R3_External_Lab_Logistic_and_Naive_bayes_questions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z66fRKOeuHPg"
   },
   "source": [
    "## Supervised Learning - Building a Student Performace Prediction System  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YXt7PB5VuHPj"
   },
   "source": [
    "## Question 1 - Classification vs. Regression\n",
    "\n",
    "*The aim of this project is to predict how likely a student is to pass. Which type of supervised learning problem is this, classification or regression? Why?*\n",
    "\n",
    "#### Answer:\n",
    "This project is a classification supervised learning problem because the variable to predict, i.e. if a student graduates or fails to graduate, is categorical. On this case this a dichotomous categorical variable where the only two possible values are \"pass\" or \"fail\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YW5BYFevuHPk"
   },
   "source": [
    "### Overview:\n",
    "\n",
    "1.Read the problem statement.\n",
    "\n",
    "2.Get the dataset.\n",
    "\n",
    "3.Explore the dataset.\n",
    "\n",
    "4.Pre-processing of dataset.\n",
    "\n",
    "5.Transform the dataset for building machine learning model.\n",
    "\n",
    "6.Split data into train, test set.\n",
    "\n",
    "7.Build Model.\n",
    "\n",
    "8.Apply the model.\n",
    "\n",
    "9.Evaluate the model.\n",
    "\n",
    "10.Provide insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0gl2MdwkuHPl"
   },
   "source": [
    "## Problem Statement \n",
    "\n",
    "Using Logistic Regression **predict the performance of student**. The classification goal is to predict whether the student will pass or fail.\n",
    "\n",
    "## Dataset \n",
    "\n",
    "This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in Mathematics.\n",
    "\n",
    "**Source:** https://archive.ics.uci.edu/ml/datasets/Student+Performance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cHpGO_AGuHPm"
   },
   "source": [
    "## Question 2\n",
    "## Exploring the Data\n",
    "*Read the dataset file using pandas. Take care about the delimiter.*\n",
    "\n",
    "#### Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q3YEgKevuHPn"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KHyQrv_muHPr",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "students = pd.read_csv('Data/students-data-1.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 33)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BuwsTmYuuHPu"
   },
   "source": [
    "## Question 3\n",
    "## Drop missing values\n",
    "*Set the index name of the dataframe to **\"number\"**. Check sample of data to drop if any missing values are there.*\n",
    "\n",
    "#### Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EWbnH1HIuHPv"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
       "       'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
       "       'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
       "       'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
       "       'Walc', 'health', 'absences', 'G1', 'G2', 'G3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "students.index.name = 'number'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school        False\n",
       "sex           False\n",
       "age           False\n",
       "address       False\n",
       "famsize       False\n",
       "Pstatus       False\n",
       "Medu          False\n",
       "Fedu          False\n",
       "Mjob          False\n",
       "Fjob          False\n",
       "reason        False\n",
       "guardian      False\n",
       "traveltime    False\n",
       "studytime     False\n",
       "failures      False\n",
       "schoolsup     False\n",
       "famsup        False\n",
       "paid          False\n",
       "activities    False\n",
       "nursery       False\n",
       "higher        False\n",
       "internet      False\n",
       "romantic      False\n",
       "famrel        False\n",
       "freetime      False\n",
       "goout         False\n",
       "Dalc          False\n",
       "Walc          False\n",
       "health        False\n",
       "absences      False\n",
       "G1            False\n",
       "G2            False\n",
       "G3            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above output, none of the colums are having any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2_P3ODMKuHP2"
   },
   "source": [
    "## Transform Data\n",
    "\n",
    "*Print all the attribute names which are not numerical.*\n",
    "\n",
    "**Hint:** check **select_dtypes()** and its **include** and **exclude** parameters.**\n",
    "\n",
    "#### Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>schoolsup</th>\n",
       "      <th>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>higher</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       school sex address famsize Pstatus     Mjob      Fjob  reason guardian  \\\n",
       "number                                                                          \n",
       "0          GP   F       U     GT3       A  at_home   teacher  course   mother   \n",
       "1          GP   F       U     GT3       T  at_home     other  course   father   \n",
       "2          GP   F       U     LE3       T  at_home     other   other   mother   \n",
       "3          GP   F       U     GT3       T   health  services    home   mother   \n",
       "4          GP   F       U     GT3       T    other     other    home   father   \n",
       "\n",
       "       schoolsup famsup paid activities nursery higher internet romantic  \n",
       "number                                                                    \n",
       "0            yes     no   no         no     yes    yes       no       no  \n",
       "1             no    yes   no         no      no    yes      yes       no  \n",
       "2            yes     no  yes         no     yes    yes      yes       no  \n",
       "3             no    yes  yes        yes     yes    yes      yes      yes  \n",
       "4             no    yes  yes         no     yes    yes       no       no  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.select_dtypes(include='object').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  Medu  Fedu  traveltime  studytime  failures  famrel  freetime  \\\n",
       "number                                                                       \n",
       "0        18     4     4           2          2         0       4         3   \n",
       "1        17     1     1           1          2         0       5         3   \n",
       "2        15     1     1           1          2         3       4         3   \n",
       "3        15     4     2           1          3         0       3         2   \n",
       "4        16     3     3           1          2         0       4         3   \n",
       "\n",
       "        goout  Dalc  Walc  health  absences  G1  G2  G3  \n",
       "number                                                   \n",
       "0           4     1     1       3         6   5   6   6  \n",
       "1           3     1     1       3         4   5   5   6  \n",
       "2           2     2     3       3        10   7   8  10  \n",
       "3           2     1     1       5         2  15  14  15  \n",
       "4           2     1     2       5         4   6  10  10  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.select_dtypes(exclude='object').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zTXZpxq5uHP7"
   },
   "source": [
    "## Drop variables with less variance\n",
    "\n",
    "*Find the variance of each numerical independent variable and drop whose variance is less than 1.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            1.628285\n",
       "Medu           1.198445\n",
       "Fedu           1.184180\n",
       "traveltime     0.486513\n",
       "studytime      0.704324\n",
       "failures       0.553017\n",
       "famrel         0.803997\n",
       "freetime       0.997725\n",
       "goout          1.239388\n",
       "Dalc           0.793420\n",
       "Walc           1.658678\n",
       "health         1.932944\n",
       "absences      64.049541\n",
       "G1            11.017053\n",
       "G2            14.148917\n",
       "G3            20.989616\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.select_dtypes(exclude='object').var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before deleteing the featurs with Variance < 1, let's check the correlations of these features wrt the target G1, G2, G3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.064081</td>\n",
       "      <td>-0.143474</td>\n",
       "      <td>-0.161579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu</th>\n",
       "      <td>0.205341</td>\n",
       "      <td>0.215527</td>\n",
       "      <td>0.217147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu</th>\n",
       "      <td>0.190270</td>\n",
       "      <td>0.164893</td>\n",
       "      <td>0.152457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>traveltime</th>\n",
       "      <td>-0.093040</td>\n",
       "      <td>-0.153198</td>\n",
       "      <td>-0.117142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>studytime</th>\n",
       "      <td>0.160612</td>\n",
       "      <td>0.135880</td>\n",
       "      <td>0.097820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failures</th>\n",
       "      <td>-0.354718</td>\n",
       "      <td>-0.355896</td>\n",
       "      <td>-0.360415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel</th>\n",
       "      <td>0.022168</td>\n",
       "      <td>-0.018281</td>\n",
       "      <td>0.051363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime</th>\n",
       "      <td>0.012613</td>\n",
       "      <td>-0.013777</td>\n",
       "      <td>0.011307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout</th>\n",
       "      <td>-0.149104</td>\n",
       "      <td>-0.162250</td>\n",
       "      <td>-0.132791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalc</th>\n",
       "      <td>-0.094159</td>\n",
       "      <td>-0.064120</td>\n",
       "      <td>-0.054660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walc</th>\n",
       "      <td>-0.126179</td>\n",
       "      <td>-0.084927</td>\n",
       "      <td>-0.051939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health</th>\n",
       "      <td>-0.073172</td>\n",
       "      <td>-0.097720</td>\n",
       "      <td>-0.061335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>absences</th>\n",
       "      <td>-0.031003</td>\n",
       "      <td>-0.031777</td>\n",
       "      <td>0.034247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.852118</td>\n",
       "      <td>0.801468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>0.852118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.904868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>0.801468</td>\n",
       "      <td>0.904868</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  G1        G2        G3\n",
       "age        -0.064081 -0.143474 -0.161579\n",
       "Medu        0.205341  0.215527  0.217147\n",
       "Fedu        0.190270  0.164893  0.152457\n",
       "traveltime -0.093040 -0.153198 -0.117142\n",
       "studytime   0.160612  0.135880  0.097820\n",
       "failures   -0.354718 -0.355896 -0.360415\n",
       "famrel      0.022168 -0.018281  0.051363\n",
       "freetime    0.012613 -0.013777  0.011307\n",
       "goout      -0.149104 -0.162250 -0.132791\n",
       "Dalc       -0.094159 -0.064120 -0.054660\n",
       "Walc       -0.126179 -0.084927 -0.051939\n",
       "health     -0.073172 -0.097720 -0.061335\n",
       "absences   -0.031003 -0.031777  0.034247\n",
       "G1          1.000000  0.852118  0.801468\n",
       "G2          0.852118  1.000000  0.904868\n",
       "G3          0.801468  0.904868  1.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.corr()[['G1', 'G2', 'G3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u8Qm0728uHQA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          1.628285\n",
       "Medu         1.198445\n",
       "Fedu         1.184180\n",
       "goout        1.239388\n",
       "Walc         1.658678\n",
       "health       1.932944\n",
       "absences    64.049541\n",
       "G1          11.017053\n",
       "G2          14.148917\n",
       "G3          20.989616\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = students.select_dtypes(exclude='object')\n",
    "for ii in columns:\n",
    "    if (students[ii].var() < 1):\n",
    "        students.drop(columns=ii, axis=1, inplace=True)\n",
    "students.select_dtypes(exclude='object').var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features: traveltime, studytime, failures, famrel, freetime, Dalc are dropped from the dataframe.\n",
    "### These features have less variance which means, they have almost constant or non-spreading values and so, lesser impact on the target variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3Y2CYTIXuHQJ"
   },
   "source": [
    "## Question 4 - Encode all categorical variables to numerical\n",
    "\n",
    "Take the list of categorical attributes(from the above result) and convert them into neumerical variables. After that, print the head of dataframe and check the values.\n",
    "\n",
    "**Hint:** check **sklearn LabelEncoder()**\n",
    "\n",
    "#### Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>schoolsup</th>\n",
       "      <th>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>higher</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       school sex address famsize Pstatus     Mjob      Fjob  reason guardian  \\\n",
       "number                                                                          \n",
       "0          GP   F       U     GT3       A  at_home   teacher  course   mother   \n",
       "1          GP   F       U     GT3       T  at_home     other  course   father   \n",
       "2          GP   F       U     LE3       T  at_home     other   other   mother   \n",
       "3          GP   F       U     GT3       T   health  services    home   mother   \n",
       "4          GP   F       U     GT3       T    other     other    home   father   \n",
       "\n",
       "       schoolsup famsup paid activities nursery higher internet romantic  \n",
       "number                                                                    \n",
       "0            yes     no   no         no     yes    yes       no       no  \n",
       "1             no    yes   no         no      no    yes      yes       no  \n",
       "2            yes     no  yes         no     yes    yes      yes       no  \n",
       "3             no    yes  yes        yes     yes    yes      yes      yes  \n",
       "4             no    yes  yes         no     yes    yes       no       no  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.select_dtypes(include='object').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['school', 'sex', 'address', 'famsize', 'Pstatus', 'Mjob', 'Fjob',\n",
       "       'reason', 'guardian', 'schoolsup', 'famsup', 'paid', 'activities',\n",
       "       'nursery', 'higher', 'internet', 'romantic'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = students.select_dtypes(include='object').columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating LabelEncoder\n",
    "labelEncoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZmNpYg_WuHQK"
   },
   "outputs": [
    {
     "data": {
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       "      <td>15</td>\n",
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       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  \\\n",
       "number                                                                        \n",
       "0            0    0   18        1        0        0     4     4     0     4   \n",
       "1            0    0   17        1        0        1     1     1     0     2   \n",
       "2            0    0   15        1        1        1     1     1     0     2   \n",
       "3            0    0   15        1        0        1     4     2     1     3   \n",
       "4            0    0   16        1        0        1     3     3     2     2   \n",
       "\n",
       "        ...  higher  internet  romantic  goout  Walc  health  absences  G1  \\\n",
       "number  ...                                                                  \n",
       "0       ...       1         0         0      4     1       3         6   5   \n",
       "1       ...       1         1         0      3     1       3         4   5   \n",
       "2       ...       1         1         0      2     3       3        10   7   \n",
       "3       ...       1         1         1      2     1       5         2  15   \n",
       "4       ...       1         0         0      2     2       5         4   6   \n",
       "\n",
       "        G2  G3  \n",
       "number          \n",
       "0        6   6  \n",
       "1        5   6  \n",
       "2        8  10  \n",
       "3       14  15  \n",
       "4       10  10  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ii in columns:\n",
    "    students[ii] = labelEncoder.fit_transform(students[ii])\n",
    "students.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4W-GbCYXuHQO"
   },
   "source": [
    "## Question 5 - Convert the continuous values of grades into classes\n",
    "\n",
    "*Consider the values in G1, G2 and G3 with >= 10 as pass(1) and < 10 as fail(0) and encode them into binary values. Print head of dataframe to check the values.*\n",
    "\n",
    "#### Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SlELxBQ0uHQP"
   },
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>higher</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "      <th>goout</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  \\\n",
       "number                                                                        \n",
       "0            0    0   18        1        0        0     4     4     0     4   \n",
       "1            0    0   17        1        0        1     1     1     0     2   \n",
       "2            0    0   15        1        1        1     1     1     0     2   \n",
       "3            0    0   15        1        0        1     4     2     1     3   \n",
       "4            0    0   16        1        0        1     3     3     2     2   \n",
       "\n",
       "        ...  higher  internet  romantic  goout  Walc  health  absences  G1  \\\n",
       "number  ...                                                                  \n",
       "0       ...       1         0         0      4     1       3         6   0   \n",
       "1       ...       1         1         0      3     1       3         4   0   \n",
       "2       ...       1         1         0      2     3       3        10   0   \n",
       "3       ...       1         1         1      2     1       5         2   1   \n",
       "4       ...       1         0         0      2     2       5         4   0   \n",
       "\n",
       "        G2  G3  \n",
       "number          \n",
       "0        0   0  \n",
       "1        0   0  \n",
       "2        0   1  \n",
       "3        1   1  \n",
       "4        1   1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades = ['G1', 'G2', 'G3']\n",
    "for ii in grades:\n",
    "    students[ii] = np.where(students[ii] < 10, 0, 1)\n",
    "students.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kO7O6UW3uHQU"
   },
   "source": [
    "## Question 6 \n",
    "*Consider G3 is the target attribute and remaining all attributes as features to predict G3. Now, separate features and target into separate dataframes and name them X and y respectively.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vCvntE0YuHQW"
   },
   "outputs": [],
   "source": [
    "X = students.drop('G3', axis=1)\n",
    "y = students['G3']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_CP-ZqsPuHQa"
   },
   "source": [
    "## Visualization\n",
    "\n",
    "## Question 7\n",
    "\n",
    "*Plot G2 and G3 and give your understanding on relation between both variables.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eh8o8rW_uHQb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4a6ec8438>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(students.G2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4a793da90>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(students.G3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4a7180a58>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(students['G2'], hist=False, label='G2')\n",
    "sns.distplot(students['G3'], hist=False, label='G3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on above Histogram and Distribution plots, G3 is greatly dependent on G2. Thus G2 can be good predictor of G3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8O10W9U6uHQh"
   },
   "source": [
    "## Question 8\n",
    "\n",
    "*Plot the number of students in each school and number of students with different ages in separate plots.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FPBcJEPvuHQj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.school.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zpl9q12IuHQo"
   },
   "source": [
    "### Before encoding the \"school\" feature, we see, there are two schools GP and MS. After of encoding, GP gets the value of 0 and MS gets the value or 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of students in GM School:  349\n",
      "Number of students in MS School:  46\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4a9700240>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of students in GM School: ', len(students[students['school'] == 0]))\n",
    "print('Number of students in MS School: ', len(students[students['school'] == 1]))\n",
    "sns.countplot(students['school'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    395.000000\n",
       "mean      16.696203\n",
       "std        1.276043\n",
       "min       15.000000\n",
       "25%       16.000000\n",
       "50%       17.000000\n",
       "75%       18.000000\n",
       "max       22.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18, 17, 15, 16, 19, 22, 20, 21], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4a9795048>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "students['age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8UP-m3juuHQv"
   },
   "source": [
    "## Question 9 - Training and testing data split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ePBT0v6TuHQw"
   },
   "source": [
    "*So far, you have converted all categorical features into numeric values. Now, split the data into training and test sets with training size of 300 records. Print the number of train and test records.*\n",
    "\n",
    "**Hint:** check **train_test_split()** from **sklearn**\n",
    "\n",
    "#### Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nu5ULZAVuHQz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of train records 300\n",
      "Number of train records 95\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=300/len(X), random_state=1)\n",
    "print('Number of train records', len(X_train))\n",
    "print('Number of train records', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GHYII1QjuHQ3"
   },
   "source": [
    "## Question 10 - Model Implementation and Testing the Accuracy\n",
    "\n",
    "*Build a **LogisticRegression** classifier using **fit()** and **predict()** functions in sklearn. Print Accuracy and F1 score of Training and Test sets.*\n",
    "\n",
    "#### Answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ne88xLZQuHQ4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sTNrahjVuHQ6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the training data:  0.9333333333333333\n",
      "Accuracy of the testing data:  0.9052631578947369\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of the training data: ', model.score(X_train, y_train))\n",
    "print('Accuracy of the testing data: ', model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score of the model:  0.931297709923664\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print('F1 score of the model: ', f1_score(y_test, predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vpy90Xe150sV"
   },
   "source": [
    "# Naive bayes with Iris data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ymp7UMwnOgm0"
   },
   "source": [
    "### Import Iris.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GNnaUGrjOgm1"
   },
   "outputs": [],
   "source": [
    "iris = pd.read_csv('Data/iris-R3Lab.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding one more step of Data cleanup, although it's not asked in the given assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               False\n",
       "SepalLengthCm     True\n",
       "SepalWidthCm      True\n",
       "PetalLengthCm     True\n",
       "PetalWidthCm      True\n",
       "Species          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It seems, there are null values in all 4 features. First locating them and deciding how to cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>151</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "150  151            5.9           NaN            NaN           NaN   \n",
       "151  152            NaN           NaN            NaN           1.8   \n",
       "\n",
       "            Species  \n",
       "150  Iris-virginica  \n",
       "151  Iris-virginica  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values with SepalWidthCm\n",
    "iris.loc[iris.SepalWidthCm.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rows with Id 151 and 152 are having three out four values as Null. It may be safe to delete those rows instead of assigning 0 or mean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               False\n",
       "SepalLengthCm    False\n",
       "SepalWidthCm     False\n",
       "PetalLengthCm    False\n",
       "PetalWidthCm     False\n",
       "Species          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are no Null values and so the data is good for the modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6UMrLVa9OgnM"
   },
   "source": [
    "### Slice data set for Independent variables and dependent variables\n",
    "#### Please note 'Species' is my dependent variables, name it y and independent set data as X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before slicing the data, encoding the categorical variable (Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['Species'] = labelEncoder.fit_transform(iris['Species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YuqCZK2-OgnN"
   },
   "outputs": [],
   "source": [
    "X = iris.drop('Species', axis=1)\n",
    "y = iris['Species']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oe4YSXwkOgnV"
   },
   "source": [
    "### Find the distribution of target variable (Class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "65wA_xofOgnW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4aa0bc208>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t386HtyCOgnb"
   },
   "source": [
    "### Plot the distribution of target variable using histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1FbADzabOgnc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f4aa0c05f8>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='Species', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AvPOo-YKOgng"
   },
   "source": [
    "### Plot Scatter Matrix to understand the distribution of variables and give insights from it( 1 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JgvP1rbgOgnh",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f4aa04deb8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dwtee/hou+9VlSEgJleO+5fUtqq40gUgAfq8fOy4rLvSdIGodvTRAQRAytr8/sB/Lu5bjjhfvAADc8NsbclL89gf2Y+O+jZnUwG2Lt8Hv9avSVmlxSlQDrT0btX5XPhvMdv2Ta5PdveduLN29FHfvuRtXn3E1osms/9HV3YUWd0vRaVIEQUxeKGLGJK93hyAx4LTpDWW75qnT6wEArx4Zwlx/nfkP9r4BpOJA2xllGwvaPsRfj+4FZp5t+mPvht7FWHIMp/hOKdtQ5vnmAQBeD7yOj07/aNmuS1QfvU5LGzo24J499xRMJZF3siSJv2p3lWQHxmlzZnK5Ab5re8NHbsBtF9yG90PvqwoIy7VniKmLnk3mWwhqHXGbYFNFyOwP7MfOAzuxdfFWANCNADMT+k4QtY7eorR3tFc3ldQu2A1T/GQBp72uHTsu34HpddMNUw8JolIEI2p73vTapoLRjmYjLpW1yZQRM1fMvyJzTntdO0TRZGQwQRBTAhJmTPLqkSEAwMn++rJdc3azFy67iFePDOEfzp1l/oNH/8xf/aeWbSyonw64GoGj+4Dzv276Y28EeITNyU0nl20oTa4m+D3+zLWJiYtevQ9JknJChbWOjTJceMUFKzKdPuRzlUKOdqEdiATQ5m1Dg6MBbptbtRO7duFauO3ucf9dxXT1IWqLQjVotGgd8cHoIO67+D780+/+KeO8f/sj34bb5kaLJ1fcoZaoxGRBb1F679578a8L/1WVSur3+mG32Qum+K3vWA+fy5eTekhCJlENoil1Ier9gf3o3NeZV2Q30/UPMK5NtunVTZnPyPZPEFMJm2CDy+4CYtljLrsLNsFm3aBqCBJmTLK/ewgzm9yod5fvn8wmCjjJX4fXuoeK++DRfYC3BfCOvztUBkEA/POzoo9J3gi8AY/dg+l108s3FgBzG+dSKtMkQet0ByIBXcdGfk/bUrXJ2ZR3h8pooQ0Afq8/Z8EwXkeIFtoTDz0hzewupVNUO+L/tuffsObja/CTS34CURAhMQlJKYmklNT9PLVEJSYLeovSQCQAn9uHRldjzvyrFcxddhfuvPBOOEQH/B4/7tt7n25hVZpfiXyUa2NEFERdewaA9vp23c8UE3HptDkz/keLuwWPv/04rph/Ba4981rDwsIEMdlJsRTAgFn1szI+lCRJ/DhBwoxZXj0yhFNayxctI3Nyaz2ePdCLREqCw2bywXJ0LzBtPhdTyon/VGD/o0B8FHCaS616PfA65jbOLbvDdFLTSdjbuxfBaJByzScZeo7N2oVrcdsLtyEQCeD+T96PBmdDxlky0+XGqNjriY0nosHZUNadV1poTyzMLPTyOfqiKKp2+gORAGKpGIajw3CIDoTiITxx8AmsvnC17vdTS1RismC0KG10NmIopt5gynRtunQ7joWPIRgLYt2edZnaXysuWIGu7i5VYVWA5lciP0bz+TzfPAzFhop61rvt+h0d80XVmo24DEaDqhpLWxdtxba/bgP+qr6e1v4JYioQS8UQHAtmNk1bXLTOkyFhxgSBcAy9wzH8/Rnj7zqk5WR/HeIpCQd7wzijvbHwB2JhYOAd4ISPlX0smHYKwCTg+BvAnMLXT0gJHBw6iE/P+XTZhzK3cS4A4M2BN3HhrAvLfn3COpSOTTQZxXuh99C5rzNTB+bm52/G9sXbM2KMXFxV2cXDbHFI052diiDfQptSnGqPQgu9QsJNNBlF577OTK2AhJTQbQtsZI9mQ9+NIJsiagW9RanP5cOhoUOG90+btw2hWAgr/7BS1TZ4476NujW/aH4l8mE0n29etBnf3P3NoqKsfC6fqahaMxGX2nO0dmxmg4kgCIKEGRMc7A0DAGY3e8p+7RNavACAd/pNCjOBt/mr78SyjyVzzcBbpoSZIyNHkJSSmNVQRH0ck8jXfDf0LgkzkxBZMOkJ9+Cm525Svdcz2gMIyOxkycVVH/z0gwC43VXamcm3ADBcaItOCsGvQQpFrGgLQGrbqmuLSwNAx+wObL90OyQmFVwgFltsWAmldRC1hl5qaj7hUxZzti3ehmgqiu6RbmzctxGBSABrFq6BqGkOSvMrkQ+j+TwwFig6ykoUxJyoWp/Lp3r2FxIeAf15evOizQVrLFH3MYIgtNDTzATv9I0A4MV6y83MJjdEAXind8TcB/rf4q++OWUfC+rbAJsr+x0FeHfoXQDZ+iDlpNHZiAZnAw6FDpX92kTtYNS6WGJSJkph66KtuGL+FbjjxTsQiARw03M34Ybf3pDTnrIU9Npw67WEVbbzlhfa8rhlB0sURVNtNInqUqg9trYAJKBuq673/3vZucvQ5m1De317ZvFphDLKoNiWqGZbsxKEVZhJ1RMFEYIgYP3e9YhLcdx63q1YccEK7DywEymWUs2/PpcP6zvWq+639R3rIQo0vxLG83kwpraDntEeRJNRlW3pIQuN7fXtaHG34NDQIdWz/9josYJ2pzdP37PnHmzo2KCqn+f3+rHjMmqNTRCEMRQxY4KDfWF4HDY0ex1lv7bDJmJ6oxsH+8LmPtD/JiDagYaZZR8LBBFoms2/wwSHhrhoMrOuAmNJX1f+DmJyYhRN4La7c6IU2uvaEYqHAJSnRodRNMI09zRTO8DaHPPjo8eplkgNUihiRa8ApDbFQlnEMZKMFB2xVWpKHdWnIWods6l6IkRcfcbVqpTUNQvXIBQL4YtPflE1/256dZOqzbD8O90LhN58vqFjAx549QHVee117Xgv9B5ueu4m09FVegKLMhJHRin66KUtAUBXdxdWLVhFbeAJgigKEmZMcLA3jFnNHgjlLrabZpbPg7eLiZhpnAWIFWor5jsB6Ptr4fMAHAodgt/j523PKkB7XTv29e0DY6xi//aEtYiCiHm+edh+6XYkpESmW4coiDnOl1yXAMiGto8Ho2iEny36makdYO1Ce7y1RIjKUKhYo12w6xaAtAv88agt4gjw/6/VKEZKNkXUKnK6pyRJ2NCxAbd03ZI3RUMUROw8sFMluOw8sBNXzL8CgHr+7eruQld3l+rzKz+2ku4FwrDO0bJzl+GtwbdUc3jnvk4A+VOblGnLEpNynv3BWFDX7pSijzZtST5HFMtf444giMkNCTMmONg3gjPbmyp2/VnNHrx6ZMhcZ6b+vwJNJ1RsLGiaA7z7OyA2Arga8p56aOhQxaJlAGBm/UwMdw9jIDpAD7dJisSknPztDR0b0OJuQau3FTsu24FoKoqklMR9e+/LdPRYu3AtRHF8O09G0QhGERSFFgDjqSVCVJZ8ESuCIMBr96oiYrx2b0YMtjJqhWyKqDUkJmEoNoTe0V4s71qOntEedMzuwOZFm2ETbIaRAaIo4pozrlEJoEqxHcg//7rtbroXCAD687lSrAGA2164LdNQANCfs7VRsw986oEc23vi4BM5wqNW9JHTlgqJkwRBADbBxjf0Y9ljLrsLNqFCAQcTDBJmChCKJBAIxzHLV/7CvzKzfB4kJYYPBsZwSlueltzJGDB0GJjz8YqNBU2z+WvgIDDro4anSUzCB8Mf4KLZF1VsKHJu7nuh90iYmaToRa3c0nULVlywAnfvuTvTOvuOF+/A1z/8dVx75rUIxUPo3NeJey66Z1zfbRSNUOoCwGwbTaK2YIwhhZTqWAopMMYAmEt1qlS3GLIpopaQF7J9Y31Y+/LazD3R1d2FtwbfyhtFpu1u1uxuxoY/b1Atntvr2uEQHejs6MyIPvL863P54HP56F4gdFGKNYFIAIFIQPW+3uaKtvD7ptc25URPLjt3Geb55uUVfShtiSDMk2IpgAGz6mdBFERe21GS+HGicsKMIAhbAHwGQB9j7Kz0sXsAfBZAHMAhAEsZY0OCIMwF8FcActXZlxljN1RqbMVwJDgGAJjR6K7Yd0xPX/tIsIAwM3SEt7OuRH0ZmcZ0UbXB9/MKM4FIALFUDNO90ys2lDZvGwCge6QbfzPjbyr2PYR1GEUjNDmbMuHH2y/drltvRulklbIwlotM3tp1a8YJW9+xHo3OxpIXAJVoz01UFgkSNu/fjCvmXwEPPIhLcWzevxm3L7gdAK+NoW3XruwmY1SrqNXbimgyOm4nnWyKqBVkIf1Hn/hR0VFk2u5mZ/vPxvKPLleln6zvWI9//dO/YiA6gFULVuHExhPhtXvR4sneP3QvEIUwG2moLfy+P7Afnfs6sXXxVgBQzd2FRB9KWyII88RSMQTHgpko5RYXRZfJVDJiZhuAHwN4SHHstwC+zxhLCoKwDsD3AXwv/d4hxtg5FRxPSRxOCzNtjZWpowIAbQ0u1XcZMvg+f22YUbGxoH66+rsM6B7pBgC0elsrNhT5gdgd7q7YdxDWYhS1oizyy8DyhgmX2lJ4KDakW2Ry9YWr4ff4ycmaxCiFPFHQL0oqCy+iqF8bY/WFqwEY1ypatWBVUYUnCaLWkYX0UDxUdLqndrEciATgsruw9hNrMd07HU7RibteuStTW0a+d3ZcvoPuG6IozEYa6kVDyqJLe71+t1GjDR2fy1e5P4ggiClDxYQZxtjv05EwymP/rfj1ZQBfqNT3lwtZLGltqJww0+RxwGUXTQgz7/HX+goKMw4P4PYVFmbSYkmrp3LCjE20we/2Z0QgorYpJWpFb2dLW+T3wMABPHHwCcMaBkYL40LFWeOpuH6RydTKivytRG2gV1fgsbceMxReWtwtWHbuMsPdV6OoL4/dk/lvM/ZIELWOLKRveX1LThRZocWpvFjeful2HAsfQzAWxLo96zJ1w+Siv0rM1nKi+ZjQYibSsFDhd5mklEQgEkAilYBdtOfd0CEIghgPVtaY+TqARxW/nyQIwl8ADANYxRj7g96HBEG4HsD1ADBnzpyKD/JwcAwNbju8zsr9UwmCgLYGl7mIGZsT8DRXbCwAeNSMiYgZAQKmeaZVdCjTPNMmjTBTbdutJqVGrWh3tlIshXv23JNx1mWRZn9gv2ENg3zFWfM57KV2vCn1b52oTDa71Qp5m17bhOUfXa5yztd3rEeTkxd8L7T7WijqC6C2vlYw2ey2FlAK6Rv3bcSqBaswu2E2jo8e54vTj6+G32u8OJXrCVz7zLWq4+Mpuj4Z52Oy3epQqPA7wEWZtwffzkTIPLT4IdMbOlNNMCS7JYjxY8kMIQjCHQCSAHakDx0DMIcxdi6AfwKwUxCERr3PMsZ+yhg7nzF2fmtr5aI1ZA4PjGVSjSpJa4MbhwdMCDMNM4BKt45umJGNzjGge6QbLe4W2MXKanut3lYcCR+p6HdUi2rbbjUxiloJRoO650tMQiASQE+4B8FoEC3uFrTXt2NW/SysvnA1nvrHp7DighUZUUa+pt7iVl4YK5HbaR8cPIglu5Zg0S8XYcmuJTg4eBASkwBkFxnyZ80W+S32b53o1ILdKu0lEAlk/h+WglbIk+sKPPjpB7F10VasuGAFNr26CQPRgcw58u5re317pp27jBzarrSjtQvXYsvrWzLnUFvf6lMLdjvZkEXKrYu34tbzbkVcimPVi6tw/W+vR1d3F6KpaMFrOEX9+dptc9N8nIZst3hKeUYUKvwO8NQmWZQBsu2zlejN77JgaOR/TEbIbgli/FQ9YkYQhK+BFwX+FEvPfoyxGNKNsxhjfxYE4RCAUwHsrfb4tBwOjlW0I5NMW6MLL7w9DMaYSq1XMfh+tgZMJamfAbz/IpBKAjZ9EzkycqSiaUwyrZ5WDEYHMZYYg9fhrfj3EaVRTEvhQjucfo8fgUgAd++529TuqVGhP1EU86Y4ldrxxsr2yVORcuyIa2vK6NUVOBQ6pCowfZt0m6lra2sVJaQEvHZvtlYBtU4lJhHyPXfHi3fkzM+CIEBiUv4oSVHUTR+BgLLOx5FkBIFIYNJHKRDjiNgVRd3C73IaKwAkUgmVfeml8enN76WmWBMEMbWpqjAjCMJi8GK/FzHGxhTHWwEEGWMpQRBOBjAfwLvVHJseyZSEnqEIzp1T+aJe0xtciMRTGBiNw1+vE6HDGBdm5nVUfCxomA6wFDDcDTTP1T2lO9yN01tOr/hQ5OLC3eFunNp8asW/jyiNYtKCzDgsZrsqAMapJsdHjxcUUErpeCPv+Ob8rSJFRFSC8Tq4Wqe9Y3ZHTvHGtdv7qaQAACAASURBVAvXonNfZ+YzctteM+jVKjrbf7ZuZ49SmGrh8ETt47a7dcWV3tFeuGyuvPeltm12KB5C575O3HPRPRDdJczHBs+etwffxt177p7waU1EYUp9RhSqHwYADptDZV/7A/ux88BObFu8DQwsMycDXOCX52nawCEIohQq2S77EQAXA/ALgtAN4AfgXZhcAH6bjgqR22L/HYAfCoKQBJACcANjzPI41L6RGJIS0xdKyow/nS7VPRjR/77oEBAPA3VtFR9LJipn6LCuMBNPxRGIBOB3V171l7+jJ9xDwkwNU4yQYsZhMRJbALXzIy9S9QSWUmvIFEIU9Hd8yfGvDON1cLVOuyygbL90OyQmwSE6MBwfVkW4rO9Yr7KnYmsVBSIBOG3Oce+MTsb6GcTEx+fyYZpnmqo2h8vuwro963DPRffknK+NWFO2zQbGNy/rdcmRa5NRlMLUoNRnhCiImOebh+2XbkdCSsAhOnJSVf0ef4593XDODWj1tmZS+fXm6a2Lt+r6HzRvE1Mdm2CDy+5K58lwXHYXbILNukHVEJXsynSVzuGfGZz7SwC/rNRYSuVYiOdLT6ur/E74tDouxhwbiuCcE3QidIbTk3tdFfI269IOTOio7tt9Y30AgOZKFyEG0OLhi/Hjo8cr/l1E6RSTFmRWMFGKLRKTMBQbQu9oL5Z3LTe1SC1GLCqGseSY7o7vur9bhxZQukq5Ga/Apue0d3V3YSVbmWmJ2uxuznHO8zndSrurlJ0BFA5P1CaiIMLn9uFomPsIcSmOdXvWZQRJpRDjtrvRP9afN2JtPPeLMpVwXtM8HAodMlWbjJg8jKeQ/6GhQ3mFb7tox6nNpxo+HwD9eXo4NpyT8rRm4RqI1pT2JIiaIcVSAANm1c/KFISXJIkfJyztylTzHE8LMy1VEWb4d8hiUA6yMOOtbBck1XcM6wszvWO9AIBmV+WFmUZnI2yCLfOdRO1ilBakjTbwuXxFLWTlhXHfWB/WvrzW9CK11BoyZv5OvR1f2gmrDOMVPsw47aIgwi7aITEJdtGu+n9pRhxx2pyq6IFyFfqlcHiiVvG5fGjztqnuy86OTtgFe047euW8rY1YG++8rEwl7OzoNF2bjJg8lPqMMCt820U7ZtTNMLyO3jx9dPQonjj4hGoDZ+eBnbh9we3oCfdQWioxpYmlYgiOBTM+U4uLNjVlSJjJw/Hh6gkzDW47nDYRx0IR/ROqKczY3YCr0VCYyUTMuCsvzIiCiGZ3M0XMTDBkMUaS+OstXbeoHKZ5vnmmBRPZefrRJ35kapFa6ZocRvUV3HZ32b6DyFKKwKbdsc/ntBeKiCkkjgSjQdzw2xtyFoPliGqpVDoeQYwXZRpILBXD4eHDWPvyWtzwkRtUQozH7ikYsTYelPeI2cKsxOSi0oX89TaXhmJDmd/16s49cfAJXP+R6/Hd331X5Sf0hHvwtWe+RmmpBEHoQsJMHo6HInDaRNS7Kv/PJAgCWuqcBSJmBMBbJQejzm+YytQ7yqNXquXsNLtImJlIKBe6Ky5YodrBLCUVQ3aeQvFQwUVqNWpy+Fw++L1+VYSE3+uHz1X5IuFTlWKKNOvZwKZPb8KOy3YgLmUda2XNi3y7poWKPVcyqqWSaVIEMR7kNBBtJKNWiDGct8tULF15j8iFWTcv2gybYKOohClESYX8DYRvURAzUS0+ly8n3Wl9x3psenUTurq7Ms8X7Tx94zk34pG/PpKT8nzrebcCoLRUgiD0oadVHo6Fomipcxq3ry4zLXVOHBsyEmaOAp5mQKySlub1A6Fu3bd6x3rhsXvgsVe+jTjAHa9jo8eq8l3E+FGGBzc5m8a9aJUXxvJuaHsd32XVW6QahSYHo+WrJS4KIk5sPBEfmvYhtNe340PTPoQTG08k579G0LOBG357AyAA7fXtaHG34NDQISzZtQSLfrkIx8LH8tqo3N5XaXdrF66FKKqL/yopV1SLcid49+d3Y8flO2iHlagJ5PvMSIiR2fL6lrz3z3jR3iOrL1yNWfWz0F7fnlPIlSCUyKKe0jbXd6zHXS/fhUW/XIQlu5bg2OixnOfJrV234or5V2R+v+G3vBiwcp6eXjcdrxx/Bcu7lmPp7qVY3rUcgUgAoXgo8/2UlkoQhBaKmMnD8VAUzXXmWqaWg2l1TrzTH9Z/c7inOmlMMnV+IPC27lu9Y71VjQ5odjdjX98+MMaqJpIRpaOMIDAT5VIIeWG86qVV2LhvI1YtWIUTGk6AIAiQmGT43TL5nJ9S055K2Z0jqoOZ1COlox2MBfPaaL72voB+V5j1HetNz5GFbJBsjahFjCIZZSFGTvUMRAJw2V2488I74RAdOfePHsXOy3SPEKWgTYESBRF3vXxXpg5Sz2gPAmMB3edJk7NJ9Xs0GVWl5klMyomiWbtwLTr3dWbOkSPH9DpNEgQxNSFhJg/HQlHM9ddV7fum1Tvxx0MxpCQGm6gRIKwQZqJDQHwMcHpVbx0fPV6V+jIyLe4WJKQEgtEgpnmq+G9AlES5c/71FsZ3vHgHbj3vVtzx4h3YcfkOtLhbMlExZoUgakU8OSlUl0Ur3BSyUafNmbe9r7IrjGyfm17dhNUXri64WCQbJCYq8n2mvX+0Qkyzuxkb/rwhs9gFsvePngADgO4JwhKSUlJlp4CxcK+MfDHqKqmqeyM6MZIYQSASyHzm/k/ej5HESKZGGdk6QRAkzBggSQy9w1Gcd2IVBYg6F1KMoX8khhlNmkKiwz1Ay8lVGwu86bbcw0cB/3zVW71jvZjvm6/zocogO2vHx46TMDMBKHfOv9HCOBQPZSIhZEfe7/HnFOY1EoKM0p62Lt5KO1cTmEIRLFrhRrZRZZcYZQ2aQsWDlV1hlKxMrSw4VmqHTUxUlPO8HMk4u2E2BAi4b+99mfvhbP/ZWP7R5Xhr8C3V/eNz+XQFmGnuabr3RLm6OBGEjFYYf+BTD+gW8b334ntVRXzvu/g+PPjagwD0U6pltJFcLaxFVaBYFERc9eRVNP8TUwqbYIPL7gJi2WMuuws2wWbdoGoIEmYMCI7FkZRYVToyycjfdXw4qhZmYmEgFuJRLNUi0zK7RyXMJKUkBiIDuGDGBVUbihyd0zfahzOnnVm1753sVKp7UbnbVOsVQF2zcA027tuYKdQnv9cz2oPOfZ1YtWAVTmo6CW672/C7jVJeesI9uOPFO2jnaoJSKIJFz56WnbsMbd42iIJoqniw0qbG0zmJ2mETtYqZFLv5zfOx/dLtOBY+hmAsiFUvrgIAlRATiATg9/pz7h8jUfJni36me08cCx/Dtc9cS1EFRMlobZoxprLBTa9tytnYuemcm7Dr0C7V8+SZd5/B7Qtux0q2clwp0D3hHpr/iSlHiqUABsyqn5XxuSRJ4scJEmaMOJ7ujtTirZ4w4/Pyejb9IzH1GyPpwrfeagoz6UihcJ/q8EBkACmWqmoqU5OL5/IGooGqfedkp9IpFOXM+c8IPZftwFhyDB8Mf4CN+zYiEAng/k/eDxGiyrnZH9iPm567Cbs/vzvvGIwW1HIkjt7OVaVbcRPjp1AESyHh0Kh48PZLt+t+33g6J1E7bKIWMft8EAURbd42hGIhrPzDysy5ekKMdp40EiVFQdS9J4KxYOYceW6WBR6aj4lC6Nn05r/fnOM7dO7rxNbFWwEgEz35mVM+k3MvOG1ORJMGzTpMQvM/MVWJpWIIjgUznU1bXNRpUoaEGQPkttUt9VUUZjxcmOkb0Uz2w+lJu5o1ZjyyMKNuU903xoWaZlf1hJlGZyMAIDBGwky5mGgpFKIgwu/1Iykl4bQ58e9/9+9wiA74PX4MxYZKcm7yReIAuTtXVA9kYmC0sDNbUNdowWi0Yz+eCDFqh03UIsU8H0q1f6NFqV7qoHJelsejTGGl+ZgohJ5NS5BybDBTA0ZRyFevVoycgiTb3TzfPAzFhoq6B2j+JwhCCwkzBhwfTgszVUxlavI6IADoG9ZEzMjCTF0VhRlHHWBzAiNqYaZ3rBcAqvrgsIt2NDgb0B/pr9p3TnYmQgqFNjrF5/Lh0NChHCdmnm9eUc6N8rqt3lbsuGwHoqko3gu9h437NmJ/YD+AXHFnoolZUxURYk4x3zUL10CEucWa0YIxZ8f+sh3wp6MYS40QK3faH0GUg2KfD2btXzn3eu1e3VpQIkTM883L6ZQjz8tAVmil+Zgwi55N94725qQurV24Fm67usaj0r4DkUCmWC+QtbvNizbjm7u/WZRISPM/QRBaSJgx4HgoAlEAmtzVa5dtF0U0eOzoD2uFmaP8tZoRM4IAeFpyUplkYcbnrl67bABocjZldjKI8VPNENpS0n90w44XbTZ0xM06N0ZRL/N88zCaGM3pmKAUdyaCmEXw9uo7D+xU1QTYeWAnVl+4OnNOPpvUKx6st2MfTY0vjD0zXmr1S9QYxT4fzMzxeoVWH3vrsZxaUF887Yto87ZlFrUSk7Ds3GU5xYO1KawAzceEMXo2/fCBh7H8vOVYtWBVNqXC08JtK9yja8tGfoCyrXYxIiHN/wRBKCFhxoDe4RiavU6I2rbVFcbnceZGzIwcB5x1gEbFrzgen24qk02wocHRUNWhNLma0D9GETPlolohtKWm/+hFpygdHxnZETfr3OSLeikk7lA++MSgxd2CZecuM7TtQjapLR48s24m1u1Zp7tjbwTVIiImMsU8H8zO8dq512P36NaCuvbMazNdmOSC3HpzczCq38aY5mNCDz2bvumcm/D424/jnBnnwAMP4lIcG/68AV887Yu46bmbdG25UA0kGbMiIT0rCIJQUlFhRhCELQA+A6CPMXZW+lgLgEcBzAXwPoAvMcYGBUEQAGwAcBmAMQDXMcb2VXJ8+QiEY2jyVC9aRsbncaBvWLMTO9qXrflSTTwtwEiv6tBAZABNribw/13Vo8nVhHeH3q3qd05mKh5CK0nAWD+CLFVSuLnerlQwNn5HPF/USyFxh/LBJwalFPe9+fmbsWPxNviTScRFqBaMP/v7n+GaM65R7divXbgWdkH/8Um1iIgJQXqORjIO2J2AtxUQs4tPs88Hsyme2rk3FA/lLb5+LHwMoVgoc99o52aaj6cQeWzVLHo2LUkStv11G/BX9bnXnnktAH1b1kuVvffie/HT136quoYZ34SeFVOYMtg0MTmptBVsA7BYc2wlgOcYY/MBPJf+HQAuBTA//XM9gJ9UeGx56R+JodEKYcbrQJ+2K1O4D6hy6hAALgZpImYGogOZYrzVpMnVhIHoABhjVf/uyYrs7LbXt8Pv8WcdAUkCwr3A0BH+KknFXViSgL4DwOZLEA8dLincXN6VUvLEwSfQ2dGZOV6KIy5HvSgxK+4oHbvdn9+dibIhB6r2EBngT6XQnkzBn0pBVEwbhuLc8FGg8yw4+99W2ciGfRvgtXuxasEqbF20FasWrILX7oXdpi/MGC1Ug9Gg7vkEUZDxzsl610vP0eg8i7/2HVBd1/D5oMFsiqd27t3y+hasXbhWNZ+vWbgGW17fkolAyHff0Hw8CTBj1yZs1Sw5Ni3m+hmyOCijtWVlquzWRVux4oIV2P3ubtx0zk1F+yb0rJiilNGmiclHRZ9gjLHfA9DOMFcAkPuObgfwD4rjDzHOywB8giDMrOT48mFZxIzXiUA4BklSrCSsFGaiISCRjeAZiFgkzDibkJASCMVChU8mSqccD4yxfuDnVwFDh+EcCxYnhKQdNZExrFm4RuXoXH3G1WjztI3LEZd3WUsVd8wuVggLKWDDhuJcup5WS9dduP/jWdsLRAJwO9yqgpBepxc+l/6cTLWIiLJSCSdeMUcD4K8/vwoY6Sla/DErdmvn3kAkAL/Xj4cvexi7rtyFVQtWYeO+jQhEAhmBptB9Q/PxBMasXRvZahlS2/X8gbUL12LL61sy52htWU6VvXvP3Vi6eynu3nM3PnPKZ3BK8ylF+yb0rJiijBrY9OjUKNdgE2xw2V2qYy67CzbBZtGIagsrasxMZ4wdAwDG2DFBENrSx2cBOKI4rzt97Jjyw4IgXA8eUYM5c+ZUZICSxDAQjlskzDiQlBiGIolsR6jRPqDtjKqPBd6W7Pf7+L91IBLAaS2nVX0o8iIoEAlUvfBwuaiG7Y4bIyfom88C9dPNXSMZz3y+5ff34v7F/4qbX15TONxcdtR+fhXEy9Zh55FndAu4jqdQHnVBKJ4JYbdKCtiwbgrEgh+g5Zl/AQCI3Xsx/+k7sOMLP0M8fBzO2Ch8zIGmxpMQlwrbDNUiqg0mnN0aUY45WYtijs4wdBgIdQNbFvHn/Vce4X5HgfB6sylF+eZeiUlw2pz45/P/GcFYMNMdb6reN5PGdvNh1q6NbDU5fvEixybTrbDzNQHIZ8fF+iaT7VkxJey2HCQjBjYdsWY8VUYQBYABs+pnZeZ/SZL4caKmiv/q/R/JyVthjP0UwE8B4Pzzz69IXksokkBSYvB5ragxwyfkvpEoF2YSUSA2wgvxVhu5rs1IL+CbkylS1uRsqvpQmlz8O/sj/Til+ZSqf385qIbtjptyOEE2J3fshw7zRe4z/4IdHbcj3noanA638aJW4ai1/P5eLDMr6BQJdUEojglht0oK2HCOYw0BLU/+M8TuvZnTxXAf/MdeBx79Kj/gmwP/0mcAJgFI8SeTgQ9BtS9qgwlnt0ZUYmFqz87RGXxzsju2RYg/xYjdRnOvKIho87YhFAth5R9WTvn7ZtLYbj7y2XW4N1t7QxD0bbVMdQ61NtnCWgracrl8iMn2rJgSdlsOBNHApqfGBqHP5UMoFkL3SHemG9rshtmGUchTDSuEmV5BEGamo2VmApD7MXcDOEFx3mwAPTmfrgKBdLtqqyJmAF7j5vQZ4NEqgHWpTECmzsxwbBgplkKjy5oaMwCoZXalkAuRAfoPDLuJHRz5GowBVzwAPHETF2fCffCLTsDmBvI5MwpHLSPo/N13EZ/xYTgdHopsIbLkK5xntOhU2LBcgwbJFODwABd/Hzi+n3/GNwf43I+B53+Y/fzQYSAxCvz4bwpGE1BUFlFWjOzZ5lQvYIspHult5TYsRywY2bxJ8accC1W6b6YAynnbSHCRksDmy7J2ufRpbpu/+Y7aVo2U8XFSzY0bsvkpiuhQ+cjwzeG/i9Vfc1qBKIg4sfFENDgbyO51sEKY+Q2ArwH49/TrE4rj3xEE4ecAPgYgJKc8VZv+EeuFmUzL7HB6sWxVVyaAO3/ghX8BWFb8FyBhpiIoUohQ35b7wPjKI9yRN3uNJf8FPHcnsOgubreRQf77P24GWMp4AaFZgIjde+F/6nt815YiXAgZpa0pbVQWSvQWnV95BBBsvH6GwwOMHFe//9XHgW88C8i5/U9/D1BE0Kh2s0xEE1BUFlE2jOw5NgI8fKX+PZAPeXHsnQZct4t/3usHnvthrs2bEeTLCN03kxjtvH3a5cCX/hP4xTVZG/7yDmD3Her0pmQMeOVBtT/xyoPApeus/XvKBNn8FMVZB1x+L+DwAokx/vsUguzemEq3y34EwMUA/IIgdAP4Abgg8wtBEL4B4DCAL6ZPfwq8VfY74O2yl1ZybPnotzBiptkrpzLJwky6XbUVqUzuJr4YSbfMlkURK4QZj90Dl82F/sjUKI5VVZS53kOHuYhy+b2A/1S+iDWzE6u8hmjjBavlNBCAO10sxQv8aRcQ8kJBkrhj9uiS4kQhYmpRqDaBKHL7+uazfGfW5uSL2P/o4OcueQzY9V315x++kp/vOwEYDQALbgR6X1fvZg0rAjjLVOOAIAqitWe7k4uMsj0D5lOPtIvjW/YDv76RC/KfXK22eXnupbauRDnQzttv7eKvS5/mUbZ2J7c1+bjM8FE+H+dEF9i50E42SRDEJKKiwgxj7CqDtz6lcy4DsKyS4zFLIMwdbrneSzVxO2xwO8RM1I6lqUyijYsz6VSmgQiPmJGjV6pNk6sJgTGKmCk72lzv7r3Aji8Cy98oXFxSdtrjY9lrDPfoh2kO9+QuIPR20a79DXe6yOEi9DBTc0MUs7Yb7s1GFgB8hyrf5z0tQMPM3N2sp2/Lni+nkhBENVDaM8AXpKXUndEujgUhO1c//0Nu880nAXYPf/5HgrnRZWYjcwhCid68/dYuHvniS1cxCHXnpje9vAn41L+o52OHl9dDevBvySYJgphU0CymQyAcg10UUOeypnWXz+tE30i6RXUmlcmiokie5kzEjJWpTABvmU0RMxVATiFSYiaMXdnusveN7DWeW8Mdp8vv5aHyskP13Br+vnIBobeL9tDn+HfL0Q8EocSMvabbrmPoCJDQdECIDOb/vCgCLScDMz8C+E4E2s7kx9PttLM7ttTakbCIUuds7eJ48P1sSP0l6fk5NgIMHwHuOx3o2VexVsXEFMOMzQo2Xj9GPs83B/i7fwb+spOnNAH89Q/3AkMf8N/JJgmCmETQqkeH/pEYGj0OCGWq+l4sTR6HOmLGWW/d7qynORO1MxAZgE2woc5hTS5kk6uJasxUArmGgdIZMpNCNKoQVV7qzDpU3Xu54zRtPtA0m5/7zIps/QKlM1bBVpjEJKWQvSoFw86zuC0pFwQvdXJhJZ+9yxEKvhN4J6anb+M1Dq7bxV+fu5MLPgRhBaXO2TbN4lgWy7X89yr+Wii6jCDMYsZmWSpbT0aea994HDj7C8Du24Ftl/PXj32bz+MyZJPEREK08blVicNLmz0EgNpql10zBMIxS+rLyDR5HJk6Nwj3WRctA/AUqv43AfCImUZno2WCVaOzEW8Pvm3Jd09q9GoYmEkhSioiEbr38lD4RXcB088EBt4BpBTQPBeIhdXRBkpnLF8HHaptQOghikDr6bw2QSoB2BxA/YysbWijsBKj6q4e4T7uBF33NABW2LZsTv2aSZTKRFhFqXO2aFOnmcrzcsvJAARu0/91XVZEl6PLSunSRxBKzNisYOOii7YDk92TLf5bI4WqCaJkpBQABvjm8nRSxrgoKaWsHhlRA5Awo0P/iPXCzJvHh/kv4V5r6svIeHw8MoIxBCIBS1plyzS6GjEcH0YilYDDNjXaylUNbQ0DMwg2tdPevZfvZl3zOK9R45vDnbB8zphRxxHPtPydd7SQiDN1kCQuFhvZhjYKK9QNvPqIuqvHH+4FPrvenM1rF7NyKhMYFZ8krKOUOTsRye2Y9/RtPI3p1zdysVMWa4BsdJnS9r+8g8/9kkQ2TxRHIZsVRf0OTOdclRXGZ5+vX6jaM6309vEEUW0SEWAskK2b5PVbu9YjagYSZnToH4nhQzOtEyCaPA4MR5KIJyU4w31AY7tlY4Hbx1vIRkMIRAJoclpT+BfI1rYZiA5gRt0My8ZBpHF4jIv8AtnwYj1nTCmkNMzItiqWHapCnXe01ypGxCEmNoVsQxuF9VIn8Kk7i28BL6O3mH3uTuDvfwRsWWTO3kg4JGoBu0H0V2Qwfb8IuRE1zjou2CRjQPAQsOtWfpzmWKLceFuBjtvVz/IvPwz8TtEau3svF2uu2wVA4DbtmaYv1jfM4PM3zbkEQUwQSJjRIEkMwdG45REzADAwGsPM0X7u/FiFnEY12o9gJIhTW061bChyNygSZixG2d66fnput4RnVvDzjMKLzQgpxdSeKUbEISY+hWxDG4UV7uNdlpTin2eaeaHEaDE72p/97nz2RsIhUSvoRSh+7sc8DdU3h9dT0ouo+YefAA9/Xn3fabvrkfBIjBe9dCcIvF22MkJmwY2A6AAaZ/LPhXv1fYDL781G79KcSxDEBICEGQ2hSAJJiVkqzPjS3x0YDGFmbNjaGjOeZgCANHIMwVjQso5MgCJiJt22m7AAvfbWi37E0z2kFLD7Dr6jpYxK0DrtjOk7Ud94FmhIL2zz1Z7RQgWEpxaFbKNQLYNihRLPNOBL/wn84prcxaxMPnsj4ZCoBeR52DsNWPoMkIzyCJjnf5iNgHF49EVIQTSeY0l4JCqJIGY7h8kbQM46XltMxsgHkAus6vkYBEEQNQg9NTUE0kV3fV7rI2ZGBtIpIWlxxBLSOY/DocNISslM1IoVZCJmSJixDqP21hAAVwPwmfXA8jeytWUAdYeczZfkti8G0k6+ostNMV1HSm0dS0xMjGxDsPGaL+FeflzuqqRtuz5qIJSMGrRbjQwAL9yd7RRy9WM8lN5s8UkSDgkrULaMHw1k5+H7Tge2LuYC+YyPAF/Ylp2vPS2599bnfszTU43mWCPhkdoXE1qUNhnu5b9r39f6C5EhQFvb0NXIbVVGEPTtMzKY/V3rYxAEQdQgFDGjQW5TXQupTGPB4/yApcV/uSg0MHwEAGojYiZKwoxlGC0yhz7gxSO1O6V6IcZM0o94EBStAovpOmJUQNhsDRFiYqG1DZsTiI0A/9Fhbsc+aUIYVJ0f5wLkW7v470bFJ43srZjoL4IoB9ooliWPAbu+q56HH76S30O+E9SfbTsDuO4pYPgoFyvlyDBtPTHZ5oePkvBIFMYoskpZB4Yh1194+ErgW12A+yP5uzkpO+/J9e6euzN7jtbHIAiCqEFImNEgt6m2VJhJR+vEhtLCjJWpTK4GQLBhYIRH71gpzDhtTnjsHoqYsRKjRaZcPFIOF65LF/CNj+U67cNH9YsGOzzq88x2HSm1dSwxcVHaxkgvd97zpcYp0+kEsbAwqERr83LxyaVP86iDQvZGwiFRDVQ2LqgXuA6vefFEFPlcnErwLnuyzTrreAoUk9Q2T8IjYQajyCplHZhrfq1vp4lIroCoRNvNKZXg9ip3GDPyMQiCIGoMEmY0BMLcUfF5rHMqXHYbPA4bUnJIvpURM4IIuJswEOFjsTKVCeDCEAkzFpKveCSQjTyQd8YW3ZXrtL+8CbjkTnXOeMNMdWhysZTSOpaYHBSKgNHu1H79mcLCoHKR6/Dk2nzH7UBDuznxj4RDotLk2Phu9T0RGSxOPPG08DlZOUe7GvkxckAMWAAAIABJREFUrd2S8EiYwUwdmOCh0kQ+vW5OVz/Gi1YD5fExCKJciLas3cs4vPw4MeUhYUZD/0gMdlFAncvaG6TJ44A4mlb7rawxAwAeHwKRIABrI2bk7w9EA5aOYUqjXGTGx4CBg1yUkettyIUiZQfppc7cEOOO2wG7u7zjoq4gUxfBlj8CRrtT+9+rgEvvMRYG9ULuv/o4jxZIxXnRyfoZxdkXCYdEJdHa+Gh/bst4o1Sk8aAqKGwygoyYmsh1YPSibWVeWAd8eQfw6JLi7dTuzu0QOW1+thOffI1wL/kJhLVIKQAM8M3l9wVjAEuljxNTHRJmNAyEY2j0OCAIgqXjaPI4YI8M5FaftwK3D8H4MGwOG+ocdZYOpdFFETOWIy8yRwOA3ZMbLiw61Gkfz/+QR85MPwtwevmCWa4HIuObU3qXGr2F9Jd38HQqUSTna7Lj8OSPgNHu1Hbv5S2Av7CN/65tn61NA5HrHFDrVaJW0dq4VhAP9/GF6mfv5/dF0+z8EV9j/er0QCA7R3vTaaqSxAUg7SKa7gtCDzN1YMJ9QGN74ejCnE6PMLZXOQWKuocRtUQiAowFskKi129tdgRRM1RdmBEE4TQAjyoOnQxgNQAfgG8BkEv5384Ye6rKw0MgHLO0voxMk8cBd2AA8FocLQMAHh8GxvrRWNdiuWDV6GzEO4PvWDoGIo1euHvDTC4kamty7L49K7wMHSm+WKReRAyQPaZdSD+6hItBu28n52uyY2SHcgSMXg2McB8/Xj+9cBoIoN96ldpdE7WCYR0kRVvsZ1Zk22LriTLyHCtJ/DNGc7QyTVWuQSO/T/cFYYSZOjBfeYTP2/mEGIcHGDkG/PzqrMBiVJtG6VMY1bgheyUIoobIK8wIgvCP+d5njP2q2C9kjL0F4Jz09W0AjgJ4HMBSAOsZY/+n2GuWk0A4jka39YFEjR4H6pKDgMfami4AeMTMaByNzgarR4ImZxNC8RASUgIO0XoBbUojikDLyYC7IVcwyVdzwKhYJMDDjLU7ZEapJckoP/YPP9F3yjzN5HxNBYzsULahQjUwCqWBAAatV6nrDFEj6Nl4x+1coAQAVz2PEMsXgdB3AOi6C/jYt4FUzHiOlr9Dnl+V0H1BGKFXB+arj/Mi7fnSjTzTgP43s5+7bhfvAKkUWMzUpjGqcUP2ShBEDVFIgfhs+rUNwIUAnk//3gHgdwCKFmY0fArAIcbYB1ZHYsgMhGOY11Zv9TDQ5HGgSRqC5DoRlu/ze3wICkCj3Vv43ArT6OI1boKRIKbX0ULbcoxqZ+Qrdqq3iLjiAeC/rsvu6Ladwc81iogZfDfb/tWosKW8kCbna/KjtUNJUjv3racb22OhNBDD1qu18cwiiIIFpguJ0rI4uegubvf1bbn3wJf+M9t9Dyi+oDAxtTFTBF1vE+ba36if/3WtuQLLC+uALz8MPPpV49o01D2MIIgJQF5hhjG2FAAEQXgSwBmMsWPp32cC+P/L8P1fAfCI4vfvCIJwLYC9AL7LGBvUfkAQhOsBXA8Ac+bMKcMQsjDGEBiN46M1ksrkF0KI2ptguRzibsaAzYaTbS6rR5IpPjwQHZhwwkwlbbfmyFfsVOmgJSJA4G2+6JULCMutjkf7jSNilO1f9RbSyk5R5HyNiwlnt8XWEtAWpZTTQK5+jOeAN7YDY4PqkPvP/di4vTZRE0w4ux0vZgtM66WFyuKkHAUzdDhbG8zTzG3+6ZXAOVdl7xW9eZe6MZWFSWu7hWxUL90o3Kt+/os6xd7DfYC3TZ3Oqm0wQN3DKs6ktVuCqCJmgzHmyqJMml4Ap47niwVBcAL4HIDH0od+AmAeeJrTMQD36n2OMfZTxtj5jLHzW1vLO6GGY0nEk1JN1JhpdgtoEcIYsVlfDIq5mxC0iWiugVrRcsRMIDLxOjNV0nYnDHIkw/BR/rsg8oKqsigDZFsdyw6UvDOrJDGWPSYXGL78XuCW1/gO2ysP8uPkfI2bCWe3RrUExvr1z5eLUsr25JsDLLgR+M0yYNvlQO//An+4ly9Sr9vFX195MHenN9zL6yeFe/nvhKVMOLutBrJoufkSoPMs/tp3ALA5s1GGynn10a/ytBEIwFu7smKMb05WwPzqr7jIfvm9QEOR3coIXaas7eqlG8mppTLDPTyCUTlfX/EAEHqf+xLbLuevD1+pnvOVG0LL3+CvcmQuzd1lYcraLUGUEbMr7d8JgrAbPLqFgUe6dI3zuy8FsI8x1gsA8isACILwHwCeHOf1i2YgzNMdGt3WCzNt9lEAwJDQCKvjQiKuekRFEc3M4oGA15gBQJ2ZapFCLav1Ihmu22XQ6ljMHxHTfLJ69yvcxwtaNqWdtc+uBy5dRy0xpyLF1hJgqfxFKV99BLhoBfCLa/R3WqnbBzERkCRgpEdftPxWF7fZrrv0o2AcnqwYI0fS1LXyLiK/viErrC9/w7q/j5h4aH0Gm0660auPAF/cDjz2NX785U3Ap1aro2PqpwNP3Ki+tt6cr5fySnM3UW1EW7aZgIzDy48TUx5Twgxj7DvpQsB/mz70U8bY4+P87qugSGMSBGGmIirnSgBVf8IPjMYA8MK7VtMqDAMAAqwRp1k8lgEbnyympVIWjyQbMTMQJWGmpjDj4OhFMkSG9NOQREVnJ2VEjP9UvkiQF8Wl1lQgJhaFRD8les69bw4/rodg4wVPlTb4hW3AdU8DYNkClEa2Rt0+iFpHnp/jYX3RMhHhc/Vn1/Nzlz4NMIkL5HLKniyEy132rniAR5XJogyljBLFYFTUX5tudPFKYI9COI8MAn/aCFy0kl/H7uQ2KgvpMmbskeZuwgqkFAAG+ObyVGrG+AaRZP0ai7Ae07kp6Q5M4y32CwAQBMEL4NMAvq04fLcgCOeAR+S8r3mvKvSPcHW9FlKZWjAEAOiTGi0eCTAg8FCZacmExSMBXDYX3DY3RczUGvkcHG8rfz8+prMo+IDviCmdrlceBBbcwB3/J27KjYhRLsjJeZr8FLurKdrUtiOHuit3o5RCjyCoI2Yigzx16bPr1fZlZGvU7YOodZTFfXUjFNOFrGUbN1o0X/cUn7NTCb7Dq211TCmjRD60867WZ3j4Sh69pRTBBRvw3u+BvzycvY5vDo+ake01leTFqZVRjV/6Ty6o54PmbsIqEhFew06O+vL6eQQiMeUp1C57BFwoyXkLAGOMlaQaMMbGAEzTHLumlGuVEzliphaEmboEr3t8NGl9u+xgkqdVTYtHLR4Jp8nVRBEztUY+B0d28PUWBa8+wnfEHl2ijph5bg1/XxslQ+HFU49idzUTEV5MWim0PHcnj4IBchedp12eP1VJ/oxRxA51+yBqHXl+NiqU/tQK3spYFjv17rmHr+SRNHKr4tnnZ1OammZz4ZzmZ8II7bz79d3G0Vu+E9SfK1S0NzIAvHC3es5/4e5ccV0Lzd0EQdQYhboyNVRrILVAtsaM9UVuHWnh4UjC+v8FA4kRAEBrbBTDFo8F4J2ZgpGg1cMglBiljyh3xfQWBR23Z1sZx8eA3jd42pIcHr/ji7xuQaHImGJSXYiJRbG7mnYn38l/9KvZY0pnW7vofGsXf136NA8pllOXZHtyeICR47kLg4YZfBHh8FC3D6J20JsL5QWoXo0YOR2p9/VshGMion/PCbbclKavPEKiDFEY7bwrF/UtJIqIIvcRlj7NI7VsDqBuutrGkzE+j8tzucyl6/KPiTo1EQRRY1ivQNQQgXAM9S477DbrHQxHNIAE7DgS8Vg9FATjaWFmLFQTwkyDs2FCdmWa1Bilj0BQtyGWFwXTzwKcXrUDJNq5o1/s7hUV8JvcFLurWcjZ1hN63trFnXjfCbn2tOQxYNd3cyN2Lr+XC4dymsc3ngVSJAwSFmI0F7aeniuofO7H6hoxyghHI6FdSgLeaWoRk2ydMIN23jXbbl2SgP4380c4GjYREPKPSdmpiTZ1CIKoAWj2UTAQjtdE4V+AR8wMi40I1ED2UDAxgnomoG5syOqhAKBUppokHs6mj8gthZ+7kxc0U7a6lBcFckX6cC+Pktl8CfBf1+W2wTSze1Vse2RiYiELLWbtwqgtqjb1SIlS6BnV2JPDqx89INuwnOYhgAs79dPJsSeswWgujAyo74nrnuJ1lWRRBlBHOCZGc1vIf+7HPEXkvtOBrZcC0RAtYgnzaOddud360qf152kZrU2fc1VWlAHUTQS09iqY6HIjd2qiuZsgiBqAImYUBMKxmkhjArgwMyI2oX/M+h7VA4lh+AQ7HJFBvktWaBeiwjQ6GzEUG0JCSsAh1oaQNuWRuyJo00dER270wpd3cKd+x+e5gCNHyQwd5mJOsXVlqIDf5MfuVrdHtbvzn69ti6qkYESNJo0jMqi/GxsZzP5O9kbUAvnmQlHkNt53gLfF/ti3efqScl6WIxxD3fpF2c+5KntN6l5DFINnWm6B3otWAPUzAFsev1tr055m800EPru+Mn8LQRBEhagNFaJGCIRj8Ne7rB4GAC7MROyNGAwzJFIMDpt1YkgwPoIm0QkxFYeYGIPkrLNsLEC2ZfZgdBBt3jZLxzJpGG+NFodHP5XJ5lCHCktJIHw8W+xX62R17zVfV0aGCvhNDoxscKyfR6Ro//+WuigsFL4u2NT29FKnvm0/d6d6PGRvhJVIEt80yTcXKqMPRnuztWY8LcATNwIXfY+f/1In8MnVuUWCn/9h9rokRhLFUK4CvXpC+auPABd/j28MFdOViSAIosYgYUZBIBzHya31Vg8DAOCI9CPhOAUAEIwyTK+zTpgZSIxguo3vUDvGBhGzWphxcmFmIDJAwkw5yFejBTAn2HhagIaZ6qiGhpn8uBy9EO4FNl8G/MNP8jtZxS5yqYDfxCefDVY7IkorMob7AGcdryMTPs4LUDrrqFUwUTvI90/XXcZ1OyRJXdS3e282wvG6Xfz3F9Zlbf/5H/L5vGUej1B76rbc1CcSIwmzJOPlKdD76iPAlx4CfnFt1sYX3MjPLVb0IQgrEG3ZVGgZh5cfJ6Y8JMykSaQkhCIJNLprIDWGMThiQaSaeavs/gjDdAu1kIH4COY7+M6DIzKImG+2dYMB0OTk/y5UALhMGNUl+Maz6nob+YrqiiLQcjLgbjAWceQFtlKMMVsAUA9lhEXDDCq+OpHJ1xK73BFRhYpF64mMjAG//nZ2YTr7fF6nAyB7I6zHKBJGbmMNcJsf6cmflte9l0eCKW3bMw2IDgEXr1SnPpEYSRSDUTSXmQK9qq5MTv6cV87P9W1cTFQKh0Bh0YcgrEBKAWCAby63f8Z4PUYpZfXIiBqAhJk0g6N897WpBor/islRiKkYkE7ZCVhYZybJUgglR1FfdyIAHjFjNXIqExUAHieysBEfM4hIiBgvlvV2ofLV9QCyC2ylGCMXALz2N7wrk9lFLnVimlzki4ppnFU4IqqYVLx8IpCe/frmAs/eqXb6w338e2g3lrAKpc2zlH4kzPI3+H0Q7uU2Xt+WK4Rr0/KUtq2cZ+vbshE0znou/NBcS5hFsOXanpkCvdquTEseA/Zu4/WOHF7eKjsSzEYwylBEF1HLJCLAWCArLnr9gNtn9aiIGoCEmTT94RiA2hBmnOlIELsnLcxEJMvGMpQIAwDqXDxKxRGpAWFGkcpElIjS4V50l8FOlq30FBK9hbIyJFkZJm/k5OdbbBe7uCZqm3xRMYVqwhQr0hmJQIkIMHSE72Apa9rMPh/41J3FRQuMt2YTQeRDr6V7vqgy2eaHDvO5V0758M0FklH9tDxJ4hE28ncMHc62h1/6dNX/ZGKCI4h8E0ZboPczBQr0ap/1Dm9uStTs83nxarl2nWzHgo3P6TQHEwQxQSBhJs1AmC82Gz3W/5PY05EgDq+csmNdxMxAfAQAUO9uBhME2GtAmHHb3XDb3BQxMx6Uzo5ROpHDU1oKSb6Fcr4FtplrtJ7OiwgaRvlQMcoJSaE6QfmisYoV6YxEoMDbfOH59d25Bam16R35nHyK5iIqjdbmlbVh9O4fpc3LETVyAe3G9tw5GeA2HA/rz7Ohbt6imGyaMIto47VgtEXUC9XV0ArpenXpwn3cjpXpTokI8B8dNAcTBDGhoBkqzcBo7UTMONKCg83TBLcNlrbMHkhwYabRUYeks74mUpkAoMnVRBEz40Hp7HTvze6i3vwqd9LbzuC1Nr7yCHdqAOMUknAv35UK92YjBfQWymP92QW27wT+Wmy6SegIsPkSoPeN7LhkKHR54qKMiln+RtYG84kfst0lIsWJdLIIpLTrKx7gi1uA11XS2pac3lHIboH89k8Q5UC7WFWKh3r3j57Nf3lHNo1EOyfLNqx3L/jmZGuPkU0TZklEuI0uuosXm150F/89Ecn/Obk2jYzcJU/rl0SGgK2XAvefA/T9rzrqkeZggiAmCNaHh9QIgZHaqTHjiPJUpqSzEc0ua1OZgumImQabBwlXQ02kMgE8nYkiZsaBNmqgey+w+/bcKINSUkjcTeOPZjFKNwn3Flc0mFJKJg6FahTJFJvG8f/Yu/MwOcpyb/zfqt57tu5Zk8kkkGAIhBCWRLZ41IiIvIARg8cjIBpEwuH1iuSnAscfnBAD/NgU5aecoB5kOeALShAEJSgGPSZ4ICFsDmRfZkkya8/0zPRez/tHTfX0Ur3MTHdXzfT3c125YHq6Z56Zeerpqrvu5771vk/ivIYAfrNqrIZMPnMr27wqdRcpKj96WV/Z6h5pc/7rf1KzYPr2AS+tVV+jl0mgzWG9Y0Frm524/Y9rK+VitavzTat/BOR3MyW1Ns1Ql7qd6Wt/ACDU10uWsewYQP0812AimoIYmBnVMxyCVZbgshnfrkzLmInZq+FxGLyVaTRjpsbmVgMzI32GjSVRlb2KGTOTkW+L6YlsIVn1h8l30cm03WR49I5XYpZP0yLA7k6/MOCWkulpvNs49CTOa19bcuFIrSB1pq1LmeZV1Qz1QjVT9xFmc1Gh5Lt+J5JlQALwxOeT56betj9t/dXW2St+rXZmGu5WP27fnrz9j2sr5TKROQuoha1Ta9P89w+A/3UvUDNbfY6vLfd2J67BRDQF8B10VO9QGB63DVKu1n0lYAv2ImqrhJCthgdm+iJ+WCQZLtmBqKMStoDPsLEkqrZXMzAzGePdOqInU2aAZMm9BSoXvdT7Lz4GvP2rsedoWT52t/72kkyBo8GOsW1XNPWMdxtHLjZXemr8Of86WmNJZ+tSpnnV+Rbwo0XA728C/vmJyc1/omwmun7nm83lqhubw+3bgVe/D1gc6nqrBWUSt/9xqwjlMtE5K1mAs1erc+/Ri9X/nr0agDS2hTrf7U5cg8kMZIua1ZXI5s5db4nKgmEZM5IkHQTgBxADEBVCLJUkqRbA0wCOB3AQwD8LIUqyd6Z3KIRqE2xjAkYDM6OdhzwOYLfPwMBM2I8aqxuSJCHiqIJ1pB8QQn0jNFC1oxq+kA9RJQqrzMSvCUnIGlAUgd7hMMLRGOxWC+oq7JDlHH/jTFktubro5Ds27WtEAuqd2e3/qZ6Q5dsdJ9NFiO8Q8Nt/5R3eqWq82zhycdUCVTPVLmFa68qqmerjejLNK+1Ea7RbiPK1P0BRFAiLHZbKBsicZ1PWhNbHYst361+ibN3PEgV6gb/cC1z6IFDTAvQfALb/Qj1GvHMB2Qpsuja5hTy3ipjKtJmzsqzfzWnp15K7hOlud8qzYDuVnCnnZ6koMQBC7YonSeo1lYiNPk7lzugr2uVCiJ6Ej28B8KoQ4m5Jkm4Z/fjmUgykeyiEaqdZAjM9iNnGAjN9QYGoImA1YNHqjQyiyqpecEQclbDEQpAjASh2d45XFleNvQYCAv3BfjTwLsikKIrArmN+fOPx7WjvD6DF68LPr16KBU1VSW+UaW+k7nrImVKTJ3IClkr7Gr429QQMALo/TGj1ehxQPSvzyVami5BAP9trT2UTTYnPRJaB2nmAsyq/QGK2eaXZ9RKin74DJ/7gg4zHUyZlfcJqQnrr48NXLcFMjxMe1xT72+R77ETDYy2JW5YCy24ETvuyul3vudXqx4nb/wBuFTGRfN/TpwR3A7D8e8lzdsVDapYkMBoQDOlvd7roHjXrkUxlWs3PiYoEgJGesZtB7nrA6TF6VGQCZgsfrwDw2Oj/Pwbg86X6xr1DYVMU/gUAW6AnKWNGAOgzaDtTX9iPKqsLABBxVI2Oz/gCwNUO9ffDAsCZKYpAtz+Ejv4RdPtDUBT9OdQ7HI6/QQJAe38A33h8O3qHw0lfa9cxPy57aCuW3bMFlz20Fbu6hqE0nDy57VD50C6EgbFWr7/9V/XxbN9LbzvU536ipjkDvMM7VRViG57e18ynWxiQuauTNq9GHxOjHW/0jqdMdI+zY/6Mxy4Vn976uPq/duCdtoGp97fJ99jJtOZ2faB+rBUF5lYRU+oZDum+p/eMdh+dUlLn7Nd+rwZlErO1BjvU7aeJ25207ahkOtNqfhIVmJEZMwLAK5IkCQAPCyF+BqBJCHEEAIQQRyRJakx9kSRJ1wG4DgDmzJmT+umJDUQI9A6FUX2c0QlEKluwF8HqeQAA7+gNqO6AQGNF6cfSG/FjnnsGACDiHA3MjPQjVDOr9INJUD0auJpKdWaKMXczyXZHAkDSHflwNBZ/g9S09wcQCEfR7QfqKuwZgzfP3bAMDVVFzjiZaIaE3nYorXAlwDu8eSrlvM1bITKyxiEti6XhZMijW/WEJEMaOjaWQTAaqDkwMFbDqL0/gHA0d5pypuPsmdXnQgjBDJpxKNS8zbQ+uu2W+N9mRrVz6vxN8jl29NbcxCwFrUD2qj+oafjcKlJQk527wYj+nA2GY+joH5l660jinE1cazV/3wh8+vb8t6NSUeQ7bzPOz0iZbOWxZEgCyPQ4lRUjIxHLhBCdo8GXP0qS9GE+LxoN4PwMAJYuXVqQW1X+UBThmGKKGjOSEoEt7EvKmAGMKQAshEBf2I/Tq9Qg0VjGjPGdmbTATE+gJ8czzaMYczeTTBd4m244D71D4aSAzVPXno0WryvpjbLF68IHR/3Y8GIrfn71UlQ7rbpvpPlcbE7aZGrWaCd0igKEhpIvnnmHNy+lnLdmlDnI2QhZluAbDsKKQVQlXBT47fX4t9+2x79Gi9cFuzV3Yb9MQYBOXwCXb3y9PFO+J6hQ81aSJN310ReIxP82A4GIbtB7Sl38Jkpdcy12IORPXj+Xfw+oamYwpggmO3flDHNWkiQsu2dLfB2Z31CJ/kBkas1XvaDh8u+p21FdNROva0eTlu+8zTQ/ZRM0XykJVy0Q8KlbmTSOagYSCYCBW5mEEJ2j/+0C8ByAswAckyRpJgCM/rcr81conB6/mj5nhq1M1qAa9IjaawAkBmZK30FmKBZEWERRPVrUMjoamLGOcCuT2WW6wAtGlLSAzR0vteLhryxBi1dN+23xunDPysXY+Nq+eEBHuzhJlO/FZkGMZ6tJptcXevsLlYVcW/1Gwgqu/m0PdoTnoE004ENpHvYrTegeigBA/CKormIsOyvTNkO71aJ7nGnfiynfpSdJAvesXKy7Pmp/G+1vMq22oSWuuVVN6oUv188pwSpLuO/y5Dl73+WLYbWoF77aOtI5EMg5X/PdEl0ymd7LLdbJnSNQyWScn2YPChaKVtdu5mlqrcSZp6kfc84SDMqYkSSpAoAshPCP/v9nAHwfwAsAvgrg7tH/Pl+K8RwbVE9yvW7jtzXYA8cAAFGHWgTK61Qf7xkp/Zthd1htjV1jVfdQRewVEJBM0TLbaXHCYXFMqa1MxaJXLFS7wEu9I2GRkBaweaW1C+suPQUbVizC3IYK7Drqx/2bd2Fnm/p3bu8PQJKAn1+9NC1rIPFi0/RKvP2FpodMQU4tW8xutaB7KIKVT+yLf/4zCxszbj/Kts2wrsKedpzds3Ix7t+8K+l7ByNs9V4KiiIQU4DHth3A3V84FTM9LhzuHcH9m3eheygU/9toafiZt3s6DP5JCoDr55QhAXDbLdiwYhHcdgtGwjG47RbEYsnbK7v8oazz1bRFWjkXpzy9+VlWOIcpA6O2MjUBeE5S09asAJ4SQrwsSdKbAJ6RJOnrAA4D+GIpBtPlDwIwSWBmpBsAEHF4AQAuC+CwGLOVqTs0CADw2EaL28gWRB2Vpij+K0kSahw1ZZ8xo9sx5CtL0FTlwMNfWYLVT+xIOqFy2fUDNruPDWHVo29iy7c/gQ0vtqZ9XlEEFjRV4bkblk2ttGealkrZuShTkFPLFvO6bNh41RJc/19jx9qa809EY6UDVmv6HbDs9ZocSccZAKz/3T/iQVLte1t42BWdtrYOBaNYtWwuvvubd9FQ6cCa8+fjB/98Gjp9gXgAW0vDN2y7J1ECq1V/gWhLWcNSC5KnztdcaxXRRGR6++LbGpFBgRkhxH4Ap+k83gvg/FKPp3t0K5PHbfxWJltA3b2lZcxIEuB1AN2GZMwMAAA81rGqwxFHFWwm2MoEqHVmyj1jRrdjyBM7cNslC/HsjjY8de3ZsMhS/OIVSM98efiqJbj1t+8DAI4OBnHf5Yvx3d+8G//8fZcvhstugSxLPBkjw5X6Lq5eFktitlh/IIIHX92N2y5ZCI/LBt/ox3detlj3eMmVgZN4nPUNh7Bq2Vy0HvGnHY9UXFrnkIZKB9Z9bmH87q4AEFUU3LLpvaSsJpusXzehZNs9iUZJOpe4LrsFP92yF4A6LzdetQQPvro76Tmp8zXXWkU0EbIsqe9hw5H4Y9o5JlG5M0cbIoN1+UOwW2RTpNLZA10QkOI1ZgC1zowRNWZ6RgMzNbbEwEylKYr/AkCVvWpKFf8thkwnTh6XDa+0dqH1iD/t7lZq5ossC3QPqcHJe1/elXQRMhKOoanaCY/L+GwyIqD0d3FlWcqaLRaOxvBKaxdeaU0uibbu0rGLl8QMn0yFD2062TUelx1N1U4ejwbQOoe09wdpDLuBAAAgAElEQVSw/oVWXP/JE+CGBbO9Ljy+7UBSIO6xbQdw98rFU3+7J00LgXAMtyfM2XBMwS/+egD/dtHJ+PrH5sEXiOB3b7fjW58+MSnomzpfc2ULEk1ETBEQQmB2rRuyBCgCiCkxxIyuX0RkAgzMADg2GIS3wgbJBBXB7YEutSOTPPanUQMzpV+wusIDcMg2uOSxN+qooxKOgc6Sj0VPtb0aBwcOGj0MQ2U6cfIF1DsRene3UjNf+oZD8SyZnW0+PLRlL2656GQAwKHekRL8FET5M9td3FwXL6kZPr9efa5uVppe4UNZlnB8XQWqnDZuISyxxADazjYfVj+xAy1eF359/bn4+IIm3Pzsu0kXtB6XHR6Xnds9yXA2q4zuoRBWP7Ej/liL14XzFzYlPfa1j83LOl9zZQsSTVQwoqBvOBS/4VBbYUONK/friKY7BmYAdA2G4DFBfRlA3cqkbWPS1NiBPQOlD8z0hAdRY61IClhFHFWmqDEDqIEZX8iHqBKFVS7PqaxX3yKxWGi2u1vaXfyRcBT3vrwr6Q7wt595B7dcdBJWPfomWryujNkIpaz1QQSU/i5upq1TTdUOBMIxuOyWrBcvqRk+ihBpx9u9L+/CT644A6hI//7cQmgM22jnkNQAWiym4P7N6t+vrsKOZo8LM6qd8XWPfysqtdT3YYsE3bl778tjRcS12nENNZnna65sQSIiKqzyvJpN0eUPor7SHCdT9pEuRO3JgRmvA+gPCsQUAUsJ3xC7wwOosbqTHos4qmCJhiBHAlBsxoa3axw1EBDwhXyod9UbOhajJNa3aKxyoMZlw91/+CBekDLT3a3Ei83bLlmoe3ctW9ZN6tcwVccGmtZKfRc309apDSsWxQOXj19zFn69+lxEYgqsFhmNlY6krU6JQSRfIKJ7vOltZSJjaC2B9TqHtCVk0ADA1puXc70jw+i9Dz957dlJwd+GKgf8wUh8y/J4alUxMExEVDoMzEBtl/2RxiqjhwFAbZc97F2Y9JjXoe7B7AsKNLhLdwLYFfahKSVIFHGovyfbSD9CBucdVturAQC9gd6yDcyk1rc4Y7YH13/yBNx68UK47NaMd7cSLzY3vrYP96xcnJSan0/WDTs2kBFKfRc309YprSZZe38AVz/yRlKgJjFAmZrhs/G1fXlvZaLS0y50Kxxqod9UqVkHZtgCTeVL731YESIp+HvGbA9uZ+04IiLTK/tbdIFwDEOhqCk6MkGJwRbsTdvK5Bm9xu0ucZ2ZntBgUuFfQC3+C8AU25mqHWOBmXKlXfRpdrb5sOHFVrjsVjRUOTJerCZebO5s88VT87d85xN4/Jqz8Ni2AzmzbsxW64PKh3YXd5bXnXWeF0LqMQYkZ5QB6YGabzy+Pd6KVsvw0b5G91AILrsFd3/hVDx93Tm47ZKFuPflXQiEi3PcKIpAtz+Ejv4RdPtD8WwQ0qdd6MZiAg9t2YtwTC28bxvtUZ6YdXDPysVsXU6G0nsfPjqgdldMXHMAYEFTJVq8LiyaVYPj6yqY6UVEZDJlnzHT5Q8CALwmqDFjC/VBErGMgZmeEQHUlWYsI7EgRpRQUqtsAIjGM2aM78xUM9q5qidYvp2ZJrqtQ0rpDKMFdG67ZCE2vrYvnnXjtFtglSUcGQikZSbYrHLe3WWIpiq9Y0yvXkNqoCax/XViho8kSbj9hfeTujgVskZOYr0Jl92CY4MhbjccB+1C9+hgEKuWzU3KbPrpFWfg7i+cCptFjndjuv1zi4weMpWx1PdyAPjl1gP4zoULkjJkAMAiS2hihVUyWKZ3Hr4jETEwgy6/eifBa4KMGVugGwAQcXiTHvdqgZkStszuDg8CADy2yqTHta1MVmbMmMJEt3VYJGTcvqQFaTbdcB56h8JpF3XzGyrRH4hAKIJbMmjaSwusAOgaCqXVa0gN1GQKtDhtMtZesCBrm9qJSq038cuvfRS3Pf8+txuOg5Yhde/Lu7AuYfuHAOCyW/C/n9qZtGYyY4aMpPde/s1PzYcQ6ZlxqdvuWLyfjCDLklrfaHjsZobLbuHcIwIDMzg2qGbMmKErkz1wDADSiv/GM2ZKuJWpK+QDANSkZcyMbmUaMT4w47Q4YZNtZR2YAcZfnE9RBGICeGzbgaxFg62ypFtD5qlrz8YVv/gf/OCLp42ruwzRVJV4jHX7Q3hoy974vI/EFLjtlqRATWKgRa845+PXnIVNN5yHSFQp6AVRar0Jt93C7YbjlJghtf6FVqw5fz7qqxyQADz9xqGk9e6xbQew7tJToCiCFxVkiMT3cm1evvROB76wZHbS8wSQFKxh8X4ySkwREEJgdq0bsqTW0IwpMcS4zZaIgZmuQfVk2gw1Zuwjamp76lYmtxWwy6NbmUqkJ54xk3yFLWQLIvZKU9SYkSQJHocHvcHyDszkQ7szpigKeobD+PGfduOr582N32X7zMJG3HrxQqy79JT4heKRgYDuRV2XP4T2/kDG7jLFaltMZAZelw1rzj8xqUX9o6s+il9ffy4i0fSuTHrFOa9+5A08d8MyzPK6s32rcdPrAFXK1uLTQaatZ93+ML5z4YK0LENFqBe4vKAlI1gkJL2Xq1vuzsSmHW048/g6uGFBOKbgoS17ccdlp8Zfx+L9ZKRgREHfcCi+1a62wgbusiNiYAZd/hCssoQqh/G/Cm0rU2pgRpIAr7O0GTPd4QEA6RkzgLqdyRbwlWws2VTZq8o+YyaX1NbYG15sRXt/AN3+MG67ZCHqKuxo9rgwo9qZdGGR2k0GUC/qtKKmet2citm2mMgMElvUj2WKfYgvn3WcblemUhbJzqcDFI/R3BIzpBRFYO0FC/CNx7fj/s27sGHFIsypc+OIL4DHth3Av110Mr7x+HZsuuE8NFY5DR45lRtZltMyZn7/bgcuPm0WbnjyraRtxpaE8m8s3k9EZD7GRyMM1jUYhMdtM0XLS3vgGKK2Kgg5PXvH4wC6RkpZY2YANsmCCkv6nZOIoxK2YXMEQ2ocNegeDWiRvsQ7Yx6XLangr5btsvXm5Wl7zb0uGzZetSQpM2DjVUvw4Ku746/XLlROaKyEy8Y96jT9pbao13z9Y/MApN95zlQkW5YkdPSPFHQrU2qh4u6hEJqqnUXZNlVOHFYZT3z9LAgBHOodwXeeeQfdQyHcd/liHB0Mor0/gGCkdO/PRJq6Cns8cJj4Pv34toNp24x//OXTUTd6ry3TjRdm0xERGafsAzMdvgDqKsyRtukY7kTEqd92qd4JHBkuXcbM0VA/am1VugGrsMuDmr4DJRtLNrXOWuzt32v0MEwt8c5Ypq0NMUXgsoe2pt1VT80M+N3b7fjWp0+MFy7tHgphRo0TLR4XL/Zo2koskqnXBSVbVyaLDN0i2e39AXzx4dcLWtthosXAKbPe4TCufuQNNFQ6sO5zCwEAt1x0EkbCater9S+0osXrYhFgMoTeMR9TFGzb34tndrTHn6cFgzWZbrx4XcZv6yciKlclD8xIkjQbwOMAZgBQAPxMCPFjSZJuB/ANAFr6w/eEEL8v9ng6fQHMri3sPv+Jsg8fyRiYaXABb3UrEEKUJLvnSLAPtfYq3c+FXR7YRvogxaIQFmNje7XOWvgjfgyFh1Bpr8z9gjKUeGdMb/vRw19Zgjteak3ba/7ktWfrZgZ87WPzeOFHZSO1SOZnFjamXdBk68o0HIrpFsm+5aKTABS+tsN4i4FTdlpgu70/gPUvtOIH/3waADVzZv0LrfHMGZtFZpcbMkTqMX90IJCzY6LelswHX92NOy9bzPWDiMggRlxVRwF8WwjxliRJVQB2SJL0x9HPPSCEuL9UA1EUgSMDQZwxx5v7ySXgGO7EwIxzdD/X4AICUcAXEvA6SxCYCfVhfsUs3c+F3V5IQoFtpBfhqqaijyWbWmctAODo8FF8xP4RQ8diVonbG3a2+fDYtgN4/JqzMBCIwDcSgddtSwu+tPcHYJH1MwMURaChhiduVB5Si2Rqx8ozq8+FEAI2q4yhYDStK5NFBjr6RyBLkm6R7EwZNmQuiYHtnW0+fPuZd3C7TuZMTFHY5YZMw223xFu9j4RjcNuTtyhl2pK57tIYuv0hBheJiAwg535KYQkhjggh3hr9fz+ADwDoRwCKrGcohKgiUFdpfCFES9gPa2Qwa8YMAHQMFX87U0SJoTs8iDpbpowZNZBlH+rS/Xwp1Y3+vo4MHzF4JOajKALd/hCODATQVO3AphvOw19vWo4vn3Ucvv3MO7jsoW1Y9eibCEUVtHiTy+G3eF2wyBLuu3xx/HPaXTeXnXvQqXzoFcl8pbULQgjM8rrRWOXE8XUVeO6GZdh683JsuuE8OKwyPveTrVh2zxYoQugeRxtf2xf/etrnuv0hKGwZaghtvezoH0n6O2iB7fjfaCgEAcBmkeLB64e27EVUgW6XG61YOpGZaAHHRInbmpfdswWXPbQVu475uSZRQWUK8zH8R2RwjRlJko4HcAaA/wGwDMA3JUm6GsB2qFk1aT2ZJUm6DsB1ADBnzpxJff8On3oCVW+CGjP2ETWwkCkw0zj6/tk5pGBRfXEvjLvDPggI1GYMzKhdo+z+LmBmUYeSU61rNGNm5KixA8lDIeduLqnbL7S7t9VOK1Y9+mbSc48OBHXTnrsGg3Cl3HVrqnbC4zI+kEmlU8p5a0b5FMlM3ErQ7Q/h6kfeiD9/YCSSdBwJALUVtqQMm/suX4xvPrUT3UMh/PzqpWiqdiAQ5h3ryRjPvM20XmrZLguaqrDphvMwEorhQM9wfAvTPSsX477NH2LVsrmQINjlhgpismtuPhe+qYXCs21rZgttyke+81aWJfUG3/BY1qjLbuH7HBEMyJjRSJJUCeBZADcKIQYB/AeAEwCcDuAIgB/ovU4I8TMhxFIhxNKGhoZJjaHTFwQAU2TMOIY7AQARZ73u5xvigZni37k4ElLjYdlqzACAY+hY0ceSi8fhgSzJODJk/oyZQs7dXFK3X2gnWFrh0kS/3HoADVUObFixCE9fdw42rFgEl92C219oxfoXWhGOKZhZ48SiWTU4frSlg96dZZqeSjlvzSg1YyJXy+nUDJvOgSAe2rIX4ZjatWcgEMGz29vwzOpz8deblmPDikW49+Vd2Nnmix+n77QN8I71JI1n3mZaL7VsF1mWUF/hgCIEXHYLfvil0/DoqrNQ7bTiy2cdB5fdgrbRi9tE7HJDEzHZNTdTHcLUxx1WOel93+PS39bM4CLlI995G1MEhBCYXetGQ5UDs2vdEEIgxvc5ImMyZiRJskENyjwphNgEAEKIYwmf/zmAF4s9jk4tY6bS+DsB9nhgRj9jpsYO2GTgyFDxW3IeCfYBQMatTIrVgYi9AvYh49tUy5KMWmctjg6bP2OmlPS2X7T3B2CRkHaXbO0FC3B8bQVqXHYEwlHs61bvCO9s8wEANrzYimdWn4uGKkfOO8tE0814Ox2lZthsfG0fbvrsgqSMtI1XLUFjpQPH/MG0DLb2/kC8HoQWINDq2TCDpjgyrZeJF6Rad6b2/gDOmO3BvZcvRiiqIBxTsP6FVgDp3beyBfCIikUIgdRLXAEgJgQ6+kdgt1ogIJIy+wDgl1/7KFtoU0kEIwr6hkPxbOzaChtqXLlfRzTdlTxjRlJD9v8J4AMhxA8THk/cFHMZgPeLPZbOgQBcNktaUTQjOIaPQEgyog79QsSSpGbNdJQgMHM0R8YMoGbNmKHGDAB4HV4GZlJk2j8uy3L8InPrzcvx3A3LsKCpClarjIYqBxxWGXarnLTN4p6Vi+OtYHPdWdZkqtdANBVpW5VmedU7fNkCI3o1Sdx2C+7+wql4+rpzcNslC/Hgq7vRlXCMJdIrDNzpCzCDpogyrZfaBamiCAQj0fi6t7PNh5t+8y5CUQUbXlSD2Nrf+derz01aWxlEo1KLCSRl6YVjCh7ashfDoVh8HRkJpQcjH3x1Dx6+akne2YFERFRYRmTMLAPwFQDvSZL09uhj3wPwZUmSToca2D8IYHWxB9LWF0B9pb0k7adzcQy1IeKoA6TMsbJGF9A2WPzATEewF5UWFxyyLeNzwi4vnIPm2D5U56rDocFDRg/DVPT2j2snWNna6cYE8Ni2A0ktNB/bdgC3f24RgPzuLDOrhspZaoYNAHzzqZ3xDDTNdR8P4M6XPtCt75TaelsLfGqB0F+vPhcKM2gKJtN66XXZ0DccwhFfEA5bciaU1uHul1/7KPqGw/AFIrj9hVb85IozMMvrNvgnonJmkYCvnjcXNz87tq7cs3IxAuEoAHUdOdAznJYd0z0UwkyPM+/sQCIiKqySB2aEEH+Dfm2y35d6LId6h9FU7Sz1t9XlHDqEsDt76+mZFcDrR4sfmGkLdqPRUZP1OaGKetR07waEkjWYVAqN7ka8cfQNhGNh2C28swOMf/uFRpKE7gmdFru0WWXdVGebdWwOZMqqYQFBKhepxYC1DDSNFmzZ2ebDvS/vwoYVi3BCYyWctvTW2/esXIz7N48Fatr7AxgJx3D+D/9SkKCnogj0DofL+kJMb730umzY0z2EowNB3Pb8+/iPK8/EPSsXJ62Nq5bNxU2/eTcedGvxukxxo4fKmyzLujdYVi6ZHX/Og6/uwcarluD6/9qRtMWy2mGD1WrsOR0RUbkytCuTkRRF4HDfCD59cvZgSKk4Bw/B37Ak63NmVgC+EDAQEqhxFO/k73CgG8e5GrM+J1hRDzkWhm24F5FKYwuCNroboQgFHUMdmFsz19CxmEm2zJhMFEU/Y2bdpacAAKyjLbRT7/BbEy7kMmXVBMJRdPtRlhd+VL70sjESgy0723xY9eib2HrzcjRWOVFfIZKybdb/7h9J2TYtXhe0w2eyNWiY3TYmcb1UFIGjg0F84/Ht+MEXT1O3kw0E8eyOtvjaGIkpcNstGbd9EhmlrsKOtRcsSDquUzPxuodCqKu0J73XP/jqbtxx2alorDLHDUsionJTtoGZLn8IoaiCpmrj7+BbwoOwhX0Iu7MHQ2aOZkcfHlRwakNx6uKElQiOhvrx0Zr5WZ8XqlCLFDsHO00RmAGANn9b2QRminWX22W3YNWyuWmBF9doHaZAOIZ7X96VdDJ378u78JMrzgDUhk3xzk+pWTWhqIIrfrG1bC/8aOqazPGWmo0hSRJuf+H9tGCLVs8kMUDQNxzCqmVz0XrEn3Q8Hh0Mxl+r1aC5fOPr4w6sMLstXTSqYFeXH4GwGmD2BSJqNsFr+/CdCxckZcz89IozcPcXToXNIseD2HdettjoH4HKTOr65HXZ4h2X3HYLZnld6B0KJQUR77t8McJRBauf2JH0tW69pPhZ2VTe8mnnTlSuyjYwc7B3GABMsZXJ6Vfro4TdM7I+r3n0wvfgQPECM+3BXggINDo8WZ8XrFDbejsGOuFvPq0oY8lX42h2z+HBw4aOo1Qme5c720Wmx2VHU7UzfkI3Eo6hqdoJj0vdIma3qneIE0/mUrs2WCSkpfzfs3IxgpEYL/xoyilEVklqNsbaCxYkBVsyFdjUOx7do63sNXo1aPI9vvKpGVVOFEWgcyCA1U/swG2XLIwHZLT17P7N6razOXVu9A+HIQDcsuk9dmEiw+itT09de3ZSx6V/XtKCaz8+N2kdqa9y4Km/H0z6Wi1eFzO+qOhkWVJv9g2PFbl32S28WUeEMg7MHBoNzMwwU2DGlX1b1YzRjJlDRSwA3B5QW2A32rPXmAm7PFAkC5yDHUUbS76q7FVwWV1o87cZPZSSmMxd7lwXmbIs4fi6ClQ5bbqBm2xFhTW59reX84UfTT09wyHd423TDeflnfKfGgyd31CZV/2n1OPRZs2vBk2+x1dqa2/ta5Zre9ze4TC6/CG09wd0AzLH1bnR6QvgO8+8g51tPnxmYSOevu4cACjb+jxkLL31SZvDmmd2tAMAvvXp+YgpAlaLjAqHjE+e1ISX3j+mmx1LVCwxRUAIgdm1bsgSoAggpsQQY7dBonIOzIzAIkuoqzT+rv1Yxkz2rUxOK1DrLG5g5nBwNDCTI2MGsgVhdy0cA51FG0u+JElCg6sBh/3lkTEzmbvc+VxkZqtNI8sS5jdU4pnV5yIaU2C1yGisTG4frLe/PfHisZwv/Gjq0TK9ErX3BxCM5LcOjzfjRi+jLfF4TKxBk2tbVC75BFrLSTgaQ+9wGC1eF3a2+XD/ZnXbZl2FHbUVdgwGI0kZMl89by7W/+4fWHvBAixocjEoQyWntz5pczjx8W37e/Ed6wLMGn2fVxSRNTuWqJiCEQV9w6H43KutsKHGZfSoiIxXtoGZAz3DaKxywGKCEynn4EFEHF4IS+67r80VwIGB4gVmDo50wSXbUWXJvUIGK+vhHGgv2ljGo9HdiIMDB40eRklM5i53xovMcAyKInJeWCiKwJ7uoawXmak1NWKKwB0vtWJnm6/sL/xo6rFkqJkkS0BH/0jOTInxZLjlE8SZ6LYoPRPt3jZd2a0WPLujLZ4ps7PNhw0vtuKnV5yJbz/zDgDEMwEbqxz4f0YzZ1qP+Lk9kwyhtz49u6MtrePSfZcvhiWh2VKu7FgiIiq9sg3M7DrqR4vXHOFZt28XQpUteT13TiXw30diEEIUpS3nvpEjaHbW5fW1A1UzUH3gb4ASA2RjMyBmVc7CjmM7MBIZgdvmNnQsxaZ3l/vhryyBoijo9oeynlxlusiMCYEPjg6ivsIOWZYzfo18LzJTLx7vvGwx1l3Kkz+aGhKzVmwWWbcT2RFfEF98OHfB3fFkuI13m2IhAisT6d42XWnZfg/8cSxTpmG0FoeWlbT6iR1o8bpw2yUL449xeyYZxWlPX59WLZuLmKKkFen/8ZfPQF3F2Gt57BMRmUtZBmaCkRgO9g7j9Nk5tuuUghKDa2Av+mctz+vpx1UDvz8EHB0WmFlZ2ItbIQT2DHfgtOp5eT0/UDUDciwC50AHgt45BR3LeM2qnAUBgQMDB3BK/SmGjqXYMmWkvNLalXSRCCBtS4TLbtG9yDw6EMR3f/MubrtkITa82JrxQnMi26h48kdTSWrWymcWNuKmz56UsQBvruCJzSrrBkNtVjntuTy+jKWtrXdetji+bnqcVlx6ektaLY7E1sPcnklGkSDBbbckrU8NVQ7c+/KHeKW1K/688WT5ERGRMcoyMLO3awiKAFq8xmdWOIcOwxILIlQ5O6/nH69eb+PDvhhmVqaf2E9GT3gQA9ERtDjr83p+oHomAMDVd8DwwExLlZpxtLt/97QPzABjF2Pd/hAue2irbs2Y3qFw2paI+Q2VafvKXXYL1r/Qivb+ADwuW9YLTRYLpekuNWtFu7i5/XOLIIRanPCbT+1MquuSLXhikaEbDLXoLN/5HF+Tad1NuaUGurr8QTz46m786hvn4NhgEMGIumYmFmB++KolsMjIazsoUSEFwjHc/kIrrv/kCXDDgnBMwXAoilXL5iZtcVTXHAnn/n9/Tjof6A9EuJYQEZlEWQZmdh/zAwDm1BofmHH7dgMAgnkGZo4bDczs7lOwvMCxkD0jaiHfWc66vJ4fqGqEkCS4+g6i/4RPFHYw49ToboRNtmGvb6+h4yi1THfYgxEl45aI4+sq4LRZ0OkLIBxTsP6FsfovvkAk/ny9C02vy5a2d33jVUvgddmK/8MSlYDeMfVKaxfWXSowy+tGtz8UvyjXZAtODodiuPflXRm3FSQGWlx2S9ZivIVo3U3jE4zE8EprF1YtmwtAbY/dUOmId2lyWGV886md6B4K8W9BJWe3qkHC1U/siD/2mYWN+PZnFqTdgInG1PqE2vnAU9eejSt+8T9cS4iITKKwKRdTxK5jflhlCU01xqd/u3y7ISAhVDkrr+dX2YE6J7Crv/AFgPcOjy8wIyx2BCsa4Oo7UPCxjJcsyWiubC67wIx2hz1Ri9cFi4SMWyJkWcKMaicqHFZseHEsKHPPysXY+Nq++NfQ22rRH4jgwVd347ZLFuLp687BbZcsxIOv7kb/aECHaKrLdExpgRetxpP2nFwFdy2SFL9w+tLP/o7VT+xA91AIFmks0HLZQ1ux7J4t+NxPtsJhlbHphvOw9ebleO6GZUkXSplq0PQOh4v16yh7Wl2ue1/eBdfolpFbLjoJAOAPRnC4bwQ723z8W5Ah9Najb51/IjbtaEN4NBATjil4aMvepE5yqW21OX+pVDKF/RgOJCrTjJn3OwbQ4nXBKhsfl6rs+wfC7hl5dWTSzK0G3usufKHBD4ba4LFWoNqafyZRoHomKrp3F3wsEzG7ajbe73m/aIWRzShTu1unXb+uhQTECwRrdWqCkRgiMQV3/+GDeJDmvssXw5qhkOkrrV1Je9cBYN2lLHxJ00M+LaQdVjnpbrTDKsMXCCMQTt8SkKmuk8tu0Q20XP3IG3juhmWYpbPVdiI1aGjiFEXAKkvxv9/6F1qx5vz5qB/dRiqApFoz/FtQqekVALdbBS4+bRZuePKt+Jrz0JVn4ud/3R9/XYvXlRaE4fylUpBlCS67BRgeu6HnsluYqUWEMgzMxBSBnYd9OO+E/OqoFJUQqOzegRHvwnG9bIEXeGqXgsGQQLWjcAvZ24P7cYJ75rheM+Q9DrWd78A23ItIRX6ZNsUyr2Ye/tbxNxz2H8Zx1ccZOpZSydSVxRcI614MHuobwXd/8258fzkAKELg28+8g+s/eQK+/rF58a0WP7niDKAi+fuxxgxNd3rHlNdli283kiQJVz/yRtoxsGHFIqx69M20LQEelz2trlNTtRMelx1HBgLjCrTw+CsdLZtJESKeKeNx21DltMFukTDb68L63/0jqdYQ/xZkhNS6SIf7hvFfrx/CL7/2UVhkCQJAJBbDtv29ABDv5PjjPyXfVOP8pVKIKQJCCMyudUOWAEUAMUVtZEFU7kwXmJEk6bMAfgzAAuAXQoi7C/n1dx/zYyQcw4lNlYX8shPiGGqDPdiLnpr543rdyV5AAHi7K4aPz5pXOfYAACAASURBVC7Mn7AnPIj2YA/O8548rtcN1R4PAKg81or+ef9UkLFM1Ec8HwEAvNP9TtkEZgD9riyBsH5di1suOiltf/ltlyxM26OupUWntt/OJ5uAaKpLbfeeWNflN9efqxtMcdst8f9PLJ4tyxKOr6tAldOWFugBMK5AC4+/0tGymW67ZCGe3dGGlUtmIxxVsK97CM/uaMOqZXPx1fOSC6zyb0FmIEsStu3vxTM72uOPfWZhI56+7hwAiK9Bay9YwPlLhghGFPQNh+I3K2orbKhx5X4d0XRnqsCMJEkWAD8FcAGAdgBvSpL0ghCitVDfY8ehfgDAiaPthI1U1f0WAGDEc+K4XrfAoxYH2nEsWrDAzDuDaorrR8aZMTNS0wJFtqLCBIGZ5spmuKwuvNP1Dj53wucMHYvR9AoCphb31faXb3xtH+5ZuRg3P5ucXaNX0DJThg5TUGm6St1u1Dsc1g2m+BLqLKVmvWQK9DRUOtIy27JdHPH4Kx1t29jG1/bhOxcuSFof71mptstuqLLjmdXnQgjBvwWZRuL2O23Orlo2FxZZwoyEq1+uJURE5mKqwAyAswDsFULsBwBJkv4PgBUAChaY2X6wDzUuGxqrjC/8W9X1JmJWF0KVLeN6ndsGHFcNvHmkcHuBdw7ug0WScZyrcVyvExYrhj0tqDryXsHGMlGyJGNezTzsOLYj95OnOb076/esXIz7N6v1EBL3l+9s8+H+zWp2zUkzqrC/exj3vrwrnqKf2jpbL0OHaLpKreuSKZCZWGskW9ZLYqCnvT+Ae1/ehQ0rFuGExkq4bLkvjnj8lYa2bUxbH+/+wqlo9rhwqHcE92/ehe6hEO76wqmYUe3kxSyZjnt0+52WkaBl9CXiWkJEZC7GV79NNgtAW8LH7aOPFURMEXhtdzdOnVVjfHFYIeDteA3D3lMAafx/hjMagDeOxuAPF2ZP5l9738eCihbY5PHH6gYbFqCiaxesAV/uJxfZKfWnYN/APnQOdRo9FEMl3lnfevNyPHXt2Xhs24F4cd+Hv7IEz+4YO9R2tvmw4cVWWGUJqx59M6luAgsCUjlL7dK0s82Hx7YdwDOrz8XWm5dj0w3noanaGW+hnSvrJTXQs7PNh1WPvgmLhPjWJzJeYrebnW0+3LLpPQwGI7BZJPy/F5+Mp687h62FyZTY9YbMjPOTKDOzBWb0jsukyIMkSddJkrRdkqTt3d3d4/ribx3uh28kgjPneCczxoJw938Ax8gR+BvOnNDrz24Cogrw3+3RSY/lUKALBwLHcHr1vAm93jfjFEgQqDn8xqTHMlmnN5wOAPhL+18MHkm6yczdidDuhs3yutHidePOyxaPteBtrMLaCxaktfx12bO3CqbyU+p5azZ67WjXXrAAM6qdmOV1o7HKiePrKuJB0NQW16lyteOmwpjsvNWC279efS7+/O1PYMOKRVj/Qitu2fQeIjGFXUSoaCY7d21W/VP7TI8TFUK+85bzkygzsx0F7QBmJ3zcAiAp9UEI8TMhxFIhxNKGhoZxffE/th6DVZZw2uyayY90kmrb/ggBCf6GMyb0+pO9QJUd+OPByQdmXut9FwBwevXcCb1+pGYWwk4PvAe3TXoskzWjYgZmVszElsNbjB5KmsnM3clKDNI0VDlgtcpJGTXaxaTHlX4RyoKA5c3IeWsGqdlneoGX1OMr2wW7XqCHx1jhFWLeyrKEpmpn/ONbLjoJG1YsinfVIiqGyc5dj8uOKqct6bEqp41zlooq33nL+UmUmdlqzLwJYL4kSXMBdAD4FwBXFOILh6IxPLujHYtbauC2G/xjKzE07vsNhr0nI2afWJDIIgPnzQD+sD+Cdec54XFO7M6dEAKbjmzDXFcT6ic4FkgS+poXo/HgNlO0zV7StAQv7X8J7f52tFSNr35POcm0v5wFAYmSFbIWAwv4Ti16XbX49yIz45wlM+P8JMrMVBkzQogogG8C2AzgAwDPCCH+UYiv/fv3jqB3OIwLT5lRiC83Kd72V+EY7kDf7M9M6utccjwQjAHP7ApP+Gv83bcL+wNH8an60yY1lq65yyArUTS0vjipr1MIn5z9SUiShKd3PW30UKak8dz9J6Lx4zE2tfDvRVMN5yyZGecnkT5TBWYAQAjxeyHEiUKIE4QQdxbiawYjMTz46l60eF1YNMvYbUySEsHsd3+EsLMB/oYlk/pa82qAxXXAw2+HMRAafxFgRSj4ycEXUGN146ya8bXsThWqbICvaSGa3tsE60j/pL7WZNU6a7G0aSme3vU0jg4fNXQsRERERERERNmYLjBTDA/8cTcO9AzjK+ccB9ngbkyz3v8PVPR/iKMLrgLkyRd7vPYUwBcSWL81CCHGF5x5ouPPeNd/EF+c+U8T6saUqu2USyFHgzjuv38MCGXSX28yVs5fiZgSw+3bbkdUmXwdHiIiIiIiIqJimNaBmXBUwb0vf4iH/7of55/UiMUtHsPGIikRzHr3/8fsd34E34xl8Dd+tCBf94Qa4EvzgU17IvjefwcxEskdnIkJBY+2/RH379+EM6rn4VzPSQUZS7CqCR0nXYTaA3/D3C33Qg4PF+TrTkSDuwFfWvAlbO3cipv+ehMGQgOGjYWIiIiIiIgoE7MV/y2IJ/5+CG8e6MO2fT3oGQrjkyc24JplE+s4NFkNe59BzdHXUXP0ddgDXRiYcS46Tlld0O9xxYlAKAb86oMIfr8/go/NsmJRgwX/enpyscoXj72Bbf2teHNgD46G+nFG9TxcP+d/QSpgFtHRjyyHHIugedcr8Bx8HYMtZyJQOxedS68u2PfI1/I5yxGMBfHsnmextWMrzpl5DuZ55mHNGWsK+jMTERERERERTdS0DMy8vq8HOw/7cPLMalx86gwsPb7WsLE09u2Au+sNhOtOhG/ujQjMOheVRfg+a/4J+NSCMH774Qje6QmjKyThXz/WmPSctw8ewd8H9uD4imZ87YTP45y6xUUJUITOWY2Oj5yPmg//gMruXagY6kboY2sL/n3ycdXJV2FZ8zK8uP9FfND3AfYP7Me3zvyWIWMhIiIiIiIiSiWNty6JmUiS1A3gUIG/bD2AngJ/zemgHH4vPUKIz5biG41j7k733/t0//mA4v+MZpy3k2HmOcGxTVzq+KbbvNVj5r8JxzYx9QA+NMncNfPvKRuOu7S0cZtxzZ2qv9NC4c+f++cv2bw1gykdmCkGSZK2CyGWGj0Os+HvxRjT/fc+3X8+oDx+xkIy8++LY5s4s4+vGMz8M3NsE2OmsZlpLOPBcZeWmcdt5rGVAn/+8v759Uzr4r9ERERERERERGbGwAwRERERERERkUEYmEn3M6MHYFL8vRhjuv/ep/vPB5THz1hIZv59cWwTZ/bxFYOZf2aObWLMNDYzjWU8OO7SMvO4zTy2UuDPT0lYY4aIiIiIiIiIyCDMmCEiIiIiIiIiMggDM0REREREREREBmFghoiIiIiIiIjIIAzMEBEREREREREZhIEZIiIiIiIiIiKDMDBDRERERERERGQQBmaIiIiIiIiIiAzCwAwRERERERERkUEYmCEiIiIiIiIiMggDM0REREREREREBmFghoiIiIiIiIjIIAzMEBEREREREREZhIEZIiIiIiIiIiKDMDBDRERERERERGSQKR2Y+exnPysA8B//FepfyXDu8l8B/5UM5y3/FfBfyXDe8l+B/5UM5y7/FfBfyXDe8l8B/5WVKR2Y6enpMXoIRBPCuUtTEectTUWctzRVce7SVMR5SzQxUzowQ0REREREREQ0lZkuMCNJ0lpJkv4hSdL7kiT9SpIkp9FjIiIiIiIiIiIqBlMFZiRJmgVgDYClQohFACwA/sXYURERERERERERFYepAjOjrABckiRZAbgBdBo8HiIiIiIiIiKiojBVYEYI0QHgfgCHARwBMCCEeMXYURERERERERERFYepAjOSJHkBrAAwF0AzgApJkq5Kec51kiRtlyRpe3d3txHDpAJRhIKeQA86hzrRE+iBIhSjh1RUZp+7bxzow5INf8TtL/zD6KGQiZh93k535bZOFgrn7fRTLscC5665lMu8myzOW8oXj6nMTBWYAfBpAAeEEN1CiAiATQDOS3yCEOJnQoilQoilDQ0NhgySJk8RCvb078GVL12JC5+9EFe+dCX29O+Z1gen2efuHS+1onc4jEe3HcSeY36jh0MmYfZ5O52V4zpZKJy300s5HQucu+ZRTvNusjhvKR88prIzW2DmMIBzJElyS5IkATgfwAcGj4mKoC/YhzV/XoPOYbWEUOdwJ9b8eQ36gn0Gj6w8tfWN4N32AVyyeCYkAL9/76jRQyIqe1wniVQ8FsgInHdEhcVjKjtTBWaEEP8D4DcA3gLwHtTx/czQQVFRhGPh+EGp6RzuRDgWNmhE5e2ve9S0008uaMTc+gr8z4Feg0dERFwniVQ8FsgInHdEhcVjKjtTBWYAQAixTghxkhBikRDiK0KIkNFjosKzW+xormhOeqy5ohl2i92gEZW3tw75UOOyobnGiXkNlXi3fQCKIoweFlFZ4zpJpOKxQEbgvCMqLB5T2ZkuMEPlodZZiwc/9WD84GyuaMaDn3oQtc5ag0dWnt7r8GFufQUkScIJDRUYCkVxsHfY6GERlTWuk0QqHgtkBM47osLiMZWd1egBUHmSJRnzvfPx5MVPIhwLw26xo9ZZC1lirLDUgpEY9nYN4fOnzwIAtHjdAIC9XUOY11Bp5NCIyhrXSSIVjwUyAucdUWHxmMqOgRkyjCzJqHfVGz2MsnewdxiKAGZ5XQCAZo8TALCvmxkzREbjOkmk4rFARuC8IyosHlOZMTBDBacIBb6QD8FoUI2ACkCBkjUqqggFfcE+Rk8NsH80ADOzRg3MuO1W1FbYsbdryMhhERVMrvVlsp83cuxE081453xUiaIn0INILAKbxQa7bEcwFuTxQgWRz3zUe44ilKR5We+qh1XmZRdROBpGb7AXUSUKq2xFnbMOditrzAAMzFCBKULBocFD6BnpwROtT+CKhVdg3dZ16BzujO8jnO+dn3bRs6d/T7x9WqbnUXHs71YDMDNrnPHHGqscaOsfMWpIRAWTa32Z7OeNHDvRdDPeOR9Votjdvxtrt6yNP/+OZXfgR2/9CD2BHh4vNCn5zEe95/znhf+JwfBg0rx8YPkDONF7IoMzVNbC0TD2DuxNOzY+UvMRBmfA4r9UYH3BPrT723Hr1luxYv6KeFAGyNyrnj3tjbW/exh1FXY4bZb4Y3WVDrQzMEPTQK71ZbKfN3LsRNPNeOd8T6AnfoKvPf/WrbfimlOv4fFCk5bPfNR7TjgWTpuXa7esRU+gp/Q/BJGJ9AZ7dY+N3mCvwSMzBwZmqKDCsTBcVhc6hztRY6/Jq1c9e9oba1/3EGYkZMsAQEOlHccGQoixZTZNcbnWl8l+vpi4NlK5Ge+cj8Qius+vsdfkfC1RLvnMR73nyJKs+7qIEineYImmgKgS1T02okrUoBGZCwMzNC7antnOoU70BHqgCCXp83aLHYFoAM0VzRgID+TVq5497Y0jhMD+nuF4fRlNfaUDMSFwbDBo0MiICiPX+jLZzxcT10YqN+OZ84pQYJWtus8fCA9kfS1RPjLNR1mS4+fBdjn9OYpQdF9nk21FHzORmWVas7nFT8XADOVN20d75UtX4sJnL8SVL12JPf17koIztc5atFS14I5ld+D5Pc9j/bL1OXvVs6e9cfqGw/AHo0n1ZQB1KxMAdPgCRgyLqGByrS+5Pu9xePDA8geSPv/A8gfgcXgMHzvRdJPvnNfOR55qfQo//OQPk55/x7I78Mh7j/B4oUnTm48PLH8Ad/39rvh5sD/iT3uO3WLXfd9gJxoqd7XOWt1jg+u0ShJi6m5VWLp0qdi+fbvRwygbPYEeXPnSlUkpaM0VzXjy4ieT3mymcFcmqVTfyCxz9502H1b8dCu+fcGJWHr82KLY0R/Ad37zDn78L6djxemzDBwh5aHs5u14TabrUk+gB9/f9n2smL8CNfYaDIQH8Pye5/Hv5/17SU6yTbI2FgPnLenKZ84nno9cdsJl+Oqir8IiWeCwOOCwOIrdlYlzt4wkzkdZknHX3+/ClvYt8c83VzTjV5f8CopQ9LsyKRHYZFN0ZeK8JcP1BHqwvXM7FjctRkyJwSJb8O6xd7G0eWmmc6qSzVszYN4Q5S3fvd+yJI878sme9sbQMmK0DBlNXaWa+t3ez4wZmvpyrS/ZPh+OhbGlfUvSiTgA3BK7paBjzIRrI5WbfOZ84vnIc/uew3P7ngMAbF65GbUu3nmlwkmcj51DnWnvBZ3DnQhGg2iubE573YyKGSUbJ9FUEI6F8d2/fTft8c0rNxswGvOZFrfdqDRY72D66RgNvDSkBGacNguqnFZuZaKyx3WPyHx4XJIROO+IJofHUHYMzFDeWO9g+unwBeC0yahwWNI+V1/pQCcDM1TmuO4RmQ+PSzIC5x3R5PAYyo5bmShvsiRjvnc+nrz4yelY76AstfcHUF/pgCSlb+H0um04NsCuTFTeuO4RmQ+PSzIC5x3R5PAYyo6BGRqXQtU7mMYFLaeU9v4R1KdsY9J43HYc6vWVeEREpZdrPWKdF6LSyff8gMclFVo+c4/zjoiKhYEZKjmtzeWaP69B53BnPI1tvnc+gzMl1ukL4Ky5dbqf87ht6BsOIxpTYLXw70LTE9cjIvPg8UhG4dwjKj4eZ9nxN0Al1xfsix+QgFrRfs2f16Av2GfwyMqLPxjBYDCKhkr9glselx0CQO9wWPfzRNMB1yMi8+DxSEbh3CMqPh5n2TEwQyWXb9ttKi6t41J9VaatTDYAQNdgqGRjIio1rkdE5sHjkYzCuUdUfDzOsmNghiZEEQp6Aj3oHOpET6AHilDyfi1bpZmD1io7U40ZrxaY8bMAME1fmdYjWZIntL4R0cTxeKRSSjyXlSWZ56ZERcZrwOwYmKFx0/YHXvnSlbjw2Qtx5UtXYk//nrxPltgqzRziGTNZiv8CQJefGTM0femtRw8sfwB3/f2uCa1vRDRxPB6pVFLPZe/6+114YPkDPDclKiKPw5N2nD2w/AF4HB6DR2YOLP5L45Zpf+CTFz+ZV6V6tkozh47+AKyyFN+ylMrj4lYmmv5S1yNZknHX3+/ClvYtAMa/vhHRxPF4pFJJPZfV5thjFz0GRSg8NyUqAl/Ih41vb8RNZ92EGnsNBsID2Pj2Rvz7ef/ONR0MzNAEaPsDF9cvxjWnXhM/sBQl8x2sxBaETqsTiqIgrDAoY6R2XwD1lXbIkqT7eatFRpXTyq1MNO0ltj/tHOqMn6Br6l316ro31Km7ZuXb3lfPZF5LNJWlzn2PwwNfyBcPyFglK6Iiit5gb9LrWI+AcslnXQ3Hwqh31SddID7y3iNQhILmyuYMX7n04ySaTsKxME6pOwULvAugCAUzKmbglLpTuKaPYmCGxs1usWN5y3JcsfAKrNu6Lt7u7MfLf4x6d33am0pia7R6Vz1uPPNG3Lr1VrZJM1hHfwB1GbYxaTxuG7cyUVnR9j9rd1EX1y/GjWfeiFUvr9JdsybT+pFtI6lc6c39B5Y/gI1vb8SW9i1ormjG+mXr8VTrU7jxzBvxo7d+hHd73gXAegSUXb7rqtPqTDsfvWPZHXBanaYaJ9F0UmGtwD/N/id8ffPXk9b+CmuF0UMzBR75NG61zlp896zvxoMygHoH61tbvqXb7iwxXfSaU6+Jvwlqr2ObNGO09wcy1pfReFx2dA0yY4bKR2qNi+tPuz7rmjWZ1o9sG0nlSm/ur92yFivmr4h/vG7rOqyYvwK3br0V1592PQDW/aDc8l1XFUVJW9tv3Xpr1uxvI8ZJNJ2MREewdsvatLV/JDpi8MjMgRkzNG6yJMMiWfJud5bYGq3GXsM2aSYQjMTQMxTKHZhx27C3a6hEoyIyXmqNC0UoWdesybR+ZNtIKleZ5n6NvSbt487hTsytmYvNKzdzuwfllO+6GlYyPE8pzfrL9Z/KUVSJ6s77qBI1aETmwnc2yktqe2y7nH+7s8TWaAPhAbZJM4EjA2oWTENV9t+7121Htz8EIUQphkVkClrNmebKZjitzqxr1mRaP7JtJJWrTHN/IDyQ9nFzxehxWNmMelf6dmmiRPmuqxNdf1PPhyfaIYzrP5Ujq2zVnfdWmbkiAAMzlAe99tj+iD/vlteJWwMeee8R3LHsDrYjNFhHf/ZW2RqP24aoItA/EinFsIhMJ1drR732vvmuaZN5LdFUpjf3f/jJH+L5Pc/HP16/bD2e3/M8jwkal3zX1Ymsv3rnwxNt3871n8pRnbNO95yqzlln8MjMQZrKd8KXLl0qtm/fbvQwpr2eQA+ufOnKpNSz5opm/OqSX0ERSl7V5KdIVyb99kRFYPTcffrNw7j52ffwoy+djqbqzIXuXt/Xiwf/vAebb/w4FsyoKuEIaRzKZt4aoSfQg+9v+z5WzF8R79zx/J7nk1o7sivThHDeljlFKOga6cKRoSPoC/XhL4f/gk/M+QSa3E2oc9bBIlkgy7IZjwnOXZPLd10d7/qb6Xx4ou3bS7z+c96S4XoCPdi8fzM+MecTUIQCWZLxl8N/wYXzLsx0DJVs3poB84Yop0z7YIPRYN4tBRPb0ZLxOvoDkADUVeTaymQDAHT5gwzMUFkKx8LY0r4lrYX2LbFb4v8/mfWNayOVK62r2dUvXx1/7Ll9zwEANq/cjMaKRqOGRlNcvuvqeNffQteF4fpP5SYcC+Pu7Xfj7u13Jz2+/LjlBo3IXIoXlpWkSyRJ2ilJUp8kSYOSJPklSRrM8ZoFkiS9nfBvUJKkG4s1RlNTFGDoGOBrU/9boirxergPdvpp9wVQW2GH1ZJ9CfC41b9x1yBbZlN5ymv9M9F6TVQURZrjPL+gcTNwveV8pYIo43MGHkPZFTM39EcAvgqgTghRLYSoEkJUZ3uBEGKXEOJ0IcTpAJYAGAHwXBHHaE6KAnS1Ar/4NPCjRep/u1oNO3C5D3b66egPoK4y9yLoiWfMMDBD5Snn+mey9Zqo4Io4x3l+QeNi8HrL+UqTVubnDDyGsivmVqY2AO+LiRexOR/APiHEoQKOaWoY6Qb+z5cB32H1Y99h9eNr/wRUNpV8OKntY8usDsK01NEfwJw6d87nOW0WuGwyuvzBEoyKyHxyrn8mW6+JCq6Ic5znFzQuBq+3nK80aWV+zsBjKLtiBmZuAvB7SZL+AiB+u10I8cM8X/8vAH6V+qAkSdcBuA4A5syZU4BhmlA0rB6oLUuBZTcCLi8Q6Dc0mjqRfbATKWqmvUZRFChQoAhl2hy0Zpm7MUXg6GAQZx7nzev5HredGTNlzCzz1kip6180GkFX8BgiShQ22YK6j9+Egco6hN21sI/0ofavP4AcnVjNASoMztsC0s5JElU2jj7eBsXmQp+EtIL+mc4B9B5nnY0xnLtZ6M1F3+GxrSHRMGC1A+4GQDb2nDGqRNET6EEkFoHNYkO9q35atwTmvM1TNKyunxfeNXZ9t/VH6uNlIqbEEIlF1P9C/a+co7RCuSjmCnEngCEATgDj2jgmSZIdwOcA/Fvq54QQPwPwM0Ct+j35YZqQ1Q4suBg4ezXwwjfVNx3PHOBLT6rRVIPfbPKhtRRc8+c16BzujKeqzffOz9q5aU//Hvx0509xxcIrsG7rurxfOxWYZe4eGwwiqoicrbI1HrcNXYPMmClXZpm3ZhGNRrB7YA/WblkbX58eWP4A/rDvD3j09UfV9eqiOzHf5irqXmHKjvO2gKx29RxEuyBuWQqcfzvw6P+CUtmIPRfdiTWvJ79fn+A5Aft8+9LOATI9PtXf3wuJczeL1LkIqOfLw93A01eOnS//y6+AxoUFP1/O99w2qkSxu3932vvEid4Tp21whvM2TzaXun4+f8PYfF3xkPp4GYjEItjjSz+Hmu+ZD5vFZvTwDFfMd8FaIcQXhBDrhBDrtX95vvYiAG8JIY4VcXzm5W4ALrxzLCgDqP99+ko1BW4K6Av2xd+4ALVq/Zo/r0FfsC/na1bMXxEPyuT7Wspfhy8AAGioyi9e6nHbcYzFf4kAAD3BnvgJBaCuT2u3rMXnT/x8/OM1r69DX1k1eKRpzd2gXuh6Ru+Cf+Lm+EVF38e/HQ/KAGPv1z2BHt1zgEyP8/2d8pI6Fz1z1PNlLSgDjG0NKcL5cr7ntj0B/feJnkBPwcdEU4wSGwvKAOp/n79BfbwM8NjIrphh2z9JkvQZIcQrE3jtl6GzjalsyDIgW/XTNadIqttEWgpqr6mx1xS0HSEl6+gfDcxUOvN6vtdtx87D/RBCQJJ4tUnlLaJEddcni2RJ+jiscL2iaUKW1eyDa/+knoOIWPz8JOyu1T0eIkpkXI/z/Z3ykjoXrfbM25uKcL6c77ltJJZ5/lOZi2WYr2WyBkYznENFlahBIzKXYgZm/jeAmyRJCgGIAJAAiFydmSRJcgO4AMDqIo7N/PTSNT1z1MfHQ1HUuwZF3Hert19ca4eWePDlaoemvWYgPIDmimbUu+pxzanXoMZeg0A0AKc1v0ACZadlzOTTlQkAat12BCMKBgNR1LiZZkjTUI51MnGNs8rWtLVtectyWGUrfnnhLzEQHsDze55n60eaXmRZ3UqtKIC/E7hmMzDcDXs0rPteb5Ntuo9bJf3jR5ZkdA51TpuaclRCNldhzpfzkO+5rc1iw/KW5VgxfwVq7DXx9wWbzHOosqeVqzj9y2M1Zt7+VVHmqxlZZavusTFdt/iNV9F+C0KIqgm+bgRAXYGHM/Vo6Zpa5W5tz6y7If+vobVkS/0aBdx3m2m/7QmeE/Dgpx5MezxbOzSthdpPd/4U933iPoSiIdy69dak13scHp6wTVJ7fwDVTiucNkvuJwOorVBPJI75gwzM/F/2zjw8iird/5+qXpLOQpJOCFsABVlEBUZAUX4zQPDylgAAIABJREFUEnUEREVHR0dxWHRUdEaGmbmiVx0VL6Mj3hHkOrhcR3FBHJdRVDZHQWcugspmQEDigiSGJTtZOuml6vdHpTq9VPWW7qSTrs/z+GBVnzpVNO95z9unzvt9DXoeYfxkoI+bc+oclhYv9W7FLS4qZt7Yedy48Ua/fOnctNyu/psZGMQXjbFiv2olyyf9iflb7vGbqwtsBUExwKJJi3hl3yua42f2+tmG5oxBePT89fVvwctXxB4vR0huWq6f/er5+/z0fOaNnRfULj/d+HmT8tjy4byF8Nov2+316peU8ymAPd2uOTaMctkKQuzVrHU6FIQpQLYsy28EnL8OqJRl+Z/xutf48ePl7du3x6u75KOju10aj8GzFwa/RYhjSbYqRxUz184Menuwavoq7On2mKsyuTwu5myYo9lvAqs3dFqeTlfa7qznPqO8tpk/XX5GRO0PHD3Bonf38eINZ/GT4fEPdAw6TErYbcII4ye1fNycU+dw3ajrcEtuzKK5K3xVT8Cw2+6GzliRbvwnNSazZlWm483HOdJ4hJrWGp7b8xwlVSUUFxVz98S7kWQJURC9izIq3WD8GLbbVej56xs/UP5VElyVqcpRxYOfPBj0tv++c+/zs9dQsXEX2rVht8lAJ/w2S2aONh3V9PkvTHuBvpl9tS5JKQ2FROyYWQRcqnF+E/AWELeFmR6PunU4Vjoh7zZUvm0sJbbVayoaK4w89ATxQ20zBZmRVWQCRWMG4KhRmcmgJxLGT2r5uJX7V3LtqGsZ2Gug4asMUgedsSJ6XBRkBwfU6uLMrA2z/M5vLt/MXfJd9M/qb4wfg+jQ89ceJ+QOTPjtnR4nm8s3s7l8s9/5uzx3BbUz7NpAk07UREpGDP2l0CRiYSZDluUgKXRZlo8KgpCZgPulNqF21ag6NVmFMGmBksvoam4vydbBHTnq265I8m21dGhClc1W+y2wFfDbM39LYUYhMrI30DO2OMeGLMv8UOdgRJ/IMw3VhZlj9cbCjEH3IBp/E07Py2qyBuVD7z66GxGoOHEYUUdzRkCg7EQZFpOFAluBkT9t0D3QiwskCQTBqy3DlmVK+/PuVMSAG48h2fKpcdbp6s2NLhjNDWfcwODswQgIlDeUYxJMFBcV+/3QDadHZ5DC6Plri03ZiRAqntWwbUkgqp3dWvOBlqaYXrtALSWI7v4GPQCzFc6ZDz+aCaJJqca0a1XKaMxYTBbmnDqHy4dfjkkw4ZE9vH3wbUN/qY1ERIrpgiCYZVn2k1cWBMECpEaR9s4inIZMRm8l77bhSHtpNrVNei5UHohZf0bVXfjrrr+yaNIib3lrLS0ZPR0arRxy334fPe9RTJhodjdzyz9vMfLP40BNk5MWl0RBduQ7Zqxmkex0M8cajIUZg+QnGn8DhNXzyrXkaOZDP/Tpn9lcvpniomJNzQw1vUltPzxvuLE4Y5Dc6MUUvUcGxwuXP6X8GH59NtQdRhp5CaUX3sP8j37nN+5Uvbm/7vor1426jlf2vcJ1o67j1x/+2m98gLKTJhI9OoMUxpav6HH46nNc97oS5756nX48q2Hb0vVvUSp6Ip8rUDRmtOaDQI0ZvXarvlzFyv0r6Z/Zn6d++hROjzOq+xv0ANLz4Iwr4ZWf+2vMpOd19ZN1CrlpuUwbOo3bPrjN0OXTIBEaM38G+gC/kWW5qe1cJrAcqJJl+c543SvlcxgjyVNsOAZ/02gzdz08Py3mHEff/Fn1LZg9zU6/rH4UZhT6TSrR5Nr6tn3mp8/gltws3ra4s/J0e3z+bUl5HZc9sYXf/3Q4E06KPPC9880ShvfJ5tnZ4xP4dAYx0uPtNhpiyu0PsXuwqvEIMzU0ZBaetZAFmxcAyg6Zu8++CwkQEDQ1Z0LkT6cqht0mG3oxhV68MP0vsOrnAFRdt5qZJY/r6s0dbz7O7PWzWXjWQpZ8tiSo3d+m/I3K5krNGCIJMWy3q2g8Bu/+zr+ijS0P3r41dDyrYdtVv/wHM3cF22KouSLS+UWvne+8seKCFZ0Z34Jht8lBXRmsvDjYXues65R0vK7mSOMRzRhp5dSV9Mvqp3VJSmnMJGLmuxc4BnwvCMIOQRB2AoeAyrbPDOJFJHmKHr18XFeHchx982dLqkpYsHkBszbM0kwziibX1retRbRgM9uMPN04UlajlMruHcWOGYC8TAvHTjgS8UgGBnElptx+Vc8rd6Dyp8+uQafk1uwvx5rjPd5cvhlJctM/qz9unfZG/rRB0qOr36ETL1gyvIfODHtIvTlJlrzjRqudW3LrxhAGBl7cTvhqLfz9elg5XfkTwsezGrbtTMuMeq6IdH7Ra+c7bxjxbYoiubXtVXJrt+9h6MVI7hT5+4cj7rOfLMtuWZbvAgYCc4DZwCBZlu+SZdmITDuKJCkr/3VlSr537iD/z320Ebw54VptTJbQ14ZBzRv3RS8vPNa29c56HG5HxNcahOdQdRMAfXulR3WdPcPK0ROtiXgkA4O4Eo2/0cXHz1oFk2Z/9c56//4FJU3JYrJotjfypw2SHrMVRkyHa16GOWuVP0dMB9GsHS+4mr2H1uaakONOHZf1znrNdh7ZY8ztBtoExr2BNgrh41lVm8YHa2tT1HNFpPOLXjvfecOIb1MUPX+aIqnO5jZdPl/6Z/Y3Ur3bSNhrCVmWHUA2cAbwc0EQZgmCMCvMZQahUHNkn70Qlp0O6xYqeYnqAPfVRlDbrlsIlz0R3Carr/Kn1rURYE+3s/z85d7BFSovPNa2z+15DnuancWTFkd0rUF4vq9uIi/DQrrFFNV1eZlWqhtbcXukBD2ZgUF8iMbfaBLgZ+1732H55Mf8+ltavJQ1pWva+5/8GHab4jsLbAUsLV4a1D6JS/8aGCjY8uG8hbDxbmU3wsa7leP9a7XjiLwh3nP2natYPnmp7rhTx+Wa0jUsmrTIr91jkx/j7YNvG3O7QTCBce+2p4NtNKMAfvFK6HhW1RLzaWPPGRz1XBHp/KLVLnDeKMou6thcZdA9ySoM/u129UvK+RTAnm7XjJEMu1eIu8aMt2NBeAkYCuwGPG2nZVmW58frHimXw6iV/z1iOly8BGTZXxvBt23ReKUqU2ZvyCmC7P7tVRY6WJUpmkpLsbRNN6eDDC2eFiRZIt2Ujt2WMNX6Hp9/e/XTWznhcHH/padFdd0H+4/xt//7jq3/eT79cgwN7ySjx9tttERVlSmQQD97zctIhz+jZsIsnKIZq+Qmt3wPdSdPxCm7sQpm7LbeiOb2HTFuyU2VowqX5MIiGlWZdDDsNtnQ05iZ8pBShSkwjgC/GEKrKpPvuFPHpSRJeGQPHtmDSTBhFa3IgtydKtIYtttZaPhjNt4dbKM3bVaqgyW4KhNEPr8EtstNy6Wu1X98QKdWZTLsNhmoL1cWGAOrMk28RfGtKUCru5WalhrckhuzaMaebifNrCuxkFIaM4mMFMcDo+RErfykEupk4mwOzkv8ai1MeyRYMMo3n7Z8e3se7oK97ZOVqqsQzaOEmGhqWmo0Jx5REBEFMeI3xlptfe9b01LTnQK4pOJQVROn9usV9XV2tWT2iVZjYcYg6YnG3wD+Abvs8feztjw86dm4LDY8soTLZEGu/ZqCQRO8b7wkWaLKUeXn9wyhX4Nuh57GjC1PP47I6tO+4NJSg0TwrsrAuKEgo8CrO1PTUkOrp9VI3zDQJtAmbXnaNupyhE/F14p55fC7gL0L7R4XFpOy0B4qRvWNfQPbac1Lxm7KFMPjgq3Llf98mXBD1zxPFyAErLUEHqcyiVyY2Qv0BY4k8B49H98Sf1MeUn4IBL4p0JqM1HzaSNpG+igBZWjVsrCBZWJ9ywPGo/Rf1OVvDTRpdro53tDK5BHR6cuAksoEcLS+RVGPMjDoKQSWUZ35up/vdGXkU3r6Jfxu441+pR2HpWVjwfBPBj0IvbjBUet/7LOIotq/Wg77/i33B5XL/qbum6DxoXfeGDcGfgTapKNW20YlNzx7sX65bA0i8d1uyc3B2oNBZa+H5w337oI05gCDqFA1PgNt2JQaOnROt5Ov678OGlOn5JyCtQO/UXsKcfcYgiC8KwjCO0ABsE8QhI2CILyj/hfv+/V4mivbfzBsWaad562lC6ORTxuNhowWNS013okHYMawGd6BpXVc0VTB/E3zqWmpifmeWveNV7+pxuEaRaixb6/oKjIB5LctzBypNyozGfQwfH0swMePwIwVXt9ZZesV5Nd+t/l3VHmUsWD4J4Meg1bccPWLsHt1+/GMFcr2+zZU+58xbIZ3UQbax0GVo0pzfOidN8aNgR+BNrl7dbA+xzWrYOM97T687rDi05srQ3Ydie+uclRp+39HVVT9GBh4sWQofjXQz/pUuevJVLdUa46p6pbqLn6y5CARO2b+OwF9pi6BKUmbHlR2zvQ5HawZ+rowoqi8LfjVBzFryAQSWP4vsOylXhnMjpb+i6n8rUEQh6qUhZk+UVZkAshON5NmFr3ltg0MegyBW+XLt8OHD8CcdcrHbWV+ffEt7Wj4J4MeQ2DcAPDxn2HstXDObcpuhQ8fgKtWei9R7V9v/ndJrqjOG+PGwA+tWNaW738sSUpavy+B5bI1iMR3uzz69htNPwYGXpxN8PmzcN3r7RozW/8HzrsLMnq+AK5RLjs0cV+YkWX5YwBBEB6RZflO388EQXgE+Dje9+yx+Ja79l2c2Xi3MilFqQ/TUdTyf+qAUsteFtgKuOGMGyjMKPT7HLRL//kKAEpISLKkK4wmCmLQffX6NQjN922lsmNZmBEEgcLsNMpqm8M3NjBIcvz0AMxmcqYuoXr4BbhMJiweDwVffYBZEECWvaUdA/2PWTRT0ViBKIjMOXUOY/uOJceaQ72znjWlawz/ZNC9CBRGze4HjUfhu3/Brpfb2wWkRAeWwQ4aJ4KZ4qJiZgybQb/MfvSy9kJA8I6blftX+rU3xo1BWIQArZjGYzGl7ltNVq9t+vpuURCpaKzAarJiMVl07Vptk25O1+wn3ZwepD1mpDYZYLZCcy1UlyqaSY5a5ThF0nhCxVQGCSyXDfxU49y0BN6vZxGu3HW4lKTAEoPPXqgcS7GXOw4s/7emdA0rLlzBgjMXsKZ0DXWtdWFLW6u5uA9+8iDfnviW2etnM+XNKTz4yYMcrD3IzLUzmfLmFGaunUlpbSmSLHW8/K0BAIeqm+mVbiYzLTbn1zs7jXJjYcagm6P6INXXvLDvFUqH/ZjZm37NxW9fxuxNv+bg8J/g/vQZWHY6BXvf0Szt+PCnDzPlzSnMXj+baUOnsaZ0DXM3zmXJZ0uYN3YeuZacLv6bGhhEiFa8cGyvUjkkTPwRqgz2okmL2PT9JuaNnceSz5ZwzXvXcOPGGylrLOPhTx9m2tBpzDl1jre9Ma8bBBFJLGvL1y4/bMsP2XVuWq7XNn1996ovV3njUIfbwbLiZbr+f+bamdS31nPr2Fv9+rl17K3Ut9ZrxrQGKU66Pbjk+3kLlfMpQF56nmZMlZee18VPlhzEvVy2IAi3ArcBQ4BvfD7KBrbIsnx9vO7Vo8uxRVLuOtLrVXIHdXinTaDyvCzLXL/uehaetZAlny3x7p7JsebgcDs4reA0v0CrylHFzLUzve3VFdNlxcv8jkEZrKumr6LAVtCx8reR06NLCV73v9uobGjlwRmnx3T9yk8OseXrKvY8cBGCEOFXdfhT5Y1rdl847XJIy47p3gYh6dF2G29UH6T6mrdnvM1tH9wW5HteOP+v9F0+DgDX1CVUnX6Zt7Tjw58+zObyzX7tF561kAWbF3iPV01dSUFWv078m3U7DLtNFqItkx0QfwSWwT7adJSa1hqe2/McN5xxg+bcrsYAK6euREbubjsKDNvtLCKJZRuPwbu/U1Lu1B0Iu1fDpUtDxruBcwFo+/LV01fjlJy4JBdmIdj/r7hgBYu3LQ7q596J93Lbh7f5nVNj2i7CsNtkoL4cnp8WbNNz16dEuewjjUd4+NOHg3aY/efZ/0k/7ZgppUo2JWLf0CvAeuBh4C6f8w2yLBtKWJESSbnrSK9XiSDnNhyB5f8qGiv88ssrmiq8ExrAxis3+l2vl48eTp8m6vK3BkF8W9nEsMKsmK/vnZVGY6ubeoeL3IwwWy5lWXkLsG1F+7lND8LPX4TB58T8DAYGHSVQD8AkmLQ1BEztAqeWDQvpN/JiyB1IRWOFX1Cuts+x5vgdO418aYPuQixlsn3wnZ/LTpQxa8Ms72d6c7t63i27GZhtlPoz0CGSWNbtVDRmAnVmpj0Ssms9bZhAX97iaaF/lvJ2X8v/28w2zX5sZlvQOUN3xgCPS9umPS7t9j0Mt+Rmc/nmoHF0x4Q7uuiJkotEaMzUA/WCIPw68DNBECyyLKeG5XWUwBKBRePhvDtB9ihvB0IJ+Wpp04B2zm1gXrlGv3qaMPZ0e9j8cqvJ6rfbRRREv/aR6NN00m6ZHs2JFhdHT7QweUTsVbkKs5VqTmU1jvALM9ueVBZlRl4CZ86C2kOw5XF46XKYuw4GjIv5OQwMwvktyeOmxlGJU3JjFc3Ybb0RTcp0F6grYBbNmvoAZtFCxa82Ym2uwb5zFWKb79TTvKp31vsdi6KJihNlWEUzuekF1LnqDR9mkJz4xhu+O3TTc5Xj8u0wYroSV9SVgdmKlG6nrrWWFtmNJEuYBROiYPKOJzXo1osN8tLzKC4qxiK2l4g15nqDIMxWxfYCd8P4xrJmK9I586mZMAunaMYqubF/9SGij71qxbZavry4qJi89Dyen/K8pl6Y1jUOt0PTxh1u/2IJxUXFfvo16m5yw+ZTDJNF26ZTpFy2fsxlaMxAYnbMqOwEBgK1KNuQcoEjgiAcB26SZXlHAu/d/VFLBL56LWQVwgUPwJrblMBJzfMuHBW8OKPm425+SMkNf+c3/tf4atOobdVSsRr9qnoMf931V64bdZ23HKaaDz40dyjLz1/OX3f9lUWTFgV9npuWS2ltqbeUYHFRMUuLl/LU7qd49LxHaXW38tK+l/jV6F+xeNJi7t1yb8jr1fPD8oYZk1cUfH28EYCivNjL8fVuK7NdXtvMGUUh9DNqv1cqdww8G866RQnmC0fB1Edg3R/glV/AzZuj2rK5r3ofn1R8woCsAVw0+CJMPuVaDVKMMH5L8rgprT3I/I9+1+4zJi9lWN5wRJPZqyuglmu8a/xdfsdqvvOmwx/z5+1/Vq6/cCnDbPmItGtq+Pok1adBe770Q5/+mc3lmykuKg7q3/BhBkmFGm9sfgjOvsU/brjsCSj9AM640rv9XjpnPt+fNZuqllq/OXvRpEW8su8V5o2dB8Dm8s2sKV3D0uKlfva/aNIiHt/xOPPGzsOepvw4VWMNY6438MOWr+hvvPbLdpsM0I+R0u2UjvsF8zf9xsfnP8awbU8jbl2uGzPnWHP8bFP11Wpqq+rLc9Nyvddo+f+i7KKgc8vPX+63iKP2PXv97KA28/45z7D5VCKzt7ZNZ8b+4rQ7kZeepxlzGRozCnHXmPF2LAhPAW/Jsryx7fgiYCrwGvC4LMtnd/QePT6H0fet8MqLI9OLiUabJoLcXT1NGGjPl7Wn23V31NS01ATl8BYXFXP3xLuRZZk5G+aE1KeRZEkzBzhBebo9Nv/2tc/LWPhmCcuuGRtTVSaAZqebG1/Yzl3TRjLvvKH6Ddf8Bkr+Dlc8A5kB/0a138P6O6DfWJizNqK0vOf3Ps/SHUuRUXzVmN5jePqnT5NpyYzp79ED6bF2q0kYv1XVeISZG+YE+4w2zZdAXQE9fasgzRgfnxP4Zj/XkkNdSxVOyY0omryLMqH672KtgWQgtew22ZEkaKjQ1j6Ys84vBqmav539zUc1dTV8tWPcshuLaCE/PZ8qR5Wf9kxJVYmi5TTtBfpm9tXV+0jScWLYbmcRSZyq5/PPf4KC5eM1rwE42nSUh7Y95H1zn5eep6k3FmiDWju7IHjni+85URC9izK+fXeyDo1ht8lAXZn2b7o56yC356d1Hmk8whyN8bpy6kpDY4bE7pgZL8vyPPVAluX3BUF4SJbl3wuCkJbA+/YcxLaSgHVlkenFSBK4HJFr04TL3ZUknC6HpiYMtOfLhtJ/8c3hHV0w2rvwoirTR6JPo3W90+NEkiXjrUKEHDzWQJpZpHdW7EMvw2omL8PCt5WN+o0ajsIXq2H41OBFGYC8wTDhJvjkcfj8WTj75pD3/PDwhzy24zHG9xnPrFGz+KLyC57/8nl+/9HvefLCJ41//1TBd5Fa9oT0W07Jre2r2jRfAnUFQmlg+F3vow2g5fNUod+KE2V+udM51hwKbAUsPGuhd9vuc3ueM7QGDJIPWYbLn1S21m9ZpsQQdYdBcis7d+sOIxWNx2nJYGD2wJDaMTIyA7IGUNNSQ6WjEoD/3v7flFSV+LV3SUp2u57ehzFOUhy3U7G9KQ+1p31sWeYX/+r6fN/UCI2Y2eVxUd1S7T0WECKyQb2YN/CcbwUmSZYosBX49W/o0KQoqj8NtOkU0aVzS27NmMidIn//cCRyYaZGEIQ7gVfbjq8BagVBMAFGvbhoCNSbgWC9GHV7f0NFZNoy4fpt68/adCyshkwo1K2cBbYCbj/zdr9Up2enPBtR33rXG1s+I6f0eCP9c22IYscWnvvmpPNNZZN+g10vKZPLyEv125xyIRz6N3xwH4yYpvuGoNnVzEOfPsTgXoO5afRNWEQLkwZMotXTysv7X+Yfpf/gquFXdejvY9ANCExdmvl6SB9nFc3a/kRs15jx/dwluTTbqz8Y268Po6vURuD9JVliwZkL/FI+Fk9aTLo5tp1rBgZxRys98LInFMH2xuNQXQoXPIBU8iql42Yyf+MNLDxroa7WUv/M/qSb04NSkxZPWsyyncu8izP9M/t7NWb0tJvCxRgGPRyLLTiVf8YK5Xwbuj7ftzS1Rhycbk73880rLlgRNxvUSs3Tsv9AHRrD5lMAc7q2TadITJBmStOMidJMxp4NgET+or0OKALeBtYAg9rOmYCrE3jfnoea/507SDnW0otprlSCqo8fUQKqUG0j6betP/vmh1g+8X7WlK5h0aRFfnXnl5+/3K8UthZqPu68MfO8iyqgvBV49LNHebz48ZB9h7p+/qb51LQYhb4i4eCxBvrn2sI3DEP/HBtfH29EMwVSlhUBs76jIWeAfieCAOf8BiQPfHC/brNV+1dxvPk41428zk8gsnhgMSPzRrJ0x1IanA0d+esYdAdU36YuxHz8iBLE6Pg4u603yycv9fcnk5dit7V93uZT1M/TTGksnrTYr71vkNA/sz/Lz1mEPcKs38D7C4LgDUBA8V33brkXSTLeTxgkCYFjrO6wojNz3p1KPPHxI7DmNmp+/Dvmb1tERVMFz+15LmjeXjRpEWtK17D8/OVIkuT9UQrtdj9vzDxv+6XFS727DALHZaQxhkEPR/K0/4AF5c81tynn27CLaSyf/JcAn/8X7C1tu3tDxMG+vvmpL54KmgtitcGalpqw9r/8/OUUZRcZNp9qyJK2TcupERPIyJoxkSpXkOokbMeMLMtVwO06H3+dqPv2SERRES371Qf61ZPUtKS6w8pbLnWLXO5g6DVAW8sjVL9t/Yl1hxm24Y/c95M/IIkZvDD1eSSIWD1eFESG5Q0j05IZtEV0c/lm7p14L/edex+SJPHCtBf89GnUvvWuN7Z8RkZDi4sj9S38ZHjHhcX659qoP3CcmiYn+YFpUZUHoOYbmHib9sW+ZBXCaT+DkleV1KaAEtpOj5NV+1dxRsEZDMsb5veZIAhcM/IaFm1dxMv7XubWsbd29K9lkMwEplyWb1fEpeesU44D/KFoMjMsbzirpq7UrMqk+qRV01d5UyLv/Nedfttql+1cxiPnPsjGC/5Xqcq0/h7Eq1ZG9LiB95dA23dJhu8ySBL00ppzBys/GMoVrQin2eq15ZKqEv5n5/+w8KyFDM8bjkUwIwom7jv3Puzpdo42HdW0+5NyTmLdz9ZhES0U2Aq8lTgCx6VRocYAAI+ObfqmljqbGLblSVad/0R7VaZ/L0P8yR1KGr9OVSaX5PKz0ZKqEpbtXMbzU58HIo9ztdBLzTs552Q2XrnRT4fGsPkUIwKb7snopq0aMRGQwIUZQRCGA/8BnOR7H1mWz0/UPXs0qt6ML766C4HlsdNzILufklbSVKkIAOstzqj96vQnlm+n4JVr9QWHwz26IJJuTvcrj60K/JrbqqTUtNTg9rg1Jybf641tztGjph4VxWPHTFsf31Q2BS/M7H8XEGDgxMg6O/0q+PoD2Hg33LRJsbk21n+3nuqWauacNkfz0sG9BnNm4Zm8tP8l5pw+JyhP26AHoZVy2XhcOa/ji0ST2av5Asq28ipHlV/wq76pr3JUUeWo8tO36p/Zn/TKrxS/B0gjL6HKZMLZVv7ad6En3P2rHFWG7zJIHrRKzeulNVcdVP7/mpeh1wCsQnvKiKr5Zk+zYxEtFGYU+s3beqlJFtGilNcWzUE/QEPp1RmkCIH2aQq2TWnkJdSYTDhPHMYqmLFbMxG/+xcFu15u7yd3EFxwX8h41WqyapbttZqsHbZDPftPN6cH65MZNp9amCza/jZFymVbTBbducEgsalMrwO7gHuBO3z+M4gHak74sxfCstNh3UKl3NqI6TB1CSDAS5fD8rHwtwuVtqG2zuv1F0lKVATY0+089dOnWHDmApZ8toS5G+eyeNti6lvrKa0tZebamUx5cwoz186ktLbUTzRNvd7Y5hwbB48p6T4D8jq+eDEgV8mB/UZLAHjfO1B4KmRE+G9iSYex10HFTji4we+jN0vfpF9mP0blj9K9/KKTLqLB2cC6b9dF/PwG3ZBIUjlDoOb66/kYuyWH5ZMfC9gG/xj2nauU60deQumFdzNz41ymvHUxMzfMobT2IJInMqE6w3cZJA2B8/yzbbGBLT94jF39Ehx831EyAAAgAElEQVT+DM6/T1k8/99i7GvvYPl5f6G4qJjbz7ydJZ8tYdaGWcxePzto3tay+6XFS3lo20Mh53qDFEbLPlsblIXBNtv098fTmblxLqXNR5Bmr416jshNy2Xe2HnemHTJZ0uYN3aeX3nsWDH8voEumQXBv6+ufkm7YEYPxJ5mZ2nx0qC5wZ5mjA1IbLnsHbIsj0tI522kdDk2rRKCI6bDtEegcj+s/UNk5bXD9XfxEkU7RGcraDRUNVcxc51/OcwVF6zQLLmpVS5Qq0RhnLd89shSgg+88yWvfn6Yv82egCh07K8oyTI3vvA5v5gwiAcuO639g5rvlEXA8TfCaVdE0aEb3r5NWcy55d8gipSdKOPity7mymFXMn3IdN1LZVnm/k/uJ9OSyeuXvo7Qwb9bN6ZH2q0fWm/5I/RFYcvw1pcjrb+TmjNn4sywK6lL321FPOtGqC+nyj6Yme//Srf8dkSPn3jf1R3p+XabbIQqPaxqy6ljzJYPjUeDymdLIy/h+PRHmL3xxqjKCouCyEPbHvKrWJbE5bDDYdhuItCzz589A01VYMvT98dTnqdAMEU1RyS6RHsS+n3DbpOBujJYfyeMvba9KtPu1crvtxQolx1Ypl7dqXb3xLvpm9lX65KUCu4TWZXpXUEQbgPeAlrVk7IsG2qt8UArJ/yrtTDlT2DJiKy8diT9xdFROKXgvEKb2RaxdoyxzTk29lbUM9ie2eFFGQBREBhkz2DfkRP+H5S+r/w56Jzgi0J2aIYx18L//QX2r4HTruC9795DQOCc/qH7EgSB8wedz4v7XuSLyi8YWzg2unsbdB+0UjkjJGwZXo8L8cB7FBx4z//CcbNg5XScC3aHLL8d0eMbvssgGdDTknE7tceYLAe1Fw+8hzR1cUTztq/dVzRW+C3K6F1jkMLo2afkgb9fD6Dvj2U3ZPeP6naJLtFu+H0DTSS38vvqq7X+56f8qWuep5NxeVxsLt8cNB/cIRlJNZDYhZnZbX/6ftMyMCTURYIg5ALPAqe3tb9BluWtCXnC7oxG3q03R9HVHPzZiOmKhkddWXuZQZcDZA+Ybfr9aZXZjgCtNwVaObcOt8Pv3OiC0cwbM8+rCZEEbxi6NZIks6/iBP/vlPgFB4PsmWz7thpZltt3qXz3L0XTKFtztTs0J/8E9rwOm/4Ep17G5sObGZo7NKItvxP7TeT1g6+z+sBqY2GmByN53NQ4KjXFfMOhW4aXNn8omhX/6BskjZiu+NI5a7GKZm0dAjGK6bMDO34MDOKGnpaM1jyvpupp6HuIJgsvTn2RmtYantvzHCVVJe1jqvGYpn0b5bANwvpBPft0NXsPrZJb245EC1WNR9rniPR8xKbj4HEpvjyrLwTMGaE0ZgwMEoZo1rbzaGKKbozFZNEcd4bGjELCIkNZlk/W+C/kokwbjwMbZFkeCYwB9ifqGbstkqTk3WqVjM3qC3lD/D8bMR3OW6hsSX5jjuIMju+HlRfD42MUDZrWhg7pOPg9no6mQ25ablDObVF2kffc6ILRLDhzAYu3LWbaP6YZOehx4FB1E81ODycXZMatz5PyM2hsdVNe61BOSB449G/oe0ZsHYomRWumupTjX7zM/pr9jOk9JqJL083pnNv/XD74/gNOOE+Ev8Cg2yF53JTWHmTmhjnx03iZvBT7e/+h6BisvFjxjyPa0uZUf/nCpbByOrlfrtXWIUiPcLFTT9fDKJdt0NnY8rW1DWz5/u08bji2V9luf9kT/voeF/wnszfeyKwNs1jy2RJuP/N2iouKWTxpMQ1Nx5He+72mfRuaGylOJH5QT08sb4j3nP3zFzU0wZbS4G72nyPqSpHW36mkWD8/TbHngDkjkRozBga6ZPXW9sNZHa+c2h2wp9k1x52hMaOQSI2ZDOD3wCBZlm8WBGEYMEKW5fdCXNML+AIYIkfwYCmbw6jm4WYVwqQFSo6iqxn6n6mIR0kSOGrad8SI5vY88WteBnOatgbNTZuV9h18qxsqb9eebg/aSQN4z83dMDdh+b4R0OPyb9/5ooL5q3fx8M/O4KT8+CzOfH28kT+u2ctT149j6ul9oWIXPDMZfnwHDDkvtk4lD6y5jTezs3nA2syD5z5IUXZRRJd+W/ctiz9dzKJzF/GzYT+L7f7dmx5nt75UNR5h5oY58dN4QcD+3n8g+qYu5Q5Sym9LbsVfrrzY6x+rrlvNzJLHY/dLoXQ9YkzP6iH0aLtNShqPwbu/C9Y2uHSpvy3Wl7fHDEXjlTgjszdVeYOYuTF4jl5x4Qru23IfVY4qVo3+LQXr7tS07yTU3IgVw3ajJVI/qLWrBvzOSel51DiqcMpurIIZUbRw7frrg3306N96K+uROwjmroec9rgi0RozSYhht8mAoTHD7PWzg8bdC9NeMDRmSGwq0/PADuDctuNylEpNugszKGlOlcDzgiCMabv+t7IsN6kNBEG4GbgZYNCgQQl47G6Amodbd9ibdwvAgr3ak9qJH9onQ1ue8qdWHq/LERenECpvVy/ntsBWQEVjRch83+4e1HWF7X75Qz1mUYhLqWyVgXYbogD7KuqVhZnv/qV80G907J2KJjj9Sj76+hV6Z/ZhQNaAiC89Oedk+mT04d1v3k3VhZmE0tU+1ym546vxUlcGgXoyqj+0n6x87uMfnRlKae2FZy30brt9bs9z/joEobboh9L1MEgYXW23SYnbqa1tMPUhxe5V2/W42m22fHu7vsfv92iOxRZ3CzeccQM51hyc6QVIWYWIGvZtaG5ERo+03Uj9oJ6emM85ESjw0ZOpOFGmPUf4VoisO6zYtQ9OjzO8bzeImB5pt4nA0JjRHHcuyRX+4hQgkb9qh8qyvARwAciy7CD8qpcZOBN4UpblHwFNwF2+DWRZfkaW5fGyLI/v3Ts1tn0FoerB+JI7SNGO0doq6tveUduuQRN4fYx6MoGoueS+RJJLHuq6cCVvuwNdYbtfVpxgkD0Dsyl+Qz3NbKJ/rq1dAPjbjxX7URf9YqRl8CS22Wz8uMUZVYUlQVCEgrcf286RxiMdegaDYLra51pFs7ZfiDUf26zjP1X/F/B5usfNgjMX+G27XXDmAtJNSun4sFv0w93PICF0td0mJXq2WHUwIGawaLazCsFjsbioGBnZOz7mfnALpdP+hGSJ38uAVKNH2m4C/aDuHNHsU2tE1WD0Id2UHtq3G0RFj7TbRKBqzPiSQhoz6WZj3IUikQszTkEQbCgCvgiCMBSf6kw6lAPlsix/2nb8BspCjYEvoilYX2bGCnC3wqvXtr+VqDusHIum9rzdLcsgo0BbnyYGPRktYs0lD3VdTUsN8zfN974VqWiqYP6m+dS0GEW+9JBlmT0/1DM4TilMvgyyZ/BlxQklZ/vwVugTo76MD583HqJFFLiosozMYweiunZiv4kArPtuXYefwyC5sNt6s3zy0mCNGFuM/kpPx0D1fwGfS6KJe7fc6+d77t1yL5L6VrW5UtvvNldGdj8Dg85CyxZnrICPH1GOVdu1ZGhqINjNGSw/Z5HfWPz9+N/zh4/+4D83b72fmpTafG4QlgT6QbuYxvLJfwmYIx7DvnNV+72ufknRYPRB8jhD+3YDg0RgzdTWmLHGP1ZPVrTGnYFCIpfn7gc2AAMFQVgFTALmhLpAluWjgiCUCYIwQpblr4ALgH0JfMbuicsBHz4AUx5qz0/88AH42bP6KUqFo5RcXrezvSrTnHXtVZky41clRBREhuUNY9X0VVGlHYW6LtFlDXsi31c3U+9wMbR3/J394PxMPvmmmhPf76KXqxn6nNbhPj+uLiFNMDPWLdC6+1W+mfJAxNcWZhRySu4pvPvNu9xw+g1R7bgxSG5Ek5lhecNZNXVlTFWZgjsU/f1hYOpRwOdOEZ1UqrZtt+G26Ie7n4FBZxFoi6AUBCj30YKoOwzOJuhzuqLJ4VPVRmw4wrD197DqJ3/AmWHH6nbSIss648OYmw18SKAfFJ1NDNvyJKvOfwKnaMYqubEf2oY49c9w0WLdqkxOyRXatxsYJIKWeih9H2a/C7IEggglr8EZP4eMni+A69IZd0Yqk0LCFmZkWf6nIAg7gYkoKUy/BSLZs3g7sEoQBCvwLTA3Uc8YK5IkU93kxOn2YDWbyM+0Ioqd+EPQbIXG4/76MiOm65dgM1vb83ZDaSGE+CwSfZd4aMCoOehqX0ebjmI1WbGKRqnNaNlVVgvAsD7Zce/7pPwMACr3/ZteAIWndqg/WZb5qHoPo7IHU3/yUPod/ID02sO05EWep3xOv3N4af9LHKw9yAj7iA49j0FyIZrMEQv9ahLg29zpuVThwSWCBQ/5kov61gZ/39WmaWBtPKJTnrVt+oykBLGebkIc6PL5qAfSI79TdQxIkvJCBhkQYPpSqPte2U1bvr3ddk1mP6FUAMxWxMbj7YKqQNUv/2HMzUlIt7Vhjxsaj4Yscx2E2Yr43b8o2PVy+7ncQTD8IsjS101UU6ACbVcQzZSdKMNislBgK8AcpxST7q6TGE+6rX3GA5MFWhrA1aJkNEhO5diUGuWiVdkKY87QJqEeQZblalmW18qy/J4sy1XAtgiu2d2WozhaluXLZVmuTeQzRoskyXx1rIErVmxh0iObuWLFFr461oAkJaa6lSaBW0LV8q4BpS2DtoqG0kII8Vkk+i7x1IDR6qvB1WCU2oySnd/XYbOY4ir8q3JSW/lt6fCnSiWwzI5tRz7Y9APHnHWM7TWEY0N+jCya6fvF61H1MaHvBEyCife+DaUvbpByBPg29yd/5WD9N8zeMJeL37qY2RvmUlr/DQ9+8qCm7wqbStWFqUpJMR/1MHrkd6qOgXd/p+jJPD8Nlp2hVB9TK4Kcf58SS4SyXQ1b71V4BkuL/cfH0uKl9LL06qS/nEEgSWnDkZTLVsu0Pz8tZJnrIGL0wVq+fWnxUh7+9GFlblg/m4O1B3FHITSvR0/QSYwXSWmfnUlmbzjjSnjl5/DEeOXPM67scBzdXehl6WXMGSFIWLlszZsJQpksy3GrBdYV5dgqG1q5YsUWymsd3nNFeTbeum0SvbPTOu9BfN8AC4JmaUtyiiC7f/uOmFDlCkH3syqTKWxJwXiWHdTra/Ulq5FkKZFvG3pUKcFLlv8bBLjn4lEJ6f/21TvZyK/JLxoO593Zob6eObye/zn0LktPvYkcSyaDvniT3oc/pWTmy7gyI7ef5TuX80PjD/zzqn9iEk0deqZuRI+y27gT4PeOzt/B7E2/DvIvC89ayILNC7zHvr5L8ripcVTqp1KF2omYQJJmPoqNpLTbbv6daqOOgSkPwca7g+d59fzc9f4xgxYBtn4EiYc/fZgZw2Z4K2ysKV3Df579n/TryC635CYpbVclKW04knLZvmXafdsElLnWJEYf7OvbBdHMw58+zObyzd7Pw5TxjZgkKc2dFHablPbZmdSVKYvigXY+Z11KlMs+0ngk2jkjRbZSKXS2BHS3Xw51uj1+zgSgvNaB0+3p3Afx3RrvW97Vp7QlC/b6T0zhtBB0PnNiCqvvEk8NGL2+Wtwt9M/qr3OVgS/NTjf7jzRw6ZjEfV8/ynWQX3UMel/W4b4+qt7DybY+5FiUnTjHTplM4fef0GfPW5RPvCnifib2m8hTJU+x8/hOJvSd0OHnMugBBPg9l0nbn+VYc/yOfX1X2FSqBKYqhSJp5qMeRI/8TtUxYMvTnufV87Ic/sdsgK27T5SxuXyz349ZgDsm3BGvpzeIkqS04UjKZfuWafdt44lAeyJGH+zr28vabNmXeGlfGDqJ7SSlfXYmklvbzuOwM6s74JbcxpwRgrgvzAiC8D9oL8AIQG6879fZWM0mLhpVyJXjBtI/J510i4lmpwdBEJAkOTE5kuHeBESicSBJys6aUO3Uz4rGwwX3Q68BIAhYZcLmA8ZTA8bIP+w4JeX1eGSZYX2yEnaP/5f+HQCOvJF0JFmq2tnA3obvmdHnbO+51sx8avqPpfeX73LkR9fiSYvs7zGmcAxppjTWfrvWWJhJYfxy+c1m7CMvQTygpLhZPB5N/1LvrPc7tiIoi95mK9jywVHdJeK9oXLxrWYTRXm2oDePVnPku8VSOtdfg1DfaeB3lWezUOtwJcd3FypOUGMER63/PD9pAeQOBlsu/HaP0tbjVjQ9ItiBIMkSZh2djnjpchhEj5YNXzSqEEEQ+KG22WurQOeNfbNVSZUbe2170Yrdq/3jVJNFu01aluKLJbeipZjVF8zx1+OwmCyatmwRLVQ5qjq0W9uIa9uxmk3c8uOTuGr8IEyigEeSeWP74ajmrW6NaIYpj8DIqe3ivwc2pEy5bGPOCE0iIsvtwA6N/7ajCPt2a/JsFuZfMJw3d5RxosXN3JWfM+OvW7j66a2JyZGMJC83XH6t2se6hfoaNGofI6bD1CWAAO//Eaq/wb72DpZPvF9X30WSJRpcDSyetDguGjCxlts2aGfX4ToATilM3MLMaPkALbKF/XLkAr1a/F/Nl8jIjOk1xO/80VOKMbua6b1/bcR9pZnSOLPwTP75/T9T8k2UgUYu/4Y5lF54N9LISwAo2P6iZn7zmtI13uPlkx/DvmVFu889tlfR59DzwYn6u4TJxc/PtPK/s8ZTlKcsjRbl2fjfWeO9P7o62n8qoved5tksft/VPW+VcCBZvrtwcYI6v+9ercQAI6YrmjIb74anfwwrp0Ptd4pO3bG94HaFjTvUcfbKvld4bPJjQeOpE9MzDAIItOGLRhUy/4LhXP30Vj9bPVTd1Hn2a8tXtBA33q3Y28a7lWNbfnubzD7Bbab8Cep/UFI/lo9V/jz+pWKjcabAVhA0NywrXobD7eiwNowR17aTm27mkrFFzF35Oef/5WPmrvycS8YWkZueIj/Ms3rD4InwwqWw/EfKn4MnKudTgPz0fM0YLD89P8yVqUGnaszEm67UmPnjJaP4r/f2JT5HMpK8XAj9dsu3j1AaNJIEDRVKALb2D3756FLReGp+8gecWYVYew3AntnH+8ZAzZ0tsBVwwxk3kGPNweF2cFrBaTFPOl2kXp8U+bfx4KYXt7P3h3oeu3pswu4x8r3LOVjt5MCEh5h9euxvfX735TPsqP+a/z71xqAy18M/eQpbUxUlM19GjvDNUkllCct2LmN58XKKBxXH/FzdiB5jt/FAN5f//CcoOHEMHLW4XS6qhkzEJXmwiCby975LvX2gUgK4uQb7zlWIY37Rnhaq6nD4Hgf64AQQSS5+R3a8dHGuf9LardZ3Wt3k9Puunv7luM6JASIhkjjBtyqT5NbWOFDn/DnrtD/36c93nF0x9Apmnz4bk2AizZRGga0AS8+uMJK0tqvia8OCIHD101uDbPW/ZpzO3JWf+51LmP1GYqNabX79Oay6stP0ONySmypHFS7JhUW0YBWtXLv22rhowyRBVaaksNuKOoemPb52yzn0T0CxiqQjxTVmqhxVvLDnBS4ffjkmwYRH9vD2wbeZfcZsvTGVUlt4E5HK9C4htGRkWe64IEUXouZG5tosnZMjGUleLoTOr/XtI5QGjSgqOeaWjKB8dLF8e3uJzAV7la13bai5sxVNFV7xTICNV26M+q/rfZS2stkG0SPLMrsO13Jqv8QpnIvuFnLq9vGlMI0DVbHbvEty80ntfibkDgtalAFl18yIrU+TX/ohVSOnRdTnqPxRZFuzWffdulRZmDHwQTeXv7laeQuLMvH1XbBXCYJqvoMNCwnyNhNvbf9/1R/6Hgf64AQQSS6+KAox/5BK+Vx/HbS+08DvqtNigEiIJE7Q06Xzba/O+XoaCD79+Y6zt755i7e+eQtQ5v0evijTLfC14R9qmzVtNcNqCjqXMPuNxEa12ghCp+pxmEWzn9BvRWNF3LRhjLhWweWRNO3R7UmRClUprjHj9DhZuX8lK/ev9Dt/7ahru+aBkoxE7Bv77wT0mTSoubt1DleHc/sjIpx+jCSBowZcjrbFEllZXPHdNRNJH74VnlzNwfnoWtep30mI3NkkeEOQcpTXOqhqdDIsgWlMmTV7EGU3R23D2duBhZnt9V/TLLUGpTGpnOg9nKacIvrufo2qEVP8FgT1MItmxvcZz+ayzTS5mshsExQ2SA10/VFzTXujEdPbAv4yEM1IIy+h5syZ/jtmHLXt7VV/6HtsTrw2QDw0ZLqy/55E4HfVaTFAJOjN8YKgzO+iGDzPa7VX53zRHFYPJN2czooLVmAz26h31vPcnueoclSlpGZGsqM3zpud/nN3Qu03lI2qWl4mjTayrH1doB5FgirjWU1WiouKgyrIGHYeOxaTqGmPZlOK/DYQzZHZdA/FGFOhifsokGX541D/xft+nY2au/vmjjIeuXJ0zLn9ERNKP0aSoOZbOL5fyQ2v/lopNRiYEx6uD99c8m1PQ84gmLGiPR9dT7umDb3c2dy0XH+thxhzcw2iY+dh5QfksD7ZCbtHduVOAFpyh3GwVsLpiS0l8uPqEiyCmVOzdLZvCgJHTynGVldG7qGtEfc7sd9EWj2tbDq8KabnMui+2K25LJ/sn7+8fPJj2HeuUhqMmK7oGLT5SunTZyi98G5mljzOlA9vYmbJ44omzeHPlPa5g+DqlxR/6HvcCdoAHdWQ6er+exKB39WbO8p46vpxyfHdac3xlz2h6Mod36cI+gbO81e/FNx+92rlfFZhSD0QSZaobK5k8bbFzN04lyWfLWHBmQt46qdPpaRmRrKjN84H52d0nv1q2ejVLyk2qsasrQ3BbcxpwbZ69Uv+ehyRaDHGSG5aLvPGzmPJZ0u8tj5v7Dxy07p9LZMuozArLch3PnX9OAqzUqBUNii2G86mezDGmApNwjRmBEEYBjwMjALS1fOyLGu/Go+BrtI7UHN3JUnCIyupIwlVtNd7E9B4DI58EaQH48U3fzdUH745vde8rARnE+dBr/4gmED2KCu5FpvuWwitnTE1LTXaWg8x5OZ2EkmRf9tRHnjnS179/DDPzpqAKUEVFkZsvoXMmr08d9JfeGQnvHdlJqcXRPemTZZlpn12H/nWXiw4eYZ+Q8nD6A8fxpndlwNXLI+ob0mWuOvfdzHSPpInL3wyqufqhvQIu40bjceQ3vu9/w6Y77YiTrxFefsqCMqiTJvPq7puNTNLHtfXpMkpgs/+BoPO8t89cPES5bMEk+iqSV1Ylanb2W3SV2VqqID6cmiqhC3LlNTl3EEwd72fzXvn+QvuA48T0nsBgrLzdtcqmHiLf3vwiyd0dZwuXkVBRlLO7fGm29tup1dlUh7Cf9fWuoXwlY+4f+4guGmzEnOqsaokwdYV8KOZIJpA8ig2evbN7XockWoxxoCurSdvHBuKpLFbt1vieGMrbo+E2SRSmJWG2ZwiO2bqymD/2uCqTKdOTxmNmSjHlKExEyeeB+4HlgLFwFx6yJcbS05/zIs5obZnup2aejBefPN3RVG5Vk17qj8MZpsSlGUVwoy/ti3EiMpE+VVAJZwFe0NOcFq5s7paD0a1nISy4/tahhRkJWxRBlkmu3IHjfbTGZqjnPqyyhP1wsx3jqP80FrNBQVjQjcUTRwdOpnBe/5B9g+7aRgQXtBYFETO6nsWGw9tpKalxniL29MI9Ivpdmg6Bh4XiGbEfmMo8HjA7QaPB1pqlEUZCMrvdmbYQ2vS/GY7bF0OgRu2pvypA48f+WJIRzRkIrlXR/vvyQQKqJoEEEWRfjk273eYNN+dqhH33BTluGi8sgCT3VcJ/q9+UVm0ObgBCk+Fc25Tfhw//ePgvibcAJc/qSxCqgs8PvGE7twuGXN7sqD+8HV5JCxtP3y1bLXL7FeWISNPsVF1wXvLsjYf7vMD3d3S5n8DXspMuMGnTYRajDFgxLGJpfuWn+kAkhs23qn858uIKV3zPJ2MMaZCk8jlSZssyx+i7Mr5XpblB4DzE3i/pEUtSXrPWyV8XdkUVLJQtzxhuO2ZZmuwHowvgToyatrTyovh8THwtwuVwGzao4AAL10Bx74M3U8UqFoPvqjaMwaJocXlYf+REwzrkzh9mfSG77G01uDIHUa/TLCZlYWZaPmoeg8Ao7NPDtu2cvDZONNzGPD5yvYf2GGY2G8iHtnD+4fej/rZDJKYQL+45QmlfOrz05RyquvvhGEXtadh7F4NE36l+L1lp0PVQT8fZ22u0fZTqiaN5NH2iTEKnHZmiWqjHHbsBH53Vz+9la8rm7jnrZLk/Q5VHY+i8e3lsJ+9UBkHLSfg8Gcw/lfKXL9yOtR9r23b1aXtKUzn36f05xMHGHN7cuN2Sxw41sDVT2/lvEc/4uqnt3LgWANudxemkQf67ZUXK3559+p2W5v2qLLbyzfmhfD+V7X7wDZx0AEzbD3+JKV9diYmHXtNEZsyxlRoErkw0yIIggiUCoLwG0EQrgAKE3i/pKW6yclNL27nynEDufPNEq/gVXmtg5te3E51k84qYXMlvHpt+5uAusPKcXOlcpzRG/KGRKYH01wJtd/Cmtv8+6s9BM1V7ee3LItIVyYS9LRnjN0LiWPvD/W4JZlTeiduYSa7Utme2pw7AlGAk3vFtjCzqeoLTrL1wW4Nr4UjmyxUDL+Q7KN76VW+I6L+i7KLKMoq4t1v3o362QySmEC/+KOZ8Nov24/HXqtxPKv9+ONHFJ/Z5uPsO1dpaNIsbdek2bVKJx+8vXJHNKjzQcTzQAfozHv1NLS+uzvfLOHKcQOT9ztUdTzOuxPe+Y3/XP/Ob5Sx8rr+WCC3TV/u40f8rzvvTr84wJjbk5vjja3Me3mHn+3Oe3kHxxtbu+6htOLZ12Yp/lk9bq6Cv8/0b7PxHvj5C8E2KvoszITSUewghq3Hn6S0z85EELX9booURjHGVGgSmcq0AMgA5gP/hbJbZnYC75e0xFxiO9z2TFEE+xCw5cK0R5RBPXd9cFUmtS817SkQ3/Pl22HTg4pmTZ/TwZoRs7q9KIgMyxvGqojuz40AACAASURBVOmrjKpMncTusjoATklgRabs49txW7JobXOqQ3rBpnIJSZYRNUpea1HlrKek4RAz+kyM+L5Vg86mX+kmBny+khNF45TdXmE4d8C5vPbVa3xb9y1DcuMmb2XQlQT6RdHkfxyY1hl4XL4dPnwA5qxTLjdbGWbL9/dT1lzESx6DqX9uT5Wau17ZZm+yKIsyptimz84sUW2Uw44dve9OnceT8jsURSgcBWlZ2rFD4FjxjoW1eDPN35ijnPe9rmC4UhSgLQ4w5vbkJinLEevFs7a89mOtGPWrtXDeHUpMqqY8ffgA/OzZ9jaq3f/qg7hXZTJsPf4kpX12Ju4WxYZD2XQPxhhToUnYwowsy58DtO2amS/LckOi7pUIwon8+R7brCbckozLLWnm8FvMYmwltsOVuQZl4smMQIDMN+2p7rCyNXnSAsjuBzXf+N+nfLuyrTQOwmla2jMGiWPX4Tp6Z6eRm5G4LYG9jn9Oc+5w7+r+0Bx47xAcqpcYkhuZzsxH1XuQkTmz19CI7yubzFQMv5CTv3idnMOfUj84/KLOuf3O5c2Db/KP0n/wHxP+I+J7GSQxgX5RTTVSj9W0Tr1jgMbjSj9t/k2EYD/l6/skSVmQkWXlzygDCF+9B7MohJwHwmnCaGlH6IkmGuWwY0fvu1PncQCn001Vsyuif4tOQxQVoX6t2CFwrIAyFgRREbJuPKYc+6JTxtWY25OXSMsRu1weRYBVkjGLAoVZaVgsnVwu21Hr80DN2m2aKuHv1/ufizGVNBYMW48vRrlss+JnA206RcplgzGmQpGwUSAIwnhBEPYAJcAeQRC+EARhXKLuF08Cc8vveauEAzrHv3llF18dbeBnKz7RzOGXJJnGFjePXjU6+hLb8dye6Zv2NGJ6e/75WzdDRkHwtro4bQM16Fx2Ha5laO/MhPVvdlRhO/EdzbkjvOeG9FL+3Fcd+duOTdVfUGjNYUB6flT3rx50Fi0Z+RFrzfRK68XY3mN555t3cHlcUd3LIEkJ9IuBqUZqyV+942j9WwdLsQbm0y9690ue1CmzHE4TJtrcfKMcduzk2SxBJV0fubJ9Hl/07pd8VdnEA+/sTT6dBL3y2eHS8sKV3Y5D+WGDxFOQYQnyMU9eP46CjPbFDJfLw4HjjVzzzDbOe/QjrnlmGweON+JyJWgnmF657N2r24/zhmjEvK8ou7VCpZImsFy2Qfyxp2vbpz298xbbupQUL5dtEJpElssuAX4ty/K/247/H7BCluXR8bpHokq3Vja0csWKLd7V3Kd/OY7/em+f5nHgZ6A4mbdum0Tv7DRvX72z0pg3eSj9c9KxWc2kmUXSLR2syhQtktRelWnlxe1vJYrGw0WLodcApXqD2QaZ8dkG2s1ImlKCsXC8oYWz/vQh1589mOmj+8W1bxX74Y2M+PhWvp3wAI7c4QC4PHDlerh5jJU7z04P20eTu4Ufb72D4vwx/KL/T6J+hvzDnzFk16t8fdH91A7RqCgSQEllCct2LmPp5KVcOPjCqO/XDejWdhsToaoymSyQ2UepxKR+bssHR3VsfrSDpVgr6hxc/fRWvzniolGF3H/paQB+u2IC5x7wn0+0+irKs/HaLefQP9em81V1WTnscCS13VY2tHLPW4qmTGF2GvlZaTS0uCivdfDUR9+wq6yOojwbf7xkFLe8pOhehfu36FQCSxMLJsXmA8dKYFpeqLLbcdhF20NIatutqHPwwpZvuWr8IEyigEeSeWP7YWZPGuK1zR9qm7nmmW1BvuTvN09kQF5GXP8OXgL9tpZfhuCYV5ag8ai+zSawXHYPIyns9ofaZl785Lsg+5x17smJs71kovGYUrQgsAT8pN8Y9qpNUgQsnUUi9001qIsyALIs/58gCN0inSkwtzxQG8b3OJxujNpXea3DG7wBbLmzOLIyhaIYv4Gqpj3VlQXnmT83VSmJnXdSfO5l0OnsPtw5+jKSaKWlV3slJYsJBmXDvurI3rT9X+0+XLKHH/WKTfOlumgc/Uo3M+Cz56k96VxlYgvBafmnkZeWxxsH3+ipCzOph5ZfzCnyPw78PFY/2sFSrFr59O/vO86900cxKN9/d1s4TZhYcvONctix4XR7eH/fcd7fp6T2/P3miVzzzDa/NqrmjO9x0ugkhIodAsdK4HW+ZbdV4lR+2CDxuDwST//7EE//+5Df+ZkTT/L+v1uStX1JIquNadmklo0GnRND22wCy2UbxB+3JGva53U+9tmjcTu1S8CffXPXPI9BUpHIhZnPBEF4GliNUqr+GuAjQRDOBJBleWcC790hAnPLA7VhfI/D6cbEkuOf8DeckWjXGHQ7dpfVYRIFTi5IXCpTr+Of48gZiiz6bzkd0gu+qIrsB8nm6i/INts4JaBcXsSIJn44dSqnfP4C+Qc/oHrklJDNTaKJ84rO4+1v3ua7+u84OSd8eW4DAy8d9JfR5NNbzSYuGlXIleMGkmuzUOdw8eaOMu98kfK5+Z2IVhyg9W9T52hPkewx/xZGjNCtsZhETVv1tU09rStzcuymiw7DXrsVZlHQts/uaHuxYNirQQgSGUGMBYYD9wMPAKcC5wJ/Af47gfftMIF5+W/uKPPLNfc9fuqjb3j0Kn3dmGhz/MNpDMSFBJYWNOg6dh2uY7A9A2uCxCdFVzOZNV8qwr8BDMmBKofM8ebQizMuycPH1XsYk30ypg4osNf2G01T7kAGbF+JEMFbsckDJ2MWzazavyrmexqkKB30l4VZaUFaJU9dP47CrOBdLHk2C/MvGM5/vbePa57Zxn+9t4/5Fwwnr21XRjR9GXSMwLl756Fqbg/4t7n9guHsPFQN9LB/CyNG6Nb0zrRq2mpvn7izMCtNU+ejW9qvYa/dinybtn3m21JkYcKwV4MQJExjpjNIpN5BPKsyRbMDJpzGQBz/gvHTruk5JEX+bSx4JJkzHtjIpFMKuGFSYnaE9DryCad9cD3f/+hOGgvG+H22pwru2grPT7NRPEhfwG1r7QFu3rOc2wdfyo9yIq/IpPk8xw8yYutTHD73Vo6NvjJs+7/t+Rs7ju3gg59/QE5aTofunWR0W7vtNnTQX6qVlNweCXOI6j2R+P9I++oGJL3d+s7dgiBo6vv8/eaJeCS5u/9bBGPECKFIatuNNI7s1KpMicaw10hICrvttN85yYxhr9GQIlupFBKWyiQIQh/gIaC/LMvTBEEYBZwjy/LfEnXPeKKVlx/u2BdJkqlzOHE4PW0p25HZVTiNgbgN5nhq1xh0Od9UNtLs9HBK78Tpy/Q6/jkyAs05w4I+O7ltnWNftUTxIP0+/lm1kzTRwqjsEI0i5EThcOp7D6PfzlVUjpyGZA0tGvfTwT9lS8UW/lH6D+aePrfD9zfoxkTrRzvoL0VRwGISkWUZS1s6QWVDa9BifVj/D5jNYkhx2SQW++12qHGAJMmU1zVr/tsAQVpBSUkn27xB5xE45iPxIwAWi6l7iq3q2bJhr92CSO3TwCAVSeTy3EpgI6AKSRwEFiTwfkmDJMkcqm7iq6MNLHr3S76tbObqp7dGlJqk5rX74tWkMUoCGuiw94d6gITqy+Qc2UJLr5ORLMGBXJYF+mbAvir9idUleXi/chdjsk8mTYxPWcQfTp2OpeUEfUveCNt2UK9BjLSP5IUvX8DhdoRtb9BD6WQ/qpWeeuBYA/e8VRI0J4T0/zHeK+6psCmG+p1W1LVo/ttYuoOmjBE79Fi0xrxHkruvrYbDsOVuj6qV5kuPsc9IMGzYIASJHAUFsiy/BkgAsiy7gZRYDq1ucvJ9dTN3vKGU2rzzzRLv6nB5rYObXtxOdZO2LkZITZrmSnj12nbBqLrDynFzZaf8vQySl/1HTmAxCQkr0yq6Gsmq2k2j/XTdNkN6wZchFmY+q/uKencTZ2lo1MRKU94gavqNpu/u1zA76sK2nzF0BtUt1bz21WtxewaDbkYn+9HqJic3vbjdbw6Y9/IOrhw30HuszgnRapJFcq9Q841BeNTvVJblID25R68a3cVPFyFG7NBj0Rrzi9fuY8XMM7unrYbDsOVuj54v7c7SGlFh2LBBCBJZlalJEIR8lIpMCIIwEahP4P2SBqfbQ4bV5C2lGc2WPVEUGNEnm7dumxS8Fd0oCWigw5cVJxiYl4EpQSkLOUc/RZTdNOafodtmSA58clSm0SmTZQ1+jg2VO7CJaZyRfVJcn+2HU6eRd3QP/Xa+Qtmk20K2HWEfwWn5p/Hsnme5avhVZFq6QQqCQXzpZD+qt207sMyy0+0J7f87cC9ji3jsqN+pKAj8ef0B/njJKG8lkSUbvuLxX4zt6kcMjxE79Fi0xvz7+45z+/nDuqethsOw5W5Pi1tiyYavguxzWU+wz0gwbNggBIlcmPk98A4wVBCELUBv4KoE3q/DRJubH6gjYxJEXB4JGWh2eijKs+mW07ZZTX4aA3k2CydaXTicHjyyTLrFRL8cm//9u6LEmiFQlfTIssz+IycYOzA3YffIOfJ/eExpOELsdhnapjOzv9rDhH7+rsUlufmwajc/yhmCRYyv22nJ7kPVwAkUfvkOR8f8HFdWaGX7K065gsWfLualfS8xb8y8uD6LQZIQym91gh8NFI2NpMyyXrqS2+2hsskZkUBnYInncH0bhEf9TuscLnpnKzaSYTVhz7TywGWnYRIFjtU7EEUxqEhA3PR9OjoPG+VZeyx6Y14UBIYUZGISBeyZVs46KReTKPB9dROWNqFqgOONrbg8kvecKAqdp1EVi10bttztMYsCM8b05dR+vZBkmX65NmaM6Zta5bJHTIex14ItDxy1sHu1YcMGQAIWZgRBmACUybK8UxCE84BbgCuB94HyeN8vXqh5uuqWUHUL+Yg+2ZqTkqojc+xEC89v+Y7bik/B4fRwxxsl9M5K44HLRvHoVaN5fst3PHLlaG86U1GejRdvOItjJ1q997poVCELp46ksqGVO94o0b+/WmJN3QKX6BJrah5k4P0KRxmLM0nEsROt1Da7GGRPpL7Mv2nOHYkcQhvmlLaFmd3HgxdmPqndT4PHwVk58Utj8qVixEUUlG2nb8kblJ17a8i2Q3KHMK5wHM/ueZZLh17KgKwBCXkmgy4inN9KsB8NnEtu+fFJ3H7BcG59eYfXtz95/Tje261Mh+pxbrpZ89pLxhYFXTuyMEtzcUZNhQqcxyJNhTIIJjfd7P33+s35w3hiUymzzz2ZW1ft9H7Hj1w5mn99dYxLxxYxz+ffKlQMETHxmIc7O3Yw6DS0xvzqm86mzuFm7srP/fzGzu+ruf3VEorybLxy09mccLj97PWp68eRYTUx67nP4mvDWsRq14Ytd3vybVbGnVzAdf+7zc8+U6Zcti0fzlsIr/2y3Yavfkk5b5DyxL1ctiAIO4ELZVmuEQThJ8CrwO3AWOBUWZbjtmsmnqVboy3fVtnQyt4f6vnjmr388ZJRWE0if1yz13v9jwbmcvfFp9IvNx2TKCDLys4Gq9mEjMzPVnzibfv0L8cFXa97/87cwdJ4TBGlCnwz8asPeqr6fVKUEoyWTQeOccPK7dx/yShG9usVlz59sTb9wLh//Jijw2dSPXh6yLa/2sT/Z+/Oo+OqrkT/f8+tSaXJmkqyZcnzhCfANuAhHaaEkDAtAoEkGAIhYEITOukXQvLrEDK+fsB7TZ67m8lJk4AJbR7GHYZOmIdgbMDGYGNjYTxKlm3NtuYa7vn9UaqySqqSqqQaVfuzlpat0q17j1T7nnPr1D17s8Bl5ZEvhSYI/vHuR3mjeQf3n3ITViMxn95P3foExUd38tHKJ/DlDF0Ou6W7hZ9u/ClLxi/hgfMfQKmM/qQmI+M2YaLptxLYjw4cSx6+djG/en7XoL790evPoKXTHbyD5ueXzsdmMUKe+/IPPh98c9X/uetuXhqxmkoGVWXKiLitb+vm589+zM8umcfXH9nMXRfPjfh6hnutRl0CNl7jsNz9Gk9pFbvhqjJd/cjmsDH6xfvfAuCVfzyb6x99b9A2v7psPjf84f2QxxJSxng0cS2xPFJpEbeHW7vCxudQ49qYkn3vrUYrLS9gEiURPZlFa93S9/+rgUe01uu11ncBMxJwvLiIdW3+wDwygf8HbKtt42sPb0IBE8Y5qSxyMrE4F1eBA4/XDNk23PMjHj9QErCo2v9vIgcjWQeZET450g7ApNLEDGglh14GoL1s0bDbzi2GLUd9IUncenxuXm/azqLC6QmblAE4OvM8LN4eKj7+87DbljhLuHzm5bx9+G1ePPBiwtokUiCafiuB/ejAsSRSnrGWTjdXP7KZVY9v5aVdDXh95qDnWgwV9rneIaosBUo8B8abNJ2UyRgen+l/fUw9ZN64SK/VqPP7xGscTua1g0iqged8IFb7C8Ro8DmKsNvk2i2DHktIjqrRxLXEckaLFJ9DjWtjiry3EkNIRI4Zi1LK2leF6Xzg5liOp5Q6ALTjr+Dk1VovSUAbBxm4Tvf06iJuP38mhuH/xCxwt0vg00e71RKSR8beV/7Nle/gRxfOZnxhDj4NhlKYpsYwVPBTDSC47S3nTGf8uBz2NXYGj396dRG3nDOd0jw7qt/zgxL5aUH/fSsla3kzwK76E5QXOMi1JyZlVEnti/TkVeHOmzDstnNL4NU6zf7jJtOK/Bd4b7fupMvsjWs1pnC6CyfQOn4eFTs2cPTUr2Hahq5Qdf6k89l8ZDO/3PxL5pXOo7qwOqHtE8OIpV9Lcg6Z4e5CGZhTZtXfTWHRlFKKnDZK8uxcMLecl3Y1BLdf9XdTmFDk5LX/cTY+U/P0lkMYhkIpFTIOBcreDvxkMWvW4qdI4PU0TROroXjx+3+HzVD8+e9XkGu3DHo9q4qdEV+rUef3iRTPFrv/k9fAOeAshe5muYtAYDXC57WyWgzW3byUtm4PShE2XrvcoZMwF8wtRynF4dau0d+B5/NCx1HwecCwyvVllooYn9kyrkmeJDGERLyTexJ4UynVBHQDfwNQSs0g+qpM52qtmxLQtoj6r9MNTK48unE/31o+NSQ/TGC9bWmencmlucE8MreeO4N//+bpgD/x77UD1ujOdOWzp7EjuP9//+bpdLl9weeW5NmC+4p0TMNQic37MnDfsy/yr3vsvw5S1vKmnZ31x5mcoLtlrD0tFDa8T9OUS6Pafm6J/98tR33BiZm/Nm6l0JrLnPzET3wcmXk+xX9bjeuTv3Bs4VeH3NZQBrcsvIVfbv4l33/j+6z9ylqc1sSUGxfDiKVfS3IOmeHyjw38+QVzy8PmlAF/tZRA3piB6+s3bK1jR/1xHlq5OJj34ekth3hw5eJB+wok7hTxF3g973+5hm8tn8of3/GPyTf+cUvY1zPw/ZG2zpDXLm75fSLFc287rL08NEfBm/dCzQuSDy7LlTrtYfugJzbt5+G/HaCq2MkfbjhjULw+uHIxRU5rcMLmgrnl3H7+LK56eNPoc874vHDs45PXk3J9mbUixWfW5JjJKRkc+1c97n9cZL2455iBYGnsCcBLWuvOvsdmAfla6w+Gee4BYEk0EzPxznfQ/1PPq4dYSx5YbxtalcmfR6bmaHvYXDFPrVoWHNwA1t54Jj9+ZkfwGIHJoKriXL6xZvDay+Aa30SuTQy379kXwVfuBa2z4VO4tFh/G4vOXi/z736Rry6q4srFVXFoWSjXZ/+PGZvuZO9Zv6ancNqw25savvkSfGWajXvOdtLl6+XsTXeyrHgO1048L+7tC2fO31Zj87rZ8Y0/gBo+Vnc07uC3H/yW8yadx31n34dtiATHaSrj4naQWPq1JOeQGS7/WKw5ZSYUOYOTMgN//sX73+KCueX8/NL5wbs0xzksUVdlyjBpGbeB1zMwNg+VU6Z/jqC7L5lHRUFOcqoyKQusOXfwOfCl/wnrVp78XnIWJEpaxm5ApBwed108l1WPbw1+f/9Vp9HS5Q65a+HXly9AoYJ3//W/bg08b0Q5Z47XwaNfHnx9+eV7/P8f+9eX6SAt4jbrc8wcr4P//tHgqkxfuRfGxf86fgzIklup/BKy9kFrvTnMY59G+3TgJaWUBh7WWj/S/4dKqZvpWx41adKk0TY1hGEoSvPs1LV1hawlDywtCgxepmkGty/Jc0BfMZzDrV0Rc8V4fCfzypxeXcT4cTkhx6hr7eYba95l3c1LI69TN03wdCdubWK4dY81L/gHzqKTdzuY2qSlpwW3z43dYqckpwQjijfA2S4Rsbv7aDsamJKw/DIv4s4po6dgalTbGwpOKYZ3670AvNm8gx7TnbBqTOE0TP0c07eupbB2CycmnTns9gtcC/j6nK/z5O4n+dGbP+Lez9+LzZJxkzMJk8g+NyiWNdeRtvV0Q1stWO2YzlJaLBbcWLBbLJSokSdUC5d/bPm0UtxeHwebO7EYCle+I7jNcDllXvsfZw+Z/+GlXQ3cfYkOuUCdmKBlimPZSOM28HoHXsfhXs+Af7poLlarEf8kqXAyp0ZAW234c8BZHPr9KK4LZJxPnVhj1+Px0dDRG5y8jZTDo8hpC/ne1Do4URNw9yVmsO853NoVv7xJPk/468sv/QZKoru+GC2J6cSKNm6zPseMz+OP/ZoXQh//0m9S054UkHMxsnT8K6zQWi8Cvgz8fV9lpyCt9SNa6yVa6yUuV3xveQzcwry3oTOYO+aCueX88Euz+dXzu7j6kc386vldNHW6McN0IP3zzvRXVewM5qA5vbqIH35pNrUt3cFj9N9+4PeB5+faDf/t+02f+j8J6y9eaxMD6x6H2LepTfa07uGaF67hS+u/xDUvXMOe1j2Y2hz98ce4RMTuJ0dOACRkKZOtu5Hi+jc5UbHUn28oSqeXwYETmkMnTP7auJUiax4z8yrj3r5IWisX4nEURJUEOOCLk7/IN+Z8g1cOvcItr9xCQ1fD8E/KEonsc4Oi6HuG3bbpU/jtfMzn/5E9rZ/GrY+yWY2QPvmqxVWsXDaZqx/ZzNn3vcHXH9nMjy6czenVRUDkPryt2wOczBsz8Oe+vjElLnlJxIjjNpBvLvA6Dvd6Br5Pan4ES4RzoLs19PsRXhfIOJ9ascSux+Njd0NHsD8KTBZGE7MD88kM7HsC58JQ20TNYgsfs0n6EERiOvGijVurocLGVdbkmEnxuZBqci4OLe0mZrTW9X3/NgAbgOE/8o6T5k43Nz22hdWv7uGeKxayfmstP/7yKcF8L+Cf1V31+NZgEt/++uedCXQ6gTW5dpvivisXcvv5M7lz/faQY9xzxcnt12+t5aGViwc9v8g87l9j/uY9cOm/nTyp47kuN7CWfYh9t/S0cPtrt1PfWQ9AfWc9t792Oy09LeH2KBJs15ET5NotlCUg50TZ/mdR2kdb5eeH37ifJX0f7P7lYAdvt+xkybiZSZ0J14aVhinLGHfoPRzH66N+3hcnf5Eb59/IR40fccWzV/Dc3ufwmQmoRiEGi6LvGXLbyx7w941Ay6JruP2NH8Stj7IaKqRPv/ns6dz6xAchY8IdT2/n9vNnAuH78AdXLmb91lqAYN6Y/j9/4JpFrHlrX/zykogRC+SbC4zNA8fowOsVeD1TkvfHsPhjvv85cNVj/tvhA9+P4rpAxvnM0dDRG8zVAf7+6Dcv7BqyDwr0M5NLcwdda/bvewLnwlDbRM2W64/RgTFrS87SFYnp9JFrNwaNgQ+uXOz/ADobpPhcSDU5F4eWVvdHK6XyAENr3d73/wuAXybr+IFbmOtau/nfL9ZwyznTURFKCoa7ldMwFFNK8yjKtbHu5qX4NOTYDMryHBw53s29f63hvq8tHHSMwhwr625eCvg/oSh22thw64qQderqxHH/baBth+C1X/rXkjuLoWgyFE6Mz7pcw/AnC/zOKxFzM7h97uDJFFDfWY/bJ2XeUmFX/Qkml+aiYrijJSpa49r7NF2F0+nNj23Na2UeVOXDc0c+xJ3jTXg1pnAaJy9jwqevUL7zWWqX3xL181ZMXMG0cdNYs2MN/9/b/x8PffQQ15xyDRdOvZASScyWOFH0PRG3BXj6eqjzr2d355bEtY/qdvu496813HXxXIqcNmyW8GWRp7ny2HjnuWH78KIcKz+/dD4/vcjEajEoy7Xx1KpleH3+73PtBv/whZncYZ0Tv7wkYkQMQzG7ooDfXL4Q0zS5+5J5dHl8rLt5KQ3tvTS097J200GuWFzNjZ+bxsRiJxXJzvvj6YZXf37yOqC7Fd7/nX/Z8ZfvGXW+DhnnM0e4ZSEv7WrgF5fOY93NS4PLm2pbOrn7knncfcm84HUlMOhas3/fEzgXhtomau5Of4x+8//5JxZNH2z6Vzj7x5Cb+LFVYjp9nOjx8cYnx/jTTUvRWqOU4s8f1HHp6RMpzkt165IgxedCqsm5OLS0mpgBKoANfW8yrcCftNZ/TdbB+5fM3lbbxqrHt/Lo9WcEH7tqcRU3fX5aMBeA12titQ6+8PH13Y3ltPkv0AN31zR29LK3X1nswDGqip08e9sKfKZ/cqi1m8GDX//yanVb/An+Asn94pksbeBa9oF/I4udyrzKkJOqMq8Su0U+4U02n6nZffQE58wqj/u+81p2ktdWQ/2cG0b0/CXl8JLnQ1y2AqblDl9mO948znG0TVhI2e6/cPiM6zFtOVE/d0L+BH669Kdsa9jGC/te4J/f+2fuff9eVkxcwSXTL+GcqnPIsUa/PxGlYfqeiNt2HIOOk0vP7F0tce2j7FYLZ04pYlpZHhZDBUt9DiyXbDVUSF6Y/rlGvF7/oBBYAGsYBpVFobdyF2XHh2UZwTBU8PVrbO/lsXf2c93yqcHlZnsaOnhqa10wYWVgYny4supxY7X7Yz6Q6Bf81wPn/ywuyX5lnM8cgWUhAxOp+rQ/Gb8GfBoa23uYVJpP/4If/eM8kmi2ia6hdtj/Fmxbe/KxQMwmgcR0+rAaih31x5k1oTCYu3NH/XG+moACFmkpxedCvVeKUAAAIABJREFUqsm5OLS0um9Ma71Pa31q39c8rXVSMyGFu21zcmkua65bwqq/m8LKZZO54Q/vc97/eZOrH9nM7mPtwQtuOJmj5vIHNrLintf5pw3b2d33/W1/2sZ9V4a/Lfqxb5/JsRO9wedd/sBGao61h+axieVW/wQqySlh9XmrqezLGVKZV8nq81bL3QQpcLC5kx6PmZD8MuNrHsO0ODg+ftmInj+vrAsjbw/VljkY8b6bJ0oNU5djdXdSvO+tmJ9rKIPFFYv52bKf8cvlv+SCyRewo3EHd7x5B2evO5vVH6ym29s9/I5E4g3oG0s+eILV59wftz6qKMfKxadVhfT93zt/FhfM9U+IBm7DdkW4vd/rNdl9rJ2rHt7E2fe9wVUPbxo0doj0FXj9r35kM1c+tIlfPb+LH35pNhfMLeeBaxbx2Dv72X2sHY/HFzL+hx3H4yXB1wMyzmcOV5497LIQn9Z8c81mzrnvDX79/E6muAq56uFNiY/NSFJ8DSsxnT4C5bL75+783vmzsqdcdpq8n0sVOReHlpBy2cmSiNKt4T7xAjhyvDtsebenVi0LfvI5XNnU06uLuP38mcyZUIDWBMuhajRffeCd4UsSxrEE7GiM4WzaaVFKMFrPb6/ntj9t439evoCpZfG7/9Pa3cTiZ1bQVnk2R0759oj28XLbe/y+6RlO6b2RO84oiFvbYqI181+7B19uKZ989V9HvTtTm9S01PBm3Zu8d/Q9ZhXPYvV5q5mYPzEOjR2VjIrbhBjQN5rOUlrcbXHpo+rbusOWjP3TTUvx+kx8pubpLYf4zudnhP1kOdLz+48dWSoj4nao1//fXt0TcudMuGuEEZUWjkaCrwfG8DgfD2kTu43tvfzurc+4cskkLIbCZ2rycyx87aHNEa9FIcGxGUmKr2ElptMjbrO+XDak/FxItRjPxaxa251uS5lSLtJtmxHLu/lOfuo5sKzqwDKb22rbuOEP77PxznNDOp+oSxLGcqt/AhnKoMxZlupmZL1d9SewGIqJcX5zN37PnzBMD82TLhzxPjZ1fIjNV8Kuuml4FzcSZsVf4ilF45RlTPr4zzib99FdOm1UuzOUwSmlp3BK6Sksr1zOmu1ruP6v1/PHC/9IZX7yqk6JMAb0jQbErY/y+Myw/fORtu6QcsnXLQ9f8jXS8/uPHSJ9DfX6P7W1Lvh9pGuEEZUWjkaCrwdknM8Mbq+Ph/92gIf/diD42Gv/4+whr0UhwbEZSYqvYSWm00PWl8uGlJ8LqSbnYmTZMz0XJdPUNLb3cri1i8b23uCtnjZLaMlU6MsrYDn5JxxYWtDjM6MqNRjXkoQia3xy5ASVRTnY4zjrYXi7qah5nPay03CPsMR1s/c4n/TsY7ptJh1uC7saUnd7alP1EkzDimvX83Hd70LXQn54xg9p723ntldvo8vTFdf9i/QRqe8fWHoW/MsL69u6Q5YpRTN2iPRk9iVOjbZctozjItnCXT+aOrRcdqSS7xKbIhWyvly2EEOQK8N+BuaI6b8OtzzfMaj84EMDSmT2z1FzenURBTnWsKWzB5YajGtJQpE1dtafYFJJfFPYV3z6BPaeZpqmXDrifbzT/iEaOLt4CnaLyea61CXK9dnzaKk8jbJPX8bwxDcnzOTCyaw6dRV72/Zyz/v3xHXfIn2E6/sHlp59cOVifvHczrA5ZKIZO0T6CVwPPPbOfh64ZtGwr79LxnGRAuGuH/McoeWI12+tHZSHRmJTpEqpM3xepKzJMSPEECTHTD8Dc8RA6Dpcr9ekoaM3WOK0PN8xqCpT/xw1Vz+yGVe+g1vOmU6R00aX28ep1eMoyRt8QZ60ag5iKGmx/jYaTR29LPn1K1xz1iQuXhifZTSGt5vTN3wed24lBxf/ZMT7ufPQ/8WrfXzbdRlP7Chn//FcHrm0kVTdIJDfvJ9T3v5X9p/9jzSd8pW47//pT5/mv/f/Nw9+4UE+N/Fzcd9/FDImbjPVwL7flWenrccbXArwi+d2DqrS1D+HTDRjRxZK67jtfz3QvyKjw2rw2Dv7WTSlNFhRZP3WWn5z+UJK8+wyjmeHtIrdgdePXp/J3c9+zBWLq4Mx+sGB5uByS4nNrJUWcVvf1s0fN+4LyYv09JZDfGvFtGzPuybCy6qOSnLM9DMwRwyErsO1WgeXOB0okKMmkDemrrWbVY9vDf58453nQpibHOJWklBkhZ31JwCYUhq/O2YCd8scnv/3I95Hbe9RDrqP8KVCfzWnBeWdfNRQwO4mO/PK3fFqakw6SqbQVTiB8p3PJWRi5rIZl7GtYRv3vHcPZ112FjbDFvdjiNQK1/e7bP5lAAebO0MmZWBwDploxg6RXvpfDzy1tS6YT+bNO87x5/Pol9MD4O5LfDKOi5QYGHeBPmlgv3TN0ilMiuM1gxAj4fGZg/IigT8+hch2MjHTT2CtbuAul8pxOTjtVkztzzsz1CcMAz+xsFn9eQUG3n0z3JpeuXNGRGNXnCdmDE8XlTsfpqNkAV3Fc0a8n7fbP8RAMc85HYA5ZV3YDJNNtY6UTcygFI2TlzJ5xwZyG2roKp8d193bDBtfm/U1Vm9bzVM1T3HNKdfEdf8i/fTvp62G4oK55SGfTq/fWhuSQ2aofl36/PQw8HVw2i3BMTxwx4zVooKv98A7pCRfh0gXNosRtk+yGIrDrV3Sz4iUihSfkndNCJmYCVGaZ+exb5/JsRM9PLpxP99aPpXvPvEBda3dwTW5sysKBg1mgbXoNz22JbjtY98+kzXXLQl5bLg1veH2E+mYIrvtrD+Oq8BBfk58TuHxNX/E3tNM3YLbRrwPU5u83bGNaY4q8iz+uwPsFs2c0i7erXPy7UXtpCqMm6uXULXrBcp3Pc+BOE/MAJzqOpVTSk7hgQ8f4OJpFzPOMS7uxxDpYWA/vervpvC982fx3bVbg/12IOdIuO379+uA9PlpINJr9Ni3z2Tdewe56NSJ3PCH90NeX4CXdjVIvg6RdkqdtrB90ss7j/Dz53dLPyNSqiQnfHyW5MjdxkLI9GQ/hqHIz7Fyx9PbuWJxNXeu3x6846WutZubHttCc+fgT/2bO93BC7rAttf9x3tUFDrYcOsKNt55LhtuXTHsIBhuP5GOKbLbzvoTTC7JHX7DKFh7W5n48UO0ly2iu2jkkxY1PQdo9h5nvnNGyOMLyjtp67FQ05S6Qddnc9Iy8XRKPnsdw90Z9/0rpbh69tW0u9v5/ce/j/v+RfoY2E8vmlIavMAEf7/93bVbaevxht2+f78ufX56iPQ65OdYuW75VG7t+4Am8LPvrt3KTy+ex9O3LOOpVcvkDa5IK01d7rB90oqZ5cHvpZ8RqdLcHT4+m7slHoWQiZkBPF6TutZuipy2IfPN9BcpN02324erwMHE4lxcBY6wF279y3N3e7xRH1Nkr85eLweaOplSFp9lTBN3/DsWTyfHZn59VPt5/cQWHMrG7JzJIY/PKevEamg216auOhNA45RlWLw9lO55NSH7n1Q4iTPHn8mTnzxJc3dzQo4h/Pr3m43tvZhm8pLYD+zvhxsrhspdNlxeM5EckV4HbWq8pg77M6/P5MqHNqG1lkkZkVYixazdarDu5qU8fO1iXPkO6WdESkTsU5M4jguRrmRiZoBAnpm2bk+wlFtApHXkgedEs21/A8tz723oHNF+RHb55MgJNPHJL+PoqGN8zeO0VX6e3vyqEe+n09fN5o4dzHNOxz4g+W2OVTO7tIvNdTmkctztLKqmc1wV5TufhwRVo7t0+qW4fW7+sPMPCdm/GNxvXv7ARmqOtSdtcmZgfz/cWDHU+DDSsUPEV7jX4YK55TR1utnXGH5c9plaXiuRlqyGChuz+xo7ufqRzfzq+V386MLZOO0SuyL5bBHi0yYT3ELIxMxApXl21ly3hPVba7nnioXBzmOodeSB50SzbX8Db59e/eoe7rsyumOK7HWyItPolzJVf/h/AEXD9CtGtZ+NHR/i1h4W5YZPHLzA1UFLt4XPmlO4hlgpGqcsJbdlH3kNnyTkEBPyJ3DWhLN4cveTNHU3JeQY2S7Vy38G9vfrt9by0MrFEfvtocaHkY4dIr7CvQ4/vWguqx7fyupX9wy6FnjgmkU8veWQvFYiLZXnO3hwQJ9035ULWf3qHsDfZ97x9Ha5Q0GkhMNmDHqvc9+VC3HY5C2pEJL8dwDDUMyuKOA3ly/ENE2eWrUMrfWQWewDz9lw64qYKmsMvH16W20b9/61hnU3LwWQzPkirI9q2yjOtVEyyjcEuS07ce3/M41TLsWbUzri/WitefX4e4y3lTLeVhZ2m1PKurAozea6HGaVeUZ8rNFqnriI6p3PUb7zefZXzE3IMS6ZfgnvHnmXRz9+lDvOuCMhx8hmqV7+E66/L3baIvb/w40PIxk7RHyFe40CcVbX2s3/frGGuy6eS5HTxsRiJzlWg+98foa8ViIt2WwW5pTns+7mpXhNjcVQfO9P29hW2xbcpq61G4/XTGErRbbq8Zjc+9eTfWpbt4d7/1rDv33z9FQ3TYiUk4mZMAxD4SpwAKElNJs73UNOzgSeEw3T1CgVvsyq3WqJaV8iu3xwqJXprnyUGt0bgskf3IPXlk/TlEtGtZ99vXUcdB/hy+NWRGyT02Yys7SbzXUOrj21nVE2fcRMWw7NVYso2/sGh1Z8F5+jIO7HGJ83nmWVy1hXs47r512PK9cV92Nks8Cyk/6TM/FeUjKwdHKx00Zrtydk8qR/Hz3cMqqhxodYxw6RGANfh8b23kHj8+/f3sfPL51PSV74nHFCpCMFuApCP8iRZXgiVfzvcULj0VVgl3gUApmYGVKiylcH9vtfH9Ry23kzgxUfqoqdPLRyMcVOKRknwmvpdHOguYtl00Z+hwvAuPq/UXTkbY7OWolpG12umufb/oZD2QZVYxpogauD//dJOftarUwv8Y7qmKPRMGU55Qc24dr1AkdPH13C40gumX4Jm45s4j8+/g/uPPPOhBwjWwWWnQzsl+O1pGRgv3/B3HJuP38Wt/Qr7dl/HEjUOCFSqyjHGrak60sf13PWdJe8viJteTw+djd0DIpdkBLvIvXGOSxh+9ZxDpmYEUIW9A0hUbkMAvtdNKV0UBnOW9ZupbU7dUs9RHr7sLYVgBkVI7/TQ5keprz/S3qdFbRUf3FU7TnmaWZzxw4W5Z5CjjH0Rd48VydG33KmVOoeN5Hj5bMZv309ytubkGOU55azvHI5T9U8RUNXQ0KOka36LzvZeOe5bLh1RVzfJA/s969YXB2clIHB40Cqc96IxGjsjFxyWF5fkc4aOnrDxu7dl8xLSJ8pRCwi9a2N0qcKIRMzQ0lULoPAfmMpyS0EwNaDrRgKpo2iVHZFzRPkntjLsVkr0cbo7s56oe1tDBRn5s8fdttcm8mMkm421+YkqihS1I7MPB9bdytlNS8m7BgXT7sYn/bx+x2/T9gxslVg2cnE4lxcBfFdVhLPctgic3l8ZtjX1WIoeX1FWhuqHHEi+kwhYiHlsoWITCZmhhCphKZSisOtXTS2946oROtISnILAbDxs2ZmlOeTYxtZjFh7mqn+6Ld0lC6g3bVoVG054evkjRNbmO+cQaEluomiBa4OjnZYOdiW2lWU7aXT6SiezIRt/4nyJeZTmvLcclZUruDpT5/maOfRhBxDxF88y2GLzGOamsb23oglh6VMtkg3gZgNXJdGil2rTMaINCDxKURkMjEzhIElNAO5Bq56eBMr7nmdyx/YSM2x9pgnZ0ZSkluI490ette1MX/iuBHvY9KH/4LF28HRWdcy2gy8/9XyOh7tZVn+wqifM8/ViSL1y5lQisNzLsTR0UD5zucSdpiLp/vvmvndjt8l7Bgivgb2+x8caB5UerZ/LjApeT12BPIFXf7ARv7vK3t44JpFUiZbpLX+MRu4LjW1HtRnPbhyMeX5kmRcpJ4rzx42Pl3SpwohyX+HMrCEplKKqx7eNCiXwIZbV8RUVWMkJbmF2LyvGVPDgsqRTcwUHHuPij1P0jTpK/TmV42qLUfdTbx4/B1Oy52Fy1Yc9fPy7SbTS3rYVJvD1fM7UladCeBE+WyOu2ZRuXUtTbO/hM+RH/djlDnL+NzEz/HMnme4cf6NTMifEPdjiPgK1+///NmPQ0p7rn71U35z+cLgkgApeT029M8X9NTWOgD+cMOZ2CwKq6FwSJlskWbC5bj6xpp3ee57y4Plsq2GojzfgW2Ed9oKEU/He308/2Edj15/BhZD4TM1T285xPjPz8Bll7elIrvJGTCM/iU0D7d2xS2XgJRIFbF689NGcmwGM8pjn0AwvD1M3/Rj3M5yGmZcOap2aK1Z07gBq7JydsHimJ+/wNXBhhoXdSesVI9LXXUmgLq5FzPvzX+hcstj1K64NSHHuHjaxbxT/w73vn8v9597f0KOIeJrYL//0q4GXtoVmsT57kt8YbcXmWtgvqCnttbx1NY6Nt55LhOLc1PYMiHCi5TjqqvXlJgVacnt9fHw3w7w8N8OhDx+3fKpqWmQEGlEljLFQHIJiFTx+kz+suMIp1cXY7XEftpO+uB/4Ww/QP0pN6Ito1tG9JfjG9nZvZfzC8+gIMrcMv3NDyxnqk39G9muoiqOTVlBxY4N5B3dlZBjlDpLuWz6Zbxy6BVeOfhKQo4hEkf6/ewhr7XINBKzItNIzAoRmUzMxEByCYhUeWdvM61dHpZNK435uSWH/sqEmsdomvQVOksXjKodH3V9yhNN/82snMksyj1lRPsocPiYWtTDWwed+MxRNScu6uZehNtZxNQ37sPwdA//hBG4YMoFTCqYxK83/5rjvccTcgyRGNLvZw95rUWmkZgVmUZiVojIZClTDCSXgEiVZz6ow2mzcGp1UUzPy23ZxYyNd9BVOJ2GmV8fVRve7djBvx9bR5mtmMuKzkaNIkHM8qrjrP14PFvqHZxV1Tuqdo2WacvhwGlXM2vTw0x9/T72fvGuUSdGHshqWLlh/g38evOvuWvjXfz23N9iKJkXzwTS72cPea1FppGYFZlGYlaIyGRiJkaSS0AkW11rF89vP8IX51Zgt0b/Zt7RfohTXr0enzWH2lO/jzZCT/cOXxfN3uOc8HUAYFM28gwn+RYn+ZZcbMpKr+lmT88hXjq+mfc6P2airZyrSy4gxxjdOTC/vJNSp4dnduVxxsReUj0enyifRd3ci6je9Tw97z3K4TNviPvkzOTCyVw1+yqe3P0kqz9YzfcXfz+u+xeJI/1+9pDXWmQaiVmRaSRmhQhPJmaESHMPvLEXDVy0IPqKPnnN25nz2ncwfD0cWPIzvDmleLWPj7s+493OHXzSvZ+jnuYh92FTVjzan5zXoWycXbCY5fmnYlWjXwdsKPji1Bb+c1cFr+1z8oXpiVlCFIujM84lp6ORym1/wvD1UrtsFcT5rpYvTPoCRzqP8PuPf4+hDL53+vdGdeeREEIIIYQQIvPJxIwQaeydz5r407uHuHD+eErzo/h0wfQxvuaPTNr2v/HZCti7+KdsM3rY1PAM73V8TIfZhUPZmeyYwNycaRRbC8k3/Ot8PdpHj+6l2/R/9ZpuHIaNclsp0+wTsRnx7S5OH9/Be/WFPPZhAbPLPCmv0IRSHDjta5gWG+O3rye3aS/7z70Dd0FFHA+hWHnKSkzTZM2ONew7vo+fLv0pZc6yuB1DCCGEEEIIkVlkYkaINPXe/hZuWbuVynE5fP2M6iG3NTwdlB76K5U7f4e7/TNecs3hRdc03m38Ax1mF3ZlY2bOJOY5pzPdURWXu15GSym4el4D//b+RH79ZjE/XNHGzFJPihtlcGjB5XQVTmDSzmdZ8J830HjKl2mcezHdxVPisrzJUAbfmvctxuePZ/2n69l4eCPXzr2Wr878KlUFVaP/HYQQQgghhBAZRSZmhEgTDSd62HnkBIdbu/nbnkZe3HmM8eNy+PGXT8FhtWDraqCl5QM87la8nhN43S2ozjraO2tp7anniEWxszCXgyVVQAeOrk+YmTOJOTlTmeGojvsdL/FQnOPlxtOO8Mft4/mnV0pYMrGXUyt6Kc01mVDgZWKhL/mNUoqmKcs4UT6bypqXcO18joqP/0xPwXi6XLPoKp2GO78cb04hPnse2rDhznfhyYu+YpZSigunXMjprtPZ8NkG1uxYw5oda5hVPIsFZQuYXjSd0pxSihxFFDoKsRk2JhVOwml1Dr9zIYQQQgghREZJv3dqQmSpF3Yc4RfP7QKgJM/O1WdU8/Uzqslz+E/T0l1Pctexp6iz2UKf6ACLI58SSz7jcydyae4kpuZOYoozPSdjBqoogVkT4C81mr8dcPD+4RwAzpmm+On5KWx/YTXHx59KR3crebXvk1v/EfmNn1Ky761BmzYvu5XWM2+M+RDFjmJ+cuZPONp5lHePvMvWhq28fPBl1u9ZP2jbJy96kvll80f0qwghhBBCCCHSl9Jap7oNI6aUagQOxnm3ZUBTnPc5FmTD36VJa31hMg4UQ+yO9b/7WP/9IPG/YzrG7Wikc0xI20ZuYPvGWtyGk86vibRtZMqA3WkSu+n8dxqKtDu5Au1Oxz43U/+m8SK///C/f9LiNh1k9MRMIiiltmitl6S6HelG/i6pMdb/7mP994Ps+B3jKZ3/XtK2kUv39iVCOv/O0raRSae2pVNbYiHtTq50bnc6ty0Z5PfP7t8/nPjWghVCCCGEEEIIIYQQUZOJGSGEEEIIIYQQQogUkYmZwR5JdQPSlPxdUmOs/93H+u8H2fE7xlM6/72kbSOX7u1LhHT+naVtI5NObUuntsRC2p1c6dzudG5bMsjvL0JIjhkhhBBCCCGEEEKIFJE7ZoQQQgghhBBCCCFSRCZmhBBCCCGEEEIIIVJEJmaEEEIIIYQQQgghUkQmZoQQQgghhBBCCCFSRCZmhBBCCCGEEEIIIVJEJmaEEEIIIYQQQgghUkQmZoQQQgghhBBCCCFSRCZmhBBCCCGEEEIIIVJEJmaEEEIIIYQQQgghUkQmZoQQQgghhBBCCCFSRCZmhBBCCCGEEEIIIVJEJmaEEEIIIYQQQgghUkQmZoQQQgghhBBCCCFSRCZmhBBCCCGEEEIIIVIkoydmLrzwQg3Il3zF6ytpJHblK45fSSNxK19x/EoaiVv5ivNX0kjsylccv5JG4la+4viVVTJ6YqapqSnVTRBiRCR2RSaSuBWZSOJWZCqJXZGJJG6FGJmkTswopSxKqW1KqefD/Ox6pVSjUurDvq/vJLNtQgghhBBCCCGEEMlmTfLx/gH4BCiM8PN1WuvbktgeIYQQQgghhBBCiJRJ2h0zSqkq4CLgd8k6phBCCCGEEEIIIUQ6S+ZSpt8CPwLMIba5Qim1XSn1tFKqOtwGSqmblVJblFJbGhsbE9JQIRJBYldkIolbkYkkbkWmktgVmUjiVojRS8rEjFLqYqBBa711iM2eA6ZorRcCrwB/DLeR1voRrfUSrfUSl8uVgNYKkRgSuyITSdyKTCRxKzKVxK7IRBK3Qoxesu6YWQFcqpQ6APwncJ5Sam3/DbTWzVrr3r5v1wCLk9S2mJmmprG9l8OtXTS292KaWVfNS4ghyTkixgqPz8OP3/ox5z51Lmt3rR3+CWLMkn5NjCUSzyJVJPaECC8pyX+11j8BfgKglDoH+KHWemX/bZRSE7TWR/q+vRR/kuC0Y5qammPt3PTYFupau6kqdrLmuiXMrijAMFSqmydEysk5IsaS+z+4nxf2v0C5s5x73r+HeWXzOL389FQ3SySZ9GtiLJF4FqkisSdEZEktlz2QUuqXSqlL+769XSm1Uyn1EXA7cH3qWhZZc6c72JkA1LV2c9NjW2judKe4ZUKkBzlHxFhxtPMoT+5+ks9P/Dy/WP4LihxF/Pu2f091s0QKSL8mxhKJZ5EqEntCRJbsctlord8A3uj7/8/6PR68qyadub2+YGcSUNfajdvrS1GLhEgvco6IsWJdzTpMbXLx9ItxWB2cXXU2z+59loauBspzy1PdPJFE0q+JsUTiWaSKxJ4QkaX0jplMZLdaqCp2hjxWVezEbrWkqEVCpBc5R8RYYGqT5/c9z7zSeZQ5ywA4c/yZaDQvH3w5xa0TySb9mhhLJJ5FqkjsCRGZTMzEqDTPzprrlgQ7lcDayNI8e4pbJkR6kHNEjAXbG7dztPMoSycsDT42IX8CFbkVbKrflMKWiVSQfk2MJRLPIlUk9oSILOlLmTKdYShmVxSw4dYVuL0+7FYLpXl2SVglRB85R8RY8Pbht1EoFroWhjw+p2QOW45twWt6sRoyhGYL6dfEWCLxLFJFYk+IyOSqcgQMQ+EqcKS6GUKkLTlHRKZ7+/DbTCuaRp4tL+TxOSVzeLPuTWpaaphXNi9FrROpIP2aGEsknkWqSOwJEZ4sZRJCCCH6ae1pZVfzLhaULhj0sxlFMwDY0bQj2c0SQgghhBBjlEzMCCGEEP1sPvwOGs3CcdMG/awkp4RCeyE7m3emoGVCCCGEEGIskokZIYQQIsDTw8a3fsk4n49Ln/snnC37Q36slGJy4WR2NsnEjBBCCCGEiA+ZmBFCCCEC3vhntvhOMN86DqUsTHvln0HrkE0mF05m7/G99Pp6U9RIIYQQQggxlsjETJKYpqaxvZfDrV00tvdimnr4JwmR5iSuxZjS2UTjljUctlmpGr+IurkXkduyj3GH3gvZrCq/ClObHDh+IDXtFEkl/ZzINBKzIp1JfAoRnlRlSgLT1NQca+emx7ZQ19pNVbGTNdctYXZFgZSHExlL4lqMOdvWss1iAjAjbwItpS6qdz1PWc2LHJ98VnCzyvxKAPYd38fsktkpaapIDunnRKaRmBXpTOJTiMjkjpkkaO50BzsggLrWbm56bAvNne4Ut0yIkZO4FmOK1vDhE2wrqcSmrEzOKUcbVlonLGDcoXcxPD3BTSvyKlAo9rbtTWGDRTJIPycyjcSZSk+ZAAAgAElEQVSsSGcSn0JEJhMzSeD2+oIdUEBdazdury9FLRJi9CSuxZjS/Bk0fco2Zy5TcyuwGhYAWicsxOLtpaD+w+CmNsNGRW4F+47vS1VrRZJIPycyjcSsSGcSn0JEJhMzSWC3WqgqdoY8VlXsxG61pKhFQoyexLUYU/a8RLdS7Pa2MzO3Mvhwe+kUTMNK4eEPQzafkD+Bz9o+S3YrRZJJPycyjcSsSGcSn0JEJhMzSVDstPHwtYuDHVFgPWVpnj3FLRNiaEMlaCvNs7PmuiUS12Js+PQldpZW48Nket6E4MPaYqejeDIFAyZmKvMrOXTiEB6fJ9ktFUkUrp97eOViLAaSsFKkhYHjdLHTJmOzSFty7ShEZJL8N8FMU7OnsYP/+8qn3HXxXErz7JQXOKgc55QkVyKtDZegzTAUsysK2HDrCtxeH3arhdI8u8S1yDy9HXDoHXZOXwKeOqbljg/5cXvpNCr3vIrh6ca0+S8mK/Mq8Wkfh9oPMb1oeipaLZIg0M89c+tyunp97G/q5Kf/9TGNHb2SsFKkXKRxeqYrX8ZmkbYcVoNfXTafXLuFLrcPh1XuExACZGIm4fonuXppVwPgnx3ecOsKXAWOFLdOiMgiJWjrH7uGoSSORebb/xb43HzscFBKAYXW3JAfdxZPRmmT3MY9dFQuBE5WZtrbtlcmZsY4w1AoFCt//25IboSB/aEQyRbNOC1EOmnudHPdf7wX0pfK+yIh/GSKMsEkyZXIVBK7ImsceBssdj72tDElt2LQjzuLqwHIb9gdfGx8nv+umr3HpTJTNpD+UKQjiUuRaSRmhYhMJmYSTJJciUwlsSuyRu27HC+dTl1vM1OcgydmvI4CenNLyG38NPiYw+KgOKeYuva6ZLZUpIj0hyIdSVyKTCMxK0RkSZ2YUUpZlFLblFLPh/mZQym1Tin1mVLqXaXUlGS2LVEkyZXIVBK7Iit4e+HodnaW+JcmTQ0zMQPQVTgBZ8v+kMdcTheHThxKeBNF6kl/KNKRxKXINBKzQkSW7Bwz/wB8AhSG+dmNQKvWeoZS6uvAPcDVyWxctExT09zpjiqpmiRIFZkqXrEby/kiRNId+Qh8bnY686AXpuSWh92su2AC4469jvJ50BYbAOW55XzS/EkyWyuSIFKfJWO5SDeR4hKgsb1XYlWkHcNQzHTl89SqZXh9JlaLQXm+Q+JTCJI4MaOUqgIuAn4D/GOYTS4Dft73/6eBf1NKKa11WtWjHK5STTiSIFVkqtHG7kjOFyGSqvY9AHbipsJeRK4lJ+xm3YUVGNpHzvE6ukumAv47Zt7ueZsuTxe5ttywzxOZZbg+S8ZykW4GxqWMuyKdBarVSnwKMVgylzL9FvgRYEb4+USgFkBr7QWOA6XJaVr0ImXAb+50p7hlQqQfOV9E2qt7D/LK2d19jGqnK+Jm3QUTAHC2HAg+Vt53d83hjsMJbaJIHumzRKaTGBbpTOJTiMiSMjGjlLoYaNBabx1qszCPDbpbRil1s1Jqi1JqS2NjY9zaGC3JJi5GKtWxmwpyvmS+MR+3te/S4ZrF4d5mqnMiT8z05JejlRGSZyYwMVPbXpvwZorYjDRupc8SqTbaPldiWKRCtHEr8SlEZMm6Y2YFcKlS6gDwn8B5Sqm1A7apA6oBlFJWYBzQMnBHWutHtNZLtNZLXK7IF9GJItnExUilOnZTQc6XzDem4/Z4HbQfZU+x/26YamdZxE21xUpPvgtny8HgY66+O2xkYib9jDRupc8SqTbaPldiWKRCtHEr8SlEZEnJMaO1/gnwEwCl1DnAD7XWKwds9izwLWATcCXwWirzy0RK/hfIJn7/yzVcsbia0jw75QUOip22VDVViKTyek0aOnrx+ExsfUnbrNbwc7yB82XgWmLJvi/SQl9+mZocf36YSUPcMQPQXTCe3H53zOTb88m15srEzBjSv89y5Tu4/fyZTC3LQ6MxTR2SA0ESm4t0FO24G8tYLkS8lObZeezbZ3KwuYtcu4Uut4/JpblyXSgEya/KFEIp9Utgi9b6WeD3wONKqc/w3ynz9VS1a7jEaTNd+fzDF2ax6vGtkrhKZBWv12T3sXZuWXsy9h9auZg5FQVhL+ikkolIa3VbwGKnRveSZ8mh2JY/5ObdheMprt+O4enBtPmTBLtyXTIxM4YE+qxnb1vBkbYeVq0NP85LglWRrqIZd2Mdy4WIF9PUdLl93PXnj0Nib+DEtxDZKOm9r9b6Da31xX3//1nfpAxa6x6t9de01jO01mdqrfclu20BwyWmau32BCdlwv1ciLGqoaM3eCEH/ti/Ze1WGjp6Iz4nUDFiYnEurgIpiSjSSO27UDqDmq56qnPKUGro2OzOr0ChcZyoDz5WnlsuEzNjjGEofCbBSRkYPM5LAkuRzoYbd0cylgsRDxJ7QkQm0+JhDJeYShJXiWzl8ZlhY9/ri1RsTYg05e2Fo9vxlc1mT2c9VTmR88sE9Ob5t8k5frIKU7mznPqOerymd9D2Hp/JB4da2byvmV4ZHzKKXAeIsUzGcpEqEntCRCYTM2EMl5hKEleJbGWzGGFj32qRrkRkmCMfgc9NXfFEekz3kKWyA3r6JmYc/SZmXLkufNrH0c6jIdtuO9TKBfe/xVcfeIevP7KZc+57gw9r2+L7O4iEkesAMZbJWC5SRWJPiMjkLAgjkDgt0HEMTJw23M+FGKvK8x08tHJxSOw/tHIx5fmOFLdMiBgFEv868wCojuKOGdOWg8dREHLHTKAyU11HXfCxLQda+Obv3qWz18tt587gH78wC5+p+eaazexr7IjnbyESRK4DxFgmY7lIFYk9ISJLafLfdDVc4rT+yQE9XhOPqTG1pqmzl7K8keXQME1NW7ebbrcPn9bk2Cwj3leqmdqkpacFt8+N3WKnJKcEQw2eA4x2O5FcHo+Pho5evKbGaijK8x3YbP5Pga1WgzkVBTy1ahlen4k1QiWHsV6tJFLsSkxnkLr3IL+CGk8bBoqJOaVRPa0nryzkjpny3HLAXzJ76YSlNLT3cMvarRQ7bdx18VyKcv1v1Ke58vjJhh1878ltPHvb57CMofNhLAp3HVDstNHc6cY0TXwaCnKsrLt5Kf7URApXnn1M93upJv1u9AaOwUU5Vho73SEVmGaX57Pu5qUhY70k/h0smviSGIye1WowozQvJPZKnXaJvSziNb00dTfh8XmwWWyUOcuwGjIlATIxE1EgcdpQ2ro8HDvRwx1Pbx9VVQbT1Bxo7ozLvlLN1CZ7Wvdw+2u3U99ZT2VeJavPW83M4pkhg1S024nk8nh87G7o4Lv9KjU8uHIxc8rzQyZnKoucEfcx1quVRIrd6UXT2du2V2I6U9S+B645fNpRx3hHCbYoLwp68soY17w3+H1xTjFWwxpMAPxPGz6mvcfLry6bH5yUASjNd3DD8imsfu0znvmgjq8tqY7v7yPirv91QKBfu//lGr61fCp3rj85Vt9zxULeqjnGJadVhVS5GUv9XqpJvxu9cGPwgysX86+vfspLuxqoKnbyhxvOwOPV3PT42Byn4yWaa1W5no2N2+1lT3PnoOvM2a487HZ5WzrWeU0vn7Z+yg9e/0HwfLn/3PuZVTxLJmeQpUwj1tzp5mBzV3AiBUZelSGe+0q1lp6W4OAEUN9Zz+2v3U5LT8uIthPJ1dDRGxwswR+H340xW/5Yr1YSKXabupskpjPF8TpoPwKuOdR0HqbaOfwypoDevDLsXS0YHn98G8rA5XRR117Hq58c4+Vdx7hiURXVJbmDnrt0WikzXHnc//KnkugwwwT6tSsWVwcnZcDfv925fjtXLpk0qNLIWOr3Uk363eiFG4O/u3YrVyyuDn5f29IdnJQJPCbxOlg016pyPRubxk532OvMRom9rNDU3RSclAH/+fKD139AU3dTiluWHmKemFFKTVVK/YtS6hml1LOBr0Q0Lp25vT5y7Za4VGWI575Sze1zB0+2gPrOetw+94i2E8nlNXX4bPmmjnofY71aSaTY9fg8EtOZoi+/TGfJNI70tkS9jAmgJ9+fU8Zx4kjwMZfTxaETtfzzX3ZTWZTDlxeMD/tcpRSXnT6R+uM9vLTr2Ch+AZFsgX6tyGkL279ZDDWm+71Ui9jvmtLvDhRpDC5y2oLfj5VrzkSL5lpVrmdjE4/rTJG5Il0re0xPilqUXkZyx8x/AQeAfwX+T7+vrGK3Wuhy++JSlSGe+0o1u8VOZV5lyGOVeZXYLfYRbSeSy2qo8NnyY7i1eaxXK4kUuzaLTWI6U9S9DxYH+3NyAKh0lET91HAls8ucZRw8XstnDR1cuagaqxF5aF1UXUx5gYNHN+4fYeNFKgT6tbZuT9j+zWfqMd3vpVrEfteQfnegSGNwW/fJNz5j5Zoz0aK5VpXr2djE4zpTZK5I18o2wxbhGdllJBMzPVrr1Vrr17XWbwa+4t6yNGOamsb2Xg63dtHY3kux08bk0lzuu3IhVcVOTq8u4tHrz2DtjWeh0ZgxzPyW5tnjtq9UK8kpYfV5q4MnXWCtbUlOyYi2E8lVnu/gwQHZ8h/sy5bf/xyob+vm2PFuGtt7B8XnWK9WEil2y5xlEtOZovY9KJvB3u5GACpjuWMmTMnsMmcZPWYnE0s0Z00b+vU2DMV5c8p5/0Arh5q7RtB4kQqleXaevOksZlXk8/iNZ/Lo9WdwenVRMMfM01sODao0Mpb6vVSTfjd6pXl2Hvu2P0bX3byUR68/g0dvOIP1W/15sKqKnVSXOFlz7dgdp+MlmmtVuZ6NjSvPzqM3nDEoPl0Se1mhzFnG/efeH3K+3H/u/ZTFsKR8LFNax/amXyn1TWAm8BIQTDyhtf4gvk0b3pIlS/SWLVsSfpxIyUxnuvI50evB4zVp7HCPKulfoCpTPPaVahlclSlpf+BkxW6sTFNzuK2LXq/GUGBqcFgVEwqd7GnsCDkH7rliIX98Zz8/+OLsQfEpVZmSGtNZH7cx8fTAP1fB3Ev5l+IiHj/8Gg/O/3ssMbxOp/31Z7ROO5uDZ/8AgA073+W5uof5UtH/4or5S4d9fnNHL7c9uY1//OIsbj9/5oh/lQyXUXEbLjH6QysXU17gQGuNYRgUO220dnvGbL+XamnU76Z17Hq9JruPtYdcRz587WJc+Q56PD6sFgNXnp3atm4ONneRa/fftT25NJcppXkSswOMoapMaRG3Ho+PmoaOkPh8aOViZvcrMiHGtmBVJtODzRi2KlNWdUgjSX+8ALgWOA8IZC/Ufd+PSZGSmW64dQWuAgeN7b1hk/4Ffh4Nw1CU5MVnX6lmKCOqmc9otxPJ09zp5htr3g1Z/1tV7OSpVcsGnQN3rt/OXRfPDRuf0VQ1y2SRYldiOgMc+QhMD7jmsO/ENsY7imOalAHodZbgaD8a/H7ngQKwQpWre4hnnVSa72DuhEL+a9vhbJ6YySjhEqPfsnYr625eysTik4mex3K/l2rS70anoWPwdeSqx7fy1KplTCrNA6CxvZfr/uO9QWN9Jl1rJks08SUxGL1w8RmuLxVjl9WwMj4vfC6+bDeS6dzLgWla67O11uf2fY3ZSRkYPplpPJOdjvXEqSK9RYo/j8+MmExQ4lNklNp3/f+65vBZ15GY8ssEuHNLgsl/j3eZfLw/H4A2T/QJfc+aWsK+pk72NnbEfHyRfJKwUmSKSON1/0pwcq0pUkX6UiEiG8nEzEdAUbwbks6GS2Yaz2SnYz1xqkhvkeLPZjEiJhOU+BQZ5dAmKKykx55HfU8LE0aQB6A3twR7RwNok7d39+Dz5uAw8mjqqR/+yX0WTS4G4NVPpDpTJpCElSJTRBqvrZaTl/xyrSlSRfpSISIbycRMBbBbKfVitpTLHi6ZaTyTnY71xKkivUWKv/J8x6DH77liIeu31kp8isxhmv6JmfK5HOg+hkaP6I6Z3twSDNOLrbOFt2t6mFCsKHaU0tRzZPgn9ynLdzC5NJeXpWx2RhgqMboQ6aQ83zEoEfVDA2JVrjVFqkhfKkRkI8kxc3fcW5GGBiYvnenKZ8OtK8Im9TMMxUxXPk+tWobHZ2KzGJTnO0aUQM0wFLMrCiIeK51kSLKzrBVLAt7+21YUOnjm1uV4vGbI8/rHpVIKi4LfXL4wbeMzGhLDWaapBrpboWI+e7v8OWJiqcgU0Jvnn8zpPHqYz45N4AsLLLTaimmM4Y4ZgMWTivmvDw/T2ummWN4QpdRQ/aVpatp6vEwscrDu5qV4TY3VUJTnOyRZZQJIvzw6VqvBLFdeSKy68uxYrSf/hpl0rTmWZWOs22wWZpWFxmdZrl360iySjXEfragnZpRSM4CKgaWxlVKfBw6Hf1ZmilSFKVJlJNPUgyrWjKaSUiYkTjW1yZ7WPdz+2u3Ud9YHywPOLJ4pJ1caiCWGo902E+IyFhLDWejgO/5/y+ext/l9DBQV9thX5rqd/omZI3trgQnMq7awo72Uve07MbUPQ0V3gblocjHPbDvM6zUNfHVRVcztEPExVB8IxHQ9IEZH+uXR83pNPm3sHFT1Zk5FwaDJmbE0pmeabI11r9dkT9Pw8SnGpmyN+2jF8hf4LdAe5vGuvp+NGZGqMDV3uuOy/VjQ0tMSPKkA6jvruf2122npaUlxywTEFpPZGL8gMZyVDr4DuaVQMJ59XUeocBRjNWL/lK43158fputoPROKFMV5inH2UnzaS5u7Oer9TC3LozjXxiuSZyalhuoDs7V/TBXpl0cvUtWbho7eFLdM9JetsS7xmd2yNe6jFcvEzBSt9faBD2qttwBT4taiNBBrtvpszG7v9rmDJ1VAfWc9bp9crKaDWGIyG+MXJIazjtZw6B0onwtKjbgiE4C22Oh1FJLTcYxZlf5hdJzdvyQqljwzhlKcVl3MW582hVRMEck1VB+Yrf1jqki/PHrRVGUSqZetsS7xmd2yNe6jFcvETM4QP3MO8bOME2u2+mzMbm+32KnMqwx5rDKvErtF8iSkg1hiMhvjFySGs07bIThRD+VzcZse6rqbRlSRKaDJWkYVjcya0DcxY/PvK5bKTAALJo6jo9fL9sPHR9wWMTpD9YHZ2j+mivTLoxdNVSaRetka6xKf2S1b4z5asST/fV8pdZPWek3/B5VSNwJbh3qiUioHeAtw9B3zaa313QO2uR64j5P5av5Na/27GNo3aoHkf6Zp8vC1i1n1+Mn1j+Gy1Q+1/WPfPhON5nBrV0hStf4JBp12C15To02NT4PWOmMSsJXklLD6vNWD1giWjOKNjoifQMWFgXkRwlVcKM2z8+RNZ9Hr1QTCLt9hodvtpeGEBvyfcISLzVgSDKcbieEsc2iT/9+K+RzsbsSHOeI7ZgAO+sqZZOxlQrE/3gttxYCiqedoTPuZN7EQgI17mlg0qXjE7REjF6m/LMqx0tjp5onvnIWhFKY28fjAaTfw+MxB47sYPemXR68838GfbjoLd9+YbmrIcxig4WBzZ7BARTT5PDJ5jE932Rrr5fkOnrl1GW6vxmdqLIbCblWUOCXfUTbI1riPViwTM98HNiilruHkRMwSwA5cPsxze4HztNYdSikb8LZS6i9a680Dtluntb4thjbFzcDkfxfMLedP3zmrr8MI/4Y00vZOu4VjJ3q57oF3Qi7yZrryg0mCXfkOfnThbB7duJ9vLZ/Kneu3Z1RiQUMZzCyeyRMXPSFZtdNQLBUXTFNzvNsbkojtgWsW8ebuBpZMLeGOp8PHZqxJstONxHCWOfA22POheDJ7m7YBUDmKC4FPeso5S72HRZtoZcFiWCm0Fce0lAmgMMfGlNJcNn7WxPfOnzni9oiRC9dfFuVYqWnoCOkXH1y5mK37m5g5flzGjdmZQvrl0dPaP6Z/ty92L5hbzvfOnxX8Ptpkq5k+xqe7bI11r9fk2Al3SDw+uHIxhTabJP/NAtka99GK+q+gtT6mtV4O/AI40Pf1C631Mq31kB8Rar+Ovm9tfV96RC1OkIEJ/l7a1cA3f/cudqsFV8Hg0tdDbe8zCZsssKGjN/j4LedM546nt3PF4urgBV7/bTMhsaChDMqcZVTmV1LmLJOTKs0EKi5MLM4NG8MB4RKx3frEB1y2qCo4KRN4vH9sjoWkmBLDWUJr2Ps6jF8IymB/11EUMH6Ed8w0dFqo8VRgwSSv52Sy33H2Ehp7Y1vKBDB/4ji2Hmqly+0dUXvE6A3sLxs73YP6xe+u3cp5cydk7JidKaRfHp2Gjt7gm16AKxZXh3wfbbLVsTDGp7tsjPXmbvegePzu2q00d0tcZYtsjPtoxfyX0Fq/DjwArAc+U0pNUkpNGu55SimLUupDoAF4WWv9bpjNrlBKbVdKPa2Uqo6wn5uVUluUUlsaGxtjbX5E8Uz4G+ln/RNeFTlt1LV2B/+N9rgicyUqdkcrUiI2rfWQsSlJMbNDusZtTJr3wok6qDwNgL1dR3DZi7Absdw0etLOBjt12gVAftfJikqFtpKY75gBmF85Do9P8/6B1hG1Rww22riN1C+aw/SLQozWaGPXa4bGaKTrzOGSrcoYL2IRbdwOjE/oi0czrT6vFyIlYp6YUUp9DzgGvAy80Pf1/HDP01r7tNanAVXAmUqp+QM2eQ5/5aeFwCvAHyPs5xGt9RKt9RKXyxVr8yOKZ8LfSD/rn/CqrdtDVbEz+G+0xxWZK1GxO1qRErEppYaMTUmKmR3SNW5jsvc1/7+Vi/zfdo68IhPArgYbTRZ/Fab8rpMXoOPspRx3N+P2xVb2c/b4AqyGYuNnTSNukwg12riN1C8aw/SLQozWaGPXaoTGaKTrzOGSrcoYL2IRbdwOjE/oi0dZHidE7BMzwD8As7XW87TWC/q+Fkb7ZK11G/AGcOGAx5u11oGr2TXA4hG0bcQCyf8CncVQyVKH2z7Sz8rzHcHHH3pjL/dduZD1W2u554qFUR9XiHgrz3fw0MrFITH4wDWL+PMHddx3ZeTYjPWcESJl9r4GBROgYDxe7eNAd8OoKjLtbLDjHOfERIXcMfP/s3fmYXJU9fr/nOp9ZpLZs0wWkDAB2SERWa5KEBXFa7wXlC2gBJQYvLmJXHeuiDder/JIML8rxI09IshivKCCQFiMgiSAoEAYEkgyTJJZevbprbrO74+a6qnuruqu7umZ6Zmp93nmmalTp05VT731Pd8+dc771gxbZnfFChMADvo8LJ49gz+1uAMz5QKruHjziiU88ep+t892UdaYVRXgZhN379+xL23b0JiZVZVbbNXt412MBepD/iw+3rxiCfUhl1cuXBQzj3sfUJCvpxCiEUhIKXuEECHgLOD7GXXmSimNOeCfAF4r4tqKhpX4X23IR08kTiSeJCkllQFdPyah6g41zY1VPLj6dDRNS7kq9UTiSCR1FT7uW3UqqibxKCCl4GB/lNkzAzyw+jSkJhFCcO0/H42iCO698tTxdWXSNBjqADUOXj9UNIIycWv8NKkRjoZzCkE5qeNiBHZuCqqq0T4QI5HUUu4Mixsruefzp6BqEq8iCPoUPnHiPPwehQe+cBqRRDJVV9MkB/qiJJIa9ZV+HvjCabauTTkuzpJ/afdY8aMoClE16t5vF8VDjcPbT8Oh7wdgX6QTVSaLFv7tGFToGPJy6nyVSKKaGUPtqX0z/YZl9n7mVhxSULvHzKvm3u37CA/GqXO/+Ew4vF6FI2fP4N4rTyWR1PAqggq/Qt3RcxEC7vn8KWhSoghBwKvQNRinNuSjO5KYGg42JcgRMvvsmkANPbEetw8fY/h8Hprr0/v0+pA/bdsYlGnriaTlAmbx1UJMBMoOZZDjuvy3RjDoteRnMFjc0uJJiTLg50RC1VQ6I50kkgl8Hh8NoQa8RS4tn2pw/F8QQnxp+M/dwJNCiIfR3ZYAkFLekOPwucDtQggP+iyde6WUDwkhvgNsl1L+FlgjhPgEoAJh4LMFfZISwBD/A/1L7dtdgxzsi/Ll+15OuShlOtRkOi19+xNHMRRPptyWbv9ztuvSHStPJqZqE6d0r2nQ/ir86kLo2Qs1C+GCu2HWURMSGDSp0dLdkmWd1lzbnOq0nNRxMQI7N4XDGyqznEZuu+w9xBIaV961w5Ln1593HD/4w046BmLcsfJkhuLJtOOduDtkXJwl/7TGI2np3ZV2j9efvp4bX7iRzkine79dFIc92yA+CPOWArB7SB//L3Yp06vt+qDJopoIgz01VEVGBmaqfSMDM4XimKaZ3Av8eVcnHz+uqahrc1FaeL36l9XXD/bzfy+1cs7x81i9+YU097qH//YO7z9iNk/vPMg/nzA/LTZOWgebEuQIVn32hmUb2PTSJra2bnX78DFENKrS0jWY5XqTSCT4103PMb82xN2fe2+WG6NVX27OiycNyiDHdflvDzt+NtdXTo/BmTLg50RC1VTe6H6DdVvXpT0bi2sXu4MzFLaUacbwz150fRm/qawq14FSypellCdKKY+TUh4jpfzOcPm3hgdlkFJ+fXh51PFSymVSyteL+UClQtdgnD1dQ6kvqIaLUj6npfBgIs1tycp1aU/X0MQq3Q91jAQE0H//6kK9fAIQjoZTnRdA22Aba55YQzgaLqiOixHYuSlYOTDtC0e4crjMiudfvu9lVp2xKMXdzOOduDukwYZ/4UhH1j2+Zts1rDx2pXu/XRSPnb8HTwCajgd04V8o3pFpZ5efoDfJ7Ko4g8FaqkwzZiq9M/EKX1EDM4c1VlHh97g6M2UGI2aet3RhalAGRtzrzlu6kK/e/zLnLV2YFRsnrYNNCXIEqz573dZ1LG9entp2Y/rYwM71ZnZ1RWo7psrR9+XlijLIcV3+22PauzKVAT8nEp2RztSgDIw8G50RN/eBAmbMSCmvAxBCfEpK+WvzPiHEp0p9YRONuJqkwu/JclEyw8ppySjP5bpkbtfc1rgp3avxkYBgoGevXj4BiCfjqQfUQNtgG/FkvKA6LkZg56ZgpYbvhOcGt4iNOTYAACAASURBVO24m8/dIQ02/ItrquU9rvZXp/5277eLgiAl7PydPijjDQKwa/AA9b4ZhDzFLRfaFfYyf2YMRcBgoIaK6EsoyQSax4cQgmp/PZ2xwgdmPIrgqLkzecbVmSkrGH28RxGWsc8ot9s/KR1sSpAj2PXZRjw3tt2YXno4cb1RBKPvy8sVZZDjuvy3x7R3ZSoDfk4kEsmE5bOR0BITdEXlhWLmTH3dYdmkht/rYSiezHJRMsPKack4Jpfrkrldc1vjpnTv9etT58yoWaiXTwD8Hj9NlelT95sqm/Cbvjg5qeNiBHZuClZq+E543hNJZNVNazePu0MabPjnV7yW97g33pv6273fLgpC+6vQuw/mvzdVtGtof9HCv4kk7OnxMX+G/lZ5MFiLQFIZMTsz1dERbbNrIieOmVdNa3eEvV1DRR3vovQw+vikJi1jn1Fut39SOtiUIEew67ONeG5suzG99HDieqNJRt+XlyvKIMd1+W+Pae/KVAb8nEj4PD7LZ8On+CboisoLjiOwEOKjQoj/B8wTQmw0/dyGrgszpVBf6eeQ+oqUK43hopTPaamu0pfmtmTlunRIfcXEKt1XNOrrGY3AYKxvrJgYK9y6YB0bz9yYelCNtbd1pi9PTuq4GEEuZ7BMp5EFdSF+MlxmxfPrzzuOTU/uSnE383gn7g5psOFfXagx6x6vP309t7xyi3u/XRSH1x4CBCw4GYCk1Hg7coCmQH1Rze3t9ZKUYmRgJlALkC4A7KujM7ofKQt/+3dMk/42ddsud9ZMucCImfdt38tNF5+U5V533/a9fP/c47hv+96s2DhpHWxKkCNY9dkblm1gS8uW1LYb08cGdq43B3uHUtsBrxh9X16uKIMc1+W/Paa9K1MZ8HMi0RBqYMOyDVnPRkOoYYKvrDwgnCaPQojjgROB64BvmXb1A1ullN2lv7zcWLp0qdy+ffuYta9p0uTKBJUBJc2VyVCnN9xvNE1DCIGUkoTJjckjSLk2GccBlo45xnmz9iFLq+BdgCJ4KdyQ8rVht99cHvQG0TSNuDZmivbjNlw/1tyF/K5MalLDO+zEIKXUyzSJz6PgVQTRYScmv08QjY9wXtNk1vGOhX9HLi6vK5NP8YGURJMxfIqXhmADXq/1iPo0V3ifUrwtKX58Mig+OPt/ANgX6eBjz1/LZfM/xPvqji64uUffDPGzHdV87bQ91IVUKmI9fHz7D/nzsVfyxqEfBmBH55M8eWALN7x3C1W+6jwtpkNKyRfvfpFTF9Xz44tOKvj6JhkmDW8TiSTtAzGSmsSjCITQV8n5vIKEKhECQNBY6acnqk4+BxsrZMboUD1EugrKQexcaXQnyyRJmcSreFPxehI5L5Y9d6NRla5IPM31pj+RTOOmVV+uKMI2N51USKowcACSCfD4oGoOeEqTEzjlqRNXJk1q45m7lA1vrfg5LYR/DYwhPycDYmqMcDSMqql4FS91wToCXttB4UkYgIpHIRozfwP+JoTYLKWcFgvBFEVQVxmAyvz16iv9li44uRwZrJTurdx07lz5Hg5N7kGUUsFbUaBqdt5qpXBDctKGIpSs0VLXiWl0sHNT8HoVmmpGppHqnBtI45zZiSmTx4oi0o4v8uIs+WfwQEuqtHS/wZonR1TbN56xgebaxSgZnZer8O7CEgdfhY6d8N4vpIp2jdKRaVfYR4UvSW1QnyQa8c8kKTzMiBxM1Znp12fjdEb3FzwwI4Tg6KaZ/PnNTjRNTs4vRFMMmiZ5s3Mwq283OzJOehcmK5hjdJEuIlb9ek2gxjJeN9c0s7t3t9vflwCaJnmreygvNzP7cjs3x0nHaU2DjtfHxPWmkLzUiv/m7emau6iqxptdg6Nz95zMGEN+TgYkkgl29e6y7AN8Hnc5UyFLmV4RQrwM7BBCvJz5M4bXOClg54JTqCODVTsD4QMjgzIwrgrepXBDKrYN14lpfGDFObMT00Q4i4QjHalBGRi+90+uIxzJ5ryr8O7CEv94AIQCh5yWKto1dACgaI2ZXWFdX0YMf0eRQmEoUJPmzFRjDMzEDhR1jmPnVdM9lOC1A31FHe+itMjlcDeh7orjiRK6iOSK125/XxoUm4+WKo+dcIyh600p89LpmrtYuYNOGUcwJ3BdmaYl752ikCHZjw//vmr4953Dvy8Gpr1SoZ0LTqGODFbt1Pi1CVPwLoUbUrFtuE5M4wM77ppdxsbbWcTOoSmuZctZuQrvLrIgJfz9fphzHIRqU8W7Btuo9VVR4SlcRyGmwr4+L2ccMpBWPhhMH5ip9umDPp1FCgAfbejMvNmZ+tvFxMHW4c7kyGgun5QuTPlQQhcRu3it2sV8t78vGMXmo6XKYyccY+h6U8q8dLrmLgmb2DklHMGcYJq7MtnFetUiv5+OcDxjRkq5R0q5BzhdSvkVKeUrwz9fAz4ydpc4OWDnglOoI4NVOz1xZcIUvEvhhlRsG64T0/jAjruGE9NEOIvYOTT5Lab3ugrvLrKw/28Q3g2Hvi+teNfQ/qKXMe3p8aGZhH8NDAZqmTE0spTJ7wkS8lTRGS3cMhugrtLP/NoQ297sKup4F6WFrcOdyZHRXD4pXZjyoYQuInbx2msX893+vmAUm4+WKo+dcIyh600p89Lpmrv4bGLnlHAEc4Jp7spkF+un8vK9QlDMU1AphPgnY0MIcRp5VVgmDzRN0tEf453uITr6Y2iaM3FkKxecO1aejESm2lJVLavtzPPVBL385JIlfOSoRv7wuaN448vHcPScEPL8zc4UvDUNBg5Czz79tza6Eeh8bkiGcFnbQBvhaJjOIf3vzkhnSpBV0zQ2nbWJmz54E7d+5FZu+uBNbPrQprxq9DWBGn607EeuE1Me2HHWXN7eF6W9L8qerkHaeiKo6ggvrLi74dPHp5yYSuos4pCfdaFGNp4xotq+bP4yfv7hnxGXSQ4M7Kd9sF3nltQsFd5vXHYjCgqdQ52Eo2Gdk0OdaIOdaec289doz8UUwD8eAMWTtoxJkxq7hw4Wv4ypW0+W58+MppUPBmsJxvvxqiNvAGv89XQUOTAD+qyZv74VJjbZ3lRPERix82BvBE3TUq51MOy2dMlSAj7BXZe/l1s/+x5OXFAzuV2YrGCO1cIDF/xSzz3mL4WLfw2X/AYklnHU6Put4qpdvA54Avzswz9jy/It/OqcXznOE1xko77Sz08vSefsTy9Zkpebdm6OZcdpqzwii6/OXG+0pErnwH7a+vbRObAfLZn91t7Mb0Uojh1CE8kE+wf2s69vH/sH9pNIps+Ema7uNLOqAvwkg58/uWSKOII5QUUjrHhAj6OffVj/veKBaeXKdOOyG7P6gKnOe6coZnjqcuAWIYQxx7oHWFm6S5o4jEb4TFEER8yewYOrTyeuJgn5PRzsi3HpTX+mtTvCh4+axZoPLk4Tu7pj5cnEVC11PqPOQy+18r9n1+AbbIU7V+tT3I44By79LShee0eEIgX6cn4uodBc28zmczZbOiYZImgNoQbWnrSWa7ZdQ9tgG8vmL2PVCatYt3UdDaEGrl56NeufXZ8mlpbzXkiNXT27uOmlm/jKyV+hLlBHQ0UDcyvnukKAJthx1kqY0izomym0FvAq/NfyY6jwexiKJ6mt9LNpxUkoilI6V4YC+Kl4vDTXLmbz2behAeFYD1c8+rkUf647/Tp++eovuerEq2iubWZx7WJu/+jtqJqKlJLrn7+era1bU5bbN75wI52RTjaeeh3Nv/8mykA72ooHaVGSrtjkVIOU8PcHYO6JEJiRKt4f6yaqxYu2yt7T46XCl6Q6kD5YMhA0LLMP0j3zUP1vX23RM2YAjpk3k0f+cYAX9/ZwymHFXa+L4mDE1A1/3MlnTnsXt//5LVYvOzwtPvq8gq/f/wqPvtquf6lYsYS5NUFqQpPUwSYTVrH6ol/Dp+/Q9997aao8M46a+36ruKoIhZn+mdx81s0oQkEIQVJLcuHDF6bF91/v/DVXnXhV7ut0YYlEIokvo0/3eRUSiSSBgH3an5nHlqUrkxU3VzwIajS77PLHIGnvIubEZMBK7HfThzax+WObczqEJpIJWnpacgqcehVvKndJaAl8yvRwlEwmNfwZ/PR7FZJJbZqI/yYhPgQPXz3C10/fpZdPA/FfTWr4PX6uOeUaQt4QETWC3+N3X4wOo2AGSCl3SCmPB44DjpdSniClfKH0lzb+GK3wmeGCM6+2gqRGWlvnLlmQJXa1p2vIss6HDvXg630btqweWYe482G44xN6B1M12/rhHSNBKUNZvqmqiYZQQ6oDMougrTx2ZWpQBmB58/JUh7Ty2JV8/ZmvFySWZrS9tXUra7eu5dI/XMoVj1xBT6xnVJ9lqqEQYUqzoK9ZaK1rMM6lt/yVy257nvN/+iyX3fY8n731eRRFoXFGoHRJWYH8VDxeGqrmogD/vnVtGn+u3XYty5uXp3jkVbzMqZxD0BvkikevYGvr1lTda7Zdw8pjV+q8+8u1hN+vd4bh3j2u2ORUROt26N0H78pexgQwL1jcQMe+Xi+zK+Mp4V8DgwF9YCZTADgcO4gmi5vxctTcmShC15lxMb4wYuq5Sxbw1ftf5twlC/jiL1/Mio/nLlkA6LH1yrt2kNQory+wo4FVrP7lp8AbGhmUGS7PjKPmvh+y42o4GubyRy5n+Zbl/PNv/pk3e97kC499IWd8d1EYOofiXHbr82mcvezW5+kcyp/LmvPYkvb/pYIVN7t3Z5fd9S+6yW7NAtuc2YnJgJXY76o/rgJBVk5shlOBUyN3WTBjAXMq50z5QRkYHT+nBAYOwL0r0vl67wq9fBogHA2z+rHVrH58NZc9chmrH1/N6sdWu7F+GI4jgBBihZTyLiHElzLKAZBS3lDiaxt3lFL4LLOtmpAvq+0Kv8eyzqwKAaKicHGocRaUMougVfur08SczNuZ+yC/WJor/OsMdpy1E1czC/oaQmvjJvhXJD/thIANXpk5Yceban916u94hT7lOB6odDk2FfGPB0DxwYJT0op3D+oDM3OL0JiRElr7vBw3K5K1b3B4CvsMswCwv46kVOmOdVIfzLaFz4cKv5dFjVVse7OTqz98RMHHuygeRjw0+mOrvtscS43tSSeQmgt2sVrxZJVnxtF8/X1mjLarbxXfXTiDqklrcVWHS/PLGlbc9BWRL+PMZKDYXNQVOLXHlOanE2iqNV+nCTfcZyM3CpkxY+jIzLD5mfQopfBZZls9kURW20PxpGWd9iEJiaHCxaHGWVDKLILWG+9NE3Myb2fug/xiaa7wrzPYcdZOXM0s6GsIrY2b4F+R/LQTAjZ4ZeaEHW96472pv/1D+qi8PzbocmyqQdP0gZl5S8CfLn325tB+aryVVHmDBTcbjigMJRRmV2Yn43FviIQnQJVJALjasMwu0pkJ4Jh51fxtXy990ant0FFuMOKh0R9b9d3mWGpsTzqB1Fywi9VaMqs8M47m6+8zY7Rdfav47sIZvIqwFlctt9kvxcCKm8XkyzgzGSg2F3UFTu0xpfnpBIrXmq/ThBvus5EbhQzM3AsgpbzO6meMrm9cUUrhs8y27t+xj00ZAoKH1FdY1vnpjj56gwtg+U3OBH8NVDQ6FjwrBczCwLe8cgvrT1+feti2tGxJiZpl7nMi4ptPdNiFDjvOzqoKZJVff95xKUHfTStGhNbGTfCvSH5mCgEbGgRbWrZkccKKN+tPX88tr9yic+jU66h7+odQs5C66kNcjk017HsO+g9kuTGBvpSpWOHf1j49YbAamEEIBoPpzkzVvuGBmVjxU5OPmVdNUkqe2+1O7x1PGPHw/h37+P65x6V+m+PjphVLuH/HvtR2WQqkjgZWsfrTd8KLm+ET/5tWnhlHzX0/ZMfVzBhtVd8uvrtwhoYKPzdn5Js3r1hCQ8UU4KgVN2sPK1lusfGMDdSFRo4rNhedrsK+TjCl+ekEVXP0eJoZX6vmTOx1jRPqgnWWz4Yb63UIKZ1NHRNCtAMdwJ+BbcCfpZRvjOG15cXSpUvl9u3bS9qmpkl6InEi8SRJKQn6PNSF/HRHEgWLoWW2NSPoIRKXCCSa1A0NKvweVE2SUDX8Xg+1IR/dkQRSS1LnjeJJDCI0FTw+/aH1eDNPoq+5VYcFzkL1EOka2bYSCbY7Nlddu88oNcLRMPFknKA3iKZpKUG0mkANPbEey31WYmma1OiJ9aCaVPFVqaJJjaAnSF0o+5gSY9yG60vJXU2TdA3Gs/ipqhrtAzHUpIbPo+BRBNFEkqBPf7OrCIipGqom8XkUAl5BNKGNreBfkZxT1QSd0U4SmopX8eJX/EghR4So1QThSAdxqRL0BNGEID4spielJJaM4VW8NHgr8EX7UufWBCn+2vFyEmBS8nZM8PuvwvZfwPmb9entw5BScsq2L3Fq7ZFcPG9Zwc0+tLOC21+aybfe9zZV/uwlK6e/tpmKxCBbzrgRgKRM8qN/fJmPLVjB8kMuL+qjJJK6MPyFJy/k2584uqg2yhxly1sjpmqaHh8Vgd5nSxBC/5ESfB6BqukuI1NGtNIco4XQHW4UBYJ1MHgQkqq+pEnxgkyC8KB6A3RqcV3A1OOjPlhPb7wXTdPQ0F2bFKHgEz4SMpHaVlBQFCUtVzCXl3E8LivuJhJJva/XJF5FMKsqQDIp6YrEU2X1IT/B4BR5I51UdT2OZGIkN5ZSL9NUnZtVc8Cb33banFv4FC8NwQa8GceZ89zM3DZX3hBX43RFu1CH85a6YB39if6JzDfKhrfRqDp1+ekEahQGOkx8bYQiZvNOVkTVKN3R7tSzURusJWj/+afJVCodjp8CKeUsIcRi4LThn/8QQjQCzwLbpJQ/GKNrHHcc7ItlOSWZ3ZScOjWZ22qsCvCVs4/g1m1v8ZnT3sVX73/Ztr3GGYFh5fnduR1sRuPCVCIHJ0MY2A5O3w5oUmNP3x6G4kMADKlDKTHh1BuKkDuaagVDrM8MTZNZrkxmt6bfvLCPc46fx+rNL6T237xiCUfOqsLnG8Mp+YqiC/EVAE1q7OrbbeuepKkJWnreYM2TXzI5K9zAYTWLaOndneWKsLi6OZV4KTjnqIsyh6bBP34DTUvSBmUADsa6GdJiNBX5RmZfr5dKX9JyUAZgIFjHrN63Ut/cPcLDzFE6M/k8CkfOmcEzLaMTb3dROKxiajyusrNjkC+YcoGbLj6Jh//2Dp88aYHjnKCsYZcXNB4JHa+nl3/6TnjqB2iDB9n10e+y5i/XpsXnRTWL2NWzK82t6crjr+RL5jhtiuNuHC4OiUSS19sH0nh584olVIe8XPSz51JlmU6Mkxaals3FFQ9CYhDuWZHOz9nHZL/MNDeVJ7cwYOanlUuT1TGa1Hir7620ehuWbWDTS5tSjpHT1QUyFlNp6RrM4uzihsqcrmFTBmoC2l+Hey9J5+usox0NJk52xNU4uy1y88OrD8c/RtIbkwkFRQMp5RtSytuklJ8HlgPrgaOBKbGUCbJdbqzclJw6NZnbWnXGIr5838spp4e87TlxsBmNC9MYOTgVi3A0TGt/K+FYmHAsnObw5LrlFI58bk3nLV2YGpQx9n/B5NRUTrByRUhz+Yh0pAZlUvuf/BKd0bC1K0LUdbqZkmj9q/7G9NB/ytq1a0hfUlSsVbbhyGSHwUAtvmSM4LCWEcBMfx0doxiYAThufg27OgZp7R4aVTsuRo+OwXjqiwToMXP15hc4b+nCgtwbyxp2ecHAgezyey+BEy4k/P6rU4MyMBKfOyOdWW5NX8qM026/Pmq0D8SyePmFu3YQV2Va2aoy7d8Lhp0r0z2ZLjeX5HW5yZdbjOYYq3rrtq5jefNyx+eaqugcyo6lX7hrxzRzZbqkYL5OFXRFuyxz865o1wRfWXnA8cCMEOI0IcR/CCHuF0L8Ffgu4AFWANVjdYHjDSduSk4dGMxt5XN4yGrPiYPNaFyYxtnBKR/iyTghbyj147rljA753Jo8ipg0qvj5XBHi0k7hPWlZnnCV36cm/vEbfVr7gpOzdhlW2U1FWGUbjkw5B2aCumV2mjOTr35UM2YAjl9QA8CTO91ZMxMNOycRI5ZOCVcmu7wgmbAuD9USr6izibOJgtyaXBQHO15mTt4yOzFOahTiypTMLZxejOOS02PyOUQ6OddUhevK5Loyua5M9ihkxsyfgAuA+4EzpJQXSClvlFI+K6WcMpHFiZuSUwcGc1v5HB6y2nPiYDMaF6ZxdnDKB7/HT0SNpH5ct5zRIZ9bU1KTk0YVP58rgl/YKbx7LMt9rvL71IOmwavWy5hAH5iZ6a1ghjdkcXBuhCMKEVVhdlX+gZl0Z6Y6+hJhYslowec00FQdZNaMAE+94Q7MTDTsnESMWDolXJns8gKPz7o80o1/KGwTZ30FuTW5KA52vMz8jmt2YpzUKMSVyZN7WUgxjktOj8nnEOnkXFMVriuT68rkujLZoxDx3zmM6MucjK5P8wLwF+AvUsrdOY4NAk8DgeHj7pNSXptRJwDcASwBuoDzpZRv57qmsRCiVFWNne39XHnnjiyNmcaqAN/42LuZWx1EQxcGbqgMpK0rNwuxhvweW42Z2//8FucuWUB9pZ9ZMwLMnRmkJ6qOCLhWeFE6XrPXgNE0iIShrw2e/B844UKobNT1O6oX6OtqDRE/TdNF+qQcEVyVw2vJ77kYqmbBB74Kje8eEfvLqG+IpAopiGtxVE3F5/HREGpAEYqtAHCmuJkh8BtVo2mivprU2Ne3j2gyiqZpaRozy+Yv4+unfB0PnrEWAy4bYbR8yOSZWUDacAfZebDfUmNmb/cQfZEE1RU+3u4cYuPjLXQMxLh5xRLmzgwgEbnFf+0EfM2CfL7hTjeZSKujJVVdpFdTURQvivCgCIEiJdFkDL/ipS7UiGJaF25e090QamDV8atYOHMhfsVPgyeEJxlnT6KP1sH9hLwhImqE+VXzqAnUsn9of9Y61ubqw+lN9DkWpU776MMigGZRyzIQDZ40vB0ztG6Hn38Q/ulqWJQt7rvixeuJanG+uuhTBTf90n4/3326jitPeodFtdaDLJ5knHOf/S9eOOJCXl58HgCv9ezgd6138e2TbqOp4tCCz2vgF3/azZ93dfHStz6Mf7LrQ6SjLHlrFVs1TZLUJImk5K3OwVTMnNQaM+b8QFN1K2yPT//d+TrMnAf+Kl3oF0CNQXgX2qtbCB/zr8QbDkcBfIqfvmSE1oF3qA/VM9M/E4/Qj0nKJJc/cnlOjZm6YB1RNZom6u5UXDVTlHUc43DZcNdOY6ahysfr+weo8HsYiidZNKuSoNdDIjnGAv+jhVV+ASNlHr9eJ94/osLtn6Hvv+fiVL6sXXw/4cpa4lpihBuStLa1UD1tQwdSotNGf95U1ZSVt6Z4pvjpT/Sz6o+rsjSVzJytCdSwr38frf2tqbykqaqJH+340URqzJQFb2MxlQODMRLqiLC6zyuYUxmYPhozg+163B0WUEfxQuWsaaMx0xXtIimTKSF4j/BQH6y305gpw0A1dihE/PcA8MDwD0KICmAlur7Mu9CXNdkhBpwppRwQQviAPwkhfi+lfNZU53KgW0p5uBDiAuD7wPkFfZpRwhBM/dFjb/CfHz8qbdDkt188nfBgnI7+GBf87FlL4V5Nk1lfhu9YeTIPrD6NhKoR8ntY/y/HogD//sHFXHlX9uBPWruz3o1yxWPZX4DNAn3vej+8/z/g15+xFuvb+t/w3ivht19MF0pTo/qAzvIfQ2AGPHW9Xu+5n2TV11Y8SIuS5KE3H+Lsw85OS6xuOusmVE1NfWlee9LaLNHelEjrsMBv51BnWp1NH9pEPBnnoTcfYnnzcvrUPpqqmrjlI7egCIWoGiU8FLYUBJ6OwmlmnhkDfl++L1tM+ojZM3hw9elpbk2aJhmKJ/ni3S+miQI2VPm59U+7+ckzb+cWuLYTh2w4Atr/oa+TrZoFH/w2bFmdVkdrOIKWnhbWPDkyUHLd6dfxy1d/ySVHXcKNL9xIZ6STjWdsoLl2cWpwRhEKzbXN3H3O3RwcOsjarWvTBcM63ibecCjrn12fKv/e+77HNdv+k5MaT+LWs28lqSXxKV7qg/XsHhbky8fXrP/78ADRj1/8MRcddRHXbrt22nOxbPDGH/TkZv7SrF1SSnYN7ec9NYuLanrfsFX2nBxLmZIePxFfFVUR01Im/7BldnT/qAZmjl9Qw2OvtbP97TCnHe4KpI4lrGKrlWD/phVLqK/yMxBNcMqiRgKTbcDMiONW+cGn74Tdz0DzWfDEd9P2a0d+nJazvpEmtH79B65Hk1pa/DXi+qoTVnHPx++hL96HqukvVW4+62a8ipeQN0RfvI8Vv1uROu6GM27gpYMvceKcE9MG1O3EVZ2IsE51CCGoDnm57bKTU19yZ4Y8tPfF+c8tf0/lmP/2wcVcdNdzlrlr2cAuv/AG4a5/0cuOOAc+8JV04dTz74LATDjnh+CrQFM8tAiVNSZubTxzI82aB8Vop2Yh6uWP0xfvy3p5Mys5K/UF0YpnNy67ke+973toUiOiRvAqXg4MHkgNQjZVNvGLj/yCqBpNey5uXHYj3z7t23wt+bVyeKEzYZAS+iJq1mDi7IpA/oOnBJIw2JEt/ltZD0z9gRkNje5Yd9ZzVzs883i6oxCNmWohxNlCiO8IIR4D9gGXAP9HngEUqWNgeNM3/JM5VWc5cPvw3/cBHxRCjGuPYQimPvpqO1feuYPzNv2Fi37+HD1RlaQG+8KR1BdgyBbutRJcvfSWvyIQzKutoK4ywKwZQSQiNSgDOQSGh1R9BkzNAv234ZZkFj9bfPbIoAxki/WdcOFI0mXs7x52e9r5MER74d5LR+pZ1A/37mHNE2v45OJPZon3tQ20pTqslceuzCnaawj8ZtZp7W9lzRNrOGHOCVz1+FV85g+f4ewHzubsB87mzZ43aR1odQWBTbASlbbipOEsMq+2RINYHwAAIABJREFUgsYZ+syu9oFYFtdW3bWD1/b385Nn3s5qIwu5xCGNTub0tSODMqY6ukhvuuDXtduuZXnzcq7Zdg0rj105LNy7jnAkfemGIhRiyVhqUMY4ft3WdXTNPyGr3a8/83VWHruS2167jcv+cBkhX4g5VXPpTfQ55msmDDG/5c3LU4MyTo5zMQ544xGYdaQ+yJyBjngvA8koTYHiHZmq/CqV/tz6DIPBOqqGrAdmRoNjmqrxKoIn3eVMYw6ngv2r7trBy629fGjDM1x22/NcestfJ5f4rxHHrfKDey+BEy+2zAfCJ12cJbTeG+vlq09/1TKur9u6joga4XOPfo7lW5bzL7/9F5ZvWc7nHv0cqqay+rHVacd96ckv8YGFH8gShnQqrjod43D7QIyLfvYcZ93wFGf+8CnOuuEp+iLJtH7+3CULssRWy1Kw2i6/6N49UnbChdnCqfesgK4W2PwpuO0cwtHurJxgzRNrCPfuSTuuS6h5RUiteLZ261q6Y91c9shlrH58NasfW52mKWNox2TmK2u3riWuxWmqakrNNp+O6IpYi/92RcqMj2OFgQ4b8d/p0cd3R7stn7vuaPcEX1l5oJCo8CZwFRAB/guYL6V8r5RynZTyvnwHCyE8QoiXgHbgj1LK5zKqzEMf7EFKqQK9QJZSoxDi80KI7UKI7R0dpSWxnWBqXE0SV5NU+D05hXtzHZ/rPAULDJvFz0K1ucX6rPabhdKM/Zm/zdcbqKRtsA2P8GQJNpmFevOJ+xkCv3ZtWB0/lQSBS8VdK1FpM3JxxxD/zaxf4fdklVm2YScOaRYzs+GknUivcd8NUby2wTbiFiJg9oJh1iK/ae2ZOOiUr5kwjp1uIpZjGXNLgr42OPAyzH+P5e6UI1MRwr9gODLlFpEEGAzUMGNwxFWhwlOFT/GPemAm6PNw5JwZPLmzPX9lFykUw9tCBPtrQr607Ukl/mvEcbv8QfFY7rcS+rXrm404mUvo0apck9qoxFWnQhwuhLtWfboiKJmJxbjCLr8w64bZcdZUx06QOh6oTD+dTe5gFiF1KuKbOciiCMVWFHuqwilvXfFfV/zXFf+1h+OBGSllo5Tyn6WU35NSPiWljOQ/Ku34pJTyBGA+cLIQ4piMKlazY7KeUinlT6WUS6WUSxsbGwu5hLywE0z1ez34vfo63VzCvbmOz3WeggWGzeJnke7cYn1W+81Cacb+zN/m640N0lTZRFImswSbzEK9+cT9DIFfuzasjp9KgsCl4q6VqLQZubhjiP9m1h+KJ7PKLNuwE4c0i5nZcNJOpNe474YoXlNlE34LETB7wTBrkd+09kwcdMrXTBjHTjcRy7GMuSVBy6P673l2AzN6AlDMjBknjkwGBoO1VEa7EJr+LAkhqPbV0xFty3Nkfhy/oIY3Dg7Q1lNQtzutUQxvCxHs74kk0rYnlfivEcft8gctabnfSujXrm824mQuoUerckUooxJXnQpxuBDuWvXpmqRkJhbjCrv8IjE0sm3HWVMdO0Fqf2ww/XQ2uYNZhNSpiK8m02dUalKzFcWeqnDKW1f81xX/dcV/7VHIUqb/E0L81u7HaTtSyh7gSeDsjF2twILhc3nRLbjHdU5qfaWfn126NBUwjHW49ZV+6iv9HFJfwfXnHWe5P9/xuc5z/459bFqxJO9xKVQ06utuaxbCthth+U0jD7mxJrdqjv77pbvhE/+bvr/2sOzjjXoW9euqD2HjmRv5zRu/4YYzbkg9UE2VTTRV6Wt3myqbuOWVW1h/+vq0/Ya4H0BdsI75M+Zn1Zk/Yz4bz9zIlpYtXHf6dVn75s+YT12gLmfb0wlm/mx6cldOTmZiVlUgi2s3X3wSs2b4nbVh5h6k8+3Td+bkZF2okY1nbEi7h9edfh1bWraw/vT13PLKLfp9PWMDdaHsTr0h1MCGZenHb1i2gfrWl9h4xg/TytPay+CgU75mwjjWiqfTlYtlgTf+MLzkc6Hl7jcH9zPDE2KmN9utKR86hxSiquJ4YEaRGpXRzlRZtb+e9khrwefNxPHzddts151pbGEVW+/fsY/vn5seY29esYT7d+xLbefsr8sRRhy3yg8+fSe8uNkyH6h7YXNWDK8OVPO9933PMq5vWLaB+mC9ddy2KL/hjBt4au9TWeVW8dUcy3PVm+qw6tP9XsHNprL7d+xL2y5bztrlF7WHjZS9dPdIrmHUOf8uqD4kg6c3ZHPDVIeahdRLry03DVjxbMOyDWxp2ZK2bR7AMQYIrdpuCLk6YfUhfxYfb16xhPpQmfFxrFDVmM3hT9+pl08D1AZrLZ8NV2NGRyGuTB/ItV9K+VSOYxuBhJSyRwgRAh4Fvi+lfMhU5yrgWCnlqmHx33+VUn461znHwiHE7Mjg93qoDfnojiRSDg0SSTSukZQQ9Ck5XZlyKd/nOk9OxXyzk4KhSA+gJXSnJW9Id2cyu+SALoxprlNRD5EufeqoL6S/IZPaiEK40fbwMZq/ih5FQUpJTIshhic4SWSas02xrkyArdsNQE+sBzWpuq5MwzDzJ+hXiCckiaSG16MwqyqAN4cYpapqtA/EUJMaHkUQ8CooishydirYlUlN6HzTVPBXQjKuL6nz+PSBG493xJVJJlGEgoLQf4RCNBnFr/ioE16URBTNFyIs0Lmk+KmTkETQKXVXMK/ipUEJ4Iv2ogWqCEuVuKbiV3woHr/eng0HLV3EFD+KootN2wnzua5MZebKlIjCDw6Fw86EU75gWeWiF39AQlP5yqLzCm7+xf1+/vvpOr5w0ju8y8aRyUBjz26W/eNWHjnlW+xvPB6Apw78lhe7nuHHpz2CIop/Oy2l5N/ufpH3HFrHpkuWFN1OmaEseZvLlSmpSTyKoKHCT188mb+/ngjYxWe7egjdccno840cwFehz0JIlWsgFDRvgDBJ4kgUFBQBXnRx/zianhsIEAgaQg14FS+qptIZ6SShJfCKEfelan81XdGurPJMVyY7lybXlUmHuU83cgBN0+gYjKNqEm+5c9YMK/5qcV1/Q1P1WQVVjRnbcwAJAwdTZVrVLMKJ/nRuZLgyUdFIVIvTHe1O5RS1wVqC3mD6JWXwbKZvJl3RrpE8JNSAR/FkcVGTWor3PsWXeh4mEGXD22hUpSsyws/6kJ9gcBrNmFCj2ZzO4N1URlSN5n3uTCjDQDV2KMSVyXbgxQHmArcLITzos3TulVI+JIT4DrBdSvlb4BfAnUKIN9FnylwwivMVDUMwFbB0WcqnZG8+3ul5DOQ9zqxYXzULPno9xAez3G+obNTrdrw+UtfCJSdlvW11nvBu6N8/csyRH6fjrG+y5sl1BbvZpH1uodi+0cr1JmG6vQXLB4M/xXDU61VoqglZ7nN4cn2GghmaBp078/JN8XhpqJid7byw/CZ4+Vdw3AWwZTVa1SxaPvpd1vzF5Hx06nU0//6bzB1oNzk1fBh69qLULKQhF6fNly+ULK45dfiwOtbFBGLPNkhEbPVlNKmxa3A/p9a+u6jm9/XqXeTsqvwzZgaGeTFzcH9qYKbWP4ukVAnH2mkIzi3qGkBfFnX8ghr+9GYniaSGzzM9RSPHA5l9czExdsJg52pjFRcVRf/Sm8ud6akf6CYBNQvhU3fA09ej7HyYhnw5RAa8ipdZFbMKclEy4my+2OzG4+w+XXcYHZocnM1EZn6hxqD99WwHm85d8MDKdKdRE++VC+6mIZOfgrS2VU1ld+/uLHeYxbWL0wZQrHg2tyo7nmfWUYTCnMo5o/t/TEFomuSt7knKz1JAjVpzetaR02JwxulzN11RcHYnhGgWQtwnhHhVCLHb+Ml1jJTyZSnliVLK46SUx0gpvzNc/q3hQRmklFEp5aeklIdLKU+WUuZsczxg5bI0oUr2ZsX609fCUKel+w1DHdl17erZnad7d9oxuhvDuqLcbFyMHcqGo4Xwzcp5YctqOPXfUseF3391alAGhjn2l2sJv/9qa6eGfJzOA9fhY5Liraf1t01zMiXLdOyPdTOkxZg3CuHfGX6VCl9uRyaAiH8GquJj5sCI2G9dQJ+afDCyr6jzm3HC/BoGYio79rjOBeOJsomxTmDnapOrr8/lznTChSPbv740fbvAeFtsjHVjc+GYVJzNh4F2awebhe8Z2TacRgvMBzojnZbuMJ2RzpzHuRgdphQ/i8E0d2Vyn7vcKOa1263AzYAKLAPuAO4s5UWVC5y6LI0bMt2YzO5KBnr26vWcODepNkFQjWe1bVa5n26uNOWMsuFoIXyzc14wnEDI4apQUTdS31eR3YYdp/NgKjt8TGm8/Qw0LLZ9y/Tm8D2dPypHJoccEIL+UAMzTTyq9RsDM6PXmTl63kw8iuDJndMjeSsXlE2MdQK72Jqrr8/lzhSqzb1dQLwtNsa6sblwTCrO5oOtg43ps+TKhXMgkUxMO+ekcsCU4mcxmOauTO5zlxvFDMyEpJSPo+vT7JFSfhs4s7SXVR5w6rI0bsh0YzK7KxmoWajXc+Lc5LUR2vL6s9o2q9xPN1eackbZcLQQvtk5LxhOIORwVRgKj9Q3OzVknqNATGWHjymL2AC0vQSzj7Wt0jL4DlCcVbZWgCOTgf5gPdUDIwlHhXcGfiVYEgHgCr+XxbOr2OraZo8ryibGOoFdbM3V1+dyZ4p0594uIN4WG2Pd2Fw4JhVn88HWwcb0WXLlwjng8/imnXNSOWBK8bMYTHNXJve5yw3H4r+pA4TYBrwPuA94AngH+B8p5RGlv7zcGGshyrJZW24W8R0Kwz0X59aYaTwSYn3Q2wpP/g+csgoCM+DeSwvXmHn2ZjjhQrSaQ2jx+1izda2txsyimkVZAn0p8bNkIiXsJKVECIHBvTEW9C0EZSOM5hRlw1FNQwvvJty7h3hFLf6Keup621C0JJriIVyzgLii6Lzw16AY+kep9bV3QWUjWqyfcH8r8YpaRNVsWgfbEAj8Hj9zKmYTUyP4tCQN0oM3GYfN52ZzGpyJX5ovP0PHYNn8ZXz55C/jEZ68AsKu+O8E4c3H4K5z4UP/BU0nWlb56mu38lzPTq5/98qCm28f9HDVQ43865HtnDKv39Exx+x5jCPfeYa7PvZLtOEk465dN9AQmMO/H3N9wdeQiYdebmPzc3v501eXMb+2cJepMsOk4G3ZxFgncKIxYzYQkJqud9C/XzcFuO+z9hozn74T1AhasJZwsJI4oCheFOHBq3iJa3ESyQQ+j7XIqSY12gb0mS6GcK/f46epqslWZD2e1EXZ+xP9rPrjKlttmgmIx2XN3UnF2Uxkiv8GqyH8NvTuHRGkrl4IHi/0H9C3G45ES8YI9+4lHqjEHxukrvoQlLrDcvb9qqayp28PbQNthLwhImqEQ2ceStAbJKElLPNYn8dHfbCe3nhvGt+AvBwsg7yhLHg7qflZCqhRa07XHTptNGaG4kMMqAMktSQexUOVt4oKf4Wdxsw0IMUIihmeWwtUAGuA/0KfLfOZUl5UuUBRBEfMnsGDq0+fOCX7pAoH/z6yHvGIc+DS3+pvC4ylHJ/9ne6kYLgtdb+tJ1pvPArv/w/49Wf0gZxzfgh1i8BfNeLcZAVFgbrDoKIOzvga3HMxSs9emo/8OJs/+jPiHi9Bb5DNH9uccl+qCdSwq2dXmkDfLz7yC/rifazbOiIYfOerd3LFcVfgQR8ZH1KHihIRdqGjLDgKaAJalCRrXvxBmljvohfvZtfSS1jz6OXp97jxSJQrHtOF/bQkPPJNtMGDtJzzfda8+APLwb/1p6/nxhdupDPSyYYzbmDxm8/gvfS3+lsGYwAGnItfmqAIhebaZjafsxlN05OnKx65wpKXToWCXYwx3t6mu8c02gv7vjnUxrwihcNTwr+VzqfX9ocaUKRG1VA7fVXzAKjxN3CgBBozACcurGXzc3t54vV2Lj310JK06SI3yiXGOoKi6LHuisesB6aNgRsrsd/zN8PKRyEZG35z64Gln4X3rdOXMGlJtFfup+Wk81nz6EhsvP4D16NqKl9/5us5hRw1qaXyAXO9OXJO1qC3VXy9++N3WzrmufE4G5OKs2ZYDSyueEB3eHz4ahNX74LHv5MaNNRWPECLUNPzj2UbaJYaSo6FAYpQUDWV9c+uT72QWXXCKq54NL3v9ypeVj+2Oq2OmcebPrSJeDKek4MuT0cwaflZSmRxevNEX9G4QdVUWgdbs/qCw7yHueK/FLGUSUr5vJRyAOgD1kgp/1VK+WzpL608YDg0zKutoHFGYPwDx8CBdJGonQ/DHZ8AoUBlg/5TswBqD4UZs3ULbEO4d+HJ+qBMz15o3Q6bPwV3flIfe8znpKAoulX2PRenzq28/hANt55DEx7qgnU0VDTQVNVEQ6iBnlhPlkBfPBlPPXiGYPDy5uX0xnoJx8KEY2FXRLgEmHCOYiPQ+Jdr6XzfumwR3yfWEI736O4IQtH5vPNhXfB32zdtBaav2XYNK49dqQuFPfklOo84Sz/W69fbUpTCxS9NMJwXFEXh37f+uy0vXTHKMoGhL+OzfsOU0JK8NXSgaOHf1tTATAFLmUL6uWaaljPV+hsJx9pJaKPXxGiqDjKnOsjjr7nLmcYT5RBjHcNwtalZMBIXDeQS+73nYr1u7aHg8cGtH9Zzhp+fBe2vwebzCL/nUtY8dXVa7OuN9aYGZYwyKyFHp4KPdvFVk1oq3zB/kXXjsTUmFWcNWPXf3W+l5aE6V1ekCVGHe/eyZuvadA5sXUc4krvfz+TO8ublWRxd88Qa2gbactZp7W/Ny0GXp+mYlPwsFQY6LDh98bQR/+2Odlv2Bd1R19gAinNlWiqEeAV4GXhFCPE3IcSS0l+aCwCSCWuRqKTNW1yzcG+hor9WbTk83kqgTxFKlmBwtb+akDeU+nFF/aYG7AQaEx5P7nts4rcTgelqf3Va21l8LFT8soDPYlyzK0ZZBogPQtuLtm5MAHsj7SRkknnB4ux09/V5mRlw5shkYGB4EKh60OzMNAuJRmd0v91hjiGE4KQFNfx5VyeDsekhFOiihMgn9msn0D5cP654s2KfXT+eKeToVPCx0PjqxuMpBKv+207Y1yREHQ9UWnMgj5hqJnfs8o6QN5SzjpNc1uWpixSmufivqqmWz4I6TT5/PhQzf+4WYLWU8lAp5aHAVehOTS7GAh6ftUiUx0YkySzcW6jor1VbDo+3EujTpJYlGNwb7yWiRlI/rqjf1ICdQKMvmcx9j038diIw3RvvTWs7i4+Fil8W8FmMa3bFKMsA+57Tk5hcwr9Do3Vk8hQ0WwYg7qsg6qvMmjEDpbHMBjjpkFoSSckzLa61pIsCkU/s106gfbi+X1OzYp9dP54p5OhU8LHQ+OrG4ykEq/7bTtjXJETtjw1acyDPsohM7tjlHRE1krOOk1zW5amLFKa5+K9X8Vo+C+4yJh3FDMz0SymfMTaklH8CnCkjuigcVXN00T3jITZE+KrmWNevaITaw2D5TfDS3fCJ/00/9oK7R7Q48qGiUa/v4Pi6YB0bz9yYetiMDmfDsg00VTZxyyu3sP709Wxp2UJ1oJq6QB11gTrWn74+7ZiNZ25MCam5mDywuv8bT72Ohmc2sPHU6+zvsYnfdU//MFXX4Iv5uPWnr+eWV27R16OecQMN2+/I5mMBnC3os5iuOd9+F+OAt/+kL4OblUNfZrANBcHcQOH3pRhHJgMDwfo0y+yaQOksswGOmDODSr+HJ14/WJL2XEwjGPExX26QGUdfuhs+dQd1z9/Bxg/8MC32VQeq+d77vpdWtmHZBhpC6TPVGkINqXwgV71C46sbj6cQrPrv2nfp5gCZOfBLd6e266oPYWMGtzaesYG6UO5+P5M7W1q2ZHF045kbaapqylln/oz5eTno8tRFClWNNt/rnOepkxm1wVrLvqA2WJvnyOmBYlyZNqCL/94NSOB8oBu4H0BK+UKJr9EWZeUQMpYwXJmSCfANT6lMJtKF/cxK9kadRET/8oIEKfX6oXqI9uj7DMHgTCHgzLa0pK4mr0AcaSm+F46G0TQNDS3ltmCo2XdHu0loCYQQIMEjPHldmSZIvb4sFOvLCpkOCTkcjrSkSjjSQVxT8Ss+6qRASQyRCFbTqcVQNRWv4qEhWK+/JTXaNWZ/JSJovhBhj4e4liDoCepOH1oCn+JDQSGWjOFVvDQIPz6pWl9PAdds+7Hz8K8M3BXMmH68/fmHINYLH/uhbZW1//gJrw7s47tHXFpw8wcHPHzx4UbOPbKd9zp0ZDLwnpYHmN37Nvd++Oepsptf/xYn1b+fS5u/XPC1WGHj4y20tPfz12+cNZnX5k8/3k4kUnmEqpsHeAO6+LpM6iLailePk1Y5hRDg8UMyjubxE5YqcTSE0EX8/YqfuBYfjvFe6oK19CcGsuKjqqm6u81wTLdyb4LC46vryjRJ4KRvNue7Hp/+8kZqMHBQnyWpePUvsIOdaXU0MOUfXuqCDSjRcN48IJOTNYEauqPdKS7XB+tRFCWtjuvKlB9TirdjATWqa8qYOT0NHJkMRNVo2nNWG6xNOfdaYNImOcWgmHlDJwz/vjaj/DT0gZozR3VFLrLh8UL1fHsrzMYjIdN+2MqJxmyDnWmxbdS1OIe24kHdccdCTR7IqzRvFj1zokTvqteXCZxYr5rqKh2v05BRV21opqV3V5b6+mKlEu/tH89qV1EUGrDmgNmVKScfDPHLUcAQAi52v4sxhKEvc9TynNVaBttoKvJtZGtf4cK/BvpD9byr/UW8agR1WJug1t9Ae4lmzIC+nOkvu7v4W2sPJy503zK5yINMd0fjDe0r90PzWenuTOYYbxFHFaDOFJ+tHPQ2LNvAppc2sbV1a1r/7VW8zKm0me1rPkeB8dWNx5MATq3cM3PZFQ/qX2LNZZlW7hfcjTLrKBqq5jo/F3qeYXYTtXJc2rBsA801zVm8teJbPg66PHUB6APi7a9nx+NZ79YHzKc44mqc3b27s56zw6sPx1+A7MBURTGuTMty/LiDMmMJO8eZgQPOnGiGOkYcm+zqWpwj3LvHVk0+n9J8MUr0rnp9maAQhyObup3RLmsnDtSc7VpxwOzK5PJhGmPfX3XHuDn2+jKRZJx90U7mFyv8W4RVtoH+4XPONAkA1/gbS2aZDXD8/GoUAU+87rozuXCATHfHnr369okXZ7szOXCxM8dnKwe9dVvXsbx5eWrbjdcuHOUTlq5Mu7PL7r0kzZXJUTsWvHbiymTlHObCxagw0G4djwemR3/eZfO9oCvaNcFXVh4oxpVpthDiF0KI3w9vHyWEuLz0l+YiC3aOM3bOTZlONGbHJru6FuewVbxPxsfEwcZVry8TFOJwZFM3oSWtnTgyZ9xktGvHAbMrk8uHaYo92/Lqy+weOoBEFm2Vva/XS3VAJVSAI5OBgWHL7GqTAHB9YDZ9iTCDamnk2GYEfSyePYPHXnN1Zlw4gF2OoHiKcrEzx+d8DnrGthuvpzmc5BNFujIV687o1JXJdYtxUVK4rkzuc5YDxawLuQ14BDAkld8A1pbqglzkgJ3jjJ1zU+aUMLNjk11di3PYKt57/GPiYOOq15cJCnE4sqnrUzzWThyallXX3K4dB8yuTC4fpineegbqD9cTdhu8OdzpzwsUOTBTpPAv6EuZNATVAyNLl+qD+pKQtsG3imrTCictrOW1/f280xPJX9nF9IZdjqAli3KxM8fnfA56xrYbr6c5nOQTRboyFevO6NSVyXWLcVFSuK5M7nOWA8UMzDRIKe8FNAAppQokS3pVLqxh5zhTNSe/E42m6QJ/NYfqjk2ZdUP1upiapsH56Qr4ddWH2KrJ2ynN1wRq6Ix0omkaP1r2o5xK9JrU6Ix00jbQRmekk5pAjateXw5w4nCkGaJ8Gpy/Ob3u+ZtpUELWThyMdEzakR+n87KHaSOpc0ZqlrwyuzJNNB8yOavJwmdWuCgC8SFoeyHnMiaAN4fa8AkPswI1BZ9Ck/BOX+FW2anjFR8DoXpq+keWLtUHdO2DtqG3i2rTCicNa8s84c6acQEjsbhn30hMNmB2d5y/FC7+NVzyG1Aj8MlN2TFeeKzbGYY5Pls56G1YtoEtLVtS20a8Ho+46cbmMkVFo64Xc/Gv4bMP679XPJjtqviZh+Gq5+GL2/Xfs47NykO0i35NZ1UDbVc8QuclD6BZtePAndGJK5OVc5hTuFx0YYmqWVnfszj/Lr18GqA+WM+Ny25Me85uXHYj9UXOcJ5qKGZ4alAIUY8u9IsQ4hSgN/chLkoCRdHFy654LFtp3q4c0oXQqmbBh9frnR+a7spUUQ/db4+IAlfNgnN+CHWLwF+FUtlIs4DN52y2VJNvrm1O21cTqMkSVPv5R36OR3gsHZ2shH4X1SyyPZ+LcUIhvOrZC0ecA5f+Vp8eryXhkW/iHTzI4uU3cfsHbyKhKPg0jQa8eGfOgyseQ9M0WhI9rPnj57KEntN4pfhRFIXrP3D9hPPBFaeeQLQ+D8k4zD4mZzVd+LceTxH3o33QQzypMLuq+KUXfaFGavtGpirP9NXgUwLsL+HATFNNkDkzA/zx1YNccuqhJWvXxSREPrFTj1d/Zi7/oz7Ycs+KkXoX3w+X/UF3vvH4IdYPP1uWUzRVEUpafA56gtx+9m0kNBXfsJPNt077Fl9Lfi3NtWas46Ybm8scahQevjqdW2ZIDSJhC1HUo1N5iOYL0RLrYs3T306/x8L0pjlf7mJUy+Cx3+Nnpm8mt519W8otpiHUgM9wjiwALhdd2EMBT0D/nuWr0GeFeQIUN1di8kFRFAKeANeccg0hb4iIGiHgCaAU6J46VVGMXfZJwP8DjgH+DjQC50kpXy795eWGa8fmEAMH4ednpa9prFmod1pVs/X9+/820mFa1SkAnZFOLn744rQ1hE2VTWw+Z3PWm4dC6o4DXCvBQmDHq8t+D7d+VC8//y545Bu2vCqz++8IZXjN04e3W/8bnr51ycrWAAAgAElEQVQeLvgV+O2XMn3w2a+zqKKJzy38SMGneP6dAD/4Uy1XLW3lkOpYUZd59J7HeXfrU2z+2C9JDi/h2LxrAzX+Br507A1FtWmFzc/t4Q9/P8CO//wQ1aHCvzxMMKYPb8ca+fp4p/WctlMExiNujmNsdrlbKJxwq7d1JHcw17ns97ozKWXZ/1qiTK/T5W05oGcf3PaxbJ5/9ndQs2DirmuccGDwAJ/5/Weyno3bP3q7nWvftLLLdjw8JYR4jxBijpTyBeADwDeAGPAoUDofUBelRz4hNCeiwAWgEPFeV+h3EsOJGHWoNievJuP9n4zXPGXw1jPDM/nsB2V6E0O0x3uZV6xVdm/xVtmpa6ichYJkZpoA8BzeGSqdxgzAe99Vh6pJHneXM01vOBVqd5ILlCgPyMR4xE03NpcxnHDLTqQ6OeKON1nu8WS5ThcTgGku/ptIJqxNQbTCXTCnIgqZN/QTwIgopwHfBH4MdAM/LfF1uSgl8gmhOREFLgCFiPe6Qr+TGE7EqCPdOXk1Ge//ZLzmKYFEBN7Znldf5o1B/T3BglBjznp22NPrpS6YIOgtbDapGX0V+lrx2v6R5Ks+OIf+RDcDidKt/F3UWEVDlZ/fvXKgZG26mIRwKtTuJBcoUR6QifGIm25sLmM44ZadSLVpKdFkuceT5TpdTACmufivz+OzNgVRJt2s3zFBIQMzHillePjv84GfSinvl1L+J3B46S/NRcmQTwitohFqD7MWBa4o/MuNnSCwlVhrIXVdlBmciFFvuzEnrybj/Z+M1zwl0Lrdkb7M68NuSAuDRQ7M9HhHpS8DMBCsJyk81JgGZhoC+hTdUgoACyF4z6F1PN3SQX/Ufds0beFQ7NRRLuCknSIwHnHTjc1lDCfcMotUG3U+fadePozJco8ny3W6mAA44PlURkOooaQi21MNjjVmhBB/B06QUqpCiNeBz0spnzb2SSlts2UhxALgDmAOupvTT6WUP8qocwawBTDmej8gpfxOrmty1zBmQNNgqEP/LZMg5YjoGej77ERcI2F9uqiW0AXYvCGotD5OExCOhtE0DQ0NTWoEvUE0TSOujQgA98R6HIn3alIjHA3b1s23v4SYVutvbf+vBo9yiOalkFRh4IDOHY9PXxIXH0TzVxLWYsQ1Fb/io054URIRy/bsrkNLqoQjHcNteKkLNaJ4rN8ojCNHJuR8eTA9eLv1e/D0D+CCX4K/yrbaN3fewdNdr3DDUZ8r+BSJJKy4fzZnHNLD2YvC+Q/IgQ+/+L/0z5jP4yd/A4C+eDc/e+M7XLRoLWfM/eSo2jZj54F+vv1//+BHF5zA8hPmlazdccD04G0p4CQm56tj7EeAGtOnzXt8+pcBc1wdrqdpGmEF4kjrGKdpqNEeOrUoCS2JT/HRUNGQ0/I0M24Wkic4/leNT2x2uVsMMjkarIPBgyP5g/HF1JxTVM0BoaQdp4XqCcczeCPJ+4xYcQPIy8nMOk45VWZ5Ari8LR+oURjo0OOw4oWqRvAGJ/qqxg1RNUp3tDslsl0brCVo//mnlcZMIfOm7gaeEkJ0AhHgGQAhxOHkd2VSgaullC8IIWYAO4QQf5RSvppR7xkp5ccLuCYXBgxXhq3/De+9En77xWxXBSvxPjs3h+rhkdyMfdqKB2lRkvz4xR9z0VEXce22a2kINbD2pLVcs+2aotTnFaHYjpS6yvZjA9v/a/UilI7X7d090hrRILPu8pvQXv4VLUsvYc1frnV0z6zuv5ZUael+gzVPrhtp44wNNNcuzhqcmQiO5OKsizHC23+CusNyDsoAvD6wr+hlTK19XjQpmFtVnOivGX0Vs6jr25PanuGrIeSpZO9Ay6jbNqN5dhW1FT5+/8qByTYw48IJ8jkuGVAUe4FeJ/mB0ZaioFU25o6pmoba28obyX7WPfmlVJ0NyzawuHax7eCMOW6OVdx2Y3MZw8zRpAoH/57twDT7mJTQL2DJf+WCu2kwc9bBM2LFt00f2kQ8GU9zEF11wirWbV2Xxkm/x8+qP64qmKcuF11YQo1D++sW7mNHlWTZaLkjrsbZ3bs77TnbsGwDh1cfjn8afP58cNz7SSm/C1wN3Ab8kxyZaqMA/5bn2P3DosFIKfuB1wA3gywlhjr0TumEC0eSLtB//+rC4TdlOY6zqm+xL9y7hzVPrGF583Ku3aZ/8V557MrUoAzoIk5rnlhDODq6N86gv6UwOs1Stz2dYft/jeTgQyasuLNlNeH3rU0NyqS1XcA9C0c6UoMyqTaeXKdfn9PP4nJk6iAR1a2y8yxjimsJdg3tL3oZ095h4d85o1zKBNBdOZeqSCeBeD+gLztqDM1jz8DOUbdthjK8nGnrznYGY9NDPHBaIVcfXWgbDvODvDF1qINOEqlBGaPOuq3r6Ix0OrokN25PcwwcGPliCvrvey/Ry81wwn8Hdaz41trfmla2vHl56suiUWfNE2to7W91eeqidBg4aMP96SHi3xXtynrO1m1dR1e0a4KvrDxQ0GsJKeWzUsoHpZSDprI3jEEXJxBCHAqcCDxnsftUIcTfhBC/F0IcbXP854UQ24UQ2zs6CkhMpjoMxfs8Lji2x1nVt9gXD1TSNthGtb869VCZ/zZQKvX5qaRsX07ctf2/2qnFW/HHhjtxxTvqexbXVPvrc/pZJiFHyhFlwdt3tkMyllf4d9fQAZJSK174t8eHV5E0hEav19Jdpa+fruvdnSqbHZxP29DbJLTScvO976ojpmo8udPtEw2UBW9LgVI4JRWYH+SNqWqchKKMylnDjdv2mDLczQUHDkyAM/47qGPFt5A3lFZml8uGvKGsMpen2ZgWvC0Fprkrk2qT36vT5PPnw7iuBRFCVAH3A2ullH0Zu18ADpFSHg/8P+A3Vm1IKX8qpVwqpVza2Dh6QbopA0PxPo8Lju1xVvUt9vljgzRVNtEb700JN5n/NlAq9fmppGxfTty1/b/aqcVb8ceGO35NHfU98yte++tz+lkmIUfKEWXB27e3AQJmW47Xp/D6wD4ADilyYGZvr5fZlXE8JegZeyrnAlDfO2KRPTu0gKRUaSuxbfaRc2YyM+Tl4Vfa8leeJigL3pYCpXBKKjA/yBtTvX58/5+9e4+PsroTP/45z1ySyQWSMEAINyUi1Qqist6olVgviK70st1WsAWpWymuCrr6ay1bl5bWXV0JotJYFbFbsN1tVVyvqzVS74qKqKBcFWK4ZMiFkEwyl+f8/pjMkElmkslkkrl936+XL8zkuZxkvnOek/Oc5/s1zX5V1pB+O7qMid2exFCBCYgt/mPYJlK8uX3usNeijWXdPne31yROu8uKuE2ELK/KZI0yvu8pP1k2GbSJGaWUjcCkzDqt9eNdv6+1PqK1Ptrx/88CNqWUPJwZq2DG+82PwRX3xV5VoadM+RG+VzJ0PKsuWMWGHRtYNn0ZZfllrPloDcunLx+Q7POS2X5gRP29OvpQlSNS7MxeTcmrK1l1zrJ+vWcljuGsmhGetX3VjMpA+2L9WSRGMsfnr0LJ8THkl6khx7Ax3F4U12m+aLRSmoD8MgAeWx4tOcUM67JiBkh4nhnDUJx9/DBe2nqIJrdUZ8ooiaiU1MfxQa99at5wnNionLEi7soa0m9nuVgr08QS/zFsEynexhSOCXttw44N3arFrLpgFWMKx0icisQpGBkl9qPkCMsww3KHRazKNCx3WJJblhpirsrUr5MopYBHgXqt9eIo25QCB7XWWil1JvBnAitoojYw67J+x1p1IVJVJsM4Vn3J6w58v4fKS2FJ1eKoypTI7PNSlWlgRK161JeqTD5v4JnwYGb5nEJob8a0OahX9CseUrkqU4rJ7Lj1tMJ/jIdJl8Hf/ajHTedtvpsjPje3nfCPfT5NU5vimg0juXyii6+P6y2ffWzO3baewvZGnrjgPgC01qz+dClnDv8GV51wc0LOEbSr7ihLn/yY33xrMnPOGtf7DsmX2XGbSJH6ZIhtPODzgFKgLIHrvsUeKDvfdXzQ9ZS99alxVGXq8zlSl8RuPLpWccwfDkcPdapMUwrWCCuuElGVjMGvypSCJG5TRZZXZfL4PBxuOxyqyjQsd1hPiX+lKtMAmA78APhIKbW547XbgHEAWusq4B+AnyilfASqPn2/p0mZrBNLZYbeqjLU74bm/bBhUWwVm6Ic04BBzTQvme0HgGli1H2KM1o8xTJz7/fBoU8iVlUwLFb6+44ZFivOglGxbSsxkrn2vRX4Q3LUqT1uZmqTT1tqOLvoK3GdZm9T4A+C0vzE5Q5oKChjTP02bN5WvLY8lFKMyB3NF0e3J+wcQROc+YwpdvCX92vSZWJGxKprn9zbeCDS96+4D95+ACpui1xlr+spe+tTDQNrXgml0bfo/ceSfjt7RKvCtPFO+OyZnitAxjImiWGbaPHW9bVYthEibj5vlKpMX408MZmB7FY7o2Ic32ebQZny1Vq/prVWWuspWuupHf89q7Wu6piUQWt9n9b6q1rrU7XWZ2ut3xiMtqWN/lZmaK2Dht3HJmXiOYbIHImo9BFrVQUh+mP3xsAdpV7yy3zZdphWf3v8FZkaA/cpRiWgIlNQQ8dS3ZIj4Xlmalp20e5vS9h5IFD16bwTnLz3RQN7XC297yDSV2/9d6TvP/XPgapMcs0XyRBtvDD1ymNfS2yKbCBjZ9GDtFyLl5X6W5nB5wFbXv+rO4jMkIhKH7FWVRCiP3a/AsMngc3R42bbOhL/xluRaWe9jaIcH4U5/rj2j6ShcDQAwxuO5ZQZk1+OX/vY07w1YecJ+trE4RgKHn+/JuHHFimkt/472veDVZnkmi8GW7TxgqM4/GuJTZHpsrwqk+iZTMyki/5WZrDawdva/+oOIjMkotJHrFUVhIhXaz3s/xBGTe110y3Ne7ApC2Nz41tyvrPexpghiV3F0m7Lpzl3GCPqPw29NjpvAgrF9qYPE3ougJJ8O5PHDOVP7+7D6zcTfnyRInrrv6N9P1iVSa75YrBFGy+4G8K/ltgUmS7LqzKJnkkUJJlpag63ePD4/NitFobl2zEM1XmDYwl9v7cO/jQ3/JnyWCsz5A2H4gkwe3V4jpmrngANNO4LXBAdw8B9OLbEryLl9BpPQcEqBsHl7pMug0t+HXjfjx6M7X0PVlXo+pxs16oK8f8wsSchFplpz98AHdPEzIdH9jDeMQKrYenzaZrbFQeOWjltZGIqMnV2uHAsIxu2B5KtKkWOJZcRjjF81rS5953jcPFJpdz1f5/xwicHuHxKWe87iKhi7k8HW97wwLW7YXdgJay3NXB9D44HuvbvnXPMfP+xQCLg4DW/p6TB0u9mjKTHckEpzPkfaNp7LGaHjoO//jLw/Z7GtF2TBheUQpRCACI9JD0ek6mgFH74VCCelQqMDYJxLbKe9GxJZJqazw4280+/30RNg5sxxQ4e/OE0Jo0sDHRQXRP4Tbos8GE2rH0fMBkGlEwARxHMfzZQncFeAM0H4A/fOnb8828N/0M7WjI2kXJ6jafODCPwvl7zUiDOWurg91f07X1XBjhKYO5fjl1crLmB1/v/w/Se7Fpkvj0bA48wOSf2uJnX9LG1eS8zhk2J6zS76gOrvMYOGZiJmePqNlPQepCj+YGB15i8cj6sfx2v2Y7NyEno+aaOLWLkkBzWvvG5TMz0Q5/602TwtcEzN4f3j0Gd+/fOVZkur4T2ZniwIvakwdLvpr2UiGVlgOntErPr4YpV4P2P6GPaaEmDR54ikzNpKiXiMdnamrrH9JAxyW6VSAFypU2iwy2eUMcEUNPg5p9+v4nDLR3P2HZN4PfZM4E/nq32QPb5vg6UDAPynVA0FoqPC0zOdD7+1Cu7J6SSZGxpo9d46ipYxcAwjq3Egtjf99Y6ePQyuP/v4L5pgX8fvSwx8ZKI5MQi/e2qDgzAe1ni++nRGjzaxwl58WX531lvQ6EZPQATM64hYwEY0XCsEtPY/HJ82svu5m0JP59hKC46qZRNnzfwSW1iyn5noz73p4Mplv4x2L8XjYWhY2DIqEDR0eCNmEj7Sb+bkVIillvr4I9zusTWnMA4tGhs9DGtJErNOCkRj8kkMS16IBMzSeTx+UMdU1BNgxuPryP5ZCIStPak6/GDiQEH6nxiQPUaT9HEG2cDGZ8DHfsi9bl2QsMeKDu9100/bN4NQHl+/BMzI/K9OKyJz8tyJG8kXksOwxs+C702Or8chcHWhncTfj6AGZOGk2M1WPv65wNy/GwQd386GAaqz5Z+NyOlRCzHG1tSZCDjpEQ8JpMk/xU9kImZJLJbLYwpDq80MqbYgd3akSMhEQlae9L1+MHEgAN1PjGgeo2naOKNs4GMz4GOfZH6tj8X+HfsWb1uuuXI55TYCim2FfT5NFrDzsM2xhQmfrUMgFYGhwvHUnr4k9BruRYHY/LL2Xz4tQE5Z36OlRmTRvDEB1+yr751QM6R6eLuTwfDQPXZ0u9mpJSI5XhjS4oMZJyUiMdkkuS/ogcyMZNEw/LtPPjDaaEOKvic5bD8jgtVMIFf8APc14S/vel6/M2PBZ5zHKjziQHVazxFE2+cDWR8DnTsi9T32XNQfDwUjOhxM6017zftpDwvvsR5rlaDpnYLYxNckamzQ0OPp7h5H7ntjaHXTij8KvvdX3CgdW8Pe8bvilPLUApWv7JzQI6f6eLuTwfDQPXZ0u9mpJSI5XhjK1hkoPN+iSwyIAZdSsRjMklMix4orXWy2xC3adOm6U2bNiW7Gf0Sc1WmrhUSIr0Ofa+m0PU4CarKZGqT+rZ6PH4PdoudktwSjEQkhR1Yg5Z1bKBiN+5M9/FW4ohzP9Pvo95dh8f0YTeslDiGY1is4cezOcD0g1+qg/Qi7eO2G3cD3FkOp3wHTv9hj5vuatnPN9/7FfNGf4Pzh03u86ne3JfDijeKuf7vagYk+S9AcfOXXLSlio2nL2bP6PMAOOpt4nefLePSsXP55vhrBuS8j7y+h5c/PUT1v8xgbEnegJyjH1I+blO6cki8fXZvFW46HzeYNNgwIh4/Ta/ziZDysdtVSsRyvNWVYtgv6phCdJYycZsS8ZhMvnY4eijw+JJhDdyAsia2EEAq6+O1I4sCQ6oyJZ1hKIYX9vBhDCbw6yxS5YSrnghUaehrNYVIx+/6dR+Z2mRHww5uePkGaltqKcsvY9UFq5hYPDFbBm1J02s8Rd8xvvc9jv1Mv48dDdu54ZUlx+JjRiUTiyZiuD6TiiACtv9fICnk2DN73fSNhkAC3a8Wjo/rVJ/W2bEZJqMKBmZSBqCxYBQeq4NRro9CEzMFtqGML5jEGwef5/Kx87EOwDLm2VNH8/Knh1j11x3c9d1TE378TBd3fzoY4umzTRPqPu25jw1OwvRSnUmu8+kl6bEcS+xFY7EGElhHO3S0MUXxiTI5k6KSHo/J5PfBoW1ZW2lMrh09k99AOopUOaFhd8pUU6hvqw994ABqW2q54eUbqG+rH/S2iNRT764LDaCgIz5eWUK9WyqCiA6fPA75w8F5Yq+bvtGwjVE5JTjtQ+I7VZ2d8UPbsA7g1VArg0NDjmNU3Zaw16eWTKfR4+I9V/WAnLck384lXy3lz+/V8OG+xt53EJkt1qpLMWwn13nRJwNY8avHMYUQqSbLqzLJtaNnMjGTjiJlt7flpUw1BY/fE/rABdW21OLxS2UHAR7TFzk+dJRM9VIRJLu01sPOv8Jx50Evd0/aTS/vNm3n5IJxPW4XTXO7Ym+jlfJid+8b99OhonIK3XUUHj0W+xMKT8aZM4qn9j6C1xyYFTvfPn00Qx02frHhY0wzfR9dFgkQa2WcGLaT67zokwGs+BV1TCFVbkQqyvJKY3Lt6JlMzKSjSNntva0pU03BbrFTll8W9lpZfhl2S5Yk9hI9shvWyPGhomSql4og2WXb/4LpheO/3uum7zftot30ckqcjzFtq7OjUUwoHrjEv0G1xYHVP+MOHnvuXimD80fNpq6tlic+f2hAzptntzLnrHF8WNPEn9+rGZBziDQRa2WcGLaT67zokwGs+BV1TCFVbkQqyvJKY3Lt6JlMzKSjSNntiyekTDWFktwSVl2wKvTBCz4/WJJbMuhtEamnxDGcVTMqw+NjRiUlDqkIIoCP/geGjIaS8l43fdH1PjmGja8URM8/0JPNB3KwW8wBrcgU1JpbTGN+KWMPvBP2+nEFkzit5Dxeqv0f/rjrXo56mxJ+7q+d4OQrpYX86pmt1DYO/OogkaJirYwTw3ZynRd9MoAVv3ocUwiRarK8KpNcO3omVZnSVaKqMg1U89KzWkPKZKzPdDFVZZJKTLHKnLg9vAvuPR2mXgWnfr/HTb2mn4q3fspXCsZw7bhL+3wqrWHR005G5rcxb8rBeFvcJ1/94q+cVLORP128hvacYzlxTO2nev+TbK5/DQCbkYOhDEzTzxB7CZNLzuaS0VcyLDf+xOwHj7Txs8c/YurYItZdc1YqVMDInLhNJ7H2sTFsl6bX+USQ2I3HAF7fpSpTTCRuU0W8FcoyhFRlii57oiDTRKvI0M+KSoliKAOnw5nsZogUZVisOAtGRfhGnNWhRGZ475FAed6JF/e66cb6LTT5WjiraFJcp9rXZMXVauX8ca1x7R+P2mFf4as1rzD20CZ2jr0g9LqhLHyj7DtMKTmH3c1bafO3orWJUgZNnsO8euBp3jr0Av806d+YXHJWXOceOSSXH5w9nt+9upuHXtvNj7/e+4okkYFi7WNj2E6u86JPBvD6HnVMIUQq6qXSWKaTa0d0MjEjhBAi+Xzt8ME6GHcW5PW+pPW/a1+lxFbI5MLj4jrdptpAqc6vDBu8iZmG/DJacoo47ss3wiZmgobnljE8t6zb602eep7a+wirt/2cmydXcsKQyXGdf8ak4Wze18h/PPcZXy0byvQTZGAkhBBCCJEKsmLNqRBCiBS35b/BXQ8n9v5Y0raj+3iz8VNmDJuMJc5HJ17fm8v4oW6G5vrj2j8uSvHF8CmU1W3G0YfSkEPtJXz3+J9QaCumatsvaPQcjvP0ioXnlzOqKJdF695nX/3gTUoJIYQQQojoZGJGCCFEcpl+eK0ykPB31NReN7/v8//FYdi5YNiUuE63r8nK3iYbU0cejWv//vhi+FQMNBNq/tan/XItecwedzVufytrt99BvPnhHHYLN180CZ9pMm/NOxw+OjBluoUQQgghROwGZWJGKTVWKVWtlNqmlPpEKXVjhG2UUmqVUmqnUmqLUur0wWhbWjJNOHoQGvcF/jXNZLdIpDuJKZFMWzdA/S6Y/F1QPed5+9vhj/hb/cdcPuJM8iy5cZ2ueo8DQ2mmjGiJa//+aM4bjqtwLCfUvBLIQNwHztxRfG3kLLY2buKdur/G3YbSobncfNEkahrczHvkHZrbvHEfS6QI6cNFqpMYFSJAPgsiisFaMeMDbtZanwScDVynlDq5yzaXAhM7/vsx8NtBalt6MU04tBUeuhBWnhL499BW+VCL+ElMiWTye6H61zB0LIw7p8dNXZ4mbt+xjtG5w7jQ2fvKmkjaffDybgdfHd5CYc4gPsbUyZ6RZ1DcvI/Swx/3ed+pJV+j1DGeP+2+lxbvkbjbcNKoIdx44US27W/mmkc30eZNzu9CJID04SLVSYwKESCfBdGDQZmY0Vrv11q/3/H/zcA2YHSXzWYDv9cBbwFFSilJsd5Vax388Upo3Bv4unFv4OvWuuS2S6QviSmRTO8+DId3whlXg2GJulmb38MNnzxAs8/NP42dic2IL3f93z530OI1OHdMU7wt7rcvhk+hzVbAKbs29HlfQxlcVPZdWnxHeOKLB/vVjtPHFfOT88t5Z089P/79JtwemZxJS9KHi1QnMSpEgHwWRA8GPceMUuo44DTg7S7fGg3s6/R1Dd0nb1BK/VgptUkptamuLguD2Oc59mEOatwbeF2ktJSNXYkp0YMBjdujdfDKHYG8MmP+Lupm7aaXm7c9xEfNn3PN2EsY5xge1+naffDnrQWMG9rGhKK2eFvdb6ZhY8eosxhz6AOKjnzR5/1HOEZz2rDzePXA0+xu3tqvtkw/wck/fX0Cr+5w8YM1b3MkQx5rStn+diBIH55RMjJ2JUYzXkbG7UCQz4LowaBOzCilCoC/AIu11l3XYEdKLNDtAXyt9e+01tO01tOGD49vcJ7WrHYoGhf+WtG4wOsipaVs7EpMiR4MWNxqDU8vBm8rnPnjqLllWv3tXPfxal6t/5gfjr6AM4aeEPcpN3xaQL3bwqXlh3tLZTPgdpWeideSw+mfro9r/3NHXEq+dQjrd1Zi6v6tdKmYNILrL5jI5r2NXPm7tzIiIXDK9rcDQfrwjJKRsSsxmvEyMm4HgnwWRA8GbWJGKWUjMCmzTmv9eIRNaoCxnb4eA9QORtvSSt5w+P5jxz7UReMCX+dJJyjiJDElkuGDP8CnT8PUq7oPUjo0+9ws/Ohe3m3czo/GXsKMOKswAXzmsvGXrfmcVtpMeXHyVssEeWx5bB1zPuMObqLs0OY+759jyeX8UbPZ27KDjfuf6nd7zikfxs0Xn8iOg0eZff/rfHog/vw1YpBJHy5SncSoEAHyWRA9iO8h/T5SSingYWCb1npFlM2eAv5ZKfVH4CygSWu9fzDal1YMA0acDNe8FFj2ZrUHPsyGVD4XcZKYEoNt37vwzE0w6lQ4eXbETeo9zfzk4/vY3vIlC8fPYtrQid228ZlgUb0WcuLTOht3vFpMSa6P2Se6EvETJMSOsnOYcHATZ338EP/79bvwWR192n/SkKl8XPAWT3zxIFNKzmFYbmm/2jN1bDH/evlJrHhxO99a/QaV/3gqM0+RVG8pT/pwkeokRoUIkM+C6MGgTMwA04EfAB8ppYK3Bm8DxgForauAZ4FZwE6gFbh6kNqWfgwDCkYmuxUik0hMicFyaBv88fuQ54Sv/7+ICX+/cB9i4Uf3cSdeB5EAACAASURBVLC9kX8e//dMGXI8LR7FptocPjlkZ7vLxmG3hTafgUVp8u0mI/L9jCr0U1rgp7TAx7A8kyPtivdqc3n1i1yKc33802m15NlSp/KBaVjZVD6b8z9Zy9kfPchrU6/vfZapE6UUF5Z9lz/svJvfffZLbpm8CmucSZGDThhRyPJvTmblS9tZ+If3ufbrE7jp4hPJsUZPzCxSgPThItVJjAoRIJ8FEcWgTMxorV8jcg6Zztto4LrBaI8QQogk+PJ9WPcPgf//xi8gd0i3Td5t3M6SrQ9iapN/mfAd2o+OZ9VWB2/X5ODxG+TZ/Iwf2sbxRV7ybCZev6LVa1DfZuPjgzZe+yIX3elyY7eYnDO6iYvLG3BYU2dSJqiuaAJbx1Zwyr6XOZI/ii0nfrdP+xfZnVw0+ns8ve9RHtu1kqtOuBnVzwQ6Jfl2ll52Mr9/83Me+Ntuqj87xH98ZwqnjSvu13GFEEIIIURkg7ViRgghRLYy/fDOg/Div4KjGC76JQwJL7rnMb08vO//qPriWYbZijnJ/D4rq8s41GIl1+rn9NJmpo1qZsyQdowe5h18JtS7bTS1W3FY/ZQWeLCm+ArhbWPPp6CtntM/+yN2bwvvnzQX07DFvP+koVOpa/uSVw8+jaEsfL/8eiyqf5d3u9XgmvMmcMb4Yh56bQ/fWv0Gl00ZxZILJ3LCiMJ+HVsIIYQQQoSTiRkhhBADw+eBbU/Ba5Vw8GMYPQ2+dlPYShmP6eWFuve5b8+z1HrqyGufwu7PvsMe0055sZtvHHeYU4a3YLN0K9IXkdWAEfleRuSnT9lnrQzenfgtvJYcTtn9v5S5tvD+V+ZQM+J0ULHNKk0fMQtTm2w8sIG9R7cz54QljC84sd9tO21cMf/5D0N4+qNantmyn2e27Gd6+TC+dfoYKiYNZ1hBTr/PIYQQQgiR7WRiRgghRPx87XB4J7Q3Q/tRcNeDazsc+Bg+fxU8R/Hml7H7tCW8VzCaI3s/p8nTxueth/m8fR/7zc/wKzf+9hG0H7yaXH08Fx93lDNKD1Ds8CX7pxs0Whl8UH45B4vKOW3Ps1z4zh205hRTO/xUGoaM40j+KDy2fLzWPJrzRuK15YXtr5Ti66V/z0jHGF6q/TO/3vxjjiv4CicXT2OUYzzFOSMoH/LVuFbSOOwWvnvGWC4+uZTqzw7x120H+Zf/+RAFnDCigMmjhzJ+WD6lQ3MYOSSXwlwrOVYLuTYDZ0EORXlSBlQIIYQQoicyMSOEECJ+R76E354b/pqywNAxcMKF7Bt2Ll9/sQz1QQ35x68K20x7h2D3nMRYyxSmDDmeyRNhZIEJ5HX8l328RaN59/gZDN+/ieEH32fcofc5oeaVsG0+OO+XHBr39Yj7jxpyIWeNOpd3Dm1ks+ttnt+3HhMThWL9Ra/0Kzlwcb6Nq53HMe/c8ew6dJR39tTz2cGjbNxex+GWLyPuc+M3JrLkov6v3BFCCCGEiIVS6ufAHMAPmMC1Wuu3E3TsZ4E5WuvGRBwv7NiBnLvpSSlVB3yR4MM6gdSpp5o6suH34tJazxyME/UhdjP9957pPx8M/M+YinHbH6kcE9K2+HVtX6bFbSSp/J5I2+LjBD5NkdhN5d9TT6TdgyvY7lTsc9P1d5oo8vP3/vP3OW6VUucAK4AZWut2pZQTsGuta+Ns56BJ6xUzWuvhiT6mUmqT1npaoo+b7uT3klixxm6m/94z/eeDzPoZB6LP7SqVf1/Stvgls32DEbeRpPJ7Im2LT0fbBuWPW+g5dlP599QTaffgSka7ZYwbG/n5B+znH0VgQqcdQGvt6jjf58CfgIqO7eZorXcqpYYDVcC4jtcXa61fV0oVAPcC0wANLNNa/6XjONO01i6l1FXADYAdeBtY1HGMhzvtt0ZrXRlLw1O8VoUQQgghhBBCCCFEr/4PGKuU2q6UWq2UOr/T945orc8E7gNWdrx2D1Cptf474DvAQx2v/yvQpLWerLWeArzc+SRKqZOA7wHTtdZTCTw2NReYCozWWp+itZ4MPBJrw9N6xYwQQgghhBBCCCGE1vqoUuoM4DwCq2P+pJT6ace3H+v0b3AVy4XAyUqp4CGGKKUKO17/fqfjNnQ51TeAM4B3O/Z1AIeA/wUmKKXuBZ4hMFEUE5mY6e53yW5AipLfS3Jk+u89038+yI6fMZFS+fclbYtfqrdvIKTyzyxti08qtS2V2tIX0u7BlcrtTuW2DQb5+QeI1toPvAK8opT6CJgX/FbnzTr+NYBztNbuzsdQgdmWnpLxKuBRrfXPun1DqVOBS4DrgH8EFsTS7rRO/iuEEEIIIYQQQgihlJoEmFrrHR1fLweKgMuBKq31v3fkhvme1vrvlVLrgQ+01nd1bD9Va71ZKfXvQK7WenHH68Va64ZgjhlgBLCBwKNMh5RSJUAh0AJ4tNZHlFJTgbUdjzr1SlbMCCGEEEIIIYQQIt0VAPcqpYoAH7AT+DGBiZkcpdTbBFbJXNmx/Q3A/UqpLQTmRv4GLASWd7z+MYH8McuAx4Mn0VpvVUotBf5PKWUAXgIrZNzAIx2vAXRbURONrJgRQgghhBBCCCFERupcTSnZbYlGqjIJIYQQQgghhBBCJImsmBFCCCGEEEIIIYRIElkxI4QQQgghhBBCCJEkMjEjhBBCCCGEEEIIkSQyMSOEEEIIIYQQQgiRJDIxI4QQQgghhBBCCJEkMjEjhBBCCCGEEEIIESel1Eyl1GdKqZ1KqZ/2dX+ZmBFCCCGEEEIIIURWaPf5z/mywf3GF4db9nzZ4H6j3ec/pz/HU0pZgPuBS4GTgSuVUif35RjW/jRACCGEEEIIIYQQIh20+/znbD949Kmf/OE9Z02DmzHFjuN+e9UZT504suCKHKvlzTgPeyawU2u9G0Ap9UdgNrA11gPIihkhhBBCCCGEEEJkPFez5+7gpAxATYObn/zhPaer2XN3Pw47GtjX6euajtdiJhMzQgghhBBCCCGEyHg+0xwVnJQJqmlw4zPNUf04rIrwmu7LAWRiRgghhBBCCCGEEBnPahj7xxQ7wl4bU+zAahj7+3HYGmBs50MCtX05gEzMCCGEEEIIIYQQIuM5C+03//aqM1zByZkxxQ5+e9UZLmeh/eZ+HPZdYKJS6nillB34PvBUXw6gtO7TCpuUMnPmTP38888nuxkic0RagjYgJHZFAkncinQkcSvSlcSuSEcStyIdDVjctvv857iaPXf7THOU1TD2OwvtN/cj8S8ASqlZwErAAqzRWv+6L/undVUml8uV7CYIEReJXZGOJG5FOpK4FelKYlekI4lbkQ5yrJY3Rxc7zk3kMbXWzwLPxru/PMokhBBCCCGEEEIIkSQyMSOEEEIIIYQQQgiRJCk1MaOUmqSU2tzpvyNKqcXJbpcQQgghhBBCCCHEQEipHDNa68+AqQBKKQvwJfBEUhslhBBCCCGEEEIIMUBSamKmi28Au7TWXyS7IaLvTG1S31aPx+/BbrFTkluCoVJqgZYQcZP4Ti/yfolEaG7zMvu+17n6a8fzg7PHJ7s5QogUJtcdISKTz0Z0qfxb+D7wWNcXlVI/VkptUkptqqurS0KzRG9MbbKjYQdzn5nLJX+5hLnPzGVHww5MbSa7aUklsZsZsi2+0z1us+39EgEDEbdbaprY7Wph2VOfJOR4QkSS7n2uyM7rjsStiEU2fjb6IiUnZpRSduAK4H+6fk9r/Tut9TSt9bThw4cPfuNEr+rb6rnh5RuobakFoLallhtevoH6tvoktyy5JHYzQ7bFd7rHbba9XyJgIOL2k9omAPJzUnmxsUh36d7niuy87kjcilhk+mdDKbVGKXVIKfVxPPun5MQMcCnwvtb6YLIbIvrO4/eEPnBBtS21ePyeJLVIiMSR+E4v8n6JRNnf1AaAaeokt0QIkcrkuiNEZCn12fC1n0Pjvjeo37OHxn1v4Gs/JwFHXQvMjHfnVJ2YuZIIjzGJ9GC32CnLLwt7rSy/DLvFnqQWCZE4Et/pRd4vkSj1LYGBY3O7j3afP8mtEUKkKrnuCBFZynw2fO3ncGjbU6yddQ6rph7H2lmBr/s5OaO1/hsQ9/KflFuPq5TKAy4Crk12W0R8inKKuKfiHm6svhGnw8nCUxcybsg40IFnCyXBk0hnJbklVF1URU1zDQ6rA7fPzZjCMZTkliS7aSKCktwSVl2wKrR0tiy/jFUXrAp7v1I5EV0qty3bHD567I5efYuHUUMdSWyNEGIwxNIHd92mKKeo1+uOENkoZcbQRw/dzX//wEnj3sDXjXvhv3/gZP6zd1M09tzBbcwxKTcxo7VuBYYlux0iPqY22dW4i9WbV/Or6b8i35bPTa/cFHZhmlg8Uf6wEGnL1CZtvjaWv7U8FNcrK1bKpGOKMpTBxOKJrLtsXcSBdTARXdcBdCr0U6nctmx0uKU99P8NLV6ZmBEiw8XSB0fbpryoPOp1R4hslTJjaNM3KjQpE9S4N/B6EkkPIfrE1CYut4vao7W43K5uWbSDSZ2qa6pp9jaHJmUg8xI8iexjapO61joWVy8Oi+vF1YtxuV1Jbp2IxlAGToeTsoIynA5n2MU/lkR0vfV7AyXTk+Slm/oWDyX5geXWrR5fklsjhBho0frgQ62HQteDenfkbRrbG6Ned4TIVi63KzXG0IZ1P0Xjwl8rGhd4PYmklxAxi6XEWeekTkPtQ1MnwZMQ/RSM/3Z/e8S49preJLVM9EdvieiSWdoxpZLkZTmtNfUtHkYU5gBwtF0mZoTIdNH64P1H94euBy2+FumnhYiR1+9NjTF0wYib+cf/coUmZ4rGwT/+l4uCETcPbkPCycSMiFksd287J3Vq8jSlRoInIRIgGP9+7Y8Y1zbDlqSWif7oLRFdMletpEySPMHRdh9evw5NzLR6JPmvEJkuWh9c3x7o/2tbatl7ZK/000LEyGpYI35erGqQs6tYc95kxElXMP/ZN7lh8+fMfzbwtTXnzf4cVin1GPAmMEkpVaOU+lFf9peJGRGzWO7eBhNtluWXseajNSyfvjz0AZTkZyKdBeP/0Y8fZcWMFWFxXVlRidPhTHILRTw691nQvZ9K5qqV3tomBk+TO3A3z1kgK2aEyBaR+uBl05ex5qM1oW2qPqxiZcVK6aeFiIHdsHcbQ6+YsQK7kYSJTGvOmxSNPZeS44+naOy5/Z2UAdBaX6m1HqW1tmmtx2itH+5Tk/rbAJE9gncOOv+R0vWuQNdEm7nWXNbNWofHlORnIr0F4/+JXU8AsPrC1ViUhRxLDsPzhmM1pDtNR70lB46l30tW28TgaWkPrJApyuvIMSMTM0JkvK59sKEMfvPWb9ji2hLaxuV2MTJ/pPTTQsRAK83zu58PjaH92s+T259k3uR5yW5aSpBeQ8Qs1ru3nRNtluSW4MyLnPwsWQk1hYhH5/h/YtcTLHppEe3+dkbmjwxNykhMp6eekgPLqhUBx1bIFOcFHllskUeZhMg6VmVlybQl3a4HRTlFkuhXiBiU5Jbw7UnfDo2Ta4/W8u1J35YxVQe5xStilsi7t1IGVqSbdC67LOKXzFUrElOpIzgxU5hrw2IoWmTFjBAZL1of/Njlj9Hma5PVMULEweP3hJXLXnXBqmQ3KWVITyL6JHhnuTS/FIADLQfiWhkgZWBFqolltUt/yy6L9NA1FoCk3A2VmEodwYkYh91CrtWQ5L9CZIDervvR+mBTm7I6Rog4yLimZ7JiRvRZIu7iShlYkUokpkVQKq1SkZhKHcEVMw6bgd1q0O6TiRkh0lksfb30wUIklnymeibTvKLPEjHbKWVgRSqRmBZBqXQ3R2IqdQRXzOTaLNgsBm1eyR8lRDqLpa+XPliIxDKUEfEzJSvPAuS3IPosEbOdklBTpBKJaRGUSndzJKZSR+hRJpsFu9WgzSsrZoRIZ7H09dIHC5FYBgbLpi/rVoLeyJApCaXUWKVUtVJqm1LqE6XUjX3ZXx5lEn2WiPKxUgZWpBKJaRGUzPLYXUlMpY6j7X5sFoXVYmCzGLT7ZMWMEOkslr5e+mAhEsswDNZvXc+tZ97KUPtQmjxNrN+6nl+c+4tBb4vH7znnsPvw3T7tG2VV1v3DHMNutlvsb/bzsD7gZq31+0qpQuA9pdSLWuutsewsEzOiz4J3EO7/4H5mT5xNSU6gJHZRTlHMxzC1SX1bfehCV5RTFPa1XPjEYOgchw9d8hB3vXMX1TXVVIyp4JYzb8Hj9+Byu7rFY9f4DX4/mBxYpJew99OwU3VRFQtfXBjKO1B1URVoqD1am/D+KVosBUlMpYaj7V4cNgsAdousmBEi3QXHsp1zzETr6+Ppg3vr22PdRohMUpJbwm1n34bP9GFqk9L8Um47+7ZBX4Xm8XvO2dm486kl1UucHZ//4yorKp86oeiEK/ozOaO13g/s7/j/ZqXUNmA0IBMzYmAYyqC8qJxFUxdxY/WNfU6Q2TXhWsWYChZOXciS6iVJT7YpskekxH/3VNzD7efcTp27jmteuCZiPKZScljRf72VQ8215lLXWsfcF+cm/P2WWEofLe1+cjsmZmxWA7dMzAiR1rquhklkXx9L3y79v8hGftNPfVt92N98lRWVDMsdhmEZvLg/7D58d3BSBgKPMS6pXuJcO3Pt3aMKRp2biHMopY4DTgPejnWflPvkK6WKlFJ/Vkp92vF81jnJblO2i1ROsLG9MTQpA70nyOx8jEOth8ISrs2eODv0AY3lWCI7xFK+uj/q2+q5/4P7ufXMW3nkkke49cxbWb15NV7t7TG2Uyk5rOi/3sqhmtrs9f3uGqs+0xdT7EospY+Wdl9oYkZWzAiReTx+T8L641j6dun/RTZyuV3d/uZbUr0El9s1qO3wad+oSDmmfNo3KhHHV0oVAH8BFmutj8S6XyqumLkHeF5r/Q9KKTuQl+wGZbNoM/qF9sKYE2R2PcbvZ/4+bN+h9qEpk2xTpIbBuJNkmiZzTp7D7a/fHjrHsunL8Jm+HuMxlZLDiv7r7f3s7fuRYrWyopKqzVVU11T3GLsSS+nD7fVjtwbeP5tF0eiWHDNCpLPexqYQf38cS98u/b/IRtHG2D7TN6jtsCrr/rL8suO65piyKuv+/h5bKWUjMCmzTmv9eJ/a1d+TJ5JSagjwdWA+gNbaA0gPlUTBGX2nwxlK1HSo9RBDc4bGnCCz612B+vb6sH2bPE0Rj5VrzcXldsmzt1ko2p2kdZet6/VZ796e2Q5+36d9oUmZ4Dluf/121s5c22Nsp1JyWNF/vb2fvX2/88qrYCK7qs1VzJ44m+qa6h5jV2IpfbR5/dg7lllLVSYh0l9vY1OI3B/HkhfGbrFTMaaC2RNnh64LG3Zs6JZYONL5ZJwrMpnVsEb8bFiNwZ2SGOYYdnNlRWXnHDNUVlS6hjmG3dyf4yqlFPAwsE1rvaKv+6fap38CUAc8opT6QCn1kFIqv/MGSqkfK6U2KaU21dXVJaeVWcTj9+B0OLn+9Ou58507ufqFq1n+1nJava0xlxDseldgzUdrwkqlbdixgcqKyrBjVV1UFXjW95m5XPKXS5j7zFx2NOxI+OMsg0liN3bx3kkK3gGLFjedv1/nrot4Douy9Bjb2VY+M9Pjtrf3syinqFv/VFlRGUp2Hlx5Fewf73znTuacPIfSvNLQOaLFbrbF0mBKdNy6PcdWzNgtBu3e9L0WidSW6X1uquhtbBqpP+5tjBFUlFPEwqkLw64LC6cuDCuSkWllgyVuRSxyLDkRPxs5lpxBbYfdYn/zhKITrlg7c+2bz3772c/Xzlz7Zn8T/3aYDvwAuEAptbnjv1mx7qy01v08f+IopaYBbwHTtdZvK6XuAY5orf810vbTpk3TmzZtGtQ2ZhuX28W2w9tY/tZyaltqmeKcwoLJCyjJKWH8kPF4/B682ovNsOF0OCPOeLrcLuY+Mzd0AZzinMLN026mNL8UU5vkWnIpyi2isb0xdAcCDXOfndvtTkIsKyb6QQ3UgbuS2O1Z15iBwPv/2OWPYWqz252qznewrn7+6qhx0/m4KytWsmHHhm6z9r849xeU5JbEtOomRVZzSdx20df3p6ftXW4XL+x+gfPHnY+pTQxlsHHvRi6ZcAlOh5MDLQeY99y8bjG3+sLVfHPDN0NfR+u7UiyWBlNaxe2FKzYyLN/O4gtP5Pdvfs6rO1x8vOySxDRQpJu0it1sFEu/GmmcUTGmgtvOvg1TmxH3i3VsEssY1uV28cs3fhlxDDJA41yJW5F0+4/uZ/7z87t9NtbOXMuogojpXQYtblNBSj3KBNQANVrrYPbiPwM/TWJ7sl5JbgnjhowLTcpcf/r13P767TgdThafvpilry/tNQdI55KEwf1+9urPwvYrcZSEXYhqj9bKs7dZLFoZy7rWum55Z8qLytnVuIsbXr6BX3/t1zHnC9m4dyPXnnotN71yU1hukKKcol7LY0oJ49QVT36int5PpRVTR07lRy/8KHS8FTNWoLQKnS9SzLl9bqD3VTASS+mh86NMNkn+K0TKivUaEFwN2bk6zMKpC6PeZITIq3mdDicHWw6yuHpx6Di/u/h3vY5hS3JLuO6067q1U1ZMikzm1/6In410fiIikVLqtpzW+gCwTyk1qeOlbxBj3W8xMAxlkG/Npyy/jAWTF4RyciyYvCA0KQM9Z5PvXJLwrvPvimm/YO6FziT3QvboHDMvfOcF1l22jkJbYcS8My63K/R6MF9RZ5HyhQCcP+780KRM8HhLqpfQ2N44iD+pSLREV7rwmJ5ucXLTKzfhMQMD7Gh9ldPhDMWulD9Nf52T/9qtBj5T4zdTZ8WxECIg1mtAY3sjVZurwiozVm2u6nEMEKm/X3jqwtCkTPB8e4/s7XUMG2mcI9cKkeksyhLxsyFxH5BqK2YArgfWdVRk2g1cneT2ZL0SR2D1Qqu3NXThiaWSUuelpLnWXNCB+vWxrIQJrpi4/4P7mT1xNiU5JTjznGHP54rM1nUlQbRVVF6/l7NKz2LeKfOwGlYevPhB/vPd/wxVxLmn4h5MM1DOuCinKLQSR6qBZaZEV7qIWkFAByoIRFvdBYH+zqd8oUegRPpq95phOWYA2n1+8uypOIwSInvFeg3w+D1U11RTXVMd9vpP/dEX6pfkllB1URU1zTU4rA7cPjflReXdzlf1YRX3VNzDjdU3ymoYIbq46/y7aGpvCn2GhuYMTXaTUkbKjSi01puBacluhzgmOKt/qPVQKIN8tEpKwbsBnZeSdn7s6dYzb40p672hDMqLylk0dVG3C5vcUchO0SrY5Nvy+d5XvseilxaF4mRlxUp+fvbP8Zge7nrnrrCyxeVF5ay7bB0ev0cq4mSgRFc6sllsEY9nM2xh51x69lIcVgcOq4M2XxsLX1wY9ojcicUnDnrVAZE4bV4/OZ1WzAReM8mT7kKIlBLrNSDea4XH7wnlXQze/KkYUxE2wTMsdxg5lpzQdcHtc0es7tTXx26FSHdWw4rP9IV9hu447w4ZH3VIqeS/fSXJpQaXz/Sxs2EnN1bf2GuOma5JVu98585QnpolZyzh56/9HKfDycJTF1JeVI5N2fBqL6Y2ybPmBZKomT0nch0AkhgtCaIl6fOZPlxuF16/F5vFhoFBm78Nn+lj7cdrefvA26y6YBVD7EOiJhKL9HowfjJoUCRx20mk97XqoioKbYV4zMiJILvG2rDcYTR5mvD4PeRZ80KPuQWPt/rC1QyxD8FrejGUwW/e+k1oUP7k7CdDk4RBwXjU6GxL8NuTtIlbr99k4s+f47tnjOHbp4/h5U8P8eCru3njpxdQVuRIYEtFmkib2M1EvSX2jfXaHs8YIFrC3p+d/TN2NuwMTcKcUHwC858LH3/MP2k+c06eg8/0YbPYsCgLVz17lYxxRVaR5L89k+kpERNTm+xq3MXqzau59cxbKckpobSglD/M+gNe09vt4th5KWnXR0bsFjt3nHcHOZYcHvjwAa6Zcg3tvnaWvr40bMKnt0SuIv1FGxhNGDqBHY07wv4YXj59OSvfX4nL7aKyopKbpt3EkJwhUR9x8unIj6AE46fz891ZWBEnY3V9X3OtudS11jH3xbkRB98+08f2hu1hsVZZUclzu55j7ba1lOWX8fAlD/PozEfxai+5llzq2+pDA+pgidPDbYfZ4tqCRVkixt2BlgP88PkfpvMEYNYKJvrNsVqAYytm2n2SrFCIwRTLZEpfru2dVztGWtXS7fymyZyT54TyLZbllwUey2hrClsBsLJiJU6HM3Qt+Fb5t5g5YWboD9JYEwQLkWkk+W/PZFQoYhJMplZdU83i6sX88PkfMv+5+SilKM0vBeBAywHq2+pxtboAQsmdOidkXTB5AbdsvIWG9gZueuUmZk+cTVN7U2hS5pfTfxlahdNbIleR/qIl6XO5XaE/lCFQ9aDN38avpv8qlKAvWPUmWiKxaK93Tb7ndDgpKwgka5U/lDND5/fV1GaPiSC7xlpwdcw3T/xm6Os/bfsTGg06sLqm6/Fuf/12FkxeAAQGHZHirr69PuL5Repr8wYGjHZr4MZdMMeMVGYSYnDFmtg3lmt7fVs9j3/2eGibsoIyHv/s8R77ZhMzNCkTPH9Te1Pokfvga4urF7Pw1IWh/eadMq9bEvlYEgQLkWkk+W/P5LcgYhItmZppBu5ezH1mLrdsvIVdDbuY+2zg/5dPX05ZfhlrPloT+v/g6pnO/zqsDpwOJ9effj1H2o+EzrPmozUsm74s9AGW5GmZJ1pcdU64GizTvvyt5czeMJs737mTOSfPCd05syhLtzhZNn0Zbp87FHfB1yV+sk9viSC9fm/E71tUYHVE5zuds56YxeG2wxG3H5k3EoAntz9JZUVlt3hc89GaiOcXqS84AWPvWDFjs6iw14UQgyORyd2VVsycMJNFLy3i75/8exa9tIiZE2aidPQnJ0xtdju/w+qI2KbxQ8aHi7XG+AAAIABJREFUrgORVlJWfVjFyoqVMkYRWSfSmD27HliKTh5lEjGJliTN5Njd6FvPvDW02qW2pZaV769k6dlLOX7o8eTZ8lg3ax0eM5BwNbgapsnThN2ws/DUhdz++u3827n/FjrPFtcW7n3/3rBjmKbJgZYD8thJhugaV1OcU0J3mVZ/YzVVH1aFlWmHYysUHr7kYW54+QYevfRR1m9dz61n3hp65nv91vXMnjibjXs38tsLf4vNsGGz2GRVTBbqLcFjtOS+fh34o3veKfPCcsY4rI6I2w/LHcYL33kBu8XOUPtQHr30UbymF6uysn7rehZMXhCWk0DuiqaP0MSMJTz5rzzKJMTgSmRyd4/p6baK5aZXbmLtzLVR9zGUQcWYirAcM4YyIrapwFYQuA74vVgNa7cEwS63C6fDKY9Ti6zz+r7XWX3haizKgl/7eXL7k4w/eXyym5USBvTTr5SaopS6Qin17eB/A3k+MXCCJWG7zux3vnvQNZfMFtcWFv11EYYyKMkNlLsuzS9l1QWr2LBjA8umL2PDjg0MzRnK2MKxOB1O8m35YascXG4XI/JGUJpfGsgT8excLvnLJcx9Zi47GnbIM4lprnNcTXFOYfHpi1n+1nJmPTGL5W8tZ/HpiynNK414N+qIJ7C6ysDgutOu48537uTqF64OrajZuHcjs8pn8ZOXfsKsJ2Yx77l57GrcJTGTZaL1XcG7kk6Hs9sKl8qKSp7c/iTQ/U6n1poVM1aEbb9ixgrshj20JN5msVGaX8rYwrEMzxvOpeWXhsXnwqkLKcopGsxfg+gHdyjHTHi5bFkxI8Tg6q0/74vOK3ODgvnporEpG9eeem1Yf55jyaHqwqqwNlVdVEWdu455z81j1hOzmP/8fBZOXUjFmIrQNpUVlRTnFsvj1CKr5FhyuLT80rCVapeWX0qOJSfZTUsJA7ZiRim1BpgCfAIE/xLSwOMDdU4xcKIlU6tvq4+5hHbn4/zi3F9gmia3nX0bBgZ+7WfhqQu5ZeMtOB3O0OoHt8/N8LzhNLY3RnyueACz14tB0DWuOlfhqm2pZenrS3lk5iMR42p/y/7Ac6nGsWO0+drY07SHe9+/N+JKG4mZ7NNbIkirYeXE4hNDK1xsRqAq04jJI7jy5CtRqLD4U0rxwIcPhK3QeuDDB7jt7Nsinr+xvTFiDhuJw/RxLMdM93LZQojBk8ik/dFWS9oMW9R9vNobcZXNo5c+GtYmNKGE88HtllQvYe3Mtdxy5i3YjMAKXikRLLJNu7894piop5Vq2WQge4SztdYnD+DxxSALJlPrLHj34oaXb2Dj3o3cPeNubn7l5rBs+V3vZEQ6TrA8dvAxqMXVi0Pfe+E7LwBI9voMFYyHaNWVgFCMda6Cs37r+lB8BY9hapMWbwsut6vbCq7g8SRmsk/XPsfUJi63K2xgH0xiHhTc3mf6qKyoDA0k3D431TXVYUvSAW41b6X2aG2PFeqCJA7TizuUYybwntoswUeZZMWMEIMt0hgyHsHVkl0r8uVaciP25RA5x0ywokznNkUbz2g0YwvH9rvtQqSreFaqZZOBnJh5Uyl1stZ66wCeQ6SAruWvg+W0nXlORuWPiulOhqEM8q35Pa64SdRzxSI1RXt23GbYsBpWlp69lCE5Qyi0FZJrzeVfz/lXhjmGhcVX17tpEjOiq1jKrXbWdUWNVVkjxtWepj0s+uuibsdLZE4EkRxtXSZmjq2YkYkZIdJVt9WSyoZP+/je09+Lem2ItT+3G1G2M6TfF9nNakQeQ1mVrB6Dgc0x8yiByZnPlFJblFIfKaW2DOD5RBLUt9Wz8MWFofLXnctpX/PCNTS2N8Z8rBJH5GeHi3KKsCqrZK/PcNGeHQdY9NIiFv11EXe+cyd7juzhYMtBfOax2fXgCojao7XUt9WHVkAk6ll0kTliLbcakQaF4uFLHg6Lq+XTl1P1YVXE4yUyJ4JIjm7Jfy3yKJMQmcBqWEP5wKwWKz964Ufdrg2N7Y2h8QUaqi6q6rU/NwyDO867I2y7O867A8OQHDIiu+VYciLm9ZMcMwEDOT21BvgB8BHHcsyIDBNcpp+Ix0YiPTtclFPEvuZ9uFpd/NfW/4prNY5ID9GeHf+y+UtqW2pDZbODeWOCA6LyonJ2Ne6KuAIiUc+ii8zR10eLfKaP7Q3buy13/9Plf6LV1wrALRtvYYtrS8TjJTIngkiOtq7Jf2XFjBAZJ9K1welwcrDlIIurF4eNLx67/DHafG1R+3Of3xda6euwOnD73FgNKz6/PK4hspvP9GE37GGfDbthl0eZOgzkxMxerfVTA3h8ES/ThNY68HnAaoe84dDLLL6pTerb6rv9YRFcrhlL4t9Yj+t0OPGZPlxuF4daD1HTXMPyt5aHnt/1az+fN31Ovi1f7jqnAa/fi8vtwmf6yLHkoJTCa3qxoygxA3eWgjHY+dnxYGygAqWzrYY1YjLfRy99NGpiaIkP0ZXdYu9W7vRA8wEUin1H9oXKqgeTMrrcLqo2V4Ul+63aXMXPzvoZEJh4GZY7LOwckZKeS6Lf9BVcGWPrVpVJ7jkJkQq6jSXtRRjuw72OczvvZyiD+SfNZ2rp1FBfP8Q+JDQpA+Hji7KCsujtweSWjbd0GxOvnbk2av4akUXi+DssU/i1n1Xvr2L2xNk4cOAxPax6f1VoTJXtBnJi5lOl1Hrgf4H24Itaa6nKlEymCYe2wh+vhMa9UDQOvv8YjDg5aqcQLSdDeVE5zd5mlk9fzt/2/Y0VM1aEstUH7yr3VBI22nEnDJ3AjsYdLKlewq+/9mscVgdOh7PbaomVFSspyimSC1sK8/q9offS6XCy+PTFLH196bH3++zbmfja/RgzfhYWg5Fi44GLHoi40sFreqOugOhLLhGRHYbahrBw6sJuK2DuePsOqmuqQ1+fWHwiVsOK1po5J88J63uWTV+GqU1mPj4ztD0Qtr+Uw84cXctlG4bCaihJ/itECog4lpxRycSXfo3x6dNRx7mR9qusqKRqc1WoL//dxb+LazV4tCTBTe1NfPfp78p4JJvF8XdYpok0pkIlu1WpYSAjwEFgQuZi4O87/ru8t52UUp935KPZrJTaNIDtyx6mCUcPQuM+aK491hlA4N8/XhmYuY0iWk4Gl9vFwhcXsvL9lXxn0ndCiX8fueQRfjX9V7R4WzjQcgCX24Wpu99Z7Om4wT+amjxNaDQLT13YbbXE4urFseWFEIMumPOlzl0Xei8XTF4QmpSBjvf7rWUcungZ5it3hMVgfVs9939wfyiebj3zVurcdaFnUoOCCcMivW4oI/5cIiKldM4hFK0/6cxn+jjQcoB9R/ZxoOVAWD6iw22HI5ZqnD1xdtjXLrcrtE/Xvuf2129Ho8O2v/GMG0OxWrW5qk/5tURqCz6yFKzGBIHHmWTFjBDJF3Es+coS6k+fG9ggOM5tqTs2Fj56kHp39/269uWHWg9FHV/0xFBGxP1sFtuxNsp4JDu11vX577BME2lM1TGkynoDNjGjtb46wn8LYty9Qms9VWs9baDalzWCM7MPXQgrT4GmmmOdQVDj3sByuiii5WTw+o+tVNDoUOLfyvcqMTFZ+tpSLn38UuY+M5cdDTu6/TEV7bidS6lt3LuR4pxixhaOlZKzaSJ4F2ruM3PD3uNoeYj2e5vZ8bXrMM1j8WGaJnNOnsOd79zJ1S9czZ3v3BkxYdiy6cuwG3ZWzQh/fdWMSgwMiZkM0DmeLvnLJVH7k6BgTph5z81j1hOzmPfcPLY3bA9NznijlGocah8a9rXX9AKgtY64/RHPkbCvG9oauPqFq1lcvZjqmmqJswzi9vqxWwwMdeyWnt1i0CYrZoRIuqh5wwpLj73QuBc8R4+NhR+6EI+3JXLf3n4kbNzRtfDEihkrsClbj20yMFg2fVm38Uqbry28jXKdyD4+T5//Dsskfu2P+Lnza7mewgBMzCil7lRKLYzw+hKl1H8k+nyiF11nZlvqAsvmOisaF3jGMYpgecDOgjP/ZfllLJi8gJrmmtA2CyYviJgLpOudgWjHDZZSAzh/3Plc99fr2N+yP+K2UnI29XS+e+XX/tD7FsxD1FlZfhn17fXc8NYy6jv1RiZmtxi6ZeMtFNoKw1bRrN+6Hm16mPjSr1k35UZe+MaDrJtyY2AJc6dzdz6fxEx66WsVpc4r7oLbd14BYzMir7Bq8jSFfW0zAgNvu1IRt9/fsr/H/SXOMke71wwl/A0KrJiRgaQQyRZtdYqRM+TYC0XjoH5X2CoFu2tn1DEJHBt3FNgKwsYdD3z4AF7t7blNhsH6reu7jVcOtB4IO5dcJ7KQUpH/DlPZ8SyPRVkifu4sypKkFqWWgVgxcznwuwiv3wNcFsP+Gvg/pdR7Sqkfd/2mUurHSqlNSqlNdXUZvuyr8yNIRw8Gvu7rfl53+Mzs6yvhivuOdQrBZxvzhkc9XLRyr06HM1AmMKeEqg+rQncHOq+MmOKcwsMXP8wDFz2A2+um3l2PqzXwSIKhjKjHDa6MCB7rvg/u63b3Id1KzmZ67AYfN3F73dx65q1McU7h0Y8fZcWMFZTll7HmozUsn7682x2kjXs3cuuZt+LuSPhsajPq89kaHbaK5rrTrgskEP70aZzrr6TsoUtwrr8S49OnKTGRMsUJkOy47WsVpc4r+TpvH1wB48x1drsDWllRyYYdG0Jfr6xYGUrWW2LCqnN/Fb79jBVh23fdX+Is+RIZt26PP+LETLs8yiQGQLL73HQTbXWK4etIb1k0Dr63DjaG3xsuqf4Nqyru6bbfmo/WhLapbaml2dsctt/htsO9Pk5bklvCdaddFzZeWTh1YUZfJyRuY6Qs3f8Ou+K+wOtZQCkV8fOqsmRiqjdK68Q+1KWU+kRr/dW+fq/TNmVa61ql1AjgReB6rfXfIm07bdo0vWlThqahiTc5VNf95v4PPHNz+OTMpMtg1p2gdb+rMpna5FDrIeY9Nw+nw8mCyQsoH1rOtS9ei9Ph5P+d+f9o97Wz9PWlEZO/Vl1URaGtEI8Zflxfpz/Sr37+6lC55AWTF1CSU8KoglGMyBuR6KRpg9YrZFrsRkqit2z6Mu59/17Kh5Yz/5T52AwbOZYcTEz2H91PfXs9G/duZFb5rG4lsEtyS7jq2au6VTT4w6w/oJQKj8OWusDy5M4xXjQOfvQSZnsz9U1f4MnJx97eQsnQ8RglEzItwVpGx63L7WLuM3O7xcK6y9ZFrHR04OgB5j0/r9v2j858lNKCUkxt8sWRL6hprgmVajxuyHEcaA1UZnL73IwpHMP4IeMDfVyLiy9aaqnxNIW2Pz5vJLmGDc/Rg9jbWygqmUhjTl63fkz0KG3i9sY/fsDbu+up/N7U0Gu3PfERE5z5PDz/7xLRRJFe0iZ2s4GrtY5fvvmrsEp7G3Zs4Bdn/QynzxcY5yoLPFgRNk7wnXsjX5w5j9qjtTisDkpyS1j53kqqa6pD2wQLD1z74rWhMcry6cspLy7vdVKl67i5KKeIxvbGbuPoQSRxmwqaD8DTN8HUK8FRDO4G2PwYXL4COj9+l6EOtBzgN2/9ptvn9bazb6M0P+LPn1UzNgPRI7QqpSZ2fbHjNXdvO2utazv+PQQ8AZyZ8Bamg3iTQ7V02W/rBvjuo+EzsxW3QWEZFI2FgpEx/ZEaLPdaVlCG0+EMXUwMZTAibwSrLliFy+1icfViVmxaQWVFJQtPXUhTe1NoIuafT/vnbslfF764EBSh4wKhMtlWwxo6dll+GVtcW7jznTvJs+UNxKSM6IdIj5vc/vrtLJi8gLcPvI3X9DK6cDQjHE5GYKUoZyh2w878U+bj9XtD733wMRWtNXfPuDtsRn359OVYtO4eh3nDA5OWnWP8e+vA9GE07MZZ/ZvASpr/+jbGH76VVQnWMkG0FXvRBsVOw05lxyqt4PaVM1bgNAJLxuvb6ln44kIW/XURV79wNYv+uoi73r0rdDyP6aFyU2XoUal6BZUfPYTH9IS+f+cH90HjvlBcWZ//KU5vG2U+P06/H0OS2GWUiCtmJMeMEMnRZTV5iYbrvhKek+66r8yhRKtj41xHSbdxguuca7nnvXtCfbvL7WL+KfPDrh33VNzDik0rwsY2S19fGpYTL5qu42arYY04jhZZ6Nzr4YXbYO1lgX/PvT7ZLRo0BgbzT5kf9nmdf8p8jAGtR5Q+BqJc9i+A55RSy4H3Ol6bBvwMWNzTjkqpfMDQWjd3/P/FwC8HoI2pL57kUKYZSG4W3G/MNJj8j/C3/4RLfgP5wwMXqKFjE7piwFAGE4snsu6ydWF3Bg7YD3Cg5UBotUtpfmmPjyT0VJa787HlbnTqifa4yYnFJ7LusnWB90wTWM31yh14vn4jy99a3m11zRbXFmpbatnbvJfff/J7HrjoAZo8TbjcLla+v5K7zvv37ic3jMBKsmteCnw+TB+88HP47JljS0Rf/iXUbMqqBGuZIlL/0lMfYPW0cOLrq3n0gvvxWizY/H6cr1ZiPf+nkFfSLVanOKcw5+Q5LHppUXg57I6Bt6nNyOWyjbzAAcZMg7OuhUcuzdrSl5muzdc9x4zNqqQqkxCDLcJqcuN7f2Dipv9i3ZQb8eSVYG+tp+S5n2N8+6Fj+3UdJ1jtoHS3vv2u8+9i7cy1aDR2ix3TNMNW0EDHuNWUcYSIk+kHix0uuxtseeBtDXxtZsdEv1/7sRpWlp69NLQK2WpY8ZMdP39vEj5q1Fo/B3wTqADWdvxXAXxHa/1sL7uPBF5TSn0IvAM8o7V+PtFtTAsWe3hyqP/P3p2HR1WdfwD/nlkzWcgetrAJCKICGlSU/kRcihbFKiqKaLVVsdRaW4u21q21tS61LnVFWwFBCm51obihaAWlgriVRURUApKNhGwzmeWe3x9n7qx3MpNkJjNJvp/nyRNm5s6dG+a9Z+6c8573lE9S05KkL1hvJrIGTUuNKm5WMFRtP/Mh4KWr1BfUFXOBf0wHlswEnHVqn52tYWPAaGQgy5IFp9dpWCBYF1r8LFaRz4a2Bo4yZLhYhZwdFkfwPfNnge0/8kJcvf4mw+wa/XkH3AfwduXbmPfGvEAmVq2zFjZTnL5k6QPqdgAtVep2w7fqHJji7xPWz6kuxjt1r1gZe4YsNlh2vYsBD1RgyL0TMeCBClh2vRsocB4Zq0bFym9Zdws06QUadkOTPuPHs4uAS1apdnbDY9HZjU17k9K2Uvo53V7YzAYZMyz+S5Rc8a5LjbLJV8yF6Yi5KPH5MMjrVVmLOf0BkyV8PyaTGpz0Z9FIKQ0XGQCCWdwmk3FhYRbtpU6TGvCfewC9BpK3Td2OU7eoN1nwzoKwrOUF7yzgctl+KfmGK6X8XEr5Iyllhf/nYinlZwk87ysp5QT/z6FSyj+l4vgynqYBbU3AmQ8HO1lOulXVirl/gqqnsf+r8GWwnzhZZcu8cydwziK1vashdtZN5DLaT5ysbifxC0RRVhHK88rxxyl/jCoQDASLbAamEHSwyCdljoSmm/izwNzZRTGXK44svhd6/wMn/BVFWdE1RcJi+f4J6jw58WZ13gAq5h2F6lw682Hg2UtSEu+UIYymtoUUOI+M1SK7cTxqPg9w32HQWqqNH2+tU2nIT5+rMmb0eANUzB2oTFnbSt3LZbAqk5UdM0TJlch1qbct+ro2t0xlHoRODZl6HbBhYbttsE/zxli21xu43dGptERxCZO6ZgiN12Pmqfv7AC6X3b5UTGUCAAghDgbwawDDQ19HSnliql6z12itAZaepT5spt8O9D8UWHKm+jAqn6RG/4WIHjXYvxNorgbaGoGXr1bPLRgaXRTVYjMedXj79g4XBW6PSZgwrN8w5Nvz4fK6UOusxd8++huuO/o65Nvy4fQ60T+nf2D0Wx/JjizayZGJzJfQdBOLygKzte43fJ8H5Q7EjZNvDExpCtyfMxDLTnwQRR8ugem4q1QZMK8bsDpU6qfPHR3LL12l4n/FXBXzBUNV2uiaW9WUJkA957I31Qga9R5GKeshbVlkrJoA43ZHb5cO7DF+/MAedSMy3gAVby01wccZaz2a0+NDUU7455BaLpudbURJE6u24k/eDH7uQ6gFLEILp+aUqLY39HkrL1Jt8vuI2QZbTBbDtt0igl+NOjqVliguqaks2+m3B2N4w2PAaXfGf24vEPO8i5cR30eksmV5BsBmADcCWBDyQ/Ho9WUqN6rlrTVfsFPmxJtV72rTd9GjBu/cqYqeWh3qsfaWxo6sYRNaJyGJo7wmYUJRVhEG5AwIKxD8u/d+h7LsMhTYCwLbcmSiZ4s73cSfyVD00TI8MPmW8Pf52N+jbM3tKBNW1Dprg/dPvgUDVlyMkgcmwfT+Ayor7ImTVdZL9Vbg7ycDDd8YZ4bpWTLnLwdMVmDZucFOGX0b1pvpnSJS1iM7mENjtQwWPDA1vND0A1PvQZGrBQBQtHWV8eNbVwV32PCtquEFBOsarbsv/HHGWo/l8vhg51QmotSKVVtR/9y/7zDA4wSOXxCebWDNVgOZkc9zFIbfjmiDS7JKcO+0e8MLxU+7FyURmbkdmkpLFI8QMTJm+sbiQyWOGOedwSqbfVEqu6e8UspHUrj/3sufWYCGb1V2TP0udXvKNWpktuFb1cMamQ3TXA30GwR4Xeqxyo2q6Kle+De/XK3GZDKFvwYQvm8g6aO8iYw6cGSil/NnMphO/ytGaxqWnboIbs0LW80XqlBf5UaMrtmGZdNugLv0YHX/qzfBpHemFAxVWWEN36qYfnF+7HOhYChQMEzFb3apGomLlT1GfZrJ58boj1Zg2YkPwm2ywKZ5VXbWULUgoOngUzF6zZ+x7MiQwpJr/gzThPOBzUvVTgqGAlkFquZMdgmw5g/hnYCMtR7N6TFYlcnCVZmIkiryuhQI/9wHVLbBMxdH1ZjBjHvU4Evo85z14bcj2mCLxYqD80dj8amL4NG8sJosKMkqgcViTdEfSAQ1KyHy+9ZLVwGXxCvD2jtYTBYcXHgwFp+2GB7NA6vJGqhNSinomBFC6OkNLwsh5kMted2mPy6l3J/s1+x19BoJb98OlBwMrH9ALXntCxlN0LNhXrpKjRRMvR4oPUR1ygizypxZcaH6cvDaDcDcF1TWQOMe9eGUVRjcRh/tTXAVKE1q2O/a3+HOE33UoavbUHJ09n2EpqmOjshpIqH3m22AyaxGt0K38WcymHxelDTvA3xe4KmzA7s2VW5EyVNnA7/4BCg8CMjxdwoWDAVmLwVW/UrddhQanwuhq+L0GxzMlNDPqZCVHELrjlCGixVzsTbvSGwLM0xjTkXJkrODsTHnGaC1VnW05JTBtO0VlGx7Jfx5+vKWemy6/F8CXPXACdcDVZ8F9zf3BVXYrmF3UqaJUvdqM6gxY7OY0MapTETJY/Q5PXsZsOqXwW3cTcbXqkUjg506+vM8LaoN97QChQdBcxRjv7M27HPBYrFiQO7A7v07qW+TPuMY7kM1VkzCBIvJAk1qsJgsHIAPkYruqU1Ql6B6Tlbo9CUJ4KAUvGbvYjIBpWOBE34DOPcD489XS16fdHPwg0fPhjl7oUrjXHunSoXTv5yOmQFc/JKqSm91AE37VN2ahm+BY68GDp8FvHNXMJum36CEMgpiLWk9unA0T6wepNPvo8FSlTh/uYrXmm3h95/5sKrp0lwdvnywzwtUfR6cA24Ud/s+Ux2Ks5epkTCvC2jep/YFhGfJ6OfCjHtUR6bVEf3FN07dEcpgsWIuxnLUHY5ts1XFjL50Zb9ytarXv36qXu+SVcYxml8OXPO56oRsawrWONA7Yn7ypupMj2x/uZx2j+Myypgxm+DVJLw+DRYz30eiLjNc0toc/NwHVFtq1B6bzME2HFDtsj7wWDAU2twXsOPATl67UvqZY2SG9ZF6mvwe2b6k/w9IKUdIKQ8CcIj/34EfAOOS/Xq9lrNOfah4XGrKxvZVwEs/C68Z01ytpiatmKsKoYWmxm1fBbz2OzVn0eMML6h2xIXqS/H2VSrboKVGvc7sZTFXMtHFWtJ6v4uJUD1Jp9/HyOJ8uWVqWeADu9VvfZ53w7cqbqdcE5wW1+ovhtq8T8VfrDpIen2O3DK1rdellsDevDS47br7VBbZhc+oL84n/15d0Alz7GOPU3eEMlSsQuUxlqOOGdstMZZg1XzAMz9SafCLZqipo89dFp46r6+QBwQ7HfVY0zzAGzeHH9/Ss9TQRMEQNQpmVNBSPx8oo3l9GryajF4u299R4/Iya4YoZYQp/LM+p0RlKIa2x7OXAa/eEGzDnfVqtbyQNnf/gW947UoZQgCzngiP4VlPIJjP0Lvxe2T7Ujmhaz2AIxO4j4zoRdDM1uCHS2jNmP6HAfY8wHUgWOQsVjHfHz4S/pjJHF5MeMNjalu9SnhOqfrimj8k6ssrl7TuHTr9PoYW59PjJ3QK0cwHVYxWbgwvvhc6Lc7niR3TVZ+r24Dxvj9bqbbNL1dfjFddGz9Dh3q29gqVG2SgxIztxj3AwpOiM1Z8EfsPbXMB9cXgzVvCV1BYcytw1kLgb0cGY7OlKlhXJjTeYxW0ZDHgHkHveDHKmAFUNk2unXPjibrMKDty9jLVBod+1s95Rl3XAmqaUnahGmjURV4PA3Dbc3jtSplBCMCeH8zw8rSq232k+C+/R7Yv6d9ahBADhBAVABxCiCOEEEf6f04AkJ3s1+u19CJo+pQNnV4zxpatRmL37zTezqhQsE7zhRcT1rNttq9S2Tf/mA4smQm4GtTocsgos76kdSguad3zdPp91OMSMC4Y/dJV6n4gvPhe6LQ4s9U4pk0W4OPl6vlnPgz42sIzcF66Cvief665uyWYpqw/HitDh3q20JgDYhcq97/fMWNbT4ePjI/I/Tvr1VTQ2UuDxXxz+qu2cdEM9bu5WnVw6/sLjXsgPN4j9x/5OGU0p1vN+7dHdMxYQzpmiCgJjLIjV1zLR9hvAAAgAElEQVQIeJ2qY/ySVer3mj+odnrRDJUl43ZGt+ERba6trYXXrpQZfB4Vw15/+VVvm7rt86T3uLoJv0e2LxXDydMB/AVAOYC/ArjH//MrADek4PV6J70I2sfL21/y+p071eOR2/UbHF0cVX9s8zLgvKeCBX8NRheQWwY07g0uUehfPrvIVsAlrXuBTi9NrsdlwVDjuAldpvrMh1XsRU6Lyx2g4i80ps97SmVpTb1OddI8dLQaITvxZpUhoe+7aZ96PG9g7NfW/82MhN4hNOaAuIXKDWN78i0oevcew+2j9t+wJxiHi2aolPip16nOGiCYIeNuCd9f6HLZofEeuX8Wnu5R9I4Xo+K/6nFOZSJKCqPswtwywJIVvbRwfnlwm/X3h19TfLw86hqjKH8Yr10pM/Tx5bI7/f2jj0h6/q2UcjGAxUKIWVLK55K9/z5DL4J2xr0qvfPS1WqJNb1oKaBO4uZqNfVjyjVAVj/gwueCBdOMiqMWj1bZNllFqn5HaLZNoAbNXLXK06IZUaPSpsve5JLWvUCnlyaPLM4XGjflk1Tc9BsMXP6Wqlt09hMqEyZ3QHDlJmedKjZ9yb8BzasyaHIHqPv12jNAMBNh+u0qS0GP1YZvg5likcXTjDJ0qGeLjDkh2i1UHhXbECh65dfBZdcB1ckiRHCVpNKxwf0DwKIfhMfhyouAH70MnPJ7lXG4eRngX0478Pp6MeDIwtIsPN2jBTpmzOH1q/SpTG1cMpsoOSw21TZPvCA4bdRRCKy8OPq6YM4zweftelfVmdOvKUwWILc0rM01ZZditACvXSn9+vhy2Z3+/tFHpHJi9DAhxK8i7jsAYJOU8uMUvm7voRcrjaTPw3379uAywfoX1/OXA8Wj1NSj0CWEm6sBs12NPOj77Dc4uCy3vu2I44Fjf65S62KMSnNJ696h0++jHpeaFlzaMrcMOOlWNZ1oxPHAUZcFL6b0jJiyQ4Ha7bFX14lVi0PPwNHr1wAqUyx0uffQGjPMSOh9QtvC0LiLsfR5WGxrGnDCb4F9nwZXrJt6XcwaNdj/tXEctu4HHp8WjOfP/OMO+vPzBsXubInVllPG0zNiIjNmrMyYIUouR7Fqm/UBmoKhwEUvxlha2H/e6e2xuxVYPKPdle9MAK9dKf2k1n5M9wH8HhlbKjtmJvl/XvbfngHgQwBXCiGekVLelcLX7t1C5+G2VAUL9uaXB78cmEzBYr76yMOGx1QGji5WVk7NVvV4AstnUx8WmQmgZxkc+/OoFRGw8iI1GmC0Os1lb6ovrXotjsiYKxiqsr30osKA6mjsNyj42mabqvlxziJmJPR2Hc1AMcq40TtlgOg4NJmN41BfhjU0no+5gvHWyzk9xjVm9I6aNtaYIUoOo6xZzWvcHtvzgKs/Vhm31mxg4dTYbTpRJhEm45hmxgghtR0zxQCOlFI2A4AQ4hYAzwI4HsAmAOyY6azQzILKjSpbpnyS+lLauEd9UXAUA9NuaHdUGYD6MpFdqjp7vF71IWjNVquQzHxQdeZMvCC4UpOjuNv/XMpgeiZAw+5gTJrMKoMmtFNw3X0qtqbfrv5ttHqNXosjMmbzBgGuRtUZAwTvdxTxy3Bf1dEMlNDtQ2NVl1vmb1d3AxCqLX32kvAVwbzO4Pb6F4aiEV38QyjTxawxoxf/5VQmosRomv9aM0aHulHWrKdFtcfOuuAKNo5i1cGut79GbTrrzFGmEiJ2TFOfl8qOmaEAQltFD4BhUkqnEKItha/b+0VmFpRPUtNI9IwF/YtraN2EWKO6kcsT/uxD1Ug0V6uliY//NfDMj9pNDyWCOSQmTZbgtKbQaUaAKnIWuqR2aBZWe5kQrNFByWJOoP08ayFw5kNqBEvPNpx4QXAfBUPVfqjXc7L4L1HXGS2FHXk9aZQ163UBEuHLZZ+1UF1n6GJl2zLDmzKRMAM+d3RMC3P851Kvl8pvNk8D+EAIcYs/W2YdgOVCiBwAW9p7ohDCLITYLIR4JYXH1zNoWtSS1cguBea+AFz4jFo+8MxHgl+CATX627QXaKxUt/sN9qfoG7zdrTWqxsz024Er1qrCwPn+L9Ljzgx2ygBchpii+bzAgUpA8wDnLlYfMM1V4fGoL2UthIqzDY8B025U8XvRv9SHUZM/xltrVHwXDAmPWT3jIfJ+6hsi20GfN7pdTHR7SHURpK/YMfX66Hh94Qr1hUBfMWHqArXSBxDsaLTYu+3Pp/QJFv+NkTHDqUxE8RkthR15PWm0gl3eINUeR7bPmq/957HOHGUqqRnHdB+qMUOxpSxjRkp5mxBiNYApAASAK6WU+rIYF8Z5+i8AbAXQL1XH1yPEGmEoHau+NOi9rT9+LXz098Sbg0V/42W5aJpapm3DY+r3yotV8db/WwBAMj2UYvN5garPg3PCx8wA5j7vX+3GIG4OVAYzZooOUhkKoUWDmZVFRozawfOeAt65C9i+KjpmIrfXi/2GFpQ8ZxFwxgP+FcHKjOO15GC1yhIAvHOHypg5dr7KoFlzq9oH9XquODVmmDFDlIBYxf0jryctWaqmnD7FQ/qMn6d5greZVUs9ieaJH9PUZ6W61doM4BkAzwOoFkIMjfcEIUQ5VKHgJ1J8bJnPaITh7duBpu/C72+pUV82yicBMx+KXoYtdFQiciRZ+tT2Ey8IPm/zUuCBCUDdjuAIhI7poX2LUcaWrnlfeKG+7auApWerDhujuNGXun7pKsDjVP+eck10tgKzsiiUUTu48qLg1KLImIncfuIF0QUln70EyBuobutTm0IVDFWp8gVDVHu3611Vy2vRDPW7uZrtYB+hd7xYY9WYYcYMUXxCRLezY2ao3/r1RUsNsPQsYNm5qq1ddi5Q96Vx+yzM4dclzKqlnkKYY8c09Xkpa7mEED8HUAXgDQCvAFjl/x3PfQCuA2A4DCWEuEIIsVEIsbGmppd/eYscYSifpLJaGveE37/uPjV6e9KtgKsh9qiEPpL8xMnAfYep3z53cEniyOfpSxIzPTQpelzsGsVL9ZZg54wvRq+/16mmeoTGzcwHVZzq23ha1b+N4o5ZWRkl7XHb3jLqobf1mIncPlaMuRrUxf+rNwDnLomOV/0iiWnyPVKy4jbmVCY9Y4bFfynJ0t7mpoIwq3ZVb0fHzACOX6AyZ/XrC0+L8XXoeQbtM2B8XUJp0yvjNhWECD8XAtccLP5LqS3++wsAY6SUdYk+QQhxOoBqKeUmIcQJRttIKRcCWAgAkyZNksk40IwVWdBsyjUq22D67eH3V25UX4ZfnB/9GBDMcjEaedZHI5z10c+LXJKY6aFd0uNiN9accH0JSrM19jKWbU3ADx9R9Y3qdoQvdR269LBR3DErK6OkPW5jFXZ01off1mNGH5nVt48VYy3+C8ftq9TvOc8ArbXBYr9n3KvuZ5p8j5SsuI1V/NdqVhfRnMpEyZb2NjcVTCbVruqrNWaXAE+fG3594TNYGru5Wk3dD13lccNjwEk3B5/HpbEzQq+M21SQMvxc0GP6tDvTfWSUAVJ5ZbkbwIEOPmcKgJlCiK8B/BPAiUKIpck+sB4jcqQ2p1R9CK27L7q3NW9g7Mf00V2jkWc9K+bj5cbPcxQxPbSvijcnPHcAMHtpeMz88FHghXnA4jNUNoLZCpjt4Utdz3wQsDrUv9fdF51dw2wECmWUsXLeU+HFeOe+oFbuaNitRmZ/+Ghw+4+XG2fE6BlcgOqc0TNoXrsBmHZDeAwyTb7Pcnk0WM0CpojRTCEEbBYTpzIRJSK7VLWrr92g2llXg6rvNXupWsRi9lJVYyPyOvSHj6rrUP15r92gMm3e/1tw38yypZ7EbFUxHBnTZmu6j4wyQCozZr4CsFYIsQpAYHlsKeVfYz1BSvlbAL8FAH/GzK+llHNTeIyZLXKkVh8JrtyoMhCm3646a/LLAZM19mN5g9T+tBijEf0GqdFhTQMuXa16czkqTPGWoBQmwJoTXqjPkhW+LWA8MnD6vcG4tjqAn7ypptUx7iiSUcaKo1i1WafdqeKnaZ+qTaAX9z13cbC4r7Me+Pz5YNsGAKuvD2ZwAeo5/QarYr+MQQrh8viismV0DqsZLW3ebj4ioh4osh03W6ML/899Ifp64f2HgJn3A5f8W13DmizAhoWqFqKOWbbUk2hedU0y5xnAZFYrjG1eBkyel+4jowyQyo6Zb/0/Nv8PdYY+UguojpPzl6u0zcqNqpf1/OXBjpdYj5lMqkDaa79Tow//ujL4QTh7mRqN4JcQiqRnKkSuCqZnErTWAG/crIqrWrMBbxvwn3vUlDs9/nIHqFGyyH3k8IsvdUBoO6jTbzdXqaLooRfz7/4FmHQJ8NQPgzGnt4U+r1qlqeqz8FWe8gYC5lR+JFJP5PL4YLcYF2W0W0xwupkxQ9Rhmhf44JHwqUxv3Ayc8BtgxYXh1wuOYiDHf72gacCE2cDWfxlflxBlOosdGPuD4FS+gqHAWQvV/dTnpXK57N8DgBAiR0rZ0onnrwWwNsmH1bPFq3XQ3mNeN9BSFb0UYWiGA1GoePGmL7UeujT7zAeB4pHqOfq2rM9BqRQzDkcZZ8CYLUD/w1QGjc/jXzJ7ADtlyJDT44sq/KvLsprQ4mbGDFFc+mICoYM0Mx9U16V69uL2Ver6tL3rBV5TUE/n86rVIEO/i5lt6n7q81J2JSqEOBbA3wHkAhgqhJgAYJ6Ucn6qXrNPiBw51pczjvcBZbEBU69Xy8RGTk1h0TSKxShTQacvtR464vXSVSrlONF9EHVVrDi8dLWqCWPEbFHTPGPRNJURxgv/Pq+9qUx2ixmtzJghis9oMQF9MYsV/ooFBUONrxeM2mNeU1BPJX3G38UuXZ22Q6LMkcorzfsATAdQBwBSyk8AHJ/C1+t74i1nHCq7FCgayaWJKXmkNI6n5n1cwpK6T6w4lJ1cFKIj7Sr1ei6P1k7HjIkdM0SJiLWYQI5/ClKsKUlsj6m3kVqMaxbGNKW2YwZSyt0Rd/EKJhn0LJnGPcbLGbfWGD/PkhUsyKpj0TQCgjHVsFv9TuSiRy8OHKpgqCrEmlsGNO0FDnyb+P6ob+tMDAKx4zC0XevIvmMtEx+rXaVezenxBZbGjpTF4r9EiYnVTueXqymnl72ppihFZiayPabeRpiMzwXBrFxK8XLZQojjAEghhE0I8WsAW1P4en1D6OhBwzeJZcDoz/n3gthLaVPf1dkRKaNljGc+CHzxKnDizcCqa4H7J3CEi+LryqioURyGtmsd3Xe8ZeKpT2m3+K/VzBozRInIKlJF1kPb6fOeAnL6qymnuf2Np4uyPabexmwDzl0Sfi6cu0TdT31eKqsdXgngfgCDAVQCeB0A68t0VejogbO+/eWMjZ7TUhW9lDZrJ/RtsUak4tUeCi3C53ECtV+opdqnXBNd8yOR/VHf1dkYBOIXg+zovuMtE099itPtQ2G28XufxVWZiBLTUgW8c1f46nnv3AX84K72632xPabextsGvHt3xEqSdwOn3ZnuI6MMkMpVmWoBXBh6nxDiGqjaM9RZoaMH6+5TGQqhq5EYZcCEPqdyY7DQ2jWfs1OGujYipRfq0zSgrRlorlYfNBzhoo7o6qhoewWmO7rveMvEU5/SbvFfqxkt7Jghis/nUasubV8Vfv/0P7X/PLbH1NtoPuNz4dTb03M8lFG6e33QX4EdM10TOnpQuVFlKMy4Byg5GLA6jFcP4YgDtScZ8RGZtcB4o45IZRvV0X1zOVYK4fLGLv6bZTXB5fZB0yRMJuM6NEQEwGw1bofN1vafx/aYehuzxfhcMHX3V3LKRN0dBbxy6QpNA4QZmL0MWHGhfwWcajUdKX9o7A+qjow4cJnYvkN/rzUNuPA5oOFrwJoNeFqBwoM6PiIVmj3DES7qiI6Oika2U45iwFln3G51ZsSVS7yTn8vjg80ce7lsCcDl9SHbxotqophyBwBznlELAujXGflD1f3xsD2m3qQr5wL1et19JdHJ9UspUMDynxeoFW9m3KOWv7blqnox7XWeJDriEPoaoV9gjCrlU88WGU+n/FEV6w193zuLI1zUUR2JGaN26rynVL2C7aui2y3GI3VBe1OZsqzq/pY2dswQxeVrC7/OmL003UdElB48FyiGpF+ZCiGahBCNBj9NAAYl+/X6jNAClpUbgWXnAk/9UOUgJfIFQx9xaK/6PZcl7DtC3+sp1wAvXJHc9z2ReCMKlWjMGLVTKy8CJl4QvB0Zv4xH6gSfJuHxydgdM/7VmlgAmCiO5n2qvmFou71irrqfqC/huUDtSPoQj5QyL9n7JHTPkoFclrDvCH2vWayXepJY7ZSjMPw245e6yOVRHS4xpzLpGTNcMpuofT6Pcbvt86TneIjShecCtSMVGTNF7f0k+/X6DL2AZahkF1TtjtegzBD6XuvLrofi+06ZKlY75awPv834pS7SO2bscTJmWtkxQ9Q+vfhvqESK/xL1NjwXqB2pyOfeBGCj/3fkz8YUvF7foBew1E/mVBRU7Y7XoMwQ+l6vuw8482G+79QzGLVT5z0FfLw8eJvxS0ng9HfMWGPWmNE7ZjiViahduQNUOx3ZbrPgKfU1PBeoHamYyjSis88VQmQBeBeAHerYnpVS3pKsY+vRuqOAJYtk9h2R77XVAfzkTcDH950ynFE75SgGzrgXOO1Oxi8ljcujAYidMWMPKf5LRO0wW4D+hwGXrlZTNsxW9UXUzKLZ1MfwXKB2pDQKhBCFAEYDyNLvk1K+285T2gCcKKVsFkJYAbwnhFgtpfwglcfZY3THkoGRr6FpQHMVO2p6o1jxxCXTKdMZxS7bLUqyeDVmAsV/PZzKRBSX2QLklwdvs52mvkqYVIeMlOq3YNyTkrKOGSHEZQB+AaAcwMcAJgN4H8CJsZ4jpZQAmv03rf4fLrGdLlw+u+/he049HWOYkiTQMZPActlE1AFsp6mvYuxTO1IZAb8AcBSAb6SU0wAcASDu+rtCCLMQ4mMA1QDekFJuSOExUnu4fHbfw/ecejrGMCWJM07HjJ3Ff4k6h+009VWMfWpHKjtmXFJKFwAIIexSym0AxsR7kpTSJ6WcCJVpc7QQ4rDQx4UQVwghNgohNtbUMIhTistnJ1WPiF2+5xShR8RtKMYwITlxq9eYiblctr/DhsV/KZl6XJvbGWyne50+EbfJwNindqSyY6ZSCFEA4F8A3hBCvAhgb6JPllI2AFgL4NSI+xdKKSdJKSeVlnLVjZTi8tlJ1SNil+85RegRcRuKMUxITtzGm8pkMgnYLSZ2zFBS9bg2tzPYTvc6fSJuk4GxT+1IWceMlPIsKWWDlPJWADcB+DuAM9t7jhCi1N+ZAyGEA8DJALal6hgpDi6f3ffwPaeejjFMSaJPZYq1KhOglsxuaeNUJqIOYTtNfRVjn9qRyuK/T0kpLwIAKeU7+n0ALmrnaQMBLBZCmKE6jVZKKV9J1TFSHFw+u+/he049HWOYkqTN3zFjjTGVCVAFgJ3MmCHqGLbT1Fcx9qkdqVwu+9DQG/7Olor2niCl/BSqSDBliu5YopsyC99z6ukYw5QEwYwZc8xt7BYzmpkxQ9RxbKepr2LsUwxJ754TQvxWCNEEYLwQolEI0eS/XQ3gxWS/HhEREVGyBYr/tjOVyW5ljRkiIiLquqR3zEgp/yylzANwt5Syn5Qyz/9TLKX8bbJfj4iIiCjZXB4fzCYBs0nE3MZhZcYMERERdV0qJ7T9TggxVwhxEwAIIYYIIY5O4esRERERJYXT42u38C+gOmaaXJ5uOiIiIiLqrVLZMfMQgGMBzPHfbvbfR0RERJTRXB4NtnYK/wL6qky9aCqTpgHrHgDuHg38uRx481bAx4wgIiKiVEtl8d9jpJRHCiE2A4CUsl4IwUXaiYiIKOM53d5268sAgMNmRou7l3Rc+LzAcz8BtvwLGFwBWLOB9+4FfB5g+p/SfXRERES9Wio7Zjz+lZgkAAghSgFoKXw9IiIioqRocfvgsMVekQlQU5la2ryQUkKI2LVoMp6UwCu/UJ0yFZcAh84ChABsOcAHDwNHzAXKDkn3URIREfVaqZzK9ACAFwCUCSH+BOA9ALen8PWIiIiIkqLV7Y1bYybLaoYmg0tr91hv/RHYvBQYfz5w2DmqUwYAjrgYsDrUlCYiIiJKmZRlzEgplwkhNgE4CYAA8EMp5dZUvR4RERFRsrS0+ZBliZ8xAwDNbV5k21KZhJxCnz8H/OcvwOjpwMQLwx/L6gcccibwydNA/ddA4fB0HCEREVGvl/SMGSFElhDiGiHEgwCmAnhMSvkgO2WIiIiop2hp8yLLGqdjxj/VqdnVQ+vMVG0BXvyZmqZ0zJXBTJlQo08BIIDNy7r98IiIiPqKVExlWgxgEoDPAJwG4C8peA0iIiKilGl1e2G3xpvKpB7vkSszeZzAirmAxQFM/Q1gthpvl1MKDD4S+HiZqkVDRERESZeKjplxUsq5UsrHAJwD4PgUvAYRERFRyrS4fXEzZrJDpjL1OGvvAPbvBP7vWiC7uP1tR0wFGvcAez/qnmMjIiLqY1LRMePR/yGl7IFXKkRERNTXtbp9yEqg+C/QAztm9n0GrP8bMOoUYOCE+NuXHwUIM7BtVeqPjYiIqA9KRaW6CUKIRv+/BQCH/7YAIKWU/VLwmkRERERJ4fFpcHu1+DVm/I+39LSOmdduBOy5wKQfJ7a9PQ8YcBiw9WXgpJvb3fRA2wGs27MOXzZ8iT3Ne9DqbYWUEqXZpZjUfxKmDZmGbGt2Ev4IIiKi3iPpHTNSyvavYoiIiIgyWKtb1YxJtPhvU0/qmPl6HbBrLTDpJ6rDJVHlRwMfPg40fAsUDI16uNXTikc/eRRLty6FR/PAJEwozipGliULALCxaiOe/eJZlDhKcMMxN+CUYack6Q8iIiLq+TJqbUchxBAASwAMAKABWCilvD+9R0VERER9SatbdbTE65jJ6okZM+8/CGQVAGNO69jzBk5Uv3e+DVT8KOyhRncjfvrmT/FZzWc4btBxOGHICRjWbxgspuBlpiY17KjfgRXbV+BXa3+F6466DheNu6irfw0REVGvkIoaM13hBXCtlPIQAJMB/EwIMS7Nx0RERER9iL7KUlacVZnsFhNMogd1zDTXADteB0aeCPgzWRJWMFQVCf7q7bC7Nanh2rXX4n+1/8P8ifPxk8N/gpEFI8M6ZQDAJEwYUzQGvz3mt6joX4G7PrwLr+56tat/ERERUa+QURkzUsrvAHzn/3eTEGIrgMEAtqT1wDpA0yTqWtxwe32wWcwozrHBZBIpex5RR8WLNcYiUXJ15Zzi+ZgeekdLlqX9jBkhBBxWM5pcPaRj5rOVgOYFRp3c8ecKobJmvloLaBpgUp1Wy7ctxwfffYCLx12Miv4VcXdjNVkxb/w83PXhXbh5/c0YWzQWw/OHd/x4KKZE2g22LZQuXq+G6uY2eHwarGYTynLtsMQptE7UF2RUx0woIcRwAEcA2BBx/xUArgCAoUOj5zink6ZJbK9qwuVLNqKy3onyQgcev3gSxvTPa/fDrrPPo54lE2I3XqwxFilSJsRtT9aVc4rnY+d1NW4bXWqByWx7/LJ5Dpu5Z2TMSAl89BRQcrBhjZiEDJoI7FwD7PsEGHQE9rv24/6P7sf4kvGYWj414d1YTBb8dMJPcdO6m/D793+Pf0z/B4RgTANdj91E2g22LZRsicat16thW1UTrly6KRB7j86twNj+eeycoT4vI88AIUQugOcAXCOlbAx9TEq5UEo5SUo5qbS0ND0HGENdizvwIQcAlfVOXL5kI+pa3Cl5HvUsmRC78WKNsUiRMiFue7KunFM8Hzuvq3F7wKk6ZnJs8cevsqxmtLh7QMfMd58ANVuBkZ3IltEF6sy8BQD4x2f/QJu3DbPHzO5wx0phViHOOfgcbKzaiJe/ernzx9TLdDV2E2k32LZQsiUat9XNbYFOGUDF3pVLN6G6ua27DpUoY2Vcx4wQwgrVKbNMSvl8uo+nI9xeX6Ch0VXWO+H2+lLyPKKOihdrjEWi5OrKOcXzMX0CHTP2+B0zPWYq0+algNkKjDi+8/twFAKFw4Gv3sF+134s374ckwdNxsDcgZ3a3fHlx2NEvxF44KMH0ObjF7NkSKTdYNtC6eLxaYax5/VpaToiosyRUR0zQg23/B3AVinlX9N9PB1ls5hRXugIu6+80AFbnDnqnX0eUUfFizXGIlFydeWc4vmYPsGOmfj/11nWHjCVydsGfPYMMORYwJ7btX0NmAB8+wGe37YSbp8bp43o4OpOIUzChHMOPgdVrVVYuX1l146LACTWbrBtoXSxmk2GsWcxZ9RXUqK0yLSzYAqAiwCcKIT42P/zg3QciKZJ1DS1YU99K2qa2qBpMuY2VQec2NvghIDEY3MrAg2OPme3OMfW7msV59jw+MWTOvw8oo6KF2uJxKLXq2FvgxPf1LVgb4MTXm/0KEci5w9RbxUa/2YT8PhFnWvfDc/HiybBbALPrRQ74PTAYhKwJfBlIctqQnOmd8xsXw24GoBRJ3V9X4Mmwudrw8ptT2Ns0VgMzh3cpd0dUnwIxhWPw8JPF6LF09L14+vjEmk3Ch1Ww8/6QoeVn92UUmW5djwa8V3p0bkVKMu1p/nIiNIvo4r/SinfA5D2qmMdKZx27xvb8aPjRmDx+l2B3zedPg7FOTaU5dkxKN8Rt5CaySQwpn8eXpg/hdXxKaUSiTW7xYTbzjwM2TYzWt0+2EOKsSVStI1FBakvM4r/RZcehb+cOwECiDqn4ok8H60Wgd889yle31LNcyuFGp1e5NotCdVNcVjN2NPgjLtdWm1eCmSXBGvEdEX/Q7EuJxvftdXjrLEXdH1/AGaNnoXbPrgNS7csxbwJ85Kyz74skXYj124O2ybXbsaOmmZ+dlNKaZqGPIcFiy49GiYBaBKwWgQ0TUPm5QsQdS+eAQZiFUVrcLoDIxMy724AACAASURBVAn7Gl24fMlGzKoYguuf+zTwu6bJjX5ZFjUaKoC6FrX9t/tbUN3kipl5U9fihqZpEELA7fX5b3OkgpIjdAS/3tkGr0+DV5Pw+DRomoTH48Oe+lbsrm+Fw2ZGca4azXf7NNyxemugIGAiRdtYVJB6m8gMMK9Xi3l7X6ML976xPSz+73p1WyBDxu3TsOK/32BfoyvuqHRdixt3rN4Kt3/uvdun4a5Xt2FWxZDAvnlupUaj04PsBOrLAPpUpgyuzdH4nVpJaeSJgCkJU1Ws2XilaADyJTCxLAkdPQBG5I/AxNKJWLJlCZrdzUnZZ19V1+LG//bUY3T/XAzIz8Lo/rnYurchqt3Yd6AtrG3Zd6At6rP73je2J9RWESWqpsWNukYnHFYTzCYBh9WEukYnavg5RpRZGTOZwqgoWmmuHd81uDDP/6X02SuPRWW9EwUOa+B3aa4dt8wcB6fbhz+v3or500bB6fZhwbOfJpx5c/1zsbcl6ozQEfzSXDuuO3VMWEwuuvQouDwarly6yfDxO2eN949kJFa0jUUFqTeJzID5/rgyXH3SwVFZYw+s+SIwGn3nrPGoaXJj8+4GHDGkAD86bgQuXfRh4PlXnTga5z32fty2XtO0qM+FO2eNR7+s4Ec3z63UOOD0IMeWWCeGvly2lDIzl3z+dAUgNWBkEqYxAWjxuvCWRcPMxmbYPS74ulqzxm/myJn4wwd/wNPbnsYV469Iyj77IqtZYnhpP5y/8INAu/HI3AoU5wTbjdJcO7KsJtz2ypbgNhceidJce+DzW2+7EmmriBKVl2XC/lYrZkfEZ14WcwWI2DFjwGpRhakq6504YkgBrjt1DIYWZQcakSOGFCDfYUV5oQMenxb4ffVJo1Hf4sFNL36OO84+PPDv4w4qxuXHH4QsqwmAQFWTE2ZhgsenQQK4fMlG3HT6uMDF9xFDCnDlCSPR0ubFvkYXBvTL4ocgdZieieX2+gJfKm86fVyg0+W8inJcfvxBsFvMuOTJD6MeB9SXvuuf+xT/vGIyqhtdsJlNePFnU5BtM8Pl8WHvARee27Q7rGibXlQwtHOGRQWpp4rMAJtVMQQvf1yJJy85CmaTgE+TeHbjt5hVMQSvb6kOnDM3nT4O857ahCtPGIl3t1cFtreYTZjz+AdRGWUvzJ+C0jw7PB4fqpvb4NUkLCaBxet3RZ2PT15yVOD4Is+t0PM+kWmxHd2+r6hvdSe0IhOgpjJ5NYk2r4Ysa4a1c1KqaUxl44D8rtWC0b1V9wnaoOH05hbk7f0EDSOmJGW/w/OHY0LpBCz53xLMGTsHubbkdPj0dm63FzUt7kCbYRICf1vzBW46fRwKHFY0OD3425ovcMsZh2LFFZPR4PQg32HF397aEb7NWztw9UmjcemiDwEAV54wMnBdCkS3VUSd0eTSYsZnP0f85xP1ZuyYiaBpEs0uL+4+ZzyeXLcL86eNggDg8mqBTpNfTx+Du1/bhgfnHAEB4KE5RwAAcrOsqG1qQ2muHQMLHKhtasNxBxVj7rHDcPdr2wI1aEIzae45d0JY5o2+f2bOUFeEjvLrMQYgEGfnVZRj7rHDcOmiDw0fD1VZ70Szy4P6Vk9UJs1zm3bj5ycdjJJsa2D7QocVj86tiMooKHRYQdTTRGaAHVSSjYH5WYEMmPJCBx6+8Eg4rMHOycp6Z2Dq0sjSnLDt9WzLUHrWi8fjw7bqZvw05NwJzb7Rt/X4M9QiCwl3tL4T60HFVt3YhsMG90toW4e/M6a5zZt5HTOVHwJ1O4Djrk7aLldVf4gSax4O9+7D/j0fJa1jBlBZM7d9cBuWb1uOy8dfnrT99lZutxfba1rC2owVV0w2zLQzCWD2wg/a3WZ4cXZgYKU4x8bsV0o6IWAYe5mYbEjU3Zg3FqGuxY2L//Ff3PXqdiyYPhb1LR7sb/Hg27pWlBc6AiMIr2+pRrPLi589vRlN/t/fNTjR6vbh6pNG49u6VrS6fbhi6kjMX/ZRWC2a+pbgF9wGpwflhY7A71gjFKwhQB0ROsqvxxaAwL8vP/4gzF/2UczHQ5UXOpBttxpm0syqGIKfLt2E2lZPYPt6pwcP+EdDVlwxGTedPg4PrPkC9U4PiHqayGVl7VZL4NwB1Lkwf9lHsFuD4xzlhQ4MKnBg3fXT4LCaw7ava3EbnmNCCFQ3twW+YOn7vv65T3HlCSPDts13WLHu+ml4Yf6UsE6UjtZ3Yj0oY16fhtrmNhRmJ7Yyot4Zk5FLZn/4d8CaDQz/XlJ2V+duwgf123BMwVi0Fh+EfpWbk7Jf3Yj8EZhQOgGL/7eYKzQloKbFHdVmaBJR15HXP/cp9PIw7W0DAC/Mn4J110/DoAIHl9SmpJMxYk+yfBERM2ZCaZqEy+NFZb0Tpbl2mEwCBdlWuL0a7litMmRy7cGMghy7BZX1TljNJlTWO3HP61/glpnjkJdlxa9XfoI/nHkoLGaBynonyvLsgcwYAIF9PLp2J+6cNR7vbq/CQ3OOBKDm/oam+D26didHKCgmo6kIbq8vMIXOYhZY+pNjcPu/t+DRtTvx0JwjkGU1455zJ6DB6cGaLVV4cM4RqG/xoCDbiiU/Php3rN4aVi/DpxnXlinLU7Hq8anip/prv76lGq9vqQ7b/pYzGMPUM4SeUw6bGUsuPRrf7G9Fts0MAYTVYYD/tgCw4orJaHX7MKosBxazgMcrISO219v8qNFCSHg0aXie6Rkx+rYWk0D//Oic71j10dxeVdw7cqoS60EZq212QwIoTGBJc0DVmAGAJleGdcy01AH/ex4YdYrqnEmCV2s2wgcNxxSOQWNzG4ZseQXWllp4ckqSsn8gPGvmssMvS9p+eyOvQZvh1TTD60ifJgNTmYQwbmt8mgxMU9I0iccvnhSVUVec4HlBZMSnSeP4ZM8METtmdJom8XWdGp35/rgyzJ82CjazQL7Dim/qWlGaZ0ObR0N9i8qcKc21B+rM6FkGm3c34PcvbcE9501AaZ4NPilhFgLfH1cWtq3NHKxhs3l3A17cvAdzjx2GVZ/swcXHjYgqvnr3OeMDF35EoWJNRRhUYA9MVQqdTlTWz46axjZc8Hiw6NqDc46A1ydx04ufh03N+PmJo7H3gAuL1+/CguljDevG5Dus+PnyzWGv3b+fnTVmqMeKPKfm/d9wnDGxPOz8uPuc8bjr1e2B4r7XnTomcE4FigM/vsFw+827G7B4/S4sv3wy9jaojLXF63fh4mOHY1hxjuG5U9YvK/CFavH6XfjTWeMNjz2yvpN+bKFFFkOnKrEelLGqRhcAJJwxEzqVKaN8vBTwuYExP0jaLv9d/SGGZJWgPKsEjaUHAwD67fkYdQefnLTXGJE/AuNLxmPR/xbhgrEXIMeak7R99zYWk4g6h61mYXgd2eD0BKYyPTq3At8fVxY2gFJe6IA5ZAqjySQwpn8eXpg/hTWoKGmyrGbD+Mzq4587RACnMgXUtbjxTV0rVvz3G9zwg3Gob/FgT4MLd6zeisIcK35z2iG49plP8MCaHWq087SxuGP11kCdjTtnjQ90ztyxeit+N2Mcrnp6M/62Zgd+N2NcYNuPvq7DoIIs3HveBHx/XBle/NlxuPrk0Zi/7CMcObwYu+udUVNGFjz7KbxcopAM1LW48a+PduPJS47CW9dOxZIfHw2PT0OTyxc13eLKpZvg8WqBlcX0++tbPLhmxcdRUzP2HnDhtle24NIpI/D4u1/h7nPGB9KaywsdeGjOkVjx32/Cpizd+8Z2eP2jbKHbcpSNkily+epkLuEaOb3nnElDo5aIX/Dsp7j6pNEAgKtPGh3WZs+qGNLu9uWFDiyYPgYmAZTm2TGyNBfXfv9gmE0C//qoEo/MrQg7dx6ZW4Fl7+/C7IUf4LZXtuCXp4yB2QTDv704xxZ27kUeW+RUpcjtea4qwY6ZxOpi5fpXyWpozaApYF43sGEh0P9woHBYUna521mDT5u+xuSCsQCA1vxB8Nhy0K9yU1L2H2rmyJk40HYAy7ctT/q+e5PcLFNUmyEgDK8jc/3FrPXrgd/NGBfV1pTlhhf1NZkESvPsGFyYjdI8OztlqMu8Ps34e07I6p5EfRUzZvzcXh9Kcm04fkx/HHB6kO3PUHl9SzVqmtz4y3mqQGplvRMvbt6Dn04bGXjsyhNGol+WBU9echTsFhMcNgva/CniKzdV4qLjhuP1LdUocNhw4eRheH7Tbpx/zDDcMOMQNDq92HfAZTjNSVdZ74THywaLoglIzJgwOCwz5u5zxsMXY0qEUdpzts1suO0hA/Jwx9mHAwBmVZRDkxJ3nH04hhZlw+2TyLGbcPyY/lFTMqQmOcpGKZPqgrWR03v06aihKuudGFmag3cWnACB8DY7VgHtESU5eOvaqbCaBRqcXly6KGSp0AuPREmuDfe8uQOAmhKlr7BSmmPDgONH4eLjRsBqMaHZ5cXMB9cZ/u2RI9w+adwO6FOVOCJubG+D+j8rSrCDKt//2V3TnEEdM5ufAhorgaOTV0D3leoPIQAcUzBG3SFMaCwbg/xvNwCaDzAlb8T7oIKDMKF0Ap747An8cNQPUeJI3lSp3qTZpWHt1io8ffnkwHLtRp/zRrdNAlh06dEwCUCTgN0iYDZzvJZSK9aUXQ8HoImYMaOzWcxw2Cy4/rlPsa/RhVa3D61uXyAL5svq5sDIwsyJg/B1bWvgsXlPbcIZD67DpYs+hMNmQWmeHfaQgpF7G9QF9MyJg/Czp1VmzNe1rfD6gPnLPgoUg2xwegKvGYqp5RRLm1eLyozRR8aM4sjsT3sOFSvmIAR+8/xnuODxDZi98ANc8PgG/Ob5z7CzpgUn//UdNLt8hgXcfDJ8lK04x4a6FndKshuo70l1wdrIYr9mkynmuTSsOCfqnIpVQHt7VRNOvOcduDxaVLHOn4YUD16xqRI2ixnDinMwuDAbNv9nyuDCbAgIXPyP/0b97fsaXYHzC0Bge4fVuB0I/TzhiHi0r2pbkG0zBzpc4umXZYUAUOv//0+7tibg3buA0kOAQUcmZZdSSrxctQFjc4egyJYXuL9hwKGwuhqRW7U1Ka8T6vwx56PN14a/bvxr0vfdW9gsZqzYVInj73obU+9ei+PvejswvSlUeaEDvpDPXr3g+Ml/fQcn3vMOTv7rO7jg8Q19vvA3pV6s+LTws4eIHTO64hxbYGT00bU7UZhjRVGONTB949G1O3H3OePx/XFlGFjgCExpCk0DfWxuRSAFPDRFXC+4OrDAEciMybaZYRIIvJ4+Jaow5DX1/TK1nGKJNTLm8vii4vPhC4/E+h01ePjCI8PuH1LkwD3nTgi7785Z49Hm9UXF4mNzK/DAGjWq39zmNXxtGVLATc9uOOvhdZhy59s46+F12F7VxM4Z6rRUF6yNnN7jdHujziW9AC8AmATCHn9u0+6oc+yRuRV4btNuAIDZZJyB4/Npcdv7WH/73gan4fnFqUqds7OmGQPzsyASXL/VbBLIy7KgtjlDOmbe/jPQVAUcdRmStQbtJ027sNtVg2P905h0B8rGQhNmFHzzflJeJ1T/nP6YPnw6Xv7qZWyqSv50qd7A6By3moVhG/Tsxm8Dt++cNR6Ry+Cw8Dd1hyxr9PS7R+ZWIMvKr6REnMrkZzIJZNvMYUV8rzt1DIYUOfDPKyZDkxI5djNunXkYtu9rQk1zG/7y2vZAVfFWtw8DC7ICo42RKeImIbBtX1NYAWBNIvB6f3ltO648YSSsZhMGlqhCjz6pGrCSHI5ikjFrSCFpXXmhA3sPuPDo2p247czDcFBpDjw+iYXv7MTKTZU4r6Iciy49GlazgMWkfu56dVtYhfzF63fhljMOxcjSXDz/0+Pg8WmwWcwwm4Aa/5eP6qa2uIVDY2U3vDB/SmDlB6KOSHXB2si2WwiBe17fHnV+6AV4NQksXr8r7PFVn+zBiismB463IMuCW2cehhtnaIEMm+jjN+GF+VPanUoU62/XR7kjzy9OVeo4TZP4rPIAJg0v6tDz8rOtqG1uw0Nvf4lGpwe//cEhKTrCOL5aC3zwMHDwqUDpmKTt9uWqDbAJCyryR4fd77M60FRyEAq+fh+Vk5M3bUp3+ojTseG7DbjxvRvxzBnPINeWm/TX6MmMznG314dVn+zBk5ccBbNJwKdJrNtRjXMmDcWJhwwItGHXnRoeo8zOpu7g8mjYtKs2bPrdW1u+Q9GhA9N9aERpx+7JECU59sDIw+bdDVjw7KdocvkwKN+BoUU5KM7JgpQykC1T09yGeU9twrXPfIKyPDsKHOGjkKEp4lrI8/TMGJ/mC4xqbN7dgNte2QIpgeIc9ZyhRdkoy8viRTRF0Yuf2swiauTh7nPG49G1O1HT3IYB+VkYnO+Ax6dh/Vd1AID1X9XB49MwpDAbgwvVEqo/Om4EbntlS6DA6I+OGwGLSaCsXxbK+mUFpjkUOMIzweJld3E5Xkq27sgCCW27B/TLwi9PGRN2fvzylDGB17OYRNT5c/yY/rCYROC8sVrNGFTgwNDiHPTPy8KjEefso3Mr0D8vK+5UIqO//c5Z6nzXRZ5fnKrUMTuqm9Ho8mJUWcc6APplWfFZ5QHc/dp2PPbuV/jGv8pjt6rbCTz7Y6BgCDDpJ0nbrVvzYHX1RhyZPwoOc/R51tD/UDgavoW9oTJpr6mzW+y4/PDLsbdlL25Zfws0yXp78WRZTTh9YjkuXfQhTrznHVy66ENUjCjB3a9tC7RRV590MOwWkdJ2lMhIrt2EihElmPP4B5h691rMefwDVIwoQa6dX0mJmDETIt7ooqZJ+DQZN1vGiM1iDjzvyhNGwiwEHDYLinLMgUKPVrMJZbl2WCxsnCg2o+V8/3nFZGj+GLJZBR6cc0RY/LYX1yaTKWrEP9aSvJH7ctjMeH7+cfB4NcPReC7HS8mWjiwQu8WE2848DNk2M1rdPthD2uiOnD8AYLGYMLZ/HlbOOxZenwZLB9p9o2yeW1/6HJt3NwS24fnVNas//w4AMKG8oEPPK8y24X97GwO33/uyFsOKu3GZ56YqYOnZgM8DnHADYM1K2q7X1H6CJp8TxxUaZwHVDxqPoZ+/iOIda7D3qB8l7XV1owtH45zR52DlFysxYOMA/HrSrxOeZtbbGRVD/+cVk6MyEr6uacQtZxyKG2eMC7Q5JpNgNh11uxa3RH2zM6zI/c7qRhTn2JCfne6jI0qvjOqYEUL8A8DpAKqllIel4xj00UUjdS1u/HHVFrVc9nOfYt5Tm1TdjYsq0M9uRU1TW8wPOH2k8/IlGwPPe/ziSSgvyOYHIXVI5PSgx/7zNVZ9XhU+PShHXbDVtbjjXnQV59jwy1PGRK1yE2vkrL1zxGjfetwnsm+iRHQkBruqrsUdVnAXUJ0f+vnW0fMHUJ0zgwrUSHWi56ku9G/XNIlfnjIGW75r4vmVBE63D0+9/w0mlOcnvCKTbmRpDt77shZF2Va0enzYUdWcoqM00Lpfdco0VQHf/yOQX57U3T+9dy362wowLneo4eMeRwGaSkaheMeb2Dvp4qTVtQk1ffh07Hftx5ItS9DiacHvjvkdrObEijP3ZkbThZ9avwtnTCzHnMeDK7/pWXmRHcCcUkzdLdtmQmFOFmYvDF+ZMNvGQWmijOqYAbAIwIMAlqT5OAy5vb7AEtmho6P98+zYUdPc7vKtnOtPyZLI9KCOLCmcythk3FNPF+9860qMd3Xpb55fybX0g29Q1+LGVdNGdfi5Rw4txIuf7MU5FUPw5rYqfFndTR0zrfuBJTOB2i+AaTcmta4MAGxt3o2PG7/C+QOPh6mdDpfaIRU4aPM/kVu1Bc0DDk3qMQCAEAIXjL0AWZYsPLfjOXxa8ylum3IbDi1J/mv1JEbt03+/bsC5Rw0Ny/LLtpnZLlBGaPOo6Yih8Rl6P1FfllEdM1LKd4UQw9N9HLHo0zL0JbIBNXK6ct6xCRU47c5RXuq9Epke1NGiu6mMTcY99WSJnG+djfFkFMfm+ZUcmiax8N2vcPjgfIwd2K/Dzy/rl4VHLqwAAGz9rhE7uqNjpqUupFPmd8Dg5CyNHerJ3W/AbrLie0Xj2t2ufuB4+D59DsVfvJGSjhlAdc6cPfpsHJR/EJ7a8hTm/HsOzhp1Fq464iqUOEpS8pqZzqh9uvqk0bjkyQ9jZvkRpZNPAj9d9lFUfK6cd2waj4ooM/S4vDEhxBVCiI1CiI01NTXd+tqxik5KabxkMQucUqhkxW4ixU9ZdJeSJZ1tbiZIZbFhnqep09G43bqvETXNbfjeqK5/wR9U4MC+Rhea27xd3ldMLXXAkjOCmTKDK5L+El+1fodXazbhpOIJyDa3X7NGs2ahfuAEFO9YA3NbajulJpZNxG1TbsNJQ0/Cv778F37w/A/wxGdPwOV1pfR1u0tHYteofRpRksN2hbpdonEb6zuTjFi+nagvyqiMmURIKRcCWAgAkyZN6tazOFbaeF2LmwVOKa5kxW4i0xdYdJeSJZ1tbiZI5XQhnqep09G4fW9HLQDgsMH5XX7twf76QV/VNGN8B4sIJ6SlFlh8BlD3JXDizcCgicl/DQAPfb0KNpMF00sT6/SpGvl/KKnciJJtq1E14dyUHJMu25qNC8ZegGlDpuGZL57B/R/djxXbVuDao67FqcNPTelrp1pHYteofZKQbFeo2yUat/zcI4qtx2XMpJvR0qPdsXwrUah4S+AyJomSJ1VLTvM8zRzv7qhBeaGjw0V/jeiFnXfWpCBzpGE38ORpamnsk1LXKbO+fiter/0Ip5VWIM/iSOg5rQVD0Fg8EgM+eRbC25aS44o0IGcAfn7Ez7Fg0gLYzDYseGcBbvjPDWj1tHbL62eCyPapJMfOdoUyFj/3iGLrcRkzmYgFGCnTMCaJMh/P08zg8viw8et6TBtblpT99e9nh0kg+QWAq/6nVl9qawJO/j0wIDWLVzZ7nbhtx3L0txXgtNJJHXru3rHTMXbdw+j/+b+wb+LslByfkUOKD8HNx96MV3a+gpd2voRPaz/FPVPvwZii5BZD7gnYrlAmY3wSxZZRGTNCiOUA3gcwRghRKYT4SbqPKVGpGlEl6izGJFHm43mafpu+qUebV8PhSZjGBAAWswkD8x3YWd2SlP0BAD59BnjiZMDnBU69M2WdMlJK3PzFUux11eGS8pNhNXVs/K6pZBQa+h+CQZuWwtZUlZJjjMUkTJg5aiYWHLUAB9oOYM6/5+CFHS906zFkCrYrlMkYn0TGMqpjRkp5gZRyoJTSKqUsl1L+Pd3HRERERL3Xf3bUwmwSOGRAx1djimVgflZypjK5GoGXfwE8fxlQNAL4wV+AwuFd368BKSVu37kSb9RuxqwBUzAmt7xT+/nm8LMBTcPwtX8BtO4vODumaAxuPfZWjMofhZvX34wb37sRTq8z/hOJiIjSKKM6ZoiIiIi605qtVRjTPxcOW/KKT5YXZuOrmhY0uTyd24GUwNaXgYeOBjYtBg49G/j+n4Cc1CwL3ehtxa+2Po5/7n0Hp5ZU4NQEC/4acecU49vDz0L+ns0Y9p/7Aakl8UgT08/eD7+a9CuccdAZeGnnSzj7xbOxfu/6bj8OIiKiRLFjhoiIiPqkL6ubsaO6GUcNL0rqfseX58MnJdZ9WduxJ/o8qkNm4QnAirmAxa6yZCb9GOjgtKJE1HuasaRyDU7/8Fa8VfsJzhv4fzh34PcgRNemFtQOOxp7Dz4ZZVv/jdGrb4a1uYP/D0lgEiacNfosXHfUdfBKL+a9MQ+Xv3451u9ZD18aMnmIiIjaw+K/RERE1Od8Wd2MO1ZvhdUscMxBxUnd9+j+ucjLsuDJdV/juFEl6JdlDT7Y1gx4WgGvC3C3AAf2APW7gN3/Bb58E3DuB/IGAcf9Ahg5rdMdMm2aBy6fG27NizbNgzbNg/2eJlS1NWCXswqfNH6FTQe+hFf6MCanHFcPm4lh2ckpgAwAew75ATz2PAz538sYv/xi1I/4HhoHT4SrYCg82YXw2XKgWezQrImt+tRZY4rG4PfH/h5vfvsm3vjmDcx7cx4K7YWYMngKDik6BMPzh6Moqwj59nw4LA5YTVZYTVZkW7NTelxERESh2DFDREREfc7lSzZiV20LLvveCAwvzkn6/n88ZQTuX7MD97y2Hb8/M6RY79+/D1T/L/oJ2SXA4COBUScDQyd3OUPmpk8exOp9xtN3TBAYljMQpw04DlNLj8DwnIFdeq1YXIefg69HHI/CrauQ/+1/UfzlW2GPtww5BpVn/S0lrx1p9pjZOHvU2diwbwM2Vm3Ee3vewytfvWK47bjicVhx+opuOS4iIiIAEFLKdB9DpwkhagB8k+TdlgDo/pzbzNcX/l9qpZSndscLdSB2e/v/e2//+4DU/42ZGLddkckxwWPrvMjj621xayST3xMeW+eUANiWIbGbyf9P7eFxdy/9uDOxze2p/6fJwr8//t/fbXGbCXp0x0wqCCE2Siknpfs4Mg3/X9Kjt/+/9/a/D+gbf2MyZfL/F4+t8zL9+FIhk/9mHlvnZNKxZdKxdASPu3tl8nFn8rF1B/79ffvvN8Liv0REREREREREacKOGSIiIiIiIiKiNGHHTLSF6T6ADMX/l/To7f/vvf3vA/rG35hMmfz/xWPrvEw/vlTI5L+Zx9Y5mXRsmXQsHcHj7l6ZfNyZfGzdgX8/hWGNGSIiIiIiIiKiNGHGDBERERERERFRmrBjhoiIiIiIiIgoTdgxQ0RERERERESUJuyYISIiIiIiIiJKE3bMEBERERERERGlCTtmiIiIiIiIiIjShB0zRERERERERERpwo4ZIiIiIiIiIqI0YccMEREREREREVGasGOGiIiIiIiIiChN2DFDRERERERERJQm7JghIiIiIiIiIkoTdswQEREREREREaUJO2aIiIiIiIiIiNKEHTNERERERERERGnSoztmTj31VAmAP/xJ1k+3YezyOcozOgAAIABJREFUJ4k/3YZxy58k/nQbxi1/kvzTbRi7/EniT7dh3PIniT99So/umKmtrU33IRB1CmOXeiLGLfVEjFvqqRi71BMxbok6p0d3zBARERERERER9WTsmCEiIiIiIiIiShN2zBARERERERERpQk7ZoiIiIiIiIiI0sTSHS8ihBgCYAmAAQA0AAullPdHbHMCgBcB7PLf9byU8g/dcXzUPk1q2O/aD7fPDZvZhqKsIpiEKeb93fHa1Pt15L3vbJwwvigVvJoXtc5aeHweWM1WlDhKYDEZf9wyBqmviXdNoWkaNGjQpMZzogsu+vsGzDh8IM4/emi6D4WIKMDj86DWWQuv9v/snXmYFNW9/t9TvUz3zMAs9LCMgAKiXo2oETGGJDIxRo0mxLiiuBtFkxDQaH7xcq+ai+bG3AAhimgSFBFxjcFEjdFk1ISsqCjGbRRFxmGZpmdglu7prq7z+6O6uqurq6qreq3u/n6eZ57pqjpVdaDec6rm9Kn3FeEW3Aj4A/C4POWuliMoycAMABHA9ZzzVxljowC8whh7nnP+lqbcnznnZ5SoToQFJC6hq68LC/+0ED1DPWhvaMfKL67EtOZp+KD/g4z101umF+wByujchTwH4UzsXPtcdUL6IoqBKIl4r+89LO5cnNTV8o7lOKTlkIzBGdIgUWtke6a467W7cMHhF+DmTTdTm8iDvYMj+HNXEH/uCtLADEEQjiEWj6GrvyvjGWl683QanEGJXmXinO/knL+a+DwA4G0AB5Ti3ER+hCKh5AMUAPQM9WDhnxYiGA7qrg9FQkU/dyHPQTgTO9c+V52QvohiEAwHkw8cgKyrxZ2LEQxnxoeSBolaI9szxdzpc5ODMurt1Cbs8Ul/uNxVIAiCyMDOM1ItUvKvHxhjBwE4BsA/dDafwBh7nTH2LGPsCIP9r2KMbWaMbe7t7S1iTQkAiMajycaj0DPUg1g8prs+Go8W/dyFPEcpIe1ax861z1Un1aavYkG6tYdR3xiTYhllSYPFg3TrTAyfKSS53TR5m2q+TRRCu8PReIFrRRDmUJ9LWEGURN0+XpTEMtXIWZR0YIYx1gjgCQCLOOf7NZtfBXAg5/woAD8H8Bu9Y3DO7+Wcz+Scz2xraytuhQl4XV60N7SnrWtvaIfH5dFd73V5i37uQp6jlJB2rWPn2ueqk2rTV7Eg3drDqG/0CJlTdEmDxYN060wMnykEud3si+6r+TZRCO0OR+mPHKK0UJ9LWMEtuHX7eCMfvlqjZAMzjDEP5EGZ9ZzzX2u3c873c84HE5+fAeBhjAVKVT9Cn1ZfK1Z+cWWyESnvewf8Ad31rb7Wop+7kOcgnImda5+rTkhfRDEI+ANY3rE8TVfLO5Yj4M+8nZEGiVoj2zPFxq6NuHX2rdQm8oRmzBAE4UTsPCPVIoxzXvyTMMYArAUQ4pwvMigzHsBuzjlnjM0C8DjkGTSGFZw5cybfvHlzUepMpLCaytRc14z+kf6c0kXUx/K5fZAkCVEp/XMJ0hlYsQ6spZa0m2vqjJ1km2RZKQaPYF62EHVzGKTbPLGrg2zltXoc4xuDfdF9uuWrRIO5QLqtEqxqWGkXymMdB6/UVCbHa/fRf+3AjU+8AQB4d+mpqHO7Cl01ovJwvG6J2iAiRtAX6UumMrX4WuBz+4yKl0y3TqBU84ZmA7gIwFbG2JbEupsATAYAzvlqAGcDuIYxJgIIAzjfbFCGKB0CE3RHMtXr80kXUe8b8Aew6NOLsGTTEkpkqALySUyymvplp6wWI20TtYNdjVop7xbcGN8w3lJ50iBRyVhtP2ZpZUo5aguFQ/0q02BERF0jDcwQBFF+YvEYtu3bRqlMBpQqlekvnHPGOZ/BOT868fMM53x1YlAGnPM7OedHcM6P4px/hnP+11LUjSgM+aSLqPe9/MjLk4Mydo9DOI9SJCZRsg2RD3b1U+zyBFFJWNU3JXGUliHVq0wRUSpjTQiCIFLQvcAcmoJAFIR80kXU+1IiQ3VRisQkSrYh8sGufopdniAqCav6tpNWRuRPWDUwE6WBGYIgHAKlMplDAzNEQcgnXUS9LyUyVBelSEyiZBsiH+zqp9jlCaKSsKpvO2llRP4MqV5lGhHJCJggCGdAqUzm0MAMURDySRdR77tm6xosnb2UEhmqhFIkJlGyDZEPdvVT7PIEUUlY1TclcZQW9YyZkRjNmCEIwhnQvcCckqQyFQty/XYW6hQdn9sHF3MhEo9A4hJ8Lh9a/cbJCuoUE59L3leURIhclPfhsjFgkZMayLG+CNhJnUkrK3ghCAIiYiT9s1kKTiLBySt4EYlHkt/GRsRI1mQnO9hJjCoBpNssZNOg5ZSlxPVurWtFaCSUXG6pa0EoEkomDAT8gTQTO4fpxSmQbqsEvbQlgQnwMA9iPJa8Zzd5m7A3shcxKQY3c8MreMEZz0h0LFTCYxETnRyv3e9seA2/fV1+XeCxBSfguINoIJhwvm6J2oBSmYyp+SdDojCok3EC/gBumnUThsVhS+lK2lSdjokd+O6x30UoHMK6t9bhgsMvwENvPYQLDr8AN2+6mdKaKgyrSRtG6R7TmqeZpi7ppTItnb0UK15dgWA4mPZZSQHJ549is3QR+mPbeVhJjTHTqNH1Xr1lNTq7O9ExsQMLjl5gmDCQT2oYQTgdRd93vXZX2j26Y2IHrj7qalz34nWm/blR+1HaV64Jj7Xe1sLqV5loxgxBEA4hIkZ0U5mmNk01G5ypGWrrTkUUDW2yUmgkZDldSZvqMHf6XPQM9mDJpiWYO30ubt50c/I3JZtUL0bpHsFw0DT1Q2+/JZuW4PIjL8/4XAjnd3KUryzyTUUyut5zp88FIPdXZnqgVCaimlH0rb1Hz50+NzkoAxj350btR2lfuSY82t232hgRJQiJ75mjcfKYIQjCGfRF+nT7/L5IX5lr5gzo612iIGiTlQDknKqj3l9JaaK0purHKN0jJumneSjX3mg/rY7Ux8sHShepLPJNRTK63oqmjPomJWGAUpmIakbRt7YdGLULbX9uVE5pX8qy3YRHu/tWG7G4BL/HhaFonGbMEAThGCiVyRyaMUMUBG2yUlgM55yqo95fSWmitKbqxyjdwyPop3ko195ov33Rfbqf800BoXSRyiLfVCSj661oyqhvUl5ro1QmoppR9K1tB0btQtufG5VT2peybDfh0e6+1UZM5PB7XQDk2TMEQRBOgFKZzKGBGcIWEpcQDAfRM9iDYDgIics3fHUyw0sfv4QJDROypispx5IkCT/r+Fmy7MaujWhvlH1CNnZtxK2zb03+pmST6sUo3SPgD2D1yaux6qRVuO+U+7DqpFW4+0t3J/1l9PZbOnsp1mxdk/G5EM7v5ChfWVhJjTHq1wDj672xayMAub8y00O+qUxmdSOIcqPoW3uP3ti1EcvmLMvQvVfw4t4v34tVJ63CjMAMw/ajtC8r7cXoWaKWnxOicQk+jzwwE6WBGYIgHEKLr0W3z2/xtZS5Zs6AUpkIy2Qz1pO4hP6Rfuwe2o27t9yN+YfPx7iGcRAgwOf2YYx/TNKAT3usjokduGHWDXAxVzKVYX90PyJihFKZagi9RA0AGbq77XO34YF/P4BvHfMtTG+ZDgC6aU7JVKZEQlMh0nAkLqFnUJ4er+je6/KivbG9XAaTpNssmCW1WOnXtNe73lMPN3MjKsnHG+0Zjb2RvYapTLkmxVS5mSnptkpQ9C1JUvIenZHKJHgxEBvAgucXJLW8omMFwmIYv+n6DU4+6GQcOPpA1Lvr0eyznsqU7VmiVlOZTl3xMuISR9eeQfzP3CNw0QkHFb5yRKXheN0S1U8sHsP+kf0YkUYQl+JwCS7UCXUYXTc67blJBaUyEYQeRsZ6609fj4A/kPyjZVHnIvQM9aCzuxOAPBq6/vT1aQ9H2mN1dnfi3b53k8cCUJPfctU6euk4eua///mX/8SNs25M01+pZqyEIiFc8dwVae/IKhqnWTPOxCx1KVu/Zna92xtT03EnNE7I6fxmZKsbQTgBK/oOhoPJQRlA1vKizkW4cdaNePKDJ/HkB08m25UyuGkFK88StUg0LqGxTn7Ep1eZCIJwCsFwEJf+/tKMZ6r7T73f9DmqVqj4r9yI0mHFWM+q+R6Z9BFWMTP3LYdmSLvVRbbrWc7rTVojqoVsJu3Ksl1tUxvRJybK5r8ADcwQBOEcyPzXHBqYISxjxVjPqvkemfQRVjEz9y2HZki71UW261nO601aI6qFbCbtyrJdbVMb0ScW50mPGRqYIQjCKZD5rzk0MENYxoqJpVWjy3wNMYnaQU8riiF0OTRD2q0usl3Pcl5v0hpRLehp2a7Jr9XjUhuR47I9Lga3wMj8lyAIx0ABGuaQ+S9hCcXcj3GGqBSFKInwuDxJbxm1sWVzXaZxH5AyZ/W5fbJJIJcyzHzV5Ypo3GcEGaM5FFESEQwHEZNicDM3vIIXnPGkPtLMVVXmv4rWFJPWfDWmZ3JZBp1qId3miVpfeibRUTGaZu7b6mvFQGzAUEO5mv3qUchjOQzSbZWj1q7ABNQJdYjEIxC5mOzHYzwGAbKxvwABgiDYMscuU3/seO0eectzmD0tgJe7enHecZNw81ePKELtiArD8bolaoOIGEFfpC/5TNXia4HP7TMqTua/BKFGST2467W7cMHhF+DmTTcnUxVWn7wa0XjUNDVEnZoQ8Aew6NOLsGTTkozyQGb6ThUlkBA5InEJH/R/YJqao9XN0tlL8dsPfouvTvtqhta8Lm9aMohVjVV5Qk7Nkk1fsXgM7+97H4s7FydTXxYcvSC5bEWP+egkV+Nggigneu1g2ZxluOf1e9DZ3Zmc+fjQWw9hwdELsHrL6uR6K+2F+mNzYnEJbheDxyXQjBmCIBxDRIxg275tac9QyzuWY2rTVLPBmZqB7l5EVpTUg7nT5yYHZQDZrKl7oFs3NSQUCWXs3zPUg8uPvDz5h7K2vFECifpYRO2RTRd625dsWoJLPnWJrta6B7pz0hjpszrJdl2D4WDyAQIA5k6fm7ZsRY+kE6LW0GsH1714HeZOn5tcvnnTzcn2pF5vpb1QOzNHjHO4BQaPi5HHDEEQjqEv0pfxDLW4czH6In1lrpkzoBkzRFaU1AMlBUeN3+23ldSkdwx1eUpXILTkmprjYi7d9X633/BY+dSDqEyyXVdtgkC2Pox0QhDWU5iU9mQ3nYnamTGSxCFKHC5BgNslIBangRmCIJwBpTKZQzNmiKwoqQdKCo6asBi2ldSkdwylPKUrEHrkmpoT53Hd9WExbHisfOpBVCbZrqs2QcCsD7NyPIKoBaymMCntyW46E7UzY2KSPBDjJvNfgiAcBqUymVOSgRnG2CTGWCdj7G3G2L8ZY9/VKcMYYysZY+8zxt5gjH26FHUjsqOkHmzs2ohbZ9+a5qQ9pWkKftbxM3RM7MCKjhV44NQH8MtTfonmumYA8sgo5xwrOlagvaEda7auwdLZS9He0I4ZgRlYddIq3PvlewEONNc1G6YrSFxCMBxEz2APguEgJE4PGk4j7RoNBxGKhApyvdSpG2rNiJIIURJ1UzmWzl6KtW+uTWpNWb/yiysxcdTEnBI8jOoBjuS/j3TqDOxch2ypLgF/AKu+tAqrTlqF+065D6O9o7HqS6syygtMQM9gDwQmFDQlhjRFOBGJSwgOB+VXQwd70Dvci73De7FraBd6BnsADqw+eXVaO1g2Z1laCpOSrmc1nUndFtTHN+qPa5VYXA71cAvywAzNmCEIwim0+Fpw95fuTj5TrTppFe7+0t1o8bWUu2qOoCSpTIyxCQAmcM5fZYyNAvAKgK9zzt9SlfkKgO8A+AqA4wH8jHN+vNlxyfW7dOilH/jcPvQO9+J37/8Op0w9Bde/eH2aCd/Upqno6u/C4s7FCPgDWHDUAkwePRmjPKMAALuHd2NR56K0faY1T9NNdCqRyR851ueIkQHvildXIBgO5n29JC6hP9KfoZnlHctxSMsh6clgRU5l6h/px+4hfe2amcgWEdKtCrumoKIkYvv+7egZlF9zC4thtDe248DRB8ItuA2P11bfhogYgVfwYiA2kGYovfrk1RjlGZWmu1w0UOUGp6TbCkXX2PfEZQADrnvxOt12IjABHuZBjMcgcQkCE5IpTHpJjlp9G7WFcfXjsHNoZ0Z/XOQ24mjt9g1Fccz/PI9LTjgImz4IYkKTD+uuMH2cJmoDR+uWqA0iYgQf7vswrc9e0bECU5qmGJn/1lQqU1nishljGwHcyTl/XrXuHgAvcs43JJbfBTCHc77T6DjU8MtLMBzEhU9fiBtn3Yg7/nlH2juD7Q3tuP/U+3Hp7y/NWL/2tLVwC25c+PSFGdvWn74+I4FEOY+VsnlCN60cMbpGN866EYs6FxXkeu0a2oVLnr1EV0/jG8bnVX87GP1b1562Vrd+RdCpFtKtCrv9RTZdZTteMfunEvZ95YB0W6Ho6XLVSauw9O9Li6ZVh/W7jtbunv0RzLr9j7jic1Ow6f0gmus9ePiqE4pUQ6KCcLRuidpg5+BO3b8N7z/1fkxonKC3S00NzJT8KzfG2EEAjgHwD82mAwDsUC13J9Zp97+KMbaZMba5t7e3WNUkLGBmCqwYOemtj0kxW8Z91WLyV83azWb0WIjrFYvHDPVUSoz+rUb1qzSdaqk03drtL7LpKlfz6UJc92rp+8pBpem2ktDTpZUggEKfU2mn1dZG8tVuNPHqkltgCfPf0n8BS9Qe1OcSViDzX3NKOjDDGGsE8ASARZzz/drNOrtk3E045/dyzmdyzme2tbUVo5qERcxMgRUjJ731HsFjy7ivWkz+qlm72YweC3G9PC6PoZ5KidG/1ah+laZTLZWmW7v9RTZd5Wo+XYjrXi19XzmoNN1WEnq6tBIEUOhzKu202tpIvtpNesy4BLgFhhExXugqEkQG1OcSViDzX3NKNjDDGPNAHpRZzzn/tU6RbgCTVMsTAfTolCMcgpkp8MovrkTAH8DyjuVp65d3LEfAH8hquKl3nkKZaRKFx8iAd83WNQW7XmZ6KiVGegz4A6RTB2C3v8imq2zHK2b/RH0f4UT0dNla14plc5YVTavU71onppox43ExxESaMUMQhDNo8bXoPnOR+a9Mqcx/GYC1AEKc80UGZU4H8G2kzH9Xcs5nmR2X3mEsHYr5r9acTzFDFeMiRC7KpsAuH1r98nZREhEMBxGTYvAIHgT8gaShZv9IPyJiJGMf7fl8bh/AgUhcv2wBofdv8yBNI2oDXpcPUSmKWDwGjyulgVyOJ8ZFxHkccR6HW3BbPlZe/xYdM0qz9pCrsXAekG41JPsdA83F4jEEw0GIkgi34EaLrwV9kb7kcsAfgMeVmomVqx4KQZk0VQpItxWMYsiu3JddzAWX4EJcioODp5n72tWs3f61DG3E0dp985N9OOPnf8H1Jx+Cv36wF7v2R9D5vTnFqSBRSThat0TtEBEjac9cLb4WI+NfoMY8Zko1b2g2gIsAbGWMbUmsuwnAZADgnK8G8AzkQZn3AQwDuKxEdSOyYJYMAgC9w70Z21r98rdVbsGdYcxqdDxlH/X2gD+ARZ9ehCWbluiWJZyDwISM2SuiJOK9vvewuHNxRpKS2YBKsVOezLCShKP3bzVbT5QOiUum6VixeCyZFqfW5Ootq9HZ3WnreisU87qTpgin0htOv/ffOvtWPPTWQ1hw9ALT9mRGtv6X+t3sJGfMuBjcLoaoSHHZBEE4gxFxBNv2bct4BpvWNA117rpyV6/slORrN875XzjnjHM+g3N+dOLnGc756sSgDLjMtzjn0zjnR3LOaajVIYQioeRDEiCbNC3800KEIiHTbbkcT7v98iMvTw7KWD0+4RyC4WCy8wXk67e4czGC4aDpfnoaWbJpCS4/8vKiayAXTRPOIdv1M9Lk3OlzdcsTBJGJXju7edPNmDt9bl7tifrf/El6zAgC3IKQNAMmCIIoN6FISPcZjPp4mZxnzDDGRqv355zT/2iVki0ZxG4igp2UE6PEp0pOXKglck1SKkXKk91zk+Yqg2zXzygRQNGWtjxBEJmY9dH5tCfqf/Mnw2OGBmYIgnAIlMpkju0ZM4yxqxljuwG8AeCVxA/NbqlizJJBckkNsZNyYpT4VMmJC7VErklKpUh5sntu0lxlkO36GSUCKNrSlicIIhOzPjqf9kT9b/5E1a8yCfQqE0EQzoFSmcyxbf7LGOsCcALn3PxdhBJA5lI6SBIw3AuIUcDtBerbACG/N9aUd77veu0uzJ0+F611rRjfOB51Qh1EScTeyF4s6lyEnqEedEzswA2zboCLuZImfACSBsEK6n2Ud8inNU/D/uh+iPHUMdUeMwF/AAuOWoDJoyejwd1QDANgMkYrMFqPmY6JHbjhuBvgYgwCcyV+hKRRsNrkNxQJ4bud301qZNmcZXALbngED3xuH7yCN6mBXM0fjQyGtecupqdNASDdqsjmURGLx/B+//tp/Y+ex0yrrxURMQKPy4PmuuY0o7pWXysGYgNJvTV7mtAfCSIqifAKbrT62yC46CEjC6TbCkQUYwhG5Me/vpH+5H16wVELMHHURATDQbT6WvGzV36WbE+/OuVXcDM3YlIMbsENr+AFZxzNdc3oH+lPtaO6ZuyP7sfuod0Zzwdt9W3yPcIZBtiO1u4f/r0LV617BbefeST+vm0vnn1zJ7pu+0qRakhUEI7WbU1RhL/VKoURcQTdg93oGeyB3+1HWAyjvbEdExsnGnnMkPlvFj6AbM5LOA1JAva8BTw8D+j/GGieDJy/ARh7eF4NXmACpjVPw7VHX4vvdn4XAX8AN826CcPicHLAZMlnluDg5oOxb2QfrnzuyuQD1eqTVwMAhqOyZLT7HDj6QNS769Hsa8aOgR0IDgcztjd6GrHh9A3YPZz5sObgP5YJyCPjh7QcgrWnrQUHR3+kH1f+IaUPxSzyosMvShr7Kia/Y3xjcM/J92BYHEa9ux7LNi9LPugr+33rmG9hWvM0U7NXI8wMhsf4xuCXp/wybYCRdFY5eF1eLPnMkuRNX/1tu0twwef2JbdzcIzyjMI5h56Di4+4GGExDLfgxv/87X/Q2d2Jn3zuJ5jcPDnDqO7ZD57F/W/frz+wM2c5prccQoMzRFUhijG8t68rbaB9zSlrsC+6L619rPziStwy+xb8P/H/od5dn/QQUA+yb9m9BceMPybtWAuOXoDFnYvT7v9+tx+DsUHM+908uvdbJOUxI5v/xuIcnHPIAakEQZSVIv2tVilwcETjUSz9+9Jkn76iYwU4ip8SXQnkMmPmGAD3AfgHgBFlPed8YWGrlh0akdUwuBv45Zfkhq7QPBm48gWgcVxehw6Gg7jw6QvRM9SDFR0r4BW8yUalsOqkVbrr1Gi3tze0Y/3p6wEAb+9923S7cn7ttgKmMdC3CUUkOLgTF/7+0oxreOOsG3HHP+/AjbNuxKLORcl1yudVX1qFa1+41nC/taetxSXPXmJbG2pNa4+rnLvA+ioWpFsVRtdVuZba7Ss6VuCOf95hqIPnznoOl/3+soztq760Cl/f+PWM8snznXo/Ao0TSvFPrlRItxXGrsGduETTh+vd99XtbdfQLt3++Ven/ApXPHdF1naYa/9eZByt3d+89gkWPbIFy849Cn/fFsKjm3fgvaWnweuu/j/6CFMcrduaoYh/q1UCOwd34lKdvwXuP/V+TNB/ZqqpEeVcvs67B8CfAGwFQC+uOgkxmt7QAXlZzN80T2vIC2Sa/vrdft11asxM/fT2z8dkmHAWURPTVa2xr/qzi7lM94tJ+gbD2bRRToNhonjYMRcHjA3GFR3Epbjudhdz6ZZPno+M7IgqI6bTh2e7bxsZwEtcstQOc+3fa5mkx4wgwOOS/6aJxSUamCEIJ1DEv9UqATL/NSeXXlrknF/HOb+Pc75W+Sl4zQj7uL3yqKua5sny+jzRGvKGxXCGeZPROvWPmYlwtu1kCFjZeE1MV7XGvurPcR433c8j6BsMZ9NGOQ2GieJhx1wcMDYYV3TgEly62+M8rls+eT4ysiOqDI9OH2523waMDeAFJlhqh7n277VMTGP+C4AMgAnCKRTxb7VKgMx/zcllYKaTMXYVY2wCY6xV+Sl4zQj71LfJ7ykqDV55b7G+Le9Dt/pasfKLK9He0I41W9egta4VS2cvTTau9oZ2TBw1MVlGvW7iqIlorWvV3Ucx2Wz1tWLiqImm27XHVrYRlUGrvw0r5yxPu4a3zr4VG7s2YunspVizdU3S50X9ee2bazN0oey38osrEfAHctKGnqbU5yZ9VSbZ+grt9o1dG7G8I12XyzuWY2PXRgDAG7vf0N3+m/d+o1te8Zhp9eff7xKEkwj4AhltoamuCT/6/I8M21vAn7nPsjnL8NLHL6Wt12uH+fTvtUxMTMVlu13yYz5FZhOEQyji32qVQIuvRfeZqsXXUuaaOYNcPGY+1FnNOedTC1Ml69A7jDroOX0DBXH/VhJsJEkCYwycc4hchMQl+Fw+tPrlByVtOg6Qnsqk3Ucx8JO4hP6RfkTEiOH2XJJ3bFBT79+W4P8z85xxEaFwL6KSCEFwG6YyRcRIMlI7Eo/A5/IBAGJSTC4PeR+lzurj2knF0UtlclDyh1VqSrdWECURwXAQMSkGj+BBwB9I+zZGq31tOkyTtwl7I3uT+2emMrVgIDZIqUz5Qbp1MEb3ByWVSeQiXMwNxgAXXACXEIOk23eq26ObmacyqZeT/XsZ7lVZcLR2f/HyNtz2zNtYc8lx+MeHe3HPy9vw5xs7MKm1vki1JCoER+u2poiLwOAuIB4DXB6gcTxQQ88METGS9kzV4muBz+0zKk4eM2ZwzqcUoyJEgRCEdPOoArp/C0z+Q1gvira9sT35oKRnyGfl2y3l+GbbK8CItSLIFilcLASXu/CGqJIEofcdBHLQOGmq+pAZXEzKAAAgAElEQVS4lDWlS++6a5fHN4xPHFDuQydo9FWn0RcZ/RLVgtn9we32YHzjBPNnC809xC24U+1JQ7Z2CFA/bZeo6lUmD82YIQhnIUlA7zs1m8okcQnb928v+d8flYLl/wHG2HzG2EU667/JGLugsNUiCsZwb6rxA/Lvh+fJ63MgFAklGxMgGzYt/NNChCKhQtWYKAFVdR0LrHGisim4tklfRI1hqQ1Ru3AsyiCMS1B5zNDADEE4gxrvO6vq748iYGdo6noAv9FZ/0hiG+FECuz+nS3xhKgMquo61rjDPZFOwbVN+iJqDEttiNqFY4nFJbgEBoGpPGZEe7YFBEEUiRrvO6vq748iYGdgxsU5H9Cu5JzvB+ApXJWIglJg929KR6oOquo61rjDPZFOwbVN+iJqDEttiNqFY4nFeXKmDM2YIQiHUeN9Z1X9/VEE7HjMeBhjDZzzIfVKxtgoAPS/WWz0TH2tvIuouH+r32Wc/yTAXED/DoDHAbcfaLB2PCXRRPtuoJ2EBAca+dUchbiOeWHF+Myq5vU0btHhnrRYfVjStp3+tL4N0vwnEdq3HdG6BnhHhtDadCAEM31pj+8fA4T35m3AThClwLANSZCfGxRNK/1u41jgxO8DrdMADln/FvRN/W9xiIoS3C55QMbjorhsgnAU9W3AJU8DYgRgDOAccPtqJpWp1deK1SevRvdAN/xuP8JiWE7vpaQ9APYGZn4F4HHG2DWc848AgDF2EIC7EtuIYpGPga8gyOWufEH+o8DjB8L9wJ63gY3X5mSWOr1lOtafvj6nh6lymc4S6eR7HfMiLgK73wQevSilv3PXAeM+lRqcsaN5rcYt/uFLWqxOsmrbZn8qMaBLiGPha3ek64QZTDnVHv/Q04ETb0zXew0Z/RGVh8CB6ZIL64+5MTUYKXIIzy0E3n06peG2w4BvdgL7e4BHLrSlb+p/i0csLsGd+L+nuGyCcBhSHAiHMp+BR02omWeCaDyKpX9fmtb3EzKWFcA5/z8AGwG8xBjbyxjbC+AlAL/jnP+kWBUkkL9RlJLU1DxJniHTty01KJPD8ZSEhPbGdgT8AVsPUWT65BzyuY55MbgrdUMC5N+PXiSvV7CrebXGG8dZurmRFqsXU23b1JZtnWiPf/S8TL3XkNEfUYEM90J48EwE1n0D7b88BYF134Dw0DmyloGUhsN75WcKZVBGvS2Lvqn/LR7ywIw8U8Yl0IwZgnAUVp6Bqxjq+82xFZfNOV8NYDVjrBEA0/OcIYpAIY2ixCjgqS+b8RSZPhGIx/T1F4+llktgjkZarFFsasu2TrTH97fUtNEfUYEYtRF/S/qyouEc9E39b/GIxXlyQIbisgnCYUiifp8pieWpT4mhvt8c21+RM8bqAHwNwHcYY/+t/BS+akSSQhpFub1AbLhsxlNk+kTA5dHXn0vlIV4CczTSYo1iU1u2daI9frivpo3+iArEqI2E+9KX3d6c+2rqf4tHNJ7ymCHzX4JwGIJbv88UbM2VqFio7zcnl3cXNgKYC0AEMKT6IYqFYm6qNGQb5qa6x2qZCsxdVZjj2UQxFVQaZclNZ4ny0zhefp9Wrb9z18nrFQqpeQNIizWKTW3Z1on2+Fs2ZOq9RP0tQeSEXhs5d52sZWVZ0XCOfTX1v8UjJqY8Zsj8lyAchpVn4CqG+n5zGOfc3g6Mvck5/5TNfdYAOAPAHr19GWNzIA/4fJhY9WvO+Q+zHXfmzJl88+bNdqpSWSjJNYCcoiTFAC7JKUr1qpQPj182k+KS/L43c8m/OU8ZoQKplBCPH2ACII7Ix5Ti8rqGsQUznjJLW1Bv87l9kCQJUckRqQysVCeqaO0aJdpYSbpJlJEEL0LxMKJchJe50eoPQHB7044heRsQikdSZdz1EGLhTG2bnTtLnaokFaTqdSvFRYTCvYhKIryCG63+NgjaFK+0HbJoUbNd8rUiFAmmju8LQIiEVNubEQrvVem1FcJQMJUq1jAOUJXPSGGiVCY9ql63FYFRWxEjwGCvPL1ecAO+ZiDSLy+7PEDDWEjhEEKIQ2ICJACS1fapnLpy+19Ha/ey+/6J7XuHcduZRyI0FMW3HnoVt535KVx4/IFFqiVRIThatzWFtn9tbJOTmWqEWDyGYDgIURLhFtwI+APwqGfNp1My3TqBXOZN/ZUxdiTnfKuNfe4HcCeAB0zK/JlzfkYO9alOlOSal+4Ajr8aeOrb6XHXQ72pmMqTbgH+frdc7h/36JcXI+kpISf9t3yMHJKZspEtbUEx5qRUhgrEKNGm7TCg9x3zpJvEvtLrj6Dr0+dh4UvXp677nOWY3jwdQvBd4OF5kKZ8AV2zr8HCF9VllmF6nEHYcJ61c1uok6JFwrlIcRFdfe9h4YuL0/XScoj+H39WUpcUs2jD4y/D9Bduh/DO74BDT4dw4o0IKGZ9eilL564Dtj4B/G2lcV+aOB9BOAajthI4GNjzTkrjOpqXLngMXfFB3PXWA7jg8Atw86abbd/Hqf8tDrE4h0sTlx2jGTME4QzESHr/qjxDjD2sJgZnRElEV38XFnemnrmWdyzHIS2HwF0jr3OZYfmvX8bYVsbYGwA+B+BVxti7jLE3VOsN4Zy/DIDslu2guHYfPS81yALIv/u2pR6kZi+SB1eUctnKA3KZfR/nlcxkhlXHbXLmrkCMEm0Gd2VPuknsGzru4uSgDJC47i8uRiicOnbo84uSgzKpMtch5PFaP7eVOhGOJxTuTQ6aABq96GE3dUn3+Nch9OkL5QLaVCW9lKVHLwKOudDS+QjCMRj2571ZNR8a+AQLN/0n5k6fmxyUAeg+7gSiqlQm5ZWmWNze7HiCIIqEtn9NpjLVxjNDMBxMDsoA8j1jcediBMPBMtfMGdgZmir2bJYTGGOvA+gB8D3O+b/1CjHGrgJwFQBMnjxZr0h1oCTX6CV6qFOVlO3a30bllX2AoiWFWHXcrjVn7qrQrlFah1HSklpPiX2jglv/uvOUU71hGcFl/dxW6kRkpdy6jUqivhaMEgzspi4ZHb8+8b6ztk81SllSa5N0VnbKrduKwKitaFNDdDQfrWtAz1APmrxNNXUfLwX5alfXY4bMf4kiQ32uRWo8lSkWj+neM2JSzGCP2sLyjBnO+XbO+XYAS5XP6nV51uNVAAdyzo8C8HMAvzGpx72c85mc85ltbVVsnqgk1+gleqhTlZTt2t9G5ZV9ipjMZNVxu9acuatCu0YJHEZJS2o9Jfb1SqL+dWcpp3rDMlLc+rmt1InISrl16xXc+lowmvJqN3XJ6PjDiW/8tX2qUcqSWpuks7JTbt1WBEZtRZsaoqN578gQ2hvasS+6r6bu46UgX+2qZ8wosdlk/ksUG+pzLVLjqUwel0f3nuERDD1maopcjDyOUC8wxlwAjs2nEpzz/ZzzwcTnZwB4GGO1/eKx4tq9ZQPwtTvT3btbpqZSEDatkBOWlHLZygNymabJRUtmsuq4Tc7cFYhRAkfj+OzJHIl9W//1AFae+NP06z5nOVr9qWO3/nkFVs7RllmG1ljU+rmt1IlwPK3+Nqycs1xfL3rYTV3SPf4ytL66Xi6gTVXSS1k6dx3w2npL5yMIx2DYn7dl1XzrqAOwcvZt2Ni1EbfOvpXu4w4iporLZozB42I0Y4YgnIK2f02mMtXGM0PAH8DyjvRnruUdy8lvLIHlVCbG2A8A3ATAD2BYWQ0gCuBezvkPsux/EIDfGaQyjQewm3POGWOzADwOeQaNaeWq2vVbkuQEhOgQwJicQgMuv5akJDAl10NOV2IuuYySysRccpl4TE5hAk+l2fjHyMdXUm60SU95JodYTVuQuIT+kX5ExAgkLsHn8smJJ+Ux/yXHei16iR1A/qlMYAhxEVEeh8/lg8RFRKWYnHjj8kGIDsmpTNJIKilHqIMgjqRSmZQ0sngiaUwckT+7vIC7Tta2yyu/YqL9XF3JOFWv23xTmST/GISi/an+yNsMQdXXSb4WhMJBVerSGAhDvarUpUBmgkLa8ljZTF0p3zgesJBKU+NUvW4dhdXkOl8rMLQbEDxyf6o8Z3BJfo5QlgU3JAAhxiExJqcygWe931doCpMWR2u34/9exPjRPiw8aToA4PL7/4ULj5+MJWccXowqEpWDo3VbU1AqE6UyGWD5yZFz/iMAP2KM/SjbIIwWxtgGAHMABBhj3QBuBuBJHHc1gLMBXMMYEwGEAZyfbVCmqrGSfNM4FjjtJ/LAjV6yEgCEtgEDO42TlxoC2c+ZY0qTnbSF3uFeSmZyImaa0EuYUSXdGJIoIwAIAJDEGLr638PCF69LT8RpPgSC2wNDBenVbe4q4I2HgRnnZ2reSmoU4VgElxuBxgk2dlClLumlv81Zjukv3KafuqR8e/XSHcC7TwMnLASOPMs8hUldnrRFOI1s93el31bSIM0SyM55AHj5J8C7T0NonoyARa1TCmPpiIqpV5kAwE0zZgjCOdR4KpPEJWzbt43uBQbYSWX6NGPs0wAeUz6rf8z25ZzP45xP4Jx7OOcTOee/4pyvTgzKgHN+J+f8CM75UZzzz3DO/5rnv6uysZJ8M3sRMBw0TlYa7pXTmKwmL9lMMikUlMzkYEqgCTkR57rMRByjxB2zum28FjjhO/qap4SmmkW3j3lxsXHqkpKQcPQ8efmYC7OnMKnLk7YIp2G1L1fSIM0SyB67OCet072+dKhfZQIAj0tAjAZmCMIZ1HgqE90LzLEz1/qnid8+ADMBvA55etEMAP+AHKNNFAIryTdWkpW0aUza7VbOWeRkkVpLZqooSqCJKDdIxOFZ3OmN6ia4KKGJSMOwjzFKXQJSCXeAsaa0KUxKeWWZtEU4Bat9ubafzNY2jI6jA93rS0csLsGlmsHkFhiiYu1OQicIR1HjqUx0LzDHTipTB+e8A8B2AJ9OOG8fC+AYAO8Xq4I1iZXkm2zJSm6vveQlm0kmhaLWkpkqihJowssMEnFYljFjo7pJcUpoItIw7GOMUpeAVMIdYKwpbQqTUl5ZJm0RTsFqX67tJ7O1DaPj6ED3+tIRi3PNjBl6lYkgHEONpzLRvcCcXF7mOoxzvlVZ4Jy/CeDowlWJSEtKmDgTuPz3wCVPA2DAeetTaUz1AeDMezWJCg/Jxr7MBTQflD15SZKA4ZD8+7wH08952R/k9f0fA30fAQO75eUCQslMDsZKuk1cBPZ1A6EPZX0onwf3AP075M/9OwAxpnsKORFnWfr171iBVs7k4w0F5f3Vnwd3yxrX1m3uKuBvP9fXPCU0VRdq3e3rlpfVSJKsk/4daJWQ2cd0LEerrwW49Gn52/+LnwIufExevvAx4LLfAxOOAha+Bngb5O1pCQoPpqcwKQl6yjJpiygXKu1jMHHPVvflx8wHvvUv4LJn5ZkufR8l+thdsqnvxU/J2696UfahO/fBdO2f80BOWqd7femIxdM9ZlyCgBjFZROEM2hsA654AVi0VX7GWLRVXq6RVCa6F5hjOZUpuYNs5DsE4EEAHMB8AI2c83mFr545Ve36LUlAOCQPmgzuln0zFMPf4aD8mlLdKNkoqn+7vBwbBgKHASP7gc7bgc9fD0hRoKFNTlRw1QGjD0ilhUiS/EdNeC/w6MXy8b+8FPC3yqbCgLG5cAFNLR2U1ECO9VrMkpbURpGNY4GTbpG1MuULwHFXyppKMzY7AnB7Mo4v7etGiEcRdbnhBUPrszfJpqzKYMsfb5EHetSfz98A1I0Ggu/I2geAloMy05rspkZVJrWlW61BqaKvcZ+S+zYdo1Np/pMI1TcjKkXhFbxoHe6H8OCZxganWjPfc9fJaTQjA3I/2zJFHtBREr78hUu0qyFqS7elwMzkF5CTGPs/lrV9/NXAU9/WNfVF82Tga3cC/7gHOOlmud901wF1TfJzhyQC4PI6G1p30L0+Xxyt3ak/eBpfO+oAnHfcJADAkt9sxYFjGrDm0uOKUUWicnC0bmuGGjf/BWzfCyiVKQuXAbgGwHcTyy8DuLtgNSJkBEGOBe7/CHj6ernxnnI78NglqXcTz3sQeO6m9HcVL3xMLn/K7cDjl6Zva54MXPlCKoFhuFfuIJQ/oPs/lmcljAyk9lHODaSM/tTHKMQ/1UaCE1FizJKW1EaRp9yeGsA74TvAQ+dkGptd+gzQPCn9GMO9ENaejkD/x5l6Vgx9T7kdeGR++ueH5wGn/xRYf07qWIq+Gwy0ZCU1inA+WoNSRV+XPQs0TdQ1OhUePBOBK18AGtvlgW5lUAYwNv895Xb5j1Rl+YLHgPu/IJdRtKbWM2mLKDdGJr/KPTs6lNK2MiijlHvs4nTNP/Vtefmhs1P9LpD5HGEDutcXn7jEIXGkvcrkFsj8lyAcg5H5r94zcpVC9wJjbA/McM4jAJYnfohiIkbTDXy1Rnx6xnxKeSPTPrVJnxiVvwXWHlO7j9kxiNpFa0atfDYyS9UzNlObUmYzmtR+VmbKqMuSNqsfIyPneOJ1uWxGp9rtVg1OtWa/pDXCaWTTvmI6aVXzSjkytq4YoolXljyauOwRepWJIJxBjZv/EuZYnkPKGHs08XsrY+wN7U/xqljDaA18tUZ8esZ8Snkj0z61SZ/bK7/6oT1mbDj1Q4aphBFaM2rls5FZqp6xmdqUMpvRpPZzbDizLGmz+jEycnYlXpPLZnSq3W7V4FRr9ktaI5xGNu0rppNWNa+UI2PrimFElPspjzs9lYlmzBCEQ6hx81/CHDsv9yqvLp0B4Ks6P0ShqW8DWqamzEw3rUg3Nt2yQX4vUW3M1zJVfqd8ywb5HXEzs9P6Nvl9xnMfSD9m02TZWLg+kN08mKhdGsen9KfW5t9+nq4p5f3ZxvGZx1CbUmr1rXjMbFqR+fn8DbLWSZu1h1p3QKa+splWa7fr9aNaM99z16Wb/ZLWCCeSTftK29F7PtCa+n7tTnk5R7NfojwoM2bcaXHZQnI9QRBlprHN4BmG+lXChvkvY2wRgE0AXuOcO2K+VU2YSykmwLGw7DnjbUw3NtUznQTkfeIxQIrJxr9uv2wCDKQboPrHyGbB4kjifDHZJJirbuLaY1SvqSUZo9lFjMmeH5IoD/JxSdZm3Wh5Roskyt8CeBuAyD55VkPj+JQBNSCbuQ7ukvXq8SfWxQCXV359JBZO/6zWeXWa+dql9nSr1oyeprRGz75WYGh3qnzDWGBoj2o5IL/3rei1sS1zeShofD4iF2pPt6XAyORcWQ8m3+8Zk2fMcglggqzreCy1rGz31ANiWE565HF5XW33t4CDtbsjNIzP39GJq78wFXMOHQsAWP7Ce+gfjuIPi08sVjWJysCxuq05xEjmM0aNGP/mAJn/GjARwM8AHJZ4demvkAdq/sY5DxWjcgTkBx8jM1MFrQmfJMnRl9pkhvoxQO87mevbDgOGPwIGdgJ/vzszraEISUxEFSBJQPBdfT1pdaZOVNIm6Ohp0qreyHC1NnG5ZaNfI9RGz0YpTlufAP62Uj+FSa1Bs6Qb6hMJp6Fncq5ouPP29Pu7lUQyoz6d2oAjiSZeWfK4UtfFIzCaMUMQTkEcMUhl+g856Y6oaSzfUTnn3+OcfxbAeAA3AQgBuBzAm4yxt4pUPyIXjJIZBncZr+/bJqfeHD0vM63h4XmJb9oIQoUdnW28Fpi9KOU+P7jL/BikN6JQGKU4HXNh+vLR81LLag2SRolKR9Gw9v5ulEimbQtGzw7UBhxH8lUmdSqTS0gO2BAEUWYG9+j3u4N7ylsvwhHkMhfbD2A0gKbETw+ArYWsFJEnRskMZmkmdtKcCAKwrzN1opLVBB2CyBcjPWpTloySZ0ijRKWjaNhKsqNeWzBqQ9QGHEcqlUlj/itasy0gCKLIUCoTYYKdVKZ7GWObADwC4ATIrzKdwzmfyTm/rFgVJHLAKJnBLM3ETpoTQQD2daZOVLKaoEMQ+WKkR23KklHyDGmUqHQUDVtJdtRrC0ZtiNqA41BmxtCMGYJwKJTKRJhg5+XgyQDqAOwC8AmAbgD9xagUkSOSBAzuln+ftz4zmaFhnP76xvGp9CdtWsOhpwMXPyV/M6Ycm6huFB317zC/5noJIOetl416tTpTJyrZSdAhCCtoNSvGgH3dQOhDAAy44LHMBAR1ypI2hcksxYk0SpQaq32y0b7MJffJ2vu7lUQy5RmB2kBFoMyY8bo0M2ZoYIYgnEHjWPmZ5MLHgEufln9f8Ji8nqh5LA/Pcc5PZYwxAEcA+CyA6wF8ijEWgmwAfHOR6khYQWtQqQyoCO5U+lLvO8CL/wuccrucrtQ4DmiaJBtptk4F/M3AaT+WExkue1Z+mBvqBR74Ghn+1Qp2jU7dPuD0n8qvwikpTA/PkwcBFf0ps2O+8cvMRBtBkI995QuUrkTkhl7fpzU0Pe9B4IrnEwlfHlmfsycAx1+V6h+/ulzu/7QaJI0S5SQf82n1vo1jgRO/D7QcBFz0pHyf9zTIoQBqbRu1BWoDFUHKY0Zl/usi81+CcA6CnF769PWqZ5T1sDdXgqhWbM2b4nK29puMsX4A+xI/ZwCYBYAGZsqJ1qDy3aeB3VvlB6nGcfK3bMr2d5+WyzRPTm3XS38a3A08cmGm4Z+yD1F9GBmd6l3z4V7gwTPT35VtniwP/D0yP11/ZuiliBCEVbSa1TM0fWS+PNjcOiW1n1ZzZhokjRLlwk6fbLZv/8fA+nNSffRzN8nHcLmttQVqAxXBiOIxo3qVySUIECUOSeIQhJpKniUI5zG4K/Nvq0cuBC59BmieVN66EWXH8sAMY2wh5JkyswHEkIjKBrAGZP5bfrIZVOZiYEmml7WHnWtuVFZt8ktaIYqNVodGhqaK4TRBVBL53IfN+mjqn6uSEVH2ztLOmAFk/xmf2vScIIjSQ+a/hAl25k0dBOBxALM451M55xdxzldxzl/nnNMcyXKTzaAyFwNLMr2sPexcc6OyapNf0gpRbLQ6NDI0VV6pI4hKIp/7sFkfTf1zVZJKZVKZ/yZeOSOfGYJwAGT+S5hgZ2BmKYA/ARhhjLVqf4pUP8Iq2QwqczGwJNPL2sPONdcrqzb5Ja0QpUCrQyNDU8VwmiAqiXzuw3r7fu1OuY1Q/1yVpFKZMmfMxOIUmU0QZadxPD2jEIbYGZ57BQAHoPeCKgcw1WxnxtgayH40ezjnn9LZzgD8DMBXAAwDuJRz/qqN+lU/kiS/My5JAGPy1HweB9x+2cBv1Hj5HUVlXYPGnG/UeODyPwBSDOCSXMboHIrBX9thZPhXraivtbdBNu+Nx2SvoSuelz+bXXNBkPVx2bNyWZdHNgE++37A45fjiPd/Iqc0CS7ZeFU5HpCuM9IVoe171JrQbvOPAcJ7U8uBQ+S+TxLlb50ax6brsmFcennt/qQ/wqkYGe9yCdjXo9L4WGBoT7rmIyHA15Qy8+dx+fdX7pD75YEegHNqA1VEVM9jRnmViQyACaL8uD3A2MM0zyxt8nqi5rGTyjQleylT7gdwJ4AHDLafBmB64ud4AHcnfhNAKl2h83bg89fLf0RvvNY4heT8DfLAjHbf468Gnvq2frpDPukPRGWhvtZTvgAcdyXw6MWp637uOmDcp1LpSUbH6H0nUy9th2Wun7sK+OMtwOAeuYzblzIOJp0RZn0PkD116dx1wEt3yMbmWv3qHVtbnvRHOBmt8W5cBHa/adwGjJ4JlL4527MAUbGkBmZUM2boVSaCcA5iBNjzTmb/PfYw+dmYqGlyugMzxloYY7MYY19QfrLtwzl/GUDIpMhcAA9wmb8DaGaMTcilflWJkq5w9Dz5m15lUAbQTyF5eJ68j3Zf5UHMrJzRdqJ6UF/rE76TGpQB5N+PXiQ7x1s9hrLfw/Pk/bTrN14LzF6UKtO3jXRGpDDre6ykLj16kbxevazoV+/Y2vKkP6KSGNxl3gaMngmUvjnbswBRsegNzLgTM2ZGaMYMQZSfwV79/nuQ+l8ih4EZxtiVAF4G8ByAWxO/bylAXQ4AsEO13J1Ypz3/VYyxzYyxzb29NSRiJV3B3yK/LmIlhUSbyGS1nNF2Ii8cpV31tRZcuaXYGOklHsue1uSpz9xOOnMkJdGtWd9jNXVJ0ZeyrOg3W3qY+lxE1eCo/rbQZOtjzZLJrDwLEGUlH+1G4xIEBrhU5r/KIE0kFi9oPQlCTVX3uYWEUpkIE3KZMfNdAMcB2M457wBwDIBCtEAj75r0FZzfyzmfyTmf2dZWQ8Z1SrpCuE9+jclKCok2kclqOaPtRF44Srvqay3Fc0uxMdKLy5M9rSk2nLmddOZISqJbs77HauqSoi9lWdFvtvQw9bmIqsFR/W2hydbHmiWTWXkWIMpKPtqNxOLwutMf7X0eOSI7TAMzRBGp6j63kFAqE2FCLgMzEc55BAAYY3Wc83cAHFqAunQDmKRangigpwDHrQ6UdIUtG2TjyrmrzFNI9BKZtmyQExkKmdxEVCbqa/23nwPnPmDfId5IL43js6c1tUwlnREpzPoeq6lLWzakLyv61Tu2tjzpj6gkjFI9FE0bPRMofXO2ZwGiYhmOxlHndqWt83vkR/3BEfpGniDKTmObQSoT9b8EwDi3F5/HGHsSwGUAFgH4IoA+AB7O+Vcs7HsQgN8ZpDKdDuDbkFOZjgewknM+y+x4M2fO5Js3b7ZV/4omWyqTWcqIel8eN05iMEtGqX70Zm0VBUdo1yiVyeWRH+DNjH/1jqHWi3o9pTIVm+rQbT6pTL5WYGi3sX6z7U/6KwfVodtyERdlz5hsqUxGfXO2ZwHCDMdq97pHtmDTB0GsOO+Y5LodoWHc+MQbuPOCY3DGjPZiVJOoDByr25pDjMieMmmpTGT8a0DJdOsEbM+b4pyfmfh4C2OsE0ATgGez7ccY2wBgDoAAY6wbwM0APIljrgbwDORBmfchx2VfZrduVY82mUGL2bZs+9otR1Q+Gde6tQDHyLJeDemMUGOmGb1t2uWmifaOTfojKhmXOzZmo1UAACAASURBVFPz2uVc+2aiYhmOxuF1pQ+w+b3yDJrhEXqViSAcgdsHNE/KXo6oOWwPzDDG1nHOLwIAzvlLyjoAF5ntxzmfl2U7B/Atu/UhCIIgCIIgiFpnOBZHnSf9VSZf4tUmepWJIAjC2eQyb/UI9QJjzAXg2MJUhyAIgiAIgiAIu0SicdRlmP/Ky8NRGpghCIJwMpYHZhhjP2CMDQCYwRjbzxgbSCzvAbCxaDUkCIIgCIIgCMKU4aiYMTDjdglwCwyD9CoTQRCEo7E8MMM5/xHnfBSAn3DOR3PORyV+xnDOf1DEOhIEQRAEQRAEYcJwNDMuG5B9ZoboVSaCIAhHk8urTP/JGJvPGPsvAGCMTWKMmaYnEQRBEARBEARRPPTisgHA53FhiF5lIgiCcDS5DMzcBeAEABcklgcT6wiCIAiCIAiCKAPhWKbHDAD4PQLNmCEIgnA4tlOZABzPOf80Y+w1AOCc9zHGvAWuF0EQBEEQBEEQFjEamGms82DvULQMNSIIgiCsksuMmVgiiYkDAGOsDYBU0FoRBEEQBEEQBGGJuMQRFaWMuGwAaKn3YPf+SBlqRRAEQVgll4GZlQCeBDCWMXYbgL8AuL2gtSIIgiAIgiAIwhKDiVeV/HoDMw1e9A6MgHNe6moRBEEQFrH9KhPnfD1j7BUAJwFgAL7OOX+74DUjCIIgCIIgCCIr/cPyq0qjfJmP9i31XkRiEvZHRDT5PaWuGkEQBGEBywMzjDEfgAUADgawFcA9nHNyEiMIgiAIgiCIMhJKeMg01mU+2jfXy4Mxu/dHaGCGIAjCodh5lWktgJmQB2VOA/B/RakRQRAEQRAEQRCW6UvOmMkceBk/2gcAeHfXQEnrVDD2fQI8Mh9YdjjwyEXArq3lrhFBEETBsTMwczjnfD7n/B4AZwP4QpHqRBAEQRAEQRCERUJDMQD6rzIdOKYBPo+AV7b3lbpa+bN/J3DfqcD7LwCt04APOoFfngS8+ety14wgCKKg2PGYiSkfOOciY6wI1SEIgiAIgiAIwg59Q8YeMy6BYfrYUfjrB8FSVyt/nr0BGNgNnHI70HYoENkHdN4GPHEl4G0ADjml3DUkCIIoCHZmzBzFGNuf+BkAMEP5zBjbX6wK1gKSxNE7MIJP+obROzACSSLXfKK0kAYJonRQe6ts6PoRTiQ0HIVbYLqpTABw5AFNeG/3IHbuC5e4ZnnwQSfw9m+Bo86XB2UAwNcEnHQL0HIQ8MQVQGhbOWtI5AD1oQShj+UZM5xz/Z6eyAtJ4nh39wC++cBmdPeFMbHFj19cPBOHjhsFQaBZSUTxIQ0SROmg9lbZ0PUjnEp3XxitDV4YzWj/1AFNAIBXtvfhjBn+UlYtd/68DKgPAId/PX29tx7ouAn43SLg11cBl/8BEOx810yUC+pDCcIY6sXKzN6haLJzAuQb6zcf2Iy9iSmpBFFsSIMEUTqovVU2dP0Ip/LBnkEc0Gw84DKxxQ+XwPD2ztQkd84dPFNh5+vARy8D//FVwKWTJNU4DjjuKqD7X8DmX5W+fkROUB9KEMbQwEyZiYrxZOek0N0XRlSMl6lGRK1BGiSI0kHtrbKh60c4EUni2BYcxASTgRmPS0B7sw9v75STmcS4hLNX/w03Pv56qappj1fXAS6vuYfM1DnAhKNkz5nIvlLVjMgD6kMJwhgamCkzXrcLE1vSb6QTW/zwuunNMaI0kAYJonRQe6ts6PoRTmRH3zAiMcl0xgwATGqpT0Zmv/pxP17Z3odHN3djfyRmul/JEUeArY8Bkz4DeBuNyzEGHHsZEO4D/npn6epH5Az1oQRhTMkGZhhjpzLG3mWMvc8Y+3862y9ljPUyxrYkfq4sVd3KyZgGL35x8cxkJ6W8azmmwVvmmhG1AmmQIEoHtbfKhq4f4UT+3SO/nnTgmHrTcu3NfvT0hxGOxvFGd39y/TuJWTSOoesPQKQfOPik7GXHHAxMPgH4571AdKj4dSPygvpQgjDGTlx2zjDGXADuAnAygG4A/2KMPcU5f0tT9BHO+bdLUadiIEkce4eiiIpxeN0ujGnwZjWyEgSGQ8eNwpPXzra1H0EUCq0GPW4BboFh574w6ZEgcsDsXkB9fmWjvX6MMbiY7JtA15EoF//u2QeXwDCpxXxg5oBmPziAD4NDeG93ajDmk/5hAK3FraQdtjwE+FuBCUdbK3/EN4CPbwBeexA4/uri1o3IC0FgmN7WiEevPgFiXILbJWBsYx31nQSBEg3MAJgF4H3O+TYAYIw9DGAuAO3ATMWSj8u4IDC0jaorUU0JIhNFg+SWTxD5YaUNUZ9f2QgCw5gGL/WVhGN485P9OKDZD6/bfCJ8e+JVp/d7B9G1exAHj23E+3sG0R1yUIT2UFCeMfMfXwMEi6+3jP0PIHAIsHkNMOsq+RUnwpFIEkdX7yD1nQShQ6leZToAwA7VcndinZazGGNvMMYeZ4xNKk3VCgO5jBPVAOmYIPKD2lBtQNeZcBJduwcwqdV8tgwAjB/tAwPw/p5BdO0ZxJRAA5r9ngwz1rLy5hOAJALTvmhvv+lfBnrfAXpeK069iIJAfSdBGFOqgRm9IVBtRt9vARzEOZ8B4AUAa3UPxNhVjLHNjLHNvb29Ba5m7pDLOJENp2pXDemY0FIJunUS1IacQbF1S9eZKBZ2tcs5x56BEUseHV63gHGjfdj0fhCDIyImtvgRaPSiZ5+DBma2rAdapwItB9nb76DPySlOWx4qSrUIc6zqlvpOgjCmVAMz3QDUM2AmAuhRF+Cc7+WcjyQWfwHgWL0Dcc7v5ZzP5JzPbGtrK0plc4FcxolsOFW7akjHhJZK0K2ToDbkDIqtW7rORLGwq92+4RhEiaOl3mPp+BOafHhlex8A4KAxDWiu92LP/pEse5WIni3AzteBg0+2v6+3UTYB3vqYnOpElBSruqW+kyCMKZXHzL8ATGeMTQHwCYDzAVygLsAYm8A535lY/BqAt0tUN13MzBsliaM/HEU4Gkecc/g8LrT4PLjnomNx9bpX0NZYh4UnTceUQAM4OERRQl84Zmr0mItxMEEUCrX+Hrv6M9geCoMBGI7GceCY+rRv4mKxOPYMjkCUONwCw9jGOng8LsPjkZ6JWkJJnFC/P7/hm8cjFpewfe8QPAmjQ7eBF4S27bT4PWn3j2afG71DUcTiUtZjWYHaqjHa/5umOhd6h6LJvu+Rqz6D93YPYuUfu9A7OIIHLp8FDo5P+obp/5IoGXsGIgCA5nprqTbtzX68tkNOZDpwTD2a/B5sCzokzejVtfKsl6kdue0/7YvAhy8BH/wJOPQ0W7tKXMI/d/0TnR93IhQJodXXiiMCR+DEiSeiqa4pt/oQGYxp8OKxBZ+BGAfinMPFGNwuUCoTQaBEAzOcc5Ex9m0AzwFwAVjDOf83Y+yHADZzzp8CsJAx9jUAIoAQgEtLUTc9zMwbAeCjvUPYvT+CGx5/A919YXz58LFYeNIhWPnH9/CTs2egsc6Na9a/mrZtwYOvGJpckeEqUU7U+mtrrMONpx6a1PbEFj9Wzz8WksQhCAyxWBzv7BnENSo93z3/WBw2tjE5OEN6JmoZbWpPQ50L3X2RtHvA6vnH4rBxozIGVLRtR+/+cff8Y/HzP76HP7y1x/RYVqC2aoz2/+bqzx+EM46emNb3/fisGdjwz+344dwjIDCG4WgcF6/5J/1fEiVld2K2S7PFGTNTAg0AgHGj61DndqG53oM+1WBv2YgOAW88Chw4G6hrzO0Y42fIM2feesrWwMyuoV343kvfw+u9r6POVYfmumbsG9mHh955CG7BjVMPOhWXHHEJDms9LLd6EUmi0TiCg7GM58iWOi98vlLNFyAIZ1KyHphz/gzn/BDO+TTO+W2Jdf+dGJQB5/wHnPMjOOdHcc47OOfvlKpuWsyMqfYORbF973DyD1cAOOvYSVjw4Cv4w1t7sD8iJgdl1NvMTK7ICIsoJ2r9LZgzLU3b3X1hLHjwFewZlB/89gyOJG+myvZrVNu1x1PKkJ6JWkJJXTqgpR7DUSnjHrBA02YUtG1H7/5xzYOv4KxjJ2U9lhWorRqj/b85e+bkjL7v+0+8gbOOnYRr1r8Kr1vIeq8niGKwZ788Y6bF4oyZWVNa8dUZE7DgxGkAgCa/FxxAqNxaff1hIDoITD8l92O4PMCkWcC7zwDxmKVdPtr3Ec773Xl4N/QuLj3iUqzsWIkfff5HuPOkO/Ffn/kvnDjxRDy//Xmc89tzcMVzV+DFHS8iLpEfSq7sDUd1nyP3hqmvJAgamtQhmzFVvdeVtl3taK91t9dzu9eaXJERFlFO1Poz0qsYlwAAosT1t0spL2/SM0GkiMUl0zalRtt2jNpjs9+Ttqx3LCtQWzVG+3/jEpjhtejuCxtup/9LotjsGbA3Y8bjEnDB8Qcml5X9egdGMG60r/AVtIIYBf6yDGg7FBh3RH7HmvxZ+VWmj/6cNdkpFAnhmheuQUyKYclnlqC9sT25TWACpjRNwZSmKTjz4DPxUvdLeGH7C/jOn76D8fXjcfYhZ+MrU7+CSaMqKkS27Fh5jiSIWqWMcxadi5kxldftwnA0nra9PxxLLqs/6y2rj2XlfARRbNT6M9KrOzG92S0w/e2qqfqkZ4JI4XEJpm1KjbbtGLXH/nAsbVnvWFagtmqM9v8mLnHDazGxxW+4nf4viWLTOzCCeq8LdTlqTRno7R0oo2Hu5l8B+7qBGecDLM9X/9qPAdw+4O3fmhbjnOO/N/03dg/vxneO+U7aoIyWek89TptyGn78hR/j2qOuRau/FXduuRNf+fVXMPc3c/HTzT/FX3v+irDooHQrh2LlOZIgahUamNFBMW9UOg7lXfExDV6MafDiwDH1+MnZM5Lbn3hlB1bPP1Z+3//FDwy3aY9l5XwEUWzU+tPqV/GwGNtYBwAY21iHuzV6vlu1XXs8pQzpmahVxjbWZdwDVmvajIK27ejdP+6efyyeeGVH1mNZgdqqMdr/m8c3f5zR9/34rBl44pUd+PFZM/D45o+z3usJohjs2h+xPFtGD/WMmbIQ+hD44w+BA2bKP/nirpOP8/ZvAZNXjn677bd4qfslfGP6N3Bw88HWDi24MXP8THxv5vfwv5//X5x/6PnwuX1Y99Y6XP381Zi9YTYu+/1lWPvvtdgb3pv/v6UKGeP36j5HjvFTX0kQjPPKnTo2c+ZMvnnz5qIcWy95KdBQB0FgyW2SxBGXOGIShzzQy8A5h9/rgihxxERJN1VDSWpQn8PjZoiJHKLEC5K0QeREyYbri6VddYqIxy3ALTCEo5m606awSBLHnsERxOIS/J6EfuMS3AJDW4MXXm/qrUcrqUyiKCWPR3ouOhWvW7uUOkko3/Np20yg3ou94Zhu+8iWBKTsK8YluCs7lcnxulWuG2MA54DEOQTGIDBA4vLrTXGJJ77gl/vK/ohICVfVj6O0e+aqTRiJSfivMw7P6RxRUcIl9/0TN5xyKL7VYW2AomAM9gL3nQYM7AS+uhJoHFuY4374MvDyHcBlzwIHfjZj8/7ofpz2xGkYVz8O35/1fQgsv+eTiBhBV18X3g69jbf2voWPBz6GR/Bg3mHzcPVRV2O0d3Rexy8QjtFtJCJibzh1XxvjJ+NfwpCauolSKzBh9/4Rw7SK0XUefBQaQu/ASFqCzS8umokJTf6Mh7G2UenfaEoST6Y73bfpQ1zy2Sn4/hNv6J6LIKygl7Dyk7Nn4I7fv4vewRH84uKZmN7WiK7ewQxd17mFtDQR9X7a1BePx4UDWupN66F3DtIzUQhKnSSU7/kkieP94JBpuo+6jSnGwYA8wPnO7gFLiU65oj4fkUIUJby7ZxAr//hexv35x2fNwNq/fphMY1QSsqifI8pB78AIDhzTkPP+XreABq+r9DNmul8BHp0PDAWBL/2wcIMyADBxphy7/dZTugMzv3zjlxiIDuC6Y6/Le1AGAHxuH45sOxJHth0JAPhk8BP8/sPf48G3HsTz25/HHV+4A0ePPTrv81QDkYiIrr1DGalM08c00OAMUfPQV9gGZEur2DM4gh2hcEaCzTfXWUthUKc7nXXspORDn965CMIKepq94fE3sGDOtKSm9vx/9u48To6q3vv499fbbNmTScgqBMMSMcQkIouyXhAEiQoKyib4gBgFwavXXcQLPnrxAUQuREA22URQQFHcAEE2SQIECEsgQDIkZA9JZu/u8/zRy/Q6PUt3V/Xk8369+tVdVadOna761amaM9XnbO8sGNdvbWwrul5/R31hpBdUUrXja7Db68voPsXOsXXbO/s8ohPKK7XvC12fU6MxnZ0zQhb1HKrNOaf12zo1ehA/ZZKkUY2R6jXMOCc9cZV0/UeleFw66hJpp73Ku41wozRpjvTSvYltZGjZ1qJbXrpF+0/aX9NGTCvvdpMmD5usL7z/C/rOh76jaDyqL/zlC3po5UMV2VatYVQmoDgaZoooNVpFdyyeNzpTbppS+afW78vITUApxWI21bFfy+b2oiPENEaCva7Xn1FfGOkFlVTt+Brs9vo6uk+hc6w/IzqhvFL7vreRsQqNkEU9h2ra2h5VZzSuUYPsn2NkQ7g6DTPOSQ98S/rLt6XJc6RjLpPG7lqZbb1nf2nramn1kqzZP1/yc5lMn5rxqcpsN8P0UdP1/X2/rynDp+j8h8/Xw6servg2/Y5RmYDiaJgpotRoFeFgIG90ptw0pfJPrd+XkZuAUorFbGoElymjG4qOENPWFet1vf6M+sJIL6ikasfXYLfX19F9Cp1j/RnRCeWV2ve9jYxVaIQs6jlU07ptHZL6PlR2MSMbwum8KuqJ/5WeWijNnC8d8j2pbnjltjV1HykQkpbdk561dP1SPfDmA/rozh/V6PrRldt2hmGRYfrPef+pacOn6ev//LqWrl9ale36FaMyAcXR+W+GeNxpa0eXOrvjCgRM67Z26r5nW3T8vGmqDwcUCQYUjTsFAlIkGNCmtm5t2NapGx57Q6cfsIt2GlmvpkhQ3bFEZ4CRYCCrc+BQICDnEtMNkYA2t3artTOqrqhTfTigXzy4XKcfsIt2GdeoWFx0BFx9vukYrT8yOxaVpIvvX6a/LlunI2aO13ePnql48hyvDwcl57SprVtf/HXPb3uvOWWuhteH9Nq6VjVGgnKSpo1pUGc0LpOpqS7RiNiQ7OC3KxaXKTGiZV0ooM6oy+rENBCwvD45fnnKXI1riigQCNAhZvnVZNwOVDX6mMnsELchEtTadzt0Zs45M25Yndq7YwoHAxpTH87ryHBbdyzdCXddSNreEVc07lQfCmhDa1fWOfjLU+ZqTGNYTqZxjWFtaEt0DBwJBdQdi+v15LnZ1hXT1DEN2nlMU9HOgnPPLw879y3FV3GbuZ/qIwHJSZvaurWltVvjR0TUHZOCgcR1PBhI/OM/1SmwmdMr77TqveObVBcOpjv+99G+Rnn5JnYfe22DTrruKX3/6D01c9LIAW/npife1KPL1+vFC48ccB4lrVkqXXtoov+Xg78z+GGx++IfF0rb10pfXSon6bQHTtOKLSv044/8WA2hhpKrl9PWzq26+KmLZWb67cd/qzH1Y6q6ffkkbjs6otra3a2uaGIAlWDAFAmZRoTD9DGDQnaoiyhnQFI87vT2lja1d8fU3hVTW1dMr6x5V8fsPUWX/OVlLTjkvWrvimV11Lv/9LE6/4gZ+q8j99D6bZ2646m3dPTek3Xlg8v1tSN2U8BMG7d36YbH3tCCQ94rk9TWFdPDL6/V0XtP1v3Pva2j956s8+98RvtPH6uvf3R3Oee0bluXFty6pGKdPWLo6O6O6eV12/M6Er34k+/X2q2dOum6p7I69P39krd10r7T9N/z90r/oTe6KayN27v1/XtfKNj571UnzdE/X16nebuMyero+vITZmtEQ0hn3LgoL1Z3nzBcv19wgLqiMcXiThclG4voIBODFQhYVnyV+w/g3IafL35kZx3/wexzJhwK6Hv3PK+/LlunHx6zh+buMi59Dh4xc7zOOWy3vI4Nf5HRSewNp39QP/v03ulrQjhouuxvy7Wlvavgurf/+62e8+eUeenvWqqRqtodJdeqzP3UPKxO/3Xk7nplzbuat8s4/X3ZGh2z92R9KeOanOr897T9d9FNj7+hcw7bTUGL6932qM6+9in2Naom9ZTL6MbB/ZRpVENYrZ0xtXVF1RipwJ8Gzkl//qYUaZT2P7c6jTKSNG0/6fErpDXP6e/d6/XMumd06sxTq94oI0kj6kZowewF+vFTP9a3H/22rv6Pq8vS8XAtWru1K+86N2Ls4J76AoaCHbNGKGBja5c6o05vb+7QptZufeOupTpgxnh96dZE536bk/MyOwK8c3GLlrZsTXcCfPy8aVpw6xIdN3eqQoGg3t7ckV5nc2t3Ot9Uujk7j003wBw2c4Le3tyhYCCYnifR2SN6t257Z8GORLui8bxOQ79x11KdeeB0ffm2Z3T6jU/rhGue1Ok3Pq3WzsJpU53/Lrh1iebPmZLX0fV5v3lWb2/uKBirqZFeIqGgPnfdU/rrsnXpNHSQicFKxdfk0Y1qHl5X1j98C3XWe/oNT2edM6ff8HS609dDZ07MOgePmzu1YMeGmZ3Enn7D03q3vTud3xk3LtKZB07v07qZHcyX6piYjrj7JnM/nX3wrvrGXUt16MyJOvuWxYnOmnOuyanOf1PvX7plsXYdPyKvHmVfo9LWbk3cG44abMNM8qdQG7ZVKF5ffUBa+bg0+6TK/nwp19R9JQuq48W79bOnf6apw6fqwCkHVm/7Od4z4j367B6f1eOrH9d1z1/nWTm8ROe/QHE0zCR1RWMKmNQYCaY75U110jiqIVy0o97MZZnpU3llrl8oXSqvVJqAic4e0We9daJWaH6hjkeLxVxm579xVzi/Qp0GZ8YqHQGj1vS1s97U+ZF7bvTWWWxv08GA9Xnd1PlT6vzi/OubzP2UOgap49rb8c98L1bnsq9RSeu2dqo+HFBDZHB9G41Mdh68fnuF+pl57OdS03hpxhGVyb+Y+hHSpA/o5uV3a3Xrap24+4meP6Vy0JSDtO/EfXXVs1fp2XXPeloWL9D5L1AcDTNJkVBQcZd4rDzVKW+qk8Yt7d1FO+rNXJaZPpVX5vqF0qXySqWJO9HZI/qst07UCs0v1PFosZjL7Pw3YIXzK9RpcGas0hEwak1fO+tNnR+550ZvncX2Nh2Luz6vmzp/Sp1fnH99k7mfUscgdVx7O/6Z78XqXPY1Kmndto5BPy0jKT3cdkVGZmpZLK18Qtrz2ERnvFW2dpcP67oG077Dd9GeY/es+vZzmZlO3vNkjakfo28+8k1t69rmdZGqis5/geL4az9pbFNEdSHT5NH1GtMU1iXHz9Jdi1bqqpPm6O7FqzQ6Oe/uxav00+NmpSuVuxev0tQxDXnpo/GYJo+uT68zuilcMN9UXgsffl1jmsKKxWO66qQ56fxT/XaMH1bn5e6BT40fVqerT56bFS9XnzxXYxsiefMvOX6Wrn1khS45flbW/EjICqZd+PDrmjK6QVedNEf3LmnJW+/yE2Zr8uj6XmN1bFNE1546LyvNtafO09imwd9IApWQG7N3LVpZ8By7e/EqSdKDy9ZkLb978ape0xeavuqkObr2kRV9Wjfz/Cl1fnH+9U3mflr48Ou65PhZ6eOaul5n7sOfHtdzL5A6Zq+v26qFOceOfY1KW7etM92oMhgjGyrYMPPEL6RIk7RblZ+WkeSc04VtL8uZ6bytbVXffjGN4UadOetMvdP2ji5+6mKvi1NVhe5PU/etwI6OUZkyZI7K5JR43C4SMrm45JR4pD0ed+llcedUHw5qTENEWzu71Z7sFLI7GlfAEsNtxpLplbN+MGDqisbTozpIUkMkKCcnF5c6o3FGZao+X/RY3xeZI4g01gXU1hlPjwgzflidwuFg1mhN4WBAoaCpoysxykx3NK5Q0NQdc4rGnRrCQUXjPaMrhUxqj8YVDpgioYBau2KqDwVkZupO/lQpYFIkOSpTNBZXqEis+nhUmKGiZuK2VuTG7PBwMG/UpVLTqVGZ+rJ+fTig1s6YQsFAelSm1DnV3BTRlo5on0ddGt0Q1ub27qLTPjr/fBW3mfsxHAwoGourLhxQR3dc4WRdmRpBJBCQ4nGl35vqAuqOmZ/3NcrLN7F70CUPadLIBp172IxBbScedzrl+qe04OD36usf3X1QeWXZ/KZ0xQekmZ+Q5p1Rvnz76O41j+mHy2/VgsA4nb3iWS393K/VNXxC1ctRzH2v36d7XrtHP/7wj/XxXT9e6c35Jm47OqJ510RGZEIRO9RFlLMgQyBgGtWY/WRKarSGy/72SnpkpsyRaa49dZ7GNdVpTFOd1JSfZ+b6qREcUqM6MXIDBqKvI62Ew0FNHt1YcN17lqzS0XtPzhr96+qT52qP8cMUTg6LXa4RXVIdtQK1IjNmo9G4Xl67Ld2x6xc/srOOmT0lb0SJzHNHUt5N5uQS02OH9XyelDMqSnO4+M9hMsvKKEwDFwiYxjZFCu6/ulBAj7yyNmv0rdRx3725SZGM40Vdh2pxzmnd1k7NnDhi0HkFAqbxI+r1+vrtZShZhicXJkZg2vPY8ubbBy9ue0v/9/XfaM9hU7XvhEPkVjynnZ77rVZ++CtVL0sxx0w/Rss2LtPFT12s2eNna+rwqV4XqeI6OqJavrE1ry6dMbaJxhns8HgMo4TUaA2ZIzP1Z9SFzPUzR3Jg5AYM1GBGWkmtmxoZLLdX/MzRvxjRBUiMfJY52s7x86YVHFHCDyPncc4OTrH999bGtrzRt1LHfT37Fh55t71b7d0xjSvTT92njm7QK++Usb+T9s3SkpuknQ+UmsaVL98+aGnfoHNfXKjhwUZ9cdpRijaN0cap89T80p8U3r6hqmXpTcACOvP9Z8o5p28+8k11x7tLr1Tj0v6/VAAAIABJREFUGJUJKI6GmRJSozVkjr6UqdSoC5nrFxrVqS95AJkGM9JKat1iI41k9orPiC6A1B2L92mUJj+MKME5OzjF9l9jJFh0ZDo/HHfsmFLxWLaGmTGNenNjq1o7o2XJT0//Supuk973yfLk10dvtq3V6UsvU2usU+fs/HGNCCWeHF69++GSi2vqk9dUtTyljG0Yq1Pfd6qe3/C8Fj630OviVByjMgHF0TBTQmq0hszRlzKVGnUhc/1Cozr1JQ8g02BGWkmtW2ykkcxe8RnRBUj0FdaXUZr8MKIE5+zgFNt/bV2xoiPT+eG4Y8e0ekvij9uxw8rTaerMiSMUd9Kjy8vwRElXm/Tk1dLkudKY6YPPr4/+seFZnfjMT9Ua7dA3pn9KUxuae4rUOEZr3nuIxr72oEa+9VTVytQX++y0jz48+cO6dum1evqdp70uTkUxKhNQXNUaZszsSDN7xcxeM7NvFVheZ2a/SS5/ysx2rlbZepMarSFzZKb+jLqQuX7mSA6M3ICBGsxIK6l1C400cjUjKgF5xg+ryxptp9goTX4YOY9zdnCK7b/3jG3MG30rddyb2bfwyNtbyvvEzB47jdDw+pBuefItDXpgkGdukdo2SHt9uixlK+Wdzs06f9k1Om/ZNWqOjNT3Z5yoaQ3j89Kt2e0/1DZiknZ56H8U2bqmKmXrq8/t8TlNaJygbz7yTb3T+o7XxakYRmUCiqvKqExmFpT0qqTDJbVIelrSZ51zyzLSLJA0yzl3tpmdKOmTzrkTesu3WiOEpEZriMfj6VFpYk6qDwc0rqmuZKeKmevHnBQ0KeYSHbcxcoOv+KbH+lIGM9JRal2TS4/+lTmaU7m2g6qpmbitVdFoPDHCWXKkpLENYW1o6+r13PFKDZ2zvozbQvtPSvQ/Eww4tXf11JnNTZGsjn+xw/BF7H7/nhd095IWXXfqPJmVp0gPvPCObnriTf3n4bvpnORIT+u2dmhbZ1S7Ng/rfeWUrjbpF3OkhlHSkf+T6Py3QjZ2bdONLX/T7av/qbhzOnb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\n",
      "text/plain": [
       "<Figure size 1126.38x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue='Species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights from the distributions of variables:\n",
    "- ID may not be the right variable and it should be ignored in the analysis\n",
    "- Distributions of SepalLength, PetalLength and PetalWidth variables are separated and are good candidates for selection\n",
    "- Distribution of SepalWidth variable is almost overlapping and may not greatly impacting the target variable\n",
    "- PetalLength and PetalWidth seemed to highly correlated to each other\n",
    "- From other pairplots, we see clear correlation/segmentation among various features, except probably, between SepalWidth and SepalLength."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vkV6DZSLOgnm"
   },
   "source": [
    "### Summary Statistics and explain insights for at least 2 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NFpTN29OOgnn"
   },
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000   \n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667   \n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161   \n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000   \n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000   \n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000   \n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000   \n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000   \n",
       "\n",
       "          Species  \n",
       "count  150.000000  \n",
       "mean     1.000000  \n",
       "std      0.819232  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      2.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ypaLrqPsOgns"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4ac5c0b00>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SIcwAkAxhBoBk/h8GIHQ0CNKabwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(iris['SepalWidthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4ac628eb8>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.SepalWidthCm.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot and statistical measures of SepalWidthCm indicate:\n",
    "- There are a few outliers on both sides\n",
    "- Mean is higher than the medium and so the distribution is right-skewed\n",
    "- Smaller IQR and the histogram pattern shows more number of data points closer to the mean\n",
    "- The distribution seems to look like a normal distribtion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4acc26cf8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(iris['PetalLengthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4acc71ef0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.PetalLengthCm.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot and Statistical measures for PetalLengthCm indicate:\n",
    "- Mean is less than the median and so the distribution is left-skewed\n",
    "- High IQR range and the histogram shows that data points are not closer to the mean\n",
    "- The distribution doesn't seem to be a normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_MsXi4JROgnv"
   },
   "source": [
    "### Correlation among all variables and give your insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ENUi26odOgnx"
   },
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716676</td>\n",
       "      <td>-0.397729</td>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.899759</td>\n",
       "      <td>0.942830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>0.716676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "      <td>0.782561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.397729</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>-0.419446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>0.949043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.899759</td>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <td>0.942830</td>\n",
       "      <td>0.782561</td>\n",
       "      <td>-0.419446</td>\n",
       "      <td>0.949043</td>\n",
       "      <td>0.956464</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "Id             1.000000       0.716676     -0.397729       0.882747   \n",
       "SepalLengthCm  0.716676       1.000000     -0.109369       0.871754   \n",
       "SepalWidthCm  -0.397729      -0.109369      1.000000      -0.420516   \n",
       "PetalLengthCm  0.882747       0.871754     -0.420516       1.000000   \n",
       "PetalWidthCm   0.899759       0.817954     -0.356544       0.962757   \n",
       "Species        0.942830       0.782561     -0.419446       0.949043   \n",
       "\n",
       "               PetalWidthCm   Species  \n",
       "Id                 0.899759  0.942830  \n",
       "SepalLengthCm      0.817954  0.782561  \n",
       "SepalWidthCm      -0.356544 -0.419446  \n",
       "PetalLengthCm      0.962757  0.949043  \n",
       "PetalWidthCm       1.000000  0.956464  \n",
       "Species            0.956464  1.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights from Correlation matrix:\n",
    "- PetalLenghtCm and PetalWidthCm are having maximum correlation (0.9627) with each other\n",
    "- The next highest correlation is between SepalLengthCm and PetalLengthCm (0.872)\n",
    "- SepalWidthCm is having negative correlation with all other features. Secondly, it's correlation value is lesser than that of other variables. It's having least impact among other variables and may not be the best candidate for prediction\n",
    "- SepalLengthCm and SepalWidthCm are having least correlation (-0.109) with each other\n",
    "\n",
    "### Above insights are almost closely matching with the ones drawn from pairplot above "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ciKj4Zf6Ogn8"
   },
   "source": [
    "### Split data in Training and Validation in 80:20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UAeLAItYOgn9"
   },
   "outputs": [],
   "source": [
    "X = iris.drop('Species', axis=1)\n",
    "y = iris['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120, 5), (30, 5))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120,), (30,))"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1-a4C3w9OgoA"
   },
   "source": [
    "### Do Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Doing Feature Scaling by Standard Scaler\n",
    "sc = StandardScaler()\n",
    "\n",
    "sc.fit(X_train)\n",
    "scaledX_train = sc.transform(X_train)\n",
    "scaledX_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "miK8grUROgoI"
   },
   "source": [
    "### Train and Fit NaiveBayes Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "29HTCg2POgoL"
   },
   "source": [
    "### There are three popular types of NaiveBayes model: Gaussian, MultiVariate and MultiBinomial.\n",
    "- Gaussian is used for Continuous data\n",
    "- MultiVariate is used for Descrete data\n",
    "- MultiBinomial is used for Binary data i.e. Text classification where many 0 and 1 present.\n",
    "\n",
    "### In this problem, since all features are of Continuous type, we will use Gaussian model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "96wnWGX8OgoR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(scaledX_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aLOFqj44OgoT"
   },
   "outputs": [],
   "source": [
    "predict = gaussian.predict(scaledX_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zkbFn_g-OgoY"
   },
   "source": [
    "### Print Accuracy and Confusion Matrix and Conclude your findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y2kBUikzOgoZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the training data:  0.9916666666666667\n",
      "Accuracy of the testing data:  1.0\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of the training data: ', gaussian.score(scaledX_train, y_train))\n",
    "print('Accuracy of the testing data: ', gaussian.score(scaledX_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mQZ7Xz8SOgoe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model:  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy of the model: ', accuracy_score(y_test, predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[11,  0,  0],\n",
       "       [ 0, 13,  0],\n",
       "       [ 0,  0,  6]], dtype=int64)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print('Confusion Matrix:')\n",
    "confusion_matrix(y_test, predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        11\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fy7LN9ZuOgoi"
   },
   "source": [
    "### Interpretation of Accuracy and Confusion Matrix:\n",
    "- Accuracy of the model is 100%; which means, the model is perfectly making the prediction for the test data\n",
    "- Precision, Recall and F1 score are also maximum\n",
    "- Confusion matrix is diagonal matrix which means all True values are correctly predicted\n",
    "- This is also evident from the pairplot also where all classes are nicely separated by most of the features (except the SepalWidthCm)"
   ]
  }
 ],
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