No-show appointments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Project2: Investigate No-Show Appointments Dataset\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row.<br><br>\n",
    "● \"ScheduledDay\" tells us on what day the patient set up their appointment.<br>\n",
    "● \"Neighborhood\" indicates the location of the hospital.<br>\n",
    "● \"Scholarship\" indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.<br>\n",
    "● \"No-show\" it says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the packages that I need\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as mtp\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "\n",
    "### General Properties\n",
    "\n",
    "Starting with loading the data and see how the data looks like.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.598513e+13</td>\n",
       "      <td>5626772</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T08:36:51Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>76</td>\n",
       "      <td>REPÚBLICA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.336882e+14</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:05:12Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.449833e+12</td>\n",
       "      <td>5630575</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:39:58Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>39</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.639473e+13</td>\n",
       "      <td>5638447</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T08:02:16Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>21</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.812456e+13</td>\n",
       "      <td>5629123</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T12:48:25Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>19</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "5  9.598513e+13        5626772      F  2016-04-27T08:36:51Z   \n",
       "6  7.336882e+14        5630279      F  2016-04-27T15:05:12Z   \n",
       "7  3.449833e+12        5630575      F  2016-04-27T15:39:58Z   \n",
       "8  5.639473e+13        5638447      F  2016-04-29T08:02:16Z   \n",
       "9  7.812456e+13        5629123      F  2016-04-27T12:48:25Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "5  2016-04-29T00:00:00Z   76          REPÚBLICA            0             1   \n",
       "6  2016-04-29T00:00:00Z   23         GOIABEIRAS            0             0   \n",
       "7  2016-04-29T00:00:00Z   39         GOIABEIRAS            0             0   \n",
       "8  2016-04-29T00:00:00Z   21         ANDORINHAS            0             0   \n",
       "9  2016-04-29T00:00:00Z   19          CONQUISTA            0             0   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  \n",
       "5         0           0        0             0      No  \n",
       "6         0           0        0             0     Yes  \n",
       "7         0           0        0             0     Yes  \n",
       "8         0           0        0             0      No  \n",
       "9         0           0        0             0      No  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data and printing out a few lines. \n",
    "df=pd.read_csv('NoShowDataset.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         0\n",
       "AppointmentID     0\n",
       "Gender            0\n",
       "ScheduledDay      0\n",
       "AppointmentDay    0\n",
       "Age               0\n",
       "Neighbourhood     0\n",
       "Scholarship       0\n",
       "Hipertension      0\n",
       "Diabetes          0\n",
       "Alcoholism        0\n",
       "Handcap           0\n",
       "SMS_received      0\n",
       "No-show           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*We notice there are no missing values*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
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       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  <font size=\"3\">We notice that the average age of the patients is 37, and also there is a mistake in the minimum age because it can not be -1</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here I will drop some columns that I don't need them for my analysis\n",
    "df.drop(['PatientId' , 'AppointmentID' , 'Neighbourhood', 'Scholarship', 'Hipertension', 'AppointmentDay' , 'ScheduledDay'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age  Diabetes  Alcoholism  Handcap  SMS_received No-show\n",
       "0      F   62         0           0        0             0      No\n",
       "1      M   56         0           0        0             0      No\n",
       "2      F   62         0           0        0             0      No\n",
       "3      F    8         0           0        0             0      No\n",
       "4      F   56         1           0        0             0      No\n",
       "5      F   76         0           0        0             0      No\n",
       "6      F   23         0           0        0             0     Yes\n",
       "7      F   39         0           0        0             0     Yes\n",
       "8      F   21         0           0        0             0      No\n",
       "9      F   19         0           0        0             0      No"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.276780</td>\n",
       "      <td>1.259761</td>\n",
       "      <td>1.218296</td>\n",
       "      <td>1.210135</td>\n",
       "      <td>1.500806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.104561</td>\n",
       "      <td>6.522499</td>\n",
       "      <td>6.527195</td>\n",
       "      <td>6.528421</td>\n",
       "      <td>6.489885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</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>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.000000</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>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>115.000000</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>37.088874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean       38.276780       1.259761       1.218296       1.210135   \n",
       "std        22.104561       6.522499       6.527195       6.528421   \n",
       "min         1.000000       0.000000       0.000000       0.000000   \n",
       "25%        20.000000       0.000000       0.000000       0.000000   \n",
       "50%        37.088874       0.000000       0.000000       0.000000   \n",
       "75%        55.000000       0.000000       0.000000       0.000000   \n",
       "max       115.000000      37.088874      37.088874      37.088874   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        1.500806  \n",
       "std         6.489885  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max        37.088874  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now I fix the negative age by replacing the mean\n",
    "\n",
    "Avg_Age = df['Age'].mean()\n",
    "df[df['Age'] <=0] = Avg_Age\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
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       "      <th>4</th>\n",
       "      <td>F</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>F</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>F</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>F</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age  Diabetes  Alcoholism  Handcap  SMS_received No-show\n",
       "0       F  62.0       0.0         0.0      0.0           0.0      No\n",
       "1       M  56.0       0.0         0.0      0.0           0.0      No\n",
       "2       F  62.0       0.0         0.0      0.0           0.0      No\n",
       "3       F   8.0       0.0         0.0      0.0           0.0      No\n",
       "4       F  56.0       1.0         0.0      0.0           0.0      No\n",
       "5       F  76.0       0.0         0.0      0.0           0.0      No\n",
       "6       F  23.0       0.0         0.0      0.0           0.0     Yes\n",
       "7       F  39.0       0.0         0.0      0.0           0.0     Yes\n",
       "8       F  21.0       0.0         0.0      0.0           0.0      No\n",
       "9       F  19.0       0.0         0.0      0.0           0.0      No\n",
       "10      F  30.0       0.0         0.0      0.0           0.0      No\n",
       "11      M  29.0       0.0         0.0      0.0           1.0     Yes\n",
       "12      F  22.0       0.0         0.0      0.0           0.0      No\n",
       "13      M  28.0       0.0         0.0      0.0           0.0      No\n",
       "14      F  54.0       0.0         0.0      0.0           0.0      No"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here I'm saparating between the patients who come and not come to the appointments\n",
    "Show_up = df['No-show'] == 'No'\n",
    "Noshow_up = df['No-show'] == 'Yes'\n",
    "df['Show_up'] = Show_up\n",
    "df['Noshow_up'] = Noshow_up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize= (13,13));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1: How many patients show up in the appointments? And how many do not show up?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patients Show Up: \n",
      "77.18204601590561\n",
      "\n",
      "Patients Not Show Up: \n",
      "22.817953984094384\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21178b65130>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aCrCf34aam6Eci/0zFay+jSPW10P/Bfvno7218wdJ7oN90N7fLcuaHrzDNH9injH8+vMUvK0OeyY0mNOnZA1/1uv/3cth7GjsIB/O5yucv1dOwvHcNKWlvw3VwAeBLwKre8zEPuD56jDXInFK7RxSr44G7+KNvYTZ9Q5j64Ow0x9qJ89j/6w5rg1sjGnxo9RmlAE9G/a1BXq6d/j5DvQrr8aewe1IEwL9jlOa2DcC+w9b/Ue5ofoE+6PgPwR/dBj4/x8C9+eToJrzsN8EZGH3pc8ObLew+2NPw/6D2tqAVb/sXMN+4utp+vVglGl81ribAv++E7TtROD9hm0MzbgD++Ceux32hfQ4Bb5v7c9j/bJoPZrYfWLg3xaXk2vCO9gHVzUMV5cFtr/dYPts7Md+OrAuaBZsNvYqNjdjH2DlaeXtLwcmBfftG2N6ErTCg4PrA61B9eMHY8+uLrMsy6nNIFJCel6dBD5l+AA42RhzcMP9xtbSsmb1M58NX2fG0XwP8o3Br03GmP2wf/c+dZhQOCqwf3tdQP0Sme8E7osXe+WX4wK3HTz25sC3DX++dkWor/UhCdNz05Qmaw/0XDdU34/eJvdV4pNmoqXeG8AVxphXsQ9C7Ifd+5vvMPYH7N67aYE/zuXAasuyvnO6Ysuy3jDGPAP8PvBHIhs7hA7Gnl0dzs73XX6LHTAfNsZ8jf3HbjZ2ALjeGPM29mxHLfZHdpnAa1bzZ8zrBHxujFmIvUTVCuw/nqOB87ED+NVW0NJurWVZVpGx145+BPjO/Lqe74XYj8MVDjPKs7GXUqv/f/D20xy2N2cW9pJxfw8cGLUau9XlYOznxMkC4AVjzJPYj8Vh2K0YX2C3/xDobd4Te7WKVrEsKy/QbtPoAMOdeJy+BS4xxtyO3Vvqxw705Tg7B3t5u5nYq6/U964fE7h/i7GXZNsZ92A/L48Eft7nYh/Iewl2T/Q9DcZ/Fqh3DEFnXLQsa7ExZgv2m7ZvLMsqbeXtP4z98fhsY8wM7F7dy7BnPvs3cZkUwGOMeRn7+IUrsVfsuaaVt9lWvsU+CO8mAqupWJb1yk5cz1XYKxJ9aYx5Hvs5ScJ+3TkB+43+rc1cfgl2H/KNgTcny/j1zK8LsT+ZcjIUyDHGvIf9Sc/vsdsTnH5P5mE/Z49gHwB+AnbgnmFZ1jdB467F/t3zBMZuwX4NzAResizLce3znWFZVr4x5hfgzMAncFuBcsuy3g/XbbDrz42jFmr/2BhTjH2A9nrs35EL+XVlHJHW2dllPfQVG1/8uiTQxBbGdQLuxf5DW4Udlv6C3S5gEbRkXWD8BdhBo4agZahwWOIu6DLnYc+mlQRuYw32UkNnBI0ZRtPLEtXfl2FB2zpjry26FTtAW4EaJmD32P2CHfJLsP9I/Qno0MJjkYI9a/cy9h/LksD93Bio97AG45u7z88SmDhusP0k7Bml8sDX1zRYRi5obP3yZisbbB8R2F4DdArhZ2Ik9puDCuyDA1/Dbl1Yg/MSd89i/zH/DjsAbMVeIaNr0Li/Ba6vUR00WOLO4eduEw2WuAv1ccJe2/VN7FYGf8OfE4fx47BnwnMDt1+DfXT/3MDPWbcG4z/HeSlFx59X7BnnR4EN2G/gNmC/IejTRD0/Bq7nvAbbXwxsvyPE3/sbsH+Xq7ED4MU0v8TdXoHndAv27+b3wFGtvK1Gj0FTj0tTv8fNXPcI7F7kksBlrKB92193Glym0f0MbO+D/RpXf/B0EfYbxAeBsa2oZSjwOvYnUBWBx+gkp/vDr0vc9cUOZfmBy8wG9m/q8cNeS39+4HlbD/wdaOdQy3js2emCoOf4RiC5pce6uccf59eAA7F/T8oDl1vTiuf4c5x/X3bpuQlj7Zdhf5K1Bft3fzP2jPhhDa9XX/pq7qt+bV4RkUaMfYa45yzLurCFcXOBtZZlnRiJuiQ8jH2WwenA7lbQUnSyawKfnFxgWVaLB7oZ+2yCq4HbLMu6tW0rE5FwUjuHiOySQC/tO+zYoywiIhLXFKJFZJdY9kk6bnO7DhERkUjS6hwiIiIiIiFST7SIiIiISIg0Ey0iIiIiEiKFaBERERGREClEi4iIiIiESCFaRERERCRECtEiIiIiIiFSiBYRERERCZFCtIiIiIhIiBSiRURERERCpBAtIiIiIhIihWgRERERkRApRIuIiIiIhEghWkREREQkRArRIiIiIiIhUogWEREREQmRQrSIiIiISIgUokVEREREQqQQLSIiIiISIoVoEREREZEQKUSLiIiIiIRIIVpEREREJEQK0SIiIiIiIVKIFhEREREJkUK0iIiIiEiIFKJFREREREKkEC0iIiIiEiKFaBERERGREKW4XYCIiIhIa/z4449pKSkpTwHj0ERgLPEDC30+36X777+/1+1iwkUhWkRERGJCSkrKU/379x/Tt2/fwqSkJMvteqR1/H6/2bZt29gtW7Y8BRzvdj3hondxIiIiEivG9e3bt0QBOrYkJSVZffv2Lcb+BCFuKESLiIhIrEhSgI5NgectrnJnXN0ZEREREZFIUE+0iIiIxCSP17d/OK8vIy3lx5bG3HTTTf3ffPPN3klJSVZSUhKPPvro2sMPP7x80KBBe8+ZM2fJgAEDfOGsqSWnnHLKsKysrOKLLrqoMHj7gQceOOpf//rX+smTJ1cALFu2rH1WVtaIFStWLIpkffFMIVpERESkFWbNmtU5Jyenx4IFCxanpqZamzdvTqmurjZu1yXuUDuHiIiISCts3LixXa9evXypqakWwIABA3zDhg2rrd9/zz33pI0dO3bMyJEjx86dO7cjwNatW5OPPPLIPUeOHDl2/Pjxo7/77rtUgJEjR47Ny8tL9vv99OjRY8LDDz/cG+DEE0/c/Z133unq8/m44oorBo8bN27MyJEjx9577719APx+P+eff/6QPffcc6+pU6cOz8vLC3lC9KGHHup9xBFH7JmRkTFi2LBh4/70pz8NCMfjk2gUokVERERa4cQTTyzZtGlT+2HDho0799xzh8ycObNL8P4+ffr4Fi9evOTiiy/edtddd/UDuPHGGweOHz++Yvny5Ytvv/32jRdccMHuABMnTiybNWtWlx9//LHj4MGDq7/66qsuAHPnzu182GGHlf/73//u071797qFCxcumTdv3pLnnnuu79KlS9vPmDGjxy+//NJh2bJli5599tm1P/30U5fGlbZs/vz5nV9//fVVCxcuXPTee+/1+vLLLzvt6uOTaBSiRURERFqhe/fu/oULFy5++OGH1/bt29d3wQUX7PnQQw/1rt9/9tlnFwIceOCBFevXr+8A8P3333e95JJL8gGOP/740qKiopT8/PzkjIyMsi+++KLLp59+2vXSSy/1LlmyJHX16tXtunfv7uvevbt/1qxZ3V577bXeo0ePHrvvvvuOKSwsTFm8eHHHL774ouvpp59ekJKSwrBhw2onTZpU6lSrMabRKibB2w499NCS/v3713Xp0sU69thjCz///POdCuOJTCFaREREpJVSUlLIysoqfeCBBzbde++96955552e9fs6duxoBcZYPp/PAFhW4xX5jDHWUUcdVfrtt992zc3N7fKb3/ymtHfv3r4XXnih58EHH1wWuJy577771i1dunTx0qVLF2/cuHHBySefXBK4fIt19uzZ05efn7+91WPbtm0pPXv23H7QY8PraM11yo4UokVERERaYd68eR0WLFjQof77uXPnpg4ePLimucscfPDBpc8880xvgOzs7K49e/b09erVyz98+PDawsLClNWrV3ccO3ZszaRJk8oeeeSR/pMnTy4DOOqoo4r/+9//9q0/cHH+/PkdSkpKkqZMmVL6+uuv9/L5fKxdu7bdt99+29XpdidPnlw6Y8aMXn6/H4Cnn366d0ZGxvZZ66+++qrb1q1bk8vKyswHH3zQY8qUKWW7/AAlGK3OISIiIjGpNUvShVNJSUnyNddcM6SkpCQ5OTnZGjZsWPVzzz23trnL3H333ZvOPvvsYSNHjhybmprqf/bZZ1fX75swYUJ5XV0dAFOnTi395z//OejII48sBbj++uvz1qxZ02HvvfceY1mW6dWrV+0HH3yw8rzzziv69NNPu40aNWqv3XffverAAw90bOf44x//mHf55Zenjh49eqwxhvHjx5c/9NBDW+v3T5w4seyMM87Yfc2aNR1POeWU/Pql8KT1jNPHDCIiIiLRZt68eWvGjx+f53Ydse6hhx7qPWfOnM7PP//8ukje7rx58/qMHz9+WCRvsy2pnUNEREREJERq5xARERFJINdcc00+kO92HbFOM9EiIiIiIiFSiBYRERERCZFCtIiIiIhIiBSiRURERERCpAMLRUREJCZV3nbn/uG8vtTp01pcd9oYs/+ll1669cknn9wA8Le//a1fWVlZ8v3337+pqcvMmDGjx9ixY6v233//qob75s2b1+Gyyy4bVlJSklxTU2MOOuigspdffnmtW8vQLVu2rH1WVtaIFStWLArenp2d3fW+++7r99lnn/1Sv+2UU04ZlpWVVXzRRRcVRrLGaKEQLSIiItJK7du3tz744IOemzdv3jJgwABfy5eAd955p4fP5yt2CtFXX331kGuuuWbrueeeWwTw/fffp4a5ZGkjaucQERERaaXk5GTr/PPP3/aPf/yjX8N9y5cvbz9p0qSRI0eOHDtp0qSRK1asaP/JJ590njVrVo9bbrll8OjRo8cuWrSoQ/BlvF5vu6FDh24/dfiBBx5YWf//LVu2tMvIyBgxdOjQcVdeeeXg+u2PP/54r5EjR44dMWLEXlddddUggKeeeqrnpZdeOhjg9ttvTxs8ePDeAIsWLeqw//77jwLweDydDjjggFF77bXXmEMPPXTE2rVr29VvHzVq1NgJEyaMvv/++9N25nEZNGjQ3lddddWgvffee8zee+89ZuHChR1avlRsU4gWERERCcENN9zgfeutt3rl5+cnB2+/8sorh5x99tn5y5cvX3zGGWfkX3XVVbsdddRR5UceeWTRHXfcsWHp0qWL99prr+rgy1x99dVbjznmmJGTJ08ecdttt6Xl5eVtv87Fixd3euedd1YtWbJk0Xvvvdfzl19+abdmzZp2t95666DPP/98+eLFixfNnTu384wZM3r85je/Kf3222+7AuTm5nbp0aOHb/Xq1e1mz57d5eCDDy6rrq4211xzzZB333135aJFi5ZccMEFeX/+858HAVxyySXD7r///nU///zz0l15XLp161a3YMGCJVdccYX3D3/4w267cl2xQCFaREREJAS9evXyn3baafl33XXXDrO2c+fO7Xz55ZcXAFx11VUFP/74Y5eWruvaa6/NX7BgwaKTTz654Msvv+x6wAEHjK6srDQAhx56aEnv3r3rOnXqZA0fPrxq5cqVHb766qvOBx98cOnAgQN97dq144wzzij44osvugwZMsRXUVGRVFhYmLRp06b2p512Wv7HH3/c9auvvuoyefLksvnz53dYsWJF6uGHHz5y9OjRY++9994BmzZtapefn59cWlqafOyxx5YBXHzxxY4nYTHGWC1tv+CCCwoALrvssoK5c+e2eN9jnUK0iIiISIhuvvnmrS+99FKf8vLyXc5Sw4YNq73uuuvyP/3005UpKSnMmTMnFez+6/oxycnJVm1trbEsxywLwP7771/+yCOP9Nlzzz2rDjvssDKPx9Plxx9/7HLkkUeWWZZlhg8fXrl06dLFS5cuXbx8+fLFubm5KyzLwhjTYo1paWm+4uLiHY6lKywsTOnbt+/2vvCkpF8fiqZCdzxRiBYREREJUb9+/eqOO+64wpdeeqlP/bZ99923/KmnnuoJdt/yxIkTywC6dOlSV1JS4pi53njjjW7V1dUGYN26dSlFRUXJwT3SDU2ePLn8u+++67p58+YUn8/H66+/3mvq1KllABkZGaWPPPJIv4yMjLJDDjmk4uuvv+7avn17f+/evev22WefqoKCgpRZs2Z1BqiurjZz5szp2KdPn7ouXbrU5eTkdAF49tlneznd7rhx46q3bt3a7qeffuoIdv/30qVLUw8++ODtPdzPP/98L4Cnn36657777lseyuMZi7Q6h4iIiMSk1ixJ15amTZu25bnnnutb//1///vfdRdccMGwBx98sH/v3r19zz///BqAc845p+Cqq64a9thjj/V74403Vgb3RX/00Ufd/vznPw/p0KGDH+C2227bMGTIkCZX/Rg6dGjt3/72t41TpkwZaVmWOeKII4rrV/Y44ogjyq699tr2Rx55ZGlKSgoDBgyoGTFiRBVAx44drVdeeWXlNddcM6S0tDS5rq7OXHXVVVsnTpxY9fTTT6+59NJLh6WmpvoPP/zwEqfbTU1NtZ555plVF1100bDq6uqklJQU65FHHlnbu3fvuvox1dXVZp999hnt9/vNK6+8smrXHt3o1+zHAiIiIiLRYt68eWvGjx+f53Yd0tigQYP2njNnzpLmlv2bN29en/Hjxw+LYFltSu0cIiIiIiIhUjuHiIiIiOySjRs3LnC7hkjTTLSIiIjECr/f7295KQmJOoHnze92HeGkEC0iIiKxYuG2bdu6K0jHFr/fb7Zt29YdWOh2LeGkdg4RERGJCT6f79ItW7Y8tWXLlnFoIjCW+IGFPp/vUrcLCSetziEiIiIiEiK9ixMRERERCZFCtIiIiIhIiBSiRURERERCpBAtIiIiIhIihWgRERERkRApRIuIiIiIhEghWkREREQkRArRIiIiIiIhUogWEREREQmRQrSIiIiISIgUokVEREREQqQQLSIiIiISIoVoEREREZEQKUSLiIiIiIRIIVpEREREJEQK0SIiIiIiIVKIFhEREREJkUK0iIiIiEiIFKJFREREREKkEC0iIiIiEiKFaBERERGREClEi4iIiIiESCFaRERERCRECtEiIiIiIiFSiBYRERERCVGK2wWIiMQyj9fXAaj/ah/0/4Zf7QMX8QW+apv5twIoBcoz0lKsSN0XERFpPWNZen0WEfF4fQboAwwA+gf+HeDwfQ8aB+O2YgHl2IG6BCgK+ioEvMCWhl8ZaSmVbVyXiEjCU4gWkbjm8fra82sIDg7DDYNyGtDOpTLDrZRfQ/UmYBWwEvgl8O9GzXCLiOwahWgRiQsery8VGA3sFfQ1FtgdHf/RUBWNg3X9/9dkpKX4XKxNRCQmKESLSEzxeH0daRyW90JhOVx8wDrsUL0Y+AH4PiMtZYWrVYmIRBmFaBGJWh6vry9wEHAAMB47LO+BwrIbCoE5wPf1XxlpKVvcLUlExD0K0SISFQLtGPthh+YDA/8Oc7MmadEGgkI1MCcjLaXU3ZJERCJDIVpEXOHx+nYDpgDp2IF5b7TsZqzzA8v4NVT/AMzLSEupcbUqEZE2oBAtIhHh8fqGAIdjB+cp2D3MEv+qgXnAt8DHwGcZaSkV7pYkIrLrFKJFpE0E+pkPD3wdAezpbkUSJaoAD/Ah8GFGWspSl+sREdkpCtEiEhaBk5XsBxwb+DoAMK4WJbFgDfBR4OvTjLSUMnfLERFpHYVoEdlpHq+vK3AUdmj+LfZJS0R2Vg3wFXag/jAjLWWhy/WIiDRJIVpEQuLx+oYCJ2EH58m0/amvJXFtwG77eBuYlZGWUutyPSIi2ylEi0iLPF7fQOB04AzgYJfLkcRUCLwLvA58okAtIm5TiBYRR4EDA08FzgQORSc4kehRxI6BWkvoiUjEKUSLyHYer68HcDL2jPPhaN1miX7F2IH6FeDjjLSUOpfrEZEEoRAtkuA8Xl8X4HjsGedM1OMssWsL8DLwXEZayjy3ixGR+KYQLZKAAsvRHQ1cjH2AYKq7FYmE3XzgWWBGRlpKnsu1iEgcUogWSSCBdo2LgauA4e5WIxIRtcD7wDPYy+ap3UNEwkIhWiQBeLy+fYDfA+cAnVwuR8QtG4EngCcy0lK2uF2MiMQ2hWiROOXx+lKwDxL8PZDhcjki0aQWeBN4JCMt5Su3ixGR2KQQLRJnPF5fP+AK4HJgkMvliES7+cAjwIsZaSnlbhcjIrFDIVokTni8vkOwZ51PQStsiISqGPtAxEcy0lJWuFyLiMQAhWiRGObx+joCZwNXA/u5XI5IPPBjn2b8nxlpKT+6XYyIRC+FaJEY5PH6hgG/w15po7e71YjErVnYYXq224WISPRRiBaJIR6vby9gOnbLhk7DLRIZ3wN3Ae9kpKXoj6aIAArRIjHB4/WNwQ7Pp6HwLOKWJcDdwEsZaSm1bhcjIu5SiBaJYh6vbyR2eD4ThWeRaLEOuA94KiMtpcLtYkTEHQrRIlHI4/UNB/6GfdBgssvliIizPOAh4OGMtJRCt4sRkchSiBaJIh6vb3fs8HwukOJyOSLSOqXYM9P3amZaJHEoRItEAY/XNxT4P+ACFJ5FYtUm4BbguYy0FL/bxYhI21KIFnGRx+sbjP1H92KgncvliEh4/Az8SUvjicQ3hWgRF3i8vq7AzcD1QEeXyxGRtpEN3JCRlrLU7UJEJPwUokUiyOP1JQGXALcD/VwuR0Tang94Arg1Iy1lm9vFiEj4KESLRIjH6zsS++CjfdyuRUQirhj4B/BgRlpKtdvFiMiuU4gWaWMer280dng+xu1aRMR1a4CbM9JSXnG7EBHZNQrRIm3E4/X1Av4OXIFW3BCRHX0H/DEjLeVrtwsRkZ2jEC0SZh6vz2D3Pd8F9Ha5HBGJbq8Df8lIS1nldiEiEhqFaJEw8nh9+wGPAge5XYuIxIwa4D/A7RlpKcVuFyMiraMQLRIGHq+vG3AHcDWQ5HI5IhKbNgFXZaSlvOd2ISLSMoVokV3k8fpOBR4EBrpdi4jEhVeAP2SkpeS5XYiINE0hWmQnBU7V/QhwrNu1iEjc2QZco1U8RKKXQrRIiAIHDl4F3AN0drkcEYlv72K3eGx2uxAR2ZFCtEgIPF7fMOB/wGEulyIiiaMIezm8Z9wuRER+pRAt0gqB2ecrLcu6xxjTxe16RCQhfQxcnpGWstbtQkREIVqkRYHZ56eBw10uRUSkDPgL8GhGWor+gIu4SCFapAmB2ecrLMu6V7PPIhJlvgQuzUhLWeF2ISKJSiFaxIHH6xsIPAcc6XYtIiJNqACuz0hLecLtQkQSkUK0SAMer+8Yy7KeM8b0cbsWEZFWeAu4LCMtpcDtQkQSiUK0SIDH62sP3GVZ1nXGGON2PSIiIdgAnJuRlvKF24WIJAqFaBHA4/Xt6ff7X01KStrf7VpERHaSH7gLmJ6RluJzuxiReKcQLQnP4/Wd5ff7n0hKStLBgyISD74Dzs5IS1nldiEi8UwhWhKWx+vr5Pf7H0lKSrrQ7VpERMKsFPtMhy+6XYhIvFKIloTk8fr2qaureyM5OXmE27WIiLShJ4BrMtJSqt0uRCTeJLldgEikfbm19mrL7/9eAVpEEsDlwNcer293twsRiTeaiZaE4fH6evrr6p5JSk4+we1aREQirBA4PyMtJdvtQkTihUK0JASP17d3XV3dzOTk5N3crkVExCUWcAdwa0Zait/tYkRinUK0xL0vttQcg2W9npSc3MntWkREosAH2GtKF7pdiEgsU0+0xLVP1pb82cD7CtAiItsdA/zg8fr2cbsQkVimmWiJSx6vL7mspPiZLt26n+d2LSIiUaoMez3p990uRCQWKURL3Hl/yZYeSclJn3Tr0Wui27WIiEQ5P3BTRlrKv9wuRCTWKERLXHn9x5Wjuvbo+Vnnrt0GuF2LiEgMeRr75Cy1bhciEisUoiVuvPL9ssw+/Qe92aFjx85u1yIiEoM+B07JSEspcLsQkVigAwslLrw2Z8U1/XcbOlMBWkRkp00FvvN4faPcLkQkFmgmWmKax+sz2zZvfKzvgEGXu12LiEicKAJOzUhL+dTtQkSiWVzMRBtjnjXG+NyuIxyMMVONMZYxZqrbtUS77KVbOhZs2/qZArSISFj1AD7yeH0XuF2ISDSL6hBtjBltjHnRGLPKGFNljNlijPnGGHOXMaaL2/W5yRhzYSBsH9rE/lsC+4dFuLSIeGPuqkEmKXlhr779prhdi4hIHEoBnvF4fX90uxCRaBW1IdoYcxAwFzgUeAH4PfAwsBm4HujjXnXipld/WHFAt569F3Tr0XNPt2sREYljBrjP4/X90+1CRKJRitsFNOP/gCrgAMuyvME7jDG9gApXqhJXvfr98rP7Dhr8TLt27du7XYuISIL4i8fr6429BF6d28WIRIuonYkGhgNLGwZoAMuyCizLqmq43RjTzxjzmjGmxBhTaIx5yhiT6jDuYmPMvECLyDZjzAxjzOCg/ccEWiEmB22bGNi2vMF1PWaMKTDGmKBt+xlj3gvUUGmMmWOMOdGhjlHGmBxjTEWgVeVeoM3CoTFmjTFmljFmsjHm+0Bta4wxMfFx3YtfL7q+/5ChMxSgRUQi7jLgNY/X18HtQkSiRTSH6DXAeGNMa886Z4CPgFrgJuBt4BLgbzsMMuYv2IvKlwA3Av8DTgVyAzPcAF8BdUBwv+1k7DM7jTDGDGiw/SsrsMyJMSYDyAUGAXcCN2DPmr9tjDkrqI404AvgEOB+4C7gCOCeVt7fnTUUeA/4Gvv+rwbuM8bc1Ma3u9PSM7PMU7O+//tue4y4LykpOZp/ZkVE4tnJwAcer6+r24WIRINoDiR3Yc/Kfh+YyX3AGHOiMaapdYCTgBzLss6xLOu/lmVdDLwLXFo/wBjTB7gVOyQfZlnWQ5Zl3YQdoocAfwGwLKsEmE/jEP0eUF2/PXB9o4EvA98b4AngB+BAy7L+ZVnWw4HxucA9QTPWNwH9gOMty7rFsqx/Y/d/t/U6x8OB6y3Lus6yrP8ARwIeYLoxpkcb33bI0jOzks/+/Z8fGTFuwi0mKcm0fAkREWlDhwOzPV5fX7cLEXFb1IZoy7I+B9KBd4BRwHXYs8vbjDF/buJijzb4/gugjzGm/l3zkUAH4AHLsrYviWdZ1kxgMZAVdNkvgUnGmHaB4Hso8DHwPXagJvCvCYwFGI8dql8Eehpj+gSCdm/gA2AwMDIwNguYb1nWZ0F1VGCH8LaUD8wIus064D9AKvZMeNRIz8zqePoV176w94HpVyUpQIuIRIuJgMfj9Q1xuxARN0VtiAawLOs7y7JOxl6zchzwR+xF4O81xlzYYLgf2NBgW2Hg3/o2jWGBf5c63NxiYPeg778EOmG/WOyFHYS/DHzVz1BPBsqBnwLf1wfkx4BtDb7uDOxLC/w7FFjmUIfTtl3R8Gw6q4LfQDS4zWFhvu2dlp6Z1fOUS3//9n6HHnZmULu5iIhEh1FArsfrG+N2ISJuiebVObYLzJYuAhYZY94HlgPnA8/uOMzyN3EVrUlhhh0D55eB7ycDpUAedtD+AvhrYIZ5MvB1UCitf1PyV+yWDicLg2veyVrBXrkE7BlkJ50C/1Y22L4rtxkR6ZlZfU697A9vTpx8xOSWR4uIiEsGY89IH5uRlvKd28WIRFpMhOhglmX9YowpAAbuxMXXBP4djR2Ig40O2o9lWXnGmKXYs84lgMeyLMsY8zX2QYfHAftg91jX+yXwb7llWbNaqGVt4DYbGumwzUl9raOBTxz2j8GeJc9vsH1PY0xKg9no+ttcg8vSM7PSzrjy+rf3TZ9yiNu1iIhIi3oDn3q8vsyMtJRct4sRiaSobecwxhxhjGlUX+AkLL1xbsloySfYBwZea4zZ/gbCGPNb7JaN9xuM/xK7L3sy9gw0lmWVAz9iHxiYzK/90GC3dawA/uR0kJ4xJvhAjJnAPsaYw4L2dwJaewrrH4EtwCWBywXfzijgaOCjwCx+sN7AeUFjk4E/YM9sz27lbbeJ9Mys/mdd/ef3FaBFRGJKZ2Cmx+vb1+1CRCIpmmeiHwS6GWPewW7lsLD7oi/AblG4I9QrtCwr3xhzK/BP4FNjzBvYS9FdA6wD7m5wkS+BK4Bu7BiWv8Reuq4a+0DD+uv3G2Muwj4AcbEx5n/Ys7v9gYOAsUD9WfbuBs4F3jXGPITdLnI+9uxxa+5LrTHmWuBl4GdjzIvYZ3MciR3EK4GbHS76C/CAMWYfYCX2kkUZwF8tyyp0GB8R6ZlZA8+99i/vj5t48H5u1SAiIjutO/Cxx+ubnJGWssTtYkQiIWpnooE/Y6/7XL928sPAidizxQdaljVnZ67Usqy7sNeP7gn8C3sJvLeAdMuyChoM/yLwbzEwz2H79w1P+mJZVi5wIHbQvhx7xZArsPuOpwWN24rdKvId8CfswDsbe+3m1t6X17BXHFkJXAs8ApyFvYrJAZZlrXC42FrgeOwZ9n9hh/obLMty7bSuU7JOHnz+9X/9UAFaRCSm9QE+8Xh9u7c4UiQOmMA5QiQBGGPWAL9YlnWk27XUS8/MGnrBH6e9P2bfA/Z2uxYREQmLVUBGRlrKJrcLEWlL0TwTLXEuPTNr9zN/96e3FaBFROLKHsAsj9fXx+1CRNqSQrS4Ij0za/iJF1758oRJGToQRUQk/owBcjxeX3e3CxFpKwrREnHpmVkjjz7j/GcPPuLog9yuRURE2sx+2Kt2dGpxpEgMUk+0RFR6ZtaYqced8mjmaedO1ZkIRUQSwifAcRlpKdVuFyISTpqJlohJz8waffARv33oN6eeM1kBWkQkYRwFvOLx+qJ5WV2RkClES0SkZ2btsW/61H8dd94lhyUlJennTkQksZwIPOvx+jSDInFDYUbaXHpm1m5j9jvwnlMu+d1vkpNTkt2uR0REXHEO9rkTROKCQrS0qfTMrP677TnyjjOvvP6YlHbt27ldj4iIuOpKj9d3j9tFiISDQrS0mfTMrN69+vb7v/Ov/+sJHVJTU92uR0REosINHq/vFreLENlVCtHSJtIzs7p37NT5LxffeOupXbv30DqhIiIS7HaP1/d7t4sQ2RUK0RJ26ZlZXZKSkv548Q1/O7VP/wFpbtcjIiJR6d8er+83bhchsrMUoiWs0jOzOgB/OOvqP588ZPioYW7XIyIiUSsZeNXj9Y10uxCRnaEQLWGTnpmVDFxy9BnnHbf3gYeMc7seERGJej2A9zxeXw+X6xAJmUK0hEV6ZpYBTj9g6lEnTz7mpIPdrkdERGLGKOBlj9enJVAlpihES7gcNXTE6DNPOP+yjKSkJC2mLyIioTga0NJ3ElMUomWXpWdm7d+1e48Lz7vu5kNT2rVv73Y9IiISk/7o8foudLsIkdZSiJZdkp6ZtadJSvrdxTfeelCXbt17uV2PiIjEtMc8Xt8kt4sQaQ2FaNlp6ZlZacB1Z/3uT3sPGDJsD7frERGRmNcBeNvj9e3mdiEiLVGIlp2SnpnVBbh+yrEnjdznoPQD3K5HRETiRj/gXY/X18ntQkSaoxAtIUvPzEoBLhu+1/hRvzn1nMPcrkdEROLOvsAzbhch0hyFaAlJYCm7E7v36nPg2b//82HJKSnt3K5JRETi0uker+//3C5CpCkK0RKqA5KSkk+45KZbD+rUpWsPt4sREZG4dpvH6zva7SJEnChES6ulZ2YNBS4/48rrdk8bOHh3t+sREZG4Z4AZHq9vkNuFiDSkEC2tkp6Z1R24dr9Dp/bc5+D0Q9yuR0REEkYf4BWd0VCijUK0tCg9M6sdcGWPPn17H3/e5UcZozMSiohIRB0K3OF2ESLBFKKlWYEDCU/DmDEX/umWSR07derqdk0iIpKQbvJ4fb91uwiRegrR0pJJwNGnXPy7gf0HDx3hdjEiIpKwDPC8x+sb7HYhIqAQLc1Iz8waBFw8buIk//6TDz/C7XpERCTh1fdHp7hdiIhCtDhKz8zqCPyuS7fu/pMv+d3xSUnJ+lkREZFokI76oyUKKBhJI9v7oGHABX+65ZBOXbr2dLsmERGRIDd6vL7D3S5CEptCtDjZDzjq2LMv7rvbHiP2crsYERGRBgzwnMfr0ySPuEYhWnaQnpmVBlw+fK99ag856hidJUpERKLVYOAxt4uQxKUQLdsF1oO+okNqJ3PGldcfl5ySogM3REQkmp3u8frOc7sISUwK0RLsBGDP8679y75de/Ts63YxIiIirfAfj9e3m9tFSOJRiBYA0jOzxgHHHTD1qKThe+0z0e16REREWqk78IzH69PZdCWiFKKF9MysnsCVHVJTCzJPO/cYt+sREREJ0RHA790uQhKLQnSCS8/MSgYuBdqddtk1+3fp1r232zWJiIjshLs9Xt9It4uQxKEQLUcD40bts59v7P4HprtdjIiIyE5KBf6ntg6JFIXoBJaemTUcODUpKWnDCRdeobMSiohIrEsHrnK7CEkMCk0JKj0zqz1wCVBy/PmXTejVt99gt2sSEREJg396vL5Bbhch8U8hOnH9Bug/YOjuvv0nH3GE28WIiIiESTfgUbeLkPinEJ2A0jOzBgInA5tOu+yaY9u1a9/B7ZpERETC6HiP13eq20VIfFOITjCB1TguBKoOO+G04QOH7j7K5ZJERETawn88Xl8Pt4uQ+KUQnXgOAUZ179W7eMoxJ/3W7WJERETaSH/gXreLkPilEJ1AAidVOQfYfNrl1x7ZsVOnrm7XJCIi0oYu8Xh9U90uQuKTQnSCSM/MMsBZQPJ+GYf13XPs3jq1t4iIxDsDPO7x+tq7XYjEH4XoxLE3cHD7Dh23/PaM848zRmvRi4hIQhgJXOt2ERJ/FKITQHpmVifgYmDbKZf+/tCu3Xv2dbsmERGRCLrF4/X1c7sIiS8K0YnhBKDr8HHjO4w7YFKG28WIiIhEWDfgTreLkPiiEB3n0jOz9gQyjTGbTrzwyqzk5ORkt2sSERFxwUUer28/t4uQ+KEQHcfSM7PaYbdxlBx9xvnj+vQbMNTtmkRERFySBDzodhESPxSi49uRwKCOqZ1KDpz6m8PdLkZERMRlh3q8vjPdLkLig0J0nErPzOoPnApsOu68Sw9M7dy5m9s1iYiIRIF7PF5fqttFSOxTiI5D6ZlZScD5QE2PPn2T9jkoXQcTioiI2HYDbnK7CIl9CtHx6WBgHLD1hPMvz2jXvkNHtwsSERGJIjd6vL7d3C5CYptCdJxJz8zqApwLbB60+57dRu6z74Fu1yQiIhJlUoF73S5CYptCdPz5DdARqDzunEsOS05OSXG7IBERkSh0hsfrO9TtIiR2KUTHkfTMrN7AMcDmUeP3Txs6cvR4t2sSERGJYg96vD5lIdkp+sGJL8cBFlD72zPOP9KYJON2QSIiIlFsP+Bst4uQ2KQQHSfSM7MGA1OAzQdMPWpo/92GjnC7JhERkRhwi2ajZWfohyYOpGdmGeAUoBrwH37CaUe5XJKIiEisGAWc5nYREnsUouPDCOyPpLYeceLpY3v2SRvkdkEiIiIx5BaP16cWSAmJQnSMC5xY5QygJKVdO3PIUcce4XZNIiIiMWYccKLbRUhsUYiOffsAw4H8o884f0Lnbt17uV2QiIhIDPo/twuQ2KIQHcPSM7PaYR9VnJ/Srl3SfulTdXpvERGRnbOvx+vLcrsIiR0K0bHtYCANKDnqlLP37tSlaw+X6xEREYllmo2WVlOIjlHpmVmpwOmANykpyeyfcbhmoUVERHbNgR6v7zduFyGxQSE6dh0GdAYqDj/xjLFdunXv7XZBIiIicUCz0dIqCtExKD0zqytwArDFGMOBU4+a7HZNIiIiceJQj9c31e0iJPopRMemKUA7oGbKsSeP6tazV5rbBYmIiMSRv7ldgEQ/hegYk56Z1RnIArYCHHzk0ZqFFhERCa/DPF5futtFSHRTiI49hwLtger0zOOG9+jdd6DbBbntH9dczOR+7Zr8+viNlwC45qQjmh234PvcFm/r3ece59bLz+GMA0YyuV87rjmp6XPblBYV8uhtN3H2pLEcObQrp+63B3+/8jzWLF+yw7glc3/gwb9exwVTJpC5ew9OHj+Um845gaU/z3G83vLSEh64+RpOHDeYI4d25crfpvPDF7NarP25++9kcr92nHXQaMf9n7//JpdnHsyRQ7ty7Kg0br38HLZuXN9oXPaL/+PMA0dxzIg+TLvwVPK9WxqNuf8vf+CGs7RSlIjENPVGS7OMZVlu1yCtFFiR4z6gBKj6872PXtin/8ChLpfluoU/fMOmtasabX/2vjvZvG41b85dQ6+0fvzwxSwKt21tNO7ff72OpKQk3p6/nnbt2zd7W6dPHE5ZcRGjJ0xkydwfGLH3BB56+9NG43w+H1ccPYn1vyzn+AsuZ9iI0WzZsI53n3scX20tz34+l36DhwDwf5ecwfxvv2LKcSczYq/xlBYX8d7zT7Jl/Rr+OeMdJh352+3Xa1kW15x0BEvm/sAZV1xH2uDd+PDl51g2/yceeD2HCYc4fzCxed0azp+8D0kmiV5p/Xn5u6U77H/v+Sf51w2/Y5+D0jnshNMoLSrkzacepmOnzjz58Xf06N0HgAU/fM3VWVM45swLGLH3BF78z73sOXZv7n05e/t1/bJoHlcdm8H/Pp3DbnuObPbxFBGJcgdmpKX84HYREp1S3C5AQjIJSAW8ex94yAAFaNu4AyYx7oBJO2wr3Obln9deygFTj6JXWj8ADphyZKPLLpv3I2XFRZx40ZUtBmiAh97+lH6Dh2CMaXJGF2DeNx5WLPiZa+98gFMu/f327XtNPJibzjmeL7Lf4vQrrwPg9Cuv5W//nbHD7R979kWcn7EPT931tx1C9Bcz32beNx5ufvApfnvmBQAcffr5XDhlAo/cegNPfvydc923/JG9D0zHV1vDtk0bd9hXW1PD43dOY/SEiTz0zmySkuwPqA49+jguPfJAXnr4Xn43/W4Acj/KZtCwPfnLg08B0KlrN+6+7jKqq6ro0LEjAP+++VpOvfQPCtAiEg/+Dzje7SIkOqmdI0akZ2Z1AE4CvAAZx5x4iLsVRbdP3nqZOp+PzNPObXZczusvAnB0C+Pq9d9tKMaYFsdVlJYA0LvfgB229+7XH4AOnTpt37b3AYc0CvDde/VmwiFTWNug9eOzd9+gS/ceHHXqOdu3dejYkWPPvohl835i45qVjWr5ZtaHfPvph1xzx/2Ota5etojSokIOP/H07QEaYPhe4xk6Ygyfvv3q9m3VVZV07tZt+/fdevTC7/dTU1UJQM7rL9iz3tf/1fG2RERizHEer2+C20VIdFKIjh0HAV2AyoHD9ug2ePfhY90uKJrlvP4Cnbt249Cjm55A8Pl8fPr2q+y250jG7n9QWG9/zP4H0r5jR566azpzvviUbZs3suCHr3ngpj8waNieHHHiGS1eR/7WTXTruePy38sXzGXEuAmkpOz4IdKYfQ+w98+fu8P26qoqHpp2PSdf/DuGjRzjeDu11dUAdExNbbSvQ2oq2zZv3N73PHa/A/ll4Tw+e+8NNq5ZyWuPP8hue46ka4+eVJSV8tjtf+V3t95DaufOLd4/EZEYcYvbBUh0UoiOAemZWe2wZ6HzAI46+ayDkoKnDGUHq5cuYsWCn5l63CnbWwyc/PDZxxTmeflN0KxuuPTpN4Dpj71IWUkxfzz9aE6ZMIyrs6YA8N+ZHrp0697s5ed9+xULvv+aw088fYftBVs3b5/NDla/LW/Lph22v/TwvVSUlXLRDU2v1jR4jxEkJSUx/9sdD6wsLsjfPhOet9luATnipDOYknUK0y87i7MOGs2qpQu54V//BeB/9/6dIXuO5IgGNYuIxLiTPF7f7m4XIdFHPdGxYT+gJ7Cmc9du7Ybvtc9+bhcUzT56/QUAMk8/r/lxr72AMYbM08IfogF69O7NmH0PYNwBkxg6YjRrli3mpUf+xbSLTuX+1z6ig8PML0De1s38/cpz6Td4CBf+accJkOqqStq379DoMu07dAzsr9q+bdPa1bz4n3u47h8PNhvau/fqzeEnns6nb7/KsFFjOeKk0ykpLOCx2/9KbW3N9tsFSEpK4rYnX+KKaXdQXJjPsJFjSe3cmTXLl/Duc4/z+IdfU1lezsN/+xPfzs6hZ5++XHbz7Rx0eGZoD56ISPRIAi4Hbna7EIkums2McumZWUnYZycsADjipDPGt6tPTNKI3+9n1luvMGDI7ow/+NAmx5WXlpD78fuMn5RB/93Cf3zm0p/ncO3JR3HGlddxzh9u4NCjj+Pca2/i70++woLvv+a9GU86Xq6spJibzj6OyvIy/vn8243Cb4eOqdTUVDe6XE11VWD/rz8aD027nj1G78UxZ13YYr1/vvdRJh97Ek/d9TfOOmg0Vxx9CO07dNh+2U5duu4wfuCwPRiz7wHb2zYe/Ot1HH/eZewxZhwPTruO+d9/zW1PvMSUY0/mrxeczKa1q1usQUQkil3s8frauV2ERBeF6Og3ChiIvawdex+YHt7m3Tjzk+cztm3awG9OPbvZAwA/e/d1aqqqWjzwcGe9/cx/Se3cpdGScxOnHEGnLl2Z/13jNamrKir4y7knsG7lcu564R32HLt3ozG9+g0gf8vmRtvzt9o9y33628uG//TV53z9yUxOv+I6tm5Yx+Z1a9i8bg01VVXU1fnYvG4NRfl52y/fqUtXbn/6Vd78eQ0PvTObF79ZzL0vZ1NeUkJSUhIDh+3Z5H397L03WL10ERfd8LfAm5iXOfvqPzHugEmcd91f6JXWn1lvvdK6B05EJDqlYbdVimyndo7odwxQDjDpqGP26Nq9Rx+X64lqOW/Ut3K0sCrHGy/SITWVqced0iZ15G3ZjL+urtF2y7Kw/H7qfL4dttfW1HDLxaex6Mfv+Mezb7LPQc6z6CP33pcfvvgEn8+3w8GFi3/6HoARe08AYOuGtQD8/SrnlpYzDhjB0Wecx18f+t8O2/sOGETfAYMA+8DLubmfs9fEg+nUuYvj9VRVVPDobTdxxf/9gy7dulPg3UpNdTW9+/96DqA+/QaQt2Wj4+VFRGLIlcBrbhch0UMz0VEsPTNrEDCOwAGFB0w56kB3K4puleXlfDnzHfaaeDCDdx/e5LjN69Yw/9uvyDj6BDp37eY4pqykmLUrllJWUrxTtQzZc6TdMpKTvcP2Lz94h8qKckaN/7Wtva6ujr9fdR5zvpjFXx/6H5OOOqbJ65163MmUFRfxyRsvbt9WXVXFBy8/y4i9J2y/3/sdehh3PvNGo6/dR+1Fr7T+3PnMGzusX+3kpf/cS8G2rZx51R+bHDPj3/+kT/+BHB3oP+/euw/JKSmsW2GfzMVXW8uGNSsbLfUnIhKDDvN4faPcLkKih2aio9vhQC1gDRgyrGv/3Ybq7BXN8Hz4DpXlZRzdwiz0x2+8hGVZzc5Wez54h39ee+kOJzUByM3J5pdF8wEoLS6krq6O5+7/BwDjJ2UwYVIGAKdc9ns+em0G0y8/ixMvuIIhw0exZvkS3nv+CXql9ef48y7bfp2P3nojX2S/xcQpR2JZfj4OCsgAGb89cXvv8ZSsk9nnoHTuu+n3bFi9krRBg/nolefZsn4N97320fbL9Bs8ZPsZEYO9/uRD1FRXkXHMCTtsf/nR+1k+7yfGBpbm+/6zT/B88A4nXnhFo7H1Nq5ZyetPPMR/3vtse+tMcnIyU7NO4bn778Tv97Pwh28oKypssxl/EZEIuwJoemZBEopCdJRKz8zqCkwGtgJMOfbkCUlJSS2f5SOB5bz+Iu3at+fwE5pfYu2TN1+iV1p/JjqcwbAlX8x8i49enbH9+5LCAp6+ezoAF/75/7aH6MG7D+epWd/zzL1/x/Phe+Rt2UiXbj2YknUyl/7l7/Tsm7b9OlYs/BmAOV/MYs4Xsxrd5qs/rNgeopOSkrjrhXd54s5pZL/wNOVlJewxehx3v/ge+x06NeT7U2+P0XvxZfZbfDf7I2pra9h91Fj+8u8nd3gD0dBD0/5I5unnMqrBYjHX3/UQ9914Nc/+63Z69u3H9MdfZOiIps/uKCISQy7weH1/zUhLqWp5qMQ7Y1mW2zWIg/TMrKOAs4B1xhj+79Hnr+3UpWsPl8sSERFJdOdnpKXMaHmYxDv1REeh9MysFOBYAr3Qk446Zk8FaBERkahwpdsFSHRQiI5OewHdgQqA/Q49TCdXERERiQ6HeLy+xmuQSsJRiI5ORxJY1q7vgEGdBw7dXQ2lIiIi0UOz0aIQHW3SM7P6Yi9rlw8w9bhTxiclJet5EhERiR7nery+zm4XIe5SOIs+BwF+wAIYNX5/tXKIiIhEl27YB/9LAlOIjiKBAwqPInBA4cTJRwzp0q17b3erEhEREQfnu12AuEshOrqMxn53Wwkw4ZApOnBBREQkOh3q8foan9VKEoZCdHQ5jECATmnXLmnI8JFjXa5HREREnBnU0pHQFKKjRHpmVi9gXwKtHAcdfvQe7Tt07ORuVSIiItKMc9wuQNyjEB09Dgj86wfY+8BDxrlYi4iIiLRsb60ZnbgUoqNAemZWEvAbAsvadeiYmjxo2J5aG1pERCT6aTY6QSlER4dhQE8CJ1iZdNQxI9q1b9/B1YpERESkNc7yeH3G7SIk8hSio8NEoK7+m70mHqxWDhERkdgwBMhwuwiJPIVol6VnZiVj//LlAXTu2q3dwCG7j3S3KhEREQmBWjoSkEK0+/YEOgPVAIf8JmtUckpKO3dLEhERkRCc4vH6kt0uQiJLIdp9BwK++m/G7HuAWjlERERiS29gittFSGQpRLsoPTOrHZBOoJWje6/eHfoNHjLc3apERERkJ5zkdgESWQrR7hoBdARqANIzjxuTnJysj4NERERiz4lapSOxKES762ACARpg9Pj91cohIiISmwZjt2hKglCIdkl6ZlYH7BC9DaBn336pfQYM2t3dqkRERGQXnOB2ARI5CtHuGQW0I3BQ4X6HTt09KSlJz4eIiEjsUohOIApt7jkEqKz/Zs8xe+/pYi0iIiKy68Z6vD79PU8QCtEuSM/M6oR9lsL8+m39dxu6h3sViYiISJgc73YBEhkK0e4Yg/3Y1wEMGzW2Z6cuXXu4WpGIiIiEg0J0glCIdkc6Qa0c+xyUro9+RERE4sMhHq+vs9tFSNtTiI6w9Mys9sA4oKB+29ARo9XKISIiEh/ao7MXJgSF6MgbAiQTaOVISkoyaQMHa2k7ERGR+PEbtwuQtqcQHXmjg7/Z+8D0ge3ad+joVjEiIiISdke5XYC0PYXoyNsfKK7/Zsy+B6iVQ0REJL6M9Xh9g9wuQtqWQnQEpWdmdQGGAqX12wbvMVwHFYqIiMQfzUbHOYXoyKqfdbYAOnft1q5X336DXaxHRERE2ob6ouOcQnRk7UXgNN8A+6ZPHZqUnJzsYj0iIiLSNo70eH3G7SKk7ShER0h6ZpbB7ocurN82Ytx4tXKIiIjEp77ABLeLkLajEB05fYBeBJ1kZcCQ3XVQoYiISPya6nYB0nYUoiNnh8Dcb9CQLt169kpzqxgRERFpc5PcLkDajkJ05OwLVNV/MyF9imahRURE4tshbhcgbUchOgLSM7OSgfEE9UPvMWqsQrSIiEh8G+Tx+nZzuwhpGwrRkTEIaA/U1m/oN3ioQrSIiEj802x0nFKIjozhwPZlboaPG9+nY6dOXV2sR0RERCJDfdFxSiE6MiYSdJbCEXuN16lARUREEoNmouOUQnQbS8/M6giMAorrt/UfMmyAexWJiIhIBE3weH2pbhch4acQ3faGBv7112/o02+AQrSIiEhiaIf9ibTEGYXotjcGsOq/McbQvVef/i7WIyIiIpGlvug4pBDd9vYHiuq/2WPMuN4p7dq1d68cERERiTD1Rcchheg2FOiHHgSU12/bY8zeauUQERFJLJqJjkMK0W2rP3Yrx/Z2joFDd1eIFhERSSxpHq9vT7eLkPBSiG5bAwhaHxqgd78B6ocWERFJPGrpiDMK0W1rOFATvKFH7z6aiRYREUk8B7ldgISXQnTbGgmU1X+z254ju7fv0FFrRYqIiCSeMW4XIOGlEN1G0jOz2gMDCTqocPhe+2gWWkREJDGNcrsACS+F6LbTL/Dv9oMKBw3bUyFaREQkMQ3yeH1d3C5Cwkchuu00Oqiwz4CBCtEiIiKJa6TbBUj4KES3nT0AX/CGnr37KkSLiIgkLrV0xBGF6LYzEiit/6Z3vwGdOqR20sc4IiIiiUshOo4oRLeB9MysFGA3gg4qHDpydG/3KhIREZEooBAdRxSi20Yadj+0v35D/8FDFaJFREQSm0J0HFGIbhuNDyrsP7CPS7WIiIhIdBjp8fpMy8MkFihEt41hBM1CA/To1Ucz0SIiIomtMzDY7SIkPBSi28ZwgvqhAbr26KkQLSIiImrpiBMK0W1jMFBR/01SUpLp1LVbLxfrERERkeigEB0nFKLDLD0zqxP2xzU19dt223Nk9+Tk5GT3qhIREZEooRAdJxSiw683DfqhB+8+XK0cIiIiAgrRcUMhOvx602BljrRBuylEi4iICChExw2F6PDrS4MQ3aN3354u1SIiIiLRZYjH60t1uwjZdQrR4TcEqAre0KV79+4u1SIiIiLRxQAj3C5Cdp1CdPgNIWhlDoDOXbv3cKcUERERiUJq6YgDCtFhlJ6ZZbDPVlgZvD21cxfNRIuIiEg9heg4oBAdXt2BZKCufkPH1E4pHTp27OReSSIiIhJlhrpdgOw6hejw6glYwRv6DxnW1aVaREREJDr1dbsA2XUK0eHVnQYrc/TtP7CbS7WIiIhIdFKIjgMK0eHVKET37JummWgREREJphAdBxSiw6s/UBu8oVvP3pqJFhERkWAK0XFAITq8+gPVwRu6du+hmWgREREJ1sPj9bVzuwjZNQrR4dWXBiG6Y6fOWplDREREGtJsdIxTiA6vXjQM0amdFKJFRESkIYXoGKcQHSbpmVntgVTAF7y9fYeOqe5UJCIiIlFMITrGKUSHTzfA33Bj+44K0SIiItKIQnSMa3WINsb0Nsb8xxjzkzHmR2PMg8aY3m1ZXIzpSoMTrQC079BB7RwiIiLSkEJ0jAtlJvoVwAucApwKbANebYuiYlRnGqwRnZSUZFLatevgUj0iIiISvRSiY1xKCGN7WZZ1e9D3dxhjTgxzPbEslQYhukeftI7GJJkmxouIiEjiUoiOcaHMRH9mjDnTGJMU+DodmNlWhcWgTjQ8W2GfvmrlEBERESdpbhcguyaUEH0F8BJQE/h6BfijMabUGFPSFsXFmG5AXfCGrj166aBCERERcaKZ6BjX6nYOy7J05r3mdafB8nZdundXiBYREREnylUxrtUh2hgz2Wm7ZVlfhq+cmNYdqA3e0KFDR53SU0RERJyo5TPGhXJg4Q1B/+8IHAj8CBwe1opiVzcahOiUdu2TXapFREREoptCdIwLpZ3juODvjTG7AfeEvaLY1ZUG7Rwp7dqF8iZFREREEodaPmPcrpyxcAMwLlyFxIGuNJyJTmmnmWgRERFxopnoGBdKT/R/+PWMfEnABGBeG9QUc9Izswz2L0NB8HbNRIuIiEgTOnq8PpORltLobMcSG0IJeXOC/u8DXrYsKzfM9cSqdthrRO/wi5CsEC0iIiJNSwUq3C5Cdk4oPdHPNbffGPOmZVmn7HpJMcmxbSMlJUXtHCIiItKUTihEx6xd6YluaI8wXlescXwzkpyimWgRERFpkvqiY1g4Q3Qi9/Sk4HD/U1JSFKJFRESkKVqhI4aFM0QnMse2jeRktXOIiIhIkzTZFsPCGaJNGK8r1jjORCdpJlpERESalsjZKea1OkQbYw43xjTXu3NTGOqJVY5hWe0cIiIi0gx1BMSwUELehcBjxph8wBP4+sqyrEIAy7I+Dn95McOxbSMpOVntHCIiItIUzUTHsFCWuDsfwBgzEDgVeAQYGMp1xDHn1TmSNRMtIr/atnnjmsryslK36xCRyKqtqe7Up//AX7r36pPXYFeRG/VIeIRyxsJzgQxgbyAPeBh7NlqamolOStLHNCKy3dKf58yf+dIzc92uQ0QibgjwVG5O9tduFyLhE8pM6b+BlcBjwGeWZa1pi4JilOPjWOfz+SJdiIhEr559+/V0uwYREQmPVs+UWpbVB7gY6AjcaYz53hgzo80qiy2OIdrnq62NdCEiEr269+rdy+0aRMQVBvU/x51QVufohv1xxFBgGNAd8LdNWTEnGYdfjtqamhoXahGRKNW1ew/NRIskLoXoOBNKO8dXQV8PW5a1oW1KikkpOPxy+Go1Ey0iv+rUpWsfYLDbdYhIxOn03nEolNU59gEwxnQlsU/x7cTxwMLammqFaBHZrn2Hju1HT5j47NKf55S4XYuIRNxytwuQ8ApldY5xwAygl/2t2QZcYFnWwrYqLobU4fDGora2RiFaRHZw4Z9uqc5IS5nvdh0iIrJrQlmC7Qngj5ZlDbUsawjwp8A2gVqcQnR1tXqiRaSh4W4XICIiuy6UEN3ZsqzP6r+xLOtzoHPYK4pNjmG5Ru0cItLYnm4XICIiuy6UAwtXGWP+D7ulA+BcYHX4S4pJjmG5pqpKIVpEGlKIFhGJA6HMRF8M9AXeAt4O/P+itigqBjmH6GqFaBFpRO0cIiJxIJTVOQqBa9qwlljmGJarqyrVEy0iDWkmWkQkDoSyOsdI4M/YJ1rZfjnLsg4Pf1kxxzEsV1dVaiZaRBoa4PH6OmWkpVS4XYiIiOy8UHqiXwceA57CXtJNflWLw8lWqioqFKJFxMmewAK3ixARkZ0XSoj2WZb13zarJLY5t3NUKkSLiCOFaBGRGNdiiDbG9Ar8931jzO+wDyqsrt9vWVZBG9UWSxxnoivKy9QTLSJO1BctIhLjWjMT/SP2iUTqQ+KfG+zfI6wVxSbHEF1ZXqaZaBFxsssrdFTedmcScEAYahGR+FCVOn3aPLeLSCStCdFnAOsty9oMYIy5ADgFWAPc2maVxZDcnGwrPTOrBkgmqF+8orRUIVpEnIRjJtoCPgNSw3BdIhL7fgFGuF1EImnNOtGPEWjfMMZMBv4JPAcUo9N+B6umweNZU11V5/f7/S7VIyLRa5dDdOr0aRawKgy1iEh8UN6IsNaE6OSgvuczgCcsy3rTsqz/QycNCFaNPRO9g7o6n2ajRaShoR6vr10YrueXMFyHiMQHhegIa1WINsbUt30cAcwO2hfK6h7xrtFMNEBtTU2VC7WISHRLBoaG4XpWhuE6RCQ+WG4XkGhaE4JfBr4wxuQBlYAHwBgzHLulQ2xVOPQmVpWXl3Tq3KW7C/WISHTbk1bOJB87asww7BNd7WD64Uck7d1/QHirEpFYpZnoCGsxRFuWdacx5lNgAPCxZVn173SSgD+0ZXExxrGdo7yspKRXWj8XyhGRKDccyGnl2OOADGCHsxx+sXr1QIVoEQnQifAirFXtGJZlfeuwbXn4y4lpVTi0c5SXFJe4UIuIRL9QDi7ciP3J38bgjVvLyoqBo8JZlIjErEq3C0g0remJltapxGEmuqSoUCFaRJyEEqI34/D68kt+XrHfsvQRrogAlLldQKJRiA6fChxm9ovytqlvXESchLK6USEOBw3V+v3+spoavcaICEC52wUkGoXo8NkGNFqyqsC7RTPRIuJkD4/X1+hMp00oaGpHSVVVk/tEJKFoJjrCFKLDp9Bp49ZN6xWiRcRJR2BgK8cWAibwteOOygrH1x4RSTiaiY4whejwKcFheZmt69eW6ayFItKEVrV0zFy2pBYoAjo03OctL9dMtIiAZqIjTiE6fBxnnP1+v1VdWakfbBFxEsrBhZuwZ693sKW0VCFaREAhOuIUosOnhCYez8qKMrV0iIiTUEL0BhxO6LS2qFDtHCICaueIOIXo8KkEanFYhqqirFQhWkSchDoT3b7hxmV5eQW/ngNLRBKYZqIjTCE6THJzsi0gH4eexbLiYi1BJSJOQlnmrgCH4y7Ka2p8lbW1peErSURilGaiI0whOrzycAjRpUUFmokWESehzEQX4LBWNEBJdbVaOkREM9ERphAdXltxCNFF+dsUokXESQ+P19erlWMLcWgXAyisrNTBhSKiT6QiTCE6vLw49Cxu3bhes0Qi0pTWLnNXiT3T1OikTnkVWuZORPC6XUCiUYgOryKnjauXLsrTgT8i0oRQWjo247BCx9ayMr1RF5EtbheQaBSiw8vxhCvlpSW1VRUVaukQESehrtDRKESvLyrSTLRIYrOAbW4XkWgUosOryVU4SosL8yJZiIjEjFBW6NiAw3EXy/PzFKJFElt+6vRpPreLSDQK0eHV5AlXivK3KUSLiJNQZqLzcFihY1t5eVW1z1cVvpJEJMZsdbuARKQQHV6VQDWQ0nBH/tYtCtEi4iSUEN1k73NpdbVmo0USl0K0CxSiwyhwwpWNQKeG+7asX6sQLSJOBni8vkavGU0oAIzTjsIqLXMnksB0UKELFKLDbx0OIXrdL0sVokWkKa2djS4DanFYL7qgokIrdIgkLs1Eu0AhOvzW4HDgz+Z1a0pra2uqI1+OiMSAVoXomcuWWNh/LJ2WudNMtEjiUoh2gUJ0+OXhsMwdQFlxkWajRcRJKCt0bMQhRG8oLlaIFklcCtEuUIgOvyaDcmHeNp1NSESchHJw4XocQvTKgny1c4gkLoVoFyhEh1/9gT+NDv7xbly3OfLliEgMCCVEO55QYV1RUanPX1cbpnpEJLZscLuARKQQHWa5Odk+7PPXd2y4b93K5Tp6VkSchNLOUYBDy5gFlFbXaDZaJDGtcruARKQQ3TYcV+hYPu+nrZbV6DwJIiJDPF5fu1aObXKZu+KqSoVokcSzNXX6tHK3i0hECtFtYw0OIbqspLimorREB/+ISEPJwNBWji0O/Nvo9bugQmtFiySglW4XkKgUottGk73PBdu2bopkISISM1rV0jFz2RI/TbSMecu1zJ1IAlKIdolCdNvYgt2i2MjWjesVokXESSgHF27CYYWOjSUlaucQSTwK0S5RiG4bXuwQ3ejxXffLMq3QISJOQgnRG3GYiV5TWKiZaJHEo4MKXaIQ3QZyc7LrsJeb6dxw39K5czbr4EIRcRDKCh2bcTj19y/5ecV+y3I82ZOIxC3NRLtEIbrtLAe6NNxYUlRQXV5SrDMXikhDocxEOy5zV+v3+8tqaoodxotI/FKIdolCdNtZBTguWeXdvHF9hGsRkei3h8frc1y6zkEhTSxzV1JVpZYOkcRRljp9ms5W6BKF6LaziSYOLtywcsW6CNciItGvIzColWPrQ3SjIF1QWaEQLZI41A/tIoXotlN/dsJGj/GSn39QiBYRJ61q6Zi5bEktdpDu0HDftvJyrdAhkjjUyuEiheg2kpuTXUsTBxeuXrqooLqyoizyVYlIlAulL3ozDit0bCkt1Uy0SOJY5HYBiUwhum0tBbo67cjbull90SLSUCgrdGzAYa3otUWFmokWSRzz3C4gkSlEt63lQIrTjo1rVqmlQ0QaCnWt6PYNNy7LyyvQMpoiCWO+2wUkMoXottVkUF6x8GeFaBFpKJQQXYjDMnflNTW+ytra0vCVJCJRqgL4xe0iEplCdNvKA8pwOPhn8ZzvNvtqa2siX5KIRLFQ14p2nHIuqa5WS4dI/FuQOn2aTq7kIoXoNpSbk20Bi4FuDffV1fmswjzvxshXJSJRrIfH6+vdyrGFOJy1EKCwslIHF4rEP7VyuEwhuu0twOHgH4ANq1ZofUcRaai1y9xVYn/S1eikTnkV5QrRIvFPBxW6TCG67a2jiY9cF/34ndZ3FJGGQl3mrtGb9K1lZWrnEIl/mol2mUJ029sM+HD42HXRnG83V1dWlke+JBGJYqEsc7cJhxC9rqhIM9Ei8U8h2mUK0W0sNyfbB6zAoS/asiy2bFir2WgRCRbKTPQGHA5cXpGfpxAtEt/Wpk6fVux2EYlOIToy5gFdnHasXLxAy9OISLBQQnQeDu1i28rLq6p9vqrwlSQiUUb90FFAIToyVje140fP7JWW5deZEUSkXijtHE32PpdUV2s2WiR+/eR2AaIQHSlrsfuiG529MH/r5oqi/PzNkS9JRKJUf4/X17mVYwsA47SjqErL3InEsVy3CxCF6IjIzcmuxV7qrqfT/g2rlq+IbEUiEuX2aOW4MqAWhwOXCyoqtEKHSHyqA75zuwhRiI6kH2hivejFP/2gvmgRCdaqlo6Zy5ZYwFYcXlu2lJVpJlokPi1InT6t1O0iRCE6kpoMyvO//WpjTXV1ZSSLEZGoFsrBhRtxCNEbi4sVokXi09duFyA2hejIKQC8QKNex7o6n+XduE5nLxSReqGE6PU4hOiVBflq5xCJT+qHjhIK0RGSm5NtAd/TRF/0qqWL1NIhIvVCWaFjm9PGtUVFpT5/XW2Y6hGR6KGZ6CihEB1Zi2jiSPqfvvpMIVpE6oUyE10A+J12lFbXaDZaJL5sSp0+bY3bRYhNITqy1mCfGKHRkfRb1q8tKyks2BrxikQkGg3xeH3tWjm2yWXuiqsqFaJF4otmoaOIQnQE5eZkV2PPRvdw2r/ul2XLIlqQiESrZGBYK8fWn/q30et5foXWihaJM+qHjiIK0ZH3Aw4HFwJ891nOggjXIiLRq1UtHTOXLfFjH7TcseG+beVa5k4kzmgmOoooREdekydWWbHg57yi/DydvVBEILS+6E04LXNXUqJ2DpH4UQHMdbsI+ZVCdOR5gUKaOPHKigVz50e2HBGJUqGs0OG4VvSawkLNRIvEj89Tp0/TijtRRCE6wlpa6i734+yFfr/fimxVIhKFQpmJ3ozD6/kv+XnFfstyXLlDRGLOx24XIDtSiHbHAiDFaceW9WvL8rZsWh3hekQk+uzyMne1fr+/rKam2GG8iMSeHLcLkB0pRLtjBVANtHfaueSn79XSISJ7eLw+x6XrHBTSxDJ3JVVVaukQiX3rUqdPW+p2EbIjhWgX5OZk1wIeoK/T/q8+en9Jnc+nvieRxNYRGNTKsfUhulGQLqisUIgWiX1q5YhCCtHu+Y4mWjpKiwtrNq1dpTWjRaS1y9zVYgfpDg33bSsv1wodIrFPrRxRSCHaPaux+xg7Oe2c/12uWjpEJJQVOjbjsELHltJSzUSLxLY6YJbbRUhjCtEuyc3J9gOfAb2d9n8z64OVNdVVFZGtSkSiTCgHF27A4YQrWuZOJOb9kDp9WpHbRUhjCtHu+pEmngNfba1/7YqliyJcj4hEl1BC9EYcDlZenp9XaFlaNVMkhqmVI0opRLtrC7Ae6Oa080fPZ2rpEElsoYToQhyWuSuvqfFV1taWhq8kEYkwhegopRDtosCJV2YDPZz2//z1FxvKS3XaXpEEFupa0Y5TzsXV1WrpEIlNRdgnaJMopBDtvnmBfx3XeF21ZIFmo0USVw+P1+d43ISDAiDZaUdRZaXejIvEpuzU6dPq3C5CnClEuyw3J7sQWAz0ctr/zawPFaJFEltrl7mrAspw6IvOqyjXTLRIbHrT7QKkaQrR0eFzoLPTjlVLFhYU5m3bFNlyRCSKhLrMXaMVOrTMnUhMKkf90FFNITo6LMZeB9Lxo9jl83/SbLRI4gp1hY5Ga0WvLy5WO4dI7Pkgdfq0SreLkKYpREeB3JzsCuwDB5o4Dfh7C/1+f6Oj7kUkIYQaohvNRC/P26aZaJHY84bbBUjzFKKjx9c4nLIXYNvmjeWb1q5aGuF6RCQ6hNLOkYfDMnd5FRVV1T6fZrREYkcV8IHbRUjzFKKjx3KgAoeDggC+/njmt5EtR0SiRFiWuSuprlZLh0jsyEmdPq3M7SKkeQrRUSI3J7sW+JImWjp++uqz9YV53o2RrUpEokB/j9fneOCxg0KaeF0vqqpUS4dI7NCqHDFAITq6fE8TBxcCzM39XLPRIomptbPRZUAtDq8j+eUVCtEisaEWeN/tIqRlCtHRZS32x7GOs06z3319cWVFeUlkSxKRKNDataIt7GXuGq3QsbW8TO0cIrHh09Tp04rcLkJaphAdRXJzsv3AJ4DjGcp8tbX+JXN/+CGyVYlIFAilL9oxRG8sLtZMtEhsUCtHjFCIjj7fYK8ZneK085M3X/6xzuerjWxJIuKyUFboWIdDiF5ZkK+ZaJHoV42WtosZCtFRJjcnuwT7AMM0p/2F27ZWrl62eF5kqxIRl4UyE70NhxU61hYVlfr8dXoDLhLd3lUrR+xQiI5Os4F2gHHa+Xn2m99ZluMqViISn0IJ0YU0scxdaXWNZqNFotuzbhcgracQHYVyc7I3AouAPk77f1k4L8+7cf0vka1KRFw0xOP1tWvl2AKaeANeXFWpEC0SvTYBH7tdhLSeQnT0+gDo1NTO7z7L0XJ3IokjGRjWyrHF2DPRjV7f8yu0VrRIFHshdfq0OreLkNZTiI5eSwEv0NVp59cfz1xZWly4LbIliYiLWrvMnR+7L7pjw33byssUokWi17NuFyChUYiOUoHl7t4DejU1Zv63ud9FriIRcVkoK3RswmmZu5IStXOIRKfvU6dPW+J2ERIahejo9iNQBbR32jnr7ZfnVVdVVUS2JBFxSSgHF27AIUSvLizQTLRIdHrO7QIkdArRUSw3J7sKyAH6Oe2vLC/3LZrzzdeRrUpEXBJKiN6Cw+v7yvz8Yr9l+cNXkoiEQTXwsttFSOgcT+ghUcUDHI/9B7HRH78PXn72h3EHTEpv36Fjo1knEYkrobRzFODwelHr9/vLamqKu3Xo0DN8ZblrzsYNvDzvZ75cs5o1hYV069iB8f0HMm3q4ew/aND2cZ+vWsmrC+bzzbq1rC8upk/nThw4eDf+dtiRjOjjuBBSI5W1tfznm1xeXTCftUWFdOvQkf0GDuLGyVM4cPBuO4y958vP+XHTRn7cuJFNpSWcO35fnjjplEbXefnbb/LCvLlN3ub/Tj6VM/eZsP37nzdv4rbZs/hm3Vp8fj/7DRzE9MOPJH3osB0ud8dnn/KPLz5zvM7l19/A4O7dm7zNK999i+fn/sRhu+/BzAsu3mHfsz/N4V7PFxRUVDJ599158Njj6d91x0N3rpv5PqsLC3j33AuavA3ZwXup06ep1SoGKURHudyc7Pz0zKxvgf2wZ5d2UFZSXLP4x++/mXDI5MMjX52IRNDuHq/PZKSltGaR+OaWuSuIpxB9/1cevl63hhPHjuOqgw6mqLKKp3/8nilPPcYbZ53L0SNHATDtkxwKKis4ccxejOzTl82lJTzxw3dMevwRZl18GRMGDGzxts59/RU+XrGCC/fbn6sPnkReeTlPzfmBo/73FJ9echkTBw3ePvbW2bPo26kz+w8azObS0iav85KJB3DYHo0/ZPjnF5+xpqiQw/b49b3TvM2bOfJ/T9K7UyduzJhKu+QkZsz9iWOff4YPL7iYSUOGNrqe+357LD0azLH0Sm16zuX79et58eefSU1pvKLit+vW8bv33uG8CfsxYcAA/vXVl1zx7ls7hOX5WzYzY+5PfHvl1U3ehjTyjNsFyM5RiI4NnwCTmtr5wcvPfr/XxIMOade+Q6Oj8UUkbnQEBmH3O7ekCDtEGxqceKWwsrJgt+49QmkNiWrXTErn2VNOo33Kr3/OLthvf/Z/5EFumz1re4i+O/O3HDJkKElJv3a5nLH3eA7473+464vPeeXMs5u9ndUFBXy4fBnXTErnrszfbt9+3Oix7P/oQ7wyf94OIXrxtX9kWE/7uPCut/2tyes9aLchHLTbkB22ecvKuOLdtzhiz+H069Jl+/bbZs8iyRg+v/RKBgRmfy/Z/wAmPPwgN3z0AV9dflWj6z9u9NhmZ52D+f1+rvvgfc7fdz8+Xdn4VAQzly1hj569ePzEk+371aEDV777NlW1tXRsZ4fuP36QzdUHT2r17L6wGrttU2KQeqJjwxpgFU2s1FFSVFC9ZO4PWjdaJP61qqVj5rIltdhnLuzQcN+28vK4+tj44CFDdgjQAL07dSJj2O4s3fbrKqCHDtt9hwANsGfv3ozr15+l27wt3k5JdTUA/bvs2LpQ38rQqd2OM7f1AXpnvLZgPj6/n3PG77vD9q/XrSFj2O7bAzRAp/btOXbUaH7atJGV+fkO12ZRUlWF399yK/wTc75nTWEBtx5xlOP+Sp+Pbh1/navpmdoJv2VR6fMB8NK8uawpLOSmyVNbvpNS75HU6dN0nEKMUoiOAbk52RbwDtCtqTEfvPzsd7U1NdURK0pE3BDKDPJmHFbo2FxakhArdGwuLaVXp+YPFbEsi61lpfTq1OR5rbYb1acPaZ278O+vv+K9JYvZUFzM3E2buPTtN+jbqTMX7z8xXKXz4ry5dOvQgaxRo3fYXu2raxTW4dcA/9OmjY327f/If+h/1x30+cffOf3lF5sI2rCtvJy/z57FtKmH07dzZ8cxBwwazPwtm3lr0UJWFeTz8De5jOjdh56pqZRWV3PLJx9zV+Zv6dzecUEpaawceNrtImTnqZ0jdiwC1gE9sD+q3UFRfl7Vsnk/fjfugEmTI1yXiEROqMvcjWi4cW1hUdyH6Ny1a/hm3Tr+MOmQZse9Mn8e64uLue6QQ1u8zo7t2vHKGWdz6dtvcOarL23fPrJ3Hz679PJdmnkOtti7lXlbNnPBvvtvb5HYflt9+vD9hvXU1tXRLjl5+/av160FYFNpyfZtPVNTufyAgzho8G50at+eHzdu4JFvv+Gwpx8n9/LfsVuPHjtc97RPPmJA125cccBBTdZ22ri9yV62hHNffwWAPp068eLpZwFw5+ezGdmnD6eO23uX7n+CeSF1+rQit4uQnacQHSNyc7L96ZlZbwLX4xCiAWa+/Oy3oydMPDilXTtNA4jEp1BC9EYc1phfnp9XaFkWxjgedxjzNpeWcsEbr7Fb9+7cPOWwJsct9m7lupnvs9/AQVw68cBWXXf3jh2ZMGAgp+29D/sNHMT6oiLuy/2Sk1+cwScXX9bkDG4oXpr3MwDnjJ/QaN8VBx7E799/l4vfep2bJk+lXVIyT875np82bQLs1UPqXX3wjm8gThgzlqOGj+DoZ//HHZ/P3t7XDPYBgy/+/DPZ519ISlA4bygpKYkZp53J348oIL+ygjF90+jcvj1Lt3l58ofv+fKyKymvqeHGjz7g41+W07dzF249/Eh+M2LkLjwice1htwuQXaN2jtiyAHt2qYfTzsJtWyuX/jxHZzEUiV+7vMxdeU2Nr7K2tunlImJYcVUVJ734POU1Nbxx9rl07+h8rPWG4iJOfOF5unfsyKtnnrPDrG5TNhQXc9jTj3P0yJFMP/xIjhs9ht8dPImZ51/EqsIC7vV8scv1+/1+Xl0wj2E9ejZasg7g4v0P4K9TDiN76VIO/O/D7PvIg3zyywpuPeJIwD7QrzkZw3Zn4qBBzF7160GDdYGDCY8fM8ZxlRAnu/fqxcRBg7e3bfzpg2wumXgAe/Xrx58+zOabdWt54bQzOXHMWE5/5UXWFMb9hx8747PU6dMWul2E7BqF6BgSOBX4m0CTh1q//8JTuTXV1ZWRq0pEIiiUmehCGqzMUa+4ujruUk1FTQ2nvDSDFXl5vHn2uYzr199x3Lbyco6b8RxVvlreP+9CBnVr8lCTHTw390dKqqs5fvTYHbaP7pvG6D59yV27ZlfvAp+vXsXGkhLO3Gd8k58U3HLYEay94S/Mvvhyci//HXOvvmZ7eB7eu+UVMXbr3oOCil//RLzw81wWbd3KVQcezNrCwu1fPr+fKp+PtYWFFFU2/SflrUULWeT1Mm3q4fabgPnzuT49g4N2G8KNk6fSr0sXXl0wP8RHIiH8x+0CZNepnSP2zMdeL7o7UNxwZ3FBfvWC73O/2j/jcOfDq0UklnX3eH29M9JSnI8O21EB4DjFWlRZWTiga9fGiwrHqBqfjzNffZnvN6zn1TPP4RCHWVywZ6qPn/Esm0pK+OCCixnVt2+rb2NzoN+4zmr8vsTn94elPebl+T8DNFqVo6FuHTty8JBfl8X7dOUvdGrXjkkNlspzsqqwgD6dfz2Qcn1xEXWWn6Of+1+jsZtKSxjz4H38dcph3HLYEY32V9TUcPPHH3LHUb+he8eObC0ro7rOt8PqIQO6dmNTSUmjyya4tcB7bhchu04z0TEmNye7DniDJlo6AN6f8dT3lRXletUSiU+tXeauCijDoS86r6I8bmai6/x+LnzzdWav+oUnTzyF3wbWhW6ooqaGk158nmV523j9rHN2OJthQ8VVVSzbto3iqqrt20b2tgP3K4GgW+/HjRtZnp/HvgNbPllLc8pranh3yWIOGrwbe/bu3erLfbVmNe8vXcJF+03cYfk5b1lZo7Ezly3l582byBzx62N02rh9eOWMsxt99e3Umb379eeVM87mtHH7ON723Z4vGNi12/bQ36dTJ1KSklielwdAbV0dqwryG53RUHg0dfq0OreLkF2nmejY9DPgxV7yrlFYrqqs8M354tPPM357/PGRLkxE2tyeQGuPfdgM9AFqgjduKS2NmxB988cf8c6SRRyxx574LYuXAwfm1Tt+zFg6t2/PRW+9zrfr13H6uH3YWFLSaNxZQQfyvbdkMVe8+xaPn3Ay5+27HwDnTtiXh77J5caPPmTB1i3sP3Awa4sKeeKH70hNaddohY+X5s1lXVERABYWC7Zu4a7AabiPHTWGvfvv2G7y3pLFlNXUcHYzs9DfrFvLbbNnceSew+nTuTMLtmzhmZ/msO+AgUw//Mgdxo7+9784Za9x7JXWn87t2/Pjpg28+PPPDO7Wnb8GHXA5qm9fxxn5Gz/6gD6dOnH8mLGN9gGsKsjnkW+/5pOLLts+C5+clMRJY/fin198ht+y+Gb9Wgorqzh57Lgm71MCqgSecrsICQ+F6BiUm5Ndl56Z9QZwNQ4hGuCjV5//ecIhGZO6du/Z+s8rRSQWhLpCx240eJ1YX1wcNydcmbfZXpni01Ur+XTVykb7lwz5E53bt2fe5s0AvLZwPq8tbNyje5bDahjBenXqxFeXX8VdX37O7JW/8PK8eXRu347Jw/bg/w47gtF903YY/9xPP+IJ6pOet2Uz87bYNQzq1r1RiH55/s+0T05udom4gV270SElhf988zXF1VUM7tadaw85lBsOnUynBmszn7XPBL5bv473ly6hstbHwG5duXTiAdw85TDSgs6CuLP+/OEHnD1+30Yz8A8ccxzXZL/HnZ/PJq1LF54/9fSQ2mYSwAup06fFzZvYRGcsh/4uiX7pmVnJwO1AJ5pY8m7yMSeOPOasC8+KZF0i0uaez0hLuaA1A48dNeZI4BzsHszt+nTq1PGxE0++qS2KE5Em1QGjUqdPa/xuT2KSeqJjVKA3+hWgZ1NjvvzgneV5WzatiVhRIhIJocxE5+GwzF1eRUVVtc+nVXxEIus1Bej4ohAd2xYAK7B7Hh3lvPHiJ/q0QSSuhBKiC2himbuS6uq4aekQiQEW8A+3i5DwUoiOYbk52RbwGtAVcFxfacF3uZvWrVg6L6KFiUhb6u/x+lp7arxCmnidL6qqVF+mSOS8p5OrxB+F6Ni3ApgL9GtqwFv/+++s2tqa6siVJCJtrLWz0WVALQ7rReeXVyhEi0TOnW4XIOGnEB3jArPRrwMdaeLECls3riub900YzkkrItGiVSF65rIlFvYyd6kN920tL1M7h0hkfJI6fdoPbhch4acQHQdyc7I3ArMB5/PcAu8+9/h3pcVFeZGrSkTaUKtOuBLgGKI3FhdrJlokMjQLHacUouPH+4F/2zntrK2p8c9+59UPI1iPiLSdUA4uXIdDiP6lIF8hWqTt5aZOn6ZPguOUQnScyM3JLgTeBQY0NeabWR+u2rhm5ZLIVSUibSSUEL0NhxU61hUVlfn8dbXhK0lEHGgWOo4pRMeXWUAx9modjt559vGcOp/PF7mSRKQNhNLO0WTvc2l1jfqiRdrO3NTp0/QJcBxTiI4juTnZVcDzNLNu9PqVy4sXzvn2q8hVJSJtYDeP1+fYuuWgybYNLXMn0qb+z+0CpG0pRMefn7FPwtLkkndv/+/R3IqyUs1AicSuZGBYK8cWY5+1sNHrfUFFpV4HRNqGJ3X6tJluFyFtSyE6zgSWvHsZ6ACkOI2pqqzw5bz+4vtO+0QkZrSqpWPmsiV+7NN/d2y4b1t5mWaiRdrGTW4XIG1PIToOBZa8+5BmDjL8bvZHq1ctWfhT5KoSkTAL5eDCTTgtc1dSohAtEn7vpk6f9o3bRUjbU4iOXx8AlUCnpga8+tgDH1dVlJdGriQRCaNQQvR6HEL06sICtXOIhFcdcLPbRUhkKETHqdyc7HLgBZrpjS4uyK+e/d4b2ZGrSkTCKJQVOrbicEbTFfn5RXV+vz98JYkkvOdSp0/TUrIJQiE6vn0PrAD6NjXgy5lvL1+3cvnCyJUkImESykx0AfYM2Q7q/H6rvKamKGwViSS2KmC620VI5ChEx7HcnGw/8Bz2x7iOBxkCvPrfBz6srqqqiFhhIhIOu3u8PtPKsQWA49ji6iq1dIiEx8Op06dtcLsIiRyF6DiXm5O9HngHGNTUmPytmyu++ug9LQgvEls6AoNbObYIO0Q3CtKFlVorWiQMioB/uF2ERJZCdGL4CPvo/F5NDfjkzZcWbl63elnkShKRMGhVS8fMZUtqsc9c2KHhvm3l5QrRIrvu7tTp0/SpToJRiE4AuTnZNcDTQDccDi6q99rjD86srampjlhhIrKrdnmZu82lJfrDL7Jr1gEPul2ERJ5CdILIzclehb3sXZNtHZvXrSn9bvZHH0euKhHZRaGs0LERhxC9trBIM9Eiu+aPqdOnVbpdhESeQnRieQ/IB3o0NSD7xf/9tG3zxtURq0hEdkUoM9EbcTjAeHl+XqFlWeGrSCSxfJI6fdqbbhch7lCITiC5OdlV2G0dPWnmuX/jyf+85/PV1kasMBHZWaEuc9doTejymhpfZW2tTrokErpa4A9uFyHuUYhOMLk52UuBWTTT1rF2xdKirz+e+UHkqhKRnRRKiC6kyWXuqtXSIRK6f6dOn6YD8hOYQnRiegsoBbo2NeCDl5/9ec3yJfMiV5KI7ITuHq+vTyvHFtDEa35RZaUOLhQJzUbg724XIe5SiE5AgVOCPw30oYmZKYDnH/jHzNKiwm0RK0xEdkZrl7mrwn7z3L7hvrwKLXMnEqIbUqdPK3O7CHGXQnTiWgh8RTNtHRVlpbWvPfHga+qPFolqobR0bMFhhY4tpaUK0SKt90Xq9Gkvu12EuE8hOkHl5mRbwKtAFdC5qXErFvyc5/nwveyIFSYioQp1mbuODTeuLy5WO4dI6/iA37tdhEQHhegElpuTXYLd1tGPZn4Wcl6bMX/VkoU/RawwEQlFKDPRG3AI0cvztmkmWqR1HkmdPm2h20VIdFCIlp+xTwu+W3ODnn/gHx8WF+ZvjUhFIhKKUEJ0Pg7L3OVVVFRV+3w6WYRI8zYC090uQqKHQnSCC7R1vAGswZ6RdlRVWeF79b8PvOarra2JVG0i0iqhtHMUAI5nVimprlZLh0jzrkidPq3Y7SIkeihEC7k52TXAf7FX6miyP3rVkoUFn7//5nsRK0xEWqOfx+vr0sqxzS1zp5YOkaa9kDp92ky3i5DoohAtAOTmZHuBx7Bno5ObGjfr7VcW/bJw3g8RK0xEWmOPVo4rB2pwOP13fkWFQrSIsy3AtW4XIdFHIVq2y83J/hnIpoX+6BkP3pVTlJ+3OSJFiUhrtKqlY+ayJRZ2IGh0cOHW8jK1c4g4+13q9Gl6kymNKERLQ28Dv9BMf3R1VWXdy4/e93ptTU115MoSkWaEcnDhJhzWit5QXKSQINLYa6nTp73tdhESnRSiZQe5Odm12G0dFtBkn+Xa5UsKZ7/3+rsRK0xEmhNKiF6PQ4heWVCgEC0SxLKsPLQmtDRDIVoayc3JzsM+0DCNZvqjP3v39SXL5v/0XcQKE5GmhLJCxzYcVuhYV1RU5vPX6eykIgHGmGtSp0/b5nYdEr0UosVRbk72AuBdYEhz41548K6PC7d5N0amKhFpQigz0U3OOJdqmTuReu/q1N7SEoVoac67wFJgQFMDamtq/C8+fM/rNdVVOlGDiHt283h97Vo5tsmgXFRVpZYOSXiWZRUCV7ldh0Q/hWhpUm5Otg94HPABXZsat2HVL8XvPvfEK3V1dXURK05EgiUDu7dybDF2O0ej1/+CikrNREvCM8ZclTp9mlagkhYpREuzcnOyC4BHgT44rC1b70fP7HWfvffG25bleDI0EWl7rWrpmLlsiR/w4rDMnbe8TDPRkuieSZ0+7VW3i5DYoBAtLcrNyV6EvfRds+tHz3rr5UVzcz+fFZmqRKSBUPqiN+OwQsemkhKFaElYlmUtB/7gdh0SOxSipbWygUXAwOYGvfb4g7krFy/4MTIliUiQUFbocFzmbnVhgdo5JCFZllVjjDkzdfq0crdrkdihEC2tEuiPfhKoBro1N/Z/99w2c8uGtSsiUpiI1AtlJnoLDq//K/Lzi+r8fn/4ShKJDcaYm1OnT5vrdh0SWxSipdVyc7ILgYeAnjjMYtWrq/NZT/3zb2/o1OAiERVKiC4EGoXlOr/fKq+pKQpbRSIxwG9ZOcADbtchsUchWkKSm5O9AvtELAOAJpfUKisprnn2vttfqiwvL4lYcSKJbQ+P19fa1/QCwDjtKK6uUkuHJAy/ZW1LMub81OnTdFS8hEwhWkKWm5P9PfAK9oGGTf4MbVm/tuyV/97/Ym1NTXXEihNJXB2AQa0cW4gdohsF6cLKSh1cKAnBsiwryZjzUqdP87pdi8QmhWjZWR8Cs4ChzQ1aNu9H78yXnnnV769Tn6VI22vtMnc+7CDdoeG+beXlCtGSKB5InT4tx+0iJHYpRMtOyc3JtoCXgLnA4ObGfvvph6s9H773XkQKE0lsoazQsQmHYxs2l5aonUPiXp3f/5Mx5ma365DYphAtOy2wYscTwEagf3NjP3zluXnzv8v9IiKFiSSuUA4u3IBDiF5bWKSZaIlrdX5/QXJS0kmp06fVuF2LxDaFaNkluTnZFcCDQCXQq7mxLz187+crFv78Q0QKE0lMoYToTTichXRZ3rZCnXlU4pVlWf7kpKRTU6dPW+d2LRL7FKJllwVODX4/9mmEuzQ39n/33PbBmuVL5kWkMJHEE0qILsBhmbuK2lpfZW1tafhKEokedZZ1U+r0aZ+5XYfEB4VoCYvcnOz1wL+BPjgcrFTPsiye/Mf/vbth9S+LI1WbSAIJNUQ3scxdtVo6JO5U+3xvdb3t//7ldh0SPxSiJWxyc7IXA09hL7PV6GPienV1PuvxO/765pb1OquhSJh193h9fVo5tpAm/gYUVlYoREtcqfL5lndISTnP7TokvihES7jlAm8BQ2hilgugtqbG/9jtN7+2bfPGNZEqTCRBtHaZuyqgFGjfcF9+RYVW6JC4UVtXV5ZszG9Tp0+rcLsWiS8K0RJWgaXv3gM+A4bRTJCuqqzwPX7HX18u2LZ1Q4TKE0kEoSxztxmHFTq2lJZqJlrigmVZls/vP6Pb3/+2yu1aJP4oREvY5eZk+4EZwLe0cDKWspLimifunPZicUHelogUJxL/Ql2ho2PDjeuKtMydxIfy2trbe91x6wdu1yHxSSFa2kRgDemnsE/G0myQLsrPq3r8zlueLy7M3xqR4kTiWyghej0OIXpFfp7aOSTmldfUfNT3ztumu12HxC+FaGkzuTnZtcBjwGJgt+bGFni3VD52+1+f04y0yC4LpZ2jAGi0KHReRUVVtc9XGb6SRCKroqZmaef27U9xuw6JbwrR0qZyc7KrgYeBVdirdjSpcNvWyv/efvNzRfnbNkWkOJH4tMtrRQOUVFeppUNiUmVtbX5yUtJhOpBQ2ppCtLS53JzsSuyzGm4CBjY3tihvW9Vjt988o3Cbd2NEihOJP/08Xl+zJz0KUkATfweKKqvU0iExp7aurrqytvbIHrdP16ea0uYUoiUicnOyy7DPapgH9G9ubFF+XtWjt930fP7WzTotq8jOae1sdDlQg8O67vkVWitaYovf7/dvLSs7Z9Ddd/7sdi2SGBSiJWJyc7KLgX8BJUC/5saWFhfWPHLrjS94N23QskQioWvtWtEWsAWHgwu3lmmZO4kt64uLbxlx/z1vul2HJA6FaImo3JzsAuBu7CDd7Ix0RVlp7SPTb3hp8/o1yyNSnEj8CKUveiMOa0VvKClWO4fEjFUFBc+O/ve//ul2HZJYFKIl4nJzsvOxg3QhLQTp6qrKukdvvfHVtSuWzo9IcSLxIZQVOjbgEKJXFhRoJlpiwrqios9u/OiDi92uQxKPQrS4IjAjfQ/2gU0DmhsbOEX420t/nvNNRIoTiX2hzERvw2GZu3VFRWW1dXW14StJJPy2lJYuXrh1y9GB1iSRiFKIFtfk5mQXYgdpLy2s2mFZFs/ed8fHc7789GPL0mulSAtCXebO8ZeqrKZaLR0StfIqyjct8m6dfMpLM2rcrkUSk0K0uCo3J7sIuBe7L3NwS+PfePI/33z+/ptv+f11jmvbiggAu3m8vvatHFtIU8vcVWmtaIlOBRUV+d+uW3do1vPP5LtdiyQuhWhxXW5Odgn2qh2raOHMhgA5r7+wIPvF/73kq63V7IOIs2RgWCvHFgN1OPw9KKio1Ey0RJ2iysriWSt/OeK0l19Y7XYtktgUoiUq5OZklwMPAEuAoYBpbvzXH89c+erj/36uuqpKZ6QScdbaZe782H3RjZa585aXaSZaokpJVVX5B8uXHX/BG6/Oc7sWEYVoiRqBMxs+BHyHPYvW7M/ngu9yNz173+1Pl5eW6A+9SGOhrNCxGYcVOjaV6HdLokd5TU3lO0sWnX3JW69/6XYtIqAQLVEmNye7BngS+Ag7SLdrbvzqpYsKHp5+w1PbNm/Ux3oiOwrl4ML1OITo1YUFaueQqFBRW1v96oL5v7/inbfec7uWWGCM+dwY84vbdYSDMeZCY4xljBnmdi0NKURL1MnNya4DXgl87QZ0aG584batlQ9Ou+6FXxbO+yES9YnEiFBC9BYc/h6syM8vqvP7dRCvuKra56t9Zd7P0z5YtvSZSN92UICrNcY0+nTHGPOUMWanlowyxkw1xtxqjOkWwmUONMa8a4xZZ4ypMsZsCgTm6TtTQzwJPJaWMcZxkYJdea6aohAtUSk3J9vKzcn+AHgce/m7zs2N99XW+p+6e/oHX38yc6Zff/RFILR2jkIclrmr8/ut8pqaorBVJBKimro636sL5t/xwfJl97u8FnQK8LcwX+dUYDrQqhBtjDkJ+AYYgf238ffAE0BVG9QmraAQLVEtNyc7F3vljp5A95bGv/f8k3PeefaxGTXVVZVtXpxIdNvd4/W19jW+yd7n4uoqtXSIK3x1dXVvLlxw33tLFt8RBSdTmQucbYwZ5WINtwOrgYmWZd1pWdZTlmXdalnW0cAQF+tKWArREvVyc7IXAP/Ebuvo09L47z/7eM3T99z2ZGlR4bY2L04kenUABrVybCH2ijiNVsUprKjUwYUScbV1dXVvLFr48JuLFk4LrCDjtruAWuyZ4xYZYy42xswLtFxsM8bMCG4zMMY8G3Rd6wNtCC31/Q4HfrAsq9GqVJZlbWyijuHGmBxjTLkxxmuMucsYk9RgTJIx5kZjzDJjTHWgReQRY0yPoDG/C9Q3NGjbqYFtHze4vo+MMT832HaEMWa2MaY0UMsXxpgMh3oPNsZ8HXjc1hlj/kILq3XtikD9TxljTjbGLAjc7lJjzDmtubxCtMSE3JzsldjvwstpRTBYu3xJ4UO3/PGpzevWLG/z4kSiV6taOmYuW+LDDtKNjj/YVlGuEC0RVVNX53vh57kPv7FwwQ0zly2pc7uegE3YLRRnGGPGNjcwEPyeBkqAG4H/AacCucaYXoFhjwNvB/5/DXBe4Ku5yZ81wFRjzB6trLkrMAv7HAx/AnKBm4DLGox7FLgbWA78MVDXlcCnxpj6kzbVr4gyOehykwE/cIgxJgXAGJMMHBI0HmPM6UB90P4/4BagR+D6JweNGxuod3fgDuBh4FLg2lbe3511EPZz9C7wF6ACeMEYc0ZLF1SIlpiRm5O9GTtIL8X+JWv257e0uLDmP//3x1cW//R9biTqE4lCoRxcuAmHFTo2l5aonUMipsrnq3nyh+8fnrls6Y0zly2pdbueBu4CqoFbmxpgjOkT2P8VcJhlWQ9ZlnUTdogegh3SsCzrG2B+4GJvW5b1QuCrvJnbvxPoDywzxnwVmFXONMY0dfB9GnCXZVlXWZb1mGVZJwHzsINpfb3jgCuAVyzLOs6yrEcsy7oaO9jvFzR2EZAPTAm6/snA69jHLE0MbNsXO7x/Gbj+zsB/A9d/uGVZ/7Ys6wHgYGAd9qfM9W7HfiM/xbKsOyzLugeYROtPHLWzxgEnWZZ1i2VZ/wYOBX4B/hV4U9AkhWiJKbk52WXAg/y6BF6zK3f4/X7r+Qf+Meuz9998u66uLlpmNEQiJZQQvQGHEL2msFAz0RIRFbU1VY988/V/P1u18uaZy5ZE3RlpLcvagj1re6oxZu8mhh2J/XfpAcuyfEGXnQksBrJ24fZnAEcDs7FD603Yfws3G2POc7hILfBUg21fAMEz2fX13Ntg3JNAUf1+y7Is7DcGkwGMMd2BvYHnsMNw/Yxy/b+ewL9HAb2wZ3b71H9hB+9ZwMHGmE6BsHo08IFlWds/QbYsaxvwYhMPSbjMtyzrs6DbrMA+YHMwMKG5CypES8zJzcn2YS9/9xT2u/IWj2zOeW3G/Ncff/CZ6sqKsrauTySKhDoTndJw4/K8vEL776dI2ymvqam4/yvPE9+sX/fXmcuWVLldTzPuxv64/9Ym9g8L/LvUYd9i7E9Rd5plWTmWZWVi/93bH7uvOgl43hhzWIPhG4ODfEAhdqhttl7LsmqwZ2OD6/0SGGGMGQBkYK/ok4sdmOtnqCcDyyzL2hr4fmTg3w+wW1WCv64I1N4b6At0ApY53G2nbeHU3G0Oa+6CjV4wRWJBbk62BXyZnpm1BftjpzTA29xlfv7my41bNqx9/Ozf33BS2sDBre0pE4lloSxzV4Dd37iDitpaX2VtbWmn9u27hq8skV+VVFWV3ev58pkl27y3zFy2pNFBc9HEsqxtxphHgBuMMRNCvLjBYSnJnayjBvgJ+MkY8xXwKXZP9WdBw3b109eG9Qb3Re8P/GxZVokx5gvg3kBf9KHAW0GXqZ+svQR7xtrJNuwVuMD58WntgYX1b74afaIW0AlwWrlrp29TM9ES03JzspcDf8cOALvRwg/+lvVryx74yx9m/PDFrI/9au+Q+BfKTHQBTfz+FFdXqaVD2kRhZeX/t3fn4W2Vd77Av0eW9z2JFWd3IESxw5oFQoyB0hQDNqWdUtrC9Jnetpe57TMzbYeZaS/tBab3QqHNQ1gvBDK9pQuUJVCIlESQEJKgLGS3k8hynNiW492SbMnaZZ37xysnsiPbOrFlefl+nkePzdGR9Noh0Vfv+b2/1/HU7l0bTZ0dv9SbTc5EjydGvwXQC+A/o9zXEP66LMp9yyLuB8YoUAP4Ivx17mU8tiH8dcB4JUlKhij7aIg4fAyAE2LW+VZcDNV7IFrQPggxqxy5LXv/roldsizvGOLmhZgEcw8eR9jSKMdi/lkiFANojHJ8uNdsiHLfBQzRNOkZDboOAE8BOA5x6WXYhQCyLGPzppf2//nF325y9nR3xX+ERAmTs7cjOGJbyDA7hnhPsHvY5o7GXpfb1f3krk9frrNaH59EARqyLFsBvAjgqxCL7yJ9ArH48Cf9HSsAQJKkuwEsB7Al4tz+RYR5sbyuJEl3DnFXRfhrtBKSkejCX/910PEfQswOXxivLMt9APYBKIf4uXeHj5shdj19NHxqZIg2QNRW/yraAkhJkgointsA4B5JkpYOuv/BGH+WHRC/+x9H/u7Dz3MbRH2zPsrjro0shZEkKQPAwwCaIXLFkFjOQVOC0aBzl5ZX/l+If9S+DqAV0S/bXHD6yMG2+pqTGx/655+XL1l+7arhziWaxJYAGPHDot5s8lZoi50AUgAMWNRldbvZoYPGVIvD0fnMns9eanY4fjvBa6CHsh5ix8AbIg/KsmyVJOkJiK4TOyVJeg+iLeu/QJQzPBNx+pHw16ckSXoXYiHglmE6dLwvSdJ5iGBbCyAZoqziIYi/4xuU/hCyLJ+UJGkjgH8Mbz++HUAJRIu7oxCt+iLtgQjRMi4uHkT4+28CaJRl+ULZhizLTkmSHgbwFoBqSZL+DLH+Yj4u1lH3B9jHws+9W5KklyB+Hw9DzB7nxfCzdEmS9CuIRZIHJEl6H+IK2zUQHwoaITqsDHYSwAfh1+yCKItZAuChcLgfEmeiacowGnR9RoPuA4jekrMQw186j8sV3PT0Y/rtb//pr36fd0LX4hFdJiUlHa2I2ubOyZloGjM1nZ2WX35ieLrZ4XhmkgZoyLJsB/DcEPc9DVEDnA8Rtn8IUSdcKsuyLeK8XRBt61YB+ANE0CwY5mV/AOAQxGTRsxCh+XaIDhmrZFmOVqoQix9DdPpYFv6ZvgHRnWJduPY60u7w11ORP0vE8T2Dzocsy+9ClH/UAvgpxHv09yAC6zMR552E6ObRANFP+p8gGgg8H+sPIsvyeoh2gi6IdoIvALgHotvIjbIsR5tQOAjg+wDugwjZWQD+QZblN0d6PYmrrmkqKi2vXAzxyT8b4pLMiP+jFy5YlMVFhzQFPV6mUf86lhMrtMX/ANG/tT3y+M0LFs55pOzWh+MxOJpeDlgspg3GvS/2yfKmCdgHmqYZSZJkAP8ly/IPRzw5Cs5E05RkNOjqIS4NHYFo0ZM20mP6Fx0e3rOTiw5pKlHSoaMJUf6unLF2sZyDRiUky/KWGtOh9Z/vWd8ny68zQNNUwBBNU5bRoHMCeDV8mwnRBm9Ysizjvddf3P+Xl363qZeLDmlqUFLOYUWUqzZdbrfXFwwOu8aAaCjBUCj4hyOH97xx9MjzAN4IbzNPNOkxRNOUZjToZKNBtw+ivqoNonvHiAtqTx0+0PbsL/75tbOnq4+MdC7RBKckRNsRpVc0ADjY5o4ugzcYcD/7+d4dW2vNLwN4S2828SofTRkM0TQtGA26NojV0h9CrArOHekx7l5n4PXf/C/d9nf+9Lbf5+UsHE1Ws/d2BLNiPNeGId4Xuj1elnSQIg6vt/uJnTsMX5xvehbAe3qzKeoHNKJEkWVZutx6aIAhmqYRo0EXCHfv+E340HzEsCvRZ1s217zy61+80tnaXB/XARLFT6yz0S6I9naXXK2xut2ciaaYtTmdbT83bNtaZ7U+qTebPtGbTexiQFMOQzRNO0aDzgyFiw5bLQ3OZ3/+T388tHvHx33BIBfE0GQTU4gOB502RPk70d7LNncUm5PtbTX/vk3/YafL9bjebGJJHE1ZDNE0LUUsOtwIBYsON296af+r/+fRl1stDbXxHiPRGFLSoaMZUXpFn3f0sJyDhhWS5dBHptP7nti5Y4snGHxCbzbVjfwoosmLIZqmrfCiQyPEosN2xLjosOlsbc/zv/zpW9vf+dPbHpfLEedhEo0FJYsLmxAlRJ+12TgTTUPyBAKu54yfb//jsaPbADypN5vaEj0monhjiKZpL7zo8CmIrVQXAMiJ5XGfbdlcs/7ff/SS6dihfaFQiAtmaCJTEqK7EKXNnaW7uzfQ18dSJrpEp8vV8ujH27ftszRuA7Bebzb1JHpMROOBIZoIFxYdboZYdCghxkWHLqcj8MazT37yxw1PbbR2tDXFe5xEl0lJOYcNQ+zw2ev3saSDBjjd0V71M/2WXU09Pe8DeGWybuNNdDkYookiGA26GgxcdJgZy+Nqjh/uWP9vP/r93m0ffcR2eDQBLdjbEUyJ8dyh29x52SuahL5QKKSvqfn0sR2fHPcGg28A+Ct7QNN0wxBNNEjEosOXIGpDFwBIGulxsixD/+bvjz336E9frK85dUyW2dGJJgwVRM1/LBwA+hDl/cHGNncEUf/8wn7jlv939LAZwNNsYUfTlcQ3eqKhlZZXZgH4KoA7ATghtkWOycqyOxaWP/Ddipy8/BE7fxCNg4oyjXprTCdqi5+CWGTrjjz+g1WrV929VFsRj8HR5NDqdDY+vXvXgWaHwwzgZb3Z1JHoMRElCmeiiYZhNOh6jQbdmwD+E+IydxFi6CsNAEf2fmr57b8+vPHQZ58YAn4f6wQp0ZQsLmxBlA4dLQ4HZ6KnqZAsy3sb6vf+RPfRnmaHYyeA3zBA03THEE0UA6NBVw/gfwN4A0A+gHmIYeFhMBAIbf6vlw88+4t/ecF84uiBUF8fawYpUZSE6POIEqLPsc3dtNTr9/e8uN/49vP7jOdCsvwnAJu4gJCI5RxEipWWV84A8ACAmyFmp2Nu51SkLcmvfOj76+YvXlISr/ERDUFfplFXxnJihbZ4DYCHAVgijyepVNKbD3z7V0kqFSdgpolzNpvp6d279ts8Hi+Al/RmkynRYyKaKPgPIZFCRoPOBrHT4TMAggAWAUiO5bEN5tP2lx77t3ff2fj8722d7efjOEyiwZTMREdtc9cXCskuv797zEZEE1YwFApuNdfo/2P71gM2j+c8gCcYoIkG4kw00SiUllemQiw6/BqAAABFu3R9+evfKim9s3JdRlZ2fhyGRxTJByCjTKMecWOgCm1xAYCnIXYvHGBDReVDC3LzlPSdpknG7vF0vnxg30fHW1tTAOwB8GeWbxBdiiGaaAyUllcWAvh7ANdAYYlHSmpa0j3f+d7K69feWpaWnpEVrzESAVhUplFbRjqpQlusBvAaRIge8Cbx2Je+fM+1c+asjtP4KMGq29oO/27v7iPuQCAJwB8A7GP7OqLoGKKJxkhpeaUEEaIfAjAbQDuAmDdeSc/MVFc8+P3V19xYektqWlpGnIZJ09sdZRr1rlhOrNAWr4coVxowA/mjm9as+fKVS8rjMThKHLff73zv1MmtH5lOeyA+PL2qN5taEj0uoomMIZpojJWWVyYDWAux+DAdQCtEqUdMsnJyUyoe+v5Ny1fedHNKatolHRKIRuG/l2nUm2I5sUJb/AiAhQAGbPV9X3HJ0u/esOI78RgcJYapo+P4BuPez20eTy6AbQDe15tNvkSPi2iiUyd6AERTjdGgCwDYXVpeeRiiXroCQAiiXnrEetReR4//7Vc27M3OzT94z4PfY5imsaS0zZ0Wg0J0Y7fdHv10mmxcfr/j3eqqLTpzjQtiV9b1erOpKtHjIposGKKJ4sRo0LkAfFBaXrkXYuHhLRA7wMW0QYGzx+5/+5UNe7Nycg/e8+B/u3H5ypvWpKalZ8ZvxDQNKFkQ2IwoXWdqu7rssixDkkZsk04T2Kn29qPP7fv8U7vHowFwFqL3M/uAEynAcg6icVJaXlkE4NsAlkHhFuIAkJaeob7z/gevu25N2drMnNwZcRgiTX3HyjTqFbGcWKEtXg7gZxAz0gO8cf8DP8tMSckZ68FR/PX6/T1vV53Ysq3WbAWQB+AdAAa92cSNoIgUYogmGkfhxYfLANwPcWm9B4Mul49EpVJJt997/7LVt60rzS/QzIvDMGnqcpRp1LmxnFihLZ4DsUvnJSH6xXu/+r052TmLxnpwFD+yLONUR/uR54yf7+z2egsgugi9qjeb6hI9NqLJiiGaKAFKyytVAJYD+CbE4q3u8E2R1betW7S2vHJt4fxFS3l5nWKkKdOoO0c6qUJbnAbgFQCNg+/79bqvfLVEM/uGeAyOxp7D67W9XV2lM5yptQPIB2AA8De92eRO8NCIJjWGaKIECofpayBmphfgMsP00mtuKPjSfd9cu3DJ0quTktRc60DDublMoz4Qy4kV2uIXAPQC8Ece/8na0rKyosV3xGNwNHaCob7Afotl78YvDh70BoOFECVkm/RmkznRYyOaCvhmS5RARoMuBOBEaXllNS6G6SIorJmurT7WWVt97MMZmsKP77jvm9cXX79qFeumaQhXAogpREO0Z9RgUIhudTq5AG2Cq7fZal47dHD7GatVBWAugO0APtSbTTH3riei4TFEE00Ag8L0cgD3QXRS8EJs2hLTJSNbR5vnvddf3C9J0v6b7rhr8erb162es3CxVqVSqeI2eJpslHToaAGwCIN24Gzq7maInqAcXq/tvVPVW7eazQ0A5gHoAvCk3mw6k9iREU09DNFEE0g4TFeXlleeBHAFgHsArIDYOa4NQEwr6GVZxoGd2+oP7NxWX7hgUdbt935jhfbaFSvSM7NiWlRGU5qSXtFNAG4ffPCMtYu9oieY/tKNV784uM8XDOZArLXQAdiiN5u8IzyciC4Da6KJJrjS8so5AL4C4FYAEsTMtH/YB0WRlKSWSu+696oVt9y+ava8BUskScWViNPTvjKNujSWEyu0xdcB+AkAy+D7/vLAt/8jVa3mJkATwDmbzfTaFwcNdTarG2L2uR3Aa3qz6WyCh0Y0pTFEE00SpeWV+QBuA3AXgFSImuney3muhUu0ebdVfn3lkpLrbkhN5wYu00xHmUY9O5YTK7TFCwE8DjEjPcAr933thwWZWWyxmEB2j6fzg9MnDVvN5nMACiF2HfwbgE+4bTdR/DFEE00ypeWVGQBuhCj1KADgg9gFccQtxQdLTklRld39NW3JyhuvLlyw6Cq1OvmSHepoSsou06hH/ABWoS3OAvACosxE/+bOu75x1axZV8djcDQ8l9/v+PRs3Wd/PnH8eF8olA1gJoCjAN7Sm00x7YhKRKPHEE00SYXb4y0BcAeA1RClHl0QW4srlp6ZqV6z7p6rim9YXTJ34eKl6uTklLEbLU0w15dp1CdGOqlCWyxB9IrugqjLv+CRW8q+dPPCRbfGaXwUhT8Y9O6zWD7//ZFDB92BgATRdcMK4A0AJ/VmE9/QicYRQzTRFFBaXpkHMTt9N8RWvh4AnYixq8dgqWnpSWvW3b2kZMWNJXMXXaFNTklJHaux0oRwf5lGvTmWEyu0xU8AyIZou3jBd6+/4br7SpZ/beyHRoP1hUJ9x1tbDm46fGhvp8vlgyjdUAP4ACzdIEoYducgmgKMBl03gI9Lyyt3Qmwrvg7AdRAhuhOiVV7MfF5P327d++bduvfNKalpSTfdUX7F8pU3lcxbfOWy5JTUtLEeP407JR06mgFcj0Eh+ryjhx064iwky3JtV2fVH44c2VVns/YAyAEwB8ARAH9l6QZRYnEmmmiKKi2vnAXgZgB3AsiCWIRow2XOTgOihvrG2+9cvHz1zcvnL75yWUpqGrszTE6vlWnU/xjLiRXa4rsgtqcfsLhwQW5u1oaKex+Jx+AIsHR3n/nL8WM7jrQ0d0AsJO7fcZClG0QTBEM00RRXWl6ZDLGBy50AiiFCdA8AB0YRqNXJyarVt60runr12pL5VyxZlprGLh+TyKdlGvWXYzmxQlu8CsCPEWVx4Vvf+s6jyUlJXIw6hizd3XUfmk7t2V1f3wRxtXgOgACAzQD2sHSDaOJgiCaaRsKz01cDKAOwOHzYARGqL/sfA5VKJa26bd2i5Stv0hYuKCrKyZ9RKElsQz2BNZZp1EWxnFihLb4CwKMAzg++7/Wv/92P8tMzNGM8tmlHlmU02O01m0+d3HOgydIKQAURniUA2wB8rDebnMM+CRGNO4ZoommqtLxyJoASiE1c+mtknQC6MYpADQD5BbPTr7+5bNHiZcsXFy4oKsrOzdMwVE8oIQDpZRr1iJv2VGiL8wA8iygz0b+7655vLZ4xY9nYD296CMmyfNZqPfVOddWeY60tnRChWQMgHcBuiN0GuxI6SCIaEkM0EfVv5FICMUO9FOLN3AnAjlEGagCYVTg347o1txQt1i4vmr1g0eLs3LxZo31OGrVlZRq1eaSTKrTFKgAbAbRiUC/y/3nb7V9ZOW/+2jiNb8rqC4VCtV1dVX+tOrH3VEe7LXx4JsTCwWMA3tObTZfM/BPRxMIQTUQDlJZX5uJioO6fZXRBBGrFG7pEM3vewqxr19xSVLS0uKhw/sLFmTm5M8bieUmRijKNemtMJ2qLn4Kozx3Qg/wHq1avunuptiIeg5uK+kKhvtMd7cfePHH88zNWa0/4cA5EgK4D8DaAM1w0SDQ5sMUdEQ1gNOh6AOwHsL+0vDIHYjHiLRDBWgURqG0YRaBub7b0frL5zZMATgLAnIVF2drrVs6bf8VVczVz58/Ln1Uwl6304k5Jm7sWAFoMCtHNPT226KdTJE8g4Dre2nL4neqqw009Pf07ReaFb+0ANgCo0ptNY/IhlYjGB0M0EQ3JaNA5ABwEcLC0vDILIlCvBXANRKAOQcxQu0bzOq2WBmerpaEGQA0ASJKEIm3JjKuuvm7uvKIr580qnDc3b+asOUlqNTtBjJ0lCs49D9EreoB6u50hehhdLlfr540NB989WX3SFwz2hQ/PgJh9Pg/Rrq5Kbzb1DfkkRDRhMUQTUUyMBl0vgEMADpWWV2YCuAJidnIFgIUQtdP9ofqyth7vJ8sy6mtO2eprTtkQnq1OSlJLS5ZfO2vhVctmz563QDNzdqEmd8YsTXpmVj4XLV4WJTPRbRAfmgaos1l7+kKhUJJKdcl901VIlkPnbFbTtlrzwXCbun6zIPq11wN4HcApzjwTTW4M0USkmNGgcwGoDt/eC9dRF0HUUN8AYBFEqA5CdPsYVagGgL6+oGyuOtpprjraGXk8Iys7+Yriq2fNK7pyVsGceQUzCmYXZOXlz8jIyspXq5M5cz00JSE66iY9faGQ7PL7u3PS0qZ9Tbs3EHCfaGs98u7J6kMNdnt/OzoJQAGATIirLH8DUMOaZ6KpgQsLiWjMlZZX5kGE6hKIUD0zfFcAIlR7xmMcswrnZswrujK/YO78vBma2fm5+TPzsvPy8zOzc/LSMzJzVUlJSeMxjkSRZRk+r8crh0It6ZlZpyFmQc+Fb3VlGvXpWJ6nQltcAOBpDNq1EAA2VFQ+tCA3T0lpyJTS5XK1GC2Nh96rrqr2XCzZkADMBpAGcSXlQwB1DM9EUwtnoolozBkNum4AxwEcLy2vfAtAPkSoXg7gWlws/5AgZqmdAMZ8J7authZ3V1uLG0Dz4PtUKpU0Q1OYPlNTmJk7c1ZmTt6MzKzc3IzM7JzM9MzszPTMzMzU9IyMtLT0zJS09MzklJT0RJeNyLKMvmDQHwz4fYFAwBf0+31+n9fj6nU63U6H09ltd/bYrb32zg5nV3uLs6O5qTfg988EUGU06DaO4qXtEH9WEgbNSNvdHvuC3LxRPPXk4wkEXKaOjirDmdrj4W25+yVD9HlWAzgKQAeggeGZaGpiiCaiuDIadDJEOYANIlggvEhxLoAFECUgWoiZu/6w4YII1oF4jSsUCskRIbtzpPPVycmqmZo5GfkFmozcGTMzklNS1SqVSqVKSlIlJSWpJFWSlJSUpFIlJamEyO9VUv/3Uv994ntJFa4nDvh9fp/H4/N6PD6vu9fncbt87t5en9vp8PU6e3y93d0+Z4/ddxlXDz0Qv+vLpjebghXaYhuAVADeyPs63a5psbgwJMshS7f9jLGx8ZjeXHPG39cXWc+cBXG1xQ/AAGCv3mxqT8hAiWjcsJyDiBKutLxSApALYB5EsC6BqNlNh1isqIII1C6ImetgYkY6KSVBzI7+j/AHmstSoS1+BOIKgj3y+H3FJUu/e8OK74xuiBOX3ePpONrSfPwj0+mqZocjsguNBLFYMAPiQ5gOwGG92TTq+n8imhw4E01ECRcOd93h2ykA28PBOhsiAM6GKAdZDGA+xGVzGRfb7HkgZki9ANgubKAQgBSI2VLnCOcO5zzEFYMBIbqx226Pfvrk5QsGPeauzpM76uqO77M0tgy6Oxni/8ckAFUQM8817LRBNP0wRBPRhBQO1o7wrQ6AERgwaz0LovPBbIgZ7EKIwJ0MERwlXJzB9kBcag+Eb1PlEpwE8fOmQJRapODiB4z+DxkygAaM/sNFc/i5B6jt6rLLsoxE14uPljcQcJ+12WoONFlO76g7Ux8IhQaH4hyI2v4AgE8A7NabTW3jPlAimjAYooloUhk0a10XeV84YGdChJ288Nc54dsMiPCdBRE+I0OSKnwsCBGSIgP3eIRuCeLf46RhvvYH4lDEYySI2WUbgEaIsoIuiNni/luP0aAbi1lSO6IEcXcgEHQHAo7MlJScMXiNceUJBHrrrF01+y2W0zvPnW3oC4UG/zmnQHwwU0F8iPgbgCN6s2lUmwsR0dTAEE1EU0Y4YPeGb5e0YwMuBO10iLCdCVHT2v/9DIjgnQ8RuPtDd/9mIv0zvED0YK0kbEsRt/6SFA9E3Xd/7Xf/970Rx5zhWy+AXqNBN17lK0MuIHT4vPbJEqLdfr+z1tpl2m9pPL3r3DlL6NKFQSqIRYIZEH8eBgAHAJxnlw0iisQQTUTTSjhou8O3EbtyABeCt2rQLdqx4Y733weI2m1fxC04mkV/48QGMSN+CbvHY5uTnbNonMcTM7vH03HOZq3bZ2ms2VNf3zTELzob4sMTAJwA8BkAk95s8o/LIIlo0mGIJiIaQTjg9mEaL1rUm02+Cm2xE6LEYUCw7HK5J1SbO18w6Gnq6Tln6mg/u7uhvi5iB8HBUiFq69UA2gH8BcBRvdk05RZLEtHYY4gmIqJYtULUCA8I0W29zoSGzpAsy10uV3Od1Vp3qPn8WWNjQ3OUMo1+aRDBWQVRIrMTwEFwUxQiUoghmoiIYtUMYBGAnsiDTd3d4z4T3eP1WpsdPZaT7e1nd507e7bT5fIOc3o6RJ2zCqKW3ADgGIB6vdk0ba8uENHoMEQTEVGszkOUQAxQ29UV1xDdFwr1Wd3uVktPt8Xc2Wk52GRpanE6R9rUJANioagKIvRvhQjOjezpTERjgSGaiIhiZcXA1oDioMft8wWDnlS1On0sXsQXDHraep1NDXa75WR7W9PBpqYWdyAw0i6VKohuKtnh/7YD2ALgOIAmBmciGmsM0UREFKvh2tzZCtRZ85Q+oS8Y9Fjd7ra2Xme7pbu7rbqtrbmqrbUrxuLkdIjZ5iSI9oJnAHwIoBZAM2uciSieGKKJiChWdlxs0zfwDo/XVpA5dIgOybLs9PlsnS5XW4vD0VZvt7VXt7e1DdM5I5okiE10siBCsx3ADoit4s/pzaaRSjyIiMYMQzQREcXKBdHXWg2xu+MFVrfLDsxCXyjU5/T57N1er9XqdllbnU7rGau1/URrS0ev3x9Q+HpJENttZ4X/uw8iMB+G2K2yg7PNRJQo0tBdgIiIiAaq0BY/DhFsB8wgL8zLy0pTq9V1VmvPMO3lRpIKUdecBjHT3AdRolEFoB6iDR03PyGiCYEz0UREpEQLRK/oASHa0t3dq/B5JIit1nNw8b2oF2Ih4CkAFgCterNppAWFREQJwRBNRERKNAFYo/AxSRCBOQvifUeGCNGtEJud1Iaf18ryDCKaLBiiiYhIiU4MsbgwLBUiLGdEHOuDmFk+CKABQBuAdr3ZNNwGKUREExpDNBERKdG/xXcWRIu5dIiZZRkXNzapDd9aIAKzlX2aiWiqYYgmIiIlOiDKMIIAzAAaAXRBbMTSpTebXAkcGxHRuGF3DiIiIiIihVSJHgARERER0WTDEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQgzRREREREQKMUQTERERESnEEE1EREREpBBDNBERERGRQv8fOKmRu2HPVbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploring using the Pie Chart \n",
    "\n",
    "Patient_showUp = df['Show_up'].value_counts()\n",
    "\n",
    "def Pshow():\n",
    "    print(Patient_showUp[1] / Patient_showUp.sum() * 100)\n",
    "\n",
    "def PNotshow():\n",
    "    print(Patient_showUp[0] / Patient_showUp.sum() * 100)\n",
    "\n",
    "print('Patients Show Up: ')\n",
    "Pshow()\n",
    "print('\\nPatients Not Show Up: ')\n",
    "PNotshow()\n",
    "labels =['Showed Up','Not Showed Up']\n",
    "colors = ['#c2e9fb','#f68084']\n",
    "explode = (0, 0.1)\n",
    "chart = Patient_showUp.plot.pie(figsize=(12,12), autopct='%2f%%', fontsize = 17,colors=colors,explode=explode,labels=labels, shadow=True);\n",
    "chart.set_title(' Patients Show up/Not Show up in the appointments\\n', fontsize = 18);\n",
    "mtp.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> We notice that in the figure above, the patients who come to the appointments are more than the patients who did not show up.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2: Does the show up affected by the patients who received SMS ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Patients')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mtp.figure(figsize=[16,10])\n",
    "\n",
    "df.SMS_received[Show_up].hist(alpha=1,label='Show up')\n",
    "df.SMS_received[Noshow_up].hist(alpha=1,label='Noshow up')\n",
    "mtp.legend()\n",
    "mtp.title('Patients show up/ no show up based on who recieve/not recieve SMS\\n',fontsize=15)\n",
    "mtp.xlabel('SMS_received',fontsize=16)\n",
    "mtp.ylabel('Patients',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">As we can see here in the figure above, patients who did not recieve SMS is showed up more than the patients who recieved SMS, Which I think It does not make sense.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between the characteristics\\n')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vIjoBP2zgWLeR/UJ7RUSsMRg9ItpEvWtckD13kE17uzaPkY1JOh94b9Wg9Nyvu8+RTWG9NdnMcfUHBd9ANk7iN7lfo+vH1Cs3XfCGnMPGlE99FOIfZF2uzo6IIxsqEBF758ZkrMvNZF28vtfQ5ATRyHVR8jQZ2DbqXPMpN0blCrIZzdbneJB9Ya79oh4Rm5D9+NJQb4Ybc3+vj4g1sgz11q3ruSr0tbdeIuLwiGjoXFZ146w7vmR9Xl+/yP0dG/WuE5T7/zAod79fROzZwP5dgW5kr521je+T1ILYVUxlJaX0ca7rxvlk1zS5LKX0dkScQTbD1hsR8QDZNLrtyT4k9yeb+/9T6zj8z8gaKP+KiDvJulzsRvaL6/+SjRep71GymcYeiOzig5XAqymlext5nEtzMZ2Tm0zgcT65jks34JyU0qR1xNqYzwJ35waQv042nWs/skxLez4Z89IUppM1jl6PiHtyj/dlsi5wv0oprXGBxnV4i+w5rXsdl63JrrVR27hLKc2NiC8DdwPPR3aByDfIxigMJRsj1YfVJ1t4lGxc1K254y8GKlJKv6xX5hyyL2J/qxfbo3ySSXq03jZSSr+J7GKf3wbej4hVM1r1BrYk68pyG9m1Ptb3HDamx3OP9cOI2Iksi0RK6ZpG91qL3MQRx5GN7/i/iHiWbNKIpWTXFNqdLPMwiNW/4DZ0rDcju6DhGODliPgH2S/pfcj+jy7ik2zl+vhpnWP/ley1ti9Zo2XVoO+8pZRmRMSfya4F9EpEPEQ2vusQsh9eXiEbN1d3n4ci4mrge8BbEfF3sokEBpC9Bz1P1vUJssztx8BXI6KK7HWVgNtTSh8W+trbAH8mG0fyL7LGWpC9t+1ONhFK3WunPEo23ulvEXEf2Q8oH6aUGvuR5tdk534q8F7ueZ9NNp32QWTv+VeSdSd7PiLeIstWfwR0B75A1k31F/UzX5JaqFQGczJ7a1031nIdlzrbB5ANnF0CDKiz/jNkXRQ+JGtAzCP74j4WOKjeMZ5o6DHIPuieJ/siVEF2zY5VfaUTa167pCvZr8FTyX7VS8Bv653LEw08Tk+yBsR7uVgryAYFH9pA2QNp/JoNk6lz/QOyrnQ/IOvONCN3/KlkA8uPqLfvFvVjXlc9NVIXk3O3HsBNZF+sKskaH+dR55o1ebwGnsg9RkeyLNuk3LE+IPsVvMFrf+TO55e5el1ONg7mbbJGzjENlB+di68y93iT623vSdbFJQHH19u2N59c++LTjZzLF4B/ArPIfvWdQXZdimuAT23IOdD4tWgafd2sJdaTyb5UL6Pe/8P1fSyyRt+PyP4vLiVrIL5H9mPAyTRwzZBG4tsb+GudupxGNgvXlzfkNV3ndf0K2fvKHLIG5GdYy/VtWMv/7Trbu5Bd1HLVtWw+Ivt/0WdtMeT2OzJ3TvNyr8uPcrHUfw/bnawxsICswdlQjHm/9vI4nzWef7KGz91k/y+X5mJ+mexHpW719m9L9r70AVnDcLXHo5FrYpHNzvZk7lyXk70f3AHsWuf/6f8jy5Cuet+ZnqvnEyngvcebN2/N+xYpFTxWT1IrFBGTAVJKW5Q2EkmS1Bo5xkWSJElS2bPhIkmSJKns2XCRJEmSVPYc4yJJkiSp7JlxkSRJklT2bLhIkiRJKns2XCRJkiSVPRsukiRJksqeDRdJkiRJZc+GiyRJkqSyZ8NFkiRJUtmz4SJJkiSp7NlwkSRJklT2bLhIkiRJKns2XCRJkiSVPRsukiRJksqeDRdJkiRJZc+GiyRJkqSyZ8NFkiRJUtmz4SJJkiSp7NlwkSRJklT2bLhIkiRJKns2XCRJkiSVPRsukiRJklYTEb+JiFkR8fpatkdE/CIiJkbEvyNi1zrbDo+Id3LbLt1YMdlwkSRJklTfb4HDG9l+BLBt7jYKuBkgItoCN+W27wCcGBE7bIyAbLhIkiRJWk1K6SlgXiNFjgZ+nzLPAz0jYhCwBzAxpfRBSqkK+HOu7Aaz4SJJkiSpUEOAj+osT82tW9v6DdZuYxxkXVbM+SAV43G0pud3uqTUIbRq1SlKHYKkVqhHx6pSh9CqTavsUuoQWrXPz/xTs/jwLfX34w79tv4mWRevVW5JKd1SwCEaqufUyPoNVpSGiyRJkqTykWukFNJQqW8qsFmd5U2BaUCHtazfYHYVkyRJklSoe4BTc7OL7QUsSClNB14Cto2ILSOiA/DVXNkNZsZFkiRJKraa6lJH0KiI+BNwINA3IqYCVwDtAVJKY4D7gCOBicBS4PTctpURcQ7wINAW+E1K6Y2NEZMNF0mSJEmrSSmduI7tCTh7LdvuI2vYbFQ2XCRJkqRiSzWljqDZcYyLJEmSpLJnw0WSJElS2bOrmCRJklRsNXYVK5QZF0mSJEllz4yLJEmSVGTJwfkFM+MiSZIkqezZcJEkSZJU9uwqJkmSJBWbg/MLZsZFkiRJUtkz4yJJkiQVm4PzC2bGRZIkSVLZs+EiSZIkqezZVUySJEkqtprqUkfQ7JhxkSRJklT2zLhIkiRJxebg/IKZcZEkSZJU9my4SJIkSSp7dhWTJEmSiq3GrmKFMuMiSZIkqezZcJEkSZJU9uwqJkmSJBVZclaxgplxkSRJklT2zLhIkiRJxebg/IKZcZEkSZJU9my4SJIkSSp7dhWTJEmSis3B+QUz4yJJkiSp7JlxkSRJkoqtprrUETQ7ZlwkSZIklT0bLpIkSZLKnl3FJEmSpGJzcH7BzLhIkiRJKntmXCRJkqRiqzHjUigzLpIkSZLKng0XSZIkSWXPrmKSJElSsTk4v2A2XBpx+Q9u4KlnXqR3r578/Q9jSh1Os9VrxDC2uvp0om0bZtzxKFN/+fc1ymx1zRn0HrkLNcuqeOf8X7LktUmN7tv3qL0ZetEJdNl2CK8ccRmLX30fgH7H7c+m3/5i7XG77rA5Lx9yCUvemNzUp1m2trn2dPqM3JXqZZW8fd5NLM7VbV2dhvZnh7EX0K7nJix+bRJvnX0jacXKte7feevB7HjLhZ/sv3l/Jv/4L0y95b7adZuddRRbX3kqz3z6DFbMW9T0J1qmil3/XXfYnO1+Moq2XTux/KNZvHXWL6hevKxo51tumqL+AYZ840gGnzwSCKbf8chqr33w9V9XtwN2YdMrv0G0bcPcPz/MzF/9dY0yQ77/DXqMGE7Nsko+/M+fs+z1DwBo270rm/34HDpvNxRS4sOLb2TphHcA6Pv1z9PvtM+TqqtZ+Ng4pv3gd0U9r+Zkh2tPo//IYVQvq+LV825m4WuT1yjTeWg/dhl7Hh16dmXBa5N55eybSCuyCyT23ufT7HD1qbRp146qeYt4/tirAGjXvQs73zCKbp/aFBK8euFYKsa9V8xTUytjV7FGHHPkIYy54ZpSh9G8tWnD1j/8D9742rWM/9yF9Dt2P7pst+lqRXqN3IXOWw1i3N7n8t5FY9jmulHr3HfJ21N464yfsOD5t1Y71uy/Pc3LB1/MywdfzDvn3Mjyj2a36kZL75G70HnLQbyw17m8e9FYtvvxNxost9XlJzF17D95ce/zWFmxmEFfO6jR/Ze9P41xIy/Obod8h5plVcy+78Xa43Uc3IdeB+zM8o9mN/1JlrFS1P/2N3yLD665g3EH/idz7nuRzc7+YoOP2Ro0Vf13/dRmDD55JOMPv4xxB11En0OG03nLgbXH8/VfR5s2bHbNN3n/tO/z1shz6PXF/em07WarFek+YjidthjEm5/7FlMuvYnNrj2rdtuQK/+DRU9M4K2Dzubtwy+gcuJUADbZ+zP0PHRP3j7sPN4++Fxmjf17Mc+qWek3chhdtxzIE3tdyGsX3cpOPz6zwXKfuvxrTBp7H0/sPZoVFUvY7GsjgKxxstOPzmDcqf/NUwdczIRv/Kx2nx2vOY3Zj7/Kk/tdxFMHfYfF735cjFNqOWpqSntrhmy4NGK3YZ+hR/dupQ6jWeu2yzYsnzSD5VNmkVasZPbfn6H3YbuvVqbPYbsz684nAFg04T3ade9C+/49G9132Xsfs+z9aY0+dr9j92P23f9qkvNqLvoevjsz73oSgIXj36Nd96506N9zjXK99tuJ2fc+D8CMO5+k7xG7571/r/13YtnkGVROnVO7bpurvs77V/0BUmqCs2o+SlH/XbYZzILn3gRg/pP/pt/n92qKU2sWmqr+u2w7hIXj36NmWRWpuoaKZ9+k75F71B7P1/8nugzblsrJM6iaMpO0YiXz732aHofusVqZHofuwby/Pg7A0pffpW33rrTr34s2m3Rmkz12ZO6fHwYgrVhJ9cIlAPQ95XBm/uqvpKosM7Zy7oIinlXzMuDw4Xx819MAVIyfSPvuXejYwP+DvvvtyIx7XwBg6p1PMfCI3QAYcty+zLjvJZZ/PBeAqjkLAWi3SWd67/0pProje+7SimpWLlza1KejVi6vhktEDIiI/4mI+3PLO0REw012qY6Og3pTOe2TL7RV0+fScVDv1cp0GNSHymlz65SZR8dBffLatzH9jt6H2X9v3Q2XjoN6U/nxJ3Vb2UAdtu/djZULl5Kqs19fKqd9Uiaf/fsfuy+z7n6mdrnPYbtROWMeS978cKOfT3NTivpf8vZH9Dk8+8LR76i96Tikz8Y9qWakqep/ydsf0WOvT9Ou1ya06dyB3gfvSschfQFf//V1GNiHqnrv4+0HrP6abD+wD1XTPymzYsYc2g/sQ8ehA1k5bwFDrz+P7e/7KZtddw5tOncEoOOWg+m6xw5s94+fsM2d19Jl522Kc0LNUKdBvVlW53W8fPo8OjXw/2DFwiW1/w+WT5tbW6br1oNo36Mre/3te+z30LUMOX5/ALps3p+quQvZ+effYr9HfshnbvgGbbt0LNJZqbXKN+PyW+BBYHBu+V3ggsZ2iIhRETEuIsb9+vd/Wu8A1cxFrLmu3q+QDRUhpbz2XZtuu2xLzbJKlr79UV7lW64163CNKmygnj8p0/j+0b4dfQ/djVn3PgdAm84d2PyC45h03V/WM96Wprj1D/DOBb9iyOmHM/yh62i7SafaX6Rbp6ap/6XvfcyUX/6Dz975PXb+03+x5I3JpJXVvv4bsrb393UVSgnataXLTlsz5/YHeOfIC6lZtpwB3/5Stke7trTtsQnvHn0x0679LVv86pKNHnpLEQ2+jtf9ObyqTLRtQ4/PbslLJ/+YF776I7YdfSxdtxpItGtL989syZTfPcy/Dr6M6qWVbH1u6+2auj5Sqi7prTnKd3B+35TSnRFxGUBKaWVENHrGKaVbgFsAVsz5wHx5K1U5bS4dB/etXe4wqA+VM+Y3UKZPnTK9qZwxj2jfbp37rk2/Y/Zldp1foVuTwacfxuCTDwZg4SsTV/vFveOgPlTNmLda+RVzF9KuexeibRtSdQ0dB39SpnL63Eb37z1yGItem8SK2Vk3jc5bDKTT0P7s/thPsvKD+zD84R8z4fDLqJpd0STnW25KWf8ASydO499fycbmdd5qEH0OGb7xT7KMFav+Z/zxMWb88TEAtvzuiVROm+vrvwFV0+fSod77+IpZ9Z6DGXPoMKgvS3LL7Qf2ZcXMeZASVdPnsPSVdwGouO9ZBpyVNVxWTJ/LgvuzBvvSV9+DVEO73t1ZOW9h059UM7D56Yew2cnZWK0Fr3xA5yF9WPXp2WlQ7zU+S6vmLqJ99661/w86Df7k83b59HlUzVtE9dJKqpdWMu/5t+m24+bMf/5tlk+bR8WEbHKc6fe+wDbnHl20c1TrlG/GZUlE9AESQETsBdihVOu06JWJdNpqEB2H9ifat6PfMfsy76GXVisz96Fx9D/hQAC67bot1YuWsmJWRV77NiiCvkft3Wq7iU277cHagdtz7n+JAccfAED34duyctFSqmZVrLHP/GfeoN9R2ViIgSccwJwHsnqe8+C4RvcfcOx+zKozjmjJW1N4dsf/4Pndz+b53c+mctpcxh9ySav60lbK+gdo37d7dieCzS/8EtN+99BGPsPyVqz6X1XPHYf0pd+RezLr7md8/Tdg6avv0XHLQXTYLHsf73XU/ix4+MXVyix4+EV6fykbCN5ll+2oXrSElbPms3J2BSumz6HjVkMA6Lbvzix/L8uiVzz0ApvsszOQdRuL9u1ttNTx4W0P86+Rl/GvkZcx8/5xtd27eg7fhpWLllLZwP+Duc+8wcCj9gRg0xM+x8wHxgMw84Fx9N7rU0TbNrTp3IGeu27D4vc+pnL2ApZPm0vXrQcB0Hf/nVj07tTinGBLkWpKe2uG8s24jAbuAbaOiGeAfsCXmyyqMnHxFT/ipZf/TUXFQkYeczLfPvMUvnTUYaUOq3mpruH97/6anf50OdG2DTP/9BhL35nKwFMPBWDG7x9i/iMT6D1yV3Z7/pfULKvk3Qt+1ei+AH2O2IOtrz2T9n26s+MfLmPJ65N5/cTsV+Yee+9A5fS5LJ8yqySnXE7mPTKBPiN3Yc8XbqR6WRXvnH9T7bbP3HEZ74weQ9XM+XxwzR/YYeyFbHnpiSx6bRLTc78kN7Z/m84d6PW5nXnnoluKfl7NRSnqv/+x+zHk9Ox9as59LzLjT48X4UzLU1PW/47/cxHte3UjrVzJu5f9mpULlqzx+AKqa5j6vVvY+vYrs+mQ//Ioy9/9iD4nHw7A3D88wMLHxtN9xG7s8PSYbDrki26s3X3q/7uVLX4xmmjfjsopM5hy0S8AmPeXRxj6k3P51MO/IFWt5MPRPyvF2TULsx55mX4jh3HgCz+jelkl/z5/bO223e+4hH+PvpXKmfN565o/sevYc9n+0hNY+NpkPvpj9t6x+L1pzH7sVfZ//DpIiSl3PM7it7PP4je++1uG/eoc2nRox9IPZ/JqnWNLTSHq93Nca8GIdsD2ZJ1R30kprcj3QewqVjrP72S/31KqTg118JakptWjY1WpQ2jVplV2KXUIrdrnZ/6pWXz4Ln/lnyX9ftxp2BeaRT3VlVfGJSKOq7dqu4hYALyWUvJnbUmSJKkQzfRaKqWUb1exM4G9gVV9Dg4EnidrwFyVUrq9CWKTJEmSJCD/hksN8OmU0kzIrusC3AzsCTwF2HCRJEmS1GTybbhssarRkjML2C6lNC8i8h7rIkmSJIlmO7NXKeXbcHk6Iv4J3JVb/hLwVER0BSqaIjBJkiRJWiXfhsvZwHHAfrnlF4FBKaUlwIimCEySJElqsWqa59XrSymvC1CmbM7k94EVwLHASOCtJoxLkiRJkmo1mnGJiO2ArwInAnOBv5Bd+8UsiyRJkqSiWVdXsbeBp4GjUkoTASLiwiaPSpIkSWrJHJxfsHV1FfsSMAN4PCJujYiRQLO7yqYkSZKk5q3RjEtK6W7g7tzsYccAFwIDIuJm4O6U0kNNH6IkSZLUwtSYcSlUvoPzl6SU7kgpfQHYFHgFuLQpA5MkSZKkVfJquNSVUpqXUhqbUjqoKQKSJEmSpPryvY6LJEmSpI3FwfkFKzjjIkmSJEnFZsZFkiRJKjYH5xfMjIskSZKksmfDRZIkSVLZs6uYJEmSVGx2FSuYGRdJkiRJZc+MiyRJklRkKVWXOoRmx4yLJEmSpLJnw0WSJElS2bOrmCRJklRsDs4vmBkXSZIkSWXPhoskSZKksmdXMUmSJKnYkl3FCmXGRZIkSVLZM+MiSZIkFZuD8wtmxkWSJElS2bPhIkmSJKns2VVMkiRJKrZmMDg/Ig4Hfg60BX6dUvpRve0XAyflFtsBnwb6pZTmRcRkYBFQDaxMKe22ofHYcJEkSZK0mohoC9wEHAJMBV6KiHtSSm+uKpNS+gnwk1z5o4ALU0rz6hxmREppzsaKyYaLJEmSVGzlPzh/D2BiSukDgIj4M3A08OZayp8I/KkpA3KMiyRJktTKRMSoiBhX5zaqXpEhwEd1lqfm1jV0rC7A4cBf66xOwEMRMb6BY68XMy6SJElSK5NSugW4pZEi0dBuayl7FPBMvW5i+6aUpkVEf+DhiHg7pfTUeoYL2HCRJEmSiq/8B+dPBTars7wpMG0tZb9KvW5iKaVpub+zIuJusq5nG9RwsauYJEmSpPpeAraNiC0jogNZ4+Se+oUiogdwAPCPOuu6RkS3VfeBQ4HXNzQgMy6SJElSsZX54PyU0sqIOAd4kGw65N+klN6IiG/lto/JFT0WeCiltKTO7gOAuyMCsvbGH1NKD2xoTDZcJEmSJK0hpXQfcF+9dWPqLf8W+G29dR8An93Y8dhVTJIkSVLZK0rG5fmdLinGw6gBe73+41KHIEkqsst3+69Sh9Cq7RkNTcYk1VPmXcXKkRkXSZIkSWXPMS6SJElSsZX/dMhlx4yLJEmSpLJnw0WSJElS2bOrmCRJklRsDs4vmBkXSZIkSWXPjIskSZJUbA7OL5gZF0mSJEllz4aLJEmSpLJnVzFJkiSp2BycXzAzLpIkSZLKng0XSZIkSWXPrmKSJElSsTmrWMHMuEiSJEkqe2ZcJEmSpGJzcH7BzLhIkiRJKns2XCRJkiSVPbuKSZIkScVmV7GCmXGRJEmSVPbMuEiSJEnFllKpI2h2zLhIkiRJKns2XCRJkiSVPbuKSZIkScXm4PyCmXGRJEmSVPbMuEiSJEnFZsalYGZcJEmSJJU9Gy6SJEmSyp5dxSRJkqRiS3YVK5QZF0mSJEllz4yLJEmSVGwOzi+YGRdJkiRJZc+GiyRJkqSyZ1cxSZIkqdhSKnUEzY4ZF0mSJEllz4yLJEmSVGwOzi+YGRdJkiRJZc+GiyRJkqSyZ1cxSZIkqdjsKlYwMy6SJEmSyp4NF0mSJEllz65ikiRJUrElu4oVyoyLJEmSpLLXKjIuvUYMY6urTyfatmHGHY8y9Zd/X6PMVtecQe+Ru1CzrIp3zv8lS16b1Oi+fY/am6EXnUCXbYfwyhGXsfjV9wHod9z+bPrtL9Yet+sOm/PyIZew5I3JTX2aLc7lP7iBp555kd69evL3P4wpdTitinVfWtZ/aVn/Te+LV5zG9iOGsWJZFXdedDPTGviM/PJ1oxiy81YEwZxJ07nzopupWlrJDocM59DRJ5BSDTUra7j3qt8zedw7xT+JZmrna05l4MhhVC+rYvz5Y6h4bfIaZboM7cceY86lQ89NqHhtEi+d8yvSimra9+jK8J+OousWA6iuXMGEC8ey8O2pxT+JFiLVpFKH0Oy0/IxLmzZs/cP/4I2vXcv4z11Iv2P3o8t2m65WpNfIXei81SDG7X0u7100hm2uG7XOfZe8PYW3zvgJC55/a7Vjzf7b07x88MW8fPDFvHPOjSz/aLaNlvV0zJGHMOaGa0odRqtk3ZeW9V9a1n/T2v7AYfTdciA/OfBC/vbdWzn22jMbLHfv1bfz8yMu5WdHfIeKaXPY57TDAJj4zOv87Ijv8PMjL+OuS8bypeu+Uczwm7UBI4exyVYDeWjv0Uy46NcMu+6MBsvtdPmJTBx7Pw/tM5qqiiVs8bURAGx//tFUvPEhjx50KePOvZmdrz61mOFLLb/h0m2XbVg+aQbLp8wirVjJ7L8/Q+/Ddl+tTJ/DdmfWnU8AsGjCe7Tr3oX2/Xs2uu+y9z5m2fvTGn3sfsfux+y7/9Uk59Ua7DbsM/To3q3UYbRK1n1pWf+lZf03rR0PHc74vz0NwJSXJ9K5Wxe69eu5RrnKxctq77fr1IGUsl+nq5ZW1q7v0KUj+KN13gYfNpwpd2Z1P3/CRNp370Kn/j3XKNdv3x35+J8vADDlzqcZfPhuAHTfbgizn34DgMUTp9Fls3507Nu9OMFLFNBwiYjzI6J7ZP4nIiZExKFNGdzG0HFQbyqnzaldrpo+l46Deq9WpsOgPlROm1unzDw6DuqT176N6Xf0Psz+uw0XSZJW6T6gNwvqfOYumDGP7gMb/mw9/iff5PKXxtB/68E8+9sHa9fveNhu/Oej/83pv7mEuy4Z2+QxtxSdBvVi2bR5tcvLps+j06Beq5Xp0LsbKxYuIVXX5MrMrS2z4I0pDD4y+wG31y5b02XTvnQe3KdI0bdANTWlvTVDhWRczkgpLQQOBfoBpwM/WlvhiBgVEeMiYtw9Sz/YwDA3QMSa61JaZxFSymvftem2y7bULKtk6dsf5VVekqRWoYDP1rsuHsu1e57FrInT+OxRe9euf+PBcVw/8iJ+P+p6Dh19fFNF2uJEg3Vfv1ADO+aen3duvIcOPbty0CM/YOszDmXB65NJK6s3epzS2hQyOH/VS/lI4LaU0qvR4P+ATErpFuAWgKcHfrlkidzKaXPpOLhv7XKHQX2onDG/gTJ96pTpTeWMeUT7duvcd236HbMvs+9+ZgOjlySp+dv7lEPY48SDAJj66gf0qPOZ22NgbxbOXPtna6pJvPrP5zhg1BcYd9eTq22b9OLb9Nl8AF16dWPp/EVNE3wzt9Xph7DFSdkYlfmvfEDnwZ9ktzoP6s3yet9rquYuon33rkTbNqTqGjoP6sPyGRUArFy8jPEXfJLhOuyln7NkyuymP4mWyumQC1ZIxmV8RDxE1nB5MCK6AWVf44temUinrQbRcWh/on07+h2zL/Meemm1MnMfGkf/Ew4EoNuu21K9aCkrZlXktW+DIuh71N52E5MkCXju9of5+ZGX8fMjL+ONh8Yx/Lj9ARi6yzYsX7SURbMr1tinz+YDau/vMHJXZufGldZdP3jHLWjbvp2NlkZ8cNvDPHbwd3ns4O8y/YFxDD0hq/teu27DikXLWD6rYo19Zj/7JkO+sCcAQ0/Yn+kPjgOgffcuRPu2AGxx0gjmPP82K+uMRZKaWiEZlzOBYcAHKaWlEdGHrLtYeauu4f3v/pqd/nQ50bYNM//0GEvfmcrAU7PhOTN+/xDzH5lA75G7stvzv6RmWSXvXvCrRvcF6HPEHmx97Zm079OdHf9wGUten8zrJ2az0PTYewcqp89l+ZRZJTnlluLiK37ESy//m4qKhYw85mS+feYpfOmow0odVqtg3ZeW9V9a1n/Tevvxl9l+xDAuefJnVC2r5K6LP/kF//TbLuF/v3Mri2dXcML1Z9Fxk85EBNPf+pC7L/8NADsdsQfDj/sc1StXsmJ5FX885xelOpVmZ8YjrzBg5DAOff6nVC+rXC17ss8dlzBh9C0sn1nB61f/iT3GnssOlx5PxesfMvmPTwDQbdsh7HbjWaTqGha+O5UJo28t0ZmotYqU55iNXLewk4CtUkpXRcRQYGBK6cV17VvKrmKt3V6v/7jUIUiSiuzy3f6r1CG0ansuX2tPehXBcTP+2CyegKU3nVPS78ddzv5ls6inugrpKvYrYG/gxNzyIuCmjR6RJEmSJNVTSFexPVNKu0bEywAppfkR0aGJ4pIkSZJarmY6JXEpFZJxWRERbclNnBcR/WgGg/MlSZIkNX+FNFx+AdwN9I+Ia4F/AT9skqgkSZIkqY68u4qllO6IiPHASLJruhyTUnqrySKTJEmSWiq7ihUs74ZLRNyeUjoFeLuBdZIkSZLUZAoZnL9j3YXceJfhGzccSZIkqRXI85Ik+sQ6x7hExGURsQjYOSIWRsSi3PIs4B9NHqEkSZKkVm+dDZeU0g9TSt2An6SUuqeUuuVufVJKlxUhRkmSJEmtXCFdxf4rIk4GtkwpXR0RmwGDUkovNlFskiRJUsvk4PyCFTId8k3A3sDXcsuLc+skSZIkqUkVknHZM6W0a0S8DJBSmh8RHZooLkmSJEmqVUjDZUVuJrEEEBH9AHNckiRJUqFqnFWsUIV0FfsFcDcwICKuBf4F/KBJopIkSZKkOvLOuKSU7oiI8cDI3KpjUkpvNU1YkiRJUguW7LhUqEK6igF0AVZ1F+u88cORJEmSpDXl3VUsIv4f8DugN9AXuC0iLm+qwCRJkiRplUIyLicCu6SUlgNExI+ACcA1TRGYJEmS1GI5OL9ghQzOnwx0qrPcEXh/o0YjSZIkSQ1YZ8YlIm4kG9NSCbwREQ/nlg8hm1lMkiRJUgFSjYPzC5VPV7Fxub/jyaZDXuWJjR6NJEmSJDVgnQ2XlNLvihGIJEmSJK1N3oPzI2Jb4IfADtQZ65JS2qoJ4pIkSZJaLgfnF6yQwfm3ATcDK4ERwO+B25siKEmSJEmlFRGHR8Q7ETExIi5tYPuBEbEgIl7J3f5fvvuuj0KmQ+6cUno0IiKl9CFwZUQ8DVyxMQKRJEmSWo1U3oPzI6ItcBPZhFxTgZci4p6U0pv1ij6dUvrCeu5bkEIyLssjog3wXkScExHHAv035MElSZIklaU9gIkppQ9SSlXAn4Gji7DvWhXScLkA6AKcBwwHTgFO29AAJEmSJBVXRIyKiHF1bqPqFRkCfFRneWpuXX17R8SrEXF/ROxY4L4FyburWErppdzdxcDpG/rAkiRJUqtV4sH5KaVbgFsaKRIN7VZveQKweUppcUQcCfwd2DbPfQuWzwUof5ZSuiAi7m3oAVNKX9zQICRJkiSVlanAZnWWNwWm1S2QUlpY5/59EfGriOibz77rI5+My6qZw/57Qx9MkiRJElBT3oPzgZeAbSNiS+Bj4KvA1+oWiIiBwMyUUoqIPciGocwFKta17/rI5wKU43N/n4yIfrn7szf0gSVJkiSVp5TSyog4B3gQaAv8JqX0RkR8K7d9DPBl4KyIWAksA76aUkpAg/tuaEz5dBULsimPzyHrr9YmF9yNKaWrNjQASZIkSeUnpXQfcF+9dWPq3P8l8Mt8991Q+cwqdgGwL7B7SqlPSqkXsCewb0RcuDGDkSRJklqFmlTaWzOUT8PlVODElNKkVStSSh8AJ+e2SZIkSVKTymdwfvuU0pz6K1NKsyOifRPEJEmSJLVsqewH55edfDIuVeu5TZIkSZI2inwyLp+NiIUNrA+g00aOR5IkSZLWkM90yG2LEYgkSZLUajTTAfKllE9XMUmSJEkqKRsukiRJkspePmNcJEmSJG1EqcZZxQpVlIZLdYpiPIwkSZKkFsqMiyRJklRsDs4vmGNcJEmSJJU9Gy6SJEmSyp5dxSRJkqRis6tYwcy4SJIkSSp7ZlwkSZKkYktOh1woMy6SJEmSyp4NF0mSJEllz65ikiRJUrE5OL9gZlwkSZIklT0zLpIkSVKRJTMuBTPjIkmSJKns2XCRJEmSVPbsKiZJkiQVm13FCmbGRZIkSVLZM+MiSZIkFVtNTakjaHbMuEiSJEkqezZcJEmSJJU9u4pJkiRJxebg/IKZcZEkSZJU9sy4SJIkScVmxqVgZlwkSZIklT0bLpIkSZLKnl3FJEmSpCJLya5ihTLjIkmSJKns2XCRJEmSVPbsKiZJkiQVm7OKFcyMiyRJkqSyZ8ZFkiRJKjYzLgUz4yJJkiSp7NlwkSRJklT27ComSZIkFVmyq1jBzLhIkiRJKntmXCRJkqRiM+NSMDMukiRJksqeDRdJkiRJZc+uYpIkSVKx1ZQ6gObHjIskSZKksmfGRZIkSSoyp0MunBkXSZIkSWXPhoskSZKksmdXMUmSJKnY7CpWsFbTcNnm2tPpM3JXqpdV8vZ5N7H4tUlrlOk0tD87jL2Adj03YfFrk3jr7BtJK1audf/OWw9mx1su/GT/zfsz+cd/Yeot99Wu2+yso9j6ylN55tNnsGLeoqY/0Rbk8h/cwFPPvEjvXj35+x/GlDqcVsW6Ly3rv7Ss/6b3xStOY/sRw1ixrIo7L7qZaW9MXqPMl68bxZCdtyII5kyazp0X3UzV0kp2OGQ4h44+gZRqqFlZw71X/Z7J494p/kk0UztfcyoDRw6jelkV488fQ8Vrk9co02VoP/YYcy4dem5CxWuTeOmcX5FWVNO+R1eG/3QUXbcYQHXlCiZcOJaFb08t/kmo1WoVXcV6j9yFzlsO4oW9zuXdi8ay3Y+/0WC5rS4/ialj/8mLe5/HyorFDPraQY3uv+z9aYwbeXF2O+Q71CyrYvZ9L9Yer+PgPvQ6YGeWfzS76U+yBTrmyEMYc8M1pQ6jVbLuS8v6Ly3rv2ltf+Aw+m45kJ8ceCF/++6tHHvtmQ2Wu/fq2/n5EZfysyO+Q8W0Oexz2mEATHzmdX52xHf4+ZGXcdclY/nSdQ1/pmtNA0YOY5OtBvLQ3qOZcNGvGXbdGQ2W2+nyE5k49n4e2mc0VRVL2OJrIwDY/vyjqXjjQx496FLGnXszO199ajHDb3lqSnxrhlpFw6Xv4bsz864nAVg4/j3ade9Kh/491yjXa7+dmH3v8wDMuPNJ+h6xe97799p/J5ZNnkHl1Dm167a56uu8f9UfIJkKXB+7DfsMPbp3K3UYrZJ1X1rWf2lZ/01rx0OHM/5vTwMw5eWJdO7WhW79eq5RrnLxstr77Tp1IOU+S6uWVtau79ClI/gRm7fBhw1nyp1Z3c+fMJH23bvQqYHvQ/323ZGP//kCAFPufJrBh+8GQPfthjD76TcAWDxxGl0260fHvt2LE7xEnl3FIqIt8Hlgi7r7pJRuaJqwNq6Og3pT+fHc2uXK6XPpOKg3VbMqate1792NlQuXkqqzJmjltKxMvvv3P3ZfZt39TO1yn8N2o3LGPJa8+WETnZUkSc1P9wG9WTDtk8/UBTPm0X1gbxbNrlij7PE/+SbbH7gLsyZO5f+u+UPt+h0P243DL/kqm/TpwW1n/LgYYbcInQb1Ytm0ebXLy6bPo9OgXiyv832mQ+9urFi4pPb70LLpc+k0qBcAC96YwuAjd2fui+/Qa5et6bJpXzoP7kPlnIVFPQ+1XvlmXO4Fvg70AbrVuTUTscaaNZIg0ViZxveP9u3oe+huzLr3OQDadO7A5hccx6Tr/rKe8UqS1EI18Hm7tp4Jd108lmv3PItZE6fx2aP2rl3/xoPjuH7kRfx+1PUcOvr4poq0xYkG675+oQZ2zD0/79x4Dx16duWgR37A1mccyoLXJ5NWVm/0OFuLVJNKemuO8h2cv2lKaedCDhwRo4BRAKO77cpRnbcqNLYNMvj0wxh88sEALHxlIh2H9Knd1nFQH6pmzFut/Iq5C2nXvQvRtg2puoaOgz8pUzl9bqP79x45jEWvTWLF7AUAdN5iIJ2G9mf3x36SlR/ch+EP/5gJh19GVQO/KEmS1JLtfcoh7HFiNm506qsf0GPwJ5+pPQb2ZuHM+WvdN9UkXv3ncxww6guMy3XbXmXSi2/TZ/MBdOnVjaXznQCnIVudfghbnJSNUZn/ygd0Hty7dlvnQb1ZPmP1uq+au4j23bvWfh/qPKgPy2dUALBy8TLGXzC2tuxhL/2cJVMcx6viyTfjcn9EHFrIgVNKt6SUdksp7VbsRgvAtNserB04P+f+lxhw/AEAdB++LSsXLV2tm9cq8595g35H7QXAwBMOYM4DLwEw58Fxje4/4Nj9mHX3v2qXl7w1hWd3/A+e3/1snt/9bCqnzWX8IZfYaJEktUrP3f4wPz/yMn5+5GW88dA4hh+3PwBDd9mG5YuWNthNrM/mA2rv7zByV2a/P22N9YN33IK27dvZaGnEB7c9zGMHf5fHDv4u0x8Yx9ATsrrvtes2rFi0bLVuYqvMfvZNhnxhTwCGnrA/0x8cB0D77l2I9m0B2OKkEcx5/m1W1hmLpAI5OL9g+WZcngfujog2wAqyRGJKKTWLEVnzHplAn5G7sOcLN1K9rIp3zr+pdttn7riMd0aPoWrmfD645g/sMPZCtrz0RBa9Nonpf3xsnfu36dyBXp/bmXcuuqXo59XSXXzFj3jp5X9TUbGQkceczLfPPIUvHXVYqcNqFaz70rL+S8v6b1pvP/4y248YxiVP/oyqZZXcdfEnv+Cfftsl/O93bmXx7ApOuP4sOm7SmYhg+lsfcvflvwFgpyP2YPhxn6N65UpWLK/ij+f8olSn0uzMeOQVBowcxqHP/5TqZZWrZU/2ueMSJoy+heUzK3j96j+xx9hz2eHS46l4/UMm//EJALptO4TdbjyLVF3DwnenMmH0rSU6E7VWkfKY8SoiPgCOAV5L+exQzxMDjm+eHelagH3fuK7UIUiSiuzy3f6r1CG0ansub2igiIrluBl/bBZPwPwvHVjS78e9/vpEs6inuvLNuLwHvL4+jRZJkiRJq2uuA+RLKd+Gy3TgiYi4H6idQL25TIcsSZIkqXnLt+EyKXfrkLtJkiRJUtHk1XBJKX1/1f3cAP1NUkpebUiSJElaH810Zq9Syms65Ij4Y0R0j4iuwJvAOxFxcdOGJkmSJEmZfK/jskMuw3IMcB8wFDilqYKSJEmSWrJUU9pbc5Rvw6V9RLQna7j8I6W0AnAqBEmSJElFkW/DZSwwGegKPBURmwOOcZEkSZJUFPkOzv8FUPfStB9GxIimCUmSJElq4Zppd61SarThEhEnp5T+EBGj11LE67hIkiRJanLryrh0zf3t1tSBSJIkSa1Fcx0gX0qNNlxSSmNzf7/fWDlJkiRJakrr6ir2i8a2p5TO27jhSJIkSdKa1tVVbHxRopAkSZJaE7uKFWxdXcV+V3c5Irplq9PiJo1KkiRJkurIazrkiNgJuB3onS3GbODUlNIbTRmcJEmS1BI5OL9w+V6A8hZgdEpp85TSUOA/gVubLixJkiRJpRQRh0fEOxExMSIubWD7SRHx79zt2Yj4bJ1tkyPitYh4JSLGbYx48sq4AF1TSo+vWkgpPRERXRvbQZIkSVLzFBFtgZuAQ4CpwEsRcU9K6c06xSYBB6SU5kfEEWTJjj3rbB+RUpqzsWLKt+HyQUR8j6y7GMDJuUAlSZIkFagZdBXbA5iYUvoAICL+DBwN1DZcUkrP1in/PLBpUwaUb1exM4B+wN+Au3P3T2+qoCRJkiQ1nYgYFRHj6txG1SsyBPiozvLU3Lq1ORO4v85yAh6KiPENHHu95JVxSSnNB7xmiyRJkrQRlDrjklK6haxr19pEQ7s1WDBiBFnDZb86q/dNKU2LiP7AwxHxdkrpqfUOmPxnFdsOuAjYou4+KaWDNuTBJUmSJJWlqcBmdZY3BabVLxQROwO/Bo5IKc1dtT6lNC33d1ZE3E3W9azpGy7AXcCYXFDVG/KAkiRJksreS8C2EbEl8DHwVeBrdQtExFCyoSSnpJTerbO+K9AmpbQod/9Q4KoNDSjfhsvKlNLNG/pgkiRJkoDUUE+s8pFSWhkR5wAPAm2B36SU3oiIb+W2jwH+H9AH+FVEQNZm2A0YANydW9cO+GNK6YENjanRhktE9M7dvTcivk02ML+yzgnN29AAJEmSJJWflNJ9wH311o2pc/8/gP9oYL8PgM/WX7+h1pVxGU82CGdVk/DiujEBW23sgCRJkiSpvkYbLimlLYsViCRJktRalHpWseYo31nF2gNnAZ/LrXoCGJtSWtFEcUmSJElSrXwH598MtAd+lVs+JbdujT5tkiRJkhqXasp7cH45yrfhsntKqe4Am8ci4tWmCEiSJEmS6muTZ7nqiNh61UJEbIXXc5EkSZJUJPlmXC4GHo+ID8hmGNscOL3JopIkSZJaMAfnFy6vhktK6dGI2BbYnqzh8nZKqXIdu0mSJEnSRpFXV7GIOBvonFL6d0rpVaBL7oKUkiRJkgqUUpT01hzlO8blGymlilULKaX5wDeaJCJJkiRJqiffhkubiKhtmkVEW6BD04QkSZIkSavLd3D+g8CdETEGSMC3gAeaLCpJkiSpBXNwfuHybbh8B/gmcBbZ4PyHgF83VVCSJEmSVFe+s4rVADfnbpIkSZI2QKppngPkS6nRhktEvEbWNawhKaX02Y0fkiRJkiStbl0Zly80sC6ATYHvbvxwJEmSJGlNjTZcUkofrrofEcOArwEnAJOAvzZpZJIkSVILldbWp0lrta6uYtsBXwVOBOYCfwEipTSiCLFJkiRJErDurmJvA08DR6WUJgJExIVNHpUkSZLUgjk4v3DrugDll4AZwOMRcWtEjCQb4yJJkiRJRdNowyWldHdK6SvAp4AngAuBARFxc0QcWoT4JEmSJCnv67gsAe4A7oiI3sDxwKVkF6KUJEmSVAC7ihVuXV3F1pBSmpdSGptSOqgpApIkSZKk+vLKuEiSJEnaeJwOuXAFZ1wkSZIkqdhsuEiSJEkqe3YVkyRJkorMwfmFM+MiSZIkqezZcJEkSZJU9uwqJkmSJBVZSnYVK5QZF0mSJEllz4yLJEmSVGSpptQRND9mXCRJkiSVPRsukiRJksqeXcUkSZKkIqtxcH7BzLhIkiRJKntmXCRJkqQiczrkwplxkSRJklT2bLhIkiRJKnt2FZMkSZKKLNXYVaxQZlwkSZIklT0zLpIkSVKRpVTqCJofMy6SJEmSyp4NF0mSJEllz65ikiRJUpE5OL9wZlwkSZIklT0zLpIkSVKR1SQzLoUy4yJJkiSp7NlwkSRJklT27ComSZIkFVmyq1jBzLhIkiRJKntmXCRJkqQiS6nUETQ/ZlwkSZIklT0bLpIkSZLKnl3FJEmSpCLzOi6FM+MiSZIkqezZcJEkSZJU9uwqJkmSJBWZ13EpnBkXSZIkSWWv1WRctrn2dPqM3JXqZZW8fd5NLH5t0hplOg3tzw5jL6Bdz01Y/Nok3jr7RtKKlWvdv/PWg9nxlgs/2X/z/kz+8V+Yest9dN1hc7b7ySjadu3E8o9m8dZZv6B68bKinW9LcPkPbuCpZ16kd6+e/P0PY0odTqti3ZeW9V9a1n/T++IVp7H9iGGsWFbFnRfdzLQ3Jq9R5svXjWLIzlsRBHMmTefOi26mamklOxwynENHn0BKNdSsrOHeq37P5HHvFP8kmqmdrzmVgSOHUb2sivHnj6HitclrlOkytB97jDmXDj03oeK1Sbx0zq9IK6pp36Mrw386iq5bDKC6cgUTLhzLwrenFv8kWgiv41K4VpFx6T1yFzpvOYgX9jqXdy8ay3Y//kaD5ba6/CSmjv0nL+59HisrFjPoawc1uv+y96cxbuTF2e2Q71CzrIrZ970IwPY3fIsPrrmDcQf+J3Pue5HNzv5icU62BTnmyEMYc8M1pQ6jVbLuS8v6Ly3rv2ltf+Aw+m45kJ8ceCF/++6tHHvtmQ2Wu/fq2/n5EZfysyO+Q8W0Oexz2mEATHzmdX52xHf4+ZGXcdclY/nSdQ1/pmtNA0YOY5OtBvLQ3qOZcNGvGXbdGQ2W2+nyE5k49n4e2mc0VRVL2OJrIwDY/vyjqXjjQx496FLGnXszO199ajHDl1pHw6Xv4bsz864nAVg4/j3ade9Kh/491yjXa7+dmH3v8wDMuPNJ+h6xe97799p/J5ZNnkHl1DkAdNlmMAueexOA+U/+m36f36spTq1F223YZ+jRvVupw2iVrPvSsv5Ly/pvWjseOpzxf3sagCkvT6Rzty5069dzjXKVdXoptOvUgZT7ebpqaWXt+g5dOoK/Wudt8GHDmXJnVvfzJ0ykffcudGrg+1C/fXfk43++AMCUO59m8OG7AdB9uyHMfvoNABZPnEaXzfrRsW/34gQv0UoaLh0H9aby47m1y5XT59JxUO/VyrTv3Y2VC5eSqmuyMtM+KZPP/v2P3ZdZdz9Tu7zk7Y/ok/uP3u+ovek4pM/GPSlJkpqh7gN6s2DaJ5+pC2bMo/vA3g2WPf4n3+Tyl8bQf+vBPPvbB2vX73jYbvzno//N6b+5hLsuGdvkMbcUnQb1Ytm0ebXLy6bPo9OgXquV6dC7GysWLqn9PrRs+tzaMgvemMLgI7MfdXvtsjVdNu1L58F+v1lfNSlKemuO8m64RESniBgdEX+LiL9GxIUR0akpg9t41nxy1uhXGI2VaXz/aN+Ovofuxqx7n6td984Fv2LI6Ycz/KHraLtJJ1LVyvWIW5KkFqaBz9u1dfa/6+KxXLvnWcyaOI3PHrV37fo3HhzH9SMv4vejrufQ0cc3VaQtTjRY9/ULNbBj7vl558Z76NCzKwc98gO2PuNQFrw+mbSyeqPHKa1NIYPzfw8sAm7MLZ8I3A40+I4REaOAUQCju+3KUZ232oAwCzf49MMYfPLBACx8ZeJqGY+Og/pQNWPeauVXzF1Iu+5diLZtSNU1dBz8SZnK6XMb3b/3yGEsem0SK2YvqF23dOI0/v2VrI90560G0eeQ4Rv/JCVJagb2PuUQ9jgxGzc69dUP6FHnV/oeA3uzcOb8te6bahKv/vM5Dhj1Bcblum2vMunFt+mz+QC69OrG0vmLmib4Zm6r0w9hi5OyMSrzX/mAzoM/yW51HtSb5TNWr/uquYto371r7fehzoP6sHxGBQArFy9j/AWfZLgOe+nnLJkyu+lPooVyOuTCFdJVbPuU0pkppcdzt1HAdmsrnFK6JaW0W0ppt2I3WgCm3fZg7cD5Ofe/xIDjDwCg+/BtWbloKVWzKtbYZ/4zb9DvqGwsysATDmDOAy8BMOfBcY3uP+DY/Zh1979WO1b7VX0+I9j8wi8x7XcPbeQzlCSpeXju9of5+ZGX8fMjL+ONh8Yx/Lj9ARi6yzYsX7SURbMr1tinz+YDau/vMHJXZr8/bY31g3fcgrbt29loacQHtz3MYwd/l8cO/i7THxjH0BOyuu+16zasWLSM5Q18H5r97JsM+cKeAAw9YX+mPzgOgPbduxDt2wKwxUkjmPP826x0xlQVUSEZl5cjYq+U0vMAEbEn8Mw69ikL8x6ZQJ+Ru7DnCzdSvayKd86/qXbbZ+64jHdGj6Fq5nw+uOYP7DD2Qra89EQWvTaJ6X98bJ37t+ncgV6f25l3Lrpltcfsf+x+DDk9mwFlzn0vMuNPjxfhTFuWi6/4ES+9/G8qKhYy8piT+faZp/Clow4rdVitgnVfWtZ/aVn/Tevtx19m+xHDuOTJn1G1rJK7Lv7kF/zTb7uE//3OrSyeXcEJ159Fx006ExFMf+tD7r78NwDsdMQeDD/uc1SvXMmK5VX88ZxflOpUmp0Zj7zCgJHDOPT5n1K9rHK17Mk+d1zChNG3sHxmBa9f/Sf2GHsuO1x6PBWvf8jkPz4BQLdth7DbjWeRqmtY+O5UJoy+tURnotYqUp6TSEfEW8D2wJTcqqHAW0ANkFJKO69t3ycGHO+cHyWy7xvXlToESVKRXb7bf5U6hFZtz+V2ASql42b8sVk8AS8MPq6k34/3nPa3ZlFPdRWScTm8yaKQJEmSpEbk3XBJKX0IEBH9gU511k9Z606SJEmS1mB3pMIVMh3yFyPiPWAS8CQwGbi/ieKSJEmSpFqFzCp2NbAX8G5KaUtgJM1kcL4kSZKk5q2QMS4rUkpzI6JNRLRJKT0eEY78liRJkgrUXK9eX0qFNFwqImIT4CngjoiYBXg5eEmSJElNrpCGy9HAMuBC4CSgB3BVUwQlSZIktWTJjEvBCmm49Aemp5SWA7+LiM7AAGBuk0QmSZIkSTmFDM6/i+xik6tU59ZJkiRJamEi4vCIeCciJkbEpQ1sj4j4RW77vyNi13z3XR+FNFzapZSqVi3k7nfYGEFIkiRJrUlNiW/rEhFtgZuAI4AdgBMjYod6xY4Ats3dRgE3F7BvwQppuMyOiC+uWoiIo4E5GxqAJEmSpLKzBzAxpfRBLmHxZ7Ix73UdDfw+ZZ4HekbEoDz3LVghDZdvAd+NiCkR8RHwHeCbGxqAJEmSpOKKiFERMa7ObVS9IkOAj+osT82ty6dMPvsWLO/B+Sml94G9clMiR0pp0YY+uCRJktQaJUo7q1hK6RbglkaKNBRgyrNMPvsWbJ0Nl4gYvZb1WQQp3bChQUiSJEkqK1OBzeosbwpMy7NMhzz2LVg+GZduub/bA7sD9+SWjyK7GKUkSZKkAtRscP6hyb0EbBsRWwIfA18FvlavzD3AORHxZ2BPYEFKaXpEzM5j34Kts+GSUvo+QEQ8BOy6qotYRFyJ0yFLkiRJLU5KaWVEnAM8CLQFfpNSeiMivpXbPga4DzgSmAgsBU5vbN8NjamQC1AOBarqLFcBW2xoAJIkSZLKT0rpPrLGSd11Y+rcT8DZ+e67oQppuNwOvBgRd5MNrjkW+P3GDEaSJElqDWpKPDi/OSpkVrFrI+IBYL/cqtNTSi83TViSJEmS9IlCMi4ArwDTV+0XEUNTSlM2dlCSJElSS1bq6ZCbo7wbLhFxLnAFMBOoJpufOQE7N01okiRJkpQpJONyPrB9SmluUwUjSZIkSQ0ppOHyEbCgqQKRJEmSWouaUgfQDBXScPkAeCIi/g+oXLUypXTDRo9KkiRJkuoopOEyJXfrkLtJkiRJWg8Ozi9cIdMhf78pA5EkSZKktSlkVrF+wCXAjkCnVetTSgc1QVySJEmSVKtNAWXvAN4GtgS+D0wGXmqCmCRJkqQWrabEt+aokIZLn5TS/wArUkpPppTOAPZqorgkSZIkqVYhg/NX5P5Oj4jPA9OATTd+SJIkSVLL1lyzHqVUSMPlmojoAfwncCPQHbiwSaKSJEmSpDoKmVXsn7m7C4ARTROOJEmSJK1pnQ2XiLgRSGvbnlI6b6NGJEmSJLVwXselcPlkXMbVuf994IomikWSJEmSGrTOhktK6Xer7kfEBXWXJUmSJBWuxoRLwQqZDhka6TImSZIkSU2l0IaLJEmSJBVdPoPzF/FJpqVLRCxctQlIKaXuTRWcJEmS1BLVODi/YPmMcelWjEAkSZIkaW3sKiZJkiSp7OV9AUpJkiRJG4czXhXOjIskSZKksmfGRZKkFqa61AG0cm1LHYCahZpSB9AMmXGRJEmSVPZsuEiSJEkqe3YVkyRJkoqsJryOS6HMuEiSJEkqe2ZcJEmSpCJzOuTCmXGRJEmSVPZsuEiSJEkqe3YVkyRJkorM67gUzoyLJEmSpLJnxkWSJEkqshpnQy6YGRdJkiRJZc+GiyRJkqSyZ1cxSZIkqchqsK9Yocy4SJIkSSp7ZlwkSZKkIkulDqAZMuMiSZIkqezZcJEkSZJU9uwqJkmSJBWZ13EpnBkXSZIkSWXPjIskSZJUZDWlDqAZMuMiSZIkqezZcJEkSZJU9uwqJkmSJBWZ13EpnBkXSZIkSWXPhoskSZKksmdXMUmSJKnIvI5L4cy4SJIkSSp7ZlwkSZKkIvM6LoUz4yJJkiSp7NlwkSRJklT27ComSZIkFZldxQpnxkWSJElS2TPjIkmSJBVZcjrkgplxkSRJklT2bLhIkiRJKnt2FZMkSZKKzMH5hTPjIkmSJKnsmXGRJEmSisyMS+HMuEiSJEkqezZcJEmSJJU9u4pJkiRJRZZKHUAzZMZFkiRJUtlrNRmXba49nT4jd6V6WSVvn3cTi1+btEaZTkP7s8PYC2jXcxMWvzaJt86+kbRiZaP7D/nGkQw+eSQQTL/jEabect9qx9zsrKPY+spTeebTZ7Bi3qImP8+W5PIf3MBTz7xI7149+fsfxpQ6nFbFui8t67+0rP+md/QVp/HpEcOoWlbFXy66mY/fmLxGmeOvG8VmO28FBHMmTefPF91M1dLK2u2b7bwV5959NX845+f8+/4Xixd8M/SZa06l/8hhVC+r4uXzx7DgtclrlOkytB+7jTmX9j03YcFrkxh/zq9IK6pp160zw286m85D+hDt2vL+zf/HlD8/CcCwn45i4CG7UDlnIY8f+J0in1XzVxOljqD5aRUZl94jd6HzloN4Ya9zefeisWz34280WG6ry09i6th/8uLe57GyYjGDvnZQo/t3/dRmDD55JOMPv4xxB11En0OG03nLgbXH6zi4D70O2JnlH81u+pNsgY458hDG3HBNqcNolaz70rL+S8v6b1qfOnAY/bYcyI8OvJD//e6tfOnaMxssd8/Vt3PDEZdywxHfYf60Oex72mG126JN8PlLv8Y7T71arLCbrf4jh9F1q4E8uvdoXr3o13z2ujMaLLfD5Sfy/tj7eXSf0VRVLGHzr40AYMvTD2XRu1N5YuRlPHPc1ex4xUlE+7YAfPSXp3juxOuKdi5Sq2i49D18d2belf06sHD8e7Tr3pUO/XuuUa7Xfjsx+97nAZhx55P0PWL3Rvfvsu0QFo5/j5plVaTqGiqefZO+R+5Re7xtrvo671/1B0j2Ylwfuw37DD26dyt1GK2SdV9a1n9pWf9Na8dDhzPub08DMOXliXTq1oVu/XquUa5y8bLa++07dVjts3S/rx/Ov+9/gcVzFzZ5vM3doMOG89GdWX3PnzCR9t270LGB70B9992Raf98AYCP7nyaQYfvlm1IiXabdAagXddOVFUsJq3MJvKd+/zbVFUsbvqTkHLW2XCJiN6N3YoR5IbqOKg3lR/PrV2unD6XjoNWD719726sXLiUVJ39Z6yc9kmZte2/5O2P6LHXp2nXaxPadO5A74N3peOQvgD0OWw3KmfMY8mbHzb16UmS1Gz0GNCbimmffKYumDGPHgMb/jrxlZ98kyteGkP/rQfzr98+CED3Ab3Y6bDdee6OR4oSb3PXaVAvlk2bV7u8bPo8Og/qtVqZDr27sWLhktrvQMumz6VTrsyk3zzEJtsO5rBXb2LE49fx+vd+7w+yG0lNiW/NUT5jXMaTTXwQwFBgfu5+T2AKsGVDO0XEKGAUwOhuu3JU5602Qrjra81OhGv8n4vGyjS8bel7HzPll//gs3d+j+oly1nyxmTSymradO7A5hccx6sn2NVAkqS6osHP24a/CP/l4rFEm+DY75/OsKP25qW7nuTo/3cq//ejP5Jq/PKcl0a/36wq08B+uUL9RuzMwtc/5NkvXUvXLQaw952XMff5y1hZJyMmFcs6Gy4ppS0BImIMcE9K6b7c8hHAwY3sdwtwC8ATA44v+rvL4NMPY/DJWXgLX5lIxyF9ard1HNSHqhnzViu/Yu5C2nXvQrRtQ6quoePgT8pUTp+71v1n/PExZvzxMQC2/O6JVE6bS+ctBtJpaH92f+wnWfnBfRj+8I+ZcPhlVM2uaLJzliSpHO1zyiHseWI2bvSjVz+g5+BPPlN7DOzNwpnz17pvqkm88s/nOHDUF3jprifZbOetOPnG8wDo2qsbnz5wGNXVNbzx0LimPYlmZMvTD2Hzk7IxKvNf+YDOgz/JaHUe1JvlM1av76q5i2jfvWvtd6DOg/qwfEYFAEO/egDv3XgPAEsmz2TplNlssu1gKl5+vzgnI9VRyKxiu6eUvrVqIaV0f0Rc3QQxbRTTbnuQabdlaeXeB+/KkDMOZ9bdz9B9+LasXLSUqlkVa+wz/5k36HfUXsz6+7MMPOEA5jzwEgBzHhy31v3b9+3OijkL6TikL/2O3JMJn/8vVi5YwrM7/kftcfd66SbGH3aps4pJklqlZ29/mGdvfxiAT4/YhX1PO5RX7nmWobtsw/JFS1nUwI96fTYfwNwPZwKww8hdmfX+NAB+sP/5tWW+8t/f4q1HJ9hoqWfSbQ8z6basvgccPIwtzziUj//+HL123YYVi5ZR2cB3oDnPvsngL+zJx/94js1O2J/pD2Z1uuzjufTbfyfmvfAOHft2Z5OtB7H0w1nFPJ0Wq7l21yqlQhoucyLicuAPZF3HTgbmNr5LeZj3yAT6jNyFPV+4keplVbxz/k212z5zx2W8M3oMVTPn88E1f2CHsRey5aUnsui1SUzPZVIa23/H/7mI9r26kVau5N3Lfs3KBUuKfn4t1cVX/IiXXv43FRULGXnMyXz7zFP40lGHrXtHbTDrvrSs/9Ky/pvWW4+/zKdGDOPSJ3/GimWV/OXisbXbzrztEu76zq0sml3BV68/i06bdCYimPbWh/z18t+UMOrma+YjrzBg5DAOfv6nVC+r5OULPqnvve64hFdG38LymRW8efWf2G3suXzq0uNZ8PqHTPnjEwC8e8Pf2OXn32LE4z+CCN685k9U5X6IHX7zOfTd59N06N2NQyfcyNs/+StT/vRECc5SrUWsrV/pGgWzgfhXAJ8ja7g8BVyVUprX6I6UpquYMvu+4TSFktTaXLbbf5U6hFZt/+VeoKOUjp7xx2bxBPz30JNL+v34oil/WO96yrUL/gJsAUwGTkgpza9XZjPg98BAsgTTLSmln+e2XQl8A1h1zZDvrhqO0pi8My65Bsr5EbFJSsm57yRJkqTW6VLg0ZTSjyLi0txy/auQrgT+M6U0ISK6AeMj4uGU0pu57T9NKf13IQ+a93VcImKfiHgTeDO3/NmI+FUhDyZJkiSp2Tsa+F3u/u+AY+oXSClNTylNyN1fBLwFDNmQBy3kApQ/BQ4jN64lpfQqWbcxSZIkSQWoidLeNtCAlNJ0yBooQP/GCkfEFsAuwAt1Vp8TEf+OiN9ERK+G91xdIQ0XUkof1VtVXcj+kiRJkkovIkZFxLg6t1H1tj8SEa83cDu6wMfZBPgrcEFKaWFu9c3A1sAwYDpwfT7HKmRWsY8iYh8gRUQH4DyylI8kSZKkApR6OuS611xcy/a1Xq8xImZGxKCU0vSIGAQ0OEd2RLQna7TckVL6W51jz6xT5lbgn/nEXEjG5VvA2WR906aStZDOLmB/SZIkSc3fPcBpufunAf+oXyAiAvgf4K2U0g31tg2qs3gs8Ho+D1pIxiVSSicVUF6SJElSy/Mj4M6IOBOYAhwPEBGDgV+nlI4E9gVOAV6LiFdy+62a9vjHETGM7BIrk4Fv5vOghTRcno2ISWRzNv81pVRRwL6SJEmScprzRQ5TSnOBkQ2snwYcmbv/L6DBaQBSSqesz+Pm3VUspbQtcDmwIzAhIv4ZESevz4NKkiRJUiEKnVXsxZTSaGAPYB6fzN8sSZIkKU81pJLemqNCLkDZPSJOi4j7gWfJpi7bo8kikyRJkqScQsa4vAr8HbgqpfRc04QjSZIkSWsqpOGyVUqpeeaVJEmSpDJS6uu4NEfrbLhExM9SShcA90TEGg2XlNIXmyIwSZIkSVoln4zL7bm//92UgUiSJEmthd2YCrfOhktKaXzu75MR0RkYmlJ6p8kjkyRJkqScQmYVOwp4BXggtzwsIu5porgkSZIkqVYhg/OvJJv++AmAlNIrEbHFxg9JkiRJatkcnF+4Qi5AuTKltKDJIpEkSZKktSgk4/J6RHwNaBsR2wLnkV2IUpIkSVIBaqLUETQ/hWRczgV2BCqBPwILgAuaICZJkiRJWk3eGZeU0lLgv3I3SZIkSSqaQmYVezgietZZ7hURDzZJVJIkSVILVkMq6a05KqSrWN+UUsWqhZTSfKD/Ro9IkiRJkuoppOFSExFDVy1ExOZ40U9JkiRJRVDIrGL/BfwrIp7MLX8OGLXxQ5IkSZJaNn/9L1whg/MfiIhdgb2AAC5MKc1pssgkSZIkKaeQwfkBHA7smlK6F+gSEXs0WWSSJElSC1VT4ltzVMgYl18BewMn5pYXATdt9IgkSZIkqZ5CxrjsmVLaNSJehmxWsYjo0ERxSZIkSVKtQhouKyKiLbmxRBHRj+abaZIkSZJKprleS6WUCukq9gvgbqB/RFwL/Av4QZNEJUmSJEl15JVxiYg2wCTgEmAk2axix6SU3mrC2CRJkqQWyXxL4fJquKSUaiLi+pTS3sDbTRyTJEmSJK2mkK5iD0XEl3LTIkuSJElS0RQyOH800BVYGRHLybqLpZRS9yaJTJIkSWqhnOGqcHk3XFJK3RrbHhE7ppTe2PCQJEmSJGl1hWRc1uV2YNeNeDxJkiSpRXI65MIVMsZlXRz7IkmSJKlJbMyGi81GSZIkSU1iY3YVkyRJkpQHf/Ev3MZsuFStbUOPjmvdpCZ2+W7/VeoQJLVC1aUOoJX74bhrSx1Cq9Z58P6lDqFVW1nqANRk1tlwiYjNgYqU0oLc8gjgGOBD4JcppSqAlNJeTRinJEmS1GI4HXLh8hnjcifZ9VuIiGHAXcAU4LPAr5osMkmSJEnKyaerWOeU0rTc/ZOB36SUro+INsArTRaZJEmSJOXk03CpO83xQcBlACmlmghnQJYkSZIKlRyeX7B8Gi6PRcSdwHSgF/AYQEQMopEB+ZIkSZK0seTTcLkA+AowCNgvpbQit34g4JRVkiRJUoEcnF+4dTZcUkoJ+HMD619ukogkSZIkqZ58pkNexOrXyInccpC1a7o3UWySJEmSBOTXVexRsm5hfwP+nFKa0rQhSZIkSS1bjYPzC7bO67iklI4BDgNmA7dGxJMR8e2I6N3UwUmSJEkS5HcBSlJKC1JKtwFHAGOAq4CvN2FckiRJklQrn65iRMQ+wInA/sC/gGNTSk83ZWCSJElSS2VHscLlMzj/Q2A+2cxio4CVufW7AqSUJjRlgJIkSZKUT8ZlElmj8LDcrX4D8aCNHZQkSZLUkjk4v3D5NFwuAT5KKU0HiIjTgC8Bk4ErmywySZIkScrJZ3D+GKASICI+B/wQ+B2wALil6UKTJEmSpEw+GZe2KaV5uftfAW5JKf0V+GtEvNJkkUmSJEktVE2pA2iG8sm4tI2IVQ2ckcBjdbblNSuZJEmSJG2IfBoefwKejIg5wDLgaYCI2Iasu5gkSZKkAiQH5xdsnQ2XlNK1EfEoMAh4KKW0qpbbAOc2ZXCSJEmSBHl29UopPd/Aunc3fjiSJEmStCbHqEiSJElF5uD8wuUzOF+SJEmSSsqMiyRJklRkDs4vnBkXSZIkSWXPhoskSZKksmdXMUmSJKnIHJxfODMukiRJksqeGRdJkiSpyGqSg/MLZcZFkiRJUtmz4SJJkiSp7NlVTJIkSSoyO4oVzoyLJEmSpLJnw0WSJElS2bOrmCRJklRkNXYWK5gZF0mSJEllz4yLJEmSVGTJjEvBzLhIkiRJKns2XCRJkiSVPbuKSZIkSUVWU+oAmiEzLpIkSZLKnhkXSZIkqcicDrlwra7h0u2AXdj0ym8Qbdsw988PM/NXf12jzJDvf4MeI4ZTs6ySD//z5yx7/QMA2nbvymY/PofO2w2FlPjw4htZOuEdAPp+/fP0O+3zpOpqFj42jmk/+F1Rz6u5+uIVp7H9iGGsWFbFnRfdzLQ3Jq9R5svXjWLIzlsRBHMmTefOi26mamklOxwynENHn0BKNdSsrOHeq37P5HHvFP8kmjHrv3Ss+9I6+orT+PSIYVQtq+IvF93Mxw3U//HXjWKznbeCXP3/OVf/q2y281ace/fV/OGcn/Pv+18sXvAt2OU/uIGnnnmR3r168vc/jCl1OC3ST2+4iiMOP4ily5Zx5pkX8vIrr69RZsSB+3Lddd+jQ4f2TJjwGt8Y9Z9UV1ez/fZb8z+3/pRddtmJ7/2/67jhp2NLcAZqzSKlpm/tvTz06PJoUrZpww5P/oqJJ13Biulz2f7e/2byudez/L2Paot0HzGcfl//PO+fdhVddtmOTa/8Bu8efTEAQ284nyUvvsncPz9MtG9Hm84dqV64hE32/gwDzz2e979+FalqJe369GDl3AWlOsvV/LlNl1KHsFbbHziMfb9+GL/5+nUM3WUbjrriNG465ntrlOu4SWcqFy8D4AuXn8ziuQt54uZ76NClY+2XiIGfGspJN53H9SMvKuo5NGfWf+m0hrqvLnUAjfjUgcPY7+uH8etc/R9zxWn8Yh31f1Su/h+/+R4Aok3wzT/8Fysqq3jpzifKruHyw3HXljqE9TLuldfo0rkz3736v5t1w6Xz4P1LHUKDjjj8IM7+9ul84YunsOceu/LTG77PPvsdtVqZiOCDiS9y6OFf4b33PuDKKy7iww+ncttv/0y/fn3YfOimHH304cyfX1G2DZeVVR9HqWPIx/Gbl/b78V0f/mO96ykiegN/AbYAJgMnpJTmN1BuMrCI7GNhZUppt0L2r69VjXHpMmxbKifPoGrKTNKKlcy/92l6HLrHamV6HLoH8/76OABLX36Xtt270q5/L9ps0plN9tiRuX9+GIC0YiXVC5cA0PeUw5n5q7+SqlYClE2jpdzteOhwxv/taQCmvDyRzt260K1fzzXKrfriANCuUwdWNbbr/vLZoUtHzLgWxvovHeu+tHY8dDjj6tR/pzzqv32nDlDnh779vn44/77/BRbPXdjk8bYmuw37DD26dyt1GC3WUUcdxu13/C8AL7w4gR49ezBwYP/VyvTp04vKykreey/rbfLII09x3LFHAjB79lzGjX+VFStWFDfwFiqV+N8GuhR4NKW0LfBobnltRqSUhq1qtKzH/rXy7ioWEX2AK4F9yT4m/wVclVKam+8xSq3DwD5UTZtTu1w1fS5dh223Wpn2A/tQNf2TMitmzKH9wD6wspqV8xYw9Prz6PzpLVn62vt8fOWt1CyrpOOWg+m6xw4MuvhkaiqrmHbNbSz998SinVdz1X1AbxZM++Tls2DGPLoP7M2i2RVrlD3+J99k+wN3YdbEqfzfNX+oXb/jYbtx+CVfZZM+PbjtjB8XI+wWw/ovHeu+tHoM6E1FvfrvsZb6/8pPvsmnDtyFmROncm+u/rsP6MVOh+3OmBOvZrPPbl2ssKUNNmTwQKZ+NK12+eOp0xkyeCAzZsyqXTdnzjzat2/P8F13ZvyEf3PccZ9n080GlyJclbejgQNz938HPAF8p6n3LyTj8mdgFvAl4MvAbLIUT4MiYlREjIuIcX9dPLmAh2lCDSXE1ugq10ChlKBdW7rstDVzbn+Ad468kJplyxnw7S9le7RrS9sem/Du0Rcz7drfssWvLtnoobdIsZa6bsBdF4/l2j3PYtbEaXz2qL1r17/x4DiuH3kRvx91PYeOPr6pIm2ZrP/Sse5LKhqo/7V1m/7LxWO5Klf/w3L1f/T/O5X/+9EfSTWmutS85PvaP+nkb3P9f1/Jc8/8k8WLl7ByZTl3/my+akp8q/tdPXcbVUD4A1JK0wFyf/uvpVwCHoqI8fWOn+/+qylkcH7vlNLVdZaviYhj1lY4pXQLcAuUzxiXqulz6TC4b+1yh0F9WDFr3mplVsyYQ4dBfVmSW24/sC8rZs6DlKiaPoelr7wLQMV9zzLgrKzhsmL6XBbc/xwAS199D1IN7Xp3Z+U8uxDUt/cph7DHiQcBMPXVD+gxuE/tth4De7Nw5tq7N6aaxKv/fI4DRn2BcXc9udq2SS++TZ/NB9ClVzeWzl/UNMG3ANZ/6Vj3pbXPKYewZ67+P3r1A3oWWP+v/PM5Dhz1BV6660k223krTr7xPAC69urGpw8cRnV1DW88NK5pT0JaD2d96zTOPPMkAMaNe2W17MmQTQcxbfrMNfZ5/oXxHHjQcQAccvDn2HbbrYoTrIqq7nf1hkTEI8DABjb9VwEPs29KaVpE9Acejoi3U0pPFRhqrUIaLo9HxFeBO3PLXwb+b30fuBSWvvoeHbccRIfN+rNixjx6HbU/k8+7frUyCx5+kX6nfZ759zxNl122o3rRElbOyj7QVkyfQ8ethlD5wcd023fn2kH9FQ+9wCb77Mzi51+n45aDifbtbbSsxXO3P8xzt2fjhD41Yhf2Oe1QXr3nWYbusg3LFy1tsKtGn80HMPfD7I11h5G7Mvv9aWusH7zjFrRt384vbutg/ZeOdV9az97+MM/m6v/TI3Zh39MO5ZUC639Wrv5/sP/5tWW+8t/f4q1HJ9hoUdm6eczvuHlMNtPpkUeM5NtnfZ2//OUf7LnHrixcsHC1bmKr9OvXh9mz59KhQwcuvuhsfvijXxQ7bJWBlNLBa9sWETMjYlBKaXpEDCLrldXQMabl/s6KiLuBPYCngLz2r6+Qhss3gdHAqk7WbYAlETE6iyd1L+BYpVFdw9Tv3cLWt1+ZTYf8l0dZ/u5H9Dn5cADm/uEBFj42nu4jdmOHp8dk0yFfdGPt7lP/361s8YvRRPt2VE6ZwZSLsv/I8/7yCEN/ci6fevgXpKqVfDj6Z6U4u2bn7cdfZvsRw7jkyZ9RtaySuy7+ZHaS02+7hP/9zq0snl3BCdefRcdNOhMRTH/rQ+6+/DcA7HTEHgw/7nNUr1zJiuVV/PEc31gLYf2XjnVfWm89/jKfGjGMS5/8GSuWVfKXOvV/5m2XcNd3bmXR7Aq+ev1ZdMrV/7S3PuSvufpX07n4ih/x0sv/pqJiISOPOZlvn3kKXzrqsFKH1WLcd/+jHH74Qbzz1jMsXbaM//iP0bXb7v3H7xn1rYuZPn0mF40+iyM/fzBt2rRh7Njf8/gTzwAwYEA/Xnjufrp334SamhrOO/cbfOazB7Jo0eJSnVKzVoyZfZvQPcBpwI9yf/9Rv0BEdAXapJQW5e4fClyV7/4NaV3TIbdC5TwdsqSWyx7xpdVcp0NuKcp1OuTWorlMh3zs0KNK+v347in3bsh0yH3IemENBaYAx6eU5kXEYODXKaUjI2Ir4O7cLu2AP6aUrm1s/3U9biGzin2uofUb0k9NkiRJao1qmvFc9rlZhUc2sH4acGTu/gfAZwvZf10K6Sp2cZ37ncj6qI0HDir0QSVJkiSpEHk3XFJKq11aNSI2A7x4gCRJkqQmV0jGpb6pwE4bKxBJkiSptagpdQDNUCFjXG6E2s54bYBhwKtNEJMkSZIkraaQjEvdSepXAn9KKT2zkeORJEmSWrzUjAfnl0ohY1x+19j2iPhrSulLGx6SJEmSJK2uzUY81lYb8ViSJEmSVGtDBufXZ75LkiRJykNzvo5LqWzMjIskSZIkNYmN2XCJjXgsSZIkSapVyHTIBwHPp5SWrqXIdzZOSJIkSVLLlpJdxQpVyBiXrwNjImIu8HTu9q+U0nyAlNJDGz88SZIkSSpsOuRTASJiMPBl4CZgcCHHkCRJkgQ1pQ6gGSqkq9jJwP7AZ4A5wC/Jsi6SJEmS1KQKyZb8DHgfGAM8nlKa3BQBSZIkSVJ9hXQV6xsROwKfA66NiG2Bd1JKpzRZdJIkSVILlLyOS8Hyng45IroDQ4HNgS2AHtg9T5IkSVIRFNJV7F91br9MKU1tmpAkSZKklq3GjEvBCukqtjNARHQDa1qSJElS8RTSVWyniHgZeB14MyLGR8ROTReaJEmSJGUK6Sp2CzA6pfQ4QEQcmFu3z8YPS5IkSWq5UrIDU6HyzrgAXVc1WgBSSk8AXTd6RJIkSZJUTyEZlw8i4nvA7bnlk4FJGz8kSZIkqWVzcH7hCsm4nAH0A/4G3J27f3pTBCVJkiRJdRUyq9h84LwmjEWSJEmSGpR3wyUitgMuIrv4ZO1+KaWDNn5YkiRJUsuV7CpWsELGuNwFjAF+DVQ3TTiSJEmStKZCGi4rU0o3N1kkkiRJUitR43TIBVtnwyUieufu3hsR3yYbmF+5antKaV4TxSZJkiRJQH4Zl/FAAiK3fFG97Vtt1IgkSZIkqZ58Gi5fAT5KKU0HiIjTgC8Bk4ErmywySZIkqYWyo1jh8rmOyxhyXcMi4nPAD4HfAQuAW5ouNEmSJEnK5JNxaVtnHMtXgFtSSn8F/hoRrzRZZJIkSVILVWPOpWD5ZFzaRsSqBs5I4LE62wqZlUySJEmS1ks+DY8/AU9GxBxgGfA0QERsQ9ZdTJIkSZKa1DobLimlayPiUWAQ8FBKtZNOtwHObcrgJEmSpJbIrmKFy6urV0rp+QbWvbvxw5EkSZKkNeUzxkWSJEmSSsrB9ZIkSVKRfTL6Qvky4yJJkiSp7BUl4zKtsksxHkYN2DOi1CFIaoXaljqAVq7z4P1LHUKrtmza06UOQc2Ag/MLZ8ZFkiRJUtmz4SJJkiSp7Dk4X5IkSSqyZFexgplxkSRJklT2zLhIkiRJReZ0yIUz4yJJkiSp7NlwkSRJklT27ComSZIkFZnXcSmcGRdJkiRJZc+MiyRJklRkDs4vnBkXSZIkSWXPhoskSZKksmdXMUmSJKnIHJxfODMukiRJksqeGRdJkiSpyJIZl4KZcZEkSZJU9my4SJIkSSp7dhWTJEmSiqzG67gUzIyLJEmSpLJnxkWSJEkqMgfnF86MiyRJkqSyZ8NFkiRJUtmzq5gkSZJUZA7OL5wZF0mSJEllz4aLJEmSpLJnVzFJkiSpyJxVrHBmXCRJkiSVPTMukiRJUpE5OL9wZlwkSZIklT0bLpIkSZLKnl3FJEmSpCJzcH7hzLhIkiRJKntmXCRJkqQic3B+4cy4SJIkSSp7NlwkSZIklT0bLpIkSVKRpRL/2xAR0TsiHo6I93J/ezVQZvuIeKXObWFEXJDbdmVEfFxn25H5PK4NF0mSJEmFuBR4NKW0LfBobnk1KaV3UkrDUkrDgOHAUuDuOkV+ump7Sum+fB7UwfmSJElSkaVUU+oQNsTRwIG5+78DngC+00j5kcD7KaUPN+RBzbhIkiRJKsSAlNJ0gNzf/uso/1XgT/XWnRMR/46I3zTU1awhNlwkSZKkViYiRkXEuDq3UfW2PxIRrzdwO7rAx+kAfBG4q87qm4GtgWHAdOD6fI5lVzFJkiSpyGo2cID8hkop3QLc0sj2g9e2LSJmRsSglNL0iBgEzGrkoY4AJqSUZtY5du39iLgV+Gc+MZtxkSRJklSIe4DTcvdPA/7RSNkTqddNLNfYWeVY4PV8HrRVZlx2uPY0+o8cRvWyKl4972YWvjZ5jTKdh/Zjl7Hn0aFnVxa8NplXzr6JtKIagN77fJodrj6VNu3aUTVvEc8fexUA7bp3YecbRtHtU5tCglcvHEvFuPeKeWrNzs7XnMrA3HMx/vwxVDTwXHQZ2o89xpxLh56bUPHaJF4651ekFdW079GV4T8dRdctBlBduYIJF45l4dtTi38SzZj1XzrWffF95ppTa9/7Xz5/DAvWUue7jTmX9j03YcFrkxifq/N23Toz/Kaz6TykD9GuLe/f/H9M+fOTAAz76SgGHrILlXMW8viBjY1N1So/veEqjjj8IJYuW8aZZ17Iy6+s+Z1lxIH7ct1136NDh/ZMmPAa3xj1n1RXV7P99lvzP7f+lF122Ynv/b/ruOGnY0twBi3T5T+4gaeeeZHevXry9z+MKXU4LV5Kpc24bKAfAXdGxJnAFOB4gIgYDPw6pXRkbrkLcAjwzXr7/zgihgEJmNzA9ga1uoxLv5HD6LrlQJ7Y60Jeu+hWdvrxmQ2W+9TlX2PS2Pt4Yu/RrKhYwmZfGwFkjZOdfnQG4079b5464GImfONntfvseM1pzH78VZ7c7yKeOug7LH7342KcUrM1YOQwNtlqIA/tPZoJF/2aYded0WC5nS4/kYlj7+ehfUZTVbGELXLPxfbnH03FGx/y6EGXMu7cm9n56lOLGX6zZ/2XjnVffP1HDqPrVgN5dO/RvHrRr/nsWup8h8tP5P2x9/Nors43z9X5lqcfyqJ3p/LEyMt45rir2fGKk4j2bQH46C9P8dyJ1xXtXJq7Iw4/iG232ZJP7bAfZ531HW765Q/XKBMR/OZ/fsZJJ3+bYbuMZMqUqZx6yvEAzJtXwQUXfs8GSxM45shDGHPDNaUOQ81ASmluSmlkSmnb3N95ufXTVjVacstLU0p9UkoL6u1/SkrpMymlnVNKX1w10H9dWl3DZcDhw/n4rqcBqBg/kfbdu9Cxf881yvXdb0dm3PsCAFPvfIqBR+wGwJDj9mXGfS+x/OO5AFTNWQhAu00603vvT/HRHY8DkFZUs3Lh0qY+nWZt8GHDmXJn9lzMn5A9F50aeC767bsjH/8zey6m3Pk0gw/Pnovu2w1h9tNvALB44jS6bNaPjn27Fyf4FsD6Lx3rvvgGHTacj+rVeYPv/fvuyLRcnX9059MMytU5KdFuk84AtOvaiaqKxaSV2VSmc59/m6qKxU1/Ei3EUUcdxu13/C8AL7w4gR49ezBw4OoTEvXp04vKykree+8DAB555CmOOzb7LjR79lzGjX+VFStWFDfwVmC3YZ+hR/dupQ5DWquCGi4RMTAivhgRR0XEwKYKqil1GtSbZblGB8Dy6fPoNKj3amXa9+7GioVLSNXZh9LyaXNry3TdehDte3Rlr799j/0eupYhx+8PQJfN+1M1dyE7//xb7PfID/nMDd+gbZeORTqr5qnToF4smzavdnnZ9Hl0GrT6bHgd6j0Xy6bPrS2z4I0pDD5ydwB67bI1XTbtS+fBfYoUffNn/ZeOdV98DdV55wLqfNJvHmKTbQdz2Ks3MeLx63j9e7+H5t3No2SGDB7I1I+m1S5/PHU6Qwav/pVizpx5tG/fnuG77gzAccd9nk03G1zUOKWmVkMq6a05yrvhEhH/AbwIHAd8GXg+IhrOtbP6FGsPLJu44ZFuJEGssa5+H8NYs0htmWjbhh6f3ZKXTv4xL3z1R2w7+li6bjWQaNeW7p/Zkim/e5h/HXwZ1Usr2frcLzbJObQU0WBF1y/UwI655+KdG++hQ8+uHPTID9j6jENZ8Ppk0srqjR5nS2X9l451XwIN1Pka7Y5G6rzfiJ1Z+PqHPPjZs3li5GV85gdfr83AqDANvf4b6ut/0snf5vr/vpLnnvknixcvYaWvcanVK2Rw/sXALimluQAR0Qd4FvhNQ4XrTrH2fwNOLGmzbvPTD2Gzkw8CYMErH9B5SB/m57Z1GtSbyhnzVytfNXcR7bt3Jdq2IVXX0Glwn9oyy6fPo2reIqqXVlK9tJJ5z79Ntx03Z/7zb7N82jwqJrwPwPR7X2Cbcwua5rpV2Or0Q9jipKzP+PxXPqDz4E+yXZ0H9Wb5Op6LzoP6sHxGBQArFy9j/AWf9HE+7KWfs2TK7KY/iWbM+i8d6774tjz9EDbfSHU+9KsH8N6N9wCwZPJMlk6ZzSbbDqbi5feLczLN3FnfOo0zzzwJgHHjXlktezJk00FMmz5zjX2ef2E8Bx50HACHHPw5tt12q+IEK6lsFdJVbCqwqM7yIuCjjRtO0/jwtof518jL+NfIy5h5/7ja7l09h2/DykVLqZxVscY+c595g4FH7QnApid8jpkPjAdg5gPj6L3Xp4i2bWjTuQM9d92Gxe99TOXsBSyfNpeuW2ezu/XdfycWvessP/V9cNvDPHbwd3ns4O8y/YFxDD0hey567boNKxYtY3kDz8XsZ99kyBey52LoCfsz/cFxALTv3qV2cOwWJ41gzvNvs3LxsuKcSDNl/ZeOdV98k257mCcO/i5PHPxdZjwwjs3q1XlD7/1znn2Twbk636xOnS/7eC799t8JgI59u7PJ1oNY+mFjly1QXTeP+R277X4ou+1+KPfc8yCnnPRlAPbcY1cWLljIjBlr1mW/fln3xw4dOnDxRWdzyy23FzVmqamllEp6a44i38Aj4vfAZ8jmaU7A0WRdx94FSCndsLZ9S51xqW/HH55Ov4M+S/WySv59/lgWvJoN/tv9jkv49+hbqZw5n86b92fXsdmUmAtz0yHXVK0EYKtvf+H/t3fvcVaV9R7HP98BFBKBECTvppanNLXwZGYZlmUdy1RM86iB2eFlncSOx+qUpVTnRFpagpV00bDwkpEmlqkRCKIS94vmpRDQMgkVjSQS5nf+eJ5hFuPeM7N1Nnvvme/79dqvWfOs27Oftdaz13Nbi90//A6IYPWUGaz83m0ADDhgL95w2RiatuvN86ueZMm5k9j07N9r9j0BNpbqklJHDh4/mmFHpWOx4FOTWLfkUQDeOuUzLDzve/zjyXW8Ys+defOkc9hu0A6sW76K+flYDB7+Gg6d+HFiczPPPfw4C8/7Pi/UOL0bjdO/drp72veqdQRKOGj8aHbOab6okOZvmfIZFhfS/NBJ59Bn0A48u3wVC3Oa9x02iDdefjZ9hw0CiUcm3sLjU+cAMPy7n2TIW1/HdoN3ZONfn+XBr09l9XUza/dFgZFP31XT/XdkwuX/xzHvGcHzGzbwsY+dx4KFSwGY9otrGHP2p3niiSe5ePwX+Ldjj6apqYlJk65hwsQfADBs2FDm3nsbAwb0p7m5mfXrn+cNB4/gb3+rnwckbPjz7FpH4SX59EVfY96ipaxb9xw7DR7EJ846g5EfOKbW0apYnyH71PfNT7bbKw+o6f3xn565vyHSqaiSgstF7c2PiC+Vm1dvBZeepN4LLmbWPdVjwaUnqfeCS3fXqAWX7qJRCi67DHp9Te+Pn1j3QEOkU1Gnx7i0VzAxMzMzMzOrpk4XXCTN4MXPvSEi3tmlMTIzMzMzM2ujkqeKnV+Y7guMBDZ1bXTMzMzMzLq/aNB3qdRSJV3FFrQJmiPJnWjNzMzMzKzqKukqVny9fBMwHHhVmcXNzMzMzKyMRn0kcS1V0lVsAWmMi0hdxB4FzqpGpMzMzMzMzIoq6Sr26vbmS3p3RNz58qNkZmZmZma2tUpaXDpyMeCCi5mZmZlZB5o9OL9iTV24rYZ7iY2ZmZmZmTWGrmxxcbHRzMzMzKwTPDi/cl3Z4mJmZmZmZlYVXVlwWdmF2zIzMzMzM9uikve4zAZmAbOBORHxt+L8iDixi+NmZmZmZtYtNburWMUqaXEZBTwEjATukTRf0jerEy0zMzMzM7NWlbzHZYWkDcA/8+co4HXVipiZmZmZWXflwfmV63SLi6Q/AjcDw4AfAgdGxHurFC8zMzMzM7MtKukqNgFYDZwKjAVGSdq3KrEyMzMzMzMrqKSr2OXA5ZL6A2cC44DdgV7ViZqZmZmZWffU7FcgVqySp4pdCrwN6A/cB1xIesKYmZmZmZlZVXW64EIqrFwSEU9WKzJmZmZmZj2BB+dXrpKuYjdKOk7SkTnoroiYVqV4mZmZmZmZbVHJU8XGA+cCD+TP2BxmZmZmZmZWVZV0FTsWOCQimgEkTQYWAZ+rRsTMzMzMzLqrZncVq1glj0MGGFSYHtiF8TAzMzMzMyurkhaX8cAiSTMAAUfi1hYzMzMzM9sGKhmcf52kmcC/kgoun42Iv1QrYmZmZmZm3VX4PS4Vq7SrWBOwFngGeG3hCWNmZmZmZmZVU8kLKC8GTgHuB5pzcACzqhAvMzMzM7Nuy4PzK1fJGJfjgf0jYmOV4mJmZmZmZlZSJV3FVgB9qhURMzMzMzOzcjpscZE0kdQl7HlgsaTpwJZWl4gYW73omZmZmZl1P+GuYhXrTFex+fnvAuCWKsbFzMzMzMyspA4LLhExuW2YpFcCe0TE0qrEyszMzMysG/PjkCvX6TEukmZKGiBpMLAEuFrSZdWLmpmZmZmZWVLJ4PyBEfEccCJwdUQMB46uTrTMzMzMzMxaVfI45N6SdgFOBi6oUnzMzMzMzLo9D86vXCUtLl8Gbgf+GBHzJO0DPFKdaJmZmZmZmbXqdItLRNwI3Fj4fwUwshqRMjMzMzPrztziUrlKBufvLukmSWskPSlpqqTdqxk5MzMzMzMzqKyr2NWk97jsCuwGTMthZmZmZmZmVVVJwWVoRFwdEZvy50fA0CrFy8zMzMys24oafxpRJQWXtZJOl9Qrf04HnqpWxMzMzMzMzFqoswODJO0JXAEcTiqo3QOcGxGrqhe9+iBpTER8r9bx6Kmc/rXjtK8tp39tOf1rx2lfW05/q1edLrj0ZJLmR8ShtY5HT+X0rx2nfW05/WvL6V87TvvacvpbverwcciSLmxndkTEV7owPmZmZmZmZi/Smfe4/L1E2A7AWcBOgAsuZmZmZmZWVR0WXCLi0pZpSTsC5wJnAtcDl5Zbr5txP8/acvrXjtO+tpz+teX0rx2nfW05/a0udWqMi6TBwHnAacBk4PKIeKbKcTMzMzMzMwM6N8bl68CJpNL3GyJifdVjZWZmZmZmVtBhi4ukZmAjsImt31cj0uD8AdWLnpmZmZmZWSdeQBkRTRHRLyJ2jIgBhc+O3aXQIukESSHpX2odl0YmabOkxZLul7RE0nmSmvK8QyVN6GD90ZKuqHCfn385cW5kbc9bSXtLWv4St7VS0pAKlt9yrCSdLekjL2W/3Ymk9W3+r/h8bmfb4ySd3xXbahSSLsh5ydKcrxwmaaak1ZJUWO7mlrSX1CRpgqTlkpZJmifp1ds43h3mdRVsq6LrspbKHK+GiX+9y3l9cczx+ZLGvYTtdFm+ZFYLnXmqWE9wKnA38GFgXG2j0tA2RMQhAJJ2Bq4FBgIXRcR8YH4V9vl54KtV2G4jqIvzNiKurNW+rXuSdDjwfuBNEbEx3/xul2evA44A7pY0CNilsOopwK7AQRHRLGl3Sj8Zs9Q+e0XE5pcb9yrmdXWrg+NlXWMjcKKk8RGxttaRMauVDltcujtJ/Uk/gmeRbgBbau2+k2uPbpX0K0kn5XnDJd0laYGk2yXt0s7me6yIWAOMAT6pZISkWwEkvVnSPZIW5b/7F1bdQ9KvJT0k6aKWQEmnS/pdrsmbJKmXpK8B/XLYlHaW6yXpR4Va2P/almlRDaXO2zbze0n6Rv6+SyWdk8PfldN9maSrJG1fWO0cSQvzvJZWnMG5RnuppPskHVRiX1taAySNlfRAXv76wvzJku7INbAnSrok7+fXkvp0fQrVF0kfkDQ3p/1vJA3L4ePycZgpaYWksYV1LsjXwW+A/Qvh++VtLMnHa19J/SVNLxy/D+Zl95b0YE7/pZJ+JukV2zwBKrcLsDYiNgJExNqI+HOedz2t5/yJwM/brPdERDTn9R5v70EyktZL+rKkucDhpfKPvNx7c9oukTQ9h+2Qj928fFxb0nxE/t1oyuf7oML+/iBpmKShkqbmdedJOiLP3ylfJ4skTSJ1yW4E7R2vTucreZlBSp5SbsmV9GNJR5fasdq0IOS0H5Gn10u6NO9/uqSh1UqAbWATaazxi36/JO2Vv9/S/HfPHP4hpd+9JZJmFVbZNee9j0i6pLCdU/MxWC7p4hx2sqTL8vS5klbk6X0l3V0usiq0tim1Qs7M0+Py8fxt3v9/vNyEsR4mInr0Bzgd+GGevgd4E3AS8CtSwe5VwDM5rE9eZmhe/hTgqlp/h3r5AOtLhD0DDANGALfmsAFA7zx9NDA1T48GniC9H6gfsBw4FHgdMA3ok5f7DvCRtvsstxwwHLizsNygWqdVF6R1qfN2b2B5Dvs4MLWQzoOBvsBjwGtz2DXAp/L0SuCcPP0J4Ad5eiKpxQzgncDiwrG6Ik+PA87P038Gti+mc55/d75+DgaeB96X590EHF/r9OyiY7IZWFz4rC6k0StpHVP4MeDSQtrcA2wPDAGeyuk0HFgGvCJfL38opPFc4IQ83Tcv0xsYkMOG5OWVz4kAjsjzrmrZTj1/gP45DR/O1/E7cvhM4DBgKdALuCN/x/V5/u75XF5Melz/GzvYTwAn5+ly+cfQfN28uuVayn+/Cpzecq7nuO7A1nnd5cCZefow4Dd5+lrgbXl6T+D3eXoCcGGePjbHb0itj8fLOF4rqSxfuTJ/7wOBecD3c/gjQP8y+x5Nvs7y/7cCIwrH97Q8fWFxuUb7AOtJecFKUk+G84Fxed40YFSe/ihwc55eBuzWco4W0mtF3kZfYBWwB6mlcnU+33sDvwWOJ90Dzcvr/iwfl92AUcD4duK7suXcJf2Oz8zT44AlpN/4IaRra9dap68/jfNxV7HU3eZbefr6/H8f4MZItXZ/kTQjz9+flKHeqdTFuhfpRtvKK1VjOBCYLOk1pB+WYo37nRHxFICknwNvI9U0DQfm5XTvB6wpsd13lVluGrCPpInAL0k3O42u1Hn77cL8o4ErI2ITQEQ8Lelg4NGIeDgvMxn4z8J2WmquF5BqsiGl/8i8jd/mGuGB7cRrKTBF0s3AzYXw2yLiBUnLSNfNr3P4MtKNZ3ewpaskpJpg0g82pBvqG5RaaLcDHi2s98tINdUbJa0hFfTfDtwUEc/nbd2S/+5IuhG5CSAi/pHD+wBflXQk0Ey6sRiWt/9YRMzJ0z8BxgLf6MLv3eUiYr2k4aR0OIqUdv+TZ28mFYRPAfpFxMp8vRMRjyu14L4zf6ZL+lBETC+zq82kAj6Uzz/eAsyKiEfzPp7Oy78HOE6tY4/6kgohRTeQbpivJrUS3ZDDjwZer9ahOgPysT2SfO1FxC8lNcRrBzo4XpXkK7NJabAK+C4wRtJuwNPx0p5o2kxrmv+ErVvnGk5EPCfpGtI1vKEw63Ba0/bHQEsryhzgR5J+ytbffXpEPAsg6QFgL1KF4cyI+GsOnwIcGRE3K7Xo7kgq4FxLOkZv56Wn5y8iYgOwId9fvZmtfy/MyurRBRdJO5F+3A6UFKQbqiDVApdcBbg/Ig7fRlFsaJL2Id0YrCHVZrb4CjAjIk6QtDepFrVF28fcBSndJ0fE5zraZbnl8k37MaQb9ZNJtVINqZ3z9jvFxXhxWnbU7WRj/ruZ1ryh1DrtPYrwWNKP2nHAFyUdUNx2pHEHL0REyzaa6Rn50ETgsoi4JXdjGVeYt7EwXUz7Uulc7hieRqopHZ4LiCtJN9KlttPxy7vqQKTxJjOBmbnAO6ow+3pSPj2uxHobgduA2yQ9Sao1Lldw+Ue0jmspmX9IOo7yx2JkRDzUZvlhhX/vBfbLXZSOB/43hzcBh+ebt+K6lNlX3WvneFWSr8wi5dF7AhcAJ5B6O8xuZ9eb2Lrbe99yC9KgadvGt4CFpMJwOam5KeJsSYeR8uXFkg7J80vlOe39PtxLevH4Q6Rj8VFSYem/21mneFzaHpOGzJOsPvT0MS4nAddExF4RsXdE7EGqCV0LjFTqo9zSzQnSRTtUaSAikvoUbsysIP9QX0lqmm+bKQ0E/pSnR7eZ9+7c/7kf6Yd+Dumm4ySlAf8t/aP3ysu/oNYxEiWXy/1smyJiKvBFUreqRlbuvN29sMwdwNmSesOWl8g+COwtab+8zBnAXR3saxbppph8w702Ip4rtaDSE+T2iIgZwGdI3Wf6V/ztuqfiOT+qvQWzWcAJkvrlms4PQKpxBR6XdDyApO2VxqwMBNbkQstRpBrUFnu25Fm0PtChrknaP7fItjiEVAvfYjYwHriuzXpvkrRrnm4CDmqzXnvK5TP3Au9QfjpZvpYAbieN31AOf2PbDea87ybgMlJ3sKfyrDuATxbifUieLF5v7yN1Max7nThebZXMVyLiMVL3oddExArSuXo+7RdcVgKH5N/rPUi19y2aSPklwL/TAOd+R3KL309J4xtb3EPruK/TyN9T0r4RMTciLiTd1+zRzqbnks7zIUpju06l9fdhFuk4zAIWkVrVNra02pSxktSCCbl1reCDkvrmSrgRpO5nZp3SE2o623Mq8LU2YVNJrQOPk8ZYPEy6oJ+NiH8qDdKfkJu1e5NqP+7fZjGub/0kLSZ1/dpEarK+rMRyl5C6ip1H6kdbdHdebz/g2khP6EHSF4A78s3IC6RauVWkwYpLJS2MiNPKLLcBuDqHAXTUclPvyp23xUdD/wB4LSltXiD1Fb9C0pnAjblAM49UuGzPOFLaLSWNTWnvprsX8JN8bQj4ZkSsK3SH6cnGkdL9T8B9QLuP6I2IhZJuII0bWMXWN25nAJMkfZl0jn8ImAJMkzQ/r/NgYfnfA6OUBns/QuqCU+/6AxOVBrZvIo3ZGUPqY99SICjV3W1n4PtqfejE74BOPfo1Ih4olX9ExH2SxgA/z+FrgHeTWo6/RbrGRLpRe3+JTd9AutZGF8LGAt/O11Vv0g3h2cCXgOskLSTdNK7uTNzrQLnjVSo9oP18ZS4pL4HWAmp7BY45pIqbZaTf7IWFeX8HDpC0AHiW1L2wO7iUQsGXdD5dJenTwF9JrSMAX88FSpEK5ktIhcoXiYgnJH0OmJGX/1VE/CLPnk0q9MyKiM2SHmPrPKaULwE/VHplwdw2835H6ra9J/CVaH2Qg1mHOnwBZU8lqX/ut7sT6SI7IiL+Uut4mZl1Vu6KeWtEHFjruJhta5LWR4RbfeuI0rtn1kdEXY+zs/rV01tc2nNrrj3ajlQj4EKLmZmZmVmNuMXFzMy6NaX3tGzfJviMiFhWi/hY5SQdA1zcJvjRiDihFvGxRNJNvLjr62cj4vZaxMe6PxdczMzMzMys7vX0p4qZmZmZmVkDcMHFzMzMzMzqngsuZmZmZmZW91xwMTMzMzOzuueCi5mZmZmZ1b3/B1faCscARLtgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap to show the correlation \n",
    "mtp.figure(figsize=(15,12))\n",
    "sns.heatmap(df.corr(),annot=True);\n",
    "\n",
    "mtp.title('Relationship between the characteristics\\n', fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Finally, the analysis shows that about 80% of patients show up to the appointments,whereas 20% of them did not show up.<br>\n",
    "- Also, the analysis shows that patients who receive SMS show up to the appointments less than who did not receive SMS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is a limitation which is, I could not have the relationship between the gender and the patients who show and not show up.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}