From b64329e20e58b353c7f6b0b91a260545bb1dcee3 Mon Sep 17 00:00:00 2001
From: mbhatti4 <nobody@makingwithcode.org>
Date: Tue, 11 Nov 2025 16:32:26 -0500
Subject: [PATCH] I finished the project I am really glad I was able to get
 through the lab. I did have some difficulty with creating the differnt
 visuals, as I didnt fully make them in the pokemon lab, so I had to use
 refrences to help me out. I was able to edit accoridngly to fit my project.

I wish I was a bit better at coding so I coudlve takeled even deeper reasearch questions.

I have now learned how to fully work with a data set, and create visuals to compare the data.
---
 .ipynb_checkpoints/argument-checkpoint.ipynb |  626 ++++
 .ipynb_checkpoints/poetry-checkpoint.lock    | 3071 ++++++++++++++++++
 .ipynb_checkpoints/proposal-checkpoint.md    |   69 +
 .ipynb_checkpoints/pyproject-checkpoint.toml |   23 +
 argument.ipynb                               |  426 ++-
 data/Sleep_health_and_lifestyle_dataset.csv  |  375 +++
 proposal.md                                  |    2 +-
 7 files changed, 4511 insertions(+), 81 deletions(-)
 create mode 100644 .ipynb_checkpoints/argument-checkpoint.ipynb
 create mode 100644 .ipynb_checkpoints/poetry-checkpoint.lock
 create mode 100644 .ipynb_checkpoints/proposal-checkpoint.md
 create mode 100644 .ipynb_checkpoints/pyproject-checkpoint.toml
 create mode 100644 data/Sleep_health_and_lifestyle_dataset.csv

diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb
new file mode 100644
index 0000000..33db885
--- /dev/null
+++ b/.ipynb_checkpoints/argument-checkpoint.ipynb
@@ -0,0 +1,626 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "worldwide-blood",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-numbers",
+   "metadata": {},
+   "source": [
+    "My research will analyze a dataset to compare different lifestyle factors and their effects on sleep duration and sleep quality. I aim to determine whether self-reported factors, such as occupation and physical activity level, have a stronger influence on sleep, or if the factors that individuals have less control over, like stress level or BMI, play a larger role. Although the stress ratings in the dataset are subjective, they still provide valuable insight that can be compared across participants to understand how strongly stress and other variables impact sleep patterns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greater-circular",
+   "metadata": {},
+   "source": [
+    "**Which lifestyle factors most strongly influence sleep quality?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appreciated-testimony",
+   "metadata": {},
+   "source": [
+    "I chose this topic because I’ve personally experienced some sleep issues and wanted to explore what factors might contribute to them using this specific dataset. While the results won’t be absolute and may differ with other datasets, this analysis can still help me better understand possible connections between lifestyle and sleep quality. It’s also simply a topic that interests me, and I was curious to learn more about how different habits and conditions can affect sleep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "permanent-pollution",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "technical-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "overhead-sigma",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_name = \"Sleep_health_and_lifestyle_dataset.csv\"\n",
+    "dataset_path = \"data/\" + file_name\n",
+    "\n",
+    "df = pd.read_csv(dataset_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "heated-blade",
+   "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>Person ID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Occupation</th>\n",
+       "      <th>Sleep Duration</th>\n",
+       "      <th>Quality of Sleep</th>\n",
+       "      <th>Physical Activity Level</th>\n",
+       "      <th>Stress Level</th>\n",
+       "      <th>BMI Category</th>\n",
+       "      <th>Blood Pressure</th>\n",
+       "      <th>Heart Rate</th>\n",
+       "      <th>Daily Steps</th>\n",
+       "      <th>Sleep Disorder</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>27</td>\n",
+       "      <td>Software Engineer</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>42</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Overweight</td>\n",
+       "      <td>126/83</td>\n",
+       "      <td>77</td>\n",
+       "      <td>4200</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>60</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>125/80</td>\n",
+       "      <td>75</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>60</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>125/80</td>\n",
+       "      <td>75</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Sales Representative</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Obese</td>\n",
+       "      <td>140/90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>Sleep Apnea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Sales Representative</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Obese</td>\n",
+       "      <td>140/90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>Sleep Apnea</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Person ID Gender  Age            Occupation  Sleep Duration  \\\n",
+       "0          1   Male   27     Software Engineer             6.1   \n",
+       "1          2   Male   28                Doctor             6.2   \n",
+       "2          3   Male   28                Doctor             6.2   \n",
+       "3          4   Male   28  Sales Representative             5.9   \n",
+       "4          5   Male   28  Sales Representative             5.9   \n",
+       "\n",
+       "   Quality of Sleep  Physical Activity Level  Stress Level BMI Category  \\\n",
+       "0                 6                       42             6   Overweight   \n",
+       "1                 6                       60             8       Normal   \n",
+       "2                 6                       60             8       Normal   \n",
+       "3                 4                       30             8        Obese   \n",
+       "4                 4                       30             8        Obese   \n",
+       "\n",
+       "  Blood Pressure  Heart Rate  Daily Steps Sleep Disorder  \n",
+       "0         126/83          77         4200            NaN  \n",
+       "1         125/80          75        10000            NaN  \n",
+       "2         125/80          75        10000            NaN  \n",
+       "3         140/90          85         3000    Sleep Apnea  \n",
+       "4         140/90          85         3000    Sleep Apnea  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continental-franklin",
+   "metadata": {},
+   "source": [
+    "**Data Overview**\n",
+    "\n",
+    "The dataset contains 374 rows and 9 columns of information. The most important columns for my research are Quality of Sleep, Sleep Duration, Occupation, and Physical Activtiy Level, as these variables will provide the most insight into my research questions. However, all of the data contributes to building a fuller understanding of the relationships between lifestyle and sleep.\n",
+    "\n",
+    "I obtained the dataset from https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset, where the main collaborator is Laksika Tharmalingam."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infinite-instrument",
+   "metadata": {},
+   "source": [
+    "# Methods and Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recognized-positive",
+   "metadata": {},
+   "source": [
+    "## First Research Question: What is the average measures of each of the cetegories?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-palmer",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "endless-variation",
+   "metadata": {},
+   "source": [
+    "I will be using each of the categories in the dataset and calculating the average value for each one using the mean function. This will provide a simple summary of the overall trends in the data and serve as a starting point for deeper analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portuguese-japan",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "negative-highlight",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Values for Key Categories:\n",
+      "Sleep Duration                7.132086\n",
+      "Quality of Sleep              7.312834\n",
+      "Physical Activity Level      59.171123\n",
+      "Stress Level                  5.385027\n",
+      "Daily Steps                6816.844920\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "Columns = [\"Sleep Duration\", \"Quality of Sleep\", \"Physical Activity Level\", \"Stress Level\", \"Daily Steps\"]\n",
+    "\n",
+    "mean_values = df[Columns].mean()\n",
+    "\n",
+    "print(\"Mean Values for Key Categories:\")\n",
+    "print(mean_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collectible-puppy",
+   "metadata": {},
+   "source": [
+    "## Second Research Question: Which occupation gets the most sleep?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demographic-future",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incorporate-roller",
+   "metadata": {},
+   "source": [
+    "I will create a bar graph with the x-axis labeled by occupation and the y-axis showing the average hours of sleep for each group. To do this, I will first calculate the average sleep duration for each occupation and then use that data to build the visual. This bar graph will make it easy to compare which occupations tend to get the most and least sleep, providing a quick and clear overview of how sleep patterns vary across different professions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "juvenile-creation",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "pursuant-surrey",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Occupation\n",
+      "Engineer                7.987302\n",
+      "Lawyer                  7.410638\n",
+      "Accountant              7.113514\n",
+      "Nurse                   7.063014\n",
+      "Doctor                  6.970423\n",
+      "Manager                 6.900000\n",
+      "Software Engineer       6.750000\n",
+      "Teacher                 6.690000\n",
+      "Salesperson             6.403125\n",
+      "Scientist               6.000000\n",
+      "Sales Representative    5.900000\n",
+      "Name: Sleep Duration, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_sleep_by_occupation = df.groupby(\"Occupation\")[\"Sleep Duration\"].mean().sort_values(ascending=False)\n",
+    "print(avg_sleep_by_occupation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "c09f8668-6794-4d9a-b094-575d0a63fb1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,5))\n",
+    "plt.bar(avg_sleep_by_occupation.index, avg_sleep_by_occupation.values, color='pink')\n",
+    "\n",
+    "plt.title(\"Average Sleep Duration by Occupation\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Occupation\", fontsize=10)\n",
+    "plt.ylabel(\"Average Sleep Duration (hours)\", fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d81599-3128-4f57-9821-a9b53ecdc27f",
+   "metadata": {},
+   "source": [
+    "## Third Research Question: Who has more effect on sleep duration: Stress leve Physical Activity Level?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "557961a6-ddf9-4744-b1af-10b9536e8884",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infectious-symbol",
+   "metadata": {},
+   "source": [
+    "I will create two graphs to compare how stress and physical activity affect sleep. The first graph will plot sleep quality and sleep duration for each stress level, and the second will show the same relationship for physical activity levels. These visuals will help the reader clearly see how both stress and physical activity influence the number of hours slept and the overall quality of sleep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faa9b939-f80f-4342-a442-cd132aeb293f",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "d98295fb-d13b-41e9-82b5-25ba5b0e90fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6fvnc5eBagj+ZqctQ/y4rtBvIgbfhoFZKneS5KlasqMYtZ5n9/PzC1L9H58AzPNknIlrgUcjnJ/uLzJi1efNmdZ3MfiUBonzOsjCgBKZSd3/u3DnVwxM+mJVAp3///urfsl8aX29M3o/sK9IzIn7//Xc1i5oc9Mq+KjMZSWZHAi/Zz6Q/43NJf4r0REiAYvjdkpnT5HOVANF4sUbJnMQn+YzHjRunvn8JLqSsTb53GZtx2ZiUMUqQFh9/J2L7HRv88ssvqo9JTlQsWbIkzN+VuPxbSGTWtO4eJyLTnBVKZpaJy1mhDLPZyKJrn3pt45lxHj58GK11LAxkppiI7pc7d+5ofT6yjoUs3BbVfPh//vnnR4/73FmhPrVOiGGrWbNm6MxEES2uZ7zVrl07RjNHCVmgLqp1LAybwYIFCz55X+M1G3LkyBHlfZMnT64W3IuO6HxexvuRzOgV1RoHUX0+MutT+LU/ChUqFOG4ZOajT61jEX4WpPhcx0JmY4pqX4uLffhTa9gYZobaunVrtGdv+5y/E5/zHYf/POT3JrLfJ65jQRQ9zFgQUYJp0KCBKvsZO3asOrsoZxyldEPOVMvZTDmTKPcxbiqVMhqZ9UXKPWSefCmpkXIGOTMvMwnJGhLG6tWrp86gjh8/Xp0RDwoKitEYZf0CGePo0aNVNkfGKKUXqVKlUjMbyRnVT9WJx4TU7UuGYuvWreqMtpR/SBZDxi1nwfPly6fKQeQst5x9NSalOfJZyGcjZUfGsxLFlMzaI+9dvhs5IyxrJMgY5POXpm+5TWZKMpDvavjw4epMsJzllTHLdylnkGXMMsOOjNlAGnplDQw5sy5n7yUrJOVCsmaAlFHJOiDGJW1xST5fmbloxowZqpxLZkqSz00yCXImXDIeUvIWUW+JnImX7IJxZiWyci3JzmzcuFGdqZdG9iNHjoS+T3meggULqrVGJBMWW5Ilkt8F+b6klEzKteQsv5TTSZO8rCPzqXVN4oK8X1m7Q2bNkjHIfiCZBulHkO9TMlSS3TMuf4uPvxOx+Y4NJNsms7NJo7msryLfmezDkrGKix4UIktgJdGF1oMgIiIiSkhSPmgcJPJwiCj2ON0sERERERHFGgMLIiIiIiKKNQYWREREREQUa+yxICIiIiKiWGPGgoiIiIiIYo2BBRERERERxZrFrWMREhKCO3fuqDnwOS81EREREVHkpGvi2bNnam2X8OspwdIDCwkqZLEcIiIiIiKKHlk4Ml26dFHex+ICC8lUGD4cNzc3Tcbw9u1btQKtrIoqK7KS5eK+QIL7ARlwXyAD7gtkKvtBYGCgOilvOIaOisUFFobyJwkqtAwsnJyc1Ovzj4Vl475AgvsBGXBfIAPuC2Rq+0F0WgjYvE1ERERERLHGwIKIiIiIiGKNgQUREREREcWaxfVYEBERkfkKDg5WdemJnbwHW1tbvH79Wr0nskxvE2A/kN4NGxubxB9YyJy4AwYMwPLly3H//n0ULFgQ//zzD4oWLRrpY3bs2IHevXvjzJkzqkP9l19+Qdu2bRN03ERERGR6c+3fvXsXT58+hbm8n1SpUqlZLLnuluXSJdB+kDRpUvU6sX0NTQOLjh074vTp05gzZ45adGPu3LmoUqUKzp49i7Rp0350/6tXr6J27dro0qUL5s2bh61bt6rnSJ06NapXr67JeyAiIiLtGYIKT09PNYtOYj8YlwV9nz9/DhcXl08uSkbmKySe9wMJXF6+fKlO8As5pk6UgcWrV6+wdOlSrFy5EuXKlVPXDRo0CKtXr8bEiRMxbNiwjx4zadIkZMqUCX/99Ze6nCtXLvj6+mL06NGJJ7AICYbVdV+kfbwPVtfdgMzlAOu4ST8RERFZIikRMQQVHh4eMJcDyqCgIDg6OjKwsGAhCbAfJEmSRP2U4EJ+h2JTFqVZYPHu3Tv1h0A+qPBvToKFiOzbt09lNIxJQNGrVy8kCmdXARt+hG3gHRSRy9cnAm5pgBqjAJ96Wo+OiIgoUTL0VEimgohizvC7I79LiTKwkNX7SpYsiaFDh6rMg5eXFxYsWKCCh6xZs0aa5pT7GZPLsiKgZEAMEZexN2/eqM1A7mv44BKyucvq/BrYLG0nSScYJ2d1gf7A4tYIbjwTupx1Emw8ZBoM+6A5NBrS5+N+QAbcFz6PfF5S0iGbnOE1B/JeDD/N5T2R6e4Hht+fiAKLmPw90rTHQnor2rdvr/op5E0UKlQIX375JY4cORJnrzFy5EgMHjz4o+tlefQEO7OhC0G1M71hEy6oEFbQQXaZoFW9sdlPrmC60xJt3rxZ6yGQCeB+QAbcF2JGZs2RxlOpRZeyEXMiE90QPYvn/UB+b+Qk/a5du1RVkTHpwUgUgUWWLFmwc+dOvHjxQmUSpGHkiy++QObMmSO8v/zRuHfvXpjr5LIscx5RtkL0799fzSJlIK8js0lVq1ZNPS4hSE+F7fHHkd8uKai3j1E7T1LovMskyJjINMhZADmAqFq1qprujSwT9wMy4L7weWQqTpk1Rxpcw5dYJ1Zy9lgOJpMlS6Z6Uhs0aKD1kEyWnECWnt2jR4/G+2vJxEHffvstTp48qU6Kx/dr697vB1LpE9MJCeQYWI6x//e//0Xrd0iOpaXvOfzvkKHaJ9GsY+Hs7Ky2J0+eYOPGjfj9998jvJ+UTq1bty7MdfIHWK6PjIODg9rCkz/YCfZH+9WjaN3NVu7H/5FYpATdH8lkcT8gA+4LMSM9m3LQJc2tsWlwDQ7R4eDVx7j/7DU8XR1RLFNy2FjH3+xSDx48wK+//oq1a9eqE6USROTPn19dZ3xsE9v3FRcqVKigTgYLe3t7pEiRQlWatGvXDo0aNUqwccj3LMsUGAdaffv2VQf7CfEZ9evXTy11YPj9NBzsx9drh7wvfzLs3zEhn4ucrJcT7JGdtDeQ55bXiOhvT0z+FmkaWEgQIZFYjhw5cPnyZfUB5MyZU+2khkjr9u3bmD17tros08yOGzcOP/zwgyqh2rZtGxYvXqx+IU2ai1fc3o+IiIji1IbT/hi8+iz8A16HXpfa3RED6/qgRp7YTcEZmcaNG6sSlH///Vcd+ElwIWfEHz2K3gnJhNapUycMGTJElcrcunVLHeA3b95crSc2ZcqUOAkMP4dkqmSLbzK5kJ+fn/retBYUFKQCvKhI8CeTHMlsq3/88UeCjEvT8DcgIADdu3dXwUTr1q1RpkwZFWwYIiN/f3/cuHEj9P4y1awEEZKlkIhepp2dNm2a6U81611KP/vTRx0WRpw89PcjIiKiBA8qus49GiaoEHcDXqvr5fa4JtPj7t69G6NGjULFihXh7e2NYsWKqZOq9epFPlOklHw1a9ZMLWiWPHly1K9fH9euXQtzHzk2kolxpKRFjrEmTJgQepvcVw7iFy5ciFKlSqn75MmTJzQbERXpTZWy9HTp0qFEiRJq7JMnT8bUqVOxZcuW0IWM5fmNFyo8fvy4us4wzlmzZqnxr1q1Cj4+PqqyRI73Dh06pMoA5YDY3d0d5cuXD1NilDFjRvWzYcOG6vkMl2W5ggIFCoQ5yy8BkIxTnltu27Bhw0efwbJly9RnL+9LjitlAqGoyGcm44uo3E76hjNmzKjGLcGWcU+ETCIkGRWZylUeK8e78l4NDJ+HsRUrVoQpfZKSK3kf8t3K8bBhDEuWLEHevHlVGZNMtSyzp0r5k0HdunXVuBOKpoGF/GJI5CcfuAQRko2QL8T4g5YdNHwq7tixY+ox8thEseq2rFMhU8oqkQQXL58AJxYk5KiIiIjMklr0K+hdtLZnr99i4KozaiKVj57n/c9Bq86q+0Xn+Qyz+ET3LLscQBrPXvmpHhw5mSr19hKU7NmzRz1HjRo1QpvWZQFhKaUaPnw4zp07hxEjRmDAgAEqK2JMqkT69Omjjqmk7EoOQD8nU9KmTRtVwiUH6TEhDcESmMiB8pkzZ9RBtxyMy/NJZmD//v3Ili0batWqFXqQbjgYnzlzpjpuND44N/bPP/+ok89//vmn6oWQz0yCtUuXLoW5388//4zvv/9eBT7Zs2dXEwiFb1w2Jp95kSJqwYAw5HhUvsc1a9aoTYK03377LfR2qbSRPhn5DiRQktlPZUyPH0fefxsRqe6R55HPWsYsn4GMWap45LuWY2YpSzPeByVYlexS+OAzvphEj4VFkHUqms1W61gg8M6H693SAkm9gRt7gZXdgYBbQPkfpZhOy9ESERElWq/eBsPn141x8lxyiHY38DXyDtoUrfufHVIdTva20ZrJSk6gSnmRLAAs/Qpyhl7OdufLly/CxyxatEidjZeDccPZbDnIlrPdclApE9MMHDhQHVQb+h7k7PbZs2dVZkEO2g169OgRWtIjpTJyRn/69OnqIDgmpHxJDspjeuAqQZJkUiRTYFCpUqUw95HyKnlvcqBep04dpEyZUl0v10nmJDISUPz444/qsxQSwGzfvh1jxozB+PHjQ+8nQUXt2rVDMwK5c+dWB++S5YnI9evXkSaNVKCEJd+JfJeurq7qcqtWrVRJmwR3kj2Qz1dur1mzprpdMjxSfSOftwR40SXBo7QHGD4HCVIkEJLvWjJeQrIXxgzjlbEbMjzxiXObJnRw0es03n21Aoe9u6qf6HUKaLcOKPN+5qodI4FVPYBgzmFORERkzuTA/s6dO6okSLIOEhxIgCEHoRE5ceKEOvCVA1hDxkPKoWRGHzlrLgex8rNDhw6ht8s2bNgwdb0x4+ZwCXLkTLyc9f4ccoY8pjMWSX9A+ABKekwk0JJMhVSwyOydMoWwcVn8p8gMRvKZli5dOsz1cjn8+zN+fZmZ1LD6dGRkOtaIyqDkgN0QVBiey/A88rlLEGU8Hin5l0xCTD9vCR4MQYWQoKxy5coqmGjatKkKWGQiJGOGWVNjMmVsbDBjkdCsbdSUsrfPBCK/TC0rZVKiykDAPR2w7nvg2Fzg2V2g6SzA4cOOSkRERJ+WxM5GZQ6iQ2aBajsz4pIaY7PaFVWzREXntWNCDlSlbl82KVnq2LGjyjpI72l4cpBduHBhVe4Unhxwyu1CDjCLFy8e5vbYrKb8qcZrKTEqWrSoumxowDYux4logTU54A0fjEhGRcqxpJRJDqKlP0ICoPham8R4tiPDWKJahE56P8IfuId/HsNzxWQxO/nMwpfQRfSZyQyq4b9TyXzs3btXrc82duxYVd514MABlakShnIr44AkPjFjYUqKdgCazwdskwCXtwAza+kDDCIiIoo2ObCTcqTobGWzpVSzP0V2vl2ul9vlftF5vpieuQ9PmpmNm2+NSTZDDuKlH0Hq9I03OcPv5eWlSl+uXLny0e2GA00D6WEwkHIaWZxYGr5jSvoG5GDbUFZlOICV+n8D6QeIDukZkSZn6auQsiQJLB4+fPjRQbwEM5GRLId8BvJc4Z9bPtvYKFiwoCori+mabfb29mHGI0GD9IcYxiOfmfSRGH/v0f3MZH+TbIiUckm/jLyWzNZlcPr0afWZyeeZEBhYmJocNYG2awGnFMDdk8C0qsCDC1qPioiIyCzJOhUypawIHxIYLsvtcb2ehZyZl56CuXPnqgbjq1ev4r///lNreclMTxFp2bKlOmsut0sjsTxGyqfkYFwadIUcYI4cOVItinbx4kWcOnVK9WH8/fffYZ5Leg3kAPT8+fNqhk4JDqQJOCpSTnP37l31WhKYSB+DLAXQtWtXNbuSkCBGFiKWmZokCJLZPKXnIzqkBEpmV5ISITnrLu83/ALIUnYk/QsyjoiyB0L6FqSvQnpSLly4oNaekAP1nj17Ijak4Voay2PC2dlZfT4yJuljkcBEyr3ks5SSNSHZJZmZ6qefflKlU/Pnz4+0HM6YfEbSnH/48GFVLiZN3bI2inGAKPtJ2bJlI11IOq4xsDBF6QoDHTcDyTMDATeA6dWA63u1HhUREZFZknUqJn5VCKncw9bPy2W5Pj7WsZDeBzmgHD16tFrtWKZ8lVIoOeiUWTIjIgefu3btQoYMGVTDrhxAysGp9FjImXohpVTS3C3BhNTeS0O4HKSGz1jIrEWySZ2+HCxLn4cELVGREivpH5Cz8PL6cpAsB+/G09nK2fEFCxaogEV6GOQAX3o8okOamSVYkMyMNEAbpmg1JkGKlP9I8CIZhIjI42RROJn1Sj4DOaCX9yeBS2xIoCMzWEmwEhO//fabyujIe5L3Jn0ysryCzKYlpE9GAkxZBFrGK5+fBGafIt+57A+S4ZEGelm4Tz4fQ5O4kKlmZZ9KKFa66M6LZiakqUfShbKGhuGXMKFJCkx2HtkRolzN8MVDYEFz4NYhwMYBaDQZyN0wIYdKprIvkFnjfkAG3Bc+jxxYy9l74/n9E8PK21GRGn05ZpFjlbhc1Vlmb5LPScpmjNd+oOiRzIN8LzLLlqnvB+vXr1fBlWTEpEH/c3+HYnLszIyFKXNOAbReBeSsAwS/Af5rC+z7ME0aERERxR0JIkpm8UD9AmnVT62CCjJd0hwtjeUxac7WivRsSObqU0FFXGJgYersnfTrXxT7Wn9540/A+n5ASOSNS0REREQU92QNDemFiMssUnxp0qTJR7ODxTdON5sYyJS0NX8H3NMDmwcAByYCgbeBRlMAu4RpxiEiIqLET5qfLawKnhKQ6YdbpCfT15X+Fmg8HbCxB86tAmbXB17GbDl4IiIiIqL4wMAiscnbBGi1HHB0B24eAKZXBZ5c03pURERERGThGFgkRhnLAO03Am7pgEeXgWlVgNtHtR4VEREREVkwBhaJlWcuoOMWwCsv8OIBMKs2cHGT1qMiIiIiIgvFwCIxc0sNtFsHZK4IvH2pX/PiyKdXaiQiIiIiimsMLBI7Rzeg5X9AgZaALhhY3RPYNgzgjA9ERERElIAYWJgDGzug/nig/I/6y7v+AFZ0Bd4FaT0yIiIiMhGDBg0Ks9p227Zt0aBBA03Gcv78eZQoUUKt8hybFcCtrKywYsUKmIILFy4gVapUePbsGUxFUFCQmmL48OHDCfJ6DCzMaTraij8B9cYCVjbAiQXA/KbA60CtR0ZERJQ4yOKzV3cDp5bofybAYrQ3b95E+/btkSZNGtjb26tVnXv27IlHjx7F+2v/888/mDXrQwl1hQoV0KtXLySEgQMHwtnZWR2Mb926NcL7PHjwAF27dkWGDBng4OCgDtqrV6+OPXv2wBT1798f33zzDVxdXdXl169fq+Atb968avXrmAZxZ86cUYvc5cuXDzY2NhgzZkyE9xs/frwKHiRIkwXxDh48GHqb7FPff/89fvzx/cnneMbAwtwUag20WATYOQNXdgAzawKBd7QeFRERkWk7uwoYkwf4tw6wtIP+p1yW6+PJlStXUKRIEVy6dAkLFizA5cuXMWnSJHWgXbp0aTx58gTxyd3dXa0krQU/Pz+UKVNGBVIeHh4R3qdx48Y4duwY/v33X1y8eBGrVq1SwU9CBF0xdePGDaxZs0YFEgbBwcFIkiQJvv32W1SpUiXGz/ny5UtkzpxZBWESVEVk0aJF6N27t7rP0aNHkT9/fhV83b9/P/Q+LVu2hK+vrwpU4hsDC3OUrSrQbi3g7AncO62fjvbeWa1HRUREZJokeFjc+uMTcYH++uvjKbjo3r27OqO8adMmlC9fXp2Zr1mzJrZs2YLbt29j6NChUZb8SFBgnHGQs9LZs2eHk5OTOiAdMGAA3r59G+nrG5dCyb937typshjyWrJdvXoVWbNmxZ9//hnmccePH1e3SyAUkZCQEAwZMgTp0qVTmQYpddqwYUOY93LkyBF1H/m3lGiF9/TpU+zevRujRo1CxYoVVQBSrFgxlRWoV69elBmgZs2aqc8mefLkqF+/Pq5dC7ve17Rp05ArVy51hj9nzpyYMGFC6G1yXxnTwoULUapUKXWfPHnyqM8mKosXL1YH9WnTpg29TjIyEydORKdOnSINDKJStGhR/P777yrAks8xIn///bd6/nbt2sHHx0cFpvL9z5gxI/Q+yZIlU4GqvKf4xsDCXKUpCHTcDHhkAwJvAzNqAFd3aT0qIiKi+CcTmAS9iN4mJcPrf5AHRfRE+h8bftTfLzrPF83JUx4/foyNGzeiW7du6qy2MTkIbdGiBZYvXw5dDCZjkRIcCTTOnj2rAoSpU6di9OjR0Xqs3L9kyZLqINXf319tEuhImdbMmTPD3FculytXTgUdkT3XX3/9pQKSkydPqjPoEgxIZkbIc+fOnRt9+vRR/5ZSnfBcXFzUJsHUmzdvovUeJIiS15LPQYISKZmS56hRo4bqNRDz5s3Dr7/+iuHDh+PcuXMYMWKECsAkK2Ksb9++anySMZHPpW7dulFmSuT1ihQpgoQk70kCNONsiLW1tbq8b9++MPeVoEzGGN9s4/0VSDvJMgIdNgELvgRu7gfmNAIaTATyNdV6ZERERPFHpmAfkSaOnkynz2T8lj56d//pDmDv/Mm7yUG2BA1y5jwicr2ctZc+g+ie7f7ll19C/y0193LALmepf/hBAqdPl0VJ9kTOdhu/nmQy5EBc6vbl4FQO3ufPn/9RFsOY3CbZk+bNm6vLknXYvn276hGQfgB5fuk5kIP+yN6b3C5BkgQ6cha+UKFCKqsjzyk9B5GVBUm2RDISknUwBEGSvdixYweqVaumSoYk6GnUqJG6PVOmTCoQmzx5Mtq0aRP6XD169FCZAiFZB8m4TJ8+PdLP8vr16wkeWDx8+FCVW3l5eYW5Xi5Lc7wx6eGRMcY3ZizMnVNyoPVKwKc+EPIWWNYR8B3N6WiJiIhMwKcyEnKwH11yYC0lL3KwLgftEmhI7X9syAFp7dq1Q0trVq9erTIITZtGfJIyMDAQd+7cUeMwJpclQxATcmAvzyW9FZJ1kOBAAgzj8i9jJ06cUOVZkrEwZDykHEqaqKWn48WLF+pnhw4dQm+XbdiwYep6Y5KlMA5yJGiIavyvXr1SZVMxId+N8TgMm2RR4ppkxaRnI74xY2EJ7ByBJrOATb8A+8cDWwYBAbeAmr8D1jZaj46IiChu2TnpMwfRcX0vMK/Jp+/XcgngXSp6rx0NUkYkZ9XlYLVhw4Yf3S7Xp0iRIrS5Wu4bPggx7p+Q0hdp0h08eLAqB5IMhGQr5Ox8bHXs2BGtWrVSZVWSAfjiiy9UZiMhyMF61apV1SYlSzIWyToYN0kbPH/+HIULF1blTuGlTJlS3S6kRExmTzImsy7FhnxXT2LYbC9Bm/SrhCfBUHRfU8Z97969MNfL5fCZICm9k88gvjGwsBTW1kCNEYB7OmDjT8ChafqmtMbTAPuE+eNARESUIKQMJhrlSEqWSoBbGv3/EyPss7DS3y73i8OTcTITkhwsS+Pwd999F6bP4u7du6rcSM6sG8hBofQjGJdSGZ+B3rt3r2pw/vnnn0Ovi2npi2RHpLQmvFq1aoU2IktJ0K5dkfdsurm5qQNm6W+Q0iUDuSylVLElDcqRrVsh2QzJ2nh6eqpxhCfBloxNZuOSICwq+/fvV30k4t27d6qXQcqjIlOwYEFVUhUTkgmJrE8lut+XBFIyi5ihCV9KweRy+LGePn1ajTG+sRTK0pTsBjSdBdg4ABfWAv/WBV481HpURERE2pBgocao9xf0dfkfvL9c47d4yfCPGzdOlRVJhkEO1mVGIzlwl4BDZncyruevVKmSur80E8tiZ126dIGdnV3o7dmyZVOlNZKlkLKe//3vf6r5OyakL+PAgQNqZiSp35eDVCFnxSVDIDMyyesYlwlFRBqfpa9CDvJlnYp+/fqpM/OyPkd0SaO0vOe5c+eqBnCZoeq///5TsyTJTE8RkWBBzuLL7dKoLI+R8imZ7vXWrVvqPpLRGTlypPp8ZArbU6dOqSyMzK5kTHpB5POTXgWZvUuyEdLIHhn5Dvft2/dRYCbBhrx3yRgEBASof0eUpYisOVvuK2OUf8tMYXLZeDYumWpWMjDSfC5ZLln3Q0q+ZJYoY/J5SI9JvNNZmICAADkdoX5qJSgoSLdixQr1UzPX9up0v3nrdAPddLp/Cuh0Dy9rNxYLZhL7AmmO+wEZcF/4PK9evdKdPXtW/fxsZ1bqdH/l1P9/0bD9lUt/fTy6evWqrk2bNjovLy+dlZWVOkZp1KiR7tmzZ7onT57ogoOD1f1u376tq1atms7Z2VmXLVs23bp163Tu7u66mTNnhj5X3759dR4eHjoXFxfdF198oRs9erS6j8HAgQN1+fPnD70sr1u/fv3QyxcuXNCVKFFClyRJEjUOGZuBn5+fuu7333//5HuSMQ8aNEiXNm1anZ2dnXrN9evXh7mPXCfjiczr1691/fr10xUqVEi9BycnJ12OHDl0v/zyi+7ly5eh95MxLV++PPSyv7+/rnXr1roUKVLoHBwcdJkzZ9Z16tQpzHHfvHnzdAUKFNDZ29vrkiVLpitXrpxu2bJlod+HPOf8+fN1xYoVU/fx8fHRbdu2Lcr3/PbtW12aNGl0GzZsCHO9t7e3er7wW3QYxhJ+K1++fJj7jR07VpchQwY1Vhnz/v37w9y+d+9eXdKkScN8bjH5HYrJsbOV/AcWRJqKJBUmUWNEabKEIDWR69atU6lF47MNCe7BRWBeY+DpDcDJA2ixGEiXsDMaWDqT2RdIU9wPyID7wueR5lw5Oy0z/MS0gTYMWWlbei6e3wNcvPQ9FQnciyj9A3L2XKailbIfOVaRKUS1Jme8K1eurLIq4WchMieSrZH9SDJDsv5GTIwfP141mst3F1ckayTHrrHZD6QnRtbY+Omnnz7rdygmx87ssbBkKbMDHbYA85sB/seBWXWAJtOBnLW1HhkREVHCkyAiU1lNhyClOoaSJFm8TWtSqiVT3soidjITlDkHFbHVuXNnNUXws2fP1MxUpkBKqPLmzav6eBKC9iEwacvVC2i7FshaFXj3Clj0FXBwqtajIiIislhSHy/9CKaQqViwYIFqCpcDZulvoKibsX/++WeTCSoMDd4y7XD4RRjji/Z7LGnPwQX4ciFQqDWgCwHWfQ9sHij5N61HRkRERBqSpm1pSJZZkdKmTQtzJ9ki6RKIaRkU6TGwID0bW6Du/4CK71ft3DMGWNYJePdG65ERERERUSLAwILCzvtdvi/QYCJgbQucXgLMbQy8eqr1yIiIiIjIxGkaWEhqTVZRlA50qf3KkiULhg4dGuXy9jIfsaw+GX6TxWQojhRoAbT8D7B3Ba7tBmbUAJ7e1HpUREREUTKsu0BE2vzuaDorlCyeIqs4yqIeuXPnVgu+SMOSTGkli5lERRZcMZ7ySlZZpDgkK4y2WwfMawo8OAdMr6oPNlLl1XpkREREHzWoSqPznTt31ArVcllOOib2Az2Z0UemATWFJm4yz/1Ap9Op55eZv+T55Xcn0QYWsvy8rI5Yu3bt0IYZmX3g4MGDn3ysBBJJkyZNgFFasNT5gI5bgHlNgAfngRk1gS9m64MOIiIiEyEHRFL94O/vr4ILcyAHfK9evVIVHYk9SCLT3w+cnJyQIUOGWAcvmgYWpUqVwpQpU9SS6rJ0/YkTJ+Dr6/vRsuoRkW59mVs5T548am7l0qVLJ8iYLU7S9ED7DcDCr4DrvvoMRr1xQIEvtR4ZERFRKDnTKgdG7969U6XW5rBY4q5du1CuXDkulmjB3ibAfmBjY6Omyo2LwEXTwKJfv35qNT9ZAEbelPwhGD58OFq2bBnpY1KnTo1JkyahSJEiKrCYNm0aKlSooBaSKVSo0Ef3l/vIZiCvZ/iiZNOC4XW1ev0Ys3UBmi+CzeoesD67HFjRBcFPriOkdG99wzdZzr5A8YL7ARlwX4gbckxhDiUwEiTJezGH90OmvR/Ia0QmJn+PrHRRdUrHs4ULF6Jv3774448/VI/F8ePH0atXL5WxaNOmTbSfp3z58uosxZw5cz66TbIZsoplePPnz1dpH4oBXQh87vyHbPfXqovXPCrgZPo20FnxDx4RERGROXr58iVatGiBgICAMP3NJhdYpE+fXmUtunfvHnrdsGHDMHfuXJw/fz7azyPBiZRQ7du3L1oZC3ndhw8ffvLDiS8S+W3evBlVq1ZNlOlN68PTYb2xH6ygQ0iWKghuNA2wd9F6WIlSYt8XKG5wPyAD7gtkwH2BTGU/kGPnFClSRCuwsNU6AgrfJCJpnphOeSWZDimRioiDg4PawpMvR+tfVFMYw2cp2QVImg5Y2gHWfltgPbeBfsYoF87MZXH7AsUp7gdkwH2BDLgvkNb7QUxeV9PAom7duqqnQsqYpBTq2LFjqgyqffv2offp378/bt++jdmzZ6vLY8aMUTM/yP1l6i3psdi2bRs2bdqk4TuxQLnqAG3WAAu+APyPA9OqAF8tBVJk03pkRERERKQBTQOLsWPHqgXyunXrhvv37yNNmjTo3Lkzfv3119D7yNRxN27cCL0sc+326dNHBRvSI5EvXz5s2bIFFStW1OhdWLD0RYEOm/Wrcz+5ql/r4suFQIYSWo+MiIiIiCwpsHB1dVUZCNkiM2vWrDCXf/jhB7WRifDIog8uJHNx+wjwbz2g8VTAp77WIyMiIiKiBMSlHCn2XFICbVYD2WsCwW+AxW2A/RO1HhURERERJSAGFhQ37J2BL+YCRTrIvLTAhn7Ahp9kAmatR0ZERERECYCBBcUdG1ug9l9A5YH6y/vHA0vaAW9faz0yIiIiIopnDCwobslK3GV7A42mAtZ2wNkVwJwGwMvHWo+MiIiIiOIRAwuKH/ma6aefdXADbuwDZlQHnlzXelREREREFE8YWFD8yVweaL8BcEsLPLyon472znGtR0VERERE8YCBBcUvr9z66Wi98gDP7wEzawGXNms9KiIiIiKKYwwsKP65pwXarQMylQfevgDmfwEc1a+kTkRERETmgYEFJQxHd6DlEiBfc0AXDKz6Btg+AtDptB4ZEREREcUBBhaUcGztgYaTgLLf6y/vHAWs7AEEv9V6ZEREREQUSwwsKOGno608AKgzBrCyBo7PBeY3A94803pkRERERBQLDCxIG0XaAc0XAHZOgN82YGZNINBf61ERERER0WdiYEHayVEDaLsGcE4J3D2ln472/nmtR0VEREREn4GBRQILDtHhwNXHOPLQSv2UyxYtbWH9dLTJswABN4EZ1YBrvlqPioiIiIhiiIFFAtpw2h9lRm3DVzMOY/YlG/VTLsv1Fi15Jn1wka4Y8DoAmNMQOL1U61ERERERUQwwsEggEjx0nXsU/gGvw1x/N+C1ut7igwtnD6DNKiBnHSA4CFjSHtjzP05HS0RERJRIMLBIAFLuNHj1WUR0iGy4Tm63+LIouyRAs9lA8S76y5sHAOt/BEKCtR4ZEREREX0CA4sEcPDq448yFcYknJDb5X4Wz9oGqPEbUG24/vLBycDi1sDbV1qPjIiIiIiiwMAiAdx/9jpO72cRa12U6gE0mQHY2APn1wD/1gNePNJ6ZEREREQUCQYWCcDT1TFO72cx8jQGWq0AHN2BWwf109E+vqL1qIiIiIgoAgwsEkCxTMmR2t0RVlHcx9nBBoUyJE3AUSUSGUsD7TcB7umBx37AtKrA7SNaj4qIiIiIwmFgkQBsrK0wsK6P+ndkwcWLN8HoOPswnr4MStCxJQqeOYGOW4BU+YCXD4FZdYALG7QeFREREREZYWCRQGrkSY2JXxVCKvew5U6SyehUNhOS2Nlg96WHqD9+Dy7ee6bZOE2Wayqg3TogS2Xg7Utg4ZfA4Rlaj4qIiIiI3rM1/IMSJrio6pMK+y7fx6bdB1CtbHGUzOqpMhoNC6bD13MO4/qjl2g4fg9Gf1EA1XKn0nrIpsXBFWixCFjdCzg+F1jzHfD0JlD5V33DNxERERFphhmLBCZBRPFMyVE4hU79lMvCJ40bVvUog5KZPfAiKBhfzzmCf7ZcQoilr20Rno0dUH8cUKG//rLv38Cyr4F3LCEjIiIi0hIDCxOS3NkeszsUQ9tSGdXl0Vsuotu8o3jx5p3WQzMtkp2o0A+oNw6wsgFOLQbmNQZeB2g9MiIiIiKLxcDCxNjZWGNQvdz4vXE+2NtYY8OZu2g0YS9uPHqp9dBMT6FWQIvFgL0LcHUXMKMmEHBb61ERERERWSQGFiaqWdH0WPB1CaR0dcCFe89Qb7wvfC891HpYpidbFaDtWsDFC7h/BphWBbh3RutREREREVkcBhYmrLB3MqzuUQb50yfF05dv0XrGAUz3vQqdjn0XYaQpAHTYDKTIATy7A8yoAVzZofWoiIiIiCwKAwsTJ9PTLvq6BBoXSgfp4x665iy+/+8kXr8N1npopiWZN9B+A5ChFPAmEJjbBDixSOtREREREVkMBhaJgKOdDf5smg+/1vFRs0gtPXoLX0zZj7sBr7UemmlxSg60Wg7kbgiEvAWWfw3s/gtghoeIiIgo3jGwSCSsrKzQvkwmzG5fDEmd7HDi5lPUHeeLI9efaD0002LnCDSeAZTsob+8dQiwtjcQzJm1iIiIiOITA4tEpnTWFFjVvQxyeLniwbM3+HLKfiw+dFPrYZkWa2ug+nCgxigJyfQrdC9qCQS90HpkRERERGaLgUUilMHDCcu6lUKN3KkQFByCH5aexMCVp/E2OETroZmWEl2AZrMBW0fg4gZgVh3g+QOtR0VERERkljQNLIKDgzFgwABkypQJSZIkQZYsWTB06NBPznq0Y8cOFCpUCA4ODsiaNStmzZoFS+PsYIsJLQuhd9Xs6vK/+66j1fQDePyCK1CH4VMPaL0KSJIMuHMUmF4FeHhZ61ERERERmR1NA4tRo0Zh4sSJGDduHM6dO6cu//777xg7dmykj7l69Spq166NihUr4vjx4+jVqxc6duyIjRs3wtJYW1vh28rZMKVVYTjb22D/lceoN84XZ+8Eaj0005KhuH462qTewJNrwPSqwM2DWo+KiIiIyKxoGljs3bsX9evXV4FCxowZ0aRJE1SrVg0HD0Z+0Ddp0iSV4fjrr7+QK1cu9OjRQz1u9OjRsFTVcqfC8u6l4e3hhFtPXqHxxL1Ye9Jf62GZlhTZgI5bgDQFgVePgX/rAudWaz0qIiIiIrNhq+WLlypVClOmTMHFixeRPXt2nDhxAr6+vvj7778jfcy+fftQpUqVMNdVr15dZS4i8ubNG7UZBAbqz+a/fftWbVowvG5cvn6m5I5Y8nVxfPffSfhefoTu84/i9K1M6FU5q8psEACHZEDLFbBZ3hHWlzdDt6gVQqqNREjRjpoNKT72BUp8uB+QAfcFMuC+QKayH8Tkta10Gi7jHBISgp9++kmVP9nY2Kiei+HDh6N///6RPkYCkHbt2oW5z7p161TW4+XLl6pXw9igQYMwePDgj55n/vz5cHJygrkJ1gFrrltjm78+GZU7WQhaZQ1BEk1DSNNipQtGvpuzkfHRdnX5kmdNnE3zBWDFuQyIiIiIjMnxdYsWLRAQEAA3NzdERdPDzcWLF2PevHnqID937tyhPRNp0qRBmzZt4uQ1JADp3bt3mIxF+vTpVcnVpz6c+Iz8Nm/ejKpVq8LOzi7On78ugJXH7+CnlWdx5gkw5aorJrUsgEwpnOP8tRItXR0E7x0Dmx3Dke3+emRJ4YDguuMBWwez2hcoceB+QAbcF8iA+wKZyn5gqPaJDk0Di759+6Jfv35o3ry5upw3b15cv34dI0eOjDSwSJUqFe7duxfmOrksQUL4bIWQmaNkC0++HK1/UeNzDE2KeiN7and0nnMEVx6+QOPJB/C/LwuiYg7PeHm9RKnCD0CyDMDK7rA+uwLWLx4AzefpZ5BKYKawP5L2uB+QAfcFMuC+QFrvBzF53VjVfrx+/TrWqRVrWczMiJRESYlUZEqWLImtW7eGuU4iObmewsqXLilW9iiNIt7J8Oz1O7SfdQiTdvp9cjpfi5K/OdByCWDvClzfA0yvDjy9ofWoiIiIiBKdGAcWctAva02kTZsWLi4uuHLlirpe1qOYPn16jJ6rbt26qqdi7dq1uHbtGpYvX64atxs2bBimlKl169ahl7t06aJe84cffsD58+cxYcIEVVL13XffxfStWARPV0fM71QCXxZLD4knflt/Hj0XHseroGCth2Y6slQE2q8HXFMDDy8A06oC/ie1HhVZipBgWF33RdrH+9RPuUxERGQRgcWwYcPUgnTScG1vbx96fZ48eTBt2rQYPZesVyFTxXbr1k1NHfv999+jc+fOKnAx8Pf3x40bH84gy1SzEohIliJ//vxq2ll5XZkZiiJmb2uNEQ3zYmiDPLC1tsKqE3fQZNJe3H76SuuhmY5UefXT0abMBTy/C8ysCVwOmxkjinNnVwFj8sB2bgMUuT5R/ZTL6noiIiJzDyxmz56tpoht2bKlKlsykIN8ySDEhKurK8aMGaP6Kl69egU/Pz8VuBgHLBLEyErbxipUqIBjx46paWTlMW3bto3p27A4VlZWaFXCG/M6FoeHsz3O3AlEvbG+OHDlkdZDMx3u6YD2G4CMZYGg58D8ZsCxeVqPisyVBA+LWwOBd8JeH+ivv57BBRERmXtgcfv2bWTNmjXCEinOtWz6imf2UH0XPqnd8OhFEFpOO4C5+69rPSzTkSQp8NVSIG9TIOQdsLIbsGMUVB0ZUVyRcqcNP8r0ZBHc+P66Df1YFkVEROYdWPj4+GD37t0fXb9kyRIULFgwrsZF8ShdMics7VoKdfKlxrsQHX5ZcRr9l51C0LvIm+Ytikw523AKUOZ9386OEcCqb4BgBs4UR67v/ThTEYYOCLytvx8REVEiEePpZn/99Vc1FaxkLiRLsWzZMly4cEGVSK1ZsyZ+RklxLom9DcZ+WRC507jj943nseDgDVy69wwTvyqMlK4Ju5aDSZLZyqoMAtzSAut/AI7NAZ75A03/BRxctB4dJRaScXh6HXh4GXh0CXh4CXh0Gbh7KnqPfx52am0iIiKzCizq16+P1atXY8iQIXB2dlaBRqFChdR1sngHJa6+i64VsiBnKld8u/AYDl9/gnrjfDG5VWE1VS0BKNZJH1wsaQ9c3gLMqgW0+A9w9dJ6ZGRKXj5+HzQYBQ+yPb4CBAd9/vO6cD8jIqLE47MWyCtbtqyalYnMQ8WcnljZvTQ6zj6MKw9eoOmkfRjVOB8aFEyr9dBMQ85aQNs1+mZu/xPAtCrAV0uAlDm0HhklpHdvgMdXjYIHvw//fvU48sfZOgLJswAeWYAU2QCPbEDyzMB/bYBndyPps3jv6Gz9fubChS2JiMj0abryNpmOzCldsKJ7afRaeBzbzt9Hr0XHcdY/ED/WyAkbayuth6e9dEWADpuBeU30Z6GnVwO+XAB4l9J6ZBSXpElfDvYlYJCMg3EJk5Q06aLoQ5LMlkfWD8FDiqz6n+7p9aV14dX8XT/7E6zCBRdGl08tBi5uBCoPAIq0B6w/zMRHRESU6AMLWSlbSmgiExzMWUwSKzdHO0xtXQR/b76A8dv9MGXXFZy/+wxjmxeEu5M2y8ibFDnjLMHF/C+A24eB2Q2ARpOB3B8WdKREIuiFUcbBuP/BDwh6Fvnj7F0iDh5k37B3jtkYfOoBzWbrZ4cybuR2SwPU+E0fqKz9Tp8lW/e9vs+n9t/6IJeIiMgcAgtZHduYTDEra0r8+++/GDx4cFyOjTQg2Ym+1XMiV2o39P3vJHZdfID6431VwJHNy1Xr4WnPOQXQZjWwtCNwYS3wXzv9QWHJ7lqPjMILCQECbkYQPFzWz7gUGStrIKl3BMFDVsA1lTQnxd0YJbjIWRvvruzC8d0bUaBsddhmLvchM9FpO3B4BrB16IcyvMJtgMoDAafkcTcOIiIirZq3w5PVs3Pnzo1FixahQ4cOcTEu0lidfGmQKYUzvp59BNcevUSD8XswpnlBVPVhMynsnYAv5uhnizo0Ddj4E/D0JlB9OEtVtPDq6fuypfDN035A8JvIH5ckebjg4X0AkTyTfsrhhGJtA513Gdw+E4j83mXC7kPyb5lAwKc+sPlX4MQC4Mgs4NxqoMpgoEDLiMusiIiIEnOPRYkSJfD111/H1dORCZCpaFf1KI3u849i/5XH6DT7MHpXzY4eFbPC2tL7LuSAr9af+vr5LQOBAxP1Z8EbTQHskmg9OvMja4g8uRbxzEsvHkT+OBt7faO0ChqMS5iyJa4z/tK83XASULAVsLYP8OAcsKrH+/Kov4BUebUeIRERUdwEFq9evcL//vc/pE3LWYTMjYeLA+Z0KI5ha87i333X8ffmizjnH4g/m+aHs4OF9/5LSUyZXoB7OmB5F+DcKmD2fX1Td2I6aDWlxmkJEsIED+/7ICSokJXQI+OaOoLgISvgngGwMaP9NGNpoMtu4MAkYMdvwM0DwORyQLHOQMWfAEc3rUdIREQWLMb/x02WLFmY5m2dTodnz57ByckJc+fOjevxkQmws7HG4Pp54JPGTa3Svf70XVx9+AJTWhVBBg8nrYenvbxN9OsNLGwJ3NyvnzFKpqNNllHrkZmmt6/eBwyGvgejn28CIn+cnZO+SdqQcTAuYXKwoP4fGzug1DdA7kb6MryzK/QZszPLgOojgDyN47YPhIiIKL4Ci9GjR4cJLGSWqJQpU6J48eIq6CDz9UXRDMjq6Youc4+o2aLqjffF+BaFUDprCq2Hpr1MZYEOG4G5TfQHydJk22IxkLYQLLZx+tmdDyVLoVmIy/qG6kjXbrACkqY3Ch6MMhAyWxIPmD9wTws0+xe4vBVY1xd47Acs7aDvwZDyKK6zQkREph5YtG3bNn5GQolCYe9kWN2jDDrPOYwTtwLQesZB/FwrF9qVzhjlNMQWwTMX0FHWumgK3DsNzKoNNP0XyF4NZut14IdeB+PgQQ5y376M/HGO7hEHD9I4zR6VmMlaGei2D9jzP2D3n8C13cDE0vqZysr/EPNpcImIiOIzsDh58mS0nzBfvnyfOxZKJFK5O2JR55L4afkpLDt6G0PWnMWZO4EY3jAPHO0sfFYkOavebj2wuBVwZQewoDlQ52+gcCIOyIPf6ReHCx88yM/n9yJ/nLUtkCxTuODh/cxLMm2vpQeicUlmsSrfF8jXFFjfD7i4HtgzBji9FKgxEshZh583ERGZRmBRoEABdTZa+imiIvfhAnmWQQKIv5rmVzNHDV97FkuP3sLlB88xpVVheLk5wqJJA22L/4DV3+qnB13dEwi4BVT82bQP7l48MmqaNgQPkn24AoS8jfxxzp4RBw/JvPX9AJRwpK+nxULg/Dpg/Y9AwA1g0VdAtmr6lb4lI0RERKRlYHH16tX4en1KxCSQ7FAmE3J4uaopaU/cfIq6Y30xqVVhFMpg4f02tvZAg4n66Wh3/Q7s+kMfXNT9n/42rbx7ow8UDMGDNFEb/v3qSeSPs3X8MOuScemSNFMnSZqQ74CiI2ctIHMFYPdfwJ5/gEubgCs7gbJ9gNI9ATsLD/6JiEi7wMLb2zt+Xp3MQplsKdR6F7LOxcV7z9F88n4Ma5gHzYqkh0WT7ESln/VNtmt667MXz/yBZnP0WY2QYFhd90Xax/tgdd0NMF5xOTYksyivY7zWgyF4eHoD0IVE/lgJhMLMvPQ+iHBLx4XYEuNCjpUHAPmbA+u+15fm7Rih3w9lDZZsVbQeIRERmZnPnuD97NmzuHHjBoKCgsJcX69evbgYFyUy3h7OWNatNPosPo6NZ+7hhyUncfZOIH6unUtNV2vRpL/CNQ3wXxv9wd3MmvrVlHeOgm3gHRSR+1yfqO/PqDEK8Inm79Cb5x8CB+PgQbIQQc8jf5y96/tpWsMFD8mz6A9GybzId9tqhX462o0/A0+uAvMaA7nqAjV+06/DQkREpEVgceXKFTRs2BCnTp0K03dhmBGIPRaWy8XBFhNbFsbYbZcxestFzNp7DRfuPsP4loWQ3FnD8h9TIDNDtV0LzG+mnzFK+i7CC/QHFrcGms3+EFyEBOunZw1d68Go/0Gmc42MlY2+xyGimZdkFWdT7vWguCfft6xvIb0WsrDe/onAudXA5W36maNKdNO2RI+IiCwzsOjZsycyZcqErVu3qp8HDx7Eo0eP0KdPH/z555/xM0pKNKytrdCzSjbkTO2K3ouOY9+VR6g3zhdTWxdBrtQWviqwrGnRfiMwvlgkq0i/nxxhRTfg5CJ95kH6IYLfRP6cTh5GC8UZLRwnTbw8UKTwZCHB6sOBAi2AtX2AG/uALQOB4/P1a1/IeixEREQJFVjs27cP27ZtQ4oUKdTieLKVKVMGI0eOxLfffotjx4597ljIjFTPnQrLu+v7Lq4/eolGE/bir2b5UStvali0wDuRBBVGgp4B59d8uGxjry9TCl++JJtT8ngfMpkhr9z6aZGl32LTAODhBeDfOkDeZkC1YYCrl9YjJCIiSwgspNTJ1dVV/VuCizt37iBHjhyqwfvChQvxMUZKpLJ7uWJl99L4ZsEx7L70EN3mHcU3lbLiuyrZVWbDIkW17oOxfF8AeZvqg4ekGeKmqZsofHmUZC5y1AS2DgUOzwBOLQYubgAq/QIU6QDYfHYbHhERWaAYd9XmyZMHJ06cUP8uXrw4fv/9d+zZswdDhgxB5syZ42OMlIgldbLHzLZF0amsfv586b/4es5hPHsdxboI5swlmmeCC7YCslXVrzvAoILiU5Jk+kUcO20D0hQE3gQC638AplYAbh7SenRERGTOgcUvv/yCkBD9dJUSTMgaF2XLlsW6devwv//9Lz7GSImcrY01fq7tg7+b5Ye9rTW2nLuPhhP24urDF7A43qX0sz8hsoyNFeCWVn8/ooTuAeq4Faj9N+DoDtw9BUyvAqz6Bnj5WOvRERGROQUWRYoUwaRJk1CyZEk0atRIXZc1a1acP38eDx8+xP3791GpUqX4HCslco0KpcN/nUsilZsjLt9/jvrjfLHjwn1YFMk+yJSySvjg4v1lmQKUWQrSgux3RTsAPY4ABVrqrzs6GxhbCDjyL/D+pBIREVGsAov8+fPjhx9+QOrUqdG6dWvs2LEj9LbkyZOHTjdLFJX86ZNi1TelUdg7GQJfv0P7WYcweadf6LTFFkGmkpUpZd3CNbJLJsN4qlkirbikBBpMANptADxz61dlX/0tMKMa4K8vhSUiIvrswGL69Om4e/cuxo8frxbGq1y5sspYjBgxArdv347u0xDB09UR8zsVR/Oi6RGiA0auP49ei47j9VsLWgNFgodep/HuqxU47N1V/USvUwwqyLR4lwQ67wKqjwDsXYBbh4ApFYB1PwCvA7QeHRERJeYeCycnJ7Rt21ZlKy5evIjmzZtj8uTJyJgxI2rXro1ly5bF30jJrDjY2mBko7wYWj83bK2tsPL4HTSZtBe3n76CxbC2gc67DG4nL6l+svyJTJLMDFWyO9DjsH6RPV0IcHAyMLYIcGIRYEnZRiIiitvmbYMsWbJg2LBhuHbtGhYsWID9+/ejadOmn/t0ZIGkfK5VyYyY27G4Wpn79O1A1Xdx8CobRYlMjpTuNZkBtF6pX0/lxX1g+dfArDrA/fNaj46IiBJzYCEkcyEZDNlkfYtOnTrF3cjIYpTI7IFVPUrDJ7UbHj4PQoup+zF3/3Wth0VEEclcAei6B6j8K2CbBLjuC0wqDWz+FXjzXOvRERFRYgosbt26pTIV0l8hs0BJxmLChAnw9/dXs0YRfY50yZywtGsp1MmXGu9CdPhlxWn8tPwUgt5xFhoik2PrAJTtA3Q/AOSorV9Nfs8/wPhiwNmVLI8iIrJQ0Q4sFi9ejBo1aiBTpkyYOHEimjVrpvosdu7cqWaJSpIkSfyOlMxeEnsbjP2yIH6okUMtCjz/wA20nLYfD5690XpoRBSRZN7Al/OBLxfpV4gPvA0sbg3MawI88tN6dEREZKqBxVdffaWCh+XLl+PmzZtqNijJWsSGNH1LnX34rXv37hHef9asWR/d19HRMVZjINMi32m3ClkxvU0RuDrY4tC1J6g3zhenbnEGGiKTlaMG0P0gUO4HwMYeuLwFmFAS2D4CeGtBEzIQEVk465iUQElQUadOHVhbx6o1I9ShQ4dUCZVh27x5s7o+qiZwNze3MI+5fp21+OaoUk4vrOhRGplTOsM/4LWaMWrlcU5rTOYnOESHA1cf48hDK/VTLidKdkmASj8D3fYDWSoBwW+AnaOACSWAi5u0Hh0RESUA2+je0dPTM85fPGXKlGEu//bbb2q2qfLly0d5RjtVqlRxPhYyPVlSumBF99LoueAYtl94gJ4Lj+PsnUD8UCMnbKy5ICMlfhtO+2Pw6rMqeAZsMPvSYaR2d8TAuj6okSfcAoqJhUcW4Ktl+l6LDf2BJ9eA+U2BnHWAGiP1JVNERGTZgUV8CwoKwty5c9G7d+8oV/F+/vw5vL29ERISgkKFCqmSrNy5c0d6/zdv3qjNIDAwUP18+/at2rRgeF2tXj8xSWIDTGxRAGO2XsakXVcxedcVnPMPwN9N88E9iR0SO+4LlmvjmXv4ZuEJhM9P3A14ja5zj2Js8/yontsLiVb22oB3OVj7/gnrg5NhdX4NdJe3IqTs9wgp3lVfMkVhSLZqv98Dlb1yv3QfJbKk5EkUC8b/P5Cp7AcxeW0rnc40pu+Q5vAWLVqoVb3TpEkT4X327duHS5cuIV++fAgICMCff/6JXbt24cyZM0iXLl2Ejxk0aBAGDx780fXz589XC/5R4nH0oRXm+1njbYgVUjjq0ClHMFLxK6RESKqdBh+1wdMguRTRgaMO7vbAT/mDYWejr1mN4nyLyXN9dQv5bv6LFC8uqMvPHFLjZPo2eOjqo/XQTMaJR1ZYds0aT4M+fNFJ7XVolDEE+T1M4n/TRGShXr58qY7R5dhbWhISRWBRvXp12NvbY/Xq1TGKoHLlyoUvv/wSQ4cOjXbGIn369Hj48OEnP5z4IuOWfpKqVavCzi7xn3VPSGf9A9F13nHcCXgNZwcb/NUkLyrnjPsyvYTCfcEySS/FVzMOx+gxdjZWsLOxVivV28q/ra3VT9v3P+3U9Ybr9Pe1C3P7+9veX6e/Tf8Y/WON7vvRY98/t+G1IxlH6Bgjem4rKzicWwKbbYNg9eKBek8huRshuPIQwNWyy1sjy14ZQoxEn72iz8L/P5Cp7Ady7JwiRYpoBRafVQr19OlTLFmyBH5+fujbty+SJ0+Oo0ePwsvLC2nTpo3x80kD9pYtW7Bs2bIYPU4+4IIFC+Ly5cuR3sfBwUFtET1W619UUxhDYpM/gwdWf1MGXecdVSt0d51/HL2rZEePSlmjLKEzddwXLMP9wNfYfO4e5n3GApBvg3V4GxyMxC0ZklqPxPe2i/Gl1WbYnFmGl2fWY5rNl1hpXxtWtrZGAdCHoORDIPMh4AkNfowDpdDHhA+m9Ncbgh7D830Igj483vh6w+M/vNaH2+OqREnKn4avv/BRUCHkOnkVub1mvrQsi7JQ/P8Dab0fxOR1YxxYnDx5ElWqVIG7u7taHE9W25bAQoICKWOaPXt2TJ8SM2fOVM3htWvXjtHjZLXvU6dOoVatWjF+TUq8PFwcMK9jcQxdcxaz913HX5sv4tzdQPzRJD+cHUymbYgIkhC+eO85Np+9i83n7uPEzacxevyMNkVQMEMyvA0JwbtgndoM/34bHKIWk3wXHKKCjnch8vP9v0Mvv7/9/f2MH2+4/u07/fO8DX+70ePD3m54TNj7G8ZgPLaIZrh6GuKEX4LaYoFVeQy3m4EC1n7oFTwD1V5sxS9v2+GoLjsSAzmP8SFbE3GQ8iHzEz4o+pANevry7fvm/YjJJyi3y4mUklk8EvQ9EhHFVIyPwqS5um3btvj999/h6uoaer0c3Ev9VUxJE7YEFm3atIGtbdjhyMJ7kgEZOXKkujxkyBCUKFFCrZ8hWZM//vhDZTs6duwY49elxE3+hz2kfh74pHbDgJWnse7UXVx58AJTWxdB+uRsvCDtyIG4rL+y+ew9bDl3Dzcevwxze4H0SVE5lyf+3XsNj54HRXimWs5Lp3J3RPkcnon6LHWIBDRqC8Hbd+GDogp4964V7pyZD8+DI+Hz5jqWOQzCvSxNcTlfH7yyS64PlCIIiiIKqgzBjf62yIKqiJ/rw/N9eG5DIGV4rvCkiDgoOARBCZREuv8s8uCDiCjRBhay9sTkyZM/ul4CgLt378Z4AFICJZmO9u3bf3SbXG+8ZsaTJ09UhkReJ1myZChcuDD27t0LHx82AFqq5sUyIJuXCzrPOYrzd5+pxfTGtyiEUllTaD00siDP37zDrosPsOXsPWy7cF+dhTawt7VGmawpUNXHSwUUnq76RT2zebqo2Z8kbDA+bDWEETLlbGIOKoS1tRXsZZP288gmgUrVFSjRDNgyEDg2B15+/8Hr9magykCgUFt5EphC5kmyL2EyN0ZBS0TZmvCBycdBjP56vwfPMO/AzU+OwbDfEBGZVWAh/QqGKVuNXbx48aN1KaKjWrVq6o92RHbs2BHm8ujRo9VGZKywd3Ks/qY0Os85gpO3AtBqxkEMqJ0LbUrpV3Ynig/3pF/ifVZi7+VH6uy1QTInO7XIowQT5bKngJP9x39qZZ2KiV8VMlrHQi9VYl/H4nM4ewD1xwEFWwFr+wD3TgFrvgOOzQVq/wWkKajp8OTviL5XA3CUabrikAQs284/UNMMRzaTioOtNdImSxKnr0tEZBKBRb169VRJkkwPa/iDK5mFH3/8EY0bN46PMRJ9Umr3JFjcuSR+WnYKy47dxqDVZ3HmTiCGNcwDBzkaIIolOQFy4d4zbD6jDyZO3AoIc3tGDycVSFT1SYXC3smilW2Q4EHuv+/yfWzafQDVyhZHyayJu/wpVjIUB77eARyaBmwbBtw+AkypCBTtCFT6BUiSFOZGvmsJJCPKXhm8eReCWv/sxs+1c6F50fQ8YUJE5hNY/PXXX2jSpIlqtn716pVaJVtKk0qWLInhw4fHzyiJokHOJP7VLD980rhhxLpz+O/ILVy6/xyTWxWGlxvLCCjmpFzl4LXHoZmJm49fhd4mx3YF0ydFFR8vVPPxUivFf84BnxxYFs+UHI/O6dRPiw0qDGxsgRJdgNwNgE2/AKf+Aw5NBc6uAKoOBfI3T9yLekQgsuyVrMLepXwWrD5xB4evP0H/Zaew7pQ/fmucD2mTMoNBRGYQWMhsUDKfrq+vr5ohSlbClhWwZaYoIq3JgV3HspmRI5Uresw/huM3n6LuWF8VXMjsOkTR6ZfYeeGBmslp+4UHCHj1NkxJStlsKVAllxcqGfVLUDyQtS0aT9OXR637Hnh4EVjRRfVhoNafgJd59dZFlb36qoQ3Zu65ij82XsDuSw9RffQu/FI7F75g9oKITMxnz81ZpkwZtRGZorLZUmJVj9LoNPuwmu7zi8n7MbxhHjQtkl7roZEJkvp2WV9CMhP7/cL2SyR3tkelnJ6qzEmCioj6JSgeZS4PdNkD7B8P7PwduL4HmFQGKNEVqNAPcPgwO2FiF1n2Sn7KCZOKOT3R978TOHrjKfpJ9uL0XfzWKC/SMHtBRCYixv+H/N///hfh9XLWxNHRUU0FW65cOdjYsK6dtOXt4Yxl3Uqj96Lj2HT2HvouOalW7v65Vi41pzxZdr+EzCImgYRsp26H7ZfIlML5fb+EFwpliF6/BMUjW3ugzHdAnibAhn7A+TXAvnHA6WVAjRGATwOzK4+KiJTb/delFGb4XsWfmy6omchU9qJOLjQrwuwFESXCwEJmZXrw4AFevnyppnw1TAPr5OQEFxcX3L9/H5kzZ8b27duRPj3PDpO2XBxsMemrwvhn6yW1zdxzDRfuPlNT0iZzjmz+SzJHMs3noauPVZAp/RK3noTtl5AAQkqcJJjI6umi6VgpEknTA83nARc3Aev7Ak+uAf+1BTJX1JdHpcgKcydBbqdymVUp3vf/ncCxG0/x41LpvbiL3xrnVRNZEBFpJcanbUeMGIGiRYvi0qVLePTokdpkqtnixYvjn3/+UTNEpUqVCt999138jJjoM+bS/65qdhVgONnbYK/fI9Qb74vzdz+eNpnMy7PXb7Hm5B30XHgMhYduRotpBzBr7zUVVEi/hAQSoxrnxcGfqmBp11LoWiELg4rEIHs1oNt+oHw/wMYBuLIdmFhSP5NUUNgFCc05e7GkSyn8VCunWitl58UHqDZ6FxYfvhnpFO5ERCaXsfjll1+wdOlSZMmSJfQ6KX/6888/1XSzV65cUatyc+pZMjU18qRCphT6vgtZDbnRhL34q2l+1MxrQesFWAD/gFdqoTrJTOy/8ijMqskezvZqkToJKKQPJ4k9SzYTLbskQMX+QL5mwPofgMtbgF1/ACcXATX/AHLUgCVkL74ul0WtmSLZC5ms4oclJ7H+lD9GNsqn1kQhIjLpwMLf3x/v3r376Hq5zrDydpo0afDs2bO4GSFRHJLZoqSpW2aM8r38EF3nHcW3lbKiV5XsKrNBiY+cnT3n/75f4txdnL4dNhOVOaUzqr4vcZKZwdgvYWY8sgAtlwDnVuv7L57eABZ8AeSoBdT4DUjmDXMnWTbJuE3dfQV/b76oZjOrOnonfq3jgyaF07H3gohMN7CoWLEiOnfujGnTpqFgQf1qqMeOHUPXrl1RqVIldfnUqVPIlClT3I+WKA4kdbLHrHZFMXL9eUz3vYr/bbuMs/7PMPqL/HB1tNN6eBTNfomDV/XrS8h2+2nYfonC0i/xvvlaSkbIzMmX7lMPyFIJ2PU7sG88cGEd4LcdKPc9UOobwNYB5kwCZlnzokouT/T57yRO3HyqJqxYf/ouRjTMy+wFEZlmYDF9+nS0atUKhQsXhp2dXWi2onLlyuo2IU3cspAekamSWaEG1PGBT2o39F9+SjXzNpywF1NbF1EzApHpCXz99v36Evew/cJ9PHv9IXPqaGeNMllTqoXqpKk1hYt5H0RSJBxcgKpDgPwtgLV9gOu+wLahwIkF+ubuLBVh7rJ6umJpl5KYuvsqRm++iG3n76OaZC/q5kbjQmmZvSAi0wospDFbFsg7f/68atoWOXLkUJtxVoMoMWhcOB2yeLqg85zDuHz/OeqP88XYFoVQPntKrYdGAO48faWCvs1R9EvIomJlsqZgvwR94JkTaLtGv2r3xp+BR5eBOQ2A3I2A6sMBtzQw9xMnMhGBZC+k9+LErQD1U1btHtkoL7zcmL0govjx2Ss95cyZU21EiV2B9EmxukcZdJl7RC081W7mQfSrmROdymbm2T0N+iVkrRFDidOZO2H7JbKkdFYlTpKZKJCe/RIUBfndlcbu7NWB7SOAg1OAM8uAS5uACv2B4p0BG/Mufczm5ap6L6bsvoIxmy+p7EXVv3diYN3caMTsBRGZSmBx69YtrFq1Sk0tGxQUFOa2v//+O67GRpRgPN0cseDrEvh1xRksOnwTI9adx9k7gfitcT442vFMeHwKemfol7iLLefuf9QvUcT7w/oSmdkvQTHl6A7UHAUUeF8edesQsOln4Ph8oPZfgHdJmHv2oluFrOp3SLIWJ28FoM/77MUIZi+ISOvAYuvWrahXr55aBE/KofLkyYNr166pM42FChWK6/ERJRgHWxu1wJRPGjcMWXMWK47fgd+DF5jcqjDSJOWiU3Ep4NVbNe++ZCV2RNAvUS5bSpWZqJzTEx7sl6C4kDo/0H4TcHwusHkgcP8MMLMGUKAlUGUw4GLe5Y/ZvVyxrGspTN51BWO2XMTW99mLQfVyo2FBZi+ISKPAon///vj+++8xePBguLq6qjUtPD090bJlS9SoYf7zhpN5k/+5timVUf1PuPv8ozh1OwD1xvli4leFUTRjcq2Hl6hJJkLWlzD0S7wL+dAvIc3WUg8uZ1XLZEvBLBHFD2troFBrIGcdYMsg4Oi/wPF5wPk1QOVfgcLtAGsbs85edK/4IXshf996L5bshcwclUdlbomIEjSwOHfuHBYsWKB/sK0tXr16pWaBGjJkCOrXr6+mnSVK7Epm8cDK7qXx9ZwjOOcfiBZT92NwvTxoUTyD1kNLNCSLKT0Shn4J6Z0IP/e+lDfJQU7B9Em5jgglHKfkQL3/AQVbAWt7A3dP6sukjs0Fav8NpC1k9uv5LOtWCpN3+uGfrZfUBAmHrj3G4Hq5Ub9AGmYviCjhAgtnZ+fQvorUqVPDz88PuXPnVpcfPnz4+SMhMjHpkzthadeSai74tSf98dPyUzhzJ0A1PtrbWms9PJPtl5BshByoSHbiTsDr0NskbijinRxVfPQzOXFaX9Jc+qLA1zuAQ9P109LeOQZMrQQUaafPYCRJBnNlZ2ONHpWyqZJDyV7IwpK9Fh3H2lP+GC7ZC1dmL4goAQKLEiVKwNfXF7ly5UKtWrXQp08ftSDesmXL1G1E5sTJ3hbjviyo1rv4c9MFzDtwA5fuPceErwpxrQSjfgnpk5CshKwz8ezNh36JJHY2KJc9hcpKVGK/BJkiKX0q/jXgUx/YPAA4uQg4PAM4u+r9mhhf6kuozFTOVG5Y3q00Ju3ww/+2XVK/x4bsRb38zF4QUTwHFjLr0/Pnz9W/pc9C/r1o0SJky5aNM0KRWZL/sUpdcq7Urui54DgOXnuMemN9MaV1EeRJ6w5LdOvJS3UAIpmJA1cef9QvUdVH3y9ROiv7JSiRcPUCGk3Rl0et+x54cB5Y2Q04Ols/e1SqPDDn7MU3lT9kL6SEsefC4ypTO7xhXqR05QkBIoqHwCI4OFhNNZsvX77QsqhJkybF5CmIEq1KOb2wXPouZh/GlYcv0GTSXoxqnA/1C6SFJfRLSKmETAm7+dx91XdiLJuhX0LWl0jHfglKxDKVBbr4AvsnADtGATf3A5PLAcW7ABX6AY5uMFe5UrthRffSmLDdD2O3XcKms/fUiRRmL4goXgILGxsbVKtWTTVwJ02aNCYPJTIL0nAswUXPhcew48IDdVbvnP8z9K2ew+wWa3vzLhj7r7xfX+LsfdwNDNcvkTG5WqhOMhMZ2S9B5kQWzivdE8jTGNjQHzi3Ctg/Hji9VL9yt1xvpgfZkr3oWSWbOlEg2QuZdEH+zq0/dRdDG+Rh9oKI4rYUStatuHLlCjJlyhTThxKZBfckdpjepqjquZi4ww+Tdvrh/N1A/NO8oLotMQt4+RbbDf0SFx/guVG/hJO9jVpfQg44Kub0RHJne03HShTv3NMBX8wBLm8B1vUFHl8Blnb4UB6VIhvMlazns7JHaYzffhnjtl3GhjN3ceDqIwypnwd18qVm9oKI4iawGDZsmFrHYujQoShcuLAqhzLm5ma+aWIiA8lO/Fgjpyod+GHJCZW9aDB+D6a2Loysnq5ITG4+1vdLbH5f9hBs1C8hZyf1q157olQW9kuQhcpaBei6D9jzD7D7L+DqTmBCSaD0t0DZ7wF7J5hr9qJXlezvsxcnVQnkNwuOqVW7JXvBCSyIKNaBhcwEJWT1beMzFlKDLZelD4PIUkjdceYUzug85wiuPnyBBuP34p/mBVA5lxdMlfyuysJYhmDi/N1nYW7P7vVhfYn87Jcg0rNzBCr8CORrCqz/Ebi0SR9knPwPqDkKyKn/f6M5yp3GXa3rI9kL2dafluzFYwypnxt18qXRenhElJgDi+3bt8fPSIgSKZkZSkoGus07ioNXH6Pj7MP4vloOdKuQxWTKBaRfYp/fo9CZnO4Fvgm9TeIGWVVcggnZvD3YL0EUqeSZgRaLgfNrgQ39gIAbwMIvgew19AFGsowwR7J2z3dVDdmLE+qERI/577MX9fNwKmki+rzAonz58jF9CJHZk5KAeR2LY8jqs5iz/zr+2HgBZ+8E4o+m+dRaGFp4+jJI9UtI43VE/RLls6cMXV8iGfsliKJPThjkqgNkqQjs+gPYOw64uAG4sgMo20ff+G3rYLYnUlb1KINx2y5h/A4/rDt1V03yIMFF7XyptR4eEWnss454du/ejcmTJ6sm7v/++w9p06bFnDlzVEN3mTJl4n6URImkHlnqjqXp8deVp9UKtn4PnmNq6yJqFe+E6peQKSK3RNAv4Sn9EpKVyOWFklk82C9BFFv2zkCVQUD+FsC6PsDVXcD24cCJhUCtP4CslWGu2Yve1XKgWu5UodmL7vOPYt3p1BhSLzezF0QWLMaBxdKlS9GqVSu0bNkSR48exZs3+pKKgIAAjBgxAuvWrYuPcRIlGl8Wy6DWdegy96j6H269cb4Y37KQan6OayEhH/olpMQpfL9EDi/X0PUl8qV1Z78EUXxImR1ovUo/He3Gn4HHfsDcRvrVvKuPBNzTmnX2Qta8mLDDTy2ot9/vEYY1yIOaeZm9ILJEnzUrlCyK17p1ayxcuDD0+tKlS6vbiEi/xsOqHqVVU7cc+LeafhADaudCm1IZY913If0Se9/3S2wN1y8hs1UVzZgMVX1SqcxEBg/znK2GyOTI73XeJkC2asCOkcCBycDZlcClLfqF9Up01a+PYYbZiz6SvfDRZy8u3HuGrvOOqilpZWpaTktNZFliHFhcuHAB5cqV++h6d3d3PH36NK7GRZTopUmaBP91KYn+y05h+bHbGLT6rFpsSsqlHGxtVJmSzKxy5KEVPK4+RsmsnpEusif9EtvO69eX2HXxAV4EfZh9zVn6JXK8X18ihyeSOvF/5ESakZW5a4wECrQA1vYBbh4ANg8Ajs/Xr32RsTTMUd507lj1TWmM3XoZE3f6YY1kL67osxc18jB7QWQpYhxYpEqVCpcvX0bGjGFnvvD19UXmzJnjcmxEiZ70MfzdLD98Urth5PpzWHz4Fi7df47mRdJjzNZL8A+Q1axtMPvSYaR2d8TAuj6h/xO+8Uj6Je6qYOLw9Sdh+iW83PTrS0iJU6ksHipQISITkiov0G4DcHwesGUg8OAcMKsWkK85UG0o4OIJcyN/h76vLr0X+pmjLt57rkpC6+ZPo3ovOEkEkfmLcWDRqVMn9OzZEzNmzFAlHXfu3MG+ffvUonkDBgyIn1ESJWLye9KpXGbkSOWKHvOP4tiNp2oL727Aa/U/4Rp5vHDlwQv1P2VjOVO975fI5YW87JcgMn3W1kChVkDO2sDWIcCRWcDJhcCF9UDlAUCR9oC1+Z0UyJcuKVZ/Uwb/bLmESTv9sPrEHezze4hhDfKiRp5UWg+PiOKRdUwf0K9fP7Ro0QKVK1fG8+fPVVlUx44d0blzZ3zzzTcxei7JeshBV/ite/fukT5GZqHKmTMnHB0dkTdvXjaLU6JRLntKLO9WGraRBASGfMSG0/dUUCFlUSUze+DXOj7Y1bciNvQqp2qZ86fnonVEiYpTcqDuGKDjViB1fuBNALDue2BqReDW4Q/3CwmG1XVfpH28T/2Uy4k5e/FDjZzqb55MZvHweRC6zD2CnguP4cmLIK2HR0SmkrGQA/+ff/4Zffv2VSVRElz4+PjAxcUlxi9+6NChMCt1nz59GlWrVkXTpk0jvP/evXvx5ZdfYuTIkahTpw7mz5+PBg0aqNmp8uTJE+PXJ0po95+9wTujkqbIdK+YBZ3KZma/BJE5SVcY6LQdODwD2DoU8D8BTKsCFG4DpC8ObBsK28A7KCL3vT4RcEsD1BgF+NRDYiUnQlT2YuslTN7ph5XH72DP5UcY0TCPmq6WiCw8YzF37ly8fPkS9vb2KqAoVqzYZwUVImXKlKpnw7CtWbMGWbJkiXQRvn/++Qc1atRQQU2uXLkwdOhQFCpUCOPGjfus1ydKaPefSU/Fp2X3cmVQQWSOpPSpWCfgm8NA/i/1uUopkVrRFQi8E/a+gf7A4tbA2VVI7L1mP9bIiWXdSiOryl68wddzjqDXwmNqYgoisuDA4rvvvoOnp6cqh5IyJOOMQ2wEBQWpoKV9+/aRTscpvRxVqlQJc1316tXV9USJgaerY5zej4gSKWnebjgJaLMGsI6seOB9dnNDv0RdFmVQIH1SrPmmDLqUzwKp5lxx/A6qjt6lJqggIgsthfL398eGDRuwYMECNGvWDE5OTqp0SRbMK1Wq1GcPZMWKFWq62rZt20Z6n7t378LLyyvMdXJZro+MLOBnWMRPBAYGqp9v375VmxYMr6vV65N2CqZzRSo3B7X2REQFURJSp3J3UPfj/mE5+DfBclkFv4NtyLso7qEDAm/j3ZVd0HmXQWInrep9qmRB5Rwe+HHZGVx5+AKdZh9G/fyp8UutnEjqZH5rfXwu/l0gU9kPYvLaMQ4sbG1tVX+DbFIStXz5ctXrULFiRaRLlw5+fn74HNOnT0fNmjWRJk0axCXpxxg8ePBH12/atEkFRVravHmzpq9P2qiVygozAg3JQuPsnE4FGzW9XmLjhvUajY60xL8JlkcatVVPxScc370Rt8/oT4yZi25ZgHX21th+xworT/hj+9k7+CJzCPIk/3QfmiXh3wXSej+Q4/14CyyMyYG5lCI9efIE169fx7lz5z7reeSxW7ZswbJly6K8n/Rh3LsXNmUql+X6yPTv3x+9e/cOk7FInz49qlWrBjc3N2gV+ckOIo3qdnY8O2NpagEodOYehq07j7tGq2bLOhY/18yJ6rnDZuXI/PFvguWyuu6mb9T+hAKlqyJ/po8Xp03s6gM4dvMp+i07jSsPX2LqBRs0LJAaP9fKCfcklv27wL8LZCr7gaHaJ94CC0OmYt68edi6das6UJfZmpYsWfI5T4eZM2eqvo3atWtHeb+SJUuq1+vVq1fodfJhy/WRcXBwUFt48uVo/YtqCmMgbdQpkA4186XFvsv3sWn3AVQrWzzKlbfJMvBvggXKXE4/+5M0akdYIKlnu3MkkNwbSJEV5qZY5pRY17Mc/t58EVN3X8Hy4/7Ye+UxRjbKi0o5eaKFfxdI6/0gJq8b4+bt5s2bqyBAmrhlpe0dO3aoaWdlhiZZXyKmQkJCVGDRpk0bVWZlrHXr1irjYCAL80l/x19//YXz589j0KBBOHz4MHr06BHj1yXSmgQRxTMlR+EUOvWTQQWRhc4SJVPKKuH/Bry/bOsI3D4ETCoD7J8k/+OEuZGZo36qlQtLupRC5hTOqg+t/azD6LP4BAJesceAKLGIcWBhY2ODxYsXqyZumebVOFsg61DElJRA3bhxQ80GFZ5cL69jIM3h0s8xZcoU5M+fX2VIpOmba1gQEVGiJetUNJsNuKUOe71kMprNAXocBjJXAN69Ajb8CMyuBzy5DnNU2DsZ1vUsi05lM0EmiFx69Baqjd6J7efvaz00IoqPUigpfzL27NkzNUPUtGnTcOTIkRhPPyu9DjpdxOlfyYaEJzNQRbaAHhERUaINLnLWVrM/SaN2gbLVYStlUpLREK1WAIenA5sGANd2AxNLAdWGAYXbysq1MLfsxc+1fVA9dyr0XXISVx++QLtZh9CkcDoMqONj8b0XRKYsxhkLg127dqnypdSpU+PPP/9EpUqVsH///rgdHRERkaWwtlFTyt5OXlI/tawhqBASPBTtCHTdA2QoBQQ9B9b0AuY2BgJuwxwVyZgc674tiw5l9NmLJUduofroXdh+gdkLIrMILGS9iN9++w3ZsmVTWQOZVUnWiJByJLm+aNGi8TdSIiIiS5c8M9B2DVBtOGDjAPhtBSaUBE4sBCLJ/idmSextVJZiceeSyOjhhLuBr9Fu5iH8sOQEAl+z94Io0QYWdevWRY4cOXDy5EmMGTMGd+7cwdixY+N3dERERBSWZDJK9QC67AbSFALeBADLOwMLWwLPzfNsftGMybG+Zzm0L63PXiw+rM9e7Lz4QOuhEdHnBBbr169Hhw4d1GJzMi2sNHETERGRRlLmADpsBioNAKztgAtrgfHFgTPLYY4ke/FrXR8s+lqfvfAPeI02Mw7ixyUnmb0gSmyBha+vr2rULly4MIoXL65mhHr48GH8jo6IiIgiZ2MLlPse+Ho74JUXePUY+K8tsKQ98PIxzFGxTPrsRbvSGVX2YtHhmyp7sYvZC6LEE1iUKFECU6dOVdO/du7cGQsXLkSaNGnUOhSySJ0EHURERKSBVHmBTtuAcn0BKxvg9FJgQgngwnqYa/ZiYN3cWNipBDIk12cvWs84iH5LT+IZsxdEiWdWKGdnZ7XmhGQwTp06hT59+qjGbVk0r169evEzSiIiIoqarT1Q6Reg42YgRQ7g+T1gQXNgRTfgdQDMUfHMHtjQqyzalsqoLi88xOwFUaKcblZIM/fvv/+OW7duqbUsiIiISGNpCwOddwGlvtGv3n18HjChFOC3DebIyd4Wg+rlxsKv9dmLO++zF/2XMXtBlKgCCwNp5G7QoAFWrVoVF09HREREsWHnqF9Ar916IFkmIPAWMKchsKY38OY5zFGJ99mLNiW91eUFB2+ixpjd2H2J2QuiRBVYEBERkQnyLqlfVK/Y1/rLsnr3pNLAtT0w1+zF4Pp5sKBTCaRPngS3n75Cq+mSvTiF52/eaT08IrPHwIKIiMic2TsDtf4AWq8E3NMDT64Bs2oDG34C3r6COSqZxQMbepZD69DsxQ3Ve+F7ibNZEsUnBhZERESWIHMFoOteoGArADpg/3hgUlng1mGYI2cHWwypnwfzOxVHumT67MVX0w/g5+XMXhDFFwYWRERElsLRDag/DmjxH+CSCnh0CZheFdgyGHj3BuaoVJYU2NirHL4qkUFdnndAn73Ye5nZC6K4xsCCiIjI0mSvBnTbB+RtBuhCAN+/gSkVAf+TMNfsxbAGeTG/Y3GkTarPXrSYdgC/rDiFF8xeEMUZBhZERESWyCk50Hgq0GwO4JQCuH8GmFoR2Pk7EGye07SWypoCG78rh5bF9dmLuftvoPqYXdjrx+wFUVxgYEFERGTJfOoB3fYDueoCIe+A7cOBaVWA++dgjlwcbDG8YV7Me5+9uPXkFVpMPYABK04ze0EUSwwsiIiILJ1LSn3motE0wDEp4H8cmFwO2PMPEBIMc1T6ffaixfvsxZz911Hjn13Y5/dI66ERJVoMLIiIiAiwsgLyNdVnL7JVA4KDgM2/AjNrAo/8YK7ZixEN82JOh2JI4+6Im49f4cup+zFw5Wm8DGL2giimGFgQERHRB26pgRaLgXpjAXtX4OYBYGJp4MAUICQE5qhstpQqe/FlsfTq8r/7rqtVu/dfYfaCKCYYWBAREdHH2YtCrYFue4FM5YB3r4D1fYHZ9YAn12GOXB3tMLJRPsxur89e3Hj8Es2n7MegVWeYvSCKJgYWREREFLGkGYBWK4FafwJ2TsC13cDEUsCRfwGdTuvRxYty2VNiw3fl0LyoPnsxa+811PxnNw4we0H0SQwsiIiIKHLW1kCxTkAXXyB9CSDoObD6W2BeUyDwDsyRm6MdfmucD/+2L4bU7o64/uglmk9l9oLoUxhYEBER0ad5ZAHarQOqDgVsHIDLm4EJJYATi8w2e1E+u7734osi6dVbNGQvDl59rPXQiEwSAwsiIiKKHmsboPS3QOddQJqCwOsAYPnXwKKvgOcPYK7Zi1FN8mFmu6JI5abPXnwxZR+GrD6LV0HmORUv0ediYEFEREQx45kT6LAFqPgLYG0HnF8DTCgOnFkBc1Uxh6fKXjQtnE5lL2bsuYpa/9uNw9eYvSAyYGBBREREMWdjC5TvC3TaBnjlAV4+Av5rAyzpALw0z4Nt9yR2+KNpfsxsWxRebg64+vAFmk7eh6FrmL0gEgwsiIiI6POlzgd02g6U/R6wsgZOL9H3XlzcCHNVMacnNn1XHk3eZy+m++qzF0eum2dARRRdDCyIiIgodmztgcoD9OVRKbIDz+8B85sBK7vr+zDMNHvxZ7jsRZNJ+zBszVm8fsvsBVkmBhZEREQUN9IV1jd2l+whq+wBx+YCE0oBftth1tmLXuXRuJA+ezFNshf/SPbiidZDI0pwDCyIiIgo7tglAaoP109NmywjEHgLmNMAWNsHePMc5sjdyQ5/NcuPGW2LwNPVAVek92LSXoxYd47ZC7IoDCyIiIgo7nmXArrsAYp21F8+NA2YVBq4vhfmqlJOL2z+rjwaFUqLEB0wZdcV1Xtx9AazF2QZGFgQERFR/HBwAWr/BbRaAbilA55cA2bWAjb+DLx9BXPNXvzdrACmtX6fvXjwAk0m7sVIZi/IAjCwICIioviVpSLQbS9Q4CsAOmDfOGByOeDWEZirKj5e2PRdOTQqqM9eTN51BbX/txvHmL0gM8bAgoiIiOKfozvQYDzw5SLAxQt4eBGYXhXYOhR4FwRzlNTJHn9/UQBTWxdBSlcH+D14gcaSvVjP7AWZJ80Di9u3b+Orr76Ch4cHkiRJgrx58+Lw4cOR3n/Hjh2wsrL6aLt7926CjpuIiIg+Q44aQLf9QJ4mgC4Y2P0nMLUicPcUzFVVH+m9KIcGBdLosxc7r6DOWF8cv/lU66ERmU9g8eTJE5QuXRp2dnZYv349zp49i7/++gvJkiX75GMvXLgAf3//0M3T0zNBxkxERESx5JQcaDIdaDYbcPIA7p0GplQEdv4BBL+DuWYvxjQviCmtCiOFiwMu33+ORhP2YNSG83jzjtkLMg+2Wr74qFGjkD59esycOTP0ukyZMkXrsRJIJE2aNB5HR0RERPHKpz6QoRSwphdwfg2wfRhwYR3QcBKQMgfMUbXcqVA0Y3IMWn0GK4/fwcQdfthy9p5abC9/ev1xTXCIDgeuPsaRh1bwuPoYJbN6wsbaSuuhE5l2YLFq1SpUr14dTZs2xc6dO5E2bVp069YNnTp1+uRjCxQogDdv3iBPnjwYNGiQynxERO4jm0FgYKD6+fbtW7VpwfC6Wr0+mQ7uCyS4H5BF7wsOSYFGM2F1+j/YbOoPqztHoZtUFiEVfkJIsS6AtQ3MjYu9Ff5snAfVc3liwKqzuCTZi4l70alMRuRI5YpRGy7gbqAcu9hg9qXDSOXmgF9q5UT13F5aD50s8G/C2xi8tpVOJ+tEasPR0VH97N27twouDh06hJ49e2LSpElo06ZNpCVQ0mdRpEgRFTBMmzYNc+bMwYEDB1CoUKGP7i9Bx+DBgz+6fv78+XBycoqHd0VERESfwzHoMQrcmAGvZyfV5UfO2XDM+2u8cDDfA+oXb4ElV61x9JGhOt1wWGacodBf1z57CPJ7aHbYRhbq5cuXaNGiBQICAuDm5ma6gYW9vb0KEPbu/bBYzrfffqsCjH379kX7ecqXL48MGTKoACM6GQspv3r48OEnP5z4jPw2b96MqlWrqv4SslzcF0hwPyAD7gtyDK2D1fG5sNnyC6yCXkBn54SQSgMRUrgdYKX5nDPxZv2pu+j130nV3B0RCTNSuTtge+9yLIuyIG9N4G+CHDunSJEiWoGFpqVQqVOnho+PT5jrcuXKhaVLl8boeYoVKwZfX98Ib3NwcFBbePLlaP1H2xTGQKaB+wIJ7gdkYPH7QrH2QLbKwMrusLq2GzYbf4TNxbVA/fFA0gwwRyndnSINKoTc5B/wBsduPUPJLB4JOTSy8L8JdjF4XU1Df+mLkNImYxcvXoS3t3eMnuf48eMqSCEiIiIzkcwbaL0KqPkHYJsEuLoLmFAKODpHZTXMzf1nr6N1v3HbLmHz2Xt48cY8Z8+ixE3TjMV3332HUqVKYcSIEWjWrBkOHjyIKVOmqM2gf//+aq2L2bNnq8tjxoxRM0flzp0br1+/Vj0W27Ztw6ZNmzR8J0RERBTnrK2B4l8DWSsDy7sAtw4Cq3oA51YBdf8HuJnPSUVPV33f6afs8XukNjsbKzW7VPnsKVEhhyeye7modb2ILDawKFq0KJYvX66ChyFDhqiAQQKHli1bht5H1qi4ceNG6OWgoCD06dNHBRvSfJ0vXz5s2bIFFStW1OhdEBERUbzyyAK03wDsGwdsGwZc2gRMKAHU+hPI2wQwgwPqYpmSI7W7I+4GvA5t3zYm7zCZkx1q5UuNXRcf4sbjl9jr90htI9efV4+VIEO20tlSwM3RgkvpyDIDC1GnTh21RWbWrFlhLv/www9qIyIiIgsi086W7glkq6bPXvgfB5Z1BM6tBGqPBlxSIjGThuyBdX3Qde5RFUQYBxeGsGlEo7yokSc1ZN6da49eYseF+9h58QH2+T2Cf8BrLDx0U23yXIUzJEP5HPpAI3caN2YzyDICCyIiIqJo88wFdNwC+I4Gdo4Czq0Gru8D6owGfOohMZOgYeJXhTB49VkVKBikcndUQYfcLiRIyJTCGZlSZEK70pnw+m2wWlDPEGhcefACB689VtsfGy8gpasDymWTkqmUKJsthVoFnCg+MLAgIiKixMXGDij/A5C9OrC8K3D/DLC4FZC3GVDrdyBJMiRWEjxU9UmFfZfvY9PuA6hWtvgnV952tLMJLYMSNx69xM5LD7Dzwn1VKvXg2RssPXpLbfI0BdInRfnsnirQyJvWHdacvpbiCAMLIiIiSpxS5we+3g7s+A3YMwY4tVg/e1S9sUD2akisJIgonik5Hp3TqZ8xXbcig4cTWnl4o1UJb7x5F4zD156oTIZkNC7ee46jN56qbfSWi0jubI9y2VKosinJani4fDxFP1F0MbAgIiKixMvWAagyEMhZW9978egSML8pULAVUH0E4KjNYrimwsHWBqWzplDbT7Vy4c7TVyrI2HnhAXwvP8TjF0FYcfyO2qQNQzIYFST7kcNTZTa4GB/FBAMLIiIiSvzSFQG67Aa2DgX2TwCOzQGu7ADqjwMyV9B6dCYjTdIk+LJYBrW9DQ7B0etPsENlMx7gnH8gTt4KUNv/tl2GexI71ZOhyqxypIz2lLhkuRhYEBERkXmwSwLUGKHPXqzoCjy9DsyuDxTtBFQdDNg7az1Ck2JnY43imT3U9mONnLgX+Fqfzbj4ALsvPkDAq7dYc9JfbcIntZvqy5BAo5B3MvV4ImMMLIiIiMi8ZCwNdN0LbP4VODwdODQVuLwFaDgJyFBC69GZLC83RzQrkl5t74JDcOLWU5XJkEBDshhn/QPVNmGHH1wdbFV5lQo0cqREavckWg+fTAADCyIiIjI/Di5Anb/12YtV3wBPrgIzagClegAVfwHsWNYTFVsbaxT2Tq62PtVy4OHzN9h9SV8yteviAzx5+RYbztxVm8jh5aoCDOnPKJwxmertIMvDwIKIiIjMV9bK+uzFxp+A4/OAvWOBi5uAhhOBtIW1Hl2ikcLFAQ0LplNbcIgOp24HqAbwHRfv48TNp7hw75napuy6Aid7G5TKkiI00Eif3Enr4VMCYWBBRERE5i1JUqDBBCBXXWDVt8DDC8C0qkDZ3kC5HwBbLhgXEzJTlMwYJVvPKtnw5EUQdl9+qAINKZuS7MaWc/fUJjKndEaF7J4q0JDpc2XdDTJPDCyIiIjIMuSoCXQ/AKz7Hji9FNj1B3Bhg773IlUerUeXaCVztke9/GnUFhKiU30Yhiltj9x4olYCv/LgKmbsuQpHO2uUyOwROqWtrCBO5oOBBREREVkOp+RAkxlAzjrA2j7AvVPAlApAhX5A6V6ADQ+NYkNW8c6T1l1t3StmVTNL7b38MLQJ/G7ga/Vv2bD6LLw9nNQsU9IELgGHkz0//8SM3x4RERFZnjyNgIxlgNW9gAtrgW1DgQvrgAaTgJTZtR6d2ZC1MGrmTa02nU6n+jBUb8aFBzh8/TGuP3qJ2fuuq83e1lqVShkCjSwpXWAlq/ZRosHAgoiIiCyTiyfQfB5wchGw7gfg9hFgclmg0gCgRFfAmr0AcUmChJyp3NTWuXwWPH/zTmUzJJMhgcbtp6+w+9JDtQ1bew5pkyYJbQAvlTUFXBx42Grq+A0RERGR5ZIz4vmbAxnL6qel9dsKbPoZOL9G3/CdPLPWIzRbEihUy51KbZLN8HvwAjsu3FeBxoGrj1WgMf/ADbXZ2VihiHdyfaCRI6Wa3pbZDNPDwIKIiIjIPS3w1VLg6L/Axp+BG/uAiaWBqkOAIh2keUDrEZo1CRKyerqorWPZzHgZ9A4HrjxWgcaOiw9UydS+K4/U9tv680jl5qhKpiTQkIX6pOSKtMfAgoiIiEjIGfDCbYHMFYAV3YHrvvoZpCR7UW8ckDS91iO0GNLEXTGnp9rEtYcv3pdM3VfBhTSBLzp8U20y/W2hDElRIYenCjZ8UrupJnJKeAwsiIiIiIwlywi0WQ0cnAJsGQRc2QFMLAXUGAkUaKkPQChBZUzhrLY2pTLi9dtgHLz6ODTQkBKqQ9eeqO2PjRfUYn7lsqdQgUbZrCnUdLiUMBhYEBEREYUnpU8lugBZqwArugC3DgEruwPnVgN1/wFcU2k9QoslC+yVy55SbQPq+ODm45f6dTMuPlDN4LJA37Kjt9UmiYv86ZO+n2nKE3nTuqsMB8UPBhZEREREkUmRFWi/Edg7Ftg+HLi4ARhfHKj9F5CnMbMXJiB9cid8VcJbbUHvQtQ0toZVwM/ffYZjN56qbcyWS0jmZKcCEgk05KdkNyjuMLAgIiIiiopMO1umF5Ctmj574X8CWNoBOLcKqP034JxC6xHSe7IWRqksKdTWv1Yu+Ae8wq7309n6XnqIJy/fYuXxO2oTksGQWaYk0CiQPilsbdikHxsMLIiIiIiiw8sH6LgV2P0XsOsP4OxK4NoefWlUrjpaj44ikNo9Cb4omkFtb4NDVObCMKXtmTuBOHU7QG1jt12Gm6MtymbTzzQlgYaXm6PWw090GFgQERERRZeNHVChH5C9BrC8C/DgHLCoJZDvC6DmKCBJMq1HSJGws7FGsUzJ1fZDjZy4H/gauy49VIGGLMoX8Oot1p7yV5vIldotdBXwwt7J1OMpagwsiIiIiGIqTQGg805gx0hgzz/61buv7tJPS5utitajo2jwdHNEk8Lp1BYcosPxm0/1TeAX7uPk7QCc8w9U26Sdfmoxv9JZPVA+u6cKNNIkTaL18E0SAwsiIiKiz2HrAFQZBOSore+9eHQZmNcYKNQaqDYccHTTeoQUTTJTlGQlZOtdNTsePX+jshgSaEiPxqMXQdh45p7aRDZPl/e9GZ4omikZHGxttH4LJoGBBREREVFspC8KdN4NbB0CHJgIHJ0N+O0AGowHMpXTenT0GTxcHNCgYFq1hYTocPpOgGoAl0Dj2I0nuHT/udqm7r6KJHY2KJXFQwUaMqWtzFJlqRhYEBEREcWWvRNQ8zcgZ21gZTfg6Q3g37pAsc76rIbcTomSrOKdL11StX1bORuevgyC7+WHoYHGg2dvsPX8fbUBZ5A5hXNoA3iJzB5q3Q1LwcCCiIiIKK5kKgt03QtsGgAcmQkcnAxc3gI0mAhkKK716CgOJHWyR518adSm0+lw1j/w/SrgD3Dk+hNcefhCbTP3XIODrbUKLgxT2mZK4QwrM177hIEFERERUVxycAXqjtFPQbvyG+CxHzCzBlCyB1DxZ8CO05iaCwkScqdxV1u3ClkR+PqtWv3bEGj4B7wOXRVcZEjuFDrTVMksHnCyj/xQXBrKD1x9jCMPreBx9TFKZvU0+VXDGVgQERERxYesVYBu+4AN/YET84G9/wMubQIaTgLSFNR6dBQP3BztUCNParVJNkP6MAzrZhy8+hg3Hr/EnP3X1Wb/fvpbQ6CR1dMlNJux4bQ/Bq8+qwITwAazLx1GandHDKzro57bVDGwICIiIoovSZICDSfqsxerewIPzgNTKwPlvgfKfg/Y2ms9QoonVlZWyO7lqravy2XBizfvsM/vEXZcvK+yGbeevFK9GrINX3cOaZMmQbnsKdVCfVN2XYEu3PPdDXiNrnOPYuJXhUw2uGBgQURERBTfpKk7fQlgXR/gzHJg5yjgwjqg4WTAK7fWo6ME4Oxgiyo+XmqTbIb0Yey88AA7Lj7A/iuPcPvpKyw4eCPSx0ugIfkMyWRU9UllkmVRXEKQiIiIKCE4ewBNZwFNZuhX6L57CphcHtj9FxD87sP9QoJhdd0XaR/vUz/lMplfNiNLShe0L5MJs9sXw4lfq2Fmu6KontsrysdJcCHlUVJWZYo0Dyxu376Nr776Ch4eHkiSJAny5s2Lw4cPR/mYHTt2oFChQnBwcEDWrFkxa9asBBsvERERUazkaQx0OwDkqAWEvNWvfzGjOvDwEnB2FTAmD2znNkCR6xPVT7msriezlcTeBhVzeKJW3uiVON1/Jr0XpkfTwOLJkycoXbo07OzssH79epw9exZ//fUXkiVLFuljrl69itq1a6NixYo4fvw4evXqhY4dO2Ljxo0JOnYiIiKiz+bqBTSfr5+G1sEduH0YmFASWNwKCLwT9r6B/sDi1gwuLICnq2Oc3s+ieixGjRqF9OnTY+bMmaHXZcqUKcrHTJo0Sd1HAhCRK1cu+Pr6YvTo0ahevXq8j5mIiIgoTsgMQAVa6FfnXtEduLoj6ur6Df30vRrWlrPgmqUplim5mv1JGrXDN28L6apI5e6o7meKNA0sVq1apYKBpk2bYufOnUibNi26deuGTp06RfqYffv2oUqVKmGuk+eQzEVE3rx5ozaDwMBA9fPt27dq04LhdbV6fTId3BdIcD8gA+4LFsrJC1alesE20sBC6IDA2wg+OAMh2aoDLikBa87BY45+rpkD3yw8oYII4+DCyuj2kOB3CdZ6E5O/R1Y6aUvXiKOjPo3Tu3dvFVwcOnQIPXv2VFmJNm3aRPiY7Nmzo127dujfv3/odevWrVPlUS9fvlR9GsYGDRqEwYMHf/Q88+fPh5OTU5y/JyIiIqKYkkZt6amILh2sEGTrgte2SfHaLine2MlPd7x5f1n+ra63TYpgG4d4HTvFvROPrLDsmjWeBn2Y+SmpvQ6NMoYgv0fCHrrL8XWLFi0QEBAANze3KO+raagbEhKCIkWKYMSIEepywYIFcfr06SgDi5iSAEQCF+OMhZRfVatW7ZMfTnxGfps3b0bVqlVVfwlZLu4LJLgfkAH3Bctldd0NiEZgoXNMBrwJhJUuGA7vnqnN/fXNqB9j7wK4eEHn4vX+p+f7n6k+XO/sqZ+p6v0CbaStWgB+CNFhv98DbNt3BJVKFkaJLCk1mWLWUO0THZoGFqlTp4aPj0+Y66RnYunSpZE+JlWqVLh3716Y6+SyBAnhsxVCZo6SLTz5g631H21TGAOZBu4LJLgfkAH3BQuUuRzglkbfqB1Zdb1bGlj1OqW/+PIR8Owu8Pw+8Pzu+3/f02/P7r2/7h7w7hWsgp4Dj5/D6rFf1GOwsVeBRugmDeYSfBh+SkDimkofhNiwDCu+2QEonc0TAZd06qdWfxNi8rqa7hUyI9SFCxfCXHfx4kV4e3tH+piSJUuq0idjcnZHriciIiJKlKQhu8Yo/exPkVXX1/jtQ+O2yjp4Rv2cUu3+JlAffBgCD/Xz7sfXvX4KBAcBATf1W5SsAOcUYYMNFYi8v2wcjNiz7NySaBpYfPfddyhVqpQqhWrWrBkOHjyIKVOmqM24lEnWupg9e7a63KVLF4wbNw4//PAD2rdvj23btmHx4sVYu3athu+EiIiIKJZ86gHNZgMbfgw75axkMiSokNtjQsqaHN31W4psUd/33Ztw2Q7jbMj769Tl+4AuGHjxQL+FLSL5mIPbx8HGR8GIF8uwzISmgUXRokWxfPlyFTwMGTJETSM7ZswYtGzZMvQ+/v7+uHHjw/Lmch8JIiQo+eeff5AuXTpMmzaNU80SERFR4ifBQ87aeHdlF47v3ogCZavDVsqk4nuKWVsHIGkG/RYVmYootAzLUHplnA0xKsd690qfMZHt0eXol2EZgo2PyrHe94KwDMtkaf7N1KlTR22RiWhV7QoVKuDYsWPxPDIiIiIiDVjbQOddBrfPBCK/dxnTWrdCxhKTMiyV7TAOQO5+fN3nlmEZgg3jYMS4HItlWJYXWBARERGRmTEuw0qZPer7vn0NvLgfrgwrgkZ0uY8uxKgM630je5RlWJ9oRGcZVpxiYEFERERE2rFz/LwyrKjKsd69NirDuhT188o6HyoA+UQjunPKhC3DCgmG1XVftcaJmo44IUriYomBBRERERGZvs8qwzKeAStcI3poGdYbIOCGfotRGVYk5VguXrEvwzq7SjXx2wbeQRG5LGucqCb+UTFv4k9ADCyIiIiIyHLLsAxZj48yH/djX4blGkUjemRlWBJUqGmHw61nImucyPUyc5iJBhcMLIiIiIjIcsuwknnrt0+VYb14+HEj+kdrhNz7vDKs0FmvUgKnl0SySKJcZwVs6KdmDjPFsigGFkREREREUZGDeDn4ly0mZVjPDNmQux9fF6MyrNAXAAJvA9f3ApnKwtQwsCAiIiIiMoUyrCs7gPNrPv06cn8TxMCCiIiIiMgUyrBS5oxeYCFlUybIWusBEBERERERAO9S+tmfpJciQlaAW1r9/UwQAwsiIiIiIlPp5agx6v2F8MHF+8s1fjPJxm3BwIKIiIiIyFT41NNPKeuWOuz1kskw4almBXssiIiIiIhMiU89NaXsuyu7cHz3RhQoWx22iWDlbWYsiIiIiIhMjbUNdN5lcDt5SfXT1IMKwcCCiIiIiIhijYEFERERERHFGgMLIiIiIiKKNQYWREREREQUawwsiIiIiIgo1hhYEBERERFRrFncOhY6nU79DAwM1GwMb9++xcuXL9UY7OzsNBsHaY/7AgnuB2TAfYEMuC+QqewHhmNmwzF0VCwusHj27Jn6mT59eq2HQkRERESUaI6h3d3do7yPlS464YcZCQkJwZ07d+Dq6gorKyvNIj8JbG7evAk3NzdNxkCmgfsCCe4HZMB9gQy4L5Cp7AcSKkhQkSZNGlhbR91FYXEZC/lA0qVLB1MgOwj/WJDgvkCC+wEZcF8gA+4LZAr7wacyFQZs3iYiIiIiolhjYEFERERERLHGwEIDDg4OGDhwoPpJlo37AgnuB2TAfYEMuC9QYtwPLK55m4iIiIiI4h4zFkREREREFGsMLIiIiIiIKNYYWBARERERUawxsEhAEydORL58+ULnIi5ZsiTWr1+v9bBIY7/99ptarLFXr15aD4US2KBBg9R3b7zlzJlT62GRRm7fvo2vvvoKHh4eSJIkCfLmzYvDhw9rPSxKQBkzZvzob4Js3bt313polMCCg4MxYMAAZMqUSf09yJIlC4YOHaoWqzNlFrdAnpZkYT45iMyWLZvaMf7991/Ur18fx44dQ+7cubUeHmng0KFDmDx5sgo4yTLJ7/6WLVtCL9va8s+yJXry5AlKly6NihUrqhNOKVOmxKVLl5AsWTKth0YJ/P8EOaA0OH36NKpWrYqmTZtqOi5KeKNGjVInpOVYUf4/IScZ2rVrpxaq+/bbb2Gq+H+wBFS3bt0wl4cPH652mv379zOwsEDPnz9Hy5YtMXXqVAwbNkzr4ZBGJJBIlSqV1sMgEziISJ8+PWbOnBl6nZypJMsiAaUxORkpZ6rLly+v2ZhIG3v37lUnn2vXrh2azVqwYAEOHjwIU8ZSKI3IGYmFCxfixYsXqiSKLI+ktuUPRpUqVbQeCmlIzkqnSZMGmTNnVoHmjRs3tB4SaWDVqlUoUqSIOjPt6emJggULqpMOZLmCgoIwd+5ctG/fXpVDkWUpVaoUtm7diosXL6rLJ06cgK+vL2rWrAlTxoxFAjt16pQKJF6/fg0XFxcsX74cPj4+Wg+LEpgElUePHlVpb7JcxYsXx6xZs5AjRw74+/tj8ODBKFu2rCp/cHV11Xp4lICuXLmiMti9e/fGTz/9pP42SLmDvb092rRpo/XwSAMrVqzA06dP0bZtW62HQhro168fAgMDVd+djY2NOiEtlS5yAsqUcYE8Dc5AyBnJgIAALFmyBNOmTcPOnTsZXFiQmzdvqjOTmzdvDu2tqFChAgoUKIAxY8ZoPTzSkBxEeHt74++//0aHDh20Hg4lIAkg5O+ClD8YSGAhAca+ffs0HRtpo3r16mq/WL16tdZDIY1OQPbt2xd//PGHKpc/fvy4muRF/v9gyicbmLFIYPJHImvWrOrfhQsXVv/T+Oeff1QDL1mGI0eO4P79+yhUqFDodXImYteuXRg3bhzevHmjzk6Q5UmaNCmyZ8+Oy5cvaz0USmCpU6f+6ARTrly5sHTpUs3GRNq5fv26mtRh2bJlWg+FNNK3b1+VtWjevLm6LLPEyX4xcuRIBhYUuZCQEHUgSZajcuXKqiTOmMz0IOnOH3/8kUGFhTf0+/n5oVWrVloPhRKYzAh14cKFMNdJbbVksMjySBO/9NoYGnfJ8rx8+RLW1mFboeX4QI4bTRkDiwTUv39/1XSTIUMGPHv2DPPnz8eOHTuwceNGrYdGCUhq5/PkyRPmOmdnZzV3ffjrybx9//33arY4OXi8c+cOBg4cqP7H8eWXX2o9NEpg3333nWrWHDFiBJo1a6ZmfpkyZYrayLLIgaMEFnJWmtNPW666deuqngo5ZpRSKFmaQMqgpJnflHGPTUBS/tK6dWvVpCnzEEt9vQQVMkc1EVmeW7duqSDi0aNHaprJMmXKqOmnw085SeavaNGiajIPOQE1ZMgQNdWs9FyZeqMmxT0pgZJeTFM/gKT4NXbsWLVAXrdu3dTxo8we2LlzZ/z6668wZWzeJiIiIiKiWOM6FkREREREFGsMLIiIiIiIKNYYWBARERERUawxsCAiIiIiolhjYEFERERERLHGwIKIiIiIiGKNgQUREREREcUaAwsiIiIiIoo1BhZERESfcO3aNVhZWeH48eNaD4WIyGQxsCAishAPHjxA165dkSFDBjg4OCBVqlSoXr069uzZE3ofOXhesWIFTE2FChXQq1cvrYdBRERRsI3qRiIiMh+NGzdGUFAQ/v33X2TOnBn37t3D1q1b8ejRoxg9jzyHvb19vI2TiIgSJ2YsiIgswNOnT7F7926MGjUKFStWhLe3N4oVK4b+/fujXr166j4ZM2ZUPxs2bKgyF4bLgwYNQoECBTBt2jRkypQJjo6Ooc/ZsWNHpEyZEm5ubqhUqRJOnDgR+pryb3ktV1dXdXvhwoVx+PBhddv169dRt25dJEuWDM7OzsidOzfWrVv32e/P19cXZcuWRZIkSZA+fXp8++23ePHihbrtp59+QvHixT96TP78+TFkyJDQy/L+cuXKpd5fzpw5MWHChM8eDxGRJWJgQURkAVxcXNQmZU5v3ryJ8D6HDh1SP2fOnAl/f//Qy+Ly5ctYunQpli1bFtpn0LRpU9y/fx/r16/HkSNHUKhQIVSuXBmPHz9Wt7ds2RLp0qVTzyO39+vXD3Z2duq27t27q3Hs2rULp06dUgGPjO9z+Pn5oUaNGiojc/LkSSxatEgFGj169Agdx8GDB9X9DM6cOaPu26JFC3V53rx5+PXXXzF8+HCcO3cOI0aMwIABA1R2h4iIoklHREQWYcmSJbpkyZLpHB0ddaVKldL1799fd+LEiTD3kf8tLF++PMx1AwcO1NnZ2enu378fet3u3bt1bm5uutevX4e5b5YsWXSTJ09W/3Z1ddXNmjUrwrHkzZtXN2jQoGiPvXz58rqePXtGeFuHDh10X3/9dZjrZHzW1ta6V69eqcv58+fXDRkyJPR2ee/FixcPM+758+eHeY6hQ4fqSpYsqf599epV9dkcO3Ys2mMmIrI0zFgQEVkIOaN/584drFq1Sp3h37Fjh8oyzJo165OPldIpKXkyLnN6/vw5PDw8QrMhsl29ejU0M9C7d29VKlWlShX89ttvYTIGUqo0bNgwlC5dGgMHDlTZg88lY5H3YDwOaUoPCQlR4zFkLebPn6/+LfHTggUL1HVCSqZkbB06dAjzHDI+4zETEVHUGFgQEVkQ6R+oWrWqKvPZu3cv2rZtqw7sP0X6IIxJUJE6dWpVFmW8XbhwAX379g3tzZCSo9q1a2Pbtm3w8fHB8uXL1W0ScFy5cgWtWrVSpVBFihTB2LFjP+s9yVg6d+4cZhwSbFy6dAlZsmRR9/nyyy/V2I4ePare982bN/HFF1+EPl5MnTo1zHOcPn0a+/fv/6wxERFZIs4KRURkweRg33h6WemBCA4O/uTjJNNx9+5d2NrahjZ5RyR79uxq++6779TBvfRvSHO4kCbrLl26qE2ayOXA/ptvvonxe5CxnD17FlmzZo30PtLrUb58edVL8erVKxVceXp6qtu8vLyQJk0aFegYshhERBRzDCyIiCyATCkrzdbt27dHvnz51ExNMkPT77//jvr164feT4IEmYJWSpRkrQuZtSkiUt5UsmRJNGjQQD2HBA9SZrV27VoVOMgsT5K5aNKkiZpJ6tatW6qJW8qxhKxJUbNmTfW4J0+eYPv27WpGpk+twxF+gTrJmvz4448oUaKEataWTIhkVyTQ2Lx5M8aNGxd6XwkaJDsj0+WOHj06zPMMHjxYlWe5u7urMjFpLJfPR8YmJV1ERBQNWjd5EBFR/JMm6379+ukKFSqkc3d31zk5Oely5Mih++WXX3QvX74Mvd+qVat0WbNm1dna2uq8vb1Dm7el+Tm8wMBA3TfffKNLkyaNau5Onz69rmXLlrobN27o3rx5o2vevLm6zt7eXt2nR48eoc3U8m9pmHZwcNClTJlS16pVK93Dhw+jbN6W/2WF36TBWhw8eFBXtWpVnYuLi87Z2VmXL18+3fDhw8M8x5MnT9TryXt/9uzZR68xb948XYECBdR4pcm9XLlyumXLlqnb2LxNRPRpVvKf6AQgREREREREkWHzNhERERERxRoDCyIiIiIiijUGFkREREREFGsMLIiIiIiIKNYYWBARERERUawxsCAiIiIiolhjYEFERERERLHGwIKIiIiIiGKNgQUREREREcUaAwsiIiIiIoo1BhZERERERBRrDCyIiIiIiAix9X8lkpQbvaAOZwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stress_group = df.groupby(\"Stress Level\")[[\"Sleep Duration\", \"Quality of Sleep\"]].mean()\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "\n",
+    "plt.plot(stress_group.index, stress_group[\"Sleep Duration\"], marker='o', label=\"Sleep Duration (hours)\")\n",
+    "plt.plot(stress_group.index, stress_group[\"Quality of Sleep\"], marker='o', label=\"Quality of Sleep (1–10)\")\n",
+    "\n",
+    "plt.title(\"Effect of Stress Level on Sleep\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Stress Level\")\n",
+    "plt.ylabel(\"Average Value\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "c04a03e1-a8b2-4c02-aab3-917be005d327",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "activity_group = df.groupby(\"Physical Activity Level\")[[\"Sleep Duration\", \"Quality of Sleep\"]].mean()\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "\n",
+    "plt.plot(activity_group.index, activity_group[\"Sleep Duration\"], marker='o', label=\"Sleep Duration (hours)\")\n",
+    "plt.plot(activity_group.index, activity_group[\"Quality of Sleep\"], marker='o', label=\"Quality of Sleep (1–10)\")\n",
+    "\n",
+    "plt.title(\"Effect of Physical Activity Level on Sleep\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Physical Activity Level\")\n",
+    "plt.ylabel(\"Average Value\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf97684-cdbf-443f-8229-8b4a01e134af",
+   "metadata": {},
+   "source": [
+    "# Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-camping",
+   "metadata": {
+    "code_folding": []
+   },
+   "source": [
+    "## Considerations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-stadium",
+   "metadata": {},
+   "source": [
+    "My results provided clear visual representations that allowed me to effectively compare different aspects of the data. They directly answered my research questions, which was expected since the questions were straightforward and focused. However, I recognize that the simplicity of my questions was partly due to my current coding limitations. As I continue to improve my programming skills, I hope to design more complex and detailed research questions that explore deeper relationships within the data. Overall, there did not appear to be any bias in the dataset that could have influenced my results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beneficial-invasion",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "about-raise",
+   "metadata": {},
+   "source": [
+    "Through this project, I learned a lot about how lifestyle factors such as stress and physical activity influence sleep. By analyzing and visualizing the data, I could clearly see patterns showing that higher stress levels generally corresponded with shorter and lower-quality sleep, while higher physical activity levels were associated with longer and better-quality sleep. This helped me better understand the balance between rest, activity, and stress management. When comparing occupations, it became clear that some professions tend to get significantly less sleep on average, likely due to longer work hours or higher daily stress, while others maintained more consistent and healthier sleep patterns\n",
+    "\n",
+    "The results made sense overall and supported what I expected: stress tends to disrupt sleep, while physical activity can improve it. What surprised me most, however, was how strong the effect of occupation and stress seemed to be compared to other factors. Some groups showed noticeable differences in sleep duration simply based on their line of work, suggesting that job demands and lifestyle balance play a big role in sleep health.\n",
+    "\n",
+    "Going forward, this project will make me more aware of how interconnected daily habits, work routines, and stress management are with getting quality sleep. I plan to pay closer attention to how my own schedule and responsibilities impact my rest and find ways to maintain a healthier balance between productivity and recovery. Understanding these patterns gives me a clearer picture of how small changes in lifestyle can lead to better overall well-being."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb832499-95a8-478a-ab91-2d4c0d1c5d1f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_json": true,
+   "text_representation": {
+    "extension": ".Rmd",
+    "format_name": "rmarkdown",
+    "format_version": "1.2",
+    "jupytext_version": "1.9.1"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/poetry-checkpoint.lock b/.ipynb_checkpoints/poetry-checkpoint.lock
new file mode 100644
index 0000000..afabeb5
--- /dev/null
+++ b/.ipynb_checkpoints/poetry-checkpoint.lock
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+
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+
+[metadata]
+lock-version = "2.1"
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diff --git a/.ipynb_checkpoints/proposal-checkpoint.md b/.ipynb_checkpoints/proposal-checkpoint.md
new file mode 100644
index 0000000..f1e7bc0
--- /dev/null
+++ b/.ipynb_checkpoints/proposal-checkpoint.md
@@ -0,0 +1,69 @@
+# Project proposal
+
+This planning document will also form the introduction of your argument.
+
+## Overaching question
+
+Which lifestyle factors most strongly influence sleep quality?
+
+### What central question are you interested in exploring? Why are you interested in exploring this question?
+
+Which lifestyle factors most strongly influence sleep quality? I want to see if the self reported factors (such as occupation or physical activity) influence sleep more, or if it is more so things that the person doesn't have control over, such as stress level or BMI. Although the rating for stress is subjective, I think I can compare all the different reports to see how heavily they affect sleep. I am interested in this because I have some sleep issues, and want to know what could be a cause of it, from this specefic data set. It will of course not be concrete and will differ with different data set, but It will help me understand. 
+
+### What specific research questions will you investigate?
+
+*List 2-4 specific research questions. Each should be answerable 
+using your data set.*
+
+Who has more effect on sleep duration: stress and blood pressure or physical activity?
+Which occupation gets the most sleep? least?
+Does BMI effect the quality of sleep? duration of sleep?
+Which combination of age, gender and occupation gets the best qualtiy of sleep?
+
+## Data source
+
+### What data set will you use to answer your overarching question? 
+
+Sleep Health and Lifestyle Dataset
+https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset
+
+### Where is this data from?
+
+*Describe the source of the data set--not just where you downloaded it, but the person or organization who gathered the data. Explain why you trust them.*
+
+I am using Kaggle to get the data set. I trust this because it was a reccomendation from the professor. I also looked up review at how legit kaggle is, and i was shown mostly postive sites that stated it is a good site to use as it is well regarded with data scientists. 
+Due to this, I feel comfortable using the data that is provided on this site to use for my project. 
+
+### What is this data about?
+
+*Describe the nature of the data in the dataset, including the number of rows 
+and some of the columns which will be important to you.*
+
+There are 374 rows of data, and and 9 columns with information. The qualtiy of sleep, duration, occupation, and BMI columns will be most important to me. Overall, all of it will help me answer my questions.
+
+
+## Methods 
+
+### How will you use your data set to answer your quantitative questions?
+
+*For each research question, explain what you will do with the data set 
+to answer the question, and how you will present your answer (e.g. a chart or a table).*
+
+Who has more effect on sleep duration: stress and blood pressure or physical activity?
+
+I will find the average hours of sleep for each of hte cetagories. I will then create a chart to compare my findings visually. 
+
+Which occupation gets the most sleep? least?
+
+I will find the total number of each occupation. I will then find the average amount of sleept that the occupation got. I will then create a table to compare ocupations and sleep duration.
+I can then asnwer both, who gets the most and least. 
+
+
+Does BMI effect the quality of sleep? duration of sleep?
+
+I will find the average quality of sleep for each BMI. Then to the same for the duration of sleep. I may then create a visual of sort to compate the data. 
+
+Which combination of age, gender and occupation gets the best qualtiy of sleep?
+
+This question seems a bit trickier. I will have to find the best qualtiy of sleep for each caterogry. I will then have to find a way to visually look at this data and find some intersection that could show me the best age, gender and occouaption.
+If this ends up being too difficult, I will switch it to just age and gender to make it easier. 
\ No newline at end of file
diff --git a/.ipynb_checkpoints/pyproject-checkpoint.toml b/.ipynb_checkpoints/pyproject-checkpoint.toml
new file mode 100644
index 0000000..85318b8
--- /dev/null
+++ b/.ipynb_checkpoints/pyproject-checkpoint.toml
@@ -0,0 +1,23 @@
+[project]
+name = "project-argument"
+version = "0.1.0"
+description = ""
+authors = [
+    {name = "Chris Proctor",email = "chris@chrisproctor.net"}
+]
+license = {text = "MIT"}
+readme = "README.md"
+requires-python = ">=3.10,<4.0"
+dependencies = [
+    "jupyter (>=1.1.1,<2.0.0)",
+    "seaborn (>=0.13.2,<0.14.0)",
+    "pandas (>=2.2.3,<3.0.0)"
+]
+
+
+[build-system]
+requires = ["poetry-core>=2.0.0,<3.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+package-mode = false
diff --git a/argument.ipynb b/argument.ipynb
index 4ed27b4..33db885 100644
--- a/argument.ipynb
+++ b/argument.ipynb
@@ -13,7 +13,7 @@
    "id": "understanding-numbers",
    "metadata": {},
    "source": [
-    "*✏️ Write 2-3 sentences describing your research.*"
+    "My research will analyze a dataset to compare different lifestyle factors and their effects on sleep duration and sleep quality. I aim to determine whether self-reported factors, such as occupation and physical activity level, have a stronger influence on sleep, or if the factors that individuals have less control over, like stress level or BMI, play a larger role. Although the stress ratings in the dataset are subjective, they still provide valuable insight that can be compared across participants to understand how strongly stress and other variables impact sleep patterns."
    ]
   },
   {
@@ -21,7 +21,7 @@
    "id": "greater-circular",
    "metadata": {},
    "source": [
-    "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
+    "**Which lifestyle factors most strongly influence sleep quality?**"
    ]
   },
   {
@@ -29,7 +29,7 @@
    "id": "appreciated-testimony",
    "metadata": {},
    "source": [
-    "*✏️ Write 2-3 sentences explaining why this question.*"
+    "I chose this topic because I’ve personally experienced some sleep issues and wanted to explore what factors might contribute to them using this specific dataset. While the results won’t be absolute and may differ with other datasets, this analysis can still help me better understand possible connections between lifestyle and sleep quality. It’s also simply a topic that interests me, and I was curious to learn more about how different habits and conditions can affect sleep."
    ]
   },
   {
@@ -42,26 +42,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "technical-evans",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Include any import statements you will need\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "overhead-sigma",
    "metadata": {},
    "outputs": [],
    "source": [
-    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
-    "\n",
-    "file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
+    "file_name = \"Sleep_health_and_lifestyle_dataset.csv\"\n",
     "dataset_path = \"data/\" + file_name\n",
     "\n",
     "df = pd.read_csv(dataset_path)"
@@ -69,10 +66,159 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "heated-blade",
    "metadata": {},
-   "outputs": [],
+   "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>Person ID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Occupation</th>\n",
+       "      <th>Sleep Duration</th>\n",
+       "      <th>Quality of Sleep</th>\n",
+       "      <th>Physical Activity Level</th>\n",
+       "      <th>Stress Level</th>\n",
+       "      <th>BMI Category</th>\n",
+       "      <th>Blood Pressure</th>\n",
+       "      <th>Heart Rate</th>\n",
+       "      <th>Daily Steps</th>\n",
+       "      <th>Sleep Disorder</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>27</td>\n",
+       "      <td>Software Engineer</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>42</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Overweight</td>\n",
+       "      <td>126/83</td>\n",
+       "      <td>77</td>\n",
+       "      <td>4200</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>60</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>125/80</td>\n",
+       "      <td>75</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>60</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>125/80</td>\n",
+       "      <td>75</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Sales Representative</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Obese</td>\n",
+       "      <td>140/90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>Sleep Apnea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Sales Representative</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Obese</td>\n",
+       "      <td>140/90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>Sleep Apnea</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Person ID Gender  Age            Occupation  Sleep Duration  \\\n",
+       "0          1   Male   27     Software Engineer             6.1   \n",
+       "1          2   Male   28                Doctor             6.2   \n",
+       "2          3   Male   28                Doctor             6.2   \n",
+       "3          4   Male   28  Sales Representative             5.9   \n",
+       "4          5   Male   28  Sales Representative             5.9   \n",
+       "\n",
+       "   Quality of Sleep  Physical Activity Level  Stress Level BMI Category  \\\n",
+       "0                 6                       42             6   Overweight   \n",
+       "1                 6                       60             8       Normal   \n",
+       "2                 6                       60             8       Normal   \n",
+       "3                 4                       30             8        Obese   \n",
+       "4                 4                       30             8        Obese   \n",
+       "\n",
+       "  Blood Pressure  Heart Rate  Daily Steps Sleep Disorder  \n",
+       "0         126/83          77         4200            NaN  \n",
+       "1         125/80          75        10000            NaN  \n",
+       "2         125/80          75        10000            NaN  \n",
+       "3         140/90          85         3000    Sleep Apnea  \n",
+       "4         140/90          85         3000    Sleep Apnea  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()"
    ]
@@ -84,7 +230,9 @@
    "source": [
     "**Data Overview**\n",
     "\n",
-    "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
+    "The dataset contains 374 rows and 9 columns of information. The most important columns for my research are Quality of Sleep, Sleep Duration, Occupation, and Physical Activtiy Level, as these variables will provide the most insight into my research questions. However, all of the data contributes to building a fuller understanding of the relationships between lifestyle and sleep.\n",
+    "\n",
+    "I obtained the dataset from https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset, where the main collaborator is Laksika Tharmalingam."
    ]
   },
   {
@@ -95,22 +243,12 @@
     "# Methods and Results"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "basic-canadian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Import any helper files you need here"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "recognized-positive",
    "metadata": {},
    "source": [
-    "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## First Research Question: What is the average measures of each of the cetegories?"
    ]
   },
   {
@@ -126,13 +264,7 @@
    "id": "endless-variation",
    "metadata": {},
    "source": [
-    "*Explain how you will approach this research question below. Consider the following:* \n",
-    "  - *Which aspects of the dataset will you use?* \n",
-    "  - *How will you reorganize/store the data?* \n",
-    "  - *What data science tools/functions will you use and why?* \n",
-    "  \n",
-    "✏️ *Write your answer below:*\n",
-    "\n"
+    "I will be using each of the categories in the dataset and calculating the average value for each one using the mean function. This will provide a simple summary of the overall trends in the data and serve as a starting point for deeper analysis."
    ]
   },
   {
@@ -145,26 +277,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 56,
    "id": "negative-highlight",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Values for Key Categories:\n",
+      "Sleep Duration                7.132086\n",
+      "Quality of Sleep              7.312834\n",
+      "Physical Activity Level      59.171123\n",
+      "Stress Level                  5.385027\n",
+      "Daily Steps                6816.844920\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
-    "### \n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "victorian-burning",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "Columns = [\"Sleep Duration\", \"Quality of Sleep\", \"Physical Activity Level\", \"Stress Level\", \"Daily Steps\"]\n",
+    "\n",
+    "mean_values = df[Columns].mean()\n",
+    "\n",
+    "print(\"Mean Values for Key Categories:\")\n",
+    "print(mean_values)"
    ]
   },
   {
@@ -172,7 +309,7 @@
    "id": "collectible-puppy",
    "metadata": {},
    "source": [
-    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## Second Research Question: Which occupation gets the most sleep?\n"
    ]
   },
   {
@@ -188,12 +325,7 @@
    "id": "incorporate-roller",
    "metadata": {},
    "source": [
-    "*Explain how you will approach this research question below. Consider the following:* \n",
-    "  - *Which aspects of the dataset will you use?* \n",
-    "  - *How will you reorganize/store the data?* \n",
-    "  - *What data science tools/functions will you use and why?* \n",
-    "\n",
-    "✏️ *Write your answer below:*\n"
+    "I will create a bar graph with the x-axis labeled by occupation and the y-axis showing the average hours of sleep for each group. To do this, I will first calculate the average sleep duration for each occupation and then use that data to build the visual. This bar graph will make it easy to compare which occupations tend to get the most and least sleep, providing a quick and clear overview of how sleep patterns vary across different professions."
    ]
   },
   {
@@ -206,32 +338,168 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 57,
    "id": "pursuant-surrey",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Occupation\n",
+      "Engineer                7.987302\n",
+      "Lawyer                  7.410638\n",
+      "Accountant              7.113514\n",
+      "Nurse                   7.063014\n",
+      "Doctor                  6.970423\n",
+      "Manager                 6.900000\n",
+      "Software Engineer       6.750000\n",
+      "Teacher                 6.690000\n",
+      "Salesperson             6.403125\n",
+      "Scientist               6.000000\n",
+      "Sales Representative    5.900000\n",
+      "Name: Sleep Duration, dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
-    "###\n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "avg_sleep_by_occupation = df.groupby(\"Occupation\")[\"Sleep Duration\"].mean().sort_values(ascending=False)\n",
+    "print(avg_sleep_by_occupation)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "located-night",
+   "execution_count": 58,
+   "id": "c09f8668-6794-4d9a-b094-575d0a63fb1f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "plt.figure(figsize=(12,5))\n",
+    "plt.bar(avg_sleep_by_occupation.index, avg_sleep_by_occupation.values, color='pink')\n",
+    "\n",
+    "plt.title(\"Average Sleep Duration by Occupation\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Occupation\", fontsize=10)\n",
+    "plt.ylabel(\"Average Sleep Duration (hours)\", fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d81599-3128-4f57-9821-a9b53ecdc27f",
+   "metadata": {},
+   "source": [
+    "## Third Research Question: Who has more effect on sleep duration: Stress leve Physical Activity Level?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "557961a6-ddf9-4744-b1af-10b9536e8884",
+   "metadata": {},
+   "source": [
+    "### Methods"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "infectious-symbol",
    "metadata": {},
+   "source": [
+    "I will create two graphs to compare how stress and physical activity affect sleep. The first graph will plot sleep quality and sleep duration for each stress level, and the second will show the same relationship for physical activity levels. These visuals will help the reader clearly see how both stress and physical activity influence the number of hours slept and the overall quality of sleep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faa9b939-f80f-4342-a442-cd132aeb293f",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "d98295fb-d13b-41e9-82b5-25ba5b0e90fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stress_group = df.groupby(\"Stress Level\")[[\"Sleep Duration\", \"Quality of Sleep\"]].mean()\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "\n",
+    "plt.plot(stress_group.index, stress_group[\"Sleep Duration\"], marker='o', label=\"Sleep Duration (hours)\")\n",
+    "plt.plot(stress_group.index, stress_group[\"Quality of Sleep\"], marker='o', label=\"Quality of Sleep (1–10)\")\n",
+    "\n",
+    "plt.title(\"Effect of Stress Level on Sleep\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Stress Level\")\n",
+    "plt.ylabel(\"Average Value\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "c04a03e1-a8b2-4c02-aab3-917be005d327",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Xhv4SUSjCziCspK+yUxslPWuFoVDf77//nkJY9e/f/6HvX05hfH1lEJ3ejLl8N7J6nVL/1kghS7Gm5gY58TuTmfvdXOT6SfyDPOR3SO436WuDlVLaZUoEpf5soaGh6rVYCoytoqlp2rRp8nuy2scG5PsogtdYWBhfR4mzICS3oKggJI+RfyAS2GmoaSGuB0FBQSn2kVoMMpgUdxfB2HwtyD+bxYsXPxQoK7OSpv7ZGg9KzW2jYcZeZsRk0GxwRZB/lLJsvK8tI4PE5557Tr1+7733kl2fRBxIUUNjJHhVAo9FiMjDQK1atXDw4EH1WkRGeqJCgrIl+FP2M9SDEAuHVAw3IEG0EtApLiDGg1bDANEwoDC2quRmbYr0AroNokK+o4bvtQSPS1CwwXVKEBElIkUGtKYGg5n9jgoS5CvXQgThiRMnUgzCMuuGZO79IoND+QxiOZLAbkPwem5YRowxvs+io6OVmDR2DzIglpPUs/WZuU6p72e5BwzJKIwRcZ1dK6U1/c6ItVG+y6aEqATyGxCRLcHrGSHtNQTCSyFJ+Y2RAHRj5D6XpA2Gz5adPjYgv1+STMTgfihB38auibkhtAkxQFFBSAbZn+SfsWQKyslB7M8//6z+aYi5WwapkjVHXE0kw5DMPstMqJipZcZXZuhkACuZngwuC8uWLVMDWVknM1ryD37jxo0p2m8sVMRVRWITZIAgj/SsI4Z/sDLwMAxoJWOIDJjkn6K4T0g7DbOC4t9vy4g14p133lEDJOmT9AalksFF4l5atGihnuV6SGE+g6AwdyZQ+qJPnz7qtVxL+Q4Ysj/JwFv696efflJuT6l9xLt27aoGGnJOY1/+3Mj6ZEocyUDakIVMYkOkHTLbLQMyGWAbsprJoEYGN/LZ5NrKd1rcS7755hu1XSxtMpA3iDjJCCXXUtbJfqYKUaZGZtDluomFydhCIsdIbWXKCHPvF5kBlkH2u+++q5YN3xlpb3aqdE+ePFn1uykk25z0u8SqiGAT5LshYkHc8cRKJtXm5TdAJhfELUuue1auk/yuSLYvg8VKMhjJ4F4GozKgluOLP7+0Q2bxU094ZAZr+p2RuCh5SAYy+Uzibinnle/kokWLkvdr2bKlmhjICMmyJe6RIqZPnz6tvhvSX2LhEHEn1k7JGHf37l0VVyVkp4+NkWsoxfoM2Z+Mi7FKzBkhuUauhYATYifZnyS7R05mfxKkBoFk/cno3MbZcsLCwsyqU2HAOJOI8aNatWpm54+XwnFpnU+yMX3xxRcPvS+nsj+ZWwwso+ttTlYf48Jl8ihevLjJPPWSIz69/pKiW8aF9NK7FpKVx5w6FZLByjjri/Ejdd0M48+e2exP27ZtS3GsTz75xOR+UpTPeL9XX30103UqTGXIMX5IZitz76XUNTXk0atXL5NtT++7kJn7Re5FKWRmvF/qWicZkfqzpfcwIFnC0qthkN59l5nrJJmX0qtTYeoezU6disz+zqQu9pbedc2oPo4Bc/pBCl2mLvyYXp0K+U6kV6fC1LXKSh8bX4+iRYuqLF+m3vPiiy+a3S+EZAUGahNiAWQG6vDhw8rtRawQYl6XGVAJspOc6JLZRzKBGAcdyzZZJ3nY27dvr2Z6xf1JLBkyi5h6Jk9y6P/www9q5iujPO2mkBk5aaPMuIkZXmbn5Dji+iGzmzJbach5b+uMGDEiReyAzBymDnYUZCZeLBgyM2+4/tJ3svz2228rt4S0fJ1TI7O827dvV7O1MisqFie5xvJa6gQYZr1lNlmy0shsuXwHxO1BtsnMdlZiTsyxUsi1kHaZQmZy5bthQOIqDDOhYjkTq5l8f+WayLWR9otVR2ZhZbsxknFLziOzt2nFbmSEZNJJHVeSlQxMmblfpB+Ms1tJ36XOdpUbSDYgsQxNnDhRucrIvS/fU3HHkests9DiFmmqLZm5TmKlku+yfN8lyFusNXIeyUpWuXJlFTsi/W5ckyarWMvvjLgjSh0ig7XAEPAs11bcFeX+lu92ZqxRUvtGrHni+iR9J59NfjPk+y7xXB9++KGyhORUHxu+i1KfR+qISLY2uZZi7ZSaHfL9JiQ3cRBlkatnIIQQK0dcWKSYnrglCMePH3/I7YgQY6RwosEFSlyLjFO0EpKXyOTCmDFj1GtxoTQUzyMkr2FMBSEk3yKWAoldEV9qg6AQKxAFBTGFBNyKv7v4tEsqYQMSd0AIIfkdigpCSL5FZpgNNQYEcRUQtwNC0krmkNplSJIsGGp5EEJIfoYxFYSQfI/4K4tv93///af8pwlJD4n/EJ9/yRo2ffp0SzeHEEKsAsZUEEIIIYQQQrIFLRWEEEIIIYSQbEFRQQghhBBCCMm/gdpSYfLq1avKH1oqXxJCCCGEEEJyDomUiIyMVDWH0qspZNOiQgRFyZIlLd0MQgghhBBC7JpLly6poop2KSrEQmH4kL6+vhZpg1SSXbVqFTp27KgqxxLbgX1nu7DvbBf2ne3CvrNd2He2S5wV9F1ERISaxDeMu+1SVBhcnkRQWFJUeHp6qvPzRrUt2He2C/vOdmHf2S7sO9uFfWe7xFlR32UUasBAbUIIIYQQQki2oKgghBBCCCGEZAuKCkIIIYQQQki2sOmYCnNJSEhQPmm5gRzX2dkZ0dHR6jzEdmDfpY34bTo5OVm6GYQQQgixEZztPa/utWvXcOfOnVw9R7FixVQGKtbKsC3Yd+lToEABdX14bQghhBCSr0WFQVAUKVJERc7nxuBICvBFRUXB29s73YIgxPpg36Uttu7du4fQ0FC1XLx4cUs3iRBCCCFWjt2KCnFnMQiKgICAXB2YxsbGwt3dnQNTG4N9lzYeHh7qWYSF3EN0hSKEEBslMQEOFzYj6NY2OFzwBcq2BBzt5Dc9MQG4sBWIug54FwWCm9rPZ7NBLCoqpOT3hx9+iMWLF6vBS506dfDtt9+iQYMG2T62IYZCLBSEkMxjuHfkXqKoIIQQG+ToUmDFO3COuIr6snxhEuAbCHSeAFTtDnv4bIi4+mCdvXw2G8Wi07PPPPMMVq9ejZkzZ+LQoUOqWmD79u1x5cqVHDsH/cEJyRq8dwghxMYH3QsGphx0CxEh+nrZbqvY82ezYSwmKu7fv48///wTEydORMuWLVG+fHmMHj1aPU+aNMlSzSKEEEIIsX23IJnFh2ZiY9K6FSP1/WwNe/5sNo7F3J/i4+NV3IP4s6f25d68ebPJ98TExKiHgYiIiGT3jNQpY2VZAk7Fb14euYWcw/Ccm+dJjbijiCh77LHH8uyctsaYMWPw119/Ye/evbned2vWrMHLL7+MgwcPqr7J6NyW5N1338Xdu3fx3XffpbufXBO5Ntbo/mS433MrVTTJPdh3tgv7znaQGApxeUobDYi4Am1CacDJBTZFQhwcYvTxX3qfLf7sRmjBzWHrxFnBfWfuuR00w8jKAjRt2hSurq6YM2cOihYtirlz52LQoEHKWnHixImH9hdLhgzWUiPvTx07IfUHJB1myZIl1TmyQ0Kihr2XIhB2NxaFvFxRt6QvnBxzzzUkLCwMn376KVatWoUbN26o1J7Vq1fHW2+9hcaNG6t9/P39MWvWLHTt2hWW5NFHH8WWLVvUa7nOEhRfs2ZN9OvXD926dcuzdpi6HpLZSQKxCxYsmOvnb926NV588UX06dNHLY8fPx7//PMPNm3aBGvj5s2bKn5p48aNKF26dJr7ybWTdLuSRU0mAQghhNgGEpRdX+In8jG7g4fjSsEmlm6GXSAZIfv27Yvw8HD4+vpaZ6C2xFIMHToUQUFBaia0bt26ePrpp7Fnz540Z1hff/31FJYKEQ0Si5H6Q0pBMxkQSbrQ1NaQzLDi8DWMXXYM1yKik9cV83XHR49WQefqxdRMrgSc+/j45JgPevfu3dWAbvr06ShbtiyuX7+OtWvXqs9k/DnFqpNe5+YFIt4kNkbEngw8L1++jCVLlmDYsGFKIP7yyy9ZPrZYsuSampuZKfX1yOja5FTfiWXt/Pnz6N+/f/J3zc3NTX2n87p/5HuTkYiWNsk9IyJM3A/TQr5vck3FPTE791BuzZpIPFaHDh1UoT5iO7DvbBf2ne2gsjyZISriH/0eWmBd2BIOV/fCedn/MtyvdotOqGUnlorVFr7vDJ5BGaJZAVFRUdrVq1fV6z59+mhdunQx633h4eFiZVHPqbl//7529OhR9ZxVlh+6qpV+Z5kWnOpROukh2xMSErTbt2+r55xAjiWfaf369enuJ/ssXrw4efnixYta7969NT8/P83f31/r3r27du7cuRTv+fXXX7XKlStrbm5uWqVKlbQff/wxeZvsK8ecO3eu1qRJE7VPtWrVMmxHq1attFdeeeWh9b///rs63urVq9XyunXr1LJ8PgP79u1T6wztnDp1qmr/X3/9pVWpUkVzcnJS23bu3Km1b99eCwgI0Hx9fbWWLVtqe/bsST5OcHCwOo7hIcvCqFGjtFq1aiXvJ300ZswYLSgoSHN1dVXb/vjjj+S+M1yDP//8U2vdurXm4eGh1axZU9u6dWu612DEiBHaE088kWKd4dwzZsxQ7ZF2P/nkk1pERETyPtHR0dr//vc/rXDhwup6N2vWTH1WA4brYYz0ufFtaziP9G3p0qU1BwdlfFSfq3r16pq7u7tWsGBBrV27duo+MzB9+nStRIkS6X6unLiHcovY2FhtyZIl6pnYFuw724V9Z0OEHNa00QU0bZRvGg8/TfuyiqYlxGs2h7T5y8r6Z7C3z2al9116421jrCI5v5eXlyqwdfv2baxcuRI9evTIvaJesfFmPSKj4zBq6ZH0woAweulRtd/92IR0j5UZDzOxrMhDZvuN40cyUrGdOnVSM+7ibiPuSHKMzp07q5lrYfbs2fjoo48wbtw4HDt2TLlXSTpfsYYYIy5Wb7zxBvbt24cmTZooFyZxl8ksYqUQl6RFixZl2sQ2YcIE/Pbbbzhy5IiqkSDWBDmeWAS2b9+OChUqoEuXLmq9sGvXLvU8depUhISEJC+nRtIVf/nll/jiiy9U7IPM1os579SpUyn2e//99/Hmm29i//79qFixorKepef+I9e8fn2VrC8FZ86cUf24bNky9diwYYNyizLw9ttvq7gY6QOJvRC3P+nHW7duZeqanT59Wh1HrrW0Wa6BtFmsgNLX69evR69evVJ8Dxs2bKisSmJhIYQQYkdcPwLM6A5ohljB1Jb4pOXO422zpoO0WdLGKkx5GWi2+9lsHIu6P4mAkIFOpUqV1MBIBrSVK1fGkCFDcuV89+MSUPWjlTlyLBmeiUtUrbH/Zbjv0bGd4OnqbLY70bRp0/Dss8/i559/Vi5hrVq1wlNPPaViFUwxf/58FVQrA3GDG48MsCUWQwaUMngeNWqUGlDL4FIoU6YMjh49qtyTZMBu4KWXXsLjjz+uXksWrhUrVmDKlClqAJwZxGVJBuSZHbSKQPrpp59Qq1at5HVt27ZNsc/kyZPVZ5NBusR0FC5cWK2XdRJHkxYiJt555x11LQUZ4EuAtYgNOacBERSG2Axx66pWrZr6fsp30xQXLlxAYGDgQ+ulT6QvRewJAwYMUOcTYSeB0nJ9Zfsjjzyitv/666/KxCnXW+4FcxHhOGPGjOTrIAJFRJD0dXBwsFpXo0aNFO8xtFfanl5cBSGEEBsi5CAwowdw/xZQvBbQ6AVg7ccmajmMt+1aDtL2PjMerlMhFK4MVMm7mE7yAItaKiTgY8SIEWqwNnDgQDRv3lwJjfzuqymD+qtXr2Lp0qXK2iDCQMSFDEBNceDAATXolcGrwdIhwcniEy+z5TKAlWeJczBsl8cnn3yi1hsj1gljgSMz8DLbnRVEMGY2VkHiAVKLJ4kpEZElFgo/Pz8VEyBB2BcvXsyUP6Bc02bNmqVY36hRIxw/fjzFOuPziwVNkOKM6aVHNhVzIIN1g6AwHMtwHLnuIqCM2yPfe7EgZPZ6i3AwCApBBFm7du2UkOjdu7cSK2IFNFUxWyxDhBBC7ICr+4Dp3XRBEVQPGLgUqN0XePUw4vsvUYHL8oxXD9m2oDAgn+HVw8CgZcDjU4AnpgKOrsCN48CZtZZuXb7EopYKyZRjyJaTF3i4OCmrgTnsPHcLg6eadqMx5vdB9VAlwAU+vj5pBhTLeTOLDFIlKEce4qYkwdBibRg8ePBD+8oAu169esrFKTUy2JTtggwuZRBtTG6lCpUga3ErMlRHN1wbYxccUynKZLCbWoiIJUVcsMSiIANoCYIW8WNw7cppjEWtoS3ppZwtVKjQQ4P21McxHCszqWvlmqV2nTN1zcR9MHWfisVj69atKoPY999/r1y6duzYoSxUgsHFyliMEEIIsVEu7wZm9gJiwoESDYH+CwF3P32bo5NKrXrlSIQeuGxPbkHyWcq0eLB8eRew/SfdOlOurfzjtWTr8h1WEVORV8igTtyQzHm0qFAYxf3cTXrrqWPJzLOfu9rPw9Up3WPlRFaoqlWrKouDKcSKIQN4iT8Qv3zjh8zsS7pecXc5e/bsQ9sNg0wDErNgQFxoJBNXlSpVMt1eiROQgbbBlcoweBV/fwPi/28OEiMiNSAkjkJckURUSNrd1AN4ETJpIdYNuQaG9LcGZKCdlc9njKRnFVeyzFCuXDlllTFujwgGiQeRvjZcM4kbMe53c6+ZfOfECiLuWxIfI+davHhx8vbDhw+raybXkxBCiA1zcQcw4zFdUJRqCgxY9EBQ5Deavw64eOlWm+PLLN2afEe+EhWZQepQjOpWNb0QJ7U9p+tVyIy8xBBIuk8JJj537hz++OMPlfozrQB2qQkhs+WyXYKG5T3iMiUDcQnGFWRw+dlnn6mCZydPnsShQ4dU3MVXX32V4lg//vijGnyKS5C4pokwkIDf9BAXGqllIOcSUSJxCy+88AKGDx+ONm3aqH1EwEj6X6k1IgJIajhIjIc5iNuTpB8WtyARAfJ5De47xq5GEq8g7TBlNRAkTkGCwCUGReqgSIpiuQ5ynbKDBFenVbAxLcS6INdH2iRxKyJKxMVLrqW4qQliVZL6K++9955yl5J6LGm5wBkj10gC8Xfv3q1cxCSAW+qdGIsn+Z60aNHioetICCHEhji/BZjVC4iNBEq30C0Ubg/cbvMd3oWBJi/qr9d+wqraeQxFRTp0rl4ck/rXRTG/lP7ysizrZXtOI7EOMpj8+uuvVX0AKXon7k8y4Pzhhx9MvkcGnlLIrFSpUio4VwaPMjA1rmsh7lMSyC1CQnztJfhbBqipLRUSvCwP8cuXgbLEdYhgSQ9xq5J4AZl9l/PLAFkG7sbBzzIrLsUNRaxIzIIM7iWmwxwkcFmEglhkJNhZRIBYZYwRgSIuPyJcxHJgCnmf1DmR7FZyDSR+RwbqIlqyg4gcyVRlqmBjesh1FkuOfCb5bBIXI22SrFmCxMWIuPz3339Ve+X6iSjLCOlz+T6IZUeC5T/44AN1fQwB4cK8efPUd4oQQoiNcnYDMPsJIDYKKNsa6LsAcE3pDpsvafKSbqmR2IpDCy3dmnyFRStqZxcJvhX3HlMV/mRALTP2MmjObuEuqagtMRahkdEo4uOOhmUKJlsoxEde2iHnN7dImzUiWZrkWomrTO3atZEfyMm+E4uDHCs7xf7yiuXLlythJZYwCcZPi5y8h3IacRUTsSXCKb8ndrA12He2C/vOipBA5LlPA/HRQPn2wJOzAJe0Lc/5ru82fQWsGQP4lwZG7AKc0y8Ka83EWUHfpTfeNsZ2R8F5iAiIJuUC0KN2kHrOaZcnYvtIILQEkWcmENtSSIyGWKzSExSEEEKslFOrgTlP6YKiYmfgqTnpCop8SaPnAa8iwO3zwL6Zlm5NvoGigpAcQGpkSOyDLVirnnjiiYeygBFCCLEBTiwH5vUFEmKAyo8CfWYCzm6WbpX1IW5gLd/UX2/8HIi7b+kW5QusfwRE8gQJdBZPuPzi+kQIIYTYFMf+Bub3BxJigao9gN7TbNqtJ9epNxjwKwlEhgC7frN0a/IFFBWEEEIIIdbMkcXAgkFAYjxQ/XHg8d8Bp3wQG5EdxILTeuSDGIvoCEu3yO6hqCCEEEIIsVYkg9HCYYCWANR8Cug5GXBiTJxZyPUKqKBXGd8+ydKtsXsoKgghhBBCrJED84BFz+qConZ/4LGfKCgyg1yrNu/pr7d+D9y7ZekW2TUUFYQQQggh1sbemcDiFwAtUY8P6P494Ohk6VbZHlUfA4rW0AsEbvnG0q2xaygqCCGEEEKsid2/A0tfAqABDZ4Bun4N2EB2QatErlu7D/XXOyYDESGWbpHdwm8oIYQQQoi1sPNXYNlr+utGw4EuX1BQZJcKHYGSjYD4+8CmLyzdGruF31KC0aNHp0glO3jwYDz22GMWacvx48fRuHFjVcE5O+ltHRwcsGTJElgDJ06cQLFixRAZGQlrITY2VqUR3r17t6WbQgghxMC2n4B/k+orNP0f0Pkz+Ydm6VbZPnIN2yZZK/ZM14vikRyHosIcEhOAc5v0DAzyLMu5zKVLlzB06FAEBgbC1dVVVWt+5ZVXcPPmzVw/97fffotp06YlL7du3Rqvvvoq8oJRo0bBy8tLDcTXrFljcp8bN25g+PDhKFWqFNzc3NSAvVOnTtiyZQuskXfffRf/+9//4OPjo5ajo6OVcKtRo4aqap1ZAXfkyBE8/vjjShSIePrmG9M+oj/++KPaRwSaFLvbuXNn8jb5Tr355pt45513svnpCCGE5AhbvgVWvqu/bvEG0OFjCoqcpEwLoGwbIDEOWD/B0q2xSygqMuLoUuCb6sD0R4E/h+nPsizrc4mzZ8+ifv36OHXqFObOnYvTp0/j559/VoPsJk2a4Nat3M1e4OfnpypEW4IzZ86gefPmSkQFBASY3EcG1Pv27cP06dNx8uRJLF26VAmfvBBcmeXixYtYtmyZEhEGEhIS4OHhgZdffhnt27fP9DHv3buHsmXLYvz48UpQmWL+/Pl4/fXXlUjbu3cvatWqpYRXaGho8j79+vXD5s2blUghhBBiQaTq8+qP9NetRuqz6hQUOY8htuLgPODGCUu3xu6gqEgPEQ4LBgIRV1OulyAfWZ9LwmLEiBFqJnnVqlVo1aqVmpF/5JFH8N9//+HKlSt4//3303XzEUFgbGmQ2eiKFSvC09NTDUY//PBDxMXFpXl+Y/cneb1hwwZlvZBzyePcuXMoX748vvgipV/i/v371XYRQaZITEzE2LFjUaJECWVhEPemFStWpPgse/bsUfvIa3HLSs2dO3ewadMmTJgwAW3atFHio2HDhsoa0L1793QtP3369FHXpmDBgujRowfOn09p/vztt99QpUoVNbNfuXJl/PTTT8nbZF9p07x589C0aVO1T/Xq1dW1SY8FCxaoAX1QUFDyOrHETJo0Cc8++2yaoiA9GjRogM8//xxPPfWUuo6m+Oqrr9TxhwwZgqpVqypRKv3/+++/J+/j7++PZs2aqc9ECCHEAmgasH48sPYTfbnNB0CbdykocougekDlR/WMWuvGWbo1dodjvrt5Y++a95DKi8vf1jMvPHwg/WnFO/p+cffSP5ac10zECrFy5Uq8+OKLajbbGBmAyuyyzEJrmTimuN2IyDh69KgSB7/++iu+/vprs94r+4t1RAaoISEh6iEiR1yzpk6dmmJfWW7ZsqUSHGkd68svv1Ri5ODBg2rmXISAWGQEOXa1atXwxhtvqNfinpMab29v9RAhFRMTY9ZnEAEl55LrIIJE3KTkGF26dFGxBcLs2bPx0UcfYdy4cTh27Bg+/fRTJb7EGmLMW2+9pdonlhK5Lt26dUvXQiLnE6tTXiKfScSZsRXE0dFRLW/bti3FviLIpI2EEELyGPk/LmJi/Wf6cvvRQKu3LN0q+6eNTMw6AEf/Aq7ut3Rr7Ir8VUFFBv+fBubQwTRlwXCcGIwMHYXeuwq4epl1VBlgi2CQGXNTyPrbt2+ruIIiRYqYdcwPPvgg+bX42MtgXWan335bRFPGrlBiNZFZbuNZdbFgyCBc/PRlYCoD9zlz5jxkvTBGtonVRGbYBbE2rFu3TsUEiP+/HF9iDGTAn9YMvmwXgSQiR2bf69atq6w5csyaNWuafI+IMLGSiCVCrA0GASRWC3H/EauMuAmJ4OnVq5faXqZMGSXCfvnlFwwaNCj5WC+99JJyvxLE2iCWlilTpqR5LS9cuJDnoiIsLEy5WBUtWjTFelmWQHhjJGZH2kgIISSPBcV/o/Q4CqHjOKCppJAluU7RqkDNPsDB+bqo67/Q0i2yG/KXpcKGyMgSIQN9c5FBtbi5yEBdBuwiMsTXPzvIYLRr167J7jR///23shz07t3b5P4RERG4evWqaocxsiyWgcwgg3o5lsRSdO7cGevXr1fiwtjly5gDBw4olyyxVBgsHeICJQHT4sp19+5dFcsxbNiw5O3y+OSTT9R6Y8Q6YSxwRDCk1/779+8rV6nMIH1j3A7DQ6wnOY1YwyRGgxBCSB4h/99Xvv9AUDwykYIir2k9EnB0Bk6vBi5stXRr7Ib8Zalw8dStBuYgX7LZT2S4W+LTCxBRsAZ8fXyUi0ma5zUTcR2S2XQZqPbs2fOh7bK+cOHCyYHUsm9qAWIcLyHuLuIyNWbMGOUCJJYHsVLIrHx2eeaZZzBgwADlSiUz/08++aSyaOQFMlDv0KGDeoibkrRFrA3GAdEGoqKiUK9ePeXiZIxYLyQmQbYL4hYmWZKMcXLKXvXSQoUKKctSZgWbxKekRoSQueeUdl+/fj3FellObQESdzv5PhFCCMkD5P+1uFbvnKwvd/0KaDDM0q3KfxQsC9QZAOyZCqz5GBjyL+NYcoD8ZamQL4y4IZnzKNcW8BVXqbS+ZA6Ab5C+n4iG9I6ViS+qZDySgbIECcsstzHXrl1TA2PjgbMMCCX+wNh9ynjmeevWrSqYWYK7ZVa9QoUKmXZ3EauIuNOkRmISDEHH4gYkcRZp4evrqwbLqdO+yrIEEmcXOYZYHEwhVgy5LuIuJqLN+CEiS9yCpG2SdSv1dnGDMmb79u3Jr+Pj41XsQlquakKdOnWUG1VmEAtI6nbIw1xRIf0lIso4Ja8IKEP2MGMOHz6s2kgIISSXSUzUi9opQeEAdPuOgsKStHwLcHIDLm4FzphOYU8yR/4SFZnB0QnobMhjnFoUJC13Hq/vl8P88MMPypVILAsbN25UmYtk0C5iQ7I4SSyDgbZt26r9JXBYCpm98MILcHFxSd4uIkLcacQ6Ia483333HRYvXpyp9kgcxo4dO1QGJPHXlwGqILPhInAk85KcJ/WANTUS5CxxFOKOJXUoRo4cqWbkpf6GuUhQtHzmWbNmqWBvcV/6448/MHHiRJXRyRRiqZHZe9kuQcnyHnGZkvNKNi1BLDmfffaZuj6SpvbQoUPK+iJZlIyR2A+5fhKbIFm6xAqRnpiSPhRrUWpRJkJDPrtYCsLDw9VrU9aJtAKxDfvLa/kM8to465akkxXLiwSai3VL6nqI6JJsUMbI9ejYsaNZ5yWEEJJF5P/m3y/rM+MyhnjsJ6Deg3g9YgH8goCGz+qv14zNVFIdkgaaDRMeHi7fAPWcmvv372tHjx5Vz9niyF+a9mVlTRvl++DxZRV9vaZpCQkJ2u3bt9VzTnLu3Dlt0KBBWtGiRTUHBwf1OXv16qXdvXs3xX5XrlzROnbsqHl5eWkVKlTQ/v33X83Pz0+bOnVq8j5vvfWWFhAQoHl7e2tPPvmk9vXXX6t9DIwaNUqrVatW8rKct0ePHsnLJ06c0Bo3bqx5eHiodkjbDJw5c0atmzhxYoafSa7R6NGjtaCgIM3FxUWdc/ny5Sn2kXXSnrSIjo7WRo4cqdWtW1d9Bk9PT61SpUraBx98oN27dy95P2nT4sWLk5dDQkK0gQMHaoUKFdLc3Ny0smXLas8884x24cKF5L6bPXu2Vrt2bc3V1VXz9/fXWrZsqS1atCi5P+SYc+bM0Ro2bKj2qVq1qrZ27dp0P3NcXJwWGBiorVixIsX64OBgdbzUD3MwtCX1o1WrVin2+/7777VSpUqptkqbt2/fnmL71q1btQIFCqS4brlyD+UCsbGx2pIlS9QzsS3Yd7YL+y6LJMRr2qLn9fHD6AKadmB+njeBfZcGUTc0bVyg3jdHlmjWSKwV9F16421jHOQPbBQJ/hX3FZnpFfcaYwxBuOK+ktlA2YeQCtoSYxF1HfAuCgQ3TbZQyKy9tEPOn2ZMRQ4g8QIya7569Wo0btwY1oLMdLdr105ZU1JnG7J2MtN3YqWR75JYhKS+RmYQ64YElUuqYGtCYmCkhsZ7771ncnuO3kM5jMQN/fvvv8oFz9gyR6wf9p3twr7LAgnxwJIXgEN/AA5OwOO/AdX1LIN5CfsuHdaOAzZOBApVAl7cliseKLbed+mNt/NvoHZWkS+YlHe3IOKeI25I4tMvKVxzU8CYg7hnSVpbKVAnGZ9sTVDkJc8//7wq2hcZGakyUFkD4jZVo0YNvPbaa5ZuCiGE2CcJccCfzwBHl+iZhp74Hahq2k2XWBDJvCVxLmEngIMLgNpPW7pFNgtjKmwI8Yd/9dVXLS4ohLlz56oAcBksSzwDST/wWgLlrUVQGIK5JbVw6gKLhBBCcoD4WOCPwUmCwgXoM4OCwlpx9wOaJ02wrf9U7zuSJSw/OiU2iQRoS/CxZD8KCgqCvSNWIvEUzKzrEyGEkHxGfAywYCBwfBng5Ao8NRuo3NXSrSLp0fA53b39zkVg3wxLt8ZmoagghBBCCMkJ4qKBef2Ak8sBZ3fg6blAxU6WbhXJCFdPPcWssOFzIJZFYbMCRQUhhBBCSHaRgejcp/Qqzc4eQN/5QPn2lm4VMZe6g4ACpYCoa8Cu3yzdGpvE7kWFoaYCISRz8N4hhBAzib0LzOkDnF0HuHgB/RcCZVtbulUkMzi7Aq1G6q83fwVER1i6RTaH3WZ/kkBUCWi+evWqqjotyw65UIJdBl6SSUfSb1pDADUxH/adaSR2RK6LZPeS6yL3DiGEkDSIiQTmPAlc2AK4egP9FgLB6ReDJVZKzSeBLd8AYSeBbT8Cbd61dItsCrsVFTIYkvz6ISEhSljk5gDs/v37KotObogWknuw79LH09MTpUqVouAihJC0kNns2U8Al3YAbr5A/0VAyQaWbhXJKk7OQJv3gT8G6aJCAri9AizdKpvBbkWFIDOsMiiKj49XmYpyqyjJxo0b0bJlSxaUsTHYd2nj5OSkUuFSbBFCSBrcvwPMehy4sltPSzpgMRBUz9KtItmlSnegWE3g2kFgy9dAx08s3SKbwa5FhSCDIhkw5tagUQZfIlqk4jAHprYF+44QQkiWuHcLmNkTCNkPePgDA/8CiteydKtITiDW+XYf6Raonb8CjUcAvsUt3SqbgH4NhBBCCCHmcvcmMKO7Lig8A4BBf1NQ2BuStatkYyA+Gtj4uaVbYzNQVBBCCCGEmEPUDWB6N+DaIcCrMDBoGVCshqVbRXIacf0Va4Wwdzpw65ylW2QTUFQQQgghhGRE5HVg+qNA6BG9+vLgf4CiVS3dKpJblG4GlGsHJMYD68dbujU2AUUFIYQQQkh6RIQA07oCN44DPoHA4H+BwpUs3ap8T0Kihm1nbuKv/VfUsyznKG0/0J8PzgdCj+Xsse0Quw/UJoQQQgjJMuGXdZenW2cBv5LAoKVAwbKWblW+Z8XhEIz5+yhCwqOT1xX3c8eoblXRuXoOBVYH1QWqdAOO/Q2sGwc8OStnjmun0FJBCCGEEGKKOxeBqV10QVGglO7yREFhFYJi+Ky9KQSFcC08Wq2X7TlGG7FWOOjC4srenDuuHUJRQQghhBCSGgnOndoVuHMB8C+juzz5B1u6VfkecXESC4UpRyfDOtmeY65QRSrrlbaFtaxZkR50fyKEEEJI/iUxAbiwFYi6rgdgBzcFbp/XXZ4irgAFywGDlwG+gZZuab4gPiERYVGxuB4RjdDImOTnG5HRuB4Rg7NhUQ9ZKIwRKSHbd567hSblcqgaduuRwOGFwJk1wPktehA3eQiKCkIIIYSkT2ICHC5sRtCtbXC44AuUbQk4OsHmOboUWPEOEHH1wTrvIkB8LBB9ByhUUa9D4VMMtorM2O84dwt7whwQIAPt8kXg5OiQ5+2IU2JBREIMQiOicV2EgjzLcpJgEPFw824MtBwwMsgxc4yCZYC6g4DdU4C1HwNDlutpZ4n1iIqEhASMHj0as2bNwrVr1xAYGIjBgwfjgw8+UJWwCSGEEGIdA2/niKuoL8sXJumz9p0nAFW7w6Y/14KBRk4zSUSF6s++JfQYChEZdhHM7IQZp3bneDBzbHwibkQlCYWIBxYFY6Eg627ejTVbLIjoKezthqK+bijs466eiyQ937obg4krT2Z4DNk/R2n5FrB/NnBxG3D6P6BCh5w9vh1gUVExYcIETJo0CdOnT0e1atWwe/duDBkyBH5+fnj55Zct2TRCCCGEpDXwlhSrsr7PDNsUFuLyJBYKk575SWgJesVsGw9mTv0JDcHMk/rXTVdYxMQn4EakLgpCjV2RImKUlcGw7tbdWLPb5OzogCI+bijs646iPm4oYiQW5NmwHODlCsc0rClieZm5/aL6HGn1XmEfNzQsUxA5im9xoOGzwNbvgTVj9RoWjgxNthpRsXXrVvTo0QNdu3ZVy6VLl8bcuXOxc+dOSzaLEEIIIekOvGWdA7BiJFC5q/muUImJ+mBdCoolPxJTLccDWup18p7U70tIdSzDaxPrUu8n2ZyMXZ5MERmix1qUaQF7DGZ+f8lhJCRoupVBCQbdunAjSTzcvhdn9vlcnEQsuKvBfNE0hIIs+3umLRbMRawYYmkRYSRHMvUZ5XPdjIpBEd8ctlY0ew3YPQ24dhA4thSo9ljOHt/GsaioaNq0KSZPnoyTJ0+iYsWKOHDgADZv3oyvvvrK5P4xMTHqYSAiIkI9x8XFqYclMJzXUucnWYd9Z7uw72wX9p3tIDEU4vKUNpoKZNa+qQ44uaUczKtB/MODfIf0LANWSHz4FWg28F29Fxuv3ItuRsWq593nb6cbzCzIviPm7jNDLIgoEDcktxSvixqtK+DhYpZYSEiIR0ICsk27SoXw/VO18Mm/x3Et4sG4UNqUqIlQisWQaTsxa2gDeLvl4FDX1ReOjV6A06bPoa39BPHlOwGOznb/mxln5rkdNC0nwmGyRmJiIt577z1MnDgRTk5OKsZi3LhxePfdd03uL/EXY8aMeWj9nDlz4OnpmQctJoQQkmW0RAREnYB73B1EuxTATe9KgAPdB6wVCcquL/ETeYAmcsPBEYlwUs+aQ9Iz9NeJhnVq+cHrRMN+al/9dWLq/VJs09e5xd5BYETGNQc2l38XN32qIK9J0IC7cUBkHBAV56A/x8uyA6LUOv21vh2ITcza7H9hdw2Bnhr8XAFfVw1+LvL84LWns3XHI0vW2DMRDoiIA3xdgHK+Gm7FAF8fckJUvAOqFEjEs5US4ZSDPzPOCffR4cjrcE24i72lnsWlANuzZGWWe/fuoW/fvggPD4evr691iop58+bhrbfewueff65iKvbv349XX31VWSoGDRpklqWiZMmSCAsLS/dD5rZ6W716NTp06AAXFxeLtIFkDfad7cK+sz0cji+D06r34BD5YOZb8wlEQsdPoVV+1KJtI+lYKmZl7N6R0GEctMA6gAzgxQ1KZm7Vw0mtS7Esz2pd6v3yWFwmJsD5hzrKxcmU9UREjgSjx4/YmyNZrmSoFRmdZE0wsijI45aJ5cy4Hhlwc3ZUsQiFvF2VEDhwWffmSI9ZQ+ujUU7HHlgBBy6Ho//vuxAdl4gn6gbh08eq5mgCIMdt38Np7RhofiUR/8J2wNkN9vz/TsbbhQoVylBUWNT9SQTFyJEj8dRTT6nlGjVq4MKFC/jss89Migo3Nzf1SI1cZEsPLKyhDSRrsO9sF/adDQX7/jnkIe9nh8gQOMt6Ww32tXdKNtTdmhIeTOalRB94OzUZboPpZV2wr/pI1Nr6svpWGnvu6DXTNByo9g7quKXtkx8dl5AsCMLuxujCIEpSosaq1KlhhmUlGGIQJ+aHTCBtKujligAvNwR4uyLA2y1ZNBhey7Nh2cvVKXngLDEVzSesTTOYWfYq5udusfSyuU39MoXww9N18dzM3Vi49wpKFvTCK+0r5NwJGr8A7PwFDuGX4HJorh7Abcf/78w9r7OlzSmOqSLnxQ1K3KIIIYTYAbkR7Etyn7hoYMGA9AWF0Hm8TfabDLpf3FsCNeNexSiXGQjEreRt1xCAMXEDsGNHcbzsdVZZDZIFwt0HQiEyJj7T5/Vxc04hEJJFQdJr2VYoaVsBT9csD/jTC2Y2HFG226OgMNC+alGM7VEdHyw5jK//O4niBdzRp37JnDm4qyfQ6i3gnzeAjZ8Dtfvp6/I5FhUV3bp1UzEUpUqVUu5P+/btU65PQ4cOtWSzCCGE5BSSPceMYF9bzbJjl8TdB+b1Bc6sBVw8geavAnumpexHVadivM1amKTasgQyh6AhVsfUR0PH4yiCOwhFAexMrIxEOAL34jB22bEMA5mNLQmFlDgwtiqktDK4u+SdAJN0sZI29kGdCp1iOVynwprp3zgYV+/cx0/rz+C9RYdQ1NcdrSoWzpmD1xkIbPkWuHMR2DlZv0/yORYVFd9//z0+/PBDvPjiiwgNDVXF755//nl89NFHlmwWIYSQnCLqes7uR3JfUMx9Gji7DnDxAvr9AZRuBrR4E/FnN2L/ppWo3aITnG20orakSt1w8gbm7riYvE4ExPbEqib3r12yAGqW8EsWDqldj3zdna26WK8Ihw5Vi2Hb6VCs2rQDHVs0sluXp7R4q1MlJaoW77uCF2ftwfznm6B6kF/2D+zsCrR+D1jyArD5a6D+EMA9B45rw1hUVPj4+OCbb75RD0IIIXZGdDhw7G/z9vUumtutIRkRew+YJ4JivS4o+i8Egpvq2xydoAU3x5UjEagV3NxmBIUUcJP0qiIkNp68gePXIjP1/nc6V0aTcrZbAE8QASHB2DePaeo5PwkKQUTfhMdrKkG59cxNDJm2C4tfbIoS/jngrlSzjy4owk4A234E2ryH/IxFRQUhhBA7jaPYN0uvOnsvLOP9PfwfDF6J5QTF3KeAcxseFhQ2hGRZOhd2VwkIERLbz97C/bgHhRHEqFCzRAG0KB+AuTsvqUDr9AKZc7wqM7EIrs6O+HlAPfT5eZsSloOn7sKfLzSFn2c2A59FXLd9X68uv+1HoOFzgFch5FcoKgghhOQcEhux/B294qwQUAGo2gPY9GXSDiaGcPdvA1u/A5q9at1J8e1aUDwJnNsIuHoD/URQNIGtEBkdp2agDULi8u37KbZLcbaWFQurR/PyhVRGJUFcYPJzIHN+w9fdBVOHNEDPH7fidGgUnp25GzOGNsx+nEuV7kDx2kDIft1q0Wkc8isUFYQQQrLPnUvA6o+AI4v0ZTc/oPVIPdWikwtQvJaeBSpFsG+Qvv7Ev8B/o4GoG0DHT4BUWQFJLhJ7F5jzJHB+ky4o+v8JlGoMayYxUcPRkAglIOSx98JtxOt5YBWuTo6oX9pfBeSKkKhczMdk3AMDmfMfxf08MG1oA/SetE0F67/xxwF8/1Qds6qBp4l8t9p9CMx6HNj5K9D4RcAvCPkRigpCCCHZm+WWDChbvgHio/UiZnUHAW0/SOkGIFmCKnc1Hey79Xtg1QfA9h91d6keP+pChOShoPBJEhSNYI1I3YdNp25gw4kb2HQqTKV2NaZMIS+0rFAIrSoVRuOyAfB0dc5UILMMMEMjo1HER3d5ooXCfqlczBe/DKiHQVN34p+DIQgq4IH3umSzanq5dkCppsDFrXqK2W75M1aYooIQQkjm0TTg8J+6dUJSwgoSwNv5M6B4TdPvSSvYt+n/AK/CwF8jgIPzgXs39YJ4rl5593nyo6CY3Qe4sFkXFAMW6cXurITY+ETsvfggwPrI1ZTVoaXQW9PyhZQlolWFwigVkPWgWxEQth6MTTKHfHc+f6IWXp2/H5M3nkVxP3cMaVYm+9aKqY8A+2YCzV4GCpZFfoOighBCSOa4ug9YPhK4tF1f9isFdPxYj53IakxEracAzwBg/gDg9H/A9O56OlNPBsrmODFRwBwRFFsAN1+gvwiKBpZuFS7evIcNp3QRsfV0GO7GPgiwFqoH+aJlBd2lqW4pfxV8S0hWeaxOEK7cuY/PV57A2GVHlbDIlstbcFOgfHv992v9eKDXZOQ3KCoIIYSYR1SontFJMjtJaKsqjPY60PQlwMUj+8ev0AEYtBSY3Ru4shv4vZM+4C2QQ1VwiS4o5PqKm4YIigGLgRL1LdKUuzHx2H5WD7DeeCpMZW0yRmpB6AHWhdC8fGEU9nGzSDuJ/fJi63KqON7sHRfxyrz9mPOsG+oFZ2Mio+0Huqg4uEBPPFHUdP0Te4WighBCSPrExwI7fgY2TARik/L81+gDtB+d8wGJ4oIzdCUwqxcQdhKY0lF3zSmSTZ9nAsREJgmKbRYRFJLuVdJ5GlyapH5EbEJi8nZnRwfUDdYDrOVRtbhv9gJoCckACeAf070aroVHY83xUAybvht/Dm+KcoW9s3bAwDq6xfboX8C6ccBTs5GfoKgghBCSdtzEyZXAyveAW2ce/NPsPCF3A3qLVAaGrQJmirA4AfzeGei7wGqDiG1GUMx6QndZk8xcSlDUy/XT3r4bi02nw5ICrG8gNDImxfaSBT2SXZqalguAjzsD9Ene4uzkiO/71sHTk7fjwOVwDJ66E4uGN8u6ZazN+3rRz+PLgCt7gKDcv8+sBYoKQgghD3PjBLDiXeDMmgcVr8UyUfOpvEn56lcCGLpC9/2/vAuY0QPoMx2o2Cn3z23vgmLg4kwPdBISNew4dwt7whwQcO4WmpQvYjJDUnxCIvZfupNcM+LglXClTQ14uDipoGg9U1MRlA7wNJnulZC8RLKFTRncAL1+2oqLt+5h2PRdmPdcY7OziKWgcCX9d/LAHGDNx8DAJcgvUFQQQghJWYhu/QRg52RASwCcXIEmI4AWbwBuPnnbFgnSHvgXsGAQcHo1MPdpoMcPQO2+edsOWyY6ApgtgmIH4C4WiiVAUN1MHWLF4RCjWg5OmHFqtwpqNdRykGBXFRdx8gY2nw5DZHR8ivdLnQhDzQipH+HmnM1iY4TkAoW83TB9aEP0+mkLDl4Ox0tz9mHygHrKkpFpWr8DHPoDOLsOOLcJKNMC+QGKCkIIIUBCPLB3GrB2HHD/lr6u8qN6VidLpkaUtLJPzwX+egk4OA9YMhy4ewNo9orl2mRLgkIKcl3eCbgX0GdMxX0tk4JCqk6nroMuAuOFWXtRzNcd1yIeFI4TCni6qMrVBiFR1Nc9Bz4MIbmP1Dv5bVAD9P11O9YeD8WHfx3Bpz2rZ96a5l8aqDcI2PUbsPZjPU4sH1jkKCoIISS/c26j7up0/bC+XLiKXm+iXBtYBVII77FJgHdhvVCe1MaQTFQdPmb17bSIDk8SFLuSBMVfQGDtTLs8iYUitaAwRgSFDJUkwFpiI6T4XI0gPxaPIzZLvWB/fPd0Hbwwaw/m7ryIEv4eGNGmfOYP1PItYN9s3Up4alW+cN2kqCCEkPzK7fPAqg+BY0v1ZRl8SkrEekMAJyv79yDioeMngFcRYPWHwLYfgLtSffsHVt82JSgkyF3S8mZRUAjLD4UkuTylz68D66N91aJZbCwh1kenasUwuls1jFp6RNWxEHe/XnVLZO4gPsWARs8BW77VrRXlO9j9JIiV/dcghBCSJ7UKNn+tz/onxAAOTkCDYUDrd62/2JxUqk2uvj0vqfr2dFbfNnD/jp6OV7LOePjrgqJ4LbPfLvERIib+ORSCfRfvmPWeu7EpYygIsQcGNS2talj8svEs3l54EEV83NG8QqHMHaTZq8DuqcC1Q8DRJUD1XrBnKCoIISS/kJioBw/+NwqIDNHXlWkFdB5vW0Waaj+tix9DALdkhpKUs9YuiPJCUMzsCVzdmyQolgLFa2b4tsu372H5oWtKSEjmpswigy1C7JF3OlfG1fBo/H3gqnKHWvB8E1QN9DX/AJ4FgSYvAes/1etWVOlufVbgHMS+7TCEEEJ0Lu8Bfu8ILH5OFxQSSPjUHH0m25YEhQHxT5a2i3uPxA1ILYvwy8i3pBAUBYFBf6crKC7duodfNpxBjx82o/mEdRj37zElKCSWtFGZghjboxq2jWyr3D7Sio6Q9bK9YZl8LuaI3SLFF7/oXVPdE1Ex8RgybaeyXmSKxsP1e/Lmad26asfYr1wihBACRIQAa8YAB+bqy67eQMs3gcYvAs5ZLO5kLUgxPKllYSiSJ9W3+0v17crId2mAlaDYB3gG6BaKYtVNCgmxRvx7KESlzDQgMdUiDLrWKI5O1YulsDxI2ljJ/iQCwjhg28FoO4OyiT0jKZAnD6iPJ37eilOhUao43h8vNIWfh5mxXO6+QIvXgVUfAOvHAzV62/5vbxpQVBBCiD0SFw1s/xHY+CUQd1dfV7sf0O4jPYDQXihSRa++LXEEYSeB3zsB/f4ASjZEvhEUMx4DQvbrgkIsFEWrJW++ePOBkDh0JaWQaFQmAF1qFkfnasXSrB4sdSgm9a9rVKdCp5hRnQpC7B0/TxdMG9oQPX/cgpPXo/D8zN2qpoXZNVcaPANs+xEIvwTsmQY0eh72CEUFIYTYE1K++PgyYOX7wJ0L+roSDYBHJmS6irLNUKCkngd+dm8949H07kCfGUDFjrBr7t0CZoqgOJBCUJwPu5ssJI5cjUghJKSadRexSFQrpop9mYMIhw5Vi2Hb6VCs2rQDHVs0SrOiNiH2SlABD0wd0gB9ft6G7WdvqeDtr/vUVi5SGeLioaeY/ed1YOMXQJ3+dplcgqKCEELshetHgBUj9boTgk9xoMNY3dxu74WXJCBy0FKj6ttPAT1+1IO67VVQSID6tYOAZyFc7rEAfx11wz/zNuFoyAMhIQP/JmUNQqIoAswUEqmR44hf+c1jmnqmoCD5kWqBfpjUvx6GTtuFv/ZfRWABDxXMbRZ1BgBbv9NTee/4RXeJsjMoKgghxB4GmJJZZPfvgJYIOLnpqVclnaGbN/INydW3Jd3sfGDJC0nVt1+G/QmK7ipN5T2XgnjNeQxWTr0mpejUZhnwNzWySBT0crV0iwmxG6RK/Ge9auCthQcxaf0ZBPq5Y0CT0hm/0dkVaP2enixDalfUHwp4FIA9QVFBCCG2SkKcLiTWfQpEJ6UCrdpDrzTtH4x8iaq+/bNey0IK5EmhvLuhQPuxdlF46uzFi/CZ/zgK3z2JG5ovno4aidOR/nAWIVG+ELrWKKZclSgkCMk9etcvqWKMvlp9UhXIK+bngQ7mFICs8QSw+SvgxnH990mKjdoRFBWEEGKLnFkLrHhX/+ckFK0BPDIeKN3c0i2zPCIeOo0DvKX69kd6kT+pvt39e5usvn06NBL/HLyGzQeOY0z4+yjreAE3ND8MiPsAQRVq47kaEvNQFP4UEoTkGf9rW16ll5236xL+N3cv5j7bGHVK+af/JkcnXUjM7w9s+wlo+DzgXRj2AkVFdkhMgMOFzQi6tQ0OF3yBsi31LwwhhOQWN8/oqQlP/KsvS/7zdh8CdQfx9yc1zV5R8QZY+j89pa5U3+49zSYCJE9dj0wOtpZsM/6IwBzXT1HF8SLCHf2xu8U0zGvUFAU8KSQIsQQODg745LHquBYRjfUnbmDY9N1YNLwpShfK4Pel8qNAYB09BfTmr4HOn8JeoKjIKkeXAivegXPEVdSX5QuTAN9AoPMEoGp3S7eOEGKrJCYAF7YCUdcB76JAcFNdLERHAJu+0Ge3EuMAR2eg4XNAq7f16snENHX66ZmR/hgMnFpltdW3NU1T4sEgJE6HRiVvK+oUiYWeE1Ay7iISvYrAb/A/eKRwRYu2lxACODs54se+dfHk5G04fCVC1bD4c3jT9BMiSNKMth/qabB3/QY0eRHwKwF7gKIiq4JiwcBUpYCSikzJekllSGFBCMniZAUirj5YJ5MVlboCR//SYwOEcu2Azp8BhStZrKk2RaXOevXtOb0fVN8esMji/8hFSJy4Hol/D4YoMXHmxt0HMedOjmhRoRAeq+iGLvueh9ONc0pkOg5aBlBQEGI1eLk54/fBDdDrp604f/OesliIK5SHazqW43JtgeDmwIXNwIaJQPfvgPwuKqKjo+Hu/qDyZr6ZRZR/+qkFhULWOegpHSt3pSsCISQHJiuuArt+1V8XLKeLiQod7T9FbK5U316Zsvr2gMU5KswSEjXsPHcLoZHRqip1QxOpV0VIHL8WqawRIiTOphISLSsWUlmb2lctCt/4O3qWpxtHAe9iwOBlQKEKOdZeQkjOIPf7tCEN8fikrdh/6Q5enrcPP/evl3bqZfn9FrdVKda5b5buqhlQDvlOVCQmJmLcuHH4+eefcf36dZw8eRJly5bFhx9+iNKlS2PYsGGwa8QtwXgW8SE0IOKKvl+ZFnnYMEKIfU5WJOHmB7ywBXD1yMuW2ReG6tszewI3T+n/0PtK9e0G2T70isMhD1WdLp5UdVrSukrtCBESyw9dw9kwIyHh7IiWFQqja81iaFelKHzdkwLJo24A07sBN47p9UbEQlGofLbbSQjJHcoX8cZvg+qj3287sProdYxeegRje1RTsRcmKdVYnyASt8z1nwGP/wZbJ9P59T755BNMmzYNEydOhKvrgwCx6tWr47ffbP+CZIj4OefkfoQQkuFkBYCYcL1aNMmZ6ttSXfz+bd0ScGp1tgXF8Fl7UwgKQZZfmLUXDcf9h67fbcaP684oQSFComPVovj2qdrY80F7NRDpWaeEkaAIBaY/+kBQDP6HgoIQG6BB6YL49snayhAxc/sF/LLxbPpvMKSUPbRQL16a30TFjBkzMHnyZPTr1w9OTg/ce2rVqoXjx5NSG9ozEjiZk/sRQkikXrQsQzhZkTN4BQCD/tZjU+Lu6dW3D8zPssuTWCjSsTHhRlQsXJ0cVEVrERJ7P+yAyQPro0ftIPgYhISByOvANBEUxwGfQF1Q2IFbBCH5hUdqFMcHXauq1+OXH8df+6+kvXPxWkDVx3Qr9dpxyHfuT1euXEH58uVNukXFxcXB7pFMLBI4KUHZJv+NOOjbZT9CCMmIa4f16qrmwMmKHK6+PQ/460Xg0B96lVupvt30pYd2lTiI8PtxKnWkWB+uGT2OXYt4yEJhip8H1EfbykXS30kEhVgowk4CvkG68KGgIMTmGNa8jKphMWXzObz5xwEU9nFD03KFTO/c5n3g2FLgxD/A5d1ACZVTNH+IiqpVq2LTpk0IDk5ZrXXhwoWoU6cO7B4Jvpa0sSqg0iGVsEjym+s8nkHahJD0uXcLWPsJsGcqoCVmsDMnK3IFZ1ckPPYLYlz84bl3MrDqfRw+eQr/FH0B1yJiEBJ+H9eTnqPjMuqj9ImMjsvYWiUxFAZBIUHZBctm65yEEMvxfpcqauJBEjI8P3MPFr7QFJWK+Ty8o2Rzq9UX2D8LWPuxnqkuv4iKjz76CIMGDVIWC7FOLFq0CCdOnFBuUcuWLUO+QNLF9pkBbcU7cDDyg9Z8A+EggoLpZAkhaZEQB+z+HVj3KRB9R19XrSdQpiWw7PWknexzskJchXacu4U9YQ4IOHcLTcoXSTs7Sg4QG5+I6xHRysJgsCyIVUHWiVCQ5dDIGMQntsILThEY6TIP1c9Pw/EzZ/Fr3DOIT/UvsqCXK4r6uqsA7GLy8HXH3Zj4jP2mk7LDpCsoxOVJgsd9SwCD/6agIMTGcXR0wJd9aqlscLvO31Y1LBa/2Ez9djyE1Bs6OB84ux44uwEo2wr5QlT06NEDf//9N8aOHQsvLy8lMurWravWdejQAfmFFYkN8HH0tygZux8zXcbDxSEBve+/h2cSG6CzpRtHCLFOzqzTU06Lv7xQtAbwyHigdHN9Wao/m6pTYQeTFSmzIzlhxqndydmROlcvnunjyWDeWCxcSxYKMbgWoQuGsKhYs47l6OCAJV594OlWDCMiv8MTThvRoEgiDjX5FoUL+qtBgIgJdxcnk0Jp6YGr6nxpOMSq90t6WZOIK624PN08DfiV1F2eCpbJ7OUghFgh7i5O+HVgfZVqVurQiLD444UmD8dS+QcD9YcAOyfr1gqZZLLBtOFZqlPRokULrF6dvWwZtowh04f8A7mCarikFUZZh2twvquvn9S/bpb+SRJC7JRbZ4GVH+g+s4JHQT1Hed1BKa0PIhykxo2pitp28ptpjAzEU/9mSvzCnXt6/ILBsqC/vm9kZYhGZHS8WeeW2g8Gq4I8i5BJYW3wc0dhbzdVGRdoB5xorKpvB9/cjOADL+pxF55eaR5fLC0ijORzpOEQq7abtMikFhTi8uRf2qzPRQixDQp4uqoaFj1/2qpq1MhvhRTLkyxwKWjxJrB3pl6g8+RKvWinjcGK2jmQ6eOKVghlcQ2BCFPLsr1D1WK5atYnhNgAMVHApi+BbT8ACbGAgxPQ8Dmg9TuAh7/p94iAsKMaN+llRzKse23+fvy++ZxyRRLBEBNvXvyCt5tz+oLB1125LKWZJ94UlR4BBiwB5j4JXNoBTO0C9P8T8AtK8y0iiEQYpa5TUSw9S4xYo8Tl6dYZwK+U7vJEQUGIXVKyoCemDm6AJydvw+bTYRi56CC+7F0r5W+TT1Gg0fPAlm90a4XUsHDMdJJW2xIVjo6O6f5AJyQkwJ6RaqmpM32IqBCCHMLUP0nZLvs1KRdgoVYSQixKYiJwaAGwehQQlZQutmwb3Y2pSGXYExK3cPNuDMIiYxEWFYMbkTG4ERWjXov70enQyAyzI92PS8TO87dTrBMxIKJACQURDEnCwVg8PORCkFMENwGGrABm9dJrRSRX366Y5ltEOMhkUkYVtRXhV3QLhViwCpTSC9uJ+wMhxG6pUcIPP/ari2em78aivVcQVMADb3SslHInqawtMXfXDwNHFgE1noBdi4rFixenWJY0svv27cP06dMxZswY2DvyzyI1V41ERXr7EULyAZf36HERYsIW/MsAnT7VZ8DNmDGXmX2zBqZ5KRSSxIJBKNyI1OMVZFlclXKCgU2C0bVGcRT380ARXzeT8Qt5StGqSdW3ez2ovt3vj3TTPUo/ZTiZJIJiWlfg9jldUEgdCnkmhNg9bSoVwac9q+OdPw/h+7Wn1e9d30ZG979nQaDpy8C6T/RkHqqGhZ0HaqfmiSeeQLVq1TB//nwMGzYM9oypDB4GS0Wgw8109yOE2DGSwWfNWGD/bH3Z1Rto+SbQ+EXA2S0Lwcw62QlmNiYuIRE3lSDQxYHBmqAvxyLMyMKQWaEgg+lC3q4o5O2W/JC87LLuzr1Y/LDuTIbHeKR6cTQqa2XWXRnsS/Xt2U8AV/fqKV/7zAQqtM/a8cIv6y5PSlAE6zEUFBSE5CuebFAKV+5E47s1p/DBkkMo5ueGtpWNahA1fgHYMUl3jdw/Cw5+wQi6tQ0OF3yBsi2tOsYux2IqGjdujOeeew72jswayj9540wfV/DAUpFhpg9CiH0RHwNsnwRs/ByIjdLXSc7x9qMAn2K5EsycWiikdjsyFgoGAZEVoRDg5ZokDlIKBcM6w3MBDxeVPjEty8ufe69kPTuStVTfXjAAOLNWj7V4bBJQs0/mjnPnku7ydPt8kqAQC0XJ3Go1IcSKea19BVUcb+Geyxgxex/mP98YNUsU0De6+QAt3gBWvqfSjDtrCVD20QuTkrIBTrDabIA5Iiru37+P7777DkFBaQey2QumMn1cTuH+pKWd6YMQYj9oGnByhf7DL77xQlA94JGJma6Iak4w8xsLDmDZwZAHIiKHhEIhH1eV/Si1eEhPKGT2nFnOjmQtuHkDT88HlgwHDi8EFj2rV99uMsJ8QSEuT3cu6MHYIij8SuR2qwkhVoqDgwM+61VDZbPbdCoMQ6ftwqLhzVAqwFPfwTtpQkpLeDhjnBRf7jPDKoVFpkWFv79/ikBtSf8XGRkJT09PzJo1C/mB1Jk+rmsFkag5wN0hDlOeKI22TCdLiH1z44Reb0JmrgVJ/dp+DFDzySxl6zCVACI1d2MTlKhISygYWw5yWyhklixlR7I2nF2BXr8CXoV11wQRk1GhQPvR6cfK3LmouzwpQVFGd3mioCAk3+Pi5Iif+tXFk79sx9GQCFXD4s/hTeHv4QSs/iCNd8m0jIP+/0fSj1uZK1SmRcXXX3+dQlRINqjChQujUaNGSnDkFwyZPradDsXCNTtw/Zo/iuMWGgbctXTTCCG5xf07wPrxeoEimUFyctVnq8VULSbrLCATMxtP3jBr38dqB6JN5SJGsQuu8Pd0tYhQyM5v5qpNO9CxRaNcr6id44hg7PwZ4F0EWDNGT/14Nwzo9i3g5AwkJqSsMeIbBMzsoQsLJSjEQmH/Fn1CiHlIBrupQxqg109bcTbsLp6ZsRtzO8TB1bgA6kNoQMQV/bfGytKPZ1pUDB48OMdOXrp0aVy4cOGh9S+++CJ+/PFHWDvyz7BRmYK4Gajh5o0iKJ54C6eOH0WdMo0s3TRCSE4ig8W9M/Tc4feSEjJU6gp0/BgIKJelQ4rL08oj1zBp/RkcuhJudoCfLaeqTv7NPKapZ5sSFAZkUq3F64BXIeDvV1QgpfpOVH8c+O+jlNXQpS6JiM+CZXVBIf7QhBBihKTHnjakgaq6vefCbcxYdQTPwAxk8sLKMEtUHDx40OwD1qxZ0+x9d+3alaKuxeHDh9GhQwf07t0btoYm5uzbx3HlwknUsXRjCCE5x/kteorYa4f05UKVgEfGA+XaZjld65J9V/DzhjNqZkpwc3aAk6Mj7sWarvNj9cHM+ZG6AwHPQsDCIcDJ5fojNQZ/aMk9T0FBCEmDCkV9MHlgfQycshP/XXbAM67IGLGG2qKoqF27tnJ5EjN9esg+mSl+J25TxowfPx7lypVDq1atYGv4FSsD3BYX2/PqOmWqgishxPqQ4NrVHwJHkmrzuPsBrd8DGgwDnDJfdO1ebDzm7ryE3zadTY4r8HV3xuCmpTG4WRnsPHdTBTPDVoOZ8yOVuwD9FuqpZk2G2QsOwIaJQJ0BVuf/TAixHhqXDcCXfWrhlbnxuKoVRHGH23BIK2eeTFIEN4VNiopz587lekNiY2NVoPfrr7+e5oA8JiZGPQxEREQkF+CThyUwnLdQYBngGFAw7joOXLyFaoG+FmkPyXzfWeq7Q6y07+LuwXHbD3Dc9j0c4u9Dc3BEYp2BSGw5Und5SRR3KPPPKxmaZu64iJnbL+J2UramIj5uGNI0GE81KAFvN/1nuF2lQvj+qVr45N/juBbx4HdOcpi//0hltd0evqv2dt85JMTDOU1B8cD/Of7sRmjBzWHL2Fvf5SfYd7ZB56qF8VanyhizeiAmuXwDzQEwTv0h/35khJzQYRy0hETxo82Tdpn7vXHQMjI/5BELFixA3759cfHiRQQGmjYTjx492mTV7jlz5qjsU5akaPh+ND77FY4kBuPHop+gUwmruKyEEHPRNATe2YlqV+bBM06PmwjzroRDQf0R4Rmc6cOFxwLrrjpi63UHxCTqEyWF3DS0C0pEg8IaXNJIEpWoAWciHBARB/i6AOV8NdBAYb1IUar6kj8+A3YHD8eVgk3ypE2EENtF04DJxx1RMmI3RrnMQKDDreRtYsEYEzcQQeXroVZA3o0z7927p8bo4eHh8PX1zXlRcfToUSUAxMJgTPfuWcub26lTJ7i6uuLvv/9Ocx9TloqSJUsiLCws3Q+Z2+pt9erV6Fi7JDymtsFtzRtDi8zHgucYrG3tGPpO4nhcXDLvzpIfkeDi3RduIzQyRs221w/2t4hLTo733bWDcFr1HhwvbVeLmm8JJLQfA61y9/TThZrgws17+HXzOSzadxVxCfrPa+Wi3ni+ZRl0rlYUzk6ZTzlrT9jbfedwYTOcZz2W4X7x/ZfYhaXCnvouP8G+s63/s62/3Kgs1o5IREPH4yiCOwhFAexMrAwNjsqCve71lnn2/1fG24UKFcpQVGQ6+9PZs2fRs2dPHDp0KEWchcFlKTMxFQYkA9R///2HRYsWpbufm5ubeqRGbhBL3yTOAaXVs79DFE5evo7IWA0FvcyJtCGWxhq+P7aAVHxOXWeguIXrDGS77yQdqGR02jNdd1Nx9gCavwaHpv+Ds2vmrJ9HroarTE7/HgpR1gahQWl/vNi6PFpXKsw4K3u978q21P2bpShVOv7PzrKfncRU2E3f5UPYd9bP7jM3k11gE+GI7YlVH9onJDwG+y5H5lk2QHO/M5meMnvllVdQpkwZhIaGKpejI0eOYOPGjahfvz7Wr1+flbZi6tSpKFKkCLp27QqbRXLUSyAngECEmZ13nhBbERQSRJy6QNu18Gi1XrbbFAlxwLafgO/qAnum6YNBSQn60i6g9TtAJgSFFK6TokVdv9usitOJoGhTqTD+eKEJ/nihqaorQUFhx4hQ6DwhaSF1Pyctdx5vN4KCEJK7hEZG5+h+eUmmLRXbtm3D2rVrlRlECt/Jo3nz5vjss8/w8ssvY9++fZk6XmJiohIVgwYNgrNzpptjXfiVAqIPIcghDOtOhOKxOixyROzDFCsWCi3t2p5quxQ2s4nsRKfXACveBcJO6MvFagKPTMhUJg2x0Mo9/tO6M8odTJCP3rVmIIa3KoeqTNSQv6jaHegzQ089bFynQiwYIihkOyGEmEERH/cc3S8vyfQoXtybfHz0yrEiLK5evYpKlSohODgYJ04k/ZPOBOL2JLEZQ4cOhc1ToCRwXRcV/5y8oQZjNjHIIiSDmfjUForUwkK2y35WXZjt5hlg5fsP6gl4BgDtPspUqs/4hET8cyhEuTkdvxap1rk6OeLxeiXwfMuyKF3IKzc/AbFmRDhU7pqyorYIVVooCCGZQOoRiWuxeAJoNla3KNOionr16jhw4IBygWrUqBEmTpyoAqwnT56MsmXLZroBHTt2zLD+hc3gV1I9lXG5pdJI7r90G/WCra/TCckvplhFTCSw8XPd3UlSwTo6Aw2fB1q9DXgUMOsQ0XEJ+HPvZfyy4Swu3rqn1nm5OqFf42AMa15GVUQlRAmIMi0s3QpCiA3j5OigYhXFtdjBxuoWZVpUfPDBB7h7V68CO3bsWDz66KNo0aIFAgICMH/+fORrpKq2FAv0iQCigXXHb1BUEJvHXBPrgl2XUL6IN6oF6rFFFicxETgwF1gzRp85Fsq1Azp/BhSuZNYhomLiMXv7Bfy2+RxuROqBc/6eLhjSrAwGNSkNP08GPBJCCMlZJPnJpP51H0qOUszCyVFyTFRIIPYzzzyj8tQa0kmVL18ex48fx61bt+Dv789gRHF/AlDaRfexFp/rNzuZN3ghxFo5Gaq7+WTEljM3VbBy8/KF8FzLsmhRoZDlfhMu7QKWvw1c1StUo2BZoNNnQMVOZqWIvRkVg2lbz2P61vOIiI5X68Qc/WyLsniqYUl4utp4/BchhBCrpnP14ipWcdvpUKzatAMdWzRCk/JFrNJCYcDs/4y1atXC22+/jTfeeAOPP/64ioFo3bq12lawIGfjkwO1ZSYz7roatxy5GoHrEdF0jSA2ibglfrfmNL7+72TyurRMse8+UhmHr0aoeIPNp8PUo0pxXzzXsgwerRkIl5yszZCYoGoDSNExhwu+ekpPg9+6pPX8bzRwcJ6+7OoDtHoLaPQC4PxwOurUXLlzH79uPIt5uy4iOk6vVFq2sBdeaFUOj9UOgqtz/q4xQQghJO9wcnRAozIFcfOYpp6tWVBkSlRMmTIF33//vap8PW3aNLRr107FVYi4kMxNQUHMdGRwf3KKuoY6Qd7YezkK60+E4skGutggxFZITNQw+u8jmLHtglp+pV0FVC7mg7HL0jfFvtWpEn7fcg7zd13CsZAIvDb/ACauOIGhzcqoGX4f92y6Cx1dqjLsOEdcRX1ZlkrGkmGnwyfAnfPAxi+BON09E7X764HYPkUzPOzp0Cj8vOEMluy7gvikIhM1gvzwYuty6FjNRrJaEUIIIRYkUzZ8qUsxePBg9Thz5oxKBfvLL79g1KhRKuB62LBh6NWrF/ItXoUBJzcgIQaPlgb2XtbjKigqiC0RG5+I1xfsVzUXxOI2uls1DGqqF3eUAbZkeZKgbIm1kOwTxgPukgU9MapbNbzariJm7bigXIhEhIz79xi+W3MKfRuXwpCmZZQYyZKgWDDw4QJjksLzT6PscSUa6Clig+pleMiDl++otLArj16DIV9Ek7IBeLFNOeXGle9dOgkhhBAzybJjcLly5fDJJ5/g448/xp9//onnn38eK1asyFJFbbvB0VG3Vtw6g1ZF76tV4gYigzS6TRBb4G5MPF6YtQebToXBxckBX/apje61ApO3i4AwJ22sBDCPaFMez7Qoo2b/J288izM37qrsSb9vPofutYJU3EWlYnp66gxJTNBrAJhMsJeEgyPQ4yeg5pP6vZiOW9e2Mzfx0/oz6v400KFqUWWZqFPK37w2EUIIISSZbEUbSgVtsVaIqJDCdc8++2x2DmcfJImKMs63UMi7MMKiYrH7/C00LV/I0i0jJF1u3Y3FkKk7ceByODxdnfBz/3poWbFwto7p5uykLHW965VUiQt+2XhWWTokPas8WlUsrOo7iFBJ1yoguf+Ni4qZQkvU7780BIW4dK0+dl2JiQOX7iSLpB61A1XMRMWiZgocQgghhGRfVFy+fFnFVMjj7NmzKp3sTz/9hN69e8PDwyOzh7PbDFCOEVfQqmI1NXCSwRRFBbFmJEB5wJQdOHvjrkqZOnVIQ9QuaV4NB3NwdHRAuypF1WP/pTuYvPEMVhy+hg0nb6hH9SBflVmpa43icDYV1G1ICZsRJvaLS0jE0v1XVczEqdAotc7N2RFPNiipzikuW4QQQgjJI1EhAdq///471qxZgyJFiqjgbAnSlrSy5OEMUAi/iLaViyhRsfZ4KN7vWtXSLSPEJKeuR2Lg7ztV7EOgnztmDGuk6k3kFiJWfupXDxdu3sWUzeewYPclHL4SgVfm7VdB3VJMTgb8Xm5GP09SndgcjPa7H5ugji2uVyKaBB83ZwxoEqzqTBT2yTgbFCGEEEJyWFT0798fXbt2xeLFi9GlSxc4puOznK9JygCFO5fQvEIh5V4hvuQXb95DqQDOiBLrYu/F2xg6bZeqAC9CYuawhijulzcWx+AAL4ztUR2vtq+ImdsuYMa282rwLxmmvvnvJPo3DsbgZqX14nvBTQHvYkDUtTSO5qBngQpuivD7cZi1/YKK3bh5N1ZtLeTtpsRKv8al4JvdDFSEEEIIybqoELcnsVAQ89yfEH4Jfh4uqBfsr3zI158MxcAmegYdQqwBSXc8fNZe3I9LQJ1SBfD7oAbw93LN83YU9HLFK+0r4PlWZZVl77dN53Au7K6KfZDXPesE4dlmJVDeo4ASFRKqbRx9kZi0HNH6Y/y08iRmb7+oKmELJfw98HyrcuhdrwTcXZJqWRBCCCHEcqKCgsJM/Ayi4rKkmVEuUCIq1h2nqCDWw1/7r+CNBQdUTQYJxv65f12LV4mWQX+/RsF4qkEprD56XcVd7L14B/N3X0KV/R+jvPNx3NdcEQkPFHEIT37fNS0AY+IGYM1CD8QnnlXrKhb1xvDW5dCtZqDpGA1CCCGE5CiWHUXYI75SBNABiI8G7oahTaUiGL/8OLaeual8vD1cOVtKLMvULecw5u+j6rVkPvr8iVpWlfJYXAY7Vy+mHnsu3MLhv3/EoLBVattLcf/DusQ6aOh4HEVwB6EogJ2JlZEIab+GOiX9MKJNBSXmJTicEEIIIXkDRUVO4+wK+BQDIkNUsHbFwLoq+PVqeDS2n72JNpVp8SGWQeozfLnqJH5Yd1otD25aGh89WtWqB9/1nM6i3u3v1Osv457AmkS9oN32RNOJD97uXBlNyjHTGiGEEJLXWM/0pJ26QEnu/dZJQkJSyxLrICFRw45zt7AnzEE9y7I9kRAfjyNb/sHuZZPVc2xsHN5bfChZULzVqRJGdbNuQYHI68D8/kBCLEKKt8MPCY9l+JbQyJg8aRohhBBCcsBScefOHSxcuBBnzpzBW2+9hYIFC2Lv3r0oWrQogoLE/SefI8Hal3eqDFBC20pFMGfHRZVadkx3Lf0iXyTXWXE4RLn/SApVwAkzTu1GcT93NcjuXL04bJ19K6cjcNsYVMPN5HUhqwriVtxAODo0xLieNfB0w6TUx9ZKfCywYIBu8StcGRdbfQ3t3OEM36YyRRFCCCHE+i0VBw8eRMWKFTFhwgR88cUXSmAIixYtwrvvvpsbbbTdtLLhuqhoWj4Ark6OuHz7Ps7c0Itv2QIye7/tzE0V1CvP9jCbL4JCMh7pguIB18Kj1XrZbuuCotbWl1FYeyAohKK4hUku3+DzahesX1AIy98GLu0A3PyAp+agfsVSSvilJcdlvWxvWKZgHjeUEEIIIVkSFa+//joGDx6MU6dOwd39wayg1K7YuHEjr2rqDFCAyqrTqKw+2Fl3/AZsARlcN5+wFk//ul0VJZNnWbblQbeIIrFQmJJGhnWy3VbFk7g8iYVCSO3VZFhudvoLtZ9Vs/t3YM9UXSo8MQUIKKeCt8WSJKQWFoZl2S77EUIIIcQG3J927dqFX3755aH14vZ07VpahanyGQWSZoLvXExeJdloNp0KUy5Qz7YsC1uYzU89tDbM5k/qXzfX3YQkqDgmPhExcYmIiU9AdDaf5Tgh4fcfslCkOKe4CYVHqxTATcoFwNY4vmOl7vKUxrhaxtvFcBNHZL9mXWGVXNwO/Pu2/rrdR0CFDsmb5Dsn370Hrms6xezIdY0QQgjJN6LCzc0NERERD60/efIkChcunFPtsiv3J0FSy8pgaNf5W4iMjoOPlVb1NWc2/8MlR1DU113VOIiO0wfs0fHpPxsG9hntp44nYiJeSppZhtDItIWHNXP/9pUc3S/PibgKzB8AJMYBVR8Dmr/20C4iHDpULYZtp0OxatMOdGzRCE3KF6GFghBCCLE1UdG9e3eMHTsWCxYsUMsSdHzx4kW88847ePzxx3Ojjbbr/nT/NhATBbh5o3QhL5Qp5KUqBW85HWa1s6oyS5/ebL5wIyoGPX/ammdtkvGiFEZzc3Z86NktjfWmni/fvodfN52z22DfRE/z0hV7+FthMoW4aD3T091QoEg14LGf5MfF5K4iIBqVKYibxzT1TEFBCCGE2KCo+PLLL/HEE0+oCtv3799Hq1atlNtTkyZNMG7cuNxppa3h7gu4+wHR4XpcRZHKanXrSoWVqBAXKGsVFebO0hfwcEFBL1dVNE0G9u4mnx3h7uyU8jkLosDZ0SFHMmaJFWbZwRDlxpVW1ERRXzebDPaVdMWLNh1Hfe3heAoDEioS6hCAyo06warQNOCfN4ArewD3AsBTswFXL0u3ihBCCCG5KSr8/PywevVqbN68WWWCioqKQt26ddG+ffvMHsq+8SsFRB/SXaCSRIXEVUzdch7rTtxQMQPWmFrW3Fn6Sf3r2VzcgSHYV+JC5MqbEhbebs6IjU+0mcrn4i42YcVxRG2fhq+df1OCQsbo8tlSiwtZDGkyCsWcrazm5a7fgP2zAAdHoPdUoGAZS7eIEEIIIXlV/K558+Z48cUX8fbbb1NQpBdXYRSsLTPgHi5OuBEZgyNXH45LsQYalPaHu0vaXwtbT91pCPaV4F5jCnm7qc995sZdvDh7D+ISLBfTYS4nr0fisR82o8COL/C5y2Q4OyQioXof7G/8FW44PCz4NEcn1KlRC1bF+c3AipH66w5jgXJtLd0iQgghhGSBTE9ZfvfddybXy6y7pJgtX748WrZsCScn25jpzdUCeEZpZQU3Zyc0K18I/x27jvUnQlE9yA/WxndrTqmMSaawl9SdaQX77r14GwOm7FCWpDf/OICv+9S2yorTYuWateMiJiw7iNEOk/GEc1Iq5xZvwqntB6jj4ICEDoNUlicJyvYoEIiqF2bC8dQK4I9BwHMbAI8Clv4YenHIBYOAxHigRm+gyUuWbhEhhBBC8kpUfP3117hx4wbu3bsHf39/te727dvw9PSEt7c3QkNDUbZsWaxbtw4lSyYNrPN1rYoHGaAMLlAiKiSu4qW2FWBNLNxzGd+tPa1eD2gcrNppr6k7TQX7NihdULl1PTt9N/7afxV+Hi4Y072aVbmp3bobi7cXHsT2Y+dUMbsWToehOTjBoeuXQP0hyfs5OTunTBtbrxnwS0vg9nngrxHAk+JuZMHPFXcfmN8PuBcGFKsJdPvOsu0hhBBCSN66P3366ado0KCBKn538+ZN9ZB0so0aNcK3336rMkEVK1YMr732cDrI/On+lFJUSLC2sO/SHTVAtBa2ngnDu4sOqtcj2pTDx49Vx+Z32mLus43x7VO11bMs24OgSA9J/ftln1pqfDtj2wV8vfokrIXNp8LQ+ZuNOHTsGBa6jdUFhYsXHJ6el0JQmMTDH+g9HXByBY4vA7b/BIshQR9/vwKEHAA8A5ICsz0t1x5CCCGE5L2o+OCDD5S1oly5csnrxOXpiy++wLvvvosSJUpg4sSJ2LJlC/I1hgJ4Ru5PQmABD1Qu5qPGVZtOWUd17dOhkXhhpsQRaHi0ZnG80aGSWi+z9xKM3aN2kHq2ZZenzCCfd2yP6uq1WG6mbM44DW1uIjU+xv1zFP2n7IB/1Cn87TEalR0uAl5F4DDkH6BiR/MOFFQX6PSp/nr1R8DFHbAIImgOzgccnIDe0x7cK4QQQgjJP6IiJCQE8fHxD62XdYaK2oGBgYiMjES+xuD+FHkVSIhLsal1Jb2egLhAWZqwqBgMmbYLEdHxqBfsjy9617LKOIK8Rty/3uxYUb3+eNlR/LknpTjMK06HRqHXT1tVfY2mjofxl8fHKKKFAYUqAs/8BwTWydwBGzwDVOulxzEsHALcvYk85ex6YNWH+msROGVa5u35CSGEEGIdoqJNmzZ4/vnnsW/fvuR18nr48OFo21bP3HLo0CGUKZPP00J6FdZdTbREvVJwqrgKYcPJG6p2giXTkT4zfTcu3bqP4ABP/DqwvqoLQXRGtCmPYc317/Hbfx7EqiO6aM6rYOy5Oy/i0e83qUxh/T22Ypbb53BPvAuUagoMXQn4B2f+wOLX1f07IKA8EHEFWPwckJhHma4knuOPIYCWANTqCzR6Pm/OSwghhBDrExVTpkxBwYIFUa9ePbi5ualH/fr11TrZJkjAthTJy9c4Oj6Iq0jlAlW3VAH4uDvjzr047L90xyLNS0zU8Nr8/er8EpA8dXADVcyOPEACtN/vUgVP1CuhxN9Lc/dh25ncn9m/fTcWL8zag3cXHVLCb2KRlfhE+wGOWhxQrScwYDHgmY2Uvm4+QJ8ZgLM7cPo/YHMe3Kuxd4F5/YH7t4DAusCjXzMwmxBCCMnP2Z8kCFuK3x0/flwFaAuVKlVSD2NrBklygbp19qEMUM5OjmhZsTD+ORiCdcdDldtRXjNh5XEsP3wNrk6OmDygHsoW9s7zNtgC4go2vlcNRNyPw6qj1/HsjN2Y82wj1CxRINcC5l+ffwDXIqLh7pSIxcF/osrVxfrGpi8D7cfogjW7FK0GSMYoyQS17lOgZKPcc0WSAKK/XgKuH9IteJJ5ysW8IouEEEIIsQ2yPDqpXLkyunfvrh7GgoKYiKtIlQFKaJsUV7HuRN7HVczZcRG/bDirXk98oiYalbWtyth5jYjA756ugyZlAxAVE4/BU3epWIecRIrtSWXsfr/tUIKiaoAjdpWdogsKqTTd5Qug48c5IygM1OkP1O6nu+gtHAZE5pJ715ZvgSOLAEdn3ULiF5Q75yGEEEKI7VgqhMuXL2Pp0qUqfWxsbMq0qF999VVOtc2OCuA9qKptoFVSalnxl78eEY2ivnkzcytF9z7867B6/Vr7inisDgd45iCxJr8Oqo++v27HwcvhqkjewuFNEVTAI9vHPh92F6/M24cDl8PV8jO1PfHenVFwvHQAcPYAnpgCVDaqOZGTiFi5ug8IPaoLi4F/SZGLnDu+uFf9N1p//cgEILhpzh2bEEIIIVZDpqc916xZoywTkyZNUnETUuRu6tSp+P3337F///7caaXNF8B7OHNQIW831CqhV9TecCJvUsseC4nAS3P2qfiAXnWD8HK78nlyXnvB280Z04Y0RLnCXqoo4IDfdqjsWdkJxv5j9yV0+W6TEhQS2zKjmx8+CHkZjtekhkMhYPCy3BMUgtSHEOuBqzdwYTOw/rOcO/bNM8DCofJJgboDgfrDcu7YhBBCCLFtUSG1KN58802V4cnd3R1//vknLl26hFatWqF3796500pbt1SYcH8S2lTOu9SyYg0ZOm2Xct9pXLYgxveqaVWVom0FCWaf9UwjZaE4G3YXg37fiYjolCmDzSH8fpwK/H5r4UHci01QfbLmcWe03NRXt2wVLAs8sxooUR+5TqEKekYoYdMXwKnV2T9mTBQwrx8QHQ6UaKBbRPh9I4QQQuyWTIuKY8eOYeDAgeq1s7Mz7t+/r7I9jR07FhMmTMiNNtouxtmfJFjVRPVmYfPpMMTG515az7sx8UpQyOx62cJe+KV/fbg656Bvfj6juJ8HZg5riAAvV+W+Jml5JUuTuew8dwtdvt2kAvWdHR3wVqdKmNP4CgotfjJpEN4QGPafLizyiuqP6zUshEXPmrSumY1815e8ANw4BngXA/rMBJzdcqyphBBCCLE+Mj2y9PLySo6jKF68OM6cOZO8LSwsLGdbZ+v4iqhwAOLvA/ceTkVaI8hPDUzFerD7wq1caYK4Oom/vgx+5VzTBjeEn6dLrpwrPyHZsqYPbQgfN2clEl6as1cFW6dHfEIivlp1Ak9N3oYrd/TaIAtfaIIRrv/AcdEwICEWqPwoMGgp4GWB4HkpRle8NnD/NvDHYCA+ZbyU2Yi149jfep0WyfTkWzynW0oIIYQQWxcVjRs3xubNm9XrLl264I033sC4ceMwdOhQtY0Y4ewK+BTTX9+5aDJdqSFgW1LL5gZSDfq/Y6Fwc3ZUgcalAjxz5Tz5kepBfpgyuIG6tnKN3154EHHxiaqWxV/7r6hnQ3HDS7fuoc8v2/Dd2tOQVY/XLYF/XmqK2oc+AVZ/pB+w0XA9vsEl+8HfWUKsCX2mA25+wOVdDwKsM8OJFcDacfprcXkq2SDHm0kIIYQQ6yPTaV4ku1NUlJ5Oc8yYMer1/PnzUaFCBWZ+SssFKjJEr1URVPehzVJde9HeK1h34gbez+F43KlbzmHa1vPq9ddP1kbdUnlfD8PeaVimICb1r4vnZuzB4n1XsPLINRUjYaC4nzs6Vy+KP3ZfURYpKXo4rmcNdK9SAPhzMHDiX92a1Wkc0GQELI5/aaDnJGBeX2D7j0CpxkDV7ua9N+yU7jolgdkSlF1vUG63lhBCCCG2KCoSEhJUOtmaNWsmu0L9/PPPudU2+8kAJbO+afiotyhfGE6ODqrugcxmlyyYM5aE1UevY+yyo+r1yEcqo0sNuqDkFm0rF8WAJsGYuuV8CkEhSBzL1C0X1OsGpf2VuCvhcheY/ihwZQ/g5Ab0mgxUewxWg2Sbavo/YOv3enG8YtUzju+IjtCFSEwEUKoJ0Hl8XrWWEEIIIbbm/uTk5ISOHTvi9u3budeifJYBSuIb6iVZEHKqEN6hy+F4ee4+FS/7dMNSeL5lHgb85kPExWnF4fQLx0nsxaxhjVAi4Sowpb0uKDz89fgJaxIUBtqN0qtsi0hYMAiIi05738REYPHzQNhJwCdQd+ES1z9CCCGE5BsyHVNRvXp1nD2rV2MmmalVYVpUGKeWzYm4CgkAHjp9F+7HJaBFhUIY26MaU8fmMhKoLRaJ9IiMicfJ3WuAKR2A2+eBAsHAsNW6e5E14uQCPDEV8AwArh0EVoxMe98NE3Q3LrG6PDUL8Na/z4QQQgjJP2RaVHzyySeqTsWyZcsQEhKCiIiIFA+ShqgwEahtoE1lPVh765mbmUpNmprI6DgMnboLNyJjULmYD37qVxcuTkwdm9uERqYvKIROjjtRdXV/4P4tILAO8Mx/en0Ia8YvCOj1qx7zsWcqcHDBw/scWwZsSHJ16vYNEFQvz5tJCCGEEMuT6RGnZHw6cOAAunfvjhIlSsDf3189ChQooJ4zy5UrV9C/f38EBATAw8MDNWrUwO7du2F37k/p5P2vVNRHBfTGSOagsw+nnjUHSWf64uy9OHE9EoV93FRWIh93po7NC4r4uCe/dkQiGjseRXfHrepZloc4Lcckl2/hlBgDVOwMDP7Hdmbzy7cDWr6lv/77VeD6UeDcJuDQQmDfbGDRc/q2Ri8AtftatKmEEEIIsaHsT+vWrcuxk0tsRrNmzdCmTRssX74chQsXxqlTp7IkTqzeUiEz1LF3AVevh3YR96TWlYpg7s6LygXKUBTPXDRNw0d/HcamU2HwcHHC74MaqIrPJO8yQIkorBW5ER+5zECgw4OaI1GaO7wddEtGYv1hcHxkIuCU6dvOsrQeCVzaDpzbCPzSAkiMT7m9cBWg4yeWah0hhBBCrIBMj25atWqVYyeXCtwlS5bE1KlTk9eVKVMGdoW7r573PyZcD9YuUtnkbpJaVkTF2uOhGNNdy1QcxC8bz2LuzkuQt3z3dB3UKOGXgx+AZIRk7/qp7mXU2vrNQ9sMguJKye4I6vqlKEjYHI5OQI0+uqhILSiEG8eBE8vNTz1LCCGEELsjSw73mzZtUi5LTZs2Ve5LwsyZM5OL4pnL0qVLUb9+ffTu3RtFihRBnTp18Ouv4sNtZ5jhAtW0XABcnRxx+fZ9nLlx1+xD/3soBOOXH1evP3q0KjpULZr99pLMkZiAOkfGK73gaEIzSPm7oPA9gJZ+xW2rJTEBWP9p+vtIILfsRwghhJB8SaYtFX/++ScGDBiAfv36Ye/evYiJiVHrw8PD8emnn+Lff6WYl3lIFqlJkybh9ddfx3vvvYddu3bh5ZdfhqurKwYNerhwlpzLcD7BEBgeFxenHpbAcN70zu/kGwTH64eRcOscEtPYz9VRr2Ow5cxN/Hc0BMFShCwD9l26g9fm71evBzQuhf4NS1jsOtgi5vSdOThc2AzniKsSzmx6u/yJuIL4sxuhBTeHrWH4fGmj5fnny6m+I3kP+852Yd/ZLuw72yXOCvrO3HM7aOKQnwnEmvDaa69h4MCB8PHxUUHbZcuWxb59+/DII4/g2rX08/UbI+JBLBVbt25NXieiQsTFtm3bHtp/9OjRqop3aubMmQNPz5wpGpcb1Lg0A2XD/sPJot1wLLB3mvutD3HA4vNOqOCbiJeqpT+rHRYNfH3ICVHxDqjun4hhlRJNzpKT3MU1PhLVL81EyTvbM9x3d/BwXCnYBLZG0K1tqH9hkt1+PkIIIYSkzb1799C3b19lQPD19c05S8WJEyfQsmXLh9b7+fnhzp07mTpW8eLFUbVq1RTrqlSpoqwhpnj33XeVVcPYUiExGVKQL70PmdvqbfXq1ejQoQNcXExnW3LcdgZY+x/KF3ZDmS5d0jxW1Zt3sfibLTh/1wkt2raHj7vp7gm/H4c+k3ciKv4uqhb3waxhDeDlZmPBv1aAOX1nEk2Dw9W9cNzzOxyOLoFDwgPrWXrUbtEJtWzSUuELmCEq8vLzZbnviMVh39ku7DvbhX1nu8RZQd+ZWzIi0yPRYsWK4fTp0yhdOqV7jsRTiMUiM0jmJxEpxpw8eRLBwcEm93dzc1OP1MhFtvRNkm4bCurXyjHiChzTaWeFYgVQOsAT52/ew84Ld9C5evGH9omNT8RL83bjbNhdBPq5Y+qQhijg/SClKck8Zn9/Yu8BhxcCu34DQg48WF+spl6HJDo8KYIiNQ6AbyCcy7bUg55tDWm3byAQEWJ1n88a7n2SNdh3tgv7znZh39kuLhbsO3PPm+lA7WeffRavvPIKduzYoTIUXb16FbNnz1YF8YYPH56pY4kb1fbt21UshggVcWOaPHkyRowYAfssgJd2VW0DklpWWHf8xkPbxFNt5KKD2H72FrzdnFUtiqK+FBS5TthpYMV7wFeVgaX/0wWFVI+u1Rd4Zi3w/Eag+/dJO6f2QUta7jzeNgWFIO3uPMF+Px8hhBBCsk2mLRUjR45EYmIi2rVrp3ysxBVKrAciKv73v/9l6lgNGjTA4sWLlVvT2LFjVTrZb775RgWB22X2p8irQEIc4JS24pPUstO2nse6E6FKRBinlv1uzWks2ntFpTD9sV9dVCluGZcvuyAxQQUgS7yAcu9JPcueEA+cXKFbJc4a1WYpEAw0GAbUGQB4FnywXtKp9pkBrHgHMA5qlhl+GXDberpVe/98hBBCCMlbUSGD3Pfffx9vvfWWsi5ERUWpuAhvb+8sNeDRRx9VD7vGqwjg5AokxAKRIUCBUukWUpMCdqGRMTgaEoFqgXrNicX7LuPr/06q1x/3qI5WFQvnWfPtjqNL1eBYMhrVl2WJF1CD4wlAyUbA3hnAnqkqo5GOA1CxE9DgGaBcO8AxDQOfDKwrdwUubAWirgPeRYHgpvYzg2/vn48QQggheScqZs2ahV69eqlsS6mDrEkayCDUNwi4fU53gUpHVLi7OKFZ+QD8dywU07eeR7PyhXAzKgaf/avXoni+ZVn0bZT2+4kZgmLBwIdjA2T2fcEAwMEJ0JLqLXgGAHUHAvWGAP6m43weQgbYZVrAbrH3z0cIIYSQvBEVEgfxwgsvoHv37qoAXqdOneDkxJlKs1ygRFSEZxxXUcRHD0ZfsPuyehioU7IA3ulsuiI3MQMpzibuOyaDjZMQQRHUAGj0HFC1B+D8cGIAQgghhBCSzUDtkJAQzJs3T7lB9enTR6WFlcBq41oTxAR+SdaFDETFisMhmLPT9D77L93BqqPm1wEhqRC3nXSLuCXRfhRQsw8FBSGEEEJIbokKZ2dnFQMhGZ9CQ0Px9ddf4/z582jTpg3KlSuX2cPlHwpknAEqIVHDmL+PpnsY2S77kSwgcQA5uR8hhBBCCFFkq2KaxFWI+9Pt27dx4cIFHDt2LDuHs2/8SmRoqdh57hZCwqPT3C5SQrbLfk3KBeRGK+0bCSzOyf0IIYQQQkjWLBWCpJIVS0WXLl0QFBSk0sD27NkTR44cycrh8geGWhXhD2IkUhMambagyMp+BClTxB79K4OdpIhbkJ7RiBBCCCGE5J6l4qmnnsKyZcuUlUJiKj788EM0adIks4fJ3+5Pmia5eR/apYiPeYXszN2PJHH/NvDHYODseqOVcv2N3chYxI0QQgghJM9EhWR6WrBggcmsT4cPH0b16tWz3Bi7RmbAhfj7wL2bgFchkzUqivu541p4tMn8RDLsLebnrvYjmaiGPfdJ4OZpwMUL6PWLLupYxI0QQgghxHKiQtyejImMjMTcuXPx22+/Yc+ePUhISMrxT1IimYS8iwFR14A7F02KCqmUPapbVQyftTeteXS1XfYjZnBmrW6hiA7X3c+engsUq6Fvq9wV8Wc3Yv+mlajdohOcU1fUJoQQQgghuRtTIWzcuBGDBg1SKWW/+OILtG3bFtu3b8/q4fKXC1Q6cRWdqxfHpP51lUXCGFmW9bKdZIBYInb8Asx6QhcUJRsDz657ICgERydowc1xpWAT9UxBQQghhBCSR5aKa9euYdq0aZgyZQoiIiJUTEVMTAyWLFnC6trmZoC6vCvDWhUiHDpULaayPElQtsRQiMsTLRRmkBAH/PsmsGeavlyrL9DtG9acIIQQQgixBlHRrVs3ZZ3o2rWryvbUuXNnFVPx888/52b77DMDVDq1KgyIgGDa2Exy7xawYCBwfpPuMNZhLND0fyaD4gkhhBBCiAVExfLly/Hyyy9j+PDhqFChQg42IR9RwLyq2iQLhB4D5j4F3D4PuPoAT0wBKnaydKsIIYQQQvIFZsdUbN68WQVl16tXD40aNcIPP/yAsLCw3G2d3daqoKjIUU6uBH7roAuKAsHAM6spKAghhBBCrFFUNG7cGL/++itCQkLw/PPPY968eQgMDERiYiJWr16tBAcxs6q2Ge5PxMyA7C3fAXOeBGIjAQm4loDsIlUs3TJCCCGEkHxFprM/eXl5YejQocpycejQIbzxxhsYP348ihQpgu7dmePfrOxP928BsXct3RrbJj4G+GsEsPpDPflu3UHAgMWAF+NQCCGEEEJsJqWsUKlSJUycOBGXL19WtSpIBrj7AW5+GaaVJRkQdQOY3g3YPxtwcAQemQh0+xZwdrV0ywghhBBC8iXZEhUGJAvUY489hqVLl+bE4ewbukBlj2uHgF/bAJd26AKt30Kg0fPM8EQIIYQQYuuigmSlAN5FS7fE9ji2DJjSSQ90L1gOeHYNUL6dpVtFCCGEEJLvyVTxO5KTGaDo/pSpgOxNXwJrP9aXy7YGek8DPPwt3TJCCCGEEEJRYQHo/pQ54u4DS/8HHPpDX274PNDpU8CJX11CCCGEEGuBIzOLuT9RVGRI5DVgXl/gyh7A0Rno8jlQf6ilW0UIIYQQQlJBUZHX+BmqatP9KV2u7gPm9gUir+puTn1mAGVaWrpVhBBCCCHEBBQVlnJ/irgKJMTTjccUhxcBS14E4u8DhSoBfecBBctaulWEEEIIISQNmP0pr/EuCji5AlqCPgtPHpCYCKz7FFg4RBcUFToCz6ymoCCEEEIIsXIoKvIaR0fAN0h/TReoB0iF8YWDgQ0T9OUmLwFPz9MLBhJCCCGEEKuGvjeWCta+fU7PABVs6cZYASKu5j4NXDsIOLoA3b4B6vS3dKsIIYQQQoiZUFRYtFYFC+Dh0i49w9PdUMCzEPDkLCC4iaVbRQghhBBCMgFFhSVgATydA/P1GhQJMUCRasDTcwF/mm4IIYQQQmwNigpL1qrIrwXwJCB77Vhg89f6cqWuQK/JgJu3pVtGCCGEEEKyAEWFJdPK5scCeDGRwKLngBP/6svNXwfafqgHsBNCCCGEEJuEosKS7k9iqdA0wMEB+YLbF/SA7NAjgJMb0OMHoGYfS7eKEEIIIYRkE4oKS1oqpBbDvVuAVwDsngtbgfn9gXs39VodT80BStS3dKsIIYQQQkgOQJ8TS+Dspg+s80sGqL0zgenddUFRrCbw7FoKCkIIIYQQO4KiwhpcoOyVxARg5fvA0peAxDigag9g6IoHlhpCCCGEEGIXUFRYOgOUvaaVjQ4H5vQBtv2gL7d+F3hiGuDqZemWEUIIIYSQHIYxFRavVWGHloqbZ4C5TwFhJwFnD6DnJKBaT0u3ihBCCCGE5BIUFRZ3f7KzmIqzG4AFA4HoO4BPIPD0HCCwjqVbRQghhBBCchGKCkthj+5Pu6YAy98GEuOBoHp6hiefYpZuFSGEEEIIyWUoKiyFPbk/JcQBK94Fdv2qL9foA3T/DnDxsHTLCCGEEEJIHkBRYSkMGZAkzWrsXdsNYJY6G38MBs5t0JfbfaRXyc4vBf0IIYQQQghFhcXwKAC4+QIxEboLVOFKsDlunATmPgncOgu4eAG9JgNVHrV0qwghhBBCSB7DlLKWxJZdoE7/B/zWXhcU8jmGraSgIIQQQgjJp1BUWIMLlC0VwNM0YPskYHZvICYcKNkYeHYdUKyGpVtGCCGEEELyo6gYPXo0HBwcUjwqV66M/JcB6pL1VsQ+twk4tFB/jr0P/P0ysGIkoCUCtfsBg5YC3oUt3VJCCCGEEJKfYyqqVauG//77L3nZ2dniTbKA+5MVppU9uhRY8Q4QcfXBOidXICEWgAPQ8WOgyUsMyCaEEEIIIZYXFSIiihXLp7UMrNX9SQSFFLCDlnK9EhQAWrwBNP2fRZpGCCGEEEKsD4vHVJw6dQqBgYEoW7Ys+vXrh4sX7azCdHoUKGV97k/i8iQWitSCwpgDc/X9CCGEEEIIsbSlolGjRpg2bRoqVaqEkJAQjBkzBi1atMDhw4fh4+Pz0P4xMTHqYSAiIkI9x8XFqYclMJw3S+f3KgYXGb5HXEV8zH3A0eKGIzhc2AxnY5cnU0RcQfzZjdCCm8OWyVbfEYvCvrNd2He2C/vOdmHf2S5xVtB35p7bQdMknY91cOfOHQQHB+Orr77CsGHDTAZ2i/BIzZw5c+Dp6QmbQ0tEtwPD4KglYFW1r3DftZClW4SgW9tQ/8KkDPfbHTwcVwo2yZM2EUIIIYQQy3Dv3j307dsX4eHh8PX1tQ1RITRo0ADt27fHZ599ZpalomTJkggLC0v3Q+a2elu9ejU6dOgAFxexO2QO5x/rw+HOecQP+BtaqSbWYamY9ViG+8X3X2IXlors9B2xHOw724V9Z7uw72wX9p3tEmcFfSfj7UKFCmUoKizvb2NEVFQUzpw5gwEDBpjc7ubmph6pkYts6Zsky22QtLJ3zsM5KkQOAotTtiXgXQyIupbGDg6AbyCcZT9HJ9gD1vD9IVmDfWe7sO9sF/ad7cK+s11cLNh35p7XooHab775JjZs2IDz589j69at6NmzJ5ycnPD0008j32BtwdoiFApXSmNjUvrYzuPtRlAQQgghhJDsY1FLxeXLl5WAuHnzJgoXLozmzZtj+/bt6nW+SytrLaLi0i7g3Ab9tWch4F7Yg22+gbqgqNrdYs0jhBBCCCHWh0VFxbx58yx5eusqgGcNtSokTew/r+uva/UFevwAXNgKRF0HvIsCwU1poSCEEEIIIdYdU5EvkZgKa6mqvft34NpBwM0P6DBWFxBlWli6VYQQQgghxMqxePG7fI/BUiHuT5ZMxBV1A1jzsf663YeAdz5yQSOEEEIIIdmCosLS+Abpz3H3gHu3LNeO1R8BMeFAsZpA/aGWawchhBBCCLE5KCosjYu7Hq9gyWDtC9uAA3P0112/YtwEIYQQQgjJFBQV1uYCldckxAP/vqm/rjMAKNkg79tACCGEEEJsGooKa0ora4kMULt+Ba4fBtwLAO3H5P35CSGEEEKIzUNRYVUZoPJYVEReA9Z9qr9uPwrwCsjb8xNCCCGEELuAosIa8LNQVe1VHwIxEUBgHaDuoLw9NyGEEEIIsRsoKvKr+9P5zcChBQAcgK5fMjibEEIIIYRkGYqK/Oj+lBAH/JMUnF1vMBBUL2/OSwghhBBC7BKKCmvK/nTvJhB7L/fPt+Nn4MYxwKMg0O6j3D8fIYQQQgixaygqrAF3P8DVR38dfjl3zxVxFVg/Xn/dYQzgWTB3z0cIIYQQQuweigprwMHByAXqYu6ea9UHQGwUUKIBULt/7p6LEEIIIYTkCygqrK4AXi5aKs6uBw7/CTg4Al2+ABzZ/YQQQgghJPtwVJlfMkDFxwL/vqW/rj8MCKydO+chhBBCCCH5DoqK/JIBavuPQNhJwLMQ0PaD3DkHIYQQQgjJl1BU5Af3Jznmhon6644fAx4Fcv4chBBCCCEk30JRYS0UKJV77k8r3wPi7gElGwM1n8r54xNCCCGEkHwNRYW1xVREXAES4nPuuKfXAEf/Ahyckipns8sJIYQQQkjOwhGmteBdDHB0AbQEIDIkZ44ZH/MgOLvhc0Cx6jlzXEIIIYQQQoygqLAWxILgF5SzcRVbvwdunQG8iwJt3s2ZYxJCCCGEEJIKigqrDNbOgbiK2xeAjV/orzt+olftJoQQQgghJBegqLBGUXHnYs4EZ8ffB4KbAzV6Z/94hBBCCCGEpAFFhVXWqsim+9PJVcDxZUnB2V8ADg450jxCCCGEEEJMQVFhjRmgsuP+FBcNLE8Kzm48HChSJWfaRgghhBBCSBpQVFil+1M2RMWWb4Hb5wGf4kDrkTnWNEIIIYQQQtKCosIaC+CJ+5OmZf79t84Bm7/SX3caB7j55Gz7CCGEEEIIMQFFhTXhm5RSNu4ucP925t+/YiQQHw2UaQlU65XjzSOEEEIIIcQUFBXWhIs74FUkaxmgTiwHTq7QC+h1+ZLB2YQQQgghJM+gqLDaDFCZiKuIvQcsf1t/3WQEULhi7rSNEEIIIYQQE1BUWG0BvEykld38tW7Z8C0BtEoSF4QQQgghhOQRFBXWmlbW3AxQN88AW77RX3f+FHD1yr22EUIIIYQQYgKKCqvNAGVGTIVkiPr3LSAhFijXFqjSPdebRwghhBBCSGooKmzZ/UmqZp9ZAzi5Al1YOZsQQgghhFgGigpbdX+KvQssTypu1/RlIKBc7reNEEIIIYQQE1BUWGv2p3thelantNj4BRBxGfArBbR4I8+aRwghhBBCSGooKqwN9wKAa1Il7IgrpvcJOwVs/V5//ch4wNUz79pHCCGEEEJIKigqrA2Ji0h2gbqYRnD2m0BiHFChI1CpS543kRBCCCGEEGMoKmytAN7RJcDZ9YCTG/DIBAZnE0IIIYQQi0NRYc0ZoFIHa8dEAive0183fw0oWDbv20YIIYQQQkgqKCqs2lKRKq3sholA5FXAvzTQ/FWLNI0QQgghhJDUUFRYda0KI0tF6HFg+0/660cmAi4elmkbIYQQQgghqaCosAX3p+Tg7Hg9MLtiJ4s2jxBCCCGEEGOcUywR63J/kpSyiQnAkcXA+U2AszvQebylW0cIIYQQQoh1WirGjx8PBwcHvPoqYwXgXRRwcAK0BGDbD8A/b+rrW7wJ+AdbunWEEEIIIYRYn6Vi165d+OWXX1CzZk1LN8U6OP7Pg9erP9KfRWQEMNsTIYQQQgixPixuqYiKikK/fv3w66+/wt/f39LNsTxHlwILBupWCmNkeeEwfTshhBBCCCFWhMVFxYgRI9C1a1e0b9/e0k2xPBI/seIdURBp77NipL4fIYQQQgghVoJF3Z/mzZuHvXv3Kvcnc4iJiVEPAxEREeo5Li5OPSyB4bw5cX6HC5vhHHE1nT00Fbwdf3YjtODm2T5fficn+47kLew724V9Z7uw72wX9p3tEmcFfWfuuR00TfKV5j2XLl1C/fr1sXr16uRYitatW6N27dr45ptvTL5n9OjRGDNmzEPr58yZA09PT9g6Qbe2of6FSRnutzt4OK4UbJInbSKEEEIIIfmXe/fuoW/fvggPD4evr6/1iYolS5agZ8+ecHJySl6XkJCgMkA5Ojoqi4TxtrQsFSVLlkRYWFi6HzK31ZsIow4dOsDFxSX7lopZj2W4X3z/JbRUWFnfkbyFfWe7sO9sF/ad7cK+s13irKDvZLxdqFChDEWFxdyf2rVrh0OHDqVYN2TIEFSuXBnvvPPOQ4JCcHNzU4/UyEW29E2SI20o2xLwDQQiQtKIq3BQ251lP8eHrw/JGtbw/SFZg31nu7DvbBf2ne3CvrNdXCzYd+ae12KiwsfHB9WrV0+xzsvLCwEBAQ+tzzeIUOg8Qc/+JAIihbCQZejF7ygoCCGEEEKIFWHx7E8kFVW7A31mAL7FU64XC4asl+2EEEIIIYRYEVZR/M7A+vXrLd0E60CEQ+WuwIWtQNR1vcJ2cFNaKAghhBBCiFViVaKCGCECokwLS7eCEEIIIYSQDKH7EyGEEEIIISRbUFQQQgghhBBCsgVFBSGEEEIIISRbUFQQQgghhBBCsgVFBSGEEEIIISRbUFQQQgghhBBC8m9KWU3TK05HRERYrA1xcXG4d++eaoOlyqeTrMG+s13Yd7YL+852Yd/ZLuw72yXOCvrOMM42jLvtUlRERkaq55IlS1q6KYQQQgghhNgtMu728/NLc7uDlpHssGISExNx9epV+Pj4wMHBwWLqTUTNpUuX4Ovra5E2kKzBvrNd2He2C/vOdmHf2S7sO9slwgr6TqSCCIrAwEA4Ojrap6VCPliJEiVgDUhH80a1Tdh3tgv7znZh39ku7DvbhX1nu/hauO/Ss1AYYKA2IYQQQgghJFtQVBBCCCGEEEKyBUVFNnFzc8OoUaPUM7Et2He2C/vOdmHf2S7sO9uFfWe7uNlQ39l0oDYhhBBCCCHE8tBSQQghhBBCCMkWFBWEEEIIIYSQbEFRQQghhBBCCMkWFBVmMmnSJNSsWTM5T3CTJk2wfPny5O3R0dEYMWIEAgIC4O3tjccffxzXr1+3aJvJw4wfP14VSnz11VeT17HvrJfRo0er/jJ+VK5cOXk7+856uXLlCvr376/6xsPDAzVq1MDu3buTt0s430cffYTixYur7e3bt8epU6cs2mYClC5d+qF7Th5ynwm856yXhIQEfPjhhyhTpoy6p8qVK4ePP/5Y3WsGeN9ZL5GRkWpsEhwcrPqmadOm2LVrl031HUWFmUiRPRmQ7tmzR/1jbNu2LXr06IEjR46o7a+99hr+/vtv/PHHH9iwYYOq9N2rVy9LN5sYITfnL7/8osShMew766ZatWoICQlJfmzevDl5G/vOOrl9+zaaNWsGFxcXNfly9OhRfPnll/D390/eZ+LEifjuu+/w888/Y8eOHfDy8kKnTp3UoJVY9nfS+H5bvXq1Wt+7d2/1zHvOepkwYYKaAP3hhx9w7NgxtSz32ffff5+8D+876+WZZ55R99vMmTNx6NAhdOzYUQkHmaCxmb6T7E8ka/j7+2u//fabdufOHc3FxUX7448/krcdO3ZMpga0bdu2WbSNRCcyMlKrUKGCtnr1aq1Vq1baK6+8otaz76ybUaNGabVq1TK5jX1nvbzzzjta8+bN09yemJioFStWTPv8889T9Kebm5s2d+7cPGolMQf5rSxXrpzqM95z1k3Xrl21oUOHpljXq1cvrV+/fuo17zvr5d69e5qTk5O2bNmyFOvr1q2rvf/++zbTd7RUZNHEOG/ePNy9e1e5QYn1Ii4uTilKA+KiUapUKWzbts2ibSU6Yq7v2rVrij4S2HfWj5h3AwMDUbZsWfTr1w8XL15U69l31svSpUtRv359NbtdpEgR1KlTB7/++mvy9nPnzuHatWsp+s7Pzw+NGjVi31kRsbGxmDVrFoYOHapcoHjPWTfiLrNmzRqcPHlSLR84cEBZdh955BG1zPvOeomPj1djS3d39xTrxc1J+tBW+s7Z0g2wJcQcJSJCTE3iS7p48WJUrVoV+/fvh6urKwoUKJBi/6JFi6ovAbEsIgD37t2bwjfRgPQP+856kR/MadOmoVKlSsoVY8yYMWjRogUOHz7MvrNizp49q9wwXn/9dbz33nvq3nv55ZdVfw0aNCi5f6SvjGHfWRdLlizBnTt3MHjwYLXMe866GTlyJCIiIpTQc3JyUoPUcePGqckYgfed9eLj46PGlxIDU6VKFdUnc+fOVYKhfPnyNtN3FBWZQAY2IiDCw8OxcOFC9c9RfEqJ9XLp0iW88soryk8x9QwAsX4MM2yCxMKIyJAgtgULFqgZHGKdJCYmKkvFp59+qpbFUiFCUHyB5XeT2AZTpkxR96BYCon1I7+Ls2fPxpw5c1QsmoxXJPBX+o/3nfUzc+ZMZRUMCgpSorBu3bp4+umnlYXQVqD7UyaQGRpRjPXq1cNnn32GWrVq4dtvv0WxYsWUmVhmdIyRjBiyjVgOuRlDQ0PVzens7KweIgQl2Elei8pn39kOMkNasWJFnD59mvedFSPZScSKa4zMvhlc1wz9kzprEPvOerhw4QL+++8/FTxqgPecdfPWW28pa8VTTz2lsq0NGDBABdbLeEXgfWfdlCtXTo1PoqKi1ITozp07lbuhuP7aSt9RVGRzNi4mJkaJDMlyIr6MBk6cOKH+gYo5i1iOdu3aKbc1mbExPGQGVczBhtfsO9tBfmzPnDmjBq2876wXyfwkfWGM+HmLlUmQlJfyj9C478RtQzKasO+sg6lTp6p4GIlFM8B7zrq5d+8eHB1TDutkxlvGKgLvO9vAy8tL/Y+TLHorV65UmUZtpu8sHSluK4wcOVLbsGGDdu7cOe3gwYNq2cHBQVu1apXa/sILL2ilSpXS1q5dq+3evVtr0qSJehDrwzj7k8C+s17eeOMNbf369eq+27Jli9a+fXutUKFCWmhoqNrOvrNOdu7cqTk7O2vjxo3TTp06pc2ePVvz9PTUZs2albzP+PHjtQIFCmh//fWX+k3t0aOHVqZMGe3+/fsWbTvRtISEBHVfSRav1PCes14GDRqkBQUFqQxC8pu5aNEi9Xv59ttvJ+/D+856WbFihbZ8+XLt7NmzamwpmQ8bNWqkxcbG2kzfUVSYiaRpCw4O1lxdXbXChQtr7dq1SxYUgnTqiy++qNLMyj/Pnj17aiEhIRZtMzFPVLDvrJcnn3xSK168uLrv5J+lLJ8+fTp5O/vOevn777+16tWrq5SHlStX1iZPnpxiu6RI/PDDD7WiRYuqfeQ39cSJExZrL3nAypUrVZpYU/3Be856iYiIUP/bRPS5u7trZcuWVelIY2JikvfhfWe9zJ8/X/WZ/L+T9LEjRoxQaWNtqe8c5I+lrSWEEEIIIYQQ24UxFYQQQgghhJBsQVFBCCGEEEIIyRYUFYQQQgghhJBsQVFBCCGEEEIIyRYUFYQQQgghhJBsQVFBCCGEEEIIyRYUFYQQQgghhJBsQVFBCCGEEEIIyRYUFYQQkstMmzYNBQoUyLXjr1+/Hg4ODrhz506OHO/8+fPqePv370deM3r0aNSuXdtqrq0lGTx4MB577DFLN4MQQsyCooIQQnJg8CeDcHm4urqifPnyGDt2LOLj4/Pk/E2bNkVISAj8/PyQ18ydOxdOTk4YMWJEpt8r12vJkiUp1r355ptYs2aN2cd48skncfLkySyLkrwSaoQQYu9QVBBCSA7QuXNnNbA/deoU3njjDTW4/fzzz/Pk3CJkihUrpgbBec2UKVPw9ttvK3ERHR2d7eN5e3sjICDA7P09PDxQpEiRbJ+XEEJI9qCoIISQHMDNzU0N7IODgzF8+HC0b98eS5cuTbHPypUrUaVKFTVwNogQYePGjXBxccG1a9dS7P/qq6+iRYsW6vWFCxfQrVs3+Pv7w8vLC9WqVcO///6b5qz6li1b0Lp1a3h6eqr3dOrUCbdv31bbVqxYgebNmyu3IRnAP/roozhz5kymP/O5c+ewdetWjBw5EhUrVsSiRYse2uf3339XbZXrU7x4cbz00ktqfenSpdVzz549VdsNy8aWhlWrVsHd3f0ha8Err7yCtm3bPuT+JK/HjBmDAwcOJFuOZN3QoUPVZzQmLi5OiRERRVkhJiZGWVWCgoJUfzRq1Ej1gxAREaHEzvLly1O8Z/HixfDx8cG9e/fU8qVLl9CnTx/V/oIFC6JHjx7K9YwQQmwRigpCCMkFZFAZGxubvCwDyS+++AIzZ85UIuLixYtqUCq0bNkSZcuWVduMB72zZ89WA2JB3ItkICvvPXToECZMmKDEiSkkFqJdu3aoWrUqtm3bhs2bNytBkpCQoLbfvXsXr7/+Onbv3q1cjRwdHdXgPjExMVOfcerUqejatatyu+rfv/9DA/RJkyapdj/33HOqzSKyxDVM2LVrV/IxRFwZlo2RzyAD7j///DN5nXyG+fPno1+/fiZdocRKJCJGjikPWffMM88oIWUQccKyZctUn8j2rCDiSK7tvHnzcPDgQfTu3VsJRbFU+fr6KhEzZ86cFO+R/pQYCRF60r8i9ERkbNq0SYlAg9g0/t4QQojNoBFCCMkWgwYN0nr06KFeJyYmaqtXr9bc3Ny0N998U62bOnWqJj+3p0+fTn7Pjz/+qBUtWjR5ecKECVqVKlWSl//880/N29tbi4qKUss1atTQRo8ebfL869atU8e/ffu2Wn766ae1Zs2amd3+GzduqPcfOnRILZ87d04t79u3L833JCQkaCVLltSWLFmSfAxXV1ft7NmzyfsEBgZq77//fprHkHMsXrw4xbpRo0ZptWrVSl5+5ZVXtLZt2yYvr1y5Ul1bw2eVa+vn55fm+w1UrVpVXWMD3bp10wYPHpxm21JfU2MuXLigOTk5aVeuXEmxvl27dtq7776rXsvnkv67e/euWg4PD9fc3d215cuXq+WZM2dqlSpVUt8XAzExMZqHh4f6jKm/V4QQYu3QUkEIITmAzHzLTLO46zzyyCNqBlxceQzI7HS5cuWSl8UVKDQ0NEWw9+nTp7F9+3a1LG474hojrjXCyy+/jE8++QTNmjXDqFGj1Ox4WhgsFWkhs+lPP/20so7IrLrB9UisJ+ayevVqZfHo0qWLWi5UqBA6dOig3J0E+WxXr15Ntx3mIBYJcSuSYxlm+8U6ktmMT/9v7/5VGomiAIzPgq9gIxqwsvEPGhQsfQELtbBSxEYEC8s8gFgFUlnoU1jYKraCpYWPIGpA9AGW78iVOEzUyQQS5ftBkN1NZm4mspwz55wbqhVURfDw8BCtSakKVBZVFyomtHzxmafH9fX1RxsZ14WWttQCR7WFa01bHGjR4vOmUpFeTwsUcym9tKJJ0qCNDHoBkvQXrKysRLsPQ9NjY2PZyMjn/14JMDvR7/9+s/4d/f20KBH4Tk5ORtCbevRTUEy7zMXFRcwaHB8fZ81mMzs4OChsvfoK52H24+zsLNZK29P09HSpthtandrt9qdzcRySHeYavlvDTy0uLkYyRpsRsyrMJZBwlbW1tRWzH7QsMQfCNU7zKmW9vb3Fjle3t7fxs1NqSeP3YGNjI1qgNjc34yeJZvq94Bj1ej2SpLzR0dGe1iVJg2RSIUl9QEUhzQv0isSBCsL4+HgE0lQlOk1MTGR7e3vxaDQakRQUJRWzs7MxK0Fwn/f8/Jzd39/Ha1NQzcxFGRzj/Pw8An3mFxLu3jMATtLDbAAVENZBwlWERCvNeXxXrSD45row/0GlohuC+aJjMpDOPANJG4nFzs5O1qv5+fk4B9WYrxIT1k315u7uLru8vIxKU7KwsBCzISSTVDAk6bczqZCkIUElggCT4JPvucjvBEVbFS037OJ0dXUVO0kVIeGYmZnJ9vf3IwEh0Ob5DBPTYkOAfXp6Gi1YtDxxB78MBso5Bu1Z+W1safuhikFSQfsX5ydwZu2vr68xkJwSoZR0kDyxOxS7VHULzjnW0dFR3P3nud1wTHalogWMJIT2ovR8kjYGqEkItre3f9zqxDES3u/c3FysieoH1SKSjMfHx3gvJHQp6WEAnx3BeC6VEXaI6nxPbDnMjk981qyVHb7YQYstevmzJP0mzlRI0pDgLjyzFQS9BKyd+Dt2UiKRIGAnuTg5OSk8Dv9GtYC+/aWlpWx5eTkqC7TecA4qDLTu0PJ0eHhY+vs0mJtIW8Hmra+vxxzB09NTBO6tVivWSUWDgJ55joSAnNkMKjAE5t1QAeJ90FpVtOtT/vxcH6ojtBHx/RkJ8wwkUiRvtH39BIkBa0sPWpZAxYPPiN2mpqamogrCDla1Wu3jtVwfKk98Dvl1M2PDTl48f21tLT7X3d3dmKmwciHpN/rHtPagFyFJekdgyV3v/HdcqDrmGPheCRICAnlJUv/Y/iRJQ+Dl5SVabRjoNaHoLwbIqZxQGWHXqNXV1UEvSZL+HJMKSRoC9Nbf3NzEDALDveof5kaYaWBOgZ2j8jtzSZKqs/1JkiRJUiUOakuSJEmqxKRCkiRJUiUmFZIkSZIqMamQJEmSVIlJhSRJkqRKTCokSZIkVWJSIUmSJKkSkwpJkiRJlZhUSJIkScqq+A8jYoOlNlp5cQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "activity_group = df.groupby(\"Physical Activity Level\")[[\"Sleep Duration\", \"Quality of Sleep\"]].mean()\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "\n",
+    "plt.plot(activity_group.index, activity_group[\"Sleep Duration\"], marker='o', label=\"Sleep Duration (hours)\")\n",
+    "plt.plot(activity_group.index, activity_group[\"Quality of Sleep\"], marker='o', label=\"Quality of Sleep (1–10)\")\n",
+    "\n",
+    "plt.title(\"Effect of Physical Activity Level on Sleep\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Physical Activity Level\")\n",
+    "plt.ylabel(\"Average Value\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf97684-cdbf-443f-8229-8b4a01e134af",
+   "metadata": {},
    "source": [
     "# Discussion"
    ]
@@ -251,14 +519,7 @@
    "id": "bearing-stadium",
    "metadata": {},
    "source": [
-    "*It's important to recognize the limitations of our research.\n",
-    "Consider the following:*\n",
-    "\n",
-    "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
-    "- *What were limitations of your datset?*\n",
-    "- *Are there any known biases in the data?*\n",
-    "\n",
-    "✏️ *Write your answer below:*"
+    "My results provided clear visual representations that allowed me to effectively compare different aspects of the data. They directly answered my research questions, which was expected since the questions were straightforward and focused. However, I recognize that the simplicity of my questions was partly due to my current coding limitations. As I continue to improve my programming skills, I hope to design more complex and detailed research questions that explore deeper relationships within the data. Overall, there did not appear to be any bias in the dataset that could have influenced my results.\n"
    ]
   },
   {
@@ -274,15 +535,20 @@
    "id": "about-raise",
    "metadata": {},
    "source": [
-    "*Summarize what you discovered through the research. Consider the following:*\n",
+    "Through this project, I learned a lot about how lifestyle factors such as stress and physical activity influence sleep. By analyzing and visualizing the data, I could clearly see patterns showing that higher stress levels generally corresponded with shorter and lower-quality sleep, while higher physical activity levels were associated with longer and better-quality sleep. This helped me better understand the balance between rest, activity, and stress management. When comparing occupations, it became clear that some professions tend to get significantly less sleep on average, likely due to longer work hours or higher daily stress, while others maintained more consistent and healthier sleep patterns\n",
     "\n",
-    "- *What did you learn about your media consumption/digital habits?*\n",
-    "- *Did the results make sense?*\n",
-    "- *What was most surprising?*\n",
-    "- *How will this project impact you going forward?*\n",
+    "The results made sense overall and supported what I expected: stress tends to disrupt sleep, while physical activity can improve it. What surprised me most, however, was how strong the effect of occupation and stress seemed to be compared to other factors. Some groups showed noticeable differences in sleep duration simply based on their line of work, suggesting that job demands and lifestyle balance play a big role in sleep health.\n",
     "\n",
-    "✏️ *Write your answer below:*"
+    "Going forward, this project will make me more aware of how interconnected daily habits, work routines, and stress management are with getting quality sleep. I plan to pay closer attention to how my own schedule and responsibilities impact my rest and find ways to maintain a healthier balance between productivity and recovery. Understanding these patterns gives me a clearer picture of how small changes in lifestyle can lead to better overall well-being."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb832499-95a8-478a-ab91-2d4c0d1c5d1f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -310,7 +576,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.13.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/data/Sleep_health_and_lifestyle_dataset.csv b/data/Sleep_health_and_lifestyle_dataset.csv
new file mode 100644
index 0000000..b7e16bd
--- /dev/null
+++ b/data/Sleep_health_and_lifestyle_dataset.csv
@@ -0,0 +1,375 @@
+Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder
+1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,None
+2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia
+7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia
+8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+14,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+15,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+16,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea
+18,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,Sleep Apnea
+19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia
+20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea
+32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia
+33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,None
+34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea
+51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+53,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+55,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+56,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+58,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+59,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+61,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+62,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,None
+68,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,Insomnia
+69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+75,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+76,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+77,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+78,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+79,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+80,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia
+96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea
+105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea
+106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia
+107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,None
+108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,None
+127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea
+146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia
+148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia
+149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,None
+150,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+151,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia
+167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,None
+168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+190,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+191,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+193,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+194,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+196,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+197,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+198,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+199,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,None
+205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,None
+206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea
+220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea
+221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+223,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+224,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+225,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+226,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+227,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+228,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+229,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+230,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+231,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+232,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+233,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+234,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+235,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+236,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+237,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+238,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+239,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+240,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+241,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+242,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+243,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+244,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia
+249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,None
+250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,None
+251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+257,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,None
+265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,None
+269,Female,49,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+273,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+274,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia
+280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+281,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,None
+282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+283,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+284,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+285,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+286,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+287,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+288,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+289,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+290,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+291,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+292,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+293,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+294,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+295,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+296,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,None
+304,Female,51,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+309,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia
+317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+350,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+353,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+354,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+355,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+356,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+357,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+358,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+359,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,None
+360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+365,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+366,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+367,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+368,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+369,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+370,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+371,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+372,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+373,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+374,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
\ No newline at end of file
diff --git a/proposal.md b/proposal.md
index e34b3af..f1e7bc0 100644
--- a/proposal.md
+++ b/proposal.md
@@ -31,7 +31,7 @@ https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset
 
 *Describe the source of the data set--not just where you downloaded it, but the person or organization who gathered the data. Explain why you trust them.*
 
-I am using Kaggle to get the data set. I trust this because it was a reccomendation from the proffesor. I also looked up review at how legit kaggle is, and i was shown mostly postive sites that stated it is a good site to use as it is well regarded with data scientists. 
+I am using Kaggle to get the data set. I trust this because it was a reccomendation from the professor. I also looked up review at how legit kaggle is, and i was shown mostly postive sites that stated it is a good site to use as it is well regarded with data scientists. 
 Due to this, I feel comfortable using the data that is provided on this site to use for my project. 
 
 ### What is this data about?