Projection of Fuel Consumption.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "coupled-celtic",
   "metadata": {},
   "source": [
    "## Fuel Consumption Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "recognized-sound",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ordered-enforcement",
   "metadata": {},
   "source": [
    "#### The variables we will use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "literary-punch",
   "metadata": {},
   "source": [
    "HBPPOPDN, HHFAMINC, HH_RACE, HOMEOWN, EDUC, HHSIZE (N), HHVEHCNT (N), NUMADLT (N), WRKCOUNT (N)   \n",
    "TRVLCMIN (N), TRPMILES (N)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "quarterly-means",
   "metadata": {},
   "outputs": [],
   "source": [
    "### import the NHTS database\n",
    "HH_data = pd.read_csv(\"hhpub.csv\")\n",
    "TRIP_data = pd.read_csv(\"trippub.csv\")\n",
    "VEH_data = pd.read_csv(\"vehpub.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "stupid-animal",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HOUSEID</th>\n",
       "      <th>HBPPOPDN</th>\n",
       "      <th>HHFAMINC</th>\n",
       "      <th>HH_RACE</th>\n",
       "      <th>HOMEOWN</th>\n",
       "      <th>HHSIZE</th>\n",
       "      <th>HHVEHCNT</th>\n",
       "      <th>NUMADLT</th>\n",
       "      <th>WRKCOUNT</th>\n",
       "      <th>URBRUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
       "      <td>750</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000008</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000012</td>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000019</td>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000029</td>\n",
       "      <td>1500</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HOUSEID  HBPPOPDN  HHFAMINC  HH_RACE  HOMEOWN  HHSIZE  HHVEHCNT  NUMADLT  \\\n",
       "0  30000007       750         7        2        1       3         5        3   \n",
       "1  30000008       300         8        1        1       2         4        2   \n",
       "2  30000012     17000        10        1        1       1         2        1   \n",
       "3  30000019       750         3        1        1       2         2        2   \n",
       "4  30000029      1500         5        1        1       2         2        2   \n",
       "\n",
       "   WRKCOUNT  URBRUR  \n",
       "0         1       1  \n",
       "1         2       2  \n",
       "2         1       1  \n",
       "3         0       1  \n",
       "4         0       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Select the columns needed.\n",
    "HH_data2 = HH_data[['HOUSEID','HBPPOPDN','HHFAMINC',\\\n",
    "                    'HH_RACE','HOMEOWN','HHSIZE',\\\n",
    "                    'HHVEHCNT','NUMADLT','WRKCOUNT','URBRUR']]\n",
    "HH_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "joined-czech",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HOUSEID</th>\n",
       "      <th>PERSONID</th>\n",
       "      <th>TDTRPNUM</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>TRVLCMIN</th>\n",
       "      <th>TRPMILES</th>\n",
       "      <th>WHYTRP1S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>5.244</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000007</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>5.149</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000007</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>84.004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000007</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>81.628</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000007</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.250</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HOUSEID  PERSONID  TDTRPNUM  EDUC  TRVLCMIN  TRPMILES  WHYTRP1S\n",
       "0  30000007         1         1     3        15     5.244        20\n",
       "1  30000007         1         2     3        20     5.149         1\n",
       "2  30000007         2         1     3       120    84.004         1\n",
       "3  30000007         2         2     3       150    81.628        10\n",
       "4  30000007         3         1     2        15     2.250        20"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRIP_data2 = TRIP_data[['HOUSEID','PERSONID','TDTRPNUM','EDUC','TRVLCMIN','TRPMILES','WHYTRP1S']] \n",
    "TRIP_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "corresponding-measure",
   "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>HOUSEID</th>\n",
       "      <th>VEHID</th>\n",
       "      <th>GSCOST</th>\n",
       "      <th>GSTOTCST</th>\n",
       "      <th>GSYRGAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
       "      <td>1</td>\n",
       "      <td>2.31275</td>\n",
       "      <td>1126.457778</td>\n",
       "      <td>487.064221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000007</td>\n",
       "      <td>2</td>\n",
       "      <td>2.31275</td>\n",
       "      <td>580.267873</td>\n",
       "      <td>250.899523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000007</td>\n",
       "      <td>3</td>\n",
       "      <td>2.31275</td>\n",
       "      <td>1027.930589</td>\n",
       "      <td>444.462475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000007</td>\n",
       "      <td>4</td>\n",
       "      <td>2.31275</td>\n",
       "      <td>93.272224</td>\n",
       "      <td>40.329575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000007</td>\n",
       "      <td>5</td>\n",
       "      <td>2.31275</td>\n",
       "      <td>2054.656806</td>\n",
       "      <td>888.404197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HOUSEID  VEHID   GSCOST     GSTOTCST     GSYRGAL\n",
       "0  30000007      1  2.31275  1126.457778  487.064221\n",
       "1  30000007      2  2.31275   580.267873  250.899523\n",
       "2  30000007      3  2.31275  1027.930589  444.462475\n",
       "3  30000007      4  2.31275    93.272224   40.329575\n",
       "4  30000007      5  2.31275  2054.656806  888.404197"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VEH_data2 = VEH_data[['HOUSEID','VEHID','GSCOST','GSTOTCST','GSYRGAL']]\n",
    "VEH_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "civil-jason",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Preproceesing the data. Deleted the rows that are Nan or no response.\n",
    "HH_data3 = HH_data2\n",
    "\n",
    "HH_data3 = HH_data3[~HH_data3['HBPPOPDN'].isin([-9])]\n",
    "HH_data3 = HH_data3[~HH_data3['HHFAMINC'].isin([-9])]\n",
    "HH_data3 = HH_data3[~HH_data3['HHFAMINC'].isin([-8])]\n",
    "HH_data3 = HH_data3[~HH_data3['HHFAMINC'].isin([-7])]\n",
    "HH_data3 = HH_data3[~HH_data3['HH_RACE'].isin([-8])]\n",
    "HH_data3 = HH_data3[~HH_data3['HH_RACE'].isin([-7])]\n",
    "# HH_data3 = HH_data3[~HH_data3['HH_RACE'].isin([97])]\n",
    "HH_data3 = HH_data3[~HH_data3['HOMEOWN'].isin([-8])]\n",
    "HH_data3 = HH_data3[~HH_data3['HOMEOWN'].isin([-7])]\n",
    "HH_data3 = HH_data3[~HH_data3['HOMEOWN'].isin([97])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "medical-boxing",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HOUSEID</th>\n",
       "      <th>HBPPOPDN</th>\n",
       "      <th>HHFAMINC</th>\n",
       "      <th>HH_RACE</th>\n",
       "      <th>HOMEOWN</th>\n",
       "      <th>HHSIZE</th>\n",
       "      <th>HHVEHCNT</th>\n",
       "      <th>NUMADLT</th>\n",
       "      <th>WRKCOUNT</th>\n",
       "      <th>URBRUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000012</td>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000019</td>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000029</td>\n",
       "      <td>1500</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    HOUSEID  HBPPOPDN  HHFAMINC  HH_RACE  HOMEOWN  HHSIZE  HHVEHCNT  NUMADLT  \\\n",
       "0  30000007       750         7        2        1       3         5        3   \n",
       "1  30000008       300         8        1        1       2         4        2   \n",
       "2  30000012     17000        10        1        1       1         2        1   \n",
       "3  30000019       750         3        1        1       2         2        2   \n",
       "4  30000029      1500         5        1        1       2         2        2   \n",
       "\n",
       "   WRKCOUNT  URBRUR  \n",
       "0         1       1  \n",
       "1         2       2  \n",
       "2         1       1  \n",
       "3         0       1  \n",
       "4         0       1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Processed dataset.\n",
    "hh_data = HH_data3\n",
    "hh_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "detected-wiring",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Preproceesing the data. Deleted the rows that are Nan or no response.\n",
    "TRIP_data3 = TRIP_data2\n",
    "\n",
    "TRIP_data3 = TRIP_data3[~TRIP_data3['TRVLCMIN'].isin([-9])]\n",
    "TRIP_data3 = TRIP_data3[~TRIP_data3['TRPMILES'].isin([-9])]\n",
    "TRIP_data3 = TRIP_data3[~TRIP_data3['EDUC'].isin([-8])]\n",
    "TRIP_data3 = TRIP_data3[~TRIP_data3['EDUC'].isin([-7])]\n",
    "TRIP_data3 = TRIP_data3[~TRIP_data3['EDUC'].isin([-1])]\n",
    "# len(TRIP_data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aggressive-delay",
   "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>HOUSEID</th>\n",
       "      <th>TRVLCMIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
       "      <td>55.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000008</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000012</td>\n",
       "      <td>30.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000019</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000029</td>\n",
       "      <td>15.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HOUSEID   TRVLCMIN\n",
       "0  30000007  55.833333\n",
       "1  30000008  12.500000\n",
       "2  30000012  30.555556\n",
       "3  30000019  15.000000\n",
       "4  30000029  15.700000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Group the data for each household.\n",
    "trip_time_total = TRIP_data3.groupby(['HOUSEID'])[['TRVLCMIN']].mean()\n",
    "trip_time_total = trip_time_total.reset_index()\n",
    "\n",
    "trip_dis_total = TRIP_data3.groupby(['HOUSEID'])[['TRPMILES']].mean()\n",
    "trip_dis_total = trip_dis_total.reset_index()\n",
    "\n",
    "trip_edu = TRIP_data3.groupby(['HOUSEID'])[['EDUC']].mean().round()\n",
    "trip_edu = trip_edu.reset_index()\n",
    "\n",
    "trip_time_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "legal-plant",
   "metadata": {
    "scrolled": true
   },
   "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>HOUSEID</th>\n",
       "      <th>TRVLCMIN</th>\n",
       "      <th>TRPMILES</th>\n",
       "      <th>EDUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>30.086333</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000008</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>8.017000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000012</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>14.090778</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000019</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.049500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000029</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>6.134100</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HOUSEID   TRVLCMIN   TRPMILES  EDUC\n",
       "0  30000007  55.833333  30.086333   3.0\n",
       "1  30000008  12.500000   8.017000   5.0\n",
       "2  30000012  30.555556  14.090778   5.0\n",
       "3  30000019  15.000000   3.049500   2.0\n",
       "4  30000029  15.700000   6.134100   2.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Processed dataset.\n",
    "trip_data = pd.merge(trip_time_total,trip_dis_total,on='HOUSEID',how='inner')\n",
    "trip_data = pd.merge(trip_data,trip_edu,on='HOUSEID',how='inner')\n",
    "# trip_data = pd.merge(trip_data,TRIP_data3,on='HOUSEID',how='inner')\n",
    "trip_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "thrown-product",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Preproceesing the data. Deleted the rows that are Nan or no response.\n",
    "VEH_data3 = VEH_data2\n",
    "\n",
    "VEH_data3 = VEH_data3[~VEH_data3['GSCOST'].isin([-9])]\n",
    "VEH_data3 = VEH_data3[~VEH_data3['GSTOTCST'].isin([-9])]\n",
    "VEH_data3 = VEH_data3[~VEH_data3['GSYRGAL'].isin([-9])]\n",
    "# len(VEH_data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "analyzed-spouse",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Group the data for each household.\n",
    "veh_price = VEH_data3.groupby(['HOUSEID'])[['GSCOST']].mean()\n",
    "veh_price = veh_price.reset_index()\n",
    "\n",
    "veh_expense = VEH_data3.groupby(['HOUSEID'])[['GSTOTCST']].sum()\n",
    "veh_expense = veh_expense.reset_index()\n",
    "\n",
    "veh_cost = VEH_data3.groupby(['HOUSEID'])[['GSYRGAL']].sum()\n",
    "veh_cost = veh_cost.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "laughing-commodity",
   "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>HOUSEID</th>\n",
       "      <th>GSCOST</th>\n",
       "      <th>GSTOTCST</th>\n",
       "      <th>GSYRGAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
       "      <td>2.312750</td>\n",
       "      <td>4882.585271</td>\n",
       "      <td>2111.159992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000008</td>\n",
       "      <td>2.266500</td>\n",
       "      <td>4002.506034</td>\n",
       "      <td>1765.941334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000012</td>\n",
       "      <td>2.465333</td>\n",
       "      <td>1158.737812</td>\n",
       "      <td>470.012633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000019</td>\n",
       "      <td>2.440167</td>\n",
       "      <td>2311.397741</td>\n",
       "      <td>947.229455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000029</td>\n",
       "      <td>2.266500</td>\n",
       "      <td>1283.764151</td>\n",
       "      <td>566.408185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HOUSEID    GSCOST     GSTOTCST      GSYRGAL\n",
       "0  30000007  2.312750  4882.585271  2111.159992\n",
       "1  30000008  2.266500  4002.506034  1765.941334\n",
       "2  30000012  2.465333  1158.737812   470.012633\n",
       "3  30000019  2.440167  2311.397741   947.229455\n",
       "4  30000029  2.266500  1283.764151   566.408185"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Processed dataset.\n",
    "veh_data = pd.merge(veh_price,veh_expense,on='HOUSEID',how='inner')\n",
    "veh_data = pd.merge(veh_data,veh_cost,on='HOUSEID',how='inner')\n",
    "veh_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "available-pickup",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>HOUSEID</th>\n",
       "      <th>HBPPOPDN</th>\n",
       "      <th>HHFAMINC</th>\n",
       "      <th>HH_RACE</th>\n",
       "      <th>HOMEOWN</th>\n",
       "      <th>HHSIZE</th>\n",
       "      <th>HHVEHCNT</th>\n",
       "      <th>NUMADLT</th>\n",
       "      <th>WRKCOUNT</th>\n",
       "      <th>URBRUR</th>\n",
       "      <th>TRVLCMIN</th>\n",
       "      <th>TRPMILES</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>GSCOST</th>\n",
       "      <th>GSTOTCST</th>\n",
       "      <th>GSYRGAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000007</td>\n",
       "      <td>750</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>30.086333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.312750</td>\n",
       "      <td>4882.585271</td>\n",
       "      <td>2111.159992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000008</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>8.017000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.266500</td>\n",
       "      <td>4002.506034</td>\n",
       "      <td>1765.941334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000012</td>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>14.090778</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.465333</td>\n",
       "      <td>1158.737812</td>\n",
       "      <td>470.012633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000019</td>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.049500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.440167</td>\n",
       "      <td>2311.397741</td>\n",
       "      <td>947.229455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000029</td>\n",
       "      <td>1500</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>6.134100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.266500</td>\n",
       "      <td>1283.764151</td>\n",
       "      <td>566.408185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HOUSEID  HBPPOPDN  HHFAMINC  HH_RACE  HOMEOWN  HHSIZE  HHVEHCNT  NUMADLT  \\\n",
       "0  30000007       750         7        2        1       3         5        3   \n",
       "1  30000008       300         8        1        1       2         4        2   \n",
       "2  30000012     17000        10        1        1       1         2        1   \n",
       "3  30000019       750         3        1        1       2         2        2   \n",
       "4  30000029      1500         5        1        1       2         2        2   \n",
       "\n",
       "   WRKCOUNT  URBRUR   TRVLCMIN   TRPMILES  EDUC    GSCOST     GSTOTCST  \\\n",
       "0         1       1  55.833333  30.086333   3.0  2.312750  4882.585271   \n",
       "1         2       2  12.500000   8.017000   5.0  2.266500  4002.506034   \n",
       "2         1       1  30.555556  14.090778   5.0  2.465333  1158.737812   \n",
       "3         0       1  15.000000   3.049500   2.0  2.440167  2311.397741   \n",
       "4         0       1  15.700000   6.134100   2.0  2.266500  1283.764151   \n",
       "\n",
       "       GSYRGAL  \n",
       "0  2111.159992  \n",
       "1  1765.941334  \n",
       "2   470.012633  \n",
       "3   947.229455  \n",
       "4   566.408185  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Final data.\n",
    "data = pd.merge(hh_data, trip_data, on='HOUSEID',how='inner')\n",
    "data = pd.merge(data, veh_data, on='HOUSEID',how='inner')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "junior-japanese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>HBPPOPDN</th>\n",
       "      <th>HHFAMINC</th>\n",
       "      <th>HH_RACE</th>\n",
       "      <th>HOMEOWN</th>\n",
       "      <th>HHSIZE</th>\n",
       "      <th>HHVEHCNT</th>\n",
       "      <th>NUMADLT</th>\n",
       "      <th>WRKCOUNT</th>\n",
       "      <th>URBRUR</th>\n",
       "      <th>TRVLCMIN</th>\n",
       "      <th>TRPMILES</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>GSYRGAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HBPPOPDN</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.205</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.201</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>0.147</td>\n",
       "      <td>-0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HHFAMINC</th>\n",
       "      <td>0.061</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.269</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.379</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_RACE</th>\n",
       "      <td>0.149</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMEOWN</th>\n",
       "      <td>0.205</td>\n",
       "      <td>-0.269</td>\n",
       "      <td>0.151</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>-0.180</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HHSIZE</th>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.141</td>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HHVEHCNT</th>\n",
       "      <td>-0.201</td>\n",
       "      <td>0.376</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>0.512</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.220</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUMADLT</th>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.180</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.146</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WRKCOUNT</th>\n",
       "      <td>0.066</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.455</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URBRUR</th>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.042</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.198</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>0.166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRVLCMIN</th>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.086</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRPMILES</th>\n",
       "      <td>-0.194</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.785</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUC</th>\n",
       "      <td>0.147</td>\n",
       "      <td>0.404</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.066</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSYRGAL</th>\n",
       "      <td>-0.195</td>\n",
       "      <td>0.339</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.171</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.275</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          HBPPOPDN  HHFAMINC  HH_RACE  HOMEOWN  HHSIZE  HHVEHCNT  NUMADLT  \\\n",
       "HBPPOPDN     1.000     0.061    0.149    0.205  -0.041    -0.201   -0.070   \n",
       "HHFAMINC     0.061     1.000   -0.094   -0.269   0.308     0.376    0.314   \n",
       "HH_RACE      0.149    -0.094    1.000    0.151   0.045    -0.065    0.001   \n",
       "HOMEOWN      0.205    -0.269    0.151    1.000  -0.110    -0.291   -0.180   \n",
       "HHSIZE      -0.041     0.308    0.045   -0.110   1.000     0.512    0.823   \n",
       "HHVEHCNT    -0.201     0.376   -0.065   -0.291   0.512     1.000    0.600   \n",
       "NUMADLT     -0.070     0.314    0.001   -0.180   0.823     0.600    1.000   \n",
       "WRKCOUNT     0.066     0.379    0.038    0.007   0.506     0.384    0.455   \n",
       "URBRUR      -0.675    -0.062   -0.107   -0.150   0.031     0.176    0.062   \n",
       "TRVLCMIN    -0.043     0.111    0.066   -0.021   0.086     0.123    0.108   \n",
       "TRPMILES    -0.194     0.166    0.009   -0.086   0.141     0.220    0.146   \n",
       "EDUC         0.147     0.404   -0.020   -0.046  -0.066    -0.038   -0.062   \n",
       "GSYRGAL     -0.195     0.339   -0.036   -0.171   0.470     0.641    0.460   \n",
       "\n",
       "          WRKCOUNT  URBRUR  TRVLCMIN  TRPMILES   EDUC  GSYRGAL  \n",
       "HBPPOPDN     0.066  -0.675    -0.043    -0.194  0.147   -0.195  \n",
       "HHFAMINC     0.379  -0.062     0.111     0.166  0.404    0.339  \n",
       "HH_RACE      0.038  -0.107     0.066     0.009 -0.020   -0.036  \n",
       "HOMEOWN      0.007  -0.150    -0.021    -0.086 -0.046   -0.171  \n",
       "HHSIZE       0.506   0.031     0.086     0.141 -0.066    0.470  \n",
       "HHVEHCNT     0.384   0.176     0.123     0.220 -0.038    0.641  \n",
       "NUMADLT      0.455   0.062     0.108     0.146 -0.062    0.460  \n",
       "WRKCOUNT     1.000  -0.042     0.132     0.212  0.070    0.431  \n",
       "URBRUR      -0.042   1.000     0.086     0.198 -0.135    0.166  \n",
       "TRVLCMIN     0.132   0.086     1.000     0.785  0.013    0.159  \n",
       "TRPMILES     0.212   0.198     0.785     1.000  0.001    0.275  \n",
       "EDUC         0.070  -0.135     0.013     0.001  1.000   -0.015  \n",
       "GSYRGAL      0.431   0.166     0.159     0.275 -0.015    1.000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Spearman Coorelation between variable pairs.\n",
    "data_corr = data.drop(columns=['HOUSEID','GSCOST', 'GSTOTCST'])\n",
    "re_spearman = data_corr.corr(method='spearman').round(3)\n",
    "re_spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "exempt-neutral",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, ax = plt.subplots(figsize=(18,16))\n",
    "# font = {'family': 'serif',\n",
    "#         'serif': 'Times New Roman',\n",
    "#         'weight': 'normal',\n",
    "#         'size': 12}\n",
    "# plt.rc('font', **font)\n",
    "sns.set(style=\"ticks\", font='Times New Roman', font_scale=1.5)\n",
    "ax.axis('on')\n",
    "ax.tick_params(axis='y',labelsize=20,width=2,color=\"black\")\n",
    "ax.tick_params(axis='x',labelsize=20,width=2,color=\"black\")\n",
    "ax.xaxis.tick_top()\n",
    "sns.heatmap(re_spearman.corr(method='spearman').round(2), cbar=True, square=True, annot=False, ax=ax,cmap='RdYlGn',robust=True,center=0.4)\n",
    "plt.savefig('re_spearman.png', facecolor='w',dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "virgin-faculty",
   "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>HBPPOPDN</th>\n",
       "      <th>HHFAMINC</th>\n",
       "      <th>HH_RACE</th>\n",
       "      <th>HOMEOWN</th>\n",
       "      <th>HHVEHCNT</th>\n",
       "      <th>URBRUR</th>\n",
       "      <th>TRVLCMIN</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>GSCOST</th>\n",
       "      <th>GSYRGAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>750</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.312750</td>\n",
       "      <td>2111.159992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.266500</td>\n",
       "      <td>1765.941334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.465333</td>\n",
       "      <td>470.012633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.440167</td>\n",
       "      <td>947.229455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.266500</td>\n",
       "      <td>566.408185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HBPPOPDN  HHFAMINC  HH_RACE  HOMEOWN  HHVEHCNT  URBRUR   TRVLCMIN  EDUC  \\\n",
       "0       750         7        2        1         5       1  55.833333   3.0   \n",
       "1       300         8        1        1         4       2  12.500000   5.0   \n",
       "2     17000        10        1        1         2       1  30.555556   5.0   \n",
       "3       750         3        1        1         2       1  15.000000   2.0   \n",
       "4      1500         5        1        1         2       1  15.700000   2.0   \n",
       "\n",
       "     GSCOST      GSYRGAL  \n",
       "0  2.312750  2111.159992  \n",
       "1  2.266500  1765.941334  \n",
       "2  2.465333   470.012633  \n",
       "3  2.440167   947.229455  \n",
       "4  2.266500   566.408185  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Drop the variables that are not related to GSYRAL and have strong correlations.\n",
    "data_final = data.drop(columns=['HOUSEID','NUMADLT','WRKCOUNT','HHSIZE','TRPMILES',\\\n",
    "                                'GSTOTCST'])\n",
    "data_final.head()\n",
    "#data_final.to_csv(\"data_final_0301.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "rapid-router",
   "metadata": {
    "scrolled": true
   },
   "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>HBPPOPDN</th>\n",
       "      <th>HHFAMINC</th>\n",
       "      <th>HH_RACE</th>\n",
       "      <th>HOMEOWN</th>\n",
       "      <th>HHVEHCNT</th>\n",
       "      <th>URBRUR</th>\n",
       "      <th>TRVLCMIN</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>GSCOST</th>\n",
       "      <th>GSYRGAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "      <td>107441.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3854.901760</td>\n",
       "      <td>6.258765</td>\n",
       "      <td>2.743413</td>\n",
       "      <td>1.209669</td>\n",
       "      <td>2.109967</td>\n",
       "      <td>1.225659</td>\n",
       "      <td>24.381493</td>\n",
       "      <td>3.442317</td>\n",
       "      <td>2.416149</td>\n",
       "      <td>1090.678649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5163.641096</td>\n",
       "      <td>2.507256</td>\n",
       "      <td>11.674805</td>\n",
       "      <td>0.407074</td>\n",
       "      <td>1.121723</td>\n",
       "      <td>0.418017</td>\n",
       "      <td>31.986537</td>\n",
       "      <td>1.053373</td>\n",
       "      <td>0.275725</td>\n",
       "      <td>1047.281142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.126167</td>\n",
       "      <td>0.027692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.266500</td>\n",
       "      <td>452.452103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.312750</td>\n",
       "      <td>850.782324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.465333</td>\n",
       "      <td>1427.638387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.434861</td>\n",
       "      <td>35303.206152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            HBPPOPDN       HHFAMINC        HH_RACE        HOMEOWN  \\\n",
       "count  107441.000000  107441.000000  107441.000000  107441.000000   \n",
       "mean     3854.901760       6.258765       2.743413       1.209669   \n",
       "std      5163.641096       2.507256      11.674805       0.407074   \n",
       "min        50.000000       1.000000       1.000000       1.000000   \n",
       "25%       300.000000       5.000000       1.000000       1.000000   \n",
       "50%      1500.000000       6.000000       1.000000       1.000000   \n",
       "75%      7000.000000       8.000000       1.000000       1.000000   \n",
       "max     30000.000000      11.000000      97.000000       2.000000   \n",
       "\n",
       "            HHVEHCNT         URBRUR       TRVLCMIN           EDUC  \\\n",
       "count  107441.000000  107441.000000  107441.000000  107441.000000   \n",
       "mean        2.109967       1.225659      24.381493       3.442317   \n",
       "std         1.121723       0.418017      31.986537       1.053373   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000      12.000000       3.000000   \n",
       "50%         2.000000       1.000000      17.500000       3.000000   \n",
       "75%         3.000000       1.000000      26.666667       4.000000   \n",
       "max        12.000000       2.000000    1200.000000       5.000000   \n",
       "\n",
       "              GSCOST        GSYRGAL  \n",
       "count  107441.000000  107441.000000  \n",
       "mean        2.416149    1090.678649  \n",
       "std         0.275725    1047.281142  \n",
       "min         2.126167       0.027692  \n",
       "25%         2.266500     452.452103  \n",
       "50%         2.312750     850.782324  \n",
       "75%         2.465333    1427.638387  \n",
       "max         7.434861   35303.206152  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "retained-legend",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(data_final.corr(method='spearman').round(3), square=True, annot=True, ax=ax,cmap='GnBu',robust=True,center=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "informational-siemens",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Saperate the data to be train dataset and prediction dataset.\n",
    "X = data_final[['HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR']].values\n",
    "Y = data_final.GSYRGAL.values\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,train_size=0.80, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dressed-seventh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HBPPOPDN', 'HH_RACE', 'HOMEOWN', 'HHFAMINC', 'HHVEHCNT', 'TRVLCMIN',\n",
       "       'EDUC', 'URBRUR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final[['HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR']].columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dietary-recommendation",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "acting-papua",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimator=50, alpha=0.0001, seed=20, accuracy=0.287\n",
      "estimator=50, alpha=0.0001, seed=50, accuracy=0.287\n",
      "estimator=50, alpha=0.0001, seed=100, accuracy=0.288\n",
      "estimator=50, alpha=0.0001, seed=200, accuracy=0.286\n",
      "estimator=50, alpha=0.0001, seed=500, accuracy=0.287\n",
      "estimator=50, alpha=0.0001, seed=1000, accuracy=0.283\n",
      "estimator=50, alpha=0.0001, seed=2000, accuracy=0.284\n",
      "estimator=50, alpha=0.001, seed=20, accuracy=0.287\n",
      "estimator=50, alpha=0.001, seed=50, accuracy=0.287\n",
      "estimator=50, alpha=0.001, seed=100, accuracy=0.288\n",
      "estimator=50, alpha=0.001, seed=200, accuracy=0.286\n",
      "estimator=50, alpha=0.001, seed=500, accuracy=0.287\n",
      "estimator=50, alpha=0.001, seed=1000, accuracy=0.283\n",
      "estimator=50, alpha=0.001, seed=2000, accuracy=0.284\n",
      "estimator=50, alpha=0.01, seed=20, accuracy=0.287\n",
      "estimator=50, alpha=0.01, seed=50, accuracy=0.287\n",
      "estimator=50, alpha=0.01, seed=100, accuracy=0.288\n",
      "estimator=50, alpha=0.01, seed=200, accuracy=0.286\n",
      "estimator=50, alpha=0.01, seed=500, accuracy=0.287\n",
      "estimator=50, alpha=0.01, seed=1000, accuracy=0.283\n",
      "estimator=50, alpha=0.01, seed=2000, accuracy=0.284\n",
      "estimator=50, alpha=0.1, seed=20, accuracy=0.287\n",
      "estimator=50, alpha=0.1, seed=50, accuracy=0.287\n",
      "estimator=50, alpha=0.1, seed=100, accuracy=0.288\n",
      "estimator=50, alpha=0.1, seed=200, accuracy=0.286\n",
      "estimator=50, alpha=0.1, seed=500, accuracy=0.287\n",
      "estimator=50, alpha=0.1, seed=1000, accuracy=0.283\n",
      "estimator=50, alpha=0.1, seed=2000, accuracy=0.284\n",
      "estimator=50, alpha=0.9, seed=20, accuracy=0.287\n",
      "estimator=50, alpha=0.9, seed=50, accuracy=0.287\n",
      "estimator=50, alpha=0.9, seed=100, accuracy=0.288\n",
      "estimator=50, alpha=0.9, seed=200, accuracy=0.286\n",
      "estimator=50, alpha=0.9, seed=500, accuracy=0.287\n",
      "estimator=50, alpha=0.9, seed=1000, accuracy=0.283\n",
      "estimator=50, alpha=0.9, seed=2000, accuracy=0.284\n",
      "estimator=50, alpha=0.99, seed=20, accuracy=0.287\n",
      "estimator=50, alpha=0.99, seed=50, accuracy=0.287\n",
      "estimator=50, alpha=0.99, seed=100, accuracy=0.288\n",
      "estimator=50, alpha=0.99, seed=200, accuracy=0.286\n",
      "estimator=50, alpha=0.99, seed=500, accuracy=0.287\n",
      "estimator=50, alpha=0.99, seed=1000, accuracy=0.283\n",
      "estimator=50, alpha=0.99, seed=2000, accuracy=0.284\n",
      "estimator=100, alpha=0.0001, seed=20, accuracy=0.290\n",
      "estimator=100, alpha=0.0001, seed=50, accuracy=0.290\n",
      "estimator=100, alpha=0.0001, seed=100, accuracy=0.290\n",
      "estimator=100, alpha=0.0001, seed=200, accuracy=0.290\n",
      "estimator=100, alpha=0.0001, seed=500, accuracy=0.291\n",
      "estimator=100, alpha=0.0001, seed=1000, accuracy=0.289\n",
      "estimator=100, alpha=0.0001, seed=2000, accuracy=0.288\n",
      "estimator=100, alpha=0.001, seed=20, accuracy=0.290\n",
      "estimator=100, alpha=0.001, seed=50, accuracy=0.290\n",
      "estimator=100, alpha=0.001, seed=100, accuracy=0.290\n",
      "estimator=100, alpha=0.001, seed=200, accuracy=0.290\n",
      "estimator=100, alpha=0.001, seed=500, accuracy=0.291\n",
      "estimator=100, alpha=0.001, seed=1000, accuracy=0.289\n",
      "estimator=100, alpha=0.001, seed=2000, accuracy=0.288\n",
      "estimator=100, alpha=0.01, seed=20, accuracy=0.290\n",
      "estimator=100, alpha=0.01, seed=50, accuracy=0.290\n",
      "estimator=100, alpha=0.01, seed=100, accuracy=0.290\n",
      "estimator=100, alpha=0.01, seed=200, accuracy=0.290\n",
      "estimator=100, alpha=0.01, seed=500, accuracy=0.291\n",
      "estimator=100, alpha=0.01, seed=1000, accuracy=0.289\n",
      "estimator=100, alpha=0.01, seed=2000, accuracy=0.288\n",
      "estimator=100, alpha=0.1, seed=20, accuracy=0.290\n",
      "estimator=100, alpha=0.1, seed=50, accuracy=0.290\n",
      "estimator=100, alpha=0.1, seed=100, accuracy=0.290\n",
      "estimator=100, alpha=0.1, seed=200, accuracy=0.290\n",
      "estimator=100, alpha=0.1, seed=500, accuracy=0.291\n",
      "estimator=100, alpha=0.1, seed=1000, accuracy=0.289\n",
      "estimator=100, alpha=0.1, seed=2000, accuracy=0.288\n",
      "estimator=100, alpha=0.9, seed=20, accuracy=0.290\n",
      "estimator=100, alpha=0.9, seed=50, accuracy=0.290\n",
      "estimator=100, alpha=0.9, seed=100, accuracy=0.290\n",
      "estimator=100, alpha=0.9, seed=200, accuracy=0.290\n",
      "estimator=100, alpha=0.9, seed=500, accuracy=0.291\n",
      "estimator=100, alpha=0.9, seed=1000, accuracy=0.289\n",
      "estimator=100, alpha=0.9, seed=2000, accuracy=0.288\n",
      "estimator=100, alpha=0.99, seed=20, accuracy=0.290\n",
      "estimator=100, alpha=0.99, seed=50, accuracy=0.290\n",
      "estimator=100, alpha=0.99, seed=100, accuracy=0.290\n",
      "estimator=100, alpha=0.99, seed=200, accuracy=0.290\n",
      "estimator=100, alpha=0.99, seed=500, accuracy=0.291\n",
      "estimator=100, alpha=0.99, seed=1000, accuracy=0.289\n",
      "estimator=100, alpha=0.99, seed=2000, accuracy=0.288\n",
      "estimator=200, alpha=0.0001, seed=20, accuracy=0.290\n",
      "estimator=200, alpha=0.0001, seed=50, accuracy=0.291\n",
      "estimator=200, alpha=0.0001, seed=100, accuracy=0.290\n",
      "estimator=200, alpha=0.0001, seed=200, accuracy=0.290\n",
      "estimator=200, alpha=0.0001, seed=500, accuracy=0.291\n",
      "estimator=200, alpha=0.0001, seed=1000, accuracy=0.290\n",
      "estimator=200, alpha=0.0001, seed=2000, accuracy=0.290\n",
      "estimator=200, alpha=0.001, seed=20, accuracy=0.290\n",
      "estimator=200, alpha=0.001, seed=50, accuracy=0.291\n",
      "estimator=200, alpha=0.001, seed=100, accuracy=0.290\n",
      "estimator=200, alpha=0.001, seed=200, accuracy=0.290\n",
      "estimator=200, alpha=0.001, seed=500, accuracy=0.291\n",
      "estimator=200, alpha=0.001, seed=1000, accuracy=0.290\n",
      "estimator=200, alpha=0.001, seed=2000, accuracy=0.290\n",
      "estimator=200, alpha=0.01, seed=20, accuracy=0.290\n",
      "estimator=200, alpha=0.01, seed=50, accuracy=0.291\n",
      "estimator=200, alpha=0.01, seed=100, accuracy=0.290\n",
      "estimator=200, alpha=0.01, seed=200, accuracy=0.290\n",
      "estimator=200, alpha=0.01, seed=500, accuracy=0.291\n",
      "estimator=200, alpha=0.01, seed=1000, accuracy=0.290\n",
      "estimator=200, alpha=0.01, seed=2000, accuracy=0.290\n",
      "estimator=200, alpha=0.1, seed=20, accuracy=0.290\n",
      "estimator=200, alpha=0.1, seed=50, accuracy=0.291\n",
      "estimator=200, alpha=0.1, seed=100, accuracy=0.290\n",
      "estimator=200, alpha=0.1, seed=200, accuracy=0.290\n",
      "estimator=200, alpha=0.1, seed=500, accuracy=0.291\n",
      "estimator=200, alpha=0.1, seed=1000, accuracy=0.290\n",
      "estimator=200, alpha=0.1, seed=2000, accuracy=0.290\n",
      "estimator=200, alpha=0.9, seed=20, accuracy=0.290\n",
      "estimator=200, alpha=0.9, seed=50, accuracy=0.291\n",
      "estimator=200, alpha=0.9, seed=100, accuracy=0.290\n",
      "estimator=200, alpha=0.9, seed=200, accuracy=0.290\n",
      "estimator=200, alpha=0.9, seed=500, accuracy=0.291\n",
      "estimator=200, alpha=0.9, seed=1000, accuracy=0.290\n",
      "estimator=200, alpha=0.9, seed=2000, accuracy=0.290\n",
      "estimator=200, alpha=0.99, seed=20, accuracy=0.290\n",
      "estimator=200, alpha=0.99, seed=50, accuracy=0.291\n",
      "estimator=200, alpha=0.99, seed=100, accuracy=0.290\n",
      "estimator=200, alpha=0.99, seed=200, accuracy=0.290\n",
      "estimator=200, alpha=0.99, seed=500, accuracy=0.291\n",
      "estimator=200, alpha=0.99, seed=1000, accuracy=0.290\n",
      "estimator=200, alpha=0.99, seed=2000, accuracy=0.290\n",
      "estimator=500, alpha=0.0001, seed=20, accuracy=0.290\n",
      "estimator=500, alpha=0.0001, seed=50, accuracy=0.290\n",
      "estimator=500, alpha=0.0001, seed=100, accuracy=0.290\n",
      "estimator=500, alpha=0.0001, seed=200, accuracy=0.290\n",
      "estimator=500, alpha=0.0001, seed=500, accuracy=0.290\n",
      "estimator=500, alpha=0.0001, seed=1000, accuracy=0.290\n",
      "estimator=500, alpha=0.0001, seed=2000, accuracy=0.289\n",
      "estimator=500, alpha=0.001, seed=20, accuracy=0.290\n",
      "estimator=500, alpha=0.001, seed=50, accuracy=0.290\n",
      "estimator=500, alpha=0.001, seed=100, accuracy=0.290\n",
      "estimator=500, alpha=0.001, seed=200, accuracy=0.290\n",
      "estimator=500, alpha=0.001, seed=500, accuracy=0.290\n",
      "estimator=500, alpha=0.001, seed=1000, accuracy=0.290\n",
      "estimator=500, alpha=0.001, seed=2000, accuracy=0.289\n",
      "estimator=500, alpha=0.01, seed=20, accuracy=0.290\n",
      "estimator=500, alpha=0.01, seed=50, accuracy=0.290\n",
      "estimator=500, alpha=0.01, seed=100, accuracy=0.290\n",
      "estimator=500, alpha=0.01, seed=200, accuracy=0.290\n",
      "estimator=500, alpha=0.01, seed=500, accuracy=0.290\n",
      "estimator=500, alpha=0.01, seed=1000, accuracy=0.290\n",
      "estimator=500, alpha=0.01, seed=2000, accuracy=0.289\n",
      "estimator=500, alpha=0.1, seed=20, accuracy=0.290\n",
      "estimator=500, alpha=0.1, seed=50, accuracy=0.290\n",
      "estimator=500, alpha=0.1, seed=100, accuracy=0.290\n",
      "estimator=500, alpha=0.1, seed=200, accuracy=0.290\n",
      "estimator=500, alpha=0.1, seed=500, accuracy=0.290\n",
      "estimator=500, alpha=0.1, seed=1000, accuracy=0.290\n",
      "estimator=500, alpha=0.1, seed=2000, accuracy=0.289\n",
      "estimator=500, alpha=0.9, seed=20, accuracy=0.290\n",
      "estimator=500, alpha=0.9, seed=50, accuracy=0.290\n",
      "estimator=500, alpha=0.9, seed=100, accuracy=0.290\n",
      "estimator=500, alpha=0.9, seed=200, accuracy=0.290\n",
      "estimator=500, alpha=0.9, seed=500, accuracy=0.290\n",
      "estimator=500, alpha=0.9, seed=1000, accuracy=0.290\n",
      "estimator=500, alpha=0.9, seed=2000, accuracy=0.289\n",
      "estimator=500, alpha=0.99, seed=20, accuracy=0.290\n",
      "estimator=500, alpha=0.99, seed=50, accuracy=0.290\n",
      "estimator=500, alpha=0.99, seed=100, accuracy=0.290\n",
      "estimator=500, alpha=0.99, seed=200, accuracy=0.290\n",
      "estimator=500, alpha=0.99, seed=500, accuracy=0.290\n",
      "estimator=500, alpha=0.99, seed=1000, accuracy=0.290\n",
      "estimator=500, alpha=0.99, seed=2000, accuracy=0.289\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "### Test the parameters.\n",
    "estimators = [50, 100, 200, 500]\n",
    "aplhas = [0.0001, 0.001, 0.01, 0.1, 0.9, 0.99]\n",
    "seeds = [20, 50, 100, 200, 500, 1000, 2000]\n",
    "for estimator in estimators:\n",
    "    for alpha in aplhas:\n",
    "        for num_seed in seeds:\n",
    "        # gb_model = GradientBoostingRegressor(n_estimators=estimator, alpha=alpha, random_state=0)\n",
    "        \n",
    "            gb_model = GradientBoostingRegressor(n_estimators=estimator,alpha=alpha, max_features='sqrt', \n",
    "                                             min_samples_leaf=200, min_samples_split=10, loss='ls', random_state =num_seed)\n",
    "            gb_model.fit(X_train, Y_train)\n",
    "            score = gb_model.score(X_test, Y_test)\n",
    "            print('estimator={}, alpha={}, seed={}, accuracy={:.3f}'.format(estimator, alpha,num_seed, score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "christian-concentrate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB train accuracy: 0.280\n",
      "GB test accuracy: 0.291\n"
     ]
    }
   ],
   "source": [
    "gb_model = GradientBoostingRegressor(n_estimators=100,alpha=0.9, max_features='sqrt',\n",
    "                                     min_samples_leaf=200,loss='ls', random_state = 500)\n",
    "gb_model.fit(X_train, Y_train)\n",
    "score = gb_model.score(X_test, Y_test)\n",
    "print(\"GB train accuracy: %0.3f\" % gb_model.score(X_train, Y_train))\n",
    "print(\"GB test accuracy: %0.3f\" % gb_model.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "chemical-bracelet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 502.064\n",
      "Mean Squared Error: 735457.929\n",
      "Root Mean Squared Error: 857.588\n",
      "R2: 0.291\n"
     ]
    }
   ],
   "source": [
    "Y_pred = gb_model.predict(X_test)\n",
    "from sklearn import metrics\n",
    "MAE = metrics.mean_absolute_error(Y_test, Y_pred).round(3)\n",
    "MSE = metrics.mean_squared_error(Y_test, Y_pred).round(3)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)).round(3)\n",
    "R2 = metrics.r2_score(Y_test, Y_pred).round(3)\n",
    "print('Mean Absolute Error:', MAE)\n",
    "print('Mean Squared Error:', MSE)\n",
    "print('Root Mean Squared Error:', RMSE)\n",
    "print('R2:', R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "worst-assault",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "plt.plot(range(len(Y_pred)),Y_test,'r',label=\"test\",alpha=0.8)\n",
    "plt.plot(range(len(Y_pred)),Y_pred,'b',label=\"predict\",alpha=0.8)\n",
    "plt.legend(loc=\"upper right\") \n",
    "plt.show()\n",
    "# plt.savefig('Random Forrest.png', facecolor='w',dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "potential-recipient",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mBAYGAlCvXj0GDBhAgwYNqFevHrGxsbRv3z7XPk6dOkVERARVq1alefPm2c5RnDhxgqSkJCDzDnMnJyf7LOiVym4XRT6wdHV1zTU4AwgKCgLgrrvusj9buHAhqamp7N69m/fff5+OHTvy9ttvk56enuP1WdPkWVPv/5T1bP/+/dnqlipVKkfdMmXKAHD06FH7HaTX0raIiEhRFR8fD5BtqblRo0ZUrVqVL774Ilvdn3/+2T7pk6Vu3brs27ePESNG2Nvq1KnTFftcsWJFtgmlIUOG8NlnnwGZEzYeHh5MmDABm83Gli1bGDJkCJA5ubR582YGDBhAp06dOHTokL2Nf/88Xr9+fZ6B5bJly1i7di333HMPEydOJCMjw152//33M3PmTE6cOMHvv/9Oy5Yt7dvxrlR2uygWeyzzcvLkSUJCQmjatKn9WePGjXnrrbc4duwYGzZs4OLFiyxcuJBjx44xf/58+2lsm83GxYsXAQgODs7Rtre3NwCnT58GMj8h5VU3a1+l1Wrl/PnzVKpU6Zrazk3W1Um5uXjxYq4BroiISEGKjY1lxowZNG3alLvvvjtbWd++fRk/fjzDhw/Hx8eHP//8kzvuuMN+p3eWBg0aMGrUKCZOnMjWrVsZPnw4jz76aI6+li5dyvbt20lJSWH58uXZsr78M0BzcXFhzpw5DBgwgD179jBp0iTuuOMOAAIDA9m+fTunT59m4sSJ9O/fnx9++IESJUpk6ys9PZ2YmBhKliyZ69c9bNgw/vOf/7BmzRreeecdAF555RUAHn74Yf766y/69OnDAw88wJgxY+yvu1LZ7aLIz1jmxWq1sm7dOgYNGpTtL3Hz5s3p2bMnY8eO5eeff+b555/HZDKxe/duZs6caa8XGxtrP5nt4eGRo/2sZ1nT6zExMQB4enrmqPvPZwkJCdfctoiISFFy4cIFxo8fT5s2bWjWrBkff/wxJpMpW52uXbvi6urKt99+C8DXX3/NY489lmt7TzzxBN9++y0VK1bk9ddf57nnnsu2jQ2gR48ejBgxgtdff51BgwZlK3vsscdo1KiR/fcJCQmMHTuW0qVL8/zzzxMWFpatfoUKFZg1axY1atRg2bJlOcazbds2mjdvfsXvgZubGw899BAzZ85k0aJF9qV3m81GiRIlGDduHN9++y1z5861v+ZKZbeLYhtYLl++nNDQUPuJrdy4uroyZMgQXnvtNQAWLVpkXxL/57T2vz9dAfa/QFllWfVzq/vPfxwuLi7X3HZu1q9fn+cvzVaKiEhh8vf3Z9SoUbRo0YJNmzblCCoB3N3d6dGjB4sWLSIuLo60tLQcZxDg8upctWrVWLx4Ma+++iqbNm3KsYz+Tx07drTvyfy32NhY3nvvPdq1a8f7779PuXLlcp0ZNJvN9OrVy76C+E/r1q3Lcxn835o1a0aZMmXsE0wffPABZcqU4b777uPDDz9k5syZ/P7771ctu10Uy8AyKiqKTz75hGnTpuX5F++f+vfvT+3atUlOTuavv/4CLi9HA7nuv8zaKxkQEJCtfm51U1NT7f8fEBBwzW2LiIgURe+88w7R0dFMmzYt1/LevXtz/vx5hg8fzsMPP5xrnZ07dxIZGQlkHmh58sknad68OadOncqz33LlymE2mzl27Fiu7fn5+QGZB3/GjRvHr7/+mms7JpOJmjVrZntms9k4deoUlStXzrP/fytdurT9XMePP/5oX3pv2rQpXbt2tfd/pbLbRbELLK1WK6NHj2bcuHG5HozJS9Zf+KzZRS8vL0JCQoDLy9z/FBsbC1w+rFOxYsWr1nVxcSE4OPia2xYRESkqLBaLfTuXn58fU6dOZeHChaxfvx7I/DmctfJWpkwZ2rRpQ3R0NA0aNLCXZ7UDmYHcjBkz7G1arVYiIyO599577eUAhmFkG8dPP/1EQkICAEuWLGHv3r0AVKlShT///NNe32QyUbt2bQAOHz7Mli1bgMztZjt27MgR8P7222/Ur18/27Pk5GSmTZtmn/j5+uuviYuLA+CHH36gR48e9lnbatWqceTIEftrTSaT/dT4lcpuF8Xu8M748ePp0aOH/S9wfpUtWxZnZ2d7YnPIPNW2Zs0azp07l23vBmTemgPY92BkleeWviirbsOGDXFzc7vmtkVERIqCnTt3snHjRk6fPs3KlSvp0KED9evXZ+jQoQwbNoy2bdsSFRWFi4sL1apVo3bt2vTt29f+c+3EiRP2PY2ffPIJPXr0ADIDw99//526desSHR3N448/Trt27YiKiuKrr74CYM6cOfY0fJGRkezcuZNNmzYB8Msvv5CUlETDhg2pVKkSgwYNYtKkSVSqVIlz584xYcIEIHNv6KhRo6hUqRL16tXjpZdeyrHtbP369TlOrsfExLB8+XJ69uxJqVKl+P7773nvvfdo2rQpnTt3zrZsPnr0aKZPn25PeVS3bl0aN2581bLbhcn490eEImz27NkEBwfTs2fPHGUpKSm5HpTJsmjRIrZv387s2bPtz9atW8fgwYPp06cPY8eOtT+32WzcfffduLi4sGXLFnuOrYceeogjR46wY8cOe14syPzHM3HiREaNGkX//v2vq+1rkXViPOvTo4iIiEhhym/sUWyWwufPn4+Li0uOoDI9PZ1p06Zd8fpGi8XCmjVrePnll7M9b9euHTVr1mTDhg32qXvI/MSWkJDAwIEDswV+WXmy1q1bl62dtWvXEhISYv9kdj1ti4iIiBR3xSKw/Oijj5g2bRrfffcdnTp1sv+67777aNKkCStXrqRBgwbs37+fu+++m1GjRhEdHQ1k7rGYMmUKL774YrZlcMjc+zBhwgQSExOZP38+kJlc9d1336VVq1Y57vJu3749PXr0YM6cOfaNyIsXL+bw4cNMnjw52z3h19q2iIiISHFX5JfCFy9ezBtvvHHFOk899RSvvPIK8fHxjBkzhh07dmAymWjWrBk1a9akR48e2Zau/+348eNMnDiRyMhITCYTDz74IP369ct1RtFmszFv3jxWrFiBq6srJUuWZNiwYVSpUsXhtvNLS+EiIiJyI+U39ijygaXkpMBSREREbqRbbo+liIiIiBRtCixFREREpEAosBQREZFrdvHiRfr160fnzp3p2rUra9asudlDkiJA+W5ERETkmjk5OTFq1CiqV69OVFQUDz/8MK1atcLT0/NmD01uIgWWIiIics1CQ0MJDQ0FICgoCD8/P+Li4hRY3uYUWIqIiEgOffr04ddffwXAxcWFcuXK8Z///IeuXbvmqHvw4EEMw6BUqVJ5tpecnMzkyZPx9fUlOTmZkSNH4urqmqPezz//zIQJE0hKSqJr1668+uqr9hR9u3fvZsWKFYSGhhITE8Orr76Ku7u7/bWbNm3i/fffZ8aMGZQtW9bRb4FcB+2xFBERkWwMw+DIkSO88sorbN26lTVr1nDvvffyyiuvcPbs2Wx1Y2JieOWVV3jrrbeu2OYbb7xB8+bNGTZsGDVr1mTq1Kk56ly4cIF169Yxc+ZMRo0axbfffsuCBQvs/bz22mu8/vrrvPDCC9xxxx3Z2oiKisJqtXLw4MEC+A7I9VJgKSIiItmcOnWKpKQk7r33XkJCQihXrhw9evTAarVy8uRJe7309HSGDBnCwIEDadCgQZ7thYWF8eOPP9KyZUsAWrZsyVdffUViYmK2ehcuXGD8+PHUqlWLBx98kN69e7Nr1y4g8zplf39/+wxl27ZtWbx4McnJyUDmcnzr1q0L8tsg10GBpYiIiGTzxx9/4OfnR+XKlQG4dOkS06dPx9XVlapVqwKZs5qvvvoqTZs25aGHHsrRxvjx4xk/fjyQuYQdEBCAm5sbAIGBgbi4uOSYXWzUqFG2m+lCQ0Pty+tJSUmEhYXZy0qWLElGRganTp2yPzObFdbcbNpjKSIiItn88ccfJCQk0KBBA2w2G6mpqbi7u/Pmm29SokQJAPbu3csPP/xA1apVWbduHQCTJk2yB56NGjXCZDIBmTOWfn5+2frw8vLKFijm5uDBgzzzzDMANG3alIkTJ7J69Wq6dOnCH3/8AWRetSxFhwJLERERyeaPP/6gT58+9OvXj/j4eCZNmkS9evXo3r27vU6jRo04evRonm106NDB/v8mk8k+W5klIyMDFxeXPF9/6tQpgoODqVatGgDVqlVjxowZLFq0iH379mGz2XBycqJ8+fLX+2VKIdCcsYiIiGRz5MgR6tevT4UKFahduzb//e9/mTdvXo6DO/kVGhpKQkJCtmfJycn2dEX/ZrFY+Prrrxk+fHi25x07duTzzz9nzJgxREVF0bJlS3x9fa9rTFI4FFiKiIiI3dmzZ4mPj+euu+6yP6tcuTLly5dn9erV19Xm3XffTVhYGOnp6QD2JfA6derkWn/+/Pk888wzuaYjAjh+/DibN2/mpZdeuq7xSOFRYCkiIiJ2hw4dwtnZmYoVK2Z73qJFC9auXZvvdtatW2ffe1miRAnuvfde9uzZA8C2bdvo1asXbm5uzJ8/n7///tv+ug8++ICaNWuSkpLC2bNnWbp0KadPn7aXJyQkMHr0aCZMmJAt+IXMA0X//K/ceNpjKSIiInaHDx+mYsWKOWYLmzdvzpdffsmlS5coWbLkVdvZuXMnAO3btwcy81hOnTqV33//nbi4OPts4+rVqylTpgyVKlVi9uzZzJw5M1s7lSpVokePHkRERLB9+3YOHz7Mm2++SY0aNbLVS0pKYsWKFQAsX76cPn36EBgYeH3fBLluJkNhfbHTrl07ANavX3+TRyIiIiK3g/zGHloKFxEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqEAksRERERKRAKLEVERESkQCiwFBEREZECocBSRERERAqE880eQH6Fh4czZcoUNm3aREpKCpUrV+bpp5+mS5cuudZfsWIFCxYswDAMvLy8ePnll6lTp06udcPCwpg8eTLHjx/HZrPRrl07Bg8ejKur6w1vW0RERKS4KhYzlklJSfTp04dVq1bh7u6OxWLhjz/+YPjw4cyaNStH/dmzZzNp0iSmT5/OsmXL6NevH3379mXPnj056p4/f57evXsTEhLCypUrWbJkCTt27GDo0KHYbLYb2raIiIhIcVYsAstZs2ZRq1YttmzZwqZNm9i9ezfdunUDMgO9v//+2153z549zJw5k0GDBlGhQgUAOnbsSL169Rg5ciRJSUnZ2h41ahRpaWkMHz4cAA8PD0aOHMkvv/zCF198ka1uYbYtIiIiUtwVi8Ayaxk8ODgYAG9vb959912qVKmCzWZj69at9rpTp04FoEOHDtnaaN++PRcvXmTJkiX2Z1u3bmXnzp20bdsWFxcX+/PGjRvj5+fHBx98QHp6+g1pW0RyZxgGaek2bDbjZg9FRESuosgHlunp6TzzzDM4OTlle+7s7EzHjh0BsFgsQOZ+xn379hEcHExoaGi2+rVq1QJg5cqV9mc//vgjANWrV8/Rb61atYiJibEHrYXZtojklJhi47PvY3ls1AXuf/EcnYedY+LnUZy+mHGzhyYiInko8oGlq6trrsEZQFBQEAB33XUXANu3bwegVKlSOeqWKVMGgKNHj5KSkpKtfunSpXPUz3q2f//+Qm9bRLKLT7LywtQwvlmfQIs6HrzaP4g+nXzZdzyV/0y6xKETaTd7iCIikoticyo8NydPniQkJISmTZsCcO7cOQD7kvk/eXl5AWC1Wjl//jyVKlXi4sWLedb39vYG4PTp04Xedm7atWuXZ9nFixdzDXBFbhUfL48lKs7KBy+XpGKpy1tJerTx4dXZEbzzaSRfvFUaJ7PpJo5SRET+rcjPWObFarWybt06Bg0aZN/DGBMTA4Cnp2eO+v98lpCQQGxsrP1ktoeHR476Wc8SExMLvW0RuSwpxca63ck80tYnW1AJ4OFu5j89/AmLtrL7j9SbNEIREclLsZ2xXL58OaGhofTq1cv+LCMjc+/VPw/LZMnah5lVnlU3r/pWqzVbWWG2nZv169fnWXal2UyR4u58hIW0DIMmNXN+KAOoWsENf28zJ86l06x27nVEROTmKJaBZVRUFJ988gkff/wxZvPlSdesJebcTlunpl6e3QgICLDXzat+1l7JgICAQm9bRC5z+d+7UkJy7rle0zMMUtMNXF20DC4iUtQUu6Vwq9XK6NGjGTduXI6DMRUrVgQuL1v/U2xsLJA5SxgcHIyXlxchISFXrZ+1l7Ew2xaRyyqUdKF0iDM/bM99q8gve5NITTdoqtlKEZEip9gFluPHj6dHjx40aNAgR1mjRo0AOHv2bI6y8+fPA9CwYUPc3Nyy1c86mJNb/ebNmxd62yJymdls4vH7fPnl12QW/hBHWnrmzKVhGOw4mMKsb2K4p64H5UvkvZVERERujmIVWM6ePZsqVarQvn37HGUpKSnceeedVK9enXPnzhEdHZ2t/MiRIwC0bdvW/uyBBx4A4MCBA9nq2mw2jh8/TmBgIPXq1QMo1LZFJLsuLbx4orMvn34fx2OjLvDSe2H0e+Mioz+MoFpFN17tH3SzhygiIrlwOLBMTU1l1qxZdO3aNVtgtWvXLl5++WU2bdrkaBcAzJ8/HxcXF3r27JnteXp6OtOmTePw4cMADBkyBIB169Zlq7d27VpCQkLo0aOH/Vm7du2oWbMmGzZssB+oAdi5cycJCQkMHDgQZ+fL21ALs20RucxkMjHgAX8WvlGKrvd64+/tRMOq7kx/MZRJz4fg6V6sPhOLiNw2TIZhXPc9aXFxcfTr148///wTwzAICAhgx44d9vLo6Gj69+9PkyZNGDNmzHUP8qOPPmLmzJmUK1cu23Or1UpkZCR+fn788ssvmEyZm/lHjx7Njh07+OabbwgODmbx4sVMmDCBOXPm0KxZs2xtHD9+nN69ezNw4EAGDhxoH3OpUqX48MMPc9z4U5ht51fWqfArnRwXERERKSj5jT0cmjKbMWMGx48f56677qJKlSrs3bs3W3lgYCDDhg1j8ODBVKxYkb59+15zH4sXL2batGlAZkL03Dz++OP2oBJg3LhxzJs3j/79++Pq6krJkiVZsmQJVapUyfHaKlWq8OWXXzJx4kRWr16NyWSie/fu9OvXL9fArzDbFhERESnOHJqxvOeee3jkkUcYNmwYAJ07d+aHH37IVufSpUu0bt2aO++8k9WrVzs2WgE0YykiIiI3Vn5jD4c2KhmGwdChQ69YJ+ugy5kzZxzpSkRERESKOIcCyxIlSmRbgs7N119/DeR+Z7aIiIiI3DocCizbtWvHBx98kGf5okWL+PrrrzGZTHTo0MGRrkRERESkiHPo8M7TTz9N7969OXDgAB07diQ1NZWtW7dy8uRJ1qxZw759+wCoUKECgwcPLpABi4iIiEjR5FBg6e7uzoIFC3j77bcZO3YsVquVZ599FsjcfwmZScPHjRuHr6+v46MVERERkSLL4QzdPj4+TJw4kREjRrBnzx4uXryI1WolJCSERo0a5cg9KSIiIiK3JocCy9WrV7N48WKeffZZWrVqRefOnQtqXCIiIiJSzDgUWL7xxhskJiZSrlw5WrVqVVBjEhEREZFiyKFT4VWrVsXNzS1fB3O2bNniSFciIiIiUsQ5FFhOmTKFO++8k7i4uCvWS0tL44UXXnCkKxEREREp4hxaCt+5cyePPfYYr7/+Os2bN6dy5co56qSkpPDDDz+QkpLiSFciIiIiUsQ5FFjOnz+fv/76C4AjR47kWc8wjKve0CMiIiIixZtDgeWAAQMYPXo09erVo1y5cjg752zOZrPx+++/c/r0aUe6EhEREZEizqHAslu3bnzxxRd89dVXV6yXnJxMixYtHOlKRERERIo4hw7vuLi48Oqrr161Xnh4OBMmTHCkKxEREREp4hwKLAGaNGly1TpvvfUWHTt2dLQrERERESnCHFoKnzVr1hXLMzIyOHnyJDt27GDjxo20bt3ake5EREREpAhzOLDMOu1tGEaO8n+WzZkzR4GliIiIyC3MocASoEWLFtSuXRsnJ6ccZQkJCWzZskV3iIuIiIjcBhwKLL29vZk7dy5mc95bNT08PLDZbAwdOtSRrkRERESkiHN4KfxKQSXAwIEDad68OaVKleLRRx91pDsRERERKcIcOhXetGnTq9bx9PTEz8+Pjz/+2JGuRERERKSIc3iP5dWsWrWKsLAwPD09C7srEbkNGYbB8TPpxCfZKBnkTLkSLjd7SCIity2HAst27dpdsTw+Pp7ExEQAWrZs6UhXIiI5bPotmfkrYzkXbrE/q13ZjcE9AqhS3vUmjkxE5PbkUGB5/vz5q9bx8PDgvvvuY+zYsY50JSKSzc+7kpiwIIqmtdx5sVcgpYOdOXoqjUU/xTNsehgzhpfgrnIKLkVEbiSHl8LHjRtH06ZNcxziMZlMuLu7ExAQYM9nKSJSENLSbXywNIb2jT15bUCQ/T2mZJAzTWp58PzkMD76LoYpL5S4ySMVEbm9OBRYhoSE6KS3iNxw2w+kEJ9k44kufjk+uHq4mel5ny/vLojiUpSFkkGFvpVcRET+x6FT4c2bN89Xvffff5/o6GhHuhIRsQuLseLlbqJsaO4HdapWyFwCD4+x5FouIiKFw6HAcv/+/fmqN3jwYKZPn+5IVyIidn7eZpLTDKLirLmWnw3PAMDfO+eNYCIiUniuaY0oMTGR+Ph4++8tFgsXL17M9Z7wLGlpafz222/8+OOPjBs37vpHKiLyP/fW9WTmVzEsXR/Pc90DspVZbQZL1ydQpbwr5UpoGVxE5Ea6pnfd8+fPM23aNDZv3mx/1rZt23y9tkaNGtc2MhGRPHh7munTyZdPVsVhtUGPtj6EBjrz19l0Pv0+joN/pfHu4BAdHBQRucGuKbCsWrUqH330EStXrmT06NEYhkFoaGie9bNOhleuXJmXXnrJ4cGKiGTp08kXsxkW/RjP0g0JODuBxQrB/k68OTCYxjU8bvYQRURuO9e1TtStWzf8/f0ZP348P/74Y0GPSUTkqkwmE707+vFQKx92HMw8JV4qyJnGNdxxctJMpYjIzXDdG5BatmxJTExMQY5FROSaebqbadfY62YPQ0REcPBU+IMPPpjvuosWLXKkKxEREREp4hwKLPMrOjqaKVOm3IiuREREROQmyddS+Ouvv87q1asZPHgwTz/9tP35k08+idWaex65LBaLhRMnTpCamurYSEVERESkSMtXYLlhwwaSk5PZuHFjtsDSzc2NjRs35qsjpf0QERERubXlK7CcP38+27Zto3PnztmeP/744xw+fJgRI0YQHByMs3PO5jIyMti1axcff/xxwYxYRERERIqkfAWW1apVo1q1ajmet2rVitatW9OtW7crvr5Fixb88MMP1zdCERERESkWHDq8YzKZGDNmzFXrJScnK7AUERERucU5fCrc1dX1qnVOnjzJli1bHO1KRERERIqwfC2FL1++/LoaNwyD1NRUNm3aRJkyZWjXrt11tSMiIiIiRV++AssPP/yQM2fOONRRxYoVHXq9iIiIiBRt+Qos+/bty7vvvkuTJk0ICgrCxcUFyNw7+dNPP+Hl5cXdd9+Nv79/rq/fsGEDdevWLbBBi4iIiEjRk6/Asnv37uzZs4eZM2dmez527Fjat2/PhAkT8Pb2zvP1mzZtIjw83LGRioiIiEiRlq/DO15eXjzzzDM5nm/cuPGqQSVkpiXasGHD9Y1QRERERIqFfJ8Kr1OnTo5n7u7uVw0qAVJSUti3b9+1jUxEREREihWH0g2Fhoaybt26q9abMmUKhmE40pWIiIiIFHEOBZYDBgzgpZde4qOPPiI2NjZH+dGjR3n++ef58ssvadmypSNdiYiIiEgRl6/DO3lp3749zz77LNOnT+e9996jXLlyBAQEkJGRwYULF4iNjcUwDIKDgxkxYkRBjVlEREREiiCHAkuAIUOGULt2bSZNmsSJEyc4ffp0tvKGDRsyYcIESpQo4WhXIiIiIlKEORxYQuap71atWnHw4EEOHjxIfHw8fn5+1K9fn2rVqhVEFyIiIiJSxBVIYJmldu3a1K5dO9eyJUuW8OijjxZkdyIiIiJShDh0eCe/YmJiGD9+/I3oSkRERERuEodnLH/99Ve+//57Ll68SGpqao60QhaLhb///pvU1FRHuxIRERGRIsyhwPLLL79k3LhxAFfNU2kymRzpSkRERESKOIcCyzlz5gDQtGlTHnroIUJDQ3FycspR79ChQ0yePNmRrkRERESkiHMosExLS8PHx4f58+djNue9XfPuu+/m888/d6QrERERESniHDq806VLF5ydna8YVGb58ccfHelKRERERIo4hwLLESNGUKpUKTZu3HjVut27d3ekKxEREREp4hxaCo+NjWX8+PFMnz6d0NBQ/P39c9SxWq3s3buXU6dOOdKViIjcYtIzDPYcSSEhyUapIGdqV3bDbNZBT5HizKHA8rHHHiMqKgqATZs2FciARETk1mYYBss2JvL5D3HEJ9nsz8uGOvPC44E0rOZ+E0cnIo5wKLB8+umnmThxIi4uLvj7++Pi4pKjTnp6OtHR0VdNRyQiIreHJesTmPNdLA/c402Pdj6UCnLm6Kk0FqyO47XZ4UweGkrduxRcihRHDgWWvXr1YunSpSxduhQPD4886506dYquXbs60pWIiNwCklJsfLY6ju5tfBjyaID9ee3K7kwY4saL08KYtyKW90eUvImjFJHr5dDhHXd3dwYPHpzrTOU/VaxYkYEDBzrSlYiI3AK27E8mPd2g530+OcqcnUw82s6XP/5O53x4xk0YnYg4yuG7wjt37oyTkxOHDh1i/fr19ucnT54kPDzc/vvnn3/e0a5ERKSYi0mw4eVhJsQ/9wWzCqVc7PVEpPhx+K7wHTt2MHbsWM6dO4e/vz/t2rUDwMvLixkzZpCcnMwbb7yR64lxERG5vQT7OZGYYuNSlIWSQTl/BJ04lw5AkF/OW9xEpOhzaMbyt99+Y+DAgZw9ezbH4ZzQ0FDGjx+Ph4cH3bt3JzIy0qGBiohI8XdPXQ883Ux8+VN8jp8b6RkG36yLp+5dbpQKdnjeQ0RuAocCy/feew9PT09GjhzJxx9/jI9Pzj0zTzzxBBcuXGDatGmOdCUiIrcAD3czzzzkz/dbE3nn0yiOnkojLtHKzkMpDJ8RxulLFgY+7H+zhyki18mhj4QHDhxg/vz5NGjQILMx55zNBQYGAvDLL7840pWIiNwiHmzpg5uLiU9XxbHh12T788rlXJgyNJTqFd1u4uhExBEOBZahoaH2oDIvhw8fBiA5OfmK9URE5PbRqZk3993txaETacQl2SgV7Ezlsi6YTLp5R6Q4cyiwLFWqFOHh4YSGhuZanpaWxsyZMzGZTNx1112OdCUiIrcYJycTdasoEbrIrcShPZYDBgxg6NChhIWF5Sg7deoUTz31FEeOHAGgX79+jnQlIiIiIkWcQzOWrVu35o8//qBjx47UqlWLiIgIhg8fzqlTpzh69Cg2W2Yesr59+/Lggw8WyIBFREREpGhyOJ/D4MGDadiwIZ9++ikWi4Uffvghs2FnZxo0aMCAAQNo3769wwMVERERkaKtQBKFNW3alKZNm2Kz2YiNjcVqteLv73/Vqx5FRERE5Nbh8JWO/5Seno7VasXLy0tBpYiIiMhtxuEZy/T0dBYsWMDy5cv5+++/7c9LlChBmzZteOqppyhXrpyj3dhdunSJefPmcerUKebNm5dnvUOHDvHII4/keD5//nzuueeebM/CwsKYPHkyx48fx2az0a5dOwYPHoyrq2uuba9YsYIFCxZgGAZeXl68/PLL1KlTJ9e619q2iIiISHHl0IxlbGwsPXv2ZNq0aZw4cQLDMOy/Ll26xOLFi+nWrRurVq1yeKDR0dHMnDmTBx98kIULF5KWlnbF+h9//DFOTk7ZflWrVi1HUHn+/Hl69+5NSEgIK1euZMmSJezYsYOhQ4faDx/90+zZs5k0aRLTp09n2bJl9OvXj759+7Jnz54cda+1bREREZHizKHAcuLEiRw5cgTDMGjTpg0fffQR27Zt49ChQ2zevJkvvviCNm3a8Nprr7F7926HBurt7c2QIUOYOHHiVeuePXuWS5cucfjw4Wy/VqxYkaPuqFGjSEtLY/jw4QB4eHgwcuRIfvnlF7744otsdffs2cPMmTMZNGgQFSpUAKBjx47Uq1ePkSNHkpSUdN1ti4iIiBR3DgWWGzZswGQyMXToUD788ENatWpFUFAQzs7OhIaG0qhRI6ZNm8aYMWP44IMPHBqoq6srZrOZihUrXrXu/PnzGTBgwFXrbd26lZ07d9K2bdtse0IbN26Mn58fH3zwAenp6fbnU6dOBaBDhw7Z2mnfvj0XL15kyZIl1922iIiISHHnUGBpMpnw8vJi0KBBV6zXs2dP/vrrL0e6ssvtPvJ/io6OZtmyZXz55ZfMmjWLs2fP5ln3xx9/BKB69eo5ymrVqkVMTAxbt24FMvdK7tu3j+Dg4Bw3DdWqVQuAlStXXlfbIiIiIrcChwLLLl264OJy9btdTSZTnqfEf/31V0eGkMPChQtJTU1l9+7dvP/++3Ts2JG3334719nB7du3A1C6dOkcZVnP9u/fn61uqVKlctQtU6YMAEePHiUlJeWa2xYRERG5FTh0Kvzll1/m9OnTLFy48IpXNq5duzbXmTvITLC+a9cuR4aRTePGjXnrrbc4duwYGzZs4OLFiyxcuJBjx44xf/58+2lsm83GxYsXAQgODs7Rjre3NwCnT58G4Ny5c3nW9fLyAsBqtXL+/HkqVap0TW3npl27dnmWXbx4MdcAV0RERORmciiwXLNmDZ07d2bp0qXExsbmSCuUkZHBiRMn+Prrr3nuuedYvnx5trIdO3YQHx/vyBByaN68uf3/X331VebOncusWbPYvXs3M2fOZMSIEUDmifask9keHh452sl6lpiYCEBMTAwAnp6eOer+81lCQsI1ty0iIiJyK3AosPz88885cuQIAPv27cuznmEYvPfee7k+v9oyuiNcXV0ZMmQIPj4+jB8/nkWLFjF06FBcXV3JyMiw18ttmd5qtWYry6qfW12LxZKtrWttOzfr16/Ps+xKs5kiIiIiN4tDgeXTTz/NSy+9RP369Slfvjxmc/63bGZkZPDHH39w6tQpR4aQL/3792fVqlUcPHiQv/76ixo1atiXo4Fc919m7ZUMCAgALi9f51Y3NTXV/v8BAQHX3LaIiIjIrcChwPL+++9n4cKFLF68+Lpen5SUlCNheWF5+OGHOXjwoH120cvLi5CQECIiIuzL3P8UGxsLXD6sk5Xm6Ep1XVxcCA4Oxs3N7ZraFhEREbkVOHQq3Gw28/LLL+er7tGjR3M88/LyYuDAgY4MId/Kli2Ls7OzPbE5QKNGjYDLB3P+6fz588DlPZtZdXNLX5RVt2HDhri5uV1z2yJya7DZDMKiLVyKsmCzGTd7OCIiN5xDgaXFYqFBgwZXrZeSkkKvXr1yLbtaDsyCcu7cOVq3bo2fn5/92QMPPADAgQMHstW12WwcP36cwMBA6tWrB8Cdd95J9erVOXfuHNHR0dnqZ+0zbdu27XW1LSLFm81m8N0vCTzx5kV6jblA79cv0Pe/F1iyPh6rAkwRuY04FFg2b978qndeJyQkMGTIkGz7EB2R1V/WAZj8sFgsrFmzJsfsart27ahZsyYbNmzI1t7OnTtJSEhg4MCB2RKyDxkyBIB169Zla2ft2rWEhITQo0eP625bRIonwzCY9mU0s5fGUKOiK2//XzDjB4VQ9y53PvoulgkLojR7KSK3DYcCy/j4eMaOHYth5P6muWvXLh588EF7svCCcPLkSSBzOTnrEEyW/fv3c/fddzNq1Cj7rGJiYiJTpkzhxRdfzLYMDpmJ2ydMmEBiYiLz588HMm/ueffdd2nVqhVPPPFEtvrt27enR48ezJkzh8jISAAWL17M4cOHmTx5sj2f5fW0LSLF096jqfywPYmRfQMZ9WQwzet40rS2B688EcToJ4NYvyeZHYdSrt6QiMgtwGTkFRXmQ7Vq1XB3d6dBgwZMmzYNf39/IPMk9JQpU/jiiy+w2WyYzWYMw7AvGV+PhIQEevfuzcmTJ+3pfAICAujRo4c9N2V8fDxjxoxhx44dmEwmmjVrRs2aNenRoweBgYF5tn38+HEmTpxIZGQkJpOJBx98kH79+uU6o2iz2Zg3bx4rVqzA1dWVkiVLMmzYMKpUqeJw2/mVlW7oSimJROTGeOPjCM6FW/h4VMlc06f9Z9Il/L3NjP9PaC6vFhEpHvIbezgUWL744otMmTKFJUuWsGDBAt544w28vLx45ZVXOHnyJB4eHowYMYJOnTrRunXrHPsN5foosBQpOp5++yL1q7gx5LHcP7zOXRbDpn0pLHor5/WuIiLFRX5jD4c2+c2YMQOAXr160bhxY3r27ElaWhpWq5VGjRrx7rvvUrZsWUD3YovIrcnDzURkXN57vqPirHi6F95FECIiRYlDeyzfeOMNAPbs2cPgwYNJSkrCZDIREhLC1KlT7UElcE3J00VEiouW9T3ZcTCFiBhLjrLoeCub96XQqn7Oq2BFRG5FDkV7X331FZ07d+aJJ57g9OnTVK9enWXLljFu3Diefvppdu3aZa8bHh7u8GBFRIqa+5t74+ftxMuzIjh8Ms1+mPHY6TRemRWOp4eJB+7xvkorIiK3Bofz3Zw8eRKTycQzzzzD0KFDcXFxoXLlylStWpURI0awc+dOHnvsMbp06cJvv/1WEGMWESkyfDzNTHkhlNfnRDBkchglAp0wm+BilJXSIc5MGRqKv4/TzR6miMgN4XBgGRQUxNSpU2nSpEm25yVLlmTBggVMmjSJNm3a5HpaUkTkVlC+hAufvF6KPYdT+f14KgZQp7IbTWp54GTWe5+I3D4cCixNJhMff/wx1atXz7XcycmJ1157jVKlSjFx4kRHuhIRKdKczCaa1vKgaS2Pmz0UEZGbxqHAsk+fPnkGlf80YMAAYmNjHelKRERERIo4hw7vjBkzJs+ysLAwzp49a//9iy++6EhXIiIiIlLE5TuwNAyDxMREwsLCiIqKump9b29vlixZwuOPP87cuXM5ffq0QwMVERERkaIt30vh1atXx83Nja5du9K9e3eCgoKuWN/Ly4vhw4dz6NAhBg0axIwZMzh8+LDDAxYRERGRouma9lhOnjyZDh062H//9ttvk5SUZP+9yWQiODiY4cOH25/VqlWLKVOmMGDAAMdHKyIiIiJFVr6Xwp2dnWnfvn22Z2PGjKFatWosW7YMk8nEyJEjswWVWZo0aYKnp26eEBEREbmV5Tuw9PDwyPVaxj59+uDs7Mzo0aMJCAjI8/V+fn7XN0IRERERKRbyHVjmleDc2dmZMmXK4OXldcXXu7i4XNvIRERERKRYcSjdUBYnJ11XJiIiInK7K5DAUkRERETkhgSWcXFx+cp9KSIiIiLFV77TDSUkJNC6detcyyIjI/Mss9lsxMXFkZ6efj3jExEREZFiIt+BpWEYXLp0Kc/yK5VB3od/ROTWFh5t4dTFDNxcTdS4ww0XZ70XiIjcqvIdWLq7u/PUU09RunTpazqsY7FYuHDhAp988sl1DVBEiqewaAvvfxPDzoMp2IzMZwE+Zh5t70vP9j76sCkicgvKd2DZvXt3hg4det0d2Wy2636tiBQv0XFWXpgWBsCLvQJpVN2d+CQbq7clMndZLDHxVgY9knfeWxGR21Fiso2fdiay53AqFqtB1QpudL3Xm5JB13RR4k2V75E2atTIoY4aNmzo0OtFpPj48ud4UtMMPh5VkpCAzLeZkkFQpXwgpYOd+WhZLF3v9aZsqPLbiogAHDudxquzI0hMttGwmjvenmZWbkngm3XxjOgbSMem3jd7iPmS78Cyc+fODnXUsmVLh14vIsWD1Wbw085Eut7rYw8q/+nh1j4s/jmen3Yk8fSD/jd+gCIiRUxiio3XZkdQOtiZNwYGE+Kf+d6ZkmZj9pIYJi+MpnwJF6rf4XaTR3p1ymMpIgUqJc0gKcWgSrncZyNdXUxUKOlCWIzlBo9MRKRo+nlnEgnJNt549nJQCeDhZmZY70BKhzizdEPCTRxh/imwFJEC5eFqws3FxNnw3ANHq9XgQqQFf+9rv7Erw2IQl2jFYjUcHaaISJHx65EU6ld1z3WVx8ls4r67vdh9OOUmjOzaFZ/doCJSLDg5mWjTyJPvtyTycGsfvD2yf35dvyeJqDgr9zXxyneb5yMyWPRjPBt+TSY9w8DdzUT7xl706eRLicDCfxtLSbNhNoGbqz6Li0jBs9rAzTXvTBmuLiasxeQMtAJLESlwvTv6snV/Mi/NCOPZh/xpUNWdxBQbP2xL4tPvY2nTyJO7yrnmq62TF9IZNj0cNxcTfTv5UqGUC3+fz2DVlgS2H0hmxvAShXIIyDAM1mxP4ruNCfx9PgOAmpVcebSdLy3rexZ4fyJy+6pWwZVvf0kgOdWGp3vOD7DbD6RQrUL+3jNvNn38FpECVzbUhWkvlsBqhZffj6DD82d5aOR5PlkVS+fm3rz6RFC+25q0MJpgPyfmjSlF3/v9uLeeJ/27+PHx6FJ4upuZvji6wMdvGAZTv4xmyqJoSgY58+oTgYzoE4iLs4k3Po5kweq4Au9TRG5fXVp4k55hMPPrGKz/2uqzYnMCB0+k8XBrn5s0umujGUsRKRSVy7ny8eiS/PF3OicvpOPmYqJxTQ8CfPK/t/L4mXSOnU7n7f8Lxscz++fgAB8n+nfx451PozgblkG5EgU3a7n9QAo/bEvi5X6BdGp2OcVH5xbeLFwTx6er4mhW24Mq5YvHDIKIFG2hgc688kQQ7y6I4tDfabRv7Im7q5ltB5L54+90urf25p66Hjd7mPlyw2Ysu3bteqO6EpEiwmQyUetON7re60OHpt7XFFQCnLqQDkCj6rm/oTaq7p5Z72KGYwP9l5VbEqlxh2u2oDJL746+hAY6sXJz8TihKSLFQ7vGXsweWYIaFV1ZvimRRT/G4eZi4q2BwQx+NKDY3FaWrxnLCxcuXHcHFouFnTt38tdff113GyJye8o6LBObYCU0l0M6sQmZu9ndr7Dp/Xr8fT6DLi1yP1zkZDbRsKo7f18o2GBWRKRqBTdGPVn0c1VeSb4Cy4cffpj4+PjCHouISDYNq7nj7mri+22JPNXVP0f599sS8fE0U7tywb4Ru7qYiE/K+whmfJINN5fiMXsgInIj5Suw7N69O59++ikeHh74+vri5JT/5SyLxUJkZCSGobxzInJtvD3NPNTKmy9/iifIz4nOzb1xcTaRnmGwYnMC3/2SwIAH/HAv4DRALep48POuJJ590B+Pf53QjIi1sOuPFJ59yL9A+xQRuRXkK7Ds3bs3R48e5ZNPPrmuNf7Tp0/TrVu3a36diMjT3fyJT7bx3lcxLPg+jtIhzpwLtxCfZKN7a2/6dPQt8D4fau3D6m2JjPkogpF9gygZlPlWefpiBu98GomvlznX/ZciIre7fAWW5cqV45FHHrnujaMVKlTgxRdfvK7XisjtzcnJxIg+QfRo68vaXUlEx1upX9Wdjk29CiV/JUDpYGfGDwrhvx9H0nfsBapWcCXDavDX2QxCA5yY9HxojlPqIiICJkNr1MVOu3btAFi/fv1NHonIrS0lzcb6PckcOpGG2QwNqrrTsr4nrtpfKSK3mfzGHgWSx3L37t389NNPxMbGMnXqVAB27tzJgQMHeOihhwgNDS2IbkREbigPNzMP3OPNA/do2VtEJD8cCiwNw2D06NEsW7YMAH9/f3tZ06ZNMZlM9OrVi5EjR9KpUyeHBioiIiIiRZtDm4Q++eQTvvvuO0wmE0FBQTlOizdp0oSBAwcyfPhwtm/f7tBARURERKRoc2jG8ptvvqFFixZMmjSJoKAg7r///hx1mjdvjs1mY9asWTRv3tyR7kREbqjYBCvLNyWwdncycYlWQgKcub+ZF13v8c6RhkhERBwMLCMjI1myZAm+vpnpPnI7NW6zZSYZPnz4sCNdiUgRdiHSwncb4tm8P4WUNBsVSrrQ9V5v2jf2wsmpeB50uRhpYdiMMOKTbLRr5Em5Ei78eTad+StjWbs7iakvhOLrdW1XVIqI3OocCizLlCljDyrzsnbtWgDc3Ir3FUUikrtDJ9J4dXY4rs4m7mviRYCPE/uPpzLx82i27E/hjWeDcS6GweW7C6JwdjKxYGwpQgIuv1X27pjOsOnhzFoSw6gBwTdxhCIiRY9Dazl169bll19+ybP84MGDfPDBB5hMJpo1a+ZIVyJSBKVnGLzxcQSVy7ryxVulGfRIAI938GXCkFDG/yeEXYdSWLI+4WYP85r9dTadQyfSeO5h/2xBJcAdpV3p08mXjXuTiUmw3qQRiogUTQ4FloMHD2bcuHHMnz+fs2fPApCens6xY8eYPn06/fr1Izk5GQ8PD4YOHVogAxaRomPL/mSi420M7xOI57/2HDat5cF9TbxYsTkBm614pcs9ejodswma1/bItbxFXU8s1swAVERELnNoKbxkyZLMmjWLYcOGMWXKFAzDoG7duvZywzAICQlh+vTpVKpUyeHBikjRcvhkGhVKOlO+RO434NxT14MfdyQRFW8lxD9/bzep6TY27Elm/5+pYECdu9xp18jzhh6WcTKDzcickfXIZRk/LT1z73hx3T8qIlJYHE6QXqNGDVavXs3atWvZtWsXFy9exGKxEBoaSqNGjejSpQvu7u4FMVYRKWKczCbSMwwMw8j18F56RuZMZX73WB45lcaYDyOITbRRrYIrJhNs+DWaeStiefv/Qqh1543Zq12/qjsmE6zdnUS3lj45ytfuTsbLw0T1iq6FOo4zlzJYuiGBzfuSsx2Kur+5d7Hctyoit74CuXnH2dmZ+++/P9d0QwCnTp2iYsWKBdGViBQhjWu4s3RDAn/8nZ5r0PfzriQql3XB3/vqs43R8VZemx1B2VBn3h8ZTOngzLenS1EWJn4exWsfhPPJmFI59jwWhpJBzrRu4MnHy2MpW8KFBlUzPxwbhsEve5NZsi6ex9r74uFWeLOoG35NYtLnUXi4m3mghReBfs7sO57Ke1/FsO33FMb9XwguzgouRaRouSFrS2+99daN6EZEbrCG1dypVNqFdxdEceZShv251Wrw5U9x7DyUyqPtfHOdzfy377cmkp5h8M6gEHtQCZlB3rj/CwEDVm5OLJSvIzfDewdSuZwrI94L5/8mXOLtTyJ58q2LvP1JFPfW8+TJrn6F0u/JC+mMmBnO259EkW6BuEQbOw6lEhrgxLjnQpj4fCj7jqfy9dr4QulfRMQRDn30nzVr1hXLMzIyOHnyJDt27GDjxo20bt3ake5EpIgxm028PSiEkTPDeXLcRepVcSPQ14nf/0wjMtZKn06+tL/bM19t7TiQwj11PfDzzpkb0tvDTKsGnmw/mMLTD/oX8FeROy8PM1NeCGXXoRTW7k4mKs5KlQquvNgrkLp3ueUrWL5WJy+kM3RqGO6umZ/5Jw4JAWDphgTGzo3ktf5B3NfEiw5NvFi5JZFeHX1xMmvWUkSKDocDy6w3V8PIeerzn2Vz5sxRYClyCyoZ5MzHo0vyy95ktuxLJjLWSrPaHnS9x5vK5fK/BzEtw8DbM+9FFG9Ps33P5o3iZDbRvI4nzevkLzh21EfLYgn0daLuXW4c/CuNxjUyT6U3qu7O+M+imL00hpb1PWhex5PV25KIjrPekK0BIiL55fA7UosWLahdu3aOe8IBEhIS2LJlC507d3a0GxEpwtxdzdzfzJv7m3lfdxuVy7rw65FUbDYD879m4QzDYPcfqdxZNvfT57eCiFgLew6nMqJPIKcuZpCSnnkoymoDw4ABXfxYvyeZrb+nkDVZ6qw9liJSxDgUWHp7ezN37lzM5rxnGTw8PLDZbMpjKSJX1K2lD+umhvHNugQe75D9Rq9lGxM5dTGDIY8G3KTRFb7waCuGAdUquhIa6MyS9Qk89+4l/jqXuXe1cjkX3F1NXIqycPBEGneVy9+hKBGRG8nhpfArBZUAAwcOpHnz5pQqVYpHH33Uke5E5BZW6043+nT0Ze7yWHYfTqFtIy9MJti4N5m9R1N5tJ0P9aveulfD+nplvpdeiLDYD0KdvpRB746+lA525pe9SfyVbrBySyIRMVbGPBVUKPs85eaJiLGwelsiJ85l4OJsolltD1o18MTVRX/OUnyYjNw2RxawVq1a4ebmxs8//1zYXd0W2rVrB8D69etv8khECt6m35JZuiGeP/7OvNWmekVXurfxoW0jz1s6kDIMg0ETwzCZ4NjpdB5u5c3eoymcCbNS4w5XEpJtnA2zANC2kSdjntI95beSNTsSmbYoGlcXE7Uru5GYbOPwyXRKBTszaUgIZUJv3W0gUjzkN/Yo9F3fq1atIiwsDE/PG7P5XUSKt1YNPGnVwJMMS+Zn3tslV6PJZOKprn68OjsCF2fo3MKb57oH8OOORL79JYGzYRaqVXAlw2oQl2i72cOVAvT78VSmfBFN5+ZeDHokwH496qmLGfx3bgSvzo7gk9dL3Tb/FqR4cyiwzIpe8xIfH09iYmbeuZYtWzrSlYjcZm7HH6J31/SgXAlnwqItPDv+Ep7uJlLTDJydTfS935cBXfxY9FM8y35JuNlDlQL09bp47izjwrBegdkOrlUs5cJ/nwnmmXcusXV/Mm0aed3EUYrkj0OB5fnz569ax8PDg/vuu4+xY8c60pWIyG0hxN+JUkFOdLnHh4uRFny8zLSo44GvV2bmjcRkm/bc3UJsNoPdh1P5v+7+ObIhAFQq40rV8q7sOJSiwFKKBYeXwseNG0fTpk1zHOIxmUy4u7sTEBBwS++LEhEpSM1qezDnu1he6hPEvfWybyFKS7exbk8SLetra9GtwmaAzQZe7nkfhPX0MJFhuYGDEnGAQ7kqQkJCePTRRylXrhxlypTJ9qt06dIEBgYqqBQRuQYdm3rj521m9JwIzoZdviYzKs7Km/MiSU4xeLi1z00coRQkZycTlUq7sPNQSq7lcYlW/vg7nSrl83/ZgMjN5NCM5aZNmwpqHCKSD+kZBht/S2bfsVQAalZyo11jTzzcbl4+Q6vNYM/hVLbuTyYlzaB8SRfub+ZFaKBuhLke3p5mJg4JZdQHEfR/8yJVK7ji6mLi8N9puLuaeOu5YMqX0AnhW0m3lt7M/DqGrfuTuecfs9RWq8GsJTEA3N9My+BSPOQ73VDbtm1JSUmhbNmyPP/88zqMcxMp3dDt6a+z6Yz6MILIWCtVyrtiNmempfHxNDPuuWBqV3a/4WOKSbAy+sMIjp5Kp2IpFwJ8zRw7nU5qusF/HgmgexvNrF2v9AyDjXuT2Hs0FasNatzhRoemXnh7KCn6rcZqM3j7kyg270umaS0PmtR0JzHZxk+7kjgfYWH0gCDtr5SbrsDTDV24cIGHHnqI8ePHXzUpuogUrLhEK6/MCickwJkpL4TaZ6wuRVmY9HkUoz6I4OPRpSgZdONmCQ3D4L9zIwmLtjB9WCh1KrthMplITrXx6fdxzFoSQ4lAJ1rU1X7A6+HqYqJDU286NL3+azKleHAymxjzVBA/7nBnxeYEZn4dY0+Q/uoTQVS/49a9GEBuPdf0U+jFF1/MFlS+/vrr7Nmzh4yMDEwmE3Xq1KFcuXIMGzaswAcqcjtbsz2JxBQbH48KIdDPyf68ZJAz4/4vhF5jzrNycwIDH75xVx4ePJHGoRNpTBgcQt27Ls+Werqb+c8j/pw4l87in+MVWIrkg5PZRJcW3nRp4Y1hGDqfIMVWvgNLFxcXSpYsme3ZuHHj2LFjB08++STvv/8+9913X4EPUERg24FkmtX2yBZUZvHyMNOmoRfbDqTc0MByx4EUgv2daFQ95xK8yWSic3Nvxn8WRVyiFT/vnOMWkdwpqJTiLN9r2h4eHrk+b9asGUFBQQoqRQpRWoaBn1fewZmvl5m0jEK/nTWbdIuBt4c519x7kHkIBTL3CoqIyO2hQDZL+vr6XrXORx99VBBdidyW7izjyq9HUrDZcgZphmGw+3AKlUrf2JPCd5Zx5fSlDC5F5Z5gb9cfKQT4mAn01WylFC8paTbCoi2kpOnqTJFrVSCB5dWm7W02G5988klBdCVyW+p2rzcXo6x8tTY+R9knq+L482wG58ItDJ8exrcb4klMcewHomEYXC1hRJtGnnh7mHn/mxj7vd5ZDp9M48ftSXS5xxsnJy3rSfFwLjyD8Z9G8tDIc/Qac4EHR5xj3CeRnLmUcfUXiwhwDXssrVYrZ8+ezbXMYrHkWmYYBsnJyaxcuZL4+Jw/EEUkf6rf4Ubf+32ZtyKO346m0raRF05O8MWaOM5HWPFwM1HrTjfik2zM+S6Wr9YmMOn5EO4ofW1JlfccTmHphgT2HUvFZkCtSm50b+PDvfU8cnyA9HAz81r/IP77cQRPv32Rzs29CfQ1s+94Ght+TaJaBTd6d7z6aoZcu1MXM1ixOYE//k7DyWSifjV3ut3rfUOzAtxqTl5I58Vp4Xi6m3iyqz93lnHh1MUMlm9MYPDkS0x/sQSVyylJucjV5DuPZbVq1RzeUHzkyBGHXi+ZlMfy9rXxt2SWro/n8Ml0+7OW9TwY83QQzk6ZCxDh0RZGfRBBcpqNz98ojXM+Zwy/+jmeuctjqVrBlbaNPHEym9i8L5kDf6XRs70Pz3XP/WDQ8TPpfL02ni37k7FYoWSQE13v9aF7a2/cXJWarKCt2Z7I1EXR+PmYaVbbA6sVtv6eTIYF3ngmmKa1c98PL1c2dGoYSSk23htewr4/GCApxcaw6WE4mU18+GrJK7Qgcmsr8DyWwFWXxq5Ep9xEHNe6gSetG3iSlm7jhWlh+Hg68cbAkGx1QgOdGfVkEM+8c4mt+5Np3fDqiZX/PJvO3OWx9Onky1Nd/ez/Xru38WHphng+WBpLoxoeNKyW8wR4lfKuvP50MDabgcWamX9RCsfxM+lMXRRN5xbePP9YAC7Omd/roWkBjP80ijfmRbLgv6UooVuPrsnJC+kcOpHGG88GZwsqITPrwpNd/Rn9YQTHz+hqRZGryfe7T1BQEJMmTaJcuXI4OeV/M35qaipr1qxh9uzZ1zVAEcnJZoPjZzJ45Yncb7apVMaVSqVd2Hs0NV+B5crNCYQGODGgi1+OD4GPtPHhxx1JLN+UkGtgmcVsNqEJysL13S8JhAY68cLjATj94zS+h5uZ1wYE8dio83y/JZGnH/S/eYMshs5cyjyAVr9q7n+/61fNTFB++mKGAkuRq8h3YPnwww/TokWL6+pkyJAhLFy48LpeKyI5Wf+3eHClZW5nZxO2fJ7hOX4mncY13HM9aGMymWhWy4O1e5KuZ6i3pMhYC78dTSXDClXLu96wvXf7j6fatyn8m6d75tL4vuOpN2QstxIPt8zvZ0y8FR/PnJ+OouNt2eqJSN7yHVi2adPGoY6GDBni0OtF5DIvdxMVSrmwZX8y7RrnnJG8EGnhz7PpPNgyf9cBujibSE7Ne6tLUqoNV2f9UE1Nt/HeVzGs252E9R9Be81Krrz6RBBlQgs35ZNhkGfeUIArFMkV1L3LDR9PMys3JzDkscAc5Ss3J+DlbrrijL2IZMr3wlXDhg0d6qhfv34OvV5ELjOZTHRv7c2W/Sms3ZV9JjE51caUhVH4eplp0yh/1yk2reXB9gMpxCVac5Slptv4ZW8yTWrd3odCDMPgjbmRbPotmf/r7s/KKWX5+f1yvDUwmLhEGy9ODyc6Luf3ryDVquzG5n3JueYzTU23sfNQKrXu1L3S18rN1UzP+3z4bmMiX/4YR0pq5qeGlDQbX62NZ8n6BHq088XDXXs9RK5GO7xFiqkuLbw5fDKddxdEsWJzAo1reBCbaOWXX5OxWA3e/U8I7vnc9Ni5hTffrItn7EeRjHkqiJCAzLeGmAQrkz6PIjXN4KF8zn7eqvYdS2P34VTeGRRCs3+cvL6nnifV73BjwFsX+PaXBJ59yL/QxtC9tQ/PTwnj4+WxPPuQv332MsNiMO3LaFLSbHRrmfu+W7myXh18SUy28cmqOL78OZ6SQc6ERVlITjPo3saHfvcrdZZIfuQ73ZAUHUo3JFkMw2DL/hRWbUnk1MUMXF1M3FPXg4da+VAq2DlbvdR0AyezKc9T23/8ncboDyNITLFR5043nJxM/P5nKi7OJt54NpjGNW7vGcuJn0dx9HQ6n4wpmWuWi1nfRLNpXwpL3i1TqOP4dkM8s5fGUirYmXvreWCxGGzal0JsgpVRA4Jo0+jqh7Ukb2HRFtbtTiIy1kqgnxP33e2l/KAiFFK6IREpWkwmEy3re9Kyfu5L3larwYrNiazYnMDZsMyTrw2rufNYe58cgWLNSm4seqs063Ynse94KjYbPPuQPx2beuF7hXvKbxcx8VbKhjrnmTqtXAkXYhISC30cj7T1pUYlN5ZtTGDL/hTMJmhe24OHWntfc0J8yalEoDN9Ovnd7GGIFFsKLEVuUVarwZvzItl+MIVW9T3p28mP5DQbP+1M4pVZEbz4eECOZVMvDzMPtvLhwVZaTv23kAAnfjuWhs1m5HqA5q9z6YQG3JgAvHpFN6oP0F5KESl6il1geenSJebNm8epU6eYN29envVWrFjBggULMAwDLy8vXn75ZerUqZNr3bCwMCZPnszx48ex2Wy0a9eOwYMH4+qa+6f/wmxbpKCs2ZHEtgMpjHsumOZ1Ls9odrvXm/e/iWHmNzHcXdMjX8t80fFWzodn4O5m5s4yLlc8mXyr6tTMm9Xbkli3J5kOTbIvN58Pz2D9nmR6ddA+PBG5vd2wwHLHjh00a9bsul8fHR3NF198waJFi4iNjeXuu+/Os+7s2bP58ssv+fLLL6lQoQI//fQTffv2Zf78+TRu3Dhb3fPnz/PEE0/QoUMHpkyZQkpKCv379+fYsWN88MEHmM3mG9a2SEFauTmBZrU9sgWVkLl8/uyD/qzdlcTqbYk83c0/zzYiYix88G0sW/Yn23Nilgp2pu/9vtzf7PY6zFPjDlfaN/Zk8sIoTl1Ip2NTbzzcTOw4mMLCNXGEBDjxUGvN9IrI7e2GRDY2m40XX3zRoTa8vb0ZMmQIEydOvGK9PXv2MHPmTAYNGkSFChUA6NixI/Xq1WPkyJEkJWVPzTJq1CjS0tIYPnw4AB4eHowcOZJffvmFL7744oa1LVKQDMPg7/MZNK6Re949D3cztSu78ff5jDzbiIqz8vzUMA7/ncbgHgF88noppr4QStXyrkxeGM1XP8cX1vCLJJPJxCtPBPH4fb6s2prIk+Mu8viYC7y/JIZad7oxY3iJXJNri4jcTvI1Yzlr1qzr7sBisbB3717i4x37IZS1dFyxYsUr1ps6dSoAHTp0yPa8ffv2vPPOOyxZsoQBAwYAsHXrVnbu3EnPnj1xcbmc2Lhx48b4+fnxwQcf8Pjjj9v7Lsy2RQqSyWTC1dVEfFLeV+/EJ9kIDcj7LeDzH+JITzf46LWS9vRDFUu5UL+qOyWXxTB/ZSz3NfEiyO/2Odjj5GTi6Qf96d3Jl8Mn08mwGFQu60Kwf7HbVSQiUijy9W64fv16jh49av/9tWQoMplMGIaR50nKa+XsnPeQw8LC2LdvH8HBwYSGhmYrq1WrFgArV660B38//vgjANWrV8/RVq1atdi2bRtbt26lbdu2hdq2SGFoUceDn3Ym0auDb46rH09eSOfwyXQezmPpNj3DYO3uJB5r52MPKv+pd0c/lm9KZO2uJB6/DfcVeriZdQuLiEgu8hVY9u7dm3fffZeuXbsSEBBwxeDu3zIyMvjtt9/49ddfr3uQ+bV9+3YASpUqlaOsTJnM3HJHjx4lJSUFDw8Pe/3SpUvnqJ/1bP/+/bRt27ZQ2xYpDI+282XI5Eu8/UkkQ3sGEuibObN4/Ew64+ZHUjbUmXvr5Z6mKC7JSmqaQbWK2U8eZ31I9PY0UzbUmYtRlkL/OkREpPjIV4TYrVs39u7dy5tvvnldnRiG4dDBnfw6d+4cAMHBwTnKvLwyT3FarVbOnz9PpUqVuHjxYp71vb0zDyacPn260NvOTVYi0txcvHgx1wBX5J+qlHflv88E885nUTw++jzVKrqRnGLj7wsZlCvhzLuDQ/NMlu7tbsZsggsRFlLTbSzflMgP2xI5H2HB28NMy/oeXIqy3PbXPIqISHb5Cizd3NwYPHjwdXdiMpn47rvvrvv1+RUTEwOAp2fOWZh/PktISCA2Nhbb/465enjk/OGY9SwxMbHQ2xYpLC3qevL1O+78tDOR42fScXFy5okufrSo44GTU97bUzzczTSr48HyTQn8vCuRkxcyaNXAk0fb+RAWbWXFpgSSUg3uKO2SZxu3K6vVuOL3VkTkVpbvNe1y5cpdVwdr1qzBZrPRpUuX63r9tcjIyDzh+s/DMlkslstLdi4uLva6edW3Wq3Zygqz7dxc6cqkK81mivybj6eZHm2vfR9kv/v9GDzpEgbwcr8gOjTxIjHFxprtiaRlGPh5mZm/IpZWDTxxug3zWv5TRKyFJesS+HlXEvFJNgJ8zHRs6sWj7X0J8Ll9DjeJiBT6Ucb77ruPe++9l5SUFHr06FGofWUtMaenp+coS01Ntf9/QECAvW5e9VNSUux1C7ttkaKoXAlnnJ3B2WxiwoIo3v86mtR0AwPo3NybDk08GTo1nN1/pNKs9s1bEjcMg1MXM4iJtxHs70T5kjd2FvV8eAYvTg8nw2LQqZkXFUq5cOJcBqu2JvLL3mTeG16C0ECdGheR24ND73YWi4VZs2axefNmEhIS7Mu/WQzDID4+nsTERKZNm1bogWVWKqKsZet/io2NBTJnCYODg3FzcyMkJISIiIgr1s/ay1iYbYsURRciLKSlw+SXQkhMMTh9MQMPNxPN6ngQ8r/0OgG+Zo6fSb9pgeXeo6l8tCyGv85eXiWoXtGV/+vuT+3KN+bU9qQvovFwM/HRayXtB6QAHmvnw9BpYUxfHM27g0Ov0IKIyK3DocDyww8/ZM6cOfmqe6WbcgpKo0aNADh79myOsvPnzwPQsGFD3Nzc7PXXrFnDuXPn7K/9d/3mzZsXetty/Ww2g1+PpLLt9xRS023cUdqVjs28iszy46UoCz/uSORCpAUfTzNtG3lR4w7XAku/VZiyUhSlphs0reVB038d1LFYDdLTDVxuwLfaMAx+O5bGqi0JnLlkwcPNRMXSLvy0M4laldx4Z1DI/2YK0/nq53hGzAxn0pBQ6lYp3ODy7/PpHPwrjTeeDc4WVAKEBjrTv4sfU76I5kKkhdLBmrUUkVufQ9dErF69mtatW7Nw4UJ+/vln2rVrx3fffcf69etZv34969ato2PHjsyZM4cZM2YU0JDzduedd1K9enXOnTtHdHR0trIjR44AZEvv88ADDwBw4MCBbHVtNhvHjx8nMDCQevXqFXrbcn2i460MnhzGq7Mj2P9nKpeirHy2Oo7HR5/np50392CUYRgsXBNHn7EX+PaXBMKirWw/kMLzU8IY/WEEKWl5Jy4vKsqVcKZUsDM/7kjKtXzr/mSSUo1CPxluGAYzFscwcmY4Z8Ms1K/qRmiAE2u2J+Fkhud7BtCstgelgzPTJ80YXoJqFdx4f0nMNeXcvR5/ncucKW1SM/cAtmktDwwDTpzLuSVGRORW5FBgGR0dzXvvvUfjxo0pX748jz32GHv27KFMmTKUKVOGsmXLMnz4cEaNGmWfpXNU1nJ71gGYfxsyZAgA69aty/Z87dq1hISEZFuOb9euHTVr1mTDhg3Z2tu5cycJCQkMHDgwW87Owmxbro3NZjBmTgSRsVamDwvls7GleO+lEnwzvjT33e3FpIXR/HY09eoNFZI125P4dFUc/e735ZvxZXhveAkWvVWaN54N5vc/05jyRfTVG/mH+CQri36M48m3LvDwy+d47t2LLN+UQGp64QWoZrOJnu192PBrMl/9HE+GJTNIMwyD34+nMuOrGBrXcOfOsoV7e9SqLYms2prIS30CmTe6JM8/FsiDrTITu4f4O/HfuZFYrZcDSBdnE33v9+Xv8xkcP1O4AZ3r//4JJ6bkHsAmpdjsYxIRuR04FFiWKlXKvvQL0LJlSzZt2pTtVHSFChVwc3O77hyY/3by5Ekgczk56xDMP7Vv354ePXowZ84cIiMjAVi8eDGHDx9m8uTJ9pyTkJkGacKECSQmJjJ//nwgM1h+9913adWqFU888cQNa1uuzW/HUjl6Kp1RA4Koe5e7fWnZ18uJ4b0DqVLelcU36S5rm83gy5/jad3AkwEP+OPhlvnPzGw20bK+J4N7BPDL3mTOh+d9T/c/hUVbGDThEgvXxFOtohuPtPGhZJAzs5bEMGx6OIkphRdcdr3Xm8c7+DJ3eSy9xpxn9IcRDHz3EsNmhFM21JnRTwYVWt+QGcR++0sCrRt40qWFt/3POSw6MxPDqCeDuRBhYeeh7O8FVcq7/q9e7h9AC0r9qu64OMOPO3KfIV+zPQlPdxN1K7vlWi4icqtxaMosKCiITz75hI4dOxISEoKrqysdO3Zk8uTJjBo1Csi8jSY8PNx+YOV6JSQk0Lt3b3tgeenSJdq0aUOPHj0YMWJEtrrjxo1j3rx59O/fH1dXV0qWLMmSJUuoUqVKjnarVKnCl19+ycSJE1m9ejUmk4nu3bvTr18/nJxybh4rzLYl/7YdSKF0iDP1quT8gW02m+jc3Ivpi2NISbPZA7sb5fSlDC5EWBjWKzDX8naNPXn/mxi2H0zh0XZXP8H87mdRGMBnY0tRMujyP9njZ9IZ8V4YHy6NYWS/wgnwTCYTAx/y5767PVm9LYkLERncUdqFp7r6c3dN90JPMxQTb+NsmIWnu/lne+7nlfnvx9PdTJkQZ/YdT6NF3cv5ZC9EWP5Xr3D/7P28nejSwpvPf4ijZJAzbRp6YjabsFoNft6VxNfr4nn8Pl883G/s30GRghKfZGXVlkTW7U4iJsFGSIATnZp60bmF9w1/b5XiwaHAcuDAgTz55JNMnjyZkJAQNm7cSI8ePXj00Ud55JFHKFOmDFu2bMFms9mvPbxePj4+rFq1Kl91zWYzAwcOZODAgfmqX6VKFfus4s1sW/IvPd3Ax9Oc5yEYP+/MwCPDYuBxgyeL0tIzl0V98whq3FzNuLmaSM+4+v6/E+fSOfC/wyH/DCohc1aud0c/Pv0+loEP+9u/5sJwR2lXhjxauEveucn6Dv07fm1QzR1/bzNL1sVjMgH/2EtpGAZfr4snNNCJWjdgpnDQIwHEJNh459Mo5q+MpXwJF05eyCAi1krHpl48+YBfoY9BpDCER1sYNiOcqDgrbRp6Ui7Umb/OZTDnu1h+2pXE1BdK4OOp4FKyc+hvRNOmTZk0aRJVq1alfv36mM1mnJyceO+990hMTOTnn38mJSUFT09P+wymSEGoVCbzBHB0fO5LnbsPpxDi74S3x41/0ysb6oKri4k9h3Nu1QA4djqN+CRbvm6t+ePvNMwmaF4n9wMy99bzIMNy+RDJrSbAJ3NGcuNvydmeu7qYeLKrH2t2JHEu3EK5Epnfy3PhGUxaGM3mfSk83dX/hiRud3E2MfbpIGaNLMHdNTxwdTHRoq4Hc14tyStPBOkWHim2Jnwehc1m8NnYUrzyRBC9O/kx9plg5rxakvBoK+9/c217xeX24PDpka5du9K1a9dsz8qVK8eqVavYvn07NpuNevXqERiY+7KgyPXo0NSb+Svj+PDbGF7tH5QtgDh0Io21u5Loe78f5ptwI4y3p5m2jTxZsj6Be+t52oMegNR0Gx9+G0uJQKd8naY2m00YZKb2cc4lQEn/34GaK32ZSSk2LFYDX6+8Z3iLKrPZxMOtfZi9NIYmNZO4r8nlfcz31PPkizVxRMbZeO/rGOZ8F0taRubXObJfYLa6hc1kMlHjDjdq3KG9lHJrOHkhnf3H0xj7TM7VkjvLutKnky8fL49l0CPWIpPeTYqGQjuW7OrqSuvWrQurebnN+XiaGdk3kHc+i+LkhQw6N/fG38fMr0dSWb8niRp3uNGz/bVfY1hQBj7kz+G/0xg08RKdmnlT8w5XLkVb+X5rIjEJViYODsnXbFqDaplpbDbsSaZzC+8c5ev3JOPlbqJahZzL1Fv3J/PN+gQOnUgDoHSIMw+29KZ7a59iNYv2UCtv/jybzrsLovj2lwQaVHUjKs7Klv0peLiZeP+lUMKirUTHWwn2d6ZZ7cxZQ5G8JKbYWLsriT2HU7DaoGoFV7q08KaEbkiyO3IyM6NCizxXSzz58NtYjp9Jp0nNm3fzlhQ9Dv8rSk1NZd68efz0008kJSWxYcMGAHbt2sW3335Lly5daNWqlcMDFfm3No28CPZ34qu1CXywNAabASUCnejfxY9H2vjc1ODC38eJmSNK8PXaBNZsT+S7XxJwcYZW9T3p1dGXO0rnb79i6WBnWtTx4KNlsZQv6UKtOzNnxAzDYOvvKSxZH0/31j45Dod8tTaeuctiqVfFjZf7BeLuZmb7gWQ+WhabuWfzmeBiE1yazSZe7hdIy/qerNqSwOb/BZS9OvjS5R5vAn2dqFHp6u0YhsGBv9LY9FsySSk2ypZwoVMzL/stQnJ7OH4mnddmhxOfZKNBNXfcXEx890sCX/0cz8v9gmh/942b6S7KzP97S7FYjFzTZWWtljhpi6X8i0PvqHFxcfTr148///wTwzCy3X3dpEkT7rrrLvr378+WLVsYM2aMw4MV+bfald2pXdmdDIvxv4M6ppu23GsYmbcArd6WyLkwC16eZto09OTzN0phMplwczXlupx9NS/3C+LV2eEMnRpGzUqulCvhwvEz6fx9PoN76nrw1L9OTJ8Jy2Duslh6d/Tl6W5+9u9H6waetG2Ywug5Efy8K4n7m+ecAS2qTCYTzWp7XPfVkYkpNt6YG8Fvx9IoGeREsL8zW35PYcHqOJ572J9H29282e0b4dCJNJZuiGf34VQsFoOqFVx5qJUPbRt5FrvtEY5ITLbx6qxwSgU7M+e1YPuHipRUGzO/iWHC51GUK+FM1Qra0lC/qjtmE6zbk0TXe31ylK/fnYSHm0nbPyQHhwLLGTNmcPz4ce666y6qVKnC3r17s5UHBgYybNgwBg8eTMWKFenbt69DgxXJi4uz6aYmobbaDCZ9HsXa3cncWdaF2ne5ERFjZdaSGJasT2DqC6F4eVzfPzdvTzMzhpdg6+8prN2VxJlLGZQv4cJzD/vTsJp7jn2kq7cm4udt5onOfjmChqa1Pbi7hjsrtyQWq8DSUeM/jeT4mXTeGRRC01qZeU+TUmx8/kMcH34bS4i/E60b3pozVT/tTGTywmjKlXCmb0df++z1O59G8fufaQzrFXDbBJc/70oiIdnGRwODs81Ue7ibGdE3kIMn0li6IYHRTypYKhHoTKsGnsxdHkuFUi7UqZy5LccwDLYfSOGrtfE81MoHT6XSkn9xKLBcu3Ytzz33HMOGDQOgc+fOOerUqFEDwzBYvHixAku5ZS1Zn8D6PcmMGhBEu8aXZ4HOR2Tw8vsR/HduBHNeLXndP8CdnUy0buBJ6waeV6178kIGde9yy3MrQKPq7sxbEXdd4yiKYhKsrN6ayI6DKaRbDCqXdaVbS2+qV8wMDk6cS2fnoVTGPBWUbcbTy8PM/3X35/SlDBb9FE+rBpf/3NIzDDbuTWLr7ymkZRhUKu3CA/d4Uyb06if5i5KwaAtTvoimU3MvhvUKtO/r7d7Ghx93JDJpYTT1q7jRptGtGVT/2+7DKTSo5p7r9gcns4n2jT1ZvunmXgdblAzrHcioDyJ4cVo4Ne7IXC3561w6J85l0LyOB8886H+zhyhFkEMfNQzDYOjQoVesk3Wv9pkzZxzpSqTIsloNlv2SQKfmXrS/2ytb8FgmxIXhvQP582wGB/5MuyHjcXM1EZuQ9208sYk23FxvjRmqo6fSePKtiyz6MZ6Swc7UvMONA3+mMnhSGJ99HwtkJtP38TTTsn7OoNxkMtGlhTcnzmXYb+k5H5HBU29fZMLn0cQl2XBzMbFmRxJPvHmRpRtuzm1O1+v7rYm4u5oY/EhAjsNinZp5U+8uN5Zvvn0CKasV3K/wd9/dzYzFWrj3yxcn3h5mpr8YyhvPBhPg48TZsAzKhbowcUgIbw0M1lWlkiuHZixLlChx1RmYr7/+GoDg4GBHuhIpss5HWIiItdI2j6XUBlXd8Pc2s+94KnWruBf6eO6t68GEz6M5fTGDCqUuz7DFJFhZuj6Br9fFYwIGvHmBTs29efBe72J5M0xKmo3RH0ZQNtSZdwaF2BPE22wGi3+OZ/7KOO4s60pauoGne977W/28M7/29AwDq9Vg1AcRmE3w6eul7N+/tHQbn34fxwdLYykT4nLdez1vtONn0qlXxT3PP99mdTz4ZOWtM3t9NdUquLJ8U0KeN3JtP5BC1VwyLNzOnJwyr6LN7YOZSG4c+mnSrl07PvjggzzLFy1axNdff43JZKJDhw6OdCVSZGVd+mK+wr8m0w2M21o39KJMiDNj5kRw9FTmLOn5iAyefeciX6/LnHF7tJ0PVcq78umqWIZOCyM+qXDv1C4MG35NJjbRxugng7PdOmQ2m+jTyY86ld1YuiGBSmVcCIu2cuZS7knk9/yRiqe7idBAJ7YfTOFsmIUxTwVnC8rdXM0897A/te5045t1xWfW0tnJREpa3rPXKWkGzrfRofgu93iTmm4w65sYrLbsM5MrN2em5nqoVc6DKiKSfw69pTz99NP07t2bAwcO0LFjR1JTU9m6dSsnT55kzZo17Nu3D4AKFSowePDgAhmwSFFTJtSZAF8zm35Lpl4uM5IHT6QRE2+jduXCn62EzFtpJj0fyqgPwvnPpDDKlXAmIsZKarqBqwu0rOdJsL8zj7T1pVdHX4ZND2f2khheG1C8VhX2H0+lekVXSgXn/jbWtpEnM76KYcJ/QgjwMfPB0hjeei4k297TkxfSWb45gQ5NvHB3NbPncCoVS7lQpXzOWSuTyUSHJl5M+zKa1HQb7q5Ff5a3SU13Zn4dw6UoS44k11arwdpdSbdVDsKSQc6M7BvIpIXRHDyRRvu7vXBzMbH9QAoHT6TxcGtv7ql7+3w/RAqDQ++M7u7uLFiwgICAAMaOHcuFCxd49tlnGT9+PL/99huGYdC2bVsWLVqEr++tnc5Dbl/OTiYebOnD6m2ZB0j+KTLWwvTFMVQo5UL9KjfupGmpYGfmjSnF2/8XTKXSLqSmGzibISMD9h1P46NlMTw++jxrtifxeAcfNv6WTGxC8Zq1NAyumGQ+67S8i4uJV/sHse94Ks+Ov8iS9fFs3JvEe19HM3hyGCWDnHmqqz+APWVVXrLKLMXkW9X+bi8CfJ0YOzeCi5EW+/PEFBuTFkZxMcpCj7a31wxdh6bevD+iBHeVd2Xp+ngW/BCHkxO88WwwQx69fU7IixQWhxdBfHx8mDhxIiNGjGDPnj1cvHgRq9VKSEgIjRo1oly5cgUxTpEirXdHX/48m87oDyOoXdmNOne6ER5jYdO+FHy9zEx5IfSGXy/pZDbRvI6nPdht09iTp7r6UyLQmcQUGys3J/DJyjg6NfMiwwInzmfQsFrxuZqt9p1uvL8khogYCyEBOd/KNv2WTI07XHF2MtG4hgfvjyjJ4p8zE8dbbRDga6ZHWx96tvfF6393yler4Mra3UlExFpyPTm8/WAKZUOd8XIvHsGHp7uZCYNDeHV2BP3+e4E6ld1wdzOx/880rFaD1/oHUa3i7Zdap/odbryu/IsihaLAdteEhITkmm4oS//+/VmwYEFBdSdSpDg7mXjj2WC27E9h9dZE1v+ahJeHmSfuz7wd5p97AG8kwzDYcSgVgBd6BuDpnjkObw8zvTv6YbXCwjWZhzeK2w0a7e/24pNVcUz4PIpxz4XY8+kZhsHyTYnsPZrK6CeD7PWrlHflv88Ek2ExSMsw8HQz5Qj229/txbwVscxYHMN/nwnOtmy+/UAym35LZlB3/2I1q3VnWVc+/28p1v+azO4/UrBYDXq296VLCy+Ci8CtQ2npNrYdSOFSpAUfLzP31vPEX3dPixRbJsMwCj23wokTJ3jggQc4cuRIYXd1W2jXrh0A69evv8kjkaLu5IV0nn77EibgxV4BOW7QiEu08vDL53FzMbFscplisW/wn34/nsqoDyNwMkObhl54e5rZ9UcKf5/P4JG2PvznkWsPAnceSuG/cyMI9HWiY1MvfL2c+PVICrv+SOWeuh6Mfbr4XIdZ1K3fk8T738QQn2TD18tMUooNsxl6tM28NepGz/KLSN7yG3vckI+rVzo5LiKFJzE580Rw4xruzF0WS5lQFxpUzTxEZLMZ7DmcOZtZo5JrsQsqAepWcWf+mFKs3JzA9gOXE6T/X/fMW4lMJhMRMRZWb0vk+Jl0XF1MNK3lQZuGnrjl8fU2reXBh6+UZOmGBJZtTCQ13Ual0q6M6BNIh6ZeV9zXKfm3/UAy4z+LonVDT558wI+yoS7EJVpZtjGBhWviMZlQAm6RYui6AstLly4RExPDHXfcgbv7lU+6vvvuu6xevbpYLR2J3CpKBjtjMkHjmh6kZRiMeC+cyuVcKBfqwp9n0zkXnnmg44F7iu/1jiWDnBn4cAADHw7IUfbjjkSmLorGzdVEncpuxCTYmLQwms9WxzFpSCjlS+Z+k06lMq683C8I+hX26C9LTrWxbncSh0+mYzZDw2rutKzveUsmoTYMg09WxdGgqjujBwTZZyb9vJ0Y8IA/AIt/jufRdj72bSQXIy0s35TA1v3JpGYY3FHKha73+nBvPQ/NbIoUIdcUWJ45c4ZRo0bZ7wR3dnbmqaeeYujQoTg5Zd8TExERwciRI9m1axeQmUxdRG6sEH9nmtR0Z/nGBGaOCOXoqQzW7koiJsFK9Yqu+HubOReRwT11b73kxwf+SmXyF9Hc39yL/zwSYN+DeTYsg//OjeSVWeEs+G/pPK++vJF+O5bKmx9HkpRio2oFVzKsBj/uSGLeiljG/yeEO0rfWkm7z1yy8Pf5DMYP8s81KHy4tQ9f/BjPlv0pPHCPNwf/SuW1DyJwdjLRtlHmHsy9R1J5c14k993tyStPBCm4FCki8r32FRcXx4ABA9i7dy+GYWAYBhkZGcydO5e33347W92NGzfSrVs3du3ahWEYdOnShZUrVxb44EVuZ9HxVo6fSedSlOWK9QY9EkBiio0hk8MJj7bQ8z4fut3rzYVIC4dPpfNS76BbclZsyboE7ijlwvBegfagEqBcCRfeeDaYsGgrG39LvokjzHQ+PIMxH0ZQtYIrX44rzeyXSzL3tVLMH1MSLw8zr8yKIDEl7yTnxVHC/7ZolMwjB6mftxMebiYuRmYQn2Tlv3Mjuatc5vdnaM9Anujsx3svlWDMU0Gs25PMqi23z7WUIkVdvmcsFy1axIULF3B3d6dVq1aUL1+e06dPs2HDBr7++mueeuopSpYsycSJE1m0aBGGYeDr68vYsWN54IEHCvNrELmtnL6Ywdzlsew6lELW5SG17nTjyQf8qF8159aUciVceH9ECT5eHsv7S2Kw2S6/ZtKQUBpUuzGJ228kwzDY9UcKT3fLfUasfEkXatzhyq5DKXRokvtVnDfKt78k4OFm4q3ngrPtc72jtCvjB4XQe+wF1u5K4uHWt06+yRKBTphMcORkGhVLZd+OsP94KvOWx5CUYrD45wS+/SWB9Ax4t7t/tg8IAG0bebFlfwrLNibQraW3tlyJFAH5Diw3b95M6dKl+eSTT6hYsaL9+b59+3j66aeZO3cuBw8e5NixYxiGQdOmTZkwYQIlS5YsjHGL3DKi4zJnzmISrIT4O9GmkRc+nrkvJpy8kM4LU8Pw93HihccDqFrBjQsRGSzdkMDL74cz7rkQmuZyj3XZUBfeHBhCbIKViFgrPp7mHDex3EoMA6w28LxCvkkPNzMZlkJPipGr2AQrFyItuLtm3vrSrrFXroenQgOdubuGO9sPpNxSgWVIgDONa7iz+Od47q3niff//r5v/C2ZcfMi8fIw4eFmYvSTQXy6Ko6TFzKYsCCKGcNL5Ejd1bKeB5t+SyYh2Yavl9IUidxs+f7Jcu7cOd54441sQSVA/fr1GTRoEFOnTgXAzc2NYcOGMWDAgIIcp8gtx2bLPMDwzbrME7ABvk5ExVr58NtYnurmx6Ptct5W9cHSWIL8nHh/ZEm8/5fUu0p5V+6t58nrH0UwbXE0i2uUzjMdjr+PU7HOEWj5397D77cmciYsAw83Ey3re9KjjQ9lQi/PfJnNJiqXdWHHwZQcKZYgcyn20Ik0+nRy7EYwq81g/e4kVm5J5OSFDNxdTdxT15NH2vrkOBiUOfZEvv0lgTOXLJfvmDdBeHTe2xl8vZy4EHnl7Q7F0aDuAQydGsZ/Jl2i532+VCrlwsQFkfj5mIlLtPH608E0r+PJrj9SSUk3iI63sWB1HEN7BmZrJ+N/tyBpj6VI0ZDvPZYJCQk0bdo017Kspe7q1avz7bff5hpU/vDDD9c3QpFb1Bc/xvPlT/H0vd+PbyeU5au3y/D1+DJ0ucebD7+N5Ydt2feNXYqysPdoKr06+tmDyixOTiae6upPZKzVnkLoVpNhMRgzJ4Lpi6MJ8nPiyQf86HC3F5v3JfPchEscOpGWrf5DrXzY9UcqG/cmZXtutRq8/000NsOgc4vrPw1vtRqMmx/JhM+j8fIw07+LHx2bebP9YAr/N+ESvx29/Odw8K9Ueo25wLQvYzhzyYLL/z7SVy3vgrenmc37U1iyPj5HHxarwd6jqdxROvfT68VZhVIuzHypBGVCnJn2ZTSDp4SRlgEBPk6M/08IrRtkHihrWM2dCxEW7q3nwc+7kkhLz77fdP2eJKqWd83xb0JEbo58z1harVa8vXN/Ey5VqhTBwcEsWbIEZ+fcm3z33XeveDOPyO0kKcXG12vj6dnehyc6+9mfB/o6MeTRAOISrXz+Qxwdm3rZZx8vRGTOWtWslPsJ4crlXHFzMd2Ss1sAX62NZ9+xVCY9H0rDf+wLfaKzH6M+jOCNjyP4clwZ+ynvjk292HcslbfmR/HjziSa1vIgKcXGz7uSOB9hYdSAIAJ9r3/2dvmmBLb+nsK454Jp8Y9T9f07+zJ2biRvzovkq7dLEx5j5ZXZEXi6mfDxMPHx6FIE+jmxdX8yM76Kwd/bTHySjY++i8282/t/M8qGYfDFmjii4qx0u7f4poO6kgqlXHh3cCiRsRbmfBfLH3+nMW90yWx7JZvX8aBUsDN7j6WSnGoQGWelTIgZq83g67Xx7Dmcypingq7Qi4jcSPn+iGexWDh37hw2my3HL6vViq+vL4ZhkJ6enu1XfHw8S5YsITIysjC/DpFiZfcfKaSkGXnum+ve2ofwGCuHT16ehcvahxYWbc31NdHxVtItBt4et96SoNVqsHJzIp2aemcLKgE83M28+Hgg0fE2tuy/fMrbbDbxav8gXnkikPgkG7OXxLDop3gql3Xl/RElaNvo+g/tZF0b2aahZ7agEsDN1cywXoEkpthY/2syX62Nx9fTTFKqjYfb+BAa6Iyzk4nWDb14/elgzoRZqHeXGzYDXpoRxs87E/l+ayLDZ4Tz+Q/xPN3NjzvL3lrphv4t2N+ZsqHOpKQZWP91AN7ZycT4QSEkp2TuHZixOIZJC6PoM/YC81bE0fd+X4f+LEWkYF3T7v377rvviuV16tRxaDAit4uk1MwfksH+uc+YhQRkPk9KuXy4pHJZF8qGOvPdLwnUr+KW4wTsso0JuDqbaFbn1stJGRVnJSrOSvO6OQ8mQebMV9lQZ46eTqdd48tBhtlsomNTbzo2LdgZv8QUg/MRFp7q6pdreckgZ+4q58rRU2ls+i2Zrvd4s2RDAtXvcMtWr0FVNyqUdKZkkBPBEU5Ex9uY8Hk0ALXvdOOtgcHcU+/W+/PMzb31PPn8h3g2/Zac7c8QoFwJZ8qVdCYm3obNMDhzKYNG1d3pdq8PVcrf2kH3zWQYBn+ezeBceAaebmbqV3XL9caquEQrq7Yksv7XZOITrYQGOtO5uRcdm3oXiTyxcmNdU2DpyLXiSgMhclmZkMx/eodPplPrTrcc5Vn7BUuHXv4najabePIBP8Z9EsWUL6J5oosfJQKdiU+ysmxjYuZ+zU6+eZ4oL86ytgOkpef+HmQYBukZBs436Et3+l8/aRl5vyempWfee52ablA6JPMGpH/nHDWZTIQEOJOYkjn+bi296dXBFyez6bb7gXxnWVda1PFg+uJozCZoWd8TJycT0XFW5q2M5cjJdN79Twh318z9w4UUrONn0pn2ZTTHz6Tbn/l4munVwZee9/nYf6ZfirIwbHoYsQk2Wjf0pHSIM8dOp/PeVzH8vCuJSUNC8XC/9d6TJG/5Diy9vLx4+eWXKV++fI5bdq4kJSWFNWvWsGLFiusaoMitqO5dbpQJceaTlbFMGBKaLYhISrGxcE08dSq7Ub5E9kMbbRp5kZxmMOfbGH7amYS/T+b+PIDH7/Olf5fcZ9CKu0BfM5VKu/DzriRa1s85g7f/eBrhMVYa36Cgw9PdTI07XFm7O4mOTb1yfHA+djqN05csPPOgP3sOp/Ln2XSa1vJgxaZE7m92eRYnLd3G8TPpVKvgSnySjbaNvPBwK/gfwufDM9h+MIXUNIMKpVxoXscD5zwyB9xMowYE8c5nUYz7JIoAnxgCfJw4fSkDF2cTL/cLVFB5g5y8kM7wGWGUCXFm/KAQ6t7lRkSclRWbEpi7PJbkNBtPdfUH4O1PInFyMrHgv6UIDbwcUhw5mcbI98OZsyyWYb0C8+hJbkX5Dix79OhBz549r6uTe++9l7Vr117Xa0VuRWaziZf6BPLKrHCGTL7EI219KFci8/7ubzckEJNgZdSA3K9B7dLCm7YNPdm0L5nwGCu+XmZaNfC0H/q4FZlMJh67z5cJC6JY9GMcPe/ztQdGf59PZ9LCKCqXc6F+lZyzv4Xlsfa+vPFxJJ9+H0e/+/3stxedDcvgnU+jKFfCmaa1PDh5IYMvfoxnRJ8AJn8RzegPI3juYX8ql3Nl4Zp44pNs/HYslfaNPXMkC3dUarqNKV9Es+HXZNxc/p+9s46Tov7D+Htmu66Lo7u7u0FAxEbFAhW7EAM7fxbYgIWKioqUNNLd3Z3Xe7HdO/P7Y+4WzrtDVEq85/Xype7uze7Ozsz3mc/n+TyPgFEvUOCUiI9WMequ+MvOHN+gF3nzgUQOngywcpsHj09iQCczvduYIhrjclx4fDvbTlyUio+eTI5UG6voRR69OY4ok4of59sZ1MVCri3E3mMB3nwgoRipBKhfXcfgXlH8vNDBfYNiyn+//xDOmVh26NDhb7+JKIrceuutf/vvy1GOKxHN6uj58Mlkvp1t591CXZ0oQPsmBl6/P7EYyXB6JNJygug0AtUqaDDoRa5qf2EmhXcc9DFrlYsjaQG0WoGOTYwM7GQmLvrSEtfebYyk5QSZMMvObytcNK6pI88eZtcRP1VT1Lx5f+JFldx0aW7k3mui+XqWnbmrXTSro8fmCrP9oJ+UOBXvPZqESiVwQ3cLa3Z4+fiXAjo3M7J1v4/hb2ehVkEoDALQp62phD/j+cBb3+axZb+Pp4bE0au1EZ1W5EhagM+n23h+vJVPnkq+LDWKdapoL8vP9V+A0yOxdqeXR26OLbWFfUMPC78sdLBkkxu1SkCjhraNSq8kd25u5Ns5do6kBWha5/K6iSnHhcM5E8uuXbv+ozcaOXLkP/r7cpTjSkSD6jrefyyJPHsYmzNMfHRxA3ObM8wXM2ws3ewmWCjPS01Uc1ufqH/kwVgaZFnm8+k2pixxUiVZTasGBpzuMJMXO5ix3MnbDydSv1rpFUG7K0yBUyLGLF4wA3ZBULw6uzY3MmeNi1NZQaJMIqPuiqdrC+Ml0STedlU0HZoambPaxbGMADqNwMghcXRvZYwk6Rj0IqMfT+Lb2TZ+X++ODG6ZDCKt6+u5d1BMiWrP+cCBE37W7PDy4rD4YlPTNStpeevBRIb/L5NJC+y8NjzxvL93Of69sDnDSDJUL6N6bjaIJMaqyLOHSU1QKylX4dO64zMRKky2EsuLlf8pXLmZbuUox78I8dEq4v9QEXS4wzz+QTZOt8TQq2NoVV+Pwy0xd42L0ZPyybOHuaP/+dNULt3sYcoSJw/fGMP13U+L8x9yhXlhvJUXP7fy0+upxaZCj2cGmTDLxrqdSm65IEDrBnqGDYy5YBWnmpW0PH4BqnsALo/EnNUuFm10Y3OGSYhRcVV7M/06lB65CMogVt0qWk5kBsnOD7N0swetRkkEKmqPmwwij9wcxz2DYsjKC6FVC4UDPReODC/b4iE+WkXXFiU1qVqNwMDOZsZPt+EPSKVO+pajdLi9Eks2uTmSHkSrEWjf2FCqS8O/FTEWFaIIxzKDpVYZXR4Ja4FyE9y8np5QGFZs9dC7bUnLp8Wb3FiMIrXLq8//KZQTy3KU4zLFLwsd5DvCfP5sSrG4whb19FROtjFxrp3ebU3nLfN76lInrRvouaFH8ZjDaLOKUXfHc8crmSzd4qFfYQv+SFqAJz7MJtai4tGbY6lZScuJrCDTljp5fEw27z+WVOrE++UKqy3EUx/lkJ0fomsLI5WTjRxJCzB2qjIoNfqxpBI6MZdX4rnPcth7LECLujqa19FxNEPRWM5a6eLthxMxntFONOhEqqdenEXW5ZFIiFGhKiPqMDlOjSSB1y+jK1/3zwlrdnh4e2IevoBMjVQNTo/EtKVO6lfT8sb9iZdcLnI+YDGKdGxiYPoyJ33amoodvwDTljkJhWV6tjYRH62iXSM946cVUDlZTb3CjoYsy6zc5mXaUie39I4q86asHFcmyollOcpxGUKSZOavc9OvvbkYqSzC4N5RTFvmZME6F3dfHfOP388fkDhwIsDTd5ReCayYqKFuVS07D/kjxPKjX/JJjlPz8YhkTIVxeo1q6ujV2sjIT3IY81M+37yY8q+p5Lz3fT6BoMw3L1WgYuLpfX4kLcBTH+fw6a/5jLo7odjffPxLPiezgox9OrmYR+XuI35Gjc3h018LePbOS5MKk5qgZulmDy6vVGrc4Z6jfswG4Yq0p7oQ2Hfcz6tf5dKxqYGHb4olMUaNLMtsO+Dn7Yl5PD/eythnkssk8v8m3H11NI+OzubJD7O5a0A0zeroybWF+W2Fk99WuLi9X1Skw/LsnfE8+5mVh97LpmltHakJag6cDHA0PUiX5gbuuvrKdKooR9kov6KUoxyXITx+GbtLokH10ktJBp1IjVRtqfGN/oDEyewgOfmhf+Q9ezYcywiw52iAO/tHR0hlEXRakaEDYziRGSyR33254kRmkC37fQy/LqYYqQSl9T7kqiiWbfFQ4DydemS1hVi2xcPQgTEljM8b1dRx54Bolmxyk+8oPSnpQqNvezPBkBIL+cfjIN0aZO4aF1e1N0c8Qstxdkxe5KBSkpoXhyWQGKPUZARBoEU9PS/fE8/BkwE27fX9yVb+HaiequWDJ5IRBYEXP8/l6hFp3P16Jks3exh+bQxDzyCL0WYVn45M5oWh8ei1Ascyg1RJ1vDuI4m8cm/CZWlrVY4Li/KKZTnKcRlCr1WmLbPySiclkiSTnR+iduXTJMjlkfhuTvEBkTpVtNzWN6pU78czodOK1K2iZcUZre4zkW4NcvBkIJJZnZ6jENomtUpvdTctfDzNGqJxLeWxcFhmzU4ve476EQRoXldP6/p6xEtc4bEWhJi5yglA8zImVzs3MzJ+mo0DJxQ/SoA9R/xIEvRoVfq+7dHKxPhpNnYf8f/p/r8QiI9Wcd+1MXw+3UZ6ToirO5uJtajYvM/L1CVOYiwqhlwV9ecbKgdhSWbNDi/3XRtTKlFqVFNH1QoaVm/3RI6PfzvqVNEy/rkUDp0KcCo7iFEv0rxO6ck7GrVAz9amEolJ5fhvopxYlqMclyHUKmX4Y/ZqF9d2M5fQKK3a7iWnIEyPwgu5yyvx5EfZZOWFuLarhVb19djdyiDKq1/l8shNsVzfvfRc8iLc0MPC/77LY/oyJ9d1M0da2A53mHcm5hFjFuleSKKKqpRWW7jUKXCrTSHEpkJ91qFTAV7+wkp2fpiKiWrCksyvi51UTVHzxgOJVCql3X+hke8I8/Ev+azZoQweAdz2cjrXdLZw76CYyOANQCisvODMydeiImBZnf7LQQFwc68o4qIU38FRY62AMrjTvaWR4dfFEG3+92sCLwbCYQhLlOl4IAgCsRYRr//CdAguJWpX1lK7crkItxznjnJiWY5yXKa4rW8UD7+fzXOfWXnwhhjqVtXhC0gs3uhh3LQC2jXSU7+acsGfvNBBhjXEZ08nFxsO6dzMwNgpBYyfVkC3FsazDhf0bG3k0KkAn00pYPZqF20aKFPoK7Z50KgE3nk4Eb1WxOuT0GkFokwC05Y5eO7OhBLbmrHciUkv0Lq+nlxbiKc/yaFCvJo3H0ikZiUtsiyz73iA977PY+QnOXz9QoVSdYAXCi6PxIgPs3F5JR6/JZbaVbQ8/F42zWrr+W2Fk+z8EK/cmxAh14s3utHrhGIt74Y1dIiCMhE7sHNJ0r5iqwdRpEw5w8VCrzYmerY2cio7pMRLJqjLzar/IrQagQoJarYd8NGnlOlnl1di/4kAt/YprwCXoxzlxLIc5bhMUT1Vy7sPJ/K/7/J48N1sokwivoBMMCTTvaWRp4bEIQgCkiQzb62Lq9qbSkwcC4LA3VfHMHeNmwXrXNx2VdlCekEQuKNfFKGwzIqtHmatcmHUCfTvYOKW3lEY9SKfTM7n9/XuSGVm4XoPOXnZvHxvPDEWNS6PxPTlTqYscTJsYDQGvcjPixyEwjLvPJIYqZAJgkCD6jreeSSJO17JYOF6959WVM8nflvhJCs/zNcvpESqpV2bG9m8z8uQvlF8N9fBzkN+mtTWsX63j58XOhjY2VKM/CbFqenUzMA3s+00qK6jZqXT+/7gyQDfzbHTrYWRhJhLf5kVBIEqKRe/KnwlYWAnM9/OsTGgo7mY24Esy0yYZSMYlOnXvrwVXI5yXPorXjnK8RdwJC3A4bQAGrVAy3r6K76V17iWnh9fT2XjHh/H0pUknPaNDcUGTNw+mQKnRJNapesDzUaRSklq1u7yEm1R0byuntSEkqf+oVMBnhubg90l0aKuHr1WYOt+LzOWu9h3LEC6NYTXL3FDdwtdWxjx+iU+n25j+yE/Nz6XQXKcijyHRDgsc1vfqIh+b+VWD91aGkv9rVLi1bRtZGDlNs9FJZa/r3fTo5WxWAt+xJA4Ro3N4bu5DrQaGD0pH6Ne4NCpIO0a6Rl+bUyJ7Yy4LY6RH+dw/9tZtG1koFoFDccyAmzY46NOZS2P31KekXyl4PruFjbs8TLykxz6tjXRpqEeh0diwVo3u474eeKW2MviJqIc5bjUKD8LyvGvwKnsIO//mF9sylijhv4dzTx4fewlSV25WFCJCpls37j0oQCdRkAUIddWckI8Oz/E29/lciQ9CMDeY/kIgtIiHzkkPtIS9fgknhubQ1KMmvHPKLm/h04F2Hfcj8cvse94AFAiJ+esdtG6gYHmdQ18/pyBn3+389VMO41r66lZUUPPVqZiLXevXyYuquwbgPgoFdaCkp/9QiLXFqZmxeIVPLNB5MMnk1mzw8tnUwoocIapWUnP0IExtGlQ+pBRlEnFJyOTWbzRw4J1LpZtCRAfrWLEbacjFMtxZUCrUeQgkxc7mb3KxezVLkAZYPvfQ4llDu1Ikow/IKPTCpd8UK0c5bgYKCeW5bjsYbWFePLDbEwGkVfvS6BdIwMur8SCtS4mzrNT4AgX08P911CU/jFnjZtrulgiQycOd5gRH2bjDSht6w+fTKJuVS1LNnn4coaN58bm8NGIZNQqgUUb3dhdEuMKSaXdFebZT3NIjlMz+vE47nsri7goxVbkvR/yeeFzK1+/UIHUBDU394pi5koXOo3ATT1LasyqpGjYcbB02yFZltl20EfdqhdXhxgbJXIyqySZzbCGOJ4RIBiSqZSk5slb4/60Kq7XilzdyczVnS5Mdvt/HcGQzMGTym9SrYLmgkWGngt0WpE7+0cz5Koo7C4JrUYoUxtstYX4ZaGDhRvcuL0yRr1Ar9YmbukTdd5CDcpRjssR5bfT5bjs8etiJ6EwfPhkMl2aK5nQcVEqbrsqmmfuiGflNi97j/nxB6TI9O5/DUP6RpFhDfLqV7mk5yjVyZkrnOTYwgSCMi3r6WlSS4deKzKgo5m3Hkxk77EAq3d4Adi010fzOnqSCzOr569z4/ZJvPVgIlVTtEiykiGcGKvm9eEJaNUCM1coFj0qlUC9aloyS/HUBLi6k5ldR/ys3OYp8dzsVS7SckIXnZT1bmNi8SY3OfnKZw4EZd7+Lpe7X89k8hInDrfEkbQgg1/IYOZK50X9bOVQIEkyvyx0cMsL6Tw6OpsRH+Vw8/PpvPVtLjbnpfEGLYJKVK5BZZHKjNwQD72bzbLNHgZ1sfDisHiu725h1Q4PD7+Xxcms4EX+xOUox8VD+W1TOS57LNrgpl97U6nt1M7NDFiMIs+NteL2yogCtGmoZ3CvqFJzbq9U1Kum47XhibwzMY87Xs2kQoKa7LwQkgxN6ut4fmjxim6jmjoa1tCyaIObbi2MhMJKq64I63Z6ad/YEGlp6zUC/qBC2g16xXZo7U4vD94QCyh+mxUTS7+cdG5moHtLI69PyKV3GxNdmhsJh2WWbvawfKuHQV3NZfphXihc183Cwg1unvwwm3sHxbBxr4/lWz10bWFg/S4vrRvoeeaOOH6c7+DjXwqINqvoVkrmdjkuHD6bUsDMlS6u7mSmX3sTJoPI+t1eflno4IkPsvn06ZTLNjXow5/y0WkFPhmRUkwWckN3C098kM3oSfl88lTyJfyE5SjHhUM5sSzHZY1wWMbhlqhSoeREaygs8+pXuTg9EokxKh69OQaXR2LBehcjPs7hmTvi6Nvu392edHkl8u1hLCaR2D9pAbZrZGDyW6ms3OblWGaQGcudXNXOVOYASeVkTaRyUq+qlilLnHh8Eka9iD8oF7OkMRlE/MEwDneYKJOKKKMYIZq7Dvs4eDLAHf1Kt1oRRYHnh8ZTt6qW31Y4+X29G4Bos8hNPc3cf13MRZcxxFhUfPhkMu//mMcb3+RFHl+zw0vvNiYeHRyLXivy2OBYsvND/DDPTtfmhv+s3OJ840hagJNZQQw6keZ1S5puH0kL8NsKVwn/1crJGto3NnD/21lMW+o4L3Gm5xtpOUqK0wtD40vYe0WbVQwdGMOrX+VyJC1QzEmgHOW4UlBOLMtxWUOlUoyHj6SVbB3NWuliwx4f0WaRjk0NEX+5a7ua+eCnfD74KZ/W9Q1n9W68XJGZG+Kb2TZWbPUQKuz6tayn5+6ro2lYo+zqnk4r0rtwP2ze5y0WQXgmZFnRrVVJVgj7gI5mfvrdwSeTC3j69jhqVdKweZ+PsCSzYJ0bqy2MQScw8pMcHrkplo17vNSoqGHpZjefTC6gfjXtWRNHVKJA33YmNu/zkZUXRqsRCIZkpixxsfWAn5eGJVx0O5yUeDVjHk/my99sTF3i4Mlb42jbsPjxIggC13S28Px4K6dyQpH9dbli/3E/y7Z4cHokKiSouaqdicTYy+cyfyQtwAc/5UeGwQAsRpHBvS3c2icqQtznr3MTH63imi4lbwwrJWno1cbEvLXuy5JYHi0clGtVv/SOSZuG+sjryollOa5EXD5XnHKUowz0bWdi9moXN/W0RDSAALNXOaldWcvBkwGuOiOGUBQFHrg+liWbPMxf52LIWbwbL0ek5wR5bEw2GrXAvYNiqFdNS7o1xIzlTkZ8lM1bDybSqv6fx8b1a29m7NQCDp4MUKdK8QVs9Q4vR9OD3H9dDACJsWqevTOetyfmsfuon+Z1dOTkhxnycgY5+WEGdTEzoKOJN77J44kPcgDQqINs3OOjTUM9z98df9bM6WBI5plPc7AWhHlpWDydmxtRibDtgJ9PpxQw4qNsvhxV4ZLcBBh0AhajSL8OpVe342OUz+T1SRfzY5WJPHuY35Y7WbbVg9srUTFRTZ92Jjbs8bJup4/4aBXJcSpWbPUwca6dYQOjua3vpT8HTmYFeeLDbFLi1bxxfwLN6ujJc4SZtcLJ1zPtuL0y9xVaOmXnh6hZSVNmznS9qlpmr3IRlmRUl9mkdZFDhdMjlTr4lVeYSnU4LUCtypoS3rPlKMe/HeXEshyXPW7qFcXyrR4eH5PNnf2jadfYgM0Z5kRWCEFQiOcfiZPZKNKghrbUSufljrFTC9DrRMY+nRyZgG1SC3q1NvHCeCujf8xn0uupZyVyAP06mFi4wc3Ij7O5rW80nZsZCIaVFJkpSxx0amqgZb3TVZWerU1UTFQzbZmTldu8qFWQkx+maoqa6qkaDp0KUiFezansEPWqaenSzEDbRoZzWhhXbfdw6FSQsc8kU7/a6Ypri3p6xjyWxJ2vZfDbSifDBsb8vZ32D1A5WUOBU+J4ZpBqpUguth/0oVZxWUzyHk0PMPLjHIIhmR6tTCTGqth9xM9HPxcgCvDcXXH0bG1CJQq4vRI//e7g65l24qNVl1wW8t0cO1FGxdKpaOjFZBB55OY4oi0qJs6xc21XM4mxaqLNIjsP+ZFluVT5QVpOCLNBuOxIJUDTWjpMeoF5a1wMvy428rgsy0xd6uSbWTYApixRggQa1tDy5K1x1Kh49vMoEJQ5kh4AGaqlajDoLk99aTnKcemvlOUox58g1qLioyeT+eiXfMb8lB/JaAZoXlfHyNtL1xAq2st/1yFuLQixYY+PEbfFlbBVKapgPvBOFpv2+mhXhq9lEfRakfcfS2Lc1AK+nWPjy99sAJj0Atd3szDsmpgSvnr1qul4Yehp4rd6u4cpS5189EsBAHWraHnuzjh6tzX9Jb3h0s0eGtXUFSOVRYiLVtGzlYklmzyXhFh2bGIgNkrkyxkFvH5/YrEqmbUgxK+LnXRtUbrB+8WEJCma4oQYFe8/lhT5POnWIHe8kokgwKnsUIRsmQwi910bQ7o1yKQFDnq3MV0yH0W3V2LVdg/Dr4spdZL6+m4Wfl7oYNFGN7f1jaZXaxPz1rhZu8tLxybFh6Yc7jAL1rvo1ebyTLkx6EWu725h0gIHyfFq+ncwo1ELfDvbxo8LnIU3w0YeuD6WHYf8TJxr54kPsvnsmZRSpRahsMwP8+zMXOnC4Vaq5iaDQP8OZoYNjC73Si3HZYd/16pbjv8skuLU/O+hJDJzQxxNV5J3lExnpa3kcIdZsM7N2p1e/EGZhBgVh08Fuf1f1gbPsIaQZcqckq5TRYtBJ3AqJ0g7/rwdbjaIPHNHPPdfF8OhUwFEUaB+VS0G/bktRp2aGenUTJkalyT+thG90y2REl82MUtJUON0XxoLGY1a4Onb43n5CysPvZvFoK4WUuLV7D3q57cVTjQageGFkoFLic37fKTlKHnwZ5LctTu9aDUC/dqbmLPaxR39oov9Tld3MvPMp1ZOZodKrcheDDjcEmGJMqtyJoNISpyafLtyDDStraNNAz1vfZvH/deG6dPWhE4rsO2gny+mFxAOw829Lt9c7jsHRFPglPj4lwK+n2enQryavccUXWmftiZGDolDpRLo0txIi7p67n87k4lz7Lx0T0Kx7UiSzJvf5LJmh5frulno0cqIKAqs3OZh2lInR9ICvPNIUpmSgXKU41KgnFiW41+FCglqKhTGERr1Ik98mM2Ln1s5cDyAyyvRtpEBiwzrdin+jEfTA3Rp/u+xiSkifLm2MJVLqV7YXWH8ARnTORLDIkSbVeekyywLapUA/6BglxKvYt/xQJmtzb3H/JHf9VKgXSMDHzyRzA/z7YyZlA+AXivQs7WRuwZEXxZRfXuP+YmLEqlfrTg58wdkDDqBbi2NzFzpIt0aLCZPiCkkof7ApdOIRplEVCIcywgUk18UweOTyMoPERetnKuCIPDKfQl8+HM+n/5awKdTClCJEAxBjVQNYx5PuiykCWVBJQqMuC2O67qZ+X29my37fahE+ODJJBrXLP79zUaRa7tZ+HKGDbdXwnRGRXfjHh8rt3l59b6EYtexOlW0tKqvZ8RHOSzd7IkMLpajHJcDLt8zsxzl+BM0qqnj6SFxvPuDQgTqF5p0H00PUiVZRZuGRr6f56BmJS2dm/07yGWtShoqJqr5bYWTZnV0JUjYnNUuVCro0KTs+LitB3ys3q5UbqtW0HBVO9MlTSsBJXpz0cYcFm5wl9D67T3mZ91OL4/eHFvGX18cNKqp491HknC4w7i9MrFRIvrLqM0oigKhMMgynHlYVE/VYHcpGlGghO5w8z4fGjWkJl66iXaTQaRTMyMzlrvo195cjDwBTF/mJBCQ6dX6NEEy6ESevzuBYQNDbNrrJRiCWpU1NK5Z8ry4XFE9VcsD12sZO7WAUEguQSqLULOSlrAENle42L6Zt9ZF7coaOjcreb43q6OnZT0989a4yollOS4rlBPLclx2kGWZHYf8zF3jIsMawmIU6dHKSLeWphKt2CLN2M29LOTZw2jUAncPiKZ9YwMqlcDhUwGmLnH+a4ilKArc2T+atyfm8emvBdzeL5q4KBVev8Sc1S6+nWPn+m6WUoligTPMi+Ot7DseoFKSMgCxbIuHb2fbGHHbpfX0bFJLR992Jt7/IZ99xwL0bqv8lqu3e5i61EmDGroyp7IvNqJMKqIuw3W6RV09382xs2mfj7YNTxONdo0MJMWq+GGenaRYFRWTTl/W061Bpixx0KOV6ZKbid89IJpHRmfx5IfZ3HV1NC3q6Mm1h5m50sX0ZU5u6W0hKa7kkpQSr2ZgZ0spW/z3IC5KRXZBOOIT+0cczwgiihBtKn5eZ+aFaFi9bCJdv7qWhYW+sOUox+WCcmJZjssKYUnm/R/yWbjBTdUUNfWr68jOD/HO9/n8utjJe48mFbOk2XrAR90qWh64vvRqV4/WJj74KZ9AUP7b+sCLjd5tTbi8El/MsDFntYvkODX5jjC+gMw1XcwRi6AzIcsyL31uJSs/xJjHkyLVTrsrzJczbLz3Qz4JMepS25AXA4IgMPL2OConqZm23MmsVS5Asfq5qr2JewfF/Gt+n0uFhjW0NKiu5cOf8nn74cRIu1sQoGV9HfPXetBrBcZNLaBJLR37jgeYt8ZFjEV1WWhEq1bQ8OETyYz5KZ+XPs+NPG42CNxzTTS39b18NZP/FG0b6Pn6NxuvfGmlZ2slfaqIYHp9Er+tcNKxiaFYKAEoHp9ZeaVHpYLid2sxXT5V9XKUA8qJZTkuM0xe5GDxRneJyeMjaQGeG2vlzW9y+eDJ01FokiSjOkuXV1N4hEuyDPx7iMt13Sz0bG1k2WYP2fkhokwqurU0lqkr23bQz95jAd5/LInmdU+Tx2iziqeGxHEsM8gvCx2XjFiC0qK97apobuoVxbGMIOGw0qovrYJzIRAKy6TnKBZVFRPVf2rXdLmhSHf4zCc53PNmFi3q6oiNUrF2pxevX0YAfAGZGctdzFjuwqgXGNDRzG19oy75RHsRalXWMv7ZFA6dKkreEWhRT39ZSQ7OJ2RZ5scFDiYtcIAAW/b72bLfzye/FnDPwGgqJ2v4dradPHuY14YnlPj7noU3xscyAiVsvbLzQ6zc5mHoZWgSX47/NsqJZTkuG4TCyqI4oJOZPn9o29aspOWxwbG88mVuMcPvhjV0LNnkISc/VGobbcVWD7Uqa/6VC1eUScWgrn/eAgxLMtOWONBrBb6fa2f+Whd925loWU+PKAqIomJN8sFP+Xh90jlPhP9TuDwSv693sWW/D0lSfqv+Hc3ER6tK+I5eSITCMj//7mDmSif5DmWAJSFGxfXdLNzUy3JZeiGWhcQYNZ8/l8KyLR6WbfGwbpeXYEjm1j4W7ugXhVaj5Gl/UTgIclNPy2VDKs9E7cpaale+8o3BJy928u1sO7f0tnBDDwvz1rj5ZZEdr19m7FQbADUrKcNIpfnB9mxtZOpSJ89+ZuWRm2Lp2MQAAmza62PslALiolT073gZ6jbK8Z9GObEsx2WDE5lB8uxherYuXQ/ZobEBo15gy35fhJj0amPiq99sPPNZDoO6WGhZTx+JBpy/zsX63T6evqN0n8srAV6fxItfWNl2wI9OK5Acr+JoWpBnP7PSsYmBl+5JQKsRiCpslwVC8jmYFP1z7Dnq54XxVtxeiRb19KhVAj8vdDDpdwfP3x1/0Sb1w5LMa1/nsn6XlwEdzXRtYUSWYekWN1/PtHEsI8Bzd8X/a4ZBQIntvKq9mSiTyKa9PkY/nkSLM6rUHZoYqVdNx12vZTB9uYvhhWk25bi48PolJs23c103c8Qo/Y7+0dzYw8KmfV4mL3KSlR9i/LPJqFWl3+zptSKjH0virW9yefWrXPRaAUEAr1+mXjUtLw1LIMp0+d04lKN0+AISK7Z6OJ4RRK8T6dTUcEXGepYTy3JcNggXuqFoymhRiqLSTpUkxSHd7grz7vd5uH0y7qwQn/6qmHinJqgw6kUOpwW5upOZq9pduXf0H08uYP/xAIO6mJm7xsUD18USGyWyZoeXN77J5auZNh6+MZYt+5Sovws1wBEKy2TlhRBFAb0Gnh9npVoFDS/fm0B8oSbW5ZX4YFI+b36TyxejUv5WlJ0sy+TZw8gyxEer/tTwe9V2L2t2eHnzgQQ6nGG03aKenuZ1FJ/EXm1MtG5w4en2yewgG/co0811q2hpXvefTTcv2axU488klUWIi1LRu42JJRvd5cSyEOGwTGahXrFC/LlLIbx+iQxrCI1GoFKi+pxN5jft9eH2ydzYo7h21KAX6dLcREKMmkfez2bfsQCNa5UtUYmPVvHBk8kcPhVg20EfsgyNa+qoV037r7oh+q9jzQ4P7/2Qj8srkZqgxumRmDjXTocmBp6/O/6iSYIuBsqJZTkuG1RJUWM2CKza4aV+9ZIG4dsP+nF6JBrW0OEPSDz9SQ559jDP3hlH3SpaZq92sWyLh8y8MDqNxEvD4unW0njFXnxzbSGWbHLz4A2x9G5r4vf1bsZNK2DU3fF0ambktr7RTF7soF0jPb9vcHNrn6jznrwSDMlMWmBn9moXBYVt5iiTiMcn8ep98cRGna6mmA0io+6OZ8jLfqYvc/LUkPhzfh9Zlpm31s3UJQ5OZCnkIDlOxbVdLdzYwxKZLl6304M/KFOzopZrupiZs8pJ41o62jc2EAjKqFWnnQR6tDLy80IHc1a7LiixdHkl3p2Yx5pCI3ONGtxemSrJal66J+FvVywcLomKZ7EQqpiojiS1/JcRlmSmLHYyY7kTa2FOd0KMiuu6Wbj5LFIIl1fim1k2fl/vxutXbmYrJam5pXcU/Tr8efKU06Ps++S40iuKKYXSnaLX/RlqVdZS6z8gH7gSsfuIn1e/yqVDEwMPXB9LhQQ1obDMiq0ePvo5nzcm5PL2w0mX+mOeN5QTy3JcNtBrRfp1MDNjmZN2jfQ0OeMuPt8e5tNfC6iRqqFpbR3z17o5kh7ki+dSIhfbR2+O49Gb4ziRGeSetzKxu6UrllSCQrTDkjJFbjGKPH17HG99l8eJrCD9O5ixGAV8fplRY63Ur6ZjcO9zs2yRZRlZ5k9JaCgs89IXVrYd8NG/o5nOTY0EgjLv/ZhHWIJJvzt55Kbi0/oatUCPVspQ0l/BuGk2pi110qW5gWHXxKBWCaza7uGrmTbW71ZyyAUBurdU9sXGvV6e/cyKQSdQs5KGW17IwGoLo9UIdG1u4JY+UVRP1dK4lo5dh/1/6bP8FYQlmRfGWzmWrrTcu7UwolHDriN+xk4p4KmPc/j8uZS/ZfadHKdi20E/kiSX+lsdOBkok9T8VyBJMm9/l8fyrR6uameiW0ular18q4cJs2wcSQvw/N3xJfaf1ycx8uMc0q1BbuxhoU1DA26vxPx1bkZPyienIMTdfzI0UyQ/mbHcSf8O5hLa5j3HlOPuUgYDlOPi4McFdqqnanj5noRIpVytEujZ2oRGLfDqV7nsP+6nXimRt/9GlB/R5bisMGxgNIfTAjz5YQ5tG+ppUF1HVn6IpZs9mPQiYx5PQhAEFm9006q+vtQ7+KoVNHRobGDRBjfXnsPwy78V4UJJgLbwLO7eykRCjIqfFzr4bEpBJFO9awsjI4fE/Wmm8Jb9PqYscbBlnw9JVgzni2LkSiPoiza62bTXx7uPJBZL9amYqCY5VjG97tnaWCIf3KATCYXlP26uTOw+4mfaUieP3hzLdd1O/57tGxtoWU9pZ1dNUfPZ0ykRc+l7B0UzZamTz6fZ2HMkwIBOZhrV1GEtCDFvjYuH38vm7YcSsRaES5h1n09s2utj12F/CR1kk1p6Rj+ezF2vZjB1iYNHbv7rOuB+HczMW+vm9w1u+rUvPux2ND3Aiq0ehl6C7PWLBa9PIs0aQq2CqimaUsn1+t1elm728PI98XRreVoS06q+gVb1Dbz+dS49WhmLySQApi1zcjwzyNink4tVlNs0NPDDfDvfzrbTq42JSkklK8Yuj8TYqQUs2aT4S46dauPb2TYGdrYw7JoYNGoBr1/ih/l2GlTX/i1JSDn+PfD4JDbt9fH44NhS5RcdmxqIj1axYqunnFiWoxwXAjqtyDsPJ7Fog5t5a13MWO7EZBC5uaeFa7pYiCtsrdpcEi3qln0SVkrWcCQtcLE+9iVBEWFbvcNLz8LEksa19DSupScQlJmx3MlXM23cf13Mn5LK6cucfDalgDpVtAy/LqbQvNzLW9/msfuIn8cGx5Ygl3NXu2jdQF8iKrJuVR1LN7lJjhOZs8pVjFjKssyanR7qVj33xXTOahcVE9UM6lLSQL2oRWkyKO3339e7cfskKiepSc9RqpjIcGf/qEgs4w09LDw/zsprE3JxuCUevvHCJf4s3eymRkUNzeuUPFYtRpG+7UwsWO/+W8SyQXUtV7U3MebHfA6fCtC3nRm9VmDNTi+/LHRQtYKGQZ0vD9P58wmXV2LCLBsLz2hRV0hQM7iXhYGdzcWO0zmrXdStqi1GKovQrYWRyVW1zFntKkEs565x0au1kZqVtGTkhliy0U2+I0x8tIruLYxMW+pk3hpXZCinCF6/xFMfZ5OVF+beQTFEm0VG/5iPXicydamTg6cC9GptYsoSZXDnwyeunPZnOUqHz690gBJiSu8eqESBuCgRj+/cb7Yvd5QTy3JcdtCoBfp3NNO9lZGsvBA6rUiFeFWxBSMpVsWhU8Eyt3HoZIDE2Cv78K6SoqFlPT1fzbTRsIauWDs1MzfEr4sddG5m/NOc6xOZQcZOLeCmnhYeuD4msp8HdbEwd42LMZPyaVVfT8emxRfftJwQN/cqWRG+prOZ35Y7qWhScyq7+G80bZmTw6eC3HtNzDl/z5PZQZrW1pVakdp12EdCtMiRtAC3vpiBSqVoOQucim6tZkU1R9JDjPwkhxG3xdG4pg6dRqBHSyMf/FxArEW8oHF4TrdEhXh1mZKMlAQ1zr+pgxQEgaeGxFEhXs305U5mLFdM5zVqxf/woRtiL5q11MXCmS3qm3paaNvIgMcns2Cdi49+KSA7P8x9ZwwrpeWEaN+4bP1sk1o61u/2FnssHJbJzg/ToIaWz37NZ8YKF0adQEqCmgxriO/m2EmKU5GWU/L6M3+tm6PpQcY/e1qiUyFBzYRZdvIdfrYdUP5p3UDP83fHl2sm/wOIMotYjCI7D/tL3MCAMoR6PDNIrzZXzpDplb3yluOyQFHbU32OU5guj8Q3s20s3OCO3MVVq6BhyFVRkcrcVe1NvD4hj637fbT4g+n37iN+tuz38dydV67NUBGevj2OJz/KYejrmXRvZaRKioajaQFWbPNQMVHDE7f8eTVu9ionMWaRe66JKUGABnQ0M3+ti99WuEoQS4NOILdwGOJMVEnR8NjgWD76pYBcncCkBXY0akUTuedogMG9FM3aucKgE8h3lHwfUMiVwyMRCMJDN8bQv4MZo15kzU4PL32ey9GM06klT3yQQ2yUiCwpFW+1SpEJXMhWeEqCmjU7vITDcqltsH3HAv9IY6cSBe7oH83g3lEcOhUgFJapnqq57CxowmGZDXu8nMgKodcKdGhiILkU39k/w4zlSov6s5HJxUhZy3p6alR08NVvNvq0NVG1gtKiNhlErAWlHzsAubZwiWlcUQS9TmDheg+7jvi5/7oYruliRq8V8fokpi1z8s1se0RDeSbmr3PRqZmx2GdrUkvPxyP0ZOYGefqTHKqmanjrgf9GpTIzN4TdFSYxVh1xhzhfkGUZt1dGRklvulz19GqVki42d7WL/h3METs8UL7DN7PtAFdU3ns5sSzHBYEsy6zY5mXaUgd7jiot6UY1ddzQ3UKX5oYyLwJur8SIj7LJzAtxfXcLrevrcXgk5q1x89a3eVhtYW7pHUXnZkZa1nPz4udWbukTRfeWRkRBEeX/9LuDxrV0dG915ZyoZSEpTs34Z5OZtdLFwg1uVm3zkBCrZtjAGK7uZD4n0nQoLUjLevoyIxXbNTIwdamzxONdWxiZv9bN0IExJWyMGtdSWr+Vk9X8vNARMUh/44EEOpylglQaujQz8umUAjJyQ6T+gYSlJKgIBKFRDW0xW5dqhcRCqxZQqeCbFyuw7aCfXYd9gFKpevlLa0RacaEwoIOZmStczFzp4vruxau7R9ICLNvi/tMhkHOBViPQsMblqc/adsDHuz/kkZMfxmwQ8Adlxk4poG87E4/fEveXojznrnHRvaURs1Fk+VYPoqD8ljEWFTd0tzB1iYN5a108eINyQ9WthZFvZttKDVCwFoRYvcPDsD/oUAVBoHNTA4s3erihh4Wbe50+rgx6MeJYkZYTKhEVay0I071l6R6tFRI0NKqpJ91adqflSsG2Az4mzLKx95hy7RcEaNtQz33XxvxjTaksyyzc4GbqUidH0pR9WT1Vw/XdLfQ/h2n9S4Hb+0Wzaa+PR97PYlBXxW+5wBFmzhoX2w74GXFb3GUZZPB3UU4sy3FB8NVMO78sdNC8ro4nb1Uu8su2eHjt61xu6xvFvYNiSv27Xxc7SLOG+GxkMjUqnr4AdWxi5MvfbHz9m43uLY0kx6l584EExk4tYNJ8O9/NUe76NCro005pA2rUl98F5kIgyqTi9n7R3N4v+i//bSAogyyf1ZbG7ZVK3ZfXd7OwYJ2bZz7N4clb46hTRYskyWw94GP0pHyqJKv5eETyn+o7/wy925qYvNjBc5/l8Oyd8TSorvj3HU0PMG+10v71+CRszjAxFuXinBKnIsYsYnNJEFQqlC3r6SORlss2u/H4OGub9HygVmUt13Uz89mUAo6mB+jTzoRRJ7Jul5cpSxQd5LWlaEevFBw8GWDUOCsNa2h5fXgidapo8fgkFqxz8+VvNnxBmZeGlYwyLA2SJJOZF8agC3DbSxnFnqtXVcsb9ydQp4qWDOvpKnX/jmamL3PyzKc5jBiiSCFAmcr/YFI+sRYV/TqUvAGtW1XLoo0edh/xc+hUgNqVtYTCMiu3efhkcgG1K2s4dCrIjkO+YlZV0WaRtJyys73TcoLEWq4cAlEa1u3y8vIXVupV0/LKvQlUSlJz4ESAyYsdPDYmm49HFL+2/xXIssz4aTamLnXSvrGBwb2iEAQlYW3MpHwOngjwxK0l9eCXGhajyEcjkvhujp0Zy51KxCfKgOQbDyTQsZQW+b8Z5cSyHOcdOw76+GWhgwdviOGmnqfv9gd2tjB5kYMvZtho07C4nRAoF425a1z0bWsq9cJzx1VRzFrpZME6N3cNiGbbQT/Lt3gIhhXrFa9fIUhH0oJMXuzA5pSIMov0aGWKVLDKocBaEOL7eXaWbPLgCyhygxfG5XDPoJhi+z4QlFm8yVMqAUuKU/Peo0m8+pWVB97JIilORTAkU+CQqFtVy+vDE/4xqQQw6kXefzSJF7/I5dHR2aTEq1CrBNJyQhh1ArEWkVy7xC0vZtCxiQGLUWTTPp9CKguRbw8RF6VClmU27/Px0S8FtG2ovyipF4/cFEtKvJqpS5zMW6tMCuu1Ar3amBh+bcwVp4M8Ez/Ot5MSp+Lth5IilT2jXuT67hZMBoF3v89nSN/AORENr19GEOBYZhCVCJ2bGamYpGbjHi/7TwS4/ZUMEmNVNKxx+rpiMYqMeSKJl7/I5YkPciLt2Dx7mGoVNIx+PLFU2YC5sNKfZw9x/9tZxEer8Pkl3D6Ztg31PHpzLLe/khkZHipCrzYmflrg4K4B0SVa/bsO+9h7LMCr950bkf43IhyW+fDnfFo30PPG/YkR+UfNSlq6tjDy6Ohsxk0tYPTjyX9r+zsP+5m6VLExO7MD0LO1iXlrXIyelE+Hpgba/gWpzcVClEnFY4PjGH5dDNaCMHqdQOKf6N//rbgyv1U5LilmrXJRJVnNjT1KDnbc3MvCvLUuZq10lSCWXr9MvkOiUc3SW3oGvUitSlrSc4IcPBnglS+ttKpv4NGblYVbkmTGTMpn/jo3B04GqJGqwWoL8+N8B73aGHn69vj/TBXzbMjMDfHYmGwkWeaWPlFUSlIzZlI+m/f72HIwiw8eT6ZBdR02Z5gxP+XjcIe5rlvpVbU6VbT88Goq63d72XssgEql6N2a1PpnqTJ/RMUkDRNeSGHTPh9b9yt2SPfU0FHgDPPZlAK+HJXC+l1e1uz0cjJbpnFNHS8Mjee7OXY27/Px5IfZ1K2qI6cgTFpOiIqJamwuidteSic+WkXfdmb6tDX9pbbsuUIQBG7qGcX13SwczQgSDMlUSdFEyMuVCq9fYu0uLw/dEFvqfu3Z2sQX020s2eQ5J2I5d40rYqE17tlkaldRrhP3XBPD/LUu3v8xn7ScMI/cVLz6UylJw9cvpLDtoJ8dh05LIVrU1Zfp1Vqk0XziljgkGQ6dCqDVCLRrZKBGRS3Ltyo+rFVTit+wXtNZ0SSP+DCb+6+PpUMTA8GQzJJNHr6cUUDDGlo6NLn8SM/5wqa9PnJtYd56MLGEpthkELmtbxT/+y6vVFnLuaBobSntetSvg4nfVjiZvcp12RBLl1cizxbGYhSJK7yp0WtFKidf2ed+ObEsx3nH4bQAbRuWrqMUBIHWDQxs2ect8ZxOI6BWUabYXpZlrDal0vDLQgfJcWpevS8hQhYXbnAzf52b7i2NLNvi4b5rY2heV8+iDW4+npyPXisy4rYrf6Dnz/Dpr/lo1PDZyAqRi13lZA3PfZZDgVPRuDaqqRiHq0SBV+9LPKsuSqUS6NjUWGK453whEJRx+yRMepG2DYtXI1xeiS9n2Ji+3MlTt8Vx21Wn5QB59jBH0gJUTlbj8kjsOx4gyiQSHyWSmRuK+GAeTQ/w4c/5zF3j4v1HkzD/jdjLo+kBTmaHMOgEmtXWlVqpVakEav+HpoA9PhlJKtsAXK0SSIxVnXPyzNxC2YNWI/Dx5AIeuSmWetV0BEOKzlEQQJYVH9U/QhSFYlKIP0OdKlpqV9bw7Vw7Hz6RXOzYdrjDTJxrp0ktXYSAFiHarOLDJ5N5Z2Ier36ViyiAJCsawy7NjTw1JO6chxj/jcjIDaHVlH2cNyjUAWf9TWJ5PCNIi3r6MteWVvX1rNxecm252MjKCzFhlo0VWz2ECpezZnV03H11dImCypWIcmJZjvMOjVo462Lh9kpoSqlgqFQCnZsbmbtGGXT4Y5Vjwx4fmbkhurQw8Nxn1ojZMCj6qx8XOOjS3MCLw+I5mh5g5TYPbRoa6N7SyOZ9XuasdqHTQN925v+szUdOfogNe3x0aGzg48n5kSpM52ZGfnw9le/n2pm82EkwCEMHxtClmYGV27xMmGnD7pZIilXRr8Nfq+4FQzKLNriZu8ZFujWE2SjSvYWRa7tZzjopmpEbYtJ8O0s3KzGNOo2S2jOkX3RkUTIbRB6+KZYxk/LJLQgzqKuFxBgVOw75mLTAgd0l4fXLdG1hxGIUmbvGhdcv07edkWfuiI8sUAdPBhj5cTaf/JrP83efe6vySJpCSouGFEBJXBncO4pbelsuC63XicwgK7d78PqVGMmuLYwYdBe+YhJlEjHqBfYd99O+sYE8e5hjGUrlr15VHYGgzKnsEF1bnNsNSa5dWaHffCCBD37K56H3sokxi/iDMl6/THy0ijx7mIMnA1Qsxbj8r0AQBEbeHs+ID7O5760MGtfWk5YdwmoL43CHUYkCLw0rvZ2bHKfmwyeTOZIWYN/xACoRmtfV/610pX8bzEaRQFAmzx4u9dzOKsxq/zs3bwA6rYDdVfbaYnNJ6C9A1+GvIDM3xKOjs1CJAvdcE0P96lqyckPMWO7iqY9yeOP+RNpdYG33pcaVf6SX46KjfWMDM5Y7efjG2BIXEJdHYuU2Dzf1LD0R57Y+UTz8fjYvjLfy0I3KBGFRpurHv+TTvK6OhtV1hMIUm+g9mh4kwxpixG1xCIJAbJSiuVy5zcP7P+bhLbQtmrfWzbRlLto10vPC0IQLajXzT3AsI8C+YwHEwkXp71izlIYZK5zIMmza56VxTT1WW5i3vs0jNdHOuw8nMvy6GKYtc9K9lZEOjQ2M+DgHa0GIrs2NVEzScOCEnw9/zuf39S7efSSphFXLHxEIyjw/LodtB/20bainYxML2QVhflvhZN5aF6MfTyq1GnoiM8gTH2ajUQvcdlUUNVI1HMsIMmuVizU7vXw0Ijmimx3Q0YzFKPL9PDsvjLcCIAqgUkGdqlreezQJi1EkKy/E1KVOurUw8vt6D20aGCLOAXWqaLlrQDSfT7dx/3WlL4p/xMnsIE9+mE1SrJrXhifQrI6efHuYmSudfPWbDbdXKnNI7WLAF5B49/t8Vmz1YDYIRJlVZOaGGDe1gJG3x9Ol+YUdGNCoBXq3MTFzhZNDJwNs2udDKuQEMWaRKilqAkGZvu3ObXjJYhTx+pWOxQ+vpbJpj49DpwJo1AJtG+n537d55NnDpdo6/R3Urqzl/ceSGDXOyqINSutbECDKKGJ3S4ybWsCbDyaiL0NHXLOS9qJoeC8ndGhsQKcRmL7MWcxPFJSO0/RlTionq6ld+e8R/05NDXw/z0G+PRzpthTB4Q6zYmvZa8vFwvhpBWg1AmOfSYkMajWpBT1am3j5Cytjfsrn5zdTr+jKdTmxLMd5xzVdzPy2wsmLn1sZdXd8hBRl54f433d5qES4ulPpi0nNSlr+91Aib32byz1vZpEUq8Ltk3B7Zdo10vP80AS0GoGUeBXbDvgi3l9FAyhxUSo8PokDJwJ0bWHk9Qm5dG5m5P7rYrjvrUxu7RtFaoKaMT/l88aEXN555PLyk7MWhHjn+zy2HTidXy0WttFGDIkrVZcnyzLp1hAOt4RRJ5AUpy6V8O0+4ufXRYpt0FsPJNKyMDHnWEaAV77M5dmxVj56MolQWBkueXtiHuGwzHcvpxZrZ+475ueZT3MYP62Ap4bEn/X7fDfXzp6jAT54PImmdU63gIZeHc3Tn+bw6le5fPtShRJatzE/5RNjUfHRk0kRG46OTZVj64kPchgzKY9PR6ZEXt+luZHOzQyk5YTw+CR2HfYzfrqNV+5NiFgh7Tmq7NMRQ+JweqRCAn16IrhrCyNjp9rYd8xPp2Z/TromzrUr055PJUd+F4tR5LHBccRGqZg4186gLuZLZtT/zsQ8Nu718eydcfRopWQSZ+WF+GKGjdcn5DL6sSSa1bmwbblBXczMWe1i414fnZoa6N/RRFZemF8XO9h5OEDDGtpz9jfs18HExLkOpixx8uANsbRrbIhUfjbt9XIkXRnqaVr7/Nku/bbCRTAk887DiVSroCHKLKLXimzd7+PFL6xK3vufnAP/JZiNIoN7W/h+ngO1Cq7vbiHarCIrTxkWXLvTy8v3xP/tSv6AjmamLXXy3LgcRt0VH7kpPZkd5N2JeWjUQplry8VAvj3M2p1eHh0cW2L6X60SuHdQDPe+lcW6XV46n8M15t+KcmJZjvOOxBg1bz+UxIufWxnyUgb1qysn/75jASwmkbcfTiIhRk04LLN+t5c9xwKoBGhRT0+zOoqo/pc3K7Jmh4cj6UG0GoEOjQ3F7v6v7mhm4jw7AzubaVBdR2qiGlGEbfu9ZOSF8QdlsnJD1Kyo4cWh8RxOC+D2ydRI1dKusQFRFHj1q1z2H/dfNvmsTo/Ekx/lEArLvDQsno5NjYTCMos3uvlqpo3nx1n58ImkYhWZ1ds9fDvHzrGM4t54zevoGHZNTDFvw8mLHVStoMbpUSa9i4hl9VQtrw1P4J43sxg3zYZGDSlxanYc8vPqfQklNHL1q+u4pU8UP8x3MPy62BIelkUIBGXmrXFxTRdzMVIJEGNR8fjgOB4bk82W/cUtW45lBNh9RHnvP3q7RZlU3H11NK9+lcvR9ABVUzQ4PBIGnVAoilcqIdOWOalfTVtq+1GWoVtLI2Mm5RfzISwaDDmXNc/jk1i1TdHxlkb2r+9m4eeFDhZv8nBrn6gSz2fnh1i0wY21IEyMRaRXG1Pks58PHEkLsHKbl1F3xdP7DOPllHg1Lw6L55H3Q/w4314msQxLMtsP+snJDxFlFmld3/C3BpsWbvSg1Qg0raNj3S4vqwr1bxUT1fRsbWTJJg/HMgLn5G14Y48opi11MmWJk7Akc2f/aIIhWLzRxTez7YiCEpxwvvwA8+xhlmxy8+ANsSUM/VvU03Nnv2i+nWMrjG4s/p5FN3vBkEyFBHWZVc0rEXf2jyYswS+LHPy80IHFqNh+GfUCI4fElRqvea6INqt479EkXvjcyj1vZlE9VYMgKB2r+GgV7z6S+KdJYxcSGbkhJFkZDisNNSpqsRjPbkl1JaCcWJbjgqBRTR0/v5HK4k1udh5WKkV925np2cqIQS9y+JQy1Z2ZFyY5TkUoDJN+d1CrsobXhyeSEq+mW0sT3VqWvv0belhYv8fHUx/l0K+DibaNDNSpouXzGTaCIRh+bQxf/mZjxG1xhCX48jcbSXEqWjdUFtKOTQ3ER6tYsdVz2RDL2auc5NrCfPtShQiZ02oErulioVqqhic+yGHNTm+khVk0CavXCui0ipG4WqUk3Ow84ueJD7J5/f5E2jc2IMsyG3Z7ueeaGHRagU8mF5Acp+LmXlEY9SIVEzUkxyn747quZk5mBxFFypxg7dzcyIRZdg6dCtCibunkJC0niMMtlXln3rCGlhizyN5jgWLE8nghSf5jolIRWtVXHv/qNxt7jvpxeWVEETo2MTDkqmjqVNFSGgVqXFOHKMDSTW7UpbgDLNnsQaPmnIzG7S6JUJgyp5lNBpGUODV5tuILiCzLfDPLzs8LHeh1AqmJarLzwvww38HVncw8Pjj2vLRyV27zEGUS6d6q5L5XiQLXdDHz/g/52F3hEqRo7U4Pn00pICvv9BBdtFlk6NXRXNPl3NuMsiyzYK2Lfh3MPHJTLDZnmHSrkrxTPVVDWIKtB3wsWOfmwRv+nFiaDCJfPp/CQ+9mM32Zi+nLXMWeb1FPxyM3nb/c952HfIQl6Nm69OO3Z2vFW3f3EX9kuEeWZX5f7+b7efbI/jPooF97M0MHxly20pvzCbFQW3hDdwsrt3mwuySS41R0bn5+tL01KylOFKu2e9hx0I8MDO4VRdcWxgvi6vBXYDIo759rC1M9teTzLo+Ex6eQ7EsBt1fiaHoAQRCoVVlzwW54yollOS4YDHqRgZ0tDOxcfDGy2pTs5grxal65TzFNlmWlQjL6xzye+TSHL0alnPUipNOKvPdoIpMWOJi3RokcBFCrlJO7wKlc1A+d8jNtmZPM3BBvP5SIqrDlqhIF4qJEPH/wobuUWLTBTbcWxlKnaJvU0tOgupZFG9x0aW7E5ZX4dEoBlZPV2JxhPnu6QqTiNXRgNA+8k4VKJfDu93lMfisVtVogFFYW5wEdTdhdEj/MtzNliZNKSWqy88M43MoC8MANsSxY5wYZRRNXSgEoXBjTWYZbi/Jc4ZOBUOn7WJKVuE/VH35mnVb5O7szXGo18ESWQjx3HPIxqIuFhjV0ZOeHmL3KxaOjs3j7oSSa1NazeFM+mbmhyP5MilMGVybMslEpSUOD6trIQrTzsI8f5tnp1doUMVkvDb6AxNJNHrYf9CEAC9e7aFxTV2JB8/oksvNDxEUXJyVTlzqZ9LuDoQOjubG7BYNeGXaYt9bFZ1MKMOkF7r/+n5Mjt1ciNkpVpo4rMUb5jl6/TPQZncNNe728/EUurRroeWmYYjiebg0xeZGDj34pQJLh2q7nRi6DIWWYok6hni7Goiq2b0URaqRqyTlL5OIfkRKvYeo7Ffl9g5u5q1y4vBKVkjQM7GKmdf2y7YP+DqTCw7Ysol90cyKdcXi//nUuK7YVn0r2BxSbnF1H/Hz0ZPIV7Vt6JmIsqr90I/JXoFEL9Ghlosdllq5WrYKGqilqZix30qp+yen1OYXOBp0ukINGWfD6Jb78zcbv6934Ctc8s0EpWtx9dfR513uWE8tyXHT8ttxFWJJ599HT5sSCINC8rp63H0li6OuZLN3sYUDHs2tl9Fol3/quAdHk28NoNQKhsMwP8xzMWqVoCWevctOxqYFRd8VTp8rpqojNGeZ4ZrBYm/BSo8ApUTm57FOycrKGU9kKqVq22U0gKJNTEOKW3tHF2qgJMWqu725h4hw7wTCs3Oald1sTNSpq2LDHy9WdzNw1IJr+HUws3OAmJz9M87oCM5Y7GdDRjFol0KyODkmGZVvcpQ5XLNnkwagXqFul7EpT5SQ1SbEqlmx0l1rV3LjHh8sr07J+8eea19Fj0gvMXu3igVJI1vs/5APwwZPJ1Kt6uro4sLOFF8ZbeXtiHt+8lMKEmSJvT8zjzQcSIsfZvddGs/WAj/0nAlROVvHpr/kcTQ+y45CfJrXOXvHaddjHy1/m4nBL1KmsxWQQWLTRw/aDfv73UGIxqcaMFU78AZlerU8fX8GQzC8LHQzsZOaOM1KStBqBa7tasDnDTF7k5LarosuUF5wrKiVrmLnSVeqQQ6hQgmLQFR+Ak2WZL2bYaFJbp/gQFpK0Kikanr4jHpVK4JtZNq5qbzqnSodGDUa9QJq19LafJMmkW4N/2XNQFAX6tTfTr/2F1dI1qK5DEGDVdk+p77VymwdRhHrVlBvjdyfmsWKbFwHl5qhI9y3JIEtwOC3I1KUO7ugfc0E/938ZVluIuatdHDoVRKOGto0M9GhpPC9BDecCQRC4a0A0r0/I48OfC7hrQDTx0Sq8fom5a1xMmGXjmi7m856bfjYEgjLPjbVy6FSAwb2i6NLcQDgMSza5+XWxg5NZQV69L+G83pSVE8tyXHSs2OahRytTqYkXVZI1tKynZ8XWPyeWRVCrhGI5wE/eFsfDN8Uydko+SzZ7uOeamGLJO7IsM2GWDUFQplYvFyTFqjicVnqOsCzLHE4LUKnQRiXDGiIhRkVOfphGNbQsXO/iaEYQnUbxlGxYQ0cwDAkxYqTCN6iLmY9/KWDNDg8dmxpJjFUz5KpowpLM6B8Vsta/g7LPKyVpaN/YwOfTbVRN0UTkArIss7YwjvC6bpazVl9UKoEbelj4fLqNBjV09Gtvily8imx6GtfUUf8PUgSDXuSGHhZ+mO8gIUbFNZ0V66lAUObjyfkcz1S+z0PvZlM9VcOgLmYGdDKj1Qg8cH0M9/0vi+0H/bz1YCLPfpbDLS9m0KGxnuOZIY6lBykqMKXlhClwuKlTRcuLw5Qp6bLu3LPyQjw/zkqtylqeuSOeCglqTmQGeejdTBzuME9+mM2EFysQCMrMXOli6lInN/eyFDsudx/xU+CUGNi59OP6ms7K0MOmvd5/XInp1drElzNsfD3LxsghcYiigN0VVir8a114Cl0SHh+TxdCB0eh1Isu2eDiaHuT+66JLrUTf0ieKOatdrN/lPSednCAIkUSUm3pGlSDLK7d7ycoL0+sczkGvX2JJYaVYRpE19G5juqCt5QoJato3NjBhpo2G1XVUOcMM/VhGgO/m2OnczEhijJrlW9ws3KhMjssonoXXdrWg0ypdg+y8MDKK9recWF4YLNzgZvSPygBPk1o6Chwy7/+Qz8S5dt59OKmE5+iFQreWJpweifHTbMxf6yIpTk2BM4w/IHN1JzMP3XD+5BrngsUb3ew+4ufjEcnFwkdqVdbSsIaOl7/MZeMe33m1QConluW46PD6JBJiyr5jS4hRcTKrdIJ1rtBqBO67NpbdRwM8OjqLazpbaFlPT4EzzOxVLnYe9jNySNx5E/qfD1zV3sz4aQUcSQuUsClZu8vLkbQg9xXa11iMIs7CfO+Xv8zF65dJTVSMwH+Y76B6qnIRdfsk9IWt5f4dzWzZ7+OVL3Np39hA20YG3F6J39e7OZkV5Nk744tVt0YOieXR0Tk89F42Rr1AlElEECAzN0y7Rnqqp2p4+L1M0nJCeP0yGrVA8zo6ru8RFalQ3tDdwsmsIGMm5TN5kUNpW+eF2H7IT7UKGl6+t3TPyDv7R+NwS4ybauPH+Q5SE9UczwhGqkA39zRTLVXLul1ePplcwI5Dfp4fGk/NSlpiLSLHM4N0bmbk25dTmbnSwbRlLnw+mWqpGm7ra6FhDeXm5ccFdrx+mU5NyyaVADNXOBFFgTcfSIyQmaoVNHz0VArvTszjaEaQwS8o+dUmg8DQgdEM6Vt8aKfos8dYSidDRY//MSbw78BsFHlscCzv/aDIAXq1NvLDfAcFjjDhM2wAD5wM8tzYXAD0hYfcFzPsLNvi5ZV7iw9upSao0agh33FuhuYAt/SOYsVWDyM+ymb4tTG0rKfH45dZsM7FhJl2OjYx0KD62fWV+477eXG8FbtLokENRSf72ZQCvpll4/X7Ey/oZPuI2+J46uMc7n0rk05NjVRL1XA0PcCaHV6qVtDwxC0KSZi61BkxaB/Y2cwTt5zOq37/sSTueCUTAXC4ZX5b4WTRBjd5hdXkvm1N9Glnuijeolcqdh/x8973efRpZ+LhG2Mj52haTrDQ8SKHiS9XuGiVy4GdLXRvaWLpZjdZeSEsJhXdWxoviZfpvLUu2jTQl5po17GpgVqVNcxf5yonlueK+++/n+XLlxd7rHnz5vzyyy/FHps5cyYTJ05ElmVMJhPPPPMMTZo0KXWb2dnZvP/++xw8eBBJkujZsycPP/wwWm3pF8e/su3/Cioladh5yA/9Sj4nSTI7D/tpdA4DFH8Gs1HkoxHJTJxrZ9YqJz8vdADK0Mj/Hrz8TGr7dTCxYL2Lpz7OYchVUXRuZiQQklmy0c0vixx0aGKIDK50bWHk61l2ALRqgS+eS6ZSspZwWPHufOu7PHRaAa9PjljnqESBxwfHUilJw6rtHtbs9KJWQbtGBkbcFlfswuP1Sbw+QYleq5ioxh+UKXBK+AOyEpVZEOLd75Uqp9koYjYI2FwSOw/7Wbsrh+HXxXBL7yhEUWDEbXH0aWti7ho3aTlBzAaR5+6Kp9tZxPaiKPDY4Diu725h4QY3adkh9h8P0L6xnnW7fLSoZ6BNQwNXtTezcpuH177OpXUDPd1bGvH45AiZjo9WUauSDq/PyXuPJtKq/unffHDvKJrW1vHw+9ks2ug+a4V8zU4v3VsaS1TIalfW8tULKYz8OAerLcz918XQop6+VJJQJUW53G474Kd325KX3m0HlSG3P8YE/l1c1d5MjFnFjwvsjPmpAAABUKth2IBoqqZoeO+HXDw+CEvKINLeYwHu7B/F4k0eRn6czRfPV4joXE9mBQmGIDH23G/GKiSo+eCJJN6emMezn1lRicp7qUTo09bE47fEndV6psAZZtRYK5WS1Ix9JiGyMFttId77Pp8XxluZ8GKFv71gn8oOsmqbB49fpnKhefyZbf64KBVjn05m3loXC9e72XHYR0K0igdviKVfe1NEI3umQf6d/aOKfaeKiRrqVdNyJC1AMASfTi6gQxMDLevrOZYR5NMpBcxZ42L0Y0mX1Y3uvwlTljionKLhqSFxEQkHKGvNq/clcNdrmSzb4uGqCyyfOBNmo3jBNKZ/Bdn54ci68UcIghJWcPBkoNTn/y6uWGJ5+PBhVq1ahUpV/EQdOnRosf8fO3YsP/30Ez/99BNVq1bl999/5/bbb2fChAm0bt262GvT09O588476dOnD6NHj8br9XLXXXdx4MABxo0bhyiKf3vb/yUM6GTmnYl5bNjjLaGv+naOnQxrCJUIj7yfRZuGBq7uaC5WSZNlmYMnA4VWLSoaVNeWqQ+xGEUeuSmW4dfGYLWFMGjFEpqzywUGnciYx5P59Nd8vvrNxvhptsLHBQZ1sXDvoJjI96yYpExxZ+eHsbslJi92cu+gGOxuiaWbPUgS+AMyTWppqVZBg9UW4ovpNlZuOx0xVq2Cmjv6RRfzcizCx5MLOHQqwJjHk2h+hj5y4x4vz4+zUnSo39oninsHKXrBpZs9/O+7PBrX1PHlDBtNayttbkEQaFxLT+O/EWVWKUnDsIExfD3Thtkg8OKweB58J5vZq10RC5guzY20bqBn1ioXoTD4g3KxafYF61w0qK4tRiqLUK+ajrYN9cxf6zorsfQHZKLMpVc7BEGZ7nb75LPGWlZMVGQeP8y307qBvtggi8cn8fVMGzVSNTSs8c9MtV1eiX3H/Egy1K2mGMTf8Gw6datq2HM0wNO3xzF9uYuvZ9qL/d3eYwEqJqnZvM/H2w8mMuzNTH5aYCclXq24CuzxEWMW/7ImskZFLV+OSmHvsQCH05Tkndb19edkCzN3tQt/QOatBxOLka7EGDWvD0/glhczmLnC+ZcHnvwBidGT8iNa4SiTSHZ+OOJLeWYakFEvcmOPKG7sUdI26kyoVRAKw/HMEPHRxb+bLMkEC6Wmr9+fUOw4OZoeYOTHOXz4cz6v3pf4l75HOYgcm3cNiC5GKvPtYXYdUabG61TVsn6396ISy8sF0SaRjDJ0zgAZ1iBRpvNbyb1iieXXX3/NuHHj6NatW5mv2bRpE5988gkvvfQSVatWBaBv375MmjSJp59+mrlz52IynV50n3/+efx+PyNGjADAYDDw9NNPc/vtt/Pjjz9y5513/u1t/5fQs5WRFVs9vPS5lX4dzHRuZiAUgu/m2jh4MohOA41q6PAFlGGHKYsdvPVQIk1q6dm638fYqQXFfBtTE9UMvzbmrEkiWo1AxcSLo7EpDaeyg8xY7mTDHh+hkEydqlqu7WopkV1sMYo8f3cCD96gRNOpRKhfTVeiUhYMKbnp9atp2X8iwNw1buaucQOnfRh1WoEqyRpybSEeHZ1NOAz3XRtD45o6tu73sXK7hze+ycPhlhh0xqRvkX9fg+paJi9yMGeNix4tjbRrZCAxVhUZRkiKFRl2TXSkOtOztYl9x/ws3uQmJV7FzBWuEvrJv4vDaQGa1tZj0KkYclU0b0/M4/PpBdx+VTRmo0irenq+mGFjXEYBPVoZi/3WubYwDaqX/TlqVdby+zo3Xp/Ewo1ulm324PRIpCaoGdDRTJuGeqpX1LBpr49hA0v+fViS2bzPR2qimqlLFd++Dk2MpQ7gPH5LLI9/kM19/8tiUBcztSppOZUT5LcVLuyuMC/fm8CC9W78AZnqFTQ0qa07ZzPpQFDmy99szFvrikx+qlXQuoEef1Am3yHRpLaOD34uIClWhf6MAZMiSGGZQ6cC/G9iHnqdwC+LnAgQ0aXGR4tYC0J/OTJREAQa1tCdk5XTmVi3y0uHpoZSK3kGvUi3FkbW7fL+ZWL5/o/5rNnhjVTTtRqBzNwQX/5m440JuVhMSWXaaP0RWo1i3ZKeEyQUhle+yGHk7fF0KrT/Wr/Lw4GTp69XDf6wD2pU1DJ0YDQf/1JAdn7ovKVs/ZcQCskYdcp54vFJfPxLPks3e4rJPvJsYqn2Wlc6erYx8d0cG8OuCZWo7B86FWDrAT/P3Rl3Xt/zijyCs7OzOXDgAG+//fZZXzdmzBgA+vTpU+zxXr168dZbbzFlyhTuvvtuAFavXs369esZPHgwGs3pi2rr1q2Jjo5m3Lhx3HLLLZGW+F/Z9n8NKpXAa8MT+Hmhg1krXcxeddqPrlkdHe8+kohGrSzKDneYV7/K5cXxVp6+I57Xv86lUU0d7z2aSK1KWk5mB/lloYNXv8rl5Xvi/5H57oXC+t1eXv0qF5NeoHtLxctt3S4vT3+Sw619okpEnwHEWlRnrQz5gzKSBDf1tFC/uo6F693sOuJHkmRqVNTQq42ZsVMKcHolnh9nJbcgjEoFPy1wEAjJxXR8n/xaQLUKGprW0SPLMmMm5RGWIC0nRIMaOtKyg7z0RS71qmnp0MQQqcxUT9UWqxCAchGbvtxFy3o69h33k2sLYdKL/9hiRasWcBRqSnu3NVHgDPPlDBszVziplqrlZFaQsATt6usZeXvxi2SMRcXJ7LI1u6eylfzy+9/JIsMaom0jAzUqath7LMDz4600qK4lGJQ4lBZi6OsZ3Nonim4tT2elv/dDHtn5YbLzw+w9GiAQktGqCxjc28Kd/aOLVdMrJWkY+3QKP863M2mBA39QRq2CTs2MCAK8ON4aaRWHwlAlWc2zd8X/KUGXJJlXv7KyZb+PW/sok5+TFztZvd3Lul0+QMkw9gdkYsynKxhNauno18HE6B/zCUuQmRfm+bvjeP/H/EiFTUb5PGaDQJ5d4s7XMnn5noRzzvj+JwiG5FItp4pgNopl2lmVhROZQZZu9vD07XH063C6glUhQTGPf2x0iB/n2c+JWAaCMl/NtHEiMxjZXx4/vD4hD4MuD61GwO46/fma1NISa1HSwfIdYSxGkWizii7NjXz4cwF7j/rLieVfhCAI1K6sZd1uLwM6mRk11srR9AAPXB9Dj9YmvH6Ju1/LxOWVefqTHD4ZmfyfMqy/upOZOaucPPVRNg/dFEu7RgYkSXE1GDetgFqVNOd93bwij+DvvvuOnJwcRo0aRc+ePenWrVsxMggK+dy2bRsJCQkkJRWP9WvUqBEAs2bNipC/BQsWAFC/fv0S79eoUSPWrFnD6tWr6dGjx1/e9n8RapXAHf2iua1PFFl5IT6anE+eLcyYx5OKVWiiTCpeHJbALS+k8/Ev+TSqqeP9x5IigxYxFhWNauh47etcxk2z0bmZ8bxlBZ8PFDjDvP51Lq3q63n5noQIGRl2TTRTljgLJ6a1tKqnZ/lWD3uPKvngLerp6dDYUOZ3MeoUH84dh/x0a2nijv7RpFuDzFrpYvtBH1sO+DiRGUIUFIISHy2SFKdmX6EWTBSgcS0dtStrmLrUxbOf5TD+uRR2HvazfrdCRL55qULk7n73ET8vf2Fl4Xp3hPQES1nQtYXefvuO+8nOD3Pz8xmIgpIff3u/KOpW/XsVzA5NDLz/Yz6HTymfb84aV6EPpnLzISPTtqGeN+4v2Urs3cbE2xPz2HfcX4KgncwOsnq7JzJM9u3Lp/1AvX6JR97PYu+xAIkxIlUrqDmRGeKd7/P5do6dhjV0rN3pxReQ0WpgSN9oBveOwuWRmL7cyQ/zHUgyDBsYU+w9U+LVjLw9nscGx+FwhzEbRT78KZ/lWz0MGxjDgE5mzAaBnYf9fPWbjZEf5zD2mZRizgZnIj0nyIRZNtbv9tGukZ4KCWoeeT8HX0AhrWo1hEKKttFqC6PVKCbzobAcmQ4PS5CaoCIjV5kcD4WVv5MleOCGGK7tYkalEtm638vIT6y88U0uFRJSill4XQjUrKRl814vYUkucROjtEC9fzmPe8U2JTu9Z+uSi6lKFBjUxcw73+dT4AyXiOU7E5Ik8/qEXDbt9TK4dxQ5BWEWrndHnvf6Tw9imY0CLo9MrzYm3pmYx7It7ggRbVVfz3XdFIL7N5MO//MY1NXMez/kM35aAbuO+PnkKWUCOizJfDnDBsCb9yfw7FjlGnY5aB8vFixGkTFPJPO/7/J46fNcNGplyCwUhjYN9Dx3V/x5N5a/4oil0+lk8uTJuN1uZsyYwYwZM6hWrRpvvfUWrVq1irxu7dq1AFSoUKHENipWrAjA/v378Xq9GAyGyOtTU0va6Rc9tn37dnr06PGXt/1fhkolUDFJw/5jAW7tE1Vq2y8uSkXdqlr2HA3w7J1RJaZ3RVHg9n7R3P92Flv2+0rEr11KzF+reHY+fXtcsZNXEARu7hXFqu0efpjn4INJ+RQ4JWpW0hAstKyplKTmrQcTS436E0WBAR3NTFni5OpOZtKtId76NheDTqRtQx1rdviQJCjqBNldEnn2QOGwjp71u33sPeZnxyE/FqNYaMFk51h6gA6N9azd5WP9bm/Ew7JRTR2P3xLHa1/nRj7D/hN+vH6p2KDK74ULa1ae0qa/vV80WXkhZq1y8diYbN5+KKnMRJ2zoXtLI9/NtfHw+9lIkiJ/aFpLh0EvsO2AD3+QMoexurYwMn25k1Fjrdx3bQw9WhkJhmS++s3Gkk0edBqB7Pwwbz6QUGxffz7NRmZemI5NDew96ueL51JYvNHDpAV2MvPCZOd7EAWwGARqV9Hy7Rw7G/d6efeRJO4dFINKVGLtbijMS/4jtBqBhBg1R9MDLNpYsoLWtLae9x9N4t63Mpk038bVnSys2elV2uQVNfRsbeSXhcpQmlqlaHEPnjp9YzD8umgG94pCkmDo65kRP8lAEO4dFMOJrCBvTMjjcFoBTWpr2XlIOT5OZoeokKAiwxrm3kHR3ND9tLawRT0D7Rrp2XrAx+TFDl4aVvpU//nCoC5mfl/v5qffHcW8PwFmLHdxND3IA9fH/KVtFpnHl7WYFmW7e7zSWYnlpn0+1u708kahZlKWZXq0NPLx5HwycxUhsygoPpZqlYAgyHw+3YZBJ3L31THUr6YlMy/EjOVOXvo8t/Bm78Jmt1+p6NPWxNYDPmYsd2ExChxOC7DzkE8Z/LOGeO7OeFrWN9C+kYHf/2PEEpSb2U+eSubgSSUuVxCgeV19mTer/xRXHLH0+Xy8/PLLWK1W1q9fz9q1azl+/Dh33303n3zyCT169AAgLS0NgISEkhfGIu1jOBwmPT2dGjVqkJmZWebrzWZlMThx4sRf3natWrVK/R49e/Ys8ztmZmaWSlr/zZBkzmrQWjRwUlZ1omZF5QSx2s49xeNiYM/RAM3r6MvU9TSvo+fHBQ6a1NLxyVNxEe3awZMB3v4ul6c/zeGbFytgLKWVPLh3FOt2e3lsTLYyqFNbR/8OJr6YYYskCjWooWXv0UBEaxQKw+odPnQa8Bd2h50eiQrxSntelhUf0LAEX89UqnJF3pmdmhrQa8AfAp1GSfJ5Y0Iuo+5OwGIU2XvMz/RlTkRBSSV5YVgCqYV2NQM6mnlhvJV3vs/jpzdS/3LSg04rUreyjlU7lFST7LwQVls4oiWsmKRi4hw7/dqbSxAGrUbgvUeTeOvbXMZMymfMpPzIcwadgFC4ayfMtFGjopIvbneF+X2Dmzv7R9Gklp7HxmRzMitE/45mNu71UuD04gsox+1TtysemHuO+nnusxw+/bWAZ++M5/ruFn763cHKbZ4S6VNnYvEmD7EWsVSzfoNepFdbEz/Oc7Bks5ekOBVRJpG5a12Mn1pAMAz3XhPNqu0ealTUEm0R+WWh8hsczwghCAIqFfRuZ2LSAjuBwt/8ofeyKFSbKFPNRwNUTVFzIkshnxnWMCa9wC29Sw6sVE7WsO9YgNXbPciyHEltSohWnffBuHrVdNx9dTTfzrazeZ+PHi2NiKJi+r9lv48be5TUKf8ZKiaqSbeGyLWFSh0g2nXEj14r/Ol3WbDOTc1KmsigmCAItGloYNLrFbHaQrwwzkqBM8yjN8fRoYmB21/OwFoQ5rm7YujUVPmtmwGp8WpGfpKDWiUQXcaAWDnODlEUeO7OeHYc8hMMyXw2pQCtWqBtIwPP3Bkf0VhXS9Vw6NT5nYD+N6FOFe0F7zLAFUgsExMTufbaawG477772LNnD8899xwHDx7k6aefZuHChcTHx1NQoNhvGI0ldUJnPuZ0OrHZbEiSsjKXVmEseszlUrSCf2Xb5VDQsIaOlds93NKn5ELm9Cj5pqBYnpyZFlKEIhPw2DI8Ai8VRAECZ7H923FIsZj530OJxchjnSpa/vdQEne8msGijW4GlXKHbdSLfPBEMk99lM2hU0G2H/SzvdCypkktLTsPB3j34UQGjUxHkpUkFElSfBZv6RPFlzNOTwVn5p3+kAadyNO3xzPio2zueTOTLs2NVE3RcDQ9iC8IUSYRtUr5XTbt9XH9s2lEm8SIv6FaBa8NP00qQSF3918Xw/C3s1i/yxuxQDpXFDjCrN7pJcokMriXhRNZIYJhmQrxKnYc8nMkTfG4XLnNU8JwW5ZlJi9ysHmfD41aiRoUUNqON/awoFHDzwuduH1hHn4vi64tjOTZwwSCMg63xNqdp42vs/JCrNru5eEbY/hsig1QiAoox/Ad/aOZMMvG8OtiiLWosBhF7K6z+z46XGGS4tQ4XBImg1DMa0+SZJZv8SADLw6Lo0K8moMng3h8EhPn2hGAJrX17D7q51ROiK0HFGLYv6OJeWvcDB0YTXKcmpOZwYjJPChVyzOXV1EgQiqV3wvqVi3dbeHgyQAxUSL2TInHxmSz56iyJUGAtg31DL8uttRKyJG0ALNXuTiWGUSvFejYxEDvNqYy9bfWghC59jB92pqoWVHD1KVOPvm1AFmGBtUVU/vuLY3nPNxUhB6tTXw+w8aEWXaevj2u2HfMygvx23Invdr8ua+ktSBErUraUt8/MUaxLpqyxEmHJgbW7fRgtYVJjBV5+Ys8mtRyUT1Vw/FMJfWpZiUNR9KCbNjtPauzwLnA5ZWYv9bF0s0eXB6JCglq+ndUhiRVooAsy2TkhggEZVLi1VeMf6YoClSroCEQlPngiaRSf5fjGcGLmnrzX8UVRyz/iIYNGzJx4kRuvfVWjh8/zpQpU3jggQcIBhUi8kftJUAodPoCq9FoIq8t6/XhcLjYc39l22VhyZIlZT53tmrmvxXXd7Pw/HgrPy90cEtvS+SiEAjKjP4xD1EQSE0Q+WWhUt07czGQZZmfFzqItYilWspcSjSvq2f8tIJSqyOyLLPvuJ+UOFWEVMqyjN0lIUmQHKeiVX09q7d7SyWWAGaDiAz0aWvkjn7RLN/q4bs5dvq2M7PzcD6hMESbRQqcEsEQ9OtgZP5aT4RUigJUSFSRnnO6dbd6m4f7b4jls2dSmLdG8e/bss8XmUxXiTL5DhkBhYBIsmL+XWR/9PZDibQs5XeoVVlLtFlJAup0Dvtu3zE/M1Y4OXAigMMtIcuKR+BV7c38ttzJ3LVuVmwJYTYKSJKMUa+0wP5ILH/+3cFPvzsYenU0adYg2w74Gf9MMrNXu/h+noOr2pnw+mW8fgBFhiAXykenLXVGsqCXbnJTo6IGWVYsjoqI5YETpw3tu7c08vl0G/uO+ameqsXuls46jGG1hTiSFuTAyQA3jkpHEBT9rCAo2eYNq2s5lR1Co4Yf5zs4nlncNkSrhv99l0uFBDW7j/gjJuf1q+mYv9bN+t1eWjcwsGyLBwQiv5HZINCrjYmDJwPsPRagWV09G/f4SIpVIYqKlGHPUT8en1TshmfrAR87DvkjnpySBC8Ni6dikoaDJwP8utjBY6Oz+PipZKqnnq6M/DDPzrdz7MRHq2heR4fNJfHJ5AJ+XujgvUeTikkQDp0K8NVvNjbv80Uea1xTxz2DomlYWHn6Jzpqs0Hk8cGxvPt9Ptl5Ia7paiEhWsWOQz6mL3Ni0IvcPSD6T7cTa1FFIlZLw8nsIGoV3PpiBnl25fzKKZCokqJGkmV2H/UTH6WKpD5d/0wap7LLtoU5F2TlhXjqo2ystjAdmhhoVlvHnmMBXv9aCUXo0tzA5EXOSHqVQaekj90zKOYfx4heDujbzsSb3+Sx+4i/hKzgSFqAdbu8PDb44ibf/BdxxRNLgLi4OEaNGsX999/Pjh07gNPt60CgZFnc5zt9QYuNjY28tqzXe73eyGv/6rbLoaBdYwN39Iviq99sLNrgpmMTA96AzLItblweKZLQ8vIXubzyZS6394uKTIX/vNDB4kKN2vkWIf9T9Glr4vt5dt78Jo/XhidEWuKhsMw3s2yEwkSmsX9f72bqEidHC62UUhPVWIwiKvFPpl5l0KgVrapGLaDVCLRpqEetUjSPOq0QsYyZv9ZT7E97tDKSEq9i0gInMoqoe8pSJ83q6mjbyMjNvaK4uVcUBc4wd76iJMu0rG+gTQMDDrfEgnWKzm3kkDhqVdZwz5tZESL2R3j9Ep4zkoDOhp9+t/P1THskVm/pZkW7+fl0G9OWOcmzS3RvaaReLwtZuSGmL3fi8cm4vcWrg16/xM+LHFzf3cId/aO59uk0BnYyEx+j5u6rY8jKDUV0oSoR9FoBt08uZrEjy9CohpZflzhpX6jjXLND2Y9Na+uYvNhB1xbFzdNlYOJcG3qtgFqlkKValTQIglIxCoUh3RrksTHZkSqiXisQDMlIMgSDMrEWkTmrlc8mFfojmo0iHZsYSMsJsudogEBIIYEmg1KF9QWU77Fwgwu1CvYc9fNj4RBR1+YGNu5Rrj8ur8xvK067MWze66NJLR07D/t56Z54ZixzsvtogJGf5PD07YqJ+fItbiYvclK7ioZDJ4NUTlbz8VPJEVlDnSpaurU08vD7Wbz+dS6VkjT4AjIaNazf7ePuq6O5re9pjXS6NcgL46w8P87Kdy9XQKUSOHDCz5Mf5VAhXs1zd8ZRs5KWU9lBfl3iZOTHOfzvocTzcvPYt52ZaJOKH+bbeb1QN6zTCPRoZeSea2LOqaXfu62JV7/KZcchHxajyLy1SspKlEmkeR09Szcpdjd925lo00DPG9/kMeSqKNbu9HI8I8hnT6dEYiJdHgmvX7k5+ruQZVnRQAsCE19JLZaatH63lxc/t7Jul5d2jfTcOyiaKJOKzfu8TF/mZPdRPx+NSD7rBP6/AV2aG2lc08Xz46zcdXU0PVqaEEVlYOu7OXZqVNTQp93l5xxypeE/QSwBunXrRmxsbKS6WK1aNeB02/pM2Gw2QKkoJiQkoNPpSExMxGq1nvX1RbrHv7LtcpzG0IExNKujVyLPNrpRqQS6NDNyXTdL5AL8yr0JjJtawIPvZkf+LjZKLDH4cLnAbBR584FEnh+Xwy0vZNC+sQGjXmDDHh959jCpCSqy8kJ8OcPG5MVOOjZRpqdVKoGV2zws2eQhNUGFJMllalAb1dSxfIuHR2+WqZ6qweuXycoL06edie/m2AEZGYgygaNwaFUQoF97Ez1aG3l+rPU0iUIhUqPG5VIhXsW13Szk28PMWuXC65d5fHBsMc/LQV3NjP4xn/d/zOP5u+Mx6gXe+jaPZnWULOd2jQ2Rad6F692Ewvxpq2/zPi9fz7RzR78o7hqg2PXsO+YnHA7iD8hk5ob56MkkmtQ+XZHIyFVa1Fv3+5BlOVLx3nbAh9src21X5djwBeRIdOKp7CBbDyoGykadomfMs0uoVdCrjZHf1532wbtnUAxb9/v4ZZGS3vTVTDvtGum579oYHhuTzSOjs7m1TxQnMhXv0W9m2TiWoVSf3vgmD4CqFdSY9CL7TwSQzuC/2sJiXREJu7armY17vBxND6LRKG3rsAxXdzTx6OA4NGqBkR8rx79eq5DJG7pF0aS2noffz8bukthxSLmpXbzRQ3y08n1XbFVugIsGSgCiTcqNiNUmseuIn4dvjKF7SxMdGht49jMrOw/7uefNLOVzqiE1UcORU8qNzzN3xJfQytqdYZwuCZtLwqAXSYpVsaZQF2vQCcVeXzFRw/NDE3jgnSzW7PTSpbmRT38toHKSQliLLGFqVtLSqZmRUWOtfPRLAd+/oj+rHvtc0a6xgXaNDeTkh/D4ZZJiVaVqmctCxyYGGtXU8synOQRDEGsRqFVJy+a9fhasU060AR1NPDUkHlmW+W6unWMZQT4akcSD72bzzWxbxBB9zhqF5P+TNvjeYwEOnAjwzsOJxUglQI1UpdKu1SjX0CK5RaOaOrq2MPLw+9lMXujgnsLI2H8r1CqBtx9O5JPJBXw5w8a4qTYARBE6NzPy5K2x/ymroUuF/wyxBKhUqRI1a9YEiEyInzp1qsTr0tPTAWjZsiU6nS7y+vnz55OWllZsuvzM13fo0OFvbbscp9G8rr5Y0ssf0aW5kY5NDGw94CPXptiBtKyvR6Mue6Hx+pU0mh2HfCArmrSerYz/2FvxXNGopo7vXkll3hoXG/Z4CYUUO6HUBKUtuWCdmx2H/Dx4Qww39TytMS1qs2Xkhlm13Vumb+CgLhZmrXLx8S/5PH5LLKmJaj6fXsCr9yWwZZ+P7HzlZqqIVIJCHuetdTNvrfKgVgOiIDD8umg+mWyjfWM963f5+HyaDYtRea5jU0MxUgmKPcs910SzcIOb1yfkkRCtItceZtdhPyu3eWlaW8drwxNYv8vL+Ok2ercx/Wn83vRlTmpV1nD31afN16unaki3hgiElGritoM+mtRWKr3bDvpZu9OLQSeQmRdmxyF/JD+6yO6lSFdVNUVJljmcFoxUKkHxHgyEFLYnCLBqm5co82l95Lvf59GxiSFiEeMLyNzQw0LlZA139Itm6hIH70zMi2zvWEaIulW1PHyj8nus2OrhsymKPtCoEyLDVUBkoCY+WiG2Py88rb0ual2DInfQqAUOnlRMjRvV1HLghEIgpy93clUHC5PfSuW6Z9IK2/oKicyzn2axWg189nQSOXlh3vouH38QtBo58l6zV7k4cDLAoC4WPhqRzLItbn6c7+BYRpBASNHVtm6gTIX/0ew8HJYZNc6KQSdgc8E918RQr6qWldvSaNtQz/hpNiolaSJVX1CqnFUraNiy30flZDV7jwV4bXhCicVfrRK4a0A0j43JZvsh/zmbl/8Re476mbHcyZ6jfkQBmtfTc11Xy1+2LAKlHd+ukYHdRwIIQIFTZtM+ZccXnQf5DuW3EwSBO/tH89a3eXwzy85V7Ux8N9eOtSDI6h0+vpll4+rO5mL6P39A4mh6EBmoXkHzp9erHYd8mPRCqfF9C9a70WkUQ/wTWaFiAxzVU7X0bWdi3loXQwdGnxfSfilh1CuRscOvi2HXYeXGsWF1bWTavxwXHv+pPZ2ZmcnAgUp0Rs2aNalfvz779u0jPz+fuLjTpsr79u0DiEyQA1x99dXMnz+fnTt3RoaDACRJ4uDBg8TFxdGsWbO/te0rDYGgzNqdHrLywlhMIp3KSM74u1CpBFo3+PN2mCzLzFvjYuzUAnwBxacv2iyydLOHCbNsvHF/YrF87AuJuCgVt/eL5uZeUYydUsC8tS7CkrLwF2HjHi+JsWqCQZklm9xs3Ovj9quiWL3Dy0e/5PPdHBsmg0jXFkb6tTdjLtREVa2g4Zk74nnvhzw27fPRqKaOVds9DH4hA0lSBiqOZQTIKVAIhlYNdatqOJIewuNTvA5VojK5bXfJ6HUCbz6QyIY9Pp4fZ+XpOxJ46Ytc2jU0sHq7B7dPolKShgbVlcGFqUudIEPjmlo+GpHM1KVOvvrNhkYFOw/7uWlUOsEQdGthZMRtJRMevD6JZVs8pOUEMehFth/0c0f/6GLi+4GdzRESLKPoDXcd9pNrC3OyUJf2yE3KQM2BE4EIsayUpFzidhzy066RgYGdLXzwUz4qEXq1NrJ4k9LSvqOfhWnLXITCivF8lEkkq5DQGXUC/oDM6h1eREFJssm1h3n6Eyvaworime19AWhRT8d7jyZFWt/TljmRZaW67vfLVE5ScSonTEqcijxHmGAInrwljgJXmDGTChBFRb94ZmXzsQ+sNKutIyVBTYxZ5NbeUbzwudLGPZkdYskmNxm5IdRnnGp/lCWEQvDIeznIcAZJVv7tdIdp38TIvmMBFm/MZujAaCXys6UJl0ciEJKV82eTJ1JxP5MIrdnpJS0nxIjbYvngpwLMBiHy/r3bKBnukxc5ihHLouMxLMlk5iofqGEZKUkNqitkKDM3BHVLfclZMWWJo5Dcqune0khYguVbPCxY52bUXfGl+lqeDeGwIifo38HE7f2i2XHIRzgM9atrWbTBzYJ1btbt8pGeE6RikoaerU04PRKfT7cRLjzO7notC39Qpn8HEw/fqEijQmGZiXPtzF7lioQCmPQC/TuaGTYwuthw15k42xBTWk6Q1EQ1R9ODpXplNqmpY+YKpSthMvy7iWUR4qJUF8XEvxwl8Z+pCc+fP58+ffrQoEGDyGOPPPIIAIsXLy722kWLFpGYmMiNN94Yeaxnz540bNiQpUuXRtrpAOvXr8fpdDJ8+HDU6tM8/a9s+0rCss1uBr+QzusT8vjpdzsf/pTPzc+n881sG1JZ4rsLALsrzKOjsxnzUwGhMFSIV8yf061hnrkzjqopGkaNy8Fa8M/E8n8V70zMY8F6F/ddG8Os0ZVY9FllKier0WsFth7w8/rXubw9MQ+bS2LU3XFk5Yc4nhnE5ZFoWd9AQoyKr36zce//MjmVFSDfHsbpkejT1sSXo1Jo21DPoZMB4qNUpMSpUKtgwx4fXr8yKQ4QCCkJKy3r6dHrBGQZ9FqRmpU0zFvrolsLZdK2bUM9NVI1LN3sRgA+nVLAy1/m8u73+Tw6Opv73spi634vc1a7iDKLVE5WNIQ39YzilzcrMvSaGBrV0BEKwbuPJPLyvQklNLBLN7u5+fl0Pvgpn1Xbvfy62IEvoBBr/xnj9HWr6ujXXln4jXoBi0nAU6jZBGheV0ebBgZCYblY9bpOFS21Kmv4bo4dr0+iZV2FtEgypBf6OgrAlCWuiFl4jEXFj6+nMuZxpU3p8cu0bWTg7YeTkGTo18HM/x5MwKQXEAWB5DgV9appee7OOB68IQYZ2H8iQG7hwMauI34yrIr27p6B0Xj8cmSS/La+UXQrXPxmrHDSp60Zi1EkJU4hbGfO98VFiWTkhli43o0vIPHq17lUraBGQCGJb32bx08LHCTEaCLfSwCuamfEpBeokqxGkpXfPxiCTk1OEziVCNEWNVv3+/hoRFLE4mfDHqWNbTaKxEWpCARkalfWoFUrleUzsWmvl2oV1GzY46NiopralbWYDQKVktSs2eWjTzsTOw/78Z3xu2bmhjicFqRBNV1keCQrv/Rzsqhye65DJl6fxM8LHdz5aga9HjnJ+Gk26lTR8u4jiQy/LpYHb4hl0hup9C40LU+3lj2IUxqOpAfJtYVRqwXmr3WhVikDUdVTtcQUputo1Mr5V4Rru1r49X+pEcJzfXcLP7yWylNDFFmBJMm8MSGXyYsc9G1nYvyzyXwxKoVru1qYtdLFC+OthMKlX0eb1dbh9sls3Osr8ZzJIBbqPwWqppQcGs0pCKMSlRjYcpTjn+KKq1g+9thj7NmzhxdeeCFSFVy3bh3bt2/n+eefL/baXr16ceONN/L555/To0cPEhIS+Pnnn9m7dy+ff/55sSxvQRB45513uO2225gwYQLDhw8nPz+ft99+m65duxbLCf+r275SsG6Xlze/zaNrcyN3D4ymSrKGAmeY6cucTFrgQEDRUV5oSJLM8+OsHMsIolHD5LdSibGoSc8J8umUAsZMyuf9x5J4fpyVWatc3HPNhf9MAAdO+Fm+1cOou+KLeRbGR6tIiVdzLF3Jw378ljjMRpFfFjlYuslDi3o6svPCPHKTUtE4maUMfQx94/SgTHKcijv6RfPUkPhi7xkMyWTlhdi414vTLVGtgoaFGzzk2iRWbfdGXuf2Srz7fT4CykAKKMd85RQN+44p7SSDTuCr51NIjlOz64ifb2fbGDXOWlj5kiN+fgBx0Spu6R3FwE5mBj6VFmkJnolNe7289W0ePVoZuW9QDElxagJBmQffzeRoepA7Xs2gQoKGpFgVV7U388StsazY6sbjk/H4wOY8TQS2HfBz60sZhMJKLGgRBEFg5BDFOun+d7KonqpBJSppJ5sKF2AZpbXdvrGeXFuYQ6eUKe2t+/2oRCWVZv8xH9/Oloi1iLRvbGDqUiUa86c3Khar2k1f5kStUiqNs1e5GDYwhsOngghA87path5Q3nN/YXb0ziM+rulkYdFGD3uO+dGoBWLMAhmFBtsdGhuYu8aF168cJ4cK9Y1FVcYThVPiItCjtYGbelp46mMrAoqtlMsr07SOnlYNDLz5zelWvSDA5v3KRjRqJWpSIeoyc9e4uaNfFOt3K4MdbRsaOHQqwA/z7Kzd6S00/IafFzoIhiRu76cMgrg8EgVOieOZXl4YGh9pqV7b1cK4qQURs/Gi+3KvX2LMpDwsRpHurYxoNQJJcSqmLXVSf1hJG59pSx2YDMpgWhFCYRlBoEQqj8sj8dTH2RzPDNKthRGjXiDDGiIrL8SD72bzwRNJ1KioRa0SeOKWWNbt8jJ7lYsHzjFzPBCUmTBT0dAvWOci2qTCagsTZSpg5JA4urcy8sUMG2oVJSIndRqBo+nBwiGamGLPbdjjY9V2L68NT6DzGZZctStraVlfz4iPcli62UOfUjxP61XT0qC6lk8m51M5KalYnntCjAqPT6Z7S0OJm7tAUGbuGhddmivZ5uGwzKKNbuasdpGWE8KgE+jSXNG6/5mMpRzlgCuQWPbt25cDBw7w2GOPUadOHVq1akXHjh0ZNWpUqa9/4403+Prrr7nrrrvQarWkpKQwZcoU6tSpU+K1derU4aeffuLdd99l7ty5CILA9ddfzx133IFKVbLV+1e2/W+Hktxio0VdPS8OO72oxFpUEeI2ebGTG3pYiDJdWB+xTft87DseoFKSmnrVtMRYlMO8YpKG14cnMuzNTGavctG1uZF1O70XjVgu3qREB/ZoXbw907mZkbFTCmhYQ8vybR56tzXSvI6eGcuc9GxtZN0uL1cXGmwHgkqWt8sjRRb4GIuyqI2elM+ijW5GP56ESlSqH9/PszN5kQNBgGizinxHGI0aDDoiOjxl8YMYs0hCrIoPfy6geqqW2pU1HDrpJ7sgTN92RhZt8DBpgYMHb4ihZT09DWskMeSlDAqcEpWT1bRtVFKeULSIhUvySn6Y76BhDR2j7jp9vCgxkcp/59okalYU2HXYz5JNHupU0USM8il8lVEPbh+R/HJBgK9n2njzgcTINutU0fLpyGS+m2Nn9Q7FBH7TPh+dmhlwuiV2H/UTDMH2g36a19Fx6FSQkR/lRHSQbRvp2bDbx4lsLzf2sPDMpznsOKSkV7z+tZWbelkihtdxUWLhgJKB1du9DBsYgyAo5HXF1tOVJJtTqdot3ujF5VHex+eHZz/N5lTO6S85denpye1Dp4IRovtHSDKs2eFj2WaF+Bn1At1bmti8z8v2g36evDUOvTYvQkjlQosoULZ3VXsT46fZaFlPz7ItHu7sH023Fka+mW1n+wEvz461EhelYujAaGpW0rLvmJ8pS5xMXepixnIX0ebTPqbtGhZP9BjU1cyeQvN8nUbgtxUOHG6ZxRvdeAMy/3swMeKlOPTqaN79Ph+zQWTIVVEkxanJd4SZutTJ9OUuhl8bg0YtMHWpg1krXKRZQ4iiEk03uFcUTQslEF/MKCAzN8S4Z1KoWUnL4OfTuaaLhZt6Wnjyw2ye+jiHyklKBbd+dR2Na+rYfcRfcscWIiwpn3f2KhdHM4JIYZlgWKn0DukbxR39Y0jLCfLlDBuvfZ3L6MeT6N7SwNLNXg6e8JOTHyIhRsWuw36+mW0nMzfEc3fFl3ifeWtd1KmiLUYqi9Csjp6W9fTMW+MqlVgKgsBL9yQw8uMc7no9k/aNDaTEq9l71M++4wFizCIb9/pYvNFN1xZGwpLM3mN+Ji1wkFMQ5uV7ogiGZF750sr63T7aNNBzcy8LefYwv693M3+ti/cfS/rbsazl+O/giiOWAwYMYMCAAef8elEUGT58OMOHDz+n19epU4cJEyZckG3/m3EiK8TR9CD/eyimVPH3Dd0t/Py7gzU7vBd8envlVg9VK2hAlku0zbQagf4dTEyca2dQF3OJasKFhN0ZpkKCulh1xeEOs3anB0mGXUeUVf+5sbmkxCtk8WBhSsS1XZR9Nn2Zkz3HAkiyMhF8TRcLD1wfi9cf5tlPrew45GfslAIeGxzHjwscTFrg4M7+UVzfXSH0VluIl8ZbOVhY+SrK/dao4PoeZto2NPDGhDy+h8cxxAAA12xJREFUn2ujVxsTGblhNCp4+MY4WtYz8N4PeSzZ5CYxVkUwBAWFBKlDY32JqhEoujtQdGdnIs8eZvcRf7HKFsAHP+eTUxCiX3sT89e52bjHF5lYP1hY5SuyAoq2iNSvpuVIepCcfKWVFxclsn63j417fbQ7g+hWT9Xy2vBE5q9z8f4P+Xz6VDL1q+tweSXufSuTnPwwBp0QiUM8c7hmQ+FjTWvrmLLESY2KmgiR3XUkwK4jedSt6uD14Ym0a2zAZBBIzwniD0jIsszq7actniomijjcMoGQjL+Q5BW9JxAZ/jgTgkDEV7M0UjmgkwmDVmDqUhdFe9KoF9FpoXsrI78udtKohhZfgIhBfO1Kao5mhAhLSoX365k2YiwigaCEzRHmzW9y2XPUTyAoM/JTK5KktKK/nWOnU1MD918Xy9WdzTz8bnakMlqEjXt9rN+TRfO6Ol4YmkBclIpuLYws3+IhPlrFlCUudBqBri2M3NDdUqyy1redGZ9f5quZNuaucRFlEnF4JNQqgaEDo+neSkmwKWqLAyTGqDiRFWTExzmMHBJH52ZGFm/0MOSqqMhQjiAqmsij6UEyc8P4gzLVUwWSYtUs3ujG4ZKokFj6DW84rGSCr9rupU0DPdd2MfPLIicmg4DXJzN5sZPOzU1Uq6DhlXsTeOT97Ij0Itoksn63l+VbT3cHqiSree/RxFITULLyQjSqUTZxq1dN0W+WheQ4NV+MSuH39W6WbnaTlh2kQoKa14cn0LS2jvd+yOd/3+Xx7g95xW72WtbTYzQoXZLN+3y883AibRoaCi3CZG6/KpoXP7fyype5/Pj6X0/OKsd/C1ccsSzHpYHDrVylKpTRKomxqDDoBezus6eQnA94/DIJ0SpiLCKb9vpKWPXERyukaMU2D8gCj43JJtos0ruNiY5NDP/IfPlsSIpTs3Gvj0BQRqtRWk6jxlrJyA3x0A0xfD/fjssjkxCjihgq59nDvPtoEklxamRZZtZKB2qVQM92Rjbu8UX0hAadiv89nMR1z6QxZ7WLVg30/DDPTrRZjBCjqzuZWbDOHSGVoBANrcj/2TvrMCnOrIv/qqrdxg13lwAhIUECIe7E3XXjvvFk4+4uxD2QAAGCu7v7wDCMS0+7VdX3x9vdM80MhGQhm/12zvNks+lpqaqurjrvveeeQzgMn4zz8Mk4YadTXBFj0foQrXMNxDQdh03GaZdxxRN2Kt0qmlpvXfPrAj8nDHKkmGKX18T4cGwtfTubk4+HIxrTlwb4NW6vMnaWF5NR4ug+ViprVWYvD3DbBRm4HAqTFvqxWiTSHQomI0lzcB1ok2+gX1cLZVUx+nYyo8gS05b6qXRrtMo1MHmhL4VYRqI64ajOiP42PvrZzZeTPTxxfTYOq8y1Z6TxzKc1tMwx4PZFkgaWFqNYiHjiFcVVW8L0bG9iQ2EEm0XCYZO444J0xszysWZbhBueLeW9fxZw1WlpvPWDm9wMhRnLAqzcUk8W91Rq3H5BOt9P91JWpdLUskZRRIW3Va5Cp1YmZq0I0rO9kZ2lwhYnQTKtZuF7+eu8eqJhsQiyEwprjJ3lSxLRl78WbVu7Rcbt0ygsFaRSluG+S7O46flSfEGNrbtFgtH6HSHqfOKDNA2y02UevDKbnaVRvpvq4daXynjr3nxGDXfw4S91FGQpPH9LDqqm8/iH1VhMEpt2Rrj+mVI6tDSxbGOIYf2sPHJNdpMLkIY48xgnxx9pZ85KkVaTZpexmiW2Fke5Mj7sctwRNs4Y6qSyNsbPc3ys2x7msM5mXvm6BqddJhzVU4aE+nWxMH2Zn/HzfPTqaGJDYZgjelq58HgXbq/K+Q/uobRSZdPOMN3apRK7sbO9zF8T5Mkbsxncx8Znv9bhtMl8+a8C4WO5JcxNz5Ux8ggb3duZSXeJ35zDKvHyHXm0zDGwZH0Qf0inVa6BPp3M+xy0cdrk5BBTUyitElrd/cFmkRk13Mmo4Y1DFa44NY0Vm4MYZJFv36WtCatZ4td5fm56rhRFkTj5KAfpToVH3q9k4Zr6CviRPa1s3BlhQdwaqhnN2BeaiWUzDgoS6SKbdoVFtXAvFJVH8Qf1fRLPg4k2eQbGzPTy2HXZTF8a4Pvp3pTM4xWbQhgUqKzVyM9SKMhSKK6I8fiHVfTuaObpf+QcEqPgk46y8/VvHsbMEtuzaF2QjTsjvH5XLnmZBr6YVMfRva3kZykEIzqTFvjJdsl0j9/oojEoqxFMoWd7E5MW+OnTqf4m6LDKdG0jiM/D74pp4SN6WtA1+H66l++meghHRZXSZpHwBsRQyuwG1ZSG0DSRrbtoXZCFawM8+n4VA7pbuO7MdDq2MuHxqzz/eTUL1wriesOzZQzrZ6NTaxPF5VFmLAuQ7pS5/3LR8qv1qtz7egWFpVH6dzWjyFBZq/LYB1UM6mXhqN5WNB2OHWjj6ieFd+LRfSycMdRFrSfGYx8KjaAkCeJrt4RpnWfi1MHCwmhy3D4ozSFTWSuI+ZaiCF//Jlrgmiba/X07m5m7KsgtL5Zz5jEOstMUzCaJDYXCX1KR4dFrhcatuk7lumf24I7PqawvFGVGf0iHkM7jH9Vgjrf73T6da58pZUjci9Ab0Hh6dL2uMVHlfP07Nw6rRIoLO/WelKoqntu/q4Xlm8J0amVky24Rg5gglWkOmbNHOBg93oPNIoj5psIIJdWClDSsIKY7hW2SroM7bp8UU0WK0U8zvLz0VQ1d2phYvD5MNL5B5TX108jRmE4sBt9O8fDcLbkc09/GTc+V8dEv7mS29Q1npyedGt6938BPM7zMWRmgxqNhNkW5//JMjjvC3ohUqprOonVBZq8I4A8K4nXqEAdt8oycdJSDddvDPDW6iooaNTmBD0JD2a7AmPRhfPazahauDYphmXXifA42qDqfPcLJlEV+JBnuujiLK/9VglERAz4vfVWDLEFWpsKYmV4evKr+N6XrYvL72AE2BvexxY+tRqZLxmlTuPfSLC5+pIQB3S0s3xhi4nw/VrPYx3/dkEPn1mJBNXzAgWnqRw6088rXNRSWRFIWaZCIEw38aZ26ruu89FUNLXONvHpHXoqh/5nDnPzj+TJKqlTysxVue7mcgiyFf5ybQX6WwsbCCOPn+VBkWL6pmVg2Y/9oJpbNOCjIyzQwsIeFb6d4GNI3NYVE03RGj68j3SE3sho5FDj5aAdfTfawfGOIi05w8sFYN0s3BBkxwE5ZdYypS0Rr8ujeFp68MSdZPVi9NcTD71Xyylc1yaSfg4lWuUbOGym2p6QyRml1lNa5Ct9O9SQHSQwGcRPq1dFMcXmUtdsjzFkZYFg/G4pSXyH8ZoqHFtkKKzaHGDfHh9UsMeQwa3LKvV0LA7tKYvzz8iwkScLjVznvn8JD9cRBdqYuESRsdbya1tA0OwFJgnmrgiAJH8eOLY08dWNOsg1mUCR2l8dE5nFJlIvj1kiL1wVJdypceIKLM4c5klZTz31Wjdun8sEDQvf28lfVzF8d5N7LMnntmxqi8UGMl76spsotiOG0JUGmLQmSm1HfptR18Y/NIrN+R5gpi/3JiXGAOp9G6zwjSzeItJGCLAM3jEonJ8PA2m0hJi/0k5elYDVLvPhFTXxf6tvMJx5lJ90h88rX1UxZ5CcSLyA15IEdWhopLImi6xCOJ+fIEvgCOpMX+mmRrfDhg/k88G4VW4sitMwx8M79eSzfFObLSXWUVcdw2sWiZsVm8R24HDKhGo3Hrs2ivEblvTFuAK46PY1t4+twNriXt85V+HS80M5K6BgUiXSnjI7CnspUQavLJuEP1tsLJbB+R5hLT07jk/Hu5N8Smt3E8Y9EhZbwH6e6eO07NztLo7QrMHLOsU4+/NlNukOcCys2hXF7NY7pb6N9CxP3XJol/nm9HFmWOHFQY/mL26vywDuVbN4lUomy0xWmLvbzw3Qvl5/iYsQAG/e/VUGnVmLi/v63KgHh97lsY4jbXi7n5dtzGD/Pz5qtIQIhHUUWlWWRWiQGqYwGiQ4tjLTON1BUFuO6p0uJxkQ85acT6oip8Pj1OazfEWbiAl/KNnoDGiWVsZQhmxbZYiG6tShMh1YmOrUyYjVJfPxwARaTxJeTPXw71UO3dn/cG3PkQBs/zfBy35uV3Hp+BoP7iEilpetDvPlDLVlpCqcO/nNSoi1FEbYURXjmHzkp12cAl13hvJFOXv/OzTe/ecQC+6acpEb66D42Th1s55LHSlm7rXGiXDOa0RDNxLIZBw03ni1SSG5+oYwLT3DRrZ2Z0qoYP83wsHJLmEeuzvpLIhfzswxcPyqd98a4Oby7hXOPdbJsY4hXvq5JPmdQr1RSCdC3s4UbRmXw6jc1lFXHDskE5I1np5OVpvDdNA+18WGH3RUqOekK/buaWbcjwm0vl3PxiS6G9bOxbkeExz+sons7E306mUlzytR6NCprhffhxPl+erQ3sbMsxrQP67V8bfMN7CyJ8e4YNxU1Mep8GtE438hwKkkS5fZpyDJkOmWq4kbaCR1esuVqkvD4dULhKIvXBenVUUT/fT6xjlqPyou35XLXaxXYrTKjHylocr93lkZZuiHEQ1dl4XLIVNbG4m25MB+OddO9nZm1W8PoOsxZKUj29aPSOH+ki5VbwrzzQ/131zpXZneFRvsWRp68IYdpSwPJ71aRRTXzilPTePbTavp1tfCv63MIhjWmLfGLiujhNmYs83NETyuPXZuNP6iR6VK45ulSyqtVJs73M3F+vXE81LenAfp1NrFya/3NNXG8EsS8R3sjGwqj3P5KBUaDlJzMV1U4sqeVI3vWL67e+qGW1VvDqBp0bW2ioibEEx9Vk+mS6dXRxLrtEUaPF7nuNR7xAQYZ1u2Il+50MbyUSHoxNjhlbRaJQEinqLyJySlg+aYwyzelajqz0hTRqpUlVm8JcfsFGTz4bhXLNoYwGkS1v12BkR7tzcRUqKoT2zRnpZ9f5+u8/WMtF5+YxuWnuJAkifxsQzKppyFUVefeNyooq45x50UZnDbEgSRJRKI6306t49MJnqRv6bB+Vn6Y7k2SX6dNxmiQ2Fka5cKHSpAVieMG2pi9MkAkolNaHUOWYMriAFMWi9+Eyy4TierYLaKSaTOLAaqzjnFy2lAHeZkGthSlJiKJ80lcHxJ62amL/YybIyQGNzxXjtkI4ShsK44yc0WAAd3MbNoZ5biBIn9+6YYAug7d2prI3U9mfAIWk8xLt+Xy1OgqHv+wCrNR5MaHIjrd25l45JrsP53pvatMHMB9mcsf09/G69+58QV1bhiV3uhaXVqtouuwuyJKnU89qN7Ezfj7QlVV9uzZw44dOwiHwwcU7NJMLJtx0NC+hYk37s7j/TFuXviingh0amXk6RvFYMO+sL/Iwj+D849zkZdp4JspHmHgDaTZZfp1NTNrRZDrR2U0qXMaebiNV76uYeXm0CEZMpIkifOPc3Hy0XbO/eceNA3euS+Pzm3i/oqazvfTvXww1k2vjiZa5xm49ox0Jsz3MW91EIdFotYjprhPPsrObRdkYDJKfPZrHdsa3MDnxCeQf5zupU8nc1KzCTBnVYA0h5wktg6LmDhNmIVHY8KMus6rsqdKpX9XE/PXhImp8Mj7Vcn36dHexKt35tGptQmnXWbFpiAmg0TXtqakeXoCq7YEkWX4dII7WVEryFI4YZCDipoIU5cEUya+AXIyDMiyxIBuFs48xslr3wqd4O4Ksd0/zvAxZqaPTJeUrLjKsogAnLrEh9un4fGLQZQl68U0eGaaQp1fIxSGCXN9XH6Ki6y4FUsiZ/2UwXbGzfFx58WZzF0VIBDS6djSmMzWXrk1gsMmEQrr9O1sprg8RlWdmiTrGwqjmE0SZdUxJEm0nX1BnW+nerji1PTk/hVXRJk435ck8Es3hnDZJFwOhfxMmeWbBXmVZeL2QTIev0ZsL/LTs4OJ4oooHp9ONFZffc5OV+jf1cLEBX6y02RKqlRkSZDvaINjbTKQrMp2bGninGNdrNwcYs6KAH07W8jLUpi3RshHovFht4RGFqB1noHPHmuB26vy0wwvn/1ah1GBi050sakwkoxjTeC3RSK0IDEN/+o3tXz2q5t7Ls2iW1szy+IDTFVulXSnzDs/upOV4u7tjPiDOm/encPFj5YRjUGvdgZKKmPJ9wPQGuyXLJE0Ggfx38GISPG6IC6R0XWdOSsDjQZn7FaZHu1NTFnsZ3d5lK9/E3nxnVubmLY0kGzN56TLdGplYvGGEJoGeyqiXPDgnuQ5LUsw5DArd16U+buELDNN4ZU78ti2O8LKLeJ33KujmW5tG1sw/REkTPxrPGqTi2a3r/74rdoaon1LI/6gRuGeKBVulU/G1dI6z8Du8hglVbFmYvn/FNOnT2fx4sXs2LGDHTt2sHPnTsJh8ZvMzMwkNzf3d99D0nVd/91nNeNvhZEjRwLiBPi7oqImRnlNDJddoU2+ockLYmVtjO+mecVUpl8jK03hpKPsnHus8w9ftDx+lflrhFdjQbaBQb2sGA0i9aTGoxGJiqGYVVtC3P9WJV/+qwUtshtfXGOqzom37ebOizI5bcihm16ftNDHS1/UoAO3nJfB2SNShfb/eL6MTbsijWIedV3nwof2UOkWN0qzUbRw9yZlJoMgJAl7mbR4PGGinWsxi8plaVXjalaCmBzeXdzkB3Y3s3JLmFOH2Pllth+XXcJokGhXYOL4I2zMXBFg8boQkiQ+U1VFpa9FtpFh/ax0bWvi9e9qqahR6dXJxHkjnBgMMrNXBJi62J/Mw24Kw/pbkXSYszLY5KDL3ijIkimvEXnfsizRroWRTTsjIuFIolFFymYRMXeaVl+VNJvgrGFObjg7g5tfKKNtgZHThzq4+YX6fHqHVVQKf+/q2a7AwJ7KWLLa1rODieOPsLOzNMqkhX4i0frko1jcviYcPxZ7Rz+C2DZNa9zWNjVxDNvmG6iLV6TDER1NT9UdNoTTCoGIIB+BkM41Z6Txybg6bjwnHX9Q48tJHlRNHK/8LIUde8QGDD3MyrzVQV65I5e+8ez2d34UyVK3nJfBC1/U8MKtORzeXSwqx8708OYP7n0er+x0BVXVOXWwnS8ne7nmjDS+n+bBYZUprVYpyBbnbMNJeRBV3JhG0kO0Tb6BnaXCEDwaE1nZgbBGYUmMfl3NoOus3BKhRbaBnh1MGA0SExf4eem2XPp3S63ozVkZ4PEPxYLqqtNdXHZyOo++X8mqrSFCYT3521NkkS61cF2AYBiuPyuN445woMjwyxwv307xosfPiYHdrclK6V+FQEjjvAf2cNYxTq47K73R39/4roZJC3xEVXEMLSZRQU5U4mUJju4jvu+PH85vpAFtxt8fPp+PwsLCJGncsWMHhYWF/PTTT8lK5J133sn333+f8jqTyUS7du1wu91kZmb+Lvdorlg245AgN9Ow39ZPUVmUO18rR9PEaryyNkaNR+XbKR5+W+TnnfvyyE7f9+ur61R+nu1l1vIA1XXCPkTX6wcgMl0yt12QybB+thQD686tTRgUmL86kELYEliwRlS2uv8JfdQfweotYbq2M9G9nYm3fqhlQ2GYEwfZcdllFqwJsmOPYIQnHZUq+q/1aFS6tWSbM9yATJiMYiq/qCxGJCZIQJt8maIyNZl5nbgXmw1iwru0Kp64YRQkRlFEJdFmkZOVo9xMhex0hV9mi9akN6DTtY0RX1DlubipuiLDBcc7+fo3LxlO4eW4pzLKF5OiKQRg3bYINXV1PHpNNkf3sTJlsZ9IVBCmaAzyM2SMRjnZtpuzIpgSUbg3FAUUqb7iVlqt0amVkSN6WvhltpdYTE9mcKOnVrE0HQIhYYx+ymAHyzaG+GW2j3BEEECx7wa27o7w1CeVKZ/ra3reKYlEJOPO0hgZThEJGQjD+h0R1u9I1ahFYyAZxfbE4t+nJNGIVAKEI/XteQno2MpAZa1GIKxx10UZvDvGnSSPNkv9cQRok6fssy2el22kZ3szUxb7GXKYlY/H1XFMPyvvj3HTtkD8DtsVGOnZwZi0R7rqdBcXHp/GfW+W88DblZw9wsmwfjb6dDbz4wwvL35Zw4jDbQyIE7VASOWdn9zJSEGLCe6+OBNVg69+81BUFqPKrXLzeel0amniy8lelqwP4g3oDOlrpm5lUJyvCmhq/THQIVnFVVXx3x1biSl6j1/nmX/k0KO9icXrgrz8dQ0r45pWkxGqPTFmLo8RU6FXB5MgnXGs3xFm3BwvhSVRLGZRoR4z08emnVEWrhUnQH6Wwr2XZTJ6fB3hqM7QfjamLxOV/w4tTWS6ZL6d4uHziR7sZrEYkYCfZ3v5cYYYMBzUhP/roYDNInPusU6+muwh3Slz+lAHFpNMMKQxdpaXn2f7uOB4J99P85LulPEHNXIzFHIyFfp1sVDn0/hljg+bRaJtfjN1+LsiEolQVFREu3btkmmAb7zxBp9++inl5eVNvqaoqIjOnTsDMHz4cOx2Ox06dEj+07JlSxRFSRa1fg/NZ0cz/iN49rNqHFYZm0ViwZog3dqZ6NfFwvrCMKVVKje/UM7XT7Zo0vqnsCTCPa9XEI7qtMg2EIroSaKl6xIDe5gJRzQRj3hzTkqueLpTYcQAG19N9tC/qyXpcwfCHuf9MbX06WROefxAsKUoIpIqykXe9TH9rAwfYN+nplSPT6AkjsGMZQFmxG9IJgO0yTeyrTjK+Lk+juxpTW5PpVsQhQynwo1nOwmEdN4b4+a6M9OYtTxAYWk0SR4DIeGVKEtwyUlOfp7txxvQcNnEwMnmXYLFqFo9iUncsIvKGrTVVwaTU8aKLKpCm4siScKoA51aGfj6Ny+Xnezi8lPTqK5TufjhEsxGCUUW29KjvZGSahWzUeLeNyowGUViUHmNSji+GCip1pBlLaU9u3c11mgQryuuUFFVKMg14PaqyW0sLBF5yP6Q0L5BPZHs1s7MCUfaeCVuvwOwbGOIf92Qw+A+Ntweldkrgzz6QTVd23ip8caojGesJ8ji/mBUSFZ8Eqj1NiaIFhOMGu7CF9QYP9dHNCaqyoGgRkwV25rhkvEFtGR1MiddeJtGovWEandFjGMH2Ji0MEBpdSzZqgbYWlRPYBWZfZJKgG27o7QvMFCQbWDeqiCKIhYG+VmGZHWyuk7l1/nRZJa0qsJ1T5dSXBFDUeD7aR6+/s2TfM9BvSw8eEVWslvx9W/epFzAaBAG/U+NFpKZXh1NSfP3Wcv8VNaqGJR6b9eFa0PJyM2G/ouJvbWYBHlP2OtPX1rP/MfM9PD4hxEi0fpjk9gHf1BnUC8LeZkKv8zxM2WxnxOOtPPhz26+neqlZY6B/t0sVLpVQmEdX0Bj+aZgcv/uviSLrDSFLX2ifDGpjskLffRob2J7cYTJC3288nVN0nNTQ6JFjoyqwffPtOTpT6t54sMqPn+8gJyMv+ZWfMWpafiCIq/884l1FGQZKKmKEQrrXHi8qGSu3BxiS1GUq09P48ITXBgUCV9A46vJQusbCOlsKIzSq2OzUfp/ElVVVWzcuDGl+rhjxw52796NqqrMnTuXDh06AKBpWpJUZmVlpZDGDh06kJeXl3zfM888kzPPPPPf2rbmVvh/If4bWuH7w+ZdYW56vpwe7U0UlUV58sacZBtN13Ve/LKGyQv9nDPCwc3nZaa8VtN0rvxXKWaTxCNXZ3HNU2XourBU6dHezII1QexWGW9AoyBbwWVXePf+/JT38Mbj3gr3RBl6mI3ObUzsLo8yc1mADJfMK3fkHfDgjq7rfDDWzXfTvORmKPTuZKbarbJqa5i2+QZeuDW3yZvGmJle3vqhFrNR4qSj7PTuZGbb7ggzlgWoiFvlyBKYTKJSMqCbhYeuyuKnmR6+muxNtv4S6NrGSDiqJ70eQVQarSaJXWUxQQgNEi6bRKVbQ5LAFq+gNERiOlrX6425LSbRLr75nHTe/sm9z2PRqZWR9x/IR5IkZizz89Qn1aQ5ZNIcMrvLY1xxShrTlvoprYqh642n0BM4e4SDEQPs3PpSeZJsNGx9nj7EzrVnpnHeAyVEYtCzvZGNu6JoGrTKNWA1SxSVxUh3ysmbelaaTIcWJiJRDbtVZsHaEJec6OSr34T+9qXbc+jf1UqdT2XUfXua3K6GqTcJ66D9oUW2gYE9zIyb408SoMR+KDIUZBsYMcDKF5O8nHK0nRnL/IQi4LBKnHS0nU6tTNT5VN79SdzQ955K79RKaP/+KPZuI+8PsgRmswg4MBlkWuWJiM1bXixHluCYATb6djJTVacyeaGQtJwzwsE3U7wpLXCAG58rY0uc7J45zM7slUFa5hg4dbCDr3/zsKcyltyuxECUyyYM0tu3MFBYsm9/x0SnwqAII/udpVFRpY7v72Unu8jNUHjpq1q6tjWh67CrLEo4ovPjsy3JTFN46N1KymtiXHCck2c/q+Gmc9I5Z4QTWZa48l8lFMV/R4osFj0SYrGRm2lAkYWOMz9LoX0LE1MWie88N1Pkqz92XTZLN4T4YboHTYfvnmrBnJVB3vlJLGTvvSzrL22L76mMMm1JgBqPSna6wvFH2JPXvCv+VUI4rFNRq5LhlMlOVygqixFThUTi1/l+enU0J23EdF0nFNExGaRD5gH8v4qampoU0njllVeSny/uZy+//DKvvPJKk6+z2Wx88cUXDBo0CIDi4mIqKytp37496enpf3p7DpR7NFcsm3HIEY2J9JFZKwL44xUZgA2FEe69NDNJKkEMt1xxahqTF/qZuMDPtWemYzbVT0Eu3RiiuCLGW/fmMXdVAFWDIX2F8bLRIPHW9zVMX+rnguOdfDfVS2mVSnFFlFYN0j2cNpnX78pj4nwfkxb6Wb4pRJpD5uKTXJwx1PGH9J0TF/j5bpqXm85J5+wRzuQUaWFJhAfeqeTR96t45/68RhrTsrjfYJt8A9ecmY7DKtOnk5nfFvmTerlbzkunZa6R1VvD/Drfx/1vVSRfpwEPXplFh5ZGrn26jF1lMUIRPXlDtlslAkGNytr62L7rT3PRroWR+9+qoiDbgKrqGBSNOr+eJEqJ7ybTVR/RF43ppDslfpwpSNgjV2eSlW7gxS+q2VOpMnKglelLg5TXxJi/OshRva3MWh6gIFuhslalVa6BNLvM1uII79yfzxcT6/hhungvk4HkgEuP9iY27YowZqYPf1BLbjekEqHx8/xMmO9PPra+MIrTJtJfTAYJi1nmxEHCYLvhObi5KIIvoAmNqUnihEGOJLHcvDNC/65W0hwKA7qZWb4pTLsWCi/dls+4OcIDVJalZJv590hlboZCSVWMLUVykgwmFgNGA/RoJyb5E4ksExf4k6k5vqDOj9N9jd5TbkBsd5VGufuSTPxBlflrQo2ee9nJThavDyXTihIY3t/KP87J4NpnylIGWvYFTYd2+SYWrA7FF2sG4cEJFGQr3HNxJlaL+H1edIKL+9+q4Lupwubn6dHVpDsVTjjSzulDHIQi4vPSHDK3X5jFsYeH+OfblXw8zk23diaKK+qJo5igN/PgVdnc/EJ5Cql02kR3omEKkc0ikZOuYDJKbNqVWp3MzVC48rR0HnmvAhBVuz6dzJz3wB7MRok0p9j+kQNtPPVJNd/85qFtgYFQWOf76R5qPBpFcUnBI9dk8fiH1XGbJ7BaJGo9MSJRkkbiyzYKPfD5I53MWhngpKPs9O1soW9nCxt3hpO59qoKRqPEyi1hLnmkhNOHOrjlvIy/hJy1zDFyxalpTf6tslblqtPS6N/VwoxlAbwBjeED7Jx4pJ3MeF59eU2MUETjh+lefp3no6JWSGoG9xWm83ubzDfjwLBw4UK+++67JJGsra1N+fuRRx6ZJJadO3emY8eOjaqPiQpkw3tOq1ataNWq1V+2H83EshmHFDUelX++VcG24ig92pvIzTCwfFP9jXDEgMb6okBI3DGCYUEG+nSqJ54bdoTJdIkovw9/dgPwwBVZyQSaoYfZGDNLZOnOWRmgtEpt8gZqNcucc6yLc45trLM8UOi6zvfTPAzrZ22k12zfwsS9l2Zx7xsVrNkaTmYYgyA5Uxb7GT7AxtL1QS56aA/HDLBRuCdKnU9LVvLeH1uX9EiUJJI6ybb5BqrqVJ75tBpFESbWiTZhom3qD+q0zVcwGvRk7OK7Y+qSdjQtsmUsJoUFca2YzQyeQL0GscajJcmmqoHHp6PpKscfYWPE4WKoqV0LE3sqg8m2ozeg8+gHVeRlCo/IUETHbJIorhB2R2u3hVm5OcSCNfWEL9H2BWH3dOHxLh56ryo5ob43Etu0d8XNGxDkuLAkSrd2RjbuCuP2aUmS3r6lkfXbI/RobyLdKTN/TYjHPqhMEvHEcS6rjlHrEaxxV4nKq99U47IphKNgMugpLXoJodMLNzF45A+K99i0M5L8DJtV2DZFY7Bmu5ASJL5TaKB/NUF+poGi8voKniTVk8p+Xc2s3Bzm5hdE1bApfDHJi83S+I/biyNc/+y+SeUDV4hJ+HmrQ7QrEAMwG3dGOKyzGbtNTLqLSpZEjUflHy+Wc/5IJ51am9hSFGbLLhE3mu1SOO4IO5W1MT6d4GbifF9SG1rn06jxxGiTb6RfFzML1oaYvzqVHGs6rN0WobAkymmDbbw3VrTYM10yZw5zMHqCJ+X5NR6dOn8Mm1nCH9STxu4goijHz/WyZEMIs1FiYA8Lq7eGk995LKajmKTksdxZFkMCvphU1yib/oUvBKk0G8X5PaCrhdkrg5gMcN5xTr6aLBYqBgWuPiOdmcsDRKI6JVUxfAEt6RtrUCS+eLyAj8e52V0eY8ThNj4Y60aW4dbzU7s0fzXS7DIllTHOG2lqUhK0pzJGbobCPa+L6/pxR9jo18VCdZ3KxPk+bn+lnCeu278LyP8aYrEYu3fvbtS23rFjB6+88gpDhw4FRGXxhx9+SHltQUFBkjDm5OQkHz/jjDM444wz/tL9OFA0E8tmHDLous4TH1ZR69V475/5yWzcipoYFz1cgg5MXhTgrGNSJ6InLvAlNZPqXpUhWZaSrdqaOAGo8aq0jFdNompcC6hItCswUlqlpphrH0xU1KrsLo9x/aj0Jv/ev6uZrDSFpRtDKcSyxiPI7kmD7Nw4Kp3xc33MXxNkd3mU3EyFljkGlm8K0ybfwD+vyMJqllm4Nsib34vV666yGL07Couf9dsjyZjMhq3STq0MbCsWDCgnXSYQ0vGH9CTxXLYxdYDk3JHCnqnWq+G0wYK1YeavrtepJchfqzwjeyqiOO0ya7eJm2R+ljDUjqlimGR7cWrLMj9Los4nEkse+0BM16Y5pDhZrX/el5M9tM4V35WmNd1ubiorG8BskohGhcZuS1GU7HTxPolJ6dVbxP5uLookj0FD2UDrPAPPf17Fb4vqCa0OzF8dQpZFS9hlF/q4iLd+EKohqcxKk6iO+zomJAY69WTf46/f2VfuyGX9jhAf/ZJKkEAM6FTUCM9Ap00kJDUk0iP6W4nFdNZujzSSE2Q447pMleRioyF2V6g4bU2zUUUWFjyJwRS3T0sOPq3aGsZllzHHLWtqvToDe1iEof1XNY2IfkWtyvfTPAzvb+P1u/J4/MOqZBUa4JYXy5CQ8AU1FFlEZ+49sZ7ulLnn9YqU967xaHz6a/0xayhPUFVw2mXOHWnnu6nelPd69Zta0h0izvLSR0uoqFWF12upsM5ZvC7El3ENIYjq8N7nXna6nHRjUDWdNIfM7JXiWBkMEuPn1ssSNA3e+qGGcFRnzCwfY2aJCrQsiajO4goVh1Vk0598tJ3zRrpQVfh4nJuLT0xLGTj8qzFyoJ1fZnu57OQ0MvfajlVbQmwpipCXZWVHSZTX7sxNqU6edYyDxz6o4plPq/jumZZYzQc/wezP4kAs7ep8KovXBQmEddrkGTmsi/mAbfB0XaeiooIdO3bQrl07CgqEp++ECRO4+eabicWalnJs3749SSwHDBjAfffdlySS7du3x2b770s5aiaWzThk2LQzwtrtYZ6+KSdJKkFoko7qY2HBmhCfjHdz2mA7BoNMJKLx7OfVKRGDo8e7icTSkqbSA7pZ+OzXOpZtFO3r0ir4bEIdD1wphgSmLw2Ql6mQ5hCpLEYD+50u/3eQuKGZ9zGgI0kSJoMwg26IxPPrfBq5maIVfs2Z6Zx+924Gdrcwfp4fWRKDGf/6uJpjD7dx+hAHn05w44179R13hJ3ThwpCHo1pnHpnccqNMEEqZZnkzTC5XQjCk6ikAXwy3oPTJnP8kTamLA6zY0+UUcc4+HmOL+XG/uXkOkaPr6NTKyO+gLAvKqtWcVolvEG9EakEKKsWn99woKXO15j0gCA+IBYOTbWb96UNDEfq/6BqJLWVeyOxvw0JCcDLX9WkkMT2BQqnD3Xy4c91BCOiGurxa8lqZUMkbIoSpHJvJAaHjj/SytTFQSwmiT6dzExeGCcbTQwFBeP74w0IU+8EUZUleOUbd/J5HQoM7CiNkZ0uU+XWqPVqZDgFgdrXsUqcQ3unLRkUuOChEgIhDZtFGIi3KzBQXafhcsj89FxLqt0qFz5cQrpDZumGEO/cl8s9l2Tyxne1zFsdpHs7I8UVKsf0t9E6z8C3UzxsKYpw2SlpvPxVTXKxkDgnkvvbgFS2L1AoLFUpaWCFJQHH9LMwe1Wo0X41XFCVVKqMHt+YrFtM9XGWFTVisnxXmTBSv/apsnq5QoMqfWaaTM/2JuauEgc/oU3WdWiZYyDDpWC3yqzbFsZmkRg50E40pvPtVC+KAhPmpepf87MUyqrVeMylxOvf1RIIaZw+VHQAThvq4JPxbuatCnDmXovtvxJnD3fy2yI/d71Wzo3nZDCwh4VoTGfmsgDv/lRLjw4m1mwNc+pgR5JURqI6Y2Z6GTfXS1m1+N5ufamM2y/IpHenpg3Z/wr4gho/zfAyaYFo1zus4ns6/zgXBQ3s5lRV58Nf3IydJYz4E+dByxwD9+wl1wIxODNnzpxG1Ue/X3znzz//PJdeeikAeXl5xGIxLBYL7du3p3379ilt665duybft0OHDtx+++1/wZE5tGgmls04ZFiyIYTLLnNEz8YXlkeuzmbUfcX4AjoXPVJCh5YmVm8NJ/0mvX6VTq1N6MADb1cmvR57djDRrZ2JV76pYVAvK5t3RZi2NECdT6V1vpEpi/yMONzG7S+ViSzsJj77YCE3QyHTJTN/dTBlSCGB7cURSqtVurdP1RulOxV6dTTz63wfxx1hQ5IkVE0nyyUn8641HUYMsKFp8M1vHsbP8eGPTz3bzBKvflPL4vUhRgywoccniBPTyznpEpXu+JS31phAJP7v3l6IvoDGhLk+BvawcuPZ6QTDGmPjpuCnDbUzYa6fWAw6tjQkp61BVPIORK+X7pRwNzEhvS+k2SXq/I2fvzcphFRycSCDNXu/PtSggKvIUFiq8sb37uRj+3u/vavqeyNx7KcuFgumSFTn1/l+CuOWUrIkNLP7QsMBqwyXONbRmKig7SiNYTSkttRrvULGYLNIKVXSfW1XApEotC1QuPbMLO57sxKjIqq6siwkEooskZOh0L6FEZtFwu2L8NmvHq4flc68eHX79KFOXviihhMH2enZwcxRva3c8GwZJZXifImpcHgPC8s2NNaFJlBYWn9AczJkYjGxT7NWhjjhSBvTlgTQ9FRjdwCrGRy2+t9BAgk/V1mGy0928sMMH5GIqALvfXQafs9uj5YklQkkSO3O0hi7ymLJlngkJtwttu8R+xmNCR3vWcMdVLtVpi4JJM+5RPTkjGUB/nlFFi1zhEbAYZWxW2V8wd//LR1KCIP2XJ4eXc2D7wi5iKaJ38zQw6xccWoa1z5dxsDu4toaieo88E4F67aHOfZwO9ecYeGdH2uprtO487UKHrwyi2MPP7Cs9AOBquksXhdM6j9bZBs4ZbAjmcueQJ1P5c5XhS79uCPsdG1rorQqxqQFPmYuD/Dy7bnJVv9bP9Qyfp6Py09J44yhDizGKNPmbeWzn9Zx9S076NeuigvPO5XjjjsOgK1bt3Lrrbc22jZZlmnTpk2KvrF3794sWbKEgoICZPnvU8E9lGgmls04ZFA1HZNRSg60NITZJHPXxZk882kNtR6NzTEhuO/U2kjhnigdW5l49uZc7BaJd35y886PtRzV20pBtoEnrsvmnjcqGDfHl2xRrtgcZmncd3HGsgA5GQrocNkpTQvUDwYMisTpQ518/VsdQw6r9+sDsVJ+9ZsacjMUju7TmHRefIKLB9+t5NVvashOU5i4wJ+cBgcxGHHnhRmEouIGNW5ufeUwENa57GQX89cEeXp0NSBW1gl6YjLJGA2q8JdUhK1LU7BZoHdHC4vXi5unjrhRd2lrYv7qIOPn+VBkOLKXlQlz/ZhNEpGIzo4GgxSDe5uZvzbc4D0lJEnHH2w8fdwUqTyih4Wrz3Dxz7cqU5I/gBRSaTXX70dT7fCGr0yQgwTB3HuCviG6tDFSUhlt0pdSirPVxHsfiN3Q7+GEI23EVFLiRX+PBDdEdZ1Gn04m1myLUOUWE9jzVwcoq9Fw2iT8IWH2HlPZL6lsCmcNd/DrfD/Pf16dJBMZTplar0bn1ia2FEWYFM/STnhxLtsYIj/Lm9SyvvhlDTaLxK7SCJ1bm3DaZDq0NCbTrxSZ/ZLKvRdBXr+GzSKT6RJJUYmIRoCsdAWbWU6SuWAYguHGX5CmCVPyDKfC55O8SDQ+h1rnKslqefJ1Oknd5d4k3GwSkoXWeQZ2lUYJhXW274nSOs+Axxejzi+01N9O8fLoNVmcMczJx+PdVDb4jb95T15KG3lXqdBYt8xNTSr6T6B1npF3789j084IG3dGUBQ4vLuFljlGar1iH7yBRAKWh3Xbw7xwqzDJ1zRhgXbCETaq61Re/KKGAd0sByWpx+NXefCdSjYURujU2kh+poEFa4L8MsfH2cMd3HxefaLae2Pc1HpV3vtnfkr60wXHu7j79XKe+riCt+/NxB0wMW6uj3MGu/ntqwd490lh2dPQMGfSOujQNidJLDt27MigQYPo0KFDygBNmzZtMJlSCa7FYqFly5b/9r7/N6HZbui/EP8tdkPz1wR45L2qFH1lQ7zzYy2TF/k4c6iD76Z50XVom2/k5KPtnDLYkdTnhCIa5z+wh9OH1idGhCIaM5cF+HGGMDAGsJolXPa4njCoccchTs8BcfN45L1Klm4McWRPMflZVacydbEfTdN5/tZcuu9jQnL8XC+vfVuLrovqnMevpxCk3h1NVLlVKmrr4wKTBt8y3H9ZJgO6WVm5OchXv3nZWbqP+Jq90PDm/eUTBTz1STWbdkVSKn2JqfDrz0rjguNdLFoX5LnPqvfZRk2gTb4hOUGb0CbG1Hp/x72RkyGjaUL/5PbqjSpICZj3MSTTFAyKILRqEzrNhpXNxH7kZsiU1YgDPKinmRVbwilZ6X8G+zo+nVoZ2FUWa1Qt3ht7b+fvwW4RpLIpnDTIxuRFTQ9DNYTNLJHuFNGPQw+zMndVPdse0lckruSkK3Rta2LpxlBSftBwXy0m4RW6eotoDze1TU1VlBNa0sR2NGUO3xRkSRynVjmCGCYWUw2jFDU9VfbREHtXPc3Geh/SxHeQKD7tfT4oSoK0GpEk8PhUquo0DusiAh8Ksg0s2xhGQrTVq+s0TEYJs1HIES4+UfjQuuwywwfY+GRcHet3hPnmqZb79L8F8VvRdLGw/U/h5hfKMJskXroth4sfLaV/Vwv3XSbshxasCfDwe1W8eU8eLXIMXPDgHq47K73JQIo/igfermDjzghPXJ+dbE+rqs4vc3y89UMtN5+bzjnHuqjzqZz/4B6uPC2N4/tHG7Ws12/cxu6iXZx/0TX0H3Er30318PptEkMHH5n8LKfTSceOHTHaW1NYncfrj57E8GOO+rf34b8ZzXZDzfiPY1BPK/lZCq9/W8Pzt+bisNa3AdZuCzF+ro9RI5xcc2Y63071cvN5GY0GeQAsJpnenSwUlkQoLImIiD5ZDGyU18QwmyAv04Dbq1HlFiTs8lNch5xUAhgNEk/dlMOURX7Gz/Px5aQ6bBaZE460c/YI5379MPMyDejxlncoorFwbYjTh9gZP8+fYg7tsst0aWNk+aYwkRi0yZMprtB49rP6qlebPPE5LbJlSqr2X1Z77LpMHvtAvPbziXXkZChs2gVd25pYvyPCXRenM6iXjfMfLMFqltlWHOGjcW6sZpmnbsrC7dX4YmJdSjs8cROuqKm/S2tafYu3KVIJNGpbQtM+i02Ryn2RhZgq/CwvPzWN5z6rTvlb4m2H9bOwdluYWq+eJJUASzeG9zkg9Hvb2PBviWrX3uRyWwMNajIRiMbt/T/KadvkGzn5KBuvfuNu9Noubc0HRCwDYZ1AWHxRDUll2zyFeauD3Hp+Bv27mvh4nIdIRE9WkTVdELLDulpYvjFELCYWCP6QnkKQs+IT3Z9M8CQft5pEbrc3oCdNztmLL0nAw1dn0b2diee/qGFDYTjpDNC7kzlOJiWCEZEvbjWLKMK37s3nx+keflscIBqrP1/MJpAQLew2+WLILbFwiUTjsZDx7TMo8MMzBdz4fEXy2pKAFk/5KSyJkpuhUBX/LlfFJ7/b5Bu5+nQXn4z30KujmRED7LRrYeS6p0sB+HaKl/wshRqPxucTxTF59NqsfZLKjYVhvp3qYeHaIDFV/ObPGObkjGGOv5xkXnyii0fer+L1b91U1KgM7G5B13VWbg7z4pc19O1spkd7kW3epY2JHXsOcFW4HxSWRFi8PsRDV2WlaB4VReLEI2TmLy7inY9mkGfujCtvANEYpJsq6dv3mH2+5+athXQYqJGdrtCuTT4vvfRSsvqYnZ2NJEms2BTinjcq6NKzxb+9D/8raCaWzThkUBSJx67N5t43KrjssRJOONJOTobC2m1h5q8J0qejmStOEatYs0nar06vui5GeY3KNU+VpTye6ZJ5576CZHxkNKbzzk+1fDHJwxE9rfRof3D81CprY0yY52Pt9jCSBId1FjGAWWkKBkXilMEOThn8x4jshHk+OrUy8vDVWezYE2Xh2jKOHWinrEZl9dYQqKJd7fVrrNoSThKZonKNtvkyu8o0ThxkJ8MlMXG+0GaGY/Vm0QmkOaSUYZkXv6hNtnkTejUQmiSA7u3MTF8aQAJe/642+bqRh9vo3NqExSSzfGMwhVgm3j2UOmz+h2A1i5v7gbab91f1K6uJ8VLcGqYpzFkZwmgQ8ZWl1fWstyFx2B95TGjrmkLiNZredOVRlqBFjiHFt7Ep3egfwcZ4y3JvSMDnv7obPUaDfWtIgBtmjjut4A3CrnJhoj15oY83v48mn9dQYhGOwomD7BTuibCuQWRlw32v9miMme1LGu4nXpeA3SoeD+xFSHXg9W9rSHcplFaJY6br4juodMewmWW2FUe577JMXv2mhmgMThnsoGMrU3IICsT5oshweDcrhaVRvH41SfRvvSCTl76sEVP8e1W4z3+wlKhKo3MpwyVR4xHvn7CoAjh/pIPvp/vYtjvKkvUhDIqQnAzrZ2PyQjEcYjaK7zsxoJQgvZ9P9NC+pYk2eant8FkrAjz1ifCf7dPJTKVbxRPQePvHWuavDvD8rbmHjFxu2hlmzqogwbBGmzwjxx9hZ3BfG7een8E7P4rrwyfj6xg9oY7iihg92pt4/DpBynRdx+PX9jng+EewZH0Ii1niiO4S7733XkrWdVlZ/X3h5dipFAzoCMAL3ylIihmnK4vuXTvSqVMHunXtRKvW7XjmGxvXXtaNmCZy6H1BnYsuuqjR524pEpZhGY7/DX3kwUAzsWzGIUXXtmY+eLCAH2d4mb7UTyCk0zLXwK3nZXDy0Y7k6nxwHyu/LfRx0QmupCelruus3xHh69/q2FIUxajAxSc6ufSkNL6ZIuLjQhGdZz+r5qXbclEUCaNB4tbzMlixKcRPM7z0uObfJ5bzVwd48pNqFBkG9rCg6/D1bx6+merhieuyUyIj/wiKK2L062pGkiTaFhjJdMnMXBbg8euyeXp0NQvWBAnE5WiJ1p7dKnz6dpUJFvLbotTJ02q3xpC+FlQVFsYznfeewE5EH0IqcSqOa8ye+LCK4kqVrDQZt1cT8oKwxrzVQarqKnnu5hxmrfj9CtgfRbsCIxt3/vuVDYB9OHukIBojhVTujf21wvdFKhu9RxOPaToppBJoctp8X7BZSJ4XB/L5e2tX9QYbpsipVdVRw52s3yGG6N6+N583f6hl3ByfaPO61SThizTxNT35cXXKMbOZIbCXvtftTWXPDT87Ub1tsHnJ1rYnoOMJiIOUIHjhqE55tYoazyG1WSSReR/SCUU0zv1ncdLkPwFVE8NDZpPEys0JqyDxgZIkSHZDgt+QZCb2rUd7IxsKo0lSuffzympU+nc1s6EwQtt8AztKYkRVnbKqCG/GB8KeuimXOp8a17NKHNHTwoZC4dt51b9Kuf6sdC443oWq6cxc5ue5z4R2tbQqRlVtjCN7WdF1WLQuyMotYV7/toa7L8lK2VdfQKOkKobFLNE619AopOH34A9qPPlJFUvWh8h0yaQ7FSbM9fHhz27uuCiTUcOdDD3Myh2vlFPnUxnWz8ptF2TQv6sladGzZluY4ooYt55/YNdITdMoKytr1Lru2rUr7QbcglGRsJiNPPvss43se2yOTHRTK3ZUtyBXE16mPTrYOLzbXFZuUXHrsMwL7nIjnYwmFKufQb0dmE2SiPCc4uH6URkp71nnUxk7y8vw/rZkEMB/G6IxPRl73NS8w6FAM7FsxiFHfpaBW87L4JbzMvb5nPOPczFrRRn/+qiK2y/MIMOp8PwX1UxbEkjeSPKyRB715l1RYqrGoN5Wzh7u5O7XK1i0LsjgvsLvy+3VOLy7hRlL/3jc3d7YXR7lXx9XcVRvK/demoU93s73BjSeGV3FYx9UMfrRgmQcm6bpLN8UYv2OMLIs0a+LmV4dzcmLusevMm1JgJKqGP6Qxq64LtKgSIwa7mT0hDp6dzJz8zlpLFgTxCDDxSe5+HGGl0BIJxDUyUkXerR9VQfnrQ7RJl9JVqLOHGZn4nz/PtvRe6O4UjwxcaOvTZIBndVbw5x9f3GyWvVHtYAJNDUIc7BI5d8RJiP07igSfWD/msjfw4GSygPB3lXShP/jnReJBJgTB9kZN8eXPHd0RCs5poLVJO1zkQKppLJfVzMGBZZu2Mck2T6g08Rwky6CFWYtD6Zs/+MfVuOyy2i6zrTFAdoWGKjxaCnuAkJTK+zIEhnusZjw4gTQD+C+u6Fw3+epIsOcuLel3SolB93eH+Pm/THiORef4KRljoGH3q3kiJ5WerY3MXpCXcrxe3+sm7XbQrh9GhsKxQ/dH9Qxxlv2WWkKt5yXgT+oc/WTJUyc7+eSk1zkZ4nhmg/GupmxzJ+s6rfNN3DJSWkcd8SBT2c/+UkV67eHeezabIYcZkWRJWrqVD742c3zn1fj8aus3BymtFp4rk5bEsBqkenezozNAuu2h3l6dDWdWxtTBhsBamtr8Xg8tG3blj2VUSYv8PLW0xdQW1lINNJ4kq6mpoYTz74Lb0Bjc5HGFVdcgcPhIK9Fe2aty2RHVR4mi6tBUpfOqUPsfD9NDJv162pi084o3dqaCIQ1Ji/00zrPkLQcuvK0ND76pY4qt8qZxzjJSlNYtSXEV5M9xFSdK09LP+Dj1hA79kT4YbqXeasDhCM67VoYOWOok5OPsh/yhKXCkghfTfYwZ2WAmCokVScOsnPxia6DMki1PzQP7/wX4r9leOePYuHaIE99UkUkqpPuTNWfDelr5dFrRd7uEx9VYTFJ9O5k5vHrcrjp+TJyMxROGGTn68melJbg+cc5ufyUNGx/crX55vc1zFoeaFJQHwhpXPDgHs48xsm1Z6azszTKEx9WsqssRoZTmGl7/Brd2pl4/NpsFq0L8s5PbjRNp2WukbKqGOGozuA+IpJSUeD5z6qZtlTEIZZWqRgN9TdWCXDawGSUG3lT/ruwmCXsFplASCUYrm+PDuxhZmdJJGlftDfeuDuXN76rSdEO/h7ys+RGPoZN4UBsg5pxcJFoyd50jhi2CIQ0zrl/D+GoTqeWRu67PINH3q/G7dWSyTV/JRIyjxtHpTFunp+SylgKSTcbBcFNVIAtJglJopH5esMFkSLXx4ruCwaFZDBDw8cavsagwDP/yOHN72uo9ahJpwGjATKdCiaTxO7yGMP6WWmZY2DcHB83jErnlW9qOXu4g0tPTuPSx0o4f6STH2d68QVE0lPvTmYKS2Poms6XT7Rg0kI/b/1Qyz/OTefcY11MWuDjxS9rOO9YJxef5OLWl8rxBTTOHuHkiJ5War0qE+b5mL86mPxeGyIY0piy2M+MZQE8AY2CLIW+nc28P7aOR6/NZnj/VINuTdO56l+lFFfEaN/CyKlDHBSXR/lljk/YQBmFE0MgDFpgGz1bVNAqrYzy0p0pUYUDBgzgpMs/48cZXpw2mY2TzsXv3g2SQkZWK/r37UynTh2TXo8DBhzOlf8qxWaRefG2XMxGiX+8UEadT+OcEU5Gj3cTVcVA4N7a7aw0BYtJSA90XWiwiytivH5XbtJnc0JcI59w55AkGNjdwi3nZ6REAh8oFq8P8uj7lWS6FE4cZCfNobB8U4hFa4MM6m3l8euyD5l8Ye22EPe/VUmGS+H0IQ7ysw1sLAwzaYGPdKfCa3flken64+TyQLlHM7H8L8T/V2IJwqZn4nwfH/3ipk2ekStPS+OFL6q56MQ0LjpBXBDHzvLy1g+1mE3w07OteO3bGtbviFBSFaNFtoFoTKe6Tpggg2ixvnZn3p9qZVz+eAkDulu4/YKmY9ae/7yawpIoz96cw3VPl5LmULjzokx6djCh67B8U4hXvq4hGtOp8QhN5PWj0slwKtR6Va58ogRvQOeIHhaevTkHXdcZM9PHJ+Prku3W/VW3/l1tXgI92psoKoumVKD2RkMtns0iEQrrTSaUNET/bkZWbPpjlciDsU/7msreH/anqfxfQoeWRkJhldOHOpmxLMDO0ijRGBzd20x5rcr2YjEwF4mkVqvtZvD/sYLkAUMCjEbh9VjjqTcqd9plhvS1MmlBfXeiTb6Bbm1NVNeprN8eJhSFXh2MIEms2x7BZoVgSGSDz1wWQJbr9bouO3j+RKNDksR5G1Prz9/EvzNdEiMOd3DR8S7Oe3APui5sirq2MbFhZ4ROrYw8dm02W4oi3PR8Oc/clMPsFX5+WxxAkWH44TZmLw9w6mAHt18orkMvflHNso0hvn6yBRPm+Xj9u1o6tTKS7lRYsTmU7Ab0aG/ivJEujulv470xtfw0w8u3T7dMJvtUumPc/VoFJVUxjuxppSBLSep1jQb45aVWWEyp181QROPs+/YQiuh882QuQa9oXa/buI1f53mIZpyPQRFWYj+9fSqe2pImj1lBq664BnzKDaPSOesYBxvWr8bucLF+dwZvj/Fx3rFObjg7tcu1bXeEe96oQJKgaxsTSzYIN47lm0RCViQqZFW9O5l5f4w76RDQp7MZTdVZtyPC/ZdnctxAG1c9WUa3tiYevCo7+f6qprNpZ4RASKN1nnG/w5f7Q6LokCh+NCxKLFoX5JH3Krl+1MGZlN8bqqZz2WMl5GYYeO6WnJTvr6Qqxq0vlXF4NwsPXJm9n3dpGgfKPf47RQPN+H8Lh1Wmc2sTMRUeujqLIYfZyMkwsL24vgp5wpF2dB2iUXjpq2o27oxQWh3DbpWoqI3htMtoutB3xlTYVhzli0l1+/nUfUMYMe97VWkyCHPzcXN8BMI6L96Wm2x9y7JE/64Whve3JrVevy3yc+1TpTz2QSU1dSrv3J9PVprMkg0hRt1fzAUPlfLOT25y0hUhGHfuv2XaFAEzN1hcN5UXvTdEtF2EtgWNV+WSVK9pa0jUgmFhefJ7FcU/SiohdZ9E3N8ffos/TCphP1Pe/G9cKBPyqx17opRUaXzwcx1bd0eTpGvB2nAyWSncgFQmzrfAPqQZOekyln/TmjGh60z8jnRdnBvokJ2mkJepJJ0RispiTF8aYPkmQSoBtuyO0rez0FsHgiRbt6pG8qZvNkrYLPVVnIaSxMR7G43iOO39q0okRd1xUTqZLnG2fPxwHmaTGPAZ3MdKZprC4D4WDArU1Kn4ghollTHOOsZJNAYf/uwmN1NhYE8L5fGqmaaB0yYTU+t9IwFOGGSn0q2yfU+EyQv95KQrVNSqLNsYIi9D4d7LMrn7kkzMJoknPqpi9Hg3l56UhtEgMWVxPXN+6uNqIlGdTx4p4Ombcrjl/ExeuyuPjq2MRGNw4UN7uOvVciYt9BGJV6jvfehVts+9l8LpF3JE/24MHjyYyy67jOefeYKNiz+lV0cTFpPEo9fmcMyQAfTt2492PU+loPf1vPr6O/z222+sW7+ZVsM+Z9RwBxcc78JskunXrx9dOndk1LGZXHqSi59n+/AFUi9wnVqbeO+f+Rx3hJ0Vm0NICLI0rJ+NSNzz99Frszn/OBcWs4TNInPeSCdrt4UZ2k9UXgd0s6AoMgO6WSjcy6JNkSV6djAzsIf1T5NKgGlL/AQjOndelNmo0zWol5URh9v4ZY6PQ1HXW7ohRFm1yk3npDdaFLTINnD+SBezVgSSw5qHAv8L18tm/BehvCbGz7OFzuuWF8q46bky2uQZmLMyQGGJuHMl2lsnHWVn9sogxRUx4Vuo6uRmKOzYE+Wq09N44+483v9nPgYFxszyoe3FNtZtD/PER1WMuq+Ys+4t5tH3K1m1JVXA1r2diQVrgo1eCxBTdRasDdK9nZlZKwKM6G9LaS+oms4TH1Xxw3Sh80ncqAJhnbmrglz3TBnv/Ojm3fvzMCjQIttIrw4m/nl5JqMfLeDw7pakDqwhfo9oNZy03Vuvn/jPa053kZOukJuhsKssRjBMMooNxE17cB8r7fINTYooE9dDo0F4CMpy45ttU5//R6Fp+yf2fwV09p+McyjxV+554hRPhINIwDVnuBjSt+nBixbZ4lx3xadldb3p77vSrSUJ3r6wr/zy/UHVBNnKcArilZtpEN6psmh9nj7Uzrv353HLeWloGnw1WVxXjHvxhVY5Rtq3MJKXqfDGXXnJxx+5Jjv53KLyGJIEJw+yo+ninLDErXkbvt+0xQF8QVHJv+ap8qTX50PvVnLXa+UM6G5BloQV09K4UfyqrSFufK6MdTsi3HdZFhKC3BsUMJmgIMuA3Soxa3mASQt9LFoXTCYtfTHJw+aiCP6QlnTV+Me5GZx8lINTBzt4+fY8rj0zjS8medhdHqVtvjGZgrSlSETuXnGai3BEZ2dxLYuXrOS8Gz9l8bR3KVn2KGsnXsmkT87mxS9quP2VcrwBjRXL5uMrm0vEt4tYLIrFYqF79+607jKSjr3P4tITHfiCOlV1Ku+99x4TJ05g/I/vktn5KqSMEfTq1YvtpQoev7ZPS7jThjgIR3WWbWyst8zPMnDzuRl0bGXi5MF2PnusBZW1Kt3aGQlFdGYtF4OFprhzwzVnpOOwSvw43UuP9qZkxK/bd3Cm1ZvC5l0ROrcykZPRNDk9ureVksrYfjtEfxaFJVGcNpmubZseXD28u4VoDPZU/oGJwT+I5uGdZvxtsKUowr1vVCRXcX27WpCQmL0yiMUEd7xSzuWnpGExS+h6vVeg0SgRjuoYDRKdWpu491InfbsI3Uyn1iaGD7AxdXGAPVUxWse1MmNneXnz+1qsZlFxlJBYsTnEvNVBrh+VzoXHixbFWcc4uXVpOaMn1HH16WnJIRxN03l/rJsaj8qZwxwsXBsUaT8NMG2Jn3mrg4wa7mDsLGGzcuv5GazeGmZDYYjd5SorNoW449VKYqrIVt+0E2avDPLBz6Jq2RSamsjdF/x7XbhOGCSOxcfxPGUlTiJ0HbLSJKrjhV1Vg/lrmoij2QvRWP2wT1P4dxfkOvzhi6/FDGcOdfLdNO+/9+F/A/yVnflE6zbRRtV0YX8jSU3rXRNEpqGe7c9837LEARuiN4X3x7oJRwUJXrYxROs8A7dfkMGE+T4eercSb0BL2XaLSSIW0zmsq5me7c0c1cfKzS+U88AVWWRnGGiZY2BPZYx+XUwM7C6SqRJV9IYLT6NBIqbqcXsjMSDX0GopgQyniOHcWBhJelxCfWX+8189DOpl4f7LM+nc2sSHP7uTxzamCj/MU4528MN0Ly9+UZPy3vNXBxnQTQyFtcxVKK1UKa9J/aIuPN7Fr/N8jJ3tpbw6QEtnDTNmrGZD1WHIkrAf27P4fnxlc5o8viHgm1sdPP5JgFe/rqHXEecSMA/h2MFduevqw5JRhSfeVsS1Z6bjC4lrpKUBact0KfTtbGHN1jDnjSRJuPc1RJLuFI/vz32hIMvApsIIuq6zfU+Ey05OIzstzGvf1qDEI029fo3ymhh2q0xZtcqdFwkpQXWdyoI1Aa76k0M5vweDQWoyBSqBhObXcAhmaIQVmkYoojWqWEL97/ZQkWpoJpbN+JtAVXUe/7CS7HSFLm1MTFviZ9HaUJL4hCIik/btH93J18xaHmDUcCebd4VZsy3CFae4yEw30qVtaspPoqWxqyRK61wj24sjvPm98F9rkW1gWH8b6DBzuZ+dpTE+GOumTyczPdqb6dnBzPVnpfPBz24WrAlyTH8bmqYzc3mA3eUxbrtArJxb5RqSMXcJjJ/rY2APS3LyW9N0XviihrYFRvKzjOwuVwlHdUriK8f+Xc20zTcyeZGfGo/WyCrlYOC3RQHMRhFxqKqpbectRYduBftXIhTm/wWp/KvRlKwiGtu3XrUh4TcbhIfqn4GmA3+gK2cxi+84gXBUVDxb5YrfeUlljHveqMRhBV8QHFZBABOkN5HwU1wRY/2OCN9OFYusXh3N+IJaksx8MdFDXpYBp03G7dPQdSgqV5ElGNTLwoK1ISzxCfnVW0K47KnZ7DazhCTDV0+2YOHaIE9+XJ20ihrcx8KAbpZkHn1xRYxZy4WtWUlljBvOcvHpr15UTae4XEwWty0wsKs0hiUeJ5lwVli+KYzJCFkuA1lpBsbN9XLaEAfr161i1apV7Nixg53zN7Po+0JCvlIWo/Mu0O2MmSBbuO+yTMboBfw6DhRzFmZna445qiuSpTWrd+XStl17iqsUhg+wMW6OD7NxCBkd4ITjMmnZMrXiqOs6E+b56N7ORGbavllTu7jsZvnGEMcf2XhSPRH72b7Fvtszpwy2c9+blcxaHsBkkPAFNB64MotnRlfz1CciGEGS4IonSpEk6NDSwKDeVgpLIjz7aTUOm8zJRx/YlPzmXWHGzvKxZpvYrj6dLIwa7thnVfDInhbGz/WxcWe4UfKaruv8tshPn07mZLrcwcRRva28/WMtUxf7OX1o48CRCfN9FGQb9nts/100E8tm/C2waH2QsmoVm0WjzqfSu5OZVVtSU1Aa3txsVrj8lDTa5Br4db5oNb/1oyi32S0Sl5ycxgXHOVE1mLG0vjUCMHq8G4BLTnKlVCEvPdnFe2Pc/DDdyxcT63j25lwALjzBRde2JsbM8jJmplcYpHexcM8lmcmJwlMHO3jm02qWbggmfS13lUY57zgXn04Q2xWJwh0XZlBWHUvROYGoFmWlKYydLfZlX6kyBwPhqCALaQ452VJrRjP2BU3/feL4Z0nln0Eo3JjsegM6d7xSCdQT5MRU9t4V74IsmdJqjcpalWP6Wal0q2wojPDV5Do27owQimhccpKLb6Z4MBtEMlBDHNXHwj8vz+LtH9xMjvvI7mlQtc90StR4dQJhnevPEjq3uSuDSUJsNMCu8hj/usFJXpbCI+9XU1wRY9xcL51ambBbjHz4iye5f2//WEfrPANnDHUwe0WQNdvC6LpOLOgm7Csi6ttN1F/ErgW7yev/BIrBxH1vVuDf8hkTx//Q6Pg5HA5kaxvy0/yUeiykOxWeefJBzrv8fp79IkzHVkZkCfp1MVMi+Wndxsgr39Rr1A/rYiEa03n7x1qcdplBvazIskSP9ma+m+ql1qvx5I2pgyE1HhH6kIjkbZkrLIg+n1jH4T0sZDjrSagvoPHxeDdd2piajAJOYEA3C8cebuPpT6tpnWtg4gIfg3pbaVdgZOmGIBlOhS5tTSzdECQUAa9f59qnStlREiU3U+GFW3IPyHZnwjwfr35TQ16mwrC4TnPOygBTl/i5cx+xwUf2stK2wMhTn1Tz1I3ZtG8h9iMc0Rg9oY4128I8deMfH545EBRkGxh5uI13f3LjsMoM629DkSWCIY1vp3qYuSzA3ZdkJr1GDwWap8L/C/H/cSr8o59r+W6alw4tjdx/WRbXPVuGBGSnC+uIxEmasKpJpMlIksjUXrMtgsUkcdM56RSWRPh5tp8LjnNSXBlj4ZogBgV+fK4VDpvMGXfvRtfFxKMsi7bBjKUB1u8Io0s6M5YEMBgkJrzS+oC3X1V1Hnm/kuWbQpw2xMGwfjYe+6CKNIfM7nJx13XahO+fQYH+3Sys2BRqRB4zXTKXn5LGzpIIP89JJZ9/ZtK5KSTex2kTZusH4z2b8f8XB8v2KRHf+HfC3j6sbfIMPHhVFl3amCmrjjFxgY8xM4WH7KCeFpZtChFTU3+LBgXyMpWklQ2INqPdKlHj0WiRY0h2JQAuOE7ING47P4OfZ3spKm/MyguyFU440s6K9bWsKZSQZRkJqCkcS13RBNTAbqLhxlX5F9+ZyI8LsvD6NaoLxxGpmo9ia41ia0P7dh14/cEB1IXTuPWlCl64NYfR4+tw+zReui2X7cURHv2gin9enslzn9dgUODcY51cPyoDb0Cjyh3jmqfKeOiqLI7sZeWJD6tYvilEbqZClkth6+4IMRWO6m3hyRtyksQlENJ47IMqNhaGOH2Ykzkrg9T5VNKdCm6PiskkccZQkZRUVBZl/FwfwbDOK3fk0rHVvokliOvul5M9jJnpTQ432cwSJw92cM0ZaZRXqzzxURXVdSpH97FgNMgc1sXM0MNsySCO/WF7cYTrny3jjGEObjkvI2kwrmo6b8XDAz54IL/J7SyrjnHfmxUUV8To2cFEukNhzbYwvqDGjWcfmonwBEIRjac/qWb+mqDQ0mcq7CyJEgzrXHFqGpee7PrDhvnQnBXejP8yVNWJHN4bz05n1soAsgQtcwwM6G5h2pIA/buZmb0iyKPX5PDKNzVs2x0lwylT69W4+sw0vp7kZfmmEK9+U0uGS8Zpk/lumheLSUKWhRjcYRNth3BUp0WOAVmWWLc9zCPvV+Lxa3RubULVdCIxiMR0duyJ0KHl/i9sCSiKxBPX5/DlpDrGz/UxdpaoPHr8WtInr2WuITllu3hd0y7XD16ZTf9uFm59sazR3/6wfQ5Na/SsFvAH61uC/1/Q7Ht5aHCwjunBJJUOq9SoEmmQhcSjYbVflgUJLMg2JBd4DWHeK/60qDzGg+9UcvclWfTuKEzdAyEdp01iyYZQ8jco3CKE6XRVncaeSjXl/Et3ynz5RAsWrA2yZH2QksoYQw+zMHdViMw0cR164/tahvWzcu2ZDl77dD2lJYUE6nZj1ooJVZTxxq87KCsro8cp35Od147KWpX8NB/ltRviWyuRk9cCv9YSs7M1Nlcb5q418tCVWdz7ZiXnnnshReXnUFgSRdPg/DPTycxysHG1KOX27mjmkWuyufv1Cq54ooSBPaxIwKcT3IBoVyeMwZ02mdkrIsgS9OxgxmGVeeHWHNbviDBrRYBgSGPoYTbq/CrfTfVyxROlDOptJRDUmLMqQEzVsZpkxs3xMXKgnVa5BrYURZi9Qiziv5/mJRTRMRklRgywcclJrgPyjlQUiStOFVZ0v8zx8vE4N+GoztaiCHe+UsHmogh5mQqv352XbL//EYyd7RVm9OdmpKTWKLLELedmMH91kJ9nexulHoGQYH30UAFzVgaSBumnDrZz6hAHLXMOXRsawGKSefLGHDbtDDN9WQCvX+Pw7lZOHGT/t6bdDxTNxLIZfwskEm0iUZ1tuyOoGpw13Mkn49ycOtiRtIUIR3ROOdrOG9+5k4kwd7wsWmBmoxjEMcjgcEp4A0L8fURPS0pUl9Uss6cixs6SCA+8XSEGfi7LokW2gUhU57S7dqPpcN+bFYx+tAVO24HpYIwGiatOT+fSk9PYUxmjsibGYx9WIssiZm7TzihWEwzqa2HFljAen96I+FmMMHaWh/WF//5deF+00f/7Mzl/S/xeyk8zqfzfwd6kUpIEqYRUCYmmiYn+BKlMnEM2SyL6Me6RGSejBoOwNHro3cqUdKiWOUZa5hhYuSWU1D5HYlBVJzw1beZ6WzCDAuU1Klt2R/D6RayirmssWL4bq1rMp58WETMN5bpz2nPZKWk8/8LLLPzhlZT92d3g/3tri+nfpxPlNSp33TSK8KWH8ckUJ4f37cjQ/hk8+kEVDptEukNhR0WMe9+sxGiAaQkJkAE6txFDQT/O8HLRCUJ3V16r0ibPyPsP5DNpgY8ZSwNYzBLlNWL/+nYxU+dXyUk3sG13hI9+cTPksHobHkmS6NVRJIs1xOA+Nn6Z42XJ+iBGReKMoU6WbwriD+q8/2AuOen1tOPiEyPc9VoFg/tYuOX8TGwW+YAqiXvDZJQ4b6SLk45y8NsiHxt3RlBkOHekk6GH2RpZ/hwo1u+IMLivtcmUHEWRGNzXmjKQ1dR2HXeE/Q8lHh1MdGtnplu7fz/W+I+imVg242+B1nGfuJe/qqFlfKWalabgC+rU+TWWrBcVPrNJStEFmoxC4H3RCWlMWexjwZoQBkX40SmKynED7dx7aaqe5KjeVn5b5OfBd4XR7lM35mC3yviDGs99VkVMhSN6Wli6IcR1T5dSkG3gqN5WTjrKjsv++5oco0GiXYGRdgVGnr05l4feqUj+LRiBmStCSJJIu9k7EeSeNyv2GdX4v47/X/XVZhxMHKigK/G0QEgXVk4StMxWKK5UaZGt8N4DBcxY6ufLyR78QXGdefLGHPp3tVBYEmHB2kAjSYqukySViS5KqG4LF179PmFvEUqsGE91EZpa36XoODSPC47vC0CZLx9JsWJytGbkkK5sKsslSCuM9taY7K1RTC5WbRPkxeRsx3HDejB6bjEFuXa+n+5JfqY/GKNzayM7S6JEYoIwjxxoBSQURWJ4fxtTFvv4ZooHp03mpxkebjonA7tFkLIje1l5+pMqtu4Wi/ifZvgYM9NHVppMlVujc2sjd13cdFBEQ+xNNrcURfhmiocnb8hOkkqPX6W6TrTDLzrBxSfj3dx8Hn+KVDaE0yZz7rEHr8UsS42jZxtCU+v9X5tRj2Zi2Yy/BY7saUOS3Og6rN4qLqLvjxGT25MX+mmZo+AN6GwpCvP5RE/ydZEobN4VZfxcL49ck820JQHe+qGW685K518fV3N4dwuyLFFUFqXKrZLhkrn4RCfTl/opq9aQJXjhi2okCZasD4rBFhmWrA9hMkpEYzoOm8xHv7j5bpqHF275fd1PQ/TtbOG4Ix2Mm+NLeVzXG8fMAc2kshnN+BM4b6STn2d7f3fgrWEVUo//T8IuKxITHZEzhjmRJHj1m1ouO9lJ/65iQO/jcXVkpSmUVPgJe4qJ+HYTiQ/PhP1F5HS/CeiHIkPYs52qzZ+mfrikYLS1xORojcNpY+vuCOPn+lhVcRTXPryAOatCZPawYDGFMGrw4YN5mIwyb/1Qm/S8rPPF0HWdDKfCz7N9eANiWr1ljoGK2hiaDg6bRK1HdEOmLQ3Sta1IAfttkT9JQltkK4yf62f8XD92q8SI/jbmrAokCfrIw62U16jsLo9RU6fRrsDI63flYjH/cX+cTTvD8Ul6K0XlUT4Z52be6mDye+jZwUQ0Btv3RBnQ7dBmWP9R9OtiZsayADefm9Go6hmJ6sxZFWDk4bZ9vPp/F83Eshn/MVS5Y0yY52Ph2iAxFfIyFGq9GmcPd/DLHB8lVSrOuGVIYurytW/dTb7X8k1hrn+2jM8fy+fHGR4++qUOp00m3Slzy4tlbGjQWu7U2silJ7n49FdBUMWUpWiFKQrEYnD5KS4q3DEKi2M8eUMOVe4YD71byQPvVPLlEy0OuLWyeVe4Ealsl29AQ2dPhXpQ4hib0Yz/VVhM4nf7w/QDs5fau/rUUI/pC2jc+lIZb96Tj9sbJeLfQ7uctkAGdT6VadNm4tn0AnW1pU2+d8SzHVt2P1QNLGnd6NDnHMzONsjW1uTktaNzx9bUBRRWbw0TAm59qZwsl8z1o7I4ZbCdhetKWLohxPD+VpZsCNGxlaj6PXdzDmfdW4w3oPPqN25e/9adUjGVJWF2nZWm0K2NiQnzRcXRFE8Jevn2XGwWmd3lUR7/sIpar0Z5jUrfzmbW7wgTCOlMmO9PHo/rR6VzwXHO5HDH/DUBHnmvig07o/Tvun/it2lnmHFzfWwvjmA2yQzuY8WgCBK/dXeY+9+qwmmXueW8DDq1MrGnMsrXv4nr8PY9EQZ0s+zzvVVNZ/G6IAvXBonGoGMrIycOOrAu0p/FGcc4GTfXx/NfVHPvpZlJX8hQROPFL2sIhDTOPKaxpc//OpqJZTMOCTRNpzA+hdYi29DI02ztthAPvlOJpsOQvlZsVpn5q/yEozpjZvnIThftF+9+9IAJvZQE2KwSFTUqD75Tha5DSXWMC4938uA7lXRsZeKJ67PF1GFplO+mefh8kof2LQx4Azpt8gxIkkTfzmYWrQtiUCQuPdnF5Y+X0rezuNBlpxt4+OpsrniilFnL/ZwwqOnEiL0xfq4vWSVIVEt2lu27rNKcV92MZhw49q7wt8gW09lNoSkLL5McxKRuZU/xTnq3Kmf5wm0Mn1BMXfVuVDXKuiOfZvCAK6iqU0GxJkmlbHRicrShRat2hKVWaKZWWDN6Jd/X7GrPrx+/1qRP4Uc/1/D1FB9Ou0y1R+PDX+r4bZEfu1XG7dVYvimctLXZVRrhuc+rUwbtjAZhGWY2in8nSGaWSyamiv+QpPoghRWbQgw5zEbrPGMyVakg28Crd+bh8avMWh7g3Z9q0XWRqZ0Ih0jg6N5WWucZmLrYn6ze7o3ymhjPjK5i7XbhztGjvQmbVeKT8W6sZgl0ePazGjJdMm/ek58cpOzV0SwiRCu9/DzLy7kjnE3a4FTUiKGqHSVR2hYYcVglZizz88m4Ov55RRbH9D80VcM2eUYeuiqbZz6tYtmGEEf1FlZyC9cGCUU0Hrwym9Z5h3YQ578RzcSyGQcd05b4+XxiHcUV4ioux+MBbzong/wsA76gxiPvV9GhpZHBfaxMXRJgT2UsJWXhQPwVjQZ49h+5vPZdTVKcv2JzGJMB2uQpLNsYoksbEy/fnpesMLbINjCwh4WH3q1k6+4ItV6Na85I48RBDqIxnU/G13HXxZn8MN1HWbXKI9fUE8jWeUa6tjWxYnO4SWKp6zrrd0TYXRHFZpYZ2MPCtt0ROrYysmxjOKVaoijCoLzxexzQIW5GM/6n4LKC5wCGzpoilWrUT9RXhMdfRMS3G1tWP2w5AwAI1G5m07SbAChflfo6STbx9cRSxq7bLTTRaV04ctRHVAVbopjq/W+N1KcWNURT2jtd15MLy2BI47yRTrq1NTFnVZDZK4R+0xvQWLQuyD9eKGXzrii6Dt3amZDQ2bgzSjgqPq9FjoHCknqmvGV3lOLKGIostJ7d2pmZt1oQIBBax+3FUWFbVKdy43NlaJpOt3YmIlE47ggb05YGKKuOpUwOS5JEq1zjPrOlf54tUsx0HVrlGrCaJVZsDtOuwMibd+fx8tc1BMNRdpfHuOQkV5JU6roImhgz08vIgXamLPazcku4UdVSVXXuf7uSUFjjrXvz6NFeVHJrvSpvfV/LU59UkZNR//jBxjH9bXRu04Jxc7ysiWtdTz7azhnDnLTIbqZQTaH5qDTjoGLMTC9v/VDLkL5Wbr8wk6w0hdVbQ3w7xcOtL5Xz9r15zF8TxBfUqHKrvP+zMN5Nc8g47RKVtUL3mKgsdG9n4oxhDj6d4E5OK0K9YbMvpPHpowVc+a9Saj0q/qCwCzq6r41vp3h59uacRm1rRZG4/NQ0bnmxnCN6Wnj+8xrmrQ4yOL4a/WmGh11lMS492dUoNcFokJrMDd9QGOblr2ooLKnPW7SYJCLRxj6R7QoMhzyrtRnN+P+E3yWVDYLK5Vg5lRs/wV9XRMRXhBpOjUHUO12aJJZRpTVGe0tatGzPUYd3xqu2ZHVRDhZXG2RzLhaTwhWnulAUiU8nSLjDPTE0uCS0yFEY0NXMlt1RthRFcdlk6uKRebe8UEa/bhZ6d7JwVG8rBkVi2cYQC9aEMBuhRwczP0z34rTJWEzxqFhdeEdqOkxf6sdpk/nX9dn06Wzh4fcqgShd2xjZVhxNksqLTnAycqCdb6d6xCS2VVggRePVy0Q84tYiQYp0IBjRad/CiEGBhWtD6IjJeV0XWdMNiaWqicjEI3s2zoxftC7IG9/VkumSaZ1n4NU78wFBYh/9oJIXv6rhoauzuPpfwj7tq8kelm4I0SpXTJoXlccYMcDGPZdkMH2pn+KKaCNiOX9NkF2lUd69Py8l6SbDqfDglVns2BPh+2keHr8u53dOkj+PFtkGbjw74/ef2AygmVg24yCizqfy/thaRg0XZrKJFX27AiND+9q44bkyRk+oIxDSsJiENVBBloGsdIUurY2Mmyu0iA2J2JaiCM9/XoOE8JwLR8S/Y3FO9vVvdSJhoY2RmctjyanPb6cIzdWk+T6KyqLkZRo4sqcFc1wj0zWe6DDsMCtDD7MxdqaXF74UN6CqOpVHrs5ixOGpFhGVtTE2FIYZOTD1ArO9OMI9b1TQoYWRl27LpW9nM9uKI9zxSnkjLZSmw87SZkLZjGb8Uei6RixYnhyaifh3E/WJKqSr1fFkd78BgHBUomrHuJTXpmfk4MpsS12sBZZM0bKWJTBYsuh7xhi8AY2V8TwCe66Q1zx8TRajx9cxekIdx/S3ccKRdn7ZSy9dUqlSUilsfSSJJKkE2F4SY3uJjx9n+HDZhc/tF5M8SAgrtRtGZbClKMLidUEiMZ1QWOWnmX627o5w50WZjJnp5dFrBaksq46xeF0QWYbX785nzbYQ971ZiUEReeFnHiOqZx6/TlaahMUkJb1yJy3wMXaWl9XxnHODAkMPs3H/5cJ7Mabq3PZSOZt2CV3B3gvx3xb6qahROaWJ+MPvpnro2cHE+h0Rrj69vovTpY2Jh6/K5raXy6moVmmdJ3xErzszjc1FEarrVLq2NXHnRZn06WymxqOhamC3NJYOzF8doFMrY5PxiYoiceJRDj4Z50bT9EOaJvPfDl3XqfVqaJpOpks5pMeqmVg246Bhetw37fJT0hq5+memKYwa7uDziR66tDESCOlcfkoa741x0zrPwNjZPs4e7mTifB+BBtPSaoMJznBcTxVuoKvavCvKbS+Xp3yWBGSmyVTXacxZFWTOKlHuMBrgrGOc3Hh2erJamOZUGNzHxilH2wmGdaYv9fPqN7UpbXmAYFiItW1mieP38iT7dEIduekKL92emxR3z1sVRNUap+WcNMjGgrXBlEzhZjTjvwW/5yX670LXddRIHVF/EZJsxpLeFYBYsJId085F15q2TQj7diFJcMeF6fy20ICn6Fph1+NoQ/v2bbnunNZkpylMXuhj4gI/ZiNccLyLU4c4yE5T2FYcxeNXeW+Mm93lUYb1szFigJ2B3a1MmOfjt8V+aj1qozzw1G3f9355/Dp3vipsx/IyFa6KG483jC2MxnRmLg+ydluYn2eLhXHvDibmrAzw3phabBaZcETDoIhYRRDerZVulYseLkl+vj+kp1y/Zi5PLffGVBg1vH7gxKBIPHtzDmffvwddFwvlljkG/EGN3xb5GTPTy8lH2RsRu2BIY/XWMHdcmMH6HRGUvWZoenYw0SrXwMK1QewWQXa37Yk2WVkcO0uEWQzq1bgqGo7quOz79hJOc8jEVHGdPfjJ2//9SGST/zDdm+yo5WcpnDnMybnHOpv06Px30Uwsm3HQUFodoyDLsM/81W7tzESiOonz2BB/2uL1IQZ0s7BxZxhJbnzzspgEwdxbeC8BndoY2FqU+gcdqK5LFTw5rBIxVUyPLlkvLDjSHDIDu4sLmSRJ2CwSpw1xsG13lBe/rGH8PB9H9LDgDWjMWBYgHNV56sYcbA1W1d6AxsK1QW69ICNJKgGmLfGhaXDZKS4+i0+fazpMXhQ4oGPZjGb8HXEwSaWuRfGWzibaoAIZ8RWhRQWpcrY8jhaHPwmAYon7J0oGTPZWmBxtMDoEcTTZW2N2tkXX4dVv3JgM0OOo66h0a2ga1AXgXx9VAfXxi7Uelaw0BadVYtPOCOt2hJm3KkjhniiaTrLt67DJXHiCiwtPcBGKaJx+V3Fy+2WpPpTBZKxPAhrc18L81fWelXvrqR1WuUlXCaNBGI4v3xTi59miMnr+Q4Iw9u5k5qZznDz2QRXLNoZoG0+RccRjWdMcMh6fhqaDGtOxmiEYFtfIi09ykpNu4J0fa4nEL5WPvl+ZJNZmo8Ss5cJqSJbhg7Fu3hvjjm8TDOtn5cLjndR61ZRM70SrPc2h0KmVkTkrg5zYQHsuSRJ2i5AGbC6KcvwRQkeZ4ajh4pPSyHQp+IIav8z28s0UT4r+siE6tjTxzVQPvqCGw9r470vXh2iTZ8BwAAQpoYNfsEZczzu1MjF8gK3JIav/L3h/rJvvp3kZ3NfK5aekYTCIwseHv7jZtCvCw1dnpaQKHQw0E8tmHDS47DLVHpVwREu2nBuitCqGJEHn1iZWb4vw44x6i5CVm0P7jCzcl7ejDo1IpSQJIhqLQTR+MW/fwsju8ijfPd2CRz+oYv2OCLvKYtx+YWNvslqPxvFH2mjXwsiidUF+ne/HZICRA+2MOsaRNG9PwONT0XRom5/6uDt+ke+0l+dlc+xgM/5XoGsxooESQRp9u4n4izBa88jqcmX8GRKly58AfW9piITBmodirJ9OliSF9sd9j8GchSTv/7YVVaGiVktW8ALxYJQ0uyBbFbUqrXIUXv2mlle/qU15rSIDujDwTiCm6izfFGL8XC+qRrJqqelCqwgiAjaBRWtT41oznAqD+1iTbfTte6LMWxlgSL/USeYaj8qidSEuO9nF4D5WbnmpnA4tjdx+QSYdW5nQdZ0ubUy8/WMt/btasJolDu9uYfbyIMGQBhJ0KDBgMMKWXbGkC8VXkxtbMbl9Gu+PdTN6vBurRaLOp9OljZGiMjEcZDVLtMo1UF6jMmtFkFkrRNWzV0czl57k4oieVpw2mbxMhcXrg4wa4eTFL2qYtMDHyUcLclnpjrG1OII/pOG0ydx+YQadWhv5+Jc6xs31kZ2uiBa4qnPeSCdXnprW5Pd58tF2vphUxwdj3dxxYUZKC3fZxiBzVgX4xzm/r3+s86k8+kEVa7eFyUpTcNhkfp7t470xbh64IotBvRtXS/9T0DSdJRtCTF7oo8qtkukS2fFH9W46BWhf2FgY5vtpXm46JzWbfHAfG4P7WHn0gyqG9LUycuDBTQZqJpbNOGg4doCN0ePrmLzQ38jbKxrT+Xm2l4E9LAzua+PHmWLqOoGmSOWfsd6RJXHzsJjriWVJZYyYChc+XJJS9ezdIB2ipCrGez/VsmBNMLkt7QqM3Hp+RtL6oymkORUUWbSPDutiIRLV+fq3umRl4JH3q1Ke30wqm/H/Cbquo8V8KEZn8r9LlvyTsLeQaKAE9NQT3pLePUksJdmAs8UIJNmEKVF9dLTGaG+FrDS2tTFa8/a5HZ1bG9i6OxbfBtGh6NPJzIK1YlAmqkKdHx66KpPPfvWwu6KxzrkgW6bWo6NGdN4b42bakgC1XpVar0a4QWvZ49fJTpNw+3QMikReloFQWE0OFzacDndYJXIzlEbazGc/r+aNHEMybGF7cYTnP6/GbpE4dbCDdKfCFaem8e5PbuauCuALaixYEyQrTWbphgi/lPvo1MrI8o0hcjIUKmpVTEZBGL0BsQG9O5opr4lRUauiaYI0yxKcdLSd8XOFoDSmiVb4Rw/l8s+3qwhF4MITXJwz3MH9b1WiajrHDbSxaH2QdIcC6DzwTiX3XJrJyUc5OGOYk9Hj3Rx7uI3Thzh48csaxs3x0b+7mZnLAmgaVNfFeOYfuVjNIhXnxEEOZiwTus10p8yIATay0/dNRbLTDdxxYSYvfVXDsg1BJAk8cWP4YEjnsK5mzhi2f/s3TdN56N1KSqtiPPOPHI7oIYIzyqpjvPVDLY99WMmb9+QnZQn/SUSiOo99UMni9SE6tTbSsaWJXWVRHv2gir6dzTxzUw7WJrSoTWH8PB8FWQpnj2jstTnkMBuHdTYzYZ6vmVg24++LlrlGTjnazts/1hKM6Jw22IHDJrNma4hXvhGWQDIwfZn/gN7vQEllYmWemyEnW+ChsKig6rpOn05m5q8JcfpQJ93bmXh6dDVmo8T81UHSHAobd4Z56atqbGaZWy/IoE8nMxW1KmNneXn8wyruuSSTUwY3feFyWGWGHmZjzEwvxx5u5+nRVazZFqZjSwPbipuHdJrx/wNqxJOsOkYbVCAjvt2YXZ1oO+xDQLQ/w94dRP2iZSwpFkEa7YI0ml2dUt63xeH/OijblyCVCfiCOss3hWmZo2A1yxwzwMYn4+p48Yua5KIPRJs6L1PBF9AprdIY3MfCgjUhojHYXR7BF6yX7Nitou1sNUNVnbg4xVSdXaVR9oYCdGtvZENhlA2FETJdEjUeHYtJdGCCYZ3rnilLRtnuLo+Rn6Xwwq25ySnuc4914vGpfD7Rw+cTPSiyuCYmFr5l1TH8QR2jQef2CzI4cZCNe96oJKbGiMY0Xrotl399XEVZdZDcDIWqOhHIkMg6P2eEk59meqmu0/hqkocqt0qLHIWzhtn5YYaX8poYb96bT7sCI4vXB3ng7UruvTSXyQv9vPFtLYP7WDn3WCert4T459uVDOlr5axjHCzfGOL7qaK6O6SvlX+cm5EyZe60yZw57I+Zih93hJ1xc31sKapvX9ksEgYD7CqNsqcilpQHNIXlm0JsKIzw8u259GvgxZmfZeCxa7O59ulSvpni4bFrs//Qdh0KvDemlpVbwjxzUw5H9rIk5xVWbA7x6PuVvPZtDQ9ceWDbWVQWpU9nyz5b3f26Wfh51oGFC/wRNBPLZhxU3HFRJkaDxMe/uPn4FzcmY30edtc2RlrkGpm44MCI5YHCZhbpPBW1qbpKT3xCc2dZFJdd4vqz0tkZvwmYTSLi7NNf65IEtkW2Qtt8I+1bmGiZo9O+hYGPf6nj7R9rOaa/DXsT+h6Aq04X1kWXP16SzAzesSeW1Iq2yFFAlyipaiaazfj7QlNDRP0iqlCLBUlrc0ryb7tmXyUqkE1g78dze9+JLJsxOlpjsOQ0GuQ7WGjK8DyBoYdZmLsqREmVyglHWhh2mJWPf6lLIZUgLMFK4qlesgzz14To1dHEuu2CVEJ9l8Ef11Ba4tc0i1liSB8z05amtr4BVGB9YT3hrPWI12a6ZEqqxHXp7ksy+G2hn1qvRq+OJk4+2pFiti1JErVeDaNBVJfSbBJZ6QYG97UyZ2WQTyfU0SrXQKZL4cxjnGzcGWZDYYSrTnMxeoKHrbsjySjIh6/OYtbyAJMW+pm/WuzYtt1hcjNkKmo1ZsZb3SWVKhc/WooswalDHLSLk7WB3S1kpSksWhfi2rPSmbrEz7QlAc4e4eTJG3MYP9fH+Hk+5qwMYjZKDB9g47yRroNWAfzs1zp2lkZ56fZcenUwE1N1rGZxfO55vYInPqri44fz93muzVkZoE2egcO6NJ4sNxokTjnawUe/uFE1/aDrDf8IvAGNSQv8XHKSq1Frvn9XC1efns67P9Vy3Vmx/VZ5E5CAHXsirN4SomdHcyMdak2diuUQ6EubiWUzDioMisTtF2Zy6clpzFkR4JMJbvKzFF68NZeWuUaKK6LMXFY/wGJU6lvWfxa+3/G421OhIgEvf1VNjUfDZRMaKVVTGdLXyvzVQUYOtLGnMsa9b1RweHcLa7aFCYb1ZDv+26kerjkjvcn3b5FjoGtbE8s21t9gtIStnk7yxtWMZvydUFf0KyH3xqR9TyxYTmI8RzFnpBBLo6M1uhatH5qJVyBNjjYYbS1S3teRd/Rfsv0NSeXe7gtzV4nfYiIje/rSphezwbDOUb0trNkWIhC/jlTWprJPo0EYjtd4NGIq1Pp0FBlCYZ0ar/hQqwmCe2nBZQnOGu5gzExfcujJE9AxGcX7vf2DIDI9O5gJRXRe/KKGz3+t47mbc2lbYKSyNsbkRX7+cU5Go1ZmuwITeyqiLF4forgixqzlfkqrVewWiQtOcPHrAj/Pf16dnA6XZYniilhykQ+welv9Bndra2TTriiPXJ1FSVWMj8fVsWR9iFBEw2KSkWUx3BiNiZzydgVGdpVF48dH4uwRTs4e4UTTxDXzYC4mIlGdX+f7OHOYI5n8k9DGZ7oUbr8ggztfq2DllvA+k4GCYWGxs6/tynQp8QFRHcX0nyOW67aHCUf1Rs4jCYwcaOOtH2pZvTXMyIH7pm879kR4/dta1sejjO98rYKsNIXLTnZxRrxa7AtqTF/q59R9dOMawhvQmLrYT51PI83x+0S0mVg245AgK03BbpPxB3Xevi83OfQyNl52T1Tz/l1SeaDQgalLBKFV4r8Lf1BPairnrwly2mA724ojLNkQomcHI5omoaqwZXeEryZ7WLsthC+o47TJ2C0S6wvD1PlS+/Uuu7j4Gg0yR/Y0s2htCG+w2VqoGX8ddF1HDdekTFpHfbtRo17aDHkn+TzP7skEqpalvDYRVWiyt0bXYslBmVZHvvS7QzP/SaTHid++sC9ts6YJg3CToX7iveEEtywLArt3NySho1yxSUwG7U0qQRDdMTOFttJpk/AGdHzxaMZKt8aQvlbuuDAz2fouKovyxEdV3P9WBaMfLWDROsF0TzpqLz9dd4yxM70s3RjC49ewmiWe/Liati0MxFSYvTyAyy6zbXcUi0kSbhafVFHprt+x527OZv2OCF9MEo4Vl5zk4uWvalm/I8xVp6fz8bg6yqpjTFnk54xhToorEsk5JlRN+CHazI0J2IF4I+q6zrbiKDUelew0hQ4tjfslorvLo3j8GkMPa1rr3qezGZddZv32fRPLNvlGFq4N4g9qTXaeVm0JkZuhsGJTiKLyGFaTxNF9rORk/LXnfCJ8w9C0sUqSUO+d8tQQu0qj3PFKOTkZBv55RRafjHODBO3yjbz2bS2BkM6g3lZe/qoaEJ6q+8P0pX5e/qqGmKoTjR3Yvezve6Voxn895q32k5epMGdFgFZ5Ro7ubaWqVlzc/hNUKxG7pmrQs4ORgT2sfDvVgxqGQEjn++n1Avstu6L07WKhyq0mW+VrtkUaVUb2hsevI8uirT59aXC/z21GM/4dqFE/0cAeLGldko+VrXoO755paLGmK3Ra1I9sFETF2fI4LBndMdnrrXsUU3qTN/m/M6kEOPdYBx/8LEiSzQyPXZfDEx9VEmjcpU6Bwypsghq2yBv+ZM0GcNoVojFR2Zy8MPC7g4YtsiSqPTrhBtJLf4PFZW66hKzIPHx1dkprsk2+kSdvzOGyx0qYsSxAKKJjMkgiazuOnaVR7n6tnGhMkINpSwJ0aWNi7bYwxeViSPHZz2ro0sbEucc6mLbUTyhCclBSAq4flcamXVEmL6y/3i1aF+LUwQ5+nOFlSF8rHVqK7tIPM4R2/M3va0lzyAzvb2P+6iDVdeqfyudesj7I+2PdKQllnVobuensjBTtY0Mk8s0TOeh7Q9NFOpC8n0LayUfZ+XyiMLu/+dzUc3zjzjBTl/gxGyUefq8Ku1UiHNF584daTj7Kzq3nZzZpD3Uo0KWtSUgy4qb3e2Nu3JO5R/t9Sww+Ge8mzaHw+t15OKwyPdubePi9SpZsCGG3Snzws5sPfnaTlabw/K255GXu+7e9YnOIZz+tZuRAGzeMyuC8s/fBePfC3/tq0Yz/SsRUnbe+r2X+qhBI8NNML3U+jXSHdMArnkOBhlndwbDOpxM8nDnMweaiCLGYWEUnMLSflQeuyObCh/ekvMcRPcwsWh/eZ9Y3gMMisaMkiiJDXoZCaRP5xc1oxoEi4i8h7NkmhmbiFciGUYWdT52JbKi/KQtSKWO0FYgp63gF0uRoA3K9hi+93Zl/9a4cMiRIJQh7occ+rCQU/v3XBUJ6I9/Jhh64XdqaufncdK5/tpwFaxpbokmSWLA2rIiWVOucNsRO23wDb/8oImsbvs4fgnOOtVNSFWP9jjCKBH06W8jPMtAiW+gAF64NMmq4k1BE+C726mimoibK/W9WoCgSj12XzbbdQlb0yDXZVNbGuPv1CuwWCatF4vHrssnPMnDVaWmMur+ESDQucVDg/bF1KdnmVrPE1CV+xr7QkpkrAtz1emVyW/dUxDjz3mIUGZ66MYcFa4O8/FU1A3tY6NZu/9ncMVV0hDYWhkUr3SwxekIdfbuYef6WHNoVGNm+J8o3Uzzc92YFz9+SS/9ujcllmzwj2ekK05f6k8bwDTFxvg9/UOfbKR5GT6ijda6BUwY7OGOoI2l7l5Nh4OZzM3jz+1oKSyKccrQDp01myfogE+b7UFXo3MHIjedk0qWNiUBIY+ICHx/+7CYS1Q94WObfRU66gWGH2fjs1zr6dDbTvkU9gdxTGeWjX9wc0cNCq9ymB5XqfCrz1wS55byMpOdny1wjHz9cwJINIRasCTBxvp/jj7Rz18ViHmJ/+HpyHZ3bmLj/8qw/lNTTTCybcdDxxne1TF7o46g+FhavC/HxwwV4/BoPvVOB2/efI1kN7wmJ4ZoOLY306mjm6dHVKc+dvjRIYUkpVW5x9b3urDQ+/LmOJRvDyFJjUtkiW6FrWyMzl4fwBITOKKbSTCqb8btoKqowp8c/kBVx467e8gmeol+bfK1iziQWrsJkaAVAZudLyeh4IUZbC2TlP2+d8lcjMdBzIKQSBOFrSCobwiDDmq1hXvyyBrNRwu3TUj4j4UZx8QkuPp/kSXnthHl+9i785mcplFWrhKM605b6+Xxi/WtkCYb1s3HXJZk4rDLBsE6/LmZa5hh496da8rIUZq8IJquid7xSgdEAA3tYyHQpZLoUzj3WyffTvJjNcPnjJQzpayM/S8FkhEh8zaxqYDaSrNDmpMsoskRZjcqPM3yUVMbo1tZErVelvCbeXdLFtexfH1URCOu0zRea8lkrAgzuY22SnGzeFeaxD6uoqFEpyFIIR3VqPBo2i8Q9l2RSkC2IUU6GgcO7W7jvjQre/rGWjx5KHcDZtDPMmm1hOrUSQ5+9Opo54Uh78jlTFvt57ZtaDAqcOcxBTqaBtdvCfPizm3mrgjx3S07S/HzUcCe5GQrfTPEkr/fpDpmcDAOyBM/dkpesTNoswhrJZpZ56asaLj4xut+p84OJ2y/M4O7XK7jh2TKG9rPRsYXQs85aESAv08A9l2bu87UisrGxf7Isi1SjQb2sLN8UJsOl/C6p9Ac1VmwOc/clmX84/rGZWDbjoKK0Ksav833cfG4Gxx9p58KH9vDK1zU8cEUW1XENVOKC/FcjoVtJVBdGDrTy6je1OG0yZiMprSuoJ59mk8T3U+OVBw1MBhpNl5ZUqZQ0IJF/1H+zGf+/ocdPiMQN0VsyA0/xVKF/9Bc3iipMb3sWZld7ACxpXQmnbU/xeky0rxVjqvDeZG/1F+zN3wMGWfgwNkQ0ljrIkwgksFuEzc/+tGl7I/HeW3enXhgMivichDfklCX+lApgAhLiWpd4PNGOjqlioC/DKRON6cRUsJglFq4NcO/rEUqqNE4dbEeWJe6/IpM7Xq5g0y5ok2egrFrl9KF2JszzE43p7NgToc6nkuZQGNzXxucTPTx5STY7SqLMWBpgy+4IdotMOKwSVaFvJzMmo0Tn1iYy0mTe+t7NUb0tlNeofD25jhMH2bn/8iy2F0e4/pkybjwnDYtJ5rupHipqxRBkSVWM8XN91Pk0Mpwyd12cyeC+9W3x8poY971ZSatcA0/fmEPHViZWbA5yz+uVWEwS/3y7kg8eyE9WEw2KxCUnp3HvGxVs3hWhWzszZdUxnvqkig2FESwmCV3X0XV4/vMavpzkoW9nM6XVMVZuDmM2Snz4YD6t4hP1Zw5zsqEwzD1vVPD5r3XccHa9efrgvjYG97Xh9qpEojpmk8Q59+/h1vMbh2UAHH+knQ9+djNjmZ+rTk8/8JPn30CaQ+HNu/P4dYGPyQv9LN8YItMlYkBPG+JoMp2o/rUykiT0ur06Nq4o+wIa1XXi3Ps9JLxb0w/guXujmVg246Bi1ooAFpPEKYPtWEwyj1yTzWMfVHLZ43uIRHUyXfsX2R9K7C3gNxokzCaShsJNQQdCETFRWBdP44jEmjZv79rGwNbi2H+ENDfj7wEtFkwZmGk4QNN22EeiHQ1EvEX4SmfVv1AyYLK3xORoi9HROqW1ndHhPDI6nPcX78nfGxKC+DW1SG3Ydk5aBf2O1rIhEnGI+0LDv6laPWFsSGgTVcI2eQYqamKEIuLvIwdambpE6OQSVkLZ6aKSqeuwOZ4kduoQsWCoiyd49e9qZtXWMJoGkxb4OHWIk+MG2rjz1QrGzfVx2clpSSLgD+lU1qrYrBIZLgPd2pr59Nc6CrIVXrmz3mQ+pupMWRRg4doQLruMx69x8tF2xs3xMnpCHR1bGTl9qBOLSWbJ+hAlVUFOH+rg+rPSsVtldpWK1uzjH1bx0m259I23qcfM9CJJ8PwtuditgqxVxrX1z96cyw3PljFzeYCTjqpfFHVuLUhhRa1KqzyNu1+vAF3n6ZtyOKKnBXRYsCbAK1/XUutV2VYcSXaNXrkjN0kqE+jR3szpQxxMWigI4d6kMTEwVVETQ9MhP7tpKmQ0SORkKEnrur8K1njF9NxjXb//5AbIcCoM7GHhp5leRg60NUrAGzvLi6rqjBjw+/rYNIdMukNm5aYQg/v8MT1tM7FsxkGFN6CR7lSSudmDell5/4ECPhhby6J1Iep8fx/WNXlR4IAri4lqpMUkEYrojV7nstXfFJrx/xsNowqtmb1RTCKKrmbbV1Suf2ufr4v4diWJpT3vKGSDVegfHa0xWvP/9gMyBxMJV4g/C4cVvMFUUmkxSbTNN7C5KLXCmLDS2R8SFUeTAVrliAXi7+GW89Lp3NrE69/VsGNPLIXQRqKCSJZWxZJV1D6dTElSmUA0Bsf0s+Kwynw52UsoomO3QMscQZSmLvbTpY2Jx6/LYeZyP69+U8uN52Rw6mAx2DFigI2pi/1cdnIaUxf7sVkkHv+winSHzGFdLHgDGp9PrItvi8otL5Zx3EA7/bqaWbIhREVtDJtFShKnO16pQJZgyGFW7rwoE4tJprQqxvw1YrsHdLMkp6rbFhh5/Lpsbnu5nM9+reOVOLGcvtSPwyZx3gN7iKo6nVub6NVRtGYNCvTramb2ilRiWRxPQkpzyExa4KOyNsZnj7WgoAHhG9rPTtsCE1c/WcopRzuoqFXxBjS6t29a63l0bys/TPdSVh2jTX7Tbex0p4LFLPLiE/nwDeENaBSXxzjuICfTHEpcfXo6t79czn1vVnLV6Wn07mSmyi0CP76f5uWiE1wH5IGpKBKnDHYwZpaXE49y0Ln1gUtr/neuZM34S9Ai28D/tXfe4VEU/x9/7bVccukNQpMaei/SpFdFwQIKIvrFgv7EDnaxKxZQQLFhQxERQUBQFAREQJqCKEjvIaS3u1y/+f2xuU2OS4BAaHFez5OHsDM7N7sz2XvvzKekZXvIzC0O4Fo7ycjt10Sz/p/jmAylh+a4EJwoDk/l8Q1gNIpSc5fnFwYfk1z6uGxHKUzfWMJp5khAqsLqHScTXqUTAAZzAgB6U3RQrEc1VWFNrV1zdEPM0Q3P/wVdJJytpUhBKbFrHS5BdkGwTfOpRKVRD3HRCsezVO/w0xGVCpCW42HF5kL2pxTXVxQYc20U3VuHseg3G3OWq3aUBgNs3VP84IiL0hFl0bH/mIevl1nRKdC6UQh/7nRic6j2bSajwr6jLjJyvFwz7qh27qRZOfyx08njo+KoWdXIr3/aWLzGyuI1arzMG/tEaKt0bo/gxY8zWFNkR7rjgIsdRbEN9Tro0TaMe66P4cnp6ew+7Gb01VH0u9xCYglP4VV/FmI0gtsNiTGBXsF6vcKQ7hG8+nkWWXletuxykJ3vIzpcxy0DIwkL1bH+HzvzVlgxGWD2z/nEReq09JegmonMWZZPYoweh9PH979Z6dgsNEBU+qlV1UiHpmZ+2VxI62Q1/mdZQc0Li2J2Gk5iS2gyKvRtr67SXtnFQsIJgmvW0jy8PkHfyy8dYZlcy8Tr9yUwaVY2D7+drh0PMyv8b1AUNw84/VXQEf0j+eNfBw9MTmNgJwsOl8B8GnE+pbCUVCg92oYxfV4Ony3J45ERsUXZI7x8vCgXuHhEZWkop7GMUlCxSYMkF5jSUhXG1LuJ0NhmANiz/yZt2xtB5yn6EEyWWiCKvyDDq15B/YFLtRVMyfklxKiQURRvslp8cYabU+H2wvGsoqw6RSkXT8Rvq+lHAHOXW4PqCQHvz8/jg/l52qNEQV3BvGtINB8vysXrU71/96W4SIpXo0ZMHZdIkzpmrnnkCFa7YNs+J1//lE9KUXKFlg1MhIbo2LTDgQBW/VHIph12LGYdLg9M/iqbuCgdiTF67rq2OJzOk9PT+WNn8d59mBmcruKwa1d3DefIcTd7jriJsij8s9/J8P6BwiPP6kURULeakYaXBa9aVY1TxeaRNDdvfJlFVLiOBrVMjBig/h0M6R7Bqj9svPBxFss3qqZSHZqqq5sHU918tjiX37ba0SnwxPRMQE1X+eF3OYy+JjooW0y1eAN/7HLSsVkoM3/IZ/0/9lK3an9ab6VWFQNJcScPkXPLlZFs2G5n7Otp3Ng3ktYNQ8jJ97HotwJWb7Fz93XRxEaeXpidi4Xm9c18OiGJbXudpKR7CAvV0aGJmbDTzDHuJ8ysY9KDicz+KZ8la63k5HtLFfwnIoWlpEIJD9Ux9oYY3pyVzeYdDlxuH/mFl4YnS3kM+yWXDj6vAwSa3WJh1l9k7piuhuxx5QbVD41rpQnLkMgGWKp01oKGq1vXtTCY41GUwIe0zhAKBG+nSc4PTnfxc+ZkovJkzoMlRaVfTI4fGcNfexz8vCF4mVRBfSE1GqB2NRMRYQp7j7iJj9Yz+upInv4gCwV1a/mmfpF8uTSPQofgnhui+XhhHjsOODGbYOnvhWoA9aJ4l0vWWNl5qFgQXtsjkm6twziW6eHht44jhEJGrheb3Uujy0yMuS6ah95KZ+TAKE1U7tjv0ETlwyNi+HhRHnlWHy3qm3B7BP8edDN+ajqKTqF1cghDukfw3EeZjJ+azo19I9VwQEddrNlqx+WBmwdGlhrjdMcBF0YDbN7pQK9TuPWqKKZ9k8OmHXbaN1H/Hnq0tfDTehs79jspsAtWb7HTZ+xhzQHKbFIYfU0UXVuG8dInmWTmevl2RQGpWV4m3B53QtxJF0lxehrVDqFlgxAmf5VNdLiepnXVLXG3RzB3eT6rt9gZPzL2lFmA4qMNTH2kCu/Nz+W9eTna90D1BAOPjYqlf8dTZ6a5GFEUhZYNzLRscHbthJl13D44mtsGRdFn0+lJRiksJRVOXLQegx6y8ryXpFgrzeu7JHod1EnSszdFhhK6WBDCi7swDbf1kLYCWTJVYZWW44mufS2gPnDt2du0cw3m+IBYj2HxrbQyc1R9anScdL4vR3IOOV3nOo8XQk0K9WuE8OnivKBynQI1qujp0cbClV0sjJ+aQd1qIdRJMjF3RQHPfJilfZbbLfD5BC3rh/D7Pw4enZpOs3oheLyqTecfO+38tN6Kvkj0/vGvg5hIPbWqGDCbdLz8aSb7jkbSp4OFkQOjmPxVDjpFFWRvPpCoreqFlHBS8fe53+Vh1KxixOFUO7OtRCpHtxf6tw/joeFqEPBX703gg+9yeeLd4liW9WsYMRnVPvVoExYg1LLzvcxfWUD31mEcOuamRX3VaWb934U8OT2DmlWMVE80UKeakaw8DwV2wZBuFlo3CiU738vOgy5+2WTj3UeraDEbr+0RwcufZnFz/whm/VTA1ivCteDpv/9t59+DLl64S40rOeGOeJ54N4P73kyjQU0jiTEGdhxwklPg4+YBkUFZi8oiMdbAs3fEk53v5ViGh9AQhTrVjOUOs1OZ0euVkwahL4kUlpIKJc/q5fkZmbRvYqZJnRA+X6I+3IS4dFYETyYqQb0OKSrPP2qqwpwi0XiYkIh6hMY2BdQt6yNr7inzXLctVfs9JKIeSe1eVIWkpYaWiUby38FoUGPRClG29YteB5ZQuOf14/h8xVl6/FRPNJCV66VXewv7UzwcSfPQtWUoXy9T09a2ahCirRiu+1u1U/vf1VH8/o8Dlwet7OBxDzoFqiUYOJruoUU9E9v2ufB6BQ1rhTDqqihmLMzl2xUFWgpGgAiL6rXr396sW83Ium12zSnmUKpqW9qsXgjjp6bj8cI1V1i4dVAUf+128u632WTlCfJtXs1run2TUNo1NrM/pSjlYrSeOtVMLFlrZdKsbDJyPfRqayE6QsfRdA/frijAJ2D0NdF8+F0uuVYvuw+72JfiwetTt8cPprpZ+5e61a1TYOywGHRFCmXBr6l0bxMWEAj88qZmWjYIYe6KAiLDFOYszyfMrLBicyELfi2gc4tQOrVQV0JjIvS8O74Kv/9tZ9WfhdjsPnq1t3BVl3Bqn0HcSX9MUMnZIYWlpEJZ+rsNt1vw914nv/+tGoyfjlPMpUZ0hEJuQSW7qIsMjzOb3APfFdk/qquPJVMVxtS/WROWJkstFJ0Jo6VGUKxHf6pCPzqjhcjqfc735UgqmLLsIUtS1rPHfRoBHLw+yMwrPtmfltEfamzMkEimzMnlzS+zOJDiplk9E3N/KaBNQzN/7HTQrXUYf+x0ckUrNbPP9v0uxhVltTnRcVCvVz2jq8bpuXlgFNveySA8VMfRDA9Gg8I918cw6soodhxw4nQJnp+RSZRFR2xUsQga0iOCt2Zns3KzjZ7tLBiLxOK8FfmYTQo+H9wxOIbwMB092hr4YV0BWXkuNu1wkJHjJiHGWHR9CvVqmKhXon9Xdraw76iLJWutbNpRvEV/WVUDz9+VQNU4A5c3M/PazGzGT0unTjUjb96fSI1EA2k5XvYdcfLiJ1nodQoOl2rrCZCR42FAR/XFbu8RF1/8mMfabXZ8PtUUocAu2LjdwcbtakikYb0jGXVVVICzjl6v0LVVGF3LyCV+KSGEYOchF6v+KMTm8FEjwUi/jpZLTuxKYSmpUP7Z50DRgdUuuK5HOIt+swbFj6wMSFF5dvi8Li1kT8lUhZYqnYlrcAughvXJ2jXjhDMVjGFJmmD0ow+JocGglUF2j5LKS0lReWJcWb8fnk8El1WJgbSc4PZO9N0z6tVYmf5zBRAdrlDoFLjc8PzH2Xh9kJGr2vx5var9Zlaeh/BQhe37HFjMCkfSPAHtxkXqaN/YzKo/7TjcfqchhRt6hXPboGg8XoHFrBAbpWfN1kKOprupkWjEEqqjfZNQftlkw+uDw2kebuxX7GhzZWcL2/Y6ePGTLBavtZIUZyA108uh4170enhxTIIWXPubZfls/le9gR4vjHgmlZ7twrj72pgAsernk+/zWPCrlc4tQmnbyIzPJ9iX4uaXTTbemZvDK/+XQI82YUz7JgeHUzB2aIyWqSbKomP9Pw68XvD5BMs32rimmxouKSpcz9F0N3/tdvD4uxkkxur5v+tjSIo3sPOgk1lL8zEY4Lk74mnTyBwUl7EyYXf4ePGTTNb/4yAuSk98tJ7lGwv55Ptc7rk+hmt7BOcOv1iRwlJy1rg9giNpboSA7AIvHg80rm1i4W/WMvNpX6qUFhhdUjpqqsJ0hPBislQHwOPM4fDqO3EXpgLBthF6U3GWDIM5gajLBhetQhatQFqqaakOS6LafUl7qP8qJf8mdTo1bWHj2iH8ucsZ9PdamqgEqFPNwP5jxUuZ7hOeXR2bmQkNUVj5h52GtUzsPlKsbM0hCnuOuPB44WCqhyqxeg6kerA5BLZUD7FROhxOH4UOyLH6WLpBjU9WO8nA2w9XIdJSLOaMBoVBXcOZt7KAmEgdj7ydzt3XRdO1VRh7j7h4a3Y2ep0a83H/UTcLVxfQu52F8DAdj4+K4/KmoSxabWXv0eKVxTpJeiItOlIy3Ez/NkfbTYoO12Fz+GjXWF1lvfeN4wzvF0l4mI6WDczERenZd9TFrKX53HFNlObp7adPBwuPTk1n6e82BnUNxxKqQwgf97yWRrN6IVjMCn/tdeLxCB4bFcuKPwpZvaVQE5Z9O4TxzfICNm6307i2iVfvTdDEY4hR4Ysf87GE6Ph5YyGdyhmk+1Jj4sws/trjZMId8VzRKhS9TiHf5uXzJXlM+yaH2Eg93dtcGvdACkvJGeP1Cmb9lM/CXwvIKVBFgt+u+9+DF3FcobNAispghPDiyNlenOtaC99zBOFzEVGtN9XavwSA3hSFx5EB+FD0YUFpCkOi6mvtKopC1VaPX6Crklyq+Hxqdpytu08vYXhEKNicBIhKgNAQBbuz+A9+/T8OQkMUHh8VS3y0nnFTix1cbHah7cyEGCEpzsCgK8J56RM1/E52XvFLlN8T2uuDh0fEBYhKP6OvieZIuod12+yEmRVe/CQLyAqo43AJNmy3s+BXDx/Mz2Xs0Biu7BJO7/YWehcF9F78Wz6TZ+ey96iXsW+kBZxvNilMuCOOh9/OoEOTUOxOwdbdTt7+WlXeeh30bm/BaIS4KD3D+gbHP2zT0EzH5qF8/1sBg7qG4/EKbugVQbUEA7//bcftgWG9I7iqazgJ0Qa27nFqtp+gOuos+s1KRq6PId3N6PUKhQ4fyzbamLEgl5YNQujSMpQP5ueSne+95LaET5eDqW5+22rn8VGx9CghHiMtesYOjeFouodZS/Po1jr0lF7uFwNSWErOCJ9P8OInmaz9y86gK8LVFFFCMG5KBh4pviodaqrCo7ish3Bbj6AzRRFT5zqt/PCae0GUYrimGBCiZABpHTW7TscYWgV9SNwl8ZCUXHhKCxEUE6mQky+ItIDZpCM9J7CCT6gib0j3cOaUEnMSoGtL1f7R/8gKMysUOtT/udyCPu1DWb5JDTPkT0fr8sDXywqonaQmg0iuZcLjLY6Ccc/1Mbz9dQ42h4+WDUJ47s54hj2RgtGgUCVOz/B+UXRqbubuiWl8/1tBqTmdjQaFF+6KZ/0/dn5cZ+NIuhsEeH2CtGwv9w2LoX/HcExGhaw8L58uzuXNWdlEWnQBtoaXNwsjNCQ3KEXlZVUNvHl/AjN/LCDMDPNWFmB3Cjo0MbPjgJPPnk1i5eZCPl2ch06n0KZhSFA8ST8tG4Tw6ffqCuhlVYz8vc/J/66ODgrT4/UJ/trt0FI/groVfmUnC18vK+CjhXnMWJSHEKptbI+2YTw8Ipb0HC/TfbmkpLsrVFgKIdh3tKSTkvGCPY/W/lWIxazQs12wI6GiqCvYz36YSVq2l6pxF79su/h7KLko+f1vO6u32Hnuzni6tVYfZH/tduC5RDy/JcEIIQIerGnbJuMq2I/LegSPIz2grjm6sSYsFUVPWFwrUBQtZM/JUhWGxjQ959ciqVyUFiIor0Cg10G+jVJzOSuA002QqPSvFoaGoGWk8eMXlaDWWbHZTnyUjsw8HwU2Hx2bmXlnbjYutypC46IMPHdnAuOmptOzXRj/7HMyf1UBsZE6jqR5uLFvJHqdQny0ntQsLw+PiNPiLbZMDmHf0bKzAul0Cp1bhNG5aAv4eJaHmyccY+zQGK6+otjeLi5KzyMjYknP9jLzhzy6tFRXtfKsXh5+Ox2jQYfD5aN5kYA9mu7mSJqHB9/KICXDQ+cWoWzaYefjp5PYn+Jm4w41HuX1vSKpW93EI1PSOZhadj+z87yEmtXnhn+V9rethVxxgjPN/JVqXMqnrwgUnAmxBhQFpj6cyMFUD3q9uhJapSj7T06+uvtlDjk7+0qHy8fqLXaOZbjJyvOyba+TI2nFL70Nahq55/oYWpUQvucLh1NgCdNhLCNLUExRbnOH69JYtZHCUnJGLFlrpVFtkyYqAf7Y5SA6XKGgUFwyoYX+awgh8DgyVC/rErEeXdYjGMyx1Or6nlbXlr4et+2I9n+9KQpjkXAMiQqMuluzy7Tzdg0SCRR5e5/ke7a0ov4dQ2lZP5TXv8zGWWStU9Jp50QHHiEgPsbA5c2MLFlrY/0/xUK0XnUTz94ZT1S4HqdLEBOhZ+LYRB5/J53sfB86BX5Ya2XOsnxcbkFcdHEQb1AFWZj59FfIfttaiMmglBqbUVEUBncL55kPMjmW6aF6gpF5KwvIzvcy46kkjqS5mfpNDscyioVUSoaHm/pGsGG7g+6tw6iRaGTtX3YMeggtEnGtG5qpFm/g8HEP+466qFcjMPNOocPHzxttdC/6HujRJow1W+08PyOTPu0tXNEqFLcXftloY+02O8P6RNC4duAKbefmoUz7Joedh1zc0Ct4u33xGitJ8QbqVS9/+CA/KzfbeGt2NjaHICJMp72IXN7MzH3DYjic6uGrn/N5dFo6E8cm0qbh+RWXtasZSc/2ciTNTc0qwde5+V875hCFKjGXhilA5XWxkpxTjmV4aFY38AHh84Fer6PKKVJoSc49XncB9pzt2NJ+Dzh+cOVI9v88mCPrxpL21+vk7JuNLW0tbtthnPn7A+rGJd9G1dbPUOuKj6g/cCn1By7lsm4fkdTmGWLr3XQ+L0ciAdQt0tK45gpVbD17RxxXdw0WXpZQhbaNTPy8wc7abXYtDJHRUCwkLaEKY64rdk7p0MSMQQ+7D7s02+qX74knLkqPyQj1axq1rdk61Yz88a+DmokG3nqoCjpFda7JzvdyXc9wkuL0tCix5X0s08PGHY6AF/NTUegQhIfpNNF3IvHReq0eqKHf+l1uISneQIemocx8NomHhqvOccN6R5AYq6fQIcjO93JZkhGHy8fiNVauaBWmxbUENQ6myajw1HsZ/LnTgSi6GQdT3Tw5PQOnS3B9T3UFVadTeOp/cYy5Npq/9zp45oNMXpiRSWqWh0dviWXMtdFB/U6IMTCwk4WPFuTy4+9WPF61favdx0cLcln5RyEjB0SecbDyjdvtvPRpFu2bhvL5s0lYzAptG5kZc10Um3c4mLOsgI7NQ5n8YCJN6oQwfW6Odo3niytahREVruO9eTm4T7AlO5zmZsGvVvq2txBazpSMFwq5Yik5I8LDdKRlB9rUVU8wkJXnlb655xlb+gYcubtKxHs8gtdVZIBviqH+wB+0uobQBFzWQxjDkk5IU6iuRJbcDo+qdeUFuR5J5aMioikYdeAuYydk3d926lQz0rVVGN3bWLiySzj3vKY6qxj08L9B0QzpHs4P62zMX1mgxbZ0e9Stcb1OdcB5f76a0CE6QmH3ERchJoVWyWZ+/bOQutWNtEoOodDho0ntEH783caATuE0qGnimm4RjJ+azsLVVnYdcmEwKLxxfyIPv53O8o2FpOd4eWRkOEIItu938foXWSTG6Onb4fSD89dIVJ+vZa1q/bXHidEAVWL1CCHIzPUGrDDqdApXXxHBxh0OFq62khSv53i2h7goPVt3O9j8r4OsPC8jTsgVfjDVTevkEHILfIybmk58tB6zSeFounruxHsTqZ5Y3B+9XmFo70iu7xlBToEPnU71Pj+Z/eJ9w2JxuARvfJHNR9/lkhCj50iaB7dHcMfgKAZ2PvO0ip8vyaN5vRCeui2OrXucpGZ5efJ/8TStG4JOUfhgfi4j+kdSJdbAyIGRPDotg12HXDSqHWz7eq4wGRUevzWOCR9kMObV41x9RTiJMXq27XXyw1qrmiL0mqhTN1QK1kIff+5y4HQJ6tUwUrd6cL73ikYKS8kZ0bNtGO/Pz+VYpodq8Qbmrchn+rxc4KS7U5JyoqUqtB3GVXAYl+0wXmeu5mUNkL3nSwozNwed609V6PO60OnVh0lSm2fRG8NRdGe+rSSRlJezFZV6PUx+MI77JmWVWp6V58Nm9zHm1eP0aBPK9v3FUSk8XnV1UadTnSCu6mLhgclpHEp1U1Ao1FiXJeJdRoQpGPU6MnK99G4XSk6BD5tDcFPfSH5cV4jDJRg7LIY3vsjmgclpXNnJQptGZto0DGHqHPWF7qouFvYccVGzikHryxtfqike07K91E4y8uq9CVrWnNNBXdXK4cMFuTx3Rzz6ImcaIQS/bS3k8yV5VIk1sHqLnd7twogO13EkLdg28un/xTH5q2yWbSzk8HE1APv+FEFcpI5X700IEKMbt9vZfdjFy/ck0LGZmW17nGzY4cDrFTS6zETXVmFl2gXqdApxpcTELA2TUeGp/8Uzor+LXzYVkm/z0b1NGP07hp92G6VxPMvDvwddPHtHPDqdQkaOuhiSXEu9xqu6hPPJojxWbylkaO9IGtRUj6fneGlU+4w/9oy4vGkobz9chVlL83l3bg4+AZEWHVd1DWdE/8hSowecDK9X8NHCXBb+asXpLv4DbFYvhHEjY6lVystJRSGFpeSM6N8pnPmrrIyfmk73NqF8/XPBKXNsS0pHCIHXlYshpDiGY+a/H1KQugq37SjCF/zl4HM/oaUitCRejt4cp4XsUVciS09VWPIzJJJLBa8XTVSWtvophBpi6PBxN598H/j3Ehel1xxX1PMVYiP1eDyC49lerIU+QkMUurUKZctuF8cyPRQUqrGD1v1tx+5U0zlm5Xn5eFEuV3UJp251E5MeTGTW0nx+WGdl/irVQahWVQNeLyxZa2PJWhvJtUw8OiqWUJPCjgMuFEV1TGnX2FzurV2TUWHczbE8+1EmY99MY0j3cCxmHe9+m0NatldLmfj27Gw++C6HVg3M/LTexo19IwO8qUNMquf4so2FDO4eTlS4nqXrrDhcgoPH3NRMNOL2qoHMv/wxn/ZNzHRoakZRFFommwO8uiuaOtVM3DG44lbUrIXqEndirHr9UeHqv0fT3dSpZiLMrCPCosNm9xUd9xTVuzBbzo1rh/DS3QnYnT4cLkFkmE57gSgvb3yZzS+bbNw8IJKruoQTYdGxcbuDTxbl8tDkNKY/VlVzkKpopLCUnDa7DjmZv7KAP3Y68PmgXg0jadlevv5ZzY0rReXJ8bltWoaZkrEe/akKG1y1Ep1BfWh7nNm4Cg4AlEhV6BeONaFEhpnYBiMvyPVIJKeLUR8ccPxM0CnQu30IyzaWHqPS41W3tX2+4p2TsUOjg7Zhe7a18PyMTJ66LY6pc7IpKBQs3VAYlNDBH6bH5hDMWJjLoK7h3DtUfTkLM+u4c0g0o6+OItfqI8SoaJlt7A4fKATYQ3Zvc/Y56bu0DGPS/YnM/DGP12Zma8fbNjIzfmQMibFG0rI9vDs3h3V/29HrYMTTKVSNN9CrnYXubULZ8I+DT7/Po1PzUMYOjdEcf96anc20b3KYUrTqajIq9O9o4f+ujw5IoXgpkRirx6CHf/Y5aVw7hLaNzMRE6JizrIDHb43jWKaHrDwv1eINCCH4Znk+VeP0pYaAOp+EhugIPYsu7Dni4ucNNsbdHMuVXYrNCLq1DqNF/RBufymVr3/O54GbYiugt8FIYSk5LX5ab+WNL7KpEmdgYKdwDAaFVX/YtDc8iYrwuXHZUjSv6+g6N2iZYtL/eZu8w4vLOFPBXZhKSGQdAKJrX0tEtZ6YwmthCE1EUaRDlOTSpFq8nsRYw2kHLC+LBrUM7Dns4ZdNwe0M6BSGELByc2HAC2696oZSBV2XlqE0qGnk3W+zSYo3UHDEjc8HcdE6nA4fNkewd7jBqK5knaix9PrgLd9z6WTRMtnMpGQzKzfbePGTLF65J56OzYudgKrEGmheP4Q1f6nCMsys4/BxD58tzuOzxXnodTCgk4X7hsUW21OH63nuzgQycjzsPORCp4NmdUO0Fb5LlUiLnitah/HtL2p2otgoPaOviWbSrGx0Osgt8GIxK9SuZuSVz7L4baudp0fHXbJC2s+yDTbiovT07xg896Mj9FzZJZzvVhVw37CYM3aKOhlSWEpOSUq6mze+zGZAZwsPDY9Fr1Ow2n38vL5Aq2M0qIbw/zVsGZuxHf+teAXSFpiq0JLYiZDIugAYwy9DHxKjrToai5xnTOG1MFqqB6QqNEc3PN+XIpGcE45leqmWYCjVVKY8Tj17Dqsn+0rUH9jRwo/rbSz9vbDUc27sW7rDg0Gv8NrYRB56K43dR9yYDGq7WbnB3kEGvboSGhuh54sf83F54K4h0afX6XPIn7sc1Kxi4PJmoYHHdzp4b56atWbbXidzX63OgWNu/tqjOu3kWX2MuS4mwPPbT0KMgYSYyiUL7hwczX1vpvF/rx/nht4RNKsbwqCuFn5cp+Zc1ylw98Q0YiJ0PD4qll6lBCm/1MjO91Iz0VDmNnrtJCOFDoHTJbQYpBVJ5ZpBknPCot+shIfquH9YrPYmt/i3Ao5n+YiL0pGV56uUotLryivatg7cuq7W/hV1Oxpw5PxDzv5vAs4rmaowYMu6/gji5La15D/I33uduDwQH60js4R4q5OkI8+mOt+cCp0OurcOZeUfdu3YfTfFMHJgJG9+lcW/B1243WA2ga0o3KQ/BE9pREfoibDoaVJHR+cWoXi9qoj8eFEeigJjroti7vIC2jcJpU0jM9O+ySEuSs/c5flc3zOiXE4lx7M82Ow+qsQatO3ys8VRFDvzxG3+b1fkU7+Gkau6WPhrjxOXR1C/pon6NU10axPG8KeP8fN6G9f1jCij5cpF1TgD08ZV4aOFuXz4Xa6WejO5lpH2TUKJi9ITH6WnQ9PQUsX2pUhclJ4tux14vKLUjEn7U9xYQhVCTOfmeqWwlJySHQecdGhqDvij+/43KwJ49JY4Hnsno+yTL3L8qQqNYdXQFzm75B5aRMb2d/G580s9x1VwUBOWoXGtiak3vCjftboCqTfHlxpaQ1EujRhkEsmJgcLPFIMeXhubyGufZ5Geq66iZOaqXtJ6Hew/VragNJsUnG5B3WoG9qV4iA7XseOAK6B/v2wq5Kou4Ux6oKp23luzs/lhnRXhg4aXndwR5PBxN8P6RDCiv7qy+dZXWfgE3Dc0mmt7RLL7sJqlZtxIC4kxBu6flIZBDyv/sJUazPtE1v9t5/Mf8th1SO230aAGEb9jSDQJ0Wf39VurqpG12+xY7T7CQ4ufLVt2Obl1UBRbdjlJjFVDA/lJiDbQqkEIW3Y5/jPCElRx+czoePKsXjJyvFhCdSTFV17507+jhbm/FLBkrZXB3QLHOTvPyw/rrPS/3HJOtsFBCkvJaaDTKUErkjkFPkJMCrN/zr0gfSovHmcOjpwdWrYZd9EqpD9VYfWOkwiv0hkAnT5UE5WG0Col0hSq/5ZMSRgW15KwuJbn/4IkknNIRYUM83hhwocZRBQJny27i0XlyIGR5BX4+GOXQ0utl1zTwO4jHiY9kEiTOibmr7Ly9c9qbMnsfB/1axhIz1b7F2XR8cH8HGpWMdCivur0lpbt5qf1NrxeNa5uWcHE/ZhNCjkFxeJ29VZ1NTS5lmqWkpOveoyDGqal4WUmDqa6yS049Qrrz+utTJyZTatkNV94fLQal3DeigLuezONaeOqnJW4HNjJwswleXyyKJf7hsVoL7M+IcjK9bB8k41RV0YFveQa9EqAOcF/iahw/SVvN3o61K1uYlDXcKbNySEty8OVXcKJCNOxaYeDz5bkYdAr3Njv1C9GZ4oUlpJT0q6Rmdk/51NQ6COiaBsnzKyQk+9j6+6yc8ieT4QQeB2ZJbyuDxNZo79mq2hL38DxP58v9VydMQKfuzifsCWxA5f1+EIN2WM4/3ljJZLzyYn20f6sNBWBooC7KKxP4Gcq/P63g4Opbnw+QdU4PcezvBxJ9xBp0fHO3BzuHBylxZrs2MzM+n8cZOf76NrKzKFUD4fTPMRF6XhwcjqNLjNiNOj496BT2+o8nYDSV7QOY/lGG/8bFEWYWYfd4UNRVK/aSIuOP3c5eHhEsedsbKSeXYdcJJwitV6hw8eUOTn072jh0VuKnWSa1Amhd7sw7nntOJ9+n8ejt8SV844WEx9tYOywGKZ8ncP+FDdXdgknMkxHVLie+ausNKlj4vpegatVeVYvW3Y7uPWqMwu2Lbl0eOCmGGIidMxfWcDXy4r9Ido2MvPQiNizXjE/GVJYSk7JVV3CmbM8nxdmZDLhjnjCQ9WwGtn55z8heMnMMI683WTv+UKzgRRee0BdY1iSJixDIuoQEllfXXW0FGeaMYXXQm8KfMjqTVFBxySSysqJuxEVuZolBLhOePesGqenU7NQPF5oUd/EvwddWhBxg17BZveRb/Px5HuZhIcqDOxs4ViGu6gcxg6LRadTuO25Y5pt5s5DanloiIJeJ2hQy0S3VqdOl3hdjwiWrrPyxPQMHrk5lmoJRgoKvXz1Uz5zfymgapyB3u3Vdrw+wdbdDnQ6TungsWJzIU63YPTVwSuGCTEGrusRwcwf8xk7NKZcQdJPZHC3CBJjDMz+OZ+Jn6txPi1mBSGgU/NQQkqYL7ncgjdnZaPXKWeVyUZyaaDXKfzv6mhu6hup2drWqWYsNWtTRSOFpeSUxEbpeXFMAs98kMGwJ1JIjNVrW1fnAp/XiduWogpGW/G2tct2mPiGdxBd5zoAhNdJQcry4hMVfUCqwpCoBlqRObohtXt+cc76LJFcCvhtE0+0oVTD0igUFAarSrMJHK7i8w0nxKQ0GWHivQk88nYGej00q2si+bIQvlleQM+2oaRkeNhz2K19Xnq2lx9/t5EYo+dw0XMkIVpHRq7q3LI/xU2DmkaiI3Rs3e3kx3U2FAVqVTEw+cEqxBY5zXw6IYlXPssKCGPkdAt6tAlTo1ecRmDppHgDr96byHMfZXDb86nEROrIKXphNhrgziHRFNh87DvqZto32didght6hWs7N2WRku6malzZHtbN6ofgcqtpF2tVPTvb607NQ+nUPJQ8qxeXRxATruOT7/OYsTCPlX8U0ql5KHanYMVmG9ZCH8/dmUBMROXfDpaohJp1dGweeuqKFYgUlpLTonVDMzOfq8a3v+QHLKufKSVTFRrMiVpIHnv2dg7/didlWXm5bEe0300RdUhoOhaTpcj+0VJdpiqUVFr0CnjPYjXRoFe3pru1CuV4tldbJVQAr08NKp4Qo8da6MXuhD7tw2haN4Rcq48qsXqSa5mY+0sByzfatDYTY/S8fl8i+4+pwtHjhbgYA5aiECaR4Xo6NgvjtZlZ6HVQs4qBOtWM1Eg08tVPajDqR2+Jo2WDEF79LItftxTSv6OFn9bbuHNIFDf0imTGwlxSMjy8MCZBE5WgbgVPfrAKh9Pc7NjvRKdTaNUghMRyZhNpVi+Er16szuothfy9z8Hav+wUFPqIidAx/dtcpn+bq9Vt1SCE/7vh1EGlw8N05BV4cbp8hJiChWN6kWmAJbTiHPpK2g7edW0MbRqZWfirlR/X2TAa1ODY1/aIOKep/CQSkMJSUg5iI/VER+jR69QvotPF67ZScGyFlmXGZTsSkKowpv4IEpveB6jb1yDQGSxFDjO1glIV+tEbw4mtf3NFXqJEctESYYHcIlPgsry2I8IU6lQ3sm2PK6js46ercv+kNI6me9l1uLj85oERfPtLAfNeq8FLn2SSZ/Oy+7CL1VsL+b8bYogusbr12Kg42jYK4ZXP1Kwvep26o7Fso434KD2ZeV5+2ViIoqhCduk6Gwt/tdK6YQhbdjkRAowGHXFRenw+ePuhKpoQHH9LHJZQHUvWWvEJ+OA71WnnsiQjkx5IpFbV0gVRrSrGsxZLJqNCnw4W+nSwMOZaHzMW5vLT+mIBHRWu48a+EQzrfXoOD93bhPHxojx+3mDj6isC7Ry9PsGCXwtoUT/krPJgn4p2jUNp1/j8rlRJJCCF5XkhLS2NN954g927d+Pz+ejduzf33nsvJlPF5UQ9X2TnezEaFSKMCrlWVV2qqQqPFolGdds6NLYFMf4ta4+dtK2vBrWl6IwYLTXQG4sf1vqQGOr1X4I+JKbUkD0SSWUmNETB7lQlo8kYaJ+Ya0UL5B0aoqOgMPjtrqBQsG2Pi4gwHTERCofTivesb3vhOEJAvs1FtXg9xzK9JETraVHfzJc/FnAw1cWG7XYGdQ0nJ9/H0XQP1z+WQo0qBkb0j6Rjs1B+Wm/jk+/z6NzczOadDrLyvPzvhVRqVjFQUOgjPFSh0CEIM4Pbo1AtwcD4kbFk53vZssvJwVQP1/WMZOchJ/VrmgJWF40GhQduiuWWK6OY+UMei1ZbeenueDo1Dz2vz4Iws477b4zljsHRHElzY9Ar1E4yBm2te72CnAIvISZd0NZ4jUQj/S638M7cHHw+6N/JgtmkIyXDzYyFeew86OL1+xLP2zVJJOcTKSzPMSkpKYwaNYp+/frx5ptvYrfbufXWW9m1axfTp09Hp7s0Yhu6XC5sNjVNlMslcNvzObzuMVzWI3idmUH1hdepCUu9OR5LlS7F9o/hNTFaamEMqxKUqlBRFAzmc5O/VCK52PGLSlC3pkvSpYWZ1Ewv+4+5g0RlyQw20eEKIwdG8ce/dk1YGg1wz/UxOJyCDxfkcjxLPX5j3wgyctTf352bg06nxqgNDdGh06mBlo+kebS81DoFBnS2cP+wWKZ+k83v2+y0bRTCqj8LcbnVVVSfgG6tLfywzsYjN8fSqHYIT7ybjtmkYDRA7/Zh7Dvqwu4sfdsjNlKvxZ9slWy+YC+YYWYdDS8LTthsd/r4amk+S9ZatZfrFvVDGNE/kg5Ni1cIHx4Ri6LA1G9y+GBBLpFhOjJyvYSH6nj69njaNJIRJySVEykszzFPPvkkTqeThx9+GIDQ0FDGjx/PyJEj+fLLLxk1atQF7mExQgiOHTvG/v37g36OHDnCwIEDefX16bw/PxevYsGRsx3hK4pLZ4opSlOoblubYxpr7SqKQo2Ob16oy5JILkr8qQIBoiN0QbERPV51y7t6op6j6V627XVRO8lIXJSerDwvnZqbubZHOBM+zMThhHaNQ/D54J99Tt6Zm0NMhE4LJeT2wOdL8jQvYb/n99bddrbsdqEosC/Fhc8HXVqG4XD5yMn38f7jVdl1yMn3v1n5YZ2NMddGM7SPusMwvF8kv22xc+CYm1fuSWDOsnx2HHBSJUbPD+tsRIQprNtWyNQ52ew+7MZogFfHJBIaoqND01AWrrby7wEnjesEijchBD/9bqN5vZCz8pg+F9idPsZNSefAMTcDO1to11h1mlmy1srj72YwfmSs5nFtMio8NiqOW66MYvWWQgodPmokGuneOrRUu0uJpLIgheU5ZM2aNaxfv54bb7wRo7HYBqh9+/ZERUUxffp0brrppvO+JZ6dna0JxpCQEAYPHgyAz+eja9euuFzB9lkAx44dIzZKz+VN1Zhy1dq9hN4cq2abMZ27YKsSyfnmxNiO5wJPCc9qv6isW93A/hQPOkVd/XtmdBzb9jpRcNCtTRhp2R6SLzPRp32Ytpp25+Bopn2Ty+HjHq7vFcF1vSLYe9jF92usWMw6bhsUxYcLcskrWl2Lj9bTqZmZ79fY2LTDhdMtGNTVQmykntk/59OjTSgvfJzFQ8PVnYOGl4XQ8LIQsvO9LNtk04Rl9QTV9vHlz7IYNzVDu2c2hxeTURWvs38qQKB6ij9+WzzVirKddGhqpnaSkZc+zeKlu+OpU019BjpdPj5bnMe2vU5evDv+3A7AGfDN8gL2p7h5++HEgNXMfpdbmDw7m7e/zqZT89AAu9Rq8QZu6iufj5L/DlJYnkOWLl0KQOPGjYPKmjVrxtq1a1mzZg29evU6p/1477332LVrlyYmc3JytLIWLVpowlKv11OvXj1cLhd169YN+qlSpQoA9aobWf+Pg/CkK85pvyWS80WIEZxF9oyxkTrsTl+AsFQU9SfKogvI1FIRxEYWx4Tdn6J+aGyUvshRxkxWnpclaz1c3zMiQLD4yS3wEWKExnVC+PC7XLw+dbWsZ9swRl0ZRVK8gf4dLbzxZRYrNtux2X1s2K4m03a6Bcm1jLRpaGb+KiuRFh0vf5pFhyZmBnQKjNXYpWUYk2Zl4/UKzd6wfk0TnzxdlW17new76sZkhPBQHVt2O7HZfVRPMDCwczhV4wK/avQ6hVf+L4HH3knn9peO07SuiehwNTON1e7jnuuj6dLi1HEozyc+n2DxGiv9OlqCtsh1OoU7B0ezbIONpettUkhK/tNIYXkOWbduHQDVqlULKvMf27p16xkLS4/Hw5EjR4K2rcPDw/n444+1el999RX79+8PODcpKYm6devSrFmzgOPLli07pU3T6aQzk0guJCUjF5QVu7EkzhJOMn6RF2ZWUACbQyCEasN4KlGZGKOnW2sz365QPYpNRtUu0VH6JgA6Rf08owHqVDORnqOuVr7/WFUttE6fDhY+WpjHZ4vzeOCmQKe241keFq62MqBTOA/cFIvd6cNa6CPSogvYbg0x6Xh6dAK3DXKzekshdqfgsqpGPF7B/JUFvPCxGlzboIdbr4piWJ9IDCc4qzicPnRFArskiqLQsoGZlg2KbQZ7tD15AHFQ8zd/9GQSv20tZM1fdpwuH1d1sXBV13CqJ1x8IXFsDkFWnpfWDUu3jYwK19OgpolDqRdHNjKJ5EKhCCEqMM+CxI/P56Np06b4fD7mz59P06ZNA8onTpzIp59+yoABA5gyZUrQ+b179y6z7aNHj2qfURo6nY4aNYrD8hQUFCCEwGAwYDQaMRgMZ2UQn1Pgw1GG4b1EUpHodMFOLCerp4a5UXB7hKYkSx5TFNWjutDhK7N9nQ4tx7THK3C6hCam/GLL7RUgis+PCNNhDtFh0EOhQ5Bn9aLTKfiE0NRsoJ+e2jmfIEDtmoyKFtKrJP42Q0wKYWYdep262mizq32Lj9ajOwsfF58P7C4f+VYfCTEGDKVEwcnM9aLTqc41/0WEgOPZHqIsesLMpd/s9BwvZpNCpEXaUEoqH6mpqej1ev7++++T1pMrlueI3NxcTfiFhgbHEvMfs1qtQWWnS61atU6rXkRExKkrlYOYCB1EyAfnhSI1NRVQV50lZ0ZU+Lmbv2FmhTBzxT5aS7ZZcvzDKyhMoU4HFrMOy0mcZeKj/5uC0o+iQFLcycc18RQ5xCsC+ff/3+ZCjr/BYDgtnxApLM8RbnfxdkhJxx0/Xq+3zDKAX375pcy2/auZJ6sjqbzI8f9vI8f/v40c//82l8L4y2Wnc0R4eLj2e2le1na7HYCYmJjz1ieJRCKRSCSSc4kUlucIi8VCQkICQIAXtp/c3FxAbmdIJBKJRCKpPEhheQ5p164dUOxsU5KUlBQAOnfufF77JJFIJBKJRHKukMLyHDJo0CAAtm3bFnDc5/Oxe/duYmNjadWq1QXomUQikUgkEknFI4XlOaR37940bdqUFStWaM46AOvXr6egoIC77roLg0H6T0kkEolEIqkcSGF5DlEUhYkTJ2K1WrWA5dnZ2bz66qt07979osoTLpFIJBKJRHK2yADp54Hdu3fz2muvkZmZiaIoDB48mFtuuUWuVkokEolEIqlUSGEpkUgkEolEIqkQ5Fa4RCKRSCQSiaRCkMJSIpFIJBKJRFIhSGEpkUgkEolEIqkQpLCUSCQSiUQikVQIUlhKJBKJRCKRSCoEGe/mEiMtLY033niD3bt34/P56N27N/feey8mk+lCd01yGhw/fpwZM2Zw8OBBZsyYUWa9hQsX8vnnnyOEwGKx8Oijj9KiRYtS65Z3TpSnbcnZk56ezptvvsmvv/6K3W6nfv363H777Vx11VWl1pdjX/koLCxk2rRp/PTTT2RmZtKoUSPGjRtHhw4dSq0v50DlJTc3l0GDBtG1a1cmTpwYVF4pxl5ILhmOHj0qevXqJSZOnCiEEKKwsFAMHTpUjBkzRni93gvcO8nJyMrKElOmTBEdOnQQycnJYuTIkWXWfeedd0Tnzp3FwYMHhRBCLF26VDRv3lxs3LgxqG5550R52pacPVarVfTp00c0atRIdOvWTTRu3FgkJyeL5ORkMW3atKD6cuwrHx6PR4wePVo0b95cXHHFFaJhw4YiOTlZNGvWTOzatSuovpwDlZsHH3xQJCcni8ceeyyorLKMvRSWlxCjRo0SXbp0ES6XSzu2ceNGkZycLD7//PML2DPJqXA6ncLr9YqVK1eeVFj6x/OLL74IOH7LLbeI7t27C6vVGnC8PHOivG1Lzp6JEyeKBx98UGRkZAghhCgoKBDjxo0TycnJolGjRmLfvn1aXTn2lZM5c+aIl156SbvHe/fuFX369BHJycniySefDKgr50DlZsmSJaJXr16lCsvKNPbSxvISYc2aNaxfv55evXphNBq14+3btycqKorp06fjcrkuYA8lJ8NkMqHT6ahdu/ZJ602aNAmAfv36BRzv06cPqampzJ07VztW3jlRnrYlFYN/Gzw+Ph6A8PBwXn31VZKTk/H5fKxZs0arK8e+chISEsJTTz2FxWIBoF69ejz66KMAZGVlBdSVc6DykpaWxvTp07WxP5HKNPZSWF4iLF26FIDGjRsHlTVr1oycnJyALynJxcnJ0nimpaWxZcsW4uPjSUxMDChr1qwZAIsWLdKOlWdOlLdtydnjcrm444470Ov1AccNBgP9+/cHwOPxAHLsKzODBw8OOla3bl0ALr/8cu2YnAOVm6effprx48cTHR0dVFbZxl4Ky0uEdevWAVCtWrWgMv+xrVu3ns8uSSoY/xgnJSUFlVWvXh2AnTt3YrfbA+qfzpwob9uSs8dkMpX64AeIi4sDoEGDBoAc+/8a+/bto379+gwfPlw7JudA5WX27NlUq1aN7t27l1pe2cZeCstLAJ/PR2pqKoC2pVaS8PBwAA4dOnRe+yWpWI4ePQqUPsb+bTSv10tKSkq550R52pacew4cOEBCQgIdO3YE5Nj/l7BarcyePZsPP/wQs9msHZdzoHJy+PBh5syZw2OPPVZmnco29lJYXgLk5ubi8/kACA0NDSr3H7Naree1X5KKJScnB4CwsLCgspLHCgoKyj0nytO25Nzi9XpZvnw599xzj2YfJce+8lNQUMCiRYu49tprWbduHS+++CK5ublauZwDlQ+fz8dTTz3Fc889V+q991PZxl4Ky0sAt9ut/V7SUNeP1+sts0xy6eAf59LG0W+L5y8v75woT9uSc8uCBQtITEwM2AaVY1/5SUlJobCwkDp16gCwcuVKxowZgxACkHOgMjJjxgzatWtHq1atTlqvso29FJaXAP6lbaBUz2+/bURMTMx565Ok4vGPc2lj7HA4tN9jYmLKPSfK07bk3JGVlcUnn3zC5MmT0emKH79y7Cs/jRo14qabbuLDDz/ks88+w2w2s3XrVtauXQvIOVDZ2LlzJytWrODee+89Zd3KNvZSWF4CWCwWEhISgOJl7ZL4t1NKM86VXDr4QxGdbIyNRiPx8fHlnhPlaVtybvB6vTz11FO8+OKLQUb3cuz/W3Tq1InbbrsNgF27dgFyDlQ2Zs6cyZYtW2jatCkNGzbUfkaNGgXAd999R8OGDXn88ccr3dhLYXmJ0K5dO6DYELckfqPbzp07n9c+SSoW/xgfOXIkqMw/xm3btiUkJCSg/unMifK2Lal4XnnlFW644QbatGkTVCbH/r9H7969AbT0e3IOVC7i4uKoU6dO0I9f8IWHh1OnTh0SEhIq3dhLYXmJMGjQIAC2bdsWcNzn87F7925iY2NPacchubipV68ejRs35ujRo2RnZweU/fvvvwD06tVLO1aeOVHetiUVy7vvvktycjJ9+vQJKrPb7XLs/4P4bdr8X/xyDlQuHnnkEZYuXRr089prrwHQt29fli5dyiOPPFLpxl4Ky0uE3r1707RpU1asWKEZ5wKsX7+egoIC7rrrrpMG35ZcHPi9+UqOYUnGjh0LwPLlywOOL1u2jISEBG644QbtWHnnRHnallQcH3/8MUajkRtvvDHguMvlYvLkyezYsQOQY/9fY/Xq1fTs2TMg1qmcA/9dKtXYV2iCSMk5ZdeuXaJt27bigw8+EEIIkZWVJQYNGiTuvPNO4fF4LnDvJKfDqlWrRHJysujWrZsoLCwstc6TTz4pevbsqeWX/uqrr0SLFi3EunXrguqWd06Up23J2fP++++LJk2aiP79+wf89OnTR7Rq1Up0795d+Hw+rb4c+8pFYWGhuPXWW8V9990ntm3bph1ftmyZuPXWW0VOTk7QOXIOVG7Wr19faq5wISrP2CtCFMU6kFwS7N69m9dee43MzEwURWHw4MHccsstcrXyIqegoIARI0Zw4MABLfxDTEwMN9xwA+PGjQuo6/P5mDFjBgsXLsRkMlG1alUeeughkpOTS227PHOivG1LzpzZs2fz3HPPnbTO6NGjAwIny7GvXPh8Pp544glWrVqF1WqlYcOG1K9fn/bt23PttddWyDjJOXBpsWHDBkaNGsW1117LxIkTA8oqy9hLYSmRSCQSiUQiqRCkjaVEIpFIJBKJpEKQwlIikUgkEolEUiFIYSmRSCQSiUQiqRCksJRIJBKJRCKRVAhSWEokEolEIpFIKgQpLCUSiUQikUgkFYIUlhKJRCKRSCSSCkEKS4lEIpFIJBJJhSCFpUQikUgkEomkQpDCUiKRXPTs3buXZ599ltatWweVvfPOO7Rv3541a9ZcgJ5dfOzcuZMJEyaUeq8k5SM7O5v33nuPbt26sWHDhoCyNWvW0L59e955550L1DuJ5OJEJpiWSP7jTJ48ma+++oqCgoKA4zqdDovFQmxsLM2bN+e6666jS5cu571/n332GfPnz2fXrl2llv/444/k5+ezYsUKunbtel76tGrVKtasWcO8efMoLCwEICIiIqCOw+HQ8sK3b9+eL7/88pz3a+bMmSxevJi//vrrtM+ZPXs227ZtY+HChXi9XvR6PWFhYQF1fD4fdrsdn89HUlISq1atquCeX3z8+uuvzJw5s8wXll9++YX8/HyWLl3K2LFjz3PvJJKLF5krXCKR4PP5eO6555gzZw4Ao0ePpkqVKmRlZfHzzz9z8OBBAIYOHcoLL7yATnd+NzuOHz9O9+7dAYIE5oIFC5g9ezbPPvssTZo0OavP2bRpE+3btz/t+nPmzGHChAkAbN++HYMh8F19//79vPzyy+Tk5DB//vyz6tvpkpGRoQnsssR4aTz66KMsXLiQNm3aMHv27KByl8vFt99+y9tvv83GjRsrrL8XO3379uXw4cPMnDmTyy+/XDu+Y8cOnn/+eYYPH86QIUOCzivvXJJIKgtyK1wikaDT6ejdu7f2/0ceeYTbbruNRx55hMWLF3PllVcCMHfuXL766qvz3r+4uLgyy4YMGcKcOXPOWlQWFhby2muvleucmjVrnrS8bt26TJkyJWgF8FwSExNzRudVq1btpOUmk4kRI0bQvHnzM2r/UqWsudekSRPmzJlTqqj0+Xy88MIL57hnEsnFiRSWEokEALPZXOpxo9HIhAkTMJlMABdEWBqNxnP+Ga+88grZ2dnlOkev15+yTnh4OHfccceZdqvcnLhqerqczrUADB8+HI/Hc0afcSlyJnPvgw8+YPfu3eegNxLJxY8UlhKJ5JTExMTQoEEDAI4ePXqBe1PxTJs2jblz556z9nv06HHO2j7f9OnT54zF63+Bb7/9lilTplzobkgkFwwpLCUSyWmRn58PQEJCgnZs3759PP300wwYMABAs0O7+eabNacWgC1btnD33XczcOBAWrVqxdChQ1mxYkWpn2O323n77bcZMGAAvXv3pn///ixatKjUurm5uXz66af079+/TBvGH374gREjRtC3b1+6du3KHXfcwbZt27Tyl156ie+++w6AtLQ0+vbtS9++fZk1a1Y57k7pPP3009rvKSkpdOrUiYYNG9KwYUNuueUWrWzBggW0atVKKyvtWvbu3cvDDz/MoEGDaN26NVdffTXffvvtWffxdMnJyWHatGkBx+6++26tz/6fDz74AIAbbrhBOzZw4EDtHKfTyZw5c+jfvz8bNmwgJyeHcePG0a5dOzp37swrr7yC3W4vtQ8LFixg+PDh9OrVi7Zt23LPPfewZ8+egDp5eXlMmTKFtm3bApCVlcW4ceNo27Ytffr04aeffirzGufNm8d1111H37596d69O5MnTy51ddblcrFkyRJGjhzJE088oR3/6KOP+PDDD/G7Lvjn0hNPPHFGY2+z2fj888/p1asXGzZsYNeuXQwZMoTOnTvz+++/B4zNxIkTGTx4MB06dKBnz55MnjwZp9NZ5rVKJOcMIZFIJEKI9evXi+TkZJGcnCzcbndA2ebNm0XDhg1FcnKyeOutt4QQQjz99NOiRYsWIjk5WfTs2VMsWrRItG3bVmtj3bp1Qggh5s2bJwYOHCj27NkjhBBi9+7domfPnqJhw4Zi3rx5AZ+Tn58vhgwZIoYMGSJSUlKEEEL89ddfonPnzlq7fjZt2iTGjh2r9evEtoQQ4plnnhFXXnmlOHDggBBCiMOHD4tWrVqJpk2bav0TQogjR45o11FR9+y7774TI0eODDpn8uTJIjk5OajMbreLQYMGlXotq1evFj179hSbNm0SQgiRmpoqBg8eLJKTk8W0adOCPuPEe3U6TJ06VSQnJ4ubbropqMzhcIiJEyeKqVOnBpWtXr1atGzZUiQnJ4vRo0drx1NTU0VycrKYMmWK8Hq9Qgghli9fLq666iqtf6tWrRLXXHON6Ny5s2jevLl2/Pbbbxc+ny/gc5544gkxZswYkZubK4QQ4qeffhJNmzYVbdq0ETt37hRCCPHFF1+Ibt26ae0cO3ZM9OrVS3Tp0kXrY5MmTcShQ4eCruPJJ58U7du31+5xWlqauPHGG7W21q9fL4QQIj09XTz22GOiffv2Ijk5WTz22GNBbZV1/8sz9j/88IPo16+f1taPP/4o+vXrJ5o0aSKSk5PFuHHjhBBCpKSkiD59+og5c+YIn88nCgsLxaOPPiqSk5PFrbfeKjweT1A/JJJziVyxlEgkQfhXJ10uF8uXL+eBBx5ACEGHDh24++67AXjxxRd59913AdXx5Y8//mDDhg08++yz3HDDDbRp04Z9+/YxYcIEXn31VerXrw9AgwYNGD9+PEIIXnrppYAwR8888wx79uxh6tSpmjNJixYtePjhh4P62K5dO6ZNm0bHjh1LvYZZs2YxZ84cXn75ZWrXrg2ozjZt2rTB7Xbz+eefV8zNKmLQoEEMGDCAAQMG0KFDBx577LFS65XVX7PZTKNGjYKO+1f0/Kt6AFWrVtWcQ959910OHTpUQVehejv7r8O/aty6dWs++eSTUutfccUVPPfccwBs3LiR/fv3A/Dll19y3XXXcf/992tRBHr37s2CBQu0sZ0xYwZjxoxh7dq1bN68mWHDhgHw22+/8cMPP2ifMW/ePFatWsWbb75JVFQUAP369WPo0KFYrVZefPFFAIYNG8Znn32mnffGG2/w+uuvs2bNGtauXUuDBg3weDwsXbo04Bq++uorvv32W5566intHicmJvL666+jKEpA3YSEBCZOnMhtt91W3ltbrrEfOHAgS5YsISkpCYAvvviCjz/+mLlz5zJo0CBuvvlmQPXm79y5M8OGDUNRFEJDQ3nhhReIjo7m999/Z8GCBeXup0RyNkhDGYlEEsRLL73E8ePHOX78OEIIGjRowH333cf1118fYF/n94p2Op3cf//96PV6RowYoZV/+eWXxMXF0bJly4D2GzZsCKhbfWvWrGHgwIFs27aNH3/8kZ49ewZ5W5f0WD+R0rx2vV4v06dPp0GDBrRq1Sqg7M4778Tr9XLVVVed3s04TRYvXqzdGyEEn3/+Ob/88ktQvZOFairNgWbevHnYbLage+AXIj6fj2XLllWYg1CTJk2Cwg1lZ2czfvz4Ms8ZMmQIq1evZsmSJTzzzDM8/PDD/Pbbb1r4qpIYDAaqVavGsWPHGDx4sBZxwGQy8fzzz/P333/z77//8t1332lj9Mknn9ClSxfCw8MD2vLPo02bNpGVlUVcXBzVq1fXyp9++mliY2MBsFgsDBw4kD179nDs2DGtjsPhYMqUKURHRwfNiVq1atGgQYNSHXHi4+PLvB9lUd6xNxgMJCUlkZqaypVXXkmNGjUAmDRpEgDbtm1j06ZN3HvvvQHnhYSEULt2bbZu3cpPP/3E9ddfX+6+SiRnihSWEokkiNdff/20HDT8X4YxMTHaF3hJ1q9fT25urmaD6UcIQXR0NACZmZkAmp1jixYtgtrx1y2N0rx2t2/fTmZmZpCoBHXVqKyVo4pCURRuvfXWcgUqL4v169cjhGDw4MFBZf77kpWVddafczJiY2N56KGHWLlyZZl1nn32WTZv3szmzZsZO3YsM2fOLDPSgF9gnfgCodPptFip27dvB9T5sXfvXjIzM4Pmkcvl0u7B8ePHiYuLC5gPJ87JxMREgADbw5UrV5Kbm0unTp1KnfNlzb3z5cDk/xz/in9J1q9fD6gr/Sf2x2q1Eh0dre0+SCTnCyksJRLJOSM1NZXk5OTT8rj++++/gTOPw1gSv+e61+s967bOFEVRtBWmsyE1NZWoqKig7dvzTf369U+acScqKopXX32V0aNH43Q6zzh2Z7NmzQA0Ewn/6uINN9xw0lVTPyduXZfE/yIkSuQFqch5d75JTU0F1PBG9erVu8C9kUhUpI2lRCI5Z3g8Hg4fPhzwRV4W/pWVioiR6P+8I0eOnHVbZ8Mjjzxy1m14PB6ys7Mv+MqT2Ww+ZerCw4cPEx8fj81m4/HHHz+tcT8Rvw2lxWIBil8O/NmfKpqKnHfnG/+9OXDgwAXuiURSjBSWEonknJGYmEhubi5r164ttdzhcGjbxX5BUdL+7UypUqUKoIboKStQ9fz58/H5fGf9WeXlZCtqpZGYmIgQgh9//LHUciEEGzZsqIiunTaZmZnk5uYGHNuxYwfffPMN33//PbVr12bjxo1n5CDlD1Jft25doDi81bp168jLyyv1nD179pyxOYB/3qWkpJzR+eWhvGN/Kvz3pqy5AQSEJZJIzgdSWEokknOG38P2xRdfJCcnJ6j8o48+0lZd/PaQ69atO2mbp7MK1qxZM20rdvr06UHlx48fZ+3atZqtX0V84Z/u6pzf7vBEYQbF279ut1s75r+Hb731VqkrsN999x3p6enl7e5ZMWnSpABbRqvVyuOPP87rr79ObGwsr732Gnq9nsmTJ7Nv374y2yl5nX4OHz4MQM+ePQGoUaMGSUlJFBYWMmHChCDzBq/Xy9tvv01ERMQZXUvr1q0BNa/6yTIvncnq64mUd+xPhX9uLFmyhOXLlweV79+/v1QHMonkXCKFpUQiAQgIaF7y95Ph/7ItK6D1Lbfcgk6n4+DBgwwdOpTvv/+e1NRU9uzZwxtvvMHq1as1QXnTTTdhNBrZuXNnqV+SfhwOR8D//V/EJbcyzWYzw4cPB9TVnOeff560tDTsdju//voro0aN4rrrrguoD5Q7oHTJvpzuPfN7Le/du5eNGzcCqtCYMGGCFuzbH7IH4MYbbyQsLIycnBxuvPFGZs+ezZEjRzhw4AAfffQRH3zwAb169dLqlxQm5REp/vt3KgG1YsUKrFartlUthODxxx9n5MiRWnamVq1aceutt+J0Ohk/fjwul6vUtkoTnbNnzyYuLo4bb7xRO+YP7bN06VL+97//8fvvv5OWlsaff/7JvffeS61atbSUoyXHsKzt7ZL3pXv37tSoUQOPx8N7771X5nWfODf8bZR2j8uaT+Ude0BbVT9x3oPqiNa4cWOEEDz44INMnjyZPXv2kJKSwuLFi7nzzjsD5rlEcj6QwlIikeDz+QIyknz//fendZ7fKzU7O7tUx47mzZvz6KOPAqq947hx4+jRoweDBg3im2++4fXXX9dWDevVq6dlMXnssce0zDwOh4NXXnlFa3Px4sX8888/gCpo/Vl0TtwOfuCBB2jTpg2gxins1q0brVq14q677qJPnz506dJFqxsbG0tcXBxZWVkcOnSIzMzMU27j+nw+fv75Z+3/CxcuPMXdUomLi6Nt27b4fD5GjRpFjx49uOKKK2jWrJnW388//5z/+7//Izs7m6pVq/Lyyy9jMBjIysriueeeo0+fPgwYMICpU6fy8ssvayIPYPPmzdrv5dki9zuCpKamlioE/XNk3Lhx9OvXD1CF22uvvcaaNWu4+uqrA+r7Q/ds376dZ599tlQB9v7777Np0yZAFWhvv/02//77L5MmTdK2qAFGjRpFnz59tGu67bbb6NatG8OHDyc9PZ2HHnpIq/vnn39qv2/ZsiXg83bs2AHAP//8owk1o9HI5MmTCQsLY+bMmbz//vu43W6EEMyfP1+bX8uWLWPLli3aiqlfGP7zzz9BL1Z+D+4///wTu92uCdbyjr3fIx7U+XXiaq2iKEyaNIm4uDjcbjcffPABgwYNolevXjzyyCMMHz6cJk2aBN13ieRcooiKWN+XSCSXLJMnT+bLL7/EZrMFHI+Li+Ohhx5i6NChpZ43dOjQgNSIAJdffjkzZ84Mqvvrr7/y4Ycfsn37dgwGA506deKhhx7S7OhK8ssvv/Dee++xa9cuatSoQcuWLRk5ciQ33XQTrVu3pnPnznTr1o309HTGjRuH1WrVzo2NjWXRokWa7ZnD4eD9999n4cKFZGRkULduXW6//fZSQ/esXLmSZ599Fr1ez/XXX8/dd99dZkiZN954g6+//jrgs/2fP378+FOuEh0/fpwnn3ySP/74g5o1a3L//ffTr18/nnjiCdLT0xkzZgwdOnQIOGfLli288847bNmyBSEErVu35sEHHwwIz/TKK68wa9YsbaVOp9PRpUsXZsyYUWZffvjhB/7880++/PJLbbUyKioqQNh5PB5ycnKw2+2YTCZ+//13wsPDGTdunPYSEhkZqYnElJQU+vXrF7BiGB4ezrJly4iNjeWWW25h48aNTJgwgaVLl3L06FFcLhfNmzfn/vvvL1UMeb1evvjiC+bOncuhQ4eIjo5mwIABPPDAA9o2+BNPPBEgwPR6PR07dmTChAmMHDmSjIwMrT2LxcJbb71F9+7dAdVOc9KkSWzYsAGLxUKrVq0YNGgQs2bNIi8vjy5dutC5c2caN27MNddcE2DTabFYeP311zXxu23bNsaPH09+fj7XXHMN999/vyb+T3fsP/vsMyZNmhQg8iMiIpg1a5YWv9PP8ePHmTJlCr/++iv5+fnUr1+f0aNHc80115Q57hLJuUIKS4lEIpGcV/zC0p9bXiKRVB7kVrhEIpFIJBKJpEKQwlIikUgkEolEUiFIYSmRSCSS88rJPKolEsmljRSWEolEIjlvZGZmapliygqcL5FILl2k845EIpFIzgsTJkxg/vz5ASuVsbGxfPPNN9SsWfMC9kwikVQUUlhKJBKJRCKRSCoEuRUukUgkEolEIqkQpLCUSCQSiUQikVQIUlhKJBKJRCKRSCoEKSwlEolEIpFIJBWCFJYSiUQikUgkkgpBCkuJRCKRSCQSSYUghaVEIpFIJBKJpEKQwlIikUgkEolEUiFIYSmRSCQSiUQiqRD+H7fwq2VZFcxMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1,figsize=(7,7),dpi=100)\n",
    "plt.xlabel(\"Predict Fuel Expenditure\")\n",
    "plt.ylabel(\"Real Fuel Expenditure\")\n",
    "bbox = dict(boxstyle=\"round\", fc='1',alpha=0.5)\n",
    "\n",
    "plt.scatter(Y_pred,Y_test,marker='o',c='none',edgecolors='royalblue')\n",
    "t = \"MAE:\"+ str(MAE) + \"\\nRMSE:\" + str(RMSE) + \"\\n$R^2$:\"+ str(R2) \n",
    "plt.text(0.75, 0.85, t,\n",
    "         transform=ax.transAxes, size=10, bbox=bbox)\n",
    "\n",
    "x=np.arange(max(Y_pred))\n",
    "ax.plot(x,x,ls='--',c='k')\n",
    "ax.set(xlim=(0,max(Y_pred)),ylim=(0,max(Y_test)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "compact-eagle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Check the feature importances.\n",
    "\n",
    "feature_importance = gb_model.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "fig = plt.figure(figsize=(18, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, np.array(data_final[['HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR']].columns)[sorted_idx])\n",
    "plt.title('Feature Importance (MDI)')\n",
    "# print(np.array(data_final[['HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR']].columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n",
    "result = permutation_importance(gb_model, X_test, Y_test, n_repeats=10,\n",
    "                                random_state=42, n_jobs=2)\n",
    "sorted_idx = result.importances_mean.argsort()\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.boxplot(result.importances[sorted_idx].T,\n",
    "            vert=False, labels=np.array(data_final[['HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR']].columns)[sorted_idx])\n",
    "plt.title(\"Permutation Importance (test set)\")\n",
    "# print(np.array(data_final[['HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR']].columns)[sorted_idx],result.importances[sorted_idx])\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "described-detail",
   "metadata": {},
   "source": [
    "## Using the predition model and ACS data to predict the average fuel consumption per household in census tracts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ultimate-lincoln",
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>geometry</th>\n",
       "      <th>popden</th>\n",
       "      <th>Race</th>\n",
       "      <th>House tenure</th>\n",
       "      <th>Vehicles</th>\n",
       "      <th>travel time</th>\n",
       "      <th>education</th>\n",
       "      <th>GSCOST</th>\n",
       "      <th>urban/rural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1400000US01117030317</td>\n",
       "      <td>01117030317</td>\n",
       "      <td>POLYGON ((-86.852665 33.281478, -86.8491899999...</td>\n",
       "      <td>2820.152853</td>\n",
       "      <td>2.074399</td>\n",
       "      <td>1.096</td>\n",
       "      <td>1.833</td>\n",
       "      <td>31.526847</td>\n",
       "      <td>2.943818</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.2158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1400000US01119011500</td>\n",
       "      <td>01119011500</td>\n",
       "      <td>POLYGON ((-88.40378899999999 32.44977, -88.403...</td>\n",
       "      <td>16.609164</td>\n",
       "      <td>1.884195</td>\n",
       "      <td>1.330</td>\n",
       "      <td>1.529</td>\n",
       "      <td>27.380769</td>\n",
       "      <td>2.386957</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1400000US01121010900</td>\n",
       "      <td>01121010900</td>\n",
       "      <td>POLYGON ((-86.118971 33.42176, -86.11435299999...</td>\n",
       "      <td>1058.486943</td>\n",
       "      <td>1.616550</td>\n",
       "      <td>1.219</td>\n",
       "      <td>1.544</td>\n",
       "      <td>22.077963</td>\n",
       "      <td>2.685673</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.1757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1400000US01125010102</td>\n",
       "      <td>01125010102</td>\n",
       "      <td>POLYGON ((-87.60330399999999 33.359237, -87.60...</td>\n",
       "      <td>12.465127</td>\n",
       "      <td>1.072123</td>\n",
       "      <td>1.036</td>\n",
       "      <td>2.172</td>\n",
       "      <td>31.481144</td>\n",
       "      <td>2.775216</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.2249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1400000US01089000701</td>\n",
       "      <td>01089000701</td>\n",
       "      <td>POLYGON ((-86.61668899999999 34.771281, -86.60...</td>\n",
       "      <td>2424.142524</td>\n",
       "      <td>9.045601</td>\n",
       "      <td>1.550</td>\n",
       "      <td>1.471</td>\n",
       "      <td>20.245846</td>\n",
       "      <td>2.551775</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.1548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0              AFFGEOID        GEOID  \\\n",
       "0           0  1400000US01117030317  01117030317   \n",
       "1           1  1400000US01119011500  01119011500   \n",
       "2           2  1400000US01121010900  01121010900   \n",
       "3           3  1400000US01125010102  01125010102   \n",
       "4           4  1400000US01089000701  01089000701   \n",
       "\n",
       "                                            geometry       popden      Race  \\\n",
       "0  POLYGON ((-86.852665 33.281478, -86.8491899999...  2820.152853  2.074399   \n",
       "1  POLYGON ((-88.40378899999999 32.44977, -88.403...    16.609164  1.884195   \n",
       "2  POLYGON ((-86.118971 33.42176, -86.11435299999...  1058.486943  1.616550   \n",
       "3  POLYGON ((-87.60330399999999 33.359237, -87.60...    12.465127  1.072123   \n",
       "4  POLYGON ((-86.61668899999999 34.771281, -86.60...  2424.142524  9.045601   \n",
       "\n",
       "   House tenure  Vehicles  travel time  education    GSCOST  urban/rural  \n",
       "0         1.096     1.833    31.526847   2.943818  2.146052       1.2158  \n",
       "1         1.330     1.529    27.380769   2.386957  2.146052       2.0000  \n",
       "2         1.219     1.544    22.077963   2.685673  2.146052       1.1757  \n",
       "3         1.036     2.172    31.481144   2.775216  2.146052       1.2249  \n",
       "4         1.550     1.471    20.245846   2.551775  2.146052       1.1548  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pre_data = pd.read_excel('Predict_census tract.xlsx',dtype={\"GEOID\":str})\n",
    "#'HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR'\n",
    "Pre_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "traditional-daughter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>GEO_ID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1400000US01001020100</td>\n",
       "      <td>Census Tract 201, Autauga County, Alabama</td>\n",
       "      <td>60208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1400000US01001020200</td>\n",
       "      <td>Census Tract 202, Autauga County, Alabama</td>\n",
       "      <td>43958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1400000US01001020300</td>\n",
       "      <td>Census Tract 203, Autauga County, Alabama</td>\n",
       "      <td>55345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1400000US01001020400</td>\n",
       "      <td>Census Tract 204, Autauga County, Alabama</td>\n",
       "      <td>59663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1400000US01001020500</td>\n",
       "      <td>Census Tract 205, Autauga County, Alabama</td>\n",
       "      <td>66108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GEO_ID                                       NAME  income\n",
       "0  1400000US01001020100  Census Tract 201, Autauga County, Alabama   60208\n",
       "1  1400000US01001020200  Census Tract 202, Autauga County, Alabama   43958\n",
       "2  1400000US01001020300  Census Tract 203, Autauga County, Alabama   55345\n",
       "3  1400000US01001020400  Census Tract 204, Autauga County, Alabama   59663\n",
       "4  1400000US01001020500  Census Tract 205, Autauga County, Alabama   66108"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income = pd.read_csv('income.csv')\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "expensive-summary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 74001 entries, 0 to 74000\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   GEO_ID  74001 non-null  object\n",
      " 1   NAME    74001 non-null  object\n",
      " 2   income  74001 non-null  int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "income.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "sustainable-match",
   "metadata": {},
   "outputs": [],
   "source": [
    "Pre_data = pd.merge(Pre_data,income,left_on='AFFGEOID',right_on='GEO_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "retained-fourth",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'HBPPOPDN','HH_RACE','HOMEOWN','HHFAMINC','HHVEHCNT','TRVLCMIN','EDUC','URBRUR'\n",
    "X = Pre_data[['popden','Race','House tenure','Vehicles','travel time','education','urban/rural','income']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "preliminary-tobacco",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = gb_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "increased-acrylic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2636.735064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2953.052851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2715.082580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2898.507068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2530.279069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0  2636.735064\n",
       "1  2953.052851\n",
       "2  2715.082580\n",
       "3  2898.507068\n",
       "4  2530.279069"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel = pd.DataFrame(Y)\n",
    "fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bright-planning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2659.353233976435"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel[0].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "thermal-preservation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>geometry</th>\n",
       "      <th>popden</th>\n",
       "      <th>Race</th>\n",
       "      <th>House tenure</th>\n",
       "      <th>Vehicles</th>\n",
       "      <th>travel time</th>\n",
       "      <th>education</th>\n",
       "      <th>GSCOST</th>\n",
       "      <th>urban/rural</th>\n",
       "      <th>GEO_ID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>income</th>\n",
       "      <th>fuel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1400000US01117030317</td>\n",
       "      <td>01117030317</td>\n",
       "      <td>POLYGON ((-86.852665 33.281478, -86.8491899999...</td>\n",
       "      <td>2820.152853</td>\n",
       "      <td>2.074399</td>\n",
       "      <td>1.096</td>\n",
       "      <td>1.833</td>\n",
       "      <td>31.526847</td>\n",
       "      <td>2.943818</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.2158</td>\n",
       "      <td>1400000US01117030317</td>\n",
       "      <td>Census Tract 303.17, Shelby County, Alabama</td>\n",
       "      <td>72527</td>\n",
       "      <td>2636.735064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1400000US01119011500</td>\n",
       "      <td>01119011500</td>\n",
       "      <td>POLYGON ((-88.40378899999999 32.44977, -88.403...</td>\n",
       "      <td>16.609164</td>\n",
       "      <td>1.884195</td>\n",
       "      <td>1.330</td>\n",
       "      <td>1.529</td>\n",
       "      <td>27.380769</td>\n",
       "      <td>2.386957</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1400000US01119011500</td>\n",
       "      <td>Census Tract 115, Sumter County, Alabama</td>\n",
       "      <td>26838</td>\n",
       "      <td>2953.052851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1400000US01121010900</td>\n",
       "      <td>01121010900</td>\n",
       "      <td>POLYGON ((-86.118971 33.42176, -86.11435299999...</td>\n",
       "      <td>1058.486943</td>\n",
       "      <td>1.616550</td>\n",
       "      <td>1.219</td>\n",
       "      <td>1.544</td>\n",
       "      <td>22.077963</td>\n",
       "      <td>2.685673</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.1757</td>\n",
       "      <td>1400000US01121010900</td>\n",
       "      <td>Census Tract 109, Talladega County, Alabama</td>\n",
       "      <td>41047</td>\n",
       "      <td>2715.082580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1400000US01125010102</td>\n",
       "      <td>01125010102</td>\n",
       "      <td>POLYGON ((-87.60330399999999 33.359237, -87.60...</td>\n",
       "      <td>12.465127</td>\n",
       "      <td>1.072123</td>\n",
       "      <td>1.036</td>\n",
       "      <td>2.172</td>\n",
       "      <td>31.481144</td>\n",
       "      <td>2.775216</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.2249</td>\n",
       "      <td>1400000US01125010102</td>\n",
       "      <td>Census Tract 101.02, Tuscaloosa County, Alabama</td>\n",
       "      <td>78457</td>\n",
       "      <td>2898.507068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1400000US01089000701</td>\n",
       "      <td>01089000701</td>\n",
       "      <td>POLYGON ((-86.61668899999999 34.771281, -86.60...</td>\n",
       "      <td>2424.142524</td>\n",
       "      <td>9.045601</td>\n",
       "      <td>1.550</td>\n",
       "      <td>1.471</td>\n",
       "      <td>20.245846</td>\n",
       "      <td>2.551775</td>\n",
       "      <td>2.146052</td>\n",
       "      <td>1.1548</td>\n",
       "      <td>1400000US01089000701</td>\n",
       "      <td>Census Tract 7.01, Madison County, Alabama</td>\n",
       "      <td>29481</td>\n",
       "      <td>2530.279069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0              AFFGEOID        GEOID  \\\n",
       "0           0  1400000US01117030317  01117030317   \n",
       "1           1  1400000US01119011500  01119011500   \n",
       "2           2  1400000US01121010900  01121010900   \n",
       "3           3  1400000US01125010102  01125010102   \n",
       "4           4  1400000US01089000701  01089000701   \n",
       "\n",
       "                                            geometry       popden      Race  \\\n",
       "0  POLYGON ((-86.852665 33.281478, -86.8491899999...  2820.152853  2.074399   \n",
       "1  POLYGON ((-88.40378899999999 32.44977, -88.403...    16.609164  1.884195   \n",
       "2  POLYGON ((-86.118971 33.42176, -86.11435299999...  1058.486943  1.616550   \n",
       "3  POLYGON ((-87.60330399999999 33.359237, -87.60...    12.465127  1.072123   \n",
       "4  POLYGON ((-86.61668899999999 34.771281, -86.60...  2424.142524  9.045601   \n",
       "\n",
       "   House tenure  Vehicles  travel time  education    GSCOST  urban/rural  \\\n",
       "0         1.096     1.833    31.526847   2.943818  2.146052       1.2158   \n",
       "1         1.330     1.529    27.380769   2.386957  2.146052       2.0000   \n",
       "2         1.219     1.544    22.077963   2.685673  2.146052       1.1757   \n",
       "3         1.036     2.172    31.481144   2.775216  2.146052       1.2249   \n",
       "4         1.550     1.471    20.245846   2.551775  2.146052       1.1548   \n",
       "\n",
       "                 GEO_ID                                             NAME  \\\n",
       "0  1400000US01117030317      Census Tract 303.17, Shelby County, Alabama   \n",
       "1  1400000US01119011500         Census Tract 115, Sumter County, Alabama   \n",
       "2  1400000US01121010900      Census Tract 109, Talladega County, Alabama   \n",
       "3  1400000US01125010102  Census Tract 101.02, Tuscaloosa County, Alabama   \n",
       "4  1400000US01089000701       Census Tract 7.01, Madison County, Alabama   \n",
       "\n",
       "   income         fuel  \n",
       "0   72527  2636.735064  \n",
       "1   26838  2953.052851  \n",
       "2   41047  2715.082580  \n",
       "3   78457  2898.507068  \n",
       "4   29481  2530.279069  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pre_data['fuel']=fuel[0]\n",
    "Pre_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "speaking-mission",
   "metadata": {},
   "outputs": [],
   "source": [
    "### save the results\n",
    "Pre_data.to_excel('Pre_result.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sealed-gossip",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "satisfied-chair",
   "metadata": {},
   "source": [
    "The Following are some ML models droped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "handy-mountain",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Linear Regression Model\n",
    "\n",
    "LR = LinearRegression(fit_intercept = True)\n",
    " \n",
    "LR.fit(X_train,Y_train)\n",
    "\n",
    "a  = LR.intercept_\n",
    " \n",
    "b = LR.coef_\n",
    " \n",
    "print(\"Intercept\",a,\",Regression coefficients：\",b)\n",
    "\n",
    "score = LR.score(X_test,Y_test)\n",
    "print(score)\n",
    "Y_pred = LR.predict(X_test)\n",
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('Root Mean Squared Error:',\n",
    "      np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R2:',metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acute-shock",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Random Forrest\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor  \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "estimators = [20,80,100,200]\n",
    "leaf_options = [20,80,100,200,400,600]\n",
    "for estimator in estimators:\n",
    "    for leaf_size in leaf_options:\n",
    "        RF = RandomForestRegressor(n_estimators=estimator,random_state=50, min_samples_leaf = leaf_size)\n",
    "        RF.fit(X_train, Y_train)\n",
    "        score = RF.score(X_test,Y_test)\n",
    "        print(\"estimator={}, leaf_size={},score:{:.3f}\".format(estimator,leaf_size, score))\n",
    "        \n",
    "RF = RandomForestRegressor(n_estimators=20,min_samples_leaf = 200, random_state=50)\n",
    "RF.fit(X_train, Y_train)\n",
    "score = RF.score(X_test,Y_test)\n",
    "Y_pred = RF.predict(X_test)\n",
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('Root Mean Squared Error:',\n",
    "      np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R2:',metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "swiss-boutique",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Decesion Tree\n",
    "\n",
    "from sklearn import tree\n",
    "leaf_options = [20,80,100,200,400,600]\n",
    "split_options = [5, 10, 20, 80, 100, 200, 400, 600]\n",
    "for split in split_options:\n",
    "    for leaf_size in leaf_options:\n",
    "        clf = tree.DecisionTreeRegressor(min_samples_leaf=leaf_size,min_samples_split = split)\n",
    "        clf.fit(X_train, Y_train)\n",
    "        score = clf.score(X_test, Y_test)\n",
    "        print(split, leaf_size, score)\n",
    "        \n",
    "clf = tree.DecisionTreeRegressor(min_samples_leaf=400,min_samples_split = 5)\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_pred = clf.predict(X_test)\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('Root Mean Squared Error:',\n",
    "      np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R2:',metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wired-orlando",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ElasticNet\n",
    "\n",
    "from sklearn import linear_model\n",
    "ENR=linear_model.ElasticNet()\n",
    "ENR.fit(X_train,Y_train)\n",
    "Y_pred = ENR.predict(X_test)\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('Root Mean Squared Error:',\n",
    "      np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R2:',metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "certain-treasure",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ANN Model\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "units = [20, 50, 100, 200,300, 400]\n",
    "aplhas = [0.00001, 0.001, 0.01, 0.1, 1.0]\n",
    "for unit in units:\n",
    "    for alpha in aplhas:\n",
    "        ann_model = MLPRegressor(hidden_layer_sizes=unit, activation='logistic', solver='adam', alpha=alpha, max_iter=500, random_state=50)\n",
    "        ann_model.fit(X_train, Y_train)\n",
    "        score = ann_model.score(X_test, Y_test)\n",
    "        print('unit={},alpha={},accuracy{:.3f}'.format(unit, alpha, score))\n",
    "        \n",
    "ann_model = MLPRegressor(hidden_layer_sizes=50, activation='logistic', solver='adam', alpha=0.001, max_iter=1000000, random_state=0)\n",
    "ann_model.fit(X_train, Y_train)\n",
    "print(ann_model.score(X_test, Y_test))\n",
    "\n",
    "Y_pred = ann_model.predict(X_test)\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('Root Mean Squared Error:',\n",
    "      np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R2:',metrics.r2_score(Y_test, Y_pred))"
   ]
  }
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