{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "\n",
    "sns.set()\n",
    "from jupyterthemes import jtplot\n",
    "# jtplot.style()\n",
    "\n",
    "jtplot.style('grade3', \n",
    "             context='paper', \n",
    "             fscale=1.5,\n",
    "             ticks=True, \n",
    "             grid=False)\n",
    "\n",
    "# jtplot.figsize(x=15., y=9.,aspect=1.2)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "# Explore the data\n",
    "<br>\n",
    "<br>\n",
    "The first thing we should do at the very beginning of any data analysis is to get a feel for the data.\n",
    "Questions on the following lines should be asked,\n",
    "+ Do the columns make sense?\n",
    "+ Do the values make sense?\n",
    "+ Are there any missing values?\n",
    "+ Are there any outliers? If yes, what's their story?\n",
    "+ Which variables are strongly correlated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"HR_comma_sep.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's clean up the data\n",
    "df.rename(columns={'sales':'department'},inplace=True)\n",
    "df_n = pd.get_dummies(df,columns=['department','salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>department_RandD</th>\n",
       "      <th>...</th>\n",
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       "      <th>salary_medium</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\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  \\\n",
       "0                   3              0     1                      0   \n",
       "1                   6              0     1                      0   \n",
       "2                   4              0     1                      0   \n",
       "3                   5              0     1                      0   \n",
       "4                   3              0     1                      0   \n",
       "\n",
       "   department_IT  department_RandD      ...        department_hr  \\\n",
       "0              0                 0      ...                    0   \n",
       "1              0                 0      ...                    0   \n",
       "2              0                 0      ...                    0   \n",
       "3              0                 0      ...                    0   \n",
       "4              0                 0      ...                    0   \n",
       "\n",
       "   department_management  department_marketing  department_product_mng  \\\n",
       "0                      0                     0                       0   \n",
       "1                      0                     0                       0   \n",
       "2                      0                     0                       0   \n",
       "3                      0                     0                       0   \n",
       "4                      0                     0                       0   \n",
       "\n",
       "   department_sales  department_support  department_technical  salary_high  \\\n",
       "0                 1                   0                     0            0   \n",
       "1                 1                   0                     0            0   \n",
       "2                 1                   0                     0            0   \n",
       "3                 1                   0                     0            0   \n",
       "4                 1                   0                     0            0   \n",
       "\n",
       "   salary_low  salary_medium  \n",
       "0           1              0  \n",
       "1           0              1  \n",
       "2           0              1  \n",
       "3           1              0  \n",
       "4           1              0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_n.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "So, no missing values in this dataset. Let's move on to understanding the correlation between various features.\n",
    "<br>\n",
    "<br>\n",
    "** Correlation **\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "corr_mat = df_n.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/skd/anaconda2/envs/data-science-portfolio/lib/python2.7/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n",
      "/home/skd/anaconda2/envs/data-science-portfolio/lib/python2.7/site-packages/matplotlib/figure.py:1743: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
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zRzWoqEyQu48Hxhe+N7OngbcIgcuK7v4JcB9wPXAIIUAhBhEHAX9LTGMbBrwJ\n7F74ZN7M/kF443wSIetTsAywUew/6ajEWGoJGYWPgD2A++K2s4EH3P242HRszBJckdh3aeBC4Dp3\nPyGx/Q3gOULg9XALLtVod/9t4pz6Ar8G1nb3aXF7T+B6M1vT3d81sw2BfsBZ7n5RYqzdgJPMbJi7\nf5o4xgh3vzB+/aSZ7RT3v8ndZ5hZIcP0srtPbWig7v5fM5sIDCJk9QpTDo8C7nL3WS0472WAPdz9\nmdjPJL4LdgpjPQ94HdjX3etju9eBZwjB8LUtOF7BgnjceYUNZrYV4VrekWh3Z2OdmNkgwnVYyIsv\n/auEIYmIiIhIqSoqCDKzpQif3B9OmILWOfHyOsAn7v6VmT1ImNI1NL62IyHbMDL20wXYnjC1rrCO\nA0Lm4WnCdLukp4sEQIXpa6cC6wHLJl5aNz6vTshe3Zfa9V4SQRAhS7IMcFdiLBCm782M42lJEDS2\n8EWcnvZ/wDKFACh6Mz5/jzBtbPv4fTo7NBI4GtgSeCyxfXSq3avAFi0YY9L1wG2FgIyQOekNDG9h\nP7MKARCAu39mZtOBNeDbYHNTwjS3+kS7Z2PQtj2lBUGjkwFQNAk43czqgSfd/fWmOnH34RQ556/m\nfJ0vYUwiIiJSxWpqqiNjk5VKm7pzESFwGUmYgvZjwnQrWDggGgmYmW0avz8UeCfxBnl5oJYwPWx+\n6rEfsELquNPTAzGzfYC7ACdknbYkBAEzEmNZJT5/nNo9/X3P+Dy+yHh6FBlPU2amvp9HWLeT3kZi\nrD3ic/pcp6deb+gYc1n4Z9AS9wCfAb+M3w8C/uPuz7ewn/SY0uNaDshR5OcZt6XPsbmK9fcLQtD4\nO8DNbGqcGikiIiKSuVxtTWaPalBRmSDClLZb3b2w3qTw6X7aWEKg0T9OdeoLXJl4/XOgHriKRTMf\nEN44JxX7JP4g4C13H5AYS0dCgFXwYXzuycLS3xcKHxwKvFHkWItkoTJQCCAKBREKVo7PzSrOUAp3\nn2dmfwEGmtkNhPVBJ2dwqM8JP8teRV5bmZB5K5gLLJVqszzFLfL74e4fAUeb2TGEQhcnAH8ys7fc\nfWy6vYiIiIhUjkoLgroSsiNJR6YbufsCM7sLOBh4GehOnAoXX58d1xNtRKi+Vsp0o66EtSBJhxEy\nTAXTgA8I2arbEtsPSO33NPAV8H13b0khgNZUWGvVj1DVrKA/IWvU0qxMIZBsbnboRkLxgFGEn/HI\nxpu3XJwioOLEAAAgAElEQVQq+TJwoJmdn6h4tyWwNgsHyu8BG6S62KOEY+aByWZ2MiHD9UMS0xVF\nREREslBTJQUMslJpQdAY4Agzm0woiLA/sE0DbUcSKrZdArzo7m+mXj8F+CfwqJndQsgc9SKsz5nq\n7lc1Yyz7mdnlhGpom8fjfTvtLK7HGUrIAPyJUG1tQ75b/F4f282KZbSvMrNese85hPU6uwI3uPuE\nJsZTFnefbGZ/A4bGjNakeOyjgWHNLdOd7DI+H2dmo4AF7j6pkeO/E8ub70YouvBFy8+iWf5AWF91\nv5kNJ2TlLgTeZuEqg3cBvzWzMwnXYjfC9WiSmS0LPEH4HSysBTqcEDQ/Vf4piIiIiEiWKm3S32BC\neeOhhDep3QnZnkW4+wuExf+rUSSrEN+Qb0UoW3w94dP5SwmFDJqT9fhzHMdBhDfVexKmcS305t3d\nrwN+S1hr9CDhHj4D48tfpNodAGxMqCL2MHAmYZraO80YT2s4nFCw4ThCYNcXOIMw/hZx91cIZbL7\nESqvvdiM3e6Nzy0tiNCScT1K+BmsRihYcTkhGP6pu3+VaHphHMfJhDVLyxOmtDXHN8ArhLLq9wN/\nIxTy2MfdX26F0xARERFplEpklyeXz6swVWszswMIb6x7x2poApjZA4QpgRu19VgqSVbV4TqMG5FF\ntwDM++jDphuV4DcDb82kX4Br3388k37n9Vgzk34BOnyd2VI96ro2tASuPLkM/0/J57L5jznLMdeT\n3ZuJmqLLWcuX5Zgz+hG2W7n6ukz6zde09PaDbS/Lv0OAzl26LHG/fa8csFtmF22Tex9f4q5XWqVN\nh2t3zGxtwpSyCcBsYDNCZuV+BUBBvKfO5oQbjQ5o29GIiIiItH/VUsUtKwqCyvc1sAlhCtyyhJt3\njgTOakkn8Qaijf021yfvfdPOPAt8SbhHz23pF1P3TlpE4Wa3IiIiIiKtQUFQmdz9A5q5oL4JfwGO\naOT1c4k3f21v3L2plGq6IuBCzGwtd5/aeiMSERERad9UHa48CoIqxxBCpqQhHyymcbSFLZp4fUk+\nd76Ym02Cr9cmO2bSL8BSc4rdt7Z8177fP5N+AU5YbbdM+r1yjjfdqEQ1GV1nAHLZTKOo67JcJv0C\n1NVnM/29Q4bvI+ozXOfQcVZG/zQus3LTbUqUz2WzViWrtTUA8zOtIZVN30vNm5NJvwBf15R63/TG\ndcll9zMUKUZBUIWImY6pbTyMNtFYaW0RERERWVSuRpmgcmhFlYiIiIiIVBVlgkRERERE2pkaVYcr\ni4IgEREREZF2plpuapoVhZAiIiIiIlJV2lUQZGb7mdlJqW0DzCzb2wwv4cxshJmNa+txiIiIiEjz\n5GprMntUg/Z2lvsBJ6W2PQps3QZjERERERGRdqjdrwly9xnAjLYeh4iIiIjI4pKraW+5jMrSboIg\nMxsBHBG/Lkx/+ycwArjF3XPxtd7AFOBoYD3gcGAp4K/AycCPgSuADYHXgaPT96kxs37A6cAPgTnA\nQ8Cp7v5ZM8faATgPOBhYFZgF/Ac43t1fT4zx2HiMg4FOhKzWCTGwK/TVETgznscawHTgJmCou9fH\nNgOAW4Ct4rh3Az6P12aIu9cl+tsCuBrYFPgQuLQ551TkHH8EnAv8JI79beAqd78pvt4pvt4f6AW8\nB9wIXObu+dS4fwL8Btg5jvscd7/ZzI4EzgZ6En7WR7n7x3HfllzDwXEc6wF5YDJwlrs/nWgzBDgH\nWBe4DtiGcK2vdPdrYpvNgReBvd390dT1GA0s7+5blnI9RURERGTxaU8h5PnAY4Q3plvHx3GNtP8d\nsDThze8VwGDgSkIA8Sfg50BH4H4zW6qwk5kdD9xJeLPblxA47Qw8ZmbNvdX1mcCJwB+BXYFfAS8B\nyxYZ48rAYcBphODlvlSbUYTA5iZgL+B64CxgWJHj3gb8O477ztj/kYlzWwl4EugGHBr7PS6eX7OZ\n2VbARGAV4ARgH2A48L1Es1uBUwiBz97Ag3HMFxfpcgTwTBz3s8BNZnYxIeg9OR5jW+DaIvs25xr2\nJgRbPycES5OBp8xs4yL93Qs8AvwMmABcbWY7w7c3dZ0EDEpdjzUIP+fhRfoTERERaXU1tTWZPapB\nu8kEufvbZjYDmOvuzxW2m9mPG9jlVXc/On491sz2JLyZ3tLdX4j71gOjCQHVP81saeBC4Dp3PyFx\njDeA54A9gYebMdytgLHunnzT/kCRdp8AP09kRj4B7jazXdz9CTPbHjgwtrkn7vNkzISdZ2YXufun\nif5GuPuFiXY7Af0IARSEoKQTsJu7fxiP+SzwDvBWM86r4FJCMPoTd/8mbvt74UUz2zAe9yx3vyhu\nHmtm3YCTzGxYatw3u/uwuO8LhABkILCWu8+O2zcATjWz2mRmq6lrCODupybGVhPHui5wFCFYTbrY\n3UfFtuOAPeK5PBlfvwG40cxWdfcP4rZfAl8RAs8GmdkgUgEUwJPPTCrSWkRERESy0m6CoBKMTX3/\nOrBuIQCK3ozPhQzG1sAywF1xSlvBS8BMYDuaFwRNAs40s0L26kV3X1Ck3b2FN+/R/cACQhD1BCGr\n8TXwSGo8YwkZlS1j/wWjU/2/CmyR+H4r4OlCAATg7u+b2dM0MytoZl0JU8UuTARAadvH51Gp7SMJ\n0xTT4/72Z+XuX5jZdOBfhQAoepPw+7oKMC2xvalrmJy6twWwElAorF9fZOzfXkN3X2BmbxKmIRbc\nQcjwHQWcH7ODA4FRqfEuwt2HUyRb9P7M2apuKCIiIi1SLVXcsrIkX72Zqe/nEdabpLcBdI7PPePz\neGB+6tEDWKGZx76QMH3vQMI0rxlmdlUMIJI+Tn4TMxyfENYRFcbThRAIJcfyr/h6ejzpc56bODcI\nAcTHLOqjJs4nqQfh9+b9JtpAyBYlTU+9XlDKz6qg0WtoZt8jZH6WJ0yJ3IYQDI0p0hfu3ug1dPc5\nhKl+R8Ws0p7AamgqnIiIiEi7sSRngkpRKHxwKPBGkdc/aU4n7j4PGAoMNbPVCcHQMGA2YT1PQc/k\nfjGrsCJQmGb1WdynTwOHmtKc8SR8mD5m1KsFfcwkZFBWa6JNod/3EttXjs/NKjDRTE1dw90Ja7H6\nufu0RLuuhCIJpbiBEFDtRljvNcndXy6xLxEREZEWUyaoPO0tCEpnNlrb04S1Hd9399tbo8P4xvtK\nMzuEUMUs6QAzuyAxnasv4WfybPz+cULVtC7uPqEVhvMscIqZrezu0wHMbDVCdbZnG90zcvc5cfpc\n/zj2uUWajY/P/YDLEtv7EzI6z5d6AkU0dQ0L2bf5hR3MbF3COU8s5YDu/pqZjScUZdiSUKFORERE\nRNqJ9hYEOTDIzI4ilJye1aqdu88yszOAq8ysF2HK1BzCmqFdgRuaE4yY2YPAy4Rpa7MIa4k2Y9Eb\nva5IWMR/M7AmYZ3PRHd/Mo7nH2b2N0IFuz8S1hrVAmsTigfs00AQ0pArgOOBx+N6JYAhtPw+S6cD\n44CJZnYFYZrb+kBPdx/i7pPjuIfGEt+TCNfvaGBYc0uNN1Oj15BQ0GABcGu8hqsQ1ge9V6yzFrie\nsD7oy/gsIiIistjoPkHlaW9B0E2ET/AvJxQwKNwnqNW4+3VmNo3wRn8AYRH9e4R1Je80s5sJhHLM\nJxIyV1MI9xlKl3i+gHC/opGx3aOECnZJhxDKRB9JuI/NHMI9eR4lkd1oDnefEcs9XwPcTpgyNoyQ\nzejdgn6eN7PtCOuebiAEZm8TgqyCwwnBxnF8Ny3uDBbODLWGRq+hu79qZv0J9216KI7zTMI0ud5l\nHPdBoA64w92/KqMfERERkRbL1Tb3zi1STC6fV2GqxS1xo88j3X1E246mfWrra2hm+xEq0f3I3f/V\nVPvGZFUdrtfX05puVKKaOen6Ea2jvmu6ZkbrOWG13TLp98o5nkm/AB0+be7nLi1X3625dV5apq7L\ncpn0C1BXn83/Vx1yTbcp1YIM/4vtNOuDphuVoG6ZlZtuVKJ8TTZv2nL1dU03KtH8dlhDaqkFX2fW\n99c12axK6JLL7mcI0Klb9wz/0tvGlNMOy+xfmLUuu22Ju15p7S0TJNKmzGxtwnTES4Cnyg2ARERE\nREqhwgjlURDUQrEscmO/dfXuXuz+M+1C6n5Ei2jgfkfV5PeEAg8vEW6SKiIiIiLtjIKglvsLcEQj\nr59LKDbQIHefync37Kw0ja4zMrO14vjbVFtdQ3cfQFgrJiIiItJmalQYoSwKglpuCJAucJCUzSTt\nxWeLJl5v7+cnIiIiIlVOhRFE2ti8zz/O5I+wrvMyWXSbqSz/Oaop+d64jTupq2XSL8CpH03OrO81\nOreouGSz1XXs2nSjEtXWZzTmmo6Z9AtQn+EvdQfa4czrXDafXOdz2U0MyLLoQnssFJHZmDN+P9q5\nS5dKnYFTsv/97qjMLtr3Lrh5ibteacqjiYiIiIhIVdF0OBERERGRdkbV4cqjqyciIiIiIlVFmSAR\nERERkXYmp+pwZdHVExERERGRqqIgqEKY2cFmljezH6e2/yhuf6bIPrea2cx4A9dyjz/VzEaU28/i\nEsc7pLXalTGOIWbWJ6v+RURERIrJ1dZk9qgGmg5XOSbE522BFxLbtwPmAD8ysy7u/nXqtYnu3g7r\npJatLzCjrQcBnBOfx7XlIERERKS6VEuwkhVdvQrh7tOAqYTAJmk74HagHtiysNHMVgd6A+PLOa6Z\ndSpn/7bi7i/HayYiIiIi0iLKBFWW8cCeqW3bAr8G1icEROPi9kKwNAG+DWbOBfoDvYD3gBuBy9w9\nH9sMAG4BtgdOAXYCngd2SQ8k9nd7PP7u7v5KU4M3s12Bk4FNge7AW8DV7n5zql13YAhwALAK8Anw\nFHCUu8+NbX4Uz+cnQCfgbeAqd78pvj4VGOHuQxL9Hgb8Afge8FocS7Fx7hTbbQ7UAU8AJ7v7e4k2\n+Xj82cCJwLLA08DRhXaxDcA5ZlbICB3p7iOaulYiIiIi5ahRJqgsunqVZQKwopmtD2Bm6wE9gYnx\nkcwSbUuYJvdS/P5WQmBzI7A38CAwDLi4yHFuJwQJfYGL0i/GIOVRYDNg2+YEQNH3CcHML+MYRgJX\nmtlxib6XAp4EBgE3EIK+Uwi/i0vFNlvF810FOAHYBxhOCG6KMrPdCdfg38D+wJ/j8Xuk2u0LjAU+\nBH4BDATWA8bF8046khAwHhsfmxGuXcHW8fnm+PXWhOsmIiIiIhVMmaDKUlgXtB3wenx+z92nmdnT\nwGAzq3X3uvjas+4+38w2BPoBZ7l7IagZa2bdgJPMbJi7f5o4zih3P7vYAMxsJWA0Ifuyrbt/0NzB\nu/sNiX5y8XyWAo4BrosvHQb8GNjD3cckdr8r8fWlwHTgJ+7+Tdz29yYOfw4hAPp5IvM1A7g71e4K\nYLS7H5QY6wvAm8AA4JpE2znAfvF6Y2YrAFeZ2aru/oG7P2dmANPc/bkmxoeZDSIEfwv597P/bGpX\nERERkYWoRHZ5FARVEHd/w8w+JgQ4f47PE+PLzwDdgE3N7G3gh4TpWhCyFQCjUl2OBI4mrCV6LLH9\nwQaGsDohcPkU2MXdZ7Zk/Ga2CmGa2+7Aqnz3+zU30WwXQtAwhiLMrCuwDXBhIgBq6ri1hKlt5xUC\noOh+YEGi3Q8I2aohZpb83f+AkBnbjoWDoLGFACh6NT6vEfdpEXcfTshoLWTe5x/nizQXERERkYwo\nhKw8E/hu2tv2xCAoBiSvxW0/AXJ8VxShMOVreqqv6anX09vTNiFMDbu9hACoBniIMA3uAmBHYAtg\nKCGrVLAC8H4jXfUg/F421iZtJULA9XFyYwxgPkls6hmfbwXmpx6bxrElpa9BIZjr3IKxiYiIiLQ6\nlcgujzJBlWcCcICZbUmo/jYx8VphXVBPwhv3whSswpv1QkGEgpXj82epYzSUeXiEUIDgGjOb7e5/\nbcG41yZkYw5z95GFjWa2T6rdJ4QiDw2ZSaiEt1oLjj2DkPHpmdwYM0QrJjYVrsNvCGuX0r5swTFF\nREREpJ2qjlCvfSlkd84GPgf+m3jtaUJBhO2BSYl7BhX26Zfqqz8wj1ABrlnc/XzCNLu/mNnhLRh3\n1/g8v7DBzDrGMSQ9AaweK8kVO/4cwnn2b2757pjxeZEQPOYSL/Vl4UD/deBdYH13n1Tk8UZzjpcy\nD2WGREREZDFTJqg8ygRVnn8DswjTykan1rhMJGQ2ViBUfgPA3Seb2d+AoTHwmATsSlgPNMzd05mg\nRrn7eTGYuMXM6pOZncZ2IwQYQ81sASEzU6xEdWGd0j1mdkEc6/LAz4Dj3P1L4HRCKfCJZnYFYfre\n+kDPZEnslHOBMcDdZnYzsCZwFuFaFs4rb2aDgftjgHUvIfO0KtAH+Lu735XuuBnnvbeZjY3HmpIq\nQiEiIiIiFaY6Qr12xN3rCUUQciw8FQ53n0JYkJ9cD1RwOKHy2XGEMs19gTOA35Y4jnOB84ERZpbO\n5hRrPw/YjxCw3Ab8KY7x4iLtdiaUlR5MCFyuJEyBmxfbPE+Y9vcJoYz2w4QKc+82cvzHCddgE+AB\nQhW2Q0mt63H3hwkBz4qEeyaNBs4DOgL/aeo8iziRcK+hMYRsVHr6n4iIiEiry9XUZPaoBrl8XoWp\nRNpSVtXh6jovk0W3mcryn6OaBpfCleekrpZJvwCnfjQ5s77X6Dy/6UYlqOvYtelGJaqtz2jMNR0z\n6RegPsNf6g7UZ9Z3ZnLZvLnK53JNNypRrr6u6UYlytfUZtJvuxxzxu9HO3fpkt0vSRv59NrTM7to\nK5xw6RJ3vdI0HU5EREREpJ2pqc0mIK0WCoKkSXF9UGN/afnU/XREREREJEPVUsAgK7p60hw/ZdH7\n6iQfb7fd0EREREREWkaZIGmOlwg3Pm3I3EZeExEREZFWpkxQeRQESZNi2epJbT2OJdXNr8/JpN8j\nN+6eSb8Aubp52fQ7b3Ym/QLUzJnZdKMSZFm84I+9Nsys76GzXsuk3y6Z9Bp8nc9m/nvnfHazeWvr\nF2TW99dkU9ChU4fs3lhltfa9JsNCAFmqqcum2Mf0b7Jb077aN1My6Xdm9zUz6begc5b/OEm7pCBI\nRERERKSdqcRS1ma2FnAVsAPhnpEPAye39B6KZjYa2B04t5F7RJal8q6eiIiIiIi0K2bWHXgKWB04\nmHDPxu2AR8ys2TGHmfUj3PcxU8oEiYiIiIi0MxW4JmgQsBqwvbu/B2Bm04BngH0JN7NvlJktA1wJ\nnAaMzG6oygSJiIiIiEj59gYmFgIgAHd/FngH2KeZfQwF3nT32zMY30KUCZKqY2ZDgAHu3ruF++0J\nXAKsCyzl7jkzOwmY6u5NfrohIiIi0loqMBO0AXB3ke2vxtcaZWabA78CftTK4ypKQZBIM8S5rKMI\nKd3jgUJ5tJOAcTQjxSsiIiLSHpjZIML0trTh7j68gd16AJ8X2f4ZsF4Tx6sFbgSudfdXWzLWUikI\nEmmeVYBlgXvcfXxbD0ZERESqW5bV4WKg01Cwk4UTgF7AkMV1QAVBUvXMbGngXODnhD/Ad4HL3f2G\n+PoQ4JzY/GYzuxn4K9AHWBM4wsyOiK9nVspRREREpILNBJYrsn15QjaoKDNbETifUAyhg5kl++gc\nv5/l7vWtOdiKm0wosjiZWUdgLHAoYb3PXsB9wJ/M7ITY7CZg//j1BcDWhD/WvsB04LG4bevYVkRE\nRCRTuZrazB4lcoqv/dkgvtaQ1YHuhOlwMxMPgDPi12uUOqiGKBMk1e5gYCtgS3d/MW570syWBf5g\nZte7+zQzK/ytvO3uzxV2NrO5wIzktoY0NL/2xFtGl30SIiIiUmVKD1ay8ghwoZmt7u7TAMxsS2Bt\n4PRG9nuLcHPVtKcIM29GED50blUKgqTa7Qa8DbycCHQAHgeOISzke601DtTQ/Nrrn5uab43+RURE\nRNrQcGAw8KCZnQN0BoYBLwAPFhqZ2eHAX4Cd3P2f7v4VocjUQswMQgXeRV5rDQqCpNr1BNYB5jfw\n+gqLcSwiIiIizZNhYYRSuPssM9sRuAq4E1hAyA6dnFrPUwPUArnFP8rvKAiSavcZIQ17cAOvv7EY\nxyIiIiLSbrn724SbpjbWZgRhiltTfWUaJCkIkmr3OLAf8Lm7v1XC/nMJ6V4RERGRxSZXW3FrgtoV\nBUFS7W4DBgL/MLPLgP8CXQlrgX7i7vs3tjOh2slPzWwPYAbwgbt/kOWARURERKQ8lTWZUGQxc/f5\nwK6EYOhEYAxwC6H89bhmdHEWMAW4H3iR4ndXFhEREWldNbXZPaqAMkFSdeLNTIckvp8DnB0fDe0z\nlSIL+Nz9NWCb1h6jiIiIiGRHQZCIiIiISHtTJRmbrGg6nIiIiIiIVBVlgkRERERE2plchd0nqL3R\n1RMRERERkaqiTJBIGxv4wx6Z9FuX4Y2Ycx06ZdNxVv0C5LL5zGeN2vmZ9AswdNZrmfV99jIbZNLv\nJV95Jv0CdMnVZdJvfU3HTPoFyGc4Z3/pz97NpN/6bitk0i/A1x26ZdJvpwxvqTg3n+Xnxdn0vUpu\nZib9Anyy9BqZ9NuDbzLp9ztdM+6/DWhNUFkUBImIiIiItDcKgsqi6XAiIiIiIlJVlAkSEREREWln\nVBihPLp6IiIiIiJSVZQJEhERERFpb7QmqCwKgjJiZvsBvd39ytT2AcAt7p5hLZtGxzUEGODuvVu5\n3z5AH3cf0sL9egNTirz0hbsvV/7IREREREQWpiAoO/sBfYArU9sfBbZe7KPJXh/gHGBIiftfQLg2\nBQvKHI+IiIjIkkuZoLIsMUGQmXVy97ltPY6muPsMYEZbj6MCve3uz7X1IJrSXn7PRERERKRhFRcE\nmdkIQlZhAHA5sAHwP2CIu9+eanMYcBmwMSGTcIGZLQdcDPQFlgP+D7jM3UckjjGEkLUw4HpgS+B9\n4BTgEeA3wAnA0sDDwLHuPjux/+rAMGB3wt23JgPnuvsjifEdEb/Ox93+6e59ik2Ha+GY1wWuA7YB\npgNXuvs1zbq4DTCz84B9gHWAOcBLwOnu/mqizfrxnLeO12U6MMbdj02MLXm+77bWlDsz2xx4Edjb\n3R9NvTYaWN7dt4zfdwTOBA4H1ojjvAkY6u71sU1n4BJgF2BNYCbwNHCau/8v0fe4+OXVhAzX+sBA\nM7sTOA84GFgVmAX8Bzje3V9vjXMWERERaUyuVpmgclRqdbgewK3AcGB/QpBxm5ntnGizPHAbcAsh\nGHnUzGqBx4D+hKDoZ4Q39LeY2bFFjnNXfPQlBEF/IwReWwCDgN8DvyC+wQcws+7AOGAH4FTgAOAj\n4EEz2ys2Oz+OYzohaNgaOK7YiZYw5nsJgdrPgAnA1anrUorVgKuAfYGjCG/qnzGzVRJtHgZ6Eq7L\n7oRrUvj9uQm4OX5dON++LRzDpWa2wMxmmtldZrZW4QV3nwRMisf+lpmtAexK+D0pGAWcHse0FyHI\nPYsQwBV0IgRyQ2ObwYTfp4lm1iU1Lov7XhbP+wVCkHUi8Md4/F8RfmbLtvCcRURERKQNVFwmKFqG\nsHj/fvj20/7/AOcCT8Y23YF+7j6msJOZ7UN4A36Iu98RN48xs17AEDMb7u51ieNc6O53xX3/B7xK\nyA5slMgabAv8nJAdgpChWhvYxt2fTYxvchzfo+7+tpnNAOY2Y4rXni0c88XuPioedxywB9AvcV1a\nzN2PKnwdg7IxhGtxMHC5ma1IyBKd6u4PJXa9Ne4/zcymxa9bOqVtLiFQGQt8RsjqnU0IwjZ2949j\nuxuAG81sVXf/IG77JfAVcGcc+/bAgcDP3f2e2ObJmJ06z8wucvdP3f0LQrCXPOfHCcHsHsB9ifGt\nCOzg7q8l2m8FjHX3axPtHmjqRM1sEKlADuCVFyt+FqCIiIhUGt0nqCyVGgTNB759s+3ueTO7G/id\nmRV+4nMJb1yTticsqL87tX0ksBuwHvBaYvvYxNdvAnngyUIAlNh+gJnl3D0fjzG1EADF8dWb2R2E\nN9rdklPnmqGlYx6dOO4CM3uTMO2rZDGTdDawESEjUrBufP4UmApcHAOif7j71HKOWeDuH7Jwlmy8\nmU0gZH6O47tCC3cQMi9HAefHwGUgMCpxvXcDvgYeMbPk7/ZYwnTDLQlZN8ysHyGTtx4LZ3DWZWFv\nJQOgaBJwppkVMn4vunuThRzcfTgLZ60AmPvVF/kizUVEREQapsIIZanUEPLTVPYDwqf0HYGVCt/H\noCSpB/BJkTek0xOvf8vdZya+XgDUA5+n9p1HCBYLv2k9Ev2lj5EjrOlpiZLHHM0FOrfwmN8ys80I\nb+S/IgQYWxGmA/670G+8zrsALxMCkSlm9pqZHVjqcRvj7q8Qgs8tEtvmEDJPR8VAeE/CNL5kUNET\n6EIIhOYnHv+Kr68A32YM7wIcOIQQHG1BKFiRvpbFftYXEqY8Hgg8A8wws6vMrGtpZywiIiIii9P/\ns3fncXZVVd7/P5UiJGGUwQTohjB2XCqiDK3080NAFAMoEgIKbZAI3WFqugO0gGGqMAQFGwRJtzLI\nINJtM4QAEpKIRiaF2InKY1bijyGgHYZggDBkIFX1/LH2IScnt6pu3Vunxu/79bqvm3tqn3322fdW\n5ay7916nt44EbWVmjYVAaARxQZtlVqv07fnrwNYV9t0mPS/rgra9DuxcYfs2qU3FIKqa+spuc3vG\nEiNRR7r7e9lGMysGX88AX00jMHsC3wT+y8x2d3cvuY2Z7xPrdz5PrMP5jbvPz/18GfAOkTSjkux+\nRMcQIzzjsx+khApbVthnvc+Zu68m1hNdlpJkHEWsG3qHWH8kIiIiUqoGjQTVpbeOBA0mFukDYGYN\nxIXmU4WpakWPEIFdcVH+V4FXgUVd0LZHgJ1SxrKsfYOIC+t5ualZ1Y7QdEeb27MR0EzuYt/MPkMb\nU+zcvdnd5wLnE6Njln60Ku1b86hU7vh7EtPSniocewHRX+cTI0HFqWUzgY2BYe7+mwqPv6RyG7H+\nfZqFfsEAACAASURBVIiOY+1oX9Xc/c/phri/BT7a2f1FREREpPv11pGg5cB3zWw48CLxrf/uxJSs\n9jwI/Aq4MSUWeIYITkYTaa7bC6CqdTPwL0Q2uEnAa8DJRCrvL+TKOTDBzE4kkjosd/dKAU13tLk9\nDwETgVvM7GYi+LiAyJYHgJl9DLiamEL2LBHcnQ68BWSr+rPRoDPN7GfACnd/uqODm9l3iCDsSWJU\nbA9iNGUJcF2FXf6DWB/0Vnp+n7v/3Mz+G5hmZv9GrN1pJBJZfAn4YrrHz0PAEWZ2FXGD1r3T+VQ1\nimdm04mpgfOIz+p+xOjYxGr2FxEREambEiPUpbf23uvEfXZOJrJufQwY5+7tZkBL08kOJS7WLyTS\nOu8DfN3dv98VDXP3t4npVo8QgcHdxNS1w939wVzRG4G7iJTbTwE/6Kk2t8fdZxLpnv8PkXr7BOIe\nO8/kir1MBEXfSGVuJdY/fS6Xqe1+YmTmLCIwur/KJiwADiJSbM8C/pVIivGp3MhN3nQiaPrP9F4U\n/T2RBGFcasMdREa/J4jplAA3ENPZjkllDiXuk/RmlW1+lMgidwsRxB5DZM6rFLSJiIiISC/T0Nra\nuxJTZTdC7aobbUr/YmZHANOAvdx9Xkfl+4KyssM1Nw4po1oAGho6LtPbNK7o7HK96rQ2blhKvQBv\nN9Q9u7RN52324VLq/fbb5S0RHLreLNau0dI4uJR6yzZ42Qul1Nuy8Val1AuwYoONS6l3SEN5kyZW\ntfbW74vbNmxVMYdS11nWWM4t8bZoWFVKvZkhm23ZB//nat+a//lpaRfxG+x1WL/rr6LeOh1OZB1m\ntgsxre3bwC/6SwAkIiIiIt1PQVA/Urg3znqquZdNV0qZ5Nr7JqG5QprztlxAJIv4H+ImqSIiIiID\nl7LD1aXXBUH5tMXSae+190Mz26mrbnJapWeBke38/EBgTjUVpc/F+LpbJCIiIiIDXq8LgqQu+3Tw\n8yUd/LyrfRFob2FK2em/RURERPonZYerS69LjCAy0KxcsUK/hN2gtaRsDn31T+h7LeU0/JxNrONC\nNbrkzQWl1S1r9cXrgqXvNndcqEabbFjehWZzSX09fKPyvuPeoI8ulx86bFgfbXnb1vxuVnmJEfY4\nuN/1V5FGgkRERERE+piGRq0JqoeCIBERERGRvkaJEeqiyYQiIiIiIjKgaCRIRERERKSv0UhQXTQS\nJCIiIiIiA4pGgvoIM1sM3OLuTT3clPWY2URgsbvf29NtaYuZjQdw91s6ud8twAHuvmP69/FV7Nbd\n92MSERGRAaZBKbLroiBIusJE4qanvTYIYu2NVm+po45LgO/nXv8DcCKwb6HcS3UcQ0RERERKpiBI\namZmQ9x9VU+3o7u4+7PAs9lrMxudtv+6xxolIiIiA5PWBNVFQVAvZGbHARcC2wMLgDMqlDkoldkb\naAZmA2e4+4u5Mq3E6EUrMAH4APBL4LR0QZ+VOxY4CfgosCGwELjU3e/LlRkP3Ax8GjgTOAh40sx2\nA0YCx5tZNlVssrs3mdmc9PoqYAqwCzCfGJV5CbgGOBJ4B7jO3S8vnOMuab/PAhunfc9x90dyZbJj\nXAFcDuwGLEp9MSdXZv9cnwDc6u7ji/0qIiIiIv2fJhP2Mml04Tbgd0SAcANwO7BFrszhwCwikPgK\ncAIwCphjZpsWqjyBCFxOIgKhjwCzzGzDXJmdgXuAcemYDwF3m9lhFZr4YyIwG0MEHWOAl4EHiWlh\n+wI35sqPAprSYxywA3AncEfa7+h07ClmdmjuHEcCTwK7AqfljjPbzPYstGkU8K30OAp4D5huZlum\nn59KBFDzc228pMK5iYiIiPQNDYPKewwAGgnqfS4iAqCj3b0VwMyWEoFD5mpghrsfk20ws6eAPxKj\nLN/LlR0MHOLuK1O5hcBTxAL/GwDc/bJcPYOI9T3bEoHTTwvtu8Pdz8tvMLNVwNI2poVtCXwyG6Ey\nsxHAVOBxd5+Utj0MjCUCogdz/bAaONDdl6dyM4HfA+el8pmtgH2zZARm9meiD0en9i4ws+XpXHts\n6pqZTSAC0XXMnzevB1ojIiIiMnApCOpFzKyRmN52cRYAJdOANanMbsTITZOZ5d+/JcQIzX6sGwQ9\nkAVAAO4+18yeBz5FCoJSnRcTI0bbsHaEcFGFZk7v5GktzE/RI6baQYxkZW1qMbNniOl/mc8TAdG7\nhfOcDXy1cAwvZGNbQEwB3KGTbS2Vu18PXF/cvnLFitYKxUVERETaNkBGbMqi3utdPkgEpq/mN7p7\nM/Baejk8Pd9GTPvKPz5BjIrkvcr6XgG2AzCzTYjAYg/gXCKI2gf4ITC0wr4vd+aEgNcLr1en5zcq\nbM8fbziRea14jhNZ/xzXOYa7rwFaqNx+ERERERngNBLUuywlRnyG5zemEaKt08tl6fls4BcV6nir\n8Hp4hTIjiNESiPUxI4H93P2x3DHb+mx016jFMuL8vtNNxxMRERHpM1o1ElQXBUG9iLs3m9lcYKyZ\nXZqbEjeGte/VQuAF4EPufmUV1R5mZkNza4L2BnYisq4BbJSe38t2MLMtgC+x/mhNW1bR9aMuM4G9\ngKe7KA33KmDzLqhHREREpOcpCKqLgqDeZzKRne1OM7uJGKWZBGQL+1vN7HRgmpkNAe4mpoNtBxwA\nPOzuP8nVtwaYYWZXEUHA5cBzxHQ6gCdS3VPN7CIiFfX5xPS7aj8fDuxvZocQo1lL3H1JDeeedwEw\nl8h4NxX4EzENbi+AYnKGKts4wczGEkHka4V1RCIiIiIyQCiE7GXcfSbwNeDjwL1ENrFx5Na9uPv9\nRMCzNXHvnhlEYoPBRPa0vJuAR4gF+TcQ0+AOdvfVqa6lxEhTI3AXESTdSKTlrtYk4HkigcNcKmRA\n6yx3f4FYm7QIuJJYt3QtsDvwaA1VXgE8Btya2thUbxtFREREekxDQ3mPAaChtVWJqfqrdGPQye7e\n1NNtkbYpO1z3aC3pj3pf/RP6Xks5DT9nEyulXoBL3lzQcSGpW1+8Llj6bnNpdW+yYXnfFzeX1NfD\nNypvos8GffT6eOiwYX205W1rfuF3pf2yNo7co9/1V5Gmw4mIiIiI9DWDNKGrHgqCZMBqJwMe8H6q\nbRERERHpZxQE9WPu3u+HMmtlZjsS65jao/4TERGRXkkpsuujIEgGqiVE4oV+q6w1MAANLeXMv28d\n1FhKvQDNJa2BGdxa3oDhitby+mNYQznvYZnrdi7Y/MOl1DtludYa5ZX5t6MsQ0pcqLLBoBL/lpa0\nomNQie9hS0n1Nra813EhkS6kIEgGpJQd7zc93Q4RERGRmmgkqC7qPRERERERGVA0EiQiIiIi0tdo\nJKgu6j0RERERERlQNBIkIiIiItLXaCSoLgqCCsxsMXCLuzf1cFPWY2YTgcXufm9Pt6UtZjYewN1v\n6eR+twDH5za9AzwNXOHu07qoeZ1pTxMw3t13TK8PAH6RK7ISWAr8Fvgv4L/cvaykOSIiIiLrUIrs\n+qj3+paJwBE93YgOjE+PWrwM7Jse44ENgbvM7FNd0bAuchrRvs8D5wJvAT8CHjazjXqyYSIiIiJS\nHY0E9QFmNsTdV/V0O7rBKnf/dfbCzH4F/IkIiH7d1k7dbEG+jcAdZvbfwDTgSiJIEhERESmXRoLq\nMqCDIDM7DrgQ2B5YAJxRocxBqczeQDMwGzjD3V/MlWkFLgFagQnAB4BfAqe5+7O5cscCJwEfJUY5\nFgKXuvt9uTLjgZuBTwNnAgcBT5rZbsBI4Hgzy6aNTXb3JjObk15fBUwBdgHmE8HDS8A1wJHEFLPr\n3P3ywjnukvb7LLBx2vccd38kVyY7xhXA5cBuwKLUF3NyZfbP9QnAre4+vtiv1XD3/zWzpem88+09\nmHivPgFsCjwDXOvuN+XKHEBMXzs8PY4CVgP3AGe6+4pc2V2BqUSfvw5cD1R9pzl3n25m04ATzexs\nd3+n82crIiIiIt1lwIaQZjYauA34HREg3ADcDmyRK3M4MIsIJL4CnACMAuaY2aaFKk8gLqJPIgKh\njwCzzGzDXJmdiYvwcemYDwF3m9lhFZr4YyIwG0MEHWOI6WIPsnbK2I258qOApvQYB+wA3AnckfY7\nOh17ipkdmjvHkcCTwK7EKEZ2nNlmtmehTaOAb6XHUcB7wHQz2zL9/FQigJqfa+MlFc6tKma2CbAl\n8GzhRzsTAc4/AF8g3rfvmtmpFaq5FlgOfBm4GvhH4LzcMYYAPwOMeN9OBj4DnNjJ5s4AhhDBsoiI\niEi5GhrKewwAA3kk6CIiADra3VsB0qjDnbkyVwMz3P2YbIOZPQX8kRhl+V6u7GDgEHdfmcotBJ4i\nFvvfAODul+XqGQTMAbYlAqefFtp3h7ufl99gZquApYXpWJktgU9mI1RmNoIY3Xjc3SelbQ8DY4mA\n6MFcP6wGDnT35ancTOD3RLAwNneMrYB93X1xKvdnog9Hp/YuMLPl6Vxrmr5mZtln8q+AbwNvAt/N\nl3H37+fKNwCPEiNrJwP/Xqhytruflf3bzP6WCIjOT9vGEyNNe7n7vFTnz4DFwJpONP1P6XmbtgqY\n2QQi0FrH/HnzOnEYEREREanXgAyCzKyR+Mb+4iwASqaRLnzT9LOdgabchTnAEmKEZj/WDYIeyAIg\nAHefa2bPA58iBUGpzouJEaNtWDsSt6hCM6d38rQW5qfoEVPtIEaysja1mNkzxPS/zOeJgOjdwnnO\nBr5aOIZnAVCygJgCuEMn29qWkcToUmY1MNrdn8kXMrNtiRGv0cB2rP0cV1o3NaPw+g/AobnXnwKe\nywIgAHd/18weIEaEqpV9bdLaVgF3v56YareOlStWtLmPiIiISEVaE1SXgdp7HyQunF/Nb3T3ZuC1\n9HJ4er6NuDDPPz5BjIrkvcr6XiEu0rOpXbOBPYisYvsB+wA/BIZW2PflzpwQsZYlb3V6fqPC9vzx\nhhNTv4rnOJH1z3GdY7j7GqCFyu2vxUtEn3ySGEH7C/CTNKoFvD+Cdh8xDe5SIlDZB7iMmI5WVOyX\nVYVy29L2e9cZWWDZ2fdNRERERLrZgBwJIu7vsoa1gQ7w/gjR1unlsvR8NuveHybzVuH18AplRhCj\nJRDrY0YC+7n7Y7ljtvUedNfowDLi/L7TTcdrz2p3/03691Nm9v8DjxNT9rL1PrsQo3jHufvt2Y5m\n9sUaj/kSkeShaESFbe05hLh30G86KigiIiJSL90nqD4DMghy92YzmwuMNbNLc1PixrC2TxYCLwAf\ncvcrq6j2MDMbmlsTtDewE5F1DSC7h8z7073MbAvgS6w/WtOWVXTdqEtmJrAX8HQXpeFeBWzeBfXg\n7r8ys58AJ6T3aQmV+3Ew60/dq9avgPFmtmduTdBGxEjTu9VUYGZfIu7f9O/uXtU+IiIiItJzBmQQ\nlEwmsrPdaWY3EaM0k4hMYrh7q5mdDkxLGcTuJqZWbQccADzs7j/J1bcGmGFmVxFBwOXAc8R0OoAn\nUt1TzewiIhX1+cT0u2rfBwf2N7NDiNGsJSkwqMcFwFwi491UYoH/VkRgRDE5Q5VtnGBmY4kg8rXC\nOqLOuphIZPCvRMpwT/VeZmZriH5fL7V5J9xKvO/3mtkkIhHDN6i8vgjgw2a2kkiE8dfAYcCxREr0\ns+toh4iIiEj1BmkkqB4DtvfcfSbwNeDjwL1E1q5x5NaQuPv9RMCzNXHvnhnERflgInta3k3AI8TC\n9xuIaXAHu/vqVNdSYqSpEbiLCJJuJNI7V2sS8DyRwGEuFTKNdZa7v0CsqVlE3OxzNpFWenci61pn\nXQE8RgQXc4kEBvW0z4H/Ak4ys61Tfx5BrL35EZEB7xEibXct9a8CPkeM/N1IvH8PE2u1KplKjB7N\nJs51M+JzdJBGgURERET6hobWViWmqle6Mehkd2/q6bZI31NWdrjWEvP8N7Q0l1Jv66DGUuoFaG4p\n52/d4NbOZFLvnBWt5fXHsIZy3sPlzeW1+YLNP1xKvVOWL+i40ADSF68KXl9ZzucZYNgG5X1f3FzS\nNdjWw/reRJ/Glvc6LlSHIRtv2u9ufrP69ZdL+3XdcItt+l1/FfW93xIRERERkYFOiRHqoiBIStdO\nBjzg/VTbIiIiIiLdQkFQF3D3fj9kWCsz25FYx9Qe9Z+IiIhIZ2gkqC4KgqRsS4jECyIiIiIivYIS\nI4j0sLfeLScxwmCNr3WLlhIHMge1lrfYu6wkFG+vbimlXoDGkrp60mblJFyAvpl0oS9eFfTVxAhl\nXYNtPbS8v0stDeX87RhU8idv6LBh/e5/xVXLl5XWaUM227Lf9VeRxtFERERERGRA0XQ4EREREZG+\nRmuC6qLeExERERGRAUUjQSIiIiIifU2JN0UfCDQSJCIiIiIiA0qvGAkys8XALe7e1MNNWY+ZTQQW\nu/u9Pd2WtpjZeAB3v6VnW9K7pHsUjSc+W4sLP2sFvq4+ExERkT6pF64JMrOdgGuAA4E1wP3AGe7+\nlzL3rUXv673eZyJwRE83ogPj00PWtSNwUXou2hf4aXc2RkRERKS/MrNNgV8Afw0cC0wA9gMeMLN2\nY4569q1VrxgJ6o3MbIi7r+rpdkg53P3XPd0GERERkVq19r6RoAnAXwGfdvcXAczsz8ATwOFAe7Oq\n6tm3Jt0eBJnZccCFwPbAAuCMCmUOSmX2BpqB2cRw2Iu5Mq3AJcR93SYAHwB+CZzm7s/myh0LnAR8\nFNgQWAhc6u735cqMB24GPg2cCRwEPGlmuwEjgePN7PhUfLK7N5nZnPT6KmAKsAswnxiReYkYzjsS\neAe4zt0vL5zjLmm/zwIbp33PcfdHcmWyY1wBXA7sBixKfTEnV2b/XJ8A3Oru44v9WmRm+wDfBD4J\nbAEsBm4F/s3d1+TKDQbOBcYRoypvEB/Kk939lVRmV+DS1HebAi8S09CmpJ83AN8g3qsdgFeAHwMX\nuvvqVOYA4luAA7PzS9vPBy5x94ZCucPT4yhgNXAPcKa7r8iVAfiFmWXV7eTui4vT4arp61x7zgFO\nB7YEngJOIT7Lml4nIiIi3aP3BUFfAB7LX6+7+6/M7Dngi7QfyNSzb026tffMbDRwG/A7IkC4Abid\nuADPyhwOzCICia8AJwCjgDlpqCzvBCJwOYm4uP4IMMvMNsyV2Zm4OB6XjvkQcLeZHVahiT8mLmbH\nEBfCY4CXgQeJ6VP7Ajfmyo8CmtJjHHFxfydwR9rv6HTsKWZ2aO4cRwJPArsCp+WOM9vM9iy0aRTw\nrfQ4CngPmG5mW6afn0oEUPNzbbykwrlVMpJ4L04FDgGuI4LSKYVy/w1cANxNfBBPAV4lvW9mtnM6\nn72Bc4BDgSuB7XJ1fAv4NjCd+KD/gAg4f1RlWyu5FlgOfBm4GvhH4Lz0s3lE35Kes755qZ36Oupr\nzOzU9PP7iGmS91PCL6aIiIhIH/Nh4A8Vtv8h/aysfWvS3SNBFxEX3Ue7eyuAmS0lAofM1cAMdz8m\n22BmTwF/JEZZvpcrOxg4xN1XpnILiW/mjycCLNz9slw9g4A5wLZE4FRcE3KHu5+X32Bmq4ClbUyf\n2hL4ZG7YbgQwFXjc3SelbQ8DY4mA6MFcP6wmRjyWp3Izgd8TF/Fjc8fYCtg3W9ifhgZ/B4xO7V1g\nZsvTuXZqipe73wXcleptAB4ngorrzOxcd29Jo3JHAKe4+/dzu9+T+/dk4rP0d+7+atr28+yHZrYV\nsbbqenc/K22eZWZrgMvN7FJ3f7ozbU9m5+qbbWZ/SwRE57v7cjNbkH62oMq+abevzayReH/udfdT\nc+fRTHxu22VmE4hgfR1P/c+8KpomIiIislZriSmy27pmIa7lrm9jty2I2UJFy4gvmttTz7416bYg\nKF1A7g1cnAVAyTQiAwRp+tnOQJOZ5du2hBih2Y91g6AHsgAIwN3nmtnzwKdIQVCq82JixGgb1o5+\nLarQzOmdPK2F+WE7YqodxEhW1qYWM3uGmP6X+TwREL1bOM/ZwFcLx/BCZrMFxBTAHTrZ1vWY2WbE\nRf1RqX2Dcz8eToxOfY6YkvjDdqr6HHBfLgAq+hQxFfGOwvbbiRG3/YBagqAZhdd/IEahatVRX/81\nMbp1T2G/u6kiCEp/NNb7w/HWuytaKxQXERER6RFtXbP0J905He6DRNC1zoWyuzcDr6WXw9PzbcRU\npPzjE8Q39XmVLrpfIU3DMrNNiMBiD2JNy37APsQF/dAK+77cmRMCXi+8Xp2ei5Hs6sLxhgMnsv45\nTmT9c1znGGmtTguV299ZNwMnE9PKPkf0TTaFLKt/K+C1bN1OG7YC/redn2fTHYv9+3Lh551V7P9V\nwJAa61qvvgp9vW16Ln7u2gr+RERERErR2lreo0avE2v0i7YkRnTK2rcm3Tkdbikx4jM8vzGNEG2d\nXmYneTZrF7XnvVV4PbxCmRHEN/gQa0BGAvu5+2O5Y7Z13t31jfwy4vy+003HW4+ZDQW+BDS5+zW5\n7bsXir4GbG1mG7YTCL1GZPRoSxZcjGDdEbht0nP2vmejevk1XRC/AL1Btp6o+Lmr9DkUERERGUic\nyut3Pgw8UmF7V+1bk24bCUojPnOBsWn9SWYMa4OxhcALwIfc/TcVHsUpbIeli3kAzGxvYCfgV2nT\nRun5vVyZLYiL/2qtomtGXfJmArsDT1c6zxrqq6WNQ4BGcn2TjC+8np3KFbcXyxxuZlu38fNfE6Nh\nXy5sz6b+PZqes6mF7/8SpCD54HaO3Z4sxXlXvX9/JqZmHlnYPrZCWREREZHStLS2lvao0QPA/2dm\nf51tMLNPEhmU7y9x35p0d2KEyUR2tjvN7CZilGYSsRgfd281s9OBaWY2hFhr8Toxve0A4GF3/0mu\nvjXADDO7CticWF/yHDGdDiKN83JgqpldRKSiPp8Yuaj23B3Y38wOIUazlrj7khrOPe8CIiCcY2ZT\ngT8RU8r2AigmZ6iyjRPMbCwRRL5WWNuy/g7ub5rZr4GzzOwlok9OoDCi4+4/N7N7ge+Z2Q5EGvKN\niHVN17r7QiLRw2HAr8xsCpFqe0fgE+7+z+7+FzP7LvANM3sH+Bkx9a4J+Im7/990rCVm9hjwzZQw\n4w1iUd4mneyPzB+J9UwnpOQRq4HfdzC1r03u3mxmlxGfp6nEGrLdWbtwsKXGdoqIiIj0ddcTtxCZ\nnq67hxK3HnmK3Lp7M/sasTTlIHf/ZWf27UrdmiLb3WcCXwM+TqQVnkCkln49V+Z+IuDZmlizMoNI\nbDCYyJ6WdxMxRHY9kQhhAXBwdpHr7kuJkaZGIgva5USK69s70exJwPNEAoe5VM6U0Snu/gIRBCwi\nUknPJtbl7M7aUZHOuAJ4jLjHz1wiuKjGscD/EBntbiHW6PxLhXJfJtJmH0tk1PsBMQVsGYC7Z8ko\nfgv8G5H04Vxi1CRzbnqMTXWcQtxj6WuFY32VSG/9H+k4j7NuWvKquftfgH8m7oP0KNE327W7U8d1\n/jtxb6UjiF/KLxHBI8Cb9dQtIiIiUq3WEh+1SBmPP0MsH/gv4vrtCeAL7p7/ongQcW3eUMO+Xaah\ntY7VTz0p3exysrs39XRbZGBLI3B3ATumALdTysoON7i8zJmS00J5HT2otbm0ulsHNZZS79uryxsQ\nbSypqydtVsotKACYsnxBx4V6mb54VfD6yvJ+V4ZtUN73xWVdg209tLy/Sy0N5fztGFTyJ2/osGH9\n7n/FMrPLbrpR/+uvou6eDifSp5nZLsQ9ph4F3gH2JEaGptUSAImIiIjUoqUvfmPRiygI6qfayYAH\nvJ/+WTpvBTGd8wRiHdorxPTKST3ZKBERERGpXp8Ngty93w/T1crMdiTWMbVH/VeDlBSj1mx1IiIi\nIl2iry5p6S36bBAk7VpCJF4QERERkX5I0+Hq02cTI4j0F++uWNnnfgmbS/q70dhQ4mLePvi3bnBL\nTdncq9KywZBS6u2LiRHK1BeTLvS93xR4c1V5iRGGNPa9xAgfGFpO8oIylfn3DmDIJpv3wb8g7Vv2\n1rul/bpuuelG/a6/ijQSJCIiIiLSx/TFLyx6k269T5CIiIiIiEhP00iQiIiIiEgfozVB9dFIkIiI\niIiIDCgKgno5M1tsZk093Y5KzGyimR3R0+1oj5mNN7PxNew3x8zmdH2LREREROrX2tpa2mMgUBAk\n9ZgI9OogCBifHiIiIiIigIIgqYGZlZNbt59Q/4iIiEjZWkp8DARKjNCLmNlxwIXA9sAC4IwKZQ5K\nZfYGmoHZwBnu/mKuTCtwCZE9cQLwAeCXwGnu/myu3LHAScBHgQ2BhcCl7n5frsx44Gbg08CZwEHA\nk2a2GzASON7Mjk/FJ7t7U24a2VXAFGAXYD4xIvMScA1wJPAOcJ27X144x13Sfp8FNk77nuPuj+TK\nZMe4Argc2A1YlPpiTq7M/rk+AbjV3ccX+7UtZnZoW/UX2nEt0AR8CDgBuL3aY4iIiIhI99JIUC9h\nZqOB24DfEQHCDcSF9Ba5MocDs4hA4ivExfYoYI6ZbVqo8gQicDmJCIQ+Aswysw1zZXYG7gHGpWM+\nBNxtZodVaOKPicBsDBEUjAFeBh4E9k2PG3PlRxFBQVOqfwfgTuCOtN/R6dhTUqCRneNI4ElgV+C0\n3HFmm9mehTaNAr6VHkcB7wHTzWzL9PNTiQBqfq6Nl1Q4t7Z0VP/7zSaCse8Ao4GnOnEMERERkU5r\nbS3vMRBoJKj3uIgIgI5291YAM1tKBA6Zq4EZ7n5MtsHMngL+SIyyfC9XdjBwiLuvTOUWEhfnxxMB\nFu5+Wa6eQcAcYFsicPppoX13uPt5+Q1mtgpY6u6/rnA+WwKfzEaozGwEMBV43N0npW0PA2OJgOjB\nXD+sBg509+Wp3Ezg98B5qXxmK2Bfd1+cyv2Z6MPRqb0LzGx5OtdKbexIu/Xnym2d2tvu7dvNbAIR\nkK7jf+bNr6FpIiIiMpApRXZ9FAT1AmbWSExvuzgLgJJpwJpUZjdi5KbJzPLv2xJihGY/1g2C/PAK\nTgAAIABJREFUHsgCIAB3n2tmzwOfIgVBqc6LiRGjbVg7MrioQjOnd/K0Fuan6BFT7SBGsrI2tZjZ\nM8T0v8zniYDo3cJ5zga+WjiGZwFKsoCYArhDJ9valmrrf6ajAChVdj1wfXH7uytW6s+YiIiISDfS\ndLje4YNEQPpqfqO7NwOvpZfD0/NtxLSs/OMTxKhF3qus7xVgOwAz24QILPYAziWCqH2AHwJDK+z7\ncmdOCHi98Hp1en6jwvb88YYDJ7L+OU5k/XNc5xjuvoZYz1ep/bWotv7O9o2IiIhIXZQiuz4aCeod\nlhIjPsPzG9MI0dbp5bL0fDbwiwp1vFV4PbxCmRHEaAbE+piRwH7u/ljumG19JrrrN2IZcX7f6abj\ndYWB8ddCREREpJ9QENQLuHuzmc0FxprZpbkpcWNY+x4tBF4APuTuV1ZR7WFmNjS3JmhvYCci6xrA\nRun5vWwHM9sC+BLrj9a0ZRVdN+qSmQnsBTzt7qu6oL5VwOZdUI+IiIhIrzFQUlmXRUFQ7zGZyM52\np5ndRIzSTAKyhf2tZnY6MC3dh+ZuYrrWdsABwMPu/pNcfWuAGWZ2FREEXA48R0ynA3gi1T3VzC4i\nUlGfT0y/q/Zz4cD+ZnYIMZq1xN2X1HDueRcAc4mMd1OBPxHT4PYCKCZnqLKNE8xsLBFEvlZY5yMi\nIiIiA4zWBPUS7j4T+BrwceBeIovYOHLrUtz9fiLg2Zq4d88MIrHBYCJ7Wt5NwCPEQvwbiGlwB7v7\n6lTXUmKkqRG4iwiSbqRz97eZBDxPJHCYS4XMZ53l7i8Qa5MWAVcS65auBXYHHq2hyiuAx4BbUxub\n6m2jiIiISE9Tiuz6NAyUxU8DSbox6GR3b+rptkjH+mJ2uOaS/m40NjSUUi9ASx/8Wze4ZXXHhWrU\nssGQUup9e3V5EzQay/t4lGbSZh8ure4pyztMSlmTvvebAm+uai6t7iGN5X1fXNY12AeGNpZSb5nK\n/HsHMGSTzfvgX5D2vfCXt0v7dR251Sb9rr+KNB1ORERERKSP6Ytf7vUmCoJkwGknAx7wfipsERER\nEemnFAT1Q+7e74cwa2VmOxLrmNqj/hMREZFeTeNA9VEQJAPNEiLxgoiIiIgMUEqMINLDVq5YUc4v\nYWsfvINAQ3kLkBvfrDd7e2XNm21TSr0AK1vKG5TcZPmfSqn39U22L6Ve6JuJEcpUVtKFshIulKnM\nxAhDNyjv71JLSZdg2y79bTkVA6+O+Hgp9W48uNyExZtuNKzf/QV5ZulbpV3E7/rBTftdfxVpJEhE\nREREpI/ROEZ9dJ8gEREREREZUDQSJCIiIiLSx7QoNUJdNBIkIiIiIiIDikaCRERERET6GK0Jqk9d\nI0FmttjMmrqoLV3KzCaa2RE93Y72mNl4Mxvf0+2Q2pnZEWY2safbISIiIiLV68/T4SYCvToIAsan\nh/RdRxCfNREREZFu09Ja3mMg6HdBkJkN6ek2iIiIiIhI71X1miAzOw64ENgeWACcUaHMQanM3kAz\nMBs4w91fzJVpBS4BWoEJwAeAXwKnufuzuXLHAicBHwU2BBYCl7r7fbky44GbgU8DZwIHAU+a2W7A\nSOB4Mzs+FZ/s7k1mNie9vgqYAuwCzCdGZF4CrgGOBN4BrnP3ywvnuEva77PAxmnfc9z9kVyZ7BhX\nAJcDuwGLUl/MyZXZP9cnALe6+/hivxbVcw5m9kHgMuAzwF8BLxPv07nuvixXbjEwB/gFcD6wLfBb\n4BR3fzpX7mDis/AJYFPgGeBad7+p0OZRwFTg/wDLgH8n3tfj3X3HXLkRwKXAF4EtiPd9srvfkytz\nC3AA8PfpXD8K/BE4GfhN6pPsff8x8A13X1PjMb4MXAt8DHgBuNDd78yVOT79O3sPf+nuByAiIiJS\nIq0Jqk9VI0FmNhq4DfgdcXF9A3A7cQGZlTkcmEVchH8FOAEYBcwxs00LVZ5ABC4nEYHQR4BZZrZh\nrszOwD3AuHTMh4C7zeywCk38MRGYjSGCjjHExf2DwL7pcWOu/CigKT3GATsAdwJ3pP2OTseeYmaH\n5s5xJPAksCtwWu44s81sz0KbRgHfSo+jgPeA6Wa2Zfr5qUTgMj/XxksqnFtbajoHYEtgFTAJGA18\nkwhgHqpwjM8Q79HZwHHAiHQOg3NldiYCpX8AvkB8Lr5rZqdmBcxsKBFo7ZrKnZzq/nr+YGa2OfAY\ncCBwLhGkPAncZWZfLLRtC+B6IkA5CmgEpgE/IIKxccD3iKlqE+o4xi3E5/0I4DngP1NAB/F+PUj0\nd/YenoqIiIiI9GrVjgRdRARAR7t7K4CZLSUuujNXAzPc/Zhsg5k9RXxDP564IM0MBg5x95Wp3ELg\nKeJb9RsA3P2yXD2DiFGJbYmL8p8W2neHu5+X32Bmq4Cl7v7rCuezJfDJbIQqjQxMBR5390lp28PA\nWCKYeDDXD6uBA919eSo3E/g9cF4qn9kK2NfdF6dyfyb6cHRq7wIzW57OtVIbO1LTObj7IuD0XD81\nAo8DL5rZnu4+L3eMjYDRuXNtBqYD+wBPpPq+n6urAXiUGOE5mRjtgXj/twc+4e6/TWVnESMrK3PH\n+xdgO8Byo4ezzGx7Iti7P1d2M+BEd38y1ddKfC62cfdDc/sels4/a0tnj3GIuz+RjvEb4JXUp1Pc\n/dn0e7CqmvfQzCaQC8gy8+fNq1BaREREpG26T1B9OgyC0kXy3sDFWQCUTAPWpDK7ESMCTWaWr3MJ\nMUKzH+sGQQ9kARCAu881s+eBT5GCoFTnxcSI0TasHbVaVKGZ0zs6j4KF+Sl6xHQoiJGsrE0tZvYM\ncfGe+TwRTLxbOM/ZwFcLx/AsAEoWEFMAd+hkW9tS6zlgZqcQQcouxJS+zN8A+Svyx7MAKPlDet6B\nFASZ2bZE8DCaCC6yflmV2++TwHNZAJTatsrMfkpMYcx8PtW7pNC/M4GrzGxjd38nbVueBUBtnX/y\nR+Dv6jjGE7k2LzOzl6nxPXT364nRq3WsXLFCf8VEREREulE1I0EfTOVezW9092Yzey29HJ6eb0uP\notcLr1+tUOYV4iIaM9uECCzeJaYsPUuMwJzCuhfNmZc7PIv227M6Pb9RYfvQ3OvhwInpUdTS3jHc\nfY2ZtRTqq0dN52BmpxNTyK4CvpHqGQT8ukLbisfIApuhqa5BwH3E+9ZEBCIriKlj+ZG5ban8nhe3\nDSemzL1XoSzE6FcWoNTzHtZ6DIg+6Kr3UERERKQmWhNUn2qCoKXEiM/w/MY0QrR1epktqD+bWB9S\n9Fbh9fAKZUYQoyUQaytGAvu5+2O5Y7bV3u76GCwjzu873XS8MhwDPOzuZ2UbzGynGuvahRglPM7d\nb8/VV1xb8xKRHKKo+DlYBjwCnFWhLESgXK/uOIaIiIhIqVoUBdWlwyAojfjMBcaa2aW5KXFjcvsv\nJNZ3fMjdr6ziuIeZ2dDcmqC9gZ2IrF4Qa1Eg9229mW0BfIn1v+lvSxnf2M8E9gKedvdVHRWuwipg\n8y6opzM2ApYXtn29UsEq64J136fBrD818ElgvJnt4e6/S+WGAIex7pqgmcQ0vefymeq6WFcfQyND\nIiIiIn1MtYkRJhPZw+40s5uIUZpJpItpd29N06ympYvbu4mpRNsRaYYfdvef5OpbA8wws6uIIOBy\nIvNWNpXuiVT3VDO7iFi3cj7wWifa7MD+ZnYIMZq1xN2XVLlvWy4A5hIZ76YCfyISIOwFUEzOUGUb\nJ5jZWCKIfK2wjqgMDwHnmNk3iXP5DJFdrRZOtPsyM1tDvK/rpU4nMqxNIjLLnUe8txOJqWr5aYRX\nEZkFHzWzq4l025sDuwMj3f0fa2xnXlcfI3sPTyQSZCxPySdEREREStNcXIghnVJVimx3nwl8Dfg4\ncC+R4WocuTUT7n4/EfBsTdy7ZwaR2GAwcXGYdxMxJel6IhHCAuBgd1+d6lpKjDQ1AncRQdKNRPrl\nak0CnicSOMylQlauznL3F4jMaIuAK4l1S9cSF9CP1lDlFUS65ltTG5vqbWMVLibSSJ9J9M3HiGQB\nnZberyOINVk/IrLTPUKkBc+XWwkcTAQcNxHv+yPp+G/myr1BJDH4OXG/qVnA94n7KVWaZllLm7v6\nGDcSn9GriAyHP+iKdoqIiIhIeRpau3k+YUplPNndm7r1wNKrpKQK84D57l7rdLx+obTscK198Cui\nhqq+l6lJ45v1DgRX1rzZNqXUC7CypaG0ujdZ/qdS6n19k+07LlSjxvK6o0+atNmHS6l3yvIFHRfq\nZd5c1Vxa3UM3KO/vUktJl2DbLv1tx4Vq9OqIj5dS78aDy+tngE03Gtbv/oLMffH10i7i99lhi37X\nX0XVTi0TqYuZ/SuxnusZ4iakJxI3yT2hJ9slIiIiIgOPgqBepp0MeECk2u6utnSx1URK7uxr4vnE\njUh1p1ARERGRTmpWdri6dHsQ5O79fnitVma2I7GOqT19sv/c/Vpi/ZSIiIiISI/SSFDvsoRIvCAi\nIiIi0ibdJ6g+3Z4YQUTWVVZihNaG8gYNm0tazTuoxDYPai1v4XRZWgc1llZ346q3S6n3DYaVUi9A\n46A+ORBemrJ6o6yECwCXlZR0YdmK8n6/h21Q3ueurPQ1w4eW2OaGcv4uDSr5vvdDh/W/xAhPLP5L\naZ32dztu1e/6q0gjQSIiIiIifYzuE1SfcvMRioiIiIiI9DIaCRIRERER6WO0Jqg+CoJERERERPoY\npciuj6bDiYiIiIjIgNLrgyAzW2xmTT3djkrMbKKZHdHT7WiPmY03s/E93Y7OSG1uTfdNqreuAyp9\nftL2LjmGiIiISHdraS3vMRD0+iCol5sI9OogCBifHgPVAcBFFbbPA/YFXurW1oiIiIhIj9OaoBqY\n2RB3X9XT7ehvzGwQUN6NUXLcfTnw6+44loiIiEhXK+uefQNFrwqCzOw44EJge2ABcEaFMgelMnsD\nzcBs4Ax3fzFXphW4BGgFJgAfAH4JnObuz+bKHQucBHwU2BBYCFzq7vflyowHbgY+DZwJHAQ8aWa7\nASOB483s+FR8srs3mdmc9PoqYAqwCzCfGJF5CbgGOBJ4B7jO3S8vnOMuab/PAhunfc9x90dyZbJj\nXAFcDuwGLEp9MSdXZv9cnwDc6u7ji/1aVM85mNkHgcuAzwB/BbxMvE/nuvuyXLnFwBzgV8BZwM7E\nyE2l9hwITAd+TLyPLWa2CTAZOBoYAbwAXOXu30/7NJFGgXLn/4K772hmBwC/AHZy98W5MpPTOf0z\nsDnwOHBS4fO1KfBdYCxxv8J7gXvS8/v1iYiIiEjv1Gumw5nZaOA24HfExfUNwO3AFrkyhwOziIvw\nrwAnAKOAOenCNO8EInA5iQiEPgLMMrMNc2V2Ji5ex6VjPgTcbWaHVWjij4nAbAwRdIwhLu4fJKZV\n7QvcmCs/CmhKj3HADsCdwB1pv6PTsaeY2aG5cxwJPAnsCpyWO85sM9uz0KZRwLfS4yjgPWC6mW2Z\nfn4qEbjMz7Xxkgrn1paazgHYElgFTAJGA98EPkH0b9HBwMnA+cChwPPFAmY2BpgBXOPup6QAaDDx\nWRgHfBs4LLVlqpn9U9r1RuCm9O/s/Md0cM5fJz43p6THnsR7n3cT8PdEXx5NBE3f66BeERERkS7T\n0tpa2mMg6E0jQRcRAdDR7t4KYGZLiYvuzNXADHc/JttgZk8BfyRGKPIXooOBQ9x9ZSq3EHgKOJ4I\nsHD3y3L1DCJGJbYlAqefFtp3h7ufl99gZquApe5eaVrVlsAnsxEEMxsBTAUed/dJadvDxGjC0UQw\nlfXDauDANGULM5sJ/B44L5XPbAXsmxvJ+DPRh6NTexeY2fJ0rrVM/arpHNx9EXB6rp8aiRGVF81s\nT3eflzvGZsDH3P21XHly//4H4D+As9z92tx+xwKfSu2bm7b9zMw2By40s/9w9z+nPunM+b8LHOHu\nzen4WwHXmNl27r7EzD5MBJxnuPs1aZ9ZZrYdMYLZJjObQATk65g/b16F0iIiIiJSll4RBKWL5L2B\ni7MAKJkGrElldiNGbprMLN/uJcQIzX6sGwQ9kAVAAO4+18yeJy6cb8jVeTHxzf82rB0ZW1ShmdM7\neVoL81OoiKl2EKMXWZtazOwZ1r14/jwRTLxbOM/ZwFcLx/DC1KsFxBTAHTrZ1rbUeg6Y2SnECM8u\nxJS+zN8QSQkyj+cDoIKzgX8Avu7utxd+9nngWWB+oZ9mpuOOIvqjs2ZlAVDyh/S8A/FZ24eYAndP\nYb+7gS+1V7G7Xw9cX9y+csWKgfGVi4iIiHSZZl091KW3TIf7IBGQvZrfmC5Gswvk4en5NmLaV/7x\nCWJUJO9V1vcKsB1AWk8yG9gDOJcIovYBfggMrbDvy505IeD1wuvV6fmNCtvzxxsOnMj65ziR9c9x\nnWO4+xqghcrtr0VN52BmpwP/DvyMmGb4t0TwSYW2tdevXwaeY/1ROYh+2pX1+2la+nmxr6pVPOcs\nAUbW7m3Tc/HzVenzJiIiIiK9UK8YCQKWEiM+w/Mb0wjR1ulltqD+bGJBe9FbhdfDK5QZwdrRgX2J\nxAb7uftjuWO21SfdFW8vI87vO910vDIcAzzs7mdlG8xspzbKttevRwK3EOuhPuvu+eBrGfAMMS2u\nkkqjeV0hS6k9HPhTbnulz5uIiIhIKQbK2p2y9IogyN2bzWwuMNbMLs1NiRvD2jYuJLJ/fcjdr6yi\n2sPMbGhuTdDewE5EpjOAjdLze9kOZrYFMaWpONLRllV03ahLZiawF/B0F6XhXkVkOetOGwHLC9u+\nXkM9LwIHEmu1ZpvZ53KB0EziHk1vuPsz7dSxCiD/WajTXCJwO5LIkJcZW7m4iIiIiPQ2vSIISiYT\n2cPuNLObiFGaSaSLaXdvTdOsppnZEGINxuvE9LYDiJGHn+TqWwPMMLOriCDgcmJq1W3p50+kuqea\n2UXEupXziel31faLA/ub2SHEaNYSd19Sw7nnXUBcaM8xs6nEaMNWRGBEMTlDlW2cYGZjiSDytW5I\n4fwQcI6ZfZM4l88QyQQ6zd1fSOmsf0kkIPicu78J/IjIAPhzM/sO8H+J4GsU8H/c/cisivR8ppn9\nDFjh7k/XeF6kZBN3Ad9Ko4ZPE8FYlrmvpda6RURERKql+wTVp7esCcLdZwJfAz5O3G9lApH++PVc\nmfuJgGdr4t49M4jEBoOJ7Gl5NwGPEAvRbyCmwR3s7qtTXUuJkaZG4C4iSLqRSMtdrUlESudpxMX+\nepm/OsvdXyDWJi0CriTWLV0L7A48WkOVVwCPAbemNjbV28YqXAz8gLiv0jTgY0Qig5qkPjmAmHI2\ny8w2d/f3iPTaPyLu6fMQ8ZkYQ4wcZe4nPgNnETdHvb/WduScCPwncb+qO4kg+4L0sze7oH4RERGR\ndilFdn0aWvvhiWY3vXT3pp5uiwwMZvY94Avu3tbapzaVlR2utaGhjGqB8r59GlRimwe1NndcqJdp\nHdRYWt2Nq94upd43GFZKvQCNg8r7fPRFZfXGpM0+XFLNcNnyWpJ2dmzZivJ+v4dtUN7nrqypA8OH\nltjmhnL+Lg0qeen10GHD+t0fkLufXlJap43dfbt+119FvWk6nEifkG7aO4q4J1MjMSJ1CjHyJSIi\nIlI6pciuj4KgAaidDHjA+6m2pW1vE+m7LyASYzwLTHT363q0VSIiIiJSlX4ZBLl7vx/Cq5WZ7Uis\nY2qP+q8d7v5zYt2WiIiISI8YKGt3ytIvgyBp1xJ0AS91KmuKfGuJ4XdrSfPYG0r8T6jM/99WbLBx\nORWvUYLEvq6sdTsA55W03uisV2pO+tmjyvodL3M94aCSGl3mOlaRShQEDTApO95verodIiIiIlK7\nFqXIrkuvSZEtIiIiIiLSHTQSJCIiIiLSxyg7XH00EiQiIiIiIgOKRoJERERERPoYZYerT5eMBJnZ\nYjNr6oq6upqZTTSzI3q6He0xs/FmNr6n29FZZb3vZvYBM2sys493dd0iIiIiIgNhJGgiMAe4t4fb\n0Z7x6fmWHmxDb/IB4CJgMfDbnm2KiIiISO/TrJGguvTbNUFmNqSn29CbqX9ERERE+q6WltbSHgNB\np0eCzOw44EJge2ABcEaFMgelMnsDzcBs4Ax3fzFXphW4BGgFJhDf/v8SOM3dn82VOxY4CfgosCGw\nELjU3e/LlRkP3Ax8GjgTOAh40sx2A0YCx5vZ8an4ZHdvMrM56fVVwBRgF2A+MSrzEnANcCTwDnCd\nu19eOMdd0n6fBTZO+57j7o/kymTHuAK4HNgNWJT6Yk6uzP65PgG41d3HF/u1KFf/VOBiYOdU/1nu\nPrtCuWuBJuBDwAnA7Wb216l9o4GNgKdTHz1QOFY17/scAHc/oLB9DfGeNeW2HQScT3xGIN7XS4Df\nA8+nbTeb2c3p3wdmfdYWM9sx7XsSMAr4GvGZuTW192+Bq4Hd0/FOcvff5PZfTIwa/iK1bVtiJOoU\nd386V24w8X5+jeiznwPfBh6rpp0iIiIi0rM6NRJkZqOB24DfEQHCDcDtwBa5MocDs4hA4ivExfYo\nYI6ZbVqo8gQicDmJCIQ+Aswysw1zZXYG7gHGpWM+BNxtZodVaOKPiQv0McRF6hjgZeBBYN/0uDFX\nfhQRFDSl+ncA7gTuSPsdnY49xcwOzZ3jSOBJYFfgtNxxZpvZnoU2jQK+lR5HAe8B081sy/TzU4kA\nan6ujZdUOLe2fIi4AJ+S2vs68ICZWaGcEcHOd4iA56n0fswBDgTOAsYCr6T2vd+/1bzvnWFmRxKf\nkTXAiem4dxEB60vpGACXsrZP5nXiEOcDmwBfJYKe04HvEu/9VKKfBgPTCp81gM8Qn8ezgeOAEUR/\nDM6VuYwIqn6Q2vp7oj9EREREukVza3mP7mJmG5vZtWb2qpm9a2a/MLM9aqjnHDNrzX3x36HOjgRd\nRFwIH+3uremgS4nAIXM1MMPdj8k17Cngj8Qoy/dyZQcDh7j7ylRuIfAUcDxxoY27X5arZxBx0b4t\ncaH600L77nD38/IbzGwVsNTdf13hfLYEPpmNUJnZCOIi+XF3n5S2PUxcpB9NBFNZP6wmvvVfnsrN\nJC6Gz0vlM1sB+7r74lTuz0Qfjk7tXWBmy9O5VmpjR0YAe7n7vFT/z4i1NOcTQUBm69TeBdkGMzud\nGAH7O3f/Vdo2gzQaxNr+reZ9r4qZNRAByZPAwVl9RFCUlZmf/vlsjX3yB3c/Kas3BbD/RLzXT6Vj\ntAAziADrl7l9NwJG597XZmA6sA/wRApe/wm41t0vyB1jE+Bf2muUmU0ggv11zJ/XmfhOREREpN+4\njZgR9a/AEuAc4Odm9jF3/99qKkgzgS4AXu3MgasOgsyskZi6dHHuwhVgGvGNPmn62c5Ak5nl615C\njNDsx7pB0ANZAATg7nPN7HngU6QgKNV5MTFitA1rR68WVWjm9GrPJ1mYn6JHTJGC3AW5u7eY2TPE\nNLDM54mA6N3Cec5m3cAjVREBULKAmAK4Qyfb2pbnsgAoHexdM3uANMUu55l8AJR8GlicBUBp/xYz\n+0/gYjPbGFhJB+97J40i+nJyob6uNKvweiHwN1kAlPwxPW9fKPt4FgAlf0jPOwBPEFPphhEjhHl3\n00EQ5O7XA9cXt69csWJgTL4VERGRLtPXU2Sb2V7EjJq/d/f/TNt+RSxt+AaR3Kwa1wH/SSw7qVpn\npsN9kAia1omy3L0ZeC29HJ6ebyOmfeUfnyBGRfIqRWyvANsBpG/XZwN7AOcSQdQ+wA+BoRX2fbkT\n5wMxdSxvdXp+o8L2/PGGE9O4iuc4kfXPcZ1juPsaoIXK7a9Fu32YU6lvtmhj+8tAA7FOq5r3vTOy\n/qkquq9Rpfe10nsK678PxX1XFcptm56L/d6pbx9EREREBrgvENdZ73+x7O5vEYMaX6ymAjMbS8zq\nObezB+/MdLilxDf/w/Mb0wjR1unlsvR8NrG4vOitwuvhFcqMIEZLIE5qJLCfuz+WO2Zb7e6ukHgZ\ncX7f6abjtaetPlxS2Fapb14nRu6Ktknl3yBGgjp63zMrgc0K5TYDGnObssDpryocty94KT0PZ93R\nyErvg4iIiEgp+kGK7A8TM5VWFbb/Afi6mQ1z9xVt7ZzWtl8DfNPd/7L+cvj2VT0SlL75nwuMTes6\nMmNYG0wtBF4APuTuv6nwKE5hO8zM3v8m3sz2BnYCsulZG6Xn93JltgC+VG27iQizq0ZdMjOJaVFP\nVzrPGuqrp40755MxmNlGRGT9q7Z3ed8jwE6p37P9BwHHAPPc/Z0q3/fMi8BuhSQChxTK/JH4jJxQ\nqC+vOPrSmzwNrGBt8obM2AplRURERKSyLVh/pg7EYEM2I6k9lxBf+t/YQbmKOpsYYTKRne1OM7uJ\nGKWZBGQL+1vTYvtp6T40dxOjDdsBBwAPu/tPcvWtAWaY2VXA5kRGt+eI6XQQazCWA1PN7CIiFfX5\nxGhCtW13YH8zO4QYzVri7sVRks66gAgM5pjZVOBPxDSvvQCKyRmqbOOENKT3AvBaYR1Re14B7jKz\nC4E3iYVlHyAymHXkZmIdy3Qzm0T068lEZP6FXLl23/ecnwD/CNxgZrcTGen+kZj+Fycan5EziGxw\nD5nZDcQvwB7Aanf/XjqnZcAxZvYHIuhYlIZIe5S7LzOz64CzzOxtIpD8NHBEKtLS5s4iIiIiXaS5\nxPv5tJXMCbg+rXGutM844EdVVD85f9uUWqQBgFOJ5F41XXt1KkW2u88k7o3yceBeonPGkVtH4e73\nEwHP1sRF9gwiscFgInta3k3EReT1RCKEBUTGsNWprqXEiEMjcdF8ORHtdSYd8SRigdU0InCp9IZ2\niru/QKxNWgRcSaxbupYYHXq0hiqvIO4xc2tqY1Mn9l1IZNI4n+ijLYAvuLt3tKO7v028V48QWf3u\nJqbCHe7uD+bKdfi+p3IPE+mo9wPuIwKDoyhMxXP3acQI0bB0zvcAXyay2pE+zBOI4Pn2kHboAAAg\nAElEQVQXRJ/sVU1ndJPziP46meiPPYmMcRCBqIiIiMj/Y+++4+Sq6v+PvzZLQghIE4KA9PpRQaQI\nfBUJRaVJV1EQAkpo4i/CV0FqCAgICqIEMKAUkaIivUMIvYQi8CWfhCIBIYRiaBJI2/n98TmTvZmd\n3Z12d3ey7+fjMY/duXPuuefenZ25n3vO+dym5e5j3X3jMo+yAVByA3EBvLvHuan8u5Tv7Vma9mkZ\nnRkDXAW8aGZLmtmSRAfJQul56S1QOmgp9NJ4wnRj0Lojwf6ss5uTSs8zsyOJbtmlsxkPK5FXdrhC\nS2ejDevXktPnRp5tzktexwKgjfyOx+ycriDOnJNfZ2jrgOZ7f+Qpr6OR51nBsYt/Lpd6j3zz2e4L\n1WiRhfJ73+V1P5ahi1Y70Kdyzfr5v8jgwQvcB8hZ97+U27/rEVuskfvxMrNRREKDxYsdIGn5RcDW\n7l5u3nqxzHvEKLLO7O/ul3S1/fz+S0QWUGa2OfB1oodqDvAVIhnIBdUGQCIiIiL91E3EvSh3I6ZU\nFDND7wJc0c26O9Exjvlt+jmS9tvedEpBUB/VRQY8YF6q7X4jJWzoavhmW61jQmvwEREE/RRYjJgT\ndgZwSg9tX0RERPq5POcE9QR3f9zMrgfOTYnSphIXlVvJZGA2s1WAl4h7Vo5O6z5QWl/qHcLdx1ey\n/V4Lgtx9geuWbJR059uXuynW0s+GwZ1AXC3ozKXA8J5oiLs/Q8x7EhEREekVzR4EJfsApxNBz2LA\no8A27v7vTJkWIjCqKpdBd9QT1DdNJRIvSLuxRLdpZ2q5cauIiIiI9JKUpOvHtCeYKldmChVMg6y2\nc6DXEiOISMgrMcKcHP+1B+Q0gTXPebEtbXNzqbcwoLX7QjXKq815eun9/Nq8cI4T1POSZy6HvKrO\nMbdFbn6z3Hq51f2dFybkVveM2fkc7K+u9Klc6gVYpP3WjQ3VttDCudRbtCAmRvjl3c/n9k1/7DZr\nL3DHq1RDu5VERERERET6Og2HExERERFpMgvInKBeo54gERERERHpV9QTJCIiIiLSZNQTVB/1BImI\niIiISL+iIKhOZjbFzEb1djvKMbORZrZrb7ejK2Y23MyG9+L2++zfT0RERKQzc9sKuT36AwVBC7aR\nQJ8OgogbnA7v5TaIiIiISD+iOUELIDNb2N1n9nY7RERERCQf/aXHJi8KgqpgZj8ATgBWAiYCPy1T\nZptUZmNgLnAn8FN3fzVTpgCcDBSAEcCSwL3AYe7+Uqbc94CDgC8Ag4BJwCnufkOmzHDgYuBrwBHA\nNsCjZrYWsAqwn5ntl4qf5O6jzGx8en4WcCqwBvAU0SPzBnAOsDvwEXCuu59Wso9rpPW2BRZN6x7l\n7vdlyhS3cQZwGrAWMDkdi/GZMltmjgnApe4+vPS4ljKzTVO9X0rH5nXgcncfnV5fExgFbAEMBV4D\nrgVGufuMburekPj7fBUYCDwIHOHuz2bKbA+cCHw+LXqFOFYXdNd2ERERkXopCKqPhsNVyMy2Ay4D\nniYChAuBy4GlMmV2Bu4gAonvAgcA6wDjzaz09s0HEIHLQUQg9HngDjMblCmzOvAPYJ+0zduAa8xs\nxzJN/AsRmO1GBAe7AdOAW4DN0+OiTPl1iCBhVKp/ZeBvwBVpvW+nbZ9qZjtk9nEV4FFgTeCwzHbu\nTMFD1jrA6emxJzAbuN7Mlk6vH0oEUE9l2nhymX2bTzqWtwGfAD8AvgWcSQRkRSsQgdFPge2AXxJD\nAy/ppu6NgQeAhYmgcK/0+31mtlIqszpwHfBC2q/dgD8AS3TXdhERERHpfeoJqtyJRAD0bXcvAJjZ\n20TgUHQ2cKu771VcYGaPAc8TJ9S/z5QdCGzv7p+kcpOAx4D9iAALd/9lpp4BwHhgeSJwurmkfVe4\n+7HZBWY2E3jb3R8psz9LA5sWe6jMbDlgDPCgux+Tlt0N7EEERLdkjsMsYCt3/yCVux14Bjg2lS/6\nNLC5u09J5V4jjuF2qb0TzeyDtK/l2tiZdYjes6Pd/Zm0bFy2QOqVyvZMPQi8SAQzy7j7O53UfQbx\n99rO3eekdccB/yLmWB0JbEj0Ph1WPAbAXd012sxGEAHvfJ568snuVhURERGZzxz1BNVFQVAFzKyV\nGN42uhgAJdcCxRPltYiem1Fmlj2uU4kemi2YPwi6qRgAAbj7BDN7GdiMFASlOkcTPUafob3nbnKZ\nZl5f5W5Nyg7RI4baQfRkFdvUZmYvEsP/ir5JBEQzSvbzTmDvkm14MQBKJhJDAFeusq2lXgTeBy4w\ns98D4939jWyB1KP2v8C+xLDAwZmX1wQ6BEFmtghxrEel58X9+4QYErdFev408Xe/0swuBO5z9+nd\nNdrdxwJjS5d/8vHH+hQTERER6UEaDleZZYmA8a3sQnefS/vJ9ND08zJi2Ff28SWiVyTrLTp6kxjG\nhZktRgQWXwSOJk7ANwH+xPwn9EXTqtkh4N2S57PSz/fKLM9ubyjwQzru40g67uN820g9K22Ub3/F\n3P09Yu7T28QQv6lmNsHMhmWKnUYEM5cDOwJfJoYU0sX2lwZaiSF5pfu3K2n/3P0FYHuiN+hq4C0z\nG2dmX6xnv0REREQqpRTZ9VFPUGXeJq78D80uTD1Ey6SnxZ6AnwP3lKnjw5LnQ8uUWY7oLYGYH7MK\nsIW7P5DZZmd/s556x04n9u/XPbS9stz9CWCX1ONTnEt0k5mtnHpl9gIuc/dTiuukwLIr7xFB2jnE\n3KhS8zLuuftdwF1mNoRI7nAGcLOZrVTSWygiIiIifYyCoAq4+1wzmwDsYWanZE5yd6P9GE4iMoSt\n6+5nVlDtjmY2ODMnaGNgNSLrGsCQ9HN2cQUzWwrYhY69NZ2ZSZ29LmXcDmwEPNugNNwzqSOhgLvP\nAu41s9OJeVKrEoHaEDLHLtm/m7o+SnOH1geOrCSYSZnmbjWz1Yg5VUvRHhCLiIiI5KK/9NjkRUFQ\n5U4iMpL9zcz+SPTSHAMUJ/YXzOxw4FozWxi4hhgOtgIwDLjb3a/O1DeHOHk+iwgCTiMm31+WXn8o\n1T3GzE4kMp8dRwy/q/Tv5sCWKZ3z28BUd59aw75nHQ9MIDLejQH+TQwT2wigNDlDhW0cYWZ7EEHk\nOyXziDows52AA4k5WVOIwOMXRDa4Yk/abUR68GeJOUS7A/9TQXuOINKV32xmFxPDFpcjepumuPs5\nZnYQMTzxlrTNFYjhgE9UMjdIRERERHqX5gRVyN1vJybZb0CkRx5BpJZ+N1PmRiLgWYa4d8+tRGKD\ngUT2tKw/EtnLxhKJECYC30g9G7j720RPUyvwdyJIuoiY41KpY4CXiWBhAmUyk1XL3V8h5iZNJtJS\n3wn8DlgPuL+GKs8gUlJfmto4qoJ1XiB6kEYRwc4FRDC2TSbZxOHADURq7KuBTwHf665id3+cSE7x\nMXA+kSjiTCLQeTQVewZYPLX9DmJo4IPAzhW0XURERKRucwuF3B79QUuhn+xoX5JuDHqSu4/q7bZI\n78srO9ycHP+1B7S05FJvTtVG3W1zc6m3MKA1l3ohvzbn6aX382vzwgvl+AbJyYA839M51TunLaeK\nc/Sb5dbLre7vvDAht7pnzM7nYH91pdJbEzbOIh1GmjdG20IL51Jv0SKDBzffB0g3Dvn707l905+/\n5xcXuONVSsPhRERERESajOYE1UdBkPQ5XWTAA+al2hYRERERqYmCoF7g7gt8F2OtzGxVYh5TV3T8\nREREpF9TT1B9FARJXzOVSLwgIiIiIp1QEFQfBUHSp6TseI/3djt6VCGfibEDc6oXoNCSz0dHIcdO\nvtk5JcMc2ITJCwBmFvI5HosNyu9LeaE8sww0obwORzOeWOWZvOCva+V3Xe7Q15/Opd7WPP9Xcvrs\nEOlpCoJERERERJrM3LYmTOXYhyicFxERERGRfkU9QSIiIiIiTaYZh672JeoJEhERERGRfqXfBUFm\nNsXMRvV2O8oxs5Fmtmtvt6MrZjbczIb3djv6CjMb1lffTyIiIrLgmttWyO3RH/S7IKiPGwn06SAI\nGJ4eEoYBJ/Z2I0RERESkcpoT1AeY2cLuPrO32yGVM7OFgP5xqURERET6nDn9pMcmLwt0EGRmPwBO\nAFYCJgI/LVNmm1RmY2AucCfwU3d/NVOmAJxMnPSOAJYE7gUOc/eXMuW+BxwEfAEYBEwCTnH3GzJl\nhgMXA18DjgC2AR41s7WAVYD9zGy/VPwkdx9lZuPT87OAU4E1gKeIHpk3gHOA3YGPgHPd/bSSfVwj\nrbctsGha9yh3vy9TpriNM4DTgLWAyelYjM+U2TJzTAAudffhpce1lJltmur9Ujo2rwOXu/vo9Pol\nwDB3X7VkvReBB4rbKJZL+34W8Dng38Aod/9LZr2KyqWyOxG9OesRx/B24Gfu/nqmTAEYDcwBDgQ+\nC/wJ+GHJ8XildB9EREREpG9ZYIfDmdl2wGXA00SAcCFwObBUpszOwB1EIPFd4ABgHWC8mX2qpMoD\niMDlICIQ+jxwh5kNypRZHfgHsE/a5m3ANWa2Y5km/oUIzHYjgoPdgGnALcDm6XFRpvw6wKj02AdY\nGfgbcEVa79tp26ea2Q6ZfVwFeBRYEzgss507zWzDkjatA5yeHnsCs4HrzWzp9PqhRAD1VKaNJ5fZ\nt/mkY3kb8AnwA+BbwJlEQFaLpYi/7VjiOD8L/NnMtq22XPrb3AC8CewB/C+wNXCvmS1WUt+BRGD1\nE2Bn4CTgj+m14vHYrcZ9EhEREamY5gTVZ0HuCTqRCIC+7e4FADN7mwgcis4GbnX3vYoLzOwx4Hmi\nB+H3mbIDge3d/ZNUbhLwGLAfEWDh7r/M1DMAGA8sTwRON5e07wp3Pza7wMxmAm+7+yNl9mdpYNNi\nD5WZLQeMAR5092PSsruJE/lvE8FU8TjMArZy9w9SuduBZ4BjU/miTwObu/uUVO414hhul9o70cw+\nSPtaro2dWYfoPTva3Z9Jy8ZVsX6pxYHh7n5tauetxP6cBNxVZbnRRI/dzu7elspNAh4i3gPnZuqb\nQ7wHZhUXpGNU0fEwsxFEAD2fp554vNsdFhEREZHGWSCDIDNrJYa3jS4GQMm1xIksafjZ6sCoNL+j\naCrRQ7MF8wdBNxUDIAB3n2BmLwObkYKgVOdoosfoM7T3tE0u08zrq9ytSdkhesSJO0RPVrFNbWn4\n2EqZct8kAqIZJft5J7B3yTa8GAAlE4khgCtX2dZSLwLvAxeY2e+B8e7+Rh31zSZ6bwBw94KZ/Q04\nzswGFIOZ7soBQ4jheSdl1sHdHzazl4i/YzYIujUbAFXL3ccSvVLz+WTGR/3jkouIiIg0TH/pscnL\ngjocblkiwHsru9Dd5wLvpKdD08/LiJPl7ONLRK9I1lt09CawAkAaOnUn8EXgaCKI2oSYNzK4zLrT\nqtkh4N2S58WT8ffKLM9ubygxb6V0H0fScR/n24a7zwHaKN/+irn7e8Tcp7eJIX5TzWyCmQ2rscr/\npL9l1ptEb92yVZRbEmih/N9iGpmhk5llIiIiIr1Ow+Hqs0D2BBEn23NoD3SAeT1Ey6Sn09PPnwP3\nlKnjw5LnQ8uUWY7oLYGYD7IKsIW7P5DZZmfHuKfeYdOJ/ft1D22vLHd/AtglzaEqziW6ycxWdvfp\nxHyhQWVWXbrMsk+bWWtJgLMcEdy9XUW5IcTfYbky2/gM8ETJsv7xqSAiIiKygFsggyB3n2tmE4A9\nzOyUzJC43Wjf50nAK8C67n5mBdXuaGaDM3OCNgZWI7KuQZxQQ5xgk8osBexCx96azsykzl6XMm4H\nNgKebVAa7pnAErWunIaT3WtmpxPzpFYlArVXgaFm9ml3/w/MyyhX2hsD0ZOzMzG8ETNrIRI5PJYd\n1lZBuf+a2VPAnmZ2cmbu2KZEBr7fVrBLM9M6894bIiIiInnrLz02eVkgg6DkJCIj2d/M7I9EL80x\nQHFif8HMDgeuNbOFgWuI4WArEBnA7nb3qzP1zQFuNbOziCDgNOBfxHA6iIn0HwBjzOxEIvPZccTw\nu0qPswNbmtn2RE/FVHefWsO+Zx0PTCAy3o0h0kR/mgiMKE3OUGEbR5jZHkQQ+U7JPKIOUgrqA4lg\nZAoR2PyCSJNd7En7OzGf6jIzOwdYkcjUVi6A/AD4rZkNJYKnA4n01l+vodwJwI3E+2As0eN3KvAS\ncElX+5V4+nmEmd0FfOzuz1awnoiIiIj0kgV1ThDufjuwL7ABcB2RlWsfMvNe3P1GIuBZhrh3z63E\nifhAIotY1h+B+4iJ7RcSJ+/fKE6Ud/e3iZ6mVuKE/jRi/svlVTT7GOBlIliYQJlMYtVy91eIuUmT\nibTUdwK/I4KB+2uo8gzgAeDS1MZRFazzAtFjMooITC8ggrFtir0n7v48sBeRrOJ64GAiLfl/ytT3\nLpGV72Dib7s+sI+731VtOXe/meitW5FIMX4WcQ+oLd39vxXs243Ee+JI4JH0XERERCRXmhNUn5ZC\noX/saD3SjTBPcvdRvd2W/q6zm6rWWq4vyCs7XEuhrftCNSoMyKcTudDSkku9kN+dtQeS33HO08xC\nPtfA3vukNBdJ4yw0IL/3RzPK63A04wnQ89M/zq3uv661SW51H/r607nUu9qS5abYNsagwpxc6m1r\nHZhLvUWLDB68wH2AbHvuA7n9s971468ucMer1II8HE5EREREZIFUaMILFn2JgiCpWxcZ8IB5qbZF\nRERERPoEBUEVcPcFvkuwVma2KjGPqSsNO37uPryR5URERESaUZt6guqiIEjqNZVIvCAiIiIi0hQU\nBEldUna8x3u7Hc2sraU1l3rzTP3YMndWPhXnlHAhVZ5LrYUB+fz9AAbMnd19odprz6XWuTkm22nJ\nqepmTZPa1rhO9pJ685PX22PG7PxanVfyAoDzVvxiLvWeNWNSLvUCum14H6LkZvVp1s9+ERERERGR\nmqgnSERERESkySg7XH0UBImIiIiINBklRqiPhsOJiIiIiEi/op4gEREREZEmU8gzi0k/sED0BJnZ\nFDMb1dvtKMfMRprZrr3djq6Y2XAzG95T61VR/zAzK5jZsAbVt2oj6yupe5SZTWl0vSIiIiLSeOoJ\nyt9IYDxwXS+3oyvD089Lemi93vIGsDkwsbcbIiIiIlIPpciuj4KgnJjZwu4+s7fbIe3S3+OR3m6H\niIiIiPSupguCzOwHwAnASsQV/Z+WKbNNKrMxMBe4E/ipu7+aKVMATiZu+zUCWBK4FzjM3V/KlPse\ncBDwBWAQMAk4xd1vyJQZDlwMfA04AtgGeNTM1gJWAfYzs/1S8ZPcfZSZjU/PzwJOBdYAniJ6V94A\nzgF2Bz4CznX300r2cY203rbAomndo9z9vkyZ4jbOAE4D1gImp2MxPlNmy8wxAbjU3YeXHteS7Xe5\nnpltSBzfrwIDgQeBI9z92ZJ6vgMcCawHzAKeTfvxUKbYp8zsQmDPVOYfqa6PUx3DgHuAndOjs3Kr\nAi8DWxX3v5I2mNlo4FvAmsAM4AngZ+7+XFfHSERERCQvyg5Xn6aaE2Rm2wGXAU8TAcKFwOXAUpky\nOwN3EIHEd4EDgHWA8Wb2qZIqDyACl4OIQOjzwB1mNihTZnXiZHqftM3bgGvMbMcyTfwLEZjtRgQd\nuwHTgFuIYVibAxdlyq8DjEqPfYCVgb8BV6T1vp22faqZ7ZDZx1WAR4mT8sMy27kzBR9Z6wCnp8ee\nwGzgejNbOr1+KBFAPZVp48ll9q1Up+uZ2cbAA8DCRFC3V/r9PjNbKbMfI4GricDk+8D3gHHpOGT9\nDvgA+A5wNnAgcGyZNlVabp4K27AiEZTuDPwwbeMhM1u+q7pFREREpG9qtp6gE4kA6NvuXgAws7eJ\nwKHobOBWd9+ruMDMHgOeJ07If58pOxDY3t0/SeUmAY8B+xEBFu7+y0w9A4j5PcsTgdPNJe27wt3n\nO+k2s5nA2+5ebhjW0sCmxR4qM1sOGAM86O7HpGV3A3sQAdEtmeMwi+jR+CCVux14hjjp3yOzjU8D\nm7v7lFTuNeIYbpfaO9HMPkj7WvFQsW7WO4M43tu5+5y03XHAv4g5Ukea2RLAKcDV2b8VcGuZzd3p\n7kcWfzezLxOBznE1liO1qaI2uPsPM+u0EoHwc0TAdFa5ujvZ3ggi2J7PE08+VWkVIiIiIoBullqv\npgmC0snnxsDoYgCUXAsUT7TXInpuRplZdt+mEj00WzB/EHRTMQACcPcJZvYysBkpCEp1jiZ6jD5D\ne+/Z5DLNvL7K3ZqUHaJHDLWD6MkqtqnNzF4khv8VfZMIiGaU7OedwN4l2/BiAJRMJIYAlva2NISZ\nLUIcq1HpebF9nxBD4rZIzzcnhvFdWEG1pYHRc8AOdZQrqqgNZrYtEVyuTwSuRWt3tV4pdx8LjC1d\nPuPjT/QpJiIiItKDmmk43LJE0PZWdqG7zwXeSU+Hpp+XEcO+so8vEb0iWW/R0ZvACgBmthgRWHwR\nOJo4gd8E+BMwuMy606rZIeDdkuez0s/3yizPbm8oMSyrdB9H0nEf59tG6plpo3z7G2FpoJUYGlfa\nvl0z7Sv+fL2COkuP00xieF2t5Yq6bUMaXngL8F/imG9GvAeeJr9jKCIiItKlQlsht0d/0DQ9QcDb\nRI/P0OzC1EO0THo6Pf38OTFRvtSHJc+HlimzHO0plDcnEhts4e4PZLbZ2XHrqXfNdGL/ft1D26vG\ne0SQdQ4xt6lUMWNeMXBdkfYesJ5WSRv2IN53u7v77OJCM1uqk/IiIiIi0sc1TRDk7nPNbAKwh5md\nkhkStxvt+zEJeAVY193PrKDaHc1scGZO0MbAakTWNYAh6Wfpye8udOyt6cxMGt9jcDuwEfBsg9Jw\nzwSWaMR67v6RmT1IDB07smToYtbDROa7HwJ317DtRqikDUOIDIPz9sPMtian4YQiIiIilWjTfYLq\n0jRBUHISMSn9b2b2R6KX5hgiWxfuXjCzw4FrzWxh4BpiiNQKwDDgbne/OlPfHOBWMzuLOJk/jZi8\nf1l6/aFU9xgzO5GYP3Ic0YNQ6bFzYEsz257ozZrq7lNr2Pes44EJRMa7McC/iaFdGwGUJmeosI0j\nzGwPIoh8p2QeUbXrHUGkG7/ZzC4mhh0uR/SsTXH3c9z9AzM7FvitmQFcRQRVmwKTS/5OuaiwDbcR\nwwwvSfuyNnH8KxnGJyIiIpKL/jJsLS/NNCcId78d2BfYALiOyLS1D5m5IO5+IxHwLEPcu+dWIrHB\nQCJ7WtYfgfuIyeoXEsPgvuHus1JdbxM9Ta3A34kg6SIiLXeljiHSL19LBC4dsoNVy91fIealTAbO\nJOYt/Y64z839NVR5BpHS+tLUxlH1rOfujxNzZz4GzicSPZxJBKOPZvbjHCIt9TrAX4kgZBsgmywi\nV921Ib3nfgJ8BbiJSKu+L/BiT7VRRERERBqrpdBPu9LSDT5PcvdRvd0W6d/yyg43oDA3j2oBaGmb\nk0u9hQH5dU7Pzumaz0IDWnKpF2DA3NndF6rRJzkNBPjPx/m8NyC/Y91UVwMzWlryOR5zczwvyKvq\nZ9/6KJ+KgZWXyC8HznkrfjGXes+akd9U29a2fD6X2loH5lJv0SKDB+f3Yd1LNjr+ttz+WZ84ebsF\n7niVatbPfhERERERkZo025wg6SFdZMAD5qXaFhEREZFe0KY5QXXpt0GQuy/w3Xy1MrNViXlMXdHx\nExEREZGm1G+DIOnSVCLxgoiIiIj0Qf11Xn+jKAiSDlJ2vMd7ux39RevcRtzqqaPZAwblUi/AgNaF\nc6k3p3neAAyaNSOXegsL5XMsAKZ9kt8BWb7l3e4L1WDokPzuIzwgpzdInklE5uQ49XbIa0/mUu+s\nz26QS70AhQGtudT71ZU+lUu9AK05Jj/JK4HBEUPWzaVegJ0mPtp9oRoMGZjvNPWt18wvwYU0JwVB\nIiIiIiJNptDW2y1obsoOJyIiIiIi/Yp6gkREREREmoyyw9VHPUEiIiIiItKvqCdIRERERKTJFNQT\nVBf1BElNzGy8mV3S2+3IMrNVzaxgZsMrKFtRuTLrTelr+y0iIiL9T6GtkNujP1BPkPRXmwMv9XYj\nRERERKTnKQiSXmVmA4BWd5/dk9t190d6cnsiIiIijdSmm6XWRUFQP2Rm6wJnEL0hiwHTgNvc/RAz\nWxb4JbA1sGJ67U7gaHef3kWdFa1nZlOA8cDDwJHA6sBWZjYOGOnuY0rqPR/YGVjZ3Su9o+FAMzsd\n2B8YCNwBHFrSjgKwv7tfkll2FHA4sDTwGHAIMLG0XCq7H3AcsDzwT+AQd3+2wvaJiIiISC9SENQ/\n3Qj8BxgBTAdWAb6SXlsamAkcA7xJnOQfCdwGfLmLOqtZ7xvAF4kg4j3gX8B1wIHAvCDIzIYA3wd+\nV0UARKr3HmA/YFXg18AcYJ/OVjCzQ4HTgfNTW9ZLP8vZGlgb+Hl6fgZwvZmt09M9WiIiItI/9Ze5\nO3lRENTPmNkywJrAke5+Q+alywDcfTLRG1Is3wo8CLxqZhu6+5Pl6q1yvcWB9d39nUz5C4C7zGxT\nd380Ld6L6Km6qMrdnOzuwzN1rw0cZmY/cPcOnxiprccC17n7oWnxHWY2Fzi7TP1DgO3c/YO0/lzg\nemAT4KHOGmVmI4jAcz7/nKCReSIiIiI9SUFQ//MfYApwegqIxrn7lGwBMzsEOBhYA1g089LaQNkg\nqMr1HswGQMk44HkiSCgGQQcCd7j7K5XsWMatJc+fAwYByxHD9Ep9FlgB+EfJ8msoHwQ9WAyAMvUD\nrEwXQZC7jwXGli6f+d/3dSlHREREqqKeoPooRXY/k3pCvg48BfwGeNnMJprZngBmdjhwHnAXsDsx\nlG2ztPrgzuqtcr0OgUhq1x+A75rZ4ma2Xlq/Q9BQgXdLns/spB1Fy6efb5UsL31ea/0iIiIi0oeo\nJ6gfcvcXgb3TMLANgV8AV6XAYy/gbnc/sljezFaroNpq1uvs0sUlRHKFvQED3rJXJnIAACAASURB\nVCDmL+XtjfRzaMny0uciIiIifULbAtATZGaLAqfRPgXiUSJR1tMVrLsqcAowjJib/gpxLvkbd5/T\n3frqCerH3H2uu08gEgm0EoHHEKB0cv/+FVRX63rZ9kwH/gocSiQxuLiSN3EDvAZMJXqwsvbogW2L\niIiI9FeXEUmwfg7sCrQB48xsxa5WSsHTXcCmwFHATsDfgFOJoKpb6gnqZ8xsfWKey9XEzUIHEwkN\nPgQeIbK5HWVmvwAmEJnQ9qyg6lrXK3U+kT67AFxYw/pVc/e5ZvZLYIyZjSGSHKxHexKDtp5oh4iI\niEilCk1+nyAz24i4AP19d78yLXsYeBn4GTCyi9W/QsxB38bdx6Vl48xsdeJC+s+62756gvqfacDr\nxJvjJuBSoAX4urtPBUYTc3OOAK4F1ge+WUG9ta43n3QT06nAnaUJG/Lk7ucRwwJ3JYKgXYAD0svv\n91Q7RERERPqJnYh51fMSU7n7h8R52Le6WXdQ+ll6jvYeFcY3Lc0eRcqCxcw2IJI27OHupdnaerot\newB/B1atIUNdxfLKDjd7wKDuC9VoQEtLLvXmVC0AA2bNyKXewkIL51IvwJsf5/f5vHzLB90XqsGs\nwUvlUi/k974bUKjmNmTVmZPjtcYhr3WarLMusz67QS71AhQGtOZS78w5+XXYtw7I74OpNaf39BFD\n1s2lXoCdJj7afaEaDBmY73X5rddcNsdvmN6xxsHX5PYl8dIFe+R+vMzsauDz7v6FkuVHEPd4XNTd\nP+5k3UWAZ4mMx4cRF9C3Av4CnOLuv+pu+xoOJ32CmX2WuH/RqcALxFWAntz+GsBBwP3AR7QnjLg2\nzwBIREREpBYLQGKEpYiem1LTiVFKSwJlgyB3/9jMtiBubD8p81JFARAoCJK+40fA8cBEYG93n+/S\nrJkNoOvuzTZ3r+dS4MfABsQQuCWAN4HLgWPqqFNERESk6XR2g3dgbLrvYbl19gH+XEH1J7n7qDqa\nh5kNBq4kMsp9j8j0+zXgWDP7yN1P764OBUHSJ6R/hlFdFDkBOLGL1y8Fhtex/anAN2pdX0RERKQn\nFdryG8rb2Q3eu3EDkWm4O++kn+8SN6svtTSRIKtcL1HRD4mgZ7XMiJ1700Xz0WZ2kbu/0/nqCoKk\neYwlEjl0pss3el/2UWFgLvUOyXE0b6EJR1Z/PCCfe9kuPCC/cewrfvJybnW/s9jKudS7ZI7vjbxm\nfbS15DNPBYAc592+tVw+c3cWz/F4DMjpeCzS4Q4NDVTIca5KTm+PvObtANz0uU1zqffGvfIdePH6\nleU6NaSR3P0DoJoJpw7sYmaD3H1WZvnngCmdzQdK1gPeKTNl4XFgIDHFQkGQNL/UUzO1t9shIiIi\n0hfk2RPUQ24iRvnsRty6BTNbjMjQe0U3604DljGzVUoCoU3Sz9e727iCIBERERER6VHu/riZXQ+c\nm+b4TCVumtpKZIcDwMxWIe5tOdrdR6fFlwJHAremez2+AWwBHE0ktfp3d9vXfYJERERERJpMoW1u\nbo8etA/RC/RrYk7RQOIGqNkgpoUIjObFLe7+MrA5kRnuDKJXaS/gl8DelWxYPUEiIiIiItLj3P2/\nwI/To7MyU4hAqHT5/wG717ptBUEiIiIiIk2mMLfp5wT1Kg2Hk1yY2Xgzu6S321HKzApmNry32yEi\nIiIivUc9QSIiIiIiTWYByA7XqxQESZ+WbnrV6u453gRCREREpLkoCKqPgiDpwMzWJTJtbA4sRuRi\nv83dDzGzZYnMG1sDK6bX7gSOdvfpXdRZ0XpmNgUYDzxMpD5cHdjKzMYBI919TEm95wM7Ayu7e02f\nBma2f9rWWsTdia8FjnL399PrtwFz3H2n9HwIcZfj6e6+fKaefwKPuPvBtbRDRERERHqG5gRJOTcC\nQ4ERwHbEjayK75WlgZnAMem1XwBfAm7rps5q1vsGcDBwHLAD8C/gOuDAbKEUjHwf+FMdAdAhwJ+I\nOwzvApxCpFa8xcyKt00fD3w18/wrxM3rl0sBI2a2NLB+KisiIiKSqwUkRXavUU+QzMfMlgHWBI50\n9xsyL10G4O6TgcMz5VuBB4FXzWxDd3+yXL1Vrrc4sL67v5MpfwFwl5lt6u6PpsV7ET1VF9W4r63A\nScAd7j48Lb7NzN4GrgS2J/LO3wOcRgRtjwPDgPuBFdLvk4AtifSN47vY3ggisJzPg489UUvzRURE\nRKRGCoKk1H+AKcDpKSAal/Kzz5N6Tw4G1gAWzby0NlA2CKpyvQezAVAyDnieCCKKQdCBRADzSiU7\nVsa6wLLAFSXL/0bcifhrRBD0BPAhsBURBG1F9JatmH6/IP2c7O7TOtuYu48FxpYun/7hjEKN7RcR\nEZF+qr/02ORFw+FkPu5eAL4OPAX8BnjZzCaa2Z4AZnY4cB5wF3GDqi8Dm6XVB3dWb5XrdQgkUrv+\nAHzXzBY3s/XS+h2CiiosVW57aWjd28XX3X0O8AAwzMwWBTYmeofGEz1AED1C4+toi4iIiIj0EPUE\nSQfu/iKwdxoutiExf+eqFHjsBdzt7kcWy5vZahVUW816nfWMXEIkV9gbMOANokemVu+mn8tlF6b9\nXhbIJnoYT8xR2pKY2/Q48BIw1My+BnwBOLWOtoiIiIhUTD1B9VFPkHTK3ee6+wTi5L+VCDyGAKXp\nqvevoLpa18u2ZzrwV+BQYB/g4tRLU6tJwFvAd0qW7wkMIub9FN0DfIrIIne/u89x97eBiUTiiC7n\nA4mIiIhI36GeIJmPma0PnA1cTfR0DCYSGnwIPEJkczvKzH4BTCBSXu9ZQdW1rlfqfCJ9dgG4sIb1\n53H3uWY2CjjPzC4i5gKtRfToPATckin+JPAB0e6fZ5aPBw6jm/lAIiIiIo3Upp6guqgnSEpNA14H\nfkYkBbiU6OX4urtPBUYTc3OOIO6nsz7wzQrqrXW9+bj7I8BU4M7ShA21cPfzgQOI+UU3AscTiRJ2\ncPe2TLm5tPcM3ZOpYnzJTxERERHp41oKBSWmkuZhZhsQSRv2cPd/9HZ7GiGv7HBDFmrJo1oACi35\n1Z2XmXPaui9Ug4UXyu9a0sD/vJxb3e8stnIu9S45KL/j0Ubzve/acvyO/e/sfN7Tiw9q7b5QjQZ0\nOuWzPi1zZ+VSb1TefNeL73x1Rm513/S5TXOp98a9jsml3qLXrxzRfB8g3Vh2lzNz+4B5+/qfLXDH\nq5SGw0lTMLPPEvcvOhV4Abi+d1skIiIiIs1KQZA0ix8RQ9UmAnun4WnzmNkAuh7e2ZYd3iYiIiLS\nzJQdrj4KgqQpuPsoYFQXRU4gsrR15lJgeONaJCIiItJ7CnMVBNVDQZAsKMYSiRw6805PNURERERE\n+jYFQbJASJnrpvZ2O2qxKPlM6C2wcC71ArQ0YUKVRVryuWJWKOQ3d/TdT62SW91L8Uku9RZYJJd6\nAVrbSm811hiFAfl9Fba25Tdhf9GBg3KpN6/kBZBfUpXCQvl93jWjIQPzS+aQVwKDb12V8/3GrxyR\nb/29QMPh6tN8KU9ERERERETqoJ4gEREREZEmo56g+qgnSERERERE+hX1BImIiIiINBn1BNVHPUEi\nIiIiItKvqCeoyZnZeGCKuw/v5ab0CWY2HLjY3VvS81WBl4Gt3H1877VMREREpHEKbboHfD0UBMmC\n7g1gc2BibzdERERERPoGBUECgJkNAFrdPZ8bcfQSd58JPNLb7RARERFpJM0Jqo+CoF5kZusCZxA9\nFYsB04Db3P0QM1sW+CWwNbBieu1O4Gh3n95FnRWtZ2ZTgPHAw8CRwOrAVmY2Dhjp7mNK6j0f2BlY\n2d27/K/LDEE7CFgH2BcYBFwK/BT4MnA2sB4wCTjI3R8vqeM7wM+ALwAzgBuAI0v2YTng98AOwCfA\nlcBznbRl3nC44r5nhxCa2WeBfwP7u/slJcfofuAYYHngvrQ/A4DzgW2Bt4GT3f3iro6LiIiIiPQN\nSozQu24EhgIjgO2AE2n/mywNzCROvrcDfgF8CbitmzqrWe8bwMHAcUQg8S/gOuDAbCEzGwJ8H/hT\ndwFQieOI4G5vIug5HPgtcBEwBvg2MBC41szm3frczA4DrgImALsRgdO2wC1m1pqp/9q0/H+JwGTF\ntM1G2jrVPRI4hAhYL03bfhjYPbXzj2b2+QZvW0RERKSsQtvc3B79gXqCeomZLQOsSfRu3JB56TIA\nd59MBA3F8q3Ag8CrZrahuz9Zrt4q11scWN/d38mUvwC4y8w2dfdH0+K9iGDmoip38zl3Pyj9foeZ\n7QD8GNjU3R9L22sDbiWCi3vNbDHgVOA8d/9xpl2TiWFtOwA3mtk30zq7u/u1qcytwDNEMNQoiwA7\nuvt/0zY+T/RQHeXuZ6RljxLB2p6U9ERlmdkIIuCdzz8fe7iBzRUREZH+oK2fBCt5URDUe/4DTAFO\nTwHROHefki1gZocQPTVrAItmXlobKBsEVbneg9kAKBkHPE+crBeDoAOBO9z9lUp2LOOOkueTgLWL\nAVDyfPq5Uvq5ORGcXW1m2ffnE8C7wBZED9pmwGximBwA7l4ws78RQ+ga5aFiAJTZB8jsm7t/YGbT\nMvtQlruPBcaWLp/54XuFRjRURERERCqj4XC9xN0LwNeBp4DfAC+b2UQz2xPAzA4HzgPuIoZcfZk4\n8QcY3Fm9Va43rZN2/QH4rpktbmbrpfU7nLxX4N2S57OA98osy7ZtaPp5HxHkZB9LAZ9Ory8P/KfM\n8Lw3a2hnV8rtA5Tfj07/LiIiIiKNVJg7N7dHf6CeoF7k7i8Ce6chaxsS83euSoHHXsDd7n5ksbyZ\nrVZBtdWs11kPxCVEcoW9ASPSTN9YwbYboZj4YB9gcpnXiz1XbwCfNrPWkkBouQq28QmRqCFr6apa\nKSIiIiJNS0FQH5BO4ieY2XHE3BIDhgAflBTdv4Lqal0v257pZvZX4FBifs357j6nmjrq8CDwX2B1\nd/9LF+UeJpIq7EwkKcDMWoh5Od15FfhcybLtq2+qiIiISO/oLwkM8qIgqJeY2fpExrSrgZeIoVSH\nAx8SCQBuA44ys18Q2ce2prIT/FrXK3U+EWgUgAtrWL8maX7NUcA5KQX2bUSK7JWIbHYXuPv97n6H\nmT1MZGUbSgQ2BxJD5rpzNXCRmZ2Z6t8M+EEOuyMiIiIifZCCoN4zDXidyDT2WeAjYvL/1919qpmN\nBpYEjiACpHuBbxJprLtS63rzcfdHzGwq8H+lCRvy5u7nmdlrxLEZDrQQQc7dzL8fuwHnAmcRQ9yu\nAG4HLuhmE5cAqxA9ZIcQSQ72IYJGERERkT5PPUH1aSkUlJhKOjKzDYikDXu4+z96uz0LsryywxUW\nWjiPaptWS1s+IzoLA/K7lvTh7Pw+nxfnk1zqLQxcJJd6oTn/hi1zZ3VfqEazBpRObWyMgS25VAtA\noSXHymWeh1/7MLe6f3D8tbnU+62rTs2l3qILClMWuDff4E0Ozu1L4pMJFyxwx6uUeoJkPmb2WeL+\nRacCLwDX926LRERERKSUeoLqoyBISv0IOB6YCOxdmoLazAbQdWr1Nndvy7F9IiIiIiJ1URAk83H3\nUcCoLoqcAJzYxeuXEvN4RERERCQn6gmqU6FQ0EOPih/rrrvuCuuuu+7GXTxW7e02LsiPddddd0Sz\n1d2MbdbxaP4263g0f5t1PJq/zc16PPToH4+uhjWJdODuU9398S4eU3q7jQu4EU1YdzO2Oc+61ebm\nr1ttbv661ebmrzvPNks/oCBIRERERET6FQVBIiIiIiLSrygIEhERERGRfkVBkIiIiIiI9CsKgkSa\ny9gmrLsZ25xn3Wpz89etNjd/3Wpz89edZ5ulH2gpFAq93QYREREREZEeo54gERERERHpVxQEiYiI\niIhIv6IgSERERERE+hUFQSIiIiIi0q8oCBIRERERkX5FQZCIiIiIiPQrCoJERHJiZvua2ac7eW1p\nM9u3p9vU08xsZTMb2Nvt6GvMbJCZfd/M1uzttvRXZrZkb7dBRHrPQr3dABFpV+1Jsbtflldb+hoz\nGwAMcPc5mWXbAl8A7nH3p2us92HgD8DV7v5xQxrb7mJgc+A/ZV5bLb1e89/QzE4ALnL3qWVeWx44\n0N1H11p/g7xMHIPH8tyImS0CbAx8BigA04DH3f2TOuvt6n+yDXgf+Ke7/7uaet19lpn9CdgOeLGO\nJnZgZuOAQ919UpnX1gYucPeta6x75S5ebgPed/cPa6z7a93VDUx291lV1jsCWMLdz0zP1wduAlY0\ns8eBXdx9Wo1t/hewW7nPHzP7AnCDu69eS915MrNWYBNgZWBw6eu1freY2XnAPcC97v5WXY3sWLcB\newIr0bHNBXffr5HbkwWfgiCRvuWSKsoWqO8Eei1gD8p/CRbc/Yc11jsX2NzdO5z0mtlGwGPu3lpD\n1VcBHwP7pboOAcak12ab2U7ufmcN9b4HXAScbWZ/Aca6+zM11FNOSxevLQ7MrrP+E4HbgA5BELBC\ner3qICidKC/h7hPS88HAcUTAebu7n19FdV0dg7qltp0J/BBYOLO9AvCJmV0EHFVHMHRJqgvm35fs\nsoKZ/QPYt8pAejLw2Rrb1ZVhxPurnMWBLeuoewrt+16WmU0BznL3MV2VK2N8Sd0tZZ7PMLPzib9p\nW4X1Hg5ckHl+NvF/fxowEjgF+FGVbS1alXjflbMI8flaETN7mW6ObVatwVUKzq4jLsSU+/+s57vl\nf4CD0nYmE3/T8cD4eoIiMxtOfE7PJS5wlAbCFR83kSIFQSJ9iLv3yBBVM9sTuCI9nQbMLClSzxdK\nVye9C9VR92bA/2ae/5zowTkCuJA4Sa86CHL37c1sJeBAYH/gkHR1+A/AVe4+o5r60tXsYZlFPzKz\n7UqKDQZ2Arza9pbo6lgvS8e/a6XGAE8AE9Lz04FDgH8C55hZq7ufW2PdDWNmLcAtxEn9tcCtwL+J\n47ISsAPwY+DzwLY1buZzxAnj+cA1wFvAUOKK9MHEe2YF4v3yS+L9WKlRwK/MbIK7T66xfZ3p7P9s\nfeCDOurdDfgd8CQdj8eXgOOJHoazzaytyoD5q8DlwPVl6t4FOAwwIrD/ADi5wnpXIf2vmdlSwNeA\nndz9djN7DzijijaW0+FYm9kg4j33bhX1XFqurhycT/Ss7Qo8R+2fEx24+wbpGA8DtiL+N7NB0T3u\nflgNVR8P/A0YUWtPo0gpBUEi/dNo4A7gB+5ezZd0WWa2OJAdX/+ZMsNmBgPfJ05sajGU1ONhZusQ\nJzbnuvvHZnYpcHWN9ZKGMp1gZqOAHYmrwhcAZ5nZFcAfqhhutxXRAwNxQlPuCvMs4qTsJ9W21cx2\nIU4Ii443s7dLig0mTj6erLb+5IvA79P2WoF9gZ+5++/M7HjipKaaICivE7u9iBPaXd39xjKvX5SO\n1zVmtpe7X1XDNs4j/v7nZJa9Bvw2DdE81d23MrPViL9nNUHQT4iemf8zsxeJCxLZY1Vw920qqcjM\n/h/w/4rrAdeZWenJ7WBgOeDvVbSx1K7Aje7+45Lll5vZGGBbd9/XzOYQAWg1QdDPgcvd/YTMsueB\nB8zsA+Bgd9/ZzD5F9AhXGgQBFHufvwbMAe5Nz18HlqmiHszsRKDYxgLwSIzUKmtspfW6+6hq2lGH\nDYB9OvmfqVv6Trk2PTCzLYjPxK2BdYhgtlrLEkN/FQBJwygIEunD0pXuHYAtgE8DJ7v7q2b2FeBf\n7v5GjVWvAvykEQFQ8lPiS66QHtd2Uq6F6k5cst4lrrhDXGGd5u7PZV6ve/J9Gl5zo5nNJo73/xA9\nRAeleRYHuvuUbuo4CTgJwMzagM3KDQ2sw6pEoFW0ER2v5M4k5uAcU+M2Fqf9CvYmwBK0nzjfBxxV\nZX1dnSRmFdy9mu+lvYAruzqZc/frzezKVLaWIGhT4NROXnuW9vfzE0SAUY0WOvYGtnTye3em0H5i\nvx/wFFAaHM9M27uoinpL7Ub0zJRzHfDX9PsdRO9hNbah8+D6PuJzBuAB4BdV1PscsHea/3cgcF9m\neOSKdDxO3RmffrYQwdAficA4q3iscwk06lTr90ZF0nDaYbT3BA0ljsUY2o9dte4iehrvrr+FIkFB\nkEgflYYU3EwMA3uX6Gk5H3iVOLmYTg09CckzxHChRrmOOAlrAf5EjLF/qaTMTMBrTWAAjANOMrOh\nwM+IITNF6wKv1FgvAGb2GWJeyQ+JIPF+4HtpO9sRQ8L+Anyl0jrzGN6YeiTOgXlzCHat45h2Ziox\n/+d+IgiflEm+sDhQ7fyaP9HxJLERNqCygOxm4Fc1bmM60fN2V5nXdkmvAyxKTNyvmLsPq7FN5eq6\nnvQ/kQLO0e7+cqPqzygQ741yx2O9zO9zqP59MoMYEleu7q+l1yHOXf5bRb2jiQszP0jt2j7z2g5U\n2WPq7vcC96aewBWBUe7+ejV1VMLMliM+g9amfPKCA2qs+hTgZ2Z2l7t/VEcTOzCz14mLAROJoPxw\nIknCO3VWfQhwrZkViGHP75UWcPdX69yG9DMKgkT6rt8Qk6Y3Ja7qZieC3kkMHanVT4CLzWyiuz9a\nRz0ApJPwpwHSl9RN7l4uI1o9jiTmC5wGPE77cBSAfYirw1Uzs+2BEcQwuI+ICcEXuHv2Cv11ZjaD\nGq/qpgCrsyxM91VZ11yid2kCcVW1nvkdnbmKmKuyDXGSmO292xB4ocr6Lmxwb1jRslQWXL2Wytbi\nXOC0lG3vWtrnqexO9IocncptQfyf9jp33z/H6v8B/DINdys9HicDV6ZyXySGslXjz8CxZrZYmbp/\nQiQ0gOidfK5sDWW4+60ps9iGwNPuns3Gdz9xUagWLcScsGuJYXUNY2afAx4E3gTWItq4FPE58hpV\nZhRMmQizVgJeNrMH6ThvqebEOMTQwlnEhZTXiDl607tcozJzicDnTDofXltLwh3pxxQEifRdOwMj\n3X1CmpeR9SpVZB0q46/EF+pDaax9uS/BNWqs+2HiSvG9pS+kpAFvuHtVJ9HF9NhEr0e5K8Bfp/qr\nzkU3E0HVwcTQqs6ye71IezKJiqSEC5cTV7dLFTNfVfvFPRsYlH7fl+gdbPQV/+OJq+6bEj0oZ2Ze\n+xIxQbkvGEzHLFHlzKbzDF5dcvdfmdmHxNDC3TMvTQUOy0z8/wORvbAq6V4129F5lsaaho/mlf2R\nmHf0KaI38pyS1/5O+7ykyVQ/bPJoYsL+YbQPfWshjutZtA/vvJPofa5Y6hXr8H/i7hXP2Smz7tzU\n85HHudSZRKKPHxDv3x+6+5NmtjURLJ5SZX1bM3/w0EZc9NmgTNl65vAtSfSWDyO+w0YTWRrvJ1Jn\n3+PutcxV/DMxKuJMYBKV/d+LdElBkEjfNYTOkwgMob4vqnvrXL8r5xBzJToEQUSvwnpEr0s1WojA\nb2ciG9h83L2e3pBN3P2J7gq5+7+Iq77V+AMxEXgkjcvC9DxwtJldQxyXHcxs3c4KV3u/j5TVak8i\nM16HEy1337XK9ubtSylNdlc+X88G3P08M7uA6JldnphT8Vo2RXN6f1TFzLYkTuaXIP4fS1M+F6hh\nDl2e2R/TRYjvpJ6VTWg/HhOyvae1TLpP9wD7uZmdRnxOFOt+Njt/0d0fqbZuM1uR+D/cAliauLfP\nc2Z2OPBoHT2VFwKHm9nN7j63xjrK2Qg4gPa/VSuAu48zs1OIHvFNK63M3VdtYNu62s7HxHDGuwDM\nbAgxL+hIIgtfgdrOPb9GJMb4c4OaKqIgSKQP+z/gW8QE41LbUcfQG3cfXuu6FdiE9vv3lLqP+GKv\nSp5XXCsJgOrwFeKL+8puS1buaGJu0o7ECcUJXZSt+n4f3vibeF5K9RPPq3FeBWVK7zdTtRTwvJoe\njXI2EdT+CJjYwJPohmZ/LCcFPPWmeO+s7neJz4qGSPfFuY/oUXmQ+Iwq9gyuRiRA+V6N1S9EpO1+\nycxupXyGv1p684YAH7l7m5lNJwLCoslEb3uflBL6fIlIjDCMCDwXJ4bu3l9jtW/QmGF1IvMoCBLp\nu34NXJkyjBUzLq2dmcOyRyM2YmaLEEMY3qvyRo+dWZSYfFxOGzGUphZ5XXElDTH5Dp0PHaooTXEZ\n71PlZPnuuPstZrY0kSnv38S8lH82chs08CaeOc9P2ar7IvXLa8ga0Uu4h7s/W0/7ymh09scOGjnP\nraTeViJI6azuWm7ieRbRO709MYwqO5TqYeq7T1D2IsRBZV6vqTePuABRHPL8DHCAmd2Unu9PBFs1\nMbN9u3i5jfjM+me6dUC1dd9IXPxZgkhe8QBx/6x7gCe98hvcljoV+F8zG9eg7ykRBUEifZW7/9XM\nliW+QIr35LiC+GIZ6e43dbpyBVIwdRIxWbh41/sngBPc/fY6qn6e6KW4rcxrO1J770IuV1zN7MfE\nzR/fItresBsHpnoPMrNb3b1hww9TXa+b2UnEMKSp3a1TpVE06CaeZlbNMLGq5qKlLF25ymvIWvIC\ncYW80Rqd/XGeMvPcimm8C9Q+z61Y9xeIY70a5dODV92zmXwF+K67zygzv/JN4DM11AnkeoPrG4kh\nYH8mAoCbiZ6UucBi1J4ZFOAS2j87s8c5u6xgZv8A9q0y6FiImEd4D/BEAy9YbQGsDkwxs4dobDIH\n6acUBIn0Ye4+xswuATYnsiRNBx6s94ZxZrYjkU53EnF/n2nEcIu9gJvN7FvufmuN1f8B+H1KuHAR\nkTVpRSL19CG0T5yuVl5XXEcSJwUHNrqHiegVW5+4GeYdlP/irvUEunhPouLwk3WIexv9swFpbxt2\nE09iGFJe8896Ql5D1iDudXOamT1cy1X3LjQ0+2OJPOa5FZ1PBJm7Nrju2XQemA0F+twNON39xMzv\nd5nZZkTv/xDgNncvN0y6Up8jgs3zgWtoz8K3J5EgZn+ip/kPxEW4im8A7O7bd1+qJsOI98YMGp/M\nQfqplkJB7xuRvsjMlsprOIuZPUac2O6S7aFIJ9M3AEPdveJJt2Xq/y1x352MlwAAIABJREFUf4hS\n57h7xV+oPcHM/gvs7O7jcqi7u6EfBXevK62rmR1KBIjFFNCbpCxS1xCZmDq7+WRXdY6nm5MKd++R\noWhdSfdJqvhLzN1Xr2EbHxFD1sr1bFYt3XQ3a11ior5TPkiueihmOi5LEUNPG5r90czep/Hz3Ip1\nfwjs43HPo0bWexORUfGbRJbJ2cBG7v6Umd0MzHD3b9dR/xBgONFzsxRxseo+4FJ3n9HFqr0ivQdv\ndPezy7x2BPAtd9/KzP6XGFZZVSbS9D2yPSXHo44LayK5UE+QSN81zcxuIYae3OjujUwJuh5wYukQ\nLXcvmNn5RKrbmrn7SDM7F9iWOMF7B7jb3UtvoNoXPERkD2t4EJTjUBkAzOwgYsjdWGIi/D8yLz9I\nzHOqOgjyBt7Es1Zp2NIsUlDXSbFLmX8Izw+Jk92biGFOywE7EenTS++TUqlGD1kbwPyBW3a4YekQ\nsHJDwiqRZ/bHhs9zy3gjp3pPICbkP070fBSAvc3sV0Qv+2a1VmxmKxD361qTuEH0NGLY1neBkWY2\nzN2r3q/U87Oyu/+1zGvfBl6to5dvU2KIXTnP0t6j/gTxP1QxM1ucGLr3FeL/923iAs3P0z2Jdqx3\nJINIoygIEum7fgHsTdyT5X0z+ztweYPmQcyg85tHLkP7ndlr5nFDwkZkFwPm3WOou23WMiH7UODv\nZvYWMcwkrxO8PBwBnOHux5SZ6zCZGLbUzLoMAtx9VPF3MzuROAndPjuHIV2lv5WOc3kq1dAhaz0R\nYOac/TGXeW7JKcDPzOyuBgzpnCf1jG5JJEA4iXhfjSQuFAxz94pvvFrGmUSP2/9kU3eb2abERYkz\niZs5V+tXdH5hZl3ic6vW3tjpwC6kNNYldqE9C9ui/H/2zjzcrvH6458bBFUqxqCI2Wrpr2quEkJb\nQ4sYamxFUEJrHpoEN8QcghpjjhhrbISKKdQYghqXSEjMYqypxJD7+2O9J2ffc/e59+x37/dmX3k/\nz3Ofe+/Z575n52Sfc9Z611rfb/aE91TMKHcX4AanbtcN2BHbrDkF84HKRAdiDoC3aEZkNiYmQZFI\nSVHV4cBw58exO/ahspeIvIG5sl+lqs97Lj8WC+xeSvpjiMgvsB7wPMIIiMh8WHtIxZPjAFWd5HYw\nn/Uctr+fjne3fVrLKq721wDY092KFlX1fq+seS4WBvYv4LmosCzpgQxYIvsj34VFZG6spWVl0lW6\njvddOxD7YG1arYa43TD86cCF+M2MHYnNS0wSkcJa1rLSYGUs7e/mxV6D09R8ePIScs5tE0zQYYqr\nGhQ2/K6qE4A+zlNqIUwNs4hWtd8Ch2mNd5GqjheRwZjKpw8/o361ZjyWxPlyLvb+vwRwC9WZoO0w\ntcm/ufttSHYrhr7AQFW9vnKDU4S73gn9DMYjCcLmNtNIfibEJCiSiZgERSIlx/lxDAYGi8ivsOrQ\nPsDh+L+Gj8BaZh4VkalYC8fimCrTq+64F049ahwmhvAiNsRakcXeDJMa9glkNky5bSHMgPXX+H2w\ngnmqBGkdCvhcVJiGJSlpO8ar4elpIyI/xtqHFsaC3g+w53oOLDD9BHveysRC1G9bWwCbTfChiTAt\na77n0hBO/GQI5tcCsA7wlIiMwGbFrvM8h+bEz212DMinlrcxVrH7gkDD76r6FVCkmuJ81De1noYJ\nGfjQnaqXUS1zA/N6rouqnurmrwZhiU+Ft7ENqwvc7yOArHLUC2ItpGm8jP/rME3tsPL+3w/7XIxE\nMhGToEiki+B2ghfAPmTa7MxnQVXfEpGfYypAG2IfTE8BZwGX52xFORP74FwJS66Ss0z3Y+0oPuf8\ncJ1Dt4nIaZiyUeYKVrKlKgBBnosENwLNTujiGXdbi4gsjyXJvjujp2MBS19Mkn0LTHb5T9g5981z\n0oF4EDhJRF5Q1cpzgbvOT8LTfLMM81FZEZG+2LUxFrsOhicOv4697r2SoJBzbqq6XFFriUh7JsJp\nj+2b1D+HKaqlDf3/2R33XXcnTKimlp0x9TxvVPV8EbkQ8wNbApvHejPp46OqWeTtK0zCzjtNvW5H\nqpX3TKjqWyk3vwU85z4bB7r1I5GGiUlQJFJyRGR9bJfrD9i8znPYDu81edZV1c+Bc9xXkfwa2FNV\n30yZU3kbq4oUzVhsdmqfAGvnIfRzcSw22P04Ng8DFtwugw2Bn+i57gaYN9VX7vdurpXqMtfSchad\nZFSagf2Be7Fqx6tUW3yWxwJ/30phV6QZuFhV93PXXTIJeg57rr7vZDHpbcG/snk88E8ReR64nmpV\nfQesGruN57rDgetce26t1cDOwK6e687EJTyv41kxrsNw4FLXanct1efjD5hPXP8CH6vCY1Rb+CKR\nholJUCRSUkRkKPZB1wv7ALwcmwMq2l2+aOagGjzX0oNifUUqrJVnXRFZHQsu6s2/9PFcOuhzoapf\nOMGIXYHfAK9hQ82nAKNyzID0AD52Q82fYMl3hSeAY3KcdhBU9RURWRkLftfFdrcnYsPwI7OoK7oh\n7NtV9cMuOpC9KlYBSuMTrM2xYURkGeAdVf3G/dwuquodVIvInFhivzTpr8WGVP6KrCp18DhjRGRr\nLBkaQtUw9mlMev8Oz3X/4ZTnhmJJD27tL4BDkjM3jdBZ17SqXu7mroZg7b4V3sNa7Ub6rNsBm9N2\nfiwS6ZCYBEUi5eWvmJzrVcD9eZWY3O54X1V9phF/FR9PFccErHKV9uG/PbZrl5k67S3dMeO/32HK\nQz7r/hKbqXkAm9MZi/Xxb4AlFV5tVI4gz0USl+hcSbFDwa9hZolgicSOQMUnZyvgvwU+VmG4RGeE\n+8rDFZhs8ofUH8iu0EL5BrI/xpLANFbEduezMBV7Ph53P9d776gkAF7eVyKyJqao9mPS559a8Jc6\nD4aq3o6ZTM+HtSv/twh1O1U9S0Quw5LChbHZvEc9JaavoJOuaVW9wM2erYrbUAFeSrbaZcU9D7VU\n3v//D/85tMhsTEyCIpHysriqFlk1GYl5NlR+DuUjMhQYKyI/BP7hHmcTERmA9Ypv7LnukJTbpmOt\nHKdQX0mpI07CEqhDMBPFo52k7spYQpTHJLPw56IzDEKBu4FNsda6SlvOr4BvscDGq83OBTJDVXVK\nyrFlMe+q/u68vxOR5Sh2iL0hknMvob2eAjEGOMaZYlYSnhYRWRC7zrOakW6CCXsA9CHce8cILMH+\nMzY7UqQ3GiKyEJYEplWY8mx2VNb4QkR+ULC896fkVOt063TqNe0Snhc7vGPjpF13X2Hv//vQcWIX\nibShqaUl1HtZJBIpAhH5KVV55UtV9V0XHL7v5npKh2sPGY7NY1R4Dfirqo6ZNWeVjoh8iLWb3IMF\n+etXZMNFZA/gcFVdPcf6hT4XIjKEbElQZvEFJ489twvAEJHfY0nbD7Ck8GKfyqSIzADWS8qyJ46t\nCTyuqr5VhLkxFcU/YDNRtcpaXlLnyVawlGNzAkvmaf/KcB5zYEn6Wh1JZIvIYpgHTk/MDHgzrKIp\n2K78L1W1dO1DIvI5sIOq5tl4SFt3XqydeAfqKOz5Xndu/T6YYMjawFzY/9N4YIiqjsuwzkbAU6r6\neUBftMJppL0uSQnbRyOzKbESFImUFBfUjcLapiptJv/CdnbPwHZKvYZBs+zI+6Cqo4HRIrIiNpz+\nkaq+5LteYJqA71S1RUTexwLoSpD+NrBCnsWLfi4Cq9lVHmM6iXklVb0NuK2g5eslT8ti8w6+nInt\nCI/Gqm5FVVGnUBWfqOX/3O2+iVuQypiqvueSyoOxWbFXMEnlEcBwzWEInGyrTTm2GjA6Ryvtc5js\ncdEMwSS3e2MqgtthFaf+2P/tznX/sgNEZFfsffoFrEI6DUs+twfuEZHdMsiR30+17fB+7LVSTxbd\nu+0wiUuY0ypjWRL7KzLct4zto5HZlJgERSLl5WRsB3dnTPXqg8Sxf2FqV76KOP0w88g2wRc2BL8H\nBaj4qOpkYHLedSqIyA+wc98I6zX/CNvhHpnD+HAilujch83wHOyMGr8FDsNmIHJT9HPRGYhID0xk\nYGHgDlX9WETmSquKtLPGHtj1BBYAXSAin9bcbR7MHPKhHKe7HTBYVU/LsUYa7XnzdMd8bXzpR4bX\noaq+1ujCrop3PMX7OfWivn/NvNgmgi+HAheJyERVfTLHOrX0BU6gOoP3hlv/fhG5ADgIk3/3YQhw\ni6ruUHP78SJyM/b8N5oELYdJVVd+DoJri/w7NuvXvc7dGk6wQrXXddXKWKTrEJOgSKS87AwMUtUb\nUuSVp2DBSB4K25HPqDzUgiUvT6tqw/MeTinpfqyn/xWsIrY81qZ1sIhsrKrv1F+hLldjqnBg0sL3\nAG+6378joxRtJ/qTBENEmoBTMXGOubH/s7WxVqoxIvKQqmYZRG5KfG+ibWLxKXAxpuTmS3egkMDZ\ntcD1Sty0hlO8SjIPlqC8ST5CVcZC0uacRaQ7tmmTp81uPGYu/LhTJawV4GhRVZ/K7I+BKa6a9hVV\nw2IwIQZf41gwFbsD6xy7CLil0YWSSW6WhNeDCzAxmfOxClYIxc4Oce8zl2Jtg2mVp/vp5MpYZPYi\nJkGRSHlZiPrO23NgvecNE3hH/goaVx6q8I2I7K+qlzZ4/2FY8PJLVZ2pqiYi62KBzDBg90ZPuIKq\nnpv4+Uknl70Ftqt9j6pmHe6t9SdZEPgRVln6ANvhnxOTKv6Y4nfqi2Aw5hPUjIkkJJOL0diueUNJ\nkJPEHQkgIuOAAYFaI2/EpHLvLWCtPbF/e4v7Oj/lPk1YkvzXLAt3RmVMRLph4gI7ki413aKqy2ZY\nrxnzpKqc82POvyYNL5VGx5nYdfcE8BLFCSO8R7XN7jWsBe5+9/uKOdd+EXuO01gG0Jzrh+A3wGGq\nmuf/qgi6Ya+Fc0n3KuqUylhk9iUmQZFIeZmE9bCnBXUb4ecYHmRHPqvykIgsAByN+c00mgT9Fvvg\nbiUrrarjRWQwcHrjZ1wfVX0Tew58/37mh7Vr4bgK2BtrmZnhAtTtsLku3xac0OyN7c6ellKFnIzn\nnJSqhjRYHQ2c7ZT47iSlIpGhXeYKLEhuwtokD6Ct0tV04GVV/cjjXENXxk7H5oHGA4+SP5m4331v\nwpKhS2lbAZuOBfx5Zsf2wK67ojcGHsQ2af6Jze80i0gvbGNiD+za8eVQYKSITANuc7OFTcDWwECq\nCW8mQgl9OL6hoDbfAqjbbtqJlbHIbEpMgiKR8nIBcIaIfAzc4G6bX0R2w9ov/pJlsU7ckW/kXD4V\nkaswk9NGmQ/b0U1jGqZc5o2IbIoFSktiw+ePZFF2qsNw4ERVvalyg5OOvVFEFgHOIttz0Fn0xOaj\n0viOHM+1m0fYHAvs0ioUvn4flUB2X/eVbNnK5F/jAq7X3Plugs0l+HizpK3dGa/D3YBjVNVLyrwW\nVX0A89FCRFowdcAQ0uVfY2p2RTOEqu/VMGzGraJ2OJqM1bwarsCqvbdg1e0P3fpzAZ8BVySqZlna\n+UIJfYAp5e0M3FXgmpFIlyMmQZFISVHV850a1DCqVY77sEHsMzSH83bgHflKr/cWtBYweFAT7umq\n+izm/dAozwH7YaIQtfzZHfc510UwU9oNad2yNoeIPIhJ9n7QzhLt8VNcMJ3CVHe8jLwO/AK73mpZ\nC5vJyoyI9AZuxdoDW2grKtCCv+lhkGvaJQBBCPw6HB9i0YrkunuNr4IF/P8pyBvnSpyqWgFrzURV\nX8Fds07U4zD3VQQPEMY3qVChj5pZzcnAziLyL+pXTWe5gpuT1M9iBxBngiKZiElQJFJiVPUIETkP\nGzheDEsm7nYf6rlw7RZbYKIAaRKpXi0prtXtdmADbGf3fWBR4EinuraV56768cA/ReR54HpMGGFx\nzPtjNWAbn/PF+tF/hs1P1LasXQycA+ziufYb2HxJmu/JXu54GRkFHC0irwCVxLVFRDbAgseTPdc9\nE5N23xt4UVW/y32mjlDJSuC2pFCVsSux67fQZKKCiOyPtcUtRlU04ykRuQkYl5yzy8hEYLCIjMFm\n0YIF5+5574Vdh97tgqrar4jzSaEwoQ/HFSm3LYu1GddSFhnr/lSToHmwFupKZ0RFinwHrBLnu3kS\nmY2JSVAkUnJUdSpwSZFrisiPsT75hbE2sw+wweE5sA+ZT/Af2D8V80/ZBbghkVTsiA1Nn4LNWGRC\nVcc449HjsfaWSovT08DWySpTRrbCZo3qtawN81wX7IN5pIishFWb3sMCx+2x56isM0EnY4nhTUBF\nevwB7Fq5AUtmfFgF2F5Vvap2s4hgbUkBK2MDgREumbiLtipr3smEiOyLyStf5Na+OXH4YSxZ9E2C\nRrjvywBbphz3Cs5F5GhgPlUd6H7vjc0uzQe8KSKbqWo9EZqsj7Woqr5fwFJFCn1AFxQWUNUrKj+L\nyN+B8XWkyG/CNsIikUzEJCgSKRFOmrdhMhraJTkd25HvC3yOVYSexYLy49ztvvQFBqrq9YnznAFc\nLyKLYrvqmZMgt87twO0iMh+2+/ffAtpwppOuTATWsuYd9KrqKGfA2uy+5sRa7p4AtlDVUvbkq+q3\nwI5O2OE3VKuQY3POSU0CFijgFFMRkf2wlsmVSfGy8WyXCeU/BOEqY2thScTiFJhMOA4FTlPVQSmi\nGROxRNeXUIH6H2ktMjEM2zw5BdtQORFL3rwQkT7Y++bawFwi8g3Wjjgkx+ulSKEPb2GBBmSsO4ud\nqb9pdDFWvT6o804n8n0gJkGRSLmYSmM90JkGvVPYABNW+Mr93s0Fvpe5ROUs/GcsFqS+tPfL2IxQ\nLlziU5SHyrVY20W9lrVr8yyuqncCd7pq2KLA+y4pLD0uyCrSgHAgcLKIPKqqhbYCikh/rAp5KmaM\neSJWWdnNfT/Fc+mi25KShKqMXYhVd/tTrNQ0WAtVvTa7/2FVLS+yBOoZg/OlcDNBItITSxJ7q+qD\nzv8pTQK90fPYFQvAX8CuuUqb1vbAPSKym6r6+BAVJvSRk45krFshIk2q2ugczwwseWxEZGNe2pci\nb9PSHYl0REyCIpFyEVSwIEEP4GPXqvYJJgRQ4QlMutqXSZjyUlqVY0csEfLCtZVtT/35ib08ln0e\n2FZEnsRae5Ita4sAJySHin3biFziM83nbzsbEXkUa026XlW/LHDpI7HndpKIKG13t1tUdVPPtQ/E\nWiXPwpKgW1T1KRE5AbsWM1VZExTdlpQkVGVsZSy5ShMRycs0t36aaMZqNBAoF0SW4PxrLIgG2Bjb\nQHnU/f4RORI3rJJ0S502rZuxa9InCeqsz4JGqCtjncLrInIpcGlHGx0uWTquwXX/hW2gpEmRn0R1\ndjESaZiYBEUiJSLPcLcL1G9T1UYc21+jKhk7EUtOKpWQrUiZIcjAcOBSEVkCq6JUBAz+4Nbu77Oo\niOwAXON+fZe2bWq+Ck0XuO9LAWukHL+w5jHaTYJE5FjgElV92/3cLgE8UYrgv9gc2pkicjVwkVPz\ny0sTdr0lf6897suKwARV/U5EvsUFvar6jYichQlcDPFYt9C2pBpCVcaep2oOWjQ3Yj47jwPPuNta\nRGR54HA6d6C+0evlaWB/EXkDk8O+x1W+wcQR3s1xDktjCXgaF2HS2ZkJqUoYmDGYR9VgEbkL20wZ\nU0D1+y/Y/NytwNc1UuSPkdEyIhKBmARFIt8LXG/+5VhPeiNJ0N3AptgO5XDgOhH5FTavsirW1uGF\nql7uWkyGYDvoFd4DDsgh7X08tqP/xwYTvUYpeg5hT8yU8W33c3u04C9AEQxV3UJElsYEAfYEBojI\nBCyguU5V/9fuAvXX3bi4s2zDZ9igO9hzvwo2qA/WNuSbFIRsSwpVGTsIE0aYqKr1/J58ORZYH3ic\nqlT6dVilbQI53jsCMgirJDyDGdEmA+a+2L/Flxdpv01Lc6zd5VDVASJyKCaMszeWtLwtIpdhm0Ne\nlUJVfQ/4pYj8FvNz6wm8AzyqqncXc/aR2Y2YBEUi3x+y7KIfhRscV9V/iMiXVM0Dz8YGTb1R1QtE\nZASWUPXAgruXcu4GLgscWHACVLgTuaoul/ZzV8NVJo4VkSFYBW9vrCo2XESuAUao6jPtLNHZPA38\nBGuLGQsc567rb7HA/CnPdUO2JYWqjF2Dve7Gi8inpCdXjZp2tkJVv3CCGbtiohmvYS1lpwCjEhWW\n0qCq453ozCrAZFX9JHH4YurPMDbCoZgCZFqb1kCsZS8zoaXZQ+JaaC/DZkxXx947DgAGJapDozPM\nDiXXHou9viOR3JTyBRSJRMKiqtNJtJOp6m2YZGwuRKQ7Zlp6kBMEeDHvmgmepf6Oa25c4LIE6Z5J\nr4Z63LLjEtfbnOLVwsAvsQrRviJyH7CPk3FvCBH5ORbcbYhVZ9ZzsztDMUNdX8W8M4Hl3c/NmNnr\n1e731/BslwlslrpxoKVDGXgCM9UDrySl9U1E5vf0AQuKqn5OisCFU5yciauqfw2sraqNJM5XYGIw\ntwDf1LRpfQZcISKV+2ZJPoNJs3cyU7Bq2FtYYv4z7LmaKCK7q2rDoiPuPXpL7L1jYWCoqr7u/Mte\nVdV3Cj/7yPeamARFIpHCUNWvnbdOiN3gA4HLReRFVR1f1KLufM/F2mLqvSd6tTs5xbKlVbXN8K+I\nNANTc7QHBscpae3lvpbFvKV2wdr9Nsd2/6/G1AYbWW9DrBVzElatODhxuAnYn3RBjQ5JtsSo6rsi\nsg6wAlbdVFX9xmfdrkhAA09E5DxVTZW4d0bJY7F2ua5MlgpcqIQzpDR7cERkbeDPWIfBHJi/2D6u\nKvdzTJHvUuDnDa7XAzPhXg+rbC6IzXO+DgzAqpH1ZrMikVRiEhSJzIaIyBQ6+OBW1eXbO94Oo4Ft\nKcCtPuU8FwIeKbjF5zLgV9hs1MsUKyd8MFUDyFreAw4BSpcEicgWWACzFaakdSVwoaom5xtuFZH/\nka2CeCpwh6puJyJz0joJepICzWNdq83kyu8ispCqfpR1HRGZQcevFW+p4oCVscr687p1pxXUqra7\niLyvqkNqHmcBLMFdtIDH6DIETDiDSbMHlLFGRAZgFaz/wzY7moErkm3MqvofERlEts+IM4AfA+ti\nra/J9+m7sfm6SCQTMQmKRGZP7qVtYLcQlgx8ieduvOOfwDlu5240przU6rEyqGkFbetx9MYEG64K\nsPYK1G8JfMkdLyO3Y0Pu+wHXtiOTPZmqYl8jrIElyND2//VDTCSgUFw163BM1GB+jyWOJf218ltM\niOGSHOcWrDImIlth4iQVxcN1gKfcrN44T+8asP+/O1widJ57rAWwgHZRTILaCzdr9JRrXas99kPg\nF4n3jkzBeRckpDR7KBlrsJnSW4HDVTVNRr3CJGBohnW3Bg5W1SdSTHpfx18CPzIbE5OgSGQ2RFX3\nTrtdRObHBssfyrH8ze77Lu7LW00rzy6rG4R+u4Hd7/ewVooQTMfkwdPoCXwX6HHzsnYjvfpuVqoj\nBbwkX1DfF2dpLBHKhGt764e1600GzlbVV53pbzMmyd4dP68WVPWEOo97BHat55nVCFIZE5G+WBA9\nFksAhycOv479n/k+H+OcQej1IvIR9n5xDzaj0dtX/csxjqryXC2ruONzuPPIGpwHwametUfD/mUu\nCawQUpo9iIy1u4b7YPM57SanqvoW2f7/foC9V9c7FnqzLPI9pNusPoFIJFIILVjVJNdAshtoPgM4\nOscym9R89Ul8VX4PitspnIIN4XbEScDhTta7aB4BDhORuWrOby6sFe7h1L+axWQZVs7IvcCRIjJf\n4rYW93wcQEbVJxHZFnsO9wXWxGYDHhOR3phXzgBsV3o1Vd29gPOfiQsYLyLfHMIaVNsli6yMNQMX\nq+qWmD9SkucwU1NvVPUWrEp1BWY6uhCwcc4ECNqfxZmXcm4abIS1Mia/tsES823c741yP5bojcMq\n6sth1/YtidvHJe7nhaoOwERg9sXECm7FqkPHu80jX77DTHR/kmONejwP/L7Osc2xFrlIJBOxEhSJ\nfA9wAVmRUr5L5DiXspj8NTTc7HyNVgamisgjtDWKbXgnN4VmTEzgJREZhbXuLAXshj3HWQKkTseJ\nRuyP+U8BjAfOyyFTPtCtMRGrILQARwCrY61UO2Vc70jM+2VrZ077Q6w97U4sidhAVR/zPNdGWJD6\nla1GKLwy5lgVqwCl8QlWtWmYOoHxXdhzvSM2xD/zflmSIRH5Ga2H47cUkVVr7jYPsDNQOpVGVV0x\n7XYRWRcb/M9SKQ0px96KEDLWTh58Cva6KJrTgWvdjN4/3G0rJ+YXtw/wmJHvOTEJikRKSkifiDpB\nTXdsB+9U/D1Vko+xEjaHsCQW/I9X1cnt/1Xn4xTcjgK+waoJtcII3m0WqvqkiPQBhmHVtW7YLMPD\nwK4NSvB2Cq61abPKOYlIL+w8F8MEI8B2XPcSkfVV9d2sj+Ha1NbEZlW2xHaO+2AVoGM6mk9IYXVg\nj0rrjap+LiJHYa+ZvxWRANW0KFWovFYGkq+aV6mM3QF85W7zrowl+Jj6GxkrYnN6WZhK/ddBE1aF\nTpJFKKIvtlmAe4xj69zvI2zgvkvgVNDOAP5Og2p5s3ADqTAZa6yTYKCI3Fukp5vzs1sU8/yqyN1f\nA3yOzQqNKeqxIrMPMQmKRMpLSJ+IqaQHNU3YB+L+vgs7NaoLMTPFZDD0nYhcDQxoZ9B+VnAcVfnW\nT4te3AXiG7rnpQfwccn+/RUWpPVnwilY0rZWxRRVRH4B/At7zvb1eRBXJeif71RnMh/wZs1tld9f\nKugx7qfta6VSZXwIz+fBUXRlrMIY4Bjn41RJeFpEZEGsDfOfGdfrT7iZi7OwtromrNKzHW1bm6Zj\n6nZe5+Dmdoaq6pSUY8sCzaraH0BVvxOR5ShGcOFNGmvJbYOIrAcso6r/SDm2I/B6XquAomWsHetj\n846vicjDtBXG8a6sq+p5InKFe4zFsMT44TL6UkW6BjEJikTKS0gIXur3AAAgAElEQVSfiLSg5its\naPpxVc3Te38m9qH6Nyx5m4aJA+yEqQF9ic1qlIUFgYtCJEBuR7+7qn7hEp8vE8fmA74usX/Nb7Dq\nzDOVG5x088nAobPutNpQLzAuan4krUXpKywIzWXOGKAyVmEwVqF6CZtLa8HEEQSrEg3JeJ5XwMzB\n93WAKUUZU6rqJ1iLHpXkI8Broh+2MdMmCQIWAfYgkZir6mt5H9CpYx6EbTj5cCo2X5PGqthGlVf7\nXEAZazB1wK+xVs5V3VeSXMm0qn7hcU6RSCoxCYpEykswn4hKUBOIPwBHq+oZidveAE4XkW5YclSm\nJOgebEA9hBTtpVg1JW0o/wLMVLaoqkjRzI/N29TyH+or3nWIiGyOzZEsjc16JGlR1d4Zl7xVRNKq\npLeJSLK10ctHKnSLUsGVscqa77nk6mAsmX0FExYYAQx3iYcP32GVsS2BQpKgGlYHfgecV3tARA7A\nzIVv91y7XvC9LDab5UUdz7XuWKWiCf9q3s8w0ZY0xtNaSTAroWSsUdXlcpxXu7gkfH3S3ztQ1Y6U\n+iKRVsQkKBIpLyF9IkIyB+nBM1ibS2epUja643gacLmIgHm2pEnR+qpe9cHaj9IYTWvp4jKQHEr/\nCFP9qqUH1oefGRH5GxbYvYPJWedt8axnNFt44iIiP8WELHpgz82DqlrPA2qW4yqbx7uvotYMOfgO\nNg90Q51jc2NzdQ0lQSKyB1bhAXsvuMCZLCeZB0s28lgCpHmZVarqN6rqJM91u9N2DrTC3FhSm5nA\nMtbBcEn9zZhhaproTQsm9BCJNExMgiKR8lKoT4SbD2gYVfWVsh6DDTvfnXJsOxoMYgqgIXU4qoPt\np1E/ccoy6J1kEeCDOseCmIPmpHYo/TfYdZhkXayy4MNfMMnmg33nO5KoahblLS9cS+OVWIUzeU21\niMh1mDBDR15U7a1fdGUsNEEG3x2rUl/q+BnqiybUoynxvYm27wmfAhdjr30v8niZdcBzWBWp9vUH\nppT3gue6FRnrLQloNOtmilYmvWJzpceSIzDlzj9jQi21AjaRSGZiEhSJlJfKh9++7svbdNSRu8+9\nQW4A/i4iPYGbMIO7xYAdMPW1vyYVtxpN5ERkX+DqNDf5WtxMU6MVp5BD3+8Aa5Hu6bEWNi9VFtLa\nWNIqNXNgrXw+zEdG2d0QOB+przFT2I4U+o7HkvdBwFXYoHdPrMXxeGzmY7DneRRdGausm9aiVWEG\nNoPzH+AcVf1PxuWDDb476lWZFiTD+52qjsRVCkVkHCbIUpRYRrs4AYpewIuq6husDweucxXqSzDl\ntqWAvbAkaFefRUNX89ws1L+w2bEWqoln8hrxSYJWBXZQVV/FxEikDTEJikTKS6GeEZ2xa+642X3/\nMbBtO8ezJnLnAsPc7vtFqjoh11k6As9H3QwcLSL/UdWZlTER2QwLnC8N+NiZaHQYXFWPqL3NJbVP\nuqHl9rgZu67L0OLZaKVwN+A4VT01cdtbwKkumdoXzySIgitjCUZi/jTdscprZSNiKyzRehDoDYwX\nkc1VNYvx5saEG3yfgPnV3JhybB88ZyRVNZj/jogcDcynqgPd772B23DKhSKymU9LnJOEXhKbydnZ\n3dyEzS8doqrX5zjtkNW8U7DzXBbbeFsXeB/bbNoJqy778Bzp7bmRiDcxCYpESkqJTEezEirgWAbb\nBe2PedU8i7VIXF2ERKrbwVyX6szHeFWtNU7NSjPwK+BOEXmT6m7uj4EnyN7eUzpcIjAOM1TtqKpy\nEHCNiJyDtUu2eX6ztHh2EosDj9c59jj5/g9DVca+xlqGtkpWIpz32O1YAvMLLFg/ngymvSEH3zEP\nmLEi8iC2QVAxF+4PrAds4buwq85sjr2PpLUdZhIASPBHWrfTDcNa+k7BVPhOxFopM6OqZzl5719i\nCcAHwKMFvN+FrOb9GhO/ecv9PkNVpwLHuveKvwPbeKx7KHCRiEzM6FsUidQlJkGRSMkJNZDtAqIt\nqN+37TVQnSV5E5EmETkWq+y0a+DoJHlPEJETsd3EP2MqR8NE5Hq3Rr1gtaPzOBlTW0oOIk8XkeGq\n6rvLXzHv/BXwJ2AzYGFsBulu4KoSy2NnpdGqSk8sCP0dZgaat8WzM3gdu97SZHk3c8d9CVUZGwDs\nV9uKparTReRs4EJVHSoilwKjCn5sb1T1XhH5Ayaznxxyfw3YUVW9pJFddeZW4EfYNTaj5i4tZFRB\nS7AUbkbOtQCvBfRW1QdFZB7Ma8cbJ3BxZ541UtiYcNW8nsA7qjpDRL7APrcq3EfV6DQr47HNlsdF\n5BPabqB4qT9GZm9iEhSJlJSQA9ki8mOsJWZhbDf6A2yncQ5MgOETClSVaoduWLVkDA262Ltd87HY\njvGSmGt4f2BPEXkOGKaqVzd6AiJyOHAUVlWqnfk4SkQ+VFVvFTeX6FxKiVrfZiEjsWvuAMzDpisM\nN18MnOIESq7Fro/Fsdfl/tiuty+hKmMLYxLnacxPNTD9yGNtIMjge+VvbwZuFpFVsH/HB6r6su96\njjOxytje2JxOUR5SYNdwRaltY6xd7VH3+0dY4uWFiCyFbc5siL0/91XVF0Tkr1il2mvTJ3A17y2q\ngi+v0HoDYW1MOc+HM7EE6gm6zntHpOTEJCgSKS/BBrKB07GgoC8md7wF8CxWsTjO3d5ZNFpFmImI\nLI0FNP2BJbGk6CaswjBSRNZS1XrS1LXsC5xRM+vyCvCwiHwO7Ef5pKy7KmsAu6nqLbP6RBpFVU8T\nkYWxhGXfxKFvgNNV9fQcy4eqjD0CnCQiL6jqc5UbRWR1rD2rIgm9Iubh1TABB99boaoT866RYBVg\n++RzUSBPA/uLyBvAX4F7EptTvWhwc6cWEVkN+Dd2nT2MJRCVSvVyWIvcLv6nHYz7gI2w9+MRwHku\nYf4G+K27zYc9gCG+HQqRSBoxCYpEykvIgewNsF21yq5cN/fBfZmILAqcRbjZHi+c0ervsTa432AV\nq8uAEapacYK/RET+ApxAfX+eWpbBkqg0xmKBjTcish+WSK1Miu+Hqpat/Sskr+CR9M5qVPUoETkV\nm0vpgV17j6mqdyXFEaoytj+2+/4fEXmVqjDC8sCbVFuSFiR7u1aowXdca2x7+M7uTAIW8Pi7RhiE\nJYXPYJLbyXavvtSfJ+uI4ZgYwBbYdZG8Nh4lh6x3hUDVvME4AQNVvcD5Eu0E/AA7Z98k5mssuY9E\nCiMmQZFIeQk5kN0D+Nj1bX+C+dlUeAI4JsfaoXgD2zl/GKtY3VhnpuYJsgU807Bd1rR5g1wy1iLS\nHzjVfZ2A7cLPwBLcGVhAOTsxCGgWkfHOiLHL4BKeOwpeNkhlTFVfFpGVMIW4dYAlgIlYBfgKVZ3u\n7neGx/KhBt8h/X2nsknwHf6zOwOBk0XkUVXNVPnqCFUdLyLLYNWmyar6SeLwxVgC5sMGwE6q+j/3\nvCaZhr0XehGymqeqH5DwRlPVczAFxLxcCWxP+vt0JOJFTIIikfISciD7NayNDCw42pHq8O1WpMwm\nlICbsYHudk0CVXU8jXsEgc0UNYvId7Sd+WjGetF9ORDb+TwLS4JuUdWnROQE4C6sCjU7cSh23U0W\nkZdoawDcoqqb+izsVLSGJqqCyWPLAs2q2h/MR0pElqMBs0jn5bOkqh6Ycuxs4C1V9d2VD1YZc4nO\nhe6rSEINvqOqc9XeJiLzY8aeg7Ag2IcjsUrYJBFRCrzuwARQSJHvVtVWxtAZ/am+oX4r5GJAHoW4\nYNW8gEwEBovIGGx+Ls08PHcbZmT2IiZBkUh5CTmQfTewKXAdVVO+XwHfYkpBJ+ZYOwiqmqstrR2O\nxXrsTwFOTtzeBPwDS4R8WRGY4ILub3ED1Kr6jYiche2QDsmxfhlowQKpRow+m7BgJvl77XFf+mEB\nf5skCKt07oEFeUDjvkhYNaVekvM0JqrhmwR1xcpYqMH3VJwc9PWuTfd8/AL0kNedz7k0wkPAASIy\nOnFbpVKzJ5DHQqHQap7bgGiYymZERiqzRMtgSXEtLRQwixaZvYhJUCRSUgIPZB+Fm09xpnxfUu3b\nPhtLwGY5zoSzYXyUtJyM8E4iMhQb6K3MfPxbVZ/Pul4Nn2Hqe2BVh1Wwdj6wXd5Smv+5OZK+qvpM\nyrHVMG+b5QFUdQaWRHaIqm5c5HmmUE/ad1lMtcuHZXASyClMJV81L2RlLNQsWqjB945QPNtHO+G6\nC8GxmILnBOy5bgF2c7Np62Pzab4UXc0LqTbXmY8Rmc2ISVAkUmJCDWS7Vpnpid9vw4wTcyEi3YHD\ngdvTAugUZmDD4R/UOX4/rXvW2yOXx4xLePImPbU8DfwEmyUZCxznEs5vsWpbRy0xs4pepATOjnnx\nDPzd4PslqtqmDU1ElgD2yaL+JCJ7YBUesP//C0Tk05q7zQP8jKoiWlY+w5KoNHoBX3quC4EqFIFn\n0UINvtfFVSd2B94peu2y4tpme2PP6XHYtXAwtomycUdtwR1QaDVPVYOL6GSo3CIiTZglwRBVzdM2\nHvmeE5OgSKTkhBjIFpHTgVENJioNo6pfi8jRNBhwOs+fPdu5S/DdPxcwLq2qx6UcawamqupIz+XP\nxBS5wNrqfgFUPIxeI8f8RCfQpqriktzNSOnHb5BmbPYsbRZnSXc8axDdlPjeRNvE4VOssunbsnYf\nbhZBVT+s3CgiC2HtbN5GpwErFMFm0QIOviMi96Xc3B1YCWtp9G6JddWqwVQ9d9Zzz8lQzID6Lt+1\nQ6GqE4A+znR1IeC/qvq/ApaeVdW8zqIbtjlyLvlmZyPfc2ISFImUCNf+9ZSqft5IK5inkSLY/MQh\nbkh4FHBNgapJz2JzRb7nNpMsu385OJj6H/rvYVLbXkmQqt6d+PldEVkHWAHbNdc66nazBJfwVRQH\nW4DHRKTe3S/yfJj2KhuL0thc0UxccjoSQETGAQNU9SXPc6vHYEyNcbKbz3gLWArYGqvoDfJduOjK\nWIKuOovWjbbJ92fArcD1qpqWJHWIiGyIzUFOwoRQDk4cbsJmLEuVBCWFPlT1KxIbB7VCHx4Er+aF\nMtPNQJeT4o90PjEJikTKxf1Y69vjdNwKlqf9qyc2XLo7tvt+oog8hCVEN9bIvGblcOBKEZmSTALy\n4tTb1k9zSReRNYHHPeccVgBerHPsJXe8EFzla3LaMeeDNBn4fc5WF1/ud9+bsGToUsxTJsl0bDaj\n4dZJEdmG1kPWx4jI+zV3mwfoTY72wFAtOar6ioishQWGv8F8fT4ARmOB6NQcy4eojEHgWTQR2RxT\nlFyatkFui6r29lk3YGXsVOAOVd3OBfzJJOhJTHK/bPQjg9BHFgJX8zrFTDcSKYKYBEUi5WI5qn3v\nwVrBnDHqaGC0k6DdAUuIRgDniMjtqrqj5/KjsCDrThGZjnlaJD8AW1TVJ7Fob2dvTuoPxXfEdEx1\nL42emD9JZ9BE+7M4QVHVB3CKUyLSglUoilAs60Vr4901aVvxmY4l/t5VFQARWRDYHGv3SgvOfTxm\ncLLbf8xzbnUotDKWINgsmpMMPwl7n5qc4xw7kzWAbd3Pte8TH1KdjykbIYQ+QtMV5bcjsykxCYpE\nSkSy/auTWsEqErSXA5eLyCbYLt12OZZ8AP+EpBUisgDmal+hpzMmTDIPsCvWuubDI8BhInJTsj1N\nRObCWuEervuX31PqzEetAggmzPFuhrXOxhQHEZEpwLZFz6K5tXtjbVM/wq6/GTV38TLadNLMPVT1\n5ZRjKwMfuZ31RtfrjMpYyFm0v2BVg4NdZTMXriWwYTzbA7+gvoHy0lgi5EWR/lQhhT46ScYawprp\nRiKFEpOgSKSkBGz/ql1rcWBnTDlqTeAT4BLf9VS1X95zSnAIFsS1uK9b6tyvCT8nedz6DwIvicgo\nLEBZCns+lsAGqWcrROQ8oJuqDnC/b495Vc0JfCoiv1bVJzKu2R2byejR0X09ORN4GdgbeFFVi6rg\nXYgl2ANSjh2EVWz+kGG9XgSujAWeRZsPk0gvZKODtsIoC2KJ7LdYy9Yi2HX3CSbI4ZME3QscKSJ3\nUFU+a3EbHQdg1TJf+lGsP1UooY/OkpgOZqYbiRRNTIIikfISqv0LZ8C6HRbo98ECjjsws9DbnXdO\nbkRkXqw1bpprwcvKrZgXSxNwGaZ0VevZMh0L7LyqC6r6pIj0AYYBR2PD2TOwCtCu2rGz+/eRLaiK\nJIAFnqOBY7Bk43h3n4ZxyoHrEm5geRVge1V9ruB1N8D8dtK4C7ggy2KdVRmrecy6s2ge3Iwlcd6q\neElUdWZw7sRgrsIS2VtcIN0Ne686A//ZnYHAeEyO/A7svfMIYHUsid3J+x9gFNK2FlLoozNkrB2d\naqYbieQhJkGRSInopPYv3N92xwL9/YEbVPW/OdZrhYhshalPreFuWgd4SkRGAONU9bpG1nHB4TNu\nzRaglUxxUajqY8CGLmnrAXysqqn+L+7/423PpK6r0BNrm6q09AjwJ1VVETkHa5/04V4s6R5XyFm2\nZhL1W57ysCDweZ1jX+ApMhC6MuZaj9YmfT4qj0LXQcA17jq4G2jzvpFDtXI4cKKq3pRYawZwo4gs\ngkl+r5V1UVV91VXPh2CCMN9h1+FY4Jisypid4U+VJWlx/9dfA2uXYNMmiPy2iDRlqD7OwLyVUtsO\nI5EKMQmKRMpFZ7R/4f726hBGciLSF7gRCzAOxwKbCq9j7S8NJUFJcnj1ZHmML2nH/NIFG1Ow4HJW\nBxsh+ZxqcN4Ha0Oq/Hu/pKo8lpXhwFVud3808C41u+g5rsmBwMki8miBcu9glcjeVHezk/QGvB4r\nZGVMRFbDqqjL1Vm/BX+Frp5YYvU7rJUs+f/XRD7Vyp/iku8UprrjXrjrynfOJY3Q/lS+59MwgWSs\nQ8lvvy4ilwKXdvT6dslSm7nGSKSWmARFIuUiePsX9scn+/5tAzQDF6vqfi5pSCZBz2GVp8w4F/A9\nsfmLeupfhclZt8Ps4D/xGPA3EZkBHIbJC1eC3RVoK53dKJUKwUBseDoN3wD6SKwNZ5Lzv6o1dG1R\n1U091r0aM0t9HbhcVb91gV0/LMk/yfN8IVxl7AJsN3xb4AWKVXAbicmEH4BJyBfSOut4A3uN35ly\nbC88E86i6SR/qmCElLFOk98Wkbsx77jHnOeRD2MwafPBInIXVlEa4yqFkYgXMQmKREpEZ7V/ufVD\nOaivigWHaXyCBVA+HIfN7DwNTKALSPOKyM9U9dl2ju+iqtfCTOWoTbC5hVnNEVjQMRpLypsTx3YC\nHvVct3YIvkiaaP3c1SarvsnryZi6WqW1Jzmsfyv5kqBQlbGfA7urasN+ThlYA9hNVetVqfMwFBgp\nIith7VTvYYnt9sD/kcPPJ6C3UWfN2hRJMBnrdkRV5gA+8xFVAVDVASJyKLALNjN2K/C2U727JERX\nQ+T7T0yCIpHy8iiwGs67JYkbIH5HVSf5LBzYQf1jTFUtjRWxQM+HfsAwVT3K8+9nBXeKyAZ15HN3\nw2Zrrq3c5rx6ZjmqOhFYSUQWTknCD8Hz/zBkS2Moo003+9VXRDYFNqNqlnq3quat4ISqjL3T8V28\neYVA1VBVHeXkwpvd15yYaMsTwBa+GzOhvY1C+VMFJKSMdeGiKhVcu/JlwGUisjqWDB0ADEpUh4pU\nLox8z4lJUCRSXs7G2sfSAuMtMWWjrTzXDumgPgbzPrmParDc4gKFQ4B/eq67IPmkbGcFjwB3u0Ro\nWuVGEfkjlgCVLThqRVoVMoD6WpdAVe+lIEW0BKEqYycAR4jIPapatKnmIKBZRMZrMWa6rVDVO7HN\ng26Yctv7BbQ8FeptlCSUP1VgQspYhxJVqWUKoFgi1wMTobgFmCgiu6vqkwU9TuR7TEyCIpHysjZw\nXp1j/ybfkG9IB/XBmOrcS1gS0IK1/QhWJRriue5YYH3sQ7qrsCsmyTtWRDZS1U9F5E/YbubxnsaP\nnYLzj9qF+oPTXtefiGwB7NvOusu3+aPG1w7V4llZfzHSz9mrFSdgZWwTrO1riog8TPp81F6eax8K\nLAlMFpGX6qztM3tVy9yYXP3ctCNW0iBFexslCeVPFZKQMtahRFUAEJG1gT9jbXtzADcA+6jqePf6\nPx+4FGsJjUTaJSZBkUh5mQ9rBUljBjB/jrWDOair6ntOjvZg7MP1FWBerFVhuKp+4rn0MOBKEfkW\nG5yuDb7yKItloeFAyimA9cUG328XkauBc4HjStgiMxMR+QmWyE4DVgKexQKbZTBRBC/PGRH5PaYc\nOBLYGksG58Rab97HPGJ8zzlIi6drDzoRC7x+VOduuU2LC2Zj7D3iC9KDwTzJQKjZK2BmknwcNofV\nhFWRnwSOVVXfSnCh3kY1hPKnCkkQGWtHEFEVERkA7IPNhk3C2iWvUNWZnwOq+h8RGUS6kmMk0oaY\nBEUi5eVlrN0tTSlpK/KZH4Z0UEdVP8V6v4usdDzmvp9M/WH0zghGMwV5qvqZG8p+EKvsHauqJwY5\ns+IYhqlH/RELjPZyFZU+wCis3cqHo7H/v6HYzvn5bt0ewP3ARznOOVSL52Hub/+K/dsPwF4z/bCK\nyIG+JwxhKmNJA9KiCTV7BTP9xf6JVZGbsXbaJYCdsU2E36vqvzyWDultFMSfyg38D60zT7gs0Fyp\nxjpRleVo3BcnlIw1hBNVORtrOzxcVdvrBphE+doPIyUlJkGRSHkZAZzjTPguwVoYlsKkYgdgH+y+\nhHZQrxi/ro4Fim8Bz7vkyJf+5NvBzo1rdenW3n1c8JLGm9hQ/QqJ++RpSwrJmrR+vucAUNX7ROQE\nLJFZ12PdVbFrb4b76u7W/VhETsQqLud6nnOoFs89sKDwOiwJGu8MKS8XkRuwgXifwDxoZawMeJh4\nNmPvR9skW9fctTEaa6X1ea5DehuF8qfqB1yIzb7Usgh2Xc5sSVXVev5KbQgoYx1EVMUlaX2AV1W1\n3UTPzalFj6BIQ8QkKBIpKap6voisjKn41CpHna2q9eaFGlm7UAf1JM7PZwj2gTcf1crJ5yIyHGsF\ny5zMqOoVvueUco6Z5opUtU+Gu/ehfrL2BdaWU6GsKkY/AL5wg9Mf0VrtbyKmWujD18CcqtoiIu8C\ny1Ot8H2OJcy+hGrx7AW84Hbbv6H1TMNl2KC374ZEyMpYKDPMrGSpnK6OVThqZcJbROQCLGH0IaS3\nUSh/Kqj//rAsdr17EUrGOknBoirfYS18W9J4tSsS6ZCYBEUiJUZVDxaRc4FNqUrz3quqtQaqPms3\n7KDuJLmfbFBp6kTgKGxA9R/YXMniWHXpGOx952ifc3bn0gOrQvTAgsTxqtqmvaUDandNN8MqYJU5\nmMWBDTCfkkz95araK+O5lJHJ2M452DxQfxEZ437fE3+Z8+exoPwurD1wkIhMwWbfhmABqi+hWjw/\npjoL9AY2k/Cg+30RbN7NlyCVsZBmmIH5H/Y6TGMRd9yHkN5Ghc1IicgeWIUH7P/qAtcJkGQeTAnt\noYznmSSYjHUIXBI8BVMIjUQKIyZBkUjJUdXJ5Jv/yYVraRmHqQY10tLSHzhFVQcnbnsZeNB9oO+N\nZxIkIidjsx5zJ26eLiLDax6vXVR1pjSxG7hdC1g32WohIkth81jjPc+1O3AbcLKq3u+zxizkNmxw\nehQ2f3U78Cm2I/tD/OdgRmDVH7CA6x6qwdxnVNvZfAjV4jkeC6LHYIPkQ0VkfixxO4x8wWioylgw\nM8zAjMVay15S1ccrN4rIL7CE0DeRDelttHHBSzYlvjfR9rw/BS7GZnd86SwZ6yI5AxgoIvcmxRAi\nkTzEJCgSKTkishBmMprW0uI7zJuVLAHED7GkKY1x2IB5ZkTkcKzCNAKblXgX+zDfHThKRD5U1eEe\nSx8OHFHba66qb4nIcdjAfWa1JKcMtx6Bgq+QqGpz4ud73L9je6xN7k5fqWlVvS7x82QR+SnwS6ya\n8oibVfA951AtnidjyQSYIMSK2E75HFjCMsD3nAlXGQtphhmSIzBftEdFZCr2Gl8cWA541R33Iai3\nUVE4yfSRACIyDhigqnmqo/UIKmMdiPVxyZuTfX+X1pXNss5XRkpMTIIikZIiIvNiO3I7UD+QLps0\nL1gQszHpbWS9MY8jH/YFzlDVZCD0CvCwiHwO7If5EWVlSer33reQb07lXizIqJcUdglU9Wng6QDr\nfoGpdRW1XsMtnhnWnABMcD9/BmwvInMDc+cU+oBwlbGQZpjBcBsPP8daLjfEzvsp4Czg8gbbcdMI\n6m0Uwp9KVTfp+F7eBJGxDszGWOX0Q6yNdNWa42Wdr4yUmJgERSLlZQjm8dEb2yXeDpN27Y/tiu08\ny86sBhFZJvHrKcAoEfkB1j70HjY4vAPQF9jN82GWoX47zFg8K0zAf7Bd4odV9b3Kjc4Y8xjyBf/D\ngatEpBvWc1+7e9lZ3kalwSk97QGshwWmb2OmuqNUtZ4vVqlQ1enA9Nrbs6qhBayMhTTDDIJrH90B\neFxVzwHOKXD5YN5Gofyp3NoLYuqDy9C2E6Alh9dYKBnrYISUfY/MvsQkKBIpL32x9pvKjMAbqvok\ncL9TSjoIf++ToplKW9nZg2mtmlUJNP6NXwVrGhbApVWY1nLHfTgQC2KmujaLStK2ARbQeu8QU616\nDaStwl+FUlTzXBtWFiPYzP41IrI8lrCugEn/TsN2dPfC+v1/m+aLkmH9zYEdMTW4tKCxt+/aGfAO\nqAusjIU0wwyCax+9DAv6C52BDOltRCB/KhHpjfni/Ah7Xc6ouUsLnn44IWSsI5GuSEyCIpHy8mNg\nipPm/QqYP3HsZsy3pCx0hofPNVjF5jtMzrUyL/AHbCfzTJ9FVfUJEVkRawtZF3Oqf8etd2aeORWs\nraerMJLw/4fnYaIW67g2MwBEZC3smj4Pm+fJjIj8DRNxeAcLottUaspIoMpYMDPMwCaeE7H3vUIR\nkWOBS9I8ZkRkCWAfVfV9TkL5U52JCcrsDbzoPMoKpWAZ6xjbVgQAACAASURBVE6hJLLvke8JMQmK\nRMrLe7hABlPyWR/zDgEbzi4NRXr4tMOx2ID0KdiweoUmTIq7Oe2PGsElOgNznV36uiOLXjMUqjrE\n929dO+TbDQTtvbGAc0LyRlWdICKDyFeh+AvWQnWwjw/VrCBUZSzNDJPi2sv6EcjEE2sBPlVEnnDV\niqJoxpQe05KxJd1x3yQolD/VKsD2ZU9KOosuLPseKTExCYpEysuD2O7wPzGp4mYR6YUpR+2B9XN3\nBi1YEjZLd9ZV9WtgJzdsvBE2NP0x8G9VfX5WnltHFORtVErcHMwUGpNQ/y/wicexRpgPGN1VEiBH\nsMpYYIKYeGKtqQsAz4vIZNIVwHzaU9trUVyUfO9tofypJlE/uZod6aqy75ESE5OgSKS8DKGqTDYM\nM0uttLSMxl8IABF5Feirqs+kHFsNCyaXB1DVGVgFptG1uwF/pv3ZjGXb/GGDuISnsKRHRJqwtrU/\nUH8AeYUc6xfibVRyGp2DuQg4RETGquo3lRvdUPyh5KsE3QxsggWlXYVglbEi56M60cSzCdCU29J+\nbhcR2YbWMuDHiMj7NXebB/s/aMT/rB6h/KkGYp5Jj+aQd/8+0VVl3yMlJiZBkUiJEJGNgKdU9XNV\nfQVTdsIFjIe5ryLoReugPMm8WDLgy+lY0D8eUxn6OsdabRCRpUkP7FDV+zyWPA4zb30ak0IurOIV\n0Nuoq9KE9fO/LiJjqIpQbIVVOB9w8xuQXf3qIOAaZ/Z4N1ZZakUn+mo1SpDKWKD5qOAmngULGPTC\nkuIKa9L2eZgOPI75CHkR0J/qSOy1MUlElIJlvbsgXVL2PVJuYhIUiZSLcdjsz+PtVWsKok1Li9uR\n34y2H7hZ2A378D8xxxptcPMTV2M94dC6J7zJffdRWusHDFPVo/KeYwqhvI26Ksckfk4zNkzOdWVV\nv+qJJe+/w9qQatUKfa+PkISqjBU6H9WJJp6FoapnA2fDTOXDbUO9l4bwpyKgrHcXpcvJvkfKT0yC\nIpFykRyy7UX9ak1mRKQZExcACwgfE5F6d78o58ONz/n3aVyGtQfuj6kmFVVhWhD/vv2OCOVt1CVR\n1W4Blx+JtYweALxEQddHYDW0UJWxYPNRgU08EZFVsMpsrVreST5iCS6hnETrykHpCSzr3RXpcrLv\nkfITk6BIpFxMAEaIyIPu97Q+9gotqpq2m16P+933JiwZupS2zuDTsZ782zKsW8uVmLFrmp9PHtYC\ndlfVWwtedyxWffNppeuIUN5GkbasAeymqrcUvG4/wqmhhaqMBZ2PCmXiKSKbYe89n2JmntMwGfzf\nYaIoW6lqpn+T8x9al4CVk5L4U33fCSb7Hpl9iUlQJFIu/oy1R/0SC3rS+tgrZNrlVdUHgAcARKQF\n8814q/2/8mIglsiNwZzS02YzfKRMXyVMIDMMuFJEvsVkdNu0Arp2Fx+CeBuVkIavRRGZD0ssNsQq\nN/ur6iQR2RF4Noc08iuEC3SDqKEFrIwFm48KaeIJnIGZQ2+pql8mHvMHmDzyGcDPPda9F5vTGed5\nXnUJ6U/lKh2DsdfKQsB6qvqUU8h8UFXvKuqxyk5g2ffIbEpMgiKREqGqk4GtAURkBtbH/niAxzmu\n9jbXhiLAY6qaxzF8LWxAeHHS5X1b8PNzGAQcJyKPqeo7Oc6vlsfc95OxYCYN31mSYN5GJaOh5MOJ\nWowDlgJexALaignwZlh1IUt1M8kgLOEcnze570Q1tFCEnI8KaeK5CrBDMgECUNX/icjp2GvGh+HA\nVU65cjRtpbfzbHQE8acSkQ2xBHYStplycOJwE9YWPNskQZFICGISFImUl02wQLFwROQ8oJuqDnC/\nb49VKuYEPhWRX6vqE57LX4jt2PWnwNkMVR3jAoNXnVpS7e62r1pSfzJW1Rol4W10AtbPviBdxNuo\nUVwQ3GhF40zgS2AlLBBNXhv3Y0p9vhyKzZBMFpGXyK+mFVwNDYJVxoLMRzlCmni+Sn1vnAWAqZ7r\nVqpeAzGZ5TR8k8JQ81enAneo6nau9SuZBD0J/Kngxysdbh6vYSqzeZFIo8QkKBIpLy9gLRCfV24Q\nkb2B1YCxqvqvHGtvQVUkAayfejQ2o3Cm+30Lz7VXxoKkPOfXBuebcgQWPH+GSdHmRlWvKGKdCiLy\nIWZ0W/l60gWMXcr5XUT60L53kk/C+WtgT1V903l7JHkbqxD5UpiaVmepoQWsjIWaj4KwJp7HAqeI\nyAtJJTfXFnYcJjfvw55FnFwdQs1frQFs636uTbA+pKqU9n2mYX+6SMSHmARFIuXlMuANbDe3ou7W\njLlk/1VEdlPV6zzX7om5blcUrgT4k6qqmyO4PMd5P48bYC2Yg4HzgQOdgWuhiEgPzIV8YWwH9mMR\nmSspXdwgtwO/wtoaW4D/icij2G70v4HxqlrY3EAIROQvmPnge1jrU1HnOwf1pWx75HmcUGpagdXQ\nQlXGQs5HhTTxPACrrDzlLAIqannLYyIJB4jIAe6+DSfiLqkNRaj5q6RSaC1LY4nQ95rQSoSRSEyC\nIpHyshatk5H9MKneZhE5EzNO9U2CPqcqGdsHax2quKZ/iQUivhyECSNMVNUJOdappTtwS9EJkIg0\nYa0nf8UkyVswRbePgTEi8lAWtStV/ZNbdymsBW4DrNXpOCww/VpEJuCSIlW9s8B/TlEcDFwB7FPw\nzMcEzEfqjpRj21Odz8qMk5K+RFXbyFOLyBLYv8VLQSqUGhrhKmOFzUelENLEswlr30tW3d6kqmLZ\nVHPfMhBq/upe4EgRuYPqxkGLiMzlHieUrH8kMtsQk6BIpLwshFV9Ku0gi+FadLDWtTz9z48Bf3Pi\nC4dhlY/Kh/cKtJXOzsI1WII13g2TpwVJK3isezOWsBXddjIYG25uxnZyn0wcG4313mcOcl3wea37\nqgTSG2BVoo2wYPJIyvk+3BO4quAECOx5HCsiP8SG3FuATURkACZ3u3GOtZsxdb80j54l3fHMSVBg\nNbRQlbGi56OSBDPxDOmNIyJbYObFK9M2kUVVl/dcOtT81UDMb20itmnQgrUDrw4sir1eZjvcZ2G9\n/0MfwZ3IbEwZP3wjkYjxPtYG8hDmjv2aqr7qjs1DvmH+IzAfjtHYsHFSpWwn4NEcaz9AGKGBW4Cz\nReRHWLKSJmXt03ayNzBEVU9L2Y2fjCWFuXDVpuWxtqdV3PduNG6q2dk8AvyUgr2TVPVeEdkOU+sa\n5W4ehrVmbqeqj+RYvr3ge1H8E4qQamihKmNdMlHJgnutfg2srapPdXDf3wM3YgnL1lir8ZzANtj7\n7FU5TiXI/JWqvioiawJDMJXN77BNoLHAMQFaEUuNa1f+F7AO9vlSuYaTnzUxCYpkIiZBkUh5uQ0b\nEv4Zph51SeLYapiSkhdOcWolEVlYVWt7yw/B5hN81+7n+7cd8E/3fX/3VVTbSU8sGE3jO8yMLxMi\nMjc2X7Sh+1of+CE2/P4QtlP/kKpO9TjfzmB/4EYReQ+4U1U/KWphVR0NjBaRFbHq5ke+ogMisg0W\nyFZIMxeeB+hNtd0zKyHV0IJUxsqSqHQCjSZzR2MS9UOxZPZ857fTA5u9+ijHOQSbv3Ky3VHxzDgF\ne56XxTZN1sUS2P7Ya+U3s+7UIl2VmARFIuXlSGwO5rdYQpRsudmWAnrCUxIgAgV7RRBqSPZ14Bek\nVz3WwoKchhGRhzCT2xnAE8DDmMjAI6raZmi6pLzsvl8DICK1x1tUtaHPDzer09F9Zv6ccW6nF62v\nizRz4enA49icjA/B1NACV8YaIktFJfE3Xc3Ec1WsvWyG++oO4MRPTgROBM71XDvk/FWkyq8xefPK\nczzDbSId667hv9N6QyQS6ZCYBEUiJUVVP8d2LdOObZB3fRFZHNiF+v3VXjuQDXg7tKhqZtlfVX0g\nwzk0YXLfFzVg/DoKOFpEXqHaltQiIhtg81In1/3LdH4J/A9rsbkPq/iUte2tHsdTXEtjrTzxgth8\nzbeYn9Qi2GfRJ1iLY8NJkKqeDZwNICJTMHPhZ9r/q8yEVEMrtDKWg4YrGV3UxPNrYE5VbRGRd7HW\n1Eqr4efY/JQvweavRGRzYEdMDS5NkKO3z7pdlJ7AO6o6Q0S+oCrsA/Y++5dZc1qRrkxMgiKR2RAR\n+QlWoZiGzac8i32oLIOJIkzOsfxGtA2gF3Lrf0znSLt2w+acxtBxa9/JwM+Am7DkBWyuaT7gBmwm\nJAtrUW2D+zuwqIi8jrXBPYQlRS9kXLNTUdUhBa410+tDRDbCksO9cUp/ItIN2A44A08DSBHpjgXl\nPTq6rweFqqEFrox1Bl3RxPN5bLPnLsy7a5BLmr/FZm7yJJ1B5q9E5G/AScA72PtxqWX1O4G3qHoj\nvYK1v93jfl+b+gIjkUhdYhIUiZQIEbkPc4x/yf3cLqrax/OhhmFDpn8EvgH2cu0sfbDKyAme66Kq\nK6bdLiLrApcS1rgwSUPBh6p+C+zoAvTfYkP0H2GGtOOyPqhrKXqKaoViFSwh+hVm9niBiHyMiQ88\nqKqnZX2MzsJV1FbBkouP3CxZHoYDJ6rqTZUbnOT5jSKyCHAWlkRmQlW/dtdXiNmMooPcYJWxTqIr\nmniOwKo/YBXie7ANCTDj5W3T/qgRAs5f/QU4Bzg4odw5O3MftsF2E/b/eZ5ry/wGe98eMQvPLdJF\niUlQJFIuXqO64/c6YVTWwGYn+ifWnwNAVe8TkROw6si6RT6gqo4XkTOw6sj6Ra5dBE5ZzkddrqN1\nJ2JB9CUAIrIONk/xO0z1qZRJkIjsi+2SL5a47T3gWFW92HPZn+JMelOY6o77ci+mnpU5cW2PooPc\nzqiMBabLmXgmTaVVdbKI/BRrW50Xm9X7wHftgP5U8wGjYwI0k8E4E25VvcBVIXfChGtOo3ybBZEu\nQEyCIpESoap7Jn7uF/ChfgB84YKuj4AlEscmYupzIXgTaz0rHSKyKbAe1t//NhYc5Q6oRWRlq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G3P0MMzsL2IjYnvVH4GF3X5Z7zwPdml8feYF4ql/Petn1QVHGNLR2VlSgc008rwWONrMrqPb1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f738e494f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(15, 10));\n",
    "# Draw the heatmap using seaborn\n",
    "sns.heatmap(corr_mat, square=True, ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "A couple of interesting observations.\n",
    "+ The taget variable \"left\" is negatively correlated with \"satisfaction level\" which makes sense because employees are more likely \n",
    "to leave if they are not satisfied with their work.\n",
    "+ Management department is positively correlated with high salary and promotions in last 5 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>department</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 department  \\\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "**How does satisfaction level vary among employees who left?**\n",
    "<br>\n",
    "<br>\n",
    "From the plot, it seems that employees who left are less satisfied on an average.\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OG0XEkcB7gb/PzH+e5NqVVNM7duvt7e1dunRpnW8pSZKkOarRVSzaISIGgfXA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f738beb4790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x='left',y='satisfaction_level',data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "**Does having more number of projects have any impact on employee churn?**\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "From the plots, it seems that people who churn (1) generally tend to have more projects than people who stay (0). From the Boxplot/CDF we can see that, ~50% of churners have 4 or more projects.\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dGS/OuYUB3U7geHOl23GklQhlkotkAuOsGmWMOccY0/uwE4mIiIiIK9YUVXFE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\nIxZYkiRJkpQj/x9R2XQj5j27iAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f738c2b8a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axarr = plt.subplots(ncols=2,figsize=(12,6))\n",
    "# plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x='left',y='number_project',data=df,ax=axarr[0]);\n",
    "sns.kdeplot(df[df.left==0].number_project,label=0,cumulative=True,ax=axarr[1]);\n",
    "sns.kdeplot(df[df.left==1].number_project,label=1,cumulative=True,ax=axarr[1]);\n",
    "axarr[0].set_ylabel(\"Number of projects\")\n",
    "axarr[1].set_ylabel(\"CDF\")\n",
    "axarr[1].set_xlabel(\"Number of projects\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "** Does staying in the company for a long time make the employees more vulnerable to churn? **\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>left</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_spend_company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "left                   0     1\n",
       "time_spend_company            \n",
       "2                   0.98  0.02\n",
       "3                   0.75  0.25\n",
       "4                   0.65  0.35\n",
       "5                   0.43  0.57\n",
       "6                   0.71  0.29"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_over_years = pd.crosstab(df.time_spend_company,df.left,margins=True,normalize='index')\n",
    "left_over_years = left_over_years[left_over_years.index!='All']\n",
    "left_over_years = left_over_years.round(2)\n",
    "left_over_years.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RMtX0F4GVgXWApQAy80lgP2DiANQoSZIkDZpmWpC/ArwZ2Ax4gAWnmp4GbNui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\ncRzHcRzHcSJcIDuO4ziO4zhOxH8A7h+6FJ3H/gAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7389b74850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "_ = left_over_years.plot(kind='bar',ax=ax)\n",
    "_ = plt.xlabel(\"number of years at the company\")\n",
    "_ = plt.ylabel(\"% of employees\")\n",
    "_ = plt.title(\"Distribution of employees after n-years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "It's seen that employees start churning after completing 3 years at the company. There's an upward trend from year-3 onwards till year-5. Almost 57% of people who completed 5 years at the company churn. Then we see a downward trend after year 6. People who has spent 7 years or more are unlikely to churn.\n",
    "\n",
    "<br>\n",
    "\n",
    "Why do people churn the most after 5 years? Is it because of promotion or some other factors are at play?\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>left</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "left                      0     1\n",
       "promotion_last_5years            \n",
       "0                      0.43  0.57\n",
       "1                      0.94  0.06"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee_spent_5_years = df[df.time_spend_company==5]\n",
    "emp_5_yrs_df = pd.crosstab(employee_spent_5_years.promotion_last_5years, employee_spent_5_years.left,margins=True,normalize='index')\n",
    "emp_5_yrs_df = emp_5_yrs_df[emp_5_yrs_df.index!='All']\n",
    "emp_5_yrs_df = emp_5_yrs_df.round(2)\n",
    "emp_5_yrs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f738993e9d0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rfTZlk+kuwK7AJzLzb4MqUqOKASUNvRew9msnNdudamp4l/TS/kwhrQZ6bNcU\n4JuUfYRnUvZ9Sf1yH5Q0xKpjaqZ30PSWpjNqDLuIeB7tj/96isy8YhjKkQwoSVI9OUlCklRLBpQk\nqZYMKElSLRlQkqRa+v/fMuaOVnuQLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f738990f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emp_5_yrs_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "This is interesting. 57% of the employees who haven't been given any promotion in last 5 years surely churn.\n",
    "While, 94% of employees who have been given promotion in last 5 years surely stay!\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}