{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# CSC 3105 Project"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cda961ffb493d00c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Preprocessing and Analysis"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "73fe8802e95a784f"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pywt\n",
    "import tensorflow as tf\n",
    "from skimage import restoration\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.layers import Conv1D, Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:52.857175Z",
     "start_time": "2024-03-21T13:38:52.843610Z"
    }
   },
   "id": "2aa3c6c09e8645d1",
   "execution_count": 39
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Load the Data\n",
    "\n",
    "This code block defines a function `load_data` to load the dataset from the specified directory. The function uses the `os` and `pandas` libraries to load the data from multiple CSV files into a single DataFrame. The `os` library is used to traverse the directory and find all the CSV files, while the `pandas` library is used to read the CSV files and concatenate them into a single DataFrame. The function then prints the shape of the original data."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e7597045d3c34419"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Define the directory where the dataset is located\n",
    "DATASET_DIR = './UWB-LOS-NLOS-Data-Set/dataset'\n",
    "\n",
    "\n",
    "def load_data(dataset_dir):\n",
    "    # Load the data\n",
    "    # file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames if 'uwb_dataset_part7.csv' not in file]\n",
    "    file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames]\n",
    "    data = pd.concat((pd.read_csv(file_path) for file_path in file_paths))\n",
    "    print(f\"Original data shape: {data.shape}\")\n",
    "    return data\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.000935Z",
     "start_time": "2024-03-21T13:38:52.996670Z"
    }
   },
   "id": "7bcd7cfc8dd11cbb",
   "execution_count": 40
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Statistical Analysis and Plots\n",
    "\n",
    "This code block defines a function `stat_analysis_and_plots` to perform statistical analysis and plot boxplots of the first 15 columns of the data. The function uses the `describe` method of the DataFrame to perform statistical analysis and the `boxplot` method of the `matplotlib` library to plot boxplots of the first 15 columns of the data. The boxplot is used to check for outliers in the data."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8cdfcf73ad317dd5"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def stat_analysis_and_plots(data):\n",
    "    # Statistical Analysis\n",
    "    print(\"Statistical Analysis:\")\n",
    "    print(data.describe())\n",
    "\n",
    "    # Plot Boxplot to check for outliers for the first 15 columns\n",
    "    print(\"Boxplot of the first 15 columns:\")\n",
    "    fig, axs = plt.subplots(15, 1, dpi=95, figsize=(7, 17))\n",
    "    for i, col in enumerate(data.columns[:15]):\n",
    "        axs[i].boxplot(data[col], vert=False)\n",
    "        axs[i].set_ylabel(col)\n",
    "    plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.026070Z",
     "start_time": "2024-03-21T13:38:53.021620Z"
    }
   },
   "id": "9e0b1ed6f23a17cf",
   "execution_count": 41
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Channel Impulse Response (CIR) Graphs\n",
    "\n",
    "This code block defines a function `cir_graphs` to plot the Channel Impulse Response (CIR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The CIR is a sequence of values representing the channel response to a single impulse. It is used to characterize the channel in wireless communication systems."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1dd92fe7b6881ea6"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def cir_graphs(data):\n",
    "    # Separate the data into LOS and NLOS\n",
    "    data_los = data[data['NLOS'] == 0]\n",
    "    data_nlos = data[data['NLOS'] == 1]\n",
    "\n",
    "    # Extract the CIR columns\n",
    "    cir_columns = [col for col in data.columns if 'CIR' in col]\n",
    "    data_los_cir = data_los[cir_columns]\n",
    "    data_nlos_cir = data_nlos[cir_columns]\n",
    "\n",
    "    # Calculate the magnitude and time for each CIR column\n",
    "    time_los = np.arange(len(data_los_cir.columns))\n",
    "    magnitude_los = np.linalg.norm(data_los_cir.values, axis=0)\n",
    "\n",
    "    time_nlos = np.arange(len(data_nlos_cir.columns))\n",
    "    magnitude_nlos = np.linalg.norm(data_nlos_cir.values, axis=0)\n",
    "\n",
    "    # Plot the magnitude vs time for LOS\n",
    "    plt.figure(figsize=(20, 10), dpi=300)  # Increase figure size and DPI\n",
    "    plt.plot(time_los, magnitude_los, linewidth=2)  # Increase line width\n",
    "    plt.title('Magnitude vs Time for LOS')\n",
    "    plt.xlabel('Time (ns)')\n",
    "    plt.ylabel('Magnitude')\n",
    "    plt.xlim([600, max(time_los)])  # Set x-axis limits\n",
    "    # plt.ylim([0, 2e6])\n",
    "    plt.show()\n",
    "\n",
    "    # Plot the magnitude vs time for NLOS\n",
    "    plt.figure(figsize=(20, 10), dpi=300)  # Increase figure size and DPI\n",
    "    plt.plot(time_nlos, magnitude_nlos, linewidth=2)  # Increase line width\n",
    "    plt.title('Magnitude vs Time for NLOS')\n",
    "    plt.xlabel('Time (ns)')\n",
    "    plt.ylabel('Magnitude')\n",
    "    plt.xlim([600, max(time_los)])  # Set x-axis limits\n",
    "    # plt.ylim([0, 2e6])\n",
    "    plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.057656Z",
     "start_time": "2024-03-21T13:38:53.050382Z"
    }
   },
   "id": "308d64639b199bc7",
   "execution_count": 42
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Calculate Total Distance\n",
    "\n",
    "This code block defines a function `calculate_total_distance` to calculate the total distance for each data point. The total distance is calculated as the sum of the absolute values of the Channel Impulse Response (CIR) columns multiplied by the speed of light in meters per nanosecond.\n",
    "\n",
    "$$\n",
    "\\text{Total Distance} = \\sum_{i=1}^{n} |CIR_i| \\times \\text{speed of light}\n",
    "$$\n",
    "where:\n",
    "- $n$ is the number of CIR columns\n",
    "- $CIR_i$ is the $i$-th CIR column\n",
    "- $\\text{speed of light}$ is the speed of light in meters per nanosecond"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "531a6ffbad89ba15"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def calculate_total_distance(data):\n",
    "    # Speed of light in meters per nanosecond\n",
    "    speed_of_light_ns = 0.299792458\n",
    "\n",
    "    # Extract the CIR columns\n",
    "    cir_columns = [col for col in data.columns if 'CIR' in col]\n",
    "\n",
    "    # Calculate the total distance for each row\n",
    "    data['Total_Distance'] = data[cir_columns].abs().sum(axis=1) * speed_of_light_ns\n",
    "\n",
    "    return data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.077550Z",
     "start_time": "2024-03-21T13:38:53.073733Z"
    }
   },
   "id": "80cfcfac265d9357",
   "execution_count": 43
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Signal to Noise Ratio (SNR) Graph\n",
    "\n",
    "This code block defines a function `snr_graph` to plot the Signal to Noise Ratio (SNR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The SNR is calculated as the ratio of the 'CIR_PWR' to the 'STDEV_NOISE' for each data point."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bfd97fbe797a7067"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "def snr_graph(data):\n",
    "    # Separate the data into LOS and NLOS\n",
    "    data_los = data[data['NLOS'] == 0]\n",
    "    data_nlos = data[data['NLOS'] == 1]\n",
    "\n",
    "    # Extract the SNR values\n",
    "    snr_los = data_los['SNR']\n",
    "    snr_nlos = data_nlos['SNR']\n",
    "\n",
    "    # Create a new figure\n",
    "    plt.figure(figsize=(10, 5))\n",
    "\n",
    "    # Plot SNR for LOS\n",
    "    plt.plot(snr_los, label='LOS')\n",
    "\n",
    "    # Plot SNR for NLOS\n",
    "    plt.plot(snr_nlos, color='red', label='NLOS')\n",
    "\n",
    "    # Set title and labels\n",
    "    plt.title('SNR for LOS and NLOS')\n",
    "    plt.xlabel('Index')\n",
    "    plt.ylabel('SNR')\n",
    "\n",
    "    # Add a legend\n",
    "    plt.legend()\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.117354Z",
     "start_time": "2024-03-21T13:38:53.112375Z"
    }
   },
   "id": "4afc8d71b3271351",
   "execution_count": 44
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Plot Histogram\n",
    "\n",
    "This code block defines a function `plot_histogram` to plot a histogram of a given feature in the data for Line of Sight (LOS) and Non-Line of Sight (NLOS) data."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "74cbef5687382eea"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def plot_histogram(data, feature):\n",
    "    \"\"\"\n",
    "    Function to plot a histogram of a given feature in the data for 'NLOS' and 'LOS'.\n",
    "\n",
    "    Parameters:\n",
    "    data (pd.DataFrame): The data.\n",
    "    feature (str): The name of the feature to plot.\n",
    "\n",
    "    Returns:\n",
    "    None\n",
    "    \"\"\"\n",
    "    # Check if the feature exists in the data\n",
    "    if feature not in data.columns:\n",
    "        print(f\"The feature '{feature}' does not exist in the data.\")\n",
    "        return\n",
    "\n",
    "    # Separate the data into 'NLOS' and 'LOS'\n",
    "    data_nlos = data[data['NLOS'] == 1]\n",
    "    data_los = data[data['NLOS'] == 0]\n",
    "\n",
    "    # Create a figure with two subplots side by side\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "    # Plot the histogram for 'NLOS'\n",
    "    axs[0].hist(data_nlos[feature], bins=30, edgecolor='black')\n",
    "    axs[0].set_title(f'Histogram of {feature} for NLOS')\n",
    "    axs[0].set_xlabel(feature)\n",
    "    axs[0].set_ylabel('Frequency')\n",
    "\n",
    "    # Plot the histogram for 'LOS'\n",
    "    axs[1].hist(data_los[feature], bins=30, edgecolor='black')\n",
    "    axs[1].set_title(f'Histogram of {feature} for LOS')\n",
    "    axs[1].set_xlabel(feature)\n",
    "    axs[1].set_ylabel('Frequency')\n",
    "\n",
    "    # Display the plots\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Usage:\n",
    "# plot_histogram(data, 'First_Path_Power_Level')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.149693Z",
     "start_time": "2024-03-21T13:38:53.143648Z"
    }
   },
   "id": "22025d6c8281fc09",
   "execution_count": 45
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def plot_features(data, labels, feature1, feature2):\n",
    "    reds = labels == 1\n",
    "    blacks = labels == 0\n",
    "    plt.scatter(data[reds][feature1], data[reds][feature2], c=\"red\", s=20, edgecolor='k')\n",
    "    plt.scatter(data[blacks][feature1], data[blacks][feature2], c=\"yellow\", s=20, edgecolor='k')\n",
    "    plt.xlabel(feature1)\n",
    "    plt.ylabel(feature2)\n",
    "    plt.title(f\"Plot of data: {feature1} versus {feature2}\")\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.190705Z",
     "start_time": "2024-03-21T13:38:53.186718Z"
    }
   },
   "id": "ac4db13fed3f9916",
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def plot_learning_curve(history):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(history.history['loss'], label='Training Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss / Accuracy')\n",
    "    plt.title('Learning Curve')\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.209130Z",
     "start_time": "2024-03-21T13:38:53.205232Z"
    }
   },
   "id": "9d9ee6b43c102fd9",
   "execution_count": 47
  },
  {
   "cell_type": "markdown",
   "source": [
    "## denoise_cir Function\n",
    "\n",
    "The `denoise_cir` function uses the Discrete Wavelet Transform (DWT) to denoise the Channel Impulse Response (CIR) values. The DWT is a linear transformation that operates on a data vector whose length is an integer power of two, transforming it into a numerically different vector of the same length. The DWT of a signal `x` is calculated as follows:\n",
    "\n",
    "1. **Wavelet Decomposition:**\n",
    "\n",
    "    The input signal `x` is passed through two complementary filters and emerges as two signals. The filter outputs are decimated by 2 (down-sampled) to get the approximation coefficients (cA) and detail coefficients (cD).\n",
    "\n",
    "    The approximation coefficients represent the high-scale, low-frequency component of the signal, while the detail coefficients represent the low-scale, high-frequency component.\n",
    "\n",
    "2. **Thresholding:**\n",
    "\n",
    "    The detail coefficients are thresholded to remove noise. The thresholding function `T` applied to the detail coefficients `x` is defined as:\n",
    "$$\n",
    "T(x) = x * I(|x| > \\text{{value}}) \\quad \\text{{for 'hard' thresholding}}\n",
    "$$\n",
    "\n",
    "$$\n",
    "T(x) = \\text{{sign}}(x)(|x| - \\text{{value}})_+ \\quad \\text{{for 'soft' thresholding}}\n",
    "$$\n",
    "\n",
    "where $I$ is the indicator function that is one if the argument is true and zero otherwise, $\\text{{value}}$ is the threshold value, and $(x)_+$ equals $x$ if $x > 0$ and zero otherwise.\n",
    "\n",
    "3. **Wavelet Reconstruction:**\n",
    "\n",
    "    The original signal is reconstructed from the approximation and detail coefficients."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "69413268ac5b549d"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def denoise_cir(cir_values, wavelet='db1', level=1):\n",
    "    # Perform wavelet decomposition\n",
    "    coeffs = pywt.wavedec(cir_values, wavelet, level=level)\n",
    "\n",
    "    # Set the detail coefficients to zero\n",
    "    for i in range(1, len(coeffs)):\n",
    "        coeffs[i] = pywt.threshold(coeffs[i], value=0.5, mode='soft')\n",
    "\n",
    "    # Perform wavelet reconstruction\n",
    "    denoised_cir = pywt.waverec(coeffs, wavelet)\n",
    "\n",
    "    return denoised_cir\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.233683Z",
     "start_time": "2024-03-21T13:38:53.230379Z"
    }
   },
   "id": "fe3089568e99a58d",
   "execution_count": 48
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Discrete Fourier Transform (DFT)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9fa7435148a660ec"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def perform_dft(data, cir_columns):\n",
    "    # Extract the CIR values\n",
    "    cir_values = data[cir_columns].values\n",
    "\n",
    "    # Perform DFT on the CIR values\n",
    "    dft_values = np.fft.fft(cir_values, axis=1)\n",
    "\n",
    "    # Take the absolute values of the DFT\n",
    "    dft_values_abs = np.abs(dft_values)\n",
    "\n",
    "    # Return the absolute values\n",
    "    return dft_values_abs"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.264050Z",
     "start_time": "2024-03-21T13:38:53.261341Z"
    }
   },
   "id": "cef7ba14c54b52d8",
   "execution_count": 49
  },
  {
   "cell_type": "markdown",
   "source": [
    "## deconvolve_cir Function\n",
    "\n",
    "The `deconvolve_cir` function applies the Richardson-Lucy deconvolution algorithm to deconvolve the Channel Impulse Response (CIR) values. \n",
    "\n",
    "In the context of signal processing, deconvolution is the process of reversing the effects of convolution on a signal. Convolution is a mathematical operation that blends two functions together and is often used to describe the effect of a linear time-invariant system on a signal. Deconvolution, therefore, attempts to recover the original signal that was convolved with the system's impulse response to produce the current signal.\n",
    "\n",
    "The Richardson-Lucy algorithm is an iterative method for deconvolution. It is particularly suitable for cases where the impulse response of the system (also known as the Point Spread Function, or PSF) is known, and the noise is Poissonian (such as in astronomical images). \n",
    "\n",
    "The algorithm works by iteratively refining an estimate of the original signal. In each iteration, it performs a prediction step where it convolves the current estimate with the PSF to predict the observed signal, and a correction step where it computes the ratio of the observed signal to the predicted signal, convolves this ratio with the PSF, and multiplies the result with the current estimate to get the next estimate.\n",
    "\n",
    "This process is repeated for a fixed number of iterations, or until the estimate converges to a stable solution. The result is a denoised estimate of the original signal.\n",
    "\n",
    "The mathematical formulas involved in the Richardson-Lucy deconvolution algorithm are as follows:\n",
    "\n",
    "1. **Prediction Step:**\n",
    "\n",
    "    The current estimate of the latent image `x` is convolved with the PSF `h` to predict the observed image `y`. This can be represented as:\n",
    "\n",
    "$$\n",
    "y = h \\ast x\n",
    "$$\n",
    "\n",
    "where $\\ast$ denotes the convolution operation.\n",
    "\n",
    "2. **Correction Step:**\n",
    "\n",
    "The ratio of the observed image $y$ to the predicted image $y'$ is computed, then the PSF $h$ is convolved with this ratio and multiplied with the current estimate $x$ to get the next estimate $x'$. This can be represented as:\n",
    "\n",
    "$$\n",
    "x' = x \\cdot (h \\ast \\left(\\frac{y}{y'}\\right))\n",
    "$$\n",
    "\n",
    "where $\\div$ denotes element-wise division and $\\ast$ denotes the convolution operation.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e1edd5ef4f54e752"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "def deconvolve_cir(cir_values, psf=None, iterations=50):\n",
    "    # If no point spread function is provided, create a simple one\n",
    "    if psf is None:\n",
    "        psf = np.ones((5,)) / 5\n",
    "\n",
    "    # Perform Richardson-Lucy deconvolution\n",
    "    deconvolved_cir = restoration.richardson_lucy(cir_values, psf, num_iter=iterations)\n",
    "\n",
    "    return deconvolved_cir"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.286019Z",
     "start_time": "2024-03-21T13:38:53.282665Z"
    }
   },
   "id": "670e8c2cf19126ea",
   "execution_count": 50
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Clean the Data\n",
    "\n",
    "This code block defines a function `clean_data` to clean the dataset. The function performs the following steps:\n",
    "\n",
    "1. **Drop Missing Values**: It drops any rows with missing values in the dataset.\n",
    "2. **Drop Duplicate Rows**: It drops any duplicate rows in the dataset.\n",
    "3. **Convert 'NLOS' Column to Integer**: It converts the 'NLOS' column to integer data type (0 for Line of Sight (LOS), 1 for Non-Line of Sight (NLOS)).\n",
    "4. **Calculate 'RX_Level'**: It calculates the 'RX_Level' feature using the formula.\n",
    "5. **Replace Zero Values in 'RX_Level'**: It replaces zero values in 'RX_Level' with the median of 'RX_Level'.\n",
    "6. **Calculate 'First_Path_Power_Level'**: It calculates the 'First_Path_Power_Level' feature using the formula.\n",
    "7. **Calculate 'SNR'**: It calculates the 'SNR' feature as the ratio of 'CIR_PWR' to 'STDEV_NOISE' for each data point.\n",
    "8. **One-Hot Encode Categorical Features**: It one-hot encodes the categorical features 'CH', 'FRAME_LEN', 'PREAM_LEN', and 'BITRATE'.\n",
    "9. **Denoise 'CIR' Columns**: It denoises the 'CIR' columns using the `denoise_cir` function.\n",
    "10. **Drop Columns with Only One Unique Value**: It drops any columns that have only one unique value.\n",
    "11. **Print the Shape of the Cleaned Data**: It prints the shape of the cleaned data.\n",
    "12. **Return the Cleaned Data**: It returns the cleaned data."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b61e454327e5acbf"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def clean_data(data):\n",
    "    print(\"Starting data cleaning process...\")\n",
    "\n",
    "    # print(\"Before Cleaning\")\n",
    "    # stat_analysis_and_plots(data)\n",
    "\n",
    "    # Calculate total number of missing values in the data\n",
    "    total_missing = data.isnull().sum().sum()\n",
    "    print(f\"Total number of missing values: {total_missing}\")\n",
    "\n",
    "    # Data has no missing values\n",
    "    data = data.dropna()\n",
    "    print(\"Missing values dropped.\")\n",
    "\n",
    "    # Data has no duplicate rows\n",
    "    data = data.drop_duplicates()\n",
    "    print(\"Duplicate rows dropped.\")\n",
    "\n",
    "    # Convert 'NLOS' column to integer data type (0 for LOS, 1 for NLOS)\n",
    "    data['NLOS'] = data['NLOS'].astype(int)\n",
    "    print(\"'NLOS' column converted to integer data type.\")\n",
    "\n",
    "    # Print line where CIR_PWR is 0\n",
    "    print(f\"Line where CIR_PWR is 0: {data[data['CIR_PWR'] == 0]}\")\n",
    "\n",
    "    # Calculate the expression inside the log10 function\n",
    "    expression = (data['CIR_PWR'] * (2 ** 17)) / (data['RXPACC'] ** 2)\n",
    "\n",
    "    # If the expression is 0, set 'RX_Level' to 0\n",
    "    zero_indices = expression == 0\n",
    "    data.loc[zero_indices, 'RX_Level'] = 0\n",
    "\n",
    "    # For the rest of the data where the expression is not 0, calculate 'RX_Level'\n",
    "    # First, update the 'expression' and 'data' to exclude zero_indices\n",
    "    expression = expression.loc[~zero_indices]\n",
    "    data = data.loc[~zero_indices]\n",
    "\n",
    "    # Now, calculate 'RX_Level' for the rest of the data\n",
    "    data['RX_Level'] = 10 * np.log10(expression) - data['PRFR']\n",
    "\n",
    "    # Calculate the median of 'RX_Level'\n",
    "    median = data['RX_Level'].median()\n",
    "\n",
    "    # Create the boolean mask on the same DataFrame 'data'\n",
    "    zero_indices = (data['RX_Level'] == 0)\n",
    "\n",
    "    # Replace zero values in 'RX_Level' with the median\n",
    "    data.loc[zero_indices, 'RX_Level'] = median\n",
    "\n",
    "    print(\"New feature 'RX_Level' created.\")\n",
    "\n",
    "    # Calculate new feature 'First_Path_Power_Level'\n",
    "    data['First_Path_Power_Level'] = (10 * np.log10(\n",
    "        (data['FP_AMP1'] ** 2 + data['FP_AMP2'] ** 2 + data['FP_AMP3'] ** 2) / (data['RXPACC'] ** 2))) - 64\n",
    "    print(\"New feature 'First_Path_Power_Level' calculated.\")\n",
    "    data.drop(['FP_AMP1', 'FP_AMP2', 'FP_AMP3', 'RXPACC', 'PRFR'], axis=1, inplace=True)\n",
    "\n",
    "    # Calculate SNR as the ratio of 'CIR_PWR' to 'STDEV_NOISE' for each data point\n",
    "    data['SNR'] = data['CIR_PWR'] / data['STDEV_NOISE']\n",
    "    print(\"New feature 'SNR' created.\")\n",
    "    data.drop(['CIR_PWR', 'STDEV_NOISE'], axis=1, inplace=True)\n",
    "\n",
    "    plot_histogram(data, 'First_Path_Power_Level')\n",
    "    plot_histogram(data, 'RX_Level')\n",
    "\n",
    "    # One-hot encode categorical features\n",
    "    categorical_features = ['CH', 'FRAME_LEN', 'PREAM_LEN', 'BITRATE']\n",
    "    encoder = LabelEncoder()\n",
    "    for feature in categorical_features:\n",
    "        data[feature] = encoder.fit_transform(data[feature])\n",
    "    print(\"Categorical features one-hot encoded.\")\n",
    "\n",
    "    # Extract the 'CIR' columns\n",
    "    cir_columns = [col for col in data.columns if 'CIR' in col]\n",
    "    # cir_data = data[cir_columns]\n",
    "    print(\"'CIR' columns extracted.\")\n",
    "\n",
    "    # Convert 'CIR' columns to float\n",
    "    # cir_data = cir_data.astype(float)\n",
    "    print(\"'CIR' columns converted to float.\")\n",
    "\n",
    "    # Denoise 'CIR' columns\n",
    "    # denoised_cir_data = cir_data.apply(denoise_cir)\n",
    "    # denoised_cir_data = cir_data.apply(deconvolve_cir)\n",
    "    denoised_cir_data = perform_dft(data, cir_columns)\n",
    "    print(\"'CIR' columns denoised.\")\n",
    "\n",
    "    # Replace original 'CIR' columns with denoised data\n",
    "    data[cir_columns] = denoised_cir_data\n",
    "    print(\"Original 'CIR' columns replaced with denoised data.\")\n",
    "\n",
    "    # List of columns to check for unique values\n",
    "    columns_to_check = ['CH', 'PREAM_LEN', 'BITRATE']\n",
    "\n",
    "    # Iterate over the columns\n",
    "    for column in columns_to_check:\n",
    "        # If the column has only one unique value, drop it\n",
    "        if data[column].nunique() == 1:\n",
    "            data = data.drop(column, axis=1)\n",
    "            print(f\"Column '{column}' dropped due to having only one unique value.\")\n",
    "\n",
    "    # Print the shape of the cleaned data\n",
    "    print(f\"Cleaned data shape: {data.shape}\")\n",
    "\n",
    "    # print(\"After Cleaning\")\n",
    "    # stat_analysis_and_plots(data)\n",
    "\n",
    "    print(\"Data cleaning process completed.\")\n",
    "\n",
    "    # Return the cleaned data\n",
    "    return data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:53.333469Z",
     "start_time": "2024-03-21T13:38:53.324565Z"
    }
   },
   "id": "685463c2d6065b08",
   "execution_count": 51
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Load and Clean the Data\n",
    "\n",
    "This code block loads the dataset from the specified directory and cleans the data using the `load_data` and `clean_data` functions. It then prints the first few rows of the cleaned data and the column headers."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8323170f6733d41d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from pickle file...\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 6000x3000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 6000x3000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded successfully.\n",
      "First few rows of the data:\n",
      "   NLOS  RANGE  FP_IDX  MAX_NOISE  FRAME_LEN  PREAM_LEN      CIR0  \\\n",
      "0     0   3.90   745.0      967.0          2          0  401549.0   \n",
      "1     0   0.66   749.0     1133.0          0          0  451165.0   \n",
      "2     1   7.86   746.0      894.0          0          0  511541.0   \n",
      "3     1   3.48   750.0     1127.0          2          0  419663.0   \n",
      "4     0   1.19   746.0     1744.0          0          0  369171.0   \n",
      "\n",
      "            CIR1           CIR2           CIR3  ...        CIR1010  \\\n",
      "0  176143.139130  172218.295753  170881.074380  ...  164661.802783   \n",
      "1  248007.108944  247273.488409  242363.138758  ...  222944.309688   \n",
      "2  282638.075317  277560.207867  262890.558709  ...  198740.768169   \n",
      "3  143584.438466  152201.642761  144111.339108  ...  138569.940418   \n",
      "4  149284.240427  145627.201594  140699.927983  ...  126080.397723   \n",
      "\n",
      "         CIR1011        CIR1012        CIR1013        CIR1014        CIR1015  \\\n",
      "0  170808.640426  169869.361433  170881.074380  172218.295753  176143.139130   \n",
      "1  232368.316737  234558.979076  242363.138758  247273.488409  248007.108944   \n",
      "2  226308.229453  238309.603004  262890.558709  277560.207867  282638.075317   \n",
      "3  145046.920891  148356.463666  144111.339108  152201.642761  143584.438466   \n",
      "4  128417.188951  130807.229975  140699.927983  145627.201594  149284.240427   \n",
      "\n",
      "    RX_Level  First_Path_Power_Level         SNR  Total_Distance  \n",
      "0 -27.806709              -32.018158  185.234375    8.164831e+06  \n",
      "1 -23.050836              -34.255986  296.375000    8.806494e+06  \n",
      "2 -28.334789              -43.509715  244.983333    8.363445e+06  \n",
      "3 -33.611812              -41.706556  115.105263    6.868560e+06  \n",
      "4 -21.081660              -23.834943  167.352941    8.364873e+06  \n",
      "\n",
      "[5 rows x 1026 columns]\n",
      "Column headers:\n",
      "Index(['NLOS', 'RANGE', 'FP_IDX', 'MAX_NOISE', 'FRAME_LEN', 'PREAM_LEN',\n",
      "       'CIR0', 'CIR1', 'CIR2', 'CIR3',\n",
      "       ...\n",
      "       'CIR1010', 'CIR1011', 'CIR1012', 'CIR1013', 'CIR1014', 'CIR1015',\n",
      "       'RX_Level', 'First_Path_Power_Level', 'SNR', 'Total_Distance'],\n",
      "      dtype='object', length=1026)\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# File='data_original.pkl'\n",
    "File = 'data.pkl'\n",
    "\n",
    "# Check if the file exists\n",
    "if os.path.exists(File):\n",
    "    # If the file exists, load it\n",
    "    print(\"Loading data from pickle file...\")\n",
    "    with open(File, 'rb') as f:\n",
    "        data = pickle.load(f)\n",
    "        # plot_features(data, data['NLOS'], \"First_Path_Power_Level\", \"RX_Level\")\n",
    "        # plot_features(data, data['NLOS'], \"SNR\", \"RX_Level\")\n",
    "        # plot_features(data, data['NLOS'], \"SNR\", \"First_Path_Power_Level\")\n",
    "        snr_graph(data)\n",
    "        cir_graphs(data)\n",
    "    print(\"Data loaded successfully.\")\n",
    "else:\n",
    "    # If the file doesn't exist, load and clean the data\n",
    "    print(\"Pickle file not found. Loading and cleaning data...\")\n",
    "    data = load_data(DATASET_DIR)\n",
    "    cir_graphs(data)\n",
    "    data = clean_data(data)\n",
    "    plot_features(data, data['NLOS'], \"First_Path_Power_Level\", \"RX_Level\")\n",
    "    snr_graph(data)\n",
    "    cir_graphs(data)\n",
    "    print(calculate_total_distance(data))\n",
    "    print(\"Data loaded and cleaned successfully.\")\n",
    "    print(\"Saving cleaned data to pickle file...\")\n",
    "    with open(File, 'wb') as f:\n",
    "        pickle.dump(data, f)\n",
    "    print(\"Cleaned data saved to pickle file successfully.\")\n",
    "\n",
    "print(\"First few rows of the data:\")\n",
    "print(data.head())\n",
    "\n",
    "# Print Headers\n",
    "print(\"Column headers:\")\n",
    "print(data.columns)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:57.839279Z",
     "start_time": "2024-03-21T13:38:53.345725Z"
    }
   },
   "id": "79c2c23691b26753",
   "execution_count": 52
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Preprocessing"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "20d838f85d847ea0"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train and Save Model\n",
    "\n",
    "This code block defines a function `train_and_save_model` to train a classifier on the training data and save the trained model to a file. The function takes the following parameters:\n",
    "\n",
    "- `classifier`: The classifier to train and save.\n",
    "- `X_train`: The training data.\n",
    "- `y_train`: The training labels.\n",
    "- `file_name`: The name of the file to save the trained model to."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3e7a8932c36118eb"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "MODEL_DIR = './models'\n",
    "\n",
    "\n",
    "def train_and_save_model(classifier, X_train, y_train, file_name):\n",
    "    if not os.path.exists(MODEL_DIR):\n",
    "        os.makedirs(MODEL_DIR)\n",
    "\n",
    "    file_path = os.path.join(MODEL_DIR, file_name)\n",
    "\n",
    "    # Check if the file exists\n",
    "    if not os.path.exists(file_path):\n",
    "        print(f\"Training the model and saving it to {file_path}\")\n",
    "        # Train the classifier\n",
    "        classifier.fit(X_train, y_train)\n",
    "\n",
    "        # Save the trained model as a pickle string.\n",
    "        saved_model = pickle.dumps(classifier)\n",
    "\n",
    "        # Save the pickled model to a file\n",
    "        with open(file_path, 'wb') as file:\n",
    "            file.write(saved_model)\n",
    "\n",
    "    # Load the pickled model from the file\n",
    "    with open(file_path, 'rb') as file:\n",
    "        loaded_model = pickle.load(file)\n",
    "\n",
    "    return loaded_model"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:38:57.845121Z",
     "start_time": "2024-03-21T13:38:57.839789Z"
    }
   },
   "id": "12e16974341e6266",
   "execution_count": 53
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Split the Data\n",
    "\n",
    "This code block splits the data into training and testing sets using the `train_test_split` function from the `sklearn.model_selection` module. The training set contains 80% of the data, and the testing set contains 20% of the data.\n",
    "\n",
    "The target column 'NLOS' is separated from the rest of the dataset, and the input data is scaled using the `StandardScaler` from the `sklearn.preprocessing` module. The `StandardScaler` standardizes features by removing the mean and scaling to unit variance.\n",
    "\n",
    "The random seed is set to 42 for reproducibility."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "686f0615a70388cb"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Set random seed for reproducibility\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Drop the target column 'NLOS' from the data and assign the remaining data to X\n",
    "X = data.drop('NLOS', axis=1)\n",
    "# Assign the target column 'NLOS' to y\n",
    "y = data['NLOS']\n",
    "\n",
    "# Split the data into training and testing sets with a 80:20 ratio\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize a StandardScaler object\n",
    "scaler = StandardScaler()\n",
    "# Fit the scaler to the training data and transform it\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "# Transform the testing data using the fitted scaler\n",
    "X_test = scaler.transform(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:39:00.691646Z",
     "start_time": "2024-03-21T13:38:57.845121Z"
    }
   },
   "id": "72c1e647b6c1992",
   "execution_count": 54
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Convolution Neural Network\n",
    "\n",
    "This code block constructs and trains a Convolutional Neural Network (CNN) for a binary classification task using TensorFlow. CNNs are a type of deep learning model primarily used for image analysis, but they can also be applied to other types of data that have a grid-like topology. Here's a step-by-step breakdown of the code:\n",
    "\n",
    "1. **Model Creation**: A Sequential model is created using Keras. This model is composed of the following layers:\n",
    "\n",
    "   - **Conv1D layers**: These are convolutional layers that will convolve the input data with a set of learnable filters, each producing one feature map in the output. The kernel size is set to 3, and the activation function used is ReLU (Rectified Linear Unit).\n",
    "\n",
    "   - **MaxPooling1D layers**: These layers are used to down-sample the input along its spatial dimensions (height and width). The pool size is set to 2.\n",
    "\n",
    "   - **Dense layers**: These are fully connected layers. The first Dense layer has 64 units and uses the ReLU activation function. The second Dense layer has a number of units equal to the number of classes and uses the softmax activation function to output a probability distribution over the classes.\n",
    "\n",
    "2. **Model Compilation**: The model is compiled with the Adam optimizer, categorical cross-entropy loss function, and accuracy as the evaluation metric.\n",
    "\n",
    "3. **Model Training**: The model is trained on the training data for 10 epochs with a batch size of 32. The validation data is set to the testing set.\n",
    "\n",
    "4. **Model Evaluation**: The model's performance is evaluated on the testing set and the accuracy is printed.\n",
    "\n",
    "The mathematical concept behind the Convolutional layer (Conv1D) is the convolution operation, which is a mathematical operation on two functions that produces a third function. In the context of a CNN, the two functions are the input data and the kernel or filter. The convolution operation involves sliding the kernel across the input data and computing the dot product at each position.\n",
    "\n",
    "The mathematical formula for the convolution operation is:  $$ (f * g)(t) = \\int_{-\\infty}^{\\infty} f(\\tau)g(t - \\tau) d\\tau $$  Where:  \n",
    "- $f$ and $g$ are the input data and kernel respectively\n",
    "- $t$ is the position of the kernel\n",
    "- $\\tau$ is a dummy integration variable\n",
    "\n",
    "In the context of a CNN, the integral is replaced by a sum over the discrete spatial dimensions (height and width) of the input data and kernel."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "862a9b7ee430a667"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1050/1050 [==============================] - 27s 25ms/step - loss: 0.5833 - accuracy: 0.7756 - val_loss: 0.4791 - val_accuracy: 0.8193\n",
      "Epoch 2/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.4959 - accuracy: 0.8182 - val_loss: 0.4292 - val_accuracy: 0.8457\n",
      "Epoch 3/30\n",
      "1050/1050 [==============================] - 27s 25ms/step - loss: 0.4589 - accuracy: 0.8369 - val_loss: 0.4072 - val_accuracy: 0.8573\n",
      "Epoch 4/30\n",
      "1050/1050 [==============================] - 27s 25ms/step - loss: 0.4420 - accuracy: 0.8444 - val_loss: 0.4041 - val_accuracy: 0.8594\n",
      "Epoch 5/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.4318 - accuracy: 0.8508 - val_loss: 0.3954 - val_accuracy: 0.8630\n",
      "Epoch 6/30\n",
      "1050/1050 [==============================] - 27s 25ms/step - loss: 0.4216 - accuracy: 0.8546 - val_loss: 0.3894 - val_accuracy: 0.8643\n",
      "Epoch 7/30\n",
      "1050/1050 [==============================] - 27s 26ms/step - loss: 0.4120 - accuracy: 0.8565 - val_loss: 0.3842 - val_accuracy: 0.8669\n",
      "Epoch 8/30\n",
      "1050/1050 [==============================] - 27s 26ms/step - loss: 0.4065 - accuracy: 0.8577 - val_loss: 0.3810 - val_accuracy: 0.8654\n",
      "Epoch 9/30\n",
      "1050/1050 [==============================] - 26s 24ms/step - loss: 0.3966 - accuracy: 0.8635 - val_loss: 0.3850 - val_accuracy: 0.8667\n",
      "Epoch 10/30\n",
      "1050/1050 [==============================] - 27s 26ms/step - loss: 0.3940 - accuracy: 0.8644 - val_loss: 0.3775 - val_accuracy: 0.8683\n",
      "Epoch 11/30\n",
      "1050/1050 [==============================] - 27s 25ms/step - loss: 0.3906 - accuracy: 0.8644 - val_loss: 0.3719 - val_accuracy: 0.8717\n",
      "Epoch 12/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3844 - accuracy: 0.8683 - val_loss: 0.3699 - val_accuracy: 0.8733\n",
      "Epoch 13/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3833 - accuracy: 0.8663 - val_loss: 0.3663 - val_accuracy: 0.8743\n",
      "Epoch 14/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3737 - accuracy: 0.8698 - val_loss: 0.3616 - val_accuracy: 0.8765\n",
      "Epoch 15/30\n",
      "1050/1050 [==============================] - 27s 25ms/step - loss: 0.3714 - accuracy: 0.8730 - val_loss: 0.3605 - val_accuracy: 0.8775\n",
      "Epoch 16/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3698 - accuracy: 0.8726 - val_loss: 0.3600 - val_accuracy: 0.8751\n",
      "Epoch 17/30\n",
      "1050/1050 [==============================] - 28s 27ms/step - loss: 0.3660 - accuracy: 0.8744 - val_loss: 0.3592 - val_accuracy: 0.8780\n",
      "Epoch 18/30\n",
      "1050/1050 [==============================] - 31s 30ms/step - loss: 0.3636 - accuracy: 0.8734 - val_loss: 0.3569 - val_accuracy: 0.8763\n",
      "Epoch 19/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3567 - accuracy: 0.8781 - val_loss: 0.3568 - val_accuracy: 0.8786\n",
      "Epoch 20/30\n",
      "1050/1050 [==============================] - 25s 24ms/step - loss: 0.3558 - accuracy: 0.8790 - val_loss: 0.3515 - val_accuracy: 0.8799\n",
      "Epoch 21/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3529 - accuracy: 0.8796 - val_loss: 0.3500 - val_accuracy: 0.8801\n",
      "Epoch 22/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3558 - accuracy: 0.8776 - val_loss: 0.3518 - val_accuracy: 0.8792\n",
      "Epoch 23/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3482 - accuracy: 0.8813 - val_loss: 0.3505 - val_accuracy: 0.8790\n",
      "Epoch 24/30\n",
      "1050/1050 [==============================] - 28s 27ms/step - loss: 0.3502 - accuracy: 0.8804 - val_loss: 0.3508 - val_accuracy: 0.8782\n",
      "Epoch 25/30\n",
      "1050/1050 [==============================] - 31s 29ms/step - loss: 0.3435 - accuracy: 0.8843 - val_loss: 0.3498 - val_accuracy: 0.8793\n",
      "Epoch 26/30\n",
      "1050/1050 [==============================] - 25s 24ms/step - loss: 0.3396 - accuracy: 0.8868 - val_loss: 0.3476 - val_accuracy: 0.8795\n",
      "Epoch 27/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3406 - accuracy: 0.8842 - val_loss: 0.3471 - val_accuracy: 0.8823\n",
      "Epoch 28/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3371 - accuracy: 0.8860 - val_loss: 0.3444 - val_accuracy: 0.8856\n",
      "Epoch 29/30\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.3356 - accuracy: 0.8884 - val_loss: 0.3431 - val_accuracy: 0.8863\n",
      "Epoch 30/30\n",
      "1050/1050 [==============================] - 32s 31ms/step - loss: 0.3384 - accuracy: 0.8843 - val_loss: 0.3415 - val_accuracy: 0.8879\n",
      "263/263 [==============================] - 1s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "# Initialize a Sequential model\n",
    "model = Sequential()\n",
    "\n",
    "# Add a Conv1D layer\n",
    "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1),\n",
    "                 kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Add another Conv1D layer\n",
    "model.add(Conv1D(filters=32, kernel_size=3, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Add a Flatten layer\n",
    "model.add(Flatten())\n",
    "\n",
    "# Add a Dense layer\n",
    "model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Add the output Dense layer\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Define early stopping\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=30, batch_size=32, validation_data=(X_test, y_test),\n",
    "                    callbacks=[early_stopping])\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
    "\n",
    "# Generate a classification report\n",
    "report = classification_report(y_test, y_pred_classes)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:52:30.092323Z",
     "start_time": "2024-03-21T13:39:00.693651Z"
    }
   },
   "id": "1c1dd203ad7db076",
   "execution_count": 55
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Plot the training and validation accuracy over epochs\n",
    "\n",
    "This code block plots the training and validation accuracy over epochs. The training accuracy is plotted in red, and the validation accuracy is plotted in blue. The x-axis represents the number of epochs, and the y-axis represents the accuracy. The plot shows how the accuracy changes over the course of training and validation."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b5f3cff03a03da6a"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.34150493144989014\n",
      "Test accuracy: 0.8878571391105652\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.92      0.89      4192\n",
      "           1       0.92      0.85      0.88      4208\n",
      "\n",
      "    accuracy                           0.89      8400\n",
      "   macro avg       0.89      0.89      0.89      8400\n",
      "weighted avg       0.89      0.89      0.89      8400\n"
     ]
    }
   ],
   "source": [
    "# Plot the training and validation accuracy over epochs\n",
    "plt.plot(history.history['accuracy'], 'ro-', history.history['val_accuracy'], 'bv--')\n",
    "plt.title('Training and Test Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
    "plt.show()\n",
    "\n",
    "# Plot the training and validation loss over epochs\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss Over Time')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print the testing loss and accuracy\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])\n",
    "\n",
    "# Print the classification report\n",
    "print('Classification Report: \\n', report)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:52:30.315374Z",
     "start_time": "2024-03-21T13:52:30.093327Z"
    }
   },
   "id": "89aa08d7d1866179",
   "execution_count": 56
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Visualize Weights and Biases\n",
    "\n",
    "This code block visualizes the weights and biases of the model. For each Dense layer in the model, the weights and biases are extracted and plotted as histograms. The weights are plotted in the left subplot, and the biases are plotted in the right subplot. The x-axis represents the value of the weights or biases, and the y-axis represents the frequency of occurrence of each value. The histograms show the distribution of the weights and biases in each layer."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dba8a77d06dc51f8"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Weights and Biases\n",
    "for layer in model.layers:\n",
    "    if 'dense' in layer.name:\n",
    "        weights, biases = layer.get_weights()\n",
    "        plt.figure(figsize=(10, 5))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.hist(weights.flatten())\n",
    "        plt.title(f'{layer.name} weights')\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.hist(biases.flatten())\n",
    "        plt.title(f'{layer.name} biases')\n",
    "        plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:52:30.700421Z",
     "start_time": "2024-03-21T13:52:30.315374Z"
    }
   },
   "id": "9739272beb4981b6",
   "execution_count": 57
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "# Confusion Matrix\n",
    "\n",
    "This code block plots the confusion matrix for the model's predictions on the testing set. The confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. The confusion matrix shows the number of true positive, true negative, false positive, and false negative predictions made by the model."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7f3da1aa73d186e1"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred_classes)\n",
    "plt.figure(figsize=(5, 5))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion matrix')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.ylabel('True class')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:52:30.791950Z",
     "start_time": "2024-03-21T13:52:30.700421Z"
    }
   },
   "id": "92964bc26d5c26cc",
   "execution_count": 58
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "# ROC Curve\n",
    "\n",
    "This code block plots the Receiver Operating Characteristic (ROC) curve for the model's predictions on the testing set. The ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The area under the ROC curve (AUC) is also calculated and printed."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2d90420f6a5cc744"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:52:30.879758Z",
     "start_time": "2024-03-21T13:52:30.792953Z"
    }
   },
   "id": "dd49203934ca9cf6",
   "execution_count": 59
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Plot the model\n",
    "\n",
    "This code block plots the model architecture using the `plot_model` function from Keras. The plot shows the structure of the model, including the input and output shapes, the layers, and the connections between layers."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "427d26655cf59f4d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n"
     ]
    }
   ],
   "source": [
    "# Plot the model\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "# Generate the plot\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:52:30.891207Z",
     "start_time": "2024-03-21T13:52:30.879758Z"
    }
   },
   "id": "81f2d793ada5c410",
   "execution_count": 60
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Learning Curve"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "225526f72ffc154c"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Learning Curve\n",
    "plot_learning_curve(history)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:52:31.015265Z",
     "start_time": "2024-03-21T13:52:30.893211Z"
    }
   },
   "id": "6b7329b28452b82a",
   "execution_count": 61
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Multi-Layer Perceptron (MLP)\n",
    "\n",
    "This code block is implementing a Multi-Layer Perceptron (MLP) for a binary classification task using TensorFlow. The MLP is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training.\n",
    "\n",
    "Here's a step-by-step breakdown of what the code does:\n",
    "\n",
    "1. **Model Creation**: A Sequential model is created using Keras. This model is composed of the following layers:\n",
    "    - Dense layers: These are fully connected layers. The first Dense layer has 64 units and uses the ReLU activation function. The second and third Dense layers have 32 and 16 units respectively, and also use the ReLU activation function. The final Dense layer has 1 unit and uses the sigmoid activation function for binary classification.\n",
    "    - BatchNormalization layers: These layers are used to normalize the activations of the previous layer, which speeds up learning and provides some regularization, reducing generalization error.\n",
    "    - Dropout layers: These layers are used to prevent overfitting. They randomly set a fraction of input units to 0 at each update during training time.\n",
    "\n",
    "2. **Model Compilation**: The model is compiled with the Adam optimizer, binary cross-entropy loss function, and accuracy as the evaluation metric.\n",
    "\n",
    "3. **Model Training**: The model is trained on the training data for 20 epochs with a batch size of 32. The validation data is set to the testing set. Early stopping is used to stop training when the validation loss has not improved for 10 epochs.\n",
    "\n",
    "4. **Model Evaluation**: The model's performance is evaluated on the testing data and the loss and accuracy are printed. The model also makes predictions on the testing data, converts the predicted probabilities to binary outputs, and generates a classification report.\n",
    "\n",
    "5. **Visualization**: The training and validation accuracy and loss over epochs are plotted.\n",
    "\n",
    "The mathematical concept behind the Dense layer is the dot product operation, which is a mathematical operation that takes two equal-length sequences of numbers and returns a single number. In the context of a MLP, the two sequences are the input data and the weights of the neurons. The dot product operation involves multiplying each pair of input and weight and summing the result.\n",
    "\n",
    "The mathematical formula for the dot product operation is: $$ a \\cdot b = \\sum_{i=1}^{n} a_i b_i $$ Where:\n",
    "- $a$ and $b$ are the input data and weights respectively\n",
    "- $n$ is the number of dimensions (length of the sequences)\n",
    "- $a_i$ and $b_i$ are the $i$-th elements of the input data and weights respectively."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "42eff9445377f73c"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.9241 - accuracy: 0.6315 - val_loss: 0.6590 - val_accuracy: 0.7523\n",
      "Epoch 2/20\n",
      "1050/1050 [==============================] - 2s 1ms/step - loss: 0.7830 - accuracy: 0.6934 - val_loss: 0.6331 - val_accuracy: 0.7756\n",
      "Epoch 3/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.7312 - accuracy: 0.7232 - val_loss: 0.6135 - val_accuracy: 0.7910\n",
      "Epoch 4/20\n",
      "1050/1050 [==============================] - 2s 1ms/step - loss: 0.6887 - accuracy: 0.7455 - val_loss: 0.5970 - val_accuracy: 0.7995\n",
      "Epoch 5/20\n",
      "1050/1050 [==============================] - 2s 1ms/step - loss: 0.6621 - accuracy: 0.7608 - val_loss: 0.5730 - val_accuracy: 0.8114\n",
      "Epoch 6/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.6332 - accuracy: 0.7740 - val_loss: 0.5526 - val_accuracy: 0.8212\n",
      "Epoch 7/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.6062 - accuracy: 0.7913 - val_loss: 0.5258 - val_accuracy: 0.8336\n",
      "Epoch 8/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5792 - accuracy: 0.8034 - val_loss: 0.5041 - val_accuracy: 0.8456\n",
      "Epoch 9/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5542 - accuracy: 0.8131 - val_loss: 0.4825 - val_accuracy: 0.8461\n",
      "Epoch 10/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5347 - accuracy: 0.8240 - val_loss: 0.4691 - val_accuracy: 0.8496\n",
      "Epoch 11/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5201 - accuracy: 0.8275 - val_loss: 0.4576 - val_accuracy: 0.8582\n",
      "Epoch 12/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5093 - accuracy: 0.8316 - val_loss: 0.4400 - val_accuracy: 0.8615\n",
      "Epoch 13/20\n",
      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.4987 - accuracy: 0.8331 - val_loss: 0.4345 - val_accuracy: 0.8577\n",
      "Epoch 14/20\n",
      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.4836 - accuracy: 0.8406 - val_loss: 0.4216 - val_accuracy: 0.8651\n",
      "Epoch 15/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4721 - accuracy: 0.8435 - val_loss: 0.4117 - val_accuracy: 0.8675\n",
      "Epoch 16/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4615 - accuracy: 0.8471 - val_loss: 0.4060 - val_accuracy: 0.8704\n",
      "Epoch 17/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4492 - accuracy: 0.8497 - val_loss: 0.3937 - val_accuracy: 0.8718\n",
      "Epoch 18/20\n",
      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4437 - accuracy: 0.8521 - val_loss: 0.3871 - val_accuracy: 0.8742\n",
      "Epoch 19/20\n",
      "1050/1050 [==============================] - 2s 1ms/step - loss: 0.4347 - accuracy: 0.8529 - val_loss: 0.3820 - val_accuracy: 0.8750\n",
      "Epoch 20/20\n",
      "1050/1050 [==============================] - 2s 1ms/step - loss: 0.4288 - accuracy: 0.8578 - val_loss: 0.3733 - val_accuracy: 0.8732\n",
      "263/263 [==============================] - 0s 573us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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zeSeOZB/ErD9mwWA2oFWt1s7uGhERERERERFRubBSqprRiBrcHt4dM7vNRK96sZAh4/ODHzu7W0RERERERERE5cJQqhqbEvMEAODbw98goyDdyb0hIiIiIiIiIio7hlLVWK96vdEyKAp55lwsObTY2d0hIiIiIiIiIiozhlLVmCAImBIzDQDwRfynMEgGJ/eIiIiIiIiIiKhsGEpVc8OajkCoVxiu5CVj1fEfnd0dIiIiIiIiIqIyYShVzek0OjzaagoA4OO4BZAV2ck9IiIiIiIiIiK6MYZSLmBs5Hj46H1xPP0Ytpzf5OzuEBERERERERHdEEMpF+Cj98WYluMBAB/tX+DczhARERERERERlQFDKRfxSPRj0Ipa7Ejchrgr/zm7O0RERERERERE1+XUUMpgMOCFF15A+/bt0a1bNyxevNhu2+3bt2PIkCFo06YNxo8fj9OnTzuwp1VfuE9dDGs6AoC6thQRERERERERUVXm1FBq/vz5SEhIwJIlS/Dyyy9j4cKFWL9+fal2J06cwKRJk9CnTx+sWrUKLVu2xLhx45Cbm+uEXlddk2OmAQDWnvoZ57POObk3RERERERERET2OS2UysvLw4oVKzB79mxERkbijjvuwMMPP4xly5aVavv999+jTZs2ePLJJ9G4cWM899xz8PHxwS+//OKEnlddUcHR6FWvN2RFxmcHPnJ2d4iIiIiIiIiI7HJaKHX06FGYzWa0adPGsq1du3Y4cOAAZFm2anvhwgW0atXK8lgQBNx2222Ii4tzVHerjSkxTwAAlh1ZivSCNCf3hoiIiIiIiIjINqeFUlevXkVAQAD0er1lW3BwMAwGAzIyMqzaBgcHIzk52Wrb5cuXkZ6e7oiuVis968YiMigaeeZcfHPoK2d3h4iIiIiIiIjIJq2zXjg/P98qkAJgeWw0Gq22Dxw4EFOmTMHgwYPRvXt3/PLLL4iPj0enTp3K/bqCcPN9riqKzsHWuQiCgKltpmHK5kfxRfynmNzmcbhp3BzbQXKK640Lqrk4LsgWjguyheOCbOG4IFs4Lsgejg0qUtYx4LRQys3NrVT4VPTY3d3danuPHj0wdepUTJs2DZIkoVOnTrj77ruRk5NT7tcNCvK5+U5XMfbO5eGA8Xh97/9wMesiNiSuxYQ2ExzcM3ImVxrjVHE4LsgWjguyheOCbOG4IFs4Lsgejg0qK6eFUiEhIUhPT4fZbIZWq3bj6tWrcHd3h6+vb6n2kydPxsSJE5GdnY2goCA8+eSTCA8PL/frpqZmQ1FuuftOJQjqm/x65/JI1GS8vHM23to2H4Pr3gNRcOqFFskByjIuqObhuCBbOC7IFo4LsoXjgmzhuCB7ODaoSNFYuBGnhVIRERHQarWIi4tD+/btAQD79u1DdHQ0RNE6QFm3bh0OHDiA2bNnIygoCAUFBdizZw/efPPNcr+uosBl3hzXO5fRLcfhnX/fwvH0Y9h8diPuaDjAsZ0jp3GlMU4Vh+OCbOG4IFs4LsgWjguyheOC7OHYoLJyWvmMh4cHhg4dirlz5+LgwYPYvHkzFi9ejLFjxwJQq6YKCgoAAA0bNsTy5cuxceNGnD17Fs888wxCQ0PRo0cPZ3W/yvPR+2Jsy4cAAB/Hfejk3hARERERERERWXPqnK5Zs2YhMjIS48aNwyuvvIJp06ahX79+AIBu3brht99+AwBERUVh7ty5ePPNNzF8+HAAwGeffVaqooqsPdLqMWhFLXYkbkPclf+c3R0iIiIiIiIiIgtBUWpWUV1KSvWf2yoIQHCwT5nOZermR7Hi+HIMbTocn/f72iH9I+coz7igmoPjgmzhuCBbOC7IFo4LsoXjguzh2KAiRWPhRlhq5OKmxDwBAFh76mecyzrr3M4QERERERERERViKOXiIoOj0Kteb8iKjM8PfOzs7hARERERERERAWAoVSNMjXkSALDsyDdIL0hzcm+IiIiIiIiIiBhK1Qg96vZCZFA08sx5WHJosbO7Q0RERERERETEUKomEAQBU2KmAQC+OPgpDJLByT0iIiIiIiIiopqOoVQNMbTpPQjzCsfV/CtYeewHZ3eHiIiIiIiIiGo4hlI1hE6jw6OtpwAAPjnwIWRFdnKPiIiIiIiIiKgmYyhVg4xpOQ4+el8cTz+Gzec2OLs7RERERERERFSDMZSqQXz0vhjb8iEAwMdxHzq5N0RERERERERUk2md3QFyrEdbTcZnBz/CzsTt2J+8D21C2jm7S0RERERERETXdemSgNRUwe7+4GAFYWGKA3tEFYGhVA0T6h2G4c1G4sdj3+PjuA/xRf+vnd0lIiIiIiIiqkCuFuAYDEC/fp64etX+ZK/atWXs25cLNzcHdoxuGUOpGmhy62n48dj3+OX0zziXdRYNfBs6u0tERERERERUAVwxwNHrgfBwBSkpChSldNgmCGrIptc7oXO3yNUCxPJiKFUDRQZHIbZeH/x54Q98duAjvN79bWd3iYiIiIiIyOFcMRBwxQBHEICZMw247z5Pm/sVRcDMmQYI9v9TVkmuGCCWF0OpGmpKzBP488If+O7IUjzXYRYC3AOd3SUiIiIiIqqirg1vAgKA9PTiX6SrY3jjqoFAWQMcADCbAUlSb2W56L4ArVaBv3/xc06fFmA2C5AktY0sFz/Xywto2VK2tP3jD+DyZY1V+6IvX18FAwZIlrbLl2uRkSEUvr5gOaYkAQEBCiZNMlnaHjigQVCQjLQ0wSpsEwQFISEKYmOLj7tunRZpaQL0egVubmpQ5+6uBnEeHgraty/ub0qKAFkG3NzU/W5ugOigS8K5YoBYXgylaqgedXshKrgVElIO4uuERXi6/XPO7hIREREREVVB9sMbL8u96hjeVIVAQFEAoxHIzwcKCgTk5akhT0iIGvAZDMDGjVrL/pK3+fkCIiMljBplBgAUFAAPPuhhOY6bmwKDAQCKz02jURAdLSM2VkJ4uDfMZtulRT17mrFiRb7lcd++XsjJsd22Y0cz1q0rbjt6NHD5su1ALDJSwoABeZbH773nhrNnbSdAjRvLVqHUmjVapKaWbqsoAgwGxapK6qOP9Ni3T2PzuD4+Ck6dyrE8njzZHVu3WkcjWq36393LS8GhQ7mW7S++6Ia9ezVWAVbJ4Ou99wqgLTzUL79ocfKkaNl/bdu+fc3Q612zAqw8GErVUIIgYErMNEzZ/Ai+jP8Mk2OmwV3r7uxuERERERFRFVMVwpvKUJaKokmTjDhzRrAZCDVqJCMqSq24SU8HPvtMj/x8AQUFsLrNywMGDDDjkUfUgCU5WUCvXp7Iz1ePde33dPRoI957T61kMhiAiRM97J7D0KEmSyil0wHbtl3/V3xJKg45rlcNJMvWj/39FWi1aqil0cDqq04d6wq5Nm2A5GQJoqiGOxoNCu8DDRtaH7h/fzOuXhUKj60ev6htcLD1cceONeHqVTOWLdMhOVmtlhIEBbVrK3joIZNV265dzahVS4bRKMBgAAwGAUajGgB6eVk1LXWugFotZjaX3n7ypIj9+22HXQDw/vsFlvtr1mixdq3ObtvTp7Oh1wOxsRICA2WkpVn/BykZILoyhlI12N1NhuO13a/gUs5FrDr+Ix5sOdbZXSIiIiIiqvZcbZ2iilrPx2xWK4KMRgEmkxoQmM3Fj8PCFAQFqd+XlBQB+/aJln3ql2B5TpcuEqKj1TTh7FkBX32lL9FOPaZ6bOCee8y46y41YTh+XMTjj7tb2hkMAnQ6BSYTcG1FUbNmMiZPth8ITZ5sRFSUGh7l5Ah47z37ZWKNGhUnH3q9YrPiRxQVeHhYh0UeHkCnTmZ4eKjTzzw81Oln7u7qvqio4sBCowE+/TQfbm7FbWbMcMPJkyJkWYAoKmjVqjjkiIvLtYRMRUFQUdB0bWD133+5KKvffgNSUvKglGGIv/qqoczHnTBBDZ46dJAsY1FRBHzwQT5697YObl580Vjm465alQ9FgWVMGgxFQZY6jbCk5583YPx4oyXsKmqvBl6C1fetRw8Jvr4KCgqKA7GitgaDYAlxBUE9pw0brL/pJQNEV8ZQqgbTaXR4tNUUvLzzBXwctwD3R4yGKDho8iwRERERkQuqDusUKYo61SsvT0Burnpbt64Mb291/6lTAnbt0lr25eYCublCYTWHgJLhjSiqFVIvveSGWbOEUsHQ//1fAe6+Ww2E1q/XYsIE+yHPu+8WYMwYNXiIjxcxZoztEAwAXn21wBJKJSeL+OQT+2VarVoVB0JGIxAXZ7/SpYgkCZg82Yjp090LA6BrAyEF9eoVH9ffX8HEiUbLPnd3wNOzuG3TpsVtfX2BrVtzSx1Tp0OpAEKnA375JR9lNXy4dXnP//5XHCbKsnXIcW0lUnURGyshJkZCXJwGMTFShVQSCYJaEajXA97e9r8vMTE2yqrsGDvWhLFlrPv45psC9O8vIj5ehCQJNaZKCmAoVeONbjkW7/z7Jk5kHMfmcxvQr+FAZ3eJiIiIiGoIV6soAip2qpvZDOTlWYdHjRvLlulHCQki9u7VIC9PDY3y8gSr9rNmGdG8ufpL9Pffa/Haa26WNrJs3bfly/Ms1Sa7d2sxfXrZlvaQZXWa2vHjtoOe/BJ5il5f/N9So1G/BzodoNMp0Oms9wcEKGjXTrKs7aO2U9vq9ep6Q0XCw2VMnWqEXq9OMVPbK5bntG1b/It9gwYyvv02r/D11HZaLfDkk+44cUKtKCoKBO6914z77y9ee+h6fHyAN94oW9WPRgNERJQ93LgVlRHgOJsgALNnGzB7thtmz3aNSqJrqxFrSpUUwFCqxvPR+2Jc5AQs3P8+PopbwFCKiIiIiByiOlQU3YgkwbK+UMl1hIYONSEuznaooygC6tSRMX26myVIev31AtSvrwYyn36qw/vv65GbK8BgKP0b6W+/5VquHPb33xrMnWs/PBo/3oTmzdX7ZrOAK1dKf6/d3RV4eSlWU60aNJDRv78Znp7qPk9PterH0xNYskSHpCTBEt60aCHjxRcNcHNDYSCkWAIfrVZd76dI794SLlzIhk5346ubxcTI+P33vOs3KlS3roKXXy5bIOTjA/TrVzqYeeWV0oGAo67AVplcMcABgJ49JWzfXrbxUV24YoBYFgylCI9EP4bPDnyEXYk78F/yv2gb0t7ZXSIiIiIiF1dZi2dLkhp4FP3ynZgo4OpVwbKodEFBUYCkhkgPPmiCe2Gu89NPWmzfrkFeXunFqgsKgB9/zLdcFW3uXDd8/LH9zrVoIeHECXUqzrXWr7de/Hj6dMESSpnNKLXgsSgq8PJSg6GSx7vtNhmDBpks+4rCo6IgqeSUsYEDzYiJyYWXl2LVXmOjwKlbNwndutmeMtaqlWQV3rz4oqHUej72aLWwXJmsqnHlQMAVAxxX5KoB4o1U0Y8EcqRQ7zAMbzYSPxz7Dp/ELcQX/b92dpeIiIiIbporTglzRWVZPLtBAwlz5rihoAB4553iX9Lmz9fjjz+0lnWRSgZHRqOA06ezLesjvfWWG77/3v4VsAYPNsPdXR0P//yjwdKl9oOmnBwgJES9X3KqGVC8CHXR7cMPm/Dss6WrmAYMMKF5c9kSDHl5qZU+Re6914w77si1Cpfc3EqvNQQAfftK6Nu3bOFJcLBSIWsIuWp4U1MDAapaamKAyFCKAACTY6bhh2Pf4ZfTP+Ns5hk09Gvk7C4RERERlZsrTAlzJXl5aqXSpUsiLl2yvk1LE7BpUx5iYiTL4r7XWrOmOCCaN88Aj8I1ss+fv/5l2fPzBctixUFBMkJD5VJXLyt6XHKKVt++ZgQHKzbbeXgAdeoUhzrTphnx2GPqwtbu7qWnoykK8O23ulILFy9ZUnDdwKNWLQW1alXd0FQQgDlzDHjxRU/MmeNa4U1NDASInE1QlLJcqNF1pKRkl+nSlFWZIADBwT4Vfi73/jIMf174Aw9HT8Lr3d+uuAOTQ1TWuKDqjeOCbOG4oCLXVhQFBHghPb34st/VsaJIUYD+/T1x4IBod0pY69YyNmzIc6lfpivL9T4vzGbg8mXroOnKFQH/+19xUDF6tAc2brT/d/Djx7Px338am9VSffqY0LRpcUA0ZYrRMs3uwAERycmCzSuduburi2RXhfWAtmyxPreSi4lXZ/x3hOzh2KAiRWPhRlgpRRZT2zyJPy/8ge+OLMWzHWYi0D3I2V0iIiKiSmK/osjLcq+6VBTJcnGViiAA48YZ8fTTti87rygCZsyoftUdjp6SqChAaqqAxEQBOTlA167F+155xQ0//aTF5ctCqSu4AcAzzxjg76/eDw+X4eWloG5dGWFhxbfh4TLCw9WwqWg62LUVRd99Z7+iqHVrx1y57Fa56lQ3IqoEkgTd7p0Qky9DDqkDU+euthd9czEMpciie3hPRAW3QkLKQSxJWIyn2z/n7C4RERFRJamsRabLIzsbyMwUkJ2tBh/Z2QJyc9X7eXkCJk40Wdp+9JEOe/ZokJMjICen+Dk5Oerl7S9dyrEsoLx16/V/xG3VqjjQmDdPj59/1sHXVynxBcv9xx4zWgKWM2cEpKcL8PVV4OOjtnF3t73WT0WqjCmJOTmAl1dx31ev1uKvv7SWiqfERAEFBcUnduIE4Oen3len5Kl90ekUhIYWh0zh4bJVdcS8eQa8+eaNQ0BXvRQ61ykiorLQr1sL7zkzoElMtGyTwsKQM28+jIOHOLFnlY+hFFkIgoCpMU9g8uaH8WX8Z5gcMw3uWvuXmCUiIqLqqyyLTF8bDBgMsAqDcnIEGI1Ajx7F1R/ff6/FkSMa5OaiRICk3pdl4K+/itdreeQRD2zZYv/H0fHjTZY/EsfFaUpdsayk3Nzi0KRBAxn16km4cKH0X5i9vRX4+RWnJomJIs6ftx/2TJhgAqC2//xzPRYtsk7p9HrFElKtWpVnWbD611+12LVLYxV2FQVZfn4KWrSQLVPRbuRmA8QjR0QkJIhITBRx8aKAxMTiaXaZmQJOnMi2fM/27NFg+fLS39/atWU0bCgiJ0ewfN8eecSIUaNMqFtXXfvoetPkdPb/k1lx5YoirlNERNejX7cWvhPH4Nr5jmJSEnwnjkHWoqUuHUwxlCIrQ5oMw7zdc3Ep5yJWHv8Bo1uOc3aXiIiIqBJIkno5+aZNJZw6Zb3+UtG6S0XBwL33emD7dg1MptKBiLe3gtOncyyPf/5Zhz//tP8jZsmpdt7eCtzcFHh7K/D2RuGtet/HR4HJVDxz4YEHTOjeXYK3twIfH1vti19j9mwjXnjBiP79PUtNCbt2LalZswwYP96I7GwBWVnql1q9BWRlCVYBlo+Pgnr1ZMt+RRFgNApISRGQkgKrUGj7dk2pAKuknTtz0LSpeuz33tNj0SKdpULLx8c6yJoyxXjDANHNTcGdd3pi+fI8S9C0ZIkOixfb78OlSyL8/NSqsf79zVYVT2FhMkJD1UowdX0YxfL7ktrvil0shhVFRFQjSRK858wAFAXXfuwJigJFEOA953mkDRzkslP5GEqRFZ1Gh0mtp+ClHS/gk7gP8UDEGIhCFVglkoiIiMotJwc4d07EuXMisrKA++4zW/YNHOiJuDjbP+BeWyUly7AKpDw9i8MgX181rChqO2iQGRERcmFwVBwe+fgo8PKyfp3PPy8o82LUakBW9uqZayvB7E0Jq1dPQb16ZQtYXnhBDbsA9XuSmwurICswsPg4sbFmeHoqyMoSSgResNwvCo4AICVFwNWrIq5etf2648YZLZVE9hZw37NH/bH+4sXioCkqSka3bmbLtLpr13QqGeT17i05fQFuVhQRUZlU5bWXFAXIzYWYngYxPQ1CWhrEtFQI6WkQ09IKb1ML76dDTEqE5kqy3cMJigJN4iXodu+E6fbuDjwRx+HV96qhyr6iQY4xGzHftESWMRNL7/wB/RsOrPgXoQrHK12QLRwXZAvHhesoGQYBwOLFOuzdqykMogSkpBQnPp6eCs6cybG0Hz/eHRs3alG3roLUVHVKnqIIEAQ1pPnnn1xL28REAYqihkteXrCs3VTVFV2Jr2hKWFW94l5KioDLl4vCq+Kgq+jrqacM8PMrfSW3IoMGmdCli4TwcAXdupmtAq9bxc8LsoXjohpxcIBT2WPDoWsvyTKEzAzrcCktDWJ6uiVkshU4CUZjxfYDQNani2AYPrLCj1uZePU9umneeh+MjXwIC/e/j4/jFjCUIiIicqKS1U7nzglW99PSBBw5Uhwebd2qwe+/Wy/iExgoo0EDBQ0ayMjPBzwLM40FCwrg5aX+blIy7FAUAfPn51uFNxV5VTdHqi5TwoKDFQQH3/h7bO8qdYsX279KHRHVXK62ePYtrb1kMhUGSoUVTKmp6q3NCqbCNunpEOSbu9KnotdDDgyCEhAIOTBQvQ0IhBxUuC0gAEpgIMSLF+Az89kbHk8OqXNT/agOWClVDTniLxNJOYlo/200TLIJ6+/ZgrYh7SvnhajC8C9WZAvHBdnCcVF+ly4JSE21/1t/cLBy08GNLAOXLwuWoOnCBRHPPmu0hAxjx7pfd4HvQ4dyUKuW+tq//KLFuXMCGjRQ0LChjPr15TJVzVSXiiIqXS21fHlepU674+cF2eKS46IqTwm7CSUDnJIf50rhh3tlLZ5902NDltXFDs1mQJIgyEX3ZQiSGTAa4X9nX4hXkkutvQSoq9wpPr4oGHUfxIyMEhVMhVVN2Vk3fU6ylzeUoCDIAYFQAgIgBwbaDJyK2sgBgdaXN70eSUJgu0iISUkQbHzDFEGAHBqGtH0J1W48slKKbkmodxiGNxuJH459h4/jPsSX/Zc4u0tEREROYTAA/fp54upV+4sf1a4tY9++XLi52d6fm6tWKBX9fLpkiQ7r16sB0vnzIoxG6x9cJ0wwIShI/eG0QQMFAQFqpVPRV8OGxY+L2gHAXXeZcTMEAZgzx4AXX/TEnDlVt6KIXPsqdUTOUu0qimQZyM+HkJ8PoaD4FnmFj3Nz4fPMNPuLZwPweWIyCv7Zo/5VQjJDkCRAkgGpMBQqDIcgSYX7JOt9sgyYzdfsKwyQoMDfYFT3FbZTj1N4THPhMeUSIdQtppsCACE7C56LPrfbRhEENVQKCLQOlAKDbIZLSmAgZP8A2P3HvSJoNMiZNx++E8dAEQSr70NRgJgz761qF0iVByulqiFH/WXiSOph9PyhM0RBxO4H9qOhX6PKezG6ZS75Fyu6ZRwXZAvHRfkUVRHZW2C66Ep1336bj1On1Gqns2dFyzS7s2fVtZ2OHs1GYKD6nNmz3fDFF8VXRdNqFdStWxw0PfecESEh6n8cs9kxazhxXFQfW7dqMHu2G157zYCePSs3lOK4oFIkCfo9O+GXl4lMTz8YO7GiCEBxUFRQACE/z3KL/GseFxRAyMuzaof8PAj51zwu2p9f4nlFIZTBUDnfjCpKKRxfgnTjzzvDgDth6nx7icCpMFwKCITi519lx6rtYDQcOfPeqprBaBmUtVKKoVQ15MgfDu5bNxxbzm/GxOhH8Ub3dyr3xeiW8IdGsoXjomqrzClh11PZ48JZ51UZsrKAK1cE/PmnFrNnu9ttt3x5HjZv1uLLL/V222zcmIuYGHVtij17NDh+XLSEUOHhitMXD+fnBdnCcUElVbuKIkD9y0JRVVFernqbnwchLw/IzYbv1McgpKfZnRIGD08Ye/cFDCVCobx8oOCaSiUnBUWKXg/FwxOKuzvg4QHFwwPIz4f27JkbPtfQpx+kFhGAVgtFIwIaLaDRQNFqAVEDaDXq48LtxftE9a8lJfdpNVA0GggaDfyCfJGRbSjcp7ZVRI3lOdCIJZ5Xcl+JtoX7IIqAIEC3Yxv8hw264Tll/PRr9b1KnYtNIWUoZYcr/IPqyB8O/r74F0asHQJPrSf+G3sIge5BlfuCdNP4QyPZwnFRdRkMQNu2Xrc0JexmVea4cOZ5AZbfPZCdLSA7u+hWQMeOEtwLM6UtWzTYtk1rtT87W73iWU6OgFWr8tCwofqNee01PT74wH5HRVFBq1YyNmzIw+LFOnz6qd5qil3Dhur9+vVl+PtX/PlWJH5ekC0cF1Sk0tYoMpstYRFyc68Jj4rvIy8XQl6JfXl5xeFSfp7180rsE/LyKuYbUA62giLFwwOKe2Fo5F5iW8nHJbaj5GN3D8DT+nHRsW2FFs4McCrtM8OF115yVVxTiipE9/CeiA5ujfiUA/g6YRGmt5/h7C4REbkEvR4ID1eQkqLYnRIWFqZAb7/wpkq62fMqCpM8PIrXXTp6VMSpUyKys4GcHAFZWYJV2PT22wXw9VXbvvGGHl9/rUd2NmA2l37dPXty0KiR+kPszp0afPSR/W9sVpaAwr+Pw89PgZ+fAh8fBYKg4MIF6x90ZVnAzJnqGkwTJ5owcaKpjN8pIqoRXKXywWiE9wvPXX+NoqemIv/o4eLpZ1bBUIlwybKvcJvJcZ+bipsbFE9PNTDy9AQMBmgvnL/h8/LvfRCmrrcD7u6WsEnx8AQ8rB9fLyhyJFPnrpDCwm4Y4Jg6d3VC724S115yWQyl6LoEQcCUmGmYvPlhfBn/GabEPAF3rf3pC0REVDaCAMycabC6ilZJiiJg6lQjUlIEyLIa2siy+rzQ0OIfxC5dEpCfr4YjsgzLV9HPatHRxZcyPnZMRHq6AEUBfH2B9HQNitYgVRRYXcHrv/9EJCWJVq9d8uuee8yWn/u2b9fg9Onitm3aqIsw2zsvQQDuvtvDUplUFDSZzYLVukuLFumwZIn98GjWLAN8fYvXXUpPL/5VSRQV+PgAPj5qoFTyis5dukgwmYzw8VHg66vu9/aG5X6TJsWNH3/chMcfNxX2XV1bKj5ehCQJ0GgUREfLXGiaiGyqElPdTCYIOdkQsou/xOxMq8dCTlbh9mu2ZZXYnpd73ZcRAAhZmfCa//pNd1URRSieXmqFkKdn8ZdH8a1ln8c1+zyvs8+jeP+1gUVZK4oM9z1QvaaEuWiAYxw8BFmLlpZ6X8mhYdV67aWajtP3qiFHl1GbJBM6LYvBxZwLeLfXAoxpOb7yX5TKjeX1ZAvHRdVkNALHj4uIjxcxb54bUlKEa6qKFMDm6hZArVoyDh0q/uVgyBAP7N5t+29MXl4KzpzJsTy+914P/Pmn7baCoCA5ubjtQw+549dfdXbP4fz5bMt0uClT3LFypf22RTQaBf7+ClJT7U/t++efHDRooA7Wzz7TYe1anSVY8vVVw6Oi+yNHmhAQoD7v8mW1kkpto5T5SszltWWLxipIXL48zyrMq874eUG2OGxcuEpFUaFbnupmNBYGRIXBUE52YUiUVSI4yoZ4zeOiNmLR4/z8yj3Ra7vdrSfMkZGFIVCJUMjDA4qnl+UWnqUDJOj1lfPBfT0uPiXMGYtnO+Qzw8U+L1wVp+9RhdFpdHi09WS8tOMFfBL3IR6MGAtRsP8LBRERlfb991rs2qVFQoKIY8dEmEzX+8Hbep8gKBDF4nVFS/L2Bvz9FQiCWh2k3qpfntcUYYWFyWjSRIYgKNDpNFAUydJeENRKoKLfB5o1k9Gxo9nqeEXtrv2doXVrCbm5sGp79aqAXbusOytJAqZNM8DfX61i8vYuqlQqrmjy8ipuP2mSCZMmlW1aR506CurUqfwkJTZWQkyMWgkWEyOxSoqoAlSJiqJbpSjq1dFycyFkZcL7+enXn+o2bRKMv65R22erYZKYnVVc1VTBi2YrHh5QvH0g+/hA8fWF4uMLxdsHio/6JRc99vW1bFN8fCEXttEeOQy/cfff8HXynpnBiqIqxDh4CNIGDnK9AEejqV7jjK6LlVLVkDP+kpljzEbMNy2RZczE0jt/QP+GAx3zwlRm/As32cJx4TiKAly4ICAhQYOEBBEXL4pYsKDAsv+++zywZUtxSOPnpyAqSkLLljI2bNDi0iXBMiUsKkrG77/nQaOpnD8aO2Jc2JvqtmFDnsP/EF7Rtm7VYPZsN7z2mgE9e7pOKMXPC7KlssdFpS2ebU/J8Cg3R13byHKba+d+jvq4xH1c+7y8XAgl5wlXVHc9vdQgqejL29cSHMklt/mUDJj8rB4r3j6A7sbVrNfFiiIqI/5bQkV49T07XOHN4aw3+qu7XsaH+/8PnUO7Yu2w9Y57YSoT/gNAtnBcVK4tWzTYskWtfkpI0BQukF3s8OEcBAer3/gVK7Q4e1ZEVJSMqCgJdesqlnDG0VPCHDUuXHmqmyvi5wXZUqnjoijoSEy0OWFZEQTIIXWQseZ3CAUFZQuPrtmGa9tWUnhk1W+drkyLdxfcMwqmrt2Kw6SicKmoWsnLu3R5rBNZAkTAZkVRhQeIjsYpYRWC/5ZQEU7fowr3SKvH8OmBhdidtBP7kv9Bu5AOzu4SEVGlS08HDh3SWIKnN98sgLe3um/TJi0WLSpeiFunU9C8uWwJnrTa4p/GRo40230NV50S5qrnRUS3QJIgJl+GePEi9Fs2WVWmXEtQFGguJyGoU0yldEXx8IDi5aWudVR0W3TfarsnFC9vG/eL26LoOR6e0O3eWabFswtGj6tWU5BcfpFpTgkjcgqGUlRmdbxCcc9to7D86DJ8HPchFvX/xtldIiKqcAcOiFi/XotDh9QQ6uJF6zX0xo0zokMH9a/sffuaodUCkZESoqJk3HabDL39i8XZJQjA7NkGzJ7thtmzDdV+elsRVz0vIrJPyMmGePEiNJcuQLx4EeKli9BcvFB8m5QIwWw/pLdF0emg+PlVeHhUWVUwps5dIYWF3XCqm6lz10p5/cpUtEaRfs9O+OVlItPTD8ZOrCgiopvHUIrKZXLraVh+dBl+Pb0WZzJPo5FfY2d3iYhqiEuXBKSm2k81goMVhIWVrU7cYFCvfldU/TRxohGNG6vP3bNHg3ffdbNqX7++bAmeatcufo0+fST06VMx1T89e0rYvj2vQo5VlbjqeRE5jTOnGEkSxCvJEC9csIROmktFgdNFiJcuQMzIuOFhFK0Wclg4ZG8f6A4n3LB95o8/V68KFhdfPNtSURTsA1NKtnrBWCKim8RQisolIqgl+tS/A3+c34TPD36MN7q/4+wuEVENYDAA/fp54upV+1f+rF1bxr59uXBzK73v3DkBv/+utSxCfvy4CLO5OOBq3VpC48bqX+47dZJw//0mREWpIVTLlhL8/Cr8lIiIyq3Sr1KXkwPNJTVc0lwoDJsuXQCSkxBw9hzExEtlqnKS/f0hh9eDVLcu5PC6kMLrQa5beFuvHuTaIWogU8bFs6trRZFLT3UjIqogDKWo3KbEPIE/zm/C90e+xXMdZiHQPcjZXSIiF6fXA+HhClJSFChK6WopQVCrpBITBcv6T336mC3T7I4fF/HSS+5Wz/H3V69+Fxkpo1mz4kVvW7eW8cEHBSAiF+BCCxeXvEpdSWJSEnwnjrnxItNFVU4XL6jB08WL0Fw8X+Yqp6LvmqLRQA4Lh1S3nho4Fd5aQqfwcCg+vmU7KRevKCqa6uYqY5CIqDIwlKJy6xbeA9HBrRGfcgBfJyzC9PYznN0lInJxggDMnGmwupJbSYoi4NgxEZ06eVs9p0MHIwCgVSsZgwaZLAuQR0XJCAtTuMYRkQur9KoiR5IkeM+ZAShKqavUCYoCRRDgPXsGMhs3gXg50RIyaS5eLA6hylrl5OdfGDbVhVy3HuS69eAV0QwZvsGQwutCDqlToaGKy1cUcfFsIqLrYihF5SYIAqa2eQKPbZqIL+M/w5SYJ+Cudb/xE4mIbkJODpCQoMHJkyIaN5Zw7pwISSqdJuXnC9DrFbRooQZPbdsWr/UUEqLgq69Y/URUU9xyVVFFkGXAaIRgMgJGU+GtEYLZZP3YZLJqB5MRgtEImEyFt0Zojx278VXqkhIR2KvLdbtkqXIKr1tY3VRPrXS6TpWTIABewT4wV+Ll3VlRRERUczGUoptyV+OhmOc9FxdzLmDF8eUY03K8s7tERC7AaAR27NAgPl6D+HgR8fEanD5dvI7U7bebcfp06UBqyhQDRo0yo1kzGTqdI3tM5CIkCbo9O4G8TOiq89W0FAXIyoL3rGftVxUB8Hl6KvLOnAZkqVQAJNgJhkptN5mst18bPkkVcxGE8pA9PCE3alx6Lae69SHXrfgqpwrFiiIiohqJoRTdFJ1Gh0mtp+DFHbPwSdyHeDBiLETB/gLEREQlKYp6Nb34eA00GgX9+qm/vMky8MADHqUqocLCZLRqJaFXLwm5uQLi49VqKY1GQXS0jJdfNnIqHtFNunaamx+cPM3NaISQmQkxKwNCRkbh/Uz1flYmxMJt6v10CIX7xKxMdfsNpqgJAITMTHi/+pJjzqeQotEAej0UnR7Q69RbnR6KXgfoih7roOhLbtdD0eshpqdD//efN3yNrO9WMNghIqJqhaEU3bQHI8bi7X/exMmME9h4dj0GNLrT2V0ioirq1CkBBw+q1U8HD6oLkaelqUF227YS+vXLAwC4uwN9+0rw8FAQFaUGUVFRMoKDi+eMNGwoW9aWkiQBM2caGEgR3aRKmeYmyxCys9SAKDMTYmaG1a2QmXHN/UzrYCkvrwLP0D5jpy6QmjazHQTpdKWCoZLtLKGSvrBtUdik05bYXjJ80t1ahZILX6WOiIhqNoZSdNO89T4YHzkRC/a/h4/jFjCUIiIYjcCxYyKuXBHQp0/x1JV77/XE+fPW1ZRarYLbbpMRHW09xWXp0vzrvkZsrISYGAlxcRrExEiIjXX8FBmqwVzoam5lWjx75jPICgqGmJNlHSJZ3S8KnQrvZ2XaDE7KS/b1g+LnB8XXD7K/v9Wt4lfyvj9kP3+1rb8/NIcPwf/+e254/LyZc6pPVZGLX6WOiIhqLoZSdEsebjUJnxz4ELuTdmJf8j9oF9LB2V0iIgfJzQUOHy6ufIqP1+DoURFGowB/fwXHjuVYKpg6dJBQq5aC6GgJ0dFqBVTz5jLcb+IaCYIAzJ5twOzZbpg9m1VS5DhV/mpuigIUFEDIzlZDpOzs4q+c4lsxOxtCdhY0p07eePHsK8kIuHvAzXXH3b04LPLzh1wYMCn+hff9AtRwqXCb1X0f35sOWOTaIZDCwlyuqsjlr1JHREQ1kqAolXUdjaoppRKvHOIoggAEB/tUmXN5YstkLD+6DHc1GYpF/b9xdndqrKo2Lsh5Ll0SkJpanNQEBHghPT3X8jg4WEFYWPkGSXo6cOyYBp07F1cljRjhgb//Lv23DV9fNXz65pt8+PjcxAlQpePnRfmVnOZWMgctqlK5pau5SRKE3JwSIVJWcYCUk1P82BIuqY/Fom25JdrcYD2lmyEH14IUXhdKYcBkVa1kCZ2K7hdWLfn64qZS5wpi+e8F2KwqcsjV9yqLg6v1+HlBtnBckD0cG1SkaCzcCCul6JZNiXkCy48uw6+n1+JM5mk08mvs7C4R1VgGA9CvnyeuXr32wgNelnu1a8vYty8Xbm6ln68oQHKygIMH1cqngwdFJCRocOGCerxjx7IREKC2jYqScfSobKl8KloDqn59hdVL5FpuNM0NAnyen44sPz8IeXlqQJRTGDLlXBMgldhW1EbMzanQ7iqCAMXbB4pP4Zd38a1cYpuYmgKPJYtveLysL76uPtPcCrl0VRGvUkdERC6EoRTdshaBEehT/w78cX4TPjvwEd7s8a6zu0RUY+n1QHi4gpQUBYpSOhkSBLVKSq9XA6izZwWEh6uPAWDuXDd88one5rEbNJCRlCQiIEAGoE6hmzvXUGnnQlQlSBL0q1def5obFAhXr8D/nrtu6aUUnQ6Kr29heOR7TajkC8Xb27JN9vG1Dp5K7vf0AsQyXBFXkqDftN7lprkVMQ4egrSBg1xnDTAiIiIXxFCKKsTUNk/ij/Ob8P3RbzGj4wsIdA9ydpeIaiRBAGbONFiuTnctRREQFiZj6FAPJCRokJ0tYMOGXLRpowZNTZrIEEV1AfKoKHUR8latZERFSfDzsz6WTlfZZ0PkQLIM8eIFaI8dgebIEfX22FFojx+FUFBQpkNIdepADguH4u1bIjwqDI28SgZIaogke1sHSjbLFytTTVg8m1VFREREVRpDKaoQt4d1R6taMTh4NQ5fJXyJZ9o/7+wuEdVIJhNQv74aJh0+LEKSSldL/fZbcZrk5qbg4kXREkrdc48JI0aY4Gk70yKq/hQF4uUkaI4chvbYUWiOHYH26GFojh2zO41O0ekgmEw3PHT2J4uqXQDi0tPciIiIqMpjKEUVQhAETImZhsc2TcSi+M8wNeZJuGudt8ApkavLyQESEjQ4dUrEyZMiTp0ScPKkiLNnRZjNAqZONSA+vnTVRYsWEnr0kBAVpV4F77bbZKuKJy+vUk8hqraEq1cLA6cj0BZVPx09AjEr02Z7RaeD1PQ2mFu0gNSiJczNI2BuEQG5bj0Edmzl8tPc9Ht2wi8vE5mefjB24jQ3IiIiqnwMpajCDGkyDPN2zcXFnAv48dj3GBv5kLO7RFStFRQAZ84UhU4iunUzo317taJpzx4N7r/fdjmTp6eC8HAFMTES4uPVailRVNCqlYwNG/K4CDm5HCE9Ta16OnLYEjxpjx2BmJpqs72i0UBq3KQweGoBc4sISC1aQmrU2O681BozzS3YB6aUbIBXTCIiIiIHYChFFUYrajGp9RS8uGMWPjnwIUa3HAdRKMNCq0QEALhwQcBnn+lx8qQaRF28KECWixOkZ58F2rc3AgCaNpVRr56MJk1kNG1afNu0qYzQUAWiCDRuLFvWlpJlATNnGhhIkWNV8KXrheyswsDpqKX6SXPsCDTJl222VwQBcoOGMLdoWRg8RcDcPAJS02blXr+J09yIiIiIKh5DKapQD0aMxTv/voVTGSex4ezvGNhokLO7RFQl5OQAp0+LJabbqbcjRpjw2GPqWjVGI/D559ZXvvPxUSyhU1SUbNneoIGCfftyr/uasbESYmIkxMVpEBMjITZWqvgTI7JDv25tqQBHCgtDzrz5Nw5wcnOhPXFMDaCOHilc9+kINJcu2n2KVK8+zM1bWKqfpBYRMDdrjopcII1XcyMiIiKqWAylqEJ5630wruUELNj/Hj6OW8BQiqq0S5cEpKbaLx0KDlYQFlb2OSySBFy8qB6vQQP1eRcuCBg82BNJSbarBps3lwGooVT9+gomTzZaKp6aNJFRq5Zy09VNggDMmWPAiy96Ys4cVkmR4+jXrYXvxDHANesviUlJ8J04BlmLlqrBVEEBNCdPQHu0cNHxo4ehPXoE4vlzNtduAgCpTiik5i1gbtFSDZ5aREBq3gKKt48jTo1XcyMiIiKqQIKi2Pmpz0WlpGRf+zNytSMIQHCwT5U9l+Tcy2i7NBIm2YTfhm9G+zodnd2lGqGqj4uqxmAA2rb1wtWr9qeY1q4tY9++3FKzfMxm4MAB64qnU6dEnD4twmAQcP/9JnzwQYHldRo08IYsCwgKKjndTq2AatlSsgRYlYHjgmyp1HEhSQhsFwkxMRG2clAFANzdIYWFQ3P2DARZttEKkIOD1eCpMIAyN4+A1KIFFP+ACu4wFeHnBdnCcUG2cFyQPRwbVKRoLNwIK6WowoV41cGI2+7F90e/xcdxH2LxgKXO7hJRKXo9EB6uICVFgaKU/tVZEBQEBSnYskW9wl3t2gpGjTIDUKfZDRxo+zJ1er2CkleOd3MDNmzIQ/36MgL4uzS5OCE1FW4/rbKasleqDQAUFEB7+hQAQPb3h9Q8onDdpxbq/eYRUGrVckyniYiIiMhpGEpRpZgcMw3fH/0Wv55eizOZp9HIr7Gzu0RkRRCAmTMNloXAr6UoAo4c0WDcOHV/165mSyjl6Qm0ayfBw0OxWmC8SRMZ9eoppZaXad3adiUIUbVUUADNmdPQnDwBzemT0J48YbkvpqeX+TC5T0xHwcOTIIfUAeeWEhEREdVMDKWoUrQIjEDf+v2w+fxGfHpgId7q8Z6zu0SEtDTg0CENEhJEHDqkgbe3gpgYCfHxIiSp9C/FHh6KJXBq08Z6kfDff89zVLeJHE+WISZeUsOmUyehOXUC2lMnoTl1EuKF83bXewIAKTgYmpSUG76EKbYP5DqhFdlrIiIiIqpmGEpRpZnS5glsPr8Ry48uw4wOsxHkEeTsLlENtGCBHnv2aHDokIjEROv1o0JCZHzwQYHNaqmPP87HPfeYWcBBLk3IzIDmRHHwpDl1Sq18OnMKQn6+3efJvn6QmjaF1LgppKbNIDVpCnOTZpAaNwHc3NQ1pZKSbIZXiiBADg1Tr1pHRERERDUaQymqNLeHdUfrWm1w4Op+fH3oSzzT/nlnd4lcUG4ucOSIiIQENXhKSxOwaFGBZf/mzRrs3l38UdewoYzISAlRUeptr16SVbWURqMgOlpmIEWOJUnQ7d4JMfky5JA6amBz7TzQm2U0QnPurFr1VGLKHc6cQtCVK3afpuh0kBo2gtSkKaQmavAkNW0Gc+Om6npP13mD5MybD9+JY6AIglUwpRQ+J2feWxV3fkRERERUbTGUokojCAKmxEzDpE0TsCj+M0yJeQIeWg9nd4tcwPLlWmzerMWhQxqcPi2UWqg8J6cA3t7q/YceMuHuu82IjFRDKB8bF4AoubaUJAmYOdPAQIocRr9uLbznzLBaHFwKC0POvPkwDh5StoMoCsTky8XT7U6eKKx8OgnN+XMQJMnuU6WQOoXVTkXBU1P1tn5DQHtzPyYYBw9B1qKlpc5LDg1Dzry3yn5eREREROTSGEpRpbqryVDM2z0XF7LPY8Wx5Rgb+ZCzu0TVgNEInDgh4tAhtQLq6FER336bD71e3b97twZr1+os7WvXlhEZKSMqSkJkpAyxxCy9YcPMN3y92Fi1WiouToOYGAmxsfZ/gSeqSPp1a+E7cQyuvWaymJQE34ljkLVoqVWAI+RkF061O2mpetKcLFzrKTfH7uvIXt6FFU9N1PCpWTP4tmuN1KBQyF43vlTvzTAOHoK0gYMqrwKMiIiIiKo9p4ZSBoMBr7zyCjZu3Ah3d3dMmDABEyZMsNl206ZNeO+993D58mW0aNECc+bMQWRkpIN7TOWlFbWY1GoK5uyYiU8OfIjRLcdBFMQbP5FqnM2bNVizRodDh0QcOybCZLIuVTpxQkRkpHoVu7vvNqNpU7mw+klG7dr2F10uC0EAZs82YPZsN8yezSopchBJgvecGYCi4NohJygKFAA+T02FYctmaM6cUkOo5Mt2D6doNJDqNyiebte0eMrdtVe4EwQAwT5QUrKBW3v7XJ9GA9Pt3SvxBYiIiIioOnNqKDV//nwkJCRgyZIlSExMxPPPP4+wsDAMGDDAqt2JEyfwzDPP4H//+x/atm2Lr7/+GpMmTcKmTZvg4cHpYFXdAy3H4u1/38SpjJPYcPZ3DGw0yNldoptw6ZKA1FT7aU1wsIKwMPu/3coycPasgEOH1LWfDh3S4LXXClC/vvqchAQNfvihuPrJx0cpsfaTjJCQ4mPHxlZ8NVPPnhK2b+cV9chxdLt2WE1tu5YAQMjKhMe3X1ttl4NrqQuLN21WvNB402aQGjSEpZyQiIiIiKgacFoolZeXhxUrVuCLL75AZGQkIiMjceLECSxbtqxUKLVjxw40bdoUQ4cOBQBMnz4dy5Ytw8mTJxEdHe2E3lN5eOu8MT5yIj747118tP8DhlLVkMEA9OvniatX7Ve51a4tY9++XLi5FW/bv1/E8uU6JCRocPiwiNxc61Dr3ns1qF9fnV7Xq5cZRiMsC5DXr6+wYolch9kMzamT0CYchDb+ILQJ8dD+90+ZnlowcBCMg4aowVPjJlD8Ayq5s0REREREjuG0UOro0aMwm81o06aNZVu7du3w6aefQpZliCUWhfH398fJkyexb98+tGnTBqtXr4a3tzfq16/vjK7TTXg4ehI+ifsQey/vxj+X96BDnU7O7hKVg14PhIcrSElRSi0qDgCCoECjAR5/3B0TJ5rQubNaxXThgoivviqu3HBzUxARUXz1u+jo4mqnmBgZMTHGyj8ZosqWkwPtkUNq8JRwUP06chhCQcGNn2tDwaNTOAWOiIiIiFyS00Kpq1evIiAgAPoSUw2Cg4NhMBiQkZGBwMBAy/Y777wTW7ZswQMPPACNRgNRFPHZZ5/Bz8+v3K/rCpUXRedQnc6ljncdjGh+L747shSfHFiIjqEMpSpaZY4LQQBmzTLg3ns9be5XFAFJSQLWrBHRqpWMLl3UsKldOwnTphkKFyGX0aSJfLMX86KbVB0/L6oTITm5MHiKhzb+IDQJB6E5fQqCUnoqq+zlDSkyCuaoaJijWkFq2RI+D42GePmyzfaKIEAOC4O5S9cK/+/HcUG2cFyQLRwXZAvHBdnDsUFFyjoGnPbrYX5+vlUgBcDy2Gi0rpZIT0/H1atX8dJLL6F169b4/vvvMWvWLPz0008ICgoq1+sGBVXOVYacobqdywu9nsd3R5bi11NrkSEmo2lgU2d3ySXdzLhQFCA9HTh1Cjh9uvTXk0+qX++8A+zbp64PVZK7O3D33UCbNkD//m4IDlbn8AUHAwsWVMRZ0a2qbp8XVY4kASdPAnFxwP796m1cHJCcbLt9aKj6hoiJsXyJTZpAFEXoSrZbuBAYMUL9V7tkMCUIEABoFixAcIh/pZwSwHFBtnFckC0cF2QLxwXZw7FBZeW0UMrNza1U+FT02N3d3Wr7O++8g9tuuw0PPvggAODVV1/FwIEDsWrVKjz66KPlet3U1Oxrr7xd7QiC+iavbucSItZH3wb9sPncRrz+51uY3/M9Z3fJpdxoXJhMwMWLAs6dE3HunIhmzWR07apWNB08KKJPHy+7xz540IjUVAOefVZjs1pqyZI89O5dPBUvJeXWz4cqRnX9vHCqvDxojx6GprD6SZtwENrDhyDklV4IXxFFSE2bwRwVDSmqlVoFFRkNpXbt0sdNyy29rccd0C9eCq/ZM6wWPZfCwpA77y0Ye9wBpGRX5NkB4Lgg2zguyBaOC7KF44Ls4digIkVj4UacFkqFhIQgPT0dZrMZ2sL5PFevXoW7uzt8fX2t2h46dAhjxoyxPBZFES1atEDida5aZI+iwGXeHNXxXKbGPInN5zZi+dFlmNFhNoI8ylfpRtdnNhePi6tXBbz5ph5nz6oh1KVLAiSpuIZywgSjZZpdgwZq6VNIiIwGDWQ0aKCgQQMZDRuq95s0kaEoQK9eEmJiJMTHi5AkARqNguhoGb16SdVuLNY01fHzwi5Jgm73TojJlyGH1IGpc1dAo7mpQwkpKcWLjx9Sp+FpTp6AcG05IADF0xPmiEiYi8KnqGiYIyIBTxvTWsvxvTYMGgLDgEG2z6mS/5u51LigCsNxQbZwXJAtHBdkD8cGlZXTQqmIiAhotVrExcWhffv2AIB9+/YhOjraapFzAKhduzZOnTplte3MmTO88l411DWsG1rXaoMDV/fjq4Qv8GyHmc7uUoW7dElAaqr9CbTBwQrCwm7uE1qWgQsXBEvQdO5cceXTuXMiRo8GXn5ZbavXK1i61HqKrLu7YgmdIiKKf+n29QXOns22+bt1SYIAzJxpwH33qQ0lScDMmQbOGSeH0a9bC+85pauKcubNh3HwEPtPlGWIZ88Ur/9UeKu5nGS7eXAtmKNblQigWkFq3OSmw68b0mi4mDkRERER1ThOC6U8PDwwdOhQzJ07F6+//jquXLmCxYsX44033gCgVk35+PjA3d0do0aNwsyZMxEVFYU2bdpgxYoVSExMxLBhw5zVfbpJgiBgaswTeHTTQ1ic8DmmtnkSHloPZ3erwhgMQL9+nrh6VbTbpnZtGfv25cLNzfb+zExYQqYzZ0TUry9j6FCzZV+HDt52j336dPF9Pz91cfKwMDWEathQRu3aCkQ7XbtRIFUkNlatloqL0yAmRkJsrHTjJxFVAP26tfCdOKbUn93EpCT4ThyDrEVL1WCqoADaY0eKp94lxENzKAFibk6pYyqCAKlxE0v4JBUGUHJIHUedFhERERFRjeXU62DNmjULc+fOxbhx4+Dt7Y1p06ahX79+AIBu3brhjTfewPDhw3HnnXciNzcXn332GS5fvoyIiAgsWbKk3IucU9UwuMndqLe7Pi5kn8ePx77HuMgJzu5ShdHrgfBwBSkpChSldPmQICgIDVWQlwdLKFVQADz+uLsliMrIsH7eHXeYLaGUv78aavn7K9dMsZPRsKGCNm28kJ9f/Nynn7Zet60iCAIwe7YBs2e7YfZsVkmRg0gSvOfMABQF1w45QVGgAPCd+gik+a9Bc+I4BKl0WKq4u8Mc0VINoCKLr4CneHMhTiIiIiIiZxAUpWbN9ExJqf4LrgkCEBzsU63P5fMDH2POjplo7NcEOx/YB1GwX1lU3WzZorFMb7NFFBXccYeEpUvV9EhRgKZNvZGdXfyrdu3axes6tWsnYeJEk2Wfoti+vKYrjAuqeK4yLnQ7tsF/2KAyt5cDA2GOal289lNUK0hNmwFap/4tpspwlXFBFYvjgmzhuCBbOC7IHo4NKlI0Fm6EP52TUzzQcize/vdNnM48hfVnfsOdjQc7u0u3LCcH8PZWp7e1bi3hwAERKFXTAciygEuXircLAvDGGwXw9VWrn+rXl+Fl/0J4rEyiGkVITob+7z/hvvSrMrXPm/IE8idNgVwnlG8WIiIiIqIqjqEUOYW3zhvjIyfig//excdxC6pdKKUowOnTAnbt0mL3bg1279bA11fBli15EAR1LSdb1VJz5xZg2DAzQkKs/2wwapTZUV0nqtry8qDbvRP6v7ZAv/VPaI8cKtfTjXf0hxwaVkmdIyIiIiKiisRQipzm4ehJ+CTuQ+y9vBuL4j9HgHsAQjzroHNoV2jESrrC1S368Uct1q9Xg6iUFOsphzqdgtxcwMuruFoqIUGEJAnQaBRER8uYPNnE4g2ikmQZ2vgD0G39E/qtf0K3ZxcEY/FaaIogwBzdGqbuPeG+/FsIaWkQbNSCK4IAOTQMps5dHdl7IiIiIiK6BQylyGlCvOqgU2gXbLu0FbO2PWvZHuYVhnnd5mNwk+tc3r2SFRQAcXEa/POPBlOnGi1XrPvzTy3WrdMBANzcFLRtK6FLFwmdOkno0EGyTLu7tlpKkgTMnMlFwYkAQLx4QQ2gtm6BfttWiKmpVvul8Low9uoNU89YGLv3glJ4UQtTuw7wnTgGiiBYBVNK4RsrZ95bgKZqBtpERERERFQaQylymnWn1mLbpa2ltiflJmHihjFY1H+pw4Kp7Gzgn3/UaXi7dmmwf78GRqP6i26fPma0bCkDAEaONCEiQkanThJiYiTLFfRsiY1V28TFaRATIyE2tvTVwIhqAiE7C7od26H/6w/otv4J7amTVvtlbx+YunWHsWcsTD17Q2rS1OZ6UMbBQ5C1aCm858yAJjGx+PmhYciZ9xaMg50XZBMRERERUfkxlCKnkGQJc7bPsLlPgQIBAubseB4DGw2qlKl8Ja9gt2iRDrNnu0GWrX8JrlVLRufOktVVI3r3ltC7d9nCJUEAZs82YPZsN8yezSopqkHMZmj/2wf91sJ1ofb9A0Eqft8oGg3MbdrB2DMWxp69YW7XHtDpynRo4+AhSBs4CLrdOyEmX4YcUkedsscKKSIiIiKiaoehFDnF7qSdSMxNtLtfgYLEnEvYnbQTt4d3v6XXUhTg/HkBu3drsGePWg01d64B/fqpvyQ3aiRDlgU0aKCGUJ07S+jSxYxGjZRbDpJ69pSwfXverR2EqKpTFGjOnILur8J1obb/DTE7y6qJuVFjdTperz4wdesOxdfv5l9Po4Hp9lv7XCAiIiIiIudjKEVOkZx3uULbXSstDVizRmcJoRITrRcl37VLawmlunaVcOBADkJDSy+eTES2Celp0G3bCv3WP6H/aws0F85b7Zf9/WHq3gvGXr1h7BkLuX4DJ/WUiIiIiIiqKoZS5BQhnnUqrJ3JBMTHi9DpgOhode2n9HQBzz/vbmmj1Spo3bqoEsqMjh2LpxK5u4OBFNGNGAzQ/bsX+r+2QLd1C7QH4qwXG9fpYOrQCabCEMrcKoZT6oiIiIiI6LoYSpFTdA7tihD3cCQXJAKwHQh5aLzQtnb7Utvz8oD//lMXJN+9W4N9+zTIyxMwZIgJX35ZAABo3FjBoEEmtGypBlFt2xZfGY+IykBRoDl6BPqtW6Db+if0u3ZAyLOeimpuEVG4OHksjJ1vB7y9ndRZIiIiIiKqjhhKkVOYTRrk//Q+MGAUAAEQSgRThXfzpVzcs2YIvrrzW4R4hkCWgaFDPbBvnwYmk/ViTwEBCnx9i48hCMBXXxVU/okQVWWSBN2enUBeJnSefjB2uv6C4EJyMvR/F64LtfVPaJKtp8/KtWrD2KOXJYiSQ8Mq+wyIiIiIiMiFMZQip9DrgcaGYYj7cQUw4CnA72Lxzqx6wIExEDp9hH+v7EG/FT3x9YBlaBPSDkajAJNJQFiYWgHVqZOELl0k3HabDFG0+3JENY5+3Vp4z5kBTaJ6QQE/AFJYGHLmzYdx8BC1UV4edLt3Qv9X4VXyjhyyOobi7g5T564w9uwNY6/ekFpGgpeRJCIiIiKiiiIoilKjFtNJSclGdT9jQQCCg32q/bls2aLBffd5AoIENNgGeCcBOaHAue6AogECTwD33w3UOgI3jRve7vk+IgxjEBCgoF69W78ynqtxlXFBt06/bi18J44BFAUl3yaKIACKgoIRo6BJToZuzy4IRqPVc03RrS3rQpk6dlYXXSOXw88LsoXjgmzhuCBbOC7IHo4NKlI0Fm6ElVLkNLGxEmJiJMTFicDZXiX2KPDwUDD6ngZoHfMn1mgmYNP53/DElsl4tNVBvBw1D4Kgc1a3iao2SYL3nBmlAikAloXJPVb+WNw8LBzGXr3VdaG694ISHOzAzhIRERERUU3GUIoc6tQpAStW6DBunAmhoQpmzjSo1VJWBHz1VR5695YAeGKE8h3e+edNvPPvm/j84Cc4nHoIX/RbgiCPIGecAlGVJSZfhtv331qm7F1P3sOTUPDQI5CaNuOUPCIiIiIicgqGUlTpUlMF/PyzFitW6PDff+oiy97eCh5/3ITYWAmtW0tISBAhSQI0GgXR0TJiYyXL80VBxIyOLyAyOBqP/zEJ2y/9jf4re+Hrgd8hKjjaWadF5FRCehq0cfuhO7Af2v3/QRv3HzRJNw6jipjbd4TU7LZK7CEREREREdH1MZSiSmE0Ahs2aLFihRabN2thNquVGBqNgl69JLRsKQNQCzRmzSqulpIkATNnGmwWbgxqfBea+DfF2N/uw9msMxi0ui8+iP0YQ5vd47DzInKKnBzo4g8Uhk/7oIvbD83ZM6WaKYIAuW5daC5cuOEh5ZA6ldFTIiIiIiKiMmMoRZWioACYOtUdBQVqutSqlYSRI00YNsyM2rWtV7wrXltKg5gYyapK6lotAiOwccRfmLRpAv688Ace3fQQElLiMavTi9CI9i91T1RtFBRAeyherYKKK6yAOn7Msh5USVLDRjC1aQtz67Ywt2kLU3RrwMMDge0iISYl2XyOIgiQQ8Ng6tzVEWdDRERERERkF0MpumVF60QdOybiq68KAAC+vsC4cSa4uSkYOdKM5s1lu88XBGD2bANmz3bD7Nm2q6RK8ncPwHeDVuK1Pa9g4f73sWD/eziUGo9P71gEPzf/CjwzokpmNkNz9IgaPu3/D9oD+6E9nADBbC7VVAoNgzmmMHyKaQtz6xgoAYE2D5szbz58J46BIghWwZRS+ObKmfcWoGGIS0REREREziUoSs26UKMrXJqyKlxm09Y6UQCwfXsubrvNfgBV0VafWIGn/3wc+eZ8NPZrgiUDv0fzwBYOe/2qpCqMC7oOWYbm1EloC6ufdPv/gzbhIISCgtJNg4IKg6c2MLdpB3NMm3JPt9OvWwvvOTOsFj2XwsKRM+8tGAcPueXToeqNnxdkC8cF2cJxQbZwXJA9HBtUpGgs3Agrpahc9u4V8eGHbvjjD02pdaJGjjShXj3HBVIAMLzZSDTzvw3jfn8ApzNPYeCqPvio7+cY2GiQQ/tBZEVRIF44Xxw+HdgP7YE4iNlZpZrKPr4wt46BOaZt4VS8NpDr1b/lK+IZBw9B2sBB0O/ZCb+8TGR6+sHYqSsrpIiIiIiIqMpgKEXXJcuAwQB4eKiPr1wRsWGDOmxat1aDqKFDS68T5UjRtVpj48iteHjDWOxM3I5xv9+PGR1ewPT2MyAKotP6RdWIJEG3eyfE5MuQQ+qo6y2VI7wRky9DG7cf2v371Kl4B/ZDTE0t1U7x8IA5qhVMMW0Kp+K1g9S4CSBW0jjVaGC6vTsQ7ANTSjbAv1YREREREVEVwlCKbDp5Ul0nauVKHe6914QZM4wAgDvuMGP6dAOGDbv+OlGOFuwRjBV3rcFLO2dhUfznmP/P60hIicfCPp/CW3/jkkGquWxPcwtDzrz5Nqe5CelpJRYh368uRJ6UWKqdotXC3DKqeB2o1m0gtYgAtPzYJSIiIiIiArimVLVUWfN0U1KK14nav7+4SiQqSsKWLXkV90KV7LsjSzFj69Mwyka0CIzA1wO/Q2O/Js7uVqXj/O3y069bC9+JYwBFQcnJckULgmd/9Dnk8LrqIuRx+6CL2w/N2TOljqMIAqTmLWBu3UZdC6pNW5hbRgHu7g46E/s4LsgWjguyheOCbOG4IFs4Lsgejg0qwjWlqFymTnXHTz9prdaJio1Vp+f171/6SmBV2QMRY3BbQHM8tH40jqYdQf+VsfjsjsXoXb+vs7tGVYkkwXvOjFKBFAAIigIFgM+UR0rtAwCpYSN1/acYdRFyU3RrwNvbAZ0mIiIiIiJyHQylaiBZBvbtE9G+vWxZS1mvV2A2C1Vmnahb1b5OR2we+TfGr38Q+5L/wQO/jsCczq9gaswTEG5xAWlyDfotm6ym7F2raJRIQcEwd+ysTsGLaQtz6xgoAYGO6SQREREREZELYyhVgxStE7VqlQ7nz4tYvz4Xbduq60JNm2bEY4+ZqtQ6UbcqxKsOfh76G2b+/QyWHfkG/9v1IhJSDuC9XgvhqfN0dvfIwYTkZOj27IRu907o9uyGNuFgmZ6X+9pbMAwfWcm9IyIiIiIiqnkYSrk4e+tEeXsrOHNGtIRSjRsrcMVLc7lp3PBerw8RXas15mx/HqtPrMSJ9BP4euAy1POp7+zuUWVRFGjOnIJu9y5o9+yCbvdOaM+cvqlDySF1KrhzREREREREBDCUqjYuXRKQmlo87SwgAEhPL76MfHCwgrAw61ApPl5E//6eVutE9e5dvE6Uh4dj+u5sgiBgQtQjaBEQgYc3jkV8ygH0W9ETi/ovRdfwbs7uHlUESYL2UDx0e3ZZgijNlWSrJoogQIqIhKlzF5g6d4WpfUf4D74DYlISBBurMCqCADk0DKbOXR11FkRERERERDUKQ6lqwGAA+vXzxNWr4jV7vCz3atWS8cknBcjIEDBkiLowecuWMoKDFdSpI1vWiapVy/Wqocqqa3g3bByxFeN+fwDxKQcw4pchePX2NzAh6lGuM1Xd5OdDt39fYQi1E9p/9kLMybZqouj1MMe0VQOozl1g6tAJip+/VZucefPhO3EMFEGwCqaKrr6XM+8tQKMBERERERERVTyGUtWAXg+EhytISVGgKLbCEwUZGQJGjPBE7doy7rzTDK1W/V1669ZcBAQ4vMtVVl2fevhl2AZM/2saVp9YgVnbnkP81YN4q+d7cNO4Obt7ZIeQkQ7dP3ug210YQh3YD8FotGoj+/jC3KFjYQjVFaaYtoC7+3WPaxw8BFmLlsJ7zgyrRc/l0DDkzHsLxsFDKuV8iIiIiIiIiKFUtSAIwMyZBtx3n73FuQWYTOo6UX37mpGTA/j7q3sYSJXmqfPEJ32/RKtaMfjfrhfx3dGlOJZ+BF8NWIY6XqHO7h4BEJMS1QXJd++EbvcuaI4eLjXFTqodYqmCMnfqAnPLqJuqajIOHoK0gYOg270TYvJlyCF11Cl7rJAiIiIiIiKqVAylqonYWAkxMRLi40VIUslqKQW+vsDbbxdgwICas07UrRIEAVNipiEisCUmbXoI+5L/xR0reuKrAd+ifZ2Ozu5ezaIo0Jw8URxC7dkFzflzpZqZGzcproLq2Blyo8ZqYlsRNBqYbu9eMcciIiIiIiKiMmEoVU3Yr5YS8PnneejdW3JKv6q72Pp9sGHEXxj3+/04mnYEQ3++E2/1eA8Pthzr7K65LrMZ2vgDlql4ur27IKamWjVRRBHmqFbqWlCd1BBKCQlxUoeJiIiIiIioMjCUqkaurZbSaBRER8uIjWUgdSsa+TXGb8M3Y9qWyfj19Fo8/dfjiE85gFdvfxM6jc7Z3at6JKl8U91yc6H771/LVDzdvn8g5OVaNVHc3WFq214NoTp2gblDRyg+vpV8IkRERERERORMDKWqkWurpSRJwMyZhgqbwVSTeet9sKj/N3h/3zt4c+88LE74AkfSDuPLft+glmctZ3evytCvW1tqUXApLAw58+ZbFgUX0lKh27O7cCreTmgPHoBgNlsdR/bzh6lTZ7UKqlMXmFvHAG5caJ6IiIiIiKgmYShVzRRVS8XFaRATI7FKqgKJgojp7WcgMjgakzc9jF2JO9BvZU8sGfgdWtWKcXb3nE6/bi18J44BrllwXExMgu+E0TD2jIXmchK0x46Weq4UFm6pgjJ17gqpRQQgio7qOhEREREREVVBDKWqGUEA5swx4MUXPTFnDqukKkP/hgOx/p4tGPv7fTideQqDV/fD/8UuxD23jXJ215xHkuA9ZwagKLh2yAlQQyq3rX9atplvaw5Tpy7qV+eukOvVr7hFyYmIiIiIiMglMJSqhnr2lHD4MJCSIl1btEIV5LbA5tgw4k9M3vQwNp/fiMmbH0Z8ykG82PkVaMTrrJ/kovSbN1hN2bMn5/k5KBg3AUpwsAN6RURERERERNUZ588Q2eHn5o+ld/6AJ9s+AwD4OG4B7v/1HqQXpDm5Zw6Smwu31SvgO+Ze+I5/sExPkRs1YiBFREREREREZcJQiug6NKIGszu/jC/6fQ1PrSf+urAF/VfG4kjqYWd3rXIYjdBv+B0+j01AcGQT+D42EW4bfocglW3tMjmkTiV3kIiIiIiIiFwFQymiMri76XCsG74J9X0a4GzWGQxc1QfrTq11drcqhiRBt20rvKdPQ1BUU/iNuRfuq1dCyMuDVL8hcp96FmlbdkAKC4NiZ10oRRAKFzPv6uDOExERERERUXXFNaWIyigqOBobR/6FRzaMx7ZLWzFhw2hMbz8DMzq8AFGoZvmuokC7fx/cfloJt59XQ5N82bJLqh0Cw9DhMAwbAXPb9pYFynPmzYfvxDFQBAFCicXMFMv+twBNzVtvi4iIiIiIiG4OQymicgh0D8IPd/2EV3bOwWcHP8Z7/87HoZR4fNz3C/jofZ3dvRvSHD0Ct59WwP2nVdCcPWPZLvv5wzB4CAzDR8LUtZvNcMk4eAiyFi2F95wZVouey6FhyJn3FoyDhzjkHIiIiIiIiMg1MJQiKietqMWr3d5EVHArPLv1SWw4+zsGrOyNb+78Hk38mzm7e6WI58/B7edVcF+1AtojhyzbFU9PGPoPhGHYSBhj+wBubjc8lnHwEKQNHATd7p0Qky9DDqmjTtljhRQRERERERGVE0Mpopt0b4sHcFtAc4xf/yBOZBxH/5W98ekdX6Jvg/7O7hqEK1fgtnY13FevhO7fvZbtik4HY2wfGIaPhKHfQMDbu/wH12hgur17BfaWiIiIiIiIaiKGUkS3oE1IO2wcuRUT14/B3su78eCvozC788uY1uZpyIqM3Uk7kZx3GSGeddA5tCs0YuVVFAmZGcDaFfD95lvotm2FIMsA1DWfTLd3h2HYCBgGD4ESEFhpfSAiIiIiIiIqK4ZSRLcoxDMEq+9ehxe2zcA3hxdj3u65WH/mN1zMuYDLuUmWdmFeYZjXbT4GN6nAtZfy8uC2aT3cVq+E/o+NgNEIfeEuU5u2akXU3cMh1wmtuNckIiIiIiIiqgAMpYgqgF6jxzu93kd0rVaY+fcz+Dd5b6k2SblJmLhhDBb1X3prwZTJBP1ff6hB1PrfIObmFO9r2RK5Q4ajYOg9kBs3ufnXICIiIiIiIqpkDKWIKtDoiHF4c888pBaklNqnQIEAAXN2PI+BjQaVbyqfLEO3eyfcVq+E2y8/QUxPt+yS6tVXp+YNH4GAHp2Rn5oDRamIsyEiIiIiIiKqPAyliCrQ7qSdNgOpIgoUJOZcwu6knbg9/AaLhSsKtAf2q0HUmtXQJCVadsnBtWC4exgKho+EuX1HQBAgCID6f0RERERERERVH0MpogqUnHf5lttpThyH2+oVcPtpJbSnT1m2y75+MAy6C4ZhI2Dq1gPQ8u1LRERERERE1Rd/qyWqQCGedcrUzkfnY/VYvHgBbj+tgttPK6FLOGjZrri7w9BvIAzDR8LYuy/g7l6h/SUiIiIiIiJyFoZSRBWoc2hXhHmFISk3Eddb1mnG1un4v+w8DNibAvefVkK3d7dln6LVwtirNwzDRsA4cBAUb5/rHImIiIiIiIioemIoRVSBNKIGb3qMxLicDyAAUEos8SQogAKgluyOS7kXMWrbeEz8D3j3AOArCDB1uV1dsHzw3VCCgpx1CkREREREREQOwVCKqCJJEh54fQU8/YCnBgAX/Yp31c0C/m89MOBkAV7oAyzoDCxqC6xv64d3u8xH7zb3O6/fRERERERERA7GUIqoAul274QmMRH3JAJDjwLbGgBJ3kBoDtD9HKApnNP3pv+D6NcxFtOOvY4zmadx365JuDdtK169/Q34uwc49ySIiIiIiIiIHEB0dgeIXImYXHxVPY0C9DoL3J+g3mpKLDJl6tUbHduPwp+jduKx1o9DgIAfjn2H7ss7Yf2Z3xzebyIiIiIiIiJHYyhFVBFkGfr1v8Hz/94pW/MQ9Sp9njpP/O/21/HLsI1o6t8MyXmXMfb3+zB508NIK0itzB4TERERERERORVDKaJbYTTC7ftvEdCjE/zG3gftsSNQALtX3lMEAVJYOEydu1pt7xjaCX+M2o7H2zwFURCx6sSP6P59J6w7tbbST4GIiIiIiIjIGRhKEd0EITsLHh8tQGCHVvB9cgq0x49B9vFF3rSnkfN/HwKCAEUQrJ5T9Dhn3luARlPqmB5aD7zU5X/4dfgmNA9ogav5VzBhw2g8unE8UvJTHHJeRERERERERI7CUIqoHITkZHi99goC20TC+5U50CQlQgqpg5yXXkVa3GHkvvgKCh4ch6xFSyGHhlo9Vw4NQ9aipTAOHnLd12gX0gGbR23DU22fhUbQ4OeTq9FjeUesPflTZZ4aERERERERkUPx6ntEZaA5fRIeH30I9x+/g2AwAADMTZshf+qTKBhxL+DmZtXeOHgI0gYOgm73TojJlyGH1FGn7NmokLLFTeOGFzq/hEGN78ITW6bgSNohPLxxHAafXI03e7yL2p61K/wciYiIiIiIiByJoRTRdWj374Pnh+9D/+taCIq6UpSpXQfkTXsaxgF3AuJ1ig01Gphu735Lr9+6dhtsGrkV/7fvbXzw37tYd3oNdiZuw2vd5mN4s5EQrpkiSERERERERFRdcPoe0bUUBbotm+E3fDAC+sfCbd0aCIoCwx39kbF2PTJ+2wzjnYOvH0hVIL1Gj+c7zsaGEX8hKrgV0grSMHnzwxj3+/1Izr3skD4QERERERERVTSGUkRFzGa4rfoRAb27wf++4dBv/xuKVouCUfcjbetuZC1boU7Bc1J1UnRwK2y4508833E2dKIO68/+hm7LO+KHo99BUexd74+IiIiIiIioamIoRZSXB/dFnyGwcxv4Tn4Y2kPxUDy9kDdpCtL2HkD2ws8gRbR0di8BADqNDs+0fx6bRv6N1rXaINOQgWlbHsODv45EUk6is7tHREREREREVGYMpajGEtJS4fn2Gwhq2xI+s56D5vw5yMHByJ05B6n7DyH31Tch163n7G7a1DIoEr/f8wdmd3oZelGPzec3otv3nbB4/2JWTREREREREVG1wFCKahzxwnl4vfAcgtpGwuvtNyCmpUGq3xDZb76L1H2HkDd9BpSAQGd384a0ohZPtnsGf4zajra12yHLmImJayfi3nXDcTH7grO7R0RERERERHRdDKWoxtAcSoDP5IcR2LE1PL/8DEJeHkzRrZH1+VdI2/0fCiY8Anh4OLub5dY8sAXWDd+El7u8CjeNG/48/wd6LO+Mbw59xaopIiIiIiIiqrIYSpFrUxTodmyD333DERjbFe6rfoQgSTD2iEXGijXI2Pw3DEPvAbRaZ/f0lmhFLR5v+yQOPHYAHep0Qo4pG89ufRIjfrkb57POObt7RERERERERKU4NZQyGAx44YUX0L59e3Tr1g2LFy+22W7MmDFo3rx5qa9Zs2Y5uMdUbUgS9OvWwn9gb/gPGwT9ls1QRBEFQ4cjffPfyFy5BqaesU67kl5laR7cHL8MW49Xb38DHloPbLv4F3os74xF8Z9DVmRnd4+IiIiIiIjIwqnlIfPnz0dCQgKWLFmCxMREPP/88wgLC8OAAQOs2n344YcwmUyWxwcOHMBTTz2FBx54wNFdpqquoADuK5bD4+MF0J46CQBQ3N1RcN+DyJs8DXKjxk7uYOXTiBpMaj0VdzQcgKe2TMXupJ2Yte1Z/HLqZ/xf7EI08nP97wERERERERFVfU4LpfLy8rBixQp88cUXiIyMRGRkJE6cOIFly5aVCqX8/f0t9yVJwv/93//h4YcfRnR0tIN7TVWVkJUJ968XwePzT6C5kgwAkP39kT/hEeRPfAxKrVpO7qHjNfZrgp+H/oavEr7Aq7texs7E7Yj9oSte6PQSHm71GESBs3eJiIiIiIjIeZz2W+nRo0dhNpvRpk0by7Z27drhwIEDkGX704xWr16NzMxMPPLII47oJlVxYlIivF55EYExLeE9by40V5IhhYUj53+vI/W/w8ib+WKNDKSKiIKIidGT8Ne9u9AtvAfyzHmYs2Mm7v55IE5nnHR294iIiIiIiKgGc1oodfXqVQQEBECv11u2BQcHw2AwICMjw+ZzFEXBl19+ibFjx8LLy8tBPaWqSHPiOLyfmorA9tHw/OgDiDnZMLeIQNaHnyLtn4PIf+xxwNvb2d2sMhr6NcLKIWsxv8f/wUvnjT1Ju9Drh674OO5DSLLk7O4RERERERFRDeS06Xv5+flWgRQAy2Oj0WjzOXv27MHly5cxatSom35dV1jXuugcXOFcykv7zx54fPg+3H7/1bLN1Lkr8qY9BVPffoAoogZ+WwDceFxoBBEPRU9E3wZ34Ok/p2HrxT8xd+dsrDv1Mxb0+QTNAm5zXGfJYWry5wXZx3FBtnBckC0cF2QLxwXZw7FBRco6BpwWSrm5uZUKn4oeu7u723zOhg0b0KNHD6s1psorKMjnpp9b1bjMuUgSsG0bkJQEhIYC3bsDGk3xflkGfvsNmD9fbVdk6FBgxgzounSBn8M7XXXdaFwEB0fiz0Z/YNH+RXhm4zP4N/kfxP5wO17p9Qqe6foMtKJTr39AlcRlPi+oQnFckC0cF2QLxwXZwnFB9nBsUFk57bfPkJAQpKenw2w2Q6tVu3H16lW4u7vD19fX5nO2bduGxx9//JZeNzU1G4pyS4dwOkFQ3+SucC76dWvhNXsGNImJlm1SWBhyX5sPY/+BcFu9Ah4LP4D26BEAgKLTwTDyPuQ//iSkZoWVPSnZzuh6lVPecTG0/r3ocO/teOavJ/HH+U2Y+cdM/BD/Iz7o/TEiglpWfofJIVzp84IqDscF2cJxQbZwXJAtHBdkD8cGFSkaCzfitFAqIiICWq0WcXFxaN++PQBg3759iI6OhiiWXuoqLS0NFy5cQLt27W7pdRUFLvPmqO7nol+3Fj4Tx5Q6CTEpCT4PjYYcGARNWioAQPb2QcG4CcifNAVynVC1YTU+98pUnnER5l0X3w1aiR+OfYc522di/5X/0OfH7ni2/Uw83uYp6DS6yu0sOUx1/7ygysFxQbZwXJAtHBdkC8cF2cOxQWXltIXOPTw8MHToUMydOxcHDx7E5s2bsXjxYowdOxaAWjVVUFBgaX/ixAm4ubmhbt26zuoyVSRJgvecGYCilFoDSijcpklLhRxcCzlz5iJt/yHkvvxqcSBFFUYQBNzX4kFsv38v+jUYAJNswht7X8WAVb1xKCXB2d0jIiIiIiIiF+W0UAoAZs2ahcjISIwbNw6vvPIKpk2bhn79+gEAunXrht9++83SNjU1Fb6+vhC4YppL0O3eCU1i4g0XJc/66HPkPzEdip+/I7pVo9XxCsXSO3/AR30+h7+bP+JTDuCOlT3w9j9vwCgVr/8myRJ2XNqG1SdWYMelbbx6HxEREREREd0UQVFqVlFdSkr1n9sqCEBwsE+1Phe31Svg+9jEG7bL+nQRDMNHOqBH1V9FjovkvGTM2Po0fj+zDgDQMigKH/b+BOeyzmHO9hlIzC1eAyzMKwzzus3H4CZDbu1FqVK4wucFVTyOC7KF44Js4bggWzguyB6ODSpSNBZuxKmVUlRzySF1KrQdVawQzxB8PWAZPrtjMQLdA3E4NQF3rOiJCRtGWwVSAJCUm4SJG8Zg3am1TuotERERERERVUcMpcgpTB06Qfb0tLtfEQRIYeEwde7qwF5RSYIgYFizEdh23z+4q/FQyJBttlMKV5yfs+N5TuUjIiIiIiKiMmMoRY5nNsPnqakQ8/KgoPRF9JTCdcNy5r0FaDQO7x5Zq+VZCxOiH7luGwUKEnMuYXfSTgf1ioiIiIiIiKo7hlLkWEYjfCdNgPvKH6Botcif/DjksDCrJnJoGLIWLYVxMNcoqiqS8y5XaDsiIiIiIiIirbM7QDVIQQF8HxkHtw2/Q9HrkfXFEhgHDkLuS69Ct3snxOTLkEPqqFP2WCFVpYR4lm1tr19PrUXrWjFo4t+skntERERERERE1R1DKXKMvDz4jbsf+q1/QnF3R+bXy2DqfYe6T6OB6fbuzu0fXVfn0K4I8wpDUm6SZQ0pW345vQa/nF6DHnVj8VDUw+jfcCC0Ij9miIiIiIiIqDRO36NKJ+Rkw+/+e9RAytMLmd+tLA6kqFrQiBrM6zYfACBAsNonFP7v6bbPol+DARAg4O+Lf+Kh9Q+i/dJovPfvfCTnJTuj20RERERERFSFMZSiSiVkZsBv5FDod+2A7OOLjB9/hqlbD2d3i27C4CZDsKj/UoR6hVptD/UOw6L+SzGr80v4dtCP+Gf0QTzRZjqC3IOQmHsJb+6dhzbfRODRjeOxK3EHFMV+pRURERERERHVHIJSw35DTEnJRnU/Y0EAgoN9qvy5CKmp8Bs1FLr4A5D9/ZH5488wx7R1drdclqPGhSRL2J20E8l5lxHiWQedQ7tCI5ZeA6zAXIBfTv2MrxK+xL/Jey3bIwJbYnzUwxh5273w1vtUXkcJQPX5vCDH4rggWzguyBaOC7KF44Ls4digIkVj4YbtGEpVP9XhjS4kJ8N/1N3QHjkMOTgYGSvWQoqMcna3XFpVHhfxVw/g60OLsOr4j8gz5wEAvHTeGNX8PjwU9QhaBEY4uYeuqyqPC3IejguyheOCbOG4IFs4Lsgejg0qUtZQitP3qMKJiZfgP3QgtEcOQwqpg4yff2cgVcNF12qNd3stwIFxRzHv9jfRxL8pck05+CrhS/RY3glDf74Ta06uhlEyOrurRERERERE5CAMpahCiefPwX/IQGhPnYRUtx4y1vwO6bbmzu4WVRF+bv54tPUU7Lx/H1YOWYtBjYdAI2iwM3E7Htk4Hm2XRuLNvfOQmHPJ2V0lIiIiIiKiSsZQiiqM5vRJ+A8ZAM35s5AaNkLGmt8hN27i7G5RFSQIAnrU7YWvBnyLfWMSML39DNTyqI0recl479/5aLc0Cg+tH42/L/7FhdGJiIiIiIhcFEMpqhCaY0fhN2QgNImXYG52GzLWrodcr76zu0XVQJh3OGZ2nIP9Yw/ji35fo0vY7ZAUCb+eXosRa4fg9u/b4/MDHyPTkOHsrhIREREREVEFYihFt0wTfxD+QwdCcyUZ5pZRyPj5d8h1Qp3dLapm9Bo97m46HGuG/o6t9+7GQ1EPw0vnjZMZJzBnx0y0XtICz/z1BOJTDjq7q0RERERERFQBGErRLdH+9y/8hw+GmJoKU0wbZPy0DkqtWs7uFlVzEUEt8VaP9xA/7hje6vEeWgRGIM+ch6WHv0afH7vhzlV9sfL4DzBIBmd3lYiIiIiIiG4SQym6abrdO+E34m6ImRkwdeiEzJVroQQEOrtb5EK89T54KOphbL13N9YM/R1Dmw6HVtTi3+S9mLL5EcQsaYF5u+bifNY5Z3eViIiIiIiIyomhFN0U3d9/we++4RBzsmHs1gMZP/wExdfP2d0iFyUIArqE3Y7P+32N/WOPYGbHOQjzCkdqQSoW7H8PHb5thdG/jsKW85sgK7Kzu0tERERERERlUO5QKicnB++88w5Onz4NWZYxY8YMxMTE4IEHHsClS7yMe02g37wBfg+OhJCXB2PvvshctgLw9nZ2t6iGCPEMwfT2M/DvmHh8NWAZetSNhQIFG8+tx33r7kGnZTH4aP8CpBWkOrurREREREREdB3lDqVeeeUVbN26FYIg4JdffsHGjRvx+uuvIzg4GK+88kpl9JGqEP26tfAd9wAEgwGGAYOQueR7wMPD2d2iGkgrajGo8V1YOWQNdt6/D5NaTYGv3g/nss7ilV1z0HpJC0z74zHsT97n7K4SERERERGRDeUOpbZu3Yq3334bjRo1woYNGxAbG4s777wT06dPxz///FMZfaQqwm3Vj/B9ZBwEkwkFQ4cja9E3gJubs7tFhKYBzfBqtzdxYNxRvNfrQ0QFt4JBMuCHY9+h/6pY9FvRE98f+Rb55nybz5dkCTsubcPqEyuw49I2SLLk4DMgIiIiIiKqecodSimKAp1Oh4KCAuzatQs9e/YEAGRmZsLT07PCO0hVg/t3S+Ez5REIkoSCex9A9ieLAJ3O2d0isuKl88LoluPwx8ht+G34Zoy87T7oRT3iru7Hk39OQeslzfHyjtk4nXnK8px1p9ai3dJIDFszCI9tmohhawah3dJIrDu11olnQkRERERE5PoERVGU8jxh2rRpSE1NhaenJ/bv34+tW7ciPj4er776Ktq1a4dXX321svpaIVJSslG+M656BAEIDvZx2Lm4L/ocPrOeBQDkj5uInLfeBUSukV/VOHpcVBcp+Sn47shSfHNoMc5nF1+lL7ZeH0QFt8bC/f8HBdbfMAECAGBR/6UY3GSIQ/tb0TguyBaOC7KF44Js4bggWzguyB6ODSpSNBZu2K68oVR2djY++OADJCYmYuzYsejcuTO+/vprJCcn46mnnoJbFZ/OVV3fHCbTRZjNKQDU/7j+/l7IyMi1nItWWws6XXiFv67Hxx/Ce+5sAEDepCnI/d8bageoyuE/ANcnyRK2nN+ErxK+xB/nN5UKoq4lQECodxj2jU6ARtQ4qJcVj+OCbOG4IFs4LsgWjguyheOC7OHYoCKVFkpVd9XxzSHLBhw/HglJumK3jUZTG7fddgiiWHGhoOd78+H15jwAQN6TzyD3hZcYSFVh/Aeg7M5knsYbu1/Fz6dW3bDtT3f/itvDuzugV5WD44Js4bggWzguyBaOC7KF44Ls4digImUNpW5qDtbatWsxfPhwtG/fHhcuXMBrr72Gzz///GYORWUgCHrodHVh/z+XCJ2uLgRBXzEvqCjwfP1/lkAqd+Yc5M5+mYEUuYxGfo0xoPGdZWqbnHe5kntDRERERERUM5U7lPruu+8wf/58DB8+HCaTCQAQFRWFRYsWYeHChRXeQQIEQUBIyBwAsp0WMkJC5kCoiNBIUeD10ix4vf8OACBn7mvImz7j1o9LVMWEeNYpU7u4K/vtXrWPiIiIiIiIbl65Q6mlS5di3rx5GD16NMTCxa7vvvtuzJ8/HytWrKjwDpLKy6sP3N3bArh2bRsN3N3bwsurz62/iCzD+7mn4fnZxwCA7DffRf6Uabd+XKIqqHNoV4R5hVkWNbfn0wML0W5pJN7+5w2k5Kc4qHdERERERESur9yhVGJiIpo0aVJqe7169ZCRkVERfSIbiqulpGv2SNBqQ2AwHLm1F5Ak+Dw5BR7fLIYiCMh+/yMUTHjk1o5JVIVpRA3mdZsPAKWCKaHwf/e1eAB1veshJT8Fb//zBtp+0xLP/PUkTqafcEaXiYiIiIiIXEq5Q6nWrVvj559/ttqmKAoWL16MVq1aVVS/yIbiainr/2w5Ob/j1KnOOH36DmRkfAdFMZXvwCYTfCZPhPsP30HRaJD9yZcoeGBMxXWcqIoa3GQIFvVfilCvUKvtod5hWNR/KRb0/hR7Rx/A53d8hZhabVAgFWDp4a/Q9ft2GPPbvdh5aTtq2LUiiIiIiIiIKky5r753/PhxPProowgKCsLRo0fRpUsXnD17FgUFBfjiiy8QERFRWX2tENX9KgA5OZtx7txwy+PatV9Cfn4csrN/hVo1VRu33XYEgqAr2wENBvg+Mh5u63+FotMh67OvYBw8pHI6T5WKV7q4eZIsYXfSTiTnXUaIZx10Du0KjWg9VVZRFOxO2olP4j7E+rO/Wba3rtUGU2Km4a4mQ6EVtY7u+g1xXJAtHBdkC8cF2cJxQbZwXJA9HBtUpKxX3yt3KAUABoMBa9euxenTpyFJEho1aoQhQ4bAy8vrpjrrSNX9zaEoCs6ciUV+/n/w8GiLRo3+hCAIMJkuIyNjKUTRC0FBUwrbSrh4cQJ8fAbB1/duiKKb9cHy8+H30IPQb9kMxc0NWYuXwnjHACecFVUE/gPgOCfTT+DTAx/hx2PfoUAqAADU9a6HR1tPxoMRY+Gj93VyD4txXJAtHBdkC8cF2cJxQbZwXJA9HBtUpFJDqerMFd4cubl/4sqVmahd+014ecXabZedvR7nz48CAGg0gfD3fxABAePh5tYMyMmB39j7oN/+NxRPT2R+sxymHr0cdAZUGfgPgOOl5Kfg64QvsTjhc8si6D56X4xpOR6PRD+GcJ+6Tu4hxwXZxnFBtnBckC0cF2QLxwXZw7FBRSotlOrduzcEwf7Vqv7444/yHM7hXOHNUdY3usl0GenpXyE9/RuYzZcs273cuiJ88RXU+fok4O6DrO9WwNS5qwN6TpWJ/wA4T745HyuP/4BP4j7EyQx1EXStqMXdTYZjSsw0RNdq7bS+cVyQLRwXZAvHBdnCcUG2cFyQPRwbVKTSQqmffvrJ6rHZbMaFCxewevVqPPnkkxg5cmT5eupgrvDmKO8bXVHMyMnZhLS0xcjJ2QhAfVLMc95QXl0Dc7sOldthcgj+A+B8siJj87kN+CRuIXYkbrNs7xbeA1NipqF3/TsgCuW+vsQt4bggWzguyBaOC7KF44Js4bggezg2qIjDp+/98ccfWLx4MZYtW1YRh6s0rvDmuNk3unD1KtwfHYirtx1HVpQOdWO2QI5WKzhSUz+BVhsKX99BZV8knaoU/gNQtRy4sh+fHPgQa07+BEmRAAC3BTTHY60fx4jb7oW71t0h/eC4IFs4LsgWjguyheOCbOG4IHs4NqhIWUOpCvuTfdOmTREfH19Rh6MKJl5Ogv/QgfDecRz1fw1BvQ7bLYGUJGXhypX/4eLFsTh+PALJya/AaDzr3A4TVXOta7fBp3csxj+jD2Jy62nw1vngePoxTP9rGtoujcS7/76F1PxUZ3eTiIiIiIjIacpdKfXPP/+U2pabm4ulS5ciIyMDq1atqrDOVQZXSGzLmz6LF87D/567oDl7BlJYODJXrYXUpJllvySlIyXlQ2RkLIXZnFz0KvD27o2AgAnw8RnA6qlqgH+VqNqyDJn49sg3+OLgJ7iUcxEA4KH1wL3NH8BjraeisX/TSnldjguyheOCbOG4IFs4LsgWjguyh2ODilTa9L0WLVqU2qbT6RAdHY0XX3wRERER5Tmcw7nCm6M8b3Tx9Cn4jxgCzcULkOo3RMaqtZAbNLTZVlFMyM7+DWlpi5Gb+6dle+3aL6FWrWcr8AyoMvAfgOrBJJmw9tRP+OTAQhy8GgcAECCgf6M7MSXmCXSq0/m6F5MoL44LsoXjgmzhuCBbOC7IFo4Lsodjg4o4fE2p6sIV3hxlfaNrjh+D3z13QZN8GeYmTZG56hfIYeFleg2j8TTS05cgI+M7NGq0BXp9PQBAXt5emM0p8PHpB0HQVsTpUAXhPwDVi6Io2Jm4HR/HLcCmcxss29vWbofJMdMwqPEQaMVbf49xXJAtHBdkC8cF2cJxQbZwXJA9HBtUpEJDqcTExDK/cFhYWJnbOoMrvDnK8kbXJMTDf9TdEFNSYG4RgYwVa6GEhJT7tRRFgiBoLI/PnRuBnJyN0GrDERAwFgEBY6HTlS3oosrFfwCqr+Npx/DZwY/w47HvYZAMAID6Pg3waKvJeCBiDLz1N/4wt4fjgmzhuCBbOC7IFo4LsoXjguzh2KAiFRpKtWjRwjKdxFZzQRCgKAoEQcCRI0duoruO4wpvjhu90bVx/8Fv1FCIGRkwRbdG5o8/QwkKuuXXVRQFV67MRXr6EkhSWuFWET4+AxAQ8BC8vftaBVjkWPwHoPq7mncVixM+x9cJXyK1QF0E3Vfvh3GRE/Bw9CSEepc/9Oe4IFs4LsgWjguyheOCbOG4IHs4NqhIhYZSly5dKvMLh4dX7aoZV3hzXO+Nrt27B3733wMxOwumdh2QuXwVFD//Cn19WTYgO3st0tIWIy9vh2W7j8+dqF9/eYW+FpUd/wFwHfnmfPx47Ht8EvchTmeeAgBoRS2GNR2ByTHTEBUcXeZjcVyQLRwXZAvHBdnCcUG2cFyQPRwbVKSsoVSZFiwpS9BkNBpx5MiRKh9KuTLd9r/hN/peCHm5MHa5HVnLfoTiffPTfuwRRTf4+Y2En99IGAzHkJb2FTIzv4O3d39LG0nKQn7+Xnh59YYgiBXeByJX5qH1wLjICRjTcjw2nl2Pj+MWYHfSTqw4vhwrji9Hj7qxmBLzOGLr9a3QRdGJiIiIiIgcqdwLnf/333945ZVXcPLkSciybLVPo9EgISGhQjtY0VwhsbWVPuu2bILf+AchFBTA2DMWmUu+Bzw9HdYnWc4HIEAU3QEAaWlfICnpGeh0DREQMA4BAWOg1dZ2WH9qIv5VwrXtT96HTw58iF9OrYGkSACAiMCWeKz14xh+2/+zd9/hUVXpA8e/d/pkJpPeqQESqkhREFAEFFAB0dUVV0EFRf3purrrYmNtiwXcXbe4qytiY7FXRKRjA0FB6RBK6Amkl0ky/f7+SDIQcgcCpPN+nicPmTvn3nsuvPeGeXPOe67HrDdr7idxIbRIXAgtEhdCi8SF0CJxIUKR2BDV6jpS6rSHsMyYMYOUlBReeeUVrFYr//rXv5g+fTqRkZHMmjXrjDorzo7pqy+JmDgBxeXCPXI0xXPfb9SEFIBOZw0mpKByip9OF4nXu4+cnKfIyOjKwYOTcDpXoqqVyUyv9xAVFRtCfnm9dZ82KkRr1yehH6+OfJO1N23gzvP+D5vRzvaCbfxu5f/Rb25P/r7+LxS6Cmrs4w/4WXX4O97d/C6rDn+HP+Bvot4LIYQQQgghRG2nPVKqV69efPbZZ3Tq1ImJEydy++23M3ToUBYtWsTs2bP5+OOPG6qv9aLFZ2z9fkxrVxNRXkxxWATKkSOE33snis+He8zVlLwyB0ympu4lAIFAOSUln1JQ8DoVFT8Ft5tMaXTsuILdu/vi9+eE3F+vjyctbSs6nfYIEFGT/Fbi3FLsLmLutreYvellsssqV0gNM4QxoetN3Nn7HrbmbWH699PIKju2emqyLZkZQ2YxptO4puq2aCbkeSG0SFwILRIXQovEhQhFYkNUq9eaUsezWq3o9ZUrrKWmppKRkcHQoUM577zz2Lt37+n3VNSZacF87NOnoc+q/JAZAaiAArh+9WtK//UKGE77n7TB6HRhREbeRGTkTbhcWygoeJ3i4g8wm7ug14djNLbB788DAlp7YzS2QVGaR4JNiOYmwhzJvX1+x9Tz7ubz3Z/w8saX2JK3ide3zOb1LbM198kuy2bK4onMGTVXElNCCCGEEEKIJlen6Xterzf4/cCBA/nrX//K0aNH6dOnDwsXLqSoqIgVK1bgcDgarKPnOtOC+TimTESXlVVju0JlYsp9xZhmlZA6kcXSk+Tkv5GenkFS0gsoikJCwnS0E1IAARISpksRZyFOwaQ3cX36BJZf/x0fjZvP8LaXhWyrUvnrqumrHpKpfEIIIYQQQogmV6ek1ODBg3n88cf58ccfeeyxxyguLmbJkiVcddVV2O12Bg4cyHPPPcc999zT0P09N/n92KdPA1VFM0WjKNgffwT8zf9Dpk5nw2hsA4DNNgKjMVWznV6fgKJEBOtPCSFOTlEULmlzKb/t+8BJ26moZDkPsyZ7dSP1TAghhBBCCCG01SkpNX36dPLy8rj99tv59a9/TY8ePejXrx9Go5G5c+fyxRdfsGLFCq6//vqG7u85ybhmNfqsLO2EFKCoKvqswxjXtKwPmYqikJz8F833/P6j7Ns3AqdzcSP3SoiW7Wj5kTq1W334e06zpKAQQgghhBBC1Ks6zfcaN24c48aNw+l0smzZMhYtWsQNN9xA27ZtGTNmDGPHjiUhIaGh+3rO0h2t24fMurZrTmy2EVgsfXG5NgJ+KmtJtcNiOZ/y8lXYbEODbXNz/0pFxXrCw68kPHwUBkNck/VbiOYqISyxTu1eWPcci/YtZHLPO7imy3WEGRt3xU4hhBBCCCGEOO3V96o5nU6WLl3KokWL+OGHH0hPT2fs2LFMmjSpvvtYr1riKgDGVd8Rec1Vp2xX9OmXeAdf3Ag9ql9O5zL27782+Lp9+0+w2y9DVf0oij64fffuQbjdW6peKYSFDahKUF2J2ZzWyL1ufmSlCwHgD/jpN7cH2WXZwRpSJwozhOEP+HEH3ABEmiP5TbdJ3NpjCh0iOjZmd0UTkeeF0CJxIbRIXAgtEhciFIkNUa2uq++dcVLqeGvWrGHmzJns2LGD7du3n+3hGlSLvDn8fqL79UCXnY2i0XlVUQgkJVOwfgvo9RoHaN5UVSUzcxgu189YLH1JTV2pWeC8omITpaVfUlr6FS7XhhrvWa0Xkpq6rJF63DzJDwBRbcGe+UxZPBGgRmJKqZoEPGfUXAalDOad7f/jzS2vcaB0f/D9y9qPZEqvqVzadgQ6pU4zvEULJM8LoUXiQmiRuBBaJC5EKBIbolpdk1Jn9IlDVVV+/PFHZsyYwaWXXsp9991Ht27dmDNnzpkcTpyKXo9zxiygMgF1vOrXzhkzW2RCCqhaie8JTKZ0EhKeCLnintV6HvHxj9Cp07ekpW0jKelv2O0jUBQjJtOxgumqqpKdPY2Sks/x+0sb6zKEaDbGdBrHnFFzSbIl1dieZE9mzqi5jOk0jmhLDPf2+R1rb9rA/658n2FtR6CisnT/YiYs+BUXvdOXVza+RJGrsImuQgghhBBCCNHa1XmklM/nY/Xq1SxdupTly5dTXl7O0KFDGTt2LJdccgkmk6mh+1ovWnLG1rRgPvbp09BnZQW3+ZNTcM6YiWfMuCbsWdPy+0sIBEoxGlOAyhFVmZlDAFAUEzbb0KppfldgNCY3ZVcblPxWQpzIH/Cz9shqynXFhAUiGJA4CL0udPJ6T9Eu3twyh3d3zKPEUwxUTvX7VdoNTO55Bz1iezZW10UDk+eF0CJxIbRIXAgtEhciFIkNUa1ep+/98Y9/5JtvvqG8vJyLLrqIq666issvvxybzVYvnW1MLf7m8PsxrV1NRHkxxWEReAYMarEjpBqKx7OXgoLZlJZ+icezt8Z7FksfEhL+hN1+WRP1ruHIDwCh5Uziosxbxsc7P2DO5lfZXrA1uH1g0iCm9JrKlR3HYtQbG6jHojHI80JokbgQWiQuhBaJCxGKxIaoVq9JqZtuuomrrrqK0aNHEx0dXS8dbCqt4eaQG71uVFXF7c6gtHQhpaULqaj4CVBp334+dvulALjdO/F6DxEWNgSdrmWM9gtF4kJoOZu4UFWVNdmrmbP5Vb7MnI9f9QOVK/xN6nEbk7rfRoKtbqv9ieZFnhdCi8SF0CJxIbRIXIhQJDZEtUYtdN6StIabQ270M+Pz5VBaupjIyAkoSuUoj+zshygoeBmdzoHdflnVNL+R6PWRTdvZMyBxIbTUV1xkO7N4e9sbvL31DXIrcgAw6AyMSR3H5F53MiBxYMh6cKL5keeF0CJxIbRIXAgtEhciFIkNUa1BC50L0RIZDPFERU0MJqQA9PpwDIZ4AoESSko+4fDh29mxI5V9+8aSn/8fAgFPE/ZYiOYjyZ7MQxc+xi+TtvHfy1/nwsSB+AI+Ptv9CeM+HcXwD4Ywd9ublHnLmrqrQgghhBBCiBZCRkq1QJJ9rl+qGqCiYh2lpV9RWroQt3s7AAZDCmlp24KjPzyefRiN7VCU5pnLlbgQWhoyLjbnbeKNzbP5eNcHVPgqAIgwRzKh603c1vN2UiM61e8JRb2R54XQInEhtEhcCC0SFyIUiQ1RTabvhdAabg650RuWx5NJSclCFEVPTMzdAKiqj4yMziiKkfDwKwgPvxKbbSg6nbXW/l7vIXy+vJDHNxjigisF1ieJC6GlMeKi0FXAuzvm8caW2ewv2RfcPqLd5UzpNZXh7S5H10yTuecqeV4ILRIXQovEhdAicSFCkdgQ1SQpFUJruDnkRm98bvdOMjOHEQiUBrcpShh2+/CqOlSjMRhiCQTc7NzZA78/J+Sx9Pp40tK2otOZ67WPEhdCS2PGRUANsOLAUuZsfpXlB5YGt7d3dOC2nndwY9ebiLK07MUyWgt5XggtEhdCi8SF0CJxIUKR2BDVpKaUEPXIbE4jPT2T9u0/JTr6DozGNqhqOaWlC8jK+j/y8/8DgKKYqkZBhbq1dBiNbVCUlr3SnxBadIqOy9qP4t0xH7Pmpl+4q/e9RJgj2V+yjydXP8b5b3fjgZX3sjlvU1N3VQghhBBCCNEMSFJKiDrS6czY7SNISvorXbpsJTX1O+LiHsFiOZ/w8CsBUBSF8PDRQCDEUQIkJEyXVcpEq5ca0YmnBz/Lhknb+eul/6RHTC8qfBXM2/42Iz4YwphPRvLJrg/x+GUxASGEEEIIIc5VkpQS4gwoioLV2pv4+Efo1OlbwsL6B9/zerNC7YXZ3I2wsGGN00khmgGb0cbE7rey4tffM/+axYzvfC0GnYEfj6zhrqVT6PN2d2b++AzZzlD3jRBCCCGEEKK1kqSUEPUsMfEZYmMf1HhHxe3ejs937MO331/KOVbWTZyjFEVhYNJFvDryTX6ZuI0/XvAICWGJ5Fbk8Nd1M+k7twe3L76FH7JWyT0hhBBCCCHEOUKSUkLUM73eQXz8n7BY+gL6qq0KOl04JlMPTKa2wbaHDk1h585uHDo0lcLCeXg8B5ukz0I0pgRbIn+84BF+nriV2SPfZGDSIPyqn/l7PuXqz67g0vcH8dbW13F6nZr7+wN+Vh3+jk92fciqw9/hD/gb+QqEEEIIIYQQ9UFW32uBZEWDlsHpXMb+/dcGX7dv/wk224hgPSlVDZCR0Rm/P6/GfiZTR2y2odjtI3A4rq7z+SQuhJaWEhdb8jbzxpbZfLzzA8p95QA4TBFM6Pobbut5O50iuwCwYM98pn8/jayyYyMOk23JzBgyizGdxjVJ31uilhIXonFJXAgtEhdCi8SFCEViQ1Sr6+p7kpRqgeRGbxlUVSUzcxgu189YLH1JTV1Zq8B5IFBBeflaysq+pazsWyoq1gOVoz7Cwi6mY8cvg22dzhVYLOdjMERrnk/iQmhpaXFR5CrkvYx5vLHlNfYWZwa3D2s7gvPievPPn19EpeaFKFTeV3NGzZXEVB21tLgQjUPiQmiRuBBaJC5EKBIbopokpUJoDTeH3Ogth9O5kuzsaSQlzcJuP3WBc7+/lPLy1ZSVfYvZnE5U1CQAfL58MjI6AgoWy3nYbJdgs11CWNgg9PrKG13iQmhpqXERUAN8fXA5cza/yrL9S2olok6koJBkT2b9zVvQ6/QnbStablyIhiVxIbRIXAgtEhciFIkNUa2uSSlDI/RFiHOW3T6MLl1+qnN7vT6c8PBRhIePqrHd6z2M2ZyO252By7URl2sj+fn/AvRYrf2Ijf0tERF1n+onRHOnU3QMb3c5w9tdzr7ivTyz5ik+3/NJyPYqKlnOw6zJXs3glIsbsadCCCGEEEKIM9Wkhc7dbjePPvoo/fv3Z8iQIbz++ush22ZkZHDjjTdy3nnnMXbsWNasWdOIPRWiaVmt59G580+kpe0kJeU1IiMnYTR2APxUVPyI33+sILTbvZvc3FmUl69FVb1N1mch6kuHiI5ckXpVndoeLT/SwL0RQgghhBBC1JcmHSk1a9YstmzZwltvvUVWVhYPPfQQycnJjB49uka70tJSJk+ezPDhw3n++ef5/PPPuffee1m8eDExMTFN1HshGp/RmEhk5K+JjPw1AB7PPsrKvsNuHxFsU1q6mJycGcAMdDobYWGDgtP9LJbzUBSZ2iRanoSwxDq1W7pvMQMSLyIlvE0D90gIIYQQQghxtppspFR5eTkffvghjz32GD169ODyyy/n9ttvZ968ebXafvrpp4SFhfHkk0/Svn177rvvPtq3b8+WLVuaoOdCNB8mUweioiZiNB77wG42p+FwXINeH0MgUIbTuZSjR/9EZuZQduzoiMu1uQl7LMSZGZg0iGRbcrCoeSgf7/qAfv/rya1f3cS3h77mHCubKIQQQgghRIvSZCOlduzYgc/no0+fPsFt/fr145VXXiEQCKDTHcuX/fjjj4wYMQK9/tgIj48//rhR+ytESxEefjl2++WoagC3extlZd9Ure63ClUtx2TqFGybmzsLl2sbNttQbLZLMJlSa60QWM3rPYTPlxfyvAZDHEZjSr1fjxAAep2eGUNmMWXxRBSUGoXPqxNVd5//WzblbuD7w9+ycO8XLNz7BWlR6dzW83Z+nX4j4SZHU3VfCCGEEEIIoaHJklK5ublERUVhMpmC22JjY3G73RQVFREdfWzZ+4MHD3Leeefxpz/9iRUrVpCSksJDDz1Ev379Tvu8IT5vtyjV19AarkXUnxPjQlF0WK09sVp7Eht7D6rqw+3eg14fFtynpORzXK7NlJRUFpA2GtsEp/pVJqkqp0AFAm727LkUvz8n5PkNhnjS0rai05kb5gLFGWlNz4uxncfxujKXx76bRlZZVnB7sj2ZGUNmMqbTOAB25G/n9S2z+SDjPXYWZvDId39kxpqn+HX6BKb0mkp6dNemuoRmozXFhag/EhdCi8SF0CJxIUKR2BDV6hoDitpEcxs+++wz/vGPf7By5crgtoMHD3LZZZfxzTffkJh4bDrS5ZdfTmFhIZMmTeKyyy7jyy+/ZN68eXz11VckJSU1RfeFaBWKir6jqGgFhYUrKCn5oUZhdJMphYsuOoiiKKiqyvr1/XE6NwABjSPpCA/vR9++a0OOtBKivvgDfr478B3ZpdkkhSdxcbuL0etq10orcZfw9sa3+fdP/2ZH3o7g9mEdhnHPBfdwdderMehkEVohhBBCCCGaSpP9b9xsNuPxeGpsq35tsVhqbNfr9XTr1o377rsPgO7du7Nq1So+//xz7rrrrtM6b35+KS29xIiiQExMeKu4FlF/ziwuzsduPx+7/fcEAmWUl6/F6fyWsrJvMJu7kp9fuaqfqgYoL9+NdkIKIEB09CPB9qL5aK3Pi572fvS0V35fWFAeopXChNRbuKHjJL479A2vb5nNV3u/ZOW+lazct5JkewqTetzGxO63Eh8W32h9bw5aa1yIsyNxIbRIXAgtEhciFIkNUa06Fk6lyZJSCQkJFBYW4vP5MBgqu5Gbm4vFYsHhqFn3Iy4ujtTU1BrbOnToQHZ29mmfV1VpNTdHa7oWUX/ONC4UxYbNNhybbXjVcdTgcdzuPQQCJSH21GGx9MZmGyHx2Iyd288LhYvbXMrFbS7lUOlB3t76Bv/b/iZZzsM8v3YGf/1pJmM7jWdKr6n0T7jwnBrtd27HhQhF4kJokbgQWiQuRCgSG6Kummz1vW7dumEwGNiwYUNw2/r16+nVq1eNIucA559/PhkZGTW2ZWZmkpIiRZWFaCjHfzA3m7uQnr6X2NgHNVoGTlogXYjmpE14Wx4d+Di/TNrOv0e8Sr+EC/AGvHyy60Ou+uRyLvvwEuZte5tyb6jRV0IIIYQQQoj60mRJKavVyvjx43nyySfZtGkTy5Yt4/XXX2fSpElA5agpl8sFwIQJE8jIyOBf//oX+/fv5x//+AcHDx7k6quvbqruC3HOMRhiiI//ExZLX6C6fo8C6IiJuSfYrqJiE0VF8/D7ZSqfaL7MejPXp0/gq18tZ+l133Bj15ux6C1sztvIA1/fy/lvd+WJVY+xtzizqbsqhBBCCCFEq9VkSSmARx55hB49enDLLbfw1FNP8dvf/paRI0cCMGTIEBYuXAhASkoKr732GitXrmTMmDGsXLmSV199lYSEhKbsvhDnHEVRSEiYDvirtqi0bTsPq/XYSpgFBa9y+PDd7NzZhcOH76Ks7FtUNVQtKiGaXu/4Pvxj+H/YcMt2Hr/oz7RzdKDIXcTLG//FwHl9+M2C61i2fzEBiWMhhBBCCCHqVZOtvtdU8vJafsE1RYHY2PBWcS2i/jRWXKiqSmbmMFyun7FY+pKaurLG1L38/H9TUPAaHs+e4DajsR2RkROIiLgRs7lTw3VO1CLPi9PnD/hZfmAJr2+ZzYoDy4LbOzg6cmvP27mx601EWaKbsIdnT+JCaJG4EFokLoQWiQsRisSGqFYdC6dsJ0mplkdudKGlMePC6VxJdvY0kpJmYbcPq/W+qqpUVPxIUdE7FBd/HCySbjR2oEuXjVJ/qhHJ8+LsZBbt5o2tc3hvxzyK3UUAWA1Wru1yPZN73kGvuN5N28EzJHEhtEhcCC0SF0KLxIUIRWJDVJOkVAit4eaQG11oaa5xEQhUUFq6gKKidwgLu4i4uGlV291kZz9IRMQ12GxDURT9KY4kzkRzjYuWpsxbxie7PuT1zbPZmr85uP2CxAFM7nkHYzuNx6Q3NWEPT4/EhdAicSG0SFwILRIXIhSJDVFNklIhtIabQ250oaUlxIWqqsFRUiUl8zl48GYADIYUIiMnEBn5G8zmLk3ZxVanJcRFS6KqKj8eWcvrm//LF5mf4wv4AIizxjOx+y1M6jGZZHvzXxlW4kJokbgQWiQuhBaJCxGKxIaoVtekVJMWOhdCnFuOn7ZnNqcTFXU7en0kPt9h8vL+yu7d/cjMHEFBwRz8/pIm7KkQ2hRFYUDSQP478g1+mbiNaRc8SqItidyKHP62/gX6ze3J5EUT+f7wt5xjv/MRQgghhBDitMlIqRZIss9CS0uNi0DATWnpVxQVzcPpXEb1yn6dOv2IxdK1aTvXCrTUuGhJvH4vX+1dwOtbZrM66/vg9vSortzW6w5+nTYBu+nUvyVqTBIXQovEhdAicSG0SFyIUCQ2RDWZvhdCa7g55EYXWlpDXHi9Ryku/gCXaxNt2swObs/OfghFMREZ+Rsslm5N2MOWpzXERUuyPX8br2+ZzYcZ71HuKwPAbgznhq43MrnnVLpEpTVxDytJXAgtEhdCi8SF0CJxIUKR2BDVJCkVQmu4OeRGF1paa1z4/UVkZHRBVd0AWCx9iIz8DRER12EwxDRx75q/1hoXzV2Ju5gPMt7l9S2z2V20K7j94jaXMrnnHYzqcAUGnSG43R/wsyZ7NUfLj5AQlsjApEHodQ1X/F/iQmiRuBBaJC6EFokLEYrEhqgmSakQWsPNITe60NJa40JVvZSWLqaoaB6lpYuBysLSimLEbr+CmJg7sdkubtpONmOtNS5aClVV+fbQ18zZ8ipL9n1FQA0AkGJvwy09JnNTt1tYm/0D07+fRlZZVnC/ZFsyM4bMYkyncQ3SL4kLoUXiQmiRuBBaJC5EKBIbopokpUJoDTeH3OhCy7kQFz5fHsXFH1JU9A4u10YAEhL+TGzs74Caq/uJSudCXLQUB0sP8PbWN/jftjfJd+UDYFAM+FRfrbYKlXE8Z9TcBklMSVwILRIXQovEhdAicSFCkdgQ1WT1PSFEq2MwxBITczedOn1Hp06riYm5l4iIG4LvFxe/z549Q8jP/zc+X24T9lSI2tqGt+OxgU/wy6TtvDTiv/SJ66uZkAJQqfxf3PRVD+EP+Buzm0IIIYQQQjQaSUoJIVoki6UniYnPYjQmBrcVF7+Py7WJI0ceISMjnQMHJlBSMp9AwNOEPRWiJovBwq/Tb+TxQX8+aTsVlSznYdZkr26kngkhhBBCCNG4DKduIoQQLUNKyhxKSj6mqOgdKirWU1q6kNLShej10UREXE9i4kwU5Vgu3us9hM+XF/J4BkMcRmNKY3RdnIOOlh+pU7v3dsyjR0xPIi1RDdwjIYQQQgghGpckpYQQrYbBEE109B1ER9+B251BUdE7FBW9h8+Xjcu19YSEVA579lyK358T8nh6fTxpaVvR6cyN0X1xjkkISzx1I+D9jHf4bPfHjEm9mpu738Kg5CFSO00IIYQQQrQKkpQSQrRKZnM6CQlPER//OGVlK1GUY4klny+HnTu7o9NZAAXQqsKow2hsg6KYGqvL4hwzMGkQybZkssuygzWkjqeg4DBHkGxLYXvBVj7e9QEf7/qAjhGp3NRtEjek/4YEW90SW0IIIYQQQjRHUlNKCNGqKYoeu/0ybLaLg9uczpWAh0CgBO2EFECAhITpMiJFNBi9Ts+MIbOAY6vtVat+/eKlL/H1DatZct3XTOo+GbsxnL3FmcxY8yTnv92NSV/dyJJ9X+ELaBdMF0IIIYQQojmTpJQQ4pwTGXkDnTuvJybmD4BRo4UOi6UvNtuIxu6aOMeM6TSOOaPmkmRLqrE9yZ7MnFFzGdNpHIqicH58X/5y6d/ZfOtO/jn8ZS5MHIhf9bNo75fcvPAG+s7twXNrn2Zf8d4muhIhhBBCCCFOn6KqaqhhAq1SXl4pLf2KFQViY8NbxbWI+iNxcWZKS5dy4MCvam1v3/4T7PbLKC1dAvix2YZVTfdrWSQuWgZ/wM+a7NUcLT9CQlgiA5MGodfpT7rPzoIM5m1/mw8y3iHflR/cfnGbS7m52ySu6DgGi0E7ZiUuhBaJC6FF4kJokbgQoUhsiGrVsXDKdpKUannkRhdaJC7OjKqqZGYOw+XaCPipHCV1PqmpK1EUhczMEVRU/IROZ8duH4nDMQa7fSR6vaOpu14nEhetn8fvYfG+hfxv21t8fXBFsD5VlDmK69MncFO3W+gW073GPhIXQovEhdAicSG0SFyIUCQ2RLW6JqVk+p4Q4pymKAoJCdOpTEjB8bWkVDWA1dofgyGFQMBJScknHDo0mYyMVPbvv56iovebsutCAGDSmxjbaTzvj/2UdRM382D/h0mxt6HQXcirm15m6PsDueLj4fxv21s4PaVN3V0hhBBCCCGCJCklhDjn2WwjsFj6AtSoJaUoOpKSZpKWto3U1JXExv4ek6kzqurB6VxMSckXNY7j9WY3et+FOF7b8HZMu/BR1t28mffGfMyY1Ksx6AysP7qO33/9W3q+mcYDK+9l3ZEfOccGSgshhBBCiGZIpu+1QDIkUmiRuDg7TudKsrOnkZQ0C7t9WMh2qqridmdQWjofi6UP4eGXA+B272L37n5YLH1wOMbhcIzDbO7SWN0PSeJC5JTn8GHGe8zb/ha7i3YFt/eI68GE9Ju5rssEYqwxTdhD0VzI80JokbgQWiQuRCgSG6Ka1JQKoTXcHHKjCy0SF02rqOhdDh++Czj2l282pxMePhaHYxwWS28URWn0fklciGqqqrL2yBrmbXuL+Xs+pcJXAYBJZ+LK1DHc1O0WLm4zFJ0ig6jPVfK8EFokLoQWiQsRisSGqCZJqRBaw80hN7rQInHR9Hy+HEpKFlJaOp+ysm9QVW/wvXbtPiA8fHSj90niQmgp9RSzJHsBr/z4Khtzfwlubxfent90m8iErjeRbE9pwh6KpiDPC6FF4kJokbgQoUhsiGqSlAqhNdwccqMLLRIXzYvfX0Rp6WJKSxdQVraKtLSt6HRWAPLyXsLj2Ul4+FhstqHodKYG64fEhdByfFxsytnIvO1v89HODyjxFAOgU3SMaHc5N3W7hcvbj8KoNzZxj0VjkOeF0CJxIbRIXIhQJDZENUlKhdAabg650YUWiYvmS1V9KIoh+Hr37gtxu3cAoNM5CA8fhcMxDrv9MnQ6W72eW+JCaNGKiwpfBQv2fM687W+zOuv7YNs4azwTut7ETd0mkhrZuYl6LBqDPC+EFokLoUXiQoQisSGqSVIqhNZwc8iNLrRIXLQMqqpSVvY1JSXzKS1dgM93NPieoliIiLiOlJT/1Nv5JC6EllPFxZ6iXbyz/X+8t2MeuRU5we2DkodwU7dJjOl0NVaDtRF7LBqDPC+EFokLoUXiQoQisSGq1TUpJdVMhRCiESmKgt0+jOTkF0lLy6Bjx6XExNyH0dgBVXUBgWBbVVUpLJyL15vddB0W56ROkV3400VPsWHSdt4c/Q6Xtx+FTtGxOut77lk+lV5vpvHwt39gc96mpu6qEEIIIYRowWSkVAsk2WehReKiZVNVFbd7C4piwmxOB6Ci4hcyM4cCYLVeiMMxFodjLCZTap2PK3EhtJxJXGQ5D/Pejnm8s30uB0r3B7f3juvDTd0mcW2X63CYI2rs4w/4WZO9mqPlR0gIS2Rg0iD0On19XoqoR/K8EFokLoQWiQsRisSGqCbT90JoDTeH3OhCi8RF61NWtpqjRx+nouLHGtvN5p44HGOJjLwJk6ldrf283kP4fHlAZVxERtooKioLxoXBEIfRKCurncvO5nkRUAN8d+gb5m1/i4WZC/AEPABYDVbGdbqGm7rfwoDEgXyZ+QXTv59GVllWcN9kWzIzhsxiTKdx9Xk5op7IzxGhReJCaJG4EKFIbIhqkpQKoTXcHHKjCy0SF62X15tFaemXlJR8QVnZd4AfgPbtv8BurxxJ5fc70enCUFUvO3f2wO/PCXk8vT6+ajVAc2N0XzRD9fW8yK/I56Od7/G/bW+RUbgjuD0xLIkj5bWnnSooAMwZNVcSU82Q/BwRWiQuhBaJCxGKxIaoJjWlhBCilTAak4mOvoMOHeaTnr6blJSXiYi4DpttcLBNTs7T7NzZlezsh9HrHVD14b82HUZjGxTF1Ch9F61bjDWGO3vfw7cT1rLw2mXc1G0SVn2YZkIKQKXyf6fTVz2EP+BvzK4KIYQQQohmSJJSQgjRghgMMURG3kSbNq+jKIbg9rKyb/H5jlBUNAePZzcQ6ldTARISpqMooZJWQpw+RVHon3ghLw57iTmj3jppWxWVLOdh1mSvbqTeCSGEEEKI5kqSUkII0Qqkpn5Du3YfERV1KzpdTIhWeiyWvthsIxq1b+LcUuItqVO7+1f8H39dN5PNeZs4xyoJCCGEEEKIKpKUEkKIVkCnMxMePpLk5H/StetuEhJmaLTyEx//GGVlK1FVmTolGkZCWGKd2u0v3c/MH59hxAdD6Du3Bw9/+wdWHFiG2+9u4B4KIYQQQojmQpJSQgjRyiiKnpiY32Kx9AX0VVv1wdf7949n164+5OX9HZ8vvwl7KlqjgUmDSLYlB4uan0hBITEsib9d+i9Gd7wKq8HKYechXt8ymwkLrqXr6x2ZsngS7+94h/wKiU8hhBBCiNZMVt9rgWRFA6FF4kKcyOlcxv791wZft2//CT5fDkeOPIzfXwSAopiJiPgV0dF3YLX2a6KeisbW0M+LBXvmM2XxROBYcXPQXn2vwlfB94e+YdG+r1iy7yuOlh8JttcpOi5MHMjIDlcwusOVdI7qUv+dFUHyc0RokbgQWiQuRCgSG6JaXVffk6RUCyQ3utAicSFOpKoqe/cOo6LiZ6zWvnTsuBJFUQgEyiku/piCgtm4XBuC7a3WvrRr9yEGQ1zTdVo0isZ4XizYM5/p308jqywruC3ZnsKMwTODCakTBdQAm3I3sGjfQpbsW8SWvE013k+N6MSoDlcyuuOVXJA4AIPOoHkccWbk54jQInEhtEhciFAkNkQ1SUqF0BpuDrnRhRaJC6GlrGwlOTkPEx//PDbbsBrvqapKRcU6CgpmU1LyCSZTRzp1+jG4Mp/fX4Je72iKbosG1ljPC3/Az5rs1RwtP0JCWCIDkwah1+lPvWOVQ6UHWVw1gur7w9/iDXiD70WZoxjRfiSjOlzB8HaXEW6SWD1b8nNEaJG4EFokLkQoEhuimiSlQmgNN4fc6EKLxIXQUte48Pny8HoPYrX2ASAQKGfnzu5Yrf2Jjr4Du/1yFEXKELYWLfF5Ueop4euDK1i87yuW7V9Mgasg+J5RZ2RQ8hBGdbiCkR2uoJ2jfRP2tOVqiXEhGp7EhdAicSFCkdgQ1SQpFUJruDnkRhdaJC6EljONi9LSxRw4cH3wtdHYgejoKURG3ozBENMAPRWNqaU/L3wBH+uO/MjifV+xeN9CdhftqvF+95iejOowmlEdruT8+L7oJKFaJy09LkTDkLgQWiQuRCgSG6KaJKVCaA03h9zoQovEhdByNnHhdu+ioGAORUXzCASKq45nISLiV8TFPYTJ1KH+OywaRWt7Xuwp2sXifYtYvG8ha7N/IKAGgu/FhyUwsv1oRnW8kotThhJmDGvCnjZvrS0uRP2QuBBaJC5EKBIbopokpUJoDTeH3OhCi8SF0FIfcREIlFFc/FFVYfTKwtOdO/+C2dypHnsqGlNrfl4UuPJZvn8pi/d9xYoDy3B6S4PvWQ1WLmlzKaM6XMnlHUaTEJbQhD1tflpzXIgzJ3EhtEhciFAkNkS1uialZNkaIYQQJ6XT2YiKuoXIyElUVPxIefkPNRJSWVm/Q6+PJCpqMiaT1PIRTSvaEsP16RO4Pn0CHr+H1Vnfs7hqNb+DpQeqpvx9BUDf+H6M6nAlIztcQfeYHsEi/0IIIYQQonHISKkWSLLPQovEhdDS0HHh9R5m586egB9QCA8fTVTU7djtI6QwejN2Lj4vVFVlW/7WqgTVV/ycs77G+23D2wULpQ9KHoJJb9I8ztmuKNicnYtxIU5N4kJokbgQoUhsiGoyfS+E1nBzyI0utEhcCC0NHReq6qO0dBEFBbMpK1sZ3G4ypRIVdTtRUTeh10fV/4nFWZHnBRwtO8KS/YtYsu8rvjm4EpffFXwv3ORgeNvLGNlhNJe1H0mUJRqABXvmM/37aWSVZQXbJtuSmTFkFmM6jWv0a6hvEhdCi8SF0CJxIUKR2BDVJCkVQmu4OeRGF1okLoSWxoyLysLor1FU9E6wMHpi4kxiYu5u2BOL0ybPi5rKveV8e+hrllRN7cutyAm+p1f0DEi6iDbh7fgg412g5l+YQuWUvzmj5rb4xJTEhdAicSG0SFyIUCQ2RDVJSoXQGm4OudGFFokLoaUp4iIQKKOo6AOKiubRvv3H6PURAJSWLsbvL8ThuAadztw4nRGa5HkRWkAN8EvOepbs+4pFe79ie8HWU+6joJBkT2b9zVta9FQ+iQuhReJCaJG4EKFIbIhqdU1KScEPIYQQ9UqnsxEdfRupqcuCCSmAnJxnOXx4Kjt3duPo0afweA42YS+F0KZTdPRLuIBHBjzONxN+YN3Nm7m9550n3UdFJct5OFhAXQghhBBC1I0kpYQQQjQ4VfXhcFyFwZCC359HXt5f2bWrFwcO3IjTuQJVDTR1F4XQ1M7Rnv5JF9ap7a2LfsNF7/TlvhV3M3fbm+wo2E5AYlsIIYQQIiRDU3dACCFE66coBuLiphEb+3tKSxdSUPAaZWVfU1r6JaWlXxIZOZGUlH83dTeF0JQQlljntnuKdrOnaDfv7ZgHQIQ5kv4JF3BB4gAuSBxAn4R+2I32huqqEEIIIUSLIkkpIYQQjUZRDDgc43A4xuF276SgYDZFRe8SHn5lsI3Pl4vPdwSLpVeNfb3eQ/h8eSGPbTDEYTSmNFjfxblrYNIgkm3JZJdlo1K7QEZ1Tanl133HL7nr+enIWn468iM/H11HsbuI5QeWsvzAUqCycHqP2F5ckHhhMFHVxt4WRVEa+7KEEEIIIZqcFDpvgaR4nNAicSG0tIS48Pud6HRWFKWyQHROzrPk5j5PWNhAoqPvIDz8akBl584e+P05IY+j18eTlrZViqjXQUuIi+ZmwZ75TFk8EaBGYupkq+/5Aj625m2uSlJVJqoOOWvXUkuyJVclqCoTVb1ie2PUGxvwarRJXAgtEhdCi8SFCEViQ1Sra6FzGSklhBCiSen1Nacy+f2FgIHy8jWUl69Br3+YyMhJGAzx+P15gFaNHh1GYxsUxdQYXRbnoDGdxjFn1Fymfz+NrLKs4PYkezIzBs+slZACMOgM9I7vQ+/4Ptx+3l0AZDkPH5ekWsvmvE1kl2Uxf8+nzN/zKQBWg5Xz4/tyQcIALkgaQP+EC4mxxjTOhQohhBBCNCIZKdUCSfZZaJG4EFpaalx4vdkUFr5JYeGb+HzZVVt1aCekKrVv/wl2+2WN0r+WrqXGRXPgD/hZk72ao+VHSAhLZGDSIPQ6/Rkfr9xbzoacn2skqgrdhbXadY7sEpzud0HiALpEpaFT6ne9GokLoUXiQmiRuBChSGyIanUdKSVJqRZIbnShReJCaGnpcaGqXkpKvqSgYDbl5d+h18fg9xcB/uNa6bFYepOaulLq8tRRS4+L1kxVVXYX7aqRpNpZmFGrXaQ5kv4JVXWpkgbQJ74fNqPtrM4tcSG0SFwILRIXIhSJDVFNpu8JIYRo8RTFSETEeCIixuNybaei4meysu4+oZUfi6UHXu9+TKYOTdFNIeqNoih0iUqjS1Qav+lWWcOq0FXAuiM/8tORH/npyFp+zllHkbuIZQeWsOzAEqCygHrP2POCdakuTBxISnibOp/XH/Cz9shqyrOLCQtEMCDx7EaACSGEEELUhYyUaoEk+yy0SFwILa0tLlRVJTNzGC7XRmqOlqpktfbD4fgVERHjMRrr/oH8XNPa4uJc4/V72Zpfs4D6YeehWu2SbSk1Cqj3jD1Ps4D6gj3za9XKSrYlM2PILM1aWeLcIs8LoUXiQoQisSGqyfS9EFrDzSE3utAicSG0tMa4cDqXsX//tcHX0dF34nbvoKzsO46vOZWa+h1Wa+8m6GHz1xrj4lx3uPRQrQLqfrVm4tZqsNInvl/VSKoB9Eu8gNWHVzFl8cQaKwrCyVcVFOcWeV4ILRIXIhSJDVFNpu8JIYRolWy2EVgsfXG5fsZi6Uti4iwURcHrPUpp6XyKiz/B692PxdIruE9+/suADodjPEZjQtN1XogGkhLehpTwNozv8isAyrxltQqoF7mLWJ31Pauzvg/uZ1AMtRJSACoqCgrTVz3EFR2vkql8QgghhGgQkpQSQgjRoiiKQkLCE2RnTyMh4YlgcXOjMYHo6DuIjr6DQKAcpWplMlX1kZv7F/z+XI4cmUZY2BAiIq7F4bgagyG2KS9FiAZjM9oYnHIxg1MuBiCgBthdWLOA+q6infhUX8hjqKhkOQ/z1d4FXJU6ThYSEEIIIUS9k+l7LZAMiRRaJC6EFokLCAQqKCiYQ0nJJ1RUrDvuHT022yVERd1GRMT4pupek5C4EABvb32DB7/5XZ3aRpojSY/uRnpUN7pGd6VrTHfSo7oRFxbXwL0UTU2eF0KLxIUIRWJDVJPpe0IIIQSg01mJjb2X2Nh78Xj2U1LyGcXFn+By/UJZ2Uosll7BpJSq+ggEStHro5q200I0gk6RnevUTkGhyF3E2uwfWJv9Q433Yiwxlcmq6K6kR3eja1Q30qO7EWONaYguCyGEEKKVkaSUEEKIc4bJ1J7Y2N8RG/s7PJ5Mios/Izx8VPB9p3MlBw9OwGYbTkTEtYSHX4leH9GEPRai4QxMGkSyLZnssmzNulIKCkn2ZFZNWMfekkx2FGwjo2AHGQXb2VGwnf0l+8h35deqUwUQa42jW3T3YLKqMmHVlUiLJHyFEEIIcYwkpYQQQpyTTKZU4uJ+X2NbefkPqKoXp3MxTudiFMWE3X4ZDse1hIdfgV5/6iHIQrQUep2eGUNmMWXxRBSUGomp6tX3Zgyeic1ko2dsL3rG9qqxf7m3nN1FO9lRlaTKKNhORsEODpTuJ68il+8Of8N3h7+psU9CWGJlgiq6K12rk1ZRXXGYJfkrhBBCnIukplQLJPN0hRaJC6FF4uL0ud0ZFBd/QknJJ7jdGcHtimKmc+f1mEztmrB39UPiQhxvwZ75TP9+GlllWcFtyfYUZgyeyZhO4077eE6vk10FGWQU7qiRrDrkPBhyn2RbyrEpgNXTAaO6YjedXSLYH/CzJns1R8uPkBCWyMCkQbKS4GmS54XQInEhQpHYENXqWlNKklItkNzoQovEhdAicXHmVFXF7d5OcfHHlJR8Aujo3HldcAWy/PxXMRoTsNtHotNZm7azp0niQpzIH/Cz9shqynXFhAUiGJBY/8mbUk9J1fS/HewoPJasyj4uGXaituHtSI/qGqxb1TW6G12i0rEZbac8n2ayzZbMjCGzzijZdq6S54XQInEhQpHYENUkKRVCa7g55EYXWiQuhBaJi/qhqip+fz4GQywAgYCLjIxOBAKl6HQ2wsOvwOG4Frv9MnQ6SxP39tQkLoSWpoqLYncRGQUZVTWrtrOjsLJuVU75Ue1+otDW0Z6uUcdGVnWN7kbnqDSshsoE8YI985myeGKtWlnV0xLnjJoriak6kueF0CJxIUKR2BDVJCkVQmu4OeRGF1okLoQWiYuG4fcXkpv7N0pKPsXrPRDcrtOFEx5+JVFRt2CzDWnCHp6cxIXQ0tziotBVUDmqqmA7GYXb2ZFf+WdeRZ5me52io72jA2lRXVl1+FucXqdmu+oC7utv3iJT+eqgucWFaB4kLkQoEhuiWl2TUlLoXAghhDhNen0UiYl/JiHhaSoq1lNS8gnFxZ/i8x2muPh9TKbOwaSUqnpRVRWdzlTrOF7vIXw+7Q/YAAZDHEZjSoNdhxDNWZQlmoHJgxiYPKjG9ryKvOAKgBkF28moGllV4Cpgb3Eme4szT3pcFZUs52G+PfQ1w9qNaMhLEEIIIcQpSFJKCCGEOEOKohAW1p+wsP4kJMygouInios/ISLiV8E2JSVfkJ19P+Hh44iIuBab7RIUxUAg4GbPnkvx+3NCHl+vjyctbSs6nbkxLkeIFiHWGktsysUMTrk4uE1VVXIqcsgo2M4HGe/yQca7pzzOhAXX0iGiY7BmVVpUOunRXekcmUaYMawhL0EIIYQQVSQpJYQQQtQDRdERFjaAsLABNbY7nUvx+4soKnqboqK30etjcDiuJjx8PEZjCn5/HhDQOKIOo7ENilJ7hJUQoiZFUUgISyAhLAG9oq9TUkpFDY6sWrRv4bFjodDO0Z70qK6kVa0CmB7dlc5RadiN9oa8DCGEEOKc06RJKbfbzVNPPcWSJUuwWCxMnjyZyZMna7a9++67WbFiRY1tr7zyCsOGDWuMrgohhBBnJDn5JSIjb6S4+BNKSj7H78+nsPB1CgtfR6eLQDshBRAgIWF6cLU/IUTdDEwaRLItmeyy7FqFzuFYTamF1y5nT9EudhZWrgh4/DTA/SX72F+yjyX7F9XYt3o1wOpkVVp0OulRXbGbTl0zQwghhBC1NWlSatasWWzZsoW33nqLrKwsHnroIZKTkxk9enSttnv27OGFF17goosuCm6LiIhozO4KIYQQp01R9Nhsl2CzXUJS0l8oK/uW4uJPKS2dj9HYAVBwuTYC/uP20mOx9MZmk3o3QpwuvU7PjCGzmLJ4IgpKjcRU9ep7MwbPJNmeTLI9mYvbDK2xf3XNqozCHews2MHOwgx2FGwnryKXg6UHOFh6gGUHltTYJ8XehrSodNKiu9I1qhtp0emkRaUTYY5s8OsVQgghWrImS0qVl5fz4YcfMnv2bHr06EGPHj3YtWsX8+bNq5WU8ng8HDp0iF69ehEXF9dEPRZCCCHOjqIYsNuHY7cPR1X/htebjcezk/37rz2hpR+DIZmKih+xWi9AUXRN0l8hWqoxncYxZ9Rcpn8/jayyrOD2JHsyMwbPZEyncSH31apZBZBfkc+uwozgiKqMwgx2FuzgaPkRDjsPcdh5iJUHl9fYJ9GWFJz+l1ZVuyo9Kp1IS9QZX5s/4GdN9mqOlh8hISyRgUmDZBVBIYQQLVaTJaV27NiBz+ejT58+wW39+vXjlVdeIRAIoNMd+w94ZmYmiqLQtm3bpuiqEEIIUe8UxYjJ1A6jsS0WS19crg0cP5XP6VyA07kAo7ENDsfVREbejMXSo8n6K0RLM6bTOK7oeFW9JXBirDHEWGuvBljkKqxMUFWNrMqomg6YXZbFkbJsjpRl882hlTX2iQ9LOCFZVflnjDXmpH1YsGd+rURbsi2ZGUNmnTTRJoQQQjRXTZaUys3NJSoqCpPpWAHX2NhY3G43RUVFREdHB7dnZmZit9uZNm0aP/74I4mJifz2t79l6NChWocWQgghWgxFUUhImF5jtFRc3CN4PJmUli7E6z1Efv6/MRrbBZNSquoD9FJvSohT0Ov0tUY81bdISxQDkgYyIGlgje0l7mJ2FmYEp/9VJq0yOOQ8SE75UXLKj/Ld4W9q7BNrjQvWqkqL6krX6G6kRXUl1hrLl5lfMGXxxFp1srLLspmyeCJzRs2VxJQQQogWp8mSUhUVFTUSUkDwtcfjqbE9MzMTl8vFkCFDmDp1KkuXLuXuu+/m/fffp1evXqd13tbw//fqa2gN1yLqj8SF0CJx0TLY7SOwWvtSUfEzVmtf4uMfRlEUAgEXTudyios/JiLi6uC/Y2HhPHJz/0JExDVERIzHYulzWgkqiQuhReKifkVYIrgg6UIuSLqwxnanp5SdhRlkFFSOrtpRsJ2dBRkcKN1PXkUueRW5rMr6rsY+UeYoyrxlmoXbVVQUFP606iGuTL2q3qfySVwILRIXIhSJDVGtrjGgqKpa+6dbI/jqq6+YMWMGq1atCm7bs2cPV155JWvXriUyMjK4PRAIUFpaWqOw+V133UVcXBx//vOfG7PbQgghRIMoKFjG7t330bnzP4mOvuykbTdvHkt+/oLga4ulI3FxvyY+/tfY7aeXoBJCNA9lnjJ25O1ga+5WtuVuC35lFmZqJqO0LJ+0nOEdhzdwT4UQQoj602QjpRISEigsLMTn82EwVHYjNzcXi8WCw+Go0Van09VaaS81NZXdu3ef9nnz80tpmjRc/VEUiIkJbxXXIuqPxIXQInHRkgwgNXUtgQDk5ZWetGVCwmuEhS2tWsVvES7XXg4enMnBgzMxmbrQufNqdDpzyP0lLoQWiYum196URvuUNK5MuSa4rdxbzqubXuaZNU+dcv8x74xlSMrFDEi6iAFJF3F+fB8sBstZ9UniQmiRuBChSGyIatWxcCpNlpTq1q0bBoOBDRs20L9/fwDWr19Pr169ahQ5B3j44cppDM8991xw244dO0hLSzvt86oqrebmaE3XIuqPxIXQInHRuihKGA7H1TgcVxMIlFFaupiSkk8pLV2M0ZiCopiD/94FBW8SFtYfs7lHrRFUEhdCi8RF82I1hNE/4cJTNwQqfOUs3b+YpfsXA2DSmTg/vm9VkmogFyQOIMoSfYqjaJO4EFokLkQoEhuirposKWW1Whk/fjxPPvkkzz77LDk5Obz++uvBxFNubi7h4eFYLBaGDx/O73//ewYMGECfPn344osvWL9+PU8//XRTdV8IIYRoFnQ6GxER1xIRcS1+vxO/Pzf4ntd7hOzs3wEqJlMXIiKuweG4FoulW9N1WAhx2gYmDSLZlkx2WbbmVD4FhSRbMnNGv826Iz+yNnsNa7N/ILcihx+PrOHHI2v41y+VbbtGd+OCxIFVxdkvol14e5nyK4QQosk0WU0pqCx2/uSTT7JkyRLsdjtTpkzh1ltvBSA9PZ3nnnuOa6+tXI3oww8/5LXXXiMrK4suXbrwyCOPcMEFF5z2OfPyWv4wQkWB2NjwVnEtov5IXAgtEhfnNrd7F0ePPo7TuRRVPbaIiNmcTmLiBEymazCZUpuwh6I5kedF87Zgz3ymLJ4IUCMxpVCZUDpx9T1VVdlbksmP2Wv4sSpJtatoZ63jJtqSGJB4UTBJ1T2mZ41i6RIXQovEhQhFYkNUq46FU7ZryqRUU2gNN4fc6EKLxIXQInEhAPz+EkpLv6Kk5FOczmXBBFVKystERt4EgKr6UZT6XbVLtCzyvGj+FuyZz/Tvp5FVlhXclmxPYcbgmTUSUqHkVeTx05G1rM3+gbXZP7ApdwPegLdGG7sxnP6JFwTrUvVN6Ef7pESJC1GDPC9EKBIbopokpUJoDTeH3OhCi8SF0CJxIU7k9xdTWroQl+tL4uP/hU4XBUBe3ksUFc2rmuJ3DWZzlybuqWhs8rxoGfwBP2uyV3O0/AgJYYkMTBpUY2TT6Sj3lrMh5+fKJNWRH/jpyI+UekpqtDHoDPRJ7EO/uAu5MPEiLkwaSHxYfH1cimjB5HkhQpHYENUkKRVCa7g55EYXWiQuhBaJC6FFKy727h1JefmaYBuLpRcOxzU4HOMxmzs3UU9FY5LnhfAH/Owo2M7aIz/wY/YPrMn6gayyw7XapUZ0Co6kGpA0kNSIzlKX6hwjzwsRisSGqCZJqRBaw80hN7rQInEhtEhcCC1aceH3F1JSspCSko9xOr8GfMH2VusAOnZcjKLoNI8nWgd5Xggth50H2ebcwLKdK1iTtYYdBdtqFVuPtcYGR1ENSBpIr9jemPSmJuqxaAzyvBChSGyIanVNSjXZ6ntCCCGEaD70+iiiom4iKuomfL58Sku/pLj4E8rKvsFgSKiRkCosfBub7RJMpg6ax/J6D+Hz5YU8l8EQh9GYUt+XIIRoAG3C23J+x+6MTBqLqkKxu+jYCn9HfuDno+vIq8hj4d4vWLj3CwCsBit94/szIGkgFyZdxAWJFxJucpz0PPU5LVEIIUTLIUkpIYQQQtRgMMQQFTWJqKhJ+Hz5BALFwffc7t1kZd0LgNXaF4fjWhyO8ZhM7QAIBNzs2XMpfn9OyOPr9fGkpW1FpzM37IUIIepdhDmSEe1HMqL9SADcfjebcjewNnsNP1YVUC90F7Iq6ztWZX0HgE7R0T2mZ+UKf4mV0/6S7MnBY2oWcLclM2PIrDoVcBdCCNFyyfS9FkiGRAotEhdCi8SF0HI2cVFRsYGjRx+nrOxbIBDcbrX2r0pQ/YqDB2/E5dpQ4/1jdFgs55OaulJq0DQz8rwQWk43LgJqgN2Fu1h75IfgKn/7S/bVatcuvD0XJg3Eaghj7rY3ap+XyufDnFFzJTHVDMnzQoQisSGqSU2pEFrDzSE3utAicSG0SFwILfURFz5fLiUln1Nc/Cnl5d9DVY2Ztm3/h04Xxv7914bct337T7DbLzuzE4sGI88LoaU+4uJo2RF+PLKmKkm1hs15GwmoWknrE86NQpI9mfU3b5GpfM2MPC9EKBIboprUlBJCCCFEgzEY4oiOvp3o6Nvxeo9SUvI5paULsdsvR1EsWCx9cbl+gRoFkStHSdlsI5qq20KIJpBgS2Rsp/GM7TQeAKenlPVH1/Fhxnt8sPPdkPupqGQ5DzNl8URGtB9J95gedI3ujs1oa6SeCyGEaGiSlBJCCCHEWTEaE4iJmUpMzNTgtoSE6RqjpQL4fFkcPjwFi6UvMTF3oygy+kGIc43dFM7QtsPId+WdNClVbeHeBSzcuwCoHD3VIaIjPWJ60T2mB91jetI9pgftHO3RyQqhQgjR4khSSgghhBD1zmYbgdncE7d7K8ePlvL5jlBc/BFlZauJjb03uD0//2UUxYrV2heLpRuKYmyCXgshGlNCWGKd2o3v9CuKPIVsy99KTvlR9hZnsrc4kwWZnwfb2I3hdIvpHkxSVf7Z/ZSr/gkhhGhakpQSQgghRL1TFIXExKdrjJZq0+ZN9HoHFRU/A8dGSKmqSm7uLPz+/Kp9LVgsvbFa+1Z99cds7tTYlyCEaGADkwaRbEsmuywbldrFZ6prSr18+WvBmlK55blsL9jKtvwtbMvfyta8LWQUbMfpLeWnI2v56cjaGsdo5+hQlaTqQY+qhFUHR6rUqBJCiGZCklJCCCGEaBA224iq2lI/Y7H0xeG4BkVRahU5V1UPUVG3UFHxMxUVvxAIFFNRsZaKisoPl2FhQ+jYcWGwvdO5HLO5G0ZjMkKIlkuv0zNjyCymLJ6IglIjMVW9+t6MwTNrJJDiwuKIC7uUS9pcGtzmC/jYU7T7uETVZrblbyWr7DAHSvZxoGQfi/Z+GWwfZgija3S3E0ZV9SDSEtXwFy2EEKIGSUoJIYQQokEoikJCwhNkZ08jIeEJFEXRbKfTmUlIeBIAVQ3g8eyhomJ9VZJqPWFhFwXb+v1F7N9/DQAGQ1LVSKp+VX/2Qa+XD5VCtCRjOo1jzqi5TP9+GlllWcHtSfZkZgyeyZhO4055DIPOQHp0V9Kju3JNl+uC2wtdBWzP38a2/C1szd/Ctvwt7CjYTrmvnJ9z1vNzzvoax0mxt6mRpOoR04vUyE4YdGf+kckf8LMmezVHy4+QEJbIwKRBMkpLCCGOo6jqubVQY2tYmlKW2RRaJC6EFokLoaUlx4XbncHBg7fhdm8Dai8pHxNzP4mJTwOgqn5U1YNOZ23kXrZMLTkuRMNprLhorOSNP+BnX0lmjRFV2/K3cqB0v2Z7s95MenS346YA9qJ7TE9irDGnPNeCPfNrJduSbcnMGDKrTsm25kyeFyIUiQ1RrToWTtlOklItj9zoQovEhdAicSG0tIa4CATKqKjYVDWiaj0u1894PHtJSnqR6OgpAFRUbCAzcxgWS4+qAup9CQvrh9ncDUWRweInag1xIerfuRIXJe5ithdsZ2v+ZrblVdas2l6wjTKvU7N9QlhizVFVsb3oHNkFk94EVCakpiyeWKtWVvW0xDmj5rboxNS5Ehfi9ElsiGp1TUrJ/8iEEEII0eLodDZstouw2Y5N7fP58mskm1yuLYAfl2sTLtcm4E0AFMWKxXIe8fGPYLcPr9P5vN5D+Hx5Id83GOIwGlPO5FKEEM2AwxzBgKSBDEgaGNwWUAMcKNkfnPpXOapqC/uK93K0/AhHy4+w8uDyYHujzkiXqHS6RXdj6f7FmsXbVVQUFKaveogrOl4lU/mEEOc8SUoJIYQQolUwGGpOp4mMvAm7fViN+lSVhdRLqoqoH6txVVr6Ffn5r1TVp6r8Mhorl6sPBNzs2XMpfn9OyHPr9fGkpW1FpzM3yLUJIRqfTtHRIaIjHSI6clXq2OB2p9fJjvxtwSRV9RTAEk9x1estJz2uikqW8zBrslczOOXihr4MIYRo1iQpJYQQQohWSVEUjMYUjMYUHI7KaTLHCqmvw2rtF2xbVraasrKVlJWtDG4zGJKD0/4MhgT8/jy06liBDqOxDYpiauArEkI0B3ajnf6JF9I/8cLgNlVVOew8xNb8LXyY8R7z93x6yuNMXHgDveJ6kxbVlfSodNKiu5Ie1ZX4sISQC0MIIURrI0kpIYQQQpwzFEWH2dwFs7lLje1RUZMwmToGR1W53dvx+bIoLc2itHQByckvk5V1d4ijBkhImC4fIoU4hymKQpvwtrQJb4vdaK9TUsrpdfJD1ip+yFpVY3uEOZK0qHTSo7qSFp1OelQ30qO7kmRLlueMEKLVkaSUEEIIIc55xxJVkwHw+524XJWF1N3u7URE3EhBwWxcro2A/4S9jeTlvUxZ2feYzd1xOMaj08moKSHOVQOTBpFsSya7LFuzrpSCQqItibeueIfdRbvYWZBBRuEOdhbuYG9xJsXuIn46spafjqytsZ/dGE56dDppUV0rR1dVfd8mvC06RddYlyeEEPVKklJCCCGEECfQ6+3YbIOw2QYFtyUkTGf//ms1WnspK1tKWdlSFMVMRMSxNgUFrxMIOLFYemA298RoTGiE3gshmpJep2fGkFlMWTwRBaVGYqp69b1nhszi/Pi+nB/ft8a+Lp+LzOI97CzYwY7C7ewsyGBn4Q4yi/fg9Jay/ug61h9dV2OfMEMYXaLSK0dXRVeOqkqLSqddeHsppC6EaPYkKSWEEEIIUQc22wgslr7HjZbSY7H0JDFxFm73NlyuLaiqu8YKgIWFc3C5Ngdf6/WxWCw9sVh6YLGcR2TkjY1/IUKIBjem0zjmjJrL9O+nkVWWFdyeZE9mxuCZjOk0TnM/i8FC95gedI/pUWO7x+9hb3EmOwt3kFGwo+rPDPYU7aLcV87G3F/YmPtLzWPpLXSOSjtuKmBlzaoOER0x6M78Y6A/4GftkdWUZxcTFohgQOIgSX4JIc6Yoqpq7TGlrVheXikt/YoVBWJjw1vFtYj6I3EhtEhcCC0SF2fO6VxWY7RU+/afYLdfFrJ9Xt7fqaj4BZdrCx7PbjhuxITJlEaXLsdGPBw9+md0OmtV0qonBkNKo9aPkbgQWiQuzo4/4GdN9mqOlh8hISyRgUn1m8DxBXzsK95bOf2vYEfVNMAMdhfuxOV3ae5j0pnoFNklOP2vcmRVVzpGpGLSn3zq8YI982sl2pJtycwYMitkok2cW+SZIapVx8Ip20lSquWRG11okbgQWiQuhBaJizOnqiqZmcNwuX7GYulLaurKOieOAoFy3O4duFxbcbm2oNdHEx//UNVxA+zY0YZAwBlsr9NFVo2o6kFY2KAa0wIbgsSF0CJx0TL5A34OlO5nZ2EGGQU7yCjYzs7CDHYVZlDuK9fcx6AzkBrRqbJmVXTl6Kr06G50iuyMWW9mwZ75TFk8sVadrOopiXNGzZXElJBnhgiSpFQIreHmkBtdaJG4EFokLoQWiYuz43SuJDt7GklJs7Dbh9XLMQMBNwUFL+NybcHl2orbnQH4gu+Hh19Bu3bvA1VLzx++E5OpAxZLT8zmHphMHVHOoNCx13sIny8PqIyLyEgbRUVlwbgwGOIwGlPO+vpEyyXPi9YloAY4VHowOP1vZ+GxqYBOb6nmPjpFRwdHR7Kch0OOvlJQSLIns/7mLTKV7xwnzwxRra5JKakpJYQQQghxGuz2YXTp8lO9HlOnMxMbe3/wdSDgwePZGUxSWSxdg+/5fEcpLn7vhP1tmM3dsFh6YrePwuG46pTnDATc7NlzKX5/Tsg2en08aWlb0enMp39RQohmR6foaOdoTztHey5rPyq4XVVVspyHg6sAVq8ImFGwgxJPMZnFe056XJXK/ZfsX8QVHU/9/BFCiGqSlBJCCCGEaGZ0OlOwttSJFMVEYuJzVdMAt+J2byMQKKOiYh0VFevQ6WzBpJTfX8ShQ3cEi6ubzT0xmzujKAYUxYTR2Aa/Pw8IaPUCo7ENinLyGjNCiJZPURRSwtuQEt6G4e2O1clTVZWc8qPM2fxf/v7zX095nFu+upEUext6xZ5Hz9jz6BXXm16x55Fib9OoNfKEEC2HJKWOo6oqPp+3qbtxSooCLpcLr9cjQyJbOL3egE53+tMthBBCnLsMhmhiYu4JvlZVHx5PZtWoqi3YbJcE33O5tuF0LsbpXBzcpiimqlFVPXA4xuJy/RziTAESEqbLB0khzmGKopBgS2Ro2+F1SkoBHHYe4rDzEIv2LQxui7ZE0zO2MkHVK+48esX2JjWik0z1E0JITalqPp+X/PwjqKrWbwqbH51ORyDQMvoqTs5qteNwRJ/1f/pl/rbQInEhtEhcnDu83ixKS78MFlevHFV1rJh6QsILFBe/i8u1EfAft6cOi6U3qalfS1LqHCfPCwGVhdP7ze1Bdll2rULncKym1MrrV7GjcDubczeyOW8Tm/M2sbNwB76Ar9Y+YYYwusf0DCapesWeR9eY7pj1Ml24JZNnhqgmhc5D0Lo5VFWloCCHQMBHRETMGRUKbWx6vYLff07907U6qqri8bhxOguxWu1ERMSc1fHkB4DQInEhtEhcnLtUNYDXuz+YpHI4xuPzHWL//tor++l00TgcV2K3D8dmG4bBcHY/p0TLJM8LUa169T2gRmLqVKvvuXwuMgq2sylvYzBZtT1/q+YqgAadgbSorpUjqqqm//WM7UW4ydFAVyXqmzwzRDVJSoWgdXP4/T5ycw8TERGL1Wprmo6dJoNBh88nI6VaA6ezBKezkPj4tmc1lU9+AAgtEhdCi8SFOJ6qqmRmDsPl2sCx2lIK1BgNodC+/Xzs9qGN3j/RtOR5IY63YM98pn8/jayyrOC2ZHsKMwbP1ExIheIP+NlTtJvNecdGVG3J3Uihu1CzfQdHx2B9ql6x59EzrjcJYQlnfT2i/skzQ1STpFQIWjeH1+shPz+b6OhETKaWMVxUklKth8fjpqDgCDExSRiNZ15MVn4ACC0SF0KLxIU4kdO5rMZoqbZt30Ons+B0rsDpXIHbvYOuXfeh11eOVsjNfZGKijXYbMOx24djMnWWaX6tlDwvxIn8AT9rj6ymXFdMWCCCAYmD6qU2lKqqHHYeqkxS5W5kS1Wy6rDzkGb7+LCEqiRVb3rFVRZW7+DoeMbPIn/Az5rs1RwtP0JCWCIDk+rnus418swQ1SQpFcLJklJnmxRoTJKUaj3qK/7kB4DQInEhtEhciBOpqsrevcOoqPgZq7UvHTuurPHBzucrwGCIDr7OzBxGRcX64GujsT12+/CqqX6XoNdHNWr/RcOR54XQ0phxkV+RH0xQbcnbyObcTewu2qVZ28phiqBnbK8aq/91iUzDqDee9ByaI8BsycwYMuu0RoAJeWaIYyQpFUJrSUo9++xTLFz4Rcj3//nPV+jbt/9pHfPee6fSp08/pky585Rtr7tuLJMnT+XKK8ee1jnqauHCL3j22ad4+OHpjBkzvkHO0VxIUko0JIkLoUXiQmgpK1tJTs7DxMc/j8027KRtKyo24XQup6xsBeXlP6CqnuB7en0s6em7gzU6VVWVUVQtmDwvhJamjosybxnb8rdUTfvbFKxT5Ql4arU16810i+5eVZ+qcvpf95iehBnDgGO1sk5Mcp2qVpbQ1tSxIZoPSUqF0FqSUi5XGWVlFQAsX76U9977H7NnvxV83+GIwGg8+W8ETlRSUozBYCQsLOyUbQsLCwkLs2I2W06v43X0+9/fy+HDh4iLi+ell15tkHM0F5KUEg1J4kJokbgQWs40LgKBMsrKVgWTVGZzD9q2fROoTEjt2TMIk6lTcCSVydShQfovGoY8L4SW5hgXXr+XnYUZbM7beNzIqs2UekpqtdUpOjpHdqFHTC+WH1hCiUYbOLaq4Pqbt8hUvjpqjrEhmkZdk1KGRujLucPvx7hmNbqjRwgkJOIdOAj0DfPwstvDsVhsVd/b0el0xMTEntUxHY6IOreNimq4YfmFhQWsX/8TjzzyOM888yRZWYdJTk5psPMJIYQQ4szpdDbCw0cSHj4SgEDAHXzP49mF270Vt3srpaXzATCZUqtqUV2GzTYkWKdKCCHOhlFvpEdsT3rE9gRuAiCgBthfsq8ySZW7KVhYPaf8KDsLM9hZmHHSY6qoZDkP8+8N/+DilKHEhsURa43DarA2whUJcW6QpFQ9MS2Yj336NPRZx+Yh+5OTcc6YhWdM4w/3zM7O4vrrx3H77Xfx3nvzGDlyNA88MI25c9/giy8+Izc3h4iISK6++lomT54K1Jy+98wzT+JwOMjNzWXVqm+JiIhk6tT/Y/Toq4Ca0/fuvXcqF1wwgI0bf2HDhl+Ij0/ggQf+yIABFwFQXFzEzJnP8NNPa4iMjOammybyl788z/ffr9Ps+4oVy7Db7YwceQX//e+/WbToy2AfASoqKvjXv/7G11+vAGDo0OHcf/+DmM1mCgsLePHFF1izZjUWi4WrrhrH1Kn/x5Ej2Vx//Tg+/HA+SUnJAMyZ819++WU9L730KgsXfsEXX3xKZGQ0P//8E3/4w8MMHnwx//jHX1m9+nuczlKSk1O4667fcskllwKEPNesWc9QUJDPzJkvBvv84ouzcDpL+dOf/ly//9BCCCFEM6PTHVs0xmTqRGrqyqqC6cspL/8RjycTjyeTwsLXiI6+i6SkWQCoqh8ARZHRCEKI+qFTdHSMSKVjRCpjO40Pbj9adoTNeRt5b8c7zN/z6SmPM2PNkzVe24x2Yq2xVV9xx33FEnPctjhrHNGWmFPWtGpIUsBdNHeSlKoHpgXzcUyZyInjE3XZ2TimTKRkztwmSUwBbNq0kTlz5hIIBFi06Es++OBdnnzyGVJS2rB27Wr+8pfnGTz4EtLTu9ba9+OPP+COO+7mzjvv4aOP3ueFF55lyJCh2O32Wm3ffvt1/vCHh/nDHx7mlVdeYubMGXz00RfodDqeeOJRPB4P//nPHPLycnj++ZMnZpYvX8JFFw1Bp9MxePAlLFr0JbfddkewHsXzz/+ZPXt28/zzf8VstvDnP/+J2bNf5t577+eRRx5Er9fz0kv/pby8nCeeeITY2FgGDbr4lH9XmzdvYtKkydx55z1ERkbxj3/8lYMH9/Piiy9hsVh55523mTnzz1x00WCMRmPIc1122Sj++MffUVbmxGazEwgE+PrrFTz00PQ6/qsJIYQQrYOi6LFa+2G19iMu7o/4/SWUlX1PWdlynM7l2O0jgm3Ly1dx8OBEbLZhwal+RmObJuy9EKK1SrAlkmBLxGoIq1NSKjWiEy6fi7yKXDwBD2VeJ2VeJ/tL9tXpfJHmyJDJq7jjtsdYY4myRKGrqsl3tqSAu2gJJCkViqpCefmp2/n92B/9I6gqJ5bwVFQVVVGwPzaNgksuPfVUvrCwyomX9ejXv76RlJTK/9Dl5ubw6KNP0L//hQCMH38db7wxm71792gmpTp3TuOmm24B4Pbb7+TDD99l79499OrVu1bbiy4aEix6fsstU7j11hspKMinvLycdet+5P33PyMlpQ1duqRx221T+ctfntPs79GjR9i8eSM33FA55Hbo0GF89tlHbNq0gd69+1BSUsLXXy/nxRf/zXnnnQ/AH//4KLt2ZbB79y62bNnEBx98Hpzu9+CDj1BRUVGnvytFUbjllsnBOlnnn9+XCRNuIjW1MwA33ngzX3zxGQUF+ZSWloY8V58+/QgPd7Bq1XeMHHkFGzf+gtfr5cILB9apH0IIIURrpdc7cDiuxOG4EqisOVXN6fwav7+QkpJPKCn5BACzOb1qql/lqn46nUyZEULUn4FJg0i2JZNdlq25ml91TalVN65Dr9OjqipObym5FbnkleeRV5FLXkUu+RXHvs8Lfp9HviuPgBqgyF1EkbuI3UW7TtknvaIn2hJTmagKiyO2+vvq19Y4YiyxxIbFEmeNw24M11xMIlQB9+yybKYsnigF3EWzIUkpLapK5JiRGH9ae9aHUlQVfXYWcZ1P/Zs+74UDKfpicb0mpqqnqgH07dufrVu38MorL7F//1527swgPz+fQCCguW+bNm2D39tslaOjfD6fZtu2bdsd19YWbLtnzy4cjohgYgygZ8/zQvZ3+fIlmEym4NS/6gTPV18toHfvPhw+fBC/30/Xrt2C+/Tu3YfevfuwYsUyHI6IGvWnLr74UqByOuOpREVF1yjcPnr0VXz33dfMn/8p+/fvIyNjBwCBQIADB/aHPBfA8OGXs3LlMkaOvIIVK5YxdOgwDAa53YQQQojjHf9BKj7+UcLDR+F0LsfpXEFFxTrc7gzc7gwKCl4mNfU7rNbKX4z5/SXodPbgCn8n8noP4fPlhTyvwRCH0Sj1KoU41+l1emYMmcWUxRNRUGokcKpX35sxeGZwupuiKISbHISbHKRGdDrl8QNqgEJXYY3kVe4JyavjE1pF7iL8qp/cihxyK3Kg4NTXYNabg6OsqqcTRltieGf725qJNhUVBYXpqx7iio5XyVQ+0eTkU3IorWTpYpPp2GpuX3zxGf/8598YO/Zqhg4dzj333M99990Vcl+t1ftCLdaolXBRVRW93lBrn5Mt+Lhs2WLcbjejRg0NbvP7/axcuYwHHvjjSRM7J3tP67cHfr+/xuvj/64AZsx4gs2bNzF69JWMH38dMTGx3HXXbac8F8Bll43it7+9k7IyJ99+u0JqSQkhhBCnoCgGwsIGEBY2gPj4R/H7Cykr+xancwUu10Ysll7BttnZf8TpXIbdPqxqFNVwjMZEoLLQ+p49l+L354Q8l14fT1ra1hr1r4QQ56YxncYxZ9TcWtPckuzJzBg886xGE+kUHTHWGGKsMaRTe2bKiTx+DwWu/ONGW1V9lVeOuqp+nVuRR155LuW+Mtx+N4edhzjsPFTnflUXcH927dNc3mE0HSNSibfGa35mEqKhSVJKi6JUjliqw/Q945rVRN74q1O2K3r348rV+E6mAabvHe+zzz7mtttu5ze/mQRAaWkpBQX5J00Sna0OHTpSWlpSYwW9jIztmm0PHNjPzp0Z3H//g/Tt2z+4fe/eTJ544lG++eZrBg8egl6vZ9euXfTufT4A3333NW+8MZvp05+mpKSYo0ePkJBQ+R/TDz98r6pw+SMAlB/3b5qVdThkv8vKnCxduohXX32Tbt16APDDD98DlUm1Nm3ahjzXc8/9lR49ehIXF8e8eW+jqpUjvoQQQghRd3p9FA7H1TgcV9d6r6LiR/z+XIqLP6C4+AMAzOaexyWo2uD35wFao8F1GI1tUBSTxntCiHPRmE7juKLjVU1eENykN5FoSyLRllSn9uXe8spkVflxo69cefxweBXLDiw+5f7/+uVF/vVL5eJMYQZbsCj8iV+JtqR6q3MlxIkkKRWKokDVNLST8V46HH9yMrrsbBSN5I6qKASSkvFeOvzUNaUaWEREBOvW/ciQIUMpLy/n1Vf/jc/nw+v1NNg527Vrz4UXXsRzzz3N7373IIWF+cyZ81/NtsuWLcbhiGDcuGtrjFpKTe3MG2+8xqJFCxg5cjSjR1/FP/7xAg8++Ag6nY7//vc/XHTRYFJTO9Gv3wU8//yfuffeByguLuJ//3uTSZMmEx0dTXx8Au+88zaTJ09l48Zf+OGH7+nSJV2zLyaTGYvFytdfryAyMooDB/bzt7+9AIDX6z3puaqNGDGS996bx9ixV6Nv4n97IYQQojXp1GktFRU/Blf1c7k24HZvwe3egtO5gsTEp9m//9oQewdISJguIwKEEDXodXoGp5x6caTmJMwYRpixHW3D29XY3je+X52SUr1jz6fQU8Sh0gOU+8rYmr+Zrfmba7Wz6C10iOhIh4hUOjpqJqxS7G1kCqA4K5KUOlt6Pc4Zs3BMmYiqKDUSU2rVf3acM2Y2eUIK4He/e5Bnn32KW2/9DVFRUYwYcTkWi5WdOzMa9LyPPvoEs2bNYOrUW4mLi+PKK8fyzjtv12q3fPkSRo68otY0OoBrrvkV//jHX8nNzeF3v/sDf//7X3jggXswGo0MH345d9xxNwB/+tOf+etfn+fOO2/FZrMzbtw1XHvt9SiKwiOP/IkXX3yBiRN/Tb9+FzBp0mR++GGVZp+NRiOPP/40L730dz766D2SklK45ZbJzJ79Mjt37qB9+w4hz1VtxIiRvP3264wYMbKe/iaFEEIIAaDTmbDZhmCzDSEh4XF8vjzKyr7G6VyBydQZm20EFktfXK6NgP+EfcMpKPgfpaVLsNkGB0diqaqKx5OJwRCLTueQpJUQosWqawH3RdetRK/T4/F7OFi6n73FmbW+DpTux+V3saNgOzsKas94MeqMtHd0qJGo6tOuF9FKIm3s7TDo6j/l4A/4m3xUm6g/itqQc7eaoby8Uk68Yq/XQ35+NjExSRiNZzaU27RgPvbp09BnHZuH7E9OwTljJp4x9b+qgcGgw+fTLlDenLhcLtatW8vAgYODdZhWrFjGf/7zDz766Ism7l3D+umnNcyc+Qwffjj/pP+xrY/4g8rBfbGx4ZoxLs5dEhdCi8SF0NLa4sLpXHaS0VKVoqKmkJxcOXXF7y9kx472ACiKCb0+DoMhDoMhFr0+Drv9UiIjbwRAVQO4XBvQ62MxGOIaZVXApire3triQtQPiYvmr3r1PUCzgHtdV9/z+r0cch4MJqn2HZew2l+yD08g9Kwbg85A2/B2xxJWwVFWnWjnaI9Jf/qffRbsmV+r/leyLZkZQ2bJaoLNTPVz4lRkpFQ98YwZR8EVV2Fcsxrd0SMEEhIra0g1gxFSTclkMvHcc08zfvx1XHXVOAoK8nnjjVcZNuyypu5ag8nLy2PTpg3Mnfs6Y8ZcLb9pFUIIIZpA7dFSOkymVGJjpxEI5OPz5WG1Hqth6fcXodPZCATKUFUPPt9hfL5j9Sd1OlswKeX3F5KZeelx79mDCSqDIQ67fSTR0ZVT+lU1QFnZ1xgMcVWJrhgUpfZiMicjxduFEKervgq4G/XGYFLpRP6An6yywzVGVu0rzuRA2T525+/G5XcFt59Ip+hoY29bOSXwhK/2jg5YDbWT/dWJthNHf2WXZTNl8cQ6J9pE8yIjpai/kSqNqaWMlALYuHED//7339mzZxc2m52RI69g6tT/05ym1xrs27eXO+64hR49evL883/DYrGctL2MlBINSeJCaJG4EFpaY1ycOFqqfftPsNtP/ouxQKAcny8Pvz8Xny8Xny8fvz8Xi6UXdvsIADye/ezdOxq/PxdVrT1KIDr6DpKS/gqAz5dPRkbHGu/r9VHBkVjh4VcRG3svAKrqp6RkfjC5pdfHotdHAQqZmcNwuTYQqni7xXI+qakr6/2XYa0xLsTZk7hoORp7qlt1bOTkFnPEeaTmdMCSyj8zi/ZQ7is76XGSbSnBJFWHiFQ6ODryyHcPkluhnZyvnpK4/uYtMpWvmajrSClJSiFJKdG0JCklGpLEhdAicSG0tMa4UFW1KpnzMxZL33pP2qiqSiBQgs+Xi9+fX5XEysVsTsNmGwyAx3OQAweuD7Y5MalUM4GVR0bGiaMR9BgMsSiKBa93f8i+1CXhdiZaY1yIsydxIUKpS2yoqkpuRS6ZxXuqpgPuqUpc7SWzeA+lnpIzPv8/h73MqI5XEGmOkhkrTUym7wkhhBBCiHOaoigkJDxBdvY0EhKeaIBRRAp6fQR6fQTQWbONydSWzp3XAJUjofz+whojsYzGY6tmqaqbsLCLqpJbeQQCRYAfn+8oAHp9HH5/AScWbwcj+flzqKj4GYulD1ZrHwyG2Hq9ViGEqC+KohAfFk98WDwDky6q8Z6qqhS4Co5LVFV+/Xz0J/aW7D3lse9beTesrCzAHmuNIz4sgbjqP8PiT/i+sg8R5shmkcA6Vwu4S1JKCCGEEEK0Wnb7MLp0+ampuwGAolSOeqpMGHWt9b7RmELHjseWcQ8EPMERWH5/Lh7PPrKzH9A4shen80uczi+DW2Ji7icx8WkAVNWH31+MwRBT35ckhBD1SlEUYqwxxFhj6J94YXD7qsPfcc3nV51yf5vRRpm3DG/AS3ZZFtnH1dMKxaQzBRNWcWHxVYmsyoTVseRVAnFhcThMEQ2SwDqXC7hLUkoIIYQQQohmSKczodMlYTQmAZUjCAoL5x5XvF2PxdKThITncLk24HL9QkXFL3g8uzGZjk0DdLk2kpk5DKOxPVZrHyyW87Faz8diOR+DIbppLk4IIU7DwKRBJNuSyS7LrlXoHGrWlPKpPvLKc8kpP0puRQ65x32fU55Tta3y+xJPMZ6Ah8POQxx2HjplP8x6M3HWeOLC4oLJq5rfVyWzrPGEmxx1SmCd6wXcJSklhBBCCCFEC1A5HXH6ccXb/SQkPIHdPgS7fUiwnd9fAhz7IOR27wLA692P17ufkpLPgu8ZjR1ITHweh+NKoDLx1RymsQghxPH0Oj0zhsxiyuKJKCg1EjhK1fNuxuCZ6HV69OhJCW9DSnibUx7X5XORV1GdtKr6sypxlVOeU+P7Uk8Jbr+bQ86DHHIePOWxLXpL7SmDx00bjAtLIMYczaPf/1Ez0aaioqAwfdVDXNHxqlY7lU+SUkIIIYQQQrQQNtsILJa+weLtNtuIWm30ekeN15GREwgPH01FxUZcrg1UVPyCy/ULHs9evN596PX2YNuSko/JyZlRNZqqL1br+URGDgF0DX1pQghxUmM6jWPOqLm1prkl2ZOZMXjmGY0mshgstAlvS5vwtqdsW+GrCCapcmuMxDo2AqsyqZWL01uKy+/iYOkBDpYeOO1+VVNRyXIeZk32aganXHzGx2nOJCklhBBCCCFEC3Gmxdv1+kjs9qHY7UOD2/z+QioqNmK19gtuq5z+l4nHk0lJyScA7NsHJlMnLJbziY+fjtncqV6vSQgh6mpMp3Fc0fGqJikIbjVYaedoTztH+1O2LfeW10pYVX5fOSKr+vvssizcfvcpj3e0/Eh9XEKzJEmpFurpp59g4cIvQr7/z3++Qt++/U/7uKqq8umnH3HttdeftN29905lx45tzJ+/mLAw22mfRwghhBBCnJn6Kt6u10dht19aY1ts7B+w20dUjaaqHFXl9R7A49mDx7OHxMQ/B9sWFLxBefmq4Kgqi+W8GqOu6sLrPYTPlxfyfYMhDqMx5bSOKYRo3fQ6fbMfNRRmDKO9sQPtHR1O2q6uBdwTwhLrqWfNjySl6sHhwwr5+aF/SxUbq5KcXHuO6Nn4/e8f5M477wFg+fKlvPfe/5g9+63g+w5HxBkdd8OGn/nb32aeNCmVm5vDli2biIuLZ+XK5Vx1VestuiaEEEIIcS4xGKKx24djtw8HQFHA4XBz+PB3uFzbMBiOJYicziWUln5JcfEHVVsUTKYuWK19sFrPJypqMjqdNeS5AgE3e/Zcit+fE7KNXh9PWtpWdDpzvVyfEEI0J3Ut4D4waVAT9K5xSFLqLLndMHJkGLm5oefZx8cHWL++DHM9/iy128OxWGxV39vR6XTExMSe9XFV9dTJs+XLl9CpUxd69erNV18tkKSUEEIIIUQrZjLFEh5+GXb7ZTW2x8Tcg9Xah4qKX6io2IDPdxiPZycez05KSuYTHX1nsG1BwZuoqqtq9b9e6HRhKIoJo7ENfn8eENA4sw6jsQ2KYmrYCxRCiCZyOgXcWyupWHiWTCZISVFRFO1kjqJUjpIyNfLP0qNHj/DQQw8wYsRgrrtuLK+//ip+vx8An8/HzJkzuOqqEVx++cU89NAD5ObmkJ2dxX333QXAkCH9+fnndZrHXrZsCeef34fBg4ewceMvZGdn1Xh/+/at3H33FEaMGMyECdeybNni4Htr1qxm8uSbGDFiMLfcciPr1v0IwJw5/+Xee6fWOM51140NTlG8996pvPjiLK6//mquvfYqysvL2LRpQ/A8l102hAcfvI+8vLyTnsvtdjFy5FC++WZFsJ3P5+PKK0cE+yKEEEIIIU7NZhtCXNw02rV7l/T07aSn76Zduw+Ji3uU6OipKMqx338XFLzMkSPT2Lv3crZvT2b37oFkZf0fVmtftBNSAAESEqbLaoBCiFatuoB7ki2pxvYkezJzRs09owLuLYkkpU6irCz0l8tV2UZR4OGH3aiq9g9LVVX4/e/dHP+zNNQx64uqqjz22DSioqJ54415PProEyxduoi5c98A4OOP3+eXX37mb3/7N6+9Npfy8nL++c+/ER+fwDPPzALg888X0atX71rHPnz4EDt2bGPw4Evo06c/NpuNRYu+DL5fWFjAAw/cQ5cuabzxxjwmTbqNZ555kl27dpKZuYeHHnqASy4Zxptvvstll43ikUf+QH5+6DoCx1u48Asef/xpnn32LwQCKtOm3c+FFw5k7twP+NvfXuLQoUP873+V1xjqXE6nk4svHsrKlcuDx/3pp7UYDAb69OkX6tRCCCGEEOIUDIZ4wsNHER//cI3aU6qqEhHxK+z20RgMCUAAt3sbRUXzKCx8DUUxAyeOAtBhNnfDah3SmJcghBBNYkyncayfuJVPr/6SVy6fw6dXf8n6m7e0+oQUyPS9k+rYMTzke5dd5uOddyoAGDbMj6KoIRNTL79sYvToiuDr/v1t5OfXzgfm5JSeZY8rrV//E0eOZPPqq2+i0+lo164D99xzP88++xS33no72dnZmM1mkpKScDgieOyxJykuLkav1xMeXrmEcKipgEuXLsLhiKB37z7o9XoGDbqYRYu+5Lbb7gAqR1GFh0dw//1/DJ67pKQYt9vNypVL6dWrN7feejsAEyfeistVgdPprNN1DRo0JJgoy8/P45ZbbmfChJtQFIXk5BQuvXQ427dvBeDLLz8Pea7LLhvFE088itvtxmw2s3LlMoYNG4Fe33qHRAohhBBCNBVFUYiLmxZ87fVmVxVS/4WKil8AI07nlyfsFcDt3s6OHYmYTO0xmdIwm7sQFjYAh+PqRu2/EEI0hpZQwL0hSFKqHihK5TQ+d4iVHBt7xPH+/XspKSlm1KhjS/4GAgHcbjfFxUWMG3cNy5YtZty4UfTp049LLhnGlVeOqdOxly1bwqBBQ4IJnKFDh7FkyVds3LiB3r3P58CB/aSlpaHTHUu6TZhwMwBvvfUa6endahzvjjvurvN1JSYmB7+PiYnliivG8P7789i1ayf79u1l9+6dwaTVgQP7Q54rJaUNJpORtWt/YNCgIXz77dfMmvVinfshhBBCCCHOnNGYhNGYhMNxJVA5kiozcxgu10bADygoigXQo6pOPJ69eDx7cToX4/HsDyalVFVl374xGI1tMJsrk1YmUxdMplQpjC6EEC2EJKVOYu/e0COXThxUs327k/Hjw9iyRUcgoKDTqfTsGeCzz8prtV23rh7n6mnw+/20a9eB55//a633bDY7ERGRfPTRF6xe/T2rV3/Hf//7EkuXLuLf/5590uPu3r2LffsyOXBgH0uXLqrx3qJFC+jd+3wMhtAhdbL3tGoFVNfAqmY6rjBXbm4Ot98+kfT0bvTvP4Bx465h9erv2bp18ynPZTAYuPTSEXzzzXKMRiM2m01zqqIQQgghhGh4iqKQkDCd/fuvrdqi0q7dPGy2Efh8R/B4duF278Lt3onFcl5wP58vm/Ly7zSOqMNkak9ExI3Exz9ceURVxefLwWCIlxpVQgjRjEhS6iRstrq3tdvh0UfdTJgQBkAgoPDoo27s9rM77plo27Y9R48eITIyCntVB376aQ0LFy5g+vSn+OqrBZhMJkaMGMnw4ZexZctm7rrrNgoLC076Q3r58iXY7eG89NKr6HTH2r311uusWLGU++9/kDZt2vLDD9+jqmrwWI8//ghdu3ajTZt27NyZUeOYd901meuuuwGj0Uh5eXlwe3l5OYWFBSH78u23KwkPj2DWrL8Ht3300fvB7092rssuG8Xll4/mkUcexGoNY/jwy+U/J0IIIYQQTchmG4HF0heX62cslr7YbCNQFCU4qspmu6TWPjqdgzZt3q5KWu2s+nM3gUAJHs9eAoFjJSL8/jx27uyCTheB2dwZk6mLxugqS2NeshBCCCQpVa+GDfNz/vl+NmzQc/75foYN8596pwZw4YUDSUxM5Omn/8Sdd96D01nKrFnP0r//hej1esrKnLz88htERESSnJzC0qVfER+fQEREJFarFYAdO7bTsWMqZvOxoc/Lli1h5MjRdO7cpcb5Jky4ieXLl/Dtt18zcuQVvPbaK/znP/9k3Lhr2Lx5I99//w0TJ95KWJiNm2++nvfe+x9Dhgxl5cpl7N27h/PP70tERCSvvfYKK1Yso3PnLrz++qvoTrLspcMRwdGjR1i37keSkpJZuXIZ33yzgq5duwMwfvyvQp4L4LzzzsdisbBw4QL+85/X6vufQAghhBBCnIbK0VJPkJ09jYSEJ+r0C0O93k5ExPga2ypHRB3F49mFwRAf3O7x7Ad0BALFVFSsp6JifY39oqPvJilpJgB+fwnFxR8FE1YGQ8Jp/wLT6z2Ezxd6MR+DIQ6jMeW0jimEEK2RJKXqkaLAY4+5eewxM4895m70WlLV9Ho9zz//N/7+9xeYOvUWrNYwhg27jHvv/R0A1177a3Jycvjznx+ntLSE9PRuPP/8X9Hr9aSmduaCCwZw992TefLJZxg6dDgAW7ZsJjv7MGPG1C4s2a1bD9LTu/HVV19y+eWjeeGFv/OPf/yVjz56j+TkFJ54YgZduqQDMGPGLF555V+8+up/6NAhlZkzXyQ2No6YmFhuuOE3zJr1DHq9jhtuuIm8vNyQ1zh8+OVs3PgL06c/hKIodOvWnXvvvZ85c/6Lx+MhJaVNyHNB5X98hg27jFWrvqVr124hzyOEEEIIIRqH3T6MLl1+OqtjVI6uSsRoTKyxPSysP926HcHjyawxHbD6e7M5LdjW7d5Odvb9wdc6naNqdFXlyCq7fSRWa+jSD4GAmz17LsXvzwnZRq+PJy1tq9S+EkKc8xRVVdWm7kRjyssr5cQr9no95OdnExOThNFo0t6xmTEYdPh8gabuRov21FPTadOmLVOm3Nmk/aiv+FMUiI0N14xxce6SuBBaJC6EFokLoeVciIvKj0N+FKXy9/Xl5evIzX0ej2dX1Qirmv/nTkycRUzMXQC4XFs4evTxGtMBjcYuHDw4oapwu9b/13VYLOeTmrqyxZaQOBfiQpwZiQ1RrToWTkVGSolzzpYtm8nI2M53333N3LkfNHV3hBBCCCFEE6pMDB37WBQW1p/27T8CKkc9HRtdtRO3exdWa79gW5drC07nMmDZCce0op2QAggQH/9Ii01ICSFEfZKklDjnrF27mvfem8fUqfeQlJTc1N0RQgghhBDNlE5nxmLphsWiXe4hLGwASUn/qFFs3ePZj6pWVB8BreTUgQPXo9dHYzDEYzDEo9fHERPzf4SFXQCAz5eH17sfvb7y/eYwze/4OlmKAqWlNioqyoKjYaROlhDiTEhSSpxzpky5s8mn7AkhhBBCiJbPZOpIdHTHGtsqR1ftpbj4Y/LyZmrspQAqfn8Bfn8BbvcOACIjJwRbOJ1LOHz4ruBrnS4CgyEumMSKibmXsLALAfD5cvB49gbf0+nqf6lvqZMlhGgokpQSQgghhBBCiHpSObqqK2bzozidS6tqS/kBPRZLbzp2XE4gUIjPl1Pjy2LpWeM4BkMKfn8OquolECjG4ynG49kNQGTkzcF2TudSDh+++7jz29DrjyWwYmPvDyawvN6jeDx7ggkunc5Rp2mEimLCaGyD359HqDpZRmMbFKVl1OcVQjQfkpQSQgghhBBCiHqmKAoJCdPZv//aqi1+EhKmo9Pp0eliMRhige6a+0ZG/obIyN+gqiqBQBE+X+5JElg6jMZ2+Hw5qKqLQKCMQKAMr3cfAFFRtwVbOp3LyMo6lsBSFHNV8ioOvT6euLg/EBY2AACvNxuPZ1fVFMI44uMf48CBX4W42gAJCdOlTpYQ4rRJUkoIIYQQQgghGoDNNgKLpS8u189YLH2x2Uac1v6KoqDXR6HXR2E2p2m2iYy8kcjIG6sSWE58vhz8/lzNBJai6DGZOuLz5RIIOFFVN17vQbzegwDExEwNtnU6l5OV9X/HnUkPGAHvCT3QY7Gcd9rXJoQQ0MRJKbfbzVNPPcWSJUuwWCxMnjyZyZMnn3SfQ4cOMXbsWF555RUGDBjQSD0VQgghhBBCiNNTOVrqCbKzp5GQ8ESDjiSqTGCFo9eHA50020RGTgjWrgoEyo8bgVX5p9l8fALLiMnUpSqBVUTlFES/xlH92O3D2LWrNxZLLyyW87Baz8Ni6Y3BkCSjp4QQJ9WkSalZs2axZcsW3nrrLbKysnjooYdITk5m9OjRIfd58sknKS8vb8ReCiGEEEIIIcSZsduH0aXLT03djVp0ujBMpvaYTO0134+MvIHIyBsACAQ8+P25eL1HOXx4Ch5PJqBSXScrEPDh9e7D691HaekXwWPo9bFYLOeRmPhcyBUMhRDntiZLSpWXl/Phhx8ye/ZsevToQY8ePdi1axfz5s0LmZSaP38+ZWVljdxTIYQQQgghhDh36XQmdLoUjMYUkpJeqFUny2rth8MxkoqKTbhcG3G5NuF2Z+D351FWtgKdLix4rLy8lygp+bxqNFXll9ncXVbtE+Ic1WRJqR07duDz+ejTp09wW79+/XjllVcIBALodLoa7QsLC3nhhRd4/fXXGTNmTGN3t9l5+uknWLjwi5Dv//Ofr9C3b//TOua9906lT59+TJly5ynbXnfdWCZPnsqVV449rXOcys8/r+O+++7i++/X1etxhRBCCCGEEGfPZhuB1dqXioqfsVor62QpioLNdgk22yXBdoFABS7XVtzurRiN7YLby8vXUFGxloqKtccd1YDZ3BWLpRdJSc+j10c14hUJIZpSkyWlcnNziYqKwmQ6tmxobGwsbreboqIioqOja7R//vnnueaaa+jSpctZnVdrSnN9TXP2B/ysyV7N0fIjJIQlMjBpEHqdvn4OfoLf//5B7rzzHgCWL1/Ke+/9j9mz3wq+73BEnPYxn332BQwGY53azp79NmFh1tM+hwhNUc4uFqv3lWn74ngSF0KLxIXQInEhtEhciBMpikJi4hMcPfowCQlPoNNpB4deb8Vm64/NVvMX5YmJTxIRMbZqVNVmXK6N+P2FuN1b8Hh206bNv4PxduTIk3g8e7BYeh1XpypR6lQ1Y/LMENXqGgNNlpSqqKiokZACgq89Hk+N7atXr2b9+vUsWLDgrM8bExNea5vL5aKgQIder2Aw6DT2OrUvdn/O2maq7gAALrdJREFUI99OI8t5OLgt2Z7Cc5fMYmznq8+4v6HY7eHY7ZXX4nCEo9PpSEiIP6tjRkfX/TcScXExZ3WuUPT6yr//M/13aIkCAQWdTkdUlA2LxXLWx9OKcSEkLoQWiQuhReJCaJG4EMeLjR1H+/bjznDvPlVflVRVxe0+iNO5AY8nm7i4Y59J9u5dSlnZZkpKPg9uMxrjsdv7EB7en44d/ywJqmZKnhmirposKWU2m2sln6pfH//B3OVy8fjjj/PEE0/Uywf2/PxSVLXmNq/XQyAQwO9X8fkCp33MBXvmM2XxRFRqHjjbmcWtC29mzqi5jOl0pg9tbQaDLtjXQKDyvMf3PTs7i+uvH8ftt9/Fe+/NY+TI0TzwwDTmzn2DL774jNzcHCIiIrn66muZPLly6dfjp+8988yTOBwOcnNzWbXqWyIiIpk69f8YPfoqoOb0vXvvncoFFwxg48Zf2LDhF+LjE3jggT8yYMBFABQXFzFz5jP89NMaIiOjuemmifzlL89rTtHz+wO1ruV4gUCA9977H59++jH5+Xn06NGT++//I506dQZg+fIlvPbaKxw9eoTk5BSmTr2HSy65FIAPP3yP9977H4WFBXTs2In77vsDvXuff5b/EmfP71cJBAIUFpZhNJ64xG7dKUrlw18rxsW5S+JCaJG4EFokLoQWiQuhpf7jIgoYhskEeXmlwa1xcc8THv5L1aiqTbjdO/F6cygsXExZWSYOx0PBtllZvwd0WK29q+pUda1TnSqP5xB+f17I9w2GOIzGlLO5uHOKPDNEtepYOJUmS0olJCRQWFiIz+fDYKjsRm5uLhaLBYfDEWy3adMmDh48yH333Vdj/zvuuIPx48fz9NNPn9Z5VZVaN4fWzaKqKuW+U6/y5w/4efT7P9ZKSAGoqCgoPPb9NC5pc+kpp/KFGcLqPdO/adNG5syZSyAQYNGiL/ngg3d58slnSElpw9q1q/nLX55n8OBLSE/vWmvfjz/+gDvuuJs777yHjz56nxdeeJYhQ4Zit9trtX377df5wx8e5g9/eJhXXnmJmTNn8NFHX6DT6XjiiUfxeDz85z9zyMvL4fnn/3zG1/PGG7P57LOPeeihx2jTph3z5r3FH/7wW9599xNcrgr+/OfHmTbtMfr27c+KFct48snH+OyzhRw5ks1//vMPnnnmBTp2TOXDD9/l8ccf4tNPv6pVv6ypaMVmUx5HtC4SF0KLxIXQInEhtEhcCC0NHRc228XYbBcHXwcC5bhcW3G5NqEohuC5VdVPUdG7BALHFsVSFGNVnarzsNmGEBl5U63jBwJu9uy5FL8/J2Qf9Pp40tK2SiH20yTPDFFXTZaU6tatGwaDgQ0bNtC/f+U84/Xr19OrV68aSYLzzjuPJUuW1Nh35MiRzJgxg8GDBzdI31RVZcynI/npyNpTNz7VsVDJLsui85w2p2x7YeJAvrhmcb0mpn796xtJSak8d25uDo8++gT9+18IwPjx1/HGG7PZu3ePZlKqc+c0brrpFgBuv/1OPvzwXfbu3UOvXr1rtb3ooiHBoue33DKFW2+9kYKCfMrLy1m37kfef/8zUlLa0KVLGrfdNpW//OW5074WVVX5+OMPuPPOexgyZCgADz00nV//+moWL15I9+498Pl8xMXFk5iYxI033kznzl0wmcxkZ2dXzX9PJCkpmTvu+D8GDbpYs6i+EEIIIYQQovnR6cIIC7uAsLALTngnQFLS33G5NlV9bcTvL6qqWbUZny+/RlLq8OH/w2TqiNncC4MhoWqklNZMDR1GYxsUxaTxnhCiPjRZUspqtTJ+/HiefPJJnn32WXJycnj99dd57rnKZEVubi7h4eFYLBbat29fa/+EhARiYhqmrhGAQuuYm5yUlBz8vm/f/mzduoVXXnmJ/fv3snNnBvn5+QQC2lPl2rRpG/zeZqscHeXz+TTbtm3b7ri2tmDbPXt24XBEBBNjAD17nndG11JYWEBJSTHdu/cMbjMYDHTt2p39+/dx9dXXMmjQEB544B7atWvPkCFDGTt2PBaLhQEDLiI1tTOTJk0gLS2dIUOGMm7cNcFRekIIIYQQQoiWSVGMREbeANwAVP4y2+s9GCykbjJ1Drb1eo9QVPS/Oh45QELCdKlbJUQDatJP5I888ghPPvkkt9xyC3a7nd/+9reMHDkSgCFDhvDcc89x7bXXNnq/FEXhi2sW12n63pqs1dz45a9O2e7dqz5mYPKgk7ZpiOl7xxeT/+KLz/jnP//G2LFXM3TocO65537uu++ukPsajbVX4lNDjMHUSu6oqopeb6i1T6hjnIrJpD1kNhDwEwj4URSFWbP+zrZtW/j++2/59tuVfPrpR/znP7Pp0iWdV199kw0bfmbVqm9ZuPALPvvsY+bMmUtc3NkViBdCCCGEEEI0H4qiYDK1w2Rqh8Nx1QnvGUlI+HNwVJXbvRM0SrGAHrO5GwcO3ILJlIzBkITBkITRWP1nMmZzd8zmTo1yTUK0Vk2alLJarcycOZOZM2fWei8jIyPkfid7r74oioLNaDtlu0vbDifZlkx2WbZmXSkFhSR7Mpe2HX7KmlIN7bPPPua2227nN7+ZBEBpaSkFBflnnCSqiw4dOlJaWkJW1mGSkysLBGZkbD+jY9ntdqKjY9i6dTNduqQBlaOxMjJ2cMEFA9i/fx9ffPEZ9957P9279+SOO+5m4sRfs3btD7jdbtav/4lbbplC3779ufPOexk3biSbNm1gxIiR9Xa9QgghhBBCiObLYIghNvZ3wdeBQBkFBW9y9OgjJ7T0Exl5M0ePPozbnYHbXfszaEzM70hMrKyX6/Vmc+DAhOOSVkkYDMkYjYlVf7ZBr3fUOkZD8XoP4fNJAXfR/MncpbOk1+mZMWQWUxZPREGpkZiqngI4Y/DMJk9IAURERLBu3Y8MGTKU8vJyXn313/h8Prxez6l3PkPt2rXnwgsv4rnnnuZ3v3uQwsJ85sz57yn3W7NmdY3XJpOJvn37c8MNv2HOnP8SGxtHmzZtmTfvLTweN8OHjyQQ8PPZZx9ht9sZOfIK9u7NJDs7i7S0rpjNZt54YzbR0TH0738hGzb8TEVFBZ06dWmoSxdCCCGEEEI0czqdjZiY/6O4+ENcro2AH9BjsfQmKmoy4eGX4/Vm4fNl4fVm4/NlV/2ZhdmcHjxO5XTBX3C5ftE8T0zM/SQmVi7S5fPlcOTIwxgMyRgMiRiNyTVGYZ1tUXUp4C5aEklK1YMxncYxZ9Rcpn8/jayyrOD2JHsyMwbPZEyncU3Yu2N+97sHefbZp7j11t8QFRXFiBGXY7FY2bmzYUeePfroE8yaNYOpU28lLi6OK68cyzvvvH3SfR58sOZqi3Fx8Xz66UImTLiZsrIyZs16hrIyJz179uZf//ovUVFRADzzzAu8/PK/ePvtN4iKiuLOO+/lwgsHAvDII4/z5puv8eKLs0hISORPf3qaDh06NsxFCyGEEEIIIVoERVFISJjO/v3VpWP8JCRMR6+3oNd3wWw+9S+yzeYutGv3flUC61jiqjqRZTQmBdt6PPsoLv4o5LHi4h4mPv5RAHy+PAoKXqkabZX0/+3deVyU1f4H8M8zG4OssgmEe4KmCCJlJV4EN8z0omlXM5dcM5fymihYmaViUmqJ3rBcMswtK7W6t5+kuYZrgmIqq6Ig4YKCLLM9vz+Q0XFmQA0GhM/79ZpXzHkO5/k+cDo+8+Wc8+j/K5W6QBBMP7BJEBSQy724gTs9FgSxJtdu1UFXrxYaPZpSrVbh2rVcODt7QC5/9P8xtTotEnMPIa/4Cpo0csezHs/X2AwpmUwCjcb0BuV1SWlpKY4dO4xnn+2q33dq9+4ErFz5Kb79dmctR1c3VFf/EwTAxcXOZB+nhov9gkxhvyBT2C/IFPYLMqU+9gtRFJGREYLS0hNQKgPQqtWeat3vVxRFfXtq9WXcvPn9naRVDjSaK/pkliiWwd39Izg7TwIAFBcfRmZmL6P2BEEOmcwdLi5vwclpPABAqy1AYeH/IJd7oqwsE7m5U83G07z5d7C17Vlt13c3rvrXN+jRVPSFqnCmVDWSSqTo+kS32g6jTlEoFIiO/gDh4YPRr98AXL9+DWvXrkJISPUPgERERERERI+ifLbUXOTmRqBJk7nV/gCqe9uTy5+Ai8sUozqiKEKrvQFBuPsxXSp1ROPGYwxmX2k0+RBFNdTqbIiiVl+3rOwcLl+eUEUk5UsTraz8UVCwCVKpM2QyZ0il5S+JxKbOPm2Q+2TVT0xKUY2SSCRYuPATrFixDJs2xcPGpny/p/HjJ9V2aERERERERHq2tiFo0+ZorZ1fEATIZE4GZVZWPvD0XGZQJopqaDR5UKtzDZIwgiCDjU2wftaVTldk4izlSxNVqlSTCSxBsIJU6gxX1xn6GVhq9RXcuLHWIIF1N5HlBIlE+bevvSrcJ6v+YlKKapyfnz9WrVpX22EQERERERE99gRBDrncC3K5l0G5tXVntGhxd4sUjeYWsrJ6o6zsLMr3lpJAqfSHjU0PlJb+ARubEGi116DRXINWexWiWAZRLINGkwNRvLtVjEqVifz8aLPxuLm9C1fXmXfqZuPPP6Oh0djrZ1+VJ7BcIJU639kPy+ERrpn7ZNVXTEoRERERERER1TMymT3c3effs4G7Dk2avANBEGBtHYAWLbbr64qiCFEsvpOgugaZzOOedpzQuPGYexJY1/RfA1pIJHeTTGp1NvLy4s3G5OoaCTe3SACASpWBS5cmGCwfvPdrpbIdFIryB1MZb0Z/v7vXRo8XJqWIiIiIiIiI6iEbmx5QKgP0G7jb2PQwWU8QBAiCDRQKGwDNDI6ZWkIIlCeydLqbuDetIJd7oVWrxbh1K0efwLo3kSWTuejrqtVXUFJyxGzsrq5RcHObDaB8v6zs7AkQBCVEsfS+mhJYWbUze21UtzEpRURERERERFQP1eQG7oIgQCp1NChTKJrB03PmAz19z8rKB02bbjCafaXVXoVWe00/SwoANJqr0OnMbXKug1L5lMHTDa9ceQcKRUuDl0zmAUGQ/I0rpprApBQRERERERFRPVXbG7ibI5M5w96+/wPVtbbuhNatD0Gtvorc3DehVmcBEAEIEARr2Nq+qK9bVnYOt25tM2pDEKwglzeHq+sMODoOAwDodLehVl+CXN7cIhu2kzEmpYiIiIiIiIiozpJIGkGp7AClEhCET+7ZW0pEs2bxsLXtqa+rULREkybzoVJlQqXKgFqdBZXqIkSxDCrVeYiiVl+3uPgoLlwYAACQyTzvmVnVAgpFS1hbPwuFoqklL7XBYVKKiIiIiIiIiB4LVe2TpVC0hIvLNIMyUdRArc6GSpUFK6u2+nKttgASiR10ukJoNDnQaHJQXHxQf9zTcwUUihEAgJKSE7h6dSnk8rtJK4Wi5Z2n/j16akWtvgSNxtzSREAmc4Vc/sQjt1/XcUHlY2rixDGYN+8dk8f+7//+i7CwEKhUKrPfn5ubg6CgQOTm5gAAgoICceLEMZN1T5w4hqCgwAeObffuBNy4cR0AsHp1HKZMmfDA3/swBg/uj59/3ll1RSIiIiIiIqoXKvbJUih8HnifLEGQQaFoCVvbEMjld58s6OAQjrZtL8HHJxMtW/4KL6/VcHN7B46Ow9GoUVdYWfno65aWnsKtW9tx7doy5Oa+hQsX/onU1I44c8YNqal+KCz8P31djeYqSktPQastrDQuna4M6endkZHxD7Ov9PRg6HRlj/CTejxwplQ1qI3MZq9eYfj881io1WrI5XKDY7t370L37qFQKBQP3N727f+Dvb1D1RWrcOVKLt57bza2bt0BABg2bASGDBn6t9slIiIiIiIiAqp3nyxBECCTOUMmcwbwtNl6jRo9C3f3RXeWBWZBpcqEWn3hzrLATIM9qQoL/4ucnMkAAKnU5b5N11vAxqYH5HJ3CIICcrkXtNqrAHQmziq5MxPrwT/bP26YlPqbKjKbWu1fZutIpW7w9k6BRGJVbeft0aMXli6NwbFjh/Hcc0H68tu3i3DkSCJiYj59qPacnV2qrvQAxPsesdCoUaNqaZeIiIiIiIiotlhZ+RjMnAIAUdRBo8mFSpUJpbLjPeWlkEqdoNVeh1Z7FSUlV1FScjeJ1rz5jjtJKQG2tn1QWnrCzFl1aNLknWp9amJdw6TU31Rbmc3GjRsjMLAL9u7dY5CU2r9/L+ztHdCpU2fk5/+FTz/9GMeOHUVZWSlatmyFt96aiY4d/Y3aCwoKxGeffY6AgEDcvl2ExYsX4tChA3B2dsGAAeEGdZOTT+I//1mO8+fPQhAE+PsHYPbs9+Di4oIhQ8o3iRsyZACiouYiNzcHf/xxHLGxqwAAp08nY8WKT5Gaeg6NGzth+PCRCA8fDABYsOB92NvbIz8/HwcP7oODgyMmTHgDYWH9HulnVNm5rly5go8++hCnTyfDykqJHj16YerUf0MmkyE19Tw++WQRUlPPwc7OHv/85yC89tr4R4qBiIiIiIiI6idBkEAuf8JoZZST03g4OY2HVltwZ1ZVxcyqTKhUmVAoWt9TW2OmdSmUSj+jPbPqG+4pVQmd7nYlr1IAFetZ34HphBQA6ODqGmGQ2TTX5sPq2bM3DhzYC6327tMDdu9OQI8evSCRSPDBB+9Cq9UhLm4t1qzZAFdXN3zyyaIq242JicbFi1mIjV2F6dNnYtOmDfpjRUVFiIh4C8888yy+/noLliyJxaVLlxAfvxYA8MUXX+n/26NHL4N2s7IyMW3aJPj7B2DNmniMGTMBsbHLsHfvHn2dbdu2wMenLdav34zg4FDExCxEUVHRQ/9sqjrXsmWLYW3dCGvXfoPo6I/x22+/YseO7wEA8+fPRZs2Pvj66y2YPftdbNjwFX7//cBDx0BEREREREQNl1TqCGtrfzg4hMPVdTo8PT9DixY7DZ7o5+AQDicnU5MgtPV+lhTAmVKV+vNPD7PHbG17o3nzbwHgTuZSACCarHv16nLY27+gf3/+fAdotdeM6rVvf+uh4gsODkFMTDSSkv5AQEAgioqKcPRoIsaMmQBRFNGtW3d07x4KN7cmAIBBg17GzJlvVtpmUVER9uxJwGeffQ4fn/KnEowePQ5LlnwEACgrK8WoUeMwdOhwCIIAT88n0L17KP78MwUA4OjYWP9fKyulQds7d34Pb28fTJxYvra2WbMWyMrKxDffrEdwcAgA4MknvTF8+CgAwLhxE7F160ZkZqbD19fvoX42VZ0rNzcXPj5t4e7uAS+vpoiJ+RR2dvYAgCtXctCtWzDc3T3g6fkEli1bCQ8Pz4c6PxEREREREVFVlEpfuLt/jOLi4ygtTQKgRUOZJQUwKVUtyjOXCgCmd8Svqcxmo0Y2eP75IPz2268ICAjE/v2/wcPDE23btgMADBw4GAkJv+D06WRcuJCFc+fOQqczN6OrXHb2BWi1WrRp460va9fuKf3Xzs4u6Nv3RWzevAGpqeeRlZWJtLTzD5Q0ysrKwlNPtTco8/XtiO3bt+nfe3ndzRjb2NgCADQac9MZH/1cw4ePxMKF87Bv3x506fI8evToDW/v8iTciBGvIS5uBbZv/w7PPx+EPn1eqLY9t4iIiIiIiIjuVbEC68KFQXdKGsYsKYBJqUq1a5dbyVGpwbu2bTORldUXpaWncTez2QEtWvwXgmBY19v7dLXF2KtXGJYti8H06RHYvXsXevbsAwDQ6XSYPn0yCgsL0aNHL3Tt+g+o1WrMmTPzgdq9d8Nymezu0/3y8//CuHEj4OPTDoGBXTBgwEAcOnQAKSmnqmzT1NMAtVodtNq7ibL7nyR4fywPqqpz9e7dF507P439+3/DoUMH8O67szB8+ChMmPAGXn11NEJDe2Hfvj04eHA/3nxzEiIi5qB///CHjoOIiIiIiIioKjY2PaBUBqC09ASUyoAGMUsK4J5SlZJIbCp5GS5Nk0pt0aTJXJQnpIDyzOZcSKW2kEisH6jdR/Hcc11RUlKMEyeO4fjxo+jVKwwAkJWVgZMnT2DZspUYOXIMnn8+CNeuXQVQeZKnWbPmkMlk+PPPM/qy1NRz+q/37dsDOzsHLF68DC+/PAx+fp2Qk3NZf7yyTG6zZs2RkmKYkEtJSUazZs0f7qIfQFXniotbgevXryM8fDAWL16GceMmYe/e3SgrK8OyZR9DLpdj6NBXsXx5HAYMGIjffttd7TESERERERERARWzpeZCofBBkyZzG8QsKYBJqWpVkdkEYLHMpkKhwD/+EYLY2KVo1epJNG3aDABga2sHiUSCX3/9BVeu5GLPngSsWRMHAFCpVJVcgy3Cwvph2bIYpKScxokTx7BmzSr9cXt7B+TlXcGxY0dw+fIlxMevw969u/VtKpXlCbi0tPMoLi42aHvgwCFITT2PuLgVuHjxAv773x/x3XdbMWjQkEe+/vT0NCQmHjJ43bxZUOW5Ll7MwtKli5GWloqMjHQkJh5EmzY+sLKyQnLySSxdGoOLF7Nw9uwZJCX9AW9vnyoiISIiIiIiInp0trYhaNPmKGxtQ2o7FIvh8r1qVJHZzM2NsGhms1evPvj5552YOnW6vszNrQlmzJiNdeu+RFzcCjRt2hxvvvk25s+fi9TUc5XukTR9+kwsXRqD6dMnw87ODoMHD8WKFcsAAKGhvZCU9AfeeWcWBEFAu3ZPYcqUt7B6dRxUKhUcHR3Rp09fvPdeJCZNmmrQrru7OxYvXoqVKz/Fpk3xaNLEHVOmTEe/fgMe+do3b96AzZs3GJQtXboCTz/dpdJzvf12JD75ZBGmTJkArVaL55/virfeKl/a+MEH0Viy5COMGzcKUqkUoaE9MXr02EeOkYiIiIiIiIiMCeKjbNjzGLt6tRD3X7FarcK1a7lwdvaAXG68F1FdJJNJoNFUvmk5PR6qq/8JAuDiYmeyj1PDxX5BprBfkCnsF2QK+wWZwn5B5rBvUIWKvlAVLt8jIiIiIiIiIiKLY1KKiIiIiIiIiIgsjkkpIiIiIiIiIiKyOCaliIiIiIiIiIjI4piUIiIiIiIiIiIii2NS6h4N7EGEVEew3xEREREREVFDxKQUAImk/Meg1WpqORJqiFSqMgCAVCqr5UiIiIiIiIiILIefggFIJFLI5UoUFRVAKpVCEOp+rk6nE6DVcobN40wURahUZSgqugFra1t9cpSIiIiIiIioIWBSCoAgCHBwcMK1a1dw/XpebYfzQCQSCXQ6XW2HQdXA2toW9vZOtR0GERERERERkUUxKXWHTCaHm5sXNBp1bYdSJUEAGje2wY0bt8HtiB5vUqmMM6SIiIiIiIioQWJS6h6CIEAuV9R2GFUSBECpVEIuVzMpRURERERERESPJU7RICIiIiIiIiIii2NSioiIiIiIiIiILI5JKSIiIiIiIiIisrgGt6eUINR2BH9fxTXUh2uh6sN+QaawX5Ap7BdkCvsFmcJ+QaawX5A57BtU4UH7gCCK3CqbiIiIiIiIiIgsi8v3iIiIiIiIiIjI4piUIiIiIiIiIiIii2NSioiIiIiIiIiILI5JKSIiIiIiIiIisjgmpYiIiIiIiIiIyOKYlCIiIiIiIiIiIotjUoqIiIiIiIiIiCyOSSkiIiIiIiIiIrI4JqXqqLKyMkRFRSEwMBBBQUFYs2aN2bpnzpzBkCFD4Ofnh5deegmnT5+2YKRkSXl5eZg2bRqeeeYZdOvWDdHR0SgrKzNZd9KkSfDx8TF47dmzx8IRkyXs2rXL6Hc9bdo0k3UPHTqEF198EX5+fhg5ciSys7MtHC1ZwnfffWfUJ3x8fNC2bVuT9QcMGGBU9/z58xaOmmqSSqXCiy++iMOHD+vLsrOzMXr0aPj7++OFF17AgQMHKm3jxx9/RM+ePeHn54fJkyfj+vXrNR021TBT/eLkyZMYOnQoOnXqhD59+mDr1q2VthEYGGg0fty+fbumQ6caZKpfzJ8/3+j3HB8fb7aNdevWoVu3bujUqROioqJQUlJiidCpht3fN2bPnm3yfmPkyJEmv//mzZtGdbt06WLJS6A6SFbbAZBpixcvxunTp/HVV18hJycHs2bNgqenJ8LCwgzqFRcXY8KECejfvz8WLVqEjRs3YuLEidi1axcaNWpUS9FTTRBFEdOmTYO9vT02bNiAmzdvIioqChKJBLNmzTKqn56ejpiYGDz33HP6MgcHB0uGTBaSlpaGkJAQfPjhh/oyKysro3o5OTmYPHkypk6dim7dumHFihV44403sGPHDgiCYMmQqYa98MIL6Natm/69RqPBqFGj0L17d6O6Wq0WWVlZiI+PR4sWLfTljRs3tkCkZAllZWWYMWMGUlNT9WWiKGLy5Mnw9vbGtm3bkJCQgClTpuDnn3+Gp6enURvJycmYM2cO5s2bh7Zt22LBggWIjIxEXFycJS+FqpGpfpGfn4/x48dj2LBhWLRoEVJSUhAZGQlXV1eT40deXh4KCwuRkJAApVKpL+c96OPLVL8Ayu8rZ8yYgYEDB+rLbG1tTbbxyy+/IDY2FjExMXB2dkZkZCRiYmLw3nvv1WjsVLNM9Y05c+ZgxowZ+veXL1/GiBEjzCal0tLS4OjoiB9//FFfJpFwnkxDx6RUHVRcXIytW7fiiy++QPv27dG+fXukpqZiw4YNRkmpn3/+GVZWVoiIiIAgCJgzZw727duH//3vfxg0aFAtXQHVhIyMDJw8eRIHDx6Ei4sLAGDatGn46KOPjJJSKpUKly5dgq+vL1xdXWsjXLKg9PR0eHt7V/m73rp1Kzp06IAxY8YAAKKjo9G1a1ccOXKEf6WqZ5RKpcEHxLi4OIiiiLffftuo7qVLl6BWq9GxY0eTyUx6vKWlpWHGjBkQRdGgPDExEdnZ2di0aRMaNWqE1q1b4/fff8e2bdswdepUo3bi4+PRt29fhIeHAyj/41lISAiys7PRtGlTS1wKVSNz/SIhIQEuLi7497//DQBo0aIFDh8+jJ07d5pMSqWnp8PV1ZV9oJ4w1y+A8t/12LFjH+i+cv369Rg1ahRCQkIAAPPmzcPYsWMxc+ZMWFtbV3vcVPPM9Q07OzvY2dnp38+ePRthYWHo2bOnyXYyMjLQsmVLfj4hA0xL1kFnz56FRqNBp06d9GWdO3dGUlISdDqdQd2kpCR07txZP8tBEAQEBATg5MmTlgyZLMDV1RVffvmlPiFVoaioyKhuRkYGBEHgTWIDkZ6ebjDDxZykpCQEBgbq31tbW6N9+/YcL+q5goICfPHFF5gxYwYUCoXR8bS0NHh4eDAhVU9VJJ03b95sUJ6UlISnnnrKYEZL586dzY4H948fHh4e8PT0RFJSUo3ETTXLXL+o2BrgfqbuNYDy8aNly5Y1EiNZnrl+UVRUhLy8vAe619BqtTh16pTBeOHv7w+1Wo2zZ89Wd8hkIeb6xr1+//13HD16VJ/UNiUtLe2B+hE1LJwpVQfl5+ejcePGBh8eXFxcUFZWhoKCAjg5ORnUffLJJw2+39nZ2WjKLT3+7O3tDZbj6HQ6xMfH49lnnzWqm5GRAVtbW0RERODIkSNwd3fH1KlTERwcbMmQyQJEUURmZiYOHDiAuLg4aLVahIWFYdq0aUYJiPz8fLi5uRmUOTs748qVK5YMmSxs48aNcHNzM5ppWyE9PR1yuRwTJ07E6dOn0bJlS0RERKBjx44WjpRqwiuvvGKy/GHHg7/++ovjRz1irl94eXnBy8tL//7atWv46aefTM6eA8rHj5KSEowYMQKZmZlo164doqKimKh6TJnrF+np6RAEAZ9//jn27dsHR0dHvPbaawZL+SrcunULZWVlBuOFTCaDo6Mjx4vHmLm+ca9Vq1Zh4MCB8PDwMFsnPT0dGo0GgwcPRl5eHgIDAxEZGWn07ws1LJwpVQeVlJQYfZiseK9SqR6o7v31qP6JiYnBmTNnMH36dKNjGRkZKC0tRVBQEL788ksEBwdj0qRJOHXqVC1ESjUpJydHPw4sW7YMs2bNws6dO7F48WKjuhwvGh5RFLF161a8+uqrZutkZmbi5s2bGDJkCFatWoXWrVtj1KhRyM3NtWCkZGkPOx6UlpZy/GhgSktLMXXqVLi4uOBf//qXyToZGRm4efMmJk2ahJUrV0KpVGL06NFmZ1bR46liBn6rVq2watUqDBkyBO+++y527dplVLe0tBQAOF40MNnZ2UhMTMSIESMqrZeRkYGioiJERkZi6dKl+Ouvv/D6669Dq9VaKFKqizhTqg6ysrIyGrQr3t+7R0hlde+vR/VLTEwMvvrqKyxduhTe3t5Gx9944w2MGDFCv7F527ZtkZKSgi1btsDX19fS4VINeuKJJ3D48GE4ODhAEAS0a9cOOp0OM2fORGRkJKRSqb6uufHC3t7e0mGThZw6dQp5eXno16+f2ToffvghSktL9RvWvv/++zhx4gS2b9+O119/3VKhkoVZWVmhoKDAoKyy+wdz4wf3h6mfbt++jTfeeANZWVn45ptvzP6eV69eDbVaDRsbGwDAxx9/jODgYOzZswf9+/e3ZMhUg8LDwxESEgJHR0cA5feVWVlZ2LhxI3r16mVQt2IpOMeLhuWXX35Bu3btjFbw3O+nn36CIAj6f2s+++wzBAUFISkpCQEBAZYIleogzpSqg5o0aYIbN25Ao9Hoy/Lz86FUKo0+PDZp0gRXr141KLt69SqnQNZjH374IdauXYuYmBj06dPHZB2JRGL0pL1WrVohLy/PEiGShTk6Oho8Pa9169YoKyvDzZs3DeqZGy+42WT9tX//fgQGBlb65E2ZTGbwBKWKv4ZzvKjfHvb+geNHw1FUVISxY8ciNTUVX331VaX7vygUCn1CCihPSHh5eXH8qGcEQdAnpCqY+3fC0dERVlZWBuOFRqNBQUEBx4t6bP/+/ejRo0eV9aytrQ3++OHs7AxHR0eOGQ0ck1J1ULt27SCTyQw2Gz1+/Dh8fX2NHpnp5+eHP/74Q/8kBFEUceLECfj5+VkyZLKQ2NhYbNq0CUuWLKl05sPs2bMRGRlpUHb27Fm0atWqpkMkC9u/fz+6dOmCkpISfdmff/4JR0dHg/3ngPLx4vjx4/r3JSUlOHPmDMeLeiw5ObnKvzyOGDECsbGx+vc6nQ7nzp3jeFHP+fn5ISUlRb/UBii/1zA3Htw/fuTm5iI3N5fjRz2j0+kwZcoUXLp0CV9//TXatGljtq4oiujZsye+++47fVlxcTEuXLjA8aOe+fTTTzF69GiDMnP3lRKJBL6+vgbjxcmTJyGTydC2bduaDpVqgSiKOHXqVJX3G0VFRXj66aeRmJioL8vLy8ONGzc4ZjRwTErVQdbW1ggPD8f777+P5ORkJCQkYM2aNRg5ciSA8llTFTeRYWFhuHXrFhYsWIC0tDQsWLAAJSUl6Nu3b21eAtWA9PR0rFy5EuPHj0fnzp2Rn5+vfwGG/SI0NBQ7d+7EDz/8gAsXLiA2NhbHjx+vdF8Zejx16tQJVlZWeOedd5CRkYG9e/di8eLFGDduHLRaLfLz8/VT6F966SWcOHECq1atQmpqKiIjI+Hl5YUuXbrU8lVQTUlNTTWaSn9/vwgNDcW6devw66+/IiMjAx988AEKCwtNbmBL9cczzzwDDw8PREZGIjU1FatWrUJycjIGDx4MoHypTX5+vn6fj2HDhmH79u3YunUrzp49i4iICHTv3p1Pea1nvv32Wxw+fBjz58+Hvb29/j6jYqnnvf1CEAR0794dy5cvx+HDh5GamoqIiAi4u7vzwSr1TEhICI4ePYrVq1fj4sWL+Oabb/DDDz9gzJgxAMr3kaq4HwXKN8VevXo1EhISkJycjPfffx8vv/wyl+/VU5cvX8bt27dNLt27t2/Y2tqic+fOiI6ORnJyMlJSUjB9+nR069YNPj4+lg6b6hKR6qTi4mIxIiJC9Pf3F4OCgsS1a9fqj3l7e4vbtm3Tv09KShLDw8NFX19fcfDgwWJKSkotREw1LS4uTvT29jb5EkXjfrFlyxaxd+/eYocOHcSBAweKR44cqa3QqYadP39eHD16tOjv7y927dpVXL58uajT6cTs7GzR29tbTExM1Nf97bffxN69e4sdO3YUR40aJV68eLEWI6ea5uvrK+7bt8+g7P5+odPpxP/85z9i9+7dxQ4dOojDhw8Xz507VxvhUg27fzzIysoShw8fLnbo0EHs16+fePDgQf2xxMRE0dvbW8zOztaXbdu2TQwODhb9/f3FyZMni9evX7do/FQz7u0XY8aMMXmf8eqrr4qiaNwvSktLxejoaLFr166in5+fOHHiRDEnJ6fWroWqz/3jxa5du8T+/fuLvr6+YlhYmPjLL7/oj23btk1/P1ohLi5OfO6558TOnTuLkZGRYmlpqcVip5p1f984efKk6O3tLZaVlRnVvb9vFBQUiLNnzxa7dOkidurUSXz77bfFgoICi8RNdZcginfWfREREREREREREVkIl+8REREREREREZHFMSlFREREREREREQWx6QUERERERERERFZHJNSRERERERERERkcUxKERERERERERGRxTEpRUREREREREREFsekFBERERERERERWRyTUkREREREREREZHGy2g6AiIiIqD4KDQ3F5cuXTR5bv349unTpUiPnnT17NgBg0aJFNdI+ERERUXVhUoqIiIiohkRFReGFF14wKndwcKiFaIiIiIjqFialiIiIiGqInZ0dXF1dazsMIiIiojqJe0oRERER1YLQ0FCsW7cO/fv3h7+/PyZMmID8/Hz98fT0dIwdOxYBAQHo1q0bYmNjodPp9Me3b9+OsLAw+Pn5YejQoThz5oz+WFFREaZPnw4/Pz90794dO3futOi1ERERET0IJqWIiIiIasny5csxbtw4bN68GSUlJZg6dSoA4Pr163jllVfg5uaGrVu3Yu7cuYiPj8f69esBAPv378ecOXMwatQo7NixAx06dMDEiROhUqkAALt27UL79u3x448/om/fvoiKikJhYWGtXScRERGRKYIoimJtB0FERERU34SGhiI/Px8ymeFuCZ6envjpp58QGhqKnj17IioqCgCQnZ2Nnj17YufOnUhMTMSaNWuQkJCg//6NGzdixYoVOHDgAKZMmQJbW1v9ZuYqlQpLly7FmDFj8MknnyArKwubNm0CABQWFiIwMBBbtmyBn5+fBX8CRERERJXjnlJERERENWTatGno3bu3Qdm9SaqAgAD9102bNoWjoyPS09ORnp6O9u3bG9Tt1KkT8vPzcevWLWRmZmLo0KH6YwqFArNmzTJoq4KdnR0AoKysrPoujIiIiKgaMClFREREVEOcnZ3RvHlzs8fvn0Wl1WohkUhgZWVlVLdiPymtVmv0ffeTSqVGZZwcT0RERHUN95QiIiIiqiVnz57Vf33hwgUUFhbCx8cHLVu2REpKCtRqtf74H3/8AScnJzg6OqJ58+YG36vVahEaGorjx49bNH4iIiKiv4NJKSIiIqIaUlhYiPz8fKNXcXExAGD9+vX49ddfcfbsWURFRaFr165o0aIF+vfvD5VKhffeew/p6elISEjA8uXLMWzYMAiCgBEjRmDHjh34/vvvceHCBURHR0MURbRv376Wr5iIiIjowXH5HhEREVENWbhwIRYuXGhU/uabbwIABg4ciCVLliAnJwfBwcGYN28eAMDW1hZffvklFixYgPDwcDg5OWHUqFGYOHEiAODpp5/G3LlzsWLFCuTn56NDhw74/PPPoVQqLXdxRERERH8Tn75HREREVAtCQ0MxZcoUDBo0qLZDISIiIqoVXL5HREREREREREQWx6QUERERERERERFZHJfvERERERERERGRxXGmFBERERERERERWRyTUkREREREREREZHFMShERERERERERkcUxKUVERERERERERBbHpBQREREREREREVkck1JERERERERERGRxTEoREREREREREZHFMSlFREREREREREQWx6QUERERERERERFZ3P8DXaqIP4B3hSUAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.3733399212360382\n",
      "Test accuracy: 0.8732143044471741\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.93      0.88      4192\n",
      "           1       0.92      0.82      0.87      4208\n",
      "\n",
      "    accuracy                           0.87      8400\n",
      "   macro avg       0.88      0.87      0.87      8400\n",
      "weighted avg       0.88      0.87      0.87      8400\n"
     ]
    }
   ],
   "source": [
    "# Initialize a Sequential model\n",
    "model = Sequential()\n",
    "# Add a Dense layer with 64 units, ReLU activation function and L2 regularization\n",
    "model.add(Dense(64, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "# Add a BatchNormalization layer to normalize the activations of the previous layer\n",
    "model.add(BatchNormalization())\n",
    "# Add a Dropout layer to prevent overfitting\n",
    "model.add(Dropout(0.5))\n",
    "# Add another Dense layer with 32 units, ReLU activation function and L2 regularization\n",
    "model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "# Add another BatchNormalization layer\n",
    "model.add(BatchNormalization())\n",
    "# Add another Dropout layer\n",
    "model.add(Dropout(0.5))\n",
    "# Add another Dense layer with 16 units, ReLU activation function and L2 regularization\n",
    "model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "# Add another BatchNormalization layer\n",
    "model.add(BatchNormalization())\n",
    "# Add another Dropout layer\n",
    "model.add(Dropout(0.5))\n",
    "# Add the output Dense layer with 1 unit and sigmoid activation function\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Define early stopping to stop training when the validation loss has not improved for 10 epochs\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "# Compile the model with Adam optimizer, binary cross-entropy loss function and accuracy as the evaluation metric\n",
    "model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])\n",
    "\n",
    "# Train the model on the training data for 20 epochs with a batch size of 32 and validate on the testing data\n",
    "history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test),\n",
    "                    callbacks=[early_stopping])\n",
    "\n",
    "# Evaluate the model on the testing data and store the loss and accuracy in 'scores'\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "\n",
    "# Make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "# Convert the predicted probabilities to binary outputs\n",
    "y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
    "# Generate a classification report\n",
    "report = classification_report(y_test, y_pred_classes)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation accuracy values\n",
    "ax.plot(history.history['accuracy'], 'ro-', label='Training Accuracy')\n",
    "ax.plot(history.history['val_accuracy'], 'bv--', label='Test Accuracy')\n",
    "\n",
    "# Plot training & validation loss values\n",
    "ax.plot(history.history['loss'], 'go-', label='Training Loss')\n",
    "ax.plot(history.history['val_loss'], 'yv--', label='Validation Loss')\n",
    "\n",
    "ax.set_title('Combined Graph')\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.legend()\n",
    "\n",
    "# Show the figure\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Create a figure for the accuracy plot\n",
    "fig1, ax1 = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation accuracy values\n",
    "ax1.plot(history.history['accuracy'], 'ro-', label='Training Accuracy')\n",
    "ax1.plot(history.history['val_accuracy'], 'bv--', label='Test Accuracy')\n",
    "ax1.set_title('Model Accuracy')\n",
    "ax1.set_ylabel('Accuracy')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.legend()\n",
    "\n",
    "# Show the figure\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Create a figure for the loss plot\n",
    "fig2, ax2 = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "ax2.plot(history.history['loss'], 'ro-', label='Training Loss')\n",
    "ax2.plot(history.history['val_loss'], 'bv--', label='Validation Loss')\n",
    "ax2.set_title('Model Loss')\n",
    "ax2.set_ylabel('Loss')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.legend()\n",
    "\n",
    "# Show the figure\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Show the figure\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print the testing loss and accuracy\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])\n",
    "# Print the classification report\n",
    "print('Classification Report: \\n', report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:53:04.001254Z",
     "start_time": "2024-03-21T13:52:31.015265Z"
    }
   },
   "id": "c8745832a585d5ec",
   "execution_count": 62
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Multi-Layer Perceptron (MLP) visualization\n",
    "This code block is used to visualize the performance of a trained Multi-Layer Perceptron (MLP) model. It generates three types of visualizations:\n",
    "\n",
    "1. Weights and Biases Visualization: This visualization is used to understand the distribution of weights and biases in the model's layers. For each layer in the model, if the layer is a dense layer, it retrieves the weights and biases, and plots histograms of their values. The x-axis of the histogram represents the value of the weights/biases and the y-axis represents the frequency of these values.\n",
    "\n",
    "2. Confusion Matrix: A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It gives a more detailed breakdown of correct and incorrect classifications for each class.\n",
    "\n",
    "3. ROC Curve: The Receiver Operating Characteristic (ROC) curve is a plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is created by plotting the true positive rate (TPR) against the false positive rate (FPR).\n",
    "\n",
    "## Weights and Biases Evaluation\n",
    "\n",
    "The weights and biases of the MLP model layers were visualized to understand their distributions. The weights in all layers (dense, dense_1, dense_2, and dense_3) are not close to zero, indicating they are likely being updated during training and contributing to the model's learning. The weight distributions show a spread around zero, suggesting the model is capturing complex relationships in the data.\n",
    "\n",
    "The biases in dense and dense_2 introduce a slight positive bias to the activations in subsequent layers, potentially affecting the model's predictions. The biases in dense_1 and dense_3 are centered around zero, with a slight spread towards positive values, introducing a small positive shift in the activations of the next layer.\n",
    "\n",
    "The impact of these biases would depend on the network architecture and data. Overall, the model's weights and biases suggest that it is learning effectively from the training data."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4114f5c851874555"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Visualize Weights and Biases\n",
    "for layer in model.layers:\n",
    "    if 'dense' in layer.name:\n",
    "        weights, biases = layer.get_weights()\n",
    "        plt.figure(figsize=(10, 5))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.hist(weights.flatten())\n",
    "        plt.title(f'{layer.name} weights')\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.hist(biases.flatten())\n",
    "        plt.title(f'{layer.name} biases')\n",
    "        plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:53:04.696985Z",
     "start_time": "2024-03-21T13:53:04.001254Z"
    }
   },
   "id": "41091791008ff727",
   "execution_count": 63
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Confusion Matrix Evaluation\n",
    "\n",
    "The confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It gives a more detailed breakdown of correct and incorrect classifications for each class.\n",
    "\n",
    "The confusion matrix shows that the model has a good performance in distinguishing between positive and negative instances. The majority of the instances are correctly classified, with a small number of false positives and false negatives."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "618bc6deb5ea296b"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 2 Axes>",
      "image/png": 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iiIgZFcN1iCdPniQqKsoecgD79++nYsWKvPXWW/Tr189h+6SkJGrXrg2Av7+//WxUgNOnT3P69Gn8/f3x8fHB19fXYTwhIQFfX99Cf38IapmKiJhTMdyppnHjxvj5+TF+/HjGjRvHqVOniI2NZciQIQQGBrJ48WKWLl1K+/bt2bp1K++99x4rVqwAoGfPnvTu3ZuAgAAaN27M1KlTCQsLo3r16vbxmTNnUrlyZQBmzZrFgAEDijQ/S0FBQYFzX3LxcwuMKu4piEn8tCvu9zcScYLSTi5f3Los+f2NCilr/VOF3jYtLY2YmBi2bduGm5sbTz75JIMHD8ZisbBp0ybmzp3LsWPHqFq1KiNGjOCBBx6w77tu3Trmzp1LZmYmLVq0ICYmhgoVKgCQl5fHjBkzWLduHSVLlqRbt26MHDkSSxEqWAWiyJ+gQJRbxemB+NhSpx0ra13h7wZzO1PLVETEhIpSOZmFTqoRERFBFaKIiCmpQjRSIIqImJHy0EAtUxEREVQhioiYklqmRgpEERETUiAaqWUqIiKCKkQREVNShWikQBQRMSEFopFapiIiIqhCFBExJxWIBgpEERETUsvUSC1TERERVCGKiJiSKkQjBaKIiAkpEI3UMhUREUEVooiIKalCNFIgioiYkfLQQC1TERERVCGKiJiSWqZGCkQRERNSIBqpZSoiIoIqRBERU1KFaKRAFBExI+WhgVqmIiIiqEIUETEltUyNFIgiIiakQDRSy1RERARViCIipqQK0UiBKCJiQgpEI7VMRUREUIUoImJOKhANFIgiIiaklqmRWqYiIiKoQhQRMSVViEYKRBERE1IgGqllKiIigipEERFzUoFooEAUETEhtUyN1DIVERFBgXhHql3diw/mRZL+zSz+/fFkRvRpax9rEViHb1aNJuPbWWx/ZyzhofUc9n2ud1sObpjI6a9nsGjik9zlZrWPlXd3Y/6EXhzbNI0fv5jO4klPUt7d7Za9Lrn9/Xj8OEOeHkiz4EA6tA3jzTeW2MdOnjzBoIH9CA0OoMvDD/HtN1sd9t2+7Vsee7QzoUH+PNW/DydPnLjV0zcVi8XitKUojh8/zsCBAwkMDCQsLIwlS/77GTlx4gT9+vUjICCAhx56iK1bHT8j3377LZ07d8bf358+ffpw4jefkTfffJNWrVoRGBjI+PHjycrKKtLcFIh3GIvFwvq5z5Dx08806/kPhk17hzFPPcjfHwzGu4I7a+cMZu3GBIIfn0b8Z7tZ89ogqlbyAGBg1xZED3mIl+M+pE2/V/GtVJ43p/e3H/v1F3vQ5N6qdBm2gEeGzqP+3ZWZP6FnMb1Sud3k5+cTNXQQFSpWYHX8el58eRL/XLSAjzd8SEFBASOGReLp5cXbq+Pp/MijjHg2itOpqQCcTk3luWGRPNrlMVatXkuFihV5bvhQCgoKivlV3bmKIxDz8/MZNGgQFSpUYP369UyaNIkFCxbw4YdXPyORkZF4eXkRHx/Po48+SlRUFKn/+YykpqYSGRnJY489xtq1a6lYsSJDh/73M7Jx40bi4uKYPHkyy5cvJzExkdjY2CK9JwrEO4yPZ1n2HjrJ8GmrSfkxnY1bD/DVzkM0D6xN84Da5Obm89qKzzl26hyxb3xGdk4uIU1qAfBMj9bMeetz3v00gYNHzvD0hLd4qJUfdWtWokxpK13aBjDiH+/y/cET7Ek6yaiZ8TwS7o+rVV9FC5w7l0G9+g14ccJEatasRav7WxPSrDnf705g547tnDhxgpdenkztOnUY+PRg/P0DeG99PADr4tfg16gRffsN4J576jJ5ynRST53iu107i/lViTNlZGTQoEEDJk6cSK1atWjdujXNmzcnISGB7duvfkYmT55MnTp1GDx4MAEBAcTHX/2MrFmzhkaNGjFgwADq1q3L9OnTOXXqFDt3Xv2MrFixgr59+xIeHk6TJk2YNGkS8fHxRaoSb4tA/Omnn0hLS+PixYvFPZW/vDMZF+k9dhmXLucA0Ny/Ni3uu4ct3yVzLvMXvCq482gbfwAeDmtC2btc2Z989V9gd1f1Ytf+Yw7HSv/pEqFN7ia/oIDHnl1I4qFTDs/n4lIS9zKut+bFyW3N27sSsbNmc9dd7hQUFPD97gR2f7eL4JAQ9u1NpEHDhpQpU8a+feB9QSTu2QPA3r2J3BcUbB9zc3OjQUM/9ibuucWvwjyKo0KsVKkSs2fPxt396mckISGBXbt2ERISQmJiIg1/8xkJCgpiz38+I4mJiQQHO35G/Pz82LNnD3l5eezbt89hPCAggCtXrpCUlFTo+RXbP+0/++wzVq5cyd69e8nJybGvL126NI0aNaJv3760a9euuKZ3Rzj08WRqVKnIR5v3sf7zPeTnF7Dwnc38X+xA8vMLcHEpydMT3iL5+FkAzp6/iK+3h33/MqWtVCx3F14e7mTnXOFf3x50OH5krzD2/vsk5y78citflvwFdGzfhtOnU7m/dTjt2ndgxj+m4V2pksM2np6epKWdASAjPd0wXtHTk7QzZ27ZnE3HiSeZ2mw2bDabwzqr1YrVar3OHtCmTRtSU1MJDw+nQ4cOTJs2jUrX+Iyc+c9nID09/brjFy9eJCcnx2HcxcUFDw8P+/6FUSyBuGzZMuLi4njqqaeIiorC09MTq9WKzWYjIyOD7777jrFjx/Lss8/Su3fv4pjiHaHnC0vw8SzH3PF/J/aFrrwc9yG1qnkxZdHHfPL1fh5tE8Cs0d3Yue8Y/z6WxtrPdjNqwAN8uyeFY6fO8crIxwAoVaqk4dhD/n4/XdsH8kjk/Fv9suQvYNbsuWRkZDA1ZiKxr0wnOzsLaynHvxxLWa1c+c9fotnZWYa/PK1WK7Yrjn/Jyu1p0aJFxMXFOayLiopi2LBh191n7tyrn5GJEycyffp0srKu8xn4z2fkRuPZ2dn2x9fbvzCKJRDfeOMNXnnllWtWgHXq1CE0NJR69eoRExOjQPwTdh/4EYDRs1xYNrUvv2TZsFhg+uJPAdiTdJKmjWsR2SuMZ6etZvriT7m7qhe710ZzJTePpfHfsPffJ/n5l2yH4w56vBWzRndj9Mx1fL698O0IMQ+/Ro0BsOXkMG7MC0Q81tXwXc4Vm43SbqUBsFpdDX9x2Ww2ypYtd2smbELOvA5x8ODB9O/f32HdjapDgMaNr35GcnJyeOGFF+ja1fgZsdlslC599TPi6nrtz0i5cuVwdXW1P/7tuJtb4c+EL5bvELOzs6lWrdoNt/Hx8eHnn3++RTO6c1SqWJaHw5o4rDt45Ayu1lI0vrcq+/7t+B1gYtIJalSpCMDlbBtPjnmDKq1HU73NOEbOWEv1KhU5nnrevv1zvdsyZ/zfiZ79PvPe/uqmvx756ziXkcEXn29yWFe7zj1cuXIFLy9vzmVkOIxlZGTg5XW1xVXJx8cwfi4jA29v75s7aRNz5neIVqsVd3d3h+VagZiRkcGmTY6fkXvuufoZ8fb2JuMan5Ff26A+Pj7XHPf29sbDwwNXV1eH8dzcXC5cuFCkz1CxBGL79u0ZO3Ys3333Hbm5uQ5j+fn57N69m/Hjx9OhQ4fimN5fWq2qnrwz6yl8vcvb1wU2qM7Z8z9zOj2T+rWrOGx/792VOXbqHABTn32UJx4O5eKlbH7+JZughjUo716a7YlHAHji4VCmP9+FUbFrmf3W57fuRclfwqlTJ3n+2SjS0tLs6w4c2E+FihUJvC+Igwd+sLe2AL7fnUAT/6sneDVp4s/3uxPsY1lZWSQdPEDjJv637gXITXfy5Emiohw/I/v376dixYoEBQXxww+On5GEhAT8//MZ8ff3JyHB8TNy4MAB/P39KVGiBI0bN3YY37NnDy4uLtSvX7/Q8yuWQJw4cSJBQUEMHDiQgIAAWrZsSZs2bWjZsiVNmjRhwIAB3Hfffbz88svFMb2/tO9+OM73B0+wcOKT1K9dmQ4tGzLtuS7MWLKRN9d/y4MtGjLsiXBqVfUkqlcYD/ytAYvf/RqA0+mZRA/qSFDDGgQ2qM4bU/vyzzVb+eniZSqUK8NrYx7nrQ+2s2ZjAj6eZe1LiRK6BZRcbZM2bOjHyy+OJ+XwYbZ8vZnXZsby9KAhBDcNwadyFSa8OI7Dh5NZ+s/F7N+3ly6PdQMg4rGu7Pl+N0v/uZjDh5OZ8OI4qlatRtOQ0GJ+VXcui8V5S2E1btwYPz8/xo8fz+HDh9m8eTOxsbEMGTKEkJAQqlSpwrhx40hOTmbx4sXs3buXbt2ufka6du3K7t27Wbx4McnJyYwbN45q1aoRGnr1M9KrVy+WLl3Kpk2b2Lt3LxMnTqR79+5FaplaCorxytesrCySkpJIT08nKysLV1dXfHx8aNCggb1v/Ee4BUY5cZZ/PVW8y/PamMcJC6nH5WwbC97ZTOwbnwHQqXVjXnqmE3Wqe/PvY2m8OPd9vtxxCIASJSy88vxj9HioKfkFBbz90U6i57xPXl4+j3cIYsU/+l/z+eo9NIEfT5+/5tid7qddcb+/kYmcPZvG9Kkx7Ny+DTc3N3r0epKBTw/GYrHw4/HjTJwQzb69iVSvUZPRY8fTrPnf7Ptu3bKZGf+YRtqZM/gHBDJhUgzVqlUvxldzeynt5DM+6o761GnHSo59sNDbpqWlERMTw7ZtVz8jTz75JIMHX/2MHD9+nOjoaBITE6lZsybjx4/nb3/772dk8+bNTJs2jTNnzhAYGEhMTAzVq//3M7J48WLefPNNbDYbDzzwAC+//LL9+8XCKNZAvFnMHohy6ygQ5Va5UwLxdqZbjIiImJB+7MJIgSgiYkL6+Sej2+LWbSIiIsVNFaKIiAmpQDRSIIqImJAulzJSy1RERARViCIipqSWqZEqRBEREVQhioiYki67MFIgioiYkPLQSC1TERERVCGKiJiSWqZGCkQRERNSIBqpZSoiIoIqRBERU1KBaKRAFBExIbVMjdQyFRERQRWiiIgpqUA0UiCKiJiQWqZGapmKiIigClFExJRUIBopEEVETEgtUyO1TEVERFCFKCJiSioQjRSIIiImpJapkVqmIiIiqEIUETElFYhGCkQRERNSy9RILVMRERFUIYqImJIKRCMFooiICallaqSWqYiICKoQRURMSQWikQJRRMSE1DI1UstUREQEVYgiIqakCtFIgSgiYkLKQyO1TEVERFCFKCJiSmqZGikQRURMSHlopJapiIgIqhBFRExJLVMjVYgiIiZksThvKYq0tDSGDx9OSEgIrVq1Yvr06eTk5AAwZcoU6tWr57CsXLnSvu+GDRto164d/v7+REZGcv78eftYQUEBM2fOpFmzZoSEhDBjxgzy8/OLNDdViCIicksUFBQwfPhwypUrx6pVq8jMzGT8+PGUKFGCMWPGkJKSwsiRI+nSpYt9H3d3dwD27t1LdHQ0kyZNon79+kydOpVx48axaNEiAJYtW8aGDRuIi4sjNzeXUaNG4enpycCBAws9P1WIIiImVMJicdpSWEeOHGHPnj1Mnz6dunXrEhwczPDhw9mwYQMAKSkpNGzYEG9vb/vi5uYGwMqVK+nYsSMRERHUr1+fGTNmsHnzZk6cOAHAihUrGD58OMHBwTRr1owXXniBVatWFe09KdLWIiJyRyiOlqm3tzdLlizBy8vLYf2lS5e4dOkSaWlp1KpV65r7JiYmEhwcbH9cpUoVfH19SUxMJC0tjdOnT9O0aVP7eFBQEKdOneLs2bOFnp8CUURE/hSbzWYPtV8Xm81m2K5cuXK0atXK/jg/P5+VK1fSrFkzUlJSsFgsLFy4kPvvv59HHnmE9evX27c9e/YslSpVcjiep6cnZ86cIT09HcBh/NfQPXPmTKFfh75DFBExIWeeZbpo0SLi4uIc1kVFRTFs2LAb7hcbG8uBAwdYu3YtP/zwAxaLhdq1a/Pkk0+ya9cuXnrpJdzd3Wnfvj3Z2dlYrVaH/a1WKzabjezsbPvj/x0DrhnM16NAFBExoRJOvOpi8ODB9O/f32Hdb8Prt2JjY1m+fDmvvfYa9957L3Xr1iU8PBwPDw8A6tevz7Fjx3j77bdp3749rq6uhnCz2Wy4ubk5hJ+rq6v9z4D9O8jCUMtURET+FKvViru7u8Nyo0CMiYlh2bJlxMbG0qFDB+BqxfprGP6qdu3apKWlAeDj40NGRobDeEZGBt7e3vj4+ADYW6f/+2dvb+9Cvw4FooiICVksFqctRREXF8c777zDq6++SqdOnezr58yZQ79+/Ry2TUpKonbt2gD4+/uTkJBgHzt9+jSnT5/G398fHx8ffH19HcYTEhLw9fU1fO94I2qZioiYUHHcqCYlJYX58+czaNAggoKCHCq68PBwFi9ezNKlS2nfvj1bt27lvffeY8WKFQD07NmT3r17ExAQQOPGjZk6dSphYWFUr17dPj5z5kwqV64MwKxZsxgwYECR5qdAFBGRW+Lzzz8nLy+PBQsWsGDBAoexQ4cOMWfOHObOncucOXOoWrUqs2bNIjAwEIDAwEAmT57M3LlzyczMpEWLFsTExNj3HzhwIOfOnSMqKoqSJUvSrVs3Q8X5eywFBQUFf/pV3mbcAqOKewpiEj/tivv9jUScoLSTy5fOi3Y57VgbBjf9/Y3+AlQhioiYkDPPMr1T6KQaERERVCGKiJiSfv7JSIEoImJCykMjtUxFRERQhSgiYkpF+dkms1AgioiYkPLQqMgt00uXLjFz5kyOHDlCfn4+o0ePJiAggF69enHq1KmbMUcREZGbrsiBOGnSJDZv3ozFYuHDDz/ks88+Y9q0aXh5eTFp0qSbMUcREXGy4rqX6e2syC3TzZs3s2LFCu6++25iY2MJDw/noYceomHDhnTp0uVmzFFERJzsDsoxpylyhVhQUECpUqXIzs5m27ZttG7dGoDMzEzKlCnj9AmKiIjcCkWuEJs1a8ZLL71EmTJlKFGiBO3atWPbtm3ExMTQpk2bmzFHERFxMp1lalTkCnHatGk0bNgQq9XKvHnzcHd359ChQ7Ru3Zro6OibMUcREXEyixOXO0WRK8SyZcvy4osvOqx75JFHqFChwh315aqIiJhLkSvEtLQ0RowYwcGDB8nJyeHJJ5+kRYsWtG3blqSkpJsxRxERcTKdZWpU5ECcOHEi58+fx8PDg3Xr1vHvf/+bd955h/DwcIcfaxQRkdtXCYvzljtFkVum27dvZ926dVSpUoVNmzbRtm1b/P39qVixIp07d74ZcxQREbnpilwhurq6kpOTQ2ZmJjt27CAsLAyAkydPUr58eWfPT0REbgK1TI2KXCG2a9eO5557jtKlS1O+fHnCwsL4+OOPmTZtmi7MFxH5i7iDcsxpihyIEydOZOXKlZw6dYq///3vuLq6YrPZGDJkCE888cTNmKOIiMhNV+RAdHFxoV+/fg7rIiIiALhy5QqlSpVyxrxEROQmupNanc5S5EDMyMhg0aJFHD58mLy8PODq7dyuXLlCSkoKu3btcvokRUTEue6ks0Odpcgn1YwfP54tW7bQuHFjdu/ebT/DdO/evQwbNuxmzFFEROSmK3KFuGvXLt544w0CAwP55ptvCAsLIygoiMWLF/P111/Tp0+fmzFPERFxIrVMjf7Qr134+PgAcM8993DgwAEAOnbsyL59+5w7OxERuSl0L1OjIgdiw4YNef/99wFo0KAB33zzDXD1OkQREZG/qiK3TEeOHMmQIUNwc3Pj0UcfZcmSJTz88MOkpqbyyCOP3Iw5ioiIk+nnn4yKHIhBQUF8+eWXZGdnU6FCBeLj49m0aRMeHh507NjxZsxRREScTHloVORABHB3d8fd3R0AHx8fXZAvIiJ/eYUKxPr16xf6jKSDBw/+qQmJiMjNp7NMjQoViCtWrLjZ8xARkVtIeWhUqEAMCQlxeLx582ZKlChBq1atAJg6dSqtWrXi/vvvd/4MRUREboEiX3bx1ltvMWLECDIyMuzrXFxceO6553j33XedOjkREbk5SlgsTlvuFEUOxGXLljFr1iyHn3oaM2YMsbGxLF682KmTExGRm8Nicd5ypyhyIP7000/UqFHDsP7uu+92qBpFRET+SoociEFBQbz++utkZWXZ1+Xk5LBw4UICAwOdOjkREbk5fvur939muVMU+TrECRMmMGDAAFq2bEmtWrUA+PHHH/Hy8mL+/PnOnt8f8sNnM4t7CmISFR5fUtxTEJPIWv+UU49X5GrIBIociDVq1ODjjz9my5YtHDt2DBcXF2rVqkXLli0pWbLkzZijiIjITfeH7lRjtVpp27ats+ciIiK3yJ3U6nSWPxSIIiLy11ZCeWigNrKIiAiqEEVETEkVotEfqhDz8vL46quvePPNN7l48SKJiYn8/PPPzp6biIjcJMV12UVaWhrDhw8nJCSEVq1aMX36dHJycgA4ceIE/fr1IyAggIceeoitW7c67Pvtt9/SuXNn/P396dOnDydOnHAYf/PNN2nVqhWBgYGMHz/e4fLAwihyIJ4+fZqHH36Y8ePHExsbS2ZmJkuWLKFjx44cOnSoqIcTERGTKCgoYPjw4WRlZbFq1Spee+01vvzyS2bPnk1BQQGRkZF4eXkRHx/Po48+SlRUFKmpqQCkpqYSGRnJY489xtq1a6lYsSJDhw6loKAAgI0bNxIXF8fkyZNZvnw5iYmJxMbGFml+RQ7EyZMnExQUxJYtW7BarQC8+uqr/O1vf2PKlClFPZyIiBSDEhbnLYV15MgR9uzZw/Tp06lbty7BwcEMHz6cDRs2sH37dk6cOMHkyZOpU6cOgwcPJiAggPj4eADWrFlDo0aNGDBgAHXr1mX69OmcOnWKnTt3Ald/lalv376Eh4fTpEkTJk2aRHx8fJGqxCIH4nfffceAAQMcrjksVaoUQ4cOZf/+/UU9nIiIFIPiuJept7c3S5YswcvLy2H9pUuXSExMpGHDhpQpU8a+PigoiD179gCQmJhIcHCwfczNzQ0/Pz/27NlDXl4e+/btcxgPCAjgypUrJCUlFXp+RT6ppnTp0pw7d467777bYf3Ro0dxd3cv6uFEROQvzmazYbPZHNZZrVZ7F/FX5cqVs/9sIEB+fj4rV66kWbNmpKenU6lSJYftPT09OXPmDMANxy9evEhOTo7DuIuLCx4eHvb9C6PIFWKPHj2YMGECX331FXA1COPj43nppZfo1q1bUQ8nIiLFwJk//7Ro0SKCgoIclkWLFv3uHGJjYzlw4AAjRowgKyvLEKBWq9UetDcaz87Otj++3v6FUeQKMTIyknLlyjFx4kSysrIYNGgQnp6e9OvXj4EDBxb1cCIiUgyceRH64MGD6d+/v8O634bTb8XGxrJ8+XJee+017r33XlxdXblw4YLDNjabjdKlSwPg6upqCDebzUa5cuVwdXW1P/7tuJubW6Ffxx+6DrF379707t2by5cvk5eXR9myZf/IYURE5A5wrfbojcTExPD2228TGxtLhw4dAPDx8eHw4cMO22VkZNjboD4+PoafGMzIyKBBgwZ4eHjg6upKRkYGderUASA3N5cLFy7g7e1d6HkVORDfe++9G45HREQU9ZAiInKLFdetTOPi4njnnXd49dVXefDBB+3r/f39Wbx4MdnZ2faqMCEhgaCgIPt4QkKCffusrCwOHDhAVFQUJUqUoHHjxiQkJBAaGgrAnj17cHFxoX79+oWeW5EDce7cuQ6P8/LyOHfuHC4uLjRp0kSBKCLyF1CiGBIxJSWF+fPnM2jQIIKCgkhPT7ePhYSEUKVKFcaNG8fQoUP58ssv2bt3L9OnTwega9euLF26lMWLFxMeHs68efOoVq2aPQB79erFhAkTuPfee6lUqRITJ06ke/fuN7dl+sUXXxjW/fLLL0yYMIF69eoV9XAiImISn3/+OXl5eSxYsIAFCxY4jB06dIj58+cTHR3NY489Rs2aNZk3bx6+vr4AVKtWjddff51p06Yxb948AgMDmTdvnv1OOZ06deLUqVNMmDABm83GAw88wKhRo4o0P0vBr5f5/0nHjh2jZ8+ebNu2zRmH+1OOpGcX9xTEJPwGrSzuKYhJOPsHgidsTHbasSZ3qOu0YxUnp93cOykpifz8fGcdTkREbiLd3NuoyIHYu3dvw81cf/nlFw4dOkS/fv2cNS8REZFbqsiB+OsXmP/LarXywgsv0Lx5c6dMSkREbq7iOKnmdlfkQLxw4QJ9+vShRo0aN2M+IiJyCygPjYp8s4IPPviAEiWceY8DERGR4lfkCrFfv35MmjSJfv364evra79lzq9+PUVWRERuXzqpxugPX5i/ZcsWAPsJNgUFBVgsFg4ePOjE6YmIyM1gQYn4W4UKxF27dhEYGIiLiwuff/75zZ6TiIjILVeoQOzTpw9bt27F09OTqlWr3uw5iYjITaaWqVGhAtFJN7MREZHbhALRqNCni/72YnwREZE7SaFPqunatWuhLrfQd4wiIrc/FTlGhQ7E/v3764eARUTuEGqZGhUqEC0WC506dcLT0/Nmz0dERKRY6KQaERETUsfUqFCB2KVLF8MdaURE5K9LN/c2KlQgTp8+/WbPQ0REpFg57QeCRUTkr0Mn1RgpEEVETEgdUyP9jpOIiAiqEEVETKmEfu3CQIEoImJCapkaqWUqIiKCKkQREVPSWaZGCkQRERPShflGapmKiIigClFExJRUIBopEEVETEgtUyO1TEVERFCFKCJiSioQjRSIIiImpPagkd4TERERVCGKiJiSRT1TAwWiiIgJKQ6N1DIVERFBFaKIiCnpOkQjBaKIiAkpDo3UMhUREUEVooiIKaljaqRAFBExIV12YaSWqYiICApEERFTKuHE5Y+w2Wx07tyZHTt22NdNmTKFevXqOSwrV660j2/YsIF27drh7+9PZGQk58+ft48VFBQwc+ZMmjVrRkhICDNmzCA/P79Ic1LLVETEhIqzZZqTk8PIkSNJTk52WJ+SksLIkSPp0qWLfZ27uzsAe/fuJTo6mkmTJlG/fn2mTp3KuHHjWLRoEQDLli1jw4YNxMXFkZuby6hRo/D09GTgwIGFnpcqRBERuWUOHz5M9+7d+fHHHw1jKSkpNGzYEG9vb/vi5uYGwMqVK+nYsSMRERHUr1+fGTNmsHnzZk6cOAHAihUrGD58OMHBwTRr1owXXniBVatWFWluCkQREROyOHEpip07dxIaGsrq1asd1l+6dIm0tDRq1ap1zf0SExMJDg62P65SpQq+vr4kJiaSlpbG6dOnadq0qX08KCiIU6dOcfbs2ULPTS1TERETcmbL1GazYbPZHNZZrVasVqth2169el3zGCkpKVgsFhYuXMjXX3+Nh4cH/fv3t7dPz549S6VKlRz28fT05MyZM6SnpwM4jHt5eQFw5swZw37Xo0AUEZE/ZdGiRcTFxTmsi4qKYtiwYYU+xpEjR7BYLNSuXZsnn3ySXbt28dJLL+Hu7k779u3Jzs42BKzVasVms5GdnW1//L9jgCGob0SBKCJiQs78vmzw4MH079/fYd21qsMbiYiIIDw8HA8PDwDq16/PsWPHePvtt2nfvj2urq6GcLPZbLi5uTmEn6urq/3PgP07yMJQIIqImJAzW6bXa48WhcVisYfhr2rXrs327dsB8PHxISMjw2E8IyMDb29vfHx8AEhPT6datWr2PwN4e3sXeg46qUZERIrdnDlz6Nevn8O6pKQkateuDYC/vz8JCQn2sdOnT3P69Gn8/f3x8fHB19fXYTwhIQFfX99Cf38IqhBFREzpdrtxW3h4OIsXL2bp0qW0b9+erVu38t5777FixQoAevbsSe/evQkICKBx48ZMnTqVsLAwqlevbh+fOXMmlStXBmDWrFkMGDCgSHNQIIqImNDtdivTJk2aMGfOHObOncucOXOoWrUqs2bNIjAwEIDAwEAmT57M3LlzyczMpEWLFsTExNj3HzhwIOfOnSMqKoqSJUvSrVs3Q8X5eywFBQUFznxRt4Mj6dnFPQUxCb9BK39/IxEnyFr/lFOP9/6+M0471qONKzvtWMVJFaKIiAmVuO2apsVPgSgiYkK3W8v0dqCzTEVERFCFKCJiSha1TA0UiCIiJqSWqZFapiIiIqhCFBExJZ1laqRAFBExIbVMjdQyFRERQRWiiIgpqUI0UiCKiJiQLrswUstUREQEVYgiIqZUQgWigQJRRMSE1DI1UstUREQEVYgiIqaks0yNFIgiIiaklqmRWqYiIiKoQhQRMSWdZWqkQLzD/evj93l12gTDeovFwsdb9nA0JZm4mVM4fOggvtWqM+S5MfjfFwLAzxcv0v2hVg77lSvvweqPNt+Sucvtr3blcswe9Dea1/fhp0s5LPj4B157b5/DNuXKlOL7ud14edV3rPwy2XCMx/52N6tGtcWty5IiHVf+HLVMjRSId7j723YgKLSF/XFebi5jn32akL/dzy+XfiZ6xGBCW4QxMjqGzzduIGb88yx5+308Knjy47EUypX3YMGKePv+JfTPSvkPiwXWv/gACYczaDZyPfdUKcfy59uQeu4yq7ek2Leb0icEX8+7rnmM8mWszBzY/A8dV8TZ9B3iHc7VtTQVPb3syxeffURBQQH9hzzLpk8+oLRbGaJeiMa3Wg16DxyKb7Ua/DvpAAAnjh+lavWaDvt7VPAs5lcktwsfDzf2Hj3P8IXfkHL6Iht3n+Srfak0b+Bj3+ZvDXwIb+zL6fOXr3mMaf1COJp2scjHlT/PYnHecqdQIJrIzxczWbNqGf2HPIvVamXv99/RvGUYJUuWtG8zd8n/EdL8apv0x2MpVK1es7imK7e5Mz9l0XvWF1zKvgJA8/o+tGhYmS37TwNgdSnBvKGteG7xt9hy8wz7t/SrzP1+VXhl7Z4iHVecw+LE5U6hlqmJbFj/LhW9vGkV3h6AM6mnqNegEXNemcyOb76iUmVfno4aiV+TQABOHDtKbm4uzz7di3PpZ/Hzv4/Bw0ZR0cu7OF+G3IYOLfo7NSqV5aNdP7J++zEARncLIPFIBp8nnjJsb3UpwbxnWv4nLPOLdFyRm0UVokkUFBSwccM6Huna074uK+sy765aRkUvLybPnEfjgCCinx9CetoZAE78eJTLly8xaNgoxk6awfmMdF4ePYy8POO/9sXces74nMembsT/7orE9m9G/WoePNWhAaPf2HHN7cd1D2TPkXPXDMsbHVecp4TF4rTlTqEK0ST+nfQDGWfP0rrtg/Z1JUuWpE7devQeOBSAe+5twO5d2/h84wZ69HmKhW+tw2Kx4OpaGoDoKTN54tF2HDqwj4aNA4rjZchtandKBgCjS21n2Yhwgup6EfN2AmczswzbNqxRgQEP1Kfps+uKfNyxy3dw5QYVpRTenRNjzlNsgbhr165Cb9u0adObOBNzSNjxDY0C7qNsuXL2dRU9vahW826H7apVr0n62asVYunSbg5jHhU8KVuuPOfSz978Ccttr1J5N0LrVeLDncft6w6euIBrqZKE1vPBr0ZF/tEvFIAyri68PqQF3VrWZuehs1R0d+WHBd0BKPmfM5fT/68vwxZu5YvE1Oset5xbKc79nHMLX6WYSbEF4uTJkzl8+DBwtZ13PRaLhYMHD96qad2xrlXV1fdrwr49CQ7rThw/Rlj7jvzyyyX6dX2QF6e9ar8uMSM9jYuZFwwhKuZUy8edd8a0o+7Tb5P6n7NIA+t4cf7nbFqN/sBh289iOjH/ox945+vDZNnyeOfr/14+EXKvN8tGhBP6/HrOXsiiYQ2Pax73bGaWwtCZVCIaFFsgxsfH8/zzz3Py5ElWr16Nq6trcU3FFI4dSSH8gU4O6x6KeJwP4t9m5dIFtOnQiU2ffsiZ1JO06dCJu+5yx8//PhbPncnwMRMoWaIkC+e8QlBoC+6uU7eYXoXcTr47nMH3KRksjLqf0W9sp2Yld6b1DWHau99z5IzjpRS5+fmczcyyB9xPl/4bbFX/c43ir/tc77gzfnM2qvw5ujDfqNhOqrFarbz66qsAzJ49u7imYRoXzp+jbNlyDut8KvsyZdYCdnyzmSF9urLjm81Mio3Dy/vq9V4jo6dQ5976TBgVxehhA/Gp4svoCdOLY/pyG8rPL+Dx6f/ick4uX/3jERZEtmL+Rz8wb8MPt+VxRX6PpeBG/cpbICUlhZ07d9KzZ8/f37iQjqRnO+1YIjfiN2hlcU9BTCJr/VNOPd7OI5lOO1ZI7fJOO1ZxKvazTOvUqUOdOnWKexoiIqaihqmRrkMUERHhNqgQRUSkGKhENFAgioiYkM4yNVLLVEREBFWIIiKmdAfdgtRpVCGKiIigClFExJRUIBopEEVEzEiJaKCWqYiICKoQRURMSZddGKlCFBExIYvFecsfYbPZ6Ny5Mzt27LCvO3HiBP369SMgIICHHnqIrVu3Ouzz7bff0rlzZ/z9/enTpw8nTpxwGH/zzTdp1aoVgYGBjB8/nqws4w9U34gCUUREbqmcnByef/55kpOT7esKCgqIjIzEy8uL+Ph4Hn30UaKiokhNTQUgNTWVyMhIHnvsMdauXUvFihUZOnSo/fd0N27cSFxcHJMnT2b58uUkJiYSGxtbpHkpEEVETMjixKUoDh8+TPfu3fnxxx8d1m/fvp0TJ04wefJk6tSpw+DBgwkICCA+Ph6ANWvW0KhRIwYMGEDdunWZPn06p06dYufOnQCsWLGCvn37Eh4eTpMmTZg0aRLx8fFFqhIViCIiZlRMibhz505CQ0NZvXq1w/rExEQaNmxImTJl7OuCgoLYs2ePfTw4ONg+5ubmhp+fH3v27CEvL499+/Y5jAcEBHDlyhWSkpIKPTedVCMiIn+KzWbDZrM5rLNarVitVsO2vXr1uuYx0tPTqVSpksM6T09Pzpw587vjFy9eJCcnx2HcxcUFDw8P+/6FoQpRRMSELE78z6JFiwgKCnJYFi1aVKT5ZGVlGQLUarXag/ZG49nZ2fbH19u/MFQhioiYkDPvZTp48GD69+/vsO5a1eGNuLq6cuHCBYd1NpuN0qVL28d/G242m41y5crh6upqf/zbcTc3t0LPQRWiiIj8KVarFXd3d4elqIHo4+NDRkaGw7qMjAx7G/R6497e3nh4eODq6uownpuby4ULF/D29i70HBSIIiImVFxnmV6Pv78/P/zwg739CZCQkIC/v799PCEhwT6WlZXFgQMH8Pf3p0SJEjRu3NhhfM+ePbi4uFC/fv1Cz0GBKCJiRrdZIoaEhFClShXGjRtHcnIyixcvZu/evXTr1g2Arl27snv3bhYvXkxycjLjxo2jWrVqhIaGAldP1lm6dCmbNm1i7969TJw4ke7du6tlKiIify0lS5Zk/vz5pKen89hjj/HBBx8wb948fH19AahWrRqvv/468fHxdOvWjQsXLjBv3jws//kytFOnTgwePJgJEyYwYMAAmjRpwqhRo4o0B0vBr5f530GOpGf//kYiTuA3aGVxT0FMImv9U0493g+nfnHasfyq3uW0YxUnnWUqImJCzjzL9E6hlqmIiAiqEEVETEkFopECUUTEjJSIBmqZioiIoApRRMSULCoRDRSIIiImpLNMjdQyFRERQRWiiIgpqUA0UiCKiJiREtFALVMRERFUIYqImJLOMjVSIIqImJDOMjVSy1RERARViCIipqQC0UiBKCJiRkpEA7VMRUREUIUoImJKOsvUSIEoImJCOsvUSC1TERERVCGKiJiSCkQjBaKIiBkpEQ3UMhUREUEVooiIKeksUyMFooiICeksUyO1TEVERFCFKCJiSioQjRSIIiImpJapkVqmIiIiqEIUETEplYi/pUAUETEhtUyN1DIVERFBFaKIiCmpQDRSIIqImJBapkZqmYqIiKAKUUTElHQvUyMFooiIGSkPDdQyFRERQRWiiIgpqUA0UiCKiJiQzjI1UstUREQEBaKIiClZnPifovjXv/5FvXr1HJbhw4cDcODAAR5//HH8/f3p2rUr+/fvd9h3w4YNtGvXDn9/fyIjIzl//rzT3g9QIIqImJPFiUsRHD58mPDwcLZu3WpfpkyZwuXLlxk0aBDBwcGsW7eOwMBABg8ezOXLlwHYu3cv0dHRREVFsXr1ai5evMi4ceP+9NvwvxSIIiJyy6SkpHDvvffi7e1tX8qVK8fHH3+Mq6sro0ePpk6dOkRHR3PXXXfx6aefArBy5Uo6duxIREQE9evXZ8aMGWzevJkTJ044bW4KRBEREyqmApGUlBRq1aplWJ+YmEhQUBCW/5ztY7FYuO+++9izZ499PDg42L59lSpV8PX1JTExsYgzuD4FooiICVkszltsNhuXLl1yWGw2m+E5CwoKOHr0KFu3bqVDhw60a9eOmTNnYrPZSE9Pp1KlSg7be3p6cubMGQDOnj17w3Fn0GUXIiLypyxatIi4uDiHdVFRUQwbNsxhXWpqKllZWVitVmbPns3JkyeZMmUK2dnZ9vX/y2q12oM1Ozv7huPOoEAUETEhZ97LdPDgwfTv399h3W/DC6Bq1ars2LGD8uXLY7FYaNCgAfn5+YwaNYqQkBBDuNlsNkqXLg2Aq6vrNcfd3Nyc9joUiCIiJuTMC/OtVus1A/BaPDw8HB7XqVOHnJwcvL29ycjIcBjLyMiwt0l9fHyuOe7t7f3HJ/4b+g5RRERuiS1bthAaGkpWVpZ93cGDB/Hw8CAoKIjvv/+egoIC4Or3jbt378bf3x8Af39/EhIS7PudPn2a06dP28edQYEoIiK3RGBgIK6urrz44oscOXKEzZs3M2PGDJ566ikefPBBLl68yNSpUzl8+DBTp04lKyuLjh07AtCzZ0/ef/991qxZQ1JSEqNHjyYsLIzq1as7bX6Wgl/j+A5yJD27uKcgJuE3aGVxT0FMImv9U0493oWsPKcdy8OtZKG3TU5OZtq0aezZs4e77rqLHj16EBkZicViYe/evbz88sukpKRQr149Jk2aRMOGDe37rlu3jrlz55KZmUmLFi2IiYmhQoUKTnsdCkSRP0GBKLfKnRKItzOdVCMiYkLOPMv0TqFAFBExIf38k5FOqhEREUEVooiIKalANFIgioiYkRLRQC1TERERVCGKiJiSzjI1UiCKiJiQzjI1UstUREQEVYgiIqakAtFIgSgiYkZKRAO1TEVERFCFKCJiSjrL1EiBKCJiQjrL1EgtUxEREe7Q30MUEREpKlWIIiIiKBBFREQABaKIiAigQBQREQEUiCIiIoACUUREBFAgioiIAApEERERQIEoIiICKBAFyMnJYfz48QQHB9OyZUveeOON4p6S3MFsNhudO3dmx44dxT0VEQe6ubcwY8YM9u/fz/Lly0lNTWXMmDH4+vry4IMPFvfU5A6Tk5PDyJEjSU5OLu6piBgoEE3u8uXLrFmzhn/+85/4+fnh5+dHcnIyq1atUiCKUx0+fJiRI0ei2yfL7UotU5NLSkoiNzeXwMBA+7qgoCASExPJz88vxpnJnWbnzp2EhoayevXq4p6KyDWpQjS59PR0KlSogNVqta/z8vIiJyeHCxcuULFixWKcndxJevXqVdxTELkhVYgml5WV5RCGgP2xzWYrjimJiBQLBaLJubq6GoLv18elS5cujimJiBQLBaLJ+fj48NNPP5Gbm2tfl56eTunSpSlXrlwxzkxE5NZSIJpcgwYNcHFxYc+ePfZ1CQkJNG7cmBIl9PEQEfPQ33gm5+bmRkREBBMnTmTv3r1s2rSJN954gz59+hT31EREbimdZSqMGzeOiRMn0rdvX9zd3Rk2bBgPPPBAcU9LROSWshToKlkRERG1TEVERECBKCIiAigQRUREAAWiiIgIoEAUEREBFIgiIiKAAlFERARQIIqIiAAKRLlNtGnThnr16tkXPz8/HnzwQd58802nPk/v3r15/fXXARg7dixjx4793X1sNhvvvvvuH37OdevW0aZNmyLv9/rrr9O7d+8//LwiUjS6dZvcNsaPH89DDz0EQG5uLtu3byc6OhoPDw8iIiKc/nzR0dGF2u6jjz5i4cKFdO/e3elzEJHbhypEuW2ULVsWb29vvL29qVKlCl26dKF58+Z89tlnN+35ypYt+7vb6e6GIuagQJTbmouLC6VKlQKutjtjYmJo27YtYWFhXLp0idOnTzNkyBD8/f1p06YNcXFx5OXl2ff/17/+RYcOHQgICGDy5MkOY79tmb7//vs8+OCD+Pv706NHDw4cOMCOHTsYN24cp06dol69epw8eZKCggLmzZtHy5YtCQ4OZsiQIaSmptqPk5aWxlNPPUVAQABdunThxx9/vOFr/Prrr+nSpQv+/v488sgjbNu27ZrbrVmzhgcffJBGjRoRGhrKpEmT7K8nNTWVAQMGEBgYSPPmzYmJieHKlSsAJCUl0aNHD/z9/WnVqhVxcXFF/F9BxBwUiHJbunLlCp999hnffPMNbdu2ta9ft24dsbGxxMXFcddddxEVFYWnpyfr169n+vTpfPjhhyxcuBCAw4cP89xzz9GzZ0/i4+PJzc0lISHhms+3ZcsWoqOj6du3Lx988AGNGjVi8ODBBAYGMn78eCpXrszWrVupUqUKK1eu5MMPP2TWrFmsXr0aT09PBgwYYA+gZ599lvz8fNasWcPTTz/N8uXLr/s6k5OTeeaZZ2jfvj3vv/8+nTt3ZujQoaSnpztst3PnTqZMmcLzzz/Pp59+yqRJk1i7di2ff/45ADExMZQpU4b33nuPefPmsXHjRvv3nqNHj6ZBgwZs2LCBqVOnsmTJEjZv3vzH/8cRuUPpO0S5bbz88svExMQAkJ2dTenSpenbty+PPPKIfZuwsDDuu+8+ALZt20Zqaipr1qyhRIkS1K5dmzFjxjBu3DgiIyOJj48nODiYfv36AfDSSy/x5ZdfXvO5V69eTefOnenZsydwNURKlSpFZmYmZcuWpWTJknh7ewOwZMkSXn75ZUJDQwGYPHkyLVu2ZMuWLVSvXp3vv/+eL7/8El9fX+rWrcv+/fv59NNPr/m8a9eu5b777mPo0KEADBo0iMuXL3Px4kWH7cqUKcPUqVPtP8tVrVo1li1bRnJyMg888ACnTp3Cz88PX19fatasyeLFiylXrhwAp06dom3btlStWpXq1auzbNkyqlWrVrT/cURMQIEot43hw4fb/8J3dXXF29ubkiVLOmxTtWpV+59TUlK4cOECQUFB9nX5+flkZ2fz008/kZKSQoMGDexjpUqVcnj8v44ePUqPHj3sj61WK2PGjDFs98svv3DmzBlGjBhBiRL/bbBkZ2dz7NgxcnJy8PDwwNfX1z7WuHHj6wbi0aNH8fPzc1j33HPPGbZr1KgRpUuXZu7cuRw+fJhDhw5x/PhxWrZsCcBTTz3F+PHj+de//sX999/PQw89RMOGDQEYPHgwr776KqtXryYsLIxHH33UHu4i8l8KRLlteHp6UrNmzRtu4+rqav9zbm4utWvXZv78+Ybtfj1Z5rcnxPz6feRvubgU7v8Kv35nN2fOHO6++26HsfLly7Nt27ZCP2dRnnfLli1ERkYSERFBq1atiIyMZNKkSfbxRx55hObNm7Np0ya++uorhg8fztNPP82IESMYNGgQHTt2ZNOmTXzxxRf07duXmJgYHn/88UI9t4hZ6DtE+cu6++67SU1NpWLFitSsWZOaNWty8uRJ5s6di8VioW7duuzbt8++fX5+PklJSdc8Vs2aNR3G8vLyaNOmDQkJCVgsFvv6cuXK4enpSXp6uv05q1SpQmxsLEePHuXee+8lMzOT48eP2/c5ePDgdV/Db58XoEePHnz00UcO69asWUPXrl2ZPHkyjz/+OHXq1OHHH3+0h+9rr73GuXPn6NmzJ4sWLeK5557js88+IycnhylTpmC1Wunfvz9vvfUW3bt3Z+PGjYV4h0XMRYEof1ktW7akatWqjBo1ikOHDvHdd9/x0ksv4ebmRsmSJenevTv79+9nwYIFHDlyhFdeecXhbND/1bt3bz744APWr1/P8ePHmT59OgUFBfj5+eHm5kZmZibHjh0jNzeXfv36MXv2bL744guOHTvGiy++yO7du6lduzZ16tShefPmjB8/nqSkJDZt2sTKlSuv+xp69uzJd999x7Jlyzh+/DiLFi0iOTmZ4OBgh+08PDz4/vvvOXToEMnJyYwdO5b09HRsNhsAR44cYfLkySQlJZGcnMzmzZtp2LAhrq6u7N69m5iYGI4cOcK+ffv47rvv7O1UEfkvBaL8ZZUsWZIFCxaQn59P9+7dGTZsGK1bt+bFF18ErlZfCxYs4KOPPiIiIoL09HRat259zWM1bdqUl19+mXnz5vHII49w8OBBFi5cSOnSpWnWrBk1a9bk4Ycf5uDBgwwcOJBu3boxYcIEIiIiSE1NZenSpZQvXx64Wq1VqFCBHj168Oqrr97wbjM1atTg9ddfJz4+ns6dO7Nx40YWLlyIj4+Pw3a/nk3797//nf79++Pq6krPnj3t1efEiRPx8vKid+/edO/enUqVKtlvPPDaa6+RlZVFt27dGDhwIMHBwfaTeETkvywFuupYREREFaKIiAgoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICAD/DxqfgbwqWkdWAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Confusion Matrix\n",
    "# Convert the predicted probabilities to binary outputs\n",
    "y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_classes)\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(5, 5))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion matrix')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.ylabel('True class')\n",
    "plt.show()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:53:04.785818Z",
     "start_time": "2024-03-21T13:53:04.696985Z"
    }
   },
   "id": "6b7d586ea49a858a",
   "execution_count": 64
  },
  {
   "cell_type": "markdown",
   "source": [
    "## ROC Curve Evaluation\n",
    "\n",
    "The Receiver Operating Characteristic (ROC) curve is a graphical representation that illustrates the performance of a binary classification model at all classification thresholds. It is commonly used in machine learning to evaluate the performance of a classifier system for two-class problems.\n",
    "\n",
    "The ROC curve has two axes:\n",
    "- The X-axis represents the False Positive Rate (FPR), which is the proportion of negative instances that are incorrectly classified as positive.\n",
    "- The Y-axis represents the True Positive Rate (TPR), which is the proportion of positive instances that are correctly classified as positive.\n",
    "\n",
    "A perfect classifier would classify all positive instances correctly (TPR = 1) and all negative instances correctly (FPR = 0). This would be represented by a curve that goes straight up the left side of the ROC graph and then along the top to the right corner.\n",
    "\n",
    "The Area Under the Curve (AUC) is a numerical measure of the ROC curve’s performance. A larger AUC indicates a better performance. In our case, the AUC is 0.91, which is considered to be very good.\n",
    "\n",
    "In summary, the ROC curve shows that our binary classification model has a good performance in distinguishing between positive and negative instances."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8645f7e159d38f0a"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. ROC Curve\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:53:04.875096Z",
     "start_time": "2024-03-21T13:53:04.785818Z"
    }
   },
   "id": "4d080bef0cf9bec4",
   "execution_count": 65
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Learning curve"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5d1867cb3af9788d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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pRwb/+HIDq/ZUsK3ITcfU2MgV10JF03//1kD9Np96bj713Fzqt/nUc/Op59EjYgs89OnTB7vdXmuRhoULFzJgwACs1tplffzxx9x///04nU7S09Oprq5m3rx5RzUNL5qlxTk5rlMqAF+szY9wNSIiIiIirVvEwlJsbCznnnsu99xzD8uWLWPmzJlMnTqVKVOmAKFRpurqagC6dOnCm2++yYwZM9iyZQu/+c1vaN++PWPHjo1U+U1mQu/Q9LvPtSqeiIiIiEhERfSitHfeeSf9+vXjyiuv5N577+XWW29l4sSJAIwePZpPP/0UgP79+3PPPffwf//3f5x//vkAPPPMM4eMQLUEJ3XPwGGzsKmwig0FLeOiuyIiIiIizZHFMFrXjMiCgsifMGexhFa/q6+W336wkm83FnLNiI7cOLqr+QW2QIfruTQu9dt86rn51HNzqd/mU8/Np56bZ1+vD6flDc20ABNrpuLNWJtPK8uyIiIiIiJRQ2EpCo3plo7LbmV7STWr9zR8eXQRERERETl2CktRKNZhY0y3dEALPYiIiIiIRIrCUpSa2Cs0FW/m2nyCmoonIiIiImI6haUodULXNBJibOyt8LJ0R1mkyxERERERaXUUlqKU027lxO4ZAMzQVDwREREREdMpLEWxfavifbmuAH9QU/FERERERMyksBTFjuuYQkqsg2K3jwXbiiNdjoiIiIhIq6KwFMXsNiun9Nw3FS8/wtWIiIiIiLQuCktRbt9UvK83FOD1ByNcjYiIiIhI66GwFOUGZyeTmeCkwhNg7hZNxRMRERERMYs90gXIT7NaLEzolcnrC3fwxdq9jOueHumSRERERFotwzDA68GoqsJwuzGqKjGqqyEQwPD7IRCo/2u/HyNw4NeBmn32fx1wWHFXuMOPMQJ+CH990OPq+nrfa2GA04klJgZLjAtiYsJfW2JiwOWq+f6g+10uLM4D76/ZP8aFxVWzr9OJxdo6xlwUlpqBiTVh6dsNhbh9AWIdtkiXJCIiItIsGIEARrW7drhx13xfVQluN8GqKnBX1exTVfe+B2wPhZGm4W6yZ25kzphw2KImYFlc+4LVgSFrfwCzZmTgOnMSltjYSFd/xBSWmoG+7RLJTnaxo7Sa2RsLmdi7TaRLEhERETkmoREaL4bPFxqp8fnAE7o1vF7weUO33tCt4av52uM5KLzU3B4SdELfU13ddD+Ey4UlNi404mJ3gM0GNhsWux1sdrDbsNjsoe12O5aaW2y20Nc191n2bbPbiE2Mw+0Ngq1m//oea6/9WGz2/c9jC33EN7yeUL881aHeejyhUTBv6Nao2YZn/9cH30/N4w2PB3y+/T+71xN6/vKGtcySkIjrtDMa8T9C01JYagYsFgsTe2fy4rw8vlibr7AkIiIijcowDKiuJlhRjlFRgVFZAZWVlDmgurA0FFbqCi77th0QeAzPAUGn5vbAx+6/z3f4whqT1YolLi70LzYeS1xsKOjExdXcxh66PS4OwvvEYomLr7mNw+KKDYWWRmSxQEZGIgUF5RhReIlNIxDYH7r2Bah9oasmYFETuPYFtfD9nmosMS6cJ4yK9I/RIApLzcTEXm14cV4e320uosLjJyFG/+lERESkJui43QQrK8JBx6isDH8d3LftgPvq2lbX1LJSM38QpxOL0wmOfbeO0NQupyN0n8NZs0/MAaGnniBzQNjZF4RwxmCxWMz8iVoci80G+0JkK6FP3M1Et4w4uqbHsbmwim82FHBWv3aRLklERESOkeH3h/4KX1UZCjiHCzoVB2yrrMCoqAydd9NY59BYrVjiE7AkxGOJT8CZlIjfYtsfUg4OLg4nlphDAw7OmqDjcOwPQfv23/f1AdtwOBRkJCopLDUTFouFib0yeeb7rXy+Jl9hSURExGThc2yOaBGA+s6jqb0vHk/jFXhQ0LEkJGJNSMASv+/7BCzxCTXb9n2//z5rfALExoZDS7RPCRMxg8JSMzKxdxue+X4r87cWU1zlJTXOGemSREREmg0jGCSYn08gbytGSUl4NbR6g04d25tsFTSbLTSNLCEeS0LiAaHm6IKOiDQOhaVmpFNqLH3aJrB6TwVfrS/ggkFZkS5JREQk6gSrKgls20pg27bQbd5WAnnbCORta7yV0WJiQuEmfL5M3EGLBfzEeTQH3x8Xp2loIlFKYamZmdArk9V7KpixJl9hSUREWi3D7ye4e1dNKNpKYPu2cDgKFhbU/0CbDVtWNtaMzP0LABwQYIiNwxoXFxqlqS/oxDb+KmgiEp0UlpqZCb0y+feszSzeXsrecg9tEmMiXZKIiJjE8HgwyssJlpeGbsvKMMrLMMrKCJaXYZSXh06qT0nGmpKKJTkFa3IK1uRkLCkpoeldVmukf4wGCZaUhEeH9hbupnzN+lAo2pEHfn+9j7OkpmHr1Albx87YOnXG1rFT6DYrO3QtGhGRI6DfFs1MuyQXg7KSWLqzjJnr8rlsaIdIlyQiIg1gBAKhVcz2hZ2ymuBTHgo+oQBUjlFWSrC8vFYYOubFAKxWLEnJWJNTQoEqOeWgQJWKNWXftpqAFRvX5NPDDK+XwI7toelyB06d27YVo6wsvF/FwQ90xmDr0DEUivYFoppwZE1MbNKaRaR1UFhqhib2zmTpzjJmrFFYEhGJFMPjwbe7Ev+WnQTLfnq0Jxx8yspCF/s8lqXFrNbQyf1JyVgTE7EkJmFJTMKalIQlISF0AdDSEoIlpaHb0pKaxQwqIRjEKCkmUFIMW4/w9RyOmlC1L2SlHBCyakawwttq9ok5dNaDYRgECwv2T5vbVnMe0batBHfvgmCw/h+5TVtsnTqT0LM73sz24VEia9t2zW6kTESaF4WlZuiUnpn86+uNrNxdzvYSNx1SYiNdkohIi2X4/aFRj00b8G/cGLrdtJHgzh0UHEvoiY3FmpiEJTERa1IylprgY00KhR9LUlIoDB0Uiizx8UcVEAyvl2BZKUZJTYAqLSFYeuj3wZKar0tKwOsBn49gQT4U5HPE68DFxu4frUpKDj1v3rbQanL1sMTF7582t2/KXKfO2Dp0DJ0jpGWsRZoFb8BLkbeQoupCCjwFFFUXUujJp9BTiGEYXNvrBjJcmZEu84gpLDVD6fFOhnVM4cdtJXyxNp+rR3SKdEkiIs2eYRgYhYX4a8JQYOMG/Bs3ENi6Gbzeuh9kt4cCTmIilqQkLAn7wk5iTdhJqjsAJSaFLtZpIovTiS0jEzKO/EOKUV0dHpkK7hulKi0lWFJ8QMgqDd1XE7IIBMDtJuh2h0aMDmS1Ys3Krpkutz8Q2Tt2xpKertXgRKKYN+Ch0FNIYXUBhZ6C8NdFnkIKPPk1oaiQMl/pTz7PkIxhTMg+zaSqj53CUjM1sXemwpKIyFEyqqrwb94UCkSbNhDYtBH/pg0YpfX8T97lwt41B1tON+w53bHldMPRvTuZ3TtRWFjRYkc6LC4XNlc7aHtkF0I3DAOjsjI8MrVvGqAlPiEUjLI7mB4SReSnVQeqwwGoyFNIQXUBRQeEoUJPIUWeAsp95Uf8nA6rg7SYdNJj0kmLySDdlUF6TDod4jsypu24JvxpGp/CUjN1YvcM/m/mBtbnV7KpsJKc9PhIlyQiEnUMv5/A9rzw1LlQOApNoauT1YotuwO2bt1DoahbKBxZs7IPmfpmsaCRkINYLBYsCQmQkIAtW+fUikSCYRgECeIJVFNYXUiRpzA0ElQTfEKjQvumxxVQ6a884ud2Wp2kx2SQ5konPSYUgNJrwlBaTDoZrgzSYjJIciS1mN+PCkvNVHKsg5FdUpmzqYgZa/L5xSiFJRFpvcKLB9SEofD5RT8xhc6Slo69W/fwaJG9WzdsXbpiiXGZXL2IRDvDMPAGvVT5K6nyV1EdcFPlr8Jdc+sJVOM3/ASCgdCtESAQDN36DT/+mq8DNfft+/7A7f5ggAB+rHao9nhC39c8fv/z1n6N2o/f/3oN5bK5akaCDgg+tUJRBumudBLsiS0mBB0phaVm7NTebZizqYgv1uZzwwmdW93BKyKtU2gK3UYCGzfi37g+HI4OXGK6FpcLe9duoVB0QDiypqaaW7iImCZgBHD73bgDbtz+Stx+N1WBqtA2f1XN11XhwOM+IPi4a+6r9ZiAm6BxxEucRI1YW1xo9OegkZ992/ZNk4u3x+tzZD0Ulpqxsd3SibFb2VbsZu3eCnq31TUlRKT5MoJBjIry0IIBZaX7b8vKCBYVEdi6Gf/GDQR37az7CazW0DV3crph79YjHI6s7bO0vLRIlDMMA1/QS2XNyM2+EZzQ9/u3VforcQdqQo7fXevrqkAl1f6akZ7gMV6T7Ce4bLHE2eOItcUSW3PrsrmwWx3YLDbsVnvo1hK6tVnt2C22WvfZLHZs1v37hLbbsVttpCQl4K7whfapefy+5ws/94HPc/Br1nzttDqJtWvF5GOlsNSMxTltjMlJY+a6AmasyVdYEpGoYBgGVFcfFHhKQ8tUH3hbc0HWfd8b5eVHfP2hfVPoDhwpsnXpoil0IiYLjeDUFWqqDgk5VbX2OfQxRzN97HCsFhtxtrhQuLHHEmsL3e4PO/GHBJ/Q93U/xmWLxWppuj++aIn86KOw1MxN6N0mFJbW5nPL2K5YNYQqIo3I8PsxysvqDjp1BCGjrIxgWWn9S20fAUtcfGgZ7qTk8HV6rMnJoaWm902hS0lpvB9SpBULGAEqfOWUeksp95VR5i2jzFdKma+Mcl8p/g0eCitK6g1C1QF3o9e0L6iE/sUTb48nribUhG9tcaFwY4+t+ToUfGIPGPWJs8fhsDo1vUyOicJSM3dCl1TinTb2lHtYvrOMQdnJkS5JRI6A4fPhX70K/4Z14PdjBINw8D8jiBEIQjAAQSN0axgYgdDtwft6HTaq3R4IHPBYo/ZzHvI6h+xnhAOSUVaKUXnkqyQdwm6vHXjqCEDh7cnJoQuzJiVraWmRo2AYBpX+yprAUxoOPPvCz/7tBwQibxkV/iNfDvqn2C124h0JNaM4oUBzcMiJPzjw1BGEYu2x2Cy2RqlJpDEoLDVzLoeNcd3T+XTVXmasyVdYEolShteLf/VKfIsX4VuyCN+KZeBp3Dn11Y36bLVZEmsCTeIBwSYccJJqf58c2maJjdNfdEWOgifgOSjw1Iz0eMso9ZVStm8UqOa+fV8HjmEBgnh7PImOJJIcySQ5k0h0JJHsTCYzKQ2rzxkKNPuCkOOAIFSzzWlzNmIHRKKHwlILMLF3Gz5dtZeZ6/L51UndsFv14UQk0gyPB9+qFfiXLAoFpJUrwFs7HFmSU7D36481Ph4sVrDu/2ex1v4eiwWLzRaa0G61gXX/raXmNj4xjiq3F8NqC4WU8P51PKfFCjYrlppbrDX72mxYrFYsiYk1oz7Joa9t+kuvyJEwDIPqQDUV/goqfRVU+iuoOOi20l9Jha+cSn8llb6K8L7l/nLKvKV4g0c/jTXGGkOic3/oSQoHoOSaMJRUs732Nrv10I+EOn9GRGGpRRjRKYVkl52iKh+L8koY3lnL4YqYzaiuxrdiGb4li/EtWYh/1Urw+WrtY0lNwzE4F8fgITgGDwld06eRVmnThxqRxuENeH8y0FT6K6nwl1Ppqwxvq/DXhCBfBRX+ykZZYtpmsdUEm2SSHMkkOhLDXx8YeMLBqGbfGFtMI3RBRPZRWGoB7DYrJ/fM4P1lu5mxJl9hScQERlVVTThahG/JYvyrV4K/9kpO1vQM7IOHhAOSrXMXTUsTiQDDMCjwFJBXsZVtFVvJq9xKkacoHHoOHPU5llGdA1ktNhLs8cQ7Eoi3J5BgTyDese82ngR7IvE19++7L9GRWBOMkoizaxqrSDRQWGohJvZqw/vLdvPV+gLuGN8dh03XFBFpTMGqSvzLltaEo0X416yGQO2/Hlsz24RHjRyDc7F27KQPOyIm8gY87KjazraKrWyr3FoTjraRV7kNd6CqQc8VWqBgf5CJt8eT4EisdRtvTyChJgwdGHoS7PG4bLF6/4u0AApLLURuh2Qy4p0UVHr5YUsxY7qlR7okkWYtWFGBf9mS0PlGSxfhX7f20HDUth2O3CHhgGTNytaHI5EmZhgGxZ7imjC0jbzKreFwtLtqFwZ1z0O1WmxkxWXTKb4THRM6k+lqU2u0Z3/oCS1coBXZRAQUlloMm9XC+F6ZvLloBzPW5issiTRQsLwM39IloZGjxYsIbFgXWkr7ANb2WaFgVBOQbO2zIlStSMvnD/rZWbUjHIbyKreyy7ODTSWbKPfVv9x1vD2BTgmd6RTfmY4JnWpuO5MVl43DqmXpRaRhFJZakIk1YWnWhkKqfQFcDv1VTKQ+wdISfEsX1yzIsIjAhvUcvDKCtUNHHINywwHJ1rZdhKoVabnKfWXkVWxj2wGhaFvFVnZW7ah3KWwLFtrFtqdjQic6xnc+IBx1JtWZqhFeEWk0CkstSP/2ibRPimFXmYc5m4oY3ysz0iWJRI1gSUnNqNFCfEsWE9i04ZB9bJ064xg8BPu+BRky20SgUpGWJ2AE2OPeXROGtoXOJao5p6jYW1zv41w2VzgMdUzoRL/2vUgNtiU7rqNWfRMRUygstSAWi4UJvdrwyvw8ZqzNV1iSVi1YXhY632jxQnyLFtYdjrp0rb0gQ3pGBCoVMZdhGPiCPrxBb80/D96A94Btoe+9wX3b9n/vDXrxHfD1/u89Nd/7Drg/9DhP0EN+dT6+n1hlLsOVGR4Z6hTfmY7xneiU0JkMVyZWS2jBIi2PLyKRoLDUwkzsnckr8/P4blMhFR4/CTH6TyytQ7CyAv/SJfgWL8S7aCGB9WsPmVZny+lWOxylpkWoWpGj4wl4KPYWUeopodhbTKk3dFvi2f91dcB9QPipCTEHhp1GWhq7oRxWJx3iOoQCUU0o6pTQmQ7xHYmzx0ekJhGRw9En6RamZ2Y8XdJi2VLkZtbGQs7o2zbSJYk0CcPtxrd8Kb5FC/EtWoB/3ZpDVquzde6CI3cojiFDcQweijVV1yCT6OIL+ijxllDiKQrdeotrvi/e/3VNGCrxljR4+esj4bQ6cVidOK1OnLaaW2sMTpsTh9VR870Tpy2mZl9Hre/3/XPUevy+r2NwWB2kxaTTLq69VpgTkWZHYamFsVgsTOzVhmfnbmXGmnyFJWkxDE81vhXLw9Pq6rwIbHaH/eEodyi2DE1FFXMFgn5KfWUHhJ3iWgFo32jQvq8r/RUNfg2H1UGyM4UUZyqpzlSSnSmkxqSGt8XZ4w8INDXBZ1+gsTlqf291aDEEEZGfoLDUAk3olcmzc7fyw9ZiStw+UmK1VKo0P4bPh3/VitDI0eKF+FYuB2/t6UOh6xwNxTF0GI7cYdja6o8D0ngCQT/lvnLKfKWU+cop95bVfF1Gua+MMm8pbksFe8rzQ1PgPMWU+8rqvc5PfawWGyk1QSd8G5Nae1tMWjgYxdvjFXBEREyisNQCdUmPo2dmPOvyK/lqfQHnD2wf6ZJEDsvw+/GvXR2eVudbvhQ8nlr7WDMy948cDRmGtX2WPjTKYe0PPWWhoOMNhZ1SXynlNd+HQ5C3PBSEfKVU+iuP6vUsWEh2JpNcM/KTUjPqEx4FqhWG0khwJIQXMRARkeiisNRCTezdhnX5m/lizV6FJYlKRiCAf/3a8MiRf+kSDHft8zEsKanhKXWOIcOwdeykcNSKHRh6QiM7Nbc14SYUeg6972imuh0owZ5IkjOJREcSSY4kkpzJoa+dSWSntsXhi6s1LS7RmaRzc0REWgiFpRZqQq9Mnpi9mYV5pRRUeMhI0PUoJLKMYJDApg37p9UtWYRRUftDrCUpKbRS3ZBhOIYMxdYlR+GolarwVbC6ZCWrSlawqngFa0vXUOYrPabnjLcnkOQMBZ5Q8Ekmseb7JEdSzdfJ++93JpPgSKg3+GgpaxGRlk9hqYXKSnYxoH0Sy3eVMXNdAZcMyY50SdLKGIaBf/MmvPum1S1ZhFFa+8OuJT4+FI72jRx1647FqulIrY1hGORVbmNVyQpWFi9nVfEKtlRsrvfcn3h7wgGBpo4RnwO2JzqSSHYmk2BPwGbV//JERKRh9H+OFmxi70yW7ypjxpq9CkvS5ILFxfjXrcG/ZhX+tWsoWr2SQEFB7Z1iY3EMHBwOR/YePbHY9WuotXH7q1hTujocjFaVrKDMV3bIfu1js+ib2p9+KQPom9qPNq62JDgSsSv0iIiISfR/nBZsfM8MHv56I8t3lbOj1E12cmykS5IWIlhSgn/t6pp/a/CvWU1w755Dd3TG4BgwMLwgg713X4WjVsYwDHZW7QhPp1tZspxNZRsJEqy1n8PqpHdyH/qm9qdvSn/6pfYnLSY9QlWLiIiE6FNLC5aREMPQjsksyCtl5toCrhzeMdIlSTMULC0JB6LQyNFqgnt2H7qjxYKtYyfsvXpj792HjBFDqczqCg6n+UVLxHgCHtaWrg4Ho1XFKyj2Fh+yXxtX25pRo/70TR1A96QeOKy6zIGIiEQXhaUWbmLvNizIK2XGmr0KS3JYwbLS/cFo7Rr8a1cT3L2rzn1tHTth790He8/e2Hv1wdazJ9b4BCB04ntcRiJVOvG9RTMMg73Ve0LT6UpWsLJ4BRvK1hEwArX2s1vs9EjuFQ5G/VL6kxnbJkJVi4iIHDmFpRbupB4ZPPjlBtblV7KlsIou6XGRLkmiRLCsFP+6tTXBKPQvuKvuYGTt0BFH7z7YevUJjRz16IU1IcHkiiXSvAEv68vWhkeNVhavoNBTcMh+aTHpNecZ9adf6gB6JvXEadOKnCIi0vwoLLVwKbEORnZO5bvNRcxYu5frT+gS6ZIkAoLlZeGRotDtGoI7d9S5r7VDR+z7QlGv3th79lYwaqUKqvNrjRqtL1uLL+irtY/VYqN7Yg/6pfYPL8bQNradlnwXEZEWQWGpFZjYOzMUltbk8/PjO+tDTAsXLC8Pn1vkX1cTjHZsr3Nfa3aHQ4NRYqLJFUs0qPBVsL5sLetL17K2dDUri1ewt/rQRTtSnCn0TekfHjXqldwHl80VgYpFRESansJSKzC2Wzoxditbi92sy6+kVxuNErQUhteLf80qfCuWh0aM1q0huD2vzn2tWdkHnGMU+mdNTDK5YokGFb5y1pWuZV3ZWtaXrmFd6Vp2VB0aqK1Y6ZrYrdaoUVZctv7gIiIirYbCUiuQEGNnVNc0vlpfwIw1+QpLzZjhqca3cgW+JYvwL1mMb+UK8HoO2c/aPisUjHr1Do8cKRi1TuW+MtaXrmNd6RrWla1lXekadlbVPQWzbWw7eib1pmdyL/qk9KN3Sh/i7PEmVywiIhI9FJZaiYm9M/lqfQFfrN3LLWO66C/DzYRRVYVv+VJ8SxfjW7IY/+qV4PfX2seSmoZj4CDsvfvWjBz1wpqUHKGKJZJKPaUsyF/A2prRovWla9nl3lnnvu1i29MzuRc9k3rTI7kXPZN7kexMMbdgERGRKKew1EqM6ppGnMPGrjIPy3eVMzBLowzRKFhejn/ZEnxLFuNbuhj/ujUQqL0MszWzDY7BudgH5eIYPARbJ52H1hqVektZV7qG9aVrWVcWCke73XWvZtg+Lis8YtQjqRc9knuR7FSgFhERORyFpVbC5bAxtns6n63ey4w1exWWokSwpATfstCokW/JIgIb1nPwhYms7dvjGDQEx+AhOAbnYs3SOSOtTam3JDSNrnRtzblGa9jjruPCwEBWXDY9k3vTM6kXPZN70yO5J4kOvd9FRESORkTDksfj4d5772XGjBm4XC6uueYarrnmmjr3/eKLL3j44YfZvXs3vXv35k9/+hP9+vUzueLmbWKvTD5bvZeZ6wr41YndsFn1gdtswcKC8JQ635JFBDZvOmQfW8dO4VEjx+BcbG3bRaBSiZRiT1E4EO2bSlfXqnQAHeI61kyh603P5J6MzBmKt8yiCwGLiIg0koiGpYceeogVK1bw8ssvs3PnTu644w6ysrI47bTTau23fv16fvOb3/DXv/6VIUOG8NJLL3HDDTfwxRdfEBsbG6Hqm5+RXVJJctkprPSyeHspwzqlRLqkFi+wZw++JYtCCzIsXUwgb9sh+9i65oSC0aBcHINysWZkRKBSiYQKXzkri1fULL4QCkf51Xvr3LdDfKea0aJQOOqe1JMEx/7FWiwWSHImUkC5WeWLiIi0eBELS1VVVbz99ts899xz9OvXj379+rF+/XqmTZt2SFj67rvv6N69O+eeey4Av/71r5k2bRobNmxgwIABEai+eXLYrJzUI4MPl+9mxtq9CkuNzDAMgjt3hEeNfEsXEdx10DkkFgu27j3Co0aOAYOxpqZGpmAxXZm3jOXFS1hSuJilRYvZWLYeg9rDQBYsdIzvFDq/qGY6XfeknsQ7tCqdiIiI2SIWltasWYPf7yc3Nze8bejQoTz99NMEg0GsVmt4e0pKChs2bGDhwoXk5uby3nvvkZCQQKdOnSJRerM2sVcmHy7fzVfrCvjdyd1x2KyHf5DUyTAMAtu2hkeNfEsWE8w/aFTAZsPes3do1GhwLvaBg7SEdytS6i1lWdESlhYtZmnhYjaVbzgkHHWI70Tv5D41U+l60T2ph5brFhERiRIRC0v5+fmkpqbidDrD2zIyMvB4PJSUlJCWlhbefsYZZ/DVV19x2WWXYbPZsFqtPPPMMyQnN3w1p2g4L35fDZGoZVinFNLjHBRW+fhxWwmjc9IO/6AWoDF6blRX49+8Cf+qFXgXL8K3dAlGcVHtnex27H364Rici3NwLvYBA7HGtb4PvpE8xiOp1FvC0sIlLClaxNLCJWwq33DIPp0TujAoLZdB6bkMTsslzZXeKK/dWnseSeq5udRv86nn5lPPzXOkPY5YWHK73bWCEhD+3uv11tpeXFxMfn4+d999N4MGDeKNN97gzjvv5P333yc9vWEfNNLTE4+t8EYUqVrOGpTFy3O3MmtzMecO7xyRGiLlSHpuBAJ4t23Ds249nnXrwv+827YdslKdJSaG2EGDiDvuOOKOG0bsoEFYdR5dWDS935pCobuQhXsWsmDPAubvns+GkkPDUbfkbgxrN4zj2h3H0LZDyYht2nPSWnrPo5F6bi7123zqufnU8+gRsbAUExNzSCja973L5aq1/Z///Cc9e/bk8ssvB+C+++7j9NNP59133+X6669v0OsWFpZHfKUoiyX0JohULWO6pPDy3K18vnI323eV4HLYzC/CZPX1PFhUiH/jRvwb1+PftJHAxg34t2wGj6fu50lJxd6zJ45BuTgHD8Hepy+WmpDvBtyVfqjUCfaRPsabSpGniGVFS1hSuIglhYvZWrH5kH26JuYwKC2Xwem5DEwbTGrMAaO3lVDQRMdHS+15NFPPzaV+m089N596bp59vT6ciIWltm3bUlxcjN/vx24PlZGfn4/L5SIpqfY5HStXrmTy5Mnh761WK71792bnzrqvTP9TDOOQwYGIiVQtA9on0S4xht3lHr7bXMzJPVr+6muG2417x2aqFi3Dv7EmFG3aiFFSXPcDnDHYc3Kw5XTHntMNW7fQrTXt0JHMaDmeolE0vd+ORpGnkKWFi1lStJhlRYvZWrHlkH1yEruFptWlhcJRSkztBTvM/vmbe8+bI/XcXOq3+dRz86nn0SNiYalPnz7Y7XaWLFnCsGHDAFi4cCEDBgyotbgDQJs2bdi4cWOtbZs3b9ZKeEfJarEwoVcmry7Yzow1e1tUWDICAQI7tofDUGDTBvwbNxDcuYOCun7rWCxYsztg79YdW0437DXhyJrdAYut5Y+4SW0F1fksLVrMsprzjvIqD13qvVtiDwalD2ZQ2hAGpg0i2ZlifqEiIiJiioiFpdjYWM4991zuuece/v73v7N3716mTp3KAw88AIRGmRITE3G5XFx88cX84Q9/oH///uTm5vL222+zc+dOzjvvvEiV3+xN7B0KS3M2FVHp9RPvjOgltxrMMAyMwkL8mzbsnz63aSOBLZvBW/cUOlt6OtYuOaFQ1K17aLSoSw6Wg6Z9SuuRX53PssLFoQUZipaw/aBwZMFCt6TuDEobwqC0wQxIG0yys+ELy4iIiEjzFNFPyHfeeSf33HMPV155JQkJCdx6661MnDgRgNGjR/PAAw9w/vnnc8YZZ1BZWckzzzzD7t276dOnDy+//HKDF3eQ/Xq1SaBTaizbit3M2ljI6X3aRrqkehlVVfg3bwqNEoVHizZilJbU/YCYGOxda6bQ1YwYObp3p02PzhQUaA5wa5bv3lsTjEJLee+o2l7rfgsWuif1ZFDaYAalh0aOEh1a6l1ERKS1shhG6/roGA0fli0WyMhIjHgtT3+3hRd+2MbonDQeOa9/5Ao5iFFVhXfeXLyzv8W3cjnBnTvq3tFiwdahY00o6hY+v8ialX3IFLpo6XlrEU39Lqwu4KtdM/lyxwzWla2pdZ8Vaygcpe8752gQCY7muQJRNPW8tVDPzaV+m089N596bp59vT6c5jX3ShrVxN6ZvPDDNn7YUkyp20dyrCNitQRLSvB+Nxvv7G/wzv/xkKl0lrQ07Dn7zyuydeumKXRSr0pfJXP2fMvMnZ+zuGAhQYJAKBz1SO7JoLQhDE7PpX/qIBIcCRGuVkRERKKVwlIrlpMeT4/MeNbnV/L1+gLOHdje1NcP7NmNd/a3eGd9g2/pYggGw/dZs7KJGXsijuEjsXfvgTW1dVw8V46eL+hjfv48vtz5Od/tmY03uP/SBH1T+jMh+1TGtTv5kNXqREREROqjsNTKTeiVyfr8SmaszW/ysGQYBoEtm/DO+gbPrG8JrKs9JcrWoycxY07EOfZEbDndsOjy1XIYhmGwsng5M3d+zje7vqTMVxa+r2N8J8Znn8opWRPJisuOYJUiIiLSXCkstXITemXy1JwtLMwroaDSS0a8s1Gf3wgG8a9agXf2t3hmfUNwe97+O61W7AMGETNmHM4x47Bl6QOtHJmtFVuYueNzvtw5g93uXeHtaTHpnNx+POOzT6VHUi8FbhERETkmCkutXIeUWPq1S2Tl7nK+WpfPxbnHHlgMnw/f4oV4Z32Dd84sgoUF++90OHAMG07M2BNxjhqj6XVyxAqq8/l650y+2Pk5G8rWhbfH2uIY2+5ETsmaSG7GUGwWXR9LREREGofCkjCxdyYrd5fz2eq9XDQ466j+Gm9UVeH98YdQQJo7B6OiInyfJT4e5/GjcI49EceI47HGxTdm+dKCVfoqmb3nG2bu+JzFhQsxCC0NZLPYGJ55POOzJnJ829G4bFroQ0RERBqfwpIwoVcm/561meW7ynlqzhZuHtP1iB73UyvYWdLSiBk9LhSQcodicTbu9D5puXxBHz/m/8DMHZ8zd++cWgs19EsdwPisUzmx/ckkO1MiV6SIiIi0CgpLQmZCDL8/uRsPzNzASz/mkRBj58rhHevc9ydXsMvuEFqgYcw47P36H3KtI5H6BI1gzUINM/j2oIUaOsV3Di/U0D4uK4JVioiISGujsCQAnD8oiwpPgMdnb+aJ2ZtJjLFx/qAsrWAnTWpL+Wa+3Pk5M3fOYI97d3h7ekwGJ2eN55SsU+mR1FPHlYiIiESEwpKETRnekXKPn5fnbeX9t76k50c7yVr5Y90r2I0NjSDZ2usv/dIw+dX5fL3zC2bu/JwNZevD2+PscYxpeyLjs09lcPoQLdQgIiIiEaewJMD+FeymzPuGs778iviKEgCCAE4njqHH1axgNxZrqi7qKQ1T4atg9u5vmLnzc5YULqq1UMOIzOMZn30qx7cZTYwtJsKVioiIiOynsNTKBSsrqH7/XdxvvYFRXARAPOCJieX7zN78mD2AS68/nyE9NYIkDeML+JizexZf7PicuXu/w3fAQg0DUgdxStZExrU/mWRncgSrFBEREamfwlIrFSwtwf32dKrffQujohyoWcFuzDicY07EMmgIP3y2gVkbC/nxs808lZRIv3aJEa5aol1hdQELC+ezsOBH5uXPpcy7f6GGzgldGJ8VWqihXVz7CFYpIiIicmQUllqZQEE+1dNfx/3he+B2A2Dr3JXYK64kZvxELPb9h8Tfz+rD7e8tZ0FeKbe9u5ynfzaI7hm6RpLsVx2oZnnREhYUhALSpvKNte7PiMngpKwJjM+aSHct1CAiIiLNjMJSKxHYuQP3669S/elH4PMBYOvRi7gpV+MceyIWq/WQx8TYrfzz3H7c8s5yVuwq59Z3lvPcJYPokBJrdvkSJYJGkI1l61lYMJ8FBT+yvHhZrel1Fiz0SOrFsMzhjO9+Ip1sPbCihRpERESkeVJYauH8W7fgfu0lPF98DoEAAPYBg4ibcjWOEccf9i/98U47j57XnxveWsrGgipufmc5z/1sEG0SdSJ+a5Ffnc/Cgh9ZkP8jiwrnU+ItqXV/G1dbhmUMZ2jGcQzJGEayMwWLBTIyEikoKMcwIlO3iIiIyLFSWGqh/OvWUvXKi3hnfc2+T6uO40aEQtLgIQ16ruRYB09cMICfT1/K9pJqbnl3Oc9ePIiUOEdTlC4R5va7WVq0ODx6tLVic637Y21xDErPZVjGcIZlDKdjfCdNrxMREZEWSWGphfEtW0rVqy/i++H78DbnmHHETr4aR5++R/28GQkxPHnhQH7+5hI2F1bxy/eW89RFA0mI0SHU3AWNIOtL14bD0YriZfgNf/h+K1Z6JvdmWGZo9KhvSn8cVgVlERERafn0SbcFMAwD34IfqXrlRfxLFoU2Wq3EnDKB2Cuuwp7TrVFeJyvZxRMXDuT66UtZvaeCX3+wkn+f3x+XQ+ekNDd73LtZWLMow8KCBZT5Smvd3za2XXjkKDd9GEnOpAhVKiIiIhI5CkvNmBEM4p0zC/drL+FfvSq00W4n5rQzibt8CrYOHRv9Nbumx/H4Bf35xVvLWLy9lD98tJp/nNMXh+3QBSIkelT5K1lSuDh07lHBj+RVbqt1f5w9jtz0oTXnHg0nO66DptaJiIhIq6ew1AwZfj+er2fifu1lAptqlmqOicE16TxiL7kcW5u2Tfr6vdsm8sh5/bn13eV8t7mIv/xvLfed0RubVR+uo0XACLCudA0LCn5kYcF8VhYvJ2AEwvdbsdI7pW949Kh3Sl/sVv06EBERETmQPh01I4bPh+ezT6ia9grBHdsBsMTH4zrvImIvvgRrapppteR2SObBSX357Qcr+WJtPvFOG3+c0EOjERG0u2oXCwrmsaBgPosLF1DuK691f1ZcNkPDU+uGkODQRYZFREREforCUjNgVFdT/dEHuN98jeDevQBYkpOJvegSXOdfjDUxMh96R3VN474zenPXJ6v5YPluEmLs/HJsVwUmkwSCfpYXL2POnln8uHcu26vyat0fb09gSPqw8MIMWXHZEapUREREpHlSWIpiwYoKqt9/B/dbb2CUFANgzcgk9pLLcU06D0ts5C8OO75XJpVeP3+bsZ7XFmwnyWXn6hGdIl1Wi1UdqGZB/jy+2zObuXvnUOYrC99ntdjol9KfoRnHMSxjOL2Se2PT1DoRERGRo6ZPUlEoWFKC+503qX73LYyKCgCs7bOIvXwKrtPPwuJ0RrjC2s4Z0J4KT4BHv93EU3O2EO+0c3FuVqTLajFKvaX8sPc75uyZxYL8eXiCnvB9SY5kTmg7mhPajCY3fRjxjvgIVioiIiLSsigsRZFAQT7uN16j+r/vQ3U1ALbOXYm94kpixk/EYo/e/1yXD+tAucfPCz9s4x9fbSAhxsYZfZt2oYmWbLd7F9/vmc2cPbNYVrSU4AGLM7SNbcfotuMY3XYs/VMHaPRIREREpInoU1YUCOzcgfv1V6j+9GPw+QCw9exN3JSrcY4Zh8XaPJblvuGEzlR4/ExfvJO/fraWOIeNE3tkRLqsZsEwDDaXb+K7PbOYvedbNpStq3V/t8QejGo7htHtxtItUQtpiIiIiJhBYSmC/Fs2U/XqS3hmzoBAaOTAPnAQcVOuwTF8ZLP7QGyxWPj1Sd2o8Ab4ZOUe/vjJah49rz/DO6dGurSoFDACrCpewZw93zJnzyx2Ve0M32fFSv+0gYxuO45RbcfQPk7TGkVERETMprAUAf4N69n+15cp/+ILMAwAHMNHEjf5KhyDh0S4umNjtVj408SeVHr8fLOhkN9+uJInLxzIgKykSJcWFbwBDwsLFvDdnll8v3c2Jd6S8H1Oq5NhGcMZ1XYsx7cZRUqMQqaIiIhIJCksmSxYUUHxL64Nn5PkHHsisZOvwtG7b4Qrazx2q4X7z+zDrz9YwbytJdz23gqe+dlAemQmRLq0iCj3lTFv79zQEt/5P1AdcIfvS3QkMrLNKEa1HctxGSOItUd+hUMRERERCVFYMpnF5SJm3Em44mOxnnsRtq7dIl1Sk3DarfzjnH7c8s5ylu0s45Z3lvPcJYPplNo6wkB+dT7f75nFnN2zWFK0iMABCzRkutowqu1YRrcdy8C0wdi1QIOIiIhIVNKnNJNZ7HaS/nwvGRmJFBSU75uF1yLFOmw8el5/bnhrKevzK7n57WU8d8kg2iW5Il1aozMMg22VW5mzO3T+0drS1bXu75qQw6h2oYDUI6lXszsfTURERKQ1UliSJpXosvP4BQO4fvpSthW7a0aYBpEaF13XijoaQSPI6pKVfLdnFnP2zGZ75bbwfRYs9EsdwKi2YxnVdgwd4jtGsFIRERERORoKS9Lk0uOdPHnhAK57cylbi93c+u4K/nPRQBJdze/w8wV9LC5cyHe7Z/Hd3tkUeQrD9zmsDoakD2NU27Gc0HYMaTFpEaxURERERI5V8/u0Ks1SuyQXT144gJ+/uZS1eyv41fsreOLCAbgctkiXdkTKfWV8tPUD3t3yFsXeovD2eHs8IzJPYHS7cQzPHEGcPT6CVYqIiIhIY1JYEtN0Tovj8QsH8Iu3lrJ0Zxm/++8qHj63Hw5b9F50d497N+9uns4neR/hDlQBkBaTzui2YxnVdiyD04fgsDoiXKWIiIiINAWFJTFVrzYJPHpef255Zzk/bCnmz5+u4f4z+2CzRteCBxvLNvDW5tf5aucX4ZXschK78bOcyzmp/XitYCciIiLSCugTn5huUHYy/zinL7/+YCVfrisg3rmOuyb2xBrhFeIMw2BJ0SKmb5rGj/k/hLfnpg/lkpzLGZYxQqvYiYiIiLQiCksSESO7pPG3M/tw50er+O+KPcQ77fzqxJyIhJFA0M/sPd/y5sZprCtbA4AVK+Pan8TPci6nZ3Jv02sSERERkchTWJKIOblHBn+a2JO/fr6ONxbtINFl5+fHdzbt9asD1XyW9wlvb3mDXVU7AYixxnBax7O4qOslZMVlm1aLiIiIiEQfhSWJqLP7t6PCG+Dhrzfy7PdbSYixc+mQpg0ppd4S3t/yDh9sfZcyXykASY5kzutyIed2voBkZ0qTvr6IiIiINA8KSxJxlw7JpsLj59nvt/Lw1xuJd9qY1L9do79OXnkez654gf/lfYwn6AGgfWwWF3W9lNM6nonL5mr01xQRERGR5kthSaLCdSM7UeHx8/rCHdw/Yx0JThsn98xslOdeW7Ka6ZtfZ9burwkaQQB6JvXmkm6XM6btOGxa2U5ERERE6qBPiRIVLBYLt4/LodIT4MMVu7nrkzU87LRxfJe0o3o+wzCYXzCPNze9xpLCReHtwzNH8rOcyxmcNkQr24mIiIjIT1JYkqhhsVi4c0IPKr1+Zq4r4HcfruLJCwcwKDv5iJ/DH/Tz1a4veGvT62wq3wiAzWLj5KwJ3DDkOtKDWRhGU/0EIiIiItKSKCxJVLFZLfz1jN5Uelcyd0sxt723ggcn9WVE59SffFyVv5JP8j7i3c3T2Vu9B4BYWxxndjybC7r+jHZx7chIS6SgoNyMH0NEREREWgCFJYk6DpuVhyb15Vfvr2BBXim3vbeCP03swVn9Dl30ochTyHtb3ua/W9+nwh8KQqnONC7ocjFndz6XREeS2eWLiIiISAuhsCRRyeWw8dj5A/jr52v5fE0+9362jl2lHq47vhMWi4VtFVt5e/MbzNjxP3xBHwAd4jvxs5zLmJB1Kk5bTIR/AhERERFp7hSWJGo57Vb+ekZvspJdvDgvj2fnbmV16QriMmcxd+8cDEInH/VN6c8lOZdzQtsxWC3WCFctIiIiIi2FwpJENavFwi9GdcbtWM5/895gibEV9obuO6HNaC7JuYL+aQMjW6SIiIiItEgKSxK1vAEvM3d+zlubXmdb5VZscWAYNnylubQPnsoto0+hXZIuJCsiIiIiTUNhSaLSd3tm88SqR9jj3g1AvD2Bszudy8D4M7j3k91sqfRyzRtLeOS8/vRqkxDhakVERESkJVJYkqiyu2oXj696hLl75wCQHpPBRV0v4cyO5xDviAfgxcvacdt7K9hUWMX1by7l/yb1OeqL14qIiIiI1Ednw0tU8AV9vL7xFa6edRlz987BZrFxac5kXhk3nYtzLgsHJYB2SS6ev2QwwzomU+UL8Kv3VvDh8l0RrF5EREREWiKNLEnELSlcxGMr/8nWii0ADErL5bZ+v6VLYtd6H5PosvPvCwbwtxnr+HTVXv42Yz07S6v5xaguWCwWkyoXERERkZZMYUkipshTxNOrH2fmzs8BSHGm8Is+tzIh67QjCjwOm5V7TutF+yQXL/ywjanz8thV5uHPp/bEYdOgqYiIiIgcG4UlMV3ACPDxtg95Ye0zVPjLsWDh7E7ncm2vG0h0JDXouSwWC78Y1YWsJBd/n7me/63eS36Fh4cm9SPRpcNbRERERI6ePk2KqdaVruGRFf9gbelqAHok9eL2/r+jT0rfY3reSQPa0SbRyR8+Ws2CvFKufXMJj53fn/ZaWlxEREREjpLmKokpKnwV/Hvlw9z03XWsLV1NvD2eW/v+mqdGPX/MQWmfkV3SePZng2iT4GRzYRVXv76ENXvKG+W5RURERKT1UViSJmUYBl/umMGV317CB1vfIUiQU7Im8tLYNzivy4XYLLZGfb2ebRKYelku3TPiKaz0cv30pXy3qahRX0NEREREWgeFJWky2yq28rsfb+P+pfdQ7C2iQ3wn/jn839w1+B7SXRlN9rptE2N47pJBjOicgtsX5NcfrODdpTub7PVEREREpGXSOUvS6DwBD9M2vsz0TdPwBX04rU4u734lP+t6OU6b05QaEmLsPHpef/7+xXo+WrmHB77YQLHX4JphWVjQ0uIiIiIicngNHlm64447mDVrFoFAoCnqkWbuh73fc82sy3ltw0v4gj5GZB7P1LHTmNz9atOC0j52m5U/n9qTG07oDMDT327kT5+swesPmlqHiIiIiDRPDR5ZSkhI4K677sLn8zFx4kTOOOMMRowYoQuBtnJ73Xt4YtWjzNnzLQCZrjbc3Pd2xrQdF9Fjw2KxcN3xnWmfFMPfZqxnxpp88ss9/OOcfiTHOiJWl4iIiIhEP4thGEZDH2QYBvPnz+ezzz5jxowZAJx++umceeaZDB48uLFrbFQFBeU0/CduXBYLZGQkRkUtx8of9PPulrd4ef0LVAfcWC02LuzyM67scQ2x9rhIlxdmscDaEg/Xv7KASm+AzqmxPHZBf7KTYyNdWovUko7x5kI9N596bi7123zqufnUc/Ps6/Vh9zuasHSgiooKnn/+eV588UW8Xi9ZWVlcfPHFXHXVVcTExPzkYz0eD/feey8zZszA5XJxzTXXcM011xyy3+TJk/nxxx8P2X7++efzwAMPNKjeaDj4WsobYXnRUh5d8Q82V2wCoH/qQG7v9ztykrpFuLJD7ev5D6t3c9t7K9hT7iEtzsHD5/WnX7vDv1GkYVrKMd6cqOfmU8/NpX6bTz03n3puniMNS0e1wENlZSVff/01n332GXPmzKFt27ZcffXVnHHGGeTn5/PPf/6TH3/8kRdeeOEnn+ehhx5ixYoVvPzyy+zcuZM77riDrKwsTjvttFr7Pf744/h8vvD3S5cu5fbbb+eyyy47mvLlGJV4inl27VN8tv0TAJIcydzQ+2ZO7XAGVkt0L7DYPTOeFy8bzO3vrWBdfiU3TF/K/Wf2YVz39EiXJiIiIiJRpsFh6cYbb+T7778nKSmJ008/nVdeeYWBAweG7+/ZsydlZWXcddddP/k8VVVVvP322zz33HP069ePfv36sX79eqZNm3ZIWEpJSQl/HQgEeOSRR7juuusYMGBAQ8uXYxA0gnya9xHPr/0PZb4yAM7sOInret1IsjM5wtUducyEGJ69ZBB3frSauVuK+f1/V/Kbk7pzcW5WpEsTERERkSjS4LCUkZHBM88885OLOgwbNoy33377J59nzZo1+P1+cnNzw9uGDh3K008/TTAYxGqte4Tivffeo7S0lJ///OcNLV2Owcay9Tyy4h+sKlkBQLfEHtze/7f0S22egTXeaefhc/vxf19u4MPlu/nHVxvYWVrNL8d1xarFSkRERESEowhL9913H9OmTaOgoICzzjoLgJtvvpnRo0dz6aWXApCZmUlmZuZPPk9+fj6pqak4nfuXk87IyMDj8VBSUkJaWtohjzEMg+eff54pU6YQHx/f0NKB0PzESNtXQzTUcjhV/kpeXPc8721+myBBYm1xXN3zOs7vciE2a/O5TFddPXfYrfxpYg+yk108NWcL0xZuZ3d5Nfee3guXwxaZQluI5nSMtxTqufnUc3Op3+ZTz82nnpvnSHvc4E+7jzzyCO+99x733ntveNuIESN46qmnKCoq4uabbz6i53G73bWCEhD+3uv11vmYefPmsXv3bi6++OKGlh2Wnh49J/NHUy0HMwyDGVtn8NCPD7HXvReAiZ0n8vvjfk/b+LYRru7o1dXz35/Vj14dUvjt20v5cl0BJZ4Az00ZRlq8udeFaomi+RhvqdRz86nn5lK/zaeem089jx4NDkvvvvsujz76KMOGDQtvmzJlCr169eJ3v/vdEYelmJiYQ0LRvu9dLledj/n8888ZO3ZsrXOYGqqwMPKri1gsoTdBNNRSlx2V23ls5b+Ynz8PgKy4bG7r/xuGZ44ENxS4yyNcYcMdruejOiTx+AUD+N2Hq1i4tZhzn5jDvy/oT4cULS1+NKL9GG+J1HPzqefmUr/Np56bTz03z75eH06Dw5Lb7SYhIeGQ7ampqZSXH/mH6LZt21JcXIzf78duD5WRn5+Py+UiKSmpzsfMnj2bW265paEl12IYRM3BF021AHgDHt7Y9Bqvb3wVX9CLw+rg0pzJXNZtMk5bTFTVerR+qudDO6bw/KWDuP29FWwrdnPVtCU8fG4/BmTVfTzK4UXbMd4aqOfmU8/NpX6bTz03n3oePRq8zvOYMWO4//772blzZ3jbnj17ePDBBxk9evQRP0+fPn2w2+0sWbIkvG3hwoUMGDCgzsUdioqKyMvLY+jQoQ0tWY7AooIFXDt7Mi+vfwFf0MvQjON4YcxrXNXzOpy2n75eVkuSkx7P1Mty6dM2gRK3jxvfXsbX6wsiXZaIiIiIRECDw9Ldd9+Nz+fjlFNOYeTIkYwcOZITTzyRYDDI3XfffcTPExsby7nnnss999zDsmXLmDlzJlOnTmXKlClAaJSpuro6vP/69euJiYmhQ4cODS1ZDmNJ4SJ+P/9X7KjaTnpMBnfn3sdDxz1Kh/iOkS4tIjLinTx98SBG56Th8Qe547+reH3h9kiXJSIiIiIma/A0vLS0NN58803WrFnDli1bsNvtdOnShe7duzf4xe+8807uuecerrzyShISErj11luZOHEiAKNHj+aBBx7g/PPPB6CwsJCkpKR6lyuXo5Pv3stfF/+JoBFgbLsT+d2Au4h3HN1Kgy1JnNPGP87pxz+/2sC7S3fxyDeb2FXm4fZxOdisOgZFREREWgOLYTR8RqTf76ewsJBAIACEVk7zer2sXr2aM844o9GLbEwFBZE/Yc5igYyMxIjX4g14uf2Hm1hTuopuiT14/IRncNnqXlyjuTvanhuGwavzt/P47M0AnNg9nfvO6K2lxQ8jWo7x1kQ9N596bi7123zqufnUc/Ps6/XhNHhkaebMmfz5z3+mpKTkkPsyMzOjPizJfk+uepQ1patIdCRy79C/t9igdCwsFgtThnekXVIM93y2lm82FHL99KX8YXwP+rbTsp4iIiIiLVmDz1n617/+xYQJE/jkk09ISkrizTff5OmnnyY7O5vbb7+9CUqUpvC/vI/5KO8DLFi4a/C9ZMVlR7qkqDaxdxuevHAgSS47q/dUcOW0xfzpk9XsKHVHujQRERERaSINDkt5eXlcd9115OTk0L9/f/Lz8xk3bhx/+ctfePHFF5uiRmlk60rX8OjKfwJwVY/rQtdPksPK7ZDMtMlDOLNvGyzA52vyuejFBTzyzUZK3L5IlyciIiIijazBYSkpKQm3O/TX9K5du7JmzRoAcnJy2L5dK4ZFu1JvCXcvvBNf0MvxbUZzefcrI11Ss9IuycU9p/fm1SuGMLxTCr6AwesLd3DeCz/y6vw8PP5gpEsUERERkUbS4LA0btw47r33XjZs2MCIESP48MMPWblyJdOnT6dNmzZNUaM0koAR4G+L/8Le6j1kx3XgzkF/xmpp8CEgQK+2CTx50UAev6A/PTLjqfAE+PeszVw4dT6frtpDUGdlioiIiDR7Df6kfNddd9G5c2dWrFjB+PHjGTRoEBdeeCHTpk3jjjvuaIoapZG8uO45FhbOx2Vz8dehD5Dg0AIFx2pklzRevWIIfzmtJ20SnOwu9/CX/61l8quLmLelONLliYiIiMgxaPDS4R9//DGjRo0iNTU1vK2iooKYmBgcDkejF9jYomEpxkgsCzl797f8ZdGdAPxp8L2cnDXBnBeOEmb0vNoXYPrinbw4bxuV3tCy+iO7pHLrmK70bJPQNC8apbT0qfnUc/Op5+ZSv82nnptPPTfPkS4d3uCRpXvvvZfi4tp/MU9ISGgWQam12laxhQeX3QfABV1+1uqCkllcDhtXDu/IB9cO59Ih2ditFn7YUswVry7ins/WsrusOtIlioiIiEgDNDgsjRgxgo8//hiv19sU9Ugjq/JX8pdFf6TKX8XAtMHc0PvmSJfU4qXEOfj1Sd14++phTOyViQF8snIPF764gMdnbaa82h/pEkVERETkCDT4orSFhYU89dRTPP3006SlpRETE1Pr/i+//LLRipNjYxgGDy37O1srtpAek8HduX/Dbm3wf3I5Sh1SYrn/rD5cNjSbx2ZtZvH2Ul6Zn8eHy3dx7fGduXBQexw2LbAhIiIiEq0a/Mn54osv5uKLL26KWqSRvbX5DWbt/hq7xc49Q+4nLSYt0iW1Sv3aJ/HMxQOZs6mIx2dtZnNRFQ9/vZHpi3Zw0+guTOiVicViiXSZIiIiInKQBoel8847rynqkEa2qGABz615CoCb+95Ov9QBEa6odbNYLIzpls7xXdP4aMVunvl+KztKq7nrkzVMW7iDX47tytCOKZEuU0REREQO0OCwNHny5J/8K/grr7xyTAXJsdvr3sN9S+4mSJBTs89gUicF3Ghht1o4b2B7TuvThmkLtvPq/O2s2l3OL95axpicNG4Z25Wc9PhIlykiIiIiHEVYGjFiRK3v/X4/eXl5fPvtt9x4442NVpgcHW/Ayz2L7qLUW0L3pJ7c3v93muIVhWIdNq47vjPnDWzP83O38v6yXczeVMR3m4uY1L8dN5zQmYyEmMM/kYiIiIg0mQaHpVtuuaXO7e+99x4zZszg2muvPeai5Og9seoR1pSuItGRyL1D/k6MTR+4o1l6vJM7xvfgZ0OyeXL2Zr7ZUMgHy3fz2eq9XD6sA5OP60C8U4tyiIiIiERCoy3FddxxxzF37tzGejo5Cp/mfcTHeR9iwcKfBt9L+7isSJckR6hLWhz/OKcfz18yiAHtk6j2B3nhh22c/8J83lmyE38gGOkSRURERFqdBv/JeufOnYdsq6ys5IUXXiA7O7tRipKGW1uymsdW/guAq3v+nOMyR0a4Ijkag7KTeeHSQXy9oZAnZ29mW7GbB7/cwBuLdnDLmK6c2D1d0ypFRERETNLgsHTyySdjsVgwDCP8oc0wDNq3b8/f//73Ri9QDq/UW8JfFv0RX9DLCW1Gc1m3KZEuSY6BxWLh5B4ZjM1J471lu3l+7la2Fbv5/X9XMTAriV+O7cqg7ORIlykiIiLS4jU4LB180VmLxYLD4SAjI0N/8Y6AQNDPfYvvZm/1HjrEdeQPg+7GatGFTlsCu83KxblZnNG3Da8u2M60BdtZtrOM695cysk9MrhpdBc6p8VFukwRERGRFqvBn6qzs7P55ptvWLx4MdnZ2WRlZXHvvffy5ptvNkV9chgvrHuWRYULcNliuXfo30lwJES6JGlkCTF2bhzVhfeuOY5zBrTDaoGv1hfws5cX8tCXGyiq8ka6RBEREZEWqcFh6ZFHHuE///kPcXH7/6I9fPhwnnrqKZ588slGLU5+2qzd3/DmptcA+P3AP9I1sVuEK5Km1CYxhj9N7Mm0KUMZnZNGIGjw9pKdnPf8fF74YSvVvkCkSxQRERFpURoclt59910effRRTj755PC2KVOm8M9//pPp06c3anFSv20VW3hw6d8AuKjrJZzY/pQIVyRm6Z4RzyPn9ec/Fw2kT9sEqnwBnv5uKz97eSHfbijEMIxIlygiIiLSIjQ4LLndbhISDp3qlZqaSnl5eaMU1eIFvHAMH2ir/JXcvfBO3IEqBqcN4fpeNzVicdJcDOuUwkuX5/K3M3rTJsHJztJqfvvhSm5/fwXbit2RLk9ERESk2WtwWBozZgz3339/rSXE9+zZw4MPPsjo0aMbtbgWyecm9eXj4ZH+xKx6E4L+Bj3cMAweWnY/2yq3kuHK5M+5f8Vm1UVLWyurxcKpfdrw9tXHceXwjtitFr7fXMwlLy/gqTmbcWtqnoiIiMhRa3BYuvvuu/H5fJx88smMHDmSkSNHMm7cOAKBAH/5y1+aosaWxQKGKxnKtpP41W9JfXM8zo2fHvFI0/RN05i1+xvsFjv35N5PakxaExcszUGc08YtY7ry5pVDGdklFV/A4MV5eVz04gK+XJevqXkiIiIiR8FiHOWnqLVr17J582bsdjtdunShe/fujV1bkygoKD+WGXCNwhKoJmPTdIKz/om1uhgAX5tBVB7/R3wdRtX7uEUFC/j9j7cTJMjt/X7HpM7nmVVys2exQEZGYlT8929qhmEwa2MhD3+9kZ1lHgCO65TC707uTtd0c5Yab039jhbqufnUc3Op3+ZTz82nnptnX68Pp8EjS16vl4ceeogFCxZw2mmnMX78eH7/+9/zz3/+E5/Pd1TFtjp2F5xwC8WTv6Ny2G0Y9jgce5eS8uHPSP7vZdj3LjvkIXvcu7lvyd0ECXJahzM5u9O55tctzYLFYmFc9wymXzWMnx/fCafNwvxtJVz6ykIe/WYTFZ6GTf0UERERaa0aHJb+9re/8e2339K7d+/wtptuuolvvvmGBx98sFGLa+mMmCSqRvyOwsnfUTXgagyrA2feLFLfPoPEz36BrXgjAN6Ah3sW/ZFSbwk9knpxW7/f6gLAclguh43rT+jC9KuGMbZbOoGgwbSF27noxQX8b/UeTc0TEREROYwGT8MbOXIkL774In369Km1ffny5dxwww18//33jVpgY4uGYc36hlitZduI//FfxKx9DwsGhsVGdZ+fcW9yDJ/s/pIkRxJPj3qRdnHtI1d8M6VhbfhuUxH/+noDeSXVAORmJ/Hbk7vTs03jX8hY/Tafem4+9dxc6rf51HPzqefmabJpeIZh4PF46tyuaXjHJpjUifLxj1F8yQw8XSZgMQJ8uu1DPtn9JRbgT31/p6AkR21UThpvXjmMm0Z3wWW3snhHGZNfW8Q/vtxAebWm5omIiIgcrMFh6dRTT+XPf/4zCxYsoKqqiqqqKhYtWsQ999zDhAkTmqLGVieQ3oeyM1/ku1Mf5f6MdAB+WVTCqZ/eStyCx8FXFeEKpbly2q1cPaITb189jPE9Mwka8NaSnVwwdT7/Xb6boP6MJSIiIhLW4LB055130qNHD6688kqGDh3K0KFDmTx5Mn369OGuu+5qihpbpRJPMXdvm4bPAmMS+3ClPQurt4z4eQ+S/uooXMtfDl3cVuQotEty8cDZfXjywgF0TYuj2O3jvhnruPaNJazarYtLi4iIiMAxLB1eVlbG1q1bcTgcdOjQgYSEBPbu3UubNm0au8ZGFQ1zQA83HzUQ9PP7+b9iceFCOsR34j8nvEC8PZaY9R8SP++f2Mq2hvZL6kzliN/i6XEOWBqce1sVzQGunz8QZPrinTw3dyuV3gAW4NyB7bhpVFdS4hxH9Zzqt/nUc/Op5+ZSv82nnptPPTdPk52ztE9SUhIDBgwgJyeHb7/9luuuu46TTjrpaJ9ODvDCumdYXLgQly2Wvw55gHhHPFiseHqeR9FlX1M+9n6CsZnYyraS9MWtpE4/FeeWL4/4wrYiB7LbrFw+rAPvXD2M0/u0wQDeX7abC1+cz7tLdxII6rgSERGR1umow9LChQv585//zKhRo/jtb3/L7t27+eMf/9iYtbVKs3Z9zZubpgHw+4F30SWxa+0dbE6qB1xJ4eTvqBxxB0FnIvbC1SR/ciXJ71+Ifdf8CFQtLUFGQgx/PaM3z/5sED0y4ymt9vN/Mzdw5bTFLNtZFunyREREREzXoGl4O3bs4IMPPuDDDz8kLy+PpKQkysrKePjhhzn99NObss5GEw3DmvUNsW6t2MJN312HO1DFxV0v4xd9bjn8c1UXE7foSWKXvYglEFql0NNlApUjf08gvc9hHt16aFi7YfxBg/eW7uLp77ZQXnMR2zP7teXWMV1Jj3ce9vHqt/nUc/Op5+ZSv82nnptPPTdPo07De/fdd5k8eTLjx4/nrbfeYtSoUUydOpXvvvsOq9VKjx49jrng1q7SV8ndC/+AO1DF4PQh/LzXL47ocYYrlcoT/kTRFbNx970Mw2IjZssXpL45kcSZt2Et29bElUtLZLdauDg3i3euGcY5/dsB8MnKPVwwdT5vLNqBX1PzREREpBWwH8lOd911F507d+bBBx9k0qRJTV1Tq2MYBg8tu5+8ym1kutrw58F/xWY9ov80YcGELCpOegj34BuIm/cPXBs/xrX2XWLW/xd3vyuoGvZLjLjMJvoJpKVKi3Pyp1N7ct7Adjz45QZW76ng4a838uHyXfzu5O4M7ZgS6RJFREREmswRjSz9/e9/p0OHDtx5550cf/zx3HnnnXz55Zd1XpxWGu7NTa8xe883OKwO7hnyd1Jj0o76uQKp3Sg/7WmKL/oEb8exWII+4pa/SPqro4ib9w8sHp17Ig3Xr30SL12eyx8n9CDZZWdjQRW/eGsZf/pkNXvL9XtAREREWqYGnbNUVFTE//73Pz799FMWLVqEy+WiurqaP/3pT1x88cU4HEe3zLCZomEO6IHzURfkz+eOH39FkCC/7v97zup0bqO+liNvDvE/PIBj71IAgq5Uqobeirv/FLC7GvW1opnmADeeUrePp7/bwnvLdhE0INZh5bqRnbl0aDYOW+jvL+q3+dRz86nn5lK/zaeem089N8+RnrN01NdZ2r17Nx9//DGffvopq1atIiUlhXPOOYc777zzaJ7ONNFw8O37j7Ni2zpumHMNZb5STu9wFr8dcCcWi6XxX9AwcG76H/E/PIi9ZCMAgYT2VB33G6p7XwgNnPLXHOmXT+Nbu6eCh77aEF4pr3NqLL89uRsju6Sp3xGgnptPPTeX+m0+9dx86rl5mjwsHWjLli3h4PTpp58e69M1qWg4+CwWSEx1ctlHV7CudA29knvz2Mj/4LTFNO0LB/241rxD3Px/YavYBYA/tTuVI36PN+f0UGEtlH75NA3DMPh01V7+PWsTRVU+AE7qkcGvT8xhQLdM9dtEOsbNp56bS/02n3puPvXcPKaGpeYkOg4+g8fX/ZP3N7xPkiOZp0dPpV1se/Ne3l9N7IpXiFvwb6yeEgB8bQZTOfw3+DqNA8tRX34raumXT9Oq8Ph5bu5Wpi/aQcCAGLuVm0/qzvl9M4mx2yJdXqugY9x86rm51G/zqefmU8/N06hLh0vj+jjvQ97f8D5WrPw596/mBiUAuwv34Ospmvw9lcNuw7DH4ti7hJSPJ5P22hhiFz2JparA3JqkWUuIsfOrE7sxbcpQhnZMxuMP8vAX67hg6nw+XbWHoH7ji4iISDOkkSWTVfkrOW/mmfiCXq7vfSOX5EyOXDE1LFX5xC16Etfq6Vi95QAYVgeenNOo7ncFvuwTmv0UPf2lxjyGYTBzXT5PztnKjhI3AL3aJHD7uByGdUqJbHEtmI5x86nn5lK/zaeem089N0+jTsO79tprOf744zn++OPp169foxQYKZE++AJBP/csvouuaZ25puuNQBSFEF8VrvX/xbXyNRx7l4Q3+1NyqO57OdV9LsZwpUauvmOgXz7mslggITmOJ79Yy9QftlHpDQAwOieNX47NoWt6XIQrbHl0jJtPPTeX+m0+9dx86rl5GjUszZs3j7lz5/LDDz+Ql5fH0KFDGTlyJMcffzxdu3ZtlILNEg0HX3N4I9jzV+Ba+Rox697H6qsEwLDF4Ol2Rmi0qf3wZjXa1Bx63pIc2O/iSh/P/7CVd5buIhA0sFng3IHtuf6EzqTFOSNdaouhY9x86rm51G/zqefmU8/N02QLPFRUVDB//nzmzp3L3LlzKS8vZ+TIkeHw1LZt26Mu2gzRcPA1pzeCxVtBzPoPcK14DUfBivB2f2pPqvtdRnWvCzFcKZEr8Ag1p563BHX1e2tRFU/M3sw3GwoBiHfauHJ4Ry4dko3LoUUgjpWOcfOp5+ZSv82nnptPPTePaavhFRYW8sMPPzB37lzmzZvHF198cSxP1+Si4eBrlm8Ew8C+dymula/hWv8hFn/oXBTDFoOnxyTc/a7A33ZI1I42NcueN2M/1e9F20t47NvNrNodOj+uTYKTm0Z35fS+bbBG6fHTHOgYN596bi7123zqufnUc/No6fB6RMPB19zfCBZPGTHr3iN25WvYC9eEt/vT++DudwWenudhxCRFsMJDNfeeNzeH63fQMPhiTT5PztnMrjIPEFoE4rZxXTmuU/M8Ly7SdIybTz03l/ptPvXcfOq5eRSW6hENB1+LeSMYBvY9i4hd+Rox6/+LJRD60GvYY6nucQ7V/SfjbzMowkWGtJieNxNH2m+PP8hbi3cwdd42Kjz7F4G4dWxXctLjTaq2ZdAxbj713Fzqt/nUc/Op5+ZRWKpHNBx8LfGNYKkuwbX2HVwrp2EvXh/e7sscQHW/y6nucR44I/fhtyX2PJo1tN8lVbUXgbBa4NwBoUUg0uO1CMSR0DFuPvXcXOq3+dRz86nn5lFYqkc0HHwt+o1gGDh2/RhaSW/DJ1iCXgCCjgQ8Pc/D3e8KApnmLz/fonsehY623wcvAhHnCC0CcdlQLQJxODrGzaeem0v9Np96bj713DxNGpY2btxImzZtSExMZPbs2Xz11Vf07duXiy666KiKNVM0HHyt5Y1gcRfhWvM2rpWvYS/dHN7uazOY6n5XUN1jEjjMud5Oa+l5tDjWfi/eXsqj326qtQjEjaO7cEbftloEoh46xs2nnptL/Tafem4+9dw8RxqWrA194unTpzNp0iRWr17NqlWruPHGG8nLy+Oxxx7jscceO6pipWUyYtNw595A8eWzKDlnOtXdz8awOnDsXULi178l/aVhJMz6E7YDFokQAcjtkMyLlw3m/jN70z4phr0VXu79bB2TX13Ej1uLI12eiIiItBINDkvPP/88Dz74IMOHD+fdd9+lT58+PP/88zzyyCO8/fbbTVGjNHcWC74Ooyg/9T8UXjmfiuPvJJDUGau3jNjlL5H25nhS3juPmLXvQM2S5CJWi4WJvdvw9tXH8cuxXUmIsbEuv5Kb31nO7e+tYFNhZaRLFBERkRauwWFpz549DB06FICvv/6a8ePHA9CuXTsqK/XhRX6aEZeBe8jNFF0xm5Kzp+HJOR3DYsOxaz5JM28n/aVhxM+5B1vxhkiXKlEixm5l8nEdef+a4fwsNwub1cJ3m4u49OWFPPDFegorvZEuUURERFooe0MfkJOTw0cffURaWho7d+5k/Pjx+Hw+pk6dSu/evZuiRmmJLFZ8ncbh6zQOa+UeXKun41r1Orby7cQtfZ64pc/jzRpJdd9L8XY9FcOZEOmKJcJS4hz89uTuXJybzROzN/P1+gLeW7aLz1bvZcrwDlw+tIMWgRAREZFG1eCwdMcdd3D77bdTWlrKZZddRrdu3fjrX//KF198wdNPP90UNUoLF4xvS9WwX1I15Gac277BtXIazq0zce78AefOHzBsMXg7n4yn+yQ8XcaDIzbSJUsEdUqN5aFJfWstAvH0d1t5b+kufjEqtAiEzapFIEREROTYHdVqeMFgkPLycpKTkwEoKCggOTkZh8PR6AU2tmhYXUQrnRyetXwnrtVvErP+A+wlm8LbDXssni4T8PQ4G2+nk8DuOqLnU8/NZVa/g4bBzLX5PDF7M7vKQhdF7pkZz23jchjeObXpXjgK6Rg3n3puLvXbfOq5+dRz8zTp0uGzZs2iX79+pKen88477zBjxgz69u3LTTfdhNMZ3ReQjIaDT2+EBjAM7AUridnwETEbPsJWti18V9CRgLfrRDw9JuHtOBZs9R976rm5zO63xx/krcU7mDpvGxWeAACjuqZx69iudMuI3MWQzaRj3HzqubnUb/Op5+ZTz83TZGHpySef5Pnnn+ell17C4/Fw1VVXcdFFF7FgwQKGDx/OX/7yl6Mu2gzRcPDpjXCUDAP73qX7g1PFzvBdwZhkPF1Pw9PjbHzZo8BWe5RTPTdXpPpd4vbx/NytvLN0F4GggdUC5wxox/UndCEjPrr/kHOsdIybTz03l/ptPvXcfOq5eZosLI0bN47777+f0aNHc9ddd7F9+3Zefvllli9fznXXXce8efOOumgzRMPBpzdCIzCC2HcvImbDf4nZ8Am2qj3hu4KuVDw5Z+DpMQlf1kiw2tRzk0W639uK3eFFIABiHVamHNeRy4d1ILaFLgIR6Z63Ruq5udRv86nn5lPPzdNkF6UtLS0lJycHwzD45ptvOOmkkwBISEggEAg0vFKRo2Gx4m8/jMoxf6Xoyh8pOfdt3P2nEIxNx1pdTOyqaaR8+LOaC9/ehX3nPAgGI121mGTfIhDP/WwQ/dol4vYFeeb7rVwwdT7/XbGbQFD/BxIREZHDa3BY6t27Ny+88AJPPPEERUVFTJgwgT179vDwww8zePDgBj2Xx+Phj3/8I8OGDWP06NFMnTq13n3Xrl3LpZdeysCBAzn77LP54YcfGlq6tFRWG77s46kY93cKr1pIyaQ3cPe9lGBMClZ3PrHLXyblvQvgkX7Ez74H++6F6M81rcPgDsm8eNlg7j+zN1lJMeRXeLnv83VMfm0R87YUR7o8ERERiXINDkv33HMPCxYs4OWXX+bXv/412dnZPP/88+zYsaPB5ys99NBDrFixgpdffpm//OUvPPHEE3z22WeH7FdeXs4111xD9+7d+eijj5gwYQK33HILhYWFDS1fWjqrHV/HMVSc9A8Kr15M6VmvUN37IoLOJCjfSezS50l99xzSXj2e+O//hn3vMgWnFs5isTCxdxvevvo4bhuXQ2KMnfX5ldzy7nJ++e5yNuTrYtoiIiJSt6NaDe9gXq+3wavgVVVVMXLkSJ577jlGjBgBwFNPPcXcuXN59dVXa+37yiuv8Oqrr/LZZ59hs4XON7jgggv45S9/ybhx4xr0utEwB1TzUc1nCXrIKP6R6kXTcW7+Aqtv/wfkQFJnqntMwtP9bALpfUL/geSYRPMxXuL2MfWHbby9ZCf+mkUgzu7XjhtGdSYzISbS5R21aO55S6Wem0v9Np96bj713DxHes5Sgy9KC7Bq1SpeeOEFNm3aRCAQoGvXrlx++eUMHz78iJ9jzZo1+P1+cnNzw9uGDh3K008/TTAYxGrdP+j1448/csopp4SDEsC77757NKVLa2WLgd5nUJExBsPnxrn1K2LWf0TM1pnYyrYSv/Bx4hc+jj+1O57uZ+PpPolAWo9IVy1NICXWwa9P6sbFuVk8OXszM9cV8OGK3Xy+Zi+Tj+vAFcM6EudsmYtAiIiISMM0OCx98cUX/OpXv2LixImcf/75BAIBlixZwjXXXMOjjz7K+PHjj+h58vPzSU1NrTUilZGRgcfjoaSkhLS0tPD2vLw8Bg4cyJ///Ge++uorsrOzueOOOxg6dGhDy4+KQYN9NURDLa1FrZ47YvF1PxNf9zOp8Fbi3DKTmA3/xbn1G+zFG7DPf4T4+Y/gT++Np2bEKZjSNaL1NzfN4RjvmBrL/03qy9IdpTz27WaW7SzjubnbeG/Zbm4c1Zmz+7fDZo3iH+AgzaHnLY16bi7123zqufnUc/McaY8bPA3vrLPO4sILL+Sqq66qtf2ll17i/fff58MPPzyi5/nggw947LHH+Prrr8Pb8vLyGD9+PN9++y3t2rULb58wYQLFxcVMmTKF8ePH88knnzBt2jT+97//0b59+4aUL1K/6jJY+ymseA82fgVB3/772g+CfudDv/MgtXPkapQmYRgG/1uxm//73xq2FVUB0LNtAnee0YcTe2Zi0f+1REREWqUGjyzl5eWFlws/0EknncTDDz98xM8TExOD1+uttW3f9y6Xq9Z2m81Gnz59+OUvfwlA3759+e677/jwww/5xS9+0aD6CwsjPwfUYoH09MSoqKW1OLKeWyD7TMg+E0t1Cc5NnxGz/iMc2+dg2bUUdi2FmX/B1/44qobegq/zyfrTTz2a4zE+vH0C068cwttLdvLC3G2s21PB1S/OZ3jnFG4bl0OvNgmRLvEnNceeN3fqubnUb/Op5+ZTz82zr9eH0+Cw1K1bN2bNmsXkyZNrbf/222/Jzs4+4udp27YtxcXF+P1+7PZQGfn5+bhcLpKSkmrtm5mZSU5OTq1tXbp0YdeuXQ0tH8OInsXPoqmW1uJIe27EpFDd5xKq+1yCxV1IzKb/hYLTzrk4ds0n+eMr8WUOoGrY7Xi7TlRoqkdzO8btViuXDunAmX3b8uK8PKYv3sGPW0u44pVFnNGvLTeO6kLbxOheBKK59bwlUM/NpX6bTz03n3oePRoclm699VZuvfVWli5dyqBBgwBYsmQJn3/+OQ899NARP0+fPn2w2+0sWbKEYcOGAbBw4UIGDBhQa3EHgMGDBzN//vxa2zZt2sRZZ53V0PJFGsyITae63xVU97sCa+UeYpc+T+zyl3HkLyf5f9fiT+9L5XG34c05HSwNXo1folCSy8Ft43K4cHB7/jNnC5+vyeeTlXuYuTafy4dmM2V4R+KdR7U+joiIiDQjDf5kd9JJJ/Hcc8/h8Xh44403eO+99zAMg9dff50zzjjjiJ8nNjaWc889l3vuuYdly5Yxc+ZMpk6dypQpU4DQKFN1dTUAl1xyCWvXruXxxx9n69atPPbYY+Tl5XHOOec0tHyRYxKMb0vlCXdROOUHKofeStCRgL1wFcmf3UDqmxOIWf9fCAYiXaY0kuzkWP52Zh9eumwwudlJePxBps7L4/wX5vPu0tDS4yIiItJyNcp1lgA8Hg979+6lY8eOR/wYt9vNPffcw4wZM0hISODaa68NLxzRq1cvHnjgAc4//3wgNOp0//33s379erp168Zdd93Fcccd1+A6o2Hdeq2hb76m6rmlupjYpS8Qu2wqVm8ZAP7U7lQN/SWeHpPA2jpHH1riMW4YBt9uKOTx2ZvZVuwGoEtaLL8cm8PonLSILwLREnse7dRzc6nf5lPPzaeem+dIr7PUaGFp9uzZXH/99axevboxnq7JRMPBpzeC+Zq65xZPKbHLphK79HmsnlIA/MldqRr2Szw9z2t1oaklH+P+QJD3lu3iubnbKHGHVkwc2jGZ28bl0Kft4X/pNpWW3PNopZ6bS/02n3puPvXcPEcalnSChUgjMGKSqTruVxRN+YHKEXcQjEnBXrqZpC9/Rdq0cbhWvQEB7+GfSKKe3Wbl4txs3r/2OK4c3hGnzcLCvFKmvLaYuz9dw+6y6kiXKCIiIo1EYUmkERnORKqG3UrRlB+oOP6PBGPTsZVtJfHr35E2bSyuFa9BwBPpMqURJMTYuWVMV9655jhO79MGgP+t3ssFU+fz+KzNVHj8Ea5QREREjpXCkkgTMJwJuIfcROHkuVSMuptgbCa28u0kfvsH0l4bjWv5S+DXCERL0D7JxV/P6M0rV+QytGMy3oDBK/PzOO+F+by1eAf+QDDSJYqIiMhROqITKQ5etrsua9euPeZiRFocRxzuwdfj7j+Z2JWvE7v4KWwVu0ic9SfiFjyOe8iNuPtdDvbYSFcqx6hP20T+c9FA5mwq4t+zNrGlyM0/vtrI9MU7uXVMV8Z1T4/4IhAiIiLSMEe0wEPv3r2P7MksFi3wcAR08p75oqbn/mpcq6cTt+gJbBWhiyoHYzOpyv0F7v6TwREXweIaT9T0O0L8QYMPl+/i2e+3UlQVWgRicHYSt4/LoV/7pMM8+ui09p5HgnpuLvXbfOq5+dRz85i+Gl5zEQ0Hn94I5ou6ngc8uNa8TdzCJ7CVbwcg6EqjKvcGqvtfieFMiHCBxybq+h0hlV4/r8zfzrQF2/H4Q9PxJvbK5KYxXchObtzRRPXcfOq5udRv86nn5lPPzaPV8ESimS2G6n5XUHT5bMpP+ieBpM5Yq4tImPsAaa+MJG7Bv7F4yiJdpRyjeKedG0d14d1rjuOsfm2xADPW5nPRiwt49JtNlFX7Il2iiIiI/ASFJZFIsjmo7nsJRZd/S9kpj+JP7orVU0L8vIdIe/V44n58GEvNdZuk+WqbGMNfTuvFq5OHcFynFHwBg2kLt3PeC/N5feF2vH4tAiEiIhKNFJZEooHVjqf3hRRf9g1lEx7Hn9odq6eU+PkPh0aa5v0DS3VxpKuUY9SrTQJPXjiAR8/vT056HGXVfh75ZhPnT53PB8t2aeU8ERGRKKOwJBJNrDY8Pc+j+JIvKZv4H/xpvbB6y4lf8Bhpr4wkfu7/YXEXRbpKOQYWi4VRXdOYNmUof5zQg8wEJ3vKPdz/xXouemkBn67aQyCoieoiIiLRQGFJJBpZbXh6nE3xJV9Qetqz+NP7YvVVErfoCdJfGUn893/DUpUf6SrlGNitFs4b2J73rjmOX52YQ2qsg+0l1fzlf2u59OWFfLkun6DO7hUREYkohSWRaGax4u12BsU/+5zSM6biyxyIxV9F3OKnSX/1eOLn3IO1cnekq5Rj4HLYuGxoBz64bjg3je5CksvO5qIq/vDRaia/uojZGwtpZYuWioiIRA2FJZHmwGLB23UiJRd9QumZL+NrMxiLv5q4pc+T9uooEmbdha1kU6SrlGMQ57Rx9YhOfHjdcK4b2Yl4p411+ZX8+oOVXPPGEuZtLVZoEhERMZnCkkhzYrHg7XIKJRd+RMnZ0/C1G4Yl4CF2+cukTRtL8n8vx7n5CwgGIl2pHKWEGDs3jOrCB9cOZ8pxHYixW1mxq5xb3lnOL95axpLtWh1RRETELLoobQTogmPma7E9NwwcO74ndulzOLd8iYXQDxdI7Ii7/xSq+16C4Uo1vawW2+8IKKj08tK8bby3bBe+QKiZx3dJ5RejutC33f6L6ann5lPPzaV+m089N596bp4jvSitwlIE6I1gvtbQc2vpVmJXvIJr9ZtYa67NZNhiqO5xLtUDr8KfOcC0WlpDv822u6yaqfO28d8V+1fLG9ctnV+M6kL3zHj1PALUc3Op3+ZTz82nnptHYake0XDw6Y1gvlbVc58b1/oPcS1/EUfByv2b2w3FPeAqPN3OBJuzSUtoVf022fYSN8/P3cr/Vu8laIAFmNArk+tHdWZYz7bquYl0nJtL/Tafem4+9dw8Ckv1iIaDT28E87XKnhsG9t0LiV3+IjEbP8US9AEQjM3E3e8yqvtdTjAhq0leulX222SbC6t49vutzFwXWkLeaoHzcjswZUh7spJjI1xd66Dj3Fzqt/nUc/Op5+ZRWKpHNBx8eiOYr7X33FK5l9hVr+Na+Sq2yj0AGBYb3pzTcA+4Cl/WyFCTGuv1Wnm/zbRubwXPfL+VWRsLAbBZLZw7oB1Xj+hE28SYCFfXsuk4N5f6bT713HzquXkUluoRDQef3gjmU89rBHw4N39O7PKXcO78IbzZn9YL94CrqO55Pjjjj/ll1G/zrdxdxtT5O5hVM9LktFk4f1AWVw3vSHp80067bK10nJtL/Tafem4+9dw8Ckv1iIaDT28E86nnh7IVriZ2+cu41r6Lxe8GIOhMpLr3xVQPuJJASs5RP7f6bb59Pf9iyXaemrOFxTVLjLvsVi7OzWbycR1IiXVEuMqWRce5udRv86nn5lPPzaOwVI9oOPj0RjCfel4/i6cU15q3cS1/GXvp5vB2b8dxuAdchbfzyWC1New51W/THdjzYNDgx60l/Oe7LazcXQ5AvNPGZUOzuWxoBxJi7BGutmXQcW4u9dt86rn51HPzKCzVIxoOPr0RzKeeHwEjiCNvVmiK3jFes0n9Nl9dPTcMgzmbivjPd1tYn18JQLLLzhXDOvCzIdnEOhoWgqU2HefmUr/Np56bTz03j8JSPaLh4NMbwXzqecMc6zWb1G/z/VTPg4bBV+sKePb7rWwuqgIgLc7BlcM7csGgLGLs1ghU3PzpODeX+m0+9dx86rl5FJbqEQ0Hn94I5lPPj9JRXrNJ/TbfkfQ8EDT4fM1env1+KztKqwFok+DkmpGdmNS/HQ6bQlND6Dg3l/ptPvXcfOq5eRSW6hENB5/eCOZTz49R+JpNLxGz8ZPDXrNJ/TZfQ3ruDwT5aOUeXvhhG3vKPQBkJcVw3fGdOb1vW+zWxltGviXTcW4u9dt86rn51HPzKCzVIxoOPr0RzKeeN54juWaTxWpRv012NMe41x/kg+W7mDovj8JKLwCdUmO54YTOnNIzE5tC00/S7xVzqd/mU8/Np56bR2GpHtFw8OmNYD71vAn8xDWbqgdeRcLxUygoN9RvkxzLMV7tC/D2kp28/GMepdV+ANolxnDOgHZM6t+ONrq4bZ30e8Vc6rf51HPzqefmUViqRzQcfHojmE89b1p1XbMJVzKVQ26masA1YHdFtsBWoDGO8UqvnzcX7eCNhTvCoclmgdE56Zw3sD0ju6RqtOkA+r1iLvXbfOq5+dRz8ygs1SMaDj69Ecynnptj3zWbYpe/jK3mmk2BhGwqR/4eT8/zwKIFBJpKYx7jHn+Qr9bn8/6y3eGL28L+0aZzBrQjM0GjTfq9Yi7123zqufnUc/MoLNUjGg4+vRHMp56by0KQjB2fEpj5V2wVOwHwZQ6g8oQ/4eswKsLVtUxNdYxvLqzig+W7+GTlnlqjTWO6pXPuwPaM7Nx6R5v0e8Vc6rf51HPzqefmUViqRzQcfHojmE89N1e437v34lo6lbiFT2D1lgPg6XwylcffRSC9V4SrbFma+hj3+IN8uS6fD5btYvGOsvD29kn7z21qbaNN+r1iLvXbfOq5+dRz8ygs1SMaDj69Ecynnpvr4H5b3EXEzX+E2JWvYgn6MSxWqvv8jKrhvyUY3zbS5bYIZh7jmwor+WDZbj5ZtYeyg0abzhvYnhGtZLRJv1fMpX6bTz03n3puHoWlekTDwac3gvnUc3PV129bySbif/g/YjZ+CoBhj6Vq8A1U5d4IzvgIVdsyROIY/6nRpnMHtOfs/m1b9GiTfq+YS/02n3puPvXcPApL9YiGg09vBPOp5+Y6XL/tuxaQ8P19OHYvBEIXt60c/huq+14CVrvJ1bYMkT7GW+NoU6R73tqo3+ZTz82nnptHYake0XDw6Y1gPvXcXEfUb8PAuelT4uc+gL10CwD+1B5UHv9HvF3Gh55Ejli0HOPVvgBfrS/g/WW7WFLHaNOk/m3JaCGjTdHS89ZC/Tafem4+9dw8Ckv1iIaDT28E86nn5mpQvwNeXCtfI37+I1iriwHwZo2kctSf8bcZ1PTFthDReIxvKqzk/WW7+WTlHso9tUebzh8UGm2yNuNQHI09b8nUb/Op5+ZTz82jsFSPaDj49EYwn3purqPpt8VTRtyiJ4ld+jyWgAeA6h7nUDnyDwSTOjZhtS1DNB/j9Y02ZSXFcE4zHm2K5p63ROq3+dRz86nn5lFYqkc0HHx6I5hPPTfXsfTbWr6D+Hn/IGbtu1gwMKxO3AOvpmrorRiulCaptyVoLsf4xoJKPlh+0GiT1cLYbumcN7Bdsxptai49bynUb/Op5+ZTz82jsFSPaDj49EYwn3pursbotz1/BfHf/w3n9jkABGOSqRp2G+4BV4Kt+Y1CNLXmdoxX+wJ8uS402rR0Z+3RpnMHtufs/u3IiHdGsMLDa249b+7Ub/Op5+ZTz82jsFSPaDj49EYwn3purkbrt2Hg3PY18d/fj71oLQCBpE5UjrwDT/dJWgTiAM35GN9QUMkHy3bx6aq9h4w2nT+wHcOjdLSpOfe8OVK/zaeem089N4/CUj2i4eDTG8F86rm5Gr3fwQCuNW8RN++f2Kr2AOBrM4jKUX/GlzWyEV6g+WsJx3i9o03JLs4d0I6JvTPJTo6NYIW1tYSeNyfqt/nUc/Op5+ZRWKpHNBx8eiOYTz03V5P121dF3JJniV38H6y+SgA8XSZSecIfCaR2b8QXan5a2jG+b7Tpk1V7qPAEwttz0uMY0y2dMTlp9G+fFNFrN7W0nkc79dt86rn51HPzKCzVIxoOPr0RzKeem6up+22pyif+x4dxrXodixHAsNio7nsZlcN/jRGX2fgv2Ay01GN832jTxyt3s3h7KYEDfraUWAejuqYypls6IzqnkhBj7gWNW2rPo5X6bT713HzquXkUluoRDQef3gjmU8/NZVa/bcUbiP/+78RsmQFA0BGPO/dGqgZfD464pnvhKNQajvGyah9zNxcze1Mh328uDp/fBGC3WhjaMZkxOemM6ZZOVrKryetpDT2PJuq3+dRz86nn5lFYqkc0HHx6I5hPPTeX2f127JhL/Pd/w7F3KQCB+LZUDf8t1b0vBqut6QuIAq3tGPcHgizdWcasjYXM2VTEtmJ3rfvNmK7X2noeaeq3+dRz86nn5lFYqkc0HHx6I5hPPTdXRPptBInZ8BHxc/8PW3keAP60XlSecBfeTie1+JXzWvsxvrWoitmbipi9sZClO2pP10uNdXBCThpjc9IY0SWVeGfjTNdr7T03m/ptPvXcfOq5eRSW6hENB5/eCOZTz80V0X4HPMQuf5m4BY9h9ZQC4O0wmsoT/oQ/s7/JxZhHx/h+pW4fc7cUM3tjId9vKaq1QITDZmFohxRG56Qd83Q99dxc6rf51HPzqefmUViqRzQcfHojmE89N1c09NtSXUzcwieIXfYilqAXAwueXudTNfTWFrlyXjT0PBodbrpet4y48HlO/dolNmi6nnpuLvXbfOq5+dRz8ygs1SMaDj69EcynnpsrmvptLdtG/A8P4lr/YXibp/MpuAdfjy/7hBYzPS+aeh7NthRVMXtjIbM3FbGsjul6o3LSGHOE0/XUc3Op3+ZTz82nnptHYake0XDw6Y1gPvXcXNHYb/ueJcQtfBzn5hlYCBXly+iHe/DP8XSfBDZnhCs8NtHY82hX6vbx/ZYi5mwsqne63phuoel67ZMOna6nnptL/Tafem4+9dw8Ckv1iIaDT28E86nn5ormfttKNhG79AVca6Zj8VcDEIhri3vg1VT3uxzDlRrhCo9ONPe8OfAHgizZUcbsTYXM3lhIXkl1rfu7Z8SHglNOOn1rpuup5+ZSv82nnptPPTePwlI9ouHg0xvBfOq5uZpDvy3VxbhWTiN22YvYqvYAYNhjqe59Me5B1xJIyYlwhQ3THHreXBiGwdYidyg4bSpi6Y5SgnVM1xvbLZ1TB3fAV1WtnptAx7j51HPzqefmUViqRzQcfHojmE89N1ez6nfAS8yG/xK3+FnshasAMLDg7ToR9+Cf42s/olmc19Sset7M7JuuN3tjEXMPmq4H0CUtlgHtkxiQFfqXkx6HtRkcM82NjnHzqefmU8/No7BUj2g4+PRGMJ96bq5m2W/DwLHje2KXPEvM1i/Dm32ZA0PnNXU7C2yOCBb405plz5shfyDI4h2lzNlUxHebi9ha5D5kn3injf7tE8MBqn/7RJJc0XvsNBc6xs2nnptPPTePwlI9ouHg0xvBfOq5uZp7v23FG4hd+jyuNW9jCXgACCS0xz3gGqr7XYYRkxzhCg/V3HveHFksYI2N4dsVO1m2s4zlO8tYubscty94yL4afTp2OsbNp56bTz03j8JSPaLh4NMbwXzqublaSr8t7iJiV75K7LKXsLrzATDscbj7XoJ74LUEkztHuML9WkrPm5O6eu4PGmwsqGT5zjKW7woFqIMXiwCNPh0NHePmU8/Np56bR2GpHtFw8OmNYD713Fwtrt/+amLWf0jckmexF60FwLBY8XY9larB1+NvNyzi5zW1uJ43A0fa8+IqLyt2lYfDU32jT13T4hiQtT9AddXoUy06xs2nnptPPTePwlI9ouHg0xvBfOq5uVpsvw0Dx/bZxC15Fue2b8KbfW1zcQ+6Hk+308H60xcybSottudR7Gh7frSjTwPaJ5HoiszxFQ10jJtPPTefem6eIw1Lrfe3rohIQ1ks+DqOpbTjWGyFa4ld+hyude/j2LMYx4wbCSRk4x50LdV9LsGISYp0tRKl7FYLvdok0KtNAhcOzgJCo0/Ld5WHA9TKXeVUegPM21rCvK0l4cdq9ElExFwaWYoA/dXAfOq5uVpTvy1V+cSueIXY5S9jrS4CIOhIoLrvpaHzmpI6mFNHK+p5tGjKnvuDBhvzK1lWM/K0fFcZ2+sYfUqIsdG/XVIoQGUl0b9dyx190jFuPvXcfOq5eTQNrx7RcPDpjWA+9dxcrbLffjeude8Tu+Q57MXrgdB5TZ5uZ+Ie9HP87YY06cu3yp5HmNk9L6rysnxn6NynFTWjT9X+us996t8+kf5ZSQxon0hOejw2a/MffdIxbj713HzquXkUluoRDQef3gjmU8//v737Do+iahs4/Nu+STY9IZUOCS2EEkSakSoWEPnAjiIiqCDKqyJgw4IoWFDRlyJib4ii+NpAUJqAtFCkhNACCSGk163z/bFhIWRDz6bw3NfFld2ZszNnHs5u8uwp41lXdLwVB/rDf+K1dR76I6tcm63hCRS3ewBL4/6g1lz2017RMa8m1R3z8+198tKpaRXuS5sIZ/LUOsKPEB+95yt8iao73lciibnnScw9R+YsCSFEdVCpsTTshaVhLzQn/sU76QMMe79Hd2wj/r9uxO7XgJK291Pa8jYUvam6aytqMa1aRWyYidgwE0PL5j5ll628tzM9n+3pBfx7zDn3aVNqHptS81yvjfAz0CbCz7WARGw9E3qturouRQghaqxq7Vkym8288MIL/P777xiNRkaMGMGIESPcln3ooYdYvnx5uW2zZ8+mZ8+eF3TOmpCpy7cGnicx9yyJd3nqogyM2z/Ga8cnqM25ADj0fpS2vpOSuBE4fCMv+RwSc8+rDTG3OxQOZBezIy2fHccK2JGez/4TxZxZ3ZOLTpxMnlpH+BLlb0RVgxaPqA3xrmsk5p4nMfecWtGzNH36dHbs2MHHH39MWloaTz31FJGRkfTv379C2ZSUFGbMmEGXLl1c2/z9/T1ZXSGEuCgOnzCKr55AccdHMO75Fq+keWhz9+O9ZTZeW+dhbnaTc15TWLvqrqqoYzRqFc1CfGgW4sOgthEAFJpt/HusgJ3HnKvv7UgvIKfEys6ybV9vSQMg0EvnTJ4i/Wgd7kurcF9MBhmQIoS4slTbp15xcTELFy5k3rx5tG7dmtatW5OcnMznn39eIVmyWCwcOXKEuLg4QkNDq6nGQghxiXRelLYZRmnru9AfWo7X1rnoj67FmPwDxuQfsEZc5ZzX1KhflcxrEgLAZNByVcNArmoYCICiKBzNK2VHurPnaUd6AXuOF5JTYmXV/mxW7Xeu8qgCmoR4O4fvhTsXkGgc5F0nFo8QQojKVFuytHv3bmw2G+3bt3dt69ixI7Nnz8bhcKBWnxo7vX//flQqFfXr16+OqgohxOWlUmNp1AdLoz5oM3fglTQPQ/IP6NI34J++AbtfQ0ra3ue8X5PMaxJVTKVSER3gRXSAF/1b1gPAbHOw53ihK3nakZ5Per6ZlBPFpJwo5oftxwDnjXNbhfu6Fo5oE+FLkHftWzxCCCEqU23JUmZmJoGBgej1pz5UQ0JCMJvN5ObmEhQU5Nq+f/9+TCYTEyZMYMOGDYSHh/PII4+QmJh4weetCcOvT9ahJtTlSiEx9yyJ9/mz12tDYd+3Ke4yCeP2jzHu+BRN/iFMq6fgveENSlvdSWn8CBy+UWc9jsTc8+pyzI06NfFRfsRHnbq58olCc1ni5Fy+/OTiEf8czuWfw7muclH+RuIifV0LSMTWM6HTXPriEXU53jWVxNzzJOaec74xrrZkqaSkpFyiBLieWyyWctv3799PaWkp3bt3Z9SoUSxdupSHHnqIr7/+mri4uAs6b3DwuSdyeUpNqsuVQmLuWRLvCxDiC41ehusmQ9KXsO591Fn78N46B++kD6DVQOgyFqITznoYibnnXSkxDwnxpUWjEIaUPbfZHezNKGRrai5bDuewJTWXfccLOZpXytG8Un7dlQmAXqumY4NA+rYKo2+rMOoHeV9SPa6UeNckEnPPk5jXHNW2Gt4vv/zCyy+/zJo1a1zbUlJSuOGGG1i/fj0BAQGu7Q6Hg4KCgnILOjz44IOEhoby0ksvXdB5s7Kqf3URlcr5JqgJdblSSMw9S+J9GSgOdIdWlN2vabVrszW8IyXtHsDSpD+oT33fJTH3PIl5RQWlNnYecw7d257mHL6XV2orVyYm1IfEZsEkNgsmtp7pvFfck3h7nsTc8yTmnnMy1udSbT1LYWFh5OTkYLPZ0Gqd1cjMzMRoNOLn51eurFqtrrDyXZMmTdi3b98Fn1dRqDGNrybV5UohMfcsifelUGNp2BtLw95n3K9pE7pfN2H3jXber6nV7Sj6Ux/2EnPPk5ifYjJo6dwwiM4NnUPpFUXhUE4Ja/Zn81dKFklH89ibWcTezCLm/X2YMF8D1zQNJrFpMB3q+5/XcD2Jt+dJzD1PYl5zVFuy1LJlS7RaLVu3biUhwTmsZNOmTcTFxZVb3AFg4sSJqFQqpk2b5tq2e/duYmJiPFpnIYSoDvaQVhT0fpPCqyfitcN5vyZNwRFMa14om9d0B6XxIyCkVXVXVYhyVCoVjYK8aRTkzV0J0eQWW1l9IIu/9mWx7mAOGQVmFm5NY+HWNEwGDV0bBZHYLJiujYNkmXIhRI1QbZ9EXl5eDBo0iClTpvDKK69w/PhxPvzwQ1dClJmZia+vL0ajkV69evGf//yHzp070759e5YsWcKmTZt48cUXq6v6QgjhcYpPPYo7P0lxx7EY93yHV9IHaHOS8U6ah9e2+dByANqWI7CGd6zuqgrhVoC3jptah3NT63BKrXY2HM5l5b4sVu3PIrvYyu97Mvl9TyZatYqO9f1JbBbCNU2DCfM1VHfVhRBXqGqbswTORR6mTJnC77//jslk4v7772f48OEAxMbGMm3aNAYPHgzAwoUL+eCDD0hLS6N58+ZMmjSJTp06XfA5a8IdkeXuzJ4nMfcsibeHKA50h//CO2ke+tSVrs3WsPaUxD+AuekN5eY1ictL2vnlY3co7EjPZ2WKs9fpUE5Juf0tw0wkNgvm5oQGhGjBedcnUdWkjXuexNxzTsb6nOWqM1mqDjWh8ckbwfMk5p4l8fY8bdYuAvd8jJL0DSqHc0VRuynKeb+mVnegGPzPcQRxoaSdV52D2cWs3JfFXylZbE/L5/TwRvoZuKZZCIlNg2kX7Y9WbopbZaSNe57E3HMkWapETWh88kbwPIm5Z0m8Pe9kzLMO78e4/RO8dnyCuiQLAIfOh9KWt1PSdgQO/4bVXNO6Q9q5Z2QVWVi935k4bTiUi9nmcO3zN2rp1iSIxKbBXN0oCG+9phprWvdIG/c8ibnnSLJUiZrQ+OSN4HkSc8+SeHtehZjbSjHu/d45ryl7DwCKSo2lcT+K40dhi+gkdz28RNLOPUulAm8/L/63KZU/92WxOiWr3LLkeo2KTg0CuaZZMNc0CSLEJPOcLpW0cc+TmHuOJEuVqAmNT94Inicx9yyJt+dVGnNFQXdkFd5b56I//Kdrs7VefNm8phtBo/N4fesCaeeedWa8bQ6FbWl5/LUvi5UpWRzJLS1Xvk2EL9c0DebaZiE0CvI67/s5iVOkjXuexNxzJFmqRE1ofPJG8DyJuWdJvD3vfGKuyd6LV9IHGPcsQmU3A2A3RVASdx+lre5EMQZ4rsJ1gLRzzzpbvBVFYX9WsWuBiJ3HCsrtbxDo5bqfU1ykHxqZ53RepI17nsTccyRZqkRNaHzyRvA8iblnSbw970JirirJwmvHp3ht/xh1SSYAitab0pa3Utz2fhwBjT1Q49pP2rlnXUi8MwvNrsRpY2ouVvupFwR66ejeJIhuTYJoF+VPsI++imtee0kb9zyJuedIslSJmtD45I3geRJzz5J4e95FxdxuxrD3B7yT5qHN2gWAggpLo76Ymw/EUr8Hildw1VW6lpN27lkXG+8ii42/D+TwV0oWa/ZnU2C2ldvfINCLdlF+tIvyp320P1H+RhmyV0bauOdJzD1HkqVK1ITGJ28Ez5OYe5bE2/MuKeaKgu7IGryS5mE49MepzaiwhcZhrX8NlgaJzpvdauRb+JOknXvW5Yi3ze5gy9E8VqZksyk1l32ZRZx5qBAffVni5Eygmob4XLHD9qSNe57E3HMkWapETWh88kbwPIm5Z0m8Pe9yxVyTsw/jrq/QH/7L1dt0kqL1xhLdFUv9RKwNErH7N76iV9STdu5ZVRHv/FIr29Ly2XIkn61H8/j3WAE2R/mDmwwa4iP9aRflR/tof1qG+aLXqi9PBWo4aeOeJzH3HEmWKlETGp+8ETxPYu5ZEm/Pq4qYq4sy0B1Zhf7wX+hTV7ru23SS3bc+lpO9TtHdrrgb30o79yxPxLvUamfnsQK2Hs1j65F8tqXlU2y1lyuj16hoHeFH+yg/2kX7Exfhh8mgrZoKVTNp454nMfccSZYqURMan7wRPE9i7lkSb8+r8pgrDrQn/kWX+hf6w3+hS/8HlcN6ardKjS2sPZb6iVgaJGKrFw/quvkH5EnSzj2rOuJtcygkZxay5UgeW4/ms/VIHjkl1nJl1CpoHmqifbQ/7aP8iK9Di0ZIG/c8ibnnSLJUiZrQ+OSN4HkSc8+SeHuex2NuLUZ/9G9n8pS6Em3OvnK7HQZ/rNHdnMlT/UQcftEeqJRnSTv3rJoQb0VROJRTwtYjeWw9mseWo/mk5ZVWKNcg0Iv2Uf7Elw3dq62LRtSEmF9pJOaeI8lSJWpC45M3gudJzD1L4u151R1zdf4R9EdWOnudjqxGbc4rt98W0MQ118kS2QX0Pp6v5GVW3TG/0tTUeB8vMDsTp7Lep5QTFReNCDXpiY88tWhEs1Af1LUgeaqpMa/LJOaeI8lSJWpC45M3gudJzD1L4u15NSrmDjva41vRp6509jod24xKOTXvQ1HrsEYkuJInW0hrUNW+CfM1KuZXgNoS7/xSK0lH88sSqHx2ZdTeRSNqS8zrEom550iyVIma0PjkjeB5EnPPknh7Xk2Oucqch+7oWudCEYf/QlOQWm6/wysYS3QP50IR9a/B4RNWTTW9MDU55nVRbY33yUUjtpQN3dueVlBh0QiDVk2rcF/aRfnRJsKPNhG+BHlX/7yn2hrz2kxi7jnnmyzV7dm3Qgghqp1i8MfS5HosTa4HRUGTdwBd6kr0h1eiO7oGdUkWxuTFGJMXA2ALbuFaKMIacRVojdV7AUJcAqNOQ8f6AXSsHwA4F43Ye7zQNXQv6Wg+OSVWthxxPj8p0t9IXIQvrSP8iIvwJSbUVCN7n4So66Rn6TSKomCzWd3vvIxUKggM9CEnp0i+NfCQ2h5zjUaLWl17fknKN2OeV2tjbregy9iMrmx5cu3xbahOm/GhaAxYo652DtmL6oItuBWoNdVY4VNqbcxrqboa79MXjdiens/29AIOZhVXmPek06iIrWeidbgvcRF+tIn0JdKvaheOqKsxr8kk5p4jw/AqUVnjs9msZGUdQ1EcHqmHWq3G4fDMuYRTbY+5l5cJP7+gWrGiknzYe15dibmqJBv9kVXoDq9En/onmqKMcvsdOhO2iI5YIzpjjeiENawdaL2qp651JOa1xZUU70KzjZ3HCtiRns+O9AJ2pBeQW1Lxy9xALx1tInxdQ/dahfte1ns+XUkxrykk5p4jyVIl3DU+RVHIzj6Ow2HD3z8YlQcmGms0Kuz2Kyr01a62xlxRFCwWM4WFOXh5mfD3D67uKp2TfNh7Xp2MuaKgyd6LPnUlutSV6I5tRG0pKF9ErcNWL96ZOEV2xhqegGIM8Ej16mTMa7ArOd6KonA0r7QscXImUHuOF1ZYOEIFNA72Ji7Cj9YRzh6oxsHeaNQX9yXblRzz6iIx9xxJlirhrvHZ7TYyM4/i7x+Cl5dnlrPVatXYbLW3l6M2qu0xLyzMp7Awh3r16tf4IXnyYe95V0TMHXY0WbvRpa9Hl7YBXfoGNMXHyxVRUGEPjsUacZXzX+RVOEyRVVKdKyLmNYjEuzyzzcHe44VsT89nZ1kSlZZvrlDOW6ehVbjJNfepdYQfIed501yJuedJzD1HFni4ACeHZmk0Eg5Rc+n1BsCZ3KvV1b9KkhAep9ZgD22NPbQ1pW1HgKKgzj/kSpx06RvQ5u5Hm7UbbdZuvHZ8AoDdN9qVOFkjOmMPbOb8LSlELWbQqomL9CMu0s+1LavIwo70AnYec859+jfdufLextQ8NqaeWjwiws/gGrrXJsKP2HomDLJ4hBBuSXZwmtowF0RcuaR9CnEGlQqHfyPM/o0wt7zVuak4E136P87kKW0D2hM70BQcQVNwBOPe7wBwGANP9TxFdMIWGgcaXXVeiRCXRbCPnsRmwSQ2cw7XtjsUDmQXsyMtnx1lc6D2nygmPd9Men4mS/dkAqBVq4ipZ6JNuC9tIp3D96L8q3bxCCFqCxmGB1itFrKy0gkOjkCn88w39rV9SFhtVNtjXh3t9GLJMALPk5i7p7IUos3YjC5tvTOJytiMylZaroyi9cIa1uHUvKewDqA/95BsiblnSbwvj0KzjV0ZBa6FI3ak55NdXHHxiICyxSOubhZCi2AvWoX5otNI71NVk3buOTIMr46bOnUKv/zyU6X733lnNh06JFzQMceOHUX79h25//7R5yw7ZMgARowYxQ03DLigc5zL5s0bGTfuQVav3nhZjyuEuDIpehPW+tdgrX+Nc4PdgjZze9nQPWcPlNqci/7oGvRH1zhfo9JgC21z2tC9q1C8av7CKkKcD5NBS6cGgXRqEAg4F49IzzeftvJePruPF5JbYmX1/mxW788GwKhV0y7Kn471/elYP4CW4b5oL3LhCCFqE+lZonb2LBUWFmI2O78d/eOPpXz11WfMm/exa7+fnz863YUNK8nPz0Or1eHt7X3Osjk5OXh7e2EwXN6bRVZlsiQ9S54j34x5nsT8IikONNnJZYmTc+EITeHRCsVsAU1dc56sEZ1w+DVApVZJzD1I2rjnWGwOkjML2XGsgH8zi1i7L6vC0uXeOg3tov1IqB9Ah/oBxNYzSfJ0GUg79xzpWarjTCYTJpPJ9VitVhMcHHJJx/Tz8z/vsoGBgZd0LiGEqBFUauzBsdiDYyltczcA6oKjrjlPuvQNaLP3oM1NQZubgte/XwJg9wnDFtkZ4gZAaCJoz/0lkxC1hV6rpnWEH20i/QgJ8eV4Zj4pmcVsSs1lY2ouW47kkVdqY+2BHNYeyAHAR6+hfbSz1ymhvj/NQ00XvWS5EDWJJEt1VHp6GkOHDmTkyAf56qvP6devP+PHT+DTTxewZMliMjOP4+8fwM03D2bEiFFA+WF4U6dOwc/Pj8zMTNasWYm/fwCjRj1M//43AuWH4Y0dO4pOnTqTlLSFrVu3UK9eGOPHP0nnzl0AyMvL5bXXpvLPP+sICAjirruG8frrr15U75HD4eCrrz7j++8XkZV1gtat2/DYY0/StGkzAP7443c++GA2GRnHiIyMYtSoMVxzzbUAfP31l3zxxafk5GTTuHFTxo17nPj4dpcebCFEneLwjcLsewvmmFsAUJXmoEvf6Ox5Sv8H7fFtaIoy0CT/CMk/EqTzwdLkekpjB2ON6gZqTTVfgRCXl1qlolmoD81CfbitQxQORSE5s4hNqblsSs1j85FcCs32csP2fA1aOkT706G+Pwn1A2gW6oNaFowQtZAkS5VQFIXSKhyypXUo2Ozlj2/Uqi/7yjPbtiUxf/6nOBwOfv31f3zzzZdMmTKVqKho1q9fy+uvv0q3btcQG9uiwmsXLfqGBx54iNGjx/Dtt18zY8YrdO+e6OrROt0nn3zI449P5PHHJzJ79ixee+1lvv12CWq1muefn4zFYuH99+dz4sRxXn31pYu+ngUL5rF48SKeeuppoqMb8PnnH/P444/w5ZffUVpawksvPceECU/ToUMCy5cvY8qUp1m8+GeOHUtn1qyZTJ06g8aNm7Bw4Zc899xTfP/9LzX+nkVCiOqlGAOxNO6LpXFf5wZrCbrjW9AfWYN3yo+ocw5g3PMtxj3fYvcJw9x8EKWx/4c9pFX1VlyIKqJWqYitZyK2nok7O0ZjdygkZxayMTWPTWU9TwVmG3+lZPFXShYA/kYtHeoH0DHan44NAmga7C2r7YlaQZIlNxRFYeRXSWxLy/foeeMj/Zh3e/xl/fC49dY7iIqKBiAz8ziTJz9PQsJVAAwaNIQFC+Zx4ECK22SpWbMY7rrrXgBGjhzNwoVfcuBACnFx8RXKdunS3bXYw7333s/w4XeQnZ1FcXExGzdu4OuvFxMVFU3z5jHcd98oXn992gVfi6IoLFr0DaNHj6F790QAnnrqGW699WZ+++1nWrVqjc1mIzS0HuHhEdxxx900a9Ycvd5Aeno6KpWK8PBwIiIieeCBh+natQcOh0OSJSHEhdF5YY3qii26K943vkDujr8w7FmEIflHNEUZeG+dg/fWOdiCW1IaMxhzzCAcpojqrrUQVUajVtEizJcWYb7cnRCNzaGw53ghmw47h+1tPeoctrci+QQrkk8AEOilo2N9fzrUDyChfgCNgrwkeRI1kiRLlagrb9eIiFN3ru/QIYGdO3cwe/YsDh06wN69e8jKynLdlPdM0dH1XY99fJy9STabzW3Z+vUbnFbWx1U2JSUZPz9/V8IG0KZN24u6lpycbPLz82jVqo1rm1arpUWLVhw6dJCbbx5M167dGT9+DA0aNKR790QGDBiE0Wikc+cuNG3ajHvuuZ2YmFi6d09k4MBb0GrlLSCEuAQqFbaIjljDO1LYfQr6Q8sx7v0O/YFlaLN2Yfp7Kj5/v4I1ujulsYOxNLkeRV+xd16IukSrVtE63JfW4b7cc1V9bHYHuzIK2Ziay6bUXJKO5pNTYmXZ3hMs2+tMnoK8da75Th3rB9AgUJInUTPIX4puqFQq5t0eX7XD8DRqjwzD0+tPrZq2ZMli3nnnTQYMuJnExF6MGfMY48Y9WOlr3a2mV9niie6SDkVR0Gi0FV5zsQsw6vUGt9sdDjsOhx2VSsX06TP5998drF69kpUrV/D999/y/vvzaN48lvnzP2Hjxo2sWbOSn39ewuLFi5g//1NCQ+tdVH2EEKIcjR5Lk/5YmvRHVZqLIeUnjHu+Q5e+Af2RVeiPrEL5axLmxv0xxw7GUv8aUMuvYVH3aTVq4iL9iIv0477ODbDaHfx7rICNqblsTM1je5rzXk9L95y6UW6oSU+HaOd8p4QGAXKTXFFt5FO6EiqVCi9d1U3SdS5j7dk3/eLFi7jvvpHceec9ABQUFJCdnXXRycv5aNSoMQUF+aSlHSUyMgqAPXt2XdSxTCYTQUHB7Ny5nebNYwBn79WePbvp1Kkzhw4dZMmSxYwd+xitWrXhgQceYtiwW1m//m/MZjNbtmxk2LARdOiQwOjRYxk4sB/btm2ld+9+l+16hRACQDEGUNr6bkpb3406/zDGvd9j2LMIbe5+jMmLMSYvxuEVSmnMzZhj/w9bSBvnOrZCXAF0GjXxUf7ER/lz/9XOpcp3HMtn0+E8Nqbmsj09n8xCC7/tzuS33c7kKczX4LrHU9tIP8J9DRir8O80IU6SZOkK4u/vz8aNG+jePZHi4mLmzn0Pm82G1WqpsnM2aNCQq67qwrRpL/Loo0+Qk5PF/Plzzvm6devWlnuu1+vp0CGB2267k/nz5xASEkp0dH0+//xjLBYzvXr1w+Gws3jxt5hMJvr1u54DB/aTnp5GTEwLDAYD8+fPJSAgiISEq9i6dTMlJSU0bdq8qi5dCCEAcPg1oDjhUYo7jkN7PAnDnkUYk39AXZKJd9IHeCd9gC2wOaWx/4c55hYcvlHVXWUhPEqvVdMhOoAO0QE8QENKrXZ2pDt7njan5rI9vYCMAjM//3ucn/897npdgJeOeiY9Yb4G1796pz82GdBrZV6yuDSSLF1BHn30CV555QWGD7+TwMBAevfui9Hoxd69e6r0vJMnP8/06S8zatRwQkNDueGGAXzxxSdnfc0TT4wr9zw0tB7ff/8zt99+N0VFRUyfPpWiokLatInn3XfnuO77NHXqDP7733f55JMFBAYGMnr0WK666moAnn76eT78cB5vvTWdsLBwnn32RRo1alw1Fy2EEGdSqbCFtcMW1o6ibs+hTy1bGOLA72hzkjGtexXTulexRHXBHDMYc9MbUQx+1V1rITzOqNOQ0MA5/A6g1GonKS3feZ+nw3kkZxZSanOQW2Ilt8TK3syiSo8V5K0rl0ydTKLCfA2E+RkI9dGj1UhCJSqnUqpyDFYN5O6OyFarhaysdIKDI9Dp9O5feJk5h+FV3ZyomqK0tJSNG9dz9dXdXPOali9fxvvvv8233y7xaF1qe8yro51eLLkDuedJzD3vcsVcZc7HkPIzhr2L0B/927Vd0RgwN+6HOWYwlgbXgqbiPNIribRxz6upMVcUhQKzjYwCs+vf8dMeZxSYOV5owXwev/NVQLCPvmJCddrjEB+9x26wW1NjXhedjPW5SM+SqFJ6vZ5p015k0KAh3HjjQLKzs1iwYC49e/ap7qoJIUSNoBj8KG11O6WtbkddcBTD3u8x7vkObc5ejPuWYNy3BIcxCHPzgZTG/h+2eu1kfpO4oqlUKvyMOvyMOpqHul9dUlEU8kqcCdUxVwJlrpBg2RwKJ4osnCiysPNYgdtjaVQnEyrjacmUnvDTEqogH73cdLeOkp4lpGepqiUlbeW992aSkpKMj49zPtGoUQ+XW6nPE2p7zKVnSZyNxNzzqjTmioL2xA4Me77DuHcx6pJM1y5bQBPMMYMpjR2Mw6/BWQ5St0gb97y6HnOHopBTbD1rD1VmoRn7eVy7QaumSbA3MaEmYur5EBNqolmoDybDhfVL1PWY1yTn27MkyRKSLF0panvMJVkSZyMx9zyPxdxhQ5e6CuOeRRgO/IrKVuraZY24ynnj22Y3oRgDqrAS1U/auOdJzMHuUMgutpRLoM5Mqk4UWXBUEp8ofyPNQ32IqWdyJVLhvoZKl0GXmHuODMMTQggh6gK1FmvDnlgb9qTQUoh+/68Y9yxCd2Q1uvQN6NI3YFr1HJZGvSmNHYw18moUQ4AM1RPiMtCoVYSaDISaDLSJcF/G5lBIyyslObOQvZlF7D1eyN7jhRwvtHA0r5SjeaX8uS/LVd7XoD0tgXL2QjUO9paV+2ooSZaEEEKIWkLRmzC3GIK5xRDUhekYkn/AuGcR2qxdGPb/gmH/L85yWiN2UyQOnwgcvpHOx6YIHKZI7GU/Fb2fJFRCXAZatYoGgV40CPSid0yoa3tuiZXkzEKSTyZQmUXszyqmwGxj85E8Nh/Jc5XVqFU0CfameagPHRoHE+mtpXmIiQDvK3thl5pAkiUhhBCiFnKYIihp/yAl7R9Ec+JfjHu/w7DvJzQFR1DZStHm7ofc/ZW/XueDwxRZlkCFn/Y4suxxBIre/eR5IcS5BXjp6NQgkE4NAl3bLDYHB7KLnb1Qx4tcvVH5pTaSM4tIziwqdy+peia9qweqeaiJmHomogOMspiEB0myJIQQQtRy9pBWFIW0oqjrM2ArRV10DE1hGurCNNSFpx5rCsq2mXNRW4tQ5yRDTnKlx3Xo/XCYIs7omSpLpnwjsftEgM7Lg1cqRO2m16qJrWcitp4JWju3KYpCRoGZvZnO5OlQnpntR3I5klvK8UILxwuzWb0/23UML52aZiEnF5JwDudrFuKDUaeppquq2yRZEkIIIeoSrRGHfyMc/o0qL2MtQVOUjroseTqVWKWXPU5Hbcl3/svOR5td+c3LHYYAZxLlW5ZE+URg9z09sYoAjeHyX6cQdYRKpSLcz0i4n5HEZsGuBR4KSm2knChiz2k9UCkniiixOtiens/29PxTxwAaBHoRU8/kmg/VLMSHUJMsaX6pJFkSQgghrjQ6L+wBTbAHNKm0iMpSeEYCdTKxSncmU4Vpzt4pcy5qcy7arH8rPZYtsBnW6O5YontgjeqCYvCriqsSok4xGbTER/kTH+Xv2mZzKKTmlLjmQO0tmxOVVWThUE4Jh3JKWLrn1K0GdBoVYb4Gwv2MRPgaCPcre+xnIMLPed8onUYWljgbSZZqqYcfHklYWDjPP/9yhX2///4Lb745nR9//K3Sexmlp6cxdOhAFi78kYiISLp3T+Cdd2bToUNChbKbN29k3LgHWb1643nVbfnyZbRv34HAwCDmz5/Dli2bmDVr7oVd4HkYMmQAI0aM4oYbBlz2YwshxJVO0ZuwB8VgD4rB6raAgsqSf2p4X1H6qcTqtB4rld2MNmcf2px9eG3/CEWlwVYvHkv9Hliju2MN7yA9T0KcJ61aReNgbxoHe3Ndy1Pbs4osrnlQe8t6oQ5nF2O1KxzJLeVIbqnb46mAEJOecF8j4X4GIk5Lpk7+9NFf2enClX31tVifPtcxd+57WK1WdLryK6UsX76Ua6/tdUE3ff3hh1/x8/M/d8FzOHYsneeem8jChT8CcMcdwxg69PZLPq4QQogaRqVCMfhjN/hjD27pvoyioCrJQndsI/rUVeiOrEKbux9dxmZ0GZth49soWi+skZ2xRPfAUr8H9uAWoJJvuoW4EME+eoJ9gri6UZBrm82hkFlo5li+mfT80oo/C8yYbQ4yCy1kFlrYnu7+2L4GbVkiZayQTIX7Ggjy1lV636i6QJKlWqpnzz68/fbrbNy4ni5duru2FxUVsmHDOmbMePuCjhccHHJZ6nXmPY69vb0vy3GFEELUQioVincIlib9sTTpD4C64Ci6I6vRp65Cf2Q16pIT6A//if7wnwA4vEKwRHfDWpY8OXyjqvEChKi9tGpVWYJjpD0VvxBXFIWcEivH8s0cyy8l3U0ylV9qo8BsoyDTuVqfOwatmjDf8r1SJ4f4RfgZqedrQKuuvcmUJEuVURSwlVTh8dVgc5TfpvU673teBAYGkpDQmb/+WlEuWVq16i/8/Pxp374jmZnHyxKqfzCbS2ncuAmPPfYkbdu2q3C804fhFRUVMn36K6xdu5rg4BAGDhxUruy2bVv573/fZe/e3ahUKtq168DEic8REhLC0KEDARg6dCCTJz9PenpauWF4O3Zs47333iY5eQ+BgUHcddc9DBo0BICpU6fg5+dHZmYma9asxN8/gFGjHqZ//xvPM6jlnXmuYcOGM3DgYACOHTvGa6+9xI4d2zAYjPTu3ZdHHvkPWq2W5OS9vPHGqyQn78HX14+bbx7Mffc9cFF1EEIIUZ7DNwpzy9swt7wNFAVN9m70qavRHVmF/ug61CUnMCb/gDH5BwBs/o2x1u+BtX538OmH/OkixOWhUqkI8tYT5K2nVbiv2zJFFltZMuVMoNJPS6yOFZRyotCC2ebgcE4Jh3Pc/92sVkGoyZlMNQ725sFujQjyPv/RT9VNPnHcURQCvrsF3bHzm6NzuVgjOpF7y3fnnTD16dOP996bid0+GY3GuVzk8uXL6N27L2q1mhdffBaTyZc5cxbgcDiYPftd3njjVT7++KuzHnfGjGkcPnyQWbPmkpubw9SpU1z7CgsLmTDhMW677S6effZFTpzI5JVXXuSzzxbw2GNPMm/exzzwwL3Mm/cxTZo05bPPPna99uDBA4wb9xC33XYnkyY9y86dO3jjjVcJDAwmMbEnAIsWfcMDDzzE6NFj+Pbbr5kx4xW6d0/EZLqwe31Udi5//0ASE3syc+Z0vLy8WbDgC3JysnnmmQk0bNiYwYOH8vLLz9O2bTuee+4lDh8+xDPPTKBFi5blklIhhBCXgUqFPbglJcEtKWn3ANgt6DK2oEtdhf7IKrQZW9HmHUCbdwCvHZ/Ar2r8Q+NcvU7W8I6gNVb3VQhRZ/notTQN0dI0xMftfqvdQUZBxaF+6QXOpCqjwIzV7lwaPaPAzNaj+bSL8ueGVmEevpKLJ8lSZWrB2MvExJ7MmDGNpKQtdOiQQGFhIf/8s44RI0ahKAo9elzLtdf2ol49Z4McPPhWnnzy0bMes7CwkBUrlvHOO7OJjW0BwPDhI3nzzdcAMJtLuffekdx++12oVCoiI6O49tpe7Nq1E4CAgEDXT4Oh/C+wJUu+JyYmltGjxwDQoEEjDh48wBdffOJKlpo1i+Guu+4FYOTI0Sxc+CUHDqQQFxd/QbFxd67Dhw+6zpWenk5sbAvCwyOIjq7PjBlv4+vrXJ3p2LE0evRIJDw8gsjIKGbOfJ+IiMgLOr8QQoiLoNFjjeyMNbIzxZ2fQGXOR5e2rix5Wo02Jxnd8SR0x5Pw3jwLRWMom+/UHWv9HthCWst8JyE8SKdREx3gRXSA+/utORSF7CKLa4if1a7QOybUw7W8NJIsuaNSOXt4qnAYnlarxnYJw/AAvL196Nq1O3/++QcdOiSwatWfRERE0qKFc6LtLbcMYdmy39ixYxuHDh1kz57dOByOsx4zNfUQdrud5s1jXNtatmzlehwcHML119/E119/TnLyXg4ePMC+fXvPK5k5ePAgrVq1LrctLq4tP/ywyPU8Orq+67GPj7M3yWaznfPY53eueL7//lsA7rrrHl555QVWrlxB585d6d27HzExzuRw2LD7mDPnPX744Tu6du3OddfdcNnmdAkhhDh/isEPS+N+WBr3o1gFIfoCCrb9hi51FbrU1WiKM9CnrkSfuhL+BocxEEtUN6z1ncuUO/wbVvclCHFFU6tUhJgMhJgMxEXWzlsGSLJUGZUKdFW4OIFWDaqzJy7no2/f/sycOYPx4yewfPlS+vS5DgCHw8H48WMoKCigd+++dOt2DVarlaeffvK8jnv6Qg1a7anV9jIzjzNy5DBiY1uSkNCZgQNvYe3a1ezcuf2cx3S3Op/d7sBuPxWHM1f2O7Mu58vduRwOu+tc/fpdT8eOnVi16k/Wrl3Ns88+xV133cuoUQ9z993D6dWrLytXrmDNmlU8+uhDTJjwNAMGDLrgegghhLiM/CIxtxhCaewQ53ynnOSyVfZWozv6N+rSHIwpP2FM+QkAu18DZ69TdA8s0d1QvILOcQIhhChP+qpruS5dulFSUszmzRvZtOkf+vZ1rjZ08OB+tm7dzMyZ73PPPSPo2rU7WVkngLMnHw0aNESr1bJr16mbCyYnn7pz+8qVK/D19Wf69JnceusdxMe3Jy3tqGv/2ZaObNCgITt37ii3befObTRocPm/+XN3ru3bT51rzpz3yM7OZtCgIUyfPpORIx/ir7+WYzabmTnzdXQ6HbfffjfvvjuHgQNv4c8/l1/2OgohhLgEKhX2oBhK4u8n/8YFZN2/nZzBiym66nGsEVehqLVo8g/j9e8X+P3+EMEfxhPwzfX4rJ2KLnUlWIur+wqEELWA9CzVcnq9nmuu6cmsWW/RpEkz6tdvAIDJ5ItareaPP36je/dEdu3ayYcfzgHAYrFUejwfHxP9+9/IzJkzmDTpeczmUj788NQNZf38/MnIOMbGjRuIiIhkxYpl/PXXclq0cA7VMxqdY1b37duLv39AuWPfcstQFi78ijlz3uP6629i587tfPfdQsaPn3DR15+Sso9169aW29ayZSu351q06BvXuQ4fPshbb03nP/95CrVazbp1a2jePBaDwcC2bVs5fjyDBx8cQ3FxMUlJW+jR49qLrqMQQggP0OiwRSRgi0iguNN4VJZCdGnrnavspa5Cm70HXeZ2dJnb8d7yXxSVGntgc2z14rHWa4sttC22kFayYIQQohxJluqAvn2v4+efl/DII+Nd2+rVC+Pxxyfy0UcfMGfOe9Sv35BHH32Cl19+nuTkPWedgzN+/JO89dYMxo8fg6+vL0OG3M57780EoFevviQlbeGZZ55CpVLRsmUrxo59jPnz52CxWAgICOC6667nuecm8dBDj5Q7bnh4ONOnv8X777/NV199RlhYOGPHjufGGwde9LV//fXnfP315+W2vfXWe3Tq1LnCuR599D+ucz3xxCTeeONVxo4dhd1up2vXbjz2mHOI4osvTuPNN19j5Mh70Wg09OrVh+HD77/oOgohhPA8RW/C0qg3lka9KQLURRnojqxBf8S5TLmmMB1t9h602Xsw7v7G+Rq1FltQLLZ6bbGFxjt/BseCxlC9FyOEqDYq5WImhNRiJ04UcOYVW60WsrLSCQ6OQKfzzLrvbhd4EFWqtse8OtrpxVKpICTE1+37TVQNibnnScw963LHW110DO3xbWiPJ6E9vg1d5jbUJVkVyilqHbbgls7EqV5brKHx2INiQFNxjm1dI23c8yTmnnMy1uciPUtCCCGEuOI4fMKxNA7H0rifc4OioC5MQ5u5zZk8lSVSanMuukxnMsXOsqIaA7aQVq7kyVavLfbAZqCWP6uEqGvkXS2EEEIIoVLh8I3C4huFpcn1zm2KgrogtSx5cvZAaTO3o7bkO2+em7GFk3eXUbRe2EJaO+c/lQ3jswc0AbWm2i5JCHHpJFkSQgghhHBHpcLh1wCLXwMszW5yblMcqPMOoSvrgdIeT3ImUNYidMc2oju20fVyh84HW2ibU/Of6rXF7t9IbpwrRC0iyZIQQgghxPlSqXEENMYc0Bhz85ud2xQHmtz95eY/aTN3oLYWoU9bjz5tvevlDr0fttA4bPXisIU6V+Jz+DW4oJvSCyE8R5IlIYQQQohLoVJjD2yGPbAZ5tj/c25z2NHk7EObedoQvhM7UVvy0R9dg/7oGtfLHQZ/bPXisQXF4vCNwu4b7fxpikIxBkoiJUQ1kmRJCCGEEOJyU2uwB8diD47F3GKoc5vdiiYn+bT5T9vQntiF2pyHPnUl+tSVFQ6jaL2w+0bhMEVh940s+xmNwzcSuykKhykCNDV7hVQhajNJloQQQgghPEGjwx7SCntIK2h1h3Ob3eK831NGEpq8A6gLjqIpOIK6MA1N8XFUthK0OfsgZ5/bQyqocPjUK0uionCYIsv1TDl8o1AM/tI7JcRFkmRJCCGEEKK6aPTOOUyhcRX32c2oC9LQFKY5k6jCo6gLjqApSENdeBRNwVFUdjOaogw0RRnoMja7PYVD54PDFOXsjfKNPtVL5RuF3RSNwyfsirhvlBAXQ5IlIYQQQoiaSGPAEdAYR0Bj9/sVBVVJVlkS5UyeTiZR6sI0Zw9VSRZqaxHqnL2Qs9f9YVRqHD5hOHyjsZsiT/VK+UWDKg6UUEB6psSVSZKlWmrq1Cn88stPle5/553ZdOiQcEHHHDt2FO3bd+T++0efs+yQIQMYMWIUN9ww4ILOcb5+/nkJr7zyAhMnPsNNNw2qknMIIYQQtZpKheIdgs07BOrFuy9jK0FTmF7WI1WWVBWmnXpemI7KYUFTmI6mMB13/UuBPmFYI7tgjeqCJaorDv9GMqxPXDFUiqIo1XVys9nMCy+8wO+//47RaGTEiBGMGDHirK85cuQIAwYMYPbs2XTu3PmCz3niRAFnXrHVaiErK53g4Ah0Os9MktRq1dhsjot+fWFhIWZzKQB//LGUr776jHnzPnbt9/PzR6e7sC71/Pw8tFod3t7e5yybk5ODt7cXBoPxwip+nv7zn7EcPXqE0NB6zJo197Ic81JjXt2qo51eLJUKQkJ83b7fRNWQmHuexNyzJN5VRHGgLs5E7eqNKhvqV9Yzpc1JBrul3EvsPuFYo7pgjeqKJaoLDr+GkjxdJtLOPedkrM+lWnuWpk+fzo4dO/j4449JS0vjqaeeIjIykv79+1f6milTplBcXOzBWtZMJpMJk8nkeqxWqwkODrmkY/r5+Z932cDAwEs619nk5GSzadM/TJr0HFOnTiEt7SiRkVFVdj4hhBDiinVyCJ5PGNCh/C4VhPhrydv5F9oja9EdXYcuYzOaomNo9n6Pce/3ANhNka5eJ2tUF+d9o4SoI6otWSouLmbhwoXMmzeP1q1b07p1a5KTk/n8888rTZZ+/PFHioqKPFI/RVEotZdW2fG1VOzlMGqMqC7TNzPp6WkMHTqQkSMf5KuvPqdfv/6MHz+BTz9dwJIli8nMPI6/fwA33zyYESNGAeWH4U2dOgU/Pz8yMzNZs2Yl/v4BjBr1MP373wiUH4Y3duwoOnXqTFLSFrZu3UK9emGMH/8knTt3ASAvL5fXXpvKP/+sIyAgiLvuGsbrr7/K6tUb3dZ9+fJlmEwm+vW7njlz3uPXX//nqiNASUkJ7777Jn/+uRyAxMRePPbYExgMBnJysnnrrRmsW7cWo9HIjTcOZNSohzl2LJ2hQweycOGPREREAjB//hy2bNnErFlz+fnnJSxZ8j0BAUFs3vwPjz8+kW7devD222+wdu1qCgsLiIyM4sEHH+Gaa64FqPRc06dPJTs7i9dee8tV57femk5hYQHPPvvSZfn/FUIIITxC54U1uhuWqG7O59YSdMc2oUv7G/3RtWgztjp7ofYswrhnEQB232hn8hTp7H1y+EVX4wUIcWmqLVnavXs3NpuN9u3bu7Z17NiR2bNn43A4UKvV5crn5OQwY8YMPvzwQ2666aaLPq+7XOTMbYqiMG7dg+zM2X7R57kYbQLb8vbV/71sCRPAtm1JzJ//KQ6Hg19//R/ffPMlU6ZMJSoqmvXr1/L666/Srds1xMa2qPDaRYu+4YEHHmL06DF8++3XzJjxCt27J7p6tE73yScf8vjjE3n88YnMnj2L1157mW+/XYJareb55ydjsVh4//35nDhxnFdfPXvC8Mcfv9OlS3fUajXdul3Dr7/+j/vue8AVl1dffYmUlH28+uobGAxGXnrpWebN+y9jxz7GpElPoNFomDVrDsXFxTz//CRCQkLo2rXHOWO1ffs27rlnBKNHjyEgIJC3336D1NRDvPXWLIxGL7744hNee+0lunTphk6nq/Rcffpcx5NPPkpRUSE+PiYcDgd//rmcp5565jz/185Opar5ox1O1q+m17MukZh7nsTcsyTenuc25novbA26Y2vQnRIAazG69I3ojv6N7uhatMeT0BQcQbN7IcbdCwGw+9bHGt3VNXTP4Rvp6UupNaSde875xrjakqXMzEwCAwPR60/NvQgJCcFsNpObm0tQUFC58q+++iq33HILzZs3v6TzBgdXHJtYWlpKdrYajUaFVqtGURTU1dBKVSrnvJoLTZbUamd5rfZUgqnROB/fccedNGzo7A7Pzs7k2WencPXVVwNQv/6tLFjwAYcP76d161aoVCrUapWrDs2bx3DvvfcB8OCDD7Fw4ZccPnyAtm3jXec9WbZbtx4MHHgzACNGjGTYsNvJy8uhpKSYjRs3sGjRj0RFRQMtOHFiNK+99kq5+p6UkXGM7duTuPPOu9Fq1fTq1ZvFi79l584k2rXrQH5+Pn/++QfvvPNfOnRwDheYOPFp9u7dy4ED+9ixYxvffbfENWzvqaeepri42BUPjUbtOq9arUKlcl7DyccjRozEaHTOw+rQoSN33TWMpk2bAXD33fewZMli8vNzyM/Pr/RcnTp1ws/Pj7//Xk3//jewefMWrFYrXbt2cXvN58vhUKFWqwkM9HHVsaZz934TVUti7nkSc8+SeHve2WPuCxE3QgfnyBPMhZC6Hg6ugoOr4ehmNAWpaHZ9jXHX184ygY2gUXdodI3zp78MtT+TtPOao9qSpZKSknKJEuB6brGUn0i4du1aNm3axE8/Vb762/nKynK/wIPD4cBuV1xD42Z2/m/VDsNzs9iAUWPEbleAC5vR53A4y59+PLvd+bhevQjX9vj4juzcuYNZs97h0KED7N27h6ysE1itdmw2B4qi4HAorsdRUfVdrzUYnIs+mM0W17bKyhqNp8ru2bMHPz9/wsIiXftbtoyrUN+TfvvtV/R6PQkJV2OzOWjbtj2+vn789NMS2rRpx6FDh7Db7TRvHut6fZs27WjTph3Lly/Dz8+/3DV37XoN4ByWeDIup9dfUZzX4HAoBAYGodXqXfv79buBVav+5PvvF3Ho0EH27NkNgMVi48CBg5Wey+GAnj378scfS+nTpz9Ll/5OYmJPQHNJC0zY7QoOh4OcnCJ0OutFH8cTVCrnB72795uoGhJzz5OYe5bE2/MuOuYBV0G7q6Dd46gshWjT/0F3dC26o3+jPb4NVc5ByDkIWz4DwO7fyNXrZI3qgsMUXhWXUytIO/eck7E+l2pLlgwGQ4Wk6OTz0781Ly0t5bnnnuP555+/LN+mKwoVGp+7xqhSqfDSel3y+Sqj1aqxUfUrs52ekC5Zsph33nmTAQNuJjGxF2PGPMa4cQ9W+lp3q+lVtniiVluxKSmKgkajrfCasy3AuGzZb5jNZq67LtG1zW63s2LFMsaPf9Ltec5Wh5Pc9dbZ7fZyz89M3l9++Xm2b99G//43MGjQEIKDQ3jwwfvOeS6APn2u45FHRlNUVMjKlcsv61wld224pqpNda0rJOaeJzH3LIm3511KzBWdCUuDnlga9ARAZSlAl7YBXdrfzuQpczuavINo8g5i/PdLAGwBTVxLlVujupQtPnFlkXZec1RbshQWFkZOTg42m831h2dmZiZGoxE/Pz9XuW3btpGamsq4cePKvf6BBx5g0KBBvPjiix6td222ePEi7rtvJHfeeQ8ABQUFZGdnnTV5uVSNGjWmoCC/3Ip2e/bsclv28OFD7N27h8cee6LcPaIOHNjP889P5q+//qRbt+5oNBqSk5OJj28HwKpVf7JgwTyeeeZF8vPzyMg4RliY81uphQu/KluwYRJAuZUU09KOVlrvoqJCli79lblzP6Jly9YA/P33asCZ7EVH16/0XNOmvUHr1m0IDQ3l888/QVGgffuOFx48IYQQoo5R9L5YGvXG0qg3ACpzPrr0DafmPGXuQJu7H23ufrz+/RwAW0BTV6+TJaoLindodV6CuMJUW7LUsmVLtFotW7duJSHB+Yfxpk2biIuLK7e4Q9u2bfn999/LvbZfv368/PLLdOvWzaN1ru38/f3ZuHED3bsnUlxczNy572Gz2bBaLed+8UVq0KAhV13VhWnTXuTRR58gJyeL+fPnuC27bNlv+Pn5M3Dg4HK9PE2aNGPBgg/49def6NevP/3738jbb8/giScmoVarmTPnfbp06UaTJk3p2LETr776EmPHjicvL5fPPvuIe+4ZQVBQEGFh4XzxxSeMGDGKpKQt/P33apo3j3VbF73egNHoxZ9/LicgIJDDhw/x5pszALBarWc910m9e/fjq68+Z8CAm9FoNJcxqkIIIUTdoBj8sDTqg6VRHwBU5jxnz9PJYXsndqLNTUGbm4LXzk8BsPs1xBbQBId/Q+z+jbD7NcTu3xC7XwPQ1o45vaL2qLZkycvLi0GDBjFlyhReeeUVjh8/zocffsi0adMAZy+Tr68vRqORhg0bVnh9WFgYwcHBnq52rfboo0/wyisvMHz4nQQGBtK7d1+MRi/27t1TpeedPPl5pk9/mVGjhhMaGsoNNwzgiy8+qVDujz9+p1+/6ysMhwO45Zb/4+233yAz8ziPPvo4M2e+zvjxY9DpdPTq1ZcHHngIgGeffYk33niV0aOH4+NjYuDAWxg8eCgqlYrJk5/jjTdeY9iwW+nYsRP33DOCv/9e47bOOp2O5557kVmzZvLtt18RERHFvfeOYN68/7J3724aNmxU6blO6t27H5988iG9e/e7TJEUQggh6jbF4I+lcV8sjfsCoCrNcQ3b0x9ZizbrXzT5h9DkH3L7erspwpU8OfwaOZMof+dPxXD+95MU4iSVUpVjsM6hpKSEKVOm8Pvvv2Mymbj//vsZPnw4ALGxsUybNo3BgwdXeF1sbCyffPIJnTt3vuBzursjstVqISsrneDgCHS6in+oVwV3CzzURaWlpWzcuJ6rr+7mGm65fPky3n//bb79dolH6+LpmP/zzzpee20qCxf+eFmWg6+Odnqx5A7knicx9zyJuWdJvD2vJsZcVZqD9kRZwpR3EE3eIdR5zuRJbSk462sdhoDTkidnj5Szd6ohDu+wGrFed02MeV11MtbnUm09S+DsXXrttdd47bXXKuzbs6fy3o6z7RM1i16vZ9q0Fxk0aAg33jiQ7OwsFiyYS8+efaq7alXmxIkTbNu2lU8//ZCbbrr5st43SwghhLiSKcZArNHdsHLGVAxFQVWagybvAJqy5Mn1M/cg6pJM1OZc1Mdz0R1PqnhcrbGsR+q0YX3+DZ0JlW80aCoueiWuDNWaLIm6T61W88orb/DeezP56qvP8PEx0a/f9a5hc3VRYWEB06a9SOvWbbj99ruruzpCCCFE3adSoXgFYfMKwhbuZlElS5Fr+J4m71SvlCb/EOqCI6hspWiz96DNrviFvKLS4PCNdiVPrkSqLLFC5+2BCxTVRZIlUeXi49sxd+5H1V0Nj2nUqDFLl66s7moIIYQQ4iS9D/aQVthDWlXcZ7eiLjjiTKBcydQh11A/ld189nlS3vVw+DXA7hOOwycMx8mfpnAcPuHYfcIloarFJFkSQgghhBBXLo0OR0BjHAGNqXDLd8WBuijD2QN1Ro+UJu8ganMemuLjaIqPc7aBeg6976kkquxn+eQqHId3KGhluF9NI8mSEEIIIYQQ7qjUOEwROEwREHl1xd2lOc5FJgqOoCk6hrooA/VpPzWFx1DZilFbCpwLUOQkV3oqBRWKdwj4R+JnCK3QU2UvS6oUY2CNWIziSiHJkhBCCCGEEBdBMQZiMwZCWLtKy6gsBc7kqfAY6uJjzp9FGaclVxmoizNQOWyoijOhOJOzrXmrqPWuYX5277Lhfid/liVXMvTv8pFkSQghhBBCiCqi6H2x632xBzY7SyEHqpJsNMXHCNTkU5C2v1wvlabwGOriDNQlWagcFjQFqWgKUs8+9M/gjy2oBfbgFtiCW2ILaYk9KBZFb7rs11iXSbIkhBBCCCFEdVKpUbxDsPuEQIgv5qBK7rNkN6MuyixLoo6hOWPYn+untQi1OQ99+npIX1/+EH4NnMlTWRJlD26J3b8RqDUeudTaRpIlIYQQQgghagONAYdfNA6/6LMWU1kKUecfRpu1G23WLrRZu9Bk7UZTdAxN/mE0+YcxHPjNVV7RGrEFxWILboE9uGVZMtUSxSuoqq+oxpNkqZZ6+OGRhIWF8/zzL1fY9/vvv/Dmm9P58cff0Ovdj3pNT09j6NCBLFz4IxERkXTvnsA778ymQ4eECmU3b97IuHEPsnr1xvOq2/Lly2jfvgOBgUHMnz+HLVs2MWvW3Au7wPNUUlLCgAF9iYlpwfvvf1Al5xBCCCGEqE0Uvcm1VLr5tO2q0hxn8nRiF5qsXc5kKnsPKlsJuuNJFW7Ya/cOKxvG1wJbSEtswa2wBzYFjcGzF1SNJFmqpfr0uY65c9/DarWi05Ufsbp8+VKuvbZXpYmSOz/88Ct+fv6XXK9jx9J57rmJLFz4IwB33DGMoUNvv+TjVmb16r8IDg5h+/Ykjh49QlTU2b9pEUIIIYS4UinGQKxRXbFGdT210WF3LoV+Mnk6mUzlH0JTnIGmOAN96l+njqHWYg9oWm4Yny24pXPFwDq4Sp8kS7VUz559ePvt19m4cT1dunR3bS8qKmTDhnXMmPH2BR0vODjkstRLOWOArbd31a7EsmzZb/TocS3//LOeX3/9H/ffP7pKzyeEEEIIUaeoNdgDmmAPaIKl6Y2ntluK0GbvOW0YnzOZUpvznNuz90DyD67irgUlQlq4hvHZglqA3qcaLurykWSpEoqiQGlplR3foVWj2BzlNxqNqM4zIw8MDCQhoTN//bWiXLK0atVf+Pn50759RzIzj5clVP9gNpfSuHETHnvsSdq2bVfheKcPwysqKmT69FdYu3Y1wcEhDBw4qFzZbdu28t//vsvevbtRqVS0a9eBiROfIyQkhKFDBwIwdOhAJk9+nvT0tHLD8Hbs2MZ7771NcvIeAgODuOuuexg0aAgAU6dOwc/Pj8zMTNasWYm/fwCjRj1M//434k5+fj4bNqxjwIBB6HQ6fv31Z0aMGFUuhr/99jMffzyfjIxjxMTEMn78BGJiWgDw1Vef8e23X5OXl0tcXDxPPDGJyMgoxo4dRfv2HV2Jl7shi8OHj+T77xfSpk1bXnvtLZYsWcyXX35KWtpRfHx86NWrH4899gQajabSc504kckjj4xm8eJfCQwMBGD37l2MGTOSJUt+x9u7dn+4CCGEEKIW0/tgC++ALbzDqW2KgroovfwwvqxdaHJTzrKgRENXL5QtrD2Whr1qVQ+UJEtuKIpC3sMPYNuxzaPn1cbF4//e3PNOmPr06cd7783Ebp/s+qN8+fJl9O7dF7VazYsvPovJ5MucOQtwOBzMnv0ub7zxKh9//NVZjztjxjQOHz7IrFlzyc3NYerUKa59hYWFTJjwGLfddhfPPvsiJ05k8sorL/LZZwt47LEnmTfvYx544F7mzfuYJk2a8tlnH7tee/DgAcaNe4jbbruTSZOeZefOHbzxxqsEBgaTmNgTgEWLvuGBBx5i9OgxfPvt18yY8QrduydiMlVc5nLlyuWo1WoSEjoTFBTMp58uIClpC+3aOd/U69f/zbRpL/LYY0+QkNCZRYu+ZsKE8Sxc+CP/+9+PLFgwjwkTniYmpgVz5rzHs89OZP78T88r9mvWrOS//52P3e5gy5ZNzJw5g+eee4mYmBbs3v0vL730HAkJnUhM7MXixYvcnuuDDz4hJCSUlStXcPPNg8v+/5bSpUt3SZSEEEIIUfOoVDhMkVhMkdCo96ntdjOanBRXL5Q2axeaE7udw/jyD6HJP+RaUCK/7yzMMYOqp/4XQZKlytSCjDcxsSczZkwjKWkLHTokUFhYyD//rGPEiFEoikKPHtdy7bW9qFcvDIDBg2/lyScfPesxCwsLWbFiGe+8M5vYWGcPzPDhI3nzzdcAMJtLuffekdx++12oVCoiI6O49tpe7Nq1E4CAgEDXT4PBWO7YS5Z8T0xMLKNHjwGgQYNGHDx4gC+++MSVLDVrFsNdd90LwMiRo1m48EsOHEghLi6+Ql2XLv2dTp06YzQaadmyNfXqhfHLLz+5kqUffviOvn37u3quxo0bj0ajJT8/jx9//I5bb72T3r37AfCf/0zgyy8/w2w+v97Em28eTIMGjQBnb9DEic+SmNgLgIiISL766nMOHNhPYmKvSs9lsZjp3bsfK1YscyVLK1b8wZgx486rDkIIIYQQNYLG4H5BiZLscsP41OY8rOEVFxOrySRZckOlUuH/3twqHYan0aqxX8IwPABvbx+6du3On3/+QYcOCaxa9ScREZG0aNESgFtuGcKyZb+xY8c2Dh06yJ49u3E4HGc9ZmrqIex2O82bx7i2tWzZyvU4ODiE66+/ia+//pzk5L0cPHiAffv2uk1mznTw4EFatWpdbltcXFt++GGR63l0dH3XYx8fZ2+SzWarcKysrBNs3bqJCROeBpz/Z9dccy0///wT48dPwGg0cvjwIQYNGux6jU6nY+zYxwA4fPgQI0a0dO0LCgpmzJizJ5KnCw+PdD1u0aIlBoOB+fPncOBACikp+zhyJJWrrrr6nOfq2/c6vv76c/LycklLO0peXm65YZVCCCGEELWV4hWENbob1uhu1V2ViybJUiVUKhV4eVXZ8dVaNY4zk6WL0Ldvf2bOnMH48RNYvnwpffpcB4DD4WD8+DEUFBTQu3dfunW7BqvVytNPP3lexz19oQat9tRqe5mZxxk5chixsS1JSOjMwIG3sHbtanbu3H7OY7pbnc9ud2C3n4rDmSv7nVmXk5YvX4bdbmf69KlMnz7VVc7hcLBy5Qr69bserbby5n22fWcmrHa7/azXsn7930ya9AT9+9/A1Vd35b77RvHGG6+e17maN48lOro+q1b9yeHDh+nR4xoMhitnOU4hhBBCiJpMXd0VEJemS5dulJQUs3nzRjZt+oe+ffsDcPDgfrZu3czMme9zzz0j6Nq1O1lZJwD3ycdJDRo0RKvVsmvXv65tycl7XI9XrlyBr68/06fP5NZb7yA+vj1paUdd+8/WM9agQUN27txRbtvOndto0KDhhV008Mcfv9Ox41UsWPC5699HH31BVFQ0v/zyE+Dspdq3L9n1GrvdztChA9m2bSvR0Q3Yt2+va19eXi433dSH9PQ0dDodxcXFrn2nX587S5Z8z403DmTChKe56aZBNGzYiKNHj7j2n+1c4Ex416xZxd9/r6Z37+suOBZCCCGEEKJqSLJUy+n1eq65piezZr1FkybNqF+/AQAmky9qtZo//viNY8fSWbFiGR9+OAcAi8VS6fF8fEz0738jM2fOYOfOHWzevJEPPzx1Q1k/P38yMo6xceMGjh49wmeffcRffy13HdNodPbG7du3t1zCAXDLLUNJTt7LnDnvcfjwIX755Se++24hgwcPvaBrTk9PY8eObQwaNJgmTZqV+3fzzYPZtOkfMjOPM2TIbfz++y/88stPHDmSysyZb+BwOIiNbcGQIbfxzTdflvXoHGLGjGlERESWDWNsxYoVy9i1aye7du3kgw9mn7U+fn7+7NiRRErKPvbvT+GVV14gK+uEKyZnOxc475m1fv06srKyXEP3hBBCCCFE9ZNkqQ7o2/c6kpP30rfvqV6JevXCePzxiXz++ScMG3Yrn376EY8+6lzK+vSeInfGj3+SNm3aMn78GKZOncL//d9trn29evXluuuu55lnnmLkyHvYvHkjY8c+xqFDB7BYLAQEBHDdddfz3HOT+OmnxeWOGx4ezvTpb7F+/Vruvfd2Pv54PmPHjufGGwde0PUuW/Y7AQEBdO+eWGHfDTcMRKvV8uuvP9OuXQf+85+nWLBgHvfeezvJyXuYPn0mBoOR6667gTvuuJs33niN+++/G4vFzEsvTQfg9tvvIiYmljFjRjFlytMMHz7yrPUZMWI0gYFBjB49nPHjx6DX6xk0aIgrzmc7Fzh7wBo1akxiYs+zDtkTQgghhBCepVLONiarDjpxooAzr9hqtZCVlU5wcAQ6XcV5NVVBq1VjuwxzlsT5q6kxdzgcDBkygGeeeYEOHSpfIaY62unFUqkgJMTX7ftNVA2JuedJzD1L4u15EnPPk5h7zslYn4t8jS1ENVq7djUbNvyNXm9wLXkuhBBCCCFqBkmWhKhGX375KYcPH+LFF6ehVsuoWCGEEEKImkSSJSGq0bvvzqnuKgghhBBCiErIV9lCCCGEEEII4YYkS6e5wta6ELWMtE8hhBBCCM+SZAlcc0Xsdls110SIylksZgA0Ghk9K4QQQgjhCfJXF6BWa9DpjBQW5qLRaFCpqj6HdDhU2O3SU+BJtTXmiqJgsZgpLMzBy8skC0EIIYQQQniIJEuASqXC3z+IrKxjZGdneOScarUah6Pm3fOnLqvtMffyMuHnF1Td1RBCCCGEuGJIslRGq9VRr140Npu1ys+lUkFgoA85OUVywzEPqe0x12i00qMkhBBCCOFhkiydRqVSodPpPXAeMBqN6HTWWvmHe20kMRdCCCGEEBdKvqoWQgghhBBCCDckWRJCCCGEEEIINyRZEkIIIYQQQgg3rrg5SypVddfgVB1qQl2uFBJzz5J4e57E3PMk5p4l8fY8ibnnScw953xjrFIUme4uhBBCCCGEEGeSYXhCCCGEEEII4YYkS0IIIYQQQgjhhiRLQgghhBBCCOGGJEtCCCGEEEII4YYkS0IIIYQQQgjhhiRLQgghhBBCCOGGJEtCCCGEEEII4YYkS0IIIYQQQgjhhiRLQgghhBBCCOGGJEtVxGw2M3nyZBISEujevTsffvhhpWX//fdfhg4dSnx8PP/3f//Hjh07PFjTuiEjI4Nx48Zx1VVX0aNHD6ZNm4bZbHZb9qGHHiI2NrbcvxUrVni4xrXf0qVLK8Rx3LhxbsuuXbuWm266ifj4eO655x5SU1M9XNva77vvvqsQ79jYWFq0aOG2/MCBAyuU3bt3r4drXXtZLBZuuukm1q9f79qWmprK8OHDadeuHTfccAOrV68+6zF++ukn+vTpQ3x8PGPGjCE7O7uqq11ruYv31q1buf3222nfvj3XXXcdCxcuPOsxEhISKrT5oqKiqq56reUu5i+//HKFGH722WeVHuOjjz6iR48etG/fnsmTJ1NSUuKJqtdaZ8Z84sSJbj/X77nnHrevz8vLq1C2c+fOnryEK5MiqsSLL76oDBgwQNmxY4fy+++/K+3bt1d++eWXCuWKioqUbt26Ka+++qqyb98+5aWXXlK6du2qFBUVVUOtayeHw6HceuutysiRI5W9e/cq//zzj9K3b1/l1VdfdVu+b9++yg8//KAcP37c9c9sNnu41rXf+++/r4wePbpcHPPy8iqUO3r0qNKuXTtl/vz5yt69e5VHH31UuemmmxSHw1ENta69SkpKysU6LS1N6du3rzJ16tQKZW02mxIXF6ds2LCh3GusVms11Lz2KS0tVcaMGaPExMQo69atUxTF+TkzYMAA5fHHH1f27dunzJ49W4mPj1eOHj3q9hhJSUlK27Ztle+//17ZtWuXcvfddyujRo3y5GXUGu7iffz4cSUhIUF54403lAMHDig//fSTEhcXp6xYscLtMY4dO6bExMQohw8fLtfm5XPGPXcxVxRFGT58uDJnzpxyMSwuLnZ7jF9//VXp2LGjsnz5ciUpKUm54YYblBdeeMFTl1DruIt5fn5+uVhv2bJFadOmjbJ06VK3x9i4caNy1VVXlXvNiRMnPHkZVyRtdSdrdVFxcTELFy5k3rx5tG7dmtatW5OcnMznn39O//79y5X9+eefMRgMTJgwAZVKxdNPP83KlSv59ddfGTx4cDVdQe2yf/9+tm7dypo1awgJCQFg3LhxvPbaazz11FPlylosFo4cOUJcXByhoaHVUd06IyUlhZiYmHPGceHChbRp04YRI0YAMG3aNLp168aGDRvkG7ELYDQaMRqNrudz5sxBURSeeOKJCmWPHDmC1Wqlbdu2GAwGT1az1tu3bx+PP/44iqKU275u3TpSU1P56quv8Pb2pmnTpvz9998sWrSIRx55pMJxPvvsM66//noGDRoEwPTp0+nZsyepqanUr1/fE5dSK1QW72XLlhESEsJ//vMfABo1asT69etZsmQJ1157bYXjpKSkEBoaKrE9D5XFHJxxvP/++8/r9+Mnn3zCvffeS8+ePQF44YUXuP/++3nyySfx8vK67PWuzSqLua+vL76+vq7nEydOpH///vTp08ftcfbv30/jxo3l7xcPk2F4VWD37t3YbDbat2/v2taxY0eSkpJwOBzlyiYlJdGxY0dUKhUAKpWKDh06sHXrVk9WuVYLDQ3lgw8+cCVKJxUWFlYou3//flQqlfxCvQxSUlJo1KjROcslJSWRkJDgeu7l5UXr1q2ljV+C3Nxc5s2bx+OPP45er6+wf9++fUREREiidBFOJvFff/11ue1JSUm0atUKb29v17aOHTtW2o7PbPcRERFERkaSlJRUJfWurSqL98nh1Gdy97kOzjbfuHHjKqljXVNZzAsLC8nIyDivz3W73c727dvLtfF27dphtVrZvXv35a5yrVdZzE/3999/888//7i+IHBn37595/X/Iy4v6VmqApmZmQQGBpb7IyYkJASz2Uxubi5BQUHlyjZr1qzc64ODg0lOTvZYfWs7Pz8/evTo4XrucDj47LPPuPrqqyuU3b9/PyaTiQkTJrBhwwbCw8N55JFHSExM9GSVaz1FUThw4ACrV69mzpw52O12+vfvz7hx4yr88Z6ZmUm9evXKbQsODubYsWOerHKd8uWXX1KvXr0KPdUnpaSkoNPpGD16NDt27KBx48ZMmDCBtm3berimtc+dd97pdvuFtuPjx49Luz8PlcU7Ojqa6Oho1/OsrCz+97//ue3FA2ebLykpYdiwYRw4cICWLVsyefJkSaDcqCzmKSkpqFQqZs+ezcqVKwkICOC+++7jlltuqVA2Pz8fs9lcro1rtVoCAgKkjbtRWcxPN3fuXG655RYiIiIqLZOSkoLNZmPIkCFkZGSQkJDApEmTKnzWiMtLepaqQElJSYU/GE8+t1gs51X2zHLi/M2YMYN///2X8ePHV9i3f/9+SktL6d69Ox988AGJiYk89NBDbN++vRpqWnulpaW52u7MmTN56qmnWLJkCdOnT69QVtr45aUoCgsXLuTuu++utMyBAwfIy8tj6NChzJ07l6ZNm3LvvfeSnp7uwZrWLRfajktLS6XdXyalpaU88sgjhISEcNttt7kts3//fvLy8njooYd4//33MRqNDB8+vNKeKFHRyZEXTZo0Ye7cuQwdOpRnn32WpUuXVihbWloKIG38MklNTWXdunUMGzbsrOX2799PYWEhkyZN4q233uL48eM8+OCD2O12D9X0yiQ9S1XAYDBU+LA4+fz0OQdnK3tmOXF+ZsyYwccff8xbb71FTExMhf0PP/www4YNw9/fH4AWLVqwc+dOvvnmG+Li4jxd3VorKiqK9evX4+/vj0qlomXLljgcDp588kkmTZqERqNxla2sjfv5+Xm62nXC9u3bycjI4MYbb6y0zEsvvURpaSkmkwmAKVOmsHnzZn744QcefPBBT1W1TjEYDOTm5pbbdrbP6sravczluDBFRUU8/PDDHDx4kC+++KLS+M2fPx+r1YqPjw8Ar7/+OomJiaxYsYIBAwZ4ssq11qBBg+jZsycBAQGA8/fjwYMH+fLLL+nbt2+5sieH+Eobvzx+++03WrZsWWGk0Zn+97//oVKpXJ8777zzDt27dycpKYkOHTp4oqpXJOlZqgJhYWHk5ORgs9lc2zIzMzEajRX+QAwLC+PEiRPltp04cUK6VC/CSy+9xIIFC5gxYwbXXXed2zJqtdqVKJ3UpEkTMjIyPFHFOiUgIMA11w6gadOmmM1m8vLyypWrrI3LBNWLs2rVKhISEiq049NptVpXogS4vi2Wdn7xLvSzWtr9pSssLOT+++8nOTmZjz/++KxzNfR6vStRAucf89HR0dLmL4BKpXIlSidV9rkREBCAwWAo18ZtNhu5ubnSxi/CqlWr6N279znLeXl5lfuCJjg4mICAAGnnVUySpSrQsmVLtFptuYm/mzZtIi4uDrW6fMjj4+PZsmWLa4UURVHYvHkz8fHxnqxyrTdr1iy++uor3nzzzbN+4z5x4kQmTZpUbtvu3btp0qRJVVexTlm1ahWdO3cud0+NXbt2ERAQUG5OHjjb+KZNm1zPS0pK+Pfff6WNX6Rt27ad8xvEYcOGMWvWLNdzh8PBnj17pJ1fgvj4eHbu3OkafgTOz/XK2vGZ7T49PZ309HRp9+fJ4XAwduxYjhw5wqeffkrz5s0rLasoCn369OG7775zbSsuLubQoUPS5i/A22+/zfDhw8ttq+z3o1qtJi4urlwb37p1K1qtttJ7vwn3FEVh+/bt5/xcLywspFOnTqxbt861LSMjg5ycHGnnVUySpSrg5eXFoEGDmDJlCtu2bWPZsmV8+OGHrpuMZWZmun7h9u/fn/z8fKZOncq+ffuYOnUqJSUlXH/99dV5CbVKSkoK77//Pg888AAdO3YkMzPT9Q/Kx7tXr14sWbKExYsXc+jQIWbNmsWmTZvOOv9DVNS+fXsMBgPPPPMM+/fv56+//mL69OmMHDkSu91OZmama3jG//3f/7F582bmzp1LcnIykyZNIjo6WpYNv0jJyckVhmqcGfNevXrx0Ucf8ccff7B//35efPFFCgoK3E7UFufnqquuIiIigkmTJpGcnMzcuXPZtm0bQ4YMAZzDjzIzM11zB+644w5++OEHFi5cyO7du5kwYQLXXnutrMR5nr799lvWr1/Pyy+/jJ+fn+sz/eRQyNPjrVKpuPbaa3n33XdZv349ycnJTJgwgfDwcFm85wL07NmTf/75h/nz53P48GG++OILFi9e7LrtQ2lpqev3KjgXLZg/fz7Lli1j27ZtTJkyhVtvvVWG4V2go0ePUlRU5HYI3ukxN5lMdOzYkWnTprFt2zZ27tzJ+PHj6dGjB7GxsZ6u9pWlmu7vVOcVFxcrEyZMUNq1a6d0795dWbBggWtfTEyMsmjRItfzpKQkZdCgQUpcXJwyZMgQZefOndVQ49przpw5SkxMjNt/ilIx3t98843Sr18/pU2bNsott9yibNiwobqqXqvt3btXGT58uNKuXTulW7duyrvvvqs4HA4lNTW1wo0O//zzT6Vfv35K27ZtlXvvvVc5fPhwNda8douLi1NWrlxZbtuZMXc4HMp///tf5dprr1XatGmj3HXXXcqePXuqo7q12pnt+ODBg8pdd92ltGnTRrnxxhuVNWvWuPatW7dOiYmJUVJTU13bFi1apCQmJirt2rVTxowZo2RnZ3u0/rXN6fEeMWKE28/0u+++W1GUivEuLS1Vpk2bpnTr1k2Jj49XRo8eraSlpVXbtdQWZ7bxpUuXKgMGDFDi4uKU/v37K7/99ptr36JFi1y/V0+aM2eO0qVLF6Vjx47KpEmTlNLSUo/VvbY6M+Zbt25VYmJiFLPZXKHsmTHPzc1VJk6cqHTu3Flp37698sQTTyi5ubkeqfeVTKUobu5KJoQQQgghhBBXOBmGJ4QQQgghhBBuSLIkhBBCCCGEEG5IsiSEEEIIIYQQbkiyJIQQQgghhBBuSLIkhBBCCCGEEG5IsiSEEEIIIYQQbkiyJIQQQgghhBBuSLIkhBBCCCGEEG5oq7sCQgghxPno1asXR48edbvvk08+oXPnzlVy3okTJwLw6quvVsnxhRBC1FySLAkhhKg1Jk+ezA033FBhu7+/fzXURgghRF0nyZIQQohaw9fXl9DQ0OquhhBCiCuEzFkSQghRJ/Tq1YuPPvqIAQMG0K5dO0aNGkVmZqZrf0pKCvfffz8dOnSgR48ezJo1C4fD4dr/ww8/0L9/f+Lj47n99tv5999/XfsKCwsZP3488fHxXHvttSxZssSj1yaEEKJ6SLIkhBCiznj33XcZOXIkX3/9NSUlJTzyyCMAZGdnc+edd1KvXj0WLlzI888/z2effcYnn3wCwKpVq3j66ae59957+fHHH2nTpg2jR4/GYrEAsHTpUlq3bs1PP/3E9ddfz+TJkykoKKi26xRCCOEZKkVRlOquhBBCCHEuvXr1IjMzE622/AjyyMhI/ve//9GrVy/69OnD5MmTAUhNTaVPnz4sWbKEdevW8eGHH7Js2TLX67/88kvee+89Vq9ezdixYzGZTK5FHCwWC2+99RYjRozgjTfe4ODBg3z11VcAFBQUkJCQwDfffEN8fLwHIyCEEMLTZM6SEEKIWmPcuHH069ev3LbTk6cOHTq4HtevX5+AgABSUlJISUmhdevW5cq2b9+ezMxM8vPzOXDgALfffrtrn16v56mnnip3rJN8fX0BMJvNl+/ChBBC1EiSLAkhhKg1goODadiwYaX7z+x1stvtqNVqDAZDhbIn5yvZ7fYKrzuTRqOpsE0GZgghRN0nc5aEEELUGbt373Y9PnToEAUFBcTGxtK4cWN27tyJ1Wp17d+yZQtBQUEEBATQsGHDcq+12+306tWLTZs2ebT+QgghahZJloQQQtQaBQUFZGZmVvhXXFwMOG9O+8cff7B7924mT55Mt27daNSoEQMGDMBisfDcc8+RkpLCsmXLePfdd7njjjtQqVQMGzaMH3/8ke+//55Dhw4xbdo0FEWhdevW1XzFQgghqpMMwxNCCFFrvPLKK7zyyisVtj/66KMA3HLLLbz55pukpaWRmJjICy+8AIDJZOKDDz5g6tSpDBo0iKCgIO69915Gjx4NQKdOnXj++ed57733yMzMpE2bNsyePRuj0ei5ixNCCFHjyGp4Qggh6oRevXoxduxYBg8eXN1VEUIIUUfIMDwhhBBCCCGEcEOSJSGEEEIIIYRwQ4bhCSGEEEIIIYQb0rMkhBBCCCGEEG5IsiSEEEIIIYQQbkiyJIQQQgghhBBuSLIkhBBCCCGEEG5IsiSEEEIIIYQQbkiyJIQQQgghhBBuSLIkhBBCCCGEEG5IsiSEEEIIIYQQbvw/Br5uIy9VfNMAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "plot_learning_curve(history)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-21T13:53:05.001520Z",
     "start_time": "2024-03-21T13:53:04.875096Z"
    }
   },
   "id": "c67bb53e5a864293",
   "execution_count": 66
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}