Case Study 1 - The ML Approach - Ryhan's Jupyter NoteBook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACHINE LEARNING BUSINESS CASE STUDY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Disclaimer: This case study is included for demonstration purposes in my personal portfolio, and the problem itself is NOT my own creation. However, the solution presented here is solely my own work. While there are various methods to approach this problem, both with and without Machine Learning, using my solution for plagiarism is not advised. Anyone doing so will bear full responsibility for their actions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Short Description of the Problem:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As Lyft's pricing manager for a new city route, your challenge is to refine the pricing model to ensure profitability and high driver availability. With riders accustomed to paying \\\\$25 and drivers expecting \\\\$19 per trip, the current 60% match rate is suboptimal. Previous tests showed reducing Lyft's share from \\\\$6 to \\\\$3 per trip increased matches to 95%, without a clear revenue gain. You must devise a strategy that maintains rider fees, adjusts driver pay, and considers acquisition costs and churn rates, all to enhance Lyft's annual net revenue for this route. This requires a strategic, analytical approach to determine the most effective commission structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detailed Case:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lyft Pricing Strategy Problem\n",
    "\n",
    "## Introduction\n",
    "Imagine you're the pricing product manager for Lyft's ride-scheduling feature, and you're launching in a new city. In this city, people are accustomed to paying \\\\$25 for rides from the airport to downtown, in either direction, one way. Drivers typically expect to earn \\\\$19 for this trip.\n",
    "\n",
    "For your launch in this new city, you decide to set the price exactly at the prevailing rate: \\\\$25 per ride charged to the rider, with \\\\$19 per ride paid to the driver. However, you quickly realize that only about 60% of the ride requests find a driver willing to accept rides at this price point.\n",
    "\n",
    "For the purpose of this exercise, we will focus on this specific route, even though there may be multiple routes available in the new city.\n",
    "\n",
    "## Current Unit Economics\n",
    "\n",
    "### Drivers\n",
    "- The customer acquisition cost (CAC) of a new driver falls in the range of \\\\$400 to \\\\$600. CAC is sensitive to the rate of acquisition due to limited marketing channels.\n",
    "- At the prevailing wage, drivers experience a 5% monthly churn rate and complete an average of 100 rides per month.\n",
    "\n",
    "### Riders\n",
    "- The CAC for acquiring a new rider is between \\\\$10 to \\\\$20, which, similar to driver CAC, is sensitive to the rate of acquisition due to limited marketing channels.\n",
    "- On average, each rider requests only 1 ride per month.\n",
    "- Churn varies based on rider experiences: Riders who don't encounter a \"failed to find driver\" event churn at a rate of 10% per month, while riders who experience one or more such events churn at a higher rate of 33% per month.\n",
    "\n",
    "## Previous Pricing Experiment\n",
    "You've already conducted one pricing experiment where you reduced Lyft's take from \\\\$6 per ride to \\\\$3 per ride across the board for a few weeks. This led to a significant increase in match rates, soaring from 60% to approximately 93%.\n",
    "\n",
    "## Objective\n",
    "Your primary objective is to maximize the company's net revenue, which is the difference between the amount riders pay and the amount Lyft pays out to drivers, for this specific route in Toledo for the next 12 months. Keep in mind that you cannot charge riders more than the prevailing rate. \n",
    "\n",
    "## Key Question\n",
    "The central question to address is: How should you adjust the amount you pay drivers per trip (by modifying Lyft's take) to maximize net revenue over the next 12 months on this route?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To maximize Lyft's net revenue for the route in Toledo over the next 12 months, we'll use a Machine Learning (ML) model. The ML model will help us predict the optimal balance between Lyft's take and driver payment to maximize net revenue while considering various factors like match rate, driver churn, and rider churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Model Approach:\n",
    "\n",
    "1. **Data Collection:** \n",
    "   - Collect historical data on match rates, driver and rider churn rates, and net revenue at different take rates.\n",
    "\n",
    "2. **Feature Engineering:** \n",
    "   - Features can include Lyft's take, match rate, driver churn rate, rider churn rate, and any other relevant factors (e.g., time of day, special events).\n",
    "\n",
    "3. **Model Selection:** \n",
    "   - A regression model (like a Random Forest Regressor) can be used to predict the net revenue based on the features.\n",
    "\n",
    "4. **Model Training and Validation:** \n",
    "   - Train the model on historical data and validate it to ensure accuracy.\n",
    "\n",
    "5. **Optimization:** \n",
    "   - Use the model to predict the net revenue for different take rates and find the optimal rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# To ensure we have the required Python libraries installed, we will use the following pip commands to install them:\n",
    "!pip install --upgrade pexpect\n",
    "!pip install numpy\n",
    "!pip install pandas\n",
    "!pip install matplotlib\n",
    "!pip install scikit-learn\n",
    "!pip install seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Necessary Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.mplot3d import Axes3D\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Set Up and Data Simulation\n",
    "   \n",
    "We'll start by setting up the environment and simulating a dataset, as we don't have access to real-world data for this scenario. The simulated data will include different values for Lyft's take, match rates, driver churn rates, and rider churn rates, along with the resulting net revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Lyft takes: [4.64644051 5.1455681  4.80829013 4.63464955 4.2709644 ]\n",
      "Example Match rates: [0.79565049 0.60332102 0.75702264 0.83389423 0.61451189]\n",
      "Example Driver churn rates: [0.06246074 0.04904336 0.05092624 0.04002082 0.05420172]\n",
      "Example Rider churn rates: [0.19521137 0.24481221 0.27907438 0.29585829 0.28777492]\n",
      "Example Net Revenues: [369.69426669711515, 310.44293918083395, 363.99845090234544, 386.48075123896683, 262.4558415315587]\n"
     ]
    }
   ],
   "source": [
    "# Correcting the code for visibility and proper prints\n",
    "\n",
    "np.random.seed(0)\n",
    "data_size = 1000\n",
    "lyft_takes = np.random.uniform(3, 6, data_size)  # Randomly generated Lyft's takes between $3 and $6\n",
    "match_rates = np.random.uniform(0.6, 0.93, data_size)  # Random match rates between 60% and 93%\n",
    "driver_churn_rates = np.random.uniform(0.03, 0.07, data_size)  # Random driver churn rates between 3% and 7%\n",
    "rider_churn_rates = np.random.uniform(0.1, 0.33, data_size)  # Random rider churn rates between 10% and 33%\n",
    "\n",
    "# Example prints for visibility\n",
    "print(\"Example Lyft takes:\", lyft_takes[:5])\n",
    "print(\"Example Match rates:\", match_rates[:5])\n",
    "print(\"Example Driver churn rates:\", driver_churn_rates[:5])\n",
    "print(\"Example Rider churn rates:\", rider_churn_rates[:5])\n",
    "\n",
    "# Constants\n",
    "prevailing_rate_per_ride = 25  # $25 per ride charged to rider\n",
    "monthly_rides_initial = 100    # Total rides considered per month\n",
    "\n",
    "# Calculating net revenue for each data point\n",
    "net_revenues = []\n",
    "for lyft_take, match_rate in zip(lyft_takes, match_rates):\n",
    "    successful_rides = monthly_rides_initial * match_rate\n",
    "    total_revenue = successful_rides * prevailing_rate_per_ride\n",
    "    driver_cost = successful_rides * (prevailing_rate_per_ride - lyft_take)\n",
    "    net_revenue = total_revenue - driver_cost\n",
    "    net_revenues.append(net_revenue)\n",
    "\n",
    "# Example prints for net revenues\n",
    "print(\"Example Net Revenues:\", net_revenues[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. DataFrame for the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>LyftTake</th>\n",
       "      <th>MatchRate</th>\n",
       "      <th>DriverChurnRate</th>\n",
       "      <th>RiderChurnRate</th>\n",
       "      <th>NetRevenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.646441</td>\n",
       "      <td>0.795650</td>\n",
       "      <td>0.062461</td>\n",
       "      <td>0.195211</td>\n",
       "      <td>369.694267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.145568</td>\n",
       "      <td>0.603321</td>\n",
       "      <td>0.049043</td>\n",
       "      <td>0.244812</td>\n",
       "      <td>310.442939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.808290</td>\n",
       "      <td>0.757023</td>\n",
       "      <td>0.050926</td>\n",
       "      <td>0.279074</td>\n",
       "      <td>363.998451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.634650</td>\n",
       "      <td>0.833894</td>\n",
       "      <td>0.040021</td>\n",
       "      <td>0.295858</td>\n",
       "      <td>386.480751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.270964</td>\n",
       "      <td>0.614512</td>\n",
       "      <td>0.054202</td>\n",
       "      <td>0.287775</td>\n",
       "      <td>262.455842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>3.293029</td>\n",
       "      <td>0.861123</td>\n",
       "      <td>0.067667</td>\n",
       "      <td>0.180417</td>\n",
       "      <td>283.570281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>4.544767</td>\n",
       "      <td>0.837995</td>\n",
       "      <td>0.051845</td>\n",
       "      <td>0.200021</td>\n",
       "      <td>380.849341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>5.815236</td>\n",
       "      <td>0.758436</td>\n",
       "      <td>0.042945</td>\n",
       "      <td>0.290901</td>\n",
       "      <td>441.048192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>3.685940</td>\n",
       "      <td>0.812475</td>\n",
       "      <td>0.062542</td>\n",
       "      <td>0.314545</td>\n",
       "      <td>299.473431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>5.031423</td>\n",
       "      <td>0.765585</td>\n",
       "      <td>0.057896</td>\n",
       "      <td>0.170918</td>\n",
       "      <td>385.198298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LyftTake  MatchRate  DriverChurnRate  RiderChurnRate  NetRevenue\n",
       "0    4.646441   0.795650         0.062461        0.195211  369.694267\n",
       "1    5.145568   0.603321         0.049043        0.244812  310.442939\n",
       "2    4.808290   0.757023         0.050926        0.279074  363.998451\n",
       "3    4.634650   0.833894         0.040021        0.295858  386.480751\n",
       "4    4.270964   0.614512         0.054202        0.287775  262.455842\n",
       "..        ...        ...              ...             ...         ...\n",
       "995  3.293029   0.861123         0.067667        0.180417  283.570281\n",
       "996  4.544767   0.837995         0.051845        0.200021  380.849341\n",
       "997  5.815236   0.758436         0.042945        0.290901  441.048192\n",
       "998  3.685940   0.812475         0.062542        0.314545  299.473431\n",
       "999  5.031423   0.765585         0.057896        0.170918  385.198298\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'LyftTake': lyft_takes,\n",
    "    'MatchRate': match_rates,\n",
    "    'DriverChurnRate': driver_churn_rates,\n",
    "    'RiderChurnRate': rider_churn_rates,\n",
    "    'NetRevenue': net_revenues\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Splitting Data and Model Training\n",
    "\n",
    "We'll split the data into training and test sets and then train a Random Forest Regressor model. This model will learn to predict the net revenue based on the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting data into train and test sets\n",
    "X = df.drop('NetRevenue', axis=1)\n",
    "y = df['NetRevenue']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Creating and training the model\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model Evaluation\n",
    "\n",
    "We'll evaluate the model's performance using the test set and calculate the Mean Squared Error (MSE) to understand the model's accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 17.377681917639283\n"
     ]
    }
   ],
   "source": [
    "# Predicting on test set and calculating MSE\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest Regressor model has been trained and validated on the simulated dataset, with a Mean Squared Error (MSE) of approximately 17.38. This indicates the average squared difference between the estimated values and the actual value. The lower the MSE, the better the model's accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use this model to predict the optimal Lyft's take. We will input a range of takes, match rates, and churn rates into the model and observe which scenario yields the highest predicted net revenue. This approach will provide a more dynamic and strategic solution, as it considers multiple factors simultaneously.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Predicting Optimal Scenario\n",
    "\n",
    "Finally, we'll use the trained model to predict the net revenue for a range of scenarios and find the one that maximizes net revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating a range of scenarios for prediction\n",
    "prediction_data = pd.DataFrame({\n",
    "    'LyftTake': np.linspace(3, 6, 100),  # Takes from $3 to $6\n",
    "    'MatchRate': np.linspace(0.6, 0.93, 100),  # Match rates from 60% to 93%\n",
    "    'DriverChurnRate': np.linspace(0.03, 0.07, 100),  # Driver churn rates from 3% to 7%\n",
    "    'RiderChurnRate': np.linspace(0.1, 0.33, 100)  # Rider churn rates from 10% to 33%\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.377681917639283,\n",
       " LyftTake           5.909091\n",
       " MatchRate          0.920000\n",
       " DriverChurnRate    0.068788\n",
       " RiderChurnRate     0.323030\n",
       " Name: 96, dtype: float64,\n",
       " 529.2065102410584)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting net revenue for each scenario\n",
    "predicted_net_revenues = model.predict(prediction_data)\n",
    "\n",
    "# Finding the scenario with the highest predicted net revenue\n",
    "optimal_scenario_index = np.argmax(predicted_net_revenues)\n",
    "optimal_scenario = prediction_data.iloc[optimal_scenario_index]\n",
    "optimal_net_revenue = predicted_net_revenues[optimal_scenario_index]\n",
    "\n",
    "mse, optimal_scenario, optimal_net_revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D Scatter Plot for Optimal Scenario:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X4PP5cM0110Rtd/LkyXVhOIlw/fXXY9++ffj0pz+NcDi87nuv14vf//73eMc73gGz2Rx1b7z1rW+F1+tNKMRqM9797nfDYDDAbDbj1KlTWF5exi9/+UsUFxcnPYbLL78clZWVuP/++4X9//a3v8X09DRuuOEG4bMnn3wSF154Iaqrq6P2x59jsVXo3va2t0Gn0wn/jj0vyfDkk0+iuLgYV155ZdRvHz58GJWVlaJWjCMItUACgyASpKysDGazGSMjI5tuNzo6CrPZjJKSkqjPKysro/6t1+tRWloq5C6kSuzv5OXlwWw2Iz8/f93nXq9X+Pfjjz+Oa665BjU1NXj44Yfx4osv4pVXXsENN9wQtV2yWK1WvPe978W3vvUtvPzyy+jo6EBFRQU+97nPYXFxEcDZ2Oza2tpN9zM3N4eOjg4YDIao/ywWCxhjUXH/AFBaWrpuH0ajEWtra8K/P/nJT+ILX/gC/vZv/xZPPPEEXn75Zbzyyis4dOhQ1HacjfJJItks96S6uhrhcBgul2vL/cTb70b7jPzdZMYaDx5nfuedd+K1117DBz/4wbjbhcNhXHLJJXj88cfxqU99Cr///e/xl7/8RTAk4x2/G264AYFAAHq9Hh//+McTHlO8a3qzzxO5XldWVvDWt74VgUAAv/71r2G1WhMaS+x1ZTQaAbw+X34eKioq1v1tvM+2QqfT4c4770R3d/e6fBv+e8FgEPfee++6e+Otb30rAKy7N5Lla1/7Gl555RU899xz+NznPoe5uTn87d/+LXw+X9Jj0Ov1uPbaa/Gzn/1MuP8feOABVFVV4dJLLxV+c25uDk888cS6/e3fvz/unLY6L8kwNzeHxcVF5OXlrfv92dnZtI8nQagR/dabEAQBnH3xX3jhhfjNb36DycnJuAby5OQkXn31VVx++eVRq2vA2dXTyFXZYDAIh8MR1zDOBg8//DCamprw2GOPRXlbuBEhFvv378d73vMefPOb38TAwACOHz+Obdu2YXJyctO/Kysrg8lkWpeEHPl9sjz88MN4//vfjzvvvDPq84WFBWF1NpJE+inw8zczM7Puu+npaWi1WthstqTHWlpauuE+gfXzT7WfRF1dHS6++GLcdttt2L17N1pbW+Nu19XVhfb2djzwwAO47rrrhM83SiZ3u9249tprsWvXLszNzeEf/uEfUi4gkC6BQADvete7MDw8jOeff35LcZsM/PzHJkADZ+/5VLwYb3/723Hq1Cl86Utfwve+972o72w2G3Q6Ha699lp89KMfjfv3TU1NSf9mJNu3bxcSu9/4xjfCZDLh85//PO69917ccsstSY/h+uuvx913341HH30U7373u/GLX/wCN910U9QzsqysDOeccw7uuOOOuPvjwjoZ8vPzowoNcGIFQ1lZGUpLS/Gb3/wm7n4iPaMEQSQGCQyCSILPfOYz+PWvf42PfOQj+NnPfhb1ggyFQvjwhz8Mxhg+85nPrPvbH/3oRzh27Jjw7//+7/9GMBiMqnISu+KeSTQaDfLy8qIM09nZ2bSqSFkslrhhJ319fQBeNxIuv/xyPPTQQ+jv78fu3bvj7u+KK67AnXfeidLS0rQNJo5GoxFWOjm//OUvMTU1hR07dqS0z927d6OmpgY//vGPccsttwjH0+1243/+53+EylLJctFFF+FnP/sZpqeno4yrH/7whzCbzTh58mRK443HzTffDJPJhKuvvnrDbfi8Yo/fd7/73bjbf+hDH8L4+Dj+8pe/oK+vD1dddRXuuecefOITnxBt3InywQ9+EM8++yx+/etfC6E0YnHixAkYjUY89thjeOc73yl8/tJLL2FsbCwlgQGc9SK84Q1viKqyBABmsxkXXngh2tracM4552wa5pXOqn4kn/rUp/DAAw/gq1/9Km688UZYLJaExwCcDWU7ceIE7r//foRCIfh8Plx//fVR21xxxRX41a9+hebm5pQEeTwaGxvxk5/8BD6fTzgWDocDL7zwQlSflyuuuAKPPvooQqEQTpw4IcpvE4TaIYFBEElw6tQpfPOb38RNN92EN7zhDfjYxz6G+vp6jI+P4z//8z/x8ssv45vf/GbcVeDHH38cer0eb3nLW9Dd3Y0vfOELOHToUFTs9sGDB/Hoo4/isccew/bt25Gfn4+DBw9mZC5XXHEFHn/8cXzkIx/BVVddhYmJCfzrv/4rqqqqUmqo9cwzz+Cf//mf8b73vQ+tra0oLS3F/Pw8HnnkEfzmN7/B+9//fmHl+Mtf/jJ+/etf441vfCM++9nP4uDBg1hcXMRvfvMbfPKTn8SePXtw00034X/+53/wxje+EZ/4xCdwzjnnIBwOY3x8HL/73e9w8803J20MXHHFFXjggQewZ88enHPOOXj11Vdx9913p7WirdVqcdddd+F973sfrrjiCtx4443w+Xy4++67sbi4GFWyOBm+9KUvCXHpX/ziF1FSUoIf/ehH+OUvf4m77ror4RCfRLjkkktwySWXbLrNnj170NzcjE9/+tNgjKGkpARPPPEEnnrqqXXb/td//Rcefvhh3H///di/fz/279+Pj33sY/j//r//D6dOncLx48dFG/tW3H333XjooYfwf/7P/0FBQUFUfkJRURH27duX1v55aeivfOUrsNlseMc73oHJyUncdtttqKqqWleeOVFOnTqFt7/97XEF/7e+9S284Q1vwPnnn48Pf/jDaGxsxMrKCoaGhvDEE0/gD3/4AwCgubkZJpMJP/rRj7B3714UFhaiuro6aW+AwWDAnXfeiWuuuQbf+ta38PnPfz7hMXBuuOEG3HjjjZienkZra+u6hYUvf/nLeOqpp9Da2oqPf/zj2L17N7xeL0ZHR/GrX/0K//f//t+k79Nrr70W3/3ud/H3f//3+Md//Ec4HA7cdddd65pIvuc978GPfvQjvPWtb8U///M/4/jx4zAYDJicnMQzzzyDt7/97XjHO96R1G8ThOqRNsecIJTJiy++yK666ipWUVHB9Ho9Ky8vZ+985zvZCy+8sG5bXtXm1VdfZVdeeSUrLCxkFouFvfe97xUqTXFGR0fZJZdcwiwWi1DalrHNq0idOXMmah/XXXcdKygoWDeOCy64gO3fvz/qs69+9aussbGRGY1GtnfvXvb9739/w8o+W1WRmpiYYJ///OfZqVOnWGVlJdPr9cxisbATJ06we++9N6riC9/+hhtuYJWVlcxgMLDq6mp2zTXXRB2T1dVV9vnPf57t3r2b5eXlCWUxP/GJT7DZ2VlhO8SpwhRv3C6Xi33wgx9k5eXlzGw2sze84Q3s+eefX1dtZrPmZvHK1DJ2tqLRiRMnWH5+PisoKGAXXXQR+/Of/7zpMdtq/J2dnezKK69kVquV5eXlsUOHDq2rCJRKI7aNfi+SeFWkenp62Fve8hZmsViYzWZjV199NRsfH2cA2Je+9CXGGGMdHR3MZDKtu168Xi87duwYa2xsFKoRbXSt8YpKW42Z3xd333238FnsPq+77jqhqlnsf5HnfKMqUrHHNd69GA6H2e23385qa2tZXl4eO+ecc9iTTz7JDh06xN7xjnesm0ssG825p6eH6XS6Dcdxww03sJqaGmYwGNi2bdtYa2sru/3226O2e+SRR9iePXuYwWCIOk/x2OpaOnHiRFQp2ETHwNjZsrkmk4kBYN///vfj7v/MmTPs4x//OGtqamIGg4GVlJSwY8eOsc997nNsdXVV+M3Yc86JN78HH3yQ7d27l+Xn57N9+/axxx57bF0VKcbOVtr7+te/zg4dOsTy8/NZYWEh27NnD7vxxhvZ4ODghseMIIj4aBj7fyVvCILICLfeeituu+02nDlzJqW8AYIglMfIyAj27NmDL33pS/jsZz8r9XAIgiCyCoVIEQRBEEQatLe345FHHkFrayuKiorQ398vhOJsVJWLIAgilyGBQRAEQRBpUFBQgL/+9a+47777sLi4CKvVije96U244447UipVSxAEoXQoRIogCIIgCIIgCNGgRnsEQRAEQRAEQYgGCQyCIAiCIAiCIESDBAZBEARBEARBEKJBAoMgCIIgCIIgCNEggUEQBEEQBEEQhGiQwCAIgiAIgiAIQjRIYBAEQRAEQRAEIRokMAiCIAiCIAiCEA0SGARBEARBEARBiAYJDIIgCIIgCIIgRIMEBkEQBEEQBEEQokECgyAIgiAIgiAI0SCBQRAEQRAEQRCEaJDAIAiCIAiCIAhCNEhgEARBEARBEAQhGiQwCIIgCIIgCIIQDRIYBEEQBEEQBEGIBgkMgiAIgiAIgiBEgwQGQRAEQRAEQRCiQQKDIAiCIAiCIAjRIIFBEARBEARBEIRokMAgCIIgCIIgCEI0SGAQBEEQBEEQBCEaJDAIgiAIgiAIghANEhgEQRAEQRAEQYgGCQyCIAiCIAiCIESDBAZBEARBEARBEKJBAoMgCIIgCIIgCNEggUEQBEEQBEEQhGiQwCAIgiAIgiAIQjRIYBAEQRAEQRAEIRokMAiCIAiCIAiCEA0SGARBEARBEARBiAYJDIIgCIIgCIIgRIMEBkEQBEEQBEEQokECgyAIgiAIgiAI0SCBQRAEQRAEQRCEaJDAIAiCIAiCIAhCNEhgEARBEARBEAQhGiQwCIIgCIIgCIIQDRIYBEEQBEEQBEGIBgkMgiAIgiAIgiBEgwQGQRAEQRAEQRCiQQKDIAiCIAiCIAjRIIFBEARBEARBEIRokMAgCIIgCIIgCEI0SGAQBEEQBEEQBCEaJDAIgiAIgiAIghANEhgEQRAEQRAEQYgGCQyCIAiCIAiCIESDBAZBEARBEARBEKJBAoMgCIIgCIIgCNEggUEQBEEQBEEQhGiQwCAIgiAIgiAIQjRIYBAEQRAEQRAEIRokMAiCIAiCIAiCEA0SGARBEARBEARBiAYJDIIgCIIgCIIgRIMEBkEQBEEQBEEQokECgyAIgiAIgiAI0SCBQRAEQRAEQRCEaJDAIAiCIAiCIAhCNEhgEAShShhjUg+BIAiCIHISvdQDIAiCyCbhcBh+vx8+nw8GgwF6vR46nQ5arRYajUbq4REEQRCE4tEwWsYjCEIFMMYQCoUQDAYRDAbh9/uh0WjAGINWq4VWq4VeryfBQRAEQRBpQgKDIIichzGGQCCAUCgU9W+tVgvGGBhjCIfDwvYajYYEB0EQBEGkCAkMgiBymlAohEAggHA4LIgE/plWuz4NbSPBodPpBLGh1+uh0WhIcBAEQRBEHCgHgyCInIQxJoRD8TAoLgi4iIgHFw5cfPBtg8EgAoGA8D0XGlx0kOAgCIIgiLOQwCAIIucIh8MIBoNCSFQ64U0bCY5AIIDnn38eR48eRX5+PnQ6HQwGA3Q6nRBSRRAEQRBqhAQGQRA5Aw9tCgQCYIxlxKsQKTh4mJVGo1nn4Yj0bpDgIAiCINQECQyCIHKC2ETubIYs8SpUfBx8LH6/X/ieBAdBEAShFkhgEASheLjXIhQKZbXaU7zf2SykipfGJQ8HQRAEkcuQwCAIQrFE9raIrBKVLXgfja22iRUcXBAFAgFhm0jBwatUEQRBEIQSIYFBEIQiiQ2JUkqfCl6BihMpOLiHQ6vVxq1SRRAEQRBKgAQGQRCKIxwOw+/3p+y1EMtYT8SDkcg+EhUckVWqSHAQBEEQcoUEBkEQioGHRPEqUXLwWojdqzRScPB9xxMcsTkcUh8HgiAIguCQwCAIQhGI3dtCDDJt1PP9xxMcfr8fPp+PBAdBEAQhO0hgEAQha7LR2yIdxPZgbMZWgmOzsrhyOmYEQRBEbkMCgyAI2cIYQzAYRDAYBCBubwsx9iO10R5PcPD/fD4fCQ6CIAhCEkhgEAQhSyJ7W0SWeZUTYiR5i0mkANPpdOsER2RIlcFgEASHHHJZCIIgiNyBBAZBELJC6t4WySIngRHLZoLD6/UK25DgIAiCIMSEBAZBELJBab0t5Dy2eCQqOGJ7cMj9PBAEQRDyggQGQRCyIN3eFlIhZw/GVmwkOMLhsCA4tFrtuhwOJZ0fgiAIIvuQwCAIQlLk2NsiUeSWg5EuGwmOUCiEUCiEmZkZBINBVFVVkeAgCIIgNoQEBkEQkqG0kKhYlDTWVOCCgyfYezweuN1ulJeXIxQKrUsa56FVcislTBAEQWQXEhgEQUhCKBTC7Ows5ufnsXfvXsUapLnkwUgUvf7sqyPSwxEMBgVhEZvDQYKDIAhCXZDAIAgiq0T2tvD5fFheXlas8anUcYtFrIeDC45gMIhAIBAlOLiHg4dUEQRBELkLCQyCILIG720RDocBnA2JUrIHINdyMNKFBAdBEAQBkMAgCCIL8MpEXFzwXAulCwxAnSFSibKV4ADidxknwUEQBKFsSGAQBJFRNkvkVroHQO0hUsmykeAIBALw+/3C9yQ4CIIglA0JDIIgMgb3WoRCobgVopQuMADyYKRDPMHBrxnu4YgVHLxKFUEQBCFfSGAQBCE6kZWFNmucp3SBoTZDN9Pz5fkZnEjBEc/DEVmliiAIgpAPJDAIghCVZHpb5ILAUPL45U4igkOr1a5LGifBQRAEIS0kMAiCEA3ekXszr0UkWq1WqChFEFuRqOCIzeEgwUEQBJFdSGAQBJE2kb0tGGMJd+RWugdA6eNXOpGCg5+HcDgMv98f1WWcBAdBEER2IYFBEERahMNhBIPBhEKiYskFA13p488V+DVHgoMgCEJ6SGAQBJESkeEpjDEhATcZlC4wyDCVL/EEB//P5/PB7/cDiN+Hg84rQRBEepDAIAgiaWITuVMRF/zvlCwwAPV5MJQ638hrVKfTrRMckR4OnjCu1+uT8sgRBEEQZyGBQRBEUmzV2yIZlC4wlD5+NbOZ4PB6vcI2XHBwDwcJDoIgiK0hgUEQREIk2tsiGaQ00MUwEsnQzB02EhzBYBDPPvssTp48iby8PMGzQYKDIAhiY0hgEASxJcn0tkgGqT0APHck3X0QuUdsl3GDwSCUVeYeDq1Wuy6HgwQHQRAECQyCILaAV+ERy2sRiVarFVaKs22UifGbajMk1TbfSCLFRqSHIxwOw+fzwev1kuAgCIL4f5DAIAgiLjwkileJyoShxPcnhcAQA6k9METm4ec39vqMLWzABUcoFEIoFNowaTzVgggEQRBKggQGQRDrSKe3RTJECgylouSxE+IRG1IVKTiCwaDwfWwOBwkOgiByERIYBEEIiNHbIhmULjDIMMx9Ur02NxIcwWAQgUBgQ8HBtycIglAyJDAIggAAwfgJBoMAUu9tkQxKFxiAssdOJI4Y+TqJCg4eUkWCgyAIpUICgyCIqN4WkUZQppFKYIglnMiDkftk6trcSnAA8buMk+AgCEIJkMAgCBWTid4WySClByMXyuwS2SMb3rx4giMQCMDv9wvfk+AgCEIJkMAgCJWSqd4WyUAhUoTckbIRZKzg4J5G7uGIFRy8ShVBEITUkMAgCBWSyd4WycB/NxwOS/L76ULGnHqQ+lzz/AxOpOCI5+GIrFJFEASRbUhgEISKyEZvi2RRepiRksdObI1cz28igkOr1a5LGpf6ficIQh2QwCAIlSCHkKh48G7eSkTp4igV1DZfQHrvRSIkKjhicziUMDeCIJQHCQyCUAHcayF1SFQ81GikE8pBqddmpODgc+ChkZFdxklwEASRCUhgEEQOE9vbQm7iApBGYExPT2NoaAgFBQUoKSmBzWaD2WxO+tiQOFIHcrtnkoWPnwQHQRDZggQGQeQoPDyCJ1Bno3FeKmTTSA+FQujt7cXc3Byam5vh9/tx5swZDA0NQa/Xw2azCf+ZTKYt9yfH40mISy4KyM0Eh8/ng9/vBwB4vV7k5+fDbDaT4CAIIilIYBBEjhEZey3HkKhYsiUwVldXYbfbodfr0draKpT/bGxsRDgcxtLSElwuF2ZmZtDf3w+j0SiIjZKSEuTl5cXdby4aoEQ0cr5/xCBScOh0OqEHx9DQEEpKSlBVVSV4OHjCuF6vl/2zhSAI6SCBQRA5hFwTuTcjGwJjamoKPT09aGhowI4dO6DRaIRVWuDsceJiAgCCwaAgOCYmJtDT04OCggJhm+LiYhgMBtkfW7FR23zVSqS3k4dMcdHh9XqFbbjg4CFVSnjeEASRHUhgEESOwL0WoVBIUS/6TAqMYDCI3t5ezM/P4/Dhw9i2bRuArb0Oer0epaWlKC0tBQAEAgG4XC64XC4MDw/D4/HAYrEAAPLz8xEKhaIq+BC5A2NMMfeS2PC5RwqOSA9HrODgng0SHARBkMAgCIXDe1sEg0FFhETFkimBsbKygvb2dhgMBpw6dQr5+fkp/67BYEB5eTnKy8sBAD6fDy6XC2NjY3A6nfjjH/8Iq9UqeDiKioqEECyCUCobiauNBEc4HCbBQRAEABIYBKFolBgSFYtWqxW1kzdjDFNTU+jt7UVjYyOam5tFN/aNRiMqKyuxurqKUCiEuro6wcMxOTmJcDgMq9UqVKgqLCxU3HkhzkIejK3nvpng8Pl88Hq90Gq166pUKfF5RRBEYpDAIAiFEgqFMDc3h/n5eezZs0exL2oxPRjBYBA9PT1YWFjAkSNHUFZWJsp+t8JsNsNsNqOmpgaMMbjdbkFwjIyMQKPRRFWoSqUkLkFkm1TFVWzFOi44QqEQQqHQhmVxSXAQRO5AAoMgFEZkbwufz4fFxUVFv5TFEhgrKyuw2+0wGo1obW1dFxKVCTYKHyksLERhYSHq6uoQDoexsrICl8sVtyRuSUlJVsZKpIaaq4SJ5b3hgoN7EiMFRzAYFL6PDamSa2ltgiC2hgQGQSiIcDiMYDAohETxcAQlk67AYIxhcnISfX19aGxsFKpEZYNExq7VamG1WmG1WtHY2IhQKITl5eWokrj5+flRHo6NSuIS0qBWIzdT4WEbCY5gMIhAIECCgyByABIYBKEAIntbRFZ2ETt/QQrSERjBYBDd3d1wOBw4evSoUPUpmyQ7dp1Ot64k7uLiopA03t3dLZTELSkpQXFxMfR6elRLhdIFfDpkK/8kGcHB+3DwkCqCIOQJvbUIQubEJnJHruJpNBrVCozl5WXY7Xbk5+fj1KlTMBqNGRjd5ohhfOn1epSVlQn5In6/XxAcQ0NDWFtbg8ViEUSJ1WqlkrhZRq2r5lIluG8lOACsy98gwUEQ8oIEBkHImK16W2i1WsWvsCYrMCJDopqamtDc3CypASj28c/Ly4sqiev1eoWE8d7eXvj9fqEkbklJCSwWS1YNK6Vfb8mitvlGEg6HZSGuNhIcgUBAaJhJgoMg5AUJDIKQIYn2tlCbByMYDKKrqwsul0uykKhIstGFPD8/H1VVVaiqqgJjDGtra+tK4hYXFwseDiqJKz5qPZ5yLdEbT3DwxRju4dBoNCQ4CEJCSGAQhMxIprdFLngwEs0j4SFRJpMJra2tooREpWtAZdv40mg060rirq6uRpXE1Wq1guAoKSmByWSSpZGoFJR+f6WDXAVGLDw/g5OI4NDr9YqYG0EoFRIYBCEjwuEw/H5/wh251ZDkzRjDxMQE+vv7sX37dmzfvl1WhoGUBqhGo4HFYoHFYkF9fX3ckrgGgyGqQhWVxE0eOV1v2UQpAiOWzQSH3+8XvB+xSeNKnCtByBUSGAQhA3hIFK8SlWjDqVwPkQoEAuju7obL5cKxY8dQUlKS5dFtjtwMknglcZeWluByuTA1NYW+vj6hJC6vUEUlcTeHPBjyusZTIVHBERtSlQtzJwipIIFBEBIT29simW62uRAitZHAWFpaQnt7u6ghUWKTjRyMdNDpdCgpKRGEWWRJ3NHRUayurqKwsFDwbmxVEletBpda550rAiOWSMHB71/uPeZdxkOhkNA0kwQHQSQPCQyCkIiNelskAzdwlWwIxBrpjDGMj49jYGBAliFRSiZeSVyevzE4OAiv10slcWOQs4DMNEp+riQKn1+s4JiamsLi4iL27dtHHg6CSAESGAQhAbymezAYBICUxAWAqCoqSn3ZRQqMQCCArq4uLC4uyjIkKha5ezC2Ii8vDxUVFaioqAAQXRK3p6cHwWBQKIlrs9kUPdd0UOq9lS5Kfq6kSuR8eTI4cNbD4fP5Ni2Lq7ZjRRCbQQKDILIM91rw3Il0Sifyv+VJ4UqEG+lLS0uw2+0oKCjAqVOnFJMbkEtGd2xJXI/HIwiO8fFxhMNh6HQ6jI+Po6SkBAUFBWRU5ThqPb+xuXBcRHCPMWNsneDgCeN6vT6pUFeCyEVIYBBElki0t0Uy8L9XeqI3L7Ha3NyMpqamjL+Yxdp/LhsQGo0GBQUFKCgoQG1tLRhjGB4exsLCQlRJ3MgKVblYEleNq/gAFB96mS4bNRmM9DbHCg6v1ytswwUH93CQ4CDUBgkMgsgCyfS2SIbIECklEggE4HQ64ff7ce6558Jms2Xtt8V62Sv12CeLRqNBfn4+zGYzzjnnHKEkrtPpxNzcHAYGBpCXlyeIjZKSElkm5hOJwa9rtRrF3IOxFSQ4CCI+JDAIIsPwkKhQKCT6S0XJHozFxUW0t7eDMYbq6uqsiguxUHoORjpElsRtampaVxK3t7cXZrM5ysNhMBikHnbSqPX8ql1gpBp2mqjg4KFUJDiIXIUEBkFkiFR7WyQDf5kpyQhijGF0dBRDQ0PYsWMH1tbWFPtiVeq4M0G8kriRHca7urqSKokrJ9R4ntUuMBL1YGzFRoKDJ417vV5otdp1SeMkOAilo4ynO0EojEyFRMVDSd28/X4/urq6sLy8jPPOOw/FxcXo7+8XjpMSUZK4yyZ6vR7btm3Dtm3bAMQviVtUVCQIjqKiIlmWxFXr+VW7wAiHwxkRwLEVA7ngCIVCCIVCQh8OEhyE0iGBQRAiw70WYiVyb4VSPBgulwvt7e0oKipCa2urUCVKKeOPhxpf+Kmeq9iSuGtra4LgmJ6ejiqJW1JSgsLCQtlURlPzeVbj3IGNk7zFhguOyHy6eIKDh1Tx/021tDlBZAsSGAQhErG9LbK14iR3D0ZkSNTOnTvR0NAQdVykEhiU5C0tJpMJJpMJ1dXVcUviMsai8jekKomr1vOrdoEhVohUsmwkOILBIAKBgPB9bA4HCQ5CbpDAIAgRiO1tkc2HvUajka3A8Pv96OzsxMrKihASFYtUAkOM31Sy90VOxCuJu7q6CqfTCYfDgeHhYeh0OslK4qrRcFO7wJBLb6FkBAfvw8FDqghCSkhgEEQa8GS9bIZExaLVamVp5EaGRJ06dWrDCkJKNtLVanxlGo1GA4vFAovFgoaGBoTDYSwvL8PlcgklcY1GY5TgyFRJXKVem+midoEh1x4giQqO2C7jJDiIbEMCgyBSJJuJ3JshNw8GYwwjIyMYHh6OGxIVi5IFBqBeAzSbaLVaFBcXo7i4WCiJu7i4CJfLhYmJCfT09GS0JK4cDc1MI1cDO1vIxYOxFRsJjkAgENVlnAQHkW1IYBBECmSyt0WyyMmD4ff70dHRAbfbjePHj8NqtW75N0oWGGozwOQyX51Oh9LSUpSWlgI427CRCw5eEtdisUSVxE21QpVSr810IYGRnSRvsdlKcJCHg8gWJDAIIgl4dY9gMChZSFQscknydjqdaG9vR3FxMVpbWxNeQVa6wFDq2HMJg8EQVRLX5/MJCeP9/f3w+XxRJXGtVmtSBpXU97gUqF1gSJXkLTbxBAdfIAsEAsI2kYKDV6kiiHQggUEQCSKXkKhYpDZyGWM4ffo0Tp8+jV27dqG+vj6p4yIXgZQqJDDkh9FoRGVlJSorKwHEL4lbXFwsCA6LxbLhNavW86t2gaFUD8ZW8IRwTqTg4B4OrVYbt0oVQSQDCQyCSIBs97ZIBikNdJ/Ph87OTng8noRDomJRcplaOV0HxMbEK4nrdDrhcrkwNjYGAFGCQ6qSuHJC7QIjVzwYW7GR4FhcXER/fz+OHj0qCI7IKlVqvjaIxCCBQRCbENnbgr9w5PZglSrJm4dE2Ww2tLS0pJxUK7UHJl2UPHY1ElkSt66uDowxrKyswOVyCSVx9Xq9IDb4qq7aULvAUEqSt9hECo5AIACdThfXwxGbw6Hma4WIDwkMgtiAcDiMYDAou5CoWLKd5B0ZErV7927U1dWldVyULDDkeD0QyaHRaFBUVISioiKhJO7S0hJcLhdmZmawtLQErVaL3t7ejJfElRMkMHIzRCpRIguYcMHBn9PhcBh+v1/oMk6Cg4gHCQyCiCEyJpW/ZOX8sMymB8Pn86GjowNra2s4ceIEioqK0t6nkkOkAPJg5BparVYQEgAwMTGB2dlZGAwGoSRuQUFBVIUqMUviygW1Cwy1hEhtRDwPDr8eNhIcm5XFVfO1pFZIYBBEBLGJ3HIXF0D2PBgOhwMdHR2w2Ww4cuQI9HpxHh9K92AodexEYmg0GhiNRuzYsQPA6yVxnU4nhoeH4fF4RCuJKyfIwFa3ByORELF4goP/5/P5ogQHz9/Q6/WyjQYgxIUEBkH8P+TU2yIZMp3kzRjD8PAwRkZGsGfPHtTW1op6bJRspCvlGiHEY7OSuH19ffD7/bBarYLgKCoqUqShTh4MEljJzj9yQU6n00UJDq/XK2zDBQf3cCjpfUskDgkMQvXIsbdFMmQyRMrn86G9vR1er1e0kKhYpGwUmO7vKlkcpQrNN5rIkrjckOIVqiYnJxEKhRIuiSsn1HaeYyEPRvpJ7iQ41A0JDELVyLW3RTJkykB3OBxob29HaWkpjh49KlpIVCxSVcFSuwFFJE6izwSNRgOTyYSamhrU1NSAMQa32y14OEZHR6HRaASxYbPZYDabZfnMIQ8GeTDEDvVLVHDE9uBQ4nuZIIFBqBiemKZEr0UkYhvojDEMDQ1hdHQUe/fuRU1NTUaPjZK9AEoeO5EY6ZxfjUaDwsJCFBYWoq6uDuFwGKurq3A6nThz5gyGhoaiSuLabDaYTCYRR586ahYY3OhVu8DI9Pw3EhzhcFgQHFqtdl3SuJLf12qCBAahOnhIVH9/P2pqapCfn6/oh5WYORherxcdHR3w+Xw4efIkLBaLKPvdDKUb6UoeO5EYYj0ftFqtUBK3sbFxXUnc/v5+GI1GlJSUCIIjLy9PlN9OFjULDP48Vev8gdfL1GaTjQRHKBRCKBSC1+slwaEgSGAQqiIyJGpkZATl5eWyWTFMFa1WK4R4pcPCwgI6OjpQVlaW0ZCoWJQsMOillvtk8tqMLYkbDAaxtLQEp9OJsbExdHd3R5XEtdlsWbsv1Sww+DknD4a08+eCg48jVnBE9uGIrFKlhOqPaoAEBqEK4vW24CskSifdEKlwOIyhoSGMjY1lJSQqFiULDEBdHgy1vrSzNW+9Xo/S0lKUlpYCOFsSl+dvDA8PY21tLaokrtVqzVhJXDULDPJgyENgxLKZ4AgGg8L3sTkcJDikgQQGkfMwxhAMBhEMBgFEP6SkSC4Wm3SSvL1eL9rb2+H3+7MWEhWLkgUGvbRyHymvTYPBgPLycpSXlwM4W9WNV6jq7e3NaElcNQsM8mCcFRjZ8palykaCIxgMIhAIRAkO7uHgIVVE5pH31UMQacK9FlxIRD5YckVgpOrBOHPmDDo6OlBeXo5jx45J9jJRusBQ6tiJxJGLoW00GlFVVYWqqiowxrC2tiZ4OCYnJxEOh6NK4hYWFqY8djUnOfMStXI571IgRw/GVpDgkBckMIicJDIkaqMqUbkiMJL1YITDYQwODmJ8fBz79u1DTU1NBke3NVL1wRDLeCCBkdvI9fxqNBqYzWaYzeZ1JXGdTidGRkbSKomrZg+G2ntgAMoUGLFsJTgArEsYJ8EhHiQwiJwj0d4WuSIwkvFg8JCoQCCAlpYWFBYWZnh0WyNVHwwxULsRohaUcJ7jlcRdWVmBy+USSuIaDIYowZGfn7/h/tQsMNTsveHkgsCIZSPBEQgE4Pf7he9JcIgDCQwip+BeC15ib7MXZK4IjEQ9ADwkqqKiAnv37s1YcmiySBVmRB4MIhGUen61Wi2sViusVisaGxsRCoWEkrhTU1Po6+tDfn5+lOCILImrZoFBHgxpytRmm3iCg9sQ3MNx5swZlJeXIz8/P6pKFbE1JDCInIBXkuBVohKpiy1WeVep2UooRYZE7d+/H9XV1Vkc3dZIJTDC4TCCwWBaQkuNLxqlGtxqR6fToaSkBCUlJQDOlsRdXFyEy+USSuIWFhYKYiMUCqny+gbIgwHkpgdjK3h+BiccDqO3txcWiyUqL4d7OCKrVBHrIYFBKJ5EQ6JiyRUPxmYhRmtra2hvb0cwGJRNSFQsUgiM1dVVtLW1we12o6ioCDabDSUlJbBarUm9VCnJWx3kogGh1+tRVlaGsrIyAIDf7xcEx+DgINbW1mAwGDA8PJzxkrhygzwYZ4+BWs73RvBnu9FohMFgiPJw8JAqrVa7Lmlc7dcOhwQGoWi412KjRO7NyBWBsVGI1Pz8PDo7O2UXEhULP2fZCsmYmZlBV1cX6urqcODAASwvL8PpdKK7uxvBYBDFxcVCJ+VEq/CoOZwk11HLuc3Ly4sqiTs4OIjl5WX4fD709vYiEAigqKhIuDcsFkvOrnCTB0OdHoxYIhctgfUejo0ER2wOhxqeH/EggUEoktjeFsmKC/43uSAwYj0Y4XAYAwMDmJiYkGVIVCzZEhjhcBh9fX2Ynp7GoUOHUFZWBr/fj4KCAqHsZ2wVHt5pmRtVsV3f1friIHIfnU6HgoIC7NmzJ6okrtPpxMTEhKglceUGGdd0DID1AiOWSMHBF/nC4TD8fn9Ul3G1Cg4SGITiiO1tkWq98lwRGJEejLW1NdjtdoTDYbS2tqKgoEDi0W1NpMDIFPy4MMbQ2toKs9m87tzHq8KzvLwMl8uFmZkZ9Pf3Iz8/XxAbNpst694XIvuoNQQu8pqOVxJ3dXVV6MERKcb5fyaTSbH3BIVIqSPJeytCoVDCgoBvQ4LjdUhgEIohkd4WyZArAoN7MObm5tDV1YXKykrs2bNHtiFRsWRaYKRaPUur1aK4uBjFxcVoamqKSoodGRlBV1eXkNPidDphs9kUc8yJ5MhVA2AzNhPNGo0GFosFFosF9fX1Qklcp9OJubk5DAwMIC8vL+GSuHKDQqTIgwGkl4cST3Dw/3w+H/x+P4D4fThy5XlDAoNQBKkmcm9GrggMAPD5fOjs7MT+/ftRVVUl9XCSgp9Hsc8FYwxDQ0MYHR0VpaFgbFKsz+fDwsIC+vv70dfXh2AwCKvVGhWjnisvCjVDHoytiSyJ29TUFLckrslkEsRGcXFxVElcuUEeDBIYgLhenMhIC51Ot05wRHo4DAaDIDjEsHWkggQGIXuS6W2RDDqdTvFlaj0eD/r6+hAKhfCGN7xBESFRsWTiJebz+dDR0YG1tTWcPHkSFotF9N8wGo0oLy9Hf38/Tpw4Ab/fL8Soj46OCl2UI/M3lPqiUDtqPG/prOJvVBKX5za53e6okrjFxcXQ6+VjjpAHgwQG8HqIVCbYTHB4vV4AwB133AGtVou77747I2PINPK5owkiBt7bIhgMihISFYtWqxWa6SiRubk5dHZ2orS0VEhWViJiezBcLhfsdjtsNhuOHDmSUcMlMka9oKAABQUFqK2tjRsyYjQaowSHnFdwN0KthjbNOz3ilcTl+RuDg4Pwer2wWCzCvVFUVCRpuKHajWtu6Ko95DObpXrjCQ6n0yncM0qEBAYhSzIREhWLUkOkIqshHThwAGazGU6nU+phpU26oSiMMYyOjmJoaAi7du1CfX191gzD2LHHCxnhK7iRTc24QVVcXKz6l7mcUaPAyGSYUF5eHioqKlBRUQEAQoUql8uF6elpIdyQeziyXRJXraKSw9+LahZZQGY9GFuh0WjgdrvR1NQkye+LAQkMQnak09siGZQoMDweD+x2OwAI1ZBWVlYUN49I+MpNOgIjEAigq6sLS0tLOO+881BcXCzeADch0WtTp9OhtLQUpaWlABAVTtXX1we/3y/kb5SUlFD+hoygHIzMYzKZYDKZUF1dDcYYPB6PIDjGx8fBGItKGC8oKMh4SWs1G9ckMM4idSUtj8cDs9ks2e+nCwkMQjZE9rbgMbCZfIkoTWDMzs6iq6sL1dXV2LNnj/DgU9o84pGOwFheXobdbofZbEZra2tWQ49SrYAVuYIb22NgfHwcAARjqqSkhPI3JEaNx16qVfzYcMPIkrgOhwOnT5/OeElctSd5k8A4i5QeDABYXV0VKhUqERIYhCwIh8MIBoMZDYmKRSmGeSgUQn9/vxASVVlZGfU974OhZLd+qgJjcnISvb29aGpqQnNzs2TzT2eVO16PAZ6/cebMGQwNDcFgMAjhVCUlJYrM3yCUhVyeJ/FK4vL+NLElcfk9YjQa0/pNtSd5h0KhlPtL5RJSCwy3252RAiXZggQGISmRvS34Cy1bDzWtViv7KlJutxvt7e3QaDRCSFQsudDsLVmBEQqF0NPTg/n5eRw5ckSyRLhMHG+NRoOioiIUFRWhsbFRKPnJOyj39PSgoKAgKn9DThV4cg0l31fpINd5x/aniXd/mM3mKA+HwWBI6jcoRErd8+dkM8k7HhQiRRApEpvIne0VE7l7MGZmZtDd3Y2amhrs3r17wwc+/1zJseLJCAy32w273Q6dTodTp07JooFXJo99bMlPv98vJIwPDAzA5/OhqKgoKn8jk8aBkq+zVFDbfCORo8CIJfb+CAQCcRtickFutVq3FORyFVfZggTGWaT0YDDGyINBEKmQqd4WycBDi+RGKBRCX18fZmdncfDgQaHSykZElnlVaiWiRM8Fz0Opra3Frl27JH8JZroLeTzy8vJQXl6O8vJyAGcr8DidTrhcLkxOTiIcDkflb5jNZlUbS0RqKNXINhgM2LZtG7Zt2wbgbE8cnjDe398vCHJ+j1it1nXPkXA4rGqvoJLfJWISCoUkDUfl/WKUinrvIEISMt3bIhnkGCLFV+e1Wi1aWloSco/migdjM29SOBxGf38/pqam4uahSInURpjJZEJNTY2Qv7G6ugqn0wmHw4Hh4WHo9fqo/I1049PVhlIN7XTJlXkbjUZUVlYKz4zNSuJyDyAleZMHA5BeaHk8HhIYBJEI2ehtkQxyC5Ganp5Gd3c36urqklqd59vJaS7JslmIlNfrhd1uRygUQktLiywbCspF3EUmxDY0NCAUCmF5eRlOpxNTU1Po7e1FQUGBYExR/gaxEblqZG9UEjeygptOp0NhYSFWV1czXhJXjpDAOIuUIVKhUAgej0eW77tEoTcLkRXC4TD8fr/kXotI5CIwQqEQent7MTc3h0OHDgmhL4kididsKdhIYCwsLKC9vR3l5eXYt2+fLN32criWN0Kn0wmhIMDr8elOpxNDQ0NYW1sTwkVKSkpQVFREhkUc5HyOM0WueDA2I15J3JWVFfT29sLj8eCvf/2rcA9xL6DJZJJ62BlH6v4PckHK4+B2uwGAcjAIYiN4SBSvEiUXcQHIQ2Csrq6ivb0dOp0Ora2tKb+85JpPkiixAoMxhuHhYYyMjGDv3r2ora2VcHSbk26TwGwSG5/u9XqF/I3Ozk6Ew2EUFxcLBpUaV29jUcq5FRs1CIxYeAU3s9mM4uJi1NTUCB7AmZkZ9Pf3w2g0RlWoysWQQ/JgnEVKDwYXGBQiRRBxkFtIVCxSCwweElVfX4+dO3em9UDfKodB7kQa6X6/Hx0dHfB4PDhx4gSKiookHt3WKNUIzc/PR3V1tRAu4na7BcFx+vRp6PX6qIRxtSKn51a2UKPA4HADO7IkLnDW4OQVqiJL4kaWjE62JK4ckTr3QC5IeRw8Hg/y8vIU3fOIBAYhOlL2tkgGnU4niVEeGRJ1+PBhYTU5HXLFg7G4uAi73Q6r1YqWlhZFvKzleG2ngkajQWFhIQoLC4WGZktLS0IybH9/v3A+zpw5kzPG1FYo+b5KB7ULjHhz1+l0KC0tRWlpKYDokrjDw8PweDywWCyCKC8uLlakoU4ejLNIGSK1urqq+AqAJDAIUWGMIRgMIhgMAsh+b4tkkKID9urqKux2O/R6vag9HKT2xojB3NwcZmdnsXPnTjQ0NGTlnIj1G7lohGq1WsFQ2r59O4LBIEZGRjA3N4fh4WGsra0JxlRJSUnccp+5glyfYZlEzQIj0U7eyZTEVVKOEwmMs0gZIsULDCgZEhiEaHCvBTd05f6Aiqy+lI2HyNTUFHp6etDQ0IAdO3aIenyUlAcQSzAYhNfrhdfrxbnnniskJGeLdA0ptRhher0eFosFKysrOHr0KHw+nxBO1d3djWAwiOLiYiFcpLCwMCeOjVLvq3RRs8BItYJWZElcxhi8Xq9QoWpqagqhUEjIcbLZbLBYLLI8xiQwziJliBTvgSHH6yNRSGAQaRMZEiWnKlFbkS2BEQwG0dvbi/n5edFComJRqgdjZWUFbW1tYIxh165dihMXgLLFXToYjUZUVVWhqqpKKPfJBcfIyIjgAcmF6jtKeJ6JjZoFRqIejM3QaDTrSuK63W7BwzE6OgqNRhMlyuUSEkNVpF4vUCNlDkYifbDkDAkMIi3knsi9GdnoH7GysoL29nYYDAZRQ6JiUWKSN/foNDY2wul0KjJWmThLZLnPuro6hMNhrKysRFXfyc/PjxIcSsnfUKN4BNQtMDLRAyQyx4nfI7wp5pkzZzA0NBRVVEFKUU4ejNftAilDpJRcQQoggUGkAfda8NUOpb2MMtk/gjEmNDbLREhULEpK8o6X5P7KK68oZvyxqNWDsRlarRZWqxVWqxVNTU0IBoNCMuzIyAi6urrW5W/IWWAq7dkmBmoWGGJ4MLZCq9WiqKgIRUVFaGxsjCqqEK8kbklJSdYqCoXDYcUsAGSKyEVTKXC73ZSDQagPOfe2SAaNRgOtVis8SMQiGAyip6cHCwsLOHLkCMrKykTdfzyU4sHweDyw2+3QaDRRfT+UbqQreezZQK/Xo6ysTLgXeDKs0+lEb28vAoEArFarYEjJKTZdredWzQJDihX8yKIKwNn3CBcc4+Pj6OnpQUFBQVSFqkyJAPJgSC8wPB4PeTAIdaHkkKh4iL3yv7KyArvdDqPRiNbW1oyFRMWiBA/G3NwcOjs7UVNTg927d0c9uJUsMJR8/aeCGOcpNhnW4/EIgmN8fBwA1uVvSHmc1XaOARIYUs9dr9evK4nL8zdiS+KK7QWkPhivHwOprgOqIkWoCl7thzcgkvoBLAZiJUdHhkQ1NjZix44dWT0+cvZghMNhDA4OYnx8HAcOHEBVVdW6baQQGGKeH6WKIzkQmb9RW1sr5G+4XC7Mzc1hYGBACBXhgkPJzaeUgpoFRjZCpJLFYDCgvLwc5eXlANZ7Af1+v+AFtNlsaZXEJQ+G9InuFCJFqALe22Jubg69vb04//zzc+bFI0aIVDAYRHd3NxwOR9ZComKRqwfD6/Wivb0dgUAALS0tG7p8pfJgiHEdK9n7Ikci8zcaGxujuiePjY2hu7sbhYWFUd2TM7naqlZDW63zBuThwdiKWC/g2tqa4OGYnJxEOByG1WpNqWy01Ma1HJCyghRwVmBUVlZK9vtiQAKD2JTY8rNKePAmQ7oeDF5mNT8/H6dOnYLRaBRxdIkjxzK1DocD7e3tKCsrw7Fjx6DXb/y4UbKRnkv3gxyJ7Z7s9/uFlVvezCzSkLJYLKIaR0q9LtNFzQJDjh6MzdBoNDCbzTCbzaipqVlXEndkZAQajSaqQtVmJXHJgyF9mBjlYBA5S7zeFnq9XnZGbLqkapgzxjA5OYm+vj40NTWhublZ8hhxuZwbxhhOnz6N06dPY8+ePaitrd3y2Mhp/KmgViNUCvLy8lBRUYGKioqoldvI/A059hZQGmoWGEpfSItXEpeHHcYriVtSUhKVL0gCQx4eDAqRInKOjRK5dTqd6BWXpCYVgREMBtHV1QWXy4WjR48KK6tSIpcQKb/fj87OTqyuruL48eOwWq0J/R15MIhUiLdyy/tvcEPKYDBE5W+k4mVU4zlWq8Dg74NcMrDjhR0uLy/D5XJheno6qk+NzWajEClIHyZGfTCInGOz3hY8XyGXXjzJCozl5WXY7XaYTCa0trZKFhIVixw8AEtLS2hra0NRURFaW1uTKqEoF4GUCkoWR7mGRqOJ6i0QCoWwtLQEp9OJiYkJodRnZP7GZqF7gHq9U7n0nE8Gfr5z2cDW6XSCmNi+fXtUn5qxsTF4PB4MDAzA6XSipKQkofsk15Dag8GrhCkZdV0xxIbw3hbBYFBwj8a+XPjNlksvHp1Ol5BhzhjDxMQE+vv7sX37dmzfvl1Wx0DKHIzIY7Njxw40NjYmfWyUbqQreey5jE6nQ0lJCUpKSgC8XurT6XRicHAQXq8XRUVFwjYb5W/I6V7PBvx6Vtu8gdc9GGqae2yfmj/96U+orq6Gz+fD0NAQ1tbWhJK4NptN9o0xxUDqHAy32w2z2SzZ74sBCQwi4d4W/GaT2nUoJokY5oFAAN3d3XC5XDh27JhgrMgJqTwAvIKW0+lM69goWWCoyRBR+lxjS31G5m/wyjuRcelms1mx12U6qFlgqMGDsRWMMZSWlgor6F6vV0gYjy2Ju5kwVzJS2jk8SZ88GISi4R25N/JaRMJvNqlDccRkK4GxtLSE9vZ22YVExSJFiNTq6ira2tqEpoLpHBslCwyAPBhKxWQywWQyobq6GowxrK6uwuVyweFwYHh4GHq9HowxLC0tpZy/oUTULDDU6MGIJTbJOz8/H1VVVaiqqtqwJG5xcbEgzpMpiStXpA6RcrvdlINBKBPe2yIYDAol+bZ6IPBtcinReyOBwRjD+Pg4BgYGZBkSFUu2PRjT09Po7u5GQ0MDduzYkfZKj5IFhpyvCyJxNBoNLBYLLBYL6uvrEQ6HsbS0JHjopqamYDabhfwNm82Ws3HpahYYPARYjXMHXg+X3uiZHq+wAhfmvCSuVqsVBEdJSQlMJpPijqfUAoPK1BKKJBwOIxgMbhkSFQ8xGtPJiXgCIxAIoKurC4uLi7INiYolW8IvHA6jt7cXs7OzOHTokBBqki5ySFJPFSWLI2JjtFotbDYb8vPzUV9fD5vNhsXFRTidTgwPDwtx6VxwWK3WnAkTUbPAUHuJ1mRDxOIJc14Sd35+PqqSG/8vsiSuXAmHw8jLy5Pkt4PBILxeL5WpJZRDZG+LVFdpEk2KVgqxAmNpaQl2ux0FBQU4deqUZA+YZMmGB8Pj8cButwMAWlpaRE1AU7qRruSxE5vDz63BYMC2bduwbds2AGfj0p1OJ1wuF6ampqLCREpKSlBQUKBYA13NAiOXipikAn8fprp6H68k7tLSknCf9PX1CSVxeYUqOb5npfRgrK6uAgDlYBDKIDaRO1UXcC56MHjp3bGxMQwODqK5uRlNTU2KeslkuorU/Pw8Ojs7UVVVhT179oi+wieVwBDjd5V0nRCpEe8c5+fno7q6WsjfcLvdguDgYSLcuxHbyEzuqFlgqN2DIXYfkNhKbpElcUdGRoRcA+7dkEtJXCmTvN1uNwBQiBQhfzbrbZEsuejBCAQCaGtrw/LyMs4991zYbDaph5U0mQoxCofDGBoawtjYGPbv34/q6mrRfwOQtsyuGKjJg6GmuQKJzTeyczIPE1leXobT6cTMzAz6+/thMpmiVm2T6ROTbdQuMNQ4b06mk9xjS+L6/X4hf4OXjpZDSVwpPRgejwdGo1EWQisdlD16YlMS6W2RLLnWzdvv92N2dhYlJSVobW2Vpas2ETIRIuXz+dDe3g6fz4eWlpaMrqYoOURKyWMnEiPZ5yZPci0uLgbw+qqt0+nE6dOn4Xa7UVRUJAgOueVvqDnRmRc9UStiLEQmQ15eHioqKlBRUQHg9ZK4TqcTPT09CAaDQklcm82WtZK4UvbBWF1dVXSIJYcERo6SaG+LZMmVECkeEjU9PY3CwkIcPXpU0Tez2B4Mp9OJ9vZ2lJSU4OjRoxlfSSEjnchlYldtfT6fEE7V3d2NYDAYlb8hdZlPNechUIiUtPOPLYnr8XgED8f4+DgYY1nJdZIyRIoLDKVDAiMHCYfD8Pv9onktIsmFECm/34+uri4sLy+jrq4OPp9P8S9TsTwYjDGMjIxgeHgYu3fvRl1dXVaOjZIFhpLHTmxNJoxto9G4zojigmN0dFSoYBVZ5jObqFlgqHnugPQCIxKNRoOCggIUFBSgtrZ2w5K4kRWqxCqJK3WIlNSLDGJAAiOH4CFRvEpUJtycSvdgLC4uwm63w2KxoLW1FTMzM1hbW5N6WGkjhgcjEAigs7MTy8vLOH78OKxWq0ij2xolG+lKfwkkg5rmmi0ijai6ujqhzKfT6cTs7CwGBgZgNBqFRFmbzZbx/A01G9lyMrClQM7zj1cSd3l5GS6XC3NzcxgYGEBeXl6UOE+1OaaUAsPtdotapVEqSGDkCJkKiYpFqR4MxhhGR0cxNDSEnTt3oqGhARqNRvHJxZx0PRi8PG9hYaEkuShKFhiA+hKf1US2z21kmc+mpqZ1VXe6urqEJFievyG2IaR2gaHWuQPS5h4kS2SuU1NT07qSuL29vTCbzVEejkTFudQ5GEqvIAWQwFA8YvS2SAYlejD8fj86OzuxsrKC8847T0i8BJQrmGJJVSgxxjA5OYm+vj5JO5YrWWCo2RhRC1Ke43j5GzxEpLe3F4FAQEiCLSkpgcViSXu8ahYYak/ylrMHYytiS+IGAoF14jyRkrhbdTPPNG63m3IwCGlhjCEYDCIYDAJIvbdFMijNIHe5XGhvb0dRURFOnTq1bvUiVzwYqRjowWAQPT09WFhYwNGjR1FaWpqh0W2N0gWGUsdObI3czq3RaERlZSUqKyvBGMPa2pqQvzE+Pg4AgthINSZdzQJDyQa2GEhpWItNbHNMXhLX6XRiYGAAPp9PqOZms9lQVFQUZeNInYOhdEhgKBTutRC7Kc5WKKVMbWSycmRIVCy5IjCSncfq6irsdjsMBgNaW1slbwIm5XkQw5iSmxFKiItcjW2NRgOz2Qyz2Yza2lqEw2Gsrq7C6XQKMelGozFKcCQS/qhmgaHmuQO5LbBiS+Kura0J3sDp6WmhJC7PPyQPRnqQwFAYkSFRmagStRVarRZ+vz9rv5cKfr8fHR0dcLvdWyYr54rASCbJe2ZmBl1dXaivr8fOnTtl8TKRygsgxm+q2RhRA0oSj1qtFkVFRSgqKkJjYyNCoZAQIjI2Nobu7m4hRIQ3/Iu3SqtmIzuXDexEUNP8TSYTTCYTqquro0riLiwsAAD+/Oc/R+VvZKs3BZWpJbJOthK5N0PuIVIulwt2ux3FxcVobW3dMqErVwRGIkne4XAYfX19mJ6exjnnnCOs4sgBKQTG7Owsurq6kJeXF1WhJ5WeH0oyQonkUaqxrdPpUFpaKoQ/RoaI9Pf3w+fzrcvf4M8Spc45XSjJWz0CI5LIam42mw1/+ctfcOTIEbhcLjgcDgwPD0On02WkJG4sHo8HNTU1ou8325DAUAjca5HtLpuxyDXJmzGG06dP4/Tp09i1axfq6+sTOka5IjC28mCsra3BbreDMYbW1lbZlcDLpsAIh8MYGBjA5OQk9uzZA41Gg8XFRQwPD2NtbQ1FRUWC4EikayzlYOQ2uXRuI0NEeP4GFxwTExNgjMFms8FoNIIxpkqhQUne6hQYkYRCIej1esEb2NDQEFUSN7J8dKTgSLUkbiwUIkVkhWz0tkgGOXowfD4fOjs74fF4ku7fkCsCY7N5nDlzBh0dHaioqMDevXtlWYIwW0a6z+dDe3s7/H4/Tp48iby8PITDYZSXlwMAvF4vnE4nnE4nJicnBYOLezfiCTO1GWBqJBfPcWT+Rk1NDRhjWFlZEXoKrK2t4YUXXoi6/sUyoOQMeTCUU6Y2U8TrgRGvJC4PP5yYmEBPT0/KJXFjcbvdsFgsYkxFUkhgyBg5hETFIjcPhtPpRHt7O2w2G1paWpK+oXNJYMQa6IwxDA0NYXR0FPv27ZO1yzUbAmNxcRFtbW2w2Ww4evQodDodAoFA1Db5+fmorq4WYnJ5w7PIhNl4Dc9yaZWbiEYt51aj0QgrtmazGSMjI9ixY0eUAVVQUCBc+xuV+FQ65MEgD0YiIis2/DBeSVzer8Zms8FqtSZ8v5AHg8go3GshRSL3ZsililRkSNTu3btRV1eX0jHKFYERGyLl8/nQ0dGBtbU1nDx5UvarIZkUGIwxTExMoL+/P6qi2Fa/F2lwNTY2rmt41t3dDYvFIqx4quXFrBaDOxK5PH+zBTeyuZhubm5GIBAQKu4MDg7C6/UK4YS8xGcuXP/kwVDHc2wzUinVG1sSN7JfDc93iiyJa7VaN/wNj8cj+3d2IpDAkBmxvS3kJC4AeYRIRRrPJ06cQFFRUcr74iv/Sn+oRnoweKK7zWbDkSNHFLHKmCmBEQqF0N3djYWFBRw7dkxowMR/MxliG555vV5BbMzNzWF+fl4IJykpKclYAiCRXdQoqOIZ2QaDAeXl5UI4YWT+xuTkJMLhMIqLiwXBka2KO2LDGFN1iBDPP1Az8UKkkiWyXw0QvyRucXGxEHpYVlYmHPfV1dWU8yRvvfVW3HbbbVGfVVRUYHZ2FgDwgQ98AA8++GDU9ydOnMBLL70k/Nvn8+GWW27BI488grW1NVx00UX49re/jdra2qTGou6rSGbE9rbIRuO8ZJE6RMrhcKCjo0M045mLilwRGCMjIxgaGkoq0V0OJFNmN1E8Hg/a2tqg0+k27PWRzvHJz89HVVUVlpaWoNfrUVFRAafTiTNnzmBwcHDDcCpCeSjlPhKLRJK7Y0t8rq6uRlXc0ev1UfkbUvfaSRSlvwvSRe3zBzKThxJ7v7jdbkFwfPe738Xjjz+Oo0eP4vzzz4dWq02r0d7+/fvx9NNPC/+Onctll12G+++/X/h3bG+cm266CU888QQeffRRlJaW4uabb8YVV1yBV199NanjQgJDBkjd2yIZpPJgMMYwPDyMkZER7NmzB7W1taIco0iBoWS46BsbG8N5552H4uJiaQeUJGJ7MHhie1VVFfbs2ZPRFyZfCLBYLLBYLGhoaBASAJ1OpxCPGxlOspl7nJAXavRgAMmJqsjrv76+HuFwGEtLS3A6nZiamkJvby/MZrNw/adaDjobUIgUCQwxPBibodFoUFhYiMLCQtTV1eFb3/oW3v3ud+Ppp5/G7373OwwNDeGqq67CpZdeiosuuggXXXQRGhsbE96/Xq8XPCfx4N6VeCwtLeG+++7DQw89hIsvvhgA8PDDD6Ourg5PP/00Lr300sTHkfCWREaQYyL3ZkjhweCVf7xeb9ohUbHkgsBYXl5GW1sbAOD48eOyK0GbCIn08UiESCG6f/9+VFdXizC6xH43ktgEQJ/PJ1SnmpqaQjgcFgytkpISmM1mWd/3HCWMMROobd7plqfVarXC9Q28ngDrdDqFctAWi0WWgpuSvKmKVCo5GOmg1+vxxje+EW984xvBGENZWRnuueceDA8P4/7778eHPvQh1NXV4aKLLsKb3/xmXHrppVHhvrEMDg6iuroaRqMRJ06cwJ133ont27cL3z/77LMoLy9HcXExLrjgAtxxxx1C6OOrr76KQCCASy65RNi+uroaBw4cwAsvvEACQynIpbdFMmTbg+FwONDe3o7S0lIcPXpU9FUvpQuMyclJ9Pb2oqGhAadPn5btquBWiOHBCAQC6OjowOrqquhCdDMSuW+NRiOqqqpQVVUlhJM4nU4sLCxgeHgYBoNBCKcqKSmhcCpCUsTufxGbAMvzl5xOpxCPHim4pczfIA8GeTAy7cHYDL/fj0AggDe/+c14//vfj9tuuw2rq6v405/+hN///vf4+te/jtLS0igBEMmJEyfwwx/+ELt27cLc3Bxuv/12tLa2oru7G6Wlpbj88stx9dVXo6GhASMjI/jCF76AN7/5zXj11VdhNBoxOzuLvLw8YXGAE5nHkSjKtEYUDu9tEQwGZR8SFQuvIpXpBkyRJVbFDImKhxwS15MlFAqhp6cH8/PzOHLkCEpKSnD69GnFhnOkKzBWVlbQ1taGgoKChDq4R/5uuiQ79o3CqVwuF8bGxoTqVFxsyGl1V42otdlcJufM85e44Obx6DykkFew4oIjm/kb5MEggSGlF8ftdgNAVA5GYWEhLrvsMlx22WVb/v3ll18u/P+DBw+ipaUFzc3NePDBB/HJT34S7373u4XvDxw4gHPPPRcNDQ345S9/iXe+850b7jeVZwIJjCyjtJCoWPiDJ5MvIK/Xi46ODvh8vqyUWFVaqVq32w273Q6dTodTp04hPz9fMHCVNI9I0hEY09PT6O7uRlNTE5qbmyW5n9IRR/HCqbix1d3djVAoJFTnUVI4FaFcsimqYuPRecdkp9OJmZkZ9Pf3Iz8/Pyp/I5MePrV7MLIdHiRHpKykxQWGWH0wCgoKcPDgQQwODsb9vqqqCg0NDcL3lZWV8Pv9cLlcUV6M+fl5tLa2JvXbJDCyiFx7WyQDV/WZeggtLCygo6MDZWVlGQmJioeSBMbs7Cy6urpQW1uLXbt2CeeAJxqryYMRDofR19eHmZkZHD58WAi/SAYxjpfYxz2yvCFf3XU6nUJ1nshwKpvNtq4CCCEuSr2n0kFKr01kx2QAQv+ZyIIJsfkbYq42q30FX+3zB6QNkXK73TCZTKL9vs/nQ29vL84///y43zscDkxMTKCqqgoAcOzYMRgMBjz11FO45pprAAAzMzPo6urCXXfdldRvk8DIApG9Lbj7VYniAnjdgxEKhURdRQqHwxgaGsLY2Bj27t2LmpqarB0jJQiMcDiM/v5+TE1N4cCBA3ErQChhHhuRrJHu9Xpht9sRCoXQ0tIieWJ7pozQyNXd+vp6hEIhoTpPZDgVDyUpLi5WvXGQCZT6vE4VOa3ix/af4QUTXC4Xenp6EAwGYbVaBdFdWFiY1tgpRIoEhpQCY3V1Na0cpFtuuQVXXnkl6uvrMT8/j9tvvx3Ly8u47rrrsLq6iltvvRXvete7UFVVhdHRUXz2s59FWVkZ3vGOdwAArFYrPvjBD+Lmm29GaWkpSkpKcMstt+DgwYNCValEIYGRYcLhMILBoGJDomLh4xfTkPV6vWhvb4ff75ek67TcDfNYY3oj12kueDASWTl1Op1C4v/+/fslr3iSzftZp9MJhhRwNiGQV6fixlZkOJVSm53JCaXeU+kg57yT2IIJHo9HEByjo6NRFax4w8tkkJO4kgKqIiV9DkY64VGTk5N473vfi4WFBWzbtg0nT57ESy+9hIaGBqytraGzsxM//OEPsbi4iKqqKlx44YV47LHHouyue+65B3q9Htdcc43QaO+BBx5I+piQwMgQkb0t+MM6Vx5aPNFbDHi/gvLychw7dkySuEc5C4yFhQW0t7ejvLwc+/bt2/QGl/M8tiKRe4MxhrGxMQwODmL37t2oq6uTzT0llRGal5e3LpyK52/wqmKR1akonCo15HKdZQs5C4xINBoNCgoKUFBQIORvrKyswOl0Ym5uDgMDA1ENL4uLi7e8B8iDQR4MqUOk0lkYevTRRzf8zmQy4be//e2W+8jPz8e9996Le++9N6UxcEhgZADGGJaXl+HxeFBcXJxT4gIQx5ANh8MYHBzE+Pg49u3bh5qaGpFGlzxyNMwj+zns3bsXtbW1W/5NJrphZ4utigcEg0F0dXXB5XLJrpGgXDxH8ZJleTjVxMQEenp6UFhYGFWdSu0rlYkgh3ObbZQiMGLRarWwWq2wWq1oampCMBgU7oHR0VGsrq5GhRTGuwfIg0ECQ8pE93Q9GHKCBIbIcK/FmTNnMD09jRMnTkg9JNFJ14PBQ6ICgQBaWlqiyrFJgdwEht/vR0dHBzweT1L9HMRqVicF/IUe7+XmdrvR1taGvLw8tLa2wmg0SjHEDZGrMRIZKtLc3CxUBnE6nejt7UUgEKBwqgRR23FRqsCIRa/XR1Vo4yGFLpdLuAesVqsgOCwWi6o9GDzyQq3z50idgyG1TSQWJDBEIra3hcFgyHrH62yRTjfvyJCorUJ+soWcBMbi4iLsdjusVitaWlqSSqRXsgeDGzOxAmlubg6dnZ3rqmaJ/bvpogRhl5eXh4qKClRUVETFrvPqPDy/gxtbGwk5JcxVLNQ010hyRWDEEhtSuLa2JgiO8fFxAGeNy4WFBeTl5cFkMuXkcdgI/v5Qu8CQMgfD4/GQB4N4nXi9LcTMU5AbqTSmiwyJ2r9/P6qrqzM0uuRJRzCJBWMM4+PjGBgYwM6dO9HQ0JD0iy0XPBh8/IwxDA4OYmxsDAcPHoxbNUsuKNEAiRe7zkNJeHf4wsLCqOpUclgMkAolnuN0yFWBEYlGo4HZbIbZbEZtbS0YY1hZWcFrr70Gl8uFiYkJ5OXlRYnuXM9hIoFxFgqREgcSGGkSDofh9/vX9bbQ6/WSG62ZIlnxtLa2hvb2dgSDQVmERMUitQcjMr/g3HPPjWpukwxSzyMdIgWG3+9He3s7vF6vLK+XeChV2HFiw6kCgYDg3ejr6xNCSYxGI0KhkCoMUED55zVV1BgmpNFoUFRUBI1Gg3379iE/Px+Li4uCd4PnMOWy6CaB8Xo0ipRJ3kp45yUCCYwU4RchrxIVW342lz0YyRiy8/Pz6OzsREVFBfbu3SvLB7KUhvnKygra2tpgMpnSzi/IhRCp5eVldHd3CyFima4qJoaRLJckbzExGAzrwqlcLhdmZmawtraGP/3pT1HVqeSWFyM2ahBTkahFQMaDJ3nrdLp1+Rsulwsulwv9/f3w+Xzr8jeUbpjzuSt9HunA36GUg5E+JDBSIF5IVOzDOJcFRiJzC4fDGBgYwMTEhOxComKRSmBMTU2hp6cHjY2N2LFjR9ov9FwIkWpra8OOHTvQ1NSkGANHKeNMlchwqvz8fAwPD2P37t1wOp2YmppCb28vCgoKokqBynEhIRWUej+li1oFBu/FE8/AjsxhAhCVvzExMQHGmCA2bDYbzGaz4o4hJXhLLzA8Hg+2bdsmyW+LDQmMJEimtwXPU8jFB/VWBvna2hrsdjvC4TBaW1tlH0+YSk5JOoRCIfT29mJubg6HDx8W7WGiVA9GOBxGT08PAGD//v2SlixOFTUZohqNBsXFxSguLsb27dsRCASE6lR8ZTeyOlW6nZXlgNLHnyy5+N5KBH4fJ2Jkm0wm1NTUoKamBowxrK6uwul04syZMxgaGoLBYIgSHErw8pHAQNTCsRRQDoYKYYwhGAwiGAwCwJa9Lbj6DYVCkjSPyySbeTDm5ubQ1dWFyspK7NmzRxErmdk0zD0eD+x2OzQaDVpbW5PuMrsZSvRgcDEKvF7DPtuke8zUaIhFYjAYUF5ejvLy8qjKPLz3AM/v4IIjPz9f6iETW6B2gZHs3DUaDSwWCywWCxoaGhAKhbC0tCR4N3p6elBQUBCVvyFHu0DK5Ga5wI+BVNe/2+2O6qqtZOR3hcsQ7rVIJgGKG9bBYFCWD5J0iFd1KRwOo7+/H1NTU9i/fz+qqqokGl3yaLVaQThmEl5ytaamBrt37xb9Qa60JG+HwwG73Y6Kigrs27cPf/jDH7IukMT4vVzMwUiV2Mo84XAYy8vLcDqdmJ6eRn9/P0wmkyA2bDabrBchUjU4lY5aBYZYSc685HNJSYlQNIHnbwwODsLr9aKoqEgQHEVFRbIw7MmDIW2JWoDK1KqGyJCo2CpRW8G3zcU8jNiQIo/Hg/b2djDG0NLSoribQ6fTwe/3Z2z/kSV6Dxw4kDHxpZQQKcYYRkZGMDw8HNWlXMmGulLHnWm0Wm3ccCqXy4WBgQEhUZYbYxaLRZWGrdxQu8AQe+6RXj7grOeWhxVOTU0hHA4LYYU2m02yppckMKRtsgdA6DafC5DA2IBEErm3IlcTvXU6HXw+H4DXV+Wrq6uxe/duWa9GbkQmV/6z2bVcCSFSwWAQnZ2dWFpawvHjx6NCopQqMNRoiKVKrKHFq1M5nU6Mj49Do9HIKpyKPBjqIlvn22QywWQyobq6WsjfcLlccDgcGB4ehl6vj8rfyNZ9IPXqvRyQMkyMMQa32w2z2SzJ74sNCYw4cK9FurF4uSoweIhUb28vpqamcODAAVk3QtuKTAkMh8OB9vZ2lJWV4dixY1kpuSpnD8bq6ira2tqQn5+P1tbWdU2rlCowAPJgpAoPp6qpqUE4HMbKygqcTidmZmbWhVPJNW49F1GrwEg2UkEMIvM36uvrhaaXLpcLU1NT6OvrE+4D3qsmU/cBeTCk92BQDkaOslVvi2TJVYERCoVw5swZmM1mtLa2Kl5tiy0wGGM4ffo0Tp8+jT179qC2tjYrLyw5ezBmZ2fR2dmJhoYG7Ny5M+7xUKrAUOq45QZP8rdarWhqakIwGBS8GzxunfcdKC0tzUo4lVrPq1oFhhzmHdn0kocV8oZ/w8PDWFtbg8ViEQSH1WoVTRSQwJBeYFAORg4iRkhULLnYzXt2dhajo6MwGo04efJkTjyMxBQYfr8fnZ2dWF1dXRcClGnkmOTN+6FMTk7i0KFDQmhMPJRqqEttkOQqer0e27ZtE8o4R1anmpiYAICocCoxK7LForZzLAdDWwrkaGAbDIao+8Dr9QrCe3p6GsFgMCp/I52y0FRFStowMR4iRY32cgjutRDbPZpLHoxQKIT+/n5MT0+jtrYWq6urOfMgEsswX1paQltbG4qKitDa2gqDwSDC6BJHbiFSPp8PdrtdyD/ZalVGqQIDUO9KdzaJ7TvAq1PNzs5iYGAA+fn5UdWpxAgjUet5VavAUMK88/PzUVVVhaqqKsEg5YJjZGQkqiy0zWZLSnjLUWBlGylFlt/vRzAYpBCpXCC2t4XYsZe5IjDcbjfa29uF3g3Ly8tYWlqSeliika7AYIxhYmIC/f392LFjBxobGyV5ScUrHywVi4uLaGtrg81mSzj/RAqBJMZ5krtBIjZyMLo1Gs26cKrFxUU4nU4hjKSoqCiqOlU6RoMaz7Ha5gwoz8DWaDQoLCxEYWEh6urqospC8zwmLrx52NVmC19Km38mkDJEanV1FQDIg6F0YntbbNU4LxVyQWDMzMygu7s7qnfD6uqqrFbK0yUdwzwYDKK7uxtOpxPHjh1DSUmJyKNLHDl4MCLF1s6dO9HQ0JDwfUUeDCJV9Ho9ysrKUFZWBiC6DGg64VRqPa9qFhhKnndkWWgAUcJ7ZGQEXV1d6/I3Io1pqiIl7TFwu91CL6FcQHUCI53eFsmiZIERCoXQ19eH2dlZHDx4EBUVFcJ3Sp5XPFL1YPCqSEajEa2trTAajRkYXeJIneQdCoXQ3d2NhYWFlMSWFOMXy4OhVkNUrsSWAeXVqebm5taFUxUXF28ZzqhkozMV1CoweHGXXCFWePt8PkF49/T0IBgMRvWhoRwMaT0YvERtrpwDVQmMTCRyb4ZOp8tKh2ixcbvdsNvt0Gq1aGlpWaem5ZhMnA6pzGd6ehrd3d1oaGjAjh07ZPFAkNKD4fF40NbWBp1Oh9bW1pTqtivVUFejIaYkNBoNioqKUFRUhMbGxrjhVHxVl1en4vezEq9HMVCrwMj1ECGj0YjKykpUVlaCMRbVh2Z0dBSMMeTn58NsNme8cIJcCYVCkpXDXl1dlazJYiZQjcAQq7dFMihxpZ8bznV1ddi1a1fch60S57UZyQiMcDiMvr4+zMzMbFkVKdtI5cE4c+YMOjo6hGaLqb6gpRIYYjwL1GqIKpHYVV2v1ytUp+JdlXk4Va6EKiSLWgWGmuat0WhQUFCAgoIC1NbWIhwOo6OjA+FwWPD0GY3GqPyN2N5FuYiUHoxcKlELqEBg8N4WwWAw6010dDodAoFAVn4rXXjjvLm5uS0NZzklE4uBTqdLSGB4PB7Y7XYAiOvZkZpsezAYYxgeHsbIyAj279+P6urqtPZHHgz5k4tzzc/PR3V19bpwqvn5eaGYRV9fn2BoZbs6nBSoydCOJNc9GJuh1Wqh1+thsVjQ0NCAUCgkePrGxsbQ3d2NwsJC4T4oLi7OyXwNKXMwyIOhILIdEhWLUvpgrK6uor29XQhv2cotyg3yXHkJJWKYz8/Po7OzE1VVVdizZ48sX0LZ9GAEAgF0dHTA7Xbj5MmTopTVU6rAAMiDkSvEhlOtrKzgr3/9K3Q6HUZGRtDd3S2EU5WUlKCoqEiWz4J0yZVne7IoPck7XSIFlk6nQ2lpKUpLSwGcLaHqdDrhcrnQ19cHv98flb+RjcaX2UDKPBS3200eDCWQqd4WyaCEUCIeElVfX4+dO3cmdGNxdZ8rFSd4iFS8l2o4HMbQ0BDGxsZEWaXPJNnKjVleXkZbWxsKCwvR0tIi2oquUgVGLrxUifjwd8fOnTsBnE2S5eFUnZ2dUeFUPGY9F64HtQqMXEvyTpbNPDh5eXlR+Ru88aXL5cL4+DgACKFUSr4XpC5TSwJDxkT2tuAPC6kucjkLjGRComLhD6BcERh8DrEvVZ/Ph/b2dvh8PrS0tMi+NnU2QqS4IG1qakJzc7Oo95YcyuymglKFEZEYkde40WiManK2uroKp9OJM2fOYGhoCHl5ebDZbCgtLVV0OJVaDW3yYCQWIsZLqZrNZtTW1kaFFp45cwaDg4PIy8sTwqlKSkoUk78hdQ6G3O2MZMg5gSFlSFQschUYq6ursNvt0Ov1CYVExcJvvlAopNgXaCSRgon/f6fTifb2dpSUlODo0aOSVZVIhkyGSEUmtx8+fBjbtm0T/TeUbKgrddxE6mg0Glgslrgx67E9B0pKSmC1WhVjtJMHQ52kumgYG1oYCoWwtLQk9KHp6elBYWGhIDasVqts36lS98EgD4bMyUTTvFSQo8CYmppCT09PUiFRsfDjK7e5pUqkwGCMYWRkBMPDw9i9ezfq6upkcS0lQqY8AF6vF3a7HeFwOKPJ7VL38UgVpVwfRPIkY2jHxqxHhlN1d3cjFAqhuLhYEBxms1m2145aBYaak7wB8eav0+mE6xw4m7/BxXd/fz98Ph+sVqsgOCJLQ0uN1CFS5MGQMXIyUuTUByMYDKK3txfz8/OirEAnWnlJCfAHm8/nQ1dXF5aXl3H8+HFYrVaJR5Ycmbj2nU4n7HY7tm3bhn379mX0wUseDCKXiA2ncrvdcDqdcDgcGB4ehsFgEIwwuZUAVbPAUOO8OZkSWHl5eSgvLxdCsSPzNyYnJ4Vcpsjy0FKdBymTvD0ejygFU+RCzgkMOT0c5OLBWFlZQXt7OwwGA06dOpVSE7RY5DI3MeDXzKuvvgqLxYLW1lZZvewTRUwPBmMMY2NjGBwczJonR6kCQ6njJrZGrPOq0WhQWFiIwsJC1NfXR4WQ8BKgcgqnUqvAUHuIVLaMa5PJhJqaGtTU1ETlMi0sLGB4eBh6vT4qf8NoNGZ8TMDZ8y91iFRVVZUkv50Jck5gyAk5GOGTk5Po7e0VveN0rnTzZoxhcnISAFBRUYE9e/Yo9sUq1jkJBoPo6uqCy+XCeeedh+Li4vQHlwBKNdSVer0QiZGJ8xsvhCQynCoYDEZVp8r2iq5aBQZ5MLIfIhYvl2lpaUnwbvT29qKgoEC4H4qLizOWv8EYA2NMUoEht/5a6UACI4Po9XrJ+kUEg0H09PRgYWEBR44cEbrWioUcxFO6RB4jnU6HmpoaRb9cxAiRcrvdaGtrQ15eHlpbW7O2cgQoV2AA6gqRormKT2wJ0NhwKr6iy//LtIdVrQJD7R4MOeSgRIrv5uZmBAIBuFwuuFwuDA4Owuv1oqioSBAcYvaiiSwQJAVut5tyMOSMnB6KkdWWslkxYWVlBXa7XTASxQiJikXp3bx5JS2DwYDW1la8+OKLip4PkH6I1NzcHDo7O1FXV5dyAYB0UKrAkNMzhxCfbJ/fzcKpxsfHhYo8keFUYq648lVcNV7XuVJ6PVXkOH+DwbAuf4MLDt6LhhdPsNlsaXXC5jYAlakVh5wTGHIi2wKDMYapqSn09vaisbERzc3NGTMSlZzkPTMzg66urqhKWrkQ8pWqB4MxhsHBQYyNjeHgwYOorKzMwOi2RskCQ4njJrZGDud1s3Cq3t5eBAKBqOpU6RhYkahVYEi9gi8VPNpC7vM3mUwwmUyorq6O8vY5nU7B28e9GzabLakFVp6DItW1T2VqiYThF2o2VsaDwSC6u7vhcDgyEhIVixI9GOFwGP39/ZiamsI555yDiooK4btcERjJzsHv96O9vR1er1fyZoJKNtSVOm5ia+RmaMeGU3k8HsHAOn36dNrhVPxaltu8s4ESDOxMwd8dSpp/rLcvHA4L+RtTU1Po6+uDyWSKyt/YrHeXlB4cLpbIg0EkTDZyFVZWVtDW1ob8/PyMhUTFojQPxtraGux2OxhjaG1tXZdIlQsCI9kQqaWlJbS1tcFqtaKlpUXyxkdSdPJeWFjAzMxMWt1m1WiIqQW5C0eNRoOCggIUFBSgrq5OMLBiG5wlE06lZoGh5iRvJQqMWLRarVDudvv27QgGg0I41fDwMNbW1mCxWKIa/kXOV8oStcBZDwaVqZUxcns4ZLIXBq+A1NfXh6amJjQ3N2dt/kryYJw5cwYdHR2oqKjA3r17475gc0Fg8BCpROKneXWOHTt2oLGxURb3TTavqciGihUVFYIxlmqpULkbomIhh+sk2yhpzpEGVnNzM/x+P1wuV1Q4ldVqRWlpKWw2GwoLC9fNT80CgzwYyhYYsej1emzbtk3o++X1eoX7gVdri8zfCAaDkuagkAeDSIpMeTAiS4kePXpU6B6bLZTgwWCMYWhoCKOjo9i3bx9qamo23DYXBAY3CDYTGKFQCL29vZibm5PkutmMbIVI8XtncXER5513HvLz86HVaoXYdofDga6uLqH5U0lJCUpLS2EymTYcN5GbKF045uXloaKiAhUVFevCqUZGRqDT6aLK4RqNRlULDLV7MKTMP8gG+fn565pfcsExMjIC4Ox1Pz09DZvNtuEzPxPw8VAOBpEwmRAYy8vLsNvtMJlMWS8lypF7mVqfz4eOjg6sra3h5MmTW7odlSCYtoKvPG1kFK2traGtrQ0ajQatra1ZfXgmQjYEhsfjwWuvvSZUWNPr9fD7/QDWx7avrq7C4XBgfn4eg4ODyM/PF8RGbC12pRuixMbkisG1WThVZL8Bq9UKQJ4VhTKN2j0Yapp7ZP4Gvx9GR0cxOzuLmZkZ9Pf3C8987hXcLH8jXdbW1hAOhylESs7I7WUgpiHOGMPExAT6+/uxfft2bN++XbL5yjlEyuVywW63w2az4ciRIwnlFuSCB4O/HOIZBgsLC2hvb0dlZSX27t0ryxdJpgUGD5Wrrq7G7t27Nz3nkc2fGhsbhVhep9Mp1GLnoSYmk4kERo6Sy+c1NpyK9xs4c+YMAODPf/4zrFar4N2IF06Va5AHQ37vhWyh1WphNBphNptx+PBhBINBLC4uwuVyYWRkBF1dXUIIrc1mE708tMfjAQAKkSISRyyBEQgE0N3dDZfLhWPHjgklC6Uik7klqcIYw9jYGAYHB7Fr1y7U19cn/LLIBYERGSLFicw12Lt3L2pra6Ua3pZkSmBEHoOtQuU2IjaWNzbUJBwOo6enJ2uN0IjsoRaDk/cbKCoqwtzcHM477zxBVI+OjkKr1UZVp5LCc55p1OzBkDrBWQ6EQiFBNOj1epSVlQkVOX0+X9x8Ji44LBZLWs+K1dVVaLVa2UUWpENOCgw5lbvU6/VpC4ylpSW0t7dLGhIVi9w8GIFAAF1dXVhaWsJ5552H4uLipP4+FwRGpAcDOJtr0NHRgeXlZRw/flwIfZArmbhvI/MtxDwGZrMZZrMZtbW1WF1dxV/+8hcYjUahERpf6SotLRW10yyRXeTyHskmPIeLh1PV1tYiHA5jeXkZTqdT6LVUUFAgiI3i4uKcCKdS8yq+mufOiRQYsRiNxnXloSMFuEajieq/YTKZkhIcPP8ilxY0clJgyIl0PBiMMYyPj2NgYEDykKhY5JSzwHNSzGYzWltbU1o9zgWBwa+NcDgslC5O55hkG7EFhsfjQVtbG/R6PVpaWjYU5uneUzqdDhqNBs3NzULlHofDAafTKXSatdlsKC0tRUlJSU6tUKkBuTxzs0W8IhFarRbFxcUoLi7G9u3bhXAqp9OJ/v5++Hy+qHCqdFdzpYJCpNQtMBLNO4onwFdWVuB0OjE3N4eBgQEYjcYowbHVO3h1dZUEBpEcqQoMviK/uLgoi5CoWOTiweDJiemW6c0FgQGcnQdPSm5sbMSOHTsU88ASU2DwnJOqqirs2bNnyxdnIqV9t/p7Tl5eXlSlEp4szl88JpNJMMRsNltOrPwSuUMi9wIPpyovLwdjDGtra0LI4NjYmJDfwa/zbPRmEgM1h0iRwEg9TEyr1cJqtcJqtaKpqQmhUAiLi4vC/dDd3S30o7HZbHE9fh6PJ6cqSAE5KjDkFCKVisBYWlqC3W5HQUEBTp06JcvVZ6mrSIVCIfT09GB+fl6UzuW5IDDC4TAYYxgcHMShQ4dQXl4u9ZCSQoxGe4wxjI6OYmhoKOGck3TFxWblgTdLFh8YGIDP5xPqsJeWlubcCpbSSffaUCLJzlmj0USFDPLVXIfDIVTjiRTVsRXY5ISajWw1z50TCoVECUHX6XQoLS0VysBH9qPp6+uD3++H1WrF888/j0OHDuENb3gD3G43zGZzSs+bW2+9FbfddlvUZxUVFZidnV237Y033ojvfe97uOeee3DTTTcJn/t8Ptxyyy145JFHsLa2hosuugjf/va308rblOddnkPodDr4fL6Eto1MUm5ubkZTU5NsX25Shki53W7Y7XbodDqcOnVKlNUx3gdBqfh8PqFT+TnnnKM4cQGkvzAQCoWE3jDZzDlJ5h6NTBaPXPl1OBwYHR2FTqeLSqSV4+KCXBZviMyQrqiKXM0FsGEFNjmGU6lRUHLUWJY4lkwdg9h+NPy5/9xzz+FrX/saGGPYt28fQqEQBgYGsHPnzqSvw/379+Ppp58W/h1vHj//+c/x8ssvo7q6et13N910E5544gk8+uijKC0txc0334wrrrgCr776asrHhARGhkm02lJkkvK5554Lm82WhdGljlQhUrOzs+jq6kJtbS127dol2oqLkj0YvCwvN0jlUAQgFXgn8lRINN8ik4ix8ru0tASHwxGVLM5zNyhZPPuoUUyJbWTHVmCLDKcaHx8HgKiQQSlzlNS8iq/muXM2S/IWi8jn/uOPP45gMIgXX3wRP/jBD/Dcc88JC4QXX3wxLr74Ylx00UWoqKjYcr96vR6VlZUbfj81NYWPfexj+O1vf4u3ve1tUd8tLS3hvvvuw0MPPYSLL74YAPDwww+jrq4OTz/9NC699NKU5pqTAkNOKxCJhBItLi6ivb0dhYWFiknIzbYHIxwOY2BgAJOTkzhw4MCmN1IqKFFgRBYB2LlzJxoaGvDcc88p1ihK1YPhcDhgt9sTzrcQG7GeN5F9CYCzXiluiPFk8UjvBiWLZwc5vU+yQaZX8U0mE2pqalBTUxOVHBsbTsXvhWyGU6nZg0FlaqU5Bnq9Hueffz5ee+01hEIh/PjHP8af//xnPP300/jGN76Ba6+9Fvv378c73/lO3HrrrRvuZ3BwENXV1TAajThx4gTuvPNObN++HcBZ++naa6/Fv/zLv2D//v3r/vbVV19FIBDAJZdcInxWXV2NAwcO4IUXXiCBIVc2ExiRIVE7duxAY2OjYh5u2fRgeL1e2O12hEIhtLS0ZCQRSmkCIxQKobu7Gw6HI8rjpbR5RJKswEgl3yLeb6ZLvP4jYmA0GqOSxbkhNjs7S8niWUKpYj0dsllJKTY5NjKcamhoCF6vF0VFRcJ1XlRUlNGxqXkVX81z52TDg7ERvEytyWQSvBfA2YIlzzzzDBYWFjb82xMnTuCHP/whdu3ahbm5Odx+++1obW1Fd3c3SktL8bWvfQ16vR4f//jH4/797Ows8vLy1kXObJTHkSgkMDLMRn0w/H4/urq6sLy8rIiQqFiy5cFYWFhAR0cHtm3bhn379mXs5leSYR4bDhSZgyJGorRUJCMweL6F0+lMqe9JJsikMarRaFBUVISioqKoZHGHwxGVLM7DqShZXDzUdhylXMXfLJxqYmICAKKqU4npxWOMURUplc6dI2UeitvtjtvFu6ysDFdfffWmf3v55ZcL///gwYNoaWlBc3MzHnzwQVxwwQX41re+hddeey3p+zrdZ0FOCgw5vRDieTAWFxdht9thsVgUExIVS6arSDHGMDw8jJGRkax0oFaKwDhz5gza29tRU1OD3bt3r3shpJPHIDWJCoxIgSVG48l0nxdSPG/iJYvz3hunT5+GXq8XKlOVlJTAYDCI8rtyerZmA6XeS+kgpzChyHCqSC8eL/mcn58f5cVLJ5yKn2u5zD3bkMCQNkzM7XaLttBcUFCAgwcPYnBwUChdX19fL3wfCoVw880345vf/CZGR0dRWVkpVLqKHMP8/DxaW1tTHkdOCgw5EWmIR4Z08Jh5pT7MuEGeiZeR3+9HR0cHPB4PTpw4gaKiIlH3Hw+5CwzGGIaGhjA6Oor9+/fHrQIB5L4Hg+dbVFZWYu/evbJ6IUpljEYmDdbV1SEcDq+rwR4bZiKn4yZ3lPqMThU5CYxI4nnx+HU+PDyMtbW1qOvcYrEkdZ3z56Za741wOKzIxU4xkTJEyuPxoK6uTpR9+Xw+9Pb24vzzz8e1114rhFtxLr30Ulx77bW4/vrrAQDHjh2DwWDAU089hWuuuQYAMDMzg66uLtx1110pj4MERobhAsPv96OzsxMrKyuyCelIB34Tiu1S5N4dq9WKlpYW0VZet0JOnclj4deO2+3GyZMnYbFYNtxW7kJpMzYTR5H5StnwaCWD3IwxrVYrGFnA68niDocDU1NTYIwJYSalpaWKaYImBeTBkC96vR5lZWVCDySv17thOJXNZoPZbN50f+TBIA+GlAJjdXV1y2t0I2655RZceeWVqK+vx/z8PG6//XYsLy/juuuui+rHwTEYDKisrMTu3bsBAFarFR/84Adx8803C17vW265BQcPHlwnTpIhJwWGnB4QvEztCy+8gKKiIpw6dSprRnMm4Q8isW7IyIpIUiS8y9UwX15eRltbGwoLCxMSXLkYIiXHfItIMpXkLRbxksUdDse6ZPHS0tK4HWbVjpzeJ9lAKQIjlvz8fFRXV6O6ujpuOJXRaERpaalQnSr2WUoeDBIYUuZgeDyeuDkYiTA5OYn3vve9WFhYwLZt23Dy5Em89NJLaGhoSHgf99xzD/R6Pa655hqh0d4DDzyQ1vHISYEhFxhjmJqaQjgcRmNjo6JDomKJ9GCkSzAYFBqkSZXwLkeBMTU1hZ6eHmzfvh3bt29P6NpRcohUPHG0traGtrY2aLVaUfItMolcBUYkkWEmvGoPX/Xt7+8XOsxSsvhZlHBOxUapAiOSzcKpTp8+jbW1NVgslqiwQf7cVPrcU0XtZWp5yLfckrwT4dFHH01q+9HR0XWf5efn495778W9996b0hhieeqpp0hgZAqeR7C6ugoAqK2tzakHl0ajEaVU7crKCux2O/Lz8yU1IOUkMMLhMPr6+jAzM4PDhw8LFVUSIZc8GHLOt4hEyfe1Xq9HeXk5ysvLwRiDx+MRBMfp06dhMBiiem+oESWf31TIBYERy0bhVC6XS+gxw0vgejwemEymnDsGW6F2Dwa3ZaRM8s5ECX4p8Pl8uOWWW3JTYEj9YOCdlYuLi9HS0oJnnnkGoVAoqw2DskG6RjlfoW9sbMSOHTskPW9yERi850c4HEZLS0vSMZlK9mBwgRGZb7Fnzx7REt8yjVKFHUej0aCgoAAFBQWoq6tDKBTC0tISnE4nRkdH0d3dDbPZDL/fj6WlpaSTaJWI0s9pKuSiwIglNpxqdXUVMzMzWFxcxMsvvwyj0RhVnSoXwpq3Qu0Cg783pfBgMMbgdrs3za9UEsvLyxgbG8tNgSEVjDGcPn0ap0+fxq5du4SyYBqNJmtN6bJJqqVqQ6EQent7MTc3l/QKfaaQg8BwOp2w2+1p9fxQugcjHA6js7NzXQNBOSP3HIxU0el0gpG1Y8cO+Hw+TExMYHJyEu3t7QDOJtHycCpKFs8N1CAwItFoNIJhNzc3h9bWViGcamRkBF1dXUJ1KpvNBqvVmpOGuJT5B3KAh4hJde2nk4MhN3w+HwoKCkhgiAUPiXK73Th+/DisVqvwXaZ7RkhFKiFSHo8HdrsdGo0Gra2tojZKSgcpBUZk+eLdu3ejrq4u5YecHIRSqvj9fgQCAXg8nnUNBOWOGgwyniQ7Pz+PlpYWIVl8ZmYG/f39MJlMgtjIpWRxNZzbSNQmMDh8BV+n00VV3uFV2JxOp5BTyRPFS0pKYDabc+J4qd2DIXUOSjo5GHLDarXiiiuuIIEhBk6nE+3t7bDZbGhtbV3nTs1VgZFsadf5+Xl0dHRs2CROSjLZ12MzeIL74uKiKBWSlBoi5XQ60dHRAY1Gg+PHj8vq2kiUXPNgbEZssnggEIDL5YLT6URfXx8CgQCKi4uF6lRKNcLUaGyrcc7AWQM73rxjq7Ctrq7C6XRiYWEBw8PD6/KUlBpOJbWBLTVSlqgNh8M5lYNhsVhw880356bAyNbDMTIkarOV51wVGIl6MMLhMAYHBzE+Po4DBw6gqqoqC6NLDv5gzebLdXV1FXa7HXl5eWhpaRElwV1pIVKR5YkbGxsxNjamyJecGg2ySAwGQ9xkcYfDEZUszsuEKtUIUwNqFRiMsS2fPTycymKxoKGhAaFQaF1Ty8jqVEoKpyIPhrRN9hhjOZODAQC7du3KTYEBJNYVOB18Ph86Ojqwtra2ZbfpXBUYiXgwvF4v2tvbEQgE0NLSIlsXIH+wZushOzc3h87OTtTV1WHnzp2i/aaSPBihUAjd3d1CvoXBYIhbPi/TiPWsUJKwyyQbJYs7HA6MjIwIRhgPp+LVe+SIGs+pWgXGRh6MzYgXTsU9ed3d3QiFQoInT+7hVGoXGFL3wAAgW/soWUKhEFZXV3NXYGQSh8OBjo4O2Gw2HDlyZMvqULzZXq6xlXByOBxob29HWVkZjh07JusqWpGNAzM5TsYYBgYGMD4+joMHD6KyslLU/SvFg8H7W2g0GiHfwuPxKEYcxSJXo0EORCaLA/E7LkeGmMgt90Zt51atAiMRD8ZWGI1GVFZWorKyUqgMxD15seFUNpsNeXl5Io0+fdQuMKQMEXO73dDpdLLu85QI/Nnxl7/8Bf/2b/9GAiMZGGMYHh7GyMhIUsm4uerB2ChEKjJ0bM+ePYroARLpwcgUfr8f7e3t8Hq9GfPmKKFiGa+WVV5ejn379gnHPtNex0yi5LFnm9gSocvLy3A6nZienkZ/fz/MZrNghEmdLK7Gc6pWgSG2ga3RaFBYWIjCwkLU19dHlX2ODaey2WwoLi6WzMBljJHAkDBEanV1FQUFBTlz/JeWljAwMJC7AkPsF77P5xOMw61ComLJVYERL0TK7/ejs7MTq6ur66ppyRmNRpPR8KKlpSW0tbXBarWipaUlY14SOXswIvMt4gn0yHKvSjRw5HrcxUTs86LRaGC1WmG1WqOSxR0OR1SyOA+nkiLERInXYjoo9f5Ll1RCpJIh1pPn9/sFT15PTw+CwWBUOFVBQUFW80n5GNWKlCFSuVJBitvdl112GS677LLcFRhiwkN9SktLcfTo0aSNw1wVGLEeDG5EFxUVxa2mJXcyVeJ1YmICfX192LFjBxobGzP60pBrmdpQKISenh6cOXNmw/4WShYYShuvXImXLO5wONaFmGQrWVwNojEWJd5/YiBGiFQy5OXlxQ2ncrlcOH36NPR6fVToYCbDqfg7I1dW0FNBSg9GLlWQinyPk8DYBMYYhoaGMDo6mlaoT64KDO7BYIxhYmIC/f39aG5uRlNTkyJfUMmW3d0K3lBwfn4eR48eFRIBM4kck7y9Xi/a2toAAK2trRvG2Cu9YZ1Sxy1XIpPFeYhJZAO07u5uoQFaJpPFlfgsSwe1CoxMezA2IzacKhwOY3FxES6XCxMTE+jp6UFhYWFUdSoxjWESGNLnYMi5AECyhEKhs/msUg9Erni9XnR0dMDn8+HkyZNplQ/LZYERCATQ0dEBp9OJY8eOCe5fJSLm6n9kEvNmRrXYyC1EyuVyoa2tLaHu5EoWGLnyYpAzsRV7NksWLy0tFSVhUonXYrqoVWBk24OxGVqtVriWm5ub4ff7hepUvb29UX1mxAin4vaJGs87R+ocjFwIkeJ5PA899BD++Mc/5q7ASOdGWVhYQEdHB8rKylIKiYpFr9fD5/OltQ85EgwGMTs7K4REKb0CglgCY2FhAe3t7aisrMTevXuz+tKSiwcj0quVaEEEpQsMJY5byUQmi4fDYaGzeGyyeGlpaVorvmozutQqMKT0YGxFXl4eKioqUFFREdVnxul0RoVT8e7iyb6LuWEo1/lng1AoJJkN4/F4ciZECgBOnz6NZ599NncFRiqEw2EMDQ1hbGwMe/fuRU1NjSg3XC56MKanpzE5OQmz2Yxzzz1XNis/6ZCuwIisnrV3717U1taKOLrEkIMHIxwOo6enB/Pz80l5tSKbHSoRpY47F9BqtUKy+Pbt2xEIBAQDjK/4cuMrmWRxNZ5TtQoMOXkwNiO2z0w4HBaqU01OTqK3txeFhYXC9Z5IJTa1V5ACpD0GueLB4PbHl7/8ZXz5y18mgcHhDeH8fn/aIVGx5JLACIfD6Ovrw8zMDGpqahAIBHLmwZSOwAgEAujs7MTKykrSVcbERGoPRmS+RUtLC0wmU8J/y40aKcafrlGlJoNMCUa3wWCIWvGN14+AV6YqKSnZ1EutpnMLKMfQFhulGtlarRY2mw02mw3Nzc1R4ppXYrNarcK1XlhYuO6aVurcxYSSvMWBX1tutzt3BUYyL4UzZ86go6MD5eXlGWkIlysCw+PxwG63AzhrPDocDszNzUk7KBFJVWCsrKygra0NZrMZLS0tkjZPkrKKlMvlgt1uR1lZ2Zb5FpuhBAM2Hkodd64Trx8BTxY/ffp0VLJ4aWkpLBaLosP10kWtHgw5h0glQ6y49ng8Qv7G6OhoVH4HD6eSskSrXJBSYHg8npzwYHDm5+dx77335q7ASIRwOIzBwUGMj49j3759qKmpycjv5EIn7/n5eXR2dqKqqgp79uyBVqvF4uJiTggnTirG+czMDLq6utDY2IgdO3ZI/oKSKkRqfHwc/f392LVrF+rr61M6DrwXiRKNOqWOW41EJovv3LkTXq8XDodDSBbXaDSw2WwoLS1V/HM7FdQqMHLRcxMZTlVbW4twOCw0tpyamkJvby8KCgpgMpkQDoclNbKlRkqRtbq6irKyMkl+W0y4J+zTn/40nn76afUKDB4SFQgEMtZVmaNkD0ZkXsr+/ftRXV0tfCfXngupslFn8niEw2H09/djamoKhw4dQnl5eYZHlxjZDpFKNd9iI5RqqKvRIMsV8vPzUVNTg5qamqhk8ampKSwvL0Ov12NoaEiIZ881IzQWtQoMNazia7VaFBcXo7i4WMhVcrlcmJqagt/vxx//+Meo6lTxwqlyFalDpBobGyX57Uzw85//HI8++mjuCozNborIkKh0QjkSRakCg3cv9/l8cUWYUue1EYkKJp/PB7vdLohTOcVOZtOD4fV6YbfbEQ6Hk8632AilCgxAHeE0uW5sxCaLDw4OYmVlBYFAQOi2zJNnS0tLYTKZcu6YqFVg5KIHYyt4Y0vuvdi3b5+QvxEZTsWv+WyVW5cCKftg5EoVKX78jh8/rr4+GJEhUbGr8ZlEr9crzhB3Op1ob29HSUnJhqV6c9GDsdV8eJ5BSUlJRvJ10iVbHgx+HEpLS7F//37RRLpSBYYaDTI1oNPpYDabsWfPnqhk8YWFBQwPDyMvLy+qs7jcngepoEZDG8idHIxU4N4bs9kMs9m8LpyKl342mUyCd8Nms+WUx0dqD0Yu5WC85S1vwTe+8Q31CIy1tTW0t7cjGAxmPCQqFiWt9DPGMDo6iqGhoS37FyhpXomwmcBgjGF8fBwDAwNp5RlkmmyIvomJCfT19WHnzp1oaGgQ9TgoWWAocdzE5kSu5m+ULM4rU62traGoqEioThWZLK4kyIOhPuKFh20UTuVyuTAwMACfzxdVnUqp1ztHyhC5XBMYTz75JDo7O3NXYERe6DxBuaKiAnv37s36RcQNcbk/uHmp1eXlZRw/fhxWq3XT7dUiMEKhELq7u+FwOHDuuefCZrNJMLrEyGSIVDgcRm9vL2ZnZ3H06FGho7KYZNtQF/N+JIGhLmI7i6+trQnhJWNjY3Gr9SgBub+nMoWaS7UmMnceTsXzDSOrU42PjwvFEfj1rrRwKilDpHKpTG0oFMKVV16Jt7/97bkrMICzN83AwAAmJiayGhIVCxc0ck4iW1pagt1uR2FhIVpbWxMqtaqGECmPx4O2tjbo9Xq0tLTI/qGZqRCpyHyL1tZWUfIt4iFFHw8xjCk1GmRqIBnRaDKZopLFeXhJZPMzbnzJOVlczQJDjfMGUhNXPJwqsjiC0+nEzMzMunCq4uJiWYcPhsNhMMYksc94KWExe69JiU6nwyc/+UkAyF2B4fV68fLLLwsGkZTqkF+0ciwBxxjD5OQk+vr6sH37dmzfvj3hh6xOpxNuzFx4MMd6ZObn59HR0YGamhrs3r1btgZBJJnwYCwuLqKtrU30fIt4KDnUSKnjJjYnlWdbbHiJ3++Hy+WCw+FAT08PQqEQiouLhXAqOSWL58rzPFnUHCKV7up9ZHGEpqYmBINBwbsxODgIr9cLq9UqlH+WWzgVf+9TiJQ4eL1ePPfcc7krMPLy8lBeXo7GxkbJjXp+4waDQUmbsMUSDAbR09ODhYWFlEJe+Lzk7JlJBq1Wi0AgAMYYhoaGMDo6KqnnKxXE9gBkMt8iHlL18UgXOb0sCfEQ61rMy8tb11nc4XDgzJkzGBwchNFoFMSG1MniSrz/xIA8GOKJK71ej23btmHbtm0AosMHJyYmACAqnCpTHvFE4e9MEhjpEwgEcP/99+OLX/xi7goMnU6H5uZmqYcB4KzxIbd8hdXVVdjtdhgMBrS2tqYU+iNnz0wqaLVaBINBvPrqq/B4PDh58qTi3JZiGejZyLeIB3kwCLkhttEZmSze0NCAUCgkrPbyZHEpk2fJg6E+Mr1IGBk+yBgTwgdnZ2cxMDCA/Pz8qOpU2RbY3IMjxXUfDodzokwtf2709vbim9/8Jn70ox/lrsAA5GWsyKlULe8+XV9fj507d6b8UOV/J5d5pYvf78eZM2dQWlqKlpYWGAwGqYeUNFxgpGMk+Hw+tLW1ZTzfIh5S3LNi5WDI5VlDiEc2jG2dToeysjKhk+9myeKlpaUZ94KrVWCoPck7W+87jUazLpxqcXExSmAXFRVFCexMnxepE7wBKG4xMxb+3JiYmIBOp8Mll1yS2wJDTsjBgxHZffqcc85BRUVFWvvTaDQ5k+g9NTWF8fFxmM1mHDlyRLEvWD7uVI0Enm9RUlKCAwcOZN0zpVRDXanXC7E12T638ZLFHQ7HumTx0tJSWK1W0Q0jta7kq1VYAdKKK71ev05gc49etsKppC5RC0DxHgxO5LVEAiNLSC0w1tbWYLfbwRhDa2srzGazKPuVel7pEhkKVF9fD7fbreiXTGReTLIvDG7A7NixA42NjZIcBykEhli/p0RhlApqmScg/Vwjk8Wbm5vh9/sF70Z3dzdCoVCU8SXGc12thrbaPRhyCXM2mUwwmUyorq4GY0yoTjU3N5excCqpm+wZDAbFlLHeCH7vHDx4EM3Nzbj11ltzW2DIaTVUSkP8zJkz6OjoyEgfECV7MLxeL9ra2sAYQ0tLC1wuF1ZWVqQeVlpwwyCZcxIOh9HX14eZmZms5lvEI9v3LM9FYowJPQ1SWRVWo0GmFuR0bvPy8lBZWYnKykowxrC6ugqn0ylqsriaBYYa5w3IV1xpNBoUFRWhqKgIjY2NG4ZTRVanSmUeUoZIra6uoqCgIGeuvcbGRnz0ox/Fpz/96dwWGHJCCoERWQ1p3759qKmpEf03lOrBcDgcaG9vx7Zt27Bv3z7odDosLS0pci6R8Idkoka6z+eD3W4XOtyL5dlKlWwKjMgyxAUFBVGrwpEx74kWQJDLYgahDjQaDSwWCywWCxoaGqKMr6GhIaE0KL+OCwsLEzJi1Cow1BoaBkhrYCdDbDiV1+sVPHpTU1MIh8PrwqkSuZal9mDkSngU55JLLsEb3/hGEhjZQqfTIRgMZu33/H4/2tvbsba2ltFqSErzYDDGMDo6iqGhIezZswe1tbXCA0ipJVIjScaDsbS0hNdee02yfIt4ZENgMMYwMjKC4eFhHDhwAGVlZQiFQlGrwg6HQ6hwYjabBSNtowZpcvKWEuKhJGM7Xiy70+mEw+GIShbnHo6NksWVNGcxkesqfjZQ6tzz8/NRXV29Lpxqfn5e8OhFhlNtlMgutcBIVPzLGf7cePnllzE/P483velNuS0w5HTCsrnS73K5YLfbYbPZcOTIkYyWfFOSByMYDKKrqwuLi4s477zzUFxcHPW90sRSPDQaTULGrhzyLeKR6XMQCoXQ1dUFl8uF48ePw2q1IhAICN9Hrgo3NjYiEAisa5DGV8hKS0uFhEO5HL9Mo5Z5cpQsGmOTxZeWloTE2Z6eHhQWFgpiIzIsUK0CQ63zBpQrMCKJDacKhUKCR29kZATd3d2wWCyC4CgqKpJFLy+32y155IAY8GP461//Gt/5zndw5ZVX5rbAkBPZMMQZYxgbG8Pg4GBWG6MpQWCsrq6ira0NRqMRra2tcVfvlDKXrdjMSI/Mtzhy5Iiw2ikXMukJ8Hq9eO2116DVatHS0pJQUp3BYEB5eTnKy8ujGqTxFTKTyYTS0lL4/f6cuHaI3ESr1cJms8Fms22ZLB4KhVRpaOeCkZ0quTh3nU4n5NUBZ8OB+TXf2dkZFU61trYm2TWfK032uEC7+eabsX//fvzgBz8ggZEtMi0wAoEAurq6sLS0FHd1PlPodDrZr/rPzs6iq6sLdXV1m/b9yAUPBrBxN2+55VvEI1MCg5ffLSsrw/79+6OugURfLLEN0oLBoODdcLvdGB4ehsvlElaF5Xh8ieTI1VXtjZLF5+fn4ff70d3djbKyMiEsUMrO4tkiV891IsipilSmMBqNqKqqQlVV1boCCS6XCzqdDr29vSgtLd00nEpsci0Hw2Kx4Oqrr8bVV19NAiNb6HQ6+P3+jOx7eXkZdrsdJpNpw9X5TCHnEKlwOIzBwUFMTEzgwIEDqKys3HT7XBEY8XJJlpaW0NbWhuLiYhw7dky2BkMmBMbU1BR6enpE9+rp9Xps27YN27Ztg9/vh9lsRl5enlDRJz8/X1hBKy4uzvkXOKFMYpPFn3/+edTV1WFtbQ2Dg4NCsjgXzrkQLx4Lb06aa6v4iZKLHozNiL3m+/v74fP5oNfrMTIygq6urqhwqkz0m+HkmsCIRJ5WhkjI6SGYKUOcx9I3NTWhubk563OWq1Hu9/tht9vh8/lw8uTJhFyQcp1LssR6MLiBLbd8i3iIKTDC4TAGBgYwNTWV8XAwjUaDvLw81NfXo76+XvBuOJ1O9Pf3w+/3o7i4WBAciVY3IaRHjefJZrOhvr4eAODxeITQktHRUeh0OsHw2ixZXEnw56WajOxI1CYwYmGMobCwENu3bwcQHU7FQwiLi4uj+s2I9VxYXV3NiRApDmMMk5OTmJqaym2BISfEFhihUAg9PT2Yn5+XNJZejh6MxcVF2O12FBcX4+jRowmv1ish3CsRuAeDd26fnp6WZb5FPMQSGIFAIKqK2mYrRGK8KGL3EendYIzB4/HA4XBgYWEBQ0NDQr8C7o4n74Y8UXKSd6rEhgqZzWaYzWbU1tZGJYuPj4+jp6cnayu9mYSfZzWKSUA5ZWozRez8Y8Op3G43nE4nFhYWMDw8DIPBEFWdKh2R7fF4csKDwUXqV7/6VTz77LPo6uoigZEt9Hq9aIa42+2G3W6HTqfDqVOnEq7TnwnklBjNlXNfX19Kq/XcMFd6LK5Go4HP58Mrr7yCQCAg23yLeIghMFZXV/Haa6+hoKAALS0tWQsH22jcGo0GBQUFKCgoQH19PUKhkJC7MTAwAL/fL4SglJaWiro6RqSP2s7FZs+/jZLFHQ4Hurq6hMRZHk7Fq6zJHTV7MNQeHgZsXqY2MveOP7+5yB4bG1tXnSpZke12u1FRUSHWVCSDPzNee+017NmzB1/96ldzW2DI6cUg1ko/T1iuqanB7t27JX8oyMWDwT06Z86cSbkbtRxK1okBYww9PT0oLS2Vdb5FPNIVGGfOnEF7ezvq6uqwa9eurD0Dkhm3TqcT+hUwxrC2tgaHwwGHw4HTp08jLy8v7W7MhDiQB2Nz4iWLOxwOzM3NYWBgQMhDKikpkXWyuJo9GGoWV5xk+mBEhggCZ8OpeDhsd3c3gsFgVLO/rRaMcqWKFJ/jT37yEwDAyspKbgsMOZGuIc7jyScnJxNKWM4WWq02Y8nrieLxeGC326HRaNDa2pqyRycXBMbU1BTW1tZQXV2NgwcPKu6FmWoeTGQDxf3796O6ujrpv5cCjUYjhKDU1dUJtdsdDofQjZnH/paWlqKgoEBx51TpqO14p+rBje0hE5mHNDAwAJ/PFxXHLqdk8XA4LPQQUhv8eavUd54YpJODYjQao0Q2D6dyOBwYHh6GXq/fNGcpV0KkOF6vFz/+8Y/xzDPPkMDIFukIDK/XC7vdjlAohJaWFlldjFLnLSwsLKC9vR1VVVXYs2dPWqswkQJDaUTmW5jNZmzbtk2RL8tUPBihUAjd3d1wOBxC87xkEENciJU7Elu7nSfYOhwOjIyMwGAwCCvCJSUlsl0RzhXU5sEQM0Q0Mg8JkHeyOBcYaoS/79Q6f0C8Tt6bhVPxnCWHw4Hnn38eb3nLW/DmN7855STvW2+9FbfddlvUZxUVFZidnRW+f/TRRzExMYG8vDwcO3YMd9xxB06cOCFs7/P5cMstt+CRRx7B2toaLrroInz7299GbW1tysfg/vvvx+c+9zn83d/9XW4LDDndMKkKjIWFBXR0dGDbtm3Yt2+f7FYZpMrBYIzh9OnTOH36NPbt24eampq098mvF6UJDF4xy+/34+TJk+jq6lKsYZSsoe71etHW1gYAaGlpkSwfKVPPmsgEW/6y4qFU3d3dkpQPVeq1lSpyeo9kmkyGCsVLFnc4HOuSxUtLS6O6LGcDNecg8MaKap0/IJ7AiCU2nMrv9+Pll1/GmTNn8PGPfxwrKyuor6/HH//4R7S2tmL//v1J3Xv79+/H008/HfV7nF27duE//uM/sH37dqytreGee+7BJZdcgqGhIUH033TTTXjiiSfw6KOPorS0FDfffDOuuOIKvPrqqykdj1AohDvuuAOPPvooLrnkktwWGHJCp9MhGAwmvD1jDMPDwxgZGcHevXvTUpSZRIocjEAggM7OTqysrODEiRMoKioSZb/8IaskgbG8vIzXXnsNVqtVqJiltDlEkozAiGyeJwfxnWnDO/JltXPnTqytrQneDb4iHFmZKluNonIZtYmpbOUiRCaLA/G7LEd6NzKdLK52D4aaxQWQvbDovLw8nH/++Tj//PMRDodht9vxkY98BL29vThx4gSsVive8pa34JJLLsHFF1+8ZfK3Xq/fMFz+7/7u76L+/Y1vfAP33XcfOjo6cNFFF2FpaQn33XcfHnroIVx88cUAgIcffhh1dXV4+umncemllyY9P4/HA5fLhdbWVrhcLqj7qsoi3BBP5IXl9/vx6quvYnp6GidOnJCtuACyHyK1srKCF198EeFwGC0tLaKJC46SjPPp6Wm8/PLLqKurw+HDh4VwmY06eSuBRAXG9PQ0XnnlFTQ1NeHAgQOSiwspjBOTyYSamhqcc845OP/887Fv3z4YDAaMjIzgT3/6E1599VWMjo5iZWVFdYaymKjJ8JQq2ZmXBd2/fz/e8IY34MiRI7BYLJibm8NLL72El156CQMDA1hYWMjIgpaaPRgkMDLnwdgMrVaLI0eOYHV1FXfffTecTid+9KMfobq6Gvfccw+qq6tx+PBh/PGPf9xwH4ODg6iurkZTUxPe85734PTp03G38/v9+N73vger1YpDhw4BAF599VUEAgFccsklwnbV1dU4cOAAXnjhhZTmFAwG0djYiD/+8Y/Iy8vLbQ+GnF4M3PjbSinzHg5WqxUtLS2yX4XMZojU9PQ0uru70djYiB07dmTk/CpBYEQm/B8+fFhwd3LidfJWCluJI8YYBgYGMDExEXfuUpGJDuTJoNVqhdXeHTt2wOv1wuFwCKUUufeDh1PJ/blCSIMcqilpNBoUFRWhqKhoy2RxsQofqNnIVvPcOVL2AeFVpIxGIy688EJceOGF+MpXvoKFhQX8/ve/R0NDQ9y/O3HiBH74wx9i165dmJubw+23347W1lZ0d3cLOXxPPvkk3vOe98Dj8aCqqgpPPfWU0A9rdnYWeXl5gheRE5nHkSwWiwX/8A//gNtvv50a7WUTLio2UsqMMYyPj2NgYEARHZc52fBg8ATmqakpHDp0COXl5Rn7LbkLDL/fj/b2dvh8vg0T/nPVgxHZPE9uxQ4AeYXT5Ofno6amBjU1NVHx7mNjY0K8Ow+nslgsCT9rlPBMEhOl98RJFjkIjFhim1ZGhgaOjIwIVXrSEc9qD5GS2gMsJeFwGIwxyY6B2+2O+y4rKyvDu9/97g3/7vLLLxf+/8GDB9HS0oLm5mY8+OCD+OQnPwkAuPDCC2G327GwsIDvf//7uOaaa/Dyyy9vakOl88zT6/X48Ic/jJGREXzuc5/LfYEh9coih6vjeKv9wWAQXV1dcLlcOPfcc9cpSjmTaQ+Gz+eD3W5HMBhEa2trxhvGyalxYCzLy8toa2tDUVERjhw5smEFIaV7MOKN3e1247XXXoPJZMLJkydFXYHPRCdvOREv3p333ZiYmIBGo4ky0KSs5kNIixwFRiSRZZ15svji4uK6pmf8Wk40WZxCpNQ5d0DaMr2hUAher1eUPhgFBQU4ePAgBgcHoz7bsWMHduzYgZMnT2Lnzp2477778JnPfAaVlZXw+/1wuVxRNuf8/DxaW1tTHkd+fj7+/d//HXfccUfuCwy5oNFo4iZEr6yswG63Iz8/H62trTAajRKNMDUy6cFwuVyw2+0oLS3F/v37s/IAkKsHg4eHbd++Hdu3b9/UAFC6ByN27FI1z0sWpYg6o9GI6upqVFdXIxwOY3l5OaqaT1FRUVQ1H7ke72yglHMqNko555GhgcD6ZHHGWFTTs42SxdXuwVCzwOA2mRTHYHV1FcDZ0KJ08fl86O3txfnnn7/hNowx+Hw+AMCxY8dgMBjw1FNP4ZprrgEAzMzMoKurC3fddVdKYxgZGYHT6UR5eTnq6upIYGSTWIExNTWFnp6ejOYUZJpMVJGKDBfbtWsX6uvrs3Zs5CYwIvMtEg0PyxUPRrrN87KJEu9d4Oy1UlxcjOLiYjQ3NwsGmsPhwOTkpODd4IJDbagtRErpPRF4snhVVRUYY1hZWYHT6cTs7CwGBgZgMpmEa7m4uFhYtFKzB0PK/AM5IGWZXrfbDQApeTBuueUWXHnllaivr8f8/Dxuv/12LC8v47rrroPb7cYdd9yBv/mbv0FVVRUcDge+/e1vY3JyEldffTUAwGq14oMf/CBuvvlmweN3yy234ODBg0JVqUQJhUL46U9/in/7t3/D/Pw8duzYgVtvvTX3BYZcQqSA10vVhkIh9Pb2Ym5uTlaJqqkgdkhRMBhEd3c3nE6nJOFiUjcOjITnW3i93qRyDuQmkpKBi6PI5nnnnXceiouLM/abYoVIKfWYRxJpoIXDYaysrAhio7e3F2azWejHYbFYVG2Y5CK5JKg2ShZ3OBzo7++H3+8X+sjINSw2G5AHI/sVpDhutxtGozGlhqmTk5N473vfi4WFBWzbtg0nT57ESy+9hIaGBni9XvT19eHBBx/EwsICSktLcd555+H555/H/v37hX3cc8890Ov1uOaaa4RGew888EDSx+Oll17CV7/6VVRVVaGlpQX/9V//hfe///25LzDkhE6ng8fj+f/Ze/PoVvLyzP+RZHm3ZVne933ft3t9my000DTQLGFgIBkCDCfMhEyYDksSJj8SkjBkIQlz0hOWAFmAA+SEwyQ0NIHudDehA013W4tted93a7Nka5eq6vfH7W8h+cq2lipVlao+5/QJufdarpJKVd/n+77P+2B1dRUqlQp37tzhfb4332g0Gs7SX30+H0wmE7RarWDtYmJZnMf6LWZnZ1O6AUl5satSqRCNRvHcc88BEDY8L1XEspHBFWq1GjqdDjqdDh0dHQiHwzg8PMTOzg7m5+fBMEycd0Nq7Z3JkisL7mTIJYFxmctm8dhkcZfLBQBYXl5mK3ZymbQmd4EhpMmdGLzT+c5985vfvPLvCgsL8e1vf/vG1ygsLMQjjzyCRx55JOXfH8uzzz4LvV6P733ve1CpVCgpKcGf/umfKgIjm9A0jZWVFTQ2NqKvry8nvtSx5vV0VDjBZrNhfn4ejY2N6O3tFey9EYPAIH2QyfgtEiFmo/pNBINBuFwu1NXVZc13A2S+iMzVRVks+fn5qKqqwsHBAV7ykpfg/PwcLpcLh4eHWF5eRmlpKTuZKttJzHyRa6LxJnJZYMRCFkElJSVobm7G3t4ebDYbtFotdnZ2YLVa47xIuVytk7vAELKC4fV6RTcNMR2Oj49RX1/P3jvUajXe+MY35r7AEMPNkqZprK+vIxAIoKmpCQMDA0IfEmeQL2a6i3KGYbCxsYGdnR0MDQ2hvr6ey8NLGSEFRmzGQybjeKVawTg6OsLu7i6Ki4sxPDwsiu9uKshpMapSqdjqRnt7O8LhcEJzLREcUq5uSO06zAS5CIxEFBQUsBN3Yr1Ih4eHcddzZWWlZKqqyaAIDOEzMKT+nbu4uEAoFILb7UZFRQVOTk5QVFSU+wJDaILBICwWCyKRCPR6PSfjyMTEdeN3byIcDmN+fh5+vx+3b9/mZJJCpgglMGL9Frdv387oOpGayTtWWDU3N8Pv90vuhismr5cQ5Ofno66uDnV1day51ul04vj4GKurqyguLmbFhk6nk8yCRm6fqVwFxmWTdyKzeOz1XFRUxIqNWLO4FJF7DobQHgy+R+9ng0gkgu9///t43/veh9LSUjz99NMoLi5WBAafOJ1OWCwWVFVVYXJyElarVbKtK1dBpi+kel7EY1BWViaqxHIhBMbFxQWMRiP7XmTSagZIq4IRiUQwPz8Pn8+H27dvw+12s5M1pIQcF2VXEWuubW9vRyQSYXeDyT0wtroh9t1gOX22chUY142pTXQ9k2TxlZUVRCIRNlm8srKSk2TxbCL3CoaQ508qGFLnbW97G4qLixGNRuHxeDA7Owuv15v7AkOILzrDMNja2sLW1hb6+vrQ1NR0ZQ5GLpDq5CUynjddjwGfZFtgEL9Fe3s7Ojs7OXkvpFLBiA3PIyLT4/FIRhxdRgrvORekep5arRa1tbWora0FwzDwer1wOp3s6NDi4uK40aFiWuzI5TMlyFVgpDKmVqvVoqamBjU1NfeYxbe2tqDVatnrWa/Xi2bz7CooihL9MfKJ0BUMqXswGIbBgw8+GJcsTsh5gZFtwuEwFhYW4PV6MTMzA51Ox/4dGVObayRbwaBpGsvLyzg5OcH4+DiqqqqycHSpkS2BwZXfIhFSqGA4HA5YLBbW1E8WNVJtNZLjoiwdVCoVysrKUFZWhra2NnY32Ol0Ymlpia1ukAWaGKbsyemzlavASHcX+7JZnIxwdjqd2N7eZpPFxRxcKfcKhiIwMoOsNxJVARWBwSEej4cdLXrnzp17dgU0Gg3C4bBAR8cfyVQwgsEgTCYTGIYR9XjebExgikQisFgsCAQCGfstEiGGSVhXwTAMdnd3sb6+joGBATQ2Nsb9vVSqL4mQ6nELyeXdYJ/PB6fTCZvNhvX1dcF73eX2mcpVYHB13hqN5p5kcafTCZfLhYODAwBg/14sZnFFYAgrMHKhRUqtVie8hnJeYGTjZskwDPb397G6uorOzk60t7cn/L252iJ106KceFFqamrQ398vakOZWq1GJBLh7fUvLi5gMplQUlKC27dv81KaFusinaZpWK1WOByOK8PzpFzBkOJxiwmVSoXS0lKUlpaitbU1LhiN9LrHTvLJljlSTgtuuQoMmqYz9r4loqCgAA0NDWhoaADDMOxo59jhB0RsCGUWl7vJWww5GLnCZbGa8wKDb2KTpycnJ9mdi0TkqsC4qoLBMAx2dnawsbGBvr4+NDc3C3B0qcHn7v/JyQkWFhbQ1taGrq4u3h7kYmyRCoVCbAXruvA8qS7U5bIoy+Z5Xg5G8/l8cLlcsNvtWF9fR2FhIWsU52txJsVrMRPkLDD4Pu/Lo51j2wMvm8UNBgOKi4uz8lkoFQzhPCg+n++eKr6UuXwdKQIjA7xeL0wmEwoKCpJKns5lgXH5vKLRKBYWFuDxeO7xoogZPlqkGIbB+vo69vb2MDIygtraWk5f/zJia5EirYN6vR5DQ0PXLgSlKjAA+S1Gs0lsdaOlpYWtbrhcLqyuriIcDqOiooIVHEVFRZwtzuS04JarwEjF5M0VV5nFnU5nVs3iisCgBGtVy5UKBrmGvvWtb+E1r3kNysvLASgCI22Ojo5gtVrR2tqKrq6upL6guSowLi/KifAqLCzEnTt3kJ+fL+DRpQbXi3PityBZH9notxTTIp1MybqudTAWMR17KshxUSYkl6sbfr8fTqcTDocDGxsbKCgoYMWGXq+XdQtIKshVYAi9yE5kFne73XC5XHFmcdIeyKVZXMigOTEgZIuU3+8XRf5XppDr5+1vfzvW19flIzC4vlnSNI2VlRUcHx+nPP0nLy8vJwVGbIsUaQNqaWlBT0+P5B5WXAqMWL9FNrM+xFDBiK3ajI2Nobq6Oqmfk6rAAJQKhlDELs5aWlpAURTberK2toZQKBRX3Uil9URuC265nS9BbOet0WjY6xW4OySFjMLd398H8HOzuMFguLF74jqEFldCI6TA8nq9OVHB2NraQmFhIQoKCuD3++HxeKBWq3NfYHCJ3++H2WwGAMzOzqZsMszlCkY0GsXq6ir29/ez0gbEF6lmelxFtvwWiRDa5B2NRmGxWNjwvFSqNlIVGGJanMgdjUaDqqoqVFVVgWEYBAIBOJ1OtvUkPz8/rvWED3OvVBHbQjtbiH2RXVhYmNAsfnR0FGcWNxgM0Ol0Ke3Ii/3c+UbIKVJ+v1/yU6QoisKv/MqvoLi4GKFQCL/3e7+H8vJyFBYWKgIjWWw2GxYWFlBXV4f+/v60vpC5KjAA4ODgAHl5eVlrA+KLTHf/yc797u6uYEJLSJO33++H0WhEQUFBWlUbKQsMKR53rqNSqVBcXIzi4uK41hOn04mNjQ0Eg0HodDp2t/hyCrPcPlM5CwypnPd1ZvHl5WXWLB47be26c5P7FCmhx9RKvYJBURTuu+8+nJ+f48c//jFUKhXsdjtCoVDuC4xMbxo0TWNjYwO7u7sYHBxEQ0ND2q+ViwLD7XbDZrOhoKAAt2/flvxuYCYCIxKJYH5+Hj6fD7Ozs4IJLaEqGE6nE2azGQ0NDejt7U1LhAtdfckEqR63nLjcehJrrN3e3o4z1pKJgFJZeHKBXAWGECZvrkhkFicVu83NTWi1WvZ6TmQWl3sFQyiBRSbjSd2DkZ+fjz/90z8FAHR0dOCjH/0o+3fSXg3yTCgUgsViQSgU4mTBSARGLtzEY7M/dDodSkpKJC8ugPQFhtfrhdFozLrfIhHZ9mDEhuf19/ejqakp7dcS44jdZJD691mukOpGU1NTXArz1tYWrFYrVCoVTk9PodVqUVpamvOfs5QX2pmQK4vsRH6ky2bx8vJyVkSXlZXlzLmni1LByJxIJAKtVouPfvSjeOGFF9hWVOmvCJMgnfYFl8sFi8UCvV6PiYkJThbPGo0GDMNIXmBQFIWlpSXY7XZMTk7C5XIhEAgIfVickM7i/PT0FPPz82htbUV3d7fgn20223VomsbS0hJsNhumpqag1+szej0ptxpJ9bgV7hKbwtzd3Y1AIIAXXniBbfuLrX7wOTZUSKT+bEqXXD3vq8ziTqeTNYtTFAWHw4G8vLyMzOJSRUiTd64keWu1WlAUhW9/+9v4oz/6IywvL6OiokIeAiMVYsPhent70dzczNmNh6jkaDQqqdGtsRCju0qlwp07d1BYWAi3250zrV+pCAyGYbCxsYGdnR0MDw+jrq6O56NLjmxVMEh4Hk3T7LWQKVIVGFI9boWrKSoqgkajQUdHB3Q6XcKdYLJ4y5XqhlyvYbns4seaxWmaZjOKTk9Psbm5iZKSEradKlWzuFQRqoIRiUQQDocl3yJFxPnq6ir++I//GO9+97sxPj6O97///YrAiCUSiWBhYQHn5+e8hMORi1iqi3G73Y75+XnU19ejr6+PvSFzNXlJDCS7OI/1W9y+fVtUN4lstBmdn5/DaDQmFZ6XClJdqOfC4jJZpPj5ZIJKpYJarWarG11dXQgGg3A6nXC5XNjd3WWrH2RxJtXqRq7u5N+EHM9brVazz63x8XEAYEfhErO4Xq9nr/tsJYtnE5qmwTCMIALD6/UCgOQrGOS7s76+jlAohA9/+MP4zne+g7y8PHkIjGQWLefn5zCZTCgtLeUtHE6lUknS6M0wDLa2trC1tYWBgYF7ou2leE5XkYzAIEGCRUVFgvstEkGM0nw9NFMNz0sFqQoMQH4Lbzlw1WdaWFiIxsZGNDY2sjvBTqcTu7u7WFpaYkPRSJ+7VBZmclxoA/KpYFyGPOvUajU0Gg1qa2tRW1vLGpBdLhccDgc2NzfZ8c7kv1zwXJLzF0Jg+Hw+AMgJDwZwt7uFvI/BYBDFxcXyEBjXwTAMDg4OsLKygo6ODnR0dPB6g5XaYpzs1Hu9Xty6dYtNaIxFDMFuXHHTudhsNszPz6OlpUUUfotEkGPierFAWsJ2d3dTDplMFiIwpLbQkdKxKqTGTZ+tWq2GXq9n/UehUIid4rO/vw+VShVX3RBze6zUvndcIaUxtVwSKzBiUalUKC0tRWlpaZxZPHYAwmWzuBTfP7IWE0Jc+nw+tg1TypDPfXBwELW1tfi7v/s7uN1uaLVaeQsMiqJgtVrhcDgwMTHBGqH4REoCIzaJ+s6dO1fu1EvpnG6C7P5f3tESq98iEeS4udxRj0ajrNDkM+tEqIdUpr9XypUXhatJ5zMtKCiI63M/Pz+H0+nE3t4elpaW4hZm5eXlolqYyVVgyH161k2feSKzOGkRjBXR5D+pmMUpimJbILMNmSAl9e8bOf6RkRG87W1vw+c+9zmEQiG43W55CIxEH6DX64XZbIZWq+XMoJoMUlmMHx0dwWq1or29HZ2dndd+CXKtggHEl8zJ4vri4kJ0fotExJ4DF7sjseF5t2/f5nUHlq/qSzJk+jvlIDCk/jBMh0zOWa1Wo6KiAhUVFejs7EQoFGKn+BwcHMQtzAwGg+DVDbkKDDm3SKVz3pdbBEmy+OHhIZaXl1FaWspe1xUVFaJ9b4UMGcyVEbUMw7CTuN7//vdjYGAAX/va17C/vy8PgXEZ0kNO2lyyefGLXWDQNI3V1VUcHh5ibGwM1dXVN/6M2M8pFWIX58C9fguhFwDJELtIzxQuwvNSgRx7th/4mS6s5LgokwNci8aCggLU19ejvr4eNE3j4uKCFRvLy8usd6OyshLl5eVZX5jJdSdfrsKKixGtsSK6o6MDkUiEFdFLS0uIRqOsWdxgMKCoqEg077WQI2q9Xq/kKxjkexPrx3nJS16Cl7zkJQBkFrQXu3geGRlBbW1t1o9Bo9EgGo1m/fcmQzAYhNlsBkVRuHPnDoqLi5P6uVwVGMRv0dzcjJ6eHsncCC6LpHRgGAZ7e3tYW1vLODwvFfho78oWUjxmhZvh63uvVquh0+mg0+nQ0dGBcDjMLswWFhbAMEycdyMbbSdyXGgnaomVC3yct1arvdIsvrGxgYKCgrhkcSHN4kKG7Pn9fslXMFQqFb797W/jhRdeQFNTE0pLS1FWVoaysrK74ctCH2A2UKlUCAQCMJvN7Mz+ZBfPXCPWxfjZ2RnMZjMMBgMGBwdT+tLlYovU9vY2Dg4OMDQ0hPr6eoGPKjViqwDpwHV4XipwWX3JJnJblMmFbF6H+fn5qKurQ11dHRiGSdh2EptRwMeCWK4CA5Dnd5hvYZXILH52dgaXy4XNzU0EAgE2T6aysjLrZnEhBYbX65X8iFoAMJvN+Pu//3uUlpbC6/UiEAggFApBo9HIQ2A4HA7Mzc2htrYW/f39grr2xSYwGIbB7u4u1tfX0dPTg5aWlpS/4GI7p0wg1aWTkxNJ+C2ugpjVUyUUCrFVrNnZWRQVFfFwdFcjVYEBSPOYFW5GiIWnSqViqxvt7e1sdcPlcmFxcRE0TbPVDYPBwFl1Q44tUuR7K7fzBrLfiqrRaFBVVYWqqioAQCAQYK/r3d3duLyZbFTtFA9G5vzWb/0W/sf/+B+IRqMIh8PIy8sDRVE4PDyUh8BQq9Xo6+u7J79BCMibLwai0SisVitcLldGO9V85y5kC5/PB6PRCJVKhZGREcmKCyC9sD0SnldRUYHh4WFBbrxCCAwurlllipQCn1yubhDvxvHxMVZXV1FcXMyKjUyqG1K/h6cDuU/K7bwBYRfYAFBUVJTQLE48ScQsnul1fRVCejByRWCQChXh/Pwczz77LP7hH/5BHgKDjAMUA2LZ7ff5fDCZTOwUrUx2CmITyqUavhPrtzg8PJT8bOpU29ZOTk6wsLCQlSyY65BqBUOOixM5IMbrUKVSoby8HOXl5Whvb48z1VqtVlAUBb1ezwqOVCYkylFgKBUMcZz3ZbN4bNWOXNcVFRVsOxUXbe5CezByoUWKcHR0hH/7t3/DP/3TP+GZZ57B7OysPASGmNBoNIhEIoIeA1lMNzU1oaenh5MpEkBmpmKhiE0pJ36Lk5MTSZ5LLMm2SMXme/AVnpcqQlQDuFhUiXExqpA5Yl9wXzbVer1eOJ1OnJycYG1tDcXFxewu8E0jQ+UoMORcwRByB/8mLlftfD4fnE4n7HY71tfXOTGLKx6MzNnf38ePfvQjfPe738Xi4iJmZmbws5/9DN3d3YrAyDYajQbBYFCQ3x27mOTSvExuUGKozKRCNBrFwsICzs/P41LKc8G0nkyLVOz5i8lvkk57l9AoLVK5idQ+U5VKxU5xaWtrQyQSwdnZGTsylFQ3YkeGxiJXgaFSqWR33oC4KhjXEWsWb21tTWgW1+l07HVdWlqa1Ocp5Pn7fL6shDvzzcMPP4z/9//+Hzo6OvBbv/VbeOihhxCJRLCzsyMPgSGmG4dQLVLhcBjz8/Pw+/2cLyZVKpVoWr+ShbSIFRQU3JNvkQsC46YKBgnPy8/PF12+R7YX6yQoiGEYaDQaSTxwFbKHmJ4fqaLValFTU4Oampq4XWCbzYb19XUUFRWxu8AVFRWyFBhyNLYTpCIwLnOVWdzpdN5jFr8uwFJpkcqchYUFNDQ0oL+/H1//+tfxpS99CXl5eXf/E/rg5IYQORgejwdmsxllZWWYnZ2FVqvl/HdIaVFut9thsViubBGT0rlcxXVVABKeV19fj76+PtE9YNKdgJUOZAY+gXw3VSoV1Go1+39vQk4VjCvPk2GgcrkArxcoLQVTWQlIfLGaS5/p5V3gaDTKVjdWVlYQiUTYISR+v1+wUe7ZhlQw5IhUBcZlEpnFYwMsrxrxTFEUL+uhZPD5fKLpGsiEz372s/B4PHC73QiHwwiHw+yoWkVgZJls7/STL1hnZyfa29t5u5FKoYIR67cYHBxEQ0NDwn+XCwLjqkX63t4eVldX0dfXh+bmZgGO7GaysVgnU89omgZN08jPz4/7/2majrue1Wo1+99Vxyxb3G5ov/515H/hC1Bvb7N/TLe3I/zf/hsiv/RLQEWFcMeXIbn62ebl5aG6uhrV1dVsdcNqtcLn8+FnP/sZCgsLWaN4RUWF5AdfXIXcKxi59rnGmsU7OzsTmsVJm2AoFEppCAKX5MoUqVe96lVX/p0iMLJMthbiJCzt9PQU4+PjbCmRL8S+KCd+A4/HE+e3SITYzyUZLp8DTdNYXl7G6elp1sPzUoVvgRErJACwlYrYagVN03GtUwzD3FjdyKXd7mTRPPEEit71LsDvv+fvVDs7KPjYx1DwR3+EwFe/CuqaB5FYkctnSqobRUVFqKysRF1dHdvjvrq6inA4zE7wId6NXBFeSgUjt8XVZbO41+uFy+WCzWaD2+3G+fk5QqFQ1pPFc8XkfR2yEBhiunlkQ2CQ1HKGYXDnzp2shKWJuYJB/Bb5+fm4c+fOjX6DXBAYsS1S4XAYJpMJ0WhUkPC8VOFTYMSKBuDq0ZTkz8nuHqloEHFyubohl4VoLJonnkDR2952tzUqwfmTP2MCARS97W0I/NM/SVJkiOn5wTfEg3G5uuH3++F0OuF0OrG5uYn8/HxWbOj1eknvgsu5giHl0fLpEDsEobW1FSaTiW0F3NjYQDAYTMssnirkO5ULFYzrkM2VJZYeab4X4qS/Ptup5Wq1WpQC4ya/RSLEei6pQBa9FxcXmJubQ0VFBSYnJyXxMOHru0rEBdm1S+XBEdseFVvdIJWQcDgMmqYRjUbvqYbkEux75nbfrVwwDFQ3iHEVTYNRq1H0rnfBu7wsmXYpMTwvsk2i3XyVSoWSkhKUlJSgpaWFneDjdDqxtraGUCgUV90oLi6WlCiTwy7+Vcj53IG756/T6VBXVwfg7uas0+lkk8U1Gk1csjiXw1ByxYNxHeJfbeQYfCV5MwyD7e1tbG5uCtJfr9FoRLXrH/t+XOe3SESuVDDcbjeWlpYED89LFT4ERmzlIlVxcZnL1Q1ikm1oaIgzjadqFJcS2q9/HfD7E1YuEqGiaTB+P7Tf+AYiv/ZrPB8dt0jle8MFyUyRujzBJ7a6sbW1hfz8fHYHOJstJ+mitEjl1r0pFS57UIqKitDU1ISmpibQNA2PxwOXy4X9/X0sLS2hrKyMFRuZJovnigfjOsT9zc9B+KhgxPoLZmZmoNPpOH39ZBBTi1Q0GsXi4iLcbnda74fUBQbDMAgEAjg7O8Po6Chqa2uFPqSU4FJgXDZzZyouLnN4eIiVlRX09fWxE0xiqxupGMUlA8Mg/wtfSOtH8z//eUT++3+XxHQpOVYw0hlTW1xcjOLiYjQ3N4OiKLjdbraViuQTkOpGSUmJ6Bbzcm6RkrvAuC5oUK1WQ6/XQ6/Xx5nFnU4nFhcXQdP0tZkyN+Hz+RQPRq4gphYpsuDh4ovt9XphMplQWFiYlL+AL8SyKI/Nd0j3/RBbNSYViNgMBoNoaWmRnLgAuPuuXmXm5gKGYbC+vo7Dw0OMj4+jsrKS/R3Avd4NIjzSHYMrJvIvLuKmRSWLimGg2t4GXC5AQgFTYlsQ80mmORgajYYVE8DPW06cTie2t7eh1WrZBVllZaUoqhtKBUNa9x8uSSUHI5FZPDZThkxdI5ky113b4XAY0WhUaZFS4BZyMV+nnJPl5OQECwsLaG1tRXd3t6A3STFUMOx2O+bn59HQ0IDe3t60318pJkkDdx/mRqMRWq0WVVVVKCgoEPqQ0oIL03SyZu50oCgKCwsL8Pl8mJmZubbMfdm7ke4YXDGhCQQy+nmV1wtGAgJDDBtS2YbroL3YlhOKouDxeNhWKqvVmhVD7U0oFQx5njuQftBerFm8ra2NzZRxuVxYX19nzeJEcFy+tr1eLwAoFQwFbokVGOkGvNA0jfX1dezv72NkZEQUu9RCGqNj/RYDAwNobGzM6PXUanXWwxAzxeVywWQyseF5pIQrRTIVeHy2RAWDQZjNZuTl5WFmZial73Aiozg5TilVN6gMp5AxEnuoym13m8+sJNK/3t3dfU/6cmz1Q6/XZy0ATc6L7FzMwUgFrs4/duoacLeTgmRv7OzsQKPRwGw2o7CwEK9//esRDocBIK0wy0984hP4gz/4g7g/q62txcnJCSKRCP6//+//w2OPPYatrS3odDq86lWvwp/8yZ/E+VBDoRA+8pGP4Bvf+AYCgQDuv/9+fPazn0VTU1MG78K9KAIjy5BFQ7oL2FAoBIvFgnA4jNnZWdGYhIRqK8rUb5EIjUbD3gCkwP7+PlZWVtDb24uWlhYA0q3CAJm1SGUyKeomzs/PYTabYTAY0N/fn9GiJNUxuGKqboTLykC3t0O1s5O0yRsAGJUKTFsb8GI7mYL44LqCcR2X05fdbjdcLhe2t7dhtVpRXl7OCg4+qxvZPGexIWdxRe61fAgs4kuKNYv/+Mc/xpe//GV85CMfQWdnJxoaGvAf//EfuHPnTspienBwEE888QT7/5NzIC3iH//4xzE6OoqzszM8/PDDeOMb34gXXniB/fcPP/wwHn30UXzzm9+EwWDAhz/8YbzhDW/A3Nwcp++HbASGmG4g6bYTud1umEwm6PV6TExMiKJ/lSDErj/5Mmm1WszOznLWEiQWP8lN0DSNlZUVHB8fY3JykvUBANLOZkhXYMS2H3EtLk5PT2G1WtHR0YHW1lbO7ydXjcFNJhQw66hUCP+3/4aCj30s5R8NS8TgDfy8RUpMzw6+EWqxrVar2epGV1cXgsFgwnGhpOWEy+qGnBfZXLRqSxWyBuP7/IlZ/GMf+xg+9rGPYX9/H3/3d3+HL33pS3jb296GUCiE+++/Hw888AAeeOABtLW13fiaeXl57GjdWHQ6HR5//PG4P3vkkUcwMzODvb09tLS0wOPx4Mtf/jK++tWvsincX/va19Dc3IwnnngCDzzwACfnDQDyvLIEJlWBwTAM9vb28Pzzz6O9vR2jo6OiEhdA9isYDocDP/3pT2EwGDA9Pc2p30AKAiMcDuOFF17A2dkZZmdn48QFIK8KRuyuPxdjaC+/NtlRHRoaQltbG+8LMLVaDY1Gg/z8fBQUFKCgoAB5eXnsgAiKohCNRhGJRNhqTbaJ/NIvAcXFYJJ8ODNqNVBcjMg738nzkSlkglh28wsLC9HY2Ijh4WG89KUvxeDgIPLz87G7u4tnnnkGL7zwAra3t3F+fp7xRopi8pbnMpDcN7PdItbc3IyXvexl0Ov1OD4+xpNPPompqSl8/etfR3d3N3p7e/GhD33o2ut6fX0dDQ0NaG9vxzve8Q5sbW1d+W89Hg9UKhUqXswfmpubQyQSwWte8xr23zQ0NGBoaAg/+clPODtPQEYVDDGRShYGRVGwWq1wOBz37FKLiWyZvBmGwc7ODjY2NjjxWyRC7ALj4uICRqMR5eXlV1ay5FLB4NPMTdM0lpeX4XQ6MTU1hfLycs5eOxWuC/m73EqVtepGRQUCX/0qit72NjBq9bVhe4xaDahUCHzta5IJ2QPkafIW42I7dlwocLdNmEym2t/fh0qliqtupDo5UDF5y/PcKYoSrBLs9XpRXFwMjUaDyclJTE5O4n/9r/+F8/NzPPXUU1hbW7vye3jr1i185StfQU9PD05PT/HJT34Sd+7cgdVqZae3EYLBIH7nd34Hv/RLv8Q+v05OTpCfn89+nwjEx8ElshEYYrppJrsY9/v9MJvNUKvVuHPnDgoLC7NwdOmRjUU5H36LRIhZYJyenmJ+fh7t7e3o7Oy88rpWqVSCT/VKl2QFBp9m7nA4DIvFApqmMTMzI5rv3k1jcLMR8kc+G+pVr0Lgn/4JRe96Fxi//+7vjfncGPJ5FBUh8LWvgbr/fk6PI1uI6dnBN2KpYFxHQUEBGhoa0NDQAJqmcX5+DqfTib29PSwtLaG8vJwVHOXl5Teej5wX2XI2eQvZHub3+xNOkCovL8eb3vSma3/2wQcfZP/38PAwZmdn0dnZiX/4h3/Ahz70IfbvIpEI3vGOd4CmaXz2s5+98Zj4+O7LRmCIiWQEBhm5SqYCif0GyHcFw+/3w2QyIS8vj1O/RSLEKDAYhsHW1ha2trYwPDycsP8yllyvYPBp5vb5fDCZTCgrK8PQ0JCoH8CpVDf4MIpTr3oVvMvL0H7jG8j//Ofv5ly8CNPWhvB//+9326kECP/MFKl+fzJBCgIjFrVajYqKClRUVKCzsxOhUIidTHVwcAAArNgwGAwJqxtSO2eu4DKPS4qkO6KWC7xeL2cDekpKSjA8PIz19XX2zyKRCN7+9rdje3sbTz75ZFz1va6uDuFwGGdnZ3FVDJvNhjt37nByTARFYAjAdYtxhmGwubmJ7e1t3lqA+IDPMbUOhwMWiyVrYktsAiO2cnPr1q2kWnXEdg6pcJN/hE8zt9PpxPz8PJqbm6+tEImR2OoGef+yEvJXUYHIr/3a3YRul+tuzkVp6d1pURJ6/65CStdApkh9sV1QUID6+nrU19eDpmlcXFywYmN5eRllZWVx1Q1yn5TjIjt2cIQcEVJgcJniHQqFsLy8jJe+9KUAfi4u1tfX8dRTT93TNjU5OQmtVovHH38cb3/72wEAx8fHWFxcxJ/92Z9xckwE2QgMMd00rxIYkUgE8/Pz8Hq9SS8kxQIfJu9Yv0V/fz/nM5qvQkyLcxKel5eXl1IyeS6avBmGYXfoAW6TuQHg4OAAq6ur6O/vj5sZLkXIoiGrIX8qFWAwSCJELxmUCoa0UavV0Ol00Ol06OjoQDgcZqsbCwsLYBgGlZWVCIfDohn3nk3kLjCEbA/z+/1pX3Mf+chH8NBDD6GlpQU2mw2f/OQncX5+jne/+92IRqP4T//pP8FoNOK73/0uKIpifRXEn6TT6fC+970PH/7wh1nf0kc+8hEMDw+zU6W4QjYCQ0xoNJp7RrpeXFzAZDKhpKQkrbnIQsN1BYOiKCwuLsLlcvHqt0iEkKGBsZydncFkMqG2tjbl3IVca5G6bOYmZmYuYBgGa2trOD4+xsTExD3mt1wgV0L+hCBXFtzJkEsC4zL5+fmoq6tDXV0dGIZhqxtnZ2dwu93weDzsgkun0+X89S93gSGkB8Pr9aZdwTg4OMA73/lOOBwOVFdX4/bt23j22WfR2tqKnZ0dfOc73wEAjI2Nxf3cU089hVe84hUAgM985jPIy8vD29/+djZo7+///u85F1yKwBCAyxWMo6MjWK3WG427YobLCkas3+LOnTu8+i0SIYYKRqLwvFSQcgXjsjji08wdjUaxsLCAQCCAmZmZtJJVpUYmIX9SvDeli1QFeibkssCIRaVSoby8HOXl5fD7/SgsLERJSQlcLhcWFxdB03TcZCqxDHngktiBEHJE6BapsrKytH72m9/85pV/19bWltR9q7CwEI888ggeeeSRtI4hWWQjMMT0JSIVjNigtLGxMTZmXopwZfJ2Op0wm82CmtuF3P2PvSYmJibu6Z9MFjGIpHSJrWDwaeYOBAIwm83Iz8/H9PS05KqGXHGdUfxyyJ8YKnvZRkzPDr6Ri8CIhWEYaLXahNWN4+NjrK6uori4mDWK50p1g7QIye3zJggtMG4a1JILyEZgiIm8vDz4/X4899xzoCgKs7Ozkt85zXTxwTAMdnd3sb6+nlW/RSKylelxmXA4DLPZjHA4nPE1kQstUnyauT0eD8xmM6qrqyUxpS1b3DQG1//iONpIJJLzrVRS/f5kghwFxmWTd2x1o729HZFIhPVuWK1WUBQFvV7PCg6pVjfkam4nSNWDISUUgSEAoVAIdrsddXV1GBwcFPUYzGTJpEUq1m8xPT3NJk4KhRDtRcmE56WClFukyLHzZeY+OTnB0tISurq60NzcLLsFVSrEVjfsdjuWl5fR3t4OAAlbqcj/zhXkdG3E+pvkxE2iSqvVora2FrW1tWAYBl6vF06nEycnJ1hbW0NxcTHbTlVRUSGZ61/uAkNIDwaXU6TEjGwEhhhummSXfn9/H8XFxRgeHhbFcXEB2TFP9aYVCARgMpnYMMFs+y0SQdqLsrWbZ7PZMD8/j9bWVnR1dXHyO6VawSDH7HQ6UVZWBoPBkLHYin3t7e1t7OzsYHh4WNItidmGTNgaGBhAfX09gHurG7lmFJfi9ycT5CowUnlmqVQqlJWVoaysDG1tbYhEIjg7O4PT6cTS0hJb3SCCo6ioiOejTx8hF9hiQOgWKUVgKHBGNBqF1WqFy+VCR0cHXC5XTt3IY1sqkr1pEb9FXV1dylOS+IScC98CI9XwvFSQYgWD9P6T7Jf19XUsLi5Cr9ejqqoK1dXVaT+waZrG0tISzs7OMD09nbbBTm4wDIONjQ0cHh7eM2HrsneD9zG4ApBL9+ibkLPASPectVotampqUFNTA4Zh4PP54HQ6YbPZsL6+jqKiorjqhpi6FeRewaBpmrPNq1RRWqRykGQSgvmAJANrtVrcuXMHbrcbdrs968fBJ+TGSVHUjV/aWL9FX18fmpubs3GISRO7aOLrBkxRFBYWFlIKz0sFqVUwYo3FpaWl6OvrA3D3u+NwOGC329l2BCI2kjVbhsNhWCwW0DSNmZkZUVTJpABFUbBarTg/P8f09PS1D8RcHIMrpe8PF8hVYDAMw8m1qFKpUFpaitLSUrS2tiIajbLVjZWVFUQikbjqhtC+S7kLDIqiks6V4hIiRJUKhkLGnJ6eYmFhAU1NTejp6YFarU6YgyF1yEPpJnM0WbQ4nU5R+C0SEbtQ4gPSFqbRaDA7O8vLgldKU6SumxRVUlKCkpIStLa2smZLh8OB+fl50DQNg8GA6upqGAyGhA8Lr9cLs9mM8vLynPE7ZQMiyhiGwczMTEoP4kRGcalWN+S02JarwOBroZ2Xl4fq6mpUV1ezAxKcTiccDgc2NjZQWFjIjsHV6/VZvzcpAkO4Fimv1yuLKroiMHiCYRisr69jd3cXQ0NDbN8yINyUIj5RqVQ3Gr1j/Razs7Oinb7Bp8Ag4Xk1NTUYGBjg7QYvhRapVJO5L5stz8/P4XA4sLu7C6vVivLyclRXV6OqqgqlpaVsYm9zc7Nk82WEgOTQlJaWYmhoKOOH8FVjcMnnL8XqRi4iV4GRDa+dSqViN0taWloQjUbhdrvhdDqxtraGcDiMiooKVnAUFxfzfkyKwBDWg6G0SOUY2WqRIrt/gUAAs7Oz95TCclFgANePqnW5XDCbzWmlUmcbkhLN9QL94OAAy8vL6OnpQUtLC68PELG3SMW20ACpJ3OrVCrodDrodDp0dnYiGAzC4XDA4XBge3sbKpUK0WgULS0taG9vl92iKV3cbjebQ9PT08P5+5ZJyF+2kdvIVrkKDCEW2nl5eaiqqkJVVRVb3SCjcDc3N5Gfn8+OweWruiHkmFYxIJTAIJ+30iKlkDIejwcmkwk6nQ537txJ6EfIVYGRqILBMAz29vawtrYmSr/FVXDZYkTTNFZXV3F0dJRReF4qiLmCEbuDDXAz1rSwsBBNTU1oaGjA6uoqjo+PUVNTA7vdjoODA1RWVrIPdDFPdhGS09NTWK1WdHV1pZUenw6phPwRESrmzQkpI2eBIeQ5x1Y3mpubQVEUzs7O4HK5sL6+jmAwyFY3iHeDi+OVewVDKIEVDAZBUZTSIqWQGmSHurOz89pdU41Gk9ZIV7FzuYIR67eYmpqKm0AjdrgSGKSaFQqFshqoKNYKBp/J3NFoFPPz8wgGg5idnUVRURG7W2S323F6eorV1VWUlJSwYiNXUnkzgWwCbG5uCjq+96aQv9hqVzZaqcT4/eETuQoMrkzeXKHRaNj7EwDWu+FyubC1tQWtVhtX3Uh3EpIyplaY8/f5fACgVDByDb5unGQE5unpKcbHx9kbw1WQG0KufcFjKxjEb6FSqUTtt7gKLgSG1+uF0WhEaWkpbt++ndWReGKsYPApLgKBAMxmMwoKCjA9PQ2tVgsgfneQzK0nRktiYiYP86qqKvbn5ALDMFhdXcXp6SkmJyeh0+mEPiSW66ob2WqlktNiW24tYQShKxg3UVxcjOLiYra6Qbwbm5ubCAQC0Ol0rOAoKSlJ+lxybYMzVYRqkfJ6vVCpVLKopMtKYPABWdgAwJ07d5K6aGJHuubSgoa0fhG/Bd9GZj65zk+SDHyE56WCmCoYxMxLWl64FhdutxsWiwW1tbXspLar0Gq1qKurQ11dHRiGgcfjiTOK63Q6dgxuKg9rKUJRFObn5xEIBDAzMyPqB95N1Q0+jOJi+f5kC7kKDLFVMK5Do9GwYgK4u/5wOp1wOp3Y3t6GVqtlx+BWVlZeu6kld4EhVIsU8V/I4bumCIwMIEFxxLic7MVKHn655sNQqVSw2Wyw2Wzo7e3NWh83H6RbwSBp0Zubm/dMD8smRGAIvWi4bObmWlwcHx9jeXk5Ld+ASqVCRUUFKioq0NXVFWcU39raQn5+PlvZqKyszClDZCgUgslkQl5eXlzFRypkK+RPDosAgtD3CqGQ8kK7qKgITU1NaGpqAkVR8Hg8cDqd2NraYjdMiOC4vKhVTN7CdJB4vd6c37wiKAIjDWIXkf39/Whqakr5NXLN6E1RFAKBAC4uLiTnt0jETSN3E0FRFBYXF3F2dsZLeF4qkJuXkIsGPszcsa+9tbWFvb09jIyM3NiWmAzEKE4e1mdnZ3A4HFhZWUE4HGaN4tXV1ZJr+YvF6/XCZDJBr9dLtsIYC18hf0oFI/cRwyYMV2g0GlRWVqKyshLd3d0IBALsZKrd3d246oderwdN04IEzYkBco8QQmDJZUQtIDOBwcVNJBqNYmFhAR6PBzMzM2n3LOeSwAgGgzCZTKAoCi0tLZIXF0DqHoZgMAij0chmfAidFp2NNPLr4NNvQVEUlpaW4Ha7MT09zYtZLtZo2dvbyyaKn5ycxBnFSaK4VBYoLpcLFosFLS0t6OjokMxxJwvXY3Bz7f25jlxZaKcCH5sfYqGoqAiNjY1obGwETdNwu91wuVzY3t6G1WpFXl4eysrKcHFxIZuWHQK5DwglMLKRcyIGZCUwMoXs/BUWFuLOnTsZqf9cSfOODY4rLi7OmZJrKi1S2QrPSwVy8xLC6B1bueBaXIRCIVgsFgDArVu3srIDp1KpUFpaitLS0nuM4sR/FZsoLtZ2o6OjIywvL6O/vx8NDQ1CH05WuC7k73IOy+XqhlLByH1iP/9cRq1Ws9UN0g46Pz+PUCgEo9HIVj+Id0Os9zCuIJ+7UAJDDhOkAEVgJM3JyQkWFhbQ2tqK7u7ujG9IUq9gMAyD/f19rK6uore3F83NzVhaWpL0OcWSbItUNsPzUkGoRVJsLzzX4oII/IqKCgwMDAgmZq8yim9vb2NxcRE6nY5NFBdDr21sO9nY2FhWcljESLLVjdigTaE/u2wiR4GRyxWM6ygsLER+fj6qq6tRX18Pj8cDl8uF3d1dLC0toaysjG2nKisry7nrgqKolMNducLr9SoCIxdJ52KiaRpra2s4ODjAyMgIamtrOTkWKQsMMpbXZrPF+S1yybh+UwUjNjwvmdHE2SbbFQy+zdx2ux2Li4uia+25yihut9vZRF4iNvhK5L0O8l09OzvjrZ1MqtwU8hcKhVgPhxxC/uQoMORSwUgE2QRSq9XQ6/XQ6/Xo7OxEKBRiczf29/ehUqniqhu54NsgBm8hPnfSIiUHZCUwUiUUCsFsNiMSiWB2dpZTY05eXp4kF+PEbwHcHcsba3bVaDSIRCJCHRqnXCcwIpEIzGYzQqEQbt++LUrDVuwuLN/wbebe39/HxsYGBgYGUFdXx9lr88Flo7jL5YLD4cDy8jJrFCeCg2+jeCQSwfz8PCKRCGZmZgT3BYmZy9UNp9OJ9fV1NDc3CxLyJwRSGtfKFXINFwSu9ucVFBSgoaEBDQ0NoGka5+fncDqd2N/fv6e6UV5eLsn3TsgJWmRMrRxQBMYVuN1udtLK5OQk5yFpUqxgEK9BdXV1whYVOVQwSHheSUlJ1sPzUiUbWRh85luQKpHNZsPExAQqKio4e+1soNFoUF1djerqajAMA5/PB7vdjuPjY6ysrKC0tDQuUZzr4EGTyYSioiJMTU2J+joVG6enp7Barejp6UFTU5NgIX/ZRq4VDKF2soUmmUW2Wq1mK7SkukEmUx0cHAAAW90wGAySqW4IFbIHKC1SOUsyN5FYb0F3dzdaW1t5uflITWDs7e1hdXX1Wq9BOqNdxUoigWG329kJPFz4cPiG7woGn5OiIpEIFhYWEAqFRB8ClwyxRvH29nZEIhE2c4Mk3hOxkalR/Pz8nB060NvbmxOL32yxt7eHjY0NDA8Po7q6GkB8dYN8n/gO+RMCOQoMOZ4zIZ0JgwUFBaivr0d9fT0YhmGrG8SLWFZWxgqO8vJy0X4PhMrAAO62SOXCpM1kkJXAuAmKomC1WuFwODA5OYnKykrefpdUBEas3+Km90Qq55QMsQIjNvdkcHBQMhN4+Kxg8Gnm9vv9MJvNKCoqwvT0dE7uvmu1WvZBTdP0PUbxiooKVnCkYhS32+1YWFhAR0cHb5sjuQjDMNjY2MDh4SEmJyevHD9OFiXZCPnLNnJcbEs5ZC9TMl1kq1Qq6HQ66HQ6dHR0IBwOs9WNhYUFMAzDTq4yGAyiatEUsoIRCATSyk6TIrn35E4Tv98Pk8kEjUZzj7eAD6SwGCd+C4ZhMDs7e+Mucrrp12KEnAsJz3O5XBnlnggBHxUMEkxFrl2uxcXZ2RksFgvq6+vR09MjiwVPrMmSBGSR6sbm5iYKCgrYzA29Xn/lomB/fx/r6+sYHBzkbBiFHCCbKCRXJRVPFV8hf0IgV4Eht3MmcC2u8vPz46brXVxcwOl04ujoiG0JJUZxnU4n6PdA6BYpMfo2+UBWAuOqG4ndbsf8/Dzq6+vR19eXlQtfo9EgFArx/nvS5ezsDGazGQaDAYODg0l9GaUgmpJFrVYjEAjgueeeg0qlwp07d0S1A5MMXAs+Ps3cwM9zGnp6etDc3Mzpa0uJoqIiNDc3o7m5Oc4obrVaEY1G2URxYhRnGAbr6+s4OjqSpFdFSKLRKObn5xEOhzE9PZ3Rd5zrkL9sI0eBIUdjO4HP6o1KpUJ5eTnKy8vZllAymWpxcRE0TcdNpuJ7Q/cyQpq8lRwMmcAwDDY3N7G9vZ311hcxL8b39/exsrKScrZDLlUwwuEwTk9PUV9fj8HBQUk+hLhskeLTzE2+h/v7+7LOaUhErFG8r68PXq8XDocjbleQoihQFJXy7rvcCYVCMJlM0Gq1vBjhbxqDe3mks9DVDTkKDDm3SGXz3C9nB5HqxvHxMVZXV1FcXMwaxbNR3RDag6EIjBxFpVKBYRh2hKPX68WtW7dQXl6e1eMQo8BIxW+RCDGeUzocHh7i8PAQpaWlGBoakuxDl6sWKT7N3MT3dH5+ruQ03IBKpUJZWRnKysrQ3t4On88Hk8mEaDQKhmHwwgsvxCWK56J3hSvIe0dCG/lebFxV3YhtqwKEbaWSo8CQ4zkDP291FWIXP1F1g3g3rFYrKIqCXq9nBQcf1Q0hW6T8fr9sNoJk+QQiU1ZKS0tx586djCa2pIvYcjCCwSDMZjNomk7Kb5EIqQsMhmGwurqKg4MDNDU1IRwOS/rhw0UFg08zN8mZUavVmJmZkcyIQzFAFsjl5eUYHByESqVijeKbm5tYWFiAXq+PM4or3MXj8cBkMqGxsRFdXV2CfMevq24kaqUi/5tP5LjYlmsFI7Z6JjRarRa1tbWora0FwzDwer1wOp04OTnB2toaiouL2XaqiooKTo5ZqBYpMq68rKws679bCGQnMA4PD2G1WtHe3o7Ozk7BbqhiWoyTzI9U/BaJkHKLVCQSgcViQSAQwOzsLJxOJ2w2m9CHlRGZVDD4NnNfXFzAbDZDr9dnZQc5lyBG+MsL5ERGcbvdjo2NDRQUFMQlisv1/SZTtrq6utDS0iL04QC4ubqRLaO4HAWGHM8ZEJfAiCW2StvW1oZoNMpWN5aXlxGJROKqG+mOL6coSrAKr9IilaMwDIPT01OMjY2xM86FQiwCg/gtuMj8EMs5pcrl8DytVouzszPJiiVCuhWMy2Zu0h/OFWSR197ejra2Nlk+4NPl5OQES0tLbAjcVVw2ijudzjijuMFgYKsbUhtekC6Hh4dYXV0V/ZSty9WNbI3BleNiW6lgiPvc8/LyUFNTg5qaGnb3n2z+ra+vo6ioKK66kezmKEVRglXMfT6fbCrKshIYKpUKk5OTolg4Cr0Yp2kay8vLODk5wcTEBCfGWilWMEh4XnNzc9xYVCmey2XSOQc+/RYMw2Bvb4/NExHzIk9sMAyDnZ0dbG9vY2RkBFVVVUn/rEajiXtIe71e2O12HB4esuFYRGyUl5fn3CKT5Njs7u5ibGyM13wjrsnmGFy5Cgy5nTNwd4HN9cYR38SGlba2tiIajeLs7AxOpxMrKytsdYMIjuLi4itfSygPBhFJSgVDgVeEFBhkegpN07hz5w5nKckajYZ9AIp9Z4Qs2DY2NhJOEMuFVPJUW6T4FBc0TWNlZQV2u/3aIDOFeyHvncPhwNTUVEYDKWJbEEg4Fsnc2Nvbg1qtjksUl7pRnGEYLC8vs++dlHufE7VScVndkKPAkOuYWik8o28iLy+PnbDHMAz8fj9bqd3Y2EBhYSE7Blev18cJCqE8GH6/HwzDSPo+lArSfnqkgVhuoBqNht19yiZc+S0SEbvTJuabF5lc5HQ6rwzPy5UKRrItUnyaucnEtkgkglu3bmV95rmUITkNoVAIMzMznL93+fn5aGhoQENDA2iahtvtzhmjOEVRWFhYgN/v5+W9E5qrjOLEP5VqdUOOu/lif1bxRa6dt0qlQklJCUpKStDS0oJoNAq32w2n04m1tTWEw2FUVFSwgiMajQpy/j6fDwCUCoYCvwix239wcIDl5WVO/BaJIGJFSAPVTZB0cgCYnZ29ctGRCwIjmQoG32Zuv98Pk8mEkpISjI6Oiva6ECPkWs3Pz8f09DTv751arUZlZSUqKyvR09MDv9/PVjdIvzNJFOdqmgtfhMNhmM1mqFQqTE9PCzIpMJtwEfIn1wqG3M4ZyD2BcZm8vDx2Y4RUN4hZfHNzkx3Rq9Vq76lu8InP54NGo5GN70152gtE7GKc7y86abE4Pj7mzG+RCLI4FavR2+PxwGg0JlW9yQWBcVMFI7afG+DezO1yuTA/P4+GhgZ0d3fL8kGeLhcXF2ylsb+/X5DFQHFxMVpaWtgdQZIovrCwAIqi4jI3xPTADAQCrKgdGhoSbN69kKQT8geIp8KfLXJ9oX0VQiZZZ5vY6gYZevHss89CpVJhfX0dwWCQrW4Q7wZf3wNi8JbLNSc7gSGWG2iswOBzd41kDUSjUczOzl5rfOICsS7Mj46OYLVa0dXVldTkIrGeRypcV8GIXXDwMfby8PAQKysr6O3tvXbakcK9OJ1OzM/Po7W1Fe3t7aK4Z12e5nJxcQGHw4H9/X0sLS2xRvHq6mqUlZUJdswXFxcwGo2ora1Fb2+vKN47oUk25I+0VMlp0S3HtjBA2CRrodFoNFCpVGhtbUVFRQXr3XC5XNja2oJWq2XFhl6v57Ry7PV6ZdMeBchQYIiFbOz2E79FZWVl1nbyhJ6OdRmGYbC2tob9/f2UxhPngsC4qoLB96SojY0NHBwcYHx8XFITe8QAEWYDAwOor68X+nASEpvE29HRgVAoBKfTCbvdHmcUr66uRmVlZdba4lwuFywWC9ra2pTxx9eQqLpxcXEBm82GhoYGVmhkM+RPKBSTtzyJnSJVXFyM4uJitrpBvBubm5sIBALQ6XSs4CgpKcnovuLz+Xjf5BUTisAQED7TvInfItkde64Q08KcmIv9fj9mZ2dTMqmK6TzSJdE58CkuKIrC4uIiLi4uMDMzIzlTsJAwDIPNzU3s7+9LTpgVFBTcYxS32+1YX19HIBCAXq9nQ/74erienJzAarWiv7//nolwClejVqvh8XhgNpvR1NSE9vZ23sbgihG5LrTlet6Eqyo4Go2GFRPA3XZLp9MJp9OJ7e1taLVadgxuOpsnfr8/Y5EiJRSBISB87PbH+i3Gx8dTmpfPBWKpYPh8PhiNRhQVFbHhealAFudSNgHGtkgRMzdfk6KCwSDMZjPy8vIwMzMjWIiRFKFpGlarFW63G9PT05IuoccaxXt7e1mjuN1ux9raGoqLi1nzJVdG8d3dXWxubmJ0dDTr9zupQ6o+HR0daG1tBcDfGFwxolQw5Ad5DibT0VFUVISmpiY0NTWBoih4PB44nU5sbW3BarVCp9OxgqO0tPTGZ6rSIpXjiGmxyPViPNt+i0SIQWA4HA6YzeZ7wvNSgdx8pSww1Gp13CSZy8ZOrjg/P4fZbBbUkCxVIpEIzGYzaJrGzMyMqMzSXHDZKE7m1C8sLICm6bhE8VRFKWl/PDk5UbJV0uD09BRWqxV9fX0Jqz7ZDPkTCpqmZTnZTk4m78uQ52Cq56/RaNjNk+7ubgQCAXYy1e7uLlv9IP8m0aamnFK8AUBad4Mcg8vFuMfjwU9/+lMUFBTg9u3bgvX5CdlaRMLzTCYTBgYGMjJ5xj5YpQqpYJCWKIB7cWGz2fDCCy+gpaUFAwMDkltgCInf78dzzz0HrVaLqampnBMXl8nLy0NtbS0GBwfxspe9DBMTEygpKcH+/j7+/d//Hc899xy2trZwfn5+Y34LTdNYXFyE3W7H9PS0Ii5S5ODgAFarFcPDw0m1lKnVanasZ0FBAfLz85GXlxc3rCQajSIcDiMajUrmvilXk7ecKxixI9kzoaioCI2NjRgZGcFLX/pSDAwMQKvVYmdnB8888wzm5ubY9Qj5nekKjE984hPstDfyX11dHfv33/72t/HAAw+gqqoKKpUKZrP5ntcIhUL4jd/4DTbT6I1vfCMODg7SPv9kkJ90FxFcCYzDw0MsLS1l3W+RCKEqGKTNxOFwYHp6GhUVFRm9Xq4IDPKw58PMvbu7i62tLQwNDaGmpoaz15YDHo8HJpMJ9fX1aVfZpIxKpYJOp4NOp0NnZydCoRCbubGzsxM3x95gMMTtNhJvVTQaVdrxUoRswuzs7GB8fBx6vT6t1+E65E8o5NoiJecpUlwJjFhiW0O7uroQDAbhcrlwfHyMN77xjdBoNLhz5w50Ol3a96vBwUE88cQT7P8fe0/0+Xy477778La3vQ2/+qu/mvDnH374YTz66KP45je/CYPBgA9/+MN4wxvegLm5Od6qWbITGGJ6kGea5k3TNFZXV3F0dCSI3yIRQlQwkg3PSwVy8xG63StdGIZBcXExuyNMTLZc9L3TNI3l5WU4nU5MTU2hvLyco6OWBzabDYuLi+jq6kJLS4vQhyMKCgoK0NjYiMbGRtA0jbOzMzgcDqytrSEUCrGJ4uXl5VheXkZBQQEmJydl2d6SLrEtZVNTUygrK+PkdbkI+RMKpYIhP8gEKT4/98LCQnbwxdbWFp588kn867/+K/7t3/4N+/v72N3dxYMPPojXvva1mJqaSuqzyMvLi6taxPKud70LALCzs5Pw7z0eD7785S/jq1/9Kl71qlcBAL72ta+hubkZTzzxBB544IH0TvSmY+blVRWSIpPdfuK3iEQigvktEpHtCkYq4XmpQMqQUqtgxJq5q6ur8fKXv5wNSJufnwfDMOwIUYPBkLL5PRwOx+0ecyHm5AQxJCtVn6tRq9XsJJfe3l74fD44HA6cnJxgdXUVeXl5qKysxMXFBXQ6nWwXSqlA0zSWlpbYQQJ8Pi/SCfkT6jOUawVDzgIj2/6TgoICPPjgg3jwwQfxwQ9+EIWFhZiZmcH3v/99fOYzn0FeXh4eeOABPPjgg2ybUyLW19fR0NCAgoIC3Lp1C5/61KfQ0dGR1DHMzc0hEongNa95DftnDQ0NGBoawk9+8hNFYOQi6S7GSXtFRUWF6HbxslnBSDU8L1U0Go2kBEYiM7dGo0FtbS1qa2vBMAw8Hg/sdju2t7exuLiIiooKVFdXo7q6+sZFh8/ng8lkQllZGcbHx2VrEkwHhmGwurqK09NTxZCcIiUlJYhEItje3kZrayvKy8vZMEJiFCeCWWmXuheKorCwsIBAIIDp6emsen2SDfkTqpVKrgttmqZ5DfgVM0K2h/n9frS1teG9730v3vve9yIajeJnP/sZvv/97+Mv//IvcXh4iN/6rd+65+du3bqFr3zlK+jp6cHp6Sk++clP4s6dO7BarexI3es4OTlBfn7+PS2RtbW1ODk54ez8LiOelWmWEFM5NJ0cDOK36OzsFE3KbyzZqGCkG56XKlLKwojtgQYS95eqVCpUVFSgoqKCnYJht9vZzAIyQrS6uvqenWEyzrKpqQldXV2iu+7EDFng+f1+zMzMoKioSOhDkhSkpay7uxvNzc0AgLq6OjAMg/PzczgcDuzu7sJqtaK8vJxtB0xmbGSuQ6aUAcDU1JTgi8rrqhtCtFIpLVLyIzZkL9uQHAxCXl4e7rvvPtx333345Cc/eeVwiwcffJD938PDw5idnUVnZyf+4R/+AR/60IfSPh6+p2TKTmCIiVQW42L0WySCjEbli2g0CovFAp/Ph9u3b/M6U1oqAiPdfIuioqJ7Roja7XZYLBYAYE22oVAIGxsb6OvrQ2NjI5+nknOQVkaNRoPp6WnBF3hS4+DgAGtrawlbyi4bxYPBIHsNb29vs0Zxkigut4pbKBSC0WhEYWEhRkZGRHf+N1U3smEUl3OLlNiuh2wh5LnflIOR7LO7pKQEw8PDWF9fT+rf19XVIRwO4+zsLK6KYbPZcOfOnaReIx1kKTBUKtWNYxCzgUajQTgcvvHfhcNhmM1mhMNhUfktEqHRaBCJRHh57djwvNnZWd4Xa1IQGFwlc5MRorGtVDabDSsrK4hEIigrKwNFUQgEAsoOfJJ4vV62lXFwcFCWC5l0iU02n5iYSGoqXGFh4T1GcbvdjtXVVdYoTqobuX4NBwIBzM3NoaKiQjLjoy9XN7IR8qdUMOSHmCoY6RIKhbC8vIyXvvSlSf37yclJaLVaPP7443j7298OADg+Psbi4iL+7M/+LOPjuQpZCgyxkEwFI9ZvMTExISq/RSI0Gg2CwSDnr+twOGCxWNDY2Iienp6s3BzFLjBiH8BcjqFVqVQoLS3Fzs4OtFothoeH4fP54tKYiW9Dp9PJ8gF9E6SlrLm5GZ2dncp7lAJkSpnL5Uo72TzWKM4wDPx+P+x2O05PT7G6uoqSkhK2QpdrRvGLiwsYjUbU1dVJdgRytkL+5FrBkPuYWqHO3efzpXU/+8hHPoKHHnoILS0tsNls+OQnP4nz83O8+93vBnD3ebO3t4ejoyMAwOrqKoC7lYu6ujrodDq8733vw4c//GE2DPAjH/kIhoeH2alSfCDu1WqOc5PAICZmsfotEsH1opzkLayvr2NgYCCrLTpiFRhkUlTsPG8urw0y9jc/Px8zMzPQarUwGAxoaWlBJBJh05hJb3fsVCqxC+BscHx8jKWlJaWlLA0oisL8/DyCwSCmp6c5mVKmUqlQUlKCkpIStLW1xV3DFouFnaxG/pNyG5vb7YbJZEJra6tknhk3wecYXKWCIT+EqmAwDAOfz5fWeOiDgwO8853vhMPhQHV1NW7fvo1nn30Wra2tAIDvfOc7eO9738v++3e84x0AgN///d/HJz7xCQBgJ1a9/e1vRyAQwP3334+///u/5/W9kOVqQEwtUolyMIjf4vDwkFcTMx9wafLmOjwvVcQoMJIxc2eCx+OB2WxGdXU1+vr67nl9rVbL7orQNM1Opdrc3MTCwgIqKytZwZHrbSiXYRgG29vb2N3dxdjYWFLTPRR+Tjgchslkgkaj4dWQHHsNk3bAWKO4TqdjxYaUjOJ2ux0LCwtxZvhchMsxuHKtYCgCQ5gWqXQrGN/85jev/fv3vOc9eM973nPtvyksLMQjjzyCRx55JOXfny6yFBhiIdFi/LLfgot+vWzClck7FArBZDKBpmnOwvNSRWwCI10zd7Kcnp6yFbOWlpYbX1+tVkOv10Ov16Onp4dtQ7HZbFhbW0NJSQnb857rrVSXwwe5CjGTC36/nx2BPDQ0lLXFT+xkNZLASxLFt7a2kJ+fz4oNMRvFSdVscHDwyjCuXCTTMbhyXWjL9bwBYU3eXHkwpIIiMATkssA4Pz+H0WiETqeThN8iEVxkRxDfiV6vx9DQkGA3AzEJDK7M3Fe99s7ODra3tzE8PJx2xay4uBitra1obW1l21DsdjtMJhPUanXcRB8pXttXEYlEMD8/j0gkooQPpsH5+TlMJpMoPAOFhYVoampCU1MTKIpiE8WJUTy2QieWz3lvbw8bGxtK1Qypj8FVWqTkh1AVDJqm065gSJXcecqngFhuKLE5GMRv0dHRgY6ODtEcY6pk2iJFJhuIwXfC98jdZOHLzE1ee2lpiTXUcrXzfrmVyu12s3kbwWCQnegjpoVaOgQCAZjNZhQWFmJqaiqnhFM2cDqdsFgs6OjoQFtbm9CHE4dGo2GrF6R/2uFw3GMUF2rYAZm0dXBwoIQ3JuCm6kYwGIwTH0ImimcbOY+ppShKkEBOn88HALKqbitPQwEhHoyVlRUcHBxIzm+RiHR3/RmGwfr6Ovb29jA6OnrPzHshUKvVgnp1+DZzh8NhWCwW0DSNW7du8Zbwq1arUVlZicrKSvT29rITqU5OTrC6uorS0lJ2oVZeXi4ZcU123q/yqyhcz9HREZaXlzEwMID6+nqhD+dayGS10tLSe4ziZNhBbKI430ZxhmGwsrICu92OqakpWe2KpktsdSMUCmFpaQnl5eUoKioSJORPSORcwRBKXBGBobRIKWQFmqYRjUbhcDgk6bdIRDoVjGg0ivn5eXi9Xt7D81JByBapy2ZuYlbkCq/XC7PZzPa8Z/OGe3mij8PhgN1uh9FohFqtZn0bBoNBtLtsxFDb0dGB1tZWyYgiMUBa8nZ2diTb1nOVUXx7exuLi4vQ6XTsdVxSUsJ51XFxcREXFxeYnp6W3TCFTAmHw2ye0vDwMPvZZDvkT0jkPqZWiOeK3++HVqvlbSNPjMhSYIhhMXB+fs7ufN26dUvSoxFjSbWtyO/3w2g0oqCgICvheakgVIsU32Zup9OJ+fl5UWQ0aLVa1NfXo76+Pq6Vam1tje15Jws1sbRS7e/vs2OT5WSo5QKGYbC6uorT09OcMcNfZRQn09Xy8/PZa1iv12e0uCGbMeFwGNPT04K0ekiZYDCIubk5lJeX3xN+me2QP6Egzxcpn0MmCCWuvF4v55sNYkeWAkNoiM+gtbUVW1tbOXXBpWLydjqdMJvNaGhoQG9vr+hueEJUMPg0cwN3F8dra2vo7+9HQ0MDp6+dKbGtVD09PWzP+/HxMVZWVlBaWsr6NsrKygTpeV9fX8fR0VHS6dIKP4eiKCwuLsLr9WJmZiZnd94TGcXtdjuWl5cRDofTFs1kwiAZ46v4fVLD7/djbm4OBoMB/f39V94/rjKKk5ZVqVc3+BpxLhWEqmAQgSEnlDtUFmEYBmtra9jf38fo6CiqqqqwtbWFaDSaMw8LsuvPMMyVN3CGYbC3t8cudJuamrJ8lMnBZaZHMvBp5ibX3vHxMSYmJqDX6zl7bT643PMeDofZ8aG7u7vIy8uLm0rF9wODoihYrVacn59jenpadg+KTIlEIjCbzWAYRlY774mM4na7PU40xyaKX/WdDwaDMBqNKCkpEXSynlTxer2Ym5tLeVIZnyF/QhGbEyJHhPJgkBG1ubShfBO5sapNESE+YGKoDQaDcX4LlUoliklFXEG+uFcJDDK1yGazYWpqStQLXbVajUgkwvvv4dvMHY1GsbCwgEAggJmZGRQXF3P22tkiPz8fDQ0NaGhoAE3T7K7wyspK3K5wdXU15z2uZOcYAGZmZmSzOOYKsjguKirCyMiIbBfHsaK5vb2d9R/FGsWJ2Ig1ivt8PhiNxht33hUSQ8a/Nzc3ZzyhkcuQP6EgxyjX76FQLVI+n092G1OyFBjZ5uLiAkajEWVlZZidnY2rVmR7l5xvyE0r0Zc4FArBbDaDoijMzs6KvkUiGy1SsX4LgHszNxmjmp+fj+npaVF5XNJFrVbDYDDAYDDETaU6OjrCysoKysrK2OpGpq1UPp8PJpOJ7dmW60M5XbxeL4xGI6qqqpRJW5e47D+6bBSvqKhAWVkZjo6O0NjYiO7ubkVcpIjb7YbJZEJ7ezvnY5AzDfkTCrLekOu1JGSLlFgG2GQLRWDwDPFbtLe3JzTU5prAIDdQiqLiFrNkF6miogLDw8OSWKjxLTBid7/4ePi43W5YLBbU1NSI0uPCBZd3hUkrld1uZ1upSGUjVYOt2+1mPULK4i51zs7OYDab0dLSIulsn2ygVquh1+uh1+vR3d2NQCCAvb097O/vAwBsNhtommav41z8LnMNyVjp6enJShtuqiF/5H9nG778fVJBKIGhVDAUOOOy3+KqXIdcFBgqlSpuYX5ycsKO9JTSQoNPgcG3mfvk5ARLS0vo6upCc3OzZN7zTLncSuVyueBwOLC8vIxIJBJnsL2ulYq8f93d3Whubs7iGeQGp6ensFqtWVvc5Rrn5+c4ODjAwMAAamtr2et4aWmJvY5JO5VYpquJCZvNhoWFBcEyVm6qbghpFJfzBClSWVIERnaQpcDge7EVDocxPz+PQCBwY65DbJp3rhBr9N7Y2MDu7q5owvNSgS+Bwae4YBgGW1tb2Nvbw/DwsOSDGzNBrVazi7De3l54vV7Y7XYcHh5ieXkZ5eXlbCtVaWkpVCoVGIbB7u4utra2ZP/+pcve3h42NjaU9y9NDg8Psbq6iuHhYfaeSapwDMPA6/XC4XCwLYFSDarki+PjYywtLcW9f0Jzuboh5BhcOQsMIf0nPp9PaZFSyIzr/BaJyLUKBnD3nCKRCEwmk+jC81KBa4HBt5mboigsLS3B7XbnTMYAV6hUKpSVlaGsrAwdHR0IhUJsK9X29jby8/NRVVWFYDAIj8eDqakplJeXC33YkoJsKBweHipjfNNkZ2cH29vbGB8fTzgAI/Y6Ji2BTqczLqiSJIpXVlbmhOcqFQ4ODrC2tibqAMdErVREbGSjuiFngRH77M02SgVDISNIK9BVfotE5KLAAICFhQUUFxfj9u3bkp26w6XA4NvMHQqFYLFYANyddCSntNB0KCgoQGNjIxobG0FRFBwOBxvup1arsb29zbZSSfX6zSZkOtzZ2ZkyxjcNSMbK8fFxSpsD+fn5CY3im5ubWFhYQEVFBXsdFxcX53R14yZxJkYStVLxXd0QqkVIDAgpMPx+v+wqurIUGFzfZMnDYW9vL+VWoFwTGE6nE+FwGDU1NRgbG5P0TglXAiM2qIm8Lpd4vV6YTCbodDpl0lEaRCIRbG9vo7i4GLdu3UIoFILdbsf+/j6WlpZQXl7OtqjIbY55MsSmSyviNnVomsby8jJcLhemp6fTHiOdyChOqnQbGxsoKCiISxSX8r05FtIWur+/j8nJSUlXHrMR8ifnCgYRV0Lcw71eLzo6OrL+e4VElgIDANtvnSmRSAQWiyUpv0UickVgMAyD/f19rK6uorCwEI2NjZK/iXEhMPg2czscDiwsLCiTetLk4uICZrMZlZWV6O/vh1qtRn5+flwrld1uh91ux9bWFgoKCth+91xapKVLKBSCyWSCVqtV0qXTgKIoNqNmenqaU8N2UVERmpub0dzcDIqi4HK5YLfbYbVaEY1GYTAYWI+SVEXh5cqPFFtxr4KvkD+hciDEgJDnToL25ITyNMiAi4sLmEwmlJSU4Pbt22n1u2o0GnZXQqqQHbjT01NMTU1hdXU1J0RTpgKDb3FBzLT9/f2CTEqROk6nE/Pz82htbUV7e3vCz6egoABNTU1oamq6cpEm11YqkhFCKmdyXbSkC9mcomkaU1NTvPolNBrNPUbx2IEHJDumqqpKMkZxhmGwvLwMp9OZUeVHKnAV8ifnCoZQI2oBJQdDIQWI36KtrQ1dXV1p35ClXsEIh8MwmUxx4XkajYb3gLpsQKZhpQopZ5ObPtfigqZprK2t4fT0VDHTpsnh4SFWVlbQ39+PhoaGpH7m8iLt4uICdrsde3t7WFpagk6nY8VGrrdSeTwemEwmJSMkTUjlJz8/H+Pj41ld9FweeECyYxwOB/b29uKmrxkMBlFWpWiahtVqxfn5OeeVHymQScifIjCEERh+v18RGHIh3RapWL/FyMgIamtrMzoOMnFJilwVnpfuwlxspFPBuGzm5lpcRCIRLCwsIBQKYWZmRvRp6GKDYRhsbm5if38f4+PjqKysTOt1VCoVysvLUV5ejs7OTgSDQbbffXNzk+13r66uRkVFRU490O12OxYWFtDV1YWWlhahD0dyBAIBzM3Niabyczk7xu12xxnF9Xo9KzjE0OJB0zQ7Bn5qakqy7V1ckkrIn5xbpIQ0uCtTpBSuJRKJYH5+Hj6fj7PRq1LNwbguPC+XKhipnAffZu5AIACTyYTCwkJMT0+LcmdRzFyedMTlblJhYWFcK5XT6WT9MTRNx7VSSXl0KKn8DA4Ooq6uTujDkRxerxdGoxE1NTXo7e0VXeVHrVajsrISlZWV6Onpgd/vZ6sb6+vrKCoqYj1IQghniqJgNptBURTvbWVS5abqhs/ng0qlQiQSyXrIn9AIJa7I+y630fHKCiVJyIOhpKQEs7OznN3YpNYiRWbd7+zsXFnBkdo5XUUqQolvv4Xb7YbZbEZdXR16enpk80DgCtLvTlEU75OONBoNampqUFNTA4ZhcH5+Drvdjt3dXVitVraVikylkgIMw2B7exu7u7sZVX7kjNvthslkutbzIzaKi4vR0tKClpYWRKNRNlF8YWEBFEWxwtlgMPBeYxGf/AAAzelJREFUSYhEIjCbzVCpVJiYmFA2WJKEVDdI9dbpdGJkZAQA0jaKSxUhW6SUoD2FhJyenmJ+fj5jv0UipLQYj0ajWFhYwPn5OW7fvn2lGs+lFinip7juM4+tXPAhLo6Pj7G8vIzu7m40Nzdz+tpygFR+iouLBel31+l00Ol06OrqQjAYZKdSbW5uorCwUNAd4WRgGAYrKyuw2+1KgGOaOBwOzM/PS/o7nJeXFyecLy4u4HA4cHBwgKWlJdYoXl1djbKyMk7vg+FwGEajEQUFBRgZGVFGcacIEReHh4fstC0hQv6ERsgWKWWKlIxI5uYXu1s/PDzMS0uAVASG3+9nx1HOzs5eOzEnl1qkgOtvSrETPLgWF7F+gZGREVRVVXH22nLB4/HAbDajtrZWFC0phYWF7OhQsiNMPA00TbO97mJppSJjVP1+P6anpxXPTxocHx9jaWkpp9rKYj1IsUZxMvSAGMVJongm1YZgMAij0YjS0lIMDQ3l1II3G1we5UsWuXyNwRUzQlUwKIpCIBBQKhgKd4n1W8zOzvJ2YUhBYLhcLphMJtTX16Ovr+/Gm4xarUY4HM7S0fHHdQKDbzM3RVFxU1LkdmPiApvNhsXFRXR2dqK1tVXow7mHyzvCJIV5Z2cHVqs1LoVZiJ2vSCQCk8kElUqF6elpUQgeqUFGSY+NjcFgMAh9OLyRyChut9uxvr6OQCAAvV4flyieLMQQr9frMTAwIPgGgdRgGIadOBgrLhJxXchfKmNwxYxQHgyv1wsAsqv+KgIjAcRvUVxczKnfIhFiz8HY29vD6uoq+vr6ki7t52IFIxa+zdyhUAhmsxlqtRozMzOyy1fgArKwGxwczHjSWzZQqVSoqKhARUUFurq6EAgEYLfb48y1xLeh0+l4f0iStrKSkhIMDQ0pLSkpEpsuLbdR0rFG8d7eXtYobrfbsba2huLiYrZKd11boM/nw9zcnGgN8WKHtDY6HA5MTU2lJOxuqm7EjsElQkMKYkOoCobf7wcA2W0UylZgXHWzOj09ZZORszHfXawVjNjwvMnJyZRMnWI9p1Qhn32swOAz3wL4ebI02bGTwk1bTJAdu+PjY0xOTkKn0wl9SGlRVFQUZ651Op2w2+2wWCwAEJdTwPUGyMXFBTvpqK+vT1nYpQjDMFhdXYXNZsu5dOl0SGQUj20LjE0UJ5spFxcXmJubQ1NTEzo7O5VrMEVICKHL5cLU1FTGrY1chfwJDUVRgmzY+Xw+FBQUyG4wgbzO9hqy4bdIhBgX4+FwGGazGZFIhA3PS4VME7DFArlJknPhe1IUeei2tbVJZsqMmCB+AZ/Ph5mZmZxJ9s3Ly0NtbS1qa2vZViq73Y6trS0sLi6m3X6SCJfLBYvFgra2NrS1tSnXYIrQNI3FxUVcXFwonpUEXG4LPD8/h8PhwP7+PpaWllBeXo7S0lKcnJygra0NHR0dQh+y5GAYhh3HPTU1xXkIYSYhf0IjlMnb6/XmfPhqIhSBgZ+Hl3m93munI/FBXl4e+8UUw8VHdi/Ly8vTHgUoRtGULkRg8G3m3tvbw+bmJgYGBnLGCJpNLreV5apfILaVqru7O2H7SWwrVSrX6cnJCZaWltDX15d0urnCz6EoChaLBeFwGNPT00pr4w3ETljr7OxEKBTC7u4udnd3oVKpcHBwgGAwyFbqlDa9m4lNOOdDXCQilZA/oVuphPRgyG2CFCBjgUEevF6vFyaTCUVFRbz7LRJBbpoURQlePiPjeNvb2zMqS+fKmFrg7rlEo1H2fLgWFzRNsyNApdzSIyQ+nw8mk0k0ycjZ5HL7CWmlMpvNAMBO8jEYDNfeX3Z3d7G5ualMK0sTYohXq9WYmpoS/F4uRc7Pz3FwcMBO2zo7O2M9SLGJ4tXV1UplKAGkeub1egVLOI+tbpBKRmx1Q+gxuEJ6MJQKhsyw2WyYn5/Pmt8iEWIQGGQc6vb29pXheamQKyZvkmvhcrlQVFTEufgkk8rC4TBmZmaUh2YanJ2dwWw2o7m5Wfa92pdbqdxuNxwOBzY3N9kFGqlukGuNjLA8OjpSBG6akDGqiiE+fU5PT7G4uIihoSH2+WMwGGAwGNDb2wufz5eWUVwu0DTNjpOempoSRfWMfCax1Y3Y/4SobgjVIuXz+ZQKhpwghsls+i0SQVS8UDv+yYbnpUIutEiRXtKmpiYcHBxgc3OTTa2trq7O+Abu9/thNptRVFSE6elpZcczDUi+QG9vL5qamoQ+HFGhUqmg1+uh1+vZViq73Q6bzYa1tTWUlJTAYDDg4uICfr8/pzwr2cTn88FoNKKyshL9/f2yX+imw+HhIVZXVzEyMoLq6uqE/6akpAQlJSVobW1lK3UkUZwYxUmlTgyL62xC0zTm5+cRDAYxOTkp2vNP1EqV7ZA/oSoYXq9XlsMeZLuqqa6uxp07d0ShKoVakAcCARiNRuTl5d0YnpcKUjd5x/aQErOrz+eD3W7HwcEBlpeXodPpWLGR6jV0dnYGi8WC+vp69PT0yHrXPR0YhsH29jZ2d3cxOjqqtPQkQXFxMVpbW9Ha2opIJAKbzYaNjQ2Ew2FotVpsb2+zve6K2E2O8/NzGI1GNDY2oqurS/kep0FsTkiykwovV+qIUXx3dxdWqxXl5eXs0IPS0tKc/lwoimKr4JOTk5LxngkV8ieUB0OOKd6AjAWGSqUSzQcuRBaGy+WC2WxGXV1dUuF5qSDlCsZVZu7S0lKUlpaivb0doVAIdrsddrsdm5ubKCwsRE1NTVLG2qOjIywvLyu77mlCxic7nU5MTU3JLriIC2iaxt7eHsrKyjA0NASv1wuHw4GNjQ0sLCygsrKSXaApbXuJIdO22tvb0dbWJvThSJLt7W3s7Oxk1JqXyCjucDjgcDiwvb2NvLy8uETxXGpfoygKZrMZFEVhYmJCMuIiEdkagytUBUNpkVIQjGwvyPf397GysoLe3l60tLRw/vpSrGAwDMPe1IDrzdwFBQVoampCU1NTQmMtqWzETj4hY5APDg5yPtWXL6LRKDulZ2ZmJisTUnIN0tITm7NCQtF6enrYSt3p6SlWV1dRUlLCXs/l5eU5vRucLCQhvre3F42NjUIfjuQg98KjoyPONwkKCgrQ2NiIxsZG0DSNs7Mz2O12rK6uIhQKxY10lrJ4pigKJpMJDMOkPe1RrPA5BlfIMbVKi5SCIGRLYJCJRSSELJXwvFQg5yOW0bs3cTmZm+yQJENsuZ6maXg8HrbPPRQKsUZFh8PB5jPIcScjU4LBIEwmEwoKChTPSpq43W6YzeZrw8tIr3tbWxsikQhrrDUajVCr1XFTqXJpNzhZiF9gaGgINTU1Qh+O5CAhhHa7HVNTU7zeC9VqNXv/ZRiG9SHFimdiFNfpdJLxz0SjUZhMJqhUKkxMTOT89zDVMbjkf1+G/JwQn7NSwZAZYlr4ZkNgXA7P49PQSb7AUhAYXIbnqdVq1lhLdoOPj4+xtrYGmqZRXl4Ou90OALK82aTLxcUFTCYTqqqqOG/nkwtk1727uxvNzc1J/YxWq0V9fT3q6+tB0zTcbjc7xScUCqGyspIVHHKoJu3s7GB7ezslv4DCz6FpGktLS3C73ZykS6cCaYmOFc8kUdxisYBhGFZsVFVVibbdiIxD1mg0GBsby3lxcZmbqhvXGcVJ5UOoMbX19fVZ/71CI1uBAdy9CMmutZDwLTC4CM9LhdjRu2JeDPKZzK1SqUDTNI6Pj1FbW4vOzk62lYr4Nqqrq1FTU5NyIJqccDgcbDaLkiydHgcHB1hbW8to1/1yKxXZDT45OcHq6ipKS0vZ1pNca6Uio3xJ5be8vFzoQ5IcZIyqz+fD9PS0IBkNsWi12nuM4na7nTWK63Q6VmyIxSgeiURgNBqh1WoxOjoqO3GRiMvVjevG4BLxIZQHQ2mRUhCEvLw83gQGV+F5qRArMMS6E8RnMjfw8x3j2IVxqr4NuXNwcIDV1VU2eEshNRiGwdbWFvb29jA+Pg69Xs/J617eDQ6Hw+z1vLe3B7VazV7PUjfWkqECLpeL95aeXIUknEciEdFkNMQSaxTv6upCMBhkjeJbW1vIz89nxYZQ13MkEsHc3BwKCgowOjoq6o07obhpDK7f7wcAdq2VzZA/RWAoCAYfFQyyuNja2sp61gfxMIjR6J2KmTvd19/d3cXW1hYGBwcThhYm49vgKm9DihAT6OHhISYmJjhbGMuJ2IXx9PQ0rw+3/Pz8uFaqy8ba2KlUUmqloiiKDS+bnp6W1LGLBeIXAIDJyUlJeKcKCwvZzSCKothE8djrOZutgeFwGHNzcyguLsbw8LAiLpLgciuV3+/HysoKGhsb78kdy0bIn2LyliG52iJFHoxutxu3bt0SpKQvxlG1sTsaQGpm7mQgizqHw4Gpqamk3vdEvg273Y7Dw0MsLy+jvLycHYErh91TiqJgtVpxfn6O6elpWZwz15DZ+MFgMOsL48vGWnI9Hx0dYWVlhW2lqq6uRllZmShaTxIRjUZhNptB0zSmp6dFW4kVM7nQ0qPRaNjqBbmeHQ7HPUbxZEaUp0MoFMLc3BxKS0sxNDSkiIs0CAaDMBqNrIcvdpOR/G++Q/78fr8iMBSEgcvFeCAQYE1gd+7cEWwHXGyjamP9FnyURiORCCwWC6LRKG7dupXWok6lUt2Yt5HLvo1wOMwaLmdmZmRZvckUMsxBrVZjampK0IXx5es5HA6zU6l2d3dFm1EQDodhNBqRn5+P8fFx0RyXlAiFQjAajTm16x57PROjOEkUJ62usYnimX73iLgoKyvD4OBgTryH2SYYDOKFF15AZWUl+vr64jYVsxnyp0yRUhAMjUaDUCiU8eucnZ3BZDKhtrYW/f39gt6QxFTB4NPMDdy9eZjNZpSUlGBsbIyzNoDLeRsulws2my0nfRt+vx8mk4ndqZP6+QhBIBCA0WhkA/TEtiDJz89HQ0MDGhoa4lqpVlZWEA6H2cVZVVWVYCZg8h6Wl5cri7o0Ie+hTqdjs1ZyEa1Wi7q6OtTV1YFhGHg8Hjbgb3FxETqdjr2eS0pKUnruBINBzM3NQafTYXBwMOc2k7IBeQ/1ej36+/uvfA/5DvkjlS85hsLKWmCI5UvLRZL3wcEBmxDNR3heqohFYPAtLkiib1NTE7q6uni7pvLy8lBTU4OampqEvo3KykrU1NQIujhLF5LP0NDQgO7ubtF8L6XE+fk5TCYT6urq0NPTI/r3MLaVqre3l00TJ62BZWVlrIDO1hQfr9cLo9GImpoa9Pb2iv49FCN+vx9zc3NsO4pc3kOVSoWKigpUVFTEGcVJ9Tk/P58VG3q9/toNlEAggLm5OVRWVl67MFa4GlL9qaiowMDAQNLvIV8hf36/n9doALEia4EhFjJZjMeG501MTIgmIVroFinSW8nnpKjDw0OsrKygr68vq4m+yfg2SCuV2Muyp6ensFqtKeUzKMTjdDphsVjQ0dGB1tZWyS1IVCoVysrKUFZWxrYGkik+Ozs70Gq1ca1UfOyIu91umEwmtLS0oKOjQ3LvoRjwer2Ym5tDfX297DcKEhnF7XY7lpeXEQ6Hrxx8EAgE8MILL8hOoHEJERekgpZptlUqIX9X3ZuUCoaCYKQrMEjPeigU4j08L1WErGBcNnPzMSlqfX0dh4eHGB8fFzR06zrfxtbWFuvbqK6uRkVFhWgeWLHTtoaHh1FdXS30IUmS4+NjLC0tYWBgIGeCnAoKCtDY2IjGxsZ7FmeRSAQGg4EVHFz4dEjWiiJy08fj8bACrb29XTT3GTGQyChut9txfHyMlZUVlJSUsJW61dVVyVQhxQgRF6TFkcv3MN2Qv0gkglAopJi8FYQhnRwMkm5cWlqK27dvi278n1AVjNjpEOQ4uCQajWJxcRE+nw8zMzOiqxDE+jYoioLT6YTNZoPFYgEgDt8GTdNYXV2FzWZLetqWQjxEoJFkabFULrnm8uLM6/XCbrezLaGkWpduINrJyQmsVmtOCbRsc3Z2BrPZzFbQFK7m8oZQJBKBw+HAyckJtre3oVarEQ6HcXp6yolRXE6Qcb7EFM+3QEs25O/8/BwAlAqG3BDLDkGqu/02mw3z8/NobW3lte8/E4SoYPDttwgGgzCbzcjLy8PMzIzob/4ajeYe34bdbhfUtxGNRrGwsIBAIICZmRkUFRVl5ffmEgzDYHV1Faenp7JKlo5tpero6GCrdbGBaERA6/X6GzcX9vf3sb6+jtHRUVRVVWXpLHILUv3p6elBU1OT0IcjObRaLcrKyrC2toa2tjZUVVXFGcUrKipYgZ2qUVxOEHFRWloqiCn+upC/xx9/HABE4UnNNrIWGGIh2cW4kOF5qZLtCkZs5YIPceHxeGA2m1FVVSX4hK50iPVtdHd3C+LbCIVCMJlMyMvLU7IF0oTkhFxcXMheoF2u1rlcLtjtdlitVkSj0bipVLGtVLEJ5xMTE6ioqBDuJCTM6ekpFhcXMTg4KOpnkZi5uLjA3NwcmpubWe8PuUcHAgHWi7S5uYmCggJWbNxkFJcTRFyUlJSIYnpebCvVj370I/zmb/4mPvWpT+Vslfk6VIwYkuYEgqKojKc3cYHH48Hc3Bxe+cpXXvlvYsPzJiYmRL9rubCwgKKiInR1dfH6e7Jh5iZGZKmaaG8i1rfhcrl48W14vV6YTCbo9fqcHl3JJyRrhaZpjI2NKTkhV8AwDC4uLthr2uv1xrVSHRwcwG63Y2JiQpZ90VxAghMV/1T6nJ+fw2g0soMFroMIaCI4IpEImyh+2SguJ0hmTVFRkejyVp555hm89a1vxWc+8xm8733vy7l1QzLIWmDQNI1IJCL0YcDr9eKnP/0pXv3qVyf8+9jwvLGxMUmMIV1aWoJGo0Fvby9vv4PvZG6GYbCzs4Pt7W0MDQ2hpqaGs9cWK7G+DYfDASBz34bT6cT8/LwyoScDgsEgTCYTCgsLMTIyouxepkDsyFCHwwGVSoX6+nrU1dUl1UqlEE9sa5kcd2W5wOPxwGg0or29HW1tbSn9LPEiEbHh8XhQWlrKDj4oLy+XxT02Eolgbm5OlOLiZz/7Gd785jfjU5/6FD7wgQ/I4vNIhNIiJQJIixTDMPdciCQ8r6amRlI7v3y3SPFt5qZpGktLS3C5XLIyIsf6NhiGgdvtvse3QQRHMkL36OgIy8vL6O/vR0NDQxbOIPcg+QxkdKVU7gFiobCwEPX19bDZbCgtLUVrayvcbjcWFxdB03Rc+rJSFboesuGitJalDxmJnK4p/vJY53A4DKfTCbvdDqPRyGbMkLHOudiKSsRFYWGh6MTF3NwcfvEXfxF/8Ad/IGtxASgVDFFUMMLhMJ588km86lWvipsGRSal9PT0oKWlRVIX6vr6OkKhEIaGhjh/bVK5oCiKl5YoMv6XpmmMjo7KtvwcS+x4RbvdjvPzc7btpLq6+h4DYmyf+8jIiLLTmSZkQo9S/UmfSCQCk8kEtVqNsbEx9h7LMAzOz8/Z6obX62XTl6urq1FcXKy83y/CMAw2NzdxcHAgiRZdsUK+z11dXbyMRCbDPMg17ff7UVFRwbYH5sI1HYlEYDQakZ+fj9HRUVGJC4vFgte//vX4nd/5HXz0ox+V/HudKbIWGAzDIBwOC30YoGkaP/zhD/ELv/ALKCgoYMd4Hh0dSXYE5ebmJnw+H0ZGRjh9Xb4nRfl8PphMJpSVlWFoaEhpRbmC63wb5eXlWF5extnZGcbHx5U+9zQh3h9lQk/6kNYy0kZx3fc5GAyybVQulwsFBQVxXiQxLWSyCcMwWFtbw+npqeJbyQCXywWz2ZzV7zMxitvtdpydnbHXNDGKS+2aFrO4sFqtePDBB/Hwww/jd3/3d2UvLgBFYIhCYDAMgx/+8Id46Utfiry8PDY8b2JiQlTheamws7PDLjC5InbONB/igngFmpub0dnZqdwgkoT4NojgiEaj0Gg06OnpQV1dnSLS0oD0uSsm2vTx+XwwGo2orKxMefJb7DXtcDjiWqmqqqpysu0kEQzDYHl5GU6nE5OTk5J9HgmN0+mExWJBb28vGhsbBTmG2ElrDoeDnbRGjOJi93ZGo1EYjUbk5eVhbGxMVOJiZWUFDz74IN7//vfjD//wD5W1w4soAkMEAgMAnnjiCQwPD2N1dRWlpaUYGRkRXXheKuzt7bFBaplCJkWRUb5cm7mBu+1oq6urilcgAwKBAIxGI7RaLcrLy+F0OhEMBlP2bcgZhmGwsbGBw8NDjI2NKX3uaXJ+fg6TyYT6+np0d3dndL8grVREQPt8PjafgLQH5iI0TWNxcRFerxcTExNKq2iakKyQ/v5+0YQ5xoZWOhwOnJ+fo6ysjBUbYjOKx4qL0dFRUW1ara+v48EHH8R/+S//BX/yJ38iKuEjNLIWGMDdVg8x8MQTT4BhGLS2tmb8QBQDh4eHODw8xMzMTEavw7eZm5T/j4+PMTo6Cr1ez+nrywWSE1JTU4Pe3l6o1eqUfRtyhwwWODs7w8TERM4uXPnG5XLBYrGkNaEnGWLbTlwuF4qKitjKRq60UlEUhfn5ebaSrpjf08Nut2N+fl70WSHhcJidSuV0OqFWq1mxYTAYBN3sjEajcR4qMYmL7e1tvPa1r8Vb3/pW/OVf/mVOfPe5RBEYAgsMhmGwvb2NtbU1dHR0oKenR9Dj4YqTkxNsb29jdnY27dfg28wdmyo9NjamlP/TxG63Y2FhAZ2dndcOI8hG3oZUiUajmJ+fRzgcxvj4uFLpSRObzYbFxcWstaJEo1E4nU5WcDAMw1Y2DAaDJFupotEoLBYLKIrC+Pi4JM9BDNhsNiwsLGBoaAi1tbVCH07S0DQNt9vNCg6/3w+9Xh+XKJ4txCwu9vb28MADD+D1r389/u///b+KuEiA7AVGOByGUG8BRVFYXFzE2dkZ1Go1+vr6ciZrwWazYX19Hffdd19aP8+3mTsQCMBsNiM/Px8jIyPKQzRNiFdgcHAwpYfoZd8GkHnehlQhCedarRajo6OSbo0UEhL+JlRmDcMw8Hg8bNuJz+djF2ZkKpXYIRO3NBqNci1mAEk5Hx4elvwz3e/3s2KDVOzINc1nxY6iKJhMJqhUKtGJi6OjIzzwwAN45StfiS984QuKuLgCRWAIJDCCwSA7s3p8fBwmkwktLS050//vdDphtVrxspe9LOWf5dvMTdp5qqurlVyBNIltLcvUKxCbt2G322Xl2/D7/TAajdDpdBgcHFSuxTQh+Qyjo6OorKwU+nAA3N3EINf02dkZiouL41qpxFaxI6nIBQUFSphjBhwfH2N5eTknBzREo9G4RHFiFCcbQ1zdq4m4AIDx8XFRXYsnJyd48MEHcfv2bfzt3/6tqI5NbCgCQwCBQYJ2qqqq2EXF888/j7q6Ol5mYwsBmff9C7/wC0n/TDbM3CcnJ1haWmLnkIvtIS8FSOXN6/VifHyc051ZOfk2PB4PTCYTGhoacsJ3JQSxpngx5zOQVipS3QAQ10oldKWAbHiVlpZiaGhIEbppQqpockg5ZxgGFxcXrNiINYpXV1ejrKwsrXsaRVEwm82gaRoTExOiWsDbbDa87nWvw+joKL761a8K/r0VO7IXGJFIhNfE6cscHh5iaWkJ3d3daG1tZb+AZJwiH6ZEITg/P8fzzz+P+++/P6l/f9nMzbW4IF6XnZ2dnNxZyhbhcBhmsxkqlQqjo6O8mz9DoRAcDgdsNltO+TbIZJnOzs600nwV7lY6l5eX4XK5JGWKj22lImFoer2eva6LioqyejykiqbX6zEwMCDZ75TQHB4eYnV1FWNjY6KpomWTq4ziJFE8mcV4rLgYHx8X1QLe6XTi9a9/Pbq7u/HNb35TaatOAkVgZElg0DSNtbU1HB4eYnR0FFVVVXF/b7FYUFpais7OTt6PJRt4vV785Cc/wWte85ob/y3fZm6KorC0tAS3242xsTGUlZVx+vpygYQQlpeXY3BwMOs7S4l8G1VVVaipqZGUb+Po6AjLy8uinywjZkgVzefzSX6Eqt/vZysbsa1U1dXV0Ol0vC74vV4vjEYjamtr0dPTo4iLNCFetPHxcWUSIX5uFCfXdSAQYEU0SRS/DEVRsFgsiEajmJiYEJW4ODs7w0MPPYSmpiZ861vfUqaqJYkiMLIgMCKRCCwWCwKBwJU7bYuLi8jPz8+ZKVKBQAA/+tGP8MADD1z70OLbzE123BmGwdjYWE738/PJ2dkZLBYLGhsb0dXVJfhCRIq+DVJF293dFZVXQGpEo9G4Xc5c2kmMRCJxrVQqlYq3Vqrz83MYjUY0NTUpwaIZsLe3h83NTYyPjyu5NVdAjOLEj3R5tDMAmM1mUYqL8/NzvPGNb4TBYMA///M/i/LZIlZkLzCi0Sjb888HZIeopKTk2qkcy8vLAID+/n7ejiWbhMNhPPnkk3j1q1995c4y32Zur9cLk8nEGmilssMtNk5OTmC1WtHb24umpiahDychPp8PNptNtL4NhmGwsrICu92O8fFxpYqWJsSInJ+fL7rALa6haTqulSoQCLAiuqqqKqNWKuID5CsrRC7s7u5ia2sLExMT0Ol0Qh+OJCBGcSKiKYqCRqOBWq0WXauj1+vFm9/8ZhQXF+PRRx/Nevui1FEEBo8Cw263w2KxoKWl5UYT5/r6OkKhEIaGhng5lmwTjUbxxBNP4P77779nhzEbZm6Hw4GFhQU0Nzcru3NpwjAMO51nZGTknrY+sXLZt1FQUICamhrBfBsURWFhYQF+vx/j4+PKQypNSFJ8WVmZLI3IPp+P3QV2u90oKSlhRXQqyctOpxMWiwXd3d05M1RECEg1UszDBcQORVEwGo0IBALIz8+H1+tFeXk5m7mRrlGcC3w+H9761rdCpVLhscceE5XwkQriqUPlEGRhtrGxgcHBwaRGz2o0Gl4rKdmG7CxSFBUnMPg2cwN3+2HX1tYwMDCA+vp6Tl9bLtA0jZWVFTgcDkxPT0tqx72goACNjY1obGyM821YLBYA2fVtRCIRmM1mAMD09HROtfNkE1IJJqOl5bhhUFJSgpKSErS2tiISibCGWjLuPLaV6qrrmoRi9vX15cxIdCHY3NzE/v4+JicnJXVvFBM0TWNhYQEURWF2dhZarZbdHHI4HNjZ2UFeXl5coni2KpaBQADveMc7QFEU/vVf/1URF2miVDA4rmBQFAWr1Qqn05lS2XR3d5f9mVzhBz/4AV7ykpewX85Yv4VKpeJ8B5IY6U9OTjLOZpAzJFU6FAphfHxc0gbaWMj0HtJKxbdvIxAIwGQyobi4GMPDwzndzsMnZJxvc3MzOjo6ZCkuruOyoTYYDMZNpSLfX9LqKLVkaTHBMAw2NzdxeHiIyclJlJaWCn1IkoSIi0AggMnJyYQbLzRN4+zsjBUc5LomQpqvSnAoFMI73/lOnJ2d4Yc//KHS+pYBshcYFEUhGo1y8lrBYDAuHCaVhdnBwQGOj48xPT3NybGIgSeeeAK3bt1CWVkZ72buSCSChYUFBINBpQ0lA8g1THrcxWS24xqSt2Gz2Tj3bVxcXMBkMsl6x50LSDtPV1cXWlpahD4cSRCbI+PxeFBaWoqCggK4XC6MjIwoI7rThGSuHB0dKeIiA5IRF4kgLYKx09ZIdYOrRPFwOIx3vetdODw8xBNPPKEM4sgQRWBwJDASheelwvHxMXZ2djA7O5vxsYiFp556CuPj49DpdLyKC7JTXFhYiOHhYaUNJU3IothgMKC/v19WPe6kNG+32+F0OuN8GzqdLqX3wuVywWKxoLW1Fe3t7Yq4SBOy4660OqZPJBLBysoKTk9PoVarodFoWJO4lEY7Cw3DMFhbW8Pp6SkmJyeVlpk0oWmaHS89OTmZ9rhXElxJBAdN03GJ4um8biQSwXvf+15sbGzgySeflIznUMzk7vZkFrkqPC8Vcs2DAQBqtRrRaJQVcHyIC7fbDbPZjLq6OvT09MhqUcwlxBTf1taGtrY22S2KufJtkKR4pcc9M0iuQKLMIIXkYBgG+/v7cDqdrI+KtJysra0hFArFTaXKlVZIrmEYBqurq7Db7ZiamkqY4aBwM1yJCwDIy8tDbW0tamtrwTAMzs/P4XA4sLu7C6vVylajq6qqUFpaeuPzLBqN4v3vfz9WVlbw9NNPK/ccjpB9BYOmaUQikbR+luxq7O/vY2xsLKOL0ul0YnFxES9/+cvTfg0xwTAMnnnmGdTX16O5uRkajYbzRevx8TEr7JT2ifQ5ODjA6uqqslOcgFR8G7u7u9jc3JTUxC2xEZsVouQKpA/DMFhfX8fx8XHCdh6GYdhWKofDwbZSketayOk9YoJhGCwvL8PpdGJqakppvU0ThmGwuLiIi4sLTE1N8RpUF2sUdzqdrFGcJIpf3iCiKAof+MAH8Nxzz+Hpp59WnoEcogiMNAVGMuF5qeDxeDA3N4dXvvKVGb2OGCB+i/39fezu7iISicTtAGfawsQwDLa2trC3t4fh4WFlMZcmxLBIBLKSQHsziXwbVVVVbBqzMg8/fchO8enpKSYmJpTpPGlCMlccDkfSz6ZwOBzXInjTokwOMAyDpaUlnJ2dYWpqSqnwpAnDMLBarTg/P8fk5GRWg+pijeLkvv3Xf/3XePWrX423vOUt6O7uxgc/+EH8+7//O5566illbDPHKAIjDYHh8/lgNBpRXFyMkZERTnr+vV4vfvrTn+LVr351xq8lJJfN3MDd3n6yA+zz+VBZWcn2t6d6syFTujweD8bHxxWjXZrQNB33Pio9xakTCoVgt9uxtbWFUCiEwsJC1NTUoKamJmXfhtyJvR4nJyeVneI0oWkaS0tL8Hg8mJiYSOt9JIsyYhQPh8MwGAys4JBDkjFZFJPrUREX6SGkuEh0LEdHR/jiF7+IJ554AvPz86irq8PFxQW++MUv4hd/8RdzeqiJEMheYDAMg3A4nPS/J33Zzc3N6Onp4ayMHAgE8KMf/QgPPPCAZEvTyUyK8vv9rNjweDwoLy9nF2U39baGQiG2J35sbIzXMmsuEw6HYbFYQNM0xsfHlfcxTaLRKCwWCyKRCEZGRuD1etlFGYC4XALlwXU1FEXFjUWWwwKWD8h0Hr/fj4mJCU7ex9hWKrvdjvPzc5SVlbGtVMn0t0sN4hXwer2CL4qlDKkAud1uTE1Niep9pGkaH/3oR/H000+jv78fP/7xj0FRFF772tfi9a9/PV772tfCYDAIfZiSRxEYSQqMdMLzUiEcDuPJJ5/Eq1/9asmVo0kyN03TKU2KIjvAJHG5uLiYFRuXe4AvLi5gNptRUVGBgYEByb1HYsHv98NkMqGkpETJZsiAUCjEjvMdGRmJExDEt0Gubb7zNqRMJBKByWSCSqXC2NiYMgEuTSiKYsXuxMQEb+/j5VYqrVbLCmm9Xi/5+0msSMvUiCxnYsWF2CpANE3jE5/4BL7+9a/jqaeeQm9vLyiKwvPPP4/vfe97+O53v4uFhQXcvn0bf/7nf47bt28LfciSRREYSQiMdMPzUoGiKDz++ON45StfKambWqywANJP5o5Go3A4HLDZbHA4HNBqtaiurkZNTQ2i0SgWFxfR2tqqBG1lAJm4VV9fz2n1TW6QFkm9Xo+BgYEbW6H4zNuQMiRzpaioSBG7GRCbFj8+Pp61ahlN03C5XKzgiEQicVOppCakaZrG/Pw8gsEgJiYmJPUcFhPEGO9yuUTnXWEYBp/61KfwpS99CU899RQGBgYS/rvDw0M89thjuP/++9HR0ZHlo8wdZC8wgLu7kVeRSXheKjAMgx/84Ad42cteJpkxeKQlilxCXPWckweXzWbDyckJKIpCRUUFWltbldntaXJ6egqr1aoElmUIEWlNTU3o7OxMWRyEw2G23YTkbRAhLSffht/vh9FoZCuScjlvrgmHwzAajWwwplD3RoZh4loELy4u2AEIUmilIm164XCY1wpQriN2cfHnf/7neOSRR/Dkk09iZGRE6EPKeRSBgasFhsfjgdFohMFgwODgIO8378cffxy3b9+WxPQUvpO5aZpmJ8p0dnYiEAjAZrMhFArBYDCwJnHlQXA9DMNgb28Pm5ubGBoaQk1NjdCHJFnsdjsWFhbQ3d3NybSR2LwNOfk2Li4uYDQaUV9fj+7ublEvPMVMKBTC3Nwc2+4oJpF2ObgyPz+frWxUVlaK6lhJe1k0GsX4+LjyTEkTMr3M6XSKblADwzD4q7/6K3z605/G448/jsnJSaEPSRYoAgN3d4Euvw1HR0fsjm+2gsdI8rXYZ7/HVi7SbYm6jkgkwu4mjY2NsTcqYji02Wyw2Wzwer3Q6/XsDrCYdkvEQOzYz7GxMWV8agYcHBxgbW0Ng4ODqK2t5fz15eLbODs7g9lslm2gI1cEAgHMzc1JogJEUVRcK1U0GmVTl6uqqgRtRaIoCmazmR14kauinm8uhxGKTVx87nOfwyc/+Un84Ac/wK1bt4Q+JNmgCAzEC4zY8LzR0VFUV1dn7Tj+/d//HYODg6KeXkD8FnxVLvx+P8xmM9uXfd0NPxAIsAsyt9uN0tJS1iQu59524Ocl/0AggPHxcVHd8KVEbOZKNrNCYif3kGlrUvdtkApQT08PmpqahD4cyUI8QFVVVejr65PUtcAwDC4uLtiAP9JKJcS1HY1G4wYMKOIiPciayWaziVJcfPnLX8bHP/5xPPbYY7jvvvuEPiRZoQgM/FxgkJ1zv98vSMbCf/zHf6C7u1uUbSxcmbmv4+zsDBaLJS0TMpluYrPZ4HQ6UVhYGNfbLqWHcKaQCUd5eXkYHR1VSv5pQtN0XGCZUJkrV/k2qqurUVFRIerda8LR0RGWl5cxNDTESwVILpD2soaGBnR1dUn+vhYMBtnKhsvlQkFBQdxUKr6u7Wg0CqPRCI1Gg7GxMcXXlyZiFxdf/epX8dGPfhSPPvooXvGKVwh9SLJDERi425JDbtxFRUWCLcqeffZZtLa2ii6qni8zdyxkAdLT05NxfzvpbSd5G2q1mhUbYuv/5Rqv1wuTyZT0hCOFxJAKUDAY5HW4Q6pI0bexu7uLra0tjI6OorKyUujDkSzEE9jW1ob29nahD4dzSCsVqW5QFMUG/HHZSkVGI5MNGEVcpAfDMFhfX8fJyQmmpqZENZyGYRh84xvfwMMPP4x/+Zd/wf333y/0IckSRWDg7uLWbDajsbERvb29gu0KPf/886ivrxdV+wCpXFAUxUtLFMMw2NzcxP7+PkZGRjhvD6NpGm63m/VtUBTFLsiqqqpEuSBLF5fLxYZApjPhSOEu4XAYZrNZ9NkMYvdtMAyDjY0NHB4eYnx8XPEAZYDL5YLZbJbNFLjYViq73Q6v1wudTsfet9NtpYpEIpibm0NBQQFGRkYUcZEm5Lt9fHwsOnEBAN/61rfwgQ98AP/0T/+EBx98UOjDkS2KwMDdhb3BYEBjY6Ogx2E0GlFZWYm2tjZBj4PA96QoiqKwuLiIi4sLjI2N8d6CwjAMzs/P2QWZ3+9nzYZiWJBlAqkA9fX1CX4dS5lAIACj0YiysrKsTI7jksu+jbKyMnbaWrZ9G2RcJckOKikpydrvzjUcDgfm5+fR29sr2+92olaqVNsEw+Ew5ubmUFRUhJGREaW6myZEXBwdHWFqakp03+3vfOc7eN/73odvfOMbeOMb3yj04cgaRWDg7q4G8RYIicViQWlpKTo7O4U+FN7N3KFQCGazGWq1GqOjo4JMErkcgEZ2yGpqakS3I3MVsSZkPipAcuL8/Bwmkwm1tbWCVjK5QEjfBklD9vl8mJiYEE17mRQ5PT3F4uIiBgcHUVdXJ/ThiILYNkGHwwGapuOmUiWqOBJxUVJSgqGhIUVcpAnpODg8PBSluHjsscfw7ne/G1/5ylfw1re+VejDkT2KwMBdwxdFUUIfBhYXF5Gfn4+enh7BjoFhGLZyAfBj5r64uIDJZEJlZaVofAKhUIgVGy6XCyUlJezub1lZmSgXmzRNs6FGY2NjkshPEStOpxPz8/Nob29Ha2urKD/vdCG97TabDQ6HAwzD8ObbiEajsFgsoCgKY2NjShpyBhwdHWFlZQXDw8NZnWYoJWKr0na7HT6fj90oIpU7khdCqpJieN5Ilc3NTRwcHIhSXDz++OP45V/+ZXzxi1/EO9/5TqEPRwGKwAAgHoGxvLwMAOjv7xfk92fDzG2z2bC4uIj29nbRzsGPRCJsOd7hcECr1bJiQyxTe8jEs0gkgrGxMWWXOAOOj4+xtLSEgYEB0Q1Y4JpY34bdbkcgEODMtxEOh+PMs7nkb8o2+/v7WF9fx9jYmGKMT4FAIBDXSlVYWIhIJILy8nJlWlSGEHExOTkp2ES9q3j66afx9re/HZ/97Gfxrne9S5TrCjmiCAzc3eGLRqNCHwbW1tYQDocxNDSU9d+dDTM3mSbDV1gZH8RONrHZbAAQN5FKiAdWIBCAyWRKKitE4Wpir8nR0VFZtpdx5duI9a4oLSiZsb29jZ2dHUmErooZr9eLubk5aDQa1ktIJlJd1UqlkBjShjs1NSU6cfHMM8/grW99Kz7zmc/gfe97nyIuRIQiMCAegbG5uQmv14vR0dGs/l6+zdwkT8But0s6UZphGLjdblZshMNhGAwG1NTUZO2BRXwCNTU16O3tVRZyaULmt5+cnGB8fBzl5eVCH5LgpOvb8Hq9MBqNqK6ullzwm5gg/e1kl1hpeUyfQCCAF154gQ0jBO6O+SXVDZ/Ph4qKCvb6lornTgi2t7exu7srymvy2WefxVve8hZ86lOfwgc+8AHl3iMyFIEB8QiMnZ0duFwuTExMZO138m3mztVWHoZh4PV62awNr9cLvV7P7v7ycZ4kCbmjoyPnfALZhKIoWK1WXFxcYHx8XFlcJCBZ34bH44HJZEJTU5MyGjkDGIbB6uoqbDYbJicnRdffLiX8fj/m5uZQXV195bCGQCDAtsC6XC4UFxez17dOp1M2bl5EzOJibm4ODz30EP7gD/4AH/zgB5V7jwhRBAbuLrIjkYjQh4GDgwMcHx9jenqa99+VDTO3z+eD2WxmJ3fkcitPIBBgxYbb7WZbTWpqajhZLJCebCm1l4mRSCTCmpDHx8cVE3ISJPJt6PV6FBcX4/DwEN3d3bLIZuALhmGwtLSEs7MzTE5OiioNWWr4fD7Mzc2htrYWPT09ST3TotFo3FQqAGwblcFgkG0r1c7ODnZ2dkQpLiwWC17/+tfjYx/7GD7ykY8o4kKkKDJdRJBeUb4hwoJPceFyufD888+jurpaFobPoqIitLa2YmpqCi972cvQ1NQEt9uNZ599Fv/xH/+B9fV1eDwepKrnSSvP5uYmJiYmFHGRAcFgEC+88AI0Gg2mpqYUcZEkKpUKFRUV6O7uxp07d3D79m3k5+djf38fNE3j+PgYW1tb8Hq9KV/fcoeM9PV4PJiamlLERQZ4vV688MILqK+vT1pcAEBeXh5qa2sxNDSEl7/85RgbG0NBQQG2trbwox/9CHNzc9jb24Pf7+f5DMTD7u4utre3MTExITpxsbi4iIceeggf+tCHeBEXn/3sZ9He3o7CwkJMTk7ixz/+8bX//q//+q/R39+PoqIi9Pb24itf+co9/8btduPXf/3XUV9fj8LCQvT39+Oxxx7j9LjFSG6v+iRGNgQG334LADg8PMTKygp6e3tFlUqeLfLz89HY2IjGxsa43TGj0QiNRsOaxPV6/bWl+NggwunpaaVtIgO8Xi87Grm/v19pgcgA0jpF/FSksrG9vZ3VvA2pQ1EU5ufnEQqFFMGbIRcXF5ibm0NzczM6OjrSfq4RMU0Etd/vZ30ba2trKC4uZq9vnU6XkzvnZPDF5OSk6Lxpy8vLeOihh/CBD3wAv/u7v8v5+/+P//iPePjhh/HZz34W9913H77whS/gwQcfxNLSUsIq7ec+9zl87GMfwxe/+EVMT0/jueeew6/+6q9Cr9fjoYceAnDX2/bqV78aNTU1+Na3voWmpibs7++LTrjxgdIiBfG0SDmdTlitVrzsZS/j5fX5Fhck4fPg4ACjo6PKeMVL0DSNs7MztpWKoihUVVWhpqbmnjyCcDgMs9kMAEqeQIacnZ3BbDajpaUlo8WH3GEYhu3JTjThKHbimt1u5zVvQ+pEo1GYzWYwDIOxsTHZtuFwAREX5PvNF5FIBE6nEw6HI66VKpeu7729PbZaLrZhLOvr63jta1+LX/mVX8Ef//Ef87J5cevWLUxMTOBzn/sc+2f9/f1485vfjD/+4z++59/fuXMH9913Hz796U+zf/bwww/jhRdewDPPPAMA+PznP49Pf/rTWFlZkd33XPrfCA4Qy4JDo9HwZjbn28xNURQWFhbg9XoxMzOj7LYnQK1Ww2AwwGAwoK+vD+fn57DZbNjc3MTi4iIqKytZz8bi4iLKy8sxODiozG7PgNPTU1itVvT09MiymsYVsVO3pqamEu6+kepcdXV1nG9jc3MTCwsL7PVdVVWVM8Me0iESicBkMkGj0WB8fFz5fmfA+fk55ubm0NbWhvb2dl5/l1arRV1dHerq6kDT9D3Xt16vZ69/Kba67e/vi1ZcbG9v4w1veAPe8Y538CYuSNr77/zO78T9+Wte8xr85Cc/SfgzoVDonntZUVERnnvuOUQiEWi1WnznO9/B7Owsfv3Xfx3/8i//gurqavzSL/0Sfvu3fzvnv/uKwBARfLRIXTZz8yEugsEgzGYz8vLyMDMzo+y2J4FKpYJOp4NOp0N3dzd8Ph9sNht2d3fh8/lQUFCA8vJyhMNhST6sxAAxxg8NDaGmpkbow5EsNE1jaWkJbrcb09PTSU3dutxqQvI2jo+PsbKygrKyMrZVMJW8DakTCoVgNBrZDJtcX2DwicfjgdFoZKfqZRO1Wg29Xg+9Xo+enh74/X52fPna2hpKSkpQXV2NqqoqSbRS7e/vY2NjA+Pj46ITF3t7e3jd616HN77xjfiLv/gL3touHQ4HKIq6x+dYW1uLk5OThD/zwAMP4Etf+hLe/OY3Y2JiAnNzc/jbv/1bNqy3vr4eW1tbePLJJ/HLv/zLeOyxx7C+vo5f//VfRzQaxe/93u/xci5iQREYIoIIDIZhOLkhkfA8mqYB8GPmPj8/h9lshsFgUHrbM6CkpARFRUUIBALo7OyEVquFzWbDxsYGSkpK2IlUpaWlon9YCU1snsDExIQSVpYBxCcQDAYxPT2ddtJ3SUkJSkpK0NbWhnA4DIfDAZvNJivfRjAYxNzcHFuZzNXzzAZutxsmkwmdnZ2imGBWXFyM1tZWtLa2sq1UdrsdJpMJarWabaWqrKwUXSvVwcEB1tfXRXmvPDo6wute9zq85jWvwSOPPJKV78zl5+t167GPf/zjODk5we3bt8EwDGpra/Ge97wHf/Znf8ZuHtA0jZqaGvzN3/wNNBoNJicncXR0hE9/+tOKwJADYlmwkRsPTdMZ72zF+i1UKhUvX0ybzYbFxUUllyFDGIbBzs4Otre3MTIygurqagBAc3MzuxNCqhv5+fnszm9FRYXynl+CpmksLy/D5XIpxvgMiUQirA9oamqKs/7h/Px8NDQ0oKGhIc63sbCwkLO+DZLNQDZilO9t+rhcLpjNZtG2PV5upXK73XA4HFhfX0cgEEBlZSVb3RC6On14eIi1tTVRpsafnJzgda97HV760pfi85//PO/ioqqqChqN5p5qhc1mu3J6Y1FREf72b/8WX/jCF3B6eor6+nr8zd/8DcrKylBVVQUAqK+vh1arjVvT9ff34+TkBOFwOKc7PnLj7s0BKpVK8BGL5AKkKCojgZENMzdZECvtJ5kRm3I+NTV1z9QOrVaL+vp61NfXx4WfWSwWAGDFRmVlpezbLaLRKDuVZ3p6WtZ9/plCWnkKCwsxMjLC27WVjG+D/L1UP0+v14u5uTnU19eju7tbERcZ4HQ6YbFY0Nvbi8bGRqEP50bUajUqKytRWVmJnp4etlXw5OQEq6urbCtVdXU1ysvLs3ptHB4eYnV1FePj49Dr9Vn7vclgs9nwhje8AVNTU/jyl7+clWdbfn4+Jicn8fjjj+Mtb3kL++ePP/443vSmN137s1qtlhW73/zmN/GGN7yBFUT33Xcfvv71r7PrMQBYW1tDfX19TosLQJkixRIOhwUXGAzD4Ac/+AFe/vKXp72zwbe4IDvETqcTY2NjohtjJyViF8RjY2MpfebEZGiz2WCz2RCJRGAwGFgTrdymVYTDYZhMJuTl5ckid4VP/H4/jEYjKioqMDAwIFgrD1mM2e12eDwe1rdRXV0tmVbB8/NzGI3GjMenKtztkZ+fn0dfXx8aGhqEPpyMIdVpu90Op9MZ10plMBh4XVQfHR1hZWUFY2Njopv26HQ68frXvx49PT34xje+kdVn2T/+4z/iXe96Fz7/+c9jdnYWf/M3f4MvfvGLsFqtaG1txcc+9jEcHh6yWRdra2t47rnncOvWLZydneEv//Iv8fjjj7ODB4C7/paBgQG85z3vwW/8xm9gfX0d//W//ld88IMfxO/+7u9m7dyEQHkKiwiVSpW20ZuYufmcFBUOh9kU5JmZGcnuKIoBYozXarVptZ9cNhl6vV7YbDbs7OzAarXmxM5vspAFsU6nU3rbM+Ti4gJGoxF1dXUphZXxQSLfhpTyNsh45Pb2dnaxoZAedrsd8/PzGBgYQH19vdCHwwmx1WnSSkXyNkKhECorK1nBweU9XMzi4uzsDG9605vQ3t6Or3/961nfKPvP//k/w+l04g//8A9xfHyMoaEhPPbYY+wQgePjY+zt7bH/nqIo/MVf/AVWV1eh1WrxC7/wC/jJT34S931vbm7GD3/4Q/zmb/4mRkZG0NjYiP/5P/8nfvu3fzur5yYESgXjRcRQwQCAJ598EpOTkylNcsiGmdvn88FkMqGsrAxDQ0Oyb8fJhIuLC5hMJt6M8bETTTweD8rLy+Mm9uQSHo8HJpMJDQ0NSvtJhpAFcVtbG9ra2kT7Xl7O26BpmhUbYvFtkFYesfoEpITNZsPCwgKGhoau7IXPJRiGgc/nYwW1x+NBaWkp69vIpJXq+PgYy8vLGB0dhcFg4PjIM8Pj8eBNb3oTDAYD/vmf/zntgRIK4kERGC8SiUTYBbqQ/OhHP8LQ0FDSX37SEkU+Rj528pxOJ+bn59HU1ISuri7RLjykAHkvW1tb0d7ezvt7GQ6HWbHhdDpRXFyMmpoaQXp+uYa0THR2dmZ9TGWuQUzWUlsQx/o27HY7/H6/4NU7siDOpd12oTg9PcXi4iKGh4dl6/Uj1TvyX15eXtxUqmQ3+05OTrC0tCRKcXFxcYG3vOUtKC4uxqOPPiq4+V2BGxSB8SJiERjPPPMMenp6krqZ8u23AO6OsFtdXUVfX58kTHVi5vDwECsrK4ItPKLRKJxOJ2w2GxwOBzQaDSs29Hq9KNtMruLo6AjLy8sYHBxEXV2d0Icjach7mQs7xEL7No6Pj7G0tCTrBTFXnJycwGq1xk3Wkzs0TePs7Ax2ux0Oh4NtpSLX+FW7/kRcjIyMsNONxILP58Nb3/pWqNVqfO9738u5KrucUQTGi4hFYPz0pz9FW1vbjQvQ2MoFHy1RJLn3+PgYIyMjouvVlBIkl2F/fx+jo6OieC9pmmbbTGw2W1ybCRnXJ0bIBLOdnR3RvJdSZnd3F5ubm6Lc1cyUWN+Gw+Hg3bdxcHCAtbW1nHwvsw3xCYhxQSwWSCsVEdTn5+fseNTq6mqUlZVBpVKxVaDR0VHRvZeBQABve9vbEA6H8f3vfx9lZWVCH5IChygC40Wi0SjnKdrp8Pzzz6O+vv7KNoVsmLmj0SgWFhbg9/sxNjam7ChkAE3TsFqtcLvdGB8fR2lpqdCHdA+xbSY2mw3BYDBuIpVYRukxDIOVlRXYbDZMTEwoD6MMiA0jFGN6L9fw7dsgY7vHxsZEN/JTapDxqYpQS41YQe10OpGXl4eSkhKcnZ1heHhYdNXJUCiEd77znXC73fjBD36Q8/cgOaIIjBcRi8AwGo0wGAwJe8qzYeYOBoMwmUzIz8/HyMiI7MadckkkEmGnbo2NjUnCtBa7K2az2XBxcYGKigq2lUqo3liKorC4uAifz4fx8XGlRzcDGIZhR01PTEzIbgOBS98GwzDY2trC/v4+JiYmlLHdGUKqQGKccCQlaJrG9vY2tra2oNVqQVEUDAYDW6EW+lkUDofxrne9C0dHR3jiiScUUZ6jKALjRcQiMCwWC8rKytDR0RH359kwc3s8HpjNZlRXV6Ovr09SPfliIxAIwGQyobi4GMPDw6JtObqJYDAIm80Gu92Os7MzlJaWoqamhp1IlQ2TOEmUZhgG4+PjiujNAJqmsbCwAJ/Ph4mJiZwfYZwMfr+fvcZT8W2QNtKTkxNMTk6KsjopJfb29rC5uSnKVGmpQQYNDA8Po7q6Gl6vl20VJK1UQmXKRCIRvOc978HW1hb+7d/+TXRtWwrcoQiMF6EoCtFoVOjDwOLiIgoKCtDd3c3+GalcUBTFm5n79PQUVqsVnZ2daGlpkfR0IaEhQq22tha9vb05815GIhF215f0tBOxodPpeDnPXBFqYiAajcJisSAajWJ8fFw0rW9iIlnfRmwVaHJyEsXFxQIfubTZ3d3F1taWIi44gGSGXDVoIBQKxbVS5efnx02l4nNjMRqN4ld/9VexuLiIp556ShmEkOMoAuNFxCIwlpeXAQD9/f0A+J8UxTAMtre3sbOzw+52KKSPzWbD4uJizo9OpSgKTqeTFRwqlYrN2uDqIUXyQkhFLVeEmhAoSeepk8i3UVVVhaqqKthsNni9XkxOTipVoAwhz5+JiQmlDz9DiLhIdiIcRVHsVCq73Y5oNAqDwcAKDi43ISiKwq/92q/h+eefx9NPP62McJYBisB4EbEIjLW1NYTDYQwNDbF+C77EBU3TWFpagsvlwvj4uGKazZC9vT1sbGxgaGhIVjszJIWWtJlEIhFUVVWxJvF0FrMulwsWiyVreSG5TDAYhNFoRElJCYaHh5XWxzRgGAbn5+c4PT3FwcEBKIqCXq9HbW2tYHkbucDW1hb29vYU/woHkFygwcHBtAzdDMOwrVR2ux0XFxdsSGtVVVVGrVQ0TeM3fuM38OMf/xhPPfUUmpub03odBWmhCIwXoWkakUhE6MPA5uYmvF4vhoeHWU8IH2bucDgMi8UCmqYlY0AWKwzDYHV1FScnJ7KYyHMdDMPg4uKCFRs+nw+VlZWsSTyZ64zMv1eyVzLH5/OxgyP6+/sVoZYBFEXBbDaDoij09vayolqIvA2pEzvFbHJyUtncyhCSHD8wMMBZLlAoFGJbBUkrFbnGU8lNomkaH/7wh/HDH/4QTz31FNra2jg5PgXxowiMFxGLwNjZ2YHT6cTIyAgAfszcXq8XZrMZZWVlGBoaUvraM4CiKNY0Oz4+rvRiX4IYaG02G87Pz1FeXs76NhK9V6QKpLTrZY7H44HJZEJTUxM6OzuVRW8GRCIRmEwmqNVqjI2NxVXlLo8HjV2I8ZG3IXUYhsHGxgaOjo4UczwHEHHR39/PW9tRbLugw+FgW6lIdeOqViqapvGxj30M//zP/4ynn34anZ2dvByfgjhRBMaLiEFgMAyD09NTWCwWNoeA6z5Ip9OJ+fl5NDc3K4uODAmFQjCbzeyiQ5ludD1kR8xms8HlcqGkpIT1bZSWlrKLDrlXgbiALDpy3QuUDcLhMIxGIwoKCjAyMnLthsxVvo2amhpO8jakDsMwWF9fZydvyW1EMte4XC6YzWZexcVlSJWaXONerxc6nQ5utxuVlZUYHx+HWq0GTdP4/d//fXzjG9/A008/jZ6enqwcn4J4UATGizAMg3A4LOjvJ2buQCAQl0Og1+vZXd9MWpn29/extraG/v5+NDQ0cHj08sPr9cJkMqGiogKDg4PKLmWKRKNROBwO2Gw2OBwONpG+r68PdXV1yvuZASS5V/meZw7xr5SWlmJoaCil65L4Nki7YCZ5G7kAaSW12+3K5C0OIOKir69P0O95MBiEw+HA//k//wdf/vKXYTAY8IpXvAIFBQX43ve+h6effhoDAwOCHZ+CcCgC40WEFBjXmbkDgQDbYuLxeNgWk9ra2qTDxmiaZue1j46OKqE2GUIMyEoVKHOi0SjMZjOCwSB0Oh1cLhcYhonb9VVa+JKHBJUpLWaZEwgEMDc3B71ej4GBgYy/536/n904kptvg2EYrKyswOFwYGpqSgnKzJCzszOYTCbBxcVlLi4u8Oijj+KrX/0qfvazn0Gr1eJ1r3sdHnroIbzuda9TwhNlhiIwXkQIgcEwDFu5AG42c5MWk9PT03tCz67qY41Go5ifn0cwGMTY2Jiya5Qhx8fHWFpaQm9vL5qamoQ+HEkTCoXiUuPz8vLYlGUiqkOhEDsysbq6WmlDuwKGYbCzs4OdnR2MjY0pmwgZ4vP5MDc3h5qaGl6ybC77NrRaLdsumGu+DYZhsLS0hLOzM0xOTiriIkOIuOjt7RXdEAyGYfBXf/VX+PSnP40f/OAHUKlUePTRR/Hoo49ifn4e9913Hx566CE89NBD6O3tFfpwFXhGERgvkm2BcTmZO9VJUST0zGazwel0oqioiBUbZWVlUKlUCAQCMJvN7AJOWZylD8kL2d3dxcjICAwGg9CHJGnIdCOyO5xoQcUwDHw+Hys2vF4v9Ho9uxCTW4vJVcQmSk9MTCgTeTLk4uICc3NzWTPH57Jvg2EYWK1WeDweJTOEA9xuN4xGI3p6ekS3wcUwDD73uc/hf//v/40f/OAHmJmZifv7g4MDfPe738Wjjz6KH//4xzg8PFTuVTmOIjBiCIVCWfk9XIfnRaNROJ1OnJ6ewuFwQKvVQqfTwel0ora2Fn19fTm1I5ZtaJpmU3uVvJDMcbvdMJvNaGxsRFdXV9LXf6w3ye12sy0mNTU1KCkpyekWk6sgWTZutxsTExNKhTJD3G43TCYT2tra0N7envXfT3wb5DqXsm+DpmlYrVZcXFxgcnJSGYWeIeTa7O7uFqW4+PKXv4yPf/zjeOyxx3Dfffdd++/D4TCnw2sUxIkiMGIIh8Pg++3gO5mboihsbGxgb28ParUaeXl5bGUjldnVCneJRCKYn59HOBzG+Pi4pB7wYsRut2NhYQFdXV1oaWlJ+3VIiwmp4BUWFrJT13Q6nSzEBkVRbPvjxMSEsoDLEGKa7e7uFk0QGPFt2O12uN1uti1W7L4NmqaxuLgIn8+nXJsc4PF4YDQa0dXVJZprk8AwDL7yla/gt3/7t/Gd73wHr3jFK4Q+JAWRoAiMGPgWGHwnczMMwyajDg0NwWAw4OzsjG0xYRiG3fGtrKxUzLM3EAwGYTKZ2PGUUm5VEAPEgJxu0uxVUBQFp9PJTutRq9Vx13kuiupIJAKz2QwAyohkDiDCV2ym2Vik4tugaRoLCwvw+/2YnJxUdqozhIiLzs7OjDZl+IBhGHzjG9/Aww8/jH/5l3/B/fffL/QhKYgIRWDEwJfASNXMnQ7/f3v3HdbU2f4B/MtSluwpKiiKgiAQsA7cGxkJravVWrXV2mXraNXaqq1WW7Wtbd9C6+rU1lcIiLsu3FYhgAzFASgiJOwdss7vj/7OeUHRogQSyP25rvd6r4aT8ITE5HzP8zz3rVQquaUSfn5+jyzjYTfPisViSCQSyOVy2NnZwdHRsd2v820NlZWVSE5Ohr29PS0xa6GGwbe1NyCrVCqUlZVxS0yUSiW3SdzOzq5DvM/r6+shEolgbGz8r30ZyL9jO8d7e3urNfi2psft29D0+1ylUiE1NRX19fUICAig4NtC2hwuACA6Ohpvvvkm9u3bh+DgYE0Ph2gZChgNyOVyqFQqtT5mSzdzN0d9fT1SU1MBAL6+vv86Hc02ymFnNurq6ho19tP1LwX2ambPnj3h5uamtcsQ2gOVSsWVp+TxeG3atbep9exs91l7e/t2uWyjtrYWIpEIVlZWj90cT5ovPz8fWVlZGDBgAOzs7DQ9nGeiLfs2lEolUlNToVAo4O/vr/PfIy1VWVmJpKQk9OrVSyubZe7fvx+vvfYa/vjjD4SHh2t6OEQLUcBoQN0Bo+F+Cz09vVY5GWAbvllaWqJ///7PdDWzurq6UaUeGxsbLmy0x5OwlmCbEfbv3x9OTk6aHk67xu4RqKurA4/H0/j+lZqaGu4krLKyEpaWltwSk/awObqqqgoikQhOTk7w8PCg4NtC9+7dw+3bt+Hn59eh6vNrYt+GUqlESkoKlEoleDxeh5gp1CQ2XLAXubTNoUOHMGfOHPz666944YUXND0coqUoYDSgzoDR2pu5gX+utKenp6NHjx7o1auXWn4H29hPLBZzJ2HsJvGOXL+cYRjcvn0b+fn51IxQDWQyGVJSUqCnp6eVewTq6+u5PRulpaUwMzPjTsLYMs/apKysDCkpKXBzc6NZtRZqWHKax+PB0tJS00NqNW2xb4NtlskwDPz9/SlctBBbJpn9t65t/vrrL8yaNQs7duzAjBkzND0cosUoYDSgroDR2uGCYRjk5eXh1q1b8PLygrOzs1ofnyWVSrkrvmVlZejSpQsXNszMzFrld2qCUqlERkYGKisr4e/v36GemybU1dVBJBLB3Nwc3t7eWr9HQC6XcydhbJlnNmxYW1tr/GSeXbKnjbXv2xv2QsKDBw90rmdIa+zbUCgUSE5Ohr6+Pvz8/LT+37q2Y8OFq6urRsok/5vTp09j+vTpiIyMxMsvv6zxz0ai3ShgNKBQKLiN2M+iLTZzq1QqZGVlQSKRwNfXF1ZWVmp9/MeRyWSNGvuZmprCwcEBjo6OWl0u8d+wV9qBf6rxUMWTlmGX8Tg6OrZKB+TW1vAkTCKRAIBGK6+xnePb0wZkbcUwDLKyslBUVAQej6fTFxIa7tsoKipCTU3NU+/bkMvlSE5OhqGhIXx9fSlctFB1dTUSExO5FQna5ty5c5gyZQq2bt2KefPmtbvPdtL2KGA00JKAwTAMV4IWaJ1wIZfLkZaWhvr6evj5+WlsyZJCoeB6EBQXF6NTp07czEZ76kFQW1uL5OTkdnOlXduVlJTg2rVr6NmzJ1xdXdvN++BxGIZBeXk5t5RKJpNxxRDs7OxafdkXu0fA19eXOse3UMOGhAEBAR16ueezaGrfBhusm7qAJJfLIRKJ0KlTJ6pkpgZsuOjevTvc3d01PZxHXL58GREREdi4cSPeeOONdv/ZTtoGBYwGnjVgPFwpqjU2c9fW1iIlJYUrTakt61wf7kFgYGDAhQ1tqs3+MLabdNeuXdGnTx/6wGwh9kq7p6en1vYRaAmGYbhiCEVFRaiuroa1tTW3lEqdG9gZhsGdO3dw//59+Pv7d+g9Am2B7ctQU1NDHaWb4XH7NtglgwqFolGZZG39jG8vqqurkZSUhG7dumlluEhMTER4eDg++eQTLFq0iL4rSbNRwGhAqVRCoVA81X3aYjN3WVkZUlNTueox2vqBzvYgEIvFKCoqatTYz9bWVmvGLRaLkZGRoVUde9srhmFw9+5dZGdn69SVdrYYgkQiQUVFhdr2JzEMg+vXr2ukrG9HxJZOlcvl8Pf3pyWQT0mpVHLNWtl9GwBgampKf081qKmpQWJiIlxcXODu7q51J+8pKSkICQnBhx9+iGXLlmnd+Ih2o4DRwNMGjLYIFw8ePMD169fh4eHRrk6GGy4vkUgkUCgUsLOz45aXaGJKveHJsI+PD+zt7dt8DB0JwzC4efMmCgsL4e/vDwsLC00PSSMa7k8qLS2FsbExFzYsLCya/bmgUqmQnp6Oqqoq8Hg8WsbTQuwGZABU3UgNpFIprl69yn12a6rfRkfBhouuXbuid+/eWnfynp6ejsmTJ2Px4sX48MMPtW58RPtRwGiguQGD3czN7rlorUpRd+7cQV5eHnx8fNptEyjgfxsK2bAhlUobhY22KGHacHO8Lp8Mqwt7MlxZWQkej9cu+ki0BYVCwS0ZLC4uhoGBATeLZ21t/dhZPIVC0ahJGV0Zbhl2j4CRkRFtQFYDqVSKpKQkWFpacg0en3bfBvmfmpoaJCUlwdnZWSvDxfXr1xEcHIyFCxfik08+0brxkfaBAkYDKpUKcrn8ice0xWbuhmVT/fz8OtQyCYZhUFNTA7FYDIlEwlUvYa/4tsaJlUKhQFpaGurq6uDv709XhltILpcjNTUVSqWSToafgF0yyC4vUSqVXLC2tbXlrqjLZLJG1XjoSnvL1NfXQyQSwdTUFD4+PlqzNLO9kkqlSExMhLW1Nby8vJr8vvu3fRv0GvxPbW0tEhMT4eTkpJX7/27evIng4GC88sor2LBhA7125JlRwGjg3wJGW2zmrq+vR0pKCvT19eHr69vhT95qa2u5mY3KykpYWVlxYUMdU+5SqRQpKSkwMjLCgAEDtK7hW3sjlUqRnJzMbfCkK8PN09Qsno2NDaytrZGfnw9zc3M6GVYDtgdLwyvt5NnV1dUhKSkJNjY28PT0bNbJcFP7Nlrab6OjqK2tRVJSEhwcHODh4aF14SI7OxuTJk3C1KlT8eWXX9K/H9IiFDAaeFLAaIv9FlVVVUhJSeGuFOnaP26pVMqdgJWXl8PCwoILG8+yBKe6uhrJyck6+/dUN/bvyZ5s0N/z2dXU1OD+/fvIy8sDwzCNgjXNsD2bmpoaiEQi2NnZoV+/flp38tbesCfD9vb2z9zTRh39NjqKuro6JCYmam24uHv3LiZNmoTQ0FB89913rfL5HhkZic2bN6OgoAD9+/fH1q1bMXz48Mce//333+M///kPcnNz0aNHD6xatQqzZ89u8tg///wTL774Ivh8PuLi4tQ+dvL0KGA0wDAMZDJZk7ezMxetsSQK+F+3Xjc3N/Ts2VPrPnzaGrtxViwWo7S0FGZmZlxjPzMzs3/9+7A9GdimRbr+92yp8vJyJCcnc3Xa6e/ZMhUVFUhOTka3bt3g4uLC9ZUpKyujtezPgG3w6OzsrJXLTtobdo+Ao6OjWk+GdXXfBhsuWhLWWlN+fj4mTpyIcePG4YcffmiVcLF37168/PLLiIyMRFBQEH788Ufs2LEDmZmZ6NGjxyPHR0VFYfny5di+fTsGDhyIK1euYP78+dizZw/CwsIaHXv37l0EBQWhV69esLGxoYChJShgNNBUwGD3W7TmZu579+7hzp076N+/P3XrbYJcLm/U2O/fqvTk5+fjxo0bHbYnQ1uTSCRIT0+Hh4cHunXrpunhtHulpaVISUmBu7s7XF1dG/3s4fd6586duRMwKysrrTsx0QZsWOvRowddnFEDti9Da1c3avhe78j7Nthwoa0za4WFhZg0aRKGDh2KnTt3ttqy10GDBoHH4yEqKoq7zdPTEwKBABs3bnzk+KFDhyIoKAibN2/mbnvvvfeQmJiI8+fPc7cplUqMHDkSc+fOxblz51BeXk4BQ0vo7mLIf/HwZu7WCBcqlQo3btxAUVERAgICqKHWYxgZGcHZ2RnOzs5QKpXcl5JIJIKhoWGjLuLZ2dnIy8uDv78/bGxsND30di8vLw+3bt2Ct7c3HBwcND2cdk8sFiM9Pf2x4ffh93ppaSkkEglSU1MBgAsbNjY2tP8F//QISklJQa9evR4Ja+TpseGiLfoyNHyvq1QqlJaWoqioCBkZGVxBBHt7e9ja2rbbvXNs9S1tDRcSiQQhISEYOHAgduzY0WqfKTKZDElJSVixYkWj2ydMmICLFy82eZ/6+vpHltCZmJjgypUrkMvl3Hvi008/hb29PV599VWcO3euVcZPng0FjAbYf/xtsZlbLpfj2rVrkMlkeO6552jddTMZGBjA0dERjo6O3JcSewKmVCqhp6cHDw8PWFlZaXqo7VrDbtI8Ho/+nmpw//593Lx5EwMGDGhWDxa2xK29vT1UKhXKy8tRVFSEGzduQC6XN9o4215PwFqiuLgY165do5k1NamqqkJSUhK3DLIt6evrw87OjjsRZ/dt5OTkID09HdbW1nBwcGhX+zbY6ls2NjZaGS5KSkoQHh4Ob29v/Pzzz626+b64uBhKpfKRFRqOjo4oLCxs8j4TJ07Ejh07IBAIwOPxkJSUhF27dnEzX87Ozrhw4QJ27tyJlJSUVhs7eXYUMB7S2v0tgH/WoSYnJ8PU1BQDBw7U6aoaLcF+KVlaWqKmpgb19fWwtrZGdnY2bt261aiLOF3tbT6VSoXr16+jtLQUgYGBHapMsiYwDIPc3Fzk5ubC398f1tbWT/0Y+vr6sLGxgY2NDTw8PFBVVYWioiLk5uYiIyOD2zjr4OCAzp07t8Kz0C7sTFD//v3h5OSk6eG0e5WVlRCJRHB1dUXPnj01OhY9PT1YWlrC0tISvXv35vZtiMViZGVltYt9G+zMxdNU32pLZWVl4PP56NmzJ3bv3t1mFyge/juw+1qb8vHHH6OwsBCDBw8GwzBwdHTEnDlzsGnTJhgYGKCqqgqzZs3C9u3b23WfsI6M9mA0kJ6ejpSUFIwdO/apOvA+jbKyMqSmpsLZ2VkrK0m0N3V1dUhOToaJiQl8fHxgaGjIVS5he23U19dz/Qfs7e0p0D2BQqHAtWvXUF9fD39//3ZztVBbNex2zuPx0KVLF7X/DrbUc1FRESoqKrjqa/b29jAzM1P779O0Bw8e4MaNG/Dx8WnWTBB5soqKCohEIvTs2RNubm6aHs4TtYd9G/X19UhMTISVldVj+4ZoUkVFBcLDw2Fvb4/Y2Ng2uSAhk8lgamqKffv2ISIigrv93XffRUpKCs6cOfPY+8rlcojFYjg7O2Pbtm1Yvnw5ysvLce3aNfj7+ze6eNhwSXtWVlabz8SRxihgNHDw4EEsXrwY+fn5GD9+PPh8PoKDg9W2N+LBgwe4fv06+vbtS1P6alBRUYGUlBQ4Ojo+tjIHwzCorq7myt/W1NTA1taWOwHr6H1Gngbb8M3AwAC+vr46uexGnVQqFTIzM1FeXt5m3c7r6+u5Kj0lJSUwNTXl3uutddGkLbF7gnx9fWFra6vp4bR7bHU4d3f3Jiv5aLOG+zYaNrLU5L4NNlxYWlqif//+WvfvraqqChERETAzM0N8fHybLs0eNGgQAgICEBkZyd3m5eUFPp/f5CbvpowcORIuLi7Ys2cPpFIpbt++3ejnH330EaqqqvDNN9/Aw8ODvt81jALGQ1QqFa5du4bo6GjExsbizp07GDNmDPh8PkJCQmBtbf3UHxoMw+D27du4f/8+BgwYQF+MasBWNmK/GJv7mtTU1HBho6qqql2u7W0NtbW1EIlEsLCwgLe3t1ZcCWzPlEol1z2ex+NpZNmSQqHguisXFxfDwMCAe69ry9Xep5Gbm4ucnBz4+/vTniA1KCsrQ3JyMvr06YPu3btrejgt0lS/jbb+bK+vr0dSUhIsLCy0MlzU1NTghRdegL6+Pg4dOtTms5tsmdoffvgBQ4YMwbZt27B9+3ZkZGTA1dUVK1euRH5+Pn799VcA/3QUv3LlCgYNGoSysjJ89dVXOH78OJKSkh470zZnzhyqIqVFKGA8AcMwuH79OqKjoyEUCpGZmYmRI0dCIBAgNDQUdnZ2//oholQqkZ6ejqqqKvj7+3fIJQtt7d69e7h9+3aLy/rW1dVxa3sbLi1xdHTUqU337EyQk5MTLdtTA7lcjpSUFDAMA39/f62YCWpYEIHtrtxe9ig9XHDAwsJC00Nq99hSyR11g3xtbS23lKot+m3IZDIkJiaiS5cu8Pb21rrP0Lq6OkydOhUymQxHjhxplaWazREZGYlNmzahoKAA3t7e+PrrrzFixAgA/4SD3NxcJCQkAACuX7+Ol156CVlZWTAyMsLo0aPxxRdfoG/fvo99fAoY2oUCRjOxsxBs2EhJSUFQUBD4fD7Cw8Ph5OT0yIdKTk4O/vjjD4wfPx6+vr40XddC7Hr2goIC+Pn5qfUqJru0RCKRoLS0FObm5lz52468yZmtxNNUTwby9Orr65GcnIzOnTtjwIABWnnizjAMKioquPe7VCrllg3a2dlp1ecU+29eLBaDx+N16H+LbaWkpASpqano16+fTvQJYvdtsDN56t63wZZgNTMz08rZX6lUihdffBEVFRU4duwYlcMnbYYCxjNgq8LExMRAKBTiypUrGDx4MMLDw8Hn89GtWzdcvHgRL774IoKCgvD7779r5YlGe8LOBFVXV8Pf379V17PL5XLu5KukpAQmJiZc2OjSpYvWXZ16VuxmWS8vL6rEowZ1dXVISkriNndq24lGUxiG4ZYNFhUVoaqqClZWVtzSEk3O5DEMg8zMTJSVlbXZHpaOrqioCGlpafD09ISzs7Omh9Pmmtq3wYbrZ9m3oe3hQiaTYdasWSgoKMCJEyeeqYIdIc+KAkYLMQyD+/fvQygUQigU4sKFC+jfvz9u3rzZqKQaeXbs5mN9ff02nwlSKBQoKSmBWCzmrn6xy6gsLS3bZdhoWDbV19eXGhKqQVVVFUQi0RMLDrQHUqmUCxtlZWWNZvLMzMza7HmpVCruggKPx9Pp/VHqIpFIkJaWBm9v7xYtLe0oHrdvg53d+LdwLZPJIBKJuAqG2hYu5HI5XnnlFeTk5ODUqVO095O0OQoYaqRSqbB27Vps2rQJPB4PV69ehY+PD/h8Pvh8Pvr06dNuTzw0paamBsnJydzGOU2GtYadlYuKiqCnp8edfLWXTbMMwyArK4tbcqKptbgdCVuJh+0h0FH+jT88k9e5c2fu/d6a4VqpVHKlknk8nlYt2Wqv2L4hPj4+cHBw0PRwtBK7J+/hfRv29vaPzFzL5XIkJSVpbbhQKBR47bXXkJGRgdOnT9NrTjSCAoaayGQyvPnmmzhy5Aji4+PB4/FQUlKC/fv3Izo6GqdOnYKHhwf4fD4EAoFWNt/RNmVlZUhJSUG3bt3Qu3dvrfp7qVQqlJWVcRWpGIbhNhHa2Nho5axVw2VmPB5PpzaytxZ2yUlHqMTzJEqlEiUlJZBIJCguLoaenl6j97u6TrAUCgVSU1OhVCq1ZoN8e1dYWIiMjIxmd5AnT963YW5ujuTkZBgbG2PAgAFaFy6USiXeeOMNJCYmIiEhgZa/Eo2hgKEGpaWleOGFF1BRUYEDBw7AxcWl0c8ZhkF5eTni4+MRExOD48ePw9XVlQsb2ngFRNMKCgqQmZnZLnqGsJtmJRIJxGIx5HI519jPzs5OKxr7Naxs5OfnR1eF1YB9j+paN2mVSoXy8nJuJk9d73e5XN6oD4s2/Ltp7woKCnD9+nUMGDCAuh0/o4b7NiQSCWQyGTp16oQ+ffrA3t5eq0KwSqXCO++8g3PnziEhIUHrvztJx0YBo4Xq6urg7+8PT09P/P77780qQ1tZWYmDBw8iJiYGR48ehZOTE8LDwxEREQEej6fTYYPdH5CTk9MuvxQZhkFVVRU3s1FXV9eosZ8mvoykUilEIhFMTU3h4+OjlbMr7Q1bKlnXG749/H6vra2FjY0N935vbv8Pdj27Nlffam/YIg66/h5VF3ZZlL6+PqytrVFcXPzU+zZak0qlwtKlS/HXX3/h9OnTWt+VnXR8FDDUICEhASNGjHimYFBdXY0jR44gJiYGhw8fhrW1NcLDwyEQCPDcc8/p1BetSqXCjRs3UFxcDH9//w6xP6C6uprrtVFdXc01f3JwcGiT5mvV1dUQiUSws7NDv379dDq8qkPDngz+/v5U8vEhNTU13JXeyspKrreMg4PDY6tAsQHY3NxcKyvxtEf379/HzZs34efnR0Uc1EChUEAkEsHIyAi+vr7ce5Tdt9GwKMLj9m20JpVKhZUrV2L//v04ffo03N3d2+T3EvIkFDC0SF1dHY4dOwahUIgDBw7A1NQUYWFhEAgEGDp0aIdeMqBQKLiNnf7+/h2yakxdXR13pbeiogKWlpbcyVdrXPkqLS1Fampqh9t8rCkMw+DGjRsoKiqingzN8HBvGTMzM27fBnvyxXaQt7a2hpeXF71H1SAvLw+3bt2Cv78/lSVVAzZcGBoawtfX97EX/Vq738bjqFQqrF69Gn/++ScSEhLg4eHRKr+HkKdFAUNLSaVSnDx5EkKhEPv374eBgQFCQ0MRERGB4cOHa9W6z5aSSqWNmpN15CDFqq+v58IGe+XL0dGRKwfaUmzVmH79+j2yJ4g8PbZsalVVFW2QfwYKhYLrrMyefFlZWaGkpAROTk7turSvNrl79y6ys7Ph7++v1kakukqhUHAl0v38/Jq9ouBx/Tbs7e1hZ2entu9vhmGwfv167Nq1C6dPn4aXl5daHpcQdaCA0Q7I5XKcOXMG0dHRiIuLg1wuR2hoKPh8PkaPHt0mS21aS1VVFZKTk3V6CY9MJkNxcTHEYjFKS0u5xn6Ojo4wNzd/6hMvdn+Aj48PVY1RA6VSidTUVMhkMiqbqgYqlYpbwqOnpwcDAwNuk7itra1OLQtVJ3bvGo/Ho6V7avCs4eJhDfcpPUu/jSc97qZNmxAZGYlTp07Bx8fnmR6HkNZCAaOdUSgUOH/+PBc2qqurMXnyZPD5fIwbN65dXVktLi7GtWvX0LNnT7i5udEVTDx6pbdTp07N7j3AMAxu376N/Px82h+gJjKZDCkpKVTZSI3YviE9e/aEq6srV4FNIpGgvr4ednZ23MlXR5qpbU3Z2dm4d+8eeDweLCwsND2cdk+pVCI5ORl6enotChdNUce+DYZh8M0332DLli04ceIEeDye2sZHiLpQwGjHlEolLl++zIWN4uJiTJw4EQKBABMnTlTLUpvWcv/+fWRlZcHLywvOzs6aHo5Wath7oKioCAYGBlzYsLKyajTbo1KpkJmZifLycvj7+2v1a99esJuPzczM4O3tTVfW1aCkpASpqalN9g1hGAY1NTVc2GCLIrD7NjrivqyWYhgG2dnZyMvLQ0BAQIcojKFpbLgAAH9//1b9d/8s+zYYhkFkZCQ2bNiAY8eO4bnnnmu18RHSEhQwOgiVSoXExERER0cjNjYWDx48wLhx4yAQCBAcHKw1V7XYq+z379+Hn58fbUJsJraxn1gsRlFRUaPGfpaWlkhLS4NcLoe/v3+7XjKnLWpqaiASiWBra0tNMdVEIpEgPT0dnp6ezbqo8HBn5S5dunDlb83MzHT+NWErmuXn5yMgIICKDqiBUqlESkoKVCoVeDxem15UaGrfRlVVFQoLCxEREQF7e3swDIMdO3Zg9erVOHLkCIYOHdpm4yPkaVHA6IBUKhVSU1MRExMDoVCI7OxsjB07Fnw+HyEhIbCystLIl7NSqURGRgYqKyvpKnsLsI0b2cZ+9fX1XOMnR0dHutLeQpWVlRCJRHBxcdG6DvLtFduU0MfHBw4ODk99f3afkkQiQUlJCYyNjbmw8W9LBzsihmFw69YtFBQUIDAwkD5L1aBhuPD399fockh230Z8fDy++OIL3L17F35+fujXrx/i4uJw6NAhjBw5UmPjI6Q5KGB0cAzDIDMzE9HR0RAKhbh+/TpGjRoFgUCA0NBQ2NratsmXs0wmQ2pqKnWSViP2KruZmRnMzc1RVFQEqVTaqKsyrWF/OmxpX3ZfEGk5dkO3uhpnKpVKbllJUVER9PX1ubBhY2PT4QtFMAyDmzdvQiwWIyAggMKFGrCFHBQKBXg8ntbttbpx4wa+/vpr/PXXXygpKYG3tzf4fD74fD78/f11LmCT9oEChg5hr3qxYSM1NRXDhg0Dn89HeHg4HB0dW+WDqra2FsnJyVwjLbrC3nIVFRVITk5udJW9qTXsbFdlBwcHCnX/gl3C069fP3Tt2lXTw+kQ2LKprbUckl06yC6lUiqVjSpSaduJYkuxvViKi4sRGBjYrop6aCuVSoWUlBStDRcAsG/fPrz11luIjo7GoEGDcOTIEezfvx9HjhyBpaUlwsPDwefzMWrUKPqcJ1qDAoaOYhgGOTk5iImJQWxsLK5cuYIhQ4ZwH1QuLi5qCRvl5eVISUmBs7MzPDw86EqLGhQVFSEtLQ29e/dGjx49HntcbW0tFzYqKythZWXFhQ3aMNtYfn4+srKy4O3t/UxLeEhjDTcft1VFM4ZhUFlZyYWNuro62NjYcHuV2vuJF8MwuH79OkpLSxEQEEDhQg3Y5cTs/jVtnPGNi4vDggUL8McffyAsLKzRz+rr65GQkID4+Hjs378fW7duxZQpUzQ0UkIao4BBwDAM8vLyIBQKERsbiwsXLiAgIAACgQB8Ph+urq7PFAzEYjEyMjL+9USYNF9+fj5u3LgBb29vODo6Nvt+UqkURUVFEIvFjTbMOjo6wtTUtBVHrP3Y/gG+vr6wsbHR9HDavYb7AzS5+ZidzSsqKkJlZSUsLS25pVTt7T3PLnUtLy9HQEAAXSBQAzZcsP1ttDFcHDx4EHPnzsVvv/2G559//onHMgwDlUpFKwSI1qCAQRphGAYFBQWIjY2FUCjE2bNn4ePjw4WN5mx6ZRgG9+7dw507d+iKsJqwV4Tv3bvX4hNhmUzGXeUtKSmBmZkZN7PxLI392quGJ8L+/v5aU2mtPWu4hIfH42nN/gA2YBcVFaG0tLRdvedVKhUyMjK4LvIULlpOpVLh2rVrkEqlCAgI0MpwcezYMbz88svYuXMnpk+frunhEPLUKGCQx2IYBsXFxYiLi0NMTAxOnTqFfv36cZvLmirfKZPJ8PnnnyMoKAiBgYHU7E0NVCpVo5M2dV4RZuuws439OnfuDEdHRzg4OMDCwkKrT7xaQqVScctNtOlEuD1je7FUVFSAx+Np7RKehu/5kpISGBkZcTMb1tbWWvWeV6lUSE9PR3V1NQICAqgEtRqoVCqkpaWhrq5Oa8PF6dOnMX36dERFRWHWrFla9Z4kpLkoYJBmYRgGZWVliI+PR0xMDI4fP46ePXuCz+dDIBDA29sbVVVVmDJlCu7fv48jR45QFR41UCqVSEtLQ21tbatfvXxSYz9tO/FqCfZvWldXB39/f7oirAbsSRv7Pm0vJ8JKpRKlpaXcex4At2fDxsZGo8tNGv5NAwIC2v0eEm3QHsLFuXPnMGXKFGzduhXz5s3rMJ+7RPdQwCDPpKKiAgcPHoRQKMTRo0dhb28PALCwsMD+/fufan8AaZpMJkNKSgr09PTg5+fXpl+GbNMndpM4AC5stOdSoHK5HKmpqVyte208wWhv2BKfcrlca9eyN0fD/jISiQRyuRy2trYaKfnM7g+or68Hj8ejcKEG7GxQTU2N1ga2S5cuISIiAl988QUWLlxI4YK0axQwSItdvXoVkydPhrW1NSQSCaysrBAWFoaIiAgMHDiQNp09g7q6OohEIq0o7cvOXrEnXkqlkrvKa2tr225e3/r6eiQnJ6NTp07w9fVtN+PWZnK5vFEI1sYSn8+CYRhUV1dz7/mamhpYW1tzS6laeyaxIwQ2bdIewkViYiLCw8Px6aef4p133qFwQdo9ChikRU6dOoXnn38e7733HtasWYO6ujocO3YMQqEQBw8ehKmpKcLDwyEQCDBkyJAOcwLSmqqqqiASieDo6Ii+fftq1RcNWwq0YRdxtu+Avb291r6+dXV1SEpKgqWlJfr3799uZ2C0iUwmg0gk0onAVldXx4WNiooKrgqbg4ODWvfvsN2klUolzbCpCcMwSE9PR1VVFQIDA7UyXKSkpCAkJASrVq3C0qVLteozXxNUKhX3Gc0wDNfnSdf/Lu0NBQzyzH755Re8+eabiIyMxCuvvPLIz6VSKU6cOAGhUIj9+/fD0NCQm9kYNmwYfXk2oaSkBNeuXYObmxvc3Ny0+gO1qau87JISe3t7rfkir66uhkgkgoODg9YFtvaqvr4eSUlJMDMzg4+Pj04FtoZV2EpLS2FiYsLN6LWkMIJCoUBKSgoYhoG/v7/WhvX2hGEYZGRkoLKyUmvDRXp6OoKDg7F06VKsXLlS5z+flEold7GivLwcdXV1cHZ21vCoyLOggPH/1q5di08++aTRbY6OjigsLHzsferr6/Hpp5/i999/R2FhIbp164ZVq1Zh3rx5rT1cjdu6dSvWrl2LmJgYjB079l+Pl8vlSEhIQHR0NOLi4qBUKhESEgKBQIBRo0a1m02hramgoACZmZnw9PRsl52kG3YRr6qqarMlJU9SXl6O5ORk9OjRA7169dL5L291YGeDrKys4OXlpVPh4mEKhYIrjFBcXAwDAwMubFhbWzf7b6NQKJCcnAw9PT34+/t36NmgttIwXGhrBa7r168jODgYb7zxBtauXavzn08NZy4WL16My5cvo7S0FD169MCWLVvQv39/Ct7tCAWM/7d27VpER0fjxIkT3G3sl8Xj8Pl8iMVirF+/Hr1794ZEIoFCocDQoUPbYsgalZKSAkNDQ3h7ez/1fRUKBc6fP499+/YhLi4ONTU1CAkJAZ/Px9ixY7W2vGVrys3NRXZ2NgYMGAA7OztND6fFpFIpFzbKy8thYWHBLSlpqyZnxcXFuHbtGvr06YPu3bu3ye/s6GpqapCUlESzQU1QqVSN9iqpVCpu+aCtre1jT4zkcjmSk5NhYGAAPz8/Chdq0LAxYWBgoFaGi5s3byI4OBivvPIKNm7cSP+WGnj11Vdx/vx5fPHFFxgwYAC8vLwwYcIE/Pbbb1T6vh2hgPH/1q5di7i4OKSkpDTr+KNHj2LGjBnIzs6m7r8toFQqcenSJcTExCA2NhalpaWYOHEiBAIBJkyY0OH7EzAMg5s3b6KwsLDDNnuTyWTcSVdpaSnMzc0brV9vjS9Wdjaof//+cHJyUvvj6yJ2b1DXrl2b1XBTlzXcqySRSCCVSmFjY/PI8kG5XA6RSAQjI6MOv4+lrbSHrufZ2dmYNGkSpk2bhi1btuj0LODDMjMzMXPmTOzYsQMBAQHYvn07li9fjj179mDSpEnccbQnQ/tRwPh/a9euxebNm2FpaYnOnTtj0KBB2LBhA3r16tXk8W+++SZu3ryJwMBA/PbbbzAzM0N4eDjWrVunk1fg1UGlUuHq1atc2Hjw4AHGjx8PgUCASZMmdbiTb7aySWVlJXg8Xptd2dcktsmZWCxGSUkJjI2NucZ+Xbp0UcsXxr1793D79u0OMxukDSoqKiASieDm5oaePXtqejjtTnV1Nbdvo6qqCpaWlrC1tUVhYSFMTEzg6+tLJ5lqwDAM10AzMDBQK8PF3bt3MWnSJISFheHbb7/V+de94bIoALhw4QJmz56NO3fuYPv27Xj//fexe/duhISE4P79+/jjjz/w/vvva3DEpLkoYPy/I0eOoLa2Fh4eHtyypxs3biAjIwO2traPHD9p0iQkJCRg3LhxWL16NYqLi/Hmm29izJgx2LVrlwaeQcfC1oGPjo6GUChETk4Oxo0bBz6fj5CQEFhaWrbrqxdsPwa2Wow2bj5sbUqlkuuoXFRUxHVUdnBwgJWV1VO/vgzDIDs7G/fu3YO/vz+srKxaZ+A6prS0FCkpKejduzd69Oih6eG0e1KpFAUFBcjJyYFSqWw0o2dubt6uP9c0qT2Ei/z8fEycOBHjx49HVFSUzoeLoqIibhn6yZMnMXbsWBQWFoLP58Pb2xv79u3D7t27ERYWBgC4cuUK3n33XWzduhWDBg3S5NBJM1DAeIyamhq4u7vjgw8+wJIlSx75+YQJE3Du3DkUFhZyawKFQiGmTJmCmpoamsVQI3azHhs2bty4gdGjR0MgECAkJAS2trbt6ktZKpUiOTkZxsbGGDBgAC2LwD+BsmEXcT09vUZdxP/ti5hhGGRlZUEikYDH48Hc3LyNRt6xsftY+vbtCxcXF00Pp0NgK3CZm5ujb9++XEPL4uJidO7cmdsk/iwhW1cxDIMbN26gpKREa8NFYWEhJk6ciGHDhmHHjh06/7mfkJCA7777DuvWrUNUVBR27dqFnJwcGBsbY8mSJdi3bx/mzZuHr7/+GsA/YWTy5MkYMGAAdu7cqeHRk+aggPEE48ePR+/evREVFfXIz1555RVcuHABt2/f5m67fv06vLy8cPPmTfTp06cth6oz2D0LMTExEAqFSE1NxfDhw8Hn8xEeHg4HBwet/lKurq5GcnIybGxs4OnpqfNXsJqiUqm4jspisRgqleqJjf1UKhVXLYbH41G4VxOxWIz09HTax6JGUqkUIpEIXbp0eaQfi1KpRElJCYqKiriQbW9vD3t7e9jY2Oj8CenjsBcXioqKEBgYqJX//iUSCYKDg8Hj8fDLL79QJSQABw8exNq1a1FbWwuxWIzLly9z502ZmZlYsmQJxGIx+vTpA0dHR5w7dw729vY4fvy4hkdOmosCxmPU19fD3d0dCxYswOrVqx/5+bZt2/Dee+9BIpFwV0v379+P559/HtXV1Vr5IdfRsEti2D0bV69exdChQxEeHg4+n4+uXbtqVdgoLy9HSkoKunXrBnd3d60am7ZiGAYVFRXcZlmZTMZV5rGzs4Oenh5SU1Mhk8nA4/F0cqlZa3jw4AFu3LgBHx+fJ1bSI80nlUqRmJgIa2treHl5PfHfPxuy2X0bcrkcdnZ2sLe3h52dHfUQ+n/tIVwUFxcjJCQE/fr1w549e+i1a+C9997Dt99+izFjxuCzzz5rtOzp9u3bOHbsGOLi4uDq6oqePXti1apVGhwteVoUMP7fsmXLEBYWhh49ekAikWD9+vU4c+YM0tLS4OrqipUrVyI/Px+//vorgH+uRHt6emLw4MH45JNPUFxcjNdeew0jR47E9u3bNfxsdA/DMMjLy4NQKIRQKMTFixcRGBgIPp8PgUCAHj16aPSEXiKRID09nUqmtgDb2E8sFkMikaC2thYGBgbo1KkTzVyoUV5eHm7dugU/Pz+qkKcmbO8QdubyaT6LGIZBVVUVt3ywpqYGNjY23KyeNpZgbQvsbLZEItHacFFWVobQ0FC4urriv//9b6tcAImMjMTmzZtRUFCA/v37Y+vWrRg+fPhjj//+++/xn//8B7m5uejRowdWrVqF2bNncz/fvn07fv31V6SnpwMAAgICsGHDBjz33HNqGzPbTI/dr/rzzz/DxsYGb7/9NsaNG6e230M0iwLG/5sxYwbOnj2L4uJi2NvbY/DgwVi3bh28vLwAAHPmzEFubi4SEhK4+9y4cQPvvPMOLly4AFtbW0ybNg3r16/Xyg86XcIwDAoKChAbG4uYmBicO3cOAwYMgEAgAJ/Pb/PZA/aEzdvbGw4ODm32ezsy9mqwnp4eDAwMUF1dzTX20+WTrpbKyclBbm4ubZJXo9raWiQlJcHOzg79+vVr8WdPbW0tFzYqKiq4HjP29vYdvqw3i2EY3Lp1C4WFhQgMDNTKCnwVFRUICwuDo6MjhEJhq3wm7d27Fy+//DIiIyMRFBSEH3/8ETt27EBmZmaTBRmioqKwfPlybN++HQMHDsSVK1cwf/587Nmzh9tIPXPmTAQFBWHo0KEwNjbGpk2bIBQKkZGR0aJ9WA3Lyj5cYvbkyZNYs2YNbGxs8M4772D8+PEA/lkp8uqrr9LywHaKAgbp0BiGQXFxMRc2Tp8+jX79+nFhQx1f+E/63Xfu3EFeXh6dsKlRTU0NRCJRo30sdXV13DKqiooKWFpacmGDAv+/YxgGt2/fRn5+PgICAtClSxdND6lDYBsTOjo6wsPDQ+2fNfX19dyejZKSEpiamnJhw8LCokMuw2TfqwUFBVobLqqqqiAQCNClSxfEx8e32qbzQYMGgcfjNdon6unpCYFAgI0bNz5y/NChQxEUFITNmzdzt7333ntITEzE+fPnm/wdSqUS1tbW+M9//tNopuNpNAwUBw4cQGJiIiorKxEcHIxRo0ahU6dOOHv2LFavXg1jY2OMGzcOp0+fRmpqKu7fv/9Mv5NoHu00Ih0au1FywYIFmD9/PsrKyrB//37ExMRg06ZN6NWrF7eM6uFNly2hUqm4kokDBw6kqkZqUllZCZFIBBcXl0bN3kxMTODq6gpXV1fU19dzYePWrVswNzfnem3oyhXep9GwAtfAgQPpb6QmNTU1SExMhLOzM/r06dMqJ/udO3dGt27d0K1bNygUCq7sc1JSEgwNDRuVfe4IBSXYcPHgwQOtDRc1NTWYOnUqjI2NERsb22rhQiaTISkpCStWrGh0+4QJE3Dx4sUm71NfX//IeExMTHDlyhXI5fIm94fU1tZCLpe3aLkk+97/4YcfsGLFCoSHh+POnTv4+++/MXbsWKxcuRIjRozA559/jm+++QbR0dGwsrLC3bt3n/l3Es2jgEF0hp6eHmxsbDB37lzMnTsXFRUVOHDgAIRCIUaPHg0XFxcubPj5+T3zF7JSqcS1a9cglUoxcOBArSyZ2B6VlpYiNTUVPXv2hJub22OP69y5M7p3747u3btDLpdzG2Wzs7NhYmLCnXSpq7Ffe8Z2PS4rK8PAgQNptkdNqqurkZSUBBcXlzZbkmloaAgnJyc4OTlBpVJx5W/T0tL+tRJbe8DOCLPhQhuDcF1dHaZPnw6VSoVDhw616hiLi4uhVCrh6OjY6HZHR0cUFhY2eZ+JEydix44dEAgE4PF4SEpKwq5du7gGqM7Ozo/cZ8WKFXBxcWnx3ohjx45h/fr1+OWXX8Dn83HhwgWMGjUKFRUVqK6uxvr16zF48GB4eHgAQIcJxbqMAgbRWZaWlpg1axZmzZqFqqoqHD58GEKhEMHBwbCzs0NYWBgiIiIwcODAZn/QyWQyJCcnw8DAAIGBgVQxRE3YTfJP24/ByMgIXbt2RdeuXRtd4U1MTESnTp24sNHeGzc+C7aTfHV1tdb2DmiPqqqqkJSUhO7du6NXr14aeV/p6+vDzs4OdnZ2jSqx3bx5E/X19bC1teUqsbWXymvZ2dnIz8/X2nAhlUrx0ksvoba2FseOHWuzZYYPv78e3t/Q0Mcff4zCwkIMHjwYDMPA0dERc+bMwaZNm5oMnZs2bcIff/yBhISEp/p8YMfA/r9KpcLNmzfxwgsvgM/nIzExES+88AKWLVsGmUyGX3/9FYaGhli1ahUVluhAaA8GIQ9hvyBiYmJw8OBBmJubIzw8HAKBAEOGDHns1T92Dbu1tTW8vb3p6oua5OfnIysrS62b5JVKJUpLSyEWi1FUVAR9fX04ODjA0dFRJ66csbNs9fX1VN5XjdglfD169ECvXr00PZxHMAyDmpoabglhdXU1rKysuH0b2jqDdefOHdy/fx8BAQFaudxUJpNh1qxZKCwsxPHjx2Ftbd0mv9PU1BT79u1DREQEd/u7776LlJQUnDlz5rH3lcvlEIvFcHZ2xrZt27B8+XKUl5c3+tzbsmUL1q9fjxMnTiAwMLBZY1KpVNDX14dCoYChoSFkMhn32VJSUoLi4mK4uLhgwoQJGDp0KLZs2YKSkhL4+vqCYRgsWLAAa9aseca/CNE2FDAIeQKpVIoTJ04gJiYG8fHxMDIyQlhYGAQCAYYNG8bNUJw7dw4zZszAypUr8dZbb+nc1fDWkpubi5ycHPj6+rbalS2VSoWysjLupIthmEbLSTpa2FAoFEhJSQHDMPDz86NZNjWpqKiASCT61yV82qSuro7bJF5WVgZzc3NuVs/MzEwrPseys7Nx7949BAYGamW4kMvleOWVV5CTk4NTp07B1ta2zX73oEGDEBAQgMjISO42Ly8v8Pn8Jjd5N2XkyJFwcXHBnj17uNs2b96M9evX49ixYxg8eHCzHocNF3fv3sXnn3+O+/fvo1u3bli8eDE8PDy4n6empmLatGnYs2cPAgICkJWVhQ8//BDDhg3DggULtHJ2ijwbChiENJNcLsfp06cRHR2N/fv3Q6lUcjXON2/ejLfffhtr167V9DA7hIZVjXg8HiwsLNrs91ZUVHC9NuRyeaOw0d478MrlcohEIhgaGsLPz69drsXXRuXl5UhOTkavXr3g6uqq6eE8E5lMxi0hLCkpQefOnTW+hDAnJwd3797V2spmCoUCr776Kq5fv45Tp061eRlytkztDz/8gCFDhmDbtm3Yvn07MjIymuzfdfPmTVy5cgWDBg1CWVkZvvrqKxw/fhxJSUlcKN60aRM+/vhj7NmzB0FBQdzvMjc3f2zAY/ta5OTkYOjQoRgzZgxqampw48YNdOnSBfv370fXrl0BANeuXcP06dMxb948zJw5E+vXr0d+fj727t1LyzQ7GAoYhDwDhUKBc+fOYf369UhISIC3tze8vb3B5/Mxbtw4+qBsgYYVuHg8nsauaDVscCYWiyGVSrm16/b29u3uyn99fT1EIhFMTEzg4+ND4UJNysrKkJyc3KGaaCqVSpSUlHD9NvT19bmgbWNj0yazetoeLpRKJRYuXAiRSITTp0/DyclJI+OIjIzEpk2bUFBQAG9vb3z99dcYMWIEgEf7d12/fh0vvfQSsrKyYGRkhNGjR+OLL75A3759ucdzc3NrsnrTmjVrmryAxu6zePDgAby9vTFv3jxs2bIFwD8bxLdu3YpTp05h6NChAP7Zo7Rs2TKcOHEC9fX1MDIywpkzZ5rs20HaNwoYhDwDhmHwxRdfYOPGjdi3bx9MTEwQExOD2NhYlJeXY+LEiRAIBJgwYYJWllLUVkqlEmlpaaitrQWPx9OqoFZdXd1o7bqNjQ13hVfb9zBIpVIkJSXBwsJCreWYdV1paSlSUlLg4eGBbt26aXo4rUKlUqG8vJwLG3K5HHZ2dtwm8daY1cvNzUVubq5Wh4tFixbh/PnzSEhIaFEDuo6grq4OQUFB3OcMu5dn9erVWL9+Pb777jtMnjwZRkZG6NatG6RSKVJTUyGRSDB48GDY29tr+BmQ1kABg5CnpFQq8e677yImJgZHjhyBn58f9zOVSoUrV65wYaOwsBDjx4+HQCDApEmTtPLLUluwewNUKhX8/f21eoaA7aYskUhQWVkJS0tLrteGNoUi4H+dpG1tbeHp6akV6+o7gpKSEqSmpqJfv37c8o+OruGsnkQiQW1tLRe07e3t1dKt+u7du8jOzkZAQECbLY18GiqVCkuWLMGJEydw+vTpdrskTp0YhsHChQtx7do1hISE4KOPPsLPP/+M119/HYMGDUKPHj2QmJiImpoaTJ8+HT169MCiRYs0PWzSyihg6LC1a9fik08+aXTbk2poz5kzB7/88ssjt3t5eSEjI6NVxqhtpFIpZs2ahfT0dBw9evSJmzlVKhVSUlIQHR0NoVCIu3fvYty4ceDz+Zg8ebJOlkZ9HJlMBpFIhE6dOsHX17ddLd+RSqVcr42ysjJ06dKl0UZZTWL7MbRmszddVFxcjGvXrsHT07PJ3gG6oqamhnvvV1ZWwsLCgnvvP8vMbXsIFytWrEB8fDwSEhK0slJYW2M3bwPA0qVLcf78eTg6OuL06dOIjY3F8OHD0blzZ2RkZCA5ORnbtm2DWCzGyZMnO+ysH/kHBQwdtnbtWkRHR+PEiRPcbQYGBo+drqyoqEBdXR333wqFAr6+vnjnnXd0ZnNzeno6lixZgj179sDOzq7Z92MYBunp6YiOjkZsbCyysrIwevRo8Pl8hIaGwsbGRmdP/urq6iASidClS5d2X95XJpNxJ1wlJSUwMzPjTrjMzc3b9DVmS6Zqsh9DR1RUVIRr166hf//+Glt3r43q6+u5935paSnMzMy4fRvNaWp579493LlzBzweD5aWlm006uZTqVRYvXo19u7di9OnT3MN4UjjkPHhhx9i165dCAoKwq5du7jXkj2GLYerjQGSqBcFDB22du1axMXFISUl5ZnuHxcXh+effx45OTk0TfwUGIZBVlYWYmJiIBQKce3aNQwfPhwCgQBhYWFwcHDQmZPB6upqiEQiODg4oG/fvh3qeSsUCu6Eq7i4GJ07d+aWUVlYWLTqcy0rK0NKSkq7rmqkjcRiMdLT0+Ht7f1IB2XyP3K5nNskXlxcDCMjIy5sNNVnJi8vD7dv39bacMEwDNavX4+ffvoJp0+fhqenp6aHpHXYSlLAPw39Dh8+jEmTJuGdd97husvr6el1qM948mQUMHTY2rVrsXnzZlhaWqJz584YNGgQNmzY0Oxp37CwMNTX1+Ovv/5q5ZF2XAzDIDs7mwsbiYmJGDp0KPh8PsLDw9G1a9cO+4FcXl6OlJQUnbjC/nBVHgMDA25mw9raWq3Pnd0b0JE3HmtCYWEhMjIy4OPj0+blSNszlUqFkpISrt8G22fG3t4etra2ePDgAW7fvg1/f39YWVlperiPYBgGmzZtQmRkJE6dOgUfHx9ND0lrNZzJWL16NQ4ePIjg4GC88cYb9Fmkgyhg6LAjR46gtrYWHh4eEIvFWL9+PW7cuIGMjIx/bRZUUFCA7t27Y8+ePZg2bVobjbhjYxgG9+7dg1AohFAoxKVLlzBw4EDw+Xzw+Xz06NGjw5yEs2vYO1Jpz+ZSqVQoLS3lwgbDMFzYaGkJUIlEgrS0NHh5een03gB1KygowPXr1+Hj40MVb1qAYRiUl5dzM3tSqRQMw6Bnz55wdXXVusIODMNg69at+PLLL3Hy5En4+/trekhar2HI+Oyzz/DTTz9hypQpWLdunda9vqR1UcAgnJqaGri7u+ODDz7AkiVLnnjsxo0b8eWXX+LBgwdaX6KzPWIYBg8ePEBsbCxiYmJw/vx5+Pr6QiAQgM/nt+sr/uyVYDoJ/t8JF1uVR6FQwM7ODo6OjrC1tX2qze7sSbC3tzddYVejBw8e4MaNG/D19W3TLs0dXV5eHm7evAknJydUVVWhuroa1tbW3FIqTVdjYxgG33//PTZu3Ihjx47hueee0+h4tIFcLoeRkRGUSiUUCsVjq4Y1DBkbNmzApEmTwOPx2nKoRAtQwCCNjB8/Hr1790ZUVNRjj2EYBh4eHggNDcXXX3/dhqPTTQzDQCKRIC4uDjExMUhISICnpycXNtrT3oW8vDzcunULAwYMeKpN8rqAYRhUVlZyjf3q6+sb9Rt40tW/+/fv4+bNm3QSrGbs39XPzw82NjaaHk6HkZ+fj6ysLPj7+8Pa2hrAP8Ue2JmN8vJyrhqbvb09zMzM2vQzjmEYbN++HWvWrMGRI0e4JnG6KC8vD8bGxtzMnVgsxrJly1BcXIxBgwbh9ddf5y4UsU33gMYhg+gmChiEU19fD3d3dyxYsACrV69+7HEJCQkYPXo00tLS4O3t3YYjJAzDoLS0FPv374dQKMSJEyfg7u6O8PBwREREwMvLSys/1Nm9Jvfu3dPatdbahGEY1NTUQCwWQyKRoKamplEX8Yazhrm5ucjJyYGfnx93skZajg3DDU+CScuxM0JPCm1sNbaioiKUlJTA2NiYe++3dnlvhmHwyy+/YMWKFTh48CDXFVvXsD1PunbtimnTpmHLli0wNjYGj8eDi4sL+vbti19++QVjxozBJ598QjMU5BEUMHTYsmXLEBYWhh49ekAikWD9+vU4c+YM0tLS4OrqipUrVyI/Px+//vpro/u9/PLLuHXrFi5fvqyhkRNWeXk5Dhw4AKFQiGPHjsHFxQUCgQACgQC+vr5aETbYqllisRgBAQEwNzfX9JDanYcb+1lZWcHBwQF1dXUoKCgAj8ejso9qxJZMpTCsXs0JFw9TKBSNNonr6+tzYaOle5YexjAM9uzZgyVLlmD//v0YM2aM2h67vRIKhXjppZcwd+5cTJ48Gfv378f27duhp6eHO3fuYNy4cejbty/Wr1+PwMBAAI1nMojuooChw2bMmIGzZ8+iuLgY9vb2GDx4MNatWwcvLy8A/zTWy83NRUJCAnefiooKODs745tvvsH8+fM1NHLSlKqqKhw+fJjrMG5nZ8fNbAQGBmokbKhUKmRkZKCiogIBAQEwMTFp8zF0NFKpFGKxGHfv3kV9fT3Mzc3h7Oz8zM3NSGPsjJC2lkxtr9g9Qi1ZxqdSqVBWVsYVSFAqldwyQltbWxgaGj7z+BiGwb59+/D2228jOjoakyZNeubH6ijYZU7Hjh1DSEgIPDw8EBAQgN9++407Jjs7m1tavXbtWgwZMkSDIybahAIGIR1QbW0tjh49ipiYGBw6dAhdunRBeHg4BAIBBg8e3CadspVKJVJTUyGTyeDv7//YDYHk6TAMg+vXr6OkpAQ+Pj6orq5u1NyM7bXR1uvWO4KcnBzcvXuXZoTUrLCwEJmZmWrdI9Rwz1JRURHq6upgY2PDbRJ/2uIjcXFxWLBgAf7880+EhoaqZYztGdvXgg0ZCQkJmDx5Mnr37o3Y2Fi4u7tzMxV5eXkYOnQoXFxccOjQIdoHRgBQwCCkw5NKpTh+/DhiYmIQHx+Pzp07IywsDAKBAEFBQa1SOlAulyM5ORn6+vrw9fWl8oRqws4IVVZWIiAgoFGlHblcjuLiYq65GbtuvS0a+7V37B6hvLw8BAQEoEuXLpoeUofBhovWLuxQU1PDhY3KykpYWlpyS6n+bWbv4MGDmDt3Ln7//XdERES02hjbC4VCAUNDQ24/SmhoKOzs7HD58mWMGjUKERER+OKLL9CjRw8uZOTn5yMxMRF8Pl/TwydaggIGITpEJpPh9OnTiI6Oxv79+8EwDEJCQhAREYGRI0eqpeSwVCpFcnIyTExM4OPj0yazJbpAqVQiLS0NdXV1CAgIeOJrpVQqG4UNQ0NDLmxYWVlR2GiAYRjcuXMH+fn5tEdIzcRiMTIyMtq8apxUKuUqUpWVlcHMzIwrfevs7NxoueixY8fw8ssvY+fOnZg+fXqbjVFbNaz+NHToUJiZmWHevHmYOnUqDA0NceXKFYwaNQqhoaHYtGkT3NzcaM8FaRIFDEJ0lEKhwNmzZxEdHY24uDjU1dUhJCQEAoEAY8aMeaY69LW1tUhKSoKNjQ08PT21YpN5R6BUKpGSkgKlUgl/f/+nmhFiOymzV3f19PRgb28PR0dHWFtb6/RrxDAMbt++jQcPHiAwMBBmZmaaHlKHIRaLkZ6ejgEDBmi0OWHDmb0lS5YgNzcXY8eORUREBAwNDTFz5kz88MMPmDlzJp0kNxAcHIza2lrExcWhS5cuMDQ05JZNJScnY8yYMQgKCsLWrVvRu3dvTQ+XaCEKGIQQKJVKXLhwATExMYiNjUVFRQUmTZoEgUCA8ePHN2vzcGVlJZKTk+Hs7Iw+ffrQl7WaNFxu5ufn16KNrCqVqlFjP6VSya1Zf9rGfu0dwzC4efMmV92MwoX6sB3lNR0uHlZTU4MDBw5g//79OHPmDKRSKZ577jm8//77GD9+vMab+2mL69ev4+WXX0ZkZCTXYJCdpWD/PzU1Ff7+/ti/fz/CwsI0PGKijShgEEIaUalUuHLlCqKjoxEbGwuxWIwJEyZAIBBg4sSJTa5PP3z4MDIzMzFlyhS4ubm1/aA7KJlMBpFIhM6dO2PAgAFqDQAMw6CiooILGzKZrFFjv5YEGW3Hlk4uKipCQEAAVd9SIzZc+Pj4aG1H+UuXLiEiIgLz58+HSqVCXFwcioqKEBwcjIiICEyePLnDlyduuKyJnZlgJSYmYuTIkTh8+DBGjhzZ6H7Z2dkwMjJC9+7dUVpaSg0oyWPp7tw4IaRJ+vr6GDx4MLZs2YJbt27hzJkz6Nu3Lz777DO4ublh+vTp+OOPP1BRUQGGYbB79268/PLL6Ny5M4ULNZJKpUhMTISpqSl8fX3VPrugp6cHKysreHh4ICgoCAMHDoSZmRmys7Nx5swZJCcn48GDB5DJZGr9vZrGVuEqLi5GYGAghQs1Kioq0vpwcfXqVbzwwgtYv349tmzZgq+//hrZ2dm4cOECvL29sWnTJjg4OGDixIm4e/eupofbathwcf/+fe6z5cMPP8SDBw+4PjvJyclQKBSN7nfu3Dls2rQJVVVVHT6EkZahGQxCSLMwDIP09HTs27cPsbGxuHnzJgIDA5GUlITPP/8c8+fPp2VRalJXV4ekpCRYW1vDy8urzf+ubEUeiUSCqqoqWFtbc5vE23O5YYZhkJmZibKyMgQGBtKSGDUqKirCtWvX4O3tDUdHR00Pp0nJyckIDQ3FqlWrsHTp0sf+u8rJyUFcXBxef/31Dh1A9+7dizlz5iAxMRHvvfceysrKcP78eRgbG+Ojjz7Cli1b8OOPP4LP58PS0hJJSUmIiIjAu+++i2XLlml6+ETLUcAghDw1hmGwfPlyfPvtt/Dx8UFqaiqGDx8OgUCAsLAw2NvbU9h4RjU1NUhKSoKDgwP69u2r8b9jXV0dFzYqKiq48p8ODg7tqnGiSqVCZmYm1/SRwoX6FBcXIzU1VavDRVpaGiZPnoylS5di5cqVGv93pQ0YhkFERAQSEhJgY2OD1NTURktgP/zwQ3z99dfcDGpeXh5CQ0MRGRmpwVGT9oICBiHkqTAMgw8//BA7d+7E0aNH4e/vjzt37iAmJgZCoRAikQhDhgyBQCBAeHg4nJ2d6cu8maqqqpCUlIRu3brB3d1d6/5u9fX1XPnP0tJSmJubw8HBAY6Ojlq9SVqlUiE9PR3V1dUICAho17Mw2qakpASpqanw8vKCk5OTpofTpMzMTEyePBlvvvkm1qxZo3X/rjSB3YMxf/587Ny5EzY2Njh8+DC3qZt15MgR3Lx5E5WVlfD09MSUKVM0NGLS3lDAIIQ0m0KhwBtvvIG//voLx48fh4eHR6OfMwyDe/fucWHj8uXLeO6558Dn88Hn89G9e3f6cn+M8vJyJCcnw83NDT179tT0cP6VXC7nwkZJSQlMTEy4mY0uXbpozeusUqmQlpaG2traf+0fQp4OGy48PT3h7Oys6eE06ebNmwgODsacOXOwYcMGrXlfasrDPSuKi4shk8nw/vvv48iRI9i3bx/Gjh3LNdsj5FnRJm+ic9auXQs9Pb1G//u3K2+7d++Gr68vTE1N4ezsjLlz56KkpKSNRqwd5HI5pk+fjosXL+LChQuPhAvgn42Drq6uWLJkCc6dO4e7d+/ixRdfxNGjR+Hj44NRo0Zxmyrp2sb/lJaWQiQSoXfv3u0iXACAkZERunbtCj8/P4wcORLu7u6ora1FYmIiLly4gJs3b6K8vFyjr7NKpcK1a9ea1ZyQPJ3S0lKtDxd37txBaGgoXnrpJXz22Wc6Hy6USiX3N2AYBpWVlbCzs0PXrl3xyy+/IDg4GC+88AJOnToFQ0NDVFVV4cUXX8SdO3fo85o8NZrBIDpn7dq1iI6OxokTJ7jbDAwMHluv/fz58xg5ciS+/vprhIWFIT8/HwsXLkSfPn0QGxvbVsPWOIZhsHXrVsyePRu2trZPfV+xWIy4uDgIhUIkJCTAy8sLfD4fAoEAHh4eOvvlz1be6devH7p27arp4bSYUqlEaWlpo8Z+7MxGWzb2UyqVuHbtGmQyGXg83lM1JyRPVlpaipSUFK1+z+bm5iI4OBhhYWH49ttvdbqhJNC4Q/fGjRtx9epV3L17F3PmzMGcOXO4vRfz5s3Db7/9hrfffhtCoRA8Hk+nvueI+lDAIDpn7dq1iIuLQ0pKSrOO37JlC6KionDnzh3utu+++w6bNm1CXl5eK42y42IYBqWlpdi/fz9iYmJw4sQJ9OnTB+Hh4YiIiNCpDuCFhYXIyMjQ6s2xLaFSqVBWVsZtEmcYplFjv9Z6nZVKJVJTU6FQKJ668zl5srKyMiQnJ2t1uMjPz8eECRMwYcIEREVF6cznSXMsXrwYQqEQc+bMgb29PRYtWoRly5bh7bffRo8ePQAA69atw7Vr1+Du7o7PP/9cwyMm7RUFDKJz1q5di82bN8PS0hKdO3fGoEGDsGHDBvTq1avJ4y9evIjRo0cjNjYWwcHBkEgkmDZtGjw9PfHDDz+08eg7FrbZW3x8PIRCIf766y9069YNfD4fERERGDBgQIc9OcjPz0dWVhYGDBgAOzs7TQ+n1bGvtVgshkQigVwuh52dHRwdHWFra6u29d5KpRIpKSlQqVTw9/endeRqxIaLvn37wsXFRdPDaVJhYSEmTpyIYcOGYceOHTrVnf5x2NmLP//8Ex999BFiYmLg6+uLI0eOIDQ0FHp6enjxxRfx6aefcks0q6urYW5uruGRk/aMAgbROUeOHEFtbS08PDwgFouxfv163LhxAxkZGY9d+hMdHY25c+dCKpVCoVAgPDwc0dHRdGVUzaqqqnDo0CHExMTgyJEjsLe355ZRBQYGdpiwce/ePdy5cwe+vr462QmXYRhUVVVxMxt1dXWwtbWFg4MD7O3tn/nflUKhQHJyMvT09ODn50fhQo3Ky8shEong4eGBbt26aXo4TZJIJAgODgaPx8Ovv/6qs+GC3cidm5uL7t27w8DAAPX19YiLi0N5eTlef/11CIVCzJs3D3v27EHnzp0xadIkLFiwAIsWLULfvn01/RRIB0ABg+i8mpoauLu744MPPsCSJUse+XlmZibGjRuHxYsXY+LEiSgoKMD777+PgQMHYufOnRoYsW6oqanB0aNHERMTg0OHDsHS0hLh4eHg8/kYPHhwuzx5YBgGOTk5uHv3Lng8HiwtLTU9JK1QXV3NhY3q6mrY2NhwYaO5JWUVCgVEIhEMDAzg5+fXLt8f2oqtcNanTx+tDRfFxcUICQmBp6cn9uzZo/Ph8vjx45g7dy5ef/11rFixAkZGRsjPz4dSqYSxsTFCQkLw8ssvY9GiRbh37x6CgoKQn5+PjRs3Yvny5ZoePukAKGAQAmD8+PHo3bs3oqKiHvnZyy+/DKlUin379nG3nT9/HsOHD8eDBw+0toJKR1JXV4fjx49DKBQiPj4enTt3Rnh4OAQCAYKCgtrFyQTDMLh9+zYePHgAHo/XqKEV+R+2sZ9YLEZlZSUsLS3h6OgIe3v7xzb2k8vlEIlEMDIy4pqCEfWoqKjgKpx1795d08NpUllZGUJDQ+Hq6or//ve/Ol8trK6uDrNnz0ZMTAyCg4MRGBiIlStXcs0lb926BYFAgKioKIwYMQL5+fn45ptv8PLLL8PHx0fDoycdRcdYb0BIC9TX1+P69euPDQq1tbWPLM1hT2Aon7cNExMThIeH4+eff0ZhYSF++uknqFQqzJ49G71798Zbb72FEydOQCaTaXqoTWIYBjdu3EBhYSECAwMpXDyBiYkJXF1d8dxzz2HYsGFwcnJCUVERLly4gL///hs5OTmoqanhjpfL5UhKSkKnTp1o5kLN2HDh7u6uteGioqICfD4fXbt2xd69e3U+XAD//BuaPn06OnfuDENDQ1y8eBEbN25EXV0dgH9m+3JycnDo0CHEx8dj9uzZEIlEFC6IWtEMBtE5y5YtQ1hYGHr06AGJRIL169fjzJkzSEtLg6urK1auXIn8/Hz8+uuvAICff/4Z8+fPx7fffsstkXrvvfegr6+Pv//+W8PPRrcpFAqcPXsW+/btQ1xcHOrr6xESEgKBQIDRo0dzV+w0SaVSITMzE+Xl5QgICHjsVXjyZDKZrFFjPzMzM9ja2kIikcDc3LxDFwTQhIbhgq0upG2qqqrA5/NhYWGB+Ph4rfj3rmlKpRIGBgZgGAZvvfUWHB0dUVFRgbNnzyI4OBjvv/8+LCwssHv3bixYsACurq5wdHTE6dOnNT100sFQwCA6Z8aMGTh79iyKi4thb2+PwYMHY926dfDy8gIAzJkzB7m5uUhISODu89133+GHH35ATk4OrKysMGbMGHzxxRdaW0lFFymVSly4cAHR0dGIjY1FZWUlgoODIRAIMG7cOJiamrb5mBp2kebxeM3eT0CeTKFQoKCgALdv3+bWlDs4OMDR0REWFhY621NFXSorK5GUlIRevXrB1dVV08NpUk1NDZ5//nkYGhri4MGDMDMz0/SQNIINFKWlpTAxMYGJiQm3yXvdunW4cOECDhw4gHXr1uHo0aMYO3Ysli9fDisrK+Tl5UGlUmnta0zaNwoYhJAOR6VS4e+//+bChkQiwcSJE8Hn8zFp0qQ2Kb/I9mKQy+Xw9/enpRtqVF9fj6SkJHTp0gX9+vXjem0UFRXBwMCAa+xnZWVFsxpPiQ0XPXv2hJubm6aH06S6ujpMmTIFCoUCR44c0flyqteuXcOMGTPQtWtXbNiwAfb29ly5WT8/P7z66qt46623sGrVKpw+fRqjRo3C0qVLH9tclhB1oIBBCOnQVCoVRCIRoqOjIRQKcf/+fYwbNw58Ph+TJ09ulSvebLlUANSLQc2kUimSkpJgaWmJ/v37N3rt2MZ+YrEYRUVFYBiGCxs2NjYUNv5FVVUVkpKS4ObmprXhQiqVYsaMGaiqqsLRo0d1vhKbTCbDwoULsXv3bpiamqJ///5wdHSEt7c3PvjgA/z444+4c+cOvv/+eyiVSnzyySfYvXs33n77bSxevFjTwycdGAUMQojOUKlUSE9P58LGrVu3MGbMGPD5fISGhsLa2rrFYUMmkyE5OZkqGrWCuro6JCUlwcbGBp6enk98rRiGQXl5OVf+VqFQwM7ODg4ODrCzs6PX5SFsuHB1deWufmub+vp6zJo1C2KxGMePH4e1tbWmh6QVrl27hq+//hoVFRVwcnLC5MmTsXjxYgQEBODevXu4fPky4uLiEB4eDgCIiorCnDlzaD8YaVV0OYcQojP09fUxYMAAfPrpp0hLS0NKSgqGDh2KH3/8Eb169QKfz8euXbu4q99Pi126Y2xsTBWN1Kyurg6JiYmwtbX913ABAHp6erC2tkbfvn0xbNgw8Hg8GBsb4/bt20hISEBqaioKCgogl8vb6Blor+rqaiQlJaFHjx5aGy7kcjnmzJmD/Px8HDt2rNXCRWRkJHr27AljY2MEBATg3LlzTzz++++/h6enJ0xMTNC3b1+uOEhDMTEx8PLyQufOneHl5YXY2Fi1jnnAgAFYsmQJunTpgmvXrkEqlSIjIwMvvvgi13DSysqKO/6NN96gcEFaHc1gEEJ0HsMwuHPnDjezkZycjKFDh0IgECA8PBxOTk7/ekJbV1cHkUgES0tLeHl50XIcNaqtrUViYiIcHBzQt2/fFs0yMQyDmpoaiMViSCQS1NTUcI39HBwcdG6vTHV1NRITE9G9e3e4u7trejhNUigUePXVV3H9+nWcOnUKDg4OrfJ79u7di5dffhmRkZEICgrCjz/+iB07diAzM7PJSlpRUVFYvnw5tm/fjoEDB+LKlSuYP38+9uzZg7CwMADApUuXMHz4cKxbtw4RERGIjY3F6tWrcf78eQwaNEit48/KysIXX3yB9PR0LFy4EPPmzQMA3Lt3Dz169OA2fxPSFihgEEJIAwzD4O7du4iJiYFQKMTff/+NQYMGgc/ng8/no1u3bo98SaelpWHnzp14/fXX0a9fP/oSV6OamhokJSXByckJffr0Ufvftra2lltGVVlZCSsrKy5sdPSyp+zMRbdu3bQ2XCiVSixcuBAikQinT5+Gk5NTq/2uQYMGgcfjNWq46unpCYFAgI0bNz5y/NChQxEUFITNmzdzt7333ntITEzE+fPnAQDTp09HZWUljhw5wh0zadIkWFtb448//lD7c7h9+zY+//xzpKenY/r06dw+C7baFCFthS6xEUJIA3p6enBzc8PSpUtx/vx55ObmYvr06Th8+DC8vb0xevRofP3118jOzgbDMLhy5QqCg4Mhk8lafHWdNMZeXXd2dm6VcAEApqamcHNz4xr7OTg4QCKR4Pz587hy5Qpyc3NRW1ur9t+raWxwc3FxQa9evTQ9nCYplUq88847uHLlCk6cONGq4UImkyEpKQkTJkxodPuECRNw8eLFJu9TX1//SAg1MTHBlStXuKV3ly5deuQxJ06c+NjHbKnevXtj1apVGDBgAIRCIT777DMAoHBB2hwFDEIIeQw9PT1069YNixYtwunTp5GXl4e5c+ciISEB/v7+GDRoEMLCwhAREYHvvvuOlkWpUVVVFRITE9GtWzf07t27TYKbsbExevTogcDAQIwYMQIuLi4oLS3FxYsXcenSJdy5cwfV1dXPtD9Hm9TU1CAxMREuLi5wd3fXylCsUqmwZMkSnD17FidOnGj1nkPFxcVQKpVwdHRsdLujoyMKCwubvM/EiROxY8cOJCUlgWEYJCYmYteuXZDL5SguLgYAFBYWPtVjqkPPnj3x8ccfw9XVFefPn0dFRUWr/S5CHodqJxJCSDPo6enByckJb7zxBhYuXIgDBw5gxowZ8PT0xO+//46///4bfD4fERERzdqETB6vsrISIpEIPXr00NjV9U6dOsHFxQUuLi7cCaNEIkFubi7X2M/BwaHdNfZjZy66du2q1eFi+fLlOHbsGBISEtq0EdzDf48n7Vv4+OOPUVhYiMGDB4NhGDg6OmLOnDnYtGlToxmDp3lMdenevTs2b94MQ0NDnS/lSzSDLrcRQshTOnbsGF566SV8++23SExMhFgsxgcffID09HQMHz4cPB4Pa9euRWpqKlQqlaaH265UVFRw5VK1ZemOkZERnJ2d4evri1GjRqF3796QSqUQiUQ4f/48srKyUFZWpvUzG7W1tdx+lraaFXpaKpUKH3/8MeLi4nDy5Mk2ew+wpYsfnlmQSCSPzECwTExMsGvXLtTW1iI3Nxf37t2Dm5sbunTpAjs7OwCAk5PTUz2mOjk7O1MzPaIxtMmbEEKeQkxMDGbPno2dO3dixowZj/y8srIShw4dQkxMDI4ePQoHBwfw+XwIBAIEBATQMqonKC8vR3JyMnr16tWmV62flUqlQmlpKbdJXE9PD/b29nB0dIS1tbVWvdZsuHBwcICHh4dWhguGYbBu3Tr8/PPPOH36NDw9Pdv09w8aNAgBAQGIjIzkbvPy8gKfz29yk3dTRo4cCRcXF+zZswfAP5u8q6qqcPjwYe6Y4OBgWFlZtcomb0K0BQUMQnTM2rVr8cknnzS67d/WBH///ff4z3/+g9zcXPTo0QOrVq3C7NmzW3uoWue3337DwoUL8eeff3JlKJ+kpqYGR44cgVAoxKFDh2BlZYXw8HDw+XwMGjSINl42UFZWhpSUFLi7uzdZElTbqVSqRo39lEol7O3t4eDgAFtbW42+1mwPEW0PF1988QWioqJw6tQp+Pj4tPkY2DK1P/zwA4YMGYJt27Zh+/btyMjIgKurK1auXIn8/Hyu18XNmzdx5coVDBo0CGVlZfjqq69w/Phxrhs6AFy8eBEjRozAZ599Bj6fj/379+Ojjz5qlTK1hGgT2oNBiA7q378/Tpw4wf33k05+oqKisHLlykdqvVtbWzfrJLsjMTMzQ3x8PMaOHdvs46dMmYIpU6agrq4Of/31F4RCIaZNmwZjY2Nug/jQoUNhaKi7H8elpaVISUmBh4cHunXrpunhPBN9fX3Y2NjAxsYGffv2RWVlJSQSCW7evAmZTNaoi3hbvtZsuLC3t9fqcLF161Z8//33OHnypEbCBfDPbENJSQk+/fRTFBQUwNvbG4cPH+Zm0woKCnDv3j3ueKVSiS+//BJZWVkwMjLC6NGjcfHiRS5cAP+Usv3zzz/x0Ucf4eOPP4a7uzv27t1L4YJ0eDSDQYiOWbt2LeLi4pCSktKs45tT6508HZlMhhMnTkAoFGL//v3Q09NDaGgoIiIiMHz4cJ1q9lZSUoLU1FT07du31SsFaQLDMKiuruZmNmpra7nGfvb29q36WjcMF9paQplhGHz//ffYuHEj/vrrLwwcOFDTQyKEqIH2LBAlhLSZW7duoWvXrujZsydmzJiB7Ozsxx7bnFrv5Ol06tQJkydPxo4dO/DgwQP88ccf6NSpExYsWIBevXph4cKFOHr0KOrr6zU91FZVXFyM1NRU9OvXr0OGC+CfCkJdunSBu7s7hgwZgsGDB8PKygr379/H2bNnkZSUhLy8PLW/1lKpFElJSbCzs9PqcLF9+3Z89tlnOHToEIULQjoQmsEgRMccOXIEtbW18PDwgFgsxvr163Hjxg1kZGTA1tb2keM//PBD/PTTTzh48CB4PB6SkpIQEhICiUSCBw8ewNnZWQPPomNSKpU4f/48oqOjERcXh6qqKgQHB0MgEGDcuHEwMTHR9BDVpqioCNeuXYOXl5fOvofq6upQVFQEsViMiooKWFhYwMHBAY6Oji16raVSKRITE2FjY6O1JZMZhsEvv/yCFStW4ODBgxgxYoSmh0QIUSMKGITouJqaGri7u+ODDz7AkiVLHvl5XV0d3nrrLfz2229crfdZs2Zh06ZNEIvFcHBw0MCoOz6VSoXLly9zYaOoqAgTJ04En8/HxIkTYW5urukhPjOJRIK0tDR4e3u3SbnO9qC+vh5FRUWQSCQoLS2Fubk512vjaV5rdubC2tpaq8PF7t27sXTpUsTHx2P06NGaHhIhRM0oYBBCMH78ePTu3RtRUVGPPUYul0MsFsPZ2Rnbtm3D8uXLUV5erlWlODsqlUqFpKQkxMTEQCgU4v79+xg/fjz4fD6Cg4PbVSMtsViM9PR0+Pj4UDh9DLlczoWNkpISmJiYcGGjS5cujw0N9fX1SExMhJWVFby8vLQ2XOzbtw9vv/02YmJiMHHiRE0PiRDSCihgEKLj6uvr4e7ujgULFmD16tXNus/Dtd5J21GpVEhLS0N0dDSEQiHu3LmDMWPGgM/nIyQkBNbW1lp5Ygn8U4UnMzMTAwYMoAZgzaRQKFBSUgKxWIzi4mIYGRlxy6gsLS2515oNF5aWlujfv7/WvgdiY2OxYMEC7N27F6GhoZoeDiGklVDAIETHLFu2DGFhYejRowckEgnWr1+PM2fOIC0t7ZlrvRPNYBgG169f58JGZmYmRo4cCYFAgNDQUNjZ2WnNieaDBw9w48YNDBgwgOtyTJ6OUqnkGvsVFRVBT08PDg4OsLa2xp07d7Q+XBw8eBBz587F77//joiICE0PhxDSimhtAyFaoC1z/v379/Hiiy+ib9++eP7559GpUydcvnz5X2u9+/r6Yvz48ZBKpY/UeieaoaenBy8vL6xevRrJycnIyMjAmDFj8PPPP6N3794ICQnBtm3bUFhY2KbvsYfl5+fjxo0b8PX1pXDRAgYGBrC3t0f//v0xYsQIeHt7Q6VSIT09HXV1dQD+qcylVCo1PNJHHT16FHPnzsVPP/1E4YIQHUAzGIQQ0sEwDIPc3FzExMQgNjYWf//9NwYPHgw+nw8+nw8XF5c2u8qdl5eHW7duwc/PDzY2Nm3yO3WFTCZDUlISTE1N0b17dxQXF0MsFkMul8POzg6Ojo6wtbXVeBPHU6dOYcaMGfjhhx8wc+ZMrZ1hIYSoDwUMQjRs7dq1MDExwbvvvvtIvwlCWophGOTn50MoFCImJgYXL14Ej8fjwoabm1urnfDdu3cPd+7cgb+/P6ysrFrld+gqNlyYmZnB29ubK7bAMAyqqqq4xn51dXWwtbXlGvsZGRm16TjPnj2LqVOn4ttvv8WcOXMoXBCiIyhgEKJBhYWFmDt3LkxNTRETE6Pp4ZAOjmEYFBYWIi4uDjExMThz5gx8fHy4sNGnTx+1nQDevXsX2dnZ4PF47arKVXvQcObCx8fniZXcqquruV4b1dXVjbqId+7cuVXHeenSJURERGDTpk14/fXXKVwQokMoYBCiQfHx8fjiiy8wb948vPrqq1CpVI1OFhiGoS9l0ioYhkFJSQn279+P6OhonDp1Ch4eHuDz+RAIBC3qoZCTk4Pc3FwKF61ALpcjKSkJJiYm/xouHlZXV8fNbFRUVMDS0pIrf6vuJo5Xr14Fn8/HunXr8Pbbb9PnGCE6hgIGIRq0atUqnD17Fjt37oSHhwfkcjmMjIxQWFgIJycnTQ+P6AiGYVBeXo74+HjExMTg+PHjcHV15cLG05zIZmdn4969ewgICECXLl1aeeS6hQ0XxsbGGDBgQIt60NTX13Nho6ysDF26dOHChpmZWYvGmZycjNDQUHz00UdYsmQJhQtCdBAFDEI0RCKRYMGCBbCwsOBKwrKee+45BAcHY9WqVejUqVOjn7GzGtXV1Th+/Dj27NkDT09PvP3221yFHmp+R1qisrISBw8eRExMDI4ePQonJycubPB4vCbfXyqVCtHR0XBwcKBw0QrkcjlEIhE6d+7c4nDR1GOzy6hKS0u5xn6Ojo4wNzd/qoCQlpaGyZMnY9myZVixYgWFC0J0FJ2FEKIhiYmJkEgkGDJkCIB/TtCAfwLE+PHjkZiY2Chc1NfX48qVK9wX9n//+198+OGH6Ny5Mw4cOIDff/8d+vr6FC5Ii1lYWOCll15CTEwMxGIxPv/8cxQUFCA0NBT9+/fH8uXLcenSJa4cqkqlwrvvvoslS5agd+/eFC7UjA0XnTp1Unu4AAAjIyN07doV/v7+GDlyJHr16oXa2lpcvXoVFy5cwM2bN1FeXv6vpY4zMzMRGhqKd955h8IFITpOs7XrCNFhly9fhr6+PkaMGAHgn54GSqUSBgYGqKmpQX19PXdscXExNm3ahMjISFy7dg1r1qxBdnY2Bg4cyM1+3Lp1C3w+H1u2bEGfPn0a/S7ay0Gelbm5OaZOnYqpU6eirq4Of/31F2JiYjB16lSYmJggNDQUEokE586dw6FDh9CtWzdND7lDUSgUSE5ORqdOneDr69vqFxAMDQ3h5OQEJycnKJVKlJSUQCKRIDk5GQYGBnBwcICxsTG6du3a6AJIVlYWQkNDMX/+fKxZs4Y+bwjRcRQwCNGA0tJSZGZmws3NDf379wfwT8BgTx7YL/nKykpYWFhgy5YtOH/+PH788Uf06tULEokEIpEIly5dgr6+Pt58803cvn0bBw4cwP79+wGgUVhp6ZpqQgDAxMSEqzhVX1+PEydO4IMPPkBOTg5cXFywY8cOREREYPjw4W1eDrUjUigUEIlEMDQ0bJWZi3/DBgoHBweoVCqUlZVBIpFg9erVOH78OEaNGoWIiAh4e3uDz+dj5syZWL9+PYULQggtkSJEEy5fvoxbt27Bx8en0e3sF7OXlxdSU1NhYWGBY8eO4ZtvvsG7776LF154AQAwbtw4DBw4EJGRkXjuuefw6aefYtasWXB2dsbhw4ehUChgYGCAvLw8zJw5E3v27Gnz50g6NiMjIxw8eBC1tbVISUlBVFQUjIyM8Nprr6FXr1544403cPTo0UYzcaT52JkLQ0ND+Pr6wsDAQKPj0dfXh62tLTw9PbFr1y789NNPMDc3xwcffIBhw4bB3NwcgwcP5jqKE0J0GwUMQjSAYRiUlZXho48+wksvvYT//ve/EIvF3D4MqVQKa2trJCYmYs2aNXjppZcwffp0GBsbQyaT4eLFi+jevTvmz5+PN998E99//z3MzMzg5+eHV199Fb6+vrh//z7S09NhaGgIa2tr7vcyDMOtnSfkWSiVSsyfPx/Hjx/HmTNn4OHhgXHjxuGHH35Afn4+YmJi0KVLFyxatAg9e/bEa6+9hgMHDtDJZzOx4UJfX18rwsXDDA0NMWnSJKxZswampqYIDw/H888/j48++gj29vZ4/vnn8fvvv6O8vFzTQyWEaAgFDEI0ICQkBPfu3cOhQ4cgl8vx6quvws3NDWvWrIFKpYKxsTFUKhUiIiLQu3dvrFu3jrtvUlISCgsLuRMPpVKJCxcuQCaT4dChQygoKMDBgwfRrVs3nDp1Cra2thgwYAB3fz09Pe6ERaVSPXbjJhtGOqL8/HzMmjULtra2MDU1hZ+fH5KSkp54nzNnziAgIADGxsbo1asXfvjhhzYarXZRKpWYO3cuzp8/jzNnzqBHjx6Nfm5gYIBRo0bhP//5D+7evYtDhw7ByckJK1asgJubG1555RUIhULU1NRo6BloN6VSiZSUFOjr68PPz0/rwgWroKAAISEhGDNmDP744w9s3LgRN27cwNWrV+Hv748tW7bAwcEBwcHBOHPmjKaHSwhpYxQwCNEAdgZhwoQJ2LdvH6qqqnDs2DH069cP+vr6CAgIwMWLF2FlZYUvv/wSXbt25WY3zpw5A0NDQ676lEwmw/79+zFs2DDusXv27Ina2loYGBjA1dUVLi4ukEql+O233/Daa69hx44dqK2thb6+/mPXS+vp6XE/UyqVHSZslJWVISgoCEZGRjhy5AgyMzPx5ZdfwsrK6rH3ycnJweTJkzF8+HAkJyfjww8/xKJFi3Sy+/qFCxeQnJyMhIQEuLi4PPFYAwMDBAUF4auvvsKdO3dw8uRJ9OrVC59++inc3Nzw0ksvYe/evaisrGyj0Ws3pVKJ5ORkANDqcCEWixESEoJBgwZh+/bt3Dj19PTQv39/fPzxx0hJScH169cxduxYGBrSdk9CdA31wSBEg1QqFZRKZaMNsQqFAps2bcLXX38NoVCI4cOHcx2+VSoVXnzxRRgaGuLHH3+Eubk57t69i4CAAGzevBlz585tshs4wzBYsWIFdu3ahalTp+LcuXMoLS3F22+/jQ8//PCRcX3//ffIysrCzJkzMWjQoEY/UyqVTwwm2m7FihW4cOECzp071+z7LF++HPHx8bh+/Tp328KFC5GamopLly61xjC1mkwme6Q/y9NQqVS4du0aoqOjIRQKkZ2djbFjx4LP5yMkJARWVlbt9v31rNiZC5VKBR6Pp7Xhori4GJMnT0b//v2xe/duCg+EkCbRDAYhGqSvr8+FiytXruCnn37C22+/jd9++w0bNmzA8OHDAfxv8/fff/8NkUiEvn37wtzcHABw48YNVFRUYOrUqdxjAv8LFnp6eqirq8OZM2ewdOlSREVFIT09HZs3b4aNjQ2A//XgYA0ePBj5+fkYO3YsLC0tsWzZMvz9998A/rkq3Z5P/uLj4xEYGIipU6fCwcEB/v7+2L59+xPvc+nSJUyYMKHRbRMnTkRiYiLkcnlrDlcrtSRcAOCW/6xfvx4ZGRlISkrCc889h8jISPTs2RMRERH4+eefUVxc3GFmzp6kYbjw9/fX2nBRWlqK8PBw9OnTB7///juFC0LIY1HAIERLGBoaYuvWrbhy5Qq2bt2KuXPncj9jT+j79euHlStXYtKkSdzPbt68CUdHR5iYmDR6vIZLnMzMzODq6oqoqCj89ttvqKysxPTp0zFz5sxGj88KCAhATEwMqqursWbNGnz11VcYMWIELCwsMGPGDJw4caJV/gZtITs7G1FRUejTpw+OHTuGhQsXYtGiRY90U2+osLAQjo6OjW5zdHSEQqFAcXFxaw+5Q2OX1axZswbJyclIT0/HqFGj8NNPP8Hd3R2hoaHYtm0bCgsLO2TYUCqVSE1N5cKFtp60V1RUQCAQwMXFBX/++SeVISaEPBEtkSJEy8jlchgZGTW7Od6lS5ewYMECdOnSBR999BEmTpz42CugGzZswIULFzBq1Ci8//77T3xcdhnMokWLIBKJEB0djaysLHz33XcAgOjoaMhkMhgZGbWrGY1OnTohMDAQFy9e5G5btGgRrl69+tjlTh4eHpg7dy5WrlzJ3XbhwgUMGzYMBQUFcHJyavVx6xqGYZCbm4uYmBgIhUJcuXIFQ4YMQXh4OPh8PlxcXNrV+64pKpUKKSkpUCgU4PF4WhsuqqqqwOfzYWFhgfj4eBgbG2t6SIQQLUczGIRoGfbKYFMnT01VdhoyZAgOHz6MYcOGISYmhtswyx539+5dZGdnAwDeeOMNvPjii9iwYQPWr1/frHHs3bsX48aNg7W1NUaOHIlff/0VkZGRAP45WX94nHV1dfj1119x9OjRp33qbcLZ2RleXl6NbvP09MS9e/ceex8nJycUFhY2uk0ikcDQ0BC2tratMk5dp6enh549e2LZsmW4cOECcnJyMGXKFBw8eBBeXl4YO3YsvvnmG+Tm5rbLmQ2VSoXU1FQoFAqtnrmoqanBlClTYGpqiri4OAoXhJBmoYBBSDvScNlTQ927d8emTZuwc+dOrucFe1xWVhY+//xzXLx4EdbW1pg1axb69u2L+/fvP/b3qFQq6OnpITU1FcXFxRg7diw6d+4MANi4cSMmTZoEDw8PfPLJJ5BIJNx9gH/KV3722Wf473//2+h2bREUFISsrKxGt928eROurq6Pvc+QIUNw/PjxRrf99ddfCAwMpKUibUBPTw/du3fHu+++i4SEBOTl5WH27Nk4ceIEfH19MWLECGzZsgW3bt1qF2GDDRcymQz+/v5a+x6qra3FtGnToKenh/j4eJiammp6SISQdoICBiEdwJOa57m7u6OyshKTJ09GYGAgpk+fjuTkZK4reFPYULBr1y74+PjAw8MDAFBdXY1x48Zh9+7dWLFiBY4fP47ly5cD+N/m8qysLJSUlODtt99W51NUm8WLF+Py5cvYsGEDbt++jT179mDbtm146623uGNWrlyJ2bNnc/+9cOFC3L17F0uWLMH169exa9cu7Ny5E8uWLdPEU9Bpenp6cHZ2xptvvokTJ07gwYMHeOONN3Dx4kU899xzGDJkCDZu3IjMzEytDBtsBS2ZTAYej6e14UIqleLFF1+EVCrFwYMHuaIShBDSHLQHgxAdUVJSgj/++AOVlZWYOHEiAgICHnssu//DxcUFc+fOxccff8zNYIjFYjg4OEBPTw8XL17EnDlzEBUVhbFjx0IqlWL9+vXYvXs3cnJyGj2WNjl48CBWrlyJW7duoWfPnliyZAnmz5/P/XzOnDnIzc1FQkICd9uZM2ewePFiZGRkoGvXrli+fDkWLlyogdGTpjAMg7KyMsTHx0MoFOL48eNwc3MDn8+HQCCAt7d3o/LNmqBSqZCWloa6ujoEBARobbior6/HzJkzUVRUhL/++oubFSWEkOaigEFIB8fORjT35Irto5GZmQlvb2+cPn0aI0eORFFREd566y3cv38fYrEY/fv3R2hoKDZu3IivvvoKERERyM7OxqxZs+Dr64uoqCgA/zS2y8vLa9RNHPineo6enp7GT/pIx1RRUYGDBw9CKBTi6NGjcHZ2Rnh4OCIiIuDv79/m77v2Ei7kcjlmz56Nu3fv4uTJk7THiBDyTChgEKJDlErlv9bYVygUMDQ0xNKlS3HixAmcOHEC1tbWeO+99/Dnn3/ip59+gqmpKQ4cOIB9+/ahoKAAZWVlsLS0xLFjxzBt2jScP38ePj4++OSTT3D06FEUFxfjs88+w7Rp09romRLyP9XV1Thy5AhiYmJw+PBh2NjYICwsDBERERg4cGCr951QqVRIT09HTU0NAgICWtxHpLUoFArMmzcPN27cwOnTp2Fvb6/pIRFC2intLFtBCGkVzTmRYqvZxMbGYtKkSbCwsIChoSHOnDmDmTNnIiwsDAAwevRoFBcXIysrC5aWlqiuroZIJIJMJsOJEycwefJk9O3bFwsWLMDs2bNhYGCA+/fvY9++fTh58iQsLCywYMECjBo1qjWfMiEwNzfH1KlTMXXqVNTW1uKvv/5CTEwMXnjhBZiamiI8PBwCgQBDhgxRezWn9hIulEolFi5ciMzMTJw6dYrCBSGkRShgEEKadPv2bZSWlnJ7L8aNG4cLFy7gzJkzcHZ2RmRkJPbs2YPNmzcD+GdvBttb4ttvv8Xnn3+OF154gStrmZeXx53QTZ06FZcvX8aCBQuwdu1avPTSS5p5kkTnmJqaQiAQQCAQQCqV4uTJk4iJicHMmTNhYGDAzWwMGzasxcuYGIZBRkYGqqurERgYqNXh4p133kFiYiJOnz5NfV0IIS1GAYMQ8giGYaCvrw87OzvutnfffRcFBQV4/vnnMXToUNTU1AAAZsyYAQDIzc3FuXPnsHPnTkRGRkKpVMLY2BgKhQIMw2Dr1q3IycnBtWvX4OzsjLfeegs7duzAihUrEBoaCgsLC408V6K7jI2NERISgpCQEMjlciQkJCA6OhqvvvoqFAoFQkNDwefzMWrUKC5oNxcbLqqqqrQ6XKhUKixZsgRnz55FQkICXFxcND0kQkgHQHswCCFPLS8vDyKRCBs3bsTly5dRW1uLzZs3Y9euXbh79y7WrFmD6OhopKamwtDQEAUFBRg6dChkMhlqa2vh5uaG+fPnw97eHtu2bcP69esxaNAgTT8tQgD8sxfh/PnziI6ORlxcHKqrqxESEgI+n4+xY8fCxMTkifdnw0VlZSUCAgKeOpy0FZVKheXLl+PAgQNISEhAr169ND0kQkgHQQGDENIsKpUKKpWqyTXqpaWlmDdvHvT09BAbG4v8/HyMHj0a77//PubPn49bt26hb9++uHXrFvT19REbG4t9+/YhLS0NBgYGOHnyJAIDAzXwrAh5MqVSiUuXLiEmJgaxsbEoLS3FxIkTIRAIMGHCBJiZmT1yPLu3aNq0aVodLj766CPs27cPCQkJ6NOnj6aHRAjpQChgEEKeCVttqqGqqip06dIFALBu3Trs27cPf//9NzIzMzFlyhR8/vnnmD59Onf83bt3kZmZieDg4DYdOyHPQqVSITExEdHR0YiNjcWDBw8wfvx4CAQCTJo0CWZmZpg5cyZEIhGOHTuGnj17anrITWIYBp9++il++eUXJCQkoF+/fpoeEiGkg6GAQQhpsaaa6YnFYmzatAnPP/88goKCMH/+fDx48ADbtm2Di4sLpFIptwGckPZGpVIhNTWVCxt37txBv379IJFIEBsbCx8fH61rMAn882/1888/xw8//IDTp0/D29tb00MihHRA1OGKENJiTZ1IOTo64ssvv0RQUBAAYOnSpZBKpfD09ASfz8fatWuxbds2lJeXt/FoCWk5fX19+Pv747PPPkNaWhr4fD7y8vLg4uKCESNG4Pnnn8cvv/yC4uJiaMt1PIZh8PXXXyMyMhLHjx9v9XARGRmJnj17wtjYGAEBATh37twTj9+9ezd8fX1hamoKZ2dnzJ07FyUlJY2O2bp1K/r27QsTExN0794dixcvhlQqbc2nQQh5BhQwCCGtQqVSNTqx6tevH06ePIlz585hwIABqKqqQp8+fWBlZaW5QRLSQgzDcCVeU1JScPXqVaSlpWHkyJHYuXMn3N3dERYWhu3bt0MsFmssbDAMg//85z/46quvcPToUfj5+bXq79u7dy/ee+89rFq1CsnJyRg+fDiCg4Nx7969Jo8/f/48Zs+ejVdffRUZGRnYt28frl69itdee407Zvfu3VixYgXWrFmD69evY+fOndi7dy9WrlzZqs+FEPL0aIkUIaRNqFQq6Os3vqbR1NIq0jby8/OxfPlyHDlyBHV1dfDw8MDOnTsREBDQ5PEFBQVYunQpkpKScOvWLSxatAhbt25t20FrGYZhsGjRIhw4cABnzpyBq6vrIz/PyclBTEwMhEIhrl69iqFDhyI8PBx8Ph9du3Ztk/c/wzDYtm0bPvnkExw5cgRDhgxp9d85aNAg8Hg8REVFcbd5enpCIBBg48aNjxy/ZcsWREVF4c6dO9xt3333HTZt2oS8vDwAwNtvv43r16/j5MmT3DFLly7FlStX/nV2hBDStmgGgxDSJthwwTAMlEolgKaXVpHWV1ZWhqCgIBgZGeHIkSPIzMzEl19++cTZpPr6etjb22PVqlXw9fVtu8FqKYZhsHjxYsTHx+P06dOPhAvgn/d3r1698P777+PixYvIzs7G888/j/j4eHh5eWHs2LH45ptvcPfu3Vab2WAYBj///DPWrFmD+Pj4NgkXMpkMSUlJmDBhQqPbJ0yYwDXjfNjQoUNx//59HD58GAzDQCwWIzo6GiEhIdwxw4YNQ1JSEq5cuQIAyM7OxuHDhxsdQwjRDtRojxDSpvT09GBgYKDpYei0L774At27d8dPP/3E3ebm5vbE+7i5ueGbb74BAOzatas1h9cuKJVKyGQynD59ulnVovT09NCjRw+89957XNPK2NhYCIVCrF69GgMGDIBAIACfz4e7u7tawjfDMPj999+xYsUKxMfHY8SIES1+zOYoLi6GUqmEo6Njo9sdHR1RWFjY5H2GDh2K3bt3Y/r06ZBKpVAoFAgPD8d3333HHTNjxgwUFRVh2LBhYBgGCoUCb7zxBlasWNGqz4cQ8vRoBoMQQnRMfHw8AgMDMXXqVDg4OMDf3x/bt2/X9LDaFUNDQ0RGRj5Tczo9PT107doVb731Fk6cOIEHDx7g9ddfx4ULFxAYGIghQ4bg888/x/Xr1595ZoNhGOzbtw9Lly5FdHQ0Ro8e/UyP0xIPh6QnLYnMzMzEokWLsHr1aiQlJeHo0aPIycnBwoULuWMSEhLw2WefITIyEiKRCEKhEAcPHsS6deta9XkQQp4e7cEghBAdw5YHXrJkCaZOnYorV67gvffew48//ojZs2f/6/1HjRoFPz8/nd+DoW4Mw6CsrAz79++HUCjE8ePH0atXL/D5fAgEAvTv3/+RfUyPExsbi9dffx1//vknQkNDW3nkjclkMpiammLfvn2IiIjgbn/33XeRkpKCM2fOPHKfl19+GVKpFPv27eNuO3/+PIYPH44HDx7A2dkZw4cPx+DBg7F582bumN9//x0LFixAdXV1s/82hJDWR/8aCSFEx6hUKvB4PGzYsAH+/v54/fXXMX/+/EYbcknb09PTg42NDebOnYsDBw5ALBbjww8/RFZWFsaMGQN/f3+sXr0aIpEIKpXqsY9z8OBBLFiwAL/99lubhwsA6NSpEwICAnD8+PFGtx8/fhxDhw5t8j61tbWPBAR2KSV7HfRxxzAMozWlgAkh/6CAQQghOsbZ2RleXl6NbvP09HxsCVGiGZaWlpg1axaEQiHEYjE+++wz5OXlITg4GD4+PlixYgX+/vvvRmHj6NGjmDdvHn766adGswdtbcmSJdixYwd27dqF69evY/Hixbh37x635GnlypWNZsvCwsIgFAoRFRWF7OxsXLhwAYsWLcJzzz2Hrl27csdERUXhzz//RE5ODo4fP46PP/4Y4eHhtK+LEC1Dm7wJIUTHBAUFISsrq9FtN2/ebLISEtEO5ubmmDZtGqZNm4ba2locO3YMMTExeP7552FmZobw8HC4urpi3bp1+PHHHzF16lSNjnf69OkoKSnBp59+ioKCAnh7e+Pw4cPce6ygoKBRoJ0zZw6qqqrwn//8B0uXLoWVlRXGjBmDL774gjvmo48+gp6eHj766CPk5+fD3t4eYWFh+Oyzz9r8+RFCnoz2YBBCiI5h+zF88sknmDZtGq5cuYL58+dj27ZtmDlzJoB/rjDn5+fj119/5e6XkpICAHjttdfQt29fvP/+++jUqdMjsyGk7UilUpw4cQLR0dH47bffsHLlSqxbt45KQBNCNIoCBiGE6KCDBw9i5cqVuHXrFnr27IklS5Zg/vz53M/nzJmD3NxcJCQkcLc1ddLq6uqK3NzcNhgx+TcVFRUwNzen5UKEEI2jgEEIIYQQQghRG9rkTQghhBBCCFEbChiEEEIIIYQQtaGAQQghhBBCCFEbChiEEEIIIYQQtaGAQQghhBBCCFEbChiEEEIIIYQQtaGAQQghhBBCCFEbChiEEEIIIYQQtaGAQQghhBBCCFEbChiEEEIIIYQQtaGAQQghhBBCCFEbChiEEEJ0Vn5+PmbNmgVbW1uYmprCz88PSUlJjz1eKBRi/PjxsLe3h4WFBYYMGYJjx4614YgJIUT7UcAghBCik8rKyhAUFAQjIyMcOXIEmZmZ+PLLL2FlZfXY+5w9exbjx4/H4cOHkZSUhNGjRyMsLAzJycltN3BCCNFyegzDMJoeBCGEENLWVqxYgQsXLuDcuXMtepz+/ftj+vTpWL16tZpGRggh7RvNYBBCCNFJ8fHxCAwMxNSpU+Hg4AB/f39s3779qR5DpVKhqqoKNjY2rTRKQghpfyhgEEII0UnZ2dmIiopCnz59cOzYMSxcuBCLFi3Cr7/+2uzH+PLLL1FTU4Np06a14kgJIaR9oSVShBBCdFKnTp0QGBiIixcvcrctWrQIV69exaVLl/71/n/88Qdee+017N+/H+PGjWvNoRJCSLtCMxiEEEJ0krOzM7y8vBrd5unpiXv37v3rfffu3YtXX30V//3vfylcEELIQyhgEEII0UlBQUHIyspqdNvNmzfh6ur6xPv98ccfmDNnDvbs2YOQkJDWHCIhhLRLFDAIIYTopMWLF+Py5cvYsGEDbt++jT179mDbtm146623uGNWrlyJ2bNnc//9xx9/YPbs2fjyyy8xePBgFBYWorCwEBUVFZp4CoQQopVoDwYhhBCddfDgQaxcuRK3bt1Cz549sWTJEsyfP5/7+Zw5c5Cbm4uEhAQAwKhRo3DmzJlHHueVV17Bzz//3EajJoQQ7UYBgxBCCCGEEKI2tESKEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFqQwGDEEIIIYQQojYUMAghhBBCCCFq83/TyGCPIEoZvwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# Create a 3D scatter plot for the optimal scenario\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(optimal_scenario['LyftTake'], optimal_scenario['MatchRate'], optimal_net_revenue, s=100, c='red', marker='o')\n",
    "ax.set_xlabel(\"Lyft's Take\")\n",
    "ax.set_ylabel(\"Match Rate\")\n",
    "ax.set_zlabel(\"Net Revenue\")\n",
    "ax.set_title(\"Optimal Scenario for Maximizing Net Revenue\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Based on the predictions from the Machine Learning model, the optimal scenario for maximizing net revenue on the Toledo route is as follows:\n",
    "\n",
    "#### - Optimal Lyft's Take: Approximately $5.91 per ride\n",
    "#### - Match Rate: About 92%\n",
    "\n",
    "#### - Driver Churn Rate: Approximately 6.88%\n",
    "\n",
    "#### - Rider Churn Rate: Around 32.3%\n",
    "\n",
    "#### - Predicted Net Revenue (per Month for 100 Rides): $529.21\n",
    "\n",
    "#### This scenario suggests that slightly increasing Lyft's take to about $5.91, while maintaining a high match rate of around 92%, could maximize net revenue. It's important to note that this is based on simulated data, and real-world results may vary. In practice, these predictions should be tested in a controlled environment, and adjustments should be made based on actual performance data.\n",
    "\n",
    "By using this ML approach, we can dynamically adjust our strategy based on changing market conditions and continuously optimize for net revenue. This solution allows for a more nuanced and data-driven decision-making process. ​"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}