Skip to content

Instantly share code, notes, and snippets.

@Keerthivasan-A

Keerthivasan-A/HrDataser.ipynb

Created November 13, 2017 10:40
Show Gist options
  • Save Keerthivasan-A/20c678e27ef7772ee4cd286ac04619cb to your computer and use it in GitHub Desktop.
Save Keerthivasan-A/20c678e27ef7772ee4cd286ac04619cb to your computer and use it in GitHub Desktop.
Download ZIP
Kaggle HR Dataset
Display the source blob
Display the rendered blob
Raw
HrDataser.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## HR Dataset\n",
"\n",
"Why are our best and most experienced employees leaving prematurely? Have fun with this database and try to predict which valuable employees will leave next. Fields in the dataset include:\n",
"\n",
"Satisfaction Level\n",
"Last evaluation\n",
"Number of projects\n",
"Average monthly hours\n",
"Time spent at the company\n",
"Whether they have had a work accident\n",
"Whether they have had a promotion in the last 5 years\n",
"Departments (column sales)\n",
"Salary\n",
"Whether the employee has left\n",
"\n",
"Source : Kaggle"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import re\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Source = pd.read_csv('HR_comma_sep.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A GLIMPSE AT DATA"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 14999 entries, 0 to 14998\n",
"Data columns (total 10 columns):\n",
"satisfaction_level 14999 non-null float64\n",
"last_evaluation 14999 non-null float64\n",
"number_project 14999 non-null int64\n",
"average_montly_hours 14999 non-null int64\n",
"time_spend_company 14999 non-null int64\n",
"Work_accident 14999 non-null int64\n",
"left 14999 non-null int64\n",
"promotion_last_5years 14999 non-null int64\n",
"sales 14999 non-null object\n",
"salary 14999 non-null object\n",
"dtypes: float64(2), int64(6), object(2)\n",
"memory usage: 1.1+ MB\n"
]
}
],
"source": [
"Source.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.612834</td>\n",
" <td>0.716102</td>\n",
" <td>3.803054</td>\n",
" <td>201.050337</td>\n",
" <td>3.498233</td>\n",
" <td>0.144610</td>\n",
" <td>0.238083</td>\n",
" <td>0.021268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.248631</td>\n",
" <td>0.171169</td>\n",
" <td>1.232592</td>\n",
" <td>49.943099</td>\n",
" <td>1.460136</td>\n",
" <td>0.351719</td>\n",
" <td>0.425924</td>\n",
" <td>0.144281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.090000</td>\n",
" <td>0.360000</td>\n",
" <td>2.000000</td>\n",
" <td>96.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.440000</td>\n",
" <td>0.560000</td>\n",
" <td>3.000000</td>\n",
" <td>156.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.640000</td>\n",
" <td>0.720000</td>\n",
" <td>4.000000</td>\n",
" <td>200.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.820000</td>\n",
" <td>0.870000</td>\n",
" <td>5.000000</td>\n",
" <td>245.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.000000</td>\n",
" <td>310.000000</td>\n",
" <td>10.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"count 14999.000000 14999.000000 14999.000000 \n",
"mean 0.612834 0.716102 3.803054 \n",
"std 0.248631 0.171169 1.232592 \n",
"min 0.090000 0.360000 2.000000 \n",
"25% 0.440000 0.560000 3.000000 \n",
"50% 0.640000 0.720000 4.000000 \n",
"75% 0.820000 0.870000 5.000000 \n",
"max 1.000000 1.000000 7.000000 \n",
"\n",
" average_montly_hours time_spend_company Work_accident left \\\n",
"count 14999.000000 14999.000000 14999.000000 14999.000000 \n",
"mean 201.050337 3.498233 0.144610 0.238083 \n",
"std 49.943099 1.460136 0.351719 0.425924 \n",
"min 96.000000 2.000000 0.000000 0.000000 \n",
"25% 156.000000 3.000000 0.000000 0.000000 \n",
"50% 200.000000 3.000000 0.000000 0.000000 \n",
"75% 245.000000 4.000000 0.000000 0.000000 \n",
"max 310.000000 10.000000 1.000000 1.000000 \n",
"\n",
" promotion_last_5years \n",
"count 14999.000000 \n",
"mean 0.021268 \n",
"std 0.144281 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Source.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>sales</th>\n",
" <th>salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.38</td>\n",
" <td>0.53</td>\n",
" <td>2</td>\n",
" <td>157</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.80</td>\n",
" <td>0.86</td>\n",
" <td>5</td>\n",
" <td>262</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.88</td>\n",
" <td>7</td>\n",
" <td>272</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.72</td>\n",
" <td>0.87</td>\n",
" <td>5</td>\n",
" <td>223</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.37</td>\n",
" <td>0.52</td>\n",
" <td>2</td>\n",
" <td>159</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_montly_hours \\\n",
"0 0.38 0.53 2 157 \n",
"1 0.80 0.86 5 262 \n",
"2 0.11 0.88 7 272 \n",
"3 0.72 0.87 5 223 \n",
"4 0.37 0.52 2 159 \n",
"\n",
" time_spend_company Work_accident left promotion_last_5years sales \\\n",
"0 3 0 1 0 sales \n",
"1 6 0 1 0 sales \n",
"2 4 0 1 0 sales \n",
"3 5 0 1 0 sales \n",
"4 3 0 1 0 sales \n",
"\n",
" salary \n",
"0 low \n",
"1 medium \n",
"2 medium \n",
"3 low \n",
"4 low "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Source.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DATA EXPLORATION"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Copying orginal data\n",
"xSource = Source.copy()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
" 'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
" 'promotion_last_5years', 'sales', 'salary'],\n",
" dtype='object')"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xSource.columns"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>satisfaction_level</th>\n",
" <td>1.000000</td>\n",
" <td>0.105021</td>\n",
" <td>-0.142970</td>\n",
" <td>-0.020048</td>\n",
" <td>-0.100866</td>\n",
" <td>0.058697</td>\n",
" <td>-0.388375</td>\n",
" <td>0.025605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>last_evaluation</th>\n",
" <td>0.105021</td>\n",
" <td>1.000000</td>\n",
" <td>0.349333</td>\n",
" <td>0.339742</td>\n",
" <td>0.131591</td>\n",
" <td>-0.007104</td>\n",
" <td>0.006567</td>\n",
" <td>-0.008684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_project</th>\n",
" <td>-0.142970</td>\n",
" <td>0.349333</td>\n",
" <td>1.000000</td>\n",
" <td>0.417211</td>\n",
" <td>0.196786</td>\n",
" <td>-0.004741</td>\n",
" <td>0.023787</td>\n",
" <td>-0.006064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>average_montly_hours</th>\n",
" <td>-0.020048</td>\n",
" <td>0.339742</td>\n",
" <td>0.417211</td>\n",
" <td>1.000000</td>\n",
" <td>0.127755</td>\n",
" <td>-0.010143</td>\n",
" <td>0.071287</td>\n",
" <td>-0.003544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time_spend_company</th>\n",
" <td>-0.100866</td>\n",
" <td>0.131591</td>\n",
" <td>0.196786</td>\n",
" <td>0.127755</td>\n",
" <td>1.000000</td>\n",
" <td>0.002120</td>\n",
" <td>0.144822</td>\n",
" <td>0.067433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Work_accident</th>\n",
" <td>0.058697</td>\n",
" <td>-0.007104</td>\n",
" <td>-0.004741</td>\n",
" <td>-0.010143</td>\n",
" <td>0.002120</td>\n",
" <td>1.000000</td>\n",
" <td>-0.154622</td>\n",
" <td>0.039245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>left</th>\n",
" <td>-0.388375</td>\n",
" <td>0.006567</td>\n",
" <td>0.023787</td>\n",
" <td>0.071287</td>\n",
" <td>0.144822</td>\n",
" <td>-0.154622</td>\n",
" <td>1.000000</td>\n",
" <td>-0.061788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>promotion_last_5years</th>\n",
" <td>0.025605</td>\n",
" <td>-0.008684</td>\n",
" <td>-0.006064</td>\n",
" <td>-0.003544</td>\n",
" <td>0.067433</td>\n",
" <td>0.039245</td>\n",
" <td>-0.061788</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"satisfaction_level 1.000000 0.105021 -0.142970 \n",
"last_evaluation 0.105021 1.000000 0.349333 \n",
"number_project -0.142970 0.349333 1.000000 \n",
"average_montly_hours -0.020048 0.339742 0.417211 \n",
"time_spend_company -0.100866 0.131591 0.196786 \n",
"Work_accident 0.058697 -0.007104 -0.004741 \n",
"left -0.388375 0.006567 0.023787 \n",
"promotion_last_5years 0.025605 -0.008684 -0.006064 \n",
"\n",
" average_montly_hours time_spend_company \\\n",
"satisfaction_level -0.020048 -0.100866 \n",
"last_evaluation 0.339742 0.131591 \n",
"number_project 0.417211 0.196786 \n",
"average_montly_hours 1.000000 0.127755 \n",
"time_spend_company 0.127755 1.000000 \n",
"Work_accident -0.010143 0.002120 \n",
"left 0.071287 0.144822 \n",
"promotion_last_5years -0.003544 0.067433 \n",
"\n",
" Work_accident left promotion_last_5years \n",
"satisfaction_level 0.058697 -0.388375 0.025605 \n",
"last_evaluation -0.007104 0.006567 -0.008684 \n",
"number_project -0.004741 0.023787 -0.006064 \n",
"average_montly_hours -0.010143 0.071287 -0.003544 \n",
"time_spend_company 0.002120 0.144822 0.067433 \n",
"Work_accident 1.000000 -0.154622 0.039245 \n",
"left -0.154622 1.000000 -0.061788 \n",
"promotion_last_5years 0.039245 -0.061788 1.000000 "
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr = xSource.corr()\n",
"corr"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2047f48fa20>"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAIuCAYAAAAYFXaJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8U9X/x/FXkoZukKGyhwi37C+CLEVAbFnur36dX3Ew\nVFRE9lBAZQoiiIMhishSf4oT3Kh8lSmrBQ4gIEuGCqV0p8nvj8TQCohI0zTx/Xw8+tDknHPzObeX\n2889+eTG5vF4EBERERGR4LEHOwARERERkX86JeUiIiIiIkGmpFxEREREJMiUlIuIiIiIBJmSchER\nERGRIIsIdgBS+D5yWrqlDtBs/Zxgh1Bs7LRbwQ6h2CjtPBrsEIqNdHdMsEMoNs73HAh2CMXGFz83\nCHYIxUabSibYIRQb1S62bMGOAQKX43TJNUGfn1bKRURERESCTEm5iIiIiEiQqXxFREREREKCzRn0\nKpOAUVIuIiIiIiHBHhG+SbnKV0REREREgkwr5SIiIiISEmzO8F1PDt+ZiYiIiIiECK2Ui4iIiEhI\nCOeaciXlIiIiIhISwvnuKypfEREREREJMq2Ui4iIiEhICOfyFa2Ui4iIiIgEmVbKRURERCQkqKZc\nREREREQCRivlIiIiIhISwrmmXEm5iIiIiIQEmyN8k3KVr4iIiIiIBJlWykVEREQkJNi1Ui4iIiIi\nIoGilXIRERERCQk2e/iulCspFxEREZGQYHOEb5FH+M5MRERERCREaKVcREREREJCOH/QU0m5FLrz\nmjUkYXQ/ll91V7BDCTi3283EabPZvms3TmcEg3p1o3KFCwv0ycrOps+IcQzq1Y1qlSv6n0/Zup2X\nXl/I1KeHFnXYAfPDym9ZtHAmDoeDK666lnZJ15+y35L355N65Fdu6fpQgedfeWE0cXElT3o+FK1Y\n8T0L5s3F7nCQmNSBjh07F2hPTU1lwvgxZOfkULZMWXr36UtUVBRfL/2K9xa9g8PhoFr1GjzY62Hs\n9vB5U3PNimW8veA1HHYH7RK70L7jtafs99F7b5J65Fduv/uBIo6wcLndbia/NIMfd/6E0xlBv4cf\noFLFCv7271auZs78t3A4HHRMbMfVHRIB6NG7P7Ex0QCUv/BCBj7ai63bdzDpxemUcEZQ86IaPNT9\nnpA+Nrau/5JlH76A3RFBo8v+TePW/ynQnnb0EO/P6k+eK5eo2FJcd98zREbFsfH7RSz/9BUio+Np\n2OoG/nX5zUGawd/ndrt5/sWX2bFzJ06nkz6PPESliif+Pny/YiVz5y/A4XDQIfEqOnfsgMvlYuJz\nUzh46BC5ubncfst/aNmiOUeOHuW5KVNJO34ct9vNgL59qFihwp+8uhRXIZWUW5ZVFWhkjPnAsqzn\ngGeNMbtP0S8C+AyIBLoYY478xe1fARw1xmywLOsdY8yNhRT3LiDBGJN1jtt5DVhgjFlSCGEFxEV9\nu1HpzmvJS88MdihF4tsVa8jJzWHauOEkm+1MfXUeY4f08bdv2b6DZ15+jcO//lZg3Nx3P+STpf8j\nKiqyqEMOGJfLxdxXJvHkxNeIjIzmyUHduKRZa0qdV9bfJyc7i5lTR7Fj2yYubdmuwPgvl7zD3p+2\nk1DvkqIOvdC5XC5mTp/GpOeeJzIqigH9+tC8eUtKly7t77Ng/hu0aXslVyUm8dabC1iy+CM6db6a\nOa+/xtQXpxEVFcX4caNZtXIFzVu0DOJsCo/L5WL2zOcZPWkGUZHRPD7gAZo0v5zzSpfx98nJzubl\nKWP5cetmml/WJojRFo5ly1eSk5PL1Amj2bRlKy/Nms3TwwYB3v3x4szXeOnZsURFRvLIgGG0anYp\ncbExgIdJY54ssK1nX3iZh3rcS/06CbwyZz5ffL2MxHZXBGFW5y7Plcvnb47hniFvUyIymtnjbqNW\noyuJK1nO3+f7T2bQoOUNNGx5Pd+8/zzrvn2bBi2u5ev3p3DfsHeIii7J3El3Uz2hJeeVqxzE2Zy9\n775fTk5ODpMnPsPmLVuYPnMWI58YBniPi2kzZvL8pGeJioqkT/+BtGzejJWr11CyZDwD+z3GsbQ0\nHni4Ny1bNGfmrNe4sl1b2rS+nHXrN7Bnz96wTsrD+YOeoXaJfSVwGYAx5tFTJeQ+FYGSxphWfzUh\n97nXN5bCSsj/aTJ27GbNzQ8HO4wis2HzVpo3bghAfetitvy4s0B7Tq6L0YN6U7VSwRNkpfIXMmpg\n7yKLsyjs37uTCytUJjauJBFOJ7XrNGJLytoCfXJzc2h9ZReuvfmeAs9v3byBH7em0K5DePyz27Nn\nNxUqViQuPh6n00ndevVISd5YoM+mlBSaNGkKQJOml7Ju3VqcTifPTHyOqKgoAPLy8nCWcBZ5/IGy\nb88uyleoRJzvGEmo25DNKesK9MnJzaZN+07ccEt4vNOWvGkLlzb5FwB1E2pjtu3wt/20Zy+VKpQn\nPi4Op9NJ/boJbEjZxI87d5GVnUP/x5/ksaEj2LRlKwCHf/mN+nUSAKhfxyJ50+ain1Ah+eXAj5S+\noCrRsaVwRJSgysVN2LN1VYE+if8ZQoPm1+Jxuzl25GeiYuI58steLqhsER17Hja7nYrVG7Bvx/og\nzeLvS960maZNvAsQdRIS2Lp9u79t9549VKxQgfh473FRr25dNiancMXll9H1zju8nTweHHYHACmb\nN3P4l18YOORxvlz6NQ0bNijy+RQlu8MWkJ/ioFislFuWVRt4FXDhvVC4E3gcqAJUAN4HhgODgBjL\nsr4DHgPuB8oCE4FcIAO4CXgZqGVZ1jTgKeAlIMq3rWHGmEWWZV3t26YN+AGYBnQELrEsaxOw0hhT\n3rKsxsDzQB6QBXT3xTgf2APU9PU943uslmVVAaYD0UAm0AO4AShtjBlpWVYksB5oCPQEbgc8eFfH\np5zlbg2KA+9+SnS1SsEOo8ikZ2YSGxPjf2y323Hl5RHh8J4sG9apfcpxbVteys+HDhdJjEUlMyOd\nmJg4/+Po6Fgy048X6BMbV5IGjVvwzRcf+p87+tsvLFowg95DnmHFss+LLN5AysjIIDYm1v84OjqG\n9PT0k/rExHr7xETHkJGejt1u96+mf/D+IrIyM2ncuEnRBR5gmRnpxMTmP0a8884vLq4kjS5pxtLP\nPy7q8AIiI6PgOcJht5OXl4fD4TipLSY6mvT0DCIrR/KfG66hS9JV7N3/M4NGjOL1l6dQofwFrN+Y\nQqMG9fh+5Roys7KDMaVCkZN5nMjoeP/jElGxZGUWPF/YbDby8lzMfOo6XLnZtL66FyWi4vhl/3aO\nH/uFyMhYdm75njIXVi/i6M9dRkYGsbEnzhH2Px4X+dpioqNJz8ggOjraP/ap0eO4+647ATh48BDx\ncXGMG/0Ub8xbwJtv/R9d/3tH0U5ICkWxSMqBRGAlMABoDcQDy40x3SzLigL2GmOGWZY1Fm8ZyPuW\nZT3mG3s98CbwHHAtUBp4EG8i29OyrKuAicaYpZZltQJGWpb1ITAVaGaMOWRZ1gDgMLDEN263ZVm/\nxzYD6GaMWWdZ1nXAs0A/oDaQhPdCYIdlWeWNMQfOMM8JwBRjzGLLstoDY4GHgGWWZT3pi/9D4GLg\nFuBy37jPLMv65Kz3qgRcbHQ0GVknqpI8Hrc/If+neOuNl9i6eT17dm2nZu16/uczM9OJiY3/k5Fe\nK/73BWlpqUx48lFSj/xKTnYWFSpX54r2Vwcy7ICYM/tVUjalsGvnTvKdQ8jMzCA2LrZA35iYGDIz\nM4iMjCQjM4NYX7Lqdrt5ddZM9u3by+ChT2CzFY8VnHOxYM50TMoGftr1I7Wsuv7nM/PNO1zFxEST\nmXniHOH2uHH4zhExMdFkZJ4o9cvIzCQuLpbKlSpSqUJ5bDYbVSpVpGR8PL/+doQBvXvxwoxXeX3B\nWzSoVwens7j8Cf/rli6axJ7tP3Bor6FijYb+53Oy0omKOfl84Yhw0nPkx+zc9B3vzxrIf/u/wVX/\nGcz/vfQw0XHnUb5qPaLjSp80rrjz/vs/8bv3uD1/elz8nqQfOnyYkU+P4Zounbiyrbe8q2R8PC2b\nNwOgRfNLefX1N4pqGkFhKyar2oFQXMpXXgGO4k2KH8Ib16WWZc0FJuGtDT+d0XhLTr7Au0qe+4f2\nn4GelmXNwbuy7gTKAUeMMYcAjDHj/6wUxhjz+/ur3wC/Zx3bjTFpxpg832tE/YV5NgCGWJa1FHgC\nuNBXXrMWbwJ+NzATqA9U883pC7zvBtT6C9uXItagTm2Wr/EeHslmOxdVrRLkiIrezXc+wNBRLzN1\n9hIO/ryX42mpuHJzMZvWcXHCmd9G7XDNLTz17OsMHfUyV/+7Ky2v6BCSCTnAf7vew9hxE3hj3kL2\n/7yftLRj5Obmkpy8kYSEugX61qlbj9WrvG/Xr1m9inr16wMw9fnJ5OTkMOzxEf4yllB36397MHzs\nVKa/8QEH9u/jeNoxXLm5bE5eR+2E+sEOL6Dq10lgxeofANi0ZSsXVavqb6tWpTL79v/MsbQ0cnNz\n2ZCymboJtVn82Ze89MrrAPzy629kZGRQtkxpVqz+gSF9ezNx1AiOpR2nSeNGQZnTuWh7fR/+228O\nj074H0cO7yYz/Sh5rhx2b1tNpYsaF+i7eO4Idm1ZDnhX0m02G+48Fwd2b+KuAfO4scdkfj2wgyo1\nQ+9zKPXq1mHlqtUAbN6yherVq/nbqlapwr79+/3HxcbkFOomJHDkyBEGDxtOt3u60jEp8cS26tVl\n5eo1AGxMTqFa1apIaCoul9nXAd/6Sjhuw1vCMd630n0x0MOyLBvg5uQLiTuB14wx/SzLGoy3JGR2\nvvangBm+1el78Ca+h4DzLMsqY4z5zbKsKcAbp9n+fsuyGhpjNgBtgK2+5z1/Y55bgAnGmO8sy0rw\nbQ+8q/GPAtHGmC2+MpYUoJMxxmNZVh9gA96LDilGrmjehFXrkrl/0Eg8HhjycHc+/eY7MrOyuC7p\nymCHV6QiIiK4/d5HGT/iETweD1e0v4YyZS/geFoqr0wdRe/B44MdYpGJiIigW/eePDFsCG6Pm8TE\njpQrV460tGNMmTyJocOGc8uttzPp2Wf4ZMnHlCxViv4DBrF9+zY++3QJ9erVZ8jgAQBce931tGp1\n+RleMTRERERwV7eHGPXEY3jcbtoldqFMufM5nnaMl6eMpd/Q0cEOsdBd3rIZa9at56H+Q8ADA3r3\n4oul35KZlcXVHRN5oNvdDHziadweD50S23F+2bJ0TryScc+9wCMDhmGzQf/eD+JwOKhUsQL9ho0k\nMrIEjRvUp0XT0EtGf+eIcHLVzYOY/9x9eDweGl32b0qWvpDM9KN89PowbnpgKpde+V8Wzx3Bsg9f\nwGa30/GOEdgd3rTlladvIMIZSfPEe4iJL3OGVyt+LmvZgh/WruPRvgPw4KHvo735cunXZGZm0qVT\nR3p2u48hjw/H7fbQMekqypUry4vTZnD8+HHmLljI3AULARg1cjg9u93Ls5Of58OPFhMTG8Pg/v2C\nPLvAsoXwHYfOxObx/J3csnBZllUTbyKdAziAIcCLQCqQjbe2vD1wAbAAby34/b6fUsBkIB1vUt2D\nE3XYLXxJ/jDgV2Av3ru31LMsqxPe1eo8vCvVj/jG9sJbOvJVvpryyXhrz13Afb7XWWCMaeGLfzlw\nqzFm12nmtwtIwLui/3t9ezTQ2xjzva+PAUYZY173Pe6PtzQnEm9pz8N431E4491XPnJawf+lFgPN\n1s8JdgjFxk67deZO/xClnUeDHUKxke6OOXOnf4jzPWeqPvzn+OLn8P6g4NloU8kEO4Rio9rFVrGo\nG/mh/eUByXEu+WJZ0OdXLJJyKVxKyr2UlJ+gpPwEJeUnKCk/QUn5CUrKT1BSfoKS8sArLuUrIc+y\nrGbAqd6fX2iMeamo4xEREREJN8Xl9oWBoKS8kBhjVgJtgx2HiIiIiIQeJeUiIiIiEhL0jZ4iIiIi\nIhIwWikXERERkZAQzrdEVFIuIiIiIiFB5SsiIiIiIhIwWikXERERkZCgWyKKiIiIiPwDWZZlx/tN\n843wftN8N2PM9nztdwB98X5L/Ky/+/00Kl8RERERkZBgs9sC8nMG1wNRxpiWwCBg4h/aJwBXAZcB\nfS3LKv135qakXERERERCgs1uD8jPGVwOLAEwxiwHmv6hfQNQCogCbIDn78xNSbmIiIiIyOmVBFLz\nPc6zLCt/CXgysAZIAT40xhz9Oy+ipFxEREREQkKQyleOAfH5HtuNMS4Ay7IaAl2AGkB14ALLsm7+\nO3NTUi4iIiIicnr/AzoDWJbVAtiYry0VyAQyjTF5wCHgb9WU6+4rIiIiIhISgvTlQe8CiZZlfYe3\nZvwey7JuB+KMMdMty5oGLLMsKwf4EXjt77yIknIRERERCQnBSMqNMW7g/j88vSVf+8vAy+f6Oipf\nEREREREJMq2Ui4iIiEhI+Au3LwxZ4TszEREREZEQoZVyEREREQkJdkdQPuhZJLRSLiIiIiISZFop\nFxEREZGQEKRbIhYJJeVhqNn6OcEOoVhY2ei/wQ6h2Gj/Xt9gh1B8hPGHhM5WeuW6wQ6h2DgaUz7Y\nIRQbl1f+MdghFBu5lAh2CPIH+qCniIiIiIgEjFbKRURERCQkhHP5ilbKRURERESCTCvlIiIiIhIS\nwnmlXEm5iIiIiIQEfdBTREREREQCRivlIiIiIhISwrl8RSvlIiIiIiJBppVyEREREQkJ4VxTrqRc\nREREREKDTeUrIiIiIiISIFopFxEREZGQoA96ioiIiIhIwGilXERERERCQjh/0DN8ZyYiIiIiEiK0\nUi4iIiIiISGca8qVlIuIiIhISFD5ioiIiIiIBIxWykVEREQkJIRz+YpWykVEREREgkwr5SIiIiIS\nEsJ5pVxJuYiIiIiEhjD+oKeScjlrbrebidNms33XbpzOCAb16kblChcW6JOVnU2fEeMY1Ksb1SpX\n9D+fsnU7L72+kKlPDy3qsIPivGYNSRjdj+VX3RXsUALO7fYw6u3P2br/MCUiHAy/JYmq55f2t3++\nfiuzvlgJNhtdLqnDHW0uAeCWCXOIiyoBQMWypXjqto5Bib8wud0eRr31GVv3H6JERATDb+1QcF+s\nM8z6fCXYoEuTutzRtom/7de0dG6bMIdpD95MjQvLBiP8QuV2u5kwYw7bdu2hhDOCwQ/cc8rzRe+R\nExj84L1Ur1yBvDw3Y19+ld37DmCz2ejf8y5qVq0cpBn8PSuWL2fevHk4HA6SkpLo2KlTgfbU1FTG\njxtHTk4OZcqWpU+fPkRFRZ1y3Geffcbnn30GQE5ODjt27GDuvHnExcUBMH3aNCpVrkyXLl2KfJ5n\n4na7eeGFF9ixcydOp5NHe/emYsUTfxOWr1hRYL6dOnY87ZijR48yecoUjqel4Xa76duvHxUrVGDV\nqlXMnTcPPB4urlWLXg8+iM1WPFdTV6xYzvx5c3E4HCQmdaBjx5OPi2fGjyMnJ5syZcryaJ/HvMfF\nKca5XC6enTiBg4cO4rDbefiRR6lSpQrjxo7hyJHfADh48CAJCXUYOGhwMKYrZymkknLLsu4GEowx\ng/5i/yjgTmPMzADGtBS43xiz5SzHPWSMmWpZVkegqjFmekACDIBvV6whJzeHaeOGk2y2M/XVeYwd\n0sffvmX7Dp55+TUO//pbgXFz3/2QT5b+j6ioyKIOOSgu6tuNSndeS156ZrBDKRJfJm8nx5XHnEdv\nZ8Ou/Ux8/2sm33c9AHluN5M//JZ5j91JTKSTG8a+RucmCcRElsCDh1ceuiXI0ReuLzduI8flYk6f\nO737YtFSJne/AfDtiw++YV6/u7z7YswsOjetQ+m4GHLz8nhq4adEOkPq1Pynvln5Azk5ucwYM4zk\nrT8yZfYCxg/q7W/fvH0nz0x/nUP5zhfLVq8DYNroofyQvIVp8/6vwJjizuVyMX36dJ6bPJmoqCj6\n9e1L8xYtKF36xIXZ/HnzaNuuHYmJibz55pssXryYa6655pTjEhMTSUxMBOCFF14gKSmJuLg4Uo8e\nZcLEiezbu5d/33RTsKb7p77//ntycnOZ9OyzbN6yhRkzZzL8iSeAE/tp8nPPERUVRd9+/WjRvDmb\nNm065ZhXZs2iXdu2XHHFFaxfv569e/ZwXqlSvDJrFuPGjqVUqVK89dZbpB47xnmlSgV55idzuVzM\nmD6NSc9NISoqiv79HqN58z8cF/Pn0qZtWxITk3jzzYUsXvwx11xz7SnHbdmyhby8PCZOnMTaH37g\n9dmvMXTY4/4EPC0tjcGDB9K9R49gTTkgiusFV2EI3/cAvMoD3YIdxGkMAzDGLAmlhBxgw+atNG/c\nEID61sVs+XFngfacXBejB/WmaqUKBZ6vVP5CRg0MnT+s5ypjx27W3PxwsMMoMmt37KNVQnUAGlav\nSMqeg/42h93Ou4PuIT46kqPpWbg9HpwRDsz+w2TluOj50tt0e+FNNuzaH6ToC9faHXtpVacG8Pu+\nOOBvc9jtvDvkPt++yMTt9u4LgGcXLeXmy/7FBaXighJ3IKzfvI3mjRsAUL92Tbb8uKtAe67LxZgB\nD1Et3/miTfNLGHj/3QAcOPwL8bExRRVuodizZw8VK1YkPj4ep9NJvXr1SE5OLtAnJSWFJk2875A0\nbdqUdWvXnnHc1q1b2f3TT3Tq3BmAzKws7rjjDq5s377oJneW8s+zTkIC27Zt87edbr6nG7Np0yZ+\n+eUXBg8ZwldffUXDhg3ZvHkz1atXZ8bMmfTr35/zSpculgk5wJ49u6mQb75169UnOXljgT6bUlJo\n0qQpAE2bXsq6dWtPO65SpUrkufNwu91kZGQQEVHwYn7u3Dlcc821lCkT+u+4/VOE5HKMZVljgKZA\nWWC9MeYey7IuAyYCuUAGcBMwFKhrWdYTxpgnT7OtNsAoIA/4EegJLAQmG2O+tiyrKfA48F9gJnAe\nUBF4wRjzUr7tjAAOGGNetiwrAXjZGNPWsqybgF6AE/AAN/heo4xlWS8CK/Gt/luW1Re4FXAB3xhj\nBvq2WwO4AKgG9DHGfHLue/HvS8/MJDbmxB9Ju92OKy+PCIc3sWhYp/Ypx7VteSk/HzpcJDEWBwfe\n/ZToapWCHUaRSc/KJj76xLsgDpsNV56bCIf32j/CYefzDdsY8/YXtK5bg+gSTqKdEXRt15QbWzTg\np8NH6DX9Hd4bfK9/TKhKz8ohPuoM+2L9Vsa8/Tmt615EdAkn761IpnRcDJfVqcGsz1cEK/RCl5GZ\nSVxMtP+x44/ni4RapxwX4XDw1PMz+HrFD4zq16tIYi0sGenpxMTG+h9HR0eTnp5esE9GBrG+Pr+3\nn2ncmwsXcvsdd/gfly9fnvLly7N69epATeWcZWRknPT3Ii8vD4fDQfof2vz74TRjDh48SFxcHGNG\nj2buvHm8+dZbVK5cmQ0bNjD1+eeJjo6mX//+1ElIoHLl4lfu5J1Xwd9vxhmOiwz//jh5XHR0NIcO\nHqRnj+4cO5bK8BEn0pyjR4+yft06unfvGeBZFT19eVDxUgI4YoxJxJuYt7AsqxJwPfAm0AZ4CSiN\nN9ne9CcJuQ2YAdxojGkD7APu9j3X1dftHt/ji4EFxpgkIAl47C/GWxvoYoy5HNgEdDDGjAJ+M8Y8\nmC+WBsB/gFa+n1qWZV3ta842xnQCegN9CLLY6GgysrL8jz0et/8PrPxzxUZFkp6V43/s9nhOSq6v\naliLz0b0JDfPzQerNlHtgtJ0aVIHm81G9QvKUCo2ml+OHS/q0AtdbFQJ0rPPsC8a1eazkQ+Qm5fH\nBytTWLRiI8vNLu57fgFm3yGGvvFxWOyLmOhoMjJPnC/cbs9fPl88/nB3Fj4/lrEvvUZmVnagQiw0\ns2fPZuCAAYwcOZKMjAz/85mZmcTlS7YBYmJiyMzM9LfHxsURExtL5mnGHT9+nL1799KoUaMimEnh\nyT9P8NaYO3y//9iYGDLytfn3w2nGlCxZkhYtWgDQvHlztm3bRsn4eGrVqkWZMmWIjo6mQf367Nix\no4hm99e8Pvs1Bg3sz5MjR5x0XMTGFXxX7KTjIvbk/fH7uEXvvsMllzRhxsxXmPrCS0x6dgI5Od7z\nzrJl39KmbTv/vg4nNrstID/FQSgm5R7gAsuy5gPTgDi8q9Cj8a5gf4F3lTz3L2zrfKAC8KavNjwJ\n72r0J0Azy7LKAK2BxcBB4HrLst7AW3ri/JPt5v/tHgJmW5b1KtDwT8YlAMuNMbnGGA/wLVDP17bW\n9989QNRfmFdANahTm+VrvDWfyWY7F1WtEuSIpDhoXKMiyzZ7S5k27NpPrQrl/G3Hs7K5d+pCclwu\n7HYb0SWc2G02Fq1IZuL7XwNwKPU46VnZlCsZ+qUbjWtUYtkmb2KwYdd+alU83992PCube6fML7gv\n7DZefeQ2Zj1yG688fCtWpQsYdWfnsNgXDRNq8f0PGwBI3vojNaudeQVz8dLveP2dDwGIiiyB3W7D\nHgJ1pF27dmXc+PHMmz+fn/fvJy0tjdzcXJKTk0moU6dA37p167Jq1SoAVq9eTf169ahSpQr7TzMu\nOTmZf/3rX0U+p3NVt25dVvlW8jdv2UKN6tX9baeab52EhNOOyb/PkjdupFq1alx88cX89NNPpKam\nkpeXx5YtW6hatWqRzvFM7up6N2PHPcPceQv4+ef8891IQkLB46JO3XqsWrUSgNWrV1Gvfn2qVKnK\n/v37ThoX57uQA4iPj8flcuF2uwFYt24tTZs2LdqJyjkLxfKVdsA2Y8wtlmWdj7ccxAbcCbxmjOln\nWdZgoAfwKn9+4fELsBe4zhiTalnWtcBxY4zbsqy38K64LzLG5PlKS743xrxkWVY74I8fc8/Cm+AD\nXAJgWVYpYCTw+xniM04k7H/8C7MF6GtZVgTeUporgNeBRngvRIqNK5o3YdW6ZO4fNBKPB4Y83J1P\nv/mOzKwsrku6MtjhSZBc2aAW35ufuGvyPDweePK2Dny8ZjMZ2bnc1KohnS+pwz3PLyTCYad2xfPp\n0rQObreHx+cvoeuU+diwMfLWDiFfugJwZcPa3n0xaS4ePDx5eyc+Xr2JjJxcbmrViM5N63LPlAVE\n2H/fF3UboTxQAAAgAElEQVSDHXLAtGl+Cas2pNBjyNN4PDC01318+u33ZGRmc31S21OOaduiCaOm\nvsIDw8bgysuj9z23ERlZomgDPwcRERF0796dYUOH4vF4SExKoly5cqSlpTH5uecY9vjj3HrbbTw7\ncSJLliyhVMmSDBg48LTjAPbu3Uv58uWDPLOz16pVK9auXctjffvi8Xh4rE8fvvrqKzKzsujcqRPd\nu3dn6LBheDwekhITKVeu3CnHAHTv1o3Jkyfz0ccfExsTw4ABA4iPj+fuu+9m2OOPA9C6dWuq50v8\ni5OIiAi6de/B48OG4PZ4SErMd1xMnsSwYU9w66238eyzE/hkyRJKlirJgAGDTjvu+htu5LlJzzKg\nf19yc1107XoPUVHedbt9e/dSvnyFM0QUosK4fMXm8RSrfO9P+e6+0gJoAmTiTVaj8ZZ0uIDJQDrg\nxpuU/wwsBz4xxgw8zTaTgCfwJu/HgLuMMYcsy6oC7ABqGWN2+RLx54FfgaNAfaAu3lX1+4FsvOUz\n6cAaX4zt8NanV/fFdwT4zhgzyrKsr/CWy3zOiZryx4BbfLEsw1siM5xT1Kr/2X46vGll6PxSA2hl\no/8GO4Rio/17fYMdQvERxif0s5VeOXwvBs7W0ZjQS3gDxRZCeUGguUOyoCAwLq5Zo1i8XfXrkz0C\ncoCWfWJ60OcXUkm5/DVKyr2UlJ+gpDwfJeV+SspPUFJ+gpLyE5SUn1BckvLfnu4ZkAO0zLBpQZ9f\nKJavnDXLspoB40/RtDD/HVRERERERILhH5GUG2NWAm2DHYeIiIiI/H02W/i+e/GPSMpFREREJAwU\nk9sXBkL4Xm6IiIiIiIQIrZSLiIiISEjQN3qKiIiIiEjAaKVcREREREKCLYxrypWUi4iIiEhoCOO7\nr4TvzEREREREQoRWykVEREQkJIRz+YpWykVEREREgkwr5SIiIiISGsL4lohKykVEREQkJNhsKl8R\nEREREZEA0Uq5iIiIiISGMC5fCd+ZiYiIiIiECK2Ui4iIiEhI0C0RRUREREQkYLRSLiIiIiKhwRa+\n68lKykVEREQkNKh8RUREREREAkUr5SIiIiISEmxhXL4SvjMTEREREQkRWikPQzvtVrBDKBbav9c3\n2CEUG19cNzHYIRQb7ed2D3YIxUaJrGPBDqH4iCkf7AiKDYfHFewQig2P3RnsEOSPwrimXEm5iIiI\niIQEm77RU0REREREAkUr5SIiIiISGmzhW76ilXIRERERkSDTSrmIiIiIhIYwrilXUi4iIiIioUHl\nKyIiIiIiEihaKRcRERGRkKBbIoqIiIiISMBopVxEREREQoMtfNeTw3dmIiIiIiIhQivlIiIiIhIa\n7OF79xUl5SIiIiISEmwqXxERERERkUDRSrmIiIiIhIYwLl/RSrmIiIiISJBppVxEREREQkMY15Qr\nKRcRERGR0GAr+vIVy7LswItAIyAb6GaM2X6KftOB34wxg/7O64Tv5YaIiIiIyLm7HogyxrQEBgET\n/9jBsqyeQINzeREl5SIiIiISGuz2wPz8ucuBJQDGmOVA0/yNlmW1ApoD085paucyWEREREQkzJUE\nUvM9zrMsKwLAsqwKwHDgoXN9EdWUi4iIiEhoCM4HPY8B8fke240xLt//3wyUAz4GygMxlmVtMca8\ndrYvoqRc/pYfVn7LooUzcTgcXHHVtbRLuv6U/Za8P5/UI79yS9eCF5CvvDCauLiSJz0fatxuD6Pe\n/pyt+w9TIsLB8FuSqHp+aX/75+u3MuuLlWCz0eWSOtzR5hIAbpkwh7ioEgBULFuKp27rGJT4i9J5\nzRqSMLofy6+6K9ihBJzb7WHUh8vYeuA3SjgcDL++NVXLljqp35PvfUvJ6EgeTWpGbp6b4e9+zf6j\naeS43PRo05i2daoFIfrAcLvdjH31Lbbt3o/TGcHj3W6lSvnz/e1LvlvD/CVf47DbubhKBQbdczP2\nM7+lXGytWL6cefPm4XA4SEpKomOnTgXaU1NTGT9uHDk5OZQpW5Y+ffoQFRX1p+OOHj3KIw8/zKjR\no6lSpQpjx4zhyJEjABw8eJCEhAQGDR5cpPM8G263m+dffJkdO3fidDrp88hDVKpY0d/+/YqVzJ2/\nAIfDQYfEq+jcsQMul4uJz03h4KFD5Obmcvst/6Fli+b+MV8u/Zr3PviQyROfCcaUzlphHxcLFy5k\nxfLluFwuulx9NR06dPBva/q0aVSqXJkuXboU6RwDLjj3Kf8fcA3wpmVZLYCNvzcYY6YAUwAsy7ob\nSPg7CTn8A5Nyy7KWAvcbY7YEOY5/AdcaY548izFlgI7GmHmBi+zMXC4Xc1+ZxJMTXyMyMponB3Xj\nkmatKXVeWX+fnOwsZk4dxY5tm7i0ZbsC479c8g57f9pOQr1Lijr0Qvdl8nZyXHnMefR2Nuzaz8T3\nv2byfd4LlDy3m8kffsu8x+4kJtLJDWNfo3OTBGIiS+DBwysP3RLk6IvORX27UenOa8lLzwx2KEXi\ny827vMdFj+vYsOcgE5esYPIdSQX6vLVqM9sO/kaT6hUA+Gj9Ns6LiWL0Te1IzcjiPy++E1ZJ+dI1\nG8nJdfHqyD5s3LaLSXMX8Wzf7gBk5eTw0lsfsXDsIKIiSzBk6my+XZtCmybn9JmpoHG5XEyfPp3n\nJk8mKiqKfn370rxFC0qXPnHBPn/ePNq2a0diYiJvvvkmixcv5pprrjntOJfLxfNTplAiMtK/jd8T\n8LS0NAYPGkSPnj2LfK5n47vvl5OTk8Pkic+wecsWps+cxcgnhgHefTZtxkyen/QsUVGR9Ok/kJbN\nm7Fy9RpKloxnYL/HOJaWxgMP9/Yn5dt//JEln36Gx+MJ5rT+ssI+Lvbs2cPmTZuYMHEi2dnZ/N//\n/R8AqUePMmHiRPbt3cu/b7opWNMNN+8CiZZlfQfYgHssy7odiDPGTC+sFwndZYgQZ4xZdzYJuU9D\n4NpAxHM29u/dyYUVKhMbV5IIp5PadRqxJWVtgT65uTm0vrIL1958T4Hnt27ewI9bU2jX4caiDDlg\n1u7YR6uE6gA0rF6RlD0H/W0Ou513B91DfHQkR9OzcHs8OCMcmP2Hycpx0fOlt+n2wpts2LU/SNEX\nnYwdu1lz88PBDqPIrN19gFYXVwGgYZULSdl3uED7ut0H2bj3EDc1reN/LqneRfRq3wQAD97jJ5ys\nMzto2cg73wa1qrN55x5/W4mICGaNeJSoSO+7R3l5biKdzqDEWRj27NlDxYoViY+Px+l0Uq9ePZKT\nkwv0SUlJoUkT7++7adOmrFu79k/HzZw5k85dulC2TJmTXm/uG29wzbXXUuYUbcVJ8qbNNG3iXYyp\nk5DA1u0n7ii3e88eKlaoQHx8nHfudeuyMTmFKy6/jK533uHt5PHgsDsAOHbsGLNmz+GBHt2KfB5/\nV2EfF2vWrKF6jRo8/dRTjBwxgmbNmgGQmZXFHXfcwZXt2xf5HIuEzR6Ynz9hjHEbY+43xrQyxrQ0\nxmwxxsz7Y0JujHnt794OEUJkpdz3dkBnIAaoCYwD7sa34m1Z1v1463heAxYCe4DqwAKgPtAY+MgY\nM8S3ySctyyqH916TdxljDluWNQZoDTiAZ40xb/lW1Q8BZYAOxpi8U8S2FNgCJOC9errF9//jgBxg\nOnAAeBrIAn4F7gX+5Yv/VsuybgYeA/KAZcaYQZZlnQ/MBs7zbfcuYCjQyLKsHoV5ZXa2MjPSiYmJ\n8z+Ojo4lM/14gT6xcSVp0LgF33zxof+5o7/9wqIFM+g95BlWLPu8yOINpPSsbOKjT6xcOWw2XHlu\nIhzef+ARDjufb9jGmLe/oHXdGkSXcBLtjKBru6bc2KIBPx0+Qq/p7/De4Hv9Y8LRgXc/JbpapWCH\nUWTSs3OJ95UnATjsJ46Lw2kZvPzVGibdlsSnyTv8fWIinb6xOfRd8DkPtW960nZDWXpmFnHRUf7H\ndrsNV14eEQ4HdrudsqVKArDgk2/IzMqmeQMrWKGes4z0dGJiY/2Po6OjSU9PL9gnI4NYX5/f2083\n7rPPPqNUqVI0adKENxcuLLCdo0ePsm7dOrr36BHAGRWO/HMGsNvt5OXl4XA4yMjILNAWEx1NekYG\n0dHR/rFPjR7H3XfdSV5eHhMnP8/93e6jRGSJk16nuCrs4+LYsWMcOniQESNHcvDgQUaOGMH0GTMo\nX7485cuXZ/Xq1UUzMSk0IZGU+5QyxnSwLKsW8AHeRPdULgKSgGhgJ1AJyAB+An5Pyt8xxiywLOtB\nYLBlWZ8BNYwxl1uWFQUs9z0HMN8Y8+4ZYvvOGHO/b3tDgHfw3s+yuWVZNmAHcLkxZp9lWb2BYcCH\n4C9JGQk0NcZkWJY1x7KsROBq4H1jzMu+W+00A0bhTeSDkpC/9cZLbN28nj27tlOzdj3/85mZ6cTE\nxv/JSK8V//uCtLRUJjz5KKlHfiUnO4sKlatzRfurAxl2QMVGRZKeleN/7PZ4Tkqur2pYiyvrX8zj\n85fwwapNdG6SQJVy52Gz2ah+QRlKxUbzy7HjlC9dsqjDlwCJjXSSnp3/uMB/XHyavIOjGdk8NGcJ\nvxzPICvXRY1y53HdJbU5kHqcPvM+4z/N6tK50cXBCj8gYqOjyMjK9j/2uD1EOBz+x263mynz3+en\nA4cZ/+i92ILwBSHnavbs2WxKSWHnzp1YCQn+5zMzM4nLl1QBxMTEkJmZSWRkJJmZmcTGxRETG0tm\nRsZJ4957/31swLq1a9mxYwcTJ0zgieHDKVOmDMuWLaNt27Y48u3L4ur3Of/O4/b4446JiSYjX1tG\n5okk/dDhw4x8egzXdOnElW3bsMVsZf/+/Ux58SVycnLYvXsPL02fwQM9uhfthP6iQB0XJePjqVK5\nMk6nk8qVK1OiRAlSU1M577zzimxuQRGC54a/KpSS8nW+/+4Bov7Qlv83tMMYk2pZVjZw0BjzG4Bl\nWfmLzr7x/fc7oAveBL+Jb9UbwIl3pR3A/IXYvsy3vev+MK4ccMwYsy/fa4/Gl5QDFwPnAx9blgXe\nT/fWBCxgFoAx5jvgO8uy2v6FWALm5jsfALx1cYMeuoXjaalERcVgNq2j8w13nnF8h2tuocM13jrq\nb774kJ/37grphBygcY2KfJ2ygw6NLTbs2k+tCuX8bcezsnlk5iJevv/flIiIILqEE7vNxqIVyWz7\n+ReG3nQVh1KPk56VTbmScX/yKhJqGlctz9fmJzo0qMmGPQepdeGJmtE7Wtbnjpb1AXjvh63s/OUo\n111Sm1+PZ3D/a4sZfHUrmtcMv3cVGtWuwbc/pJDYojEbt+3i4ioVC7SPfuVNnM4IJva5L2Q/4Nm1\na1fAe468v2dP0tLSiIqKIjk5mRv//e8CfevWrcuqVatITExk9erV1K9XjypVqrB///6Txl3eurV/\n3MABA3jo4Yf9pSrr1q7l1ttuK7pJnoN6deuwfMVK2rS+nM1btlC9+onPTFStUoV9+/dzLC2N6Kgo\nNiancPONN3DkyBEGDxvOQw/0pPG/GgGQYNVmxksvAHDg4EFGj3um2CbkELjjwlmiBO+99x433Hgj\nv/32G1lZWcTHn3mBTIqvUErK//hJjiygAt7SkUuAfafpdyrNgEV4y1WSfdv4yhjTw/dVqo8DP/r6\nuv/C9poAe4HLgJQ/jPsFKGlZVgVjzM9AG2BrvrE78V5oJBpjcn2lOuvwJuWXAusty7oC78XDRxSD\nzwFERERw+72PMn7EI3g8Hq5ofw1lyl7A8bRUXpk6it6Dxwc7xCJzZYNafG9+4q7J8/B44MnbOvDx\nms1kZOdyU6uGdL6kDvc8v5AIh53aFc+nS9M6uN0eHp+/hK5T5mPDxshbO4R16co/0ZV1qvP9j3u5\na/p7eIAnb2jDx+u3k5GTy02X1jnlmJlfr+NYVjbTl65l+lLvZzReuKsjUc5QOk2fXrumDVmx0XDv\niEl4PDC85+0s+d9qMrJzqFujCu99vZzG1kXcP9qbbN3W4QraXdooyFH/PREREXTv3p1hQ4fi8XhI\nTEqiXLlypKWlMfm55xj2+OPcetttPDtxIkuWLKFUyZIMGDjwtOP+zN69eylfvnwRzezcXNayBT+s\nXcejfQfgwUPfR3vz5dKvyczMpEunjvTsdh9DHh+O2+2hY9JVlCtXlhenzeD48ePMXbCQuQu8pTuj\nRg4nMt8HXkNFYR8X5cqVIzk5mUd798bj8fBgr14h8Y7JOQvRi/a/whYKn1rOd4uZQb7yki3Ag3i/\n5nQ33oR8N96a8gXGmBa/9zPGVPdt44AxprxvNXwX3pXwY0BX4KhvW5cCccC7xpgn/8qdWnx9juCt\nO08H/ov3a1bvN8bc6utzFfAU3kT9CN56+PqcqCm/0zcfhy+2e4BYvCvl8XgvNO7DWwP/OTDNGPPc\n6WJauSW1+P9Si0DDHQvP3Okf4ovrTvpG4H+s9nOL74paUcu9qH6wQyg2DpWpHewQio0Id26wQyg2\nXPbQ/cBxYat50UXFom4k66OXA5LjRHW5P+jzC4mkvDj7u7dY9NWN/9cYU+g3bVZS7qWk/AQl5Sco\nKT9BSfkJSspPUFJ+gpLyE5SUB154vC8aYJZlVQVeP0XT139ze02ACXg/uCkiIiIif0VwvtGzSCgp\n/wuMMbuBtoW4vTVAaBZLioiIiEihU1IuIiIiIqEhjD/oqaRcREREREJDGN+nPHwvN0REREREQoRW\nykVEREQkNITxBz3Dd2YiIiIiIiFCK+UiIiIiEhrCuKZcSbmIiIiIhIYwvvtK+M5MRERERCREaKVc\nREREREKCJ4zLV7RSLiIiIiISZFopFxEREZHQoFsiioiIiIhIoGilXERERERCQxivlCspFxEREZGQ\noA96ioiIiIhIwGilXERERERCQxiXr4TvzEREREREQoRWykVEREQkNIRxTbmSchEREREJDfbwLfII\n35mJiIiIiIQIrZSHodLOo8EOoXgI46vps9V+bvdgh1BsfHHHjGCHUGy0WPtqsEMoNjye8H1L/GyV\ncGUGO4Riw1XCGewQ5A90S0QREREREQkYrZSLiIiISGgI41siKikXERERkZDgCeOkPHxnJiIiIiIS\nIrRSLiIiIiKhQR/0FBERERGRQNFKuYiIiIiEhHCuKVdSLiIiIiKhQeUrIiIiIiISKFopFxEREZHQ\nEMblK+E7MxERERGREKGVchEREREJCR7VlIuIiIiISKBopVxEREREQkMY15QrKRcRERGRkOBB5Ssi\nIiIiIhIgWikXERERkZAQzt/oGb4zExEREREJEVopFxEREZHQEMYr5UrKRURERCQk6D7lIiIiIiIS\nMFopFxEREZGQEM4f9FRSLn/LihXfs2DeXOwOB4lJHejYsXOB9tTUVCaMH0N2Tg5ly5Sld5++REVF\n8fXSr3hv0Ts4HA6qVa/Bg70exm4P3X9gbreHUW99xtb9hygREcHwWztQ9fzS/vbP1xlmfb4SbNCl\nSV3uaNvE3/ZrWjq3TZjDtAdvpsaFZYMRfqFyuz2M+nAZWw/8RgmHg+HXt6Zq2VIn9XvyvW8pGR3J\no0nNyM1zM/zdr9l/NI0cl5sebRrTtk61IERftM5r1pCE0f1YftVdwQ4l4NxuNxNmzGHbrj2UcEYw\n+IF7qFzhwgJ9srKz6T1yAoMfvJfqlSuQl+dm7MuvsnvfAWw2G/173kXNqpWDNIPCtWLFcubPm4vD\nf+7sVKA9NTWVZ8aPIycnmzJlyvJon8eIiooCICsri2FDh9D70T5UqVIlGOGfE7fbzaSXX+HHXT/h\ndDrp/1BPKlco72//buUaZi98G4fDQeer2nF1UnsWf7GUJV9+DUBOTg7bd/7EO69NIz4uFoCpM2dT\npVJFruuUGJQ5na0Vy5czb948HA4HSUlJdOx08u9//Lhx5OTkUKZsWfr06UNUVNQpx+Xl5TFl8mT2\n7tuHDXjo4YepXr06P27fzogRI6hYsSIAnbt0oU2bNkGYrZyt0M2GAsiyrCssy2ro+/8DZzm2umVZ\nywMTWfHgcrmYOX0aTz09hrHjJvDJ4o85cuRIgT4L5r9Bm7ZXMv6ZZ7moZk2WLP6I7Oxs5rz+GqPH\nPsMzE58jIyOdVStXBGkWhePLjdvIcbmY0+dOel9zBRMXLfW35bndTP7gG6b1+g9z+tzBwv+t5cjx\nDABy8/J4auGnRDrD57r4y827yHHlMafHdfROupSJS07+3b61ajPbDv7mf/zR+m2cFxPFa92u5aW7\nOjLmo/8VZchBcVHfbjSY9jT2qMhgh1Ikvln5Azk5ucwYM4wH7ryZKbMXFGjfvH0nDz4+ln0HD/mf\nW7Z6HQDTRg+lx203Mm3e/xVpzIHicrmYMX0aTz09mrHjnmHJKc6d8+fPpU3btox/ZiIX1azJ4sUf\nA7Bt61YGDujPzwd+DkbohWLZilXk5Oby4vin6XHXbbw0a46/zeVyMfWV2UwYOZTJo0bwwSef89vR\no3Rq35bJo4YzedRwrJoX8Uj3u4mPi+Vo6jEGjBzDd6vWBHFGZ8flcjF9+nSeHjWKcePHs3jx4pN/\n//Pm0bZdO56ZMIGaNWuyePHi045bscJ7jp04cSJ3de3K67NnA7Bt+3ZuuOEGxo0fz7jx48MvIbfZ\nAvNTDCgpP7V7gYrBDqK42rNnNxUqViQuPh6n00ndevVISd5YoM+mlBSaNGkKQJOml7Ju3VqcTifP\nTHzOv+qTl5eHs4SzyOMvTGt37KVVnRoANKxekZQ9J67hHHY77w65j/joSI6mZ+J2e3BGOAB4dtFS\nbr7sX1xQKi4ocQfC2t0HaHWxd/WuYZULSdl3uED7ut0H2bj3EDc1reN/LqneRfRq7333wIN3n4W7\njB27WXPzw8EOo8is37yN5o0bAFC/dk22/LirQHuuy8WYAQ9RrVIF/3Ntml/CwPvvBuDA4V+Ij40p\nqnAD6vdzZ7z/3Fmf5D85dzb1nTsBcnNzGfb4E1SpHLrvGGzcZGjWuBEA9azamO0/+tt+2ruPShXK\nEx8Xh9MZQYM6CWxI2exv37LtR3bu2cs1Ha4CIDMri7tvvYnEtq2LdhLnYM+ePVTM9/uvV68eycnJ\nBfqkpKTQpIn3nNi0aVPWrV172nGtWrXikd69ATh08CCxsd53D7Zv28bKVavo378/z02aREZGRtFO\nNMA8NntAfoqDMy7TWZZVEpgJnIc3UV0I3A7UNcZ4LMuaCnwBbAemADbgV7yJbWNgHJADTAcygV6A\nE+/f4Bt8fV8AmgIHgBrANUCeb0y0b1wPY8ye08Q4ArgYKAeU9W3v30BtoKsxZrllWX2BWwEX8I0x\nZqBvXA3gAqAa0Af4BegIXGJZ1ibf9ksBPwC1jTF5lmWNA9YYY948zW4737KsRUAFYIMxprtlWdWB\nWXj3uQd4xBiz3rKsA8aY8r7XWQC8DFT37T87MBy40ze/aGCyMWYOQZSRkUFsTKz/cXR0DOnp6Sf1\nifGdIGKiY8hIT8dut1O6tLe044P3F5GVmUnjxk0IZelZOcTnW/F02Gy48txEOLz/wCMcdj5fv5Ux\nb39O67oXEV3CyXsrkikdF8NldWow6/PQfqcgv/TsXOKjSvgfO+wn9sXhtAxe/moNk25L4tPkHf4+\nMZFO39gc+i74nIfaNy3yuIvagXc/JbpapWCHUWQyMjOJi4n2P3bY7bjy8ohweC9QGybUOuW4CIeD\np56fwdcrfmBUv15FEmugnXzujCbjFOfO35Or/O1169UrukADJD0jg7h8F1j2fMdCekYmcTEn2mKi\nozmefiKZnPv2u9x9y7/9jytceAEVLryAFT+sK5rgC0FGerr/7yJ4f7+n+tuZ//efnp7+p+McDgcT\nJ0zgu+++Y+jQoQDUtiw6dOxIrVq1WDB/PvPmzqVb9+6Bnp4Ugr9yaXAxsMAYkwQkAXcBG4DWlmVF\nAu2AD4AZQC9jTFvgY2CAb3yUMaa1L5GsDXQxxlwObAI6ANcCZY0xzYD7gN8L5SYAU3zbmwCMPUOc\nmcaYjsD/AZ2NMdf4xtxqWVYD4D9AK99PLcuyrvaNyzbGdAJ6A32MMWuAJcAAY8xuAGNMKrAM6GBZ\nlgPoBCz6k1hKAvcALYH2lmVd4JvDZGPMFb7XeuUM8zni208rgSuAG/FeLOSdYdz/s3ff8VFV+f/H\nX2mkAlJUQlfEk4QmooBYQCURRFf9ru7aBQUE6b0FBAWkKFV6UVAQ0d/u6lpwWQsrSpWahBy6UkQs\nlJBMSJn5/TFDSDBUmUwmvp+PRx4w95x753Pu3Jn5zGfOveM1by14g4ED+vLyiBfJyDj9QuJwZBAZ\nFVmgb0REBA6H+wU1w5FBZKS7Iux0Opk3dzYbN25g0JBhBBSTr4wuVWRYKdJPZuXddrpceQn5KS0b\nXM/yEZ3Jzs3l32uT+dearay2e3lu6hLsgcMMefsTfjl+oqhDv+wiQ0PO2Bfk7Yv/JO3maMZJur61\njPlfb+LTLTv5YMN2AA4dO0H7+R9zX4Pa3NvgOp/ELt4TER5OhiMz77bT6cpLyM9naLcOvDt1DGNm\nvIkj86S3QvS6hQveZOCAfrw0YniBqqXD4SAyquC3Ze7XTsfp9siS821aZEREwWPBdfpYiIwIJyPT\nkdeW4XAQ5UlE006ks+/AjzSsX7doA75MFixYwID+/RkxYsTvHv+oyMLeO/M9/lFRRERG4jjHen36\n9mXO3LlMnjKFzMxMmjVrRu3a7g+7zZo1Y9euXZQkLgK88lccXEhS/hPwoDHmbSARd5V7DvAM8ADw\nobU2B4gFphtjvsJd5T1VCrL5tnUYWGCMeQOo79lWLLAKwFr7M5Dq6VsPGOzZ3jCg4JlBv7fB8+9R\n3Ak/wBEgDIgBVltrs621LuBr4FTZYaPn332evmczB2iLOyH/r7U26xx9d1trj1hrnZ4xR3jG+T/P\nODdx+sNHfvmPCuvpmwb0xP2twbuAzyaiPvVMO8aMfZW3F7/LwR8PkpZ2nOzsbJKSthITE1egb2xc\nHe5F6N0AACAASURBVNavWwfAd+vXUaeu+8X09amTycrKInHo8LxpLP6s4TVVWJnirvxu2XuQ2pWv\nzGs7kXmSZ6e8Q1ZODoGBAYSXCiEwMIA3uj/G/O6PMa/bo5gqVzHqyXupWMb/33gbVq/Eyh3uL7O2\n7PuJ2lefPuH1iVvqsqTzQ8x77j6evf0GWte/jgduvJ5fT2TQ6c1P6ZnQmIcaGV+FLl5UP6Y2qzZs\nASBp+y5q1Tj/9ItPv/qWhf/4CICw0FIEBgYQ6Mcf4J9+pi1jxo5n0eIl/PjjQdLS0vK9dsYW6Bsb\nV4d169YCsD7fa2dJUDfWsPo791tust3OtTWq57XVqFqF/QcPcTztBNnZOWxJ2UadmOsB2JK8jRv9\nNCEHeOaZZxg7bhyL33mHHw/mf/yTiIkt+PjHxcWxzvPeuX79eurWqUO1atU4WMh6n3/+Oe+++y4A\nYaGhBAYEEBAQQGJiIta6U69NmzZxXe3Cv42S4udCzjLrA6yy1s4wxtwJtME9XWUc7sT71PeKFnja\nWvuDMeZW3FM3AJyQNwVkBHDqWbgcdxKaBDwFTDLGlMNdTQd3cv6qtfZbY0wMcL4zFVznaEsF+hhj\ngnFXmu8AFgINzrKekzM+sFhrVxpjJuOu5ideQizbgNuBD40xN+CeqgMQYoyJwj3FJ//3k6f2WzTQ\nyFr7kDEmDNhnjHnL80HIJ4KDg2nf4XmGJQ7G6XISH9+KihUrkpZ2nCmTJzIk8UX+/ujjTJwwns+W\nfUKZsmXp138gO3fuYPl/llGnTl0GD3J/kfKXBx6kWbPbfDWUP+yu+tezyn7P0xMX4cLFS4+35pP1\nKWRkZfNwswbce1Mc7aYsITgwkOsrX0mbm+LOv1E/dVdsTVbt2s/Tsz/ABbz0UHM+2bzTvS9uji10\nnbkrNnE88ySzv9rI7K/cb9bTnm5FWAk6AfbPrnmTG1m3JZmOg0ficsGQLs/xn69XkeE4yYMJLQpd\np0XTRox6fR6dE18hJzeXHu0eIzS0VKF9/Yn7tbMjQxMH43S5SIhP8Lx2pjF58kQSE4fx6KOPMWHC\nq3y2bBllypahf/+Bvg77srm96c2s37SFLv2H4sLFgO6d+e+KlTgyM7n/npZ0efZp+g0fhcvlovXd\nd3JlhfIA7DtwkOhKV/k4+j8uODiYDh06kDhkCC6Xi/iEfI//pEkkDh3Ko489xoTXXmPZsmWULVOG\n/gMGnHW9W2+9lQkTJtCvXz9yc3Lo+PzzhIaG0rVrV2bMmEFwUBDlypWje/fuvh76ZVVc5n97Q4DL\nda5cFjyJ+FTcc7+PAnWBONzJektr7V2efo2A1zg9Z/o53HPQO1lrHzXGBOCu9NbEPa/7CPAtMBp4\nHff880NAY6AJ7orwDNzV63Cgh7V21VliHA4cstbONMZ0AipZa4cbYx4EWllrOxljegN/x51srwR6\n456vfWq9GGCmtbaFMeZ53B82/g58mW/Od2/gEWvtLefYXzVxT/dp6rm9GvdcdnBX20Nxf0PQzVq7\n3hgz1HM/u4EgYLxnH8VYawd69tsM3N8s5AIfWWvHnu3+AXbs+v7cD+qfRLUdy30dQvFx/KivIyg2\nPn9ijq9DKDaabnzD1yEUG0fCo8/f6U8iMvuYr0MoNjJKlfF1CMVGrWuvLRZfV/2cvMYrOc6VdZr4\nfHznTcq9zZMM32CtXWKMqQAkAzWstcVuAqExph/wq7V2vq9jORcl5W5KyvNRUp5HSflpSspPU1J+\nmpLy05SUn1ZskvKUtd5JyuMa+3x8xeE74n3AWGNMT9yV4gFnS8iNMf8Ayp+x+Ji19gEvx4gx5k3c\nlf/7Pbc74r4KzZkGna2iLyIiIiKXzlWCr+bt86TcWpuO+4TRC+n7f14O51z33faM27Nxn3wpIiIi\nIvKH+DwpFxERERG5EC4/vhLT+ZTc7wBERERERPyEKuUiIiIi4hdK8iURlZSLiIiIiF8oLr++6Q0l\n9+OGiIiIiIifUKVcRERERPxCSZ6+UnJHJiIiIiLiJ1QpFxERERG/UJIviaikXERERET8gk70FBER\nERERr1GlXERERET8gk70FBERERERr1GlXERERET8guaUi4iIiIiI16hSLiIiIiJ+oSTPKVdSLiIi\nIiJ+QdNXRERERETEa1QpFxERERG/4IvpK8aYQGA60AA4CbS31u7M134/MAzIAeZba+dcyv2oUi4i\nIiIicnYPAmHW2luAgcBrpxqMMSHARCABaA50NMZcfSl3oqRcRERERPyCiwCv/J3HbcAyAGvtauCm\nfG2xwE5r7RFrbRawErjjUsam6SslULozwtchFAvpVeN8HUKxUSrzuK9DKDaabnzD1yEUG6sbtvN1\nCMVGbOonvg6h2AjNSvN1CMVGSb7Sh79yBfjkRM8ywLF8t3ONMcHW2pxC2tKAspdyJzraRERERETO\n7jhQOt/tQE9CXlhbaeDopdyJKuUiIiIi4hdcLp9Uyr8B7geWGmOaAlvztW0DahtjygMncE9defVS\n7kRJuYiIiIjI2f0TiDfGfAsEAO2MMY8DUdba2caY3sBnuGegzLfWHriUO1FSLiIiIiJ+weWDmdfW\nWifQ6YzFqfna/w38+4/ej5JyEREREfEL+kVPERERERHxGlXKRURERMQvqFIuIiIiIiJeo0q5iIiI\niPgFVcpFRERERMRrVCkXEREREb9QkivlSspFRERExC/46Bc9i4Smr4iIiIiI+Jgq5SIiIiLiF0ry\n9BVVykVEREREfEyVchERERHxCyW5Uq6kXERERET8QklOyjV9RURERETEx1QpFxERERG/oEsiioiI\niIiI16hSLiIiIiJ+wVmC55QrKRcRERERv1CST/RUUi5/2HdrVvL+kjcJCgzizvg23N3qL4X2+/iD\npRw78iuPt+1cxBF6j9Pp5NU5b7Fj7z5KhQQzqHM7qkZfXaBP5smT9BjxKoNeeJaaVaPJzXUyZuYb\n/HDgEAEBAfR7/mlqVa/qoxFcfk6nkzFvvMeOHw4SEhLM0PaPUq3SlXnty779jneWrSAoMJDrqkUz\nsN0jBAaWrJl0Oi4u3BWN6xMzui+rWz7t61AuG6fTybRp09i9Zw8hISH07NGDypUr57WvXrOGxYsX\nExQUREJCAq1btTrrOrt27WLGzJkEBgYSEhJC3z59KFeuHABHjx2jb58+TJ8+nVKlSvlquJfE6XQy\nfu4idu7dR0hIMIM6PUO1Qp4j3V+ewODObalZJTpv+W/HjtNuwMtMHtq7wHJ/4nQ6mTRzLrv27CUk\nJIR+XTtRpfLpsXy7dj0Ll7xPUFAgrVvexX33tARg0Xv/5Nu168jOyeGB1vfQJuFuXho/kd+OHAXg\n0OGfiTO1Gdavl0/GJX/MRb8TGmPCjDHtjTFtjTGFZ1/FnDFmiTGmha/jKAlycnJYMHcqQ16ewPAx\nr/Pfzz7k6JHfCvTJOnmSKeNH8J+P/uGjKL3nf2s3kJWVzZxXEun85CNMWbCkQPu2nXt4YegYDvx0\nOG/ZyvWbAJg1eggdH/s/Zi3+f0Uas7d99d1WsrJzeGNEL7r9/X4mLvpXXltmVhYz3vuYWUO6Mn94\nT044Mvl6Y7IPo/UOHRcX5to+7ak3aySBYaG+DuWyWrVqFVnZ2UycMIF27doxZ+7cvLacnBxmz57N\nqJEjGTd2LJ9++ilHjhw56zozZ82ic6dOjBs7llubNeO9994D4LvvvmPIkCH8duSIT8b4R/1v3Ub3\nc2T0YF544q9MXfhegfZtu/bSedg4Dhz6ucDynJwcxs56i1A/+xByppWr15GVlcW08aPp+PQTTJ+/\nMK8tJyeHaXPfZPxLiUwaPYKPPvsvvx05yqatySSnWqaOHcnk0SP4+ZdfARjWrxeTRo/g5cH9iIqM\noMtzbX0zqCLicgV45a84uJTyVCWgvbX2TWvth5c7IPEvB/btpVJ0FaKiyhAcEkJMXH22JW8q0Ccr\n+yTN727NQ38vOZWwUzZv20GThvUAqHt9LVJ37S3Qnp2Twyv9u1IjXzWneZMbGdCpLQCHfv6F0pER\nRRVukdhkd3NLg1gA6tWuybY9+/LaSgUHM394T8JC3W+oublOQkNCfBKnN+m4uDAZu3/gu0e6+TqM\nyy45OZlGjRoBEBsTw44dO/La9u3bR+XKlSldujQhISHUqVOHpKSks64zaOBAatWqBUBubm5eRTwg\nIIBXRo+mdOnSRTm0y2bztp00bVgXcD9Htp3xHMnKzmZMvy7UqFKpwPKpC9/joYTmVCxXtqhC9Yqt\n27bR+MaGAMTFXM/2nbvy2r7fd4Aq0ZUoHRVFSEgI9eJi2JK8jXUbNnFNjeoMHT2ewSPHcsvNjQps\n843FS3moTWsqlC9XpGORy+dSpq8MAeKMMU7gBSAVGAScBKoBM4G7gAbAZGvtDGNMc2AUkAvsAp63\n1mYXtnFjzAvAM4ATWGet7W6MeRMI8Gw/CnjaWptqjOkGPA64gCXW2imevieBmkA00NZau8EY0wVo\nD/wIXHWuARpjmgCTcH9oOQA8AcQAUz1jyAQ6eNrfBfZ57m8JUBdoCHxsrR1sjPnKs49iPGP4O/Az\nMMsznmjgQ2ttYmGxAxWBDtbaRzyxfQM8Yq09eK4xFBVHRjoRkVF5t8PDI8hITy/QJyqqDA1ubMxX\n//2kqMPzugyHg6iI8LzbQYGB5OTmEhwUBED9mNqFrhccFMTLU+ewYs0GRvXtUiSxFpV0RyZR4WF5\ntwMDA/L2SWBgIBXKlgFgyWf/w5F5kib1jK9C9RodFxfm0D//Q3iNKr4O47LLyMggMuL0h6rAwEBy\nc3MJCgoi/Yy28PBw0tPTz7pO+fLlAUhJSeHfH33EuHHjALjxxhuLaDTekX6e50iDQp4jH3/5DVeU\nLU3TG+qy8J/+/X6SkeEgMrLwYyTDkVGgLTw8jPSMDI6lpfHT4Z8ZPXQgP/50mMRRY1kwfTIBAQEc\nOXqMDZu30uW5Z3wxnCJVkueUX0qlfBSQAryUb1lV4K9AZyAReApoDTxvjAkA5gD/Z61tjjvJbXuO\n7bcDulprbwG2GWNOfXDYZa29CxgOjDPGxOFOcG8DbgceNMacenf/3lp7D+4kuqMx5mqgB9AUeAA4\n3/des4BnrbVNgI+BWM8YunrGMB2Y4Ol7LfAccB/wMtAbaOJZdsq31toWuBP4wbiT8dWeGBsDnfL1\nLRA7sByoZ4wpZ4ypA/xSHBLyJW/NZsTArox7eSCOjNNJuMORQWS+JL2kiwgPJ8ORmXfb6XTlvamc\nz9BuHXh36hjGzHgTR+ZJb4VY5CLDw8jINx7XGfvE6XQyadG/WJNkGdfzWQICSt4LrI6LP7eIiAgc\nDkfebafTSZDn8Y+MiCAjX5vD4SAyKuqc66xYsYKpr7/OiOHDuaKsf1eIT4kMDyc9/3PEdf7nyEdf\nrmTt5hReeHEcO/bu46Wp8/j1yDFvh+oVERHhBY4Dp8uV93hHhEfgyLdvHI5MoiIjKFM6ipsb3kBI\nSAjVq1YhJKQUR48dB2DFt6tp2fy2vG2If7pcZ1cleSrfR3Enz1nAESAMuBJ31Xepp2qcANQ4x7ba\nAV2MMSs8/U69Y3/h+fdbwOCuSNcAPvf8VQBOfbTe6Pl3nyeGWkCytfakJ8615xlPJWvtNgBr7Txr\n7QagsrX21LyM/wF1PP/fba095hn7T9ba36y1mbir96ecGftvwM3GmEXARCD/hMoCsVtrXcDbwGOe\nfTPvPLEXiUef6siLY15n9tv/5tDBA5xIO05OdjbbkjZxfUxdX4dXZOrH1GbVhi0AJG3fRa0a5z8x\n79OvvmXhPz4CICy0FIGBAQSWoMS0wfXX8M2mFAC27tjLddUqF2gfPW8pJ7NzeK3Xc3nTWEoaHRd/\nbnFxcaxbvx6AbampXFOzZl5btWrVOHjwIGlpaWRnZ5OUlERsTMxZ1/niiy/cFfKxY4mO9s+TGgtT\nP+Y6Vm3YCnieI9XP/43JjJcGMOOl/kwf0Z/aNasxrNtzVPDTaSx1Y2NYs34DACmp27m2RvW8thrV\nqrD/4I8c9xwjm5NTiIu5nnpxsazdsBGXy8Uvv/5GZmYmZUq7i2AbNm2hcaOGPhlLUSvJc8ovZfqK\nk98n867COnr8AuwHHrDWHvOcHHriHP07AJ2stZnGmM+AZp7ljYCVwK1AMmA9/7a21rqMMb2ALcDD\nhcSzA6hjjAkHsnBPL3n7HDEcNMbUttbuMMYMALZ7ltW31m4BmnuWnW/spzTy7INTsbcFjlprnzfG\nXIe7mn/qiChse2944o0EBl7A/RWZ4OBgnm7flVHDeuNyOrkzvg3lK17JibTjzJwyhr5DRvs6RK9q\n3uRG1m1JpuPgkbhcMKTLc/zn61VkOE7yYEKLQtdp0bQRo16fR+fEV8jJzaVHu8cILUHJ6Z031WfN\nVsuzwyficsGLzz/Osm/Wk3Eyi7hrqvHBitU0NNfSafQ0AB675w7uvLmBj6O+vHRc/Lk1a9aMjRs3\n0rtPH1wuF7179eLLL7/EkZnJva1b06FDB4YkJuJyuUiIj6dixYqFrpObm8uMmTO56qqreHnkSADq\n1avHU08+6eMR/nHNGzdk7ZYUOgx5BVwuhnRpx2dfr8GRmcmD8c19HZ7X3d60Md9t2kLX/kNwuVwM\n6NGF/674Gocjk/tbxfPCc8/Q/8VROF1OWre8iysrVODKChXYnJRC5z6DcLqc9OjUPq8y/sOBg1S+\n+urz3GvJUJKnrwS4XBeSU55mjAkDVgOfAXtwz5fuZK191BgTA8y01rYwxlyBe4pGjDEmARiGO5k/\njntO+OGzbL898DyQhnuqSwfc89Qr4Z52EoR7nvgeY0w/4EHclea1QDfcleQl1tplxphWwKPW2rbG\nmGc97T/jrp4Ps9Z+dZYYbsY9PcWJew76M0AcMBl35T4H9/QUp+e+mnr2S6q1tqZnG4estZU83w4c\nAcoD6bin9lQCFgPHOD0X/27cU4N+F7tnex/irvYPOucDBGza8fPFPaglVLWTO87f6U+iVOZxX4dQ\nbGSFlfF1CMXG6obtfB1CsRGb6t9zlC+nK9J9PkOy2MgM9c9KvDdUNvWLRTa8zh71So5zs7nC5+O7\n6KTcFzwnQC6x1i7zdSwXy5OUd7LWpv7B7XwE9LTW7jxfXyXlbkrKT1NSfpqS8tOUlJ+mpPw0JeWn\nKSk/rbgk5WtTj3klx2kcU9bn4/PJjwcZY6oDCwtpWmGtffHPEsOF8Ey5WQl8cSEJuYiIiIj4H58k\n5dbaH4AWF9G/ra9j+AP384fuw1rrwD0nXURERORPzenrALzIJ0m5iIiIiMjFKi5XSvGGy3VJRBER\nERERuUSqlIuIiIiIXyjJl0RUpVxERERExMdUKRcRERERv1CS55QrKRcRERERv6DpKyIiIiIi4jWq\nlIuIiIiIX3CW4N8sV6VcRERERMTHVCkXEREREb+gOeUiIiIiIuI1qpSLiIiIiF/QJRFFRERERHzM\npRM9RURERETEW1QpFxERERG/4NSJniIiIiIi4i2qlIuIiIiIX9CJniIiIiIiPqYTPUVERERExGtU\nKRcRERERv6Bf9BQREREREa9RpbwEutJ1yNchFAtHIyr5OoTiQ/siT0k+SehixaZ+4usQio1tMff6\nOoRio8LWtb4OodioEnjA1yHIGZwleE65knIRERER8QslubCi6SsiIiIiIj6mSrmIiIiI+AVdElFE\nRERERLxGlXIRERER8QtOXRJRRERERES8RZVyEREREfELJXlOuZJyEREREfELuiSiiIiIiIh4jSrl\nIiIiIuIXSvIveqpSLiIiIiLiY6qUi4iIiIhf0ImeIiIiIiI+5tJ1ykVERERExFtUKRcRERERv6AT\nPUVERERExGtUKRcRERERv6ATPUVEREREfKwkJ+WaviIiIiIi4mOqlIuIiIiIX3C6dElEERERERHx\nElXKRURERMQvlOQ55UrK5YI4nU4mz5jDrj3fExISTN9unalSOTqv/du163nrnfcICgqiVfyd3HdP\nPAAde/QjMiIcgEpXX82Anl3YvnM3E6fPplRIMLWuvYauHdoRGFj8v7RZs3o1ixcvJigoiISEBFq1\nbl2g/dixY4wbO5asrCzKV6hAr169CAsLK3S95cuX89/lywHIyspi9+7dLFq8mKioKABmz5pFlapV\nadOmTZGP80Jczn1xytGjR+nerRujRo+mWrVqjHnlFY4cOQLATz/9RExMDAMHDSrScf4Ra9as5p3F\niwgKCiI+4R5atfr9Pho/bixZWScpX74CPXv1JiwsDIDMzEwShwymR89eVKtWzRfhXzSn08m0adPY\nvWcPISEh9OzRg8qVK+e1r16zpsBj37pVq7Ous2vXLmbMnElgYCAhISH07dOHcuXKAXD02DH69unD\n9OnTKVWqlK+G6xVXNK5PzOi+rG75tK9DKRIb137NB+/OJTAoiDta/oUWCQ8W2u+zD9/h2JFf+dsz\nXQFY9sFiViz/gDJl3cdE286DiK5ao8jivhycTidTps9i1569hISE0Kd7lwLvqavWrOOtJUsJCgyk\nVfzdtGmVQG5uLhOmTmf/gYMQAD1f6MQ1NWuwY+cuJk2bSUhICLWuvYYuHZ/zi/dU+T2/SsqNMZ8D\ng6y1a40xpYCfgZHW2vGe9q+AntbaTefZzl4gxlqb6eV4BwJfWGvX5lsWBqRaa2te5LaqAw2stf++\nvFFemJWr15KVlc3rr44mJXU7M+YvYGTiQABycnKYPvdNZkwYQ1hoKN37J9Ks8c1ERUYALia+8lKB\nbU2YNpOuHZ+lbmwM8956h89XrCT+zjt8MKoLl5OTw+zZs5k0eTJhYWH07dOHJk2b5iUKAO8sXkyL\nO+8kPj6epUuX8umnn3L//fcXul58fDzx8e4PLtOmTSMhIYGoqCiOHT3Kq6+9xoH9+/nrww/7arjn\ndLn3Rbly5cjJyWHqlCmUCg3N28apBDwtLY1BAwfS8fnni3yslyonJ4c5s2cxcdIUwsLC6Ne3N02a\nnLGP3llE8xYtiI9PYOnSd/n000946KH/Y8f27bz++lR++fUXH47g4q1atYqs7GwmTpjAttRU5syd\ny4vDhgGnj5nJkyYRFhZGn759adqkCSkpKYWuM3PWLDp36kStWrX45JNPeO+99+jYsSPfffcd8994\ng988H9ZKkmv7tKfKk38hN93h61CKRE5ODovnTWT4a28SGhrOyIHtadj4dspeUSGvT9bJTOa/Pord\nO1K46ZY785bv3ZVKx57Duea6WF+Efll8s3oNWdnZTH1tLCmplpnz3uDloYMB976ZMXc+0yaOJyw0\nlB79B9OsSWNSUi0Ak8e/wqYtScx/axEvDx3MxNdn0OX59tSJjWH+W4v4YsX/aHlnCx+OzrtKcqXc\n3z5KLQdu9/z/duAz4F7IS3ZrAJt9E9rvWWvH5E/I/6C7gFsv07YuWlJKKjc3ugGAuJjrsTt257V9\nv28/VaIrUToqipCQEOrGxbAlOYVde/aSeTKLfkNfoveQ4aSkbgfg519+o25sDAB1Yw1JKduKfkAX\nad++fVSuXJnSpUsTEhJCnTp1SEpKKtAnOTmZRo0aAXDTTTexaePG8663fft2fvj+e1rfey8AjsxM\nnnjiCe66++6iG9xF8sa+mDt3Lve2aUOF8uV/d3+L3n6b+//yF8oX0lZc7dv3A9H5xhpXpy5JSVsL\n9ElJTqZRo5sAuOmmm9m0aSMA2dnZJA4dRrWqVYs87j8i/2MeGxPDjh078trO9tifbZ1BAwdSq1Yt\nAHJzc/Mq4gEBAbwyejSlS5cuyqEViYzdP/DdI918HUaRObh/D1dHVyUyqgzBISHUjm2ATd5YoE92\ndha33tWG+x9pV2D53l2pfPT+AkYO7MC/33+zCKO+fJKSt3HzjQ0BiIsxbN+xK6/th337qRwdne89\nNZYtycnceksTend7AYDDPx8mKjISgJ9/+ZU6ee+pMSQlF//31D/C6fLOX3HgV5Vy3En5UOA13Mn4\nXGCsMaYscCOwAmhpjBkJZAK/As8CNwBjgSxg9qmNGWM6AQnAY9bak2femTEmCJgFVAOigQ+ttYnG\nmNqe+y4FZACPAlcUsmw8sARYCSwCygE7822/HjAFCMgXa0NggCfWaz3rjwEGAhHGmG+ttR9e6g68\nVBkZDiIjIvJuBwUGkpubS1BQ0O/aIsLDSU/PILRqKH976H7aJLRk/8EfGTh8FAtnTiG60lVs3ppM\ng3p1WLX2OxyZv9v1xU5GejoRnhdAgPDwcNLT0wv2ycgg0tPnVPv51lv67rs8/sQTebcrVapEpUqV\nWL9+vbeG8odd7n2xfPlyypYtS6NGjVj67rsFtnP06FE2bdpEh44dvTiiyy8jI4PIiIJjzTjPPjrV\nHlenTtEFehm5x3z6dSAw32tE+hltecfEWdY59QEsJSWFf3/0EePGjQPgxhtvLKLRFL1D//wP4TWq\n+DqMIpOZkU54RFTe7fDwSDLSTxToExlVhnoNm/L15x8VWN7k9nha3vsI4eGRTHmlP5vWfc0NN9+O\nP8lwOIiMzHfsB53xfInM/54aRnp6BgBBQUGMnTCZb1atYdig/gBEV7qazVuTaFCvLqvWrifzZPF/\nT5XC+VtSvhGIMcYEAHcAg4H/Ai2B+rgr57OB26y1B4wxPYBE4CMgzFrbBMAY8zLQDXey/oi1Nvcs\n91cNWG2tbe+pxO/3bO9V4BVr7TJjzF9wJ9JdCll2SicgyVo7xBjTBHfVG2AO8Ky1NsUY8xzQH/cH\njxqe8YQCB621o4wxY3BPuSnyhBwgIiIch+P0bB+ny0lQUFBeW4bj9FeuGQ4HUVGRVK1SmSrRlQgI\nCKBalcqUKV2aX387Qv8eXZg25w0WLnmPenViCQkpvofhggULSElOZs+ePZiYmLzlDocjr0pxSkRE\nBA6Hg9DQUBwOB5FRUURERuLIyCh0vRMnTrB//34aNGhQNIP5g7y1Lz748EMCgE0bN7J7925eBzEi\nBwAAIABJREFUe/VVhr34IuXLl2flypW0aNEi71gr7hYueJOUFM8+MgX3UWRUVIG+v9tHkVFnbs6v\nnBrPKU7n6deIyIiIAq8RecfEOdZZsWIFS959lxHDh3NF2bJFNArxtvffnsGObZvZt3cn115/+gOo\nw5FOZOT5vwFxuVzcc/9jRHieLw1uupXvd2/3u6Q8IjycjHzvqS6nq8DzxZGR/z01s8Br7IDePfjt\nyBG69h7AvBlT6NezG9Nmz+Ptd5ZSt04cISEhRTcQH3DpkojFg7XWiXt6SivgkKe6/SnuaR23AV8A\nx621Bzyr/A849ay3Z2yuJXDFORJygN+Am40xi4CJuJNkAAOs8sT0obX2P2dZdsr1wFpP2xog27M8\nFpjumQv/LHCqTLLVWptjrU0HisUEw7qxMaxZvwGAlNTtXFujel5bjWpVOXDwR46npZGdnc2W5G3E\nxVzPp8u/YMa8hQD88utvZGRkUKF8Odas38DgPj14bdRwjqedoFHD4puUPvPMM4wdN47F77zDjwcP\nkuYZY1JSEjGxBeczxsXFsW7dOgDWr19P3Tp1qFatGgfPsl5SUhI33HBDkY/pUnlrX4wfP55x48cz\ndtw4rr32Wvr07ZtXKd20cSM33XxzkY/1Uj39TFvGjB3PosVL+PHH/GPdSkxMwX0UG1eHdevcs9vW\nr19Hnbp1fRHyZRMXF8c6zzc821JTuaZmzby2wh772JiYs67zxRdfuCvkY8cSHR195l2JH3v4yc4M\nGjWTKQuWcfjH/ZxIO0ZOdjY2ZRO1Yuqdd31HRjpDuj1KpiMDl8tFypb11KwVc971ips6cbGsXf8d\nACmplmtqnn5PrX7Ge+rWpGTiYgzLv/iKxUv/HwChoaEEBgYQGBDAmnXrGdS3J+NHv8TxtDQa3VB8\n31Pl3IpvifLsluOukL/jub0SGAa4gMNAGWNMtLX2R6A5sN3Tz3nGdh4A5hpjOllrZ57lvtoCR621\nzxtjrgM6eqr024Cbgf8aY54Ayp9l2SkpwC3AB8aYhsCpj7EWeNpa+4Mx5lbcU2TwjOVMTnz4Ieq2\nWxrz3abNdO03GFzQv0cXPv/qaxyZmdzXKp7O7dsyYNhInC4XrePv5MoKFbg3/i7GTppG9/6JBARA\nvx4vEBQURJXK0fRNHEFoaCka1qtL05uK/1fSwcHBdOjQgcQhQ3C5XMQnJFCxYkXS0tKYPGkSiUOH\n8uhjjzHhtddYtmwZZcuUof+AAWddD2D//v1UqlTJxyO7eN7YF2fjz/uofYeODE0cjNPlIiE+3z6a\nPJHExGE8+uhjTJjwKp8tW0aZsmXo33+gr8P+Q5o1a8bGjRvp3acPLpeL3r168eWXX+LIzOTe1q3p\n0KEDQxITcblcJMTHU7FixULXyc3NZcbMmVx11VW8PHIkAPXq1eOpJ5/08QjlcgoODuaxZ3vy6vDu\nOF0u7rj7fspXuIoTaceY//ooug8aV+h6EZFRPPzUC4xJ7ExwSCni6t9Mg5t8drrVJbvtliZs2LiJ\n7n0H4nK56NezG59/9T/Pe2oCndq3Y+Cwl3A5nbSKv5uKFStwW7OmjJ80lV4DhpCTk0PnDs8SGhpK\nlcqV6T/kRUJDQ7mhfl2a3NzI18PzquJyoqcxJhx4G7gKSAOesdb+XEi/QOBj4INz5JsABLiKy+gu\nkDGmBrAXuMZau9ez7F/AJmvtcGNMS+Bl3EnsEdyJdV2gk7X2UU//vUAMEIG7gt3aWruDMxhj6gCL\ngWPASdzTWe4GwnHPNQ/CPX/8SdxJ+JnLJuCeE/4VsBCoDKQCt1trjTGmEe758cG4E/HnPH3yx3rI\nWlvJk8wvAV601i451z46sH2rfz2oXpIZHHn+TvKnU5K/+rxYgb+rVfx5bYu519chFBsVtl6u6xP4\nvypBB87f6U+iWu24YvHi+eZXhRYu/7C2Lbio8RljegNlPLnno8At1toehfQbjXva8pslLimX81NS\n7qakXAqjpPw0JeWnKSk/TUn5aUrKT1NSXpAx5h/AOGvtas8FR7611tY5o8/DuM9fzME97fqcSbk/\nTl+57Iwxwzh98mV+7ay1e4o6HhERERH5PV/Ukj0X4+h1xuKfcM+kAPf0lbJnrFMXeBx4GPc06/NS\nUg5Ya18CXjpvRxERERH5U7HWzgPm5V/mqZSfumRQaeDoGas9jfsCHl8ANYEsY8xea+2ys92PknIR\nERER8QvFaNb1N7h/M2ct0Br4On+jtbb/qf8bY4bjnr5y1oQclJSLiIiIiJ8oLr++CcwAFhhjVuL+\nwcfHIe8E0J2X8rsySspFRERERC6CtTYDeKSQ5RMKWTb8QrappFxERERE/EIxmr5y2fnVL3qKiIiI\niJREqpSLiIiIiF9wluCfV1BSLiIiIiJ+QdNXRERERETEa1QpFxERERG/oEq5iIiIiIh4jSrlIiIi\nIuIXitGPB112qpSLiIiIiPiYKuUiIiIi4hdcXptUHuCl7V44JeUiIiIi4hd0oqeIiIiIiHiNKuUi\nIiIi4hdK8i96qlIuIiIiIuJjqpSLiIiIiF8oyXPKlZSLiIiIiF/QdcpFRERERMRrVCkvgT7/sZ6v\nQygWbqu6y9chFBtBrhxfh1BslMpx+DqEYiM0K83XIRQbFbau9XUIxcav9Rr7OoRio8y2L30dgpyh\nJE9fUaVcRERERMTHVCkXEREREb/g8tqkcv2ip4iIiIjIBdGJniIiIiIi4jWqlIuIiIiIX9CJniIi\nIiIi4jWqlIuIiIiIX3CW4EnlqpSLiIiIiPiYKuUiIiIi4hdK8pxyJeUiIiIi4hdKclKu6SsiIiIi\nIj6mSrmIiIiI+AVnCS6Vq1IuIiIiIuJjqpSLiIiIiF9wOX0dgfcoKRcRERERv+DS9BUREREREfEW\nVcpFRERExC84S/D0FVXKRURERER8TJVyEREREfELJXlOuZJyEREREfELzpKbk2v6ioiIiIiIr6lS\nLiIiIiJ+wVWCS+VKyuWSbN/8BSs/mkZgUDANbv0rDW//W4H2tKOH+XB+P3JzsgmLLMsDz40nNCyK\nrav+xer/zCM0vDT1mz3EDbc94qMRXByn08m0adPYvWcPISEh9OzRg8qVK+e1r16zhsWLFxMUFERC\nQgKtW7U66zpHjx5l8pQpnEhLw+l00qdvXypHR7Nu3ToWLV4MLhfX1a5NlxdeICAgwIejPj+n08nU\n6TPzxtire1eq5Nsvq9asZdE7SwgKCuKe+Jbc2+oecnJyeG3SFH46fJjs7Gwe//vfuKVpk7x1vvhq\nBR/8+yMmvzbeF0O6ZE6nk4kz57Fr7/eEhITQr+vzVI2ulNf+7drvWPDu+wQFBXFvyzu5L+FuPv38\nK5Z9sQKArKwsdu75nn+8OYvSUZEAvD53AdWqVOaB1vE+GdPl4HQ6GT93ETv37iMkJJhBnZ6hWvTV\nBfpknjxJ95cnMLhzW2pWic5b/tux47Qb8DKTh/YusNyfbVz7NR+8O5fAoCDuaPkXWiQ8WGi/zz58\nh2NHfuVvz3QFYNkHi1mx/APKlC0HQNvOg4iuWqPI4i5qVzSuT8zovqxu+bSvQ/GadWu+Zek7CwkM\nCuLu+NYktLqvQPvxY8eYMH4kWVknKV++At16DiA0LIwd21N5Y+50XC4X5cqVp2ffIQQGBvL6pHEc\nPnyI7OxsHvn7kzRuequPRiaXSkl5MWCMaQvEWGsHFtJ2LfAJsAZ4FShnrf1f0UZYUG5ONv9d+grt\nBr9PqdBwFox9jNoN7iKqTMW8Pqs+m0O9Wx6i/i0P8r8Pp7Lp6/ep1/QvrPhwCs8l/oOw8DIsmtiW\nmjG3cEXFqj4czYVZtWoVWdnZTJwwgW2pqcyZO5cXhw0DICcnh9mzZzN50iTCwsLo07cvTZs0ISUl\npdB15s2fz50tWnDHHXewefNm9u/bxxVlyzJv/nzGjhlD2bJlee+99zh2/DhXlC3r45Gf27erVpOV\nlcXk18azLTWV2XPnM2JYIuDeL7PmzGXqxAmEhYXSq98AbmnSmLXrv6NMmdIM6Nub42lpdO7WIy8p\n37lrF8v+s9wvT+RZuWYdWdnZTB83kmS7nRnz32LUkH6Ae1+8Pm8Bs14bTVhoGF0HDqVZ40a0vrsF\nre9uAcCkmfO4t+WdlI6K5Oix44yeNI39B3/k71Uqn+Nei7//rdtIVlY2c0YPJmn7LqYufI9xA7rm\ntW/btZdxs9/i8K9HCqyXk5PD2FlvEVqqVFGH7DU5OTksnjeR4a+9SWhoOCMHtqdh49spe0WFvD5Z\nJzOZ//oodu9I4aZb7sxbvndXKh17Duea62J9EXqRurZPe6o8+Rdy0x2+DsVrcnJymD9nGuMnziQ0\nLIzB/brRuEkzrihXPq/P0ncWcEfzu7krvhX/b+liPvv039z/4MNMn/Iq/QePILpyFZZ/9jE/Hz5E\n6rZkSpcpQ8++g0lLO07vbh1KbFLuh28PF0xzyou/24CPrbXPAH8F4nwcD78c2kW5q6oTHlmWoOBS\nVLuuEfu2ryvQJ/5vg6nX5C+4nE6OH/mRsIjSHPllP1dVNYRHXkFAYCCVa9bjwO7NPhrFxUlOTqZR\no0YAxMbEsGPHjry2ffv2UblyZUqXLk1ISAh16tQhKSnprOukpKTwyy+/MGjwYL788kvq16/Ptm3b\nqFmzJnPmzqVvv35cUa5csU/IAZJStnFToxsB9xi379yZ1/bDvn1Ujo6mdOko936Ji2NrUjJ33HYr\nzzz5hLuTy0VQYBAAx48fZ/6Ct+jcsX2Rj+Ny2JpiadywAQB1zPXYnbvy2r7ff4Aq0ZUoHRVFSEgw\n9WJj2JK8La89dccu9uzbz/33tATAkZlJ20cfJr7F7UU7CC/YvG0nTRvWBaDu9bXYtmtvgfas7GzG\n9OtCjSqVCiyfuvA9HkpoTsVyxf95cKEO7t/D1dFViYwqQ3BICLVjG2CTNxbok52dxa13teH+R9oV\nWL53Vyofvb+AkQM78O/33yzCqItexu4f+O6Rbr4Ow6v27/ue6OgqRHneN2Lj6pGctKVAn20pSTRs\n1BiAG29qzJZN33HwwD5KlynDh/96jyEDenAi7ThVqlan2W0tePzJZwH31UlOva6Kf1FSXowYY7oZ\nY1YZY741xnQ3xlQHBgOPGGOGAG2B3saYxr6MM8txgtDw0nm3S4VFkuk4UaBPQEAATmcus0fcx/d2\nDTVjmlL+qhr8cnAnJ47/QvZJB3tSV5GdlVHU4V+SjIwMIiMi8m4HBgaSm5sLQPoZbeHh4aSnp591\nnZ9++omoqCheGT2aK6+6iqWeqviWLVt4tl07Xn7pJf71r3+xf//+ohvgJcrIyCAyMjLvdv79kpHh\nKNAWER5OekYG4eHhREREkJGRwcujx9L26SfJzc3ltclT6dT+OcLDw4t8HJdDekYGUZEFH++cvGPE\nQVS+YyEiPJwT6aeP/UXv/5O2f/9r3u3oq68iztQugqi9L93hICri9GMalG+/ADSIqc3VFcsXWOfj\nL7/hirKlaXpD3SKLsyhkZqQTHhGVdzs8PJKM9IKvnZFRZajXsOnv1m1yezxtXxjIwJensyNlM5vW\nfe31eH3l0D//gys7x9dheFVGRgYR+V4fw8LDychIP6NPel6f8PAI0jPSOX78GHZbMvfe9xAjRr3G\nls0b2LJ5A+Hh4YRHRODIyGD86OE8/vSzRTqeouR0urzyVxxo+krxcS3uqvhtntvLgc+AMbintowy\nxoQAh6y1a30R4Ff/msi+nRs4vN9S+Zr6ecuzMtMJiyj9u/5BwSE8P+IT9qR8y4fzB/BUv7dp+bdB\n/L8Z3QiPuoJK1esQHlWuKIdwySIiInA4Tn+V6nQ6CQpyVyIiIyLIyNfmcDiIjIo66zplypShaVP3\nm26TJk1YsGABcbGx1K5dm/Ll3clJvbp12b17N1WrFu+pPWeO0eV05e2XiIjwAvslw3E6ST/888+M\nGPkK97dpzV0tmpNqt3Pw4EGmTJ9BVlYWP/ywjxmz59C5Y4eiHdAf4D4OMvNuO10ugvOOkXAyMgvu\niyjPvkg7kc6+Az/SsH7JSkBPiQwPJ/0s++VsPvpyJRDAui0p7Ni7j5emzmP8gG5U8NOq+ftvz2DH\nts3s27uTa6+vk7fc4UgnMvL3r51ncrlc3HP/Y0REuhP6Bjfdyve7t3PDzf7/TcqfzaKF89iWspXv\n9+ymtjk9FSnT4SAyMqpA34iISByODEJDQ3E4MoiMjKJ06bJUiq5Cteru8wka3tiYXTss9RvcyC8/\nH2bMyKG0avMAd7RoWaTjKkr+OL3xQqlSXnzcBNQAPvf8VQCKVamsxYO9eKrvW/R89RuO/PwDjvSj\n5OZk8cOO9VS5tmGBvp8uGs7e1NWAu5IeEBCAMzeHQz+k8HT/xfxfx8n8emg31Wrd6IuhXLS4uDjW\nrV8PwLbUVK6pWTOvrVq1ahw8eJC0tDSys7NJSkoiNibmrOvExcWxbp17uk/S1q3UqFGD6667ju+/\n/55jx46Rm5tLamoq1atXL9IxXoo6cbGsXXd6jDVrnj7xrHq1ahw4eJDjnv2yNSmZuJgYjhw5wqDE\nF2nf7hlaJbhPYIwx1zNnxjReHTOawQP6Ub16Nb9KyAHqxhpWf+eeipBst3NtjdOPX42qVdh/8BDH\n006QnZ3DlpRt1Im5HoAtydu4sYQm5AD1Y65j1YatACRt30Wt6lXOu86MlwYw46X+TB/Rn9o1qzGs\n23N+m5ADPPxkZwaNmsmUBcs4/ON+TqQdIyc7G5uyiVox9c67viMjnSHdHiXTkYHL5SJly3pq1oop\ngsjlcnvi6ecYOWYSbyz6B4d+PEBa2nGys7NJTtqMiSk4OzUmti4b1q0BYMP6tcTVqc/VlaLJzHTw\n48EDAKQkb6Va9ZocPfIbwxP78XS7jrRMuLfIxyWXhyrlxcdmIBxoba11GWN6AVuAu/L1cVIMPkgF\nBYfQ8pGBvDPpOVwuFw1u/Stlyl2NI/0oHy9M5OHOr3PzXU/x6aLhrPxoGgGBgbR6YjiBQe7Dbd7I\nhwgOCaVJfDsiSpc/z70VD82aNWPjxo307tMHl8tF7169+PLLL3FkZnJv69Z06NCBIYmJuFwuEuLj\nqVixYqHrAHRo357Jkyfz8SefEBkRQf/+/SldujRt27YlcehQAG6//XZq5kv8i6tbb2nKho2b6Nmn\nPy5c9OnZgy++WoHD4aBN61Y83/45Bg99EafTRauEllSsWIHps+Zw4sQJFi15l0VL3gVg1IgXCQ0N\n9fFo/pjbm97M+k1b6NJ/KC5cDOjemf+uWIkjM5P772lJl2efpt/wUbhcLlrffSdXVnAf+/sOHCS6\n0lU+jt57mjduyNotKXQY8gq4XAzp0o7Pvl6DIzOTB+Ob+zq8IhUcHMxjz/bk1eHdcbpc3HH3/ZSv\ncBUn0o4x//VRdB80rtD1IiKjePipFxiT2JngkFLE1b+ZBjeVzJP4/iyCg4Np1/4FXhraH6fTyd0J\nralQ8UrS0o4zbfKrDEx8iUcefZIpE8aw/LOPKF2mLL37JxISEkKXHv2YMH4kuFyY2Drc1PgW5s6a\nSvqJNJYueYulS94CYOiIsX7/uloYl9PXEXhPQEn+GsBfnLr6CvAr8CAQCqwFugFP4bkyizGmDTAe\n6GKt/fJs21u4Aj2owG1Vd52/059EkKtkz8+8GKVySu4VHS5WaFaar0MoNraXqn/+Tn8Sv9bz6WlL\nxco12876VvunE3dd5WJxjd7+Mx1eyXHGdQr3+fhUKS8GrLVv5rt55sWZ38zX72Pg4yIISURERKTY\ncZbgYrKSchERERHxCyV5hofP5yeLiIiIiPzZqVIuIiIiIn6huFxT3BtUKRcRERER8TFVykVERETE\nL5TgKeVKykVERETEP7g0fUVERERERLxFlXIRERER8Qsl+TrlqpSLiIiIiPiYKuUiIiIi4hc0p1xE\nRERERLxGlXIRERER8QsluVKupFxERERE/EIJzsk1fUVERERExNdUKRcRERERv1CSp6+oUi4iIiIi\n4mOqlIuIiIiIX3CV4B8PUlIuIiIiIn7BqekrIiIiIiLiLaqUi4iIiIhfKMnTV1QpFxERERHxMVXK\nRURERMQvlORLIiopL4GaV7G+DqFYyKaUr0MoNlyBIb4OodjIKaV9cYorQF+WnlIl8ICvQyg2ymz7\n0tchFBt7Yu/0dQjFRlx28cgtSnJSrldkEREREREfU6VcRERERPyCUyd6ioiIiIiIt6hSLiIiIiJ+\nQXPKRURERETEa1QpFxERERG/UJJ/PEhJuYiIiIj4BWcJnr6ipFxERERE5CIYY8KBt4GrgDTgGWvt\nz2f06QM8DjiB0dbaf55rm5pTLiIiIiJ+weV0eeXvEnQGtlprbwcWAon5G40xVwA9gFuABGDS+Tao\npFxERERE5OLcBizz/P9ToOUZ7enA90Ck5895vg1q+oqIiIiI+AVfnOhpjHkO6HXG4p+AY57/pwFl\nC1l1H5ACBAGvnO9+lJSLiIiIiF9wOc9bcL7srLXzgHn5lxlj/gGU9twsDRw9Y7XWQDRwjef2Z8aY\nb6y1a892P5q+IiIiIiJycb4B7vX8vzXw9RntRwAHcNJam4k7ab/iXBtUpVxERERE/EIxuiTiDGCB\nMWYlkIX7KisYY3oDO621HxpjWgKrjTFOYCWw/FwbVFIuIiIiInIRrLUZwCOFLJ+Q7/8vAi9e6DaV\nlIuIiIiIX9AveoqIiIiI+NglXlPcL+hETxERERERH1OlXERERET8girlIiIiIiLiNaqUi4iIiIhf\ncLqK/seDiooq5SIiIiIiPqZKuVwQp9PJ1Okz2b1nDyEhIfTq3pUqlSvnta9as5ZF7ywhKCiIe+Jb\ncm+re8jJyeG1SVP46fBhsrOzefzvf+OWpk04cvQok6a8TtqJEzidTvr36UXl6Ggfju7CrFmzmncW\nLyIoKIj4hHto1ap1gfZjx44xftxYsrJOUr58BXr26k1YWFih6+Xk5DDhtVf56fBPBAUG0q17T6pV\nq8bYMa9w5MhvAPz000/ExMQyYOAgXwz3nNasXs3ixYsJCgoiISGBVq1/vy/GjR1LVlYW5StUoFev\nXu59cZb13n33XdasXk1OTg5t7ruPe+65J29bs2fNokrVqrRp06ZIx3ihLue+yM3NZcrkyew/cIAA\noGu3btSsWZNdO3cyfPhwKnuec/e2aUPz5s19MNoL43Q6mTRzLrv27CUkJIR+XTtRpfLp5/i3a9ez\ncMn7BAUF0rrlXdx3T0sAFr33T75du47snBweaH0PbRLu5qXxE/ntiPvXqw8d/pk4U5th/Xr5ZFyX\nwul0MmX6rLx90ad7lwL7YtWadby1ZClBgYG0ir+bNq0SyM3NZcLU6ew/cBACoOcLnbimZg127NzF\npGkzCQkJoda119Cl43MEBvpXbW3dmm9Z+s5CAoOCuDu+NQmt7ivQfvzYMSaMH5n3Otqt5wBCw8LY\nsT2V/9/encdrPef/H3+cFqftZCm0yDo82xiUUJYs2QbfMWMGww8lZQ+pSA2RCJUYOxEqy/jyHbvB\nYCzRqv1F1lQKg9I5ref6/fH+nM6ixHTO9b46n9f9drtu51pPz8+n61zX6/P+vJcH7ruDTCbDlltu\nxcWXXUmNGjX42y03snjxV6xatYo/nXQaHfbrFGnLqs4WHfag5ZDLGH/46bGj5ITq3Kc854pySQcB\n35vZNEn/a2Z/qKTf+xnQMlnq9Fdn+ZnnTAaWJDc/NbOuG5MzV73z7nhWrlzJyGE3MXvOHO65bxSD\n/joAgNWrV3P3vfdx24jh1KmTzyV9+rH/vh14f+IkGjYsoN9ll7Jk6VLOvbAX+++3L/eNepBDD+nM\nwQcewNQPpjFv3pc5X5SvXr2ae++5mxG33EqdOnXoc9ml7Lvvfmy55ZZrnzNu3BgO7tyZLl2O4PHH\nH+OFF57nuOOOX+fr5syZw5o1axg2bARTJk/modEPcuWAgWsL8KVLl3LFFf04u0ePWJu8XqtXr+ae\ne+7hlpEjqVOnDpf17s2++1XYF2PH0vmQQ+jSpQuPP/44L7zwAscdd9w6Xzdv3jxmz5rFzcOGsWLF\nCp588kkAfvj+e24eNoz5X37JH088Mdbm/qzK3hezZ88GYNiwYUybNo2HRo/mr1ddxUdz53LCCSfw\nhz/+Mdam/ipvjZ/AypUruf2mIcya8yF3jHqI6wb0A8I+u/2+B7lr+A3Uyc/nwn4D6dihPV98OZ+Z\nc4zbhg5mxYoVPPbUMwBrC/ClP/7IJVdezflnnRlpq/47b49/j5WrVnHbsKHMmmPcdf8DXDuwPxD2\nxZ33jeL2ETdRJz+fXn3703HfDsyaYwCMvOl6pk6bwaiHx3DtwP6M+NudnN+zO21atWTUw2N47Y03\nOfyQzhG37tdZvXo1o+69nZtG3EV+nTr073MhHfbtyBZbbrX2OY+PG81BBx/GoV2O4snHx/LSC89w\n3O9P5I5bb6Zv/0E0bdacf770HF8v/oo5s2dS0LAhF1/Wn6VLl3DphWdXu6J8597daX7a8axZVhQ7\nSs6ozkV5Lh5idwOaAVRWQV4ZWdZFUh0gz8w6J5dqWZADzJg1m/bt9gagVcuWfDh37trHvpg3j2ZN\nm1JQ0IDatWvTpnVrps+YyUEHdOKM004NT8pkqFmjJgAzZ8/m62++oV//gbz2+hvsscfuWd+eX2ve\nvC9o2qwZBQUF1K5dm9Zt2jJjxvRyz5k1cybt2rUHoH37fZg6dcp6X9e8eXPWFK+huLiYwsJCatUq\nf3w8ZszDHHfc8Wy1VaOsbeMvNW/ePJqV2aY2bdowY8aMcs+ZOXMm7dq1A6B9+/ZMnTJlva+bNGkS\nO+60E4OvvZZBV19Nhw4dAChavpxTTz2VQw87LOvb+EtV9r7o2LEjF/XqBcDiRYuoX78+AHM/+oj3\nJ0ygT58+3DJiBIWFhdnd0F9p+uzZdNh7LwBat9yND+d+vPaxz+fNp3nTJhQ0CJ8Xu7f4/radAAAg\nAElEQVRuybSZs5kweSo77bA9A4fcRP/BQ9l/n3blfucDYx/nhN8dTaOttmRTMmPmbPZZuy/Ehx+V\n7osvkgaJkn3RtnUrps2cSaf99+XSC88DYPHXi2mQvA++/uZb2rRqCUDbVi2ZMXN2lrdm43w573Oa\nNm1Og+R936r17sycUb7Na/asGezVLnwG7N2+A9OmTmLB/HkUNGzIP55+giv79eLHpUtovt32dDyg\nM385rRsQFpQp+Y6pTgo/+YJJf7owdgyXJRtsKZd0JvB7oABoDFwDDAI+BFYC5wCPAA2T3zfAzF6T\nNB14E9gDmAMsAg4CVgDHAPUrvg74ATgK2FvSLOB9M2siaS/gNmANsBw4m3BAMQ6YB+ySPPfcX7A9\nbYHhQM1ke841s3ckPQD8BqgLjARmlc1iZl+s49f9Fqgn6eVkG/oDs4HJwG5mtkbSUGBScv+tQB7w\nLaHg/xG4G2gBNAX+YWYDJD0INEou/wM8lmxvHeAcM5u6oe2sbIWFhWsLBIAaNWqwZs0aatasSWFh\nUbnH6tWty7LCQurWrbv2tdcOGcqZp58GwKJFiylo0IChQ67lkbGP8vgTT3LG/zs1uxv0KxUWFlK/\nXuk21q1bl8Jly376nGQ/lDy+vtfVrVuXxYsW0bPH2SxZ8gNXXX3N2ud8//33fDB1Kmef3bOKt+q/\nU7hsGfXql9+mZRvYF8uWLVvv65YsWcLiRYu4etAgFi1axKCrr+aee++lSZMmNGnShIkTJ2Znw/4L\nlb0vAGrWrMmwm2/mnXfe4corrwRgN4kjjzqKXXfdlUfHjWPsmDF0P/vsqt68/1r4TKi39na5z4ui\nwnKP1a1bh2WFhfywdCmLFn/NkIGXs3DRYgZcN5TRd4wkLy+P777/gckfTOf8s86IsTkbpbCowr6o\nWbovlhWW3xf16tZh2bJwwFWzZk2GDh/J2+++x1+v6AtA0ybb8sH0Gfx297a8+/5Elq9Ykd2N2UiF\nhYXl3vd16talsLDi30vp30bduvVYVriMJUt+wGbP5OxzetG0WXOuG3QFu+wq9vhtaCgqKizkpiFX\n85fTu2VvY7Lkq6depu4OzWPHyCnVeUXPX9pSXh/oAhxBKGi3AK41s5MJxfQ/zewg4E/A/ZLyCEX8\nWDM7EDgQeCd5zmZAm3W9jlDMvgj0rVAE3wtcYGYHA3ckGQB2A84COgDHSGryC7alDdDbzA4DhgJd\nJRUQDhj+QCjE15jZpPVkKasQuBk4knBwMgZYBrwFHCmpJnA08HSyDeebWWfgeaAvoRgfb2ZHJttw\nTpnf/ZqZdUzu/zb5PecT/i+yrl69ehQVlZ4+yxRnqFmzZvJYXQrLPBa+hELMxV9/TZ8rBnDYoZ05\ntHPoA9uwoID99w0tIfvtu0+5Vvdc89DoB7m8Xx+uGXR1udbJoqIi6jdoUO65ZfdRUVER9es3+Ml+\nK3nd00/9L3vv3Y5777ufv91+JyOG38zKlSsBeOutf3Nw50PW7t9cMXr0aPr17cugQYN+si8a1C//\ntvzJvmjQgHr161O0jtc1LCigXbt21K5dm+22247NNtuMH374ITsb9V+qqn1Rovdll3Hvffcx8tZb\nWb58OR07dmTXXXcFoGPHjnz88cfksoqfCcWZMp8XdetRVFTai7CoaDkN6tejYUED9tlrT2rXrs32\n2zWndu3N+P6H0DPwjXfGc/jBB+Tc38QvUa9uXQrLbG/Zz8769epRVFj2s3N5ufdBv0t78eA9tzP8\ntjsoWr6cPhdfyLgn/pc+/f/KFptvTsOGDbO3IRthzEP3M+Dyi7n+mivL/b0sTz4ny6pXrz5FReE5\nRUWF1K/fgIKCzWnStDkttt+BWrVqsdfeHfj4o9DF55uvFzPwiks4+NAuHNT58OxtlHNV4JcW5W+Y\nWbGZLQK+A7YGLHmsFaFFHDObT+hfvU3y2OTk5/eElmeS19fZwOsqalamdfhNQmENMNfMlprZGmBh\n8ns3ZD4wUNJo4ESgtpktBS4G7iG0Suf/gt8D4WzBI2aWMbMPCcVzU0IBfiahkH7FzFYm23uHpNcJ\nreTNgf8A+0gaA4yo8O+W7N8XgLeB/yOcpYgyF1Cb1q14f0JosZw9Zw477rjD2se2b9GC+QsWsGTp\nUlatWsX0GTNp3bIl3333HVcMuIruXc/gqCO6lP6uNq15f+IkAKbPmMkO22+f3Y35FU4/40xuGHoT\nY8Y+ysKFC1iabOOMGdNp2bJVuee2at2GCRPeB2DixAm0aduWFi22Z8GC+T95XYOkMAMoKChg9erV\nFBeH/9qpU6fQvn377G7oL3DGGWcw9MYbGTtuHAsXlN0XM2jZqvy+aN26NRMmTABg4sSJtG3ThhYt\nWrBgHa9r3aYNEydNIpPJ8O2337J8+XIKCgpibOIvVlX74tVXX+Wxxx4DoE5+PjXy8sjLy2PAgAGY\nhY+EqVOn8pukQM9VbVu15L2J4eN/1pwP2XmH0r/xHVo058sFC9d+XnwwcxatW+7G7q1b8f7kKWQy\nGb759j8sX76chgWhYJs8dRod2u0VZVs2VpvWrdZ+3s2aY+y0Y+m+2L7Fdswvsy/CZ6f452uvM/bx\nMLYiPz+fGjXyqJGXx3sTJnLFZRdz05BrWLJ0Ke32/G2Ubfq1Tj39LAbfcAsPjPlfvlo4n6VLl7Bq\n1SpmzvgAtWxd7rktW7Vl8oT3AJg88X1at9mDbZs0ZfnyIhYumA/ArJnTabH9jnz/3X+4ekAfTu/a\ng8OPOCbr2+XiKC4urpJLLvilAz3bAUjaltDdZDGlxeFsQkv4FEnNgS0JxSnAz51jWN/rivnpwcIC\nSXskAy4PJhTDG/r963MrcKqZzZY0CNhRUlOgnZmdkPQTnyfp4fVkKasbsDtwnqRmhH2z0MzmSRpJ\naMUfkDzXgNPN7AtJnQjF+5mEgaQ9Jf0G6JGcZYDS/ds5+Z1HSNofGAIc8l9s90bptP9+TJ4ylYt7\n9yVDht4X9+K119+gqKiI3x19FD27n0X/gVdRXJzhqCMOp3HjRtxx9738+OOPjHn0McY8GgqN6wZd\nRc/u3Rg+8jaefe4F6tWvxxV9Lsv25vxqtWrVovvZPRg4oD/FmQxHdDmCxo0bs3TpUkaOHMGAAX/l\n5JNPYfjwm3npxRdpuHlD+va9fL2v+/0Jf+CWEcPp26c3q1at5owzulKnTjimnP/llzRpkrsDX2vV\nqsXZZ5/NgCuvJJPJ0OWIMvvillsYMHAgJ59yCsOHDePFF19k84YN6duv33pf17hxY2bMmMHFvXqR\nyWQ47/zzN5kW0creF506dWL48OH06dOHNatX06NnT/Lz87ngggu48847qVWzJltuuSUXXXRR7E3/\nWQfu14FJU6dxQd+wff16nc8rb/yboqLlHHdUF8476wz6XnUdxZlijj78ULZu1IitGzXigxmzOLf3\nFRRniul1Tve174Mv5i+g2bbbRt6q/84B++/L5ClTueiyy8lkMvS5+EJeff1NipYv59ijjuCc7l25\n/K/XkCku5qguh9G4cSMO6LgfN91yG5f0u5LVq1dz7tndyM/Pp3mzZvS98iry8/PZc4+27Fuh332u\nq1WrFl27n8c1A/tSXFzMYUccTaPGW7N06RJuH3kzlw+4hj+dfBq3Dr+Bf770LAUNN+fSvgOoXbs2\n5/fqw/CbBkMmg1q1oX2H/bnv7ttY9uNSHn/0YR5/9GEABg4aSn7+L21Xc5ui6jzQM29DfXOSPuU9\nCV01NicUmXeRzGQiaStgFLAVoT/2QDN7sexsJ5LGAyeb2WeSngZuIBTW63pdT0I3jZOAf5XpUz6S\n0B97NaHYLQYeNbP9kpxr/431bMdnQEvgPEIx/R3wJaFf+RHAnYT+72uAZ81saNksZvaTETWSNgMe\nBLYnHCD0M7N3kscuBf5kZvsnt9sBwwgHQplkG2oDYwl96VcQurMcBlyXbNuLkhoBjybPrQVcY2Yv\nr+e/C4DP51r1fcf+CqvyNosdIWfk5flbwv1U3VU/xo6QM9bUyLnJyKJZmrdF7Ag549NWWW8Dy1m/\nW2V5G35W1Tv27FlV8oX27L2to2/fLy3KW5rZ5VlJVE1I6gN8a2ajsv1ve1EeeFFeyotyty5elJfy\noryUF+WlvCgvlStF+e+6z6iSL7Tn7msbffuq1aeQpA7Ajet46DEzu3Mjfu8dQOt1PHS0mf1k8tBk\n9pRmwHH/7b/pnHPOOefSY4Mt5W7T4y3lgbeUl/KWcrcu3lJeylvKS3lLeSlvKS+VKy3lx3SbXiVf\naM+P2j369vmnkHPOOeec2yRU54Geubiip3POOeecc6niLeXOOeecc26TUJzJjTnFq4K3lDvnnHPO\nOReZt5Q755xzzrlNgvcpd84555xzzlUZbyl3zjnnnHObhExx9e1T7kW5c84555zbJHj3Feecc845\n51yV8ZZy55xzzjm3Scj4lIjOOeecc865quIt5c4555xzbpNQXI37lHtR7pxzzjnnNgnVefYV777i\nnHPOOedcZN5S7pxzzjnnNgk+JaJzzjnnnHOuynhLuXPOOeec2yRU5ykRvSh3zjnnnHObBO++4pxz\nzjnnnKsy3lLunHPOOec2CdV5SsS8TKb6ngZwzjnnnHNuU+DdV5xzzjnnnIvMi3LnnHPOOeci86Lc\nOeecc865yLwod84555xzLjIvyp1zzjnnnIvMi3LnnHPOOeci86LcOeecc865yLwod84555xzLjIv\nyp1zzmWdpCaxMzjnXC7xFT3dRpN0xPoeM7OXs5nF5R5J3c3svjK3LzKzW2NmygWSaphZ9V0vegMk\nvQV8DdwPPJ/yfXGsmT1b5vafzezxmJlikdTezCaWuX2wmb0RM1MskvYA6gPFwBBgiJm9GjeVq0pe\nlLuNJumB9TyUMbNuWQ2TIyR1AS4F8kvuM7ND4yXKPkmnAMcDhwCvJXfXBNqaWZtowSKSdCqwhvC+\nuAm40cxujpsqHkmtga7AgcCrwP1m9kncVNkj6VigE3AKMDa5uyZwvJm1ihYsAkkHAq2BS4Dhyd01\ngfPNrG20YBFJege4ABgEXEf4vDgobipXlWrFDuA2fWbWteS6pN2A3wDTgAXRQsU3ArgYmBc7SEQv\nAguBRsBdQB6hxefjmKEi6wUcDTwKtABeBlJblAPzgU+AdkBbYKSkmWZ2edxYWfMB4e+jCLDkvmJg\nXLRE8XwHNCEcsDZN7isG+kZLFN9yYCawmZmNl7QmdiBXtbwod5VG0gXACcBWwIPAroSj/DT6wsxe\niR0iJjP7Dnhd0pfAPmY2TtINhAI9rZYnP5ea2QpJqf0MlvQ4oRB/BDjNzBYk90/82RdWI2Y2Dxgt\n6eE0d99J9DazrpJWmdmQ2GFyRAZ4CHhe0p+BVZHzuCqW2i8EVyVOBg4CXjWzkZImxA4U0WJJdwFT\nCB+smNk9cSNFMxronVx/ntCH+LB4caKaC4wHLpF0FeGMUlrda2b/XMf9B2Q9SXz9JPUDCglnlDJm\n1ixypmzbT9JNwJ8kNSj7gJn1j5QptpOADsALQGfCd6yrxrwod5WpBqEALRmosCJiltg+TX76DBOA\nmY1Pfr4pKc2zPj0MXGRmP0qaaGZfxQ4U0deS7gDqlNxhZt3MbPnPvKa6OhloZmaFsYNEdAzhgOxY\nSrvypN3TZlZykPqvqElcVnhR7irTOOBNYAdJzwNPR84TjZkNkvQ7oE24af8XO1NE30vqAbxLaPVZ\nGjlPTINKBmqlvCCH0MXtb6R73EWJTwn9ylPLzD4FPpX0OuFAbVfCmaT5MXNF9h9JvQgHKcXgM5pV\nd16Uu8p0J/AKoZ+omVlqT81Lup7wpfIWcIakA83sssixYjkDGAD8HpgNpHJGnkRG0lOU/5JN66n5\nr8pOlZlymwHTJU2ntLvbX+JGiuZ/8LFJJb4F9kwuEN4bXpRXY16Uu8o0HXgGuM/MPowdJrKDzKwT\ngKSRhH7EqWRm30h6FtiZsB9+jBwpplGxA+SQzyRdTvlxF2ktOIbGDpBDfGxSouzMZgCSmq7vua56\n8KLcVabfEualHi6pDvCAmY2JnCmW2mUWh8mjtJ996kgaAmwHtCKMM7iCMC9zGn264aekRj6g5ALp\nbgWcDPQDmgHPku4BwD42KSHpGuBcwpmUesCHhC6RrpryotxVGjNbCfxd0leEOboHAGktyh8D3pY0\nHtg3uZ1WB5jZQZL+ZWajJZ0bO1BEJdueR/hy/YwwDiN1vBWwnFGEGTYOBr4izFB0cNRE8YwF3qB0\nbNJTkfPEdDyhQWMEYUGlO+LGcVXNi3JXaST9FfgzodXnVjNLZbEBYGbDJL0EtCSsUjgjdqaIaiVn\nTjKSahJWtEwlM1t7hkDSZkAql1IHbwWsoJGZjZJ0mpm9k8YZipJxOCWt4wuB5oR5/RtFCxXfwmQ9\ngwIzm5t8ZrhqzItyV5m+AzqZ2Q+xg8QiqbuZ3VfhC2ZvSWke0DcCmARsDbyX3Hbh83fn2CEi8lbA\nMiS1TH5uB6yOHCeGOWWuG2FNg7T7UlI3YFnynbJF7ECuanlR7irTU8DtkrYBngCmmdl7kTNlW8n0\nbnMq3J/aPuVm9oSkV4DfAJ+a2TexM8UiaSHhvZBH+Py9JW6iqLwVsFQv4AHCuIu/A+fFjZN9ZjY6\ndoYc1JNw4PoEcCaQ1hl5UsOLcleZ7gaGAQMJ/WRHA/tFTZRlZvZScnUfM1s7jZekhwjLJaeGpAFm\nNljSOMoclEgCWAk8a2Z/j5UvBjNLc7/pirwVMGFm04H9Y+dwOac+0IPSAcAr48ZxVc2LcleZ6prZ\na0kxZpJStzKfpPMJA1y3lPSH5O48YFa8VNE8k/y8ax2PbQbcSGgVTA1JuxMG9W1HGNDXzcymxE0V\nTepbASV9SvmzaKuA2sAKM2sVJ5XLIT4AOGVSN5jEVanlko4EakrajzBIJ1XM7PakNfQaM2uWXJqa\n2WGxs2WbmX2QXJ1CWDq7L2EBoelm9k/grFjZIroV6J68R7oSVrRMq8ZAb8Jg1x0Ig/vSpiXQmrCE\n+slmJuCPhEXHnGtkZqOAVWb2Dl6zVXv+H+wqUw9CodEYuIzS6d/S6C5Jp0g6XdIZkq6IHSiiUcAX\nwJWEKQAfBDCzyfEiRZNXcrBiZlNJ54C+Eo8Rxl5cDnwCPBw3TvaZ2QozWw7sYmbvJ/dNoXTudpdy\nPgA4Xbz7ittoZQZoLQZOj5klhzxFWFJ+D6AIKIwbJ6pGZnZbcn2qpBOjpolrjaRjgX8TVi1M7cIo\nAGZ2Z3L1A0l/jhomru8lXQu8D3QknWcN3E9dRMoHAKeNF+WuMhg/nV2kZBXLtE75lmdm50gaBXQn\nFGFpVVdSEzP7StK2QM3YgSLqBtwM3EAYZ3B23DhRzZF0KqHrRjvgW0m7AZjZh1GTZd+pwDmEbl4z\ngaujpnG5oiVwoJl5C3lKeFHuNpqZ7fRzj0vqaWZ3ZytPjlidLJhTn3Bwkua/tQHAO5J+ABqS4kLU\nzD5PVjQteV+kWcvk0r3MfXcT9suhURJlmaT2ZjYR6ARMTy4QBvO9HC2YyxXtgQHJlLL3m9ns2IFc\n1UpzoeCy5yTCl22a3A5cQvhinUe6B25ta2Y7S2qc5jnKASTdQyg4F1N6Nqlj1FCRmNkhsTPkgMOA\nicAplM5fX3Kw5kV5ypnZ5ZL6A0cDgyU1Ae4FxpjZqrjpXFXwotxlQ17sANlmZk+WXJf0hJktiZkn\nsh6EL5FUF+SJPYBdzSztreRIGkyYgWftvjCzZvESZZ+ZDU2u9gH2MrN/SroAeCRiLJcjJOUBRxDG\nau0AjCFMpPAMcFTEaK6KeFHusiF1BYikf1FhwRwzS8Up+XXIlzSF0rEHGTNL3ZzUiQVAAZDmg7QS\nxwI7mlmqB7smxgEjk+v/IRTlx8aL43LER4TxSLea2dsld0pqEy+Sq0pelDtXNc5JfuYRBrHtGTFL\nbP1iB4hN0ruEA5JtgI8kfZI8lDGzVHZfIcxfX4eUz0CTqG9mzwKY2VhJqR134crZG/gRaCqphpkV\nA5hZ17ixXFXxotxlQxq7r1iZm3MkpXGhnBKzCXOU70aYWeK6uHGiOPnnHpS0r5m9l60wOWIGsFDS\nVyR9qc0srbM1rZTUBRgPdADWRM7jIpJ0v5mdRZgKcQzwLVAgqZuZjY+bzlUlL8pdpZK0DaH1CwAz\n+4KwkmOqSOpR5mZToEGsLDngseQyijDLxMOk7NS8mX2+gadcT0pmHCnjJGAn4PvYQXJAd8JUmbcS\npsrsGTeOi6xkRrPrgKPN7CNJzQjdnA6OF8tVNS/KXaWRdAdwDKHf7NqZJcxsQtRgcTQtc305kOaF\nUTCzu5KraV8kZn1SdzYJ+BxY5n3KAfgOuL3MQM9vYwdyOWGNmX0EYGYLJPkq7NWcF+WuMnUAdi7p\n95ZGJYufEFo0ytqs4nNTxBeJ2bDUDYYGWgAfe/96AB7FB3q6UptLmgTUT7o+jgGGEQ5kXTXmRbmr\nTHMJXVfSvKT8+uZjT82CKOuQ+kVi3DqdFDtADqk40LP7hl7gqi8zaycpH/gt4fu0mLCw1P0AkvL9\nDFP15EW5q0zbA59LmpvcTl3L1/oWRJGU2pbyn9knV2c5Si5LY/eVNcAIoDXwIWGxrbSqONAztWcb\nXZAU3e+XueuuMtdfwBs0qiUvyl1lOiV2gFwhqSdwKVCbUHCtIsw+4kodFDtAtkk6EXjazFZXeGhs\njDyR3QvcCbwJdCa0Ah4WM1BEPtDT/RppPIhPBS/KXWXylq9S5xMKjQHAE8DFUdPkpjR+sbQHBkr6\nJ3C/mc0GMLN748aKoo6Z/SO5/rSkS6OmicjM5gK/L7ktqenPPN25NI5BSQUfyesq072E6e46AaNJ\n+r+l1AIzWwgUmNnrwOaR8+Si1H2xmNnlwF6EQa+DJb0t6UxJtSNHi6GWpN0Bkp+pez+UkHStpK8l\n/SBpFfBK7EzOuezzotxVpjpm9g8z+97MniZ03UirHyT9HsgkXVkaxw7k4pOUBxwBnA7sAPyd8N54\nJmauSC4CRkn6knAA3ytynpiOA7YjzLLRCpgfN47LcWk8y5gKXpS7yuQtX6W6E6avuoLQl/zCuHHi\nSQrRdUnjF8tHhFlHbjWz9mY2wsxuBhZGzhXDLKCHmW0HDCGs9ppWC5OBfQVJV5bUDgx3pSRtV+G2\nkquzIsRxWeB9yl1lKmn5akZo6emxgedXZw8RuvNMNbPescNE9hKhdbii07MdJAfsbWZLKt5pZl1j\nhIlsDPAcMIVw4Ppn4C9RE8XzpaRuwDJJ1wNbxA7k4pHUFmgODJVUsiJ2TcLKv3ua2fnRwrkqlZfJ\npLkx07mqIakd0BU4AHiaMKhvXtxUcUh6jDC7iJFM9Za2RYMkLeSnZ47yCNOGNosQKTpJ75rZ/mVu\n/2t902dWd8lKjS0ICwedCbxqZrMk7WBmvmBMykg6EOgGHAW8mNxdDLxnZvdEC+aqnLeUu40m6e9m\ndmKFwiPVBYeZTQImSdqSMO3bXCA/bqpotqH87DOpWzTIzJoCSGpR9uBMUst4qaLLSNrNzD6UtAuh\nJTCVklWQS4rv28o89AAp+1txYGb/Bv4taW8zmwzhwC3Nq2WnhRflbqOZ2YnJ1Q5ecARJS8eZwD6E\nKREvixooIjM7RNLmwI7Ax2b2Y+RIWZecjm4G3CipD+GgtQZwA7BnzGwRXQw8JmlbYAE+N/e6pHHc\nhSvVStJuhAadGyXdlIxBcdWUF+Vuo1Xo/+YFR3AxcA/Q3cxS3UdM0h8J87XXAh6XlDGzwZFjZduW\nhMW1tqW033QxcEe0RJGZ2fuE6SHLkXSVmQ2KECkXpfqzw9ELOBp4lLBi9suERaZcNeVFuasMWwIn\nEwqOUwhFeaoLDsIMG2cCgyS9Bswws2/iRormUmA/Qt/IwcDE5GdqrOt0tFuvg2MHcC5HLE9+LjWz\nFZK8Zqvm/D/YbbSyBQew2My+lLSPmU2InS2iuwin5LsAEwizsRwTNVE8a5IvlIyZZSQtix0ookaS\nngfqlNxhZt5nuDzvslHK90W6fQyMBy6RdBUwLXIeV8V8nnJXmXoQWswBTpM0MmaYyHYxs78CRWb2\nDOle0fMtSeOA7STdRThISasRwHDg3DIXV17qumxUXNE1GfgK8FqEOC5HJFOl7mVmzwJ3m5l/XlRz\n3lLuKtPeZnYOgJn1kvRm7EAR1ZLUGEBSAclUgGlkZv0lHQVMBmYnXzBp9YWZ+RLqrqJxkv6UnEnq\nCfQGdjOza2MHc/FI2g/omhy05UlqZmZHxs7lqo4X5a5SSWpkZt9K2oJ0v7+uBN4GmhJOP6Z2CXFJ\njQjdeARsJenfZvZD5FixLE7OFkwhaRH2eYd/Io1dNl4BHko+N78D9o2cx+WGO4EbgROB6fhKr9Ve\nmosmV/muASZK+o7QXSO1q46Z2ZuEVZG3Br4pmYFFUk8zuztuuqx7CHgm+XkgMBr4fdRE8Xya/GwS\nNUVEkg5a32PJ301qVnqVVFJkjQIaAIcB3eMlcjnmGzMbJ+kIM7ta0huxA7mq5UW5qzRm9qykF4DG\nhAGfqesbWpGZfV3hrpOAtBXldczsruT6B8kUialkZoMk/Q5oE27a/8XOFEFJv9hdCC1/EwhTI/4I\ndE7ZyrdG+QXXyt63c5RELpcUS2oD1JMkYKvYgVzV8qLcVRpJxxNax0v6vzU2s90jx8o1qTk1nyx6\nAfCNpD8B/wY6UNpanDqSrgd2Bd4CzpB0oJmlamEpMzsFQNJzwP+Y2WpJNYHn4ibLPjPbCUDSaWb2\nSOw8LudcSjiAvxUYSzij4qoxL8pdZRpMWJXvHOBfwOFx4+SkNJ09KHtG4LzkAunaBxUdZGadAJLZ\nicZHzhNT0zLXawHbxAqSA84GvCh3QLluTR8lF4D9I8VxWeRFuatMC83sXUnnmNmDks6MHcjFY2aH\nxM6Qg2pLqmFmxYSzJmk+QLkfmClpBqE1cGjkPDHlS5pCadeVjJn9ZQOvcdVX2bf/7yEAAAx+SURB\nVG5NFXm3pmrMi3JXmVYkg7hqSzqS0LfclZea7islJA0GzqLMl4yZNYuXKKrHgLcljSfMsPFY5DzR\nmNntkp4g9C3/KMUr3gL0ix3A5Y6Sbk3rk9IJA1LBFw9yG01SycI45xL6kw8mLCSUqqXUy5I0oMLt\n65OrfSPEie1YYEcza1ZyiR0oFjMbRuiq8DbQw8xGRI4UjaQ9gasJ++NGSWnuLzuF8HfSlzAz0fS4\ncVyOOyl2AFc1vKXcVYbngAOAAWVWHEvlDBuSziJMadZK0jHJ3TUJBytXmFkaV7OcQlhWfkXsILFJ\n6kBY9bYOcKgkzOy8DbysunoQ+BuQptlW1mcU8AYwBjiYsG+OjxnI5bTUnXFNCy/KXWVYJWkCsKuk\n35Z9wMw6RsoUyyPAq0B/4LrkvmJgcbRE8c0AFkr6iqQftZmltV/kaELf6e9iB8kBX5nZfbFD5IhG\nZnZbcn2qpBOjpnG5Ls1jUao1L8pdZTgcaE5YfSytrX4AmNkK4DNJlwBbAqsIXXkeAj6PmS2ik4Cd\ngO9jB8kBH5nZg7FD5IjPJF1O+dVNX44bKZq6kpqY2VeStiWcXXPOpYwX5W6jmdka4AtJJwFbUL4Q\nTau/A3cRuvHMAu4BjoyaKJ7PgWXJAUvaPSnpUcJ7AgAzuyZinpjyASUXCIV5qopySXuY2TRgAPCO\npCVAAeHz07n18e4r1ZQX5a4yPYEXoiXqAf8AepnZ6ZLSPGd7C+BjSZ8ktzMp7NZU4nzgSfysAWbW\nNVlg6jfANGBB5EgxjJS0PaE/eX/gNTNLc1c3V4GkbQhjUAAwsy9I54QBqeBFuatMXoiW2gzoBUyS\n1BqoHzlPTD5TQKlvzSzN83GvJekC4ATC0uEPElY6vSBmpmwzs0Mk5RMWhukMnCWpBvC6mV0bNZyL\nTtIdwDGEA9aSdQ06pnTCgFTwotxVJi9ES10G/A9hsOdphP2SVmes4760dtn4RtLdwGRK+1HfEzdS\nNCcDBwGvmtnIZLB46pjZCkmTCAcnBcDewF5xU7kc0QHYOVlszKWAF+WuMvUmzLGb+kLUzN5Oums0\nBJ4BUjs3N7Ao+ZlHKDjSvD7C3ORnk6gpckMNktUrk9upG3MgqTehJXQL4BXgWeByM1sVNZjLFXMJ\nXVcKYwdx2eFFudtokrYzsy+Bb4D7gG1I2YCtiiTdTzglXZ/QredjYL+ooSKpuPKcpBdiZYnNzAat\n635JT5nZCdnOE9lY4E1gB0nPA09HzhPDQOBF4HrgDS/GXQXbA59LKjmYT/N4nFTwotxVhkuTy92E\nVq+SkeEZ4NBYoSL7LdCGsE/6E2ZjSaVkMF+JZsAOsbLksC1iB8g2M/ubpFcJfydmZmlcxXJr4EBC\na/kQSQuBF4DnkwF9Lt1OiR3AZZcX5W6jmdmlydXhZvZMyf2S/hwpUi74j5llJNU3s28kbfgV1VfJ\nwRrAcsIBnCsvdYuBJAdrNxCmRJwhqbeZpWou/6Rl/LXkgqSjCAfxt+NzlTtYA4wAWgMfApfEjeOq\nmhflbqNJOhboBJwiaf/k7hqEgY6PRwsW10RJlwELJI0D6sYOFNGDwOWUTut1B5DWFT1dqYeAQcA7\nwAGE98khMQNlm6T2hJbyA4GWwAeEVV9Pi5nL5Yx7CYvyvUmYned+4LCYgVzV8qLcVYYPgEZAEWDJ\nfcXAo9ESRSLpekq78DRNru8GvBczV2R9geOAebGDuJyyzMxKxhc8JymNZ1BuIIy/GQxMMbPUnTFx\nP6uOmf0juf50Sv9GUsWLcrfRzGweMFrSw2WnbpLUNGKsWOas47409pUt6xMzm7vhp6Xad7EDRDBP\n0gBC1412wApJRwCYWSoGiptZmtdycBtWS9LuZjZd0u6ksJtb2nhR7irT1ZLOJcxXXo/QB65N3EjZ\nZWajY2fIQYXJjCtTKZ2bu3/cSHFIag4MJcxQ9AQwzczeM7M/xk0WRQbYJblAmDrzlOT+VBTlzm3A\nRcAoSc2A+UCPyHlcFfOi3FWm44HtCANThhP6Djv3fOwAOeQeYBhhKrw3Cf2H0zpVZldJNQldvfYH\n3jOzlZFjOZczzGwKsE/sHC57vCh3lWlhsjpdgZnNlbRZ7EAuPj97UE5dM3tN0gAzM0nLYweKRdIt\nwGzCFJl7A18BZ8bM5FwukPR3MzsxmSKzpMtKHmGe8jQvRFftpXllPVf5vpTUDViWDHhM3dzLzm3A\ncklHAjUl7UeYIjKt9kkWltrfzI4CWsQO5FwuMLMTk6sdzKxZcmlKetf9SA1vKXeV6VqgATCBMCNL\np7hxnMs5PYCbgcbAZcC5ceNEVVNSO+Cz5KxaQexAzuUCSW2B5sBQSX0IreQ1CLP17Bkzm6taXpS7\nyvQwcDVwPvAXYAgpm3fYuQ2oQZgissQqSbVTurz6Q4RxJ92AGwmLTDnnYEvgZGBbwncphGmGfZxW\nNZeXyfgMO65ySPoXcDjwkpkdLulVM/OFDpxLSJpGGAw9hzB/fSGhcaSvmT0SM1uukHSVmQ2KncO5\n2CTtbWaTJW0NfFt2ymFXPXmfcleZahNavN6UdAhhakTnXKlPgd3MrCOwK6GrV1vgwqipcsvBsQM4\nlyM2l/QJYYrQTyR1iR3IVS0vyl1l6gp8TJiHeWvgjLhxnMs525rZNwBm9l1y+z+EU9MuyIsdwLkc\ncS1wgJntRRijNThyHlfFvE+5qzRm9hHwUXLz8ZhZnMtRkySNA94lzM09VdJJhIVzXOB9Kp0L1pjZ\nAgAzm5/mKVTTwoty55zLEjM7X9LxQCvgETN7TpKAZyJHc87lniWSLiQsNHYQ8J/IeVwV8+4rzjmX\nJZK2AuoDC4HGkq6woDBytFzi3VecC04DtgeuI8zj3y1uHFfVvKXcOeey5ynCKpa7ExYOSm0xLqkm\nYQXPHYDXgBlJf/vTY+ZyLof8SJhquC6hW9euwPtRE7kq5UW5c85lT56ZnSNpFNAd+HfsQBHdDSwA\nuhBmoXkIOMbM5kVN5VzueJ4wi9l3hDNIGeAPURO5KuVFuXPOZc9qSXUIXVgypPszeBcz6y7pADN7\nRtLlsQM5l2PqmJlPEZoiaf5CcM65bLsduJgw7/A84K24caKqJakxgKQCfFpI5yp6U9KRhC5vAJjZ\nFxHzuCrmRblzzmVPHTO7AUDSE2a2JHagiAYAbwNNgfGEgxXnXKltgVuA75PbGaBjvDiuquVlMj4l\nrHPOZYOkN/x0dHmStjazr2PncC7XSHrTzA6KncNljxflzjmXJZLGA/mAkXTXMLO/RA0ViaSeQE+g\nTsl9ZtY6XiLncoukvwGPAFNIFtUys5VRQ7kq5d1XnHMue/rFDpBDegHHEGaWcM791EHA78rczgA7\nR8rissCLcuecy57JhMK8GfAsMC1unKimAfPMbE3sIM7lIjPbA0DSNsC3/rdS/fmKns45lz2jgE8I\ni4B8BdwfN05UrwGfSHpN0r8kvRY7kHO5RFJnSZ8ALwEfS+oSO5OrWl6UO+dc9jQys1HAKjN7h3R/\nBvcE/gycC5yT/HTOlRoMHGBmewGdktuuGvPuK845l0WSWiY/twNWR44T05fABDPz+cmdW7c1ZrYA\nwMzmS1oeO5CrWl6UO+dc9lwEPAC0Av4OnBc3TlT5wAeSZlA6s0QqZ6Jxbj2WSLoQeJMw6PM/kfO4\nKuZFuXPOZc8uQCdvHQbg+tgBnMtxpxEW2boOmAV0ixvHVTUvyp1zLnsOBwZL+gdwn5l9GjtQtkk6\n1syeBbSOh9/Idh7nctidfvYoXdI8yMg557LKzC4E2gFTgdslvRI5UgxbJT/vAJqUuewULZFzuSlf\n0h6S6kjaTNJmsQO5quUt5c45l10dgCOBbQn9ytOmtqR3gWXA0cl9NYDawBXRUjmXe3YjrGewNbAY\nWIMvHlSteVHunHNZImkW8AFwr5l1l1Q7dqYIHgFeBfoT+soCFBOKDudcqauA4cAcoCE+bWi150W5\nc85lz1jgdGBfSXnAKkJrWGqY2QrgM6BH5CjO5bqBQAczWyxpW+AZ4OXImVwV8j7lzjmXPX8GDgZe\nALoSZlRwzrl1+dbMFgOY2SJgSeQ8rop5Ue6cc9mzwMwWAgVm9jqweeQ8zrnctVTSS5L6S3oSqCdp\niKQhsYO5quHdV5xzLnt+kPR7ICOpJ9A4diDnXM56usz1+dFSuKzJy2QysTM451wqSCoAfgMsAnoD\nzyQt5s4551LOi3LnnHPOOeci8z7lzjnnnHPOReZFuXPOOeecc5F5Ue6cc84551xkXpQ755xzzjkX\n2f8HP/bMBcfyF50AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2047dd3a320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12,8)) \n",
"sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, cmap='coolwarm', annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the correlation matrix, it is clear that the following columns are having good relationship:\n",
"\n",
"number_project and last_evaluation, \n",
"\n",
"number_project and average_montly_hour\n"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2040f5964a8>"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAAEeCAYAAAD4jwBAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJElV+l+mWQ7RMHFOhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2040f67c128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(7,5)) \n",
"sns.boxplot( y=xSource[\"last_evaluation\"])"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x20401733b38>"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAboAAAEeCAYAAAD8etB9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni06SVDSLTpJUtP8FuCl2qxbrsukAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20401610940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(7,5)) \n",
"sns.boxplot( y=xSource[\"average_montly_hours\"])"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 14999 entries, 0 to 14998\n",
"Data columns (total 23 columns):\n",
"satisfaction_level 14999 non-null float64\n",
"last_evaluation 14999 non-null float64\n",
"number_project 14999 non-null int64\n",
"average_montly_hours 14999 non-null int64\n",
"time_spend_company 14999 non-null int64\n",
"Work_accident 14999 non-null int64\n",
"left 14999 non-null int64\n",
"promotion_last_5years 14999 non-null int64\n",
"sales 14999 non-null object\n",
"salary 14999 non-null object\n",
"IT 14999 non-null int64\n",
"RandD 14999 non-null int64\n",
"accounting 14999 non-null int64\n",
"hr 14999 non-null int64\n",
"management 14999 non-null int64\n",
"marketing 14999 non-null int64\n",
"product_mng 14999 non-null int64\n",
"sales 14999 non-null int64\n",
"support 14999 non-null int64\n",
"technical 14999 non-null int64\n",
"high 14999 non-null int64\n",
"low 14999 non-null int64\n",
"medium 14999 non-null int64\n",
"dtypes: float64(2), int64(19), object(2)\n",
"memory usage: 2.6+ MB\n"
]
}
],
"source": [
"# Changing categorical data to hot code encoding and contatenating it to the source data\n",
"\n",
"salesDummies = pd.get_dummies(xSource['sales']).astype('int64')\n",
"salaryDummies = pd.get_dummies(xSource['salary']).astype('int64')\n",
"xTemp = pd.concat([xSource,salesDummies,salaryDummies], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Dropping the categorical columns and using hot encoded values\n",
"xTemp.drop(['sales', 'salary'], axis=1, inplace=True)\n",
"xSource = xTemp.copy()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 14999 entries, 0 to 14998\n",
"Data columns (total 20 columns):\n",
"satisfaction_level 14999 non-null float64\n",
"last_evaluation 14999 non-null float64\n",
"number_project 14999 non-null int64\n",
"average_montly_hours 14999 non-null int64\n",
"time_spend_company 14999 non-null int64\n",
"Work_accident 14999 non-null int64\n",
"left 14999 non-null int64\n",
"promotion_last_5years 14999 non-null int64\n",
"IT 14999 non-null int64\n",
"RandD 14999 non-null int64\n",
"accounting 14999 non-null int64\n",
"hr 14999 non-null int64\n",
"management 14999 non-null int64\n",
"marketing 14999 non-null int64\n",
"product_mng 14999 non-null int64\n",
"support 14999 non-null int64\n",
"technical 14999 non-null int64\n",
"high 14999 non-null int64\n",
"low 14999 non-null int64\n",
"medium 14999 non-null int64\n",
"dtypes: float64(2), int64(18)\n",
"memory usage: 2.3 MB\n"
]
}
],
"source": [
"xSource.info()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = xSource['left']\n",
"X = xSource.drop('left', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(14999, 19) (14999,)\n"
]
}
],
"source": [
"print(X.shape, y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TRAIN AND TEST DATA"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MACHINE LEARNING"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model evaluation"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,mean_absolute_error"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def modelEvaluation(orginalValues, predictedValues):\n",
" print(\"MEAN ABSOLUTE ERROR: \\n\",mean_absolute_error(orginalValues, predictedValues))\n",
" print(\"-------------------------------------------------------\")\n",
" print(\"ACCURACY SCORE: \\n\",accuracy_score(orginalValues, predictedValues))\n",
" print(\"-------------------------------------------------------\")\n",
" print(\"CONFUSION MATRIX: \\n\",confusion_matrix(orginalValues, predictedValues))\n",
" print(\"-------------------------------------------------------\")\n",
" print(\"CLASSIFICATION REPORT: \\n\",classification_report(orginalValues, predictedValues))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Random forest classifier"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rf = RandomForestClassifier(n_estimators=1000, random_state=40)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=1000, n_jobs=1, oob_score=False, random_state=40,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"predictions = rf.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN ABSOLUTE ERROR: \n",
" 0.0133333333333\n",
"-------------------------------------------------------\n",
"ACCURACY SCORE: \n",
" 0.986666666667\n",
"-------------------------------------------------------\n",
"CONFUSION MATRIX: \n",
" [[3418 10]\n",
" [ 50 1022]]\n",
"-------------------------------------------------------\n",
"CLASSIFICATION REPORT: \n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 1.00 0.99 3428\n",
" 1 0.99 0.95 0.97 1072\n",
"\n",
"avg / total 0.99 0.99 0.99 4500\n",
"\n"
]
}
],
"source": [
"modelEvaluation(y_test, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x204101ce940>"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAqkAAAI+CAYAAACfRZM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSpIkqTj/A3NeEFJT61blAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2040ff52208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(10,10))\n",
"sns.barplot(rf.feature_importances_,X_train.columns)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment