsQHDGK0eLy1c3pdPPyLoLYSKg+Op4xrxraU7V2RDV54xjXG+fvfqiqq24e+w6mj5vZDhQNnh7/K4Li/oUX1yJOYYPrvsqYrtalxzkoTr8N7Fj/Zar49c8V42dge9n3f971/40JGTmNCcjLavj9ZIGnezCu5eE7P9NRQnmXzf8urGbuKuw7qtWQqeT9x1+OFvX4quzJgwKokxJrosJxlzWL8tV9EbvOyieby2KRNcQtqVxxgY25jgL6XLnDu6cnz/nCPJFnzGlz778oq7SxbO5nsPP1/RS+0Y06tK84FtjXxkaTAE0U8vOBqg1/dAuO14flBVeuG/9T6pEcb8IF62PyLiVQngwFMCuPv6itktmSy5oiVf3D5udCJmSMYMo9NKAPcGb3T24FkPx7hRAuhbD9e47Fu7G3B4XAI6GIwxJwPfIej9/jdr7TUDsdxd/YLr6/K51qYE7Z0ZunJexYFte2eGi25bxU8+Mbdinre2ZisquHV05WhKxrj1E3MBolL1cdfBt8E1wOWDAIdnbRcvmMEND7/Iwnn7V4z5l0640QHy9We+iy+fPp3WxkTpy92y8N+2XyYXFqwJl714wQwWHTc1alv4PmpV9pThpb/FRHY0XX8v8w2HPfj16vUVvQ4Anz5hGi0N8Sj5u27BjGjMSggGqa+uYLjouKls6MrxjV+t5qsfms7Zcw+I4hlj2dpToDkdh658FNvVA7K3d2ZwHKKiM+G4f186bTpFz+fLpwf3Fv70gnlsyxZIxV2uuf/ZigG0f/zYK9GQFxAMY1F+H253rkh7d+3x5BzHUPB8rrn/WS47cRrf+c3z/P0ph/OxH/0v3/7IzJrFkjIFj9c7e0Ziz/mQGej7h/tKJjd057jm/me56pTDGdOYwEDFybDqExmTxqS59RNzo+QPgs/0jc1ZzipV0v2XM2eyfluOLZk8B7Y18qXTDmdjd54bHwnG+rtiWTDu5RdPnc4XT5vOKx3dPPrcOq465fCoQNI19z/Lp084mPOOmRJV5Q3jesnCWVxy+xNc98AavlYqZFP+PfCVX/6/6OTH3596eM0YbEnHaxbu2tHYr/oeENkzYgaKjsGUSsEYgvvrY/rq2GskYg5vbslzye2Vw/9MHL3rI8GP6ATQGOMCNwDvA9qBx40xd1trV+94zp3b1S+4WgfC186fwZubs3x22VN9HuTFXKfigLH8y7qlIc7odLziDPC182fgeUUOHj+KQqlc+dc+dAT/cNp0HMfQnSty3jFTAJg/exL5omXKuAYMhjXrtvH1u1dH9wL61lZUc7z6jHdUvN9LblvFl0+fzq9Xr6e9M+hJrE5Yw2k1ZEN9Kr/fKlMosm5LjmzBZ1N3T81EqCHh0rEtR1tTks994FC2ZAoV0zy5djPXPbCGn3xybnQG8+/ueoqLjz+Ijq4cX797dXB5aCxOa1OSZMyhtSlJT77IpDFpvvF/3xEUwvB8rvzAYdFg15PGpOnKetz71OvcfP5RxByD6wYFN5Ixl6vveYaWdILLTzqYhkSMRMxw1SmHc/lJh5COuyRjhi+eOp0bH3kxWt7z67qYMq6Bhf/2x6inpK0pyQ3nzKoYT+7a+TP45r2rOXvuAXz82Kl8/+EXOO+YKWzsztPemelzEPKX1nex6JbH9/Ze9rdlIBORHSWTnu/z8WOncl7p6ozlFx/D/NmTox7jL58+vdeJsU2lz7e6vWEl3efXd3H1PaujHukr3n8Ih05o5qsfPAKLZfFHZrChK0/B82htSnDw+CamtjXiGthndIrxzUkuPf5g0gmXT9/xZNTmGxfO4oaHX+TDs/arSPrirsF1DJPGpKPtYvWb2/jy6dN5dUPt7TWMzeoKqhq6R2T4yXsQdyFb9ljcDR5XQd+9R3M6xi2L5kbjAO5uwfyRfmvoXOBFa+3L1to8wfASZwzEgnf1Cy48EL7rwnksv/gYvnz6dL714Bp8aysO8spNGpPmrS1Zlp47O3quoytHOuGSTgSfaNw1fOLdB/KLS4/llkVzmdLawP6tzXztV8/w3sWPsmDp79nYlecf713N5Xc8ybZskat+/mfOvumPXPGzpyj6wXXCP/3jq1z0k1VR8hd+uZe/t4ZEZRSFZ4DL//esrfk+NGRD/Qp7Bq2FTT15rvjZU3z3oRe4dv6MKFbCRChb8NjYHVy+9oUVT0cHxOU6unI8v66LjV15nl/XRUdXjqWPvsS182dE4wxe/tMnyRY8NnUXaO/soStXZNEtj/PB7/0Pi255HM+30XiH4WsvX/kap87Yj0W3PM4J3/4vzrnpD6zbmuN7D73A2XMP4KL3HoRvg154rzT+WkPCJVZKuv7pvtUsW9Veqvg4i+8+9AKOMRU9JU+u3YxjggJLd104L9oP/Hr1ehoSLl9YEQw8PnF0mo3deYDovZWvq8ULZvDdh4JhW8KkJpxethvIRKSvZDK475mKYU02dudpbUxE/4eff/XyqmN7xaq10f5+6aMvsXjBjCghvOrnf6ZjW5ai75MvWrpzXlTgK1vwsdbi+5Zc0bKlp8BLHd383xsf499++wo3n38Uj3zuvdx5wTzufep1lq1q5+bfvcK45iRX/Owpzr7pj1z18z+TK/pR8he+x5Z0nO8+9AJLFs6qiMElC2exYtXamoW7witeyul7QGTPirvQnQvGkoYgOejO+cS1WUoNI7oHENgPWFv2fztwdPVExpgLgQsB9t9//34teHfGpgvPzi9Y+vvosTDxCw/yyu8xWrJwFl/55TN87UPTK8Y9W7FyLR+dewAxx/DWlhxFzycZc7DWsjlToCkZ47xjpvD5kw9jQ1ce39po/LJv3vss3z/7SMY0JqIhIH72+Frec+gE7v1/6yruAbnugTUV760nX3nQVJ0khlUcq99HrQME2X27E6/DgVsa2Ly9M0N7Z4ZvPbgmqhg6vjnJ3y0LevJWrFrLF04JLjmrtV2El22G04bPh+PwTRnXwOudGcaPSvLM61tpSLg4Bu644OjtlbF8n384fToXvucgNnbno3v87nv6dX56wTze2JypqH67bFU7d104D4CzfvgHfvCx2VHvDAT34v7NSdP40mnTcYxhc0+ejq4cjjEVPSXtnRne2JKtmBe2b0vtnRlaGxMkYg4rVgW7rifXbo7e2+Sx6ajXMzxIh+Hfu7KnYnYgxxDdUTIZnsgLLX30Jf7lzJnRa9fqxQ2TvfL7vj9+7FQeeXYdtyyaGxSFAZZddAxvbM6QLXjEYw7fvPdZLjn+IMY1pfBtMGzPj38XjHsZ3W/alIhOECxb1c6yVe1AMMzKrCmt3HX4PoxvTtKQcKNtcGxjIjopUr6uNmcKdHTl6MoVKyr8jm9O8s0Pz6h5+XF/KzwPVyN1Hyv1qz8xW/ShMemwLRMkgY4J/i/6NSeXEaivqi27U81lpCeA/WKt/SHwQwhunu3PPLv7BVd9QBKe5b1y+dO9BpJuaYjT0ZXja3cH9zZtyxZpSceZd1AbW7MFDFD0fa76+Z+jNtxwziy+ee/qaOzAz33gUD59x5O0NSUrlr25J18q05/irlXtPLymI/pyt8C4pgQdXTkgOAj4zlnvYlQqFrU9vJQoHDw+fP8TRiUpFH2WXXRMv6qAyq7bnXgdDhKuw9jGRBRD4YDx4eXFT67dzIpVa7n8pEPo2LZ9UPkwUTy4rQljiO69W/roS1GhlvBe1bGNCZY88hKPvbyR2z91NKm4E1UDff/08Vx1yuFY4JUNPTy0eh2nvHMiB7c1MX/2ZH782CtcduI0LDaaJxSeAMmXBnavTkw7unKk4g7rt+YY05hgQ1eexQtm4JhgoO+bf/dKNH35Nl+d1E4ak2ZsY4KYC59894GsfnNbtPxEzOG6B57jH06bHm2b5e0bzr0reypmBzIR2VEyabEVzz25djN3/u9fogSv1me+6LipjG9OcNeF88h7wZAO33owqKw8a8pYEq7DZ5f9ids/dTQFL9jPh0MxtDQkeH1zMLZmeB/gR+ceQNx1eGtLlg1d+Zox8saWbMU2d8iEporLTD/3gUOjmCu/tzXc14e3Gtx03hzamlN97tdH+tA9I3UfuzfZ1cI6I7hozIDob8xmCzZKBmzp/4Q7MrZL2TnrW3IFD9fZfjyQK3g07MbxwYiuAmqMOQb4mrX2A6X//x7AWvvPfc0zWFVAy+epvo/klkVH0RB3o7H7nNJA1nHX0LEtz0WlghhfOOUw9hkVnPUNKx1+7UPTyZQNiH3/n9/kw7P2iw403j99PH9/6uEYgsHjM/mgGuLksWkaEy6u67Buay4aLyos7rJvS5JMPhiKIuYYtmUL/Oh/XuWUd05kyrhG1m3N8vNV7ZzyzolMHddIQ9JlXGNyxHzBD5A9/mZHUkn9YtHnja0ZNvcUKoZ/uOljc5gwOkkmH2xHo5MuG3rybOwqcMntlQNpNyZjdHbnowqbYXwD0ZhoHV05Fi+YwS+eeJ2F8/ZnU3chus9p0pgUi0uXXIaxPioVY3NPgZaGOI0JFx8bbHdl1RmvP3MmoxvifOvBNRWFPcIy/B3bcjQnXeKxYOzBhOvg+Za4a9iaLbJ2U4ZxTQnSiRixUuW19VtzNKfj0UD2HV05blw4i5aGOM0pl3zBRsOplA8RMSYd54WOrt0tbFJ3MTtQVUB3dRiUGxfO4t6nXmfWlFZaGxPsMzoZ7YfDqy8SsWDIn4JnKXp2+1Ueq9by+ZMPI1f0ufvJdubPmczrndkojg8c30guH/QIbssWARidjpOMORRL13dt6SlUVHEuH7pn8YIZtDUnaWmIs25LLpouHC8wW/BoTMSC7yILjYmgVHyh6A91Mjci4lVVQPe8YZQADtuYzWaLbC0UelUBHRWPk0rVRX/PXi+bLdK+NcPaTZno+2Ly2DSTRqX7+oz3zmEgjDEx4HngJOB14HHgHGvtM33NMxQHJzs7IKkeX6roWXJFH8cESaG1UPCD+z1ScYdiaWRha8GzllQsKP9a8HxipQMNH4vvE2306YRDV9bDcQwNCYdMfvuYIcmYg+sGHgCNhwAAIABJREFU44iE4xKG5egTMZcx6Xg0buFIO7M7wPb4mx5JCSAESeCmnjx5zy/Fr8u4pt4nDnzf0pnJkS2dhHAcQ8J1gjj3bRT/4fhqrgkOrAulpCtmDJmiTyrm4FlLwbMkXIdEzJAt+NF2EHOCA3C3ajtxHLA+QRXP0nPx0rThtuaHJ2zCG60dQ6y0fXbnvWj8HQi2pfB9xB1DIm7I5i1g8cuWlU64tKT7dyJpN5Maxezb0N9hUOIxh4Rr6M55eNYSdxwcA7GYIV+w0TARcTeI20TMUCxa8qXxLuOuQzph8DzIF31iMUOhaLeP65Rw6Mn7JGIOmVKsxVxTGvTX4pbGfw3/jxlDzHXIFrxoW3IMZIs+rgPWBmPFhtuTb+l3LA6yERGvSgD3PCWA2+0oZrPZIhs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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.pairplot(data=Train[['FULL_Charge','FULL_DAYM780201','FULL_OOBM850104','AS_MeanAmphiMoment','CLASS']])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms\n",
    "plt.figure(figsize=(15,15))\n",
    "Train.hist(color='red')\n",
    "\n",
    "plt.subplots_adjust(bottom=1, right=2, top=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the above graphs we can tell that we have 2 categorical attributes NT_EFC195 and CLASS but the numbes in each group do not balance out.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6)Preparation of data for Machine Learning\n",
    "### Pre-processing of Data is carried out in this section\n",
    "* I will need to preapre my data for prepossessing \n",
    "* My preffered method for Data transformation is \"The Fit and Multiple Transform\" method form the scikit-learn library.\n",
    "* Split the dataset into the input and output variables for machine learning.\n",
    "* Apply a pre-processing transform to the input variables.\n",
    "* Summarize the data to show the change.\n",
    "### a)Rescaling the Data: \n",
    "* Rescaling is done since the data has various scales and these need to be into a scale that fits all\n",
    "* From numpy, the set_printoptions module is imported to determine the display of number of floating point values other Nump objects and from scikit learn preprocessing module.\n",
    "* The MinMaxScaler option is imported to rescale the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.457 0.    0.348 0.545 0.33  0.483 0.    0.005 0.508 0.415 0.182]\n",
      " [0.435 0.116 0.322 0.489 0.276 0.458 1.    0.011 0.421 0.586 0.475]\n",
      " [0.467 0.116 0.246 0.523 0.288 0.565 0.    0.011 0.442 0.273 0.666]\n",
      " [0.457 0.089 0.275 0.399 0.403 0.647 0.    0.011 0.405 0.153 0.424]\n",
      " [0.511 0.183 0.323 0.373 0.342 0.595 0.    0.011 0.5   0.196 0.536]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from numpy import set_printoptions\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "array = Train.values\n",
    "# separate array into input and output components\n",
    "X= array[:,0:11]\n",
    "Y= array[:,11]\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "rescaledX = scaler.fit_transform(X)\n",
    "# summarize transformed data\n",
    "set_printoptions(precision=3)\n",
    "print(rescaledX[0:5,:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* No rescaling of data was done since predictions with rescaled data had a poor result in my analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Looking at when my data is binarized instead\n",
    "from sklearn.preprocessing import Binarizer\n",
    "\n",
    "array2 = Train.values\n",
    "# separate array into input and output components\n",
    "X = array2[:,0:11]\n",
    "Y = array2[:,11]\n",
    "binarizer = Binarizer(threshold=0.0).fit(X)\n",
    "binaryX = binarizer.transform(X)\n",
    "# summarize transformed data\n",
    "set_printoptions(precision=3)\n",
    "print(binaryX[0:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Since my data was not boolean the data binarised was not used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Features:  6\n",
      "Selected Features: [ True False  True False  True  True  True False False False  True]\n",
      "Feature Ranking:  [1 5 1 4 1 1 1 2 6 3 1]\n",
      "Num Features:  6\n",
      "Selected Features: [ True False  True False  True  True  True False False False  True]\n",
      "Feature Ranking:  [1 5 1 4 1 1 1 2 6 3 1]\n"
     ]
    }
   ],
   "source": [
    "#b) Feature Selection\n",
    "#Select an anttribute will will give the most to the prediction variable \n",
    "#a)Selection is by The Recursive Feature Elimination (or RFE) \n",
    "# This works by recursively removing attributes and building a model on those attributes that remain.\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# feature extraction\n",
    "# feature extraction\n",
    "model = LogisticRegression()\n",
    "rfe = RFE(model, 6)\n",
    "fit = rfe.fit(X, Y)\n",
    "print(\"Num Features: \",  fit.n_features_)\n",
    "print(\"Selected Features:\",  fit.support_)\n",
    "print(\"Feature Ranking: \",  fit.ranking_)\n",
    "print(\"Num Features: \",  fit.n_features_)\n",
    "print(\"Selected Features:\",  fit.support_)\n",
    "print(\"Feature Ranking: \",  fit.ranking_)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c)PCA\n",
    "Principal component analysis is a dimensionality reduction technique. It uses linear algebra to transform the dataset into a compressed form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance: [0.605 0.242 0.103]\n",
      "[[ 1.409e-01 -3.557e-01 -4.743e-03 -4.935e-01 -1.975e-04 -5.101e-02\n",
      "   2.877e-03  6.020e-01 -4.950e-01 -8.765e-04  4.186e-03]\n",
      " [-8.867e-03  3.090e-02  4.994e-04  3.921e-01 -5.282e-04 -8.492e-03\n",
      "   3.437e-03  7.629e-01  5.129e-01  5.018e-03 -2.000e-03]\n",
      " [-2.731e-01  8.713e-01  8.049e-03 -1.349e-01  4.214e-04  8.143e-02\n",
      "  -2.941e-03  2.312e-01 -2.966e-01 -1.359e-03 -5.700e-04]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a2015a690>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "array = Train.values\n",
    "X = array[:,0:11]\n",
    "Y = array[:,11]\n",
    "# feature extraction\n",
    "pca = PCA(n_components=3)\n",
    "fit = pca.fit(X)\n",
    "# summarize components\n",
    "print(\"Explained Variance: %s\" % fit.explained_variance_ratio_)\n",
    "print(fit.components_)\n",
    "\n",
    "high_dim = Train.drop(columns='CLASS')\n",
    "\n",
    "scaler.fit(high_dim)\n",
    "scaled_data = scaler.transform(high_dim)\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(scaled_data)\n",
    "\n",
    "x_pca = pca.transform(scaled_data)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca[:,0], x_pca[:,1], c=Train['CLASS'], cmap='Spectral')\n",
    "plt.xlabel('First Principal Component')\n",
    "plt.ylabel('First Principal Component')\n",
    "labels = np.unique(Y)\n",
    "plt.legend(labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the PCA plot, Two distinct classes are obseved.  The Note is in the class distribution and region of overlap between the two classes.\n",
    "The overlap problem is a critical problem in which instances appear as valid for more than one class which may be responsible for the presence of noise in datasets and thus affect the performance of the learning algorithm (Gupta and Gupta, 2018)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.078 0.152 0.082 0.085 0.053 0.064 0.037 0.318 0.052 0.033 0.047]\n"
     ]
    }
   ],
   "source": [
    "# Feature importance\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "array = Train.values\n",
    "A = array[:,0:11]\n",
    "B = array[:,11]\n",
    "# feature extraction\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(X, Y)\n",
    "print(model.feature_importances_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BN2N1+li99a4Fu/ZjN16xYNeWtHhc0UtS41zR67j8DUJa+lzRS1LjDHpJapxbNzqtuFUkzT9X9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG+YUpvewt1Je0/IKWTheu6CWpcQa9JDXOoJekxhn0ktS4QUGfZEOSvUmmk2ydof7MJJ/u6r+QZHVXvjrJ00ke7H7+6/xOX5I0m1k/dZNkGXAT8HbgALAryVRVPdxrdh1wpKrekGQT8DHgXV3dI1V1wTzPW5I00JCPV64HpqtqH0CSHcBGoB/0G4EPd8e3A59Iknmcp9SMU/1xTj8+qiFbNyuA/b3zA13ZjG2q6ijwN8APd3VrknwlyeeT/NQc5ytJOkEL/YWpvwZWVdVTSf4BcGeSN1XVd/qNkmwGNgOsWrVqgackSS8vQ1b0B4Hzeucru7IZ2yRZDrwGeKqqnqmqpwCq6kvAI8CPjQ9QVbdU1WRVTU5MTJz4o5AkHdOQFf0uYG2SNYwCfRPw82NtpoBrgT8HrgLuq6pKMgEcrqrnk7weWAvsm7fZSzrt+P/+nn5mDfqqOppkC3APsAzYXlV7kmwDdlfVFPBJ4FNJpoHDjN4MAN4KbEvyHPAC8L6qOrwQD0SSNLNBe/RVtRPYOVZ2fe/4B8DVM/S7A7hjjnOUJM2B34yVpMYZ9JLUOINekhrnfzwiaUnzUz6zc0UvSY0z6CWpcW7dSNIJWIpbRa7oJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXGDgj7JhiR7k0wn2TpD/ZlJPt3VfyHJ6l7dr3Xle5NcNn9TlyQNMWvQJ1kG3ARcDqwDrkmybqzZdcCRqnoD8J+Bj3V91wGbgDcBG4Df7q4nSTpFhqzo1wPTVbWvqp4FdgAbx9psBH63O74deFuSdOU7quqZqnoUmO6uJ0k6RVJVx2+QXAVsqKpf7M5/Abi4qrb02vxF1+ZAd/4IcDHwYeCBqrq1K/8kcHdV3T42xmZgc3f648DeuT+0Qc4BnjxFYzleG2M63tIebzHGPFXjnV9VEzNVLD8Fg8+qqm4BbjnV4ybZXVWTjrc0x1uMMR1vaY+3GGMuxmMcN2Tr5iBwXu98ZVc2Y5sky4HXAE8N7CtJWkBDgn4XsDbJmiRnMLq5OjXWZgq4tju+CrivRntCU8Cm7lM5a4C1wBfnZ+qSpCFm3bqpqqNJtgD3AMuA7VW1J8k2YHdVTQGfBD6VZBo4zOjNgK7dbcDDwFHg/VX1/AI9lpNxqreLHG/pj+l4S3u8xRhzMR7jS8x6M1aStLT5zVhJapxBL0mNM+glqXEGvSQ1zqAHkrzX8Zb2mI63tMdbjDFbH+8lY/upG0jyeFWtcrylO6bjLe3xFmPM1sfrOy3+BMKpkOShY1UBP+J4p/+Yjre0x1uMMVsfb6iXTdAzepIvA46MlQe43/GWxJiOt7THW4wxWx9vkJdT0P8hcFZVPThekeRzjrckxnS8pT3eYozZ+niDuEcvSY3zUzeS1DiDXpIaZ9BLUuMMeklqnEEvSY37f3yiVYNZNnqQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate feature importances\n",
    "importances = model.feature_importances_\n",
    "# Create decision tree classifer object\n",
    "clf = ExtraTreesClassifier(random_state=0, n_jobs=-1)\n",
    "\n",
    "# Train model\n",
    "model = clf.fit(X, Y)\n",
    "\n",
    "# Sort feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Rearrange feature names so they match the sorted feature importances\n",
    "names = [Train.CLASS[i] for i in indices]\n",
    "\n",
    "# Create plot\n",
    "plt.figure()\n",
    "\n",
    "# Create plot title\n",
    "plt.title(\"Feature Importance\")\n",
    "\n",
    "# Add bars\n",
    "plt.bar(range(X.shape[1]), importances[indices])\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(X.shape[1]), names, rotation=90)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## According to feature importance all the features in the data set were useful hence could not be lost and therefore I kept them all therefor no rescaling was carried out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7)Evaluate the Performance of Machine Learning Algorithms with Resampling\n",
    "##There is need to know how well the algorithms will perform on unseen data\n",
    "##The best way to evaluate the performance of an algorithm is to make predictions for new data to which  answers are known\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  91.63533834586465\n"
     ]
    }
   ],
   "source": [
    "## Split the  Data into Test and Train\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "test_size = 0.35\n",
    "seed = 42\n",
    "\n",
    "X_train,X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n",
    "random_state=seed)\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "result = model.score(X_test, Y_test)\n",
    "print(\"Accuracy: \",  (result*100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "2\n",
      "387\n",
      "371\n"
     ]
    }
   ],
   "source": [
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  91.54135338345864\n"
     ]
    }
   ],
   "source": [
    "# On rescaled Data\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "array = Train.values\n",
    "X = array[:,0:11]\n",
    "Y = array[:,11]\n",
    "test_size = 0.35\n",
    "seed = 42\n",
    "\n",
    "rescaledX_train,rescaledX_test, Y_train, Y_test = train_test_split(rescaledX, Y, test_size=test_size,\n",
    "random_state=seed)\n",
    "model = LogisticRegression()\n",
    "model.fit(rescaledX_train, Y_train)\n",
    "result = model.score(rescaledX_test, Y_test)\n",
    "print(\"Accuracy: \",  (result*100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "2\n",
      "752\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber1_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Despite having a similar accuracy of 91.82,the rescaled data was giving imbalanced false a.nd true values and therefore i would not use it further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: (88.66787208086441, 18.91491119508906)\n"
     ]
    }
   ],
   "source": [
    "#Classification accuracy:is the number of correct predictions made as a ratio of all predictions made.\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "array = Train.values\n",
    "\n",
    "num_folds = 20#number of folds to use\n",
    "seed = 42#reproducibility\n",
    "\n",
    "kfold = KFold(n_splits=num_folds, random_state=None)\n",
    "model = LogisticRegression()\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n",
    "random_state=seed)\n",
    "model.fit(X_train, Y_train)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "\n",
    "print(f\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "2\n",
      "387\n",
      "371\n"
     ]
    }
   ],
   "source": [
    "\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber2_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.91      0.92      0.91       521\n",
      "         1.0       0.92      0.91      0.92       543\n",
      "\n",
      "    accuracy                           0.92      1064\n",
      "   macro avg       0.92      0.92      0.92      1064\n",
      "weighted avg       0.92      0.92      0.92      1064\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#To check out the classification report \n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "array = Train.values\n",
    "X = array[:,0:11]\n",
    "Y = array[:,11]\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "rescaledA = scaler.fit_transform(A)\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "rescaledA = scaler.fit_transform(A)\n",
    "test_size = 0.35\n",
    "seed = 42\n",
    "rescaledX_train,rescaledX_test, Y_train, Y_test = train_test_split(rescaledX, Y, test_size=test_size,\n",
    "random_state=seed)\n",
    "model = LogisticRegression()\n",
    "model.fit(rescaledX_train, Y_train)\n",
    "predicted = model.predict(rescaledX_test)\n",
    "report = classification_report(Y_test, predicted)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: (-0.26434068269258093, 0.156909136567752)\n"
     ]
    }
   ],
   "source": [
    "# we then look at the regression M\n",
    "# The Mean Absolute Error(MAE) is the sum of the absolute differences between predictions and actual values.\n",
    "# Its aim is to give an idea of how wrong the predictions were. \n",
    "from pandas import read_csv\n",
    "from sklearn.linear_model import LinearRegression\n",
    "array = Train.values\n",
    "X = array[:,0:11]\n",
    "Y = array[:,11]\n",
    "kfold = KFold(n_splits=10, random_state=None)\n",
    "model = LinearRegression()\n",
    "scoring = 'neg_mean_absolute_error'\n",
    "results = cross_val_score(model, rescaledX, Y, cv=kfold, scoring=scoring)\n",
    "print(\"MAE:\",(results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: (-1.2197243963358124, 3.6591731890074377)\n"
     ]
    }
   ],
   "source": [
    "#The R2 metric provides an indication of the goodness of fit of a set of predictions to the actual values\n",
    "#Cross Validation Regression R^2\n",
    "array = Train.values\n",
    "X = array[:,0:11]\n",
    "Y = array[:,11]\n",
    "\n",
    "kfold = KFold(n_splits=10, random_state=None)\n",
    "model = LinearRegression()\n",
    "scoring = 'r2'\n",
    "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
    "print(\"R^2:\",(results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8)Spot-Checking Classiffication Algorithms\n",
    "##Algorithms Overview\n",
    "##looking at six classication algorithms that will help spot-check on your dataset. Starting with two ##linear machine learning algorithms:\n",
    "\n",
    "8.1.1)Logistic Regression.\n",
    "8.1.2)Linear Discriminant Analysis.\n",
    "Then looking at four nonlinear machine learning algorithms:\n",
    "\n",
    "8.2.1)k-Nearest Neighbors.\n",
    "8.2.2)Naive Bayes.\n",
    "8.2.3)Classication and Regression Trees.\n",
    "8.2.4)Support Vector Machines.\n",
    "8.2.5)Random Forest\n",
    "8.2.6)Gradient Boosting for classification\n",
    "8.2.7)An extra-trees classifier.\n",
    "8.2.8)Neural Network-MLPC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## 8.1)Linear Machine Language Alogarithms\n",
    "### 8.1.1)Logistic Regression\n",
    "Logistic regression assumes a Gaussian distribution for the numeric input variables and can model binary classiffication problems.\n",
    "****This is done using the Logisticregression class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 91.54051589369463\n",
      "MCC: 0.8342865299822478\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression Classification\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "#for model set up\n",
    "model = LogisticRegression()\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "2\n",
      "383\n",
      "375\n"
     ]
    }
   ],
   "source": [
    "#Converting the LogisticRegression predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber3_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The difference between Accuracy and MCC model is slight but giving the same output pf 383 True values and 375 false values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1.2)Linear Discriminant Analysis or LDA \n",
    "-This a statistical technique for binary and multiclass classiffication. \n",
    "-It too assumes a Gaussian distribution for the numerical input variables. \n",
    "-You can construct an LDA model using the LinearDiscriminantAnalysis class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 91.70574952232066\n",
      "MCC: 0.8377162908048379\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "#Model set up\n",
    "model = LinearDiscriminantAnalysis()\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "2\n",
      "401\n",
      "357\n"
     ]
    }
   ],
   "source": [
    "#Converting the  LinearDiscriminantAnalysis predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber4_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 Nonlinear machine learning algorithms.\n",
    "\n",
    "### 8.2.1)k-Nearest Neighbors\n",
    "##The k-Nearest Neighbors algorithm (or KNN) uses a distance metric to find the k most similar instances ##In the training data for a new instance and takes the mean outcome of the neighbors as the prediction. #You can construct a KNN model using the KNeighborsClassifier class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 90.5863066851643\n",
      "MCC: 0.8690586462107053\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "#Model set up\n",
    "model = KNeighborsClassifier()\n",
    "# For cross validation of data\n",
    "num_folds = 5\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "2\n",
      "402\n",
      "356\n"
     ]
    }
   ],
   "source": [
    "#Converting the  LinearDiscriminantAnalysis predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber5_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.2) Naive Bayes\n",
    "-Naive Bayes calculates the probability of each class and the conditional probability of each class given each input value. \n",
    "-These probabilities are estimated for new data and multiplied together\n",
    "-The Assumption is that they are all independent (a simple or naive assumption).\n",
    "-When working with real-valued data, a Gaussian distribution is assumed to easily estimate the probabilities for input variables using the Gaussian Probability Density Function. \n",
    "##You can construct a Naive Bayes model using the GaussianNB class4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 91.93622980719125\n",
      "MCC: 0.8407203694376205\n",
      "0.9193622980719125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "model = GaussianNB()\n",
    "\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n",
    "\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False]\n",
      "2\n",
      "370\n",
      "388\n"
     ]
    }
   ],
   "source": [
    "#Converting the  Naive Bayes predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber5_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2.3)Classiffication and Regression Trees\n",
    "Classiffication and Regression Trees (CART or just decision trees) construct a binary tree from the training data. Split points are chosen greedily by evaluating each attribute and each value of each attribute in the training data in order to minimize a cost function (like the Gini index). You can construct a CART model using the DecisionTreeClassifier class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 89.20379537953795\n",
      "MCC: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "model = DecisionTreeClassifier()\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False]\n",
      "2\n",
      "388\n",
      "370\n"
     ]
    }
   ],
   "source": [
    "#Converting the DecisionTreeClassifier  predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber6_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2.4)Support Vector Machines\n",
    "Support Vector Machines (or SVM) seek a line that best separates two classes. \n",
    "The data instances closest to the line that best separates the classes are called support vectors and \n",
    "influence where the line is placed. \n",
    "SVM supports multiple classes and that of particular importance is the use of dierent kernel functions via the kernel parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 90.81618030224075\n",
      "MCC: 0.8187103751493263\n",
      "0.9081618030224075\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "model = SVC()\n",
    "\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n",
    "\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "2\n",
      "397\n",
      "361\n"
     ]
    }
   ],
   "source": [
    "#Converting the SVC predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber7_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.5)Random Forest\n",
    "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.\n",
    "Random decision forests correct for decision trees’ habit of over fitting to their training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 93.68030224075041\n",
      "MCC: 1.0\n",
      "0.9417394042035783\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "model = RandomForestClassifier(bootstrap = True,\n",
    "                              max_features = None)\n",
    "\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n",
    "\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False]\n",
      "2\n",
      "383\n",
      "375\n"
     ]
    }
   ],
   "source": [
    "#Converting the Randomforest predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber8_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.6)Gradient Boosting for classification.\n",
    "GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. \n",
    "In each stage n_classes_ regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function.\n",
    "Binary classification is a special case where only a single regression tree is induced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 93.15442070522842\n",
      "MCC: 0.9092011960769395\n",
      "0.9315442070522841\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "model = GradientBoostingClassifier(learning_rate = 1.0,\n",
    "                              max_depth = 1)\n",
    "\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n",
    "\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False]\n",
      "2\n",
      "382\n",
      "376\n"
     ]
    }
   ],
   "source": [
    "#Converting the GradientBoostingClassifier predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber9_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.7)An extra-trees classifier.\n",
    "This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset.\n",
    "It uses averaging to improve the predictive accuracy and control over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 93.77898645127671\n",
      "MCC: 1.0\n",
      "0.9364751606739621\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "model = ExtraTreesClassifier()                              \n",
    "\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n",
    "\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False]\n",
      "2\n",
      "384\n",
      "374\n"
     ]
    }
   ],
   "source": [
    "#Converting the ExtratreeClassifier predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber10_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.8)Multi-layer Perceptron classifier.\n",
    "This model optimizes the log-loss function using LBFGS or stochastic gradient descent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 92.39708181344449\n",
      "MCC: 0.8470217206261198\n",
      "0.9213359822824387\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "# For cross validation of data\n",
    "num_folds = 10\n",
    "seed=42\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n",
    "model = MLPClassifier()                              \n",
    "\n",
    "#For accuracy of model\n",
    "scoring='accuracy'\n",
    "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n",
    "print('accuracy',results.mean()*100)\n",
    "#Training the model to make a prediction\n",
    "model.fit(X,Y)\n",
    "kmodel=model.predict(Test)\n",
    "#With Mathews correlation cofficient on the model\n",
    "kmcc=matthews_corrcoef(model.predict(X),Y)\n",
    "print('MCC:',kmcc)\n",
    "\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False]\n",
      "2\n",
      "356\n",
      "402\n"
     ]
    }
   ],
   "source": [
    "#Converting the MLPClassifier predicted model first to data frame then a CSV file\n",
    "out= model.predict(Test.values)\n",
    "\n",
    "Eva = pd.DataFrame(out) #Converting to data frame\n",
    "Eva.columns=[\"CLASS\"] #Naming the column\n",
    "Eva.index.name=\"Index\" #Creating a column index\n",
    "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n",
    "\n",
    "Eva.to_csv(\"Amber11_csv\") ## Writing a csv file\n",
    "print(Eva['CLASS'].unique())\n",
    "print(Eva['CLASS'].nunique())\n",
    "\n",
    "#printing the numbers of False and True\n",
    "print(Eva.groupby('CLASS').size()[0].sum()) #\n",
    "print(Eva.groupby('CLASS').size()[1].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9)Compare Machine Learning Algorithms\n",
    "It is important to compare the performance of multiple different machine learning algorithms consistently.  The test harness as a template on your own machine learning problems and add more and different algorithms to compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('LR', 0.8353580423831856, 0.27188952844394665)\n",
      "('LDA', 0.8535044293903076, 0.2571395669719574)\n",
      "('KNN', 0.8027933385443807, 0.2521136100771112)\n",
      "('CART', 0.7345470731283654, 0.2898967460848411)\n",
      "('NB', 0.880815746048289, 0.11642272449162755)\n",
      "('SVM', 0.8350280093798853, 0.25836507020625044)\n",
      "('MLPC', 0.8291112992878235, 0.2688132800232323)\n",
      "('EXTC', 0.8370375195414278, 0.25047049509079944)\n",
      "('GBC', 0.7899437641132534, 0.2887124941351131)\n",
      "('RDF', 0.8093625151988884, 0.2921369545256496)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare Algorithms\n",
    "from matplotlib import pyplot\n",
    "# prepare models and add them to a list\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "models.append(('MLPC', MLPClassifier()))\n",
    "models.append(('EXTC', ExtraTreesClassifier()))\n",
    "models.append(('GBC', GradientBoostingClassifier()))\n",
    "models.append(('RDF', RandomForestClassifier()))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "scoring = 'accuracy'\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=42)\n",
    "    cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)\n",
    "\n",
    "# boxplot algorithm comparison\n",
    "fig = pyplot.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From the alogarithms above the best was the Gausian Naive Bayes with the highest mean value pf 0.88 followed by LDA with 0.85\n",
    "# The least fit alogarith was DecisionTreeClassifier(0.73)"
   ]
  }
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