{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c17f691c",
   "metadata": {},
   "source": [
    "# Lesson 12 activity solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7bc4fe9",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Import the required libraries and load the weather dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "576d7092",
   "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>weather_condition</th>\n",
       "      <th>wind_strength</th>\n",
       "      <th>temperature_c</th>\n",
       "      <th>rainfall_inches</th>\n",
       "      <th>humidity_percent</th>\n",
       "      <th>pressure_hpa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.13</td>\n",
       "      <td>48.8</td>\n",
       "      <td>1016.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Snowy</td>\n",
       "      <td>Gale</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>89.6</td>\n",
       "      <td>1009.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Strong Wind</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1003.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.62</td>\n",
       "      <td>49.3</td>\n",
       "      <td>1006.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Calm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>38.6</td>\n",
       "      <td>1016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weather_condition wind_strength  temperature_c  rainfall_inches  \\\n",
       "0             Sunny  Light Breeze            8.2             0.13   \n",
       "1             Snowy          Gale            1.6             0.29   \n",
       "2             Rainy   Strong Wind            7.3             0.01   \n",
       "3            Cloudy  Light Breeze           21.6             0.62   \n",
       "4             Sunny          Calm           12.0             1.09   \n",
       "\n",
       "   humidity_percent  pressure_hpa  \n",
       "0              48.8        1016.5  \n",
       "1              89.6        1009.4  \n",
       "2             100.0        1003.3  \n",
       "3              49.3        1006.9  \n",
       "4              38.6        1016.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib.patches import Arc\n",
    "\n",
    "# Load the weather dataset\n",
    "url = 'https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/weather.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53591171",
   "metadata": {},
   "source": [
    "## Exercise 1: linear algebra - finding similar days\n",
    "\n",
    "**Objective**: Apply concepts from linear algebra to gain meaningful insight from data.\n",
    "\n",
    "In linear algebra, we can treat each row of data as a vector and measure how similar different vectors are. This is useful for finding patterns - for example, finding days with similar weather conditions.\n",
    "\n",
    "**Tasks**:\n",
    "\n",
    "1. Select only the numeric columns from the dataset: `temperature_c`, `rainfall_inches`, `humidity_percent`, and `pressure_hpa`\n",
    "\n",
    "2. Extract the **first row** (day 0) as a reference vector\n",
    "\n",
    "3. For each row in the dataset, calculate the **cosine similarity** to the first row using:\n",
    "   $$\\text{similarity} = \\frac{\\mathbf{a} \\cdot \\mathbf{b}}{\\|\\mathbf{a}\\| \\|\\mathbf{b}\\|}$$\n",
    "   - You can use `np.dot()` for the dot product\n",
    "   - You can use `np.linalg.norm()` to calculate vector magnitudes\n",
    "   \n",
    "4. Add the similarity scores as a new column to the dataframe\n",
    "\n",
    "5. **Sort** the dataframe by similarity (highest to lowest) so the most similar days to day 0 appear first\n",
    "\n",
    "6. Display the **top 10 most similar days** including their similarity scores and weather conditions\n",
    "\n",
    "7. **Interpret**: Look at the top similar days - do they have similar temperature, humidity, pressure values? Does this make sense?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7f50bc",
   "metadata": {},
   "source": [
    "### Cosine similarity function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3c53856a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to calculate cosine similarity\n",
    "def cosine_similarity(row, reference):\n",
    "    '''Calculate cosine similarity between a row and the reference vector'''\n",
    "\n",
    "    return np.dot(reference, row.values) / (np.linalg.norm(reference) * np.linalg.norm(row.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294631a1",
   "metadata": {},
   "source": [
    "### Cosine similarity for day 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3dc683f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 most similar days to day 0:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>weather_condition</th>\n",
       "      <th>wind_strength</th>\n",
       "      <th>temperature_c</th>\n",
       "      <th>rainfall_inches</th>\n",
       "      <th>humidity_percent</th>\n",
       "      <th>pressure_hpa</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.13</td>\n",
       "      <td>48.8</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>50.1</td>\n",
       "      <td>1011.5</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Strong Wind</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.38</td>\n",
       "      <td>48.2</td>\n",
       "      <td>1018.5</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.32</td>\n",
       "      <td>48.9</td>\n",
       "      <td>1017.4</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>49.9</td>\n",
       "      <td>1018.3</td>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Calm</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1017.7</td>\n",
       "      <td>0.999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Moderate Wind</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>46.5</td>\n",
       "      <td>1012.4</td>\n",
       "      <td>0.999996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Moderate Wind</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.33</td>\n",
       "      <td>50.7</td>\n",
       "      <td>1017.5</td>\n",
       "      <td>0.999996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1.01</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1011.5</td>\n",
       "      <td>0.999996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Strong Wind</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.17</td>\n",
       "      <td>45.8</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0.999996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    weather_condition  wind_strength  temperature_c  rainfall_inches  \\\n",
       "0               Sunny   Light Breeze            8.2             0.13   \n",
       "41             Cloudy   Light Breeze            8.0             0.01   \n",
       "319             Sunny    Strong Wind            6.8             0.38   \n",
       "26              Sunny   Light Breeze            6.5             0.32   \n",
       "100             Sunny   Light Breeze            6.6             0.25   \n",
       "57              Sunny           Calm            6.0             0.29   \n",
       "346            Cloudy  Moderate Wind            6.5             0.06   \n",
       "283             Sunny  Moderate Wind           10.4             0.33   \n",
       "233            Cloudy   Light Breeze            6.7             1.01   \n",
       "18             Cloudy    Strong Wind            6.9             0.17   \n",
       "\n",
       "     humidity_percent  pressure_hpa  similarity  \n",
       "0                48.8        1016.5    1.000000  \n",
       "41               50.1        1011.5    0.999999  \n",
       "319              48.2        1018.5    0.999999  \n",
       "26               48.9        1017.4    0.999999  \n",
       "100              49.9        1018.3    0.999998  \n",
       "57               50.0        1017.7    0.999997  \n",
       "346              46.5        1012.4    0.999996  \n",
       "283              50.7        1017.5    0.999996  \n",
       "233              51.0        1011.5    0.999996  \n",
       "18               45.8        1011.0    0.999996  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select numeric columns\n",
    "numeric_cols = ['temperature_c', 'rainfall_inches', 'humidity_percent', 'pressure_hpa']\n",
    "\n",
    "# Extract first row as reference\n",
    "reference = df[numeric_cols].iloc[0].values\n",
    "\n",
    "# Calculate cosine similarity for each row using apply\n",
    "df['similarity'] = df[numeric_cols].apply(cosine_similarity, args=(reference,), axis=1)\n",
    "\n",
    "# Sort by similarity and display top 10\n",
    "df_sorted = df.sort_values('similarity', ascending=False)\n",
    "print(\"Top 10 most similar days to day 0:\")\n",
    "df_sorted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73403d23",
   "metadata": {},
   "source": [
    "**Interpretation:** The most similar days have very close values for temperature, humidity, and pressure. Day 0 itself has similarity of 1.0 (perfect match). Other similar days show comparable weather patterns.\n",
    "\n",
    "### Extra: visualizing cosine similarity in 2D space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "42452340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual explanation of cosine similarity with subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# --- First subplot: angle plot ---\n",
    "ax = axes[0]\n",
    "\n",
    "ref_f1 = 0.9\n",
    "ref_f2 = 0.01\n",
    "mid_f1 = 0.73 \n",
    "mid_f2 = 0.69\n",
    "dissim_f1 = -0.81\n",
    "dissim_f2 = -0.23\n",
    "\n",
    "# Draw vectors from origin to each point\n",
    "ax.set_title('Cosine Similarity: Angle Visualization')\n",
    "ax.arrow(0, 0, ref_f1, ref_f2, head_width=0.05, head_length=0.05, color='black', label='Reference')\n",
    "ax.arrow(0, 0, mid_f1, mid_f2, head_width=0.05, head_length=0.05, color='tab:blue', label='Vector 1')\n",
    "ax.arrow(0, 0, dissim_f1, dissim_f2, head_width=0.05, head_length=0.05, color='tab:orange', label='Vector 2')\n",
    "ax.set_xlabel('Feature 1')\n",
    "ax.set_ylabel('Feature 2')\n",
    "\n",
    "# Calculate and display angles\n",
    "ref_vector = np.array([ref_f1, ref_f2])\n",
    "mid_vector = np.array([mid_f1, mid_f2])\n",
    "dissim_vector = np.array([dissim_f1, dissim_f2])\n",
    "\n",
    "def ccw_angle(v1, v2):\n",
    "\n",
    "    angle1 = np.arctan2(v1[1], v1[0])\n",
    "    angle2 = np.arctan2(v2[1], v2[0])\n",
    "    angle = (angle2 - angle1) * 180 / np.pi\n",
    "\n",
    "    if angle < 0:\n",
    "        angle += 360\n",
    "\n",
    "    return angle\n",
    "\n",
    "mid_angle = ccw_angle(ref_vector, mid_vector)\n",
    "dissim_angle = ccw_angle(ref_vector, dissim_vector)\n",
    "\n",
    "mid_similarity = np.dot(ref_vector, mid_vector) / (np.linalg.norm(ref_vector) * np.linalg.norm(mid_vector))\n",
    "dissim_similarity = np.dot(ref_vector, dissim_vector) / (np.linalg.norm(ref_vector) * np.linalg.norm(dissim_vector))\n",
    "\n",
    "ref_angle = np.arctan2(ref_f2, ref_f1) * 180 / np.pi\n",
    "arc_radius = 0.5\n",
    "\n",
    "arc1 = Arc(\n",
    "    (0, 0), arc_radius, arc_radius,\n",
    "    angle=0, theta1=ref_angle, theta2=ref_angle+mid_angle,\n",
    "    color='tab:blue', linestyle='--'\n",
    ")\n",
    "ax.add_patch(arc1)\n",
    "\n",
    "arc2 = Arc(\n",
    "    (0, 0), 1.25*arc_radius, 1.25*arc_radius,\n",
    "    angle=0, theta1=ref_angle, theta2=ref_angle+dissim_angle,\n",
    "    color='tab:orange', linestyle='--'\n",
    ")\n",
    "ax.add_patch(arc2)\n",
    "\n",
    "mid_label_angle = ref_angle + mid_angle/2\n",
    "dissim_label_angle = ref_angle + dissim_angle/2\n",
    "\n",
    "ax.text(\n",
    "    0.19*np.cos(np.radians(mid_label_angle)), 0.15*np.sin(np.radians(mid_label_angle)),\n",
    "    f'{mid_angle:.0f}°', fontsize=10, ha='center', weight='bold', color='tab:blue'\n",
    ")\n",
    "\n",
    "ax.text(\n",
    "    0.2*np.cos(np.radians(dissim_label_angle)), 0.2*np.sin(np.radians(dissim_label_angle)),\n",
    "    f'{dissim_angle:.0f}°',\n",
    "    fontsize=10, ha='center', weight='bold', color='tab:orange'\n",
    ")\n",
    "\n",
    "ax.axis('equal')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# --- Second subplot: cosine function ---\n",
    "ax2 = axes[1]\n",
    "\n",
    "angles = np.linspace(0, 365, 361)\n",
    "cos_values = np.cos(np.radians(angles))\n",
    "\n",
    "ax2.set_title('Cosine Similarity vs Angle')\n",
    "ax2.plot(angles, cos_values, color='black')\n",
    "ax2.set_xlabel('Angle (degrees)')\n",
    "ax2.set_ylabel('Cosine similarity')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Mark the two example angles on the cosine plot\n",
    "ax2.scatter(\n",
    "    [mid_angle], [np.cos(np.radians(mid_angle))],\n",
    "    color='tab:blue', s=80,\n",
    "    label=f'Point 2 ({mid_angle:.0f}°)'\n",
    ")\n",
    "ax2.scatter(\n",
    "    [dissim_angle], [np.cos(np.radians(dissim_angle))],\n",
    "    color='tab:orange', s=80,\n",
    "    label=f'Point 3 ({dissim_angle:.0f}°)'\n",
    ")\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68fd20b",
   "metadata": {},
   "source": [
    "## Exercise 2: data types and visualization\n",
    "\n",
    "**Objective**: Understand statistical data types and visualize interactions between variables.\n",
    "\n",
    "Understanding data types is crucial for choosing appropriate statistical methods and visualizations. In this exercise, you'll identify data types and explore how numeric variables interact with categorical ones.\n",
    "\n",
    "**Tasks**:\n",
    "\n",
    "1. **Identify data types**: For `humidity_percent` and `pressure_hpa`, determine what type of data they are:\n",
    "   - Are they **interval** data (no true zero) or **ratio** data (has true zero)?\n",
    "   - For each variable, explain your reasoning:\n",
    "     - Does zero mean \"none\" or \"absence of the quantity\"?\n",
    "     - Are ratios meaningful? (e.g., is 100% humidity \"twice\" 50% humidity?)\n",
    "     - Can the value go below zero?\n",
    "\n",
    "2. **Create visualizations**: Choose an appropriate plot type to show how `humidity_percent` and `pressure_hpa` vary across different `weather_condition` categories\n",
    "   - Consider options like: box plots, violin plots, bar plots with error bars, or scatter plots with color coding\n",
    "   - Create **one plot** that effectively shows the relationship between both numeric variables and the weather condition\n",
    "   - You might use a single plot with subplots, or find a creative way to show all three variables together\n",
    "\n",
    "3. **Interpret your visualization**:\n",
    "   - Which weather condition tends to have the highest humidity?\n",
    "   - Which weather condition tends to have the lowest pressure?\n",
    "   - Do you see clear differences between weather conditions?\n",
    "   - Does this pattern make sense from a meteorological perspective?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d334d5",
   "metadata": {},
   "source": [
    "### Data types\n",
    "\n",
    "`humidity_percent`: ratio\n",
    "- 0% means 'no humidity' (true zero exists)\n",
    "- Ratios are meaningful: 100% is twice 50%\n",
    "- Cannot go below 0%\n",
    "\n",
    "`pressure_hpa`: ratio\n",
    "- 0 hPa means 'no pressure' (true zero exists, though not in atmosphere)\"\n",
    "- Ratios are meaningful: 1000 hPa is twice 500 hPa\"\n",
    "- Cannot go below 0 hPa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb8dd7c",
   "metadata": {},
   "source": [
    "### Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "074a162c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plots showing both variables by weather condition\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "sns.boxplot(df, x='weather_condition', y='humidity_percent', ax=axes[0])\n",
    "axes[0].set_title('Humidity by weather condition')\n",
    "axes[0].set_xlabel('Weather condition')\n",
    "axes[0].set_ylabel('Humidity (%)')\n",
    "\n",
    "sns.boxplot(df, x='weather_condition', y='pressure_hpa', ax=axes[1])\n",
    "axes[1].set_title('Pressure by weather condition')\n",
    "axes[1].set_xlabel('Weather condition')\n",
    "axes[1].set_ylabel('Pressure (hPa)')\n",
    "\n",
    "plt.suptitle('')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbb7d21",
   "metadata": {},
   "source": [
    "**Interpretation:**\n",
    "- Rainy weather has highest humidity\n",
    "- Rainy weather has lowest pressure\n",
    "- Clear differences exist between weather conditions\n",
    "- This makes meteorological sense: low pressure systems bring rain and high humidity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a8d8b34",
   "metadata": {},
   "source": [
    "## Exercise 3: analyzing distribution skewness\n",
    "\n",
    "**Objective**: Use measures of shape to describe distributions.\n",
    "\n",
    "Skewness describes the asymmetry of a distribution. Understanding skewness helps you choose appropriate statistical methods and understand the nature of your data.\n",
    "\n",
    "**Tasks**:\n",
    "\n",
    "1. Calculate the **skewness** for all four numeric variables:\n",
    "   - `temperature_c`\n",
    "   - `rainfall_inches`\n",
    "   - `humidity_percent`\n",
    "   - `pressure_hpa`\n",
    "\n",
    "2. Identify which variable has:\n",
    "   - The **greatest skew** (furthest from zero)\n",
    "   - The **least skew** (closest to zero, most symmetric)\n",
    "   - Print these findings with their skewness values\n",
    "\n",
    "3. Create **two histograms** (side by side) showing only these two variables:\n",
    "   - One histogram for the most skewed variable\n",
    "   - One histogram for the least skewed variable\n",
    "   - For each histogram:\n",
    "     - Add vertical lines showing the mean (in red) and median (in green)\n",
    "     - Include the skewness value in the title\n",
    "     - Use appropriate bin sizes\n",
    "\n",
    "4. **Interpret** your findings:\n",
    "   - Why does the most skewed variable have the sign that it does? (Think about the real-world meaning)\n",
    "   - For the skewed distribution, how do the mean and median compare? Why?\n",
    "   - What does the skewness tell you about typical vs extreme values for this variable?\n",
    "   - Why is the least skewed variable more symmetric?\n",
    "   - **Bonus**: Explain why skewness matters when choosing between mean and median as a measure of central tendency."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cc98e1",
   "metadata": {},
   "source": [
    "### Skewness calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e740fe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most skewed: rainfall_inches (skewness = 2.103)\n",
      "Least skewed: temperature_c (skewness = 0.179)\n"
     ]
    }
   ],
   "source": [
    "# Calculate skewness for all variables\n",
    "skewness = {\n",
    "    'temperature_c': df['temperature_c'].skew(),\n",
    "    'rainfall_inches': df['rainfall_inches'].skew(),\n",
    "    'humidity_percent': df['humidity_percent'].skew(),\n",
    "    'pressure_hpa': df['pressure_hpa'].skew()\n",
    "}\n",
    "\n",
    "# Find most and least skewed\n",
    "most_skewed = max(skewness, key=lambda k: abs(skewness[k]))\n",
    "least_skewed = min(skewness, key=lambda k: abs(skewness[k]))\n",
    "\n",
    "print(f\"Most skewed: {most_skewed} (skewness = {skewness[most_skewed]:.3f})\")\n",
    "print(f\"Least skewed: {least_skewed} (skewness = {skewness[least_skewed]:.3f})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12a5f17",
   "metadata": {},
   "source": [
    "### Histograms: most and least skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64fd3582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histograms\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Most skewed\n",
    "axes[0].set_title(f'{most_skewed}\\nskewness: {skewness[most_skewed]:.3f}')\n",
    "axes[0].hist(df[most_skewed], bins=30, edgecolor='black')\n",
    "axes[0].axvline(df[most_skewed].mean(), color='red', label='Mean')\n",
    "axes[0].axvline(df[most_skewed].median(), color='green', label='Median')\n",
    "axes[0].set_xlabel(most_skewed)\n",
    "axes[0].set_ylabel('Frequency')\n",
    "axes[0].legend()\n",
    "\n",
    "# Least skewed\n",
    "axes[1].set_title(f'{least_skewed}\\nskewness: {skewness[least_skewed]:.3f}')\n",
    "axes[1].hist(df[least_skewed], bins=30, edgecolor='black')\n",
    "axes[1].axvline(df[least_skewed].mean(), color='red', label='Mean')\n",
    "axes[1].axvline(df[least_skewed].median(), color='green', label='Median')\n",
    "axes[1].set_xlabel(least_skewed)\n",
    "axes[1].set_ylabel('Frequency')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0dc5ba8",
   "metadata": {},
   "source": [
    "**Interpretation:**\n",
    "- `rainfall_inches` is right-skewed because rainfall can't be negative but can have high values\n",
    "- For right-skewed data, mean > median (pulled by extreme values)\n",
    "- Most values are low (typical), with few high values (extreme events)\n",
    "- `temperature_c` is more symmetric with balanced distribution\n",
    "- For skewed data, median is better than mean as a measure of central tendency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7e2228",
   "metadata": {},
   "source": [
    "## Exercise 4: exploring relationships with correlation\n",
    "\n",
    "**Objective**: Apply covariance and correlation to describe relationships between variables.\n",
    "\n",
    "Weather variables often have meaningful relationships. Some pairs of variables are strongly related while others have little relationship at all.\n",
    "\n",
    "**Tasks**:\n",
    "\n",
    "1. Calculate the **correlation matrix** for all four numeric variables:\n",
    "   - `temperature_c`\n",
    "   - `rainfall_inches`\n",
    "   - `humidity_percent`\n",
    "   - `pressure_hpa`\n",
    "\n",
    "2. Identify the pair of variables with:\n",
    "   - The **strongest correlation** (highest absolute value, whether positive or negative)\n",
    "   - The **weakest correlation** (closest to zero)\n",
    "   - Print both pairs with their correlation coefficients\n",
    "   - Note whether the strongest correlation is positive or negative\n",
    "\n",
    "3. Create **two scatter plots** (side by side or in separate figures):\n",
    "   - One for the strongest correlation pair\n",
    "   - One for the weakest correlation pair\n",
    "   - For each plot:\n",
    "     - Include the correlation coefficient in the title\n",
    "     - Add appropriate axis labels\n",
    "\n",
    "4. **Interpret** your findings:\n",
    "   - What is the strongest relationship? Does it make meteorological sense?\n",
    "   - Is this strongest correlation positive or negative? What does that mean in real-world terms?\n",
    "   - What is the weakest relationship? Why might these variables have little correlation?\n",
    "   - Compare the scatter plots: How does the pattern differ between strong and weak correlations?\n",
    "   - Based on the correlation strengths, which relationship is more predictable?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87efb68b",
   "metadata": {},
   "source": [
    "### Correlation calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ba6665c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strongest correlation: pressure_hpa vs humidity_percent\n",
      "  Correlation coefficient: -0.471\n",
      "  Direction: Negative\n",
      "\n",
      "Weakest correlation: pressure_hpa vs rainfall_inches\n",
      "  Correlation coefficient: -0.066\n"
     ]
    }
   ],
   "source": [
    "# Calculate correlation matrix\n",
    "numeric_cols = ['temperature_c', 'rainfall_inches', 'humidity_percent', 'pressure_hpa']\n",
    "corr_matrix = df[numeric_cols].corr()\n",
    "\n",
    "# Find strongest and weakest correlations (excluding diagonal)\n",
    "mask = np.triu(np.ones_like(corr_matrix, dtype=bool))  # Upper triangle mask\n",
    "corr_values = corr_matrix.where(~mask)\n",
    "\n",
    "# Find strongest (highest absolute value)\n",
    "abs_corr = corr_values.abs()\n",
    "max_corr_idx = abs_corr.stack().idxmax()\n",
    "strongest_corr = corr_values.loc[max_corr_idx]\n",
    "\n",
    "# Find weakest (closest to zero)\n",
    "min_corr_idx = abs_corr.stack().idxmin()\n",
    "weakest_corr = corr_values.loc[min_corr_idx]\n",
    "\n",
    "print(f\"Strongest correlation: {max_corr_idx[0]} vs {max_corr_idx[1]}\")\n",
    "print(f\"  Correlation coefficient: {strongest_corr:.3f}\")\n",
    "\n",
    "print(f\"\\nWeakest correlation: {min_corr_idx[0]} vs {min_corr_idx[1]}\")\n",
    "print(f\"  Correlation coefficient: {weakest_corr:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4b3eb9",
   "metadata": {},
   "source": [
    "### Scatter plots for strongest and weakest correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e449fecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Izs5u91pubi6ff/55u8V2bc/zkbYXFRXx888/d3oBOtJifI/Hw7Jly3jttdcoLy8Pm2rlcDg6fd+RHDp0iMGDB4ddTOB/06WO9jcRCjB6KwAUBKFrFAoFU6ZMYd26dQSDQX788Ufi4+Pl794pU6bw/PPPA8jBRSioKCoqQpIkBg8e3OFnt57uUlJSwr333svHH3/c7t952+8dr9fL7NmzGTt2LO+++y5q9f9ueWw2Gw0NDaxYsYIVK1Z0eNzQ99/dd9/N2rVrmTBhAoMGDeL888/n6quvZurUqV0+P621vgZ0RXe/Fzu7BnT0WnfOfUc+/fRTHn74YXbs2IHP55O3KxSKI76vM51d87RaLZmZme36mpqa2u5YMTEx/Pzzzz06vnBsRFAhdJlWq2X8+PGMHz+eIUOGcO211/Lee+9x3333den9nWUa6uqXT2cZIlrfzHZVMBgEDs/17Cg4aH3xufXWW5kzZw6rVq3i888/Z+nSpSxbtoyvv/6a0aNHd/vYOTk5wOH6H61HXnpLZ+e5o+3BYJDzzjuPu+66q8P3DBkypNPjLF68mNdee41bb72VyZMnYzabUSgUXHnllfL5Pd56829CEIRjM23aND755BN27dolr6cImTJlCnfeeSfl5eX88MMPJCcnk5mZCRz+HlIoFPz3v//t8N90qIhdIBDgvPPOo66ujrvvvpucnBwMBgPl5eUsWrSo3feOTqfjoosuYvXq1axZs4Zf/OIX8muhfX/1q1+xcOHCDvsTWv+Rm5vL3r17+fTTT1mzZg0ffPABf/vb37j33nt54IEHun2eWl8DjocjZfVr+1pXz31Hvv/+ey6++GLOOuss/va3v5GUlIRGo+G11147YelwxTWgbxFBhdAj48aNAw5Pzwnp7pOJjIwMgsEgRUVFYQt3q6uraWho6FHe6oyMDPbt29due9ttoUVi8fHxzJw586ifm5WVxe23387tt99OUVERo0aN4q9//Stvv/020L2+z5kzh2XLlvH2228fNagInYO9e/e2e62goACLxXJMKQGzsrJwuVxdOgdtvf/++yxcuJC//vWv8jav19suT3t3zk1GRgY///wzwWAw7KlcQUGB/LogCH1T63oVP/74o7zQGWDs2LHodDq+/fZbNm3axEUXXSS/lpWVhSRJDBw48IgPMnbt2kVhYSFvvPEG11xzjbz9yy+/7HB/hULBP//5T+bOnctll13Gf//7X3kRudVqJSoqikAg0KXvP4PBwBVXXMEVV1xBc3Mzl156KY888ghLlixBr9d363tuyJAhZGdns3r1ap599tmjVv4+nt+LXT33Hfnggw/Q6/V8/vnnYclQXnvttXb7dvX8tL7mhYJOgObmZg4ePNija5Vw4og1FcIRffPNNx1G/KG5/K2HKA0GQ7cK/4QuKm0zDD311FMAcuaI7pg1axYbNmxgx44d8ra6ujr++c9/ttvPZDLx6KOPdpgL3GazAeB2u/F6vWGvZWVlERUVFTbU252+T548mQsuuIBXXnmlw2JAzc3N3HHHHQAkJSUxatQo3njjjbDP3717N1988UXYhbknLr/8cjZs2MDnn3/e7rWGhgb8fn+n71WpVO3+Np577jkCgUDYtlDQ05Xzc9FFF1FVVcW///1veZvf7+e5557DaDRy9tlnH/UzBEE4OcaNG4der+ef//wn5eXlYSMVOp2OMWPG8MILL9DU1BRWn+LSSy9FpVLxwAMPtPtOkSSJ2tpa4H9PpVvvI0lSu0KsrWm1Wj788EPGjx/PnDlz2Lx5s/xZ8+fP54MPPmD37t3t3he6BgDy8Vt/5tChQ5EkSb5+dOd7DuCBBx6gtraW3/72tx1+z37xxRd8+umnwPH9Xuzque+ISqVCoVCEfecXFxd3eF3r6jVy5syZaLVali9fHtaeV199FYfD0aP7AuHEESMVwhEtXrwYt9vNJZdcQk5ODs3Nzaxfv55///vfDBgwgGuvvVbed+zYsaxdu5annnpKLoY0ceLETj/7jDPOYOHChaxYsYKGhgbOPvtsNm/ezBtvvMG8efM455xzut3eu+66i7fffpvzzjuPxYsXyyll09PTqaurk5+WmEwmXnzxRX79618zZswYrrzySqxWKyUlJXz22WdMnTqV559/nsLCQs4991wuv/xyhg4dilqt5qOPPqK6uporr7wyrO8vvvgiDz/8MIMGDSI+Pr7T9RoAb775Jueffz6XXnopc+bM4dxzz8VgMFBUVMQ777xDZWUlf/nLXwB48sknufDCC5k8eTK/+c1v5JSyZrOZ+++/v9vnqLU777yTjz/+mF/84hdyet6mpiZ27drF+++/T3FxsZwesa1f/OIXvPXWW5jNZoYOHcqGDRtYu3atnCIyZNSoUahUKh5//HEcDgc6nY4ZM2YQHx/f7jNvuOEGXnrpJRYtWsTWrVsZMGAA77//Pj/++CPPPPNMlxc2CoJw4oWmyH7//ffodDrGjh0b9vqUKVPkkc3WQUVWVhYPP/wwS5Ysobi4mHnz5hEVFcXBgwf56KOPuOGGG7jjjjvIyckhKyuLO+64g/LyckwmEx988MFR11BFRETw6aefMmPGDC688EK+++47hg8fzmOPPcY333zDxIkTuf766xk6dCh1dXVs27aNtWvXUldXB8D5559PYmIiU6dOJSEhgfz8fJ5//nlmz54tfyeF+vqnP/2JK6+8Eo1Gw5w5czodSb7iiivYtWsXjzzyCNu3b+eqq64iIyOD2tpa1qxZw1dffSVPITqe34tdPfcdmT17Nk899RQXXHABV199NTU1NbzwwgsMGjSo3ZqGrt4fWK1WlixZwgMPPMAFF1zAxRdfzN69e/nb3/7G+PHjwxZlC33QCc01JZxy/vvf/0rXXXedlJOTIxmNRkmr1UqDBg2SFi9eLFVXV4ftW1BQIJ111llSREREh8XvbDZbu89vaWmRHnjgAWngwIGSRqOR0tLSjlj8rq2Oiqdt375dOvPMMyWdTielpqZKy5Ytk5YvXy4BUlVVVdi+33zzjTRr1izJbDZLer1eysrKkhYtWiRt2bJFkiRJstvt0k033STl5ORIBoNBMpvN0sSJE6V333037HOqqqqk2bNnS1FRUUctfhfidrulv/zlL9L48ePlczt48GBp8eLF0r59+8L2Xbt2rTR16lQpIiJCMplM0pw5czotftfRee7s/EnS4fS6S5YskQYNGiRptVrJYrFIU6ZMkf7yl7+EpQ6mTUrZ+vp66dprr5UsFotkNBqlWbNmSQUFBVJGRka71IEvv/yylJmZKalUqi4Vvwt9rlarlUaMGCG99tprYfu0LvLUVtt2CoJw4ixZskQCpClTprR77cMPP5QAKSoqSvL7/e1e/+CDD6Rp06ZJBoNBMhgMUk5OjnTTTTdJe/fulffJy8uTZs6cKRmNRslisUjXX3+9tHPnTgkI+57oqPid3W6Xhg4dKiUmJkpFRUWSJB3+vrnpppuktLQ0SaPRSImJidK5554rrVixQn7fSy+9JJ111llSXFycpNPppKysLOnOO++UHA5H2Oc/9NBDUkpKiqRUKrucXvarr76S5s6dK8XHx0tqtVqyWq3SnDlzpNWrV4ftd6zfi0e6PkhS1859RyllX331VWnw4MGSTqeTcnJypNdee00+Vmud3R90Vvzu+eefl3JyciSNRiMlJCRIv/vd7zotftdWR+0UTgyFJInVLEL/d+utt/LSSy/hcrk6XdglCIIgCIIg9IxYUyH0Ox6PJ+zn2tpa3nrrLaZNmyYCCkEQBEEQhONArKkQ+p3Jkyczffp0cnNzqa6u5tVXX8XpdLJ06dKT3TRBEARBEIR+SQQVQr9z0UUX8f7777NixQoUCgVjxozh1Vdf5ayzzjrZTRMEQRAEQeiXxJoKQRAEQRAEQRCOiVhTIQiCIAiCIAjCMRFBhSAIgiAIgiAIx0QEFYIgCIIgCIIgHBMRVAjCSVBeXs7ll19OdHQ0JpOJuXPncuDAgW5/TkNDA/Hx8SgUCt5///2w1xYtWoRCoej0v/LycnnfL774gt/85jcMHz4clUrFgAEDjrWLgiAIQjcc63Vh/fr1TJs2jcjISBITE7nllltwuVwd7rtt2zYuvvhiYmNjiYyMZPjw4Sxfvrzdfs3NzTz66KPk5OSg1+tJSEhg9uzZlJWV9bifQv8lsj8Jwgnmcrk455xzcDgc/PGPf0Sj0fD0009z9tlns2PHDuLi4rr8Wffeey9ut7vD12688UZmzpwZtk2SJP7v//6PAQMGkJKSIm9fuXIl//73vxkzZgzJyck965ggCILQI8d6XdixYwfnnnsuubm5PPXUU5SVlfGXv/yFoqIi/vvf/4bt+8UXXzBnzhxGjx7N0qVLMRqN7N+/v12g0NLSwuzZs1m/fj3XX389I0eOpL6+nk2bNuFwOEhNTe318yCc4k5qPW9BOA09/vjjEiBt3rxZ3pafny+pVCppyZIlXf6cXbt2SWq1WnrwwQclQHrvvfeO+p7vv/9eAqRHHnkkbHt5ebnU3NwsSZIkzZ49W8rIyOhyOwRBEIRjc6zXhQsvvFBKSkqSHA6HvO3ll1+WAOnzzz+XtzkcDikhIUG65JJLpEAgcNQ2aTQaadOmTT3okXA6EtOfhH7pnXfeYeDAgajVaqxWK//v//0/fD7fyW4WAO+//z7jx49n/Pjx8racnBzOPfdc3n333S5/zh/+8AcuueQSzjzzzC6/Z+XKlSgUCq6++uqw7cnJyWg0mi5/jiAIwqmmv14XnE4nX375Jb/61a8wmUzy9muuuQaj0Rj2/pUrV1JdXc0jjzyCUqmkqamJYDDY7jODwSDPPvssl1xyCRMmTMDv93c6Ki4IISKoEPqdN954g6uuuooRI0bw7LPPcsEFF/DMM89w/fXXH/W9brcbu91+1P/q6+t71LZgMMjPP//MuHHj2r02YcIE9u/fT2Nj41E/57333mP9+vU88cQTXT52S0sL7777LlOmTBFrJgRBOK305+vCrl278Pv97d6v1WoZNWoU27dvl7etXbsWk8lEeXk52dnZGI1GTCYTv/vd7/B6vfJ+eXl5VFRUMHLkSG644QYMBgMGg4GRI0fyzTff9KifQv8nggqhX2lubub222/n4osv5uOPP+amm27irbfe4ve//z1vvfUWP//88xHf/8QTT2C1Wo/63+jRo3vUvrq6Onw+H0lJSe1eC22rqKg44md4PB7uuOMO/t//+3/dCg4+//xzamtr+eUvf9mtNguCIJzK+vt1obKyMmzftu9v/d6ioiL8fj9z585l1qxZfPDBB1x33XX8/e9/59prrw3bD+Dpp5/m22+/5aWXXuK1117D6/VywQUXHPWcCacnsVBb6FfWrl1LbW0tN910U9j2xYsX87e//Y3//Oc/jBw5stP3X3PNNUybNu2ox4mIiOhR+zweDwA6na7da3q9Pmyfzjz22GO0tLTwxz/+sVvHXrlyJRqNhssvv7xb7xMEQTiV9ffrwtHe3/q9LpcLt9vN//3f/8nZni699FKam5t56aWXePDBBxk8eLCcNaqxsZHt27eTlpYGwIwZMxg0aBBPPPEEb7/9dk+6K/RjIqgQ+pVQ+r3BgweHbc/KykKpVFJcXHzE92dmZpKZmXnM7fB4PDgcjrBtiYmJ8kWno3m8oaHnI12YiouLefLJJ3nhhRcwGo1dbo/L5WL16tXMmjWrW9mlBEEQTnX9/bpwtPe3fm/o/1911VVh+1199dW89NJLbNiwgcGDB8v7TZ06VQ4oANLT05k2bRrr16/vvKPCaUsEFUK/UldX1+F2hULRpfe7XK5O83q3plKpsFqtnb7+73//O2woGQ6nc42NjUWn08nD1a2Fth0ppeu9995LSkoK06dPly+EVVVVANhsNoqLi0lPT0epDJ/ZuGrVKtxut5j6JAjCaae/XxdC0546e3/r9yYnJ7Nnzx4SEhLC9ouPjweQ14WE3tN2v9C+rddpCEKICCqEfiWUxaKoqIiBAwfK2/ft20cwGDzqGoS//OUvPPDAA0c9TkZGxhGfbs2aNYsvv/yy3XalUsmIESPYsmVLu9c2bdpEZmYmUVFRnX5uSUkJ+/bt6/Cp2e9//3vg8EUhOjo67LV//vOfGI1GLr744k4/WxAEoT/q79eF4cOHo1ar2bJlS9j01ubmZnbs2BG2bezYsXz55ZfyQu2Q0LqLUFA0YsQINBpNWJHU1vseKXgSTl8iqBD6pRdeeIHzzz9f/vm5554D4MILLzzi+3pr7mxSUlKHi+YAFixYwD333MOWLVvkbB179+7l66+/5o477gjbt6CggMjISNLT0wF4+OGHsdvtYfvs3r2bpUuXctdddzF58mQMBkPY6zabjbVr13LVVVcRGRl51L4JgiD0R/31umA2m5k5cyZvv/02S5culQOQt956C5fLxWWXXSa/9/LLL+exxx7j1VdfZcaMGfL2V155BbVazfTp0wGIiorioosu4tNPP6WgoICcnBwA8vPzWb9+PTfeeONRz4dwGjrZhTIEoTfdd999EiCNGDFCmjNnjvTCCy9Iv/rVryRAuvrqq0928yRJkiSn0yllZWVJ8fHx0hNPPCE9/fTTUlpampScnCzV1NSE7QtIZ5999hE/75tvvjli8bvnnntOAqQ1a9Z0+hk7d+6UHnroIemhhx6SsrOzpejoaPnnjz/+uNt9FARB6CtOh+vC1q1bJZ1OJ40ePVp68cUXpT/96U+SXq+Xzj///HbHuu666yRAuvzyy6UXXnhBuuyyyySgXZG9PXv2SEajUUpKSpKWLVsmLVu2TEpKSpKsVqtUVlbW6+dAOPWJoELoV0IXj7y8PGnBggVSVFSUFBMTI918882Sx+M52c2TlZaWSgsWLJBMJpNkNBqlX/ziF1JRUVG7/XojqJg0aZIUHx8v+f3+Tj/jtddek4AO/1u4cGF3uiYIgtCnnC7Xhe+//16aMmWKpNfrJavVKt10002S0+lst19zc7N0//33SxkZGZJGo5EGDRokPf300x22aevWrdLMmTMlg8EgRUVFSXPnzpUKCwuPtatCP6WQJEk6oUMjgnAc3X///TzwwAPYbDYsFsvJbo4gCIJwkonrgiCcGKL4nSAIgiAIgiAIx0QEFYIgCIIgCIIgHBMRVAiCIAiCIAiCcEzEmgpBEARBEARBEI6JGKkQBEEQBEEQBOGYiKBCEARBEARBEIRjIipqA8FgkIqKCqKiolAoFCe7OYIgCL1OkiQaGxtJTk5GqRTPk45GXBcEQTgd9Oa1QQQVQEVFBWlpaSe7GYIgCMddaWkpqampJ7sZfZ64LgiCcDrpjWuDCCqAqKgo4PAJNZlMJ7k13RMMBrHZbFit1lP+6WN/6guI/vRl/akv0LX+OJ1O0tLS5O874cjEdaHv6E/96U99AdGfvqyrfenNa4MIKkAe2jaZTKfkxcPr9WIymfrFP4D+0hcQ/enL+lNfoHv9EVN5ukZcF/qO/tSf/tQXEP3py7rbl964NpzaZ0wQBEEQBEEQhJNOBBWCIAiCIAiCIBwTEVQIgiAIgiAIgnBMRFAhCIIgCIIgCMIxEUGFIAiCIAiCIAjHRGR/Ek6IYDBISUkJjY2NREVFkZ6e3uuZFbpzjNb7GgwGABobG+X3ms1m+f1tPzc1NZWysrKw4wDtji0IgtCbTsT3qCAIQk+d1KBi3bp1PPnkk2zdupXKyko++ugj5s2bJ78uSRL33XcfL7/8Mg0NDUydOpUXX3yRwYMHy/vU1dWxePFiPvnkE5RKJfPnz+fZZ5/FaDSehB4JHcnPz+ejjz6ioKAAr9eLXq8nJyeHSy65hNzc3BN+jNb71tTUUF1djdfrxe/3EwgEMBgMZGZmMnHiREaOHMnPP/8sf67P58Pj8RAREYFOp0Ov1xMbGwsc/ltsfex58+bJrwmCIByLE/E9KgiCcCxO6iOOpqYmzjjjDF544YUOX3/iiSdYvnw5f//739m0aRMGg4FZs2bh9XrlfX75y1+yZ88evvzySz799FPWrVvHDTfccKK6IBxFfn4+y5cvZ/v27VgsFrKzs7FYLGzfvp3ly5eTn59/Qo/Rel+FQkFNTQ11dXVUVFRgt9vR6/X4/X4OHjzIZ599xt133813332HxWIhLi6OgwcPkpeXx8GDB4mNjUWhUPDxxx/zySefoFAowo79/PPPU1paesz9EwTh9HYivkcFQRCO1UkNKi688EIefvhhLrnkknavSZLEM888w5///Gfmzp3LyJEjefPNN6moqGDVqlXA4S/aNWvW8MorrzBx4kSmTZvGc889xzvvvENFRcUJ7o3QVjAY5KOPPsJutzN06FBMJhMqlQqTycTQoUOx2+2sWrWKYDB4Qo7Ret/c3FzKysrwer0olUo0Gg1qtRqPx4PVasXv91NZWYnD4cDv92M0GiksLMTv95OZmYnf76ewsJDS0lJ0Oh06nY6ysjKUSmXYsTdu3HhM/RME4fR2Ir5HBUEQekOfXVNx8OBBqqqqmDlzprzNbDYzceJENmzYwJVXXsmGDRuIjo5m3Lhx8j4zZ85EqVSyadOmDoMVAJ/Ph8/nk392Op0A8o3nqSQYDCJJUp9s96FDh9i7dy9paWntKjUqFArS0tIoKCjg0KFDZGRk9Kgv3TkGIO/b2NhIXV0dERER1NfXExkZCYDX66WlpYWIiAiqq6tJTk6mrq6O8vJy6urqiI6ORqVSYTabqa6uBiAmJgZJkqirq8PpdGI2m1EoFPLai5KSEgYMGHAMZ7Jv6Mt/a93Vn/oCXetPf+nr6aakpISCgoJOv+NSU1PJz8/vN98zgiCcuvpsUFFVVQVAQkJC2PaEhAT5taqqKuLj48NeV6vVxMbGyvt0ZNmyZTzwwAPttttstrCpVaeCYDCIw+FAkqQ+t2Cvurqa2NhYkpOTO2ybwWBAkiSqq6uJiIjoUV+6cwxA3tfpdJKZmYlWqyUmJgadTgccDjitVisARqORxMRE/H4/JpOJzMxMoqKi5MXbtbW1AMTFxQHIiydNJpN8bJ/PR01NjRy0nMr68t9ad/WnvkDX+tPY2HiCWyX0hsbGRrxer5xQoi2DwUB5ebn4/QqCcNL12aDieFqyZAm33Xab/LPT6SQtLQ2r1SrfEJ4qgsEgCoUCq9Xa526OPB4PdXV1KBSKDs+r0+mkrq6OhIQE4uPje9SX7hwDkPeVJIkDBw6gVCqpqKhAo9EA0NLSwoABA2hubqa4uJi6ujrUajXR0dEcOHBAXqDt9XrlEa7a2lokScLr9YZlY3E6nWg0GuLj4+X+lZaWysFHWlpan/udHUlf/lvrrv7UF+haf/R6/QluldAboqKi0Ov1NDU1dfgd19TUhF6vJyoq6iS0ThAE4X/6bFCRmJgIHH4SnZSUJG+vrq5m1KhR8j41NTVh7/P7/dTV1cnv70hoDnxbSqXylLzBUCgUfbLtGRkZZGdns337doYOHRo2dC9JEqWlpYwZM4aMjAy57d3tS3eOAcj75ubmEhsbS2VlJXq9nsbGRhQKBUajEY1Gg8PhwGw2U19fT3Z2NikpKRQXF1NZWYnFYsHhcMh/Y5WVlSgUCpKSkuSLviRJlJWVceaZZ5Kens7evXv7ReaWvvq31hP9qS9w9P70l36ebtLT08nJyen0O66srIwxY8aINNaCIJx0ffYqM3DgQBITE/nqq6/kbU6nk02bNjF58mQAJk+eTENDA1u3bpX3+frrrwkGg0ycOPGEt1kIp1QqueSSS7BYLOTl5cmLnh0OB3l5eVgsFubNm3dMNzvdOUbrffPz80lJSUGv1xMMBmlpacHv96PX66mpqUGtVpOUlITZbEatVtPY2MjgwYNRq9UcOHAAtVrNkCFDSE1NldfopKamEggEwo49adIkCgsLReYWQRB65ER8jwqCIPSGkzpS4XK52Ldvn/zzwYMH2bFjB7GxsaSnp3Prrbfy8MMPM3jwYAYOHMjSpUtJTk6Wa1nk5uZywQUXcP311/P3v/+dlpYWbr75Zq688kqSk5NPUq9OTcerqFJubi633HKL/JS+vLwcvV7PmDFjmDdvXq88pe/OMdruGx8fjyRJREZG4vf78fl8GAwGBg4cyKRJkxgxYkRYnYqBAweSlJSEXq+nrq4OvV7P3Llz5YXahYWF8rHnzp1LdHQ0r7/+OjabjZSUFDweD8FgELPZzNChQ8nLy2PVqlVkZ2eLmwJBEDp0Ir5HBUEQjtVJDSq2bNnCOeecI/8cWuewcOFCXn/9de666y6ampq44YYbaGhoYNq0aaxZsyZsbvA///lPbr75Zs4991y5+N3y5ctPeF9OZce7qFJubi7Z2dnHtRJs62M4HA75OKEF4K2P1bY9R6uofdFFF3Wronbo81wuFwUFBWzatAm73U5RURF+vx+1Wo3FYiEnJ0dkbhEEoUtOxPeoIAjCsTipQcX06dORJKnT1xUKBQ8++CAPPvhgp/vExsaycuXK49G800KoqJLdbictLQ2DwUBTUxPbt2+ntLSUW265pVcCC6VSedxvmpVKJR6Ph88+++yoAVJ32tPRvh29d8CAAeTn5/POO+9QUFAgj3r8+OOPmEwmEhIS0Gg0tLS0yDUwxo0bh9frFZlbBEE4qhPxPSoIgtBT4hHHaay/FVU62VVn2x5/8ODBeDwevF4vLpcLv9+PUqlEp9NhtVpxu93s2rULnU4nMrcIgiAIgnBKE0HFaaw7RZX6uu5W1i4uLmbXrl0UFxf3StDU0fGbmprw+/3ExcXh9Xqx2+14PB5cLhc+n4+oqCjKyspISkoSmVsEQRAEQTil9dmUssLxdyoXVWq7sDwYDHYpQPr666/ZvHlzr68f6ShA8/l8BINBrFYrLpdLnvLUOn2uXq9n6tSpYl60IAiCIAinNBFU9HHHKysT9M2iSsFgkEOHDuFyuTrtb0cLy2NiYqipqZHrUbRlMBjIz89nxYoVSJIkrx9xuVz88MMP7NixgxtuuIEZM2b06Px2FKDpdDpUKhWBQAClUokkSUiSJAcdwWAQlUrVaVAnCIIgCIJwqhBBRR92vLMy9bWiSgUFBaxdu5atW7fi8Xg67G9nC8sLCgo4cOAACQkJDBw4sN1nu1wuqqurUSqVjB8/HoVCgc1mo6CgAJvNRl1dHUVFRSxYsIBLL7202+e3owDNZDLhdDqprKzE7/djNptJSUlBpVKhUqlobGxEpVKxadOmHgczgiAIgiAIfYG4i+mjTsSi494sqnSs6xTy8/N5/vnnOXDgAHFxcR3290jrJsaNG4dWq2Xbtm3tji1JEoWFhcDhitqhgGLTpk1UVlZiMBhITk7G6/Xy5Zdfct9997F27dpu9SEUoJWWlsoZzRQKBRaLhUAgQEtLCzqdDqPRKBfTi4yMZMyYMezdu/eUWLciCIIgCILQGTFS0Qe1vnnOzc3F6XRit9vR6XTk5uaSn58vF0w7Vr1RVOlYR1Ra93fSpEk4nU4AeZF1qEDcFVdc0em6CaVSyZgxY9i4cSNbtmwhOztbHsUoKyvDaDSSkJCA0WhEkiQKCgpwu91YrVYUCgUul4va2lr8fj/79++nqKiI+fPnM3/+/C71IRSglZaWkpeXR2pqKgaDAUmS0Gg0aDQaIiMjqaurk6t15+TkEBMTQ2FhYZ9ctyIIgiAIgtBVIqjog0KLfiMjI/nxxx+x2+1hRdNSUlLkrEy9MTXpWIoq9Uadi1B/U1NT273WepF1YWHhEReWp6enU1NTw6BBg6itrQ0LkMaPH8+bb75JU1MTwWAQu92O2WxGoVDQ1NREaWkpPp8Po9FIbGwsjY2NrF+/nvLy8i7X6mgboFVUVJCQkEBSUhLDhg0jNjYWn8+HTqeTj+1wOE74uhVBEARBEITeJoKKPqixsZGamhpqamrwer2Yzeawomn19fXEx8f36tPtnhRVajsdKTR60HaEITs7+4gBSlezUAFHXVgeHx/PrbfeilKpbFfxevPmzWzfvp24uDj8fj8ajQZJkrDZbDQ1NWE2m9FqtWi1WhQKBenp6XIq2iP1oe1i+rvuuouysjKcTifBYJD4+Hh27twpBxIhJ2PdiiAIgiAIwvEggoo+yGAwUF1djcvlIjk5Wb4RDRVNq6ioQJKkk541qDt1Lo4UsLRe5BwTE9Pu9VAWqiFDhnRpYfmAAQM6DABC05MOHTpEMBjE5/PhdDqpqqoCwOPxUFxcjFarJTIyEr1ef9Q+HGnq1/Dhw6mpqeHSSy+lvLw8bFpUaFpWd9atCIIgCIIg9FXiTqYPa3uj3td0ZYTB6/UedUQltMi5rKys3WuhYCE3N5cBAwYc08Ly0PSkKVOmoNfrKS4upry8HEmSMJvNREVFoVarcTgcuFwumpubj9iHoy2mLygoACAnJ4dbbrmF0aNHU1tbS2FhIbW1tYwZM6bLU6sEQRAEQRD6MjFS0Qc1NTWRkJCAzWbDZrNhMpnQarU0NzfjdDoxmUxYrVaamppOajt7q85FaJFzWVkZJSUlKJVKIiMjO3ya35OF5W2nJ91zzz1MmDCB22+/naqqKjQaDWq1mkAgQHNzM0ajEb1ez969exkxYkSHfejK1K/Vq1ezcOFC4NjWrQiCIAiCIPR1Iqjog6KiooiPjychIYGysjLsdjuNjY1y1qDU1FQkSTrpi3t7s85Fbm4uN998s1ynoqysrNNgoTs36J1NT5owYQKZmZmYTCb2799PY2MjERERGI1GLBYLKpUKm81GYWEhZ555Zrs+dGXqV6gGRmJiItCzdSuCIAiCIAinAhFU9EGtb9anTp2K0+mUswaZTCby8/P7xOLeztKo9nS9QE5ODtHR0cyZMyesojZAcXFxuwDiaDfoR8pMtWPHDnw+H+eccw6ZmZls27YNn89HXFwcOp0Or9dLXV0daWlpDB06VM60FepLY2MjHo+HlpYWqqurwzI6weGpXxUVFXg8np6fYEEQBEEQhFOECCr6oNY36/n5+aSmphIXF0dTUxP5+flhN+vdLTLX23qjzkVrSqWSjIwM+ea9pzUwjjY96aeffqK6upqmpiYyMzNRKBTs3bsXp9OJQqHA6/USDAZpbm7mn//8Jx988EHYcaurq9m3bx+7d+9GqVTK6X5zcnLkqWl6vZ6IiIgO2yamQQmCIAiC0J+IoOIE6s7NZG/frB9Px2u9wLHUwGhd+8LhcLSrDzFkyBAqKyvZunUrarWa2tpaWlpaAFCpVHg8HuLi4hg+fDhGozHsuLNnz+bTTz+lpaWFQCCAxWLB7/dTWVmJw+FgwoQJ2Gw2xowZg9VqbdenYykUKAino2XLlvHhhx9SUFBAREQEU6ZM4fHHHz9qAdD33nuPpUuXUlxczODBg3n88ce56KKLTlCrBUEQTi8iqDhBenIzeSot7u3t9QLHWgMjVOujtLSUurq6sOKBoUrWofoXPp+P+Ph4oqKiaGpqYv/+/QBMnz4ds9kcdtw9e/bw3HPPERUVxVlnncXmzZupra3FZDIRFxdHVVUV69atY9q0acydOzesbb1RKFAQTkffffcdN910E+PHj8fv9/PHP/6R888/n7y8vE6zz61fv56rrrqKZcuW8Ytf/IKVK1cyb948tm3bxvDhw09wDwRBEPo/EVScAMdyM3m6Lu491hoY1dXVHDhwAEmSsFgsYcUDHQ4HQ4cOpampiZSUFHmkor6+nmAwKNeoqKmpYciQIfLxFQoFJpOJbdu2cc455xAfH8/EiRMpKCgIW0yv0WhYsGABOTk51NTUAL1XKFAQTkdr1qwJ+/n1118nPj6erVu3ctZZZ3X4nmeffZYLLriAO++8E4CHHnqIL7/8kueff56///3vx73NgiAIpxsRVBxn4mayZzqrgSFJEg6HA7fbTW1tLQ6Ho917g8EgGzduRKvVEgwG5QrZoeKBNTU1bNq0icjISMaOHYvJZJKnSDmdTrZv305UVBR2ux2Hw0F0dLT82Wq1mpaWFlQqFQBWqxWLxSK/X6VSUV1dTUJCQlibeqtQoCAIyP/uY2NjO91nw4YN3HbbbWHbZs2axapVq45n0wRBEE5bIqg4zsTNZM90VAPDZrPJowIejwe/38/bb7+NVquVR3qCwSDr169n06ZNZGdnc+jQoXa1Pvx+Py0tLSQmJmI0GlEoFHLgoNPp0Gg0APj9fnw+X1i7/H4/Go2GQCAgb1MoFJjNZhwOB7W1tQQCgXbBUFcKBZaXl3daKFAs7haEw4LBILfeeitTp0494jSmqqqqdsF9QkICVVVVHe7v8/nC/r07nU75eCc7IUZ3BYNBJEk65drdmf7Un/7UFxD96cu62pfe7KsIKo6zY72ZPF21rYFht9vZtGkTbrcbk8lEc3Mz8fHxHDx4kOXLl3PLLbcA8NFHH7FhwwZ27NhBdHQ0BoMBk8mEx+ORpyelpqYSDAYxmUztCveZzWYsFgslJSVERESg0+nk1yRJwul0MnjwYBwOB6mpqSgUCjnYsdls1NfXY7FYWLlyJZdeeqn8JPVYCgWKxd2C8D833XQTu3fv5ocffujVz122bBkPPPBAu+02mw2v19urxzregsEgDocDSZL6xcOH/tSf/tQXEP3py7ral968/xRBxXHWW1WnTzet0+ru2bOHyspKXC4XZrMZp9OJwWDgjDPOwGKxkJeXx4oVK/B4PNTW1hIfH09MTAxqtRqn00lERAQjR47EaDTKQUJtbS2JiYkUFxczdOhQAHkKU1JSkrweAw6PToRqb1itVhYtWsRnn31GXl4eERER7Nq1i/r6eiRJwmw2M2LECHbu3ElFRQXXXnst8fHxPS4UKBZ3C8L/3HzzzXz66aesW7eO1NTUI+6bmJhIdXV12Lbq6mq5GGVbS5YsCZsu5XQ6SUtLw2q1dvjd3ZcFg0EUCgVWq/WUvzGC/tWf/tQXEP3py7raF71e32vH7PNBRWNjI0uXLuWjjz6ipqaG0aNH8+yzzzJ+/Hjg8A3Zfffdx8svv0xDQwNTp07lxRdfZPDgwSe55Yf1ZtXp000ore4//vEPtm3bhlqtxuv1kpSUJNeDAEhJSeHbb78lKSlJ/rsoLi6msrISi8WC3W6noqKCadOmAZCXl8eYMWO4+OKLef7559m4cSMul0suMujz+TCbzYwaNYq6ujoqKirapfPNysriww8/5O2336aiogKlUolGo0Gv11NeXk52djZ2u52NGzcyevRo1Gp1twsFivU4gnCYJEksXryYjz76iG+//ZaBAwce9T2TJ0/mq6++4tZbb5W3ffnll0yePLnD/XU6XdjIZIhSqTwl/30pFIpTtu0d6U/96U99AdGfvqwrfenNfvb5oOK3v/0tu3fv5q233iI5OZm3336bmTNnkpeXR0pKCk888QTLly/njTfeYODAgSxdupRZs2aRl5fXq9FXT/V21enTTW5uLr/61a/kcxcRERFWuRoOjyTU1dUxYsQIeXtOTg4OhwO73Y5Op6OmpoaysjKcTqd8znNzc5k9ezYPPvigXBU7tJjbaDQSGRnJggULSEhIaLeOITc3l/Hjx/PKK68QHR1NbGwsRqMRl8vFwYMHsdlsjB49mrKyMkpLSxk4cOARa49cfPHF8qhH6FhiPY4gHHbTTTexcuVKVq9eTVRUlLwuwmw2ywUmr7nmGlJSUli2bBkAf/jDHzj77LP561//yuzZs3nnnXfYsmULK1asOGn9EARB6M/6dFDh8Xj44IMPWL16tZw28P777+eTTz7hxRdf5KGHHuKZZ57hz3/+M3PnzgXgzTffJCEhgVWrVnHllVeezObLTqVCdn2R2WwmLi5OXh/RVigTTKimBBzOyhRK91pTU0NDQwM1NTVMmTJFPufBYJCff/6Z9PR0Jk+eLI9SNDc3A3Do0CE2b97MPffcAxC2UDo1NZXPPvsMv9/PgAED8Hq9lJWV4Xa7CQQC1NbW4na7mTFjRth8xY5qjzQ1NbF69ep2ayaGDRsm1uMIAvDiiy8Ch2vHtPbaa6+xaNEi4PC/z9YPZ6ZMmcLKlSv585//zB//+EcGDx7MqlWrRI0KQRCE46RPBxV+v59AINBuxCEiIoIffviBgwcPUlVVxcyZM+XXzGYzEydOZMOGDX0mqIBTq5BdX9N2Chn8b/2DVqvFbrcTGxuLWh3+5xxK91paWorNZuPuu+9mypQp8jkPjQSkp6fj8/nYsWMHxcXFeDweALRaLcXFxSQkJFBdXR1205+QkEBeXh5GoxGn04nNZqOlpQWdToder8fn82Gz2aioqGg3r7t17ZH8/Hyef/75DtdM5OXl4fP5xHoc4bQXWt90JN9++227bZdddhmXXXbZcWiRIAiC0FafDiqioqKYPHkyDz30ELm5uSQkJPCvf/2LDRs2MGjQIHkIvDtpA+Hkpg5su3biWI93uqQ/mzdvHmVlZWzatAmXy0VjY6P8e4yPj2fkyJGUl5djMpnaTRVyuVxMmTKFSZMmyccB5DUUHo+HH3/8kfLycpRKpXwD73a7KSkp4cEHHyQ3N5ehQ4fKN/0///wzxcXFxMTEUFJSQjAYlNPTwuGFT83NzajVajZv3sw555zTLoAMrZmora3tdM2Ez+ejtLS0w/U45eXljB49Ws5mdbydLn9rp6Ku9Ke/9FUQBEHom/p0UAHw1ltvcd1115GSkoJKpWLMmDFcddVVbN26tcefKVIH9k1H6ktsbCwXXngh77zzDiqViri4OLRaLZGRkeh0OoxGI3B4BMNisaDT6fB6vVRUVJCamsro0aOpqakJ+1xJksjIyKCyspL4+Hji4+PDFmoGAgGcTicqlYqUlBQ520xMTAzR0dGo1Wr0ej0GgwGFQoFWq0WlUhEIBORCeGPGjMHpdLJnz552wW91dTUOh4MzzjhDnhfe2hlnnEFVVRV6vT6sXz6fD7vdTk5ODjNnzsRut/fa7+BITpe/tVNRV/ojpskJgiAIx1OfDyqysrL47rvvaGpqwul0kpSUxBVXXEFmZqacGrC6upqkpCT5PdXV1YwaNarTzxSpA/umI/UlGAyya9cumpubGTx4MM3Nzeh0Ovn3lZ+fT2pqKlarlcLCQmpqaqipqQEgPj6e2tpatm3bxrx588jJyQHAYrEgSRLffvstPp8PvV4vT6GSJEmuMaLRaKiqqqK5uRmLxSIf0+FwsGPHDjQaDUqlEo/HI/cBIDMzk4iICPLy8lAoFMTHx4f1qaamhuLiYoYMGUJLS0vYa5IkUVdXx759+5g/fz7V1dUUFRWFrbmYO3eu3JfQOSotLZWn16WlpfXq38Tp8rd2KupKf/pC4gpBEASh/+rzQUWIwWDAYDBQX1/P559/zhNPPMHAgQNJTEzkq6++koMIp9PJpk2b+N3vftfpZ4nUgX1XZ31pnQmpo8AvJSUFu93OzTffzIEDB1ixYgUKhYLs7GyMRmOHtR2USiXTpk3jnXfekYMHhUIhjzQoFAoUCgVer5eqqirWr1+P0WjEYrGQk5PD8OHDKS0txefzkZiYiMViwev14vF4MJvNjBkzBp/PJwc/bftkMpnQ6XTt1kyEiulVVFTgdrv573//y9ixY/n1r3/dYSYqOHEF8k6Hv7VT1dH601/6KQiCIPRNff4q8/nnn7NmzRoOHjzIl19+yTnnnENOTg7XXnstCoWCW2+9lYcffpiPP/6YXbt2cc0115CcnMy8efNOdtOFXtSVyuRer5fGxkY2b96MJEmMHz8es9mMSqWS1ynY7XZWrVolzy8/44wzyM7ORq1W43a7cblctLS0YDQaiY2NpaWlhUAggFqtJi4ujsjISCorK9m0aRMej4czzjiDQYMGUVdXR1NTEwqFgvT0dCZOnEhcXJw8TamjOiShBeilpaUEg0EaGhooKCjg+++/p6KigkAgQFZWFunp6fz444+sWLGC6urqDgOK5cuXs337diwWC9nZ2VgsFrZv387y5cvJz88/Pr8UQRAEQRCE/1+fH6lwOBwsWbKEsrIyYmNjmT9/Po888ggajQaAu+66i6amJm644QYaGhqYNm0aa9asEUP9/UxXK5M3NjZ2q7ZDKBAIBAIEAgF0Oh0ajYbY2Fhqa2vlrDPR0dFERETIU0xsNhvbtm3jyiuv5OKLL+axxx7DZrORmppKUlISbreb/Px8eZpSR0+JlUolc+fOZcOGDaxcuRJJkuSsVqEMUwkJCezatQubzUZNTQ27du1i9uzZXHvttQwbNkwUyBMEQRAEoU/o80HF5ZdfzuWXX97p6wqFggcffJAHH3zwBLZKONG6Wpk8Kiqqy7UdQulclUoliYmJVFdXEwgEcLvdFBcXy1OitFotSqVSTmHb3NxMS0sLfr+f8ePHExUVxeWXX84PP/xAVVUVRUVF6PV6Ro8ezcyZM8PWPbSWn5/Pyy+/zK5du6irq0OSJDktbWja1a5du/B4PPIxy8rK+Ne//sW6deu49957GT58uCiQJwiCIAjCSdfngwpBgK5XJo+IiOjSiIbBYOCdd97BbrczadIkBg0axNatWykuLsbtdssF8EaOHMmgQYOorq7GbrfT2NiIWq0mNTWVlpYWPv74YyoqKmhqaiIyMpKsrCzOPPNMzjjjDFJTUzvNzJSfn8+zzz7LDz/8gEaj4YwzzsBms1FWVoZOpyMtLY3S0lLq6urQ6XT4/X70ej1KpZLY2FgqKip46KGHuOuuu6itrSUiIoJgMNiu2rgokCcIgiAIwokggorTUDAYPGWK8LVt68033yxXn+6oMnkwGOzSiAZAQUEBqampNDQ00NDQQFZWFjk5ObS0tMiLpYcPH056ejqDBw+WpybpdDrq6ur47rvvKC8vlxfIajQaioqKKCsrY8iQIZ2e09CUpVAFYIvFgkqlIjo6mrq6OgKBANXV1QSDQTnFcVRUFIFAAKVSicFgIDIyktLSUh5//HHsdjtFRUVERETIi8itVisgCuQJgiAIgnBiiKDiNHOisgT1hs7aOnfuXK6++uoOg6Kujmg0NTVRU1NDQUEBJSUlchXtiIgIBgwYwBlnnEFlZSVlZWXy1KLo6Gjgf0FBTU0NWq0WtVqNSqVCr9ej0Wj46aefWLFiBU8++WSH/QplsoqLi+PQoUPy+iCdTofBYMDhcODxePD7/fj9fvl1n8+H0WiUM0a53W5KS0uxWCw0NTWhVCqprKzE4XAwceJELBaLHER1tFBcEARBEASht4ig4jQSyhJkt9tJS0uTb7bbplrtC7rS1hEjRnT43tzcXG655RY5IGk9onHxxRcTERHBl19+ye7du3E6nWg0Grl4nsfjYe/evdTV1ZGamkp8fHy74GTLli1UV1ejVCoxGo2o1WoCgYAcCGg0Gr799lsOHTrU4dqOUCarhIQE1Gp12DqKUFrattOV3G43Go0Gi8UCHK7F4vf78fl8NDY24nA4qK2tldeU7Nixg6SkJKxWK/PmzeuzI1GCIAiCIPQPIqg4TZxKWYJ6o625ublkZ2eHTZ1qampi9erV5Ofns23bNux2O5IkYTAY5NEAtVqNy+WiqqqKgQMHcvfdd/PJJ5/IwUlolAAOZ4Rq/T6VSoXb7UalUlFbW0thYSGjR49u17ZQJiu1Wo3FYqGyshKr1YpCocBgMBAfH08wGKSpqQlJkvD7/ZhMJiwWCwaDAY/HQ0NDA8FgkEAgQExMDBaLherqapxOJwAtLS1MnTqVa6+9ts8EioIgCIIg9F8iqDhNtC4e19ezBJWWlvZKW5VKpfx6KNOT3W7HZDLJ6WN9Ph8NDQ2YTCb0ej2BQABJklAqlQSDQaKiorjnnnvk4MThcHDffffJhfLatk2n0+HxeDosrhjSOpNVdnY2DocDm82GyWRCo9HgdruxWq2MHTuWffv24XQ6iY+PR6fT4fV6sdls+P1+FAoFsbGxmEwmOSAJva7RaLjqqqtEQCEIgiAIwgkhgorTRFeKx/WVLEG90dbWC7wNBgMffvihPPJRU1NDS0uLnC7W5XLhcDjkFLImkwmVSoVSqaSxsTEsONm1axc6nY7IyEg8Hg9qtTosuFAqlXi9XiwWC0OGDOmwba3XfdhsNoYOHUppaSlVVVW4XC4iIiI477zzuOGGG9i/fz8PPfQQVVVVciX40HmJjIzEYrHIx1coFERERGA2m3E4HPKIiiAIgiAIwvEmgorTRFeLx52sLEHBYJBDhw5RXV0trzHoaVvbLvD2+/0cPHiQkSNHyqMJkiTh8XgIBAL4/X6CwSCSJMmF8EJZltoeIyoqiri4OJKTkykuLpbbolKpCAQC8o38lClTyMjI6DSlbOt1H3l5eUiShE6nIzU1lV/96lf88pe/RK1Wk5uby8CBA3n99dcpKCiQK4HbbDYiIyOJjIwM+1xJkuSATGR8EgRBEAThRBFBxWmiq8XjTkaWoFAQsHfvXrmSdXV1NTU1NUyaNKnDto4ePZpgMMiuXbvCMkB1tMD70KFD2Gw2du3ahdFoRJIkfD4fLS0tBAIB4PBTfoVCQUtLi1wbIj09nfT09HajHjk5OdTU1NDc3IzNZpPTvkqSRCAQIDMzk9tvv/2oa1Nyc3MpKirigw8+YN++fTQ3N1NUVMTu3bv56quvWLJkCbm5uQwbNozHH388bArWE088QXFxsTxtKlSUL7TwfODAgZjN5uP3SxMEQRAEQWhFBBWnia6mWj3Ri7TbBgHJyckoFAqqq6spKytj48aNDB06NKytKpWKmpoa7r///napZlevXt1ugXdsbCyxsbFyFW1AXijt9/tRKpVyrYmWlhaUSiWRkZEoFAry8/PluhihY8XGxhITEwOA2WymtrZWHhEZNGgQS5cuZdiwYfKoQmc+/fRTbrvtNiorK9FoNMTExBAMBmlsbGTVqlXYbDaeeuopcnNzw6ZgBYNBJk2aJAdGtbW1clG+pKQk1Go1kyZNEmlkBUEQhKM6lWpXCX2bCCpOI0dKtRoqHncidZTlSalUYjKZmDRpEhs3bgTAbrfLbU1NTaWyspLy8vJ2qWbz8vJobGwkKysrbHTDbDZjtVopKSmhoqIChUKByWSioaEBhUKBz+eTRxnUajWRkZFMnDiRAwcOsGzZMnw+H2lpaURGRlJZWUleXh46nY6cnBzq6uqIiYlBqVSSk5PDeeedR1JSEsXFxaSmpnbad7/fz7PPPovNZiMiIgKDwSC3OVRcb9u2bXz44YcsWbIk7Au+7ZqMAQMGyAGS0+kUaWQFQRCELjmValcJfZ8IKk4zHaVaPVlPJY6WkWro0KHY7XZuvPFGzGYzBoOBlStXUlZW1mGq2Q0bNlBZWdmufoVCoSAnJ4f6+noqKirQ6XTo9XoA+YY+OjoavV6PTqfD7XYTGxvLxo0bSUpKYvLkydjtdnbu3IndbqelpYWmpiZ8Ph933HEHSUlJVFdXs3HjRt5++228Xi86nY7ExESmTZvGyJEjycjICDvHGzdupKCgQC6a17r/oUxOHo+H77//nl/+8pftsly1DRBdLhd6vZ6xY8eelABREARBOLWcSrWrhFODCCpOQ62n0pxMXc3yZDabGTFiBMXFxRQWFpKamorD4cDn86HT6TCbzSgUCtLS0ti3bx+VlZWkpaWFfZbVamXEiBF4vV5cLpdc5yEqKoqEhARUKpVcwVqlUtHQ0EBTUxNpaWnY7XY2bdqE2+3GbDaj0WjQ6XTs3buXN998kyuvvJLPPvtM/mL2eDz8/PPP/PDDD3LgMGHChLAnP6EF6UqlEpVK1a7vWq2WpqYmnE5np1mu+lKAKAiCIJw6TqXaVcKpQwQVwknT3YxUjY2N1NTUUFpaSl1dHX6/Xy4gl5OTQ2JiIgaDgdLSUlJTU9st8Ha73Vx11VU4HA527tyJy+Wiurqaqqoq3G43wWCQYDBIXFwcZWVlGAwGEhMT2bhxo1w7IvSZUVFR+Hw+qquree6554iKimLYsGHY7XZ++ukn3G43iYmJaDQaampq2LZtW9iTn4SEBDQaDc3NzfK0q9aam5vlqWBHyuJ0rAGimEsrCIJw+jmValcJpw4RVAgnTXczUlVXV3PgwAEA4uLi0Gg0tLS0UFlZicPhYNiwYWRmZmI0GjtdjL5gwQIAli9fzqZNm6isrKSlpUUeLVCpVDgcDlQqFWlpaVRVVWG32+XRkJDm5mZ5cfWWLVs455xzACgoKJADEKVSSUREBPX19WRkZHDo0CF5jcSkSZPIyclh8+bN8mhN6PMlSaKpqYnIyEjOPPPM47bgWsylFQRBOD2dSrWrhFOHeCQpnDShBccWi4W8vDycTieBQACn00leXl5YRqpgMCgv3A7VlQjVnLBarbjdbrZt28aECRP44x//yOjRo6mtraWwsJDa2lrGjBkjjxLk5uZy4YUXYrPZkCRJro4dKngX+kyNRkNJSYlcKC9EkiScTicWiwWz2UxLSwtqtRqHwxEWgDQ1NdHQ0EBFRQUbN27k4MGDvP/++3z99deo1Wr+8Ic/YLVa8Xg8OBwOmpub8Xg81NbWolAoGDNmDBMnTmTPnj0UFxcfNZtUd4Tm0m7fvh2LxUJ2djYWi4Xt27ezfPlyOUuWIAiC0P+0ninQkZNdu0o4NYmRCuGkar3geO/evUiSRF1dXbuMVF9//TUffPABfr+f+vp6amtrMZlMJCQkoFaraWlpwe/3M3HiRIYNG0Zubm6n03qCwSDff/89CoWC3NzcsPUUCoUCj8cDHM7CFAoOdDodUVFRci2IyMhIcnJy5Crcfr8fn8+H3+9Ho9HQ1NREZWUlBoMBjUZDXFwcCoWCiooKVqxYQUpKCr/4xS8AePTRR8nPz5ezUZnNZiZMmMCAAQN48803e30UQcylFQRBOL315dpVwqlLBBXCSRdacByqqJ2QkBCWLSk/P58VK1Zgs9lISUkhNjaW6upqnE4nLpeL+Ph4UlNT0el0JCQkAEdeaxCaSxp6ChMaKQitq5AkCZfLhUaj4dZbb+XFF19k7969+Hw+NBoNSUlJ5OTkYLFY2LNnD4MHD8bhcJCSkoJaraa5uVnOEqVUKomKiiIiIgKfz0dMTAwul0u+af/FL37BBRdcwPr169mzZw+RkZEkJCSwatWqDtPm9kZGDjGXVhAE4fTWV2tXCac2EVQIfYJSqSQjI4OIiAji4+PDRhU++ugjXC4XsbGx8joFg8GA1+vFbrdjsVgYOXIk9fX1XRqqbWxsJBgMotfrcTqd1NTU0NLSgk6nk0ctHA4HxcXFREVF8fTTT7Ns2TJqampIS0sjMTERt9tNXl4eVquVRYsW8dlnn1FeXo7RaKSqqkqeh6pWq4mLiwPA6XSSlJTEkCFDwm7a1Wo1Z511FmeddRbBYJDHHnuM2tra4zaK0NlcWkmS5OCqtrYWh8PRo88XBEEQ+r6+VrtKOPWJoELo00JP1bOzs/H5fFRWVspZmCIiIrBarTQ2NlJUVNTlRc1RUVHExsZSX1/Pvn37kCSJyMhIAAKBAC0tLcDh6U+bN2/mnnvu4U9/+pP8xVtUVNTuizcrK4uPPvqITZs2UVJSgtvtxmKxEBsbi9PpxGazyVOmjEYjFRUVHS6AOxGjCB1l3bLZbBQUFGC32/F4PPj9ft5++220Wq24sAiCIPRTR0tNLjIECt0hggqhTws9VTcajeTk5OBwOLDZbJhMJrRaLZIkUV9fT25ubpeHatPT08nNzeXQoUMEAgEkScLr9eLz+WhubpYXZkdGRspBwtG+eFu//sUXX/Dss88Ch7NEeb1eecqU1WrF4XB0ugDuRGTkaDuXtnUdDpPJRHNzM/Hx8Rw8eJDly5eLAkiCIAj9WGfThUWGQKG7RFAh9Gmtn6pbrVYmTpwoP1EPTWOyWCxcf/31Xf6SC80l3bFjB3v37gUOT00KLdTWaDQkJibi8XjYvXs3O3fuZMCAAUetCRF6/be//S02m43169czbNgw0tPTMZlMKBSKoy6AC/XX5XIhSVK7An+hjBwGg4Hi4uIePT1qPZd2z549VFZW4nK5MJlM2O12dDodAwcOZODAgeTn58vTrQRBEITTg6i2LfSECCqEPq3tU3Wr1YrFYsHhcOD1eikpKWHq1KnMmDGjW5+bm5vLDTfcQFFREWVlZSgUCtRqtZymtqmpCa1WSyAQ4Mcff2TOnDndumm/9NJLKS8vx+l0olQqCQQCXVoAl56eTmxsLF9++aX8vlCBv+zsbGw2G6mpqaxcuZLCwsIePz0KzaX9xz/+wbZt2wgGgzQ0NMiv//zzz1RUVIRNtxJZQARBEPq/7mQIFITW+vTEuEAgwNKlSxk4cCARERFkZWXx0EMPIUmSvI8kSdx7770kJSURERHBzJkzKSoqOomtFnpT21oWDoeDQCCAQqGgtraWjIwMLr300h7N8ZwxYwbnnnuuvD4jIiKCmJgYTCaTXHciGAxSUFBASUlJtz47NzeXm2++mczMzE7rZXRk7969VFVV4fF45Glfer2ekpIS1qxZI6eq3blz5zHXl8jNzeVXv/oVaWlpREZGEhUVxYABA0hOTiYyMpLKykp27dpFTU2NKIAkCIJwmujO2j5BaK1Pj1Q8/vjjvPjii7zxxhsMGzaMLVu2cO2112I2m7nlllsAeOKJJ1i+fDlvvPEGAwcOZOnSpcyaNYu8vDz0ev1J7oHQG45XhgqlUsnUqVP5xz/+gd/vJyoqCrVaTSAQoLm5GYPBQEREBAcPHuxRJqScnByio6OZM2cOLpfriNOUgsEgxcXFvPjiizidTs4//3wKCwux2+34/X4iIiIIBoM4HA4iIyMZNmzYMWWGCi2+Ky8vl9PfJicny5+p1WqJioqisrISn89HREREt/svCIIgnHpEtW2hp/p0ULF+/Xrmzp3L7NmzARgwYAD/+te/2Lx5M3B4lOKZZ57hz3/+M3PnzgXgzTfflPP8X3nllSet7ULvOtpC6Z6yWq0YDAa5+F1zczNKpRKj0YjFYpHTy/bkyzMYDGKz2VAoFJhMpk7bG1oMt2XLFrZu3YrBYMDn85Gdnc2IESPkdRUOh4PvvvuOnJycY8oM1Xrxnd1up7KyEoVCQXR0tDxv1m6343a7aWpqwuVy8fe//53rrruO2NjYbp8HQRAE4dTRUYbA1kS1baEzfTqomDJlCitWrKCwsJAhQ4awc+dOfvjhB5566ikADh48SFVVFTNnzpTfYzabmThxIhs2bOg0qPD5fPh8Pvlnp9MJHL4JDAaDx7FHvS9UrO1Ua3dHutKXtvP6j7XfRqOR2NhYeaQitIZBp9MBUFNTQ1RUFEajsVvHKigoYNWqVTgcDg4dOoROpyM7O5t58+aRk5MTtt/zzz+P3W7HaDTKxwoV95s4caJc0M/j8cjt64jBYKCiogKn09lpW1sfLzU1lcjISAoKCmhoaODAgQPEx8fjdDrlfx8mkwm9Xs+uXbt44YUXWLhwIRaLpcvnISQYDFJaWioHhGlpaSc1LWF/+ncDXetPf+mrIAjHl6i2LfRUnw4q7rnnHpxOJzk5OahUKgKBAI888gi//OUvAaiqqgKQb7pCEhIS5Nc6smzZMh544IF22202G16vtxd7cPyFpsRIknTK547uaV9CIwJutxuPx0NERASRkZFYrdajfo5CoeDMM8+kurqaQCBAZGQkarUav9+P2+3GarWSkJCAQqGgpqamS+0pLS3lk08+obGxkYEDB2K1WvH5fNTU1PDOO+8wZ84c0tLSCAaDrF27Fq1Wy6RJk2hqasLr9aLT6eQ1HV6vV35S5PP5GDlyJHFxcR0+PXK73QwYMABJkjpsa9vjweEnTuPGjQOgrq4On89HfHw8Wq0WnU4n1+8YPnw49fX1/Pzzz6SkpKBSqbp0LkLnY/369ezfv19eWJ6VlcWUKVNIS0vr8uf0pv707wa61h8xVUEQhK7oTrVt8bBCaK1PBxXvvvsu//znP1m5ciXDhg1jx44d3HrrrSQnJ7Nw4cIef+6SJUu47bbb5J+dTidpaWlYrdYOb9b6smAwiEKh6NINdF/Xk76ERgQ2b97MwYMHaWpqwmAwMHDgQCZMmNBuZKAti8VCcnIy+/bto6WlhYMHD+L3+1Gr1cTGxqLRaBgzZgzDhg3rUpuCwSD/+Mc/KCgoIDc3F7/fj8vlAg6PouXn5xMREcGdd95JaWkpW7duJS4uDqfTiSRJ1NXVUVVVhcViwefzcfDgQaKiojCZTHIbi4qK0Gg0copap9MpZ8KaMmVKp209dOhQ2PGAsGNqtVoOHjxIcnKyXK+iuLiYxMREeXF8UVERM2bM6HLhvYKCAp566in27NkTdvH59ttv2bhxI0uXLj3i7+d46U//bqBr/RFrzARB6CpRbVvoiT4dVNx5553cc8898jSmESNGcOjQIZYtW8bChQtJTEwEoLq6mqSkJPl91dXVjBo1qtPP1el08vSW1pRKZZ+4wehuBUuFQtFn2n6sutOX/Px8nnvuOQ4cOEBlZSUtLS2YTCY8Hg8HDhzA4/EcNZ926ycyNpuNAQMGyKNiDocDq9XKvHnzOp1y1FYoa0ZqamqH6x5SUlLIz8+nrKwMl8uFx+ORF8MpFAqys7NpaGigpqYGo9GIz+fDbrfLT4cWLlzIZ599Rl5eHpGRkZSUlFBVVUVjYyORkZEMHjyYoqKiDvvb9nhtj1lXV0dLS4tcVDBUBTw7OxuFQkFkZKS8xqKrAdaKFSvYvHmzXGtDo9HQ0tKCw+Fg8+bNvPzyy/z1r389KX+7/enfDRy9P/2ln4IgnBjHay2j0H/16aDC7Xa3++NVqVTyE8+BAweSmJjIV199JQcRTqeTTZs28bvf/e5EN7dXiAqWXRPKo22z2WhpacHv9xMfH49CoSAqKgqbzYbf78dms3WYEalt4HbzzTezevVqCgoKaGxsRK/XM3bs2G4/kelO1oyOFsO1LvBXUVEhL5YeP3683JasrCxeeukl/vOf/+DxeDAajQwePJj09HTKy8s7rYLd2eK70DE3btxITU0NDQ0NmEymsCrgcPjfYygrVFcUFxfz3XffoVKpsFqtcpCl0+mwWq1UVFTw7bffUlxcTGZmZpfPsSAIgnBiHK3oqyC01qOgYt26dUyZMqXd01u/38/69es566yzeqVxc+bM4ZFHHiE9PZ1hw4axfft2nnrqKa677jrg8JO5W2+9lYcffpjBgwfLKWWTk5OZN29er7ShM90dTegKUcGy60IjAmazmaKiIrniNCBnW7Lb7WRkZLTLiNRZ4DZ37lyuvvrqY/qddidrRmeL4axWK3FxcWzZsoVBgwaxYMECzGaznFY2Ozsbq9XKoEGDSE9PR6/Xy/2XJIm8vDw++ugjuYhf6FhHWnxnsVgYMGAABoMBnU7H0KFDiY6OlvcJLc4788wzu7wOorCwkLq6OhITEzsctYmNjaWqqorCwkIRVAiCIAjCKa5HQcU555xDZWUl8fHxYdsdDgfnnHMOgUCgVxr33HPPsXTpUn7/+99TU1NDcnIyN954I/fee6+8z1133UVTUxM33HADDQ0NTJs2jTVr1hzX+cPHYzShOxUsxdDj/0YE9Ho9fr8fjUYT9rpWq6WxsRG1Wo3L5ZIXqXYlcBsxYkSX2tBRYNn2xr211lkzUlNTKSkpYdiwYeTl5bFnzx65CF1lZSVlZWXodDoUCgWvvPJK2N/ZhAkT2Lt3Lzk5Oe0Cl1Ahv/fee48tW7agVqvD/j6PtPguPj6ea6+9ls8++4yKigqUSmXY61arlUmTJvXa31/rIpaCIAiCIJzaehRUSJLU7skjQG1tbafTPnoiKiqKZ555hmeeeabTfRQKBQ8++CAPPvhgrx33SI7XaEJ3KliKocj/jQiEUqy2tLSErZNpbm6WsziFRgZ6M3A7UmAZunHPz89n5MiR+P3+sKwZI0aM4IknnpDf6/P58Hg87Ny5E5vNRlNTE2q1mubmZg4ePMjYsWPJzs6W/8527NiB0+kkIyNDbo8kSTgcDqqqqti9ezcNDQ2MGDGCjIyMdn+fR1t8l5WV1eHrc+fO7VadiiFDhhAbG0tdXV1YYb1Qe+vr64mNjWXIkCFd/kxBEARBEPqmbgUVl156KXD4JnfRokVhN3GBQICff/6ZKVOm9G4L+5DjOZogKlh2T2hEYNu2bcTFxVFVVSXP2w9lREpKSsLpdDJ27FjS09M7DNxCN+M+n4+oqCjy8vK6VDzuaIFl6Mbd4XBQXFyMTqdjzJgxjBgxgs8++yzsvS6Xiy1btlBSUoLFYmH8+PEcOHCA8vJygsEge/bswWg0YrVaGTp0KD/99BPV1dW4XC7MZjM2m42CggJsNhtVVVXyYmxJklCpVO3+Pu+++27uueeeTqfvdbY4D+hyWl04XKzy7LPP5uOPP6ampgaz2YxWq6W5uRmHw0EgEGD69OkiSBYEQRCEfqBbQYXZbAYO34hFRUUREREhvxbKfX/99df3bgv7kOM5miAqWHZP66xNoWlO1dXVRERE4PV60Wg0qNVqOXuTUqlsF7iFbsbtdjt+v1++qd65c2env7+uBpZ33nknV1xxBQUFBUyfPp3s7GzS09N54oknwt5rs9nIz8+nqKhIrkuh1Wqprq7GaDQSERFBY2MjBQUFWCwWFAoFQ4YMobKykr179zJgwAA2b96M2+2Wg/xQMoO8vDxMJpMcbLX9+zzS32hHi/O6m49cqVRy4403yqMnDodDfk2lUjFhwgRuuOEGMZ1PEARBEPqBbgUVr732GnD4CeQdd9zRq1OdTgXHczRBVLDsvtZ5tDdt2sSBAwdwOBxynYpJkyaFZW9qHbj5fD42bdqE2+2WU502NjZSV1fHe++9x5AhQzp8Wt82sGw90qHT6UhJSWHjxo0sWbKEmpoaefpPdnY2EyZMCHuvzWZj06ZNNDQ0EAgEMJvN+Hw+8vLy5NEGrVaLVqslEAjQ0NCAQqHA7XZjMpmIjIxk3bp1+Hw+EhMT5aDEYDCQmppKU1NTWDByMka7cnNzue+++/jwww/ZunWrXEdk3LhxIqOZIAiCIPQjPVpTcd999/V2O04Jx3M0oTsVLIX/aT1Vx+FwyAGA2Wxul72p9ZSpuro6uWJ2KDjw+XxkZWXh9XpZsWIFcXFxFBYWhq2ZGDZsmHzj3nakQ61Wo9PpqKysxOPxMHz4cHktQdu1EJIkUVBQgNvtJjo6GofDgUKhwOPxoNVqUSgUBAIBNBoNHo8Hh8PBN998I+/j9/tJSEiQ96mrqyMYDBIZGUlCQgIGgwGVSkVFRQX79+/HYrEAnJTRrtzcXJYsWSJynQuCIAhCP9ajoKK6upo77riDr776ipqamnZZXHor+1Nfc7xHE0QFy57pah7tUOC2Z88e9u/fT2xsrBxMOJ1OIiMjyc3NpbGxkc8++4xBgwaRk5MTtmZiz549+Hw+SkpK2LNnjzxqEAwG8Xq9FBYWyjUzTCYTSqVSnhrVei2EJEnY7XbMZjOSJMnTs0JTCwF8Pp/8mtfrpby8nMGDB9Pc3IzVaqW+vh6fz8fkyZMxmUxotVp27dpFVVUVLpcLu91OfX09zc3NGAwGgsEg5513Xq+NdnUnrbLIdS4IgiAI/VuPgopFixZRUlLC0qVLSUpK6jATVH90IkYTRAXLzvVGbZDc3Fwuv/xyfv75Z/x+P7W1tajVarnQm8ViIS8vD7fbTXp6ujwiFQoM9uzZg8fjYd++fQSDQYxGI9XV1bjdbpqbm3G73ajVaoqKisjKypKP23YtRHp6upwKV6FQoNVqqaurQ6/Xo1arMRqNtLS04HK5aG5uRqVSAYczrJnNZkaNGoVaraawsJBDhw5x7rnnolAoyM3Npaamhv379yNJEhqNBqPRiMfjIRAIyMc/1gBVFGkUBEEQBKG1HgUVP/zwA99//71cxfp0ciJGE8RT3fZ68yb2jDPOYNSoUfJ6BZ1OJxePa2hooKqqCpPJ1K7WiUKhIC0tjV27duF2uwkGg9TV1clpbeHw706tVrNv3z6Ki4s544wz5PcbjUYSEhIwGo2UlJQQDAbx+XxyUB56byAQQKFQyAuvg8GgXI8jNjaWcePGYbVakSSJ1NRUSktLaWhoICYmBovFgtFolFPSKpVKJEkiLS2N7OzsTiuMd6RtEJeamgpAQUEBzz33XKfZr26++WYMBoMIioVes27dOp588km2bt1KZWUlH3300RELnH777becc8457bZXVlaSmJh4HFsqCIJw+upRUJGWlnZaF67qz6MJx6NS+LHq7dog6enp5ObmdjiNzev14nK5GDx4MCaTiYaGBnkRttlsltcqJCUlUVVVJWdrkiQJo9GIUqlEp9PhcrnYu3dvWFDR1NREfHw811xzDRs3bqSyspKKigpiYmJIT0+XszaF6laE1kfU1NRgMplQKBRMmjSJmJgY4HCQM2LECKqqqsjPz2fYsGG0tLTgcDiIjo5Gr9czbNgwEhMT5aBJp9N1KUNZR0HckCFDGDt2LGvWrOHQoUOMGzdO/tsIjeRs3LiR2267jfj4eHw+nxjBEHpFU1MTZ5xxBtddd52c2rwr9u7dG7b+rW3BVkEQBKH39CioeOaZZ7jnnnt46aWXTtsn6v1xNKEvTmk5HrVBjjSNraSkhIiICKKiovjxxx/DFmFbLBZSUlLk7F/19fVkZmaiUqlQq9VotVpKS0txOBxyNqmmpiYgfM3NjBkzmDFjBpMmTWLFihVyEBN6v1arJSoqiqioKDweD263G5fLRXx8PC0tLWF9iYiIYPjw4QwZMoTq6mp5KlZWVha5ublYrdaw/buSAaqjIK6kpIR3332XTZs2sX37dkwmEz6fj5ycHPkYdrudyspKHA4HqampDBgwoFcKQwr9h9Pp5OuvvyY7O7tbfwsXXnghF154YbePFx8fT3R0dLffJwiCIHRfj4KKK664Qr5xiYyMRKPRhL1eV1fXK40TTpzjVSn8WB2v2iCdTWObNm0aFouFH374AZ1OR3R0NBqNhpaWFioqKjh48CAXX3wxgUCAPXv2YLFYUCqV+Hw+3G43UVFRNDQ0oFKp8Pv98vbS0tJ2a25mzpxJSkqK3AadTidPVwpleYqIiJALxkmSxObNm5k4caI8/am0tJTs7GyuuuoqmpqacDqdvPLKK2RkZMh1ZVo7WoayjoI4m83Gnj17CAaDBINBWlpaiIqKkgOIiRMnYrFYKCgooKWlRU6F21HhvZ4UhhROXZdffjlnnXUWN998Mx6Ph3HjxlFcXIwkSbzzzjvMnz//uB5/1KhR+Hw+hg8fzv3338/UqVM73dfn8+Hz+eSfnU4ngPx3fyoJBoNIknTKtbsz/ak//akvIPrTl3W1L73Z1x6PVAj9x/GsFH6kY3ZUsbmt41kbpKNpbKmpqdxxxx0oFAo51Swg3+iH/pszZw6ff/552NoIv99PIBBAp9MRFxdHS0sLtbW1uN3uTtfctG1DZWUlTz75JIWFhRiNRoLBIFlZWbhcLoLBIA6Hg7y8PM444wzy8/NpaGigpaWFZcuWodfr5SJ7ZWVl8pSpkKNlKAsGg6xfv54NGzbI00Rap76Nj4/HaDTi9/vlDFShtLrDhw/Hbrej1+sJBoPyehA49sKQwqlr3bp1/OlPfwLgo48+QpIkGhoaeOONN3j44YePW1CRlJTE3//+d8aNG4fP5+OVV15h+vTpbNq0iTFjxnT4nmXLlvHAAw+0226z2fB6vcelncdL6LsilD3uVNef+tOf+gKiP31ZV/vSm7WrehRULFy4sNcaIJx8x7NSeEc6m2Y1b948YmNjw/Y93pXG205jKy4upq6ujmnTplFWVobdbpcrdiclJZGamkptbS2DBg1i0qRJfPrppwQCAXkKVOgpvc/n45xzzuGaa64hMTGRjIyMLqVbjYqKIjExkfT09LBF5Ha7nYKCAioqKjhw4ABqtRqn00l0dDRZWVnyyNKOHTtQqVSoVKpuZSgL/U42bNjAjh07iImJobi4mOTkZDn1rUKhQK/Xo9FocDqdmEwmTCYTdrsdu91OS0sLkiSRnJzcbpTkZBTeE04+h8Mh/5tes2YN8+fPJzIyktmzZ3PnnXcet+NmZ2eTnZ0t/zxlyhT279/P008/zVtvvdXhe5YsWcJtt90m/+x0OklLS8NqtXb43dOXBYNBFAoFVqv1lL8xgv7Vn/7UFxD96cu62pe2SWmORY+CCoD9+/fz2muvsX//fp599lni4+P573//S3p6OsOGDeu1BgrH3/EcDWjrSNOsysrKuPbaa8MWUx7v2iBtR0xCVamzs7MZMGBAWLVss9lMIBCgsLCQxsZGYmNjiY6ORqlUEhkZiV6vR6lUyu8xm81kZGSQmJjY7h90ZwviGxsb8fl8DBgwQE4jC2C1WrFYLNTW1lJYWCjfuA8bNqzDkaXU1FS5eN/RMpS1/p2E5qCr1WoqKyupqqqS+wKHa9DExMSgVqux2Wxy6tumpiaampowm83k5OS0C06PNfgTTk1paWls2LCB2NhY1qxZwzvvvAMcXo/UmxeyrpgwYQI//PBDp6/rdLqwEbYQpVJ5St5cKBSKU7btHelP/elPfQHRn76sK33pzX72KKj47rvvuPDCC5k6dSrr1q3jkUceIT4+np07d/Lqq6/y/vvv91oDhePveI8GhBxtmlV+fj4bN25k9OjR8h/58awN0tGISUJCAj6fTz4XbRd5hs5FY2MjdXV1nHnmmfKIRlNTE2q1muTkZFJSUqitrcVms7VLYXmkBfFH+l0oFAq57oTT6WTAgAGdjizV1tayePFiOVDpLJNX298JwMGDBykpKZFHSLxeL83Nzej1etxuNykpKQwZMoS9e/dSUVGB2+1GoVCQk5MDIFfvDumN4E84Nd1666388pe/xGg0kp6ezvTp04HD06JGjBhxQtuyY8cOkpKSTugxBUEQTic9CiruueceHn74YW677bawG80ZM2bw/PPP91rjhBPjeI8GhHRlmlVZWRmlpaUMHDhQfu141AbpbMTk4MGDcqrYSZMmAcgjD1qtlvLycsaOHUtUVNRRRzSKiorweDxdOm7rGg85OTls27aNlJQUmpub5c8EKCsrIy0tjYqKiiOOLJWVlVFYWEhKSsoRUwO3/Z3YbDbcbjf19fXU1NSgUqloaWmhtLSU6OhouUhgXFwcarUan8/HpEmT+NOf/oTP5+P5558/boUhhVPP73//eyZMmEBpaSnnnXee/PvPzMzk4Ycf7vLnuFwu9u3bJ/988OBBduzYQWxsLOnp6SxZsoTy8nLefPNN4PC6v4EDBzJs2DC8Xi+vvPIKX3/9NV988UXvdlAQBEGQ9Sio2LVrFytXrmy3PT4+HrvdfsyNEk6snowG9KSexdGmWUVGRtLU1NThNKverA1ypBGTYcOG0djYSENDAxs2bJAzKoUywyQmJrJo0SLMZnPYiEJnIxoRERFdOm5o2tLHH3/M8OHDWbVqFT/99JM8JcNkMmE0GsnMzGTOnDm8+eabnY4slZaWsm/fPlasWIFarT5iauDWvxObzcamTZtwu92kpqbicDhobGzE7/fjdDrlp81Op5O1a9dSVlaGWq0mLi6Od999l0suueS4F4YUTj3jxo1j5MiRHDx4kKysLNRqNbNnz+7WZ2zZsiWsmF1o7cPChQt5/fXXqayspKSkRH69ubmZ22+/nfLyciIjIxk5ciRr167tsCCeIAiC0Dt6FFRER0dTWVkZ9jQZYPv27aSkpPRKw4QTqzujAT2tZ3G0aVZut1uu0dCR0ILmUECzZ8+eHgUXRxsxGTp0KDt37qSmpob6+nr5xt5qtWI0Gvnss88YOHBgl0Z3QjUcOsqs1Pa4qampbNy4kd27dxMTEyPXuvB6vZSVlZGYmMjs2bOZMWMGmzdv7vDYNTU1/PDDD0RERJCRkYHRaDxiauDQ78TlcslZnqxWKwqFgujoaJxOJy6Xi5iYGGJiYnC5XKxdu5bm5mbS0tIYMWIEERERYZ9/zz339LkCisLJ4Xa7Wbx4MW+88QYAhYWFZGZmsnjxYlJSUrjnnnu69DnTp08/YsHV119/Peznu+66i7vuuqvH7RYEQRC6r0dBxZVXXsndd9/Ne++9h0KhIBgM8uOPP3LHHXdwzTXX9HYbhROkK6MBx1LPoivTrM4880zS0tI6bWNvFOg70oiJJEk0NzdTWlpKQkICZ511Fi0tLWFTkEIjCnPnzpVHd1JSUvD7/TgcDmpra0lPT2fu3LkolUoKCgpYtWpVu8xKrQvHweGRmgMHDpCUlMTkyZOB9lOvdu3axUUXXdThyFJjYyNfffUVzc3NTJkyRU4pe6TUwKHfyQ8//IDNZpP76PV68fv9uFwu0tPTGTFiBHV1dcTExJCZmUlOTg7R0dHy77D15999990ibawAHM6otHPnTr799lsuuOACefvMmTO5//77uxxUCIIgCH1fj4KKRx99lJtuuom0tDQCgQBDhw4lEAhw9dVX8+c//7m32yicQEeqFH6s9SyONs3KarUyceJESktLcblc7YKajgIal8vFDz/8wI4dO7jhhhuYMWPGUZ+KdzZiEqq5cOjQISorK5EkiT179sg30CGhFLtXXXUVt9xyCy+99BLfffedXPQxNjaWIUOGAIdHRZ577jmqq6uJiYnBbDbLmZVCheNCgUVo2+DBg3E4HJjN5rDjKpVKObVv25Gl0PaamhoiIyPZtGkT+/fvZ9SoUVitVhwOB3q9np9++oni4mIyMzPDfic7duygrq4OSZJobGzE5XLR3NyMWq3GaDRSXl5OXV0dRqORYcOGdbiIXNSiENpatWoV//73v5k0aVLYQ4Rhw4axf//+k9gyQRAEobf1KKjQarW8/PLL3HvvvezatQuXy8Xo0aMZPHhwb7dP6EN6o57FkaZZDR8+nI0bN7J161Y8Hk/YKER2dnaHlZ4LCgqw2WzU19dTVFTE/PnzmT9//hFHLToaMWm9nqClpYXIyEji4uI6vPlvnWJXrVbj9XpJSkpixIgRctBQVlbGgw8+iEaj4dtvv0Wj0ciVsT0eDykpKXLtCYvFgs1mY926dTidTvLz89m/fz8Wi0UezZAkiZaWFqqrqykoKCA9PV0eWfr6669ZsWIFERERKJVKuehedXU1+/fvl7NPNTc343a7eeaZZ/jd734nn6Pc3FxuuOEGdu3aRVlZmVzALyoqikAgwIEDBzhw4AAGg4GoqCiSkpI6nL4malEIbdlstg6n+zU1NbX7DhEEQRBObT2uUwGHc5AfaaqK0L/0Vj2LjqZZNTU18cILL6DVaomLi2s3rWr+/PntshSFggCz2UxERASNjY2sX7+e8vLyI07DajtikpKSQl5eHg6HA7VajclkkmtOtK4abbFYUCgU8iJsg8HAO++8Q21tLePHjw+7SfL5fKxevZqMjAzcbrc8ehIIBOR99Ho9JSUlfPPNN+zfvx9JkoiJicFkMqFUKsNGLg4dOiTXjHjppZfYtm2bHGxt3rwZh8NBc3Mzfr8frVZLREQEXq+X+vp6nE4nQ4YMkadD7du3j+XLl4edo+nTp5Oamkp9fb08lctmsxEIBDAajTQ2NqJSqWhubmbDhg1MnTo1bOoWiFoUQnvjxo3js88+Y/HixQDyv5FXXnlFnuInCIIg9A89Cirmz5/PhAkTuPvuu8O2P/HEE/z000+89957vdI4oW/pzXoWradZBYNBHnvsMex2O5MmTcLpdALh06o++eQTPB4PBoMBSZLaLSoOBoPy/H+73d7hNKy2GatuvvlmVq9ezZYtWzhw4ACRkZEkJSWRkpJCYWEhdrud5ORkuWp0aEpSaBE20OHIjSRJbNu2jcbGRoLBIFqtFq1WSyAQwO124/V6qampAcDj8VBcXIxKpZKL5NntdlJTU7FarRw4cIDCwkIUCgWBQACDwUB1dTXfffedHGzl5+fjcrnw+/3ExsbicrnQaDT4fD6USiXBYJDa2lpMJhNJSUmMGzeO/Pz8sHNUVlZGZGQk8fHxeDweGhsb5XS2zc3NREREYDAYiIuLY/fu3eTn58tBVqjPohaF0Najjz7KhRdeSF5eHn6/n2effZa8vDzWr1/Pd999d7KbJwiCIPSiHqVkWbduHRdddFG77RdeeCHr1q075ka1Firw1fa/m266CTi8oPSmm24iLi4Oo9HI/Pnzqa6u7tU2CIeFpg2Vlpa2y8QSuqnMzc3t9k1laFpVampqu9dC06pKS0sJBAI0NTXhcDiw2+2YzWb5pjY0/1+v14dNwwrJz8/nscce49577+Whhx7i3nvvZfXq1cydO5cbbriB7OxsOVD4+eefqa2tpa6ujvz8fJqamuQpRXl5eXKK3aampg5HbhoaGuRAIRTUtLS04HK58Hg8eL1empqa5DZHREQQERGBx+OhubkZp9NJWVkZ9fX11NXV4Xa7gcNBXWJiIg0NDdjtdg4cOMAnn3xCbW0tjY2NREdHY7FY0Gg0uFwufD4farUahUJBQ0MDKpWKlJQUbDYbJpOJvLw8+Rw1Njai0+mYOnUq0dHRuFwuJEnC7/djNBpJS0tDo9FgtVqJjY1l//79lJaWYrfb2b9/Pz/99BNxcXG9XosiGAxSXFzMrl27KC4uJhgM9tpnC8fftGnT2LFjB36/nxEjRvDFF18QHx/Phg0bGDt27MluniAIgtCLejRS4XK50Gq17bZrNBr5KXNv+emnn8KmjOzevZvzzjuPyy67DID/9//+H5999hnvvfceZrOZm2++mUsvvZQff/yxV9shHL/q1l2ZVqVSqUhOTqa0tJS4uDj8fj8ajQY4HNA4nU6SkpLkwnOtp2EdLWPV/PnziYiIYNeuXfj9fsxmM2azGYPBQGVlJaWlpXINjfHjx8spdouLizscuamtrcXj8RAZGYlKpcLr9dLS0kIwGCQYDKJUKgkEAvj9fvR6PSqViqioKDweD2q1mqioKCRJoqKiQh5tMBqNJCcnyyM1NpsNl8tFSUkJPp8Pr9dLdHQ0Op2OlJQUKioqaGpqktsUCi527tyJ3++Xf0c7d+5kwIAB8ihUREQEI0eOlAMPjUaDTqfD5/MRCASIiYlh8uTJfPXVV6xfv14u7td6cXpv6Y1MX8LJl5WVxcsvv3yymyEIgiAcZz0KKkaMGMG///1v7r333rDt77zzDkOHDu2VhoW0nbf92GOPkZWVxdlnn43D4eDVV19l5cqVzJgxA4DXXnuN3NxcNm7cKFdEFnrP8ahu3XpaVUxMTLvXm5qaiIiIYM6cOXzwwQccOnSIYDCIz+dDoVDgdDqJjIwkJycnbM1DVFTUUTNW7dmzhzVr1lBTU0NNTQ1DhgyRb7hD2ZoKCwsZMGAATzzxBJmZme3SsXaWIrelpQWDwUAwGKSlpQW1Wk0gEJBHeTQaDWq1Wg44dDodbreb5ORkvF4vERERcjrZ1NRUuZBeKE2s0+kkOjqatLQ09u3bR3Nzs7zWIzU1FZ/PR0tLS9jUK6vVKo9k1NbW8u677zJkyBCys7PlviQlJaFWq3G73Wg0GjkjVCiocbvdSJJEYmIi6enpYYvT267V6KljSV0s9C3BYJB9+/ZRU1PTbqTprLPOOkmtEgRBEHpbj4KKpUuXcumll7J//375Zv6rr77iX//613FdT9Hc3Mzbb7/NbbfdhkKhYOvWrbS0tDBz5kx5n5ycHNLT09mwYYMIKo6T3qxuDf+7Od+xY0e7KVCt5+rPmDGDlJQUPvzwQyorKykvLyc2NpakpKSwLEmt5/YfKWOV3W6nsrKSzZs3y6MJBQUF8ohHaCpSfHw8VqsVtVod1sfORm60Wq08GhEVFSWP6jU3NyNJEpIkoVAoiIqKQqfT0dDQgNvtxmg0yiMZzc3N8jEiIyPR6/VhbddqtdTV1aFUKrn66qvJz8+nrKyMlJQUdDodCoUCpVIpr8VQq9UkJyejVCqRJAmv18ugQYPw+XxybYm5c+eyfv16PvroI2w2G36/H0mSUCqVGAwGBg8eTDAYZPv27ej1es4666yw8xGaUnWktMJdcaypi4W+Y+PGjVx99dUcOnSo3ZTJ0N+mIAiC0D/0KKiYM2cOq1at4tFHH+X999+Xp0ysXbuWs88+u7fbKFu1ahUNDQ0sWrQIgKqqKrRabVguf4CEhASqqqo6/Ryfz4fP55N/Dk3ZCk1POZUEg0EkSTop7W67duJY2jBv3jzKy8spLS1FoVAQGRmJ2+2W61fMnTsXgOzsbO6++24mTJjAK6+8gsvlYsiQIRgMBnktQuv9nU4nPp+v3dQqu93O5s2baWpqQqVSYTKZMBqNVFVVUVFRIS8KT05OZvDgwdTV1eF0Otv1MTs7m8WLF7Nq1Sr27t1LRUUFOp2O4cOHU1ZWhtfrlatTe71eGhoaAOS1FCqVSp4CFcoSJUkSKpWKYDAoZ6Hy+Xzo9fqwNSQtLS3k5uYyY8YMfD4fDz/8MDU1NXIF8LS0NKqqqnC5XCQnJ6NQKPD5fDQ2NmIwGMjNzUWr1VJQUMBXX33Ff/7zH7Zs2UJtbW1YP4PBII2NjXIlbbfbzbRp09rd0CsUCtLS0uRaHxkZGT36Wzh06BB79+7tNHVxbxzjZP67OR660p+T0df/+7//kzNAJSUliTSygtBDbRONHMuDPEE4XrodVPj9fh599FGuu+66E75u4dVXX+XCCy8kOTn5mD5n2bJlPPDAA+2222w2vF7vMX32iRYMBnE4HPIT5VNVbGwsCxcuZOfOnezbt4+mpia0Wi1nnnkmkyZNIjY2Vs6YBIen4N1xxx1s2LCB8vLyTveXJIkBAwbI6wVCysrKSEtLkxdIw+G1G6NGjaKuro6oqCiys7MxGo243W5MJhOSJIW1oXXbFy1ahM1mw+PxyNOW/v3vf+Pz+eRFz3B4wXboyb/f75enREVHR1NfX49SqZQzY/n9ftxuN4FAgGAwSEREBNHR0SiVSmpra8nKymLBggXY7XbGjx/Pc889x1dffUVZWRnBYFDOwrV37155ChRAZmYmKSkpJCYmyovfP/vsMw4cOEBmZiYDBgzo8AZUoVBgsVgIBoOkp6d3WqtCkiSqq6vDznd3VFdXExsbK4+sHI9j9Jd/NyFd6c/JqB9SVFTE+++/z6BBg074sQWhvxDry4RTRbeDCrVazRNPPME111xzPNrTqUOHDrF27Vo+/PBDeVtiYiLNzc00NDSEjVZUV1fLBb86smTJEm677Tb5Z6fTSVpaGlartcMbpb4sGAyiUCiwWq2n/M2RxWIhJSVFvhGPiooiLS2t037Fx8czevRoSktL5ac3bfe3WCyYzWZ27NhBbm4uCoUCh8PB1q1b0ev1NDY2kpCQgMfjoaamBovFAkBpaSnx8fEEAgHy8/MZPXo0w4YNO+I5bvs3ZzAY+PLLL1m7di0VFRUolUrUarU8xQkOBxlGoxGz2YxWq8VsNhMZGUlLSwsej0cOsAKBAIFAQA5IUlNTuemmmxg3blzY+RgzZkzY+fD7/dx99900NDRQW1srr0PJy8sjLi6OlJQUdu/eTVRUFPn5+TgcDuDwwwNAnqoVmk6lUqnIy8ujsbGR8847r92TZ6fTSV1dHQkJCR0WPesKj8dDXV2dvHakrd44Rn/6dwNd60/bKXQnwsSJE9m3b58IKgShh07V9WViZOX01KPpT+eeey7fffddp1WTj4fXXnuN+Ph4Zs+eLW8bO3YsGo2Gr776ivnz5wOHn8qWlJQcsbBSaHpIW6GnxKea0A3fqdj2tlQqFQMGDOhyX5RKJQMHDjzi623XPTQ1NcnpYJVKJU1NTdTX11NbW4vNZpPXOtjt9rCsVmp19/655Obm0tTUxFdffUVTUxNqtRqr1YrX66W8vByPx4NGo5Fv1jMyMpg4cSI1NTV89913FBcX43K55MXdKpWKyMhIMjMzeeGFFxgxYsQRz0d+fj4ffvihnF43VNgvPj4erVZLRUUFhYWFGAwG0tLS5NS1oZGRUMAQGrUIBoMEAgG8Xi+7d+8mKyuLrKws+diSJFFaWsqYMWPIyMjo8d9jRkYG2dnZnS6A741jQP/6dwNH78+J6ufPP/8s///Fixdz++23U1VVxYgRI+SMbSEjR448IW0ShFPRqbq+TIysnL56FFRceOGF3HPPPezatYuxY8e2m69+8cUX90rjQoLBIK+99hoLFy4Mu7Ezm8385je/4bbbbiM2NhaTycTixYuZPHmyWKR9igoGgxw6dEgeqeiNpxttM1bV1tbK9RdCi7FjY2MxGo3U1NTI00gqKys599xzu5XVqvXTmcjISDZu3Chnrtq7dy92u51AIEBycjI+n48JEyZwww03yGlsg8EgV111Ffn5+XJ1bL1ej9/vp6WlRU4fe7QCg6GnWzabDb1ej0ajkTNjVVRUEB8fLy8aDwQC8lPs0M+h6VqtF9eGblpDBfG+/fZbFAoF6enpx5xWuLXjlbpYODFGjRqFQqEI+9u57rrr5P8fek0s1BaEjoWuIwUFBWzZsoWMjIwO15e1rsl0Ih/yHsmpOrIi9I4eBRW///3vAXjqqafavXY8LhRr166lpKQk7MIU8vTTT6NUKpk/fz4+n49Zs2bxt7/9rVePL5wYBQUFrF27lq1bt+LxeHr16UbrjFUOh4O33nqLNWvWEAwG5arcOp0Oo9FIeXk5AKNHj+bOO+/s8ghF26czoaxLoUrVVqsVh8OBz+eTR8rq6upIT0+XLwhbt25lz549+P1+IiMj5RtnlUqFRqPB7XaTl5dHbW1tpxeR1k+3UlJSKCoqIiMjA6fTidvtpqmpierqaoYOHUpMTAzbt2+Xq3X7fL4OA4oQtVqNTqcjNjYWrVbLrl275HUkx5JWuK3jkbpYODEOHjx4spsgCKes1teRqqoq9u7dS319PUOHDm2XYt9gMITVZDrZTtWRFaH39CioONFZRM4///wOb3Dg8DzhF154gRdeeOGEtknoXfn5+Tz//PNotVri4uI6fbpxLPM0Qwug4fCi/I8//hhJkuRaEKFRi+joaIYOHUp1dTVlZWVyatojHbOjpzMlJSVUV1ezb98+DAYDVqs1bO2P3++noqIi7IKwceNGvF6vPC2qbfvVajVer5eNGzd2WpG4dRpdj8eD3+8nLi6O6OhofD4fbreblpYWRowYgdFopLCwkIaGBhISEqirq+v0/KlUKrlK+MCBAxkxYgSlpaX85je/kQvfNTU1UVxc3GsjTL2Zulg4MXqakUsQTndtryNms5nS0lI5cJg4cWJYYNG6JlNfcKQU7n11ZEXoXT0KKloLzZcThJ5q/XRj0qRJcorftk83gsEgq1ev7pV5mgkJCWRmZuLz+airq6OxsRG1Wi3XvIiJiaGwsJCdO3eycuXKIx6zs6czsbGxuN1uDh48SEFBARaLJeyLtqMLQij7WDAYlCtfhxZKtw6sKyoq2LVrV4c32q0rlAeDQbnAnk6nQ6/Xo9VqsdvtHDp0SK4gbrFYKC4ulmtqtLS0hJ2v0NQnOLwgPDc3l6ioKNRqNT6fj3feeee4zJ9tHQgKp55ly5aRkJDQbpT5H//4BzabjbvvvvsktUwQ+paOriOSJJGcnExFRQVutzvsOtK2JlNf0Pra05G+NrIi9L4eBRWBQIBHH32Uv//971RXV1NYWEhmZiZLly5lwIAB/OY3v+ntdgr9WOjpRtvCd/C/pxsbN25k9+7dNDc398o8zaioKOLj44mLi5NHK3Q6HWazWc4Q5fP5+P/Y+/P4OMt6/x9/zp7Zk8lM9q1blqYrbWmhoIgVULYeUI98PSouuJzDongQ8XPQIx4VcQGhHBGPgJwjckQQqEURigJtaUtL9ybplm2SzGRmksy+z/z+6O++TvYmaZImzf18PHjQzHLPdd1zz31d7+31/v3vf3/GzxzJOyN1Co/H43R0dNDX1yc6ho+0IEhGRCqVIpVKoVAoRHRAep9CoWDbtm0cP3582A18/w7lksHQ1dUl0rzcbjcdHR04nU5SqRQ6nU4oTBUUFBAKhYhEIiQSCTEGqQC4sLCQJUuWYLfbxTl67rnniMfjcv6szBB++ctf8swzzwx5vL6+nk984hOyUSEj8/9nuHVEoVBQW1uL3+/H7/fT2dmJz+dDo9HMyPqy/mvPcMp9My2yIjP5TOhK/P73v89TTz3FAw88IDybAEuWLOG//uu/Jm1wMnODM3k3DAYDp06dwuPxsHjxYiwWi2hYt3jxYrxer4hkjBWpi7fT6cRqtVJYWEhubq7wALW3txOLxYjH4wM+02w2U1JSwsmTJ3nyySdJpVLDjt/j8bBjxw6CwSDBYJC2tjbefPNNXC4Xfr+fo0ePDlkQGhoaaGhoQKfTiahEOp0mkUiIsSSTSTQaDStWrKCmpga73c6+fft4+OGHaWhoGDC39vZ24HSXeYPBgMfjoauri+bmZhKJBEqlEqvVSnFxMZ2dncJDdsMNN/Dxj3+cm2++mbq6OtEzorCwkOrqaux2+4BzFIvFJu17kTm/cLlcFBcXD3nc4XDQ1dV1DkYkIzMzGWkddDgcrF27lrKyMiKRCMePH8fn83HBBReM22mTyWRoaWnh0KFDtLS0TPq9uf/aMzhlXXKk1dXVzZjIiszkM6FIxdNPP83jjz/OBz/4Qb785S+Lx5cvX05jY+OkDU5mbtDfuyF58vvjcrkIh8OTmqd5JoWhnJwckskkFRUV4jM9Hg+NjY14vV7RQyKbzXLllVcO8M54PB527dpFLBZj5cqVlJWV0dnZicfj4bXXXmPJkiWsW7duQMGxFPpubW2lpKSEEydODLjh909/stvtpFKpARv4wQVwg+e2evVqDh48yHvvvSea7VksFux2O0ajEZPJxOHDhzl06BAXXHABPp+PvXv34na7CQQC9Pb2otVqaWpqQq/XE4lE0Ol0Q87R2X4vMucX5eXlbN++fYjs8/bt28+6iamMzPnEaF5+h8PB8uXLycvL44tf/CK1tbXjri+bDplXWblPZkLfbEdHx7DNjDKZzJBcbBmZM9E/ajAYySNuNBqH9XjC6TzNWCw27jxNSWFo5cqV+Hw+jh07JjxAH/vYx9DpdMJrJBkKXV1dQs1JrVZz4MAB/vCHP2Cz2WhvbyeTydDY2EgkEsFut4uO2UuXLuXGG29kwYIF1NTUcNdddw24kbe1tYnjp9PpARFA6TwAlJaWYjKZaGxsFI8N3sCnUil8Pp+oDfF4PPT09GAymdDpdFRUVLBo0SIqKirE/PR6PVarFa/Xy6FDh3jzzTdpbGwUzflUKhWZTIauri7efvttysrK+PjHPz7gHE3W9yJz/nDLLbfw1a9+lSeffJLW1lZaW1t54okn+NrXvsYtt9xyrocnIzNjOJOXv6OjgzVr1nDFFVeMq5cT/F8B+L59+7Db7SNGuSeD0dZVOR32/GdCkYrFixfz9ttvD1H5+MMf/sDKlSsnZWAycwfJu+F0Omlra0OpVGIwGIR3o6CgQHjHJztPcySFoba2Np5//nnC4TBms1kYClJdQiwWQ6/XU1dXR2dnJ2VlZeTn57Nnzx46OzuxWCzE43H8fr94nc1mo76+HpfLhdPpHOC99/v9nDp1inA4TCgUIpvNYjAYRN8IyVi3WCxYLBa8Xi9+v1+oSUkFcH/60594+eWXOXHihEiVKi0t5R//8R9FN+zS0lIhkyvVk6RSKWw2m+g2HggERPdtqUlgXl4eRqNRSMouXbpUzp+VGZW77roLn8/HP//zP5NIJIDTin13330399xzzzkenYzMzGGqvPznQuZVVu6bu0zIqPj2t7/NZz7zGTo6OshkMrzwwgs0NTXx9NNP86c//WmyxygzB6irq+PWW28VfSqkFKQLLriA6667jpdeemnEDstnq4AxnMKQ5DXat28fJSUleL1eUcSdzWYJBAIUFRUBpzdJra2tfPnLX+ZPf/oTR44cEX0vpHoEu90OjKx+EQwGCYfDRKNRkskkKpUKtVot5qpUKonFYng8HvLz8wkGg8TjcfH+cDiM1+vl5z//OdFolIKCAmGYHT9+nAcffJCPfvSjoteFxWIR74lEIqJbtvQ5UndtlUpFTk4OOTk5pNNpYeDs27ePT37yk+IcTcX3IjP7USgU/OhHP+Lee++loaEBvV7PokWLRJ8WGRmZ/2Mq+vOcK5lXWblvbjIho+L6669n8+bN3HfffRiNRr797W9zwQUXsHnzZj70oQ9N9hhlZhkT7SVRW1tLbm4u11577ZCO2kqlcswenNE+f6xjUyqVXH/99Rw5coTdu3cTCAQwmUzEYjECgQAKhYJIJMLf//53EokEkUiELVu2cM011+B0OjEajdjtdsrLy4VELozsvTebzWg0Gnw+Hzk5OSQSCaH0JP1fpVIRiUSE/K20Mctms7S1tdHe3k4kEmHBggUolUrC4TB9fX1kMhk6Ozt5+umn0Wq1om7D5/MJqVmlUklPTw9arZZIJIJCocBgMKDT6dDpdKLLtlSYHgqFCIfDMz5/9mz6mshMHiaTiTVr1pzrYcjIzHgm28svy7zKTCcT7lNx6aWX8tprr03mWGTOA862GEypVFJZWTnkBjqcB0en0zFv3jzWr1+PTqfj1KlTHDp0iG3btuFyuYjH4wM+Hxjz2BoaGnjppZcIhUL09PTg8XgIBoPk5+eTl5dHKBQiEAhgtVrF5v748eO88MILVFRU0NHRIVKCstksfr+fWCxGW1sbl1xyyRDvvaTC5Ha7SafTqFQqksmkqMlQKpViUejq6mLhwoUYjUb8fj9Op5N0Ok00GqWwsFAYFB0dHcJoMBgM9PT0oNfrCYfD+P1+1Go1ubm5pFIpIpEIWq0Wg8FAb28vgOiTIf2nUqmIx+OiM7fZbKaqqmrGdr4e7Vqsqak5q2PLxsrYCIfD3H///WzdupXu7u4hajOnTp06RyOTkZm5TKaXX5Z5lZlOzqr53Z49e0SBz+LFi0fs8CszNxiuq/Rk9izo78E5cOAA27Zto6uri1/84he43W5isRihUAilUklZWRlLly7FYDCwb98+Dh48CJyWaD3T2PrPY8GCBSxZsoS//OUvOJ1OtFot2WyWTCYjOpt6PB5KSkpYvXo1DQ0Nor6ioaGBefPm8e6779LV1UUoFEKv11NdXU1TU9OAc1FRUcGaNWs4duwYKpWKbDZLIpEgkUiISIJerwdON+6z2+2iT8UFF1yAwWBg7969og7D6/WSTCbRarXE43Gi0SjpdBqHw4Fer8fn85FKpejp6SEnJ0ekdvX29oroiBSZgNNpUBqNRpxDl8vFsWPHKCsrQ6/Xc/XVV/O+970Ps9mM1Wo955vsM12Lt912GzabbcLHnmoVlfOFL3zhC7z55pt86lOfori4eEj6hYyMzNTSP5VXTlOVmWomZFQ4nU5uuukmtm/fLgpF+/r6uPjii3n22WeHbWImc34zXcVgSqWSaDTKX/7yF7xeLwaDge7uboLBIH19fcJo6O3tZc+ePaxdu5a6ujpeeOEFFAoF//AP/yA+f7ixAcPOY926dezcuZOOjg7S6TTz5s0TXnuDwUBtba0wZnw+H5/+9KfZsmULjY2NnDhxApPJxKJFiygvL6ejo4OHH354gCGjVCq5+eabeeutt3A6nZSXl5NMJunp6SEcDouu2kuXLuUnP/kJZrN5gJd8x44dol5Cq9USCARIJpOiiV0mk0GhUKBWq3E4HEQiEUpKSggGgxQUFKDVajl27BgKhQKtVivSryTS6TSRSEQ0w+vr6+OOO+6grKxMpEn131yf65SnM12LL730Ep/5zGfGfeypNpzPN/785z+zZcsW1q9ff66HIiMzJ5FlXmWmkwldRV/4whdIJpM0NDTQ09NDT08PDQ0NZDIZvvCFL0z2GGVmAeMpBjsb+m8Y6+rqcDqdxGIx8vPzUalUKBQKgsGg2Dg3Njbi9/tFkXH/+obhxjbSPBwOB+vWraOoqIhIJEJPTw/RaJTi4mLWrl0rohaSjKrD4SA/P5+SkhKuvvpqrrjiCi699FLmzZs3YmO4+vp67r33XkpLS+nt7SWZTJKfn8+iRYuYP38+F1xwAV//+tepr6+nqqqKpUuXCmnBdevWsXDhQmFghcNhksmkmINUlyHVWABoNBry8/Npb2/nyJEjhEIhUqkUmUxmiKShdAw4napls9no6Ohg//79nDp1ivz8/CmTKBwvY7kWGxsb8Xg84zruYGNFbvZ3ZvLy8iYcEZKRkZkcZJlXmeliQpGKN998kx07dgzIS66pqeGRRx7h0ksvnbTBycwepqsYrP+GMRAICFWmZDJJJpMR0rPxeFxIr/p8PvH+/opJI41tpHk4HA7Wr1+Px+OhtraWhQsXirQhCSk/NRgMcvz4caqrq4f0bhlNceOaa65h3rx5PPXUUzQ2NhIIBPD7/SgUClKpFP/zP//Dnj17hqTaqNVqbr/9dr7xjW/Q2tpKJpNBq9UKY0qShE2lUvT19aHX6/H7/SQSCaLRKHA6xUmqm+h/XEnWFk57vRKJBB0dHcTjcdRqNW63m3379nHllVdOmUTheBjLtdjZ2SnmPVbOlYrKbOZ73/se3/72t/nNb36DwWA418ORkZmzyDKvMtPBhIwKKTVjMOl0Wu6SOkeZrmKw/htGr9dLKpVCo9GQzWYHKDylUikMBsMQI2Y4KcvBYxttHhqNhoKCAjKZzBCDon9+qtlsJhaLia7TgzEajTidThobG4fc4Ovr6/nRj37EG2+8weOPP47RaKSmpgaTyTRqqs0111yD2+3m//2//0dvby+xWEyoRlksFgwGA8lkkmAwSFVVleiWrVarUSqVpFIpMVa1Wj0kYiEVbMdiMTKZjDBCUqmUMKDmz59/zjfXY70WpRqVsSKrqIyfn/70p5w8eZLCwkKqqqpEXY7Ee++9d45GJiMz95BlXmWmmgkZFT/+8Y+57bbbePTRR1m9ejVwumj7jjvu4Cc/+cmkDlBmdjBdxWD9N4w6nQ61Wj1A4UhSNVKr1SQSCdRqNTabTaRGDd5kDje20ebR0dHBZZddRjQaHTU/Va/Xk5OTMyQyIilBnTp1ipMnT/LYY4+RTCZRKpXU1tZy8803U19fD8Du3bvJZrOsWbNmzDUqF154IcuWLaO9vZ22tjYRYYhEIuKcpFIpQqGQqJGQDLJMJoNSqUSj0aDRaIRRAqcdBlLDPCn6IUncSgpVTU1NzJs375xvrsd6LUopa2NFVlEZPxs3bjzXQ5CRkZGRmSYmZFTcfPPNRCIR1q5dKzYaqVQKtVrN5z73OT73uc+J1/b09EzOSGVmNNNVDNZ/w1hXV4fdbqerqwuHw4Hdbqevrw/4v817fn4+nZ2dLFmyBECoM402tjPN44tf/CLAqDKqmUyGmpoauru7RUTD4/HQ2NhIe3s7HR0dKJVKgsEgOTk5ABw8eJA333yTL33pS5SWlrJnzx4qKyvHlWrjdrs5duwYwWBQRHBSqRSpVAqv1yuUpbq6ukilUlgsFhwOh5DITafTorBbqVQKFSjpWFL0QiroVqlUoqjb4/GIVK1zubkey7V4/fXXj/talFVUxs93vvOdcz2EaUWWGpaRkZnLTMioeOihhyZ5GDLnA1PRDXQw/TeMDQ0Noqi5s7MTgOLiYnQ6HS6XC7Vajd1uZ9WqVVx33XV0dXWxefNmWltbUalU6PX6Ycc21nmMlp+qVCrZuHEjzz77LHv27EGpVAoJ0mAwKDblUn2DJM3a2NjI3XffTXV1NR0dHfT29rJ48eIhXvXhogGZTIbNmzfj9/tJpVJYrVaUSiWRSEQ8JhkIWq1WGAgmk4mCggKcTieJRAKfzyeiFGazGaPRSCgUIhQKiVoNlUpFOp0WqlQKhYJQKEQ0GqWnp+ecb67P9B1KBt94kFVUZEZDlhqWOZ+RDWaZsTAho2KsUoz3338/fX19QnZW5vxnOorBBm8YCwoKRO5/YWEhDoeD4uJi1q9fz/LlywmHw7z00ks0NjaK4tzi4mKuu+46Lr/88mHHNpZ5jCU/VaPR0NzczKlTp0QzO+nxTCZDbm4u0WgUt9st6hQSiQSxWAy9Xi8Mh/4KUzB8qk1LSwtvvfUWFouFRCJBPB5Hq9WKKKKkSlRSUkJBQQHd3d34fD48Hg+VlZWYTCbcbjc2m01ENaqqqlizZg1NTU3s2LEDlUqF1WoFIBAIEIvFUKvV6PV64vG46L8xEzbXo32HE1Vomg7DebZjs9k4duwYdrudvLy8UXtTnC+RbFlqWOZ8ZiobicqcX5xV87sz8YMf/ICPf/zjslExx5iOYrDBG0apeDYcDg/YPDY0NLBp06Yhi317ezvPP/88paWlIy72ZzMP6XOl4melUonZbCaTyRCLxUTKUDqdRqfT0dfXh1arxWw2i6iA3W6nt7dXSOPa7XbRmG64VJtjx47R09NDUVGRaFAn1U4olUpR42E0GtHr9djtdiKRCL29vej1enp7ewmFQuTn57NgwQK0Wi09PT1s3bqV8vJycnNzycnJEePOyckR/S8SiQRKpZJly5bxla98ZcZsoKbiWpRVVEbnwQcfFMbuXIhqT1ePHhmZc8FUNhKVOf+YUqNiOK17GZnJ4kwbxrEu9osWLcLpdE7aBlH63JMnT6LVanG73aJpnFTTIP0XDoeFKpPRaCSTyQgZ14qKChKJBH6/n87OTnw+HxqN5oypNtFolGAwSDKZFL9BrVaLRqMhkUiI4m2j0Uh5eTltbW14PB5isZiQnl22bBlVVVX4/X7RGdxqtaLVarFaraLZnUKhwOfzkU6nKSws5KMf/eic8FzJKioj0z+SPZ6o9pe//OVZ6YCSpYZnBnJ6zuQzlY1EZc5PptSokJE5l4xlsd+5cyf33HMPbrd70vKg29ra2LVrF+3t7TgcDvR6vag/kOoapHqGZDJJIpEAED0iJLnToqIi8vPzOXr0KKdOneL48eMUFhaOmGpTXV2NwWCgra0NjUYj1LESiQTJZJJYLIZGoxHGhtRh22azicZ3Go2GeDzOgQMH6OjooLa2lvr6erxeL9XV1fj9fsrKyvB6vYTDYdEsLx6PEw6H+dWvfsW2bdvkPHKZcTGbo9qy1PC5R65nOTMTMbrG00i0qKhoKocvM0uQjQqZ85YzLfbRaJTDhw8TjUZZsmTJpOVBS5KxUnM4jUYj+kBIBdLSRl+SxFWpVKKvhVarxeFwCNWo5cuXk5eXxxe/+EVqa2uHXQwymQyZTAadTkcikUClUgGnDRVJHjabzWKxWLBarXg8HsxmM36/H4De3l60Wi0lJSWimWBXVxd+v5/Vq1cTj8e55JJL2Lt3L729vVRWVhIMBjl06BDxeByHw8HFF1+MXq+X88hlxs1sjmrLUsPnFrme5cxM1OiaqkaiMucvslEhc94y2mKfzWY5dOgQqVSKuro68fxk5EEHg0HC4bCIEvT29pJOp4W0q9Q0TqVSkUwm0Wg0ohGbwWAgLy+P2tpaUT/R0dHBmjVruOKKKwCGeJuampr44x//yJ49e+ju7katVosGdWq1esDnGQwGampqaG1txel0olKpRG+K8vJy8vLygNNNAh0OBx6Ph0OHDjF//nyWLFmCTqcjEAjQ2trKiRMniEaj1NbWUltbKwrJ5TxymbnE+S41PJPTiuR6ljNzNkbXVDUSlTl/mfFGRUdHB3fffTd//vOfiUQiLFy4kCeffFI03ctms3znO9/hV7/6FX19faxfv55f/OIXLFq06ByPXOZcM9pi39fXh9PppKysbEjKxdnmQZvNZlQqFV6vl8LCQrLZLGq1GoVCIdKdVCoVJpMJk8lEVVWV6B2h0+mor68nLy8Pv98/oH5CMh76e5tsNhsul4t0Oo3JZCI3N5f8/HxcLhfJZJKcnBxMJhMWi4VsNovb7cbn8zF//nzWr1/PwoUL+fOf/4zb7aavr0+kRWWzWRFpaWlpoaamht/97ncEg0E6OzuJRCIkk0lWr17NkiVLBpxbOY9cZi5xPksNz/S0IrmeZXTO1uiaqkaiMucvU2pUXHrppWdlwfb29rJ+/Xo+8IEP8Oc//xmHw8Hx48eFNxXggQce4OGHH+Y3v/kN8+bN49577+XKK6/k6NGjoqmYzNxktMW+oaEBtVrN0qVLh5W8HC0P+kyeO7PZLIqtFQqFqDmA/0vzUCqVXHzxxXz/+9/HbDYTDAZxu93s3LmTY8eOcezYsQFSpcAQb1MoFOK1114jGo1y5ZVXimJsg8HA4sWL6ezsxGazsW7dOnJzc/H7/bS1tQ1Iozpy5Aivv/46y5YtY8+ePXg8HjQaDX6/n1AoRDweJ51Os23bNjo7O7n00ktFpOPIkSM0NzdTVFQ0pj4aMjLnK+ej1PBsSCuajnqWwff7srKySRX2mErO1uiS1tC2tjbeffdd8vPzsVqtqNVqOjo6JtxIVOb8ZUJGxfvf/34+//nP87GPfWxUo+GVV16Z8MAAfvSjH1FeXs6TTz4pHps3b574dzab5aGHHuLf/u3fuP766wF4+umnKSws5MUXX+QTn/jEWX2+zOxnpMV+xYoV6PV6DAbDsO8bKQ96rJ67nJycIQtd/94I6XQalUqFWq0WN/OlS5dy+eWXDzFYAH74wx/S2tpKRUWF6HadyWREStWBAwe4/PLLB3QYz8/PJxqNisXE6XSycOFCiouLxTik8LbBYGDt2rXs3buX48ePk0wm0Wq1GI1GITvr9XpF/YfNZsNmsxEMBgfI3Z7p/MnInK+cT1LDsyWtaKrrWQbf7+PxONFoFL1eLxTwZlLkZjCTZXTl5OTQ1dXFkSNHgNO9aC677DK++MUvTqiRqMz5y4SMipUrV/Kv//qv3HbbbXz84x/n85//POvWrZvssfHyyy9z5ZVX8rGPfYw333yT0tJS/vmf/5lbbrkFgObmZlwuFxs2bBDvsVqtrF27lnfeeUc2KibITM6hnQjDLfZlZWU88MAD48qDHqvnLhwOU1BQgMfjEV2pw+GwkHJVKpUoFAoOHz7Mz3/+c+644w6xIA0nVfr666/z3HPPEQwGaWpqQqvVYrFYiEajdHV1oVKpOHr0KNlslsrKSvx+vyjEljpkNzQ00NfXRzKZ5Pvf/75YDK+//noR3q6rq8NgMGCz2dBqtfj9fgKBAIlEQkjZnjhxggULFmC1WnE4HEKO1u/3izSy8yGPXGZ6Oduo9kzhfJEani1pRVNZzzL4fh+JRNixYwc9PT3YbDbWr18/44Upztbo6n8OLrvsMlKpFH19fXi9Xrq7u0VEXafTTfVUZGYJEzIqHnroIX7yk5/w8ssv85vf/Ib3ve99LFy4kM997nN86lOforCwcFIGd+rUKX7xi19w55138q1vfYt3332X22+/Ha1Wy2c+8xlcLhfAkM8rLCwUzw1HPB4X6ShwujMw/J+CzmxCkiadrHE3Njby4osv0tTUJDzxNTU1bNy4kdra2kn5jJGY7LkMZvDCsnHjRpxOJw0NDZSVlWEwGIhEIjidThwOh4h+SdfFH//4R3w+37Ceu4aGBtHzQqqVMBqNGI1GIeEqpSfpdDrS6TRarZb29nbxvuEMt8bGRn7605/S2tqKRqMBTkc5WltbhbKU1M26u7ubZDJJdXU13d3ddHV1EYvFcLvdBAIB8vLyWLBggTCGtm/fzoEDB7jiiiuw2+3s3bsXt9uNwWCgu7ubaDQqZGkNBgPxeJyuri4RXamrqxMLTE9PDyaTacTzN9OY6mttuhnLfKZrrtL9dCxIG52zjWrLTC6zRSZ3qupZBkdqAA4cOEAqlWL+/Pl4vV6OHTvGJZdcMqMiN4MZr9HV36FoNBp54YUXhkSrMpkMHR0dbNu2jYMHD7Jy5UpWr17Nhg0bxLmSmbtMuKZCrVZzww03cMMNN9Dd3c3jjz/Ovffey7e+9S0+8pGPcPvtt3P55Zef1eAymQyrV6/mBz/4AXA6QnL48GEee+yxs2q28sMf/pDvfve7Qx6XGoDNJjKZDH6/n2w2e9Y3s/b2djZv3kwgEKC6uhqdTic2q88++yzXXnst5eXlkzTyoUzmXMaCzWbjs5/9LO+88w4dHR2Ew2G0Wi2XXnop69atw2azibCu2+3G7/ezfPnyYT2qy5Yto6+vjyNHjuBwOFi+fDnRaJTFixeTSCRE4zupF4UUOl+wYIF432DjOJPJ8Pzzz6NQKFi2bJnY4IfDYZLJJICQq1Wr1RQUFBCNRjEYDFx11VWcPHmS4uJiVCoVHo9HLByBQIBAIIDBYMDv9/PXv/6V+vp64a2Kx+Pk5uZiNpsxGAz09vaKsRcUFJBMJrFYLFgsFoxGIy0tLeTm5o56/mYa032tTTVjmc90bQBzc3OHrVPqj1RvJEXvZGYWs0kmdyrqWQZHaiTnidVqRalUYrFY8Hq9IkI7UyI3gxmP0TU41SuVStHc3MyyZcvE79nj8bBr1y4ikQj5+flCJv3UqVNs2rSJ2267bcZFa2Sml7Mu1N69ezdPPvkkzz77LAUFBdx88810dHRwzTXX8M///M/85Cc/mfCxi4uLh1i+dXV1PP/88wCi2Yrb7R6QJ+52u1mxYsWIx73nnnu48847xd+BQIDy8nIcDsewN9CZTCaTQaFQ4HA4zroL9BNPPEFjYyN1dXUkk0mxcbVarTQ0NKDX67nrrrumbBM2WXMZDwUFBaxcuZL29naRGiUZTv0fy2aztLS0UF1dLc5Lf1KpFC0tLSgUCoqKirjooot49tlnUSqVHDp0CL1eLwq2NRqNKGwuKSmhu7sbhUJBQUHBgGM2Nzfz4osvEgqFSCaTYtPe19cn+l7AacMiJydHLGZNTU10dXVRWVnJRRddxP/8z/+Qn59PIBDA6/Wya9cuotEoFouFdDpNe3s7oVCI3Nxc3G433d3d4ncQCoXE8zk5OSxatAi32y1+K83NzSxfvpxPfOIThMNhcf5m+kb9XFxrU8lY5jNdwhV/+9vfpuVzZKaO2SaTO9n1LIMjNfF4XDQHBdBqtQSDQZHxMFmRm6lIPR6L0TVcam9ra6uQFDeZTNjtdhobG4lEIjgcDrLZLD6fD61WS0VFBTt37pyR0RqZ6WVCRkV3dzf//d//zZNPPsnx48e59tpr+d3vfseVV14pbj4333wzV1111VkZFevXr6epqWnAY8eOHaOyshI4XbRdVFTE1q1bhRERCATYtWsXX/nKV0Y8rk6nGzYHUKlUzsofg0KhOOuxS56ZsrKyYXNoS0tLaWhowOl0TqknZjLmMl6USuUAAYDhirELCwuJxWKjeu50Oh0WiwWlUskHP/hBrrzySk6dOkUymSSVSok0ovz8fCKRiIgi9H9ff44fP47X66WoqIhMJkMsFhOdr7VaLQCJRAK73U5RURF+v1+k9i1YsICvfOUrpFIpotEoRqORbDZLQ0MD4XAYh8MhlKlCoRAmk4kDBw7gdrsJhUJirna7HZvNRiQSwe/3o1aryWQyeL1e4em64YYbWLBgwdR+SVPAubjWppIzzWe65vn+979/Wj5HZuqYjTK5k1nPMjhSI0WJk8mkaDCqVqvFPmIyIjdTKd87mtE1UlH+YDGOJUuWiGiNQqEQkuPSOZip0RqZ6WVCRkVZWRkLFizgc5/7HDfffPOwGsXLli1jzZo1ZzW4r33ta1x88cX84Ac/4OMf/zi7d+/m8ccf5/HHHwdOL6Jf/epX+Y//+A8WLVokJGVLSkqEDKfM2JgtObRTzUjF2JIoQCwWY926dWf03CmVSm655RZ+97vf0dTURDQapaCgAKVSSTAYRK/XU1xczNGjR1mxYgVlZWWjjstoNFJaWorL5SISiRCLxURK1fz581EoFAQCAaEqJXnU+i+OkjEgLQpw2ihJpVI0NDQQj8fR6/UkEgnxeCwWo6CgAIPBIGo4IpEI4XCYNWvWzFq5TJmp4+DBg2N+7bJly8b0urfeeosf//jH7N27l66uLv74xz+e8R7/97//nTvvvJMjR45QXl7Ov/3bv3HzzTePeWxznfNRJnesDI7UWK1Woaxnt9sJBAIUFxdjtVonJXIzHfK9IxldIxXlDxbj8Pl8IlqTzWbFOZCcbAaDgVgsdt7vEUbjfBO5mQgTMiq2bt3KpZdeOuprLBbLWYfB16xZwx//+Efuuece7rvvPubNm8dDDz3EJz/5SfGab3zjG4TDYb74xS/S19fHJZdcwl/+8he5R8U4mU05tFPFaDKK9fX1BINBUf/Q/8Y/kueutraW6667jlAoxJYtW+jq6sJsNmM2m0kmk7z99tuo1Wr0ej0PPPAA119/PUajUdyQFi5ciM1mo6enh5KSEhKJBOl0ekjqk9PpRKPRYLVaAcjPz6e1tZWHH36YW2+9VSyOUg6sZHBks1n8fr/o8l1UVITP5yM3N1cIHYTDYdxuN4sXL6a2tpZsNsuCBQu4/fbbqaqqmnM3TJkzs2LFCtFAcTTGU1MRDodZvnw5n/vc57jhhhvO+Prm5mauvvpqvvzlL/Pb3/6WrVu38oUvfIHi4mKuvPLKMX2mzPklkzsehovULFq0CI/Hw6lTp7DZbCxatIhAIHDWkZtzLd87kkNRoVBQW1tLb28vHo+HcDiMUqkUkWyDwUBtba0YbyQSOe/3CKMx0xtFThcTMiq+853v8MILLwzpRBwIBNi4cSNvvPHGZIwNgGuuuYZrrrlmxOcVCgX33Xcf991336R95lxktuXQTgVnklFcvHgxp06dYt68ebjd7jF57srLy/nxj3/M1Vdfzcsvv8zRo0dpbm4mlUpRVlbG0qVLMRgMvPnmm7z44osUFxeLIu7q6mqWLl3K3/72Nw4ePEg0GhUbNSmHPpPJ0N3dTVVVFYFAAKPRyPLly7Hb7Rw9epSXX36Z66+/nvb2dlpbW0VdhxTZkNKZrFYryWQSjUbD8uXLOXr0KH6/H5vNRjabpaqqCq/XS21tLZ/4xCeYP3/+tHwnMrOP5ubmST/mhz/8YT784Q+P+fWPPfYY8+bN46c//SlwenO8bds2HnzwQdmoGCfni0zueBkcqYnFYsybN4/i4mJycnLo6emZlMjNuZbvHc2h6HA4WLp06YDoo8/nY+HChdTW1g7IUnE6naxcufK83iOMxGxoFDldTMioePPNN0kkEkMej8VivP3222c9KJnpZzbm0E42Y0kB0+l0/NM//RNWq3XMnjulUsmGDRu47LLLuOeee1AqldTV1QmVHI/Hg9frxe12o9Vqueyyy4hEIhw4cIBwOAxANBolk8mgUqnIZrOoVCqUSiUqlYp0Oo3b7aa+vn7AjV5ajG666SZuv/12nn/+ebq6uujs7CQvL4/i4mIcDgeHDh1CrVbj8/koLi5m3rx5mM1mGhsb6e7upq+vD4/Hw8UXX8yGDRumXFpYZnYj1bydS955550B/YsArrzySr761a+O+B5Zanzmcq7mU1NTwze+8Y0Boh2lpaUiFbi/MMVYxzZ4LoFAgHg8Puq609nZSSAQmJL5l5WVUVtby/79+6mrqxviUIxGo3z0ox/lE5/4BIcPH+a5554TCoapVErU2810GfGxMt5rbTxy89O9fxrrXCbz+xqXUSFZq9lslqNHjw7oBZFOp/nLX/5CaWnppA1OZnqZyzm0MPYUMKvVOqzHaHA+5eA6CafTidvtZsmSJeL42WyWxsZGotEopaWlBINBocRUV1fHCy+8gEqlIjc3l0QiIQwKKYUqGo1SUlJCKpViyZIl5OXlic/rXwezdOlSvvWtb7Fu3Toef/xxQqEQ1dXVpFIpMpkMLpcLq9UqwtkOhwO73Y7T6aS7u5u77rqL0tJSPB4Pra2tVFZWntcG5lQyF/Nujx49Sltb2xBn1HXXXTcln+dyuYbtXxQIBERH5MHIUuMzl3M9H71eL66Znp6eAX97vd5xHWvwXKRIcE5OzrDXZSQSoaqqimw2O2US3R/60IeIxWL4/X7sdruQk5ei01dccQVGo5G1a9dSUlIyRIZ90aJFLFu2bEbLiI+V8V5r45Gbn6webmNlrHOZzDqYcRkVUq6sQqEYtgeFXq/nkUcembTByUw/czWHFs4uBWykfMoPfehDQio2GAwSiUTw+Xw4nU7RJE8qntZoNCJfVYoOSAXZJpMJm81GJpNBrVaj1Wrx+XwEAgFSqRRKpXLIhk1Siuro6BDf44YNGygtLRVjjUaj5OTkkEwmufDCC4eILgQCARYuXMhbb73F8ePHRY1HTU3NnMsVnQzmWt7tqVOn+Id/+AcOHTo0oM5C+m3NpD4VstT4zOV8ms/gudjtdqxW64iRgoaGBlauXEl9ff2Uzb2goACj0Tik8W1tbS3XX3/9gOj0YBl2k8mETqejsLBw1n83MP5rrbu7e8xy84Nl46easc5lMmuQx2VUNDc3k81mmT9/Prt37x6wAdFqtRQUFKBSqSZtcDLnhrmaQzvRFLCR8in3798v0qkWL17M7t272b59O5FIREQcTCYTCoUCq9UqFJcOHjxIOBwmFArh9XpRqVRYLBZUKhUGg4FwOEx7ezvhcJh4PE5raysWi4VQKCQ8Id3d3bz11ltoNBp+/etfo9frB2xe+xuObrebP/zhD3g8HnQ63YA5q1Qqurq6cDqdlJeXU1JSgkKhmJO5omfLXMy7veOOO5g3bx5bt25l3rx57N69G5/Px9e//vWzkhs/E0VFRbjd7gGPud1uLBbLsN5EkKXGZzrn03z6z2Ws645afdZtxUZFEuMYi0Oxvwy7VNd3vnw3ML5rTZIcHqvc/HQzlrlM5rjGdZVKubKzOV9ORmY0xpsCNppyR11dHX6/n5deeokTJ07w05/+lEgkgkKhwGKxkEql6O3tJZVKkZOTQzqdJhaLoVQqyc3NRafT0dvbSyKRIBaL0d3dTV5eHp2dnSSTSdRqNUajUaRB7d+/H6PRSDQaZdu2bWSzWS699FIqKiqG3bxKhuPSpUsHRC+kOa9cuZLu7m46OjrE3KRustOhSnI+ca4VXs4V77zzDm+88QZ2u10sbJdccgk//OEPuf3229m3b9+UfO5FF13EK6+8MuCx1157jYsuumhKPk9GZqJkMhn0ej1XXXUV27Ztw+VynbPU47nqUDwbZJGbgYzZqHj55Zf58Ic/jEaj4eWXXx71tVOVJysjMx2MJwXsTModdrudhoYGXnnlFQKBAIsWLaKzs5NYLIZOp8Nms+FyuWhra8Nms6HX6ykoKBDKTmq1WkQoYrEYra2tpFIp9Ho94XAYtVpNeXk5ZrMZl8vFtm3bhEzt+973PhFuPdPmdbg5ZzIZ/v3f//2cqZKcT5xrhZdzRTqdFhKTdrudzs5OampqqKysHNLYdDRCoRAnTpwQfzc3N7N//35sNhsVFRXcc889dHR08PTTTwPw5S9/mU2bNvGNb3yDz33uc7zxxhv8/ve/Z8uWLZM7QRmZs2BwOqROp6OoqIhLLrmE5cuXz5nU49mMLHIzkDEbFRs3bsTlclFQUDBq06HxaI/LyMxUxuqxOZNilE6nw+l0cvz4cfLy8lCr1ZSWluL1eolEIsJLFY1GicfjFBYWks1micfjBAIB0fk6Ho+jUqlIJpPE43FCoZCIeFgsFi644AJhdGg0Gurq6kTfCgmpM/q7777LX//6V2prawcsWoPnfOjQIbkh4iQxV5tLLlmyhAMHDjBv3jzWrl3LAw88gFar5fHHHx+XLPGePXv4wAc+IP6Wah8+85nP8NRTT9HV1UVbW5t4ft68eWzZsoWvfe1r/PznP6esrIz/+q//kuVkZWYMjY2NPPLII0PSIVtaWoSQxlzZiM525rrITX/GbFT0T3mS059kZE5zJsUor9dLa2srfX19pFIpenp6MBgM5OfnU1hYKJrYNTc3i1Smrq4uUVy1fPlyAN577z1RQCc1urPb7ZhMJgKBALt372b16tWi74TJZBoyFo/Hw9GjRzl16hQPPvgghYWFoxYJyw0RJ4+5ei7/7d/+Tcgi33fffVxzzTVceuml5Ofn87//+79jPs5ll102ajO9p556atj3TFV6lczcYrIV2zKZDC+++OK0pEPORbW5c8FcFrnpz9RW/sjInOeMlk/p8XiE91Sr1aJWq1Gr1YRCIeLxOKWlpZhMJvx+P0ajkfLycmKxmJCODQaDvPfeewD09vYKZQnptZLXW6fT4fF4OHTokEidGrx59Xg87Nq1S3xWdXU1Go1m1CJhOVd08phL5/LgwYMsWbIEpVI5IDKwcOFCGhsb6enpIS8vb0gamMy5Qd50js5UKLZ5PB6ampqmPB1yrqnNnWvkmpRxGBUPP/zwmA96++23T2gwMjKzjZHyKUOhEG+//TaVlZVceeWVvPrqq3g8HnJzczEYDEQiEbxeL3q9Ho/HQ2VlJYlEArfbTVlZGVqtFr/fT1NTE9lsFqPRKJSipFxN6bMUCgVmsxmn08lFF11Ebm4uO3bsoKKigpycHCwWC42NjYTDYTQaDcXFxdhsNtElfCSv2OC5SYZMIBCgvb19zuWKng1nm3c7mzZ+K1eupKuri4KCAubPn8+7775Lfn6+eN5ms53D0cn0R950js5UKbZFo9EpT4eci2pzMueeMRsVDz744IC/PR4PkUiE3NxcAPr6+jAYDBQUFMhGhcycYrh8ylQqhUajYfHixej1ei688EJef/118TvRaDT09fURjUbJz89nwYIFuN1ustksgUAAs9mM3+8HTm8ok8kkZrOZ3Nxcurq6CIVCuFwuqqqqSCaT+P1+1Go1VVVVnDhxghMnTnDo0CGMRqMwXDQaDTabTTS4g6FesYqKigGb15qaGjE3ycDp6emZk7miZ8tE825n28YvNzeX5uZmCgoKaGlpkdNlZyjypnN0plKxTa/XT2k65FxVm5M594zZqGhubhb/fuaZZ/jP//xPfv3rX1NTUwNAU1MTt9xyC1/60pcmf5QyMjOcwfmUHR0dPPHEEzgcDkKhkPid7N69m56eHtHJuq6ujs9//vO8+eab1NfXE4/HaWxspLOzk76+PjQaDSaTCY1Gg1arRavVUlpaisvlEoaFVKOh0+l45513SCQSXHTRRTQ0NNDS0iIa5OXm5g7b0VPyih04cIBnnnlm2M3rN7/5TVpbW3G73RQWFsodtSfIePNuZ+PG78Ybb+T9738/xcXFKBQKVq9ePWL/olOnTk3z6GRA3nSOhZEU27LZLH6/n5ycHN59911aWlrGJToA4HA4qKmpmbJ0yLmqNidz7plQTcW9997LH/7wB7FRAqipqeHBBx/kox/9KJ/85CcnbYAyMrOF/vmUZrMZvV5PPB4Xz9fU1LBo0SKcTifd3d0kk0keffRRotEor776KkajEYvFgt1u58SJE+zcuVMYCz6fT0QvHA4HVVVVuFwuVq5cSXFxMU6nU9Rq1NfX4/V6yWQyWK1WbDYbTqcTjUZDIBBg165drF27VjSvlJroPffcc8Tj8RE3rzU1NULydq5uNCaDsebdjmfjN5N4/PHHueGGGzhx4gS33347t9xyy3lXgD7bkTedZ2Y4xTaPx0NjYyNer5dEIkEkEuGhhx7iK1/5yriMe6VSycaNG6dMhnQ61eYymQxut5vu7m4sFsuMTs2UmXomZFR0dXUJ1Zr+pNPpIV1MZWTmIhUVFdTU1NDd3Y3VahULt1KppLy8nGAwyPr165k/fz5tbW0DQuGS8pPJZBIyshqNhpqaGo4ePSo6X0vh8c7OTvR6PalUSni2GhsbiUQilJSUAKcNh0AgQHl5OaFQiMbGRux2OwDt7e1CVaq+vn7Ezetdd911Ds7k3GU8G7+ZVuB91VVXAbB3717uuOMO2aiYYcxViePxMFixTRK7iEQiWK1WcnJyUCgUnDhxgocffnjcUcPa2topkyGdLrU5KTXT7/fT0tKCTqeb0amZMlPPhMzJD37wg3zpS18SyjRwevH4yle+woYNGyZtcDIysxXJE2WxWGhoaMDv95NKpfD7/Rw9enSAJ0pSBmpvbxeymZJkbF9fH4FAALvdLrT+i4qK6OnpASCRSHDBBRfwsY99DJ1Oh9FoxO/34/V6hTGjUCgoLCxEqVTi8/nQ6XR0d3cLL5lkoFRUVAyryGM2m9mxYwfvvPOOnB8/jYxl4xeLxWb0xu/JJ5+UDYoZSP9N53CcrxLH46H/fTmTyQhHjcPhQKvVEgwGKS4uZvXq1Xi9Xl588cVx3x/r6ur45je/yX333ce9997Lfffdx913333WG/Lh1hQJKb2qrq7urJwRUmrm/v37sVgsVFdXY7fb2bdvHw8//DANDQ1nNQeZ2cmEIhVPPPEEn/nMZ1i9ejUajQaAVCrFlVdeyX/9139N6gBlZGYrtbW1XHvtteTk5IzqiRpJGai0tFTUMkkF2qlUiry8PC655BI+/vGPi66rbW1tPP/88yKVSSoUl1Cr1RQUFJCfn08wGKSvrw+Px8PFF1/M4sWL+e1vfztk8yqF+ru7u+nr6+PHP/4xGzZsYMOGDSxevHj6TuQc5XzobREOh7n//vvZunUr3d3dQzZdck3FuWEuSRxPlP735T179tDZ2YnFYhGNSQ0GA7W1tSiVyrNKF5sKGdKp7vI8ODVTr9eTTCblmhyZiRkVDoeDV155hWPHjtHY2Aic3kBVV1dP6uBkZGY75eXlfOMb38DpdI5amDuSMtCll17K8ePH2bdvn0iDKisrG2BQ9I927Nu3j5KSEtRqNclkEp1OJxSlysrKWL9+PR0dHXR3d3P33Xezbt06du7cSTQapbOzk7KyMhQKxYBQv06nIzc3l4KCAk6dOsWmTZu47bbb5PD2FHM+bPy+8IUv8Oabb/KpT31KFG7LnHumetN5viDdlx977DGOHDmCQqEQsty1tbWiLm0mpYtJ8tOpVIobb7yRXbt20dTUNKnpVcOlZkoF7PF4HLPZzNGjR+d0Tc5c5aya31VXV8uGhIzMMGQyGaGWFI1Gx6SWNFgZyO1284c//IHi4mLKysro6+ujs7OT5uZmHnzwQV577TXWrl0r8lelTUJHRwcmk4menh4sFgvBYFB41RQKBYFAgIsvvpi8vDweeOABoRJ18OBBFixYQG1tLU1NTUQiEex2O16vl+LiYkpLS7FarezcuVP2Qk0D49n4zdS0tD//+c9s2bKF9evXn+uhyAxiohLHc426ujruuOMO2tvbMZlM2Gy2AXVyMHOihsPJT1dXV/PpT3+awsLCCfW4Ga5HzuDUzEAgwPbt2/F4PKRSKXH8AwcOyEbFHGPMRsWdd97J9773PYxGI3feeeeor/3Zz3521gOTkZmtSDf2pqYmbDYbPT091NTUjKl4TQqFZzIZNm/eTHNzM8lkUkQXMpkMFouFbDaLz+fjvffeGyAtKm0Sdu3ahcvlwuVyUVZWxtKlS9FqtaKeY+nSpWzatElIla5fv54dO3Zw7NgxOjs7SafTmEwmvF7vAIMEkJVhppHZvvHLy8uTm93NYMYrcTxXqaqqYvXq1ezbt2+IQTFToobDyU+HQiF27NjBwYMH+eIXv0h9ff24vtuReuRceOGFIjUzkUjg8/lwuVyYzWY0Gg3BYJCenh6ee+45qqurZ/x9SmbyGLNRIaVfSP8eCTm8LTOXGXxjLykpQaFQDOkrcKYOyW1tbezatUvUUUQiEVQqFQaDgVgsRiKRwOPxsGrVKjo7O0XkoP8m4cCBA2zbtg2Xy0VPT4/YjF533XW89NJLA6RKLRYLl156KQ0NDTQ0NBCJRFAqleTm5lJTUyOUogAxhpkQ6p8LzOaN3/e+9z2+/e1v85vf/AaDwXCuhyMzDFOR03++MdaoIUBLS8u0/06Hk5+WauI8Hg89PT0cP36cj370o9xwww1j2uSP1iOnra0Nm81GW1sbvb29QlgEThtZ8XicBQsWEIvF5Kj2HGPMRsXf/va3Yf8tM3s400ZW5uwY7sauVCqHFK9lMhleeumlUTsk+/1+Tp06RSqVwmKx4PP5yMnJQa1Wo1arCYfD9PX1EY/Hh0QOpE1CVVUV11577ZDvvKWlhT179mAymfD7/cLz5nA4sNvtaLVaduzYIQyHAwcO0NHRQV1dHRaLhUgkMiNC/XOJ2brx++lPf8rJkycpLCykqqpqgHgAMEBBUEZmJlNTU8ONN97Iyy+/TGtrKyqVCr1eL6KGAPfff/+o9/WpYnCNw3Dyt8FgkO3bt+N0Os8ofzuWHjllZWVotVpOnTrFhRdeKIwJqYi9rq4OrVYrR7XnGGdVUyEzexgpjCnrSU8eY+krsHPnTo4cOTJqk7m6ujqCwSDhcJjc3FzS6TSZTEZ0JVYoFKjVauLxOIlEArvdPmKR4ODNaENDA4899hh79+7FaDSi0Wiw2+2i6NDr9dLd3S3Gn5+fTyqVoquri0AgQH5+Pk6nk5UrV87oAmGZmYG02ZKRmc30Xz+j0SgAJSUlXHvttVx++eU0NTWN6NXvf1+fKvrXOGSz2QHytwqFgkwmQzgcpqKiQsjfjhY9GMta5vP5uOyyyzhy5AjpdJqenh6USuWAIvZUKjVjCthlpocJGRWxWIxHHnmEv/3tb8PKBMrep5nFaGHM6bjhzRXO1FfAYDBw6tQpiouLWbduHYFAAK/Xi06no66ujoaGBnGzN5vNGI1GotEoFosFpVJJOp1GrVaTzWaFZKxWqx1zkaB0HbS2tmIwGNBoNGSzWdra2vD7/Vx44YU0NTURDAYpKipCq9Xi8/mwWCzk5+fT3d3NkSNHcDgcsjKMzJj4zne+c66HICNzVoy0fra3t/P8889TXFw8JJ0UhjYPncoUoP7y05lMZkCfIjjdz0itVpOTkzOmmrixNkesqKhg+fLllJWVUVBQgFarHfC5M6WAXWb6mJBR8fnPf56//vWvfPSjH+XCCy+c0jqKf//3f+e73/3ugMdqamqElG0sFuPrX/86zz77LPF4nCuvvJL//M//pLCwcMrGNJsYSxhzruQ8TnX615n6CnR1dREOhzGbzWzfvh2v10sqlUKtVmO32yktLRU3e6vVyvz582lubiYYDKLVaolGo+h0OhKJBCqVitzcXHQ63ZiKBPtfB5WVlTQ1NdHZ2YlWq0WhUNDT00MoFBKNksrKyqipqaGpqQmv10swGEStVqNSqcackysjIyMzmxnL+vmb3/yGrq6uUb36U50C1F9+WoouS6mGkqR4cXExVquVdDp9xujBWHvkVFdXU1tbS3d3t4iKSMyUAnaZ6WVCRsWf/vQnXnnllWmTCayvr+f1118Xf6vV/zfsr33ta2zZsoXnnnsOq9XKrbfeyg033MD27dunZWwznbGEMedCzuN0pH+Npa+AWq2mubmZWCyG1WpFo9GQTCbp6uqit7eXgoICgsEg9fX1rF27llgsRjKZJJ1OEwqFCIVCmM1mtFqtSHsaS+RAug4MBgPvvvsuADk5OSQSCZLJJIlEAr/fj0qlwm63U1BQQEFBAQ6HQ2iPS0aFbLDLjIbNZuPYsWPY7Xby8vJGdTpJneFlZGYiY10/M5nMiOvnRHpYjNcB1r+QvLW1lUwmQzweFxLi/RX8xhI9GGuPnKqqKjZu3Mizzz5LQ0MDpaWlcr+TOc6EjIrS0tJpDWep1WqKioqGPO73+/n1r3/NM888w+WXXw7Ak08+SV1dHTt37mTdunXTNsaZyljDmOdzzuNE0r8mEtUYrBAifVYgEKC9vR2Hw0F7ezvBYFCoQgHodDocDgednZ1ks1mMRuOAY3k8HubNm0cgEODUqVN4PB4UCgV2u51Vq1aNSVo0GAwSjUbxer1EIhGRE9va2koymRTpVdLcDx8+TF5eHgUFBeTm5gKntcg1Go0cypYZlQcffFBcIw899NC5HYyMzFkwlvUzk8mgVCrP6NUf631zog4wSX76+eefp6uri87OTvLy8gbUOIw1ejCeHjm1tbVce+215OTkzErZa5nJZUJGxU9/+lPuvvtuHnvsMSorKyd7TEM4fvw4JSUl5OTkcNFFF/HDH/6QiooK9u7dSzKZZMOGDeK1tbW1VFRU8M4774xoVMTjceLxuPg7EAgApzdTM7WJ1EhkMhmy2eyI4zaZTOj1+hFveJFIBL1ej8lkOudzP9NcJnrMP/7xj/h8vmHD11Idw6JFi4TR0NjYyIsvvkhTU5O4qdfU1LBx40Zqa2tH/byamhpuu+02XnzxRY4dOwac9sZecMEFXHDBBTQ2NhIOh1EoFEM8X0qlEqVSKc5B/2M1NTWhVCqpr6+nqKiI9evXs2zZMsrLy0dtfpbJZGhvb8fpdBIKheju7kav1xOJRAgGg+j1enJzc4nFYoTDYbLZLOFwmFgsxmuvvcYVV1whFqOOjg4uueQSSktLz/m1crZMxbV2LhnLfKZrrp/5zGeG/beMzGxjLGlANpuNoqIiWlpaRvXqjyUF6GzrH+vq6vjWt77FunXrePzxxwmFQlRXVwulv/FED8bTI6e8vJxvfOMbOJ1OWV1yjjMho2L16tXEYjHmz58vCj77M5kh7bVr1/LUU09RU1NDV1cX3/3ud7n00ks5fPgwLpcLrVYrPKkShYWFuFyuEY/5wx/+cEidBoDH4yEWi03a2KeDTCaD3+8nm80O+wPW6XSsWrWKU6dODXtT7OvrY/Xq1eh0Orq7u6djyCNyprlMBLfbjd/vZ/ny5ej1+iHPL1u2jL6+Po4cOUJhYSHt7e1s3ryZQCBAdXU1Op2OeDxOd3c3zz77LNdeey3l5eWjfqbNZuPmm2/G4/HQ19dHbm4uDoeDtrY2Vq9eTSAQIB6PYzAYUKvVog9FcXExFosFj8cjPGP9jxWNRtHr9TgcDnF+vF7viONob2/nnXfeoaOjQ6QvWa1WdDodABqNRvwXjUZRKBSoVCrUajXpdJpsNovb7cZkMhGPx6mtrWXFihV4vd5Zv1hMxbV2LhnLfM51NFLqr9Kf4e5JMjIzhbGmAV133XVs2rTpjF790Zis+kelUsmGDRsoLS0VBkFnZ+eEogeDe+RI61I4HKalpWWAoTRbZa9lJpcJGRU33XQTHR0d/OAHP6CwsHBKC7U//OEPi38vW7aMtWvXUllZye9///thN4lj4Z577hnQFTwQCFBeXo7D4Zh1i1wmkxE9Bka60WzYsIFNmzaxc+dOysrKMBgMRCIRccP74Ac/OGx62XQzlrmMl+7ublpaWqiurhbNG/uTSqVoaWkR6URPPPEEjY2N1NXVkUwmxXusVisNDQ3o9XruuuuuMY2voKAAj8cj5hOPx4nFYuh0OjweD83NzaJQOz8/H6vVSldXF8lkErvdPuAzioqKRNTB6/ViNptFlGI4GhsbefLJJ/F6vZSVlZFIJDh69Cjt7e0olUpMJhORSIRsNks6nUalUmEymcS4o9EoPT096PV6mpubueGGG9i4cSM2m21Sv59zxVRca+eSscwnJydnmkd1evNx99138/vf/x6fzzfkeSnlTkZmJjLWNKDxePVHYrLrHyeraaZkLDQ0NPDss88OScuS1gUZGZigUbFjxw7eeecdli9fPtnjOSO5ublUV1dz4sQJPvShD5FIJIQ3WMLtdo+6SdbpdMJb2x8p/WS2ITVZG2nsixcv5rbbbhuSp7ly5coZl/N4prmMF4vFgk6nGzV8rdPpsFgsOJ1OGhsbKSsrG/amLqkzOZ3OMXtk+s+nsrKSmpoa9u3bx8UXXywiFqFQCKfTyZ49e7BYLDz++OO8/fbbA3Jox5Nnm8lkePHFF/F4PCxevBiAAwcOoNVqRS+KSCRCOp1GoVCIjV0gEECtVuP1etFqtdhsNpYvX45CoeCmm26iqqqK7u7uWfs7GcxkX2vnmjPN51zM8xvf+AZ/+9vf+MUvfsGnPvUpHn30UTo6OvjlL3/J/fffP+3jkZEZL2M1GM52Ez8V9Y+TFT0YLS3L6XTy2c9+loKCArnBrszEjIra2lrRAGa6CYVCnDx5kk996lOsWrUKjUbD1q1bufHGGwFoamqira2Niy666JyMb6YyWV6L2cZYw9cVFRUcOXJkSova+3u9GhoaKCsrI5vNcuDAAXp6erDZbFx88cUYDIYBObTAuPJsB3u8+vr68Hq95ObmYjKZSCaTRCIRDAYD0WhURGT0ej0WiwWVSoXf78dsNlNQUEBvby/hcHhCc5aZ22zevJmnn36ayy67jM9+9rNceumlLFy4kMrKSn7729/yyU9+8lwPUUbmjIx1/TybTfxYZVynWyjjTGlZDQ0N7Ny5E71ez8svvyw32J3jTMiouP/++/n617/O97//fZYuXTqkpmIyU4j+9V//lWuvvZbKyko6Ozv5zne+g0ql4qabbsJqtfL5z3+eO++8E5vNhsVi4bbbbuOiiy6SlZ+GYS7mPI5HxWI6bur9vV4NDQ3s379f1G/U1dXhcDgARA7tCy+8ADCuPNvBHq94PC50y3U6HRUVFbS1taHX6wkGg0K9xGg0olKpiMfjGI1GcnJyOHz4MAUFBXR0dGAymYaN8MnIjERPTw/z588HTl+zUr3dJZdcwle+8pVzOTQZmXEx1evneBxg08lY0rKamprYvXs3sVhMbrA7x5mQUXHVVVcB8MEPfnDA49lsdkA6xWTgdDq56aab8Pl8OBwOLrnkEnbu3Ck2Xw8++CBKpZIbb7xxQPM7GRmJsYavx3pTLysro6WlZcIRH8nrtWPHDn70ox9RUFAwJOVKulnv3bsXgKqqqjHn2Q42jnQ6HWq1mmQyKf5dVFRETU0Nu3fvxuv1kkwmSaVSKJVKcnJysFqtpFIpDh8+TFlZGb/+9a8xGAysWrWKDRs2iLQqGZnRkBo4Sr+t3//+91x44YVs3rx5iMCGjMxcZjwOsOnkTGlZer1eRHAkZ67U26ikpISOjo4502BXZoJGxd/+9rfJHseIPPvss6M+n5OTw6OPPsqjjz46TSOSmY2MFL4GBhgI119//ag39aVLl/LAAw+cdYhXqVRitVrR6/UDelb0R/psqXfFcPRPyZLyWf1+P4WFhTQ3N1NfX4/VasVut9PV1YXdbhfdVfPz80V0IhKJoNFoSKVSxONxnE4nkUgEOF24XVtbSzgc5tSpU2zatInbbrtN9jzJnJHPfvazHDhwgPe///1885vf5Nprr2XTpk0kk0l+9rOfnevhychMGoPrCcrKysYtsToZBd+TzZki+C6Xi3g8Tnl5OV6vl8bGRrxerxAhMZlM7Ny587xvsCtzmgkZFe9///snexwyMlPO4PD1SMXPV199NQcPHhxyU1+6dClbtmyZsIb4YPrfrM1ms/Du6HQ6rFYr4XB4gITfaClZbrebzZs3i7nE43FcLhfBYJDFixezaNEiPB4Pp06dwmazsWjRIhKJBOFwGKvVSl1dHfv37ycSiYiohlKpFIXbPp9PFLTv3buXF154gXvuuUf2PMmMSDKZ5E9/+hOPPfYYcFqFrrGxkb1797Jw4UKWLVt2jkcoIzM5DF5L4vG4kADX6XQDnE81NTWjHutM9RvTXQw9lgi+2WxGrVaza9cuIpEIVqsVjUZDMpnE5/Phcrk4cOCAbFTMASZkVLz11lujPv++971vQoORkZkuztRk6NZbb+X/+//+vwFepwceeOCsNcT7I92s33zzTXHz7S8xq9FoxG9p//79I97Qy8rK+MMf/oDP5xswl1gsRm9vL6dOnUKn0zFv3jyKi4vJycmhp6cHnU5HbW0t2WyWaDSKxWKhrKyMaDRKR0cH2WxWGDevvvoqZrOZBQsW0NLSQmdnJ2vXrh3QeFJGpj8ajYaDBw8OeKyysnJaGqbKyEwXg9eSaDTK9u3bRxTfuO22284owTpS/cZEu22fDWdKy3I4HFitVg4cOEAkEsHhcIh1SnKQuVwutm/fzrXXXis7os5zJmRUXHbZZUMe67/ZkbXHZWYyY2ky9PLLL3P33XeLG2BLS8tZa4gP52FatmwZv/3tb/H7/TgcDvLy8giHwxw7dgyr1cry5ctZsGABTqdz2Bt6fn4+2Wx22I7h69at48iRI8yfP5+bbrpJRD6kyIhkMPzwhz/k7bffJj8/H51OJxShjEYjRqNRNIXMzc3FbDZjMploa2vjgQceAODyyy+XFwqZYfmnf/onfv3rX8vysTLnJYPXEjgt351KpZg/fz5er5fjx49zySWXiLXlpZdemlCn+bPttn02jJaWde2117J582ZeeeWVIX3LstkswWCQsrIyurq65BSoOcCEjIre3t4BfyeTSfbt28e9997L97///UkZmIzMVDGRJkNnqyE+2MOk0+koLCzE7XaTn59PQUEBPT099Pb2olarqa6uRqPRcOjQIT7ykY+MeENfs2YNTz/9tOjy3dfXNyCFqry8nKamJp555hncbvcQ71Z9fT0f+9jHxELo8/nIZDIYDAYKCgoIBAKk02k0Gg1KpZJEIoHL5SIajXLo0CHuuusubrzxRm688Ua5xkJmCKlUiieeeILXX3+dVatWDfn9yHUVMrOZkeS7rVYrSqUSi8WC1+vF7/eTm5tLWVkZjY2NeDyeYXtpjZTaNFndts+G0eoST548iVqtxu/3A6DVakkkEgQCAQwGA0uXLqWnp2fCcuwys4cJGRVWq3XIYx/60IfQarXceeedQrFGRmYmMhED4WzkZhsbG3nkkUcGhMcPHjzIG2+8QSAQwOFwUFVVxfLlyzEajcIgCAQCwrgZ6YYu9daIRqMcOHBgQIGc3W7Hbrdz+PBhotEoS5YsGda7tXz5clasWIFWqxX/HTp0iLa2NlHAnclkRIqW1MOipKSEYDDIjh076OjokGUDZYZw+PBhLrjgAgCOHTs24LnhxAlkZGYTo8l3w+nNdTAYJB6PA6fXls7OzmH7fI2W2qTX6ye12/ZEGS4tK5PJUFVVRX19PR6Ph1AoRDAYRK1WU1xcTG1tLVqt9pz02JCZfiZkVIxEYWEhTU1Nk3lIGZlJZyIGwkQ1xKXu1pKHyev18u677xKJRMjPzycQCAjvfyAQYO3atUJqc7BxM9wN3Ww2E4/H2b59O6lUakCBXGdnJ0eOHEGpVFJXVyfmOti7ddddd1FXVzdgbnV1dbjdbtxuN2q1GqvVSm9vL1arFaPRSFFRETqdjlAoREVFBV6vV5YNlBnCdCoFysxNzmUX5zPJdycSCdRqtejvI60ter1+wHHOlNp01VVXTWlj1rPF4XBw4YUX8t5771FaWkoikRDOMYCjR4+ekx4bMtPPhIyKwcV32WyWrq4u7r//flasWDEZ45KRmTImYiBMVEPc4/HQ1NQk0pMaGxtFMZuUphSLxSgtLSUYDNLY2IjdbkehUIyp2Z5UWC01GZM+X6fTYTabaW1tFYV0/env3XI6nUPmlpeXR01NDR0dHeK8hEIhDAYDpaWlGAwGYrEYarWanJycafOUycjIyEhMd+HycLKx/deSkeS7rVbrgLXFbrfT2tpKKBTCaDTywgsvjJratH37dnQ63ZQ0Zp0Mo0ypVLJx40ba29vp7OwU62MgEDinPTZkpp8JGRUrVqxAoVCQzWYHPL5u3TqeeOKJSRmYjMxUMVEDYSIa4tFoVHiY/H6/yLdVKBTodDpMJhM9PT2kUqkB+bdWq3VMHVSdTid6vR6bzYbX68VisYh8Vq/XK7poBwIBrFbrANna/t6tpUuXDpmb0WjkwgsvJBaLUVhYyMGDByksLCQWi5HJZAYsmOl0+px6ymRkZOYWZ1u4fKbN9ODnw+EwL7300hADZtmyZWItKS0tpbCwkPb2diG2YbPZcDqd9PX1odfrMZvNPP300zQ0NBCLxUilUjQ3NwuJ5cF1cVKRc0FBAQ0NDZSXl5NMJkVKkcViGbJWjNVQGM4oq6mpYe3atRQWFo7LyKitrZ1xPTZkpp8JGRXNzc0D/lYqlTgcDnJyciZlUDIyE2E8HpeJNhk6k4b4YPR6vQiPD863VSgUWK1WgsEggUAAm81GIpHA5/PR0dExJu+O3+8nlUqJqEL/fNaioiLU6tM/cZfLxcGDB+nq6iKRSKDVasnLy8PhcAjv1nBzC4fDbNq0idbWVhHWj0aj+Hw+tFotpaWlwNl5ymRkZGTGw9kWLp8pwjFc3wmXy0Vubi719fVDDJirr76a119/nTfffJOenh4ikYiodfP7/Wg0GnJycrDZbOzatYvq6mr8fj9Lly4lHA7j8Xh49913aWpqIhqNDqiLk3oMZbNZEbWA0/UaFosFvV7PkiVLxFrRf+zRaJR0Ok15eTnXXnvtAKW+4YyytrY2fve73/Gb3/yG+fPni8anUn+NM617410fZc4/JmRUVFZWsnXrVrZu3Up3dzeZTGbA83K0Qma6mUgYfKI3wJE0xIfD4XBQU1PDvn37KCkpGZBvm81mSSaTLFq0CIPBQFdXF5FIhHA4zJo1a87o3WloaOC///u/OXToEAqFAoPBQF5eHhUVFRQVFWGxWNi6dSvNzc2899579PX1DfitdnR0UFVVRTgcBkY2ym6//XZeeOEF3G43Ho+HtrY2MpkMJpOJAwcO4HQ60Wg0XHbZZXLOrIyMzJQzEQU/iTNFOK6++uoBTU4NBgN/+9vf6OjoIJ1OE4/HsVgsAwyYrVu3Eo1GKS4upry8nJMnTxKPx0mn00K9KRKJEAwGMZlM5OXl0drayrvvvktdXZ2IGnu9XubPn49WqyWZTNLV1YXT6SQej1NRUYFOp0Or1ZJKpUSUw263Dzs3g8GA1+vF5XLx7rvv8uc//5mPfOQjfOlLX6KmpmaIUebxeDhy5IhoCZBIJMjPz2ffvn0cPHiQoqIienp6hqyvg5v5jWd9lDn/mJBR8d3vfpf77ruP1atXU1xcLKt4yJxTziYMPtU3wP65ph0dHSLdyWKxEAwGMRgMrFq1ivz8fPbs2cOiRYu44447qKqqGtW4aWho4Lvf/S6HDh0SKVbhcBifz0cgECA/Px+FQoHRaCSTydDR0YFWq8VgMACnIwvZbBaPx8OvfvUrbrnllmFD+5JRds8991BUVMSLL74IQFFRESaTaUBPjaVLl8oeKRkZmSlnohLfZ4pwHDlyhEceeQSz2Ux9fb2QiQ2FQpSVlREIBAbUvSkUCkpLS/n73/9OcXExq1evZvv27WSzWUpLS8lmszQ2NgKnnbHHjh1Dp9Oh0Wiw2+10d3fjdDpJJpMD+nsplUp0Oh12u539+/ej1+tRKpWoVCrq6upIJBKkUin8fj/l5eWkUileeOEFALxeLw6Hg927dxOJRMjNzcVut+N2u9m6dSuxWIyPfvSjA4yybDZLQ0ODSL1Np9N4vV6y2SwOh4NXX30VvV7Phz70IXHfH08zP5m5w4SMiscee4ynnnqKT33qU5M9HhmZcTET9LvPRP9c0127duFyuXC5XJSVlbF06VK0Wi0NDQ1UVlby5S9/mfnz5496vEwmwy9/+Ut2796NTqejpKSE7u5u4RlzOp288847zJ8/H4fDgcFgIJlMolariUajJBIJ4HTH41AoxG9/+1tOnDiBXq8f0SirqanB5XKRn59PeXm5UK3SaDRDemrIhoWMjMxUMlGJ7zNFOKxWK++99x4f+MAHxPNS2qqUbtS/7wSc7sXS09PDkiVLCAQCA+rm4vE4mUwGhUIhhC3i8TjJZBKFQoHFYsHlcgEI8QtJxjsUCuHz+UT9ndvtJjc3F6VSKVLN1Wo1Pp+PefPmCSn/yspKDh48OKS7tc1mIxKJ0NbWxubNm4lGo8Ioa25u5ujRo6RSKfr6+sR7urq68Hq9KJVKlEol2WwWlUo1YH2daDM/mfOTCRkViUSCiy++eLLHIiMzbs4mDD4cUyVP2D/V6sCBA2zbtg2Xy0VPT8+4i9laWlp48803UalUFBQUoFAo0Gq1eL1ekT7V0tLC5ZdfTk1NDdu2bWPBggXE43E6OztFxEKlUhGLxejp6WHPnj3ccMMNI8rOrlq1ihdeeIHKykqRLmU2m6mpqWHevHkDemr0P8/nUu5RRkbm/GSiEt9ninCoVCrhgJHoLxM7uO8EIBq+5ebmDqmbS6VS4nUKhQKVSkU6nRZRCUlUQ6FQUFhYKPo8uFwuYXikUilCoZCIGvRHGo9KpRLR51QqNcCwGfza/Px82tvbAUSt3969e4lEIpjNZmH4RKNR9u/fD5w2SEKh0IB5S+vraM38ZOYeEzIqvvCFL/DMM89w7733TvZ4ZGTGxdl2uu7PVMsTSqlWVVVVXHvttRPebB87doyenh6KiorEomE0GjEYDMTjcUKhEL29vXzwgx8UzysUCgKBAHDaGOj/OJxeTJuamgZ4tqRFY+fOnWzbtg2Px0NtbS35+fnE43G8Xi979+4lm81SUVEx5DxPt9yjjIzM+Ukmk8HtdtPd3Y3FYqGiomJYBb9QKMSxY8cwm82sWbNmyHHOFOFIp9NoNJoBxkB/mVhp0y31nchms/h8Pmw2G2q1GpVKNaBurr9xotfrRW8f6V4viWYAxGIxKisriUQi4nPVajUnTpwQEY9AICAiJNL71Wo16XRarIF9fX0DDJvBr7VarcRiMUpKSmhra6Onp4dEIoFerxf3/nQ6LYRDwuGwGIs0b4nRmvnJzE3GbFTceeed4t+ZTIbHH3+c119/nWXLlg25eH/2s59N3ghlZEbhbDpd9+ds5QnHy1TUcigUCnJycshmswSDQRQKBdXV1eTl5dHZ2Uk4HBYLGJxeEKPRKCqVivz8/CFhfTgdkj916hS5ubnYbDZSqRQ+nw+fz0cikRDGxYIFCwYoSU33+ZSRkTk/kZwTfr+flpYWdDqdcE70V/BraGjA7XYDp/coTz/9NLt37x7gxDhThMPv97No0SL8fj9lZWWibqK2tpa+vj46OjqorKwUEuFOp5OKigqqq6txOp3U1dUJA8ThcKDVaoUBodVq0Wq1mEwmoQrV29tLXl6eSF0FhFqTQqEgk8mg0WhQq9Vks1k6OzuxWCwiFSkQCFBUVITf72fVqlUAbN++XURc+hs/kgS4Wq1Gr9dz7bXX8tRTT3Hy5ElsNhvJZJJAIIBKpRI1H1I6lMfjYf78+UP6HY3UzE9m7jJmo2Lfvn0D/paa3B0+fHjA43LRtsx0MtEweH9mQ11Gf6qrq7HZbPT29pKTkzNkzj09PdhsNqqrq4lGoxgMBo4dOyZqICR9c0kJymQyDZC97Y/L5SIcDrNo0SK6urpob2+nqamJdDqNQqFAo9EQDAY5duwY6XSacDg8686njIzMzERyTvh8PpYvX051dfUQ58Q3v/lN3njjDR5//HGUSiU1NTVDioklJ8aZehQ5HA5uvvlmtmzZMuB5rVaL3W4XMq/Hjx8fkLYK8PDDD9PQ0EBpaSm9vb10dnYCpxUAk8kkzc3N2Gw2ampqiMfjHD9+nEwmQyqVoqKiAofDQXNzM/n5+WSzWeLxOIFAgIKCAtF8NBAI0NHRgcViIRqNotVq0Wg0OBwObrjhBgDa29tpa2vD6/VSVFQkjAWDwSCkxy+44AIuv/xywuEwBw4cIJ1Oo1arhRHlcDjQ6/WiJ5EkWz7S+jo4LUtm7jJmo+Jvf/vbVI5DRmZCTLSRXX8muy5jqqmqquL9738/mzdvxuPxDGh4FwgEyGQyXHbZZUSjUTZt2oTZbKawsJCuri4RnZDSkQoKCsjPz8fn84nwvEQ2m6W9vR21Wk1DQ4MI82cyGdH4Usr7lUL9L7/8Mnq9fladTxkZmZnHYOeEXq8nmUwOcU7cdddd7N69m2w2y5o1a87oxBipR9HKlSu58MILKSws5MYbb2TXrl00NTWJ5y+77DKuu+46jEbjsGmr/Y8pGQIAhYWF6HQ6YrEYyWSS48ePs2jRImpra6mqqsJiseD3+wmFQuh0OhENVqvVFBcXU1tbC8DRo0dpampCqVTi9/sxGo3MmzePdevWDajHu+OOO8jJyeGVV16hubkZs9lMUVERFRUVeDyeAWvi8uXLWbFihYiihEIhOjo6RDQ6k8lQUlLCggUL8Hg8omlq//X1+uuvl51DMoIJ1VTIyMwkJtrITmIy6zKmA6VSyZe+9CVcLheHDx/G7/cLWUC1Ws2aNWv43Oc+J0LbdXV1zJ8/n1dffVWkB6RSKWEQZLNZYrGYMChSqdQAz92JEydwu90iPN/fUMhkMqhUKpH2dPToUY4dOzarzqeMjMzMY6zOnp07d47biTG4R5Hb7Wbnzp08/fTTwuFSXV3Npz/96TF3lh58TOn+Fw6HMZvNlJSU8P/+3/9Do9Gwbt06Ee0FKCsr491338VgMHDBBReg0+lER23pNcuXLycvL48vfOELWCwWzGYzVqt1yLjq6ur42c9+xjXXXMPLL7+M0+lEpVKRzWaHrIkVFRXU1dWJSH9hYSHz58/H7/cTi8Voa2tj/fr1bNy4UUiOD15fa2pq6O7uPrsvW+a8QTYqZM4LzqaT52TVZUwndXV1fOc73+H555/nvffeIxwOYzQaWbVqFcuXL+fpp5/m+eefR61W43a7sdvtIi84Ho9jNBpRKpVoNJoBi0RPTw+dnZ3i75UrV/LGG2+QSqXIZrPiPSqVikwmI5o75eXlEQgE6OnpAZh151NGRmZmMVZnj9vtnpATQ6pra2ho4Pnnnx9S/3XgwAE6Ojq4/fbbxxxRHa1WrqWlBbfbTX19PSaTSQhnAKL+raurC5fLNSDiAqejxh0dHaxZs4arrrpqTA1aN2zYwOWXXz7qmjhSpF+hUODz+aisrOSGG26grq6Ourq6YY81uPmxzNxGNipkzhsmWvw8GXUZE+Fs5Vbr6ur41re+NeAY4XCYTZs2cfLkSdRqNQUFBaTTabq6ukTI3Gq1Eg6HRThemldhYSFf+tKXiEajYjx//etfRXqTpEoihfXhtGoU/F/kQ6lUUl1dfU7Op4yMzPnDWJ09hYWFE3ZiTGf915mMJJPJRGFhISaTacKpvIMZy5p4pkj/okWL2LZtG263m8LCQi688EI6Ozs5cuQIRqNRNFGNRqNUVlbKqVBzHNmokJnzTEZdxniZLLnV/otGJpPh/vvvx+v1UldXJ1KWdDodZrOZjo4OrFYr5eXlBINBent7MZlMhEIhotEozz77LB6Ph3/6p3+ivr5ezFej0WCxWEThoZQGpVKpUCqVpNNpUqkU8Xicuro6qqqqpv18ypz/PProo/z4xz/G5XKxfPlyHnnkES688MJhX/vUU0/x2c9+dsBjUl67zOxgsLOnP/2dE+vWreOtt96akBNjOuvp+htJeXl5Q54Ph8MUFBTw6U9/mt27d08olXeijBTpf+WVV7j99ttpamoiHo+jVCoxGo1UVFSg1Wpxu90olUpWr15NLBajpqZGlgyf48hGhYwMZ1+XMR6mSm61/wJpNpsHSBum02nR7E5SFUkmk0SjUXQ6HYlEAr/fz5///Gd27NjBRz7yEb70pS8JpalQKITD4RC5vpJaSDKZRKVS4ff7qays5DOf+cyoxZBTvTjKnJ/87//+L3feeSePPfYYa9eu5aGHHuLKK6+kqamJgoKCYd9jsVhoamoSf89UZUK5QeTwDHb2LFy4kJ6eHqFm5HA42LhxI2q1esJOjOmsp5OMpP3791NWVjbguf7Gz+WXXz5q2tLZXC+jvXdwVONPf/oTX/3qV+nu7katVpPJZIhGo3i9XtxuNwUFBSiVSlQqFYFAAJ1ON22S4fJvZuYyq4yK+++/n3vuuYc77riDhx56CDjdMObrX/86zz77LPF4nCuvvJL//M//pLCw8NwOVmbWcTZ1GWNlKsPt/RdISVvd7/cL1Q5Ju9zj8RCPx8nJycFkMtHR0UEsFkOr1VJRUUFfXx+vv/46sViM2267jfe///28/PLLKJVKtFotOp2OeDxOPB4nm82K9917773U19dP6/mUmRv87Gc/45ZbbhHRh8cee4wtW7bwxBNP8M1vfnPY9ygUihnf5VduEDk6dXV1XH311TzyyCMcPnyYgwcPolKpWLRoETfffLM4RxN1YkxmPd2ZNrqSkeR0Omlra0OpVGIwGIY1fkaqUzjT9TLaGEZ77+D7dElJCT/4wQ9wuVzk5OSQk5MzoGlfOBwWNR4qlYp4PI7H4+Hiiy+moaFhSiXD5d/MzGbWGBXvvvsuv/zlL1m2bNmAx7/2ta+xZcsWnnvuOaxWK7feeis33HAD27dvP0cjlZnNTEVTuv5MVrh9uMVj8ALpcDhYu3YtjY2NeDweMpkMiUSC3NxcEYJ3u92iFsJsNmMwGFCpVEQiEdra2nj55Ze55ZZbcLlcHD16lJycHNRqNalUCp1Oh8Vi4aqrruLOO+8cYFBITPX5lDn/SSQS7N27l3vuuUc8JhWivvPOOyO+LxQKUVlZSSaT4YILLuAHP/jBsNfouUJuEHlmGhoa2LJlCxaLhUWLFlFSUkIqlcLv97NlyxYWLFgwwLAYyYkx0mZ7surpxrrRraur49Zbb+X1119n7969OJ3OIcbPSMdatmwZW7ZsGfF6ufrqqzl48OCwYwBGvNYOHjxIUVERPT094n06nY4jR46g0WgwGo2k02nR1TudTos+GseOHaOyspKysjKam5sJBAJTKhku/2ZmPrPCqAiFQnzyk5/kV7/6Ff/xH/8hHvf7/fz617/mmWee4fLLLwfgySefpK6ujp07d7Ju3bpzNWQZmWGZjHD7SIvO9ddfP2SBdDgc2O12+vr6ePfdd4lEIiKdKZlM4vf7Rd2F3W5HoVCg1WoJBoPk5+fT0NDATTfdxHe+8x1eeOEF3G43iURCqJVcd911XH755XL0QWbK8Hq9pNPpIdHnwsJCGhsbh31PTU0NTzzxBMuWLcPv9/OTn/yEiy++mCNHjgxJPZGQom8SkjpPJpOZdIUbKWLp8/mGjVhK3t5FixZN6Lcl9ZKZzco8g8+R1WpFq9UCiI3rcOdosAEgRX+bmprE/bKmpoaNGzdSW1vLxo0bcTqdNDQ0UFZWhsFgIBKJCEnt66+/XoxnOBobG9m0aRNer3dA6tX+/ftxOp3ceuutotcEnG5earVaueaaawiFQpjNZsrLy1EqlRw9enTYY+3bt4+XXnqJvLw81q5dO+R62bVrF//xH/9BeXn5gM32/v37aW9vJycnZ9hrraCggFdffRWj0cgHP/hB8b4333yTaDSKzWYTkQhJAleSpwWIRCK4XC4WLlwonFY2m43Ozk7RM2mymOrfTP/Pme2/HYmxzmUy5zorjIp/+Zd/4eqrr2bDhg0DjIq9e/eSTCbZsGGDeKy2tpaKigreeecd2aiQmXGcbbj9TJ6aq6++etjc4s7OTpYvX87VV1/Nm2++SWNjI93d3YTDYTQaDWq1Gq/XC/yfklM4HCYUCtHY2MgVV1zB3XffzZEjR1AoFFgsFjmVSWbGctFFF3HRRReJvy+++GLq6ur45S9/yfe+971h3/PDH/6Q7373u0Me93g8k17g7Xa78fv9LF++HL1eP+T5ZcuW0dfXx5EjRyaUypvJZPD7/UIGejYy+BwZDIYBz4/lHLW3t7N582YCgQDV1dUidbO7u5tnn32Wa6+9lvLycj772c/yzjvv0NHRQTgcRqvVcumll7Ju3TpsNtuIfRgymQyvv/46Wq12wH4jLy+PsrIy2tra2Lp1K7m5uQNqIgKBAFarVczJ6/WOeiydToff7ycvLw+LxTIkyl1QUIBKpWLp0qWYTKYBYzh+/DidnZ0sW7ZsyDl0Op3U19ej1WrJy8vDaDSSl5fHRRddhFqtRqvVYrFYCIVCxGKxAWp/cFqxSqVSYbFYqKmpobi4GIVCQVVVFdlsdlL7V0z1b0bifPjtSIx1LpPZM2rGGxXPPvss7733Hu++++6Q51wuF1qtltzc3AGPFxYW4nK5RjzmdHqkppq5aFXPFoabT1lZmSjWq6urG1aLfOXKlZSVlQ05D2Px1Bw6dIh/+Zd/4eWXX6apqWlAzwkpklFRUcHWrVtJJBKo1WpycnJQKpWiz4TkkfJ4PCgUCn72s5/x3nvvcf3111NQUIDD4RiwQM5G5sK1NtxrZiN2ux2VSiUaN0q43e4x10xoNBpWrlzJiRMnRnzNPffcw5133in+DgQClJeX43A4hnUAnA3d3d20tLRQXV1NMpkc8nwqlaKlpQWFQjFiIfpoZDIZEakcTxFve3u7SBGSvOfniuHOUf/eDmc6R5lMhieeeILGxkbq6upIJpPiOFarlYaGBvR6PXfddRcFBQWsXLly3PNvbW1l79695OfnDxibhFKpZM+ePVxzzTVUVlaKcQ333Qx3LK/XS2NjI06nk87OTtRqNW1tbaxcuRK73Q6cztg4cOAA8XicysrKIeciHA6ze/duCgsLsdls4nG/38/evXvR6XSEw2EcDod4r0ql4ujRo8TjcfLz8wmFQqRSKTF2qXFqYWGhEOwIBoMUFxfT0dHB/PnzyWazRKPRSbuOpvo3IzGR385MZaxzycnJmbTPnNFGRXt7O3fccQevvfbapE56Oj1SU81ctKpnCyPN50Mf+hCxWAy/34/dbhfeM6/XS21tLRs2bBBRg/6M1VOTTCa58MILKSoqQqfTiU6pSqUSl8vF3//+d1avXk11dbVILdFoNEQiEXGz1ul0KBQKISfb0tLC73//ezZs2HBefD9z5Vrrz2ztYK7Valm1ahVbt25l48aNwOn5bt26lVtvvXVMx0in0xw6dIiPfOQjI75GUjYbjFKpnPRrxGKxiM3cSBFLqWZpop+tUCjGPPaZWPx6tudIql8rKysbtn6ttLSUhoYGnE4nVVVVKJVK5s2bd8Zx9a/P6OjoIBKJjFhzYTAYcDqdA4qcpc8f/N1I0t5SaqzH42HXrl1EIhH0ej05OTkkk0m6urqIx+OsXbsWu92Ox+Ohr68PvV6PRqMZ9jxmMhl6e3sHGBWxWIxEIoFWqxUiHBK5ublUVVXR2NhIX18f2WxWCH1IG1WNRkMwGCSdTlNSUoLb7ea5555DpVLR09NDS0vLpF5H0/GbkRjPb2emM5a5TOY8Z7RRsXfvXrq7u7ngggvEY+l0mrfeeotNmzbx6quvkkgk6OvrGxCtOJMHazo9UlPNXLSqZwsjzaegoACj0Tgkz7d/XcRwjMVTs2PHDt5++226u7tJJpNoNBoWLFjArbfeytVXXz3AG6bVamltbeXEiRNicchms2SzWTQaDTqdjsrKSlwuF4WFhXR0dKBSqSguLhaL8Gxlrlxr/ZlMx8x0c+edd/KZz3yG1atXc+GFF/LQQw8RDoeFGtSnP/1pSktL+eEPfwjAfffdx7p161i4cCF9fX38+Mc/prW1lS984QvnchqCc9VwczhmavHrWPtUjHSOpkIudrDxlUqlaG5uxmAwDGuQjEc9qn9qrNlsprGxkUgkgsPhAE5HLeLxOHa7nVAoxN69e9Hr9bS1teF2uzEYDBw8eJDFixeL9wCo1WpsNhs+n4/58+cDp6MU0j2/p6eHiooKrFareI9CoeCCCy6gp6eHSCRCKBQik8mIzalOp8NkMglHhlqtFhv+dDpNZ2cn+fn5aDQa3nvvvUm5jmbSb2YmMdPkdWe0UfHBD36QQ4cODXjss5/9LLW1tdx9992Ul5ej0WjYunUrN954IwBNTU20tbUNyKcdzHR6pKaDuWZVzyZGms/ixYupra0d183gTJ6aQ4cOcejQIXJycigpKREFh0eOHOGb3/wmCoWCyspK4Q2zWCxcccUVGI1Gtm3bBvxfiozU2M7r9WI2mzly5Ij4zAMHDrBq1apZL+E3V641idk8z3/8x3/E4/Hw7W9/G5fLxYoVK/jLX/4icqcliU6J3t5eoVqWl5fHqlWr2LFjx5DN6bniXDTcHI7p7Cg9Xvqfo4aGBpYtW0YqlRrzOZpMuVgY3vgKhUIcO3aMt99+G6PROCDtZrwb3f6b5pKSErxeL1arFYVCIaS7TSaTMIIaGhpQKBQiXTUej3P48GG6u7tZtWoVRqMRrVZLR0cHl112GdFolJ07dxIKhQgEAsRiMTwej2heN3iTHolE+PjHP05ubi7/8z//Q09PD7m5uVRUVFBWVsaxY8fwer1CRSuTyeByuUin03R1ddHT00NRURF2u51gMHjW19FM+c3MJGZihHFGGxVms5klS5YMeMxoNJKfny8e//znP8+dd96JzWbDYrFw2223cdFFF8lF2jKTwlR6AcYrtzqapyadTrNz506y2SzV1dVijBaLBZPJxKlTp3jkkUd49NFHhyy0wWBQ9J8IBAIolUpyc3NRq9UEAgGCwaDIn9XpdBiNxnPuxZSZe9x6660jpjv9/e9/H/D3gw8+yIMPPjgNo5o4M6FB5HR2lJ4I/c+R3++npaUFnU43pnM0mZ7tkYwvq9XK+973Pl599VXeeustNmzYgMlkmtBGd7ARFY1GMZvNxGIxAoEAeXl5LFq0CLfbzcGDB4VRlJeXh9lsxufzEQ6HOXnyJC6XC5vNRiKRoKioiJtvvhmA733ve7hcLnQ6HXq9nvnz54s0K41GQ0VFxYCxf/GLX6Smpob169fzy1/+Eo/HQ2lpKQqFAr/fj0qlwmq1UlpaynvvvSciGnq9nkwmg0qlwuVyoVar2blz51lfRzPhNzNTGEuEsaamZtrHNaONirHw4IMPolQqufHGGwc0v5OROVtmmhdgNE/Nvn37iEajlJaWDlnAlEolDodDqID0X2j9fj+BQACtViuUPbRarehFkUwmSSQSmM1m1Go1arWa/Px8LBbLOfViysicD5zrBpHT2VF6otTV1bFo0aJxK89Npme7ra2NhoYGzGYzJ0+eJJvNYrfbyc3NpaCggEsuuYSDBw/S1tYmxC/GutEd7Li69dZbeeqppzhx4gTd3d3o9XqKi4upra3F4XDgcDjYv38/Op2OhQsXimiGUqkkHA6TSqUIhULY7Xax2fzTn/5ETk4O5eXlrFu3jkQigU6nw2q14vF4eOuttzh06BDRaBS9Xj9k7KWlpZSWlnLixAmam5tJJpNEIhFqampYs2YNOTk59Pb2kk6nxbUUiURQqVQ4HA7cbjenTp3C7/eP8VsfmXP9m5kJjDXCeNddd0372GadUTHYI5WTk8Ojjz7Ko48+em4GJHNeMlPzjEfy1JSWltLY2Eh+fv6w7zMajXi9Xjwez4CFVsqzz8nJEcZFNpvF7/cTj8dJJBJkMhmSySSBQACLxSIkDc+1F1NG5nzgXDaInOwUoalCqVRSWFhIQUHBuDaPk+XZPnDgALt27SIQCBCNRgHQ6/VUVVWxatUqysvLiUQifP7zn6e0tHTMG92RHFef/vSnxefW1dWRm5srNo4+n49YLEZ+fv6A9CjpuzIajUQiEWpra1mxYgVwunlwV1cXl1122YDaCThd47dhwwba2tr4/Oc/LxQC+3filtbCyy67jFQqRUdHB7t27RLnQlLW0mq1KBQKUqkUSqUStVqNQqFAr9eLOo7JYK43VR1rhLG9vX1YUZepZNYZFTIyU81MzjOG4T01bW1tvPPOO0QikRE3BxqNhsLCwgEL7Z49e4hEImi1WsxmM+l0WjTEUyqVZLNZsUgEAoEBi9tM8GLKyMhMnLlQ/Hq2nu2GhgZ+9atf0dnZiUajEX0gYrEYTU1NBINBVq9ejV6vp7a2dsyb3cbGRh555BHR6C6VStHb28tf//pX9u3bx1VXXUUwGKSzsxOlUikcW1L9kHQvzmazBAIBAoEAGo0GpVKJSqXCbDaL7zM/P58jR46I/hKDMZlMqNVqSktLB4x/pLXQZrPR19fHsWPHaGxs5KKLLkKj0QhxkHg8jslkQqfTkc1mRTTsXBun5wvjiTDKRoWMzDlmpucZw1BPTVlZGQsXLuTo0aOYTKYBC2Ymk8Hj8VBfXy9qjaSFtqWlhZ///OccP36ciooKXnvtNcLhMEqlUmiRa7VaCgoK0Gg09PX1YTQaUSgUM8aLKSMjMzHGmiIE0NLSMmvTTSbq2c5kMrzwwgs0Nzej1+uF0pFCoUCtVhOJRPB4POzdu5ebbrppzMZXOp3mySef5OTJkxQWFnLgwAE6Ojro7e0lkUiQzWY5fPgwH/rQhygtLcXn89HR0YFOp6Ouro6Ojg5CoRBarRa3200wGCQajaJWq8lms6L2VEKKTvT19Q0bzR7pXt7S0sKePXuE0pMUGVEoFNTV1eH1ejl58iRLlizBarXi8/mEvK3NZiMejwtjZ968eUOiJDITYyZHGGWjQkZmELMhz7g/mUwGp9PJddddR3NzM6dOncLhcIjNgcfjwWq1ctttt6FW/99PXqlUMn/+fL785S/z8MMP09raikajEY2L/H6/yLGtqKgQKVBmsxmLxXJeeDFlZOY6Z0oRArj//vunpLZspslh9ieTybBjxw7eeOMN4vE4JSUleDweIpEIOp0OlUqFSqUiHA4TDodZu3btkLEPN7/GxkZeeukl/vjHP5JKpdi7d6+ICEupQrFYDJfLxf79+1m8eDGf/vSnCYfDbNu2ja6uLtEEr729XdRTpNNp0uk0cDqq3l92fLCs7FgiUg0NDTz22GPs3bsXo9GIRqPBbrcPqO246KKLeOutt+jr60OpVIq+RhqNRkjuFhcXo1arWbdunbxWTBJjjTCWl5cP2/NqKpGNChmZQcxkL8BgBufklpeX09bWhtfrxev1otFoqK+v51/+5V9YsmQJhw4dGrJ4S5uKxx57jCNHjojFYeHChRQUFHD8+HG8Xi8Wi4VUKoXP55uzEn4yMucjI6UINTU1TVlt2UwTwhhubO+88w6HDx8mGo1is9lEd+lIJEI8HhdGQElJiZA3HnyM/vPLy8ujubkZq9VKNpslk8mI9KBMJoPNZkOr1YqocCwWo7W1ld/85jdkMhni8TgVFRWsXLmStrY2ISebyWSEHLhSqSQWi/Hee+9x5ZVXAgyQlZUiUgaDga6uLpxOJw6Hg+uuu25IHUVraysGgwGz2YxSqaSrqwu/38/atWsBOHz4MMlkEqVSSXFxMTqdDofDQXl5uRD7CJe4/c8AAFWZSURBVAQCOBwOea2YRGayvK5sVMjIDGK25BkPV0xeVlZGXl4e6XSaD3zgA6xatQqr1crmzZv5wx/+MOLiXVdXxx133EF7ezsmkwmbzSZC3TabjcbGRlwul1hIV69ePeck/GRkzmcGpwhNZW3ZTBLCGBxNCIfDbNq0Ca/XS0FBAXl5eSJKG41GheqTSqUinU4Ti8UoLCwc4GQabn5tbW08++yzxGIxLrnkEnp6eojH4xgMBhGliEQiaDQaIcfa3NyMz+fjnXfewWg0UlNTQ0FBAe3t7Wg0GlQqFdFoVKRlSU0wY7EYjY2NlJSUEIvFMJlMXH311RQXF/PSSy+xa9cuTp06RTgcxmg0otfreemll1AqldTU1IjvffXq1cTjcbq6ukR0Qkr3SiaTuN1uqqqqRDH5kSNH6O3txWAwoNPpyMnJYdWqVfJaMQWMRYRAMjSnE9mokJEZxEz2AkiMtuAvWbKEo0ePEolEyMvLY9OmTSIFSq/Xk0qlhu1yWlVVxerVq9m3b58wKAAcDgf5+fns3buXtWvX8slPfpJ58+bJXicZmfOYqaotm0lCGIOjCTqdDrfbDZzetMViMQwGA+l0mng8TiaTIRQKYTKZ0Ov1qFQqDAYDq1atEk6m/vOrq6sjEAhw/Phx9u/fTywWEymoer1epNBmMhlR6JxKpYhEIqRSKVKpFHl5eWSzWSwWCy6XC4/HQzAYRKPRkEgkUCqVAyIVUhpUMpnk9ddfp6ysjPLycp5++mlqa2vF+lBUVCT6e2UyGbEm3HjjjeJ7VyqV1NbW4vf78Xg8ou/RyZMnUalUlJaWsmLFClQqFRaLhbVr1/Luu+9is9n42Mc+JgrXR/seZ3IK3ExnJsrrykaFjMwwzPQmO2NZ8I8ePUpPTw+nTp0imUxy/PhxUqmU6DUxuMvpmYypyspKPvzhD8sGhYzMHGCqastmihDGcNGEzs5ODh8+TCaT4dixY0QiEcLhMLFYbEDdQzqdFt2kL7nkEm644QZxT5TmZzAY2L59Ox6PB5fLRSQSIZPJYDAYSCQS5OXl0dPTQzKZJJvNishHJBIR6ns5OTniuGazGb1eT3t7O8FgkEwmI8YlnTtJsU/6LxwOEwqFSCQSKBQK3nvvPV566SXUajU6nY7GxsYha8LmzZuJRqPie3c4HKxdu5bGxka8Xq9I/6qurmbdunXY7XYAvF4vDQ0NdHZ2cvToUfx+P6tXrx41nW0mp8DNFmaavK5sVMjIjMBM9AJIjGXBP3bsGO3t7fT09JBKpbBarcIbNlKX09GMqeuvvx6bzSY+Q/Ywycicv0xVbdlMEMIYKVoSDoeJRCIkk0lCoRA5OTlDNutS4zej0YjBYKCoqIhFixYNmF93dzcej4doNIpOpwNORyYCgcCATX5ubi59fX2kUikSiQQqlYqcnByRZmowGEilUuj1epRKJQqFAqvVSk9PD4lEQhgfgPi/ZGRIj4XDYdrb2wkEAixevJj29nZRv9F/Tejq6hICHbm5uYRCIaHW5HA4sNvt+P1+Tpw4QSwWY/369cKgCAQC7Nq1S1wrCoUCk8k0ajrbTEqBk5k8ZKNCRmYUZpoXQMJsNqPT6ejs7ESr1YruqP0XR4VCQVdXF0qlkoKCAvGcVEzX3d09bJfTkYwpgO7ubkD2MMnInO9MVW3ZZBgrZ+vQGC5aks1maW1tJZVKkc1mSaVSGAwGstkser1eFCRnMhmMRiM2m41MJsPf//537rnnHj73uc9RV1eH0WjE7XYTDocpKiqir6+PZDKJXq9Hq9USi8WIx+O4XC5RCwEIgQytVkswGMRgMIh+FHa7nUAggE6nw2QyoVKpROQkk8kIo2dwHwqpUFqhUBAIBEgmk0KVqbKykmw2K4yoaDSKz+ejvb2doqIi2tvbed/73odOpyMej6PT6USqlCQxLp23jo4OotEoDoeDeDyORqMRRstw6WwzKQVOZnKRjQoZmVlIOBzG7XbT1NQ0RO7PbrfjdDopLS3l6NGjAxrWSSgUCnJyckbscjqcMSXl7PZv2iR7mGRkzg1THSmcqtqyszVWJsOhMVy0pLm5mebmZlE/IfXiSafTqNVq1Go18XgctVpNIpHAYDBgNBrp7u7mwIEDPPzww9x+++2i+VswGCQSiRCNRsX/0+m0iHhIjeqkiINUaO3xeEilUmi1WhwOB6tWrQJg165deDwedDodRqORRCJBMpkUxxypsV0qlSIcDqPVaoXRpFKpcDqdIkISjUbF/T2TyRCNRvF6vTzzzDPk5+ej1WrF8Wtra3nf+96H0+nEYrEQCATw+/3odDpCoRCBQICKigrh5BounW2mpMDJTD6yUSEjM8toaGhg06ZNIhQuedA6OzvxeDwUFxczf/58rrjiCnbs2EE0Gh3QXRUQHiqNRoPL5aKlpeWMm5JMJoPL5eLJJ5+ktbWV1atXi9fLHiYZmeljuiKFU1FbdjbGymQ5NAZHSyRFo0gkgl6vJx6Pi4261PsBEKpMWq0WlUolUpPq6uro7Ozk8ccfJ5VK4fV66evrI5vNotPp0Gq1wqiQVJskg0BKYbLZbLzvfe9DqVRy8OBBXC6X6DUh1TU0NDRw8uRJ1Go1BoMBrVZLIBAYMMb+SB23pTUim82STqdFqpXBYBBF4ZJylFarxWQyEQwGicfj+Hw+tFotiUQCOG2k2Gw2VCoVR48eJZFIiBSrWCwmohTNzc0YjUahUNXfeTUTUuBkpgbZqJCRmcEM9kaWlZWJsPG6devwer2igE6hUOD3+ykuLubWW2/FaDQyf/58mpub6ezsRK/Xi8I/j8dDOBxGo9Hw5JNP8vLLL4+6KZE2MS6Xi82bN4uFV2qEBLKHSUZmOhhLLnpNTc2kfd5U1JZNxFjJZDK8+OKLk5Iy0z9aUldXR2NjI4lEQtwfpZQkadPfv3ZBrVajVCpRqVQEAgGKi4tFbcSWLVsoLi4mlUqh0WhQKBTCMJEMEqk2Qq1Wi/4UUvRDakhqNpvZuXMnbreb/fv3c9lll6HValGr1eTl5ZGbm0tXVxfhcBilUklfXx/AsNEK6fMkw0OKVADEYjERpZCMjkwmI7pgS/K0BoMBu92O0WjE7/eLKJNer2ffvn0UFRWJOg2dTkdzczMnTpwgLy8PjUZDTk4ObrebpUuXArOrF5TM+JCNChmZGcpw3sjCwkKOHTvGggULUCgUAwro4vE4iUSCRCKB0WikoqKCefPmcfToUQKBgKiHkMLuCoVCNFKKRCIjevukTYzP56Oqqgqj0YjRaBzQCEkyLGQPk4zM1DHWXPS77rprUj93rLVl40nJGq+x4vF4aGpqmpSUmf7Rkj179tDZ2UleXh6xWIy+vj40Go1QRVKr1SJikM1mRfF1IBDAaDRSW1sLnE7p8fv95OTkCKUnlUolGtxJ70+lUqLYOycnh2QyKd6zZ88e4PR9dPHixeh0Orq6uti3b58Q2MjLy2PlypXU1tbyxhtv0NPTI5rgjTRXyViQzpU0p1gsJowN6f+JRAKv1yua10npUKlUip6eHrRarajRSKfTLFq0CJvNRnd3NxaLhc7OTvFZkpGjUqn4wx/+QGlpKXV1dbOmF5TM+JGNChmZGchI3sgDBw5w8uRJSkpKhIdHUhGB0zfxY8eO4ff7eeONN2hsbCQej2OxWCgqKiKdTuN0Okkmk1RVVbFixQrUavWI3r7Bmxi1Wi2MEqkRUmNjI3a7XeQgyx4mGZmpYay56O3t7ej1+mkd20RSssYjhBGNRic1ZUaKlvziF7/gyJEjQm1J6kWRSqUGePmVSqWIWqRSKUpKSkSktq+vj9bWVhKJBH19feTk5AgHj1RDAQz4zqTj9perdbvd/PWvf8Vms6HRaMjLyyM/P5/c3FwOHDiA3+9HoVBw4MABTCaT6G8Rj8eHzE+Kikg9LNRqtUjZkiIxkoztYKTISv9xS0pUoVAIv9+P3W7H5XJx+eWXU1ZWxrFjx0TNhslkIpFI0NvbS0VFBZdeeikej2fA2jLTe0HJTAzZqJCRmWGM5o2sq6ujqamJQ4cODVB0kgiHw8TjcZ5++mneeOMNvF6vkCiUvG1SkbbZbBaSgNLjg719gzcxRqOR/Px8Ojs7cTgcWCwWvF4vfr8fq9Uqe5hkZKaQ8eSiT6dRMR3yoFL65mSmzNTV1fHVr34Vp9Mp7m0+n4/XXntNFFFL6ULFxcWiqZzVamXJkiXk5eUBiMJmnU5HcXEx8Xh8QCqStDlPp9Mi9UhSlZJSkACh+CQpPDU3NwtDIpPJUFpaSiwW49ChQ0Lhz2AwYDAYSCaTIsIi1T9I0QaDwUBxcTFdXV2oVCoqKiro6OgYteNyf2laqSg8EomIuR09elSkSklOqfb2dpRKJdFoFIVCgU6no76+noKCAnQ63YC1Zab3gpKZGLJRITNtyH0NxsZo3sjc3FzKyspob2+nr69PLGpwehE4evQofX19JBIJ4vE4eXl59Pb2CtlYk8mEUqmkuLiYaDQqNMklBnv7htvE1NbW0tfXh8fjwWQykUwm8fl8dHR0yB4mGZkpZCbmok+XPKjD4aCmpuaMKTNlZWW0tLSMeZ2pqqpi9erV7Nu3D6vVSjwex2azYTKZSKfT+Hw+CgoKqK+vJxKJcOTIEfr6+vD5fKTTafx+P8ePHyebzWK328nJyUGr1dLb2wucdtZIEQGpcBr+LxogGQwqlUo8n8lkBqRRScdJp9NCHSqVSonPSiaToiZDSk+SjCGj0UhxcTHd3d2oVCoKCgpER+78/Hz8fj+xWEycj/4Su9Lf8Xhc1G3AaQNPKgA/cuQIixcvFjK7UgRFiuoUFRUBw0eSZnIvKJmJIRsVMtOC3Ndg7IzmjVQoFCxduhSXy0VDQwP19fXCKygZGrm5uVRWVnL8+HF6e3tJpVJYLBaxcKRSKZH/OjhsPnhTMtwmxm63iw6rnZ2douvsmjVrZA+TjMwUMtZc9PLycrxe77SMabrkQZVKJRs3bhwxZSY/P5+CggLuvPNOnE4nKpUKvV4/phSs/qk4FotFqDOFQiFRRL13717S6TTRaJR4PM7OnTtF9Fev14t+DnDaoJKayUk1DVIqlbRZl7z/RqORVColiqKNRiPpdFrUJpSWlhIMBslms7jdblGDIalJSRFkKSIhRRKkSIHRaBTXghS56OvrIxaLYbVaRbSk/3cGDKgDkVK+pBoTaU2wWq0EAgHa29sxm81C2Uqv1wslQqmB3kgG70ztBSUzMWSjQmbKkTtnjo8zeSP1ej1Llixh4cKFNDc3Ew6HMRqNLFiwgGQyyYIFC0in06JzqyQnK+X5WiwWent7hXygRDabpb29nfnz5+P3+2lpaaGsrGzAJkbC4XCQn5/Pnj17WLRoEXfccQdVVVWyh0lGZgqZibno0ykPWltbO2zKTFlZGT6fjx/96EdEIhEsFguFhYVUVFTw3nvvcfToUT72sY+xfPnyYT3h/VNxGhoaiMVieDweYVB4PB6sVisOh4NQKCRkWVesWEFlZSWhUIgtW7bg8/lETUH/LtyAUJXSaDQDFKT6F0pnMhl0Oh3pdJr8/HwA8vPz6enpEUXb0qZcqpeQNvySKpS0Lni9XhYuXIhCoaC3txez2YzX68VisaDVamlubiYQCKBWq4WxI41RipYMjqxoNBqy2ayIWkhGSmtrK2azWRgTWq0Wg8FAbW2tOIacGjs3kI0KmSllPKFxmdOMxRs5f/58kbYk3fSlTq1Go5FAIAAMLAyUurCazWYCgYBo0GQ2mwmHwyJ1KplM8v3vf19Ek5YtW0Z7ezsNDQ0sW7ZMNFNyOp1UVlby5S9/mfnz50/fCZKRmcOMJRd9tFz5yWa6U7IGp8y43W6ee+459u7dSyKRoKCggGw2i8vlwuPxYDQacblcHDhwgBUrVlBXVzds5EI67htvvIHH4yEWixEOh4XnXWrsptVqycvLQ6vViuZ4ra2tKJVKIREbiUSA0xt/nU4HIIrApftwJpMhHo+Tk5MjUp8k0Y0VK1aQl5fH3//+dyH9XV5eTmtrK7FYTBgCUidsg8FAQUEBsViMwsJCVCoVV1xxBTfddBPPPPMMzc3N1NXVsXXrVtxuN/n5+RQVFeH1esW4pK7fOp2Ovr6+IVEnSQVKpVINKCJ3Op2Ew2EOHTqESqUikUiQl5dHfX09eXl5+P1+ufh6DiEbFTJTynhC47IH4zRn8kaqVCq6urpwOp1C4jUcDtPY2MipU6coLCzEYDBgMpkIh8NEIhF0Op3wmiWTSUpLS0mlUng8HkKhkMiZzc3NZcGCBUOiSVdffTUHDx4UEQydTicX1MnInCNmUi76uZAHlVJmMpkMmzdvpqGhQdQ4SL0b1Go1wWCQnJwcSktL+f+1d+bRUZf3/n/PvmQyk0wyySRkIeskrArUEHBBi6KlXlDaej3eskirbcGqtF4PbbW1Pb2g3lYFEUuPRW8toiiCO0VQKAoUYsJmNgjZk8lkmyWzzzy/P/h9HzNJgAnZZsLndU4OzMx3nu/zme/M9/N8ns8WCAQgl8sv6yE/cuQIxGIxpk+fjqNHj/IFt1Qq5d4EoYR2Y2Mjz3FITk7mC2+ZTAa5XM69FWq1GhMmTEBbWxvsdjv3UshkMt4jIhgMIikpCTKZDJWVlbjuuuugVqtRU1ODnJwcZGVl4fTp0+js7ITdbuedtL1eL5KTk3lH7vr6emRmZmLFihVQqVQwm81Qq9XYt28fmpqa0NnZCbPZDJVKxZvyCR6HQCCArq6ufvkfva+nRCJBXFwc4uPjYbFYIBKJIJPJkJaWhsLCQtTX1/PSvC6Xi5KvrzLIqCBGFOqceWVcbDfy2muvRVtbG5qamvp5fmbNmoW6ujocOXIEM2fOhEwmQ1JSEmw2G5xOJ9/RysjIQFpaGoLBIB588EHExsbi9ddfh0wmw+TJkwf0Jp06dQq//OUvUV5eDpFIBK1WSwl1BDGGREos+liGZNXX1+Po0aNobW2F0+lEbGxsSN6A0IROJpPB6/VCLpdfMnl8//79eOedd+DxeODxeHjjN61WC71eD6fTifr6eh6m1NXVBY1GwzfNZDIZ6uvrodFo4HQ6efK2XC7nY/n9fjDGkJCQgNTUVN612m63800gs9mM6upq3tn77Nmz6O7uhtVq5R4FoRS43W7nFfhSUlIwd+5c3H333SgsLMSpU6dQU1ODmpoa3ldCKDHrdrshFoshk8mQnJwMu90Oq9XKw7EGMioA8D5IPT09vFO34B1PTExETk4Ozpw5g+zsbNx3333Q6XSkK64iyKggRpRIrFYSLQy0GxkMBvG73/1uQM9PR0cH5HI5GhoasH//fq4cjEYjJBIJEhMTMWPGDGRlZaG8vBwzZszAnDlzUF9fD7PZjIyMjEt6k5qampCcnIykpCRSEARBcMaqPKjVakVNTQ2vliSEEPXOERBKtkqlUigUiosmj5eXl2PLli1ob29HfHw8bDYbr6InbMwI1fbcbjd8Ph8vLyvcN6VSKYxGI2bNmoX29nacOnUKJpMJEyZM4PkTFRUVkMvliI2NRVdXF++JIeRpAOCLdeHPZrOho6ODewpEIhGSk5ORnJwMj8eDuro6JCUl4emnn8b8+fP5/bmlpQUVFRVwOp38c5HJZFw3MMYgl8vx5JNP4tNPP8Vnn30Gs9nMe3L0TdoW/t/c3MyrTKnVasTFxaGxsREejwcikQjp6elobW2FTqeLCMOXGD3IqCBGFOqcOTT67kaeOnVqQM+PxWLB0aNH4XK5oNfrYTAY0NHRgba2NtTW1iI3NxdFRUVQqVQoLy8P2T2M1Nr3BEFEDpcrCR5OSNZwlxUXdvd1Oh0vTiF0sQbAF8c2mw25ubm8ElFfD7mQ++dwOHjIUCAQ4InawWCQl+qWyWRwOp28IZ5QtUnosp2SkoL09HSkpqbC6/UiPz8f3d3dPBRr7ty56OnpQUJCAsrLywGAV19SKBSwWCwIBoNQqVRQq9WwWq3cQAIubNRJJBK0t7cjEAjwIh2JiYnIz8/nxwWDQezZswcej4cbEUIuhtAETwiJ/eKLL9DT0wOTycRL4QqfoZC4LVS6Eubr9/uRkpKCpKQkSCQSbrQN9PkSI0skleuPeKNi8+bN2Lx5M2prawEAkydPxpNPPok77rgDwIUv9y9+8Qts374dHo8HCxYswEsvvYTk5OQxnDUhMBjX+GgmF0YrA3l+GGN8N0qn00GhUGDOnDkQiURoaWlBaWkpgAueDJVK1W/3kLxJBEFcinBLgl8qJGskyorHxsYiJiYGbrcbiYmJ8Hg8cDqdvMGc0IAuJiYGJpMJVquVhzYpFAp+TxNy//Lz81FZWQmbzcYX0oJe8vl8CAQC0Ov1cLvdPK/h/PnzvHt1UlISr3jU09ODpKQkPPLII3zzRigXu23bNuzbtw8ikQgJCQmora2FUqnkoUmCh0LoPSEg5FBMmjQJXV1dPCFaKpWiubmZ9yMCLoRyvfvuu9wAAC6ELgkeC+CCJ1osFuPTTz+F1+uFSqXi1aQAhFSG6ouQdK5Wq+F0OpGQkHDZ8rHE8HOp39VYFMCJeKMiLS0N69evR15eHhhjeO2117Bo0SKUlpZi8uTJePTRR/Hhhx9ix44d0Ol0WL16Ne6++2588cUXYz114v9DnTOHj4E8P1arlZcKFHbK4uLieCWRlJQU1NfXY8WKFdBqtbz0n6C8IrH2PUEQkcFwlAQf6hjBYBBms5n31xF2YnU6HbKzs3lpbYPBAKvVysu+BoNB6HQ65OTkoLKyEu3t7fD5fOjp6UFBQQF6enoAfJP7FwgE4Ha74fV6eaiRWCzmu/qC0WI0GqFUKmGz2eB2u7kRI9D7vimU2i4vL8f27dtRWVkJsViM1tZW7lUROm0L/SikUil8Ph9EIlHI/4X+GUJ+nLA7LRgCmzZtwsKFCxETE4Nnn30Wra2tAELzIwTvhJBE7nA4AAA6nQ56vZ439hM+d6HKVN/rEQwGUVdXh46ODtx0000wmUxUPnaUudzv6qGHHoJerx/VOUW8UXHnnXeGPP7jH/+IzZs348iRI0hLS8Mrr7yCbdu24ZZbbgEAbN26FYWFhThy5Ahmz549FlMmBiCSqpVEMwN5foQkbCGBTtgpExCSBvfs2YOurq4BdwkjrfY9QRBjz3B0yx7qGMJObO/Kc713YouKiniOQ0dHB9RqNVQqFVJSUtDe3g6NRoPa2lregZoxxnfUX3zxRfz85z/n3trOzk50d3eH9JAQQocEHA4HlEolioqKYLFYUFFRAZ/PB71eD6vVirKyMqSkpMBgMPD75kCLP2EB39zcDL/fD4fDwRfrvT0FgmEjFou558TtdsPhcMDpdMJgMPAQp08++QT79u2DTCZDe3s777At5FT09jgIHhihulV8fDwcDgdSU1Nhs9m4Z2YgL4WAEHLW3d2N1tZWmEwmuFwu0hmjQDi/q927d2PZsmWjOq+INyp6EwgEsGPHDvT09KC4uBglJSXw+XyYP38+P6agoAAZGRk4fPjwRY0Kwf0pINT0F37Q0UQwGOQ1r6OBvjsXvecdbbJcjpGSx2Qy4aGHHsKuXbtQWVmJjo4OBINBJCcnY/r06UhMTAw5vrGxEbW1tZBIJDCZTNxgKCsrQ2NjI1avXo2CgoKQMZubm7k3adGiRTCZTOPq+lxOlmAwiIaGBm4Ap6enR7SCDOfajIfrRowuw9EteyhjCIvxjo4OTJ8+Hfn5+f08HMKGiMViQVZWFjcGrFYrJk2ahLNnz6K5uRkxMTGQyWRITU1FQUEBEhMTuUHz2GOPoaCgAO+//z6vEtU7WVkwMoQFf3V1NRwOB2JiYnjIqMvlQjAYREtLC+bOnYsVK1bwniF9F39OpxNutxsikQher5cnaAsIv1XBQyJ4FYTHTU1NvHmp1WqFy+WCVquFwWBAe3s72traQsa8WAiTgMPhQH19PcRiMZRKJfR6Pc/ZuBxCx+2qqip0dXVh4cKFeOCBBygCYYQJ53dVUVEBi8UCo9E4avOKCqPi1KlTKC4uhtvthkajwbvvvotJkyahrKwMcrmcNwETSE5O5m6/gVi3bh2eeuqpfs8LDW+iiWAw2C+RK1oZT7IAwydPMBiExWKBy+WCSqWCwWCAXq/H8uXLYbFY4HQ68fnnn/MKTr0R3NtTpkxBcXExv/nEx8cjLS0N9fX12LdvH+Li4kLG7H0usViMtra2cXV9LiVLQ0MDDh8+jKamJr7AmDBhAoqLi5Genj5GM7404VwbSpokBstwlAS/0jH6LsZVKhV8Pl8/D8fjjz8eEl4rlE4Vci26urqgVCohFouh0WiQn58PmUzGQ6m+/vprNDY24q677sLnn38OADyciDHGjQqRSMQX+yKRCH6/HyqVCjabDSqVCtOmTYNKpUJTUxPuu+8+mEwm1NbWoqKiAsePH0dmZiZEIhHa29v5RpDgDbgYwkanXC4HAD4Pl8sF4JucEY1Gg7S0NF6NqaWlhTfZExLKL2cgdHd382TzwW4eBQIBaDQaaLVaSCQS5OXlhf3eaCKSEqLD+V01Nzfz78poERVGhclkQllZGaxWK95++20sW7YMBw4cuOLx1q5dizVr1vDHNpsN6enpMBgMAyaqRjJCzKOw+ItmxpMswPDIU1FRwb0HQtiSyWTC4sWLUVBQwHcgdDodXnzxRR4WKCTPVVZW4ty5c5g9e/aAil8sFuP48eP47ne/i8zMTAC46K7GeLo+F5OloqICW7duRXt7O9LS0ngDwUOHDqGiooJ7dSKNcK6NUqkc5VkR0c5wFHG40jEG4+Ho3Q37vffeQ3l5OcrKynhYaGZmJhQKBcxmM9577z1oNBpIpVL+Wzlx4gQWLVqEpUuX8ip6fr+fnzcQCITkFUilUr5YMxgMsFgsaG5uxtSpU5GQkACLxYL169ejoqICra2tqKysRFdXFwoLC1FVVcXzQk6fPs0X+4InZCCPglCJSpiz8HuPiYmB3++H1+uF0+lETEwM92gIZWnDNQ4YY5DJZPB4PHz8S3k3+s7PbDbD7/dj586dEIlEuP/++8eVt2IkCg0MhXB/V6NdsTEqjAq5XI7c3FwAwMyZM3Hs2DG88MILuOeee+D1enknYAGz2XxJd49CoeClz3ojxC1GG8JOSjTOvS/jSRZgaPKUl5dj48aN/ZKwvvrqK5w5cwY/+MEPMH36dG5ELFiwAIcOHUJrays8Hg+USiVyc3MRCAQuusOuVqvR2NgIh8MR1hzH0/XpK0swGMSuXbtgsVj6xagWFhbyGNWCgoKIlP9y1yYS50xENsNREvxKxxish6OyshLvvPMO994qFArodDrU1NSgqakJBoMBTqcTPT09kEgkmDhxIhwOBzo6OvDWW28hPz8f//mf/4nXXnsNx44dg8vl4oaE4CEAwD0FQgK3UqmEVquFxWJBVVUVTCYTduzYgYaGBiQkJCAlJQUNDQ1oampCR0cHfD4fUlJSIJFI+iV4C49jYmLg8/l4LoRarebGgdBrQ6gWBVwI6W5paUFycjLMZnNIvw7hdy94LIRE7d5GjOCFERLFr4TeCecnTpzAhg0bwkrijwaGo1jBcBPu70ro/j5aRIVR0ZdgMAiPx8O7Bu/btw9LliwBcOHGUl9fj+Li4jGeJUFcORdLwvJ4POjs7MS5c+dw8uRJZGVlwe12Q6VScWPZaDTi+uuvx/Tp03mzPCoXe3mGI36cIMYTw9Et+0rHGIyHo/f9csKECaiurkZcXBzkcjn0ej06OjrQ2NgIqVSK2NjYkI7Zubm58Hg82LlzJ+69914sWrQIVVVVcDgckEqlkMvl3BsgFouh1Wp55+veHao7OzthMpnQ0dGBkpISBAIBVFZW8m7ewIXchd4GibDwl0gk3JMYCASgVqt5tSm5XI6ioiI0NjZCJpPxPhqdnZ2847bQK8Nut8Pv9yMhIQFer5eHMslkspDyuMJ5AIQ0C+z9em8jKhxDgzGG7u5uMMaQnJyM9vb2yybxRwPDUaxgJAjnd7Vo0aJR/+wj3qhYu3Yt7rjjDmRkZMBut2Pbtm34/PPPsWfPHuh0OqxcuRJr1qyBXq+HVqvFQw89hOLiYqr8REQ1Ay1whQZ3TqcTer0ePT09qK6uht1uh16vx9y5c6FSqVBbWwuHw4H8/HyYTCZqPhgmwxE/ThCjxWjFdw9HSfArGUPYif3qq6+QlpbGd9IFA6P3vav3/VIIXZLJZBCJREhMTITdbofNZoNOp4NEIoHL5YLFYoFer0dBQQEcDgfefvttlJSUQCqVIiUlBS0tLQgEArxLtGCQaDQaXhVKCK8KBoMwGAyYNWsWNm/ezHf8hQW/gEql4h2++/akELwIgndByJdQqVRISkpCc3Mz4uLi4HK54HK50NPTw98vvKenpwcymQzx8fFQqVSoqqqCy+UKKUzTF2GevRHkFUrrhoMw/0AggPLyckyaNGlcbMJE8mbT5X5XJpMJbW1tozqniDcq2trasHTpUrS0tECn02HatGnYs2cPbr31VgDAc889B7FYjCVLloQ0vyOIaKbvArd3gzuDwYBgMIimpibExsYiOzsb7e3tqKqqwvXXX98viZHKxYYHNQEkooXRju8ejpLggx1DLBZj2rRp2LVrF0pKSjBt2jTU1tZCo9FAo9EgOzub37t63y+DwSDv7aBQKBATE4OkpCTYbDZ4vV7e8yElJQXXXHMNgAvFYNrb2zFlyhRoNBpUVVXxfAQhwVuhUMDtdsPpdEKtVkOtVmPWrFnQaDSor6/H3LlzcfLkSZjNZgDotwAVi8VQq9UQiUTo6upCIBBAUlISOjo64PF4uDdDoVCAMcYNhNjYWO5FsFqtsFgsPFxVaE7n9XpD8i46OjqgVCohl8uhVqvR3d0dViWn3kilUu6JCcewED53ocx5Q0MDN+iimUjfbLrU72osKv5FvFHxyiuvXPJ1pVKJTZs2YdOmTaM0I4IYefoucIUGdzqdDiKRCA6Hg1dCEVzy7e3tsFqtiIuL65fESM0HL89wxI8TxEgTTnz3SHTSvVS37JEYo7y8HB9++CHi4+Mhl8shl8vh8XjQ3d0No9GIhQsX8ntX7/ulTqdDQkICGhoaoNPp4PP50NXVxRfmjDHo9XpMmzYNiYmJOHToEOx2O+Lj4yESiXDs2DF0dXXx37/gIREW+VKpFHFxcZBKpVAoFOjo6EBmZia+9a1v4W9/+xv8fj9f3AuhQ0LidE9PDyZPnozc3FzI5XJMnToVJSUlaGhogMfj4bv9NpsNYrEYycnJ8Pv92LdvH7xeLy9D6/P5IJPJeJPT7u5unmDtdrt5x3CFQsFDvQabtO12uyGXy8P2VgjNVuPj4+HxeNDa2or4+Hi+CRNJlZMGQzRsNg3Hb3O4iHijgiDGMxe70fZd4Ho8Hu7SF5SOsIsFXChmYLfbuZu77+4JNR+8PMMRP04QI0m48d2PPfbYGM/0yhDuh1arFa+//josFgsPZZZKpTAYDJDJZGhqasKpU6fwne98p9/9UkjI7uzsREtLC0+2lsvlYIzxfhXHjh3DpEmTYLFYAACJiYlobGxEV1cXz58Qqi2p1Wq4XC7I5XJMnDgRZrMZWq0WXq+Xb86cP38e7e3tkEgkPE9BQAhncjgcyM3NxYoVK/Dpp5+ioqICGRkZPB8hGAxCIpFArVaHhENZrVY4nc6QkrZKpRKBQIB7OGJiYvi8/X4/D5MCBg5xCuda+P3+yxojgqxSqRSJiYnc69Ld3Y20tDRkZGREXOWkwUCbTYODjAqCGCMud6PtvcCNjY3lbn6PxwO1Wh3i4vd6vXznDBh49ySSdjMiFfLqEJFMuPHdDQ0No15K8koRDIkTJ07w6nWdnZ0oLy9HSkoKkpOTYTAYeBgTAN6lWohjFzYETp48iU8++QQSiQSpqamor6/nu/tisRgqlQpKpZKHBJ08eRIdHR1ITk7GhAkTcOLECXi9Xvj9fuh0Ot5ZWgiBcjgcsFgsmDt3Lu655x5Mnz6db878+9//ht/vh0aj4Qvr3t2xhTKtcXFxUCgU+M53voMbb7wRsbGxPNn87NmzCAaD2LFjBw4cOMAX50IOhoCQxO3z+SCRSCCVSqHVarnHQmjad6WVnASEuQt5GwP1rxDOIYRdCdUHxWIxHA4HPvroI3z44YcRVTlpMNBm0+Ago4IgxoBwS9QJC9zy8nIAQGdnJ3JyclBQUIDKykq0tLQgMTERNpsNKSkp0Ol0tHsyRMirQ0Qqg4nvjgajQthYOXr0KE6fPg2/34+0tDSkpqZCKpWis7MTR48exezZs0NCTwaKYzeZTDAajXyXX6iyJDQLdTgcvHJkfX09byqamJiIqVOncm+EEDoklUoRExMDu90Oh8MBv9/PqzwplUrk5+eHbNIIXpRAIAClUskNEuCbhTdjDNu2beNjKhSKkI2k3Nxc1NTU8B4b3d3d8Hq93BMQCAT4WMLi3ufzQSqVwu12hxgBQiL4YOlrjEilUj5W70aAvXM0GGM8eVyhUEClUiErKwtWqxW///3vER8fj9mzZ0dM5aTBQptN4UNGBUGMMoMpUdd7gXvixAns2LEDbrcbCoUCeXl5sFgsqKmpgV6vR15eHmw2G+2eDAPk1SEikWiI7w4XYWPFYrGgvb0dcrkcBoMBXV1dsNvtEIlE3FtQUVGB7Oxs/t6B5Kyvr0dnZyduvfVWMMbQ3NyM0tJSGI1GuN1utLe3w+12Izs7GzKZDE6nE+3t7VCpVGhtbUVBQUFIp+xgMMjDh4RSrsCFa9DY2NivD0NBQQFSUlLQ1NTES70KXa2FHX5h976hoQE5OTlQqVT9NpKqqqrQ2dmJnp4e7nHo3XNCWMwL/waDQR4CBYD3zRAMEuBC/4tLJWr3NiR697UQztm7l0Xvnh19kUqlvFSuyWRCUlISjh8/zjuC9z1nNJXpps2m8KBPgyBGmcGUqAO+WeAuWrQIv/71rzFjxgx0dHSgs7MTWVlZmDx5MrKystDZ2YmOjg7MmDGDJ2vW1tbi1KlTqK2tHZNKEARBDB9CfHdDQ0O/0BbBQ1lYWHjRZpeRQt++Eg6HA3FxcVAqlTAYDPD7/fD7/bDZbIiNjUVHRwd6enoAhMrZ2xMreHE0Gg3i4uKQmpoKtVoNn8+H9vZ2BAIByOVynrOg0WiQlJQEvV6P7u5uNDY2QqvVIhAIoKenBx0dHXA6nfD5fPD7/XC73ZDJZNBqtSgoKEBdXR1efvll1NTUIBgMYuLEiViwYAHvct3bU9C7H4UQHnXq1CnExsZi0qRJvKdD72Z2brebl2gVEr37GgYXMxQYY5BKv9kzDufe33tx3Pt44bzCuXqXux3ougrzamtrg9vthlgsRltb24Df2ZiYGN5fIxoQdPHUqVN52B0RCnkqCGKUGUqJuoF2S9LS0tDY2Biye1JZWYn169dHZWIcQRADM17iuy/WVwK4sADXarXo6uqCRCKB1WoFcGGh3btUaV85+3pxdDodEhMTUV9fj56eHkilUr7YFopdpKSkYOrUqTh//jyys7Ph8/lQXV2N7u5unqQtVFsSi8W8w/Xp06fR3NyMEydO4PTp05gxYwaWL1+OBx98EBUVFTh06BAPWxKqPwn/CnkPTU1NA1bry83NBQAeOjVY3G53v4pN4eRWCJ+lsNEllUohkUjg9/u5QcEYu+y8JBIJPB4Pqqqq0NXVha6uLvj9fhw6dAjp6ekoKCjgXZ6jybNGhAcZFQQxygw1hGGg0Jzej8PN1xCI1lJ/BHE1Ek58d6R7JS/VVwK4UKlJKpVi8uTJaGhogNls5p6Di8WxD1Slp6CgAK2trXC73ZBIJDznzGKxQK1Wo6CggHsWioqKcOedd4Ixhs8//xwul2tAz4CwgeP1euFwOHD27FmcPXsWBw8exBNPPIFf/vKXOH/+PJqamnhpVgC8mhRw4Z7b1taGkpISzJw5k28knThxAqdOneLvuVLCuf5KpZKfRzB4hPcKzfiEErzhJn0Hg0Ge99HW1sbDxnp6ehATE4OWlhZYrVYUFRXxaluU+ze+IKOCIEaZkSxRN5h8DaGCSrSW+iOIq5Voj+/u21ciMTERLS0tMBgMvJmbVCrlfRrmzp2Lu+66C0ajEZmZmXzB21f+vl6c+Ph4mEwmNDU1AQBPaE5JSUFBQQEYY9izZw9aW1uxadMmxMXF4ezZs1Cr1XA6nf3m7ff70dnZyasuSSQSKBQKiMVi1NbW4ve//z1eeeUV3Hnnndi2bRtsNhs/RghnEhbnfr8fx48fh9VqRWxsLBQKBd566y10dXUhLi4ONpttyNWbLkVfw0Umk3GPioCgP8KdR+8E7kAggNjYWJ6X4fF4EBsbC5vNhrKyMqSkpMBgMESFZ40IHzIqCGKUGWoIw6U8C4PJ13C5XIPyaBAEETlEczGBvhsrBQUFvFt0bGwsrFYrEhIS0NTUBIPBgOXLl0Ov1yMpKemSmyGLFi3CkiVL8N5776Guro7nT0ydOhU9PT0oLCxEYmIi4uLiUFVVhYMHD6KrqwsqlQoWiwUtLS04d+7cRXMGBOx2O29u53A4eL5EeXk5nnvuOfzgBz/Azp07YTabIZfL+4VRCXg8HnR2dqK1tRWMMXR0dGDixImQSCRQqVQDGjYjhUQiAXAh7EoohSvIGC59jQ+bzYaEhATk5+fDbDbz3JaWlhbMnTsXy5Ytg0ql4vkl0WQYEwNDRgVBjAFXWqLucp6FcPM1rFYrrx0ejkeDIAhiuBhoY2XWrFk4deoUGhsbeSO1mTNnYvHixTCZTGhrawNw8fDOAwcOYNeuXUhJSeHVhpRKJWQyGeLj49Hc3IxDhw4hLS0NEyZMwMGDB7mXICMjAz6fDzU1NZc1KASEPBAhZEtoOLdjxw40NDTwZnt9E517wxhDS0sLgAubRd3d3aitreXJ4UKJ2qGEs/UNXRLK0/bNjfB4PCFGRO/8CSEPRQiHChe9Xo+ioiIYDAbk5eXxJn5NTU0oLi7G7t27yUs+ziCjgiDGiMGGMISTKxFuvobdbg/boxGtu6EEQYwtl/Kq9t1YEcq9zp07F3Pnzg1pLCcsqgcK72SMob29HfX19Whvb4fP58PcuXNhs9nwxRdfgDGG66+/HrNnz8aZM2d4RTyv14u4uDgkJSUhJiYG9fX1lyy7OhC9+zYIC26Xy4X29nZcd911aGho4InOwrF97++9DQbBgPB6vQgEAjy0yuPxDPFKfMNAydZCyVmhU7dQbUosFvMwr3ArT/XOizGZTEhMTAQA3vhP8Oq89tprcDqdSE9PR0ZGBpxO56h5ySmPcOQgo4IgxpBwQxjCzZV47LHHwsrXiI2NveIKVMTYcTUrw02bNuHZZ59Fa2srpk+fjo0bN+K666676PE7duzAE088gdraWuTl5eHpp5/Gd77znVGc8dVNOPlag91YaWhoCNkMsVgsKC8vx8mTJ/m9qry8HN3d3SFjfPHFF4iJieFeAyGsyO/3w2w2o6urCw6HY9C/JSEpmTHGqz2JxWLYbDbExcXBaDSipaWF96y4nMeBMQaHw8EfD4cxMRjPghD61LsHRjAYDDFClEol97yIxWLIZDJIpVLenE8ikcDn88Hj8eDIkSM4d+4cTCYTsrKyAABnzpzB+fPn4fF4oNFoYDabkZiYiIKCglHxkkdqHuF4ubeTUUEQUUC4uRKNjY1h5WuoVKpx00TrauFSytBkMo319EaUN998E2vWrMHLL7+MoqIiPP/881iwYAEqKyuRlJTU7/gvv/wS9957L9atW4fvfve72LZtGxYvXoyvvvoKU6ZMGQMJri4GU4FuMLkhvcM7LRYLjh49CovFwg0CsVgMn8/Hy5j2Dk2Sy+Ww2Wzo6urii2bh/udwOLihEW6lIwFhgS0YDkKFJ5/Ph6KiInz88cchzenC8YYMdg5DRajwJCRaX+rcHo+H6yDB6yD8X6giJXhumpqa0NraiurqamRmZkKtVqOtrQ09PT0wGo3QaDTw+XwhVaFG0ksezvdyLO6lkWroXAnRZwYRxFVIOLkSQhMhIazg2muvRUdHB6qqqkKa4glNo8JpokWl/iIDQRmWlpYiMTGRhxWUlpZiw4YNqKioGOspjih//vOf8eMf/xgrVqzApEmT8PLLL0OtVuNvf/vbgMe/8MILuP322/HYY4+hsLAQf/jDHzBjxgy8+OKLozzzq4++XlWtVguJRMK9qn0bvQ0GIbzT4XCgoqICTqeTVywSGtvJZDK+w+50OuFyuSAWi2GxWNDd3c29CQDg9XrR09MDmUwW0k8iXIQ8A4lEwhfYcrkccrkcCoUC+fn5uOGGG6BUKnnys8ClEqDDmUPf98tksn6dq4X8CSFESwhn0uv1UKvV/Y7re+7e3bx7z00kEkEul/OcFa1Wy+UXQrYUCgXvKO52u3H+/HnU1dUhJiYGcXFx0Gg0/DiDwQCn04mKigqo1eoRaYg3kt/LoXC5e3t5efmozmeokKeCIKKAwfa2uFxYwXhponU1EE7o2+7du7Fs2bIxnunI4PV6UVJSgrVr1/LnxGIx5s+fj8OHDw/4nsOHD2PNmjUhzy1YsAC7du0ayakSGFwFusHuRAvN0w4dOgSLxQKFQgGPx8NDi4Su2QqFAt3d3fycHo+HN9cTDAifz8fL1wo9JIQwH2G8gRb3wvuBCwnMSqWS79ZLpVLIZDKkpKRAp9MBAK699lq0t7ejuroawWCQV4saTFUl4EKuQm/DRDAC/H4/XxwrFAq4XC5YrVYEg0GoVCoYjUbYbDbeEE8IWxIMIQGxWMyNJAHGGP/chM9D+FcikSAzMxNmsxk2mw2BQABqtZobF1qtlodzyeVybixIpVIeUiV4koSGh+3t7WhtbR0RL3m438uGhgaoVKphPffFGGwJ+GiAjAqCiAKupLfF5cIKrrQCFTG6hKMMKyoqYLFYYDQax2iWI4dQhjI5OTnk+eTk5It6aFpbWwc8vrW19aLn8Xg8ITHsNpsNwDdx5dFE7wXgaGOz2eDxeC7pVW1ubobNZgt7fr0X+IsXL8aJEyd4N2qxWAy5XA6PxwO5XI7Y2FhIJBI4HA7ueRCLxVCr1bDb7TzxWAjXEfozCOFTIpEIarUaarUaVqs1JHRJKpUiPT0dTqcTTqcTsbGxiI+Ph1gsRlNTEzweD5KTk/vdo7VaLVJTU9HV1cWNgd4ek94I8xIMj949LoTFuZC7IHT5lslkEIvFvAxsXFwc/H4/TCYTkpKSeJdrr9eLiooK2O12yOVyxMTE8IpPIpEILpeLz9fpdPJ+HEKDQuCbUDKZTAav14uEhATIZDIkJiby35hMJuOGlkajgdfrRTAYhEKhgM/n42WDFQoF/5wUCgUcDgcaGxsxb948pKWlDev3dzDfS8GzNNLU1dWhsrLyovf29PR0VFRUoK6uDpmZmYMeP9z7wHDKSkYFQUQBI+VZiPYmWlcD4YS+NTc38wUBcWWsW7cOTz31VL/nLRbLkDscjzbBYBBWq5UvqEcTxhgmTpwIpVI54I6v0+nExIkTedflcOgtj16vx4oVKwAALpcL8fHxPIZfCKcBgMzMTG4kKhQK7untnfvg8/kglUrh9/uhUqngdruh0+lCDExhV18wOvLy8pCcnAy73Y7a2lqeWJ2dnQ2JRIL4+HgYjUbuRREqQeXl5eGDDz6Aw+GAXq/nFZUuFuokEomgUqkQHx+Pzs5OHmYlk8l4wrlYLIZOp0NsbCxcLhd8Ph9kMhliYmKg1WqxZMkSqNVqqFQqJCYmor29HTU1Ndi/fz/PL9FoNCHJ6zabDTKZDG63m59TSEIXMBgMkMlkyM7O5u+Jj4+HTCZDQkICvwYCLpcLarWal6bNyclBS0sLPB4Pf97lckGj0WDSpEmYP38+2tvbw/puhEu430sA6O7uHpXfjtlshl6vR2pq6oDniomJAWMMZrP5irwn4d4HhjPUjIwKgogSRsqzEM1NtK4Gwg19Gy2X/WiTmJgIiUQCs9kc8rzZbL6oZ8ZoNA7qeABYu3ZtSMiUzWZDeno6DAbDgJ97JBMMBiESiWAwGEbdqEhMTIROp0NZWRkKCwv7eVXLy8tx7bXXYvLkyWHPra888+fPx/Hjx/Hll1/C5XKhq6sLOp0OnZ2d6Onpgdvthlqt5jvkwIX7nBACpNVq4ff7odFoEBcXB4fDgdTUVKhUKigUCmRnZ6OjowMikQgJCQmIjY3FV199hby8PDz00EN817iurg5VVVUAgPz8fLhcLrz33nuorKzs15QvPz8fdXV1OHz4MNxuN86ePQun08nLxwoIHgypVAqj0QiLxcK/f+fOnYPNZoNcLodKpYJGo8GUKVOQmJjId+LlcjmampowadIk3HjjjSGfcUpKCqZOnQqTyYSdO3fy0Jv4+HgkJiYiPz8fe/fuRVtbG/fe6HQ6novi8/mgVqvhcDhgNBp5lcC2tja0tLTAZrNxo6R3wrrD4cCkSZMAAM3NzcjKykJCQgIqKipw/vx5+P1+OBwOmEwmLFu2jB87nIT7vSwsLERHR8eo/HZcLhc6Ozt5+FdfbDYbOjs7kZycPGBBissR7n1AqVQOeuyLQUYFQUQR5Fm4+gg39M1gMIzhLEcOuVyOmTNnYt++fVi8eDGAC8py3759WL169YDvKS4uxr59+/DII4/w5/bu3Yvi4uKLnkehUPTbYQUwYG+BaOBifRFGmnC9qsKiM1x6yyMWi7FkyRI0NTUhEAjA5XLBbrfzhGGtVguj0YiUlBQwxlBWVga32w2j0Yi2tjbY7XaoVCrExcXBZrNBoVDAZDLhu9/9Lj788EM0NzeHzLuiogIZGRl48MEHkZOTw+eUk5MT8hgAJk2adNH785IlS9Dc3Ay/34/U1FR0dnZyY0DoE6FQKKDRaODxeNDT0wONRoPp06dDqVTycK9FixZh4sSJ2LlzJ9ra2ngoE4CwPmOhi/ns2bOxZcsWOBwO5Ofnc0+B2WxGMBiEXC6Hy+VCMBjkeSkajQZKpZJXSbJarTAajfB4PAgGg+jq6uLlYgOBAP8cpk2bhpqaGiQlJaGxsRHp6ekoKipCS0sLGhsbYTAY8Ktf/QqTJ08e1PciXAbzvRyt305mZiZMJtNF7+0NDQ2YMWMGMjMzr3gu4cgynHKSUUEQUQZ5Fq4uwlGGixYtisqFb7isWbMGy5Ytw6xZs3Ddddfh+eefR09PDw+DWbp0KSZMmIB169YBAB5++GHcdNNN+NOf/oSFCxdi+/btOH78OLZs2TKWYlw1jEa+Vu9zHD16FDU1NbBarYiJiUF2djZmz57NjdC//OUv+Oijj2C1WqHRaKDRaABc8F6p1WrceuuteOCBB1BYWIicnJwhzftS9+fCwkKsXr0an376Kdra2nj4U++dYqlUivj4eB5ipFQq0dnZCaVSiRtvvDFkHunp6Vc8V8HjM2HCBD5Gc3MzdDod7rrrLlRXV3PjCLhg3Gs0GqSnp2Pq1KmQy+X4+uuvYTAYcMcdd+Djjz8GcMHgr6urQ2dnJ8RiMWJjYzFr1iy0t7cjOzsbCxcuxMmTJ0PmfPPNN49KHl8438vRzEMajwVTRGw0iyFHKDabDTqdDlarNSrd3G1tbUhKSoqqL95AjCdZAJInkolGWQaqZV5YWIjFixfDZDJdVp5ovs8BwIsvvsib311zzTXYsGEDioqKAADz5s3DxIkT8eqrr/Ljd+zYgd/85je8+d0zzzwzqOZ30fx5Rcr3e7gael1KHuEcVquVn0en04WcKxgMYv/+/Xj//ffR0NDAu3Snp6fjzjvvxC233BIy7kg2IgsGg2htbYXH44HNZoPdbucLSeFfQYa0tDQ0NjZech7DMdeBxggGgzhy5AhaWloAXFgAHzlyhM9duP8sWrQIer0enZ2d2LVrF6/sZTabIZFIkJaWhuTkZH6vEhbuY+ltv9T5x+K3c6l7+1AMrXBlGc57HRkVIOURKYwnWQCSJ5KJVlkupgzDkSea73NjQTR/XtH6/b4YwyXPWC9mhTlE67UZ6PMDwOUBwF8XwrEEIylawnTH6vqMxHdzLIwKCn8iCIKIEij0jSCuHPr9DI2BPr/e4UL0+V454+Wzi3izcd26dfjWt76F2NhYJCUlYfHixaisrAw5xu12Y9WqVUhISIBGo8GSJUv6Vf4gCIIgCIIgCGJkiHij4sCBA1i1ahWOHDmCvXv3wufz4bbbbkNPTw8/5tFHH8X777+PHTt24MCBA2hubsbdd989hrMmCIIgCIIgiKuHiA9/+uSTT0Iev/rqq0hKSkJJSQluvPFGWK1WvPLKK9i2bRtuueUWAMDWrVtRWFiII0eOYPbs2WMxbYIgCIIgCIK4aoh4o6IvVqsVAKDX6wEAJSUl8Pl8mD9/Pj+moKAAGRkZOHz48IBGRe9umcCFJBXgQmzgaJYTGw7CbcMeDYwnWQCSJ5IZT7IA4ckzXmQlCIIgIpOoMiqCwSAeeeQRzJ07F1OmTAEAtLa2Qi6XIy4uLuTY5ORktLa2DjjOunXr8NRTT/V73mKxwO12D/u8R5Jw27BHA+NJFoDkiWTGkyxAePII9eYJgiAIYiSIKqNi1apVOH36NA4dOjSkcdauXYs1a9bwx1arFRkZGVAoFMParnw0CAaDcDgcUCqVUb84Gk+yACRPJDOeZAHCk8fr9QK40KmVuDzC5yR4sqOJYDAIu90+rr7f40We8SQLQPJEMuHKItzjhkM3RI1RsXr1anzwwQc4ePAg0tLS+PNGoxFerxfd3d0h3gqz2Qyj0TjgWAqFAgqFgj8WPtDMzMyRmTxBEESEYLfbodPpxnoaEY/g2UlPTx/jmRAEQYw8w6EbIt6oYIzhoYcewrvvvovPP/8cWVlZIa/PnDkTMpkM+/btw5IlSwAAlZWVqK+vR3FxcVjnSE1NRUNDA2JjYyESiYZdhpHEZrMhPT0dDQ0NUdegqS/jSRaA5IlkxpMsQHjyMMZgt9uRmpo6yrOLTkgvRA7jSZ7xJAtA8kQy4coynLoh4o2KVatWYdu2bdi9ezdiY2N5noROp4NKpYJOp8PKlSuxZs0a6PV6aLVaPPTQQyguLg678pNYLA7xfkQjWq026n8AAuNJFoDkiWTGkyzA5eUhD0X4kF6IPMaTPONJFoDkiWTCkWW4dEPEGxWbN28GAMybNy/k+a1bt2L58uUAgOeeew5isRhLliyBx+PBggUL8NJLL43yTAmCIAiCIAji6iTijYpwEkeUSiU2bdqETZs2jcKMCIIgCIIgCILoTXSnthNQKBT47W9/G5J4Hq2MJ1kAkieSGU+yAONPHmJojLfvw3iSZzzJApA8kcxYyCJiVF+QIAiCIAiCIIghQJ4KgiAIgiAIgiCGBBkVBEEQBEEQBEEMCTIqCIIgCIIgCIIYEmRURCB2ux2PPPIIMjMzoVKpMGfOHBw7doy/bjabsXz5cqSmpkKtVuP2229HdXV1yBhutxurVq1CQkICNBoNlixZArPZPOJzP3jwIO68806kpqZCJBJh165dIa8zxvDkk08iJSUFKpUK8+fP7zf3zs5O3HfffdBqtYiLi8PKlSvhcDhCjjl58iRuuOEGKJVKpKen45lnnolYef74xz9izpw5UKvVIV3fe1NfX4+FCxdCrVYjKSkJjz32GPx+f8TJU1tbi5UrVyIrKwsqlQo5OTn47W9/C6/XGzLOaFyf4bg2//Ef/4GMjAwolUqkpKTghz/8IZqbm0ddluGSR8Dj8eCaa66BSCRCWVnZmMhDDD+kGyJDN5BeiFy9MBzyAJGjG6JNL5BREYH86Ec/wt69e/H3v/8dp06dwm233Yb58+ejqakJjDEsXrwYNTU12L17N0pLS5GZmYn58+ejp6eHj/Hoo4/i/fffx44dO3DgwAE0Nzfj7rvvHvG59/T0YPr06Rct7/vMM89gw4YNePnll3H06FHExMRgwYIFcLvd/Jj77rsPZ86cwd69e/HBBx/g4MGDeOCBB/jrNpsNt912GzIzM1FSUoJnn30Wv/vd77Bly5aIlMfr9eL73/8+fvrTnw44RiAQwMKFC+H1evHll1/itddew6uvvoonn3wy4uSpqKhAMBjEX/7yF5w5cwbPPfccXn75ZfzqV7/iY4zW9RmOa3PzzTfjrbfeQmVlJd555x2cO3cO3/ve90ZdluGSR+C///u/B+yOOpryEMMP6YbI0A2kFyJXLwyHPEDk6Iao0wuMiCicTieTSCTsgw8+CHl+xowZ7Ne//jWrrKxkANjp06f5a4FAgBkMBvbXv/6VMcZYd3c3k8lkbMeOHfyY8vJyBoAdPnx4dARhjAFg7777Ln8cDAaZ0Whkzz77LH+uu7ubKRQK9sYbbzDGGPv6668ZAHbs2DF+zMcff8xEIhFrampijDH20ksvsfj4eObxePgxjz/+ODOZTBEnT2+2bt3KdDpdv+c/+ugjJhaLWWtrK39u8+bNTKvVhsg43AxVHoFnnnmGZWVl8cdjcX2GS5bdu3czkUjEvF4vYyw6v2sfffQRKygoYGfOnGEAWGlpKX9trOQhhg7phsjUDaQXBiYS9AJj40s3RINeIE9FhOH3+xEIBKBUKkOeV6lUOHToEDweDwCEvC4Wi6FQKHDo0CEAQElJCXw+H+bPn8+PKSgoQEZGBg4fPjwKUgzM+fPn0draGjIvnU6HoqIiPq/Dhw8jLi4Os2bN4sfMnz8fYrEYR48e5cfceOONkMvl/JgFCxagsrISXV1doyRNePKEw+HDhzF16lQkJyfz5xYsWACbzYYzZ84M65wvxZXKY7Vaodfr+eNIuD5XIktnZyf+8Y9/YM6cOZDJZAAiQxYgfHnMZjN+/OMf4+9//zvUanW/cSJFHmLwkG6IDt1AeuECkagXgPGlGyJRL5BREWHExsaiuLgYf/jDH9Dc3IxAIIDXX38dhw8fRktLC1cAa9euRVdXF7xeL55++mk0NjaipaUFANDa2gq5XN4vTjM5ORmtra1jINUFhHP3vkkKj4XXWltbkZSUFPK6VCqFXq8POWagMXqfYzQIR55wx4lWec6ePYuNGzfiwQcfDBlnrOUZjCyPP/44YmJikJCQgPr6euzevTtknLGWpfe5LiUPYwzLly/HT37yk5CFV99xIkEeYvCQbogO3UB6IXL1Qu9zjQfdEIl6gYyKCOTvf/87GGOYMGECFAoFNmzYgHvvvRdisRgymQw7d+5EVVUV9Ho91Go1PvvsM9xxxx0Qi+lyEqNHU1MTbr/9dnz/+9/Hj3/847GezhXz2GOPobS0FP/85z8hkUiwdOlSsCjsCbpx40bY7XasXbt2rKdCjBCkG4hIZ7zoBWB86IbR1gt0p4lAcnJycODAATgcDjQ0NODf//43fD4fsrOzAQAzZ85EWVkZuru70dLSgk8++QQdHR38daPRCK/Xi+7u7pBxzWYzjEbjaIvDEc7dt9JI73kZjUa0tbWFvO73+9HZ2RlyzEBj9D7HaBCOPOGOE23yNDc34+abb8acOXP6JXNFgjyDkSUxMRH5+fm49dZbsX37dnz00Uc4cuQIH2esZel9rkvJs3//fhw+fBgKhQJSqRS5ubkAgFmzZmHZsmV8nEiQh7gySDdEvm4gvRC5eqH3ucaDbohEvUBGRQQTExODlJQUdHV1Yc+ePVi0aFHI6zqdDgaDAdXV1Th+/Dh/febMmZDJZNi3bx8/trKyEvX19SguLh5VGXqTlZUFo9EYMi+bzYajR4/yeRUXF6O7uxslJSX8mP379yMYDKKoqIgfc/DgQfh8Pn7M3r17YTKZEB8fP0rShCdPOBQXF+PUqVMhCnPv3r3QarWYNGnSsM75UoQrT1NTE+bNm4eZM2di69at/XZBI+H6XOm1CQaDAMDj0yNBFiA8eTZs2IATJ06grKwMZWVl+OijjwAAb775Jv74xz9GlDzE0CDdcIFI1A2kFyJXLwDjSzdEpF4YdGo3MeJ88skn7OOPP2Y1NTXsn//8J5s+fTorKiriVQfeeust9tlnn7Fz586xXbt2sczMTHb33XeHjPGTn/yEZWRksP3797Pjx4+z4uJiVlxcPOJzt9vtrLS0lJWWljIA7M9//jMrLS1ldXV1jDHG1q9fz+Li4tju3bvZyZMn2aJFi1hWVhZzuVx8jNtvv51de+217OjRo+zQoUMsLy+P3Xvvvfz17u5ulpyczH74wx+y06dPs+3btzO1Ws3+8pe/RKQ8dXV1rLS0lD311FNMo9Hw8ex2O2OMMb/fz6ZMmcJuu+02VlZWxj755BNmMBjY2rVrI06exsZGlpuby7797W+zxsZG1tLSwv8ERuv6DFWWI0eOsI0bN7LS0lJWW1vL9u3bx+bMmcNycnKY2+0eVVmGQ56+nD9/vl+Vj9GUhxh+SDdEhm4gvRC5emE45Ikk3RBteoGMigjkzTffZNnZ2UwulzOj0chWrVrFuru7+esvvPACS0tLYzKZjGVkZLDf/OY3/UrMuVwu9rOf/YzFx8cztVrN7rrrrpAf+Ejx2WefMQD9/pYtW8YYu1AC7YknnmDJyclMoVCwb3/726yysjJkjI6ODnbvvfcyjUbDtFotW7FiBb/RCpw4cYJdf/31TKFQsAkTJrD169dHrDzLli0bcIzPPvuMH1NbW8vuuOMOplKpWGJiIvvFL37BfD5fxMmzdevWAd/fd39iNK7PUGU5efIku/nmm5ler2cKhYJNnDiR/eQnP2GNjY2jLstwyNOXgZTHaMpDDD+kGyJDN5BeiFy9MBzyRJJuiDa9IGIsyrJOCIIgCIIgCIKIKCingiAIgiAIgiCIIUFGBUEQBEEQBEEQQ4KMCoIgCIIgCIIghgQZFQRBEARBEARBDAkyKgiCIAiCIAiCGBJkVBAEQRAEQRAEMSTIqCAIgiAIgiAIYkiQUUEQBEEQBEEQxJAgo4IgxhETJ07E888/P9bTIAiCICIE0gvEaEFGBUEQBEEQBEEQQ4KMCuKqxOv1jvUU+hGJcyIIgrhaiMR7cCTOiSAuBhkVxLhg3rx5WL16NVavXg2dTofExEQ88cQTYIwBuOD+/cMf/oClS5dCq9XigQceAAAcOnQIN9xwA1QqFdLT0/Hzn/8cPT09fNyXXnoJeXl5UCqVSE5Oxve+9z3+2ttvv42pU6dCpVIhISEB8+fP5++dN28eHnnkkZA5Ll68GMuXL+ePr3ROl8PpdOL+++9HbGwsMjIysGXLFv5abW0tRCIRtm/fjjlz5kCpVGLKlCk4cOAAPyYQCGDlypXIysqCSqWCyWTCCy+8EPb5CYIgIgHSC99AeoEYFRhBjANuuukmptFo2MMPP8wqKirY66+/ztRqNduyZQtjjLHMzEym1WrZ//7v/7KzZ8/yv5iYGPbcc8+xqqoq9sUXX7Brr72WLV++nDHG2LFjx5hEImHbtm1jtbW17KuvvmIvvPACY4yx5uZmJpVK2Z///Gd2/vx5dvLkSbZp0yZmt9v5fB5++OGQOS5atIgtW7aMP76SOV2OzMxMptfr2aZNm1h1dTVbt24dE4vFrKKigjHG2Pnz5xkAlpaWxt5++2329ddfsx/96EcsNjaWtbe3M8YY83q97Mknn2THjh1jNTU1/LN88803r/j6EARBjDakF74Zk/QCMRqQUUGMC2666SZWWFjIgsEgf+7xxx9nhYWFjLELN9XFixeHvGflypXsgQceCHnuX//6FxOLxczlcrF33nmHabVaZrPZ+p2vpKSEAWC1tbUXnU84ymOwc7ocmZmZ7L/+67/442AwyJKSktjmzZsZY98oj/Xr1/NjfD4fS0tLY08//fRFx121ahVbsmTJZc9PEAQRKZBe+GZM0gvEaEDhT8S4Yfbs2RCJRPxxcXExqqurEQgEAACzZs0KOf7EiRN49dVXodFo+N+CBQsQDAZx/vx53HrrrcjMzER2djZ++MMf4h//+AecTicAYPr06fj2t7+NqVOn4vvf/z7++te/oqura9BzHuycwmHatGn8/yKRCEajEW1tbSHHFBcX8/9LpVLMmjUL5eXl/LlNmzZh5syZMBgM0Gg02LJlC+rr6wctH0EQxFhCeuECpBeI0YCMCuKqISYmJuSxw+HAgw8+iLKyMv534sQJVFdXIycnB7Gxsfjqq6/wxhtvICUlBU8++SSmT5+O7u5uSCQS7N27Fx9//DEmTZqEjRs3wmQy8Ru8WCzmcbsCPp9vyHMKB5lMFvJYJBIhGAyG9V4A2L59O375y19i5cqV+Oc//4mysjKsWLGCEgYJghh3kF4ID9ILRDiQUUGMG44ePRry+MiRI8jLy4NEIhnw+BkzZuDrr79Gbm5uvz+5XA7gwm7N/Pnz8cwzz+DkyZOora3F/v37AVy4Kc+dOxdPPfUUSktLIZfL8e677wIADAYDWlpa+LkCgQBOnz59WRnCmdNwcOTIEf5/v9+PkpISFBYWAgC++OILzJkzBz/72c9w7bXXIjc3F+fOnRu2cxMEQYwWpBfCh/QCMVTIqCDGDfX19VizZg0qKyvxxhtvYOPGjXj44Ycvevzjjz+OL7/8EqtXr0ZZWRmqq6uxe/durF69GgDwwQcfYMOGDSgrK0NdXR3+7//+D8FgECaTCUePHsX//M//4Pjx46ivr8fOnTthsVj4DfiWW27Bhx9+iA8//BAVFRX46U9/iu7u7svKcLk5DRebNm3Cu+++i4qKCqxatQpdXV24//77AQB5eXk4fvw49uzZg6qqKjzxxBM4duzYsJ6fIAhiNCC9ED6kF4ihIh3rCRDEcLF06VK4XC5cd911kEgkePjhh3k5voGYNm0aDhw4gF//+te44YYbwBhDTk4O7rnnHgBAXFwcdu7cid/97ndwu93Iy8vDG2+8gcmTJ6O8vBwHDx7E888/D5vNhszMTPzpT3/CHXfcAQC4//77ceLECSxduhRSqRSPPvoobr755svKcLk5DRfr16/H+vXrUVZWhtzcXLz33ntITEwEADz44IMoLS3FPffcA5FIhHvvvRc/+9nP8PHHHw/rHAiCIEYa0gvhQ3qBGCoi1jfAjyCikHnz5uGaa67B888/P9ZTiWhqa2uRlZWF0tJSXHPNNWM9HYIgiBGD9EJ4kF4ghgsKfyIIgiAIgiAIYkiQUUEQUcK//vWvkJKCff8IgiCIqwvSC0QkQeFPBBEluFwuNDU1XfT13NzcUZwNQRAEMdaQXiAiCTIqCIIgCIIgCIIYEhT+RBAEQRAEQRDEkCCjgiAIgiAIgiCIIUFGBUEQBEEQBEEQQ4KMCoIgCIIgCIIghgQZFQRBEARBEARBDAkyKgiCIAiCIAiCGBJkVBAEQRAEQRAEMSTIqCAIgiAIgiAIYkj8PxyFz+lmftuUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create scatter plots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "# Strongest correlation\n",
    "x1, y1 = df[max_corr_idx[0]], df[max_corr_idx[1]]\n",
    "\n",
    "axes[0].set_title(f'Strongest Correlation\\nρ = {strongest_corr:.3f}')\n",
    "axes[0].scatter(x1, y1, alpha=0.5, color='black')\n",
    "axes[0].set_xlabel(max_corr_idx[0])\n",
    "axes[0].set_ylabel(max_corr_idx[1])\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# Weakest correlation\n",
    "x2, y2 = df[min_corr_idx[0]], df[min_corr_idx[1]]\n",
    "\n",
    "axes[1].set_title(f'Weakest Correlation\\nρ = {weakest_corr:.3f}')\n",
    "axes[1].scatter(x2, y2, alpha=0.5, color='black')\n",
    "axes[1].set_xlabel(min_corr_idx[0])\n",
    "axes[1].set_ylabel(min_corr_idx[1])\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a211213",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- Strongest: pressure_hpa and humidity_percent are strongly related\n",
    "- This is a negative correlation: variables move in opposite directions\n",
    "- Weakest: pressure_hpa and rainfall_inches have little relationship\n",
    "- Strong correlation shows clear linear pattern; weak correlation shows scattered points\n",
    "- Strongest relationship is more predictable"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}