{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "6204641218"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exam Instruction EE522 Fall 2022:\n",
    "    \n",
    "1. Answer all of the following questions. Each answer should be thorough, complete, and relevant. Points will be deducted for irrelevant details. Use the back of the pages if you need more room for your answer.\n",
    "\n",
    "2. The points are a clue about how much time you should spend on each question. Plan your time accordingly.\n",
    "\n",
    "3. There are 3 questions. The total score is 100 points accounted for 25 percent of the total scores.\n",
    "\n",
    "4. Exam Date and time: October 1, 2022 from 1-4 pm. \n",
    "\n",
    "\n",
    "5. You have to submit (1) the Jupiter Notebook code (.ipynp) with the name: your student id.ipynp i.e 6504610069.ipynp \n",
    "\n",
    "6. You have to submit your code on BE-moodle.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1 [50 points]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Set Information:\n",
    "\n",
    "Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. \n",
    "\n",
    "Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\t\n",
    "=========================================\n",
    "Dataset characteristics\n",
    "======================\t\n",
    "hour.csv has the following fields:\n",
    "\t\n",
    "\t- instant: record index\n",
    "\t- dteday : date\n",
    "\t- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "\t- yr : year (0: 2011, 1:2012)\n",
    "\t- mnth : month ( 1 to 12)\n",
    "\t- hr : hour (0 to 23)\n",
    "\t- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
    "\t- weekday : day of the week\n",
    "\t- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "\t+ weathersit : \n",
    "\t\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\t\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "\t\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "\t\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "\t- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "\t- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "\t- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "\t- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\t- casual: count of casual users\n",
    "\t- registered: count of registered users\n",
    "\t- cnt: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.1 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plots to visualize the relationship between temp and target values (cnt). Then, plot the heatmap to represent the correlation matrix between features and target values.\n",
    "\n",
    "Also, calculate the median of cout of total bikes (cnt) separated by weathersit, season ,and holiday. Is there any interesting thing you found? Explain carefully.\n",
    "\n",
    "\n",
    "[20 Points]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"hour.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"cnt\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"temp\",alpha=1, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404046</td>\n",
       "      <td>0.866014</td>\n",
       "      <td>0.489164</td>\n",
       "      <td>-0.004775</td>\n",
       "      <td>0.014723</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>-0.003416</td>\n",
       "      <td>-0.014198</td>\n",
       "      <td>0.136178</td>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.009577</td>\n",
       "      <td>-0.074505</td>\n",
       "      <td>0.158295</td>\n",
       "      <td>0.282046</td>\n",
       "      <td>0.278379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>0.404046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010742</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.014524</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.149773</td>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.178056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>0.866014</td>\n",
       "      <td>-0.010742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.250495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>0.489164</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.120638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>-0.004775</td>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>0.394071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0.014723</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>-0.030927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>0.001357</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.026900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>-0.003416</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>0.030284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>-0.014198</td>\n",
       "      <td>-0.014524</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>-0.142426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.136178</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.404772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>0.400929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>0.009577</td>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>-0.322911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.074505</td>\n",
       "      <td>-0.149773</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.093234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.158295</td>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>0.694564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.282046</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>0.278379</td>\n",
       "      <td>0.178056</td>\n",
       "      <td>0.250495</td>\n",
       "      <td>0.120638</td>\n",
       "      <td>0.394071</td>\n",
       "      <td>-0.030927</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>0.030284</td>\n",
       "      <td>-0.142426</td>\n",
       "      <td>0.404772</td>\n",
       "      <td>0.400929</td>\n",
       "      <td>-0.322911</td>\n",
       "      <td>0.093234</td>\n",
       "      <td>0.694564</td>\n",
       "      <td>0.972151</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             instant    season        yr      mnth        hr   holiday  \\\n",
       "instant     1.000000  0.404046  0.866014  0.489164 -0.004775  0.014723   \n",
       "season      0.404046  1.000000 -0.010742  0.830386 -0.006117 -0.009585   \n",
       "yr          0.866014 -0.010742  1.000000 -0.010473 -0.003867  0.006692   \n",
       "mnth        0.489164  0.830386 -0.010473  1.000000 -0.005772  0.018430   \n",
       "hr         -0.004775 -0.006117 -0.003867 -0.005772  1.000000  0.000479   \n",
       "holiday     0.014723 -0.009585  0.006692  0.018430  0.000479  1.000000   \n",
       "weekday     0.001357 -0.002335 -0.004485  0.010400 -0.003498 -0.102088   \n",
       "workingday -0.003416  0.013743 -0.002196 -0.003477  0.002285 -0.252471   \n",
       "weathersit -0.014198 -0.014524 -0.019157  0.005400 -0.020203 -0.017036   \n",
       "temp        0.136178  0.312025  0.040913  0.201691  0.137603 -0.027340   \n",
       "atemp       0.137615  0.319380  0.039222  0.208096  0.133750 -0.030973   \n",
       "hum         0.009577  0.150625 -0.083546  0.164411 -0.276498 -0.010588   \n",
       "windspeed  -0.074505 -0.149773 -0.008740 -0.135386  0.137252  0.003988   \n",
       "casual      0.158295  0.120206  0.142779  0.068457  0.301202  0.031564   \n",
       "registered  0.282046  0.174226  0.253684  0.122273  0.374141 -0.047345   \n",
       "cnt         0.278379  0.178056  0.250495  0.120638  0.394071 -0.030927   \n",
       "\n",
       "             weekday  workingday  weathersit      temp     atemp       hum  \\\n",
       "instant     0.001357   -0.003416   -0.014198  0.136178  0.137615  0.009577   \n",
       "season     -0.002335    0.013743   -0.014524  0.312025  0.319380  0.150625   \n",
       "yr         -0.004485   -0.002196   -0.019157  0.040913  0.039222 -0.083546   \n",
       "mnth        0.010400   -0.003477    0.005400  0.201691  0.208096  0.164411   \n",
       "hr         -0.003498    0.002285   -0.020203  0.137603  0.133750 -0.276498   \n",
       "holiday    -0.102088   -0.252471   -0.017036 -0.027340 -0.030973 -0.010588   \n",
       "weekday     1.000000    0.035955    0.003311 -0.001795 -0.008821 -0.037158   \n",
       "workingday  0.035955    1.000000    0.044672  0.055390  0.054667  0.015688   \n",
       "weathersit  0.003311    0.044672    1.000000 -0.102640 -0.105563  0.418130   \n",
       "temp       -0.001795    0.055390   -0.102640  1.000000  0.987672 -0.069881   \n",
       "atemp      -0.008821    0.054667   -0.105563  0.987672  1.000000 -0.051918   \n",
       "hum        -0.037158    0.015688    0.418130 -0.069881 -0.051918  1.000000   \n",
       "windspeed   0.011502   -0.011830    0.026226 -0.023125 -0.062336 -0.290105   \n",
       "casual      0.032721   -0.300942   -0.152628  0.459616  0.454080 -0.347028   \n",
       "registered  0.021578    0.134326   -0.120966  0.335361  0.332559 -0.273933   \n",
       "cnt         0.026900    0.030284   -0.142426  0.404772  0.400929 -0.322911   \n",
       "\n",
       "            windspeed    casual  registered       cnt  \n",
       "instant     -0.074505  0.158295    0.282046  0.278379  \n",
       "season      -0.149773  0.120206    0.174226  0.178056  \n",
       "yr          -0.008740  0.142779    0.253684  0.250495  \n",
       "mnth        -0.135386  0.068457    0.122273  0.120638  \n",
       "hr           0.137252  0.301202    0.374141  0.394071  \n",
       "holiday      0.003988  0.031564   -0.047345 -0.030927  \n",
       "weekday      0.011502  0.032721    0.021578  0.026900  \n",
       "workingday  -0.011830 -0.300942    0.134326  0.030284  \n",
       "weathersit   0.026226 -0.152628   -0.120966 -0.142426  \n",
       "temp        -0.023125  0.459616    0.335361  0.404772  \n",
       "atemp       -0.062336  0.454080    0.332559  0.400929  \n",
       "hum         -0.290105 -0.347028   -0.273933 -0.322911  \n",
       "windspeed    1.000000  0.090287    0.082321  0.093234  \n",
       "casual       0.090287  1.000000    0.506618  0.694564  \n",
       "registered   0.082321  0.506618    1.000000  0.972151  \n",
       "cnt          0.093234  0.694564    0.972151  1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_data = df.corr()\n",
    "correlation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jk5zBERHYW9uoH9tZWxOfmEhCUpLGlL4Z69bSqkEjirq4aixfunARjlzypnyxYqTI5Zy9eR1dnU8/LGMjIzC3/DB9zczSipSkRFKSkjJMf8qqLjYqAjOrNK9ZWRMW+BqAu5fOo1QoKF+7PleOHVTXWNk5ULVxU9ZMGoWBkTEGhkZ0GPLxUeaYSM1tmVtak5wub3Y1ZpZWtOk7KNN1G5mYUrpqDUpUrMLrZ0/YtWw+PUZPwjzNurKToR0yacvsamIjM74W+uYVgHqanO9lzTPCSqWSg2uXU79VW6Q6OlrlhLzf7zo6usTHRLNxxgQS4+No0b2fOu+hdSuo1/LXHOUF7dr3Y3WmFpb82CvzmQm+796r5WrV49rxQznKlpnIsDCs3n1hArC0sSUpMYGkxESNaVPvp+j5+WiOKEdFhLNj7UoGjR7PhRNHPztPljnDw7C2+ZDTysaWpISMOd9P0XuQRc4/vMZz/vjn5cx4DFhnPIayqYmNDNc4Xs2srAl98xpre0dkKSk8f+BLodJleRvgT9jbN8RFRxMRHIyegQH7Vy8hIjgIc2sbGv3SPke5zY0MiUn4MHU8JjEZQz099HV1NEaMtKnzcHcmNimZx4Eh6rrEFBn3XwXxMDAYFxtLfqnhwerTl9WdrJyKCAvF2vbD73ErW1sSM9nnnfr8DqR2utIKCnxDoago5kwYQ1REBMVKl6Ft1x6flCU7IcHB2Nt/mFZpZ2dHfHw8CQkJ6qlyjk5OODo5AalTD5cuXECtOnXR09PD84fmnDt9mratfkKhUFClWjVq1cn9mT2hIcHYOTjkOGfN2nXe5WzG+TOn+a11yw85a9fJdFvfNKm4N91X0wIymYzt27czcuRI5s+fD8C8efNYs2YNu3fvxtnZGX9/f7y8vLC3t2fx4sW8ePECf39/tm3bxrFjx3BycmL//v0AhIaGUrNmTfbu3UvVqlXZvHkzrVq1omzZskyePPmLd6C+VkqlCgkZh0p10nwh2nnwADo6Ovzk6fnlgkmkmU5zQqlU/1PPpQCWLZvxZtREXg8eSdSBo9gP0hwpM/dsRPT+w3kWU6lSZTrSLE1zweWuUyfR0ZHyY736Ger+aNcBiURC13FeDF8wj6plyqKbwy+jaalUKjLZnUjSfdhlV6dSpvs/qVRIpFKCX73A58IZvm/XJcNyAX6+PLlzkz6TZvP7lDkUKV+RI5tWfzxvFu+/tHm1qclMm76DKFmpKhKJBNeixXEuXJQAv/sfzZR2u5k1UvpsWdWkPwus0iLz+X07cSlaHPdSZbTOCXm7398zMbeg35Q5dBgymqOb1hARHMSF/Z+WV50lE9LMMmtRl1bIqxfcvXiORr91ynGurChVykyfzy7Hewq5nDXzZvNL155YaNmJ/1RKpTLTfaxtzlXzZvNrt9zJqVKpMr0OROMYyqYmdd+neU2lQiqVYGBkROu+f3Dl2AHWThnD/asXKViiFDq6OiiVcp7evUWdH9vQbdRE3EqUZu+KhTnKndW1K+nfitrUVS3qxsWH/hqv77rqw8PAYABeh0fxJiKaQvY2fCqVUpXplCdt9jmAQqHggc9tfh82krGz5xEfF8uuTTmbAqkNlUqpdc7ExEQmjBnNm9evGTo8dTrfhrWrsbCyYteBQ2zfs4/YmBh2bN2S6zmz+l6UVc6JY70IfPMmTc41WFhasnP/Qbbt3ktMTAw7tuV+TiH/fTU3lqhbN/VsQrFixYiKigKgYcOGtG/fnsaNG+Pp6UmpUqV4/fq1ehk3NzeGDx/Ov//+y/Pnz7lz5w4FCxbMdJ35dR3U187R3o77jx6qH4eGhWFuaoqRoaH6uYMnTpCUnEzHAf2Ry+Qkp6TQcUB/5k2chJ3Np3/wZ0ceEYFBEXf1Yx0rSxRx8ahSUtTPGZUrTfITf+QhYQDEnjyLdYdfkJqaoIyLR7+gC0h1SHr4JE8yAjja2PDA/5n6cWhkJGYmJhgZfGi/Q94XSE5JpsuY0cgUqe3XZcxo/vl7KAqlggFt22HxbrrRugP7cUlzBkwb3gf38OzeHQBSkhKxLeCifi02OhJDYxP0DQw0ljG3stG4ziltnZm1tcb1RXHRUZhZWnH/6kVSkpLY8s9U9fOH1q+gfqu2BDy8T5FyHpiYmQNQsV4j1k0Z89Hs5tY2BKbNEZUxrzY16SUlxHPr3GlqNm3x4YuOihyNlphZW/M24MO+zWy72dWYWdlotmNUJGaW2X8xfXDtEsZm5jzxuYUsOZm46EjWTR1Lt1ETM9R+qf2enJjAy8d+FKtQGQAHVzfsnF0JC3zNg2uXMTYz08i7fto4uo6ckOn/7/KhvTzz9fmQ2clFY3sGxsbopctsZmVNUMDzj9ZptuNlUpKS2D53OgDx0VEc3bCSOi1/pUg5jyyXy461rR0BTx6rH0dFhGNsYopBms/KrLx49pSwkGB2rV8DQExUJEqlEplMRuffMx9J/VTWdpnkNNUuZ8Czp4QFB/NvJjm7fELODO+3zI7vbGrM0x9D0VGYWlmjUirRNzCg/V8j1a+tHD8cKzsHQi1e41y4mHp6YLla9Tj172ZkKSnZTqmqV6oIxZxSR3P09XQJjY5Tv2ZmaEBiigxZuksBohOSKGBlkWWdg4UZUqmEl2EfZlUY6OlSubArlx4911iXQvnp1xrb2Nnhn2a6cmR4OCZa7nMAS2trKtWopR61qlm/Ifu3b/3kPFmxd3DE78GH6+xCw0IxMzPDKN2NRoKDghg9fBhu7u7MWbgIg3e/Ty+cO8egv/5GT08PPT09mvzQjPNnT9O2fc6ma348pwMP05xwC8sqZ3AQXsP/R0E3N/5ZsAiDd+9r7/NnGTg4Tc6mP3D+7BnatsvdnPnuK75W6Uv5ajpR7998ac/seHl58fDhQ86dO8ewYcMYOHAglStXVr/u6+vLkCFD6NatG56enkjTnf1Nu84vcTOEb1H1SpWZv3IlL9+8oaCzM7sPH6JezZoaNevmL1D/OzA4iPb9+rF58ZL0q8pViff8sG73M7oOdsiDQzFrVJeE25pT8lICXmH+XQOk5mYoY2IxruyBPDQMZVw8AIYli5Pkl7PrYHKqWtmyLNi2hVdBQbg6OrLnzCnqVdScrrlm3IcvlG9DQ+noNZINk1KvBVy6cwfxiYkM7dyViOhoDpw/y6TfB+YoQ50WranTIvXOWvGxMayfOpbIkGCs7B3wuXA20y+NbqXKcHbP9kzriparyL3L3hQp60FKcjIPb17j+3ZdcC1WAn75sI4VY4fRvGsfHN0KkRgfx53zp6nauCn6BoY8vnMDp0KFM2w3vUKlynJ61zYiQoKwtnfk9oUzFKtQMcc16ekbGnHr3CmsHRwpWakqQa9e8DbAn+Zde30003vupcpydvd2IkOCsLJ3xMf7DEXLV9S6plj5ivhevkDRch/asUn7jKN4afWfNk/975ePH3Jqx6ZMO1Dw5fa7RCrl6Ka1GJua41ykGGFv3xAR/BYn98L8PnWuZt5/N2fZgQKo2bwVNZu3AlJvKLBp+jh1lrveWWQuWYYLe3d8tC6tBj+3g5/bqR+vHj+cpl16f9bd+UpVqMiuDWsIeRuIvVMBLhw/QoWq1bVatnCJkkxdtkb9+OCOLcTFxOTJ3flKV6jIzvVrCH4biINTAc7nIGeREiWZvvxDzgPbtxAXG/PJd+dzL12WM7s/HLt3LmRyDGVTU7R8Re5dOv/uGErC78ZVmrTvChIJOxfPoXW/P3FyK4Tfjavo6uph5+yKSqXCx/sMUWGhWNra8eTODWydnD96Tcp5v2ec90s9IWJsoE/v72piZWJMZHwClQq78PhtSIZlnoeE07hc8SzrCtpa8SI0QmOZFJmcyoVdCY+N51FgCA4WZhSwsuDgTd9PamOAMh6V2L52NcGBb3Ao4MzZY4fxqFZD6+Wr1KzN9Yve1PveEz19fW5fvUKhYhlvpPG5qlSrxrJFC3j96hUurq4c2LuHWnXradQkJMTz96ABNPmhGV179NR4rVjx4pw9fYqKlSojl8u55H2BUqXL5k3OxQvT5NybYdpgQkI8QwYNpMkPP9Cle/qcJTh3+rQ65+WL3pQuk/PReuHr99V0otKTy+U0a9aMjRs30rdvX2QyGX5+flSvXh35u7uCXb9+nWrVqtG+fXsiIyM5e/YsTZpkf3toHR2dfLuxxNfI2tKSMX/9zYgpk1Nvh+vkxPihw3jw+DFT5s/L885SVpSxsYSt2oD9wD6gq4M8JIywFevQdy+IbY9OBI6dSpLfI6KPnMBx5F8gV6CIjydk/jL1OnQd7JGHhedpTmtzC7x69mbU4gXI5Aqc7e0Z27svfs/9mbZmtbqzlJUuzX9k4opldBw9ApUKerf+mdKFP975yIqJmTlNO/Vg/+rFKOQKLG3t+KFLasch6MVzjm1ZR9eRE7Kt86jbkKiwENZPG4dCIadC7QapHahslK1Rh+jwMDbOmICOrh7m1jb80KlntssAmJib07xLT/asWIxSIcfS1p4W3Xrz9sVzjmxaQ4/Rk7KsyY5UKuXn3//kxPZNeB/ci1RHSste/bW65bo6m5k5P3Tqwb5VS1DI5Vja2dOsSy+CXjzn6Oa1dBs1McuatO24burY1Has0wDXYiW13n5O5PV+b9VnEKd3bUWpUKCjq0uLbn01rmP5FMZm5nzfoTuH1ixF8W6/enZKvQ4j+GUAJ7aup9PwcdnWfUnmFpZ06f8nK/6ZnnqrdQdHug38ixfPnrBp6SJGz57/xTNlxtzCkq4D/mTF7OnI5XLsHBzpPugvAp4+YeOyRYz5gjlNzMz5oXNP9q1crD4+mndNPb6PbV5Dt1GTsqyB1BHtqLAQ1k4dg0KuwKNOAwoWTz2GWnTvx7HNa1Eo5JiaW9K63x9IJBIcXN34/rcu7F2xAIVCgaGxCS2zuG4uKwnJKRy8eZ821SugI5UQGZ/IgRv3AHC0NKd5pdKsPn0l2zoAa1NjouMTNdatAv69fIcmFUpSr1RRlCole6/5kJgi++R2Nre0pMegwSyeOS31Txc4OtHrzyE8f/qEdYvmM2HeomyXb/RDc+Lj4pgw5E+USiVuRYrQtbv2J5y0ZWVlzbBRXoz3GoVcLqOAszMjvMby6KEfs6dPY+W6DezdtZPg4CC8z5/D+/w59bKz5y+k/x+DWTDnH7p2+A2pVIdKVarQrmPuTdlNm/N/I0czYcxo5HIZTgU+5PxnxnRWrF3P3l273uU8j/f5DzcLmzVvAb8P+pOFc/+hW8d2qTkrV+a3DrmfM7+JW7aDRJWPQzRpbywxcOBAqlevzuvXr+nSpQunT5/m4MGDLF26FAMDA2xsbJg+fTrm5uZ07twZPT09Zs+ezcCBA0l6d6OBUqVKoVQqmT17NiVKlODRo9RRiN27d3Pt2jWmT5/O6tWr2bZtGzNmzKBSJe1u8PCkzhe8FugT2W9Y9vGir0DkhJn5HUEr5v2653eEj9oV9+m/dL8kXZ2v5tLLbH3OdJov6VsZVZcrM7+G6GtTwilnfz8ov3wLf9fyWUjEx4u+AsHRsfkdQSv1S336CbUvyd02b6/zyy3fymeny2dcH/clPWv688eLckmRoxn//MXXIF87Ud8K0YnKPaITlXtEJyp3iU5U7hKdqNwlOlG5R3SicpfoROUu0YnK6GvtRH210/kEQRAEQRAEQfgKiVucfz23OBcEQRAEQRAEQfgWiJEoQRAEQRAEQRC0J24sIUaiBEEQBEEQBEEQckKMRAmCIAiCIAiCoL1v4W43eUyMRAmCIAiCIAiCIOSAGIkSBEEQBEEQBEF7EjEOI1pAEARBEARBEAQhB8RIlCAIgiAIgiAIWpOIa6LESJQgCIIgCIIgCEJOiJEoQRAEQRAEQRC0J/5OlOhEacN+w7L8jvBRIV365XcErZwc45XfEbTSbMO2/I7wUcrWbfI7glZS5Ir8jqAV6TfyC0FX59uYQCBXKvM7glYquDnldwStfAvvTx3pt/HefPA6OL8jaCU+OSW/I2jldUR0fkf4T3Gxt8nvCIKWRCdKEARBEARBEATtibvziWuiBEEQBEEQBEEQckKMRAmCIAiCIAiCoD1xdz4xEiUIgiAIgiAIgpATohMlCIIgCIIgCIKQA2I6nyAIgiAIgiAI2vsG7hia18RIlCAIgiAIgiAIQg6IkShBEARBEARBELQm+Ub+LlxeEi0gCIIgCIIgCIKQA2IkShAEQRAEQRAE7YlrosRIlCAIgiAIgiAIQk6IkShBEARBEARBELQnrokSnai85H3tKkvWriVFJqNooUJ4Df4LUxOTTGvPXrrE+NmzOLt7zxdOmTWH0UNJ9g8gauvOL7rd5/fvcvHALhRyObYFXGjcvhsGRkY5rouNjGDbnKl0Gj4OI1Mz9fMv/O5zYf9OOg0flyt5jcqWwrJlcyR6uqS8DiR803ZUScmaNRXKYdnCE1QqlAkJhG/agTwsHImhITadf0PP0R4kEuKv3CDm+OnPyuPv64P3gd0o5DJsC7jQpEP3TNsvqzqlUsm5PdsJeOCLUqmkyneeVKjTAIDE+DjO7NxCeFAg8hQZ1T2bU7paLfU65TIZe5cvoHzt+hSvWCXfcr58/JDze7ajVCoxNDGhYZv22Lm4AvD66SPO79uJPCUFAyMjPDv1xNLWLsucz3x9uLB/Fwq5DDtnVzyzyJlVnVKp5OzubQT4+aJUpOb0qNsQgLcvnnNm11ZkycmolEqqfd+M0tVqolKpuHhoD4/v3ATAsWAhvm/XGT19g2zbFODpvTuc27cThUyOnYsLzTr1zJD3YzUxEeFsmDmJHl6TME5z7ABEhYWybtp4fvtjKE5uhT6aJzt5eaz737vDsc1rMLOyUde1/XM4+oaGn5z3orc3y5YsRpaSQpGixRjl5YWJqalGzc4dO9izaydIJDi7uDBi1Gisra3VrwcHB9G7Rw82bN6CpaXlJ2f5WM4lixchS0mhaLFijPYakyHnvzu2s3vnLiQScHZxYeRoL6ytrUlKSmL2zBk8uH8flQrKlC3D0P8Nx/Az2i0rd29cZ9fm9chlMlzc3Ok24E+MjI0z1KlUKtYsnItLQXc8W7VRPz+4awesbD7sX8+WbahRv2Gu5yzl7ECzSqXR1ZHyNjKG7ZdukyyTZ1nfrnYlgqJiOHv/qfq5ib/9QFRCkvrxWd8n3Hr+Otcy+t66wYGtm5DLZBQo6EaHfgOzbMtNSxZQoKAb3/3YCoCUlGT+Xb2CF0+foALcixbj15590Nfi8yan7ly/yr8b1iKTy3B1K0SvP/7CyDjj9yKVSsWKef/g6u5Os9a/ALBw+mSC3waqa0KDgyhZthx/eU34qnIqFQo2LF/Cw/v3AKhQuSrtuvdCIqa//eeIbmQeiYyKYtKcOUz3GsPOVatxdnRi8dq1mda+fPOGBatWolKpvnDKzOm5ueI8fwamDep+8W0nxMZyfPNamvfoT1evKZjb2HHxwK4c1z24dol/588kPjpK/Zw8JYVLB/dweN1yVEpFruSVmppg06UdoSvWETh+OvKwCKxatdCokejpYdu9A6Er1vF26j8k3L2PVdvWAFj+9AOKqGjeTppF0PR5mNWrhX4ht0/OkxAby7HNa/mxZ3+6j5mKha0d3vszdoKzq7vrfZbIkGC6jppIx2Fe3DpzgrcB/gAc27QGU0srOg8fzy8Dh3Bm51ZiIyMACHz+lG1zphLo/zTD9r5kzuTEBA6sWky9Vr/SZeQEGrftzMG1S5HLZMRGRrB/5WK+a9uJLiMnUMyjMqd2bMomZwxHN62hZa8B9Bw7DQsbO85nmjPrOp93ObuNmkSn/43h1tnUnCqViv2rFlO7WUu6jpzAz/3/4szubUSGBPPE5xYBfvfpOmIC3UdPRi5L4daZk1q0awyHN6ymdZ+B9JkwHUtbe87u/TdHNfeuXGTznGnEpTl23pPLUjiwdjkKRdZfILWVl8c6QODzZ1Ru5Emn4ePUP5/TgYqMjGTKpIlMnT6DbTt3UcDZmSWLF2nUPPTzY8vmTSxfvYbN27bj6urKyuXL1K8fOXSI/n36EhYa+sk5tMk5eeIEps2YyY5duyng7MziRRlzbt60iZVr1rBl+w5cXQuyYtlSANatXYNCoWDT1m1s2rqV5ORkNqxbl+s5Y6OjWbtoHv2HjWTKouXYOTiya2PG7QS+fsU/40Zz8/JFjeeD3rzGxNSUcXMWqn/yogNlYqDPb7Ursf7sNWbsPUV4bDzNK5XOtNbewpR+TWpT3q2AxvN25qYkJMuYc+CM+ic3O1CxMdFsXrqQnn//jzHzFmPr4Mj+LRsz1AW9fsXCSWO5c/WyxvPHd+9EoVAyYtY8Rs6aS0pKCif2ZjwWP1dMdBQrF8xh0MgxzFy6GntHJ7avz/i96M2rl0z3GsH1Sxc0nh80wovJ85cwef4Segz8E2MTU7r0HfjV5bx49hRv37xm6oKlTJ6/hIe+d7l+8UKG5b95EsmX+/lKiU5UHrl66xalixenoLMzAD+3aM7RM6czdJSSkpIYN2smg/v0yY+YmbJs8xMxB48Sd+b8F9/2y4f3cSjojpW9AwDl6zTg4Y2rGdotu7q46Cie3b1N6/6DNZYJeHgfWUoyTTr1yLW8RqVKkBzwCnloGACx5y9iUq2SZpE09UNAapT6BU5qYIBKnvolNHLHHiJ37QdAx8IcdHVRJSbxqV48vI9jmnapUKchfpm0X3Z1T+/epmyN2kh1dDA0NqFE5Wr43bhCYnwcLx49oOYPPwFgZmVNh6GjMXw3unr77Cnq/PQzjlqMTuRlzsjQEAyMjChYIvWLjrWjE/qGRrwNeMaTOzdxL10OB9fUjmr52g1o2KZdljkDHt7H0a2QevsedRvid/1KhpzZ1T31uUXZGnU+5KxUjQfXL6OQy6n1w0+4lSyjbk9jUzNioyIo7lGZ9n+PREdXl5SkJBJiY9TtnJ3nfr44uRfC2t4RgIr1GvLg2mWNvNnVxEZF8sTnFr8NGprp+o9v20i5mnUwMjHN9PWcyMtjHeDt82e8evyQTdPHs2PeDF4/ffxZea9dvUKp0qVxLVgQgDY//8zxo0c18pYsVYodu3ZjampKcnIyoaGhWFhYABAaGsr5c2eZu2DBZ+X4mKtXUnMWVOf8hWNHj2TIuXP3njQ5Q7CwsASgYsVKdO/RE6lUio6ODsVLlCAo6G2u57x/5xbuRYvhUCD1d2SDps24euFshv1/5shB6jZuQpVadTSef/rQD4lUyozR/2PcXwM5sGMrSkXunBxLq0QBe16FRxIWGw/ApUcBVCrsmmlt7ZKFufrkBXdfBGo8725vjVKlYkDTOgz5sSHfly+Rq98LH/rcoWCRYtg7pXbe6nzflBve5zO05fnjR6jV6Hs8atTSeL5IqTI0bfMLUqkUqVQHV/fCRORBR9/39i0KFyuO47t93uiH5lw+l/F70alDB6jfpCnVamd+Ilcuk7Fi3j907NUXG7usZxHkV06lQklychIyuQy5TIZcLkdPXz/Xcwr574tP5wsKCmLo0KEkJCQglUrx8vJCKpUybdo0kpKSsLKyYsKECbi6unLt2jXmzp1LUlISMTExjBw5ksaNG3PgwAFWrVqFjo4OLi4uzJo1CwMDA5YtW8b+/fvR0dGhdu3aDBs2jLdv3zJw4ECKFSuGn58fNjY2zJ8/P8+mUbwXHBaKfZqD297WjviEBOITEjSm9E1buIDWPzSjaKHPmxaTm0LnLgbAuGqlj1TmvtioCMysPkx9MbO0IiUpkZSkJM3pO9nUmVpY8mOvARnWXbR8RYqWr8irJw9zLa+OlSWKyCj1Y0VUNFIjIySGBuopfarkFCK27MRx6B8o4uORSKUEzV74YSVKJTbdOmJSqTwJd+4hCw755DyxkVq2XzZ1sZERmFpqvhb25jVRoSGYmltw8/RxAh74opDLqPydJ1bvvow3794XgGvHD+drTis7B2QpyQT4+eJeqixBL54T/jaQ+OhoIkOC0DMw4NDaZUSEBGFuZUP9bDpRsZERmFtqlzOrugzvVStrwgJfo6unR7la9dTP+3ifJSU5CSf3IgDo6Ohy69wpLh7cjamFFcUqfPx4jEnXXuaW1iSny5tdjZmlFW36Dsp03T7e51AoFHjUacClIwc+muVj8vJYBzA0MaFk5eoU9ahMoP9TDqxcRMfh4zTWlRPBwcE4vOvIAdjZ2xMfH09CfLzGVDldXV3OnT3L9CmT0dPXp3ef1OPCzs6OaTNnfdK2cyIkOBgHhw857T+Sc+rkSejr69O7bz8Aqteooa55+/Yt27duZcSo0bmeMyI8DGtbW/VjKxtbEhMSSEpM1JiG1rH37wDc97mtsbxSqaB0eQ9+7twNhULO/MkTMDQy5vsfW+ZqTksTI6LiE9WPoxMSMdLXw0BPN8OUvj1X7wKpHa+0pBIJT96GcujWfaQSKb0a1yBJJueC37NcyRgZHqYxrdHSxoakxIxt2bZH6slav7t3NJYvVcFD/e+I0BDOHDlA+3ftnpvCw0KxTjN12trW7t0+T9CYKtelX+ox7Xv7ZqbrOXfiGJbW1lSpWTvXM+ZGzrrffc+1ixf4s1snlEoFZT0qUbFaDf5zvuIRoi/li49E7dy5kwYNGrB7927++OMPrl+/jpeXF//88w979uyhe/fujBkzBoBNmzYxefJk9uzZw+TJk5k/fz4A8+bNY82aNezevRtnZ2f8/f05d+4cp0+fZteuXezZs4cXL16wbds2AB4+fEj37t05ePAg5ubmHDjw+V8APkapVCEh4xtMR0fnQ1scPICOjg4/eXrmeZ5vRVZTGqXpLmDUti7PSSVAJlmUH57TK+CERbMmBE6cwZuRE4g+ehK7Pt00ysPXbebVsDFITYyxaN7kk+OoVKpMP9gybb8s6lQqlcbcbZVKhUQqQalUEB0ehr6hEe3+Hkmz7n05t3s7wS8DvqqcBkZG/NR7INeOH2bDtHE8uHYJ1+IlkerqoFAoeHb3NrWat6bz8PG4Fi/FgVWLP5Iz4/Pp/8hgdnUqpUrzv6BSZVj+6vFDXDq8j9Z9/9A4Y1mp/ncMnLmIYhUqsX/1kixzqledxedO2u1pU5Ne0MsAbl84Q9MOXT+aQVt5faz/2GsAxSpWQSKR4FykGE6FivDy0YNPC8u7dsvsvZjmM/29+g0acOTESXr27s1ffwxCqVR+8nZzSqlSZn7MZJHz2MlT9Ozdh8GDNHM+9POjX+9e/NK2LXXq5v7UbpVSu8+ArNT7vikdevfDwNAQYxNTmvzUitvppqnlhqyuY8nJ9PurT16w59pdUuQKkmQyzt1/RrmCTrkVMcNn4Xs5/X340v8Z88aNpp5nM8pWrppb8dSy+uyRSjO+N7NzdP8eWrZtn1uxMvjcnHu2bcbMwoJFG7Yyb80m4uNiObIn96dHCvnvi49E1axZk0GDBuHn50f9+vWpX78+S5Ys4fffP5z1iIuLA2DWrFmcOXOGo0eP4uPjQ3x86nB6w4YNad++PY0bN8bT05NSpUqxf/9+mjdvjtG7M5g///wze/fupX79+tjY2FC6dOrUnmLFihEdHZ3n/09HezvuP/ow4hEaFoa5qSlGaebkHzxxgqTkZDoO6I9cJic5JYWOA/ozb+Ik7NKcVfqvu3xoL898fQBISUrE1slF/VpcdBQGxsboGWhe4GpmZU1QwPOP1uU1RUQUBu4frmHSsbRAEZ+AKiVF/ZxR6RIk+z9HHhYOQOxZb6x+aYnUxAT9gi7IAt+iiI5BlZxC/PXbGFcsn6MMFw/txf/eHeBd+xVI236RmbeftTVBL/wzrTO3sta4HiY+OgozS2tMzS0BKFsj9eyflZ0DBQoXJejFcxwKun81OVVKJfr6hrT983/q19ZMHIWVrQNhFq8pULioempYuZp1ObtrK7KUFAzebdv74B6eZZEzNjoSQ2MT9NPlNLeyUV83lr7OzFozZ1x0FGaWVkDqtJSjm1YTHhRIhyGjsbBJPTMf8volKpUKB1c3JBIJ5WrV4+bZEx9tY3NrGwLT5ojKmFebmvR8r1wkOSmRjbMmq/8PB9Ysp2Gb3yhWoeJHc733pY71pIQE7nqfoer3zTS+XGbWkdCWg6MD9+/7qh+HhoZiZm6u/p0D8PrVK8LDw6ng4QFAix9/Ytb06cTGxGCRx7Mf1DkdHLnvq5nTPF3OV+9yerzL+eNPPzFz+jR1zhPHjzFrxgyGDPsfnk2b5klOazs7nj95pH4cFR6OsakpBlpet3b57Glc3Avh6p46i0OlUqGj++n7Ny1Pj5KUcU3t5Bjq6fI2Mkb9moWxIQnJKaTItZ86WLmwK4GR0er1SCSgyMWOtbWtLS/STFeNjgjH2ET7tgS4efECO1av4NcevalSp97HF/gENnZ2PHv84XtRZHgYJjnY5wABz56iVCgoWTZnvydz4nNz3rh8kc59+qOrp4eunh51GjXm2kVvfmj9c15FzhfZnXj7/+KLt0DlypU5dOgQderU4fDhw0yaNAkXFxf27dvHvn372L17N1u2bAGgQ4cO3L17l7Jly9KvXz/1Ory8vFiwYAEWFhYMGzaMffv2ZXqmT/7uuhODNL9sJRLJF7mBQ/VKlfF9+JCXb94AsPvwIerVrKlRs27+ArYtW87mxUuYO2kiBvr6bF685P9VBwqgZvNW6gu/2/09iqAXz4gMCQZSbx5QpJxHhmXcSpbRqi6vJfo9wqCQG7p2qV9+zerWItHHV6Mm5dVrDIsVQWqWOpXG2KMc8rAIlPHxmFT2wKL5u5FIXR1MKnuQ9OhJjjLUbt6KziPG03nEeNoPGc3bAH91u/h4n6NouYxfct1Llsmyrkj5ity/4o1SoSApIYFHt65TpHxFLGztsHd14/7VSwDEx0QT+PyZVh2oL5kTiYTdy+YR9G6E7NHNa+jq6WHr7ELRCpUI9H9KdFjqfP8nPjexcSqgMfpTp0Vruo6cQNeRE+gw1Etz+xeyeD+WKpNlXdFyFbl3+UPOhzevUbR86tS8wxtWkpyURPu/P3SgAEIDX3N00xpkKalTQu9fu0TB4qU+2saFSpUl8PkzIkKCALh94UyGTo42Nek1btuRvhNm0GP0JHqMnpQ6ja5H3xx1oODLHev6hob4XDjDU59bAIS8eknQi+e4lyqbo7xpVateg/u+vrx6+RKAvbt3Ubee5pfNsLAwxnqNJioqCoDjR49SuHCRL9aBgtTpeL6+vrx8l3PPrl3UrVdfoyY8LIwxo0epcx47eoTCRVJzXjh/njmzZzN/4aI860ABlKlQkWePHxEcmPo78uzxw3hU1X7K05uXL9i3bTNKhYKU5GROHzlI1SyuocmpY3ceqm8AseDwOdzsrLA1S53GVbNEIXxf5ewaMUdLM5p6lEIiAV0dKbVLFuZOwJtcyQpQsrwHAU8eE/LuznXeJ45Rrko1rZe/d/M6O9etYsDocXnWgQIoV7Eyzx49JOjdPj995BCVqtf8yFKaHvreo3T5Cnl6p7vPzelepCjXvFOvKZfL5dy6eoWiJUrmSVYhf33xkaiZM2fi4OBA165dqV69Oi1btsTIyIgbN25QpUoVdu3axYEDB1i4cCEBAQFs2bIFfX19Zs+ejUKhQC6X06xZMzZu3Ejfvn2RyWT4+flRo0YNli5dym+//Yauri67du2iRo38m4NqbWnJmL/+ZsSUycjlcpydnBg/dBgPHj9myvx5bF788ak5/x8Zm5nzfYfuHFqzFIVCjqWtPZ7vbgQR/DKAE1vX02n4uGzrviRlbBxhG7Zh16cbEh0dZGFhhK/bin5BF2w6/cbbqf+Q9Ogp0SfO4PjXAFQKBcr4BEKXrQYgYtc+bDr8itOYYQAk3LlH7JlPv4uPsZk5TTp258DqJSgVCixs7WjauSeQOiXrxJZ1dB4xPtu6CnUaEBUWwsbp41Eo5JSvXR/XYiUA+KnXAE7/u5m73qkXgNds+qNWN5L40jmbde3Dia3rUMoVmJhb8FPvgUgkEuxdCvJd207sX7UYpUKBgbExLXpkPfffxMycpp16sH/1YhRyBZa2dvzQpVdqzhfPObZlHV1HTsi2zqNuQ6LCQlg/bRwKhZwKtRvgWqwEgf5PeXz7Blb2DmydM1W9zXotf6VMtVpEhYawceZEpFIdbJ0K4Nmx+0fb1cTcnOZderJnxWKU746LFt168/bFc45sWkOP0ZOyrPnS8vJYl0ql/NR7IGd2buHKkX1IpDo069ZX408d5JS1tTWjx4xl9IgRyOQynJ1dGDt+PH4PHjB9ymTWb96CR8WKdO3WnQH9+qKro4OtnR3TZ+X9dVDpc44ZO5ZRI4Yjk8lwcXFh7PgJ+D14wNTJk9m4JTVnt+496N+3Dzo6utja2TJz1mwAFs6fh0qlYurkyep1lq9QgWHDh+dqTnNLS7oP/JOls6Yhl8uxd3Sixx9/E/D0CeuXLGDcnIXZLv/jb+3ZsnIZ4/4aiEIhp0rNOtRtnPtT4+OSUth28TZdG1RDRyolPDaeLd6p18C42FjStlZF5hw4k+06jvs8ok318gz9qRE6Uik+AW+4+uRFrmU0s7Ck4++DWD1nVuqfgnB0pPOAP3n57Clbli9mxMy52S6/d+M6UMGW5R+mNhcuUZK2PfvmWkZI3ee9//ybhdMnq/d537+G4f/kMWsWzWPy/I9/Lwp++wbbNNcm5oXPzdmhZ182Ll/M8N97IZVKKV3Bg+Ztfs3TzPlCXBOFRPWF76v99u1bhgwZQnx8PDo6Ovzxxx9YWFgwZcoUkpOTMTU1ZcaMGRQsWJBp06Zx6tQpdHV1qVGjBkeOHOHMmTOcPn2apUuXYmBggI2NDdOnT8fGxoYlS5Zw6NAh5HI5derUYeTIkQQFBdGlSxdOn0792zsLF6Z+MA8alPnF05mJ9n/+8aJ8FtKl38eLvgInx3jldwStNNv79fy9rqwcbd3m40WC1qTfyC8EXZ1vYwpFUjZ/R+dr0rZGhfyOoJVv4f3p+yoovyNoZe+N+/kdQSueFYrndwStWGTyt92ET1e9xNdzo7HsBHTN/ZuPZMV9/dIvtq2c+OKdqG+R6ETlHtGJyj2iE5W7voUvqSA6UblNdKJyj+hE5S7Rifr/SXSiMvpaO1FffDqfIAiCIAiCIAjfMOnXf2Inr30bpzQFQRAEQRAEQRC+EmIkShAEQRAEQRAE7UnEOIxoAUEQBEEQBEEQhBwQI1GCIAiCIAiCIGhPXBMlRqIEQRAEQRAEQRByQoxECYIgCIIgCIKgvW/gzy7kNTESJQiCIAiCIAiCkANiJEoQBEEQBEEQBK1JxN35xEiUIAiCIAiCIAhCToiRKEEQBEEQBEEQtCfuzic6UdqInDAzvyN81MkxXvkdQSuNJ03O7whaOfwNtKfuN3JRp1T6bQx4K5XK/I6gFbni28ipUqnyO4JWjvk8yu8I/xnPQyPyO4JWijvZ5ncErRjofhtf0ezMTfI7gla+lc8k4dvxbRyhgiAIgiAIgiB8Hb6RE7l56ds4RSwIgiAIgiAIgvCVECNRgiAIgiAIgiBo7xuZqp+XRAsIgiAIgiAIgiDkgOhECYIgCIIgCIIg5ICYzicIgiAIgiAIgvbEjSXESJQgCIIgCIIgCEJOiJEoQRAEQRAEQRC0JhF/bFeMRAmCIAiCIAiCIOSE6EQJgiAIgiAIgqA9ifTL/eTAgQMHaNasGU2aNGHz5s0ZXr9//z4///wzP/30E3379iUmJuaTm0B0ogRBEARBEARB+KYFBwczd+5ctmzZwt69e9m+fTtPnz7VqJkyZQp//PEH+/fvp1ChQqxevfqTtyc6UYIgCIIgCIIgaE8i+XI/Wrp06RI1atTA0tISY2NjPD09OXr0qEaNUqkkPj4egMTERAwNDT+5CcSNJXKRUYWyWP3aEomuHimvXhO2ehOqpCSNGuPKFbBs3QKUKhTxCYSv3YQ8JAy7gb3Rs7dT1+na2ZL06Akh85bmWr7n9+9y8cAuFHI5tgVcaNy+GwZGRjmui42MYNucqXQaPg4jUzP18y/87nNh/046DR+Xa5m14TB6KMn+AURt3flFt5tWXrdtTjzz9eHC/l0o5DLsnF3x7NA90yxZ1SmVSs7u3kaAny9KhZIq33niUbchAC8f+3Fuzw6USgW6evo0+qUDTu6FUalUXDy4h4e3rqGnb0CBwkVp2KYdunp6mWe8d4fz+3Yil8uxd3ahaaeeGTJmVaNUKjmzayvPH9xDqVRS9bumVKzXCIDE+DhO7thE+NtA5LIUajb9kTLVa3Pl2EEe3ryqXndCbCwpyUkMnrMsX9vzvaiwUDbNnMgvA/7G0a0QAPtWLib0zSv0DAwAKFi8JA1/bp8vOd++eM6ZXVuRJSejUiqp9n0zSlerCcD1U0fxveyNVEeKkakZTdp1xdLO/qPtCu+Ph90oFO+Ph64YGGZ13GRdFxsZwfa50+j4v7Hq4+bVk4dc2PsvSoUCIxNT6rX5DTtnV61ypfX47m1O7d6BQi7DwaUgP3XthYGRcY5rti+Zh5mlFc06dNV4/rb3OR7evkH7QUNynC23ciYlJLB//UrCgt6iUimpULMudX74EYDnDx9w/N/NKBVKjE1N8fytE46ubp+cs6ijLQ3KFkdXKiUkOpaDN31JkSu0rpMAnhVLUdDWGoBnQaGcuvdYY1kLYyN6fleTrRdu8DZK+6k6/r4+eB/YjUIuw7aAC02yOIayqlMqlZzbs52AB74olanHUIU6DQAIfxvIiW3rkSUngwTq/vQL7qXKcu34YR7duqZed0JcLLLkJAbOWqx1boC7N6+zZ9MG5HIZzm7udO3/B0bGxhnqVCoV6xbNw7mgG01atsnw+tKZU7GwsqZD73452r62rl2+xNqVy5HJZBQqXITB/xuBiYmJRs3p48fYuX0rEiQYGBrSb9CfFC9ZEoDffmqBrd2H70o/t2tPo++b5EHOy6xblSbnsOEYp8954ji7tm1FIpFgYGhA30F/UrxEas52LX/UzPlbOxrmQc7/L2JiYjKddmdubo65ubn6cUhICHZp2t3e3p67d+9qLDNixAh69OjB1KlTMTIyYseOHZ+cS3SiconUzBTbXl14O3kW8uBQrNq2wqptKyI2bFPXSPT0sO3bnUCvKchDQjH3bIR1x7aEzF1C6KKV6jr9Qm7YD+xNeJplP1dCbCzHN6+l7eARWNk7cGHfTi4e2EWjtp1yVPfg2iWuHN5PfHSUehl5SgrXjh/C58IZTC0tcy3zx+i5uWL/90AMS5ck2T/gi203vbxs25xnieHopjV0+HsUVvYOnNv7L+f37+T73zprXefjfZbIkGC6jZpESnISW/6ZgoOrG/YuBTmwZhm/DPgbB1c3nt27w+ENK+k5dhq+V7x55utDp2FjMTQ25vKR/Xgf2E2DNr9lmvHIxtV0GDoaa3tHzu7Zwbm9/9KkfRetanwunCEyJJgeXlNISU5i06xJOBZ0x8m9MIc3rMLGsQA/du9HbGQEayZ7UbB4KWp4tqCGZwsAkhLi2ThzIk079cjX9nRyLwyAXCbj8IaVKORyjXUGPn9K5/+NxdTSKl9zOroVYv+qxTTt2B23kmWIjYxgw4wJOLkXJiYijHuXL9BxiBcGRkbcPn+ao5vW0O6vER/PHBfLiS3r+PXP4VjZO+C9fycX9++mUduOOarzu3aJK0c0j5vkxAQOrV5Ks+79KFiiFBHBbzmwajEdh49DVzfzjn1m4mNj2LduJT2Gj8XGwZETO7dxcvd2mnfsnqOai0cP8vLJI8pUraF+LjE+jlO7d3Dv6iXcipfUOlNe5DyzbyfmVta0/f1PUpKTWDJuBG7FS2Ln5MyOpfP4td8fFC5VlrC3gWxbPId+46ZleYIkO8b6erSoXJb1564RGZdAw7LFaVS2OEfv+GldV86tADamJqw8cRGJRELXBtUp6ezAwzfBAOhIpbSsWg6dHN45LCE2lmOb19Lur5FY2Ttwft+/eO/fyXcZjqGs6+6+O4a6jppISnISW/+Zir1LQZzcC3NqxybK1qhD2Zp1CXn1gh0LZtF/+nyqNWlGtSbNAEhKSGDL7Mk0ad8tR9ljo6NZv2g+/5syE4cCBdi1cR27N62jY5/+GnVvX79iy8plPH/yCOeCGTvCR/fu4onffarUqpuj7WsrKiqSOTOm8c+iJTi7uLJ6+VLWrljGwL8+nEB4/fIlq5YtYdHK1Vjb2HLtymUmjx3Nhh27eP3yJWbmZixevTZP8r0XHRXF3JnTmL1wMc4urqxZvpS1K5Yz4K+/NXKuXraEhStWYW1jy/Url5ky1ov123fy+uVLTM3NWLRqTZ7mzHdf8O5869evZ9GiRRmeHzhwIIMGDVI/ViqVSNKMXKlUKo3HSUlJjB49mnXr1lG+fHnWrl3L8OHDWbFixSflEtP5colR2VIk+wcgDw4FIPb0eUxrVtMskkoBCVLj1DNbEgMDVDLNL07o6GDbuysRW/5FERGZa/lePryPQ0F3rOwdAChfpwEPb1xFpVJpXRcXHcWzu7dp3X+wxjIBD+8jS0mmiRZfSnOTZZufiDl4lLgz57/odtPLy7bNqYCH93F0K6Tehkfdhvhdv5IhS3Z1T31uUbZGHaQ6Ohgam1CiUjUeXL+Mjq4u/ab8g4OrGyqViujwUIxMTAEIfvWCouUrYvjuzGcxj8o8vnMj04zP/XxxdCuEtb0jABXrNeTB9csaGbOreexzi7I1P+QrWbk6969dIjE+jhcP71O7eUsAzKys6fy/sRimO3t4Zvd2CpcuT+Ey5fO1Pd87uWMjZavXxsjUVP1cVFgoKclJHNu6nnVTxnBk42oS4+PyJadCLqfWDz/hVrKMul2NTc2IjYrA2NyC73/roj5b71jQneiI8I+2K2RyPNRuwKObWhw3aerioqN4du8OrX4frLFMVGgI+kZGFCxRCgBrByf0DQwJeu6vVbb3nt2/h7N7IWwcUt+HVRt8x72rlzQyfqwm4NEDnvrepXL9Rhrrvn/9KmaWVnz/a9aji18qZ9N2nWnyawcA4qKjUMhlGBoZExEShIGRMYVLlQXA1qkABoZGvPZ/8kk5CznY8jYyhsi4BABu+b+kTEGnHNVJJBL0dHXQ0ZGiI039USiV6mWbepTi7otAEpJlOcr24uF9HNO8zyrUaYhfJp/j2dU9vXubsjVqfziGKlfD78YVAFRKJUkJqf+flOSkTDuh5/fuoFDpshQqUy5H2R/43MataDEcChQAoL7nD1y9cC5D9jNHDlGn8fdUrlk7wzoe+d7j/u1b1G/yQ462nRO3rl+neMmSOLukjgi3+KkVZ06e0Mipp6fH4GHDsbaxBaB4iZJERkQgk8l4cP8eUqkOQwcN4PceXdm8fi0KRcZRzM/PeY3iJT7kbN6yFWdOpcupr8efQz/kLKaR0xcdqZRhfwykf89ubFm/Lk9y/n/StWtXTp06leGna1fNkX1HR0dCQ0PVj0NDQ7G3/zAz4vHjxxgYGFC+fOrv/99++41r167xqf6znahhw4ZpDNF17tyZ8uXLM3DgQDw9PfHz88tm6ZzTtbbS6PTII6KQGhshSTPXUpWcTPj6LTh5DcVl3jTMGzcgcscejfWY1a+NIiqahJs+uZovNioCMyvrD9uxtCIlKZGUdNMNs6sztbDkx14DsHr3xfa9ouUrUr9NO/Q/Y17ppwidu5jYE2e+6DYzk5dtm+MskRGYW2qRJZu6DDmtrImLSn1v6+joEh8TzXKvIZzb+y9VG6f+wnVyL8yze3dIiItFpVRy/+ol4mOis8yo2Q7WGTJmVxMbGY55unyxkZFEhYZgYm7J9VPH2Dx7Muunjyf4VQB6+gbq2rC3b3jqc4s6P7b+Ktrz7qXzKBUKyteur7G+xLgY3EqU5vt2XegycgL6BoYc25z12de8zKmrp0e5WvXUz/t4nyUlOQkn9yLYFXDBtVgJIHVE7fz+nZSoWCXLnJpZIjVG2UzfZ0lOnznrOlMLS1r07J/huLG0d0CenMyLh/cBCHrxnIigt8THRGmV7b2YyHDMrWzUj82trElOTCQlKVGrmtioSI5u20SbXr8jlWr+uq3S4Dvq/9g6RyNjeZVTIpEg1dFh96olLBk3EvfipbBxdMLGwRFZcjLP7t8D4M3zZ4S8fUNsVNQn5TQ3MiQm8cP+jUlMxlBPD31dHa3r7ga8ISlFzh/NGvBn8wZExifw5G3qlyYPd2ekUgl3Al7nOFvGz5ysj6Gs6mIjIzBNd3zFRaYe643aduTaicOsGDOUnYv+4bu2nZDqfPh/h78N5Ond29Rq3irH2SPCQrG2tVU/trKxJSkhgaTERI26Dr37Ub1ugwzLR0WEs33NCnoOHoJEmndfC8NCQrCzc1A/trWzIyE+noR3nUsABycnqtWsBaSOIqxYvJDqtWqjp6eHQqHAo3JlJs+czaz5i7h1/Rr7d+/K9ZyhoSHYpvni/T5nYtqcjk5Uq1lTnXPlkkXqnEqFAo/KVZg0YxYz5y/k5vVrHNiT+znz3Re8O5+5uTkuLi4ZftJO5QOoVasWly9fJiIigsTERI4fP069eh9+f7m5uREUFIS/f+oJtVOnTlGuXM5OWqT1n53O9/PPP7Nw4ULatm3LmzdviIiIoEKFCpQoUSLTIcHPJpGiyuz5NGfI9FwKYNmyGW9GTUQeEobZ9w2xH9SHwDFT1DXmno0IX5vxloyfK/0ZqffS/2LXtk744GtqW5VKBZmMsKf/xZhdnUqp0ryOU6XSWN7E3IJ+U+YQ/G5Kio1jAcpUq0VcZCQ7FsxCT9+A8rXraXxBSL9tSSYXiqbdRnY1qe2Y5jWVCqlUgkKhIDo8FANDIzoO9SIyJJgtc6ZiZe+IY0F3AG6cPk7F+t9luFYkK3nZnsGvXuBz4UymU9+c3IvQqs+HKQq1mrVk6ai/UMjl6Ohm/Nj+Evsd4OrxQ9w6e5Kf+/+Fnr6++vmE2Bj2r16CgaExdX/6OeMGMqFSKTPdx1JJ+sza1aVlYGhEi14DuHRoD977duJcpBguxUog1cnZrzyVUpXpRc0a79UsalQq2LVyMZ5tO2KmxZTMz/E5OdPWtOnVnxadktixdD7nDuyhYcuf+W3AX5zes4MTO7dSsFgJCpUonel7UBsSCagy+U2Z/qMxu7q6pYuSkJzCvINn0NPR4ZeaFalezI0XoRFUKuzKhnOfdlY59djI5H2W6TGUeV36zy2VSoVEKkEuk3Fo7XKadupB4bIVCHz+jH0rFuDoVkjdIbt19gQe9Rpp/dmUPpMkkwNbm98tcrmclXNn0bZ7LyzTdA7zglKlzPTzRyeTnEmJifwzfSqhISFMnjkbgB9a/KRR0/rX39i3eyetf22bqzlTPwu1a8+kxETmzJhGaEgIk2bOAqBpix/T5WzL/t27aPVL7uYUMnJwcOCvv/6iS5cuyGQyfvnlF8qXL0/v3r35448/KFeuHNOmTWPw4MGoVCpsbGyYOnXqJ2/vP9uJql69OmPGjOH169fs27ePli1bcuHCBfUQXm6TR0RgUMRd/VjHyhJFXDyqlBT1c0blSpP8xB95SBgAsSfPYt3hF6SmJijj4tEv6AJSHZIeftpUifQuH9rLM9/UEa2UpERsnVzUr8VFR2FgbKy+aP09MytrggKef7Tu/7uvqW29D+7h2b07H7IU+JAlNjoSQ2MT9NNtw9zKhrcB/pnWmVlbE5fm+pK46CjMLK1ITkzg5WM/ilWoDICDqxt2zq6EBb7GyNSUklWrU92zOQBv/J9ileaMY7bbjsqYMbsacyubDPlMrawxtbAEoGzNOgBY2TvgUqQYbwP8cSzojlKp5PGdG3QdMf6raM/7Vy+SkpTEln+mqp8/tH4F9Vu1xdDYhKSEeIqWrwikfqGUSCUaX3i/VE5IHWU6umk14UGBdBgyGgubD2e9Q9+8Ys/yBRSrUIn6rX/L9ovb5cP78Pd9nzkJWydnje1ldjyYW9kQ/CJnx41KqUTPwIBfBg1TP7d+8mitb3jxnoWNDW+eP1M/jlG/Dw0/WhP69g2RoSEc25F6UiwuJhqVUolclsJPXXvnKEde5tQ3MOSp710cXFwxs7RC39CQstVq4nfrOiqlEn0DA7oN81Ivt3D0UKztMz+2M1OvdFGKO6Ve6K2vp0to9IdpqWZGBiSmyJClm+oUk5CEs7VlpnUlCthz3OchSpWKZLmcey/fUNLZAXMjI/R1denWoLp6mZbVynPq3iP1SFV6Fw/txT+LYyguOjLzz3Fra4Je+GdaZ26leQzFR0dhZmlN2Ns3yFKSKVy2AgAFChXBxtGZtwH+mFlZo1QqeXLnJh3/N1bLVtVkbWvH8ycfbq4RFR6OsakpBlrMDnnx7ClhwcHsWJd6m+eYqEiU796nXfr/8Ul5smJv78CjNLOAwsLCMDUzwzDdzTtCgoMZP2o4rgXdmDFvAQbv9sGp40cpXKQohYoUBVI7j7o5PDGiDTsHBx75PfiQMzTrnBNGjcDVzY3pc+enyXnsXc4i6lrdTzzx8DWTfMFronLixx9/5McfNTuyK1d+uO9A/fr1qV+/fvrFPsl/dnhBIpHQqlUrDh06xJEjR2jZMvU6ic+5lWF2Eu/5YVCkELoOqb8szBrVJeG25pS8lIBXGJYohtQ89e5RxpU9kIeGoYxLvdWiYcniJPk9yrVMNZu3otPwcXQaPo52f48i6MUzIkNSL769632WIuU8MizjVrKMVnX/331NbVunRWu6jpxA15ET6DDUi7cB/upt+FzIIkupMlnWFS1XkXuXvVEqFCQlJPDw5jWKlq+ERCrl6Ka1vHmW2skPe/uGiOC3OLkXJvhlAPtWLEKhkKNUKLh2/DClqtTIsF0A99JlCXz+jIiQIADuXDij7ixoU1O0fEXuvZsGl5QQj9+NqxQrXwlLWzscXN24f+UiAPEx0bzxf6oehQp98wpDYxMsbOzIzpdqz0a/dKDnuGnqbZlaWNK8ax+Klq9ISnISp/7dor4O6vrJoxT3qKLRQflSOQEOb1hJclIS7f/W7EDFRkawY8Esav7wEw1/bv/RM981m7Wk4//G0fF/4/jtr5EaWe5dPEfhshkzFyxZWqs6DRIJ+5YvIPhlAACPb11HR1dP40uyNoqULsdr/6eEB6e+D2+cO0VJj0pa1bgWKcZfMxfQb9xU+o2bSpX6jShTtUaud6A+NyfA/RtXOXtgNyqVCrlMxv0bV3EvWRokErYsmE3gu4637/Ur6Orp4eBSUOts5x88ZdWpy6w6dZl1Z65SwNoCK9PU0ZZKhVx5HBiSYRn/kPAs64KiYijlktqJk0okFHOy501ENCfuPmTZcW/1tmITk9l37W6WHSiA2s1b0XnEeDqPGE/7IaM1jw3vcxQtVzHDMu4ly2RZV6R8Re5f+XAMPbp1nSLlK2Jpa09KUiKB/ql/ryYqNITwoEDs37VjWOBrDIxNNI6tnCjtURH/x48IDgwE4NzxI3hUra7VskVKlGTGirWM/WcBY/9ZQL0mP1ClVt1c70ABVKpajYcP7vPm9SsADu/fS83adTRqEhISGD54ELXr1mfkuAnqjglAwPPnbFyzGoVCQXJyMgf27KZeI81rDXMlZ5WqPPR78CHngX3UyCTniL/+oFa9eowYO14j54vn/mxcmy5nw9zPKeS//17XOI02bdrQoUMHihYtioOD9mfOPoUyNpawVRuwH9gHdHWQh4QRtmId+u4Fse3RicCxU0nye0T0kRM4jvwL5AoU8fGEzP9wi2VdB3vkYdpdlJ1TxmbmfN+hO4fWLEWhkGNpa4/nuxtBBL8M4MTW9XQaPi7bOiFzX1PbmpiZ07RTD/avXoxCrsDS1o4fuvQCUq8LObZlHV1HTsi2zqNuQ6LCQlg/bRwKhZwKtRuor3tp1WcQp3dtRalQoKOrS4tufTGzssbMyppXTx6xfuo4VColRctXonKjzG/namJmzg+de7Jv5WIUcjmWdvY079qbty+ec2zzGrqNmpRlDUDFeo2ICgth7dQxKOQKPOo0oOC7u5u17vsHJ7Zt4PaF06iUKmo1a6m+C15kaDAW1jn7kpLX7ZmVwmXKU6nBd2ydMw2VSoltARc8O3TLl5yB/k95fPsGVvYObJ3zYdpDvZa/8sTnJrKUZG6dPcmtsycB0NHVpdOwMR9t2/fHw+G1y1Ao5FjY2OHZqSeQetyc3Laejv8bl21dViQSCU279OLUtg0oFHJMzFOvOcxsik52TMwtaNm9D/8uW4BCLsfKzp7WPfsRGODP/vWr6DduapY1X9Ln5vRs24GDm9aydPxIAEpWrEyN7zyRSCS06dWfAxtWo5DLMbW0pN2Av3Lcju8lJKdw8KYvP1f3QEcqITI+gf3XfQFwsjSneeUyrDp1Odu6k3cf4elRir5NaqNSQUBIOJcfPc9us1oxNjOnScfuHFi9BKVCgYWtHU07p77Pgl4GcGLLOjqPGJ9tXYU6DYgKC2Hj9PEoFHLK166vPtZ/7DWQM7u2IpfJkOpI+b5dF/XIaFRoMBbWNpkH04K5hSXdBvzJ8tnTkMvl2Dk60mPQ3wQ8fcKGpQsZ+8+Cz2yd3GFpZcVfw0cyZdwY5DI5TgUKMHSUF48fPmT+rBksXr2WA3t2ERIczKUL57l04cNNo6bNmUfHrt1ZMn8uv/foikKuoG6DBjRt/mM2W/yMnP8bwdRxY5HLZTgWcGboyNE8fvSQBbNmsmjVGg7s2U1IcDCXL1zg8oUL6mWn/jOXDl27s3T+PPr37IZCLqdO/YZ4Nm+R6znz3Sd+DvyXSFRZXajxH9GhQwc6depEs2bN6Ny5MwMHDqR6de3O0LwX0PX3PEqXe4506JDfEbTSeNLk/I6glZNjvD5elM90v5Hr1L6V6+mUaa5fFD5f+ilaXytL44x/B0j4NM9DI/I7glZszUw+XvQVKFkgZ9NP84urjWV+R9DKt/J1t0iBvD3pn1tefeL000/hOnPiF9tWTvxnR6JUKhUhISGEhYXRuHFjADZu3JjPqQRBEARBEARB+NZ9G6eIP8GxY8do2bIlf//9N/pp7iIlCIIgCIIgCMJnkEq/3M9X6j87EtW0aVOaNm2a3zEEQRAEQRAEQfiP+c92ogRBEARBEARByAPixhL/3el8giAIgiAIgiAIeUGMRAmCIAiCIAiCoD0xEiVGogRBEARBEARBEHJCjEQJgiAIgiAIgqA1yVd817wvRbSAIAiCIAiCIAhCDoiRKEEQBEEQBEEQtCeuiRIjUYIgCIIgCIIgCDkhRqIEQRAEQRAEQdCeVIxEiU6UFsz7dc/vCB/VbMO2/I6glcNjvPI7glYaT5qc3xE+6vTYMfkdQSt638wH7bcxMK9UKvM7glb0db+NXy+lXRzyO4JWpN/A1JlC9tb5HUEre2/cz+8IWrExM8nvCFqRyRX5HUErSlT5HUErRQp8G59JguhECYIgCIIgCIKQE5Jv48RjXhItIAiCIAiCIAiCkANiJEoQBEEQBEEQBO19M1P1844YiRIEQRAEQRAEQcgBMRIlCIIgCIIgCIL2voGb3eQ1MRIlCIIgCIIgCIKQA6ITJQiCIAiCIAiCkANiOp8gCIIgCIIgCFqTiFuci5EoQRAEQRAEQRCEnBAjUYIgCIIgCIIgaE/c4lyMRAmCIAiCIAiCIOSEGIkSBEEQBEEQBEF74hbnohOVmy7eucPSnTuQyWUUcXFldM/emBgZZVp77uYNJq5czqllKwGIjotj1oZ1PHn5AkMDA1rUqcev3zfJk5xGZUth2bI5Ej1dUl4HEr5pO6qkZM2aCuWwbOEJKhXKhATCN+1AHhaOxNAQm86/oedoDxIJ8VduEHP8dJ7kBHh+/y4XD+xCIZdjW8CFxu27YZBJm36sLjYygm1zptJp+DiMTM3yLG92HEYPJdk/gKitO/N0O/6+Pngf2I1CLsO2gAtNOnTPtM2yqlMqlZzbs52AB74olUqqfOdJhToNAHh27w5HN63G3MpGvZ7fBg9H39CIu95nuX3uFBKpFAsbW5p06KZVWz+9d4eze/9FIZdj7+xKs849M+T9WE1MRDjrZ0yk55jJGL/bZljgG45sXktKcjISCTRo1ZbCZcp9SpNm8OzeHc7v24lcLsfe2YWmnTJm/lhNTEQ4m2ZNotvoSerMn5XJ14cL+3ehkMuwc3bFM4v9nlWdUqnk7O5tBPj5olSk7nePug0BePnYj3N7dqBUKtDV06fRLx1wci8MwL6Viwl98wo9AwMAChYvScOf22eZ8fy+nSjkcuycXWjasUfGdsui5n2+5w/uoVQoqdq4qTrf03t3OLJhFWZW1ur1dPh7JPqGH9Z94/Rx7l06T3evyZ/Ryh/cunaVrWtXI5PJKFioEP0GD8HYxESj5sLpk+zf+S8SCRgYGNKtX3+KFC+RK9vPzs2rV9jyLptbocL8/lfGbOdPnWT/zh0gkWBgYECP3wdoZAsLDWHU4EHMXrICcwuLPMl558Y1dm5Yh1wmw8W9ED0HDcbI2DhDnUqlYtX8Obi4ufND65/Vz586fJDzJ46RkpKCe5Gi9Bg0GD09vVzPWdLZnqYepdHVkfI2MoadV+6QLJNnWd+2ZkWComI47/csw2ud61UlJjGJfdfvfXauh3ducXznNhRyGY4uBWndsy+GRsZa11w5dZwb508jT0mhgHth2vToi66eHiFvXrN33UqSk5KQSCR4/tqeYuUqfHZeAN9bN9i3dSNymQzngu507Dcwy32+cckCChR0o/GPrQBISUlm++oVvHj6BBUq3IsW57eefdDXN8iVbOlzHti6CblMRoGCbnTIJuemdzm/S5PzX3VOcC9ajF/zKKeQv/6z0/kWLFjAjRs3AOjcuTNXr17N0+1FxsQwZfUKpg38g+3TZ+Fsb8+Sf7dnWvsqKIiF27eiUqnUz83fuhkjAwO2TJ3BqjHjuXzPB+87t3M9p9TUBJsu7QhdsY7A8dORh0Vg1aqFRo1ETw/b7h0IXbGOt1P/IeHufazatgbA8qcfUERF83bSLIKmz8OsXi30C7nlek6AhNhYjm9eS/Me/enqNQVzGzsuHtiV47oH1y7x7/yZxEdH5UnOj9Fzc8V5/gxMG9TN820lxMZybPNafuzZn+5jpmJha4f3/oydtuzq7nqfJTIkmK6jJtJxmBe3zpzgbYA/AIHPn1GlUVM6jxiv/tE3NCI6LBTvg3toO3g4XUZOwNzahkuH92mRN4ZDG1bRps8g+k6YgaWtHWf27MhRzb0r3mz6Zypx6fbvsa0bKF+rHj29JtGscy/2rlyMUqHIaZNmmvnIxtW07DOQ3uOnY2Frz7m9/+aoxvfKRbbOnZYh8+dkOrppDS17DaDn2GlY2NhxPtP9nnWdz7v93m3UJDr9bwy3zqbud4VczoE1y2jSoRtdR06khmcLDm9YqV5n4POntBs8nK4jJ9B15IQsO1AJsTEc3biaVr0H0GvcNCxt7Ti/L2O7ZVXj432WiOAguo+eTOfhY7l55viH96X/E6p+15Ruoyaqf9J2oF4/e8K1k0c+r5HTiImKYumc2fztNZZ5q9bi4OjElrWrNWoCX79i06qVjJo8lZmLl9OmXQf+mTwh1zJkJToqiiVzZjN0zDgWrF6Hg5MTm9eu0qh58+oVG1etYPTkacxespyf23dk1qTx6tfPnTzO2KF/Exkenmc5Y6KjWb1gLgNHjGb60pXYOzry74a1GeoCX71k5piRXL/krfH8jcsXOXnoAMMmTmXKwqWkpCRzbN+eXM9pYqDPrzUrsvH8dWbvP01EXDw/eJTKtNbe3JTejWtSzs0p09frly6Ku711pq/lVHxMDLtXL6PDwL/4a/pcrOztOfbvVq1r7t+4xpWTR+kxzIs/psxGnpLCxWOHAdi/YTWV6jZg0KQZtOnZl61L5qHIhc/O2JhoNi5dSO+/hzNu3hJsHRzYt2VDhrqg169YMGkst69e0nj+2O6dKBUKRs2ax+hZ85ClpHB8b8bvBLmRc/PShfT8+3+MmbcYWwdH9m/ZmGnOhZPGcufqZY3nj+/eiUKhZMSseYycNZeUlBRO5EHOfCeVfrmfr9TXm+wzXb9+PVcOem1d871HqUKFcXV0BKBNw+84dvmSRkcJICk5mfErlvJn+44azz8KeM4PteqgI5Wip6tLrfIenLl+LddzGpUqQXLAK+ShYQDEnr+ISbVKmkVSCUgkSI0MUx8aGKCSp551i9yxh8hd+wHQsTAHXV1UiUm5nhPg5cP7OBR0x8reAYDydRrw8MbVDG2aXV1cdBTP7t6mdf/BeZJRG5ZtfiLm4FHizpzP8229eHgfxzRtUaFOQ/wyabPs6p7evU3ZGrWR6uhgaGxCicrV8LtxBUj9wvzqsR8bpo1j+9zpvH76CAClSolSoSAlKQmVUoksJQVd3Y+fEfZ/4IuTW2GsHVKPm4r1GvHg2mWNvNnVxEZF8vjOLX77Y1iGdStVSpIS4gFISU5EJ5fOUD/388XRrRDW9u/zNOTBdc3M2dXERkXyxOcWvw4cmit5AAIe3sfRrZB6f3rUbYjf9SsZ9nt2dU99blG2Rp0P+71SNR5cv4yOri79pvyDg6sbKpWK6PBQjExMAYgKCyUlOYljW9ezbsoYjmxcTWJ8XOYZ/d5v2/HdthvxIF3G7Gqe+NykXM266nwlK1fnwbXULy9v/J/x4rEf66aOZcucqbx68ki9zviYaE7t2ESDVm1zo6kB8Ll1kyLFi+Pk7ALA9y1+xPvMKY3/i66eHn0H/42VdeqobeHixYmKjEQuk+VajszcTZetSfMfuXBaM5uenh79Bv+NlU1qtiLvsslkMiLCw7h26RJeU6bnaU7f27coVLQ4jgWcAWjYtDmXz53J8J49dfgg9b73pGptzZNQF8+comnL1piamSGVSun6+yBqN2yU6zmLOdnxKjyK8NjUz5IrjwOoWMgl09qaJQpx/elL7r4IzPBaYQcbihew4+qTgFzJ9cT3Ls6FimDrmNphq97we3wue2u0X3Y1ty+ep3bTFhibmiKVSmnZtRce79pYqVKSFJ/6/01OSkJXTz9XMvv53MGtSFHsnQoAUPf7plz3Pp9hn587foRajRpTsUYtjeeLlipN0za/IpVKkUp1cHUvRERoaK5kS+uhzx0KFimmzlnn+6bcyCTn+eNHqNXoezzS5SxSqgxN2/ySJmfhPMkp5L+vejrf1atXWbZsGXp6erx+/ZpGjRphbGzMyZMnAVixYgWtWrXC09OTmzdvoqOjw7x587h58ya+vr54eXmxaNEiAHbu3Mn06dOJiYlh9OjRNGqUux+2wRER2Ft/mOJkZ21NfGIiCUlJGlP6ZqxbS6sGjSjq4qqxfOnCRThyyZvyxYqRIpdz9uZ1dHVyf/foWFmiiIxSP1ZERSM1MkJiaKCe0qdKTiFiy04ch/6BIj4eiVRK0OyFH1aiVGLTrSMmlcqTcOcesuCQXM8JEBsVoTE9x8zSipSkRFKSkjSn6mVTZ2phyY+9BuRJPm2Fzl0MgHHVSh+p/HyxkVq2WTZ1sZERmFpqvhb25jUARsamlKxSnWIelQn0f8q+FQvpPGI8VnYOVPnOk3WTR2NgZIy+oRHth4zSKq95mhzmVtYkp8ubXY2ZpRU/9/sj03V7tuvMlrkzuH7qGPGxMbTq1R+pjs5HM2mTWbPtrDO0cXY1ZpZWtO476LNzpM9kbqndfs+qLsNxZGVNWGDqftfR0SU+JpqNMyaQGB9Hi+79AEiMi8GtRGka/doRUwtLzuzcyrHNa2nVJ+P/T5vjObuazN6zoW9eAWBkYkKpKjUoXrEKb549Yc+KBXQbORETC0sOrl1O/VZtc2XfvxceFoqNnZ36sY2tHYkJCSQmJKinzdk7OGL/ruOvUqnYsGI5VarXRDcPppulFRYagq2d/Ydsdplkc3TE3vFDtvXLl1GlRk309PSwtrFl2NjxeZoRICIsFGtbW/Vja1tbEhMSSEpM1Jg21blvfwB8083MCH7zhphi0cweP4aoiHCKly7Db9165npOSxMjouMT1Y+jE5Iw1NfDQE83w5S+91P0ijnZaTxvZmTAj1XKsub0FaoXc8+VXNER4Vik+c5hbm1DcmIiyUmJ6ul62dWEBb/FJSaadbOnERMViXvxkjT9rQMAP3XuweoZk7l4/DDxMdH89vuf6OTC8RMVHoaVzYd9bmljS1Jixn3+W48+APjdvaOxfKkKFdX/Dg8N4cyRA7Tv3f+zc6UXGR6mPsGQmtMm05xts8zpof53hDrn77meM9+Ja6K+/pEoHx8fJkyYwK5du9i8eTPW1tbs3r2bEiVKcOjQIUJDQ6lZsyZ79+6latWqbN68mVatWlG2bFkmT55MiRKpc7zNzMzYs2cPXl5eLF68ONdzKlWqTN9P0jS3gNx16iQ6OlJ+rFc/Q90f7TogkUjoOs6L4QvmUbVMWXRz8Zf+h0ASQJXxeWWas5QFnLBo1oTAiTN4M3IC0UdPYtenm0Z5+LrNvBo2BqmJMRbN8+barfRnfd6Tphva1bbu/wOVSpXpB1umbZZFnUqlQpLmNZVKheTd+/in3gMoXrEKEokE5yLFKFC4KC8ePSDAz5cnPjfpPXEWfSf/Q5HyHhzbtEbLvBmfl6TJq01NenJZCntXLaF5194MnD6PTkNGcWTzOmIiPn96Uvr2ySrzx2pyk7ZtlF2dSpnuM0yl0ljexNyCflPm0GHIaI5uWkNEcBBO7kVo1WcQ5lbWSKVSajVrib/vXRTyjNeLqJQqMtu4RrtlU5P+OFelWbZVn0GUqFQViUSCS9HiOBcqSsDD+5zftxOXosVxL1Um43/6M6iUSiSZ5JTqZNy/SUmJzJ06iaDAN/Qd/Heu5sg0Wxb7OKtsc6ZMIujtG34fPCTPs6WV1TGi7ee2QqHg/p3bDPjfSMb/M5/4uDh2blqf2zGRIMnsNyZKZea/d9KTSiR0qFOZgzfuE5uY/PEFtKRSKbP4ziHVqkapUPD0/j3aDfiT/uOnkhgfx4md25GlpLBtyXx+7tWP4XOX0GvkePauX0lUeNhnZ1aqlFr9fvqYl/5PmTtuFPU8m1GuctXPzpXe574333vp/4x540ZTz7MZZfMgp5D/vuqRKIDixYvj5JQ6FG1lZUXNmjUBKFCgADExMQDUrZs6BF2sWDH1dVDpNW7cGICiRYsSGRmZ6zkdbWx44P/hItLQyEjMTEwwMjBUP3fI+wLJKcl0GTMamUJOckoKXcaM5p+/h6JQKhjQth0WpqnTZNYd2I+Lg0Ou51RERGHg/uEaJh1LCxTxCahSUtTPGZUuQbL/c+RhqV84Y896Y/VLS6QmJugXdEEW+BZFdAyq5BTir9/GuGL5XMt3+dBenvn6AJCSlIit04dpE3HRURgYG6svYH/PzMqaoIDnH637r7p4aC/+9+4A79qsQNo2i8y8zaytCXrhn2mduZW1xrU68dFRmFlak5SQgM+FM1Rr0kz9C0alUiHV0eHZPR+KlPXA2MwcSJ2GtWHq2I9mN7e2JvD5h+MmNioSQ2MT9NPk1aYmvdDAN8hSUihW3gMA58JFsXNyJvC5P+Zpzsx+CnMrG/W1OFlm1qLmc3kf3MOzLPZ7bHTm28uQK02dmbXmfo+LjsLM0orkxARePvajWIXKADi4umHn7EpY4GsSYmNISoinaPnUM8QqUjvcmXUWzayteRuQ/X7MrsbMykYzX1Sk+n155/xpqns2//C+BKQ6Ojy4dgljM3Oe+NxClpxMXHQk66aOpduoiVq2cuZs7e15+uih+nFEWBgmpmYYGmreJCMsJIQZ48fg7FqQcTNm5+r+zzKbnT1PHvp9NFtoSDAzxo3BuWBBxs34B4Mv/HlpY2eH/+MP0y4jw8MwMTXFwNAwm6U+sLS2pnLNWuqRgZoNGrJ/25ZcyfZ9+RKUdkkdqTPQ0yUoKkb9mrmxIQnJKci0vFzAxcYSa1MTWlRO7cibGRkgkUjQ1ZGy64rPJ2e0tLHllf9T9eOYyAiMTEzQT/OdI7saM0srylSuph61qlCzDmf27yL4zStkKSmU9Eg93gsWLYZDARde+z/FMs0o0qewtrUj4OkT9eOoiHCMTbTf5wA3Ll5g++rltO3Rm6p1Mp6Qzg3Wtra8ePpY/Tj6E3LevHiBHatX8GuP3lSpUy8vYuY7ifg7UV//SFT6O+1kNqT8/sNfIpFkOSrxfrnMzi7khmply+L77CmvgoIA2HPmFPUqak7fWjNuApunTGfDpCnM+WsoBvr6bJg0BTsrK/acOc3KPakXHkZER3Pg/FmapJtnmxsS/R5hUMgNXbvUD0OzurVI9PHVqEl59RrDYkWQmqV26Iw9yiEPi0AZH49JZQ8smnumFurqYFLZg6RHT8gtNZu3otPwcXQaPo52f48i6MUzIkOCgdQbHhQp55FhGbeSZbSq+6+q3byV+iYP7YeM5m2Av7otfLzPUbRcxQzLuJcsk2VdkfIVuX/FG6VCQVJCAo9uXadI+YroGxpy58JpnvjcBCDk1QuCXjynUKmyOLgW5Pn9u6Qkp14f9+TOTfXd27JTqFQ53jx/RkRw6nFz+/xpilWomOOa9Kzs7ElOTOT1s9T3ZmRoMGFBb3AoWPCjmT7GvXRZAp8/IyIkNc+dC2fUnYic1HyuOi1aq2/m0GGol+b+vJDFsVKqTJZ1RctV5N7lD/v94c1rFC1fCYlUytFNa3nzri3D3r4hIvgtTu6FSUlO4tS/W9TXQV0/eZTiHlUyPWPrXqosgQH+RL5rEx/vTNotm5pi5Svie/mCRr5iFVLfl7fPn+LxndT3ZfCrFwQF+FOodDn6T5unvtGEZ8fuWNraf3YHCqB8pco8eejH23fTXE8cPkiVdyf43ktMSGDC8CFUq12HwSNHf5EOFECFyprZjh86QNWamr9LEhMSGP+/IVSvXYe/Rnp98Q4UQFmPSjx79JCgwDcAnDl6mIrVami9fJVadbh+8QIpycmoVCpuXblMoWLFcyXbibuPmH/4HPMPn2Px0QsUtLXGxix1KmSNYu48eB2k9bpehkUybc8J9fquPHnB3ReBn9WBAihatjyvnj0lLOgtANfOnKRUxSpa15StUp171y8jS0lBpVLhd+sGzoWKYGPvSFJCAi/eXVcYHhJESOAbnNzcPysvQKnyHgQ8eUTI29RrxrxPHKN8lWpaL3/v5jX+XbeKgaPH51kHCqBkeQ8CnjzWyFkuRzmvs3PdKgaMHvef7UAJqb76kahPpaOj80VvLGFtboFXz96MWrwAmVyBs709Y3v3xe+5P9PWrGbDpCnZLt+l+Y9MXLGMjqNHoFJB79Y/U7rwx7+E5pQyNo6wDduw69MNiY4OsrAwwtdtRb+gCzadfuPt1H9IevSU6BNncPxrACqFAmV8AqHLUu88FbFrHzYdfsVpTOqF/Al37hF75kKu5wQwNjPn+w7dObRmKQqFHEtbezw79QAg+GUAJ7aup9PwcdnW/X9jbGZOk47dObB6CUqFAgtbO5p2Tr1OIOhlACe2rKPziPHZ1lWo04CosBA2Th+PQiGnfO36uBZLnRbbss8gzvy7mcuH9yGV6tC8ez+MTM0oU6MO0RHhbJ45ER1dPcytbbTaBybm5jTv0os9Kxal7js7e37s1oe3L55zeOMaenpNyrImO4bGJvzc7w9O7NiMQiZDqiPlh47dsbL7/NFdEzNzfujck30rF6OQp+Zp3rU3b18859jmNXQbNSnLmrxiYmZO00492L96MQq5AktbO37o0guAoBfPObZlHV1HTsi2zqNuQ6LCQlg/bRwKhZwKtRuo93urPoM4vWsrSoUCHV1dWnTri5mVNWZW1lRq8B1b50xDpVJiW8AFzw7dsm63Tj3Yt2qJuk2adelF0IvnHN28lm6jJmZZkzbfuqljU/PVaYBrsZIAtO77Byf/3cylQ3uRSKX82PP3XLltfFYsLK34/a+hzJkyCblchqNTAQYM/R/PHj9i+fw5zFy8nKMH9hEaEsL1S94ad5YbM20WZubmeZqt/9/D+GfyRORyOQ5OTgwcNpxnjx+xdN4cZi9ZztH9ewkNCeHqpYtcvXRRvey46TMxM8+b25mnZ25pSc8//mLxjKmpfwbA0ZHeg4fy/Mlj1ixewKR5i7Jd/rsfmhMfF8v4v/9AqVTiVqQo7Xvk/jEWn5zCv5dv06leFXSlUsJj49l+KfX6LGdrC36p4cH8w+dyfbsfY2puwc89+7F18VwUcjnW9g780nsAr58/Y8+aFQyaNCPLGoDq3zUhIT6OxeNHolIqKeBWiJbte2FoZEzHP4ZwaMt65DIZUqkOrbr1xubdzV4+h5mFJZ1+H8SqOTORy+XYOTrSZcCfvHj2lM3LFzFq5rxsl9+9cR2oVGxe/uG9UaREKX7r2fezs6XP2fH3QayeMyv1T4A4OtJ5wJ+8fPaULcsXM2Lm3GyX37txHahgy/IPl44ULlGStrmcM99JvvpxmDwnUWU1dPMVuHr1KosWLWLjxtRbSzZq1IgNGzbg4uLCwoWpNzpYtGgRjx6lnjHZvXs3165dY/r06axevZpt27YxY8YM5s6dy8CBA6levTqvX7+mS5cunD6t/d82iric+3fJy22xG7bldwStHG7VOr8jaKXxpNz5WzJ56fTYMfkdQSsGet/GuRqFltc45DelUpnfEbSSV6P+ua1K4czvtPa1kX4D7RmfnPLxoq/A3hv38zuCVqoWcf140VfA0kj7aW75SZnp1W1fnyYepfM7glYC/8n+REduKjBk4BfbVk581Z2or4XoROUe0YnKPaITlbtEJyp3iU5U7hKdqNwjOlG5S3Sictc304mak/s3actKgb/z9y7LWRFjcYIgCIIgCIIgCDkgOlGCIAiCIAiCIAg58G3MsxEEQRAEQRAE4esgbnEuRqIEQRAEQRAEQRByQoxECYIgCIIgCIKgPXGLczESJQiCIAiCIAiCkBNiJEoQBEEQBEEQBK1JxDVRYiRKEARBEARBEAQhJ8RIlCAIgiAIgiAI2vsG/gB4XhMjUYIgCIIgCIIgCDkgRqIEQRAEQRAEQdCeVIzDiE6UFnbFyfI7wkcpW7fJ7wha0f1Ghn9Pjx2T3xE+qtHESfkdQSvnxo/L7whaUalU+R1BK5Jv5BhKkcvzO4JWouIT8zuCVpTfwNtz0fGL+R1BK3941s7vCFop4+KY3xG0YpAQn98RtKL6Rj6ThG+H6EQJgiAIgiAIgqA9MRIlrokSBEEQBEEQBEHICTESJQiCIAiCIAiC9r6RqeV5SYxECYIgCIIgCIIg5IAYiRIEQRAEQRAEQWsSqRiJEiNRgiAIgiAIgiAIOSA6UYIgCIIgCIIgCDkgpvMJgiAIgiAIgqA9iRiHES0gCIIgCIIgCIKQA2IkShAEQRAEQRAE7YlbnIuRKEEQBEEQBEEQhJwQI1GCIAiCIAiCIGhP3OJcdKI+1TNfHy7s34VCLsPO2RXPDt0xMDLSuk6pVHJ29zYC/HxRKpRU+c4Tj7oNNZaNCgtl08yJ/DLgbxzdCgHg432WW2dPIpFKsbCxxbNjd4xNzTLN6O/rg/eB3SjkMmwLuNAki4xZ1SmVSs7t2U7AA1+UytSMFeo0ACAxPo4zO7cQHhSIPEVGdc/mlK5WS71OuUzG3uULKF+7PsUrVsm3tnz52I9ze3agVCrQ1dOn0S8dcHIvjEql4uLBPTy8dQ09fQMKFC5Kwzbt0NXTyzJnXrbns3t3OLppNeZWNur1/DZ4OPqGRtz1Psvtc6fU+7xJh24YZbHPc4PD6KEk+wcQtXVnrq73ma8P5/ftRCGXY+fsQtOOPTK0X1Y17/fx8wf3UCqUVG3cVL2PI0OCOLppLYnxcegZGNCsS29sHJ001nvj9HHuXTpPd6/JAKQkJXF00xrCgwJRqVSUrVmHqt81zTL3l35/AuxbuZjQN6/QMzAAoGDxkjT8uf0Xbdu3L/w5vXMrsuRkVCol1b5vRplqtdTHz+M7NwFwdHPn+3Zd0NM3yGLvf9zz+3e5eGA3CoUc2wIuNG7fFQPDjO2cVZ08JYUzO7cQ/OI5KlQ4uhWm4S8d0NXX/+RM6d27eZ09mzcgl8txLuhGl/5/YGRsnKFOpVKxbtE8nAu606Rl6wyvL505FUtra9r36pdr2dLn3LdlAzKZHBc3Nzr9nnXO9YtTc37/U2rOxPh4Ni5dSFDga1RKFTUaNMKz1c95khOgcmEXOtatgp6ODi9CI1h8zJvEFFmGunqlitCqajlUqEiWyVl9+grPgsORSiT0+q4GZVwdAbjl/5r1567nasZvZb+ndemiN8uXLCFFlkKRokUZOdoLExNTjZpd/+5gz+5dSCQSnJ1dGD5yFFbW1nmay/vKZRavWkVKioxihQvjNWwYpiYmGjU79uxh5/59SCQSXAoUYPSQoVhbWTF8/DhevXmjrgsMCqJS+QrMmTIl93NevcqSNatJkckoWqgQXn8PyZhz3152HTyIBAkuBZwYNfgvrK2siI6JYcbCBTx+9gwjQ0NaNPHkt1atcj2jkP/+s9P5rl69SufOnfNk3QmxMRzdtIaWvQbQc+w0LGzsOL8/4xfO7Op8vM8SGRJMt1GT6PS/Mdw6e4K3Af7qZeUyGYc3rEQhl6ufiwoLxfvAbtoNHkG3UROxsLHl0qG9WWSM5djmtfzYsz/dx0zFwtYO70wzZl13913GrqMm0nGYF7fOfMh4bNMaTC2t6Dx8PL8MHMKZnVuJjYwAIPD5U7bNmUqg/9N8bUuFXM6BNcto0qEbXUdOpIZnCw5vWAmA7xVvnvn60GnYWLqOnICpuQXeB3ZnkzNv2zPw+TOqNGpK5xHj1T/6hkZEh4XifXAPbQcPp8vICZhb23Dp8L6Ptuun0HNzxXn+DEwb1M31dSfExnB042pa9R5Ar3HTsLS14/y+f7Wu8fE+S0RwEN1HT6bz8LHcPHNc3XYH162gQt0G9BgzhdrNW7F/1WJUKpV6va+fPeHaySMa27p+6ii6+np095pMx6Hv9sWL55nnzof3J6QeR+0GD6fryAl0HTkhyw5UXrWtSqVi38rF1G7eim6jJvJL/785u2sbkSFBPPG5yXM/X7qOnEB3r8nIUlK4eeZE5jtfCwlxsZzYso7mPX6n6+jJWNjYcnF/xuMxu7prJw6hVCroOHwcHYePRy5L4Xq6/f45YqOjWb94AX2HjWTigqXYOjiyZ/P6DHVvX79i7gQvbl25lOl6ju3dxdOHD3ItV2Y5NyxZQJ+hI5nwLufeLHLOm+DF7XQ592/fjKWNDWPnLGLE9H84f/wI/o8e5klWcyNDBjaty6x9pxm0ZhfB0bF0rpfxpFsBK3O61q/KpF3HGLJhHzuv+PC/lt8BUL90EZytLfhr3V7+Xr+XMq6O1CzunmsZv5X9nlZkZCRTJ09i8rTpbN2xkwIFnFm6eLFGzcOHfmzdvJllK1ezccs2XFxdWblied7miopi4syZzBg/gV0bNuBcwIlFK1do1Pg9fsSmHdtZs3AR29esxdXZhWVr1wAwY/wEtqxcxZaVqxg9ZChmJqb8788/8yTnpNmzmT52LDvXrMXZyYnFq1eny/mYzTt3snrefLatXImrszPL16e+L+YuW4aRoRHbV65izfwFXL5+jQtXruR6znwnkX65n6/U15vsKxbw8D6OboWwsncAwKNuQ/yuX9H44vaxuqc+tyhbow5SHR0MjU0oUakaD65fVi97csdGylavjZHphzNHKpUShUJBSnISKqUSWUoKOlmMnLx4eB/Hgu7qbVeo0xC/G1czZMyu7und25StUftDxsrV8LtxhcT4OF48ekDNH34CwMzKmg5DR2P47izN7bOnqPPTz+rRs/xqSx1dXfpN+QcHVzdUKhXR4aEYvTsTF/zqBUXLV8Tw3dnEYh6VeXznRpY587I9IfUL86vHfmyYNo7tc6fz+ukjAJQqJUqFgpSkD/tcVzfr0bLPYdnmJ2IOHiXuzPlcX3eA3/v9l3q22KNuIx6k28/Z1TzxuUm5mnXVbVeycnUeXLtMbFQkEcFvKVW5OgCFy5QnJTmJkFcvAIiPiebUjk00aNVWI49KqSQlKQmlQoFcLkOlUqGjo5Mxdz69P6PCQklJTuLY1vWsmzKGIxtXkxgf90XbViGXU6tZS9xLlgFSj3MjUzNioyIp7lGFDkNGoaOrS0pSEgmxsersn+Llw/s4pDluytduwKObGY+v7OqcixSnWpPmSKRSpFIpdi4FiY0I/+RM6T3wuY1b0WI4OBUAoL7nD1y9cC5DxrNHD1HnuyZUrlk7wzoe+d7j/p1b1Ps+81HP3OB39zbuRYph/y5nvSY/cC2TnOeOHqL2d02oVEMzZ9vuvfm5Sw8AoiMjkMtkmY665AYP9wI8DQrjbVQMAEfvPKRuqSIZ6mQKJUuOexMZnwjAs+AwLE2M0JVKkUokGOjpoasjRU9HB12pDjK5Itcyfiv7Pa3rV69SqlRpXAsWBKB1m585ceyoRuaSJUuxbecuTE1NSU5OJjQ0FAsLizzNdeXGdUqXKEFBFxcAfv6pJUdPndLIVap4CXZv3JSaKyWF0LAwLMzNNdYjk8mYMGM6fw8YgKO9fa7nvHrzJqVLFKeg87ucLX7k6On0OYuza+06TE1M0uRMnSHy8MkTmjVujI6ODnp6etSuXp3TF3L/96qQ//7TnaiIiAh69+6Np6cn/fr1w9/fn6ZNm9K+fXu6d+/+yeuNjYzA3PLDkLeZpRUpSYmkJCVpXRcbFYGZVZrXrKyJi4oE4O6l8ygVCsrXrq+xPis7B6o2bsqaSaNYOvpvXj95RI0mLbLMqLH+bDJmVRcbGYFpuvxxkZFEhYZgam7BzdPH2TZnGptnTiT41Qv1VJ7m3fviXqps9o2oRRtpW5ddW+ro6BIfE81yryGc2/svVRv/AICTe2Ge3btDQlwsKqWS+1cvER8TnW3OvGpPACNjU8rXaUDnEeOp89PP7F+5mNjICKzsHKjynSfrJo9mudcQXj99TDXP5h9v2E8QOncxsSfO5Mm6M+yjTNovu5rM2jU2KiK1TS0skUilaV6zJjYqEqVSycG1y6nfqi2mllYaeap9/wPREWEsHfUXy72GULJydexdCmbMnU/vz8S4GNxKlOb7dl3oMnIC+gaGHNu89ou2ra6eHuVr1VM/7+N9lpTkJJzci6iz3zp7kuVjhpAYF0uxCpUyzaeN2MhIjX1k+j5fcvp2zrrOrWQZdScxJiKcO2dPUtSj8idnSi8yPAxrG1v1YysbW5ISEkhKTNSoa9+rH9Xq1k+/OFER4exYu5Kefw5BKs27X72RYWFY2X7IaWljS1JixpztssgpkUjQ0dFh7YJ/mDRkEMXLlMWhgHOeZLUxMyUsJl79ODw2HhMDfYz0NU8UhcbEcdP/tfpxtwbVuPHsJXKlkjP3nxKflMyqfu1Y/Xt73kbFcMP/Va5l/Fb2e1rBIcHYO3zoXNjZ2xMfH09CQrxGna6uLufPnaXNTy3wuXObZs0z/z6Re7lCcUjT6bG3syM+Pp74hIQMuc56e9O87a/cvnuXH5v+oPH6vsOHsbWxoWHd3J81ARAcGoq9nZ1mzoSEzHNevEiLDu25fe8eLTw9AShTsiSHT55ELpeTkJjI6QvehEVE5EnW/CSRSr7Yz9fqP92JCgwMZOzYsRw5coSwsDAuX77M8+fPmTVrFmvXZv6FRBsqlQoy2aeSdB+Q2dWplCrNu0OqVEikUoJfvcDnwhm+b9clw3IBfr48uXOTPpNm8/uUORQpX5Ejm1ZnqPuw7YwbT/8hnl2dSqVCkuY1lUqFRCpBqVQQHR6GvqER7f4eSbPufTm3ezvBLwMyzZKdvGzL90zMLeg3ZQ4dhozm6KY1RAQHUaZaLUpUrMqOBbPYMmca1g6OSDMZidDcft60J8BPvQdQvGKV1LnpRYpRoHBRXjx6kLrPfW7Se+Is+k7+hyLlPTi2aU2WOb9WKqWKzHZg2v2UXU36s74q9fPKDMuoSN3/5/ftxKVocdxLlcmwzhPbN+Fesiz9p82jz8RZPH9wj8e3M45E5tf708m9CK36DMLcyhqpVEqtZi3x972rMb1Xvbo8atu0rh4/xMVDe2nT70/00lxjVKlBYwbNWkwxj0rsW7UkYwNoSaVSahwb70kl6dv543XBr17w7/yZlK/bkMJlK3xypvSUSmWm+1ibL8YKuZxV82bza7eeWKTpsOYFpUqZ6fP/x959xzV1/X8cfyXsvYcDwYFbFPdetdW6R+veVltb7bettc46q93bVqt1i3vg3nujoihOZLjYe0PW749gJBIgIAj2d56PB4+HJJ/kvj03Ocm5595DUb/Aj/l0Cj+u3EBaair7t28piWh5M0nU79eXKVV5bwMwMTLky16dqGBrzV+HzwMwsHUjktIzGfv3JsYv24ylqQm9m+p3EE8fb8p+z02lVCLREVoqzfsZ175DR/YfPsrYceP54rNP1f/f0sql0p3LQEdbdmzblmO+uxk/ahSTp32llWvTju2MG146l2uA+j2kq5/RmbNNG45u38H4ESP5dMYMlEoln334IRIJDJ84kanz5tKicWOMSukMEqFs/acXlqhduzZubm4AVK9enYSEBBwcHKicM5VcFOf27SL41g0AsjMzcKz44jlSkhIwNbfA2ET7omprOwet65xy11nZ25OalKi5LzUpEStbO25fPk92ZiYbf16suX3/2uV06DuQsHu3qd6gERZW6qlt7/adWbPoa81znN/vS0g+GVOTEjAxN9dcpP6clb09kY9CdNZZ22lnTEtKxMrWHktrWwDq55wGYufkQsVqNYh8FIpLFY9y05ZZGek8fnAXz4bqo9Eubu44VXIjNvwpZpaW1G7WghY5szrPQh5i5+Sitc3X1Z6Z6ekEnD1J83e6azpulUqF1MCA4FsBVK/fCPOcfd6oXWfWLZ5TaBuXN1b29kSEBWt+T0nMu58LqrGyc9Dex4kJWNnaY23nQFpyotYANS1n/9/xu4C5lTVBAf7IsrJITUpgzeI5jJ65gKAb1xg9ayESqRRLG1tqNW7G46B71PRuWi5en+kpyWSmp1HDyxt4PjCU5BnclGbbgvrazIPrVxIXGc6wL2djk3NEPvrpY1QqFS5u7kgkErxad+DayWN5shXk4oHdhATeANQLfThWeDHbkZqUqPP9ZW3nQFSua9derrvv78fJbT50HDCU2k1bFClPYeydnAgLeqD5PTE+DnNLS0xMTQt9bFjwQ2Kjoti2Vn0AJDlnplQmkzFy4uSSzemoI6eFfjkB7tzwp2IVd2ztHTA1M6NZm/Zcv6z7Op/iGNzGm2bV1bO+ZsbGPI59cYTewcqclIwssmR5DxY4Wlkws9/bPI1PZM7Wg2TnnLLX0tODf49fRK5UIs9Wcup2EK1qerDnamCJ5H1T9ntuLi6u3Ll9W/N7bEwMVtbWmOVabObpkyfExcXRsFEjAHr06sVPP3xHSkoyNja2pZPL2YXAu3c1v8fExGBtZaWV68mzZ8TFx9OoQQMAer/7Lt/99ivJKSnY2thwPygIuUJB44Yld4DkZa5Ozty+9+I6wJjYWN05ExJoVF89YO/VtSvf/fE7yampZGZmMvmD8ZrTEFdv2kjlihVLLW+ZEX8n6r89E2Vo+GKMKJFIqFixIqZ6fpC8rG3PfpoLvId+OZuIsBASoqMACDh7iuoNGuV5jHudevnW1Wjgza2L51AqFGSmp3Pvmh81vBrT+b2hjJv7rWZblja29Bg1gRpe3ri4uRMSGKA5xeXBjatUqFpNs702PfpqFiUYMmWW9rbPnaZGA+88GT1q18u3rrqXN7cvvch43/8K1b28sXF0wtnNnds5H6xpyUmEhwbrNYB6nW0pkUo5tGE1z4KDAIiNeEZ8VAQVPKoR9TiM3cuXoFDIUSoU+B05QJ2mLbW2+bra09jUlBtnTxAUoF7pLPrJIyIfhVK1Tn1c3KoQevumZp8H3bimWb3tTeJRpz7hYSEkREcCEHDupGaAoE+Np5c3gRfPau1jz4beWNnZY+vkwr1rfgCE3rkFEglOFSvz8be/MXrmAkbPXEDXYWOwdXRm9MwFgHrAct9f/ZjsrCxC79yiYs5pauXh9ZmdlcnxbRs110FdOXaImo2a6jz6XVptC7B/7XKyMzMYOmWWZgAFEPPsKQfXr0SWnQXA7cvnca9ZO0+2grTq3odhX81l2FdzGfT5DK32u3X+NNXq523nKrXr5lsXEhjA6R2b6Tfx8xIfQAHUbehNSNB9oiLCAThz5CANm+m3neq1avPdP6v4+qff+fqn32n/djeatm5bKl+k6zT0JjToPtE5Oc8WISfAtQvn2L9tMyqVCplMxrWL56hV36vE8m0+f50p63YzZd1uZmzcS80KzlSwVX/ZfKdhba4EP8rzGFMjQxYM6s6loDB+2XdKM4ACCImKo3Ut9fW3BlIJzapX4UFETInlfVP2e27NW7TgdmAgTx4/BsB3107atWuvVRMXF8u8r2eTmJgIwJHDh6harVqpDaAAWjZtSuDduzx+qj41c8fevbRvrX0NWWxcHLMWLiAxSX16/aHjx6ju4YFtzvVa1wICaObtrXOmqKS0aNJEnfOZOufOffto36qVds74eGYvXvQi54kTVPPwwNbamp379rJ8nXqRibiEBHYfPEi3zp1LLa9QdiSql8/l+I+4fPkyS5YsYf369QBMnz6d5s2bs2TJEk6cOFGk51px9Hye20Ju3+Tsnu0o5ApsHZ14d+QHmFlYEvkolMMb1zBqxvwC65QKBad2beHRvTsoFHIatulIsy55LzpdPmcqvcd9jKt7VfWywvt9ue/vh4GhEdb2Drw9aARWdvY6T38IuX2Tc3t2oFQosHF0otuIceqMj8M4unENI6bPK7BOqVBw2ncrj3MyerXpQNOcZaCT4+M4sc2HpNgYVCoVjTt2wStnue7ntv7+A43ad9Za4lyqo+MrzbZ8EnSfU7u2oFQoMDA0pH3v96hSqw4AZ/fsICjAH5VKSQ2vxrTrPUDzJfV1t2fk4zBObvMhOysTqdSADv0HU6VmbVQqFRcO7OZBrn3+1sDhWNnZ03nBwjwZS4LLzClkhT4qsSXOT8+bC6i/5J7ZswOFXI6tkzPdR35AUmwMh3xWawY3umpy7+Owu7fV+7htR5rnXD+UEB3J4Y1ryEhNxcDIiK5DRuUZ0D9+cI/jWzdoljhPiovl2Jb1JMXFIpFIqNWkOa269dKZv6xen1eOH+LWhbOoVEocK1am69DRmJpb6PzyUBpt+yzkIRt/XoSds6vW0v8d+r5P1boNOLdvFw+uX0UileJYoRJvDRym9ecWsnWceliQ0Nu3uLBPvXS5jYMTXYePw9TCgqjHYRzbvJZhX80tsG7totlkpaVhYWurec6KVWvQ6f1hBW63biWXAu/P7Zb/VXxzlrp2cnFlzOTPiYmKZP2yJXz90+9atWuW/EZFN3edS13v3bKR1JTkIi11rSzCJ3Wg/1V8N65DIZfj6OLK6EmfExsdyYalS5j1Us61S36jYhV3zRLn6WmpbFy+lPCcxVkaNW9Jz4FD9Tp9bcmRvJ+VhWlctTLD2zXF0EBKZGIyfxw8Q2pmNtVdHPi4a1umrNtN/+ZeDGnbmMexCVqPnbv1EADj32pFVWf15+Ctx+GsPXUFeQGnpX3aNe/iDwUpq/1er7JrkXLmdvHCeZb9/RdymZxKlSsxe848wsOf8d3iRaxZ7wPArh3b2bljOwYGBjg6OvHF1KlULMb1byYvXWtVkPOXLvHXvyuQyeVUrliRedNn8Cwigm9++pGNK/4FYPvu3Wzb7YuBgQFODo589b//UamC+s9WfP/7bzjaOzCuGKsvq4rQJ533u8xfq1Yhl8moVLEi86Z+xbPICBb98gs+y9SrGG7fu5fte/ZgYCDFycGBqZMmU6lCBdLS05n7/fc8DQ9HhYrRgwbzbpcuem/bxj3v9bnlUZTP1te2LZdhAwsvKgNiEKUHXYOo8ia/c8jLG12DqPLoTWjP0hpElbTng6jy7k3pCkvzCGxJKuogqqwUZRBVlooyiCorxRlElYWiDqLKyqsMol6nogyiylJRBlFlSQyi8iqvg6j/7DVRLVq0oEWLF1Pu3333HQD9+/cvq0iCIAiCIAiC8OZ7Qw7olab/9DVRgiAIgiAIgiD8/7B37166d+/OO++8g4+PT577Q0JCGDFiBL1792bcuHEkJeX/520KIwZRgiAIgiAIgiC80aKiovj111/ZuHEjvr6+bNmyhYcPH2ruV6lUTJw4kfHjx7Nnzx7q1KnD8uXLi729/+zpfIIgCIIgCIIglILXeDpfcnIyycnJeW63trbGOmcpeYALFy7QsmVLbHMWGOratSuHDh1i0qRJANy+fRtzc3Pat1evVvnRRx/pfF59iUGUIAiCIAiCIAjl0tq1a1myZEme2ydNmsTkyS/+ZEB0dDROTk6a352dnbl586bm98ePH+Po6MjMmTO5e/cu1apV4+uvv6a4xCBKEARBEARBEAS96frj76Vl1KhR9OuX988G5J6FAlAqlVor2KpUKq3f5XI5fn5+bNiwgQYNGvDbb7/x3XffaRafKyoxiBIEQRAEQRAEoVx6+bS9/Li6unL16lXN7zExMTg7O2t+d3Jywt3dnQYNGgDQs2dPPv3002LnEgtLCIIgCIIgCIKgP4nk9f3oqXXr1ly8eJH4+HgyMjI4cuSI5vonAG9vb+Lj47l37x4AJ06coF69esVuAjETJQiCIAiCIAjCG83FxYXPP/+ckSNHIpPJeO+99/Dy8mL8+PF8+umnNGjQgL/++ovZs2eTkZGBq6srP/zwQ7G3JwZRgiAIgiAIgiDoT1o+/9hur1696NWrl9ZtK1as0Py7YcOGbN++vUS2JU7nEwRBEARBEARBKAIxEyUIgiAIgiAIgv4kYh5GDKL0YGhQ/l8o2XJFWUfQi/Q1Lon5KozK6TR1bqfnzS3rCHrpMG9+WUfQi8/kz8o6gl7uRUSXdQS9tK9dtawj6OXWk8iyjvCf8XYDz7KOoJeHUXFlHUEvT+KSyjqCXqKTU8s6gl6MDQ3KOoJeJrtXKesIgp7EIEoQBEEQBEEQBP29AQebS9ubMS0gCIIgCIIgCIJQToiZKEEQBEEQBEEQ9CYpwt9v+q8SM1GCIAiCIAiCIAhFIGaiBEEQBEEQBEHQn1idT8xECYIgCIIgCIIgFIUYRAmCIAiCIAiCIBSBOJ1PEARBEARBEAT9iSXOxUyUIAiCIAiCIAhCUYiZKEEQBEEQBEEQ9CeWOBczUYIgCIIgCIIgCEUhZqIEQRAEQRAEQdCfVMzDiEHUK3p46wand29HIZPjVLky3YePw8TMrEg1yfFxrPthIWNnL8Tc0gqAoJvX2b/2X6zt7TV1w6bMxMRU+7kLEhIYwLm9O1HIZThWrMw7Q8fkyVZQnVKp5PSuLYTdCUSpVNL0ra40bNsRgMcP7nFm1xaUSiWmFhZ06j8Ep8puADx9eJ8zu7cjz87GxMyMrsPHYevopDNj8K0b6lq5HOdKlemmo/3yq1EqlZzcsYnQO7dQKpU0e6sb3u07A5CRlsqxrRuIiwhHLsumVbde1GvRhkuH93Hv2mXNc6enpJCdlclnvyzTu11BvU9P+W5DIZfjXMmN7iN07/eCapLj41j7/QLGff2NZr/Hhj/joM9qsrOykEigY9+BVKvXoMAswYEBnNm9HYVcjlOlynQbNjZvG+ZTo1QqObVzs7oNFUqadelGo3adAEiIjuTQhtVkpKViZGJC95HjcXCtoPW8V08c4daFM4yZ/Q0A2ZmZHNqwirjIcFQqFfVbtaVDkVpWfy6zviQrJIzETdtLaQtF41nBiS5eNTGQSolKSmGPXyBZcnm+9X2bNyA6KZUL90NLNZe3RyUGt2mMkYEBj2MT+OfYBTKyZXnq2tauRq8m9VAB2TI5a075ERIdB8DyDwcRn5quqd17NZDzJZA79PZNzu/diUIhx7FiZboMGaWzjyusLiUhni2/fsuwr+ZgZmlFXGQ4h9at0NyvUqqIi3hGj7ETqdGwcZFzht2+ycX9u1DI5ThUrMRbg0dhrCNnfnXy7GxO79hI1OMwUKlwca9KhwFDSY6P5cj6fzWPV6qUxEeE8+6Yj6juVbScr5oxKyOdE5vXkRAdiUqlonazVjR5q5vWY+9cPkfIzRv0HD+pSNlept6fO1DIn+/P0To/mwqrS0mIZ/Mvixk+bS5mOX0owKO7tzm7ZzvDp80tVr7S6lOfS4yNYf3383l/0hRc3asWKyNA0K0bnNy1FblchkslN3qOHJ8nZ341mRnp7Fv3r6av9mrZjtbdegIQHhbCka0bkGVloVQqad21Jw1atil2ToDHd2/hd2A3CoUM+wqV6fD+cJ2vT33qjqz9B3NrG9r2G0xCVAQnfFZp7lOqlCREhvP2yAlUbeBd5Jxv0vtIKHtlMoy8fPkyI0aM0Lu+Vq1aAGzatIlNmzbluX/nzp1Mnz69xPLpKz0lmQPrVtJvwiQmzP8OW0dnTvluK1LNrUvn8fnlW1KTErUe9yzkIc27dGPsrIWan6IMoNJTUjjss5pe4z5mzNeLsXF04tyevF82C6q7ee4UCdFRjJq5gGFTZ+N/8igRYSFkZaSz99+/aN/3fUbOmE+XgSPYt3opcpmMlIR49qz4i7cGDmfkjPl4NmrC8a0b8m2/g+tX0mfCJMbP+w4bR2dO62i//GoCzp4kITqKsbMXMXLaXK6dPEJEWAgAB9b9i5WtPaNnLmDQp19xbKsPKQnxtOzak9EzFzJ65kIGfzYdIxMTeo/7WO92fZ5p/7p/6T9hMh/O/x5bRydO7tpapJpbl86x4efFefb74U3r8GrdnnGzF9J9xAf4rvgLpUJRYJZD61fSd/wnfDD3W2wdnTizO28b5lcTcO4U8VGRjJn1DSOmzdFqw31rltOwXUfGfr2INj36suffv1CpVJrnfRochN+xg1rbunL8EIbGRoyZ/Q3DvlS/Zh7IsvRvXD0YubtR6ffvsezYrkSf91WYmxjTt3kDtpy/zpKDZ0lIzaBLw5o6ax2tLBjVsTl13VxLPZeVmQkfvdOGX/ef4ot1vkQnpzCkTd4v5xXsrBnWrgnf7jrGdJ+97PS7yRc9O2ruS8vMYrrPXs1PSQyg0lNTOLpxDT3GTmTUrG+wcXDk/J6dRa6763eB7X/8QFqu95KDa0WGfTVX81OlVl1qNm5erAFURmoKxzev5d0xHzF85kJsHJy4sC9vzoLqrh47gFKpZMjUOQz+ai5ymYxrxw9i71qRwVPnaH6q1KqHZ+PmRR5AlUTGywf3YGlrx9Bp8xj4+UwCz58mIiwYgMy0NE5u3cDZXVtQocrzvEWRnpLCEZ/V9Bj7MaNmL8LawYnze3cUue6O3wW2/a693+XZ2VzYt4sDa/5Bpcy/3yw4X+n1qQBymYz9a5ejKOAAiz7SUpLZu3Y57334KR8v+BFbR2dO7Nqid83p3duxtrXnw7nfMXbGfK6dOc7T4CBUKhXbl/1B+179Gf/1IoZ8OpWj232Ij4osdtaM1BRObVnH2yMnMOir+VjbO+J3wLdYdTdOHiEy9KHmdzuXCgz4Ypbmp3LNOlRv1LRYA6g36X1ULkgkr++nnHqj5uKGDBnCkCFDyjqGRujdQCp4VMXeWf1lyLt9J+74XdT6ollQTUpiAkEB/gya/GWe534W8pBH9++y8puv2fDTYh4H3S9Stkf3buNaxQM7ZxcAGrbtxN2rl7WyFVb38OZ16rdsg9TAAFNzC2o1ac7dq5dIiInGxMyMKrXqAmDvWgFjUzMiwoIJunENj7oNcHFzB8CrTUc69R+cb/u5ur/UNlfytl9+NQ8C/Knfqq0mX+0mLbjtd4GMtFQe3btNmx59ALCys2fEV3MwtbDQ2v7JnVuoVteLavW8itS2IXcCqeBeDXuX55k659nvBdWkJCbw4IY/gz6dmue5lSolmelpAGRnZWBgZFRglrC7t3F1r4pdTvs0ateZO1cuaWUpqCYo4BoNWrXTasM7fhdJSUwgPiqCOk1aAFCtnhfZWZlEP3kEQFpyEse3bqBj34FaeVRKJdmZmSgVCuRyGSqVCsMS7gBt+/cmed8hUk+eKdHnfRXVXR15Fp+kma25+vAxDapU1Fnb3NMd/5An3HlS/C8l+vKqUpHgqDgiE1MAOHrzPm1rV8tTJ1coWH70AonpGQCERMVha2GGgVRKzQrOKFUq5r3fje+H9aJ/Cy8kJbBPH9+7jUuuvserTUfuX8vbRxVUl5qUSPCtG/Sd+Fm+23kW/ICHAdfoPGh48XLev4Ozmzu2Turt12/TgQe6chZQV7GaJ03f7oFEKkUqleJUyY3k+Hitx4cHBxEccI1O7w8rk4zt+g2iTe/3APX7WyGXaQ7cPbxxFQsbW839ryLP/mzbkXs6PpsKqktNSiT45nX6ffyZ1mPC7t1Glp3FO8PHFjtfafWpzx3bsp76LdtiZmlZ7IwAIXduUTHXZ0yTDm8RePnCS59D+de8M2gEXd5Tf59KTUpCIZNhYmaOQi6jXc++VKtTHwBrO3vMLa1JToynuJ4+uIuTmwc2Ts4A1G3VnqDrfnn2eWF14cEPeHr/NnVa6j6AFhESROjN67QbMLRYOd+k95FQPpTZ6Xzx8fGMHz+ex48fU7VqVf744w/27t3L6tWrkUgk1KtXj6+//hqLXF98//zzTwAmT56Mr68vS5cuxdLSkkqVKmFubg7AwYMHWb16NZmZmWRnZ7N48WIcHBwYNWoUJ06cQCqVcvnyZVasWMG///6rM5u+khPisbJ7cbqdta09WZkZZGdmaqbUC6qxsrWj/4eTdT63mYUldZu1pJZ3U54GB7Fj2e+MnbUQ61zPVZCUl7ZrZWtH9kvZCqtLSYjH0lb7vthnT7FzckGWnUXY3UA86tQn8lEocRHhpCUlkRAdiZGJCftXLyM+OhJrOwc65DOIyrtt+zwZC6pJSYjTag8rO3tinj0lMSYaC2tbrhw/TOjtm8jlcpp36ab5IAGIjXjGwwB/Jiz4Qa/2fDl37u1a2+Xd7wXVWNnaMeCjT3U+d9fBI9j46/dcOX6YtJRk+n7wMVIDg/yzJBa+nwuq0bX/Y549Ue97G1skuc55trK1JyUxAafKVdi3+h869B2YJ1vzt99l02/fsXTm52RlZuDd/i2q3SraAYDCxPz6FwDmzYo+q1BabMxMSU7P1PyenJGJqbERJoaGeU7pO+B/B1APvEqbg5UFcSlpmt/jUtIxNzHGzNhI65S+mOQ0YpJf1I1o35RrIU9QKJUYSCXcehzBpnP+GBhImNanCxnZMg5ev/tK2VISErC0tdP8bvn8dZmV+dKpevnXWdrY0rOQmeRzu7fTuke/Is3k55b6Uj9oaWNHdmYmsqxMrdN8CqqrUrue5vbk+DhunDlOp4HaZ2Oc37udlt376jx16HVkNDY1Q2JgwJENKwkOuEa1Bt7Y5gwS6rdRn5R71+9CkbO9TJ8+q7A6Sxtben3wSZ7nruHlTQ0vb54E3SvVfMXpUwFunj+NQqGgYZsOXDq0t9gZQf3dwtreQfO7rs+hwmokBgb4rlzKXf8r1PJugoNrBaRSKd45p+0D+J85QXZmJpWq1ih21rRE7fewhY0tMh2vz4LqZFlZXNi9le4fTObOpbM6t3N5/06adetdrPcQvFnvo/JAIv5OVNnNRIWHhzNnzhwOHjxIbGwsmzZtYtmyZaxfv569e/diZmbGkiVLdD42KiqKn376CR8fH7Zs2UJamvrDX6lUsnnzZpYtW8aePXv44IMPWL58Oe7u7lSuXJnLl9XXwvj6+tK/f/9X/j+olCok5H0R5f7iqU+NLv0/nEztxs2QSCS41ahJpWo1CLt7W/9sKpXOKVDpS9stqE6lUmkdcVapVEikEkzMzOg9fhJ+Rw6w7tu53PG7gFvN2kgNDVAoFATfvE7rHv0YMW0ebjXrsPffv/LNqOuItlb7FVCjPjqU6z6VCqlUgkKhICkuBhNTM4Z9OZveYydyYvsmIh+HaUqvnjiCd4e3MDEz15mtIOo2y3v7y7kLq3mZXJaN779/02PUeCZ99xvDp8zkoM8akuPj8s+ifKkNdGUpoOblI2wqze3KPI9RoUIilXJm93Yq16iJR516vOzolg141K7Px9/+xoQFPxJ65xbns9Ly1P3XSCToPDlDqSrbUzak+cwYKZW6c5kYGvJZ9w642lrzzzH1B/2JwCDWnPIjSy4nPUvGfv/bNKte5ZWzqVRKne9tqeTlPkq/Ol3CQx+SkZpKrSbNXyFnPn1QnpyF10U/ecTOP3/Aq20nquaaAY8IDSYjNYWajYuXsyQzvjN8HOO++YXM9DSuHN5XrDyFZdVF52eTHnUlrbT61KjHYdw4d4p3howswZx55f5iq09N33ETmfLz32SmpXF23y6tuvOH9nJm704GffI5RsbGxc+qUuaTI+97XfcTwImNq2jV+z3MrW10lkSGBZORmkoN72avkPPNeR8J5UOZzUTVrl0bNzf1QgTVq1cnJSWFTp06YWenPgoxaNAgZsyYofOx169fx9vbG0dH9ZHcXr16cenSJaRSKX/99RcnTpwgNDQUPz8/TYc7YMAA9uzZQ6NGjbh06RLz5s175f+Dtb0D4bnOdU5JTMDU3AJjE5Mi1bwsMz0N/9MnaNWt54s3qooCZyQAzu/3JeTWDQCyMzNwrFhZc19qUgIm5uYYvbRdK3t7Ih+F6KyztrPXumYnLSkRK1t7VEolxsamDPzfV5r7Vi2YiZ2jC7E2T6lYrYbmFIwGrdpxascmZNnZeTphazsHrXPFdbZfATXWdg5a+VKTErG0s8fSxhaA+q3aAmDn7ELl6p5EhIXgWsUDpVLJgxtXGTV9XoHtmR9re3vCQ4MLzq1Hzctiwp8hy87G06sRAJWq1cCpQiXCQ0O0jibmZmVvrznfOr/tFFRj9XIbJiZgZWuPtZ0DacmJWh8W6v1vxx2/C5hbWRMU4I8sK4vUpATWLJ7D6JkLCLpxjdGzFiKRSrG0saVW42bcPHGCNibap1L+F3Sq70mtiurTTkyMDIlKStHcZ2VmQkZWNrICrmcrLe+3bEST6uq+1czYiCexCZr77C3NSc3M0rnghYOVBV/17syz+CQWbD+syd6udjUexSbwOOd5JEhQKPP5slOIiwd2ExJ4A1AvQuJYoZLmvtSkRJ19lLWdA1GPQgut0yXI/yq1m7Us9KDVyy4f3E1oYIAmp0PFwnNa2dkT9Tj/nA/8/Ti9YyPt+w+hVs5pspqc169Qu2mrIuUs6YyP7t3GoUIlLG1sMTYxpWbj5gQH+OudpyAX9/sSrMmagWOF3J9N+WeNDCvefn8VpdWn3va7QHZmBj4/LdL8f/atWU7HfgOp4VX063ds7B0Iz5UhWZPBVK+a4Ns3ca7khpWtHcamptRr1pJ7/lcA9XVbe9YuJzbiGaOnzc13YaiCXD28l0e3bwLqU9PtXV+8PtOSEzExM8fIWHtfWtraE53rYOfzuoSoCJLjYrm0R31NXHpKMiqVEoVcRof31TO6IQHXqNmkRZm/10vzfVTu6HEg67+uzFrA0PDF+E0ikWBtba11v0qlQp7PhZcSiUTraM/z50pLS+O9997j6dOnNGvWTGvxim7dunH+/HkOHz5M+/btMSmBjrhqnfqEhwYTH62+tuH62ZN4NvQucs3LjE3N8D99nPvXrwIQ+eQREWEhha7S1qZHX0ZMn8eI6fMYMmUWEWEhJERHARBw7jQ1dFxo6VG7Xr511b28uX3pHEqFgsz0dO77X6G6lzdIJOxc9ptmZuf+NT8MjYxwrFSZGg0bEx7ykKTYGACCAq7hUKGizqNYHnW12+bG2ZN5PkwKqqnh5c2tC2dy8qVx9+plPL0aY+vohIubO7cvnQfU5yU/C3mIaxUPAGKePcHU3AIbh6J/MABUrdOAZ6HBmgttr585oWO/F17zMjsnZ7IyMngaHARAQkwUsZHPcKmS/1F/jzr1CQ8LISGnfQLO6WjDAmo8vbwJvHhWs4/vXfPDs6E3Vnb22Dq5cO+aHwChd26BRIJTxcp8/O1vjJ65gNEzF9B12BhsHZ0ZPXMBAC5u7tz3Vz8mOyuL0Du3qG1Uul96ysrJwCCWHTnPsiPn+ffYRSo72GJvqZ7ZbFq9CvfCo8sk17ZLNzQLQHy9+QA1XJ1wtVWvXNbFqxZXg5/keYypkSFz3uuK38PH/HHwjNbgz83RlvdbNUIikWBkYEDXRrW5+CCsWNlade+jWexh0OcztPqeW+dPU61+ozyPqVK7rl51ujwNvo9bzTpFztni3T6axR7e+2w6kWEhJMaotx944TRVdWzfrVbdfOtCAwM4u2sLvT/8LM8ACtTXelSuWbtMMz68cZUrh/eiUqlQyGU8vHGVyp61ipQpP6169GX4tLkMnzaXwV/MJPJRsGZ/3jx3iuoN8mZ1r11Pr7qSVlp9auf3hvLB3O80faeljS09R08o1gAKoFrd+jwLeaj5jPE/c5yaLy2cUlDNnWuXObNvl/q7lkzGnWuX8aitvs5596qlZGdkMPqrOcUaQAE07dpLs9hD38lfEf04lKQYdZ949+JZ3Os1zPOYyrXq6Kxz8ajGsNmLNc9Xp1U7qjVsohlAAUQEB1HJs2jvIXiz3kdC+VOuljg/ceIEH3/8Mba2tmzdupUWLfJ+2AA0adKEhQsXEhUVhZOTEwcOHMDa2pqwsDAkEgkfffQRKpWKr776CkXOlwEzMzPat2/PL7/8orm26lVZWFvTY+Q4di3/C6VCjq2jMz1HjyfiUSgHN6xi7KyF+dYURCqVMmDi/zi6ZQPn9vkiNZDS54OPNctg68Pcypp3ho1h78q/USoU2Dg60W3EOAAiH4dxdOMaRkyfV2Bdw7YdSYyNZv1381Ao5Hi16YBbTmfQfdQEjm5ag1KuwMLaht7jJyGRSHCuXIW3Bg5nz7/qVeVMzM3pOXai7vazsubdEePYveIvFHI5tk7O9Bilbr/DPqsYPXNhvjWgXqwhMTaa1Yu/RiFX0KhtR6rkfBHp9+GnHN28jutnT6BSqmjdvQ8VPNQX1CfERGFjX/zrUdT79AN2LV+CQqHO1Gv0BCIehXJg/SrGzV6Yb01BTM0tGPDRpxzd6oNCJkNqIOXdYWOwy7l4Nd82HD6W3f/+rWmf7iM/IPJRKId8VjN65oJ8awAatetEYmw0axbPQaGQ07BtR9xyPoh6jfmQwxvXcOnQXgyMjOgz7uNCj/J1HzWeY1vWc/vyBSQSCbWaNKfT5etFbOE3T1pWNrv9bjGwjTcGUikJqensuqw+ClvRzprezRqw7Mj5154rOSOTZUfP83mPjhgaSIlKTOGvw+cAqObswIS3WzPdZy9dG9XGycqCZjWq0KzGi0H7NzuOsP1SAGM6teDH4b0xkEq5HBTGicCgV85mbmXN20PHcGD1MhQKOTYOTnQdru57oh6HcWzzWoZ9NbfAusIkxkRj7aB7FrcoOd8aMpqDa/5BKZdj7ejE20PHanKe3LKOwVPnFFh3fs92VCoVJ7es0zxvhao16PCe+gL4xNhorF+hTyqJjG37vM+pbRvY9MN8AKo18KZh+7eKnamgrG8PHcP+VUvVfaOjM12Hv8h6dNNahk+bW2BdaSrNPrVEc1rb0GvUeLYv/wOFXIGdkzN9xnxIeFgI+9evZPzXi/KtAXj7vaEc8FnN8gXqM35qNWpK885deRocxF3/K9i7uLL2h4Wa7XXuP4jqRVyE6TkzS2s6DBzJ0fXLUSoUWDs40nHwaABinjzizLYNDPhiVoF1hUmKjcbSruzf66/rfVQuiGuikKjyO/G4FF2+fJklS5awfv16AKZPn07z5s1RKBSsW7cOmUxGvXr1mD9/PpaWltSqVYv79+9rLSxx6NAhfv/9d8zMzKhRowZSqZRFixYxdepUbt++jUQioW3bthw7doxTp04BcPHiRRYuXMiBAweKlHf1iYuFF5WxbPnrP22oOAwLOSWxvDB4AzoHuaJ4p1S9bh3mzS/rCHrxmfxZWUfQy72IspnhKqr2tYv/929eJ0U+140IRWdo8Gac3mP0hnwOmRiWq+Pc+YpOTi3rCHoxNnwz9vvk7qX11xVLVuyxk69tW45dOhVeVAbKZBBVFhQKBb/++isODg6MGTOmSI8Vg6iSIwZRJUcMokqWGESVLDGI+v9HDKJKlhhElSwxiCpZscdPvbZtOb7V8bVtqyjejHdoCRgwYAB2dnYsXbq0rKMIgiAIgiAIgvAG+38ziPL19S3rCIIgCIIgCIIg/Af8vxlECYIgCIIgCIJQAsQS52W3xLkgCIIgCIIgCMKbSMxECYIgCIIgCIKgN8kbsABXaRMzUYIgCIIgCIIgCEUgZqIEQRAEQRAEQdCfRMxEiZkoQRAEQRAEQRCEIhAzUYIgCIIgCIIg6E8q5mFECwiCIAiCIAiCIBSBmIkSBEEQBEEQBEF/4pooMYjSh0KpKusIhZK+IS9mpVJZ1hH0VP4naVWq8v+6BPCZ/FlZR9DLsD9/K+sIerm8aGFZR9BLpkxe1hH08qa8j5RvQM6MbFlZR9CLqdGb8dXnDflYx9HKoqwj6OVN+Z4kvDnejJ5EEARBEARBEITyQfydqDfgcLsgCIIgCIIgCEI5ImaiBEEQBEEQBEHQm0Qi5mFECwiCIAiCIAiCIBSBmIkSBEEQBEEQBEF/YqEOMRMlCIIgCIIgCIJQFGIQJQiCIAiCIAiCUATidD5BEARBEARBEPQnljgXM1GCIAiCIAiCIAhFIWaiBEEQBEEQBEHQn1jiXMxECYIgCIIgCIIgFIWYiRIEQRAEQRAEQX/imigxiCqO4MAAzuzejkIux6lSZboNG4uJmZleNUqlklM7NxN65xZKhZJmXbrRqF0nrcfeunCGoAB/+k/8LM+2r544wq0LZxgz+xu9cp7dswOFXIZTJTe6Dh2TJ2dBdc+zht0NRKlQ0vStrpqsEY9CObljE7KsLFRKJc3f7k7d5q1QqVSc37+LBzeuAeBapSpvDx6BkbFJmeR8LjE2hg0/LOC9T77A1b0qALtX/EXMsycYmaizValZm04DhhTarnly3brBmd3bkcvlOFeqTLfh4/K+HgqpSY6PY8OPCxk9ayHmllZFzqC1rVJsz8cP7nJ611aUSgWGRsZ0fm8oFTyqASXXni/zrOBEF6+aGEilRCWlsMcvkCy5PN/6vs0bEJ2UyoX7oa+87ZLgMutLskLCSNy0/bVsL+jmDU7s2opcLsOlkhu9Ro3Ps//zq8lMT2fvun+JiwxHpVLh1aodbbr1BCAjLZVDm9YRExGOPDubtt1749WqbZGylVbf+fjBXU7t2opSocDQyIi33h9GBY9q6v5oX67+yN2DtwePLLA/ep6htPukWxfPqvv5j/6nuS3g3Cn8Tx1DIpVi4+BI12FjCuwPQgIDOLd3Jwq5DMeKlXknn5z51SmVSk7v2kLYnUCUSnXOhm07AhAXEc7RzWuRZWWBBNr1fg+POvUBuHnuFNdPH9fkfGfoaMz07Lce3bnFpQO+KORyHCpUotOgERib5s2cX11WRgantq4jIToKVEpqNW2Fd+euxEeGc8xnlebxKqWS+Mhwuo76kGpe3nple+7hrRuc3r0dhUyOU+XKdNfRpxdWkxwfx7ofFjJ29os+PSIshGPbNiLLzkKlVNHine7Ub9G6SNlyK633eti9OxzbsRmFQo6RkTFdB4+gUtXqxc758NYNTvpuQyGX4VzJjR4jPtDZnrpqZNnZHN68lvCwEFBBxarV6Dp4FEbGxoSHhXBsqw/Z2ervIa269qB+izbFzhl06wYnc7VVz5E62jOfGll2Noc2rSU8LBiVCipVrU63IaNIjI3Bd+XfmscrlUpiwp/y3oefUrtxs2JnFcqHcn86X61atQqt6dy5M0+fPn0NaSA9JZlD61fSd/wnfDD3W2wdnTize5veNQHnThEfFcmYWd8wYtocrp08QkRYCKD+knJk01qOb9+ESse2nwYH4XfsoP45N6yizwefMG7Ot9g4OHFmT94vcQXVBZw7RUJ0FKNnLmT4V1/jf+ooEWEhqFQq9vz7F22692HUjPkM+PhzTu7cTEJ0FEEB/oTdvc2o6fMZM+sb5LJs/E8eK5Ocz8llMg6sW4HipS/e4aEPGfzZNEbNmM+oGfOL9YU/PSWZg+tX0mfCJMbP+w4bR2dO++Z9PRRUE3jpPJt+/ZbUpMQib19XntJqT4Vczt5Vy3hn6GhGzVhAy649ObBuheY5S6I9X2ZuYkzf5g3Ycv46Sw6eJSE1gy4Na+qsdbSyYFTH5tR1c33l7ZYEI3c3Kv3+PZYd2722baalJLNn7XLe++hTPln4I7ZOzhzfuUXvmlN7tmNtZ89H875j3Mz5XDt9nKfBQQDsXr0cKzt7Jnz9DcO/mM7hLetJTojXO1tp9Z0KuZy9K5fSdehoRs9cQKtuvdi/Vv26DAq4RujdQEbNmM+Y2d8gy87m2smjhecsxT4pIy2Vo5vWcWL7RlC96OkTY2M4t3cngz+bzuiZC7BxcOTCft8CcqZw2Gc1vcZ9zJivF2Pj6MQ5nTnzr7uZk3PUzAUMmzob/5Mvch7fuoH6LdsyYvo8ug4dw75Vy1AqFCTFxnBu3y4GfjaNkTPmY23vwIUDuwts0+cyUlM4sWUdXUdNYOj0+Vg7OHJp/64i1V05tAcLGzsGT53DgP/N4PaF00SGhWDvWpGBU2Zrftxq1aWGd7MiD6DSU5I5sG4l/SZMYsL877B1dOaUjj69oJpbl87j84t2n65Sqdi1fAntevZj7KyFvD/pC05s30R8dGSR8j1XWu91hVzOjhVL6DFiLB/OWUzbHn3wXbWsWBmfZ9i3bgUDJkzmo/k/YOvozMldeXPmV3P+4B6UCiXjZy/ig68XIc+WceHQXlQqFTv/+YN2vfrxwexvGDT5S45t30h8VPHbc+/a5bz34ad8vOBHbB2dOaEjZ3415w7sRqlUMOHrxUyYsxiZLJvzh/biVLES479epPmpVrcB9Zq1+k8MoCQSyWv7Ka/K/SCqvAm7extX96rYOau/qDVq15k7Vy6hyvVhWFBNUMA1GrRqh9TAAFNzC2o3acEdv4sA3Pe/gqWNLR37Dcyz3bTkJI5v3UDHvnnv05nz3vMMLjkZOnH3pZyF1T0M8Kd+y7aarLUaN+fOlYso5HJav9sb99r1ALCys8fc0oqUxHhqNmrCkC9mYGBoSHZmJukpyZhaWJRJzueObV1P/RZtMLO01NyWGBtDdlYmhzetZc2irzm4fiUZaal6tW1uoXcDcXWvin3OvvZu34k7Vy5q5S+oJiUxgaAAf96f9GWRt61LabangaEhHy36GRc3d1QqFUlxMZhZqNu0pNrzZdVdHXkWn0R8ajoAVx8+pkGVijprm3u64x/yhDtPivchWtJs+/cmed8hUk+eeW3bDLlzi4ru1XBwUb/WmnZ4i8DLF7T2f0E1XQeN4O331IPf1KQkFDIZJmbmZKSlEno3kA49+wFgbWfP2BnzMDPP/739stLqOw0MDflo8S+a12VibAxmOX1OzUZNGTplZq7+KEXzms03Zyn3Sff9r2Bha0vHfoO0nk+lUqJQKMjOykSlVCLLzsbAyCjfnI/u3ca1iodm+w3bduLu1ct5chZU9/Dmdeq3bPMiZ5Pm3L16SZ1HqSQzXf2+y87KxDAni1KlRKlQkJ35IqehYf45c3ty/w7Obu7YOqmz1GvdniB/vzyZC6pr03cgrXsNACA9JQmFXI6xqanW48NDggi+6U+H94bqlSu30LuBVPB4qb/2y9un51fzvE8fNFm7T1fIZbTp0QePOurPTWs7e8ytrEhJSChyRii997qBoSGfff8HFap4qN9PMdGYF/KeKUjonUAquFfDPidD4/aduf1yexZQU8WzFm2690YilSKVSnFxcyc5PhaFXEbbnv2omjM7am1nj7mlNcmJ+h/Yye15Wz3P0KSA9tRVU6Vmbdp276PJ6ermTlJcrNY2Hgfd566/H92HjSlWRqH8KfHT+Xr16sVvv/1G9erVmTJlCpaWlsyfP5/r16+zdOlSmjZtysGDB1EoFLRt25apU6cikUjw9fVl7dq1KJVK6tWrx9y5czExeXHKhb+/P9OnT2fFihXY2NgwdepUIiMjqV69OllZWQCkpqYyc+ZMoqKiiI6OplWrVixatIivvvqKZs2aMXCgegAyYsQIvvzySxo2bFjk/19KYjxWdvaa361s7cjOzCA7M1Mz7VtQTUpC3vtinj0B0JzuEXjxnNY2lUol+1b/Q4e+A5EaGOiXMyEea9uCcxZWl+f/YWdPbPhTDI2MaNC6veb2gHOnyM7KpIKHerrfwMAQ/9PHOb9vJ5Y2dng2bFwmOQFuXjiDUqHAq00HLh3ep6nJSE3GvVZdOr8/DEsbW05u38Rhn9X0nTC5kJbNm197f9rnfT0UUGNla0e/D4u2zcLylGZ7GhgYkpacxPrv55ORlkrPMR8BJdeeL7MxMyU5PVPze3JGJqbGRpgYGuY5pe+A/x1APfAqD2J+/QsA82b5v/5LWnJ8PNb2Dprfre3syXpp/xdWIzEwYNfKpdy9doXa3k1wcK1AxKNQLG1suXTsIA8Db6KQy2n59rs4uFTQO1tp9p3PX5frvptHRloqvcZO1NQZGBjif+oY5/Toj6D030Oafv6Sdj9v5+RCsy7dWLVwJiZm5piYmjF0yqwCcxbWnoXVpSTEY/nS/yH2mTpn54HD2PbnT/ifOkp6SjI9Rn+I1MAAOycXmr7VlTXfzMLEzBxjUzOGTJlZYJs+l5qYgKWtneZ3Sxs7sjMzkWVlap3SV1idxMCAYz6rCLnpT9X6jbB11p59vrh3Jy3e7aPzNMHCJL/UXta2Ot5DBdRY2drRX0efbmhkTMM2HTS/3zh7iuzMTCoW8zS50nqvAxgYGpKanMS/33xNemoK/cd/UqyMAMkJcVjnbitdOQuoqVa3geb2pLhYrpw4zLvDxmBoZEyjXO15/exJsjMzqFS1RjFz6tGeBdRUz5UzMS4Wv+OH6TF8rNY2ju/YRKc+7+s85faNJBXzMCXeAh06dODiRfURtwcPHuDv7w/A2bNn6dixI4GBgWzfvh1fX1+ioqLYs2cPQUFBbN26lc2bN7N7924cHBxYuXKl5jnv3bvHrFmzWLZsGe7u7vzxxx/UrVuXvXv3MmzYMGJj1aP9U6dOUadOHbZs2cLhw4e5cuUKt2/fZsCAAezerT7d4NmzZ8THxxdrAAWgUqqAvFOLklwvpoJqXj7ipnrpsbqc2b2dyjVqao5g6ZVTpdIVIc+2CqpTKVVozaKqVHkef/nIfi4c2E2/Dz/FyNhYc3vjDm8x6YcleDZszJ5c5wO/zpxRTx4RcPYkbw8emedxFTyq03fCZKzt7JFKpbTu3oeQnC+HRaFSqXRONWu9HvSoKSmvY79bWNvw0aJfGDplFoc2rCI+KrLE2jNPHgk6T21VqnTdKqhfa3lvl+S6AFifmn7jJvLlL3+TkZbGmX27UCoUJMbGYGJqxphpc+g//hOObvUh4pH+152Vdt9pYW3DxMW/MmzKLA6uX6l1Wk/jjl2Y/ONfeDZqzO5/8++P4PX1nS8LuxtI0I1rTFj4ExMX/UJ1L28ObliZb716+3kDSHXm1F33ct+kUqmQSCXIZTL2r/6HbsPHMmHhTwz83zSObVlHSkK8OmfANcYv+JEPv/mZ6l6NOLxhVZ7nzzezrv0r0ZG5kLouw8YyZsFPZKWnc/XIfs3tkaHBZKal4OldvNOlVEoVEj1ep4XVFOTi4X2c3beLAR9/pvW5WaScpfRef87S2obPfviDMdPmsnftCuKiIoqdU1eIlz8jC6uJeBTK+p++oUnHLni+dIrmhUN7ObN3J+9/8kXx21Op+zNFqz31qIl4FMq6H7+hace3tXI+CX5AWkoK9Zu3KlY+oXwq8ZmoDh06sGbNGlq2bEmNGjUICQkhLi6OM2fO4Onpyc2bN+nfvz8AmZmZVKxYkZSUFB49eqSZKZLJZNStW1fznOPGjaNbt25Uq6a+gN3Pz4+ff/4ZgGbNmuHm5gZAz549uXnzJmvWrCEkJITExETS09Np0aIFX3/9NU+fPmX37t306dOn2P8/K3t7IsKCNb+nJCZgam6Bca5Zs4JqrOwctM6TTk1MwCrXkUBd7vhdwNzKmqAAf2RZWaQmJbBm8RxGz1ygVXdu3y6Cb90AIDszA8eKlV9kSMqbE8DazkHr+qHcdVb29tpZkxKxyjk6KJfJOLRhJXGR4QydMgsbB/XR/+inj1GpVLi4uSORSGjQuj3XTmlfg/C6ct6+fJ7szEw2/rxYc/v+tcvp0HcgpuYWZKanUSOnk1Oh/vJQ1IFNnlw6Xg/61LyK19WeWRnpPH5wF8+GTQBwcXPHqZIbseFPSU9JLpH2BOhU35NaFZ0BMDEyJCopRXOflZkJGVnZyBSKIj/v/wfW9g48C33R9yRrXmumetUE376JcyU3rGztMDY1pX7zltz1v0LD1urruhq2Uc9A2zu74FajJs9Cg6mQs1BLYUqr78zKSOfR/bvUbJTzuqzigXNlN2LCnyKXZWv1R16tO3BNxzWar7PvzM/DWzeo3qARFlbWAHi378yaRV9r1Zzf70tIPjlTkxIwMTfXLOzynJW9PZGPQnTWWdtp50xLSsTK1p7YiGfIsrOoVl99sLFi1eo4uFYiIiyEJ0H3qV6/EeY5ORu168y6xXPy/X/5HdpD2O2bOZkzcajw4nTctKRETMx0ZLazJ/pxqM66x/du41ChEhY2thiZmFLDuykht66/aMeAq9Rs0rLYB6ms7R3Uixjk0Nmn61Gji1wmY/+6f4mNCGfEV7OxdXAqVsbnGUrjvZ6Znk7Y/TvU9m4KQAV3D1wqVyH66dMizTw/Z2PvQHhowe/7wmpuX7nE4U1r6Tp4BPWav1iIQy6TsW/tCmIjnjHqqznYOha/PW3sHQgPK7g9C6u5feUiBzeupduQkdRvrr1gyJ2rl/Fq2aZUDp6WmXJ8rdLrUuJ709vbm3v37nHhwgWaN29Os2bNOHToEHK5HCsrK0aNGsXu3bvZvXs327Zt46OPPkKhUPDuu+9q3T5nzotO+aeffuLIkSPcu3cPUF/MlvuopEHOKW7r16/nhx9+wN7enuHDh1O9enXNkba+ffuyf/9+Dh48+EqDKI869QkPCyEh52LQgHMnNV8c9anx9PIm8OJZlAoFmenp3Lvmh2fDgi98/fjb3xg9cwGjZy6g67Ax2Do65xlAAbTt2U9zUf/QL2cTERaiXsEICDh7iuoNGuV5jHudevnW1Wjgza2L57Sy1vBSnwpzYN0KsjIzGfLFiwEUQEz4Uw5tWIUsW32K5W2/C1SpWadMcnZ+byjj5n6r2ZaljS09Rk2ghpc32VmZHN+2UXPdzpVjh6jZqGmeo7iF8ahbn/DQYM3FwTfO6ng96FHzKl5Xe0qkUg5tWM2znIUGYiOeER8VQQWPaiXWngAnA4NYduQ8y46c599jF6nsYIu9pTkATatX4V54dJGf8/+L6nXr8yzkIXE5szDXTh+nVqPGetfcuXqZM3t3oVKpkMtk3Ll6GY9adbFzdMa1igc3L6hPQUtNTuJpyEMq5qzMqI/S6jslEimHNqzSLIARG/6MuMgIKnpUI+bZUw6uX/miP7p8HveatfNke519Z35c3NwJCQwgO0t9+uqDG1epUFW7fdv06MuI6fMYMX0eQ6bM0t7+udPUaJC3X/GoXS/fuupe3ty+9CLnff8rVPfyxtbRmezMDMJDHgKQGBNNXGQ4zpWr4OJWhdDbNzU5g25c06zQqUvzbr01iz30//Qroh6FkhijznL74hk86uc9K6RyzTr51gUHXOPqkf2oVCoUchnBAdeoVOPFAlThwUFU9sy7j/VVtY52f3397Mk8n9H61Oiyd80/ZGdmMGLqqw2goPTe61KplL1rV/Dk4QMAosOfEhsZQaVqxTvtsGqdBjwLDdbMDPufOUHNl06pLagm6OZ1jm5dz5D/TdUaQAHsWb2MrMwMRr7iAAqgWk5bvchwPE/OgmoeBPhzeMt6hv7vqzwDKIDHD+4V6Wwi4c0gUb18jkQJ+PzzzwkMDGTZsmWEhoYyf/58+vfvT8OGDfnjjz/w8fHBxMSEMWPG0K9fP+rWrcuHH37Izp07sbe356uvvqJKlSpMnjyZWrVqcf/+fXx9fdmwYQNbt27lu+++w8DAgGnTpnHz5k0GDRrE0aNH+eabb+jRowe9evXi1q1bjBgxgqVLl9KqVSsiIiIYOnQoNWrUYMWKFYX/J3L599gFrd9DAgM4s2cHCrkcWydnuo/8gKTYGA75rNYMbnTVmFlYolQoOLVrC2F3b6NQyGnYtiPNu7yr9fyBF89x/8ZVBuhY4vzxg3sc37ohzxLnunZjyO2bnN2zHYVcga2jE+/mZIh8FMrhjWsYNWN+gXXPsz66d0edtU1HmnXpRnjIQzb+shg7ZxcMjV5Mnbfv8z5V69bn/H5f7l+/glRqgGOFinR+f1jBy/SWUs6XLZ8zld7jPtYscX7l+CFuXTiLSqXEsWJlug4djWnOhfJF+fKfe0lmWydneowaT2JsDId9VjF65sJ8a16+wP2Hj0cz6Yc/9V7iXKlU6ry9NNvzSdB9Tu3aglKhwMDQkPa936NKrToFtuez+GS921IXzwpOvJWzxHlCajq7Lt8kI1tGRTtrejdrwLIj57Xqi7vE+bA/f3ulnPlxmTmFrNBHJbbE+eVFCwu8P+iWekljhVyBvZMzfcZ+SEJMNPvWrWTCnEX51phZWJKZnsb+DauJybl+p5Z3Uzr26o9EKiUpLpaDm9aSEBODSqWixVtdadKhc745MmV5T+Usrb7zSdA9Tu3cgkKhwNDQiHZ9BuBeS302w7l9u3hw/SoSqRTHCpV4a6B2f/Q6+87cAi+d48H1q5o/ZaH+8xC+3Pf3w8DQCGt7B94eNEJz7Y2uU1hDbt/k3J4dKBUKbByd6DZinDrn4zCOblzDiOnzCqxTKhSc9t3K45ycXm060PQtdc7HD+5xdvc25DIZUgMprbr1pkbDxqhUKi4c2M2DXDnfGjgcKzt7MrJl+b4ennt09xaX9/uiUCiwcXCic04/Ef3kEae2rmfglNkF1mVlpHNm+0biIsMBqFa/Ec269tQc4V8x41OGTJuvdU3Vy2zMTfO9D9T99Snf7SgVcmwdnek5Wt2nH9ywirGzFuZb83Kf/t3E0Xz6o7pPfxbykPU/foO9syuGxi8W4ujYb6DWdT+5GRVyDXRpvdcf3b/L0e2bNP185/4DqVo7/wGAIp/T3J57eCuAU75bUSjk2Dk502v0hyTGRrN//So+yPkuo6vGzMKSZXO/IiMtTWsmt3J1T+q3aM26HxZi7+Kq9T2kc7+BVKvnpTOHtJCZk4e3bnDCV91Wdk7O9Bmjbs/961cy/utF+daYWVjy95ypZOrI+e7Q0QB8P3kcExf8qHXtV35GdGxeaE15kHAz8LVty86r/mvbVlGUyiDK19eXn376iXPnzpGcnEzLli3x8fHB29ubv//+m/3796NQKGjXrh0zZ85EIpGwbds2zcISderUYfHixZiYmGgGUQAjR47krbfeYsCAAUyfPp3Q0FCqVavG/fv3WbVqFU+ePGHevHkYGxtjaWmJqakp3bt35/333wdg6NChDB8+nO7duxfp//PyIKo8KoXd+P9acWZQXrf8BlHlzasOol6X0hpElbTCBlHlha5BVHn0pvSdb8J1gPoMosqDwgZR5UVhg6jyorBBVHlR2CCqvBCDqLz+Xw2iyhuVSkV0dDQjRoxg3759GBfxwkMxiPr/RwyiSo4YRJUsMYgqWW9K3ykGUSVHDKJKlhhElaw3ZhAVeOe1bcuuft3Ci8pA+f+mWAIOHz5Mnz59+OKLL4o8gBIEQRAEQRAEQcitxFfnK4+6detGt255r5ERBEEQBEEQBEEoqv8XgyhBEARBEARBEEqGrr9/+f/N/4vT+QRBEARBEARBEEqKmIkSBEEQBEEQBEF/UjETJWaiBEEQBEEQBEEQikDMRAmCIAiCIAiCoD+JmIcRLSAIgiAIgiAIglAEYiZKEARBEARBEAT9iWuixEyUIAiCIAiCIAhCUYiZKEEQBEEQBEEQ9Cf+TpQYROlDpVKVdYRCGRq8GZOKcoWyrCPoRaks/znflD90dy8iuqwj6OXyooVlHUEvLWZ9XdYR9HL120VlHUEvBuKUlBJjbGhQ1hH0kimTl3UEvWy/fKusI+hlSo8OZR1BL8/ik8o6gvAf82Z88xYEQRAEQRAEoXyQSF/fTxHs3buX7t2788477+Dj45Nv3alTp+jcufMrNYGYiRIEQRAEQRAE4Y0WFRXFr7/+ys6dOzE2Nmbw4MG0aNGCGjVqaNXFxsby/fffv/L2xEyUIAiCIAiCIAh6k0glr+1HXxcuXKBly5bY2tpibm5O165dOXToUJ662bNnM2nSpFduAzETJQiCIAiCIAhCuZScnExycnKe262trbG2ttb8Hh0djZOTk+Z3Z2dnbt68qfWYdevWUbduXRo2bPjKucQgShAEQRAEQRAE/b3Gxa3Wrl3LkiVL8tw+adIkJk+erPldqVRqLbqlUqm0fn/w4AFHjhxhzZo1REZGvnIuMYgSBEEQBEEQBKFcGjVqFP369ctze+5ZKABXV1euXr2q+T0mJgZnZ2fN74cOHSImJoYBAwYgk8mIjo5m6NChbNy4sVi5xCBKEARBEARBEIRy6eXT9vLTunVr/vzzT+Lj4zEzM+PIkSMsXPjiz5d8+umnfPrppwA8ffqUkSNHFnsABWJhCUEQBEEQBEEQikIqfX0/enJxceHzzz9n5MiR9O3bl549e+Ll5cX48eO5davk/+6amIkSBEEQBEEQBOGN16tXL3r16qV124oVK/LUVa5cmRMnTrzStsQgShAEQRAEQRAE/b3GhSXKK3E6nyAIgiAIgiAIQhGImShBEARBEARBEPRXhD+C+18lBlHFFBwYwNk9O1DIZThVcqPr0DGYmJnpXadUKjm1czNhdwNRKpQ0fasrjdp1AuDxg7uc3rUVpVKBoZExnd8bSgWPalrPe+3kEW5eOMuYWQvzbFOXh7ducHr3dhQyOU6VK9N9+Lg8eQurSY6PY90PCxk7eyHmllZaj02MjWHNt/MY9OmXVHCvqlemwtpI37qC2jLiUSgnd2xClpWFSqmk+dvdqdu8FQBXjh8i8OI5pAZSzCyteGfwKGydnPNs93XkLGif717xFzHPnmBkYgJAlZq16TRgSL4Zz+zejkIux6lSZboNG5snY341z/OF3rmFUqGkWZduudoxhBPbc9pRpW7Hes1bo1KpOL9vFw9uXAPA1d2DtwePxMjYJP8drgdvj0oMbtMYIwMDHscm8M+xC2Rky/LUta1djV5N6qECsmVy1pzyIyQ6DoDlHw4iPjVdU7v3aiDn74cWO1PQzRuc2LUVuVyGSyU3eo0an6dt86vJTE9n77p/iYsMR6VS4dWqHW269QQgIy2VQ5vWERMRjjw7m7bde+PVqm2xcxaHy6wvyQoJI3HT9te2zQc3r3NsxxYUcjkuld3oPXo8pmbmRa7Z/NevWNna0WPYaADu3/Bn16pl2Dg4aGrGTpuDiWne96o+7gdc5+iOzchlclzd3Og7ZkKeDAXVfPvpBKztXmRp260HDVu15d6Na+xcuQwbe0fNfR9Mn6OzT/mv5Cytz6GMtFSObtlAbEQ4clk2rd/tRf0WbYqcL7fQ2zc5v3cHCrkcx4qV6TJktM7/c2F1KQnxbP5lMcOnzcUsJ2/ko1BO79yMLDsblVJJ0y7dqNOs1Svl1aVZdTdGdWyOkYEBYdHx/HbgtM5+tGeTenT3roMKiExI5o+DZ0hKzyzxPAC3rl1h98Z1yGRyKru7M3zip5iZm+epU6lUrP3rNypV8eDt3uqlrTPS0li/9E8iw5+iUqpo2bEzXfsOKJWcQbducDJXX95zpI7+Pp+azIx09uXu71u2o3VOfy/8t5T6IGrnzp34+fnx3Xffad0+fvx4vvnmG1xcXF7p+f/8808ArT+2VdrSU5I5tGEVQ7+YiZ2zC6d9t3Fmz3beHjRC77qAc6dIiI5i9MyFZGdlsvHnRbi4ueNcuQp7Vy3jvU++wMXNneBbNziwbgXj5nyred5nwUH4HT2IqYWl3nkPrFvJ8KmzsHd25eSurZzy3UbXISP1rrl16Tzn9u0iNSkxz/PLZdnsXf0PCoW8XLWlq3tV9vz7F92GjcG9dj1SEuJZ9/18KnhUIzk+llsXzzJsymxMzMy4fuYEhzasYvDn0197zsL2eXjoQ0Z8NQdLW7vC23L9SoZOmYmdsyunfbdyZvc23h48Uq+agHOniI+KZMysb8jOysTnp2807bh7xV90Gz4Wj+ft+N08KnpUIyb8KaF3Axk1Yz5SAwP2rPybayeP0rJr8T8wrMxM+OidNszdepDIxBSGtm3MkDaNWXXyslZdBTtrhrVrwgyffSSmZ9DIoxJf9OzIpFU7qGBnTVpmFtN99hY7R25pKcnsWbuc0V/NwcHFlWM7NnN85xa653xxL6zm1J7tWNvZ8/5Hn5KdlcmyeTNw96xF5eqe7F69HMcKFen3wcckJ8Tzz/wZeNSui7WdfYlkL4iRuxvOX0zCtG5tskLCSn17z6WlJOO7ejnjps/FwcWVo9s3cWzHFnoOH1OkmnMH9/I46D71mrXU3PYk+AGtu/agfY8+r54zOZldq/5h/My5OLhU4PC2TRzdvpleI8bqVRMTEY6ZhSWfzP82z3M/fhhEm6496NCz7/+LnKX5ObR/7b84VKhI77EfkZwQz8qFs6lSs06x30PpKSkc8VnNwM+mY+fswtnd2zm/dwedBw4vUt0dvwtcOrCHtFx5VSoV+1ct5e2ho6lSqy4pCfFs/HEhru7VsHN+te9DuVmbmfJZj45MXb+b8IRkxnRszphOzfn78HmtuhqujvRv7sWkVdtJz5IxrnMLRrRvxpJDZ0ssy3MpSUms+/sPpn7zPc4VKrJrwxp8fdYyZPxErbqIp0/Y/O8ywh4+oFIVD83te7b4YOvgwIQvp5OVmcmCLybhWace1WrVLtGcaSnJ7F27nNFT52Dv4srxHZs5sWsL7w4drVfN6d3bsba1570P1f39P/NnUCWnv/8vkUjEFUFl1gIrVqx45QFUWQm7dxtX96qaDq9Ru07cvXIJlUqld93DAH/qt2yL1MAAU3MLajVuzp0rFzEwNOSjRT/j4uaOSqUiKS4Gs1yDpbTkJI5v86FDv4F65w29G0gFj6rYO7sC4N2+E3f8LmrlLagmJTGBoAB/Bk3+UufzH9m8ngat2mrl1FdptqVCLqf1u71xr10PACs7e8wtrUhJjMfc2oa3B43UHFlyreJBUnxcmeQsaJ8nxsaQnZXJ4U1rWbPoaw6uX0lGWqrujHefb9s1Z9udufNSxoJqggKu0aBVO02+2k1acMcvpx2798EjVzuaWVqRkphAzUZNGTplJgaGhmRnZpKeklKs10FuXlUqEhwVR2RiCgBHb96nbe1qeerkCgXLj14gMT0DgJCoOGwtzDCQSqlZwRmlSsW897vx/bBe9G/hpfVXy4sq5M4tKrpXw8FF3W5NO7xF4OULWm1bUE3XQSN4+z317GFqUhIKmQwTM3My0lIJvRtIh57qI63WdvaMnTEPM3OLYmctCtv+vUned4jUk2dey/aeC759i0oeudqqYxduXT6v1Z6F1YTeu8PD2zdp2vEtred+EhxE6L3bLJ03g1XfLyDswd1i53x4+yaVqlbDwaUCAM07dSHgknbOgmqePHyARCrl32/ns2TONE7u2YlSqVTnfPiAkLu3WTJnOv9+O5+w+//tnKX1OZSRlkrYvdu0zRk0W9vZM2raHMwsiv8eenzvNi5VPDT9uFfbjty7ejlPf19QXWpSIsE3r9Pv48+0HqOQy2nRrRdVatUFXvSnqYkJxc6rS+NqlQmKiCE8IRmA/dfv0LFu3i/xDyNjGf/PZtKzZBgZGOBgZUFyRunMQt29eR2P6p44V6gIQPt33sXv7Ok87Xr60H7avPUOjVtqzyYOHDOeASPVBwaSEuKRy2Q6Z7Fe1fO+3D6n72lSQH+vq+adQSPooqO/F/57CpyJ6tWrF7/99hvVq1dnypQpWFpaMn/+fK5fv87SpUtp3Lgxe/bswcDAgDZt2jB16lQiIiL44IMPsLOzw9TUVGuZwUWLFhEXF8ePP/7I22+/zbp16/Dz8+Ps2bMkJSXx5MkT2rRpw7x58wD4+eefOXz4MHZ2djg5OdG5c2f69+/Pv//+y9atW7Gzs8Pa2hovLy8ANmzYwO7du8nIyMDIyIiff/6ZqKgofv/9dzZv3gyoZ8YCAgKYP39+sRstJSEea9sXR7isbO3IzswgOzMzzzR+fnUpifFY5TpKZmVnT2z4UwAMDAxJS05i/ffzyUhLpeeYjwBQKpXsX7Oc9n3eR2pgoHfe5ATtbVnb2pP1Ut6Caqxs7ej/oe6ZvoBzp1EoFDRq25ELB4t+1L8029LQyIgGrdvnynqK7KxMKnhUx8jYWHO7XCbjzJ7t1PJuWiY5If99npGajHutunR+fxiWNrac3L6Jwz6r6Tsh7/7I8/w6MhZUk5KQ976YZ08wNDLCK592fJ7d/9Qxzu3biaWNHZ4NG+fbjvpwsLIgLiVN83tcSjrmJsaYGRtpnYoSk5xGTPKLuhHtm3It5AkKpRIDqYRbjyPYdM4fAwMJ0/p0ISNbxsHrxfsCmBwfj7X9i1OdrO10vIcKqZEYGLBr5VLuXrtCbe8mOLhWIOJRKJY2tlw6dpCHgTdRyOW0fPtdzRfd0hbz618AmDd7tX1WVEnxcVjb5+pv7OzJysggKzNDc3pZQTXZWVkc2rye4Z99xdXT2kvUmllY0qBFa+o2ac7jhw/YvOQXPpq7GJtc+0b/nPFaj9OdM/8apVJJ9br1eee9wSgUCjb89iMmpma0fuddzCyt8GrZmnpNmvM46D4+f/7CJ/O//c/mLK3PoYSYaCysbfE7dpiQ2+r3UPMu3TRfbotDn760sDpLG1t6ffBJnuc2NDKifqt2mt9vnT+NLCszzyn7r8rJyoKY5BcH3GKT07AwzduPAiiUKlp6uvNp9w7IFAo2nLlaolmeS4iNxc7xxWmhtg6OZGakk5mRoTUYGvyB+vPvbsB1rcdLJBIMDAxY/cfP+F+6QKPmLXGpWKnEcyYn6NHfF1IjMTDAd+VS7vpfoVZOf/+fI1bnK3gmqkOHDly8eBGABw8e4O/vD8DZs2fp2LEjJ06cYMeOHezatYtHjx5pBiqhoaH8+OOPrF69WvNcf/75J1FRUfzwww8YvDQAuH79On/88Qd79uzh5MmT3L9/nxMnTnDt2jX27dvH8uXLuXPnDgC3bt3SbHP16tVERkYCkJqayrFjx1i/fj379u2jY8eO+Pj40LJlS2JiYnj8+DEAvr6+9O/f/5UaTaVSgY7XjuSlPwhWUJ1KqdJ+/alUWo+3sLbho0W/MHTKLA5tWEV8VCRn92ynco2aeNSpV7S8ShUSHUFyb0+fmpdFPg7j+tmTdBs6qkh5tLK9hrYEuHxkPxcO7Kbfh59qDaDSU5LZ/tfPGBub0q53/udWl9U+r+BRnb4TJmNtZ49UKqV19z6E5HzZzpNRqULXxl/ez/nVvHw0UKXj/3f5yH7O7/el/0f/02rHxh27MPnHv/Bs1Jjd//6dtwGKQJpPx6xUqnTebmJoyGfdO+Bqa80/xy4AcCIwiDWn/MiSy0nPkrHf/zbNqlcpdiaVSqXz80KS68JafWr6jZvIl7/8TUZaGmf27UKpUJAYG4OJqRljps2h//hPOLrVh4hHxb92602gUunub6S5X6v51KCCHcuX0HXQcKx0nOI6+JPPqde0BRKJBHfPWrhV9yTkTmAxcyp13q6dM/+aph0603PYaIxNTDEzt6D1O925638FgKGTPqf+85w1a1OlhifBt4v3xyDfhJyl9TmkVChIilO/h0ZMnU2fcRM5vn0TkY/CipxRk0Olu6+R6urv9ajLz5WjB7h4cA+9J0zGMFd/WhIkEgm60inzyXwp6BFDf1/HxrPXWDiou6533itT6vE61ceYT6fw48oNpKWmsn/7lpKIpkWVz2eNVn+vR03fcROZ8vPfZKalcXbfrpINKZQLBc5EdejQgTVr1tCyZUtq1KhBSEgIcXFxnDlzBk9PT3r06IFZzqh8wIAB+Pr60qFDBxwcHKhcubLmec6cOUN8fDzbt2/H0DDvJr29vbG0VJ8C5ObmRlJSEhcuXODdd9/F2NgYY2NjunTpAoCfnx8dOnTAImeqvlu3biiVSiwtLfn555/Zv38/YWFhnD17ljp16iCRSOjXrx979uyhf//+xMXF0bBhwyI31Ll9uwi+dQOA7MwMHCu++P+lJCVgam6BsYn2xfTWdg5EhIXorLOyt9c6rzs1KRErWzuyMtJ5/OAung2bAODi5o5TJTdiw59yx+8i5lZWBAX4I8vKIjUpgbXfzmXUjIJn1aztHQjPnSMxb159al4WeOk8WZkZrP/xG83/Ye+qf+jUfxCeDb3zfdzraktQzzId2rCSuMhwhk6ZhY3Di6NgMc+esOufP/Bs2JgO/Qbl6cjLwz5PT0kmMz2NGl7q9lShQiKV6PxSYWVvT0RY8Itt69iHBdVY2Tlo50tMwCpnVk0uk3Fwvbodh305W9OO0U8fo1KpcHFzRyKR4NW6A9dOHsuTrTDvt2xEk+puAJgZG/Ek9sWpLfaW5qRmZpGlY+DoYGXBV7078yw+iQXbDyNTKABoV7saj2ITeJzzPBIkKJS6P8D1YW3vwLPQF+2WrGk3U71qgm/fxLmSG1a2dhibmlK/eUvu+l+hYWv1EemGbdQzffbOLrjVqMmz0OAiL9DyJrGxd+BZ6EPN7ymJ8XnaM7+amIhnJMREc3jrBkB9uoxKqUQuk/HOwKFcOXmMdt17a07fVKEq0sy9Vk4HR56G5Hq/JMRjZvFSzgJqblw4i6ubO65uVbSyZKSn4XfiKO179HmRUwVSHZ+P/5WcpfU5ZGljC0CD1urFWOycXahcw5PwsBBc3T30zndxvy/BgQFATn9f4UV/n5qUiIm5uWZxn+es7OyJDAsttO5lcpmMIz6riY8MZ9DnM7Q+l17F8HZNaOHpDoC5sTFhMfGa+xysLEjJyCRLpt2PVrCzxs7CjDtPowD16dOfdGuLpZkJKRlZJZLrOXtHJ8KCHmh+T4yPw9zCEhNT0wIe9cKdG/5UrOKOrb0DpmZmNGvTnuuXL5RoRlD3PeFhBff3BdW83N/Xa9aSezkHJf5TxOp8Bc9EeXt7c+/ePS5cuEDz5s1p1qwZhw4dQi6XY21tnadenvMlx/SlN0SlSpVYuHAhCxYs0JxnnZtJrg5HIpGgUqmQSqU6a5/f/9zzQVlERASDBg0iJSWF9u3b069fP01dv3792L9/P/v27aNPn+JdbNy2Zz9GzZjPqBnzGfrlbCLCQkiIVnc6AWdPUb1BozyPca9TL9+6Gg28uXXxHEqFgsz0dO5d86OGV2MkUimHNqzmWXAQALERz4iPiqCCRzUmLv6VUTMWMGrGfN4ZOhobR+dCB1AAVevUJzw0mPho9azd9bMn8wxy9Kl5WZeBw/hw/veMnbWQsbMWqk9fGPthoY97XW0JcGDdCrIyMxnyhfYAKiUhnq1//Eird3vTacAQnUfCysM+z87K5Pi2jZrroK4cO0TNRk115vWoU5/wsBAScvZhwLmTmsGXPjWeXt4EXjyrle/5vty/djnZmRk6BqJPObh+JbJs9Yft7cvnca9Z9It8t126wXSfvUz32cvXmw9Qw9UJV1v1SlZdvGpxNfhJnseYGhky572u+D18zB8Hz2gGUABujra836oREokEIwMDujaqzcUHYUXO9Vz1uvV5FvKQuCh1u107fZxajRrrXXPn6mXO7N2FSqVCLpNx5+plPGrVxc7RGdcqHty8cA6A1OQknoY8pGIJn9pT3lSv14CnwS/a6uqp49Ru1ESvGrfqnnzx459MnPstE+d+S9MOb1GvWUv6jB6PiakZV04e1cyiRDwO41loCDXqF/3AGUCNeg14EhJEXFQEAH46chZUE/XsKcd9t6FUKpFlZ3P5+BEaNG+FiakZl08c5c41dc7wR2E8Cw3Gs77XfzZnaX0O2To64eLmTuAl9YIJaclJPAt+SIUiDKAAWvXoy/Bpcxk+bS6Dv5hJ5KNgTT9+81w+/X3tenrVvezw+n/Jzswo0QEUwIaz15i8aieTV+3ki3W+1KrkTEU79Xe17t51uBT0KM9j7C3MmdbnLazN1N/DOtarwaOYhBIfQAHUaehNaNB9oiPCATh75CANm7XQ+/HXLpxj/7bNqFQqZDIZ1y6eo1Yx3zMFqZbTl8fn9D3+Z45T86XT1AuquXPtMmf25ervr13Go3bdEs8plD2JKr/56Byff/45gYGBLFu2jNDQUObPn0///v1p1KgRS5cuZd26dRgaGjJ58mRatGhBly5dGDlyJCdOqM9Tz70636hRo+jSpQsjRoygc+fOmmuicq/eN2LECCZNmkRGRgYrVqxg9erVZGdn079/fyZOnEjt2rX53//+x44dOzA2NmbYsGF06NCBWrVqsWXLFlauXElmZibjx4/H1dWVH3/8EYAxY8bw6NEjNm3aVOQFLVYcPZ/ntpDbNzm7ZzsKuQJbRyfeHfkBZhaWRD4K5fDGNZrBTX51SoWCU7u28OjeHRQKOQ3bdKRZl24APAm6z6ldW1AqFBgYGtK+93tUqVVHa/uPH9zj+DYfzRLnhgYFT4cHBwZwync7SoUcW0dneo4eT2JsDAc3rGJsznPoqnl5kYDvJo7m0x//zLPEOcDfs6bQb8KkAo+gyxV5B8al1ZbhIQ/Z+Mti7JxdMDR6capE+z7vExRwjTt+FzQLLAAYGBoyfOrX+WYvq31+5fghbl04i0qlxLFiZboOHY2puYXOhRJCAgM4s0e93K6tkzPdR35AUmwMh3xWM3rmgnxrcucLu3tbna9tR5p3eZdnIQ/Z+PMi7JxdMTQy0myrQ9/3qVq3Aef27eLB9atIpFIcK1TirYHDtF4fxwKD8m3T/DTyqMSQNo0xNJASlZjCX4fPkZaVTTVnBya83ZrpPnvp06w+g1p58zguUeux3+w4QrZczphOLfB0dcJAKuVyUBibL1zXvbEcPb3rFHh/0C318uUKuQJ7J2f6jP2QhJho9q1byYQ5i/KtMbOwJDM9jf0bVhOTcw1cLe+mdOzVH4lUSlJcLAc3rSUhJgaVSkWLt7rSpEPnfHO0mJX/a7S4XGZOISv0UYkucX7120UF3v/g5g2O71QvX27n7Ey/sRNJiI1mz9oVTJz7bb415pbafdLJ3TtIT03RLHH+LCyEgxvXkpWZidRASrdBw6laO/9ToA0KOZr64OZ1jmzfgkIhx97JhQEfTCQhJhrfNSs0q9npqjG3tCQ7K4v9Pmt4EvwQhUJO/WYt6NJ/EBKJhGehIezfuEadU2rAu4OHU62Ip2qXt5xpWdkFZiytz6Gk+DiObF5HUqz6PdS08zt45/x5Bl0yZYWvJKteunwnipwcXYePxdTCkqjHYRzdtJbh0+YWWJfbb59+wIeLf8XM0orw0GC2/votds4uGOT6XGrbewAedeprPW6ff/EXGwFoWt2NUR2aY2QgJSIxmZ/3niI1M4saro78r3t7Jq/aCagHWD2a1EOpVBKXms7Sw+eJSkrReztTenTQuzbQ/yq+G9epl4R3cWX0pM+JjY5kw9IlzPrpd63atUt+o2IVd80S5+lpqWxcvpTwJ+rBYKPmLek5cKjepwM+i0/SO+fDWzc44avuy+2cnOkzRt3f71+/kvFfL8q35nl/f8AnV3/fqCkdcvp7fYzo2FzvnGUpOSr6tW3L2iX/Pz9TlgodRPn6+vLTTz9x7tw5kpOTadmyJT4+Pnh7e/P333+zf/9+5HI5bdu2ZcaMGURGRuY7iAoNDWXIkCH4+voydOjQAgdRLVq04Ndff+XYsWPY2NgglUoZOnQo3bt3x8fHh3Xr1mFtbU2FChXw9PRk7NixTJo0iaioKFQqFc2aNSMoKIhNmzYBsG3bNo4cOcKKFSuK3Ei6BlHlTWGDqPJC1yBKKJ5XWW3udSrOIKosFDaIKi9KYxBVGgobRJUXhQ2iBP0VNogqL/QZRJUHrzqIel2KMogqS0UZRJUlMYjK640dRJWV69evExYWRr9+/ZDJZAwaNIjFixdTu3bRTxWSy+V89dVXdOvWjXfeeafIjxeDqJIjBlElRwyiSpYYRJUsMYj6/0cMokqWGESVLDGIKlnJMTGvbVvWTk6vbVtFUW6/eVetWpV9+/bRu3dv+vfvT48ePYo1gFKpVLRr1w6JRKJZnEIQBEEQBEEQBKG4ircU0Gtga2vLypUrX/l5JBKJZpl2QRAEQRAEQRCEV1VuB1GCIAiCIAiCIJQ/b8olBaWp3J7OJwiCIAiCIAiCUB6JmShBEARBEARBEPSn55Lt/2WiBQRBEARBEARBEIpAzEQJgiAIgiAIgqA/cU2UmIkSBEEQBEEQBEEoCjETJQiCIAiCIAiC/sRMlJiJEgRBEARBEARBKAoxEyUIgiAIgiAIgv7E6nxiEKUPuVJZ1hEK9SZkBFCpVGUdQS/GhuX/rZEtl5d1BL20r121rCPoJVP2ZrTn1W8XlXUEvTSdMausI+jFZ/JnZR1BL0rKf9/5hbdnWUfQy+5n8WUdQS+1KjqVdQS9HAsMKusIepHJFWUdQfiPKf/fFAVBEARBEARBKDeU4poocU2UIAiCIAiCIAhCUYiZKEEQBEEQBEEQ9KYs/2cYlzoxEyUIgiAIgiAIglAEYiZKEARBEARBEAS9Kd+QhcJKk5iJEgRBEARBEARBKAIxEyUIgiAIgiAIgt7elD9ZU5rETJQgCIIgCIIgCEIRiEGUIAiCIAiCIAhCEYjT+QRBEARBEARB0Js4m0/MRAmCIAiCIAiCIBSJmIkSBEEQBEEQBEFvYolzMYgqEaG3b3J+7w4UcjmOFSvTZchoTMzMilyXkhDP5l8WM3zaXMwsrbQemxQXw8YfF9L/4y9wqeJR7nKG3LrBYZ9VWNk5aOoG/m8axqamxcy5E4Xi+fZHYWKaX87861IS4tny67cM+2qOJueToHuc9d2GUqHAzMKS9v0H4VTJrdBMwYEBnNm9HYVcjlOlynQbNjZP2+VXo1QqObVzM6F3bqFUKGnWpRuN2nUC4OGtGxxc9y9Wdvaa5xn6xQyMc/0/rp44wq0LZxgz+5uiNaQOr9q28uxsTm7fSNSjUFSocHWvRqf3hmJobFymuZ57eZ/HRYZzaN0Kzf0qpYq4iGf0GDuRGg0bF5qrtPb74wd3ObVrK0qFAkMjI956fxgVPKqhUqk4v28XD25cA8DV3YO3B4/EyNhE77Z8cPM6x3ZsQSGX41LZjd6jx2NqZl7kms1//YqVrR09ho0G4P4Nf3atWoaNw4v3+Nhpc3Tup9LiMutLskLCSNy0/bVtsyCeFZzo4lUTA6mUqKQU9vgFkiWX51vft3kDopNSuXA/tNSz1azgRBevWhhKpUQmpbDb71aB2fo19yI6KYXzubKZGhkytnNLfP1uEZ6QVOIZz125wtJ1a8mWyajh4cGsT/+Hpbm5ztrTFy8y79dfOLl1GwCZWVn8uGwpdx48QKWCerVqMvWjiZia6P9eyc/DWzc46bsNhVyGcyU3eoz4IM/7Pr8aWXY2hzevJTwsBFRQsWo1ug4ehZGxMRlpqRzZvJ7YyGfIsmW0ebc3DVq2eeW8z9Wp5EKPxvUwNJASnpDMlgv+ZMny3+dD2jQhIjGJU7cfam5bMKg7SekZmt9PBgbhH/q0xDIC1KrozDsNa2NoICUyMZmdl24W+Np8r2VDIhNTOHcvBABDAym9m9bHzcEWkPAkLoE9VwORK5QlmrN2JRe6N66DgdSAiIQktl28UWB7DmrjTWRCMqfvBOe5b2SHZiRnZOLrd6tEMwplq1yczjdjxgyePXsGQOfOnXn6tGTfsJs2bWLTpk15tlUS0lNSOOKzmh5jP2bU7EVYOzhxfu+OItfd8bvAtt9/IC0pMc9j5TIZh9b9i1KuKLc5w0ODadK5K8OnzdX8FGcAlZ6awtGNa+gxdiKjZn2DjYMj5/fsLHLdXb8LbP9DO2dWRjr7Vy6lbe/3GD59Hp0GDuPAmn+Qy2UFZ0pJ5tD6lfQd/wkfzP0WW0cnzuzepndNwLlTxEdFMmbWN4yYNodrJ48QEab+MAgPCaLZW90YPXOB5if3AOppcBB+xw4WuR11/j9KoG39ju5HqVQwbNpchk2bh1yWzZVXzFea+9zBtSLDvpqr+alSqy41GzfXawBVWvtdIZezd+VSug4dzeiZC2jVrRf716oHekEB1wi9G8ioGfMZM/sbZNnZXDt5VO+2TEtJxnf1cgZ9/BmTF/2EnZMzx3ZsKXLNuYN7eRx0X+u2J8EPaN21BxPnfqv5eV0DKCN3Nyr9/j2WHdu9lu3pw9zEmL7NG7Dl/HWWHDxLQmoGXRrW1FnraGXBqI7Nqevm+hqzebH5vD9/HDxDQmo6bzeslW+20R2bU++lbJ4VnJjQpTWOVhalkjEhKYlvfv+Nb2fMYNuyf6jk6srfa9borH0c/ow/Vq/SWlJ5zdatKBQKfP5cgs+ff5KVnc3abdt0Pr4o0lKS2bduBQMmTOaj+T9g6+jMyV1530P51Zw/uAelQsn42Yv44OtFyLNlXDi0F4B9a1dgZWfPuFnfMPSzaRzdup7khPhXzgxgYWLM4DZNWHPqMt/5HiM+JY2ejevprHW2sWLiO23xcq+odbuTtSXpWTJ+3ntS81PSAygLE2MGtGzIxnPX+HXfKeJT0+naqLbOWidrS8Z1bkn9KhW0bu9UzxOpVMofB87wx8HTGBkY0LFujRLPOai1N+tOXeHH3ceJT02ne+O6OmudbSz58O3WeFWpqPP+jvVqUNXFQed9bzKVSvXafsqrcjGIunz5cqk20pAhQxgyZEipbOvxvdu4VPHAztkFAK+2Hbl3Ne82CqpLTUok+OZ1+n38mc5tnNzmQ90WbTC1tCy3OSNCg3ny4B4bvpvH1t++5+nDByWTs01H7l/TI2euutSkRIJv3aDvRO2ciTHRGJuZUaVWHQDsXSpgbGJKZGhIgZnC7t7G1b0qds7qLxmN2nXmzpVLWpkKqgkKuEaDVu2QGhhgam5B7SYtuON3EYBnIcE8enCXNYvnsPGXxTzJ9cU1LTmJ41s30LHvwKI2o04l0baVqtek+Ts9kEilSKVSnCpXISU+rsxz5bfPc3sW/ICHAdfoPGi4XrlKa78bGBry0eJfcHFzR6VSkRgbg5mF+otqzUZNGTplJgaGhmRnZpKekoKZhf7v++Dbt6jkUQ0HF3Weph27cOvyea3MhdWE3rvDw9s3adrxLa3nfhIcROi92yydN4NV3y8g7MFdvXO9Ktv+vUned4jUk2de2zYLU93VkWfxScSnpgNw9eFjGuTzJaq5pzv+IU+48yTytWSr4epIeK5sVx4+zvcLXgtPd66FPOX2S9laenqw/XIAKZlZpZLx8nV/6nh6UqViJQD6v9udQ6dP5XnfZ2ZmMu/nn/nfuA+0bm9Urx5jBw1GKpViYGBArWrViIyJfuVcoXcCqeBeDfuc90fj9p257XdRK1dBNVU8a9Gme29NH+ni5k5yfCwZaamE3g2kXc++AFjb2TNq2lzNe/9V1arozJO4BGJT0gA4fz+UxtV0n2XRtnZVLgeFEfBI+4Cyh7M9KpWKSd3a8WWvzrzjVQuJpETiadSo4MTTuETicnJeDnpEI49KOmtb1vTgavBjbj2O0Lo9NDqOk4FBqFAvbhCekIytRcke0Kn5UntevB+Kd9XKOmtb16rK5YePuPkoPM991VwcqFXRmUsPwko0n1A+FOt0vl69evHbb79RvXp1pkyZgqWlJfPnz+f69essXbqUpk2bcvDgQRQKBW3btmXq1KlIJBJ+/fVXLl68SFJSEs7Ozvz666/s3LmT6OhoJkyYgI+PDwB//fUXd+/eJSMjgx9++IGGDRvy6NEj5s2bR2JiIqampnz99dfUrVuX6dOnk5iYyKNHj5g6dSpXrlzh/PnzSKVSunTpwqRJk/jzzz8BMDEx0dqWnZ3dKzdgSmK81qlYVrZ2ZGdmkJ2ZqX0KXAF1lja29PrgE53PH3jhDEqFggat2+N3ZH+5zWlqof6SWKNRE8JDHrJ3xRKGTZur9Vx65UxIwNL2xX6xfL79rMyXTtvKv87Sxpae4z7O89y2zi7Is7J4dO827rXrEfkolPjICNKSEwvOpEfbFVSTkpD3vphnTwAws7CgTtOW1PRuyrPgIHYt/4PRMxZgYWPLvtX/0KHvQKQGBnq2XsFKom3da784spkcH8eNU8foPGhEmefKb5/ndm73dlr36Kf37Elp7ncDA0PSkpNY9908MtJS6TV2oqbOwMAQ/1PHOLdvJ5Y2dnjqMWv2XFJ8HNb2L7ZpbWdPVkYGWZkZmtP1CqrJzsri0Ob1DP/sK66ePqH13GYWljRo0Zq6TZrz+OEDNi/5hY/mLsbGvvSPsMb8+hcA5s30b4vSZmNmSnJ6pub35IxMTI2NMDE0zHNq0gH/O4B64PW6siXpmW1/TrYaL2Vbf+ZKqWaMionFxfHFNp0dHUlLTyctI0PrlL5v//qLvt26UcPDQ+vxLRu/eC1EREezec8eZnwy6ZVzJSfEYW330vvjpfd9QTXV6jbQ3J4UF8uVE4d5d9gYEqKjsLSx5fKxQ4QE3kQul9Hi7e44uGjPshSXrYU5iWkvTsNLSs/AzNgIEyPDPKeg7bx8E1APFHIzkEh5EBHNfv/bSCVSxndpRaZMzpm7eU9PKy4b85dem+n5vzb3Xg0E1AOv3B5Gxmr+bWtuRptaVdnld7PEMgLYWpi91J6Z+bbn81P0alXQbk9rM1P6NGvAv8cv0rKmR4nmKw/K8wzR61KsmagOHTpw8aL6SPqDBw/w9/cH4OzZs3Ts2JHAwEC2b9+Or68vUVFR7Nmzh0ePHhESEsLmzZs5fPgwFSpUYM+ePUyYMAFnZ2eWL1+uGdTUqFEDX19fRowYwcqVKwGYNm0aU6dOZdeuXSxcuJDPP/9ck8fW1paDBw9Sq1Ytzpw5w549e9i0aRMPHz4kK+vFUTRd23pV+b2IpFJpsepyi37yiJvnT+t95LwgpZkToNcHn+Dp3RSJREKl6p5UqFqdx/fvFCOnEomOQ19Sycs59avLzcTUjJ4ffMKVowfw+X4+965cpLJnLaQGBR9LUClVQN5tSXK1SUE1L7epKtdj+06YTK3GzZBIJFSuUZNKVWsQdu82Z3Zvp3KNmnjU0X06RnGUZNtGPXnEtt9/wKtdJ6rVb1hucuUnPPQhGamp1GrSXP9cpbjfASysbZi4+FeGTZnFwfUriY96MRPQuGMXJv/4F56NGrP737/1z6xSIdGRJ/f7N78aVLBj+RK6DhqOlW3e/nHwJ59Tr2kLJBIJ7p61cKvuScidQL2z/ddIJOp9+rLycLG1RCJBpSNdecj2nEql0vl+Nsj1Wt2+fz8GBgb0fvudfJ/n7sOHfDhtGu/36Enb5vq/vwvKpWv6RfLSe6iwmohHoaz/6RuadOyCp5c3CoWCxNgYTEzNGPnV1/T94BOObfMh4lHJXB+nfj3m3b9F+aJ7KSiMXX43yZYryJTJOH37Yb6zq8Wl7ntK5rVZ0c6GCW+35uKDMO6Hv/osZG75TcDpm1MqkTC0XRP2XA0kJaN0ZnOFslesmagOHTqwZs0aWrZsSY0aNQgJCSEuLo4zZ87g6enJzZs36d+/P6Ceiq9YsSJ9+vRh2rRpbNu2jdDQUG7cuEGVKlV0Pn+XLl0A9WDq8OHDpKWlERgYyIwZMzQ16enpJCQkAODl5QWAi4sLJiYmDB48mE6dOvHll19iUgIXmb7s4n5fggMDAMjOzMCxwosp3tSkREzMzTF6abtWdvZEhoUWWpfbHb+LZGdmsuXX7wBIS0rk0LoVtO3zPtUbNCo3OTPT07l57iTN3u6u9aGo7wzKxQO7CQm8kZMzE8cKL6b289u+tZ0DUY+KllOlVGJkYsJ7k6dqblv7zSxsnZzzfQyAlb09EWEvjsSlJCZgam6Bca5tFVRjZedAaq7rdFITE7CytSczPZ0bZ07QomsPTbupULfbHb8LmFtZExTgjywri9SkBNYsnsPomQsKzPqy0mjb+/5+nNzmQ8cBQ6ndtEWR8pRmroIE+V+ldrOWWl9yClNa+z0rI51H9+9Ss1ETAFyqeOBc2Y2Y8KfIZdmoVCpc3NyRSCR4te7AtZPH9M5sY+/As9AXF4mnJMbn5DEttCYm4hkJMdEc3rpBnTcpCZVSiVwm452BQ7ly8hjtuvfO9VpVldgs6ZuiU31PauUcvTcxMiQqKUVzn5WZCRlZ2cgUxb929VV0ru9JrYou+WZLL8Nsurg4ORH44MXpyzFxcVhbWmKW61ra/cePkZmVxfBPJyOTy8nKzmb4p5P5de48nBwcOHLmND8uXcqXH35E144dSySXjb0D4aEFv+8Lq7l95RKHN62l6+AR1GveGkBzYMKrtfq6PntnF9xq1CQ8LIQK7lWLlbVbozqaa9lMjYyISEh+kdHclPSsbLKLcC11k2puhCckvXgeCSiUr75YQ5cGNald2SUnpyGRiS9em9ZmpsV6bXq5V6R30/rsvRpIgI7T6IrjnYa1Ne1pYmRIZOKL9rTOaU+Znu3p5mCLg5UFvZvWB9TvQYlEgqGBAdsv3iiRvGVNWX6OyZSZYg2ivL29mT59OhcuXKB58+Y4ODhw6NAh5HI5VlZWjBo1ijFjxgCQnJyMgYEBgYGBTJkyhdGjR9O1a1ekOo7UPmeQ88H8/MNaqVRibGzM7t27NTWRkZHY2toCYJrT6RoaGrJt2zb8/Pw4c+YMgwcPZv369cX5LxaoVY++tOrRF1BfWL7hu7kkREdh5+zCzXOndA5w3GvX46zv1kLrcus4YDAMGKz5feW8aXQbOV7v1fleV05jU1MCzp7EztkVz0ZNiH7ymMhHobwzbIx+Obv3oVX3PrlyztNs/9b501Srn3f7VWrX5UyunPnVaZFI2P3PH/T64BNcqnjwwP8KBoZGOFbUfZ7zcx516nNq5xYSoiOxc3Yl4NxJanh5613j6eVN4MWz1GjQiOysLO5d8+OdISMxNjXl+pnj2Lm4Usu7KVFPHhEZFsK7I8ZpPngBHj+4x/GtG4o8gIKSb9uQwABO79hMv4mfF3uVyNLIVZinwffpOGBokTKW1n6XSKQc2rAKcytrKlf3JDb8GXGREVT0qMbjB/e4cvwQw76chZGxCbcvn8e9pu6LrnWpXq8BR7b6EBcViYOLK1dPHad2zmCtsBq36p588eOfmrqTu3eQnppCj2GjUSqVXDl5FEfXCtRt0pyIx2E8Cw2h75iPitSmb7qTgUGcDAwC1BeeT+zWFntLc+JT02lavQr3SvhoeFGcCAziRK5sn3Rrp8nWrLp7mWbTpYW3N7+vWsnj8GdUqViJnQcP0K5FS62a1b/8qvl3eFQUQyd9woY/1K/Rs36X+WX5cv5YsJA6np4llqtqnQYc276J+KhI7F1c8T9zgpovnVJbUE3Qzesc3bqeIf+bSgX3aprH2Do64VrFg1sXz9G009ukJifxNPghLd/pUeysh27c5dAN9bWJlqbGTO39Fo5WFsSmpNG6VlUCn0QU8gzaKtha4+VekTWnLmMoldK2djX8Q159YYljtx5w7Jb6OmkLE2P+170DDlYWxKWk0dzTnbtPo4r0fLUrOdOzST1Wn7zMs/iSWzXySMA9jgTcU+c0NWZKr06a9mxV0yPPdYMFeRSbwKIdRzS/v92wFhYmxmJ1vv+YYg2iDA0N8fLyYv369SxbtgwnJyfmz59P//79adiwIX/88QcDBw7ExMSETz75hH79+pGUlETz5s0ZMmQICQkJnDp1infeUU/RGxgYoCjgKISVlRUeHh7s3r2bPn36cP78eebMmcOxY9pHaO/cucPChQtZv349rVq14s6dO4SGak+VF7atojK3subtoWPYv2opCoUcW0dnug4fC0DU4zCOblrL8GlzC6x7HUozp1Qqpff4SZzcvpFLB3cjkRrQffSHeZZpL0rOA6uXoVDIsXFwouvwcZqcxzavZdhXcwusy49EIqHbyA84vnkdCoUcC2v1NV66TinJzcLKmneHj2X3v3+jkMuxdXKm+8gPiHwUyiGf1YyeuSDfGoBG7TqRGBvNmsVzUCjkNGzbETdP9Rfjfh9+yrFtPlzY74tEKqXXuImYF6Pd9FESbXt29zZQqTi2ea3meStWrUGn94eVaa7CJMZEY+1QtGt3Sne/T+bk9o0oFAoMDY3oOeZDrOzsqdeiNQkxUaz/fgESqRTHCpWK1E9YWtvQZ8yHbF36Owq5HDtnZ/qNncizsBD2rF3BxLnf5ltTEKlUyuBJX3Bw41pO7t6B1EDK+x9OwsKqdF6rb4K0rGx2+91iYBtvDKRSElLT2ZVzrUlFO2t6N2vAsiPnyyzbLr+bDG7TGAOplPjUdHZeDsjJZkOfZg1YeuRcmWR7zt7Wlq//9z9mfPstcrmcSq4VmPvFF9wNCmLRn39oBkv5+WOVerW+RX/+obnNq05d3U92UQAA/rxJREFUvppY8Gu5MBbW1vQcOZ6dy/9EoZBj5+RMr9EfEvEohP3rV/HB7G/yrQE4vmMTKhXsX79K85yVq3vSbcgoBnz0KYc3rcP/zAlUKiVte/Shoke1/KIUSWpmNpvP+zO6YwsMpFJiU9LYdO6qevsOtgxq7c3Pe08W+ByHA+7Rv0VDpvZ+CwOplICwZ1wKCiuRfM+lZWWz/XIAQ9s2wUAqIT41nW05MzOV7G3o18KLJQfPFvgc73rXRQL0a+Glue1xjHqZ8xLLmZnN1gvXGdGhGQZSKXGpaWw+p750pbKDLe+3asSv+06V2PbeROKaKJCoitkKvr6+/PTTT5w7d47k5GRatmyJj48P3t7e/P333+zfvx+FQkG7du2YOXMm0dHRTJo0icxM9QWFderUQalU8tNPP7Fo0SLOnDnDv//+y6hRo1i3bh2VK1fm8uXLLFmyhPXr1xMcHKxZWMLIyIh58+bh5eXF9OnTad68ueb0we+//56TJ09iZmZG48aNmTFjBkuXLgVg8uTJWttycyv87wMBLD1c8Bta0N+b8qYzNiz/f0Itu4C/qyEU3Zuwz0F9JPdN0HTGrLKOoBefyZ+VdQS9KHVegVW+fOFdcjNCpWn3s5JZVry0BTwumdPUSpupkVFZR9CLvqfilbUfR/Yp6wh6eRr9aivzFkVl5/K5RHyxB1H/n4hBVMl5U15ub8IXajGIKllvwj4HMYgqaWIQVXLEIKpkiUFUyRKDqJL1ODq28KISUsX59axsWlTl4u9ECYIgCIIgCIIgvCnejEOvgiAIgiAIgiCUC2/KmUWlScxECYIgCIIgCIIgFIEYRAmCIAiCIAiCIBSBOJ1PEARBEARBEAS9ibP5xEyUIAiCIAiCIAhCkYiZKEEQBEEQBEEQ9KYUU1FiJkoQBEEQBEEQBKEoxEyUIAiCIAiCIAh6E0uci5koQRAEQRAEQRCEIhEzUYIgCIIgCIIg6E1cEyUGUXqpVcG5rCMUqqF7hbKOoJfDAffLOoJe6lZ2KesIhUpMyyjrCHq59SSyrCPo5U05NcFAKinrCHrxmfxZWUfQy7A/fyvrCPqRGpR1gkIt+/zzso6gF0tTk7KOoJevG1Qt6wh6MazmUdYR9KKMTyjrCMJ/jBhECYIgCIIgCIKgtzfkuGOpEtdECYIgCIIgCIIgFIGYiRIEQRAEQRAEQW9vyinwpUnMRAmCIAiCIAiCIBSBmIkSBEEQBEEQBEFvYnU+MRMlCIIgCIIgCIJQJGImShAEQRAEQRAEvYlrosRMlCAIgiAIgiAIQpGIQZQgCIIgCIIgCEIRiNP5BEEQBEEQBEHQmziZT8xECYIgCIIgCIIgFImYiRIEQRAEQRAEQW9iiXMxiCpRt65dYffGdchkciq7uzN84qeYmZvnqVOpVKz96zcqVfHg7d798tz/z4+LsbGzZ/AHH5VKzvPnzrHs77+QZWdTvYYnM2fPxsLSUqtm+9at7NqxHSQSKlWuzPSZs7C3t9fcHxUVyfixY1nnsxFbW9sSy/bg5nWO79yKQi7DpXIVeo/6ABMz8yLXbPn7N6xs7eg+dJTW7dfPnebe9asMmTylxDLn5u93mU2rVyKTyahStSoffTYFcwsLrZqzJ46xZ/s2JBIwMTFl9EcfU71mrVLJk9uta1fY5bMOuVxOpSrujPw4/9fnmiXq1+c7ffK+Ppf+sBhbe3uGlODrM+z2TS7u34VCLsehYiXeGjwKY1Mzvevk2dmc3rGRqMdhoFLh4l6VDgOGkhwfy5H1/2oer1QpiY8I590xH1Hdq7Fe2YIDAzi7ZwcKuQynSm50HToGE7O82fKrUyqVnNq5mbC7gSgVSpq+1ZVG7TppPfbWxbMEBfjT/6P/aW4LOHcK/1PHkEil2Dg40nXYGMwtrQrNez/gOkd3bEYuk+Pq5kbfMRMwfen9UVDNt59OwNrOQVPbtlsPGrZqy70b19i5chk29o6a+z6YPkdnW7wqzwpOdPGqiYFUSlRSCnv8AsmSy/Ot79u8AdFJqVy4H1riWYrDZdaXZIWEkbhpe5ls37xlMxw/HI3EyIis4FCiv/8NZXqGVo1N/17Y9u+FKiuL7EdPiP71b5QpqUitLHGeMgmTGtVQZmaSfOAoSTv3llrW6i6OdKhXAwOplJjkVA743yZbrtC7rm9zL+wsXry+bSxMeRKbyI5LN14pV+jtm1zctxOFXI5jxcq8NUR3n5RfnTw7m1PbNxL1OBRQ4VKlGh3fG4qhsTFPg+5xfs92FAoFhkZGtO8/BFf3qq+U97y/P39v3oRMLqNGlSrMmvARFjr6d4DTV64w/+8lnFi9VnPb9iOH2XPyBFnZ2dSuWo1ZH36EsZHRK2XS5dy5cyxZupTs7Gw8a9Tg61mzsHzp+8eWbdvYsWMHSCRUrlSJ2TNnan3/AJg6bRqOjo5Mmzq1xDPmyex3mb9XryZbJqNG1arM/uxzLF/6XH/u1IULzPvpR07t3FXquYSyV65P50tJSeGTTz4p6xh6SUlKYt3ffzDhyxnM/2Mpji6u+PqszVMX8fQJv82fzfVLF3Q+z5HdO3h4906p5UxISGDRwgUs/u57Nm/fQcVKlfj7ryVaNffu3mWjzwb+WbkKn81bcHNzY8U/yzT3H9y/n48nfEhsTEyJZktLSWb3mhUMnPg/Jn3zE7aOzhzbuaXINecP7eNx0H2t2zLSUtm3fhWHNq8vtWU5kxMTWfrLT3wxew6//bsaF9cKbFy9Uqsm/OkTNvy7gpnfLOaHv/6h/+Ch/PzN/FLJk1tKUhJr//qDD6fOYEHO63NXPq/PX+fPxj+f1+dh3x08vFeyr8+M1BSOb17Lu2M+YvjMhdg4OHFh384i1V09dgClUsmQqXMY/NVc5DIZ144fxN61IoOnztH8VKlVD8/GzfUeQKWnJHNowyr6fPAJ4+Z8i42DE2f25P1iXFBdwLlTJERHMXrmQoZ/9TX+p44SERai/j+lpXJ00zpObN8IuV6XibExnNu7k8GfTWf0zAXYODhyYb9voXnTkpPZteofhnzyGZ99+zN2Ti4c3b5Z75qYiHDMLCz5ZP63mp+GrdoC8PhhEG269tC6rzQGUOYmxvRt3oAt56+z5OBZElIz6NKwps5aRysLRnVsTl031xLPURxG7m5U+v17LDu2K7MMBjbWuMz4nIivF/Fo+ARkEZE4fDhGq8bM2wu7oe/z7PMZ/B97dx3e5PX3cfydpO7uArTF3d3dYQ7DYQMG29iwDR86YGNDN2y4u7tbkaLFKUXrrmns+SMsECqkbZD9nvO6rl7XknyTfLhz27nPuc8e9xlM2rmLuA37FgDXwV+hzsjgUff+POn/A9Y1q2Jdq/pbyWppZkrrKmXYGnyNRYfOkJiWTsMyQfmq23b+Gv8cPcc/R8+x9/JN5AolB67eKlSujNQUDq9dRuveA+g2ahJ2zi6c2ZnLPimXugsHd6NWq+gyfBxfDB+PUpHFxUN7USmV7Fu+kMafdafL8HFUa96Gg6uWZPvs/EhITmbS3wuYOuQHNvz+B15u7sxbuybH2scREcxZrX8cPHo+mI379zFn1BjWzvgNuSKLdXt2FypTjjkTEpgwaRLTp05ly8aNeHt7M3f+fL2aW7dusWr1apYuXsyGtWvx8/Vlwd9/69UsX7mSy1euGD1fjpkTE5n4++9MGz2GTYuX4O3hybx//smx9vGzZ8xevOj/zdTfGo3mnf3lx86dO2ndujXNmzdn9erV2V4/dOgQHTp0oH379gwcOJCkpKQCL4MPuhGVlJTErVuF2xm+K7euXaZIQBBunl4A1G/eivMnj2f78Y/v202dJs2pXLNOts+4G3qd0Msh1Gve8q3lPB98jlKlS+Pr5wdA548+4sC+fXo5S5YqxYbNW7CxsUEulxMTE4O9vT0AMTExnDh+jFmzZxs924PQ63gXKYqzu/aEqFrDJlwPPqOX7U014Xducv/GNao0aKz32aEXgrF1cKTZJ18YPfe/roZcIqB4cTy9fQBo1rYdp44e1stvYmrK19//gKOT9kp/seLFSUxIQKlQvLVcADevXsY/MAj3F+tngxatCM5h/Ty2bzd1mzSnSq3s6+edG9cJvRJC/WbGXT8f37mJm68/Dq7uAJSt04C7l4KzZcurzqtYEFWbtUEilSKVSnH19iU5Pl7v/c8f3OPB1Us0+qSrwdnCb4fi4V8URzftd1as14hbF85ly5ZX3f2rIZStWRepTIaFlTUlKlfn5oWzANwJuYC1gwMNO32m93kajRqVSkWWPBONWo0iKwuZAVeF74dew7toMZzdPQGo3qgpV8+d1subV82T+3eRSKUsnjqBuWNHcHTHFtRqNQBP7t8l7FYoc8eOZPHUCYTfeTv75gAPF57FJxGfmg7AxfuPKefnlWNt9SB/QsKecPNJ5FvJkl8OnduTvGsfqUdPvLcMVtUrI799F8XT5wAkbduNbTP9nk/zEoFkXLqMMiYOgNQTp7GuXQNMTDAvHkjK/iOgVoNSSdrZC9g0zL4/MIaibs5EJCSRkKb9rS8/fJpjg9iQOqlEQtsqZTh87Q4pGfJC5Xp8OxQ3vyK6fU25Og25k9M+KY8674DiVGv+yj7Jx4+UhDhkJib0mjAdVx8/NBoNybGxWFjbZMuQH8HXrlKqWAB+ntptunOzZuw/fSpb3ky5nPHz5vLtl931nt978gRd2rTF3sYGqVTKiD79aFmvfqEy5eRccDClS5XC78X5x8edO7P3tfOPUqVKsXXTJt35R3RMDA4vzj8ALl66xNmzZ/moU/ZREm9DcEgIpYsXx8/bG4CP2rZh39Ej2ZdtZibjZkzn+6++eie5hJxFRUUxa9Ys1qxZw7Zt21i/fj3379/XvZ6amsr48eNZuHAhO3bsoESJEsyZM6fA3/dBN6ImTZpEdHQ033zzDdu2baNTp0506NCBn3/+Gblcu5OsU6cOY8eOpWPHjvTt25e9e/fSpUsXGjduzPnz5wHo1q0bkydPplOnTrRu3ZpTp04ZPWtCbCyOLi+HuTg4u5CZkU5mhv4Qis/79qd6vQbZ3p8YH8eGfxbR+7sfkUrf3s8SFRWF+4sTPQBXNzfS0tJIT0vTqzMxMeH4sWN0bNuGK5cv06ZtO229qytTp8/Az9/f6NmSE+L0hhHZOTohz8ggKzPDoJqUxAT2rVtF574Dsi3Dqg2b0KBdJ0xMjD884V9xsTE4u7rqHju7uJKRnk5GerruOTd3DypXrwFor+KsWPg3VWvUwuQtDJt4VUJcLE7OL9dPR2cXMtOzr59fvGH97PMW1s/UhHhsHF4O1bCxdyQrMxOFPNPgOr+SZXQNmOT4OK6cOExgxSp67z+9cxM1W3fMcUhOblIS4rF75TttHRzJyswgKzPT4LqUxHhsHV95zdGJ1MQEQNvYqt2qPTIT/ZHVjq7uVGvakqUTf2bBqB94eu8ONZu3fWPepPh47J2ybx/yV7ahvGrUajUBpcvS/YcR9Bk5lvs3rnHu0H4ALG1sqdaoKd9MmEqzjz5jzdxZJMXHvTFTftlbWpCc/nL5JmdkYmFmirlJ9tHne0Jucv1xhNEzFFTMrHmkHDz6XjOYuLmijI7VPVbGxCKzsUZq9XK9z7x5B8vKFTBxdwPArnUzpGamyOxtybx1B9sWjUEmQ2JpgU2DOsicnbJ9jzHYWVnoNXiSM+RYmJpiZiLLd12FIt6kZsq5G1H4ERIpiQnYOjjqHtu82J5f3yflVafdJ2kbesnxcVw9fki3T5LJTEhPSeaf8cM5tWMTlRu3KFTe6Lg43J1fbtNuTs6kZWSQ/tr+fdriRXRq0pRAfz+95x9HRJCQnMz3U6fQdfgwFm/aiG0uQwELIyoqCnf3l+cfbi/OP9JyOP84dvw4rdu14/KVK7Rrq933xcTE8NvvvzPpl1+QyvTXkbclKjYGt1eO624urqSlp5P2ynEdYOqc2XRq1ZrAooUblvlfotZo3tmfoc6cOUPNmjVxcHDAysqKFi1asG/fPt3rCoWCcePG6dbDEiVKEBFR8GPIB92IGj16NG5ubnz//fds2LCBdevWsX37dpydnVmyRNv9HRsbS/369dm2bRtyuZxDhw6xZs0aBg8ezPLlL4crpaamsnXrVn777TdGjhxJVlaWUbOqNeocnzfkhFOlVLL0j5l83KMP9o5v52D1L41ag0QiyfZ8TjukBg0bsvfgIfr068eQbwfrrki/zWzkkE3yyjLMrUajgc2L5tHi0656B7V3SaNWIyGnZZt9HcjMzGDWlIlEPn/G19//8NazqdVqcohm8Pq5+I+ZfNLz7ayfGk3O66REIs13XfSTR2yZM53ydRtRtEx53fMRDx+QkZpC8cr5G5ak0WhyXG4SafZsudVptzm94mzvf134rRvcu3KJrybOZMDk3wkoX4m9Bgz50RiwH8qrpmqDxrTt2hMzcwssrayp3bw1t0IuANBl0BDKVq2BRCLBv3hJ/AKDeBB6/Y2Z8ksiyXnqXHETs4EkkhyHv2he2X9nXgslftkaPCePxnfhn6DWoEpKRqNQEjtvMWjAb8kcvCaPIf3CZTSK3O9HK1RUyDnra88ZUlct0M9o98RptxFD9klvrot+8ojNs6dTrl4jipapoHveytaO3hNm8Mn3Izm8dhkJ0QXvTVVrcj4uvrrdbzqwH5lMSrtGjbLVKVUqzl+/xuTvvmfZlKkkp6by1/p12eoKS53LPlyWw/lHwwYNOHzgAF/17cvg774jKyuLUWPG8MOQIbi8csH6bVOrNTke11/NvGnXTmQyGe1bFK4xLOQuOTmZp0+fZvtLTk7Wq4uOjsb11UavmxtRUVG6x46OjjRr1gzQ9h4uXLiQpk2bFjjXf2JiieDgYB49esSnn34KaFuSpUuX1r1ev76229nb25sqVbRXery8vPQW7r/vLVWqFK6urty5c4dy5coZLaOTiyvh9+7qHifGx2FlbYO5hcUb3/vowX1io6PYvHwpAMmJCajVahQKBd0GDDZaRgB3D3dCQ2/oHsfExGBrZ4flK/c2PH3yhLi4OCpUrAhA23btmTFtGinJydgbcRKJ19k7O/Ps4QPd4+TEBCysrDEzt3hjTUzEMxJiotm/QTv+NTU5CY1ajVKRRfse/d5a5le5uLlx/85t3eP42FisbWyxeK3nIzY6ml/Hj8Hb149xv87EzNz8rWdzcs1h/bQxbP0Mf3Cf2KgoNuawfnYv4PoZvHc7D29cBSArMxNnL2/da6lJiZhbWWH62nKxdXR6cZN2znV3Q85zfPMa6nf+ghJVaui9997lC5SsWuuNjReAU7u28uD6lRfZMnDx8tG9lpL07zqpn83O0Vl3n9PrdbZOTqQmJerlflND//71KwSUq4i1rR0Aleo3ZtnkMW/Mbu/swtOwl9tHSkI8ltavb0O511w5cxIPX388fLVXqjVokMpkZKSncf7IQeq36aA7CdJoQJpD71BBNCobRAkvba+IuakJUUkputdsLc3JkGehUGWfbEDIThkVg0XplxPVmLi4oEpOQZP5sidHYmlJxpXrJO8+AIDMxRnnPt1QJ6dg4uZK7IIlqFNSAXD88lMUz54bLV+9UgEEemhPcsxNZcQkp+pes7UwJyNLgUKl39BPzsjEy8k+1zp3e1ukEgmPYxMKnOvcnu08vHEFgCx5Js6ehuyTnIl6lPc+6dim1TT4qItunyTPSOfpvdu6+zLdfP1x8fIhLuKZrucqv9ydXQh9ZchSTHw8dtbWWL6yf99z4jiZcjndRg5HoVQiz8qi28jh/D58JK4OjjSsVl03EUXLuvVYsmVzgbLkxcPdnRs39M8/7F47/3jy4vyj4ovzj/bt2jH111+5eesWz549Y9YffwAQFxeHSq0mKyuLMaNGGT2rLrObK6GvHNdjYmOxs7HRW7a7Dh4kUy6n6zcDUSq0y7brNwP545eJuL7SQ/i/5l1e11q+fDlz587N9vygQYMYPPjleYhardZrqOd28fXfORdKlixJp0IMDf1PNKJUKhWtWrVi9OjRAKSlpaF65YBqZmam+++crmi8/rxarcbESAf/f5WqUInNK5YSHfEcN08vTh7YS4VqNd78RqBYiZJM+Wup7vGuDWtITU5+K7PzVa9Rkzl//smTx4/x9fNj25bN1KuvP/Y5NjaWcWNGs3zVahwcHDiwbx/FigW81QYUQEDpchzYsIa4qEic3T24ePwwJStWNqjGNyCIIdNf3qd1bMdm0lNTs83O9zaVr1yFlYv+JuLZUzy9fTi4ZxdVa9XSq8lIT2fCiB+p37Q5n3Tt9s6yla5QiU3LlxIV8Rx3Ty9O5GP9DChRkml/v1w/d65fQ2pKcqFm56vRqgM1WnUAtJMyrJ0+gcSYKBxc3blx5jhFy1bM9h7fEqU5tX1jjnUPb1zl5Nb1tP/6e9z9imR77/MHd6n/kWH3w9Vt24m6bbU71bSUZJZPGUtCdBSObu5cPXmMgHLZs/mXKsOxretzrAssV4nrZ08RULYiWXI5ty+dp9nn3bN9xqvcff25cuII1Zq2xMzcgrtXLuJZtNgbsweWKce+9auIi4rA2d2T88cOU/K1YY151UQ9e0ropfN88c0QVEolwYcPUKFmHcwtLAk+chAXDy/KVK3O80fhPHv4gM59vn5jJkMcvXGPozfuAWBtbsaAlnVxsrEiPjWdqgF+3H4ebZTv+f8g/UIILt/0xdTHC8XT59h3aE3aqXN6NSYuTnjPmsrj7l+jTs/AqdtnpBw+DoB9h9ZIra2I+WMBMkcH7Nu2IGL8NKPlO3nrASdvaRvxVmam9GlSC0drKxLS0qlU1Id7Edl/64dRcTQuWzzXOl8XRx7FFLwBBVCzdQdqtn65T1rz6/iX+5rTxymWwz7Jr0RpTm3bkGPdwxtXObFlHR36D9HbJ0mkUg6vXY6ljR1exQKJi3hGQnRkoWbnq1G+PLNXreRxRAR+np5sPXSQelWr6tUsnTRF99/PY6LpOmwoK6dNB6BRjRocPneO9o2bYG5qyvGLFygdEFDgPLmpWaMGf/z5J48fP8bPz4/NW7bQoJ7+JCyxsbGMGjOGNatW4eDgwN79+wkoVoyKFSqwe+fLWSL/XrSIxMTEtz47X43KVfhz0SIeP3uGn7c3W/bspv5rx/Vlf74893geFckX/fuzet781z9KKIQePXrk2Nixs7PTe+zh4cHFixd1j2NiYnBzc9OriY6Opk+fPtSsWZOff/65ULk+6EaUiYkJSqWSGjVqsHTpUgYMGICTkxPjx4/Hz89Pr/X5Jnv27KF8+fJcv36d5ORkihfPebangrKzd6D7wO9Y+Ns07VSn7h70HDSERw/usWrBXEbN/NOo31dQTk5OjBozllEjR6JQKvD29mHs+PHcunmTaZMnsXz1GipWqkSPnr34pv/XmMhkuLi6Mm3GjLeezdrOng69vmLjX7NRKZU4urrRqU9/noeHsWP5YvqPm5JrzYfA3sGRAUOG8vvkiSiVCjw8vfhm6HAe3L3D33/+zvR5f7Nv53ZioqO5cOYUF868vDdvzNQZ2L62MzAmO3sHenzzHQtnTkOpVOLq7kGvwUMIv3+PlX/NZcx7XD+tbO1o8kVP9i77G7VSiZ2LK8269AYg6nE4R9ev4PNhY/OsO71jExqNhqPrV+g+17NoIA0+7gJAYmw0dk75HwJibWtHyy97s2PJPFRKFQ4urrTq3heAyEcP2b9mGT1+mpBnXcV6jUiMjWb51HGoVEoq1GmIb1DeU9qXrVmXpLhYVv46AZmJKXZOzrT6ss8b89rY2dO599esnfcnKpUSJ1d3Puo7gGcPw9i2bBHfTJiaaw1Ao/ad2b16GXPHjEClUlK2Wg2q1G+ERCKh6+Af2b1mGUe2b0IqlfFp/8G6njJjSpNnsf38dT6tUwmZVEpCajpbg68B4OVoR/tq5fjrwGmjf+//ClViElHTZuH5y89ITE1QPIskcvJMzEsE4T78Wx73GYziyTMS1mzA9+9ZIJGScT2UmFkLAIhftQGP0UPxWzYfJBLilqxCfvveW8manqVgd8hNOtUoj1QqITEtg10XtT0VHg52tKpUmn+OnsuzDsDRxoqk16ZwLwwrWzuadunFnn/+Qq1UYu/iSrOu2u0v6nE4R9Yt54vh4/KsO7V9IxqNhiPrXt5W4FkskIYfd6VNn4Gc3LoOtUqFzMSU5t366t3vmV9O9vaM6T+An//4HYVSiY+7B2MHfsOtBw+YsuhvXWMpNx81b0Fyaio9fx6JWq2mRJGifPel8S/yOTk5MXbMGEb89JM2p7c3E8aN4+atW0yaPJk1q1ZRqVIlevfqxVcDBmjPP1xcmPkOzj9yzezgwJghPzBy8iTt/x7E05PxQ4dx8+5dJv/5x//rxtK7nIXQzs4uW4MpJ7Vr12bOnDnEx8djaWnJgQMHmDhxou51lUpF//79adWqFQMHDix0LonmA56LUaFQ0K1bN0xNTWnfvj3Lly9HrVZTqlQppkyZgrm5OSVKlODOHe101iNHjqR69ep07tyZ4OBg5s6dy8qVK+nWrRt2dnY8f64dkjBu3DhdV7Ehjly78+ai96yCv+f7jmCQ/Vc//GUJUNrH/c1F71limvFOGt6m6x/IzGlvYmH6QV9T0rG3evMQzA/Bzaf/jd6jrnP+eN8RDCN9NzfSF8bmIUPedwSD2Fi8/SHUxtDV/e1dWDMmk2JF3ncEg6jjC9db+a7YF/tvTE4Rcv/RO/uuyoGGT2a2c+dO/v77bxQKBR9//DH9+vWjX79+fPvtt0RGRjJ48GBKlHh5IbNs2bJMnjy5QLk+6EaUsXTr1o1BgwZRo4Zhw5deJxpRxiMaUcYjGlHGJRpRxiUaUUYmGlFGIxpRxiUaUcb1X2lEXbwX/s6+q2pQkXf2XfnxQc/OJwiCIAiCIAiC8KH5b1x6LaSVK1e+7wiCIAiCIAiC8D/h/8FAtjcSPVGCIAiCIAiCIAj5IBpRgiAIgiAIgiAI+fD/YjifIAiCIAiCIAjGoRaj+URPlCAIgiAIgiAIQn6InihBEARBEARBEAymQXRFiZ4oQRAEQRAEQRCEfBA9UYIgCIIgCIIgGExMcS56ogRBEARBEARBEPJF9EQJgiAIgiAIgmAwteiJEo0oQ0gl7zvBm0kl/4GQ/yH/heUpphc1LnFAMC71f+WmY6nsfScwjFr1vhO8kansv7EsM7Ky3ncEw0jFYCFB+JCJRpQgCIIgCIIgCAYT1x3FPVGCIAiCIAiCIAj5InqiBEEQBEEQBEEwmJidT/RECYIgCIIgCIIg5IvoiRIEQRAEQRAEwWBiMibREyUIgiAIgiAIgpAvoidKEARBEARBEASDiXuiRE+UIAiCIAiCIAhCvohGlCAIgiAIgiAIQj6I4XyCIAiCIAiCIBhMLUbziZ4oQRAEQRAEQRCE/BA9UYIgCIIgCIIgGExMLCEaUUZ1/dIFtq5egVKpxNvPn+4Dv8XSyipbnUajYdncP/D2K0LzDp2yvb5g+hQcnJz4om//t5Lz9KlTzJ83F0VWFoFBQYwaPQZrGxu9mo0b1rNl02YkEvD28eGnUaNxcnIiMzOTmdN/5WZoKBoNlClbhqHDR2BhYWGUbHevXebwlg2olArcffxo36Mv5pZWBtVkpqezY/kiYiMj0GjUVKhVj7qt2gHw8PZNDmxcjVqlxsrGhhaffYmHr79RMl8KPseaf5agUCjwL1qMAUN+xMraWq/mxOFD7Ni0ASQSzM3N6T3gGwKKl9C9HhsTzc/fD2bm/IXY2dsbJdfrrl+6wPY1K1AolPj4+/PlgNzXz+XztOtns/ba9TMjLY2VC+YQ+fwpGrWGmg0b06LjR0bLFh56jbO7t6JSKnH28qbJ5z0ws7A0uE6ekc6RdStIiI5Eo9FQslotqjRpqffem8GnCLt2hbb9BuUrW9iNq5zauQWVUoGLlw/Nu/TC3DJ7ttzq1Go1x7euJ/zmDdRqNVWbtKBC3YYAxEU85+C65SjkcpBAvfYfU6RUWQCunTrG5eOHkUil2Du70LxLTyxtbN+Y987VyxzcvA6lQomHry8de32FxWvbUF41U7/9CjtHZ11t3ZZtqFCrLrevXGLLkr+wd3LRvdZ35Ngcl0VBFPd0pWn5EphIpUQmpbD9/HXkSmWu9Z2qlyc6KYXTdx7qnrMwNaF345psO3+d5wlJRsn1Kqua1XD5uicSU1PkDx4S/esfqNMz9GrsO7fDoXM7NHI5WY+eED1rPuqUVKS2Nrj9OAjzwGKoMzNJ3nOQpC07jZ4xP9xHDUUeFk7i2k3v7Dsf37rO+T3bUakUOHn60OCTL3Pc1vOqWz5uKDb2jrra8g2bEVS5OtFPwjm7fSPKrCzUGjUVGzYnqEqNAuUs5u5M/VKByGRSYpJS2XflJllKVb7qKhbxoby/FyYyGVGJyey7chOVWoOHgx2NyxbH1ESGRCLh/L1wbj6NLFDOf50OucT8tWtQKBQE+vkzqv8ArHPYvwMcv3CeCXPncGT5St1zLfr2xs3JSfe4a7sOtKxXr1CZcnLq1CnmLlhAVlYWQYGBjBk1CpvXzj/Wb9zI5s2bQSLBx9ub0T//jJOTE6mpqfwyaRLhjx6hUatp06YNPbt3N3rGbJnPBzP/n3/IUigILFqU0d8Pwea1Y/y/jp05w/iZMzi2ZetbzyW8fx/0cL5u3bq97wgGS0lKYvm82Xw97Cd+mb0AF3cPtq5enq0u4ukTZk0YTci5Mzl+zv5tm7l/++Zby5mQkMCkXyYw9dfpbNi8BS9vb+bNnatXc/vWLVavWsWipUtZs34Dvr5+LPxrAQDL/lmKSqVi1dp1rFq7Frlczoply4ySLS0lme3LFvHpgO8YNGkmDi5uHNqy3uCao9s3YefoxMAJ0+g36hcuHj/Mkwf3yExPZ8OCP2j28RcMGD+VNl17senvOSgVikJnTkpMZP7vMxk6ZhyzlyzD3dOT1f8s1qt59uQJKxcvZNSkqcyc/zcffdGVGRPH614/fugAY4f+QEJcXKHz5CYlKYkV82fz1dCfmPBi/dyWy/r5x4TRXH5t/dyxfjUOzs6M/X0uI6f9xokDewm7c9so2TJSUzi8bjmtevXny58nYu/sypldW/JVF7x3BzYOjnQZMZ5Ph/zMjdPHiQh/AEBmWhpHN6zi5Nb1aMjflbP0lBT2r/6Hdn0G0mvMFOxdXDm1I/sJZ151104dIyE6ih4//0LXYaMJOXqQiPAwAA5vWEXZmnXpNnI8Lbr0YtfSv1CrVCTFxnBq11Y+/X4E3X+agJ2TM2f2bH9j3rTkZLYu/Zsvvvme76f+hqOrOwc3rTO4JibiOZbWNnwzYarur0KtugA8vn+POi3a6L1mrAaUlbkZHauXZ93pEGbvPUFCajrNKpTIsdbF1pqeDatTxtdD7/kgT1e+alobF9ucT24KS2Zvh/tPQ4gYM5lHX36FIiIS56976dVYViqPY5dPeDbkJx73GUzauYu4DfsWANfBX6HOyOBR9/486f8D1jWrYl2r+lvJ+iam/r54//krNg2Nf5Kcl4zUFI6tX0Gz7l/x2fAJ2Dm5cH7PtnzVJUZHYmFlzUc/jNL9BVWujkaj4eCKhVRp3paPfhhFqz6DOLtzE0kx0fnOaWlmSstKZdh24RpLDp8lMT2D+qUD81UX5OlK5WK+bDgTwtIjZzGRyagS4AdAh2rlOX0njOXHgtl09jINyxbHwbrg21JCchKTFsxn6g9D2fDHbLzc3Zm3ZnWOtY8jIpizcoVeL8Kj58+ws7Fh5fSZur+30YBKSEhgwqRJTJ86lS0bN+Lt7c3c+fP1am7dusWq1atZungxG9auxc/XlwV//w3Agr//xt3NjQ1r17Ji2TI2b9nCtevXjZ5TL3NiIhN//51po8ewafESvD08mffPPznWPn72jNmLF/2/6aHRaDTv7O9D9UE3os6fP/++Ixjs5tXL+AcG4e7pBUCDFq0IPnk8249/bN9u6jZpTpVadbJ9xp0b1wm9EkL9Zi2zvWYswefOUap0afz8tDvzzh99zP59e/VylixVik1btmJjY4NcLicmJhp7ewcAKlWqTK/efZBKpchkMoqXKEFkZIRRsj0IvY53kaI4u2tPjqo1bML14DN62fKqafl5N5p/0gWA1KREVEoFFpZWxEdHYm5pRbEXV/hdPL0wt7Dkadi9Qme+FnKJgOLF8fT2AaB5m3acPHJYL7OpqSn9v/8BR2ft1f2A4sVJTEhAoVAQHxfL+TNnGD15WqGz5OXWtcsUCQjC7cX6Wb95K87nsH4e37ebOk2aU7mm/vr5aa9+fNS9NwBJCfEoFYoce7EK4vGdm7j5+uPg6g5A2ToNuHspOFu2vOrqdfqMOu0/BiAtOQmVUoH5i6vW969cxNreQfd6fjy6HYqHXxEc3bTfWaFuI25dzJ4tr7r71y5TtmYdpDIZFlbWlKhSnVsXzwGgUavJTE8HIEueiYmpKQBqjRq1SkVWZiYatRpFVhYmJqZvzHs/9BreRYvh7O4JQPVGTbl67rRe3rxqnty/i0QqZfHUCcwdO4KjO7agVqsBeHL/LmG3Qpk7diSLp04g/M6tfC/P3AR6uPA8Pon4VO2yuHD/MeX9vHKsrRHkz6Wwp4Q+0b9yXzOoCJuCr5KSKTdarldZVa+M/PZdFE+fA5C0bTe2zRrp1ZiXCCTj0mWUMdoLIqknTmNduwaYmGBePJCU/UdArQalkrSzF7BpmP048C44dG5P8q59pB498U6/9+ndW7j6FsHe1Q2A0rXqc+/y+WzbU151UY/CkEil7Jg/k02/TeLSwd2o1WpUSiVVmrXBp3gpAGwcHLG0sSUtKSHfOYu4OROZkEximraX8crDp5T28cxXXRlfTy7cf0SmQtubeuDqLW4+iUQmlXLmThiPYuIBSM2UkyHPwtay4KM5gq9eo1RAAH6e2u/u3Kw5+0+dzLZcM+Vyxs+dzbfde+g9f/3OXaQSKV+PG0PXYT+yZNNGVOrsvW6FdS44mNKlSunOPz7u3Jm9+/bp5SxVqhRbN23SnX9Ex8Tg8GJ0xtAffuC7b7UXJWJjY8nKysq1R8hYgkNCKF28OH7e3gB81LYN+44eyb5sMzMZN2M633/11VvNI3xYPojhfEqlkvHjx3Pv3j1iY2MpUaIETi+6lT/55BM2btzIiRMnmD17NkqlEh8fHyZOnIijoyONGzemTZs2nD59GhMTEwYOHMjSpUt59OgRI0aMoHXr1owcORJzc3OuX79OWloaAwYMoGPHjkb9NyTExeLk/HKYi6OzC5np6WRmZOidbP47RO/m1ct670+Mj2PDP4v4dvR4ThzYZ9Rsr4qOisLd3V332M3NjbS0NNLT0vSG9JmYmHD82DGmTJqImZkZ/b7W5q5Rs6auJiIigvVr1zLy51FGyZacEKc3jMjO0Ql5RgZZmRm6IX1vqpHIZGxZPJ+bly5QqlIVnD08UcgzUcjlPAi9TkCZcjx7+IDoiGekJCYWOnNsTDQuLw70AM6urmSkp5ORnq4b0ufm4YGbh7bRp9FoWP73X1StWQtTU1OcnF0YNnZ8oXO8SUJsLI4uL9dPB2cXMjOyr5+fv1g/b722fkokEmQyGf/M/o2Qc2eoWL0m7l7eRsmWmhCPjcPLYSQ29o5kZWaikGfqDfN5U51EJuPAqiU8uHqJYuUq4eCmXeZl6zTQ/pvO59z7m5eUhHhsHV9+p62DI1mZGWRlZur1wuRVl/JablsHR2KfPQWg8add2ThnJiHHDpKekkybnl8jlclwdHWnapMWLJs0CnNLK8wsLPnix5/fmDcpPh57p+zbhzwzQzdcL68atVpNQOmyNP/4c22P8x8zMLewpHbzVlja2FK+Zm3KVKnO43t3WD3nd76ZMFXvswrK3tKCpPRM3ePkjEwszEwxNzHJNqRvd4i2pz7Qw0Xv+ZUnLhQ6R15M3FxRRsfqHitjYpHZWCO1stQN6cu8eQeHj9pj4u6GMioau9bNkJqZIrO3JfPWHWxbNCbj+k0kZqbYNKiDJo/him9TzKx5AFhVq/xOvzctMQEbh5fD8KztHVDksK3nVadWq/EOKkn11h1Rq1TsWzIPMwsLytVrQsnqLxult86dJEueiZt/0XzntLU0JyXj5fqYkinH3NQEMxOZ3pC+vOocbaywMjfj45oVsbEw52l8IsdD76FSq7n++LnuPeX9vTEzMSEivuDDT6PjYnF/5fzDzdmZtIwM0jMy9Ib0TVv0N52aNiPQT38ou1Ktolq5cnzTpStKlYofpk3F2tKKz9u0KXCmnETlcv6RlpamN6TPxMSEY8ePM3HyZMzMzOj/omEikUgwMTFhzLhxHD5yhIYNGuDvb5xh+blmjo3BzdX1ZWYXV9LS00lLT9drwE2dM5tOrVoTWDT/69t/lTqfIzv+F30QPVGXL1/G1NSU9evXc/DgQVJSUqj3oit548aNxMfH89tvv7FkyRK2bdtG3bp1mTlzpu79Li4ubNmyhYCAABYuXMjSpUuZMWMGCxcu1NU8efKE9evXs3z5cqZPn05MTIxR/w1qtRok2Z+XSt+8iFVKJYv/mMknPftg/8qJ2Nug1qhBkj2oVCbL9lyDhg3Zf+gwffp9xfeDB+uuSIN2yF//fn35+NNPqWukbn+NWpNjNskry9CQms59BzJ81gIy0tI4vnMr5pZWfPbNEE7u2c5fE37m6tlTFC1RGplJ4a8haDSanH93WfbfPTMzg98nTyQy4hkDvv+x0N+dH2qNOsfnDVk/X9Xr2x+ZsWQVaamp7N60/s1vMIBGo0GS028qkea7rvmXfegz6Xcy09O4sH+XUbLluL1Is2fLre713BqNBolUglKhYPc/f9Pyy958NXEmn343gkPrV5CSEE/4rRvcu3qJfr/M4OtJvxFQviL7Vy01IO+bf+e8aqo2aEzbrj0xM7fA0sqa2s1bcytE2zjpMmgIZavWQCKR4F+8JH6BQTwINc5QGolEkuNQS/WHNIxDIslxWInmlf1i5rVQ4petwXPyaHwX/glqDaqkZDQKJbHzFoMG/JbMwWvyGNIvXEajeD+NqPclt3VPkm17yr2uVI261On4GaZm5phbWlGufhPCb1zVq7tyZD8XD+yiZa+BmJia5TunJKedOtlvpM+rTiaRUsTViR0Xr7Pi+HksTE2pW0p/SGD1IH/qlCzGluArKNU5/5sNoc7tOPTKct20fz8yqYx2jRpnq+vYpClDe/fB0sICW2trvmjbluMXggucJ6+cOe3DZTmcfzRs0IDDBw7wVd++DP7uO73zj4kTJnBo/36Sk5NZvGSJ0XPqZVZrcvydX828addOZDIZ7Vu0eKtZhA/PB9ETVa1aNRwcHFi9ejVhYWGEh4eT/mKIC8DVq1eJiIig+4sbCNVqNfav3Hxfv359ALy8vHBzc8PExAQvLy+Sk5N1NZ07d8bU1BQPDw8qV67MpUuXaNnSeMPmnFxdCb93V/c4MT4OKxsbzA2YcCH8wX1io6LYuFx7kpScmIBarUahUNB9wGCjZQRwd/cg9MYN3eOYmBjs7OywfOWq+pMnT4iLi6NixYoAtGvfnunTppKSnIy9gwMHD+xnxq+/8uOw4bQw4jK0d3bm2cMHusfJiQlYWFljZm5hUM39G9dw9/HF1sERMwsLylavxa2QC2jUaszMzek5bLTufXNGDcXJ7eUVsYJycXXj3u2Xw5riY2OxtrHF4rUbpWOio/h13Bi8/fwY9+tvmJubF/q788PJJYf109qw9RPg5pUQvPz8cXByxsLSkmp16nM5OP89O/8K3rudhy9OfLIyM3F+pVcrNSkRcysrTF9bRraOTkQ9fphj3aPboTh7emNj74CZuQXFK1fnwdWQAmU7vXsbYdevvMiWgYuXzyvfmZBzNicnIh+F5Vhn5+hEalKi7rW0pERsHZyIjXiGIktOsbIVAPAqGoCzhzcR4WE8uXeHgLIVsbK1A6BivcasmDL2jdntnV14GvZy+0hJiMfS+vVtKPeaK2dO4uHrj4evdriNBg1SmYyM9DTOHzlI/TYddCdBGg1IC3EhonHZIEp4abdBc1MTopJSdK/ZWpqTLs9CoTL+kKKCUkbFYFH65X1aJi4uqJJT0LwyfFBiaUnGlesk7z4AgMzFGec+3VAnp2Di5krsgiWoU1IBcPzyUxTPnvO/7uL+nTwKvQZAljwDJ4+X23paciLmllaYmulvTzYOTkQ/Ds+x7u6lYJw9vXF+Zbv8t7GgUio4tn4FCVERdBw0HNt89JLWKVmMQA9tj4OZiYyY5DTda7YW5mRkKVCo9Bs6KRmZeDra51iXminnbkSMrufq5tMIapUoBoBMKqFVpTI421qz+sQFkl/pzSoIdxcXQu+/HJ4eEx+PnbU1lq/s3/ccP0qmPItuw4eiUCqRZ2n/+/eRP3PxxnUC/YsQ9G+vjkaDTGb800MPd3du5PP8o327dkz99VeSU1K4desWgQEBuLq6YmVlRYvmzTly9KjRc+pldnMl9JX7f2NiY7GzsdFbtrsOHiRTLqfrNwNRKrTLtus3A/njl4m4Ohe+p/5D9SFd43pfPoieqMOHDzN06FAsLCzo3Lkz1apV07vio1KpqFy5Mtu3b2f79u1s2rSJ2bNn6143NX15r4BJLgf1V68aqNXqXOsKqnSFSoTdu0NUhPageOLAXipUM2xWoIASJZn291LGzPyTMTP/pH6zllStXdfoDSjQDse7ceMGjx8/BmDr5s3Uq99AryYuNpYxo34m8cVwt/379lIsIAB7BwdOnjjB7zNn8uecuUZtQAEElC7H07D7xEVp73W4ePwwJStWNrgm9GIwx3ZuQaPRoFQoCL0YTJGSpUEiYc3smTx/cTP/jQvnMDE1xd3Hr9CZK1Spwr3bt4h4MTzrwO6dVKtVW68mIz2d8cN/pEadugz5afQ7b0ABlKpQiYf37hD9Yv08mY/1E+DSmVPs3rgOjUaDQqHg0tlTlChbvsB5arTqwOfDxvL5sLF8/P1IIsPDSIyJAuDGmeMULVsx23t8S5TOte7+lYtc2L8TjUaDSqng/pWL+ATlPDHBm9Rp05FuI8fTbeR4vvhxFBHhYSREa7/z6qnjBJarlO09RUqWybUuoHwlQs+dQq1SkZmezp2QCwSUr4SDixtZmRk8D7sPQGJMNHGRz3Hz8cPd14+HodfIkmtPru5duYRnkWJvzB5YphxPwu4RF6W9T/H8scOUrFjF4JqoZ085vG2j9iJOVhbBhw9QrnotzC0sCT5ykJuXtL1Szx+F8+zhA4IKsQ4cuXGPBQdOseDAKRYdOoOvswNONtqhR9UC/Ln9PP8TArxN6RdCsChdElMf7b1a9h1ak3bqnF6NiYsT3n/+itRKe1Lo1O0zUg4f19U799FOliRzdMC+bQtSDh17d/+A96Rqi3a6CSA6Dh5O9OOHuskebp09iX+ZCtne41OiVK51CZHPuXhgF2q1GqUii9DTxyhWsSoAR9YuIyszkw6DhuWrAQVw+rZ2ooflx4JZfeICXo52uskeKhTx5n5k9tEr4dFxudbdeR5NSW83TF408AI93IhM0F7YbVO5LGYmJqw5WfgGFECN8hW4ce8ejyO02/TWgweoV7WaXs3SKdNY89vvrJw+k99H/oy5mRkrp8/E1cmJB0+esGjDelRqFZlZcjbu30fT2rVz+qpCqVmjht75x+YtW2jw2kiW2NhYfh49Wnf+sXf/fgKKFcPB3p6Dhw6xcPFiNBoNWVlZHDx0iKpVqxo956tqVK7Cjdu3efzsGQBb9uymfq1aejXL/pzNur/+ZvW8+cya+AvmZmasnjf/f7oBJWh9ED1RZ8+epVWrVnz00Uc8efKE4OBgatWqhUwmQ6lUUqFCBUaPHs3Dhw8pWrQo8+fPJyoqimnTDL8Zf+/evbRs2ZLnz59z7do1Jk+ebNR/g529Az2++Y6FM6ehVCpxdfeg1+AhhN+/x8q/5jJm5p9G/b6CcnJyYszYsfw8cgQKhQIfHx/Gjp/ArZs3mTJpEivXrKFipUr07NWbgV9/hUxmgourC9NnaIdPzvnzDzQaDVMmTdJ9ZvkKFRg2YkShs1nb2dOh11ds/Gs2KqUSR1c3OvXpz/PwMHYsX0z/cVNyrQFo8WkXdq36hwXjfwKgZKUq1GzSAolEQue+A9m5YgkqpRIbBwc+/2ZIjsMK8svewZGBPwzjt0m/oFQqcff0ZNCwETy4e4cFf/zOzPl/s2/HNmKiowk+c5rgM6d17x03bTq2dm9nOvPX2dk70H3gdyz8bRoqpRIXdw96DhrCowf3WLVgLqPesH5+1KM3axYuYOKP2oZ9xeo1adS6nVGyWdna0eSLnuxd9jdqpRI7F1eaddFOYhH1OJyj61fw+bCxedbV7fAJxzauYu30CQAUK1eJCvWbGCVb86692LlkPmqVCnsXV1p26wNA5ONwDq5ZRreR4/Osq1C3IYmx0aycNh6VSkn5Og3wfdHAa9d3EEc3r0WpUCCVSWn2eXccXN2wd3ElKT6O1dN/QWZiip2TMy2+7P3GvDZ29nTu/TVr5/2JSqXEydWdj/oO4NnDMLYtW8Q3E6bmWgPQqH1ndq9extwxI1CplJStVoMq9RshkUjoOvhHdq9ZxpHtm5BKZXzafzDWL3rKCitNnsXW89f4vE5lZFIp8anpbAnW9lR6OdrToVo5Fhw4ZZTvKihVYhJR02bh+cvPSExNUDyLJHLyTMxLBOE+/Fse9xmM4skzEtZswPfvWSCRknE9lJhZ2plN41dtwGP0UPyWzQeJhLglq5DfLvzkNv8lljZ2NPi0OwdXLkStUmHn7ELDz3sCEPPkESc2ruKjH0blWVelWRtObVvHpt8molarKFa+MiWr1yEqPIyH10Kwd3Vjx9yXw/2rt+mEb4nS+cqZnqVg7+WbdKhWHplUSmJaOntCQgFwd7ClZcXSLD8WnGfdlYdPsDQzoVvD6kglEqISUzhw9S5ejvaU8HYnPjWNLvVeNnSOh94j/MVkE/nlZG/PmAED+fn331Aolfh4uDP2m0HcevCAKX8vYOX0mXm+v+/HnzBz6RK6Dv0RpUpFk5q16NC48PvPbDmdnBg7ZgwjfvpJm9PbmwnjxnHz1i0mTZ7MmlWrqFSpEr179eKrAQMwkclwcXFh5owZAAz57jumTJvGZ120E0g1atCALz77zOg59TI7ODBmyA+MnDxJ+7+v8fRk/NBh3Lx7l8l//sHqefPf/CH/oz7kWfPeFYnmA1gKd+7cYejQoYC2V8nb25tixYoRFhZGWFgYW7Zs4fTp0/z555+o1Wrc3d2ZMWOGbmKJFStW4OPjw5w5cwAYPHgwT58+pXv37hw5coSRI0cSHx+vm83lhx9+oHHj7OOCc3Ps+p238u82pgr+Oc9k9aHZe8U402K/bWVfmz75QxSbkv7mog9AaCH//yfviplJ9nH5HyLHQkyF/C7deBr1viMYpNu8uW8u+hC8hdnSjG3H8MJfTHsXcrtH9EPT19/tzUUfAJMihR/V8S6o4/M/U+P7YF/svzE5xb4XFwzehZaVy7yz78qPD6InqkSJEuzcmff/dLBx48Y5NnyOHDmi++/Bg18Of/Px8dF7rWXLlnTu3NkIaQVBEARBEATh/68PauKf9+SDuCdKEARBEARBEAThv+KD6Il62/Jz75QgCIIgCIIgCLn7AO4Geu9ET5QgCIIgCIIgCEI+iEaUIAiCIAiCIAhCPvy/GM4nCIIgCIIgCIJxqMVoPtETJQiCIAiCIAiCkB+iJ0oQBEEQBEEQBIOJiSVET5QgCIIgCIIgCEK+iJ4oQRAEQRAEQRAMJnqiRE+UIAiCIAiCIAhCvoieKEEQBEEQBEEQDKYWPVGiEWWIB9Hx7zvCG8mk/41OxYcxH/6yBCjq5vS+I7zR3AOn33cEgzQrF/S+IxgkI0vxviMYxMxE9r4jGOSHSv+N3/2vIUPedwSDmMo+/N+9/fRf33cEg/zY7pP3HcEguy/fft8RDOJkY/W+IxgkNVP+viMY5MDoAe87gmAg0YgSBEEQBEEQBMFgoiNK3BMlCIIgCIIgCIKQL6InShAEQRAEQRAEg6kRXVGiJ0oQBEEQBEEQBCEfRE+UIAiCIAiCIAgGE/+fKNETJQiCIAiCIAiCkC+iJ0oQBEEQBEEQBIOJnijREyUIgiAIgiAIgpAvohElCIIgCIIgCIKQD2I4nyAIgiAIgiAIBlOL0XyiJ0oQBEEQBEEQBCE/RE+UIAiCIAiCIAgGExNLiEZUgTy4foUT2zehVCpx8/ah5Zd9MLe0NKhGrVZzdPNaHt68jlqtplqTllSq3xiAR3ducXTLOjQqFRbWNjT5pAtuPn4APLl3h2Nb16NUKDC3tKR19744uLgVKP+1ixfYvHo5SoUCH/8i9PzmOyytrLLVaTQals6ZhY9fEVp07Kx7/vseXXB0dtY9btGhMzUbNCpQlpwEerjQsGxxTKRSopNS2HXpBllKlcF1EqBFpVL4uTgB8CAyhsPX7+q9197Kkj5NarH25EUiEpMLlffKxfNsWrFMuzyLFKXP4O9zXZ6L//wdH/8itOr0ke75w3t2ceLgfrKysigSEEjvwd9jampaqEy5qVLMh671qmIqk/EoJp55+0+RkaXIVle/VAAdq5VDgwa5QsmSI+d4EBWHVCKhb5OalPH1ACAk7CnLj18wSraHodc4vXMzKqUSFy8fmn7RM9t2ZUhdSkI8636fwpcjxmFpY6t7/tGtUE7u2MSXI8YVOOOjm9c5t2cbKqUSZ09vGn3WDTOL7Blzq5NnZHBswwoSoqNAo6ZE1VpUatyC+MjnHFq9VPd+jVpNfORzWvT4mmLlK+Ur4/3rVzi+fRMqhRJXHx9a57B/elNNcnwcK6ZPpPfoiVi9WIYZaakcXL+K2IjnKBVZ1G7VjrI16uQrW25OXbjAghXLyVIoCCxShFHffodNDtsQwPGzZxk/63eObtgIQKZczoy/FnDz7l00GihTojjD+g/AwtzcKNleF+DuQoMygcikUmKSU9kTEprj/im3uo7Vy+No/fLfZm9twZPYRDafu1KoXI9vXef8nu2oVAqcPH1o8MmXOa6bedUtHzcUG3tHXW35hs0Iqlyd6CfhnN2+EWVWFmqNmooNmxNUpUah8uaH+6ihyMPCSVy76Z1956uqFvOle4NqmMikPIqJZ/bekznuNxuWDqRTjXJoNCBXKll06Cz3I2OxsTBnQPM6FHVzQq5Qcuj6XXaH3DR6zppB/vRrWgtTmYywqDim7zhMujx7zk7Vy9G+alkAnsUnMXPnURLTMgDYPrwPMcmputp1py9z6LXjZ2EV9jg0rH0jPBzsdHVu9rbcfBLJ1G2HjJqzeqAfvRvVxNRExsOoOH7fdZT0HHK2r1qWtlXKgAaeJyTxx+7jJKZnYGYiY1DLepT0cgcJ3H4Wxdx9J3PcXwj/Tf+Z4XzBwcF069btfccgPSWZvSuX0OGrQfQbPw17FzeOb9tocM3Vk0dJiI6i9+jJdB8xjktHDxARHoY8I51tC+fQqNNn9Bo9ieZfdGf74vkoFQpSEuLZunA2zT7vTq9REylesSoH160oUP6UpCT+mfsHA4f9xOS5f+Pq7sHmlcuy1T1/+oTfxo3i0tnTes9HPnuKtY0N436fo/szZgPKysyUtlXKsvncFf46cIqEtAwaly2er7py/l4421iz6OBpFh86g5+LEyW93XXvlUmldKhWDplUUui8yUlJLJk9i0EjRzFtwSLcPDzYuOKfbHXPnzxm+pifuHDmlN7zF8+e5tDunQz7ZQqT5ywgK0vO/u1bC50rJ3aWFgxqWY8Z248weOlmopJS6Fa/arY6L0c7ejSoxsTN+/lxxXY2nbvK8A5NAGhQOgBvJ3uGLNvGD8u3UcbXg1rFixQ6W3pKCgdW/0Ob3gPpMXoyds6unN65Od91N8+fYeOf00lLStQ9p8zK4syurexZ9jcadcEPXhmpKRxZv4IWPb6iy8gJ2Dm7cG539t8qr7oL+3Zgbe/I58PG8tF3PxF65jiR4WE4eXjx6Y+jdX++JUoTWKlavhtQ6SnJ7FmxhE5fDeKrCdNwcHHjWA77p7xqrp87zerfp5L6yjIE2L18MbaOTvQe9Quffzecg+tXk5wQn698OUlISmLSn38w9aef2PjX33h7eDB/2bIcax8/f8bsf5bqXQFdtmEDKpWK1XPmsnrOHORZWSzfuDHH9xeWpZkprauUYWvwNRYdOkNiWjoNywTlq27b+Wv8c/Qc/xw9x97LN5ErlBy4eqtQuTJSUzi2fgXNun/FZ8MnYOfkwvk92/JVlxgdiYWVNR/9MEr3F1S5OhqNhoMrFlKleVs++mEUrfoM4uzOTSTFRBcqsyFM/X3x/vNXbBrWe+vflRs7Swu+bV2fqdsOMXDxJiITU+jRoFq2Om8ne3o2qs74Dfv4ftlWNpy5zE+dmgLQt3ENMhUKBi3ZzLCVO6hSzJeqAb5GzWlvZcGIjk0Yu34v3eeu5nlCEl81rZ2trrinK5/VrsSgJZvpNX8tz+KT6N1I2yD2dXYgOUNO37/W6/6M3YAyxnFoxo6j/LhiOz+u2M6CA6dJl2ex8PBZo+a0t7JgaLvG/LJpP30WrCUiMZk+jWtmqwvycOHjmhX4ftlWvlq4nmfxSfRoqF0/utStgkwq5euF6+m/cAPmpiZ8XqeyUXO+TxqN5p39faj+M42oD8XDWzfw8C+Kk5v2Snyl+o24eeGs3o+cV83dqyGUrVUXqUyGhZU1JavUIPT8GeKjozC3tMS/ZGkAnD28MLew5PnD+9y5fIFipcvj4VcEgIr1GtL44y4Fyh96JYQigUG4e3kD0LBla4JPHsu2kh7du4t6TZtTtXZdvefv376FRCrl11HDGTdkEDs3rEWtMt5VlaLuLkQkJJOQmg5ASNhjyvh55qtOIpFgaiJDJpMik2r/VGq17r0tK5bi2qPnOV6hy68bl0MoGlgcjxfLs1HLNpw9fjTb8jy8Zxf1m7WgWh39k4HTRw/TskMnbGxtkUql9BgwmDqNGhc6V04qFvHifmSsrudt35Xb1CsVkK1OoVIz/0XDFOBBVCwO1paYSKVIJRLMTU0xkUkxlckwkcpQGOGq2uPbobj7FcHRTdvYLV+3IbcvBmdbjnnVpSYl8uDaZToN/F7vPeG3Q1FkyWn+Ze9CZXxy5yZuvv44uGq/u0zt+twLOZ8tY151dTp+Su122l7I9JQkVEolZhYWeu9/HnaPB9dCaFCAbfzhrRt4Fnlt33M++/4pt5qUxATuXQ3hs8FD9T43Iy2V8Nuh1G3TAQA7Ryd6jBiLpbV1vjO+LvhyCKWCgvB7sQ11btWafcez75MyMzMZ/9tvfNenr97zFcuUofdnnyOVSpHJZJQoVozIt3SCX9TNmYiEJBLStPudyw+fUvpFr2x+66QSCW2rlOHwtTukZMgLlevp3Vu4+hbB3lU7OqF0rfrcu5x93cyrLupRGBKplB3zZ7Lpt0lcOrgbtVqNSqmkSrM2+BQvBYCNgyOWNrakJSUUKrMhHDq3J3nXPlKPnnjr35WbSkW9uRcZQ0SCdr+59/ItGpQJzFanUKqYu/ekbr95P/LlfjPAw4WjN+6j1mhQqtVcfPCYOiWKGjVntQA/bj+L5ll8EgA7Lt6gabnsFyDvRsTQdfYq0uRZmJnIcLG1JjkjE4Ayvp6o1Wpm9+rMkgGf071BNaSSwl9sfJUxjkP/MpFKGdyqHkuPBBOXkmbUnFWK+XLneTTPE7TLc9elUBqXzX7B5F5kLL3mryVdnoWpTIaLnTXJL7bn64+fs+bUJTSAWqPhfmQs7va22T5D+O/6Tw3ni4+Pp1+/fjx+/JiiRYsyfPhw+vbty5EjRwCYM2cOAIMHD6ZOnTo0adKEa9eu4eLiwkcffcTKlSuJjIxk2rRpVK9evUAZUhLisXV00j22dXAiKzODrMxM3XCYvGpSEuKwe/U1Rydinj3Fyc0DRVYWD2/eoGjpskSEhxEb8YzUpCTio6IwNTdnx5L5xEdFYufkTOOPvyhQ/vi4WJxcXHSPHZ1dyEhPJzMjQ28IWtd+AwAIvXpZ7/1qtYrS5SvyUbeeqFRK/pw0AQtLK5q161CgPK+zs7TQ7dABkjPkWJiaYmYi0+sCz6vuWvgzSnl78G3rhkglEh5Gx3EvIgaAikW8kUolXAl/Sp2SxQqdNz42Rm95OrnkvDy7fT0QgBtX9Jdn1LNnJAclMXP8GBLj4yheugyf9exT6Fw5cba1ITb55YEmLiUNa3MzLM1M9YZSxCSn6g3n6NmwOhcfPEapVnM09D61SxRlcf/PkUmlXAl/xsWwJ4XOlpL4+jbjmG27elOdjb0D7fp+k+2zA8tXIrB8JZ7cu12ojKmJCdg4vBzqZGPvSFZmJgp5pt6wqTfVSWQyDq1eSti1EIqWrYiDm/7J9dmdW6jRqkOOQ7HeJPm1fY+dgxPy15ZjXjW2Do50/npwts9NiInG2s6B84f2ExZ6DZVSSfWmLXFyz96AyK+omFjcX9mG3FxcSEtPJy0jQ29I39R58+jYsiWBRYrovb9m5ZdXdiOio1m3Ywc/fTOo0LlyYmdlodfgyXX/ZEBdhSLepGbKufti31QYaa+tc9b2DihyWDfzqlOr1XgHlaR6646oVSr2LZmHmYUF5eo1oWT1l8M2b507SZY8Ezd/4zYCchIzax4AVtXe39V7F1trvf1mbC77zejkVKJf2W/2aVyT8/e1+827ETE0KhvIrWeRmMpk1C5RFOUrF/aMwc3eRm+/HZOcio2FOVbmptkuGKrUauqWLMqw9o3JUqpYejQYAJlUwqWwpyw8dAYTmZSpXdqRLs9i07mrRstpjOPQv5qUK058agbB9x8ZLd+/XO2yL09rC3OszEyzDelTqdXULl6EIW0bolCqWH5MO8T9UthTXY2bvQ2dq5fnj93HjZ71fVF/wD1E78p/qifq+fPnjB07lr179xIbG8vZs7l338bGxlK/fn22bduGXC7n0KFDrFmzhsGDB7N8+fICZ9BoNEhyuDIjeeXqSF412iuDr7ym0SCVSjC3tKTT199ybv9O/pk8htDg0/iVKIXMRIZareT+tRDqtutMz59/wb9EabYtnFOw/GoN5JBNKjVsVajfrCVd+vXH3MICK2sbmrfvyOVg43WjSySgIfuG+fq2mlddvdKBpMuz+GPXUebsOY6FqSk1gvzxcLClcjFf9l423lj03H5rQ5enSqUi9Mplvhn+E+N/+5O01FQ2rSr4+pkXaS7LLLcdobmpCUPbNcLTwY55+7XDOj+tXZGk9Ex6z19Lv7/WYWNhrhtbXxi5dde/vhwNrXsbsm27L0gkOWXMu65p1970+mUm8vR0Lh7YrXs+8uEDMtNSCKqUfbiQQRnVGiQ5ffer+ycDal6nVqlIiovB3MKSbsNG06HPAA5vWkvko/AC5dTLnMs2JHslz6bdu5HJZLRv1jzXz7l1/z5fjxjBJ23aUreAF8neRELO6+DrzxlSVy3QjzN3Hholl0aT8wn5679pXnWlatSlTsfPMDUzx9zSinL1mxB+Q//k+cqR/Vw8sIuWvQZiYmpmlOwfutx6YvLab47o0BhPRzvm7j0JwNIjwWg08EfPTvzcuRlXwp+hVBm3ESWRSHJc59S5zEN96vZDOkxfwrJj55nRrT0SCewOucnsvSfIVChJzcxi49kr1DXCxcZXGeM49K92VcqwqZD3EuZGIpHkkDL3nGfuhvPJ78tYefIiU7u01dvDBnm48Hv3jmy/eOOtNPiE9+c/1RNVsmRJfH2144gDAgJISMh7OEH9+vUB8Pb2pkqVKgB4eXmRnFzwiQTsHJ2JCA/TPU5JTMDCyhqzV25izqvGztFZ716D1KREbByd0KjVmJmb88WQn3SvLRo/AkdXd2Lsn+JdLEg3/KZc7foc3rgaRVYWpmb5O5A5ubry8N4d3ePEuDisbGwwf21IUW7OHjuCT5Gi+BbRXoXUaDTITGT5yvC6+qUDKe7pCoCZqQkxSS+v/thampORpUDx2pDB5PRMvJ0ccqwr4eXGgau3UWs0yJVKrj9+Rklvd+wsLTEzMaFnwxq693SoXp7D1+/oeqryy9nVlbC7L5dnQlws1vlYng5OTlSpVVvXa1WrYSN2rFtToCw5+bxOJaoFaCcnsTQz43Hsy3tYnG2tSMmQI1cos73Pxdaanzs142l8ImM37NVdPa8ZVITFh8+iVKtRZqk5FnqPWsWLsOPijXxnO7t7Gw9enKRlZWbg4umjey01KRFzKytMX5scwNbRicjwh2+sM5bz+3YQHnrtRcZMnD29dK+lJSVibplzxujHD3Ose3w7FGdPb6ztHTA1tyCwUlXCrr/snbx/9SLFq9TMs0GTFzsnZ56/af9kQM3rbOwdACj3Ynivo5s7PoFBPA8Pw8O/SIGy/svd1ZUbr2xDMXFx2NnYYPnKNrT78CEy5XK+/HYwCqUSeVYWX347mFnjxuPq7MyBE8eZsWABQ7/uT4uGDQuV53X1SgUQ6KHdP5mbyvSuTtta/Lvf0T8hTs7IxMvJPtc6d3tbpBIJj2MLPiTu4v6dPPp33ZRn4OThrXstLfnFOmem/5vaODgR/Tg8x7q7l4Jx9vTG2evldvjvxQmVUsGx9StIiIqg46Dh2Do587+sS93KVA/0B8DK3JRHMS9/J2dba1IyMnPdb475uDlP4hIZtXa3br9paWbKsmPnSc3U9k5+UrMCES+GiRVGr0bVdcMCrczNCIuKeyWLDckZmWS+ltPbyR4nGyuuP44AtMMTf2jbEFsLC2oE+fMgKlb3ORIJekPhC8rYxyGAom5OSKVSQp9EFjrfv7o3qEatoCKAdnk+jH5ledpZ57g8vRztcLSx0uXYf+U237aqj42lOSkZchqWDmRQq3rM23eKo6H3jJb1QyA6ov5jPVEmJi/bfP9euXz1yotSqb9ym73SwJDJCnei/68ipcvy/OED4qO1G8yVk0cJfO3m77xqAstX4vqZE6hVKjLT07h1MZig8pVBImHTvN+JeKQ9+bp1MRgTE1NcvX0JqlCZZ2H3SIzVnujfu3IRF0/vfDegAMpUqMSDu3eIev4MgGMH9lCxWvabJXPz7PEjtq9bjVqlIksu58jeXdnu88mvEzfvs/jwWRYfPsuyo8F4OdnjaKNtVFQu6svd59nvbwiLjsu1LjIxmVI+2vtRpBIJQZ5uPItP4uC12/x14JTuu1Iy5Gw/f63ADSiAshUr8+DObSJfLM+j+/ZQqbrhy7Nq7bpcOH2SLLkcjUZDyLmzFA3KPo69oNadvqy7AfenNTsp7umG54tZjZpXKMmFB9mvilmYmvDLZ605dy+c33cd0ztwhUXFUfvFQVsmlVAtwK/Aw5FqtenIlyPG8eWIcXz+w89EPnqgnbUOuHbqGAHlKmZ7j3/JMgbVGUv1lu11kz10/nY4UY8ekhij/e7QsycoUrZCtvf4FC+Va92Dq5e4eGA3Go0GlVLBg6uX8A4soXvv8wf38AkqWeC8RUvp73sunzxKUIVK+a55nYOLK+6+/tw4p70SnJacxLMH9/EsZAMKoEalSty4c4fHL7ahLXv3UK+G/jb0z++zWDtvPqtmz2HWuPGYm5mxavYcXJ2dOXk+mN8XLmT2LxON3oACOHnrgW4iiBXHzuPlaK+bXa9SUR/uRWTfPz2MisuzztfFUe/EvCCqtminmwCi4+DhRD9+qJvs4dbZk/iXyWHdLFEq17qEyOdcPLALtVqNUpFF6OljFKuoveH/yNplZGVm0mHQsP/5BhTAmlMhfL9sK98v28qwlTso4eWGp6N2v9mqYkmC7z/O9h5LM1OmdGnD2bvhzNxxVG+/2apSKbrW1Q5JdLCypFmFEhy/+aDQOf85el43AcTAxZso7eOB94vGe/uqZTl9O3tPp5ONFWM/boG9lfYiRdPyxXkYHU9yRiZF3Zzo3agGUokEMxMZnaqX58iNwp/4G/s4BFDGx4Mbj58XOturVhy/wIDFGxmweCPf/bOFUt7ueDlql2fbymU4ezc823ucbLQNPTtL7fJsXDaI8Jh4UjLk1AzyZ2CLuvy0Ztf/XANK0PpP9US9ztbWlsTEROLj47GxseHkyZM0amS8meJyYm1rR6tufdi+aB4qpRIHVzfa9OhHxKOH7F+9lJ4/T8y1BqBS/cYkxkbzz5QxqJQqKtZtiF9x7UlT21792b/6H1QqJTZ2DnTq/y0SiQR3X3+afdadbQtno1KpsLCypkMO934Yws7BgV6DvmPBjKna6dc9POn97Q+E37/H8vmzGfd73sME2332BWsW/cW4IYNQqZRUrVWXek1bFChLTtLlWey6dIOPalREJpWQkJbOjgvaXg5PBzvaVCnD4sNn86w7dO0OLSqW4uvmddBoIDw6jrNGGjbzOjsHB/p8O4R5v055sTw96Pf9UB7eu8vSebOZ+MfcPN/fpFUb0lJTGP/Dt6jVavwDAvmid7+3kjUpPZO5+04yrH1jTGRSIhOTmb1Xe8N2gLszA1vU5ccV22ldqTSudtbUCPKnRpC/7v3jNuxj6dFg+jWpxexenVFrNFx//Jxt568XOpuVrR3NuvRi99IFqFRKHFzcaPFiIoiox+EcXLucL0eMy7PubbOytaPR5905sHwhKpUKe2dXGnfpCUD0k0cc27CST38cnWdd7fYfc2LTGtbPnAhAsbIVKV/v5UQiSbHRhTpJtbazo033PmxdOA/1i+XTtqd2/7R31VJ6j5qYa82bdO7/LQfWreDyiSPaSTLadMCzSOGH+jg5ODDmu+/4aap2n+Tt4cm4H37g1r17TJ4zm1Wz894nzV6qna1v8pzZuufKlyrN8AEDCp3tdelZCnaH3KRTjfJIpRIS0zLY9aIX1sPBjlaVSvPP0XN51gE42liRlJ5htFyWNnY0+LQ7B1cuRK1SYefsQsPPewIQ8+QRJzau4qMfRuVZV6VZG05tW8em3yaiVqsoVr4yJavXISo8jIfXQrB3dWPH3Jm676zephO+JUob7d/woUpKz+TPPccZ2bEJJjIZkQnJzHpxX0ughwuDWtbj+2VbaVO5NK52NtQMKkLNF70ZAGPW7WHTuasMadOAOb07I5FIWHMyhPuRsUbNmZiWwa/bDzPh01aYyqQ8T0hmytaDAJTwcmNY+0b0/Ws91x9HsPLERf7o2QmVWk1sShqj12mHFC8/foHvWtdn6cAvMJFKOXbzvtGnYjfGcSg1U46no73ePWjGlpiewcydRxnzcXNMZTKeJyQxY7v2/vsgT1d+aNOQAYs3cuNJBGtPhTCzWwdUajVxqWlM2LgPgH4vZkf8oU1D3eeGPo1k7r6Tby33uyTuiQKJ5kOeO/AVwcHBzJ07l5UrVwIwcuRIqlevTkREBJs3b8bDw4OgoCBcXFwYPHgwJUqU4M6dO3q1nTt3zvY5hlhi5Kkz34biHi5vLvoAnLgd9uaiD0DjHGZf+tDM2PXfuEG1WbnsMxp9iHIaTvIh+vcK8oeuk89/Y5/0V2j4+45gEFMjjaZ4m9pP//V9RzDIj+0+ed8RDPLq5EkfMiebnP+fbh+af4dTfugOjDb+BaC3YfmLCUnehR6N3t3/ly4//jM9UTVq1KBGjZcLcdq0abr//uab7L0y/zagXq99/XMEQRAEQRAEQTBcThOE/H/zn7onShAEQRAEQRAE4X37z/RECYIgCIIgCILw/v1H7gZ6q0RPlCAIgiAIgiAIQj6IRpQgCIIgCIIgCEI+iEaUIAiCIAiCIAgGU2ve3V9+7Ny5k9atW9O8eXNWr16d7fVbt27RuXNnWrRowahRo7L9P2bzQzSiBEEQBEEQBEH4T4uKimLWrFmsWbOGbdu2sX79eu7fv69XM2zYMMaOHcv+/fvRaDRs2LChwN8nGlGCIAiCIAiCIBhMo9G8s7/k5GSePn2a7S85OVkv05kzZ6hZsyYODg5YWVnRokUL9u3bp3v92bNnZGZmUrFiRQA6d+6s93p+idn5BEEQBEEQBEH4IC1fvpy5c+dme37QoEEMHjxY9zg6OhpXV1fdYzc3N65du5br666urkRFRRU4l2hECYIgCIIgCIJgsHc5xXmPHj3o1KlTtuft7Oz0HqvVaiQSie6xRqPRe/ym1/NLNKIEQRAEQRAEQfgg2dnZZWsw5cTDw4OLFy/qHsfExODm5qb3ekxMjO5xbGys3uv5Je6JEgRBEARBEATBYGqN5p39Gap27dqcPXuW+Ph4MjIyOHDgAPXr19e97u3tjbm5OZcuXQJg+/bteq/nl+iJMkBUUsr7jvBGN58WfEznu1Tc0+V9RzDItouh7zvCG33bos77jmCQ+1Fx7zuCQSxM/xu7w0xFwadjfZe2P4t/3xEMYmNh/r4jGCQjK+t9R3ijH9t98r4jGOS3nRvfdwSD3J41831HMIharX7fEQwilYp+g/917u7uDBkyhO7du6NQKPj4448pX748/fr149tvv6VcuXLMnDmT0aNHk5qaSpkyZejevXuBv++/cdYgCIIgCIIgCMIHIT89RO9Su3btaNeund5zixYt0v13yZIl2bRpk1G+SzTLBUEQBEEQBEEQ8kH0RAmCIAiCIAiCYLB3OTvfh0r0RAmCIAiCIAiCIOSD6IkSBEEQBEEQBMFgatERJXqiBEEQBEEQBEEQ8kP0RAmCIAiCIAiCYDBxT5ToiRIEQRAEQRAEQcgX0YgSBEEQBEEQBEHIBzGcTxAEQRAEQRAEg4nhfKInShAEQRAEQRAEIV9ET5QgCIIgCIIgCAZTi54o0YgyhgAPFxqVCUImlRKdlMLukFCylCqD6zrXqICjtaWuzt7aksexCWw6e4VAD1faVS1LcnqG7vWVJy7k+Pn5UcrbndaVS2MikxKRkMz6M5eRK5S51n9epzKRickcC72ve+6Xz1qRmJ6pe3zsxj1CHj7Nd5awG1c5tXMLKqUCFy8fmnfphbmlpcF1arWa41vXE37zBmq1mqpNWlChbkMA4iKec3DdchRyOUigXvuPKVKqLOcP7OFOyHndZ6enpqCQZzJoxrx85wco6e1Gy4ovl+emc1fyXJ6f1qpEZGIyJ249yPZat/rVSM7IZPuF6wXKkpvrly6wdfUKlEol3n7+dB/4LZZWVtnqNBoNy+b+gbdfEZp36JTt9QXTp+Dg5MQXffsbJdeDG1c5sX0TKqUSV28fWnbtne33z61GrVZzbMs6Ht68jlqlplrTllSs10jvvYmxMaz8dQKfDPoRD/+iBc55//oVjm/fhEqhxNXHh9Zf9smW8001yfFxrJg+kd6jJ2JlYwtARHgYhzauQZElR6PWUKN5a8rWqF3gnAAPQ69xeudmVEolLl4+NP2iZ47b1JvqUhLiWff7FL4cMQ7LF3kjHz3k+JZ1KLKy0KjVVG3aklLVauU74/3rVzi6bSMqpQI3b1/adOub4/LMqUaRlcX+dct5Hh4GGvAqWowWn/fA1MyMjLRUDqxbSWzkMxRZCuq0ak+5mnXyne/15XR21xbdcmryRQ/MLHJenjnVKbOyOLZpDVGPHwIa3P2K0fDjLpiYmfH03m1O79iESqXCxNSU+p2/KNB6WszdmfqlApHJpMQkpbLvys0cjxN51VUs4kN5fy9MZDKiEpPZd+UmKrUGDwc7GpctjqmJDIlEwvl74dx8GpnvjDmpWsyX7g2qYSKT8igmntl7T5KRpchW17B0IJ1qlEOjAblSyaJDZ7kfGYuNhTkDmtehqJsTcoWSQ9fvsjvkplGy5Zf7qKHIw8JJXLvpnX/3zcuX2LthNUqFEk8/Pz7tOxCL1/bvudWkp6aw+Z9FPH8Ujpm5OdUaNKJu89ZGy3brSgh7N65BqVDg6evPJ337Y2FpZVCNWq1m24olhN3W/qYlK1SizefdkEgkPAm7z47Vy8mSZ6JRq2nYpgOV69QvcM63tQzv37zBrrUrUKlUmJqa0bF7b/wCggqcU/hwGGU43/Xr1xk1alS+3lOiRAljfHW+PH36lMaNGxv1M63MTGlbuSybz13l74OnSUzLoFHZ4vmq2xJ8lSVHzrHkyDn2XL6JXKFk/5VbAPg42xN8L1z3+pIj5wrdgLI2N+OzOpVZfuw8v247TFxKGm0ql86x1s3ehv7N61De30vveVc7G9LlCn7feVT3V5AGVHpKCvtX/0O7PgPpNWYK9i6unNqR/QCUV921U8dIiI6ix8+/0HXYaEKOHiQiPAyAwxtWUbZmXbqNHE+LLr3YtfQv1CoV1Zu3ptvI8XQbOZ5Pvh2OqZk5bXoWrFFgbW7GJ7UqsfLEBWbuOEJ8ahqtKpbKsdbNzoZ+TWtRzt8zx9cblA6kiJtTgXLkJSUpieXzZvP1sJ/4ZfYCXNw92Lp6eba6iKdPmDVhNCHnzuT4Ofu3beb+beOdoKSnJLNv5RI69vuGvuOm4uDiyontGw2uuXrqGPFRkfQaNYluI8Zy6egB3W8PoFQo2L18ISpl7g1aQ3PuWbGETl8N4qsJ03BwcePYtuw586q5fu40q3+fSmpSou45jUbD1oVzqde2E71HTeSTQT9wZNNa4qMLfoKanpLCgdX/0Kb3QHqMnoydsyund27Od93N82fY+Od00l7Lu3vpAmq17sCXI8bRsf93nNi6gYToqHxlTEtJZteKRXz01WD6T5iOg4sbR7euN7jm9N4dqFVq+o2eTN8xk1FmKTizbycAu5YvwtbRiT6jJtHl+xEc3LCS5IT4fOV7VUZqCofXLqN17wF0GzUJO2cXzuzckq+6Cwd3o1ar6DJ8HF8MH49SkcXFQ3tRKZXsW76Qxp91p8vwcVRr3oaDq5bkO6OlmSktK5Vh24VrLDl8lsT0DOqXDsxXXZCnK5WL+bLhTAhLj5zFRCajSoAfAB2qlef0nTCWHwtm09nLNCxbHAfr7I3I/LKztODb1vWZuu0QAxdvIjIxhR4NqmWr83ayp2ej6ozfsI/vl21lw5nL/NSpKQB9G9cgU6Fg0JLNDFu5gyrFfKka4FvobPlh6u+L95+/YtOw3jv93n+lJiexftE8un83jBEzZ+Pk5s7u9asNrtm+ahnmFhYMmz6LwROmcPvqZW5evmikbMlsWDSfboN/ZPj0P3F2c2Pv+jUG14ScPkFMxHN+mPIbQybNIOz2La5fOIdGo2Hl7N9o3ukThkyaQe+hP7NzzQpiIiMKmPPtLEOlUsHKub/zSZ/+/DjlN5p2/Ii1C+YUKOOHRqN5d38fKqM0osqVK8fkyZON8VH/OUXdnYlITCIhLR2AkIdPKOPrUaA6qURCuyplOXjtDikZcgB8nBzwd3WiT+NadKtfDV9nx0JnLuHlxpO4BGJT0gA4cyecysVyPujUKVmM4HuPuPboud7zRdycUGs0fNOyLj+2a0Sz8iWQSPKf5dHtUDz8iuDo5g5AhbqNuHUxONsNi3nV3b92mbI16yCVybCwsqZElercungOAI1aTWa6dplnyTMxMTXNluHEtg0ULV2WomXK5f8fgPbk40lcInEvlue5u+FUKuqTY22tEkW5cP9xtuUJ2ivExb1cCb4XXqAcebl59TL+gUG4e2obww1atCL45PFsy/nYvt3UbdKcKrWyX7m/c+M6oVdCqN+spdFyhd8KxcO/KI5u2m2hYr3G3HxxgDSk5t7VS5SrVU/325esUoOb58/q3nto/UrK1qyLpY1NoXI+vHUDzyJFcXqRoVL9Rtw8f1YvZ141KYkJ3LsawmeDh+p9rkqpoE6bDhQpVQYAO0cnrGxtSUlIKHDWx7dDcX9lWylftyG3c9im8qpLTUrkwbXLdBr4/Wt5ldRo2Q6/EtqLLraOTlja2JKamL+8D2/ewNO/GE7u2mVVuX5jQl9fnnnU+AWVoE7r9kikUqRSKe6+/iTHx5KRlsrDWzeo17YjoF2ePUaMw9LaOl/5XvX4dihufkVwcNUup3J1GnLnUs7LM7c674DiVGveRpfX1cePlIQ4ZCYm9JowHVcfPzQaDcmxsVhY539dLeLmTGRCMolp2hELVx4+pbRP9gs1edWV8fXkwv1HZL7oQT9w9RY3n0Qik0o5cyeMRzHahmhqppwMeRa2lhb5zvm6SkW9uRcZQ0RCMgB7L9+iQZnsjT+FUsXcvSdJeJH7fmQsDtaWmEilBHi4cPTGfdQaDUq1mosPHlOnRMF7nAvCoXN7knftI/XoiXf6vf+6e/0qvkUDcfXQ/pa1m7Tg8pmTeutoXjVPw8OoXKc+UqkMExNTSlWszLXz54yT7cZVfIsF6L63ZuPmXD77WrY8atRqNVlyOUqFAqVSiUqpxMTUFKVCQdNOnxBUtjwADk7O2NjZkRQfV7Ccb2kZmpiYMnb2QryLFEOj0RAXHYWVbeGOR8KHw+DhfO3ateOPP/4gICCAH3/8ERsbGyZMmMDly5fp1asX5cqVY+XKlXTr1o1y5cpx6dIl4uPjGT16NA0aNODp06cMGzaM9PR0KlSooPvcs2fPMmPGDADs7e357bffSE9PZ8CAARQrVoz79+/j5eXFjBkzcHBw4MSJE8yePRulUomPjw8TJ07E0dGRa9euMXXqVDIzM3F0dGTChAn4+vpy8+ZNXS9ZyZIljbz4tFfSkl8Z0pacIcfC1BQzE5lej5EhdRWLeJOSKefu82hdXUaWgtAnkdx+HoWPswMf16zIkiNndY2sgnCwttQdRAGS0jOwNDPF3NQk2xC0rcHXAG3D61VSiYR7ETHsDglFKpHSt2lNMhVKTuYwPC0vKQnx2Dq+7HmxdXAkKzODrMzMbMOKcqtLSYjHxkH/tdhn2l6xxp92ZeOcmYQcO0h6SjJten6NVCbT1cZFPOf+tcv0GTc1X7lf5WBtSZLe8szEIpfl+e8QvSBPV73nbS3NaVe1LEuPnKNGUJECZ8lNQlwsTs4uuseOzi5kpqeTmZGhN6Tv3yF6N69e1nt/YnwcG/5ZxLejx3PiwD6j5UpJfPPvn1dNTutFzLMnAFw7fRyVSkWFOg0496KXoqCSX/seOwcn5K/lzKvG1sGRzl8Pzva5JqZmVKjTQPf4ysljZGVm4lU0oMBZDVmmb6qzsXegXd9vcshrStlaL6+2Xz99HIU8E88ixfKVMTkhDrtXl5VjTssz95pipV9e8EiKi+XCkf206tqLhOgobOwdCD60j7Ab11AqFdRo1hpn95x7fg2RkpiArcPLi1c2L5aTQp6pN6Qvrzq/kmVe/tvj47h6/BCNPusGgExmQnpKMutmTiQjNZWWPb7Kd0ZbS3NSMl4eX1Iy5ZibmmQ7DuVV52hjhZW5GR/XrIiNhTlP4xM5HnoPlVrN9ccvL/qU9/fGzMSEiPikfOd8nYutNbHJabrHsSlpWJubYWlmqjekLzo5lejkVN3jPo1rcv7+Y5RqNXcjYmhUNpBbzyIxlcmoXaIoSrW60NnyI2aWdhi4VbXK7/R7/5UYF4eDs7Pusb2TM5kZ6cgzMnTD0fKq8Q8IIuT0CYoWL4lSqeDahWBkrxwnCyMpLg57p9e/NwN5ZoZuSF9eNVXrNeTa+bNM/q4/KrWK4mXLU7pSVQCqN3g5sujc0UPaf0tg9pFAhniby1BmYkJKUiKzRg8nLSWZboN+KFDGD424JyofPVENGjTg7FntFd67d+8SEhICwMmTJxk+fLherUKhYP369fz000/8+eefAEycOJHOnTuzfft2Kld+uaOZP38+48ePZ8uWLdSuXZubN2/qvqNLly7s3r2bgIAA5s6dS3x8PL/99htLlixh27Zt1K1bl5kzZ5KVlcXo0aP57bff2Lp1K7169WLMmDEAjBgxgqFDh7J161Z8fHLuHSgMSS7dL6+vW4bUVQv05/TtML3XNwdf5fZz7VCZp3GJPItPoqibM4WRexbDN4jge4/Yev4aWUoVmQoFx0MfUM4v/ycqGo2GnLqwpFKpwXUajUbv36TRaJBIJdqhXP/8Tcsve/PVxJl8+t0IDq1fQcorQ3tCjh2kYv3GmFtmvzfIUBIk5LTk1GrDlqdUIqFL3SrsuhhaqMZxXtRqNeTws7++nHOiUipZ/MdMPunZB3tH4w411Kg15BRM8kquvGpeX2c1L56PehzOlVPHaP5Fd6PllBiQ8001eTm7fxcnd23lo4HfY2pmVvCsuWzHOW5TBtTl5sLBPZzdu4P2Xw3GJJ95c9ue9ZanATURjx6ycuYkqjRsSlD5SqhUKhJjYzC3sKT78DF07PsNhzauJuLRw3zl08+qJsf1T/L68nxzXfSTR2yePZ1y9RpRtMzLi4lWtnb0njCDT74fyeG1y0jI53DOnNY7bSaNwXUyiZQirk7suHidFcfPY2FqSt1S+r1C1YP8qVOyGFuCrxiloSLN5ViU28mZuakJIzo0xtPRjrl7TwKw9EgwGg380bMTP3duxpXwZyhV77YR9b5pNOocj+v621PuNe269AAk/D56GMtmTad42fLITIxzy3xu3ys1IJtUKuXg1o3Y2NoxZu4iRv3xF+lpaRzfq39R7OjObRzcsoGeP4wo8L7zbS9DW3sHxs5ZyOBxU1i/cB4xEdlHowj/PQZvJQ0aNGDZsmXUrFmTwMBAwsLCiIuL48SJE3z55Zd6tfXqaa9UBgUFkZiYCMD58+f57bffAGjfvj2jR48GoEmTJgwaNIimTZvSpEkT6tSpw9OnTylSpAg1atQAoGPHjgwdOpQ6deoQERFB9+7aEyO1Wo29vT3h4eE8efKEAQMG6DKkpqYSHx9PdHQ0depohyZ17tyZzZuz3xuQX/VLBeh6EsxMTYhJenmFzNbCnIwsBQqV/n1LSemZeDna51rnbm+LVCrhcezLYTHmpiZUKebLmTv6JwAqA0/OX9WiYknK+GobORamJrrhEwD2Vhaky7Pyda9VlWK+PE9I0n2ORAIqAw+qp3dvI+z6FQCyMjNw8XrZuE1NSsDcygpTc3O999g6ORH5KCzHOjtHJ737TNKSErF1cCI24hmKLDnFympPVryKBuDs4U1EeBi2jk6o1WruXblE1+FjDf53/6tZ+RKU9tEOMzI3NSEy8eXytHuxPF9fB3Lj4+yAk401bator1bbWpojkUgwkUnZfO5qvrPlxMnVlfB7d3WPE+PjsLKxwdzizUNywh/cJzYqio3LlwKQnJiAWq1GoVDQfUD23pX8sHVyIiL8Ze9lSmICFlbWmL3y++dVY+vorPfbpyYmYOvgROj5M2RlZrB6pnaYcWpSIruWLaRhp08JLF8p3zntnJy1kxjkkdOQmpwoFQp2r1hMbMRzug0fjYOza571OTm7exsPbmjXlazMDFw8X92mEnPephydiAx/+Ma6nPIeWP0P8ZHP+WzIT9i/0sNpKHsnZ54/zPt3f1NN6IVz7F+7nBafd6NMde1EHP/2BJWvrT0GObm54xtYnOfhYXjmY7KGc3u28/DGFUA7DNjZ01v3Wu7L05moR7kvz7sh5zm2aTUNPupCiSraY5s8I52n924TUF57YdHN1x8XLx/iIp7phq/mpk7JYgR6vDgOmciIeaVH5+XxRX+fnJKRiWeOxyE1qZly7kbE6I4DN59GUKuEtodRJpXQqlIZnG2tWX3iAsmv9GblV5e6lake6A+Albkpj2JeHvOcba1JycjMcVIeF1trxnzcnCdxiYxau1uX09LMlGXHzpOaqb0A9UnNCkQkFL6X7L/EwdmVxw/u6R4nJcRjaa2/f8+rJiE2hrZffKmb7Obw9s24uOe9/hmezYXHD15OSJWcEI+ltTVm5hYG1dy4eJ4O3XphYmKCiYkJVes24Nr5czRo1Q6lQsH6RfOIfvaMb8ZOwslVf8RM/nK+nWWYkZ7G/dAblKum3eZ9ihbD08+fiCePcfXUv9f8v0aT4+Xj/18MbkRVqlSJkSNHcubMGapXr46zszP79u1DqVTi6anfA2H+4qDxeov936tiEolEdxWiZ8+eNGrUiKNHjzJjxgyuXbtGu3btMHmlBa/RaJDJZKhUKipXrsxff/0FgFwuJy0tjejoaHx8fNi+fTsAKpWK2NhYJBKJ3pU4Y3VPn7j1QDermpW5Gf2a1MLR2oqEtHQqF/PhbkR0tvc8jI6jabniudb5uTjqxpz/K0uhpEoxX+JS0rjzPBp3e1u8HO3ZdelGvjPvv3Kb/VduA2BjYcbQ9o21QylS0qhVoig3nuTvZkwPB1vK+3ux7FgwMqmUOiWLERL2xKD31mnTkTptOgLam/FXTB1HQnQUjm7uXD11nMBy2U9yi5Qso7uB/fW6gPKVCD13ioCyFciSy7kTcoEmn3XDwcWNrMwMnofdx6tYIIkx0cRFPsfNR3uzdOzzp5hbWRfoJPDgtTscvHYH0E4sMaRtI5xtrYlLSaNmUJF8zVz1ODaBqVsP6h43LV8Ca3Mzo87OV7pCJTYtX0pUxHPcPb04cWAvFV7s1N8koERJpv29VPd45/o1pKYkG2V2viKlynJsy3oSoiNxdPPg6qmj2Ro5edUEla/EjbMnCSxXkSy5nNuXztP8i+74BpWk8cdddJ/x95ihtO35VYFn5ytaqixHNq8jPjoSJzcPLp88SlCFSvmuycnOZX+jzMqi27DRb2xw5aZWm47UemWbWjXt5TZ17dQxAspVzPYe/5JlOLltwxvrXrd/5WIUWVl8NuSnNza4clO0VDkObVpLfFQkTu4ehJw4QvEKlQ2uuXftMgc3rOSL74bh6f9yKKGDiysefkW4fvYUVRs1IzU5iacP7lOzeZt85avZugM1W3cAtMtzza/jSYyJwsHVnRunj1OsbMVs7/ErUZpT2zbkWPfwxlVObFlHh/5DcPcronuPRCrl8NrlWNrY4VUskLiIZyRERxq0np6+HaYbuWBlZkrPRjV1Q7UrFPHmfmRMtveER8fRsExQjnV3nkdT0tuN64+eoVSrCfRwI/LFRbI2lctiIpOx5uSFbA2z/FpzKoQ1p7QjWeytLJjT+yM8He2ISEimVcWSBN9/nO09lmamTOnShiM37rHutP5Q41aVSmFlZsrfh87iYGVJswolmL79SKEy/tcUL1eBnWuWExMZgauHJ+cOH6BM5WoG15w9fIDMzAw69+hLSlIiwccO86WRhpwVL1eBXWtXvvzeIwdzzJZbjXeRolw7f5bA0mVRKZXcDLmIf6B2Zru1f81BkSXnm7ET9RplBc35NpahVCplw6L52NjbU7R4SSKfPiEm4rmYne9/hMGNKBMTE8qXL8/KlSv566+/cHV1ZcKECXTu3Nmg99euXZsdO3bQtWtXDhw4gFz+4qrRJ58wYcIEevbsiYODA4cPH6Zdu3Y8fPiQW7duUapUKTZv3kz9+vWpUKECo0eP5uHDhxQtWpT58+cTFRXFL7/8QlJSEhcvXqRq1aps3ryZnTt3snLlSry8vDh27BgNGzZk165dBVtKeUiXZ7HrUiida1RAJpWQkJbBzovak18PBzvaVC7NkiPn8qwDcLKx0ruvBrRDkzaevULzCiWpXyoQtUbNtvNXc5z+NT9SM7NYd/oyPRpWRyaVEpeSxppTlwBtr8intSvx+86jeX7Ggat36FyjPEPbN0YmlXI1/BnB9x7lO4uVrR3Nu/Zi55L5qFUq7F1cadmtDwCRj8M5uGYZ3UaOz7OuQt2GJMZGs3LaeFQqJeXrNMA3SDv7Y7u+gzi6eS1KhQKpTEqzz7vj8OJqVWJMlN447IJKk2ex8exlvqxfFZMXy3P9Ge2B3tvJno9rVuTPPccL/T2FYWfvQI9vvmPhzGkolUpc3T3oNXgI4ffvsfKvuYyZ+ed7yWVta0erL3uzffF8VEolDq5utO7el8hHD9m3+h96/vxLrjUAFes1IjE2mmVTxqJSKalQtyG+Qca/99Hazo423fuwdeE81ColDi5utO3Zj4hHD9m7aim9R03MtSYvz8LucyfkIk5uHqyaOUn3fMNOn+rd95MfVrZ2NOvSi91LF6B6kaPFl70BiHoczsG1y/lyxLg863Lz/OED7l25hKObO+v/mKZ7vm77jyhSqqzBGa3t7GjbvR9bFs5BpVLi6OpGu55fE/EojN0rl9J39KRcawAOb16LRgO7V75s3PsEBNHyix581P9b9q9dQciJI2g0auq26YBXPu/ZepWVrR1Nu/Rizz9/oVYqsXdxpVlX7b4n6nE4R9Yt54vh4/KsO7V9IxqNhiPrXs6I6VkskIYfd6VNn4Gc3LoOtUqFzMSU5t366t3jaYj0LAV7L9+kQ7XyyKRSEtPS2RMSCoC7gy0tK5Zm+bHgPOuuPHyCpZkJ3RpWRyqREJWYwoGrd/FytKeEtzvxqWl0qffypPJ46D3CX7vwl19J6Zn8uec4Izs2wUQmIzIhmVm7tfvKQA8XBrWsx/fLttKmcmlc7WyoGVSEmq/cMzpm3R42nbvKkDYNmNO7MxKJhDUnQ7gfGVuoXP81tvb2fPbVN6yYPROVUomzmztf9B/Mk7D7bFz8Fz9MmZlrDUDj9p1Z+9dsZowcAhoNLT76HL+A7BN8FISNnT2f9BvAqjm/o1IqcXJz5/OvB/Ek7AGblv7FkEkzcq0BaNelB9tWLmHGiO+RSqUEli5LgzYdeHTvLtcvnMPFw5N5E8fovq/1p10pUb7iB7UMew4ZzvaV/2i3cVMTugz8Tu/eqv+q/NwC8r9KosnHUti2bRszZ87k1KlTJCcnU7NmTVavXk1WVhZz587VTSwxaNAgatSowdOnT+nevTtHjhwhKiqKYcOGkZiYSNmyZdm3bx8hISGcPXuWqVOnYmJigpWVFZMmTcLExIRPPvmEihUr8vjxY0qUKMGkSZOwsrLiyJEj/Pnnn6jVatzd3ZkxYwaOjo5cvnyZyZMnI5fLsbGx4ddff8XPz4979+7x008/oVQqqVixIidOnODIkfxdpZqy5UC+F+y7FpeS/r4jGKS4Z/57fd6HsOjCnRy8C60qvPv/TUBB3I8q2GxJ75pMWoDpJd+DzDz+/2MfEqtC3Nf1LqVkvp37EI0tIyvrfUd4o5O3w993BIP8tnPjm4s+ALdnzXzfEQyifscTeRSUofd7vm/tqhXsAtq7NmP7oXf2XcM6NH1n35Uf+WpEvSuvNr4+BKIRZTyiEWU8ohFlXKIRZVyiEWVcohFlPKIRZVyiEWVc/5VG1K/b3l0jakTHD7MR9d9YowRBEARBEARBED4QxpnD0sh8fHw+mF4oQRAEQRAEQRBe+gAHsr1zoidKEARBEARBEAQhH0QjShAEQRAEQRAEIR8+yOF8giAIgiAIgiB8mNRiOJ/oiRIEQRAEQRAEQcgP0RMlCIIgCIIgCILBxMQSoidKEARBEARBEAQhX0RPlCAIgiAIgiAIBhMdUaInShAEQRAEQRAEIV9ET5QgCIIgCIIgCAYTs/OJRpRBGpQq9r4jvFGaPOt9RzCIucl/Y5VztrV+3xHeqIyPx/uOYJAncUnvO4JBJJL3ncAwm4Kvv+8IBinh5fq+IxhkTLmi7zuCYaQf/sCR3Zdvv+8IBrk9a+b7jmCQkkOGvu8IBnGaPOZ9RzCI/NLV9x3BMNXKve8EgoH+G2e0giAIgiAIgiB8EMTsfOKeKEEQBEEQBEEQhHwRPVGCIAiCIAiCIBhMdESJnihBEARBEARBEIR8ET1RgiAIgiAIgiAYTI3oihI9UYIgCIIgCIIgCPkgeqIEQRAEQRAEQTCYmJ1P9EQJgiAIgiAIgiDki+iJEgRBEARBEATBYGrREyV6ogRBEARBEARBEPJDNKIEQRAEQRAEQRDyQQznEwRBEARBEATBYGI0n2hEGdXVi+fZvHI5CoUC3yJF6DXoeyytrLLVaTQalsyehY+/Py07fqR7/sieXZw4dABFlhz/gEB6DfoeU1NTo+e8EXKRnWtXoVQo8PLzp0v/QbnmXDV/Nl5+/jRp1xGArCw5G5cs5NH9e2iAIoFBfNLnK8zMzI2e89qlC2xdtQKlUoG3fxF6DPw215zL5v6Bt58/zTt0zvb6gulTsHd0oku//kbLdvtKCAc2rUOlVODh40enPl9jYWllcM25wwe4eOIIyqwsvIoUo3PvrzExNSX62VO2LVuEPDMTiURCi0++IKhcBaPl/teZ06f4e/58shRZBAQG8tOo0Vhb2+jVbN64ga1bNiORSPD29mHETz/j6ORk9Cz3rl/h6NYNKJUK3L19adu9H+aWlgbVZGaks2vFYuIin6PRaChfsx61W7YF4Hl4GAc2rEIhl6NWq6ndoi3latYpeM5rVzjySoZ2PXLImUtNZno6O1/NWasedV7kDL99k0Ob16FSKTE1NaPF593wLhpQ4JxvUi3Alx4Nq2MqkxEeHc8fe46TkaXIVte2ShlaVyqFBohMSGb23hMkpWe+tVylvN1pU7kMJjIpzxOSWX8mBLlCmWv9F3WqEJGYxLHQ+7rnfvmsNUnpGbrHR2/cI+ThU6NlPB0Swvx1a1EoFQT6+THqq/5Y57BPAjh+4QIT5s/lyD/Ldc9tOrCfHUePIM/KomTRYoz6uj9mb2EffzrkEvPXrkGhUBDo58+o/gPyyHmeCXPncGT5St1zLfr2xu2Vbb1ruw60rFfP6DkBagb5069pLUxlMsKi4pi+4zDp8uzrY6fq5WhftSwAz+KTmLnzKIlp2t96+/A+xCSn6mrXnb7Moet3jZbx5uVL7N2wGqVCiaefH5/2HYjFa8szt5r01BQ2/7OI54/CMTM3p1qDRtRt3tpo2QrCfdRQ5GHhJK7d9M6/+/S1q/y1dQsKpYIAbx9+7tEL69f2o7M3rufoxYvYWVsD4OfhwcSvXh6/o+Lj6TdtMivGjMfB1vat5DQv6o9d3ZpIZDIUsXEkHjiC5rX9pImLE/aN6iM1N0Oj1pB06BiK6BgkFuY4NGmAqasLaoWSjNBbpF25/lZyCu/X/8vhfCVKlDD6ZyYnJbF0zh98M+Jnps5fiKu7B5tW/JOt7vmTx8wY+zMXz5zSe/7S2dMc3rOToRMmM3H2AhTyLA7s2Gr0nCnJSaxeMIc+PwxnzB/zcHH3YMealdnqIp8+Yc7EsVwJPqv3/IEtm1Cp1Iyc8Qc/zZhFVlYWB7dtNn7OpCSWz/2T/sN+YuKcv3B192DLqmXZ6iKePuH38aO5dPZ0jp+zb9tm7t0KNWq2tORktiz5iy6DhjBk2iwc3dzYv3GtwTWhF89z7tA+eg8bzbeTZ6LMyuL0/j0A7FixhMr1GjJ44q907vM1a+f/gUqlMmr+hIQEpkyayKSp01i7YRNeXt4smDdPr+b27VusXb2avxYtYeWadfj4+rJo4d9GzQGQlpLMzuUL+fjrbxn4ywwcXNw4snW9wTXHt2/CzsGJr8dNo/dPE7h04jBPH9xDo9Gw6a/Z1G/XmX5jJvPFt8M4uGk18VGRBc65Y/lCPu7/Ld9MnIGDqxuHt2TPmVvNsR2bsHN0ov/4afT5eQKXjmtzqpRKNi+aS5tuvfl67BTqtunAtqV/FSijIewsLfi+TUOmbDnI1ws3EJmYTK9G1bPVBXq40Ll6eYau3M43izfxPCGJbvWrvbVc1uZmfF6nCsuOBTNt2yHiU9JoW7lMjrVu9rYMaF6X8v5ees+72tmQLlfw286juj9jNqASkpOZ9PcCpg75gQ2//4GXmzvz1q7JsfZxRARzVq/UmwL46PlgNu7fx5xRY1g74zfkiizW7dlttHwvcyYxacF8pv4wlA1/zMbL3Z15a1bnnnPlCr2cj54/w87GhpXTZ+r+3lYDyt7KghEdmzB2/V66z13N84QkvmpaO1tdcU9XPqtdiUFLNtNr/lqexSfRu1ENAHydHUjOkNP3r/W6P2M2oFKTk1i/aB7dvxvGiJmzcXJzZ/f61QbXbF+1DHMLC4ZNn8XgCVO4ffUyNy9fNFq+/DD198X7z1+xafh2fs83SUhJYfLyf5jSfyDrJk7By9WV+VuyN+SuP7jPL199zfKx41k+drxeA2rv2TMMnPErsYmJby2n1NIChxaNid+5j+hla1AmJWNXt5ZejcTEBOfO7Um9eJmYVRtIDb6IQ+tmANg3qItaoSB6+Vpi127CvKg/5kX931re90Wj0byzvw/V/8tG1NsQeiWEooFBuHt5A9CoZRvOnTiW7cc/snc39Zu2oFrtunrPnzl6hBYdOmNja4tUKqXbgEHUbtjY6DlvX72CX0AQbp7aE5C6zVpy8dSJbDlPHNhL7cbNqFhT/4AWUKoMLTt/jFQqRSqV4VukGPExMUbPefPqZfwDg3D30uZs0KIVwSePZ8t5dO9u6jZtRpVa2XsY7ty4TujlEBo0b2XUbPduXMO7aAAuHp4A1GjUjKtnT+lly6vm8ukT1GnZFisbG6RSKR169KViHe1BTa1Rk5mWBoA8MxMTUzOjZge4EBxMqVKl8fXzA6BT5484uH+fXv6SJUuxbtNmbGxskMvlxMTEYG9vb/QsYTev4+VfDCd3DwCqNGjCjeAzelnyqmn+WTeafvwFAKlJSagUCswtrVApFdRr25FipbRXru0cnbCysSM5Mb5QOZ1fZKiaR86calp81o1mOeSUmZjw/a+z8fQrgkajITEmGqvXegSNqXIxH+5FxPA8IRmA3Zdv0rB0ULa6+5Gx9Pt7HelyBaYyGc621iRnvL1eqBJebjyJSyA2Rbvun77zkMrFfHOsrVuyKMH3wrn66Jne80XcnNBoNAxqWY+h7RrTvHwJJBLjZQy+dpVSxQLw89Ru052bNWP/6VPZ9kmZcjnj583l2y+76z2/9+QJurRpi/2L7X5En360rFffeAH/zXn1GqUCXs3ZnP2nTuacc+5svu3eQ+/563fuIpVI+XrcGLoO+5ElmzaiUhv3Qs6/qgX4cftZNM/ikwDYcfEGTcsVz1Z3NyKGrrNXkSbPwsxEhssr62MZX0/UajWze3VmyYDP6d6gGlIj/vB3r1/Ft2ggri/25bWbtODyGf3lmVfN0/AwKtepj1Qqw8TElFIVK3Pt/Dmj5csPh87tSd61j9SjJ97L95+/GUop/yL4ursD0LlBIw4EB+styyyFgnuPH7Nq316+HD+WnxfMIzIuDoCYxAROXLnMrO+HvNWc5v5+KCKjUSVq18v0qzewLFX8tRpflElJyB8+AiDzwUMSdu0HwNTdlYybd7Tj3dRqMsPCsSz+9kYXCO/Pex/Op9FomDlzJocOHUImk/HZZ59RqlQpZs2aRWZmJsnJyfz00080bdqUnTt3snjxYmQyGT4+PsyYMYMrV64wd+5cVq7U9qaMHDmS6tWr07lzZ2bNmsXZs2dJSkrCzc2NWbNm4eLi8lb+HfGxMTi5uOoeO7q4kJGeTmZGht4QtC+/GgBoG12vinz+jKKJifw+YQyJ8fEElS7Dpz16Gz1nQlwsjs7OuscOzs5kZmTP+WnvrwC4de2K3vtLVaio++/4mGiO7t3JF/0GGD2ndnm+/K0cnV3IzGF5/jtE7+aVy3rvT4yPY/3ShXw7ZgInDuwzarak+DjsnV4uQzsnZ+QZGcgzM3TD9fKqiY2KwCc5iWUzp5KcmECR4iVp+VkXANp3682SXydx+sAe0pKT+GzAd8hkMqPmj4qOws3dTffY1c2NtLQ00tPT9Ib0mZiYcOL4MX6dMhlTMzP69vvKqDkAkhPisXt1OTk6Ic/MICszUzdU7k01EpmMbUsWcCvkAiUqVcHZwxOpVEqlug117wk5cYSszEy8iwYWLGe8ATnfUCORydi6ZAG3Ll2g5IucADITE1KTk1g8aQzpqSl07vdNgTIawtXWWm/YU2xyGtYWZliamWYb0qdSa6gZ5M+3rRugUKlYdeLtXT13sLbSDc0CSErPwNLMFHNTk2xD+rYEXwOguJeb3vMyiZS7EdHsDglFKpHSr2ktMhVKTtx6YJSM0XFxuL+y73RzciYtI4P0jAy9oXLTFi+iU5OmBPr76b3/cUQEpQOS+X7qFGISEqhYsiSDunQ1Sjb9nLG4O7/cd7o555Jz0d90atqMQD/9K+RKtYpq5crxTZeuKFUqfpg2FWtLKz5v08boWd3sbfTWx5jkVGwszLEyN802pE+lVlO3ZFGGtW9MllL1f+3ddXhTyR7G8W/qLaUtVUpxd3d3t+KLu1x8cYfFKb64u7ssxV1KcQotDhWoK9XY/SMQCC3QQiDp7nyeZ597k0ySl9Pk5MyZ38xhw3kPAAwNJNx+6c+aM9cwMjRgdsdmxCUmse/Gfa1kjAwLw+azv7u1reo3MzE+Xl3S9602OfLk487VS+TKXxCZTMoDTw+t79NTK2SRquLAolxpnbx/UHg4Tp+ViTpkykRsQjxxCQnqkr7QqEjKFCxE35au5M7iwo5TJxmz4m82TZyCg00mZg/4dfvHjwwzWiKP+fS5lMe8x8DUFImJsbqkzyiTDYrYOKzr18LY3h5lYiLRl68BkBQYhHnhAiS9DURiaIh5vjwoFYpfnvt3E0uc68FIlLu7O3fu3OHo0aPs3buXAwcOsGLFCmbMmMHBgweZMWMGS5YsAWDx4sVs2LCBAwcO4OLiwsuXL7/6um/evOHly5fs2rWLkydP4uzszJEjR37Zv0OpUJLSaU8Dg9RtYrlczuP7dxkwahyT5y8m9n0M+7dt0XZMlEolkp/I+ZHvyxcsnjKB6g0aU7SM9st8lEolEn4sp0wmY+0iN9r16I1NJu3P4VEqFSme4f4827faKORynj96SIeBQ/nf1FnEx77n9L7dSJOS2LViCa1792fMohX0HjeVQ5vXEhkWqt38CsVXtm3yH/bqNWpy/ORpevbqw5/DhqDQ8g+BUpHyTlhiIElTm5a9BjBiwQoSYmO5fEyzDPaq+1EuHT1A+4HDMTb5sZE91ffmOzlT0ca11wBGLlxBfGwslz7LaWllzbB5S+kxZgpHN68lLOjdD+X8HolEQkpb82s/hjeevaHjki3suHyb6e0bp/Cp0VYuUKaQLC1lHDeevebgzQckyeQkSKVcfPScYtmzfP+JqaRQfn8fv+/USQwNDWhWq1aydjK5nJsPHzBz6DA2zZpN9Pv3rNq9S2v5NHMmv18j58mTGBoY0qxW8mqHlnXqMrJnL8zNzMiYIQN/NG3KRU8PreeED5/HFP7Giq9856/4vKLFvPVsunATty7NkUjg+J3HLD1xiQSpjPcJSey9fo+qBXNrLaNqX558g0qS7e9TbtOsYzdAwsKJo9i0aB75ixbH0Ejn5691IjXHH1nsHVgwZBh5XLIikUjoWL8BASEhvNPy7+A3fW0k8/PPpYEBprlyEPfgMaE79hJ77yG2rs3A0IDoi1dBCQ6d22HbvBGJb/xAy2X5gn7Q+TfZ09OTRo0aYWJigomJCYcPHyYxMZHz58/j7u7O/fv3if1Q3lSrVi3++OMP6tatS4MGDShUqBAeHinv3HPkyMGYMWPYu3cvr1694t69e2TPnj3Fttpg5+DAy2dP1LcjwsLIYGmJqZlZqp5vY2tL6YqV1aMslWrU4sjund95VtrZ2tvz5vmnevGo8DAsMqQ+J8Dtq5fZs34NbXv2oWxV7ZejANjaO/Dq2aeckWFhWKRye7558ZzQoCD2bFoPQHRkBAqFApk0ia7/G/LT2Wzs7PF7+Wkye3REOOYZMmBiapaqNhltMlGkTHn1qFWJSlU5f2Q/QQF+SJOSKFiyDADZ8+bDKUtW/F8+x8ZOeyOoTk6Zefzo0zyx0JAQMlpZYf7Z5F5/Pz/CwsIoUbIkAE2aNWP+vDnExERjbW2jtSzWtna8ff1ppCA6MgIzC81t+a02Lx49wNElGxltMmFiZkaRchXxueMJgEwq5cjmNYS+C6D7mCnYfDZSnFZWtnYEvPp2zm+1+TJn0fIV8b7jSUJcHK+fPKZgqbIAOOfIiVPW7AT7+2Pn5PzDeT/XuVoZKuRTjTZYmJjwOuRTSaNdxgzExCckG+1xzmRFpgzmPPYPAuD0gycMbFgVS3NTYuITtZKrYclCFMmmKn00Mzbm3YcSQ1DNlYlLTCJJlvoDjzK5s/E2IurT60hUoxfa4mRnz6Pnn77TIeHhWGXIgPln+6R/Ll0kITGRLmNHI5XJSExKosvY0SwcPRYHm0zULFdePRrUsGo11h/Q/nxSJ3t7Hj1/9u2cF8+TkJhEl9EjP+UcPZKFY8dzy+sheXPkJF+ODyNUSiWGhto7VOhRqzxVCuQCwMLUhJdBYerH7DNaEh2fQMIXn0cXW2tsLS146Ks6uXDirjd/Nq1JRjMzKuTLwYugUPXrSLT8d7exc8D3xaftGRURjvkXv5nfahMRGkLTPzpjYalaAOHs4f3Yfyj5/a9xsrXl0atPJ79DIiPIaGGBuemnhame+/vxzM+PRpU+m0qgVGL0G0fv5DExGGd2Ut82tLREkZCAUvbpcymPjUUWHoE0ULWPTHjxCut6tTCytkYplRJ9+RrKBNW+0rJ8GWQfSgP/TfR5rtLvovORKCMjI40zE/7+/nTs2JEHDx5QtGhR+vf/NKFw4sSJLF26FGtra0aNGsXhw4eTncmSSlVDrV5eXvTq1QuFQkGDBg2oW7fuL/2DFylZmpdPnhD0VlWnf+HkP5QsXzHVzy9bqQqeV6+QlJiomjfjcYNc+ZLXhv+sgsVL8vrZU4LfvQXgyumTFCubfGL51zy87cm+TesYOGHKL+tAARQuWYqXT58Q9FaV8+KpE5QsVyFVz81ToCBz12xk8oKlTF6wlOr1G1G2cjWtdKAA8hYtjt+L54QGqn7Qb54/Q6EPB8GpaVO0bAUeel5HmpSEUqnE+84tXHLlwc4xMwlxcbz50BkPCw4k+G0AzjlyaiX3R+UrVOCRlxd+vr4AHDp4gGpfzM0ICwtl6qSJRH6YvHvqpDu5cufWagcKIHfhogS8fK5e8OHOpbPkL1E61W0e3/bg0rGDKJVKZFIpj297kLNgYQAOb1hJUnw83UdP/qkOFECeDxnCPmS4ffEsBUqWTnWbx7c8uHT0s5y3PMhZoDAGBgYc3bwWvw8nNoLf+hMa+A6X3Nqrn992+TaDNxxg8IYD/LnlEAVcHMmSyQqAxqUKcePZm2TPsc1gwZgWdbAyVx3c1CySlzchEVrrQAG43/NWLwCx5J8L5HDIhH1G1WpclQvkwssvbaNxzjZWNCxZCIkEjA0NqFowN/deB3z/ialUoXhxvJ49w/edKtfBM6epVlbze79hxix2uC1g65x5LBwzFlMTE7bOmYeDrS21KlTg7I0bJHz43l+85UnhPNqfJ1GheAnNnKdPUa2sZrXAhllz2LFgIVvnzWfh2PGqnPPm42Bryws/P9bu2Y1cISchKZG9J92pWzn5Yg8/auP5m+oFIP63bh+Fs2bGxVY137J52aJc9XmV7Dm2lhZMbtMAawtVx6Vu8fy8Cg4nOj6BXI629KxVAQOJBBMjQ1zLF+ec17Nkr/Gj8hcrwZvnzwj5sC+/cfYURUqXS3Wb62dP4b5ftcBMTFQkHhfOUqqSbhZ20LXyhYvw6OVL/IJUHY9DFy9SrWQpjTYSiYTFu3fyNlQ11/rAxfPkyZoNx19QVfI1ia/9MHF2wtBG9bm0KFGEhOean8vEV74YWVth7Kj6bTFxcQaUyKKisSheFKvKquMqAwtzLIoWIt5He59JQX/ofCSqXLlybNmyhQ4dOiCTyejVqxfv3r1jx44dmJiYMH/+fORyOTKZjMaNG7N161b69euHVCrF29ubwoUL4+fnR2JiIvHx8dy+fZsqVarg6elJ+fLl+eOPP4iIiODChQvUr1//l/07rGxs6Dl4GMvnzUYuk+KQ2ZneQ0fw6vkzNi1bwrTFy775/NqNmhD7/j3TRgxFoVCQI08euvXorfWcGa1t6DRgMOsXuiGXSbHPnJkuA4fi++I5O1YvZ+y8Rd98/qGtm0AJO1Z/Ws0td4GCtOvVT6s5raxt6D5wKKvnz0Ymk+GQOTM9B//J6+fP2LLybyYvWKrV90sLSytrWvfqz87li5DLZNg6OtGmz0D8X73g4IY1DJ4+96ttACrUqU9c7HuWTx2HUqEgS45ctPijN2bmFnQaMoLjOzYjk0oxMDCkZfc+2Dlq96xlJltbxk+axMTxY5FJZbhkdWHi5Kn4eD9mzqyZbNq6nRIlS9G1e3cG/68/hoaG2Ns7MHuem1ZzAGSwsqZZtz7sW7MUuUxOJgdHWvTox9vXLzm+dT19Js38ahuAem068s/2jaz5axwABUqWpXztBvi/eIb3HU9snTKzed509fvVbtWePEWK/1jO7n3Yt1qVwdbBkRY9VTmPbVlP38kzv9oGoF7bjhzftpHV0z7kLFWWCnUaIDEwoN2AYZzcvQ2FXI6hkRGuvQdg9YsOGKLiElh8/CLjXOthbGjAu8hoFhy9AKhW5BvauDqDNxzgkX8gu6/dZXanZigUCsLexzFj/6lfkgngfUISu67eoXvNChgaGBAaE8vOK6o5WFntbGhfuRQLjp7/5mucvO9DqwolGNW8DoYGBtx/HcCNZ6+1ltHW2ppJ/QcwfvFCpDIZWZ0yM/l/A/F+8YJZa1ezdc68bz6/df0GRL9/T/fxY1EoFBTImYuhnbtoLZ9GzgH/Y/zCBaqcmZ2YPHCQKufqlWydN/+bz+/dpi3zN6yn08gRyORy6lSsRIvadbSeEyAyNp65h88yrV0jjD8sbT/r4GlAtdjIqOa16L1qNw9937H10i0Wd3dFrlAQGhPLxF2qlQ03X/RkaOPqbPjfHxgZGHDh8XOO33mstYwZra1p33cgW5bORy6TYefoxB/9B+P38jl7163iz1nzv9oGoHbzVuxctRS3scNBqaRB6w5kz/NjczPTO1srKyZ078GE1SuQyuS4ODgwuWcvvF+/Zs6WTWyePJU8LlkZ3qEjo5YtRaFQ4pgpE9N6a38+7rco4uOJPHUO22YNwcAAeVQ0Ee5nMHZywKZebUK27UYRF0f44X+wrlMDibERSrmciCPuIJfz/uZtbBrVxaFrB0BCzPWbSIOCf+u/4XcQA1EgUerBeNyiRYs4d+4cCoWCTp068ebNG86ePYuRkREVK1bkxIkTnD9/nnPnzrFy5UpMTU2xs7Njzpw52NnZMXnyZK5du4aLiwv29vZUqVKFKlWqMGjQIBISVCv4FCpUCIVCwfz58ylQoABPnjz5TqpPrno//34jHYtNTNJ1hFQxTSe14CEfVgnTZzW0WPf/K7nfT/13TZe0uZLbr7Tz2j1dR0iVAll+bvTvd5lULJeuI6ROGuet6oLrId2s+pZWI5rU0HWEVCk4fKSuI6SK7cxJuo6QKom3tbPYyK+W5c9fv3iGNozacvi3vZdb1xa/7b3SQi+OaIcPH87w4ZpLVo4bN079/6dOnQpA06ZNadq0abLn//XXXym+7t69e1O8Py0dKEEQBEEQBEEQPhGr8+nBnChBEARBEARBEIT0RC9GogRBEARBEARBSB9SuizFf40YiRIEQRAEQRAEQUgDMRIlCIIgCIIgCEKqiTlRYiRKEARBEARBEAQhTUQnShAEQRAEQRAEIQ1EOZ8gCIIgCIIgCKkmqvnESJQgCIIgCIIgCEKaiJEoQRAEQRAEQRBSTSmGosRIlCAIgiAIgiAIQlqIkShBEARBEARBEFJNLHEuOlGpktPeVtcRvss/PErXEVLFwSqDriOkilQm13WE7zKNi9V1hFQJjn6v6wipYp8xfXw2RzSpoesIqXLG65muI6SKUe6cuo7wr2FraaHrCKmiUCh0HSFVbGdO0nWEVAmfMF3XEVJFYmqq6wip8+dAXScQUkl0ogRBEARBEARBSDUxJ0rMiRIEQRAEQRAEQUgTMRIlCIIgCIIgCEKqKcRAlBiJEgRBEARBEARBSAsxEiUIgiAIgiAIQqqlpzlRb9++ZdSoUYSFhZErVy7mz59Phgyai0kFBwczbtw4QkNDMTAwYPTo0VSqVOmbrytGogRBEARBEARB+FeaNm0aHTt2xN3dnaJFi7JixYpkbebNm0ft2rU5fPgwCxYsYOTIkcjl316pWXSiBEEQBEEQBEFINaVS+dv++xlSqRRPT08aNGgAQKtWrXB3d0/Wrl69ejRt2hSAHDlykJiYSFxc3DdfW5TzCYIgCIIgCIKgl6Kjo4mOjk52v5WVFVZWVt98bkREBJaWlhgZqbo8Dg4OBAUFJWv3sZMFsH79egoVKkTGjBm/+dqiEyUIgiAIgiAIQqopfuOcqM2bN7Ns2bJk9w8aNIjBgwerb584cYLZs2drtMmRIwcSiUTjvi9vf27Tpk3s3r2bbdu2fTeX6EQJgiAIgiAIgqCXunXrhqura7L7vxyFatSoEY0aNdK4TyqVUqFCBeRyOYaGhoSEhODo6Jji+8ybN4+LFy+yfft2MmfO/N1cohMlCIIgCIIgCIJeSk3Z3tcYGxtTtmxZ/vnnH5o1a8ahQ4eoXr16snabNm3Cw8ODnTt3pvq9RCdKEARBEARBEIRUSz8LnMOUKVMYO3YsK1euxNnZmYULFwKwc+dOgoODGTJkCMuXL8fS0pIuXbqon7dmzRqcnJy++rqiE6VFN65dZd3qlSQlScmdJw+jxk1Itg796ZPu7N6xHYlEgqmZKYOH/UmBgoWQy+UsXbSAB/fuAlC+YiX6Dxz8zbrNH3XP04O9WzYilUnJliMXvYcMx9wiQ7J2SqWSNYsXkC1nThq7tgHg7zkzCHr3Vt0mJCiQgkWLMXziNK3nvHn9GhvXrkYqlZIrdx6GjR6bbHueO3WSfbt3IkGCqZkZ/QcPJX/BggC0b94UewcHddvWHf6gdr36Ws/pdecWh3duRSaV4pI9J536D8LcwiJZO6VSydYVS8mSPQd1m7UEICkpkd3r1/Dm+TOUKMmZNz/te/XFxMRU6zmv3LjO8nXrSEqSki93biaOGoXlF9tzz8GD7DtyGIlEQtYsWZgwYiS2mTIxZuoU/AIC1O3eBgZSungJFs6cqZVsvt4PufnPYeRyKbbOWanRtjMmZuY/1O7U5tVYWFlT1bUDEUHvOLd9g/oxhVJBROBb6nXtS65ipdKU8fnDe5w/tBe5TIqjSzaadOmNqbl5qtpIk5I4uWszb1+/BCVkyZWbBh26YWxiwtvXLzmzZztJSYkoFQoqNWhC0QpV0pTtax7e9uTwji1IpTKy5shB5wFDvvrZ3Lx8MS7Zc1KvuapkIj42lq0r/ybwrT9KhZKKNWvToGVrreRKSYEsjtQvURAjQwMCI6M5cOMBiTLZV9u3qViCwMgYrvi8BMDI0IDmZYuSzc4GkOAXFsGRW17I5AqtZbxy5QrLVq4kKSmJfHnzMmnCBCwtLTXa7N67l/3794NEQlYXFyaOH4+tra1Gm1FjxmBvb8+YUaO0lk1bOd+/f89fM2bw+s0blAoFTZo0oXvXrr8kJ0CZ3FnpVK0sxoaGvAkJZ/nJK8QnSZO1q14oDy3LFUOJkkSpjPXnbvAiKIxRzWuR2ebTWWNH64w89gtk9qEzP5zJ+94dTuzdgUwqxTlbDtr27o+ZuUWq2igUCg5tWc9Ln8cAFCxRiiYduiCRSPB7+Zwj2zeTlJiAUqGgZpMWlK6S/Iz4j7j64D6rDh5AKpOSxyUr47v1IMMX+6ele3dz/tYtrD7s97Nnzsz0vv3VjweFh9Nnzky2TJqKzXcm1P9qThNGkvjyNZE79+nk/S0qlMWud1ckxkYkvXxD0PylKOPiNdpYt2yCdcsmKBOTSPL1J2TpKhQx78k8ZQzGWZzV7YwyO5HwwIt3k7TzeymknYuLC1u3bk12/x9//KH+/56enml+XZ0tcf7w4UMmTJjw1cf9/PwYP378L3nvsWPHcuDAAa2+ZmREBPNmzWTqjNls2bmbLFlcWLtScx16X983rF6xjLkLFrF20xY6d+vBlPHjAFXnys/Xl3Wbt7F201Ye3LvHxfPntJoRIDoqkrVLFzJ43CTmrVyPY2Zndm/emKxdgJ8vcyaOxfPaZY37B4+dyIwlK5ixZAU9Bw3FIoMlXfsN0nrOyMgIFs6dzcS/ZrBu6w4yZ8nCxjWrNNr4+/qybtUKZsybz/L1G+nQpSszJk9QP5bRKiPL129U//crOlAx0VFsXfk3ff4cw5TFK7B3cuLwji3J2gX6+7F0+mTuelzTuP/kgX0o5HLGuy1mgttipElJnDq0X+s5IyIj+WvePOZOncb+LVtwyeLMsrVrNNp4P33Ctj272fD3MnZv2Eg2l6ys2qjqgMydOo0da9exY+06JowYScYMloweOlQr2eLfx3Bh9xbqde1L+9HTsLK15+Y/h36o3b3zpwh89Vx9O5OTM63/nKD+L2v+QuQpWTbNHajYmGiObVlL676D6T9tHjb2jpw/uDvVba6eOIJCrqDPxJn0njQTWZKUa+5HUSqVHFi9lGrNXOk9cQbtB4/kzL4dhAcFpilfSmKiotiyYil9R45j2tKV2Dtl5tD2zcnavfP3Y/G0idy9ofnZPLJ7OzZ2dkxeuIyxcxZw6dQJXj7x+elcKclgakLriiXYceU2i45dIPx9HA1KFkyxrYOVJb1qV6RodmeN+2sVyYeBgQFL/7nE0hMXMTY0pGbhvFrLGBERwbQZM5g3ezYH9u7FxcWFZV9ca8Tb25tt27ezYd069uzcSfZs2Vi5erVGm81bt3L33j2t5dJ2zpWrV+Pk6MienTvZsmkT+w8c4MHDh78kq5W5GYMaVsPt8DkGb9hPUFQMXaqXTdYuSyYrutUox/T9Jxmx5TD7btxndIs6ALgdOc+ILYcZseUwK09dJS4xiTVnr/9wpvfR0exZu4Iug0cwet4S7BwdObF7R6rb3Ll6iZB3b/lz1gKGz3DjpY83Dz1vqE6iLV1Afde2DJ/hRs+R4zm6Ywshge9+OOtHETExzNy8kVn9/8eu6bPI4uDAigPJOx8PXzznr7792Dx5KpsnT9XoQJ24fo3/uc0lNDLyp/P8DOMc2XBZMhfLmtV0lsHA2grHUUMInDob3+7/Q/ouEPve3TTamJcsRqYOrQkYOQm/fsOI87iF458DAQicNhe/fsPw6zeM4IXLUMTGErJ0dUpvla6llyXOfyWddaKKFSvGzG+cxX779i1+fn6/MdHPueV5kwKFCpE1WzYAmru24uzpkxp/fBNjE0aOGYedvT0ABQoWJDw8DKlUikIhJyE+HqlUijQpCalUiomJidZzet29Q+58+cmcxQWA2o2acP3iuWQf0rPHj1KjfkPKV0l5RyaTSlmzeAGdevfD7rPRHm254+lJ/oIFccmq2p5Nm7fk/JnTGjmNjY0ZNmoMtnaq7Zm/QEEiwsORSqU8fvQQAwNDRg4eyICe3di+eeN3L5r2I7zv3yNHnrw4OmcBoFq9hnheuZRse148dYLKtetSqmJljfvzFipMw1ZtMTAwwMDAkGw5cxEeEqL1nDdueVK4QAGyZ80KQOvmLXA/e1YjZ6H8BTiwdRuWlpYkJiUREhqK9Rd1wVKplGlz5/DnwIFk/srEzLTyf+qNQ7acWDuoXq9wpeo8u3sz2Tb8Xru3L57i/+QRhSqm/Jl99/IZrx7cpVrrjmnO+OqxF845cmPrpJpoWrp6bR7dvK6R8VttsucrQJXGzZEYGGBgYIBTthxEh4cil0mp2tSVXIWKAmCVyRYLSyuiI8PTnPFL3g/ukjNPPvVns3r9Rty8fDH5Z9P9OFXq1Kd0Rc3Rr3Y9+tC6a08AoiLCkUmlKY5iaUNeZwf8wyIJi4kFwOPZG0rmdEmxbcX8Obn1wpeHvpoHn6+Cwzjv9QwloFTC24hobDIkH838UTc8PChcqBDZs2cHoE2rVpxwd9f8DhUqxMF9+1TfocREgkNCsLG2Vj9+6/Ztrl+/TusUJkjrS86Rf/7J0CFDAAgNDSUpKSnZiLW2lMyZheeBobyLVC1d7H7Ph2qF8iRrJ5UrWHHqChGxqpGAF0Gh2GQwx8jg0yGMkYEBgxtVY8M5D/Xn6Ec89bpPttx5cMis6qRXrF2fu9cva2y/b7VRKBQkJSYik0qRyWTIZTKMjI2RSaXUdW1LvqLFAbCxtcPSyoqo8LAfzvrRzcePKJQjJ9k+lB21qlGLUx4eGpmTpFKe+fqyzf0EnadOZvzK5QSGqd47JDKCS/fusmjY8J/O8rNsWjUn+pg7789f0lkGi7KlSHzyDGmAah8TdeQElnVqaLQxzZeHuDv3kYeqtuH7K9fJULE8GH1W4GVkhNOYYYQuX4csJPS35Rd+n58q5/Pw8MDNzQ2FQoGLiwsWFhY8e/YMuVxOnz59aNq0KVKplClTpnD79m2cnJyQSCT873//A2DZsmVs3bqVjRs3cvDgQQwMDChevDh//fUXM2bMwN/fn2nTpjFlyhTWrFnDiRMnkMvlVK1alVGjRhEQEEDv3r3JlCkTZmZmrFu3jnnz5nHz5k3kcjmtWrWie/fuKJVK5syZw4ULF3B0dEQul1O+fHmtbMCPgoOCNFb7cHBwIDY2lri4OHUJWmZnZzI7q3a6SqWSlX8vpXLVahgbG9OgURMunjtHu5bNkcvllC1fnspVtX8mJiw0BFv7T50eW3sH4uPiSIiP0yjp69pfdUbF6+7tFF/n4umT2NjaUraSdsqOvhQaHIyDw6c6VHsHB+K+2J5Ozs44fbY91yz/mwqVq2BsbIxcLqdkmTL07NsfmUzGlHGjsbDIgGvbdlrNGRkWSqYPnTgAGzt7EuLjSIiP1zjgbN+zLwDeD+5pPL9QiU8jImEhwZw/cZQ/+vxPqxkBgoJDcPrs8+n44fMZGxencYBkZGTEhStXmDHfDRNjE/r16KHxOof/+Qd7OztqVdPeZzM2MgJLm0zq2xmsbZAmJCBNTNAo1ftWO2liItcO76Fx78E8vqE5evqRx/EDlGvYPMUywe+JjgjDKtOnkiyrTLYkJsSTlJCgLun7VpvchYup748KC8Xz3EkadeqBkbEJJat8+nG+e/k8SQnxuOT6+RGUiNBQMtl//7PZobfqbLT3/bsaz5dIJBgaGrJx6QLu3LhGyfIVccqScsfmZ1lbmBEVl6C+HR2XgJmJMaZGRslK+o7e8gJUHa/PPQ/8dJBiY2FOlQK5OHjzgdYyBgUFadTGOzo6qr5DsbEapXJGRkZcuHiR6TNnYmJiQv++qu9+SEgICxYu5O8lS9h/8KDWcmk7p0QiwcjIiElTpnD23Dlq1qhBjhw5fklWu4yWhEZ/6vCExcSSwdQEcxNjjZK+kOj3hES/V9/uXrM8t174IlN8KtWsUyw/4e/j8Xj+5qcyRYWFYW1rp75tbWtHQnw8iQnx6pK+b7UpW60mD25eZ+bQ/sgVcvIXLU7hUqrRtfI1aqufc+P8GRLj48mRN/9P5QVVGZ7TZyWjDpkyEZsQT1xCgrqkLzQqkjIFC9G3pSu5s7iw49RJxqz4m00Tp+Bgk4nZAwb+dA5tCFm0HACLcqV1lsHIwV6j0yMLCcXQMgMSC3N1SV+Cz1OsXZth5OiALDgEqwZ1kZgYY2iVEXl4BABWjeohCw0n9uoNnfw7frXfucS5vvrpkajXr1+zefNmcuTIQZEiRThw4ADbt29n1apV+Pn5sWvXLuLj43F3d2f27Nk8/KIsQC6Xs3r1avbv38+BAweQSqUEBQUxceJEihYtypQpU7h06RJeXl7s27ePQ4cOERQUxJEjRwB49eoVbm5ubNy4kT179gBw8OBB9u3bx9mzZ7l16xYnT57k8ePHHDt2jCVLluDr6/uz/+xklEoFpDB/ycAg+SaOj49n2qQJBPj7M3KMqpxvy8b1WGfKxP6jx9l98DAx0dHs2bkj2XN/OqdCiYSUchqm6XXcjxykRbs/vt/wBymUClKIiWEK2zMhPp5ZUyfzNiCAYaPGANCoaXP+N3Q4ZubmWGbMiGvb9ly7ov0zW4o0/N2/xfflcxZNGU/1Bo0pVqactuKpKZWKFP/uKW3PmlWrcubQYfp068bgMaNRfHagsnP/Pnp17pLsOT+bLSWSL7J9rR1KOLdjA5Wat8HCyjrFJoGvXxD//j15S/3YtlUqlSn+nT/PmJo27968Yuv8GZSpWZd8xTVLCq+5H+XS0QO0HfgnxloYhVZ8ZXul9bPZY8gI3NZvI/b9e47v2/39J/wA1Wcz+Q/yj/xIZ8lkTd96lbn+9DVP3gZrId2nLCnNUTU0TL7vrFmjBmdPnaJv794MHjqUpKQkJkyaxJ/Dh2P/Wcf2V/iZnJ9/16dPm8aZkyeJjo5m3fr1vySrgQSUafi7mxobMbJZLZxtrFh+8qrGY83KFGHfjXs/nUmpVKS4/Qw0vutfb3P64F4sM1oxadlaJixeRVxsLBdPHNVod/7oIU4f2EP3P8do5buu/Mrf/PPMWewdWDBkGHlcsiKRSOhYvwEBISG8CxMjJF+SGBikvGrCZ9+PhIePCd+6i8x/jSfrigUolQrk0dEoPzvpY9OmORHb9/yGxIKu/PTCErly5SJjxoxcu3aNhIQE1URVIC4ujmfPnnH16lXatWuHRCLBxcWFSpUqaTzf0NCQUqVK0aZNG+rUqUOPHj1wcnLi9evX6jbXr1/nwYMHtGrVCoCEhASyZMlCmTJlsLOzI+uHEqXr16/j7e3NjRs31BmePHnCixcvqF+/PsbGxtja2qa4tOHPcnTKjPfjx+rbIaEhZMyYEfMvJnYGBQYyYcwocuTMycK/l2FqagbA5YsXGTz8T4yNjTE2NqZ+o8ZcunCOdn+kvfToW+wcHHjx9NO8hoiwUDJYWmJqZpbq13j94jkKuZyCH8oSfgVHRyeeeHurb4eGhmKZMSNmX2zP4KAgpo4fQ7bsOZi7eCmmpqoFGc6ecid3nrzkyqM6o69UKjEy1P46Krb2Drx+/kx9OzI8DIsMaduet65eZvf61bTr2YdyVWt8/wk/wMnRCa/PtmdISAhWX3w+/QICCAsPp2Qx1ahJ80aNmLN4EdExMdhYW/Pk2TNkcjmlS5T46Ty3Th7lzSPVKEFSYjy2mT+NcMRGR2JqboHxF4trWNrYEuz7Olm7iKB3RIeFcuPIh31PTLTqB00mpUZbVYfv5f3b5C9TIVnHLLWsbe14++qF+nZMZARmFhkwMTVNdZtHnjc4uXMzDTp0oUj5T2WdMqmUY5vXEvougG6jJ2Njr53yWFt7B14/e6q+ndbP5uN7d8iSPQc2tnaYmZtTrkr1ZHP6fkbdYvkpmFU1YmJmbERgZIz6MStzM+ISk5CmsQS3eI4sNC9blKO3vLj/5u33n5AGmZ2c8PLyUt8OCQnByspK8zvk50dYWBglS5YEoHmzZsyeO5fH3t4EBASwaPFiAMLCwpArFCQlJTHpG3ODf3fO6JgYvL29yZsnDw4ODlhYWNCgfn3OnT+vtXwdqpSiXB5VqaG5iQm+oZ9KV+0yWhATn0iiNPmCIvYZMzDetR7+4ZFM3nOCJNmnz0YuR1sMDAx45Pfzcwlt7OzxffFpXmV0RDjmGTJgYmqWqjZet27SoksPjIyMMDIyomzVGjy4eYMajZohk0rZvXY5wQEBDJw8A1sH7ZREO9na8ujVS/XtkMgIMlpYYP7Z/um5vx/P/PxoVOmzknKlEqMUOtf/ddLgEEwLfhohNLK3Qx4dgzIhUX2fxNychPtexJw4DYChvS12PTqhiFbtx0zy5kZiYEj8fS/+rfR5rtLv8tMjUWYffpAVCgVubm4cPnyYw4cPs2fPHqpVq4ahoaHG2a2UrFixgqlTp6JUKunduzc3b97UeFwul9OtWzf1a+/du5f+/ftrvP/HdqNGjVK32717N23atEEikWj8sY2MtH8wXbZ8ebwfeeH/YR7X0UMHqVxNs7MWFxfLn4MHUq1GTSZNm67uQAHky5+fC+fOAiCTybh25TKFChfVes5ipcrw4okPgW9VK62dO3Gc0hUqfedZmny8HlK4eIlfsnLgR6XLlcfn8SMC/FXb858jh6hUpapGm7i4OMYMG0yVajUYN2WaugMF8PrVK7ZuWI9cLicxMZGjBw9QvXZttK1Q8ZK8fvaE4A8rFl45fZLiZVNfKvrw9k32blrHoAlTf1kHCqBi2bJ4eXvj6+8PwP6jR6leWbMUMzQsjAnT/yIyKgoA97NnyJMzp3quxO379ylXqpRW/u5lGzRTL/bQcvBogn1fERWiGjXwvn6ZHEWSd9SyFiiUYjunnLnpNHGW+vUKVapG7hJl1B0ogHcvnuGSL+WFClIjV6FiBLx6oV7w4c6lc+QvUTrVbZ49uMvpPVv5Y+gojQ4UwJGNq0hMiKerFjtQoCoVffXZZ/PyqROUKFch1c+/fe0Kx/fuQqlUIpVKuX39CgW0eOLkzMOnLDtxmWUnLrPy5FWy22XCLqOqtLR8vhx4+wel6fUKujjStEwRNp730HoHCqBihQp4eXmpKxn2HzhAjS/KWkNDQxk/cSKRHybnnzh5kjy5c1OyRAmOHz3Kjm3b2LFtG61ataJe3bpa70D9bE4ba2tOnznDmnXrUCqVJCUlcfrMGcqWTb7Yw4/adfWueiGIcTuOkt/ZEecPq+vVL1EQzxfJy/HMjI34q31jbjx7zcJjFzQ6UABFsmbGy1c7f/P8xUrg++KZesGHG+dOU6R0uVS3ccmZiwc3VQtbyGUyHt+5RY68+QDYuepvEuPjGTh5utY6UADlCxfh0cuX+AWpvjOHLl6kWknNkW6JRMLi3Tt5G6qac3vg4nnyZM2G42clyIJK/K27mBUugLGLarqAdbNGxF7z0GhjZGeLy8KZSCxUJydsO7Yj5tynUnLz4kWJu6e9cmJBP2mtN1GxYkV27tzJjBkzCA4OpmXLluzatYvKlSvzzz//UKdOHYKDg7l58ybdunVTd2rCw8Pp1KkT+/bto1SpUgQGBvLkyRMKFSqE7MOwaMWKFVm6dCnt2rXD1NSUgQMH4urqmmxeU8WKFdmzZw+1atUiKSmJjh07Mm3aNCpVqsT69evp0KED8fHxXL58WX0GTlsyZbJl1PiJTJ04HplMShYXF8ZOnMwTH2/mz5nN2k1bOLR/H0FBgVy5dJErly6qnzt/yd/8b8gwli5cQLeO7TEwMKR02bJ06NRZqxkBrGxs6DP0T/6eMwOZTIZjZmf6DR/Fy2dP2bBsMTOWrPjuawS9C8De8evr5muDTaZMDB8zjplTJiGTynDOkoWR4yfy1MeHJW5zWb5+I0cP7ic4KIhrly9x7fKnUr3ZCxfTqVsPVixZxICe3ZDL5FSrWZOGTZppPWdGaxs6DxjMuoXzkMlkOGTOTNeBQ3nz4jnbVy9j/LzF33z+ga2bQKlk++pl6vvyFChE+179tJrTNlMmJo8azdipU5DKZGTNkoWpY8fx+MkTZsx3Y8fadZQqXpwenTrTb/gwDA0NcbCzx236DPVr+AX44+z0/St4p5W5pRU12nXl9NY1KORyrOzsqdmhOwAhfm+4tHcbrf+c8M123xMVGoxlJrvvN/yKDFZWNO3ahwNr/kYul5HJwZFm3fvx7s1Ljm/dQO+JM77aBuDs/p0olXB866fl1rPmyUfRCpXxueOJrVNmtrhNVz9W27UduYv8XIfFytqGrv8bypoFc5DLZNg7Zab7oOG8efGMbSuXMWH+km8+v3W3nuxYs5LpIwYDULJ8RWo11v53CCA2MYl9HvfpWLUMhgYSwt/Hsff6PQBcbK1xrVCcZSdSnuv2UaNShZEArhU+bTffENUy59pga2vL5EmTGDNunOo75OLCtClTeOztzYyZM9mxbRulSpWiZ48e9B0wACNDQ+zt7Znv5qaV9/9dOYcPHcqsOXNo31FVBVGrRg3+aN/+l2SNiktgmftlRjWvrV7afukJ1b48j5Md/2tQlRFbDtO4VGEcrDJQIV8OKuT7ND9ryh533ick4pzJmuDP5kz9DEsra9r2GcC2vxcil8mwdXSiQ79B+L18wb4Nqxg+w+2rbQCadezGoa3rcRszDAMDA/IWLkqNJi148+wpDz1vYJ/ZmeXTJ6nfr3G7ThQoXvKnMttaWTGhew8mrF6BVCbHxcGByT174f36NXO2bGLz5KnkccnK8A4dGbVsKQqFEsdMmZjWu+9Pve+/lTwyiuB5S8g8ZSwSIyOk7wIJmrMI0/x5cRwxCL9+w5D6BxCxcz/Zls0HAwkJXt6EfrYCn3FWZ2SB2isn1kcKMRCFRPkT43EeHh7qxSHev3/P1KlT8fHxQS6X07dvX1xdXZFKpfz111/cvXsXBwcHwsPDmT59OvHx8ernbtq0id27d2Nubk6uXLmYPn06iYmJdOnShUKFCuHm5saKFSs4fvw4crmcatWqMX78eAICAujatSvnzqmWApdKpcydO5cbN24gk8lo1aoVfT9Mll20aBEnTpzA3t4eCwsLGjdurC4P/J6AkJ9fKetX8w+P0nWEVHGw+jWrPGnby6CfXzHpVyvvkPL8H32z9vavWRZb2+wzpo/Ppuo6SPrvjNez7zfSA+MaV/1+IyFVuq3T7qVDfpVu1croOkKqVE2I+X4jPRA+Yfr3G+kBian2r7/4K+Q9e0TXEVKl67Ltv+29tgzq9NveKy1+qhOVGhcuXECpVFKrVi1iYmJo2bIl+/fvx8bG5le+rVaJTpT2iE6U9ohOlHaJTpR2iU7Uf4/oRGmX6ERpl+hEaVeXv7f9tvfaOlj7lVnaoP3JQV/IkycPo0ePZvGHCbVDhgxJVx0oQRAEQRAEQRCEz/3yTlS2bNnYuXPnr34bQRAEQRAEQRB+A7E6nxZW5xMEQRAEQRAEQfgv+eUjUYIgCIIgCIIg/Hv8yMXQ/23ESJQgCIIgCIIgCEIaiE6UIAiCIAiCIAhCGohyPkEQBEEQBEEQUk1U84mRKEEQBEEQBEEQhDQRI1GCIAiCIAiCIKSaEjEUJUaiBEEQBEEQBEEQ0kCMRAmCIAiCIAiCkGpiiXPRiUoVcVVm7Ukv21KRDoaplTKZriOkiomRoa4jpIqBRKLrCKkSEB6l6wipIpXJdR0hVRThEbqO8K/xPiFR1xFSxcAgfRThJN6+r+sIqSIxNdV1hFRRJqaPz6eQfohOlCAIgiAIgiAIqZZeTor/SunjdIwgCIIgCIIgCIKeECNRgiAIgiAIgiCkmkIMRImRKEEQBEEQBEEQhLQQI1GCIAiCIAiCIKSamBMlRqIEQRAEQRAEQRDSRIxECYIgCIIgCIKQamIkSoxECYIgCIIgCIIgpIkYiRIEQRAEQRAEIdUUYiRKjEQJgiAIgiAIgiCkhehECYIgCIIgCIIgpIEo5xMEQRAEQRAEIdVENZ/oRGnVjWtXWbd6FVKplNx58jBy7HgyZMig0eb0SXf27NyBRCLB1MyUQUOHU6BgIeRyOX8vWsj9e3cBqFCpEv3+NwiJRKL1nPc8Pdi7ZSNSmZRsOXLRe8hwzC0yJGunVCpZs3gB2XLmpLFrGwAUcjlbVq/A59FDAEqUKUeHHr1/Sc6b16+zad1qpFIpuXLnYdioMVh8sT3PnT7F/l071duz3+Ch5C9QEIAOLZph7+Cgbtu6fQdq1auv9Zxed25xdOc2ZFIpWbLnoGP/QZhbWCRrp1Qq2bZiKVmy56BOs5YAJCUlsnf9Gt48f4YSyJk3H2179cXExFTrOa94eLBiw3qSpFLy5srFxD9HYPnF9txz+BD7jx1DgoSsWZwZP2w4tpkyERUdzdy/l/L0xQvMzcxoWr8B7Vu21Fq2148ecP34QeQyGXZZXKjToRsmZuapbpcYH8e5XVuICA5EqVRSsFwlytRpqPHcxx5XePngHk37DPqhjM8e3uP8wT3IZFKcXLLRtGsfTM3NU9VGmpSE+87NvH39AqUSXHLloeEf3YgMDeHQ+hXq5ysUCkLe+tOm3xAKli73Qzm1lTkhPo5jW9YRFvgWpVJJ8YrVqNyw6U9n+pqCLk40Ll0IQwND3kVEsff6PRKlsq+2b1+lFIER0Vx8/CLZY11rlCM6PoFDNx/+srxXbnqwYuPGT9+nYcOTfZ8+unDtGlPnu3HhwMFfludr9D1n+bzZ6VmrIsZGhrwKCmPhsfPEJUmTtWtetihNyxQBJbyNiGLx8YtExsVjYmTIoIbVKJjFCSTgExDEMvfLJMnkP5zp8d3bnNizHZlUhnP27LTr/T/Mvtinf61N3PsY9m9cy9s3rzExNaVcjVpUrd8YgOePvTi2cwtyuRxjYxNadu1J9jz5fjjn50xz5cCqakUkhoZIQ8OIPHUO5Rfb0cjeFuta1TEwNUGpUBJ15gLS4BAkZqbY1KmBsYM9CqmM+EfexN77Nd8diwplsevdFYmxEUkv3xA0fynKuHiNNtYtm2DdsgnKxCSSfP0JWboKRcx7Mk8Zg3EW50//nsxOJDzw4t2kmb8ka2o4TRhJ4svXRO7cp7MMgu7858r5zp8/z8aNG7X+upEREbjNnsnUGbPYvGMXzlmysG7VCo02fr5vWLNiOXPmL2TNxs107tqdqRPGA6rOlZ/fG9Zt3sraTVt4cO8uly6c13rO6KhI1i5dyOBxk5i3cj2OmZ3ZvTn59gjw82XOxLF4Xruscf/VC2d5F+DPrKUrmbFkBT5eD/C8ejnZ839WVGQki+bNZsK06azdsp3Mzs5sXLNao42/ry/rV61g+jw3lq3bQIfOXZk5eaL6MUurjCxbt0H936/oQMVER7F95d/0+nM0kxYvx94pM0d2bE3WLtDfj7+nT+aex3WN+08d2IdcrmCs22LGuS0iKSmJ04f2az1nRGQk0+fPZ87kyezbsBEXZ2eWr1+v0cb76VO279vH+sVL2LV2LdlcXFi9eTMAi1atwtzMnN1r17FhyVKue97k8o0bWskW/z6Gs7s206hHfzqPn461nQPXjh1IUzuPE0ewtMlExzFTaTd8PF5XL/LutergOiE2lvN7tnH54G6U/Nips9iYaI5uXkObfkP4319u2Ng7cu7g7lS3ufLPYRQKOX0nzaLv5FlIpUlcdT+KQxYX+kyaqf4vd+FiFClXSSsdqJ/NfPHwPqxsbOk3ZQ49x03j9qWz+L949tO5UpLB1IT2lUux5YInbofPEv4+jsalC6fY1tHakn71KlM8e5YUH69ZJC+5nOx+Sc6PIiIjmb5wIXMmTmLfuvW4ZHZm+Vd+V3wDAli6bq1OlgPW95zWFmaMbFabv/adpNfKnbyLjKZX7YrJ2uXLbE+biiUYtukgfdfsJiA8im41Vd+RjlXLYGhgQL81u+m/Zg+mxkZ0qFL6hzO9j45i99rldB06ijHzl2Lr6MTx3dtT3ebwtk2Ympkxat4iBk+bhc/9uzy+ewuZTMrWZQtp26s/I2YtoG7L1uxc+fcP5/ycgbkZNg1qE37UneBNO5BFRWNVtZJGG4mREXatmvP+1l1Ctu3hvcctbBrXA8C6RlUUUinBm3cSunMfprlyYJorh1ayaeS0tsJx1BACp87Gt/v/kL4LxL53N4025iWLkalDawJGTsKv3zDiPG7h+OdAAAKnzcWv3zD8+g0jeOEyFLGxhCxdndJb/XLGObLhsmQuljWr6eT99YFCqfxt/+mr/1wnysvLi/fv32v9dW953qRAwUJkzZYNgOYtW3H29CmNHyRjYxNGjBmLnb09APkLFiQ8PAypVIpCoSAhPgGpVIo0KQmpVIaxiYnWc3rdvUPufPnJnMUFgNqNmnD94rlkP5xnjx+lRv2GlK+iuYNQyBUkJiYglUmRSaXIZL8m5x3Pm+QvUBCXrKrt2aRFS86fPa25PU2MGTpyDLZ2qu2Zr0BBIsLDkUqlPH7khaGBAaOGDOJ/vbqzY/Mm5PIfPzP5NT7375E9Tz4cnVUHdFXrNeTWlUvJtuelUyeoXLseJStW1rg/T6EiNGzVBgMDAwwMDMmWMzfhISFaz+lx+zaFC+Qnu0tWAFo3bYb7ubMaOQvlz8/+jZuwzJCBxKQkQkJDsbbKqPp3PntG47p1MTQ0xNjYmCoVKnDu8iWtZPN98hjHbDmwcXACoGiVGjy97ZFsG36rXTXX9lRprhotjY2OQi6TYvphJOv5vVtksLZRP/4jXj5+SJYcubF1ygxAmRp18PK4ppHxW22y5y9I1cYtkBgYYGBgQOZsOYgKC9X89z17gvedmzTu1OOHc2ozc/32Xajb5g8A3kdFIZdKMTVPPsKqDfmzOOIXFkFoTCwA15+8olSurCm2rVwgFx7P3/Dgzdtkj+V2sqNAFkduPH39S3J+5HHnDoXz5ye7i2o/2rppE9zPJ9+PJiQkMMVtHsP69v2leb5G33OWyZ2NJ2+DeRsRBcCx24+oXTT5yMyzwFB6rNhJXGISxoaG2FtlIDo+EYCHvm/ZceU2SlQHdc8DQ3GyzvjDmZ4+vE+2XHlxyKwa8ahcpwF3r13W2GbfauP/+iWlq1THwMAQIyNjCpUszYObNzAyMmby0jW45MyNUqkkLDgIi4yWP5zzc6Y5siMNDEYeqdqOcfe9MC+U/4s22ZBFRZH46g0ACS9eEXHsJADGTg7EP36iqs9SKEh4+Rrz/Hm0ku1zFmVLkfjkGdKAdwBEHTmBZZ0amjnz5SHuzn3koWEAvL9ynQwVy4PRZ4VTRkY4jRlG6PJ1yEI096O/i02r5kQfc+f9ee38Dgrp07+inE+pVDJ//nzOnDmDoaEh7du358yZMxQrVozbt28THh7OxIkTcXFxYdeuXQBkyZKF1q1bay1DSHAQDk5O6tsODg7ExsYSFxenLunL7OxMZmdndeaVfy+lUpWqGBsb06BRYy6dP0d71xbI5XLKli9P5SpVtZbvo7DQEGztP5W42do7EB8XR0J8nEZJX9f+qjM/Xndvazy/Wp163Lx6maHdO6NQyClasjSlyic/c/izQkKCsXd0VN+2d3AgLjaW+Lg4dUmfU2ZnnDJ/2p5rVyyjQuUqGBsbo5DLKVmmLD369EMmlzFl7BgsMljQsk07reaMCAslk92ns942dnYkxMeREB+vUdLXrqfq4MT7wT2N5xcqUVL9/8NDgjl/4ih/9Bmg1YwAQSEhOH5W2ujo4EBsXByxcXEapT1GRkZcuHqVmYsWYmJsTN9uqrOERQoW5J8zZyhRpAhJUinnLl/ByMhQK9neR4RjaWOrvm1pnYmkhASkiQkaJX3faycxNOTUtvW8uH+b3MVKYeOo6hgUraL6kfa+ee2HM0ZHhGNl++nvbJXJlsSEeJISEtTlcd9qk6dwMfX9kWGh3Dx7kiade2q8x9n9O6nVom2ycjtdZTY1V23TQ+tX4n3HkwKlymCX2TnZ+2iDTQZzImM/lfRExSVgbmKMqbFRspK+jyV6BZwdNe63MjejRblirDt7nYr5c/6SnB8FhX7xfbJP+fs0+++luDZqTN5cuX5pnq/R95wOVpaERH86qRkS/Z4MZqZYmBgnK+mTKxRUzp+T4U1rIpXJ2XzBE4DbL/3VbRytLWlVvjiLj1/84UyRYWHYfLZPt7ZV7dMT4+PVJX3fapMjTz7uXL1ErvwFkcmkPPD0wNBQta80NDIiJiqSRRNHExsTTZdBf/5wzs8ZZrREHvNpO8pj3mNgaorExFhd0meUyQZFbBzW9WthbG+PMjGR6MuqfWJSYBDmhQuQ9DYQiaEh5vnyoFQotJLtc0YO9hqdHllIKIaWGZBYmKtL+hJ8nmLt2gwjRwdkwSFYNaiLxMQYQ6uMyMMjALBqVA9ZaDixV7VTDfEjQhYtB8Ci3I+PeqZ34mK7/5KRKHd3d+7cucPRo0fZu3cvBw4cICQkBKlUyu7duxk3bhxLliwhb968dOjQgQ4dOmi1AwWgUCiRkHxekIFB8k0cHx/PX5Mn8jYggJFjxgGwZeMGrG1s2HfkGLsOHCI6Opo9u3ZoNSOA8qs5U3dAfHDXdjJaW7Nsy04Wb9hG7PsYThzUfvmZUqFMcZ5VStszIT6e2dOm8DYggKGjRgPQsGkzBgwZhpm5OZaWGXFt245rl7VfdqhUpj7nt/i+fMHiKROo3qAxRcv8fCnXlxRKRYo5DVPIWbNKFU7v20+fLl0ZMm4cCoWCYf36IZFA5wEDGDV1ChVKl8bYyFgr2b62DSUSgzS3q9+5F71mLCQhLhbPk8e0kg9Un8eUSAwkaWrz7s0rtrjNoGzNeuQrXkp9v9+Lp8TGxFC0fKWUXuKHaCtzy14DGLFgBQmxsVw+9mvmynxtRmVqyzgMJBI6VivDkVtexHwYofiVvra//3iwDLDv2FEMDQ1p3qDBL8/zNfqeUyKRpFhg+7W/+7Wnr2m7cBNbL99idsemGv+yfJntWdi1JYdveeHx/M0PZ1J+ZV8p+Wxf+a02zTp2AyQsnDiKTYvmkb9ocQw/G0XJaG3D5L/XMHjKLHavWU7Iu+Qjqmn2tTnJn3+/DQwwzZWDuAePCd2xl9h7D7F1bQaGBkRfvApKcOjcDtvmjUh84we/oHJDYmBAyn/wTx22hIePCd+6i8x/jSfrigUolQrk0dEoZZ9Opti0aU7E9j1azycIafWvGIny9PSkUaNGmJiYYGJiwuHDh+nSpQvVqqlK0fLly0dkZOQvzeDo5ISP9yP17dDQEDJmzIj5F2eVg4ICmThmNNlz5GDB0mWYmqoWELhy6QKDhv2JsbExxsbG1G/YiEsXztOuQ0et5rRzcODFUx/17YiwUDJYWmJqZpaq59+6fpUuff+HkbExRsbGVK1dl5tXr9DIVbudUgcnJ554P1bfDg0JxTJjRsy+2J7BQUFMGz+WbDlyMGfREvX2PHvqJLnz5CVXnk8lCUZG2v+429rb8+b5U/XtqPAwLDKkfnsC3L56mT3r19C2Zx/KVq2u9YwAmR0ceeTz6e8eEhqK1RefT7+AAMIiIihZtCgAzRo0YM7SJUS/f09CQgKDe/fB2soKgI07d5A1S8pzUlLD48RhXnndByApIQG7D+WlAO+jIjG1sMDYVHNxjYyZbAnyfZViuzc+j7BzdsHS2gYTUzPyly7Pi/t3fjjfl6xt7Xj7+tMCBtGREZhZZMDE1CzVbR55XufEjs00/KMrRctrlnU+vuVB8YpVNA7UdJ35xaMHOLpkI6NNJkzMzChSriI+dzy1lq9+iYIUyaYaLTQ1NiIwMlr9mJWFGXGJSUhTuThANjsb7DJmoHlZ1Wc3o7kpEokEI0ND9l2/p7XMH2V2dODRky++T5aWmH/2vT92+jQJiYl0Gvg/ZFIZiUlJdBr4Pxb/NR0Hu187Z0ufc3atUY5K+XICYGFqwqvgMPVjqjK9BBK+GH3MksmKTJYWPPILBODkPR+GNKqOpbkpMfGJ1Cycl0GNqrHc/QrnH/3cvD0bOwd8P5v7FxURjvkX+/RvtYkIDaHpH52xsFSVFJ49vB97p8zEx8Xy/JEXxcpVACBrrtw4Z8/BOz9fHJx/fF8KII+JwTjzp0oYQ0tLFAkJGh0PeWwssvAIpIFBgKqcz7peLYysrVFKpURfvoYyQXUCwrJ8GWQfSgO1SRocgmnBT2WGRvZ2yKNj1O8LIDE3J+G+FzEnTqv+Lfa22PXohCI6BgCTvLmRGBgSf99L6/mEtPnROcb/Jv+KkSgjIyONs0L+/v7ExcWpD6h/xcpxXypbvjyPHz3C388PgKOHDlG5quZ8ori4WEYMHkS1GjWYNG26Oh9AvvwFuHjuHAAymYzrV69QuEgRrecsVqoML574EPg2AIBzJ45TukLqz37nzJOXm1cuqXPe8bhB3g+r4WlT6bLl8PF+TIC/anv+c/QwFb8ob4yLi2Ps8CFUrl6dsZOnamzPN69esnXjeuRyOYmJiRw9eIDqtWprPWfB4iV5/ewpwR/OJl45fZJiZcun+vkPb3uyb9M6Bk6Y8ss6UAAVypTBy9sb3wBV6cuBY8eoXknz7x4aHs7EWTOJjFL9eLqfO0funDmxsbLiwLGjrNmiWmQiLCKCwydO0LD2j2/PCo1a0GHUZDqMmkybYWMJfP2SyBDVj7vXtYvkKloy2XOyFSj81XbP793C8+RRlEolcpmU5/dukTVfgR/O96XchYsS8PI54UGqg7g7l86Sv0TpVLd5ev8OJ3dvpePQ0ck6UAC+T33IWUi73/efzfz4tgeXjh1EqVQik0p5fNuDnAVTXuzhR5y678OiYxdYdOwCf5+4RHb7TNhnVJWYVcqfU33AnBpvQiOYuf+U+vWuP33N/dcBv6QDBVChdBm8fHzwDVDtRw/8czzZ92nTkqXsWrWa7ctXsGj6X5iamLB9+Yrf1oHS15xbLnoyYN1eBqzby9CNByjk4kSWTNYANC1dhOspzGeztczAeNd6WJmrOjK1i+bjdUg4MfGJVMyXg/81qMq4Hcd+ugMFkL9YCd48f0ZIoGrezo2zpyjyxUIv32pz/ewp3PerFmeJiYrE48JZSlWqhoGBAXvWruDVh5OYgf5+hLx7q5XV+RJf+2Hi7IShjWo7WpQoQsLzV5ptXvliZG2FsaOqvNPExRlQIouKxqJ4Uawqq363DCzMsShaiHgf7S8iE3/rLmaFC2DsoioLtm7WiNhrHhptjOxscVk4E4mF6gSfbcd2xJz7VEViXrwocfceaD2bIPwIifJfUNR4+vRptmzZwoYNG5DJZLi6uvL+/XsWLFhAhQoV8Pf3p2vXrpw7d44VK1aQmJjI8OHDU/36/p+dKfsWj+vXWLd6FTKZFOcsLoydOJl3bwNYMHcOazZuZsfWLWxct4ZcuTUnbLotXgrA34sW8PzZUwwMDCldpgz9Bg7G2Dh1JVMBEdHfb/TB/Vs32bNlIzKZDMfMzvQbPorgwHdsWLaYGUs0VxRcs3g+WXN8WuI8JjqarauX8+blCwwMDChcoiR/9OiDUSpz2mdM/cR0zxvX2bR2DTKZlMxZXBg5bgLv3r1lqds8lq3bwO7t29i6YR05c+XWeN6sBYswMTVl5ZLF+Hg/Qi6TUbVGLbr17pPqDvWLVP7NAR7dvc2RHduQy6TYZ85Ml4FDCQsKYsfq5Yydt0ij7dYVS8mSLbt6ifPpwwYS9/491raf5vrkLlCQdr36ffd9K2RK26Tkqzc9WL5hAzKpFJcsWZg6ajQBge+YuXAh21epVjjad/Qo+44cwdDQAAc7O0YNGoyLszOxcXFMmTsX/7dvUaKke/sONKpbN1Xvu+XRq++2ef34IdePH0Qhk2Fl70C9jj0xy5CBIN/XnN+9hQ6jJn+zXWJ8HBf2biPsQ2c2d7FSVGjYTGNkx/vmNZ7fv02zPoNTzGBj8e25SM8f3uPcoT3IZXIyOTjSokc/IkKCOb51PX0+LLGbUhvzDJasmDyKhNhYMtpkUr9e1jz5aNSxOwBzB/diwF9uWGWyTemtf9jPZE6Ii+Wf7RsJeavqeBcoWZYazVqlerTsge+7NGUt6OJIo1KFMTQwIOx9LLuu3CE+SUpWOxvaVirJomMXNNq3r1yKwMiUlzivV6IAGUxNUrXE+cSqxdOU86OrN2+yfJNqP+ri7MzUkaMIePeOmUsWs3255n70bVAgf/Tvz8WDh37ovX7G78zZdod7mp9TLk92etaugLGhIW8jonA7fI6YhETyOTvwZ5OaDFi3F1B1sJqXLYpcoSDsfSzL3C8TGBnD+gF/kNHMlLAPi5IAPPIPZJn718u3Bzf49nxj73t3+GfPdtWlFByd+KP/YMKCg9i7bhV/zpr/1TYWlhlJiI9n56qlhAYFglJJ7WatKPPhBNkL70cc3bEFhVyOobERjdt1Il+RYl/NUSYNi/d8XOIcAwPkUdFEuJ/ByNoKm3q1Cdmm6tSZuDhjVb0KEmMjlHI50eevkPT2HRJjY2wa1cXIxhqQ8N7zNvHeT7/9hp+JO34y1W0typdRLXFuZIT0XSBBcxZh7JwZxxGD8Os3DADrFk2wbtEYDCQkeHkTsnQ1yqQkAOyH9EMeFvFD5XzKRO2X+jqNH0HiqzdaXeI835XUb09dajjr962M6D7++8dEuvCv6EQBLFq0iHPnzqFQKOjUqRMnTpxg0KBByTpRnp6ejBkzhh49etClS5dUvXZqO1G6lJZOlC6lpROlS2npROlKWjtRupKaTpQ++F4nSkibtHaidOVHO1FCcj/SidKF73Wi9EVaOlG6lJZOlC79ik7UryA6UcnpayfqXzEnCmD48OEao0sdO36aS5Q1a1bOfSiVK1eunPr/C4IgCIIgCIKQNv+SMZif8q+YEyUIgiAIgiAIgvC7/GtGogRBEARBEARB+PXESJQYiRIEQRAEQRAEQUgTMRIlCIIgCIIgCEKqpfZi6P9mYiRKEARBEARBEAQhDUQnShAEQRAEQRAEIQ1EOZ8gCIIgCIIgCKkmqvnESJQgCIIgCIIgCEKaiJEoQRAEQRAEQRBSTSwsIUaiBEEQBEEQBEEQ0kSMRAmCIAiCIAiCkGriYrsgUYqtIAiCIAiCIAiCkGqinE8QBEEQBEEQBCENRCdKEARBEARBEAQhDUQnShAEQRAEQRAEIQ1EJ0oQBEEQBEEQBCENRCdKEARBEARBEAQhDUQnShAEQRAEQRAEIQ1EJ0oQBEEQBEEQBCENRCdKEARBEARBEAQhDUQnShAEQRAEQRAEIQ1EJ0oQBEEQBEEQBCENRCdKEARBEARBEAQhDUQn6jd59uxZsvvu3bv3+4P8C/j4+Og6wr9Or169dB0hVfr06cOJEydISkrSdZSvevDgga4jfNe0adPSRc6oqKhk9wUEBOggyffJZDIePXqEj48PSqVS13G+KiwsjFOnTnH27NkUt68g/G6HDh365n/65uDBg8nu2759uw6SCLpmpOsA/3a3b99GoVAwceJEZs6cqf5xlclkTJ06lZMnT+o4oabo6GiOHj1KZGSkxoHAoEGDdJhK0/Dhwzlx4oSuY6TKhQsXWLZsmXp7KpVKJBIJZ8+e1XU0DfHx8bx79w5nZ2ddR/mmPn36cOjQIdzc3KhRowaurq4UL15c17E0uLm5ERkZSYsWLWjRogUODg66jpRM8eLFWbBgAeHh4XqZ8927dyiVSvr27cvatWvV+yK5XE6fPn1wd3fXcUJNV69eZcyYMTg6OqJQKIiOjmbx4sV699k8fPgw8+bNo0yZMsjlcqZOncqMGTOoUaOGrqMlc+vWLTZv3pyso7dlyxYdJfqkdu3aSCSSrz6uL/v3ggULqnN+2bGXSCR4e3vrIlYyHh4eAPj6+vLmzRtq1KiBoaEhV65cIW/evLRs2VK3AT/YtGkT79+/Z9euXRonc2QyGceOHaNTp046TCfoguhE/WLXrl3j5s2bBAcHs2TJEvX9RkZGtG/fXofJUjZ06FAyZsxIvnz5vvkjoUt58+Zl2bJllChRAjMzM/X95cqV02GqlM2cOZMJEyaQN29evd2eAOHh4dSuXRs7OztMTU3V9+vLwcBH5cuXp3z58iQkJODu7s6QIUOwtLSkTZs2dOzYERMTE11HZOvWrQQEBHD48GF69uxJlixZcHV1pU6dOhgbG+s6HgCurq64urry7t07jh07RocOHcibNy9t27albt26uo7H0qVL8fDwIDg4WOPAxMjIiJo1a+ou2FfMnj2bdevWUbBgQQAePnzIlClTOHDggI6TaVq5ciUHDhzAyckJUI3q9e/fXy87UWPHjmXQoEFkyZJF11GS2bp1K0qlkuXLl5MtWzZatWqFoaEhR48exd/fX9fx1NJL1cbs2bMB6NKlC0eOHMHW1hZQjUQPHDhQl9E05MyZEy8vr2T3m5qaMmfOHB0kEnRNdKJ+scGDBwOq4Wp9OZvyLaGhoWzcuFHXMb7p0qVLREVFcfPmTfXZNYlEohdnKL+UMWNGvTzo+9LKlSu5du0aERERuLi46DrON3l4eHD48GGuXr1K9erVady4MdeuXWPAgAGsX79e1/EAcHFxoWXLlhgZGbFr1y62bt3KokWLGDlyJPXq1dN1PAD8/Pw4cuQIx48fJ0eOHNSrV48TJ05w6tQp5s2bp9NsHw+q1qxZQ9++fXWaJTVMTEzUHSiAYsWK6TDN12XIkEFjxNHFxUVvOvZfcnJy0tvfzI/7yCdPnqg/qwA9e/akVatWuor1VeHh4Rw5coTY2FiUSiUKhQJ/f3+df8+/FBwcjI2Njfq2ubk5ISEhugv0hZo1a1KzZk0aNWpEnjx5dB1H0AOiE/WblCtXjrlz5xIVFaUxrP75DlgfFCpUCB8fH40DAn1TvHhxwsPDadmypd6VIX3k6ekJqEbNZsyYQZ06dTAy+vR107dRMzc3N0JDQ8mdO7dGmYKrq6sOUyVXq1YtsmbNSuvWrZk8ebJ6JLJChQq0bt1ax+lU9u7dy+HDhwkJCaFly5bs2LGDzJkzExQUhKurq150ov744w9CQ0Np0aIF69atU5/tb9myJdWrV9dxOti9ezft27cnKSmJZcuWJXtcn8qLAcqWLcuECRNo164dhoaGHD9+HBcXF/V+QF++78WKFaNPnz60bt0aQ0NDTpw4gaOjo3reiT51Wrp06cLIkSOpWLGixr5TnzICXL9+nUqVKgFw8eJFDA0NdZwouWHDhuHs7My9e/eoW7cuFy5c0MuOfs2aNenRowf169dHqVRy4sQJGjVqpOtYybx9+5bRo0cnO57Tt8oN4deTKPV5Buy/SNu2bSlbtmyyMjl9O0h1dXXFx8dHXdalr3N43r59y6FDhzhx4gQuLi64urpSu3ZtvTmr2qVLl68+po+jZg0bNtS7eSYp8fX1JXv27LqO8U2jR4+mdevWVKhQIdljJ0+epEGDBjpIpenzAz99tGvXLjp06JBiBwr0rxOVXr7v48aN++bj+nRSr0+fPiQmJiYbGdenjI8fP2bMmDEEBwcDqhGqefPmkTdvXh0n0/Rx/z537lwaNmxI9uzZ6datG0eOHNF1tGROnjzJzZs3kUgkVKpUiTp16ug6UjINGjRg7NixyY7n9L2KQ9A+0Yn6TVxdXVNc0UXffG3lK33cObx9+5Zjx46xa9cunJ2dCQ0N1atyKVCtypgvXz6N++7du0fJkiV1E+gr+vbty9SpU/Vy/sHn7t27x+rVq4mLi1OXpbx9+5Zz587pOpqGx48fqzPK5XL8/f1p06aNrmOpvX79mm3btmlsR39/f71cYSopKQkTExPevHnDq1evqF69OgYGYmHZf7v08psJEBERgUQi0ShF0yft27dn9+7d7NmzB6VSSfv27WnevLledqJu377N06dPad26Nffv39ebUdzPdejQgV27duk6hqAHRDnfb1KmTBnOnTtH1apV9WLy+9dkyZKFnTt3cuPGDWQyGRUrVqRz5866jqUhPZRLpZdVGbt06YJEIiE8PJxmzZpRsGBBjXIUfTmD/tH48ePp1asXBw8epEuXLpw6dYrChQvrOpaGiRMncvPmTaKiosidOzc+Pj6ULl1arzpRf/75JzVr1uT27du4urpy+vTpZJ19fbB8+XJevHjByJEj6dSpE/ny5ePq1atMmDBB19E06PNKcp9zd3dnzZo1yXLqW6UBqMq2z58/T/Xq1fWyRA5UJx0nTpxIQEAA27dvp2vXrsyaNYusWbPqOpqGihUrMmTIEMaMGUPPnj159OiRxqJM+mLz5s2cOXOG4OBgGjVqxOTJk2nTpo3eXYKjTJkyzJ49m2rVqmksxKSPHT7h1xKdqN/E3d2dbdu2adynT0uMfjRv3jzevHlD69atUSqVHDhwAD8/P706aPH09GTw4MHJyqWcnJyYMmWKjlJpSi+rMn5c+CS9MDExoXXr1gQEBGBlZcW8efNo1qyZrmNpuHbtGidPnmT69Ol07dqV+Ph4vVu5SSqVMmTIEGQyGYULF6Zdu3Z6M6fsc2fPnmXHjh1s2bKF5s2bM3r0aL2cuK/PK8l9bu7cucybN0/vc4Lqb797926N+/TtN3Py5Mn06tWL+fPnY29vT9OmTRkzZozejegOHz4cX19fXFxcWLhwIZ6ennpXEguq6y/t2bOHdu3aYWNjw759+2jbtq3edaIePHhAWFgYjx8/Jj4+nuDgYHLmzKl3J02EX090on6TK1eu6DpCqly9epVDhw6py2Vq1qypdwep31pRSB/mm0D6WZWxfPnyuo6QJqampkRGRpIrVy7u379PpUqVkMvluo6lwdHREWNjY/LkycOTJ09o0qQJMTExuo6lwdzcnKSkJHLmzMmjR48oW7asriOlSKFQYGZmxvnz5xk2bBgKhYL4+Hhdx0pGn1eS+1z27NkpU6ZMuiiHTA+/mREREVStWpX58+cjkUho166d3nWgAPXCIXfu3AHAxsaGa9eu6d1n1sDAQKNSx9TUVC9HIevVq8eBAwfYunUr/v7+9OnTh8aNG+s6lqADohP1m6SXJUblcjkymUy9I5PL5Xq5E0sv0suqjOlF9+7dGT58OH///Tdt27bl6NGjFC1aVNexNDg5ObF69WoqVaqEm5sboJrXo0+aN29O//79mT9/Pu3bt+fy5cvqawfpk0qVKtG0aVPMzMwoV64cnTt3pnbt2rqOlUx6WUmuZ8+edO3alXLlymns1/VxVCI9LCpiZmZGYGCgenGBW7du6WW5/seL2YJqFPr27duULVtW7z6f5cuXZ+7cucTHx3PmzBl2795NxYoVdR0rmT179rB3714AsmbNyoEDB2jXrh0dOnTQcTLhdxOdqN8kvSwx2qxZM7p27UqTJk0AOH78OE2bNtVxqvRr2LBhlC1blrJly+r1xXbTi0aNGtGwYUMkEgn79+/n9evXFCpUSNexNMycOZOLFy9SvHhx6tevz7Fjx5g6daquY2no3LkzLVu2xNLSkq1bt/Lw4UOqVq2q61jJVKhQgS5duuDk5ISBgQGTJk3Su783wP79+0lMTOT27dsa9+vbQerKlSvJlStXujsxJpVKuXz5MiVKlNB1FA3jxo2jX79++Pr60qJFC6KiojTKt/XFlyftIiMjGT58uI7SfN3o0aPZs2cPBQoU4NChQ9SoUUMvOyZSqVRjJWB9WRVY+P3E6ny/SXpaYvTSpUtcv34dpVJJpUqV9PJq9ulFelphSp+lh6WZ3759+83H9WEeytfO7n+kT2f5AZo0acLx48d1HeO70sv3vHXr1uzfv1/XMX5IUlISPXv2TDa3WNekUimvX79GLpeTO3duvRyJ+lJSUhJNmzbl1KlTuo6ioVevXnpzwfRvcXNz4969ezRq1AiJRMLJkycpXbo0w4YN03U04TcTI1G/ibW1NQC5cuXCx8dH786ofZSUlISjoyNjxozhyJEj3Lhxg2LFimFra6vraOlSelmVUd99nLt1/vx5YmNjad68OUZGRvzzzz9kzJhRx+lUOnfujEQiITExkbCwMLJly4aBgQF+fn5kzZpVb1ZkBNXE6MDAQBo2bIiRkRGnT5/Wy8sYZMuWjXHjxlGiRAmN1cT0bYQnPawkB1ClShW2bdtGtWrVNM6e60MH/3tiY2O/e6Lid4uKisLNzQ1fX1+WLl3KlClTGDt2rPr3Xl98XIUVQKlU4u/vr5cnR+Pj43n37h3Ozs66jvJNo0aNwt3dHU9PT4yMjOjatSt169bVdSxBB8RI1G+yaNEiXr16pV5itEKFCvj4+LBnzx5dR9MwdOhQsmbNSoMGDRg1ahTNmzfnwYMHrF69WtfR0qWqVasSFhaW7H59WmEqPWnbti27d+9WT4xXKBS0a9eOffv26TjZJ8OHD6dTp07qxRoePHjAunXrWLp0qY6TfdKhQwc2btyIubk5AImJiXTt2jXZami69rURSH0Yefxc1apVCQ0NBVQryH28SLm+fc9Tmk+mjxdTB1XWzw/8o6Ki6N27NwMGDNBxsk+GDBlClSpV2L59O/v27WP58uV4e3uzZs0aXUfTcPPmTfX/l0gkZMqUSe8uCAyqcu3Xr19jZ2eHqamp+nukj59PQQAxEvXbfL7E6IIFC7h165belc4A+Pv7s2TJEtzc3GjdujV9+/bVy6WP04vDhw9z/PhxoqOjdR3lXyEmJobIyEj1yGhoaChxcXE6TqXpxYsXGqvdFS9enFevXukwUXIfLw76kVQqJTIyUneBvuJjZykqKkrvzu5/Lj2sJAfo3UWpv2X9+vVcu3aNiIgIAKysrLCystJxKk3+/v60b9+enTt3YmJiwvDhw2nevLmuYyVTsmRJXr58ScGCBTl69Cjnzp2jT58+eldhsm7dOl1HEIQ00f91Tv8lBg8eTPbs2QEoWrQo3bt3Z9SoUTpOlZxcLic8PJwzZ85Qs2ZNQkJCSExM1HWsdKtv3774+PjoOsa/Rv/+/WnevDlDhgxh8ODBtG7dmqFDh+o6lobMmTOzZMkSnj17xtOnT3FzcyNnzpy6jqWhbdu2tG7dmrlz5zJnzhxat25N165ddR0rGR8fHxo2bEiLFi0ICgqiXr16PHr0SNexkklKSmLVqlWMGTOG9+/fs2zZMr1bkRFUndGJEyfStWtXIiMjGTdunN6e4HFzc+PQoUP4+/sTEBCAt7e3xipz+sDQ0JCYmBj1CYnXr1/r5fLxo0aN4ujRozx48IC///4bS0vL784z1QUXFxfu3LnDnj17sLW1xdPTUy/LjAXhI1HO94sNGjQIb29vgoKCNJYQlsvlZM6cmV27dukwXXJHjx5lyZIl1K5dm/Hjx9OgQQOGDh0qroHwg9LzRG59FRwczN27d5FIJJQpUwY7OztdR9IQFRXF0qVL1SU0lStXZsiQIWTIkEHHyTR5eXlx8+ZNJBIJlSpVomDBgrqOlEynTp3466+/GDFiBIcOHeLq1assWrRIr8o3ASZOnIitrS3nzp1j7969TJ48GaVSyfz583UdTUN6KT+DT4sx6bPLly+zYMEC3r17R5kyZbh37x6zZs2iZs2auo6m4ePvkJubG9bW1uoKE337bZo/fz6BgYE8evSIvXv3MmDAAIoUKcLYsWN1HU0QUiTK+X6xOXPmEBkZycyZM5k4caL6fiMjI707+APVEuefX1z3n3/+QSqV6jBR+la3bl327t1LxYoVNSacp4eJ3Ppk9+7dtG/fPtnqck+fPgX0a1W56OhoJk2apL6tVCrZtm0bXbp00WEqTR8vvvmxnMfHx4fXr1+TO3du8ufPr8NkmuLj48mTJ4/6dpUqVZg7d64OE6Xs0aNHHDx4kEuXLmFubs68efP07iLlkH7Kz0B1YeC3b9/q9b6yWrVqFClShAcPHqBQKPjrr7+wt7fXdaxkPq8w+fvvv/W2wuTKlSscPHgQV1dXLC0t2bhxI82bNxedKEFviU7UL2ZpaYmlpSVLlizRqEl+/PixXtYknzt3jsWLFxMXF6e+KHB8fDw3btzQdbR0KS4ujlmzZpEpUyb1fWKibNqlpwHz3r17s2bNGnLkyMGTJ0+YOHEiGTJk0KtO1NmzZ3n8+DH16tVDqVRy4cIFHB0diYuLo1mzZnTv3l3XEQGwsbHBx8dHXS515MgRvZwbJZFISEpKUuf8cs6ZvkgP5WcfV5ILDw+nWbNmFCxYUOME1JYtW3SYTlN0dDQrV67kxo0bGBkZUb16dQYMGKCxkqQ+6NWrF+3ataN27drkz59fXWGibz5+Fj9+PpOSkvTu8ykInxPlfL/Jx1Xv6tevz6hRo2jRooVernpXr149pk+fzsaNG+nfvz9nzpwhPj6eyZMn6zpautS0aVP27dundz+qwq9z584dJk6cSOXKlTl16hR//vmn3i3J3aFDB9asWaOeqP/+/Xv69+/Ppk2baNWqld5cv87X15cxY8bw8OFDzMzMyJEjB25ubuTOnVvX0TQcOnSIvXv38ubNGxo1asTp06cZNGgQbdq00XU0DZcuXWLhwoV6XX72+UpyKfl4uQN90K9fP3Lnzk3Lli1RKpXs37+f8PBwFixYoOto3ySXy5FKpXr3u7RmzRoePXrEw4cP6dq1K0eOHKFevXp6tSKjIHxOjET9Jp+vetemTRu9XfUuY8aMVKxYkTt37hATE8OoUaPEfKif4OLiQlRUlN79WKU3BQsWTPHMvj4uJV26dGkWLlxI7969WbBgARUqVNB1pGQiIiI05miZmpoSFRWFkZGRXo2gZM+enZ07dxIXF4dCocDS0lLXkVJ09uxZ/vrrL27cuIFCoWDVqlXMnj1b7zpR1atXp2jRojx48AC5XM5ff/2ldyve6VMn6XsCAgI0ToROmDCBpk2b6jBRytJLhUnfvn25fPkyWbJk4d27dwwePJhatWrpOpYgfJXoRP0m6aUm2czMjFevXpEnTx5u3rxJxYoVxZyonyCVSmnSpAn58uXTuLilPpWkpAfpYYXDzzt6Hwf4u3fvrpcdvfr169OtWzcaNWqEQqHg1KlT1KlTh0OHDuHg4KDreGoPHjxgw4YNREREaJR06sv35+PCQcHBwTx+/Fidcf369Xp5wdD27duze/du9ciTQqGgRYsWHD16VLfB0qm8efNy69Yt9SUNfHx8yJEjh45TJTd79uwUK0z0zfTp05k0aRLVqlVT3zdmzBi9nAcpCCA6Ub9NeqlJHjZsGIsXL8bNzY01a9awe/duvTubmp70799f1xH+VeLj41m2bBnXr19HLpdTsWJFhg4dioWFha6jpYuO3kcjRozg/PnzXL16FUNDQ3r37k2NGjW4d++eXpUijRkzhs6dO5M3b169GiH7KL0sHNS1a1d1mVyhQoXU9xsaGqZ4AV4hdV6+fEnnzp3JlSsXhoaGvHr1Cmtra/WFgvVl7qu+V5hMmDABPz8/vLy8ePbsmfp+mUxGTEyMDpMJwreJOVE6IpfLNSbL6it9v8il8N8ybtw4zM3NadeuHQB79uwhJiYGNzc3HSf7JCkpiQ0bNvDq1SsmTZrEpk2b6Nu3LyYmJrqOpuHZs2dERUVpjPCUK1dOh4mSc3V15eDBg7qO8a/x8Sz/x7IuuVxOQECAXp7QSw8CAgK++bi+XOOoY8eOzJw5k6dPn/Lw4UOGDBlCkyZNOH36tK6jAaivBfblyQhDQ0Py5MmDjY2N7sIJwjeIkajf5PLlyyxevDjZQYu+nKn6KCAggIkTJxIQEMD27dsZMWIEs2bNImvWrLqOJgg8evRIY9GDyZMn69UZVYC//voLW1tbHj16hKGhIW/evGH8+PF6dc2gadOmcf78ebJly6a+TyKR6E2Z3Nu3bwHVqMmmTZuoU6eOuESAFoSHh7N161Z8fX0pW7YsHh4elC5dWtex0q0MGTLw+PFjKleuzOrVq3n06BEjR44ke/bsuo6mYfjw4UyfPp1Vq1axdu1atm3bxh9//KHrWGpZs2Yla9asHDlyhODgYBwdHbl16xY+Pj4UKVJE1/EE4atEJ+o3mTFjBmPHjiVfvnx6WZby0eTJk+nVqxfz58/H3t6epk2bMmbMGLZv367raIKAUqkkOjpaPRk+Ojpa70Z008M1g65evYq7u7veLnjSuXNnJBIJSqWSGzduaHTu9KlMKr15+vQpp06dYubMmbRu3Zphw4YxbNgwXcdKt0aMGEHlypUBcHd3p1u3bkyYMIGtW7fqOJkmb29vwsPDMTExYfHixfTq1Usv525NmTIFqVRKz549GTFiBFWqVOHu3bt6dQJKED4nOlG/SaZMmdLFKjMRERFUrVqV+fPnI5FIaNeunehACXqje/futG3bllq1aqFUKjl//jx9+/bVdSwN6eGaQdmyZdPra2+dO3cOgMjIyGSlPP7+/jpI9O9gZ2eHRCIhV65cPHnyhJYtW4qFg35CVFQUvXr1Yvr06bi6utKyZUu9Gc393J49e9i7dy+gGvU5dOgQ7dq1o0OHDjpOpunhw4fs37+fZcuW0aZNGwYPHqyXqxgLwkeiE/WblClThtmzZ1OtWjVMTU3V9+vbHAQzMzMCAwPVB323bt3Su7kcwn9Xs2bNiI2NJSYmBmtra7p06YKRkX7txrp27UqPHj0ICQlh5syZnDlzhoEDB+o6lgZra2uaNGlCqVKlNL7fs2fP1mGqT969e4dSqaRv376sXbtW3eGTy+X06dMHd3d3HSdMn/Lly8f06dP5448/GDlyJMHBwXrdmdZ3CoUCLy8vzpw5w7Zt2/D29kYul+s6VjJSqVRjddjP/78+kcvlKBQKzp49y7Rp04iPj9fLVQQF4SP9Ovr4F3vw4EGKyxzr21mrcePG0a9fP3x9fWnRogVRUVEsWbJE17EEAVCtHhkSEkKePHk0RiT06WK2LVu2pGjRonh4eCCXy1m5ciUFCxbUdSwN1apV01hGWN8sXboUDw8PgoOD6dSpk/p+IyMjvbowbHozdepU7t69S968eRk8eDDXr1/Xq9UY05tRo0Yxb948evToQbZs2WjXrh1jx47Vdaxk6tatq76kgUQi4eTJk9SpU0fXsZJp2bIlVatWpXTp0pQoUYLGjRvTvn17XccShK8Sq/P9YpMmTWL69Ol06dIl2WP6NJH7owcPHnDz5k1q1KjB9OnT8fHxYf78+VSvXl3X0QSBhg0bpotRiKNHj/L8+XP69evHqVOn9KaTFxISgoODg3rhhi/p24INa9as0btyTUFIj9zd3fH09MTIyIhy5cpRt25dXUdKkUKhwMDAAFAthGJra6vjRILwdaIT9Yt5eXlRtGhR9TU6vqRvV2dv164dQ4YMISIighMnTjBp0iQGDRrE/v37dR1NEOjbty9Tp07Vu4P9z82fP5/AwEAePXrE3r17GTBgAEWKFNGLM9T9+vVj9erV6uvYfLwQ8Mf/1bcFG8LCwjh69CixsbEolUoUCgX+/v7MmzdP19GE/7DPL6wNqhFSQ0NDEhMTsbS0xNPTU4fp0p/PTzanNH9U3042C8JHopzvFytatCigf52lr1EoFFStWpURI0ZQv359nJ2d9bLGW/hv+fjjGh4eTrNmzShYsKDGqnz69CN75coVDh48iKurK5aWlmzcuJHmzZvrRSdq9erVAJw5c0Z9tvejoKAgXUT6puHDh+Ps7My9e/eoW7cuFy5coFixYrqOJfzHfbyw9pQpUyhdujTNmzdXl8ldvnxZx+nSn48le4MHD9ZxEkFIG9GJEjSYm5uzYcMGPDw8mDx5Mlu2bCFDhgy6jiX8x6WnH9ePnZOPZ1STkpKSdVh0bdSoUeoVOAG2b9/O8uXLuXbtmo6TaQoODmbLli3MnTuX+vXr07t3b7p166brWIIAqMrfp02bpr7doEEDVq5cqcNE6dPHk81fjkJJJBJMTU01LmshCPpEdKIEDfPnz2fv3r0sXboUa2trgoKCxMRjQefSy0guqOZtDRs2jKioKDZt2sThw4dp2rSprmNpsLW1Zfjw4fTt25dp06ZhYWHBjh07dB0rGWtrawBy5cqFj48PJUqU0HEiQfjE3Nyc/fv306hRIxQKBYcPH1Z/ZoW0W758OV5eXlSqVAmlUsnNmzdxcXHh/fv3DB06VO/2o4Ig5kQJgiBoUb9+/ahZsyYPHz7EysqKChUq6OU14pYtW8aKFSuYPn263l6LZdGiRbx69YoxY8bQs2dPKlSogI+PD3v27NF1NEEgICCA6dOn4+HhgUQioUqVKkycOBEnJyddR0uXunXrxuzZs9VzXoOCghg/fjxLliyhS5cuHDx4UMcJBUGTGIkSBEHQogEDBnD58mWePXuGXC7HzMwMOzs7ihcvrutojBs3TuN2pkyZ2LNnD7du3QL05zpRHw0fPhxfX19cXFxYuHAhnp6eenfNLeG/y8XFhVWrVuk6xr9GcHCwxqJBTk5OBAcHY2lpKa5nJuglMRIlCILwC4SHh+Pu7s6qVasIDw/Hy8tL15G+eybX1dX1NyVJvY/Lxffv35+TJ0/qzXLxgnD58mUWL15MVFSUxkG+vq1ymV5MmDCBhIQEmjVrhkKh4Pjx42TIkIHatWuzZs0avSw5Fv7bRCdKEARBi6ZNm8bt27cxNDSkXLlyVKhQgfLly5MxY0ZdR1Pr1asX69ev13WM79Ln5eIFoUGDBowdO5Z8+fJpLIrg4uKiw1Tpl0wmY9euXVy9ehVDQ0MqVapE+/btuXr1Knny5CFr1qy6jigIGkQ5nyAIghZFR0ejVCrJlSsXefLkIXfu3HrVgQJISEjg3bt3ODs76zrKN+nzcvGCkClTJr2c75heGRkZUbNmTbJmzUrVqlV59+4dRkZG1KhRQ9fRBCFFohMlCIKgRR9Xs3zx4gXXr1+nf//+xMXF6dX1Y8LDw6lduzZ2dnaYmprq7cV208Ny8cJ/V5kyZZg9ezbVqlXD1NRUfX+5cuV0mCr9+ueff1i5ciUJCQns2rWLDh06MHr0aFq0aKHraIKQItGJEgRB0KKXL19y/fp1rl+/jo+PD8WLF9e7M6nr1q3TdYRUSQ/LxQv/XQ8ePADg8ePH6vskEoleXfw7PVm7di07d+6kc+fO2NnZcfDgQXr06CE6UYLeEp0oQRAELRo6dCi1atWie/fulCpVCkNDQ11HSiZLlizs3LmTGzduIJPJqFixIp07d9Z1rGRu375NzZo1yZAhA4GBgQwZMkSUTwl6Y+vWrbqO8K9iYGCApaWl+rajo6MYeRb0muhECYIgaNHRo0d1HeG75s2bx5s3b2jdujVKpZIDBw7g5+fHhAkTdB1Ngz4vFy/8d02aNInp06fTtWvXFB8XI1E/Jl++fGzbtg2ZTIa3tzc7duygYMGCuo4lCF8lVucTBEH4j2nevDmHDh1Sn+WVyWQ0a9aMEydO6DhZyvRxuXjhv8vLy4uiRYtSqlQpxowZg5mZmcb1jcqXL6/DdOnXkCFDyJEjB9euXUOhUFCxYkUGDhyoMTolCPpEjEQJgiD8x8jlcmQyGSYmJurb+lh2+OVy8VOmTBEHqILOFS1aFICNGzdy+fJlLl26hFwup3r16qLc9CcEBAQwa9YsRowYoesogpAqohMlCILwH9O8eXO6du1KkyZNADh+/LheLtiQHpaLF/67SpYsScmSJenUqZN6pHTdunVipPQHGRgYULt2bXLlyqWx2qEojxT0lSjnEwRB+I/p168fNWvWxMvLCysrKypUqEDNmjV1HeurPi4Xv3XrVr1bLl7470oPF9ZOT27evJni/WL0WdBXYiRKEAThP+bjgg1Pnz5FLpdjamqKra2t3i3YkB6Wixf+u8RIqXaJzpKQ3oiRKEEQhP8ofV+woVmzZtSqVYvq1avr7XLxgiBGSgXhv0l0ogRBEP5jRBmSIPy8r42UiovDCsJ/gyjnEwRB+I8RZUiC8PPSw4W1BUH4dcRIlCAIwn+UKEMSBEEQhB8jRqIEQRD+Y8SCDYIgCILwc8RIlCAIwn+MWLBBEARBEH6O6EQJgiAIgiAIgiCkgYGuAwiCIAiCIAiCIKQnohMlCIIgCIIgCIKQBqITJQiCIAiCIAiCkAaiEyUIgiAIgiAIgpAGohMlCIIgCIIgCIKQBv8HcFyc9HkE1vMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, calculate the median of cout of total bikes (cnt) \n",
    "separated by weathersit, season ,and holiday. \n",
    "Is there any interesting thing you found? Explain carefully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cnt\n",
       "weathersit     \n",
       "1           159\n",
       "2           133\n",
       "3            63\n",
       "4            36"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weathersit')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " weathersit : \n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "    - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When weathersit=3,4 people are less likely to rent bike, especially for when weathersit=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          cnt\n",
       "season       \n",
       "1        76.0\n",
       "2       165.0\n",
       "3       199.0\n",
       "4       155.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('season')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the median for spring is very low compared to other seasons. People are less likely to rent bike in spring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cnt\n",
       "holiday     \n",
       "0        144\n",
       "1         97"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('holiday')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People are less likely to rent bike on holidays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
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       "      <td>0.75</td>\n",
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       "      <td>3</td>\n",
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       "      <td>7</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
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       "      <td>23</td>\n",
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       "      <td>49</td>\n",
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       "<p>17379 rows × 17 columns</p>\n",
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      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by training our data. Please split the dataset into 80 % train data and 20 % test data. Then, use two features which are atemp and weatherlist to train the Regression algorithm and make predictions. What is the MAE and RMSE of train and test data in this model? Interpret the coefficients of these tow variables carefully.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "y = np.log(df[\"cnt\"]) \n",
    "df[\"log_cnt\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "      <td>4.779123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17376</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>4.488636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>17377</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>4.499810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>17378</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "      <td>4.110874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>49</td>\n",
       "      <td>3.891820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt   log_cnt  \n",
       "0              13   16  2.772589  \n",
       "1              32   40  3.688879  \n",
       "2              27   32  3.465736  \n",
       "3              10   13  2.564949  \n",
       "4               1    1  0.000000  \n",
       "...           ...  ...       ...  \n",
       "17374         108  119  4.779123  \n",
       "17375          81   89  4.488636  \n",
       "17376          83   90  4.499810  \n",
       "17377          48   61  4.110874  \n",
       "17378          37   49  3.891820  \n",
       "\n",
       "[17379 rows x 18 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df,columns=['weathersit'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'cnt',\n",
       " 'log_cnt',\n",
       " 'weathersit_2',\n",
       " 'weathersit_3',\n",
       " 'weathersit_4']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"cnt\",\"log_cnt\"], axis=1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import (linear_model, metrics, model_selection)\n",
    "\n",
    "X_train,X_test,y_train,y_test = model_selection.train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'weathersit_2',\n",
       " 'weathersit_3',\n",
       " 'weathersit_4']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 5.049856007249537,\n",
       " 2.1972245773362196,\n",
       " ...]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model: log(cnt) = 3.0388 + 3.2614 atemp  + -0.0561 weathersit_2 + -0.5514 weathersit_3 + 1.3197 weathersit_4\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "model = linear_model.LinearRegression()\n",
    "\n",
    "model1 = model.fit(X_train[[\"atemp\",\"weathersit_2\",\"weathersit_3\",\"weathersit_4\"]], y_train)\n",
    "\n",
    "beta_0 = model.intercept_\n",
    "beta_1 = model.coef_[0]\n",
    "beta_2 = model.coef_[1]\n",
    "beta_3 = model.coef_[2]\n",
    "beta_4 = model.coef_[3]\n",
    "\n",
    "print(f\"Fit model: log(cnt) = {beta_0:.4f} + {beta_1:.4f} atemp  + {beta_2:.4f} weathersit_2 + {beta_3:.4f} weathersit_3 + {beta_4:.4f} weathersit_4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse_train =  1.8879628802932704\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predict1 = model1.predict(X_train [[\"atemp\",\"weathersit_2\",\"weathersit_3\",\"weathersit_4\"]] )\n",
    "\n",
    "\n",
    "rmse1_train = mean_squared_error(y_train, predict1)\n",
    "print(\"rmse_train = \",rmse1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse_test =  1.7673825175598807\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predict1 = model1.predict(X_test [[\"atemp\",\"weathersit_2\",\"weathersit_3\",\"weathersit_4\"]] )\n",
    "\n",
    "\n",
    "rmse1_test = mean_squared_error(y_test, predict1)\n",
    "print(\"rmse_test = \",rmse1_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If atemp increases by 1 unit, log(cnt) increases by (3.2487x100)%\n",
    "For weathersit coefficients, those are compared to the (omitted) based case where weathersit=1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(df)\n",
    "df.info()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.3 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the cross-varidation by spliting the dataset into 10 folds and train the model with Regression algorithm by using the two features as in question 1.2. What is the MAE and RMSE in model?.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[[\"atemp\",\"weathersit_2\",\"weathersit_3\",\"weathersit_4\"]]\n",
    "y=df[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))     # compute rmse for the estimation rmse test data\n",
    "    err_test += np.sqrt(np.mean(e_test*e_test))  \n",
    "rmse_train_5cv_model1 = err_train/10              #average the rmse\n",
    "rmse_test_5cv_model1 = err_test/10              #average the rmse\n",
    "print('RMSE on 10-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE on 10-fold CV on the test data: {}'.format(rmse_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_10cv_model1 = err_train/10              #average the rmse\n",
    "mae_test_10cv_model1 = err_test/10              #average the rmse\n",
    "print('MAE (Model1:Linear) on 10-fold CV on the train data: {}'.format(mae_train_10cv_model1))\n",
    "print('MAE (Model1:Linear) on 10-fold CV on the test data: {}'.format(mae_test_10cv_model1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last excercise, using all features which are\n",
    "- holiday : weather day is holiday or not \n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "    - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\n",
    "to train the model with the same process in question 1.2 What is the MAE and RMSE in this model? Interpret the results carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'weathersit_2',\n",
       " 'weathersit_3',\n",
       " 'weathersit_4']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model: log(cnt) = 4.4259 + -0.2229 holiday  + 0.0148 weekday + -0.0798 workingday + 0.2678 weathersit_2 + 0.0846 weathersit_3 + 2.1620 weathersit_4 + 0.3733 temp + 2.8644 atemp + -2.5508 hum + 0.5021 windspeed\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "model = linear_model.LinearRegression()\n",
    "\n",
    "model2 = model.fit(X_train[[\"holiday\",\"weekday\",\"workingday\",\"weathersit_2\",\"weathersit_3\",\"weathersit_4\",\"temp\",\"atemp\",\"hum\",\"windspeed\"]], y_train)\n",
    "\n",
    "beta_0 = model.intercept_\n",
    "beta_1 = model.coef_[0]\n",
    "beta_2 = model.coef_[1]\n",
    "beta_3 = model.coef_[2]\n",
    "beta_4 = model.coef_[3]\n",
    "beta_5 = model.coef_[4]\n",
    "beta_6 = model.coef_[5]\n",
    "beta_7 = model.coef_[6]\n",
    "beta_8 = model.coef_[7]\n",
    "beta_9 = model.coef_[8]\n",
    "beta_10 = model.coef_[9]\n",
    "\n",
    "\n",
    "print(f\"Fit model: log(cnt) = {beta_0:.4f} + {beta_1:.4f} holiday  + {beta_2:.4f} weekday + {beta_3:.4f} workingday + {beta_4:.4f} weathersit_2 + {beta_5:.4f} weathersit_3 + {beta_6:.4f} weathersit_4 + {beta_7:.4f} temp + {beta_8:.4f} atemp + {beta_9:.4f} hum + {beta_10:.4f} windspeed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse_test2 =  1.5872066572330235\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predict2 = model2.predict(X_test [[\"holiday\",\"weekday\",\"workingday\",\"weathersit_2\",\"weathersit_3\",\"weathersit_4\",\"temp\",\"atemp\",\"hum\",\"windspeed\"]] )\n",
    "\n",
    "\n",
    "rmse2_test = mean_squared_error(y_test, predict2)\n",
    "print(\"rmse_test2 = \",rmse2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse_train =  1.6583576240153652\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predict2 = model2.predict(X_train [[\"holiday\",\"weekday\",\"workingday\",\"weathersit_2\",\"weathersit_3\",\"weathersit_4\",\"temp\",\"atemp\",\"hum\",\"windspeed\"]] )\n",
    "\n",
    "\n",
    "rmse2_train = mean_squared_error(y_train, predict2)\n",
    "print(\"rmse_train = \",rmse2_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.4 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should we use all features or only the two features? Why? Is there the problem of overfitting in the model you select? Why? [10 Points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All becuase RSME and MAE are lower"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.5 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the model you selcted in 1.4 to create the dataframe that contains three columns which are : Actual value, Predicted value, and the error/residual. Then fill in these values from the test data.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 [20 points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 The below information shows the U.S unemployment rate (unemp)in 2010 from January-December \n",
    "\n",
    "\n",
    "Task: write down the python code using control flow  \"if\" to print the message ONLY the months that have the unemployment rate above 8% i.e.\n",
    "\n",
    "\n",
    "- The umemployment rate in Apr is 14.8%\n",
    "\n",
    "- The unemployment rate in May is 13.3%\n",
    "\n",
    "\n",
    "\n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp= [3.5,\n",
    "3.5,\n",
    "4.4,\n",
    "14.8,\n",
    "13.3,\n",
    "11.1,\n",
    "10.2,\n",
    "8.4,\n",
    "7.8,\n",
    "6.9,\n",
    "6.7,\n",
    "6.7]\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-74-95e87eeb2ad5>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-74-95e87eeb2ad5>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    print(f\"In{month}unemployment rate is above 8%\")\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "for i in unemp:\n",
    "    if i > 8:\n",
    "    print(f\"In{month}unemployment rate is above 8%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 write the list [.] of even numbers from 2-500. Then using control flow to find out the summation of the log of even numbers from 2-100. i.e. log(2)+log(4)+....log(100) \n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 is an even number!\n",
      "4 is an even number!\n",
      "6 is an even number!\n",
      "8 is an even number!\n",
      "10 is an even number!\n",
      "12 is an even number!\n",
      "14 is an even number!\n",
      "16 is an even number!\n",
      "18 is an even number!\n",
      "20 is an even number!\n",
      "22 is an even number!\n",
      "24 is an even number!\n",
      "26 is an even number!\n",
      "28 is an even number!\n",
      "30 is an even number!\n",
      "32 is an even number!\n",
      "34 is an even number!\n",
      "36 is an even number!\n",
      "38 is an even number!\n",
      "40 is an even number!\n",
      "42 is an even number!\n",
      "44 is an even number!\n",
      "46 is an even number!\n",
      "48 is an even number!\n",
      "50 is an even number!\n",
      "52 is an even number!\n",
      "54 is an even number!\n",
      "56 is an even number!\n",
      "58 is an even number!\n",
      "60 is an even number!\n",
      "62 is an even number!\n",
      "64 is an even number!\n",
      "66 is an even number!\n",
      "68 is an even number!\n",
      "70 is an even number!\n",
      "72 is an even number!\n",
      "74 is an even number!\n",
      "76 is an even number!\n",
      "78 is an even number!\n",
      "80 is an even number!\n",
      "82 is an even number!\n",
      "84 is an even number!\n",
      "86 is an even number!\n",
      "88 is an even number!\n",
      "90 is an even number!\n",
      "92 is an even number!\n",
      "94 is an even number!\n",
      "96 is an even number!\n",
      "98 is an even number!\n",
      "100 is an even number!\n",
      "102 is an even number!\n",
      "104 is an even number!\n",
      "106 is an even number!\n",
      "108 is an even number!\n",
      "110 is an even number!\n",
      "112 is an even number!\n",
      "114 is an even number!\n",
      "116 is an even number!\n",
      "118 is an even number!\n",
      "120 is an even number!\n",
      "122 is an even number!\n",
      "124 is an even number!\n",
      "126 is an even number!\n",
      "128 is an even number!\n",
      "130 is an even number!\n",
      "132 is an even number!\n",
      "134 is an even number!\n",
      "136 is an even number!\n",
      "138 is an even number!\n",
      "140 is an even number!\n",
      "142 is an even number!\n",
      "144 is an even number!\n",
      "146 is an even number!\n",
      "148 is an even number!\n",
      "150 is an even number!\n",
      "152 is an even number!\n",
      "154 is an even number!\n",
      "156 is an even number!\n",
      "158 is an even number!\n",
      "160 is an even number!\n",
      "162 is an even number!\n",
      "164 is an even number!\n",
      "166 is an even number!\n",
      "168 is an even number!\n",
      "170 is an even number!\n",
      "172 is an even number!\n",
      "174 is an even number!\n",
      "176 is an even number!\n",
      "178 is an even number!\n",
      "180 is an even number!\n",
      "182 is an even number!\n",
      "184 is an even number!\n",
      "186 is an even number!\n",
      "188 is an even number!\n",
      "190 is an even number!\n",
      "192 is an even number!\n",
      "194 is an even number!\n",
      "196 is an even number!\n",
      "198 is an even number!\n",
      "200 is an even number!\n",
      "202 is an even number!\n",
      "204 is an even number!\n",
      "206 is an even number!\n",
      "208 is an even number!\n",
      "210 is an even number!\n",
      "212 is an even number!\n",
      "214 is an even number!\n",
      "216 is an even number!\n",
      "218 is an even number!\n",
      "220 is an even number!\n",
      "222 is an even number!\n",
      "224 is an even number!\n",
      "226 is an even number!\n",
      "228 is an even number!\n",
      "230 is an even number!\n",
      "232 is an even number!\n",
      "234 is an even number!\n",
      "236 is an even number!\n",
      "238 is an even number!\n",
      "240 is an even number!\n",
      "242 is an even number!\n",
      "244 is an even number!\n",
      "246 is an even number!\n",
      "248 is an even number!\n",
      "250 is an even number!\n",
      "252 is an even number!\n",
      "254 is an even number!\n",
      "256 is an even number!\n",
      "258 is an even number!\n",
      "260 is an even number!\n",
      "262 is an even number!\n",
      "264 is an even number!\n",
      "266 is an even number!\n",
      "268 is an even number!\n",
      "270 is an even number!\n",
      "272 is an even number!\n",
      "274 is an even number!\n",
      "276 is an even number!\n",
      "278 is an even number!\n",
      "280 is an even number!\n",
      "282 is an even number!\n",
      "284 is an even number!\n",
      "286 is an even number!\n",
      "288 is an even number!\n",
      "290 is an even number!\n",
      "292 is an even number!\n",
      "294 is an even number!\n",
      "296 is an even number!\n",
      "298 is an even number!\n",
      "300 is an even number!\n",
      "302 is an even number!\n",
      "304 is an even number!\n",
      "306 is an even number!\n",
      "308 is an even number!\n",
      "310 is an even number!\n",
      "312 is an even number!\n",
      "314 is an even number!\n",
      "316 is an even number!\n",
      "318 is an even number!\n",
      "320 is an even number!\n",
      "322 is an even number!\n",
      "324 is an even number!\n",
      "326 is an even number!\n",
      "328 is an even number!\n",
      "330 is an even number!\n",
      "332 is an even number!\n",
      "334 is an even number!\n",
      "336 is an even number!\n",
      "338 is an even number!\n",
      "340 is an even number!\n",
      "342 is an even number!\n",
      "344 is an even number!\n",
      "346 is an even number!\n",
      "348 is an even number!\n",
      "350 is an even number!\n",
      "352 is an even number!\n",
      "354 is an even number!\n",
      "356 is an even number!\n",
      "358 is an even number!\n",
      "360 is an even number!\n",
      "362 is an even number!\n",
      "364 is an even number!\n",
      "366 is an even number!\n",
      "368 is an even number!\n",
      "370 is an even number!\n",
      "372 is an even number!\n",
      "374 is an even number!\n",
      "376 is an even number!\n",
      "378 is an even number!\n",
      "380 is an even number!\n",
      "382 is an even number!\n",
      "384 is an even number!\n",
      "386 is an even number!\n",
      "388 is an even number!\n",
      "390 is an even number!\n",
      "392 is an even number!\n",
      "394 is an even number!\n",
      "396 is an even number!\n",
      "398 is an even number!\n",
      "400 is an even number!\n",
      "402 is an even number!\n",
      "404 is an even number!\n",
      "406 is an even number!\n",
      "408 is an even number!\n",
      "410 is an even number!\n",
      "412 is an even number!\n",
      "414 is an even number!\n",
      "416 is an even number!\n",
      "418 is an even number!\n",
      "420 is an even number!\n",
      "422 is an even number!\n",
      "424 is an even number!\n",
      "426 is an even number!\n",
      "428 is an even number!\n",
      "430 is an even number!\n",
      "432 is an even number!\n",
      "434 is an even number!\n",
      "436 is an even number!\n",
      "438 is an even number!\n",
      "440 is an even number!\n",
      "442 is an even number!\n",
      "444 is an even number!\n",
      "446 is an even number!\n",
      "448 is an even number!\n",
      "450 is an even number!\n",
      "452 is an even number!\n",
      "454 is an even number!\n",
      "456 is an even number!\n",
      "458 is an even number!\n",
      "460 is an even number!\n",
      "462 is an even number!\n",
      "464 is an even number!\n",
      "466 is an even number!\n",
      "468 is an even number!\n",
      "470 is an even number!\n",
      "472 is an even number!\n",
      "474 is an even number!\n",
      "476 is an even number!\n",
      "478 is an even number!\n",
      "480 is an even number!\n",
      "482 is an even number!\n",
      "484 is an even number!\n",
      "486 is an even number!\n",
      "488 is an even number!\n",
      "490 is an even number!\n",
      "492 is an even number!\n",
      "494 is an even number!\n",
      "496 is an even number!\n",
      "498 is an even number!\n"
     ]
    }
   ],
   "source": [
    "for i in range(2, 500):\n",
    "    if i % 2 == 1: \n",
    "        continue\n",
    "    print(i, \"is an even number!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "cannot assign to function call (<ipython-input-84-7ff64f7b4923>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-84-7ff64f7b4923>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    X(i) = math.log(i)\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m cannot assign to function call\n"
     ]
    }
   ],
   "source": [
    "for i in range(2,100):\n",
    "    X(i) = math.log(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 [20 point]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.read_csv(\"TSLA.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Using the library seaborn to plot the distribution of TESLA log returns. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>260.333344</td>\n",
       "      <td>263.043335</td>\n",
       "      <td>258.333344</td>\n",
       "      <td>258.493347</td>\n",
       "      <td>258.493347</td>\n",
       "      <td>53868000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>259.466675</td>\n",
       "      <td>260.260010</td>\n",
       "      <td>254.529999</td>\n",
       "      <td>258.406677</td>\n",
       "      <td>258.406677</td>\n",
       "      <td>51094200</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>265.500000</td>\n",
       "      <td>268.989990</td>\n",
       "      <td>258.706665</td>\n",
       "      <td>260.510010</td>\n",
       "      <td>260.510010</td>\n",
       "      <td>91449900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-05</td>\n",
       "      <td>261.600006</td>\n",
       "      <td>265.769989</td>\n",
       "      <td>258.066681</td>\n",
       "      <td>260.196655</td>\n",
       "      <td>260.196655</td>\n",
       "      <td>55297800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-06</td>\n",
       "      <td>258.733337</td>\n",
       "      <td>262.220001</td>\n",
       "      <td>257.739990</td>\n",
       "      <td>260.916656</td>\n",
       "      <td>260.916656</td>\n",
       "      <td>43898400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2022-09-26</td>\n",
       "      <td>271.829987</td>\n",
       "      <td>284.089996</td>\n",
       "      <td>270.309998</td>\n",
       "      <td>276.010010</td>\n",
       "      <td>276.010010</td>\n",
       "      <td>58076900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2022-09-27</td>\n",
       "      <td>283.839996</td>\n",
       "      <td>288.670013</td>\n",
       "      <td>277.510010</td>\n",
       "      <td>282.940002</td>\n",
       "      <td>282.940002</td>\n",
       "      <td>61925200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2022-09-28</td>\n",
       "      <td>283.079987</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>277.570007</td>\n",
       "      <td>287.809998</td>\n",
       "      <td>287.809998</td>\n",
       "      <td>54664800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2022-09-29</td>\n",
       "      <td>282.760010</td>\n",
       "      <td>283.649994</td>\n",
       "      <td>265.779999</td>\n",
       "      <td>268.209991</td>\n",
       "      <td>268.209991</td>\n",
       "      <td>77620600</td>\n",
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       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2022-09-30</td>\n",
       "      <td>266.149994</td>\n",
       "      <td>275.570007</td>\n",
       "      <td>262.470001</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>67517800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2021-09-30  260.333344  263.043335  258.333344  258.493347  258.493347   \n",
       "1    2021-10-01  259.466675  260.260010  254.529999  258.406677  258.406677   \n",
       "2    2021-10-04  265.500000  268.989990  258.706665  260.510010  260.510010   \n",
       "3    2021-10-05  261.600006  265.769989  258.066681  260.196655  260.196655   \n",
       "4    2021-10-06  258.733337  262.220001  257.739990  260.916656  260.916656   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "248  2022-09-26  271.829987  284.089996  270.309998  276.010010  276.010010   \n",
       "249  2022-09-27  283.839996  288.670013  277.510010  282.940002  282.940002   \n",
       "250  2022-09-28  283.079987  289.000000  277.570007  287.809998  287.809998   \n",
       "251  2022-09-29  282.760010  283.649994  265.779999  268.209991  268.209991   \n",
       "252  2022-09-30  266.149994  275.570007  262.470001  265.250000  265.250000   \n",
       "\n",
       "       Volume  \n",
       "0    53868000  \n",
       "1    51094200  \n",
       "2    91449900  \n",
       "3    55297800  \n",
       "4    43898400  \n",
       "..        ...  \n",
       "248  58076900  \n",
       "249  61925200  \n",
       "250  54664800  \n",
       "251  77620600  \n",
       "252  67517800  \n",
       "\n",
       "[253 rows x 7 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1[['Adj Close']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['return'] = np.log(df1[['Adj Close']]).diff()\n",
    "df1[['return']].plot()"
   ]
  },
  {
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