Audio_Module.ipynb

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  "metadata": {
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      "provenance": [],
      "collapsed_sections": [
        "9ji_ksU636Va",
        "7qr5fbw-73V0"
      ],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "sj6iCWJdGLUy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63cc358f-8885-49cf-a38c-73ec8d2a19b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#install packages"
      ],
      "metadata": {
        "id": "9ji_ksU636Va"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pyAudioAnalysis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2SA2IXgGqOZ",
        "outputId": "f1c39011-d64d-460f-9e3b-075ff5ab6013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyAudioAnalysis\n",
            "  Downloading pyAudioAnalysis-0.3.14.tar.gz (41.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 41.3 MB 9.1 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyAudioAnalysis\n",
            "  Building wheel for pyAudioAnalysis (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyAudioAnalysis: filename=pyAudioAnalysis-0.3.14-py3-none-any.whl size=41264389 sha256=98a547ff31a248dd673e3e6341ca77dd5e97466f4db9dfb971be4555f8866a1a\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/b4/52/c6a5bdc8517a9bc4cc95aca9839f118d25139aee7881a8d210\n",
            "Successfully built pyAudioAnalysis\n",
            "Installing collected packages: pyAudioAnalysis\n",
            "Successfully installed pyAudioAnalysis-0.3.14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install eyed3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64ISb-0-G0Nl",
        "outputId": "f23fc510-0be1-4a2b-8995-79cbf7092b2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting eyed3\n",
            "  Downloading eyeD3-0.9.6-py3-none-any.whl (245 kB)\n",
            "\u001b[K     |████████████████████████████████| 245 kB 5.2 MB/s \n",
            "\u001b[?25hCollecting coverage[toml]<6.0.0,>=5.3.1\n",
            "  Downloading coverage-5.5-cp37-cp37m-manylinux2010_x86_64.whl (242 kB)\n",
            "\u001b[K     |████████████████████████████████| 242 kB 58.2 MB/s \n",
            "\u001b[?25hCollecting filetype<2.0.0,>=1.0.7\n",
            "  Downloading filetype-1.0.13-py2.py3-none-any.whl (17 kB)\n",
            "Collecting deprecation<3.0.0,>=2.1.0\n",
            "  Downloading deprecation-2.1.0-py2.py3-none-any.whl (11 kB)\n",
            "Collecting toml\n",
            "  Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from deprecation<3.0.0,>=2.1.0->eyed3) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->deprecation<3.0.0,>=2.1.0->eyed3) (3.0.9)\n",
            "Installing collected packages: toml, coverage, filetype, deprecation, eyed3\n",
            "  Attempting uninstall: coverage\n",
            "    Found existing installation: coverage 3.7.1\n",
            "    Uninstalling coverage-3.7.1:\n",
            "      Successfully uninstalled coverage-3.7.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires coverage==3.7.1, but you have coverage 5.5 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "coveralls 0.5 requires coverage<3.999,>=3.6, but you have coverage 5.5 which is incompatible.\u001b[0m\n",
            "Successfully installed coverage-5.5 deprecation-2.1.0 eyed3-0.9.6 filetype-1.0.13 toml-0.10.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pydub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSPX1xmrG5Zn",
        "outputId": "73773a7e-9428-4744-9bea-cf4d56fdb024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Installing collected packages: pydub\n",
            "Successfully installed pydub-0.25.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install hmmlearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPBaFmnUG_E8",
        "outputId": "c1aee64d-334f-4d07-823c-78a355756619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting hmmlearn\n",
            "  Downloading hmmlearn-0.2.7-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (129 kB)\n",
            "\u001b[K     |████████████████████████████████| 129 kB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.16 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.0.2)\n",
            "Requirement already satisfied: scipy>=0.19 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.10 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.21.6)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.16->hmmlearn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.16->hmmlearn) (3.1.0)\n",
            "Installing collected packages: hmmlearn\n",
            "Successfully installed hmmlearn-0.2.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#### DO NOT IMPLEMENT THIS CELL ####\n",
        "## to remove the error of incomplete wave chunk- \n",
        "# open files with librosa and write them back with scipy.io.wavfile.write\n",
        "\n",
        "import os\n",
        "import sys\n",
        "import glob\n",
        "import numpy as np\n",
        "import librosa\n",
        "from scipy.io import wavfile\n",
        "\n",
        "def main(pathAudio):\n",
        "\t# for dirs in os.listdir(pathAudio):\n",
        "\tauds = glob.glob(os.path.join(pathAudio))\n",
        "\tfor aud in auds:\n",
        "\t\twavs = glob.glob(os.path.join(pathAudio, aud + '/*.wav'))\n",
        "\t\tfor wav in wavs:\n",
        "\t\t\tprint(wav)\n",
        "\t\t\ty, sr = librosa.load(wav, sr = 16000, mono=True)\n",
        "\t\t\ty = y * 32767 / max(0.01, np.max(np.abs(y)))\n",
        "\t\t\twavfile.write(wav, sr, y.astype(np.int16))\n",
        "\n",
        "if __name__ == '__main__':\n",
        "\tmain('/content/drive/My Drive/audio_data/audio')"
      ],
      "metadata": {
        "id": "wHGrupsRTNhi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# segmentation"
      ],
      "metadata": {
        "id": "Na9cgcMUGIbz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from pyAudioAnalysis import audioBasicIO as aIO\n",
        "from pyAudioAnalysis import audioSegmentation as aS\n",
        "import scipy.io.wavfile as wavfile\n",
        "import wave"
      ],
      "metadata": {
        "id": "ub1mWhqFGE6h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VvoYHbC5Fxn5"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "A script that iterates through the extracted wav files and uses\n",
        "pyAudioAnalysis' silence extraction module to make a wav file containing the\n",
        "segmented audio (when the participant is speaking -- silence and virtual\n",
        "interviewer speech removed)\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def concatenate_segments(participant_dir, partic_id, remove_segment=True):\n",
        "    \"\"\"\n",
        "    A function that concatenates all the wave files in a participants\n",
        "    directory in to single wav file (with silence and other speakers removed)\n",
        "    and writes in to the participant's directory, then removes the individual\n",
        "    segments (when remove_segment=True).\n",
        "    \"\"\"\n",
        "    infiles = os.listdir(participant_dir)  # list of wav files in directory\n",
        "    outfile = '{}_no_silence.wav'.format(partic_id)\n",
        "\n",
        "    data = []\n",
        "    for infile in infiles:\n",
        "        print(\"infile:\", infile)\n",
        "        w = wave.open(infile, 'rb')\n",
        "        data.append([w.getparams(), w.readframes(w.getnframes())])\n",
        "        w.close()\n",
        "        if remove_segment:\n",
        "            os.remove(infile)\n",
        "\n",
        "    output = wave.open(outfile, 'wb')\n",
        "    # details of the files must be the same (channel, frame rates, etc.)\n",
        "    output.setparams(data[0][0])\n",
        "\n",
        "    # write each segment to output\n",
        "    for idx in range(len(data)):\n",
        "        output.writeframes(data[idx][1])\n",
        "    output.close()\n"
      ],
      "metadata": {
        "id": "F8lAr7oFHQ1e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_silence(filename, out_dir, smoothing=1.0, weight=0.3, plot=False):\n",
        "    \"\"\"\n",
        "    A function that implements pyAudioAnalysis' silence extraction module\n",
        "    and creates wav files of the participant specific portions of audio. The\n",
        "    smoothing and weight parameters were tuned for the AVEC 2016 dataset.\n",
        "    Parameters\n",
        "    ----------\n",
        "    filename : filepath\n",
        "        path to the input wav file\n",
        "    out_dir : filepath\n",
        "        path to the desired directory (where a participant folder will\n",
        "        be created containing a 'PXXX_no_silence.wav' file)\n",
        "    smoothing : float\n",
        "        tunable parameter to compensate for sparseness of recordings\n",
        "    weight : float\n",
        "        probability threshold for silence removal used in SVM\n",
        "    plot : bool\n",
        "        plots SVM probabilities of silence (used in tuning)\n",
        "    Returns\n",
        "    -------\n",
        "    A folder for each participant containing a single wav file\n",
        "    (named 'PXXX_no_silence.wav') with the vast majority of silence\n",
        "    and virtual interviewer speech removed. Feature extraction is\n",
        "    performed on these segmented wav files.\n",
        "    \"\"\"\n",
        "    partic_id = 'P' + filename.split('/')[-1].split('_')[0]  # PXXX\n",
        "    if is_segmentable(partic_id):\n",
        "        # create participant directory for segmented wav files\n",
        "        participant_dir = os.path.join(out_dir, partic_id)\n",
        "        if not os.path.exists(participant_dir):\n",
        "            os.makedirs(participant_dir)\n",
        "\n",
        "        os.chdir(participant_dir)\n",
        "\n",
        "        [Fs, x] = aIO.read_audio_file(filename)\n",
        "        segments = aS.silence_removal(x, Fs, 0.020, 0.020,\n",
        "                                     smooth_window=smoothing,\n",
        "                                     weight=weight,\n",
        "                                     plot=plot)\n",
        "\n",
        "        for s in segments:\n",
        "            seg_name = \"{:s}_{:.2f}-{:.2f}.wav\".format(partic_id, s[0], s[1])\n",
        "            wavfile.write(seg_name, Fs, x[int(Fs * s[0]):int(Fs * s[1])])\n",
        "\n",
        "        # concatenate segmented wave files within participant directory\n",
        "        concatenate_segments(participant_dir, partic_id)\n"
      ],
      "metadata": {
        "id": "PpGwpWKEHFts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def is_segmentable(partic_id):\n",
        "    \"\"\"\n",
        "    A function that returns True if the participant's interview clip is not\n",
        "    in the manually identified set of troubled clips. The clips below were\n",
        "    not segmentable do to excessive static, proximity to the virtual\n",
        "    interviewer, volume levels, etc.\n",
        "    \"\"\"\n",
        "    troubled = set(['P300', 'P305', 'P306', 'P308', 'P315', 'P316', 'P343',\n",
        "                    'P354', 'P362', 'P375', 'P378', 'P381', 'P382', 'P385',\n",
        "                    'P387', 'P388', 'P390', 'P392', 'P393', 'P395', 'P408',\n",
        "                    'P413', 'P421', 'P438', 'P473', 'P476', 'P479', 'P490',\n",
        "                    'P492'])\n",
        "    return partic_id not in troubled"
      ],
      "metadata": {
        "id": "CH7_feK9HMMP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    # directory containing raw wav files\n",
        "    dir_name = '/content/drive/My Drive/audio_data/audio'\n",
        "\n",
        "    # directory where a participant folder will be created containing their\n",
        "    # segmented wav file\n",
        "    out_dir = '/content/drive/My Drive/audio_data/interim'\n",
        "\n",
        "    # iterate through wav files in dir_name and create a segmented wav_file\n",
        "    for file in os.listdir(dir_name):\n",
        "        if file.endswith('.wav'):\n",
        "            filename = os.path.join(dir_name, file)\n",
        "            remove_silence(filename, out_dir)"
      ],
      "metadata": {
        "id": "2QI4P0a4HUsE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Augmentation"
      ],
      "metadata": {
        "id": "77A2QF-zF9sL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import librosa\n",
        "import os\n",
        "import csv"
      ],
      "metadata": {
        "id": "Y8dy3GT9DAsa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## required for adding noise\n",
        "# FUNCTION TO ADD WHITE NOISE\n",
        "def noise(x):\n",
        "    noise_amp = 0.05*np.random.uniform()*np.amax(x)   \n",
        "    x = x.astype('float64') + noise_amp * np.random.normal(size=x.shape[0])\n",
        "    return x"
      ],
      "metadata": {
        "id": "uhI5Jw0jGAXl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# FUNCTION TO INCREASE SPEED AND PITCH \n",
        "def speedNpitch(x):\n",
        "    length_change = np.random.uniform(low=0.8, high = 1)\n",
        "    speed_fac = 1.4  / length_change \n",
        "    tmp = np.interp(np.arange(0,len(x),speed_fac),np.arange(0,len(x)),x)\n",
        "    minlen = min(x.shape[0], tmp.shape[0])\n",
        "    x *= 0\n",
        "    x[0:minlen] = tmp[0:minlen]\n",
        "    return x"
      ],
      "metadata": {
        "id": "nldpjq0zGCWs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# FUNCTION TO STRETCH THE SOUND\n",
        "def stretch(x, rate=0.8):\n",
        "    data = librosa.effects.time_stretch(x, rate)\n",
        "    return data"
      ],
      "metadata": {
        "id": "I58NBG0KGCUX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir_name= '/content/drive/My Drive/audio_data/interim'"
      ],
      "metadata": {
        "id": "uR1BpF8KKlln"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## To add noise"
      ],
      "metadata": {
        "id": "HDhFqz3J4uLB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generating a dataset\n",
        "header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'\n",
        "for i in range(1, 21):\n",
        "    header += f' mfcc{i}'\n",
        "header += ' label'\n",
        "header = header.split()"
      ],
      "metadata": {
        "id": "-Dk8Gef3QYox"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file = open('/content/drive/My Drive/audio_data/noise_data.csv', 'w', newline='')\n",
        "with file:\n",
        "    writer = csv.writer(file)\n",
        "    writer.writerow(header)"
      ],
      "metadata": {
        "id": "eDhhqlwaQZ6i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for subdir, dirs, files in os.walk(dir_name):\n",
        "        for file in files:\n",
        "            if file.endswith('.wav'):\n",
        "              wav_file = os.path.join(subdir, file)\n",
        "              png_name = subdir + '/' + file[:-4] + '.png'\n",
        "              print('Processing ' + file + '...')\n",
        "\n",
        "              x , sr = librosa.load(wav_file)\n",
        "\n",
        "              aug = noise(x)\n",
        "\n",
        "              chroma_stft = librosa.feature.chroma_stft(y=aug, sr=sr)\n",
        "              # chroma_stft = librosa.stft(x)\n",
        "              rmse = librosa.feature.rms(aug)\n",
        "              spec_bw = librosa.feature.spectral_bandwidth(y=aug, sr=sr)\n",
        "              #display Spectrogram\n",
        "              X = librosa.stft(aug)\n",
        "              Xdb = librosa.amplitude_to_db(abs(X))\n",
        "\n",
        "              zero_crossings = librosa.feature.zero_crossing_rate(aug)\n",
        "\n",
        "              spectral_centroids = librosa.feature.spectral_centroid(aug, sr=sr)\n",
        "\n",
        "              spectral_rolloff = librosa.feature.spectral_rolloff(aug, sr=sr)\n",
        "              mfccs = librosa.feature.mfcc(aug, sr=sr)\n",
        "              print(mfccs.shape)\n",
        "\n",
        "              to_append = f'{wav_file} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spectral_centroids)} {np.mean(spec_bw)} {np.mean(spectral_rolloff)} {np.mean(zero_crossings)}'\n",
        "\n",
        "              for e in mfccs:\n",
        "                  to_append += f' {np.mean(e)}'\n",
        "              to_append += f' {file}'\n",
        "              file = open('/content/drive/My Drive/audio_data/noise_data.csv', 'a', newline='')\n",
        "              with file:\n",
        "                  writer = csv.writer(file)\n",
        "                  writer.writerow(to_append.split())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q28IKFlQKHmV",
        "outputId": "cad05510-096f-48fe-db52-c444463dde10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing P301_no_silence.wav...\n",
            "(20, 40907)\n",
            "Processing P365_no_silence.wav...\n",
            "(20, 75003)\n",
            "Processing P325_no_silence.wav...\n",
            "(20, 46369)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## To increase pitch and speed"
      ],
      "metadata": {
        "id": "2SwXIYhA42z4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generating a dataset\n",
        "header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'\n",
        "for i in range(1, 21):\n",
        "    header += f' mfcc{i}'\n",
        "header += ' label'\n",
        "header = header.split()"
      ],
      "metadata": {
        "id": "LWjFCqWqR4JF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file = open('/content/drive/My Drive/audio_data/speedp_data.csv', 'w', newline='')\n",
        "with file:\n",
        "    writer = csv.writer(file)\n",
        "    writer.writerow(header)"
      ],
      "metadata": {
        "id": "KhuXev-aR4F6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for subdir, dirs, files in os.walk(dir_name):\n",
        "        for file in files:\n",
        "            if file.endswith('.wav'):\n",
        "              wav_file = os.path.join(subdir, file)\n",
        "              png_name = subdir + '/' + file[:-4] + '.png'\n",
        "              print('Processing ' + file + '...')\n",
        "\n",
        "              x , sr = librosa.load(wav_file)\n",
        "\n",
        "              aug = speedNpitch(x)\n",
        "\n",
        "              chroma_stft = librosa.feature.chroma_stft(y=aug, sr=sr)\n",
        "              # chroma_stft = librosa.stft(x)\n",
        "              rmse = librosa.feature.rms(aug)\n",
        "              spec_bw = librosa.feature.spectral_bandwidth(y=aug, sr=sr)\n",
        "              #display Spectrogram\n",
        "              X = librosa.stft(aug)\n",
        "              Xdb = librosa.amplitude_to_db(abs(X))\n",
        "\n",
        "              zero_crossings = librosa.feature.zero_crossing_rate(aug)\n",
        "\n",
        "              spectral_centroids = librosa.feature.spectral_centroid(aug, sr=sr)\n",
        "\n",
        "              spectral_rolloff = librosa.feature.spectral_rolloff(aug, sr=sr)\n",
        "              mfccs = librosa.feature.mfcc(aug, sr=sr)\n",
        "              print(mfccs.shape)\n",
        "\n",
        "              to_append = f'{wav_file} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spectral_centroids)} {np.mean(spec_bw)} {np.mean(spectral_rolloff)} {np.mean(zero_crossings)}'\n",
        "\n",
        "              for e in mfccs:\n",
        "                  to_append += f' {np.mean(e)}'\n",
        "              to_append += f' {file}'\n",
        "              file = open('/content/drive/My Drive/audio_data/speedp_data.csv', 'a', newline='')\n",
        "              with file:\n",
        "                  writer = csv.writer(file)\n",
        "                  writer.writerow(to_append.split())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQ7Sz_RIRa5z",
        "outputId": "0da89d3b-f6e6-4564-a7f4-e80e6f208999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Processing P339_no_silence.wav...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## To add stretch"
      ],
      "metadata": {
        "id": "wqsyB-mL49_3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generating a dataset\n",
        "header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'\n",
        "for i in range(1, 21):\n",
        "    header += f' mfcc{i}'\n",
        "header += ' label'\n",
        "header = header.split()"
      ],
      "metadata": {
        "id": "wLzHp7krRvoR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file = open('/content/drive/My Drive/audio_data/stretch_data.csv', 'w', newline='')\n",
        "with file:\n",
        "    writer = csv.writer(file)\n",
        "    writer.writerow(header)"
      ],
      "metadata": {
        "id": "WDwYR9mARvlS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for subdir, dirs, files in os.walk(dir_name):\n",
        "        for file in files:\n",
        "            if file.endswith('.wav'):\n",
        "              wav_file = os.path.join(subdir, file)\n",
        "              png_name = subdir + '/' + file[:-4] + '.png'\n",
        "              print('Processing ' + file + '...')\n",
        "\n",
        "              x , sr = librosa.load(wav_file)\n",
        "\n",
        "              aug = stretch(x)\n",
        "\n",
        "              chroma_stft = librosa.feature.chroma_stft(y=aug, sr=sr)\n",
        "              # chroma_stft = librosa.stft(x)\n",
        "              rmse = librosa.feature.rms(aug)\n",
        "              spec_bw = librosa.feature.spectral_bandwidth(y=aug, sr=sr)\n",
        "              #display Spectrogram\n",
        "              X = librosa.stft(aug)\n",
        "              Xdb = librosa.amplitude_to_db(abs(X))\n",
        "\n",
        "              zero_crossings = librosa.feature.zero_crossing_rate(aug)\n",
        "\n",
        "              spectral_centroids = librosa.feature.spectral_centroid(aug, sr=sr)\n",
        "\n",
        "              spectral_rolloff = librosa.feature.spectral_rolloff(aug, sr=sr)\n",
        "              mfccs = librosa.feature.mfcc(aug, sr=sr)\n",
        "              print(mfccs.shape)\n",
        "\n",
        "              to_append = f'{wav_file} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spectral_centroids)} {np.mean(spec_bw)} {np.mean(spectral_rolloff)} {np.mean(zero_crossings)}'\n",
        "\n",
        "              for e in mfccs:\n",
        "                  to_append += f' {np.mean(e)}'\n",
        "              to_append += f' {file}'\n",
        "              file = open('/content/drive/My Drive/audio_data/stretch_data.csv', 'a', newline='')\n",
        "              with file:\n",
        "                  writer = csv.writer(file)\n",
        "                  writer.writerow(to_append.split())"
      ],
      "metadata": {
        "id": "MtUgAn9zGCRx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Combining all data"
      ],
      "metadata": {
        "id": "_TBQm3C15EL2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/audio_data/data.csv', header=None)"
      ],
      "metadata": {
        "id": "xSlGA8BDd1xK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "noise_df = pd.read_csv('/content/drive/My Drive/audio_data/noise_data.csv', header=None)\n",
        "speedp_df = pd.read_csv('/content/drive/My Drive/audio_data/speedp_data.csv', header=None)\n",
        "stretch_df = pd.read_csv('/content/drive/My Drive/audio_data/stretch_data.csv', header=None)"
      ],
      "metadata": {
        "id": "dXaMjI-VGAL9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#### concatenate files\n",
        "combined_df = pd.concat([df, noise_df, speedp_df,stretch_df], ignore_index=True)\n"
      ],
      "metadata": {
        "id": "93iLGbR8SiRd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "_uSV71X6d_oN",
        "outputId": "19867ad4-16d5-4afe-affa-268cdfbd0a74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a209b56b-f654-49d9-9ddf-e8358ec4e476 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a209b56b-f654-49d9-9ddf-e8358ec4e476');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qvic_tJzeDTZ",
        "outputId": "8a4d66b8-e3a0-47c7-c78e-5ecc081f1dcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(640, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = combined_df"
      ],
      "metadata": {
        "id": "rJSRpyVPeSrX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('/content/drive/My Drive/audio_data/combined_df.csv')"
      ],
      "metadata": {
        "id": "ZJqwHwhFeYc0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cci3F-X8eWCA",
        "outputId": "3e29e318-01b9-4572-e474-3668479762f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(640, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MFCC"
      ],
      "metadata": {
        "id": "6J3FYByhT3RU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import librosa\n",
        "from matplotlib import pyplot as plt\n",
        "import librosa.display\n",
        "\n",
        "import csv\n",
        "import os\n",
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "aqY975NyT09U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MFCC for single audio file"
      ],
      "metadata": {
        "id": "-XqWE8Y65aux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "audio_path = '/content/drive/My Drive/audio_data/interim/P301/P301_no_silence.wav'"
      ],
      "metadata": {
        "id": "jzpdicwjVlh-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x , sr = librosa.load(audio_path)\n",
        "print(type(x), type(sr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fY0674M_V-cZ",
        "outputId": "d51ee152-109c-404b-8fa4-9feab6767c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'> <class 'int'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display Spectrogram\n",
        "X = librosa.stft(x)\n",
        "Xdb = librosa.amplitude_to_db(abs(X))\n",
        "plt.figure(figsize=(14, 5))\n",
        "librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='hz') \n",
        "#If to pring log of frequencies  \n",
        "#librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='log')\n",
        "plt.colorbar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "_Lk3UKG0WBXt",
        "outputId": "3447bee3-bd7f-41e8-913e-59f4ba86e9e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f0e689d72d0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ],
            "image/png": 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s+ux+ew0SCd99Pd9jAWP3vezow+Txas1DJjwUllq91m739d2N9cKh4TDaI7SOn3rRqn2LxS8edwMAKvniQ2lHMPzw3IK9BDuh/IzM3tu+ddGYoVxNjB99EivmDg/d6UdJcTgUvPLZWfvzMIat1X0AwPbyYHHN02cOVmQO+/u94A0F4HAMPh0VUtm+v9Nu9Tf2BVMz/Ssw/RgJBrU4TizODvT9XsKjWZCuVZsIxuyV5IMMJmNzvSvkPSwP19mXB7MUN5uH95teXr0+l5tDOWpfye3bKyhmTr50xvL/Wq2/xjbqzUMNTh/96IO+znk/mT3BFZb7PY9lkoN5veqVGo6dP3qI7YP0htar9a7PMvc5ly6f776GHQ8jfOm+8gDLjj5onmqFYPvOBjw+7rrtd/Ednwyg2WgeeEzrHgWN+4HHaxU82i0eUvLWG0uHfvd+Wu/e/d77Pf82iGvZzpKyvbJtc6Sh/LRa91aEKpUyYoX/8lvvHvl7lXy56zOz5yaUiFlCfQ5jbCqMZtNeySyXmxiZ4oLU+VfuzeItcHkOtpzm9vsXug/D7fNiduHoC1yv8SqU9H4sbdPHeodimen0uh11QTOTGD2ax8zpdiEQHSy8iFeiu3dGZ+yfS6vRkL9PTQcxs2A/jt2+3mEx2QOEp3xyYM/3kVi+eneg73/qxW6ht1OJyu3aW6Pvd/h0o1KFP3a4gnrigr0QHIgEZf++8dbHlr9trB6uRFjaUmtC7eGhFrz+G6/Yfv4whD+/n89vGzdX7+t5YwN6OD0BHwrZ7jWjF728T/cDr7+7g77wxecGO2eHkjg1ebSwuYcRvnRfIQ/Bk8nQ5AjaGhfaG5VqX5PR7nYJodjBVpCZY48ultvttioEwgMyvchzCA6qrZ/f716Qj7LY2PHa332+598yW/deJcsucXZy4WAPwMuvHj3+38yrLxqL29/9Gi+EdZSkwF/4tfNdn7n9XhmvGx4K48wr57qOMVuuzDkfeztFeDyGRcEsFPt8TjQb/B17PIOFqtXKhgJkF3p2lNjpfnGqTowmutvdK/Stl7Ds9XMPWD+hQ7NTBwsvgmbNqgCEQod/r7P9w8PuI1+rnCsc6dhORL84eX5cfmYXAnAYvZ6hOSnT4WBo9TB3V4slW0ux2++FP9zb0/awLIInL93bfCCwywfcWV63/L9XjsVIuGH7+SCU0rlDj5metR83z74wieEZPtd0hvO89tn+9jvy+FxweQ7u5zub9h7qUvbe+vxhmL1V5TI35B2mtPRLr2d7FNx+L9w+L2aOaJwAHuz+OHZGoWplsD7bGZWQzhxsUBUclDv3WHL/k4ofGk+1QjB1LCGFyEtfvAjWx8uZmw/B6+cTSi9rpcNx9PMdtgj2mwysdCzmQvDcXOZC+EHKjAitMS/mvuC9xTZOTzyYakx28b8z84aF2RsKdIUeXDjev0UXAJI5Q1AVcujNd64d+r3lFfuQoeQWtxquX19Berd7EU9t78vfFacxRD/xUhzvvrUr/9+oGPsjnJpnqFb4/fl8gykEo9MxGcrUKQQD6GucHJWR6SF43d0WbbvrA0Crh3duYporru0DvEGdnqnd5L15jiLhw59zZ/uLxaNdK5SIoVaqHHhMrzlj/hwXdO/cMFz8lVL3XhqHJYu6XPbhbOZ2fXRlB15f7zHeKQQ4VBW1UgVDI709JY0e7/x+0+v+jspWsnscRMeHLcaCQtb6DoUy9dMP7n846fSZY4dW0Hn3x6u2nxeLTWzdWgPQHUKnKP21NTrkR7lwcLikXIc65uhgnI/fe8n36vQKmhP4zfmCQ3G+bh82voCjRw74Y2H81b/6yZGOtcPt86JWriCdOrxNgnvJlzoqdspNPlOGNxS451y8Zz5jVTSPzRxNNsgnH5wn5L7D2H1PKn6YPF6tecg4nQrKBT4A3/vuZZT6sMjNjWuolrnGbBbMzOzvH31wH2YZ2V+3D4fpxdCQ1fqR3efn/8JXFjB1ag5L79/q+d3tJe5KN1d9cTgc91SurX6AUUFYbfpN6AYATeu2Sn58ZRMAX0yqxTICUd5+YR29vXXw5N5rovub727K39ttPglf+uLFQ9uYz3b3i1I6Jz0j5z79DE6cG7c9RrbJ5FL8wz/4ISLx3oqZsORVqlahs9/Sr7G4RypcdtaZWOxoVu5+WL1+F6XK0Re4Xta97U3ezw9a7Ds9U8emj9b/hIIsrP7VWv9C3Ym5oy2m+WQan/zqiwce08uVLpTVsxdGbfu0EMJWP7x94PmHh+3Hu9k48Uu/OIrV2/u2xy1cPGmpXqN6PWg1GvBFQhgd4WPRTrGfWOgeE/1w5Hj6TPf47Gcuena+u48pigJf0BgfwYjxDP2xsOyXL5/nfT06PtyzUEC/3P34DrzBQJdgPDw3gZmzvCqeJ2D/bDxeo5+cOGkN3XOr/fXzzH7p0CpSr3+VV+DrDA9zOHg7jhJK2el96vQam9swcdx4xk4nf/YzZxcOrfrVqFSPJACX0rl7rrgFALVyBb5QAOOTR082F+v0/cI8bpZudIe6KQ4FlXyxrzBXy/ed1rGVKx6tX00uztzT9Yj+eWoVgvBIHP6AEzU9eebYhcW+yh7+8M0SFk7wiXP8hH2HnexjcM+cPrjT95sA1WhYB5sIHxCeXBEPaLcgC2+Cuc62Q3VYhKyj5hlMDHFLrt1C+8xneEiNXUK36vUcmJg2Oj/Z9dn4LBde280WtHZbWrzy6YKlLb1gTJGT/9lPGuE+v/YrhiX2j/8VF7Z8ulX0IAEi3FFa0Ol2YWJxFgsXT8Lt9yK9m8PanYMrXkSGjQXr4uefw4kTwa7nkpgZx9I6EPDy5/iZC9Z3f9TSr8JyvrNZlB6mzhhop9sFv4/f8/0M7Xj5C2cwEu1eaHqFqgUi/Bl09t/5hQiYoliErM6Sip2K7fK6fchLp/VSWLuF1f/2TS7M2PUBc7vHFqaN4gX6qxCKluhvnecIj8Rx8wNr+EknvbyG4t6PTTG5eF96eVp+R4RzdFrsOgXplTv2IShmw0mupPRMyl66fAPpXWu+SWJmHC987gwyWW4pMOcjiHfp899bKIdQ1MwGGnP/MN9fKBHD1Z982HWOfopLuB3d1o5Ws4VMsiivu7VqeGk8ppyKXIVbrMu5ApLrh8dIH2aMGZoaQ3xyFNmdZJdg3Kg1sHaV546ZQ1KYoshn5nUb/W9l2SrMt9qHK+pmJeSFFxN4/vWDDSY/+SE3snTOZZ6AB4mZwxVCxeno8j51hueZx5S4fwAIBfj9hGJ+hIcitsebOYoA7An4ER8ywuDsztV5r+aQn9hYAg7VgXMLhz9r0Rc6x6v5/u/Fim8eN/l0d0jXzHzMdt0/ainlWMzahxfGjxYydOHF7rX+sYZChp48CqksFmcV7K3wiWnj1t2+rPC//kUNoihKryTL4fjRH29u31hk7YT0zVv9WQPmOsLpoxG+ALlVDevXV2RCD1NYl0Vp9y63+CUmjcG/eXPVcly1eLTkp40kv67dQmueQDtpVKoHVkISwr7AoapoNttwqCrcfq9FSauV+USX8JcOtB62Gg2Eh/mCmUnm5Xt456qxIMye4g92ZyPX874EnYndvlAAxWwB+XQBtVIFbq8LbptYzdd+9RNyQdm6Y/TJSNSDkVi767kk17YQiyj4+TU+wa4mrRb8neVNHAXxnM6fj+DkpRO2AmezVofHzSex+5HsJRauUrGJZK57EesVMuT2qLaKeCzigNZuo1EzhLXlj+5Yjml1LPAnZpnsF+ZY+84wOdH/xXP56uvcICD6gLlvCS+PLxLC9tLdLi+icIMLYUOcQyz21WIZwahVgOhceF1eQwCwE0CW7hqK4N4ev/7++rb8XjFvbVNnG0VRhE5BxuydmYzX8doXjAW7c3wFTPfQqFRRzOTxt//mTUxPdnuZhHKwemOwKllmRdWscFgEnmQaX/udzwx0nf1yt7cuFA9hZCKMufPcgHP87ITsN/lUVlrqx8K8LTOnZuHTn6/b7+1ZCrWzzwqC8SicbhdarRaiIxHbY86/aORKLB5zy76itdtyfGXzTVldaGIyYBEuzw0fPs6bpuf80dWcxbtkp8yIkL7OuUxRGJJrWxiem7DNHxJ90U5I75wrtHZbPs9TnzgrPxfRQ7G4F3mTN9ZuLg8lYgeWpxVtjI0P4dyiMX/Znev5185aKgOZ801CsQCmjo3g3Y8OF5JH9cU9MWXN7RD3rzgd92zFF8qYKFBhxuXi/aZzHjrqniJbG9bj7mwfrez7gLURHj6UVPzkERqKQVGMRV5Yv49Sm9oXCaHWUrG3xy3oZisDYAg5nVUUDzq3eXIwL2LifL4+9g2Ijg/D67JOSDu7vK0LQ3mcfOkMkil+jWat3mVREgLD3RuG0O10uyzHdX7HGwrIhdg8keseYFtBfKVHqAFTFDhU1bZSjHi2nQlVrUYDN969DafLiVqpgq1ba1K4O3GBh8zc2g2j3Wz1TFQFjHASp+qU72Fx3jh+cTGkt5F13VenVWYoZn3f+WQaXr8P8dEoHKoKl0dFIcufo9vvle36wZ+8KRcUc/jExdMKmi3DojC2MC0nZ79Hw3On+QR7e8kmlMHGYtS5UIvF+Yc/2MDSR6s9FeR09t4WGzvEwrV6exdNfS0Ui4M3FIBDVW37ztr1VRQzBXiCVqWyVOGrx9ickScwNj9hWXA68wuqdeOZ7m8ZfTKza+2f4p0L13epZh3gvUIHzcQDvE+J9yveubhHUZknEA1jdCIC1euR3oZyNm/pu9k9I7bWLIB49bCQU3PGe9/fLcjrCWUpmzw4CVVY6kU1NTvB7sotB/weY8U2P4PR+SlEEwH5vROXTqGSL+LEpVO24VbiHYnCB4KDqpGJ+/NFQnCqKpxul62iepA3615CFgGgUu/+3tbSOkIhF7J7/Nlur2dlv5lanJaW6tV9wyAzNsfbVitVLOPU/K575cwUUhk0a3VktvZ6lu394Z++JZ9hyNeSfcWsUCfiqixE0Zl/9mfvDct5xs4a7FBV+COGd318IoC9PT4HRceHu5QZXySEFz85Zev98wU8iI4PY29l09YYIOYou3fW+Y5H56fkHG6uuOX38Pv3+3vnAIn7rJUrXeuxGdHGWrl2aCnZVLJkqQzEFEUqCK1WGxOTfsRiLttnbB57yQ2+RnUaxQTtZsuixIt+dJD84Qn44Q0FkFzjynind9sfC+Nnf3Md1WL5UAWgl1c3nrCu5ycmjOcqDHF2iL4E4Ejeo0cK5RA8eQxNjSG7k0Qqp+C5z3CriD8WxtDUWNfgtxtEoVgYlYYTExN84Z2Ys24EJYSc9W3rRHjQxHIQ7WbLNoGol6sus7WHP/urjEWYcKq6ENNw9Vw4xKQjJprTLxjWUvPkLMIdzAuWy+uWC7HTFH+ezhpVnAAezyqIDvFJS/V6LNZord1GYmoU1WJJhj8Y+0Xwid0uFvazX31WKnbxyVH5zJY+5N4VRdEQSkQt9+L2e+VzjIwmZBiF2XIfDRiL8ZX3eAiA6nJCcTosApB476LPMNatCMXHIqiUamg1GvD6XTj7HLeu1koV2S5zuMv1N6/K33/wZgWFsqnKkFuVk3OzxRDy8P51/oz1mqPzEwjGu62HnXH243qc7Ze+PNllHTcvwFfePjiU5V44+9wkMjmrtbySL6JRq9sK2lMnZ5BPprsqq6QzDThUVSZrq14Pmo2WRWAODVmtX/UGk9a2Ypo/T0/A3zX2Rb8WXkXhGLTLsxDjbnh6BJ6AX4YfXVvTw2L0PifeubjHfDKN0fkppDZ2cPODu/CHg5Z7NPdd8z2ZF+H9Ta7I1JqK7JMvfWJEfr+Y4fcYjJmEOBtvy8xsQLYtGI/K/mL2HL5ypgqzg9TcTyYXhnHl+1fgDfLzuDwqTr50Bi6PKkMazYKLy8u9BrmU1dhwUDUycX+tRgPVYgnNWt12XuxUEpii4OPrXLjs5eU7yHDQi8nFGfzkrz9EKM6fbWo7JftWcj1pWGD1KkPp3SwUfT5efOG0pa/3etdmQomYnFMrJW706axPb55bb9015g+zQBkKMHh8XKH64L0ty7X/3vMbMrbfThhsNRpQHIYik07XsLXKQw0zW3uIjnZvlJjcr9tWRVLdDmS29rBw8aRlHjTnNFE6iE0AACAASURBVHkCftv1sPMd728aBRjMa57HyfvMx+/vyPvqRM6rdasi1kvYze6mkCsdLE59/NOPLP/X2m0UUhlu0W+1EYsolmsDRtiiea4enub9qTMU0tzv65WabKt4lwfJH9ViyeKt6SyZXkrnEElEMXt21vb75vBK83s1G/Xcbuuz3i8Y8+tBEQfVcgOK04HwSFwqLI81FDL05MCYIi2ffq+Gt797BQAwd3oGTGFdlTfsqpVUy1W4nC3Ewvxl9hKwXz1/9KRiMYkzRbHdmKdsEz7TS1NnioJfeD2GRqUqB2dFT4BeT3tx450bFuv10NSYxQIhJuIPfmhsHnPpixflxl9CMHKYrFnmWPNa2bjv6TH+jIT1RghTAN/UCOACR6c12qE64A0FUMoV5TGAsTD6g90KQbOpgSkKVK8HikNBcoML75FhLgzfWWt2vU+n24W6fm5vwIv0Hn+momIPANSbxvt99VVuaRse5RNd5wLhCfjlxPu9P79uWeBnzi7A6VQwMhGGPxaG3+/C7nb3e93fNAQgs7Dp8TjhcRnPUnEoCI/EkZgZR9DbgoPxZ1MsWyeZzZurCESs+TF2fcyp+9KdDg2+oM9irRTP3RcJ4XOvz3Z9t1+cbpdFePzwnbtIxLsX515Wd4V1e2gErUZD7iLcqFSxu2r0OafbZemfqteDnWQLyXUuPIgEtmqxOzFSWPeE8Pzlk6v6sdbFLDo+jFI6B6YoWP3wNqrFEkrZAs8hGeaCcC9BJDwSR1tr4+RLZxAeCiG3l+ppITffh3kRFvttuJyaFGpX71bBFAVMUeTCL8YQV5q6LdA+vcStJ+BHIZWR80IgZDzzXNUtQ8gAq+BaLTdkXDsApHayuPHWx1DdDowM8Xdv7mMjs1xYHh63jqmjlEw1C0wzp6xJuqKfma2LWrvdM2la0KzVDyztOBe197C8/isXsH6TGyEufuoE9jf5/YeGwvD43NyLUeRjbf70OJwqfwbLHy1bzmMWiHt5MVqtFooZ7v3xBT0IxqMopDKWTelOX5zB/GlueGg2NduyxmF/G8ntPJq1Oo6ftoai7NUOL/vYrDXkWBwe9si5BIAU4sTfy9k8SiV74bRebWLxhdPYurNlCcUVyiJjCjStLRVJs9Le+a7MSo25vv9WRlfKgx6LcmtHZ/GKTiVGrImv/J2L2E8ba4ut9yMS6rJwi/Ce5fdvYW+/hTffWEF4JC7HRTHT3cc8Pj6mb7133aK0muer6OiQbOtRc72cbpd8nmcWuz0Es8eHENOLWnR6nM3J4ea+ao4kyOVqPRUquzLiADcYjIwFEBqK9dzT47GDQoaeHMQAd6gqhoINnPuk7iEIupBc28Lda8uWQWZnmcntpbC868HlD/jC+tZf2W9WlakeXO3CPJlV9cVda7dtN+YZmuCTtFnj7mXB0tpteFRru4XwPRWrYHJxBsl9Y7LcX99GrVRBu63BGwpgbpFPIM9+9lk5iReyFTQbfMKrV2qIjCZQrxhJx+ZKNj5d+MztprCX5sKnXRWlqRlu0egMRXD7vUhtJsEUpecmRSsfd7tLRxJOjMxOyBjn2ZN8EQxGeFhJOOTsmlRK6Zz0OjRqDeR1C7HLw0OWFKcDDIa7WXXy35tNDe1mCyHTBBcdH7ZMbL/wq6ctSViZ3TT2d3Lw+1VebShV7qq8AACvfOWC8fsXTslF5MsvtzEdLyOv78bbajRRTOfRbDTxs8t1lBoqgvEoJhOGcKd6PTj1ibPIJjOWSdz87gRCWfK4NNy9tmwbh1qvVHFijPfVUCLWlajZSS83dbNWt8TNz58aw1jsaElmAJBJ5uCLhLoUBrEXw8SU8V5OvWiUgWzW6halsFGpwu1W5HnKBS44eEMBi2XVoYejmMk2uaLp04sRCCGlki8hOj4Mrd2WcdnBeMQioAgBB+DvSJw7kohib2UTqe0MfAEPtHZbWsg7n2+vOGEhCGSLirxms9mG1uY/Ip5aWCwblSrSW8mud/XBh7yfiT7dqHHvy93bhvI+FcogmbJvR7lYRWYnKQWEzE4Kl754EdVyAxs7LR4zb1KMRLndSskaKiJ2TT0oUdIXCclnaPaqAXw+jI4PI7tnnUuCAUXec3R8GE63y2Jdd6iqDOW0u/a/+Fa3ULt+fRXhEL9fp5sr/GKOqFfrSG2n0KzVMRnl/e3q27eR2hHj2Xo+cxWZXh6CWqkin2EpV0YhlcHCxZPYNG265fc7cflvfg4AuHMrjbK+YaJDVZHZ4c/8+rKGZb3yXDptNWTlqm6pDNt5X4QSIsZQpdLC8AS/ZzF3mb2pqteDWIzPG53eNbHGMIVZBFxVF1azO0nUShXZx1uNhuxflVK3lVlc/+S5bqHYH3QfWLUNODihOBiPYunyDQBAudxAMGDM5XZVztw+D1S3dYyJ84+fmIHPq+C5l2eQ203Jz+2un9WNVs9+9llpnLPcVywsDWyK03FgrpfZQ9es1aUnudbRtUvpHGIxF1Zv7/Zsl0DkbnR6qsJht0WhSgSNcS6UOafbZRlr1WIZCzMOBKM89PBe90N6ePTpHSAPwaPFoTp5XGOjgTvbLikoX39vGUNTY10Ld+eE5XS7oLXbiIfaOH+GD6ZXfvEFyzFCaI94uoUuc3x0r4Q3O8QGatViSS5i5nYetk+BSOC9tuHFcy+OY/NuVlpphctZtOHKj3klnXKhJj/bWNrE0ChfDAqpDGKjUcsiZU5C8vqNuG6Hg1sPoqPdVqa2Htd58tIJy+e1UgXNRgNun6dLCDNCh7oXSE0DSrkixo+No91qY/XGBgDgox9xT4fbxQef+ZxjC9MIJfTkUE2TCkMpV0ajVke72ULA3ZTvLZ3n50jvl+F0u7CzzK8RjEel4CbauLRctmzy5lCd2Ly5imZTQ2JmHDNzYQT1EoXmSfCjt1fk7+t3C7La0x//dR03Nn3ynaxfX4HT5US1WMbcrBfJnBOFVAZru9w65w0F0KhUUS5wT5F5ErezyKxf59dN5ZWeFZ5UtwuVBj9/Ppm27JBqp7yZF+xe1tZaqYJMuiz3exALSa94Uafbhexe2tbKnkmV4Qn4LUJNqWAdW52Cb8BnTMqNWgNuvxeVfNHitWo1GlIprxT5uV0KH7/ivQtPQbVYkp+JXV+HJnj/F4q6eZFu1upyLDt0BTE+FkU4ajUodD7fXgJyMM5zVFpt45mbheyYPheYQw6atXqXQPrpl62L7/76Ni8basrbUFkTJ2btF7VCpgiXxyPH6uLFBRTzVexvpfHMQhu1UkXeE1MUKSwEw9b7zu5xgblTCOn0YPVKQAf4OxqaMDwtDlXF6lpZGogyW3to1upS8DV7+uyuDQC/8R90CyetRgPf+XfL+u9NzMyFMaaHlI5MDXV5nUamR6SF//hz3BMixp7Z0nqQYi1yEC69zN+1EFKFomkuQ/y5zw5JbxBTmFSOn1tsS8+BUKrF9fNVp/zdLuykkMogMTMu291oajh/hgtwybUt+CIhy/NzOByIRvi7s7P6hqJeuDxuSzUdX5C/D5lrYzIiif6V2011zVvCO7G+ZgiiEzF+D68874NyyF5BZoOHJ+C3zEnmvgIAo3HDcGTXF70Bb1fcv1Cw5hZHcGe5gNmJ7rlXcTosfUF4zfc2M7bzeCmdk2tcZ781n4cpCsJDhtDu9ntlIYOmjW3mvZ+tY+vW2oHV/8z9pHMn5U4n19K28Y6F4t3U11z5HYcDd9ZbWL++YlEEH1sYKIfgSaLdbMnFejzewuYSnzBOXJhDpVSG6rIKoKGY1SIi3MhetQWPyz79XSwy6XJ3rW1zfLRdkp55sPUqNWeelIXVsDPk5tqaNWEqGNDrO7c1XH5rE+eeHZZWWs1UlrKSL2JsXlh1jO5x/Jk5uTPr+IkZFHPWiei9714GwCfH7J4xyYt86/R2Uj+nQ06CqX1d2bjDrQ7Cms4tdRE4VWfXxCqtUDbJZfliG56AF0uXbyC1sSO9EosvnIbidOD/+/ZNBOPWHIJipiD7Q8UU+rG9dFc+54CrJt9bPMyf1ebyjsXK7Q1xISkYj8o2NuotuH1eqVQ6HA6c+/QzKJcbSK5tYW0lh0iE9w3zJGje+KvZaMlF85WXwvB1OJ2a9SYSUyOIBDUcH+HvRBwjnlFsJAhPwG/pTwdN6henU4iOdFv7Va8HlXwRtzf10CJ90RGTtFg8zYKaNxSQ8b7ZvXRPr5bL5YB4LcJb1itetFmrY/rkDEJD3eFap05H4fZ70G7y9zI8NyFd7IJOoezkRFl6uJyqU1r3OuvDi911M1t78AT8uFuIyWsAB1ecWr3KhcQRv3Xc+GNhy/fyKd5nl67cxu5W7+Q9h6r2tNK1Wi20Gg04HcZCq+oJo6rXg+31LBSnAxtLB1efyhX5e+ssNTg8aQgRtzPDuLtjv3u2y+OSG00BwM3LS6hXGzh1YRo/fM8qcZj74/qyVUgUIVCdYTPm+zeHPNmFkQ3PTcgQFKYoXMEb8vR8htVi6dCdYD3OpuzP5sTNi6/MIBSPQGu3kcvV8f4b3CCxfnsTw3MTcKgqrm/ogtDqjiwcsL/FlSMhpIt+4VDVA3fVLWRLYIqCy2/x9yneV6NS5cpmoy3b12oz+PS5SmtrRgimq45Tz4xiYnEWz5yPyOs73S6EvU3EdWW41/4e2b20nG9Gh1WUq0a5YzHe5P4dxRIUhcn3aX5f0SE/Nlf24Q145bW4UaMkfwdgyVkQOFTV4v0zbxA3d8zIoRL5xSubQLloXV86LdDyek4HVLdLzkliLl184TSqxRICQRcyhW5xyrz2h+NB2wo9TrcLKzd3EY160Na6Ky+JQhjiOYm1JhDqXaWvl3Is3rcn4IfWbss8C8XpgNPtwsbNNaheD1TVajjzhgKIj4YxsThrW/3vIEOdeKalonXMR0OafMfjJ2Zsw+ImTkzD53XY/u1xRAOgMdbXz+PEk/GU7yOi2sDQ1BgKFYesQBMOuzG9OIXIsHVCEC5MM7n9DK6tObG0xv/203/3juXvopN71FaXpUngDQVsJ1fzYBOTHlMU1Cv2A1xY2BSnwyKIxSO8o/ljYYRH4tjZ1UtKzlbgC3pQKhn3lVzbsgx+1aULfKbzZVMl1Gr8O8VMwZILAECW0xOJfQC3fuzsc0tveJgLT4wpcKpOhBIxqYj4w9zCEh/jxzRrdSTG48js7PdMnBZx1eaSj+GggvRWUgpoYnG4/fObaDdb+MyXTnRZLTwBQ0huNRrSOm0uU+dUjElOWHhfePU4FKdDTnZ7K5tQvR7L+Z9/LoBKvigF+v31bVx/+zre++5lOFQV45NB25JqrVZLKhFuj1Mu8IwBHlfbYqVqNRoo58uYiJSxss+f4+q6tb9Vyw14Ar4DN+sKJWJykfs/vu1AuVDuUkgblSoSM+Oyzb2UNbOQJTayUb0eS9/o9LxFYl4sjNUs5zmIQMiDQNgH1euRi0W1WMLiVBOKw4GIXvPa4/PgxlsfW4Tazj0AAMg4b1/IJy1+e2tWJXt7xXDDV4slzIf2MDQ1JsdCp4tcJMIB3LPi9ntx5W7YInQIZUoIxIFoAOGROBYuWHOZzOMgPjlqO6eIdjt1L2izZSzqIkmwUaliai6GdrOFoQmroN/pcdje1asR6TkAoo35dFleK+ar4pl5wxNqfnfPvTSF5NqWfC5nXlrE8vu3UCjU4OwIlStnjTK/F18atwgAop1H3ZlV1PsXxhJvKIBKvozEpDFnKE4HomGjQpHd5mA7y+td809iZlyOi2F3ygjJ0v89+zIXQsdmxbWA6dO87KfqcmFvZROK04Hj4zUkZsYxNmd4dlMbO13rglg/QglDoDWHRwHceOF0u/Ds89xKb/ZKCoRCEfC0pJXa/L6/8UcFFAsNbN9Zx8a2MX9rbQ2ru6ptxTFRBQywvne3i0FhxmfiPYhn5An4sbxS4p72yVHLd5evc4E7vWWt8NXpHRPrpPn9MIXJUEimKJbd5Hd3DGPP5Zv6Hiu5Rlc/LKV5KGIwHpXvWSjf5h20a6UKgvGovEa7pSESaHcJruYqaEvvL3UZI5xuF5q1Orx+DzZWs3j/akXO72Zls1GpIqKvoaFYEExRUKseXKREjFFvKNBliBGeBfFO2s0WFKbA5XWjUakimWpZKlvVyhWcPBmSFcwEQhEQ71Dcv7i22++Vc9z8XEdosLMt50LVpcpxaZ6rN26uwu9l0NrtA41Yjw+McgieRFqtFupNoFTgE0ixWMfO2p50t4qFXMRxWqwYo3G8diaFuSlriEMn5bqjZ7LMQTX2xURdyRf1SbmN2Gj3Ncxtmj17TApi4ZE4np/kE3gpnUNuN4Vint/nds6LY8ejSCbLcjJXvR4ZDgEAE9N6dQKTYJfZTSOV5PcSG4tbktYAI9GpM8np9TNbXCjQN5Jyed3IJ9PIJ9OyzJ1Lr+hz58pNuXikd7PwBvw9E6eFVV8sCE7ViTvLvEScEM5jY3xRnjrJlT7Vad0ISlSrEEmi8+ePwR/m7cyljJyHStOw8nxwkwtWCmNw+7wWgU4sum6/F0xRLOUsAWD2/HFMHJ/G869fRKvRQDpVxd3V7vt74dXjUolYvb4pBTK3qiHgbnVZzvfXt7GT9+LqjQrcfi+mJqzhBasfr8okv9H5KRkSYyafTCOku4+fvxhBq9FErVTpKv2aXNvCEfc5s9Ap5Od2U7KGOgCEwyrWU9ba9AeFwd26cgc7a7tyEzrBfkGFL+hDq8Ub6fKomDo1J58hwGN5PQE/2s0WIqMJrCR9crFVHAocukIshG4xzuJjxsZ3AOBkTWm9BgzPhhAkXB4PyvkSwiNxVPJF1EoVjMebXUpAYmYclXwRqtcDh0NBrVQFY8wS32weB6mNHVtPS7XIcxfyyTQyW3tot41ylamdvOz7P/zTtwAYVnXp2emwuvp8DkvZ13arxROlry6jWuR7elSbKt65bqrINT4s7yuZrOL51y+iXq3Kak++SAgz0wFcOuvkoSAmgS4xxUNfrr6ftLzT7F4GTrfL1pov7sncv4qZHCKjCSlEVvJFFDM5WfIzlOAKkSifW87mkd1Ly3LHZjrnn+TalhQ639uekgKQaMfSR6u4/NM1WfGnrWmyYpkwRjUqVXicTSTXttCoN6VnYOHiSatBSFc83X6vxQBTzualICcVvkYD2WzDdidZf8CJ4Wn+bBnj85Dq9cAb8ElDzq98NYFivoZ2s4Uf//l78jqtRgMnJqq2lbQq+aKtYjo1VMcHHxiGkXwybVGCq8USrr7Nd8nujG93qk40ag05Hnshxlg5m5f9TWtrRvinqf843S4ZGsQUBS+d1j0wGvDhj62Vf8Q5C6mMrI4l7rFaLFmeg9vHq/gF41Ekd4vwqO0uC7k5xCUQDVnC9DoV8PMXEvjMC4YXolPZFJ97/W5u3d9KWr5vLj4QjEflPFbJF209Bp3KSyGVkcro4iyzlEZuN1vIF9qynC7AjY2d87r4TrVYslQmA4Cbt4uW+758rSWfjxib7WbLUqSh3Wxhb5/PU+1Wy7YU+WMHKQRPHm6PG5FAGy3dA1AtNxDWQxCYokiBTGjr5o5fyhXx1p0hCAX6pc9by38Jgh7rZNlpcTVrvENTY4ZlW5/Q3X6vXJDWPl5BJx6fl7v3vB5s3jZKQeZ2U6i1rQKDiBuO+BpYXspibi4oJzpFYRZrWLnMb0zrkPxEHeHcfg6bN1ct9yM2VuuceH5wi9/TnSs3AVgVoelpLmisfHgHTFHgi4RQ0BNm99e3US1XDnUVlrN5RMeH0ajV8ekX+GZVnoDfUrlILMhrdyvwh0P6vbVRr1axt7KJrVtrel6JUd41rJcN9AT8MBsmTx3jk1m9bt0grJIvyndVK1Wgtdtotawu6OTGHoYnwnj3O5fh9nsxPOKDx9cdG/yd//encnL/1OsnZThLucbw8Uq3q3zq1BwmIyVcOOtBrVTB9JD1HYwfm5DPolIq9/QUDI3zRfjEaAnNRhPxydEuhVb1evDcMT4WeoW02Xl1RN82v09RQx0AqtU2RiO8PzrdLjjdLtlmuxCQsflxFNP57kTMZBuZ3TRuXOGK/MqHd7B+fcWitCtMkbk42Z0ktnZbUvGvlWtdcarmnA1ze/ytHLI7+13HiedbLZbQajSQ200ZO8KqhrAWG+Ox5WKh9/i8qJZr8EeCuP7mVSS3cl3PTCCeW+ezyWztSa+g32uEhCycGpbziuhbW7fWLInZ/o5KVBsbXOBrVKq8OlOpAsXhsJSR3C+omBoz2pfZ2kMlX0QoEcP23Yyu4FTQqFSRTfHKTYViC2/+3Cgna54HHaqKY4tW4bPVavUMgXB5uvcB0dptFDM5y3MzC2oilNCt784bHR/GyOwYH7Om/nRQ0QYAGA4ZIYPi2ZbSObz82pzcHd3vV3HrvesArIn817f4XLq1tC73hdi6swVvKCCNIuKctVJFCu5mrxNgtfCKEJ3O0L1GvY0dXaH42eU6amWeGzY8PYxils9h6aIDuQwXxP6T/+ply9jeynh6loV0qGpX/3z3uoLxSWtfElW3AL6m/mf/xXnbPRcYY1DdqmVfHm8o0DWnmOcwsU61Gg20m4aV3lzad/njTf3YNjJlPueWinVETH25U0DP7iQRnxy19E8hF0THh7G/vg1fkFfgCkU8cDDremk3NlM7Gdm+dtPo107VAbeLIeypWapACcyCcEMvhVruKNRhLs9bSGV6xtuL/mMeE6JNYr3yqq2uXMUrb921hAN3hooKxFzf6Y0/Ns/3OhD9+jMXjOdl9gp0rjlNPfyzVqr0NLA+TlDI0BOEoggLMXdrDo/zSeODH34Aj8+F6TPHEIiahLi1LShOh6VOt+py4dRkReaDfO+PfsY/N03EitOBUs0q7HXWNBaTrCfgx/76Nip69QfhvpahRwE/nv3MOQA8XEDGbufy3KJSqXYJearSlN8FgB/9GbcK7uVcSG6kUK225cIjXKHlbB6hRAw7G1woD0WNGv2JyYQcmKFYCG6/13I/M4sjchExV58Zj/PB/+xnn5XHijZducyFqZG5cWjtNsrZvBTIxXU7LS52wpFICLyxqmDr1hoaei1yMcmIBXnxuE+GCPFNYSKyLZmtPeys7cqE0etvXpWbse2XjAlvIszfkVO1DmTV68FLX3nespCGA5pFuatXatA04MUvX4LicGBjvWDJFzDT1IWEvb0K1q+vwBsKwK1qGE1YNyYDuKDabCsynKlUMxY21euxloncTXUlygmW3l+CJ+DHGx+44Q8HpPXO/MxD8Qg+WuPfF31OnE8sIOVsvmuxFxbOXnH25XITHy0LgU6zCIBmZVycNxj2Ijo6xBPjRHiV34vjU3oVKF1b9wR9SMyMy8UpMpqQwoYQ/i6cMNq0vXS3p6Ijdk8VVTSKjsiBeQMA9075Y2F5ziGvEU+d38/JNolKLYnxCFIbOxiem8Dznxi3fWZi/EfHh23Dq4RXcCpWkYrQR++aNhlUeZLo6PyUxQJuTvB0+7148SIX6jq9gSPTQzLU49zonmWzPEE+mcbMwhA++Ml1+Vm9WkcwHkViyIlLz/Adc8vZvFSsd5bX0Wo00Gppln7ZuSeG8Cx15qq4PB64/V6Mzk8hMTXatfuzQLz3t3/Mn0k5V4DX7+6yPpr7oFmhFPPKG+9ZEz6F4BgNMRRS/N2m9svSKmzOeQn6eD+NDMel90o8C+HZMF8zn87D7fei3eQ5RUJwc/u9sqzkUJxvahUZjlkErys/uyM3Bxsd80jlt1yoyB2GG01Ij9QbP+BV58R4PjeWhCfgswjl4tnbeQiOTTlw9rhiOd4T8COmK6K1chXNFrPmgIj9OOoN7h0zCYiVfBHhIT5X21WZqRZLcmMth+qU793ctxOTfI5gioJag3uCRkZ9iI2YDAUOh0X4Z4qC1MaOxfAjxp7Yy0Mo8ys3thFw142wPT3Mx8zM2QWkNnbsC2K0NYQDGjYyPhQyVkE/MTMuPUUAMDUTRnR8GPPPHO86j2h3J8JwCBjjvDMx27yx6GZale9W3POFl6YtORqy3wesuQx7K5vGRnimymiZXMvyLDczhkc4u5O0rUwEABdOOaB6PT3n5ccKRiFDTxQi4dcf9sHlbMvNMsYWpjE6EUJ8NGSp/ZuYGcfo/KSlOoDqVhF2l2D2zEZGE5YY6nazhZVt6+ONT1rriXsCPnhDAan1CqFFCMViQq4WS0hu6wvMxo6cPEfnJ+XOpsY1+KBeyQ7J7zJFwRd+7WXeNo233+FgcHncsr0An+TzyTT8QT74Crkqytk8/LEwSrkyPvwpD6dye1ScemHRshjkMhXUShUMTY2h2Wgayb/6bp5a26iJLiZwESqUMtXdF20pZ/NwOBxdrurOydQsFCzO6AlTQR/84YBcaIXF4pv/7IdyAtfabeR2U4hPJIwFdWIIz31yAYrTgWA8alQWKhhhRrtFPvnt71l3tk5MjSC5nUetVJHXC3iMCbBWqmD82CQcDoaN5X1U8kWcORPG8LB9aVrRBz0ep6wdrzo0RPwt6TEQwuvYwjSWdv3YzejtZMZk3ahUce2dm/K8iZlxy0JltrgMT3OPwOSoE6nN3a5kUgDIp7IYG7JawsT5RH9IzIxbFnuXjYVfcTq6EklPzbbleexCFACjf+zv5BCIBvQyvXzc1EoV7OcdYAqT3r7wUEQu3gBfeDrPnS07LTve9vKg7K1swqmqsiKPinrPdna22anyEJn9itjpWkEhlcH0mWNgCpNjf3N5D0zheTa5vH0Z1matDqYoPTfsEkJTpmyUi0yubSEymoDidGBmkVvDq+XeuRq1UgViaO1v7Fo2N5qZCyOnC63fvzaMancxNQB8Z9ZPfvm8/L/L40K9WkUq3YTX1R1eIeeHakv2S6fbhVDMam0u6yEQnbkq1WIJtVIFO8vr2FvbBlMUKE4HmvWmDE8yzxdf+cVZAHx36BtvX5PXFAmWlmcaDkBxOqRXKT45iq+81EC72YJDdcowvMUXtNzh6gAAIABJREFUTuPuZkNeJxzxwOt3wR8Lo1Y2HtRkhBsWOktlispA8clRi4U1s7WHWqkiBaxO67BDVVGucKNKcm3LEu++cM5Ijj413ZTP1eVxYWSKr0nRYBvpvQImFmcxNReTidduvxd75TByuymLgC2evUhONbO62Ua2qMgqYMKwIgwM+WQaHrVta/nPJ7PYvLkKrym3KxiPYmd5g+/n0WH1Fudo1Opo1OqW0ECzEDk1w8eE4nAg4m/y6mcMlrC8Zq1uEVjtBHezRwYwhOrPvn4M1aaKarGkGw34HGnub81GU5Yh7mRvYx+3V5sIeNpd41qsWWJe2tnmVcyCEXsB2a7djUq1S0Fp1OoWY0W9wr2jzVodIZ+hTDdrdUTHh+HzKpZNHYVhxKywi/vN6Z4Ec+6Hz2v1wIxHrV4/u8pEwXgUd3f5fGiXSP5YQmVHnxxEHOfS5RtIF5z48J1VAEB4KIhctopywZi0RZ1moQwI7T+fzsGnVLC0XIUvEsKFz12wTERjC9N8AfJaX7awkopBWMkXZeyweeIQ1zML3PMnrcqE289LmInryth4vYO5nEZiKlMYImEnvKEAJqI1TB5LIBxULCEZTDF2NS3mKnrYC39WpXQO/rAPgSifvJfev2V5TsNzE0ht8zhfl9eFxOSwXLhE6e+7t7fRrNXhdLswe/44T17SPQy+cFA+W7OVzhvyd4UtCV788iUA1gTW3awTE4uzqBbKljj7uZM8Fv3MK+e6zrN9Z10uVJViFTc/2ka72bLU1f7UzBqYovAwoyZ/vl6fCsXp4NVc3C4efqFv9JTZSVmSbwU7K5sYGvJgdCqGmbMLePPHW7j2kTUOFODeFFGnPh53yYkw7Kkj4GogtW1YcoOxCK+I1AYWxnhoR6HisCwKz376jFRShMeLP9+AxZo0Oc+VyKFQE0NTxoZS5pr5jUoVmsbdzp2uYlmmcJ9P6nb7EgB6GJbDYVl8715bxn7eqIwlrFjmc5itg96AB9WizQ7GQ3U4HA650FeKvB+bv9tZ6vBYPC/7USBqFT7N9xYeiVsUqDvFSRkOZN6Hw/xc1q4uycTyRqWKv/yRbpHV+1ZuP4vszr6saDM0HoPWbqNWrkFxMFuLaHgkfqBnQrGxOk2fOYbsThJunxceD3/O5jkLMARy0T+aLX4vDlW1jM9GwyjxeXqmgZ/9pDvhFOD7HBQKDSlw7K5uS0H1w9vG3Civp/+tUKjJtjRrdRluCHABodVoyOdSr1YRHR/uCl1xupxw+7hFXYQ9+YJ+y3xx7UYFvkgI+WQawXhEKqhun1fmXihObp3cWV6X5wrGo0ht7CDmzsk2CmHt5jvXwBhDvcLXBqdTweqNDZTSOUtibq2lW9jrRkljEerYrNUtsfVmy6gQsMzx3flUVi/vazz76OgQHKrKvQotw+O2mzPCQIqZIrx+Pe/J2cbUfBybN1fx/s9uy/PXShUEXLWujcCEJdc8HsR84vcpKFU0FFIZKVCaUZwONFqMV9rrKJsmqjttL92V/aKQyhjVkPT37gn4jRBXPdfBHwlaFHSz5zyb5etVYmoUqzsO2Z/F/gz3SlwvZfs3f3ELih4yJJQpwJRv5HYhm8zIMsSif4u1NxgNYHHeiXxFsXjTzfOfyKny+fk72FzeQzAetXjDzYhz90rGbTd5DL94RiIfyu33IpnlnUn8LbO1h3KlbdlHIZSIodVoWMK7OvdPMCvWb75hDXsOuQ3DiwgT7aSQyqBUbsMT8GH+3FzX3x9LqOzok4PYyGPq1ByaLeDc8zwUKBLzweVyoNXilitR3UEIsYAx+ZXSOXz3xiRefEYFUxg2l60a/fbSXTQqVbnRUqfgZB6EDlVFo1K1LFT+WFhOvMKqdesjayKpOXZWuJIBo/zo+r4Lud2UTKC7fi2rW5nb2LiTRKnChSQRdmAWMGIjQX2PAmPR3l7ZxsgUFxjbzZa07gNc0cnvZ+DyejAyFcPa1SW5cNUbfJKs5HkSYrNWx87yFkrZgrHRWbWGhv6czZbBzNae7U6NAKDqu3uahZXRaJPvyhuzbni2dpOXVptfMCp1iEnSrIBsL91Fo84ncrNwUdPc8vmcGOL35XJxBcoT8Es3ai5l7KqcXNvCzTWlS6BzuRR4vE6sXV1CvVq3KDzimu+/8b502a+vl+Ti8s5NNxTWtuwCmt7ew/DcBM5N5tBuM14KtUMRefc7l5E0VcwRfaWSL1re+/bdDEKJGLZSTjgcRnnYTot5KsdzbOJj1kVInEuEJ4g+YLYsincshASx6I/MTiDi598zWw3Nz8dsHQxGvHIhGpmdkJ9X6vx8YvOsZq2B2PhQzyR+byiAOyljQRVWTSFY+GNhMEVBYmYMTJe4xN/mg5v6NerYu7sjzxGI8jFr5+L++5837tET8IMxBbHxYXkvYlOtSrGMOzdT8p6HpsZkKGIxnbfsPtq5kAorW9RXw9DUGMIjcRlCVckXsaeXM1W9HovAIeY6ETJ5a7khhSoRslAtlrCzbQiBi/4VfPGLRlvMwsdLX3ke+7sF2X9G57nnSFUVRMIOOcd1JsFeOB+yCJGij4gqXsF4VD4XUUa61WjI/jd95hgfP3pSpS8SQnxytKtSTqlYQzmbl0qB2+fldftN46LdbBm75OrjUzzfVC3SZeGOjPLQSqEkrK9kbGvt7+b4OavFkiURsxOmKNLzakaWckzn0KjwpO1arW2q9laBPxJErVSB15SnZNYjVbeKD3/8Ee9TVQdKegnOi5+y7gvDoJnKPDPLMzBTyRfhDQWgacCP/mYVitOBWokXOhD9QuxJIAwrZmt4MB5FMB5Fbj9jKatrTj53edwYmhpDtVhCZDgq54ehKT4+GVPkuJP7AY0mcPokv/7u6iYuzRdQLZYQizhs4/Udqmp5r3aCqngOwnjn8rjQ1vhnQtn3RUKWCkuldE62aWze2PvH7fNidDqG731/Hw6Fz/8Ct88DbyiAYDwq+6/IFSmkciikMjwp3qTcxydHER6Jy3XdnP9hLiQC8DVSPC+hnNUrNbhd/H2YDZVTYw5Z/jWUiCGfTEtvuvmcgshoAs1aXZ7jxPkJy/Nsto0KerHxBDR9Y1TznCSO1doaPv7pR7b5ZI8X/eUPUA7BI8ahOjA6P8UtlDXDoi7i5rW2xivEmCbnTgvH6PwUnj+WR6HCO3ralO1vFiSvreiWH5/XNvkKsHoBxMDS2pr83O3zIDwcRyDks1gBzDHRYtCIiQAAbi/xRVgIZS++wM8dcReR28/C6eCLSeeE7An44XTyHU6FmxUATlyYR9VU+vT2ldsWgadWqvAt6XNVI0FMURAOGvcsd6p0u9BqNPD8i7yyUTAS0hd0Hlbki4Tg0mMe7SqLeEMB/PxHPDZZ09pGyJX+PtqmJCR+PS4YfPzBrmyzWOCGJhKWxdbtEROQsXL6mNEXdkt8oWhrRhUQUSf67sd3LO1cXS1YEgkbtTo+/PmuzMU4d2kKn3jFEKa0dhvDcxNy8Wy3WpgYN+IzxxIK7uz54QlwwWVsYVoPT2vj3TshqA59TwS31Xr8ya++KBewg2qrKw6+M/RwhCfZ9qrwVK3xBclsGQLMccVN24otgDGWRLyoqAT1yc/NQc/ZtTx7O8EDABwORZZfNLdjZVuB6nZhZnFcfn/r1poe2mFtj+LkXopGk8kFVcRXyw3q0jlo7Tb2VjZlwrvYq8TR5kK2SGrP7iShOB1ot3jIoFmREuMyVeHvs5zNyyTe1MaOfC/m0qHm3I/9de65Uhxc0Tho91FBQK3q32tbQh5FTk2jUu0q59is1WWfPbXAvWDnP81L8Ir7qZvKHa7WprCywceoNxSwCBC1WhPnLwzL/Kv0dgqR0QQ8HgV/+90Vy6Z1Zq7dtIbRiD4i5g/zrrmq19PhVQzg7sd3MDQ1JpWGcjaP1MaOHPvie/MLYfn3YxcW5f13Is4jyiIKGi2la4wUUlkk98rSe3jhkjFnmwXLkXANDlWVSatmQcd8nBgLQhgUuynbJb5n9svI6XkphVRGVvfZ28pLBSLoa0uL8vbSXbh9Xrg8HizdbSEc8SAxM450ir9nGf5RM9pmfj5moTmUiMGjV4WbHWvj1LOGkl4rVVDJF+XfEzPjGAryPiQ8l6LNhVSGV9cLBYy8m4lh+ZwzW3vYX9/GxOIsauWaYaTLFRCMBuROxgDg9nNFIruTxN4+76Ox8WF8uM7bfWe5gr217nGkOB2W99rZJ8RaLivcuV2YmE8gX7Xu/dNqNCzjS/V65AadO6v8fXoCftTKFZSLdXzpC3GoTg3PfOYZ+ewzOzy81DwP3np/FQD3oAvDgvk9pDZ2kNtNybHl0A1IDlWVm7uZCyCI51XTQwhdXjdu36mgkMpY1t9SBYjrhRDK+vrZbraMPJRK1WKoyO4kLWt4u63xEL5aHaFEDM22Mfb3VjbRavAqWf6wUc5Wa2tIp2tgCsOZV84dqST1I4WBcgieJJr1JnaW15FP5xHyQ9bWP34qgWq5gb27exZ3np0rbmd5HT5HDdvJNkrpHF78ouHiMwszRoJjw2LB6kVnDoEn4Ecxk0d2Zx+1agO1ckVauwTZnSRK6Ry8oQAyO0kpyLz6gjE5OVQV23u6Fbvux9SJSVy/lrX83e33opDiOx9efZO76K9e3pDHLF+9iztXbsn41mAsgrYpL0CWGtxKwWuaoKYTDTlxiomrkOIWoLd/xifFrB5X1Go0oOolzsrZPCLDMakkma0GjVodc2e4kGGJI64xPWzJWr5ScXArRjnPJz/zop5c37VYDsWuqGY8zZL8TrXBh0yl3OiKkxdCheCZ89aybO1mC9VyTVrsSqUGdpMNeAJ+OQHurWyippev1NptKA5+T95QAFevVzA9xCsjaW1NCq9MYXj5eEbul5DOG8OaC6hGcrM5brxTGBPVmK7esQ/TEkLF2VljgTQLL+IaIrGvM+HQLEDw/ROKqJWrGJoaQzzCMDvBlQihrJkXuvjkqKW9d2/vYHR2tGuxPjXdRDlfxN6msYAKYVy0R5xLtDcWtI/V90VCljafuLQIpvBQO7ffi2CVJ8VntvYQjEfkM7CzCAshO+EzPBWpjR3pARMW/NmzswCAsblRjIx1l9hzeT2odyyK5vFm5uZeFKPzU907hup9uTOkS1YYCemWVV8T3lAAm8t7cKgqQokYHKpq8Q7G3XlMjjr1GHm+k6iYn/a3svjJ9++gkOIJsZ6AF5rWxngCUmC0iwt++aLHMq8U0nxM2pUEbVSqsmKUyLVx+73I7Fjr2IdH4lLw0dpt/P/svVmMJGl+H/aLjIy8M/KIzKw8Kuvq6qqu7p5rZ2Y5uxSXu16KEinIpmETlv1CCIYN2A988oMhwU82bD0IehAM2TAgQ3wwDNMCLJO2TGm54l6zM5zdmZ7p6Z7uqq4rK6vyvq/IyMjM8MM/vn9EVM/KWmu5GmL5AYOZ7qnKjOM7/sfvMPU5Wq057+9nj44RugVfuU0ydjvuyooCLeTg+MU9R5NxTEc6Y63f+z51kfy2D4UYNz3Ctw/swE3sE9ntIs/pz4Pc7Tzc+4lJciDk58RWdAqmvSHSuRgnELIPUNMJJnG+81dehWWt8cYh8OgHJxi0etjfVz0V+v/mv3rvc6/FLfk5avcwn0yRKRfwo0+XuH+Xft99z4LP1qk2YK5oLbcubj6/cGCayNvJgtjn3OPm+JKDUrFnVJ9dOCaQisK8B+rE0e91rxvIJW14YFxBupjhnwdsONPniBi4h4CgiaQhW85DliUcaU3+mVAsCmOq85ry+WVE1Cjqp1cs1xnXUiRlmtPw9N1PYZgSTi4tfPLdT7j7LH+OEpOQxvbZpPzF3LleUbQAgM1DOiOXxsLjXeI2uQtGw3yPbpU8TfOeoQAgy+QLICr/4pr00YQ/o3vd4HsWXQqA1k4wKPOcGrV7aE+CL81XgPxfxBm+Mk186ZUw+rUWTj568RMl3v9i/GzGL1xCsLI9uSPxCDJxE9GYXT1+dIP6VceDHTf1OaZD2vRvQwAGiyi+fEiH/NMfvSwJ6vPLrPryL/MccA/RwnfLFoq2n2VZLG2pBBxtcLGRpfKaB0bkl52gbmWaKG6QGsZiKcOYm/jmrzrVf9FaBmjh7r9+hyqDX3aqlvPJDJlyHrFUAt3rBsF89DkTRgW/oF9rwe/CGUqSxRuNOBBecj7dzrNijj6aIL9XRH6vDGM2Z1dGt5b10ligduFsvmxu4qfNzid7tcHXK+IEvPI23Y87kC/ecSpUANioTlYU3oA/mR291Kk4/fQakaTKB6Ix1XH26BhhNcYdB6Ek4h6FbQ3NGs2p8mYIxbzCCaOYX+vlij+31zcRUcng7OFRGLU+PW93sD0dTlAdJtAY0+8fFBw4mrW28On7L/hn3a1lUTlyvBkkyIoCw1ijfLTLxmuiGrayOR9PK86B4g7IxfNK5rxEW3FIzwYO1CWaTjCJrFOt4/0PBlAjK9aivz36Da82fTgahrYR9xxoks+HgJ+I3IKoroRDsNbWS9AO92cJiJUb7pLMZzEbjDxBTyBA3IxoOoFELo2lHOTvcDvl+vwyMuWCLUhQZn15WVEQkXXP94hr4wqwrVrTuu6wcZa7zT8bjDyynwAFT7ehaWE1htfzdTQvbziYE2pBYi4vzaUnoXPjhQHg5NqPaW+IdqWOUDyCUbsHf8CPtBbmYFlbNnAnR2RP8bnd6wY5Y0eC+OrX9xDXiKjdq7Wx0A20+0C7NUNcS/G9JPNZnv+nVZpXYXve33/nHoDPV7QRIxgN870YU52DHkGkFtCLaDqBRCaFsBpDLhfi9SArJHXpDkx+ksShgFJ053H+TnHPo3YPX3o7D31Me/7hww1kygXIfhnT3pDvcS9LEqwRNQbTWCC3WyLsts19ym4XX+reAIBpLLmqLoIj0YF5cORwgoSuO0B7lfj7jz5bol1t8HoY9KhIslxJ2Hu4Bb/ix5/+oMrPOq6l8Lt/61fYxd3nl5Eq5hCMhj3Ju5ijnWod9/aDvKaEgZeYL9aaILmJEJ2d2mYefhva5V4XS3PJ9+A+dyWfD0o4hNxuib9bdNq0zTxDgtPFLEqHO1QVX62gpbxQQID4hI3za8bCA7Q23KpagZDXK6Owv+VRHAyrMdRPr3D25AZ/9CTP1xuMhhDXUo48cDCA6WCMSFLFtDe0C1R0/SuT3mk0uMZe2cek9vlkioVu8FlP0MUixl3ak+Lp+Ev+HLLi5+dYO73mdzaf6eyPI85j8U7WyxW2H+4jsaEhFIvSeT/3GmACQOV6AUnyec4QN8lanOuig+pWw5oNRshofq9XQsjZM29eXDH8SHQKJJ8P/mAA07mEwv4WtEL2JybDX6RhSb6f6p8v0vhiXc3PYYQitFD7zR7GcxnmggKDhBZHeiOJN775hifQE8mBrCh8YIpD4INjCghuk/MAWiDf+RFNeAEHcOMVhe+A2GyEStFCN5AuOgGzCHaiahhqNo1oOoHGedXJvG23SCG3KRb5wq7AiM306mYJa73GSS2IlblEre3Ie7qDmdlghPpFi5wZF2si6U6mKNzZxNZdh2gq5Dtvt8zdlSRZ8aPacfgXYTWGUCyCxdwgEphKsqaj7oirQIkNDZePX1AXp93zGPIIF0nAiz0V1a9kdGnjfy0kNjTeKE1jSYdtywtF8Pll1C/qnrb1dEIHlTv42I4773dl40R375dsmVQ6oMShlspr6Np28LGQN4koH+0inQ4hlaYk59PHfcg+Z1N1Q0z00QS53RK2NwMYtXsIqzGkYkssV/DAcSJJFbPBCM8ugM0EJRpPq+IAIXOZVP7l6p4SDiFxS+9cn9B7ubMdQPXZBVWpImEiuU51rkQdls3PVQjh+7ilXuOumAt+wbQ39Dz37V0V5lLywH9EQBbXUlgvVx6t7Hw5idYNcXHE9VvrNXpTglIJUyFTnyMUCyMYIeJ+JKnyQQzQWq51qcLtTtwHjTYfiKK6/8l3P+F307q4wR/W3uTvEHNQ28xjvVyhU62jXamhcV7F9bEjc/pRveD5nmgy7llDvdYQ2e0CgqEg/vj/Ilje6lZLXgSNrLeuUgXYXc3URxOcDgiSN2r3kNst4eb4Ev5ggO/LbXBFpHmL7wEA7paWFFDLMha6AW0zT2IDFimkAcAPBq/iuh/xfLcSDmExn+PuvQymM8cZ17IhAwBw5w6pufXqLX7e20c7dJ/MO5rYydaaZVJ/0nA/Q+GKLT6XlNwItjLtDdGukpRkrebsB4FwEHEthcqTU4+5GgBP0g9Qh3NlmhjoAaxXZLaWKubg88tQs2lUrqaIJChI/dN/8RxKUGGFIIGpl2CxMaJiy/K61/9t93gxKk9OYRpEbBaV4tpJBdsP93FRMWCt1xxIiZEppVkRprARQHF/E7FUHOliFrXLNiJJFRuxKcpbceijCb76q/ScExsa0gUNm8kpErZpoeBsGFMygdQ28/Z9u2RZo855kt8rIxQLI5qM87xK5rN4Wo0irqWw0A3uBLjXhduYzN1FEp1H97kwaveoo7FeY2RXyMUaETzAZpu6XcNmF1OD1o3fT3PenXj5gwHcHF/ynzd2Cjy31Gwa9dMr9GodnhP6aIJMuYBgJIivHNJ/x1KkyjTu9pl3RM9rieIdB8rIhmczHfpohuu2DxvxucefwFqvEbXnkuCm5bZyFNiPZy91SN1KSf6An/fOaW+ISCLOBQ0ArMqVKRdQfX6BYbOL+WSK+WSKr71meoQ5UsUc0inlpaQ8u12kYH0zz91OUUQFvP4Tz58NPQIPmzF67qXDHYRjUWwe7vDeHNdSSOQ04md1VpjP5tAKSXzxx0+pMPQXHIJ/s2M+m2M6HNEmbfrw3j//FP5gAMPuGOP+FLOxwXrQks/HAZ974S2NBQLyElsFepnuwAZw5M5+6XVKGBrnVUTTCQ/GvFOto3Vxg9lgRBWUSIigOFoCIbuaVTrc4UpXq9pBNBHzEJMA4hisTBOZcgEr0+SDMCA7OPpgNIzZbIloOoHXtqeoPrvgQzeZS/GGJysKMuUCFraOYO16hMY5bbyBgN+jyJDKJjzQJZFURBMxlhxbGgtqUduB53wygz6aILtJG5pQS3AnVNba8mgm3x7ug9/dPoxrKcQCBL8Jx0Ievf1MkRwTuw0v7tZaW9h7uINRe8DPtHZW52fxeXjihWmrOClOm9XU5xygm4bJlffF0udJkKrPLhAK+TAckNHQ9m4CKxeKjIjcDilOH83QHzpdpkhgiXv5kQeOI973N16dQDcDSGxo2Msv+fmLISQYC/tbrGbScyVV2mYexsxAqphDu+dclMCqk+mPXVlek8W9+1244TzBSOhzYQDaZv6ldy1GPuNDQfVWG8UQVSEBy5n2hphNTYY4uUcqYqJ+euUho1tri5x7l9Q9cGtpL40FtMT6c0182pUa4lqK34fbUAgAXtv0wssy5QI/L3HoyYrCiVQkqWI/69JGt6vAud0SCvtbiCRVLPQFjNkc3XobD97aduGh6Rrc3AFrTdKN4u/clULJ50M8uGDce+vihiuW+e2XYZDuOdW9bkAJhzDU/bbEJgkPiO/JZQMYNNqQfD48yDSwq42RcXV2hEhCp6Pjo3fP+e/VLGHftQSQ00i1S3JVyAS3YTx2iP2mPkflWRXWeu1JFgXMzp2IZEo5InJqSU9105jqtpIQ7Rcr07QhDH5+tqKLmNstYdjqQh9NWDlKBH0r03SgJbKMkjqxE1WJVFgGI4zaPUQiChcsdu+XPdA+cT3feUwE7VG7x+vothqMmO+3hyzLFFCuVrzHVZ6c4o0HBP1UsylPFylfUlkk47XtKdZrC62LG7QrNdx9WEIwEoIFCbUb2ksySdqzh80uKk9O0ZpEsDQdNaTEhsaV3u51A4otACHemWVJ+N4PaT01zqtoV2roXjc42Bs02rhbNJgzUH3udNjjWgrlo12EY2HutCZzaf6+7Yf7PM9FtZ/gODGM2tTdF89RBPbBSBjTqQm/zVtqtKlglEzIHs8hfzDAkr7iPbs7LTNOUKmCLebTbDyFElTw/ond1a21OEh2S9la6zXqF/WXOCOmPsd8piMRk5gH5h6DRhvRdILn9HgwwUKfY+LyKwi7OBfiufiDAd47lXCI35HflskFqLsk+EnuhFvxeYtZ69UKBVdjUqAZ2pUaRr2hRw49ZEuhi+sVyekvvZ2k/aSQQaqYw3DhvKdxt4+rp2d8XaaxgD6ekIv7fIVULomUFnmpCPVFHD/rDoEkSf+zJEktSZKeuP4uLUnStyRJemH/+2eCpfqFSwiUYIDITaMpZJ+FN7/+AEtjgaW5hBJUMOyMXVbo1N70+STPxhFJqmhPowgqFNC4qxWAI3eWidJh/nkkMDHEom1e3qB73UD3pgmfTFjYm+NLGFPdDhQMPljcGECRjQv2fygWxf6b9+CTLCb5RRJxrFcWpr0hxkYA7/zm2xDCLLWTChM8Q/EIZuMp3v46tejvHaUZyxeMBLhVmirmIPkkT6VVbDb10yts7JQ4wO4N1i9J5dXOrumArw4wG4wYLgTQRpAp5RBNxD0ukWLzFZvM9sN9T/tw3O3jZkCVwc4NBZ2isjKbzOHzy6g+u/DAlRI5DeZiiflkym1nIYsoyFcAcD6iw0gJh7CRMBBNJ9BtTzyJmRJUkMxn0a7UKBFSFHz/gxnzF8Sm+Af/6PuQJAnDzhCP3r9Cp+cNTn2y7KlEitZ7JKni3cd+hP0OSVlgmkOxKP7ZjyM4bYYxavdx0aTDTCQjV0/P4JMJv1k/veJDKpnP8LPvXjcwn83Rr7Xgkxy8/20cdSgWhWWBtf9FAnObH8N4VdfBJ/Cl4nB3V6Bmc+Af/C8z/t1QLOqQ3O1nJ+aR5PPhxUenMFzkVnGQdCb0s+71Oh16tcWlW5tw0O9cuzjoxKE67vYZsy/Ux8QYLUKeZ9Sp1vn3BK/HnUjNBiOEZJpfYq5EkipaFzdoVerk4tsbYNjsIpH8c/n1AAAgAElEQVRJIRJxeEACenF7CGUXt3SluNZ4wPFFEfcCOE6nYmS3iwhGw57D1tTnUPw0+ea2WZ9H1tM2aBsto6iPY2hd3HgCxfxeGeZihV//t4lXE4yGeY+YLySIXPB2xdEfDOD+QciGu9F7SeZevm8xh8X8jyRVJsIHQgHP3FoaCyTzXrjBQp+jXApCCYe4m6Rt5tG6uHF1zOY8fzvVOvy2GIL4TDFrbu/thrFyYIMJryngnTcO4Q8GPGZ4D//Sq/Y9eTuY4nkJ4qh4PwJe1q7U4HMpwQ0mPvhkGcNmF+1KjQ0iP/vIWfPtSQjz6ZwT1kFPR7/WgrmWsbYsWsuWs9bUbBpbiQEWc4MTmmGzy+s/mk7AmOouRZ8MMtEZAoGXsffuOarIK96f3AWAcbePyXAKSZL4PoedAT8LEaBH0wksdJKnlRXFU3HnCrm9byzmcyQSAf781ZruQUtYXLyJJB1lq3QxxxDOvdcdxaWlsUAspSJin0kigZwNRshvJvHvPjxDr95CIqdxkCzmjzjHXvvKIcFidxwhi9xuCaFIGIrfgizR3BMIAjHm4xlGnb4HNvulrx3xc9dHEz43REdWnEMAredMuUCJvv3/ExuaxxhTVhw56v485Pp74mOEFMcF2i1IIDpVgscz7vZtp3kqIIh1fNO098HRDP1aiz19AJoTajZNz3KvzN2LUCSIenWIi8enOPus/i+FDX5hxs++Q/CPAPzVW3/3XwL4tmVZdwF82/7zv/b4hUsIJEmCmk7g4VePkI0voKq0YHbvFTBoDZDaSHggNICtoDOaoHS4w1CURMhAPGhCzaZf6hAAdOCnghPbUZMes7vaKPDFYhMSgfd6uUIi5SyUsBpDNBF7KfgFaEGLzc8tXadP5vjohcIBcb/WwpdepQUeCZioX/VRqdNEFIecrPihphMIxyLo9ai9/expl1u3508uEU9GkSkXoI+mGLRGnhav+KxXvvYa/IrMB9D9HcJkCu8FNZtmuFPlySlh9RUZud0ScrslZEpZO2idYaEb/B3jbp8SsUoN2mYelkWEZrFBZsoFrNd0T+vVCj6/zNXo/XtZpAs51mIH6HBazA106/QsRXJz8AbBupRwiBOvuwk6mFQtid40gGlviKQWwdIOXAGqig8abYab+QN+vPaQVC/G3T6/56/91jvY3lWRKWnYupvjTo2aTbNCTW63hGQ+i2Q+i4/ed6qiD+/6MFuSqopbi34+mSKZ8CMVpwT23uYcajYNa+04vq5ME8l8Fvtv3mNFkH6txUmmrCgI2bCa7aJTzbztOOzzy0iEDGzd3+N5Jw4igb3tVOv8vUJVA6BE0if5oI+cqpmsKJAVBRcVHeVdmmuUCEQws82mlsaCA6xoOgE1S2Q82e/A4sQ1zOYSigfbePjlPf55UYEtHe4QGVijORNJEnny+z9yjNDEQecm561XDjlSmOrFtRQK4R7/nZhTItlIFXNMDgdgw0N8qI4ouJ30hzZkgtZC0v7+jR1KeKOJKPS5kyy63Zbd7wNwIE0CdyzWhLn+fCMfdxAgIEiSRNhit8RjpS6RIo+9zsR8G08dSENENnBapfU37g54DjXOqyiWYnj0EZF7w/EYEhsagtEwzq8WXEzxXJeP1M06A4t19MX13sYzJzY0rvTPJ1NPECiIm2JEkip3LcV7DMei+NGftmweVMADwRPf1anWYeq0lrYf7r/UMbQs4ty4ZaIlnw9Xp6SEo2bTuDhuorC/xfOrU+sgosYYQx+MhvHkB4/p8+znu/1wH9ntIlLFHJRwiE3XdBecAnCCcX4m0TXShQyZix3tYqETz8s0TA5Iz2s+9G0hCgB4/sFzFA+2EZRNxONBGFMddRcfe9Tu4UeXGqupiSGeo8CLzyckvDDpj/BPvuvH/n7MQ8oPxaKIxKNIFXOYT6b44dMgdx2sNcmlis9cGiaMmYG4lmKYmkg0tc08crsl+CQfYikV1tpCLKVyIu9OWg/fomR077W7WK0p+I+mEzjcprn33XeHiCZon3J3O4MROosDoRAmQy+PpF9rIRKntaxm09BHM+R2S+h3pvijF9S90McT3ntVLUmiENEIrf9zqvZXn13wewyGqCioxUxes8FIkN2uxXmS3y1hPp4hW0yjsL8Fn0/ivWf74T5DIrPbRVuOO+IJoDvVuqvAFybFsrBT6BMeTZLPh9Nrmfc5oQY2mXs7KoCtnJRNsPz29sM7BLmzDeuUcAgbO0W6Lk1mwn8oFkU6YjC87c5re9AnMyTzWU5M1GwatdNrZDZiuP+VB5j0x575/oUcfwZOxZZlfQ/AbULRvwPg9+z//j0Av/WzuPxfuIRgMTfQOK9iOpzjrBFEvT7lzfzel3awXq1Z1zmaTqB4sM0kstqLKwybXVIiWMvoTgOYjSZIbyRfwnsOGm00Z0ksjQVn5ONun/GQAl8cSarI7ZZ408uUC3j+4xM+0PTRBNPhBEdfecifLTY/8bmpYg57r+zyZnhzfIm3Dwz4/KSk4vPLmOgUBMSUOSpPTpGIS3wAi+sdtHp2pZjUfl59PcMBY347j+MPPkOnWqdWtw1dEsYoPr+McCyKT7/3CarPnBbwP3/XwM3xJXsvrFYrhhpFkiosa43r4wpVSS9u+HcX8znimuo52MWm3b1uIBQOEFGrNyQH2GodPp/F6g5uY5RuR4e5WKCwRWTCxIYGWfFjNhihU6178ONrC6wCIb7PgsTfe6jRaTkZGVgaC+ZGtC5ubCMYias1vlvJPxF2l5BlCacfPieVJj/90KjdQyAUYolLc7HApD/E1765zfe+XAEzM2Brl1vwKwoHGmpMwlZiTIGoKdtBbZ+NfgCSaM1sxFkRxI2VXpkm3vzlbZLn9Dl46dvyewt9Dr9v7Wmli9G5Ju5ENJ3gg8rdPfPZ1aP5ZMpuxkJZqnE9xGtHtA7H3T4C4YDnMBOH9LQ3xLDZxVu//iY6N/QuBJ5dCYfg81HXq3Ez5J93QwiMqY5Rp8+J/co08etf9b2kDLReOvN0vVwhkogzhEQO+DHu9lHX01zl9Pll5Pc2CQv/cJ+lR8V7p0QigtVaQqZcwMZOEfOZjqXtFiqk+2qnVajZNPrNHho3zrMXFW+h9JMq5hjeIBL6bDmP/F7ZkfmzvNu7SKquT244uRMwIPG+3PPeL9M8kCQJ097Q7gz68PyxQ+i/maRQzksMO6ifXrHE65/8wccsXTxsdREIBbFartBuTDCaeheHrCi8N8aj9Hni/VeenHoq/gBVQYWMZTKfZXiNgKEVdp0K7Gww8hht5XZLGHf7uP8KYSAEdHNpmpgNRljbOD5tM89661efEfTJXfwZzAlqRDhpyf7+NRa2PPOo3cP2fg710yueX8NmF7PRBJJtYmVMdVYnE4TOypNT9BsdhsCIwErAssbdPiSfz0NSDqsxfPJ8iXgqCmNmQJ/M2TU3norz/pZNSSx3WjzYRnF/E+PuCNVBHB/88acAgB/88SlyuyWUj3aR2NDw9k7XA1/SNvM8R0TxBaB1Yupz/PtfX2A0XjMpX8h4d6p19Gst7Lx6Fw/2bDd6xY/cdgHNyzrvOfpkitJeFtPhCJIkYWWaFIgnVfRqLayXawTCQeJZBPzU4bATeXen8um7n1Jw++FzZNN+zCczhKMRfPQZFam+8ZdULOYvw0JrJxUUD7Yxn+nsSyKC97Aaw2w8td+zgD0NMepN8NbugJO02WBE52R3QElutU6wvNXaU+QCgF6zh4d/6VVU2gH87b/1EWRFwc3xJf9cMBJGKBbBzYsrrEwTN2cN1E+v8OH3njFReDFfIJHTIMsy2pUaUvmMR3o0mc8iFItyIjhq99CvtXD+8QmaFzWEYlEszSV8fnJ6f31vDreDeiQZx2T2sgy0qc8x7IwhB0hZ7vLxCwwaHUg+CdomqcDdHF9iNhhhOLYQCIV4z68P6bxbr1Z4+u6n5E8xNxjqNWr3YFlrvPuHH2A+W/z5IBQDPy8fgg3LsoQ8YgPAxs/i+n/hEgJJkqjqMp1juXLsxbutCT7+3mdQbHKoqc8xH89QO6mwU+Jtctl//999D0tjgefvP30Jr50pF1CKdnB7uKuGAG2KrYsbG284w6Q/QmZz46U29LP3niCaJty+LMvcroskVfRrLTx991OvfrsR4GBMzaTRbFOQ8MMXKbzytdfQ6qxegnnwBhTyY9Ds4vRsDDWbRjKfhWVZDLcR15AuZjCzTXziWhLjbt9TyVPCIRze9eJijanOQUggFIQk+XD3S45cZzBK2NH1ksiI7ufgyCLGoE/njsKFvWmfnDscirAaQ1iNYWkssL9PZLLJyGDXWPG+7rxx6DH6qp03Pd4BAFCfaY69+5pwuls7TqIiWv1ucikAhlwAYF3yZCqIeJQMy5LJILKaY9AkDtbcbokt5EUHAQACfgv1QZAlHtfWGsNmF5lyAXdyU4RkA/1ai7uQIhEIhMkFctjs4tP3T9jMrV9refD056dUFR9Ofdwdua1eszQWmC6c5NfNkRDr4vbcjaYTSOazGLiCbhFAiWDhwWs5hFxVY3cikSkXmMsCUJLXa40xaveQ2NA42IqoUQzHjhM54O30iKQ9GAnDNExWxnpaIe4F///Pwev3ay2YBnUOxXxJhyZIZOj5iGA4mk7g6rNzzEZOZXFlmoSFHU0g+yxKQuNh7pS5n9fW0Q4paiViHNy6lTlG7R7SxSxCEeoUiTW8NBaon16hcV7ldSGqjfm9MmRFYVhh8U6B4Wk/aSTzWYwmlDAJ6Ea/1oK1XmP/KMcBUj42RCrqKGQJ4QTLWuPtf+shPn7/EgAlbdMhSYIWN+O4ujaw8+pd/r2VafL8/9b/c+m55580RMA7aLRZhWxlmkgVczj98DkTfd2jsL+F1sUNsttFPH7U5Ar2vXceYNgknwTx3rvXDYzaPa5iq9k0Whc3fG1LuyMpEkfBOSsfOHyKTDboqVrndkvIlvOwLCcQ6NQ6fD/zyZS5Hu6EWAR64n4EOXRlS3taawtq3I/n7z+l+WVXvpfGAoMWQVui6QSKKRIO6NVathDF0ia/AvfevgsA+Ppf2efizLDZxc04AWM650TCrVgzHYxfUmBrzaL48pHTZXefM8FoGJePX+D0xked0JwG06B9W1Sql8YCN+dtSJLPQ1AVhnGdah3d6wYVP24Jfdw2cBPfHVAIatup1rG7FYAxM2CYPk+iuff6gaPQc1KBqc8xaPVQPNjmYFQfTfjsnU+mPH+OXi8hKntJvtpmHqY+Z1hl8WAbCS3Oz6uwv8XmbU9+8BjLJfD3/+4Rv/fy0S511BdUVAzYRUrhFpwpZrHQDUSScdRPr9CvtTDu9kkW1EY5CJjjoNFmGW/BYdA28zZcOGl3ExzBh94syPBZgNzPR5O1R6VJzaax9eAOOjdN3sMEfHTU7mE+0/n5y4qCYMBJKMbdPh7kqFsvnsfKND3CKGJ887e/itVq/bnSt1/I8dN3CDKSJP3Y9c9/+tN8nUWtnc/XCv8pxy9cQhAIB2Hqc8SSUURCEh4cEXYwGgtiPpmi36aKRvloF6F4hKv2CRvfBtDEtSwJ2w/3EddS3D5M5snkSmxatZlD7hFBkXvzdJPI4uk4t4a393MEOSrmoGbT0AoavvnbXyU5xesGUnmN29RKkKqFd944RCSp0qazocFYSsjvlUlvfbWCGieiVECx1YXCPt6QkvmsXYEqQNvMYzzQEUur8Pt9kBUiFjavmhg02gjFoogmogirEdROKtA2cyTjF6UqrVAREMTVu0U6+IVp2spcIrFB17+5XyCTpUaf2+rGVMd0OP5c/Wef5EPxYNujOU1VNfpuNU6BmsCXCrhKr0+bY6nslY8DgNlY54DSHwxg886GR4f6tV99DfEAcUKS+SwS/hFMY4HpdMkBt5pNcVCQKuY4iLbtFbD14A5iKXomve4c9Sa5Vq5WFsJBx4ROyHIKAqjPL+PHf1qH5KMEot6V8SDfZTOZ+XhGRkL1FnqzEG4mNBc/OvZh/8177LqcysSYUHfvS3eYfB7XUuzLENdSaFQIp3tWWcCY6UhsaB4Su9iQx4bCz9mt0JEq5jx8EDH8ih+Sjyp9B28d0UFpmAx70jbzSCV8kCRHgcL9Oe5qsUjy5jODoCD2uxMKItkUGH4GUBAwatOhLjS9TWPB3SHJ50O7a3qSxEAoxPKxAoZAkC4bomTr1R+3NTaCEkHitDdE8e4WSndpbgSjYZQOd9CuUBXuqkVzVBBoAVumtJghk0GXBGxMDWHn1buM1xZBeLtSYy6I8AXJlAseZ1bx/BIbGhrnVey+eseR/50tPHBF8TtiyIqCQaONB3e8x0N+r4z8Xhm16xHG3T6p+SwD0MKzl1r5gVAIteqAk3UhmTztDZFQ/XjroR/T4ZQ7o6XDHXJ3399CTI1gvVp59sewGnsJmrn14A4SNk4eAMLRiN2dogRdEMkBWttCJSaupdCu1FA9Js7Bzqt38fz9p8iUC/Arfja8AyhIFPtLNBFjB3UAKMWHCMUiGDS7iKgxaKUNSD4JiSTBru68cYgXx33uLAWjYcQSUU6Gw7ZCXGrDpajjlxHXVOZ1iJHbzqN0uANrbfFeoxUyWBoLxNNJBMJBBBSJk7+lSQpRh1++z2pt8/EMH58q2DzcQX5vEwCRfv3BAK6bEirHNZJv9UuewC/oJ/PHlWlioRvc7S3e3UIgHGRHXrF2g/IKn5wptqoczclQLIpkPotYimSM41EJwVAAw06fg9dJf+wRVRDyk/tv3kNut8R7RVil9yASc9EtKx5sI5qIcgJWOtzhNWMsgFgqjsSGhoy6hLlYIBSgz9t6cAfJfBbnH5/wuxbnz/0vH0CfON417kRVEIX10QTt1gw/rBQRScR5n+zVWkjms3Q+lHKY9Me4OXWSOp/s8ygKRcNAZZDiAtFqtUYsFYcsywirMeR38rYMMCUxmbwKa+0IIpQOd+zu1wCDRtsl5kF7feemCZ/fh2gijkA4iOymhuL+JixrDSWoYNQd8HcH/GuPy7A/qODFcY/flRIO0byqNNjhPJnPYmV3B0uHO3xdlMQEMNMtZMoF5PfKKB/t4sNrh5uXzJNMbCKTQiyleiSSW80pBq0BRu2eBynxRR0WpJ/qHwAdy7Lecv3zP/0rfE1TkqQCANj/bv1//Py/0viFSwiEJGIqHcHxqY5aYwHJJ8Hn91HmHCQyW/XZBaa9IfTRFJkSuf+5D81ceIjf+E3C9RbvbrFTqaimKeEQ8mGnxTXtDTFotFk33eeXGc8XVmO4fPyCN+HRcA5zseBOgD6Z471vPYFiE6jqp1dQAgEmNy3mBs4eHUMfTdC8qGHY7KKUmKLf7CBTzkPySZhMSUXk1fIIZ48vkEkRWWu9XGHSH0IrZMlwKRJEtkCBVutmCGM2RySpYuuwzFAQvyJDTcehhENoXdygcV5F56ZJxL3eAFuHZcc9eO7H5eMX6NVaGDa7KOyXSfqtN8TzHz2nDXUy87TVMyVKnG5bmAvX2biWwuvfeB2BECVxpISSh0+WOIiNJuMsd/it/+2HAIBWc8qBUv30CokNDfXTKz7816sV1hYFlOIgOXl0hoFBh8Sg0caNnrMdY0kiUxCu1nYFsV9r8UYo4EBXT88YpqOqQeSytOnOZkssV5LHKEpUJ4VLZGk7RW3V5Qrl3BKtWdyjbd2u1BBWY6i2fEiE5vDJMhSFIEnDdo8w4vUB5pMpxt0+GtUeB1q5rRx3YxbzOeIp4pz4JMdgiyQb6V2u7G5CPk5/FtchPm/SJ/lYd5VOBO39WgthNYZmtcU+FuJ9T/pDDEdrdMd+ls8U1exgNMyBk9ClBwAloODgDeIJUGJM73C5krBerqGmbUK5HQTrE0qi3PKaAFXQ9racjofPL7Nbqj6dcQcMAM+ncZegGpEAVcIIquRjcv6g3UflyRnjn6c2Dtmy1vj6Xdof3J2X9XKF9nUb0+EYZ5+ckvb2cIJgkNbOyiQMuGkfvB51L2OBXq0FfTpjHPfKXKJ+eoWPzx0C4emHz/k7J4MJv0vxroSSiXgGAPkBAE6y0DivonFe5UqiLMuQJQs3ozgHYdZ67XAZYkE8eLPMRRMxNjcshAMrDmz6tRYmA6oEj3pD3HtIcAd9NEGqmMPRVx6y1KsYxYNtXD09QywZQ7a8Yftl2NCmPBUKRGECgK1C07PfH+3Lv/EfvA0AuHz8gqCFN010b5qYDUaIpRKIaymcf3wCv80TqJ9eeZKo9840LHQDcS1JakFjwtn3OzMkMklMhzP4FRlXT8/gDwZgTHUktCh6tQ5akzCW5pLkl+MOgXO9XKFX68Cv+D3V76unZ+hcN2Gt17xHXD09Q2JDw6RP6kbBgIT5bE7zJUacpurJNb+/lWkip/kwHU45KRFz6LVdEhSQFQXf+sMTxJJxJzC1OyE7r97FuNvnfdmYGVjotirfZMbFg29/YOFwe81O7kJtJ1PS4JOp21JILzHqkUKbSLBG7R627u9BzaQQijnPZDqc8bsX9740FizNKhLm2kkFlSenvF5HnSF0e++6uNT5jOmO/XS+9WlODjsUQN9545CfwebhDpRwCB/+8Ufo11qIJlTk98pQggonLGomzWqEwZAfAT+Q3khh1O5x927Y6tJ5N6TOglsQZD6d8/rK7ZZQqS3x+MSCZdEaGrTovFuaJKd9/vEJf3c0nUAy5fUiujm+ZMdf8TzFWh53+yjaZ2/XruhXPqtwF6hdqSEUcbqWNx2/Z832ay3cPUxj2huisL/lMVYTUqt+xY/S3S2CNbmkWwEqxDabVOxrnFdRfXaBzbRB+5o+hzHTcXN8iU61DiWo8O9Tku5DOp/CO7/5Np699wRf7CH9vHwI/gDA79j//TsA/s+fxdX/wiUESjCAB7/8Cj59/wXefi3I7nnXp0381d/+EvwKVcxF+zIQDuL5+08BwOMaOF8F8fgpLZ5QJMhERTFMfY7jHsG63IeIwLcLJ0uA1HD8wQCSWaoKj/pk9NOp1imzr9bxxq/cw3rtVJPH3QHG3T5moylWiyWy20XEUglWx2mOIzCmOtoVShCCQXrV9XEMgVAQ3/oWHXzFg22szCXzGWonFUwndL2ZvMo40GfvPcG0N4SsKDj/+ITJXKJKvjQWWMwNql7rLviUfe7cDqqE2+F8MuN7EhubUANyV5/dzojjbh/nT64w6jgJV/e6gbwGTHoUALp/97f+5q8AAF5/LYF+w4GtiIBXCQbY4KV23kTr4gbjbp9J2xPD76iNTCmJe/efPgLgqKQYszkicQo+dl69S5jMpYWDt44AONjjb//vP8Qf/R9UHU4kA7hpENY8rqX4mkWAnd0uIp2m79NHE4x1GcmQzkGc4C9Me0PkNQsnTar8qDGfXUFT0L1uIKY6JPWb40uu/p89OkanWiepWd2AmqLrP9hzVD/cELfZYAxZUfDDZ7bXhQ2tErKM4h13qnWusgqiKs0Bizk4094Q/mAASjiEeDqJjz9sQPZRJVEkI5GkykGRqBaKkdSikG0DOje04qZJHgDnn7zwmMQtDdO+hjWMme6pNF5WF7zeYylHTlfAtm7Pp0wpS2ZOawlxLcWSnCvThJpJYNobwlqvMekPkd0uMpHdmOroL6hLlcgkmWgPAPntDciyjFA8QrLGoSDyBScoElVvJRjgoEh2cUhEErperjjg3dDA0ABtM+9U7BJRtCs1FPa3PC7d4v+LoGBzg8i8AlYmSK4JLWKbFVKwdVyRWOf++rjCz2y1srCyky83bGSi+9AYeJMwNa3yNeQ0eq+F/S30ay30m0P+OTF6tQ6y20VUnpyiV+tgPia4JQAsdKpszscz7hIqtqSvWFvRdAJqzEniSne3EFZjfJ1CDACgYEq9BZ2TfD4UtRVy2wUMGm2Uj3YdXL0Fhm9VTyjpWRoLqNk0Hn37EeaTKXIxHaZOxZan7xJ2X8y7+WTqkZQUgdnnESoJJ27zkCY0t0OxKEPmVqYJ2Ta6AkjNq3vdwHq59jzPVGjCBHxVi6Nx7gSWP/qMPv/yMRkcCklfd+dOzJn5ZIpgUEY8aDK/S8xXY2YQgd3nw5+87xQE3KNhy6FWn13y3wkeWygWRWF/i92oxXN1m6iJvSaaTsDn9yFgm5j5ZImTw+cv6FoHwxW61w0Mm12o2TTOHh3T+1uR7n04FuHPG3f7aJxX2S9CVhSs7Gq5mk0hoMi4tzFE66rlWRtiPnGxq5j2GPjR+5UQiUegxmT88qtLdngWncjZYMRdJmOq4+CtI4L19Gg+iMKiGG7VJndh7ebkiq9JJIn5vTL8wQD7RaSKOeqmRS1WpgNo7d3fXaOwv8XvY9ztc3wy6Q9hLhYY2eevQEsABA2WFT/2dqPQRxPes5ZrUlOMJFUEwkGWanU7U3eqdZx+fIlxfwLD+HxH+S/c+BmTiiVJ+l8BvAfgUJKka0mS/mMAfwfAX5Yk6QWAX7P//K89fuESguXCxPMPnqN0t4iSOsFq5UjrvTgewFpb0McT1E4qSBVznkBg5tIeTis9HNwV+uQS2hU6WN1t7r/3X38fAD7XUGfQaHP7LRAO4ME7R6g8OSWi43LFgeS428edNw4x6OksHeZ2LjV1Io8pQZJeI0hPHDYHjjfB+Zw4EPGgif1Xt5DK0nW2q82XuATVE6q8fPbBCTuI7r1+ADWbRn6PAtvVak3VcJekozgQL584esL9iUPKCsWiHBgPGm28/o3XkS7mkLd12IPRMKLpBDJlOmS3HtzxHOAABWzk0kjBoru1PZxIyG0XPNXXVDGH6YykCU/P57xRq9k0264vdAN7rxD2ubBDbUwivRKZLxZcQpbJtff3f/8KSjiEN7/xCn+HaB2bC8Iv95s9xFIqVmsHGtKu1KGEQ3jjm2/gr/1795HMZzGdLLGRJQ7BpD9khRdx4LUrNYSCkhNQW0BvFmE1CMAx8Gn2JOxmyechoAB+m8gKAGqaDjZxoIqAW4xOtQ5Z8UMJyth6cJ3wmrMAACAASURBVAf9Ec2bVDHH5Fpy/LW7THeW0DbzCEbCPBcn/RETD4UUn5B7NPU5VwvLR7t8SPkVhTtTf+03C5jqFFAIqT9xIC7NpceRmJ65Dy8+uUR+r8zETwDIpEmxKpXPQh9NOOAVwZ3PL2P31X10r8mcamkscHeXMMUABf4L3fBUx8SIphPI7ZZYeeT7H8xeIrqFIk7XLxyPvWTSppt+Cs6HZJIVCBEuuPLskuQRM/QMExkVTx93cPDWEYoH20jb6ixC/1y4q4r3KOaIW5q0mCTH3n6jDS2fYkUP4YfiPnjd9yfGcCJhNp4ypKVfa2FpLHDy8aXTATSDsPMyW43EqTyHIgoaNyN2Lhd4a8sCvvOdJnK7JcYwV59d8F5Vb6081yf2GBGcCEMvEVyGVdLYF52fTrWO7Cat41GPkrJERkVh39mHp70hvv+dG6TyGVIBM5eIpeJ8DTuv3vV0KOunV575FwgHsVxLfI3VZxe8Fq5Paa2H1RgKuxv8XkbtHpkwqTGMDJonC32O0uEOJJ8P3esGf8a422eJXjahtJ+fIJaLdy4SZTXmQzASxmq1wt7rBwTNSSepu53TyCk7RHti47yKlB0UZsoFDIwY7r5GQedrb+Ypuc2moW3msVt2OFbu9wA4DtGCg1M+2kU+p+DDU7o2N/yy+uyCoT/vfCnMqlPinjPlAkGRDrY9Sb5IlOIadWlkRUFu62VHc3/Q8Y4hWBNBPVsXN5hOFzxnv/ambMPzJIYAuc95a73GdOh00T5vrEyTA/Fhs4vpxMAH5yrG3b4He18+2uU1FUmq6Ldflh+fT2a4fPwCakyCuSJXeCUQwHwyc+nyL1HY30JcS6FZbSGupXDyyCa6b3k5LMV9goOJ+SHuLRSPYOvBHT5f9dGEIGOKgtZVDe1KDf1aC6l8Bk+OF4gm4zznwmoMxxUf6qdXzPMT3dSwGkM0GYdPljlJGjTaCIQJKh1PxRGOhfHZ0wGS+SwCIVsgYkHrKRKPUpei2kD3usHqb2JOPPilfWSKaVw+u3mJI/KFG9LPnlRsWdZ/aFlWwbIsxbKsTcuy/qFlWV3Lsr5pWdZdy7J+zbKsl23N/3+MX7iEIBAOYmWaZEK2DGA+X9lBdAzdxgCFMhFsiLhJm5kgyumjCWNnT4ab0Od0AMl2G1RIxAm+wH/7d94CABhzh6QqFCXiWgpJ292xVanj4jPqzwuFARFIJvNZnD06xtrG5lNLLYDtB7vYuudYqNdOKkjmszBmOgaNNjJpWmxiE3z3W8+o9Wj60a4Nce+AYBhaIcvcg9lgZEt/agjHorj/5QMMGm1WGhm1e2hdNbD9cB9dW+Fl0h8yDGfv9QNWOTGmOkqHO5jM6BkJdRnR+o2mE2hUuujVWqg8OWV1EtEZAaglflvaVGxkIvCK2f4BYTWGzewSS3PJusgABTGtBkEpVJVgJ3EthelgjKWxQGGfWpwnP35GLrYTIs+1KzWsLVKcSocc2b+v/uo2EpkU2nWXM6qdWHSvGwSNiUbQqdYxHtPmmNstsW77fGbi7GKG9WqFi+Mm1MjaDmYIszkbjDwymr3+ktvMh7k+IoGlh+xaP70i6cW5hbVdsc4ll/zelXAIuVwYsRTJM4oAxq057/PLhAP1+3D19AzmkuaNaJ8L+UIxzupUpRa4YjHP+rWW3T53qr+iOi6CiOqzC6jZNCb9IatVhdUYeiMJsYiFSFLF+ccn9pwZcAWe5qCzXa2XayztzpYgx2qbecQjFhb6ArIiv0RCE12g9driwNnnl7FaewnEwUiI17MYqWIOmWIWpmFi0OigdLiDewcxTpSFVnyjQp+jjyYYNNosdSjGck2Bn2lQJ2/SHyISd5SxInEKAE4/fA41FcbJj5+he9PCqDtghax0QeNkia7XafX7/GSkmNjQsF5LdL/LFU5+/IznGADmF4k54rcVu4Rals8v48MPO5j2hh7iubVecxIiKwrCfhOhkHOoBUIhftbjgY67h2mGhIiCwXIF/NI7G5j0x7As5/PmE1J8WywcIiIAaCWXI5L9cwvdsKV11ywpOp9MUTrcwcFbR+jVu+we3q7UcP7xCXcnxciVErzX1F5cEc7eXjfdmw5km08A2LyMO5R83nnjkHwhQktP4ijWwspcMgxCn9LeP5/plCBsazBmOqsMCRUWUZSJuHD3096Qn112u8jPb9TuEQFT9yabvcEK4VgYPp9kd3HX0KczPjdWpomAYsHn8yGZz7LRVqdax2ShoHJMvhiyDzZpWII+mSGrOgGuKCyI4o0wcBzbajrVZxcoaCtOEkm1xnlGYv+fzknZi5yv1/YzbyIYDUOfOP49YTWGtZC77Q5thb0Uhp0hw8LEEKR3gM6XsBpFckMj19skzcv8XpkN97aLZCj3eY7Q096QITTi/Bf7SfFgm7xobF8VANCnC4SCEhPbxbMy5gu0Lm5QOtwh9S27sCTG9sN9MuvcLqLbX2MwUzAbjGDMdCzNJRuLNs6rmI2nnq5VKBJGXEsxj0n4JgmlvtvzY6EbBD2r1pEq5jgZE5KyCftZtSs1fP8Pf4TVasVzLruZw2ZeYp6fkA6VfFTlnw7G6Ndani537aQCn0RnSu2kgldeSdqw6gYSGxrafR/Pv/LRLpbGAko4hNlgxM9Q8kl4/5/+CLl8DLnNjKej+UUc1s8PMvRnMr5YV/NzGNbassmtK3QnAVxfkrSVVkjizv0CTHMFJUCHo1gMwkFPVhREkipMfY58bIRG04axKFTlNfU5t+YHjTZqQ4JqiE09rMa4LTnu9jHujeyMvozcFlUqRHYPeFt/3UafCHkbGgLhIM4/PsHZo2PezEqHOxg02rDWawSjYby53UcwGqbq2IaG3ftlUgaKzNG+bqE3IAWRxnkVs8EItZMKkQQnOtrX1C5v151qxvadNFdqYokw5jaUxFqvuWpz/vEJookIgtEwcrsl3Bxfot6wCVP2JpjY0LD3+gGmvSH2jgpcZcntllgKU/xZdFYE/laMYDSMQMjBTZIe9ASdkR/tSg36aMYHuc8vo3XTp2QnI2M+oaquz0/VcOKPkINvLBWHthHn7xEQir/3ewZXLQ42l1jMDWTycbZtT+S8gadwVI3H/SQP2+qR+Z2xwN5+EqlUEKN2Dw/fLOF77425Gi8OM7eu/HBo8Dx80UlBN/0vEXeNqY7tIqnKjLt9DKYUICfzWSjBAE6fdxlOIYaQa1TCITZ36ncoGNrKW6yvTuovPq5yBaNhhIL2v2+pQYj5oE/m/LPinQPgquao3UO6kLPnG0moFrQ15gsJs8GI5/TSWCCuOTrmIsADqPqcKVGFUyQdk/4QlkV/DoYCnqpfYkNjDW1jZmA6HDlzRHJ8AgDH+Cm3lef52K+10LpqYNQmxbGb40v4fMBspCMYCbHMoBIIOAo8e2VOEMV3TQx6N4FQEKm8xvKJYTVGAZbsyATG4/QcVrac43q1YvdY03AqqIJcDFC10tTnmA7GeFoNsW+CGJGkSl0ia42IMOPT5wxpERXp9XKF116n97a2K+9iP/r1v/HLPDee3sSxkbY81yLggIGQwspV7k7eJ4+HUBQgHIu8pFCzMk1kNIVN3QAgHHUgEW7CqVAWWi9XDI0SxRQROAm5VPH8RfAFAIv5kp9J2kUWFUZXwn0YsPlDNtzu7NExtM08npz7WX1OECmD0TBeeWefnttyxZ1YUydVtHZtSJAQ2blvITcqElARREfTCd4DB62eHeR6idYABc7i3VhrC+lCFokNDWqWMN+q3XWVFQW1NiW/+niCiBpDRKW5YS4lyLKM2WCEcEiCMdURTcRgTHWYKydMEN0+QS6OpeIUtN96j/p8zSIatzvQ0XQCAZcCm3Pta8TTST4vZcVPylyyjEy5ANNYIJFJceA5n0w9Cl2HX74PWVFQPtrFtDckn5nrBsJqBB98+ynUbBpra42QQvC2P/gDSk7EuhfCIHEtxWpTcS3FHTXdhu0J9TvxfAGgWW3hD//xMfLbTuciokax0BfI7ZZQP6tiY6eAw9dKDL3N75VReXLK/joHWxbEo9JHE5QPaZ/PbRHkb9Tu251WOt9iSfInEmIfyXwW2mYeyXyWC4/ivvJ7ZU/XpV9rkQy4aXrmlD9Ia++/+Ntfwco2MAxGw2hdNfE//N33GOq4sVtEMpeCVnJg0UI1TMC6AGBpd5oA4Affq9nv6QjDZhf3yzOYOpnk1W0VudtJTLtSw+vfeB3DwRzxZBh/LsbP3pjs5zZ+4RKCxdxAOBpBu9rAZY2Y/sZUx3Q8h2Es0W9PGZIhJre2mWcDL3FAnPVSePKjS2S3i7h4WoGadTYRsQC0qCOhpWbTWJpLNqAC7ABmvcbV0zPkSwnyCxg68msr00Q6T8FFLCEcSC2PXKCo3rWunO9dry08qZOCTCKjwq8oGPWJdNjXg9waFtclfARUmxwnOhd+xcGePvuYnJTJZn3BCzexoWHn1buOUs6YyJuT/hilwx0szTVSxRxkWUZ2u4hhs4srGx/6w//7Aw70Rbt/PpnhzhuHGLUHSGQoYEnmNA8PIxSLYOFyqR21e6TuE1ojknTatokNDT5ZxsErFAA02ytOQEx9zpULIRk56Y9xfda2dd0VPij+k/8oiWSOKtXNoUJShJLEai8C0hCKRSErChn9RMMob1gYtXtUBRP4+s4chkEBeFKVUd4i3L84BOlZrFl6cGPD2QS3UhNoYSJgi0BBBMrTOeGyU8UcZJ+jFDMbjHD4IANVS75k+x5RIx7sr5BqnC8k9ngACLIjKnvGVEdZM+CTZcRSKhNG3cGMElTYWwNw8LMr0+TDPZaiQ+jw7SOSpJz7EAlaHEwK6d7aSYUxuUIuNazGMOjOsDSJOyPWizHV0epRInUb8jNsdrmd3arUIUk+xFIkAfjhx2PMJ1MPpAQAxr0R+rUW7r3zgKq7e0WsTJMrhvsbOkNXxKE6HY45GBWQwF7debcbcXomiUyC8chizAZjHH/wGUG1Vis8+5ieedg+7I2pzlKEggSsbebJx8BYcCJSOtxBRI3hqGzwu/2l33jL/o4RwjESA7gtDztq9zCwK38AcFMzkMxncXN8ie2H+xw8NOsT/t2Dwgy6Qc9a8hHcQSRogYCM9dqypWVn/E56rTGa7RV3ZW5L267Xlqcr4V7rghgulG2mvSG7zHavG57kRzgQ92stdhLP7ZbQrtRw+OX78PlJ0WZlmtAnM/iDAUSSKvOwhFeDuzBjTHVsPbiDeDqOtw4MUngqZREIBzHqDCFJPjz6/nMH3qM6yUx2uwi/QhXiD46DzKFJ5TWbdJsjXpjtmzAbEAFTwOyS2ZQHjiI8AMiXQEI04kPjvMrkWZEQ10+vOMktb1jw+clgqnvdwOY+7Y2K30IgbLv5Tmiv6tU6WJkm4kGT7yeupZh0Lfl8tiJawvPcZ/Z8GLa6fI/uoRUyCChOl06WZca0C1+YZD5LUpl5ghTq0xn8AT+vpdswXCUcwvUp8R5uXlzZXgljqmjP5igfbLLaUmsgI7ddQGHL9haIRRh2qI8nMI0FS3sKGU/AG6yWj3YxavdYDWr3fhm/+59v4+zRMfJ7Zey9foBhs4tBo43WxQ3ShRyMmYHjT2hNr0wTrSsKkK01yXn+j//gM+TsbkymXOBOqT6ZQ5KIp7OxU0LehmQmtSg/w/lkCstyCOdCypb2TOow3E7aANsLaG2xSlu/1kIik8JVy8dcs/Xawmwwwt/83a/i6ukZwbFmBvyKH8FIkAuFrasmGdvZQh2lwx2G5CY2NFw+foHC/hYuPj1H6XAH9WGY5wPgSI4LvgRAa6bfntCeUf2ZoGL+zMdfdAj+nA1zYWLn4R5SCR/e+QpluPPJHD/6Zx9Si/66wVUQqn4TCVJUDwBAkS385b9+gHalhoWtlDBotMnZzz7MLJCToOTzYdTusXkKGwe5Fuizjyoo3KGKYmk3w8H9bEQBw/nHZFYWTZASUvFgmxViANqsRIvb1OfoDSm4Ov3wOfqNNqIqLb6Qf4XaZZsVcERbMFPOo1droXS4w6ZTgZCDyUxvUHUqVczh7NExP4dhs8tks9xuiSEpWkHDzfElAiEZsyFpxgtVCMXGnN554xDRdAKRpAq3M+7Zo2MsTROnj14gUy681CYcNrsIhBRkygXGRfdrLaSjpud5CClJVSXy5Wy25ADMjcEVAYlf8TPpWMB4kvksdNPPwZ1I6M+f0Wae3ytzcCQgNLWTCoypjt/7h8+5oikOzOvzDrrtGQLhIL7/LypoNGaY9Id8H4LsKpyZx+MlJ5AnzThqYwpyRrYpkTGzJUgN4IfvU1XTshz4QjAaxulJH7PRhCvqQuJ1oS88GNeDt47IlVexmEy88+pdj8Y0AMyXMuPzhUcHJ4g5qgS7FWHEM07msyjd3cRibuDy8QuY+hzP3nsCfTxBvb1GLLTmYNIny9zVmE+m1H6f6pADVDXs1HqIJ6OY9EceKMhyBUSTcW6buxODcbdvy96VsF6t+Bq/+csRZLeLqJ1UGN4V11JMEq9fNFjlI1MuYNCigP/JVQiL+YK9DgRfQgRPnWod+2/eI6iPzQ346Iy6hmKNCXk9obEOUHI8avewd5SHxGZuMxQPtlF9dsGB3rQ3RL/RJg36bBoxWyVqMV9g1O5hasvDhtUYPvrOE0oegwGuuItAV+w1Qm9fwGay2SD08YSvlzk3ixXr0FuWhP7IIQ6LawCAQXeKVDrCbtZaaQMHbx3h137d6YICjmCA4CrVGwtep8Lsz28T/5P5rB0sk7JOJKmyrG0wGiYlryzprQvpRVHUEMEZAFRPrrFe2jCdRJxMsmwuEOB0TYR5XSSp8p539fQMl49f4Lsf+9GrtVB9dsGqccFoCOWDTTbkEhw1JRzCbDzFwf0spr0hvnTX5PsWEJn66ZUneBbXUn12QX4Q103PfKZCgwM9sv3UUD7a9RiW5ffK3HEJB9bIbRXRvW5A28zj7JNzpIo5jHVH6SwW9THnR/L58Ow6bJuIKciWs6x4Ja5PQK1EkP4n3+shHPIxRO121XfUHWFhSpBt2dDcdp7XfVxLIRAKwlws2GMiVcwhmU0hEAox/2jY6XueRaaUQ2mvCCUcQjBC957dLtiwpDkyG04VXI1aqJ9ecbElGAmxIo8x1WEaC5iGCX8wwHh7wPE6SGxoaF01WE54tVzBmC9hrnzIbhfRuWni/OMT7opF0wn0Gx0szSX8tgtvJKlivVwhUy4gosYw6Y3wzjeP0B7Td3SqdeYMdG+aLNTQvLzhtfLJdz9hQ8PEhsaCGoNGm/+bJKYdCBaAl+R7fX6Zz7dQLIrGeRXbG1Q8Gza7yO8Qbn84triz0b1ukGzwzGDc/2wwcjw6jAWrkREkaY2/8Z99jZS6Fkss5gs0OnQtV0/PqPi1UySPiXoXpcMd+PwyBq0eKk9OEVXDCEdDnk7jF3P8dJKjwvT0izL+jSQEkiQlJUn6x5IkPZck6ZkkSV+RJCktSdK3JEl6Yf87Zf+sJEnS35ck6VSSpMeSJH3J9Tm/Y//8C0mSfucnf6Mzgrbqwag3xsPSCKfn1AYMhAM4/PJ9bD24w6TEuJZEXEuhdXHD+r6i+nfd9mGm26102zIcoKBDQD6C/iUTTtVs2oN5FFVNUVk8fG2LiY2L+ZIrQY3zKrLbRfzSb7yFSDxKmPF4BI3zayhBhQmYQvJSdDB+5d6QN7KwGsOn3/sEpcMdnDWC6N402UU3FCFYEVV6khh1qOKWzGeRTBPUIVXM4em7n5ID72zOTp/uQVjzBasLVJ9dQPL5sL9DB7nbdCaZI5fls0fHiCZiyG1tIBKnoEy0+IV6xE/CDJ78mBRysttkspTfK+NpJcCJhZtc2WqR18Dr9xUOwAQGN66l+H2N2j3IAS+BbtBooz9V0LogQpPg86ay9B6FPObnjV/760fIbG7Y75Ra7cUdDaVynEiMagQ721GomTQkn4S4RgRAEYxHEzHE437Gzv+T3z+BZYFxlj5Z5qRltQa++au2bKQLv2tMdQzaI+ijCa6eniGSVLmC2KnW0blpY9QeEHxHJwOcalPiw/7y8QtOSMRBKyqAADyBB1XyZxzgiANUCYdIVUjxo35R90B5IkkV67WFYEDCbOHzSCQaM0f/29RJTlEkJ6mNJFrXHebsiLFdsGyDKao6bd3f43cO0EE76lBnjr0K5DW6N00o4RB6tTZS+Qz0yRSN8yqi6QRiNrlu59W79Pv29c90i7tmC90gGIZ90AvTn35zgGlvyHKsoaBzAAhYnSB7B6NhHPy/7L1ZjGRZet/3i+XGvu8RmZGRe1ZWZlV1ddX0dKtnpkk23SOSsknB24NkGYZtvRiGX/1gQID1ZNgwYMCAANqSLcOUaZKSaVrikDMkhzPsnpludndVV9eWWblHxpKx73vE9cN378lKkhJBQRKHtg7Q6KzMiMwb957lW/7Lw121n9jtVjwhP6n1LL5wQCU+r5NbLRarCo4bJVnv9cKV8TOZrMNOD7fPg9Prkk5C0IXmdt3A6Up3y37jmc7numDlDfUWryFFGYl5cHrcao44Hde6+rX8NRfj+NEB4YhD/WzQ7dOstnE79RudcpNIOu4POXl8SDole4YpSegPuUkY61x4Gdc+GN6gj2AsiNMjvBu74Tvx+v5kusUv7ayqgGLQ6hCOeQgmZN80eQHKnNHrVv4I5utf520ArK9oZLZWJInU7FgsInmbzBjk6cWCeFySr1BCuhnf+qUPJckzkrU/Giw73M4bHQkQ+Jjd6bjB5THPG9NPIJiMEvRbyO6u0bhqYLFYlRnesD8gGBNY0XBiVV0HkGDN5XHxT36zROW8RL/VxeO6LlZZrBalFDWfTikeFZRfDgi3xuwSmuvl/Z+IMJvp4oS8nVPcl/TmigryL690unWBkzXLDbUWTXfg1xW+msUKhYOzG890MZ/jCwfV5ygdXZA/zONwOxl2esxnkvB7QgHm8zlOp3QmnV433/uwRTiToFKRe2kWHiJpCWYD8TCRVFg5DL+uaLSyt2HITOuUjgUamlzN8OKHTxlNbdg1u7ruyXCs9j6H20ktXyKSkM5WYiVJKBXHYrUohbI3d8Gp6eKvYUhiz4zC1PUcdBuQwYniH6Q3V2hf1UnkJHA30QzmHDH3K4tVOA5mUhxKxQklpOPzeqcYYDixqvVkFlf8XguL+VxBYp0ep1JCNJWXrMbhk1rPMuj2lXDAZDRS8dJ8OqV6XuTt7Q6NYpX05grxXIbSSUFgRx4X5ZOCcr72R8M4XXYaV60bho8/ruNfdwj+7ON/AH5L1/VbwD3gBfBfAr+r6/oW8LvGvwF+Btgy/vubwN8BsFgsEeBvAV8F3gL+lplE/LOG5rBx7717hONBXpYDTCdzwpkE0VSAje0IX313iel4Sr/VFVKY0W5fzBZqY1jaWeVrWzUuzsVIxTxIzMNmbW9NgoGBk8VsjjcSpN/qKoxxZjtH8fBcLWjzfQ63g+2Hu1RLLTSnQ1WVq+dFPv7Wpwo37A95uP32HlabVXUQzGomyOHwa99zKpOXxWzOww8eMBlN8Hl0gokodrsYqw06PSajMenNFXWQ3nlnG3/Yx/nhFZPhiFFvwOrdLWLZtDiBGsGw3ekgnsuo6lMkFVZeDCt7G2S2Vji/nOLxeyWBMZwW3T7B+6Y3V5iOp5w9ecXF8xOq50Vi2TTJlfh1Oz2TUGZfr+uvm9Af0/iofJJnLzdh2OmxsrdBt95UWPvRwCD7LSw0SjV53sbvsjs1UqtpFbzee+e6a2G+36WJgU0tX8KMxfSFLpt9q4vmdilc8uuwnFJ5ROHgTLX8F7M5DoeNb//qJ9g1O8Goh3pDZAGtNhtOl1OpAJnXV7jsqcBt540sdptO7vYqcK0u4g16CXih0hQZRZe2ILWeZXV/Q13L3ruiiuQL+4ll04o/YMIVpsOR0ssvl0bk9jfVfHe4hEhoVtEeP58Sy6YVLEiq0wHxMogGWdpIKbKh5nbh8rilNa3rqlJusVoVUXI6HDEYLhiMLLTKVcV7yRhSkGrtuq8Jq6GoF6vVqnwnzHFwKu1vlxG8XR6cUzg4I5SKs/1wl/vv38dqlwpop9oiupzi1/5xi1g2RSgRIbaUZDadETOwsf1Gm05N+ERnT16p57vz1m3iETEsy+6uMZ/PCSYiikvQb3XxR0M0DCiYSZ4zBDYk0TBggFa7TRL9oJ/jL47o1MU3ot+XBKh8kmc8GCkll0Asokz5zLmxendLwVkWszmhVByXfa4SjZWdZeyadNW67dGNQNT08zDJ64GYJACzma4geL5wgEGnj91hp1rqKpjeaGZjYIgr2DVNzTl3wMdXvvmA/mCu5mpmI4M/7OPV2ZxEzIbL5yW1nmVpZ5V6oYLVbiO6nCKfl+q8yycFiXa9z9Vp8UYyDyhZ5fyLUyX77Db2zNz+plIkMZ9ZvVCVdWbM6+J5i15T1u/l4YXah8OZhEoUWobGvd3poNuQAHbzwS2iyyksFp1Rf0TltEAoHiaaDuMOeHj2qfCaNLeLo1fXsLvMdo6f+nfeIbu7xnh6bf5lsYrazcreBv5QgOhygtW7W+r5DLs9cTdeLFQVftDqCOzC6Mi2r+rkC1N6bcHVj3p95fMw7o8EMjKfY7FIIOj2umgUK9x//z7tWou/8nNpNu9vYbFaOM1P2XxwC5DEIBO7xvubHg8Wq1Xh2gOxINGlpBB+I0EuyzqhoJV6QdyQxd23ydVZgaWtFfbevcPTx1e4/B7lH2EWJabDEZPRWJntZbZzRJdTKvAPZxLYNDvZW+IebNPs5PY3cQd8ONxOIsmIyCiPxmw/3FX7y2SyoN/u4vS42dwK0ixWWM251WfKbOeIJENybhcrqsIfziSYTcSEMpSKMxmKRPF0KJ4vK3sb2DU72w930XXw+N0KmhZbko5hKBXHG/Tjj4Z59qMXAs2rtOjUGkwMwZFgMkq1bSfonnDx7FhJYlus10WS9Te2NqephAAAIABJREFUVVEmEIsoWJ1dswlZutZSxTRPKEBq9fq+eSNBPH4v/UZb/c7xYEgg4ruGbmmacrTOX0nHzdwHbZrGy8MhoVRMdQFMvtNsOsUXEajdoNMjt7/JaCBFksppAYfbSW53lXZbzg3zXH1REqijKm7eWefsySvCiRDLO5JMJtaW2L6/RqveZzr5492mfz3+xY5/5QmBxWIJAt8A/i6ArusTXddbwM8Df9942d8HfsH4+ueB/02X8SMgZDizfRP4jq7rDV3Xm8B3gL/8p/394WBMtdSifH5FPDjj5efHNIsVLo+uaDUnXOQHWK1WaZdarIRTMQ4enYs0o00w9YWDM6YLjW++51X4P32xUEF5vdwknIrx//zWdWtfJPmshjxoR8ykXkrm3b6q8/G3H+FwOcSYSLMr0pRJGDOxuZrbxfmLPE8/fELh4Ezh15Vp1HqWfqvLew/tqlW/mM9xaDYJuP1ToumI8FmsghVvX9VvSBBWSx2cHieRZJD5dMZiPqfwKk8oEcQXDpI/vARQ0phmYGpiHsOZBMWjPIWDMzZydka9ocr2YxlxhNQcGsPeUGHnzYqU2+diMpJ2utvnweP3KLMvh+s6SRoaChnmda/sbdDoi2nMxbPjG/Als01bbcr77Jqd+mUZze2iWaxQK1QVqfLFozMhpA1HVM5LgqGuWRU287Is11kt1lXVOpSI0Kk1Fb7WHNGoUR2MXyuVPP7ogF/4G28zn89ZzXmx260sFgs8AR/lk7xK+haLBdXzItGYx9DUtrG77eaiYufos5eiEGW4P09GE3QdtjLSVXHaF4ZZXE1gSKkQh58LrKtdaVDLl3D7REGjWa4rVRrzM2YyAoUZ94fKVGjU6xPPZXC4XHztvlW6C/myERQIHGwxm6M5NI4eH93QJ+/Wm4STYro2MpS7dOPzgSQHqbiNjz6qklrPqntQOs6ralt6c4VgTA6u2XhCo9IlmgrTvqqrORHLpimXRYHGH5LvJdcyIj3b7nL46Qse/e4j0mspFrM5o16fdrXBT/5EjMppgcV8wUIXOd3ua9rfpsqNqWoDcPLlCT63QGQWC53pcHQDJmUa5rl8HrK7a+p9mbB8HpfHTfmsTHZ3DbuhRNQsVvBHQ/jCAYKJKB6PxuaDWxLEG54j/qgodfRbXSajkfrsJmyvmpfuQKtcxeuQw3bcH1Ip1KlflqnlS0r3/prLJHPW4XaiuV1Uz4tMhyPiURs2m5goNouVa7UnowvXLFZw2ed0uzMRVRhPZG27Xdg1jW57RCwiTuqhVJyrsyuyaxECfhu6LvdI1FPE6djl84pvhl9TruaT4Yhus0d0OUHx1fUeZfIGrrkbPTFGrDfJ7W9y/vSIYX8ghmGta+hZp9pk3B/iCQXIbYQV3M3t971mlNdR0EFzX3K4XcwNuMrRZy8ZDYZMZxaCMVnb50+POPjkOR6/x5D5FM5RNH7tAVI8PKdRH4p62tii/p4v6KVZrHDx7Jhuq0PltKCeJ6A4WXAtWGDTNLoGLMRqF6ndXm/CfDrDHfApGFhibQmH26mgVv/jf/sxrXKV8vkVDreT4yfnDFodnh5OGfbGksRrVs6enqhAcTlw7YlgOmTriwXT8YRELk3h4Ay7ZlcCE3a7hZBvobrcpngDwNVZiaPHR6xtxeg32pJwG2fddDjiK998wGQoMJRBt0+/3cNqs6oqdbNYYTwYcfSZ8DRalRbnT48YD4bYNY1hb0QtX2LU6yulvvTmCn6/xrAj/IDzCzmvBkNdqeEUD8+5eHkhRnFG8al9Vcdms1375NhtXJ0VVKfOPGvOnx5h02xM5xaOHx0w6omRWuWijNPrpl2pK2nnBz91VzhQ5Sq+cJD2VV0UDfsjwr45J1cu1U2///XbDFod1RVr1zoqWXL73AQikjDkX5xSL1WZjic3uGgnjw+5PDjHEwrIvW73sGmaMisNJSIcfHJNwL/11i00h0Z2exmbVeZdIOJThOSDLy7oVJuKi2V6DsymM5XADDs99TwsVotSITr89AWz2QJfWDqFNk1jKSLzY3lHCqQHnzwXdMHBBedPj9R9/ux3PieTDasu34/1sPCvScV/xrEGVIH/xWKxPLJYLP+zxWLxAkld1018SBlIGl8vAfnX3n9pfO+f9v1/5rDb7YwHY8LJCL/8Dyus7q5gtdsIxgKMxzM+/tanCkLiDoiBTzAWvAFBAaj2vfziL77CHw0rvJ5J9gwnQ1jtVv7tnwup13sjQRUEmU6o5mFjdzp452fepPAqLxX42HVVdD4XDOvWvTXiuYxB6h2qqpepkmMe6pWLorTWB9fQF38kxNwISBo9jUF3SKc3V1jj1yvvAKPBmGFvRLPaYeX2Onang803Njn67KUc2AZO2DSXUvfW+Ho+nalA7g8+akr11KiGnT89QnO7KJ/k6dTE5fV1gljppKCMzTrVxg3HQ32hK2dE+OPtdpemK+xx5bSguhdOp10MV2wWFfCYaiqm+RjIxjZoi+qPPxoiEJeOh8lvjOcyVCry+50up5Lda5ZrKlAyqyea20UqJhX7Fx/LwTTqDVjdXaF8JQodxdKIcMjGeDBSbd1avoTFalUb+2g0w2qxojkdeJwL1pJTRQJvVxv0W12RifuozlVb7v9vfm+o7p8vHOTph08IREPGwSM/e12lxuRb7Lx1G7vTQbU2wWFIh5ra6Ln9TdWNydc0ZSDWNGBp5igcnKm/YWJ8TfWXpZ1VRdYz54oZZBev5tg1G8P+gFa5SjIrHghrd9axWK0Muv0b8DF/UCQ5X3dSruVLfPWBX3Czw4k67KPpCNOhdD3ks8/wR8OGslSQVleutXVVp3JawOl1MxmOlUFe+UQSl/hyXKm/TIcj2n1xPTbnqNnBeJ1gOeoNyL84ZTaeEM9llOtru9pAc2rM5wulXgKgOaVCabPZOHtVZdgb0W1IJ2M6HNFrdkitZxW+3azqO73ShVnM59cEb4soCQXiETTHzfvuj4aVupQ5/02FIpD9qtFaKMdsu9OhgsyDT56rKvvnxy5iUc3AKstz8IUDjAZDJqMp3/v2EcFYkFZZApbZbMF4ouNz6yqYN+e6xy9qO9/+5Y8UKXY2nhBJhm74OXgjQaYGidp0Mw7GQnRqDVb2Njh/eiTyrMnIDSUecXG+lsl9/riI3TB3s2t2BdWZDqWDEohHJFldzzJodXAYMrHrb2zj8rhJBqecPD68QYJs19rsvHUbf8iDO+CjlBepzHZFzK+SaSGIu5264kQMeyO1Vkyn7j8KGwrEgqqzJ58npPYMfzTEfDonGnUxn8/JbCzh8rlZ2lnFbsDATGnW/+S/eJv05grhZERMru7LtcciGv12n9z+JsGADW/IrwLFg0pQKSiZY+et2zhcTiHoG3Ahm10I07EwFCoWoktJXD4vF8+OxW8jGMAfDeL2+3j7jgH5MuB+5tyqljrKi2TQ6igXXfOMMp8FoIJlze3C4XIxHU/wBDxKXtjci0tHF3zrlz4EpCC0fztAPJfh5dPaDfjd6z4pA6NzP51M1D33+K9hcq93Jr2RIE6XnaeHEiiHEhHlUj7uD4nnTCPJAaUL8byIZdMMewNi2bTq6AA8f9kjmgphdzo4PSirc9Mkvg9aHeqXZZHpfs1x/fU5C9L1W9pZJZJJMJ9ORejC48LpddMs15hPZwx7Q2599bb6Hc8++pJ6ocrVRZWg30j6q9KhjC6nuPf2GvFsSs27TlV4HJ6AF6vdhscwggwmoyRXhRthKrsF4hHKl236bSnYzKdTal05V7pN8VYRd+b6DQERu2bn3nv3ADl//6gD8o/fsKBj/TP99+M0/jyuxg68CfwdXdfvA32u4UEA6CLzo/8J7/3nGhaL5W9aLJZPLRbLp5NRg2a5ylIuwgcfZMitiWzdbDpnc9PHz/71r2F3asqhc/9rd2lVm/SabVw+N/FsisTaEk5tzts/uU16LaXMukAqOEefvaRyWiDslsAotZ4luZJk4/6OCma3H+6ysrdhEC1XcDisTA2Tmi++9wUWq5X9r93FromRUSLlRV/oRJdT7L19S1Uy5Pcvk1xNG26DXmLZNFvxNks7q6zsbdBrdohEXKJQ4pljs1nxe22E4wE27u8wm07V+zS3i2gqxHgwxmazEop6WVrP0Gv9ceyeNxIkkomz/7W74sw7nvDgp99k2BsQTEbZees2mWU/mlNDX+jKmGw6HLH54JbSZQ5EfOo+OdwumlcNQqm4wqeGUnGyu2s0r8SddD4XKERuf5OVvQ2BvsR8nJasbD64xe47+2S2c0xGYyLpGEfPC8ynMzQNVWX2+KVy5wv5VdUhthRn841Nogb5ttfsiNNseyYazWEfb933EohH2L67RCQZwRcJsLIr77dpdjpVUaVwedyEvHO8IT+RTJzdd/aJLsWpl1rkT+qk1rPEY06OT+S+hlJxBRPIGbCLW2/vMZ/rOD0u3H4fDrtOpaMx7g+FRLe1QigRJZ7L8PV3o9isuuEDsWDnrdt4I0G69RaJtSXDnVQyG08ooILr6XhynWBNZsSzcvDWilUJ4N0uQskohVcXeAJC2nQ7IXc7x9LOKv5oiFg2jT8aZvXuFnvv3iGzLT/zhSWQcLhEcSKWCtJrdlnZ22A2NlrvhlzdxVmb0WCiqqal07KoDB3LdS5mCxV4CZncQiAeYdQb3EhoTy7kM14enKEZOHTTfbzf7uP0upX0r8zzIbGQGVTJOljeXmHlVo5APMJ4MFR4XYdLI76cUNjgZGhOq9JQh3A4FZXP9hqhO7cvnKTMdo5wPIiuC5zGxLWXjvJSofYKh8aE49XyJUonBQoHZ8SWk2gOjY37O2Q2llWVWLwE5OAe94eiX27MjXguQ77lZzaeYLXZSCzL85mNJ2w/3KVbbxqKRVK0WNpZVevT9J7IZSx4QgGCySjekJ/pWEjoqfUs/oi8z+mwcFWZqMTeEwowHohsqdvrJBwPEgzLWtv96jYHj/N8+J0DruqiFvb68AQ8jHp9Nu7vsHp3S5FjB13hAGV311jZ26DfaDMdjvCF/dz5xj3sml2phVltVkPzfUG71kZzOlRguPnGNrn9TcKZBC6fl+xGnMRKglFvgDfoJZyKEUxG2f/aXTbu7xBfjpNaTRFNh7jzjXsMWh3W722hOey0qw1GM5viwGw/3CW7u8ZkNGZjO8KwP2bY6bG5EyGeTZLeyGK12fB7bdjsNtLBEcnVjCRLlTrbD28pmMz6vS3W7l7D/cyutMPtoN+Wav1ifh3whuJhXF4X4/GCxWxO46pJYimqKvf9Vpf0Zpa9d+9wXljgcDmwazYy2zk+/fZnAIrb0ml0+N7vnLGysyxuzvEIe6k6LaOzYj6TV58fMBmNyWwuq7VosQh8djK1sJebUD0v4vSKQZsvHFAGcMLLshNJRti+vy4mVzabOIunAuy8dZthb6SEM8wEMLu7xtK67M3xXIY3fvKN6yJZr09iJcHFs2O23txm7907OF3XCk+5/U3lFRL2L/D4PWzsRIkvJ1TQ7fS6adfaLG0tMx1P6FSbygnaarcyHV/7TsSX5dzbfWcfu2bn8Xcfs7upqfOpbQTNoVRckXrDqSgOl0Y4FcXpcQqvx+Nk9e6WkNZdU37uGxout53YUlJ1OUEKFtv319W/d966zeVhAZtmN7gIVnL7m2zczakOwmQ0oddsSyei1iKUkL3FHw2RXkuhOTXGwwn33rvH6t0t7r13D5fPg9PlJBaYGX5DVwKdSoZwOm2UT/KkN1cMuN+yIUIwQHM6SGRjeAKypluVFuFkSHVXg7EgP/lTKeqFK5KroiQU90+oXJRJrsRV8hnPpgilYuy9e4dgMkrx8JxeZ0S/N1beQz/OQ+dfvDHZv8ph/9Nf8i98XAKXuq5/bPz715CE4MpisaR1XS8ZkCCz91UAXtcYWza+VwB+4o98//f/pD+o6/ovAr8IEF++r+sLnWK+yZt3Xfzm/y6VA3/Iw5MvGvQNTP5ivmAyHPP0wyeAqZM/pFptiDujNYPVCt1WX8EgwDAcS4SoFaqUuwa8pT9goS8YD0ZKMrRarDPs9PGG/NSKVe49TCtTMtOY6ujxMRarhX6rS7mUVEGcsoLvX1eCQSqx8+mU6WTCeStAr3XJdDzF6XXhdFgoHl3itCWxaXbsdhR0ILG2JMo9bqdUPuuiIPPgp9/k8997DEBmS3B/nlCArf0l/CHxVLBrdp5++ATN7cIfDfPZ73xu6N9rHHzynL2737jhimtWQo4+EwWe6XiiWrvlk7wEJDaRrrw6KxJJx6kVKjjcDmUYBoZKkNPBbCrwoi++9wUPH7xHu9ahXrjCGwzg8rkpHJxx7717fPG9L/C6dGXKYhLCBt0BgUiA6HKKi2fHwvcwKj9m9X8tu8V3rupSQf3pNJ1qg7NXYjq1mC2UjKppchOIBrg8OOOLV7rBy7AqgtvOgw30hc6TPzjlt361wcOf2hcjFq9btYYvD6RVWjotY7OJ8oI74MNqgVRIYEGmY6TbL+3ng+MVsktOqvky8eUIB588x+50EMnE0ReiONWu1Akmo8QyMY4fmSoUM6OdrbG8GuHFowuZcw0xzNIXsg6Sqxlm0zmJtSVqLZ2LlxeK/6I5NaU4FM4klEkRXDsEh1JxrFbxGSgaVeji4TnxXIbqZYWv/7U3eHnY49i47wPDBBAkgHZ6XLQqLax2G7V8ieXNJOOByLma6yGYjBLwS/AfjAWVko++0BXBHiTRql7WpXMSj/Cb3yrTqTYEhz8Y028PVNANAjloVZucP72exwBHBSv+aFCpaTTLUmFPb64wGojyWPWygj8khODVu1vkaxqegM9IkD3MpzO69SbBWIDLwwsaxapI1t7KYddsvPzRMxqlGp6Aj1a1zaAjbf9IJs6wP6BbbyklrmAyKpX1Rht9ofP7H3aYz+YGVjmlWv3VolSrTRiGNxJUlTdTWcYXCdAdyPMyJRlNKI7FalUcnm//xgF/+ed3mA4Fp26qjVisVp599CUPP3jAp9/+DJfPS7veJ5QIsrImSXmrLJA20yDw4tkxNk0jnQ3x4W98rPabYNRPHmjXWsym4thaOrqgVWlQOrpQZoPDXv8G1Mbl86LrC4KJCP6wX+0zZkL5ybc/VxyvcNxH9bKiqtJLO6sUDkRW+uTxoerIHn32ktz+JktbK+RCLeVJIa72TeLZFB63lVqhRnpzhXp9jMVioXAgrtpn533ql2WctrSSc52NJ1xdVOnWW2Ka2Wgx7g9Zf2Obar6qzDCr+SvVwbkmilrVPPf63eJU7nZycSCwTlNDv3h4TunIys9/8Da/848u/1h3NejTCcYCXLw85/aDVV49vZalfX61T7/xVPakk4IQvA0p4k61QXpzhUDEx9mzU3zhIM9e9pluireK0yVKNbElUSdqFiu4fF7OCuJX8uT7T7E77IqU+um3P1NzQrrqOvPpFLvPQ/7FKfkXp3gjQarnRfptgQB5I0EmwzGdRpe9d++oc830d5iMRgreMur1GU2sdBodLs7cdBtd5ZQ77g+x2mwcffbSUGEbi9rgaGzA+cKqQ1AvCf6/32iTWFtCc7uoNCCSDHHwyXN1X02VJJBOxc5bt5VARTAWVA7X8WyKzlCj3rVhtc64OitQPrn2b/CFg5y9KJDZzlE5L3HwyXPSmytMRiNjj8pyeXBOryXJRqsi6m8un0d1ec+fnymY60VPXO0bRU3d/3BGkuPkSpw/fLpQ86ptnH2373wdgPLJJcnVJa7OCoQzCXxBL4VXF7z44VOlVOaPhimdlpSk6Hg04R/83Uci1hLycJWv0Z/YiaRj6qwKp2KUTy5Zub2unmEsm8btdVI8q1I9Lyo41Y/z+HEjCv9Zxr/yK9d1vQzkLRaL6ZzxPvAc+A3AVAr6D4H/2/j6N4C/YagNvQ20DWjRbwMfWCyWsEEm/sD43j9zLOZzcvsSlNXbFnbf2VebfSTmYdgbKkOToUGQEZktMUOJZdNMhmNsFp1sWqAMZstboCYBZtO5KPLMDD3mqzr9du+GFnqn2kRziuNrv9Em6LcSSkZvwDpMd1ztNVgOoMh35kYDolrkcIk6RzKbYCkgf8/t8zAdT8nn+6LAMLWTf3HGaKSrykL9soInFFCHTL0kB/Vnv/O5ISUYZDqZEkxGcfs8nLwoU3h1SSybxuFyvkYGG2F3Okgsx5Q2e7E4VIorZuvVlMF7HY9sjul4isVqYTqeEltO0qo0DBy6EDs9IWn3ak6HQBBew6k2Ozq1fJnk6hKhREglD09/IBv0r/wfRyKdmggp4m6v2WE2vXb/3byzKv83SHWz8YQf/Oha3cJiketf3Yrj9jrxBn2KPAsQTsXo1DssZnNyGcm3X1eJmI7nHD46Ib2RJZFLMTQIz2ZrdW4kOC6fHFLmxjibzugMrVzWHa9di1UFul6vnU9+VGE+nfKXvhpS1149F0t6l+G+G8vEaNc6ar4C6rlUr3oiozsSObzqeZHkagabZqf46oJBR7CxIb+FYExavKPegEapqohiNpu4Hptdh+hyyuhStfEGPUJWfa0lPJvO6DfaHJ8Mya2IA6rZxrcb8rT6Qseu2XEHro2sWvX+DQ1/kErpanrBsNujUboO3iuXtRsShWdPz9Tz1pwO4mmpHJ49eUW71rrBH/BHw4aLtjzD1yEGDg1VgQ3EIwqCVjq6YDaeKklaM7k4e/KKeEgSmLU7a0wnE6VcU7kQo6DM5jLBRIR2rUM2F1Tu5wCNYkUgMqkYLo+TQCSA3emgURJCrpl49xtthp0e739D9rXk6hLPPvryNUdTXUEFTdEBE4ceiEeU3OCH35dAIpK+hvtZ7TY27m/jcLkIpeL8+39tG6dDeEOvu+nqiwXrb2xjs1nYuL9DOBVlPltw9NlL3G4bZ+d94rk0QYN8a/J95tMp49cqsZPhiBcfv1BFC32hM+yJ+tSw02P9jW2GnR4WqwWPAQ8y9xtvyC/8idOCCozjOUmwXR43X/83vyLPOBSg2xre4P+YhofdutyzQatDdDklvKMrkUKcLwz1La9bYc1rhSuKxZFIZ46nvHqSp1ESTfhhf4DHI1CgLy6uIZ8ALo+LSEbW4bg/xBsJKk7W0s4q0+EIuyEr/br0Ym5/g+2Hu9JRjXvVPDBhmjtv3VbrXF8s+OLIqrhw4UxCqfvU26IKNR2OqFf7VM+l+GR3Ojg8mSoVsdl4QvHwnMppQXlgNEpVzp6dCkRmMKRW7jKbg9VqpV6qiujEfGE8J+Hn3dnUaVXqbN7fUqIF8+lUdYnNeWy63rev6uJxYhCXvZEgg1aHlVs51TEKRQMcfv6Kjfs7+KNh6RrkUvijIaxWq3q+46kUb6w2K96gl1qhogQyzLVmtcn8slqsKjHq1pss7ayS2c7h8XuUFHnltCBwHZ/lRjLgj4ZlLtpsyq/n4JPnag6aCfl8OqVeqlKs27BZ4eNvfaoM7wBFmJ6MxhQPz0muZohl05SOLkRFrlwVyeRcmvplmeKh8EJMNT9znplJ4HQ8IbYUF6K04TwMkMol5HWTGcGgzFPzbIgup3h10OT++/eFmzgaiwRsJECn0VH7snnWdetN1vdXuXh2zL337tGtt/naX76Dx++mdFqhclrApc1VIWI+nTEejIhkEsqsDWA6mfDi4xdUz4us7G38MbfxH8fxF1l29M+jQwDwnwO/ZLFYHMAJ8B8hycmvWCyW/xg4B/4947W/CfwscAQMjNei63rDYrH8beAPjdf917qu/6nOFRaLhZX1KPP5gq+stTk8dDBodZiMptQnM77y9XU+/eiUUDzMZDQhEPEydmm4fKu0Ki3GgxH6YsFCh3bPwmQ4ZmVvg1aliTfow2azEkkH6Ld75MJdpT6jOTVWd1conhqSgEYbMp4T5Y2TsyH+iJ9ULs5oMOH40QG33t7j4JMXhFNRakWpvqZyCebTuap2ZXfXsGl2Lg/OVaDVi4Vw20W7u3VVJ5SMspLzcfbSh2ZbkNtboz+QymFma4VIMkir2hETq7duS0W13mEynJDbyfDsRy/wBqVKqzk0shtxRoMRw/6A+WTGyq0cl4cXJFZSRhXXaNvPF7jcQuANJqMklmOMegM6jTbeoF8FN26fG5fbQbPaFkJfscIbP/kGj7/72JBGi+DxC9TD4XKyMAS359OpVF1fnOFwaWSTgksOxQO8+OFTVTE1X/dT72f4jV87pHhUIL25QvnkkvV7W8pXwR8VP4Tc/iYnX7wikk7QbbR4+DDMxUFEkpCZSI2GQhqVso7mtJNYWyKZjfHsB0+xWC24fW4smYRKJDyhAJpTY9Dp06x2uP3WFp//3mPW7mySTHnYuL+jYCwun5fkqlQwS8eXvPGNPYpndQqvLhhNLCxHJdge9gf0G21239nn5IkEOz/zzRiNqzTFyuJGlXzj/g7lczFtMpUhAALxEJXTvkoqzEq322NXBkGNUg1v0M/OW7vyWQIeBiOdhb5g/c4Gh5++ECm782t8fzyXoXpeFILwZMrFs2PSmytEol4sb2xSKzZUomcm0+3WkG53zLg/NBQzmixtLbOYLygeFSif5FV1aPXuFnZNIAZLW8v022LWdv70iM9vxXH7faTXkrSqfkpHF6zuLnHlc+PyOOk2e7z57hq//Q8+Ir25QjVf5t7eDi8/dxFKRESDe3+T6mVF1KOycTbfWKVZlY5FdClJpybwCZfTgsUiHJ5wMkJ2WwLv7Ye7zGZzTh4fKhx3q9okGAvR7lvxR8O8+OFTNu7vMJ8vqOVlP3AHfKRWInSaQ159fsCjH+h4g35awxGZ9STx5SitSodqvowlEyezGmc8mlA5LSiZP1/YT/HwHH2x4KxkIZKJM5vOFAzI6XUrs8P6ZZl4LqMIhqWjC7yRINlbOa7OrnjzK0LjMuemPxqmW2+SzAS4PMgTXYrx+OmISrlDt97CFxYypj/ix2qVe2OxCNFy88Et7JoUMvqDOSsrXj79tlRKV27lmE5mdJs3V6jbAAAgAElEQVRdqcT6RT7YatPYebjD1UUNX9BLN+AjEAlSL1XZfniL0mmZTr2rPvtsOuP++/c5+OyYRC6N5tQIxrYpHhWwWC3sv7NDo9Kjei7KKL2uwFBmU4GMruxtYDFa+OdPjwglwjhcGq1KG0/Aw3w2p11dGN93YrHo6r6t7q3hDXrpt/vUqyJcMJ/P8fq9OFwOgrEwwZifwrm4zT5+3GTzwS1sNiurd7fQFzrNq+vjy/SW8EdEncsfDTMaDFm5laPb6hHLphkPRpw9eaVMvOz2NZa2VhgNxkzHU+bTGfnDS7be3GQ2nVPJ18imJSDP7W9isVqIpwNMx1OCPoso7SSCvP9ekJc/koA0nIoSjWjYNY3ESsLg+Yy5eHZMs1wltb5Mo1jD6XWR3ljm7MkrvvlBkoUuHYfpeEJmI035zDQUW2bYG9IfS+Gg05B15Y+GiWRihOMBbJrN2Ofn9Jpt431ZxqOx7K0WC2u3czz98ImaU/XLMuXzK5a2svTbA9VdvHh2THZ3jWDUL2TvVJzLosBtvT4H4GM6TkjSP1+wcX+HyWjCaDBm4/4Og+5QXL39Xmw2Gy6PU60HkxycWs9SPslTbci5lFhbIp6JUC02mE/nykOgfFJk9519Lg+vxQf2v3aXYX+M2+vk5UGHD77u4Ru/8DaPPzoku70k0LPeiGDMz8VLgUe6PE7cPlEtW39jG81hp1ZsYNckYdIXCzbubdJt9SVRz0SpFuss5nPDyVqEKGZTkTd3B3zsPNjk8Xcfs7K3wcnjQ9a2Yqze3aLwSvbdYW/I2kaIq7LwnUw4ltlFSK1naVUaZDaXcHudFI7LNK7aZLZzPP3Bc1b31+l1p9RLdeLLCQLRbY5LoiQYTUs80Wu2CcYjrN7d4uzJtQdRPJchlYtTPq/+MUO6H79h+QvdIfhzSQh0XX8MPPwTfvT+n/BaHfjP/im/5+8Bf+/P9rfh+7/+IzFhwkkqLSou49EUr99F/qIr+P9MgmGnT24nzXgw4ezJK6LLYqDi9LpZ6FYS4YWh795TLWYAi3WDTrXBXF9XmW2r0qR4eK6qO26fl+l4SrfewuFyYLNZ6bf7nD89Jre/QSAe4eJlXik33Hp7D2rQa/Upn10HX6WTArPxhHAmIWRETUNzaDwtx/BHayzmcybDMVflkVEVXefk8SHxdACrzUbh4IzSsU3JmR188pylnVUqpwXuv3+fq3wDfzSEN+hh0B3SvGrgD4sqhtkerBVrTMcTikeXJNcyN4iW/9ZfXeOHXje9hrTizYpvLV8SLHOrT61QJbWaYjwYCe72wS0ef/cx2d018i9O6WpCWjQ1rP3RMKn1jJIrXczmHH32ksNbMdw+Dy9++FQdICD40bMnr+D9jIJd9ZoOVbnyR8Ms9IWogBRrjHp9Nh/c4uizlwAUyzPVPbFad5jP55yedHj5o2fqOZgKM5XTgsjddfsk0z6aO6tMJ1M0hyaQo4mhImRgrPv9GcePDtRn9YUDlE8LCo979OUlFouoQUX9c5p9TX0Gq93Gix8+ZWlnledPruj3o9TyJRaLDVrlqqhJWa1MJzMiSYGImImkea1mez66nCKW9HPqdtHrCa706Q+ek9tbI39wQSgRVPfjr/wV0V0/fSYVrn6jrYLF8WCE3ampDoEZYJWOLlTAabVYCcbCysNBc2rcvRfhB9+XdbfQF/jCAY4fHRrSe6JWUTw8J7qc4uzJK+69dw99oXP02UtlRNWtN3E6bej6gla1w3QsBOzyeZ12TeAY4/6Q5x6nOInWOrh9Xg5PBc8/CYj8bv7lKYuZ8FROHh8SziSUvnb1vKieVSI4F/nYWIj8y1MFBzz+4oitB9vEsgIL0dwu3D4PF8+O0X7u2sOjcdVU0oGdagNvJMgf/vZnBk5/mb37S3zn//wB/miY6mWDsQFdED5Ejy++V7yxt3WqDTVPNx/cIuS30G+Lq3AwGaXf6gpErFBTrzO7aGYlsN9oc2RURPuDBZWLipIo7DWlIjqfC7m/edXgr/+7MX7tn9jQnA68QS+VizKdeot4Nkl2I47TKQTZ40eH2DQ7W29u43RaiYVkXpimS6t3t64Vx3RY2lqh0+jw7KMvBW5mE7USU3f95Y+eiVmZpikYYjAZ5fTZJYOWFDfiuQzBmBDwB60On37nETbNjtVuw2Kx4vU5lCrN+hvbqiIPktReHpzhcLlY2lrm+NGB6m61ay0sFiuP8yHsmgG9mwvEIpZN02310Zx2yi/ybD/cVUGT07NJu9YmEIvw8x/4+O/+m0d4gv4byjDmeowvJ+i1elitFvIvzoTgOhxx/OjAcFG34DDkjk0oxae/9yWeoB+Xx0W3LkTVxNoS08mMQVf2/0pjm8Takromq3UbfaHjd8+ZjqccPT5m+9YD4rkMNpsNfaFTrU+ZjicM+yOKh+cs7ayS3lxhNp1RPDzn1tt72GxW1c3sjyz43Donjw9JrC1x/MWxIrw2y3XjeUmBQV+I2VW3LuITbq9L5GGNTtxiJiT5eqlKMBamUaxhsVrU+WLClKLLKTSHxnw6Ux05IYVbyL84JWDILrfKVT761oDxYEgm4+bX/9fPVYesflmm12y/JoqwpGKB6nmR7Ye7HH76gqWdVSajiVo7lYsiD376Tfxeq9obzPPAJIeb3YlWtSOFBYP3ZEKSAf6rv/0Ol3U73//1jwBYen+X7/zKDwnEw9QKIjQwn805fnSAJxRg++Eu5fMrhr0B0+EISzatugDtWledNVf5Ct16i+hSkup5EX9EIIzpzRV1ljz+rkCD++2+FJOCdi6en2C12SgdSRzS6axRNtyCzXtsJmNOjxOXz6PWUGo9i67rXJ0WCcTDFF5dsrwqKnznrQ7eSJDaTkxBDc3hDXpV4vTih08FfTCespiLIMsfJdv/OI4fN17An2X8xU1l/rmHcJUzuRD//f/URLNbDJb/iNV1qZRHl1M0ixVGvT6FkyqHn75Qknia24UvHMRpm3J4ds17drqvoRwXz46J5zKUu4az62sqMp1qE28kyEJfoDk1FrM53UaHj7/zBS6PUyp7T14puTybJlKaw564JlfzFbI7KyrgCsYjLO2s0q7U5bCeTpmMJkS8M4adPu2rOt16k/OjCsFkVKpaqThOpx2Pgb1NrGTIbAhcwKZppHNRAvEIk5Fs+MFYkONHB5SOBF9eeHVtuW6xWsWoZTbHYrUIVvVU4BjhTAK3U6RPTXUPu6Ypd81uq890PMUb9HHy+FB9v10T3HL5TPD+5nsX87ly8T178kqInavXhO75XKpSibUlyid5ddAurxmuzyNdtZNFW92P2+tkoS+YTwTKpRlqKvXSdbVuOX2dNy8WcojVy60b1YrXN6rKZQ2rzcbOul3plJeO84ame5toTFris9mcgQEZ0pwagXiEQVegMPFsgvTmCpn1JLqu4/S4Sfn7+FxzpXi1mIljbOHgjDffSrO/I07H5dIAd0A4HvpiQSDspW0YzoEkEubXZjJQvyxz9LyAzWbD7dEonlVFOWYgZkjnz88UiX2hCybZPHyiyykJxr1uIQ6OxVCufJKnUarc8BJwuJx0600cbgcOlxNf2E+v0aFQHPP+v7FEs1hh2BkwHU+VI61pgiXmWRIoXOUbBOIhw0+ggS/sJ5xJkE7Y6DU6NEpVhv2BOog1p2YEUlaiqSClkyL9lhA0m7WBcl81MelLO6uMBiOiyykCkQDe4DXBr9sUWMH3/3DMxfMTes0uvnBQkctdfg/D3vjasXyxUIIAXuc1LrhZrNzwD7FrdiksvLogGPXjdttkDQU8+Iwk3FQSMtceSPA6nUzVHFy9u8XRZy9Ziw+VYprD5WRmuMuOByLxmdnOqXnQKlfJbOduQKLiURtWu9WotIcNxZIZ+eOqWveVtoNvvh8RrflaW82Jav6KfndCryucF5th2HT65Snj8YL+UKA2JsTg6rUix+//wx+Sf3GqTIh6zTatyrXXhAkvii0lb8Ap+62uqiibfKPmVYtQIqyeg9wDnelkwg++9blSZwMJIE2COMj6CiUilM9lP5sOR0I8varTKlfJxSc0rhrYnQ7qpQbxXEaItqsxBl2Di2ZU9wEKry4IJULMpzN6YzENNPcoE3Jowg/Pnx6JTGxBClD9VlcFkSascNDqUMtL1bt4eM6dd2/j9rllri3F8UaCtCsNXv7oGRfPjrE7HaylF/Sa4p+zcX8Ht9epDMKq50XR8g9aqJ4X6RiGeqGAXWR0KyKYUDg4k7PICIhLp2WVDPijYX7nt84pXMleXjktkF4XovqwN1COxosFvP2zXyGVi4qyXiYhSfPLc0a9vkrqQGCI06EkbvP5XMF6TA+g+mWZaCrM7TeX8QavieoWqwWLxUosm+bky1PiuQzR5RS/8B88YPuhQELv/8Q9IYTfErK0aSRprkczFgjEIzSu5D4VDs5oVa7Ph7U7mxROqjx/2eHy4Byn160glPOpQG1N351gzI83EmQ2nar9B6TbsFhYiPgXav42GiOCCelyuzwi6GA6hw9aHRa6bux9AVEranfVXCuf5Bn2BjSLFZwecXiunhcJxCMqmarmr7lXgIpxbHYbXrdFxFaMPWNpZ5Ved4zVgLiFUnE27u/Qb3WlGHnVYNQbXHsUaDaa5brq1I16fQVDEqEJJ1ab/K7c/iYb93ewaRqtSpPUekZ1q/WFuNdXLuuGnO6fajX15zpEDecvLmTo/3cJgb7QufX2HmevavynfyON01BX2NzP8Fu//DGdRofJcKyqjpXzkqFmIofTdDhiPpvTmzjJLUm715QzdAd8Yi4TDdOqNFgspOJjSuat7G0QTETRHA4CkSCz6UwFNMnVDOORKK88/OABVruN3P4m3pCf7K0c8YwEY/FsgvNnpzg9IntZvyxj1+wkV5dolauEUnGq+TIv83ZVIYpl08QzIRwuJ+cVB9PJBL/fzngwJLoUo1a4olVtM+wNmE+nXOUbwg1wSHBg4rFtmsbKThZ3wKM2ntvv7JHdXePW23v4oyGWdlYZGBu22+fmk0djcvub7H/trpCghmMmoxErextcnRUEF+v34PJ56dab7L17B32h4/F7cbid+IJedUhrhlvnzlu3ye1vMh2OKB1dKFjWbCZENRNCkVhbYmVvg0BAgoZkRMdms+LySaJWy5cZdEc3ZN+m4wnx5TiRpHy+QDxCqTJXONzzyymDVkcqZ6+5spr4VxDcY7NY4Q9+2FZYdYtFuilf/9l7PH9UJJSKE417ubXt49bbe5w8PqRTbeDxe5XTc+nogi+//wWheJBOtcFp3U9/bGM6nihlJJsmOu9by3PmC5GzjMbcCk/tjQTpdYQst5hJQrWYzSUZMhLChbHh+4Je5vM5oYAdf1j0p+2GisV0OCKajuEJBQi4r036AEKGMo4nKIpNprKEJxQgnIrj9LgJJqMiXehyKvWI6WSC1WIV3sN9G//416VqORmNSCzHFCmwVa6qNag5NPzRMC6fi/l0rqrypaMLFVw53E4SKyncXg/bD3fxh6QbF4yFpNLVHCi31dl0yu29kFyjMc/6jTa6rhOIBKhfljl/esTFs2O+8s0HqksF8NPv2Fm7u4nL5yYYCyoJSn8owHgwplWuCXxoPKFbb2K1WYl6RWLy3nv3sDsdlI4ucPm84szr9YiogMeN02UnnxfTwOl4Sv6lYGcHrQ7x5TiegI/1e6JK1a3LPDOf+dmTV2S2c/zwueM6QD+/7iYkVzOMen2FNQY5lE2lHpDALl+Qzkkg4hfCsF+eeeHgDH/U4KnM4fBMFwPBVARPKCDV4GiI2WxOKOxSZkkiXyzqOYORruRsc/ub+MIBleA8/OCB8mcACZKnE4FDBpNRbJrG6t0tAhEfVrvIwO5/7S6hRJTMZlatx+hyimFvQKNYU+tFc7uwOx30G23u/8QdmVNuF6dPjm50WEx53OlEukrBZFS4T36fdIzsNqwWXTnqSsfUQjAWJBpz4QvJs3C6NHK7WaLLKZKrGSajCQt9wa/+6oVyaBXn2mulLE9A9prMdk4Fv2KYN1UGkeYzMh25A/EIjUoXl8dJ+UQ8YPTFtQyz0+sW8nJT9gF9sWA0GHPy5Snz6ZTf/v2ukaDpVOoLJfsJkMsIudNmE4Wk7O4ae+/eUfc0vSbXnt1dk8KA28F4IglwKBXn4tkxTq8bb1DkquulKlar7NfTyZydt26TWI4p129/NMzKnsyTzQe3yN3OKa6D2+dh/Y1t4rkMvnCQzQe3cHrd5A/zzOc6h5++VN0uX8gv62cywelxK77Gb/1fLzj45Dnj8YJ2c8DlUUH4MTYb9UJVQRMXswULk//V6aHrOt5IkNW7WzdI2d1Wj3AyyLAvwbNN05QEOYDT46J+WSaWCVMvNek32kTTUTSng/vv3wekUFbvaVyU5Ro37m0SCDjwh32s3t2iW2+y/7W7JFZSWKxirDgejIWnkI5Qy5ekqPeaRLppAmm1iHTuxv0dOoYoij8aJrkq56Y36FPPMp7L0ChW8Lr1Gw7wvVaXZFo6gOaoXFSILsVF0Wg4NnyNZN0UDs7IbIgX0GQo59XLpzV239lnPp0TjAWxWkSmu11r02l0iWdT2DQ7Z09e0W31SW+uKD7JqDcksZJS8rk/tsNi+f++U7HFYvm6xWKx/ZHvvfkv55L+5Q6rzUqn0cOu2Xh+4eDsXDBxh19e8sZ7dxh0+obqR1B5AHSqDbXJAERSYfzOEa0uFF7l0XUdzWE34EPHIum3s8JScMBsLFg9kM5Bq1ylVa5SOrqg32iT3pCFOugMmE/ndOsdGpUui9mcXqtHp9qgeFxU8nDlsxLR5YSYkjVE43o6ka6AOWbjCamoVOgrpwXGgxGj4ZRGscpacoLL4+b4leCgp+Mpmc0s9cvyNfm1O0TXddVGNDcRu8NOvdzErtmVosyzj76kfFbi9MsTuvUWg6585o37OyxmC4IhB/mXp7x6JIRNq92Kw+Xi4tkxi9mcTqNN/uWpCsirxQbD/gCnx0kql2I+X6hD2u0XR+SDT54jSDKjsmJwCrJLDlU9dHqcWC1WLp4dc3bSwmK1YrPCsD9SkqOBWES1znP7myTWlnj4U/scPzogHDcOdI+LTkfurcVqZTyWgKnf7rG0vcLDDx4oEuON1mckyF/6akhVx+fTKUtby7z4osjWnkDPGrUBJ2dDXv7omaqsWKwWmob6CsB7f/VtqpcSgCaDY3Jhcbwe9UfKibPX7FBu2gl5paLz8XdfYrWZpMwxi7lUWRxup6oMVk4LaE4HC31Br9mhcHBGrVDDZrMRDVsJRr2EEgHCMcHJ7rx1m2r+ikGrw2xhUYcJwNnTE1LrWZrFCvVClbMnr1h/YxtvUD67ri+YTWeUjvOMBiNVLe832qqK/p0/GLL/QKpq4/6QZrWNzWFX88KmiZeEmPF1lJv4Yj435E/FVyAVEgPA6VjUkw4/fUHfqNaaZlM3oCHZJB9+94JWuSrJtUFkHnQGXB6c4w74VLXvD3/7sxsylkHniJPHh9LSXgh/pV6qs9DFGE5fLKhc1tTfMmXz0ptZzl5esrK7yvbDXUM/3cewP1BGPi8+OaB8LtebXIkrbkU4k+D40QH1y7LCMo96fcOb4rpjUDw8Z39D8MzhTIKN+zsqiasVqup1pkDB6/h1IdM2VUWvfF7GEwoIfK8u2Pdhb4DL52U33WE+15WxlhlE1i/LhCIenE6rOsRn4wmdZh+n00q9LhXu86dHQpivNdVacWg2hr2R6m7svXuHWCamCgPzqbibH376gl6jQ7/Rpl5uUcuXuDw4Ey7G+vWeZrXbaF41lH+E2+chmIzSqPRwB3xs3NtQFePX75/5OeyawP18Ya+xf9dwetxUOg71ervTQeW0QP7FKccHdc6fHpFaz3J1UaNSqItZXthLOC4mUe/99CqNkiinJFYSXJ1JwhbOJPCF/CTWltQ1jPtDyid5JsMx50+PFb+nW29itVqx2W2ieLbQubq4utG59EfDbNwX/Q6r3cbp+RBvSNzKfUEP2/c38EaC/MxP+nH75f6fHLVplJuqCDKbW+g1BepSvyxzeXDOs4++NBLDMPnDS1w+r0Bz4hGWVyOUy33ms7mqsjtcTjwBD6n1LJmNZeptC06njYNPD1RHGQRq0mu2addauAM+jj57aRiGzQ3fFztXF1fiTbAU4+izlywWuijm1Ies39tSXaLCwRkrt7LoC53Nuyt0qi0JzJeiotpmtXD7Thynx6W6h06vS9338kkeq9UqEOH5XAmAtCotdb2xbJrKaUHgLQ47K3sbKsAun+RZvbulzEO//P4XNIpVtQfN53MuDgxZZX2BU1uwlNBZf2Obw09f8Hu/Jp0yUznLhD+l1pcNDxKBWl2dXYmU8XTOYr7AYrUIAdyIO+qlKuWTvOp0jftDuvUmxVcXCn5YODhT7tbxXJrf/37d2KtWDEhXi6tSn/TGMiBcsm69SeHgjOLhuWFeKaITmw9uobldHD86ENnuchVd10lkApw8OaZ8kufsyStKxR7deotWuaqkWf9f9t4sxpEtPRP7IoJBRjDI4L4nk7mwMiursrZb1XfpvrrtUbekGcnTI409ggzYfvKjH/3gdz8a8IPtAfwwgKGBYcxgYFsYLXaPRt263be776raMisrV2aSyX2NIINBRjBIP/wRJ6tGhjQCdAdXkgNIdN3qzEoyGOec///+b/FAHY9i6pcCbgPEw7ZsFG/fZH58U6+/CxOCHwL4Ecdx6Tf+7p99Da/na784nkcqF0U6q1I4l0Xpk+99tIUPvhXCe79CIRj6QH/LdcJDqyKZBGrHVaxWHP7siy5scwZDM1h6L++jGPGpbsJyqIcyBtpb3bbnvBLJkFd0s9LEh98vQ/TTAo+nCUnxaAo+0YcPf/Aecec3cuhf06LxBfxIFtIY93UoEcV1AaLo9KvGki2ucX+IdrWL7FYBx9ci+tctxn8ftgeY6tQULVyv56k+YQUlQJvC5r0tSvgMB9E4uYIv4Mew0cHt9++yBNNIKo54JobC7gYa5yQETScJkQoEZVhuANZUn7AGy9u8vMvQJpCCMrrXHVy9qryVCmmOJ4ziUD08J6eiqXljw2qtEIyqkNUQrg7O0KnSIXv0iwOslkuIwpIlW9J9GeGuyy2dT+fo1Vo4/JJ48ddndO+6Vw28/9AP3icgVy7izg4hFvntHOy5jeppC/3rFhzbJs/+bArGgHio9gIsrRMgy8L8RgLt5gSJtSysmc2mF95n1b1qgPcJyGzkIYUU6Poc4biKaDYFgVvhs1MXZbtuQR9MGLd4bKwQEOhZ/s3f2X8rfM2cEMLpBdtUXbRZ7w4okEmgQCGO5zCbGDg4MqAofrz+9BDLFT3TWm+M2YSoNcaMR795U+g6tg2tR7x4z45S6924N2ntPoyBhpgrfjQnUwxdLY7l6jyUkJ/pDWQ1RNqbXSpsvETpyZBcpnLbRQRkPwq31hBORFE/vsS4P6SJygXxxT17SvodFqLpGGsueJ/AxuTVw3P8xj8sUqLzWhaLuQWO42GOJ7j1eAdSKMiaTM/ZZtggwbE2l8D7BGom6l3EshRyp3UGRF8qrzM6xtbDHYw6IxiWH42TK2jtPi4PznHy5RFSxSyUSAiRZBQ5F+Eu3i4hkY3CsRY4/vyIHHzGBtMceMWz5/0/HekIRd1no07GBUeXHDqVOoaNDs6fHrMgo+mIws2oqMpgNjVZ2GAsn2bPbLtFKCilhAcYSptIh4izzHM466poNia0FxVzrIBIrGUhyT48+7yG5eImmLB+UsXMdNC6pqLN01nMDZN9Pl/96UvwPp657hx99ooZCvilt73I43mixjTPqlBTcTdsLYjWRQ3hRAx+OQCf6CP6pTtN0rsDaO0+Gud18D4B1dc1HH/+Crffv4v1u9vIllLsM/NccQBCRAGyYI5lE1g49LzOJiZi2SRruOz5gnQXsznEgAjLtOA4Dl5/esioNVdXNw5ZgsDDJ4pYv7uNqTamKZF0Q4Xypmuem5IXpuhzXer61y2MWj0oYQmRZBSmQdQNy5xBEH04f3qMQJDcpO7uykhkYy4wI8I0LCiREPpjAWvlHOy5hU69D63ThzWjaVa9vWL2lrIaAu+6FCnxCNQk2TRv7G+waXe/a0AQeKxWSwxbPXAcx5xw5rM5rg7OEFdXGA1M5q7mufPoAw2C6INfCrDpiGWSdWsil6SkYsMkG1bXHS9ZuClLRh2NXIXcc7hd7cIy57DnC+TL9PwO2yOEYipEkcPFmYb5dIagGoIgCJgbpHWQQgo1+NctzA16nd4ZLrj/dnqz8FZYYjanQFFl0gYOyHr38sUpsyYFwHJevHXoUSB9og/dEY8/+VjDxbMTVyf3NiV1PJq63ytg4/4tcBzHnnFTn2DpOHBc4bA9txCOhch0xH3uZeXt3I+dJ7cxanXfcCKiM79TqWN9IwrDpYwVdih3oFxWMR4QzaldIW3km8GkHqB49YoyYOj+tJDfoSTig88vABClMb1ZgBLyI5qmfWHcH6J1UXvLQSu7VaRmYrmETxQxaPTeAi6+qdff+gkBgGMA/z2AjzmO+7b7d9+s1ubf8/L5eNQvOqhfDTAazpkF38unLfz+7zfQqpNzy5v8Pu+K5dMszXO54hFP06Hcv25h824J0WzKpWMo4HgOc4fCXzj+xroMAPySH52rFiYDHfmdEiUu6guMRzoTugKA9UZi79VpBwvbhj1fIO0mH/pEkWwS+0NUD88RSUbRurjGqNXF/vYKvE9gAV/b+0XUjy8xNVco7G7gl//TD3D3O/egxiPwiT42Jp1N5yg/3IY1m+PBdx8gvUljv9ZlB/mdEtqXTdz76MENZcAmi8pUKY/+dQvhKB2KyUIKaioOTV/S6DgdQ7KQQa68jnQpxyz0jIGGZIHsT4t7m2QhN7cgBkTky0Xkd0oQRJH5VHve9+QJvWI8+nAihoCfc8V2Aex9sI/sFqEZ//i/+uZ/OAAAACAASURBVIgOtp6PCTpFWUK+vIbq62uG5iQKGTY98L+hCXlxQoE/k+EYf/CviUv/+tNDcDwHx3bYuJWaw7Gb1JjHyfmMpjVbRQiiiGBUxbBrIBKVMWh00DivswTN8uPbLAgskSOe7sK2oaoBdKokdjuoSlBDN8vOcRxo7T755I9smLYPYsCPV6/pIJ25CbiDRhd+KQApKLNxt3dAzVxer1f0pUp5bG4EsVgs8eC7DzAdE491OjawWpKIPhsh/22vKJVCCpRI2B1jU6HjLBzoXbrXhd0NctOKhxnVa7lwGNqkpuJoN3S0GuO3kC3PPck2Z4yK4hN94HkOfklEOEqHL9PTZBIo5Tms391GPBNltnxiQETj5Io1ocuFw9a3rIZg2RwWc4u57pjjCeK5FF5/eohho8N0Jx6dxntNorCEElERy6dRfrjN3H0Kt4pQ4yqaZ1WE4tTAXjw7wbg/xI8/W7D3LKtEB2hd1OATBTTOakw4PR2b8Ik8Mhs5+AJ+SMEAoukY7DlpBVbLFSKZBLq1m8mexw3ObBTA8TzyaaLTBKPEMfaJPkbH8KZZC3uB6WjMGu+Vi7YGFBm3yjJi+TTta46DaILeiyTR2vUKto1NFYu5hV6tiUGzy1JVaxcDxNMk+FQiIWw93EFxbwOz2YIBAb1ak5oHN/k6vVnAL//mYzg2hRaKsgTOPTTVRBjz6QyiLKG0X2aOaF6SsjdJNHUD4UQMhqazxOPC7gZLhPYKO1OfYLVcMZrDeGhgqk/x8ifPUdzbBC/wTKP0JkVuPNBhaBN42sFxn6amoVgEkUwCVwdn8MsEmIw6A5jjCSxzhgfffcDsWTNZiVGRbGtBerWTKgSRKHGeJSVAttV6T2N0v0QuwcLt9j+8D4Com6lsCFpvhEBQQnotSROZHt0Tr+Gb28Cor6NTqeP6rIlULkwWkP4VDj55AVlVUL5TYL9rtVzi6RdN9uyv316HFA5C8AmwzDlGXXJN6jeH7HtS2RDWCkFo7T7CiSgrmj0QCQAmJgfRL8CxFyy00hfw4867t+B7I28GAIp7G7Ati022oukEA6FSa2lMhrSWX39xzGzAlQi5FQ0bHWQ2chADPlw8O0E0m0IyH4c1m6PXs5DOKq7LILnczCYGjNGYWX5HMgn2bPHuczhsddGttRjyn94sYOP+LZwcdtC9Jn0J5TPcFPSeyLh1UUP58W2EEzFEMglGJ+YFAXNrhYcPY3jw3QcUvrmZRHqzwPKF1jZoQqYPdNSOLiEIlAcyc/dcfaBDdrN9Nva3SIg/mWE+NbHzZO+t5qW4t8lATO/aeXIbpf0y8jslzGYktA4nYpTPEwnh5x9fUUCZIqO4t8E0OW8mdXvBo2SxbSOeT8N00f+H3ykzClunUsfdXUpN9vQz3rPuUbA71QYCCu1BgWCAkqR3CvimX38XJgSr1Wr1BwB+AOB/5jjuv8ZfY5Lwf8hrtSKHG8HHI5ORYVsOFeyage51B2sbZAnoecGX9svgfQIW9gLDRofx1c2FiPJWEMGoSpqEoypD9IftEfSBhnRwzIoEDz2L5cniLJZNwOf3scCicMgH0e+HGPDj+owQPi/oJ1nMYblcYTahwKTmWZWmASptZt6Cqh6eIxgNQ03FMTJ8iGVTWK2WCMfCTMhayHAQfAKe/vwcJ392ikiS+MGeW0T3qoHVcgWt3cfzj59j2OpDlCWMWl0Y2gT57QIGbQ3DRgeJtSzOvnpN91OgzePw09eoH1+idlSB3h2g2SIEPxiWyCWl1Ydfoojy6UiHL+AnjmlQZhu+3h0gt5lF/aTKChVfwI+5QUFUWruPQFBiibDeCPS8MnNFqyMM2xrUOI1ujw4HyGwUsJGhEJtoNgUlEka31oHj3Hghd68aWK1WdHC1BuB4HtFsCs8+vQTHczAnU7z/UQk8x7toJHnKvxmn7tg2O8ALeUIzO9UGEmtpBMMK2rUOlqsV1nZLeO9X7uHbv/EucuV1XB5cUBMwtzBsE/rulyVIMgnqFnMLR680zC0qTjievxH1nVWxUfTj2fEK/esW7t+lQp0VbPvkdvVmcFOnUkc4EXtLRDdo0Oh2qDmYmTZ6LQ1XryoYNjqMvhFOxNDS6H3NXNrIbGLA0MbMM9xDdjwxeL/exWSoI5pQWOiaIIoYtgeYGzOYkylWyxU+/Hb8Lb/sN/nP05EO3icgtZYiNyZVQrcxcoWVdNApkRCOzhaoHp6jWWkxSoz3DOm9AWQ1hEffe4RwIuqO2ReYzmgr8xI+47kU4/YDgDWzkCqmSIyvyCjtl4mLzi8x7g8hCMJbbiEXz07Qvmxi74N9ll/gXaKfKAhiwE+ooDvtG7QHN1kUCwf9egfPfvwM0zGlgJqTGaPkiAE/HMeB6Cd7TlOfYPvRLkRZIicce0HrZLJiQESv1kRmI0c0BTcALBhVaR0X6BlIFnNMH+FpMwCwAufkyyMIoghVFdmafJDrYLFYMWTP+8ximThiqRDKt+g9NU6uoPV0aD0NxtjCu++lkC4RWtq/bmE2IXeTcDSEVsvAqN1H64JSnO98cAe337+LqW5iqtMEsVWp4/jzV5iODQTVEDiehxRS6B4E/FAiVNBmt4i/Xz++RDQdR3qzQNOqfBp7H+wzj3sppKB+fAklQoVpIBiAoU3Rvqyz4KvcJglDvQnD2aXNJkHDVh/OwsFsMkWqlGcFYH67gLlhsvDEYDhI4s6QlzBNtDNZDWFtt4RoOoZ4LsHE3W86aPlc0fjCdtjnenFAgWfnzy8w7JEVsazIaF212Tp7cw0Y0xVkt7mZT2fgOGrMnr4kwfSw0cHzXxzfBLbl08itx9jnffz5K0xHY4TjUfA8h9Vyhdx2kU02o9kUUgkRV9UJeJ+AyVBjeoA30fHlEsjkFPA+HuP+EGu7RSzmFk6fX0JNRZHfKSFXXme5D1q7j9xmzhXQE1CWLGTQve6A9xGnfuveFtLrGUancmwyYBg0+8ywwi/Te5hNpgiHfXCcFXtuPZDL27cAQtU9O28vM0QKKZDcHAJRltCp1NG+bMInCizkUU3F3/LMX7+7DVGWsPvuHUyGBtZ2bnI1AEry7XTmqNXnOH1egalPUL/oQPTTRF+JR1A975Elc7uPzGYe1ddVLJdLNgGgqdc1rNkM49EEZ1+9Zg3HyZdH4H0CAoqMgCJjNp0jWSCw0DtP2tUurg7OaJIzX2C5cLBcEaDXvWqguJ3C2VevEc+lcPniFLWjCnifwJrXVCmPcf8GKErk4ggEA2wK/OrPqth+tItRZ4RgVMWf/mkHobj653KWPIOBZDGLYCQMOSQjllIpz8CdknxTrxX+DmgI4E4DVqvVKYCP3K/7X9eL+jqv1Ypiv8ny0UEw5MdqSZZW//g/fwSeoy5XiUcwmxi4Pr7C2u4GcpsZBKMqW8D5YB8cR2O/ywPi8/euaRMOBAPsYPScPzz0bDIkBx0PvfRQd+/SuwMsnSVFmSdiNO4eG5BDElKlHNRUHNmtIiLJKEYtKhxtc8YaFWOgYTLUcHxhQRAppKZz1cL5MY0FA+IK1cNzhCIKxIAfnWvq0JPFHM6enjJKDuBuYgE/clskztW7Q8Zb9lC2D3/wHgBqXsb9IXYf77z1/7//Dh0+58/PmXOTZ4cGuMFQpTzjIAoixb/36gNI4SA4nr8J63L/2/P190SUXkP07gNCoMhGjsfVqyvUjy/h2A5aFzV0NBJuKhEFUlAi7nWYCgDvAF4tV8wtx3M42Lm/BlOfwDZneHVArk16d4DWRQ2pUt7lb9MYd7lw3I1xiPFkiVg+Db9Eh0ZQDSJbymDQmaB2VMGrP6tiOCAPfZ97cG/eLyOSJrcTn+jDD/93sqBLFnP4+38vhOUKCMdJHJsrr7PphGGu4O0tsznRW7zwobWNGNRUHIu5hd1379Dzu1MiL/Y3JmFqKob0ehrmlMbOq9UKj/4jolR5tIlxf4hYaOHeqyVEl3PPAn18FG8vqyFWULCfHZlIrKVx+/27rHGaTQwEVQViwIef/WLI/h1BFNk42vvf5cLB5cEFFQXzBeouXxygRsWe25AknsL5NjIwtMlbqD7HET+/WelB6/QRdFHQ/mCBjfu30L9uUTPh2qPOJgZ2nuyR9d5FA4lcHMFIGIZmkGvHRML63W2YxpQ9P9mtoit6DuLixTmbSHnvKZelxnbjdh5qXIUUJIpfPBNngILWodTUX/2d77iWqxyKO1l2L9KlLGuavGLl/CkVcbY5u7ESJVAY05GO8uPbuHxxirUymQw4to1kIYVkMYdBs+N+dvwN4KFP8NlnPUy1tyeljm2jeqmzIrc/CyMZF5h7mvdZtS8bcJwlzs91RJK0p3jUPiko4uCV/hbtjJ6tESovz5DOBBGK05ry8kymY7InXMwtyGoQc8NE+fFt2HMLU3dtRtMUDjfVJ2hf3iCyXlKrB3YARNHzJlBeUyuIIiajCVJFAjqsGQmhLXOO7NYajj9/hWGrj/W725BCCgIBHlJQgqFN2DPulyT0ai2EE7Tmeo0eW4vrd7cZeHAzxTOYbkTraZAVCZPRhBWnxmgMWQ1SM+oWpP1mDwFFZkFwRI+KIJYMori3CWtmwZ5TmJhtzlhz551F9dMq8jslpNbSaNUJFX/ygM6s0n4Zqrt3R1MxDBsdrBUoLNCjd/jlAHq1JvyShLVyAe3LBjnpFDIEBCyJBhVJJ1C6s4lhi6YUnjNPYXcDf/yHF1g6K0Y/8iyNi7fI6vNNQblHaWlWmuheNdh/ey5insvdxcsLWDOL7SGdaoP24f4QT//0OXLldUhBiaakcwv9/hy1CiUtb9y/xSZMwA19JqjeOAZNhjqlXy+XSK0RTSmWpXsSScZQ2o4jmk3h1uMd+KUAwokYCaY3C+jVScN1/PkrcsBrU0OndweEwr+6QDLhx8f/16fMlSsYDrLnRQrK8EsievU20psFtCsNJAopN3CSMivSmwXaj9MJ+EQfOJ5n55gUUsBxPORwCMk1sh+tn1SxdBwEI2HE82lm6R3LpxGNEehjDDRY5hzpzQIE9554k5FgVGXnnUfzDCcibMJI6+8afimA7FYRs4mJUVeDmlARDCt4931iXAiCwCha3poACLSSgqTpqJ3UYUzm+Jtw/a2fEKxWq0dv/HmyWq1+G8DW1/aqvsZrYZM95uHPXmKjKKK0fsNfH2oOOi2DvJHdh9SxbYh+H4ZtSso1jSmUeAQ/vcjDpUtjNjGgpuKsCfBLfqzvbeBf/CiIzGaeodLJtRubvJ0ne2Qnp02wfnsdX/z0EqNWF+nNAvwSuRBNhhqkkIxELoHp2EQ4GmKFaDAswS8H8Pj772D33TtU5IYUQkUFAR8+pPRLr7DxDkLFTwVSIBjA9v1NJHJxZDdyjLNr6hPUTutUQNoLTEc6akcVVA/PEc+nIashchxYz4DjOHz6w6dv3d+JbmL70S54jkd+p4S+xrMxIkDUmOYZHezpzQKWjgMpGECymMPabskVcC2h9Ya49WCTLF4VmQK71jPMinLQJC56ab8Mv0xUl2afmoW9D/bROLmCqU+wfncbt/fJicNaEEI0GY2JIrSWBe/jcff9PYRiRK1QIkFIbibDm83RzhPyzd4qx1DaLzOaFMdz7HCovrpANJtiwjFNs6B1+ggnIijsbuD6+BKF9QhMgxq44nYGxVKEPUPhRBQXz07QqdRJaNkdsIarV2vi4JzDYrHCdGwgVcpD641YE/ov/5ef4PmnxKMe6Q6SxSwKt9Zh6gY++YOvEMvEkSuv4+TL15BCChXLjoNIMgo1FUeqlMew0UH99BoLe4lmtYdENoqv/uTZn1tD0/nNtsH7eHAcFeGeK1BAkREIyjAGGqbamCHig/YQol9k+pVkMYdkMYeJ6zf/3rs3lnIrN/hs+9HuW3Q7x7apKBh6fFoR2a2ie/CtIAgUxtWpdpAs3BgBhBMxOLbNXLiWC4c580QjPjY58ahz8QyN57uNPjrVFsIJst4dtXpkf2jb+Ld/6vr1DzTwAlm5TscGOJ6mcHPDZFqhrYc7WK2WGAxdUfpkjl69i2Q+jlGrS97lroOH54by0z96Tk4nCwfWjJqwxdyCX7oBGbzLa5yj2RTjHncGYH8+++o1IpkElqsVow51rloQAyIT6nUqdTcwkQrI/JqK3PYaE9wDBBLIip+hqD0jAL9IzWFmPcMalMXcwqA9RvlWBH5JZNOV/GYGvZaOX3ovhMx6hqXpRjJkPxkIyuT+0u6jcGsdves2Dn/2kiUNx/JpRt04f3qCSDLGdBWtixpuv38X8XwKq+WSgJNMAhzPY2N/Cw+++4A1J7xPwNb9bbLWnRhIFnMkts3HMRmSfaNfIo2W52YGkMub41os16pjNM+qN6YHIRmZjQzi+TTG/SGi6RgrVhdzKtKlkIJgVEU2GwTvE1yKUJQ5w4y6mpugS1MPjucwGY4xanXRqzUhyhKMgQaOo8RhBkQ4S3zx41fQBzqaZ1XKbXEtq99Evp2l9wz5MZ/OmSHDs1c2th7uYNDsY/tODolCCtMxTTt+759/ivS6m/AcUjA3TNISaTrOnp1gMbcQihKdS4lH0G7PsLcbwmJuY9TVsLZDwvbMRg5KPAK9p+G3f2cDlkWGBkE1hMRaFrF8GgefvEA4EUP9pIr2ZR2rJYEqoixBEATsPNlD+fFtsk4OKxQ65qYA57ZIiO2XSCS8sb/Nnv/cdhHWzIKsBLC2QVqTSNSPdJ70J5cvTsnVz22GfAE/dt+9A17g0TyrsuTjWDaBwq01tC+bKD++Dcd2yA3KmKLTMjAZamhWWhh1+jSRPawQyPTGdAEAomly1cqV12HNZohlU+B4Dh/+4D2mxxMEntFxgmEZshJAUA0xfUk4pqB5VmVNcadSRyRJqcyNk6u37J5lVUE0k4BtWXBsB4m1LPLlNTe4c8GcAa3ZDNZsDiUovPGzQUpjdsEVy5wjkklAjUdQ2i9DCgaYdmw+naH8+DbmhonJ0EAknYCpU2jgh792B4FgAD5RIBc3ZYVced21YPezRk5NxZkmyedqLDbv0Of474II38RrxXF/pa9v0vUXNgQcx/1PHMf9j/9fXwD+m/9Ar/Gv9ZqbM1y6Y9ahvoISpA8kV17HcGBBVkTMJiY7lNVUHFevKqyAnBsmTH2CUpoOPiUewdbDHWzfLzEfa58ooFfv4Te/Sz7+c8PE0qWmhGJhssJrjxiadvz5K5R2aWwvCALEgMhcSnyiD7WjCoYtEsHtPNlDYXcDlZcXuPXoFrpNDcefv8LVwTnmUxOzCSF3zaEf5sRk/vqjLgmbWiMRoiyB5zh0rwe4PLjAwiY3iE6ljjsf3IVfChByEVWwfncbuXIRex/swzLnEAN+bN4pklCs00dmg7ydI5mE66M/RafaQSwbh9YbYTZfMbQnGFVhzxfM0aNTqcNxN5loOoLaUQWWSQE6SiSMdm0AazZDJE2Wk2+6w8wNE0FVwWQ0wWw8pSJRd2hic3jJPLurh+cIyhzUVBx+HzDqDBnCFIqGoHUGOHl6Dr07AMdzuD6uQesOkN4sMFF5cU1igV5/9L99QoV/OoJhq4dOpY5eo4vCrXUsFw4EnwDHpiRM0S8w/+f68SWWCwcvPrsELwiYDHV0mxquLm4QZK9pAwjVjeXTOH5eg5qKkw1tnocS5CAIArpXDUxHOmtC7330ADv3C5jqE/R6ZL1qukLNxFoatmW7wks62PTuEOF4FIZmQO8OIAgCNXI+Af3uBKl8HAefvGQIu1cQ5srrqLp1qChLmE2mLrIUhV8OYDrSwXE8S/CcGybmhsk4+96zAFBBu7AXkMJBvP/r38Lv/tNfMAcfNRlH66qN+uk1OxQ99BoAeJ5jVAqPDy+HJIQVHjzHY+fRJisiiRpE9I5xf4SdJ7us8FeTEbjAFxX0+hT3PnqA+Ywog8NGB1v3NjHuU46DFAoiWcyB9wlQIxJ8ouBO86LIbeaYdWX/mpx52peEaM5NC6FYBJkUuSXpgwkTiXoTLq8RlUIKvvVrj1HcocRmU59AH0zY973pye9dHt1o1OqisEVC2//zn/2Eib15n8Ce+/ls7mZBBFjCs3fp3RETASsKNUpaT2MUmOrhOS6OGkgWc5SwuwKqdWoOzMmMfVbhRAylchLDkQ1zMofeG2C5XGGimehed/Dl8zkunp1Aa/dRO7mG1u4z0fbhl1eMRx8Iyth+tIvH3ydTOy94ynOcWa3IWjNVyiOciOH0K8oz2Xq4g9ZFDX6Jsl1aVy28/NkBEwZLIQV+/03R4zgOUmsptKtdjPtDTPUJOWO5uRThRAzpzQImmgHHJi/877wfYRO3WD6NUWeA6+MapvqERNu1Nk153Slo86xKPvwbWdxaJ0qEY9uYDDWM+0MUtrMYtfuYjskSlhcozddbg55JgDfR8CZxtaNL9K9byG3moLXJrz2SjsNxHAiiiMWcPh+O5zEY0Ll1+eIUrYsaHJfS9uSeiOZFA7yPhyBwpA3geQwaXXz3B08YjVFWCXQy9YnrdEPvrX58CXtuuc5hK2jjpfv7BVSPa7BmcxgaaZXG/SF++G/7kGQBpTsbWC1X4AUew0YH+x/eJ0FsIgolQgYRw0bHtSNVsVxRGOGamxNSO6pAa/dRPyUdzu67dzCbGJDCQfTqPea81a93wQs8Otc9DPtTFPc20apP4HP//8RaFlcHZwx8iGUT0AcT9Op9hrQDRM88++o1TH2C86cnmAw1ZhKwczsKvyxB9FO43GJuQQ6HoMbDGPdHJC4u0VkZTyiIZpOuk45EmpuagY11Cbffv4uFbaNT7WDUGbmfcQVan1wHJxq9v9efHiJZzCGgSFCiYWZ36pkCcDyHrYc0sR82OswlyZuaeQCEGPBjYS8QjKpIFNKUaO6/KVQtk+zQ4wmvqSF9iGlMUXtdQTytQvSTKYMxGmPUoQBD3sezSfx4MMLFGYWzdmtdLJdLVK6XGLbIcGI2NSkwkOextb+OZCGDXq2J2XSO7FaRxO+hG6H5N/larbi/0tc36frLJgRfAvjK/frBG3/2vv7GXcSZDCK1FsdWfolGizbLQDAAZ7nEzFxg3KeicWHbiCQjiKYTyJXXMdUniGWTWC4cxCUTcmAJY0CuAJdHhOrOpybOvnoNvTtAzwi6G/KN00z3qslGrsZAY9aMC3uJvQ/20TyrQuuRM4nn117aL6P8cBumPmGFaWYjj6PPjhCOBl2B7RKJQgaJAnnFPz+YYDo22Chc6w6gdQa4rFmIZRMIR2WIAR/SpRw6V02MOpSeeP78AktnifRmAQvbYULHo18cMBT22Y+f4fzpMXJbBVgziznJ2HML474GNRlB9fAci7mF2+s2E0evlmQ/qQ8mDBELKDI61RbOviLkWnDRyd1HJdaUecUIcEMfefz9d6C1+xi2ulBcboRhLBBQaLwtKTdCpbnlCrMbCybABIBOtQWOI+EmQEhvvlxA4dY6OpU6pT7LEqP+ADTuXi2XOPvqNUuIpg2NxpkeLcIYaFjYSyZk5X0Cth7uIBQLISCROFqNKczNSYlHMNUnTGsCkDjSms1huu4+M4uDbRMq/+b0AgAEgYNp2hB8AnJZGYXdDTapSOWJzyqFFCb0jWaTGA9GTBvQb3ZhGjPYcwvZgorLoypuv3eHTUbm0xnTvyhB4mszYZ87XVgtSX/hjXynY4M1ElI4iHAsjGF7gMRaFuFEjFA/nwDHWiCRCCBZyMCazZlVXSxDjWDj7NpdOzdCw2BIQqpIha9XKE/HJg5faWhf1tG4pAImv1NCLJuAPadpzdJx8PqzV5jqU8Ybb3e90DwfpKCEQVuD6BeR3SKP+KPPXmE+NcnBRZ9g1OljbXcDhYKExhmh6p6Tj/dZA+QwNTdMxPJkK2mZc6xWdDgnsoTaWjMbsSx560fT0RuQ4M8q2CjHsXVvi1kK5rfX/pyzCeBSkTZv+NnV0xbCURUf/uA9LOwFth/tYvMehf9oPZ0JO2OZOKNGeM+g4KeGTWv3cXLUZ+nsgigy9DSeIaHo5YtTzCweEZV+hswHmu77muH6coB2Y4z6aRWhWAQ8z2EymmDvSRnJ1I1b0HRE5gpeWvov/UqZFTV6dwDTmOHsJYmtvURnY6BhtVxh5DY5ckjGbEoBiBzPoVuj1xxLRaCm4jAGGoVAuq5PkWQUwx4VF+FEDLF0FNXXV0xDQQi7z12HEyQKSSpceR7LFe0hn36hM3ccx17A1CfMaap1UcNsMkUoFkZuq8AC0KSQgvppDf/Df/dTohFGVfa+r88acGwbUlCGrIYY7cJbT97adRyHuPqlPIyBhlCcUms3yzTVym1R47xcEEDiFbM7T25jLR9gyLQSjyAUIf74ZZNDbisPvxTAZGwxD3vHthEMCohlkqw4n09nzMEruZZhVLeFvUCqlIck+9BsTpmttej3I5FLvpWK/Vu/EUOvY8KczFjCOaWKd7F+e51ZW3v3BgA6V01MxySQrR1VILt5Mt50KRiS0KxQI+4Tfchv59hnGM/T75eCElJZKrDv3YtCG80QzabYvu09447twJpZzOXGuzwDA4DOs+Vyxfb40xMNcijIzi2ALIMBsIwTyyQK1M//8HMIArmdjftDTEc63ntHgb1YYbmgCdlytcTCthn67p2jXoMXzaZgzUjbEoqGkS7RxFXvDvDguw8gK0G0LluUAeHSNz0dAQAk1zIsjMwTAmu9EbbubeFNWr/eG2DQ6KHfoym/t0bmxgx+ScLVcQO9WhP96xbi+RSCahC2ZWGqk3341sMdOAsHrUvadwiE4bFYLKl5c8+SWDaF0v42sztPFnNQ42GIAR+UiIROfYi99+7g/7++vusvbAhWq9Xvel8Ahm/+t/t3f+MuvxTAarWEbS3w0y9miMdow8gUIqidd5HLh5hvc1ANYTo20as10TyrIhCUWZGlWxI++czAxv1byO+UkFlPQQ4RtzVZzKG4twlRoI1ETcXZSC4YDcOxFuQCslNCv94hnuxqeYMgbwAAIABJREFUBUMnXqxnD7Zako0mx3N4+ZPnN4mXx5e4Pr6EY9s4f37BDk/HoU1sOtLxvW9LjPbg2DaF4phzvHNHgNYZYDwyMZ9ZaJxcIbtVQCwTBy+QxV7/uoVOpY7iVpylryrueHDcH7INsHZUYd7qxOG23MOwTgicJOGr1wIchxBHe269JcAt7ZfZ+Bugg29umEhvFjCb2lgulohlkgzN3bh/izVX/Q6lbSYKGehdKjJDIR+h0QONJSoCwHBEG2gq7mMIg2fjN3OFtt5BoPV0DJrkdBB0ObhRVcB8Ss48D99dw8b9W6xBEAM0ltd7N0i/t+EuFktWTAmiiKvDCkKqzHy3o3GZrEfdgiVdymHU6TOXHQDM1YruE7CRsRHL3NDTAEK3JvoMPM9RKvQSWDpLrO9tEMVLo+9d2DY73Dz7Pq9YSBUzbip1DJWTLoJhBTzPoXPdQ/nxbWqQ3YPo+fMhwokbG92lQ6J5T/joBQfxgoBOhehnqbW06zxE6HksG3dpFBZmEwPnp0M8eH8dqbU0+vU2aQjccTHHcyjubYLjKXV0/e42WSP2qMiyzRlLWwao2dm6TaFIHEfp2XJIQSSdQCSdwHd/6z0sHYcVWtbMYRS5UYc0PNbMgiDwJHDfLuLWO7sYNOmzXN/bgKLKyCVXdKC5BZwnrE264kmy5qRnW/DROugOvIkdWajWT2tYLpYuXSaAdCmHnUdbSK+n0e1OMdEoRK15VkXtdYVCfdzfE1BkiLKE7FYBJ18esWcynomyqYltznD+9BjnT4+hD8bwS35Goxh1yftflCXkyuswBhrWygW2vgdtahg9upVfCrg0xxthX7O7xNy6mWzZ5gyF3Q1wHI+HT7LQ+pQVIQb8UJMxSIqExmUP/f4csXyaZQuYE0pVTqxlUa0amE/JWjKSSTAXGWM0ZtaivgDRlhzbdkWSM5Yk3L1qQArJiGWT6Db6rCmKZBLE/3bR+urhORJrWcymJsZD0iGUH9+GL+BnaCtAk6PWRQPdK2rqJkNKZ9WHU7bW9e4AUkhhPyPKEtGoinHUjipETXNNHuaGiff+wRMkiznIoSDbk5QIhRT2r1sw9clbttcA7eOeS5gckqHGwwhGVeS3cqi+ruKP/+XP6fsWS8gKBXGpqThRL1xBcKtjs+Rs0e/HoK0hGA0jFePQb/aJBx4JYLmgYpTjeTy6tSDAwLU69SYn6fUsmuc11E9rSBZzjBaajIuIxSRX+2VDDIion1TZucrxHK7aPlRPW0xPFsvEKZSxEEftpAaO50gAro0Z9WUxt9A8q6F+0SDRfG/EKIeCKEIfEl0vEJSgtfs4f37OBK9qPIRglJy/grIAWQ3hi8/pvPdAqK2HO2xC4Nm5Jos5Br6kSnkMmh1mA5vdKsI2Z9B7A/zGf/Eh5QC4ekTPTer481cIqRKbhGdLtHbVVBzJQhyin3Repf0yZhaHbs9Gs9KCmooT/ccnsCZWHxDyPu4PIfr9MLQx9C4lUXeqLTTPqozW9fzj5+jVmmyKHM/EmYGApyWZuG5K3hpMlfJQIiE0zpt4Q1eN1XKFaDqOQEBgewxA9cZsYkAOyaxRGvc1VA/PKa+h1YWhGVjYDpRoGH6ZrGp9og9KRMF4bDNKs7cOPeqm4zjo1ZoYdTUEwzKWiyW5xVXa+GZfHFbg/0pf36Trr/Jq/ka6Cv2712q1ghIJIRyR8K2HQQyGN8XXg28VcXHaR6/RQzSbovF/f8SEm6NWF1KYuJ///F8NkM0FUX11QamkHZ0lecohCfZ8gW21yYLNyKpr07VrpHHhZDhGopCGT/Rh5xah3M1KE+V9QnikkAw1HsGg2ce3fu0xxv0ho01s3itTcqb6hgai0aFRs0/AwuEwn1nMwmtmzAihX3KIpOM0OnWDmmIp1Q0mocLRQymbNQ0+F9k2BhorIDyEILtVvLHs6w9Zse7YNlarJfxyALubhJ5yHI/F3MLOkz10ay1wPAetR5uvV5h5yDEl5q4QcFM3OY5DMKri8sUpK1YEgYcg+sj9YG8T4XgYUZXoG4m1LLq1Dit89nfoNW5nCQ3ffrTLDgGvuDIGGnnKt0k0fHVwBilEn9ncWiGapmbMspa4fHGKgCuU6lTqmBsmCrduLObmhonC7gbOXhKyHVRDSORScGwbV0ckvi7tl2EYFkxjBr07YK4Ui7nFEDfHcfDgu5SLkSzmcDurw3Z4DJr9t5ww+tct5ItRPL7vBq6FiPdqTmYu3UR/i0uc3qSiz7O9BMCeG+/qXjUw0UxMhhrjn/vlAHifgAcPYuheNaDEb1IxPXeN2cRA9ejyLTR/PjUx6oxgWza735ZJqZ7mZIpwIoZ79xNQggIuX5wilk1BiYbRrDRR3NtEspBB7ajCQttqRxWkCwn0r1tkmxvwwxiNkSrl8eSdCIaNDoZ9ojJNRmM3D2BI1APLwvmr1lsCwi9+9ILpZdRUFOGoQjSvU/r8Bs0eaifXUJMxRDMJXDw7wdEvDsBxcB3IbLrPrgVjp1KnZ0wUsfXgFnyiD+WHZUTTcZhTcm/Zup1hh3Wv1iRR/1evEQzLeP7xCyQzYcTjMrnjuNbBWw9uIRQNIVOkYkMQRQRVBfPpHNFsiiH/x5+/ovG9YbHitrRfBsdzqB9fonVRI7RQoMyGNznyx5+/wmRI7ik+0cfoLOFEDHJIpuIirTIqAgC0WjcNgufoI6sKTl6PICsSNva30b9uYeksEYoEsbAXuLUtYzG3mahS6/QZfcZxw5WyGznXAYh0VFIo6FKAViyjAAA2722Rd/rdbdYod68aGDQ7GDY6mI50dK+7mAx0l7M9x8O/99B9DudvCbG9yZ9j3zyroViYobRTt1BfOg5+6bs5hGIRVmB5lrte0R7JJNC47EJWQ0wrcuvRLQQUGe+/IyMUI5/7b/3aYwDUFHn6Gm9dAXD3+RBkNcQKJn2g4fzpMTiew8WLc5TulBit6urgDN3rjvtMDkgsXqSE21xGJFG542A+NTFodmEMNDw7MBgwo41miKZJ+BnPp/HJM2r0th7uYDG3mDanV+8SX18Q2NRqOjYwGNpwnBULuBMDIqKZBJoV+p5ho4OgBLz70RYkl1vvOfRMxzfJ2p1KnaaQ0xvwY7VcsulFJBnFfDqjiXBQhk8UMBno0LtDls/iaUlef3rIGqyTox4iyRiSmRCOnjcYkFA/rbHP0uP6q4kwfKKPkPfeEMliFpFkFNPxlJ7RrSLUZBxf/ewK0/GM6VmGjQ66VyS2fv7xc9iWhe/9k2+jfkFrNFvKoF3tQgyIiGdiqL2u4PzKwl6ZJoV6dwBZkaDGIzeUsUwcs8kUex/sEwpvzlghLrqc+/W726wm8Oh7AUVG/bTqmoSImLr25lq7T5oOdy/07vm4P8Qf/h+v2OTVF/CjfVnHelFmVtZbD3fY6/KKd289efcuvVlA96rBaJOyIsOxFqi9rmBhL7BRkiH6/e4eJcAYaCjubTKNW0CRkczHcX1Wh+OsYGgG2+O+qdcKfwdExX/brn69g1Z1AElcIpMiWogxtjCfO7Bm5H7iFb22OWPFoxRSYAw0BIIyfvu3Uri9QeP6zTsFSnzVqLDdvpNFv9nF735MugAvh6B2VCEhK0/pnQt7gX69g6uDMzRbFq7d9OFP/vVnhHpNKKGSFwScPCP/e2+TMbQpxiOd+ci/eYViEfTHAqIJlfGDJUViB1ynUkc0HYUcChIid3zN+NheAQYAmbyKi5fn7H1p7T5DszykqF296djzOyVqEmQJC2uBaCqCiUmPmHc/JzolGevdAUatLo133QaEF3hs3F3HxbMTWDMbI3dE7oVnxfJprNxxvU8UWIE7Hk4I5ZeJfhBNRbB+e52Nbt1QY0QCtIHmisSdB8jVwrPFEwM3Ysl7Hz2AHJLJPnXsQOuN0KnUMejPaBpi2RBEgW26E82AEo8wuk/9+BI792+aBNOYYv3uNkKxMLYe7qD66gLWbMEcG7wphyhLuDo4Z6mqJ0/Pya2k1sTv/VTEH39CKJjXpHqbdutaw+tzm5wf3PfrFXlqXEU8l0IsHX0LsTZ1um8cT371y4VD4rirFh5//x1cHZxB8AlQU3FY5hxTndxPOj2brYVxfwhBJKeQqOvZ7TWGo1YX24924ZckEtoKAvOZ9oKjbJMoAz/+v0/R61lsfK93B7DMGWRFYu4ky4WDoBpCMBqGPqTPb9DsIlnIYDYx4Jf8+MWnA/gCflyfUcOitftonlVR3NtkupK9BzdaBAC4/507mI4IsZ9PZzh/TmvGa5Asc45QLIzuVYNZABKix7tWq0Q7iLp0wvxOCeMR6QOsGQUEHX9JxVs0ShTC68shW0cBRWZWimdfvcZquYRtOfiTf/Vzdh/FgIhOtYPG+TWuz+rsYM8U0zCNKcSAiEgmgWg2hXsfPSB6Y0xCvlzE0nHQve7AJ/qIpiWKGDY6zK7Qe5Y8pNAniogkY8y+dP3uNsb9IcwJiUlDaoB5uosih1hMYs+VR0ELSAHoQwOyEsBUN8HxPPrXLYRUCZZpodsnJ7BoNoWpy0f3NFqRqOTyqqk47FTq6Na6TFgbzyehdQaU2bGWRfU1TUOqh+csHDCaTVFjGb8p2NOlHBb2guhQTy/w4Q/eYyBLLJ9mk8j1u9uIZlNMc9Gp1JEskDFBQJGhpuLgOB7PnlHyrbfPeGnaly9OkSxSM1PczsDUJ6QT2y/j+qxJOrQ5hVbxPgEHvzhm9wwAQ4G9z4NyKkR2j9fvbmM2pkbaC0E0jTnjeEezKcwmBrYf7dLr6AzQPKtitVzi8EiH4ziYDKmpKT8kwOjJgyDbo3M5BdXXNXSuiAYSiYhE63AnhMPWAMliztWskdD29vt3AQCOtcDtbRGz2QLBMBWGgsAjFA1hOtJR3NuErIaQCDuYzZZsGjrqashuFeGXROS3C2xtJNay4AVC9GP5NJLFHPuZhb0gjVmSzq6A7IeaisHn970VYNe6qOHJrz5mYFG2EGETtEyR7rEoS4ik6c/bj3aZ0Pzi2Ql4t+GZGyY6lTqqh+ew3NC5frPrGkvEUdxKsGfGc/3xaF7GQEPltAdrNoeaimPQHiGVT8B2hdeBoIxcxo/RmAMv0LnSbw4YaAcA0/EUfjmAYVtD6+IaAUVGr9YkBsJIx3w6Q/uygcWcmtmF7UAKKYhliO5mTkwY2gTpzQI7x4NhBduPdhm108tI+J3/cg/JAq0Jr7EaaWSrm1jLIhyRoUTDiGQSCEUUhBMxpqWaTQzKCorSZIfjeOw82cPVwZn7vNDkdjZbQYkoWMwtNgWsn1YRTccRTccgh0OwrQUSuSTatR4W7jTwm359HQ0Bx3F/n+O4Y47jzjiO+2+/rtf+l4mKxxzH6RzH6QDue3/2/v7relFf57WwF3AWDpRIEPUeRblH3Y1A1y1EEjfK/IAi495HD9C+bCAYVSH4ffAF/Mht5RGTZvji5QK2OcOX/+Yr2HMbw0YH63e38dkfv8T67RL+4bfpQPNoGhzPIxiW4Zf8KNxax3SkI1GgsXko5EP54Q5EWcK9jx5g6Qam5XdKGLW6uP3OJolWRRF+KUA88rBCAuf9MrPtimQSEEQfqtcWTr48QiSTgBRSsLaVBMfx6I141ykkgNTaTfHlUUc61RbbLKsXfcghBf3rDjt8pZDCiuBerYloijYCUZbQrjTQb1J6s2PbjFMdjKpso/G8mT17s9VyiXyZrBqXzhIvf/IcADAzicfqib3CCZWhA/mdEnqNARU1oohRq4tkMQfRR5Xw+dNjCKLAAlEmJnltd6YKsqUUJuM5Q4UOPnmB+gkVzh7dCwD0gYHq4Tndu9wN1ShfUIg2lYpg0OxBdrUKk+GYxvzjm+bs9IAQZiUSwmSgQ5L9aJ5V0W/2aYPtjfHw24TQ+wJ+SIqEoKrg1js3zjq//I8esn9v/7aMXI4O2frxJdbvbpPwPOCHrPgxMx1Y5gz62Hkr5bJUTkKNh1A9uqRRe2fArBg9GoWH9iwXDm69UwYvcOB4HgtrgVAsjO37WwjFVGoKTRoVe82IGg8jkoqjf91iXune5T0DYkBk9+bq4AyyGiKKkVsQf+ujbSSTfoxaXSjxCGQ1hGg6gZMvjyCFaPLh2bkmcknmXDM3TPa87j8uYDa1EFBklo7s8We7NRLVyUoQ1csRBFFkSBYvcOw1JnIJ1mh6YU6JtTQCkp9Np1JraVgzC4Z54z4lBqjIDkZVtCsN18knSpMNjnOpLE1omg01GcOoS/7smY0888IHgEffe0TUk2qf+OXu6+c5HrwgMK7vZDhGskhUkWiKJjZ+KUCORScNBNUQLk566Fy1mDMXQGLJjX0yiMusJ13b0R5uv38XeneAVCkPvxzAqEN5IbIaQvXwnIR9usHWQSSTwGwyRS4JPPvsku1xHr9eH2jY3c/g4JMXKN5Ks4Avn8hjPjVhWUvMjRlxtN2fGTQ6kEIKLk97mE0Mhjrmd0pQk+TUJoUUTHXip+e2Cuhft1DaW2e0DkEQkMiRZW//mtykfH4foukYRp2BSydSkCyk0GmQcL97RUVU4+QKAUVG9fCcFcdeEe4ZGkxHOtGDghIiMSpO0qUcK1S8Pcp77d0mrWMppFBCb5YCqX74/9TZevPMBJYr0m55v9tDbj2wxuNbmxMTSjTM9uwnv/oYzsLB4VP6N7WOa8P5/JQaqJw7VRVFfO87ChK5OOZTExzHwxgTzUqbcMiV1xHJJPD5xyTG97RZT79oYjYxUDuqoLC7QXoclyLJ8TyUCAlcPWrP4YkF0S8Q9zsewUQzsFqtkFjLonZUgRIJw3I4fP6jIxQ2SfMWTUUwbPdw63YSnWoHu+/eQbKQQf+6Bb8sIaiGEIooMLQx1Di9LntuI1FIM+GwFKQ1uLazju5VA3sf7NP7iEfQa+kw9QmCYRm9zgRSSIFlOZhNibYSTcfRqdSRKmZReXlG2SeuBsr7PDwrcN4nkC3zcsnCEs9fVPDpD5+yyY7jOLDnNxTN9GYBF89OkMglEAhK6NWaMI0ZIkmVAsT0CebWConIErXXFdSOKoimo4zSkyuvYzG3iZZrUG6LNw1TIiGWvQCAaSABMPH51oNb6F41EAjSRCdXXncbfQ3V11cYtvrYuH8LZ1+9xrg/xLMD0xU8+1my++GzJtRUDJY5x/OPn1MdIggYdigc1TJnCKpB1gzTpI9HupRl2kcv/Tkg+/F7/+tPIYckssp16WZqMk5ieYWyjxpndcgKTQV3Hm68RZX9pl5/3Q0Bx3ECgH8K4B8AuAPgP+M47msRU/xlGoLwarVS3S/fG38Or1Yr9S/62W/q5fMJtNE0h4iFV9gsEee0edmBqvoxHRNXXFZDsOcWXv7kORKFNFJraZj6BIu5hdpxFa8aIWyWAsjvlBDJJKB3CWmqHp4jqIYwaA/x8UGYcdMBKvpqRxVovRHjNpIVnYzRkAp4KSjj/EWFFWjNsxp2nuzh/KAOU58w28WpPmVjbinohxINY+fRNjmaTGcYjwlx1NoUQX990YOaUPHD3z9F9fAcpjF3C2cR63e3WSLp3DBZ4eP3+5DZyMCxbcihIAsQqh1VsJhbiGQSqB9fIhCUaGTr9yFZSDOeoayGMJmuMB3pbKPxLkPTKa1YDWHYGmC5WjKeZXFvkwmrF7bNbERj+TT07hBab4TcBv0OD9EfD0b4wz+6EVvyPAdDo6Kp27/hix988gLdxoht3DTVoGIlGFXZQX51QIdC4dYaBqObgrlyriG/U8L1SRX7H+yyjW460inNeHDTJ//6b1KxH1RlLB0HlcPKW1kW6bUY/H7epWeIWK0oEO76rM4ak1Zzyr5/OgM6nZsNsXp4DsdeYDG3UCiGIcnkFa5pNlGteB6yGsLJizquz+osVVJNxpDdKiKeTzEEaz41qdjQJ+g1hmhfjyj9tJil2PlPiFYTy6ehqpQR4HGGtZ4OvT8ilLVM9/LN9Mlxf4hOpY7yvRJSpTwKuxts9BxQiIby6nkLfreXMAYaTH0C3sczihodeBp730qIil/v9yTWsnj6i0tYMwtqPMLQfU9TwvGUYNyrNWFO5kiXcuyZKBYVtlZqx1Wk15JEx3FTqzuVOhpn13DsBU0fjq+gDzRUrkwsFi71otEloedaGo5to3PVZMVhs9J0RbBL7G4HKE1b8lNhO51DEMWb5OipjcxGDoY2wfvfv4tYJsmcQbz07OxWkUwLak1YsxmWzpISoF0EV/AJ0PsjfO/7lO9R2i9DVkNonFyhU6mj36TC//BnL9GrNUlo/ekhglEVg0YXjkM2meOBzu4jiWTpfsXjAZYhoQSWyK0n2XpSIiHwPqIivfyC9q7pxGJN21c/eoHiThGhkI/9e97aiqQTcByHJbJ617DVRzQZhtah3+kVaAt7AVGWcPSLA2ztb9D9mxg4f3qM1WrlWhg7iKTjlL0SI6qTlwp88uURhg0CO7y9KRCUUX58m01SsltrCCgyBNfC05s02ZaNd++T/qxxcoXMRh577+2xKV84EXNzZMgW1C8HkCvT9NMvBZDKR9jZ8Kbveveqyf5+/e42CX9jYZbkPJsYFAL2RnBc86qPmTFjAlqvCF0tl8jvlNA8qyK7VYRj2/j5V2S9GUknwPEcZCVAVL/5iuWheOh32E3Z/p3/JM1AIKKWGoim48hsFKgonpMmLllIIZZNIBAQ8MWPX1G424BCLKuH50Tx8zQjcx6//k/uwZoRVa9xTtTLn/2bVxj3h8yv37v61y03vO9m/xw2OjA0stvkfQJ8PgqMHHVpnzh7duOeFYkFkdkgo4xCMYJQTEWnruHVp4cwBhqjQJrGFMuFA8cVZHcqddbsFXfWaHKTJETecS1b51Nq0kt3NmG7dtre+/Y+x06lTnvm3Ma4rzFhtKHROZ4q5RFWOEznN+nYtaNLANQYdmstLGybdI26wcJOPa3Lm9bMHkDod+1J1VQc50+PSfAdlKD1Rmhf0qTDLwcgh4KIJGNonl+j/JgcAP1+mgzXT2uUIu0CPZpLqfXe97BBtDwClla0FtZpmjYejF29yvKtfBOafvTJXvb0mtUTANlNp0p5XLsg3ea9TcSSChK5GLNe/mZff7Vm4N9zQvAugLPVanWxWq0sAP8CwD/6Ol793znKkDW3IIUkBMNBXNaXiIRWCCeo6xUEDoLAk2WjfhNqZM9t1F5XEE5Ekd4swLFtlDMmWl2yFV0rE6rmoU+7D9YpPj3IYeraLfI+4UZ0VUixMb3H21uuVkiV8oimo4imScRX3NvEarnEyZdH2N6nTUJNxSk5c3YT0lF9XSWOap9Q2+lIZ0VOqpSnxEF38/rV//gWC/tRU3GGiJm6wURo4/+XvTf9keRO8/u+GZGREZkZmZH3fVVV1tVVfbHJJjnkkDvXjrX2aCWsDAkCbBgw4FeWBfil9dr/gl/YgE/BWhtaY3cErSR7POOZJZccDpvd7K7u6qquKysr7zszIjOOPPziifhV1SxgSIsdmcJsAA2Sze6qrMyI3+/5Pc/3+/n2hvBHFBx/ecgCY8adPpRkFLvv7zNzmJPwKUoi1IE9ulZnGNTbSKxlUbxThGFSd8wpLBJrWbhFD5bzBbSRit5VE5ybg6HNCJm4XUK/1UdmPc1SiV//8hVhYettcDxpDQ8+ec46vIJXgjcgIxyX2dhfHc0QjIbg4lwwDNtIq7pJuz/SWCezfkzhZW7RwwJ5AAqvM7QZmpUmJuqCyYqicR9DyfbbKnbf37+l93XSOl0ch9Nznd0/6XIe+e0Cth9tMFmFEpJQrYwQtMeszvQknCRkIAAY+vy6WPEAfr/Auj/A9ebfaGj46T/9DL1aC5/8+Jd0n+VTmI1VKLEA2zCqh+cwphTyREFN9PflsMJGvR//oIRg2I+Xn76AL+hlRfeg3sZyscCf/uNPIIcVuDiObXJuwY1xZ4DJQL2VOwFcByI9+clX6FTqRBrKJpFaz9vFOod0IQxdXzFZD21CNJlxfA6LxQLpcoHJonhBYKjOxXwBdTBBJBHEsN3H2t3yLQOcXwkwDCnv5tiBM5ZPg+eufTPWTMegM0I4FcN0QiQiF8dh7d4G3KJAeD833YPJpIROtYNYPkVrQy4GyyST+M1U1ulwzO7V56+m4FwcRB+RxiLJEBaWxdj6h58doHJwgsx6GhcnPUiyBG9QRnG/zFKPeYHuMcErYTlfoHFyyYgoAGzTdhSDMRVONGG7pjENmx3mvQml4pTCWy4gUaDDvz6hw9q404fWH9lTR/oMvUEZg4HJ1g5z4UIs4b/RnRwxbGk4qWAy1GAZc8bfz24WcPzlITRtwZ5V571Zrah4+OTHv2TBdgAVEXNrgfzu2q3JV/24gkQhBbfoQbc+YJ/n1tu76F01kVqjlGDnfVsulqifUPIwhT9RCKJzrxJBjLCWzpTTwTgGoiEs5gtKyQ5Rl/PPn5jg3IS6HPfGePGLrxGKK/B4RRaINVN1VA4Ia9w4oUCwTqWOj9/1MsmWE24IAIU76+xZ5XiSmjZOLiGHb/ffnM+aFwR4ZQmSX2L3N0DNjZtpuZ1qE4m1LKpnHRx88gKiROz4w88O4JFEGObKxu6quPPOBjLraXhlul/+l/+1xmSjjjSoU6mjW2tTN7hLh11ecKNxconToy7uv7+N6WSGaC7FqEbF/TJp42UJbn6F/mCO46c0hS0/oPX17ntlZux21tzpcMye9eViaXsnOERzKYxaPYRScSznC1SOmwxaEErFUdgpMhLT1z//GtOJBkEU8NM/+pz0//kQ1u9vwhuU7dAyL5MnzQ2T5aQ408SjL14xb43glVhexbjTtwPdfNBVjVHxnLWscUb/PTdMLJcrrFZLmOacMOBnVey+v09NydYclSsLo+7Qljcl2DMg+r24/+EuPJKHrdmiT4RsH9qUZBThDIEb5tYcuqphuSDz/bjTZ4X+5ctTiD4Joo/WdQeT7EiiWhctthZOxyqUGBEMA5EAfvSjLLu/1h9sYdIbILWzy70xAAAgAElEQVSeR3G/jNVyhYVlsUYDLwjsADjuj9Gp1PGtf/8xSzYetXr4W3//HpG57HWac/NYzsmU7VNoCnT42QFOXlxh3FfhkdxMkvtNvn4D2NEsgOqN/76yf++v/PqtOxD4g34sbdPaagXw3IpIJ9MZDGMJQ7dQ2Nu41hvbD+tyTpxqy7DgV4KoDSVYJj2YLz99cYuPOxoSalCRXWyM6hwunClC7eiCnZqdf/ZqLagjDS4XERbGdoeu/GgH3eaEdZedDWP9wRbpbqczSF4BlYMTtrm12xQeMu4O7DRk6gh0enNoIw2WYREnmufpkLNYYGGHYYVTMSa3KO6XWcGrq1McfnbAZCCS7EduO89Gkt6gjECYft72eQ3D9hitpg5d1W7h4xxmurOwjVo9ZMp50hNPpvArMnrNAUN5xvIppqV0JgLlRztMSmLNSHow6mlonFTZ1+ldNUkOMKdR/L/8FzX4AhL8ip+9h4m1LOktDRNj+/dCqThOnpJ5T+uPcHebZ5vt81+eASAef/uyjWGHsH7O5+y8lmA8DEUhSc6oS6xqB0+bKKaQLhfw5qCB2ps66+Q4lzbSwHEcdVDOmuz3a80FdLtL4haJ2OD4QmaaiUfffwuhVIxkbeUCIx85aERn8uNQK+i9TTOSVbyYQTQdRbNtYTwg8kPzoolo+trAOWr18B/9g4/JYC/7KEHaJ7H7f9IbEWrwxoRgYdNKnNc9HY4RjAbQPKuy5M/XX13g05+eol9vI5xJwB8KoHVeR7pcoGlPfwQ5TJSQ6XCM6USHz5YdATThiKajME1ixGujKbyyl6VQ9xttRHMpxIsZ1E9qTELTq7VQvaLsDvYabQrGbDyFPxRANJvEyZPXcAtujFo9pOxuvjZdkHdoMMaw2UH7qovpeApzZrC04/0P7yG/uwZ/KEByA5cL2kjFarkitK+9Ljg+CYA29zdfHWOm6rg6qkD0edE8r2E6IYRw7egCfsUPn82EB67DzHyhIH73732AUbsPn3SdpZLMx9ja4EgpqofnGDY7kMMKoqkQam+q7B65iSkEgHAqBtHvhccr4pMf/5JMqdoM1tyFq8qQfd7OfTBq9TDqTZBdj2PcJ5qXX5Exs0f+kTDP5HaC6LGTc+OsUzgbq0zCF4iGcfTFK1y+PCVPlfuadkLZLgqTsHSrDRx/eYhoLsUMnv1GG96gTLplVWP0tUA0xJo40VwK9TeXcIsCAlGS9ZQf7djZE4RDLuyWbh0yOd7F1nWnswngVrfWF/AilIpRwSn70a8TDvdP/rTLJEE0MSLN+6Blh5wFZUg+D3tGu9UGAtHwrQMRAGTKOXRrPdSOLrD/4T34QkFsPNzGarlEJB1l68PCsmAZFro1wmhSuKUfD7/3EIGwDCXAMayuz+emQuzJa8SLGXz3d4uY9IYo7G3g9ecv2T1izXQiwTQ7cHEcRl0CM0QSMmqVPurHFQZI8Ege5mebWwvopgu/+vkbzMYq1u6uodekNalWGaDy6gJee13whYLsfeV4DlM7BdeRnmw83CZEcSbBJnoArQf10xo6lTqK+2UU98sIJcK4fHmKrbd34Av4oE8tjLpjeLwiaxAM6mTGTq3n0a02GKnHmeI4P7M102HODLb+bO8n8fT/vh3S6chSV0uSzmS3S1itVkzCld9IonRvE1fHNZQf7SAc4uHxcIzW1z6vsWlIIBTE5UmbQTwAmxLm90GS/YQpXSyQ3SxAVnz2QcbNJvnG1GCExEG9zQh6zkHfOXhwboJ/NK9GmBsmutUGwin6+wAYTMCR0BGueoZxp4+d9/agq3QIXFjUGBl3+gjah5ajry8Ry6fYBEObrcC5eTZJWc4XUAcjovLx13jUVDGOTCmGs5c1hOLfbGHKX9JUHHO5XF/e+PWf/f/1+n/rDgT6VEe31oFb4LFVcuH50Rxzy4IgejCZGPAFJHSqLcztGxqAXTBxaF20EEmG4VdknF8t8OYlMX6juRQKexsQ/V4kiilcndRhTHW4+RXTfTrXTfqKM6bsVOpQxwYi6QSmowlLn3VSSk+evMZisWRMaX9EgVf2YdybQPAICMYiuDqlSHNHwuSXqXgxZwbqxxVWhFcrhANrnl2B53nIYUqYJESiy8ad0UMXyyVROThhBe9N/d7Gw22EEhE2QWhWmoRJDXgh+r0IZxJonlVRq/SZFCUQDaNTaWC1WiKaS9nBTBx7f4zpDAtrjn69g627uRsGahWrFWUBOJ20aOKadgTQQuX1i0iWSPPJ8zTy9IWCiEQ8WM6X+NGPMjj87AC9GpE/orkU4pkIwnaBMB2OEcunMWx22IQmXszgsumCWyQawjsfUbfaxbkQzcbQrpDE6Nc7HW5BwL/8w19i1Oqxjb90bxORZBgulwuNk0s8eJyDWxSY18FJ+Y1loxg0KSCpsHnNvfZ6OehTix1gtP6IkSKG7TFOD6oY1NuIZOLo1toMR5tYyzKe/M2OUygVhzoYE3+6RcjB5kUTy8WKjfinwzELQAJIz+xguWPZOAX82KN9fyhAnel6G76gnyVMi34vVsvlLWxsv0nkmtVqSamjQR+27+cRySQwbHYxU4ni4Qt4cfbsGIW9DbZxpNbzCEX90Kcz1mUm/8YMh58dAKAkUIfaJPq82H5nF3JIJrnFDZrSarlEOCyyf995bw9KLMDu+95VE167S9+p1MnIV+9QwVigrxNJ04h82Owgmo4gnCLfTiwTx8Enz0kaMJxA9ElwCxy00RiVgxOE4wrCMdn+mShYK17MYNTqYeN+GdpIhRKPMHTtdDhG+eEmAtEwWhd1CB4PkxvUjyt48J0HmA7H+Pk/+4oC4cwVo6VVj+vYeLhNWvbDS7hFDx5+7yFCqTgsw0Tjoo3VcomFHRQXCJFnZzlfYNwhTrwcJpO2kx7tZGMMOiN22BC8EvvMM6UYzl/VMBmO7YTaASvaprMVKq/OGb2qflxB9XWFrQXhTIIVJPp0hrsf3WcUsuV8cYs2EgjLiKRjyG6XIPq9WH+whd5Vk8kyOZ7HcrHAqDuA4JXY7zvrh65qWNrShnAiBH06Q66cQeeqQxQmg7JWmhdN+AI+VthGIh52WFktVxg0u5hbhLB1DpenT48wqLcx7o8g+iWI9iEoHPMjlIojXS4gmY+xQm9mS+k8XhHTCZnVHRnj3LLQa3TYhBYgoII6oEL88JeHmA7HOH16hNlYReeqDSUWQmFvA+VHOxjU29i4t86KOn2qo3pMAVXt7pxBCJ782TGiuRTJ9dp9eAQ6EC7mRMi6qal3nr9oNsnul+VihcrBCXvOeMENdagyeMGw3YduAN/+IfnKzl+QjDYQDYPjXAiniM3vC9AUhed5eLwiJSErAQQiIXagP316hGCckJpXRxXE8mmEUnF7qkS5NJWDE1QOTjBo0jqcK4ZQOTjBxqZia/HJNDuwwxQBMMqNWyQfjVv0sOdTSUYheCWEU1Fwbh6BaJgFTIYzCQSiYTYVT6xlEYiGsFou0b5son5cgTYimMOwp0G3jfNza4FO10IqzoPjXAjGqQnHsngu65CDPpbv40Adbkr5Rq0eOldtVI+v0Ku10L5sIbtdguAhqfKvk+kErwS3nTviV2RbkUAHENXOaQEo/8HUTbT7BM/Y++Au+zpe2YdIkjxXrz9/icJODhxPe7ojH6weniO1nocv4AXn4tjz8+Z4RNlJuRT9/1AQd97fIzWFSUGOgldC9U0NgoenDCnfbY/aN/H6SxwIuqvV6u0bv/7bX/uSNQA3OwE5+/f+yq/fugMBz/PQVQ2VVxf4x//TG5TyREWRwwFMhjObXqPYppgFDj87gGUQInHSG+D06RGaZ1XsbXDYvZ+xI7qJvWtoM+hTHUosBLfghjV3Md0ngFuovmguBV8oCK/fB7foYR0SgHTPC2uBSW+AcCbBphUAdQ8p9MqCHJZtAoUby+USgZCMxkkVk94QkRA96IFoCP6IgliWdL6iREav1XKJcJLShAUPdaJ8AT98AT+bAKxWK1ZAhhIRZg6O5lJYrVZoXdTY5sTz9L4tFkv4lADTpv/w38tCDtOBytR17H9Ii4lTzK2WFE5izqgrIsleMpO+uGLmpOlwjOxmHqN2j20E3ebkVod8MiBZwWKxgC8UpAC0Th8eSYTf6yIDtI861bpKUeq9qyZefvoCbZuURFxw2ygbj6DX6GHSG0FwgyVPiqJNTWoPMGwPsbAstokM6m07JZSQmHIkCCVJBI5oLgWvX0S/NcCgReFK7fYMH/5gG+Nu3/6eIfiUgE2aoYX565+TyTq1nkc46ML5q+qtDn+6XMBsrOLD761h+2ER/oiCR98iqZmSiKK4X0YkGcKw1QMvCFiYcwSiISbR0lUN4UyCdWNEn4STVw32PQE6PJCmn8ekN8BwTF3R6iGZ35yDmhJTWKd71OrBG5Dp6yepYHaQiQCZcCe9ASNguFwu7O+IJPVYz4Hnybjn3IsOM170eylhdbFkCZuO/M4X8LLkWJ7nGPnCxXFMigMAhZ08+/kC0TBCyjVtp/KqwtCPjlyg3+oz877LxYHnecQyMbx4TYcbp3sGwF4frrBaLW8lADsm2VBIgF+h13T0xSs8+clX7L3hBTdjehu6hY17RTLg+b3svj/64hX06QyhRBTdaoNJUADg2c+eIRiPILuZR353DSenE0TTEbsY7+P06RGKd4qYDscIREPotyZ0+F0siJ5kG1W1/gjDdv86zTkeRjCmsANmJEGHadHnxUZCY94O51m0DBPhZAw870I4GWLhixzPIxDykVehT8+TL+CHNpywrrMjW1MHYzLay37E80m8+MXX0IYT6NMZy3BxOsf1kysbp0o69MlARbyYYdMhF8dB9HlhzXR2II3l09h5b4+Z2mdjzc7s0OALklxutaT1L2UHFy4si+WbZLdLKGWAcMoGUnT61Fk9r2Hc6SNZus5zcK5gJMjkfrmcnxXWl0dXrPBjvpxWj9C8q+W1nM0wWa6EI8HyeD2I51PIbhawsCzsvLeHeDHDsk0uX57i8uUpWhctglcERfQaPWS3SxjU29TtNkx0OlP2GabXUlgul0ivZ8DzPNq9JSzDQs2m4KXLBZKnWGTEjuXTzEwKEHhh74O7yG2X0K11IPq9EH0iUus5LMw50usZuFzA1ZUKvyLbQZlBLBYLjHsTJrmxTJJLcm4O+a08BNEDdTBGv9G+tZc666EcVti0TvR7UbxTQrJEcqzC3sY1DYonjPVqBZTv5uAN+mEZpi2DpD8TSoQQScexWq4gh4PIlPNknNUNuFwchXfZP5NlmJgMaY+XfJKN6VWYLNbxS0XTdAB0MNOToYpxf4RkKYlJf4JkwoOxukI0myBClY0pF/1eLOcLeP0e5mdxagqnaelc0yF581bLJcyZju5Vi9adG/eiLxRkuRGGNrPv/ym6tQ7MmQHBK1F2S5DoQR6vRLI5vwvt8xpefvoCAE2xPJLI1ksAuDi8ZOvR3Jrjznt7dmJ7DXLIj+lEQ/nRDpKlNP7m9/3o1zuwDBPtyzpi2Tgmwym8dhJy76oJJRbGqNVDtzVBqhj/C1OYb+L1G/AQ/ArApsvlWnO5XB4Afw/Aj38Tr/237kCwWCwRjEUo9TMfw8ErGnHPxlNIXg/SGRmDJmnjJZ8XvlAQvqAfqfU8ODfPivN/8k8qODqgQtLR4AJEm2mc0abwR394dEvzdvbsGLF8Gkoyyrqv3WoDc8Nkkhink9+3dfiST0LvqsmKKc7ufM+tORqnV9QV84kwdTIJZzbp++nGCvndNXtkp2AyUOEWBSghCXPDRLyYIYmRzb2XZErNdbTrot+L+nEFy/mCLTqOOXjY6uHs2TEk2YfOVccOpRrS+N1Li7ajof4//rdjqIMx5DAVFI3zNtyCYGtjq0STyKeZxrV9XiOz5MyEbMuP/BEFZ8+OEUknmPnS1E0szDlD0C0XFDbH84TT7FYbyG6XwLt5xEI2i/pSYBhFZ+PdfnwHxV16z0bdAYWtyX6bEmFiblm4qExR3Kfch5NjOuj4An4EwjJS6ySZchZct410DMYjSBWTEH0SmhdtjDp9HH52gEG9jUAkCEOboX7Rw8kxmdTKj3awsBaMy60kokxDS5ScJp48HeH9H+xC9HlZsd6tUVfuzfEIikImvssLGvdORxOoQxWtyw7i+RTCqRh0VWOHDafg09UpG2Pr6hTpQhQujsNMo43M0GbgBQHL+YI2i7rGZBUUwLRiZnNn8+PcPCt4mmdVFlYGkMnOMizE8mnIYZIGZEpRvD6lDpY2UjHpDagAswkXDo6Ut4PP2rUBghEF7fMaC7uzjDkMnb7G2YtzlgDq4A+d6/XnL+EWPRi0SL7xq1+2ULq3idrRBZO8xIsZW0ZHgVzkMfFDVzWMO33U3lzh03/+BH5FxqhLYX2OTne1XLJpmqO5BmhSY1krMvLZE5f9D++xQsqv+DHujaGNJogmAnDb9BSXi8ySjnTPmuns2XA04k73ddzp4+LgFKMuJWVPJ2SqDkRD2P/wHo6/pEyJ2Vhj8hRrprPJhHMZ0xlLUx61SJIy7vTJnHxBf27Y7ODLEx8efvsOeEHAarmCNdORKeegT3V4fQICIQoVknx0qPT5bQ66lzJD2hUi2Iy7ffaeRdNRJhmgZ3DOJglkKl2ifly5Tr5NRnH3o/uQI0E2QXSmOXJYQXo9S4VnJoGIPcWTwzK6tT4y5TwMbYbinSIi6RiZPwWBacM5F0fEFxu44EwXulctuHk6qN5MF5dkH1sTnGcBAMNOO9ef/uGvEM4kIIjkCXKK4Jt+pOZZ1Q4EdBJqrxPv/aGAbTCm0MxOtY10uYB+a0hTv7F6q2h2cS6EEmFcvLaZ+/Yz45iVTX2O7cd3yDQ6IN772bNj6KoG2c9h0huy99YjeWBMqZgsP9oh2VB7wNKY76wt8fLTF7AMC9PhGFp/BHNmonFyCUH0oHXZQqc/x2Q4w6g7oIRru7EVjAbYOuHIUlwuF46/PIQckrFa0dS8U+2w98qRng6bHUSzcerqB2SM+xO4BTK4r23FIcleRo2Lpgm+cHZYh6kbmBsmBNsPJcl+VA/P0Ti5RCgZRadSx+XLU5J/2tKcxnmToat1VUNpM4l/+I++DXUwwe77+2xfcgAJACCIbgQjlEA97vTRPq+xPVX0ifjJP38Dt5uCFHlBoEC7EO0VpXubqBzV7WbhdadfsA+9Dr6bwBt9hDMJeGU/vAGZPc/OwZIkvCJ5725M/imROw6e57HxcBumbiCSJvP5uNOH4L72RXiDMj3PQZr4bb29C18oyPxxnJuHV/aiWx/AF/AT+fCoinGnj5MnrzFo9fHZ8xWKezSVWM4XaF3UoU91lngPEK77u3/nffA8hzdP39yajn0zr38z/8C/jodgtVrNAfznAP4VgEMA//tqtXr5//23/nLXb92BQPC4iWdd7yAa9yMYvDbQrFYrnB51sfUWyUJCcQWZDUr4bZ5VbewndRPfer+ARJaMlavlErFMmHVZ43ni+P/+f3h98wai4esE5IAfvEABMZLsJz14KMD0lrwgQElEKZjFJoc4D7yuTmEZJkLxMO5+cAe6Rl2A2ViltMbuCA9/5z4k0YXq4TncIqEuI0kKcYnHPDC0GQSRwor2v7WLUXdE5I0jCpRyeOAALRJ+RUbj5BLF/TLS5QKTeZQfbCCzkYZlmgjGwxQq4hMoQdQ+WIQSRHzxKzI4nkev1oJs84kl2Yf6KXWjfUGZGSuL+2Wk15JQByoErwTB40FiLYtBs4v2ee3W2N8pbkWfiNZlC/pURySTQOneJoypAY/kwXJJm2s2sYIk+zG35mwc2q33WRiOc0jQVQ3RNDHlI5k40ikaOzfPqjB1mjJ0qw24OA7Ns+qtMbojr/IFyITWPq+RVCcUYPeHZVpIr2cxaPXROG/BMk2cPyeJjjO2dwtuuFwubL29y7rz04kBw1hiNlZZwqdbcKP8aAczzcTrF02k1vNQRzMW0tS7aoLjOBYew7l5eO1FfNIbQ/CS/n/UHbJF/vjpOfK7a4yYpCSjLBHW4xUx6mngbT29k5q6fp+eGedAm97IM9kEQJu7Y2RdzBeMKS9KNAIOhURcXQxYIeYcQv0KdVUdNOV0OMbl4QUypRiaZ1WkywVGQGmeVeEPEB0mEFWYhMuRXNy8HLSoqeuovLpA86x+iwimqzPo6hSle5sYtXuI51MMiUsmQx/ufrgHr9+LcIoQnI587qYh1tQtVkitliv0+zpj17s4Di6OplfeoIzmGd0rs7GKp//P14xPH4yHIIcDiOepw8i5KcDN2ZzJW7RkBeNb330AvyJDG8/QrpD2fNTqYdAZY7VcwiOJiGbj7NnhhWskrFNcrJZLzDSDbczOAQagyaGjj84lVjj44gTZrQLUASWpdmuUGdCojtCq2ujMSACJtSxefEZFnttNmnVHsuB8HrF8Gq3LFkNeLiwLs/EU6/sFBCLXhJl0uUAY51AQvasmLHPOponaSIWSjCKajmHc6RONa0EyI+d1D9tDdKsNTAYTFPfLmGkGmrb50zJMhBKhW8X5TXmiM324bLrY/aWNaCqx+2idoBWhIIp3KJfFeQ78EQXRXArZ7RL2399l9J2bXzeej0P0e1G6t4nd9/eZ7MZZOySZmjfacGI3a8LwRxRkylloIxWWYTEanrN3SLKfvQ9O7olzOaFfmVwAR1+8Ai8ICNs6bRZs5QHcHjfCyQiUZPTW5GvQou58ei1FPpGIAje3wrt/423W5EqsZZkHYqZqEDweGMYSfllkPjB/REEkHSUZrCiwILbEWha6pjOpnjcgo7C3gUA0eIta5/gKLNNCppxHei2J9nkNZ8+Osf5gC+GQgPZ5DcU7RVw15qi9uUSjNoE2UpnnY7kkP5CzdwHXPoCbOQzjTh/ZcgadSh3ZNZq8a6qB9oDDpDfAm6+Ob0nanAO/6BOhT014JImhRJ2rflzBpDdCu2Ox+x4AEgWqJS6ev2GUM4C8WE62By8ICCXCUOIRjNo9Jg/zKzLzV0iyj0mgkqUs+o0u/EoAfiWAYDwCyzCRWs+jdnSBaDaOxYIO8ZWDE/b+tLqUXh2IhjEbqwjGIyz74/zlOR30ZR+riZwCv1ttQBA9KO0WEIiG2ZTkvbsuqCONKSGSpQy61SYmvQHW724gGI/g7ntbMIwFjr54BZ8SYGSnb+q1ArCE69/o17/W112t/nS1Wm2tVquN1Wr1X/+mXv9v3YEAdpGl9UfY25aQTHhsH0ACDx9G4OJcePnpC4av69Z6DLlFiZq0wXW7OgIBD3z2xtWsdGBoM/gVGc2zKyjJKC6qtCg7jODTp0foVqmjs7AsmLqBzbfKCKdisAwTg1af+RmcoCJ1MCYtfIACPGZjFebMQO3oAgd/Tp0FQ5tB9HkhBXyY9AZ48pOv8NWXNprPNhUdffEKHknEP/3vfgGAFqBhZ4Bxf8o4/om1LDYebrNFMF7MwBukw0AoFacAtZNLBGMRLOcLViCNWj2EkxEYuonjp+ek/bPNeWdfv4Ev4MeoO0Dxzhpy20U07W6YMZ1hOlahDsgb4WjRKwcnTAu+sCyM2tRBcxZJj1fE1tu70FWN/d50PEV+K4dkgTrJC2uOZCGGQbMHc06JknHZQDgVhcvlgqnrjKoh+SjN1cVxzBxpGRYSxTQEj4Cd0pIt3o/fJ21taj2PXoM6zFp/xMxZgldCKEEsZaeLT4z3AQxtRgWxRbSSVCmFdz7ehEcSIdnaaqfTO9OmaJ7XoM9MLOcLWDMdO/sxlPIko3C5aCFxCwLUgYbiRhjvfpBFt9bCxk4c1UPSZ5fubUKfzljHNJZPoX1egy9EG6pzCAonIyg/2MDGw224RQGNsxrkCOVLjFq9W4bJy8MLpmsHgObZFQYtSvSe9AbY//CejaOlIsbBJwIkM3IKL+e5ca6PP04iko6y1+OPKAjGFIh+LzSb1uXc0506bVKNk0t2OEyt53H3Xhi+oIz2ee0vpFo6WR2ZrSJ8AR+i2QQMbYb7395DMB6C1h9RcbVVhCR7WXBbJJ1gBzX6Pln4gj6USgH0Gz2slivwdmGbLhfg8dKkJrGWRTpPeueNh9sYtvuQZXvsz/NUnHt45rFZWBY7xCiJKNLrGRtpPGSmdNEnMiOr4BGQLhcQSoQheCXEckmEMwk8+clTNE4uce8hUYOcwql2dIFgPAKe52FMDbht3XpqPctkg4U76yykzUntBagx4GiznUR2APj5p2PEsjHM1BmFH01UlmTL8RxmKiUxXzx/g1G7z4hLZ2+IuuNycUit59l0olttIL2WRr/ehtYfsfv0658/JxqYjdZ0Pg+nceHIFpRklNabVu9Wsd21J5DpcsEuHE34QkECKfTHSOZC7FAy7vQxndDXvVl0AtfmS0Ob4f7atSlzOlYxbPdx9KyCXq0Lc6bj4uU5mmdV9hwE7MNL7egCwx5N2XhBYF+jU6nDMiyYMwP9Rg+Hnx2wzzpZoudPVzX2S/R7MekNofVHOHt2jHGnT5hVW7rlPBeiX8LOe3uEQbUnEk5BGrVDKC/PqbBfLhZ4+ecHt/4+z9GUipCjU6TW84yQNFOnNklGZWvEQHPjy598zSZhzvTDLQqIZOKwTBPLxQovPj2AIApIrech+bwYdUcIJRSYM6I3zcYqm5DzggDBQzkl5sxEIks6/Vg+DX9EgaHN4BYEyIofjbMa2z8AoH5SQ6NJn+fZizOIIknIjn51yMhySpK8T6n1DHzB2xNFzs3bXXUKvePcPPv6kxEFK7oFHpkofVY+e38AaD13ko4fPkpg1B2BF9yMOnVT8vOd33+I4UBHNJdieNubMksX50KylKF0dFvGDACxbAJza45EPob8zhosY86mVJybhzoYs7WweGeNJI3LFTUf7Cmwk1cgeCVYpoXiRpTVAc5hVBBcWM6XMHWdEaMAQPJ5WRN03KFgSL8SQGoti1CCnqn0egaCh0eiQGupJPuxWLnYvaHEFBNem3IAACAASURBVFy+PEUgGkK8mMGJfT/32yquzvt49P23YEx1NE6v8E2//jqp+N+hS/DwUMdT7Ly3B1FY4ovPmiyBsN40MVN1uyCgRcHpDNGJ3EXdda+E77wnYj4n4k8sn4YgCswgvJwvoA0naNQm1D2y0YpOx4lz88hsFTHu9HH8hDqn6mAESfYinosjkiHNtSSTRMGY0mjV6TQ6unUHVUmLjgDOxbFO3n/ww+uFZtIb4gd/91sYNjv4L/6rb2P9wRYFgyUj6FwRIcIySbbkaLYBIBxXEIgqkGTSuzodSafr4OI4CB43adOtOcJxBev7VDi7XC7E83F89PuPMWxTwvHp0yNcHVWwsAPWlnOSZNwMuQLImFXcL6PfaEMOKyjcWWf/j3PzLLoeAEuXHDY7eOedGOqnDSjJKCYDFVcndcLA9Wn02BxL8Cs+rFYr5u0QRA8uXpLhKrddhBKjz6jf6KDf6KB2dAEPv0S/Qcz7SHAFnucxGY6RtLs30RzJcQCaMjROyLS5dS8Lf0SBy+VipAmP5GELrT418OzzC+Jp212YSCbBxszBWBhrW1RcZbaK+PH/+GdIKItboU1zywLn5tDtzlBvkEa6fkWd4HFngF6ti/13N7Hz3h67z4Hr0C1nsa8dXeD4yTFqb67w3ne2sH53Hcl8gmmmqVgxUdjbwPbbNOnKbV9rzJ1CBwDODuifjh68dVFnOnKSbcTZ8zW3LHiDMr3+1gKmTgWoqZvgeR7+AOUUKDbmEqCibHwj78HxEHBuDgcvhvAGfbdoLI40ytSJTNQ6r6N2dIFJb0yekqnFDvpaf4ROtUVQAFtfHowG2HRPSUYh+cgEOxzPwQtudCp1FLZtv4VPhMuedBlTHZ/+sy+w/fgO2pdtKLEwFnagkLOZ9toTdv87xA236IGpG+g1ehi1ekgUrz1ElYNTmkDZ+vHGySX69S58QT90jbjgTgEai9DrCKXi2HlvD3sf3MW400fzrIrVasWKhEl/jNlEZZMHhwT160SbhS3dOfv6DVtnRoMpAgqlqq+WS6Q3cqgeniMQDePwswOYOtHOAtEwDG2GeDED0S9Bn5pIlwswphQsl90uEQwhotjZDFT4uwU3gvEI1u6W2esJZxJ0AMoTzSSWT7PPh+N5CKLAutvO/ZJaz2HQ7LFJiBILsZAxXZ1CFIks5XTwteEE2U2bbmNPTUKpOPHlm13E8yk0RvSZxYsZLBcLG1VK6blzw2SHleIduv+0kcqQxO+8l6R0Xb8Xm4+2kFrPIxiPoHp4jogt+whEw0z245hub97zhFRdMASnk67sEIscKEIoHsbxl68xHavIlalbPekNIYgCYtkIeEHA1i4dIoLxMDskANQUGk2uD7xOEKA+JRmLX5Gx9XANboFn04diRMN/8g++BXUwQmYjyz4LjiOkt9YfodfRKNSq2cOg1UXvqknS0qAEbTSB1h/hB3/3W5Q5ElEwHY5Zsv2g1cWznz1j66DWH7HANMuwSLa2VbyV77FV9kNJRuEW3Hh/d4bZWEUsn0Kn2kRmq8gOpBfP38CcGXBxHMKZBNYfbDFEpzHVWehbulygw4hM73W7NsDpFXkTnGeMPmsXlqslBs0u/s8/eYmdt0rsWQtnEmxCc//j+3C7XXj7YQCjTh/Vw3P0f62hceedDZi6yT6frbd38fB7DyH6RLTPazh5doJhZ4DKwQk6VdpjeEFA0D7ozg0Tp0+PsPFwi3kQtP7olpyI41zw+r0YDSmPqbhfpvU3GcXr15RMb2gzpPLXe3B2M4PVcsW6/9ZMhyAKcAs8O9CcPTuGW+DhFnjwgkDSJNcK6XIBGw+3cfH8DZGqWj24BTciaWqMXL6+xGq1ghygNWlpTwi/sdfqN4Id/bd2/dYdCBbzJeLpEJSwFyeXK/A8jZ3zu2u4uhgilY+gcVJlZstH338Lc8uiTntMQTAeQSgRgTV34Rd//Dli+TQiyRDRA+yiLr+7Rpg+yc2+r8NpBoBEIcNG34Y2A+fmEYxFoKsznDx5zagz4RRpuWmTowdi/8N71M1oddnmslouoU9nEH2U6JhYy+LVGR0oaFzox3RK3ONqy4XWZQvZzQy0kYbMRhpKIsq0qsF4hBVYx18eYtTuQ1c1Npp3JE6mTnKCi5eXLJF1sViiVSWuceuC9L2iSBgzZ/Tt4lzEY14sWFEheCVwnOt6RC2JqL25tBnwLqZ1BghNNh1PMZ+TedgpcAGgUjWIzjHWkN/KsMIsl1hB7Y9RCKuQvBQIRczjBWbqlG3cljFnUfCGNmO/P5y6WRDXf//ffAUX50IsE4cSJezjqNPHpD+8hSQs3lmDppqIZeI4/OwAGw+3sf14F81KE+/93juELa00UNq51h87Ovn1B1vwK8TTb9ao8K0fV/AP/9G38S9+NmFG2WA8AkH0wJyZWFsLIB6jzSmRomI/EA1hsVigftGDqV/rTgHyuqTLBaYDFrwSDG1GnomzIcZ9FWcvThkPmqgacdSOL9nYtnJwwjj2Ny/HAJtYy94wF88I8RqjQ7Ouasw0Loge3NsP4uHWknV+GyeXmFtzHH95CMFLXUKPJDGjXXY9hcRaFvFihh1E+vUuBJHHqN1HMEL3llMEUCBbhMl1nE7varnERx+QyTqaSxGbPp8Ex/OQwwq7B1yci/08+tSALxREMi6Ad/NIredZx/Di+RsMmx2myS/ul3H24gz6lDrle5sChbvFqRDTRhq7tzySxBjo+a0cdt5aQ3a7hFF3iNR6njwxghuZDcpC6dY6CMYjMHUdo1YPok+00ZRUyH75Fa1Hlmmi8qqC2ikVP/ndNfSumojl0whnEpiOVazdXcfW23YokZc6oTeDoQDqls+tOd7/G4+uk7vzJJdwcS52oKDJIplMV8sV5XCspRCMR6AOxsiVM2zaGEoRHcjUTboXbCIK7+bhFj1kNO30YeoUbtatUuCbNdPRqdQRy6cpKfbpEclL1CncAg9fgJDJvlAQ8VwCjZMqMwfTlNW8FZbUrI1QO7pgB1td1djUYTlfQA4HMGx2aNoVCiAQ8kOWluB53n4dKYZ6TG/kGBEJIJmqk8w9bBL28/TsWn8+GWiYaVN2SHQCGie9AeonNsPeus4jIfLadSI67S08Iin6fg69rtcgcphXFuGRJERzCbz4xdd47/feIUjGYILXn7/EwrIQDXMs4dvRhRf3yxg0u/BKLtbUcho3Hq8IbaTBmOp4+rPniKYU5hc47cho95bwSBJcHElX9z64yxohANC67IK3s3kMbWabV0W8/tUb+BWSV756Vkd2u8SaCelyAYIosGfSMdgCQL/RhWWYaF407D1xhbV7G+hdtWmiYqww6Q2xcW8dw5kEJUmS3Llhon5cgeCVWMNtNlaxWi4h+SR61oMy5HCQmfEBSuLuVhuYDOm1iz4RguBiEwdeEDBsdsh35SEZ7ajVQ/WUlARKIortB0UY2oymKLUBnj9p4OjMZFO5dDnPDjSxfBqcy4VOpc68M/rMRL81YaSiO4+3oatThFJxlpm0Wi7B8Rxb3/O7a0za6AvRBFj0eVnugk+hqafg4dE+r6FycILSvU0IHg8evx1Ccb9MBzTtej+ZDDSMupRW7OCWOZ7DZKgiXS4wEmPtrIN+a4hMOWdLYmmd77cG7D4GyKOSK9OhJxBVcPetNEZDHan1/C0E9jf1+usJwb9Dl64ZePP1OdSxgXyax979OGEXuyNEEjJefn6E1ZLIDoN6G4vFkm1+hm5CHYygqzO8vgDe+eEjWx/nvmXg6lTbFJ2+q7DupJKMMpOZZVqsU+UNyqQFjQYhhwMIZxKQFT8Ejwf6VIck+yCHg+w1HHzyHKNWD8lSBro6tTcDAeFUFJZJeLx+vYPlioyeg3qbZFC/Oseo3UenY2BhziF4qLM56k7glemBTZayZKTjXAhnEth+fAfBGJlHE4Uknf4NE6V9Cs+ZGyZMnbj7qfU8o/wEomFEMnEM2kM0a/S6c9slCF4JcpjY7JLsYz+TW3Ajmk3g6qiC4n4ZAHUzHBymYyoErs2q/iB1QUv3Nlkhnkl7MB2rsGY6+i1Kpy3ulzGeclhYFhKeLpJp0lUqCUJDru2t4e5H98kkGyOd87jTv9UdjcomM6b+wX/8CJPeEOlCGL0mBa7MDRPmzLglUbk6vkQ250fl4AS8IOD06RGG7TH8ioz5fIlAyA+PV0Kp6Efj5NLe5Im2spgvmZxnYyvCNoW4bOB7HwWRKqUQSpE+ejpWEctGEAu5cHpKUo/lkrwSiUICXtmHZJ6ISZLsZ9SjVCmFsS2RcYKY8rtrmBsm3norCo/kwcb9MpMGWDOdbWx371EITmFvA8NmB3NrTibq9TxJplJxIjb5r6UlLs6F9d0M6xjtvr/PtPOZjTSu6hamFn/Ld+AcLEKJCCMcOTzvxWKJ9nmN+W4AIFPO4oPHARjaDJmiPapeS8GcUcbAsEm42Y37ZbgFNyuwqs0VCnsbttSMyCmjdg/DZgeZrSLOnh2j1+hDHUwgyX6ki2R6dfMumDMD3VrrL6wznUodhb0N8G6OGgi2NCsVpC7++j5RYZRYEJEMbXLGdEajda8EU7fg9QnoXrXA8TxCiSC8Mhm425dtSLKPAgztg3V+dw3mzMSkN8CHH1Ea9ONHyg0vBgWq3SQi+YI+DOptOqDPl3j9+Uvmf9HVKT740WNkt6/Z+46xVg5QEZYuF/DeWyLuPaSOtiNv6Vab6F21IYdlhJOU6nr85SFcnAv3PtjBybMTSLIfpXubGNTbaJzSwYPoKVOUH+3A0Gbw2CFQhb0Nyme5cW+woDRZwkybIl7MsEBJfWpgtST05XQ4truPLgzbQ3gk0QZFXDPrAeDs6ze38NDOz+sUiZyLGkej7tBu0LiRVwaY25LF1XLFDivqQMXQTnTd++AuTJ10/Q4kALhOx5ZkP8p3ridAW2/v3noNzpRNiYXYhEztj9nkOl4kXK2uztC8IGlNNJeizIexCn9EwdEXh9i4v86kgv22ytaU/O4awpkEzi4MVmg7Rml1aHsR5is7iXYJr635d9J9U8UkintrePazZ+DcPK232gputwvGlBpcwXiEQg5D1wx5OXwbY61Pyfy+cZ/oPU4SszbSoNsNssbJJQuZk2T/LYPtbKyiuLeGzEYOq+US04mG5WIJX1Amw/uKPqODT56j3uMxavVY1oTo98ItuJmcjQWiTTRoI5XlxDiGXQDQ7dDCRCbI6Ftut4t5DBeWhWiOQvNqRxfsfdY1QskaUx36jF67OTPQqTYhK17sb11jNZ1k8fUHW5hONKyViKLmC5F5Pl0Iw8W5GBb66rRFBwyZpGTOgadTqcMtuBGIhlE9JH+YNygTZjubYHv2sN1DPBPFTNUQiUpsL1YHKnq1FiTPCqZu2kjbPtuTA2E/uy+8QRluwY3a0QXa5zVKyV7LIp6L4v3fKdGEvNFD87wGyU2EuOlownwWqfU8VqsVXnzyEtFcCtpIRS7Fo10bQIkFIPpEfLOvv3pT8b/N67fuQOCRPPAF/MgVgxDcK6gq0QAoHXbJumvOKd0vexizfrVcsVjxq8sJZvYp+fTrM0STAbZhRTIxYvzyYMQA1Q4Zo3CaNkatHqWuBonV6ws4tJYpZvaJf1CnjV8djJHfXWM0i1AqDq9fZGO/3HbxVojJ3DCxXaLXEsunyZSbi8HQZnh01wNd1bCxEYTo9xJ9Ik7d/5u8eYAMt51KnTobhxeYTjREMnG47UWPwm/ssCt7433wLunI2+c1GFMd775D2NPLl6fgbFpB9U2NbVYAdQHqxxWslkssFws0z6r44d//AJLsR353DbqqwSOJ4AUBkXQCSjKK7W0F0XQUF8/f3CrEnbyDVD4CQRTg4lzQpvS+XM1SePJnb2jsXG8jnk+gfdWFNp7BMkwcffGKjNfFDATRzUyLmiHQoip6UEqSdtPt5qBEbxgt7e6G8zMtlyu8PuhC9Htx7yMKgmqeVckLsVji8vUl8ls5nJ3RoUgbTliomambjH0tSRzDOk5NNwYTDv0mET2czWw60dEZrPC9j4LwSCK2NiToqoaTJ68RTobw9GfPwdm43bk1x+vPX6J50YRfkZnkIF7MMBnOWF2ifnqFbr3PpElkeqX7Q9WWdmo0/b1gNIRAJAjdzrMYNjtkNI/4WSE+avXQbxPbO7GWZR312VhF/bSBdkvD4ZnNgs8kkC4XsP/hPfq5JxorNiWvB5KPElZj+TTk4PWhY9Qd46xKnwPnIs9P86KNmTbF5ctTZLaKFHilW3ALAvY/vIdoLoVixsX8IAB1HpOlLCTZj/pxhelwgxFKoe3UaXQueoBANMikU07B5kwALl+e4uzZMYrbaYh+L7LbJTTHdlpwj57dkyevIfkkuEVKII0mAxBED/rNAUJBe6KYi+H15y9x8uS1XeC4kV5LsalkMKLA65cYeeiP/ucn0EYa4kGLpQ13qw2Ebfzs3KL3snVRZ1IBQXTbnU4vsf9VDV/8hHT7pm4gu12iw59hQp/R329XGmj1OXh+DQ1OeQYWwnEqDhxz6N7bVGglCil8728/ZNMESSYcoSMfHPcnyGwRHpXjKRTw7uN1ZkAl4lKIgtN4Ku4cwk04k8D2/TwM3cLW27tsTQ7GIkSdsr/P3JpjOtFYIeJICJ1gL4BkiNMh4U93HubsHJghlosFtImO80GEmeidzJSH33uImTZlyOGAIkG1J5KL+YJNoYZDiwUCDvszjFo9BKJhXL6+xLhLybfXoZZEVXLwvTcpM5KPUpHHnT4ef28PC8vC0KYW8YIbhjbDW999gE69DxfHobhbwNVJDYNmz14fOQzqbciym+WVOOnH0VQYbtGDjfQcbhuCwfEkd02t22jbZ8dwuVy4//F9yodo9ZGLL5GMgkk9XZwLGw+3b00Oq4cXAKgALN3bZAfmTr2PxmkVs7GK/EYcw2YH406fTVGDMYVp+gPRa5BHMB5Bt9aFqZtwix6MbBKeOhhBjgQRUVwsZ2Q3R9S40k4O4/4Ec3NO3293DdFsHJcvSZa3Wq4wavXICzPWWMAWQGQrzs3j5ICmnRcHZxA9JL10puF9+7Bd3C/j6qgCWfHDF6BifToc49nPngEAyg83ocQjePdxBDHZPlzY+QeS7Mfl4QWi6Si06ZL2KG2G6XCMXpuyJgDykjkTGCdkzUl1d3EcjOkMHq8IJRnF1VGFNeMaJ5eEfeZc8AVlDHtjPP7+A4SCPCoHJ8xv4w3KOLlcwBegtdHQDTYZzuSC7HvPxipNK280g0zdxOnXp6jVpqgdXYDnebhcHIYzOixF0tdZG82zKuonVSwsC5JfwnQ4xptz064PVgxC8U29/pLBZN+Y67fuQDCfzxFKKDj8uoneyMVCloadAZqXffRqXTZKAwC/3x5J1tvQRiqmEw2WYWJrW4FboLdv860yXnz6mo0TtRF1qQ9e2R1Om9KSK2fh8YqseNRGKniBotLdbh7NsyvE80mW/ghcpwpGEgGisvhE6OoU7asuQ6FWDk6ocxDws3CeL782WMLlsNmBOp5i6+1dnNdoFPzH/8OfweXiiMffHNAmYFNmxn1iGec3U6wDv7AsCB4qxpxR+k2JTDQdQyyfRqNOY0JH9/nHf3TCtPaO/m/U6oFz8xA8lLY7alO3IV0uYDqZIV0u4LP/6xDZzRz6rT7CqTgrirvVBjySiNOzCUsyZgEqneuNsnLUgOSTUDk4hSTSQ6eaAorbGXhlHxJrWTTOakiVEtCnBiTZh/zuGgvo6jV6kMMKRJ8ErzBnUo7zJhVQ50ct9vk7vHoXx8Hr95EER/RgczcKl4tD+2qA9Qdb2Hp7F7vv72M5X6K0X8LRF6+wuxOAJPvZvQNQJ8shhXCu6/TXQrCHxYIKFWum4+zrN4gkI3j3gyzuFOf48rnJRrHONWyPkN8tYdKzZSe2UXXj3hpDtDr8bsuwkFjL4tkXV3j3B/exmFP3WfR7MbfmdmdVxs/+5ClK9zbRuaJJWCASgDE1EE1H2GcxavUw7hPb3ZFOjPsqRJ8X5sy8de8Q3hH4zn2DhQw1TkiaVH60g4CdVuoWPZgMyez/6PtvwStLOPjkObsX06UEMknqUp4ddzEdq/DKJIMIpeKY9Ch4Tgn7MFOnOPzlIXpXTTw/vB7TA2B5Ik7R4Zi6SzspiD4J4biM8qMdvHilolNtsamHR6I1Q1b8iGVjRLrJpfDVT58hko6jeVZDo0vFizElVHE0l0Lros6+vzo2EMvGsVot8fN/dQSfQvSXD//mu7R+WRYW1hz61ITo9yK/S/pt0edhr2PnnU0Mmx0cXpJUSvBK+E//y49RfV3Bxv0yvLLEJmBzw0QgGkYyLaO0V7hF03GKtOlwwrr4AFA5pTC81WqJibbERF2yzjRAno1QKo5gUET7sglzZqB9XoPX54Y2oZ+zck6F9qQ3wmq5hNYf4e3ffUR/vxRH/bgCXygI2Z4AeX08ew5cnAvDZgfJQpwRb5SYHRoVU3B+1EL7solWtc0CpYbNDt77vXegT6ZQEhFEEgqCEYUVIs4hIJQIo/bmCvndNfKPxKnor1eGrEgzNB0Xz9+g3nUhlKDgLwfZefq8gvxWDoZGMq43z6tIF6JYzhdMkuQLyhiP7QyG/giCSAc/kmRZiKQT6NY7iGXjbDIGgEm4vEEZoVQc8WKGoSkTa1loKhWfue0iXBxn5+soqF90oQ4mhPfNBbH37ibcghuST8KgSWtvqzklDOZYhdvjxtxa4OqEOsytERWbclhGJBXGpDdGr9FBr9FDLJ+Ci6N8lHgxg3Gnj7M6hxeHOioHJ8T/d3HoNfrsgBOKBrH19rad9l7HxfM3rICklPAgODePTmPMuvWdapPtyan1HABgsVhgblJDzzJMjDt9FDYJt+mPKCjsbdC9NZzg6JT2itefv8TPn1F+iV8Wb8Eqhu0hppMpeEGAHA4ykIFXlpAspTG35qxrDgCZjQxSRZo0J4pprGx5eySdQDiTgJKIsj+7Wi7xB3+Qu4Wedb7W0RevKBPntYY/+amBjYfbDCBS2i8hUUxjuVxhPKGf9WbyuxILIb+7hklvgO/+nfcheKmIXlgWvDL9u6MiCEQCGLV6iGaTLEAtXS7AnOlQByPMrTl0dYZua8LqIqfA9ysyygUec2vBktYd/8Orr65uBYwCYDIwB5HuTBDKj3Yw6Q3Ae9yYmRzS5QIaJ5cIZxK3jNjrD7Yw7AyQLhdQyHnw4DsPIPo8OP7yEH99/eau37oDgcvloq6c14M3JyrqV9S5X7uThzrSkCwl4Rbc8HhF+CMK0kk3losF3KKHjGhjDYGoAiXgwtkB6Z1f/OJrbNxfx2KxQHG/zDr1uZzMTKX+iIJmpYVxp494MYOtt3cRiASxnNNm2LigUKsrWw/tDRI3O7+7hulYBc/TR+Vs+qIkQpIlVkgeffGKkoGPLmBoM/ytj0kP7wsFkVrPQ4kGcP7yHJq2QOXgBB/86DEzK7770To83msEnK5O4ZE8ePazZ+g3etQdsaUHqq1d3X1/H8vFggVDOdQbbTxjOvCP//Z7+M4PN3D27BihVBxuwY1wJoF4MYOc3XEEANFHRVvj5BKWYcLUTWTLGVjmHAt7wY/nEwx96Qv44JUECCIROkS/hHAmgXKJivVJb4ButYFkLgRJ9oHjqHtbCPRxddpCKE4ki7lhot8aIl2IYrVaYude+tZYnxfcpFdecKwY4Tjqau3cy6BVJTxmt9og+UciimG7h+mEOjiqOoccDpKBdaCicniBw88OMBnNoE9NbDzcxmhCK686GDMsodOtzWwVIQgulkA9sXys2HdM2dXDc9RqOmYWj++/SwjL1QpMEqDEgvD6Rey8s4PmWRWnX58gGAmi1xzCxbnsomyASDoGORxA+7yG3fsZdJokJePcPCLpOKyZDnNGyMzH37+LxukVk1E1Tq8oEKgzQiQTYzK5UNRPKDpbnnH3nTw4nlIolZiCdLnAjGsPH4ZwNfRREW8XDt16H4LHDcu0mDRr+36Wci+G10x4R9735ukbNNp0f997K4XkWgamTubVYbMD0SehV2tBUw0osRArBNaKIpMvFPY2ULq3ecN8OWCemnBYRL/eweEXRxj3J7i/L+Px9++x/AFtNLY1/yO0LlsYtXrIbSSxdreMSFJBKBnFZo5kdcGIDF/Ai3QpATmsMANzPCnj8uUpHv/ONnYfliBK1NV7+slrQjoKFOjWq3UhyT4YuklFoV9gG3Ik8v+y92Y/cuXpmd4TJ+LEvu+RmRG5ZzKZTC5FFotVrKW7epO6JU3LGlvyzHiDAcMYGDBgGL7zH2DAN4bhubDhWTxjj2V5PDNSS+NudUtdrapqVhVZXJPMfYnI2Pd9PSd88UUedmF8YV25G9MH4B23jDjnd77lfZ9XiiuzIqQYl8/DP/offk4wEWE4GFPJViicZFjYSM0yROqcHFRmMjvFGCqktldRLGauvbtD6uqKMU1/815CAqpWk9RqI06OGqg2q7HO73ckcbzZGPDGB1cNSdRgMOHFx8+42D9nedUneSjJqGGCPnsl2ujNTa9sDRotypmS4DiDAi8IzEXxBqVgHPReN5aBmA+nx8nZs0O++RuLRFNxlFnCbbtaJ7W9yoM/+2K2ufBxcSTpqzaXg7mNRUrpmcG62jIyNcILsZnszkF8wcfND3aw2m14I/7ZzzOlfFGi3+kbKe+tco1QxMXiVQlOiybDlPNNfLEQN79+k6Xr6ygWhVTKRXUmNfN6Bass4Ak/o8EQfaJRzb/OhQCM+8wdEDqSalOlaQp4iMwFqVe6gkc+zTLV9Zlno0m32WHUHxCYi1Kr9Tl8mkbXNAJh8TU0i1WubnkILcTRNY1ht0+/0+fq3XXxtzklZTx/fGFsl0AkfaXTLIsrQRa3koa2fX1B4/aO3Avp3WMahfLMOzEltBBnMtGoF5s4PQ48Ib9hOLe5HKTWoiSW4yzvrOENOg2JizvgI7mZQhtr9Gzs5QAAIABJREFU+EIeaTqjAQPm0J+dG/l0HW9YQsG8AZFIOr1uI9Nj8+5V3tnRKJxksagScOeLhYznwGw2GwTAyyydo0d7nL84Irt/xvmLI2wuB9WLAvVig15bhkmNUp1EcMLcxiK1fAnVpuINyUahlJbp+X//3zwQwtnsfIvOy3YpnEwwt7HIZKzz3ltujh/vE19JsnhtjWalzVSf0qo0abVE53/5HB492iOSEP9eOJnAYlEM6WdyaxmP7/WUPr6UoJQu4vR78YY8zK/ECc2HKZ1LDtKlhMjlc1Mt1Dk968p3HwkaoW7TqSCHVZuVYU+yaVxBH0tX4ngjAWwuu9G0XTayvVaHYMyPWbVQLXXIn0oD3q01cdrk3bd0fR1f2IfVYSWcTDAZjqRmiATw+F1EfRqZwwLPf/b0XwMd/DJev5YM/QpdyswYVS3USSZdHH55IIavRp+v/eYV5lN+Rn1Zh3VrTZ69aKHNJoSXkoh6oYJignvfEL3ntXevMxlrmM0yrV++Kjetpk2Ndb0v7Dd0kY1SjYOHrxgNRsaEavNGiq07a18NAmu2ybw6RbVZ6XZkwnFJF+o2OzhcNlSr9StJxpdXoS3FQa/RYjKecG1HUKFzcXmJBIM2GqU6ikmh19OwOmxoszAVbTbNuvHBDZweF/6ovOgqmTyBqB9/PEL+tEirXCcQ8ZHdP6NerKHaVKJzPiPspFruYbeZpAFYSzAejnC4HZTPc+RPsvjjEax2G4FY0NCXbt1ZoZYr8ernL0jvHouEoNpgPJT/27Db5/zFEaOxRu7g3DBr+cI+7FbdmEbb3S5sNgvugJf9QzEO/+DLEG99bY3jx/uMBkP88QiJVJjcWYVmsYrJZMLjl+LVHw0QSsj3sOJ7rRGfTmVSV8i1yR2cG5pSEHSaO+AzsIyFrATPLV5bo3yeY+P2hkw6HFbqRUmOHY0F4zbuD+QwviigaeIh0Cc6w9FUCFgH5wStTbyu6WxlP8/GnS2iy/PsXHXw+dMRnzyTKU22ODHM1seP9ymclwxTsTYec7F/Tv4oQ+k8TyAum51LzSdAwG+m0+xRzhRQTAqNkgTdXE6xr21amV9PEkvKyzi8EJPshtn0MHdwLiFJpZaQWWb0kfRpA39U8J66JgZiu9OOarPwZ//yiEJFJtb+GTlq+84ir37+gupFwfheX3x+hmpTiSQ8gvL7xi0x4W4tC2vbJjKvyWRK7uDcyHaIryRxep3EVxYonhUNXTDAbMMPQDDqoXCSYzKTd0gBKkz09FkTbTxm6+4mpdMsNnVKMGijeFbEFwuhzKhImqaR3JAppmqzsHE1wqvPXuLyudg/V4Sykq+TPbyg3x2ysBpj8eoyZlWl3R4SmIvy53/4KYrZxHAwRDGbiS/GZwFpKvObS1gdNlKbC5ROsyTWUrz8/JhIUorjezvS2MxFZDBxKfXJH6U5f3HEzr01Q6t7iR10eewEY5Jg3ihLgvTF/hn6RGOqTzl7dkh8ceZ1GInnol6s8c4tlXfeDjHodLE7bTMk4pjxcMT6upfJRKd8nuPWN26xvOhg5/0b3Pz6DR789Hgm6crTLEnh26zIJLzdeS3dXLu5Svk8RzQgMqTLM1GfaIyHY+o5CeQymxX0GT/+H/13H4nszu0wtpCq1WKkE+9/Lt9FNBnGGw6QOzhnYUMM6r+IGc3un1E8KxJLBjl4luXhjx7Rb3UJRHzsvH+DteQUl89N+TyHarXQrjb44HfvMRpp9LtDyuc5Tp4d8/7XFxgPxxw+PRX8arHKZDI1GqFivkP5PC9FfFASe/utDqpNNSRJrqCP4pmcQ+XzHDanHZPJZBTlqtVMalmekfn1S+KVHX80xPLVJKktSSauFdvMr0rOjNVu4d5335T3X0enUawSTcWFuT8aMdWneMNB/vjPyjQrTUwmhW6ziyfknWXyyPa8Wunh8diM5uwkZ2Ywmk2wYyHjfWZ1SBJ5JVuh3+1x9uJEgtBmn7muT1lMOei2+1hUM/pExx3wSZ6OakGb6OhTCaBslOrkjzJGUwUQno9w9515HG67+O66Q9lUlmtosyHd8dMjXmXk2T98IZhXp8dFp96aFeUTA4Pr8sm7ILGWwhsJcuODGyIfnn+93bTaxTy8eXuVVk88OvpEaoFLc3jqisidAvEwZrPCeDhibmMRXZtiUhTC80GsdivNeo9yQ2H11ibFsyxmi0L+KI3VLkOb5SUXlUwed8BNeNasdFriSZhOpwQDZsLJxCx7Z8rZ3mtE56A3xBv0EYgF8QacnLw4+8pm5nIgopgV4osRTCbx/bXKNfInWWx2KxfFKTtvJFhYjXH3G9eEqjWaGF6Iy1waEAleOJkgtSWbcF3TWN0MGSGXgbko9Y6Z0rkgiavZCundY7whjxihLWay+2dkDjIcXii4fC5ufeOWQU/6Zb70v+avX6br37iGAGQVHooHyGS63PraDopJQbVZ+JN/8oBOe8TO/SvG771+TQpVTyhAPVdibmOR8HwMi3nK0Z5MuPvdIYpZMYpDX0BWzYPB62nK5YQ/sjhnSIY69RZmVQLCFLOJaklWlNItv55aBmJB9IlOOJmgcJIhujzPwsY8VqtZwqbMgki7cm/b+Pt8DpECeEIBus02LqeJ2PIcewcdQokQ7c5EAlZWIvR7E1SrFV/Yx5V726zsrBJaiNOodimlczQr9a+Ypi8nPt5IANUmBB6r3Ub+KE2j1jPkLg6XyuGRMPA7rT6T4QiTySSYwBurjAZDtPHEMP8B9LpjEmtJQz/uj0dweiXH4BdNhQePX69eE2spVjbCNLsK8+tzJLeW+dv/yR32HqdplGpc3XQSWohzc12nWh2INGmGseu0BsSSMgXpdsd0ZyFlFtWC02PHpCg8zEnxHZiLcpaecftDTm5949aMhCT/r1q+yqDTI3c8S1ANyQtlYVkanuc/e0qr3mPvs5csXVng3nffZHNJQZ9o3Pz6TaYzM/Bl+myzUifoM7F9f4e5jUXGU5VOX5Iss/tnDPojSqdZ/K4Ji0k7Oxuire3OfDGX19XbSwz6MikX9rlE2y9dW0G1qVQyedZuXzEm++fpPrV8RcymU/0rzSJAq2vi5MkBR0+E835J+WnVOlgdNoOMs3o1TqNQFrPgrU22r4e/Eqjl8Mo0/OjRHvc/fF3AqjaVjTtb+L0Wtt4W/8VoMMAXC7F8dY7xcMxkoqPaVPKnFbyRINnDNJGFCK92a8a9BDJ9SqylcAdcpHePKZxccOP+hkFpMSkK06lskCKLczz7q+csbCap5uu4/W7quRKF8xKxpQRWu0USr784ZH5ziRf7I44O6gTiQWLJKNHFBOevzhj1h5IOvTjHwx894vOPjth66yq5owybiwIsiKXCzK3NM+gN0bQprVqb8HxkhkCtEl9JcvD8gnquhK5ppPfOGXR6dOotKhdFqhcFjp+dEl2eR9M0gokQmVenmBSFf/BPstQLNQ7Opsa25b/4r98jtb0qJJ7+hMJJhnZdQpkUi5njpyfY7BaRhXlcFM+yLO+sYXe7DFnWdKaJePowT7fZFR30/pTPPq9jsVk5eXIwk/fVcXhd5PIDnvxsl/hKkm5zgMUCNruFWrHN9h251y5NoICBR0yfiR9JsZjpdQbi4egrRtMXX0mycnPDkDHlj9LY7BYyr06Z21hk5eYGmqbRrsoz7vF7aTc6bN9ZZO/BLnMbi2zuJFAUE56ASOKsVqEF/WIavdyjTiZj3UieHXS6M4JKD7uq0W/1ZLN7lCe2NI/FonBxXKaar0iAVtCPywEOtxNf2G8YMft9TWhlyQTzSS+ekAxaWrWWZBx8cAOr3UYwEcLhdbN8dRGrw2ZMhyuZPIWTLPObS4STCfYfHaFp8v2cPDkgsZaaNQ42zvYuCIRlW339doI/+J4UgfVKl8xxGafXjc8rlCFJancSSoRRzPL5tGptbLPNSCgeoFlpoI3HeCMiCWlUuxRzTcPLpSjwP/63HwmOdaIbGNRoKjpLZxbJjjYeM7+5JNPsUIC51QX291sEIl7sDpWdHT/tap0bH9yQbbdH0J/dZo9eoyVTa103mP0un5P/83/6GZ169yuNAkClKM97amsJj8sk//ZKhPBccAZQWGHUH4nRvlgR39veOdfevU69IE2FSTEZW/QbH9wgsRQlMS/yLX2i8+nnLWxOaYzyR2nqxRquoI/jp0eGfNfhsrOwmcTjd1GvdPDHw7jcNuYXA1zdiRD0Tjl+vM/Vt7eNpObxSD6nVuf1sLCWK0s6sMfOsNdHtarMh3W6zTYLSwHsTpuxwZ3fXCKWDNNtdtDGGqcvM4yHo1masRNXULZ1ibUUDred3U+es7Qofza0EDdQ3OenLbIXMtxpNgYz2uCAYqZmZNVc1gneSBCrw0qr2jawrsOhhCf6YyHWdmZN66whuSQwnu/Ke33p2ioWm5XFq4uszou37OlHz5hbW+CX/fr1huBX6Lo8hF0+O2urLgrp2mxVqBBJxgmG7Hzxw0eM+wNMioLfrc+wmTJ5zB2c06zU+af/eI/VTUkpjC/4mE6nKBYz/ngEt9vCVJ/idluMyXfhTLT8tVwZq0Mc/ImVOWwuB8WzPNGozTDrZl6dMuxK4blyc0PSAtuCLQzMRSmf53n18xcsLkt6cfk8J5KJRwdo47GRYuvyuem1OvRbHf6v//U5gYgXj0cSDl1O+bdK2Tpuj4piUeh3BtSKDVw+wTy2ai1C8zF84QCD3lBSczsDwskEG3e2GA/HOFxWJsMRG9eFMJM9zBmoMV2bEovbsbkchoxqNBhhdVjZe7DLzftX6Hd6hiQDIL1/gcNlZ3FZdOOXhtTYDBkYWoh/RavoCvoY9Aa8eJTGZBJJWObVKU9fdNi4nuTaO1t8/kWdTr1JzN0mFLIbSMrI4hzHj/dJpjykd4+5dkW+C4BBb4DHI34Pr0OnUZVwl7duzMK4sk0yBwUGna7w19dSxJZiMwmErE19PhsrNzdIH5Uxm80k1lJcvxVjYXORSqGJpulouuBW243+TMMpgUOCjvPRaE2ZW/BgUS2cNCLYVNE73/jgBqnVME6/l598OsBug91jKaw2Vu3GZM7hdXPw7IJqvsLiRoxBTxqz+EqSo0d7FE9zBmlo2BsKVtElSb8A2miCw+U0ml2by0G3PyUwFzXSZP3xiPyKemekHjFoPv/s1EA7Hj/e59XujPXfaGN3WA0NamghzvXlEXZ1KgVrT+7Ds7M2iiJBgsOuGC8L6RrNSp1eV0hdl1hKfaJx9GiPK1fFx+B0SIHjD7nwhT1MxhpzG4tMdZ2LsxqN2VTa4XVTrGhY7XbK5zmCiSi9dh+z2Uww5sETCqCNJ9jsKhaLJFMPOl3yxxmajQGhiAttolG6qFAvVJmMJoz7A7xBL+XzHB/+zbfZvJHCH3RgUVWGE9F2WywKzUqLerHGyYszhr0B6d1jLKoZT8gvBm6XoDNHfcHpxpakkf7FQUG/1aPf6jGdymcXX1mgXW3Rbbb49Meit/VGgnz+dER691h04t2RTM1n4YGBuEz+K4WWBML5XEx1nWZF5DOX8rPL7IA33pqTafLyPD6vmdu3A0yGI6LL8wY7Xdc0TCYTidUFzDOPw2QC/e6I8xdHOB1m9KmOx+80ioJarkRya5nCeRnFYia2NE/+ZJbr0ofwnF88PpkCJ08ODKN5ZHEOh1Mltb1K6TzP/fcSLG7EaVfrrN7aRJ/qtKtNEvHXW6EHP97l7FWakycHM+68vJgv0+hVh53VW5uUz/OEYy58YTnHw8kE1UJdErFNU0aDAf1Wh1AiSLfZoZCT9Nt+q0PuKEO32eY0M6F6UWDnzgKJZWk22m0hD1UyeZwOhXa1QafeRNc0PCE/Tz96ymgwpHCSw6Qoht/F6nj9M2jjMe2aFMYWVWU41Fi6vm5gNwHjrKsUWkKXU008PZXzqdfu06o0hUzX0I1GulEo06q28PttZF6d8jv/1hq+sIdKpkD2JMeoL8jpYExSxbXxhORKkMVra1y5t42uw3f+1n1cPhftah11FoB39GiP0WCIy+cilorh8LoZdAfkji6wOe1UcmVUq5lipkIxU+Of/S+PADh9mTFABOs3lnD5nFx79zr6JXo6XaBZrjEeTrj/23fRpzr6ROPq9aiB633x8TOR7Z4ViPtH4gH65CUXB1mGvSG7nzwHBAIy7ArxKJKMkT0p4I9KiFu/O6JdbUgOyVmZFx8/o98XQmD6MM+//R2VdrVl3Ce9RovwXITNO5tUMnne/M5t0ntpjh8fkDm44K13EvjCPh79+Eu6nRF//A//CpdN8moOvjzEFwuhOuzkDs6pF2tEQ7KFAtk2mBQTJy/OmIwnqDYLlZYZiyogDbNZoXQu77LpdMrFUV6khvkyLp8bh9uJ2+ciFBePlGIxixfk2TFX7m2zt9ck8+qU6kWBZqVO5tUpyytefuNrLgb9McPBBE/Ijz7R8IU9OP1eLKqF/IkMw1rlGuVMkWq2yKgvkqRgQIIUqxcFOq0h3f7UuOcu3wXqTDp5vnuKrmmUL6pYzFNUm8r2O9e+4sH4Zbz+uobiX5uK/3++9ImGy+fi8U8e43KYcLjkcEzvXaDaVC7SrV+gBUU5OJPwDG/wNfrToqo43A5Oj2pYHTYsFgno0icazVIVr0fBYlOZTOSG98cjuANedt6/IWvvRktSgqttTIpJzFGTKXNJmRKBmAdNikIlW2Hz7lV6bVntT4ZjprrO/OYSnc6E+fWUMdm9vEwmEy/TNqwOK/5YiPhKkrnVhMGP//BbC6gW0bwub8Zwu4U5XMnkMZvNvPhUCom7X1tnNBhiVs3kDs7x+B3kj9KoNpWDh69welw0ql1CC3FOXuVnaDOhzQy7fUJhB9Gg6NTDcS+uoJhXY0mZcnz6p5/jDnixO21GURBLRul3Bzz5/AKnx4knFMDp9zIaCN2nelEwJEzhZEKmbmEf9UKVqE/j6ImYDGMxB2eHRXx+O2/dFY1qf2KjVhtSzcuKulWpE1qI43XL912svEbMms1mPv3Tz/FGgjzc1Qw5zY9/JlucuaRftKIzzWTh5ILYnNdYb89tLOL1mDl7ccx4OCE0H+bD7yxzeNBgfTtOMObj2ccvOTidkHl1ytXrUeMlnryySKNQpnSaJRWf8ud/+Cnp3WOKDTN2q46m6ew/OsJsNqFPNIqZGnuHfd69Nmbp+jr9oaxvR/2hmLdtKrFUjE5raKx0NU0TxOl4bEySqhcFBr0BNptCs1Qjtb1qJFhe3mNmVeVwv0Gv2UaZbb08ASFRpPcyxhbLZFJ471vrlM9zhBbimBSF9c2AwZZPLsuLSAoInS8PVY6yCja75CpMxhPmF9zsfvKcg4evZAIY9KFpOqs3VnG6VBwuJ+1qE9VhJ7I4x82v3yTgNQlNaTLFE/SjaVNqxQbli5KxeXF7Hfhnhr+prrO8oKDN5Cbf/K11cgfnkvJpMglNyGmncF6k3ZTPzqQozK+ncDqtFLJNeu0euqah2qyGL6FwmiW+kqTXm+APWInFhPz0bE90xuJj8MqEbta4J7eWRS9crvP+9+9hd1pZ3pTG0B8JcPbskL0Hu8YEOxyXBPR2tU5695jkaoz8UZpvfn8Hk0nhw9/axu6WZq7THhrPUrclBc/GzpxhZB10utjsgjm92D9nbmORYExSQ/ut3mwaKN/L0aFIp8rneWIh+OM/kvNiPDubbC4HrXIdn08CC7P7Z3g8NiwWOHl+OjOagjfoI3OYZdjtCzFqJg9579vr2N2C43UHBLHodorZ3un34vJ7jCCz+c0leu0upVwLX0iM/7nCmC9++AhvJMjx431quTLRVByfe+Zzanbwhf3EUmKunOq64QcDaJUbjPsDsocXzK2nOH5Z4Pjxvkw6K3Vuvb1ErdhkigmHx83GnS3KF2Vh87cHfPv717j+3g4A8+sLXFkRmerDnx2z92BXkJkzCaTN5cDhUGbmZ2l4tJkvq1msMujIJuYyX+byHr4067erDSM0cGnRIZsVs9loHArnJVKbC9y4k5CQxtIQt0s+B4fLztK2yFkiwdehlpdoZ6tVnm+nTafTFLPtZaE8GY64OEhjczm49dYCU33K0nqYXntAr69z54rIzMQ7Zmbt9hWBYJgU0rvHaBM5a91+N+u3N8T7pE959vFLJuMJoYSf3/qDNwQ92Wgb56/Xe/lzSVDmJYnqMnTLbFZwepx4I0FKJQlR67V7RgPRa7TojSx0mz02bm8wGY8lTdrloNuUsz21vSq5CHYrw94ATdMoZwpU83VSW0ucvTjGF/bywe/eo9kYkD1I02916Q7F32Oz2wypqK5p7H7yHLOq8vV7DjwhH2bVgtls5uCgzdmzQ7bevsZUn/K133ubJ/tTwy/RLFZRFJHc9hotxpMpU13n7Nmh4M6tFkKJML5wAJOisJHo4Y/6sdkUsic55jdSxj09vxo38k7GQ6GPTcYatWLDAEuAAFD2HuwSjclQYO32FZKbKVZubqBP4eWpQmxO5FiNglD00nsZka7NGhBPKEA4mWDcH+AJ+alk8vijQV6+qBry0cJ5iXZHmh+T8tqjN+h0GXb7TGdNndVu5dPHGktrIZ7/7Cm/CtevNwS/Qpem6Qx7Q979nbfo9KYsrUlx2q7W2b6ZQBtrLF8X93+30ead7dEsPKNGZDFh6ExjySD50zzViwKJuJVaUSg4U13npz88xWa3YbEIbs08K8ouCz6AnbfWpLO3WvGG/AxHOudHFYOVfqmLbpVrFNNlfuP7W5KWOYsCr+UrzCesVHJl9Ilo7pxe0TpPp1PsNhPaRCO5HqeUFo3r3oNdbDYzP/2LHHuvargDHnR9SqUi2vC5jUXyR2kSKzIRfPEoi9VuYzwas3n3KibFxJ1v32ZuKczKTTnEjx7tUS+UuffBkiAde695/Psvinz0VxV8YT8Hj0+NVMR2o28Y5Oq5EoGI2ziQsic5VKvK4lqUYW+I3e1g1B/g9EgIzc77N4xk0167y1Sf0q61cfk8ZMqy9QgnE5jNIq3RtSnd/pTFa2uEbXWSCw7e+poYh4fdPvHFCJPZqn08mRJOJoguz+OPeNm8e5VWucbv339t7tu66sOsqqRPqtz62g2jwN68u0U+I9MbXdMpnFxQb0wIJqK8981lzp4d8hc/PGUy1nC5LOx9sc/S9jLxqOgpXU6T8d322pICqjrslOoKG3e2uP3NN9hI9EjnoXCSwx3wsrxow+F1Mb8cxu1RuahLQdDtSeNnUiRQaKpP8YVcxBJuAzOpayK5ETNdE7OqzmQGecxmE4tbS6R3j42f+7IQ6TVadFp9ArGwhB553bRqLexOh0FtUR12VJuVJ48K+OMRGsUqi9dWSZ+3iS3NkdxM0WpJgm2vPWDYG7C5OMWsvN6kNQplHHbFwA2OhyO00UR8Afk6Xq8V1WYhdSVlhFSZFJNhug4HpchPzLmZW5KE2UvDuM1uoV4QzKg74CVbkp/LFfTRbIkPZX5zidxZBYfXTTUrBuFaUcLnzKqFSrZMtzM07vduo23wyJ1+L/d/8yaqzYLVasaqmigWRfpyflxj1B+hazoWi5lBTyR12nhCNV/FZrewsLnIwbMs3oAD1WJCn+iGLOud793F7pKsh2ZdijTFYmb11iaxuMgZVud1AvEw1epI8LAWM2trXlw+N6krSUEAW8x0ZsSfqS73/dHjQ3R9SiQZp1lpcPDwFXanjfl12WQOeqKLd87Ia+FknHId3ri/hllVjed+2JXU4j/9xx8bhsfseY1qXWeqT7HaVSqVAd1mh2F3gCcUkJCnah1vJMjjL/JGwFP5PEdiOcGTF10mY7ln+50e7ZpklGT3z1CtVk6eH9OsSlGROZUE4F6rw9L1dSLJOA63jaMzadYGnR5vvbfIZKxJJsTNDcP3JXpxv0g+O12y+2ds3RAyV+7gHF84QLczIX+UptRSGQ2G1MtNIgsRTCYTb9xNYDbDoC9hlqfPjtk9FA/ava+vkVhLMeh0cbnE26BNNLK5obFVumTdX37fl8GSi1tJeY5nU+N+q0O7Wued793BGwny1rd2ePi5+CmmU51WVShOzVKVi6M8P//LY3yxEB//8WfEA9KMHD/ep98Vb9rLfZF6mlXV8Ab85Z+IVKzWUqhcFBn35R7uNjtYbFZp5BZihAMK+0+zfP6TF8QW/AT9Zn76hfwbjUJZfHBWixTnrQ5rt69I0b8Qp9PoGP6mQCzI/d+8iSfgQdN0Xu7K4OZyWLRyc4PUgpXs4QWVTN7wFgUTsildu32FarnLXDKAO+AhGnUQTiao50p4feIrWLq+jmKaSvaL327o+0f9oVB9rq/TqrYIxIOcPTuk12hhnjUclUyeZqWFPtEYDcY0GwOm+hSX38N0qlNqiT9vPBqjOuw0ynUqWZEVa+Mxn3w5opYTP49JMXHrhof4SpLxaELurMxooGFSIJgIoZgVUtuyDRz25B3jc5tIba/y5nduE1mcY//RAbEFP82KDAT+/AsLg96Q9UUzyY0FQnHZavVaPbrtgeEBW7+2gFlVGQ1GRhPUKtcMzxXA2VGVuY1Feb+XGlTzVSrlIYOBzs/+xQOyhxnBhOtT1m9IoOgbH143oAcW1SJDy7rURfmjNMurAUM+1m91CQeFsHiJ2AbZ9i1eW8PlE2xvKO4nGrHy8x89Z25j8Ss401/W69cbgl+ha6rr2Jw29h6n0TQM3SVAtzthdTNEq9pm7fYVBp0u51W5AYOxIKpVePQiaxizcm1JqDAWk/B1VTkQ/sbvrVA4yeB0mLg4SFO9KFA+z2OzWyTFz2JmMJhgdVilELgo4HWbWduKEk4mDLPZpc65USjz7KloowtneWwuh6RtjqeM+kMKJxn0iYbNKXrCyXjC+cWI7P4ZU32KYjZz+OWRGFDXwBdwsroewOlxcH5YIhqxklhLGUXfpV42GPMxGU8YD8cUzkWT+fBHj2jWe+i66JNXbm5I4u9QitBoMmwk3zrcNvSbYXtrAAAgAElEQVSJjsNl59Z7m9jdLrbfuUan3qV4VuTKvW1JXzWbjIMoPCcoQYuq0KzUjcO4XhQqzvOfPcUbCUq4W6NFv9OnelFg2OtTqY5xesV4lYgIukxVRSOePUxTHASZaOCwK8Zk4uIoh90mMrJ7WyMjBv7i4HUCcnfyOsQonR6gjcdYVDOPfvylYXbde7DLyZMDFIsZxSzm3lZrSCWT59Hn8rNdv5skPu+l15MC4Wz3lEJJjJHDEYZcKZLwUy/WGPcHdPtTHC4r9UqH/tjCB9strry5TiwVpj+YGuF5FrOJVKgnxdeF3DfDXt/AUlqtZor5joHkGw9HmM1manlJ8owk49IMriXZfVrk4OErvBEJRbssEC6v1EoI1SYH/lSXe3A8HBlTSdVmxeF1sX0jhifgxh30cvbskMlYCttiusgXP3zEqD8kvXtMr9Hi5Ql4XCbsToehs84XBsbkvl1t4Am9DvprtUZc7J9z916UpevrAFQLLUwmwfm93JMV9GCg0arLdi22IIVetdQ26Fx2p41IwITD6xaev0NhbjVB5aKIalPRNY33fvtNAK7cSIqJdWUe1WZlecVLeM5PYi3F3NoCnpCPcFImsaORTq/dx243Y7ebiMXs6BONzW0JJXrn3QQOlxW708HW29dIXV2h12hRr3REMjgY4nSq6Lp4jeY3lwS9qiosrIRpFMps34gJXtDrxht8fY/+4EdSSDlmskB9orG2IFPO85fnvPO9u+gTzSDGWO02/FEfS9dWGPcH5I/SKCZFJtD1DnsPdunWmkYI3CWxpdfuYlVN2Gz/76+R3/2P3+dbv/8OLp8kjtusJr71e2+IxtluwR3wSFLvzFQaWZxjMp4Qm/dROMngCQUk4TZfQ1XN9LtDTCYFu9OB1W4zGjxtPOGtb9+k3+nji4XYvhEhEAtjUhR8ASf5ozTdZg/n7PMYdvscHTYZD6UQPXlywNOPns4+Cyu33l7C5rTjCQVQHXaO9ysGc72SyfPxH3/Gv/t3P2A0NuHyuTEpJiPl/MvP81jMJjw+2RwubC4SDMjGsN2Ws9Ris6LMmvXJcMRcQp6b8xdHjAaCnbzEitZzJfzRIIePDxkPx7j9HpSZvEl12PnkTz5nMp6wlLJJg1OosnP/Gtp4QmprCX2iGcQsb9BLanuV46wUYkvX1xkN5P/j81tnn+XYGHKsXpdCL5MVY7vN5ZAt+CzV/jKvYDyR4cO1tzdpVnsUyxPeu6N+BdHZrLZJbi2jjcdSZBZr2F0iTT15csDitTWy+2e4XBaq+Qq9toRLVi8KhBIh5jYWKaaLRsDYJaK2Xa1TL9ZIbizgcNlY3/BTKrRolOrEwnJfrt2+gscnZ9jZs0PsqoYvFsJmM+P02EhuLWN1SKGaP76gUSiT3j3GH4+wdvuKISe7TGW3zNLhS9k6i8te8ReYFBZDs3yAiwLjvmBnLz0HAM8+PUQbyxY3EAvyV39VppwpcPRojzffW8HuMLO+KGCSUrpE9iAtCcoel/hidKE2eTyyuRj3B5zsZhnPCFJv7SiUz3McnmtU83UcdnUWgtY30oxT26ukUnahTMX8r1UPs4L8yr1t+RxO80bjV8+VaJXrxBN25uIWrr17nfvfvSVggl/Yrn3xw0e0q/XZoEcgA6H5mPEcH+9X8Uel0R90ugxHoqAYD0fsvLtthJr12j0sNpVmsUoxU8FigZWdZXqtngFm+aW9pqD/NX/9Ml3/xjUEqk3FbFEYj0bsvqgxHGrMby5JiqtPJXfRNibfALmihivoQ9en6JrO4rU1lq8kuHMnRL87pHpRYHaWYPc46TVa7B0Ncfq92KwmgzQw1XV63ZEQViYayaRrRmURrGE0iPHCuoygX1iNEVmcm/Ht5YEd9wd4giIt+uG/eGUc3iAyEJNJIRjzs/dYCAPhqIvEyjw7969QL1Q5zSs0ql1urOnkT/OsXImhqiZMisnQCucOznEFfTTKLZHjzILUOi35t1SrhU5ddLLJ5RlBYsbkN5sV6kVZuV/ZCmK1q5w8PaRUaDPodEmmPFgdVvqdLpVsjW6zS+7s9QS+lC5w8+s3OTssM7c6b1BQAMP4ZLXbBD3qldRhk6KweXsNn/e1xrJcn5JI+mk2h6wmJkyGI16kneRyPXK5AeFkHF80RLNYpdubomkaf+/vF/AEXESX5/GGfdTLMt2o9p2YFMU4TAExgm0uGcXMJWHDFw2h2lRG/QGtet/4vABubgh68cnPTwBweNwGrrVWG+EOePGEAjz96CnD7kACfDJdaqUW/c6ATMVKZ2TD7lDZf7hvFAZ7D0/IXnT4i0cq7oAbm01Chi6LvvnlMN32iKMnx7Sr9Vlx56Z4ljV02IWTDLe+cUu+e6/DmFS2q82vBCGBYDqDUcGpRhfj2JwOw6gMMm3vNdvGhLhZlO83teQ19M23vnGLfqsjmQi3NomGzXhdQley2m14QgGWFx14gx7MqoovGqJdbeJw2YklwywvOUldXeHJkzqNUgOn38v3/8YcxYpO4TTLeCLT389//JzTZ0coFjPTKShmM3bna/9C5tUpvQHc/cY1CemZFRx2t5PSeQG728nFWZ2l6+v87F88IDAXJfPqlFa5hsspGRPdZofCWV4Y3hkxl+YzDax2K6l5C9HAlGx2RvNxKticdnqDKU8/ekq30ebVz1/QKNcJJxM43XJ/1XMlTg9EtrFz/wqegItus0P6pMpwIA17vSYTv26tydt3PNjt8gyevzyXM+xVBcVixqyq/M9//xRtPEaxmHE45X7cuSYv50omT6PUxO6UM8Y80yFfykk27myRWEsx7A1RHXaKOdH9KiaFhw+r+Lxm/LHQvza9cztNPPyrI7xBF9VsBZfTRDSk8PjjA8zm15MxV1AavdFgiNPjwmQSX40+1dn95Dn+qB+PR8XpsYtJvdnCH/UZnqvEcpyFeTvBmEhuHj3IUjjJMO4PjEI/lgwSDSl4I0E27mwx6Ave+PJau32F7fs7dJtdHv7sGKvdyqDXn03unaizgskfj/Dh33ybUnnMcITkXJRqxJPSYLo8duw2eSYVixmbXWUwnOINukmfVKlkBPMYi1qNM8PrMhkFGUDuOEujUDaGT4WTDO9+9xZ2p53s/hnz60lDkhFfSQppraXz4TfmJCRr74LQfFjIMpEgq6te3v76Kpqms341RqerE1tKMOgMmOrikSsV5f60uRySHr6xyObm7P4oSaDhJVt++77IoRY2Fzl5ekjUr3Hn/iKKYsLpsaFpU6ZTIWyFFuJce/c61WzJ0H9vvX2N+FLckMFu3NnCbFFQLGZOj5uSzWC1EJ2X96DVbqWaLcm7U5dicv/RiSFxcrid1IoNgiEHjx6IdLPXaHF0NsIX9jIajNl/UcKiyrl4WrAy1af85I8+xWazUErLcx5OJhh2+4bvqVEoY1HNXL8r2yPVptJrdVh/Y4Nmpc63v7tEsdhn9dYmkWSUF2l5di8zQy4zVS7/PotNlUTtdHGGhp2ijcdce/c656dNohGVds+EzeUgPB/G5fcQiIfod3uMh2OsFvEJtduSuTC/uYQ+k+h16i2CDnk/L85ZKJ/n6PUklyY8HzGGNZPxBE1jFgKqfyUPBqBZaRNaiJNYTtAsVjHPEGyekJ9HD3KMx7B11c/+U3nGJsMRq9cXDbOz0++dNUIOWrU21WyRdrXOwZeH3LoTMeqqa+9ex+00yYakP6DbFC9O/iiNN+ihnisZdL7Dww7dtgReXnrbflmvXweT/YpdZosZl9dBLBnF6bKy+/CU7P4Zm7fXGAw02s2+ER/v9Hv5zi05oLL7Z3Tqbc5fHBEK2UlfDPneb4jefz44MMJEAN6+acYd8HB8OsDhdqE67Fy5t43XbzdSDNPnHRrlFpHFObL7Z5xkdKqVIe6Am5MnBzJhd1np1CVYxOeTXASby8F4NELXZPITmJPk3pWbG4JmG4+pZGv8O3+whElRaLWG1Io1Bv0J/miQUnnE5rUYpZZKeC6Cpk2xWUVPWjrNYpsZGbu1JsGo18g9ACmCV25u0KxK0+QK+igVZmFBJplYnb6UtWNgLsoP/rfPaFbbvPHhTdp1CanyehS0icZkOOLtD9dEGjLzcQA4vW6e/OUTtm7E6TZ76BPdIM04vG5S61EqGUGV9Vsdrtzbxh3wsftADprL0JpKZcTnP35Gq9bjzz8VxOjnPy+wsuzC7jDTrjaNteZkMmU0GPKbv72MxSJ+isicH5vdit3totqRYje9e0ytfjlVtZBYDBnfp9PjFBRjrsR4OEbXNFq1joS6zbwbf/YXHYrZJoGYXyhOhTI766IRdToteAJuIskIH/zuPUmrzZXZXHeTXIkQivtpdaZ8vm9j/8k51lkStjKTnSRTHmq1PmfPDrGoigRBed0oZgVN06kWGnzwOzdxeIU2kj9Ks/6GhDFdbjke/+TxDDk4YdgbSA6CzWqQTS7D1/6Pf/DI8I6kd49plqoMewOC0ddJnsNun9vffOMrz1420+b6ezuzqZB8js1ilUa5yU9+KMV4YC7KZDyhXa3PNLPy0pTGzyRGbE3n+TNpxOxOlWapSq/R4h//w302UlMxIFd7Iq+wWfFFQ4TmY5hMMJ3qxn12eQ2GUz77czEVvtqtEoy6jWTndrWB1a6ysBRk5/0bBnYX4MsvqzTKdexuh0zpYvJSzB+laVZa5I/SvNzrMxwr2GwWfLEQpbJIDzodjZ33b+CNSLCVoHO9ZA6FHPOt33+H9O6xmE+7Y/Ye7NJrtFhYChIMSUaA2/N6c/Ps1dBIDH7vuzdmdLEeLp+X2PIc/+XfTcw+LynaTYpCty+afLtbsIsOl3zX3oho0xtlofSULiq0atIcXwbigUxnFcXEP/17H2EyiZ4+sjg3M8QHGI3FDGhRFbrNFs2WzsNHDcJzYT76lw8JRLw4vG66tSbF0xz3v7VFJZPnwZ99QSldoFtrisdE07FYTBw/k4JSn2i0am3Ss7Tbg4evcDtNDAdjIY5diTK/uYTFJkV3aCHO/qMTNF0awstwI6vdij8eYX5ziaNHe+x+8hxvyMPOW8vkDtPGZzXVpwRiQUILceGvm03kLlocHHUY9AYEExE++1cPMasq2eMCzfZ0lu0RxaSYUFUTew92CcdFhphYS5HO9GiWqoYfZDIcsX1/h16jjc3pMJ65yy2Gz2eh15Yk5ZMnBzPijYtGqYbd7WQ+ZuLZboe5jUV+43e38QWcFE9zdOpNjg6b/PRPdylnivzkjz7l/rURZ88OiSWDBn7XahUgxrDbx+52Ui9U+cE/FVPvfNLLys6K6PmbLUoXVXyxECbFJKm1E4XlebDbLUzGOmtLKtmKBZtdlaRgrw2nz2Pcq7226NhHgyEOr5vzV2fUSw3sbhfv3/eLz6XZwxeQ+/z48T7Dbl9kcrPcFpfPbUj0JrPkabvDzMa1hLHBUkyyuahkyygzr17pNMuz5w3MqoXv/0fvEQoJza9dbVDJ5Nm4s2XICYXtb+ZH//vPWby2RiwZZm5tgexRDpvTQbUuifXnL09plBpUqhOWrq/z5c9ekdpepVWu8eZ3bhv5DG9/uIHD7aDXEJLUu+9F2b6/w4uPn5E5yHJ80uXwSOSYuj7FE/TSqbeF0DMYUW1C6TTL+UGBcDJBpyH0p8TqAu6Al+cZkd8EPeJT8gcdNEpVrDNfFoh8qFZ/TYyr/wIEwO6WJOXqRYHF1SDzm0s4PLJJ6tSbhONeCqUx5eqYqT4luSUbpCd/+YRWuWZsbSw2K9pYY+tWivl1yTrZeGOdSnWC6hDgxYuPn5EvaRROMsaG95IWdf5SnvNOQ2qLr7/jpFPviCza83rj8st6/dpD8Ct0TadTXn32ivxpgbl5F8FYAIfXzdOPnnJyUMViMXP8VIypgViQH3wuB1loQagVW29fY+9FCZfLwkefiv6u3LZx/HgfxWzGYrPy5SuoXpTweERP5w352XuwSyHTMCayJsVE/ij9Wp7jNxMK2+jUxVyoTzSefvyKfquDK+jD4zYTnosQW5pjqk+JLESolQT11q42yJ/kjIOnkslzeC4GpOlUJohzC24CER9er4pJgfEE7E4ri0k7rc4Uf8hFcmtZAmJm8eDtZh+n32tMEdweK9V8FYtqkWlarUm1IIVZpTzE5fPgC8ukrJ4r8Vt/6y3ufy3J458+xe600W910DS4fmdB6DevZHKsabqBhxz1hyS3ljnerzIZT+jUm0aIDcBn/+oh8LqIbdU6xpR+PjqleJqbMeFNxJcSeINOIhEHjUKZ3/9+iH5f59WTLC6/h8Sy+BTqjTHdWpOof0Kn1Tf+7syrU4a9Pjb19eG3kpIGoFXv8/BHj4zJYXb/jPSuEBp67a7xPddzJdxeaXg+fM/L9o0YR4/2jBfxP/+TEq1KHbdLoV5qGKi5drUuaZXDKfp0ikWVyfrOisab78vK2eWYsvXWVdZurhELK1y76sXmcjAaaTMNtETFTyaCTcym20zGE7q1Ju/+zlsUMyVJlJ6tjZNby6g2K3aHSq/Rollp0CpLvsSlAXmq6/zev3+bYMyDyWTCpCis3trAG/Tx9KOnhvnXGwmSO5MX62WT6XLbjIntePQ69VMxK+zcnqdYMzEaDBn2Bly5t02+MGI8C6Yzqyo2pwOXx87duyE6zT7j0Zjr225jM1HPlSg1zKS2VwlEXDSLVTZvrxjGy9OXOTq1FqPBmMiCyJLmN5fEb3NpKv56iJNXQu1Kba+iTzTsThWX20Ig6ODkyYERqLe86ufKG6uG4dHtk82KxWalXZNk21TKznK4y93rFmLJKPGYVZCFMQuNcotRf4RqteDwSiDRtburZPfP+Owv9rj9zTeIRGw4Zpp9xWKWFN5CF22iEfCbjaCeb75lIpcfEVmcY33JTLfZYufNJO1qndJ5HrMiP5/dZWd73SwUoeaEXqMlOMn5CO1GH8Wi4Av7DGml0++lUSjjjwSYWwqT3FrmzW/dMs7Tt+4G+e7feZeFtRhmixmHW55Td8BDLDjlzXeXcTpVlnfWMCkmvvehC2/QyXu/c4fdT54TScYILcTRxmP+7J98jMUmn094IYZiMbP3YJd6oUavp7G4lWLp+joOr5vSaZa5tSSumddgIThkNBC/V7Xcm03SU9iddqLJMFaHDbMiMrro8jy335QzrVNvMp1OBXMbj+Bw2chnmsytp2hX60SX57E7VMoXJaoXBfqtDrGIyvKqn/fuOimdZskfpQWRuL3Mf/6frfLwszx3vn0bl8/FynqQ8zMpZtbX3BTOi5gUE0fPL2aEFi+7+7Kt2v3kOVNdF6nfRpybX7+JoiiEFuKYFdF5XxLNdE1n0OkSW0rgjwTIFER7kDs459OPMrz49CXuoOQFLK/6+J3f38FsNnPn27f5v3/+eiPh9NhnZ/AUu9NOansV1aYyHo74G3/nNhableWkBcWs0KmL16iWE/Jb+aJE8SyLy6ahT03YHWbcXhutLqQiY1SrmWG3z/qqk9FgaNyr5y+OyLw6xRv0YXM6GHb71HMlbE47f/LHmVlTYsfttpDePf4KhU7Tptz44AYmxYTL75HGxPSaqvTs8zPsDjmj6/UB1XyNUCLE1k6UQMRHcmuZt+/68Qa9PHlUoN/XjPetK+ij3egymSE5K5k89XKLd753h8JplhcfP0O1qbTKNRqFMi6nid+4L1KqudUEV1YtwvZ32hn0BqS2V9n/8hSH143d7WIhbia9eyxewHhEJEB7FzPkqjSA62sOimdZzp4dkt0/m2WPWFm/Nk88NGXp+jrf+q6cm2s7i5gUk6CcE0FcDoim4mRKFnwhD7GIitli5vjxPoOZD8ETcONxz5QIUQ92t4vQQpzQfEz8aIsB/PEIpUKXTqON1WHFarVgMilMxhqRsAWbTcHqsBobn/e/fw9PKMCTj57jjwZwuF1MxmNSCzbCc2Iq3n+4TzhkIbmZYjwc440ESUQlVT798oRBf4SuT6leFIw8kVFfMlmCzgGRhRA7968Z0qdf5ksC3P6///pluv7Nawj0KYtXl1m9vsiDnx6zuR0xdHSb2xEcLitX374KCIv+ctrQqTdx+r3sffaSuVSAz356hMs9O1StOqGFOFaHjclwxBuyYGBrRbq/S328za6iOuzMby7h9sifredK2N0uXA7o9TQx19U7qDbrVwxPP/xnj+m1e2QP0ticdpqVFmazhJjMr6cMMpI/HuHOt2+zmpIVrN0u8oCf//kr+t0BLqeJqQ4PH9ZwemxszA+YTKZMxhrNSpPFa2tMZodj/iRHr9GiVRYaz0f//AGRhQjteptyroY/HiGWDAvf3qcaAUOX6cj7e3VGY/j+f3ifyViQeBaLCbtdqAJOj2it5hcDmM0K3WYHd8BDrVhjYSlAu9Zgqutomobb7zK+p0upjsPrRp9IMbh2c412T0Ebjxl2B8zHrYTiPiqFFu9em2UPjM2sJaf8e387RbNcY++LfTZvJrFaFdZuX+GsMNNcT3WcTquRGaHpJuPwS/iF1qJaLVy5t22smFWHHW8kyFSfGs3AZbMXCjvlRdpSeP5lnt/+D95jeTNOYi1FfN5vBN1E5kJiOhxMsNisqA47L19UyRyXyZ2VGU+mnJdUptMp2f0zDk7G7H7ynEa5xXAEXteU0HyURMKBy/e6UJ7qU269vczLB7uGzODpJwdG+N6lJCjz6pR2tU4xUzPMvCAT70ahbDSGJhMcPzvH6XUSnIvSrLTod4VEU70ooE+Esb56NWH8HZPxhFKhZcjSrHaV8xfSeFcyBXlZpgcEY0F8YWmgzw7LhrH5UsfbqLQxK7C8GSGS8HN+MTZW8v/pf/U+g+EUi2qmUmgTTiZ4+KNHuIOyuUhuxHEHvQx6Q0PGVc2WiQZ0kpvy8+bKcO22JCh3m12RZgw1HDaFyfh1sqZiMfP8UZZ24/VGUTEr9Ftd/NEQ3/jd21x58wp/8odPKXfsfPlSIzrnxe00kT0pUalpswwEXcydrQ52t4vMSVWCAxMhNE3HZIJHP3nCxp0t0YOvhLl5M0DxLIdqeT1denKk4nCa8QTc/OAHgurMXYgXam4tyR/+WAqX7P4ZE03+XDhkEeP6aZbCSZZKrkbpNEuj1MCsqlSyVcxmsxFcZ7MJ67/bHuH0e1EddjK5MY36iNxZ+f9h781iZMnuM79fRGRkRu57Vi6173Xrrn1v783uaTZJkU2JkmjJoi2P/aDxPPjB8zKYgeZlYL8YBgxoDBuWYY8fxgN7SK0WJZGgeiG72Xvfre6turXc2rKysnLf94yMSD+crGgSGGNGAy80wAAaXbnUraqMOCfO+f+/7/dZlKp6vkS31WHK16fTNVhfcRJN+PB5JH700z52u0K92kO2KQQjHuJzUYuaMxoMaTVEDgbArTdu0e/2SEypnDw+oV0TWRc3X78pfpdqQyQ4yyaay0FsIWXRSNLbh4Sm/NjtCrGZCBcFg6n5BMPekO/9j+8LGYZTo1Guc7R1SD1fIhx14ws6yR9ncYf8LKwnmE45kWSJ9Rc2kWSZw+MuDz/LkKvI1obEExRIy1LLzgsvJ3A4hBb8h//bB8iKhCTJ/M3392kUKoLYNpHC9dp9AgE7Nofd8sdoHhcP3nlAs9ZlpOvU8iVCfnHOLmUVw56QpcZSfiRZYnkGlpfc+KIhZhajPP+1W1Yom8Mu8fGHeRS7jX5XJxi0E5tLEAg5SU578E+FmZ11kz/OUC/W0AciEV6WJUFtKpoM+zp2TcMfEenAroCPYW9AYmmG3lAm7B7y6bsH3PvxYx4/qrF9olDMinyKBw+ETNHhcqB53JZHQnDxR9aYahQrPPuykEB5fE4O9ypEZhJML0YIxKPEpiM4NZnscZFiOkc4ESaSFH4ab9CDaYh5bmwKGdrLz3nRXBqVXAWnJpM5OEeSZeIBYepdXI2g2mVhenZqIj13Unjqt4WcadAdkDmuMOiI69VmU6z5xjDgw0cyU7NTjM0xnb44R6+/uU7xJMvZzhGeoBd9ErZ476EoIEanY9TzJU7SwnvlDfvw+jX6vRHlqkF07ot50+VzYVNV9h6coakmjXKDTFZ4086e5pmaERI4RZGZ8gukr6rC7sfbHB13MM0xS7fWsDtFt6zfHXByLCR/tVKb+avzyIrwHnQbLTRNIRQPEgg5aRQqFE+yHNzdxdB19u8dsprSiUdtPw+cyNQFaGU+ycVBml67gy/ko9EyefDOAwBSK7Ps7TY42xWbnFaljj4SY1RIW0cMugPrvapTo98VG8WzqgubqlArt5Ftv+hLVgnzb/nfL9Lxi/7p/t9+6MMRRw/2AaH/bDYnzP6Qn0K+S63ctvSt+ZMsVxYFeUIfDJnbmGXlmTWCQTuaWyRFJpZnsckmbr+oSAaTMXJVG4GpMN/93pnVsnYFfBiGydyVebL7pzzdzrF4c5Vrr95Addjp9qHdHk4QmwNGuk6r3rEmH7ffi12zE56OsXFrlnImN9G5DoQO+mcY0tnjEvWWRGw2Sbko2sgzqykkWWYqNMbhkHnmmRC9zpBGz45Tk1hcFpXii6Nz0jsnFnUBIDwdw+UVVd7sUxGWNOwNJ7xjiV67x+5WHkW1UTgRm4j8cYZEyosswcVFj4W1KKePnuJ2QrMpbgLd1gBJlrnI1Dl6sD+RZ4hcg1BQxRsKWDrCTlNU0SRZJn+c5YU3n6XXbJM/FuFE61dCuDSx3R7pOj/4813y6QqF0xz1nqiC5coS/81/+QF/877YcNlUGx/+5Wc4nQqH9/YwDGG4tmsOGvWepeF12U2Sq3MCI2dKKKqN3GkRc2QSnUsK2s4l3rHRxRv2ozo14nNR0TH68BBD12l3x2zciJPPdzl8IiqLL922Uy/WyJz3xMRrGHRaQ4LxCJrLydVrYStFuNHQmYvpyIrwe7jdNoLJGNVcif5gjGoT6bzN5kik+c4lJ1WvHvc+OGLhmqgsNco1kksJ5jfnrXGRWpufEHRUUotRuq2OZRoCDr8AACAASURBVBwEJvjJjuDX20T37GzniEapSr/dQ1YUlm+tcPP1m+iDIY1CheO9vDCKDgV6UnOKzxzApopcD5vDLvw1fbi+qZE7ziIrMoqq8uKXUizfXhcLx7i48beqYvOj2iS23tvinT/+yAoh+os/OyUWEibRowf71IsVrr16Q2hhVRV9IFJ3o4kAnabofjl9bkp1kcEBQmp2dlKjeJIlHA/SrjUZjQxK5QEf/fVnANam76tfn+XgrthoA3SaPVG1nY3gdomp9caLa6i2sZDmORSa7TF2p535lCwQr64v5HKz67P0u31WbizQqgokcT4n0H3egJBPbH/6lP2nbYE91bCQsPGohGmMKWfLNKtiLrhzJ4QsCR/TwUMBPXCH/DjtJk6fB30EicWUkLXpOvG5KOsvbNKuNZhem8Mf8Qs87cQIeH4q5rdKvo7qUJFlCbtd5un2ObdeFKbnYW9oJb3eO3TS742oNcfs3T/FpsDXX9U42Drj8Qc7TM2n6HWG9DpfzDWAMI+aY+yaxtZ7j/BHgjRaJmu3l/GFvfhCPi5OSxaZrFNtoJsKxw8PaFUafO07L1v/VrXQoN/TURQZwxhTPCsw7IuFR2gqgC/kp9/qEp4Wm91uV6dwLoK3RoMhWz/d5e2/3KVTbbD/2S6ugJf1VRdfe3OeG3Md/BEhIQvFg3TrTd5+p8RyUrdgFb5oiLUVj1jkBjy4Aj4Sy7NkJnKnX/nVBa4ujRkNhqQWIpbufPPlaxw/PKBRqGCODN55t/BzBKBLHG6t3ME0TDIFie/+4fvogyFbH+9TzIuU9KnZKXweiUjcS6faoFnrsJCC2eUYJwcl9reFP+zjn4jfZ9DtCYRmtcHurpCJTUVkhn0x35czOQbdPjbVRnxebBQNUyLfcLD+zDyay8m160FyuS6+kAd/NEQg5CS2kCK9fUi/3aF0XmLz2QXcfo8VSFU4zQnJiiZw2OndDPPLIcoZAVow9BHZ4wsGwzG+sBeXz0Ov3f+57J5wyIaqSqSPa/gjAfpD0YXv1ps47BK+kB9vwMXnuwr1Yp0rKyqhgE0EfPnck6Tiny/mlDM5Shkxv5gjg4O7u1Zx4tHDMivzNnrtPu1ml7mI6G4XCn2LEjfShX+tUa5x7+37OH0eyhcl1p67gqwIRLLTLbKPbKrMcxu6VUG3OewUTi4wDIOv//oaja6CoY/odkURJDkfJb1/wcHdXfY/e8JZWRQZNbvIiXny2VP0Xp+jB/sUT7K0KnWCUTFmbA47vU6f7NMLyhkh8fVFgng8CpVchaUFgXIOJmNWSOhr37rJcd7O3kGHmY0FJFngdM2JJLZVbaJ53IwGAs6hOSQUVSW+OMP5fprVNT/ecIB6vsTYNOkPviiPj82xdW9oVps4PS70Xt/KcVIUibOdI6sA+It6jPmlZOj/V8el9nLQH+ENuOh0vjDmqXaFm88mqVc6gtnt9zIyxYI3GI9ycHefWqmB2yVx+8UZolGRzuuyjyicCrmOoY8YDEXr68tfm0eWZSrnebr1Jv3ugEZZ3Kyf/9Is3WaPx+9v4Ql6+dFfPsUwxiIwJOhlNBiiuRw4J/rkXkeg9sqZPO//H5/gDvkZ9ocEIkLS5PSJSk8gHiU5H2EtKdquyWkfrUoTWRH85629EZmzFj63GIztnizMYJqEaRg4PS7GY8F6/+rvvMTVV67TqjTQByNScyGLBS7JErWLIm6PnXq+RCwVwOF2WsZFRVX59N0nzMVN1lddOLVJm9JjMJ0UN7U7z0ZRVBs2VZkQa0zS24d4QwG27hWIz0W5ciMuwpMiQvM9e2URQ9ex28W/d1mhe7pf57//r94nsSx0iy+9sYon6MbhctLXJVxeJwsJg//sH7/KV77koddsYxoGN167QTImBuVc3KSUq2MaBu2G2IDYVBvdoSxST0s1Uu6KJRlp1TuU0hciofGqqDAXz/LU8iLC3e2x4/J5+PpviAry6ozB0X6Fz390j9SiIEr1dRFl73AoJFfnKJ5kWVjyU8kW6LU7xEICNekNBxkMRgScfbJnLaLJEAspmN9IMej0ePyoQtg1YP2FTWJRlbOdIzSXA7dfSFG+8qur1kZYkmW8ASc29YtU1ma5QbfREvScebcw33pc+KJCPlVKX6A67HSqDbwuE4/fOeF1B5heTTHsDzjby4iAtcEQSZbxhTwTpKaOfyrMxpUAvbb4XP0BjW69yWgwZHZzibmYzu7TIdNrc2T3T1m+tYI+EhU6c2RYut5mqcp6qkckrPLV33lJGOsmHZmvfGOBYlXgc+evrzAaDMmnRWJwdCaOokg0S1V6nSGFs4Kg+lwUkWVw+4U2dSpmJzLlJTqXpN3sMuj2kCQJTbOxeHMVV8BndTY8TpPNl6+hOsRC2+1zEl+cQXOq7D6ps/PRNqsrTkLOHoOBQa87IhqE6kWZTl+i3+5ZcqPEsthcLG4kebp1gubWmJv1EAjaaVeb3Hv7PrIis3FnmblZN5m9E87zImzt4iBNuytRrw+IpCJ867c3JgUIKGSKZPdP+We/72V6OUV/YuTsNduMx5A7zuLQBGnF43OQfXrBaDCkcJrDoQnp2KUR0K4JtOjK1SS2ycL09prBf/C7S5ynm5TSF0RSYfLHGXzRELeXe/h8dqJBiSu3F0iFhjjVEd/89gbf+o9fxOV1cnB3l+OHB4Sn40zNT6E6Nd741nWC8TDjscnCtWXhV3JJjEYmxw8P6LZ62FQbiYU4gXgURVXx2sWmLBALUci1Wbq1Jvjm7R6yIuP22tE0mXAiTLfeZHZziXqlOSG96ZbkpFXvYddUNI8bu6bhiwb4xrevkFqbxxXw4vK6KVUMShWDfMvJxUGaer7E8cMDFFXl6s0oqmKSP6/jDvm5+fIqn3wsFpTf+a2pCTa1x5e//SyekI+tx01c9hG+aIhhX6THxxZS7Hz42JLYJJZnmV0IWmjpyyM2HcHpduANuAj5JX7777/K1HyC6HSMeMovtNwjk2rdJJkU13ez0uTe4wG794555rkkhVPhBZtdERsip9dDdEZ8vXlFzK2Fskm/O7CyX+xOB81SVSzCp+NoqslsuEut3MHpc+H3jPnGl+w4HDZUu8qVVdGF8k+Fmb++QqfeotMeYhiGNZf2mm269SanZ32apSq+iJ/jp6LjnJ9UoONzcWJhGVmR8UX8OD0iH+QyC6bWMLj3YZp4SgSIanZBwrvMsagXqxTOyvh9MnNrSWpNiWp9RD5doF1tCvloQYQn6r0+Tp+HYDLG2u1lkqtzFtWqelEivjjDr3wlzDMJUdip5SsEtS6Z3RNiMY3TR09JLKZoFKtoHtHd3njxqsC2Vhvsf/aEerVnhWPe+dptpqc9/NN/8ilOj1PQAgdDi/r0gz/fxW4b0yxVuXlNzKuXkI/k6hw3X7/JdHiIfyqM22GiKAqv/9p1y3gNEJtLUK80Od4+YzQY0mkIue3YNPGFfKgOlXxOFCkkScz5NtVG9lgoHD55Zw/DhGTSRbMqsLaJxRTBiPiceu0u/XaHQDxK+skp1dqIhetLgjjkdbGUEqCHmY0FFm+uojkkK2Q0d3hmFfcuc3GcPg9uv4f1WJ3zwwKrdzYsL9Mv8vFLydC/wyFJkiJJ0gNJkv5q8nhBkqRPJUk6lCTpe5Ik2SfPOyaPDyevz//Mv/H7k+f3JUn6lX+bnzsaihCeQU9UpmqltmW07LSHPHlc4vTRU1qVGppLo9qUcfvdVM7zLN1YplVpkDnvc3t1yIfvHhGdS5KpOhlZWmcbna44y8XyiNHPEFqquTKl9AXz11fI5/pWpSF3eMavfXuFTmuAJEmTgC8hS2mUBTVn9dYihiEqnDdeu4HL66aSq7Dz4eOJRtGFOTKp50uMdINM1SlSUA/KAgUW0ES7ds5OPl3hnZ9UhHwq0qXRNDg66WKODAzDIJSIkTvJc37W5PDhEZIsE0kGuf/eDlPzCcKJMKGp4OU5EH9rts6g0yO5OkdwojffuLOMxzFi63GTz98XLUanOmJ7pyW0yU1hLr44ypE7yqA6Na69KmLq169GaVRajEYmsk1m6z2BGz3fF2jUs2MRlR6aEmm8qVkv/+S/eInpJYEtPUu3mEqK9MSUv0u31WM8hr0jnceH4vyEEtGfw87mqwpOt0ajUOHZF5NEZhJcHKSxKWM0jxNzZNLQxQSr2sWNXXVq1ItV2s0+DrcTT9BHMD4JvRoLetH3v/sQh9vJTz/XWVoLc+drt2lWuzTKNUwT/JEgbrfN8hU0mzrmyMA0DAxTYqgbBOMhVpc9/NHbEqGIC5tNJlOQaFQ64oZwK0yhpTHSDcL+sZCf7J6g2lVuvXGLH/7pNoqqsvHiVYa9Pls/3UFz2iwKluZxcvXFDUa6zpOdGqs3RDDOpeksEI8y7IsbUH8o4/VrnO0cUbsoUi816VQbLF6dJxTzWDi6bmuCL7y1JjoGx21albpI30zXRZVrdY6RPuKzHZhJOWhVWzh9Hs4Ps/zZP3+f9gS9a2EOfR5qPQfFks5b3/uI5HzYOn+ffVpCUSZpo12Rc3B5U1vcSFjtZrumoiiKxXifiQw5vLdHZCbBWabL6V5ebHy9TsamicttR9dN7JqKXXNYEiG7bczR1jF2TQQ22e0KpUyelRUPgZCTtec2yGR1yh0X0aiG22PDHAtJyGxkSCAmxtDs5hIjfSQS1N0qN19ex9ANHnyewzDHqA47V1+5Tu7onPR+jv39BtNr81xd+iIfYn5qaC2YHaogopyeCfSh6tR453COg7u7xOYSfPJwiOZx83S/jifoo9fpUi/WmZpyWkbNYDyM269hc9it8xSMTALsSh1BV/N68NgHXJQkzMmCulaoM399BZfXzcgUn/f+8ZBatUulrfLWZzIPH9Y4S7cZj8e4Q34xFxQquD0OzJHB3/zJfWr5iijI6IZFtbk4KVjSyNxhhla9g6GPCEyFKbQFHSx3eIY/6ERz2ZnZWGDt1hxP7x8w0k0GQ5PkvEjwPts5wuV1WdKHciaP5nHT7w7JneTot8V1p7k0fvhnT3B6NKZmp8RCrD0iHlVI+sR7Lqu5hq7z5tUsqmzw7AsJZEmmVGhz5znRecvXhI/E7rRztF9GtduplQROuFmqWqS3YU9skuvFqnV/uLpqQ+/1aVVqlsdp/7MnLC35aNXFJq9U0Tl99JS5pTD1ao9gPMLqZgxZkfjz/+V9AG68uEQiruFwaezv1Xj9WzcZm2O6HSETqudLeALiPHf7Y7zhILHwF8uES/6/zWHn+W/coV1r4LCZnFVcLK0KNHehAp2BSL7NH2dYjAqghCfg5fTRU5ZvrUyCr8asvyBwk5cerZlpsbg3DZPZhSCJ5VmquQrz11c4erBPpyeus+z+qdVBWby2iCvgY2Fa4aUvL3Dw+IJAPEqtKRFKRFAmqMpgPEwg5mcwhFKuTr6oM5OwMej2MHSdynmeYW9gGV/9kSDeoJdaqU3uMIMxSUY2DWGGPTwbc9qKTbqnbR5fBAkmY3R7glx4tnPE1HwSSZbwT4UZ9nX6LYGGvvn6TaYSbkrpC9Zf2GTnk31SUxL/6J++gmpXGXR6ltlWdWr8nW+s0x3IBJMxvv9nk4KET2N6OYEkSdRKbT7flUVSsSRQxemTJj975A4zjM0xz7y6NgnkG1sytfT2oRhTrQHFkywh74QY1+qwcWsegPVnFgh6xzx+WCSSFB3ss50j3B47qkPl5qubeMNBeq02eq/P7Ssy5WwFh0vIsc5LogOQP81x/PCAp4c99MEQ2ySdGMA/FcamKigTOtKg2+ek5md+I4ldU63U61/k45eUoX+34x8Auz/z+L8G/mA8Hi8DNeD3Js//HlCbPP8Hk/chSdIV4DvAJvB14H+QJOnfClJ72Y51uh3Mr0ToNdt0qg00pw1/8AsXezVXwuceY+jCgT8aGfTbHVwuG8cFjWdfWRBptx6d6EzcaveuzIpBHAwodOpCNxiIR0mtCAJO4TSHahdt2MsKrSSBTVUYdPtcHKRpVWpUC3UL2RibctGpNlh7boOt97Ymch1RVe412xNzcQ1JlilkqoS8Izr1Fuf7aWILKR59/BSHWyPqH9GsNnj1lQiVXI1szYnLKeNy2Sy6kGyT8QS8uL0O5q/O06032f7gEYFYaMJIzlvV5vvv72Jz2DnbOcLu1BibYyRJsvwL7YGN+XkvqcUYkiyTqzt47UW3qAYdN5BtCs1SFXMk2Pxn++ckV+fQ9TFuv4u3//gTQLDlR/qI5LL4DFc2ooz0EfWiSFrsdQ2GhszFaQWnz8Pmpp9yoY0+HJJruUgtRinUbWiawvt/tYXmcWObsM0rdWF8bXdMKrkqsk2h1TEITQUIJmP84R/cpZS+wO50kG2KDUF8Low/4hfJj70+pmHiiwTpNUVYmi8aot0c4A34RCBbp4fTZcOuykLX7tMIxsPsHemoDhW7KtGqimvl8El+4o8w+O7/uo2m2bg4zLD/tM1Ld1xUSh067QHN5gin24FqV8nlhxxnTJrVFvpIQvkZjXw8Ljjwhq6z+/E2o8GQ5796g8zTolWhA6iV20yvzbG4HKCUa1o0HW84SLvWwDTHqE4NRR5jtwuW+eqdDfyTLtX2B4/4/Ef30IcTGZ5PTPJHD/YF1jcgPDbhRJSDu7uU0hfU8kJTvbGkEAuaSJIk9PQuJ9/+e6+iKAqaRyz2DF3H4XLidw5JxgXnPBTWLINk+aKKSxPISpFsPebm6zdx+100G322P9q1qE/JpQROjxNfNEC9q5JcnROVaLfK9HKMxEKMclaMvXtv30eSoFqo0yyLhVt0LoksweYLa1RyNWSbyKUwdJ3dJw0GgxFnexlCQRt+5wCXUyIcspEvjdGHQ9Ilu2X0G/aGQsPb6tDvj8gcl5hZiZGaC5HPtnn+q9fIp0soqkpoKoA/oNFpdNhLi3Em2xT+6C/qLCyIoKO33xYyi9UlsbjSe30ePqyRWJ7F7Xexseqk3+4Qirio58s0ChWRdpwWaGCbw47L6+RsXyAy9YGO5nKgTJKpLxeg9XyJ3kjl4f0i4ahbXLOmyfl+mlqhzDufSbicMj/504+FHt2Q+NIzEssrfka6QSVXIRAN0m93MHSdkycZDF3nua9cw+lzo7k00tuHHG0d89N3z/BH/LgCXnrtLivPrAlEYcQvOgD2EaV0jvB0nEjETq3UFOne2QaSLLPzyT4PPj7laDtLsyQW2oGwWPjGFlL4Jr+HYpOJz8UtiMTZzhErN2Y5vLdnGf4lWSJfMpAlkRjvcGnEJrrvf/lelH/1A51O16Q3KTzZFIF1XogNGPR1iidZBj2R2/HCywnGYzH/1CsdDN3A7Xfj8mo/V0zaPRKp3qIjYrMWix++KzY023s92i2xuBqPYdjXcXqc7G8XkCUsGVX2rM5c3CQQ9uEPOsnlRBLylSt+5jcX0DxuGpWWyOuoDAXcwJhIOhxfYGlHgyHnx2XmNxdo9GzYlDHZTIv51Rghv0S9o+CbZDH0dBvD3oBqrozmcSMrMrtbFzTLNfY+2SE6M4XDJfxkTocIILNrdjodXWSjLMQt2l23N8YbcFpFPIBaqUG33mSoS2w/LNFr93D5XGxt1dCHOomFODv7YqydPDpiLjGmelHm85/s0etjSc8AfNEAsizRKFRolEWwWHr7EF80iGITZnxzZIggx5iCTR5TSl8QTMZIBIeEpoIoskSz3CC1Nk+/20cfDOm3u/yd1+OEklESCzEcmo1WQwSYVQt1/JEglTp8eF/4ABLLs3QaHSFX7vU5OW4S8hg0ihVmV8SaoZCp0qoLA316+5B41Ea/2+Onn/fFtayIuTQ8HWfp1hpj07SKkLWLIsmlBLW8IEY53E4cLifxhBgTxZoY65rLidsj/HKBoIOQR0eWJOqlhgggc9jptMVc220P0TxOiwh1UVFoVUR+kGxTuIwQGJumBVxwuJ0oNoXF66L7MrOSot3oCNR5uYE/4idTkChdNEjvZqz10C/s8cscgr/9IUnSNPBN4J9PHkvAl4E/mbzlXwC/Mfn61yePmbz+xuT9vw58dzweD8bj8QlwCDz3b/rZDqeD0kWVl3/tOWw2GdMQu2Sbw86wP6JRE7jC5Oqc0NRpBubYRLHbWFiJiJacS2GoQ69nYJpjfNqQkT7CG/IRW0hxfI6V9ntJwmgURQUMxORqGGOqF4JcIckyH35UJpH00J2w2UHoGGc2FgSucMKJ3vtkh5mNBaoXJYb9Pt1m9+f+vvjiNCN9hE028YYDjAZisL7wxgaSJNPsKvhCfp4c9Fm6msKujjnP9nj3Tz62dJPJ+SjZ/VOOtjPog5G1abkksazeXuVr33mZxZurJJeSVqry2DSZW52ili8TTk2xc/cEj2PEh+8eUSu3GZsmwxEcnwsNY+GsLDIZJtUhcS3IojI7HrP3yQ6/+ndfpt/u4vJq+CMBSpnipMPSod/uWummJ/sFHh2MUWwKvWabB/fKaE6VmZUU9bagUCSDOjMJhW/8+88w6PYoZYocPdjnLCM+Q69HZn4jhT8WZqSPyR5fYBoG//AfC6qKPhiij8SEuvXeI+qlGrfeEK/5gi5K6QvmrsxRSl/QLFUxDNP6TAESUyoup/j+Rq1L8SRreTDqzRGSJFka4UtpwO/83au0WwNCySivPOdiOJJYXA7g0FTWl2y4PHahze2PuLNuUErnuCiadCat1X67w9lpg5Ub8z93nRw9ybF6PWXhDUvpC4IRD6ePnnJ22sQfdlubhW6zLRaGvT7un8EHSrLMwd1dnG47Tp+HyEyC+OIM9UIFzeNm/3Ox308szxKMh8llhclV82gkV+cIT8fRB0OcPg9Rz4CtPaFXdwV8qHaVSkWMK9Vht3S1/oifvXONkM/EG3Bxnm7icDtZurXGl99c5TwnEn8r53kcLg19aFAvNnC6VYLxMP12l15nwOGDp8LofJLF7xxZ56nbHXHv7Qc8fn+L2HSE1No8b/z2S1RKXcLxIFPzKSJTHkzDpDMQmR5Xnpllem2exZurYvxMe4nHXazeWmTrfgmfvUu7I8LjALwTXezsSpxgMoY+1JndXBKLmqFBcj5M+iCPrEjINpn3/vwTUosxuvWmSBUO2+nUWxjGGH8sjF3TuHozysVFD9VhI5oQqcSPn7QnUhcfX35VhAwe3hOLIAC/X2VsmmgeEXwUCIkNnMPtpF5qEJ0WEsSp2SjGyOSTH3zO4s1VznaOWLy5ytpzV1CkMV/7SoyT/YIVwjQaDBl0erx6W8Icw2/9p6/S6wz49G6DXM1Op2vQbvatpGFhQpcZdAXhy2YTcsRmtUEgHkV12PH4XdjtNmbXZujWmxY6tHxeQFEUym078cVpeu0uuztVbKoNSZZRFJnFa4v02x2S81Hic1Hr+r2UCTk0O063y9KQdxpdhr0BqbV5a3xE55IklmdxuJ006n1Ojxvs5f1W1/eyU5NMOIjGXDQaQ+auLBBL+slkB6RWZqn3VGyqQmwhRa1QpdfuMdTBY9ctups/4qNeqnFwd4+5KwuWbGj73jn50yLD/sAiy8QXZ7j27BzdZpeXn7HTbvYJxMJ4vTaCUbeQeOWr7GxXOdovE4hHWVoLc++J0MLfvOpkPAmYPHja4uDuHv12h05DeKySCTuyTaHWMK38hOXb61YYXnb/lKf3RWHI7xwRjLjIpmu4NZOTsyFnx2Lxdl514PJ5rM6py23HE3Dj9Ipuol1TLVmaLEMpIxbhe/cFyGE0MogtpJBtCo/u5ylf1InOTFnnsZwRkpZyzSQ+7efmy8t0Gm3WNwKWKVaWJSLJIDdfu4ZNHpNcTvHN376GU8MyEvunwjjdTiRJttKUA7Egc1eXcbi0CTRAeF1MwyDsNZh2iznyMk04+/Scbk+sGZwekY1xOR4arfGEojbkZC/Hww/3CCeipBZj5I8zKApk0zVBIDIMBt0+xXRu4rMaU2womCODYNBBfHEGT9CNaZisPXeF5dvrXE2JTm23oxNfnCGeEnNy5TyPXVMtSWq5KDpbDodtIv+qiXTxscndnx4SmUmgOYQUqXKe563vfcTeJzsossSTtIrLK4pQIPIJBv2R8FrpBvpAJ7k6Rz1f4t2/OcUd8OLxu4VxeCS8aoFYmNFgyEfvZ+nWm0Smp9j9eFsUNzsDYtMRAGvjfn1BYNYV1WbNsb+oxy89BP9uxz8D/hFw6RAJA/XxeHy5ejoHUpOvU0AGYPJ6Y/J+6/l/zff8Xx7jsVjwHu3k2HpvC1mRrMn8yWf7TCXFzdQcmZTSFzS6ClMzEVxeN7IM3WaH3Z0KPrfQG8uyxEXDSataJ7t/SqvSIJfviQTW3phiWvC0JVmyNH/9dodSvsXUfApvOIjd6eBX3gjR6egYuk4kKQaE0+OkUW7g8rqx2xWRiLo6R/Esj9Pj5vorVwjFg9YG4rJlbnfa+WxHplkSOM5LFrbqUJkLdagXqywvanz2o/vUWjKtRp83fvslK6hHkoWxa+3mHMGoR6RFTseFRngmQe6kSDbTxBiZP7dh6DXb7N0X+m9fyEuv2eGjxzLPvrLAzKLQj/pcJpGQjN2pMbMqvu+yOgRC6tCuddhYEt2Pw4PLyWqMrMj02x26ra4VHhWdS9Kpt3D7XTy3aTI9H0Z1isl7acXP3ud73JqpsvPJPrI8JpMz2N8Vusl+u8P6C5t8+sO7uPxePv24yMMfP2Q00MlmRP5Eo1Ch1BSTX7NUJeoV7XxP0E+jUKFZFXQdz4SY1G50LUxeKOzG7rSz+fI1lm+vU2sYlMpiYXuZSqxpykTmZeKP+DB0XRBdXA5Up9ByPvzxQwbdPn/4BwK5qqoS/e4QRR7T732x4QhpHbzhAJpDssxYgXiU1IwPm00mtTZvZVm4vE72tjKWaQ/EJgXg7GmOXkdw0S+RkJetZdWhUmlAs9EXZj23E5/PIQgcqo38cYbptTn67Y6F8uu2OjSrgkdezlaoF+t4fC4q53mBVm22McYya4s2N1FgrgAAIABJREFUDu/tCaNnt4/mVIjPx3D7PTg9TlxeN4Gwm9XkgIPTMS+9EMQwTAadHmd7abbulwgGFHGTmUmIRXurTzgRJBp1Egj7RLJyf8jG8xuWlEA3ZMus5nLZBH7VqTEcjmjXW7z3/bs43Sr1UpORPsLhUAjHg8R9fUrnJQq5FuWsWBhEZhLc/eAYwxjz8McPObi7S7XvYWSMOc8NcTplVIeNoNekkKnSawr5zWUy6v13H9Ht6JbBbn7eywtvPsve3QOcPg+qw0azNSI6E6NWHeINekkupzCNMS6XoAAdP7lgZilKJCJIMd16E1URlUxXwEfAOyY8Hecv/8VPJxCDPr6gC30oNvyKouD2u7HZFIb9AR6fxng85tqrNzh+eCCwl+aY8kWV3XMnAZfwUI0GX1S0HW4nDsUgk2kjSWCMTK5u+tlINKjVBvhCLtwhv0WdcricxGbjjHSdVMKONxy0ZHNT81NMzwUYjQx2P962OleXiFd9qBN065SzRZweF9dvhHB5Na68uMlIN/D4NG69cQuHZiO9+8UtQ3OpLN1ao93oUM4WqBdruH2CKGR3Osjun1LO5NCHBv12j9zhGau3V7lxzcetWyFME5JLKQv8AKDrYpORP28IUEKjj90uU74o8eRQLFhtqg1/JEC30eLdv9rFrQ4JxKPsf/YEh1P4dDxBP3ZNtXTVr311Hn04xO33iPTieJRWvclwaDKzGucH7zQJR93owyHbD/Lkzmocbz3l+ksrrK0HST85wdBHXJy3WZwVn2+5JjCepmEQDGq4AmKzrw90+t0exZJOamWWRuOL81o6/0LzL+YDO4YJB+c2MsdVimcFml2ZcEglPi0yBRJB0WkYdAf02x0yRwV2P97GH/FTL1apFxu89M3nBE62YuKLiOvizqvCB+L2CbmmOTKIJnzEUkFsqkJiIYEvGiK1IvxbyZhENl3jJ3/6MQ6XxmA4tq6vQEDFblfInVWQJbHp6A/GyBIWQW7YG5A/yeKLBji8t4feEwW39PYh7VqLsTkmsTzL3NVlxqbJw/0xu5UEc1eXUR0q5xU7dqeDSqnL1OwUx1tP2ftkx7of5AsDkosJDu7u4tDsItU4V+LBOw+Y2VjgvbdOef6lBKF4EHNkiqLSxNy+suon4DGRZJm3vvcRtUIZb8BJt9mleF4mEHbz0b44f/MLPhRVoVEfIttEGN+gN6SSFfLPflfc5w8fn9JvdxmbJt5wkPU7q8ytJQlNBbDbxtSLogPwK/+h6C65nDJX5nSOHqWt67JdE+oBb9iPLygkZ6kFsX759r83R3Q6ysWh8EkVy+Le1ijXWL69zpvfTFkby9nNJQonF8IAfV62rjdPwE2m4uDZl2eo50uWhPQX+filh+BvcUiS9KtAcTwe3/t/8Wf+fUmS7kqSdHfQraIPDZavCo14Kd9GdWqs3Vlj+eYypVyLYW+IOTZRnRq9gUTupEgpfUGrpaMoCtG4l7feyrG9XUcfDEn5u3iCfvxTYUb6iNkZF7JNodsVEiPN4yacmqKSE+mCqlNj7UoYh8uB0+dCVhTcdgNNs1nGJRAdgpE+Ind4xt79E3KHZ5gjcxKkUiPztCg0wy6NxPIs7VoLm8POxUGaF68apFZnSSzPYugG7faITqNNpu6m3+6QzemsP3+Fp4c9bDaZd/74Iws39+CdByLE5aLB2dO8lUJ47dUbOFwOkYL68JD09iFzy5FJNXayIK41qBbq1EsNUZWLq/z1v/yAfm/E6aOndAcyIY+BPxKYSFlE9X7u6jLz11copS/IH2d4+EQnOR/Bodkm6MGhSMadTPDbHzwCRHXI0HVsNoW+rtCo91i6scTicoDjoybXXtmkrQt5zue7MvMpmdX1IJGZBIF4FHMkuN61iyLD4Ygbr92g1+6QmhHn0+nz0O1jmbOqHWHYXbouFsidRpfRYIjbI3wf0WSAZllU54e6wcVBmny6RO4kh90u02yKqveLXxO66GKxN0nRHXP0YJ/VOxsiqMwrzOTneYObr9/Erjn4B//wNvPhLj9965Buq0e7J9OotPGGg8zOuDisiAwIw8SiTunDIZIMiYRATi5cmUNWFHwhF8n5qJC+2ER+xuXNPjYdoVlts/PhY6oXRSRZJhj1IdsUSukLZFlCHwqM7KDTo9vVcfs9Vqp0o/yF8UvgBoWGdHrazdRsFJfPhS/kYu7qMsu315ndXKKnK3idBos3VwVj3O9GkUWgU/44I0yCHo3tj57w+Z6NwcCg1hzjdAsq1SUBZOKBxjAMZEWmXqwTT/lwOWUkWcLh1rg4SPP4/S1LsjcYSehDwR4PBMT5Xby2yOmjp9g1B7Mb82IekYX0490/+Vj8nX07w97AktKcbh8x6PZ57WvLXGTbvPRN0bB02Ya89UefIEuwlhrg9rs4zcLC2hSeoA//VFjIC9pdrrx4hcfvb9Gutfjg+5+y96RK/rzOwrVFTMOgVmqiaQr50xzBkJ1GuU6/O8DplMmeCQne6vVp7v9km7mUTO44S3QuyV/8oMzVV67TrTfRVBNZkbn5+k0SCwk0j5vscZFYTCO2kKJZqlLKFPD4HAKG0NM52zni4N6B6OxNOOmV8zy5wpB8XUhI3H4P9XwZ1akRTsX4/MDB9LSHvb0GxbM8Z+cDck0PyaTwwAiQgzrBeUq89NqMkG/2xrj9HuxOB3MbM2Jxpn9BF2lVRDL14pVpUWyRJPq6gt7r0yhVSZ/1KJ1XqOTqpLcPKeebnB8W6HZ0gvEQieVZEQxY6ZJP56ldFFm6sUy33qTXGYgNbKXO+gubROeSaE6VxauzzGws8Pj9LXb3Oxyf9JiL9mlWW5bhfWZjgaWUwWvPmCytRYjMJOh1hC+sU23wzAY8uXdKp9FmeimK5nHx9d/c4OOnAtJw7dUbjMeCEBOdjopq6YJYNJ2cdulUG3QabYErno8RmgoRjTroNPrMzvsIhx1EkhG+8rUUU9MBzJHBh3/5GaenLV76xjOYY5NE0sNsZCB8ZV4Jn18EcxnGmE61IRCk6zNoLiej0ZjM7glut83KFgknRPHp0sg76PToDyViIXC6xfWSzenUGyPazQHdepMHB4oAUUz8ZsGon5uv32TQHRBORbGpNgoXDXpNQfjL7p9i6Abv/PFHaB4XpztnVo5BIuGiVm7T7w4meS4OMrsnRGYS7B3p+EIuXnjzWfSBLoIIJ10gm03idO+C2eUYW09FcJxdhWZnbBU+TMNg2BvQrrW48ZoI93P5XFaHYNgf0u8K75/NYWd9Ueb4QqZRbjC7Ns1SrIc35EOWBOnMHBlEZhIUz0QH44Pvf0qt0CC5Oke70SG+OMPM2qyVfv4735mj2xuT3jmx5lIBbghydNwiX5FwuJx843dfITgVIZ+uoA+HDPsDCpkqyahAfu/vlJFlibPDIoZuCAP4/hn+mLiPX0o4U4tJXH4vizdXaVVqbH/0hHqlw2hkUGuJc2XoI+6+f4R/Ksx5tkfU1Wbz+WVuvHYDALtToGTNkUksJjotsizhDQc5y4l7miRLzF9fYXXBRnxxhsVri1ZH6PLod/v4okFSa/OWbw0EPrXRGpPPiWLqJYThl8f/M8f/Fx2Cl4FvSZJ0CnwXIRX6b4GAJEm2yXumgcsznwVmACav+4HKzz7/r/menzvG4/H/NB6P74zH4zs2u4+dDx9Tq3SZ30iy8+FjoQMfjPAFNNx+UbkunmTRe302Uy18YS+SLFOvdi2z2X/+H2kkkpNEU8OGaZo0ChVhpp0VzOaZlMry7XXCKcFcNg2Di6dn6L0+j+7nqeYqeAOCeKMqJna7TOH0As0ltJregMda2L30lQ3RAlcV1l/YJLk6hz/i5ZVvPT/B53ksasvac1f4eFuEoIwnYR9rSw5Wby2yFBbaRlWVaVRaxBMaXr/Gq7/xAv12F83j4tYbt4hOxwT+UVFo18RNt1poWBSG1MrMz33GjbJAjq0/u44/4qNynie+OEN/MObbf+9Vnm6dApAMDml0FXKHZ5wc1YjOJeh1+qS3Dymc5rj6ynVmN5eYitnJpSu4XHYevPPA0pAWT7L0ml2uvXpDhH5N9J+Ly+L/g75Ida1UBiRTXxAWCqdZRvqYZkfix3+9K4yU+RKdVo9rd2aY3VwimvARibkJJaOEgjYahYrQsztgZlX8vRclYRBTJ5Sj/HGG8HSc83STYDyEJEsM+wI753YL+kyjVBU+goHJ2oqH4FSIXLZFIB6l39VZeWaNs0OxGD/aOrTSSRdvrrI8K5FPV3B5XaiKSXuoorkcwv8wgFq+yuqtBQpFUfW5xPiBWKDIkszTJyUuLros315n+4NHQlL1zgOqRRFUZk6So2dWxGbr2o0I0USA1No88cVpPEE/Hp8wfK6/sEnYP+bipECr2iKYjBGLOSmeZAV69Pa6FXevOjUUVbUm8b0nVbqtHubIJHtconiWt0LwHDaR93D88IBhX2z+puMKS7fWvtAKF6o8+9WbPH9FZ2HWztt/tcfZfs56/ZVvPc9ZboxsU4hNRxh0+3iDHtqtIZlz0VW6RJDObi4JPGqpyr/6348n57pDr2cys7HA/mdPuPrKdQbdPnZNpVJocXGQpniS5flv3KHfG5ItKyh2G0dbx6gOlZn1BZw+F52uSSXf4OBRhi//1ov41Tav/voXasb0kzSrs2N6PZ1uq4Ndc2DoBrPrs7jcKjdeu0GzVGXtuSvMzAmamKLIDDo9VLvKaDRmdn0Ou13G5XUTjPoYjcY8vb9PbDbJZ28/YnZ9joviGG84QCl9wbd/NUKj0mLx5ipjhP/IpoqNaLfexO130+0Z1gKq12wjTxZwtZKYgwYdYVR96c071sb89WcM7OpYyAw8Gte/dE10TQ/SvLrZwmGXuHLFT6/Z5qMf3kc3JAxjTCzmFOGGjQ65ExFGdPdTYRqOBCTsTjuNQsXKFOi0h1Z1cGZjHk/Qy7237xNMxlAdKooseOsOt5PBYMTytWmuPDPNC28+y6ArZDblixrNikiQrpznSc36GfbEortwVmJmY4HM7ikur5v1Z9fIPr1AURQMw2TrvS1yx1nmri5zY9PN1Q0nhYYDRVHwR0MT06ybkSHhVgcUCx3KmRzL61HSx8IIvH+mWGZ9n89Bo1DhvbdOuTI7pFmqMT3jpdPoUrsQUka3V9B5TMNAkUXmgS/kx67ZOdvLUMoUqVaHFM/LeD0yzZZAar/3XoFhXwT/uQI+uq0B+XPR8ZxO2Ci17ISn40T9IzTNxtKtNTweGxsvXkUfiKT7frdHLCruQ6GAIOPMrk1jm4xtvSeSzJOrc/jdJkcZg6vXAsKAHld56ZrILogtpJiKKOiDIfVihfjiDOWLKg9//FBQf04v8IU8lidtZmrM2nNXmF9P8pu/9yo2VeXGy6skFuLkjzN43TJrmzH0gU5qbd7qcJYzOVFYmvVw+PhcdM5iguQF4PNIBKcCfPKDz/G6J+GSLZPVlM6gLwzFg06P2SuLgDAd64MhFwdp0tuHePxu7Jodh+agki2LcVWXeWOzjDfo4fH7W6iKSXb/lK33tpjdXBKd46AHfaLRm91cQrbJdJtd+u0uDpcDY2Ti8QvdvizBbEJ4vRILCa6+cp1KrszcxgxXr/iYmzLwBH3c++iEQX+ANyBQ5/1Wl0F/SCYvxvKVaxEyuydoLgd2zS4Shz0uOo0289dXeOVbz4t7YqYovIe1tiiQTYVZvRKjlCkR9I6ZXZ/BHwtTOc/TKFS4edVJb+RAUYTaIbk6R7feIrUyLbKC3DKLN1c52s7SrjVYnR0L0lezTaPc4PBMmLEzB+cAvPVWAbvTwezmEqV0Tvwu1SZzVwXVKRCPcvWV69xY6NPriy71z1KTflGPX+YQ/C2O8Xj8++PxeHo8Hs8jTMHvjsfj3wV+DPzW5G3/CfAXk6+/P3nM5PV3x+PxePL8dyYUogVgBfjs3/TzL2kYg77OoD/ihTefBSB3kqff1XE4bGSPxQ7V5rCTb7k5fnhAIB5BkiTcIT+PPz3kpOYnfSxMvDbZtDSEbr+PH32oozrsxINCs5vdP6Vbb5JcSuANiyqu3W6jValx9GCf5OochaYdm00iMj3FzoePUVTVmiQBms0hsqJQL9UonJXQXA5Od07IZersfbKDrMhWuE2z2mY6YbNu2gAPHrd5+OOHlDruCTt6TK/ds1rcpjm2+PGyJDSqlVzVWjCWMzmy+6dEUlEWb64KM6tTo9Ua4A75mV9P0G93OD/MWbt/RVX44MdnZLNd3H4xkP/8b7pcFEWLUlFkiidZErNhUZ2Nhdj+4BG1fJWpsJCnpA/FubgMylJUkf487Ou4Aj4cLo1gMsbedplMUSYQdqM6NT75wedEwwrH22fohoIkybjdCoYJS1dn6FQblj632RxytnOEz+cQZCDd4MlOjdnNJdwhP5W6WGwF4lFiIRh0+lSLIkgqsTxL5TzPzoePaVaaVAutyWepc/D4gk61wWvfugNApzOi3jQIRLyEo25C8SCvvOgnny4QSwnpl6Hr2J0CfXn88ICP7nZZvZ4i+/SMzlAhXbSzdCUuKndOmF5Nce/t+8Sidqa8fZqlGr2eKTS3ioLNoeIPu3G7hU43tpDC5rDzwpvPsn4tTrvWFDf1qTDnhzkC8Sij0ZiTJxnyx1l67R7m2KScb+GfCmOOTCKeIbHpCN6Ql3atSaUyIDKTYGyOqRdFuE90LkkkJfj2YiOrMuzrNMoN8scZCqdZ5jfnKV1UUe02XKpOX5dFOJWui6pVFyq5KqpdLEwahQp339nikx2VwRBeeH0V0zTpNdtIssTB43McdomZ9QW8AeeE3CITm3KhaTayxwIN/OxXb3K2c0SrUmc0GPKbvyXyGfTBkG53RCAq5FbbHzyiWaoy7IsNWng6juZxU6+INnvYP8bpdjG7Potdc1A6LzLo9tn6PMPKZpzQVIDz0zrZTphh38Awxnzvr9oE42H+4odVxmPRyQlNBZBkiWqhbs1PMxsL2FSFT97ZpVGuo9pFldXl1VBViW6rx3BoUs4W2fnwMYOhyEaIJINcfWkDfTiiUOhZVKc73h3K2RKFiaQjf5whl65wNuHhO90O3C7RAbI57ATiUSolUY2OJgLEF2dQVFUYwSvC/xKZSfDf/c9ZQu4R/sj/Sd6bxUZ2pml6zzkRJ/Z9XxgLyeCWZC5UplJLZkmlUrWqqpfqru6pnnE3DHhsY2BM2wbcN/aNrwa+HPjGgAc2uo3xGDNT7jG6xt3Vtam6JJWWVCpTuTGTyZ3BiGBsjH2POHGOL/5QqMpow9PlQVtABUBAIiUyyBPxn///vvd7HhHHyh+VZ6ZZO5ouYzFLNNtTMXwe9OGxqbicBs6yHU6eCmSnURFUlcJBHrPNSqWm/QISd3l7jWTq843A6eMDGqXPhwuLh2eAyG8PO31CYRvqRKNY6FCvdHEHnESTAXRNJ7H6ebzn4GmJQDwknBNRH7ndE+ELaXR48t4jTFYzkixx/ycPBQlqJAY+d3YHfPq4y90HPUrHOWr5Eg6vi5MnJ/zTf/I+Q9XEV247MdutVKt9nB4rFoeVu++fUasI8/xwKKJ+G9fiBGwdZKOBd/7iIXa3DaPZNEePGs0mvOEA2cMKJouZ0XAkBkjTESRZ4uk98Td89qxFODg7XFsUHC4zjXKd1e0lVjb8pDOiOvxsr4+uQ7Nc4727E37yZx9yunNM9qRNq9bBqBjxR73Y3Q5aM19MvTmdvx9y+zkMijCXl0/O6bf7NLsyL1+a8smdMpPRhPffK9EciOdSOSkQ8qi4A9456nLtapLtN7fZeGkVdTSmWWtz+TVRka82ZWrFBqOhykc/FaLE/Sd59u4+A+DRowZ3frqPP+qlsHc6f324w36ubih893/5mbhX7Z9RKOs4/a759e53BvgXIlgtwh/kcRvwWvoUDvIkN5exOOzUi+J1db6fRddFV8od9jMajpmMJ5SOc3Pktq7r5DteLgpVrr5+lYOyfX6vOnt6hDoac/r4YL7+XX5BUOu06ZRBu0t2R3TZPUGXMEQbNMo1UQg6vP+ck2dZpmOVnfcfk82NOKsYGQ9HZC7F5xJQ/0KE8GKMUNxP0CdkpPfv5LE47Lz4coTi4RmFvVN8UT/9ZofTxwcUc02G3R7qRJ2/p+1uO5IkUbsYYDQrHJyqNCttFLPC1u0rpK+scF7ReXhqYzSa0u8MKB3ncYf8HD3Yo3Fe4aKmioPaUghd06i2DPjjYSHga/ewmsV2U1OnWF0OXrkVoVWucVGoEk6LvUq30WLUH1E+ORdRWnXKSdWK1aKwuBZGmQ22f5Eff5eRIUmSvi1J0lNJkjRJkm783772t6ZwfpE8BP818MeSJB0iZgT+ZPb5PwH8s8//MfDfAOi6/hT434FnwA+AP9J1ffr/9kM+s7c2yk1MJgO5I0HvWNpKYrEpNOt9qllRddCmU/70f35OZCmBzWljKePBHw2I/1bRCEacuPweQpYGS1tpYqspxsMhimIQm/2CAXfYT2orIwgfDsFvNtut1MviRr156zLFwxyrwTYWs0S3KZCcnrCftZuXhOBqKcFFuYt/IUQgFqRxXmHYH5HeXCQcm0mD9gvEMgmquTL9Tg9JEvQSm8uKYrXw4J0nM83753+LF25liIVkLipdWk1xoLE4BJHGEwly88srhBb8BBJRrC4HazcvUcmWsNrNPP3gyYxAMiUQCzLoiU2TySpuZu6wn8RykGs3FwgELBQPzzDbrayvu4mFZGSjTDDiwBX0cfWyk1alRvHwjPWXN5GNMp2+RLfRxTHjw39GR/AvCH5/ctGDwys2pM3SBbJRJhXWuPej+9jdTv74v/0S7a7OQibGUDVw+fYmLoeMpukEQlYsDmHm7NTb+P1mzHYrRkWiWukLpKLdxEWhyngwotGcoGsag04Xn2OCJEscPdgjshSbb0aSm8uYrCZG/RE2l51GuY5iVsRGOyvQsVc3FEwmmacfPGH306w4YJo1jIpCKVefR780VeP0uWgzx2M23vvuHcLpGCHHgO1kC8UoWrAnZxOKJ2XWbl4il+/jsfRIbS7OUarZnUPaVYGcNRplgqkYmqqRurTI048PufvOPpPBUFChDAaii2GG3T5Oh0w8E5vLwIyKEatdVDT37+3iMImqeb0ofAunz0u/UKlTJ1Oq2XPqxeocQRpZijMZq4SSgnG+8sIax4+PhLNhrPI0b2c8EdK3VrnGVNX48L0Cgbh/fu1BDKy+dEml1pii6TqZywnSW2Kzdv3VRSxmiWq+QnavSDSTRJIkElEDreaQxKogVH3y44d8+fdewRMRWdd33hXdGYvTxvHzMtpUDNp+Nuy+kPbRbw9ozdCQ0YSb3O4Jra4QNW1dC1LNnhNbjjHqDdl+KYHHo9DrDFjZ8BO0tQlHLLhcCtev+1FMCn/wO04+/euHpK+ssHf3GcXDMy5yRcZDFcVspHImDpnugBur3cbDnz7EZDHTKLewWWWBolzW59VHEHGBVq3D6ooTu8vKeDydD4P/oHCFQbsrIhEzLPKwNySYiCDJMrsf7RAVfw4xY+KyMZ1qDPtDVFWjdJzjq9++KYZMZx0Dxazw935/mf7EgM1p4ezZMf12V3QGuz3GUyP15pRWc0ww6iQQ89Ebia6E1yecJZ8dysXaY2PY7RHwyRw/3EeSJPwLETx+OysLGla78DwoVguhZASryyFemy4Hui4JLLOu8eRenmathyxL5A8L5PbzdFoDFjIh8odFbB4Rf3N47BQPz/AvRObFl41Xtj7H6/YHxJeCRJcTTNUprqCPrdtXSCTE++mztdQd9tMoXeD0uwkkorz70MjDXVUMktZ6ZJbFmvqb30xhtiiMByPK54LiJUkSRklcJ3fAy/lRke0vX8bpEQO36mhM6ThHYjlIqyIwwAZFoVYU3cvEqiAi/b2vWzAaxOzX1mUvo9EU2WCgXukQCRp4/kSsJzabkfWQyKaPRiqrNzbwxYL0u0MxH2OzsHf3Gbqm8+PvfCiui0XGHRa4VnWi4p+ZdtNbS3QbLQKuKY9PTHz5jQgGo4Hbr0WIugbz6xp3NrnIFbG6HHRqDe6+/RCXy8zpbgGLw07lpMD5cZlOrUEiOKVerGCxKaxdFZvE8UDE+byxEHanidRa7BcO6eOhiGEWLyRe/Y2bJDYWmU4mtNuTuf324SNRqEivxzApEE6IA9JFX+BPz54eMez26NQaeMPia6OekBe2yjUmown184tfuBcFPLB3ZmDY7VMtNrGaRXfy9lfSWF0OQotC+md1CTFdp6POI3mJjUWuvXEN2Whg5/0nQvLYNxD2Q+lUXKvpWGXY7SHJMpdWTcT8U+xuB72ucLM4PVYsdouwS49VrsTrcyhF6lKK0Uz8ZfO4sNhMeCIBrr5+db5nSK4lsHucGM0mcs9PmE6nhMI2FjJRxmMNi8PCZDTheOeU08cHGI0SX1s7w+s1z70toQUh00ttZUjEhXm71xXo1nZXxxf2sPXqJXyxAEti+cXucRJMhDk+ERCB8WDIaDjCFfShqaKLMJ1MSGws0qwIEaXbYyJ/Up8PGn9RHzp/u4Hifw9DxTvA7wLv/fwnf1kK5/+vBwJd19/Rdf03Z/98rOv6TV3XM7quf1vX9dHs88PZv2dmXz/+uf//v9N1fVnX9TVd17//7/IzHW67aPu+vMh4PCWVEVVMo2LAalNYXvGydHWFxIZAsP3nf5RBkiX6nT6DwVSg7HJ13JYRBoNEYiXCSSvExXkDk8WEyWIhEhaiH12H5Jqg/vRaXS5KQogTWAjPqjBO6uUWwVQUWdJotlQcHqdgpIeFnTSSjgpbqN82x/AFUzHq5xcEwmJxiK2mCCU/j/gsbSWpNTQSqzGMRpGtfevbN2mVa+yeGdm8dZlWc0y3O2Y4ho1NP0ZFJrGRxukXi4XFZuFkv05q0Y3L78Qb8XPw6R7prTTNiw7bb24TzSSwO00YFQNXr3qJLCVIrUZIrkR0EYvtAAAgAElEQVRmGdgxg8GUwWDK9a++gD8eolAY4rBMaZVrWC0GLA4rTpswp/oXItgdZiRJJu6bYHfbOXokmMuKSZh3LTYLF7kiP/7Oh7QqYoPmi4V47XaARc8FrqBvXgVUVR2TZWY/bPSpNabYrRLqRMcT8omFbCNBNGQgvpLAahbzDGs3L3H7po3FzRSJtSQ3NsX7yB3ycVIykbm2zItfu854OBYitLU0vZagmKTXI3jDPoyKkVdfi4soSk9cox//tIHXJbH95ja3vrpCvdwgW5ZxeMTGxuVzMh6OyVxeILoYEAZUj8zX/uAWF/kymi5z2nAyHmuM+iNqF33iyxFaFx021uxU+y4G3SHVqqjgrt7YwL8QYtCf8KN//QHeoJtOvUk1X2V1e5Ff++YGmevrs2H45tz6+dGHZdr1Ll96a0Vwtds9LDZxuDGaTYymYsDWbLNg97m5/GKCrdtXcAfcpK+s4PTYyVxfJ5QUbX7RHRgTjLpxeoS19PyoiC8aFN2nQhWnTWem8hDtdsXAt383wuH95wy7fUKLceJraTZvXSZfNyNJMBlrdNsj1ImoON1995C//lGWSDrCytYCgZgYPqw2dHY+fMbpzqkQpL20wcFOEZtT5IM7zR6XX7tKr97i2k1x1xp2exSPC6L79ChH6TjHtdcvCz6/TUSjnu93MdutZE+7JDYWObz/HF8swN6zCwwynB+coesw1Q0YZKhdjHDZdXRdp9wyE19JYlQM3P7mS2y/uS06MDrc+9F9LA4b8bU0Lp8Dl1/gREf9AS6fA6dd4tZv3eS8ZiS+lmbr9hUW43DppkBRhn061XyNzQ0X8bU00UwSn1Ow0SPpKMOh2Cj0213qxSrBVBS7z83u4YT1lzcxKkZ0Tcfjt7OQiXL2XKCAh4MpS1dXWFwLc/X1qzQrdfojiVJNIrd/zuqNdYKJMPJsgN1t7pGOy6SSwlhcypZZcHeo1VUa9QGugBeDIgaYM9fX8UVm3PmWzsYrW/RaHQyKgWd3Be6zdFKg3+kRW17AE3Cydl10diRZJuZsk7mWIZyO4w26sNrN1IpNFjdTeMM+TneOGPYnxBbD9JttYXq2m8RmyW7h1d+4iSvoY//eHtde2wRm8jZNZ+t6HKvDQmI1TvG0wmm2j92u8MIVB5u3LhNbirL58gbaVON3//4qkiTN5WjRhIePfnbOeDDmJKficJlZ3FzEajejTadcW5cxMMUV9LJ5PYGu6ZTO6gx6Q8IJn7hfJKIspm1YXQ5iqykhfzQYZq/dAYuX4jw8MmO1gGww8NMfHlGvdHD5Xbi8dsYTiCa9oor9tMTDvJ/VGxu8dN1BLOXB7rbzlTej1M+rVLPnolKfPed3/uGXcIf9tFoq3rAPh9fJZDDEbBGd6MlYJXVpkaku8eJKnzsf12fRM53jqp1QzIV/IYJREnW62HIc/0KEy7c36ffFPJ6uC3RrIOYT/gqTyuLlDMtLdvYeFUhfWcHqsDIejggngyymbYSj9nkGXzEp2JxW/NEAk4nO3bcfirU67CezaGbrxTSprQyv3nTh9FgZDVV6Ax2bzcRwpDPVJRSzifWXN1m7eQlPJIgn4JzHEPvNNkvXVmmcV7C67HPzeeb6On/6Pz7k1zZFdVuSJFLeDplrq3zwThZfNDCPewUXQlx74xr50zqtmVui2+px9DiLLxoitpJk45UtNE2iXINrt9YFNe13trF5XHM5ZLNnmBuj/QsRRkMVXddJbSRpXXSo9h0EE2F+7etJogn3fAOe2kiyvOLFH/WxuOTCZDEQTMU428uRXFsgdWkRXdNRzAoWi8xkrOLzmTh9fIDL7+KN37oi4q1DDQMqZydNHC4LoWSMdr3LlVdWiCQ8fPJJHZPVRPm0zGQw5NryiHq5id1hpt/uU20Ks3EgJrpxVzYt2N0O0ltiPiq5tsDl166yvL0m/E67J0QXAyxHx3Q6Eyw20xy3+4V9/B1jR3Vd39V1fe9v+NIvReH8InUI/k4e3VaP7M4hf/1vPmIyVul0RnhjIe6//Smt5pDv/Yv3KZ4U6bZ62Jx2LjoK7brg4aqqjicSJL0S5rhqJ3tUI3dQIuzozFFwmq5xeXFE6bRIsTTkyXuPaJTrjAcjKmcl1IlKYe+Us+dntCo1xsMxg3af3bKXRExB13XMNguH95+ze++Q/F4Wg8HAzscHTEYTopkk7oBLYDOLHQ4enXC+n+XowR4mkxFX0MPe/SMiQYnsbo7xWMXqcjCZiJiONqseB4OiyrXztINBhr37R6K67RSVQZvLSmbdz1/9b+9z/HBftFA1nWalTWHvlN2PxedcLhP1coOHD0W86ME7T2jX+xzef850qmO3iw15s9ajVqiwtWFFMYpsYThoYNDuU6hIjHoDHB4H99/+FIvNQqmpYDDK+GKCKFI+LVLNns9FQnafm1FvwKDdpZYv8dHdJk9KQrbSaw9odmV6PZV+Z0RnYMAbcNDrqQxG0GqNmIwndGpNBr0RF3UhdJpqUK8Ii+S9x2O6rQHdVo/v/6RFaDFO5aRAr6/x5L1HPL93zPl+lkBMtK7tbjv5wzLHu+dU8xXa1Tq1uopsNNC6aFLJFgmGHeSLKsGgjdFYo3FeIR2ZzlvHh/efY1AMFE4u2Lt/xKDT5Z//Dx9ydiI8Bu2hib9+ryXiQQnfbIZFxh/1oKpicSkenjGcqe337+1SOSkQi9nYvHWZ4UDg79wBNw/ffcKnn5QZdIcitx0Pz4b3hoQiLhbXwjz4pEhoIcBkMKTXGWGyCI9Af6wQSfqoZkUk6sff+ZC9+2LTpms6uf0cvVZ/LpGaTiaoE5XFRQcX5000VcPhdXJRqDCdTOg329jMGuXqVJi5R2LxH4xlEWGa4UELe6dU8jUsJh23y0C52CUad9Jv9xm0uzg8DjKXIpROS+SOqxw+PELXdYwGQUOJryzQKtfYef8xsiyGF7vNLm/9epr92fM/PmzSbQ3mJJtBu0flpEByc5mjnRyVbJHv/Yv3Od055pMf3ie9uUgsbkediIHoz6RoBgOE03GKxR73Tn00mioTdUqtBWdPj/jkYY/WRYtmpcXRsyLHO2fElqI8++gZ0UxyjuQcDSeokykvv55m0O5y8Okep7kx93/6lJhfZdgbsvP+Yw6yOsVsjdzuCX/yzx5jtpn56Y+zFPZOKR6e8ab+Awp7p+R2T2bD0TLeiJ9RT1RyM5fThIIKz+88pVtvY7aZCQatPHnvEWabRbguci0O7z/ng7+4K3LS60mmU4gFdFx+F7Vig2quTK/VpbB3ykA1c1rQuKipFM9qJNcWOKq5CQUULs4bVLPnFPZOKeyfcXj/OdVcleLhGeXycEah8WK2mFAnKlNNdBAMipHsziG5/QIPf/qQbqMtJHiqiWa1Tek4z40bPga9EdVcib17eyKiFw+jqlOefvAEm8dFt9Xj/tuf4vK5qZyVOM81BMp1MmH/kehmJtYX6ffG/OT/+ITD+885e55H0zTsdgWjInF2PuX4yQnPP37GybMcskFGnYr1xWiUOXqwR78/YfvFKAajAbfLwN6DLPv3djl8eES7Wuef/pP3eVaLMBlNODkQEsnicQGHy8ajdx8x6A64yAmARa/emhPxGucVbE4L4+GEeqXDC8tDHFbBmf/GNzNos6r30eNjxhOdUq7Jo3cfYbKYuGho7N/b5UdvV6iWupw+PiBXVFFnUrpOTdDpCud9vGEfJwcXdBvdOaUmv5flfD/L2dMjeq0+2aLEB08tXLvmY9gfUq6MCTgnfPzjR/TbXU5bfhIbixw92EM2yNRKbSoFgbX1hgMU9k7xBwXyWMQodAZDneCCT0RcDs9IriWYjFQcNon3/vIRlZMCpeMc/U6Pi3MxBJ+Kwa1ff2F+r6/WNd798zvi3lzQaNf76LpOp6txsJPHpMCfffeCyWhM6bTC3t1nJFZjNC86KCYhtotmkkQTIubbb3Wo5uu4w35yz7P84//yCqW+h06zLWZxVBO1UoMbrybF77QgIjE2p4WHP33I6c4R2gzzKssyb/zWlhC9Far0O0MePe3hdkpM1CnteoedB0X02WxioawjSSLqpZhEwbBV67C0HqGUrVA6zuEwTUSEq6MzHk7JlUX1uVltc5btCJzuRKfXHc/X7qcf7nD0YA9dE3bjk+M2vVafex/msLocnD094oMf7wnKm1XmbnmJeNKNYjJQOs4xGo64qHT5+Pv3ePrBE4qHObwR0WHZL4jB85O9kiAdGiGUjHB+VMQd8rMRbtCZmdDdAQ/Z3Ry5fUEaas9IRJV8g4uOQrcjHB4/D6z4oj6+IJShX4rC+St3IACBq9t4ZQu7wywEIbUmsdUUNpvCm99+FaNipHEu8sCtriBEnD4+4PSgyqg/IH9ao9bUZ9SBAVPNQCjuxaAoOD0u/upnQrKVTlqF3nsxiifkE4eCbBFJlvFFA+iaRrsmtOxRz4hqXeN8P0t8KYRiFWQWXRPt+vUXlpmMhKileCIWCrPNhDfsm5Ni9u/tMmj3Z+IcoTkPRoU5t14bMh4OsVpl8ofnaJpOvz9hY92J2SSxur2EJEtiRiFX5/jhPqoqCAzBVIxgKsbKC2uUTwtCEjVzEkRCCrIsYjB2tx2D0TA3wpotRj69kyMWMXH0YA+zzYrLNqXeMeIKevnp2wU6tQbPd0WkJrtzyLU3rlE6zqEYBf5OMSnIRgOuoGf+PABefOMSwBy5evjwiKPclF6rzfHDffpDnV5vgs1p5snzMd32kPe+ewe3Q+fZ3YOZ/0FiMlKJh6V5dOHV11NIsszmmoXKWYnKSYGFlItoUuQpklGILCW4dFPQoHrtAYFEFJtTdC4Uk7DW2n1uTCYZTZ2ykBGuhrde0sikDHQ7EyZjjcz1dWwm0UZOX1khkIjSbXTotnqCajEY8fV/8BL2GdI0aO/xH/22jNMuYbEqtJpj/EEbDpcZgwG6IyGXS6TcczY3wPl5X+SRFQPpKyvkdk8w263EU+Iw4/C6KR3nePmrG5gswqi7lrGgTTVss5/t9QnPgjvsp9pWOH4qNsnxtTQ33rqON+KnddHC6bGxur1M8fCM8WA87yq4A24OD9t0Wz1G/SHFwzMcXhcvfu26oKY4u2wsfW7uzh+KvH+zVBU3q1krPL4UwmjQUVWd8tkFTz7JzglXL9+OCazwcox+Z0A4HcXtt3P/bglfNEA45sYbC/H6t15mcU1wzJ0+JzvPuoL05XMz6I1Y3QwxGY9RR2NimTi3v/kS3oADXdPF51ZThFJRfucffgld07HbJCajCSaLMCknM0FOswNKxzka1S5L4RGyDF6vBe8s0vz7b2p4wz7cARdrV+MoJhO7H+1w7fXLOD12kpvL3HjrOhtbQdIrQUplEWOxeZxsrRqRZInHBxK1vLjOCxERxVm9sYFRMVLYO+VLX0kKBK/RwGPH66SvrLB28xL5fA9fLIQ/4hGox5MCxbMLFKNEaiuDw+cit3uKoogIykWuiK7rtOtdkpvL86HEvbvPKBTHtPvyLG5iJpyO0irXxHCvasTnMbC2JIoCg96IgGNM/nzE0kaE9JUVFKuF6UzANeyLw8n2lthIlI5zlE7FWtfoGjDbLHMCiTswgwlcXmTQ7uI1dykenuGLhfizf/6AaELMZUSX4oSjDlw+J51Gj+TmssAsep28/q2XZ/Z3DavdzPrNdV79jZtsbKcJp2O0621sdhOvfuMF1l/enNvr/T6FG+s6DrvMZDTG4XXPiiV1/vS/f5ev3VJ4dlcU7ex2E5GguE5el0R8WcxF/PYfio3r1u0rpD0tAvEg/bb4/a0OO/v3dolmkvP3sc0uXv8/H7Gq5mssrgZpXbTpjhUMkk56I87eQX8uXRu0u5TKYxxuQYBpXbS5sixacUJqaCCylODsRGy0PluLbB4XoZCVZqXJiy9H5pFAIUcLzClDZpuZ1cSUgM/Aj/7yQMjh/CYc5jGZa0Imma8KHK7FYWc8HNEo1bE6zOx+tEPpOMfqjQ00XRSJCg0z3VaPblfF7vg8L96qdeg0u9gtGvGVBTEUnYphc9qp5UtCmnnO3PgeW4qSiEhkrq8LwZXLQLve4ekHTwj4DFgdVhIhjW98PTyLCol70JP3Hgk3xnRKbveEfqdHrdoTAkCDAU3XaFcbOLxu3rkz4Lhkwua0i0LAxEAtX6JWEzGlZrlGaivD/r29+RzBZCAKMFN1SqUi3tOSLGKOG2t2nj7vc/y0IDo1HhtGRcHisLOW0qm3dGKZBfJHVQ7vP2dlM0Kt2qNZEgSxmLlEt9EiGdE5zzUoV8YEUyLWmt3N0SjVUFWdQMAqcK4+9y+src1KHYtVYflSBH9EgACMZhNbN5doVupoOkRcQ8ZjDa9XXP/GeUXI5VIxsXcym6gVL9i8dZnFyJiLXHE+czGeQLfZYTD7e3fG1rkvx+W106k1GHSEh8nmdnJ4/zmdepu9YxW3x0J252juKPgiP34JU3HgMwrm7OMf/fz3kyTpbUmSdv6Gj9/+9/3cf+UOBAbFiMNtp3hSplJoCD55yE+tUKHfF0NWJsvnXPzFsJCwuII+glE32nSK2+/A7ZRwuGwi89YVA2/TyYReq8uL12yiFW0WmefWRZvyaYHwotgYOv0ejIpRDOwlwihWC0PVgNEocGaDnsj9bt66PMeQSrLwCChWCwaDgauvX+Xphzu0L1o0K3XMNivRTJJOrYFiMhHyy6y/vInJZGDQHXD/7U/n1cCFjNhUX5w3+exXNRjETV02CJ58IBHFZpWZTjVqhTJWh5X9e7v442G6jQ6rL6wQW00RD6h4wx7+8I9eZzKeEE7HGHQFIu7+258SSfhw2XXia2na1ToXbQNJ/4BWucb2i1Hia2kyK+45Leh0tyBahq4xLp8QQNndLobdAf2OGISSjQb2HglyTbfexuZx8cLrl3A5DLzwlWtYXQ5sFglFMfDgJw+4tGqiXm7yH/zj1wm5Jvxn/8XVeUs4u3PIcU4cuhRFolQeYTSbOMmpSLIsjJg2ccjxL0RodATpqXjWILWVoXAgDuEXhTqXX7tKLC2qYb16C4ddvL3cHitL11b58V2Zdk9CNkj0ZjMX7YGCrun4Q865dOjGlzJkLovsvckkEw5bxe9cdlHpOsjmJ9hsRqaaRq3a5+Pv32M01hlPJM6eHVOt9vHHw3Oz6GioEltN0Wl06Xf6JDeXsTntPPjZHrLRMK/onOeFqfaTdw/49FGbxVWBZZWNBiaqOAC2yjXSgd6MGtKgsHeKzaZQy1cEjk8Rv7NitTDsC/mUzSWqf2trLuJLEQZdsTFrnFc4fJKjeHhGtWfnvY/7NCuiAjfs9vE7JshGA+mtpfmmsZyrs3us0e5OGfT6JFci8+HCYmnMwc45zUqL9e0kuqazd/8I2SDj9NopZOuMhyO8HoX8aZ3k5jKnjw+IRm24A2KQ8nTniPxZm0hKbNzqpQZ2h5FOa4B9RgNZvxKjfHJOKgZOj5XqhYov7BEI1IlKMmFlbcWGQVF4/Y0YRllcx/FEo9MTJuUP95x0m13Ojwrc/clTzDYzBkXh/tuf0mn2CEbd3H/7AYoisf+4QDBowen3EogFqTQkUpdSrKalucVZlnVi6QD793b5vT/Y4ht/eJtaXWUyGBJdTlDpWikc5Ni7+4xue8RkNObJe4/mPHmXz8lZfkS73qZVrmH3OLn/UU5EhyJBFleDlI5z9Fo9Oq0hNo+LrdtXxOvbNsVqtzDsDek2uixdW0U2GIjY6kxUUQKLJv0c3n/Ogr3KYsqMxWLEZDJiddjmr1N/NIjd5yZf0ueABG/Ej8FowGbRqZwUsHlcGBSF/F6W5OYyTz94AkBPtWDzCLrZr3/7GpXzNk6/B5PFxOOPT9F1HafXTiVbYtDuYndZcbnEQOXS5WV2P9rh6QdPuP/OE57cOaCSLRFJhQiExCyGpmokN8VBt9fX2MsZabZUTFYLk5Hg7E8GQ1782nUC1g7rL66RvrJCtzOiUNaJLCX4yU9KBCMOLA47u89aYnYt48YkTxj2h7zyhvBfWBxW0ldWSK2GCc4MyKsZO2a7lWF/KCyvm8tEUkEevL8vhppNE3ojmQc/ecCNq1aGA/F+yVxf55VrBjYuidhJYjXCYCxm25IpJy63hdJxjt2PdnAFfVROBFLWG/YRDQkPhd0qMJJmm5WTJyKxq47GLF1bxWIz4TBNuBTr8soby6y/dIlYUBBzxsMJFpuVjQVRNBn1xZC70azgdIt5KaPZRDlXYTKeCgu5TawzRqPMnb/6BBAZ+E6tjdVupdOX6bX6hBIBwokApeMc3liIRqlGOg6t1pjCQZ7dj3YwGnTsDjNWh5VyZYxlVjhw2nTCCx7MRo3hWGLjlS1MVjPBVAx32I/T76VdFcb6zOUUtWIDTRUUsFDcjzvkp9tosbxk5/ZyCXdAnPLNyhSzXbxeXEFBnJuMJ1x65RJLV1fYfHkdgPFgiD/q43j3nGAqxguvX2IyGuNxaCymbWQui6yP022ZzeTEuGgbMJtEAavXElSg83yb6WSKPx5mOtV4XE+T2V7h7oMRL9yM4vMqaFMxM+nwOhl2eywmjLTbY2IrSYyKkWAqhi8WJLQYJxAP4vaYOM82yWRcpLYy2D1OtKmOOlHRdfBb2jidCu//4AmhxTiSLPPyr7+IyWIin23iCflYyMSp5GuYDeJanu9nBS3OCpqqoY7GrN5YQ9Xkud+m0+yT2sqIuZsZ8Q0gkgpzec2A2WwgtpKcU9C+qA+dXyoydPEZBXP28T/9wvfU9a/qur71N3z827/pOcwe/84Uzp9//ModCKYTldZFC4vNQm73hHp9yEWuiMVho1UTL8LxUMSIOrUGrYFxjpzMZByMegO0qcZkIrBdoWSE8VSaZ9U7tQbjiYSmTml3hRjFZDWhaxqdmkBztqt1sjuHKGaF4mEOdTRGnUqoqk58JclwMCa+lkadiJmFyFKCg0ei9Z9cT9Gu1lFVjRd/bZvpVHQjZKOB5UtRjGYTzVKVXHFK9lmW5w/O5hlsTyTIcKTjD9rJn3Vw+518dKeOokDuqEzlpEDjvMLaVpiLXBFNB4/PhiTJ86jOqC+EVOWzC873s/zbH7Q4eXxEfyjIOmdPj3D57DQrdV76xg2On+Vx2aYiVuNzz9tkkixzctymfdGaLZxjJFlmbTtFs9ri7lN5XtWOLUfxhn3YnHYsNgveSBCnx45sNMyNvhaLEZMicfDolEG7S7WuUS11WN5eQ51K+MIe6k2V8VTm3Y969OotEuuLYmBriRmHWsfnMzEZDBlPtBmO0cliVCyqtXyJg6M+w96QyWiCruvCCVFv4vI72Xn/CYrRwHA2yNZsiwXx8FmB44f77D85x2aBVMJCrdLl+NEBilEIo5xuM4rZNI/gKEYD8bU0hwctul0Vp9/DZrTJ/pmE3W7AYjGgTjT6nSGRpQTTKXT60gw1KrCNRkURrP+MYIAnVyIMuwM6jQ7T6ZSv/95lgaUtXswyqWITkd6I4/NZefD+/nyAzuUyzyk77zw0Y7KY6TZaOP1ePv7xI7yRzys3Vrv4G0bSYoB4PBCHrG5P43jnFHU0RjEZZxl5cTMdTmRiMRvjwQib04475OO8YeLyra1fYFb3Wl28boOwhQ9GxOJ2Aoko7rCf3QdnvPLlNHa3nYtyl8LBGbHlGIf3n/P8zlNCcTfugIdcrsdU1SifnhNajKOqOjaXQKf642EuzuuUsiU8kSAGo4F6bUi71hFRIEUhuWBiOpnw4b2+uPm7RMXeaJSZDIbs7rbo9XUSG2lGY51//b0BsiwR9CsUy2PUyZRYENTJhEG7y5VX15hOxEDsS9+4IXLkuTq6pqEYYX17AbNJmsudQl5h8T48E2SwykmBUlUnf1gRxQu7xoOPsjSbI9xhP+PhGIuiMRkMyVxfx6gY5hb0z0hkuedZ+v0J/VaHxMYi8UwMxazg9LtwB9y8++d32H5zG0mSUFXx3iieVuj3VR7va+T3z4ikgtSLwo0yaHeZaArf+Wfv4bJNOX0uOj75XhBdh0FfZf/eLu1qHZNVmKttLlExLJWHmO3WOebV4rBRrgnp23gwxBcLsrCWmg/6Ll1bpTcxE0mLQ9xorGNzmjFbzBw92MMTcnP8cJ9I3EVkSRRD2vUu+7s1AomoOIClYgId6nWztJVk2O1RyV3ww3/5AT6fmXq5OV8DV1IS44nORBWYR6ffTea62Oxllh2ctTzoms7p4wMm4ymKIgaefSEHF+Ue/niQ4tkFJquZ0VjjXi7EdDLl0f0SuiZiP+XTIkdPC0xGYvD6LD8inBbXRB2NqRUvKJ5WSKyK6+c293nwRBz2v/+DMtlnIlp0eP85w4nM+z85IX1lBXWikS3J9JodKpUB7333Dt5YiK/9wa25NRygclaiWJky6g149KTDZDSmXa2LaKfbjq5pQnZVbFDuWHhwaqdcHtCqdXj4dEh7aMIbFEPEDkVUwg2KEbPNSnojTqc1ZPVyjOiS6I7F4sKPY5BF7NBmlVm9sQHAQiZOp9agXW+TzYsu+dleju6so9KpNQmlIthNU9YyVsLpqMCEd2UGPYEOLRY6DPsjJFnm6Ezj9HkRSdKxW8QaOmh3qWbPGQ+Eo0EAGKZk94qEE2JtO/h0j+JphclYRC8HQx1VN9Bt9EhfWWGsGogsxgmFrHQbLbyRAAaDTL8z5OjBHg6nmUAiijoaE1lw85u/k6HbaHN2WCWSjhJ0DGdrrWlm7R1jNBk5fXxAwDXlyeMGslHGF/XP4Q2V/AUXuSIGg4zTMqGar7G+aqVQ6KPpzA8r5/tZrC4HpwWV8rmI/boDHmxOG51aC1mSadfbRIJGDIqBw8M22Z1DWuUaT+8dMRkMGQ41yn0P+8+qbL60QqtSxxX0MhqJGLTJIg7Yhw8OaF802DmzzItq/c6AVlf4H1xBH8ePj6i0TfOh7/OjPNmdQ5x+L5nr60xm8bX9e7ss++rsP6sgyxK9zufD6h31CrIAACAASURBVF/UxxckMvRLUTh/5Q4EismEL+rH5XPw8q+/yMffF/ZXXdMZdIe89I0bIjNrnmHT6lAvNwnEA0iSQJFOpxrxwBSHx4lRMWCQdEEHsVrmqDzFbOKTO0Vq+QrdRgdX0Een1ph7DICZJ0AjsrRAo2ug1ZrQa/Vwemwzo18HSZaxuay0ygIz9lk2OxJz0u+J/LXRbCK4IDZf6khYL8NBA7qmE1zwY7Ka8YU9NEtVxmON+kWf6VTHaldYyoj8+fYracx20VbOnwmKiCyBqmpMJxPcYT+bty5jNCukr6zMW8iZVS+hVJSPf5alfSGyp7n9cwwmI8P+hCsvLdHsGZBkGVmSsVt0HKYRuqbxm2+KNuFntA5d0/j4+/fYeCHFYkKIvgbdIZOxSqNcp5o9x2wzU8uXmE6FubJTazBod+l2x0T9U156Y53M9XWePCjh9FgxWRT2j0Z4A3bKxR5xZ5NvfkVBksUCuP7SJUKOgbAxe2V0XVS337w+RZJlRv0hnYFhbrL1+cyokwnj4Rhd0wSrvjfAaDSQvLSEw6mgTlSWt9fozAyfr31VdHn++B/5Uafw1z88YSHtxepyYJA+XxF6zQ4Ar/7GTc5zovru9VkJBU2CD963sxCRsFkldB3cHrHheePrSxiNEq8v56lkS/R7Y+rnFwy7PSZjIejJXIpgNhtpV+v4wl4a5xWOj7sYFCN2t7BROz1WrC4Hg96YxaT4PY4eZ9HUKYe7FYb9AQaTkcsrElaHBU2dEkqG2HplA5dfdDj8ARter6jEaaowa8pGA+pozMXFkGu3xMZp0BtS2DsVMzKKwnEerBYh/mrXW3QbHfxOlUfvPppblxWrBV/Uz3CoY5AFC/0v/vnP6HfEe+rG7SVOTjpUzkrzeA/Ar/39V1m6topiNNCsNFhfcxKMuZmqUwbtPh63geOH+yhWi8Bqli5QTCYsNgtmm5nNDQfLW/EZknZCwDXFEwni9VowW4zsPq1x+vSETmuI0+8lFnfQaAoRn8su8fWvuPF7ZYYjnbOTBsXDM7oDifT6Alu3r/D040N6LTHAajQKgdqoPyKaSWJSJO6/85zRWLxOMlsxugPR7t9a1vHFgvPYnD/qYdDu8mRPHERPnxcJxAJUs+fz19hixsfR4xOsDhvJTBCbU9BCossL9LtjnD4Pud0Tus0ep48PsNjMePwCr2s0ykynU/xBO+6w4JPf+atPiIQULt+6RK3YxGSxzH/WYSPA7/6nrzFWZV59c4XLr11lwV7loj6dI003b12exePGxJKiin3runkeJbjx1nXsbgeHR8IHY7ZbaVbqTKcakYRPVHINMm7TAJtD/OzpVCcWdyDJIgLVrIjvNehPiKfE+27YGwpUs2IUHarsOU6/m0gqyKN3HxFajKOYFLbf3Oa80KXbaM8PT9lz8HlkvB5xj6hmz6kV67iCPhbDE96/N8I8KxC5PVaSER1tqvHCFQeD3ojC3inhRIBQUhQhJqooNnz2+2cuxeco3Xa1Qek4h8kkU8mWcPmcs0OdEcWk8PSDJ2y8ssX37rtxu820LppcvR4muhSbG3JDjgHhhI9uo0sobCcV0bA67Cgzp0DjvII21XF4XQzaQnQIkIjK2FwOkikH6a20oNTJ8pww43DbUMwKSW+XVzN11IlGt9HBZjNyf0/CbldYurZKc2TjIlfEbLcSXYrx4CcP2L/3nNODC4b9EfVihUZDFISaPQPugAuLRWLQEwdDSZZY3l7DYDDw0hYzD4xJHBw8LryRgOhGDow8ftpF1/X5YXr/3q74G0QcrGwtkNpaxucx4PI5KTZM1NtiC/T5zJB4XdqcNpaureLwOlDVKRaHHcVswhN0o6miE/DO957SGDrQdI1YwktnaODsmUC4SpK4d0ynGv3OALPdyvv/58fzOG2/P8Fq1sUcz3BMs9LknQcKThvIBgmDQSa24GT79S0CiSjNngF/0EY1V547eUBEKhWrhVqpyXnDgkExEHBN2VizYzRKlE5L8w6c0+/mh//yA9G5cdjxR1wUDs5QzCaG/SGBWABVhWvbfoHonVHWbr+1gc3jYjSa8jRrQp1M0aY67pAPTdVweyyCCFRts3bzEi+9tc2oN+ByaojT78HhdVE8POPp0xarNzYIJUOkNxdZD9Xn97zPaGkLq3HGw8m8IwzwgwdeXno1ij/iRv+iqX3/hsffMXb0W5Ik5YFXgO9JkvRD8Rx+OQrnr9yBwGRRsNrNpDM+fD4zV1+/KvCEssRrX00Ti4msdK8l8nNBrzAGt2sdplMIpaJIksRP7wzwBl0CeaZJnJ9WSa6L9leuOMXpd3F5OyLwbv2ByLPOiDQgsqOfMeG7jQ6nuQmVUmdeaQSILwVJbYnZAf9CBLPdOu82RMMKsQUHW7evoJhNmC0KreYQq8uBxWbBYdUJp6P0O0NsTjuRuIula6s4HTKqOkXXdTY3HFxa1DCboF4bzvnSZosRxWohXxA0jWgmyUImhslixB8VN5at21dYurbKcDilUaqxshllMstXr22n6NVbLC65mIw1kr4+4XScQbdH1P05JtFpHouORnNCci0xX7iefHyI1SwU9p26yHbKBnHzOn18QDAVI7Ig9Pb+hQhmm5V+b8K6r8hwOEUxGfm93w5jtytkd89wuxWqxTaZjIvvf+rlvQcGopkEjfMKZ3s57MoAd9hPqTql11PxhHz87LGCrmksrMapt3TiCSdOv5duV6VVrhFcEAv5zvuPWd5eI7XsJRR30+1MsDqsnD3PYrKI5/zd//UOdp+bQttB7lxgS81mGZPFzO6Zkdhqimefikx9bDWFUZEJRMQmeDyeEvSCyWLm3/xlk/FE4tHDOtGQgeyRiL0cHHYxGgTNRjGb6DZ7c4qSw2WjXBkTCZs42SuxdG2V1LKICAVCVsx2K+FEALvPjaZq2N1OHC4LD5+IynXmWhrZaOC1N+Jo6pRhp0++KmNzWnn9Wy+LwcmeqHr36i3MZplSqYc77MftFxZug0HklN98WVzfpWurGBUhRPosDhQNyrgdEp6QuNm7fG6Ozw1EM0kcXicgbhoevx2PW8ZuE7Sm+Foau9uB1W4jn+ugazradMr2jSCrNzZoXbSJRRTUyRSjIjMeDJlqOu16n1AqSqfWYLYnnsf0vvb7AsawtBEVG3VdVO0+y7j/q+/kmYzHVKt9LBYjpdMKC6tJkmkPnVqD80KXclFEcRSjjtU0ZTXS47zQwxuwY/O4MBqgcFxhPJywur1IMBHE4XVjsxmJpQNs3Uzj9NjRdVi/scSzxxVSWxkefbCP1yH+1m7LiPFQVNgURcLtEQf6YEBU+T47CCQ2Frn3XKBqPW4jnpCXWr7E8W6J4lF+/r4yGuVZ5lesP9e/+gKT0YROa8ArX05jtSpYbBYURSYQC+D0e/mP/6vXOS8Jp4vL5+Dy7MAXWUqw6qvQbE5wW8f0+1OKJxUOmhEWF2SG/TGKWaFebs1ABlOOnhXJXF/npCgLK20mSXavSDDq4c1bwgejmET3qXScp9cdkdhYpJqvUuw6yR8WSV9ZIeg38ODOGcXDMwxGmcVLsXlEcP9xnkG7i8liYv2ST0QFrRbWX96kVakTCNnxRILIkszyZhSn00w05mDY7YlN/GIcn0cc7iRJYJqDqRjLWwlxWFUN2O0KG+vO2etcFBlq+RLdPqxtBolmkqQXXagTlZ37ebaTLYKpGC9dFQeMD793l9UbGyyuRzFZBRL58qqBYbdHs9pCnUyRJOGSeOsf3CIUcfCtFy+4lJEFHrMhNlPdZodXf+MmpbaV7W0P4+EIs1nmom2YdbKnRDPClOt0GnF6Hdh9bpLrC1y5dQmXVaNTa/D979xHUzX8UT8LaymGfXHQjsRdXNpeQEKnr1rIZBxs317F5zWyuGCg358w7I/oj4X0rFdv0W8PCKZiXL61hTadijiqy8FkMsUd8uOyTTl+dIAkSbx0O4E3HGA6FR0oh9eByzJm4/oS6Y04jXKd4EJIuDMUI2vBGl+7bcbpEfQwh1UUcYKpGAdPizy7f4Ku6XR6GvVyg7PChJBnitNjY9QX94BgKkYg5mXUH9G6aNOpd0ik3BhMRtwhH76QEIm6Al6+/R9e5pLxGR6/C0mGR7sTAokIDpcZk1V0UV0+B8mVEEtXlvEvCOGpK+ijVevx6NnMinwzRXozyUtbOjaLRqs5plas0WqOGY1UooshPPYpFxWxrqe2MgQSUeJJ4QcJJSM4PXaGY1HYHE4kuj2dg/0W7oCH9NYSitWCy+fkW//Ja0xn8SeA6JLovlgdVurlBnYrPHxQw6gYWN0S9LH8WQdPyIvVauBKeshX34ozHk+pnBSwux0oRuFF6bZ61MtN7vzwPgAf7JhYvCTSFdFMkt99yyq8Ao+P6DR75Nse4isL84MHCDNxoyRITNtvbpO+ssKNNRWPUyJ/WMbmtP4/be2+EA9dB02X/lYf/99+nv7nM6+XWdf1sK7rX/u5r/2tKZy/cgeCXrtHt9njPN/hyf0CkiyhToRQ48N3c5RLw3muTR2NGU0ktOkUk9VELj/AH/Gw+9EOdoeJcu5CcHeHMm/95grnRwUcHgf33j9G13Qe3s2LKvMsF9drdRn2xIZ45/3HVE4KqCOBEVtZNLK6LnCVn+Ef77/9Kc1KE0mWqeVL+KLBedt693mHi8qA3Y93heTkWRabXWHY7QuRVFO0fSVJonh4xp0fPqZRaXJeHBNPuBn2x3z0YZVPdjTcdo3hQCUQD6KYFabT/4u99/qRLE3P/H7HxQnvfWSkr8zKyixfXW2ne3pM9xhSq6FZihIkSpCIxUqQAEnQPf8A7ZUESVxQu5QErWZAEhyS403PdLWp6u7ylVlpKn1kZnjv48Q5RxdfdgwI6GYFaXcI8gB900BnZx7zfe/3vs/zewTf/GS3RK3axxgabHz0jONtETq2emuGYq5C8bhIJKySnE+z+TiH7nYx6o8o58WEwemUeP7ggP2yB92tE4iFcaoGzaH4qL/zExOHy0mp0OV094xQMsob/87LeIM+9k8gmQ2zdHOJw/U9wknRtVp744pg3FcEWrN6UsAXCeAP6Hx8NMWd796jWqjzeEtsBsZwhCTBpStxanWDL13tMJXWJrSOizcWGFuCfR8OKgz6gqGfSgh95ebddSxbaFMdLp2Li6L7ffBsn5ffnBfJjI+2+el3Pmb97janRzUcTlG0fPSDRwQSEd753dt0a01kCdJJDWts4dJles02V+bESNs0RPhb8eCMxTkdVZWZvXKBUEgnFRjQKJT54lsxTAu+9FaY7d0+U7MhVq+luHHFS7E8Jt8LI6sy2fkYrVoT0zTRdBVZkajVx8yvJJEVmbOTFp5wgHhEpVtr8uzOEwbtHk/ef0Ill6fXHVE6bfDo/XVkScjfRoZNv9XBNAyuz/UIRT3sPDvDHwvj8emT99KhSRhDgZU92SsL/na1jmmavPeJQanQ4nB9T0wfHm0zvyS629ORPrWmTSWXJzEdQ3frvLTYwe1zoZ6nL/siIfwBJxubHQxDeGBuvzGNogiPx2+940LVFLIXZ2h3BEnlpS/M8+BBneONPUzTIr2YZf1ZQ+QonHPFm51ftWk2PnqGokg0CmUKpw0R3iODZQkSD8C735hh0O4xNeXFtm1e/tIS1XyNx58cCqP1lJfRQPgfOn2JDx7a3N108fJNLxeXRKK4rtncfmsBT8DJ2DDZf7zDoNfHsiCecFMt96gW6hweD6gWRaOg0+iw+vIihimK++28h1a5Tq/VJR6G7SfHwjRrw2tvTaN7XPTafar5KrGwSvEwz+FRl8VLQpc+tRDDsmz850m7wbD4LqeWZzBNi/W7m7h9LnwBF5XKiETcQS1fxrRsyidlkbVyOCSd1DjazHG8dcxn58WAw+WgNvTh96mYlozTKQ4b/ZFMKtBn2BtOTJu1fIXxOTDh6PkhPo9Ev9tDVmT8YR/Neo9GVyS0J6ZFJ3fx2hK9tlhLu402imSTnI1TOi7i0m3e+aYgxFiWzYOfP+byK0s0qj2mFhPMX1vCE3Cz9byGqjvQdAfW2GLY7fP+X96jUSjTrNTZ28jTbg/xemTcQT/P7jxh2BtwWhyTCNsU8gMGne7k/Tx8+oIXx7C3XWZrp8P+4x3ufPceY1MS2TMNk0f3juk2OxwfCtnGH/7HGQxLHN4+eWKwcFWktBZzJQq5Bv1Wh2G3z4N1g9krF6ieFBgNRrSrYl/o9cY8+nCHT4+TtHsyvUaLcqGDdZ7K/fH3PyXiHbG7L3IGTnNt8sUxa29c4eywwtRCjF6zzc5Wg2q+KsgzHz1j8/4uA0MUK2//o+t0Wj32H+9QzVcnHepPfvqMk6MGTnVERs9zeNTj6Sf7yLLExXiDTNpNo1THpY3ZvLsOQDknphyBkIvX3hIZIqKRoNIolPn0qaDidboWv/zRCwr7OXYfbKG7XRyt77Fb8vDJjx7QaYlG19H6rggb8zg5rIc4Kmm0Gz2O1nc5LUl89fdeA+Da7SwLa1mKh3n6fZNANMDVZZmBIU/S2z/Hg+89PaCwn8OyLKonBRwOmW6tyag/olntAdCpt/j0foOmI0a1UOfZvV2ur2ooijgQBqKhSXryJz+8z+bddVZuiLqiVa6RygYJhx30Oz0ajSHdVp9WX+WDe13cbhVrbKI5RHn27M4Tdo4lGpU25aMzbNs+lwlJDAdD6sUaR5uHpMJjykdnRLwjhiObG9eDGEOD4mERp9vFsDfkYL9FMhsWBLqTOrnNA2RVweUVhv3nOwOyMwE6rQG//O5nYp2b9pGZi6KpEppisXc04vYtQV4q7OdotUZoDo2Z5TTDnvAsASzPSWzd36F8dEZh/4T9ok7ltIIn6ENzaHQGgsSV2zwgPpeZQEM+B5YcbJwQTfrJeKq0upCai/8t+eiv6/VrIhn6f3X9vTsQ6G4nldMyrXr3PIpcpV6oEEqECcb89AeG0Nt53MJc1LFFiuhZhXBYZ/fxnjidJx0sXREZAw7V5kd/tT1JH3zrnUU0XWPlavpcEyoMqQ6nkLukFqeZWRMM7Wg2haKpVOpQKQ/J7x5TLbUnYR/NUpV+p8/SrRUqp6VJpwtgack30XjfeGuVcNiJ0+smFAuQOxmgu10UzjsSoWSEWDrCwXYRn1chGPHg9TsJBDQGIwmnS8UYGpSPzpjKio5sZj5GOOIiEPUTn8vQ74jFsFLq4vKK4mE4gvJJCV/QSyAqivZ+Z4DT6yERtknNJXCoNo1ynepJAcuWyTdEXHkxV6PXaGGMxrTKQhK08+yEN788Qygg06z36HeHuPxe9h/voHtcrH/4FACPT58QN0aDIYoicyNb5rVv3sYYGhzt1/D7NWYuzU5oDl6vyv/2NzbZ6IjIVJLM8iy97ojG0I3uceHSmSQje5w2bp9bkICcEjtPT2iWazgdFpGpJDe/eJl2Z0yn2SW7Msfy7Ut89beu89V3M0LnPZfhP/qngsaytS5+Zm8oUamZLF/N0GqPSc5l2M3rzKwtEkqIBdY0DMI+k+ef7WEaY/oDk+ZAdNaDnjHnDWGq5S6F0xaWbRP1GXS7BiFnl1a5TqXYxhvy0a016bYH6LrMXFbj2b1dWrU2O/e3kCWZjz8siPH7OenlS7/zKiDISeG4D5fPi2ULTN7nfohQOo5bHRCPO7mwlhZ+FkNIaDLLs0TDMoUjEfS1ci0zkaG1KnVuX3OiOVTmLi8SivnJrsxNwo/8jh6PH4r7NBwYHK3vUu25qOZrAu+aFROoxx/tkEq5ycbH1Io1fvbdZ0iyGLH/4AMDXVdwuhwTGVqjPqJ0WsUTDrD+6R7HG3usXQ4yGhiT1EtZFpQpl98r0qndMje/coNBb4jTLX7/ve0yu88OxXeX7GIaBvX6CM2hsPW0SGI6RnYhwfHGHpYFs4siZMc04daaStAv83zX4P33Tsgsz9LsSOTzXSpnDQ6eC2P6eDhCUSTWH57i8+kEYwHuv7dOrVDH6XKIg+bIRFNsvH4XF5J9nF434+GI9e0Rt95c4Hhjj2gQ/uyf30GSRICZaRh8Y34TYzhi45MXtJrCc5RKufGG/EiyJPC95fNip9nFH/Zy4foFXjzcJp708MZ1GcMQnpmZKcckICgU1Gi2La6/scSg0+XiK6siLKo/ojtyMBwJlgYIWUbMO+So4mZmOYFtWQw6Ahn7eXaBbVn0+jbW2KKWr+ALuvD6XaiKLWQmG4KQtXN/k/RMmPy+aKqMLZmDZ/vEpxN89rDD3oHw+TRKDSRJ5vGd58iK2O72H+/gC7pYuxzG4XJiWxaNaovYTJprb18DILuUxRvykE57aLZMeg3R5GgWqyxOK5TqMgvzIigtv3tM+ax5/u5I/Ae/E0NRBCDi2tvXeDm5z7DbZzotaHQAU9MiW0JXx5y13OdUH5GMnJqO4PZ5Juu8rCq8cV3maH0PTzjAhdUUkSmRBttpD7n+xpJYXwZiMjM7H8AXdJNdmSOYjBFxtnn9hpAGbt3f4dKiTKfVZ/ZCgs9+/IBht8/FlSCDTo9oVhwWe40WXl18893uGK/fPfn3n1+a7sDh1AjKDR5X5wiHnUwtptg/6FIbeMjluvjDAbZOfkUL8kWCVPM1Xqyfsf6sjnouWzo9quP0erh1WUAWmk2D3/ht8XfdeucmsakY6QvTPN/qc+HGslgD82Kd6bU6jA2TekchHRG+u+nVBdwuiZ/92T0GnT7jsU02K3wKn/5ym9zmIWH3gN1jc7KnujxuQonw3wpCBNh4UhRrSaGML+iaSKoiMTd7rRQOpwNvyIci2ZimiaIIM7EvEmL70+cT8308rvPSuzcBONgu4nZJjIcjOq2hIOMNZdZWfdSqfQadLu//5T26TbF3JaPyBJ7gdDmIzaSplnu0qw2mFjNIksyTHfF7P9nXaXfGPF0Xz6pdrdOu1um1+/gDTnpdMeFZvJQkmk1hjU1y28f4IiGureqoqsSgNxIhl+eBameHVe69f8jmmQePW6HVsSd+E4DhYIjXpzPs9ZmaF56LkSGRvTjD7JULeEMBslGDVrlGIBrgaH0Xp0NIvj6fnFRPizjd+mQiPLuSYWyYbFSSKDIcb5/gj4X5db/+4UDwd+iyLdHpDES8tGptMhmxoWq6htMlOj3z15YEr7haZzC0cXncjA2D7Y3y5OSfL44o59sYoxFnJXuC/5teXWAmaXG8sYfTKeOLhLAti1AiiKZr+GNhTFOw5yVZdM1kWebwqIusiA/a6xdFyNH6Lt5QgG6zQ7VQF8SNc+nM2WGFTld0PD+/An55Mi2Yn3WhqMLkZFs2vqBIy4wkA3z60TG6LoyQNxcHhL0Gly568QU9+GNhHt09wun14HSppFM6R88P0J0CcTp/bQm314HT5UBRFFqtMcFYCNu2Kezn8Ed8RNMhBp0ug5GM169zVrInZKP9ihefy6RRErzmtTeusLQS4aV3b+IJB/jS1xYolQ2OTkY4XUIn+rmeeNjtTzZst1tDOZ+kNItVTo/qdA0XL9ZPcHldBCMeXmzXsC2buVk3D+/l+OX3N/j62z5++P6QZrlGp9HG69cZWxLGcITTYbF4SXhAPnnQ4WT7iHqxwXBkE4j6WX1lhfWdMf1Oj1ZzgN+nUskVaJQa7D/b56//9ANKVUtIFRSFQlkgR1//QorU4jTGWGj/S4UOTz855Gh9l3zRoHxSIhQVBYAnHOD7P23QKtfIbR7w/OExAec5H7yvoDvAo1tkZwI4nBqZhEqtq9JpDRmZKrZlEUv5GPVHTK8uMD0fYW+7Sr1l8+631hj1R7z01eu0q3VeeiVBs1jFG/LjCQeoVcXBMzEVpNMaMLWYot0QWQ/KeRiOoigUu34aDYNmXUho+t0RDpcDY2Rg2zC7cm7cbI0Yj8ZklmfJXpyh2pTZ/myTvUfbjA2B9fvmf/iGyMewZbx+oQGfmg2zdGuFgSHTrjbQPS4quTzGcMTS9TlqtREf3DeQJZl//AdXGPQG5PdPmZn20O2MONrMEfSLd8MYm1xYy9BrtPn9P1jF5fcykzBxODXUc5+QqoA35KHf6tCsNERwVqXD3HICRVMI+0zeeDNNt9YkvTRDY6CTXppBUSQxWUv6cXkceH0OUovTzGdlivkOkizz6b0Sigydns2NSyqXb6Q43T5kJm6QTHrIbR4ws5I9X5ss6vUBF1aTqJpMq9bmv/pvr/PaV5bx+nVq+TJjw2R9z8YfcHJSd4pno6ncWlM5PmjiDvr58G6T5duXeO1rV0SQXLfPP/vBFJkL0yxcmcMfEP6PVnss9OPn/hjTFAGBobg4MHn9gnxUyHf59l9WCAbEdvGT7x9M1hxjbPP4szPcbpX4XIb8QYFRf0izUifi6lGp9PE4RmQSItG10tWRZZvtR2JCF51KTA6Nyawo0CNBCCXDBONhWvUe07N+nA4bh9OJ7nYRyQi/1MYnu8Sy50bisYQ74KN0XOT337W4tCTeJWMkChuX38OF5RCDniGIKlHx//SHBV7R6XZiWzbFXA3N5aR0UsHrd1KpDEglhORl4foymsvJwYmFS4fnm200XewZnUaHfqfPV5ZP8etDTNNGURUSKQ//6/uCGlOsWAyHY3xhP8ViH1mVKbacfPJogGkYXJ0fUjmt8fSj5/TaXfrdPpGpJKFkjM+eSyzfXiE1l6JU6FA6OEVWFS6viWLW7bSZSxhousrpSYd2o4fu1mkUyryoRjgsiClEfDrJXg6qpxW8Pk14NMIBtjYb6G4X/a44FAaTMUIu8X2PRsIQ/fnk/PNDw8Vb80zP+FhvzBL2DNl4nGfYG3H5kgfTkrh13ScoQH5xqAFolqo0CiJvIhRx43Ce44RnxJ5hWuLQ8eXbNj/4K7FH2pZNt9mjUa4Tj+vkdnKc7Z2RXhD3debSDC6vzlKyS7WtML26QK/VIxka86XffoXEbIKRYaHrEvG5DK98+SKL1y8wHKtkEuqEKCarMp1Ge7Lf1M9KJOezu9jREAAAIABJREFUvPNOekLzAhgNBlz/wiUs0xbv9rzISjFtiUQ2yoVZgR9tV+u89O5NjjZz6B4X47EInQO4+lKWZsti7soikZhY+xUZQj6LzJSX2SsX+NZ/+ibFXAlVd1Cu/6pylGRJ1DFdgUbutsUB4tK8mKIuZU3CIY1A0Ikn4GHp1gouv1fAHVSJVq0jEpgbA1RNwB2M/oBQMsxgJFEs9IilA6iqIjCi5S7BqI+pxTiSBDNpmZVpg8Rs6txTaTMeGpTOWmQuZOl1DWbWFjkr2YwNE4dDxel18WT3HEfu1gkkIpxVJOLTCYHkTcfFmlRrkchGSc5naVS7eLwOqk2ZF3tdJEme4IZ/na9/k8Fk/19ff+8OBJ9jK5/deUKv3aWQHxBIRDjZPsLhEObRdl2EXbmDfk5OOhT2c4L+0xNYyHf+vdcJBDQCETeDdo/+0OK3/rM3iUwlqeYrPNo0CSQi/Pyvn9I7x/rtPtjCH/bSropgps+NgKruEEmLUSf9882q1xmIk3QiIk72jRbVkwIujwvlfDz9uek5t3kgPnbD5Ad/LsayKy8v0e6IjIN+t38u2bCoFRvMzvoYG2OajT6l0wZbpy7aAxXTFAYs0zTxhs6JD4pMvjhC0x24PE7WbmbYf7yDosjobodgeNs2tXyFg6e7BJOCpX28LXTJpZrNzuNDnE6Jblt0JZOBIYYpDj6zaagWGjSbBp/9+AHmaMy9j4RZORrWWP/wqdB+646JWftzNrXPp046JvG5DNGkj4jeIJoOc/nWFG63RvmkyqA35PGjKoX9U37zH1+m0VV4+aYXl9dDs1jlaKfA994TG9/mnknhtCU40abF3JUFJEnC75WwTIuD5zleuSzwr+sfPmVrvcL1L16lXa0TjIe5+MoqHrdMIBERHP6RxXAgiuT87jEu3UZVJWzbZnYljTvoZ2VRFRISXZiozdF40rEW74YbwxKb6d/8dQ5VgWZPoVLpE4m58bkFOi895eOk6SU5n2X7cY5GoUyjVCeVdLB8KYosQ7VmEJ+K8uSDTaLZFLW66OzXz0p0a02R3tkfMRqMxZTHrbH7YAtVFx336kmBeqGCQzVJxB1cWg2iuZz02n0URaF0cMrI+Dxp18ONK14s00TTNar5GoWSQTgVF0bj827ts4d52tW6SOquCt53pz1k5/4mHocpDO3xME6vwC5KEkQjDmanXbSrdXYPRIKppju4OjcgHHFx+ZULk4XWtsHr03AHfaiKKEzOqiqtehdN18TkYWxPjKfeoI9IWKVd77D9OEftrMxpRWEwFD+wsH+CRzMY9obMzeiMxxbtRp9ee0g85iC/e8xpUSRk25Y1SdputcaU6jKFfI/kfJZKW0M95/5XCw0Wrouup66rmKZNvdan2+ywcyxxmmuTTDgJJaKsXAqwPCtzsF0g4BbrjDcU4Hs/rrG4FJp0cE3T4r0/vzuhPx1undEo1+m2+uw+z6PqjgnCr12t4w768fp10VDYOKBZaWGMTELJCMfbJ8RSfmaiYjz1m//uHNFsimAyxslxm8s3UpSKQsKXmksKX0YkQHPoJB530R9rtHuicMsG23h0i7nVKVZeXeN0+5BwShRU+eMqvUaLrT2Dl1/PkN89RlUVqtUBlYaMw6XjC/vRHAJs8I3fvUyjVMMd9ONzjknPJVA1lX/2xzV6AwjGA8xdmiGS8LF6a45kVCaeFLkLrdaIzecNLNtCczlp19tUcnnCiQBGf0B2Kc3WZy/Yf37GxmZnYhJ2ul3EIgq5M4OZWS+l44LAJE9FCMUC/PEPg+wUvZROGwy7fQ52anzpJSHLWsyKBGnbtvH7HdQLVWQZrlxy44+G2MzpOFwOLr4k3gXTMAXX/qRAp23QrAgDZq8tZK3eUIBKTXTxq02JF6eC95+d9tFtdhkNjMkkulwRPjTTtCiXByiayie/2OLBzx7RrTVZ//ApwbhA74Lohld6YiqgKBJ7G6cTmeXsSlr4LGSJX37/GWlfC8NU8AXdjEZjfvi9I/z6kGrD5vqXr9MfSizdEt6SCcJ7Js3qspPOuafAOn8XP/e/9w2Vr35jEUUTjavYVJhurUkyJqZZnoCX4+cCgbp1b4ODZ/scVDyUqhaSJCRnpiXx+O4+xnBMOKRxdCwIST/59l0KR0WaA41yzaJRbgrKWm8w+ftTi9MEkzGi6RAb231i2ThOt5Nyvsnc5XmePzjgyd0X9AyNs8MKvqAXrz4m9+KUrb0xK6+usXRrhcOtPDMrWYzhiBfbdfxhL9mVOT59f5dmY8T+4x3KhbZA6RZMHm+OqVQG1Is1Wq0x7WqDcDqGaYqMkYXry+zc36KSy1MrNnD5vZxuH7J8+xJjS0JWZUoNFYcGo4HJ8fN9TFOgo30RYQw+3T7ENAymZ3w0SjUhLZZlznZzOFSbBz97iCTB3pNdxsMRUzN+TnbPBEwlbPBn397lwbaCMTSYv7xAvdzBNE127m+y/3iHZ3eeCCxwCmIpP/6wG0mWSMYUZFWh2+zhcOpsbIh8p2g2RSwt6iFJkmnVupSOz7hx+/zgmekTj7swRqOJof3X9bIB25b+tf75dbr+3h0ILNMkOT/FtbevCVa8T0NWFBKzaWxLECpUTRRnvUaLZNLD/DUxunztrWkqpyWcThlJgoPnZ5iGwUsrFo2GcU5q8ZOIO/AGfVx+ZelvueWr+Tq2JbpxY2MsAnIiQVweJ9GQytlhhVF/ROGwgKJppOaSk4Vp/toS9WINTdfEoaHV4/CgJQKVAj4RkBTwEplKUjiq0usLOlC9UCWQiBCMeCgfnWGMbS7fmiEQdOHxu4gELNo9mXJNhNVMLWbwnZsT7//yOY2a6BBrusrTT4UB7/Gd5yiKRGouxaBvMuj0mL96gcUr01TzZcaG+FmDgUhZbLUtghGPGE1WdXJFCd3tZHPPxLZsHtzZJDmfZdDpcv2lFA6HuL/ppRlS0yHGwxGXXxbGI10/7zQ1jQkZIpYOc7CVp285qRbq7G5WCATEgUnTNRwOhcRcmgcPKoS8Y06K9iSNc+ValrVVQY64sSLhcIricXbWh+7UUDUVSRIyA0138N4nBs1iVWwWCR+VgijAFlYzfO0rYaq1MW9/4xLRbApZkVA1hU5XPPNmR6JSHoqY+bMmDqfOo/WBYEGHHbSrdXSPk+WVEA6XOPgcPn0h4ktkmesvT9Hs2OiaRTzhRtfF57uY6PNiq8JUQBxea/kS06sLtMo1Gi2TwcDiQsag1xszHBgMOl0quTyzU0K3+7n0yukSqcqyKhNNBjjcPOPWOzcZD0dC2z6XIT6TwrRkTk8HWJbwmuQ2D9DdukCeygLhKskS//y/fx+X30uz0sQb8DA3rYnNfSZFKVdh6dYKkaR/8hwvXUkgKwrzC8JQvV9QJwfnQadLr9Gi3RwgKxLnTWVWLjjYureB7nbx9NDJ2UmLUqFFJmoKqVO1ywc/eEZqLkWlgcDNRg0htzjHrz56UMF1Tqg53T4kdzIQWtyUePdURRCQZq9cAODOE5XqSYFm2568j76gi1hIGBh7fYujHdH1euM3biLLNtdXZH7x0yNGI5PCfg5VsXmxXSOZDeJ06/jDQp5WLXdp1Pr4fDrRTIyj4w5vf8HP7osmzUqdVttCU2wK+yc825XwBgXd6fXXxT2MTCWJxj1MzYZ47Zu38YfF+P2//i/O049f5Lh2O0s0k8A0f0USSc2luP/TR6I4M01qZyU8Xgelg1OWrs1yddVDwt1EVhWGBgJLbJr4Ak7WHxVQNZn4XIbTF6Ki6zY7DE2Fo4MWR2Un6+sNvH4n3/mZgmFKtBt9RueY2/zuMZdfWcIYCp389pNTtraaBBIRPAGB6D046uMJiOJnNBhNdOySLEhB4oAlJkqvvDXL8clQpAtvn5BMunE4ZPaODFRV+AE++eF9pmf9xDMRjP4A/TyQpXBUJjaTZn/9kEGny+WXZli+4OVoWySoWraFS7cpFbp89lGOYbePbdskM3527m9Szrd48KhJICLyBhRNIeESE7/OQCYQFQjgcEiQzlYiRWJ+A1VTKVfG1M4qOBwK/vODz+eNkC+9Ir7Ng6d7qJpoXBnDEaoq4fdrLKRG6A4RJPfhT7eZv5QmkvRztL5LpSEzN+3Asiz6nT5XV900CmXm16b50m+/AojJ9tgYE4z4Jzz+lFccQC6vOImkQmgup4BgtAY43U56XYPYVBxNGlPpOOh3hzjdDmRZIq0XcDklrLHFYkJQ2GRVZNzEpmJC216xmJ6PkJyJ0WkPz9d3iczyLFuHsgjzS0ZZ/3iLduNXciaX38vtN+eJZBK4g36WbolAxaBnzK3lEbMXonRrTZ5sW6Rm4xw/36dYHHC8K5C4X//3XxNZGw6TTEJCVkTAnDE08AU9k3fSF/Li9esMh2KaOegNuHE7zda9DRqFMr/5u2uUmgKd6XQ7+HRDxhv0kYxr1ItNdu5vMjbG1EpNQskYXr9O6aRKq9bi5hsL1Gs9YeKeCwoaj0cmGdfw+x00i1XOTlpcuLFM6eAUl1OidFLB5dF5/TdeIppNcfH6NJYpmjqmafHiyKbbaKMqkDsZMByOCafjGKMxTrcLSZLodsS0OT6d5PmzspjKGAJ0MR6OkGVh5tVUheR8Bl8kRLUyID2fwrZsnh+p/Od/OM3agk2v3RPo4Fp7QtwDQYHyBH385L0asizh9TkY9Ufc+0TkJeV3j/EEPHzzy14yy7MMewN8QZG1Y9sWR+u7uPxeisUB5Xyb7TMX9z86IhAN/trnEPCvKRf6B8nQv+VLkiQuXknjdGkCo6lKuLwuTrcP8QU0XB6NbrMz6cQPhybNSguXV+fP/+RDlm8u0e2a3Pnxi8m47fmRRjik4QmLxNezvEDLhcM6meVZgskYa29cmSSqqppKZj59nrToplFtsf2iQyQZZNjrc+PNFULJKI1Si/zuMf6In+PNQ3S3k1AsIAJ6/G4WLwQIp2M0SgJJunw1w6DXJ5oOMR5bzK4tCLZ9o83Wwz0CiQgf/XSbzSennOXq5A+KLEZqhLwmM2mVyFSS4nF5glDtNVqk0l7icxn2nx3g9rkEj7jT5fEvHrP3aAfNIeP0utl7tM39nzzA6A/wBf1C5/tcbK6dzpjymaAFLSR6uHSJ8tEZ7bZBr93lwtU5MvMxJFkm4JOQJHj2tEq72uL5Z3v4IiEcmizu45IqEqF9Go1CmfTSDOWzGnMXU/z5Bz7mLqZIpAMcH7ZYvjqNMTS4eSOIP+zFGI4xTJnhyELVRTR9oyamA5rLiaZY9LtDZElme6uOZQkZVKlqcf3L10nNxrlyyT3ZpFvNAdl5UbAebhWoNmUGA5OtzRrtWoNuZ8zWvQ3ScWEqfPiwRq3apdvs4PY5aRTKBIMONF1jOLKIZkXK63BokZ4JYxpj5q8t8fTAQWI2w/rjIiE/dPris202RmiKzYuCi2alxcNDEdpkjU0qp2Xmry3x43/1EYe7Vf6P75RZvuCmfCJ8KMFkjI1t8bcX9nP4Y2EiYU0kfxZaRONCYlY6Fc/tx9+5izUWcqist0wi6eQ7/8udSer3yfYRF1aT9Po2vVaHfqvDl3/3NfzhALpTJ5oO0u7auLwuBp0+08tic795zU84GWJkqbRaBrZlc+fHL7j1zk3CfpFhUDo4ZenWCsFkDM2hUqmM+Bf/w10AFmPCIN0olHnysIzHq9OqtfE4RLDcpcsxpi9mOds749O7QtZ3WtPI75/iDweon5WYmgkSz4Qmmtjx2OLiK6skM+Jg0mhZuJ0wGowIJWNI5waF4dCi1zOol5skU25yBZvaWRm/T2HlWhZFUfD7NcamxN/8qMbv/E6WdkNkgtRagtJy/2dP2H+8g6oKiVC31afTGpDbL6NqCqmUhz/9ky16nQHzl+eIRRQR7ndpnoBf4XT7kF6jRb1l49QlQokgoaDKi/U8pmXRbQoJyMcbDjxhEQB4mmuLdepYmApB4Ixvv3uD0UAggVXdQetcv3x6UOHh0w7vbcaYWp7lxa54b1rlGmfHdTRdRT+XzqQXUvhjoptr23BxJYgkwYWlAOGIk4V5D7WWjC8o3lkQMrnDrQLJmRj1Yo3f+p3Zc19RnXjCjWUK6MHYEM80v3sMwE/+eov4dIJWuYZlSXQ7QxauL5FN2Lx6TRzUbry5jKpKvPfndzHPiwlPQBQihmFNApEy8yIULTUrqDXTy1kWb14kf9rm4LDPa19eIpSO0601efSsx6svB7j1mjhIq6rC/PR5CGBviNvtYDQwiGXjBEIu/uSH5133NuguB7rLwZ2f7TG3OsNPn4u1pHSUZ2VRZdDp8uiXTyjnSrRqTbqNNvPXlvjfv13CNAxmVueIJoUUR5JllqZtnj0qcOeBje4Qz+Tdf3SRk/0KrZp49k4dDo5HtMp16mcl1rf6XP/yddxubULDiqUCnO0ccbBxgKIouDxuKn3RDNs/NsnOBAglI4yHI0YDg3qxwslunqnZMIVekHSgh6oqBEIuvvzuNHUzhEuHcr5Boy/ujdPrweHU6XcHaLqGxy3Tbg/pd4fEEx6cXg/likE4EeDW0pC9/T62JWh57Vobb8jLv/qX6wSiIfZ3GzQrYnq+c3+TdrXOSVllv+jkw+89QNUdRCOaIOpZFg/ff86121kiU0kMw+K1b96mPVBQFei1u7TKNVRNpXBU5PKbV8V7/+KYvY1TpjJuMsuzlA5O2X3RnLDyD476pIJDoukwh5vHLM4oEzlgYT+HomkkpmOc7RxRPSnw7CMB5LDGFnvbFUYDg6lFIUNy+934PBK1ujmRDutOlV67z8L1ZXK5AYlp4U1pNweMBkO2Hh0TTsVoFMpU8zXSCYF3jvkN7v3oESsXfVRPCgQiAivdabQxTZGNlNs84PI1sXbbli0Q0ueSKJFkLKAki9dm8Xg0nG4Nj1dMFe+uK9zfsKmflVi6tYI/7COcitJp9UXzcm0aT8DLm29EOdmvcPiiQqNQ5saN8ATrms6GKNTFlL9drdOodnE4nYz64mAYm4rj9zuQVRndAW9+ZZaznSNatc7/Q1X363X9g2To79Dl9Lh4sSH42YPeiF7fnIxCP/3lC2qljuisFoRhKZ0UZr6texukL0yL8ZsMsakIsakwnUabWEii1TYn2sPx2CI5nyUSEgvEsNdn67MtRgNxOg/G/ciqQMT12mLUvrjgYf3Dp6Ir+KIkKALnGvlGqc7lN1axLXvyQSSmgnS7JpIsCd1t1MUv/+Iu3VqTeEJ0OcyxRTQTI5SM8tLbl2gWq1x5ZYHiwRmhmJe12/Pk2wFMWyIb7uP2uWgUykzPBkjOpvjq773G4X4D0zC5+sYKiytRMep9/TJLt1aYvjSPbUMoGeH6l68DiANQ3M+w22fpUozrr86haTJrN8Vis3ni5vD4PLlyOKbbaON0aew+O0JWFM7R8dy8GcUfDUyQjBdmFRqFMns5wYJ//Mkxmss5IfTkj+u8dFnFsmwkWeheP/7+p9TyFbZ2+ui6SnIqwGgssZAVC2du84CpaR/TMZGo2R8pXL4Wp9/p8tYXwmzeXSeYjBHwybRqPdY/fEouL8b4pjGmXm6h6wq+SIj5lRS1hkkq6SCR8jHs9vF4xYHxvferqLqDV14Ok0j58IZ8TE37kVWFmbRM6ShPMd+l22wzvbrA+qMCsZhgO8fTAaaTIikylvKjazYu3abfM/H7HXQGMjOxEbMXUyykRniCPkLpOMZQjKNf/votghEP/+UfePG5bbJLUzicOje/cIHZaRfRbArNJbrjTx9XaJRqVPMV3vuLeyysZibF79vfehnbtnF6PayXUzh1idd/8zbegIeV1TCxbBLLhk7XIhgX1Jrx2KJVazLoDYQkyiNxvLFHq1zjcPOUernJhx9V2H+8g0cdUDhtYhoGX/zaBe7/5AGDkZAHuIOi+9oolKmcCRnUN35fGLZflAKM+kMyy7PcvB1HkiV8QS8fPhHElXhEpl0X04WLa3GyK3N4nDYur4fCvjDznuaanB1WiGZixGbSOF0q8aQPh0PIv3o9k0bbpt8ReRWphEowGePRvWMGPYPpC0lCAYVLc2IqNzJsivk2qibkP7pq4fI42DqwMI2xwPW5JE4O6/giQRauL/P0w+fIkszLr2dQFJmFS0lhpNdlvvj1i4wNk4ONI1LhMZoKoZgXRWayiaeigvCU2zriw58fEIr5GQ7GOL1OPOEA3e4YVVMJRP0Mh2KC5/G7JoegYNyP0yloW6F0HKfbxdMPnpFemuHqS1nWLnk5OOggK6KIA1HIByIepudCRCKC1BOKCl67y++l2FAxLXDpgsizPK/icUkoCrRqPaKZMMn5LJdfEdSffneIw6nj0S1yOyeouoNKqY9l2ZTzTcpHZ/hjocn7+srbS1imOEi7nRa+oIvdB1vcudvmwwcGq69f5pd/cZde32R6dYFETGMqrRFJhbh4RfhcjtZ3KR0XON0v0601KeYqguZTqJ8jbC3SaSdb6+UJ4vXamoedfYNWa8zslQv4w25aHRtF0/jv/kmYuz+8z/ScyHOIxZy8ctNDcj7L7XkBUFBVBW/Aw8nuGd2excaBgupQOS3BwvVlVl6+xKAjGiWeoI/9xzvEM0Hi00mRR1IQnfv5tVn2TiQyMyGurDjo9m1WXl3j2//zHRrlOosrApM6G+3j9aqYhsFr37xNr2dwsHFCr2egKJIIjuqOuPrWVeLTSQr7OQr7OVzamGq+yvZ6kZPj1nlInIfphQjDbl8kja+f4df7fLThRJIlGtUe/YGNSxnQH8L0YpzhWOZ4+4RgPISsysQzIRxOB7GQxclukd0HW7RaIwadLtmMg1Tai23DyrLwDkWTfga9PrV8lRtvLtPv9phfFPcD4OWv30JWFfYOekT8Fq9/U0w1q7UxwbAbp9fD6ivL54jsskieVyRGY4lfftDA7fNM0tubxSrFnKCPLb+0wtKVKb77Lz+gUa4TSsdZuhgkPi1oN826ODCXTipMLWZwOmx8kRC5ipjs+CJBtu5tsPLqGgBrr11k69EJ3WaLYMTDaDBi+9PnOBwy/rCXX7wn1pZYWBUy2ahbUAybPXo9EeYIIvhx1B+yeHmK/O6xQN+mI1Qborrczqm8/s2bbG61cXo9PLvzhJm1Rdw+D26PRqfeJpCIMB7bbN5dx7YsMsuzpGdjuB1imt2o9RkPRxxsnOD3azz6+SO6nRFXFwyuX4RoRPwujWqLvUfbFPZP8fpF/fDo548AKFQsZpfiBCMe0aiyxNRf0zUqxTb9gU1qcXpSmw17fdx+r/ATmBajkZB/RXxjen2b2SsXJs2Af7j+/7n+3h0I+p0egYiPy29eJZbyk9uvEUoLk1r9rITb5/xbRdJgKBJ4AeaWEwD0emM8Pp1nd56gu534XCYjwyI2IzYZr0+jsJ/jKDfEHfSj6Q4Ss2kkWUKSZY63cqiqKHDHQ4PsyhyFksHrvyn455eui59TPDwjmk3RKtcoHFWFmfn8gzh4fkYoqNCuNuk1WtRqA669fY3syhy6LlOr9kXSYKUhkHY/fSZQkzEH7/7ey0xNeRkOxwzHMkd5ic5Im3QLVVUit3lAId8lEHJRPSnQ747weUSgy8ZHz3B5HEiyhKbJuDxO2o0+Lr+XVqVJ5bSGJMvkjlr85NsfUcx3uLwoCstMdEwkohPNhJmd9TGzOofPp9EsVjENg3REZAE8fFgVaYoBncJhgXv3OwQSEZIxBZffy0tvzExwf9WTAppDxec06LSG9HoG3VZ/kh5q2TbtZp9apYdHN/nssQiyklWFD370HIcqeNsXIwWePS6JzUQ0xolPRdnb72OaliB8zEjIqoIkSzjdOua5VOvwRZG1BZv3frg7GQMqskQsHaR4LIqNJ+sdwmEHjVKdcqkncIe2oMs8/sVjgvEw5VyReCbAL7+3zvHGnkirlAVmTtdVPvyki9dpTrwITodNqaWxuODlb37Wm9Bf3H6PQCpqCoO+wcfbfrLBLvmDApFUmLOTFk+e1IVZtz+g3+mxsBQmNS/Ms6nFLIO+GCVHsynufO/hJD3yRvyQkM+meNYiGPOzv9eisH/C3vMCHrfY3GzLZnZaP0979rD77GSyYQFEUmHKR2e88yVBjWiOvIzPuejv/2SX2SsXSAYNbn7lBsF4aPLfhRMB6g2TWFgU660uuLxuRoMR/9f/9D576zkyMyG+9ap4gGGv0MwuXF8mm1LotfsMRhKpOVFMxGbSBEIi/Ci3eUD56IxyocPmo2MWZgR5K5vRGBmgKArxuQyWJTTWC5dSbN5dR1bEVGvzUCyn0ymJ8kkVTVeF8diQCYZdLE7/ijJTKJtcuBhl2OuTP8gjSTL1YoXDoy6dVo/RwGTl1TUGA4ujYzFRSs4mUSSbc9UhDg1KByKN/vDUYnROAfKHvVy+IhCtx8/3CcZCDAbC/F85rZCeEpOPZ3eeTA5cldMap8cNNJeTfqvL2DBYfW2Ns50jmo0RH39YIp3xUDoucvGiMPY53S7q5Q5bT8+IhyXmLqXpdkaYY5NgPEzUb7L7osmzrRFzs66JqV5TweUR09Jus8O9H3xGu9Hha++I7vxPP+zicOoY/QGZKQ/dZndCjuk2hDxhZm2Rdlsko1dyeU5Lv8pv+ebbbi5ecNGqdSZyuMs3UqSiNmcFg0apxZ3vPcTlEg2XsSEyFGbWFvGHfRw+fYE34GHQG3L0/Ij7n+S5fC2O/5xG88MfnHBhzoFhWJSOi9TLHZazY2ZW5/gXf2Xxh//NFzjaq6NoGvX6iMFIorCfY7/mp1PvivTV5Si628m3bhVZzArD9PFRB9O02PjoGQBb93fwnUu+3nrZRTUv5CaD3hCn14NpWpgWxKI6D58OCHglDjcOya7M8dpXV/G4FfqtHl1DoKQ94QDdzpC3XvXiCXjw+XSSGS/x6SShsJvnn27TabSJTCVRdQeqJChQulPjxo0IiqaJPIaYRigdJ5pNEUkF+e4HOm9d7mGaFgsPWlA3AAAgAElEQVRLYXK5AYFxlZDPot8dEXQbk/tcPSkxHIyZWYzzcF1Q7QKJiKCDeT3UmxbrD08wbZnc2ZhgMsZwMGZ6OYtt2UTDQuL73l89FvtDIkKnNcQam6ytuBmNJYpnLWbWFllZkPH7Hay9dhG3W6NW7eMNBSgUB+w9z7OarJKeOtfRHxUoHZ+x8uoaw96A6dUFtj/bpNUc8jt/+KbwL8zEGY9t9h5tiwJ6yseiN4c/7McYjfn5ndZ5romN0yMmwDNri9SLolF444qX2YtJrLFJs97DG/RM9uv0lJ9XX0/y5GmTzz4t4w0F+PmffTyBdYQjLpxuDa9fZ3HejeJQJwGQg06XQX+EU5dIL82QiduUCm00h8Kw12dmbZHScQHdrdOs94VnzS3qm2g2JaR+24c8/sVjzqoKB7s1gmEXsiL2uf3dBqF0nMJRFVm22TyUmUuZZJZn6dTF4dQb9pOd8RNIRM4lXA4++/AIXVdoN/p4gj7Gpk1+N0e32SUQcdNomfQ7fTzhAJouPFeKplA/K1E6KtDpjOh3hzw/kHG7JEaDEbfeucmv8yU8BH93JUPKH/3RH/3b/h3+jV7/4x//6R+9/s1/KsJ1ok76fZOTFyesvr7GK28v4fc7KBe7TC2mCaeiZKbc1Bomsqph2RL5gwKd5hCQiE7FmZqNkk0rlOuw9XAfazwmlIrj8HjxeJ10OkNqpyWmlqaondVw+b1cvDmP06Vx8uKMUX/AwtU55mbcVGsjbGR218+IZhPMr07jj3jRnC5imRCphRTRTBzN5cIX8tJoCmJFZiFNtzVg+VKYVttkecmD0+VghJPF1QzNxoCLtxbRnU4U3YHXq2Lb8ODODr/9VRU0F159zM6JWJRjqSCq28flK2H6fRun348kyywuuHj86SmL1xZYvhhg8/Epy2txPv7BQ6YvTuH2efCFvEQzQdwBPzPzIfojmVgqQKUhobi8xFN+khGboxODr79qsZtXmZtxM8TD7EqWekdGUWSmpz3sbZcIJsJIisaF5TCGpYLioN0eE0n4sGWdSCpKKBVlZj5MIqpgKW6K+Q6vvZGkUjEoHhZ46QtzrH92iMfnFvd/QeP58zZzl+fwBr0sLrg5zCssrcbwRYKcno1Iz4TZfnxMejFDZsqDpKhYlozm9oLqRlIUFlcS+LwqlXKf1HQYl9eJ5vYSjTpQ3AFsW0JWJJbXErQ7NhfXYjRbY4Lx4Dn+zcQT9OPwBbj9xSWazRHugJc3Xovg8AcpF1qs3swyMBTGkptbNwLs7XVAc3Ny0kF3afh9KqUafHYvz9tfjGKoQWRVJZIMEYyFKJ7UufFSksUpk9bQQbnlYO/xLhevzXD9ipeNpxV80RBI8NIraT77YA9/JMS7v7HI7nadzEyYSNLPpetZqpU+qqbxxst+bFljJHv58G8+Q9Gd+CIBIokAPr/Ox99/QGphCkvW0dxeXF5RmCbn0zSbI3zhAJG4n2AigqK7abVtllcFdUJWFM72TphZmcKUXdTrQ9q1LuF0jFAqSuGgwPJaAiSJ3a0Kv/s1F3tFF+bY4vf/k6vEp6I8/PiAxdUUhbaHTNbL8rVZiqUBly/qHOTGvHrDyS9+dozLJ3IE4ukQoZgwO4ZTURaXw3gCXuJRlb39LpFkkKsLYz6+WyKaDPPmLZWOHKde7rL28iKlfJtkykMoILO33+H6jRif3T3FH/EzNiUuziuclSVm0za7RwbjscRLt2PsH/SQZA1Zkbn8yiKd9oj55Ri2LYMk8fT9J2QW0xgji2TKy4OfP+a1t6ZxaEICsnpBYz9no7mcZOcieN0ysjsoJHQXwmxvVlEcDjw+N//kWybvfzLAG/IxOx8gd9TA6A9Ze+OKCIWK+vH6naRm4uS2jvFFghyt7+MJB4ikQugujdUlB+tPKriDfnI7p2i6A1/Yx9H6Hpdvz/KD//MuutvN/83ee8VIlqZnes85J0547216V5VZvqq72vfMdM9MD81QtENqCC0XEoWVgSAJC10JoKCrvdICWumCu1iBWi5nOUszHMuZnmHb6u6qrupy6X1kZIb3/sSJOEcXfzFHEiABhMwOQf5AXmZEZMY5//m/73vf5/UExHe+NO9AsjqYyygc53SuLwxRLCKH4LN1jcJhkcSMkHCsXJ2hWBkzwcLsQohIMsBoomCxiYAnfWwSTYcIJUJEM1FkRWZ6xkOzJUzBt2+H2d7pEMnECETcuOwGjx9ViWZCuN1WAn4LboeJPlHQdBmLzU6zoRFIhAnFg3gDTg6fHJJeTDBzIcPsvJ92S+fmK3PMzHrpDQwqxS6dWpNf+fplsqc6VpuM6nARTQgj+tMnDZZXI1ydHZCt2qiVWqTnopRKOlisyDY3qsOGJxTg9LiGrMjslP28sjpgr+zh9nN+6m0TbyhAYi6Jw+NCVS24g34OjjVCiSBzK3EU1YIv4mfzo3XefGuGs+KYZNIGSFjcftLTfh5+dMTSxTCF4oDZhQDf+eN1gokQk7HBaXGCy2MnmXTi9ShkD5uCgBfyEYj48AbduANe3KEgNl8Q0wRNFx11fzTI0oofzbARTwXI7Zf47V/28PjYxd2fbHLt+Qx33t7m51+R2W/HaHVhLFnRJlaOnuyz8vxFMtM+cscNkmkPsZkEM4vC/2L3enjlugVN9nJ9tk0wYOXe/QYurxPTNCmfFLn9SoaPfrTJc29cJbd3JnCuy2l80SAXluy8834Lp8tKrzPk2iUXTzZ6uD02wmEr7/zZXULpKN3WgJnlGE3dSzSkcP/9A6IzSSLpKMP+iEDER6va5oU3L2ECb//xx8TnMviCLhHaZ7ETywR49OEul5+f4s/+aB2LzUZmLgwWOw6vm80Hx8yuzdGqtrn9+jy7j0+otlW6rSGj4YjZC0m8Pjv54yrzSyFkWcbvlbBYrbjcNsYTmTd+8aIgEa1NY7HIxKJ26jWN5TmVnZ0uVqcdyWLDYrNiTAy+8nkX3/nmBq98borDrE4i6cKwONAGGopiAQmefzHB+v0snqCfl1/009HtVM7qIvw0FWNlLUS9OcHrtaG63Iy1CdGUj/2He8RmEoQTfrK5IaWahD4Gf8TH7GoGh9tJuzXC5rRTLzWJpgK88WYcj9uCIVs5eJrl6nMZuiObAEpIMmsX3VQbJqnZKLViC8MEX8hHOBWh2+zw/CtTSIqV3Y0SyFY0bYKsyNx/+58Ufu/3fu/3/12eI/+v1j/9Z7//e6/93O/+jX7n2//rf/cz8/f8nZsQ9Lsau0/zHGwUODluE0u4GWsjSrkquVyX48MW+d0sG3eesnNvU2AiT8rooxHhmAdZUXB6HUwmBvGUl/3NM7aOhJ5YHwiDqNOpcvxkj4lh0KmJ7sCgpwmtXLHCpz98wM6jrEgVdrs42jhlPDapFNp0mz2uvTiLJEnsP83y8CcPUVQLobCThz95yN7DPYa9IS63jXTGw/YnG1SLLax2lT/7F+9TzlXJF8fkcj0kSeJwu4hpmNTLHXbvb9Go9jk6bFOvj5hbm+LD3RCFmsxJ1cZYH4uu98Qkf1CgWBrR6WjP/g8m4wm0a01qxSZPHlWJJENIkpAJWVSF3ftb7N7fwjREF6XRGJLfFV2C2Skr+w+2qbdM9LHE8cYJ//0/2ePssMxffOMJqtXCwXqOl6/CxDBpNMd4gn4iEeEeLZeHbH28TsCvkN/Nko5b6Lb61EtNmuUWT+4eM+8r0etNKJ1UePKkydSMl4svXESRYW5tinjax0p6xMcP+iRm4xhjg0jcQ75moVNvo0/E7aCPRui6KN2fvv+YdmdMrzvibO+E4dCglCvj9jk53q+hKBKnO1kURebevRozUzZ2t5usf/iEj753D20o5BmdZptIUObTdza5//ZDNj87od/poVqk847g6e4JZ3s5gd7r6/jCfmrVIYmwSafZozeA69dDpOMSldM6reYQWTa5vdSjdFxga2/E4/ceo2s6+aMSxxtHxDJBxmOTd+9qHOWFgS6cilE8bVGuGVgdNlrlGh6/lw/fLzIZjRn2B7z/zhmTscHdtx/TrA/4/h9+SGH/hNRclGLfT6Ov8vSzAi/9wnMsXxIozcfvPebosM2NN67j9rtIxG0crx8y7I+48MIaraYwI1ayeR6/9xiP30GjMaKSK7Lqz+JyWdm5t8lbX3uenQcHXJgac7pfopIrMhlPROiU10m1NsLrgkGny/c+Vjl4fIAxMbj7aZP+QMjo9LHE/oNtZkJ9/vX/9B7bn2ywcSDM4Z88BYuqYpoGbp+LwVDnznfuMehpaEOdw/0mJwdVxhOJUDzAYsakNbAwtZxm9/4W79wXoYFOj412WyMQFonCzbYIg6q3Za68sCwC9UJ27JYxs1Mqd58a+Px2TNPEopjMz7lEUFK7z4ffvos36CEStPD4vce0632cfi8+n0o05qRS7qOoKp2BwqOtMc1aj2ZPYayP6bc6HB00GWgmj997DMB7bx/TrgvZUuW0wr/4jpVOTSSvaprBoN3l0qtXzgMS9x9sMxzo5A5KqA47NruNldurLF2bQ5Yl5udc+BwiL2Qq40BRVcb6mLE+4daXblCuCg2y2+/i4OEO2lDn06djfB4JTZc4PW5S69nZyco8PXGRnvbTrtQ5O8wTSUfIHVZ48tEelVyJlTmZQq6Fx/8MfZz2cO3FebJbOWqFJk/ff4zFonCS7dLv9Gjky9hVsR9nt044yI54sjPG7XPRrvdIJyx4XYKL3h8YHG+f0q61WV4N43LbOHx6yP6TE3RtxPHWGdndEu/9YBOXz84n7+zz9EmNXncs0NRuF//yf3iPWETl6f1TQVQ5ruOxjykd52k2RtT7dp5+sku/2WYqbWN2RnT3f+G5JtVim35nQCwTpN/ukzsocdz0Uc3X+c63jqjmm0wmBtmtHPVig1KuTP7gFF/IycHjQ473qvQ7Ght3ngp9f87AYpGxWSUKpTHDgc6ju8d87q1FtJFJfjfLvYc9bn1uhZONA9IzAWQJNG1Mo6nz/k+y5LaOePTOI7Y/2aBWbNLvDMltZ4n7de79+AmTiUm92ie/m8Vqt3J4rPHwJw853isJCtpEweuS8EWDnOQGrD23wEDxUGua6PoEm1XC5bESnU2x9fE65VKPg4c7zKZldH2CphkoisTmx5sUmyq9nk5z6OLOY4lGvky32WPvwS6SLDMY8izTR0x3JFmmfNagVmjQ6cv84ptuPD4b/XafWsfCr37RSqsxIBMzeevffxlv0M3a9dR5AnzYO0ZRVWpnZUzTJDkdpFFp0arU0bQJoZCdqdV5WlUBoUgnbYyGIwrZGr/5O5fpjaysvXwZ1aZimjDoDajXNfrNtiAZVeo8vp9HddgZDUfnmR/NWh9JgoVLaZqtMZGgTK1p8vE7B7z9xx/RbfcpFEc4XFbu/+gBkbCVUEBGG46RZajnK6SSTs52jknNxXF6nKwfi4lBZ6AQjok9af/BNpVciWa5htbXOM6KSX7xMEe+IlEvd2gWKyxcW8TmtOF3GxSzNX7ybz/ibC/PWB9jGCbhTIJitkinD7/y6oibqwpn+3m2Pl6n3RAT9MnE4ODhLpnFOEebOT78qEmxMsHvtTDRxeeu5qu4fE4urAbp9k32PtvhYD3H1EKUYCzIoDfgbE+kif/4u7s06wPe/FIan9+K22tn0NP+/zgm/j9af+8h+Fu0DMNg5XISl89Jtdgie9jA5nKQnImw+1Agyv73K5cTCNC5tSmO90rihu4OWVmLcHrcoHx0xuuXetQqApk4vZxgKi1uzEalh+1ZsNHxk71zF77D6yY+HWXQ7jLs9vBH/QS8EoePdkVyoCKRXd/HHwlgczk4frJH/Zn5VesNzjMPej1h/j3bOaZebgnjZcRHwK+wtORBH43J72YJJkJcvhbD7nYJdrFukEnbGfRGpCMG1ZpOxCe0zf6Il3RSxeVzY7XKFLNC/z7oaXznT3aITsXPJSG9zoBP7+Q42zlm45Md0am8MEuz1nuWsmtn7uoSXq+KNgKby8Hy1IRmR6JTa2CMJwy7AovabQ9oFiv85O4EWRJFgS/sZWKYZNcPWJwXQXGHRz388Qg21RR/d0H4LRYupemOXUQjKjdfWeDyZT+7W1XsDgsWiyRC0Po6Hz2V+fwLootRzJbInzQolkVgStTZodcXuRNv/8k9ABZurFA8a+NyW5m7NM/irAVjbCDJEv6Qi15vgsVqYTIxeOuNAJ2eKQxYQR9f/NpLtBt9/F6FXr1FpW7w6luXMA2DaCpEu9LgB9+8Lw5XE5P4bAqn18XJmY4+Et2QRqVLdyCMlOOJiSwLWpEv7GFp0UPYo3NYdRNORTBNQXioFWoEYwEUVaV82mCkm6yuuEiEYe+zfSRZolZsMHkmPbG7XQRiPp57IY7L78HudBCIuGmU6lx+6SIbd57yla+/DEApVyfk6DIcyWTmQgwHY3o9nSuvXeHzv/oC0ZiLBz/+jK2P1ymVRzi9brqNLv3OkEDAzmQyIZSO85Wvv8zpfpl0StwrzYmfD799l8yFWR7fz3PphSW8Ng2XT/hh/to8G02FSCVtmCbYnA6uXlTRB0OcHgfJtIfjfXFPBpwjLDYrzaF4/ZtfvEGlIkxv89MqtdMisqKQ2zrCYhGEnMNHu8RSPuJJD7IiI0uw9fE699fHNLsyWv+ZZGpNFaN+uwVFkRmPDayqxPaWwABaLCI1OBCPMBhM0CYKW7sDnr8k88kPn9As1Wi0xd4ye2mW1GLyfBQ+MZ4FMSU89JttwgGZj368RSjsxGK1YFFMXrgsEU/7cTsMuo02Wm/A6pro5FpsVsLJIOFkgNRclFgmiNYfMDPjoddqn+8dxnhCp9lHtiiEMwmef+smAIN2H30wRFEV3F47jUqX0lmLB5+Wef/BhPhchlZb4GAdbidWq4VOa0h/MKF8dMbsvP98v/vS7QnJgI7LLoK/qm0FVZWwKMK3AYL6FIy6yW0dcfnFRTLLU3z3+yVxKN7MEo+q2GwyiiIJ6eQzM60kS3h8tnOJXL6mMNYNwbC/MeHigkqn0SWe8XNWGnPvszbf+FfbxCOiiBoNNUa6Qf64gj4Y4g15CaVitCsNhv0hU8tp0mk3qzdnmJ7zY1GF52tqZYpf/Y9exec2ycxH8MVCHK8fUG5ZCcTD/KM3z/jBOz9l2X/3T7eYiYnivtr3IMsSimrhcP0EX9jHb399FotsUsnmcXqETOPg4Q6dWgPFouDyudEHQ5675mJmbYbScYFoUki+uq0ez10Y4/MqvPuTMwJ+hZ0H+3zlqwu8/e0tZAm8kSAv3XLS6wnZTqs55Oc+76LfGaJpE9ZupMQ9cW2Z6bUFIf17uEMgFsZn19AHQ2IJN4frx1hsVg4e7rA0L+6pQMRLMObBMCUcNoPFtRTRqB1JhtbYS6s9plrs4HcbNGt9QZMJBeg0hYyl1oJuW+N4r0L2oMFE17Eo0GkNhRF91sL8tWX6nYEo8gdD/uqHR0QzYQ42xXVsGgbNchNjYuB2GDw9VNhbLwgPngx/+Bc9GtUu1aZMo6Fxtpfn5LhFKunEaTUwTYjPpdB6Ayq5Cs26+Jz+aAhFkXjn24+Ipf1YVJWTvQLbOx0sqkK72qJYNbjoOcSiyoTifm5eEmQ7m01BddhxelxceWUNp8eON+SnVW2iKDKXXr1CasrHcDjmyZ1tknGVUnWCyyHx2hcX+LXffZV+u8/dtx/z+L3HuII+Ts8GJAIjxuMJ+lgic2EGl1MmPpdh/cMnRJI+Is+UlenAgE7rpwdnfTDEFwni8rmIROwE4gKEsTYzYtjXBPJ0/4x6oYppgjfo5otfewmLTVD2nE6Vaq5AfDrO9Zk2/9U/fkS5pdCu1IlMJ0lP+6me1VldCwpUttNKt9Fi7ZKfUrFPvaGzeH0Z1QLJuQTtehevWyJfEPupYlEYjw1yW0fomo7L7yE6m+LLX12mmK3Q7Zt88s4udqeKovyMH1n/njL0t2s5XHZUq4zTY8fhtpPbPUXrDRgOdG597iKzSz9NwnN43bz5gszc1SWK2RqZ+Qg2u41oKsAPvvExxWwR2aJwXPegDXWM8YQHP35IoyVMbJIsCZPu6jwApVyVxMIUg3aX8jPjUmJhCpfHzmjMOYt8OBBs6UjSh6KKTSaREONKXyyEzeWgXm5jtyvMX5kXpjqPg+n5AGd7pzTbE2r1Me1a+/y9f/DN+0QyUV55NcpXvuBlPDYZ9DQkCYIBCydlC9NrCxxvnLC5LfCVO+sl0vOR80TX//I/ncITcDE14ye3KwqBVz8vsKixmQSzq7MM+xozi2GOn+yhqhIXLkWp1zW2d7tovQHHRYVme4I3EuTnv3ad5euz/OI/eIXKaVkw1T0qqlVmYcZKs9IiFbOw8vxFTk7FhGVmWsiS9o7H598RgNdrpT+2Uq3prD84RVFg+WKY4+0Ci8mR4ItrY168ZPDBpzqjoUZiNk6tUCMWEQe82sDNYGhgc9q49LJIXB10h8wvBvjk+5/iCzn53ndyTF/IkD8o4HA+O6yoKruPjsmVJDwuCa/XSq/eotXSyMwGefCgysrtVaYSEg6HjDcSZHXNj9Pv4a1fv3n+nfdaPVql2rkW2u4UxBy71eTN33iReMikXBuzmu7R7wzwuCSqHZWwR0dWFNqtIa1Sjee+sCoe3NfmmF6Ikowp2K0mm/sCORpNBfCFxaFC6w/wRwNsfbyO1wVOj4NIOsRLz3u5dHuBXlc8WAoF4bsY62PinFGsGrRbGjabBbfbymg0oVbps7te4MprV1h+7iI2m4wn5D0P4luZFymk7VqTSwvCKP3uD4+EEdewMHN5kdzWEWvXEtTKXe4fuMSk7Rm61xX0kd3Kcf9uif4QolNRfvhjQdiyqAqxiILTY2P+2jL3dmzYXA4sssFLv/Ack4mB12tloo/59p8dEp/LiM6yW3TSkjMRAskoFotM9rCO1teot4RR9PYVhUbLON8X9rKmyFYodzndL+Owq9SbE65eDRCIhyhXhfHcardSzrexKRPSKTt7pwrzV+ZILqRxOSTm5xwcPT2iXe9yslfk4OEOFkXkOGx8eiQ67g6D5FyC7GGd6FScUl3i000Jq1V0SJVnRsOP3svy5LMSM2tzdJs9Ll/y0673efiTh/iiQX7j0jbhTJzMhVniEfG78bSfcCZOo1il39O5eVNkFoTScYqHZ8/yFfr4Qy5eeDHGjUtWDNPA6RAFlCRJQg+/GCAVt7JwY4WATyG1PIMvFqKnqeSqKqW6RGIqzGxUo94YM9JNtj/ZwB+PoKjq+d/y6M4OrWqbt74cJxr3MrUyRe50SKMxQtNEQnh8LoPT76Fd750jFD2hAE47HGwWSCxM8d0PDLJ5g0o2T6sxwOVUWFr08OIXltjY7jO3NoM/GuCD7z8llgkzd3WJaMqPRbUwf2WB+HSUVq3LyXEbTRtTLvXRRxNsDuGbuvthjnwF5maFCdXl8xL3jxgNNf5iYx5FEVk102sLfPEXV3h6qFDJFYm7mngDgvSl9Yb0Wj0yvjZRdx9/PMJbX47hcNvwRoKoDjt2lx3VKvb/h0+FuTSUipw3n0LxAN9+R+PJoyq/9svxc7zx9/58j//iP5unWNIYDTTsqkE4LLxxiiLzyVMTu8PKwpyTgE85T+C1qAovvHWTmcuLWB1W+rrYF03T5ObrF5lZmwPg/mctptcWODsoYrUqOFWdozPYfHDMcGgwGk1Q5AmGIaaeCe8Af8iJRVVxBzzUSw0SUwFGOgRCTpLTQQIRF6F0HLfD4Ggji1UZ8+BxD4/fgd1lJ5gUFLqL19LYnSqXbqSQZJnIdBJ3wMOg26fckLg0N2H+gvAHnRYnfOH1EK1qE5cDEgmRXeLx2cnlerz9YY8fvtdjoovnbb/d5Xj9GH8kIAz4PpWVW4vkj2v0212mFhNIskRmLoIkS2JaPI5zul9CUSQKNZlGUezf7oAXu9vO4w/W0bUxhmGwfEM8i/udIbVKn1DIzsqtRT6+U6ZUGrCQ0Pj+v32ELIspdSgVZebyIqFEmEjEzlBXcLisuGwGvVYPbWSKNO2laRGK6BMF12cHdhYX3Xg9MqF0nNTyDJF0CNVqwW6T8AZF8Fm5LYAp8bkUVruNxFwSRYbnX4gyHE6YX8tQPMyhaRNsLgd2p5Vsw8N//d++jDaCmcuLGBODQX9MNVfgT/75+/jCAbYeHhPOxCmWdTIZFw6nwqA3RB9DOVclEPHy7l/lScStmIZ43u49PhYp54kgL72xSLvS5J23j/GFvXR7BqFE8Fm+xM/2hMAEDONv9vOztP7OFQS9do8HHxziclsJhF1cfWkFi81K4ahIp6Xxwz+6c56q6I8Geec+HK8f4A266fd0WtUGhgmL15dx+dwsXF3i8abG4op4mMqKQqulUz2rEAi7SCxMUcmJ6HOnx0mv1SU6myKSDpFcmqawf8LGnadoI3B67KSWZ+j3xQFo894eTo8wHpWKfWxOB61SjZnVWXJbR3Q7OjabhU5dML7f/uOPBDt7QWL7aZFmscLJxoEwRq5Mk9s6wm41ebBpUChqeAMuqm2FQnGEywHVfIXRcEg87kRRVSIJH7VSB5dPkC5GE4Xj9WN6PZ3n37wEwPHJAKvDisfvpHBUFA/hpqAv5PM9rFaZ1WUHX3lVFBX9gYnHLeP0uIgEoP/sod6rt2iVaqTjFhw2md4Aes0OI110ab1eVbD1uxM6jS4ejzgIDTsCrWezyZimRLs9Ymoxyvpml+HQYOlymrubMqpVEQ+Cmo2PvncPfzRAu94lvZAk5BPdidO6jWp1SHZ9nxtXvSiqoES5XSJxevPTfdaup1j/8Am3Xl/hYLOAphnYnPbz7IXHT1q4XOKzLc67yB7UMA2T3fvbjCewtVHH6XEhyxJOj4tqbcRE13nvzz+h3+nh8LqZSipowzEHDwVPvNWVqNeGbB0YOB0KG6cuLlxJsnugYbeajA2JsT4mkXTj8Lp5/LFAtW5/uodqlXoo3tEAACAASURBVDEMeLKl8fJVztn8h492SUZlfNEgjWKN6GyKT+410YYj9h/u8XR7xKM7OzTLbVZfukQiITr1S5czHOhzBHwyHo+NkT6hVu1TOCqhaWNe/fwU0biL3O4p7fYIi2ohlAgyf22Zelsgd90BL6cVC/1Oj+UrgpITUpu88aYw0//Vtx7icNm4ODX6P9y7DpeT6QsZrt+K4XWZ5Laz+EMiqKZV7fD4aZtqvkmn2WUhI95rqCvkDmsUjqq43RasDhsr19I4n6WB291OOs0Bj955RLvSwO1Rzw2vnd4E1WZl61im1xPBOwBWq8zJ1jFziyESs2F8fivDoUG+OCK/m+X68oRLr16helZmfll04wrFEckIqFaFQXfIpw+a/MW/ecrM2gxWu8D5eSNByjVxHcVnopSPzqi1ZEzTxBdwMpkYhHxwbQUa9QEPN3QcLifhTIKFiwkSGb+gn1RbPHkqTNX+eIROrcU//2gBu9OO0+Ng9CwapXAipimmaTDWJ9SbE8a6yFORLQrzS0F6rS4WVeGjD4vsn5iUj85QFGFmlmSR/DocGjTbE8IxDw8eVDnbORYHZZtOsazTbE/wBx2U21YyKZWQT0zeLKog3xxsFEgtz3Dx1gL9dhdFNvnsvQ0alRbBoI3P3t/C7bYwGmgUD3Ni70j6+PSHD/DHIwz7AyQJXnh9hsL+CdVih3y+R3Q2xfYnGzz4JM9Ih4UpidkZJ0/ff4zW1/jCV68IjGOjSykndNTaUGfr43VCcS/VYotqsUOj0mU4GJPdOGLn3iaXb6YYj02arYkoJqai/OTuhFapRu60T/msQanQJbu+z0gX9KexNmJiyvj8dlSrlauvXqSaK7Bf8+O0CIzkadGkVmiQWkhy+aWLTPQJldMyIMAIY32C1tdolFrCmFrr8OItD+GYm+/+qIHXo6BrI77wlQU+eCThdFrwhHz0NIWNxyWquQKHm0KeGkl4yJ1pjMcCrnC0maNWqPPR9+5x/GQPu8OKPpGp5MoYJgwGOvsPtnF43SSSbrS+hkW18PiDLU6bTpamTJavTjOZmHRaQ0o9L06ngi8aZKBbePrxLm6/m+pZGW9QNCOm4iYffvsuG3f3UWSZfruLpkv83K9fZWzItGp9TvdLRJJ+6vkKc1cWmZ1Sye0Wefe7T4hMJ9D6Q8rZAv1mG78Hmn0L7daQcCZBPKJw524bfySA025QLovnktNpwaLKrCx7eeMVN/ZnGSQTXeerX79B/uCUWqHC+qMSuw8PuXwzjcvvoVpsEYs52Fs/xR3wkIxbaGoOUnNRJhPR6p2+OIPdLtPIlxkNR0xdmGHY1zAmE3x+O7VCncJRgVTGw3A4oXBUZfFCCG045smRlVufu8jBYY9gLMjiaoLScYF+u893/uADnNYJg96IB5sGFtUiqF+ShNbX6LQ0sjUHgXiQpcyERktMHEBMV4vHZQ4e7nCS6yNJEsdP9kThoYmQRdWmUj2rYrUYDDWTo50Sjz/YwOZy0GkNScyniSeFwf2sKhPymZSOC8iKfE4te/M3XsQ0n10H+gSXU+Gjdw/54R/dETkOmolqVdm485QLl2L43BJOv5deq4vb72H3/jZaX+PosM2w22P1WoqT7SyxsIJFFVOEv062/llefz8h+Fu07M8kPPd+9BnGxCQWtwt9m6IwftYp8IT82N2uZ2mzE4zxBG2os/3pDlp/IOQPYRdrN6Zo17v88isaJ8ctwpkEr//SLQBxSBwb9FpdjIl43ez6PqYhHqrRuIf8bhZfLMTqS5ewqrD+4VN6rR57jwVJKDGboFNvkts6Ihr7aUJf/qDAiz/3HJIM3fYQWVGolzvE5zLY3S6aPYWVS6JLojrs6CNddLqvLeN3jqlV+9y8LFJpnTaTqbQNt8PAG/Sh9QacnnaxWC0sLrrJbhzRawnyhISJ1WGjkGuy+dkJkixz45KN8tGZYElPRUkuTZ8zyce6Qcgv0eqa3HksNqdoSEaRBXVjJVrnZPv0XK+fuTDLvftNLBaJTESkpA41k5Xbq6zOGZxsHHBy1KJZrNBqiU3IGwkIH8ZBi6Ctw8qSi7s/uM+rzzvRdYOHH2yzMiuz+/AQRZGYjgz5j//xa1TPKiI92GWlNxS3wXy0Rywmro+hJqgyAI3mBIfXSa/eopDvoqgq20/yzC7HUVWJYbfPrc9dRJZh9aKPD9/eweF1o49NJvoYb8CBy+dloEm8+EKI9EKUz+5XUG0iT8EXC/Gl33oJ0xDabr9rzESfMH9tmVtXnVzM9DnZKzKbsRDwSiRDY/r9MWvLViyyyVnVwq0XMxwdNBi0u6TmRFqsJEucnTQZaPBrr/W5uy4xf3mO050st750Q3Rsjs7ExOrojLPDMuWjM25/6Rrt1pBLt5dYuZYmmfZgVSUUVUVWJKL2Bv2Byd76KdpwjGpVuPHKotB4aibt9oh+s00m7URWZPYfbOMPuTjODtA1nUa+TNgv5B1Op4X4XIa9VhLTFNfA8o0FZAm6moW1ly+zcnsVh9dNNVegUWlzdNwTicXpGG+8YCWcSdAoVem1hiytJQjG/PgcI0YDjXZf4bXPp/GFPeeb7xs3J1gsCqF0nGaxgt0hik3TNMgkLLz6og9f2IMxMZlMJkRDMq9cNQSWcTbFC8tdIpk4vZ6Oz2/Hbpex22USMVH0tgcWgiEHs6uzhIMKw7GFZMJK2D0iGnNhmibXr/mZvZhh0NUwDSEZ0bURQb8Fu9vFlashVm6vkgpPeP3VEKm0C9MwaXQkBiMZY2KwumSlmiug9Yd0OhpOp7jvPv/zqxxvnDCZGCRmYwTiISRJ4mz3hGK2hD4W/wgR0phBUVUGPQ2nQ2bQ7mJzOZjoOi6nzGQ0ZufBIdPzQYJ+BV8shEWRmL+2jCRLTPQJiZhKq6WjaWNm539KhFLlCc+vmcxnlPOgwIvJDo83h8RTPpYuZ5BkmUu3MgRjPoaDMVaHjdOyxMqtRSGjcUhkltIUCj06tQbhTIKxPia7U+D1X3mBZlFIfiyKSacj9oTf+XUfb77sQpZkbn3pBpm5EFNxE59jTLkiqqEbL88T9MvYnSqVbJ7F1Ti1sxLZ9f1nWR4GCxfjLF2M8MUvJrm05mHluRWisynScQVNM4hFlGfo6AFXLooOfCzm4B/+dpJQROzXiTC8vNzEFwtx0hKyLrvTTjLpEBOvvIlb7mK12zjL90nORBgNdTbv7ZJZjJ5Pxy4uOynsnyA92zs9oYBALhuQTNi5dj2MLAlZpjaCqxcsBPwK9XyFveyE1HQAbySIzW7jy7fHnB7VcbksaCNBOIpPx3C4BYY4tTyDw2Ul7Owz7PawqjLRqAN/PMLM6gzFYg+rw4o/6uOVn7tC2t/n4EymeNqkXhcT1WpHJRSQuXA1w/aplZe/fInRcERiLoU/5OLxh1tsHYp8l1/57ctomvjuF8It9vbaOC0jXngpisNtx2a3MNZGtOsdbKqJO+Amko4y0Sf0mh2sDhtzV5dw2ky8zgmKIjPo9Wm0TXJ7RSyqwllZYnbawepLl6iU+4xGE3TdZMrXxDRMHF43qkNkq+iDIfpgSHomyI1XL/DhX27QrtTxBt2cnnSIpsXU4eR0xKpjh+37Ip13NqZTOCpQrfy0i334aJeVy0lapRob97PEZwStymKR6PfHvPKG2COuXPazNqPTag7pNAfkto7welUxCUoE+LXffRWnquNwWdnfKqPaVEajnzYpZEViMIReq4dFNgkHLNit5rlEMDYljNurKy7O9vMiNE82xcHd50K1qujaiM/Ey2EYJqFEBK03YOPOU8JxLzabjN+pkzsd0ulLuANeKtk8qbSL1/692zgcCqpVxTRN/BEf6bhCai56nsW0kBwzmUxw+r2US30GGvSbbVqlGqPhCHfA9+ysI9QQ/b64Jr77rX0URcbnsxGKuv9vz3c/C+vvC4K/RUsbDAklAlx4/iIOp8rhfovYTIpQIsjsnJff/s9f48KNOdRnwVU//4pIKDUNk7lLc0SnkuijMZo2Jn/aZvlKit2Kl3qphdVh5en9LLo+ITkbI5MRD/HMyjRLNy9w7QvXmEwmLD93EVUV8h9jbIj0wq7Jc1+6jmpTSczGiEwnsagKWk/gM4vFHoF4iLWXLxOIBwkErRRPW1TzVeYvzxFJ+Ji7kMDhdXFyNmZ3s8zM5UWhjw16CMd9nGxnqXZUEkm3SCc+rVCoCrRqwCmSGpNL0wz7OuG0qOBvf/kaM6szzKwkqXattCt1XF4Hl25OM702T74ikiUbpSb5gzPsTtv5weTp+49x2Q38Hol0UhVx9xL4PSbLz13k9/9cplNrUC6JLr9qU/nqm04q1RGPdsRhbDAU3ctmT2Hu6hLXr4uO68KMeL1+q0MkI7CxLqXLk/U2b/7Gi/Q1iY37x0xfmGIu2OSFN9dY//QYTVeoNMRd+NfJlyHPmMTCFM2BHZdTfC8hn0iyXXv5Mm63gqIo3PziDWx2C8ZEjKXLxTYn2Q5L1+ZxORUCXnA5IDUXJ5yKoI/B7Xehj0RBGPAYWBQonzVZvhg6z78Y62Nq1SG6NmLt5cvc3xD+jGFf40dvV1Akk3a1gccxIeydUOtYuLhkZybU5bPNCS4HhAIyL78UPk/9jc6mcAe8jPUJmciEHz3ysTRrYefBLsmFNN22hj4WpjwQB/H/4B/Mk7kwy/bDE6amPRROhJGu3RZmONmiUDxroUojnA4Jp8eJ12ej3x1xmm1iVRVcDolauYtsUajWdY6f7Inro9olk3bgDXlYuLFCviJx9XNXyWXb+KNevPYRG9t9tOGITmtAo9ol7unhC9jptfpEp+LMX1smGPPx/HUXkiQefp9uSoyGGlpvQHLaTzyqMjXjp9Kx4Q54KVYN9LEpkjjH4gFZaNnxBp3UTotEZ1PIFpmTjQOM8QRFhnc/aNAst+l1x/ijQVodk2rXSqXQpNfq8r2PrXQbHdwelWZ9wNFhG6dDZqSLLr+mywSDVhqVFv/qf3wPl3VE0AuNvkr+tIOsyPhcBqGIE0mWcLhtLN28gN3tRBuZ2N1OjrPinjirKnz6WZd8vo/H78KqQsCpMzMnMjWuvHaF9FIKp1NlYVph7eXL/OCb94nNxLh2K4HLY0PXdL7+Up7YbBJd0zk9G5JcmiY+FcTmtKEoCqnpALGQwLBabFaM8QS7TeLC80tceWmJtSULrY6BJMm0uwbteodIMsjzry/wh//sPTwelSfvr/Po7glLNy+wdPMC3ZGN9sBCpQGDwYRCFXZLHn71NY3x2KDfFz6PnfUSxsTAmIhp21JmQrcturmSBLIiY4wNZi4vUs0VsDvtWO1WPvnhE4Bnsi8Jl1Nh9aVL/OXHEt/45hnlkzz97ohKsUNvKPP0SGVhRhQmT++LDApVFb9zuFtlem0eTyjApZdXOdrM0e2M8PsUjnI6LrvJ6X4eb9BDuW6yOKNwnBuRnAkhKzK1FvRaHX7+SpF5T4FQUGXp5gVyRZN/81c2LKrKSuAMn0+l3+lRLmvEp+P81pVtdltpHG4HM9Muuu2hkEoupnG5rUxfmMHp9/LgsyZLNy8wvZxg/toyimph5fYqNtWkXBnRbE2480EeYzwh4JW493hEqaKj2qyUCj2SCTu+sI+Va2m++cMxFlURUsJn37FqtVDYP8HpdVI8PCMad3HWdpO5MEu9PqTTGZ9jnuNxF+VskVa1LcL5dJXr80NuPp8gmXRy5+0dUv4htYbBk7uHLKaE0dkX9jDoDti8t0NqMY1Vlel3NO7fb2BRZVSrleZQIIA7IzuNljCqulwqqsNOs1yn2gSXx87CheizPBgdq8PG4aNdHm2NOata8Phs9OotAl6JX/1NIcNNhE22tjsUsxU2P94knXYT9EloExW700ogHiI5n+aXfucVgTyeyyArEpo2xuF1klya5njjGF/AQbc14NrLK9y+JFGXo3jDAVZfukRfV/CF/dif4Wx7rS7+eIT8aZurn7t6nhoeywTRRgbBoI1yZXQ+sQvae5zul/D4HSSXpqnXxTViURWePKoiSwaFoyqvfyFFJVfipds+Vm6vsnJtinTahd8j5JgOdUzuTOP9j1qiufDaFY43jli4sYI2gmAidC41DMR8Yro8HcKYTLh10eTouEc0HcAdcDF/bZmrn7uKzaawNCMRcvR57aYFn0v46FxBH+3O+FlxaaANNRRZxmpXebo5wBgbONx2wqkQf/r9FrFMmIvPLeJyW/G6RFJ6anmG6eUEgZiYJJ9mm5iGQSHXFAGBF1N0mn02H54y1n/GNDb/p2X+DQ3Ff28q/ne8HG4HVrsYWz2+s0s8JSrOg4e7NJo67/3VKa3GAIfXSW7riFzdITobUwHKp1XsbjurVxPUim3iKS+PPtpja2/EC6/P0Cw38AY9hEJ2SidVyuUhvVab0nEJbaijKDLGeMLOvU2q5R6hRAh3wEOjWCWf71MtdlBtKs5njH99NMYbCXLwcFfolAsVjjaz9Fo9Op0x5ZMyFlWlUWnz8N3HFM+aDLt98qcdkpkAhYNTHF43xsRAtVmIZGLMRzrU60PKNYO15+a5MjfC5ZQ4q9uJpEOUswVcHhuyLNEbQKsh6D4PfvwZZyVxkI8nXETCgtHudj3r/GfCpBbTAin2bN144zrbRwaFikHAY9BrdQh7dKpN2Lm3iaJITK3Ok0w9i4s/zPMHf1TE6bSQSQp0ZtCvMBrqDDTxPt/6xiMAZsM9msUKmZVphn2NGzcCHPeSPH/Dw713d/C7Jrz4hUW8fju5lo96fUByLoY2lplNiINHNVfA6VRxW3UK+ydYZGGwbpVqSBJMJhPWP3xCs6lz8Vqadr2PIsssP3eB97/1CQDJtJvCSRVdN1mOtnj33TKjoc5kYvC9b3xKZtpHvyP8D92BjE01uHorycFeU2gwTZN+s0Mk4mCsjeh3h+f8aZfHwdrVCCd1J95wAJd1zOM9k5BnzP7xCMOUiUWtrMZr2K0m+hgh/xiLAmSsjzENk6SnzelJh+5AGPP6nQFHT48IeAwi0wmmVufJbR2xeQhjfUKv1aFW0whE3FRLPZq1PrlcD1mWGA117OMeNhWCMQ8bnx4JA64iMxjqVOsTGqUmM2vz54VQ+aTIwkqEbs/g8NEupeMSpfKIw/UTllb87NzbImkrMjvjpJorMr8c5s0vpan0XIL+0x1iTCYcPNyhVeuSrwjt5etvrZA9bBBMhPBGgjSqfVQVtp8WuRipoPWH5HJdtjZbpDI+BkPxeWyqee7TKB+dEYk4mV5bwGKzYprw2ssBvEE3obCNbqNNuzvBaTXw+F0oisIvvSSM8C6ngttjY2HBy/5Bh/7AIDoVRbWYjMcmTo+Tr/2jV3GrQ/74X+/isY8Jx1xUsnkmhkSnM8Ljd3K2l8fls4scFLtEYjbG6ooLXRuTCk+4csmDaQiZhyJDsSUkTamAJszynSELcw4kSfiUbn/xChfWIuzvtSjl6vRaXf7kQVrQuRZSvPUi5HezDHo6rWpLBAI5Lewejalk84L3nklwVhhRPm0wHhv8i//5MfGwTLNYwWKRMCYGud0zgn6F/+S/eZXplIw/FuK1N2dEiFytjd2isxYpYrdJuN0Wrs5rKDIMxio2myIM99Ego8EIwzBJpj1o/SHffbtDMuNn4cYKPrdEIuV5Voz1SCxMnWex/NJvCyO2PxpEH0PQL1MrNPnqyyN+49dSGOMJ4aiL1JSPTs8k6JP47InIMynsn9BoGfR6IzbuPCUU9VA7q+LwOqmXO4wGGsOBjs0KqYTKScEkvZCkXe8Q9Em0uhJzU1Y+e3dd6Lk1E9Mw+dNPo0gIk3kxW2I4nPDqLRutSp31WpqphEDrzk7bOV4/ZKu/wKyvTGH/hKEm6Gx2l5CW7K0XhGZ9Kc3snJdOs0f+uIo36CSaDlM5rZH2dYmErdjtMq+8lsQ0TPpDk4tLdvxeC6u3l5mZ8fD0ifDaPLqzz+P3nhBL+jBNYWKvlzqUclWWbl7gZOOApZvL7G6UGRsim2Pjkx2OdoTkaOvjdQ736iKfJBnEMEzsFp1szUH2pEe5PKRZrHDWtCNJcOHaDMclK7XTMv3OEItqYfX2Cqkpv8hSMUyuXg3Q64zoNtq4rZrwy0mQjknkd7M8+GCXUCKCPxpk/WkDn9/Bgw/2hOzMZqVVqqGoKpdXLNis8O6ffsz8tWVCngk/+P4p/c6A/RMhuZpaThBMRuh0xxxkdSLWGjv3NmlVm+QPTvmLP7gjSFxDjUFf5+4P7rNwMYVpmPjCAex2BafHTv6kwVldFJd2pw2fX0yW87tZej2duatL9Ns9MaELOnn0ziNa1Ta797c5XD+h09KRJDjeq1Kt6Tx+0kQ3xDTAZlMwxgbjsUGtUKeSr7N0IYQ2trB0OYVNFVJH05TY/mSDSqHD8XGHelsiMZfCZ+uxOGfjF99wEZ9LsvHJFon5NN1Gj4Empta9eot6W6ZV63C2cyyoZj4PnaEiDv+LHvYfbAuDe3PA0U6JTl+m0PHw53/ZZn1XFIh2p4PRcCKISVMKwViAfl+kWc/POZ4R0zrsP9jma7/goZSrkt0p0KgPmAr0yG0dofU1tOGY3PYR2U2RhRSZTrKyFkWWJVaWHERTAlbQaQ7+XzkH/n+5TNP8G/38LK2/cwVBv9PHfFaWLV2bxWaVKR7m+Pyv3KZU6OD02Nn6eB2PX+ixI94Ro8EIWZKY6GMKB6f4vKKjWMp3WLk+yy+8OKRUGmJz2rmwFmFimDTLNdotjQvPX6RTa4hRtCIx7PYEPrA14Gwvh9PjYOXWCitLLgIRN3anjWjMKdJMvQ4CsSCyolA8a6L1BowGGqpVpVEf8Fv/8DJOj4N6ocr1z18lkfETm0mwuubH5bawcHWBQbvL4aNduu0hgYiXz47ctJuC4pBO2dnL2/C5DPpDKGZFKNeFZUH9WEhouL02QmkhPwoFZMqnVaxWmU7XYDQcMZmIDdFmt1A+KVM5rRAMqDi8biqFFgGfwmLGpNaS0QdDWgMLna5IRrx104/DZWMuLaRN/miQ19+Yot0esZpooSgy1bqg7RRKYw4f7fKbv3MVSZap9+3YXCKZtJLNs7c/YGLIvP12idmLGT64r+P3yjRrfUwTHHaVk51T7KrBn/x58VwOdOd7D6j1bUyvLeC2jQgGLESmk5TqwueQWp4BwKJKDAcj+v0Rujbm1V+6TTjmIXvYpJ6vYLPJfLDtY9Ab0uuIzsjX/sPnyB42iCZFYItNFdedLMG1a0F0TefaMiQWMqiqmEQdPtrlf/mnM7SqTXbvb3F01CEd6FPJ5vnGt1o4bDJJT5uZjBWLPCbgMbFIEz6538Uw+N/Ye68gydLzTO85Lr3PykpTlVned3VX+5npscAQAwyBAR1oFiTFIMUNShG6kEIR0o2uV3shRihCK4ra3VhxYylxiSWJJQiAIAbAeNPeVHV5m5Xe+zyZec7Rxd/bki4paVdEiH9E3XRXZHdV/vmf7/++931eYlNREYTWaKMoCt1WF1kyuX4tQKtjMXd5iYWLSfrtDi67SfE4w9nWocDiDUyquRKp5RSqKvTOly4F0ftDep2BIFzlyvQUD60uZI6KTK9M8NLNAMPBiMPHp8ylZPH6XZ3ImGCuT61O02jolEo9rr91lValxsuXZZqlKof7DVKrs+g4uH+3hDkyONwt02hZLATzXH3zCiCmRwC+oJtUzGIq1GF3p8ZY1MPJ432apSpXrwSoN0xmFiOU+j68IR83rviYTHmpVnvIkpiEGCa43eL15i4vMT6mcrp5wNhElJEBP/pJmdxxnicP8vSabdqtIaYlLrEAd49FsXq436BR7zObMJmZ8QofwWGW06xFPt8VxuSagVvp8uZXFxmMFCZiNiJTCQKuEbIssfnRE1S7RiEtaDm7+22a1Talqkmn0cWmGuzs9Vhc9PLxd26jKrASrSHLEuW2TYQqneXxugTKtFURHpbv/NGHpKa8TC2Ms3JtnnJZhC3VSg1UWaT7/jvogWWKRND1RfGZ6NabWJaFrotQt3ZT59d++zIep8nitRXCAQlvyEuzVOXu3TKnWZM//9Yh3pCXas0gsThFo1glYOuwW33m90h3KDbtJEM9sg1BvXH7nOjdPvGZMU63zwiHRFLyL37ZhcejUs5UqLcsqpUe4ViA0mn2+T7od/q8+1f7hCdjlDMFxgMjdvZ6tOpNdgs+CjURIteo9wmHNCQJ2l2LZFIYgQOxCKGATO5YwB28fjueoJdOo02n0SE2m8AfcNLXLf7qz/ZYTFloNiERarQsVBXu3KuzemMJX9jHYGjhCfo42C7zZw+muPN5kWapyqPbp8iyhTccQMKiXIdBX6fTE4nr2ZqNSO+M8GSMrUeF58mtLq8Lp8fB7u2n7N3d4dv/4kNyB2d4A+5nnqRthvqArayPf/NPP8AwLIJeE18kgCRJpHMGJyct7r17n6Upk/GYl6E+Yu36DP/tPxKSwJlIj6BPEO2ml+O0m2Iqdbab5tK1OKdZ4UHxBH0kpsVk9gu/9CJTs0FcPg+pGT/1ag+nOmQm0mUq5cZmU3j5nZuoCtTrQ7rdAbnCkMVrSwx6A3IHZ7SbfT7564dUqwM6rR5b203iEx767Q77RR+He1VaukY6bwmJ1nyC0VBcVpdWgvgDNl55a/nZVMlg+uICxnDI/qmF2yEmSYO+8B/92jdihGMB4uMKq2uB58nilmnx1vU+FjLx+RROt4vkUgrLNIlNRUQ4WdLFy+/c5Pa7D8kdnKFoCpv3z7E7VPpdncnwkInuHrmjDJIsYANX37yCpinkjrKE4xFOt8/45Lu3ic+nmLswwatfvyGSioM2dp+WmV4YY2pSo1FpYyHRrLaRJOEJU1WZ+YspFEWhVhsgSeB2q+wc6Az6OqWaxdSFeS5sRIhEXLw0X0aSJP76QZC58Q4P92VsNpXotPBm9bt9MpkuiqY+hymEYwEmlqYZDEa0KnUMU6LTHhD0Hsvj+gAAIABJREFUwcqLF4QM2WVDs2mMDHBoBu+85eeNKwaNcp1mpY5hmmTO2xSr0Kq3CY+5CI17qNQMll9Y4+pLM/giIcptG91Wh9JpFtMw6QxtaE6BYS5nBXVQURXypwVKp1m+968+YuvjJ+SKBg6HytRiFMP4uz0hgL+XDP1ULbvTjiQLCoQki67z7MYi1WqPmbkgqekAsdkk+dM846kYAUcPu8tOs9EX0euKwmAIG29soPcGaJpCe+AQSYsuB9ubJaYSKte+eIlqoUG7ISQ/i9dWGA1N/NEwC1cXCY55mFmfpdPosvnRY0YjsEwLzaZiGBaXXruEy62hagrGcMjFy1FSa3Mkl1LYXXY2LgV4tNXF6XagahqJhAvDsDh5vI9pCuNj8bwi6DZrc8+1vfnikIWlIG6XzKOHFfweC0lCYC+nxsVDtGXy0s/eYD9rx25XWL6cYunGKg6bxfx6kif3cxzu1whFA9g0cQgEQk6SixMkFybo9U0WL8+xvB6j2TLpDWQuTLZJrswgSZCICrPuwnhbvCeqeJj2u32y+SEer0ZrKLqgg4GJMRxxeVnoYw9OBowlY2weCgN3cmXm2dTCgUMdEgi7KWZqvHFDYTwgOsKXwid88t3bbLy0wPaZytKFKC6fwJh+4ReuU2vJaHaVRt9OOi2kCuNBk8hkmEgigGaT6fcMInE/bo9A7z34cJfTvQL79/ZYuraExy2jyPDO15MsrcfJH6XxOC2m54P4fIKu43UaVBoy3Z7J2blOZveEXFXFH/by+L7QenrDQR4UkwQiQTSn4zmBJT6fYmElzGBosVsMkisahLU6jbZE2CqQmvLw8JHQw0ZTEZKLk9TyZa7cmuG0HqDTg/XpAR6fg1ajz/TFBfG7ee0Syy+skZibJDKmsnBZ/LnbrSLJEgdHHRwuG9evh0gsTrF4eRYFgw9+dIrdYeNo84z+QPCul67MUKjAVMpJq9JkOLKY35gmNRMkFHLgcmkcbWWYvrjA5rFCam2OhUU/vU4Pw1I4enLE9beu4vU72dmu4zOqpA9FZ7JwJvj4lXydelum1nfgcGk8vXeCogl5xh/9D59QKuvPP9eD/gBNtYSZdMZLtdJjfCJIMtDCssTlqFVvc3auCznUUZqPPy4JTvx0lPC4GPHPzzp5egTX37qKZVpMRka0ax3GY25SU+J7LAtGIxOnx0XAJxMI2Hn9rRnarSFtw02tbhBy9UlndBRFodFTMU2L2UsLKIrQ3i6/sMbcrJtgxIdhWNicNtq6ymBoUCwNkVUFTbU4b/o4P2+TDHZFsNPaNFFvn54uY1nmcyLPyBAc+PRhgXdekzjfPWFyPkp3KDCC3VYfh0dM5/p9g5BLaJ/DkzEGPZ1U0ok3HETVZEpVk0pTNARcdpN2rc1YMk6j3GLzQR6nW3RIu12R+mtzOJAlk0l/C4/DQO+P8DgMzipO7KpJIV0lGHLSLFV5/MEmN764xp/8wQcEE+N0Birvf+8JkixRrY04fpomnvCweG2F1rNk1Mm5KFduzVI5zzO1OiPOmBUnM6tT9HUoVw2iyTEcTo39/SbZ3ICAV6LRGLF0Y5WX3lzi6ERn7WqSiaVp3C4Fza6xfGWOyrnIbsmcVnE5JF7/8gLZisKTj7ZwuBx43BLRwIjIuItGrYvb56RY7BGOB7lyPUqx2OONN8aJz6f46s/N8/0ft2mWalwL7vKDb2/Tb3eeP4+Wok3KziThWJAvfSmG3WUns3tCKOrHH3Rx4eWLLN1YEV6IlRlW1sKEx1xMLE3jDfmwqfD2r79MsdhHliEcC3J9poHHLRNPeEgsTvHhneFzUMWDD3f4m7t2Bv0hBwUn1YYoul0ujXatxdSFeSZmE1SrA9wuWQAjgh5GQ5OVFy9QKnYxDCG9q1b6qJrMTiFAuuqkVh/x8XfvMRa2EfHqTCTsjIYmX7nRZfvTTaJJcYYArL2wzHTKjjEUZltdN1HtNko1i0jMS9zTYmlKTNmCYRfldE54GOIWWw9zdDojnG470xfmhHl3bY7pCRlJgkjcR2jchyRZ/P4/vke72aNYEWbZeqmJZlPo90dYljgn8kfn4uf0O3nnt15h6+Mn+McCPLhX5GAzy1f/wU1RExxluPnKFP3ekG6zi6aYNNxxRvoAzaYwFlLInpTxejV6zTaGYdCtN/FFQoRjAVJJF+/92adozyaRc0sCbfrpZ1WuvTjB0FS4eCPJ4pyDQV9nZdFJq95Ds2tcWLYzMMSz4JUrwuuTyXTJHp6zv9cgmVDJtAK0622+eLHNv30fYhEFwzCRZTHRC8dDrK+6URSZqdUp7DbYu7eHaZhMTAUJxsaYCHTp9wT+1RgaBKN+TNNi9VKMat1kylOg3VdwagO8AR+x6TimaTEcGIT8giYWCqrc+cE9zk6aXFgP88m723hDPuotiU61gdPnYWExgCYbRJJRXF4ngYiP2Y1FFq8uEo6HmVia5uZXrpFYnKLTGREM2rDZlJ8KU/HfU4Z+itZQH9Fp9th7WqRV76GqEpn9NOm9PLWaTrXap5TO06k2cLhsNHUnmd0TAiEnC6tRJFnGNCzqlQ6Z/TSdzgBJsp4bCjsNYWbauX/C3EqM4UDQGSRZQrMpNAoVdj7bYmVZhIrVCmWmLy7wx//kffT+iOCYm25XPATvv7eJxyc64a22QSVX5mz7RNyoqwbXLjnR7CqyKlMs9mk3xQP9wb0i52ctBn2d9N453WaXk6O6CLNJ2fiZi1V+9P0DYhM+4v4+R+cwFx89n4z0egabtw8YGRajkcnH37lNNOHjwRPxb6xtxJlI+ske5hjzifwCt0vhZPsMp1tjOLQ4PyygaZLIArBZbJ57aFabjAw4PR8SiEXINFy0G10+fzxClmWcHieHuyUUWWLc2cDp1ggGRDem3FSZWEgyEbOh2TVWZ6HXbOPyOjl8dMBoZOG19fD67Vx9MYVpShiWxNxiiPdO53jpZ29wsl/mxcUWk3EVY2TQqtTodkdU6yaFkwKaYjIz7SSaHCNfkcge5TjaPOP0qI7brdJp62jPCvT5jWnKmSLrL69RKzUploZ8YaVIuQ4+r/p8IrB5L8PhXhVvyEepoeJ0wMmBMPTZ3U5WEh16HZ25pQhOt53wxBjf+tNTYskQw16feqWL19Yjd3DG0rREozFgMtgl4FdQGbI80SNrJRmNLIIh53OT2c5nW0ytzZA5axF0DfjCYob3H0iomsLiclDs7a6gi3RbfXqdHq22SXo/w8nWMZGwwkd/dY/lRTc7n23R7oiQI6dD47Q3QXIuQjQZYmI+gSQJedidH9wj5Jd49LDC/MY0HrfMgx894Oy4hiwLElQwGqBRbiBLgqp1+5MMwYgfD03WXlimUmgxNu7mzdf9NJUQxeMMAG6/h8Tc5HNpxXAksbzk4423lwlPjrN3d5vXvn6NZkOnVu6ScAm6U7YE1aaMyyHRqve49+596n0n2fMmhw92MUcmXq/KzPo8ABc3xphbjaEoMjMzHpZurBINGCzPSPh8dlqVGhf9h+SP0uw8ztHvm1RaCpZpMZ2yi2LSgoVplZ2dpthPZAkFxb6plrsMB0NKNZiecjMcjAiM+zndPGDns63nHaPNBzlq+SoBp0D3xcY14nNJHJpJwtvmwY8e0NJtePxOeh2d3lDFMOHmz1yi3x+RmI0S9CtUim2MoUHY3kSz28gdl3l4oJJYnEKzC+JTeDKGro+4fyhCmNx+N/1uT1z2KzWmp70iOLFiPM+FaFXryKrM1GKUS9fiRBJ+4RdJiGKzVakR1U/xq03affGz+x06L0ye4XWMCMf8nJ9UcYf8vPiVKwyeyblq2SI2xeRrv3KJkT5E101e+8oqlXKPeqVJv90VWRXvP0LXjefnuiJDKtyjmKnw0myRiwsW5wc57v3kCTOzPlYXxfk8ndTYvf2UZnPI2JiNH33rE+xOG9lsB1VTOHp6DkD+OIPDZaOnw8P7IrRq47V1LNOi3bGotASWcqgLKtHbr4pQvS+vnpM+LPHkaY9Oo80PvnfKm696kVWFne4cX/vGKk6fB5dDFKMxWxEFg+J5mUqd55dQu0PldDfH2e45O59tcXndQ2b/jG7PfD65UTUVSRKF38vX7AxHEuVslTvHfvb3m6iqxOxyjBuX7SQSLvJHaTZuLXN6VCWe9PPgQZ3BwKSczmO3K4TiYTS7Sr3S5KO//JzL0w2apTrLF8a5sOZl7+4u1UKD4f9Jxz0ammwkChQqMJnQuPDSKk6nIL5lsjrJKR8DQ+jVd+8dkN07RbOpVIstcoURsakwk3EbT++dMtIHXJ3vs7FmR5Is7m4JFPPxbkGgZX0uJCxC415MSxC79N6AerFGs9LErlkokgj4PHhywod3hnzpl64RCLtx2CVcDonYVJjPvnfn/wiMNDxYpim8fJkan71/RHgyxlAfcv1mFFmVuf2xMHN7wwGy2S7jMS+WZVJq2RihMbE0zfFuCafdYqgPnxOHxifDLN1Y5fLLi2x+9Ji9XZFNMbGQFBcau4zdrhAIORmNLLoDjR/8rx9TqpoEoyH2j3TajS65oww9XUKRTZrNAQ/2ZGYvLXD1oovU8hTxhIe+DiFnj3atwWHFx1dfkQl6DIpnRdLbx6TmxjjfO+Mv/1zk0Ozefsq//IPPef3rVwjHAjz4cAdv0EO24eLatTHqLUlMu5s98qdFSqUe7/7FAx6XJnjwpE+9L2oPSZafn1knmRGP3n/ED/9ymwsvX+TSpSCtjsFLb67Qrrew22Dx2gr+sSCdrkF/pArpYrtPYtJHr92nUWnj9jkZDobUK10Kx1lUVeLoQAT2qZryf6/w+w+0/rbTgb+fEPx/vEzTIJEKMjkdollt43ZK6B0RfLL/OI0kQXJlGhAow3lPmqkL87RbOg8/PWI8FQUgNRtm5cYSD370gKSrSKky4sXXp+l3RadGs2ts3T2h/ayjVTgrcfBEPGzGZyZwOwXHd2wySrvW5tf+09cEzcWmEArZ8AS9fPmXrzMamYTiEZITGp1qg5E+4GzrkOx5i+2DEfVyi2hynEjEwcKS0Nm9/EqU4JiLcDyMwyW48npPGFZDXoO7p2MUjzO0Gjq5hoP1WQOvfcD4zASHD3aRn4UATUVNNJvCC29fx+1WmZhwoCjC/X96VEXRVBRJ7GiPWyY+E+f4afY5/q7VGqEoMvW2jM9tEZmM4HGYvLRu4vK5+PEHDcZifsrFDqPhkOzeKV/6UoJQQOEP/9JB9qSCqgo02ue3a2T203hcgpXf1cXW3b39lPnLCywkLZxyn0a9z2hkYVNNPr7TxW6XGQyh1dIJhN18+0ON89yI3GEah8eN329jPCwjqzKqbLG5Wefg4SFetySC4VwO3B47p8cNHE6NeFTw1rc/3+PGmxdp1kSC53BocjcdJeyH4Ugk1nZ1iUvXJ2mUWzTLDQJugz/9l0+o5Gu0WkP0To90zUX+KEu91mfQH5LePmH9WlIk4QZ8TKT85Nt+AIo1mdlpJ92hht0Gf3JvhkbfhmFJSBKEwzaWX1ijXmwCwox5+PiIT7ZsfHo6wUsXJc7283z6k2PsdpWZcAtZUTjfPaF4nOHx3YzglntcnJ4P+MLPX8ftEMYvh10UMJ989zYhe4uDJ+dkT8rUCg0++0wEwL38zk0kySIx6eNo84xmy8Tp8zA1G2Jns4SumxTPirQqdYpFnVatw/RChL27O2SHcUJhJ5n9NFv30nx8p0vPcHD5i5dJrc1RzZZo1wUBp9e3sKsm2ZzO5sMSpdMcKy9eIDlh48kHj2g3+3x6OsHLb85zdtYhER4R8Fp0Gl3Wbq0TdnZZvyiwv+W0KOqLZ0WWbqyiDywGfYNKrkYu32f39lMGhsTD7RG3fywuW+9nloSZrtrk/LRO2GswFpIxDDFtTIQN+gOJbktnZFjYR11CPtANhdCYi363x8FBm/d+cABAbMJPIBbh0muXkJ95hzauJwhPjPHBA4mffHdbpJPmyrR6Co/OhZnQZ9cZDQ0KJzki7g61psAF+v02VE2m0TRp1zpUzvPsVMaZWZ9F7+t86WKF7N4pY+MeNLtGo1Sl2xlyaVZHs6m0ai1uvX0ZgPVXL2GYsLFgEvSLyV6jIxObEbrqWrlDJCRTzNSZ3Vik2TJpNzos3VhFtgwOm3HsmkWj0mZoKmS6EQ5ydrZv73LxcpSRPqDV6PPo012WbqySXJnBppqUyiNalRrVZ8nqqyteiscZ/GNBMvtn2N1OcmlBduu2eqiyxY/vKQx6A364FWH39FnqeK+PrpvUmvDnf7pPqSr25Pb9UxQZvvLNlzl6uEco5KRwVsAyLWRVYfWF5efFx+R0kLAfHE7RfBkLQtQ/pNsz6La6uEN+3v3UYGJpmt1agl/4xSmWF53YXQ6W1uPMBKuE4xEyNTthv4neETpoWVVI9+O4Rw38Y36KpQGlnAiY7HUGrF6dwhP04vC42TseYI4MMukmNpuMK+CjcJJDVSx29npsHUKlAc1yjTcX02xc8iNJsPfknKBrQLU6IJgYp9sd8F//hqDZfPFVH9MTMprdRqczRJYlvAHREFm7tU667qXf7rD5IMf+QQ9PyMfU/Di6PiKxOIXbYyM55aMx8LAwaaKpUK90OEv3+P5HI+IxG+Vyj5C9RTAxjt7t4fC42b+/R3I2TKOhoygyLgeEogGWbqwy5mjwdH9If2RjY0Vl5XKKzO4JazfnqWbLZMoya6t+JmI2gTitNlm5Ni98CyWJs4LMynqU6ZUUVy46+Py9Q7Zv73J01ObD98558KMHvPzOTVJTXrJNF2G5TGJxCodHmJa9AQ/BaIBoaoxqfYQ34EbVhOG6ni89y1vp4B8LMO7TiTb2CEZ8XLoWp1CB6ZUEg6GQLR0+OsLh1Gi3hHzp0fuPmLu8RPYgQzxmY3oC8vkOlgmNxpC2LjI8KmXR2PP5NHwhD/Mb8zjtFk51SLXc5cbKiOxhlv2TEWMxL2cndQzTImKrEp1OEPP18dl75KsKnUaTuctL7Dw4Q+/0CEUDRJ9JWP+z//I6mbMW7WYPu8vByeYhHoeBz21xcCSkRaV0gcVLKQrpKldfX8PrGPHVV0x6AxHqaJnmcwn2eFhFtdu48vI8p9tpdvfbfP7jPXTdIDYVZf9I5/wgQzgeoNcz8Dv6wvuVL3PnvR1CUT9nW4d0W31kSWZ6PsjC1UVWFzS8fgfhsP3vfA4B/L2p+KdqOVwOHn+6D0AtX6GvCwNdvVhFs2lUi21a1dazcJ40fUuktlaLLV54Yx6Hy0YqLugXh49PWbqxSmPkYzA0+et/85DFSykCXolwPEi/22M8JQg4E3Mxfvu3RTCJZtOIB3Q6jQ6Z3RNAGFEKuTaf//AR9z5NC6zZSZP1i0HqxSpHJ/pzPXtqbY6NjQC3LlnYnTb27+8S8Mn89bfuA5DNj7BpMkcP97A5bOzf3wNg86PHgkrjsPjV/+RVojEX2aJFoaHR7NsIhH3IqkKvaxCZSnBakImENXR9xMlRHbdL4ulnW1QrPVbXI4RiQeLeBonFKTJZnU6jy2g4IpsV6c5TSTsOp8ZyvMX12AkH93aY9DXJ1e1k9055/ZUAqqZwYT1IfCbG2q11/uSPnjAeNPnNt8XvxO2UcLgcbGwERZZCzsDpttPsSNifyRR2Ptui3VdwmS0urPqQZTjM2/nP30pTqQy4OVvG67XjdNv4zS/UubAgP0tJ7tDvG0yGdGRFwWPr88KNAIN+n5BHhGlJskR4zEk07qHfG1IsDSmcFEgtp3C7NXrtPk6fh1BQ6JRzZXHrd4f8z8gnMi+8Nk232SboGnDl1SWSCzGmU4J9HXQPmVoVY/TDB7s4vC5+68Y+sxuLpJaT7G/lcdsGXH/rKn/+rx7w3rtn+O19qnWT+ZSETTHxqR0e3c2RTnfQbCqdRlv4ER7tc/HWMjOTMusTdfYzKuOTYWqFMu1mn5OqF2M4YvHaMvNXl7n1epLkYhKbw47Xo3K0X+HR0z5DfcRnnxZIrc3x1j+4xdBSeeMrC1SzZSq5EmPjHh5/sk/6qELILZKLg9EQTqdMYDxEvdYnmvDT64lE6JE+QNVk8kcZ1pdtXHrtInFbgXsf7hGMjjG9HGN83IlT6TPUDc62Dpm+IOQh0ekE80nYPFZERz5fY+ONS3SaPdKZAS+/c5O5pTEuTgjs6sK8h46uUKrB2tUkWx8/4TufubDbJIa6kFHYbIJ21ay2sSxYWXJSzRWZTon9lfB1uLiskpgTVKrXE9v0W13C8TBTs0H+8H/aptODa9M12o0OhglOu8XUXJDkuEnVHufOww6GKSMrEsZAoFpf+9I8zUqTXLrOwqUU2ZMSQa+Fxycu6aXzIi9dgq99Y5W9gw6p5RTJUI8LE03qxSZOVadaqBOKj6FIBuXyAEmWScYVdu7ss7tVJBwPsPzCGgGngBrUskW8Sgt3yE8+02CoD1m5uUKn2SPkaNPv6vTbXXLpBpIElXwdXTf54Sc6qiokbT959xyX14HL68Lh1Gi2LcYnAuRP8pRKPcKxIGc7Z0iWiaaY+BxDjGdGd6c2YHmix6tfvYyqSniCfkYjkws3F4gnxSWr2tEIBQWO9vDxEQ6nSjYvporTyzHGUwlmL87x4q1x7C473VaH/lDixrqMN+ghEpS4vtQnmorw87/zKqoqEQ1ZfPM35lhIWrj9Xn79P1pgZECvbzA+M8GPvvUJms1GKBbEHBkcb51ztnOO0w6DvsFZzqJR6+PxuUjnTGyqQaM+wOFyMHthmt/9mSKqprJ5CLci2+zs9SidZnE4FD4/jmAYBr/o/h4RT5/JpSkUBZLLMxTbTlRjQGb/jFsbwpxvc2iMhgaDgYEkScysz3D4NM/c5SWCYRfN5oDAuJCAXoud0e0OSMYVFFk0t77zaIKt7TbJmJBwONUhuXSd2NQ42ZMS/+xvQkISVZKotySmVqYZDAzajQ6dRp/ccRG9P2QpLPwVaxtxHnz4lGapxnBkkEi4yO6JFGnDsAjb62weiimN3tXJnzf4+muC7BOPuzhujPGzv7BCeCKKw+Ni47V17v74CT//OoxHXRycDFhdH2Pv7g4SFpIkMa0e0+zIfPDtz5i/uoxhmARjYU5Oe6gq2G3igqZoKvfevY/D4yYasphNGLRaQ7Y+foIiW7z+5QWMkcHiopd33png0muXaNT72G0S494+RSOKqqlE4j6CsTCdRpehPiJ7XGAiquLy2nF5Hc/rh8lJD16/g+xBmkzVwcAmoCR2m8TWoxKdlsgEUDWFSHIct8dOMVNj+uIC1750FUmWCMbCjAXgziOdzU+26XYHVCs9errMzVdSxOIOmtUmtdqA7U83adc7TAb7mJaMMTToDRUx6TmssX33iNCzCUjPdJLZOyPXcJBt+ag1BDJ9YiqI2+8WIWcRNzsPzmgUKhgm+PwOWtUWzVKNhStLTPkq7B6N2H14ysnjfeKzCUq5Jt1WD9OwmPDU+MHnKnZVyBLT28dIsoTNoTIYWgTGwyzOakytJJmZdnPj9UWcToVGuYWqSsRn4vS7A3LpOtmmm9FwhKyK1ONqQUxQoglBPvK6FfrdAZ/f7+AP2FBVibGo9/95Efjvef39hOCnaMmyMDOW8i2mL0xzebZLYNyPfyxIs9rAF3IRjAaoPzOemcg8/MlDzrYOabdHNKttcmXRLZVkiexhjs5QFFBLV+dQVZlqw+JsJ83M6hQen4P4fIqtj58gSRZ2t5N2vcVuxk6/3UNzOoimxhgPWqyuBZhYSLK6kaDf6rKy6kdTJfGQn7BTyZQAqOWr/NF//z7/3e8/JTUXZnJpip29NktX55ndWCQcUpFkmFgSQT1Xv3CRwlkBWVWQJAmv0yB93mdhSuLaos6f/tFjukOhzzNHBm63Kgxck0Pu3ymwc2efXkfHsmDx2jLLy34WUxYuj4Ncy4/H5+Lquu05YejimoPYbJIP389SzjfINt382QNR9HZHNlZjAmeZ8HepVzpMRoSpsXBW5he+uc7+GRS7biZmxpAkCIz7ODjq4nDa6PcNTrfT/PhvTvAE/c/f11zJpCd7+OAnGT7+4S5zMZ0jfRpdH/Gv3/PQ6w95+MFTdqtR7j4ZojkdXH3zyvMbejgewjAVOj2YvjCHYYrip3icQVUlarU++/f28PlEYajZVYoFEazVa7YpVwa8mjxkPCS69U63i5EB+sDC7ZKZWExhU0ZEwho+v4PtnRazG4t4bEN6HZ2ZGWFi71QbfP94BVmR2flsC7ffRbnj5M4P7vHmz13i5stJPFqPy/NDNMUi5mmyWYqxeilGs9qldF7FN+ZHliXWXrpAu6kT8Q4IqA3sNgmXx8Zv/N4NZhfDjHkGRKej1ItNaoU6SxMD9u5uUzrN8uJKj5ULESYnHDSrDd55OyI8Hudtzup++rpFdDrO0lVx4E+tJPH4nUTcHcbHnXQaHew2iVI6j2GYTE05MQ2L6YsLRKYSJOIiIfPe4x6FdJW6GSAxG6d4luWz790h4FPIdMYYDkaMz0xwcG8HQIz3LTAMi9PzAb/664uc7eZolOvc+ckOTqeCrhv41SbN5ghFBr9zhNsp4fcLVGOnM2QyMsIb9KDabeTzHa6+eYVqThB03n+/wHgqQaki9OanVQ+qbNFuCNPlbneW6QuzmKa4xP/H/3CFoNei2ndTy1f4/NEQVbEol0XnNtbYZXXZw1nZRrslEK6/+Wabam1Ir9UmEvdx5wf3KJ1mCbpHPL29R72r0qk2KDVtz+SBLvKnBdzaAIc64OKNFANTxeV1kdk9waX00PUR/rCHrd0+erfH0cM9Dh4KhvfLJ/8Mh1Pj1tdu0Bj56VQbVHJVyukcdoeKqir8z99W0bs68xtzhCMeYkEx4ZuMq/R7I6YjOqF4mN/9ZpjtTzeFBHBksvlEGKIjk+MoitArj4YjLEmmP1Jo9jVmlqLEnDUCWotq147bJZCXY4kQsiJz9DTL0U4Ry7RYGKuSiFjkj9L87K/lqt1nAAAgAElEQVRcYX7Og6KIM7eUa5I/SjMcjCiWDdr1FmOJMVx2k4BDp93o8MrkAT+8o9Kqdfj8w1OBm23IzIcq7JyIz/v2oUmlMmDQNwhG/Ky/eglf2IckSbz+iy8yNhHm9a9eYGevRyBoE6Ziu5gYzCUlWn2Nhx88pdfuUUiXef9kSnhhTAt/K8ObL4qcC7tdxu0UydaHgevoIxVVEz60dr3Ny/5HVO0xRvqAfF3DH3ThDzoYDkZ89r07GEOD7U83+Y1fjROMeFA1mVTKjcNlp9/ucDefwuUScih9YPHFb7yE0ykzOemm2pTodQY4VTEdPdtNs3BhggsrLoqZGjvbDT7/rEAlX8M0LMKxIIPBiGhqjOCYG78kzunzdIuvf/MKocQ4pmE9nxaORsJ7YlgKb11uEPUPiEyGSM6GqPcc7J1YeN0y+kjmw/cyrF6dei45840FyTWdbD3MEQhoZLJdgZrshohHNVpKEK/LZHxmgvTOKYP+iMJJhlev2/net/dpti3sdhVZlll58QL9dgdZBq99yERC+M88dgNNE5fOs3SP43OT/UfH1EtNSpUhMUeFu8cBjJHB/R8/Jrt3Sv4ozenmAbVskU7PIpFw/1/qhycPxHmWWplmLV6l5Ewx6A+JBOHNL0aol5oEgg6G+ojR0OBkv8DMUpSTx/tUiy1qhTqNcp3P73cIhezcePMigZCT1275MS043G/Q74vgsXjMzs//zqvkjjJ86691/LY2e3e3sasmxXyTGy+Ms3FrkWy6hk0FA/GcMUyJpUCWsZDYg81Gn3gqyOnmIbPTTq6+LBqTTpuJ26Mylgjh8IrMmM9OokTGVObXk6LGkSRsDo31mzOomoxHavHihoJNNVm6lGIsGcemKdhsCuXKiEqmgGlKz2S0AxRFol4fYHPaSE7YON87o3ReYjgYMRxJz0iLPqYuzGN32Zm/usz5SYVyOsdgaNGqtnj1hpPjPXHGnO4V/tY133/oZZnW3+rr79L6/92FYKAPmb+6LDprYTeabFAvNjAMg8XLs9RKbQ7u7TC1NkM9X8Iu6cTnU/zS775K+rhKdu8Uy4Jivi24ztdmWZOfsJiyqOSFYWl/v4nL6yYa93B+UCAQ8eH0iU5CeGIcVdMYjSwmFyeYWZvh8NER+lDmh9/Z43TrmMHAJBANkxizqDdNbA47yXER0vHC29exuxz87G+8zK//zgaKLNFr95mdcXPrpu+56ebfsf1Ta3NomsJIHxCMRYhHxd/rukGhKtHSNf6b/2oGj31EtdjAFwk9P0hOSzbe+EKMiYVJUrNhNFUcEI2mQamhEI170Ecye3e3UWRwe504XA52DododpW3vxLHGBoshsp8bSNPZCrBuKOGKo9Yf/USMUeFflcn5u0QDLm49MIMn3xU5Ef/9hEO1WDQH+F2mM/Qak3S+xlarQGtSp1f/uUUlfM8G29sEJ6MsZC0KOkhli9GqedL2JQRZxUnhYzoOiiKzMKVeVbDWX7uVh9ZlsielHE6FCKuJsePD/HZOqQiQ4b6kHxVEWjJkB+bJlE8r5GYn+TTnxzTrLbR+0Nm53wc74pLWinf4rSb4EK8zNUlg3I6x0l6SLmkEwmaFE/zhG11Mb3YzjM17cHpttPSbXSbXZEemoxidzt5/8fnlM7F64bCblbCOVJrc3Q6BunzLvvVCJWOjXpHFCg21eI83WLv7rYwotZblNIlGpU2kZiHek/joJlgeaKH3a5imlCt9HFqAl2bP0pTy5fYOrWx8uIF1m6tk6m7+OiHu8iyRGIuQaUp02m0Ods95w3tfcJBmcU1Mf1KTWj0ewNiCS/nDS8+j0y71kRVYKQPCIWd1BsGqiYLn0Q8yNlZH8uyeOmqg9zBGZODQ+ITXsyRwdqtdfLFIbWuhi/oRJZkpi4IjX+/q7O1bzAWUvB5VTZ3B4wlQtgcdn7lty7SaY/odofIkkG/P2I+rrPoT6MPBPUks5/la6/KpIsq2YMM0xdmSSTc3Hv3Poompjy/+LUwsytxlmdlytkq69ECXscQX8iDLxIChP8hEveRmPTisY8wLfDYdMLxCNcu2siWJLxeO4mogiXJJMcGzI332H90gtvv5bAW5vykzuXXLmBZFi+8fZ3U2hxDQ+bSyysoskVicYp6y+JrVwUyslWp88FTL5u5MWq1Ph61x3AwxB3yk25HeHr7kGqhgSxLXPnCBisvXmBqZZqznXMeLv46m59uU8g2cSk9Fq+tEIoGsbudDPURNofGP/w54Wd48sEj3B6Vb3+/jqwo6AN47Zafek/jdPMAr61PfD6FaRg43TaSU37Gx53YHRqKLLN7+6kId1KdNLsKharIZQiNCowsFVW2RCCWhqAeaTJTS3GMkYHe7bFTDPPMOsJ4SOSkTMZV6vkSiiITmUpwcG+HZFyhXW3SaXQJOvpkG05C0SDpfpzXLlvcfEWkxCei4nNy3gqyPC38D263gtOl0Gz00ftDvF4757sn9Dp97n+wy9xSmK1HBbrdAVeWDD662ycScVEt1LGpFnPBMi99+SKB8QBTizHemDnC4XLwe5fuc+q7iCxZDPUBxWKfd9zvIkkSxa6fo7wNWZLo9UxkRaakJhhZYhqiD0BWZT7+zm3CUS8vv3OTfreP3e3kf/njjMjeaA9E2rlHpNqf5SzWV5002hZfXTuhWu2xf9CmVh+iDyzsLht+ucHlS16iqSgOh8oveH/AaDhiPObmxZeiVM7ztOo9dj5/ijfgYuvjJ7QafWTLILU2h89vZ2urQTVb5N679+n1TGKzSUJ+Cbdb5Q//ykWx42VgCDLZ2WGFnVOJNy52yBeHBF0DQlEv5ydVMrsn9DoDppfjpAsSZ1uH+DzCnxOfT5F0Fwn7TRQMpgIt+u0eb33jGsExF5rdhmnB2z+3QDggcXZYRtEUTMN8DkmodW3Issg48doH/Ok//YR6vsTZQYmvXK6SmEvgdDuwaTI2dDammgwHIq8htTbH7/wXr+HwuAnEIgS8EuVyn/WLYcZnJli7tc7kdIiDJ2mG+pDdYpBk+T6Hjw74H//xB5RqMJYI0mzqDPoDXF4nLq8Tv19jfGaC1FyYynmebr3JS9dcVMo6yUkHnfYARbZody1S0z5iEYXSaZaQX+L8vMPy9WXW1wNYloRqtyFLFuVMBZsGzUZfYER74JDFZKLRttitJ7BrEIj48XjttJs6yZUZnHaolHtIssxxTqHXM2iUW3SqDTIHWZbiHVptE6dTY/riAp2GSDZu1PscbudxDlt47QN2zm0cbecop3NUKx0+//5dxsIq/vEwAbfxnAY2GJi4XOrzCWF8bpLk4gQvvjJBpmDRbnSIJATByuNz0ig36Xf7LF5bwW6TmJyLshAqsbASZjpusbIx8f9eMfjvYf19DsFP2VI0hcMHe4TjQT757m2OK17K6RyyLDM97WXpwjiL11aed7v/+Y+j5A7OOD5uUzgrMD4zwc52XRh7MyWGA4M/P7/Be592cXkdjIYmobDQ7R/vl7Esi/xpkcXLc2TKwkTTbbapN0YcPDwgd5wnlBjj0VaXX/nmAon5SRJxO/PrkzzcHjEYmtTzJT64LeQWn33vDuV0DsuCZgcq5e5zc9m3/vUh6b1zjo47uFwaTo+DwkkWAL3TY3IuSl+3yNdUkkk3hgFPj2XuH4vLSnbvFE/Q+8z9btJsi0OqeFbk9KBMOjfC7lB5cjfN7n6Xux/silTkF9YwLdi7u03+KM3agkZ6+5hs0aLb6tA17PTMZ8EyepBiV3T2/+D7IYIRH5mmh8HQwDAt+r0Br3/1Im1dpVZu49BMbn3tBi6vg0ahQr83YuONS3SfpQvvPzpm0BO6QlkysUzwRUKclN2sxOqMxXx8+UWL6Sk3h4+PqegB/uwDG9HpBLmDM9KnTco9H8HYGK2hi/duj6gX6+gDi0AsQmwqxuMHBYpnefxhL/GZMSRZ4vDBHjYNPH5BYtm9/ZShqfAkO0axqTF/dRmvR8EfsFFrif9rz3QwHEFyLkL/WeDah/dN/GM+JFnCMoW+2OV1cOHGHIvXVnC6VHTTRr1Y4/y0TqPWI+jUWYpUqNQt7PKA2UCFTrPH9MUFus0uvpCPVqVG4UQw5U0TrtofsX3uRNdHtDqiu5ZtCMpLZCrBys1VllNCVlIvt6g0YO3aDDYNZhfCLMR6GIMRGy8tYMkKueKIs+M6hXSZk7RIa62Uu0wF6vzxP3mfXrNNr28xsTTN9sOMkBaEHdQKDcG5jwjZWLYscfmLl9EGHfa3BGlp6+MnzE9rzIer7D86I3+UJnt4TmQqQek0i9er4rDBpz855GS/jCRLqJrKk6cddh+doakK/m4Bj1fjpGTnH/1vPupNg0KujcPj5KDgpNUxcXhcqJqCokjc/Mo1fCE/Aa9EtqJSKbXZOTJxeZ18fBQjU3XQrneQFQWbarD1ySaWBbIMbtsAj8Mk23STP0qTr0hcnevywbc/45///vvkfYvUOhojU4Qv+cJeFkNFBoMRO/dPWF5043Kp5I4yPNq38Ho1zosSelfHNOFuehxVsbj02kW+eilL1D9g0B+R74q0Wr3Tw66OUO0amd0TDp7miMddjMc8yKqMJ+hlO+dn5cYSnWdTjvxpgdC4B/94iHazT6+jU+8LAklqbY7D3TK3Xhoje3guOnVdiYhHJ5gYZ68UwOV10ihUuPs39/jwe4/YfJBj7+42uj56ftb2NC9j3iHrqQ6jkUVP8zLR2eU4J5PLtsmXDIrHGWqlNp2WjtvrFMSoSI2VaZPlF9bIleHooMFwJBCxwYgbX8jLtS9dxWk38YYDQtppqIKMYpgokoVLG5LJdJlZm8FlNxkLmJwWVM5LMi6/l2p1INJcXRrtWhvNJuMbE5c9T9BLIdfBHxahgvd3FRbm3GTPRU5MqaGwWRjnYLuI2yt8VeluHFmR+bR7lY8OovzF3/ToNts4HCrf7b1JvVTjqnKXV+eytJtdWm0DX8jLB/tRqroX/5iXeNhgqBu88PZ1Ie+qdPGFfZimMPTf+cE9JhIuFpaCHD89JbN7QjwiC7nabItv3Uuxc2dfIBonbQR9YlpTNwIUKyIDZPtRhnu2V0Wat0/FpvE8dXr20gJPPnhEfD6Fx+egIYXwhz2MRiY2m4In6GfjjQ1mJ8X7m84ZhIMKv/fVDuPuFl1dppRvUSvWubGkc1L1sjAjfGaLC16cbtG5L55XqBRaXJ0XXopEcMjpUZVBf0DPdFCsydRHfjJND7MXUhwdNNi8fcLkYoqAc8Dt2xXefVckEBePM3RbPU4e75MpSox7epimRb835OOnTm5+6TLx+RSp+QjpZpDccQ7lmTfkbmmW9sBO8TiD3u1xvnvCd/7iAKfPjaIqFKsWoZCDSnVIp9Fm6+MnLM07WLs2QzjmZ2RIlMNLuHwevviNl2g0DZ588AgQOM/DB7uYhkk8Is7/cqHD2q11Fq+tIEmwMO/k0w+zyLLEzrHJeUbn/mdpfvTDDN5wkJEBmqrgdGs0WiZ+qSbklrKJoip0+xZ2hzBsF8tDdNP2v7P35rGVpel53+/cc87d933hcrmTRbJY+9LVXb1Nd8+uGUnRaAmSsR3LhhAYARxHVgQjQRbBQIAATpDIli1bgiBbkUYzk5np6ZlRd890dVV3dXVVV3ErFlncyctL3su7r2fPH6fVmQCCHdmyIGfyAQcscDkoEvee73vf93l+D9uPNrh/r0Ta3+ajpTZHWwWW3n9KrdzE0O28gHjSw8jZceJh8Holep0ubr+PsbPDrBV8uFy2VKtx2uB4+wClp+Jxy4TjAdz9Bst7HsIByI0mEGWZcNRLMBHl4KBPv93lh3d7uL1OJkZdlE46qKpJYX2X47LOyW6R8lGVre0O58Y1+u0uleMa5cMS2yu7NE9rZPMJDjcL9BWLdrPPO0/T1Osqf/S1Q1T1rxiW5/9j6yeuIDB0g5Gz4zy5u8rVz1xCN+y0T3/YT08xMQ2b8axrBuF0gnOzLtKjg6iqQSgeJjMUZ+ZMGEkS8Qb91Mptnhs94jPPuxgeDVM6anL9rGCjvkyLSCrMwHiG2mmb1bUm+bMT9Jptmk3Vxpomwxxt7DGS97KyrtKsNjk47HO0W2FixInH5SCSTTIx7mH84jSj5yZtaUlHt6v94wb56TQry3VefG0Mh+BgctzH4U4Vf9BNKBnlcLeKJ+hn8Z1FHt4/odYwuf3mJgeHPVJxB2MZHd10kJ0cpnxwQqdrUjsqUa2qVGsG4WSEarGCZdqd9hsv5ll7sM3AeBbLgvJhhXy4wbNftPFsfrdBIBYhnRC49PwUQanD8lGUcNLecA1ToNPs8eH3H3C4WURyWBztnjKYdfKpVweIRUS+9d0Khm6weyyysXTI8q1FArEIwZCLlfdsc2ckmyQUD9Oq1OipDnTTwQ/+4A5nr08gSxbfvOOh01J4/7GTtcc1Lr1who7m5JVrtlY3lIoxMRXme3cMasenPNwNMJL30KrUGM2aRNO2TndmPkk8l2T1vRU0xaBVafLM5y5RaxicP2//TpOXZsj4akynbILO5oMnvPejbQzDYiCmofb7lLtBDg46ZDIetjdrVE/qhMMy/a5Ct2fQOG0wfnGay5fjGIZFNOnn8cNDurqbcDLC/EKc3GCIoKtHpRewjXyChmrKbD/awO114pAceHy25nX60iTBoEy75+Aj9SyfmthD103uvXeExyvRV+23f6fR5nivzPqBzN7aAZqqcT7fQVF0fvcfvQPA02OPzQlfLVL0TRANS4xORO3u3KDM9Nksc7MhTMvBL/+XzzN15QyGaWuK5y8NMpEXOTnuMnk290koXXZyGFkSePTDRTY8F/GHfczfXGBodoz/4/ce09XcyC4ZXzREJB2jelS2u+EHHda3VWbOD3H2YgaXyzb9vXZDxul2Utir8si8wP5OA9EBN5+NY5rg8Tkp7x1ROjV4snKKx+9h77E98auVOxiGQb1lkYnaXbNzUwLHu0VmBzocn5rMLGQxNJ21Qy9Tl2fYeVzA4RDwSn28TttI7Pb78HsFik0bYfjKV54h1i+QCfXpaRLhZIjtRxtUlCBnL6SpH5ftjbLYZmR2hKm8g/2d+icd5KVHp1weOqHds4lNv/N2nGpHxumWSHoaXPzUBaYvT7Nf9aL27QP7cy8Ns7lR495bK8hOibEzGX7e+j2iMQ/ZfJztRorceJa9DRsXqvZVRNHB40OPjccNe+2OdsVGFgK8OLJDseEmlomSi/TYWd7k3IvnmLw0w+DkIBev5YgNpG05xewYQ7NjmIKDO4sC5bYdUiWZKqvyJRIRCIXdnBm1i/dULoTbK5PMBsmMZLi7FaXekXhyd5VoSCCd8+NwgCSL9LsaWw/X6Xc1HiyrhJNhZm/M4xIN6m0HB2u7xFx1/sm/apEf9pLIBHDLFh6nSTRo4XELRFMRpsdduJxQ2C4zdW6QTNqJodlhS/VSDaWvEwq7iSV8XJnRuX//lJXbS8gumbDfZPWpRmogzNHOCaWjBl5JpV1rc2/FoKdYfPlVD6FElJlxiecSq7i9HlaEcyiWE13T2Xl6itpXOZ9v4ZVUNu4/weu0u6jtpsL+Zol40ofaUwnFI/yv//Ms+bMT7O620HULpdPj6mcu8fq393A54RvvyMyMwPnnzpBJu3jze3tUGxamadFQvERC9ns9GPXz+m2L4s4xh4UenZ5F9aSGrtnBfwDFzX0iERfZ3ib1cpOdJ0UCIZuw1e9pfPBIoXZySjDgoNEyaWo+iu0gi2saA8NhakclDqoe3LLJ0mof2WFw/4MTDjYKhNMJOo020WSAnbKX2Rvz7Jdl5s4mqBwe0zecjKQ0urqTwVCTeqVDq97l5msTHO8dU2q5SKT8xNMBpudsTPblG4OMX5ymXNHoaLZ86sndVa5OK1QrHXwhL3e+fY8fvNvF4/PSqLTwehyMx6o4HBYz1+c+Jg0ZJHMxHA4HycE4o1mTrfUKhmHRqTZw+TxUaiaNep9HP3zEWKKNbCoMTQ8QjUjMjDn47H/8LF6vzMrtJdKjg7Rqbd65Vaa0U2Dl9hLHe2X6PZXldZ3N7R4ut0w06iaXsg/202ez3Hwx90nA4fZakaeL+7z/9iZHWoZQKkbaU2FgLIXPI3C4fcrB2g7xmEzSLJIeHWR0ImanhFe79Jpt1J7ySQhWwGPQbmlsP9ogGVRYWzwinIjQb3c43qug6RZf/2e3ADh7ddR+L4/FWF/cJzcYxF3aYTyrMRDpkxvwMf/sLJLkwBeygxyj2TiDgz6yA0Hev1vF6RRpt2xDezwikRnN4g/5CIVc/Le/fpdIOobslHnus/OYuoGhG5wcVAknIySiDvaf7DGWUREEgYvXBrj9rQ/+PZ0M/+LW/+8h+A9o6apGZjBMfDDDB2/cp9mxODmoIkoOdjaqHBc7HxMRFGSXTD7aROkrdiLt5gFPF3f45u/do1pqMDqbo1qs0DU8yKL5ic5NMxwc7ZwQSXwsC6l3CUZ8uFzSJ4Epfr8TSZbw+NxMXpohGTHJpJ1MLOTRNINuq0e7C1tbTeavjPInb+zSrLbIDkXQFBWXS+TOnTKVYoV6tcsLz4WZyGn4IwEqNYNE1mbmR1NhxqYSpPIZzr14Dn/QzVdnPuLSs6OkU27iQR1ZNJEcpk3L6fUxLZt7HwzK5Ackkrkwl1+cZnJE5MGbHyEIcP7ZSU72T0mHNXvkrLqpVbqE4gFqbck+pCsC1UoPEwc3BveIJELkXEXC7h7NSovpa7OE4iHafQeT81lUzeJ7r++ytd3jp78QJZoKkU/bnbRXf/4G/kgASbIDzh5v9Ok2WgiC3d395jf2SLpr/MKvPM/ZGReCAGuPDrAsi17PID8a4sniAVF3h3pPZnfpKQPjWbo9g15HJZyKEfRBJGgRG0gzFTkinQvTaXTZ3W6QzEVYeP4s1VKDWC5Oo9ZnOCvRbJs4JAehmJe+4UK3HER8OvmzE8xdHKJy2qNQlUkOZZn2btGs9ahUVBbOxcjmEzgEKKzvsrVe4dyNSTRF5913jrAsi7vf/ZD0cAxJMNEUDUmC3c0K+/UQMU8Lr1Nno5ElILWZvTHPUD5MOBbk6UfriLJMqVDD5xVwOCDpbfHO7jBLt1aIp4PUa32OT02mrpwhMZBE7Slcn2gyPDNIIhvlqOklk/HyzOeuoGomkmh7Ks5fHWSw+hCnbHP3B8ZSWBa8+/pDJAk2K2FW13vsru58XOTUaDQU2j2BeNKDqhrMXJ+zE2vbPS7lq1imaevhfU40VWf+QoZAxI/oMCjvHTE6l6e0U8CyTDIjGZ6/6mFuUsYwTPZ3m7i9MoMzI/zGf/+A+UuDWJZFvSfj9sqEfCbRgI6um/h8MrGBNKJom9O8AQ83P79A5VSh31Nxup04ZVB0gXAyiGoIeIM+np74mB62KB61aVVqzAx02VvbJ56N0u0adHU3hilwNbtPOBllMNZHdMC3fuddej0D0yHRVmX2Sw62F58SiNmF5uJ9Wwo2kJaoFOvUyg0SfoVEOsALZ3u0a00icR+/+ycBBqIKssfNz7/QYiphT8r2W1EEh8DK7SVSQRW314NpGLz79h61kwbRTJzyYZVAQGZt4LOcFNtEY27qXYlmtU02n2D/8TaN0wamZZGMmPQ6fQzDxOmW2dlqEEnF2d5u83t3Blh+otoIZSwcosjTxR321naZmo2jKCa+kI9ozMP+6hb7q1soppvBnMyZ2BEul4Ng5wS3qPLu+w0Odqp0FQfJoSQ7T4p0Gn1mp9wEwh4+P7HO6rqdldFXIOgXSYUNNh8+JZ70Eckm0VSdLz6r0a616XUUvFIfv8fEIYmsVTL8g6+q3HpzF1U1cMsGSW+HdLCPS7ZhBbsFncOijsvr4mC7wlROxRP04g24mbk0Tn40TCQsU9irsbTj5LRQJX92gmalyd4RnJuRaFS6yC6ZwZEokkMnMRDj4e0N5vIaXlnFNE2W1lRCzUPatQamKbBSjDM8mebcJduQKQkmR60gowsTHNWc5IYCBENubr48TOm4jT/iw+lxcncrTiwZYGoqiCQJ6IpKvdLl7/5nYTwui5euiUS9fWrVLn/0W7eYPZ/j8LBD+bCCT1ZxyXbQVCji4VdeLZIeTpEf9qDrNmhj6+E6szfm+crfvsnM9TkO95v0nUGCUT/nruZxCBAfzHBaqPLCZTuvJBwQ0HWLjuaicCoxN+UkGJS4+KkL3H/Y4uGawebjAh889fHCi2mSQ0mCsSDZsSwnB1VaHYtw1MPqWpNcyk6tFwWL4eAp1Y6LnVoIy7RQugr9vkk0FSXgNggEJGIxN5Ik4BAclEoK7VqHuUmZelei17ezFWLupm16r7aYvDTDZ256aFXr5CdSNFs6Hc3DZtGNL2BPLs6/fB61b+9n20tbBD0aG/fXyKSdeMNBAPI5SGd8JIazvHHPib99gq4ZuF0CsmhxsN8inXLyyleeIRj1E0mEePnFOOnRQUKpGO1aA13TyWWc7G+WUVUdRTFIhHQCARnLsuj0LNS+ilOyEGU7H+OVL0wyIB2SGkyimk7SOT+6AZVCmclLMzhlOHFkyYzEP8HxDo9FGD03ydTFcXrtnk0W8/XJZtzMXJ9js+hG1wxOj8pcevUihmFQaxj87f/qJudePEdhv46maDhlB3OX81SrfUyPnz95T2P9yEO9rrH4zhKhkEyz2qDT0WnXWrz17VV03WJqJsKNKz76PY2zV0d5st7iYH2fg3U7afwXfuV5Oo023qDH3v9unkFXVAJhH4Ig0GzbAIB57zoul0irbfAfwjJN6891/bssQRD+J0EQngiCsCQIwjcEQQj/2Nd+TRCETUEQ1gVBeO3/zf1+4gqCQMRPudjC+DjhUBIFjrcP2H60QSThIxR2kxtNMzqTJjUYZ/kwxOTCEJXTPp/+hWe4/sqcnU4Z9dPv6Zx/booPdyL8439eRNcNNu6vcdoUSQ8ncTptPnm1WGF6JoxDEPAGPFz77BdE3AsAACAASURBVGUUxSAQ9uLxOem07NHpUVGlXukwOe5jYCyFbths+b3NU5v5Pmqb9q69dp6t9VM0VWdoepDGqY0g3TySKW7uI0kChmGysXTI0VaR5ft7XH0mg2FYrLz3mDvt8+xs1TAt7NCcvq33m742yws/c51E1EE2H2cmb+F2mnRaCoIgoGp2mm69YZtj567k+ZP3bLlOylNDkhy06h0cDsueZLgsdM3EJSg8OBkiMxiirCVwiRrT54e4dClOejDMmVSV4kGDdMzi7/yNBB++vURfc/zfRsLDMopi4PG7qVZ6PP/la4iiwOWXzxKM+tl/WuLv/60Aid4+a2sNiqfgECx+4RdHaVbbXD8r4HQ6uHpzlONOgPcf9Ji/uYCm6Ny/tUW90mFoKsMb3z1kY8eOt69pQQp7VXwhL51Wn3qlg8stMX9pgFgywNx8mL0jnXwWpi+MoPZ1VEPipOVl/UAiPxHn7a+9z+Cgj5OyQX46jSmIpLJBmo0+Bwd9Ft9ZZCgDc8+e5dnnUhwfNpBkEUkW0T9G12mKQVo8IpuPc+utPXTNQDMEDhoRdkou8sFTalqQWtlGjSp9jTPXZ5l/dtZ+7dUMLAvWy1EcAuTnRhkc9PHcNR8fB1gyMpVgaHqA/UYA2SXy+P1V2j0Hj5fLVCsdohGZXFQlO5ZhMOPgoe8F7tw+QRAEkmkf9ZbF+LlxSqcGs8kSkuRA6fRQFJPnPneeD964z717FRp1lXKxiWmYSBKMzw/yD/5hgeG5cYJyB49XprhzzMlJj1c/M0S1axcky7cWmXv2LBdeOkftpM5h2UGhZBckAJ2WyvBEkl/+L67xgz+4QzIX5mJ8m35XQ3JYSA6TR3d3Gcy5GJrK0GhodNoK3oCbdkujUe8xdz7D8HiS0bTOP/of32VkLELEqxKKh3E7Le4u2kFUVz9ziWbfSXYsS24oRCQs4RI1nJLBeiPH8fYBawcuvE6d8YvTfPjWElVnmmpLwusWeO4LlxmaHmCv4iORDZGfSFAsGUyezdGuNREdJp2OyvKBn16zzUDOw1deUtk7dXHz8xfQTJk7T2MMDgVI+1poim26fv2WRrfZJpaJMThmU20K67tcez6PrptsViK4PTIbqyWGox272ZEPYJkmr315jrmzCTxOE9O00DUDUXQQjXsZnkpx+YKf1y73uTxvm7JLbQ9zz5xhaGqQ+Rtn6HR0Vh4WGRyNoesWoVSM8y+fx8TBD767C4AswWlgmLhYRumqjE3FqTSgUqyycGWYZDbIypMe5aM6d44nuTBnd+x3du1n42lT5PIr5zBMi2gqwvBohH/5hsnlm2N0mz0sBLKhHuduzvKs/wFvPh22ZXgWLO/I3NsOMuQr8nRHRZJEXE4HQzmJ0ck4kiyyvOskmYtRLzV5uriLohh0OgbDY1FmhnTyM1nOXUwzsTDEk7UqzY6Dfk9Fdtqkna7uJjsQ5MXPnuG6+iZxZw1RFIlGZL6nv8rkxUly3jJjCRtD/fZ31lh4foHN0yBhT59+V8HttFhbPCYWd/EHv3mLF29GKR+WmbswwOJijU5bodkyKRx2WHh+AbdH5oOtII+fajgEeLjtZWQszN/5r59Dlm1dvjfoxbAEFA2UnoppWry9O4bskhAdAuGg/YxNDGcpHVZQFAtRdBCJejkWcoTCHgoHTe6+uUo0FcYh2SbhaqlBsWySTYlE3B2msj3aPYFOxyAUdjE/F2ByVObnfn6M+bxKX4FA2IPaU7l0JUmz2iQTt8gPeViYD1JtCiQyIUTB4A/fixP2qES8GpGEn8RAlMJBi2gySE8Tabd1JEmgVOoTy4RxuUTGZjPEfCpBj87EoMXQRBqAZMpHaadAMOrF69SZv3GGg+0yLpeI06GTCJsofZ1nPneFwnYJX8jNjS9cYfrSJFF3m/mbCxSPVSYWRpi7PoNLMtF0i/nLw9xYEKgHB3G6ZbZ3e+wdO9h4sMFpRedH3/qIWrmB7JJQdTv/IDWYZPLiJPmJFO2uRTxrN+tCIRlFd6DrJoZhsb/fQVN0gh6dbD7G8FSGp5tt9rUhDMMkYRxxuNdEdMDYwigb99fodC0Om2F0zWRj2ZYJu90ijdMmsYSX3GiaS69epKvZicMOh8BkpsfUQg5RFDncLFE5LGGZUDixiMW9jIxHyebj+HwiPp/E08VdKulZnrvsZiytEI87yU4M0WhoTJ4fpdvViaSiZEbS7G9X6HQNSjUBXTM43K0xMuInMZhi8sIETqcDSQRBcCAIAqGIl83lQ9vblPQzOJawAwCHEhyTo9lUmJ0QCaVi/97Ohn8Ry+IvfULwJ8CcZVlngQ3g1wAEQTgD/DwwC3wa+N8FQfg3hjj8xBUEumbQbnbxBrxMzCTwum0aT3wwQ7ej4fGIrNxeonhQJxr3EvKZVEptdh4XWFs+4e0/vkss4SUQclM+rPJk8YCL+QZDE0mm55Lc+MIVJtMdonEv4YiLRqXFyJlB7r1XIBZ3cVqosPTeOr2uRirjZ+PhFsmBCD6nQTbjpNfuY31MUXG7BHo9zcZ6OUU2Hm7TbqmcltoMj0W5fC2NpugUN/dxOh2kohbPfO4KTtn+/jMXhsiMpBkcS3Fa0el1FMbPT9Dq2a+LRkPjq1+AoVADyxKYmIrydKVIsWQbQL/9gzp91YH4sZ+iVDHxBdwk4zIjGZPiQYMz0367u4DIUD5IJBGk0RZIZOyPK7eXqOu2Z+C01CEot1BNiY9urTE/2KZR61FTfFy6nEA3BH646OJnvnqFnuJAdopIDotLz0/h80l0mz3GJ0Lcf2eNkWEXd7//kGDYzchUmp7uou2Okcr4ODnuc1gSaPcc+INefviBTrWq0mrrOEWTkbzXDqe7v0Y4EeLFT+XQFIMv/ZTNkNY1E8O0D+af+0yS4bEYtZMqgiBQ2G+xt16kVNYonXTpKvYDzed34ZX7XA8ucXm8Q/m4zZf/xk0e3D3EtCxEUWCpMYaumbg9Mv6AXYSt75oEQ26iARPBIZAZDNNr91H6OsNjMbrtPv9qcQqf38nIVArZJWGYAkl/G5/bIkCDvu4kmrT/xsPjcVbvLLO9ss/wVArBAdWGPSoejCm4vU6qNY3jisB797vIToknDw/pdRTCHo3SYY2hM6N0+/YGLkkie7ttdk6cSLLI0x2doLPLCy+kaDQUVh8WiIcFkmk/2ZTIainJwMeUj1zaycqDfeZvLnDjmRher4Tskli/95igT6BZ6/E//P0sbq+TB4dJ2i2Fqy9OM5Dz0u7anULBYReq3XafXsc+gA0kTFxO2FopEEt4WftgjU5HZSF9zOyNeZJJD2UtQXYgyEJ4kx99aHLmwhAHBYXizimppBNF0Vm+tUij1mdqJsLOVp21h7sIAvz6f/cMPq+DO0sS2eEoB8cW587I3H1zlaPdCoYp4PG5EAS7Q2paDvbKbvLBU6avzXIu32XzSCadCzF9aRKf2eRirogsQbXSQ3aKxPwaLpdoywpDIvG4k8svzdFRbcnDWNqGGcTCApYl4PdYH3uSBIaSJuVyj8NmEI/PiSiKXFzw88VfvMD4dIK99RN++ucmeOFnrrP+xA7fC3p0Rkf9ZIYiCIJFu9am2dJ59otXMUxYflTi69+uMjyeJJ4KsHJ7iTuvP2BrpUCrIyAJJrfu9fAH3QTdup1m+rEWWxAErt7IcXLURFF0Bsaz9LsauiVy8foQHrPNaUVnr5vhSMsQjHpxu0XODCnMXhpBUUzaLYVmQyEQ8TGZqFNpOBgcTVA6buJ2CQzFFYr7NSzTsj0866f8tc8bvPH7twkng7gcKo92vWiqwW8+vMDz40eoPZWtlQMGUxaXRps8OMkTDsu06h3Ojuk4JYtiocX2ow3e/9EutXKThWtDXH5hmnKpi6rZ9LOvv9HEMi329tv0ezojYxFMCzw+F+F4gF5XQzcdvPVH77H5tEElMs5eO8Xk2UFScQfT8TL764esVnI0+m4KO6e8+LkZTAsOTyz8ch9N1RiLNZg4k6LV1vlbf+95dEPA4/Oyt13j2WfC9LsqggCNWteeakY9rCzXGBlyIjsMRjImnY7O//Ib72JZ0Ggo1Es1DEvE57ZQeiqRiJv9Q5VWvUurY9LpwchUGm/ASzwbRdUsjvfK1KpdUhzRqPeIJXzMXBpn4/4apZ0CfpdOIhPmw9t2lsPDgyjbJQ+HRxobj0v2JCpgYpoCD1f6LO86+c7XVtha2iOSCtHpWtx4ZYbjioAkgSRC0GfLt56UokTCEo2+k82ii42H26QzPs6ft4sR3YBmU8EwLBRFR+lr5Adlmg2Fw6rLpnI1RTothbbm4WCvwcz1OR69s0xLkVH6dpJ2NiXS0ly4JYNYwsu9Nx8xOpPl7FyQO9++R+20jWbIOJ0i1xZETg6qlAo1dEMglZApl7oougNDkBgaDtCo9Tkq9nG63STi0sdNwwCtepde35a7Tc3GSSS9VMptFMX8OPDPtEP/ag5MEx5/tM+nbrgZGE8SdCn0ezqP79mYdK/cZ+vhOpKhYBgm3b6F022HDLY6BgmfHXJ57uoQxYabWk3F5XXR6Wis3F7CKYuUmjKqalI6rHBQ9XBy1GL+2jiheICxhXGyaQlVs8PvTNPOlfjg3T2qVYXGSQWP0uDNd9t0VYmDgw6JbBifX6LbUqiddnB9jMwdm0qgaxbjWY1A2IOuGdz54S6hWIBQ2IMkCQzGNabODxNPB6iUWuRGk3gDLtYf7dFuKoR8Juv3HhMSbTDIg2WFoamBv4xj4r/9+ksOJrMs6weWZf2pcesu8Kd/oJ8C/sCyLMWyrB1gE7jyb7rfT1xBYBq2UTAcD3Cw1+Dw2KC0f4wv5CMYcvH40RGSy4nX72Z/u4LXZbL54An56Syjk3HC6TjBgESvozEwnmRoIk3fkLh2wWaWd9rqJ1jEu289IT0UZfGdRdweJ8Vil9xYmngugcsl0elojM6P2GSGsszKcp1AxIdhwuajLZ5s2AjH2YUUsZib+WuTZHM+JEmksN/gyZr9RhmeG7e16qE2mm4wNaiztVLgcLfG9vI27WafrfUylWKVy1cSRAM6jVO7U1Xve6j1vfQ1B2srZeLpEF6Pg83lQy5diuGULU6PaiiKQTRsa+83tzvcXdQ53jthda3N0e4p3/kozuriCf2uylBSZ+3+NpIIC88vIAoGw5EW5aMqG7UMu9Ug3XqTX/3VB+wsb3N/3cmtt/eptwR+9soJi4+qGCb0OhpbRYnD3ToAzWqDO29voXR6NFoWkXScD964b+tE5R6mIJKI2gFq83mVYsnA6ZYJR5zEYk72tyqs7ct0e/bmOH5xmnDMx2zW7q4HPTqyJLC7uo9X7hOO+Tg4EdjZKDN3ZZwPvveAfk9l5EyW93+wzOO7Twj7DO5+90M03UDAYkmZ5eGuj6OdE56u1xk7k6bfM/jgB4+YC++iKDrplBtZFph79iy9rsHRQY2TqgNN1bn9rQ/4e38zyPRMmOJBg2DEy/kJnU5bJZtxc7R1RLsnsH1q5y2ogpuL3bftIvaoza1v3rW9EQt5Ft9dQ3QIuF12p/Ste/aIslLucGW0yfNXPfR76icTsuOGk8xwjEDYy0hK43ivgmGYPHjzI0wT1u+vE/CLdHU39aZFfthLfiKFbtgsf4cAk/Eq9z8sM39zga0d2/AeS9gI1vkpiXQuyODMCIUTg/V7j1ksxNBUnYsDJTTVDl5af1IjE7fYaSawTPAH3fiCHqondsfto8cGrbZJaihOKunEskzWH2zxtJbk8furKIotQXI4oCv4OTPpxul0kEk7EWWRTtek31U/eSbcfmuL/fUDZs7n8TlV9ssyT9bqDGQkOm0Vn9dBreVgeGbI7uTKBj6/k631MgcHHcodH5mIxn4zRrfV57TjZnawjygKnB43aAhRvvVRkr4KvY6Cyy3hkXV21k8oHTXxeeDB3SNuffMuEU+X06O6TXw6l+Pk1OSwaRv9tx9toJkipYZIMunBssDpFG3Z0JMeJ2WNtcUj5i8PsrjapXzcZuFcFJ9PZjRgG7bTKTdPigEbOFBs29CDYp8Ll1N4fE4a9R53v/sh09dmmbxkm009bni4H+Kl627KRw0U3UGn0UXpqrSbfZ4sHfHRhydkB8PIsog/6MLtldFMmfMTOg9qk2TTMnnfER5JIRh00mhofOcdW8q1v12hVLARl0/urnLQCFKtG7z3+j0G8hGSYQOnqGMaBp2ObWD3B908PbWlV4GQ7Zm5MtqkctzgP7m6zUo5zcB4iusvTbJ7BDuVIB+tqCiKicfv5p/9i0N+/3dWsSw7eC89HCM5EOG01OO01CEUdlM77RKNSPz0Z4LomoGumZiGXYwdFDTCMS9Ot4TsFHEI9rQqGvdStyIM+08o7FX5aKmNU1C58sIUHcXBcV2m1+4hOqBRaXNztoXLoVLaKfD2chBRFBgbdvKjd8qc1i3i2QjdVo+eItBv2yFrlmnZWQW6SSrjp1IzGXfvsLEvcFJs8Su/ehNNMxnIefGFbGDER8s9wvEAkiTwpestPD4X7bZOX7E43K2QHgxjaAadjj0NObsQ5YkyweqdZTptlQdvfsT4xWnyZyfwSBobD7cYnkyRj3eYzTaZyTSJhCWcTondtX0erekUTgxCIScel8DVF6eZPD9Cu97h27/7Lrpu8aPv79DtWfjcJk93VNLDMc6li9ycrrK+Z08u4rkEAGvrHUzdJOZX0RSDH357iX5XY/PBEzo9i15HweOykESLat3k9KhKoenH7ZUp7pww/+wslZbE8q1F9p8compQarroqCI762XiuRTba0f88M1D29Cr6Rw2A8xMB/jt396mtH/E8ESSdKBDrW43zI6rIn6lxkfv72NZFom4iws3p5BEm8vfaXQ/of49emeZb//uu2xvnH6SlD01ZCA4BBoNDY8bOh0Nj9/Du/c1RFFg4yTAwcYRmqKSH/bx8CDOuRfPceIapllts/ioSqfR58qNIdIJCcWQ0RR7epKL9NB1k+rRKUe7FWIDaR4/2OHJlobT6aB2fErEb4NNysUWjdMWh5sFQj7bR7n3tMTjh4eofZVzVwYJfNzAcvUb+PxOOoqD8TE/pUKN7//LO3aCd6OLrhuMTsR4snTE1TlY2pJYvbuOJItcuD6EJIuYlkW3a7C8LbK1UmBrpUAo4mXxnUXbwxSxC6mdAlz77GXS5WW2VwuM5j2fmKj/6i4L0/rzXX+B668Db3z87xxw8GNfO/z4c//a9RNXEMhOiYnpOB6f004q9tqa9MxQBJfLTuLUFRW319aZ1zsil169iGlZpJJOoqkI3Z7J4WYRURSIRt2EnR0UTeDDd7fJ5vzsnvq4/9Yiobgt5womoiQyAQTBlnTsr25xXKizt3FC+ahKv6czP9gmNxTA1E0qVR3RKREKudA0i/29FpVKn3KxiWFYlAoVghEPk1Mh8uMx6uUaqYTE0kGQVqOPotsYP7Wv2pzjmBe318nzr02xf6iQ8rUZmUrzzjfuopsO2oqEWzbptuzEZp9XYOHaCB9+eEqrK5AZiZNJuzg40li+tUinpTI+4kLXdObO+CntFZnOw0A+SvWkRrsv0qrUCfktJFnEJ3Z5chxkeDKNWzYYCHe58YUrDM6McO3VBUQHDI0nmMgqbDVSGJrB0YlBKOImnzYobB/h89oHsvlLwyw8N8fm0wYTcwOcf/k8mqJjWg5aZpDtnR6WBX1dZGJYIDsQ4PykvVlLkkguafHSXOMTI2W/p+GWVHwBJ08LMlOjtv/BMG2/hNslcOm6/T4aOjNKbihMMOgiNzHAF3/pMvWOyMVPXWBz+QC3Q0F0mAR9AsGojaR0uRxcmpPITQxhIOIPOGm2dOJREbdXxu+XPum0BiNeMuND3NkI0+2ZJLNBezLgL1I7bbO712FwcoBcVGNzzzaxiugsB25y8VMXcAjw0s9ex+V10272WXhuBoB2x6SnOvi7zyzidIqc7J9S6vq4+0jFIQiMnZ9i7tmz+N0mhmGycnuJ5W2Rz35xhHrZTrr0eWBgaphYxEHCWf1EauB0i7idFppub8ZH7RDXn0mgawaJhNtOfhVsdKSm/+nPOEknRMYvThMP6rg8TkTB4HivTPmoiqbqFEoCuiHw3uv3aFa7tOp26F231WMw5+TkpEe/qzI31GXq8gxqr4+qC5x9bp52WyXpqnB02MIpKOgGdLs6O7tdwvEAnY5OeiDM+MVpPD6Zibkc116e5WC7jFvU8bqxD7Q6bDzYYCSl0WiZXLwYY2phkK+93rSLQEVjdNTPUU2m1pGotGXcHidJf5fDmofD3Ro/9aUhZEFjZgQaTZN6qcHy+xu0VZlXPpPH43PaE5ywF1GWcYsq2XyMcFBgb6vKG394n4X4LplQn1e+8gxHTS8T6S57O00Cbp3le9v4g17yQ3ZybzYfw++3N81mtY3bZWdoFLoJklHo9kwSIR3DMAmFPSiKXZACPH8jiEN0MHXlDC6XhC/gIj2coFK3eGlsj5O6xN7KJn3NwdB4gs1HG4QiHhLZMM88m8btFikdNVH6Ov2uhkMwefN9g6Fwg6DPwrLsaUcoZPs/IhE39WoXp1MiNRhF1wwy40O0eiLz43DzS9cYyNgd36biwhfykh/2kRqMcrRbZjDc4dkvXuXgaQndEvFJXcbOpAl1ikzGK1w8H6LRUPF6BM6mjvmbLx+RzzlI58J87kujfPrLs0iSSKVY4bmrfo73Kmw83EFRdMrHLSJxO8Cup4l2sVRpc7RzwtioD+vjjbxWbmMY9vPFFw1RPmnT1rxE+kXi6SALc35WKzmKhTaj8TaqBuefGUMUIZEJUWr7aeleJi/N0GxqFA6aGB8Xwa2WTjTuJTUYRdVsqku91ieS8GNZdmEfjcjUmzo76jDhkIP9Jwe8d6/JyKCMptsFhSgYfOl5i0jMy713tvi9H7iIxH1sb5ximZbt9+jrxFMB0iknuvYxItJT5cYXrmAYJv/5r90kmQ6SyAR58yMX4+fG8Holvv62RUNxU+l5WXpY5qWXEpy5PE4u46Td1vF6HIxlVCZHRBIJDxOzab70155j80mZ6y+OUCj0qbcdVCs9auU2AD98HOXipMZQQmNqLoVh2FS0eNKHA9scO3t1gkzODqmaH+5x8GSPTKiHYUI26SCaCtNXBZIpL6F4iGzWh99jcu2zl/ncz80znFSJ+DTCHo1rz2Y5c2GA4ck0+6tbpPIpRqfTZAJtDgoKz7w8zmtfuU4k4qTS85IfkKietMjGDBTZy8y5HKZpIcsCxYMG3b5FPO7hePsAy7R4/Y8foysqz3/5GrFkgPMvnycQkPmn/3SL40LTlt5EbF/g7LkM8zNuVNWWenqDXnITg5TKKumwRqvewye0mZzP4vbK5MejfPNfvIssgaqLeANuymXbyD2S97Lw3AymaRGIBsjP5Kiddmk0NM6/MI8gwNVXFogm/YSTQUbn8hyVBYZyMv6wj4GxhI0klgXe/vqHvPaLN1jzX+eFyxIhj067YzI0nuQL/+lzOCQHC5cH2H60gd8vMncxR6kpU6nYuSa7K9vsbjdYvrVIJOJialRiJm8xPj/A6UGRdrNv70FBF/1un4GRKAGfg15HZS1yE0EQWFtv/5Xj9v9ZyzL/fBcQFwTh/o9dv/zj9xME4U1BEFb+jOunfux7fh3Qgd//d/m//8QVBJ1mj6X7h1imRW4kzut/+Ijpa7Pc/e6HFPYbvPAz15m8NIPXa1fEPUWgWe0iig7eu1X4hMTw3GvTeLwyqw8LdHQ3rY5AejjBwX6LsUSbZz9/kUDYy4fff4DT7WJn/YSzs352lrbIjA+RzIb4ua8M06o06Pc0Qs42TtlBvdKkXO7SqTYIBUUkSaBZ7fDo1qrN7E+IOAQHPr+TQqHP2mKBVqVOvWmSi9uTo5OaiKoaKH2VO9++Z2PoRsOsP6lx5/UH3FkPUa10eO0XbzAaPGY6doxlQfXoFH/ARbli0GqphCIeVA10zWT/oMsrF/sAjE+ESIR0BsZzZCMaw2dGCLh1ej0bI1ZvCSw8fxbLgt0nR1TVEBcHSrSbCj5ZZePYx9OVAn/9l1I8XS0ynTd58YrIZtHFQUlibCpGPufAH5BpdkUWrk+hG7D2/gqNhoLLZXedS8dNijundBpdLARKvRAT4/akJuJRODgRqFb7/P7Xytx+a4vt5S1UTWD5KMLspWFiCT+bjzaxLIFaucPUgEa5ZkvI7myEGZwZoVrXqVRUCnu2YbzT0Xj6+IQz8wlWlqt0erD/9JhIMszKaZZR7yGJoEav06PRsP0Vj7dhai6FA5No1InsdHBc1rn/gwf4vA6yg2Hmhzo8+uEjoqkQf/Rbt4iERHpdjUd31jFxcOFymn7P5sUXqjKphES7a9Gx/EiCidtjIxRdLpF2rcXW4ibdjobb7cDtEjiXPOQfL12k1ejhDXrZPBRZOOMmOxym8PSQldtL9FQHGw+38UVDfPjuLg8eNrj5qTyjkzFU3Z6uvfd+hbbp46Coo2rQaij43SYf3VrH4zTxOzXqDZPtpS0+untAcXOf7Y1TBEFAlix21stsLz6lWDJQ+xoz4QO6rT6GJTJyJoem2CPmo2KfiUiJS69epN9TMQ2TsdkclcNjag0Tp2x3xr/+psGVKzF0RWUg3GHj4RbhiIuyEmVuPkJdD9uJnEHnx+QYDy6XyPLdDTRF58PvP8Drlbj39hpHm4csFUJIokWt3MbrhpkrU2wVJRyCXVhtPS7y1S97+I9++SbH2wV0A/IJBVmy6PbtsELTcnB4bBAIe/C7TXxmk9OmSLdn0mm0GJoe5K33+uwdqiy+s0guaRGKuEkOZyi2w7jdEsWSgT/o5uIL8zw4yfP40IOmW1yOb/LOIyfTM2GqHRm3120XcSt1MoMhGrUe3/jtW7hcIvVSnU7X4sPvP+DDDRcBj4HggIy/xdHGHj6/RGG7zLM3s7icAn/0h3s4BFsqNjwSZP3BNsWdEt2erplcfAAAIABJREFUwVubw5RODS69epGRSIPCXg1TN1h/uEe3pdBqmxQOmjjdMsu3VzE0g7ri5eysG4dg0mgLNIwQlZ7fxhDu1ZgcEXF7ZIbHIuiayfhkhOLmPlcHDlh8anHrm3cJByxqDZO4t4Pa19jZabP+YIsLN0bYKvu5/a0PEGURSTD4zW95eXh7gz88eZ4YZRZX2gzk3FRrBj3DzePaEP/bP7zFe6/fY2m1TSpqy480RaXdc+ANeJi/NmEnqe4ck07K3L51hGHC8JAHp1tmYDzDk7UGm2slTgoNlK79PLIQ6NZbvPZyhJ1TH1V3Fl0zKBwbpAMdhkeCrBYCZKIGd99cRVEslu48ZrPgoKO5aFbbvHLFZHQ8jKbZz5RAQOLem8sc7Z4S8lsofZ1SoYbLJSJJDsJRL+dG+8iSwJCrYAdHnh8lN+Bna09je7OOrhnU+x7irhq3v/UB4/MD5LJeDMPk2o0M0bAD2ePm0Q8fcXrSQhDs1/DBQZ+e6WZrtUiz1uXw2OLu9x+wt3HMZy93qBzXUVWTr366h246OBt4ysRMjK09HdOw+M4fr6GpJo2mTl8TiflU3vj927z9tffpKSZevxtFMZkY9yBLEI3ZORYOwSSbgK4q8Rv/zXtsrJZ4+2vvMzPh4sG7T2mrMp1Wnw+//4BuV8cT9PNo28PE+QkKNQ99VaDTg1Q2SMhrcH3W7sKrqsm7dzsU92sclzROWzJP9mWKdRfVms7aowKNSpdALEIg7OH2tz6g1vNQrfTweR28/9Y6um7R7ov0FBs3XW2LqKKbVkslmfYTC0On1UOWYGu9wvmXz+P2OvnMT58BQJIc9rRVN3m8eEwkFWZsIsJIxqSrirg9Io26yoPFDk6nSD5uG4ETmaA9nUKg8PSQEy1BvWoXUNtPK7z0s9exLBAEu5Fx87KLtupi/6BHMumhsL6LL+Bm4+E2n37RTzQis7l8QF9zUK/2WP1gk92VXebPRpjI6bzxnV1KB6dUS23OXBhgIC1w7dPnefj+LgG5zR98o8L9NYGg34Gi6FSrCsl0gGZTY/7mAjs7Lfo9g5DXoNNW7amoz0MobO/N3a5uy+FkncXba8zfXKBx2uT0qEa/pzMxl7NlWw2DcNRDoRnguVcnyGS8HB9U/3IOiv+Wy/YQWH+uCzi1LOvSj12/9f+4p2V9yrKsuT/j+j8BBEH4KvB54Jcs65ORQwEY/LHbDHz8uX/t+okrCGSXjOySebq4y8MfLTN2doSN+0+QPW4apy0a9b59KD9pI7mcZKI6um6wt17k8vUs8cEMldMeb397yZZx5GPU+x4Gk7YmeWwsSKPvIhiQaDd63PjCFU4PiviDXr73+h7p0Ry14wqJhJvldZ2h6SFE0UFT87F43zYDeb0yiWF7kwYYm04wMj9KZjhGvWXRqjdp1Pvkcm6UroIvFGQ4KzARKlIvNfm/yHvPGEnv/L7z86R6KufY1TlPTw6cGXKYxKXIDeLuSlrZOq2kdcLK0J6luzNs6A4H+HBJd76T4TMErA6GZUvWrpKt1UYul0tyOUwzw8mhc6qurtCVc3rSvXhGhN6cJVmyJEB/4EFXdRWeqkZX/Z9f+P6+n7nkEF0zaFebhMbiHBXa3PlgnxMnw7z0ty6xvtYgPRGgVh3QNdzcLY4RdA2YWpnC4RCpVu0NVxAE1taatBs9uu0Rj3I+Fi8cY2enxeqeSO2owU5BpnxYptBQGR/30G91UB3w4N1HeFST6mGRsKPJQTtMfreAhcBMrE+zVMOtaBS2s4Rdfa7esqjWDc5M2fId3RB45zsPGOkC7faQvd0WydkJVFXiwzfvc2LRdo8wDINus4NfapNw2wHQg3sVCi0XsZBAJOokPRVi5dwk+tCWiUxEhuiaye5agRd/7DzfvhXgYP2AWlfBMECSJRyKwLHTYwyHJrvrR4zPhDl9IcVwoFPJlSkUely4EGFtrcmg00cbasS8AzL9NJuHMqW9HKmkC1kSmJsUuXt9H8nS2d1uMhyavP/aKoIoMhzZgU9fk20gV6bM05++xHvv5Dlx3Ed6dgwBi7e/t02r1mX3oa1zHwztGZOu7sQj9zjYtkFmogjBeIgTT63w4Oo9LAuyh302m2PMjEsMekMOHu0wmbT44GabfKbBoNMlkIjQHwooDgeBaJD/6R/Zeuzt7Q4PPszS7Vm0qi08PpVMI4Qk2vr5aqnNekbgyksrCAJkay62Nhuk5sbxBtxExpM43Q5UB8iixcRsBMs0kSQBQze4lp+mUa4jCQbtRp/nXl6ikm9w8pgTw7I7J7FUgINHO9x8w/b4drsEfAHFrqI2+pim7YDy779lMHtyhm5H58Tgmt0Z0VUCHpPSUY+JSR/5bJ3Xf+8DADIPt4nPpCkWuoRTNvF7LKRRbcKFSwmyBYPcboljEyNmx23Hr9FghGaIfOMrH+KPhbh/+4irdwT6Q5Fu36KSr9m8AVmgVe/hVAxaQojd/SEBv4Q35EdxyMzPeZibcrB44Rjffb3Ke9+8ga7phFw9Wq0humF7cIuSwJPRdWQJ8pkG651pnlix6PZMvKpBYfuAQW/IsZUAR/k25cMqx548QSrpIL0wxlFpxImnT/EL/D94HRo/+PptNstB0kvTVCp9xudidqVzZHHhyhT33r5Hq9ZD0yye/OEVm2TsEJlNjnjn1Ufc/N4tTEtkNBiRmp+0h1YNk3BQRBvqPHz3PoamUS3WGekSb79TwykO2T8YoAg6IWePg0wby7T4+h9madV7FAtdeu0B3/yNd+z/SyfBjz5RAWAwElictBjoDlrVNqJkg/IA0mG7QNFptFGFIV98ZUggGuRvx97kQXuB3G6Z7e0O4ZBEseuj0pK58spF4jNpvviJJrcf2ommoWn0h6CNdBSHSG4rS6tszwx95pUk7Z5Iu2PSrLTYub9HesLHZz8zhmmYdOptLMsi7TwiOTvOxp7FeKiP22xTr3QI+EVSjiPabZ2p2IihLnD2mWVMC7T+AIcC7YFCr91lI+/CqQrEQiZOt0ou18Pl95DfzPDmm2Wa1TZPPTfBUb5Fs95j7dYuDw+cHJuTOBimyRyOcLsdbK1VGUsqjE/6EUWBYkOho9uwLUkSuLzcZ9DXqTcNRhrEJ5NMn1pg98EOi2m7Ch2NqRQ6fqqFMrmtLMORxcI5Wxd/az+IJIkc7tcodIIkPW2y2gS72w1W5iUyGwU+/4Vl4nEVn08m7W/SGcpMnZi32S4xmYfv3ue1372OIECjZWEYFtViHbfZ5nwyS64i8d/8j8/QrNhFuhu3W5iGQb0jEwjZHZVBX2PUH3Jmts9ooOFQLCI+A0mCXKZOwDnkW29reP0uXv/d9/nU8yqSLNLtjBiMBJ6Y7/Joo084JGMYBuPTQeZOTVEvdxhbnMIhm6hOmbe+u0ujWMYwLOYiTSTJZqVMRAZEG7uEI04O9+usbY3QRhqiKJAcD7D36BBdM8ge2p9Tj0di4tgMhztHTM1Hef75BNXqEEmEnZyIzyMx0gyWFrxkNo74cFMlMp7k1ht38QUUKi2ZSDpGpechEnVz+UqKRqnJ7Xe3UWQLl6wB8K//9SZOWWNp3sU7r61z5ZWL7D3KkJpJMdBE9jNdpo+l7Q5K0kNiOoEvEuDO7Qq3NgRiqSCKQ+HkmTjVsl3gs0xYPDlOdJjjRz4ZZ2FaQpbhcPuIbmdEvdZHkgQeXL2H369SyDbxqjrayLA7S+NxIlGVubNL6LrJWGjIRs7BqD/kwdV7GIaBN+Th7tsPaNT6KC4nM+MilgXngpsUCn2cqsDcseR/ocjwL2hZPOY4/emPP88SBOHjwD8FPm1ZVu+PPfQN4CcFQVAFQZgBFoAbf9L5/sYlBKPBkNLBEYFogBNPrZBMeVi5fBytPyA2HkYb2gGm060QTduWirt3bZAZwLA3YHnJx8oT8zSrPXYf5Vj2Z9g+lBBEke2tJi5Fp983yDzcptUcEp9JE4p5CEZ9NMp1DMPg2g92WL+fJ79jy0FaQ5Wnnk0TigXQdZNyJo+mWzTqIzI7NRSHLQMYDEyCsRCxmJutrdZjlwuDrYzBbitJ/ahCvq5iaIbNOyjXqeRqlPZyVGsaggC/8sxb7G1V8Psfg2JqYFkC/pCLg706qaSLk8+eRhQETp4IcLiRYXLajyzZkieP10G3qzN3PG1bz2k6nZ49pDx7ZhHDBEPT8Kj2/Y7hodSQaR5V8StdPMqI6HiCB1kvlmmyXvDRbg8ZS0hslfwcZtuU6xYf+8xpPrzZoN3osbTkp3SQx+93MLk8xXe/VyIxEcXtc9Ou1qlrQYaGA123E2RZtC/MR4Uuzz8ho40MxhanKFbt6pOmGfS7PW69lyEUkPAEfbgdJi6nTc9MhE2azSGhoEK1UEUQBNwuAUmycev5/So+t8WLz3jptToEIj7ODt6hN5JZHLc7Nf2h7eOeyVtUskWGgotKsYmumbz42ZNYpokoYPu3WwLaUKd6WOTvvVDi0z+SZHvXHnwuDSOcuzLDzFLMBpepFrNjBgGPiUce8KCUJBDx2Z2cTBtf0EO93OHSJy5weNhlftbNGe866eCAeDpEaCxOoyNy5aIP07KYPD6H2+cBYO7EBNVcibVKnKVjIYIhJ+nZGE5VoFEsMzbmpt6RiEclFict9u9vMRYXePUr7+J2GCzFm0TjHvbvb+H1O9E1Da/f+dHwlKraQf7HTjYJxfwsxxsIgki2E6FV67C/3+b85XF+7Z9fxSkOcLttecnZj50lPpXi/Ivn+J0vX0WWBKrFJp/+ZASHAqpb5QuvCDQqbTupDcxgmpByHuFzGuiaQbOpcbRf4Of/6dOMz6fxhAOIgojbozAajHC6HeTr9tDy1e/v43SKOJwOfv2rZRLeHrpmoLpVnLLGsYtLNIoVJEnkk5f6OB0mMykTb9CLVxlQKPbJbhzw9dd7jCyFj1/WUR0C3WYHXTP4nS9fZaRBp9UjGHEzfWoBf9hHV1Px+VRkSaDX7nPhlIPXsytkskN27m1z3LPNdl5GdQgMdImpE/OEYn6uvVcgMeZjYiHJpUsRen0LUzepVXpU8nX2z/wEV+8phBJR6m2Baq6Mqsp8+Not8lWJWAimxuwChK4bGIbFG//xBseePMHOVoOUp056wQ7Eq303YFeSa3m7Q/fqN/c+Aj4BDHp9XIpOKu1DwCIQUPALDZojF1cuB5BkkU/+yCT1UgPTMCnsFXj+x59E9bhY9Oxz1Lflll6XiWYIOOURbr8bt1uhsFegdNSjr9muR82jKi3Dh0vsU84e8dtHL7BblFHd9v7Watsdhu294Ufvb6OWJJVUicQ8CILI5mbbpub6FQzN3rc+eDfPnUcayaCGqookJqJYpkmno3NYsvc91e20AxhrRGH7gFptwJPF38UQZA439vG4BN7OzpPL1NnM2V3no3yLSnlIcnaCczMdYp4+kiKTCBns7nbYPQRv0MPivAdfyJbF/A8/3SW7tsdRWSOa8FErVEnNJKnWdJpdkc2ih2RcIZepUc5X+fDDCsVCl2KmjG5Ae+TE5XXhdMq8dU8lmfKweidPvWnQa/fIru2zcG6Rb7zRZ/vWOv2+wVIgRyAawhcOouv2gGco6uHUZBtPwJahzvkL/Mvf7BNwdFhaDqFIFg6Xg1/95as0Wjql0pAH+TClhoSu6TSKZfYyQxYvHOMXf+ky2ZzGlaUmjVofURKxBJFbxQnOTze5v66TnktyuF/n7Ck/bp+HoMeg07ID7Htv28nnq++LRBM+xvxdSg0JSQRFldkpebh83kMxUyY6kULAshkgR21aHYsPtz08edbBRNwkMRHl7a/f5KVnvRiajq7puGSNeNzFf/tzcU4/dxpZFri6FkASsRkDA4WR6kMSBWYXI7z3nducvDCFSwVZFmhXGxysH7A450RWHZyZt68LtXwJgIV4h25nRKUlkY5Dt2dSzttzgaZlMpkS6DbaRCeSeNwSfreJN+Bm1l9ie61EPGQRSYWYOzGBLMGsuM2dN+7w5AuL9DQF07Lp5sXDJqrbSWIsQLEqcPvNu6xeW6fbFyjk2hT2CvjDPi5dinJpxcTrV2mUarQ7Bt3OkHjIRNMNsrtVBoqXb3+3jCCArtvyz537dmfxj6SKG/dy1I4a9v40E8AwTTIPt1m9X6Ld6HD77VVubSpMJQxOPXsCgPRsku1b68SnUqQn/KxcXOLW/b4NSBPChEIqqkOgfNT5zwn7/lLXf0aH4M+zfhXwAa8LgnBXEIRfe/weHgG/B6wC3wW+ZFnWn+jb+jcuIVAcDlIzKdIzURSHRDyqcLBxiDvoZ9gbMTHlZ+WJee68cYdg3M9mVmLy+Bzr1x4hSeD0uvjgvQL5/SqbN9fwBr0cDMbo9mzyqdOtMNBlJtMKk8fnyG7mkSSJwkGdYNiF2+fBE/AxuZBkZikB2AmHKhlsbXXYuLGKotjJxc5ul72NItpIp1nt0Kh2cTpFPD4X+cM2iZSH2WMpxubGMA2LuKdNKBFlOWFvKoIoYmgapy5N44uE8HhkWi2Nq65XmJ6P4HSKqOKIpQkNCwHTsEimA/i8Ig+u3mNy0kk2P0JRHYSDEhcnCpiGQbPeZyzpsO0M9zXcPg8nJ7sM+gaCIDAcWVx55SKmKdCud0iS45XEdSLjSd7fjbNRCtBptLl5s8KZHzpDt28RCjnp9CyuTOzi8TqYSFjousXZs0GcbgcBr0AgHiGbaZHbOmTxWJS162tkHm6TXpr+yL0oGROYnAlyPJLjg1UHW/f2+JV/fo+jfIteq2cDn0SbmhuMhZg7nuLbv/eAZrlGrSPRH9iE4vlQhUBApVDo4w8HqJa7lMoaikOiWWmijTSyRYv/+39+l/CYPfh2FFxGN0UKdYXoRIo7H+yz8ajCSDOZXJklMCrjDbiRFZGv/ZurTBybIRwU8YV9SIINxBNliW8+mODNq02eOK0ydWKeeXGTdtMOShTVQb5ksZOT2D6w8NMg6R9QL7fI7hzhcEgYhkl6KmwzG0YGrY7J//IHY2RrLu6+dZfEZIywzyCTtynUB4928AU9jEdG9LojFs4usL5nJyv5wxaBoEqlZsPmXvv9mywkurz7zhG6KRBMxhhpAk996iKmBbczAZwuCafXQ3rcw8RCmk5rQK1hMhls0u1qto+8KXGwcchuLUCjWMayBJZOp0mPeznM9fi5f/Ic0W6Geq2P06Vw5407WKZFpdjiZ/7Rc4SDEsGYn0pL4rCoU9jJcmsvYFM/7+d51Jgim2nR1AO4FJ1EysvFUzITixM4FTu4MXXjoyB2ZimJL+hiMLRo9SSmFhO02zqmYfL3fipGfeDkYCNLKVMg4mhQytXxhgJ4Ak46IyeKZCGJ4PY5qfQ9/NBlFUmSWDkeZKq/xrfesbfak08uYlkW/+svX8Sl2tTqYraBrEh4/U66QztoXH9YpllpsLpt8tL4Q04uO+zBSGJcnm+SOegQ9fSp5Mtkt3L82GeS1Cs93G4HB4cjjo76GIZJNOFBlEV+7TtBZiZkAlEfLy/uYVkm3c6Iy598glJFpze0B89PPH2KQMSDoggsX1zGH3QyNu7j2n6CK09FCY3FiXvspNPUTRLTKZbPjvNjn5vmYCPL4oVjpJemkSQJQbA4tyKhMkDTLNoEyFadXL/VZm4pytf+wzZLZ6ZsAuz5OdwuiWG3jy4o1HsO5s4uYVnQH4pUeh7a9TaDgU631iQcduFTR+S28kQnUgSkJr/13hhTK1NMxTVeWdzEF3STHvfQqI94mAvg9cr23FCnj1MxEEUQJYHkbJrJKS+Ky0mh0CM+OYbikEmkA5w65mCgSxxk7EDE7fcyOa7iUm069vh8grFxH33Bwwufe5JeV0MLJekLHiaWZ5AleGZyl9REkOzhgGZXJBjx2ATzowrbJR8RtcHU0hhuh8HKMR9+r20wkM2PKGeP8EVCvLo+zeTxOcrFDvff38AyLTxelXDIptRPRkc8uF8nnPBx7so86YkAE5M+OnUbzreec2EYBrVaH12z2HxU4qVPTOL3SdTzJcaXpihlK/zsJ21ycbejYyJSyRaoZAsoisjxKyf58LVbPMr5GA3savTQUvmlvytS6IZodwwk0aYqf+EXn/tI7/30xB5T8RGjwYjJ43N4vTKGYbJ1AAszMsWO15a49gYMBReKbDEwZC4cty2Qd+9tcVjQSU5FmAlWWDoew+FUOP+iTSE+c1ylctRGEQ3iQYP33iuzeXOdkNfgV3/5KsmpGInJKAHVTiRcHpWgz7Zc9Tk1Gl2JarGB0+fm/Tv239WuNpFEA7dL5G7GS6PaRRDgqaU2kgihRADdFHC3ChzstyjkOiiqg8NMg5EGg77B9IlZjl9eYnNnYM8S/GCIpMhEJ5IEAgofrLuZnPJRb9nzV+mkxNxyDIcCwYifdk/g5JVl4ukIpdIQp2yQTPt5YyOFP+zhrXdbuDwO2o0+g5FATpjCEw4QCUks+Q5odSw+/pOX0EY6kVSYw/0qsRA88cNnGfWHOBTw+FRi43F27mxQqRl4HUNiURVP0Ee/p9Nt9oh77bk8l1dlKLo4cSrGUqzKYX5IYSfLuWeXaDf77GzVOfbkCUKJAMF4gCc8D5gcEylmGzz3o5cJxbwoDoX4ZJJj0xY+VSO7WSQ2NUY46iY0Fsftc1GvD2yGTNBBemmaXCdEPCqhG38hAfR/0WUBpvVnO/5cr2dZ85ZlTViWdebx8Q//2GP/m2VZc5ZlLVmW9ep/6jx/tP7GJQSSLFHMFMntVRBFgbt3a7TKNbvSU22RzbSwLLtqWy3U0XWLRqmOPxam0zUZm44RinrJbeyzdHEFl1flpHmbREwiNTtGbreMIFisb/U5eLTDyvkZtKHGqD+i2xlRzuRpFMvIisj+ZskmErqceJQRkaiL5cvH2XxwiNNrD9AJokA47sMf8mAatgPAaKQzMeUn6Jeo13ps31oHICzVKB3k+XAvgCiLiJLE8SsnyR00SUwneHqlR3pM5d/+Vo52e4SmWTQ1L29eM3DLA9qNPu9+4zqra23cQT+GAaIA/VaHG9dKVId+VLfK2Lif4ciWNFQrPSrZAsW2i9kZFzt3Nuj1LTbvH1LvKaycm0Q2RlwbnMcX9jEWMQh67ArMxFSArXt7VKo6Gw8K1Oo6/93/pZFMOHHIFn6fiKpAJVdjqEGrXGd+wabWBvzSRxKg3MY+fr2GgEXuyMLjlhAx6HR0zj97jIVz87z0YpxGsUw0LDHpq/Hua6uEYl5ufP8+H/v0iY/O5XIKON1O2pqb0lGXTmvIxEIc1Snz8NYBR4cNgvEQTzw9w0wavvRLzxIfj6A6ZTqmh6OGxH52RCVbIDUZZWwqSCggsX9/C9EyGJ8Kkowr/MQXnyW7tsf1GzUicR9eh02ZnT29QLOpMTHl/6idqEkqtWoXlyoSjIfxeUVeWikwnpRwDxscVF0oqoIkSbjcCtu31sln62zczRJPeJhMCXzpx3U+HrvJiadPkd085J0PByxMQjQdBaBarPPGdYP166vsrWaYTsu2TGApRO6g+bi63eLMs7YP/XPPJdg+sAhEA6yMNSkVW6wfKKyMdwkFJBwulXy+x/bdHRSHzOKkhWFKrCzZRF2PPCA2HiPssS/AU74y1XKPcFDCoUh0+1DzjOPzqXRaQwRRpJQpEIp5+cqX37V97fN10mGNg70mpm6gaRaSIhOI+DjnWyMSc9PTneimiCwLjPtb7D3aQzcF5s8v4/S66TTaSKJIJKpSzjfxuASCHsOeORia+IIe9ssqcXcXWZHRhyM2m2MsHE/hcNm2iANd4likQNg9YP/RPu2BzOqeiGXZ9qgNzxiJpJPhyCK7U2b92iOKLSfT0S7XX73Jj/zIGF6/k9xuCVm0WQA/9EKSUX/AyQWBb++fsOdx1nI4BI2vvetibtZHpeti2O3TKtf5zus1nn/aj9enkDtosjDnodu0O8iBiI///dS3kESLYMSNiUR6YYJ4wsPuWp78YRvLgqOq7W6W3SxSqQxwuhR2V20Z44sz23R6FqF4kIHuYOPGKr12l2alycMb+6iKxeknlzAMk9zGPqmZJPf3XRyWRUxB5MVzfaoDPyfHaqiqTLnUY2Y5xZ23H/Kxn3jK7kbO21dHAxm3YjDoDXn1tQphn45L0WlXG7ZneSLC5sMCQ902ABj2BriMDudXRDKrGS447/F6ZomLF4J849+9g8st8/T0IZ85d8T8jEooESagDsjlhxzsVHG6VQ4Pu8yenKWcb+L2u+h3h5w45mHnwGQwEmjWe7g8CtXDItu7PbK5IYZuV6s13aI2CpDZqXHxQpCeJ0Zj5CO3dUC2YDC2/x7NxoC/9XyfZEhHEMDrEUnOpClVTRqaj7tv3aXasT3iHYo9x+HzyiSmUxiGwXe/9ohw3Ifb62Dx7Cyq28n991Y/Ikl3hjLTswG7K/RYvuF2iUwdmyYe1O39ut4mmfTw8hMDFo/H+d6rB+i6hTvoxxd0k5qOIwqPoXQbRXZbSY5fOcnSxRVmJ0QevfcAWXUwHRuiDTUCIRceoUO2HaE9VEjGFaLuHsnZCW7erOF2y6wsOnl9a4bOUCY9G6dRqrMwJbD/cJd63Zbu5CqyLW+SJFSrz3igTaXr4iu/c4hlmpy8coL7Nw64+9ZdvEKb7fUq6UkfN793i0apxlbGwBNw4pfbvPl+l0+8FGHxwjKdgchzP3qZermFKImIgkl0IoXikCjXTHQDJMHCNCG/maFba1It252YfquDJFj0+iYu1e6GhQISA11hqNk2oiNNYOQOEQy7mJ0PcOLx598wIRJV2bmzwa3v30FWRPyxEM16D1EUKGfytDs6W5sNvv8Ht5hKWbR7Ilu7Nu8n6jco7BWJBQya1R6r1x5xekVl81BGG5m8tGzPQC4vB9h9eIBhmPQHEJGrTB+b5OBwiGRqhPwCB5k2hm5QLdQ42rcd6nTNJDqRpNWx7bALO4d4wgFyhx2KbQ/hoMjS6UkM0ySeDqG0sMTPAAAgAElEQVSbIu1G306S6xv4vCJHXT/hsANTNygVO9SKdZ6+EmXtg4ds3txgNNC4OziOqli4vCqt1pBAQGU0GOENuBAEC0GwqGQLdJsdbv5gFUVV2LmzgWlYXH/1JrJkJ90T3irbu30CXuEvIUL8cy4LLNP6Mx1/ndZfekIgCMKEIAhvCYKwKgjCI0EQfvHx78OCILwuCMLW45+hx78XBEH4V4+Ja/cFQTj3x871hcfP3xIE4Qt/mtdXHBKDdo9KrozTKSPLIpKiMOj06Ld6yIpIIKDSOKrSa3e5tDzE6XWRnh9jIiVy7+17Hw22pCYCuDwOhooXj9NCliX6nT7tgV2JSi9Nk0w5cfvdjx1S7Ja1KEvcevM+vpCHSr7C/Jl5VosByuUehb0ioiDicKlMJi28fjeZjTzhqJtytoyqChxuZGi3NdtB5KhJbGqMcqnHv/hmHJffy8nJLoGQi8sfP0ur1mFpJcL2rXX+4AcCIw1+5qfGGfR1RppJT3Pw/EUZRbRdJgBcboVANMhU0mQi7cATDpBZ26czdJBZ3aff01l9VCca9zI758cXsSFO//E3bxEZT9LvGzjdKmGPxvZqEWe/TtLbYf/+FkuhPGlfk26tycSYwvSxST51sUd8PMRYUuEXfn6KXN4OZOoNg3uPeqRm4ow0WDi/yB/+23eoF6sclewA/uSzp4lNjXFoTbFX9aHrFrW6Rl0PcWbFdvH59Isebj/oE59JEwua/M7VAIrqILtVZGxunDPzdlDqkC0sC7SRxsiwK8fpSR/ZrRJen0p6No6sSBw82uHB7QJDTWR9Rye3U6RcaBIQm7w4s82Tp+yN69hKgFhE4Xe+fJWxxSkE0+DWO1t847fvIop20jm3ECS3W2bjyI+i2sF8r6ezs1Hl6gdtRoMRFeL0O0MKxT6F7QNMEzbrCUJeHVkf8N4HNbShRqNUY3e9iOpxYWh2e7peHyAKFpu1KF87vIDLo3D26WVuvXmfoSawe3cTl9+LL+zjwimVubOLON0uegPodnX8XttqNhoWSUynCYedxL19EgGNpWmBaNJPV1MJhNyk4xYWAjdvlHD7PHh9DgadLg+u3qPekXBKQ+7cb+EJeJEF2zFo1nNIcnaCvunkwdV77GUGOF0Sv/3/vk9ukGDtzj653SKWaXLuhVPsrWb51OefotG2B0KbfZkLFyI4vR5OzwwQRYFGuUXBGmc4NBg+/j+u3j9isxwklIzQ7olkVvep50uEEmHy2Tq12pBgxMNOZkSzJ/HgZpZE3EF2M4vXZVHqeVg4PY0nHOBZ3rIvgvkSy+fmME2B2ijAQc3N9PFpml3bgjUQD3P9epVYfQtZgvt3K+Q3M/beEeiTb7o58fQpDgoWikPC4XLw2jsDDnYqNNsW+nBEsSZxcaaC02ExtTRGR/fwU8/VuXPziLi3jz4cEZ1IcvpMhFxJoNPWyG4esrXTZel0mmKuRSzh5V78k+znbKbK3dI4B2v7VCs9JEmyE6e+RSICg06X6WNjqKqM1+fA5XXRaAzZ6kyyMjmknC1ze89Dan7SdrOqN2nXGkwGmuSzdQ43D5g7u0RmNcOLy3kCXoGe6aHWd3GSOwhYXP/uLQzDYnbGgz7SabWGPLq2wW//fpHZM4uMLAebhzKFnSzVYp3Dssxbt0TG5ieoV2z7yZmlBF6HvZ/6In5y5iQB15BoOsa7nXM8PX3I97+X41M/8zTDoUGxH2G9luT23SaZh9u45CHRiIPdu5v02n2mpzxs3FhFG2mEol4KuznuPewQDUtEfRqXLicYDXSmTszjdNkVbkMz2L61TqPW58aWl507GwQ8Ju+3z9DTHejDEeGgxKvez+NwSNw+CFNqyJQLLba2WrRqLX70zD7yY8tSWbL48PoR9x91ONw+olDosX9/i1F/wL/47wMcbh+hqjKqUyacCDJ/Zp6Tk10sCxbCZYJ+CcOweOkZN5IkcJjrIykSHkUjEYFeo0W3q/PVV20A1rHTY/bAfThAs9oms5ZlvRRk8+YGiqrwJO/w6L0HADxYHxGfSTO1MsPukUp2bQ/DsFhtTiGLJlPBBooC17d8NCt1Ykkvm6tl6i2LY+N9ChWBu2/dRXU7GWoCJ58+zqXTCl//vTW+9+1tCrkO3WabNrbz0mSgydS8fT0TRIEf/tQs0YkUomXg8jj4wddvA6C6XZSObKvetu7l2cteilWBjRtriAL4/QrVXJlkysNePYA/4uPh+6v4vSKGAeWOyuvftcm5gijyU6+4mTw+h+JyslYMUih0CfsMLNOicKRxf9+JYdg8I4diMVT9BAJ2V+/D127hdDkIeC12txt4wgHOvXCG+WmHXQTxqnZSF/SzOONgbNyHO+AjUxAoVU2cLolarU9vJNKu1jEtgcJeEVM3OKra1eT3v30Dt9mmWW0jitAq12y5owruUZPZ+SDPn4f90STNtkW70SO3sY834CE+mUSWIBBU6dRbqA6Bh9e3GHb7+IJ+Bn2NsHvIcATFXANFkRgOdVRJY2klgmmYKLlt1jY6BNQB+/ttVI+LSMyDJEvcude2CdHjcXqtHmFnn7Ud2ya3mKmyv12lU29RLTQ4KMl45CH+WBiX101sPE5pL8f8+WXqFbsb5/WIuLwqt/MpwmGV2WjnI+n0X+f1lwwm+wtdfxUdAh34x5ZlrQCXgS89pqr9EvCGZVkLwBuP7wN8AnsgYgH4IvBlsBMI4J8Bl7CBC//sj5KI/9T6o5bT2WdW0HWTZNpLfCqFaRisXJwjFnfz3qv3CCWjJKeS1HsOEhN25ut0WKSXplFUheXLxwGIJ9yIlsHV95vUjhrMn5rE59QJBhVCMT+P7ldweVROX54hHPcSmxpj+sQcF188Q7fZIzYe++i9HWVrLJ+b4eOfnmViIU13IGIYJivnpxkOdbwhH72+xYmnVjixrOJSLTwBN8mpGC887eNzP2x/WbaPPBSzDQZ9nezaHqsPyqSXpvnE0zKxkEC+KhNPeKhXeiTcDUaGSMQ4QtMNVI+L8bTK2HSU3lBkpMHk0gTDbh/hcYLucsucOh3mcL9mV1TjQTTDTmIWTk3gcknE0yEUyUAbaQxcIZzSkBNPn8Kn1yl2/EydmOeV5A1yO0V0S+TOG3dotkyGmsTtN+8T9/YYDAzOn3Lhe4yWb5Ra/IN//BzBeJjZKQeTx+fI7x4RigXwK20SgSGjka2bDsgtChWBzbUKr7075GC7hD9sJzyff66GN+ilelgkPRUm5a5x8tnTmI+/oGOzCXyOAdrI4DDTIjkVod0csLToY/fuJmOLU6QmQ7z3QZ1QUMY0DE5fGKOiRelZthbfHfRTrekspHX+j//zAvMrKdRejfh4lGc+eRJFFhAlu3Jd3M3SbFvMr6RJzU/ypZfytGodzpwO4At68IpdLMsilXRx5ZWLJMMGi6Ej6h2ZtjvO00+FcbpVhr0+y6fGCCWiVAtVjl+Y4tRxL4ORSNLX59REi/Wbu0QjDj77s5c5Hi8zd3aJs8+sEIp5yZUESgclqodFNN1i61GR3/rVtzl2PEzAbRAdC1EsdjlsuGkNZLYOYDQyGOkS+f0KsmhDls6cixMbD2Matvzqhc89SSygIwi2rr20lyPTjhEdC/Hv3p3A7Xdx0Ahw/sVzpMfsgfzP/f0ncUkacycmGJtJkF6apnhQ4+SledwuAVGAxEQYr9PgK19+F2044q3bEnNLUc5cmqQ28OBwSMiiybQnz4VLqY++Z0GvydjcOBPHZti/v8XcUsyG4CkSHrdEbwBnL0/y4H6V+GSSkSYw5m0yMeFh+dwcd9Wn6Lb6nHrmJJNTXsZ8LdvmL9Jm7YOHJII6/aFJq9zg0y8HGLhCXH8vx8VLMeIzaZYurrBXdpMK9PEHnMykwTTsatHLzziZXY4TCcL0qQV8HnugVhQsBn0NSTR47WGC46fj9DRbmuZ0q3S6Jk5VIJly8sX/+jRer4OHH+7jCzjJbFd4mPOTz3UY9obMhFuceGqFRMpDJGnrw1sdA90QmDg2g9Mps79VplTs4PG7cDgkVFnnqKkyfXyS+TEdURIpZ/Kcff4kqblx2iMn1VwF07Q4WM8wfWKaru6m24eQWSboHJJVF3BJA869cIYHV+/Raht8/CcvEYu5uPzyKZwuB4pD5uFRnLm0gcvv5eJz8yTDBudXJBxOhVjSR6/VYTjUEbBsyWGzQ0rK8eGGk+FgRMrXoW86yTzcJrPXQlUlAmqXq9d7vPSslydePs+dwxitts75F89hmXYylJqfZGYpQb3S4flPn2Nh3kskYJKrOXjztT3uvX2PuWV7v376vEJsPMwLn3uSdqNPOCCweOEY793s872326xlVVSPi27fYj5Uod3oU64a9AYWHp+TcMRFs1RlaDqZHT5k8cIxliIVXvxYnETCdmM5d8qLO+hHH454LzOB6laZn3XSbdsuSKVshQ+33LQ6Ftf2E+zs9QiGXXz9uw221itIksD0bJBqT8XlMJk8PsfexhG53RK9nu1X71IF2o0Wk/Mxli/McjpVsiWicR89dxRfJEQk5iUcdjA2bUt10lHb3cfplDnp3+GYa4udqk30Pj0z4NSVZarlHpefSrL6qI5THuFyCsyeWSScCFKpQ6c1YDcv8vJnlrnywhzzCwEC0RCFboiBJuCTOwRDDgLREG63g9W1Nu1ag0etWQzD4nN/5yJPfeoi6bkkMzM+XB4nXV1lqAkosoBlmpgW1GpDJpcnMQyL5/03ESWRY5eO8Wi1SdhvUm2KLBxPEhlPMnVijo28C7fPiSfg+2hoHaB8WCIeU3hpJY/fYzEaaAxGAu5uibUHJeIRkZd/6gqyIhLx6rRqHZJTSaqlNp2ehcOp4vGqbN3bZ+H0DG+8UeRj50ZIkoQswcIkTKft63fApTN3dolSXeTUUwtc+sQFLi50cCg2pHEoupicj5HNDnj2s5cxDJOTY3UUrcc3f+MdXnvfYMW6z1jM3lN+9O8/SyQZIJ4O0enZ8c/E4gT1pkFyKonL7yU9GyM9EaA3kokETFITQSRJIJHysV70MxiYdJp9tNQMP/vykJijSq89tLus7SHzJ8cZS3voNjucuTSN6laJKWUsyy7ciJKIy2PbdY/Px5mI2XL2cCqCKImcOJtk6sQ8vqCLcNzH1Il5RhokUj7eeqtk08A1hUq+/ieFeH/lyzStP9Px12n9pScElmUVLMu6/fh2G1jDBiZ8BviNx0/7DeCzj29/BvhNy17XgKAgCCngZeB1y7JqlmXVsRHOH/+TXl8bGfgiQY7yTcqFNsOhSbPSYOHsAnevPuLBh1kGnS4urz2EczqyT373CE84QMrXo5or4/KoNCttdlaLWCb4avs8edEmKXZaA26tSxxk2mgjnU+9bH/gNc3k/vsbdruw3uEo3yS7tkdu65C1Dx5yaqyKP+xl/fYeiaDt1jEb7dBt9mjWB/j9DlS3ymBgUM43SAWHRLw6iirTbvRs2zx5hOJwEPUbKKqMqsoEk/YFbHw2yq9/tYwsWexlbKlPJOamNfLQ7ksYoszUpF1FCPpg+0GGxViDdNRg48M1Pv+l53ApOlp/QLersb3TYeVUgrDfpHRQZNJru4Js3M1gWXDr+7fpazIr5yYxBYm3N+Ns3Nqkr3i5rFzH6XbwqzfP4gl42K14EWWJgF9k7UDmiR8+w1HbTT7b4L3rLUrFFppmUdzN8uq3MhR3s5Sqjw18x8K4PA6OBmFCapd+T2dqxk9gZA/TReJeYjEXH//UNLmtQ/xOjdceJXF5VcYWp3j/2zcYWQr53SMsS7Adkq7abjb97pBipsz6h1uEom7cToHnf/xJ2tUWB1sldN1gfa1Bq1zjm7/xDutHAd7fTxFy9XD7PMSjNpL+D9+2h6EaoRnGp4PomoVuWJQzeWIhgdBYnEQEHlzbolVr8tr2PMfPT/Bw1a6UGEh0m13GkwLlQpszkT22Gwn+zb/6AMnSWd8eMj4d4uJL57jz/g7aSCM1k6JW7ZPN68T8GgvCOntVH5Ii43KJ1Bs6pX6ASr7C+9++weadXSTJ3qDdQT+yJHDm4jj/1c8/x80PclRaEjv393C7FZZjFVKBPheXhowGGi5F58T5cVTZ5Fd+vcfV7+8gSQJer0yjWGZ7rUTa12RgqCTTXpKzE8z4j8jvHnFi0b4Ihj1DRiODd97MEPCL/O6vXcUEYjEX0biHVqWJpMg0anZi6lQFcrslLjrv8hP/4AqCKDA75eD7v3+NV7/yLn7Vfp4qaex3x9jb67Acr9Fv9dgvCPiCbnJbdmVwODQIhxQ8Xgc+r4giw1Gxx5mzEbuarJo8KkYAWL2xyaSao16sPaalmkTkKhulAD3d1rwOdRG3S2LQ6XLUkCmpk/zDnwkxGFpMLSbZvrOF323S7Kvsbx2xlbHld9pQQ5YsquUe2YJJr93DNAVc8hCvqmOZFvcOw3xh/houp4D4uOWeXdtjaVqg3jTI7Hd490aPYr5NJBnCsmD+WJyJ6AiP18FwMGKp+yF337qL32tT2XOZOqm4TKNtkV3b491vXMcf8tDvDu1uS7bBlHqI12mS3z1i/0ihXWsRTMZQZFsOp0gm7Wqd2ZOzuLxuivslil0fmm5hCSJueUDCyKGZCpGoi8/+3WeIhiV2txt857feJbtbw+WxpUjL8SqHZZlurcn9m4f4nBr3N21ApMcrY1kmwZDKyJS5+9ZdFk5PYwgyiajIqD9iZMrs10M88fJ5aqXmR3u/263w7s0RxYMasgRjSQfrt3eoH1WIeEa4fS4O92rs3t0ks1Mnk+nTG4icGGvwmR+b5viVk1z7/iMUWeDBtp2UvvkfPmD5RIwP77Q4WD/g+qs3+elPyZyd7jLs2s4oDnGEKIv82IUCbqdA6bBKudQjnIrz1XejGLLK4XaOf/lV0HThI1lJpWEHU/5Y2C7++Fz84M08Lo+CrCpMLCRptXRmxixOjzc5f8q2y/zJV3ycOhtH1032dxukfD0yRRFZkVg+leIXfm6c4VDn1IJFwGsx6g+5//4W7eaAm9kEk8uTlPMN/I0MiekEuUyNp5babNza/qigZpkW5WKbe415vnd4gpecP8DrNHnjlkSvq+EPqLzx6i7PXAkRVGwIZr3UYOPGKvWGjtPl4M1vPSLos8+VjAlUsgXGPDUMU0DEpFodUNzN4nBKbD/IEJ9MshDI0+sMePO1Pd7/9g3ajR63ruUZmwpyTFil3RVod0wmjs0w0gSOL7notvtEwgoNNUH5sEwlX+fShQBOxeTybJl4zJaCDToDXpg/IL9ToFWpEVLtbtQl3wN6jRabm226upuHGyNcHpVkYETbN0Ys5ac/gNJRj537GRKeFjNLCWpHdVwelYuzNun6wmmVyaVxRiODZ55LUuq4qGQLxEIw1ARqTQttaOB2aEiSaEvRKrbDX3PgpFrT0YY6ijVClkUunlUpH9ne/BYCaqeMLxIilXJxY3SBwUjE7XPyra/eoFHtIisiZ6Z7uFwS/e4AVRVxuh30Wx06rQFv/P77uB06PqfB+p0DpidV3v7aNTxOk4P9BrqmY8kO3loNUxzG2X2wy/j8GG63g+0Hh6gOgdh4HMuymDuW5EF9hn7fIBr3kpqK0G70CITclHINDkoilb6H8mEJp1tFNyyCEQ+6ZtLvjoinAwwGJg6HyD/5aY2AX+JRxoFh/IlzsX+l6886UPzXbSbir3SGQBCEaeAscB1IWJZVePxQEUg8vv3/R1z7U5PYBEH44h9BH/qdMuFkiPljUTZvrtFsDBh0bI/r5145R3E3yyc+/zSD3pBatUvfchOI+plZmaLQduPye9BGOuVskfSMrb82JYVW10JxKKxfe0QkJKE6ZVq1DoWqSKPcol7rE0qE8UVCWKZFvzsgvTRNv9Xh9HOnaQ7d+AJOmkdVwK4OjgwJT8C29Xvn2/coH5ZIpxRyG/sYpkjQ2Se7eUiv3UM34FEhyGgwZGQIVHJVXG4ZQRRIpgPsrRf4xCfHebgxYjjU2X14wAvnDY46bhKBEa5hk1pdI7u2x0izHTQArt3VMHWDdtdEN0XboabWIx53MRyarO+Z+KMhFEGjeVSlni+hKII96KcOWL19wIPhCnMpO5kYmC42lZPIisTdD3ZRXQ5EAT77d64gCPD8Sh1JElgIl3G67KFgj1eleGRr7J94agJPOECpNKDb7LJxY5XCQYX20IEi6iwuuMjnu6zqKzy51EFRJJ45OeC172Rs16WBwuKEwfq1R9TyFebPL5NthhifS2CYoMg2SO7/Y+9NYyTLzjO9566x70tGZEZkRu5bZda+dXVVr+xmk2wuEilqGXrIkSmNKHhkYCxjAMMwxsYYggfjwcBjWZAscyBSIzYlkRRXsVd219q1V2VWVe57RkRm7Pty497rH7dE6I9nRvYYlAAdIFGom6gqIOrmOd/5vvd93kzdQ/9gAEVVmDo9zvKDfVTFJLtfJToYZXA8yrlzYexOBW8kyC995TkcNoMXhjdpaDbyuxmGohpLWzA57uTY8RAmAooqoqoilarB6PFJFpdaKDaFdlcgGA/RKFbYy3TRNIPiYY2D3UOyrRCFvSyqbBLu87BWT1KsS3zln56lJgXw+RQ6HZ2NJ2l0rUegz08k7mVvLUMoKFNrS/z48CSLy12OXRij1TIYHlQQBJPUVIKRYxM0ihU8LoFmrUn/aD8XRzN8+MEGtz48QJSsItkfDZDL1FgrhtgpOLm1YkNRZfING4YBlZbMf/9lgVc+PkZ2u8DCnX38sQh2h8pe1Y9mSGhdg0giyJNCnJHZAZ6s62zcXyGg1rE7ZCbm+rlzp8SF188gCRaOsF7r0pfqY+vhKkPD3p8a1+ulGn++c4rtnRaJySFcdoOzHz3Ji589zxtvS2R2K4iAS7GkJYneJoZpEAkIOFwKik0lNpLE61WIh2Fvq8h3/+Q+zbaJP2Dn5vUso8cnqTVF+nxdvG4BWbHkAZFkhP2NLPV6j41mApfN4LBqe4pQNRmMmXgjQXo9sIstvvljS8d955376JqGKhukCxIDw2GCfolCpsT82RTlpszBbp5WS6dyWMTr1BExCdrr9A/50XqwLM7R6ZjY5R5T52YZOjJGsSZy9a1lGvUOd96+x8vP+9lYWCe9lWdzNc+0e5N2q0dh75Ce4sDhdbOxYeEVx6cj3LlbJhIAfyzCkWfnadbbTM/1kVnbQbUr3C6M4bF1aTdbBLwmoiTRbXeolFuMnZxCNwU++cWLlHIV3AEP5WyOKd8uM4MdSoRJ1310ZQdX16M0Gho3r+6QObCSUi3KlZ1ozGXhHgWNUkXHGwny+qcGebJrYzwlW8hCzcTo6exsVqi2VSbPzHD7zTuktTj1JsiKzO1VB/PhbXKZKtNHrSOh2LbMwKeO2mjV2yQCLVbXG6SmB+k0WhQaKsWDEi99ZICJU9M0ai06nR49HR7s+fnB93YpZMo0y1VcTolYRCKffUqEMayAstBAhEB/lGFpg8U9Nw6vG0mEr70foVXv8PCwn5hfwxv0MDrqxeG2c2JGRtZaDM8O8YnXwrz//gEup8Dxl45Tq/WIDcXwhX0MBesoNplTZ+PYbDKdZpvFa485fUSg2RHYqXiotwSmzx/h3Vuwv9/kwx/dxu21IQgWplYQBHa3K/zoqpUts7YnUW0IaK02PU3D4VLZTVv7vmJTkOslcns5gn0e3n1gIaWzW4cMeQtMnR7H6bERdLZ5LrXBe9pzrOwIeNzW+2u3W2Fz7a6Ay6iystpi9EiS+Nggs+NWmvWXf32alQ2N7Z06i0sdHF43HUOlXBMQBZ3jc07OvnaKRt1KegarORIb8NOfss7enUfrXHphANMwKduiXBzL0h+1ZLUep8HyeofxI3F291q4tRLDM0kSoxHuLzaotST2agEe3LckwJ12h5VKP81q3TrzNAcffcHNHy3O8+Jnz+Nyq2yWfITDKotXHrKTt3EoxPH5bKxtNGjWOrgDHrJ1L4oiEoj6cXtt/O432pimwZvvFum2NfbX0vzRv73Cn387gycUwKEaaD2Bre0G+XSJ+xsO4kkfiixQL1vPTBMmhq3miatdpFrtcPNeh1bD2tt0U+CO71USEwO4nQL7RYVWxypQj12apdvuousmdqlLNtOkWWv9tOsfG0midXtPvTwGqqxTLZTJHPSYODWNXTGZng0RjvmQW1UGYyaGCSeeP0K1WKdaaTN1fJB0uk0lX6HZtIIPn7HfJB6z0W5bmTvbi2sc7luXJFkS6BkipmHS03SePMiyvZxmZ3mPeqXF3nqORqPHUEJBFTrIEpwdr9Go/B2gDP3Ng8n+1qyf2YVAEAQ38OfAf22aZvWvf+9puMJ/tquTaZq//1ehD3ZXmI37Kxi6ia8vRClXx+52Ee7zYJomgf4om+slFJuCaZjcy8TIbKSRFZGp0CGjR5KMT4UZmRvF41FIxGUqwWEqVQNZkQn0R2m0TNpNDX/Ey52bh7h8TitYaTqGN+yjWWvgcNmJJYNMnz+CrEhIokGz3mXyzAzX71uaRZvcY2g0yN7KPh/7xZM0ihUaTZPYiJU3cXfLy+kXZnC4HER8PZw2A63TJZMXiKWipPeqDE320+n0MHoGsmTi8ch86dUmF16ZIVe3MRwoYpiwrU6S2a8xcmwCVTGRJIl0zcvspJ3+iSEqFQ232sYb9HL0WAiPW+SdP71Geq+OalfZbsRITg8TG0nS6VhvuWGKTMwl6HcV+fo3D1Ecdho9J7oh0WlrJMetz3Yo2ODBnQPefWuPy0sB4jE79Z6DaqnBF3/NkvLIisjITILHC3mMns7RWSeeoIexk1PIisyVOz1sQofdfY1w2MGAM8cPrqs8ubvN//7VPLPHB5AUhZ2sQL0t8dxnzpE6kqJVb/MXPyry4P0HFjrNrlPNFTni36LV1LC77Wwvp+m2u1TqArIsUS3W2Fk9pNE0SQ258IUtFB3AZr2fasu6JCxsSPSFJW7eLHDtg31kQ2MTZjAAACAASURBVGNvu4phmgwnrD8Qj9mpFSrUmya5vUPLg9HUKOQaxJN+EmP9rB1YEoJ0XuDu+48IOho0WpDwN6hqHvqjAsmEg+ljScaPpsjvF9jfzBOKB/G6IBWoMRhq8YlnuhykrW7dt99YptuTUGwyG/dXrP932ZItHe4c8q/fUHj2pVGef76P6bk+hiNNQjE/q3eXCTi7nB/Y5NmZBv6gg+nQAQ9ubKIbIKHz4GGZ7MYuZ59NUs7mKBeqTAbSqGKPQq5JdjtHWxMpHNQolTpMnZtlrRQmFnMiiQK/9hkDp1Nmr+LB65HoH3DhctuQbSo2m0jmoEsm22Hy5CgBr4ndIWMaJm//pEwwaMNmk7h0xoHDpSIKBm/ddRKLO1G0BlMnRvnwdgWXW6Uv1Y8gCnQ6Bj+5XKJZbTH/zCS9Hmyu5nn+hTjr95Zx2k38thaZgx5z58a5cThOJV8lFA/S0wyccpedA5FzsXU6jRYRdxvdEIgkIsgyFLUg+xuHjCXBFw1x7mOnKdZkcsUey3c3sdtAlEVqtS4O1eDUhWGOTcuMHRujVJfYKIfo6AqLN7eI+A3GWcLjFslUHSzdeMTO4w3aHZNyNsfBTg7TMPg3/+IyY8fHye0ekBwO8s7OpHXhOjVJydWPPxrE7pARBIHt9RJnT1uYz1qhTKvRoX8wQLFoSSaiMQ+5kki7Z3XtP7zbeApGkAmEnIxOhriz5kDXTfqSYdxeK3/CpVWQRYPBxmMmfGlyepRPp+7TH7fzysdSpPdqfPwXT3P0VD8ut8rudoX0yjY+o8DMiKWPtikmZ0aqRDxdnB47+7sVXEEfwbCTlK/A4Z41mRyUtnm8VKWn9bj67jqPS4MkUkEaDQ3dMBl17HAqVUKWrGJ3u+DgyLSLvTXLNC1LptVM0Sy08viROGNjblTF5GiizPGzCRqVOr6+EM2Wwf5Bj/hQiLOvncJuFzn/bD+lgyL//J9G8edXmRpoMjCeQOuZnJ23Ep7fv1ql31Pm3DMx8oUuLp+Tl+R3adt8dNoa86Ftfu4TYdKZLo9vLFGtdqkWa1TyFR7ueKhXWjSa1s9GLBXDHw2xmZHYSRt47T2296z0dYdT4si0i2c+fobD/QqaIbHyJE+r0SYcddOod/B4bIT8FlkqNT+OPxpk4cojnpnTWbn9BLtT5Vu2L+CPBFi5t8HPn7Z6dfVShZB+gGmYOOwK+Yadh/khjoU2ScYEQgERURCpVjv0JzxIIjyoTXCQrmAaJs1aw9LG75TYzorYbBIHu0USAzaSE0lkQWdto4Fuylz/sMKT2xvkMlUe30/j9rtwGjV2N3L0xZwce+EYTr+Xr/1v75PZrSCjcX0rRtijYRoG7a5IMKiSy9bx+VWyUoL7792nUmiiKBJep85f/qRBpM9t1QLpQzJFmdlzVsJ7S7PgAJdm66wv5TAMk+ORbR7eOyQy1M/2noYqaLTbOtOTLgaH/fgjXu6tiKSSCpmNNIZu8vzzfVQOCsQGvDg9Nk4/P8Wv/MazePxOTr8wxequgCyZnD/pRHWoTCY19rZKZA40kiMRshu7ONUemTyodoWMfYRKocEzJ1UcLhvh/gCq2EOVehSyJbZ3O1wa3iUe1HG5bZTyDbSORqfdo9lTSW/lsTltpAasMyi7scvG/RWcTomVrAvDEBicGkLXTWrlBpWmhNtp6YW3+s4zGcrzR99p0W5quP0udpb3uP3uAi+ft4hVqUEHXq/KfeMED+9b0rX42CBnXzuFIIrYHQqdrkVz67baZNZ2SI1HkGSJwckEsiKh2lX6oiqGAX+5PIIiw8NdL89+9Mh/rrLw/7dlmObf6Otv0/qZXAgEQVCwLgN/bJrmt54+PngqBeLpr4dPn/8/Ja79v0tiE0UGZ0dxOSWmTgwzOhlh5EgKh1Pmwc1tfGEfPU0nvbLNwU6OT7jfQ7GpNGsdRAxGR1wM9ktktw94fD/N8lqL0OK7DA2I5NM55s6McGm6wtS0D63b4xc+5cfttTMzG6Db1fGHPSTGBnB67MiKSKPawu1ROaw7UFQJf8jJ/LTK9uIabtmiRcw/M06lYnVubKrA0GQfXV3k+fEMiixQOihy+ZaG02Zw9OIskwmN4dEAg8M+eprB1pKFjgMoFbv84F6I4YSIIln65FJDwTBFzp4N4Qu58Dl1bE47cU+NQtnCyMVjKrtlD51mh07H5DCn8ZHPP0Mi6aGwnyPlSiNKEoE+H6oq4vQ6idiKtNs9Fg+tYY835CcmpZk0FhAFgVcuuUnNpkhXnciKxAsvJ/jITJa9/Rb3t30cPdXPV3/vvjWRydbJ7BRot7o4vW5u3Cyz9XCVtTtLVPJlPvaMjt1o4PPJlj4WjRdPm5x5bozR6RjDSRlvJMDLMwdsp020rsHSjUfsL2+RGPIzff4IbofBd76fY/r8EQxBJBiyE0v4GZlJ8OLHJrhz8xCbXcYTcBPuD7C/3+ba+9tIioxugN+h8WhbJemrEoyHSO9b7PNA0Imh6xSlKKkRH2tP8qQPrY3g9FiDSNL6fGRFxuVz8auvVJiZDWC3y4QiLiZiDQRRJJfvMTQzRNzcI53pUGnbUaUuN25Z9+mVhTS1covUtNUVzWxm8Dh0eqbIkz0H3/qJRF+/h6F+iKeiZMsKrYZlzt5+tEnQrRGO2KjmilTyNdLpFoWy1aXvGSKPrz/mmY+fImiv0zKdrOc89HoGXVNh6vig9fOFztnTfp795Fly+S4Tp6aJ9Adp6E48sqX97rY7zEZzON12Xn/e6nq5bT1q9R4Pbmzy1R8qCIJAwNnFbhN49ODAKhomh3A5BEYGVUzT5N4793jFfYX1x1mKmQKvv+KlXu9RLLYxEdhetoq95451GeiTaDqCPLi8yLnTXkaHVHYerZNZ26HR0Hj9FS9jR/pJJhyM9BvEBvwYpqXZddt1GppKf0zm2g9ucqJvl3B/kI0Hq3h8CiYCkgSSodHrdOkZIoYB6/eWOTt0wHbZy4UXR7n5oMvITIJapc18fw5JtC4NpaqJza7idCo4FJ2DbJNcWaScr5E51En5iwiCydhcgrfer5JWhrCpMBXOMXlmhsTkELGQ+bS5IPOxf/AsL33uGVZuLzE0k0LrWgVso97F67fjbecRRIEjU1aKaSLl5879KoYB51+zmA1Op0wy4SQ+NoiuGzjsMOPZID42iMej4usL0W11SCbstJo6z02X2N+rs/DBAyRFolGuoUk2Gh2FoidJUfPRM0UMUWJ/v41NgQvnLcvXtXdWqVc7tOodps8fYUcfYnnbKkDaXZH9qhtNF2k1OoQiLk5emqZe7VDXHJTSh7z+Dy/SkZ2cOeGlUanxhX8wTMJbtvZLm8T9q2vsdAZYyT1NrQ66ebzUolQ1qRwULMa8aJKcSOC0w8BQEEUWaLZMDovwL/7NIdffW2f27BiCIBINy9hUEZdL4cMf3cblEKhUdWKpGD+44yMdO0GpaaNarKEqAjF3ncHZUX7704dcXglTLOukBu2YhsljxxnKSgSX28Z+u4/bj3SEp4b058/acHqc2N0OC3ntVAkHJd78xlUKmQKheICfH1uwCi5XiY+eseAIsiSwl+1xkK6QGg/T7UkcOxElPhhCkgS++LqFW52L5yyPxOouAyMRep0u+brK6VdPktsrcCn6yOLxu5wsFePEx6xwxxVtnHKhQbPZJemrMBoooCORCtbYz2gkxqIcPeJm8e4+iWCHSd8+dqdKT9NRVJWuJrC9uMabf/EIQYBWvUW1ZrBy+wkh84DhlJOy5uPzrymEB8L4Qy7Gj8R5cn0RTbIxNR8nEpJo1jp0W21e+twzfPb1AN7WIedSBzQ6Eqn5caoNuHNjn/6Eh3hUxiU2mTo3y97aPsmEnZ4u8FufKmO3S4TiIU69cpITg0VqlZYlYbW1uTjbQBJM9ld3KBWa3M8Pce58H6mpOC+e1Mi2LJx30GNQq2k8ub7IF088RpZg5swE0T4n55J7jBybIH/QIBh0kD9sEPIZfP5jdg6zDaIhEbfd4M5Ch3azjaaLzJ/ow+eTESWBgckUDzbtpOIm+2tpUsXbjE1FEAVQVYlauUlQLJAQdwnFApw/JrFRjWFXdPY3cvQ0HU/AjcutErTVGByP4vG76PYEgn0eRo5NMHFqmp2tKuOxJu2eRCDixumUsDlthD0aTrvlcQxpGX5wL8SFZ8LouhXEeOGVGcaOjbFXUJ9iqK2zbdKxzvGTYXKZKsE+H9Vym51H63S7Ok6HQK0pcOy5Oev8WcvjC3vJbB4yNh2mWWvS6ZroBsRDBrfulFldb7Jwe+8/VuL9/fr/sH4WlCEB+EPgiWma/+tf+9Z3gb8iBf1D4C/+2vP/4ilt6BxQeSot+jHwiiAIgadm4leePvsPLlkRrZdSs9BWdy6vsnjlIQ9ubBLs8+MLWKE7/liEYCzApnseUZKQFImuqbC33+b7390mGLc0xfVqh+rUBQxDwDRM9rdL6KaA2ymgqDIfLgp4fXaKpR43fngLj8/Oyu0nTE76yGUsHFpmt8JkKMfhfgm7XUbrCVY3uxbkMGuhLz1uialzsyyt1MnslLhyp8dX3/KR7FfwhX3c+ckCmaJMvdpmYVMhk66T3a/TanSo5ks4vG4abYHhlIO9nRqZPCysmexX3UxH8wTkEpnDHvl0mXxVJjUVJ1PzMDvU5dKnz7G13aJQEclu7LK5UeXx3T06HYOg30pfreg+gn0edE3H7bTCvR4VBtA6PYYCNX7pc32WkZcIHdVN8aBM3FMjvZHBrhiMTQbJHPR463GMxICD14cX2Nur8/NfOI4sSwwkvUQHAiRHQticNubmrI7mJ794kW6rQ6cnc/Vwiny+i8ulECmt4lNbvP2t2/zCpSq7GZ2RmQTZZoDj4z0+fPMeqflx7G4XkbDMk+uL6AYcPdnHk+uLVHUvbrfMw2vLlAsNypUegihgmCb1ijXGnZtxcObCIFsPV+n1TDYObJwYbbKYCbD1cJVTxz1USi2GU5YJbLMSRpEFhkaDREOi1c2TO4iSiN0mcO6labqtLn96I8Kf/v4HAHzwnRvsFJ1MnZ3hIGNRFopSlIBf4cdXOuRbXnTd2oBDMT+qXeH+e/fpT4V47eePEvPU8Ss1ZBkunbbhckrsHwr4AnYAVLuMbFPRNQ1NF1lZssbn/+orlqm6PwLry1YX9sSL8zSbGuvFIOFehum+ErIsYpoCa4tp3r9cpGZ46OmW/CbgV+h2e9QrLWTBCs4rHpQZnkmyXw+Q3c7xx9+qUsnXiDnLdNs6k8eS/MYn6vTHZPZKdnTD0h7HhkIU9vNIEtQaJhdPKXz+H1/iQ/0c47MxEhMDFGsydrtEOGxnYw9OPzdOP7usHzi5+7BOQejDHw3R5+3S063OqCCKlPINHm9ayavVus5GWmRr9QBdh6UbjwDwqG0L06co/OE7EVqNDhOnpvB7JQpNB4c5Dcns4Y9FWNyxs7atM3thjuVChNlwBsOEV8+Z5DIVREnk7m6EYrFDaiyE3yPgC3l4fHeHUlNhatLDULSLw23n9ZN57u5GkASTW2/d5+J5HyIm6QOd7aq1B/kjXmyyQV+/h8mjSRbu7NPp6Pzcly7gD7ko5Op8+fgCwyNe/AEbzkoaWZHReuAK+igWWjRrHQYCLZYf7FLJV3nzG1dpNA0OtzO0Wz1abTjoxcis7TAypDJ+NIU/GuTe3TzRiMqN9SBen525S0cpZEsoNhV3K4/f0SZU2eRJxodfqaELMh6fwl5W5+bdKrl8h8Jelm5bI5b0IysSc7UPLPThmRk6GngdVsjh2p0lul2dUrHFzKwfSbDe+1yujWCaLK11GBhPUm5IvPUwSCCgsrKQ5uj5UWL2AsUqKJIlURgYsMLyosMDrC1s0eyIrNxewu/SabU1brz9hEcPDjg60uGf/ZM+zj0/iiyLVA4L7GW6nJ1q8+T+LlPnZmm0LESyospsrZX4oysD3H6kEx8MMxZv83tvdFBUmYflUVRFYCIl0GybLN98zOXHbtxGBVmRqLRsnJkTmR5X8QY93H1iUjqwfC+ray0KmRLlqsFnv3wJUZIQJZFr5SPEIyJrxTCbeRfdtkYkJOF1S8STfirlNm61g88jsL2c4eGHG3z9LyUe3d60pFk1EaNnBfcdf+k4Q4EaNpuM3tMpClH2l7cYnulnxG953C59+hwxR4HRqQg2u8yTgwBhI0vbdCAJJn0RhXq1zfe/vcHzLw9S6yg8yCd5dHWBQNhJfDjK/oHOK794gd/4yhRLi4f8xq+PMTYocPrVk5TECE67QLVr5/oTB4IgMDbm5cH1dQZnRyn3/BQLbTxOk/HpEL1Ol83VPKv7Mj9pnGW9FCZflXB7HTjtMHciTrFgTboe5hPWeTQ9iMNmASQkrAZCp9Wl3dR4b9FPdivLyNFxtooe7m15qHTsDM0ME4q4KNVEcsUeczNu9ksOvGobXTdJ5wUCARtnXztFTojhd1mymR/98RV+52sKes8gPuBm6WEaXTfJFgQMBOIDbvoDXcoNibERB7IiE3E1uHllh8GYycrCHopNIegTKNctlHgxOMbSQpZmV6TXMxgYCiCaOovNCfLpIm9ebvJ4W0aVdFw+J6OTEXqaTmLAQdewmi2yYgWdVYtNZFmilKvgcKl0exKmKVA8rNHrWbr+lT2ZRsuCODRVH7965B66AZVCDUEUCAcl+hMeEqEu5WyOoE/A7RLZ0Qbp9aB/MMBgysfklNUA0Lo91jZaACzf2WDu0lHmT8YJ9XnI72aoVTRGZwfI5zq47CaTwQPOnvbz+iVwehz/sRLvZ77+3kPwN1sXgC8ALz5NVrsvCMLHgN8BPiIIwirw8tPfA/wQ2ADWgD8AvgJgmmYR+J+AW0+//senz/6Dq93q8szHzyDLAhdeP8Olj04xenySUDxAKOJiZzVD6aCM0+tkdDJMUCigOlTsToWxwnWCQRvPvjDIhYv9HDuToC/uxnO4isNmoGsaEzMRurpMV4ORMT8Oh8T+dgmv1+rcaV2dky+fIF/UCPV5GJ2MMDoZQhW6mIaJqooUKyYXPn4Sl6IxkPQQijhRFAGjZ2CzSUQH/CQGbLx+CboaTM3H+fQXTmNXwWaTOTpiGQiLh1UEUWBoOsXkyTFME0oVnfEJH30h6HQMBn01vnc7wGo1gaGbOD0O7KrJ4vVlvPYuuwWL8PPSOYWxuKVX7PUMnntlGEURSUZ7JCdihMUcWkcnGHXT7pj0TwwRdHUYGfOxV3GzmbG6DjfWwxwKcebPpvjXX61z9oUpTFPgycIh2xslbKrVbd3WU/T3uzgs9Aj3uUjEZYIhB4VcA4Bvff0ekaF+TBOOXpxlr2gj6u3QbGoMJ2WygWkOG26e+egx3l4IMZESGEi4CDuq7BVUXv7cGQIRN6ZpMDfUsrwdJlx7d5NwMs5Y5TZ3P0wzOj/MwFCAw2yDCxciP013nj+TROtBKCBy8uUT2G0CHqfJXsn500Lj7beziLLI3buWLyTkbBEKiLjdMpW6yd7aPgcNL76AE5sCoaDM6Gwcu11i7tJRAn6Z137lWVTZJD7g+WnQVLYZ4N6tDF9+zZL/zM/5qVR1vD6ryP/kFy+yvrhPs2WQb7oQBYPp/gb/yz+/wtpykfWNGooqsbWr0Wp0ee6Tpzj58gnur4Dba+fZT57lGw8nef68E92AuWNRqi2FcNjB1pN94h4LC7tW8BMMqtjELqpD5Tc+C7Wug7BXx2ZXKJU1Ygk/o1MR3GKNuLvMJ39ujIPdIrceC3hDHlZuP7EoTpqTwUE79WqHrWqEg5xOqyMgS6BrPfriLmqFEj6XQX/E5Hf+h6sossCsew2wuOCtDhTyLZ4sHDKSsN7vgtSH025y8qgbQTA5/+IYLU0m4tMZnYzQlxpg/liYVkvHH7IIUbGwxe43TatgHvNlqWt2gj6BYH8Er1clNW5ROVRFoM/VIBJW6EhOkhP9DIQNGg2NTluj2hA5aAX56Lx1qQrFvOws7+F2GASD1ru08LhuscqDHnyOHqIIWwcqgbCLWzt9xPwahZaT4y/M8857eQptDzabyLxnFUEUiPa5SPkKpJIqmqYTivn49MUOf/YHH+B0Kqh2hf/ma32Yhkm9ppGPTBMd8HPtep4XX5/H5VZ57ZUQmi7RrDWYP5viuc+cIxmXOP/aCdYXd5lJNlEkjZMvn2A/q9N4ilAWBAGvW+DoUAOHU8LlVhkcj3Hu5Wns5QyaIfGW9iJnkhmyrRCS2cPQLf75pXNu5qbtVkKxU8UwTGRFpO4d4Mb1HHaHQqVmUmyoHFTtvPS5Z4hGHRzs5imWe2TrbryRIBdO2vC2DnlwfZ1mrUnMr9HpGmxtVhkYiWCzSTiMOn4PZAoSwaib+VSber3H4HiMZz96hFSoTmIyRVsTaFTajB1L8eJLcbxqm6iSx+u2vD9DR0at9PKGnaNnhui2NWwqFEs6oxNBBgZ9DMRVhhIqh/slcjUbM3NhkiNBDBOL9PU09G/u0lF+dfIGddHH+uIuJ7xPeLQhsrSu4fE7OD0DJy5NEoyHmJpwkBjrw24TeHgvhyiKGLpBsy2ytNYh4Ozic+okUn6eS20T9Am0mhpHZn3YJI0///ePiSRC9A1GiMWdnLgwRr3nYjbR4PxrJ4hEnTjsCqWWg0e3N5k6kSLd8FueDlVioLOGw6WSO6hzYzdBt2uwt5Gn0RJYbE8Ra28SVIosrdRp1tqcvphic7tNwNHheGSLL/xXz1GrdGjW2pya7BENy6zsSpw8E2M9I+N39tC6OiYCd+9XcCkd+sIikbgXQYCxuSSDo2EkQUdRJWpNiyb0zMfP0Gl1WVwokvDVmApm8LkMsts5/C6dnm4yNWnR5fwO7Wn3uc1guMOTTXBqVerVNorNCiidHDLoH+2nP+lndUtnqK/HsDvN+r1lXC6Z4WiHcMA636PeLl1dRlEE+sMmG6tFCod1+rVt3KpGudDg1V++wK993snAUJAL8waBiJfFKw/54J1tvv9+j2q1y15BJejpMRDUGByLUuvYOXtxEJeqM3U0SSjq4S9/tM9MfwWXz4VsdEmkQlTqlkE9GrVTEYK4VY1QPMD8ES9nJ5qsZh3k03mu/ugBI1NRfB6BumbnxJylRihURY6f6qNarDMy049pmuwVbaR8BWwOlUq5SyTqZHRAp1jW6fUMbL0m68IEq+stJo7EGZqI4XOb5A6bbGRVxk5OMRfPoWkmPrXOzWv7eDwKP/rjK+TyHc6+dgpZkUgM2Al4DPpS1nT8we00t358h9Hjk4iSgCgITE04GAlXWS1FefedDN/7ABKp/yhI8me6TPPvKUN/o2Wa5hXTNAXTNOf/WrraD03TLJim+ZJpmuOmab78V8X9U7rQbz5NXJszTfP2X/u7/q+nSW1jpml+9T/l31dVhWs/uEm7bWC3y+xs19hZ2mb17jJXv3eT/uE+zjw3RqfZYeXRIdHd29QKFfLpMhuhc9y6vEX2oMv6ZpMb762h6yaGaqPVEZm7MIMowhs/0lAV2Nqo4HKKrN9bZnOjit0uUsrX6XZ16rUut358h7tX1zBMEwGTwfEob71xDdOE2z95TK5us4y7D9MUihrdbo92y5IO9Xrwg8sClZo1qmy1TYoVi1ayuKViGlCvNGjV2wyNBohEnRgG9PdJlMo9vA6Dg3SN7bKHpYcHzHg26I8p9DSdhaUuwXiYfMNGxNfj8ndvUm7KLG6pAOytpVFkgbtX17i1aJJPlymaIVqNDsXDOqJoBb141SYP7x7Q0wVabZPFa485NVzGKbbodHQicR87W2VW90ScbjujE0Ecdit/IKBW2dupsfo4h2lYJqZu12BuPoRpmHz5N49id9rY2qzSqHVQZJPdgp0Lp53EfB2cehWfvYPTKeNyivjsGkuLeXarAU4kcxweNKmVW5x+aZ5GR6FWKOFQDWZOJOhpPfKBMS4+P0Ag6ODe5WVOnvCTKxqWKbY/yPLiIbtpDbtqUi408LpM/M4e3Z5Asw1OjxNv0MnitSd87uOWtGYpY6VS5nOW7Co5kUCRDJbvrFFvwp/87vvkMjVuXd76KenoycIBHrvO3StrhCIuq+MpP7CMdAh8+80WDx6UuHNlnf3tIqv3Vrn69iqJsb6fYmIP2wF+748rnHrlJEMjAWIxF5MjKrGogi/gwOmUuPO2xfWennSz/jiDLAtsZkRsirVheR0a965vc/T8KO/cs1OSo6iyycpSkX/7bQfPvZjg+nqImL3Ak00IhF1W5/iHt3j3z67TNh00NAdv/LuH+CNejk9BIGx1HLWO/tMuu8dnp99Txu+zcHyjsQ6eoJdioU18bJBKQ2RzH37rv7tIvWHwQXaKZrPHzvIegxHNQkBGPbz1fpVOp4eIweq2TrUBse42G2tlUr4Cra7I9bcWaVTq3LyW5uUTHXLpMtffesLVmzUCQSfNtklqepC7mQGccpdGCwr7B5yZ7rFwcwNNM9jPdNkoetlPWwVyJOok4m7z8lmRUNTNWF+DWkfhjcs+3GqHRqVN5aDA+q6VedHp6Fw646BcaKDaFW4sioz0dRiLd6gUmjxaanBjwWTEkwUgMeSn3lW48f4Wh0aMpRuPuPbjh9zZ68PrNGjUunTaPb7+Qzj3sdPY7RJLt5bZW96m2TLwelUeVkZJb+UZnwxSKnW5+/5jvvnNHbIVlU6jxUGmjter8MbXFsnuVwjGAlx7ZKPds4rcXs9gb2Wfw500oYiLbE6n3ZPZXCmidXW2lzPs7VRpBpPE7AVesl8m2wqwU7C6e36fTK9nUmmIRL2a5dVxyHz2Rcvs2lI8iJKV+eJyCAgCxLwtdjdLqKp1cforRCPA4w0oOgb4yCen0Toa91dFZkdFvD4bj24s027rrLWHqdQFnh/fp1nvcu2Rit8nk97Ks7VWQhQMa3LcEwhG3TjsCkG3TkOzcSMzwuZWi2q1y9BoiEKhy9ouvPutW8iyRKVqYJomK0/yfP5CgZsfkGuQowAAIABJREFU5viLNx7jC3vIlWAsaVIptzka3OQbbwocGRUI+QUeXVsk5xhEQsfpdfKkNc74IDgdEvfeucfSrsy9KyuUDooMR1qkt6xCa3giSDQZplqscy6xw7EZhXJLRZV1DBOu7w0xEyvi9qjsZbpUOk5+/pdneHJ9kWatzQvzdfpjCi1d4bBukYneeuMatVqHjazM7KlhapU2blXD4bJz571HfNg9TSFb4XA3z0CggyhaNCStZ3JUXeCufpKuaSMYtHPsdD+RoEguW0OVdGRD409+/zqyYp2FtY7CrZs5Hj3MI4rw7o/WWM+oPLm5jCxYF4N2T6FQtjqo8bCJ328j1mddoGN9dqp1E61n8uj2OqZpkhzy4ZQ6aKaCQzVoVGp47D1+/O+vsr7ZpD+gYZqwt13F4baxuGX5keqKNWl2uCypZLpgvVcffOcG4ymJfneFt1aHAchmG/zh1w+wqfD2DzcoNhQamqVzFzDRuj1KB2UeG7N4bS1sdoXZUSsQ7fGdTf7gj7IsXnnI6PFJPvnpIatBl2vgtJtsZSUKdQWXS2G3YGMq2aPekeh2de6/v8CR4zF2y5bPURNtbC5nrfM8FWHpcZFwL0PPsKZU+aLO5UU7N28WaNebTJ0eZ2+rxJ17FQ7rDt6/UmJv7QC/2+Aw1yU+HGZvI0+13OZIf5mH2QjBsJO9rQJvvXGNUl0mGZfw+OwstqeodOx8/IJBu91j4eoS9xbbNGsdfvy9VSr5KtmGj8tvrRLqZfnIqwne/OaH/MpvPofbo1Att1m68Yhk1OBr/26Z9HqahQ8e8I9+OcLxl46j6wYrC3tUK238bgNF1BkL5OgfDCAKAjvrhf/kWvNntf4+h+Dv0GrWm7iCPk7PmKT3qhQPa7h8HpJTwySnh5FlkQe3drG77NTLdaqxKTwhH/6Il77eLmcupXj1bI/sXpmxuQT1epeWN85gqMXtN+9gGPBffsoq2nXdoFjuceH1M8T73Sw/KbFxf4W1++vY7TLHXjiGrMi89c0bZNshVh7sINtUgj4L35XJC2xvFEmvbOP3K3j8ToIhS3uqKAKnj9mYTpmsPtxF00zu3Tmkkq/SF4L7V5eYPp4iORrhrTeuEQzIbO92WHjcQDdMPLYuwbCTdlfkt/+Ryv/x3hBf/91r5PZyDCZsjE7HkEWTVldk8sw0lbrApakisxfmmD01SrGsE+wLcHRKol5pIGCS28uxcvsJrbaVfrmYCTMxE3lqxhXQtR6bRS/Zdoir37uJIFomXa9bIBh24nSIdLrg80rEa6sMj3g5eSZGsdBiYbFG/rDO2nqN/P6hxZu2KZRzVbRuj/ShyVCoxXYa1jI2XM0CcdsBvZ7Br0Tf4v/8mtWtH/bl+frbLgttFvNy5bsfEnC0cPq9tDXRGpNG/XSx0emaLD3YZerUCNW6SSQo0mpbOt3RyQiz4xK7WZP1e8ukc5CvKficOpWqhUZrVNqMzI3w7TebhMN24gGN6cEe135wE1ESKOUqlJoWtaZU0Xn1ly9QLdb5l//E4B//t5d4+3tPsNkVFMmgnM1x4YTM9NEBKrYI6xstVKHL/LyfgUEPhT1LRz8yP4rb70ZWJIJ+60BTRJ1XXulnctxNsdgmHJR4/3Keyz/ZI7NTYnXZGqwF/TKKDEMTfcyluqwslREE2NqqIWLi9DrotnUCfgkTAcOE/oSXr3ymw8JihTMjZWq6m9GkwMaTNFvbDZLT1mFqE9v41DqBWBDTtA7A3fUDCrkGzbo10m82e1z57odk6n5EAXo6JFwWy/ruuw/J7WaRRDg71UYWTQbjkAy2EAWBkSODPFyX+MRrYSIRB4GgJTeoaG6y+3WSEZ0HvaPk0kX2alaX6fjFKSZPjnD8dJyDupNuu8vFj87w4jMu/AGVRlOn3exyIbaCIJgEvSYun5dvfLeKJEkcpKvUal1MEwIBlccVy4gXtpW4vSyzvXyAboiM+Q9YWUgzwA4rt58wMJnC7bK23ofXV2lrIo1KA7fXhtcj8d5tyNetULTnztjpi6rkuwG0Tg9ZEZAEk8RohJBsHY7ugJcXEsu0u1ZuyZc+o+LzW9OWXs/g5Atz/Kv/eZJ71zfZ3a5y0v0Y1a4yPIBV8MfDzB4f4GS/lStQLdaJBEWS4wOUDysEox7WlotUOnYyG/toPRN3wEM4GaPR6BINS4QcNRwu1ZpYJEIUsiWcxV06ho1t5wwhe43xaBVfaYs//f0PcDlFcgWd7ZxqhW3ZZFYP3WjdHgYil5618Lc3rh/gVHQuL9hxemwIAqzdWUI3wGXTGBjrZ2YEbGaLe3cOCUT9DPWLfOd7B0TCNlrVOundMkfEh7w6scn17QGy2zm8HpGH9/PIisz6gzWr8Dg5xZFYnsxOCY/PQpQCzMcOGBl24Pao2GwSw0M2Ls3WeO5Tp9hb27dyHKpdQlE3X33Lx5c/p/KRT05TydfY3W1z5XaHSqHBRjPBL71qkPDVKNdMLn3qDHuNCCLWftEzRBTJYLBfJDk9TCJq4g166bY6bBccxAfDlMoaqiJyuJvH4bbz4f4gW2lY3hbYzFrEtpdjD6l1HdhsEpMjKi6ly517FYaOjHHsdD/v3Hfzzo+3CNsqDPosUlI4GWfhgwfMDbVYvLnBo6sLtDSZfDrPMx+dZ8a7CUA0Gea92zA+YiO9tkepovOwe4QLje/T0K1J9vX31lnf6nD6TITvXZV5VB1h7Pg4DqfC4OwohyURQRT47Cf8JKMG40cSTCdaqHY7LqPKiRNByi2VpcfFp14ijXe/dYvVlQqSYFCt9ZgZ6vGDr12hcmAlTaf3arjFGm6hRsTVYPLkGNcWJH79t58jNeTAQOAvr1rncretsb5RYyhupeVGYx5EUWDu0lEWFiscbB3g6wuhyCYdXeGViQ3Ofew0TqfCL/3CAOtbHSbmB0j4GyiiTiZdp1SXqJXqFo/fkcMnVcini+znBNJVD4pNodPscPLlE3iDTpw2k7MnXASjbkKuLtcvp1l43MLrlRmJtrj20GQ0WGL5/jZHnpnhzT+7zZC/RnI0wlLdykJqdUzy2Qouj42O7KTcUln44AGVisZUCr70GRVJUWjWOqTX05w77cXv0BhIegnFA5YEeK9C8aBGNBEgMejlzpafwaA1AR6f6SM1P27tMXbdQkmrHQxDoNOTufPeI9r1Bu1Wj1jCx6/9aorYUARFMhiZHaAkR8nmdPrHEjSaBsm4jKEb+GMR7j0xePVTU7h8bo48O89axkZPM9heXKfb6uL12Yl5GnilGiPpD5iZsNPp9Ogf/Ns9IYC/Tyr+O7XsTjuq3YYkmgSCTvqHgpx5fgJv0MXQeJRA2InDZcfmUJk6PkhVDTMxlyA17EXt1rl/K813PxAYHAnh89molFqIps6jXav7Va12aWp26k2ToRE/qmJ9xKVSh5ExP0efO4ooSwwl7TidKopN4Re+fIEj+l0uvjLJ2VeO8Wi5xcBQkGSfydyxKP0TQ6TiJit3VpBlEZdb5doH+/zF97IIgsn0iSEcDpGXXuxj9MgA127VaJar2B0S7ZbG5JkZ8oUebrfMqxcUKuU2LU0mNejA49B5fynMF1/M81v/7Bzx4Rh+j0m10ubty3UUycTltnHlgzQP9kP4/A5GRxycmNRxeuys7sDITIKImWV4JslHPv8MhUKHieMjnB3YIZdrYbdBuaxx8ZNneM5/j357jtd+5Vl+9bUGgykvfrdBt6vTahucHz4kHjapuOM8Xsjz/W/cJTXsRVYszf1LF1yceXmeRMDituu6wfq9FVTF0mQ2mjpnRorYDze5kxumWGhzS7nIL30+wdx8iK6h8KWPVGjW2uxvF5k8M0OlY2f+mUnaXRGXS0ZVZXRTIh62ULJOp4JuWPKse+/cYyDppdHQEAWYGjL5+BeeJeAV2MtaI+/5cZNQ1MPGwjoT0yE++bILr0fix1c6bB0ojJ20vA5jMwNE3B2OXpzG6xE5PGgyMBLhd/40yOpmj1gqyrETYYoNhU9/6SJbWYl2u0cst8C5EzZrqhTRcNhETr96klqhRCDoJDrgY+XeJhG/gSzo1Lp2vvedTZZX6wwOumi0TI7Mhbj0QoJEKkQo4uLcx06TjOjohuWLqbQVLpwPEHG36Yu76HNYuDifX2UgYuKmyonwJutLOXTz/2bvzYIkS8/zvOesue+ZlVlZWfteXdVb9b7MvmKAAUCQBAjSBE2aDEtiiHLY4QvLDjksKUJBMijSYUpWiLIIggyCFAYYDDAYbLN0T890T+/VXUvXvmRV7vu+nXN8cRDwha9oURIZ5h9RtxWRcU7++f/f937PI7Iw72E77yEoZgm7W+QPMxybdRIdCTB7cZ5kK4yLMs+9GGNg0MOlySKT8wO8/JyXYyfCKJKOy6XwzBcuEnMVWH1SwarCG3f6GZ0O89mvXGT+0hxW1SBTteJ3tGl3Rc7v/6npkmj1GIuZ33GPW+TccRmrTcat1IkNuQg6GpzRb+ILeZhz79LTBCrlNqu3N5FlAaelx8LZYVxOkZ2ESDrVYHxQopAu8t3NaYa1LZI5GJ4d5Iuve4hNhBElkbkZJ4pssHQ3QcDWoNvR0QyJM9M9jp8bwqW2yLe9/PwXh0kKpgztaH2Pnx1fMrtksRAhV4ep4zEkSURRBD5/pcmJcILhuWHsao9SWaOrmzndoE+m0ZVwuVTqhpPFF04TGQ7REax4HRpaV2Np34XTKVOrNLFaJSJhK4+O/Jy5MkYw7CDBIDaHebienAkSinrx+2RUwbxAWOwWkhmNYrbC/LkRRkccLJwIkqsqLD53HFGAyHCA+cUhRkZcbGw2SVY96AZ4PBbsP4kNCbqGWygx2N7kxmaQasdK0xXm5S9fpqfBSyfLXB09RBCgVGhSb0L2MI+9V2Vts82554/x+qdCOCxdri60GIg5abd1BNEc2i7UVWRFQpYMwvufMDrhp3/QS9Tb4jd+wc3Deyme+cJFpueCVGwh3nkywqvRhwxORpiJdTh+Mogn4GTy9BTVjo3x6QBbhQB9Ax50zWBuxoZmCCTqfja36/i8Csv3D8nkNEotG/vbBaZOjXGwWyIctmGxyOxvpJnsraAqAoIocPyYlblpO7MLfQzaM9ilNqWWjWpN5+FHWww4cuhI+EJuzip3sSnaT4llAA6PnUuvHGf/0OyeVSptej0Di92C3WnlC8J/oNs1uDjb5PWxx+xt5bmeXyBXt9AflhkPVenoMmdOe4gMeul2DcIhCZfXwVE9QK7p4MUvXmLm1BCnnj/FTsbO+PwgX/mtp0mWLMwujrG3lccQRMKDfvKpIhdOyOSLOsefmkcUBE7KD1kPPk1bU5AkgfxRmtFhCytrFeZnLDxTfYNOq4skCoxMhuj2DGwOlQcbIvGMiMvz/6B8Zb1LItVFMwRm5vwkD4oE7VXGT0xw6pSXXNM0A9vVLq//ylUufOos49NBE6+deoiitXDJDZr1Ds+farH8pInbITDgKrEw5zCRxc0O//2nU5RqIvZOhVPHFOxOlWa9zWvP2ll8ZobFq1M02yJetUpFc3OwlSGXrvGNb2cYilm4cEJmTNiirUk06x2cNp3haRNB6ulkKfZ8ODwORiIas4EkA2N9zC0Os70cZ3zMhabDYiTOzlqCH3+i8w9+2c3nnu6xspQF4JVzXTZyfiLDfUQGnJx/8QQdXcZqlZlzbDM9H2Z3t0oo6jUtyt0IUVeNxRdOc2VRZm0XrJIpPovEPEwvjtPpChTqCo1Gj1q5wUi4w9xCEJfXTretkc00WBwp8caPdQZjVhKHFRrVBiF3h2MB8+IbtmSxKT2yVZVXv7jImZcWWZh3IQjwJ3+epNMyZ9GmJpyoQptYRGL+9ACFYhenTafd6jIyO8DggMLWdp3Tl0fp9TTOjeZxuSwsvnCK/tEwoyM2cnU7Ml0+8nyWYsXgF17Wsdmk/3yHxf8Py/grEob+jjL0X3gpqszUiSEmnHEajQ6SJCBJAm6P2ZL0+xTySVNOs72SxNUtUCo0KRY7iFqXidk+2u0ejUaXdLLGk1srtBUnY5EugViEQMCCZgg4bAKrDxKUy2ZF2eFQ6A+JtJpdXF43xbKGbpg3xK//6+sorQpv/fGHtJo9JsdtbK0ecf2TBrVaj3q5Rq0p0q43yWabZNM1RiaDXLjcT1cTySQqbG6UWd/pkIqXOHfazclnT/LeN27i8VpRreZGPTIgUmnJvHjFQcxV5JNbaZodkdmhLncTA5TrIqpV4XvvpLBYZcbG3ZTqEtVSk5deijIZrqMbBh9eS7C6r9CotrCoAp2ORlwf4mAzSSpZx++38PD9h8QbfUxOONk/7LIwLfPo1jZxcYztaj+pVJ1rG32sPkrzrTcTHJ+zEwpIfLTdx6P1Hl1BZePuGscvz/Ddr33E6IjpY3jz7RylQpNqW8Hf58bf58Ed8lGta9xeU1BVkds7fqqxY9hUHX/AykHOysaBwNZ21YwGVINYbSqDYwEUVWYroZjCOptGJt3A7rKgI+K0aPhCbgRRoFLpUW8aWBw27n+0g8OhoMo66ZJEOt1ka7fN2KCIKBjceqRzsJni2c+d4dG9JD++2eF7byzj81mwWqCYLjE/rbK5fIhhmDGJckXn0lkX++tJojEX8d0iA4MeHDaBRFbg3q1DHDYBTTMo+0dZeqIRTd9jJ6VweNQgsZfnqc9doJCvUyk2mT83Qb0loood8jWFyHCAy2ftFIpdul2DbK7D299cY2/T7KIUc3Ueb8FIyJSN5csiu/Ee8YINn0ci0QhgtSmIIqQLIh3ByttPxnB57XhE0/oZdrXYbIxw7aHK4OwITzYarN7exh+041Fr5PQQ2bxGLlPn/oGf7ZUkX/uTbd75+ic0uxKaZtBsdOnqMsdm3ZSqBlcXWuyup/nmH12nP+pkc08j6GhjkbqE3B1uD/8i/RNDrHz0mHevlSlURSRR4N/84QrJeJmtYsj0PrSsHDpn2Lj7hL3WIFt7XbYf7eLwOIkfNuhpAtVqh0JR49H9NFabzKO1NkOT/XxmYo0dcZKjowa9nsZXv579aWVHEuHOUptzlwZIVp1Uq22KHTc7aQuKIpCsuXh/yUIiKzDY26Hb7nHq+VP8/vVp2m0NwzA4yFmI75ioWLsVREGn3rPz5NYKs9YNDuNVig0Lh/Eqjx6X2DvCRAJ27TTqHUrZCuHyBj+8XmPj7hrbuw36+yS0bo9CoU21pvHRxzne+bMbOOwS37xhYpDTBYEffeM26XiBWt1EBXu8VpxuK+/82Q0WLw6Szza4/XGCx0s5ZvpKWK0ymmYSnp48SnDr2h4AqZLC4+tLtFo94psZDrcyLPe/ymZ9mB1ljlem97h2z8BeTrC3VWRzvUS1YyPf9mK1SdQqTfKFHv0jfQAUcg0+eOMm8bSAR61zZ9NO0G9+1+YuHqPf1+UgJfD4+hKPtwS+7/5FVFUkl67xrXd7/OuvlTh/qZ9SoUkiUaeuOel04R/9kY/Ufp7DgoVG0xStWawyOxkbH37vMdOBLLpm0Gj0yBV09rNWrHKXVqOLLMHYTASHXSRkr3FiMYzbY6VRbbK+mkfXDc5fHeb9+gUsKvj7PLQ6Zm5v7XEGAYP9ip852waZTJNSKstg7j513U692uJW5yw9XaDVEShlStx+2KLV6JA8qvLa2Rrbq0keXV+mWu0yeawfSRJ4W/kCTx9vcWfTzqP6DOPTQayKTqUucu9ekT/6yyaaISCJkM/UqFa7zA+UGZnwUWrIpmX8sMrWcgKLRebD6xn6wnbiRx1mIyWSB0XKuQr5XoBSvk4g4uPeqo7VKiBJIk6HSEt1MRd/mydJB0eHdSZPT/N4ucr8nJuv/p/3+b7t5wn1u6lU2lx/8xaqImCxyFitpoDwYLdMsaGS3ElQlgOkElUqdZFiqYuuafxwyU+lUGN5tUazK2NRIVO18p0/+YjUYYkb7zzCapOpBMZIE0UzJGwOlY20k/MnLZRrBtfWAmxstzl50sfR+h4H7QEsCuwZY/z4gxKFTA2X18a//9Mjlm/vcbhXYjfe4/r2AF1NIToSZGo2wMhEgIBHZ8yb5Vb1OHalS7fTo1ARsVplttdzfFI/SVhM4u9zcZSTOawFiW+muP7mLeYWRwkH4KM7Df7DnQF8IQ+SJJKqOriz5USURQ6LVv7wj/PUmgJrN5eplrtkEhUcSpNWq0dN8HD9uw/weK0szHtot3pElST/7k2NYq7G0oZArdYjIOZwBz1omk611CSZ0ej0BHo9HX/YQ6pkdkE1TWdgyMXQsAvDEPi5F0U+fO+A/gEXQ5MRvv2jBt9dHkQSRe5nRpi0bAOwuV5iby3BzY+z7G5k+fznYyS2k+ylVdodAwORvSPTiB3tV+n2hJ+ew7J5c5D9cL+C3tPZyvvMOSq3iiSJrKyWCdibCIZO1FHgwx/vYpfa9AWV/xzHxP+o9Xcdgr9Fq93q8sk7d9ltDNDr6qQTFQqFFgdbWbK5DvduJRiZHWDk+CSqTUUTzUxhvd4h4ZnlvW/c5LVnrMiyiGqVWXjqBA3VTaqk4Am68bjEn4qQLHazDbazliIYkMmVTLKEL+yhv09i81EcTdOYPjfHgf80n/qlK+yvJxnta1FM5QmHbTz4eJduu4vPaUpCAJ6+6qdW7VAsa5TrIht31+i0ulTLbdqNNumczsP3H3LhU2dptcw2aaulYbfoHKYF/uRr+1S7dqw2lYCzi0tt0dMEag2D7Ue7DI0HqJRaRAJQqRmk4xkEAaKWFMVcnam5PtxOgUqhypmJBo+vL1Fp27DarTy+voTdJhCdGmbUfsS92xkCfpnrt2o0qzW28l5aXZM9vbdv5ixHp0K8975Jdfns6CPmpxTc7TwXPnWW+x8s84Vfu0KhqOH125mdD6FpOk61S73WRrFICIJ5CIyEJGIRibOjRdRWhRfyf0o20+CL6hv4PCIH60nC2hE2pUdf1M3a/X1yiSJWi8DW432KNYmTx12k9vOMH/wIt7VD/4CTvc0crVYPp13g8qsnycWTVCptbEqPRKrL6u1NAgGVbBH2kiL1Wod2o0Wj0aOULXJsxsHUyRGunNDYi3c5dmaEbBEEUWArbSMVLzAUlej2TFtnvdbj+KkQDodkDqdHddx+J9t7bTKJMrLWwe+TOQifx+M0CIVsFNMFUodlntxaoZgt8/Hbt1FkA4deIeDs8vRFJ29+O0mz0SWVatBuaxQTGbLxNPc/WObgyQGaZtDqmUOXDpvBxXkNi2LQ6ZpZx63H+4iiGRWyaVVEUaBSrOMv7/EH//xDovYcfbYyR/EKye1DRkfs9LpdNM3AJjSIcMQnH5g/Ji67wfOvjlHJFZk9P0vA0aJa7fDk/i6Vjp3DZIfRfo0+a5H0boKTz57knT+7wdXjXSRRJ1t3EHZUcKsNnnt5lNBwlBMnfATdGo2WwZVXjtE/6MGh9n4aDRguPWTsxCQOpY3bLdMoVajmy7QaJsVm+eY64ZDMb3zJyeFugTPHVbLJEnudIfrlFIoiUS83MAyDpWtLP5UCDcasVKoadrVHs95mQtzAZTdt1O2eyM+czfLy3BEVS4D+0SDlfJ3/4fIjSoUmiirz1LAZx7A5rIQ8PfqNQ2Sxx8D0CGXR7DD57G18fhser5VkqkkmVePU7tcpZSscru/zRvlFJibcBGIRbv/wPom0hmJRsFgkJMkknp19eZFarccrF3qs315ldrDNmedPkNw6YH7cQNa7LN/eI76Z4sTTJ3jrjz/EYpVp1pu8+lKAicYDMqkafSETO6qoyk/JW1F/h+BgP+32T8gmx/qJiAlW9lWiQpyOYcFqk9BlFdWq4PXbGLHGGVATHO5XuHC5nwc3d1m+8YiOZCW5Z1KnT441eZINMhg26PUgNmBH1w0m3YeEfALeSIiZUbPD82Q5S+Ywh8UiMzgW4NFSgc2lXRrVNl7y/NyxJ7zy2jDhQT/lqkEq2aCcLbC/nuBkLM/gVIyx7E22Hu9Tr3WwWAQUGVo9BV/QzuZmhVq1TSxsRklWl/MIApy5NMjcQpAbb33Cr41/yO0Hps/gaCeFLMHS4yq9rsa/eifAcc82PVFlaMgcYG+6wlQ6dlRVJlOSuffEnJ1RbRZ+7aUitWKNtZvLPMl4GZuNcOrZ49SqbdMg67QwGchjk9u8tnDEhDtBqdRmyFPiRKzApQs+Tp0J4VYbPF5tkk8W6I9YeLjvoVruEvPWyVclVj56jNVppVpu8as/5yC+X6HR6JGtO9hf3sJit2CROsSf7BOOuohFFSIB8zDjcggc9mJcD34Jv0vH47Vgd1loNbusrdf49JdOc9Vxh/RRmdRBgb7RAVx2czbg4lSVX/lHT9M/4GLAXcPb50czJGLDbqL+Dq+db6FaVY6Pa3Q7XQYHHfR0kWJZw6FqfOaXL1MtVPl7v3UKVZWoKAEma/dYzYY42skw1tfgjW8l8boEgj64cso87px9eRFZ1Nnc7aBKPSanvOwt71DM1viFLw4ysRCj1eyQTta4OpbgWOFdCpkqiiJwbsHsTqWbXiZ9GTRdRJREblxPYLfL1MsNXJYO1m6ddLzAWKRDyF6h02zze79zkmarS6EicOmMnWSixvrtVZ47C/2uGqP9OpEBN+eicRYWBzg9mGf+ynF0w+D8xT5aPRVZNj/D5KlJLBaRnb0mwbCDlmDnyiU/6YM0k8MCp+cVdEHiaH2Peq2D1aZy6ViHZlugv9/G4+tL5Ao6m2tZ9lb20DWDSNBE/EYtKX7xSzEUVaSUr/PS0y58boG+PgvD3irvp+Zx2TSWbzwiF08yPuVHViRuP2wRHe/HbgWHXUCix2hMQhDAaTedF1a7SiBoFv+euuhi5aPHbD3cYDG8x/Zais2VFJsPNtldjVPtqFh7dYbe+T1CMT/3DvtIpjv/yc+I/7Hr7y4Ef4uWxaowfW6OsM3kVE/OBHnmvIVsPIOmGUzM9mG3q5QyJYqpAiUxSDDsIJcoESss8bNAYhUFAAAgAElEQVS//hQeS4t6rYOuGcQGXfircUaCTXYebvBwqcRQd5Nq3SDQ52R42IbdZaPRNGi3dYKD/aiqxNp6E1ESsVhVnG4rQ8UHjA2KBAf8tLsSi88uEItIqDaVwakYox5TLONwKIg/uWh73RLJjMb8leOMTfoZG3OST2bRDXOgML6dJX1UZm/lgMkxCzfutllfL2N1Wim3LHQ7PdyWJpsZN4WyQcADnaaZ595b2WNzr4ffIyAIIk6rxlZtkKFRL06nxEx/nYvPjvPGe6aRtd9RJDzoZ3B2FFEUcHkd3MuOYrGrDPXpyLKIpCiMB8p0egI2h5Vcukr/oAe3W0HXdDa3G3zv8DiqbPBeaZFirs7xy3PIMkyNSqze2WZ9Jcfe8h6qpJka9MMinqAHl1PkMNUj7OnwIO4j7ZniG9avkDkq8m1+BoDYeJiUFOO4fpf1pTihWABRFvnkkzyKquJ1aOSLOoF+L4gyubqV+H4Zp9tGNlnFZTeZ/IFYhGq5RbWlsDAl0T/az8aTIvsHTWQZsskK/aMRGo0uDo+TkKdHtdxiec+CJAms3N3D4xKYnB9gwN/FYlMplA2sqk7f6AD/8LltvvFvr/Pxu1u02ga7SZFozEW3oxMb8WNpV8kXzM6TKMDqUorpxXFsDpWFp06wcGaY+SvHEQWDkhBgJ6Xy1f9rnVK2SDhsIxi0cWnRpLa4A148QR/HL88wOSLR7IhsrxzxfGgJUTSIeWr4PQLHrOtEx/opFNr4XAa6IHFxPI/b52DFdp5/8s8u8CAd46Dq57MvuXD5vezuNRieG+bW9+6wUY6hCTLFVI6la0tMBousb9ZYfGYBj9dKumLF67WwcH6COXGFD964yW5SYrMUYebcDA/ff8j5V8+wnrChiBrNjsj1JwFm9t5maaloug0G66ztws5WmVSqjqEbCAJUGyKJgkrSN0enZZJBvvXvrnPmpUWGZoaYn/dy47GFiZPjrK5VWU046Xa6bO6bDo5xZYem4MBikRBEkXOXBjh2eYGxqaB5YGwZtFoapYaC1aZSVfzMh7NsrmaxKjr7lSB3jmJ8eDBKKGRHUWVK1ggerw23z0amE6RvwMvG3TVqLYk9fRQ/OXKHadx6gTd/UKXeVmg0unS7Gp95SsTjs3E08wLBqI/h+XF+zv4dmi2d/GGKf/u7wzy4dUAg7ELTzHf2Z1+xsn7fvHgELWZu3Gepc+t7d7jy+nk24hKS3sXutpM/TNFqdnnqcxd4/NEqT70wzo8/KHNPvsThxhFv/dkd811I5rG7bGw+inN/XWJucZheVyOfLJiHkG6N52aztGQH4dYeTx3v0LF5iW8k2HmSYrs5RBUPVpvCrY+SnL06Rt/oAAXdNHOHhqPU2wqJLIz5y1gtsL1dYevhFo/zQ2TyZlejXBe5VH6LwTE/oWiAwUErU+NWOq0ukydGGZ/ykzP6OOoNcHyggNWusLtbw+FUcQW8NCt10xTvtZHoO4U35GN/PUGlquO0ajQ6Ev1hlclJNwMxJ/eXuwzKcZ7cWuH2u8vE43W2NsssvnCa37l7gUq5RSJlxjX2D7sMxBz4gk7+xxe2WKuNovaarK8VEUSRQ2UMAYPMYY4vWr/JK6fLhNwd+kf6+GBzgImFGN6IyZzfXU/j8VgYHXObe/Rmmo6mImBw8yCGYrR5/NET3rnv5Y0PHWSLBoVij54uc/WswsjsALt7ddLZLvGdLO8v2c39LhbBG3CgqBK/+38c4XCpJA+KbBzKeCMhZEVGQsPl97J0cxurahJ7XB4L61tNTia/jdfSJJETmBxVadY7hCJOwmEbhgG2appIzMtrnxmk3WhRqIDfb+Wd21baHdjfLmCTW3iCLsKdA/a2imwlFL7xvoRqVdhJKTz76hROu0CrK3B+TsdhMTud0bEwP3y/jKFDsHFAyjONx9ajLxagp4smCls3O3m//y+XWXpUwu224FMr5LMNNnM+vB6R40/NI0ki33+vzK3v3cEXdDI86sZA4LbrVRxuGyuPcnzzOwXuLrXNToXQIluzMDTq4+lno+RzTTIHCRTJfC8DES/5msJmPkBgIMhfvGcKFMsVDb+jy5nTHl7/lau41Rb/9Hf2qbfNwlbbsHB+rossaFjtCuVik3xRw6PUKBdblLoumvU244MS96+tUq912K+FGQo0aJSqxNMC23FwNzPY3E7sdpV8qkRXl9jebaEqEJ0a5sJMk+igj4HJGIV8i2RW55TfLNis7ECp2MbltdHTBV4cWKbdMejqElejG5QbZnRncHaU7Q0zUVEuNmk1OjTbUG8YhNNLpPPwnT/5iHYHnFadcq7K0q1dAi6Nm/fMWYWR+XG+8ltx3H4n8bVdnv7MaWqFCp2ehC5IFF79b5iY9ABw5/0n/4lOhn9NywD9r/j3N2n9/+5CoGlmjk3AIBpzkUrVEQRYuDyDz6fi95mc7IHxCCPHhkjU/azc3WfxQoykfx5Nhzc+kHE4TeJGudShZg8hiuaTfXx9iaQ6SqttIqX+/F9dw+m2ksk0OTtjsuxbzS7PnJPptNrmsFGrR9Ud4zBlEI252U0rHO3m+OBalom5fuIbh7y7Fia+tkupYPJ77/34Ptd+uMvBXgWn20KzpbH8uMDpZ+Z58XiBxL45KCorEtV8kWRGY/G4jWjMyamz/YSdVdZvr1Js2rk8uIPfI3D7QR2720k+28DhcREOKdx9UGV4OspmXGDImeFHf/Ex928nee+BhVvXdnj2gg1JkTEMgWyihCCaJlm3z8ZMMEs05mQ3KTI96cTmtPMk7UUQIBhxMTrhx+WUmRvV+bUveZibtvP0yD71lsjl0BpDo15GR50cHrVZ3jAP15VCjWMXpvn2dYleVyO9l0AQBVLpDj6PxCerIgP+Lq5ugdFAndHpMJkCFEo6pXwdl1glYZ8kPBhEEARGJsMcm/fj7fNgUzUymTaFdJk3pC9xwr/L9tI28c0jOp2eeUHyq8TGw/gDDvqcDTYPzM8yMubhwmkrboeAy2snfZClUW0T6veytify5NYKrbZZGcsfpkhmNG7/+BF/8WaeyIAXt1NAFGF+cYiHtRnzhyLgIhoysFoEKpUOQ0NWqtU2umTK1wZz9ylUIDLoJZ+qsHp7nf21OOlkjeRehuUNk7IT8Wt8+guzTCwMk8m0qNd7rGzrlIotGpUaoiwiyyKyZHD7YYtg1M/vvjdJsaHS1hTefy9FUQpRrzQJBCwUqwKy3qXQclLIlJnt3OcHH2ucCCfYT8tsJ03BWn+/Db2n8/KXLzPoLuCuJfnyf3uJS6+dI172YLXK7G9lsFgk6i2Rux9usfMkxZoxxz/+3y6h6zDrjSNKIsPzEzy8vkKhpPEk5cZr7+FxCbzl/GWGR93MLo7x9kciXrfE6JibXKKEphlspywYBigyRLMPaTXa3HxiZ/rcHHd/eI+jnQSFksb0qBk/mZpyMRZuMTodZmJIMDn13XE0Q8LnNfnsD+9niA27+eDN21gUnWZbp5BroOv81O9Q6jgYGg8w795h80jGZdM4P5jgYK9Eu9VFMdrohkH6sIRLadBqdJlYnCFbhEE5Tp4QvnAQTVRYOO6j3RMJh208vrnB7/z+Nn19VkLFTWRFJLl9yP++9ynWV3MsvnCaNx8MsHBmkPhmhnq9Q6HQ5Np9kauvzGG3y6zkIoiyRFtTuPCps9x46xPUn3Ti+4d8nHlpEZtDxe9TCI9EeXA3zeaDTULWCtHxfmbPTZNLFJg4OYLTbSV/mOIrp1ZJHZbZfrSPzWkjHS/wSes0xY6T9dIgBgL3tmwYCFRyBcZmIvyT/+kTal2biUYeD9Dt6thddg4rXn71v3sah8eBIun4PQLFlp1y1WByysPosVFinioet4jL52TA32Ep+DKqYg6r7uzWuX2nyNhUgGa9w/ZGAd0QGWOTD1ZN4d/J4yaKMjoaxh3yspezUchUCdb2qBQqvP5zs2SzTfJVCUk0fipJUmQBv0+hIvj40t97ir6hCENDDtweC9EBB8ODFhSLRCHfJBdPcuWEhs8jUq+1EQ2Nf/a/3KQj24hEXVz+9FlmH30Np9Kimi+x03eZ1ZQfgPjmETMDTY52c0RG+liIZOh1e6zci3P34zjP/exFfCE3pbbNNMKHa3x3YxJ/NMizJxpMjlnwewSsVgm3XCVVlMkcFnn+kpUvX0zQN+BDUQQsFoFAxIesSNQqLRavjtGod+kb8HB+smpiLhWJUteFalM5cXGcREan1papVTtUym22h17ErxTIF3q4bKYdt17roKoiu7s1NNWOJAmIAthdDpx2AYddZGRQJZnuMDUXQhG6tBodlE4DRZWpNwx+4+UC/qCdpybT7B/UqdZ0eppAvS1hk7rs7ZRYvvGYY/Neuj2NI9skFqNJyF6j1eig62asSZbMZ/czv3SKbqfHUbzMVimMx2fDqpgI2IP1JNnDLF6/DYvDpH51uwa5tnkQzadKXLwUYmTCz9mTFiY8KW4ejRJxN6nXuyw9KhPpt3Px1UUe7NoxBIFWs8NOXEcSwRdyomkmBVAQQJV6XBg85MlyjuWEh9/7xz7+8F9cZ/XBIeWOk9trCvWelbs/vIfDZeHh7TjFjptozEW9o9I/6OX2gzrPvn4SSRIJ2GrcWrMydWaGQqFDOCiyLi3g9Lmx2kxnhUXuMRC1UKpoNGtNvn9bweGUGRr1kTzIk0o1KBk+ZL3DzAgoikR8I0WrI3KgDeN2iYQtWWqYF9Irr58nEPEgiAJPf/4Cx0/48YWcpDJdAl6BUnASt1PguS9cQJGh3RMYGAsRigXIlCQKmRpTZ2bZW97m3/zLKZK7aT7zlavUql0Ui4rf3iInRggdPaBY6lKumjCDv8nL4O86BH+rVqPaoNs2LwSPbh/Q6+r80R/tcOcH99jbrfCtr96i29HZXtrh8fUlArYaIzP9KIpZlo/H64yO2NnfSBMe9PPx27cpEkQWzKrA5c+cwyK2OT3Z5XA7+1PuriAI6IZAMOpn/fYqy7sS/aMRdh5uIIgCrsoh82M9Vu8fUij1qBZrjEyYE/WKReHZmSz9E0N0OhrvXTPjNK+8PsbUjJf1h/v0ujqlbIVP3rnLZs5PvVwnf5Rl6555o/7RG3fJl2F8UGLpXoqNrI/Xf+Uq4+4kG+VBnDadzz0r0W13CITsGIbBvXt5JiacJA9yTA4a7FX7cIf8jEwGGR2UefrFcTYOBNx+c4Oolaocru8RDilkkxWe5EL4PBKDYYOPbyTJH6Y4GU0T89TYWU3w62fXkESTz798YGcv3uGw0YfTqvGwMoXXI/PgbtocTDttkNw64MLTI9z78X0++5RG5qjI/KU587k2urQ7MDkskSgqHAnDuNU6mmYwO2TGZDqtDketMKOZj9ld3Wft5jK5dJXPz22wde8JiqQzOWGjnCvxqu8mnnqSX/3N84zMxBiZDLI4J3Dv1hH1mhkVSVUd2G0Cn7xzF4tF5KM7DRTZ4Oyil1Iqy9lzIfoiDl46nmfhqRPMjhjIssjJZ08yGhNxeFw882yEQt50KzxY6fLeN26ydSihKmbMo1gVabYMBgbsJJId+vsdiL0OoZCV96RPEQ2a/3NwLEC32cLfH2D5xiMKiQxzkwr7lSC6IfDOm+Z7NjFmp1JuMTEk4XSp6JpGJVsil67R52rx/AWFtZvLnDjm4M23UngtNV57tQ+fliV3lMXnMbeMmuwlYi8iKzLvtq7y+ad1vvOwH4/TwGYVONo8QJYguZtiay2HT8/yo/YzbGzVSSfKTPnSOJwyL7wyjMUisrnTJjwUYmiij2lpnT//Zo5oyODt1RH21+JUChV84SCqInBlaJ96W6LZgudcn5BI1IlvZ3ntsk6hpGGxCFx5doj+iIXF4SI3Psywc9Cl5B/D5rTx0kIOX9CBxWHDE/QS368gCQZTZ2bp9Qw2jqzsbWRIFwSq+TIz4hpDxQdUqhpH63t8+tUQ+VyLK58+y5CvykBYZnDYTTIH96+t0kMmZC3xwRs3sXcqXJ4qEXS0uH0YRe/pNKoNNFGm1zNRl9vFINVSA4tVMWVhrQJD7Q16XTPut7JSJuKqo+swNj/CP/j702QyLUruIR68+4B2vcmvL9zjytUwxVyNw8MaPq9pGZ2ectFqdFm+d/CT70mPx+s93EE/Q9I+vZ7OL/3m04z2a4iGOdy/8WD3p/ulosoMjvnxhYMsp0PIioTFKhOM+tl+tP/TeNG+NoIoi/SPRui2u0wtRDnu2sQidRnzpAG49v0NDi0TXPrUGVJHZX7nt08x3XtENGon2m+SYkqZEqJo8N03t6gVazzeUxkMtBhxpnjt2AG6ZnC0k+DDJx6++gfXqJXraLqATW5z78Y2m/fXkSSRV55zs3R7H0WVuXw5BMA2U1ydqXD73WXyRXO/3l3Zp1VrEnT36La7iFqXYiJD/KiD221Se1pdkW7X4MRYm2SqSb2hEeilqFR1RiaDuBwCpUKTq8fqtNoGz1924HRZsDod3NtQ+eofXMMXdJBRYvzP//QimiDTavVIHZVJnXgNzZAIDUbYLQUxDKi2ZEbnBrmxrKBazUz1W3eDeANunD4HL74yxP52gUK6zLjrEEnUsMltPj21idNt5wd3rOwedHBYdd77ziN6hozDapjZ+YzMdrWfUr7O4lSH93+wzcbdNSqFhokKDsgMDrkYGXbwzm0rsiKT3Ekw37xFrVjFYhEJB0Ucao+7P7yHP2Aj0tqlorlx2CXsqsbBZgpJMoltVpuMZf0e+WydqLdFNp6i3YEnayWi/g6DUZWzsxo9Q6bT6mA9esKxeS9Oh8g370W5f22VcsfJnR/c48JMk2wRpvwZcg07uUQBT1+A/YMmhUyNyftfo4Ybn1JE03RGnCnu/vAeOweayezP9dB7OmMTPgQBM9vuqqEbkD9MoVhUXrkITp+HXCKLxy0yYE3js9YIRLw0WwZDAzKpvEBHV1mMJii3LGwsHRDud1AqmRekkYiGYBjsLG3yM2dTZEsiD959QDRqRl6dDhFF1FjNR1EsMgsDJVxakd/+7dM89cIIAGdnegTVIudfPUO33WPhTIxxcROHQyLmzPHo5ib760nq9R7JgyKPE35CfpFcosAXr5b5wfcO6FNzFFM5Oh0dp8eOTeridQmcmoZiIsMXrxTNAt+NLWZPDNCsd3GIDQQMDASK+QaeoJtT0RRT2jKVqs7j/BAiOk6rTjCgUsrXmT0WYOnmNjfePyC+mWKg39zHAsvvkcr2UFURQQBBgFqlhdtrZXagzvSxEL2eRv/4ILfj/bh8TiqVLlabxMBkjN2cg3Avjr7ykOGYynOzWeZOxf5az4N//euvJiX7OzHZf+GlWi04PXaGM7eYmI8yPe3hV/7rcQCOHfNw6dXT9HoGkbEoQ8fGcUk17vzgHpIEqVaAaNTOM5NJBsZMQdHsxXkCRpqwJUtkbJBw2IqvnaariTRrTYJBKza7giyLDDjylPNVxk9Nc2m2ifSTymc+VULJJ1jekRmZibC1lsMwdErFNplUFW/Ix7WNEIVklnDUxWsv+theOSJ+1GXjSQl/2MfG40Neem2UkeOTDHgajM4NMDAZ47X/6gonnz3JyaeOmTnwfY2dhxtcu1HirT/+EEnoka0o5MsiubqJ6RuMqiiqwomTfkRRYHgqgsOiEXGUCcVCTI2q/OAHCWRZ4OJMA3/YhVOsEh7sY+7CMZot07A5GcjT68HmAcyfDDN+apq2ptLoqWT3E7y1dxyrVeDDFQeFUo9ORydsKzLqK+C2dlhbLXHxcoSNnS6ZisriC6eRZYGR45NErBnCg34EUaBRbaDIEk67gCobBFwadqnJxzthmvUOd9YkXr2qYnfZcCotOo4AM6fHmb04j24YeGpJps/NUW8rLK9UmTxhojLfbVym3oSpaQ+SKLC6K3L56RgOp4VuT2PQU0GRBWYvzrPyKMfm0gFee4/vfHOT2YvzPH5cQpZFvvYjOy6XhXxVQpYFSvk6VkVn5vQIPQ0GBj0sLdfweBSmzszSbOlYLQIT0358Lp2Rfh2f2xzM2t8tU3VGKJe7bB6KVBoiB1tZ9jYyTCzOEBvxc/7VM4SG+9mJ68x69inXRdwBF1fO2SmWNaqlJq2OOVw2e26ambOTFNIlCnULbksbd8jPZ10/5nOvR9jMB6g1RXJixMzc1w2S6Q49Q+baZoQTZ6I87bzDbt7B8fEe6Tz4XTq9dof+oEFgIMjCyT422+O8aL1uzik02oiCTi7T5M2vLyMIcHpeIb5xyN6TFCkpxuxCHwcpAYsqEBkOU0xkmJiPMjOiI9EjXZToaQYVWx+hkJ2+mA+/pUq51EGRzWrkzk6NasfKU0/34ffJtGU7ByvbRLoH2O0yTp+HmeNRRsc9SCIEwy729us4bPDU88McHLTwRQLUVS9J/zx+n8T8leN881sJ7v7wHsGgilNuUqwYtNsafo/A8NwwXV0h2D5i4akTdGQryZoLVexRaxjERrzYHDYqmilcmj43R8jRRJJFWo0OQa+A3KnTkU1jur1dYmd5j/W0m0ajR2o/y/V7GpGwlYwe4dJr53jmCxf5UeEs9YZBbMSPqpqzJ4pFJhIwGe3/4rdUjo2B1Sbx7GmNQL8fb26TVqPLw4dFHm8JCIZOLOYkMhxGUc1IQKjfjdctE4z6UBWDSqHG6Igdp9vC8OwgfYNBrHaFSWMNr9+OJ2Cn02qbMUBBotSyoQgdlE6df/j3h8k13WyvJrHaVEotGynbGC6HiK4bPP+0F8WiEHMV6RsIEBkO0esZPNhUqekOch0fmg6B/iBXpiu8/OXLjM3FcFvaDDWfYHVYOf3cSXLpKn/877e5/Nw4DpdKtqDzgwdubHKbG+tu5s5NUan2ECWBiy/OE4qFcFm6jE6HibvnefGLl1h7eITNJjHW1yRdEJgeNsjXVHKpKoIg0FDcvP/WQ1bu7tPrQavR4f0lO7/h+jon/Lv09Vk489w8Jyd6zF6cxx+wMvrj36fSlHG2i4wM2eh1NTbrw5RaNkIxP09J11iImgI7q03h1DRIkomSlWWB/kEPiirzvW/vEIy4cHjsvPFgBK9Yoth28I1HE2QOc3zmfI2TszLlusjQ9CCy0KNQgVPPn8LnMhAFGBj2sbRj4fJz4/iiffRF3SiyhChCMlnn6KhFX0hhcCxAs1LjvnqFsfkhREHg7W/v0upJnHr+FMemVCzNIrOH30OWBXzWBu1Gi3a7x3BUoFZpUz3+DM1am35bjolTk5waKjM140WRdDpdg/VDhWrXjtvvRE8n0X8S8fnZxUOe+vQJPGrNrBx3FPr8sJINkygqXH1xgkC/n1aziy/kJHfyFWxCg7+8O0zmIM1urZ//9Z9fYCQmo+sGa0sJej0NWRaoNCSsVolq1+wgHru8gMVu4S+/32bxyjj1QhlBENirR/n2J14K6TK1us577yZxOQR6hsR6IUy7JyKKIm6nxPyMjWqpQboko3TrXP3MWdq6isdpcOr5U8TjNRoNjYGQwbixweYBuDxW2ppCXfJgl1t0ujDNCo92VVSjRavRxem28PjuIYfCCE67QLblxelz8fO/MGF2gsIuxkJ1FNnEwn6y30ej2sTdznPi6jy1n0gEm5rC49Um91YNPOEA5Y7pgSmlsricEgNDLiKFFVJ6lKUnGr6AHZvDwu2DCCnrKA9u7uOwaIytfJOVbVh9nMMbcPBktcDE8SFiY0HqpSrtn8T89VCUmVGRgE+i1Tajm6m9DMVsnUrbQibTxO2zE4p62dxp0+tqlApNZFmk19U40Z+hqvopvvjLTPU32CsH0P6GVdT/X+vvxGR/u5ZqkbE5VI7CZ9A1wxx+Ec3q4PLjEs1m16weKBLRYT/5rokKXVkpI4s63a6BIpiG18ShmWe9XZpDRyK1EzezoehsHMq0G032dyvcfW8ZwzCQBI3Exj7J3SS5uhWbQzVvibpBPTZHvaHRbvcIRz20ag3219Pkk0UsVoXTo3V84SCpowrbhwKBfh8Oh8TouIdyrkKtWGVzq8nAsA9BMFi9vUmo383bX7vB9iNzGNRqMYlKxy4vcPmij5e/fJmOYeHT/hsM93XwWLtMnZlFFM2B1929Bm/+6T32N1J0NJGwdkSz3mJjt8OZ8/08fJBHETWWP16jYTiolkymfK9nUE7nSdU9JFMtTkz0TDb1g3UexH2UmyqRsUHe/9E+VovpKHhtsUxfnwWPnsclVam2VaZmvGQLPeanZFodgaOdLGNRHW/AwUfxMR6+/5BH15cpJLIMD9u4/v4RtabIjbstOoZCyGtWPK1WkaOCyvaDdfZLXnZsCyx9uEwxXTbbdoLAwZMDZFHH6VLJpcpUrUHzAKvD/Ttp3G6ZUxNd9veblIsNNpcOCEkZkukuqioxtxDk5c/NmhjYU0Ok9jN4fDbufbyL329lfemAQtkgn2szMOwjV5FIHhTpdGFzNc3iCScuhzkg/ptjP6LbM8jlWhxmBL75Voa790pUazob9zfpihamxi18euGQTF5HlCQ6rQ77q3tsryYByOweoekGLWwcpnrERvx8950shgH1coNkxjR+Ll17RD5VJjYR5r2P65RbFtqNJlvO0zztvMOwt4rLriMJGoFYhMmYRuqoQqSxzbOTCd75+ick1VG8Dg2LpHFyvM1Hd5sEB/tptEUKyTxHiQbHhQfU7QG67R6SLLGaj5pW2+kYzabGxp7BpRePMXc6xt2DEOODAqoCmg7D4z68kRA33vqEWksi3upnJNzh5odHvLs7zuO7cXpdnUepMIoqsrxS5uHDAhMTTrq6xJt/uUG1pjOwfZ3QcBRbI0erqeH7v9l7ryDJzvNM8zk2vTdVWd5XdZmu7q72jQa60SABEIQIEiIpUuRwhqLcSmN2ZidiFJLGSLvamdBG7KyZ2dVqZrQjkhJFUiAJkARhGo0G0GigXVV3eW+yXGZWenvy5DlnLxLBm92bnQuFGMPvum4q8pz/fP/3vUZgaL4AACAASURBVO/7tPhJHpZJJqvEUxLJwwIdHS4m2o7YiVe5ek7G4bJTttzolsLSUuGnZtfhs6PcvRUHmp6FWtVAlqChG9hEjT9+Z4LZdx5i08t8/S92aZX2meiusjp/SKTNR39xGodDIRB2sXHkRpSa0CZRtFBzCRb0EY7iB6yJxzhxaZh0zsLrVZi63MfxYzaKJYOgnKFSqbMyu09PqMSFwQKGYfIHJ14jHi+T2k1RqYmsLCT5x/+6RDwl4XRI2OQG69PL1F1NmKBlWfR2iKTlVjbX83j8DmIxJweHVSyrKbdo6/BwLJQg1OpjaanA0r3mBlUUmxuyQ7WbWlWnkK0Sbg/z7mtLrFW6KWkygmWR9vVSqCr0u+LIisTBVpJ8VcYuVEke6dhtApcDj9AqNdYyIYJhJ8v3V7h0rILdLtDW2GbQmGd/r4ysSGxkvBTzOvlshUzVzr5zkO6BKKsPtzhxKsrJi73MziSplJvn+dXJCt21JZJJjftvPiAaUSjmNd5/7RG5VNNTsbmcwCWUuPXaHGce62Z54YhbcyoHh3VuT9cp10SKuRIH+yU+POxDEEVGTnZRLJt09wdJpar8ef0LLBe72FgvcBDPsXOk8tzH/Lz2F7dIfPxXEUWYN8Y4TOqIokC5LlPSZGbfeUja18NRpRnhWKvqLGyKrM2ssPTBPB87/hGUzW/n177aQa2i090X5OX/+13upfoYtG/SFYPhk928NedrShm9BiOjQQJaAp9bYPr6NK+/mWRSneXOm7O4HAK1mkkhleW9lz/EtCxee2WDjg43R4kix7srvPfyhwB0OA7ZXt7n9vVlPv1iD6VaM30ulYUf1J7lB/YvsblZYjvnpbW7hfjKId/4s0V8AQeOYoLugTDz6XYKmRJLCR+HBzV0Q8TvEajrIAkWqd00YjjK6mqBSs1iPtPB6kIKEZPZ6UP++9+/zXu383jsBqWyxY0fL1HIFIm1uTk6LGAJInnTR2dMRLXbSJcUvvd6FUEATbN47vlOAG69uYogwMriEYWqwtpGlZ2lXZweB/WaztyDXTyhANWqid9Wwe2WMXSD9dUsZ863kslbbOf82GQTv0Onoet85/96h4OUiarKJFIGZUeI+GaGpVSYzV2T+Mohf+dZk714nqVNix2pH0URyGervHFP5fpqF/d2ImxuFLhdmSLgFagIblpibmRZ5PFr3XQbq0gS7OedePwukhn45p/eR5JECpoNrWkrI+Q1+ewXh8jYWlm8t0aszY0gCriVGh6fQlurQudgO0E1T2K/gOJoBhWkj2rEAyfwykUmRyS8XoX59+d45aVVHuy1YnfbcSl1Hgx9ha42icFjIfY2mt+55fsb+AM2uka6ME2L3YMGNV8rNtmiUrMIeC0kEaIdYVS7glvVsdtlEvEMxVyVzz5eRBAFNK3B8lyCYIuHVM3LkR4kTYRvv1onV5bYWEr+TbaL/0X18w3Bz1BpNZ3NhTgB7ZCNxQPWNqrsHcm4fHY6u72MjrjZ3cqSS+ZxuRRCSo6WmJtnr3o4LLpoa5GomA4uXwpQLWlMXh5jMrRFW2qGgakRLl0I4tlbYLizQbVQ4tion8FTA7g9CivZGAAOt5O7s82VayFdINjiw5VYoz2mkD4skNjPo5WrnLzQzZWnB8ilCyzsuwm0+OgbCDDaYyAIAnu7Zep6k8rYf7wXt0fBMkEVG8iKzN03Zph8YpK2/hjhsB2PsznJ/OLzdmJ+jUy6RmtpDXshwV++lGEvayMQcfFgOofT46C3x8kvfmWKaqGC366xK/QQjProbFOZ6NU4dSrEeropF5IEg1MXutiYWaGjtWm+9NlqxDfSvH3XRFZkIt1tDMUq1OoChmHwzHPdSGJzGvQnLwlUqyY5McxetYVX3yrRaFjcfmORVE5ElpoTz9k1mLkxw2Md68g2lQvPnkKSJUzT4pln27natsCFKQft+iZdvjwnToaY6G02BT3HBxEFi7GNlxi/OMrQ8XYqxSpZdweRzhbCzgqxVpXMwRHOeoGEpx+HrXk5dNoFXGqdbKZCpVjh2c+MYgkiz56tMDjkw+0UiMebwCxZFnB6nNx7e4Ff+kIfUyMGl54apKvVwu1R8PtVvE6T3eVthto0dubX6Qw2Dcmdx3p56LzMYHvzdG8NwdUnY5w740eRBY5fGsUSBKYfFhGwONZjcuZCG8V0noETAzz1bC9rs7uEO2PodZO87masX2R1bh9fwInXI3Lhai9njzUwDQvLNDncasYZvviUgiBAMBahbsj8p8UzlHWVvaSAbsl09Lfwb/7lLVrbvajVHNeXY0w9eRxVrJMpyjxYb0a8ut0qhmEgChDtiqKqEo70DvZ6EbtTwRtw8Uztu1SrOp1dHnw+hcFugbdeuovfr/CLwbe48V4BWRIY76qwvZ7F6XVy8bmzxHxVRpnlIKsydb6dzwXfRFYkthfjXA0/xOdTePyCh6eu+JElgZouceWZJlwn3zGBalcxRZnlhzus3V8ik8wTX0/R3dKgtd3HznaJtXSQ8ydUerxJ3F47LqFErLLG4KAXT9DD5SsdhCJuahUNRdC5cK6pM3bZTdIHGfYrYf7F8Vc5cfUEnvgsv/Mbbjbr3SSLdhq6QSTiYMZ2kbtvzXHv+kMC7gZHe2l6jg8iiVAPxOhwNvPIJ/JvszS9w1CnwWAX7O+WePN6CsO0eJTpIbmXxTItbs46aVgiWytJ7nie5fkrMhefOkYqa/HE1Ta8IS+maRHwCuxk3UR72xEsi2q5xseu+un0l/FaWexOhYfvzmFZsDy9jderkj7SWF8+4lGila3FPRq6STGdxd8aQavpXHr+LH2p27g9NrSaTv6owOSFfqKOPDFPheub/cTid/jmN7fRsBNtDzAw0YHeEDARGe6TqWkW11OTFFIZ2r0lMkcVJEmiR94kGrBYNke4rZ1BlAQqhSqLaw3cHqUJmdqTqJsKawt7jJzq5anRBIM9Mg3dINbmxqYKlOsqG+oYk6M2xi5NcJjUaWt3odhUuoZjZCo2JEVGMnXGzg3idYsMj4a5crzK5DGFz1+p4HGaDI63c/Gcn084rvP0iyeQFZFYROSt794mkyzSGda5uyjyxEUPdqeKTYHvfj/B01+8RFIL47U3OC5MMzYo4Qu5+Jj+AwaDKWSbSkKLoBsihbJIfPWQjhaBnvF+fC0hYmacjZU0druMKhmIskgmU+V3/+AiY+FDtupdOFQTRZHobQefR6TLn2d2Jol37m1Ot+/hCQX48os+CmqY9sEOFBkaDRND1wm0RSnma3zqxT4qFYNwi4flg2YSUt+JITrW38bQG/zWfzPIrQ/yDEcy2OwKsizwbOA2T7veYWTYw0AwTTFXZvxsD7/268OYpsVO+DQPb69y1r/A/so2DQOGBh20uXK47QaryzkqDZX07iF6rJeRES8Xhopc4zWCUQ9duYe0dQb4vT+8wNXLXnz2GuM9dSYv9FOvaUzf3qZWqhH+8Pu4xApjrWlGTvUQcuv89qfL2BSTZFJjbkmjrSvA+JkeFNmiWqpR1kQ+f6VCMZ2loRt0dPtp6QxSTGd5/XsP8ckFetssfGEPQyNBqjWLtgh0+3MEHE0/39TlIX7xVx/H6xY5cy7KWD9NnsLaHmPRJL0dIoZh8J0bNo6NhRjvB9MS+Wr3TU6cDPH0mRpfaHmLj3Uv09PjwWOrM9hSZvqgjfnpPY6SZdKZBnXZgSxB2F2nZyBASwgGTvQzMuQmV5YYbStSypWJuqvcfDdDw5JxBzz4fRL+oJNqw4bDJjIcK3O0n+WtlRgOl0q0qxWASMRBV/o+t+OdlKoiQb9E93g/f/CPvLzgeYOuvhCGJTCZu046ZzHS0xwc5jIV2vpjqIqIy2f/yGcjYbv9Eyp1kbODFX74owPqDcgkcnh9dny2Mk6njD/iZX16mfnDEN6gm+2Fbbbn1ujt9eCQdSTBYjj+E6KtLjQdQq3ev5E+8b+0fu4h+BkrXavT0hnFXj7CH/WyPn+ATYWFD5Y4PCyzvVvn1JkWLMtic/WIouEm4Jdwqg0ELNa366RrHvYSFttza2RTJTxampo7QkM3qGqAXkcQLK68eIFyxUSr1CmXGnz4qEH/yWHqVY1/dG6GsUsT5A5ThCIujjpPUdOsJuzoeJTOY71omkGlamKZFp3hOtWyxq03FpleEYm1exgZ9hCLygRaQ8iKSF+XRLmkEZLTnHp8GKfXzfL9NZbvLPDo3h7FikC5rPPyTYu/+mGJoQE3JWeEVMs4n/xElJnZErl0hfFxP4VMkaWlAttxjWI6y//5zTy6KVGv6UT9BsZH8W9XQzPo1Ro100ap1MDXEmJtq06wLUqxbifa7uepcwJf+XInik3h1pwNh2rS3hflmdgMsgQ2VeAXn3VxfkwnrO/Tak/zDz5dZHuzQKAlSFvIoNVfJ5PI8PGTRbrHB7A3yoycGWFldhenz0PIB6msxW6jg7sPNbalQf70exZDbRr/9n9ZZrilgM2uEHFV+br8NXLpMu98/wMUm8Lra/3szK+zlfWwvlGmZ6yHB/Xj+OtJtDr4Q07sNkgUnVy7EiS5ucfmVoV4LcZRxcXiQpZ80eTKWRmH3PQB7C1vce1Tk/zwxwlKmkyjYbG63bws5PM65ZpIx3A3f/DP7zDx+CTZiophQCFTYGbLyXJcQRAENndNLAvagzWCfhGbTaZgeHnigouZZAdlTWJ7p4xiU1m8PceDBxnSu4c43HZqVYOVhA+vXedXvtIOQKMB+bzOd1+tMNRvY+j0McLtUY6dO8bcjp1sWcbpcTBW/ZCtzQIhe5GOFgu3WKLRMPmjf326CbbztPDk8CGyLGJYEqJo8fR4gsGWMj6fjNvnIuLV2V2Oc+uVO6y0XSPt6OAwnmPuvUeUwz1UyzqJwwrpdB29IWDoOj/4z7f5j9tX+bUXGoR8JrohUavWUVSFdKqEW6kx0ziBy25SqRp8I/EU46faGTrZy6bVT9Av8s7tIn/xjXXefnObd+5q1HULm01kUxjA7rSh2f1MPdaUCU5d6GJ/ZRuvTeNwL8/EuIfJyC6v3yxSNezsb6VIN0Js2MYpV0yyiQzf/Hc3mbm1RDlfomw4cdtN/AGVQkUku5+kzXnEQctJthb3OOo7z3IqRL5qo81bwRt04/dJxOxHvPh3pjh27hj1hkj3cBuqKlOuCUhamYV0GwBvSs/x+NNDWJbAWhwGh3y88FyIWIvKcPCQaqlK/ijH86dS9AlrONx2drMObtwXyefqnBuqUCxb/MaXAwx36IS9BlVNQFEVbAu3cXocfP/lQw6KTu5lh7n3+n2uvnCazfU8n//yKH6fzN52hsmpFoYiWRxuBwODPoZOH8PutONy25i5tcwb8vN4PApdfc2EoORhCYdYZTvj4mLPAfOxZ/jiL/dQMRzU6wYfvnqPxVWNeKUVt91guENndbPpmXj1jp3HL/npGO7EWc2gyhYTjXtcq3yPSMRB93ALkbCM0yEiCAI+j0BESPC1v9tNqaDxjRt+MgUYPBbhg+uLzXNVbEaFLq7p2B0Km8vNC5dWqbLwwRKtnjLlfBlv6YC12Tha3aJYbJCp2tnah72ij6omUipoaDpcbzxJvd48mw9SJs/+8mNo1Toum86jO3EO003Pmd6AJ6628fYPHjCZeYPpVZm67GRrH4q5Kq/waWqGjb6JfrqlLXIVGVGEzsFWskWBQNiN2+/Bl1qlsy9IrdZAEQ3MhomuGdz4sMFOKcxx/S4/+EmO+XubrO1YrG1UyFRdKKoMdgdbhSih9jBlTaElt8T69DKpjEk4pNA9PkBbbxO0t7GjI4rg86tEfAZ9J4YQJZGjnjMoNpUf3ahw7XEvS8kgTrdKo2HxYf0M88pptuM1LJrG0cO9AnPrICsiHivHb/zmCAvVISafmORER5qQxyBd8zAR2vnpt7lrrB/xwS0sC1aTXg6DY2SSRarOMP6Aynsz0DAE7q65kQSThQc7tHa38Llf6kGraRSmnqbQ8GAXNbaWDulz7/Hj2TZavVVOTtiZHLURDKq4XDKiAOcudzEczfOffih/9CxoBAPNJKCe44NIksRRPcD1dwos31mgNSwy/zDJB/fLdNVXeLDl5e0HArdenSGba9Ad1Xnp6/d5uGwSF3qxuezc3WmhVBXI7idR7RLZnE6mJOGWy1QcQZIpnaCa55XikzzK9+NyioxJ87x+V2EkmmHqQidur41gQCa0fZ980cKp1DlKVpElC6dL4ZXvzFGrC/zLP1rD4bLz1rSNqakQVcNOPplh/0AjFLIBzVSbuiGj2GReGJjH5VbZX90hvp5ClgXkxA4dQY2JtiOWVkr4Ak4MS2TOcZ6t1RQVXaHia8PvFciVJYItfhS1Sfu2qc0t1EvfmGb2URbj9OO0eGq8flfh0uU2UmmDUCzwUw9eNCwTa2+ahKfa9tlfPyAYC3Pu2dPs71fJVB1UGipLnc9y6pjAQEuVocG/3aZirJ9fCH6myuawk/1oRWx3KPSPxVjfriMpCrWKzsKDnZ9GOuaPCqSrbra2K5Q0mRZPBY9bpqwpvPKf320660+1sGiNMW2caup3FaBaptOd5nCvwNJckpV7i6iqyBcfzyArEt6wj0XGibS6ufT8WUzTQjIbjHXWKGVLBP0i8cVNImGFw4MKnoCbO/MiyZ0EQyd6KJcbpFMVDpM6D2cL5JJZJie8zMxVEUSB/3Czk2JeY/LSECNnBjl57SQTU00QSzpZolbRuXYliFa38Gc3ibz3LQxT4Nc/kSe+sku1apLa3scfdNDVYWNgaoRf+GQrITWHL+TknTtVdtMq8zOH7NBH9/gAEiZut4zZMOntak57DFPg3uv3efNDi9feqWA2TJ49leXeXAObXUb99r/Hppj0ttbpd+9SqcssGqNkdB/TiY7m5HduDZfNoMd9SDmT59V7zfVnSfZTLWtE20MU0zkeLWpomkndlOntduCWy1y+FOAvv58n1tvK//7nRUr5ChVd4cvmn+HyNNNgapUa09MZBFHkZDRONOrA0A2OO5ZwlhLYbU1yryxBMiexuds0I1qWxencT2hxlQhFXHhcIusHMtWGwtf+yRMIosiH72wQ6/RzOrRGJqMx3g+pVBWfT8HnMkntJvncrz3G7DsPCTjruBwgCCKnektUqiaGYXJ0VKW/pcL9FRW9AQ6njIDF8paF19HANCEcdtAz1kW0t53uXh+R7jaO9lKMDDVJ1Kmiyhvv6+ysHjLarSPLIk9d8TEzW8LmVClk8j8liV6/cUT6IEPSN8AfPrvIXCKCXTFZzHWytbiDXy0Ti7lwFA55Zy3GzK1lNvIRsgV4c6GFxX0XXnezGUrkFeq1GmeeniJeCLBfjXD+sTYGpkZYE0YACIbsOJwStbrAyPkxTj15nC8NP+Ct+RBrOxZLuyoTp2JkExmW7ywgYDHs3MAwBSwTyhWTZKJJJgVw2iyGh714gx7OXOzkc1d1oiGRUqnBscpd1qeXcZQS5PMadreLlcU0U0+dwinXqZZq2BQoGm5OnfQD0NodISIlGcm+Sz5fx+Fy8qXffoJwe4Sz18bYzvkpVCQM08KmNA/3qmlHtppETqeWI5GGx+V3SZYc5I+KHKV17ie62NisUsiUSeVE/EEHpUIFl92i7gwwEjok0BblivIuicMaHzxsEPCJFIsNppdMFhfzrOdbGD7RjcPt5Otv+figeJyOnqbOPuBXmqTyhozDJvDBkoOSJuOy6XysfY5SroigqPhDLo6fakVvCIyG9rn0/FkO9krIisif/PFNXE4Bm13h3GCZhcMghUyeeLxMvd4gf5Rle3mfjoF2Lrqmm2yLj1LQnnwiyFy6g+5gGVEw6TLW8Tnq1E0Zp0th/LHjXDwp0erM4FAamBbUag0uPneWi5Mwt1znYPOAlLuXXFnCVkiy2HKNXLbO2uwuiixQKDaaYLc9nRv7o7zxvkbuqMjp4yrt4SbVtP94N91tAn5bhWpD4bETsD4X59S5Nh58EKd3og9oblX7x9opuaIEon5URSCVKHHzTp2JPoOyJtEwILmXxe82MUyYf5ggElYxDItCoc7e8hYnXv0d/BEvLodAtayxsl5joKXK8188w3Tgaa5NfBT56hOplmo4VZOlZPNZq8kuwh4dWbKoFDVcDlh6sE56L8Vt//N4PTKSKOJUatTrBsGwk196ssL6gUrRFeUfft7g1KUBkskqvT1OnIpOtNWN5fEz6tnANJpnl3q4yeMvnCcSbH7+40ub1Cp15t+f48pJvclBGbRwqQ1c3ibB2Z/ZIL17SG+fl9WPevjhQTd2m0DEVUbAorfbTqLc9MYsfTBPudxAVUScWo5vfTfBmGOZ1YebxMw4+YpE1JHjO/e7+PLzCmsJB76QG6uhk80ZBD0NvPUjBkYi5GxR7txcJ5epYlnQ1dL8PwZG26lV6ixvmXiDXnTJhmYoFHU3k+e7aNu/h6aZ3F118sG9EtffTiMK8ONvvEeuJFKuGAhYjI36kG0q4bYATx07ZObGDFuPVnnmxQmGjDl+9/kdJEVBliw+/XyE8VE3OUcrlwZSPHNWZ+LSKKoqspdRuPoLJzg7Bi6pQj6R5kxXgnzRItwZw++VsdlEimWLSH2Xf/HX7UiSwGahBYfN5LRrjoNEnaythc9dytBVX0EUIJsqMzebJdVzFllubrtESeDuQ41g0M7znx0n5DH40tdOcrid4DdP3G8mYok6rb3teL1K0wxuO6LRsAg7CgSjXo6EplfIMk0Gx2IUSw3iY5+k05WkUHdybNjN/AeLDKRv45arVEtVQo4KB0oPybRJw4D99QNGR33IisLd2/uc/vgUn/7lk0Ra3TRUF7tZBxPDMqYFNptINpVHtcu061tYFmytpYl0t1ExHIyfHeBgPU4oZKfRMCnVJOoNidcfBtlPy3jUGpr2t6uB/n/Xz0nFP1NlWRa1UhVTUgmGHMzcWibgV6hXa8iKxIlzPZgW5BJpcok0fnuVzg4n1bpIrmqnUNC54LzPP/tXl8jnamQydQbkdabEu2wuJ1jb1DCjHaQ1L26vnTPnWzn98Smy2RqzyVYSOylMw2RYXMI0LBL7BXKZKkv6MA6ljupQgea0pFqzePBWMyrx9DGLwcleFFmir8dGd68XVRVpbXXh8rmp1ix8fhWvz84/PXUbURJ47+UPyaZK7G0kKRR0NuM6zz0TYXzcT1+wwI1XV5CqRVKP/RINA16bb6NrpJNoSKT/5DB9XSqZnMHRXhq3zUAzbRSyVa6ed5DKmE0tdGMeva7TsCS8HgnDMChXmweg16YR7oxx7axAR4ebYq5AquLliSmBdLJE/Av/A4YpcPuRwHdnelnfk3ApGrO7Plw2A3/Azj/5/cfIlBVqpp2pp04x0C0higKh6i61ikZ8dY+uYz1EozZawhIBtcTOrkZB99ATqtDdH+DF5/y88KkYe8tbiKJFou8ily8FGDp9DG/Qwx9/7C69xwdIaiEAVu4tApAMjbB72ODkySDlKgzEaqiqiCcUwOezIZZyrB35uPvWHG+9tsnCQp5MWWFlXcMyTT7xqQHSqTILhV68XpVcWcLpVJqcBpuOoTcvnmOXJqjUZT68l6dWqjD68u9w9phOJlkiFnOyn3Pgcop8/X+7SanUbHyrVYNx5wp+ZwOHQyQQdJDc3CMUkNAqNVp6YmQLFh2+IpYFlbLOr3y1l4YhkM3WcCoGz1xW0esN2vvaWLm3SCxkEIq46R3rYDrVzXvVs6SyzfempouE2yI8OowwN32AJclcGdyjY6Cd4751NjbLnOqvYFNgY6tpEOuLlDEbBi63yrXyS3Q79sjmDNanVxirfsj2YpxMusbOZp71LY1qSUMUBZbNEeYfprgwWudsX4G3fzTL0MneJglcPOJWcoQ33kzh9Uh89sQGI8MeHrsY5NTuX3OYBlUR+MJngsTjZdbTXrZ3dRwOib9IPsWJqydYC5zj0a0l3AEv6w/XKBVqVBoqDredOw8K9GoLJI8M+kvTiALM5vtYCV4iFLLR0Bv88LsLhFq9pFMVBAF2Dw0sE9x2g4vPnUUWDLzVJJ1dHnLOGE8dO+SdxmXWdwWcXgf93TI22cTtUZBkEb1h0dNlQ6to2BQLwTR4e7Udh9vBD4tPcHFKpSWqYlchlazQ16XQ1e1hxBcnFLJhc9o4OW7nqnSDxH6Rz+/+ETa1mSi0dmDjIFFne7PAQVrEKddJEmsmg/lDrM/FSac1Ai4dh1VmdzPD2LifzYU4U0+dwudqNl+v3lEpV0FWZPwBO4belEQOHO+iUqySUttZnNmjkGkmZl1/O82x0AH7BSevzMTI2lp4+S0dj1xBEkUqpRqHWZnNfJTvvaHR5srxxGkZQQSbZJA5qlDO5GnNLTYJq+4Qo/uvYXdIqHaV9nADu11id3mLzjaFE21JquUm08OlGhwVJK6OFzhz0ktdF7g+62d5z8b95Sbb5epoBk/ARb2mM3BiAIC9zSMks8HBxj6tQYNTp0K8eKXB+oGMKFgUShYun5ORUJKRcAqP38lrL81is4n09Tj4vT+8wPanfpdrV4LE93WqpRod7TZevQXJVB2XUiVbc5JTmsTuUq7IVfMnDEfzlItV8g0fDUPArlq0dfsJeRqMnOrH4XVxtnodgO21JHMHYTYerfPeD+/y2oyfywNJNuvd3FyJIisCL1wVWVouELWlGei1UY70UcSHw2Vj80DkYOQpFEUkFtCpaRZ9k4OsTy9z5YWzpEp2AkGV7/84y417zRjtuft7ALiCTfN9o2GRzMDWTq05RZYL7OWdiCJ0uDNYFpx79jSfvKAhywLfil/k5JkYeTFEJVcgTi/v3ToiVl2np11kJ+uiO6yhVeqIE6c5fcwi5Khgr2Z5cGuDR0cdvPC5IX7rUyUk0aJYlTjI29ndSlOv1XnhTBrTMPBnNkiWHBR1G29+5w5vOj6Dxy3icgicPtmU/NhswkesHDhKVllL+xmIaUS77qvTIwAAIABJREFUY2wvxvm331bpOzGEJxSgplnkHS3cyo6j2FTWtg0eLBgA1C0bC4kwRU2ls9OFojR/N1kW2EooP40rzWpuAt4mcHJtNY8oCJwfyFNT3HzxeSdut0SLq8CxUAJfcoUz4yJOs8i3bwWRjDpvv7pEtVxjfCKAXS/haA76SSeKWCaEAhLT0xlmlkwGowU6h9pZFUfJ5Q1ur0fwh1wUCs10oXyjeVl7bzXMw5sPiTV2ePflewDM32vC+DoXf8zd/Q5kwURVYOz8MT50fRwTgWMnu2gT4vTmH5DJ1Nna0Qi0BHn0MIM34uc3v+xHVSSWV0uIAizLx+kMVknnm21m0CdwFD/EZpP5q80pqh8pIgRRoLu+zO5WmnBnKwcHZVRFIuarosoGXzo+z2FS5/UZL3X9b1cD/f9VP98Q/IyV3e1AoDnV6RhoJ5XSaGj1ZlOVKKPIAp6Qn8u/cBpZbHBwWCPsrlM3RDZXUmxJQ7z0Soa2djcH8TxpqYWiI0JrZ7AJL2rU0U2JndVDiiWTclFjdMRLm6+CXq9TzpfIqK08vL2KrEhkknlC9iL95jInznbSGmiQP8ohywKf/upjyIrM3KZE6iDH3J01Hs4WkCSBjdUMO1t5HG4HP/7OLD3tMqlECVOSmb21xMlrJxmdbMUf8hIIqHS2KeTLIkfZBh+ue3nyE0MgiAQKO4Q9Oj0xk64eP1UNcqk8hymDSEgid5hClU0EwSIUcdPpy+J0CNQqOo70Dvsr21QbNvYPNGK9MWRJQFZklg/dTJ7vZWFHRdNMuoY7SRYUyppEfGmb6/NhNF3AZhMJ+kVO9lexiXVG20oEHTXCIZkfv5njKAchK0HqIM/X/8MsNof60xSWY1N97Cxu0RqC5bUqFcNGS1QlKidYTbjo7VT4kz/dQNObE+iypmDXSyyu1pEUia25DSStzP7aHjtZF3PTh4ycH2OuNoKvkiDgk5idzeFygFOuE/ILFNNZXC6Jtf7nUGSLk5dHOf9ED59/1oYiWQz02vBGghwkG7jcNp7c+vesLzfTQ3w+hUf3D/hXv/cBo+dHiMdrHG6n6PMl+MWPq9RKZeae/ze8ftuifziMYVgsrumc7Cly7bMXcbkUDEuirVVlXe/j/UcWr730kJ2NNJ5QgNvvJ2jtbqFeq2OZFg6pRjoPNrvM91/NEXGV8XptuGw63/jOEUsfzLNyb5FIdxtrcYGJUQeZRBGfo8Hd2QaTvTWKVYmRcIpAxI3PaTB5ug1lbZaba+2sP1zjoNHK5bNOfvhuM15ue+2IUrZI3ZBx+r2szO6zHHsSA4lazcAXDbHjnWDs3ADbywdIkshgnw2bXSEadSAK8MJzIRxKnRtzHrRylYPtNOndQ1bKPRxvOSS+Ese04H99tZWGYWFZEO+7isclUCyZvPG+jt0hIwgw1CvjdYt8tuM2iipxVPHQ0hNDlEXGzh9ja36TkqYSbvHwmWsKliDicorM2c8z9/4Cp11zGJZELCIS640yeaGfRDzTjFSUTQa7m1K8m3fqTL+7AMCGOkZ8p4hiaixnWmj35nlirEBru59kBk6ENqnVDIJRD4IgIAjg9DjYPxJQK1nO9aWRFZkL7Zu8e7eGxyXw4FGJaIuLzXiDu+9ts5Tv5Edff49yvkStLiCaDQqZEh9M/rd8pn8WTWtg/6iBCEddlMomxbqdhUSQg409qNdo72uls8NOTZdYq3YjySKZbIPJC4NEW5x8+6+2GRkPc3XKZLyzTD6RprtDQdcaOLxubDaJUKuPaD2OL+whm8gQ7oxx7lwYn35EIiPwheNLaKaNzk4XDqGCpjWaML6ciYnAExc91E0Fh9JgdW4PwxL57CccAKx6zhAOSthyB9z0v0ij0YwfXtpq/kaq3c6tWylsosYvPWdHliW+8e0kWzsas3t+Hi1UiPrqPDme52NDO0RCErVSlW++2fzbjZkV7A6FfN3B0HiMmuJm6soYt+5rfPPf3eQndx0c69Tw2A32D5rQxtVMBM1QKReqtA+2kcvpHCZ1bJLBRi7EM86bHOuXaOsJ43YK7MezaFqDdn0TCwHDkkilqgyd6OEgNEGloeINugkLSVTZRBIs8rka6aLM2uw2AxOdKPEVVEXg7GNddIfKjF84RktPO4PdAvPJCGfjf8lEd5WbL8/w/RsmhUyFqungzr0ctkqGjuIC1bJGb8wk9uAHiCIc5hRmZ5Ks3V8i2tvO+lKK7798SKnU4LmnQzw+JRBoi+LyOEiHh/EGfTyaTuF2Nd/zaNSGx6Pytf9uB00X8btNNnKRn9J0v3ez6Un6e85vsbSYQRF0Tlw9QVG30RLzkHZ20h0s8d7tPA6l+ewKpsHKrsyDLS85byfZwyOeK3+ThaUqf/ZGEK+jKSur1QV8ITcHazt8/U0vx0+3kw4PMxnaQhZNPverF7lqvYbLIeC0QzJjcf92nPt3kwyNx5AlC4dTwW03WNm3kdw+wBP08tzH/Hh8DjwhL3XdxFM7Yj8lUCuVOT4ksLOZpVS2MJDoCFS4Mw+aZvLw3gF3H5SIhkTCfhO71KB7fICtIxfFsoXT7+VffGobn1fksOSmIAS4kPwO54dKeKUCwUaCcqib126WqYgehvoUyo4Qv/1bA7R0BpElgXVxmErNIugoY5oW0Wjz5T4+GSQSlnnlfRulfJWoesRjExpXBvfw+OxUKjqFUjNKtLdDZKSjOVS6kT3FJ754nkvPn8UX9jJ7a4n9Yx+nM1jFQkCrw95GkvOln7CYDLO7leEbj8ZYdF/g1LiKz6dgWRYdXV5EQeSDJQemZZHaz7O/W8Aha8zt2PF7moZi0wSzYZDP1fhc3wM8LgFZkahX6+zbmrLdzv4WFj9c4ShRZCHuJJ5xsFAd4sJEE/7o80p/Ax3if731X92FwOFUKedLVB1BOtqdnD0Xob3NRmygC7tdYmLch6qKjE71/pQInDmq4LdX8Tvq/LNfUSnrNkbGw8zPHOINOplLRNnXY6zPxZuJNl0XOVu9ztBEB5YF+XSRqmaxnXbROdTBxPkBDEti/Gw/lWINp8dB2DzEEBU21rJYlkDXcAdHR3VyOZ3eoQhnhjROnO3k6U+P8/TjTm78aJEzZyOMTwRQVJmnPzPB3FKVlramxq59sINCpsJuvMj4iWZ8n1ZvTm9Nw0JVBAwTRL3GmvsUj9ZlKpqIphmUKhaDxztpa5EwTZh4fBLTElg6ipA8LKAbCoYBLTE35UgffSeG8KklRgZseIPNRn30VAcnO4+o1RpM9VdYmj0k0uJmJJojV5aYujKB1y0gibC+kmErXufRloNExYcgWNycdSCKAuMTQew2gZLkx+5Q+c3fHsNml3EX9+keiCIKAuMXR7GrFna7zFjpNu++uUnOCjDUUmJ5XeOf/v0O9hIWLTEPEVeZA7mLK1PQ2+9n6snjmPdvE+1u5anAXXoGw/gCDrrdCSSjTmvIQrXL7OwbeJRmOk/XWD+qAgObP2mCbaSmjvn+mh1Ftojv65x6fLgJyep1szTxRTr7gvRHmpOTaLuP//l/mmT6+jRPXZCoFEo8SraRrag88enzOOUah7t5fF6JbKbOxUlYOvBw4bjF7N1tRMFEq1s82nJwbcrg7//DcXLJHH3jXTx5tQVBFBgcizVlGEYTRJNLl3n6WhBZbOBySXTZ9/naFwJMPXUKTyhAW08E/0eMge6BMI+VXuGrV/Z4sG5nZbPBbrEZ8XrSs8TGWo69c5/l+d5Zpp48TjebOBSDy2cU1rZ0LlxuI9oVpd8Vx+a04/G7sAl1HEYJl0tm+GQ30DQ0PvOpIbw+OzOzJWrVOqZpEVTzvHevTjzn4enJNFNPHqe1M8gTnz7PhdrrzCZb+fxXp1Bk+PVnc4R8TW1s5/pb+N0mbVGBj11skrb0hoBNsdjZ1XireA5RFHAqOkOjUXpHYnh8djpHuvHa61iWxXrSgaIVKVdMRvX7DJwc5GZmkqCS4d50Ea1S5+SYgiSJHO5k8NlrdPqL9HY7mDruoG+iF92SGahMc+60j6Lgx21rsJIKcGfDT1ubnbBfYC7XQzSiYrfLBL0WXeE6/aOttIUtKr42NrIBjIbBw6Merp1XCXoMLp91sraU4sSwwKkLXYz5tvjElx5j8sIgg9ESdbuXYIuPvZydby9PUC3X8TkNZFnk0qRFNCTitVWJenUmL4+jt/QgKyLD7XW6/Vki9gJOt53uDoXEfoHDwzLDk+3YVIG37gq8O2fnxNUTPJovMXqqA9VhI5+t0dXtwVE4pJApMX52gIGJDgwD/mplkuH2OgkzhlfIcW0iQ87wk89WiLS4cTlFQo4KA6EMlYbK+/MquqajSg2+8+MqZ56eoqe+xPpmjXKkjyu7/xFBgOe/cpmRHpNkqk5LTwxFldkstvDNlyvYnCq//zWLsSGVug6nJx3MbcikKm42iq1kciat3S3YbDL+oINnf/kxxsc8SIKFYVo49CLbq0na2hz8wt+9zMdPa7w/JyOJFl6vyuBImNOBZYaLH9I7GGZqKsxAj0p7TOFbP6rS48/wSDlD0KUjSgJHGZMXP9XCzDvzvJo6S7qkIGDR3+fC6VR4dNRByPYRXFJ00zCb2uxoi5PBaIljU31kkiVq/SeZeZCkVGqwlnAhKyLdQy0UKyLHokdkes9yb9VBz3gPX3jKwBt0EjH26ej0IOk1DrzDyIrEO3c1jPZeVFUkFqgzdCxMtLedkclOSrkS/+DLLhIHRbYOBHRDpF7TiLZ5icbv0T8aY3gsTE2ziIUtjo7quFwyf/U/urnctoJNtujw5jhKFAmH7XzlqRzlisG98PPoWgMZHcu0OC49wumUCVXiqGKDet1gP+/AMAz0mbu88fI8k91l3LU01148iyUpFPI1Tozb8dlqyJKF3oDePi99J4b4zJU6719fIXLwiCM9jCRYvHN9m7InhiJDpQZDnQZPfrybJ6+2kDgo0TAE0qkyDqVBV7RBoDWMJEvMr0MxX2Vooo2gv2kyt9sEzn/iDNW6yCefjTSHHoUQDlnnk2eL2O0iZy+2YbPJeJ3N5CGvUiQQcXO1bYGAV6BvvIc57RiGAR6bjirUaawucuOhk2huhZnqGFl7jCcuelCoc6wli7uc5P/4k03cHpVs3mCgsYDLIZAoeX4qeZ2bz5MvGPjcAucnZWJdAaL5Nb5/w+SDnaYkqbXVyWi3jk8uUKwIDDq3eOwXznE18KDpZyvV6egJUCuVceoFnHIdv1qkJWAQ6QhR8ndysnWPwdEIl0arjObfYT8lcHBQ+elAo2eklZEeE71ucOpcG6dPhxnZ+hH9sQaqbHHzepNLc+XFC3R1exDNBh6nRVePn8kL/fRm7yLbVCrlOlNXx3D57Ay11+gMVlk7sLGZUHlyymCoTfubbBf/f5fFz1OGfqaqVq0zcWEIaDYRSyslPK5m558+qvLOjT0sC97/0R0efrBJse4g0uKmrNtY3ncQ4AhRMFmYTeENujAMk6nWOH2skjtMEQ6IdKxep+jrJLlfoFCoYzZMRjqbFMdyvsLS9A5uClgmpOIJQlE3B0IH9mqWtelVDBNWH6yxs5FuRnjmarx6C7Y382xslHi0KnDmiUFe/9Ea1ZqFrEg0GhbPXoSFBztknM30nPXpZapljUxW551XZ3E54J3vf8DqYopzvSk8LoHCW2+RKHs5MdDgO99ao1zWKZUafPiT+8wulLGpTUqjYQocpps3ekXS2d2rks9reBZvYZoWAhav/GCLYq5Kvmiys5nljUdBvF6V1YSLf/z3XGyvHXF91ocgQLmk8WXzzzBM+OdfyJA9qhD2w6Qyi4DFl06tsL1dRVUESmWL7tXXAFBlg2yqhOYI8P6P7mB3SMzcmGFmsYFhWmR93Tz9fFMXPLfr5uSYwsNtF0NdFjPvr3JUcbKZDbKwo7KzVWjmiotNCJhk1CkUNJYebFI17YiGTlUTqdcahIMS8WKI+YcJdubX2d2rUYoNo0om1UqTovjw/iGmKXDv3TVmP9zE5xYoFA16iw/Z385xb91FS1hErxtUGk1p2Ft3TAZO9HPVe5cOX5Gb3/uAmqHi8TvIFwxaWu3MbzWb262EimVaGKaEyylwdTjB0p6DN96tMjjZRa2qM79cRbUr3Lu5iNctspLyc3IYvvKil7szGkcVD6IAdWy8O9ukZOtaHVmR0HW4N13k1o/u85L5Ge4e9nJ+qERfl0KrO8/m/C5zlSEmjgdoX73BN2fHiK+nyMpRchWZrUOJEyMijQasTy9zUG+hpSvCztIOnblHSGYDl1Mkvp7CJmiUixp//ef3KZc0JsfdRNubQKK9coiPXxSJeatsZEOUi3Xuv/ng/yHvvYMrO887zeeEe27OCBc5Aw000EDnyG6ySTaDaFKBypalsWRZHo/ttWyPZ8thx/bO2luO4x3ZY1tpLHMkWckiRVGkmLrZOaIb3WjkjAvg5nzPvSftH4erVU1NzdjWrnen/FWdKtxzUQe44Xzf+73v731+pBJlZkInGGvaRpEhlda5utHOnfs1dlJvG01tQlekzM1ZiUBAYWq6yvPfTTI6pHAqdIvt9RwBpcwrX7nAlZeuE2t2EmsLsl10c+Wl64y351DWZzn30hRb3gE8fienQzfQLIVgyEk2meeLn53BF/KS2cmQrriJF/ykswbxJORSRRRBY9Jx1M7oq6s0egrkijYxaX4uz1/+wVmOuG5Q1yzSyTKpHLbRWkUjkRXxZNYZjKQIRHy0Boq8dK6KV9HJlUT2H4qxFBeRJIHFcgeb60Xm78a5s+bDEBU2FrZoCdW4cyuJWqmztiOSTlX52otFltfqDGpTtHizrM5u4VibJZss8eI5jbCQodGIUy3X2EoY+AIuYjEvHrfEzEweSRL46Pg9aqrO8YMebl9eJr+TxumSef6Lb7HTNEq4MUBV1YivZpAkeM/wfZZ27CzmpZ0B3piOMpeKsHBjhoYGhbX1KnVTJiKkiTiLNm6zVCFXdXH4UJhKqc6ktY+7V5fQZRdvtn8cSRJ46StXWN6SiDUr1Co1BFFgt3eBib1RRAH+8KtuXn0tyeRkFo9i8sy4jYdVNYmGsEguXeCpY3VqNZ3JK2vUNZhccrE4vQ1AQ2sIj8tu/r+24OX0uMrilkIoKLGxXmSm0kcmZEMf5hZKCAIsLKmEox5aP/9LXJ5xY1lw7WX7M379YplYTxvHO5boDJdxCiqCYJuZ9UfSeIUSc5MrXN3swO3QcTstMmmVdMWNJAkUcyVc969w8HAzgiDQ1aji8ylkU2UWlmtYCLi/+R/pbTWplmq8eNWD16swZwxSqeiUwx00Vtdwe522qWbTEeLxMoWqTH+nQGJ50wYsKA7+9a9P09UbwiHD5Bw4FIVsusKN5nfaEtRsnaaoyPyqRW+3k0rF4FJhD99fGaRQFZlLRairGuWKwZ2dGNlUhYPJ53nnU42ULD8bizuYgoTLJVJ0N6KZEt29IZI50d4MtrXx/o+Mopu20+7U9XVSreM8fCrIq68lyVbd5EsCrVGd6akkumbwZ89V6B/rINu8iy5rAa+jyv6jHYTmLpIrWvYaVrSbtfMli2ijB9OEVDyD31HjW9/JkFrfpl6tIwiQ3kozNxXHsiy70hevU8yrSKLFG28VSCTr7PNOs5INUNWdNIYFMlmdfK6KR7FlemulJuIrSf5ubpiAx2BtdoPbiw46mi3ub9jVr9snf434RpGlwD6KqkTH+gVmlk1ks856Pojm9PFbn46wNJPA6RSZEfcQ3zFsXK/bgdcj0t8fIOAXKZRhKW7PrZlAF8cOeGgI6Jz91mVcToF8VUY1XczOVzAEmY2VDKrDRzRq98DUaga7juwmkFzAJ5fJ133cmDZYurNIcG2Si6vtxBpl2pVNLrnO0BUzGRrwMfnGJLWanXS4NQvBkJOZ6Qy1usVOz1F6A9tEPDXe8WMdCIIdn5QrBkqtSDILr3zlAuWyRjoywPDhYQ4ciJLcKqJWNObjLgqqwqMDyzgVuLno4ua84//1GPFHGv/EPgSCIPyOIAh3BEGYFAThFUEQWt8+LwiC8KeCICy8/fy+v8/1/tltCCRJIp0oUZfdLC8VAHj1+3G8QQ+Ld9fJp+zFb+jQCLv2duJ11AiFHFyZcXKid4s5tRe3rDGxr4mluyvceu0WAKuCrUNd3dDIde/HFEQWby8gSSL5VJbJedtCva2ngWgsTBk/F1+8isOpsDyzRVRMUXcG6Bzu5vJknb7xXp58ogVdM0lv59k75mLmqu06PNILxaLGyL5OanUTURK5dW2L+W03wxMdtK1fpqE1zMHH9hNrC5LaKdMz0oVDtvjoL5zi0x91spyN8MqLywROn2av+y6TCzK/9nMNxFdSbG2WsEyb4T85VcIyLdIlmX09ZTq6gliWwIFxN8l4nszow3j9LhLVEMPjbaQ203g9ArVKjb39GvMzKQQBvvQ9mVQ8yUOjRVY3bO28ldgiV7DYqLdx5FCIsFfjm+sHSJa93Cv1EworxCI2aUmN9ROMegi5qrT3RJC1KgfO7KdYrP/gs52/u8Wd4gCJlIFTrKPpFk7ZZPpeDlUTqVdrSILF0cAdSmUTl8dBR28E6/BpLMti1bULWRZp72+lpHlwJlaoqHYm2+0Et0Pn6adi7H/EvrfOVQ6zllKYv72CxyXwiQ8EMC344Ed20b2rlcVVjVe+coF0sId6XefuVIZSxcam1nW79On3O9heTeIu7pAsezn42H4mLv4+43uC6LpJPF6hVNKRJQj5LbxBL5opcX+2TNDKUFEtlqY3CIddePxOOtpd1FUNj99LMqPjcZqsJ2S++LU8VVVjK+fE5RIIaikOj5g0xbwM7OtH1wxMC2qqjuJ28ua5NI3+GvGCF0m02CoG6R5uI+hU+cqfn8MIRJnot/G2M5kYZVWgUrWIpyUCPvv+EQWbwjK4t4+3xId5MzPB1O0U8blVvjfTSSFT4qOf2ofDKZPOmmg1Ww6wV7rJ/XWFiuagxW83uI6dHLeNa2oecnU/5SoUCjXuTNsSFNO02Ow/RTpd4/nzErlsnfXVAo8clXjs0SY2dixyShOBiJfm+joHzuzn2DsOUamaBAIOgi6NR99/DK9UQWvr57F3j+MQNGZvLPJmfh8BPYMkCQiiwPt+fASPV2FgvIegq8ZyXKCqGvS0WvQNx8jWA/iVKr09XtaUQS4vNvBMzx0yZSe3z97mwz97iucWD7E4nyOXLLC3p0JboARAvmjyVeN9aJaMJImEHQU8HgcrSScbWzovf+sutbrF5LU423kFX8BJdjtNsWyyLvfSPdzG8a0vc+bRRsKNPjTNIp+t0NzqJ52sIGkqF5ea6RpqwcymSaxtc+yAB7dWZEYbItYeJJupU8xVCQUlLAtiLV7efSzPZ6+PoCgS22mB9IYtrWtocnPkyYM49Qr3LkwhCgK6pvP1L03iMGoMt5VRRI0jsUVWV0qMNNoGjsWizpEJhc2sm6TRxHSyieOjNaqFEqom8Pk/ect2m/XGeeTHhrmv7WLEu8j1s7N4Q34Uh8CltzbJ7qQYGQmyrnfQ06zZzYsumScfbyAUcdPgrZDXA4RcVZp8Njq0pbOB7121JUxqRWVhsUSsAXqGmrlYGKep2UPAa5FJVzEtuLXi5YOxN1FrFrVKnU5fgqTRxOWXb+H1KThkC103UVWdysd/nfnZLOmywvipcQC8XoVyvsx6pYV0xU3eCHLndgZfwMnQxit8Z6aPUGOY3bE0r99woOkCfb1eRNHiwgtX8fg9EG1ioLWOIML0mhPDtEjFM5zaL5CsBIi/9zdo9ldYm15iuF/G73eQrTiZv7dF0RlFk+0g1KlAR+4OdVXn5t06X//6GrFem88fbQnx9EeOsLFa4PZkmofGazS1N+ALOAkrBSYemuDAHoXP/dFZLr56n6pqMXV9ldZAkdO9S5QqAhGvRiDsJZ2skMpBraZzv/UM56+riJiolSqrQj/5vMb3lobIqW4ODesMttbYd6KfytAhylX40rdKlOpOAhEfd4v9lKoiTz3RgGEJOBXbt2Bjdp21e4tMHIihVnW2rHZSjlZ2ygGWlwpUuvdwbCBLtmCRL1pcvZpma7tOY4NC2Gfg8XtQDZlg1MPI0d0oboVPDV9k7HA/Hr/bxmfKGd5zvEC1XKOuC/T0BujudHIhO0p7sMT3ryu8eTZJrWaQ2EjT5CnSEDLxKyqdAzGaIxa5soQ36GN+LkciK/Ldb91ns9pEXnXS3unHJ5YYaMgw2/YIbqeIbNrrY1kJ8a8+PY1DkVlayBNWCjgcAiGPjiAKPPeZs9TqFl/583N0NBo4FYHN2RViC+cwTIGOQJbxU+Pk8jpexaDRiNPa6iatR0luJCjjp1I1sEwLRZFoaPLxDeu9LOSayaouutoVYj1t1Brtiu7sfIUiQdbTTvIVifnFMk09Nr2uXK5z+bUZpq6t0tLmJ5fXUfQqmXqQVneSV19L4JAtIs0hTMNCsAxuT6bpGu23k1KCk0pRRZLAMEwqRZWJrjyWJeA0qxxs32KkS0P6HyBi/SfuIfh9y7L2WJY1AXwH+M23zz8BDLx9fBL487/Pxf4HeHv/nx2iJFDOV5gqDuByy7S0eFAUGa/PyeHTg7T1xRjohGq5ZrtEmhLVqsHCXJZrGy2Uaw5KdSeFokGwIcSD7zmKgzr5movho6Ooqo63EKcgRuja3UM0qqCWKpwYVUnmZfLZKm6vwpvzLZx4+jCFZAZ/yMeK2oEvuUC02U+9btDQ5LVlEB0u3F4XC2sG4VgDTqeMV9FZmdnGNCx0zcLlcbB7Isb16xnSyQp3W54g1uKlUqqzvpRGkgQGd4W4O1unULJ44WqA1W2Rgye6IJ0g72igKSryP//2Em09DRw4EGbs5DiiINDa6kEQBbaTFpJooCgiQ6mz1DUB0zCIbEyS2EiznHSTSpRp6WnG4xLYe7idcl1mcLiBkNcgnSjSO9pNUXPS0erAtODi+KfZ2lY+QWrsAAAgAElEQVQp1hUyeYubsxKSBB5F50///X3CQYnx6Ar9nQLXhKNkU2V6zXkW722RDXTaZjQWnPnAcXw+mYlD7ZwQzzM/k8JtlbEsuL8i8v4nHFy8VqRtoB2PojOp7ubEWI1sskRyu8QF+TTDY00MJc4CIMsiA8IMCAJuJxw+FOHGZJH1jJurkzbhIxp18rD8Oh6nxZl3jmKYFmfv2K6Xi+sG/qCT9lYHex/ey2qllcGRJt7/lI8XvzFN16i9eRw7OY7XI5JcjWMoHjoCGTYWk/yh5zfxuuyApL/Xy/W3FhAE2E6BaRgEHGUePqJwLbuLzpjFez80zL1bm9RVO6COL25x4EQv5ZJOzF+ircHg8ceaef8ZicaAxvqGSmBnlst3Rd568RYut0y1XCeXNxgdC9G5q5P/MP41VpMuGn0qhingduis3N+kS1xh4qEJysF2FrcUmttDHApN86XPXOTk7hIj7VVSWRPZIWFZAuEGL7WaTos/z9GmOcINXh58z1Ee3bWJL+jhwuU8Pr/C7EyWnt4AO0mNJXmEpeUyhimwd/0bWKZFS6uPts4gHd4EHodKc8TE43Hwc4+us3/cy8pSnobcEr3dbg5POHn3g3UGd4XIV2W2UpDLaayXm5AdIre1MbsRUBFJp1XKZZ1yXea1b1yhbjlwZLZYXi5SNj0MjPcw2rjNktGHYVjsP9bDy9/bxOuzM2vxvC2Ra4spGKa9mHkdVZazITbjNSq6i3DA4gu3dnPrvoEnFCCTMxjp0jh6JELXYDOv33IiiXaWLb5ZpruhyvWVCMVchf/wQpBazaC7sUbAL/GuD43Zbt0DjRxuXcOhSLQNtDPSYzISfxlFkfim52M87n6DbLLE7h6bKf/pfVd47+NOKp4GFpeqTF+dw9hzhN6xHr7/RhZvIU7YVaJS0QhHFPqGoqytVymXdRwOkV/8tSVqNYPbZ29z+XycI08epJyvkEpUMQyT6/lheicGiURdtPU0MLi3l7v1EUo1hcVslOvJXj78mElAyOELexElgfl1gdaQimZKhD0ah1f+mr69QyiyheJ2Yuom22oTV86vc6D6OtezQxSSGbp32Qz9J9/RRnN3K7WaRabq5gtf2mBnPcXPPlPiP31+ll0DLjRTot1a4fqSnzfveHj9rQLNLT4kyXbBjrZE8PoUOkJlRElgNLpBKlFlOw2iACeGCjgVgWnnftqawON3UjY8xIRN2gY68ftk5lcNmppc+PwKK2oHyY00TtmkWq5jmBaPHoG+0TYu3FUQBBt/KssiO/ECH3/5FMcGsjS1BanqTk7uNd5OQkCuLPHJXz6FaZhkuvZzfhIyaZW+Ft02B+xq5M6iTMyTo614n7Wsj/FTe8gVBWo1A8uCYNSPX02xSg+JjTSaBo6NORSXTCSi8MDpLrp32UkO07RwKgKiZOON57a9mJaFIAh0Zm9hmRZ3ZnQ+/RsP0D/WheIQGD/UhVdW+catbhQHpIoOCtky1XKNj4e+ydieKH6pRKlQo245KGfyLKYDqFWDR/qXGPXNc2dZIVd1kNgu41m7y9xCiQ8+E+DGrER6K8tgcIOr13NEPDXSRZlU1iRdctA20E4o1ogogFbXGdn5PgErSyKv4PUpuHJbxIsBNM1+XU88EsbtkRAEWNsR0eoamYqLM8edhCJudk+08I3EA9w8dx+318niUpWMHuG3/jhDe3cE3RBoiIjU6haJrMjkaoAnDlXo7AkxMuhi94EuZpMRttMCpmWHVh2hEpoOfSMt1FSNgVaVn/npfnbJM3QHkmysFWlfOktFc5FVfbhcAnk5yuVJjWhmnv/1dw8RafJx/EgQRahjGJb9HVJ13vGREzQ3iLz/UyfZyUns7igTbY+R7T7A3TmNfN2ee/cMijR6y7jVHCNdGmE5y8TxIRShRjGvoesGLpddoXwicglJsJBFW5baPdCAJcpcu5Ej1uzi3HwzpzvnaApoNDS4aO1uJBxyIAoCv/Bzg3QNtXDjrTlESaAmexiu3WQ218qnPuBiY9uitz9MJOwgF+jk2NEGmtqCbK/aCODm1iALSypur5NcMstWwUejt8xfXexHEepMrzoQ/i8d9/9vxz9MLvSjSoYsyyr80EMvtmoJ4Bngry17XAZCgiC0/Peu989uQ6DVDdr7Gmn3Z5Ek0S476gblUo1ySef+lWmWNmwpTyGvspAKcO/WJg8/GKFUsXnW6xk72zJxqINsqkLV8tDuSyGKAt2dbjR3kMV8Ey0dIRIJld3HRpEEiwc6l2ls9lEt1xntrODxyIyfGker6+yxbjDT8ijpnSL9fT7eeuGa7XWwViWxkaKrVSbaEsYwTaqazNjBDhwOEY9Hsl2TN0u88zE/bq8DyxKYu5dgezXJI2fayCQKnHt5ntEhmxtdqejs7rL7E8xKmduJNqoqPPWBfdx4bZKllRpNzV4cioTHLdA90Ey5YrCR97O9VSHevJ9YqE5bTwOFlhHcPjddjSqiLFIu2Fm1S28skitLyLJ9A0/sj7F4Z5nlhAuPy+LexbvcmldobXGxlVVYWS3T2yGyndS5t6rwq788gMMBq5VWFtctHtj5zyzcmEHAZPf+DlYqbUiSiFbTmbxiI8xuXVnnTe0BZIdEwQrREIJISGRqzcvDx72Eol5KdQcni9/m2qwLt9fJvQtTLG8rTN3cQqirhEJOSgWVb63tZ6fvBG6nybUbOQYGfJSr0NTkIpcs8PwX32LJP0EyC/ens8S36uiaRSIn4fdKvPmNS5gmaDWDw5lvs7qU5cIdkaMPD9HV30C5LrMwucjKapnWwS4krUpaDeAPeZElgTv3a6zOxsnkDH72Z/rYSpp4PQLFbIlENcSVu7CTEUlkRc6eS9LaHSWbLNDWYJtG3b62TnK7iG7K7OQkvvfyDp/5YhpJtKhUNCrRLg4MW3SNdFPIVjEti7aYxHZCY2Nhk3MdH2dvR4qIs0giY6HqMg6ng5//Mx+SJOKqpgn57czSqt7NMx89yoX7Pm4tuQgHRbLJAnVT5vJ3r1HIlGiytpivdLO9kePNb1xiKtmKZVpkdvLM393Csixm7mdoiykEHEXOHHdSqkm81fwhZIfE1PV1kokKy8Vm0lU/papIe6uT/+P7HZy/mCPa6GXOe4BEWkc34I8/a+uXy6rI4nyelpjCkHuRW6/dYtg9z7mX57h9aZH1xSSJbZte0j8xyEDqArmufezdE0ASTO5enGYu20yvtIjTKbG8mKVvVyOyLFDIlNF021E5mdapaQJTlxcoa25yJZGWmJNW5zb90RwjvXB6v8EDT45x7qUpNjMK12/mmb+zzpn9Zcqakxtv3KOjw0und4vDPUmyOxl++6FbhMNOrs/KrG9UuHkrx9xMFpdL4t99yUUuU7XxnHmZq43PYBgWN6eqfPr5UWLtIW7NiVz+7jXe97tuvvGqhlMr8fFHUwiiSDbUw70LU0iSSC7cQ1C0KShvvjhFVTVI7ZRsOaAAP//LB+jpctK5u4/9R1rZWsviC3m58epN7pyfJuypEYx6qVYNrr18g8EBP13ebVYTDoajWxyNTDO9HWRLi1EpVimXdBrCIstJey79zpt1LnR+DF/AhSKZ/Mq/mSAZT9PkTKNrBlfdD3M0cIeH33uMbKrEVsJgLW5SSNtZ093BFY4+2E0g4uen/qcF2npjvPH6No3OHBYCB3qLPLM/waEDIc5/bwqvR+LLf3YWf8jN7UuLKJLO9M01Vkoxbr15G02z2DUc5NsXXRTLFm2OOMksNLd4Ccp5Jku7MA2DdKpGwCfZeMhUBVGw+2JkyTZ9A/jccynuXpnn0YkSLb4imimxMrOB7JA4+VA76aqPay/f4NvnJGqaRL5oEgrY/VVnzyURBIFwfMrGNxoWuYrMzOV7rNxfY3mpwE4lSNy/i9ZghZ31DJpuYVqQLdlVSFXxE5DLJFfjiCJ8hp8jFvMy0GFR1+w10bQs1mbXmbyVZteuIMMjQXJFi/RWhtW5Hd4Uz7A6G6e3S+Hl13OYlsVgS5VCoc6NjWYeGCnhc5u0BFXW7y8Tinp50/005YqJhE4o4qZQ9+CNBBltTiBKAt+f7+Vvbg2RSKhEvHV03cAq5GzDRAsGugQ6BmOEq1uM7wkhCBZBj4HiEPC7DfwhN03tDSwsFNBqOsnWcaqij5ZQjVKhxmLrKQKuOsmkiizDzLJFrWayuanS2WzSM9RMxKPyndfKLN3fZvr2Nh8IvEhjexOr99dpjrlwSnUeeWqASsXGwd66XUQQBA715hjpqJCpeLj+1gLnzqfQ6gb9DXnm5ov86q/eYGc9w714AMOAmVtr/MsPuDBMke2cg6TcSucrf0pbh58LsQ+wVfQw7JhheaWCItRobHSSDfcSUCpsr2V49bUkRcPHzk4VVbP9gNZWi0xNl7l2eZta3eJvX9ZxepyoDh8n98KQNEs5X+GVs0VEwcSUZLayCkXTz+K9LSZTXfj8DmYu3yOdqnLv8ixFdyMOyW5sz+YNzn7rMo5Khny6xLWL60x0lXgrPsjkvEg2UyexmSWT1dhcSvLV5/PM3VrmoSdHCAUkFqudTDv3c7r2PMmyF1kWuPT6HOvrdn9Irmhx5/w0pmGyUGhlJ54nGHSg6waKy4nfpbOR99HZriCj4XEJrK+X/z+IGv/+w7LAMs1/0PGjDkEQ/p0gCOvAh/m/KwRtwPoP/drG2+f+m+Of3YbAoUjsbOSIWAn8fgfRJh/vfadtKlar6Ywc2U00LNI10sXkG5M0B2s89Gg3XqfB/p4imi5wb7ZKqaghywJenxOnqOK27M7/RErDszlDXzBB7m0m993zdwgpRVYrLciygGGY1HSZ43t04itJ9h9pY9M9QLc6jSxLXDofxzItbl5etydrw7C5v5rB/etLRD1V3G6JYFAmFBCZuXyPgYEAU0v2a6voCkOjzXQMxPjcH51leLyVtt4mnvvifTKZGu97sEpedSBKAvWJUzylfwOXE3J5nUNn9lGtaty7sU57m5tkSqery01bi4Owp042VaJt+zp+RWXpfpx5dtHaFcGyBLq77Ybm69ezlPMlAh6T57/4FgAz9zM0dTYz3FJidknnkWeP8NBoDr9XIOQ1ODjhpVQVKJd0MlmdpYSbWh3SFSf9nQJX297H8NFRnGqeO1dXCLnKuL02flV22BSZQ8c7OVP/O8b2RPGIZTZ37FJxfKvGUlwkly7T6svzd8KzjPdpZJMFTr7zCD8R/Dua20MIusaV12fwh9yc7IvTtHaVUlVEUSS8boFS2aQxItI3EuPZnzqJTywjywKlvO0tMDoo4VRga1tl4qEJdpI6vf1BPl9+P70DEQ7thskrazgcIt2hDNVCiUN7vYQbA6SiQ5TqDnbWEzQ3SGTTFY6eHiQUlNAMkdEeA5/bIhAN4JBMDo/CMz13aI6YPPtUiJYWD8GoH6ds/kDSdOpkI4mSG6fDdpT81EcbWNpWcLsc+Hbmub8m4wu47IU1FkCtQbWicfzMKPfXHGwVg4iYREMCPYEd+na38Ys/3URnl498oINSVcDlkhkyp2hthP4Oi729KqYJjS0hshWnXbJOZHkrNcqAZ4W6WqdjuIepWZ3NpTi797ahllVOn4oiSSIb8RqblQbeuKLTEqjywPbfoFY1BsdaaWzy4JINNEMk4rerIe1tbk4eD2GaFu3WCk5FJOLV6BuOMT+dpCmgcfBACFEU+JPvd/Hge46SFGKceXqIQNT+7iTWU/gUjUiTj9XGQ3gqKT7/x2dJVQM8/J5D9IbSXC3sZnu7zOZ8nENjIisLaQZ3N9EcrHO0P4fLJaLWBVr7WtBNkRPdG8zPF2jMzvPHX6yQK0tMrbpYuJ+iY7CdoNdk164Arb3NfPNNhYiziOJ2srVdJVFrYDkbYeL4IGfrJ4iEZR4YVelo93DyWJCh4TDrq3l+8yMlnC6ZB54cI+o3OLzxZQqZCo8flzh8tInL371GwC/y5I+f4KFn9uLxOJA0lawWIBAJ0jT1Ch3DPfzE0zIeNctrK/1EG9ycedc4ddXA5bb1uo1RiT/8nfNcuZKmnC9zdzJJR2+UcMTD2MlxnnjfAea3PRQyFeJr2R9UwBpKK8gy1E2FF5ZGee6vblCqOwk3BtA0A00Hp8PCKWm8+xEJwxRJbGbJV2VePl8nEAmQ04MMjrWy37jMF26PEd8o4A24CfhFnE6RPUf6KFdMVqt283O4wcsv/cYJxsfDPPtMA1XTxbLVx3bRy3KhkWTG5MTjYzw0muPMB46zeGeV4b3dFOouREFkXJmif2KQxohIsWTy7ANFvG4BwTJxyAKyJNCYW+SU8QrHTnXQ2upCrVkEAyLvfdzJyFd/ng9Hv4fbYeANepAlgf7hJk49OYZhCXjlCpJgceThXcxcvsfHrM/ikm2JyM88ukmi4CDoF1lafduszzBZvbtAtaGLi9crrC/uEPLonHznEfafHKanN0DMm+NuMsa1eQ+j+9tZXasSDjnY15Hk8OEohiBTMdwMHRrBIcNTYxu8+cIkggD1ukmpoLK1nEJ2yPzkuxx87S/P8bXPXuLIYAlf0Is/5OWo8xrt/S1sbhs8djrEwb22Y2xTk5unGi6xkfeRzouUNQePf/AYPr/C6eyXCfhFtmrNhEIKHrlO51AHEoadDOss0NwgkctUaXQVMHWT+q6D7B33s5Fx2kAAp8wfXz/AcHuFXmUFn1NnpEvDsuy1vJgrc2h/kIaYH6deIaMFMSyBrZUEXflJhqRZRoY8RPwmJ8dURvodGKZJTReRJJG6IbNnT4hoLEStUuPr5Xewa08zLT0xdrZVJAwqVYt7l2cBeM8jIumMzqu3/eimyNdfyHLggX5Gx6I0N7uQBIPRkQB/8PsTBKN+TnWvspPUaO5sYC3vxyEa1OoQtDLMPfIr5PN19usXWdmEWWOIwX4v84V29vWpuLUiO+UAHr+bk6eaCEgFWls9KJJJIOwmHHHz7EMGD5xqoavZ4MHjfsKNAby1HL/965dYo5fmjggPHA1SMxzUHV7UGjRa24SaAhyMznPzwhK9E4PomsGnf2Wc1WobOwUFh2Syq0fkE790iqv+J/jJDzVy7FQnfeI871Kex+8T8QcdlLJFQiEH20vrPHEmiuJycv7VBRyywF7rGvvu/DkrTUcZD8zjdgn0jXZw9KAPQ1JQVZPePX2MHuymzZdl8dYs6XSN5EaagdF2NjJOihWRh7sXaJl9HY8Lerq9/xRh4o80/hE9BA2CIFz/oeOTP3w9QRBeFQTh7n/leAbAsqxfsyyrA3gO+Fc/yv/+z25DIEkCuyda+NzVIbraHTx6zMZgdvZGOXbAw+mTQXQDcokCB87sp8mdZ3VdZX5DIqrkuDFt8Aun5qmqGuvrJYrFGp56gclsH4Zm2GjPrr3cTcbwB12EQgqnnz2KYdkmG69//RIADtFgbtNF964WkskaO5UQWV8bLR1BHjzdyu5jo+w/2kFPj4/hA71Ua7YMo5DMkCy7bddaReDudIHxU+M4FYF4vELPYCNeR41CoU5Lq4/f+98PkNgpMzwS5GOf2MWFF67y/GU/OxmR7g6FrL+da5F3MNBcZu5egtROkVjMQ7DBj8Mh4PFIqDWL+7NltnJOYm0hhLpKL/NMHO5mIvcKxbxKqSaTzWm43ArDIyF6R7v58pdXeexDx9FNgYMHG/CHPHz7nC3BymZVhndeI+wzOBi6zyuvJsjkTD5xeouH99dpCBhcPLfJo47XmVuxmFDPM3d9lqIvxoETvTSQoJAtUyjqaDUNpwIrK0WyjYPkCwaKqTLRr6HrFg/uh3S6jtNlBzgHWtZ5+aKBy+Pkyvdvk2kYJJ+uUL16hZ6RDqpl22k60z5BvmiRS9vNe8GAyOf+6CzrSxkSyTotqTvEt2p2xnSrwtSsTle0ytL9bbZX05TL9kL/4bZzGIZFVZN4z3u7EUVwinWOveMQDtmirSNAoLJDxF0lv5PmXZd+hnc97iORqLK0XKZck7i/JpPIQM9gIy2uHf7o928Tp4Owp06q6MCpCMzfnGMz4yCbKtHUFmZuuY7bYdAWUfnE+3xcmXERi9gc7ZnOJ+htNdE1k82lJFdfnSSdNQiFnayv5hls18mUZdK1AJIIS/lm7l1ZoFdcIJFQcdfydgNeqkLc2UezX0XVRDZzLjobNXbWMxQqEsvT63QMttMbKaDoNr0jGgvyL06s0dzZTKmo0drbzPq2/T54PDKJgpPmmAtF1Ml27aeYq6DrJpl0la++VKPdn/lBSfvdwzMsrhuUijXCC5fQNAvdFHjfyQLRZj9TSxLXrueo1S0+/cgS2VSF/vx1pu/l6OiN8t53t9De18x6xk0k4qSkeyl7Gvncn/SysONmdSmLXyzQH05SV3U6d7Xzne8X8Qfd5HI1XLLG928HyOc1DAObGibXuZNopbXNR9HfylNPtTIzX6WuQXt3GK/PyQnfTa5d3iGXLPC+h1Q8QpkH37Gb8d1eGpQsQ5EdVuaTxPwlNuM1EiU3VdVkfsVgba1MpMHDTL6ThgYXL//nC9R0gZX+xwhGPRy7+XtoukXn7j52tdfx++xpfmzYhYDFtcUALT0NzI19kEqxynMvGoiGxqHOBPl8jempFPN3N4g2elhfSuN1WRx58iC/9Z5Nene38c53RKlWNFI7Rdo7/KyulTjatcXeA81Em/0cPNpq43R9/Vw4v4NqOOmJ1fnwT+1HFC36BkK4PQ7UmkVz0JZV5atORMEitZlAliwaGtz0DDWSLHsJhxxcE47y4f0LdPeGiDZ62dqu09FsUavpNDXYXPp0RsfjsRMXN26kmFoQ8Ell9t3/PIsbAnVdpL1ZIJ2q8t0bAe7fjhNpiSJKtnGZJ+Bm3trFnr02SaanXWQ9H2aso0iaRvJFE0URSYb6WA3v463XV5m5n6MjJlDXoMOzhfrjv8htzwNYFvgCLnTD4uEDOqpqsJH34bbKKKKOrlnsf2QftaZuupwbjJ7Yw2S6h9ZwHbVm8fSRMmGfzoffFeL4jx3CEB309/noHbYdrGs1nVpN58blDXxCkacrf8NIl06tZqLrJtmchiwYrG3UqQsu4sUA0UYfhgldi68yfmIYj2IginZfTKwrSiASYD4R4IP/8hQf/OljnJ2ykwwbC5uIlm2E5vOK3J4xmFuu45BMTNPiueUjFCsiXjc0eMpkMiqL09vovjALi2X23/2PjPVb6KaIoRlslBuYmdqmxbHNs6Hvs2c8AkCpUOFr2UdY3dDoabSdeLW6PV9phsSLSyPMbCgsbStYCGRTZZrawly5lkOWRXy5dfxyBb9S5/gjAyjpOPe0YZ5svYXbYfDCeXvT7nLJRLwa9brBdl4h4LVwOCWaOyI8GbvB5lqRfKpIrMXFQq6ZyWtxWnpbcTosbi56CIckHhhVuXBH5l//eJ2eDgcup8DaWpmVXNheizIB3F4HX7naxviQiMvt4PzVCg7JRJZBMVTuxKNEo04caoFHxtL4HVXuz5bwKRpf+NsczmqWk+qL9A9FmJwqUTACTE/ZiOqWFg/FYo2/+luVO3cLzK6KtAVKlIsq19VxPvyzp3BJNVZnt+iMVIgqOUxBIps3eCu5G6/PyVShj0On+mjtDLNrOMgXn9tkjFt0RyuousT1OzVyBYvd0j3O3RK4eHaNL9zZw9eqT2OaYJkwMN7N1laFg4/tZ3rBRKvX8QQ8zC+WWVD2UBk5hluoUBddKA4QBNhJg6ti92Q1t/pRVZ1Mzc/xHztEY6OL/rF2HIqIS7HobKjx2ko/N3s+yLtdL1AoGf90weI/cvwjJEMpy7IO/NDxl//F9R6xLGv0v3J8+7/4088B73n7502g44eea3/73H9z/LPbEOQzJdZXCzx9OM+bb+6wmlB+kImZmtVZiUNLRMcX9qJrBmXdxntpmkVeDyCJAmmpmUDASTjsQnaI+Erb7A/NUi5WmVtUCezM0R4qY1kW8XgZt1siRBpBsNh9fMxuFnSVONqXRK3qBIMOOr07uLUSWt3E57ZJCEG/yMJCgZqqszibxuN38u5PnOS49waZnEnYb7F3T4B63eDGjTQfe7xKYrtEj3afqctzuN0iL5wX2bUrwMxMgWRWZOKhCe7dWGe0s0IybdD06heo6xL/5lev86s/6SC5kaRWM2lo9rGrXWWoyyKZrDF1aY6zb2Uo5FVq4TZu18eIhCXUQIxcqkhboMjuAQeNLX6ao/ZN8fs/q3HzwjJhT53FpSqKIvGB0xUeOSwQCDi5HnkS3RSYr/XR3h2mvwM+81IjsmjSEczy+BNtbAZGqNdNlNQGYyd2Ezr7VQQBgoV1DN3gxpszRFvC7KTsjOal7G5cLhHFUPnGy1V8XpFLdyUOjsmolToWAslahI89WuDQ0RhtAx1E43fYWt5Gevyd3D57m0DYTbwSRTJ1WhstUvEUR8YlWiI6p589yuKtWdtQLtzD8QmRgXYDtaLR2a6g6jKf/FgrxVyB1hYXmmbxlnYCn08iVxJZ3jBpa3GgmQ58fgc7GYH7t+PMOCao6TLv/9RJpt75ByzEHTidEo8ddxDxamxslOlotvB4ZIqGnw984gCvTQW5cEfmS391i63tGu1DXbRFNBSXg2DIRXK7RLO3wFbOyYVpN7eubGBaAkf2OQkJWSQRevqCRGO2U2ooKKFrFr6Ak0ZPiWezf0azK8PuWJqwu0pTZxP/9u86WV9KYwkiA7EKbe0+FLHOdsGFWhfojFTQDJGh8Tb8boOxI/24vQpRJc1tdYT1pQzbq2nOb/bjDbh56LDM2uwGpmmRz9eo1Qx20uB1C9xYCbIldiAIAnN3NhBEgQ8+6aCkudnOKdQ1i7+dGiLgk7h99g7pwRO0Nku8fL7Oa/caKOZVTo0WefwhHwGfQFZspCnm5ab75A8267myLblrDdcQBBhNvExN9pDXAuztyFIt18ibQVqrC/iDLqrlGk8/6sPhlLEsUHUHY/0WjQ0Ku9vL+EMeUhUPB5qWUZTS/cAAACAASURBVBwComWwL7bBL524x74+FadTwudXuFjey9HjMRpaQ5yfDZE3g5TLBm6nScn0UtK9VEtVaoaD956qAKCqJvGNIo8/oNDd6eZk/SUbzXtiDwcalxCwkCSR/xT7X/hI7DXau6PcWVYolkw21osUywKz3oNsbdfRNZMQaQ6c7KNvIIhcK9FSX0at2JvYfcd7kGWRtq4Ia1sWnzyT4iszYzhkieUtkXSiSKTJx/xMCq1m0JW+zrlXF2lsdBOPqzgUEcOS+IUP21SyLn+SoeYC/e5Vm0oUL9LXZpCtKESdOVwOg25vnN6xXhp9NUJBiakry4z551ldKzHmnOZvrvdz6bVZTMNiYsRBPCmwNp9A1+EJ71nGBmw/iM9+OctPvttJPl+nNTPFzp7HeWxPkoFIkvVty6603t7myac6URSZjnYXWdWLaVr4HBUWFgp878VVDBO2MhIvXlIYnv4KTVGRoF+g6YXPUNLdNLWFCUXcOGSLxpDJTq2RV+Oj3F33IosmTqdEQ1jmxbcEXv/6JQ41zPPG+iAZ1U2pVMcwTF5SH2a+0o1hmGxnJByiQSQoUNJcNHvL3FlWCAQUvJk1Al64/N1rKLJJa6uHQk7l1z6poOJmoesMLb4cPp/Mpx9fp1SsIwl28NSSnmIiOEdTk5NYWGe97zSXv3uNxS0FSQRBEIhE3Zimxd37VW7dTJHOGhSLOsGwi47BdrbcfdTrBn6vQCAg0xh10CfO09Uq8qHeqzzTdBHdgCvzAUqFGgePd/CS9Q5uvHqLyT0/xRuXa8iiyfK9ZUwL9h9pI1xY46Zygs4mnbJu44k/vPO7rC3n8DlUDkdnESVbcnp7SeGJvlm7GucEWbTYf7CJXLrM3r0hyqU6lUArLrGKIFhIksBW3wMcv/vv+cKdPawmHASDCsWyhSAKZMoOEvE8Ia9BIgN1VSebKiF/7S+oqxqVYhmvW2AsvMzvfdxOAGwmBF7+5hQ3riVYSnp59nCCguYnlbPYSWo0NrrpCWfoaawQC6gUslWOjepUaiJen8LQgI+CqlAoWbjUPAsrdWRZQFTLaKaDwexFzhx30uxMcfLBFpTcDoVwNytLeY4d8BAR0xw+0ky2HuDWlQ3UisaJB5rYMxpgsNPiuZcsnC4Hp9f/gr6WOgYird2NNtwBey7KZes8HL5BqaAyGIwjy4K9sbyWYPfeNhbl3QwIM6wmFCZ2u3hsd5zA1jSPHNDYtaeV0T6LjmiNgE8gHJJwKJLdnF/VmZtOEOtq5vipdgb6vIwmX8GTWMJfs2MfWYL1+QQup8BqaIKAT2QnXiSdKNHgyrOxnKFc1lErGuGQg12NKTYyTh7qWWJP4U2uex9he7P0Tx0y/sOG9Q9rKP5Rm4oFQRj4oYfPADNv//w88BNv04aOAHnLsrb+e9f7Z7chCIR9ZBJ5nMLbxk1OONS2zlC/m9e/fol7txOcu2pnuqMNHmqGg1vnZ4iERFqFdVKJMrPpRoJBuzFMqxkU/a34S9v0DcdwuyXSrWM0KFlCYScej4P7UwmKQoiiKgPYDXv5CDfXG9m1K0g6XSNUS2AJAg5FRDdsLVq5ajEwEKCm6uzd30RDg9t2iVQ6bGOwumDrPLeznD4V4fx8A0O7wuTcMU7/2B62t1R+8uEUMzMF9k0E6W+pcfvsHfKpLC9dFDkyXIc9BzlWfpHf+d8OcXuribHD/ZTLGpIkkig42UpLnDog8YF/sZd/+2ycljYfliBwyLrIzEwB941XKWbsvha1bk/eq3E72/vi/CCHTvZS1WQKeRXZIbJdClCqyaiqzlLSi89poJkSAb+MYcJPPKZyftrL1875OftWiu5zf0EwKBPvf5D1+W3qhx5lz4DFLdcDtPc28PQH9tDZHcLvE0luFTjjeI25mQze0g4trT7Argq9dqFMW1cYt6jSJ87z168GmV8oIQgCxuQ1FLcTRz7B4ScO4HY72Mt1QptTdIeLRFuiOGWTvuAOAb/M8NFR2jqDGKKD169obKZk2jr9hHwmimzw/Bs1uoc70TSLoR4BWTS5cTnOeHuGgF96m4JUI5tVaY5YDOyOMVK7gddRp1K1MC0RSRSoVDSWthXavSmOHfAQdNcpFOpIgolhwPGRKod2/5/svVeUXOd5rvnsVDmnruqcu9Hd6EYOBIhEEgRJgEEiRVEiLcukg+QZ6/hItseybMlBHvvMjG15dKzkY2WLFkWKYgRIMABEBhqp0Q000DmnynnXDueitHx9ju2j5Vmeb626qYtaq3bt+vf//d/7vo/BH/1+Jw6HzIbNUYYmJBqbfRQKFfoHAjikIm2hLI9sXOAjj9cRdJb4/vfGyeJFMwTUikFvn48jT+8AIBpRaGhwcmXWz6nm5xhN1pKr2Ahb4ihWmb27/QxsqUWTrGRKFrqbofHEt6j1FemOpilrMgFHieFL00RcRVRVZ8smL3cy9WyRB6mp9bI0MUtTME9vr5fbczIOt5NUSqW1xUljvQW7TeCD9+fpb8zRvfwuG7fGsLvsqD+/Zxqlacoq5PM6u9blqAnChv0DJMUw9YEydrvC5vYSaqnCxJqHsiZikcEqlHn7n84woJ4jELAweHqapVUdf9jJnQUrFovItfAhZEOlb/lNfHKKmnofbamLzNs76O9xEKxxc/qqQXOTk3JJYylVPVAolgxOj9jxBex0+WY5MdNGWTXxTQ1y/HYDP53bwuVxG7PTaS6duMleyykWlyuk43n2dCfwimnmZ9Ncu6UhCzoZ1UZmLUmbMsHZMT/NviT5fIXNm/1Mr1jQDZOx4E5qIyKlospEoR4Bk2CwmkDzVzf2UtGqG8J4vIQoVH0O9eYUv7PlHJEaJ8G1US6dHCeVqpAIdnBF21g1gxsmTbUyuWyFxkYbd0YTfONYkP4WlZHBSdIZnWi9j6lbS/iCTkzT5G3hQbbubmFlpYgoCezfAu3TbzE4E6KpdItsxcVbg3Z8mVli7Y1UVI1bUyJNvszPU5lKLJfDFLJFKrrIxGQeq8PK1Uwnzc1uLJU8v7b+EnVtUQzT5OpIhYBXYMfeZs6dXf5nEvTidJw/+BWTtwdt3LNd4opjL7Kh8taNCOena3A6BEIRJ888WcNKwqShxY8owELSSnotTbzk5rkjKrv3NzKzYLCxMc3jd2f5wsKvIEtQF6hw4+AX8So5+nq9NDfaqPfmkCU4O+qiIVhidr6MVdZwOGTmFsrURG089MxuFiox7FYTwxAIhWxcfe8qYXeZVucckaiLR1quc2HUituuIwpVbXFNECbHk4xE7iVfhKc+vZcG5wqZjEZ9o4ezU1EcZo728ddJlV1cODHOicV1bNngREHF51PQLU6m1EZSGY1cSSJnOHnw6d3UhXTqowLzYwsEAwr1rSF+7b4lmlp96LrJwe1G9RmnarQsfIDDIZMvmiwsFPC4BAoWD6omkLZFeDe3g9ZIkXvXLaFYJKanchxJf5v//Ad3sWHqBT6830AzRNo3tGOaVRDfrKcPh6xy8YaBWykQqnGRG7nNkw97OXHTx18fa0AUBT7f8hJ3dWW5mWthMefmyrUspUoVQGiaJsVS9TlpKyVZKYeA6oFCbPgYVzZ8iud6L9FVW+S9V67idQvURS2E3WUcrqpfxe8RqIm5CMe8XDjyXwnH3Bz5yHr8boOhZAvHZ9cB0Bg1+S9fqOHB+8OcvZDi8mKMy9NeNM1kfWc1SjVVclJQFertS4SjbmbWbITcFQQB5pdUUgWZoE9A1Mv8buJ32dJeIF43wFzazWXXAf78i2comTbOn48Tb9iIIUo4XBaujlRIGEFcDphNubjn/kacbiuyBHfGirx1MsdD+x2MXhhhvPfDvHQ0T9hcYmxoiohljUB5CU1U2LdN5t3UZvwhJzndSTpdoaLqrOsLUxNWaDbusCw3sLU5TiIjMJqIcjbyBNNxJ+3NCv3ecQbka4Q8Gu21GufeuMjZ96fo7fUSjLix2KqwMo/TpOCvx5AtiIaGbFQYHi3Rv62RiakiEXWOYtlkXW+AxhY/minhCThJxKuhK2/++BIz6QCdNVnqbr6FPDcGQEen9xewS/yXl8kvPGXoL34uH7oOHAQ+8/P33wAmgDHgW8Cn/0c+7D9cQ6AoAoceaubCXC0PHKqhuybNSLyWxRWdL/zpTh44VEN/r42+9T4sFpEG2yL7HlpPWTUpSB7u2eMhV6xeNk0z+NAhJ45iAgQRn09hoEvE89rfIwkafq/E1gErsQYfVqGEJEJTS/WG3uoaYlfTLLNzVZ+B++rbiIZOIGChK5JG16vsgtqwyeLkMoMXlrDbJdLpCkFtiYvnlvG7dNrrDPbc24ZNMbh1K0Mqo1E/+CI2q0A+V+boSD2BoB1Fhgs3ZQb29vP1P/XT2WZjPmVDii9ywnaEKxM2Khr0dNrYtclSJWv6CjRENDzWMvVBlQ/W+rh/SwFbcgG5nKW93YPWt51IY4R02Y7XaeByK0SCIhN34jzQOU5TrUjMleHZR6uj2pIm4bOrFAoVeqJpIs4smZKM1y3y1rtJ3rzs577+BJ/vPca9+4Nc3P57TE/lqB0+ygOPdmFfnuDMFZ31+iBXTw5z40aKmakUtSGT7r4wL6bv4/57AuRcUT7X/DMmpwo8vWWMlhY3hmFSMqy4E5N8aJ9KR7sLXTcw7z7E+u0tCMk1zr95CZ9PYcbWxUzzPtYKDvbtjzE4KhEqz5Mv6LR3+mlusFBz7Q0CAStel0ldjUyuKDIyY2XPDhc1MRfdzQaFkkjIUWDT9lpm01666qon0T+7WofTZaFcEYjVWCjafOQrFpwOAadSxu00qZR1PE6T6ysxSqrIzJqNgR4bNqHE9espOrnJXNzCzQUnuWyF+YUCasWo6kJbnCwslqmYCumyjdeH6rg4pHFr3sFTH2/DZ8YRBROrRWR6pkgup+FxQr5g8NLfnyToMdAMgUZvAkkw8ZTj9K0Psi88RG1ExJ2eJebKMrkoktr1YWpsCVbzTooVmQt3HNz/cCfJkg2LReLddxbZbLnKJW0z67rsdO/oxW/NsbqmEfTBvoMt/zxNcdlNDjcP8ZlPuIjaVvjjm4epqxGw2KoN+J6GcT5Y62MlrrG9D55/SyCVFejp9lCfHSGvyuzbIjC1ZmfPvhgum0FfaAGLYmIg8qHn9lBwhAj4RHo21bOhw6C1xUl7rIymmQysHsWXmGClbhM5w4XdrvC2eYi2099gbEqld52LrjYrN4bihCMOLIrJ+JxALCJxb3+KUlFjthCjMVSiUNCY67iHBzvHWFeX52DfCq3tPj7xqwMk3A3IikBbV4jxhB/FKBMMO4nVWFCECkF7lo17+/CmpmmO6ljECoGAlRvDWdpjJZJpnZHlELOLOjt21rDRvEgsOUI6XeG+rjnCoaoBdWNriS0DDu7d4yYYkHEWVrkqbiPgl3lDeJhd93bQ3y3jyS3Sbx1GVXV61oewKCa1tTacdoEDe4McvltEMwSOPNHDnoEKXR0OHJ5q9HBHp48Hsj+kJiQxdn0Gh0NmeNrC5drHkEQQdY32yhA2m8TrhXuwOqzs2FlDX6uGaQr0lAcpaQqXxp1EGwPYFY1nD6YJx3zcLZ7ENExSjhhvrm1n69YAum6Qz6lk8ibxuMqHDoewKxoVzWT3/kZ++EGYSEhhctlCzLaGf+kmB3rjPFh/HV2Hk69dJluSCHgFmhuqiWd1gRIDO9votE0QL7mpCZgEfBInR9x85Yc6fxb4Kops4rcX2Dj9YyR05hZVFpcrJIt2VE3g+uVlWv6fx5mfSWGYIrIs0tJkZUdnnrW1Emdvu7nHd5GoK1NdRzZ3s/3kH2GvZCmVNBZoYFtXmWROoku8hSgYeB06DU1e2ovX6K4rsrBY5oOpBta1y7hcMrGAhiYqZOv7qLOv8MCjnRyIDhFPQdZwk81qLLo78FgKdLcpFMsC3XPH0A2TgKNERRPYuq+b26NpmpscHB9rIRyQSSRKLKRshEMWBjZHuRR8iIY6C1tas3g8FrqiWerGT2JVTHRkHqq8yKsnDY4NR2lp9RAI2viR5VnePZMn29jPyIKbrGqjq9vPdvk8mmbSfuunOOQiTqdMtuKo+gya6phetXB4/Ryf3z9Mb7eTK+HDVAyJ2TULHptKbZ2Tel8eTTMJx6rPU4sioEsW+nKnCFmSVDSTXOsmOl/4HHfEHnKqwq//1kZOn4njdZnMrNnYvNlPn+0WhRI01FnI58pstl6rTmHzBqNTBv2+cQB03SSRFTl2PUy+JPL7jy6yMbbEo01XiIVFphYFgj4Bi6Rz+prJSLKRWNSKVamawK1WiV/bNU5TIE/ApWOZGkHZtJ13Bi0Elka4fttgnTDMZ/9wN5op0T8QICFGyEteNg84qY1ZaMtdoaLBQGSedNbEoki47Ab37xKoqDprWYVn/ve9tE0c5TOPFwhdPUrnxlaKpoOixY2ASdiZ55D9PZxOhZm0j3UdFhqb3KyuVg3w9vwKMXWSjOog7DPoCczTYR1nYaXaoKZNP+eKm0jmZJbTCvc9eRctXVECHvD5bXi8VhojBvE0vLa2E1EtYc2toehl9m0V0TSDjX02UtYIHpdAvqATDik4xQIWSxUMu32jjY/8yja6/AvcXnbzdvQ5pjY8wbVJG/nCv96E+7+6DNP4n3r9a8o0zQ//XD7Ub5rmEdM053/+vmma5m+aptlmmuZ60zQv/Y983n+4hiC+kuXtozMcjF3n+HtxFrIudrmuMD4a5/X3S1y6VqQrkiabM6iLShRMJ6GAxKULKwgYbAxO8JjnOFfOz+HzKSwkLdjiM9juDBLwiYgCyFt3EZsfZOh6kql5mJ2IA9DsSzJ2K47VIpCQa7CaRc69cZHuTgfldduxFxPkchoVQ0JVdc7fgNllAbVUZubmFLdvJWios1JU3Nx/sIZSReRb37rNjaE4kgi/9Uiaxdk005uf5MrgCps3+XioZwqnU8bv0misldi22ctX34gQ9VXw2A3Wjr1Hv2uU2jDsbZ6gXIGAo4Sum4yvOBmfl3AoJfJliePvxbk47qHsreGS815sVgHL/BitHUE67RMksiLFgs658wn8ISfXE83cmdLIV6x88S9nWV7MstV/i5AtTU+3h57UCSRBxyobRHw6n3q8urBbBZXbgV0AhK0pDu+zMNn7KHfG85RDDcSiFuyJOe5/YgtNLR5qYj9f7IISTyW/wnJCwLt6h5POI3x4n8ZwppVDvYsYukm9NskZ50N8+2Wd2bkS5UKZcfdGhs5PUujYwmPP7iGVqhAylwnlpxmakBgZLXDvQBYTkcY6hanJDImUgRGKkc9rnDqbRpHB59RZ16AScKpYrRLX7whsq58jrKySz+u89laSTEmhISbyTO91ioUKraEci8sqSTlCtmzB6xKQPn0YtSKgWCVi3hJzSya5qmqE8VmdW6k6PvagzJDeh2nC3EIFn78KKvrQ1lX23N/BlUurrO+2sFzwsZZVyOd19m+BUtlEFCGwegvDFEilKkiS8M9xdvVRgQ89t4cHcj9km3ielYIXUTAQjQqraypnUn1cGy6g2n2sFZ3UhiEw+gHB8gKpvEy9J8UTPTcxTLDJOrUxGwcPRslZA+yb/iaaDlarzPnpGnTDJOjScFaDZigUDeaWTG6VO3jzkoOM7uFzu4cIuargtGy6iIDJfZ6z3NWnkStLbN3koqO2xPvHp4FqVGODa5Wbt4uUVark1LKfiiaQrHjI5XTspSTFkommmRw7VSaT1SlpEktLBa6GH0C6c51QfBQRE0UReTDxD9y+61O0NFp4+fkR9teO4PbamBqLs5aCg+vXcFgNjl/3MXj8MqJgsL3wNplMmfrhN7BXshQqCvM5P7mcxpkLGY6OttIQk8nnNJp8WSbUZtLJEvmCwYnxeuJFN+GwjaVgH7MrMrKgEYtIPHW/wZ0FGxXV4GHlNfra4MqVBFeErci3r+B2Kwhf+FVWViuszCc5dk7g9PkMB7Wf0VJTYdrTz5UJG7oBD/Aafq+I11ZBMA0ko0Jfnw+1YuKwGIT8cONmnnJF4PK4DVk0mV1QOXlNIZMzae8OU9cUYGm5xHjTfVwfztG3rZVMRmV5RaVWWWRbwyIXhZ0MiZv4pb7r3F1zk+13xSiVTRTJQDcFdNlCk2MBp0OgocGJJOicGKulvtHNmqcFl1MkcvQbPBC+QDxpEA7b2Tjg5tKFFVaXcwQcJe5e+REPb1phfCLP0nyGzroSNX6dhqkTpCOdZFQHpxJ9FIoGjz6zjbmV6jRzaraC0w7Fioxa0jER2GScJ+wqM3onxyMDs3zp6TSlrm3YFIOyrvBB7Bkab75ONKKwvksi4swhSyaKVSb6yafZeVcNfkuG9352BUmEr/0wQ2ODg08Lf4dcKbJWdJPPqfSsD7P8wG8wVO6httbB3z1fIV6wEvJoWEtpXHK+Sga/HUd8/xU2qadY12Hh8eD7zCyaTE5kuEs5h1UrMGdpw0BkLaFzPtVLS8ygpFlwOiQC6hI1+jxDN0u0RoosN2ylISZTrMjU+ktVec3MGgDJtM7Ckorfb6XGW2ZlVSWT0VivXWJxWePMqJvdAzqXJ12YkkTEU6b+3W/wjuMxHt4rcLhvlsmJDG63zKOBE7S1unGPXWTwcgq3pcTqWpkVRzODZ2cp1XVR0OzIskCjNM3r3z8FfVvI5EwqpsKPZ7Yzu1Dh6rSLsC3F/pZJorY4+/uqSU2ZTJVBc+zlm2RyBqYgYkjVRJqmGoMppZvZp77MV7+bJluUiLiKPHnYRSIDmg43hrNk5AClssl7x+eIRN24V+5we3iFsmpQVyMjYLKraRaXx4rDalJWTfwunVuFNvz6KiklTKEEa2sqkzMV/JYM/V0y6/xzlMom22snEYTqlDoycYbvvFwm6Ciy2n8/cw134XRKiJUSv7H5Ou7ZIayygSzo3N25hlPI8d54I+PTGrUhk5LdT31QRUGlNgyRiJUGb4bzIwoP3etFEk0mp0vkz5zm1asxjJp6ero9VAyZQHIcVbJT1hX+31t7SKfL7LGdZXbRwOmogg4XllSm/JtxrU4QsSWp9+XxlxaxVvJ8dMMdbt4pc2Emwj7zOKJoEvWpiJJAT7edbEHg7g0GDofMmSsV6sImD779y5iri8yEtuAoJrg+LhPwK7z66gKh/DQtkRL5nIYsCdj1LLV1TiwKNPszjE8Umc7VABByFAgVZznSM0FT3b/zLav5C58Q/JvWv/Or+29f/pAbp9fBlNFCMVdmZAJeX96CpEj4/dUTzZM3vUxPZjj+9sLPs3/hY4/5KZoOVKzVUafbzuRkFqfNwJCr1NlKBRq9CVIvvYTqChKKOBloq7BpWy2ecpx40YXVYWFlpcx4KoK7tEZzfweVCuS/8w2UUoZiUUczRLLpEvdtUTF0k1hLjD/7k35q6z1YLZAz3SysmLQFMzz1TDcATmuFv387jMWmUJscxu60ksyYnFtoxuMSGZuTqPdXjVqBoI1ezwSKZBDavYVFs56+yDLfPF6NqS1UFC69NYgsQdAHbdkrXL1l8GcfnuVQ9wxSpUgfV8nlDaionDo6hGRqBNwGk6PL7N/jJxp10Olfxu2WqbEleObZPubvLPDizXWkVRfDI2lO3P07mAjsEd7nyk2dmbSPT3RfYrXsoy13hVxRIJa/w+U7Cg1Hv4IgCIj6z2U1Vjsn3hxhciJDLqvismoM38wx2P0srVENsZSnVJH43msGC0mFy4sxRgYnEY0K/f/4HMsza5TLGg63nYqu4PK5sBRTzM3lWVnKEjj7Eo7hM4QDVbKwqsvIWpFCCfKZIvlCVY5x9yaBhw44kSUTUYA25yyvvFvh7Duj9LXBSsmP8jefR60Y/MFHq1naQZdGXvFiGCapoo3VlQLNi6eJunKUVGj5qy/jshts3ehhLmnnsQ1TDA6ucflahpBf5t7sC+imwNiijYPNo3xi1wz5vEYmkeWli2FGbyZZml5mYtZgKV3VCa/vEJiNW+lrLjO3LJANtLDHcR6fTyGXqcL3uiMp2oJpTh67wzu+p/j2xG4AVEPGWkxy4qfniLkL2Owy1vPH2KW9y4+fH0cP1zGvtBD1lplJ+1g1azh7qgrOeuGbJ6nxqFxabQVdZ+RWDsOEh2OXSCWK/LfvzOKwGlw8t4jVKtJUC7tmv4fTIbFa8HAqt5m5hBWrVWbi6m3ShpecLcitORt3pk0eT3+d2/M2ejbUYrt1kYlplcGFWv7TnlGWV1TevOyj3jKPIpv0HP1j5mczKKffJJHUKBYr7NjsRJEF/PYyFVVnOuHCNAzO2g7SmB/h6tkJZjvuo/v6D0ikTdr7G/mH8x3EYg7CMQ/rm4r4iKMbAvesTwPgkFV+lD3Ckwclik19HF/dwOXRaqjA0nyGzRu9PFv+WyZnKlXznFSmXRknEHIgywIfqTnBjuJbrKwUib72twgCBMsL+Jw6J4fdPO17lfhqAXl5hsWERLTWzc7BvyS96RDlsk7+T37A7ze9SEtXDW0tNoZODfON1ce4PSfjErIc6Zvh3Mlp5HwKw4BvfW8RJZ8kYa3+/xtrRdxWFdMUiEXt1aSuhQK7R7/KB69coqO5OqkcvbGM12ehp8tBy4kq9yYUtOD1WQiFLETmLzM4H2Pb279LyJrmxYkBSqKTc6cWsFkF4jkLzcIECXsdacNLdyzPyI04ObUqe3rzR2cJZSYxTYjf/yxvJ7ZitQiMDq9SHyjzqx9xIopClTJd+xH+7mU7TqfCV/af5PlX82QKElfrHsF3+U3euVSVaz6yaZlKxcRhEyiVIZUssRI3CNiLVDSdoujitL6LkTk7Y0OzTOejfPdcC7aVSeZWRRRR5+61f0KtbWd1TSOerv5+kmBSKqgkY708Vn+JrObA5qrSYtdvjDI7V6TU2Ius5ulwzeB2W7h4ahrJ0NidfpnZ2TxffnKBnf5hllMyeWeYpWKQnOSjoTXAuf1/QdzbQsxX5rZtI+GASFeXh6Pp2XTc7QAAIABJREFUXTjKKWLf/E+IGLicIo3eNIMjBm3mbQRRwHvzFHnFy9JcmkTBylSpgfklHbuisZC0cfn0BO09dcwvlHly8xSN9RYe2Z4iX1aw2STeeeEMvPFP5LIV7u9b5vR1mT0dqxQibdjlCvMHfo1N7psUVIW46sMXsHPj6iqj1g30NqlkL1zigf3VBLpCocLNeIzejXUoxRTLeSefbD1FcHqQgb0DLHz1mzzRe4u5XIBtrSlCwaoEJal6uJFo5OJCPa+cs2MYAts32nC6LOw51E1djYD2o29hGbnAXCkKQN/4C3R+8FX+z+fyJDIwtuwkWVDQdWiJlHG5LaiGhXt7ltEqGnNTCYaiD2LoOnPTGaK+CkOZNl69Vse5Ny5is5g8tDlBoSwSdmRQ9BIj8VqWVzVamqpE7kvztVhkk5Jpo1w2+JPvOGjxp1layvOC/Vk+esTJxJoLZzFOVnchCmAuzJC1BLjdeoRUQWa54OOLf5VANlSeCh6rKgPyIt6Ji+TLEkndz+ySidUq8N3XBQI+ibAzz4HQVQIBCyvP/DGt9SCuLSLLMJ/1knfHULGSUxU+sm2RlfkUi/Y2elvh1miGjX02ImGFluN/Q9lbQ9GwceyijWGzn4StlldGO7HZJO5pHmPR38NAbAVJMJmfyfKTH45gUaBYkcjnNA7v0qvT5w8/zdL2J2i98iOuKdvY1FFhbr5ItMGH9exRfNYCo9dmeeOnN9FEC9cuLTB8q8BSzoViEYk5k+xumKT32JdwTV8jkJlmOf6/dn/4ry2TX6yH4N+6/sM1BJWKwaeeEPEpWZ56ooZ1LXC45gLdPQE6WxV2bxSRJcgk83j9DixmicHBOAVVojlxiaVSkLlcAEEUEAWBiLtEKVAPkkwipfPB7RCBHRtBECmVNNIlhTu3U+StPpIFBbfbWgXg+Ke4IWygvjnArdEs+v/2JYSKSjhsJVW0EV9MYpgQCog0tQWYSzmIrxY4dz6B10ywpyfL2JqHZFYgEHSSKSoIIgTDDoqOEP6AnXLZ4EHvKTI5g01tBSTRoD+6zBPbljgx30VZE1k8dgpRMHjxXJBPHZglmzdZSln4+G/upSFQZDkOSU8jDXUKr81vZKUcRFmawp5ZpLUeSq0DBGNBbpfbKVcEInX+f77WBiLZrEZed2CakI0n+fC6m0RtK0yOzDIw/CLpspMJ9wYa6y2MzQo8P7mV9fogSnaNqF8j4W7gwPoMqft+CVEQUObH8LhEUsE2dt23jq4uL2tLGQL2PKGQnY1rbzC7pkBihQPicZpbPDwSu4DHbtC3rZVlWzOLz/0VD32ok55uFzW1Xrrz55i+MYY8c5u6WgdaRWdh9zN82/97bG9cJJ0sMJ92cEXYysxskbauEEuLebTBc5imwHxCQRCqp9NrWohPHU7Ts7mFxYTE8KyT9Ge+wuYeiXNLbSQLFlIFGQOJiqqjSAZDJ69x3ncY3azSfm9aNnH+SpFkprpY5HUXO3aE6O/zoOkmxWADDbZFPlH8KqeXOvnrV8NcOztOe08dO3t1hk8PsWVvF811IrtqxzFNOHtZ5WcvjDKfsNEYNbEX4gwLA8zP5RFEgeXlEnfWfORUKz2bm1hMyDy8foZcWcYwBCS1yPYHttBlDJHLqpS3388x/SA1DSESoU6+/YaNYkWmO7CIX0qiVzT219ygb3c/Y4tWHjRfIb1uNw/vlZAkgQulDQydGub3Ph1gagHuubeWhYUCtyYNLjV/nN09WXrto2z13STs1RBlkV1HthFkleDaKHs7lljXKvBq6Ner0LbFPCtbHqa7TeG999d4b22A2qiFx7cu8MNLbYiCiXrvEwyfHqKy6wEMA5bnUkBVRpgqVeFEDws/xVRVuh3j3LFvwGKzUPfBd7jc81yVGp0p8eTOZaAqI4ja4miihUxeQBQMBvYOYCLwUc/rnB6xo5x6nQe9p/hM46t0+hdp7Qhw4sQyIz0fZ3tfFYLnEPOMV1pxuapG5SVXO6/ph0nFC5zb9+d01xUZNtZT0QXu60/w3cQRXB4rmCYNoQq3bywytutTeC8fpVzWKWkWfiI+RaFQ4UDrBE/9+l1UKiY/+dZJgulJ6ubP4/Y5edn+DPmiyZ98WkbMJmmcOcmPv3WGW+MVzgxbsFsN6qMCsgz/x75hZjY9wbrt6/DaNbJZlfk7Mywt5Vla1TF7NrEyn0I3YG46Qy6ngyhxIHYD9cGPs1T0MzVdpG7pElabwsqaRmcozpJUj2paiOmzrH/j85iGSdCeY2d7kp4dvYhGhWRax/PCVzjkOcXZkzPUN/uZWrVy7KIFt9dGxFUkaM1gsckUChW+fPMhvvCRNV58fpR6cZa57R/j13eOsiM0yqX5WhRF4GnvK/jcJqIkYLcJZMpVv5enEme3eIpY0CDaFGF8ycrhbVmKNa1016skinZ0u4sVbzter0zAY5AouzERqKnzkpX8ZG0BVvNOHB4HF28YHBxI0Nvt4CfxfRSdESYK9TTUWYjU+Qm/812W6zYhSQJLUj2yrlIXqKBoZVqtU0SzY1gUkd1X/4KpQh1jizYCSoLeWJp/+vpJjljeQK4UcXz0GeaLEY6+OMRUysvWPoGjq1vY0ZlHq22hJnETm8PCuctldsx8H6tFoKTJCIJJZ38DZ16/wPSdFd4ebyOTMzk+FKSgiiQSJWSrhdJjv8b02AofjEV5bHucwbkIzpkb5FULDed/iCu3TJN7lVplkVJRw2JTCCopSppE6RO/w1f+70E8Sh6LRaI3OI/LJWFevUCrd5X3S7u40fgIdQ1ugusaOLfWRa+zqhk/+c40vxQ9jkMu0ROYZ0/0Fh/elUEUTW6OG5x8+RwOu8DCioln911onQM0W2aqzx9vkMV9n+AHl9rpbyqwsWENu8WgtVZnNWvhwjs3qC2Pc3E2SnNHDbEGH/Xf+A1ESUKURHRTYF/lTX658zwb9m/AMOGVC35cNoOppB9bfo19ttMc2ZbC7TBxOCTWRVP0BOaxCSXUisFnnlY4fdtHOp4nV+Dn/gmTrCNC14m/5rmW9zGbu7geb6Jz8nVcNgPThN/+zUbWzAhH1ftwOkS2NiyhxtqYXBRxSXmmJzP43AIfPSSzmtA4f8fN33ywjkxWo/HdrzEQmiXXtQOrAi5LhZLsJJyfQhJMrizXU1PvozZ/m3RBYm0hxXK8ai43tu6jZPcTZZ49G3U2l07QdONnPNQ9QXO9THh1hGh6lOdP+lhMWRFEgUee7CFfNDl7VWd5Ic1716qATuvYVSLvfodC1zZaLVNcuCnj8Vg4/+YlirsOM7IUwBvykFxYoSJYUSwy4bCNq7erE5Wbq2HOLTQzfP+XWOncy/MrB5ClX+Bm8V9Yv0gw2b91/YdrCERJ4DOfHaIhdZ0zV3TuzArMyO0sL5UYua3y5skiTVGDQqZAOlmopql0+Wn0psj4Gjk1VD0J6OkLIisitxcdOBbvYAYiNNWJbG3LUBybxJqYJRi0sZoUGT49hKKX2BqZ4OaVKVaXssyVomxde4V0qkRtnZPInQ8QKyUcNgGbotO7pYm1vB2nzSCdKtMWTJNOFugfCPDWzDqyFSt1/iINYZ1EPM/giM5je6ojVFsxTjpZxOMWWXB0MDeX470rFpazVW3GsZE6Il4Vq2wQ3tZL4/c+yye3jzGUbkUSIeypcPb0IpduW+mor2rcrApYZAhaU2TbtjAV2UmuKGKbGaGuJUTInmHolsrY0DTvf5BClgVcZKiNKrilHBeu5Nl6/2aGsh2cX2jBXxPgSrabnKpQ1Kx0x/I8OjDNE21XkbUS8/U7KZRF6sZOcHPJh6uwxvJCmtXeewEIXj3G2Xdvc+d29VTWJla1+ct1m2gMVyAQ4Yy0n95Wgz98tYMO/xLlskbFlIlpMywsVXA7TGqiNlb9HbRv7saINnJ1cIlCtkj9tVd4OvA6QytR1m+IsD18hy7rGLMTcVwumYqqUXjwE4QceUplk2xBYH/tCADHR+vw+S1cOL9KT0OBtpGXWMtINPrzlDWRckVgJl+D01XNe16/Z4AtpffIV6ycfn8ai6jR2e7E6RBoC2cp6VacNhPDgKVllRVHM6/dauN61zPYLCabN3i4/9F1rC1n+eCqyEPP7GZ+Jo0iVxeb9dEVfvPeGZ56phNFNtENmHBvqJprgzasNhldNwm5VJJFKxffvcHBHz9KKDdFozfNdLwK2/N4LJwpbaOl2Yny3sscjv89fX0+fKkpDu2x0e8b53YyRtbwYLFZ+N7VHnwBO7PzZW4G9uK9fRZFMvB4bQyNCWw60M+5MS8rKyU0HbxeK36vxJZbf8/5Ox4UrYwvOcnXv3Ybp9PCnRvzzOt1SIU0I6sRNB3qvHlqg9VpjUUvEfOWeOSBAB+cTVPRTN4fb2Bjp4GqCbjGBwGwT9/A75Pp3RCj3ldgciqPqlXlfidsRxDbunj59jrWT/+Utp4Yl7Z+jmZxkl1b7Hj9dl4fqmbeW60SVrMqY1qJa2RUBzaHQkfxKvFABwPtGhMHP8eqs5mf6Y9wcb6eQkFj51019Nx6npODOu+8cIa07qVbuElDTKKtzkBBZb//MopFps4Z5+yIhW2JVwm7ylyYDPFJ549RLBKprl2IokliOUHb+3/L0KZPszibwmfJIokma0sZfjbUgtUCv1X8c376l3bKNi+nvY/Q3hXg0fx3KRYNfutLq5BNYYoSH/uNu9jRL1JbI6FWBGrcJRJJnS+90UnjnbeQFZHxRZmLxwbp2NRJU6OLdS1gKDaaO8JomkkuXUStGGgOD5fS67Dl19h1+2v85wNjCGqJjnVBGutkfnzSw1KuqgGfoZXz9/1fyIqEYYp8/Scmre1e5FIOm00k8eTnOKPtZOtdDei6yUB9iqd3L6FpBnNJB+1XfsgX9l3HNGFTn8J3z7fR2lvHhVQPDZf+iS/9pJYzq90UShAJwDeXj1CuCPh8VgShulHTNINZWrimbOODCwXaO/3c1zZOrmIj44yymLJSVEUMxYZDywKQLYg4ZJVMoUr3rX/7v/LacAuyaFLTEGBnv8BXnzdZWtX52OwfY1FzvPBGke9+5QQr80mkphZK2Jm8tcgPjzsYNbqpcWZwpuc4s9pN3hHk5GtX0VNpdt7+OitxjVvpBmbSbnp3rafsCGAikPvBd6i3L3Hvo+sZGtWZXJR4nOf56QmR6979nLfeQ2OzF5/fQqJtB26XSMSexmM30I3qGtTYHqa/IYMkwtauEneHbgDwxLM78XzwEwzdwOc2eflikNZIgZsdH6KsS2Q2HiTlaWCxGGCq3EBtrZ2D+72EclP47WW8r36Tv/7TZhbyAZxOBXclwcXTs7y3/c9oG/w+75zM0J38gFJJx3LfYb721xcZynYQlOP0bKhlzdvKjaUQC8UwFxJdnBkLUdZkaqMym+/dhCAIaJpJtnkjcr7a5GuGQObln2LT8jw3cJWljJ0fvG0nV5K4MyehyCYHHtnAJW0zS6s6TpfMzNgqV5/5Hs0dIdo7vIQcBUa8e3h5eScer425ZYFDm/MYJsiSyVKgl2VnK0s5Lw2+PE6HQLzoYLUcwFVJMjm6ynTKx4F1KwQibvIFg1xZxiKbVISqquAHi/sR5ybIFETytV1kiiJea4kbMzYCYpxD1ndIZ3S+87YHZfg88bhKTXYcWZFYSZjUWpfYus7gwd5ZjuzSSK4V+E79n/H8hTq+NbKNYhnmknYkU0PSyjiUCjsid1BLGlOOXkYnVDZsq0fTQZZg3LMZ78INipKL1ayV255tDHU/zZe+40AUTX6QfhjL+HXu367REckQDDvI5EwkUeDQDo2KqnHPhiIee4VSxyak1k4QBOKEWVzMI4iw59EduKauEvaoeP0Ourb14KokqW/243GJnDp6g3y+wqbINF2RNGFLdSzgc1clW/+uywTDMP6nXv+e6j9cQ6CpBpGWOi7b9xAMWjh9/A5XZgMEQ1bOvHmZQMDOmUGV7o2NuDx2/JkZaiMCmiFjVXME/RJ7Uy+yuyuD12vl9ZduQ6nARP0+SqrA7WUv9o0bycR6uHVjhbvbl9h4z0aKkhtnOUXP5maaWv1sKJ3iQvAR/AE72ZwGpoExO4nbaZIry1it1cxkQYCpWwucvOnFH3bRENa5r2kUq6QjUNV9B4JODm6t8OYFB6ZhooxcpK7BzeqaxtByDZ0dblwumYBTZSIV5tatNNukC8RcKdiwE/vhx3hrZh1H38vh9whMryg0twfZ0lkmUxRxleK8f2KV3liCVMWNrRDHr6+ymjQZ6n4av99K+9y7/NK+VQ4c7uXR+528/O1TRG8cZWGpQrLi47OHZpi9s8K1MZGemjU61lWJiEVVRBSq9OUbiUaOr25A0FQabrzKybNZnrd8koZAkaLNh9tnx//O9ymWTTANtu3rYP16HzMjE5gIOOwiY7kGBMGEchFVk1hNyzx4j4eLc3UkVnJ0D/+IO2Y3v7xjArUi4PcICKaJWqogZpMcOhSjsT0C5RLK0hSSaJLP66zqEbKSn4cfruPd14bZuCnEilRHQaum4hSKJuPlFlorI9SFDCoVg8ce8nPiqoWXAp8mmzfxWzK0BdNYFZMG5wrz00k8lgJdXV5ue3ewmrXgDbrxf/lptrWs0RKpGr3yFQuyZPLGT0epr7XSPPQS93QtspRzsct9hQ11q+TyBrVNPkJBGVEUaO8KkCuKSEaFjOrkGyeamV6oLqYhZ5n6ygQJTxMul8zyfJp8TsWhVFAkg67N7cQ/998wRYmZtBeH1UAwdS68P0rAUeKFb56kfM/jvOT5VcqqyahnJz5bkcVKlC3uYaKVGdZviPDk5ilSiSKN9VYWc26Gup/m0pgDQYDf9n+fieF5pmeK9HQ5GJ+sftfuepXl/kMc7FngeGIzJ5VDhOqC+P0W7j/cTv/ymwzXHaY3vIwswcCpv6TDv4TNLiP/41dpciwgiiZfeGSWWKgaB7nbfJeoV2Ww45dp39zNcPdTqJUqgVczRLZtdNLoTfPuT86y1XqZqaYD7O1cZartPpobbfSr53llqp+VZHW5dNgEgj4Ih6wsaTVUsLCjR6fZNouumyy52ykJDiaXLLS+/VecnW0gkYED0SFqY1bCPgPT5eEje/N8+vf2EBJXsRXjTMxUKKoiad1LyhLB67exWAzw691nSEbXkVMV9rfNMFJzH/UxC97LR3n3nE4xk+O9zV+iXb2O22vH9uVP0Rgo0NoZQpGhOVTka54/4OWFbcSttfhsRRRZIN60GUGAj36yOtFUvdGqhOhb4yyu6MwuVcnsLpfE4/dbGWz8KL09Xu7rnqdzyzpidW4yWY3pJRElPo/VWr0+d++ro69TofzT56l151AWJkAQmTObKPuqsqTgz8FyewuvcGE2hkvOkynJyLKIV85w8ICf+bk84vIcuZxOODXOPZkXmJ7JEYlYubHgYzwTw+lUsFsMEv338f2JnTQ3ObnLd4OGWhmHQ2FiDua2PMHnn0zSGsjQXVfEbjGYGM+QzVeb7Ey2CqpSVZ1WbQTTFNi6wUm5bDCWa+DmnIPIuRdpC2eZmAPjxDGGS100Rk0iPg2bVCbo1nh2/zxSZw+ZnE6dK0ks5uT9ixq9/SGunJ9F79tO0ebjDx+bo3fXerbsaiLTuIGGt7+KYlH44pb3aZfHKesK8VAX93rOkRTD2FwOzLsP8Y/+3yYalnFZKkRcRbrW+VmytaDaPHifeAKLUcLrFvhs3/tsbCmg292sLWfpKV2kwzFFPq9RH5WYMZswTRhPBllOSdhsMoZuUC7rhCxJmqIG02sOrue7GT43it0KUqyO1nVRwm6Vx7ct8+YZkY74GZI5mX8Y2oDv5b9DxKSgKiwtlkjnRX71HxrQDJEzB/4LP3jHTYdzmkxGRcDkoSMNrPdO8KPI7xKKOJFTK2SzKslAGz/+koBFNqhZGcLrkZks1LE3epNkwUJbIIFhmDS7liiVTUIROw92jrFnfQnX5bcpBhspiG5sso78id8kePUYN7ReCmWRP7zrIjXuEh6XQK0nTzarYVc0PrR5gYW5LOWSyt7y67hcMppmUtKqqTs+l8HMRJyt7XkqhoRN0dkrvEc0MczxW7XcZZ5AM0XyBZNW9xLzaQfjRgcDW2LVAyDdwujgBA8PzFHjymORTSR0ru/+Xa4MroHDyUeUF9EkKw2BIlFpAVkSkEyNFW87uZzGh/dp0NCG1SqRcdZQW+ckk9F4f7qN8UULOhL9uROoqk5bTOXBzRme2XCzCvMK5UkSQtRVbLJKID3J9OgC665/n5qwwsJ8ntXVMsWSSdvrf45pdaCYKrJk4pXSrL/5PQ7cE2MtCbvalrm94RlqbAlmUy6W5jPUBKt8gddPi/RuiFGoWJAEk7wzjLk4h6WQxETg2YfKzM9kuW+bwUrXPqZXLNjsMm2dASqSFY9HIV8wePDxAdaWsoQyk/jlJDWvfZXQwjU0HVZWSr/I7eK/qP5/ydD/h8pqk3jqY610SaPUReCPfjvM/GKFuhqBz/7+Vu7eYNDfa0MUBe7a4ceaWuTs+arByzV1lUzOYKlhK9MpN7EamZaeWrTZaQLaMpUKuOwGiCKXS/20d4fJVhz4/TYi6Tu4Vsf+mV4sGDqt1il03eDKqdvcaH8SOvpYS4Jd0bFbRfqjy+SKIo9+pIuPd1+lvt5FzJ1lulxPh3CLqC1OrTuDWtFZy9upjSpEwhYGN/82bpdIW5PCEelVxsZyRAICxYrMbutZdu3wcjS9C5tYRhy+xGq4h93Nc3zusTX+O3vvGR3Heeb5/qqqq7s65wYaOYMAATCKOYqkSAWKtLIta+Qkp3EY29fjmR3P2mPPjMPa4ziWHDQKtmVZlpWzKIqkGMUEAgQBIufcQOdY4X7oPff77r13dufMPufU5+o+p+qt532f///313RYimpomkE8ayaagCVrObfu93Pyurc48jx/AkdqjucfPUGN1k91uchU5SYe+vII758cp8Uxwj2f3MZk+0FuaIWW+Ele6G+hsjHElzxPUpPsZl2rQL1zBlUTaE2dRRQN7GYVl1VH0DVO1j7Ezi1O7lv4IU5zlncXV2GSJc5t/QYVIQMtWpwMlAdUtty6HouQI5XW2fjO16h2LpC6cJFdJ77C0Fiem+Z+zfnLcTrWlqD7i0myP3q9nEQaxiYL+J/7MW1rwkw9/SJjU3oxoXn9QWZb9yFLBjPTSc4MejAMgQpPmm03tbCiIkvT6GvkVQm/Uysyy2MKI3ILq/yjVJbJ2OUCNRUmbBadoFcgrVm5Ou1BNhn0RMopKXfjkFKk0jots0dYWzqJ3Wkh9feP8cIZN+9eFBlfdtAhXGaTv4+9tzWxEFH5k+8LjMaDbLZd5IfHW7k6G+D0O/2UhmQ+XHGCXE4nldZoK4viiwzQxDU+vWOYz9a8hduqouoitvdeJHzmj6ytz/LgPX7cHoW8JtHmGaXz3U5ev+ThuqmDKneMyXkBUzbJnR9qY4Xexce+tBPLu89xePBbVJYYVGlDnOl3smb4KTKyA1UyMzIcZzJbisdnJZUx2LfwOI2ZTu5o6UMQBX4nfoR9B5v4653Fqcr+TQbtjQJHz0NGsHN1Icz2kl5uHPk5HWtC2G0CE9N5rpftpe3q41iFNI2+BY6u/SY53Ux9nZ3JB77LW4P1AJyLrsRi0tluP88lyw7sco6sZqK03I1TSnJTRwSnQ8QiFRia0JiMu/jAx3fgnL1OIDtJw+xxqk8/jsteXLArgiomE6xcYecB78uEXAUmJlKsjJ9kMe9FMwR+dbwaSRKoGXyT4O+/xb3lJ9E37eG+yI842NSPWc0QWSo2wqPVN3Jx3I+qCchajrQ9xMRYDJNkYJMyTKaDzExEyasSj49sY14I0+4eZjAWpq3z15hl+I37bzi8U+WBv9zGrvxrvJXcxrq1Hga++Cx10hCSJHBv7UUyBRMBj0AsrlN55GHaLj7M5FSaWcpZWspxKHSa8XX3YInOYDHD3/9ViLf/fAG3UySWsXCwbZwy2yIhS4RMrmj4tNrNBAMyt23MsKExyVTjbjStaFgvqMWJmXPTBlqSZxhaeSfdKx/kj0dNGK/9iXxex2fN8uCueb47eJByX55/e9vDckKkvLIovzEM8AesPOH4IuESE3FHmKvhmxGFYgBUfShFo2uSynILFe4E1uwy6xvS/PnRk/ypv53bg6f56M5pPmd6mLhWTG8t6BLjESsmyeCLh6I0VxSoqZSJxYoepA/tU7kutrO6cIagM48kCWzSjtNWmSKzakcR6+oV6bz5O2yLPEMkJpLJi0RyLpyWAnHNxWz1ZrxuCacYJ5vV+C/rT/FQ+ZvcfrgaTTKTkH3MGBXs211Eew4JTUzs+xzN7WHkqUGUfBzdEFEKKWaVWmoXzrL3YAtiPsPGqpniNDInY5dznHhzgIaLTwIQDTRiy0UpFOCV9F5++ugC/Z7N/O0DeZSBS5RcehmPR2Z7xSDeb9yD363T7h9nX00/i/MpKqpdmGWJZy9WYDHpjE2r5FSRLfs7eP3lEXSnt5iZY8lwtL+cv9i9hGl2lM3lI/yV+n3ihz6FR0mxTXuHlmYrggC/KfsXTKLOWnMXpWGliM10mknLLjx2jZKJ80xMq8zPpUhUttPc6MD2hx8za65m2/Av6fHuZnGpQLVtBnsmwv7sn2mePsLtzf3YtRjH3x5lajzBQiFIMi9DZT3LjnJ8uVnKnFHsXcfoXvUJ/EqCteXzvKdtQxJ1cnkok2coC5sJWqI8djSIYpVZs7kGabyf4cEoqZRKiXUZ1/B5djgvsXV7GQYCv/htDIe5wIKnnkHPBh4yHkEzWZBFjZKASDA5wu3xx1g3+xwtNToHhRdRpBz772in6v0/4JTTlDmT6IKI1ZRDLWhgc5Dw1WA//iwFTWJWK+MDVRdZJERU9aBpOr0zLoRYhNHBJSIEKQmITE/G+ZD6GCOjafoipbyu3cyHDtrYrJ9gLulkuFBLJGp1CVnpAAAgAElEQVQwHbNSlbqGvDCJT1pixtNKdHaBbP0aZmbzrGp3IkkCbqfAwRN3EQm14E5Mscl7jfDcZQDu055ElgVqF8/R1P8cCzkfNd4ErW1+EimBVMbgSzv6uHppiqWUjGLK47/2LkZlHWlnmIblc4xGfXz8sMDkshVrIcHa6ih1NVbMsoAtFyWRVCkLCZT4weO3c0zdyXvjtURu/RSj5TtYVzb979Yn/s+WgYFh6P9D1/9O9Z9uQ+C2Fjh3OYtrfoCRySKJ4dDGKPsCl5hcMPHK8QLzS8X46Z7rGRBEbr3RjkXMYkSXef/EKDkUwq4MCxGN1hUORLudIb2RpvIsbf5JNHeAetcMhmHgkDN0nx9j2VVF3l00O83NJFny1hPqfouJ4QjrtjfRdulXiHOTDA7GaXaNMzgQw6Mv4rFrZHPw4vgaBvuXuT7vZsPEH5gQ6ygYMvFc0UgXcmTYUz/C1FSW9T2PMDmVRTHrdLl3s6q9aOgqd0bpM63irsSvCTrzuNQltPaNhI48Rla3cDVaw/ScRk25SCpVYLt8kk31UURBIxIX+ZTyBOWmKYwNu+ixbuTgg9u5kFvD2JTOfD7Ab/6lBrvTygu9TcX7TZxlLiYT9dZS4jOYm4zykvNBhu2r6BkWKX3lp4zPGJgXJ9mQO8aW9OvMxyTydl8xyEQ0SK/YiG6I7A5cYbBrnM1LL7AQFcluvZU3nzrF08/OFTWrRhaLWaD7wD+SUm1YQgEmbv8aX+04QW/TXXy/40XmZrPQ302TPMA3dlykMqgRLpGxtK8iEslSft8hJsbiXDo/i+e1R/G98gg2s4Za0NjVPM9Uyk/vdJGZ3zlk4VjwQzzxTAQDgfUNaQ7xHKXCFKem6hkazfHORYlTZ5Zo8U0zPa9TU+ijJpDBbta40F1AliWSmh2/V0K/eolTY5XY7TLPnPJgt0u0NcmsKZlkyVrOheVm5hYKNNZI3F14ktX2XuSXn+RLu66zu+Qq9z+4goVFlR91bkb47291z4wHQzKxZAkzkK3l58P7MJt05uJmhJZVnOr4Cscum5iJWigvsxCyRpnNh9hz9xb2rE6ymLbRPe2lNqyzHGzi5ecGOBLfxMVLUeTWdjo3fpkjx6Jc01eytFxguPl2THqBGaGC+allblh4idOvXUBVDTSXj2lHE9NaGSPXF7ipqpfx8SyPXF5NLg/JvExnn86ndwxTPX6cJx7tI3D+Rf7rwqd56bdncTsEErEcdjFNtn4NF+ZrqUj1sV08QTxvY2Y2j2ZIrK9ZRjFprPaPMDYn8ebSBmqUCfzmKB5LmmQihy8zRV4zsxzVeOuilWSiwFb7RRw2gdGaGzHnEpxwHmJh053ML0GfdR0blE5yeZhd0Ph+334uD5goDdvQzMVcBI+S4e9aX8fhMINJxrL/IH+Y3EbUWUG8ZTsj2SquqKtwOIq0k9orz7KjdgLZZBA3+XBEJ3B7rciSQe3oO2ydfRqL1Uy7/ToPhV8jwDxQnGicbPkiZtngY97nmYrZefo37/OKfpASZxaPQ+fo+zrff6uOvq4ZTibXYjbpbK8Y5OaOOQy1AKUVRCMpGl76B+prbXQaa/FmZzjt/wCzCxoXRr3svWM9BdXApeRJ6Taeu1iGiolEQmU2VQyym53L806Xg2NdNkpnOxGEYiPf1xujrz+F7vIz5W2jevE8Le98l4/dFEfeczM9l6dYzirMZv18aNs8xy4YbFmn0BxOca1rDkVPYZIgm9W4L3CEmmAOWy5Ke++TjA/MYrPJLKasDCfL6emNoxkChacfZe3yG2y/fQMP1JzidG4DX/2RylDrHaw4+VOOLK3DaspxT/5xoimJnGEhlZOYXdBIJfNEMzILKTtrO39Gl3kjY4sWui5M8kJiHxeHbCjRGXKazPhkng1XfsLp4N3FYLq8SKkSKeJ2RwK8MdCAbBKw56IcffYMV5VNfLdrN8dORMg/8zjXlsopEWfoHdaZm03T9tY/UH38V4SCMnNt+zEEEY85gTU5Rzg7zHRoDfMLeaRMgtqp49SUapzp1JhOuGhor+CVmv+LjOwkJ9mQ8yme//1lbr/yNf75swZWMYurEEEvr0WrqOPKhRnShh3pnx/BqaiYjALThTD9lwbI5XTOvXWZh1rfJ5I0cfeGWQ5Ef8vEyDIunwMxFsEki4SFSXY2zjIYLeFi08fQMHG6/Ut4336SkDFDweIgXygS10bu+CbllhkWzWWEfFChjtDfPc1ktpT+CYmnuZ+mWolv39wLgsDcQgHjjo/yyqUASy27aHn7H3E5TRQw82ZiG7/N3sOV0C0sGz4WhFI27axBVYtTnWbnOKlADaFIH6pkZjnrQG1aQ9v0qwAIgsEN9m7KrXPEExoJ3AwOpUhrVg5uVRm4MorVInKu7XOUlDkRBAGvOs/wikP8aWw9b7x4HZec4uG7rxGwLDOUqEBCR3d6EQwdWdSYW9SZsjfzXumHMWSFoSmJx6OHsQkpNB2+OvcZJpM+Lo66eau/jgvjQbbtKEV1+piUapnf9wlc5ix9c25+crqN3jk/tYVe0ukC+yu6KZTVkc3kaRl+kYkZlc2b/Lzh/QsObJU4kH6GDcFBXjqu8YeFvSylTFRbJnE5BFYG5zAEgWRVO/7YCFWnnwBAmeojnS4wOaNy/Pmz+J0aP/teA3lJQTeZuRRvYSi4jXTdGs6W3A2AlE2x0LSDMsss4zEnsZhKS0WGl393liG9kTUbK9gc6GM+6SC6YjuIRdF/j2sb6929/NtLxYkFQCRjQ9eh6/IctqUJ1IKObsCm0AA9Z6+zP/EUN5V3M10IowgZlvMu9m42/f/dIv6/q/9DGfqPVQZw1+5iGmdlWGRg3sFixsWlbAeiCJqmUxvWaapXkM0SWs9l7JYCWd2KIJvo2FCFLOSpksc5/94w8aRBcmCETMHE+KLCT553Ic2N0xMpp69rBrOQp2NDDcGFayjjPVjtZgRRoOTaEZ72fRGzIjPYO0/32k8Ra9mGz28lh4LHZ8WsZsmrAk67wX2BI9Q1eFgbnmGk8WZMokpJdpRW0zV8vqIe8aWrNSwvpTla/wVUVUeRDdZefwzdgMZQDKuYpTV3kV9qD7F95JeooowmWzm/9euouol90adorQPFXHxQr0gbiOaK+MdU2qC35jai+MkpbjqWjxQJPJrI678/ycpXv85Tp0opq/bgdQmYZYFXrfdSH0rhP/E0271dlFX7uH3iX2haPo2uG5zZ9R3yBZ18oILL1u38KXeYZMpg1lqHU85x/FwG28gVdOBj3xXZsreZVy33sLKyOGXZdvtGPvVhD063hZxgZefKOAHLMlYpS3rXXVxfKuG1zB4kdGaqt1BQNcSSME/1tPHdcxs4e0UllTH4tfZx8nkNw2rngYMyHWtKyO69B33nbUTTJsLlTk4Pl1DjmOVw+BzXOydoqVLxKRn+6aMJxmcFusftnHbexhxlHFZeJZnIY5ZFWld6eaW7ArutmBRplfNMLcn4/WaiSxnC+gSaDsv7/oK7XW9gt5uwWSXsNpF8QcCgiEf02XNsXVWUkGmD11mWggwe/jYRUykD+QZMkkHAb+LzN3SSTBRYnEuxoaJ4omISCqywDLC5NU88K7M6PIc4O86mzFvcuiFNdSBNJmvg1iN4zXGyWRWzqNLimWBz1SSqLmBWM6zZXItD0VheTKKbrbQnTvCRQyZKrEtIokDd2BE0wcRSxk42neWI8252Hd5AR73GSGgL81kv3ZNuapuD+Bd6Ge6d4aF1V+m5FsNmVgkFZY5OtTBUuYdnHrhKcvUevql8n623rCXgLBQ514UIplyCVaEpVJPCuKONEmUJm83EpXEfflOEVVInVyI1XHh/nkPK60wXwkiCyoVRHz6/lVF5BfVqD1s7VA5tjOLzWXhjcQOz8wUCT3yDJXcNO+IvEJy8VPQKSAWen1xPbUmBXE7nv5h/VOTFL+WZcTYSGDmHJBicUG7F5TJxInAfUibJ/aVH8S9eZ8JUR/uzn2fbyK9RVQOXnGJi1WE0QyLozDOZCnLVsxOfT0EUYKB6P7GKdlat9vH6eBtnLXux5mJ0RetxWvJsTr7K+LTOi/oHWBccoa6jnpucpzjba6bDP4bbLWOzydQ2l2A26Swmi2m2ac3KtT1/x0Llehwuheu3fYtMzqBV7kPOpyizRYgu57mxYZzjr3bRVpVFMwQWMy6+ZHmYkViIVFJltXyFqhoXhbxOfaXA4XVzXHAfIFwiEwpITI8usLrNjtF7hVB6lJ+N38q1G/8WB3HoPMOmHdV0OAdYH3+LyqOPFJ9pHU5eVdi0NYwjs8iBwvPEY1ley+zBb00St4ZI16/llsONOJ0mNru68FuLqaWTMSen73iMn818gHCpmUmliX0Tv+CWw43UX3+RsZ2f5I6Zf6Hp7e/RXXWYSl+GyaQPq1lDFAWymQJ2s0YyJ5FYvY8b5l9gIaKx/5ZqcgX4SPk7zIdXsVo9h6YZdK75SxYSFhrCOSZmNBQjTa1rjjdeGmBbwxwPRr6HKplZf9M6fv74EqtaZLZuCWD64EOIAshajj2rMwiiUFwD1+xEVSE0cYHXlrYgYKDJVuaUGhQ9hcUicjZwB7+O34sgQGw5Q6VrGcMw2Ok4j2So/OKtMOKpN/nwJ9bw7qZ/5pWeYtbOw+daEUauo54+TkNLiGB+Cl96itmoGWd2kXJ5mtqVteSzGh/9whYe79+IrgsMxkrpq7mVNeuD7NsTYHLlLcxOROnNNNIXCbHCO8WqE/9E2XIPfiVB756/wRWbQCpksZhheFpkxdlHeLGnnqRqp9yTZVBopra5hLXHv8225jiyyWB2ER7t20hBUkglVWaVWj7T9B55SUHqWIfXLVI7/DbbQr3c736ZNWPPUB+7SH30PK3VKlMD01xlNTkUzPkki74mvBdfxWUpfiNesX6QwSU/4ewwv77cTsEw43SINJx9lHWrHLQNPM3AvIO9h9roqFdZd+ybRBbSaLrBnFROzYlf8hf9X2HFmioAnlrYh9VIsS35MlXJq1wKHkSJjOMxxdjWmqYyfpXV8hU0qxNZFviI5wVkPcfA9SjVVQ5qnXPsbpzE79a5X/gtbrsOgkA0Z8OVmSejyawpn+MzO4ao8qV5amwzLpeFMa0GsZBl9Q1l6DYXiViBi5eibLVfpNI+z9uOexjLVWA2S9xVfoa7Zn7IO+PN5AtgFdIIuoYls4yUTRC74Vbu+MQODKudGzdb0TSDUG158XuoKuR0BXNqia1KcRpgPv0am6afJpXWORc4TEp0oSLjVgpcOTuCLGrc8sGNLKSsWMwCs3oZM0sSuiCR8ZazaC6jfekdCpLCypVueqftnEqto8KxxHvHJmlYESTlr8bplBkaU0nhxFvqZ7DqJgYL9Sifuxnv0z8gmlGYjZr/vVrF/5T1n25DkNdMrJp9DXF2nHxBoNSTp8K+gMuSw+fS+W8rn6PWu8T755cRBZBcLhI5M7961YGezTI1Ecfzu+9yan4F+w82Ulli4FrVRqVjgUp/joN7bej+MBXuBGs2VrCY8xJdziLoGnOtewmFrAiigOH0sLlyglw6x7pN5bSPv4BcSNNQY0Y1TJhkkaglxOC4wXIcrlg2E/RLxFU7VXPnmEl6mFVqmZcrGBmOc3nExmdNv6SiysUm01kcDjPZgsCv5c9jGMX/bTVSnBc243UJTHXcRnDiAsriOH4lzoroSd5w3Y/PniPsTOJwmmmjk2brEPWJy4R8sJB2EdBncYxdoWDzMbeoEXYmuPuTO4ge/iz3blnALIvkVcgXDDb6+vBakojlVfzkvRUsL6ZYXH2AMf96JscTnOwEURAQ1TwmUWdH5RCfiv0TXnWeE9ecfGifBmYLL7xn4ftfL0EQoMyV5ninCSVeNBRem7QzN53ARIG3O528erUCtx5BeuYRDnR/k6bgMpXPfAMVGatVJl53A/e29/HgjctUVVhIJlU6Ly2Sz6oI+Sypgpmuy3OY1CySmiVXKL4iK8rT/OFsOUnFjyfkZmjGRGOuk+lCmDX1GTbXR3CZs9Snu7hs34XFYqKxRqK+XOPj1e/S0xMjYQ1SJYzREk5ya/sUtfVulk0hGsvyeF57lNcLNzE8uMyXvU+yv2mEmkAKT2EBmxbHZU6jGQKVnhTPtfwTpdkRagp9JFUrl4YUfvyP7zE0nOJXXetY126hvcPLcDxEyhnGlo/jneujoInIkk5VvJtU6xZeVm9jLOoklrHQc2Ued3SMvqUwseUsnZN+/vbnMJvxMzhukDJ7mJ9LsevMf0EtFGUvL+dvoXfGxULWQzyeQzdbcWXmMZs0FJvCnivfRpIExhdkql/9b4SUZbbWTFMRNnM9sJNd+2s5FeugqtpJlW0Opw3utL1KReIaX3jvRhYt5WhtG/H5zAzNmCgNyeQlBU224ipEiCjlDEf9RPIeZFnAYTVwp2fp0lcRcmSorvNxzrKHevpxp+e4pfYafp+My5QgqfiZiVrws4BiEbjV8x7VFTKj9/+AqVyYH8/cySvWD6JqBldnAuyonyKviRiGwZm2L6DqIh6vGRGddFkzNinD7pnHGBmOs6P/Zwj5LM8t30jSU0nDi99g6L4f8kbZZ1BVg1NDIUpi/Szl3SgmFcWksjJ5hrISiZMX8jRe+i2OE8/R3bXMLVXdrBPPY4gSgmCwPnOUf505jNctsTk8xHwhwIpWH7/p30JbvUFMc/Mly8N8Yes1ysJmdhVex2LS6Z4P83ank8ZcJ67UHKESOx1jzwLwiR+YmHS2EsqMoek6x0eqaF5XTyxrJpWTWSuc50/ez1HhXKaxwUrC4md+PsPsVJSu6yqD0RKqLZNcuLBILg+rN9UwOasxuudzRG1hHmp9n47Bp/jjlUYSu+4lkVAZzVcxHNxMYf2N2Gwmhifh5JvXSKUNktYAp523oVhldga6yWkyuiBhPvUqmga6DpfTK6nQRggGbTT4IhwY/AGfajiBpkFON3Os5tM4rAZ9TXdS2/MCn+t/kOWbPkLr0jGW0xbqnLPIko5iEait92IYYLPo2BIz5DxhXv/DGTQdbi+/yAV5G87sIhFXFWVhCyvTZ5DE4sleMqmSwM1S3s22vQ0sZlwsr9mPkovRUG/n0w8G2OM8SypjYBu8hG6AJpowDAG7Xebo+zrK/AghHwipOKvL5vAXZrD0nsNpRAkOnyGT0bghfYRP2n/H6csagZCdWN6Oz6fw6sJGZinnv254D7m+kbwKe1LP8WD1CRpCKdJplZkjp8nd8gCxWA7HwiD5Jx9hKWZgn76OIxuhYYUfsyKRzRXX7Upfho3KJaoyfczO5RmfMbDoGULlbtaJ5yl3JVENE2N7v8D7ll30LQRom3qZEc96LMvTzMxr7G6NMLblI9zUOkOt1keNbYq1/U/g91swVVUzHnNyyPIaikXA5xFJmdzYHSZq508zbm/FvzyI0X+VbM5grG4Pb0+00ufZxsmyB5jwdJCxh1hMyBz6YAdBJUpKszHgWIdSSGJUN6HpElI6zj7LMW6LP0na4uErFc+jGyJuh0B69S7Wl08x0nI7kgjnT03wra+fofvGr1NV7aSQ1/CxyLWtf8WnZ79MVaUVRcxyU8MQspbjz9qd/KJ/Jw2PfYrzJXegIXFt2kHEXVuc2Chukimd6dAaApOXKa9yMtAfxWJk6JwrZ3Je4D3/vSwnRK57t9L6+MdwjF1BEgzOj5dwbKaVnCbxwNXPk04XaE+dote/m5HhOE/l72ZuOsaeHS66tA6m0wGavDOUKhHaWxQe7dvIyRWfZ3v1GKpmENU8KFPXGfWuYzG0Eud7fyaRUPmd/gBua475uRT7bmlg13qBzjEXnbNhZoPtdBprKQQr6d39Nc6W3UddBWzq/QUCBqXRXmxygb23NTKXtJFKaXhtOY69OUh9rpv5iMqIVoclFcGlL5FzhrAWEgQ8RfPyWk8/1UsXqVsR4q2nT2FNzhPwSeRyOlMpP/WtYeoWzuA1J2n656+RuvsvOdWpc+L4/P+axvF/oP7PhOA/UEVjGsc9d6KX12K1GAxMyfQvhznebWVgzOA564Ncmghy9WQXsllEz2Z56plZbttlJnXDAeJLSRbv/3tkk0EqYxCJiRRqWogWnLjMWZI5E/PhVUSzVl56/D0AFKsJIZ/FH+lnbCRGZaUDY6Sfkme/RzadY3o6zVPSR0AQGJtWcRtLDFydYToTRFFEpqZztGmX6LwSo0G9hhRdZCEuU7twFq++gCSJHGweYLl+E2//8TS/HdvGxMgysmTw8cVvE08ahJRl3IkpTKLOzYFzXI9WkvOWM153I41jb/Jc9iALMYnltIWpuKN40mZpQDB0dFFG0wW2dX6X9+MrSdSupce8nvISic4JD8feHMSeXSKp2rHbJUwSKBaB3nQDSzknZNN8fucAmVSOpOShfvgNWld68Hhk0mkV3WSmWesmtHiNF+r+jlmpguZqnemEg776Q3x18yVODJRw/uQIZkllVbOIphR1j3saxhBFgbLuV8nlino8b2QQ8213cXL91wkbk/R84AcEkyPouoFjYZCv/9bH0f4wNSV5rnVOs3ptgGCpgxOlD/DkM4vYHArW0W5M1ztRNbDbTSwkFRqqJR59r4ryKg+ySeCn3Vu4OFKUYzVGz9I2/yaW7pO0cBWzIlHuSZMtiPxqeBdbNnoIvf8cANdnHRQMGbdTJJCfJpYx8aPA97jZ9AbpRJYn+ChnpmqpUGZZNIXxdx/h6kwAk6hjNeU4rLyKKlmwvPscqwef4rPGz7j30ztobnTg80j0DGjMLxTYoHTSrzWhiSZeM99Jz5iZoD1F2hZAE83U+uNoukAqJ3Lv4aIzrMazRCaVY3JGJVDmZT5hweeRyAo21q91M3jTV2loDfN04W42hEe50p3gWKeFulo7i6VtOHreI5WTqWoMsbztLvq6ZpBNkNtzFw+/4iCjK/zu58dp7f0DyzGNC115ZmbSxDUnPqdKt3MHr6d3c9ONPmqvv8pxcS8D16MIgkBXdxSlkCLhKCUjO/EUFrhRPILXHGd+PsuO4FVUSUESDNZe/DHrWkXGFy2IhkbMVsIP36hicChBTHUxmS8nlhR4Y6yVxnKVjOLhNz88TkBaQBI0Pt7RyfrgMIsRld2lV3niqB+7WWWgZ5Za6wQmUcdkEtAMCUMQmUr5QRSpqXGSXrGRbKiWA+VdCIaBse9O0qqZjc5uJEnAqkDO4sJjTpDMy7ToVxj1rOH0mQh/t+EUWkUdZ7f8PdW1bv7lSB2mfAoDkQs9BnJ0nrWNBRIpnTcGGuidcZNMqridxc1e97SP78Q+xXfebODS+QUu2PfiteWxmnUqSiWUyDgTX/8HFEUkeuRddM1g1eZG3MYSry9vxuEwUxvK0brCRSYvMrEg0S+3U1Dh7Z4gqbSBPzaC3S7jCznxeWVKHUmGMlVs2RzAYobxkSgVpRJmIc9cPsj5wnp6m++mUDAwq2mmJxPMJax49QU0WSHgk3A5RLYfaOXCqTFcqTmyahHLezbWhs8c49xsHYt7P0pPb5x0WmWVow9NlOl6f5y6RCezN3wAKZ/i0vlZqrVBZqNm3jkWIaNaGG79AD/f9FqRxuOppMyZpOT5H2IYAsmUzsx0kl/9ZoRkViJn96OMXeX+z2xjKarTq7Wydfr3zCk12PJxhkdSvJHfRzYv8ttnl6kst1C9fAlFytNzZYGFpIW47MecXmZkNE29PMxDvwqRKwLgWOUbwbvQz7VpB7Foli9vvYoxP4MoQrRhE0s5J/b4NKmOXUi6SrK8FbNZJOmuwBgdJJdTqSyXsZtyDPQt8srzw7ikBN3WrWjeELGEwUn3YU6qW1GkPF/YO4bFbceci5OIZrgQOIhne/FQCEliWG5lqH+JTLrA9GyeL3sex2HOcTG/GumtZzn+/Fkun5vEP92FJIn0mVbRkrsEQM38GVrM/Vy+muV65QEcYpG8NDYSxypmuTRTjoHAqwsbOTbegOH2M3B9mUxlKw6Lyr/N3QLA4pL23xHVBaRkjL7FAIOeDfTu+AqjY2lCyWG2Vk8gCjpmSaUi3oNJy/L+pThLUY1riyVcm/VRoY0watSRdZUQkBc57TnEL/p38uv8g0QIEgs2YhXSnD4XxRoZ5/JMGTXjxzjoPIpskdn5gU3k9aIkRTZLpEUnLZF32XtTMaPBoUYpu/QCuiCyKjxPZVjC9sEHWSH0YtfjNJZmMASBY9MtnC+sZ3gwSudCJZNV25ieTJJJ5ZAMlRtKRwh4BJ54eoHp2QI2KYN393ai9RvJqiaudCfQdHCZsyzc9VUWZuIUzHZKhSm6T1zhwzxJa0eIpYSISdSJZc3UDLxB5bFfk80LeFwigzMWnn6/nE31UUyCRqJuHeHsMN74OP27/or52RT3ed5gatnK2jUejh0ZI6+JbK2fp9qXwp1bIFMwkXKVUaUP0TmskM0LzL9zilNjlYy61vDWBQvpTFHam0zmeem4gTfoZtjShtctceKqlai3lguxFk5kNyPpBa4NFIgs6/zuUhOjvvUYusHGm9ejyVYKahEDPBkxMzm6xIvaIZouPV4MQc0neGjHGHMT/7tvCIx/16Ti/6/rP92GwKyYuDYmE/fXYZENxsYznLqUx+2SiESyXB9WWVjS2f+hrexelUMKlfLhe0qwyQUkNcev7r6KLzeDS1ExdIPqUAGp+xwmQS/yoQMzZAQ7TkuW/R/aShPXaG9RyHnCmEZ7Ka90saLaQKyu55f1P2XrjfUoiomO8mWmrI1UlJpwZCM0toXZNPQoDeU6DXUKMXuYmhonl7W1DDbdjtdRPNE26QVKwzYWtBD/1tlOuKGKbc3LNLb4UWSdz49/Br9HwEKWLvNGRNEgpoS4cfAnKCPdONQo/5p4gIMnP8PO2nHyqsDxM0kWFlVMgoYtu0xG8VDtT3Fq9dfYbj3HmNRIrTRM2JPjlqpuSioDFB77GaKgIwjQXraErsP1SUg0BeIAACAASURBVBm3OUWhoollMcjOGysoefqfMGQzS1GVm9umsdtNSBdOcDS5iaPSLRwe+y4SOuPzJmo8S9S+8C0i1gpqSwo88OFaBhbcFDQB1aTg81lQMWFWTBieIA9tus7Hqo+zFGjigmUnsYyJ54c7AHg7vR1RFLgSuoUD+0vYVF80bO7YU03/QAK7TWKFbYiP3OvH7VW42PgRjq38Ki3hGLIsFBOnZw0+t6kHm83EhtoIa1sE7q1+H58lQcTXyAX/rcxuuQ/74khRGiBq5AsCrbU6Vf402ZVbAFhTuUiiYMPtMLCm5gnYc/x14AkuWndz+FAZsgliSYOsYaUsM4gWrkbV4GQnOKQUhsnMdX0F4vqtdDd+kD+4v8CqmjR2K3w0+1Nu3ZAmGDBxLLaWdvUC3uNPs819hU9M/m0xQMlkx/r2Hzg34OLdE8sIRVUSUjqOSdBo7Qjx18rP+NHal6jwpHA7jOJmT9ExCwU+tHOZD4p/4E8Xq/jwAY3PrL6IbComcWab1tPuHCCVyOPvPc6WnZV09SQZkVv4xt4e5lNO9ty9hf7We1jdBN8se5ypkUUakpcwSzo51UQ0IVDri6N5Q+w48dds2ujlrpqLReSl0IQuSDizi9hS8xxnD2/2lNFQZ+NSooWLuVWElGUGNn0Sl5Lnrq4v86uudRQMMx85kMGqyASledZN/gmrBW6sGyanipjVDPf/5U4WtSCV0jiPXOzgqfO1lJXK9GUbWdms4FEyHLy9kuDCNWJZMxazQLTgRNA1VstdxKtW03lxFqmQRem/wLBWzxsL63l0dCc3xF/Hc/4VABpDSeRCBknQEAV4amwzr3UG2b/Hy6RrJfRcYuvwb+jpnCWXVbmsbGdUbKStSSJd2kDXiJkV1UUEX1tZFLfLRDZn8OTT8zSXFCVfHq/CZ+4zc3VcYaXYzZ7Ob7G3tIuZ6i3IP/ktAFMP/ZTSoMDfrD6GpZDGY9fweU24LRmWYxrrSieoD+e5OuVma/UEu1uXiohOQcTjkfF6LZhlkEQNu5wjXwBRAKfHyuBogcq+11l1+geouoBTTPCx8w9gH73CDTf4KHOlccfGsU/14rIbmEywogbqW8Mow1fIqyIlpTYclmKOSdidpfTqG+zc7CjS4SIr8Z5/hX231vHY1F5OTDXxg76b2LC5lFPJNewtu8o/3jnB6gs/ZTrtB00jMH2FvKmIXn57+4/wWNJ43BJ19S4+9+lqfPYCY+Zmkg03EI3rlJcItHOZx/k4F6bKAIhG0lR5E9ziO81nPmQlntRRzXbqk5cRTSLZvMh4MoQpMk0qkWcgV8+NB+pZWsrTvfJBemI1iOkEVovBwnSUi7lVJNfuY2BMQxVlHHKWRX8ztqUJLiZbsS+O4vHInIl38Puqf2DzOitH356kUh+muTXA974IVi1BR+oE0uQQZhmml83sST/PO91uRvLVOB58CGVmkEJepVSJMLHyNgZHcqSDtdSo13G4FMKlVtqaTDxt+ThWKYvLkkM9cC+f+MpOLDYLXSU3MzWyyJ/eEeiSbyBWcKCbLNgyS3x97XEUMcfJyXrSwVoamz28P1nBB8a/y3LexYHSy+yoHi6S4Tb4mbI3U+Ocx2QSaK1Icee6KSxGhsoKKyP1N7HfchSToFGf7aKl2U7EUcVs1k9d6gpNUj+CrmGbH+bwHjOKIuKz56n0ZShIFhxyBlMhw0yuhBXKAIIAHyl5E8MQeGdhFdcTVezc6ibrqyDszqIP9vJaajfDnf0c2pJlde40DVUCsWiWmtGjIIh8MPUwJ144y3ND7bxW/SVmjXImE15udxzha+/cQE624YmOUm2dQjAMbvWfZqt+lK2bfdw2/D0q+15HLWj8+J4hkqIbb3IKs2xw8y1lNNeZ8BQWyFS2EpMDWE0Fqmsc3Jd7lLmUg4rxkySXk0TsVVxOrGDH4U2kTp2kJCDSEk6y7r1vM7csMdxwC+c2/C0+p87m2nkEAdrqi1KkubQb23svFk3tjlJq0j3/T15RtS9FLK6z90A1fmuavsUANjlL1FLCFvksttQ8mihz+8phDEMg8pVfcXvoDPWLp/n82qI/LOxMYLGY+JudV5mbmKdl6Th2q8AXKl8urjOLEgemH8YeGWPbarBYBHZ3pPDp82xfK+D3KwiGhtshcPFYH4rZoKLGxx7/JVIrt7Fp+N+wXXwb/9IAN9+x8t+rVfyfKuM/uIdA+uY3v/m/+jf8u9Yjv/zlNzfu+yQtwUXmCwFuaZtCdvmJpwX2r81QWVpM573cnWY+YWVLYIgx60qmolYqPXEmrCuY0sroGlGQJIESr0aVfYklVw3d82UgyrTNvondBhdmSpnQKpmYVtlUMslk7U6GFl0gStQH41RVSSQNN6XBot6uWb/GlF6O5LKTF534mgJEcm48dpV6tY+Tk+Vsr5+heu4cCVcVDpuBYBi82uliTZPGIfXPRKp2EnAbRDMWphcEvtH+JkOmFhKaixu0k+TtHi5MV9AUjJIqa8Ez0UmuopXxhlsYXvLSGIyymHEhSQK35l9gJrSaBaOEN89buaP0DElrgHBqACUd4fhiC+cngqzrUHDv2oRFzLOcd5PRLKiaQH1pnrRqoTLXz1MDa4gsFZhdeYhZ10oqQwZzKScBDzR4Fol561lvusg3em+nvtqMx2FQoiwyXHeIhsRFcs4gk8t2tpf1U5DsyDaJtOilyTqGt6IUKejlSz8CvXYdAY+Gy5xmw+JLNFbkCD37HXxb2hhe9nPAdZqj4zUccJwi7/AzOq+wqV0i5IOVS8f57bUWKsoUVpdOUiVO0BuvREfk1hVDVJVoqCYLkYIHp81gW+wldMXOlFHFhakw+1N/RDLLnDDdRHmJgFVWi819RiZTMBF0Z5kxKvBJS6iYGVmwUVZm8NqVIOmqVeyYeoI5fweqUfQPlHpzOI04qtlOo3OOlL0Cnz3H6Vg7jZ5ZrhqraZAGGUmXMjgtU1NS4Kp1ExnVjMUMG4KDTEl12CpKuJTtoCe4j0hKIWm4ybVuYF1wDMNbxbvHIxiyndaqNGcjzdisIj22zXSxCpdV59WjKfauXGIi4eOG/Hv000rKV8mGsimuRsq4OF9JeUDDKCsnsNRHj3kDquREWdHE1VGZumqFfUu/44/pQ6wPj5ESA2iCGQOB940NrGjx4AgovHnJjmQpNpnjEYWXh2q56N2PYhFJmEP4fFaavbNcWaqlSe1m2tvOhWE3H614h+5UHYsxAbNZIKNbeX/QSUNJiuGqA6ytWiaad9I358XtkSn15En6qlnKO4kWXJy+XKC50cRi1kUspyBbLRwIXKKmRuKHPx3mszunGcuXkS6YmZgT6Bda8Dk0Tr2foKXeTG+mFqdTJ7h4jZPRFta1QaR8DeXaGGunnmWDuYtfp+7H3L6aJx4fRPKWU1ktEUqPcS1VzW2llygrN/Gz38yxanWASmGKkRUHuTrlpLnJxYaSUTKGjcllG63KCEqJnysjCjetnOP7v0nTvMJDR3WaAxtyPP66wvYNCmNTKn98eoTvVT7BOd9hzlr3MhAvYTTmYy7tJJ0VuMlygj92ljMut7DCN0+NMMrDL+isXOnlsX89z47dlRR0E2F3ltPDQa5PKUQiBeqabVweKlLNdF0g6FbRDZGjZzI4nBbCJRYqSkWqQjnU8noWxTAWWaO3+X7Oahs5c3axOFFSqjGFPIwl/NgVg9tGfkCXYwdrq2O8NlxHaVAi6MqhmAqUmBd5LnkzgiAwMJTmzo5RRsO7mIlZOdTQw/rYm9xQvcz5pVpayxP0LFcwkSuly7GTZt88ryd2IJRVIEsqI/Egi3GJAhb8Lo3rwyqK3cLcssQO81mskTEKFS1ohkjSHGSH5wqK20J/qor2FRbqLWO8u7SGSMrCyqosecWNS1tiQlpBOKBjN6uUKDFOzteC2c5HI99hNLybfek/kw1UkfFXkNVtmBweUjkTvqCZyaiDnclXSPprCSaGmQispUKZwxGfQStvYGfmZVpdU5i8TkprSpnMlxJLClwec+ENOZk3V+MMWlmUyvA7Ciy7avG6RXRDpKrQTyrUSHesii01C4SOPIpr61ZKChNMWZuZirsJ+SUMBAZG8tSUQVo1U5O4zHN9tThcCtVlErLbzwc2xei49iTD3o10ZluwehV+37cKUXGw3deFSctzJVrFjQ0T/HzkJqpLoTdezrlBD+t8Y7x4vZo7Cs8Q8a/AkK0sJIqBVFfmwoxPF7A4XZQ6EpT3vclcxXomE15afv+XfOvyJpytHZyZDFNTXuCt3B4EQaB/WOWWugF6lspwOeH5c35ygVpqHDO8v9hINi9S8Fdhk3NsUY9TLk5zfqmOcKnBkas+qrfUkMaBs6KGSNrKkF5HQRUwm2XaKhOck3cz4lpLnACf3tBL0J4kbdjpHLURqlS4J/II10puxuRUeLWvnozgJmYK0Zlu5uTZGMmVe1FqylnWgkxQTQ4bw/kqqj0Jtp//B1rip5BdTibc7dSNvM0VcR1rqpbolDZhMemYgh6U8gY28H+zd15Bct3nlf/de7v7ds65e2Z6csBkzAxyJAgwACTFoBxs2bIk2+Ugl9eWLdvgWrKyFUhKjKIpiUGkKOYAAiRAZGAADOLknKenJ/R0jncf4CfX7tb6aenaPVX/h1t1/w/f23e+/znnO4fequbyhInt243MSOWY5QynDXdS6szQkDhHX76Gl343h97jQxRAp1EwyVmWEjKGpmqKokRvogrRqGc0YmKzuY8xpZJoSiKfh13GboazZZi0eezCEr+42krSVsGFOR9zSTszCwXcDpHjMxW82l+GxhdEI0tcGFAT8Gr43QUru24JMiTU8upLw1g7NuDUx4lkbcw41xNSRnj2ejWKIhBN66izLvDGFTcajYg/pGMxZSJU42VyNk9dlQa/Jc6KykPe6WOqZCcnoi1UetI88/NvzR08ePDx/9u95P8M3/7eIwcDlR/7d2bwf3ZGrz3+kann/7kXAlnKce1KBF16ld++FsGanCeTF6n05RlcNDMa1jO9pCGdypFM5m9mOcfUOE05zKuTFIoS60/+C42hLNWBPPmCwHRgI4WixC7bJUr081C8qbFOJvN8Tn4Bv1dNUmdnKWPFYhJZWVPQLU+jIYNeLtJzPc1rh5P0i82sJUUcLDI5kyMwdpIqyzzDs2rm9FVYzCre7i3ljPlOzOoExsVhVPkUNpuW8vQNznoe4OTRSd7rVmPS31wu81z6PhyGLEY5y2lhB0Yhhqwu8lLsDgzRaZ7I/R5WbYpdow+zX38Em7TC8nKGMr/I8+LnsWXmcUlh2upFdHNDnFuqZc3o45ByO1W+DHe2hoklReaSDrpng4xP57kxUiS8VOSdk3ncuigD9q18afBPkWWJTwnPsvv833NjTGImIjG5IJI1u0lk1IR1ZTidOuZjOjZk3mcwWkKFOMyhwl5OD9l449UxXr5Rw9VxHZZ0mJU1gd/0NTI4o0YuJvnTr5TxMceHWJRlSo/8jKfSn+VitpXJT/0LmaIWvU7ghfBuZI3AN89vxayKIWsEdl77NnPLKvq9t7CzXSGylMOSXECTjRMwx1mLK7w/XoVGzPLEew4q3SkalKuM+rczoGrGKa9Q5UkQ9Tcix8LsWXyG/nGRTF7FwIyGM91x/OYktrefpDp5ibhiQhAUllYKuGcvc2D9ElWWeZ4W/4hTVwT29/wNnxv/e6IZA7rYAsLvniai8ZPOCuQViXtWHuOVniBlxgVso+fZ6e+jOljg5GXYv/Qkk2GR85fTXF6pxCktos4lkESFvdpj+CxpoikV9bPv8cLVWjyWLB0dTgDiWgeyWiGWhPBSkXQGVGKRvTtMyIUkMwsKpzS3cq5XjVVc5cRcDXZDDqtJIZkVySsSaasPgypDKl2kYf4Q4YUkQ8MpzgY/g8NcJKvIqCSFXeFfcuxclk8mHuXS1TjhtA2PS43DmMNqyPOF+E/42oZL/GX52/QNxFEUuN6X4tFDTqrsEebsjeiUBF+yvMQbiVtYiRb4fOlx9hhPU2OZ4quWF1CLObZc/SELSQs2zRrJNFiNRTJFDZKQ5+K1DDXWORrrdCxnLYxN5bln+oe4pDCncpsYiXr51BfbeKi7FUWBTF6kzKfwgPMYXmOU9jYrbu0Kt4rvoS/GuGC9DVlWkRYNBOe7eeRCC70Nn+EF/R/x5eyPab7xC/be08iW+iQZRebVxa3EUyLXCo30R1zsvbMKkyZF0leDlhS1NSYyGYULS1VkCmpagxH65HbODJqQROhfcvOjLy6RzcGFYT1f+othSkoM7D7113xt/Cvkc3lONfwZzfINhkYztJcs8un809zLb5FEGLGsZ32zjs8730aXXuWpgY24fFZuTGho391MunBTPjG2ZOAP1r5HqU+grETGmZ5m8MbNKaTDoqBXpbk0ZiAUMqFSCczO5zDIBVKyBf3kdUbmZV44bmVkRuJT/IrKahuyusCGxd8Rw4JKArcpw99G/gSVSkA9ep2vWZ5mfCqLXpUlndfw6GE/n7G8wbkLUQIBPTM5PxXZGxw/OsOi4OVD+yf45tmNWEwCjdHjHFj4GbWORe5Vv8rFWT+fXvweTVefIqXoKbUsc0fpNQpFMMlZKkIyWk2RSl+Wnw3u4Jr/LsYXbw41Arowvx5az199Y5hy8yKprMTLffV0eCdRFAGbJo43N4luYZSv+N9km+4cb55QmLWto6HOyK6qKZ5x/R0mvcIV337KM734l6+RyN6USQWcBYKpAVKpIq8Yv4g7PYFYzDESdfPuSA3jJdtJ5NTEbWWMm1oYW3WyHBNpdYxht4r8We0x/PI8oxEj+ugMaklheFZN6/J75Isil4dVpF75DfrYHGqNhHvgGPGdH2cpqSNhcJEsyIwMLJHLQyYHf7KxF4s6fjNeOp/HYlHTWCdTLY8QTxRZSFo4Vf1lbHKCu3K/wVJc4guNV+jpL/LqeCsz+hrMRoHFjJ2vrT9PqWEOvyVFXajIrKsVp0NDbmSIpYyZtaRE0JYkXVCx09fL4nycfQPf5Xy8maMlf4SmmObYsQUiX/0RT993iS2Gi/yB+bdYYjPck/k1P3+kj6pymaMLjRi1BcqXzrO7LcOtc0+QUbSscy1QVEASijgJ8/jM7ZwobkcrQx4VX6o9jffYM7Roe5mdTVDuyfHJ1Z8yFy6wupYnYihja/RVdo4/SkWlmZ5UI/N5D5GUgU9XnKd04RzvN/4d69cO44iOsbVm6aZ3sHCOWyznWZxe5h71G0RzRnq659BqFGzaNJ3WPjRSlsie3+dpzz8BoP/mHzJcfhsGuUDPlJNbpx+lwjjDeMLPakzhrfReLoXLCPploqYAl/vyXJ4ws9d1EZ0qhzo8yS3CYe64M8D92tf5dPpxrg0puLSrWHU5AjPnmMt5sWpvpvVIksCMpeHffVGg0wpkNCbumfo+Ls0Sk7lS/iz0Ni3WYX4v/yj3iy9RGxLoOPkt9lUN8w/rj9JgGmU2rPDF9X347AW+sesaJ0+EqXVHqW4MskV9BlUxi1WfZ4N8kYirnnQqz7ljwwScBTIqPbIsolEL6JQEoqDgthXZ1KSg1ShkBS2VI2/z60s1GMUExaJApvARX1X8/18I/mvhiYd/fPBP//ZrBFRzrFnrae95CKWpg/bEUQSni6yioagIFAWZploVdZke1H4PJ24YaPfMc361FrmpgTfO6BifKdJRnaLm6nMYLSoSWjvuSC95g52U1spo3MuifR0gUK2fpnz6KM/3VdHZJCF6nPzr76yUBm9OXz7VOoJFWmMu7aRWM0rvWpA29yxTqipWU1p+/NAYHRu87KiapTLfT1jwE7OUkNGYmFixonU7iSR0yCYLf1n5Dm+OVBDLymyqiHBtykypPUmZdoYba+Ws/8UDrN9kIa/Ro3K6mYsZqNTP8efvbeDWhhWSOj+XriX549BhLkmb0GnyFAU13UoXtxpP0ZNpZk/2dQ7NNbLOs0jfvJUDhZeplceZ0dVT4oFESuCP23q4kahEq87zhngv4XAKQ0MDUn0tovZmCkNkBZr8K8wXPKglhX22bnzGGEeTm/GZElhZoVw9RaN1hsO9Nv5pw2l+fdbBvooZRvIhtlUtMLViYuvMc9zQb+SFi17MHgdev4reVAW5okQsp6VSO8kLR0Ru6RK5xX6J2xIvE/Wt4+hlDbvNVzijbGK9a4KZtJM3fjeMo6mZ8XwpXfnjTIpVlLtSlOcHmFdVMDQlovU4yRXVtI/+mkua7WxefZUL6u0YrBJD5o0sJXS4rTm81hx31Ywyn3eRqt9ASrbhS48xWSjlykCephaZqbSX2uQlKtxxwvjpsdzKcs0O1GIRnzKNtDCFPuhhKF1GuWWJD3K7ub/0ArZ3n+KDhv/G+Uk35a40qHWYK1xMRq0EfWqqnKtMJtw4dAnsmihX8y1cG5fZEArTp2rDaBC5MSbhsAqYDBAwreHSxxmYN/PlhX+gpSzGuKqWREbCYcry/BtxdnWI5EQdbcVujs5UkEdN0Jbmw+4c+0pHUGeTzEsl5AUty9YaPjgyQ+t6NzqtSIklSn/ERSwpMmZso7lK4WxxI0vLBXbULlKQ9Gwrvo/FUuAd5U7Wnf0ps633EFVcxFMibz17mu/8qUCsYCK0cpEVfZB3Vzdzj+l9xoUq3hsOsKAuR68XeSu8nmbnDCPeXbTkzjEthmh1TdE9bmedJ0Ja0TG5YmK9d5bxuIvdqdeZ1Dcx592ATlbQqQvEczKFokBVUGGH4Rw6i8xM1Mhve7xUlwq8fTRBZ4OCpBa5HK9j28LzZCrW05o8ybKzBqPLSnv4Lc6nmkiUtWP3aOhbC2A2CFQKw3TGjrBoa6DSNEuJPsL5MSt+p0Dp4jlkJcOx2RD3r5+mJXuenmQdaxkdbeorjGWC5HLwMecJegrtZPIS+QL81WcFXA6Rt4W7SHftJ6O2UxEUEWQ1O978CpZrR5BLggjZNC8MVmP3WIinRCalStZNv0G2so2LVxPs2aTm9NlV7uqKcnzASYkzz28jm9lXO8k759Ssr8thLivjyPth0gUNpT6JnbbL/Ns7AlXVZhYjOeJpFZtM/ax5auiQeugsW6Hz8k9ZWn8nMclF0LzGormacNJCJi9i0eXQWwxo1AK+GjO/Cu9jYSHNfb4LLKl97KqavZnAI6nRaQVsJgV/cYqkp4loRodKAo32pqZ5yVTFkqeFoiJyKtbCPucFrnnu4Ip+Kx59lCffMXFg+VfkqxqZWTMyv6igVotMhUV+r66bjMZIuqgjnhKpNIdx2NX8/u4Ek5kAc6sa7i27xJHpeiz6AlZdmpxahym/wmn97cwrPu6uHqI/Xk4iI+ExZ0gUdJzqjmN1mFCMFmzFJa7FyxmfzvLZob+hp+JzdF9Ls799mYTKSkQO0pK/iNOnwrc2yOVYJZXGOS4uV9LonCEv6smg5/KgQMEVIqkY2GS4ApKKw2Nl3NY4x6DUjFMXp8abwK+NEildT0Tx0qftxGdPMbZix2oq4hLCzCulpDMK22sjHJuqJIOBdcZhpkyNRHNGZuaL2N0Gnn9ulL9o7SG03M0VqROV20a44ObDyRBf9r+N7HNjlVZ5/7IBQdajtevJKDIVxUHysonSRB8LmhDrLLNYjAUuRUq49fRfc0Szn/WGQfKeGuqNM7hsOdDKODMzLOrqMJskxlV1ZDRmevKtaG1aiiYrNZ3VCKLIndJbYLMT1fsxqpJIZhP9qQqqGaTDNIxRnyMtGtiu7canXaZgsOGUloio/ejdVq7kWwlHb+7WGLN2IkoSKyt59rj7GLJuYNC0iYl5gTsDVxjPBBAFOB8uwxo0IYoiAzRgN6R46pgXs01PmXqGOV0lamcJEWMV7cIFrsYqCXglzNocF+aDGHVwI+LFbipSO/Em0oFP8OpwAy5LEZ8lxeHUVuxmaEke5/xKFTuq5sgpWiIxFete/HOGax9gfUWCkXSQq2MysUALEbmUpbiav/rePKP+O/nK5gFurIWIptQEbQm0mjwvnXXzcdUrfOvZHF/smCOtc7CwpsVlLXJ+xstaeRcVhSFiKgeLcogMWsKORk4k1hOJipSdeoxI5wOkDXbcayMo7iDLBTtHuwt4yu2MhrXsL+1D6/eT1trRqHIEVXOcjbcgawUW4ibu2muhwTxGTLEwtqjn5Aej3LU+Sd+qD5cpS7Pcy/uDHjY5Rzki3M62ygUc2Tlev+zA61bxmyf++0dmov4f8e3vPnzQX3kPN/Ms/8/O2I0nPzL1/D9HCB569MmD32lOk/JWYXWoKVY1UBRUxAxeBAG6Lv+EessC781UEfCpqM1cxlxcQR0IEpo/xcvj62gsTVNTUmRj5Qo2ltANXkRxB7BOX2U8uANTdgljdJrv/qbIX+ya5PS0n3rPMv2mzdhdN7ei1oy+yW2+QSL2etbSapD1LGSdjM+raHAucm3JR5ttEpVORRwLTe0e5sIKLZ4F+nJ11BWv4YoOoVXS5Kx+LHKK+qf/gDOOuxkWG6gpA70Wiqjp9E7gyU5xI1NP36SK2NaPUz/2CtHSFkre/iGBBieHc7dw94Y1PEu9HI/U0lCl4mq6lt3ySUyZCN2rNdy3+FMiJe00Tb5G5uwparcE6U9WYtRDZfoav0rcT9CZxWVI4LfneHe4nK7SeXRShjJnhkBAx/Syjlr9FGmVidWUjKKIrDNPUXH0JxhOv8KR0J9TpZlgKBlElERGkz40Jg0j2RAmp40RpYqtrWDRJEirbYwsWRBFgUbdCGFLLWqdjq2WK4yqG9l58m8IXX2equnDGMMjpDv3U2mL8J3XPLw+18j9pdeYketYDHRR6swSePbvWG6+g/omD7cnX6QmfZlldz3LBTvbzj/IUu02GiwzZHUuPMYY9Ye/hSRrUJWXMqVvpOHlr2H0OwlrQ8QyWoKWGPGslkN9PhoCMWojH5I2OZkolqNTZTHbjZTK88iywplYM4qsR6MWUQSRoiLQWn3hvQAAIABJREFUZuxHF1tguv0BzK8/SqZ1B0VUdKkv8kFsA1UVEjUTb9NmHiNl8yNIalJFHZGYGlmjIEkS5aYFCpKKh98LsJrSsKU+QW3sPFGdD5UIW0onWSua0WkUysVxkpKZjGhk2LubKXU1PmOUZF6LVU4hWAPc0vMgQ55byJi9NAZWCVjiLKWMWGwyLmuOrGxClvKkFAPb029h69xApTvJxpP/yGLFdraHX2DZ2UjItgKIpAsqnA41Wlkkr6gomK3EFRM+U5yFil1cX3CzElXwuRQ2bi/DYUhhEmOYViaxRifIeUMIBi1nh0zs74qxq++HuHVx/GUyrqV+1gw+IqoAJlWSC/OleGxFKqVR4qIFl0MiI+iJ5zRErRUksio85hQ29RrzSRsB4ypHe9RIGg0XwkGsZhG9RqEsIFKnHqS9SUU0ZyQtGmjLnmHQs4MzfTqSziqCqjl6owEUj59tR/6StaZbeLGnlNoyqLbMsoiHC0oXdn2GrCLjzU6gcblRiUUku4k3FjawvjLF+XEXa8Yy/KYE5aY5hnNVWA0F8oqaOaGEGuscPeMmWsoz9Iad2PQ5nOYiAzMabm1N0vTm36CvCjHQ/iUy62/h5fBWxMpa0oKJT4Z/wJR7E15TCn/4AsHBdxC37SeWVtGyzoBTl0DSGujQXkHncrCYtpBHy/bih5xPrKM8ZOT2pgU0Up4zizWUlZvZGRrF5jLTVBLFffo5rvnvYaRYxWCqBGdzCTlJSzhtJWBcQRQU6rM9ZIxOfv7sGvG0Co9LjcuaA42ehgoBoyHPYs7Bz1/V8Efre5FsThzmIh3KWa6qOkGUuGv8+5QNvUlN3wssNN3F7cKb/Px0CEU2EbRnODpeQsCeRZLAqEpzZ6CX4bLbmYtb0EhF2kuXKAoydb4Yx2aq0OtEZFURQRRJC0YujRk4PmhnY8UyWUFHVHCwsKrGZiqylNRj1WUQtRqiWOmd0tJ66BtUJXu4YtlFNKvnzu6/puSOrXQd+TqrddtZUvkw6BRMZg2/mt/K70nPYm1voe7YD8hUNlM2d4Zh+wbSBS3etT7G1bWEXvw6zfIQvbbduPRrGFVJwikriiDSabrBa1PtaJ0WRhe0zMUtbLXfQC8kyYpaZu1tuLLTnJ8PsqNqnuCRx6h3LeOY7EYwWXjprIm/7TzLtFDObcmXWLZWEVdMNPf9ihFLFyWeIlvHnmLEsxcpWM6MsYHxsIzJKFLBEL2rfpK2EGqpgFoqoreaqXEts5o2sC55lg8zW5HVCkW9noyio+jx4VwZpG3md7zX/k2cliLVa+e5oTQypl1H8+hvUb32C/ravoSoUuE2JHHKUYqIePRrqIU8WVFLPKdnflXDhKoanUahJtbNhFSNLBepTV/me+e6kPxl9Mx72SB088PrO7EFbZRoZpDzKa6ullOlDODSrLIilzE5W8DrgMUVgXVVAo8eMrNlXYaCosJiUXFjNcgdqRdZs5ZT41gknjcS/PmXiXbchUuMIDn81NtnSWvMjKx5MOmLOA0pppQSbHaZSscq5cUhwoqHQlFkdEYg6CpQKk7zN8e38N/WvsFi1U5aMqfRuByoxTxnYi2sxsDtEFEUgXBUxeaGFKpQBX55gcszTj5Z3k3d/PtonEZmU04+8zELbRVJ3uwrp9KTxq5PsaB48RVnKC2TeXF6Awduc2E25RlY9WE3FUhlRdzWAg2mMQzJCCqtgFaVIV4w4hNmGFz1YNIrvCHdRUdFDFEo8sZ0Gy5TjpB+hovjFuaiOra1Cdi0CbSywtkxF3XWeVTFHAYDGMU4ObUVuz7NRMJN58CTJCs72NJpRCNDvGDErkvhVMLEdAFWBRc7C4ewr46inewjuLGKC6Mm3nvhwY9MA/0f8S/ffeSgr/ye/4xiiPHe/08I/q/h+z956qD2wNNsyn7ImLaRsZibrCLTt2BjPGIkXdPJocUWotE8+5tmMI9e5LOvb2Zrl5Gsy8+FQS0er57pVSNXpy047BIXzfu4HK+i1F/EM3MeSSkQdVSiKW+htXgRc6kLtVSgf8lNviBwdUxLm2eeId8ujlwycPnKKn+QexxD0M7VsJuQL8fAvJkNQ79gsuxW5tf0VDjWOH2lwI7QApVrF4iZA1wX1uNTphnOhtCqixQ23oKkN/HJ8Pf5sLCNzaVTaFUF+pb92MxZQoVhNqov8o1fKFTdcwulsWvc8UwzLXta2Xntu9jTsyiyFvwhnnl2ln/w/JJIsA1BFMjrLLgNCfTJRZb9zVwJfRq9rkDTwiHOpVu4TjOfGvpbLpj3UW5d5li/g6/KT7HqqEZFAX98iOFcOQdGv4N27CrLZRuof+KznHbfR8cH/8TQvd9G6uyi/eT3yIfqqdZO4pUWqO95kmJpJfVXnqFHu5VPa15k0VhJxeg7XNN00eUZYzpmZ1jTRKNtks0jTzPq20m2qOKp8D4c++8g2byNbGUjk1Eb25ZeZk/8NQ40zDET3Mxs3MJaUsRlzjJTfxcOXZyheQPaUi+SUc+UUko4piVWs5lgcZyrmUZWkyq2ZN7nVOWXeW1lC5GsjcW4loZWE3nZQMn4B5zNd7DR0su5+RL+MPLfyYXqsIYHyZpdlC+dZ0hah1ZdoGb+A+LWUtZ3/wg5FODosBe1SmB+UWF74Sjv6++lUhoj2ryLWN5IlTKINrFIbfYqJ+Tbsfr1xC0BBmIlaFUFNvT8K6p1TaxlZNab+/GNn8Y80s32tTfpSr7PVOktlMSv4xv/kIinhUde0bKpsYhWVaBs5SJ5vZkLUy5ktUAsJREwJ9Cr8+ilFKt5M1Mlu/GZk7Rf+inhQCdHbrj4ROxR9JV+yq6/jk7MkdHbeO2sibS3nkRaosEywUn7x9k68jjDdfeQUzTEs1oqteM0nfwBK9U7aBCuc2K6jD2jj+CcOoczOsSQeSN7Lh+k0LKFdmMfvcte2uXr5FRaxvUtOPJznE820bX8NmKoFkEAi1vHoGkDKrFIXO+h8uKvcUcHiLmr2XDtYZZLO6kcfA2dSY1aL3FxxodNn2fb1NO8ON7Ep/RvYl3oI+GuZC5hxe8R6fKPs2voZ1w27WItpWJf37+QKGtiOuPDL4epGHmHQ/J9bB56nEx1BzvO/D2G/BrF0koaBl/kVMffs2n2ebYIp+lRbySDno5z36em0MuYtQOrnMC13MeVfCNTSzIbrz5EU/wEPx/azF+JP6Y8fQObNsWKxk/96kkuJBtIZwXuW3scy0QP8YoufMZVNuaPcy1Tz/CsmpZQkjODRrw725gtBhiaN3LoVIFNLQJnemU8DhFdbTmDYTN7r3+TSNc9vJD/BAYdbLdf5Rfv6amu1FOpm+Raqo5tvT+hdOoD5kM7qM1eZUBqYm6xgCAbKdGH6Rr9FX2GjRj1Aidv6LBZ1RRqmmjueZyS4bep109y3bCNuotP8c33SjjQkaBk7BjXHbsxqlNg8vG1wvf5eU8tn1VeJKSappdGWhfeZljXwh+WfsiCqYqhiJVcQaQ3Uc7e/u8y7d3MSkkbl+x3kOu4hZllGcXl57PRh2mcf5dQ8hodUg++3ASlSxewrY7QY72VbFFFPKMmnRO5PG6kxJFFIxXYk3wFxeFgNWdkdE7NndcPEugq447IL1n1N2KWM7RMv0rb2vuM2jeyQ3USe3wCXWKRYyvNuKwK0oYtGM++zkrT7cRSEgtVeygoKhZrdmNWJ6m7/Ayj9k0srKowm9WUVcmkRSM99jtoiZ+gqNHhGTuJKz/LiqceSdYSb9/LoGMbTeJVBpLllEvjrEpu7p77CWpZRdvUy3iSI8RK2rkv8hBT3s2YC8u80l9Nl3sM28g5uosdbDFfp6fk48zpq1H7nOgyaxCspS9dTqN1nJTZi0ZdpOrQ98i2bmdeDDITUeGrtTCTdrP/yt9RYksxJDawZ+nXXDHtYn/kccoiZzHbVJjSEQYyIZz6JC3nf8JizQ5auh/C3XcIRyFCv64Tn36ZhNHDS8m7SGcFtj33AOG9X+GtUwJOh8yT1+vw3v8xNo4+zbsrneznDUy5JdzdL+NYHsSyMoZ57AJnNbuQRLg3/FMcxixFScaXHMR0/h2S5a3sXX6W0MIJ6oZeQiyrYLu+B//yZYbNXSQEC2sZmbrh3zFRsovltJ4d1Qt0zb7Eq5N1BH1q/oyHsccn8ScGOBpr5173CX61fCdN7jm0xSQlhx9i6lP/Quv8m+iWJhnQttOUv4T58K+pjJ6nz76TnUMPUzJznGumHWw+9nViDVuw6jOsZIyspSQO6I4ghae4Im+mdasLUaviUqqJzUOPcUa1iwMD/8x82U42C6cw6vP0zDgxV5eglbLMp53sDz/GG9yDqcxOJO/CosvSMvUqtuQ0HerLGMwSeZWWpp4nSPmrGc8GuW/uRzxyrYWP6T5A8noYjVhIZgSs+gLVuV6mTOsIznejP/U6Xt0ahoVhmlY/wFVlRet00Xrhp9gSM7Toh4nbS7AUlvGWmLj93F9z2nKAjfH3iFlL2Dn+JDoxhzaxhH3sHKaVcVSlARREus59l8mNn+Nkv5WQO0NN5ASLhkpGwgYWBS/XhxQesB4mq7fx2ORefK0BbPkwTo+axx76/kemgf6P+PZ3HjnoK7/rP+UhmOh76iNTz395QiAIwm0PPvjgWw8++OBfPPjgg/qDBw+e/N/9/8jPHzt49ye/hM5n5Zl3tZQFNXx4Po/JpKbCmwUBpsMSao3EUs6Oub6E2zcVOTvhpVN3nTMzfj4z800OZXcSXcujM+nZl3uVStsyYjHHgG0b3v7DrJU0caLfyrSunqE5HevNo0xkfbz88iQmmwFPjYvzYy4WI1mCJUZ+ca0KlbeCltI1HCxyacaJtnMjHs0iC2kbiayGoZE0LetUXMq3k5P0tGTOoE6t8spwDctJLWt5AzMLcEnegs0skFb0ZBQNFm0Wm7jC9WwDoYWTPOC7jBx0s2woZdOuKnpnDJxTb+eVyXpCzR6eP6LG47dQrO+iXBlhTevi+oKbS7EqfAGRa2uVNBqHeLY7RGmDlfFVK3vKBnjf+AnOXYzRM6JFr1dzsdiO166QUWTenqhlfgkuaLaibu/kX59cZXL971MoKLTtr+HIcAmKxsRDfV00Nel5qbeOOn+MXyYeoNKT5N/Ct6NSwYi6kblVmbKQRPecn5hiJRoX+Ljqt/zkUienChu41ddH8Njj9FhvJYdMHg1uXZTDl/RkQm2clPdyeKWVhmCCd89KlAXUjIfV1HtXODviYnm1QEoxMpXxsGvycd5Y7MBsVpHS2FlKygxNFBkR65mYF/hifTcljgQd537AycAXsBlSPLd0G8mUQsAvgSTz3TMNbGqReHJ4Ez6fTMbsJI+at06JlLQGiOZNPL+yF59PzY++0813nE/QPPESQkMbktWI/fVHuBa8n8eeCeOur+bVkRpUoUq29v2UV5X7uDzrpCmwwqk+E5cN25ld1ZLMiDgcImYpyXzlVu7/mQ/5ri8hSiKCy0nOW8rb133cvS3Hc4dgW32MrNHGmmAjI+i5U3wTm19HOG3jyqSJduMgR4Z86PUiek2RaGkbx3rtJBIFVkq7sOtSDJo3YdGlGc5WMjor8Pu21zkVrWcp70CjUniyrwmf30Ayp+Kl11dRucu45txLQRGRjAZ81izTng2clm+hV7+B28Z/zHOBb6DXKCQlC8sJDVaHRBo91StnSJm9nJkJYKgIYNEk+OV7WoylJRy/pkNnMuCUo5ySb8cWNGDMLvOy+GlG59UY11WhaGX+6UmZz+1a5Xi/lRlnJ9mCRL/QSDLQQDIns5ZWcbG3gGiwc7SwnR2hcfoX7Ay7dlCrnaA/UYpTn+CSeiubdBc5ZbqbVE5FuGIHcsDBbNpF3FPDsas6zqbbOFfsIp0V6AiGmQ1u4+34DoxaBVEUkI0qjvQ6qC0pMOnfxoR3KzlFy29nWxm2bKYvU8nkqomr6VqyuZvG2tXSjRxObuLlF4eoa/FzJVXLpetZ/nz5H7CcfpHBio8xHTXR7hxnY/QdbrVdoXTkHdomX+JnA+0sFT3IGpHXVjdxZ/Y1Vn2tjM5KyDYLpSU61mkGODrfyPWhIgP27ZwUdzAzX6CtMsmxcT/Xe+aJpjR0VaxyUnMbx06t0VQtspbVMRuR2KU6QTS0nueKn+b1qXru953nmP1TLETVdNTlOCtsp0EzQBQbDc4F3ijcRTonsqEF/u7kZq5cWaVXu5Hm0hTP9zVwG4foyTVy5UaKzzT1cdFxFx+cK9Bameb6hB6PLc+NMRFZJ/PcVCf93r1c0W5i2d/OmLaRU9lOPBVGnGKEipOP8vTkRhJpCVEUGJ0RUWm1HFpoxOcS+OHP5iirsDPsuwXUOk4o22nT9ZOSTLw4v5Up9ybO9WRpq8lwXViPymbgSI+eL5l/y4lUFzXrDHSvVFLvWwNBJJlVUVREtOo86UA1hy5bOH1sgn+2PsIx4330TWtpDqyA0cBzYxuZc3cQt1fgy0/yWn8lJc48I4tGDDaZ/nkTUdFF7yj0GjdT647yy/QnUFfV8MSvI5wqbuJT6tfIG20siEHqXvk6xa5ddC+F2DryJOO+nYgoWDRxiio1T72l5mpPmFBDkKODHqo9cY6b7mW8UMrkgohaJWA2q8gqGpTG9bw41kbIm+dCsYOt8lm+dnIXw86dBEpUhFVB+md0+Kx5Tmv3otGqeGpmN4WuvZj8JnojHjb1/IhUWSPLBTuf4AWm9v4Z3uwES9oKPnbuj2m4bxMDizZ6dZ2sRBUqa2W+d6KZq9Y9THi2oq0s5Sd9O7l6OUI0IXA6vwF/jY15KYhiNvLb7L002OZ4qfhxXl/ZwETVAZqMo7yYvodMoBa1WKA62YPGquc95TauTeg4/M4EKmcJr0+v4wu7VzjZa+CssJlooJ2z2XYWwjlwBlmJS5Q74lxbCTHi38P4kpEbQjO6kI+hsJlpoYy1uh2cVO8hsgqvzLfyxKVSBNlIr+8O2hwznJyvYZ1rnlJHihlVOe7hY3TO/Y5ibTMplQmPboVfR26jxpfihPpWRifzzMtVqHQ6Lg8U+XjxRbrFzdSZxnkjczu3uS8xlCnnZ88s09mq59mxdmZNzYQtDZgNGXqXAjzW28wd7mus6oM8PrIVn0+Hr9aORsxyesBMZaBILK3i9d4Ad4tvEw608dlna2m6swtcLn6TuJuL007uPvMnPF/3Awb1ncwaG3jlAwV3mYuTN3R8KO9leirJhmZYk+x8u7uL8vYAPcV25lzrWXI2cH3ehceU4MW12zFbZE51J9ndGGVKrmdqxcDEbIF8UcXKSo6OigS9NBFyZamNHOeXM7toc03z44ce/sg00P8R3/7uwwd9obv/M4ohJvp/8ZGp5780IRAEQQLeBfYB3wZ++uCDDx4/ePDg4v/qzsM/e/zgpz77eaoTPaxvVKgr9vLesIfmWhVbxFME8yNIXj9eBxi0RRpjpzAkwiybKqmbOYSpeR2rFV247SLzq2o6yldwj5ygYHGxZCrDUVxkLdiIp/cIa2WdfCzxNGveJipyvTSsnsS1dTMBj0Dn/Cu0Lh9CadpMmSvHjvYio2E9da4IztgYOWcZjaobTBVLGZ3XUulOoDJaMOnBo4/iFWZ5bWETfj9orHa2BYbpuPII0ert1PtiDM3KtAUXKSgqrk8bmUm5yOQliiWVnFLfSnv/v7ESaKHuzCO00UO8fD13146wXHCgMZppr86xlNBgsxbxrvSjdZnYNfQQBiFD+Y2XEE6+B9vuoEIZpmBwUDf4MqlgPX6/nooSFTX+NHXeKAFlgqFkCQ9kfkki0MSO0lEC2VGqO0I47Gr8HpGcZODOiZ9QMvgmxt230bzyPu228ZsRmUY/sbwBs0kkWxCpdCc43ZPnNv1p1MESCkhoNALV6R4c68rZFFpgqlhGuqad2wa+R8P8u5SNvIMlPEi6aSf19hnQ6KgNZpGlHGaHhWxeoMSZo+XoN9F3tuF1COwdf4j6xDkwWjDUV7Gt/yGm3BvYlj9CwVPOAekNNq2+gXDlHBe9H8dTYab6wlMYs6tUl4Noc+LWLNF45qestR6gxrbAvsVf4crPMmtsIFWQWRfK0zD7Lhm7j3JvjiIiLZuruWrbAx3buZhsQFZDZt0GGt//FhX376NFvMyO7GFmLE3o/XY6+5+ig26ivkYUtZ7tZWPIBh02Y5GpVROizYIjN8/uO8pxmrIIgkhtuocxsRZUMhZtlmBAx8SqhaJaj1u1SE7SY9fEsC8PEsgMow24CU6exFhXwZZT/4jS0IxWzOC0iWwpm+HQJROVgSIKAu+Pl7HHfA6VN0jK6qfMmcQgK2y7/q/kW3ayRTrFghTkj20vc3StlQMVN0hKFnxyGESB8VUrRm2RO1MvMFG/H5c5y0LcQK6gQpKgTjPCZCZASaqfbtV27lt9lNfXdmI1CXxe8xIlQ2+xRTpPaPEUstlAmTxHVHaTVekJp610hpbQihkMhTXKG/1opAKrWROdwVkiGQuf072EQ5fkeqwMpylLTTCPSgKXpYBGpaCRVWwxXWZZ9jIcsVBhiVAfP8cZZStVljn++QfjHNitw1Jc4cayH6O2wD19D9K+Xsdm/VVU/hJMqpsGOr8tjU6dZyWtwy7HsDoN6NQFvLoVHPIaRrOG5soiFe4Em0zX0Nv0BGwpDHqBMr8KtUrhgPcC1nXN6NQFApY4pT6JbsttZDpvY50nwuYPv8FS3W50qgIFg5Xp6lv5nfBJqqtN/OH8P5Jbt5HNVSvYIv2Ept6nUN+JVpVjJaWlrngDndPAZv8km5TTLFlrcTtEmubeJuLroL7eRHNlgfGYizLrKrWVMpPLRspdaSIxFa1XHkNv1pF2V9FVHccVH8FuyVNaYUUr5ViXv4xp5ALzzlZkMceLh/J8/vYCRiHO7d5epOpW2sqTVChDGNw23pxrZV1pippSgaxKj0+3iGR2suHMN+lIH8c/d5Zi81Zu7f8eHV0GfD6JDcZeFvGyLfMOLcJVwqZqApOnOFn9J2j0OtoqMxQFDbIs0hGcR2vQsZLScud2FahkNvlGuDznYWVNQeuwYVIlsFtEQpYlyoIafPlJQsvnmTasQ2cxMqhqxKrPM1oop8yRoKhItL/3dW54b2fPub/Fq17BkF8j7a5m32aJfKiK9quPsVS2mY7jD2Iwy3RKPYTNdZQa5rCsjCP4S2k59i0ak6eZ8m0jldfQ6RxiNu3iY6EeLNPXWXI20ypcpKK9HJPNyLKzHo8YZlHwEm25jdD8CdKlTYTLNrPh1IPYQzYcl99FrdciV6+jvdlEqSnCrf0/5IZzH2XWFbYMPEqxthWrsUjHlUeY8WwkW1TTElzGpV2lo3iWw5md7OtIYTSqeafbQHWggMkgEJIn0epVVGeu4K+0cPK6jqbAKm+e17OlS0NWraf9+hMM1d9PLGegbPUS45p6lhr20nbtcWqn3mE+tINKb47hqJuvys/gbgrSrrmOvphgh3+c2vV+SgJaNlcvU0Sk8fozTLk2stHSy7BSS8gepa40j9eSZbBYTcgeQyPl0UoZRopVuNWLrC+cY1PmA0J7NqLTCtxbdplVyYnJrKHSm2UDZyi3ryLZ3JSaItwy+jOSgVqa4qcwOLV0fHCQFm8Ec24JS8BIuX6esswAZqdMrGDkQOsCuzdpqQiKKCqZgHEFkwlMrDGfcSEg4FcvMbnx87hPPU8q1IjvxK+obHfgK0yht+uoDWYwaBUW4noaK4qUZAYYkZvw6lbwWtP0JSrY3vtj7qmb4ZXwRj7b0kdroZuk0cdMwkHIssTu2gioNDgKC1RVq3HaJJYyZmyaGB6XiqIiodcU+Gz+aQoWJ1PaOj65K0de0FAyfx5zmZMSV4F4006qHCuYdAohwyxah5tNnET0BGgNJfH79aj0ahRF5K7QAEZljajkpEYexqBKIWi0pHIyLcFlGs89hLThFkRJpESYICo4qPDmqXTFKPOLuIQwFQOvYTvxPEJknveKu2gri/GTnz70kWmg/yO+/d2HD3pDB/jPMILJ/qc/MvUIivLRcjn/ZyAIwibgoKIo+/79++sAiqJ8+391p66xXfn586doEi5zJtOJU58kkZMZX5Sp8qTQq7IMR8wsRSHkLVBlXSCWM6JTpfFkp+gtrmMlqaHWvsD4mgu7PsloxEyTN0ww0Y+gKEwb63il20lHnUKpKUKqoKU/bCVfEKh2xwnHdbTbh+leqsasvZlI5NZHCSVucEXdhVu7wltXfPxR2WGGTR2Mrti4M/Ubev37qFs6TlGt5UcDe/hr3/MIyTjPG77Kfvc5jqx2cercGn9x9zKj8QB6dY72pbehWEAo5EAQuew9wNSqEUGAnaZu3olsILKqUF+aJ5pS0ekdI1Ewspo1sE65gmF2gLVgE49daWVXS4aW+DHmHE2MrPlw6hPUrxznqmUXVydNHCi/xuNn69jZlsegzhIQJ0lJRvzT5+hx7efDazo66goIKLx+NENzo5mQK4NFTnNpwoLdXMRvSdG+9DYnzXfTIZ6nT2qlKX2GcVMLV+bc3Gk9QUEloyDyxNU2vtpwmkt0Ec+oSOdEtOoi++IvcNZ5L9cntTSHUqwkZdaSEstRha+onmC0Zj+uzBQRbZCeuZv54smMgN1UJGRdYXzVhtuYon/OQIM/xo0ZE+tLl0gXNMhSjpdPGPjCzjBjMQ9vHEny/c53ueK+g7mYkU3mqyypvJwc8bKraopfvO/kY9uzGFUpjvR6CPmK7FSfoChK/Lx3M/d1zjGTdOLVrVAxfYwez356xk3YTMrNZVKWGEspPYoioFEVsGvjpPMaGlLnOS9tw29YZnDZjUpSKDGvkMxryRUlEhk123PvcsGwh+WkjE5d4MMLBT69c43SRC+zxhpCM8fJG210a3awllKzL/M7skYH3eIWdOo8QwsGtpROUuBmssOVBT/VzhWWUkbs2gSNE6/yuukLGOUCpeYliopI9fRhYp5aDkU6eGDxRxwK6KHbAAAV7UlEQVSr/BO8hihrWT0aKY9NvUYsbyRXlJClHKJQpKiIVCSucIZtuPRxbOoV+lZLabBM0B8tRS0VCRnnWczaaUyeRr0WQVFpKGgNjFk7qAqf4LJ9LzdmTGwJzTKdcKKRbmbji0KR+oX3yZpcvJvcQYNrkZJkH1I+zeMTt3J/8zCXlqv+R3v3GiXHWd95/Puv6urbTM9d0oxGo/tItiTfDZaNsYVNFgOOL+Dses8JBLKcAAF2OXmxG3bPJoScfZFNXrCbPVkOIU4MCRhwwBDHGLzB2A4OwrYsy9bNkkby6DIXzb17+lbV9d8XVbLGgyzL8WVmuv+fc/qouupp6amfqurpp6ueKra3vsBMogNFeH60h/d7P8EtznCg+2YuGvkpoz2XMeR34zk1Tsy0cLM+TCXbwXhmFc21KTr3PcqBrf8eV0IyUiTAY/Wpf+HAips4VWjl6JDLjZtO89zQcrb1jKEq9NaOsXP2ct5b/HuKHX08W7uSlU0TLK8MMpxay3S1mZ70CLnyBKPJVZRrKfpnnyE5PcKuFbdzcW03D029i5Sn/Hr+60yvuoQfnryKtuaQw4PKZRtD9g8m+EzuG3xh/4e4bFszG5fPUqm57NzncWm/MpFP0L98huNTzQwOQfcy4a7qN3i658MApN0qY6UmAPyaQy0UNrWPMOXn+NmeLP/26kF6B5/k1JprufeJVXzy2oP8dPgSALZ0T5B2KmSkSEmz/PNADxf1Ftl3PMtvdj7EiY7L+IfdPWxZF+IHQqnq0JKt0eT5FKoeg6MJrlk/iSL06VHa9v6M7634PMtyVd598l7+X/dvMzqVwEvAwKDPh7ZPUArSpN0KL0500ZSs0ZYusSJ5mscGN1D14Y6+Z0iVp3m8diO9LdOsrR7gB6PXcXHPDGtlgHRlmoRf4m9G3se21WXWZY7TOXmYkx2XsualRznU9z66/UEG3Q0AtCTy5PwJWiYG+EF4J73tJfYdz+I4sLIz4KURl22ry+QrHsOTCfq7S1zCs9x3/Dq2b5igFKRwpUaoDplEhTZ3ih+9uIE7NjxPLn+KE62XMO038/RAju5O5dbyt/lB8m6uXnGMw/lewlC4rPkAx8PVbJt6jOdabqIzNcXap79Jpf9KXkhvZ7oc3dmrWnMolF1eeNHn1u0laqFLWzLPkanlHDrh8NFNT3HM3USTWyTllNk3uZre3DQjsy2cnvHIpEJ25J7Cq86SqBR4xPkg7x/+Cl/hd7nx4nH6J/4FqfmcWPEOMmGBx4YuZmV7mWcPJVm5XLgj/C6feuTd3H1nFztmH+CvC7+BI3DxqhKHhjOkPFjTVSRfSTKRT3D9qiMcKaxiZCbJ+q4CANVagg2po1ScDM+eXoMfCDd37uJgeBFb9TkmMz2crnZyxeB32bfuDjJumWMzy3nP8a8ysfndeEGJQrqTZWP7edy7hTUtYwxML+PpvcqvXV0lk6jw7PEOOnM13tG2nyFdRTFIcrqQ5ub0E+zUa9mcG+RwoQ/PDWnyKrQnJqlomrFKK2tSJ8j4eQ6EFzOaT7N7n89HbxzludFeHAc2dYwxUcmxOnOKvVNrGBxN0NGi9HdNMRukaE3Ocmi8k3Xt02wsPstQy2aOTK/gmvQuMvkRHk28n2wyIF/2SLohCVe54cU/Z+eWTzNb8VjRnOfgaDsnRpSbtk3TmRgnFIepoI31pedRxyWfWcbPhzdxh97PQ8kPMzblcFFvCRFl90CGa/pnuPzkD/jFit+gUPXIpaK7fR2daCGbClnRVEBQlrmjHC6tYWPmJR48tJlsGtqba+xIPMbPghu5uOMUy2aPMda0hoqmWFk+wh/+ZCt/sv2feCJ7K2tzoxye7iaoCTeknuTJ4FpaUhXGZjNUfOGmjl0M0E858GhPF/BDj4xb5pL+nmdU9eo3+evkmyLXdpFefuNfvq7P/PMPb1g067PUOwR3Abeo6ifi9x8BrlHVz77aZ7Zdcpk+8MADeFqlKml89Qg0Qdop89DePu7YcghfUrgEhDi41KiSIhdMMp3oZPfwSla2lfCcGnsGc/ybjUcoaZaOYITQcam4WXaNraO3pUBv4iS+myIR+vx8eBPbewZwtMZ42MXRyTau7jxMIB75MMeJmTY6MmXy1SSXZg5QSLThSsB4tZM2bxoAVwKmgjaybonhYgcZr0pPYhhFqDnRLQL3T61mc+tJjuR7WNE0Q7tMEIpLslbiheImHAHXCVmTHaIQNtPqTJPx84y6vcwGadqTM0z5OZoSZQTFEx9FOF5YRmemQLmWJFSHlanh6OmSYY4mp8hMkMN1ajQ7s9RwGau0c3wyS197kc7UFKPldlamTzPpt9HuTfG9Z/t476VTpJwqlTAZPdQMpdkpMOF34Lk+GafMwPQKNrYOUQwzeBLQopNMSScA1dCjFrrM+kk2po+SlzaKtQx7TrZz7ZqT+KHH6sILjLeu5cXpPp7YFfKRHWOkKFMjQQ2XtBaZCDtZwSmGdBUvnm7jip5TOIQUak1k3RJpSjhaY1I7CNVh73An160aQFSpkMYPPUKEhNSiB4k5HgdlGxucQxwMNrM6c4pkrcQ/HtvGjg3Hmai2sjw1hqMhVVIISqAJQhxOFDpYlxshrUV8SdIxe4JfhtcwU0pw1YpjTPgd9OlRak6CU9rH6vAIs8k2ZsIWTuXbWN86gidVRBWfJI8f7aPqww39I3ji01EZZiLVDUACnxAXjyo+yZe/+E9UWlntDdI2dYxDrdewTEY4UNxAb1P8GPpElgf2rOM9WyZIOj6nS610ZWaoqUPKqdJdOMwzsp10ImC9e4RKIsuR2T5ElJ8+JVyx1WNb1ylcanTODjLWtAZfPXrz+/lJeQcHj4Vcv80nnfApBkl6MhOcrrSxwRsgGZQJHZeqm6bsNNFeGWYi1UM2zFNymqnh0lKboJxoYrbWTDX0SDo+M34TK1PDHC32sjI7jkuNfK2ZpOPTpuMUnRyVMIUjIWkpka7NMuu2kg+a6XJPMxF20umMkaeVrMyiInTODBI6Lvccfhfv3FwilyzRxShuGDDmdBOoS6czRlXSOFKjpgmePL6aVR0VNmZfwpcU00ELuUSBQBP4miDrlBCUqiZJSpVmfxLfTVF2mjhZXMaWxD5OJdawc6CLD6zfj++mmPA7gOjLaaAJhkvtpNwanakpmsIZSk4zipCkwpjfRdKNHpt7fKadK3P7KbnN0e1gJeBkcRmeG9LsFZmqNDNTTtLXOkktdBmezTFZSPCu3iO4GiAaouLw1Hg/q1rzLPfGSAezFBM5EuqTp5VAXZqd2ZePpyXNMlHJsSZ1gpI04VIj508w7XUhKC4Bzf4Uo24vrTKJG/pU3QwlzZKQgEATNJEnTyutOkHgJDlR7sYVpZ8DHHP6WZ4YYZYcxwudrM8Nv7wfF2sZpitZ1mZPMh500pyYxSHElYBymKGJPK5GP9DMOi2E6lBVj8lyM23pWQ6Pd7CqtUBbMk/ebybtVqiGHrlEgXwQZbzMHSUQj5laS3RcDl081ycpPjmdwqtVmEwsZ6jUwbL0DAfHu/Bc5dKOYzhai45rkmY6aKFQTbG6aYhSmKUzGCZwk1ScDC9Or6Ip5UdnlXScaemgSQpUSCMok34L7d4My2aP8XjluuiLWfElhjIbKNYyrKkdpuw1MUUnNRxSUsWVGh2VIYrJVtJ+gaOykYSELHNHUYSyZFGEUB2amWEk6CaEaP2cGu3eFDVc2qsjDCVWE6pDpeaRr6bpbRpHUNISHUefHt9Ib0uUo0PIvvGVbOoYJcQhIQF+6MXtXY3ZIEuPe5KhWi/FIMnqzCnC+G7phbCZNpkkFJcKaUJ1KIcplsswM9JORzBCMdlCIvS558n13L59htFijrW5UUKN/i1FXt4+EgRMBa1RXd0KK6qDHE9sIOn4AFTCJJ4EtMokI0E3abfCeDlHb/Y0u0b62L78EDUnQa48wUSqm7FKO81ekYxTphhmSIqPJz4eVaqkGJjpZizvccXKYVJSYdJvI5sokZQqHlUCvJfb8RXhKUJx8N00NVyKYRMiSpuOR/ty0IcfuriiJJyADm/65e8vhbCJlFRRhJxOEYrLYKWX7nT0/5LUMmXJkg3zHA/6CNUhmyjT7M6SCQsEThJHa1QlzbjfRqtXYLzSRos3Sw2Hntpxpr0uHjm4iuv7x2hyioQ4ZMM86WqeyXQPAJmwgO+kOFHupsmr0OpG32vG/A48p4YrNWaqWbboHo56FzNdydCRLjA43cbatgmWh0NUElnWb+xfNF+g52tu26yXv/v1/dj/8wd3LJr1aYgOgYj8DvA78dttwAtva0UXpy5gbKErsQhYDmdZFhHLIWI5RCyHsyyLiOUQWYgc1qjqsrf537wgIvIwUSavx5iq3vJW1Of1Six0Bd6gk0DfnPer4nmvoKpfBb4KICJPL5be2EKyHCKWw1mWRcRyiFgOEcvhLMsiYjlELIdXWixf7P+1lvqTip8C+kVknYgkgbuBHy5wnYwxxhhjjFkylvQZAlUNROSzwI8BF7hHVfcucLWMMcYYY4xZMpZ0hwBAVR8CHnodH1kUt3daBCyHiOVwlmURsRwilkPEcjjLsohYDhHLoY4s6UHFxhhjjDHGmDdmqY8hMMYYY4wxxrwBddMhEJFbROSgiBwWkd8/x/KUiHw7Xr5TRNbOWfaFeP5BEXnf21nvN9sF5PAxETktIrvj1yfmLPstETkUv37r7a35m0tE7hGRURE57y1mReQdIhLEt7A9M6+ecugTkUdFZJ+I7BWR/3SOMreLyJ54e3haRK6fs6yeskiLyC9F5Lk4iz86R5nfi7PaIyL/JCJr5iyrmywgetK7iDwrIg+eY9kNIrJr/r4RL6ubHETkmIg8f2bbP8dyEZH/HR9P94jIlXOWNVIOO0Rkek678Qdzlp23zVlqRKRNRO4XkQMisl+iB6Ceq1zdth0isnnO//VuEZkRkc/PK9MQ7UZDUdUl/yIaUHwEWA8kgeeALfPK/C7wlXj6buDb8fSWuHwKWBf/Pe5Cr9NbmMPHgP9zjs92AAPxn+3xdPtCr9MbyOIG4ErghdfI66dEY1DuqtMceoAr4+kc8OI5tolmzl4+eClwoE6zEKA5nvaAncD2eWXeA2Tj6U/POU7UVRbxOv0e8E3gwXMsWxtvC18/s2/UYw7AMaDrPMs/APwo3na2AzsbNIcdr7KdvGabs9RewL3AJ+LpJND2Kutd123HvHUdJrr//9z5DdFuNNKrXs4QvBM4rKoDqloF7gNun1fmdqIdHeB+4GYRkXj+fapaUdWjwOH471uKLiSHV/M+4BFVnVDVSeARYMneU1dVHwcmXqPY54C/B0bnzKu3HIZUdVc8nQf2A73zyhQ0PpIDTcCZ6XrLQlW1EL/14pfOK/Ooqhbjt78gerYJ1FkWIrIK+CDwtXMtV9VjqroHCOctqqscLsDtwNfjbecXQJuI9NB4ObyaN9LmLDoi0kr0Y9JfAahqVVWnzlG07tuOOW4GjqjqS3NnNkq70UjqpUPQCxyf8/4E0CsiXxKR2+aXUdUAmAY6X+2zb3mN3xoXkgPAh+NTffeLSN/5PvvWVvftJSKfEpFPxdO9wJ3A/51XrG5zkOgyuSuAnXOziJfdKSIHgH8EfjueXXdZxJfJ7CZqyB9R1Z3n2D/O+A9Evw5D/WXxZeA/M+cL/3lymKveclDgJyLyjERPtGfevvFq69toOQBcG19u9yMR2RrPq7cc1gGngb+OL6f7mog0NXjbcTfwLfjVbaJR2o1GseRvO3o+qvoHr12q/s3L4R+Ab6lqRUQ+SXTW5KaFqdnbS1W/Muftl4H/oqphdKKovolIM9EvWp9X1Rlgbhao6veB74vIDcAfA+99+2v51lPVGnC5iLQRre+2cx0nROQ3gauBG9/uOr7VRORWYFRVnxGRHWfmN+jx8npVPSkiy4FHROTAvONEo3itHHYRXTJSEJEPAA8A/QtS07dWguhS08/FPxb8L+D3VfW/zynTMG2HRA98vQ34AvxKG9ow7UajqJczBCeBvjnvV8XzzllGRBJAKzB+gZ9dKl5zXVR1XFUr8duvAVdd6GfrzNXAfSJyDLgL+AsRuYM6zEFEPKLOwN+p6vfOVza+1Gq9iHRRh1mcEV8G8CjnOJUtIu8F/htw25x9pZ6yeBdwW7zt3wfcJCJ/e4GfraccUNWT8Z+jwPf51ctFX219GyoHVZ05c7mdRs/+8er0GHECOKGqO+P39xN1EOZqmLYDeD+wS1VHzleoUdqNurfQgxjejBdRr36A6HTfmYFNW+eV+QyvHFT8nXh6K68cVDzA0h1UfCE59MyZvhP4RTzdARwlGgTUHk93LPQ6vcE81nKeQcVzyv0NrxwYVjc5EA2G/Drw5fOU2cjZwWFXEh28pQ6zWEY8QBDIAE8At84rcwXRIMn+efPrKos567WDcwwWnbP85X2j3nIguu45N2f6SeCWeWU+yCsHFf+yQXPonnOMeCcwGGfymm3OUnvFx4XN8fQXgT89T9m6bTvidboP+PirLGuIdqORXnVxyZCqBiLyWeDHRCPi71HVvSLyJeBpVf0h0SChb4jIYaLBpnfHn90rIt8B9gEB8BmNLitYci4wh/8YXyccEOXwsfizEyLyx8BT8V/3JVV9rUG5i5aIfIvoy06XiJwA/pBoECl6nksC6i0Hol+DPwI8H187D/BfgdXwchYfBj4qIj5QAv6dRkf5esuiB7hXRFyis6PfUdUH5+0ff0p094zvxpcDDKrqbXW4XfyKuTmIyDuIfiluB35dRP5IVbfWWQ4riC53gOiL7TdV9eEz10jH+8ZDRHcaOgwUgY/Hyxoth7uAT4tIQHSMuDs+RpyzzVmIlXgTfQ74u/hymQHg4/OyOKc62yYQkSbg14BPzpk3N4dGaTcahj2p2BhjjDHGmAZWL2MIjDHGGGOMMf8K1iEwxhhjjDGmgVmHwBhjjDHGmAZmHQJjjDHGGGMamHUIjDHGGGOMaWDWITDGmEVKRDpFZHf8GhaRk/F0QUT+YqHrZ4wxpj7YbUeNMWYJEJEvAgVV/bOFrosxxpj6YmcIjDFmiRGRHSLyYDz9RRG5V0SeEJGXRORDIvI/ReR5EXlYRLy43FUi8piIPCMiPxaRnoVdC2OMMYuFdQiMMWbp2wDcBNwG/C3wqKpeQvQE0Q/GnYI/B+5S1auAe4D/sVCVNcYYs7gkFroCxhhj3rAfqaovIs8DLvBwPP95YC2wGdgGPCIixGWGFqCexhhjFiHrEBhjzNJXAVDVUER8PTs4LCQ6zguwV1WvXagKGmOMWbzskiFjjKl/B4FlInItgIh4IrJ1getkjDFmkbAOgTHG1DlVrQJ3AX8iIs8Bu4HrFrZWxhhjFgu77agxxhhjjDENzM4QGGOMMcYY08CsQ2CMMcYYY0wDsw6BMcYYY4wxDcw6BMYYY4wxxjQw6xAYY4wxxhjTwKxDYIwxxhhjTAOzDoExxhhjjDENzDoExhhjjDHGNLD/D+MfGaGEO0q5AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#feature extraction\n",
        "##zero crossing rate\n",
        "x, sr = librosa.load(audio_path)\n",
        "#Plot the signal:\n",
        "plt.figure(figsize=(14, 5))\n",
        "librosa.display.waveplot(x, sr=sr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "7W3vNdEmWuz4",
        "outputId": "e42dc1e0-0075-46f0-fc55-94ce2e2fddf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-019b1deb7ad1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#Plot the signal:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaveplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<decorator-gen-118>\u001b[0m in \u001b[0;36mwaveplot\u001b[0;34m(y, sr, max_points, x_axis, offset, max_sr, ax, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/librosa/util/decorators.py\u001b[0m in \u001b[0;36m__wrapper\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0mlineno\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mco_firstlineno\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m         )\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__wrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/librosa/display.py\u001b[0m in \u001b[0;36mwaveplot\u001b[0;34m(y, sr, max_points, x_axis, offset, max_sr, ax, **kwargs)\u001b[0m\n\u001b[1;32m    723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m     \u001b[0;31m# Reduce by envelope calculation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 725\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__envelope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhop_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m     \u001b[0my_top\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/librosa/display.py\u001b[0m in \u001b[0;36m__envelope\u001b[0;34m(x, hop)\u001b[0m\n\u001b[1;32m    587\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0massumed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mmulti\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mchannel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mof\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn_channels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m     \"\"\"\n\u001b[0;32m--> 589\u001b[0;31m     \u001b[0mx_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhop_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    590\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/librosa/util/utils.py\u001b[0m in \u001b[0;36mframe\u001b[0;34m(x, frame_length, hop_length, axis)\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mParameterError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Frame axis={} must be either 0 or -1\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mas_strided\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrides\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mas_strided\u001b[0;34m(x, shape, strides, subok, writeable)\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0minterface\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__array_interface__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m         \u001b[0minterface\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'shape'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstrides\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0minterface\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'strides'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrides\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'tuple'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# zooming in\n",
        "# Zooming in\n",
        "n0 = 9000\n",
        "n1 = 9100\n",
        "plt.figure(figsize=(14, 5))\n",
        "plt.plot(x[n0:n1])\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "N5LMTJ5MW1zo",
        "outputId": "b63329ac-6c67-4dc5-a7ed-18a4cea905f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zero_crossings = librosa.zero_crossings(x[n0:n1], pad=False)\n",
        "print(sum(zero_crossings))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tel2Ol2IW31W",
        "outputId": "67d86650-8f0a-4415-c30b-d7943e51f410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#spectral centroid\n",
        "#spectral centroid -- centre of mass -- weighted mean of the frequencies present in the sound\n",
        "import sklearn\n",
        "spectral_centroids = librosa.feature.spectral_centroid(x, sr=sr)[0]\n",
        "spectral_centroids.shape\n",
        "# Computing the time variable for visualization\n",
        "frames = range(len(spectral_centroids))\n",
        "t = librosa.frames_to_time(frames)\n",
        "# Normalising the spectral centroid for visualisation\n",
        "def normalize(x, axis=0):\n",
        "    return sklearn.preprocessing.minmax_scale(x, axis=axis)\n",
        "#Plotting the Spectral Centroid along the waveform\n",
        "librosa.display.waveplot(x, sr=sr, alpha=0.4)\n",
        "plt.plot(t, normalize(spectral_centroids), color='r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "gU-95IuyW6W0",
        "outputId": "447c84ab-9cc9-43a0-ad83-75d6c1a64c71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f05464d7410>]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# spectral rolloff\n",
        "spectral_rolloff = librosa.feature.spectral_rolloff(x, sr=sr)[0]\n",
        "librosa.display.waveplot(x, sr=sr, alpha=0.4)\n",
        "plt.plot(t, normalize(spectral_rolloff), color='r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "cTtt1oZPW97H",
        "outputId": "e9339990-2959-473e-fd29-d037ed869ded"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f054334e910>]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mfccs = librosa.feature.mfcc(x, sr=sr)\n",
        "print(mfccs.shape)\n",
        "#Displaying  the MFCCs:\n",
        "librosa.display.specshow(mfccs, sr=sr, x_axis='time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "dnObaEkSXGW0",
        "outputId": "7f057bac-d4e2-4cd9-9655-27f45d78cd25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20, 20293)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x7f05432cd050>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MFCC for all data"
      ],
      "metadata": {
        "id": "cTNtkOfg5fkL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dir_name= '/content/drive/My Drive/audio_data/interim'"
      ],
      "metadata": {
        "id": "YMlYK4_JYWZ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generating a dataset\n",
        "header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'\n",
        "for i in range(1, 21):\n",
        "    header += f' mfcc{i}'\n",
        "header += ' label'\n",
        "header = header.split()"
      ],
      "metadata": {
        "id": "dgdJqFD-bkNo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file = open('/content/drive/My Drive/audio_data/data.csv', 'w', newline='')\n",
        "with file:\n",
        "    writer = csv.writer(file)\n",
        "    writer.writerow(header)"
      ],
      "metadata": {
        "id": "hHL1_Zokbt50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for subdir, dirs, files in os.walk(dir_name):\n",
        "        for file in files:\n",
        "            if file.endswith('.wav'):\n",
        "              wav_file = os.path.join(subdir, file)\n",
        "              png_name = subdir + '/' + file[:-4] + '.png'\n",
        "              print('Processing ' + file + '...')\n",
        "#sr=sampling rate\n",
        "\n",
        "              #load the audio file\n",
        "              x , sr = librosa.load(wav_file)\n",
        "\n",
        "              chroma_stft = librosa.feature.chroma_stft(y=x, sr=sr)\n",
        "\n",
        "              #indicates loudness(root mean square energy)\n",
        "              rmse = librosa.feature.rms(x)\n",
        "\n",
        "              spec_bw = librosa.feature.spectral_bandwidth(y=x, sr=sr)\n",
        "           \n",
        "           \n",
        "              X = librosa.stft(x)\n",
        "              #convert and amplitude spectrogram into decibel scale(to indicate sound)\n",
        "              Xdb = librosa.amplitude_to_db(abs(X))\n",
        "\n",
        "              #number of times a signal croses the horizontal axis\n",
        "              zero_crossings = librosa.feature.zero_crossing_rate(x)\n",
        "\n",
        "              #Frequency band where most of the energy is concentrated\n",
        "              spectral_centroids = librosa.feature.spectral_centroid(x, sr=sr)\n",
        "\n",
        "              spectral_rolloff = librosa.feature.spectral_rolloff(x, sr=sr)\n",
        "              \n",
        "              mfccs = librosa.feature.mfcc(x, sr=sr)\n",
        "              print(mfccs.shape)\n",
        "             \n",
        "\n",
        "             #append all features together in one file\n",
        "              to_append = f'{wav_file} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spectral_centroids)} {np.mean(spec_bw)} {np.mean(spectral_rolloff)} {np.mean(zero_crossings)}'\n",
        "\n",
        "              for e in mfccs:\n",
        "                  to_append += f' {np.mean(e)}'\n",
        "              to_append += f' {file}'\n",
        "              file = open('/content/drive/My Drive/audio_data/data.csv', 'a', newline='')\n",
        "              with file:\n",
        "                  writer = csv.writer(file)\n",
        "                  writer.writerow(to_append.split())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYgnjUaFYVH5",
        "outputId": "c88490e2-b1b1-4ba4-831e-fe390a5f17bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing P365_no_silence.wav...\n",
            "(20, 36988)\n",
            "Processing P325_no_silence.wav...\n",
            "(20, 23195)\n",
            "Processing P336_no_silence.wav...\n",
            "(20, 20308)\n",
            "Processing P332_no_silence.wav...\n",
            "(20, 18074)\n",
            "Processing P340_no_silence.wav...\n",
            "(20, 9001)\n",
            "Processing P326_no_silence.wav...\n",
            "(20, 13207)\n",
            "Processing P334_no_silence.wav...\n",
            "(20, 22033)\n",
            "Processing P383_no_silence.wav...\n",
            "(20, 39964)\n",
            "Processing P327_no_silence.wav...\n",
            "(20, 13516)\n",
            "Processing P319_no_silence.wav...\n",
            "(20, 12355)\n",
            "Processing P324_no_silence.wav...\n",
            "(20, 15593)\n",
            "Processing P369_no_silence.wav...\n",
            "(20, 33104)\n",
            "Processing P328_no_silence.wav...\n",
            "(20, 26429)\n",
            "Processing P320_no_silence.wav...\n",
            "(20, 14550)\n",
            "Processing P318_no_silence.wav...\n",
            "(20, 11848)\n",
            "Processing P333_no_silence.wav...\n",
            "(20, 20720)\n",
            "Processing P409_no_silence.wav...\n",
            "(20, 30217)\n",
            "Processing P312_no_silence.wav...\n",
            "(20, 26790)\n",
            "Processing P377_no_silence.wav...\n",
            "(20, 45108)\n",
            "Processing P307_no_silence.wav...\n",
            "(20, 33406)\n",
            "Processing P311_no_silence.wav...\n",
            "(20, 16572)\n",
            "Processing P337_no_silence.wav...\n",
            "(20, 56556)\n",
            "Processing P313_no_silence.wav...\n",
            "(20, 18099)\n",
            "Processing P364_no_silence.wav...\n",
            "(20, 53101)\n",
            "Processing P331_no_silence.wav...\n",
            "(20, 18510)\n",
            "Processing P323_no_silence.wav...\n",
            "(20, 20630)\n",
            "Processing P322_no_silence.wav...\n",
            "(20, 25293)\n",
            "Processing P304_no_silence.wav...\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pev9xv71alK_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Models"
      ],
      "metadata": {
        "id": "R01Sx-eJzw3d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "jiOt-B8dzyzG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "################ DONT EXECUTE FOLLOWING CELLS #################################\n",
        "################ IMPLEMENT FROM \"START HERE\" COMMENT ##########################"
      ],
      "metadata": {
        "id": "UX7GPVSJ18O2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/audio_data/combined_df.csv', header=None)"
      ],
      "metadata": {
        "id": "S-5DpDII0AtV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "0UnIN5YU0Jvc",
        "outputId": "36892915-efa3-43bc-ff44-8163d5e8e91d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         2         3            4            5            6         7   \\\n",
              "0  0.569320  0.017554  1179.037573  1526.276326  2431.840489  0.047237   \n",
              "1  0.358157  0.027760  1358.624741  1415.536135  2604.327427  0.065088   \n",
              "2  0.440538  0.027978  1197.019446  1392.292686  2500.084620  0.049712   \n",
              "3  0.463018  0.013304  1498.399575  1611.700862  3021.572803  0.066246   \n",
              "4  0.461996  0.028187  1304.979257  1494.103505  2674.776216  0.054946   \n",
              "\n",
              "           8           9          10         11  ...        19        20  \\\n",
              "0 -378.723999   89.888130  34.436951  23.434534  ...  3.854689 -0.833143   \n",
              "1 -358.913391  100.120476   7.161822  15.062524  ... -3.080664 -0.269885   \n",
              "2 -379.248291  104.145782  13.680271  29.738724  ... -2.571175  0.443118   \n",
              "3 -408.170197   76.551636  10.367766  16.630489  ... -0.760586 -0.163474   \n",
              "4 -353.486176  112.376045  10.709586  24.575321  ...  2.108073  6.677998   \n",
              "\n",
              "         21        22        23        24        25        26        27  29  \n",
              "0  2.360463  3.653419  2.640903  2.180515 -0.018859  2.898318  0.768065   1  \n",
              "1 -1.964366  3.397129 -3.236047 -1.490361  1.393715 -2.004146 -2.106658   1  \n",
              "2  2.786901  0.584616  1.343224  0.827890 -0.831182  0.379470  0.331436   0  \n",
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              "4 -1.790910  2.357386  4.616292 -0.267873 -0.669441  1.354952  0.971203   0  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <td>104.145782</td>\n",
              "      <td>13.680271</td>\n",
              "      <td>29.738724</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.571175</td>\n",
              "      <td>0.443118</td>\n",
              "      <td>2.786901</td>\n",
              "      <td>0.584616</td>\n",
              "      <td>1.343224</td>\n",
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              "      <td>-0.831182</td>\n",
              "      <td>0.379470</td>\n",
              "      <td>0.331436</td>\n",
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              "      <td>0.066246</td>\n",
              "      <td>-408.170197</td>\n",
              "      <td>76.551636</td>\n",
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              "      <td>...</td>\n",
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              "      <td>-0.163474</td>\n",
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              "      <td>-2.229471</td>\n",
              "      <td>-0.469260</td>\n",
              "      <td>1.866178</td>\n",
              "      <td>-0.473763</td>\n",
              "      <td>-0.938451</td>\n",
              "      <td>1</td>\n",
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              "      <td>1494.103505</td>\n",
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              "      <td>10.709586</td>\n",
              "      <td>24.575321</td>\n",
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              "      <td>0.971203</td>\n",
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              "<p>5 rows × 27 columns</p>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-95e1f67a-6a53-4da6-b6ae-b9bbf2c04362 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95e1f67a-6a53-4da6-b6ae-b9bbf2c04362');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.drop(df.columns[[0,1,28]], inplace=True, axis=1)\n",
        "# df.head()"
      ],
      "metadata": {
        "id": "e83zM5540V2y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "M2fSrHy-1VVO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "758559ea-728a-4ea6-b2ef-b2ba4d0f842f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(640, 27)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1miYEhuQ4kXh",
        "outputId": "868b7628-290e-4a05-b8c4-37e8adc4da75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 640 entries, 0 to 639\n",
            "Data columns (total 27 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   2       640 non-null    float64\n",
            " 1   3       640 non-null    float64\n",
            " 2   4       640 non-null    float64\n",
            " 3   5       640 non-null    float64\n",
            " 4   6       640 non-null    float64\n",
            " 5   7       640 non-null    float64\n",
            " 6   8       640 non-null    float64\n",
            " 7   9       640 non-null    float64\n",
            " 8   10      640 non-null    float64\n",
            " 9   11      640 non-null    float64\n",
            " 10  12      640 non-null    float64\n",
            " 11  13      640 non-null    float64\n",
            " 12  14      640 non-null    float64\n",
            " 13  15      640 non-null    float64\n",
            " 14  16      640 non-null    float64\n",
            " 15  17      640 non-null    float64\n",
            " 16  18      640 non-null    float64\n",
            " 17  19      640 non-null    float64\n",
            " 18  20      640 non-null    float64\n",
            " 19  21      640 non-null    float64\n",
            " 20  22      640 non-null    float64\n",
            " 21  23      640 non-null    float64\n",
            " 22  24      640 non-null    float64\n",
            " 23  25      640 non-null    float64\n",
            " 24  26      640 non-null    float64\n",
            " 25  27      640 non-null    float64\n",
            " 26  29      640 non-null    int64  \n",
            "dtypes: float64(26), int64(1)\n",
            "memory usage: 135.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[:,:-1].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "LehPHWlRT8l7",
        "outputId": "2157348c-0c0b-49b0-87e0-11a7cc18e5ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         2         3            4            5            6         7   \\\n",
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              "1  0.358157  0.027760  1358.624741  1415.536135  2604.327427  0.065088   \n",
              "2  0.440538  0.027978  1197.019446  1392.292686  2500.084620  0.049712   \n",
              "3  0.463018  0.013304  1498.399575  1611.700862  3021.572803  0.066246   \n",
              "4  0.461996  0.028187  1304.979257  1494.103505  2674.776216  0.054946   \n",
              "\n",
              "           8           9          10         11  ...        18        19  \\\n",
              "0 -378.723999   89.888130  34.436951  23.434534  ...  3.535516  3.854689   \n",
              "1 -358.913391  100.120476   7.161822  15.062524  ...  1.107063 -3.080664   \n",
              "2 -379.248291  104.145782  13.680271  29.738724  ...  7.812723 -2.571175   \n",
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              "4 -353.486176  112.376045  10.709586  24.575321  ...  3.695489  2.108073   \n",
              "\n",
              "         20        21        22        23        24        25        26  \\\n",
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              "\n",
              "         27  \n",
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              "      <th></th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>...</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
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              "      <td>23.434534</td>\n",
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              "      <td>3.535516</td>\n",
              "      <td>3.854689</td>\n",
              "      <td>-0.833143</td>\n",
              "      <td>2.360463</td>\n",
              "      <td>3.653419</td>\n",
              "      <td>2.640903</td>\n",
              "      <td>2.180515</td>\n",
              "      <td>-0.018859</td>\n",
              "      <td>2.898318</td>\n",
              "      <td>0.768065</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.358157</td>\n",
              "      <td>0.027760</td>\n",
              "      <td>1358.624741</td>\n",
              "      <td>1415.536135</td>\n",
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              "      <td>0.065088</td>\n",
              "      <td>-358.913391</td>\n",
              "      <td>100.120476</td>\n",
              "      <td>7.161822</td>\n",
              "      <td>15.062524</td>\n",
              "      <td>...</td>\n",
              "      <td>1.107063</td>\n",
              "      <td>-3.080664</td>\n",
              "      <td>-0.269885</td>\n",
              "      <td>-1.964366</td>\n",
              "      <td>3.397129</td>\n",
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              "      <td>-1.490361</td>\n",
              "      <td>1.393715</td>\n",
              "      <td>-2.004146</td>\n",
              "      <td>-2.106658</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.440538</td>\n",
              "      <td>0.027978</td>\n",
              "      <td>1197.019446</td>\n",
              "      <td>1392.292686</td>\n",
              "      <td>2500.084620</td>\n",
              "      <td>0.049712</td>\n",
              "      <td>-379.248291</td>\n",
              "      <td>104.145782</td>\n",
              "      <td>13.680271</td>\n",
              "      <td>29.738724</td>\n",
              "      <td>...</td>\n",
              "      <td>7.812723</td>\n",
              "      <td>-2.571175</td>\n",
              "      <td>0.443118</td>\n",
              "      <td>2.786901</td>\n",
              "      <td>0.584616</td>\n",
              "      <td>1.343224</td>\n",
              "      <td>0.827890</td>\n",
              "      <td>-0.831182</td>\n",
              "      <td>0.379470</td>\n",
              "      <td>0.331436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.463018</td>\n",
              "      <td>0.013304</td>\n",
              "      <td>1498.399575</td>\n",
              "      <td>1611.700862</td>\n",
              "      <td>3021.572803</td>\n",
              "      <td>0.066246</td>\n",
              "      <td>-408.170197</td>\n",
              "      <td>76.551636</td>\n",
              "      <td>10.367766</td>\n",
              "      <td>16.630489</td>\n",
              "      <td>...</td>\n",
              "      <td>2.336517</td>\n",
              "      <td>-0.760586</td>\n",
              "      <td>-0.163474</td>\n",
              "      <td>2.424940</td>\n",
              "      <td>2.928302</td>\n",
              "      <td>-2.229471</td>\n",
              "      <td>-0.469260</td>\n",
              "      <td>1.866178</td>\n",
              "      <td>-0.473763</td>\n",
              "      <td>-0.938451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.461996</td>\n",
              "      <td>0.028187</td>\n",
              "      <td>1304.979257</td>\n",
              "      <td>1494.103505</td>\n",
              "      <td>2674.776216</td>\n",
              "      <td>0.054946</td>\n",
              "      <td>-353.486176</td>\n",
              "      <td>112.376045</td>\n",
              "      <td>10.709586</td>\n",
              "      <td>24.575321</td>\n",
              "      <td>...</td>\n",
              "      <td>3.695489</td>\n",
              "      <td>2.108073</td>\n",
              "      <td>6.677998</td>\n",
              "      <td>-1.790910</td>\n",
              "      <td>2.357386</td>\n",
              "      <td>4.616292</td>\n",
              "      <td>-0.267873</td>\n",
              "      <td>-0.669441</td>\n",
              "      <td>1.354952</td>\n",
              "      <td>0.971203</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81f1146d-1cae-4021-8191-358a3734dc57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81f1146d-1cae-4021-8191-358a3734dc57 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81f1146d-1cae-4021-8191-358a3734dc57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df.iloc[:, -1]\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3vegqMs6iCz",
        "outputId": "78da8222-e1c2-42d9-cdea-8db3400fce39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    0\n",
              "3    1\n",
              "4    0\n",
              "Name: 29, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "5cMFV-sl618I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "R7bbl3vXgyf9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjqyNyHIYtaW",
        "outputId": "714d5187-5e35-4abc-e716-629ba86bf9bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-6.41113139e-01  6.39490264e-01 -6.78568656e-01 ... -1.89074930e+00\n",
            "  -1.77751176e+00  2.17086370e+00]\n",
            " [-1.54367598e+00  7.81024766e-01 -9.03912024e-01 ...  2.26483228e-01\n",
            "  -3.51914418e-01 -1.12104387e-04]\n",
            " [-8.86728123e-01 -1.29453987e-01 -9.94904484e-01 ... -2.29044270e+00\n",
            "   1.82873117e+00 -1.06253078e+00]\n",
            " ...\n",
            " [-1.15656325e+00 -4.20244381e-02  1.35317762e+00 ...  1.09713318e+00\n",
            "  -1.76823169e+00 -1.69800910e-01]\n",
            " [-4.38262990e-01  1.84421272e+00 -9.26034105e-01 ... -1.90818932e+00\n",
            "  -2.53762471e+00 -1.55675474e+00]\n",
            " [-1.09712073e+00 -5.20688100e-01 -4.06987094e-01 ...  2.09638178e-01\n",
            "  -5.61189943e-01 -1.67388188e+00]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/drive/My Drive/audio_data/xtrain.npy', X_train)\n",
        "np.save('/content/drive/My Drive/audio_data/xtest.npy', X_test)\n",
        "np.save('/content/drive/My Drive/audio_data/ytrain.npy', y_train)\n",
        "np.save('/content/drive/My Drive/audio_data/ytest.npy', y_test)"
      ],
      "metadata": {
        "id": "oNcyCgizXzp9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "###################### START HERE #################################################"
      ],
      "metadata": {
        "id": "rO9eH03g2Em7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.load('/content/drive/My Drive/audio_data/xtrain.npy')\n",
        "X_test = np.load('/content/drive/My Drive/audio_data/xtest.npy')\n",
        "y_train = np.load('/content/drive/My Drive/audio_data/ytrain.npy')\n",
        "y_test = np.load('/content/drive/My Drive/audio_data/ytest.npy')\n"
      ],
      "metadata": {
        "id": "2ZwFQrcLfTAl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# #normalizing\n",
        "# scaler = StandardScaler()\n",
        "# X_train = scaler.fit_transform(X_train)\n",
        "# X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "Y8ewafqC5vd4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XocLM-VILqRr",
        "outputId": "b8665af8-32f3-4eb7-81fb-2d4a7550e677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "#Keras\n",
        "import keras\n",
        "from keras import models\n",
        "from keras import layers\n",
        "\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "7-wxldtV6XPH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# models\n",
        "from sklearn.linear_model import LogisticRegression, Perceptron, RidgeClassifier, SGDClassifier\n",
        "from sklearn import svm\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier \n",
        "from sklearn.ensemble import BaggingClassifier, AdaBoostClassifier, VotingClassifier \n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import metrics\n",
        "import xgboost as xgb\n",
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "uqL9pZdG9tDF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model tuning\n",
        "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe, space_eval\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "Mwz26kal4q_7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a model\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(128, activation='relu' ))\n",
        "\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "\n",
        "model.add(layers.Dense(32, activation='relu'))\n",
        "\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "TE0q5DWf7AVR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,\n",
        "                    y_train,\n",
        "                    epochs=100,\n",
        "                    batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wv21Sr_h7D9m",
        "outputId": "cd2f5c67-d070-4499-d9d5-5ac4df919be9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 4s 3ms/step - loss: 0.6745 - accuracy: 0.5820\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6137 - accuracy: 0.6797\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5843 - accuracy: 0.6797\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5591 - accuracy: 0.6875\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5398 - accuracy: 0.7109\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5128 - accuracy: 0.7402\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.4848 - accuracy: 0.7617\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.4600 - accuracy: 0.7793\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.4270 - accuracy: 0.8047\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.3982 - accuracy: 0.8301\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.3634 - accuracy: 0.8516\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.3418 - accuracy: 0.8691\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.3185 - accuracy: 0.8828\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2881 - accuracy: 0.8984\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2710 - accuracy: 0.8906\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2502 - accuracy: 0.9180\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2226 - accuracy: 0.9258\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2078 - accuracy: 0.9277\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1928 - accuracy: 0.9277\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1867 - accuracy: 0.9219\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1708 - accuracy: 0.9434\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1800 - accuracy: 0.9121\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1483 - accuracy: 0.9375\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1418 - accuracy: 0.9395\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1344 - accuracy: 0.9414\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1289 - accuracy: 0.9492\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1147 - accuracy: 0.9609\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1177 - accuracy: 0.9512\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1081 - accuracy: 0.9492\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1086 - accuracy: 0.9512\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1028 - accuracy: 0.9512\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1019 - accuracy: 0.9590\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0989 - accuracy: 0.9590\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0914 - accuracy: 0.9590\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0902 - accuracy: 0.9629\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0888 - accuracy: 0.9531\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0835 - accuracy: 0.9668\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0856 - accuracy: 0.9590\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0781 - accuracy: 0.9648\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0762 - accuracy: 0.9727\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0751 - accuracy: 0.9668\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0753 - accuracy: 0.9668\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0753 - accuracy: 0.9668\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0694 - accuracy: 0.9727\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0667 - accuracy: 0.9746\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0703 - accuracy: 0.9766\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0910 - accuracy: 0.9570\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0780 - accuracy: 0.9629\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0631 - accuracy: 0.9727\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0601 - accuracy: 0.9746\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0660 - accuracy: 0.9746\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0620 - accuracy: 0.9707\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0565 - accuracy: 0.9727\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0508 - accuracy: 0.9824\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0550 - accuracy: 0.9688\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0519 - accuracy: 0.9785\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0555 - accuracy: 0.9746\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0683 - accuracy: 0.9688\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0650 - accuracy: 0.9707\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0565 - accuracy: 0.9824\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0556 - accuracy: 0.9785\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0480 - accuracy: 0.9766\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0619 - accuracy: 0.9746\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0471 - accuracy: 0.9824\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0454 - accuracy: 0.9785\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0423 - accuracy: 0.9805\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0418 - accuracy: 0.9883\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0406 - accuracy: 0.9844\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0420 - accuracy: 0.9805\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0391 - accuracy: 0.9824\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0384 - accuracy: 0.9883\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0391 - accuracy: 0.9902\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0494 - accuracy: 0.9766\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0553 - accuracy: 0.9746\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0595 - accuracy: 0.9688\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0977 - accuracy: 0.9531\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2757 - accuracy: 0.9199\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0883 - accuracy: 0.9707\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0750 - accuracy: 0.9688\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0504 - accuracy: 0.9805\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0401 - accuracy: 0.9863\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0386 - accuracy: 0.9824\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0357 - accuracy: 0.9863\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0354 - accuracy: 0.9863\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0354 - accuracy: 0.9883\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0376 - accuracy: 0.9883\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0340 - accuracy: 0.9883\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0322 - accuracy: 0.9863\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0353 - accuracy: 0.9863\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0403 - accuracy: 0.9824\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0354 - accuracy: 0.9863\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0331 - accuracy: 0.9883\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0318 - accuracy: 0.9902\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0324 - accuracy: 0.9863\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0295 - accuracy: 0.9902\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0285 - accuracy: 0.9844\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0377 - accuracy: 0.9863\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0388 - accuracy: 0.9844\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0288 - accuracy: 0.9883\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0337 - accuracy: 0.9883\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate accuracy\n",
        "test_loss, test_acc = model.evaluate(X_test,y_test)\n",
        "print('test_acc: ',test_acc)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3zLT_Ty7HgG",
        "outputId": "0216f094-787f-4713-ffa7-c0c4180552ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.9635 - accuracy: 0.8281\n",
            "test_acc:  0.828125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predictions\n",
        "predictions = model.predict(X_test)\n",
        "# print(predictions)"
      ],
      "metadata": {
        "id": "PHK8UNQUmyHq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, predictions.round()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du6CAPDYhss2",
        "outputId": "cd9e7999-2eab-4a58-d850-c426e14aab95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.88      0.88        96\n",
            "           1       0.65      0.69      0.67        32\n",
            "\n",
            "    accuracy                           0.83       128\n",
            "   macro avg       0.77      0.78      0.78       128\n",
            "weighted avg       0.83      0.83      0.83       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, predictions.round())\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='Blues', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "-U8VUQ2G22Rh",
        "outputId": "85533b29-230d-4a6f-9d77-198f595f0a4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logmodel = LogisticRegression(max_iter=400)\n",
        "logmodel.fit(X_train,y_train)\n",
        "\n",
        "pred_test_log = logmodel.predict(X_test)\n",
        "pred_log = logmodel.predict(X_train)\n",
        "\n",
        "print(classification_report(y_test,pred_test_log))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Em-YHT7F_oLo",
        "outputId": "e4c1afed-5db9-4a13-fc86-1cd934f2c16b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.90      0.83        96\n",
            "           1       0.38      0.19      0.25        32\n",
            "\n",
            "    accuracy                           0.72       128\n",
            "   macro avg       0.57      0.54      0.54       128\n",
            "weighted avg       0.67      0.72      0.68       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, pred_test_log)\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='YlGnBu', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "31ue5F4O3MBl",
        "outputId": "d9ee009a-d213-40d8-af8e-ea3421475044"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = svm.SVC(kernel='poly')\n",
        "clf.fit(X_train, y_train) \n",
        "\n",
        "pred_svc = clf.predict(X_train)\n",
        "pred_test_svc = clf.predict(X_test)\n",
        "# pred_test_svc [0:5]\n",
        "\n",
        "print (classification_report(y_test, pred_test_svc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pzfm_DcT_1Mg",
        "outputId": "0d567a88-31be-4031-de70-e7d432fce16b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.99      0.87        96\n",
            "           1       0.80      0.12      0.22        32\n",
            "\n",
            "    accuracy                           0.77       128\n",
            "   macro avg       0.79      0.56      0.54       128\n",
            "weighted avg       0.78      0.77      0.70       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, pred_test_svc)\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='Greens', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "X5bsjmqd3n5Y",
        "outputId": "f5191a16-1f3b-45da-b614-defc8381f78a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = 45\n",
        "#Training and Predicting  \n",
        "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
        "pred_knn = neigh.predict(X_train)\n",
        "pred_test_knn = neigh.predict(X_test)\n",
        "pred_test_knn[0:5]\n",
        "\n",
        "Ks = 50\n",
        "mean_acc = np.zeros((Ks-1))\n",
        "std_acc = np.zeros((Ks-1))\n",
        "ConfustionMx = [];\n",
        "for n in range(1,Ks):\n",
        "    \n",
        "    #Train Model and Predict  \n",
        "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
        "    pred_test_knn=neigh.predict(X_test)\n",
        "    mean_acc[n-1] = metrics.accuracy_score(y_test, pred_test_knn)\n",
        "\n",
        "    \n",
        "    std_acc[n-1]=np.std(pred_test_knn==y_test)/np.sqrt(pred_test_knn.shape[0])\n",
        "\n",
        "mean_acc\n",
        "\n",
        "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyKCSlsx_5S2",
        "outputId": "e4e098ce-df9e-4056-d765-4e55bb0dde10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best accuracy was with 0.8046875 with k= 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "neigh = KNeighborsClassifier(n_neighbors = 2).fit(X_train,y_train)\n",
        "pred_knn = neigh.predict(X_train)\n",
        "pred_test_knn = neigh.predict(X_test)"
      ],
      "metadata": {
        "id": "APMRJxheiJf5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,pred_test_knn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzUO5vtJbDFV",
        "outputId": "37b89557-bd37-47a0-b994-7702bb0fdf1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.97      0.88        96\n",
            "           1       0.77      0.31      0.44        32\n",
            "\n",
            "    accuracy                           0.80       128\n",
            "   macro avg       0.79      0.64      0.66       128\n",
            "weighted avg       0.80      0.80      0.77       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "import pickle\n",
        "import json"
      ],
      "metadata": {
        "id": "exxeIyXFnlbJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(neigh, '/content/drive/My Drive/saved_models/audio_knn.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFksyGYlnt6Y",
        "outputId": "3371d801-a2d5-4b9a-f026-7ebbfe966e46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/saved_models/audio_knn.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XMznmaIMnt30"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, pred_test_knn)\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='Reds', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "A40vCArh4GN_",
        "outputId": "371fb7ba-6765-46dd-8673-5535b732f538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DecTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 5)\n",
        "DecTree.fit(X_train,y_train)\n",
        "\n",
        "pred_decision_tree = DecTree.predict(X_train)\n",
        "pred_test_decision_tree = DecTree.predict(X_test)\n",
        "\n",
        "acc_decision_tree = metrics.accuracy_score(y_train, pred_decision_tree) * 100\n",
        "acc_test_decision_tree = metrics.accuracy_score(y_test, pred_test_decision_tree) * 100\n",
        "\n",
        "print(\"Accuracy train : \", acc_decision_tree)\n",
        "print(\"Accuracy test : \", acc_test_decision_tree)\n",
        "\n",
        "print(classification_report(y_test, pred_test_decision_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5XgZFpVAOHb",
        "outputId": "1d358cfa-5efd-4388-f3b0-608263649047"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy train :  79.296875\n",
            "Accuracy test :  68.75\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.85      0.80        96\n",
            "           1       0.30      0.19      0.23        32\n",
            "\n",
            "    accuracy                           0.69       128\n",
            "   macro avg       0.53      0.52      0.52       128\n",
            "weighted avg       0.64      0.69      0.66       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, pred_test_decision_tree)\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='Blues', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "L69oF2am4u5R",
        "outputId": "7299425c-1e99-4a70-effa-0b1ff05ca87d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def hyperopt_xgb_score(params):\n",
        "    clf = XGBClassifier(**params)\n",
        "    current_score = cross_val_score(clf, X_train, y_train, cv=10).mean()\n",
        "    print(current_score, params)\n",
        "    return current_score \n",
        " \n",
        "space_xgb = {\n",
        "            'learning_rate': hp.quniform('learning_rate', 0, 0.05, 0.0001),\n",
        "            'n_estimators': hp.choice('n_estimators', range(100, 1000)),\n",
        "            'eta': hp.quniform('eta', 0.025, 0.5, 0.005),\n",
        "            'max_depth':  hp.choice('max_depth', np.arange(2, 12, dtype=int)),\n",
        "            'min_child_weight': hp.quniform('min_child_weight', 1, 9, 0.025),\n",
        "            'subsample': hp.quniform('subsample', 0.5, 1, 0.005),\n",
        "            'gamma': hp.quniform('gamma', 0.5, 1, 0.005),\n",
        "            'colsample_bytree': hp.quniform('colsample_bytree', 0.5, 1, 0.005),\n",
        "            'eval_metric': 'auc',\n",
        "            'objective': 'binary:logistic',\n",
        "            'booster': 'gbtree',\n",
        "            'tree_method': 'exact',\n",
        "            'silent': 1,\n",
        "            'missing': None\n",
        "        }\n",
        " \n",
        "best = fmin(fn=hyperopt_xgb_score, space=space_xgb, algo=tpe.suggest, max_evals=10)\n",
        "print('best:')\n",
        "print(best)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJdWoxYzAyOB",
        "outputId": "d8a146ec-a2a6-4e06-c80e-c48ee1d82264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7090120663650076\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.755, 'eta': 0.155, 'eval_metric': 'auc', 'gamma': 0.885, 'learning_rate': 0.0393, 'max_depth': 6, 'min_child_weight': 2.35, 'missing': None, 'n_estimators': 644, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.585, 'tree_method': 'exact'}\n",
            "0.6972473604826546\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.52, 'eta': 0.035, 'eval_metric': 'auc', 'gamma': 0.585, 'learning_rate': 0.0234, 'max_depth': 4, 'min_child_weight': 5.8500000000000005, 'missing': None, 'n_estimators': 290, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.73, 'tree_method': 'exact'}\n",
            "0.6855203619909502\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.7000000000000001, 'eta': 0.48, 'eval_metric': 'auc', 'gamma': 0.505, 'learning_rate': 0.0018000000000000002, 'max_depth': 9, 'min_child_weight': 7.45, 'missing': None, 'n_estimators': 768, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.985, 'tree_method': 'exact'}\n",
            "0.707051282051282\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.805, 'eta': 0.13, 'eval_metric': 'auc', 'gamma': 0.8200000000000001, 'learning_rate': 0.034800000000000005, 'max_depth': 4, 'min_child_weight': 5.3500000000000005, 'missing': None, 'n_estimators': 805, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.905, 'tree_method': 'exact'}\n",
            "0.6739064856711916\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.66, 'eta': 0.485, 'eval_metric': 'auc', 'gamma': 0.76, 'learning_rate': 0.0267, 'max_depth': 3, 'min_child_weight': 3.45, 'missing': None, 'n_estimators': 137, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.52, 'tree_method': 'exact'}\n",
            "0.6933257918552037\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.6, 'eta': 0.11, 'eval_metric': 'auc', 'gamma': 0.895, 'learning_rate': 0.0324, 'max_depth': 9, 'min_child_weight': 7.800000000000001, 'missing': None, 'n_estimators': 875, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.71, 'tree_method': 'exact'}\n",
            "0.681711915535445\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.67, 'eta': 0.48, 'eval_metric': 'auc', 'gamma': 0.515, 'learning_rate': 0.0039000000000000003, 'max_depth': 4, 'min_child_weight': 2.1, 'missing': None, 'n_estimators': 164, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.905, 'tree_method': 'exact'}\n",
            "0.7089366515837104\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.505, 'eta': 0.145, 'eval_metric': 'auc', 'gamma': 0.775, 'learning_rate': 0.0322, 'max_depth': 10, 'min_child_weight': 8.525, 'missing': None, 'n_estimators': 945, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.995, 'tree_method': 'exact'}\n",
            "0.7088989441930619\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.9500000000000001, 'eta': 0.115, 'eval_metric': 'auc', 'gamma': 0.515, 'learning_rate': 0.0475, 'max_depth': 4, 'min_child_weight': 8.6, 'missing': None, 'n_estimators': 441, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.835, 'tree_method': 'exact'}\n",
            "0.6914027149321267\n",
            "{'booster': 'gbtree', 'colsample_bytree': 0.8, 'eta': 0.165, 'eval_metric': 'auc', 'gamma': 0.59, 'learning_rate': 0.0345, 'max_depth': 9, 'min_child_weight': 6.925000000000001, 'missing': None, 'n_estimators': 396, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.51, 'tree_method': 'exact'}\n",
            "100%|██████████| 10/10 [00:43<00:00,  4.36s/it, best loss: 0.6739064856711916]\n",
            "best:\n",
            "{'colsample_bytree': 0.66, 'eta': 0.485, 'gamma': 0.76, 'learning_rate': 0.0267, 'max_depth': 1, 'min_child_weight': 3.45, 'n_estimators': 37, 'subsample': 0.52}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params = space_eval(space_xgb, best)\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9x5ry_5JA6zQ",
        "outputId": "b81038a8-ce5f-468e-99cf-7fcfc89ec63e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'booster': 'gbtree',\n",
              " 'colsample_bytree': 0.66,\n",
              " 'eta': 0.485,\n",
              " 'eval_metric': 'auc',\n",
              " 'gamma': 0.76,\n",
              " 'learning_rate': 0.0267,\n",
              " 'max_depth': 3,\n",
              " 'min_child_weight': 3.45,\n",
              " 'missing': None,\n",
              " 'n_estimators': 137,\n",
              " 'objective': 'binary:logistic',\n",
              " 'silent': 1,\n",
              " 'subsample': 0.52,\n",
              " 'tree_method': 'exact'}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "XGB_Classifier = XGBClassifier(**params)\n",
        "\n",
        "XGB_Classifier.fit(X_train, y_train)\n",
        "acc_XGB_Classifier = round(XGB_Classifier.score(X_train, y_train) * 100, 2)\n",
        "acc_XGB_Classifier\n",
        "\n",
        "# pred_XGB = XGB_Classifier.predict(X_train)\n",
        "# pred_test_xgb = XGB_Classifier.predict(X_test)\n",
        "\n",
        "# acc_XGB_Classifier = metrics.accuracy_score(y_train, pred_random_forest) * 100\n",
        "# acc_test_XGB_Classifier = metrics.accuracy_score(y_test, pred_test_random_forest) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RrAc09aQA7lT",
        "outputId": "811d4c0f-2e07-4e3d-9fad-619d9c45976f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81.45"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_XGB = XGB_Classifier.predict(X_train)\n",
        "pred_test_xgb = XGB_Classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "vyFDKT5Lig8i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print(\"train acc: \", acc_XGB_Classifier)\n",
        "# print(\"test acc: \", acc_test_XGB_Classifier)"
      ],
      "metadata": {
        "id": "qMIzOfixpWgd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc_test_XGB_Classifier = round(XGB_Classifier.score(X_test, y_test) * 100, 2)\n",
        "acc_test_XGB_Classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqCQq47TA9t9",
        "outputId": "e21e2437-e6f0-40ad-b9c8-bf84115214a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75.0"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, pred_test_xgb.round()))"
      ],
      "metadata": {
        "id": "56bH1EdVnmAX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de181e20-6225-4165-b72b-2c61697a5ab8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.93      0.85        96\n",
            "           1       0.50      0.22      0.30        32\n",
            "\n",
            "    accuracy                           0.75       128\n",
            "   macro avg       0.64      0.57      0.58       128\n",
            "weighted avg       0.71      0.75      0.71       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, pred_test_xgb)\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='Blues', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "yBZH75Rn5c-S",
        "outputId": "706ad87d-44bf-47b9-af0c-4fc5b457d22a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "def hyperopt_lgb_score(params):\n",
        "    clf = LGBMClassifier(**params)\n",
        "    current_score = cross_val_score(clf, X_train, y_train, cv=10).mean()\n",
        "    print(current_score, params)\n",
        "    return current_score \n",
        " \n",
        "space_lgb = {\n",
        "            'learning_rate': hp.quniform('learning_rate', 0, 0.05, 0.0001),\n",
        "            'n_estimators': hp.choice('n_estimators', range(100, 1000)),\n",
        "            'max_depth':  hp.choice('max_depth', np.arange(2, 12, dtype=int)),\n",
        "            'num_leaves': hp.choice('num_leaves', 2*np.arange(2, 2**11, dtype=int)),\n",
        "            'min_child_weight': hp.quniform('min_child_weight', 1, 9, 0.025),\n",
        "            'colsample_bytree': hp.quniform('colsample_bytree', 0.5, 1, 0.005),\n",
        "            'objective': 'binary',\n",
        "            'boosting_type': 'gbdt',\n",
        "            }\n",
        " \n",
        "best = fmin(fn=hyperopt_lgb_score, space=space_lgb, algo=tpe.suggest, max_evals=10)\n",
        "print('best:')\n",
        "print(best)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "343fRr8VBDnK",
        "outputId": "5b2f271e-3a2a-4b49-b5f8-f63c35d02684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7207767722473605\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.625, 'learning_rate': 0.0337, 'max_depth': 6, 'min_child_weight': 1.8, 'n_estimators': 883, 'num_leaves': 2444, 'objective': 'binary'}\n",
            "0.7012066365007541\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.505, 'learning_rate': 0.0114, 'max_depth': 4, 'min_child_weight': 8.875, 'n_estimators': 345, 'num_leaves': 1690, 'objective': 'binary'}\n",
            "0.7480392156862745\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.93, 'learning_rate': 0.031400000000000004, 'max_depth': 10, 'min_child_weight': 1.3250000000000002, 'n_estimators': 299, 'num_leaves': 2936, 'objective': 'binary'}\n",
            "0.6972096530920061\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.9550000000000001, 'learning_rate': 0.0223, 'max_depth': 3, 'min_child_weight': 6.2, 'n_estimators': 524, 'num_leaves': 2458, 'objective': 'binary'}\n",
            "0.7070512820512821\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.9450000000000001, 'learning_rate': 0.0044, 'max_depth': 7, 'min_child_weight': 1.0250000000000001, 'n_estimators': 626, 'num_leaves': 3612, 'objective': 'binary'}\n",
            "0.7069381598793363\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.995, 'learning_rate': 0.0374, 'max_depth': 3, 'min_child_weight': 4.65, 'n_estimators': 469, 'num_leaves': 990, 'objective': 'binary'}\n",
            "0.7382352941176471\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.735, 'learning_rate': 0.0058000000000000005, 'max_depth': 8, 'min_child_weight': 1.1500000000000001, 'n_estimators': 973, 'num_leaves': 410, 'objective': 'binary'}\n",
            "0.6973604826546003\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.725, 'learning_rate': 0.0364, 'max_depth': 9, 'min_child_weight': 6.175000000000001, 'n_estimators': 925, 'num_leaves': 2126, 'objective': 'binary'}\n",
            "0.7051659125188536\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.625, 'learning_rate': 0.029900000000000003, 'max_depth': 7, 'min_child_weight': 5.5, 'n_estimators': 923, 'num_leaves': 1990, 'objective': 'binary'}\n",
            "0.6992458521870286\n",
            "{'boosting_type': 'gbdt', 'colsample_bytree': 0.65, 'learning_rate': 0.0333, 'max_depth': 8, 'min_child_weight': 5.7, 'n_estimators': 905, 'num_leaves': 1262, 'objective': 'binary'}\n",
            "100%|██████████| 10/10 [00:59<00:00,  5.99s/it, best loss: 0.6972096530920061]\n",
            "best:\n",
            "{'colsample_bytree': 0.9550000000000001, 'learning_rate': 0.0223, 'max_depth': 1, 'min_child_weight': 6.2, 'n_estimators': 424, 'num_leaves': 1227}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params = space_eval(space_lgb, best)\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tR1J6ND7BQzv",
        "outputId": "ff54cc8c-fbeb-4fcc-d246-2e5edf68578d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boosting_type': 'gbdt',\n",
              " 'colsample_bytree': 0.9550000000000001,\n",
              " 'learning_rate': 0.0223,\n",
              " 'max_depth': 3,\n",
              " 'min_child_weight': 6.2,\n",
              " 'n_estimators': 524,\n",
              " 'num_leaves': 2458,\n",
              " 'objective': 'binary'}"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LGB_Classifier = LGBMClassifier(**params)\n",
        "LGB_Classifier.fit(X_train, y_train)\n",
        "acc_LGB_Classifier = round(LGB_Classifier.score(X_train, y_train) * 100, 2)\n",
        "acc_LGB_Classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_x8pkF9NBSqq",
        "outputId": "78477e29-3afe-413a-9e6e-ef5d424e4444"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.55"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc_test_LGB_Classifier = round(LGB_Classifier.score(X_test, y_test) * 100, 2)\n",
        "acc_test_LGB_Classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rfjt5usUBVLh",
        "outputId": "bf180eca-658c-4b27-f30c-38e4658d7a5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75.0"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_lgb = LGB_Classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "e4GfF1L5kRyS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, pred_lgb.round()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iJvIS11keIe",
        "outputId": "24ab22cf-fbf9-4ad3-e488-b0d7b6cb80bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.86      0.84        96\n",
            "           1       0.50      0.41      0.45        32\n",
            "\n",
            "    accuracy                           0.75       128\n",
            "   macro avg       0.66      0.64      0.64       128\n",
            "weighted avg       0.74      0.75      0.74       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, pred_lgb)\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='BuPu', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "rwGrg5pr6A5K",
        "outputId": "bbd57dfb-2450-420d-86b1-8e27719ab036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bagging_classifier = BaggingClassifier()\n",
        "bagging_classifier.fit(X_train, y_train)\n",
        "Y_pred = bagging_classifier.predict(X_test).astype(int)\n",
        "acc_bagging_classifier = round(bagging_classifier.score(X_train, y_train) * 100, 2)\n",
        "acc_bagging_classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnaUVo8KBan2",
        "outputId": "2e5d69dd-14aa-4ba2-f00f-fae896c08fba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98.83"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc_test_bagging_classifier = round(bagging_classifier.score(X_test, y_test) * 100, 2)\n",
        "acc_test_bagging_classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQKUU8jJBcns",
        "outputId": "3472b15f-865a-4cce-fefa-662ab410e702"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.34"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_bg = bagging_classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "xiG1GXeHlIEb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, pred_bg.round()))"
      ],
      "metadata": {
        "id": "0ZTebC1fBiRB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b3d508c-061d-4c52-e3a2-688414dc92b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.91      0.86        96\n",
            "           1       0.57      0.38      0.45        32\n",
            "\n",
            "    accuracy                           0.77       128\n",
            "   macro avg       0.69      0.64      0.65       128\n",
            "weighted avg       0.75      0.77      0.76       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, pred_bg)\n",
        "ax = sns.heatmap(cm, linecolor='white', cmap='BuPu', linewidth=1, annot=True, fmt='')"
      ],
      "metadata": {
        "id": "Yragbllasisx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "da59005a-e11b-4d6b-ea99-8bfb22722e04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD4CAYAAACt8i4nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAT0klEQVR4nO3de7QdZXnH8e9zzklIoAm5UGIkIhRSKEWIhcZoLFKCFiqFVJEFrTW6gqctWq9LIFZllaUVu6yAXbb1lAhppQjhljRrlTamIEURCYIXjHKJcs0FCBFapSHw9I8z4CEmZ/ZJ9uw9Z/L9ZM3ae2b2fuc5kPXLe96ZeScyE0lSdXq6XYAkNZ1BK0kVM2glqWIGrSRVzKCVpIr1deAYXtYgqVWxqw2cHCe1nDnLc8UuH68VnQhaTo6TOnEYjRLLcwUAzzz3fJcrUZ2M623uL9gdCVpJ6pSeGo6IGrSSGqU3ertdwi8xaCU1Sk90ZNh1RAxaSY0SDh1IUrXs0UpSxezRSlLF7NFKUsW86kCSKuZ1tJJUMYcOJKlingyTpIr1hEErSZXyZJgkVaxn12dabDuDVlKj1HGMtn4VSdIu6IloeSkTER+MiLsj4vsRcUVEjIuIAyPitoi4LyKujIixpTW15SeTpJroGcGf4UTEfsD7gKMz83CgFzgd+AxwYWYeDDwJLCyvSZIaJCJaXlrQB4yPiD5gT2AdcBxwdbF/CTC/rBGDVlKj9EVvy0tE9EfE6iFL/wvtZOYjwGeBBxkM2J8CdwCbM3Nr8bGHgf1Ka2r/jylJ3TOSk2GZOQAMbLediMnAKcCBwGZgKXDCztRk0EpqlDbegns88OPMfAwgIq4F5gKTIqKv6NXOAB4praldFUlSHQQ9LS8lHgTmRMSeMTigOw/4AXAjcGrxmQXAsrKGDFpJjdITPS0vw8nM2xg86fVt4HsM5uUAcA7woYi4D5gKLC6ryaEDSY3SzltwM/M84LxtNq8FZo+kHYNWUqN4C64kVczZuySpYmGPVpIq1mPQSlK1fJSNJFUreh2jlaRqOXQgSRUzaCWpWi1Of9hRBq2kZrFHK0kVs0crSRXr9XHjklSpcOhAkipm0EpSxRyjlaSK2aOVpGp5C64kVc2hA0mqWA2HDurXx5akXdETrS/DiIhDIuKuIctTEfGBiJgSESsj4t7idXJpSW374SSpBiKi5WU4mfmjzJyVmbOAo4CfAdcB5wKrMnMmsKpYH5ZBK6lZ2tSj3cY84P7MfAA4BVhSbF8CzC/7smO0kpplBFcdREQ/0D9k00BmDmzno6cDVxTvp2XmuuL9emBa2XEMWkmNMpJpEotQ3V6wDm1vLHAysGg738+IyLLjGLSSmqX9Vx2cCHw7MzcU6xsiYnpmrouI6cDGsgYM2oqc/IFTeNOZbyITHvjeT7j4XRdx/spPMn7CeAD23ndv7v3WPfz1H36qy5Wqmy7/l3/mmqVLyUze+ra38fZ3LOh2SaNf+4P2DH4xbACwHFgAXFC8LitrwKCtwJSXT+UP3vcHvOews9jyzBbOvvIcfuf0Y1h0zDkvfubcqxdx27Lbuliluu3ee+/hmqVLufzKqxgzZgxn9b+bY95wLPu/8pXdLm10a+MNCxGxF/BG4E+HbL4AuCoiFgIPAKeVteNVBxXp6etl7Pix9PT2sMeee7Dp0U0v7hs/YTxHHHck37z+1i5WqG778f1redURRzB+/Hj6+vo46rd/m1VfXdntska/Nl51kJn/m5lTM/OnQ7Y9kZnzMnNmZh6fmZuGawNa6NFGxKEMXs6wX7HpEWB5Zq4prXI3tenRJ7j+s9ex+MFL2fLzLdz5n3dy18o7X9w/Z/5r+c6q7/Dzp3/exSrVbQfPnMnfXXwRmzc/yR57jOOWm2/msN88vNtljXp1nOtg2Ioi4hzgK0AA3yqWAK6IiB1epBsR/RGxOiJWDwwMe0KvkfaatBevOeU1vPvAhbzz5e9g3F57cOwfH/vi/mPOOIabr/ha9wpULfzaQQfxrjPP5M/OPJOz+t/NIYceSm8NQ2LUiWh96ZCyHu1C4Dcz89mhGyPic8DdDI5V/JJtLpnIFX+6fFfrHFVmHT+LDT/ewFOPPwXArdfeyqGv+w1uuvwmJkydyMzZv+5JMAHwlreeylveeioAn7/wQqa9rPSSTJUZhXMdPA+8fDvbpxf7tB2PPfgYh8w5hLHj9wDgyHlH8tCahwCYe+pcVq+4nWf/79nhmtBu4oknngBg3aOPsuqrKznxzSd1uaIGiBEsHVLWo/0AsCoi7gUeKrbtDxwMvLfKwkaze751D1+/+utc9O2LeG7r86y9837+Y+AGAH7n9GO45oKlXa5QdfHh97+fn27eTN+YPj76sY8zceLEbpc0+tVwmsTIHP6mhojoAWbz0pNht2fmcy0eI08O/5XWLyzPFQA885y/FOkXxg2OT+9ySv7NiUtK79R6wdn/vqAjqVx61UFmPg98swO1SNKuq2GP1hsWJDVLDU+GGbSSmqV+OWvQSmoYhw4kqWI1vOfDoJXUKNFTv6Q1aCU1S/1GDgxaSQ3jVQeSVDFPhklSxeqXswatpIZx6ECSKlbDoK3fdRCStAuiJ1peStuKmBQRV0fEDyNiTUS8NiKmRMTKiLi3eJ1c1o5BK6lZ2jsf7cXADZl5KHAksAY4F1iVmTOBVcX6sAxaSc3SpkfZRMTewDHAYoDM3JKZmxl8huKS4mNLgPllJRm0kpqlfU/BPRB4DLg0Iu6MiEuKx49Py8x1xWfWA6XPHzJoJTXLCIJ26INki6V/SEt9wG8B/5CZrwb+l22GCXLwyQmlE4171YGkZhlB93GbB8lu62Hg4cy8rVi/msGg3RAR0zNzXURMBza2sSRJGgXaNEabmeuBhyLikGLTPOAHwHJgQbFtAbCsrCR7tJIaJdp7C+5fAJdHxFhgLfAuBjuoV0XEQuAB4LSyRgxaSc3Sxt/TM/Mu4Ojt7Jo3knYMWknN4qQyklSxXoNWkqplj1aSKmbQSlLFanjRqkErqVns0UpSxTwZJkkVs0crSRUzaCWpYp4Mk6SK2aOVpIrVL2cNWkkN01u/sQODVlKz2KOVpIq18BjxTjNoJTWLJ8MkqWL1y1mDVlLDOHQgSRUzaCWpYm0M2oj4CfA08BywNTOPjogpwJXAAcBPgNMy88lhS2pbRZJUB2163PgQv5uZszLzhYc0ngusysyZwKpifVgGraRm6RnBsnNOAZYU75cA81spSZKao7092gT+MyLuiIj+Ytu0zFxXvF8PTCtrxDFaSc0ygom/i/DsH7JpIDMHhqy/PjMfiYh9gZUR8cOh38/MjIgsO45BK6lZRnDDQhGqA8Psf6R43RgR1wGzgQ0RMT0z10XEdGBj2XEcOpDULG0aOoiIvSJiwgvvgTcB3weWAwuKjy0AlpWVZI9WUrO0r/s4DbguBgO5D/jXzLwhIm4HroqIhcADwGllDXUkaJfnik4cRqPMuBpOZ6cGaNNcB5m5FjhyO9ufAOaNpC17tJKaZXedVOb+jU934jAaJQ7adwIATz3zbJcrUZ1MHDemLe2EjxuXpIrtrj1aSeqUGuasQSupWaKGSWvQSmqWGl7MYtBKahR7tJJUsXDib0mqmD1aSaqWPVpJqlr9ctagldQsngyTpIo5dCBJFbNHK0lV84YFSaqWPVpJqppBK0nVqmHOGrSSmqWOE3/XcNhYknZeRLS8tNheb0TcGRErivUDI+K2iLgvIq6MiLFlbRi0kpqlTY8bH+L9wJoh658BLszMg4EngYVlDRi0khqlnTkbETOANwOXFOsBHAdcXXxkCTC/rB2DVlKzjCBpI6I/IlYPWfq3ae0i4Gzg+WJ9KrA5M7cW6w8D+5WV5MkwSY0ykltwM3MAGNhuOxEnARsz846IOHZXajJoJTVKG+c6mAucHBG/D4wDJgIXA5Mioq/o1c4AHilryKEDSY3SrqsOMnNRZs7IzAOA04H/ysw/Bm4ETi0+tgBYVlaTQSupWWIEy845B/hQRNzH4Jjt4rIvOHQgqVGqmCYxM28CbirerwVmj+T7Bq2kRqnffWEGraSG6XHib0mqlpPKSFLFooaDBwatpEaxRytJFTNoJaliPspGkirWY9BKUrVqmLMGraRmqWHOGrSSmsUxWkmqWA1z1qCV1CyeDJOkitUwZw1aSc3iGK0kVax+MWvQSmqYGnZoDVpJzeLQgSRVrI5XHfhwRkmNEtH6Mnw7MS4ivhUR34mIuyPir4rtB0bEbRFxX0RcGRFjy2oyaCU1SruCFvg/4LjMPBKYBZwQEXOAzwAXZubBwJPAwrKGDFpJjdJDtLwMJwf9T7E6plgSOA64uti+BJhfVpNjtBV5bMN6/vZT5/Hkpk1EBCec/IfMf9sZPP3UT/n0eYvYuH4d+75sOovOv4AJEyZ2u1x1yPmf+Bi33Hwzk6dM4cprrwfg4s99lv/+2tcYM6aPGTNewSfO/yQTJvp3YmeNZIg2IvqB/iGbBjJzYMj+XuAO4GDgC8D9wObM3Fp85GFgv7Lj2KOtSG9vH2e+54N88ctL+dwXL2XFtUt58MdruerLlzHrqNlccsV1zDpqNku/fFm3S1UHnXTKfD7/D//4km2vmfNavnLNdVxx9XXs/8oDuGzxJV2qrhlGMnSQmQOZefSQZWBoW5n5XGbOAmYAs4FDd6Ymg7YiU/bZh4MPGfx/sueee7H/AQfw+OMb+eYtX+P4E04C4PgTTuLW/76pi1Wq037rqKOZOHHvl2yb87q59PUN/nJ5+BFHsGHjhm6U1hg9ES0vrcrMzcCNwGuBSRHxwmjADOCR0pp25gfRyGxY9yj33/MjDj3scDY/uYkp++wDwOSpU9n85KYuV6c6WX79dbxu7uu7XcaoFhEtLyXt/GpETCrejwfeCKxhMHBPLT62AFhWVtNOB21EvGuYff0RsToiVg8MDOzoY7uFn//sZ3zqY2fT/74Ps+dev/KSfRFRy0cjqzu+9E9fpK+3lxPffFK3SxnV2njVwXTgxoj4LnA7sDIzVwDnAB+KiPuAqcDisoZ25WTYXwGXbm9HMc7xQsLm/Ruf3oXDjF5bt27lUx87m2PfeAJz33AcAJMmT2HT448zZZ992PT44+w9eXKXq1Qd/Nuy67nl5pv5+4FLanln02jSrv98mfld4NXb2b6WwfHalg0btEWSb3cXMG0kB9rdZCYXXXA+rzjgQN5y+ttf3D5n7hv46g0rOO3t7+SrN6xgzuvf0MUqVQff+Pot/MtlX+KLiy9j3Pjx3S5n1Kvjb4mRmTveGbEB+D0GL8p9yS7gG5n58haOsVv2aO/+7l185D1ncsCvHUxPz+AIzYL+szjksMP59CcW8djG9ew7bTqLzv80E7Y5OdJ0B+07AYCnnnm2y5V03l+e8xHuWH07mzdvZuqUqfT/+Vlc9qVL2LJlC3tPmgTAq151BIs+fl6XK+28iePGQBsm37plzYYdh9o2Xv8b0zqSymVBuxi4NDNv2c6+f83MP2rhGLtl0GrHdueg1Y61K2i//sPWg3buoZ0J2mGHDjJzh7eWtRiyktRRdRzj9s4wSY1Sv5g1aCU1TA07tAatpGZx6ECSKlbHib8NWkmNUsOcNWglNYtDB5JUsRrmrEErqVkMWkmqWB3nOjBoJTVKT49BK0mVcuhAkirm0IEkVcwerSRVzDvDJKliPTV85GwNS5KknRcj+DNsOxGviIgbI+IHEXF3RLy/2D4lIlZGxL3Fa+mD/wxaSY3SxqfgbgU+nJmHAXOA90TEYcC5wKrMnAmsKtaHZdBKapSIaHkZTmauy8xvF++fBtYA+wGnAEuKjy0B5pfVZNBKapSR9Ggjoj8iVg9Z+rffZhzA4KPHbwOmZea6Ytd6WngiuCfDJDXKSGbvyswBYKCkvV8BrgE+kJlPDW0/MzMiSh8GadBKapR2Xt4VEWMYDNnLM/PaYvOGiJiemesiYjqwsbSmtlUkSTXQrpNhMdh1XQysyczPDdm1HFhQvF8ALCuryR6tpEZp4+0Kc4E/Ab4XEXcV2z4KXABcFRELgQeA08oaMmglNUubhg4y8xZ2nNvzRtKWQSupUep3A65BK6lhajjVgUErqVl8OKMkVax+MWvQSmqYGnZoDVpJTVO/pDVoJTWKPVpJqlgNH4Jr0EpqmvolrUErqVEcOpCkitUwZw1aSQ1Tw6Q1aCU1StlDF7vBoJXUKF51IElVq+HZMINWUqPUL2YNWkkNU8MOrUErqVlqmLMGraSGqWGX1qfgSmqUnmh9KRMRX4qIjRHx/SHbpkTEyoi4t3idXFrTrv1IklQ3MYKl1GXACdtsOxdYlZkzgVXF+rAMWkmNEtH6UiYzbwY2bbP5FGBJ8X4JML+sHYNWUqOMpD8bEf0RsXrI0t/CIaZl5rri/XpgWtkXPBkmqVFGci4sMweAgZ09VmZmRGTZ5zoStAftO6ETh9EoM3HcmG6XoAbqwFNwN0TE9MxcFxHTgY1lX+hE0NbvWosuiYj+4l9Q6UX+vWivcb2Vz3awHFgAXFC8Liv7QmSW9nrVJhGxOjOP7nYdqhf/XtRXRFwBHAvsA2wAzgOuB64C9gceAE7LzG1PmL2EY7SStAOZecYOds0bSTtedSBJFTNoO8txOG2Pfy8azjFaSaqYPVpJqphBK0kVM2g7JCJOiIgfRcR9EVE6CYWab3szQ6mZDNoOiIhe4AvAicBhwBkRcVh3q1INXMYvzwylBjJoO2M2cF9mrs3MLcBXGJwBSLuxHcwMpQYyaDtjP+ChIesPF9sk7QYMWkmqmEHbGY8ArxiyPqPYJmk3YNB2xu3AzIg4MCLGAqczOAOQpN2AQdsBmbkVeC/wH8Aa4KrMvLu7VanbipmhbgUOiYiHI2Jht2tSNbwFV5IqZo9Wkipm0EpSxQxaSaqYQStJFTNoJaliBq0kVcyglaSK/T+eBJnyH9OY6gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STOP STOP\n",
        "## experiments- 84%"
      ],
      "metadata": {
        "id": "7qr5fbw-73V0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "############## STOP ############################################################"
      ],
      "metadata": {
        "id": "pN3J43Rg72Zb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "############## STOP ############################################################"
      ],
      "metadata": {
        "id": "woiBB1kt7pjO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a model\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(128, activation='relu'))\n",
        "# model.add(layers.Dense(64, activation='relu'))\n",
        "# model.add(layers.Dense(64, activation='relu'))\n",
        "\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "# model.add(layers.Dense(16, activation='relu'))\n",
        "model.add(layers.Dense(32, activation='relu'))\n",
        "\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "qCPXbUN8MteZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,\n",
        "                    y_train,\n",
        "                    epochs=100,\n",
        "                    batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tqIp5VgRNVoY",
        "outputId": "38ead2a7-8f10-4f08-8ff8-f20ecd96459e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.6344 - accuracy: 0.6758\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.5903 - accuracy: 0.6797\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.5651 - accuracy: 0.7031\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.5400 - accuracy: 0.6934\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.5186 - accuracy: 0.7539\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.4871 - accuracy: 0.7539\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.4630 - accuracy: 0.8066\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.4473 - accuracy: 0.7715\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.4098 - accuracy: 0.8301\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.3941 - accuracy: 0.8574\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.3523 - accuracy: 0.8613\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.3191 - accuracy: 0.8828\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.2876 - accuracy: 0.9082\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.2608 - accuracy: 0.9082\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.2481 - accuracy: 0.9141\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.2233 - accuracy: 0.9141\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.2058 - accuracy: 0.9199\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1879 - accuracy: 0.9355\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1801 - accuracy: 0.9355\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1843 - accuracy: 0.9199\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1543 - accuracy: 0.9414\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1381 - accuracy: 0.9531\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1195 - accuracy: 0.9609\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1249 - accuracy: 0.9492\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1265 - accuracy: 0.9434\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1133 - accuracy: 0.9551\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1004 - accuracy: 0.9590\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0997 - accuracy: 0.9609\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1058 - accuracy: 0.9473\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1020 - accuracy: 0.9551\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0925 - accuracy: 0.9668\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0865 - accuracy: 0.9707\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0782 - accuracy: 0.9648\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9648\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0742 - accuracy: 0.9668\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0766 - accuracy: 0.9648\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0746 - accuracy: 0.9746\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0726 - accuracy: 0.9766\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0808 - accuracy: 0.9629\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0735 - accuracy: 0.9668\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0706 - accuracy: 0.9727\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0753 - accuracy: 0.9707\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0665 - accuracy: 0.9688\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0618 - accuracy: 0.9766\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0632 - accuracy: 0.9746\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0611 - accuracy: 0.9746\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0600 - accuracy: 0.9727\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0553 - accuracy: 0.9805\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0540 - accuracy: 0.9805\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0514 - accuracy: 0.9805\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0514 - accuracy: 0.9785\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0550 - accuracy: 0.9766\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0557 - accuracy: 0.9785\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0518 - accuracy: 0.9785\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0549 - accuracy: 0.9785\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0505 - accuracy: 0.9824\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0545 - accuracy: 0.9805\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0503 - accuracy: 0.9785\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0499 - accuracy: 0.9766\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0480 - accuracy: 0.9805\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0503 - accuracy: 0.9766\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0424 - accuracy: 0.9824\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0460 - accuracy: 0.9844\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0471 - accuracy: 0.9766\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0528 - accuracy: 0.9805\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0607 - accuracy: 0.9629\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0625 - accuracy: 0.9707\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0694 - accuracy: 0.9707\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0493 - accuracy: 0.9766\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0482 - accuracy: 0.9805\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0527 - accuracy: 0.9824\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0823 - accuracy: 0.9648\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0662 - accuracy: 0.9766\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0627 - accuracy: 0.9727\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0492 - accuracy: 0.9824\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0416 - accuracy: 0.9844\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0356 - accuracy: 0.9844\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0346 - accuracy: 0.9844\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0362 - accuracy: 0.9863\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0340 - accuracy: 0.9902\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0421 - accuracy: 0.9824\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0349 - accuracy: 0.9883\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0341 - accuracy: 0.9863\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0392 - accuracy: 0.9824\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0378 - accuracy: 0.9844\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0481 - accuracy: 0.9766\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0519 - accuracy: 0.9727\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0340 - accuracy: 0.9902\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0316 - accuracy: 0.9844\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0305 - accuracy: 0.9922\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0311 - accuracy: 0.9902\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0284 - accuracy: 0.9844\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0339 - accuracy: 0.9883\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0303 - accuracy: 0.9883\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0312 - accuracy: 0.9883\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0311 - accuracy: 0.9883\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0323 - accuracy: 0.9844\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0323 - accuracy: 0.9863\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0269 - accuracy: 0.9902\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0253 - accuracy: 0.9922\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(X_test,y_test)\n",
        "print('test_acc: ',test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GC-THV_AM0_E",
        "outputId": "912c6572-df69-4c45-c557-93d2a4a710b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 2ms/step - loss: 1.1337 - accuracy: 0.8359\n",
            "test_acc:  0.8359375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(X_test)\n",
        "# print(predictions)"
      ],
      "metadata": {
        "id": "FEQFADA9N12U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, predictions.round()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhdHb5KIluFm",
        "outputId": "c7ce9ec3-c8dc-43b8-a6e6-0a8decb12a7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.89      0.89        96\n",
            "           1       0.67      0.69      0.68        32\n",
            "\n",
            "    accuracy                           0.84       128\n",
            "   macro avg       0.78      0.79      0.78       128\n",
            "weighted avg       0.84      0.84      0.84       128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a model\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(128, activation='relu'))\n",
        "# model.add(layers.Dense(64, activation='relu'))\n",
        "# model.add(layers.Dense(64, activation='relu'))\n",
        "\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "# model.add(layers.Dense(16, activation='relu'))\n",
        "model.add(layers.Dense(32, activation='relu'))\n",
        "model.add(layers.Dense(16, activation='relu'))\n",
        "model.add(layers.Dense(8, activation='relu'))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "yHBdTQ98luDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,\n",
        "                    y_train,\n",
        "                    epochs=100,\n",
        "                    batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sInjtIthmNCe",
        "outputId": "298d9a1f-104f-4e3c-f9a5-8d0e621a65b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 2.6036e-04 - accuracy: 1.0000\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 6.3265e-04 - accuracy: 1.0000\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0601 - accuracy: 0.9824\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.6979 - accuracy: 0.8984\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.9617 - accuracy: 0.7773\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.2807 - accuracy: 0.8809\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1906 - accuracy: 0.9277\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1396 - accuracy: 0.9453\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.1012 - accuracy: 0.9668\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0784 - accuracy: 0.9746\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0617 - accuracy: 0.9805\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0559 - accuracy: 0.9844\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0473 - accuracy: 0.9824\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0435 - accuracy: 0.9863\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0380 - accuracy: 0.9844\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0459 - accuracy: 0.9824\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0426 - accuracy: 0.9883\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0390 - accuracy: 0.9863\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0291 - accuracy: 0.9941\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0303 - accuracy: 0.9902\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0287 - accuracy: 0.9902\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0625 - accuracy: 0.9805\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0362 - accuracy: 0.9922\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0313 - accuracy: 0.9902\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0225 - accuracy: 0.9961\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0225 - accuracy: 0.9902\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0183 - accuracy: 0.9980\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0261 - accuracy: 0.9863\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0210 - accuracy: 0.9941\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0188 - accuracy: 0.9941\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0132 - accuracy: 0.9980\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0140 - accuracy: 0.9980\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0222 - accuracy: 0.9902\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0157 - accuracy: 0.9980\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0139 - accuracy: 0.9961\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0147 - accuracy: 0.9961\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0295 - accuracy: 0.9863\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0176 - accuracy: 0.9961\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0115 - accuracy: 0.9980\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0135 - accuracy: 0.9980\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0136 - accuracy: 0.9961\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 0.9980\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0172 - accuracy: 0.9941\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0224 - accuracy: 0.9902\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0152 - accuracy: 0.9941\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0122 - accuracy: 0.9961\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0093 - accuracy: 0.9980\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0091 - accuracy: 0.9961\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0112 - accuracy: 0.9961\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0080 - accuracy: 1.0000\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0077 - accuracy: 1.0000\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0060 - accuracy: 1.0000\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0103 - accuracy: 0.9961\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0131 - accuracy: 0.9941\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0232 - accuracy: 0.9980\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0114 - accuracy: 0.9980\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0145 - accuracy: 0.9941\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0151 - accuracy: 0.9922\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0144 - accuracy: 0.9961\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0232 - accuracy: 0.9883\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0372 - accuracy: 0.9844\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0425 - accuracy: 0.9824\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0293 - accuracy: 0.9883\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0246 - accuracy: 0.9902\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0236 - accuracy: 0.9863\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0109 - accuracy: 0.9961\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0081 - accuracy: 0.9980\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0084 - accuracy: 0.9961\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0064 - accuracy: 1.0000\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0054 - accuracy: 1.0000\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0051 - accuracy: 1.0000\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 1.0000\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 1.0000\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9980\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0061 - accuracy: 0.9961\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0074 - accuracy: 0.9980\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 1.0000\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0035 - accuracy: 1.0000\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0077 - accuracy: 0.9961\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 0.9980\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 0.9980\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 1.0000\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 1.0000\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9980\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9980\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9980\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0047 - accuracy: 0.9961\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0197 - accuracy: 0.9961\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0142 - accuracy: 0.9922\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0075 - accuracy: 0.9961\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0135 - accuracy: 0.9941\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9961\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 1.0000\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 1.0000\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 1.0000\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(X_test,y_test)\n",
        "print('test_acc: ',test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2uJ5IwvluBT",
        "outputId": "fb6e9fd2-c39a-4025-9d4e-d9ae04f7d323"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 2ms/step - loss: 1.6760 - accuracy: 0.7969\n",
            "test_acc:  0.796875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "guGhuj6jlt-i"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}