mogreps_data_basics.ipynb

{
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    "Met Office MOGREPS data\n",
    "=============\n",
    "\n",
    "The Met Office has made data from its world leading MOGPREPS weather model available for research purposes. In this page, we'll go over the basics of how to download the data, how you can look at it, and what it means.\n",
    "\n",
    "Data from two models is available:\n",
    "\n",
    "1. MOGREPS-UK: a high resolution weather forecast covering the UK, and\n",
    "1. MOGREPS-G: a global weather forecast.\n",
    "\n",
    "Amazon Web Services are kindly hosting this data for free as part of their Open Data Initiative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How is the data structured?\n",
    "Our weather forecast models predict many weather parameters, such as air temperature, humidity or rainfall. These are predicted for every point on a regular grid, that is, as a function of latitude, longitude, and (when appropriate) altitude.\n",
    "\n",
    "With the advent of our latest supercomputer, we now regularly run \"ensemble forecasts\". This means that instead of starting one model run now to predict tomorrow, we start many, purturbing the intial parameters to give several possible futures (or *realisations*). The spread of these different realisations gives an indication of the uncertainty of the forecast, and can be very powerful information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/ensemble_forecasting_schematic.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Each forecast point also has several measures of time. Firstly, `time` indicates the real world time that the data is forecast for. Secondly, `reference_time` is the time that the forecast was started from i.e. the 0th `time` point. Finally, `forecast_period` is the difference between the two i.e. the number of hours that have elapsed since the start of the forecast.\n",
    "\n",
    "## Downloading data\n",
    "Here, we'll show you a simple way to download files using Python. The data is stored in AWS S3 buckets hosted in the eu-west-2 region, named `mogreps-uk` and `mogreps-g`. If that doesn't mean anything to you, don't worry - have a look at the Python functions below.\n",
    "\n",
    "`make_data_object_name` takes infomation about the data you are looking for and constructs it into a data object name (you can think of this as a file name). We'll go into what things like forecast_period and realization are in a minute. `download data` then saves this object to the same directory as your notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "\n",
    "def make_data_object_name(dataset_name, year, month, day, hour, realization, forecast_period):\n",
    "    template_string = \"prods_op_{}_{:02d}{:02d}{:02d}_{:02d}_{:02d}_{:03d}.nc\"\n",
    "    return template_string.format(dataset_name, year, month, day, hour, realization, forecast_period)\n",
    "\n",
    "def download_data_object(dataset_name, data_object_name):\n",
    "    url = \"https://s3.eu-west-2.amazonaws.com/\" + dataset_name + \"/\" + data_object_name\n",
    "    urllib.request.urlretrieve(url, data_object_name) # save in this directory with same name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can also use Python modules such as Boto to list, filter and retrieve these files.\n",
    "\n",
    "We'll use these functions to download one data object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obj_name = make_data_object_name('mogreps-uk', 2013, 1, 1, 3, 0, 3)\n",
    "download_data_object('mogreps-uk', obj_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and here is is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 niall  staff    22M 21 Jun 14:33 prods_op_mogreps-uk_20130101_03_00_003.nc\r\n"
     ]
    }
   ],
   "source": [
    "!ls -ltrh {obj_name}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data\n",
    "\n",
    "This data is in NetCDF format, which is a type of the widely used HDF format. You can use any compatible tools, but we recommend the Met Office's Iris Python module. First of all, let's make sure we've got Iris instaled using Conda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching package metadata ...........\n",
      "Solving package specifications: .\n",
      "\n",
      "Package plan for installation in environment /opt/miniconda3:\n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "    conda:     4.3.22-py35_0 --> 4.3.21-py35_1 conda-forge\n",
      "    conda-env: 2.6.0-0       --> 2.6.0-0       conda-forge\n",
      "\n",
      "conda-env-2.6. 100% |################################| Time: 0:00:00 396.65 kB/s\n"
     ]
    }
   ],
   "source": [
    "!conda install -y -c conda-forge iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load the file we just downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/lib/python3.5/site-packages/iris/fileformats/cf.py:1143: IrisDeprecation: NetCDF default loading behaviour currently does not expose variables which define reference surfaces for dimensionless vertical coordinates as independent Cubes. This behaviour is deprecated in favour of automatic promotion to Cubes. To switch to the new behaviour, set iris.FUTURE.netcdf_promote to True.\n",
      "  warn_deprecated(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: cloud_base_altitude_assuming_only_consider_cloud_area_fraction_greater_than_2p5_oktas / (kft) (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "1: stratiform_snowfall_rate / (kg m-2 s-1) (time: 36; grid_latitude: 548; grid_longitude: 421)\n",
      "2: wet_bulb_potential_temperature / (K) (time: 4; pressure: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "3: wet_bulb_freezing_level_altitude / (m) (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "4: cloud_area_fraction_assuming_maximum_random_overlap / (1) (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "5: air_pressure_at_sea_level / (Pa)    (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "6: air_temperature / (K)               (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "7: air_temperature / (K)               (time: 4; pressure: 2; grid_latitude: 548; grid_longitude: 421)\n",
      "8: air_temperature / (K)               (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "9: air_temperature / (K)               (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "10: dew_point_temperature / (K)         (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "11: fog_area_fraction / (1)             (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "12: geopotential_height / (m)           (time: 4; pressure: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "13: high_type_cloud_area_fraction / (1) (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "14: low_type_cloud_area_fraction / (1)  (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "15: medium_type_cloud_area_fraction / (1) (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "16: relative_humidity / (%)             (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "17: relative_humidity / (%)             (time: 4; pressure: 2; grid_latitude: 548; grid_longitude: 421)\n",
      "18: specific_humidity / (1)             (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "19: stratiform_rainfall_amount / (kg m-2) (-- : 3; grid_latitude: 548; grid_longitude: 421)\n",
      "20: stratiform_rainfall_rate / (kg m-2 s-1) (time: 36; grid_latitude: 548; grid_longitude: 421)\n",
      "21: stratiform_snowfall_amount / (kg m-2) (-- : 3; grid_latitude: 548; grid_longitude: 421)\n",
      "22: surface_air_pressure / (Pa)         (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "23: surface_altitude / (m)              (grid_latitude: 548; grid_longitude: 421)\n",
      "24: surface_downward_eastward_stress / (Pa) (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "25: surface_downward_northward_stress / (Pa) (time: 3; grid_latitude: 547; grid_longitude: 421)\n",
      "26: surface_downwelling_longwave_flux / (W m-2) (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "27: surface_downwelling_shortwave_flux_in_air / (W m-2) (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "28: visibility_in_air / (m)             (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "29: wind_speed_of_gust / (m s-1)        (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "30: x_wind / (m s-1)                    (time: 4; grid_latitude: 548; grid_longitude: 421)\n",
      "31: x_wind / (m s-1)                    (time: 4; grid_latitude: 547; grid_longitude: 421)\n",
      "32: x_wind / (m s-1)                    (time: 4; pressure: 2; grid_latitude: 547; grid_longitude: 421)\n",
      "33: y_wind / (m s-1)                    (time: 4; grid_latitude: 547; grid_longitude: 421)\n",
      "34: y_wind / (m s-1)                    (time: 4; pressure: 2; grid_latitude: 547; grid_longitude: 421)\n",
      "35: y_wind / (m s-1)                    (time: 4; grid_latitude: 547; grid_longitude: 421)\n"
     ]
    }
   ],
   "source": [
    "import iris\n",
    "mydata = iris.load(\"./prods_op_mogreps-uk_20130101_03_00_003.nc\")\n",
    "\n",
    "print(mydata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see here that the object we have loaded from our file contains many different meteorological parameters. iris.load returns a 'CubeList' object, which is essentially a list with a few helper methods.\n",
    "\n",
    "## Exploring Data\n",
    "\n",
    "We can find our more information about a particular parameter..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: air_temperature / (K)               (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "1: air_temperature / (K)               (time: 4; pressure: 2; grid_latitude: 548; grid_longitude: 421)\n",
      "2: air_temperature / (K)               (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "3: air_temperature / (K)               (time: 4; grid_latitude: 548; grid_longitude: 421)\n"
     ]
    }
   ],
   "source": [
    "air_temps = mydata.extract('air_temperature')\n",
    "print(air_temps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there are four different forecasts for 'air_temperature'. I'll show how to dig into them, but essentially there are three forecasts for ground level, and one for particular pressure levels in the atmosphere.\n",
    "\n",
    "The three ground level forecasts are:\n",
    "- minimum forecasted value over an hour period\n",
    "- maximum forecasted value over an hour period\n",
    "- forecast for an exact snapshot in time\n",
    "\n",
    "Lets grab the snapshot forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "air_temperature / (K)               (time: 3; grid_latitude: 548; grid_longitude: 421)\n",
      "     Dimension coordinates:\n",
      "          time                           x                 -                    -\n",
      "          grid_latitude                  -                 x                    -\n",
      "          grid_longitude                 -                 -                    x\n",
      "     Auxiliary coordinates:\n",
      "          forecast_period                x                 -                    -\n",
      "     Scalar coordinates:\n",
      "          forecast_reference_time: 2013-01-01 03:00:00\n",
      "          height: 1.5 m\n",
      "     Attributes:\n",
      "          Conventions: CF-1.5\n",
      "          STASH: m01s03i236\n",
      "          source: Data from Met Office Unified Model\n",
      "          um_version: 7.9\n",
      "     Cell methods:\n",
      "          minimum: time (1 hour)\n"
     ]
    }
   ],
   "source": [
    "surface_temp = air_temps[2]\n",
    "print(surface_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So here's the metadata for our chosen cube. We can see a lot of information here. \n",
    "\n",
    "- The cube shape is (4 x 548 x 421)\n",
    "- There are three numeric dimension coordinates (time, grid_latitude, grid_longitude)\n",
    "- There are two coordinates that are not linked to a dimension (forecast_reference_time, height)\n",
    "\n",
    "We can drill into the cube in various ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DimCoord([2013-01-01 03:30:00.000006, 2013-01-01 04:30:00, 2013-01-01 05:30:00], bounds=[[2013-01-01 03:00:00, 2013-01-01 04:00:00],\n",
      "       [2013-01-01 04:00:00, 2013-01-01 05:00:00.000006],\n",
      "       [2013-01-01 05:00:00.000006, 2013-01-01 06:00:00]], standard_name='time', calendar='gregorian', var_name='time_3')\n"
     ]
    }
   ],
   "source": [
    "print(surface_temp.coord('time'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the underlying numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 284.125,  284.125,  284.125, ...,  280.75 ,  280.75 ,  280.75 ],\n",
       "        [ 284.   ,  284.   ,  284.   , ...,  280.75 ,  280.75 ,  280.75 ],\n",
       "        [ 284.   ,  284.   ,  284.   , ...,  280.75 ,  280.75 ,  280.75 ],\n",
       "        ..., \n",
       "        [ 280.875,  280.875,  280.875, ...,  279.125,  279.125,  279.125],\n",
       "        [ 280.875,  280.875,  280.875, ...,  279.125,  279.125,  279.125],\n",
       "        [ 280.875,  280.875,  280.875, ...,  279.125,  279.125,  279.125]],\n",
       "\n",
       "       [[ 284.25 ,  284.25 ,  284.25 , ...,  281.   ,  281.125,  281.   ],\n",
       "        [ 284.25 ,  284.25 ,  284.25 , ...,  281.125,  281.125,  281.   ],\n",
       "        [ 284.25 ,  284.25 ,  284.25 , ...,  281.125,  281.   ,  281.   ],\n",
       "        ..., \n",
       "        [ 281.   ,  281.   ,  281.   , ...,  279.125,  279.   ,  279.   ],\n",
       "        [ 281.   ,  281.   ,  281.   , ...,  279.125,  279.125,  279.   ],\n",
       "        [ 281.   ,  281.   ,  281.   , ...,  279.125,  279.125,  279.125]],\n",
       "\n",
       "       [[ 284.375,  284.375,  284.375, ...,  281.125,  281.125,  281.125],\n",
       "        [ 284.375,  284.375,  284.375, ...,  281.125,  281.125,  281.   ],\n",
       "        [ 284.375,  284.375,  284.375, ...,  281.25 ,  281.125,  281.   ],\n",
       "        ..., \n",
       "        [ 280.875,  280.875,  280.875, ...,  279.   ,  279.   ,  279.   ],\n",
       "        [ 280.875,  280.875,  280.875, ...,  279.   ,  279.   ,  279.   ],\n",
       "        [ 280.875,  280.875,  280.75 , ...,  279.   ,  279.   ,  279.   ]]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surface_temp.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can slice a cube by index to extract particular pieces of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "air_temperature / (K)               (grid_latitude: 548; grid_longitude: 421)\n",
      "     Dimension coordinates:\n",
      "          grid_latitude                           x                    -\n",
      "          grid_longitude                          -                    x\n",
      "     Scalar coordinates:\n",
      "          forecast_period: 0.5 hours, bound=(0.0, 1.0) hours\n",
      "          forecast_reference_time: 2013-01-01 03:00:00\n",
      "          height: 1.5 m\n",
      "          time: 2013-01-01 03:30:00.000006, bound=(2013-01-01 03:00:00, 2013-01-01 04:00:00)\n",
      "     Attributes:\n",
      "          Conventions: CF-1.5\n",
      "          STASH: m01s03i236\n",
      "          source: Data from Met Office Unified Model\n",
      "          um_version: 7.9\n",
      "     Cell methods:\n",
      "          minimum: time (1 hour)\n"
     ]
    }
   ],
   "source": [
    "# The first forecast in the dataset\n",
    "print(surface_temp[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "air_temperature / (K)               (time: 3)\n",
      "     Dimension coordinates:\n",
      "          time                           x\n",
      "     Auxiliary coordinates:\n",
      "          forecast_period                x\n",
      "     Scalar coordinates:\n",
      "          forecast_reference_time: 2013-01-01 03:00:00\n",
      "          grid_latitude: -1.77995 degrees\n",
      "          grid_longitude: 360.911 degrees\n",
      "          height: 1.5 m\n",
      "     Attributes:\n",
      "          Conventions: CF-1.5\n",
      "          STASH: m01s03i236\n",
      "          source: Data from Met Office Unified Model\n",
      "          um_version: 7.9\n",
      "     Cell methods:\n",
      "          minimum: time (1 hour)\n"
     ]
    }
   ],
   "source": [
    "# All forecasts for a particular point:\n",
    "print(surface_temp[:, 100, 300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Data\n",
    "\n",
    "Iris also has built-in functions to make plotting the cube easier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/lib/python3.5/site-packages/iris/coords.py:826: UserWarning: Coordinate 'grid_longitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n",
      "/opt/miniconda3/lib/python3.5/site-packages/iris/coords.py:826: UserWarning: Coordinate 'grid_latitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x10447cb00>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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iIjLMpq3nF2sax6LkWplo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dy4vQmh77uup8gCcMfXA/c/o1MX6DB4wwU4TH9weRDpKBEZhX2gEhxV535oJqfAZkYAVJ3DnJi6\nrmT0ts5BER5/ZDIhmqwJW0UcVvP3T0MTwtoB3iIwX7nWjADWbMB1paWpdC/bj59HqgZH79Q1kBaV\ntte0X9xwkzY9ToM+emfu2N6LMLShGrgDiuNwMNZ8aDImNN9iGt60dBLe82YuJTgfnzqYDTa7wo8L\nJocxNywz2ReR4+z+eXMfa/f1g/bxXsq6vm43lA2xqAmxbDbEttmWNXCVMZSR7JNl21k32zQhaBJs\nB2ZDEEIIWQb+6wAhhBBCCCGEEEIIIYQQQjYGjQhCCCEz0IQ4fHaRkdAF9vG61zXmeWbGv/3dVyIi\nMinX09946v6YWYxde6ierk3lezLRinrNe6iHbgfe+17nOD6V/NSd49+j7+QJKY/dMeVQ2zSZDpma\nFMhywHZkOZx+5L7zkqmeV2o1sfZz/H37NcI4KIZh9THyEWBCYL3sp8H4U5NjkKhE4HMPBuH1esxX\nNMyOGNk4HAewZgashFythuzcKR+wEpKyqXytNeugGLrBwYTIRm5wdZ4Gx01PLv9rh23HZkdUaM/Y\nL7OGQpiLYOvC0a41GsphaOhYbBbFjFlh9y+ZCWGXEhnHxWwId3xLRfuGq9yXNSE2xbazIa5iD6wr\nG2LRMXTB2hDZfhYELQhCCCGk29CIIIQQQgghhBBCCCGEEELIxqARQQghJIA2BFkHzIa4ejbEruyN\nfua+A65qRsCEKM/Ccv2kpxWrmglRw15QG0HUkDh/7vSGwT2nMUyqI0l0TvIzd6ifYm9GhNWw2SjV\npcmQULxJgOrhBO/1d+vH37VXycOsQNV7qeZCNpbgeIxvcuL+BzkGvbOwKh/ZEbAJUP1fDPPgPD9u\nrbJPtfrfVt0jcwLL/EyzHtRsgKVQDrNgnBgP7IA6dxOEnAVkRYg0hgPGbrfPZCXAUECGg89uQNt6\nvmZOHGvGRHmUB8dVJlMiNTkeYNFCbGtGxIwFe7y/vgXNB4B7GevXZ0pgPQ8Nj1YW/aoxVfKJzWKY\nkw0RMyHmse5siG2bEF2gK4bDPLZtQACaEIQQQsh+wH8lIIQQQgghhBBCCCGEEELIxqARQQghRERo\nQlwX9jEjYR3s03WvOtb/9C+PgvW//PA7ERH5/ZfviYhIYipGP374o4iIHOfTlfqL8cfP77v/MTkN\nWK9RB4MKX6349u/EN4WtqH7O3xrJNHWWRJ1mYdsKsiH8emFyADQTIlVz4ewTDWU4hxmgVfqaERH7\nWoQBEQNV7+dvhpkQMCZgXFgTIim1+n1okw30eiJFvzAf0iJcZqMwb6HquYHAeEAuQ9k3GRd63X4e\nvL2g5x0lkk5xrYuZALV5rJvjItkKA/c/yKkAVb8fnJ/YzJGivXIc2+v+PAOi3Xjw/S9oPtj9WJaD\npHW7P16fnbJv8jxMNkQrpmo+NrZlTYh5XDUbYlETYld0IRui6zALghBCCCGLQCOCEEIIIYQQQggh\nhBBCCCEbg0YEIYRcc2hCkHXCbIjt400IVDXXrmQaJgR4751fRETkRs9ZAPmaKljRDjIxfvvR9yIi\n8tkPb4uIyMN7z0RE5Ktv77lhjvUZKXRph4FHSO0GVBSnWSLDt104RHXP7Zv86BQHb0LoFGTnrhFk\nSQA8nmd/60Ie6pH+UVitjKNv3fr4Ldcn5JTMV9uH7QCYDjATbLU7ljAL8nM1F/R4ZDXATEDVPMwK\nm4NgMyBgBYAmoyIJlt6EONL2s/byetgOvVPNltBsiOJYx9lLBZNd9lwb2RQ2STgGOyYAk2LmOD3/\n9TvOfBi8LM01hcfj2tKJsT/6oY0yOyfhOOaB62z6vfx4e13RLArzjJy/bc9rbx/2TltZ2cw5MH3m\nmBAxYobEPBNiXjbEsibEtrMhumBCdC0bYlcZEGCfTYhszjNRVvvx56d510EIIYRcxn78tCOEEEII\nIYQQQgghhBBCyF5CI4IQQq4pNCGuF/uUkbDvFCtaIeu+Rw/e+1lERAaZK6fPF6xinJTrGcdv3n0i\nIiKFVnliPN9+96aIiCSv9I+hKPiF1JDrOLUS+OzFkW+z1syCG3ed6pDfPw36PHt84pp6HY4FRbzl\nX7vj63PXd6J99b8OQx8GT7WaHnkFMCJQXa+GgjUh/DhRpa9mweCVa6D3Wu0OPX5yKzQhYEDMVv27\nZT5y5/VftpsKtro/ieQ3YFzIpAC4jv4Ld0DvmZojvUz7ORYRkeHzUqY3XGM+m0F/pKRFu51hx4Ab\nXvXC83E8xoJ+MGa0by0TzMVMxgNskEg2hM2AiBodmR3/5cSsmJgJMXprTvtLZEPM3R7rwhzfFRNi\nVa5LPsMm2bUBAbpsQqzLENiUaTDPtKDhQAghZJvQiCCEEEIIIYQQQgghhBBCyMagEUEIIdcEGhDX\nk+tuQlyH64clkGjOwaMHP4mIyCBXE8JUtPaz7XwXfPPLHRERKYxhkfZc/+WxjnsS1sXcvv9SRESe\nP7kpIiL1tKm8RxbE2ZMbbp9e8623nAJx+8EL18gDt6i0EnRaqDGguRSpnpd9MRQRkclDl5vR/8bl\nEmRuVWoYCqj291X5oseFld3TG2HZeu+19jMOMyGmJ248kxOYEJG8hKoOttvqfxDLOcB2m5OQaZ5C\nNgpzFZIptrsLrIZuAqa3B8Hxk1u5NweGr0rdV2pfYR5F2cf9s9aIjlFzKlIx+7NwPz7KqbkXxTCc\nw5lrj3wFxPIxxPezWtW+NTH8dm3XZ1XoMmpCzMuGuKRvy7xsiJj5MNPOjkyIVbMhVuU6ZkN0xXyw\ndNGE2DeDYN/GSwgh5LChEUEIIYQQQgghhBBCCCGEkI1BI4IQQg4cmhDXk22bANMVcxE2xS5MiFWz\nIa7Kv//tlzvtP8bDN56LiMiff74rIiKFtxFcdeavHjwTEZFx4f44WqrtAHshKbSivIAtkPhtyGgA\np2e3RESkGrq2337vFxERUbFBeuZPvNMvXNZB8ehc+3BjG993ZfZHX/eC45FjYKvxUUQ8uenGVWic\nRab7UZVfDlC1H5oQPkvC5x7EzAYJ9pfDTNclON9X2+t2GBqz1f9hvoO3GXo6DzePgnZA4S2HRAaa\nU5G/chebqk1RaRtNNoMEcwB8poSaB36MkY+uNyh8WEIdtGMNCLQXtQQi2RXWaLBzGmvPnhfLoMC9\nhAlhsyKs8VDHLAKfNdGyP1ZNv6BZsKgh0RzfLRNiVSth2xbCLuiq+WChCUEIIYQcJt36GyshhBBC\nCCGEEEIIIYQQQg4KGhGEEELIAbGrTISeVll2xYyACXQdMiKAzYIAuzYl3lMzotS8BowH68+fOzuh\nPHPaQXrutmdT887/Xu2rw7ORO6Z36vahCn1yyy2fnr8pIiK3PnzeOqbk/TN3XtE+N6P7Trk4/sr9\nUdlOLcyHqqfGQb+1GV/lXhgboKm2D00GVPlbMwL7YWYUWktksx78+I7bn3tvSmg+R6FmRVqqIQFL\nQQ0LWAwYP8bRP60kPwttO29CYJm32x5lP8yE8HaJmhEz2RCRv63AjMj0PLQbMxdsTobdbteb3I7w\nHtncDX9eFp4f+9j5TIjcHq/V+Pb8pH3/ZdcAZrIh7P41ZUOsm22bEOti1/0fAl0yIWhAEEIIIeun\nG/9aQAghhBBCCCGEEEIIIYSQg4RGBCGEEHIAdKXyv6tmBOjKPG0TmBKLmhH9zM3ZpFxurpbt5/GT\n2yIiUr90Zf69U9df7mQFSTUHohjqCceXVLnrsdmkyZMQEXn55R23/VcuC6LfdwemyA/QgldUhg++\n7QV9+5IdVMPrlGS+Oj7s31557HXstsreVuFnE7e/dxZWhqPqH/YAzkd7VY58Bh3n2G3P/X7N4TgO\nbYOm+t+PMGgH5KM6GJ9IY0Cg78qbCe1WBzIhLH4uzGOHe1EZg8D3b46PzTnaaebatDMz3nDOQBXG\nh1w4Hx217x/d1ePMdVR5uwkxkw1h203r4PiL22ZMCGMYxEyIXWdDrGpCXJXrkA0BSn1gupYV0QUT\nggYEIYQQsnm68a8EhBBCCCGEEEIIIYQQQgg5SGhEEELIgYMKcFsZTsgm6UWqLbtiSnSRQ8+18NWm\neDSeu2CF/kvNJ4BVYCrXUTaTTpKZavdSbQlUt5dH7oBU8yVsRsS0aJ/b3leuoViRcG3/xDwNx4ax\n916oyXAe5h8UwyRYemNhAsPA93TpOBorAAZDavYnZn97pXfsY2hzFFJvmsyaEDAgLKkeY+esGYua\nE71InyYzAhZIbOzIkrDtxLIgrAUCoyIxPyKR/2EzH6a32scxL5tixniIbYdFYLMhwCUmxKrEDYnY\n8d2qHGc2w/6ySxOCBgQhhBCyffivAYQQQgghhBBCCCGEEEII2Rg0IgghhJA9Zt+q52OmhGVT5sQ+\nWAebGuOyGQ7r5ssf3hIRkfrZQESc4SAiUg7DauxJL6wITwr3P/lZIqmaA7iE4lir5o/dtb37m6ci\nIvLDF64vm3eBd8FP/nRysYu5pTmoosdhyK1AngUea5gQ+Xl7tkM5kNb9PmsiUs0PZqrzj1G1nwbn\n+ep/reove+2ZDxYYEMiCaLbD1LhgRBhTIRtVwTF1FdoZdT+0NJKy3XSAoYAxg3lfHdaIsNtjJoRl\n9KbZEDEc+s/D/fi4Yv/kjXC9Wc7JhLAmhDcnWkyIC9tFVs+GsMRMiEXZVjbEVU2IdWVD0MjYL3Zt\nQuRzvsx29TN6F8ybixhdyzghhBCyONfnpxwhhBBCCCGEEEIIIYQQQrYOjQhCCLkm2OpqZkaQLmPN\niXUbEvtkRoBtj9XaBItizYtcq0+rQu9hrhaDViM/+uSxiIiMS/fH0ifP3Ev4i1MXIpCduv7r9EJG\nAv4Eq00++PiJiIh8/+SOiIi8++unwZhKHcvLL13baYoKf1d+3lTLa7NqXsB4yEZuiRwB5BvAcADj\nm66fYhDmG+C4bIzzwmp/5CLAPLCZDE2mRJg1UeVhP/54vWWF6ccCAyItw8pwtI/2YBE0xkXi5wAZ\nDXk/1XPCz643ItJwHW2hHWBNiKYdCcbij3dRIxfGFl4L+rOZFABzP7rX3h+wBgSewTppP74yf8vq\nogmxqCHRHL/eKuRVTYjrQKnfTeuyNrrGNrMhtm1ArFrlv+z518mcIIQQcjjwpxchhBBCCCGEEEII\nIYQQQjYGjQhCCLmm0JDYb7pcyb8JYEhsKjtiH1iXxbGurIjff3VfREQ++cAZDX/65lciIvLxwx9b\nj//o4ZNgvaxc/9/+7CyGWteLM/fHU5gQiVbtV3kt9TBsE4Wj3/3R9V0N3QZUwKIPcOtDV9Y+LVzb\n489uaTthlT5A1XvvedhfrVX0yIqYMSPeUEtATYrsXK+hF7bj+9FhwlBosiJQDR0aCjAWfLV/DoNC\nx/ta52Hs9k9O0qCffKzH6xKGxvQoCcaBDIqGZt0+PhhLMcyCsYLSZEpYC8P3AFPC/C3FZzv4/Iv2\n82ayJ0x2gz0eJsRVDQj0M76L/XMMCD+Q9uMlVglvtgc2xBVNiFg2xKImxKLZEFc1IZgNcXVgiW37\nXfuHZEJc1XxYd/80JAghhOwD/GlFCCGEEEIIIYQQQgghhJCNQSOCEEKIiDRV1jQjSJdZtxmxD1kR\nll1nRwBUVRdqHdRqFXz653dEpDEgfnjhrIPJxP2xE1kRPjMCJeZjt55O3BImBMpmHv71977i85uv\nXBl7Mgmr3x8++ql1rKj6LUVNiC9CE6I27+HPdDuyIlB4ijyBqRoQyIzwmQzH4Xacj/1N1b7u10wI\nGAigyTMIn3OYEBa0CwshP3MdVLnOtdoE1kqACYEsCVgNTe5CvHIcx17YotvDsdbGCMC1VtJ+LT4H\nxGRKxEyIWTOjfbxNhkTYT++lHmDNhTw8z1JHPnb+WcJ1mAwIe9xMWdgCmRAi+2lCXJWumBCk+2zC\nhti1ATEPGhKEEEL2Af50IoQQQgghhBBCCCGEEELIxqARQQghJKCoMloRpPMwM6Jh01ZHP3PtT8qw\n/b94/4f2E0buuC9/eEtEmmrrqtDK9lINCrzL3+czhHbCw7917V+sbP32J5cnkT6uYFEAACAASURB\nVPTdmB59+PTywaM6X/t8+ZU7P/XV8chWwFj0NK3ahwGBJUAB+vSG7lfzIRuHx+PxtCYEzIXeWVih\nDYugyE3Oga/iD7MikPWATIhUt09P3B/xYUCgXVgARdr+uYEBgfGifYwb7dVpY01YAyKWf2H7mGcy\nzBoXDj+35nGPZUCAqt9+3Dxi47Q2DLIhvFlhTIh5BkSsP+xPrmAdLGtCLMqyJsSq2RBdy2Lo2nj2\nCXwXbyor4jqaEDHWlQdFCCGErBP+VCKEEEIIIYQQQgghhBBCyMagEUEIIWQGW1lNQ6J77GO2wSZY\nlxnB+VwDdfgu//JMX+aPamyYD2pG3P+1sxkGmdMPUMma6xLZExf54N6z1q7LlmNFRL79wmVJiPaJ\nGnu8vz+dYExu3ZsLMCP0qw+5BNMT7W+I890yH4XrNhcBNNkQ4XZU1VeRXAKYEDjOZ1mosdCYEO6A\n6XFoQFhi9gDGNXip90KzJsqhWiyZW5YttoIfWzQ7AfvDc5Mlf7zYOahNpoQ/zp6XhMfZOUaxft0u\nYvj9eCbscf3nblmpNTJ+E+ZHxAJY0ITw+0szb1vKZSC7pazC+77unItSH7xsy1X/MCPAugwJ/CxY\nhxmxryaEpUtmxKHMKSGEkNXZ/U8jQgghhBBCCCGEEEIIIYQcLDQiCCGEzIWGRHeJ3QtW9pNt85cf\nfhes2+pLGA6xitU8st5mRljQljUjHvz6JxER+fazt90GGA/WSNA/EVemWr6YhMchZwCmQe+VjvW1\nW6ZFrfvVSFCTwmc86HqTr9BuLlgzA8f78frz3NKaEOgvNaZHYwO4cRZHkfJ/perrPcOyNzvemUwI\nYyw0Y27fLsYCmTUDLh1i064xHuz4fDxBpCA3ZkIsut/2N/g5DcaT6bN0/o47AM+cz4Cw8yiRDi+p\niLdZELFsiG2zajYEmQWGxKGYEWDT2RFkN2YEDQhCCCEWGhGEEEIIIYQQQgghhBBCCNkYNCIIIYQs\nDQ2J7jPvnhyaMXGdsyK6OtaZSki9NdZ8mNvOEmZEjHsfuGyJn768q2OBOaDv8Ud+hRlacSx6nC71\nT875mVtmY2k9D1ZAOQi3p1O39MaEsQFgVGTTsNoZ1fTTG6FpMTkJxx3LP0hLrbrXdkrNMcjPQ4MD\n5zemhFtaEyKpmrEC7KskbGtmbnAbI8ZEbN1irRGf/2GyHmwx/rwsiNi4543D31tsNwZIqs/K0WO3\nwZsRkflIpDb79R7abIikXrv5kKzh/frrIO3IOLrIpsyIXbMuM6Ks0pVzIg69in/e9a1qTBz6vBFC\nCFkPNCIIIYQQQgghhBBCCCGEELIxaEQQQgi5MjQk9o9DzZZYlxlB1s8u3k/97Rf3RETk3sNfRETk\n7qPnIiIyLdxzfvan2yJyoapeMxVgIGB9eiLBcdl5uA5gEsCE8NXwZfvxFmQ6oLp+niVQDhYLLpiq\nL4B2s4mros7HmhWhx8GMGOl1JGbcsCDyce33NZkQoZ3hryGSGRG7Jnsv5s1ZLFMC5oO1RfAVVy/5\nVRezTmz2hL1uMc+AqBkxfOJ2jO6pGaEGhOSLmxCrkuzJ12OlFtS6zIhDtAhwTeCq17brrIh1saoN\nQbppNtj7ue/PJyGEXGf25I+hhBBCCCGEEEIIIYQQQgjZR2hEEEIIWTuoqqcZsX/Ye7bvhsSqrJIV\nse2K/+t2b/K0mpsTgarJEsdptTCyIWBEjP/pjoiI9LTqvvfKLVFkPrnllnXfLcuhmgBnahbAFDBf\ncciQgBGBos3zN8P14+/dEtXzSVVru3WwDssAmQ5oH9X3tS0KnfPoVX58Whk+hsWgWRGaPVH1w36a\n4lN3fCGNveFNA5MzgbZq87eN2McDBoS1QmLnWdPCjxF6h8loQCbEPEPDFtpaEyKGNSNiWBOjzowJ\nkYcV7es0IfYNZkQsz7qsj12ZEVfNhrhS36yy7xw0Wwgh5PCgEUEIIYQQQgghhBBCCCGEkI1BI4IQ\nQsjGoBmx/6xiBnQBZkWsn37mnoVJebVnYZvmSN13faVnbswv/5tTE/oj3W+GgCwIbC+HbplNtMpY\nsyNSLIuw6rjKwve1j95y+298r+bBnNwDnJ+iit/kGaCqPzGmgrUObFU+joPpgFHjvKQIbYYmnyBs\nr/D2QRLNgLDnxjIg7FhjJoQ9zxsQpRmiXhu+qnxGBNpFP3peMWwf76wFshjeuLD3Ds8SxqfL8/tu\nIN6AOKDcArJ79i0PY10mBCvoDwPeR0IIOVz4t3NCCCGEEEIIIYQQQgghhGwMGhGEEEI2Ds2I/Wdf\nsyNoRsyyrQyLeaxiRuRaJWmzIj77/F0REUnUXHj0yWMREXn4/lMRaTIjnvzT2yJyMXvBnY/q99Jk\nQqRqCsCASEzVfnEUmgTlkVvChDj5OjQhUPUPfNW8Vi9XWh0/PdYshqGO01bvSzgOwXhu6DglHK81\nKCpjWoixCOpQ7Gg9rjJzZ/Hmgo4hG+uYC+RgtB+PezPPpLB5Frh3APaKvXYx9xB5HrHxL5opETNB\nkMsxftsYEHOYyYZYI8gWSbrxVUA2CMwIsKwhsemsiF1mQvgxbCkbYl6VfzknA+k6QBOCEEIOH/60\nI4QQQgghhBBCCCGEEELIxqARQQghZGvQjGhnYMukLzCuuvmjel+zI5YlT8vOXWPXxnNV1pkZkWj1\nr23r8Z/uiYhIVoWGAar1kReAEp3Bc32/+rlbRzU/CmdhHuBWwITAxxXHZZpF4fMM1AYoB2pA9MPz\nsrHaANquz3RA/gBuvc9qkFa88aDWQIY8h0huQ3Ni+2Z7/MWa6gS5FabQ2s+Bzl3vzB2Qj2BE1Nq2\nzoWOdXokwXY/F7ofuRZVZLu/FPyYMebGvDlAO35pjpvcqYPtvZdhxTm2T+6aEItYJfoV3+GfdDgD\nILUPRUfYt/yEdbLra9+UAbFKJf2mTIhVq/px3nU0I2hCEELI9eH6/ZQjhBBCCCGEEEIIIYQQQsjW\n6GaZJSGEkIOGZoTjMhNimWNEdmdO7IsZsc2siHVW+F8nlpm3WFZEHanyfefjn0REZFy4z8mLP94V\nkaaa3Wcn6Mco1Sp+a0JUphofZsToHfc5SEduPDe+CavkZ/INbLU9MiZwHebjjMJyn2ugFkAyCNuH\n4VEeh+3DKkjMV26dta/b4yz2PBHxNoUtgvdGA+YOYy9D46HsIScjNCRiZkKsf8ylz5BAf5FMCpvp\nMLNfmZ60P1uTN3DhOO/ySvOZTAm8w39ehXrELqgvZABYOwL7lrUmav1cJVesUq5U5emqGUG2Rxey\nIDbFuqv5r5MZQROCEEKuH/xFBCGEkINhX37BsegvF67SZldf6UT2n37mPl+TcrlfPv3+q/siIvLJ\nBy5Iep2vxcAvAOaBX0BkZ/hHb7cdQ8n1VUr+VUs9s11f81PoP/SP33Gfu+OvXUP4BQb+kbnQdjJd\nx6uX8HEtzS82fBi0jgdfZfgFA9qd3tL2cZr+ggKvksI/rk/tLyQW/HetK/0Ozf5CQOdsor9gmN4I\nf/lj+7SvXgIzr37S9ZlfruB/zDXP+6fwRX/xEG9Ax1MhbFs3+Os0v5yS8BVP9hcStf6ixodW15F3\nYF0cwoq/eFiVSseYzgnWXvUXEpX+Q2zasX+s7Oq4FmHbr2Ta9C8gdvFKpk3/4/kh/QKCv2gghBBi\nOZyfcoQQQgghhBBCCCGEEEII6RwslySEELIz7Ot8rMnQ9df9LMsmTIh5fW3LjNiXVzStwqrXdt1f\n0fSHr98VEZFq4uYNld14ndJVKmX/+LmzK/LnaiLo9qqngciRub/922ciInL6X94MtttXL/kwa4RT\nn4T9j99xCsKNL/D+H7fAV5gPlzZmBF6tlFjToR+274Oez8PteI0R2q0G4Xa0h/NsmPaqtD3CsfBn\nXKOvu17yK6H1tU8X2rXCG4a2pKDT9LeoCRH5GDevmIK5oNsrE2KNHcaAmGdGzA44bPcyQ8KfsuFX\nNC1qRqxKVw2Ero6rjVVNiGxFe6CLr2Ja1YRYd1X/IRkPF6H9QAghZBEO86cgIYQQQgghhBBCCCGE\nEEI6AY0IQgghnWFd1fRdyYrYpgHRFWL2wKL34iCNigM1I+ZlRdSmIvzRAxcYnV+havLT3z8QEZHB\nc9dnE/rsqn0/+IsfRKSpzCxKN+ePP70nIiLpVCvD1UBIJ26ZaQZEEclUwEd58p47IRlrpbhuh+GA\ndrCOMGtrJqBfm2PgQ6drs165DWWWBP3C3MAdKGFIGDsBoP91PIozbSBw2+RuzMUeZ9uFrYLQaTM3\nAHNuxxXNojDL4sRmOlyONUJiZkRzvO5f1IzAeEuTLXGJdTDPeLBmRI3na87zsC4z4qrh1ftkIHSF\nbWVCbNuA2Eb1PU0IB00HQggh62Q/fxoSQgghhBBCCCGEEEIIIWQvoBFBCCHkYNmVGdElE2LbWRFg\n1Tk/5KyJ68plFdwXmWeMfPF/fSDHL/XY43Dfw7/9XkTilZv1Lfc5KFBdjspwPXx6Wyu+y3B7Urj1\nsh9ew/FX7Z+nqRoQGfIM1HyojalQqZFRqNHgzQiMV4eJbIneax2PfqxgOmAeSpMxAXvAGhEwKfA3\ngGpBe8GbI5PmeGt52K+Y2O2cMRnM/hmjAdt1iWv1BoTJzVi0zGquFbJgOzEzwvcTyY6YMSMs87Ii\nWrAW0qKZEOs2I+ZRmZyLVQ2JrtBFU+OqJsSq2RBdZNlsiH0xIWgqEEII2UdoRBBCCCGEEEIIIYQQ\nQgghZGPQiCCEEEIIWYCr2hrXLSsizd31fvjuUxERGWbLmUKf/rPLgxg+du0ePxOZnrh9KHAtj8I5\nzcwL/r9/ckdPQHUwMhZM1XqvvrjbV9+j4LR6Z+zG8Mdh0D+q8mEgZGM90Vb9o/kiPL42ZkJq9oNy\nEI7X77c5BybTAhkS8yQtazPg+mBCwKzA+NNC/N8ibNbCPMti2cffmwZmvV7ybzGx7IjpLVM5vuLH\nM5ZFETMkFjYj5nHRLjBmgc2E2BbzsiKu3H4HDYSusK1MCHCI2RBdh3NACCFknzmsvwkTQgghhBBC\nCCGEEEIIIaRT0IgghBBy8GwrK6JL2RCWXWVFrAqzIvYPa0b87sHjK7WXTF1Vda75CNOTpgq+OHZV\nv+/97kcRERnMsy20ajyZqDlxFtbiVJoBUaemWv3+yC1VNfBV9bABjBVQmhKf2FeOtQfGb7kK18FT\nt2PylhvH4FkS9qvAdGguQHTcYbu2PzCT0zBn3RoabTZCzAiIYbMVVsWbB3MMDG8+4LgNlWP1nxsD\nwvavbkedm8p12AtSB+fZrIi6tKkaF3JYYEfMMSNipkRtn4PIHC2aFbGoGYHMiEPIitiVpbEuE4LZ\nEOsH7W4qK4IQQgjZJ/jTkBBCCCGEEEIIIYQQQgghG2M/yiIJIYSQDtNlE4KE9LRCcnqFMmzaGpcT\ny4xYlE///I6IiGRqRExu645UpFRz4d2/cbZFzIT48093g3VUkeenakSM3HrVd/tPfvNCREROv77l\n9r99LiIi5cRdQ+/7gVvX42PV/D5bQbMiUF9bDvT4iKkAEwMmA3IEqlzHeTN2nvaL7TggbT/er2Oc\nOn2wTrJpeBwecRgRF82IxNoec4yIRY0J2C7z6J22mweWdWVARNHr6r9E/ki4G6OEKdHcO32+77oG\nfFaEhGYEqGXWhBARb0pchjUf1pUZsWsz4jpnRWw7C2LXbDIXYdOZC+syIZgNQQgh5BCgEUEIIYQQ\nQgghhBBCCCGEkI1BI4IQQgi5RuxbVsQhgvdWF1d9Of6e9r8o5dCNs+o3leB4rz4qTEt9iT0qRT//\n5m233td3co/cc9576kr58zPXjjcPtKoYFdW9d90BqOBOng70OHe8Nw/ysB0Aw6BSAwLV8jjeZjv4\n89PwuPy1ZkVotTxMDlTbeyMD48GtNHkJ0VuM7doesiyycz0PMouOxxao18lsbsS8vhd93PKz9sr/\n4iQchDUdcjUk7HEbQ69zgEwIk9MB5mVY9J/pvX7DmBE+G0LPt9kRbSxoEiAjYuZ0ZkZ0nq6YEHnG\n6nxCCCGELE+3/wZKCCGEEEIIIYQQQgghhJC9huWQhBBCDga8u5+QbbCrrIhDz6j45AOX//DpPz9w\nG7xVUMvd958v1EY5dnOTvXR/1PWmAkwIswS+AluntsxsFXpY/W7btVhzAeD46V3XQFK4dqd3yqB9\nML3ltvdeuoElk7B9tId1mBM2QsNeP9aLY92ul4dpsO2BujdrYdjcinWTn0YyEpSNmRALFn7PzcDA\nXEbO7/+SBudNbxtDwrbXVhlft7eOjJRmsMvNFUyJeYZEbd6FHzMkFjUjFmXVrIhSzZCuWAa7JIsp\nO3tK3qHrgbG3rqwIQgghZJ/hT0NCCCGEEEIIIYQQQgghhGwMGhGEEEI6A40Gsi16Wi053WFOwlWz\nGq5qRmwrK+IPX78rIiJ16fr56P0fRURkqOX6/+0zZz70n7jQAVTf93yuQlOtPMjdzixS+VxP3Fyk\nI31nfTXbxkXQ18uv7oiIyMn7L0VEZFqEc5qqsTBT9Y52sN0YChhmqgYDptobDD1UvYfjzU5d/+VJ\n2KE1I/x1mHwCmBrIfPDzcKRLDLfvljAiimF7OzPZFPnFnI1weVXmZSrEiBkT8zIrsL84Xq4qHtkQ\nM7bIvHnAdZnj7PV6Q0LneXJX71pqMiQknvnQak243tx/s3mZEDAhkmDdHr9qdsQ8M2JbWRE0I7pL\n7Lu+622vk30ZJyGEELIINCIIIYQQQgghhBBCCCGEELIxaEQQQsies0/viz9U42GspcGDtJhzZHfA\nWMexl9vvmH14nsGuzYRd92+ZlG4cv//yPRERyX52pkPVd9XG/j3ZOtz8uXsGj100hOAyUL2faLXy\n9FZTrYwx20rRpO/mApXaZa6V4lrpnExc373nxpjQPnp5+B011tNnqv4juQiJ3Y6Pl341pMYsSDK9\njp9hgyR6vhtvdhbek/LEjQ9ZEtlpuB/mhjcuBrqMVO3bW56Y/aX9erhCYW7McJgZwxWLfxc93x6X\nn7mrj5kRMCDE2Cdg2Y+PtVhsO36J8akFU9xBMMkFCyJWyW9MiRkrxI4JXyFJuwlhzYtlsyNiZsQ8\nFjUjVs2KuM4cWjZEl1k2K4ImBCGEkEOERgQhhBBCCCGEEEIIIYQQQjZGN8sgCSGELMTFSjZrG+yi\novxQjQdyuHQhKwLs2oxYld8/fkdERP7yHac0IPOh99RV+WcjdxxyCJDzAGPiSCvNfY6C5hTYvIX3\nfvujDLLFrKMHv/5JRJo5/fLPb7sxPVMj4lTb1oyECtkMenyZhpXfsDlmTAcFX8UVnII8bDc/C4+v\nf/3a/Y9mWnijQvdnkyTYjv6RHTFjJvjsCZ9aEYzTGhF23Is+cpEkgvZjTZ/zCq9jZoQdY2ys89q3\n7cSO778M80AWtUli40jm/FiMGhGZ2a/Pav+pu6k+M6ISid4Zk0fh2zI5E7U+N3WJ7AgzpjVlR8TM\niHlZEYQsSr6A4bFr02CeGbHr8RFCCCGbZPd/6yaEEEIIIYQQQgghhBBCyMFCI4IQQg4U2gnbh1kR\n15t1GQm7MiOW7fefvnFGA6qf//Gz90VEJH3t+sXHANYBqvPRTz11/eQqB+ARLPX44sQtH/y770Rk\nsSrRjx4+CdY//8aZENlL1zjMBFvVDuMg18yGUnf4SnFUfOvYBOZBAZtDj9d2bvz6hYiIvP7itog0\nORfoD5XfFSrLdR3HoQofde7YXmr7M6VEpvK9MSP0/Hwxl2Felf/F/dHCY1s9v2RmA5axr9GrZkrM\nGAux9RWzJvz2MtK+3prE5oVEzI9qGu4vvRnRfL5jVgWsCZs94rMj9Dlp9uv5aDdiKMSyI6wZsSzr\nMiNWzYpAVkx2xevYBF0cE1kvNCEIIYRcB2hEEEIIIYQQQgghhBBCCCFkY7D8kRBCCFkz+2xGgF0Z\nErvINhFZb1bEvpsRMf7rn50BURV4z7t5L7xWM2dTk68Aq0CXyIZIJuFxUzUgYE48/A/fiIhIfqFK\ntIi8U9vy5Q9vuT5eupyK/DTsq9QcCp8RoabB91+/KSIid955KSIi9z54JiIiPz25HVwjQCW55dVj\ndzG3f/OLiIi8+Nqdjwr0oZbFp7m7tsq0m5pqefhtqWZXYLzNCXLpei2XV1PDuLgSprJ/nnlgH+tY\n9oLP37jiV9K8jIgrmxV6k5DtUJuPHSRFmBC4x+nUjMu0MzMuXEd+YX8kMyR94hoZv1lp23qfdVEX\n4fGJPic1MkZwbbiW5PLnaNaU0NMWzIqYR1WrMTFnHGTzFKUabdn+VPF3zTjo2ngIIYSQbUAjghBC\nCCGEEEIIIYQQQgghG4NGBCGEEEJ2zq5MiH1g22ZErL/aVM3b6mfYAVWvDpb+eJtboOdPbos5bqFh\nXko1ddeajbSCWiu/S29A6PpxFYwN1ed4Hzuqfd9915kNPzy+E/Rz78PnIiLy05d39fzwXfto5/b7\nLjMC767Hu+jTTOdM25vJDUDVPGwBMzcL5yXEMiV8O+335jJmzAL0MceImJdDYdtf9PgY3i7BUo2D\nahBpPzL+WL9Jefm6NyNMZgRMiJnjIsCoqGFQXBjHjI2Bc/SaBz+7i5zcac8kaYwZ/UwvaEYsmg2x\nrBmx66yI60C2rAJECCGEELIGaEQQQgghhBBCCCGEEEIIIWRj0IgghBBCNsQ+ZkUAjHlXWRG7ootZ\nEb69K5oRq/L/ffeuiFx473uJUviwKroxHvBS//aq+nrqzr/9yNkEr4o3XHOmYn5c4tlb/vODLAcY\nBMUx2nZj9CaEZi2kI9dpdubu1YtTZzi8/VdP3HatqH5w32VGfPfYjRnGxIOP3XHffnEvuJZnP90U\nEZG7917pyEJ9oKrCCvVMczNQvZ9NwuvCx9FnUyB/Y1l7YEFDwhoebUXUl+1zYzXr7YfNtDePWBaF\nB3bL2C1hFNiPY3TcMbNhDqkxH2Qcrmd+Xc2hPJzjmes322fMkeySscG+gBnxzPU1votzI2bEvHtQ\nI2ui3YxY1JRYFWZF7J59yoYghBBCSHegEUEIIYQQQgghhBBCCCGEkI1xvcocCSGEkB2wz2bEtli3\nPUDWR1VorsFZL9ie9NQGeO9nERH5/smd4Ph64u5lgip/tRRQpX3cdy+7v/lXj4N28yu8u7zUd8IP\nbruy80nu2ip1TAnMg4F73sqJ1uSc6350rcZBFnm3/Dtvv2jtN7s1DdvVym28j72UxZ5v5Ad4M0K3\nI9uiRuYFSopWLS2K5iAkZt3uX7yt2HHoYdVsiJk8DZvBUIbH+XbxGC+aARE5zloI6C9Vi8XfQ28l\nhDkk1nCw22Hz1EYhaZuXuRaJyfGoTfaCzwhZ9DkyJoI1HzZlQuwD/z97b/YkuZFm+zkQS9bG2lis\nJtls9q6h5o7N1Wp60h8uM73JTC8a3TujO33V0+qeZm9cqorFIllLZgQWPfh3HOEH+BKIzIjMyMzz\neyAIwOHucDiQWVXn+FEuxeVw2s8N7zsuhBBCiItHjgghhBBCCCGEEEIIIYQQQuwNOSKEEEII0eOy\nsiHkjDg84HCAswG5BD/75HkIIYSjeZSc/7efRGfDf/1zzJSAJhpOiJm5DqC+/up3MU/hx599Has1\nRes26lU4EZinD2Imw7MQMxqa2smrqOI8x9lOjR77+NffPw0hhPDRL15M7lNWv7VbTww+wGvXktOB\nVfGpHLZY69/aS+JgUsKPMeaE2Iak+PfyKEYcCb0nhnrIQYAcDTgfYDyDQ6JZDG+9dj1SvzCx2YHR\nc0K0eX/c+82f3dQ/ncGRkU0tZ6xT362PR9/EgidPpj3gYnY+h0NvDqTjh6lUrxs4og7H2XGIfRJC\nCCGE2BY5IoQQQgghhBBCCCGEEEIIsTfkiBBCCCEuiH1kRYw5F65qLoWcEZfPv/zpx/F/3uZzDAps\nOCF+95cf5edJVQ8nxOJ13G/m5hKwcp4TYm771YbrwXNAVOQ48MrB3ZFcHpYFAUdBdc9k7uaI+OAn\n38bj9TTtztPH34cQQvj6xf3YjlPu+xd3Qwgh3H54HA/YkMHhANU+lOz1bdvewn5eM9T05YocEQY7\nPbhjcEL4qv28nowRUTs+UT2nA6n1U1t1vp+yGCgLYsY5GifUro1hSy4TzpToteMAJ8RYRkU3Rja3\nyjwbYltKqhd6+Hbo0+89P7rXMuVmDKvrO0fOdur7m5wNwRxqVsTsPDanC0S5DkIIIcT1RI4IIYQQ\nQgghhBBCCCGEEELsDTkihBBCXHlY9X9Z+QZT4f7ts/+HPhZjXJYzYmGq0fXEtf0vAlb977sduAY4\nZ+CzX/8thBDCnfk6uy45ISzQoDyOF8zfxsNwRFR3rJhNTShf52dQwHpOCDgYHtx5F0II4Ztv78Vb\nWds8OrHrqjz34vEn31k98fiqzucdr8/O6mK0+6MnuTNiWzA2MGJgzKo7efu9TAiiINcBnlEzn6Zc\n77kVTnlE3NbYNZ4zoVeOzk91QjRL29Kno8ynbeeU4MwHtMs5G5y7AMeCtcMOkI0Ekux4cjiUcOXk\n7XDOR3KSDH0KR14ddntg7O58EU+8+7CxvlAf4erAdSNZEZhfcEacNRuiPGcmhdg/+NbNZ4frXJCr\nQgghhDg8DudP10IIIYQQQgghhBBCCCGEuHZcbZmkEEKIG8XUvIPz5iJctIvgqrsWLgJlRlwcyIZo\n4YhYRnWy54T4737+1xBCCP/8x0/idZRXMDsebuc8037MCVHb+VevopUguTvYCWE8+Dg6GNbVzOqz\nvvfWvCeFbRnr85wR5XxYkXv/yZusPWQ3YEys2lCbqh9OiJTx4AjGO0V7fn+s4p/h/pMqf7g+7/op\n5pyeQ8JxEgBW7XOmAzsZcDxlQcDAY2PWFvnxMWcGHBCc/QB4vvIYdDkctqX6NzwNeXlyUnjuBnfM\nN8p7z2dsDFJn2f1xTlo86ysqffO/A2JfyMUghBBCXG+u6K+FQgghhBBCW8UPVwAAIABJREFUCCGE\nEEIIIYS4CkiCKYQQ4mA5r7PhotqVo+HiuErOiEPp41n7MbsV34Nf//hZCKHvhGD+/tMvQwgh/Obz\nj0MIIRSmuodCfI01+5F/cDsqX09qvD+xvW2yIl58H7Mf3rsdbRdwQkDJDCdEu5plfUrAiWCOCjgU\nQGNdeffqVnb84dMfsnY8Z8Sj995m/WKOfziyhvLjaYxukRPCISneeX8krwHq/5adEZSHwJ/Eopnm\nihiCsxh6bTt9TeVsKOB8AJjmPSeF1x4enU3r+Tvbr/KxbuYF7dv12HoOhiLvp+dS6MnCuD7qb3KO\nDDghtgXP//bXsZGUFZGcEflYwO1UoA+FzU/KhuD9XVMWciiIyPysk/8AgJMPyA0ihBDiJiBHhBBC\nCCGEEEIIIYQQQggh9oYknEIIIcQ5YQeFHBL75yo5I84L5yHsm//407+d6/qULWEg56AllX9jjoik\nCh15lKweDSGExmTivUyItWUvrOPxwvIqkJ0AhwG2cEKgvsaU39XL6ISYHVs9Jlj9/u3DEEII93/2\nyvp2ujNilJRvYP3C/hH2h7Mf2lmuDO9lQzhwfsPU7m6W2/Yz57UBhX/PrVH3y4Yw4Axw2oETgh0G\nqT0butlJ3M6P44HZKm7rZfKXoOa4h2cz3zwaehkYKfvBtt5b3Hu9nYJTnSPesSltH72IB06ecBYK\n9QHtkDPivJSzw3Q6nCcrAm6rUmr3G8/Qz7Ch83JGCCGEuM7IESGEEEIIIYQQQgghhBBCiL0hyaYQ\nQoiD4bIyIYQQZ+ef//hJCGFjXXhTNVf3oyS9vk0q/qOo9vzJz56HEEI4mk3PhqhSBkTcwrkAxfLd\ne1He/v2Lu7EvcEJYuSRfN2Xz0YNYviIXR/UiOiEW30fJ9+zYLrcuVndiRd/99UEIIYQHn3w32vch\nbr0X23+3vh3vZxkbKOZQ39Ma/eyMIIF2lzsAJ4Xdf8iLu9kRY4/AUeUP1cmk7AZzKjSL4XKcIZGO\n87065o9UzukPzhf046asW2cfbhpyRiAjwu4r/fiCM4KzHZzcjrH78Bwg25hu2F3CdfHzXLzKD6we\nxwoKOIkwow7UwSB84B7b2rXlUNWxvvnsYlX8Y9kQh+AqGHNAjF13CPcghBBC7Bo5IoQQQgghhBBC\nCCGEEEIIsTfkiBBCCLE34HDwMhOuqwNi7L6FuE40x3GeF1Wev/DzX38VQgjh8789CSGE8MnHL0MI\n2zkgxrh/L1oV4Ij44VV0FhSW6YBsh7Su/y1r82hETfs2XrB4HffLk/x8p6Y3x8SnZ1OGY93424/f\nhRBCePfytnXUqQ8ZF+ZtSE6PXsVx46nw3f6s7TpW37OLodxwAHg4rouU0VAPn0/7zhCkco6cavL5\nkPenOioGz2MfGRKgtvL8p6mUu2Hj09qYJeeD45Tg68fOp/2BLA0e41Snk0fh5U/gPJxBqGf15PSH\nX4xkKbSm+C7oGwCH01hWBLJcyonZFLvKabhOWRG7dkacF6n/hRBCiJuBHBFCCCGEEEIIIYQQQggh\nhNgbkmoKIYTYO9fV+TCGnBH7o2pm44XOwXpoEfwrxrZjVG15z//ypx+HEDonRFobH6aDeZz//+Fn\nX+yl/RA6NW9tzgTIyFOGAvrkXJ8U1Xb5+ss7IYQQFsf59c2R1WOfsvkxKoibb//wKIQQwpNffXNq\nP0OJe2TZPjkjXt3KK2iG1fpTSc4I2ge9vARzRmB4eCoV1fS+DCn2Qxh3QPTq93ItPHionfYxFtVt\n5Ink58uKsiHIZYKzyIwIzo+71umPdx/JpeC8xpvj6mZ+cNsMvbNeufXjeFNwOyEHxrOtIC9m40je\nP9zbDXRGiJsDXB5TsyLkChFCCHETuPp/yhZCCCGEEEIIIYQQQgghxMEiiaYQQoi9ASfATXVEADkj\ndse+nRBiOv/xp38LIYTwn3//aTxwEvUtv/z1lyGEEOaXuPZ4W+Yq9qRCpy4lZXSDLIl4XbOMh/nL\nBSdEiyyFEUnPfBbrr+q8YKekdpwRD2NDUHyffBctGVDdswsFgvN2lt83bg+OB1c5jwwNyzOAI6I3\nXuvu/1MWAtqemGmQjm/pgPDKbfZpk/po+DjoORucnITayY7AHOHy7XK7dvk442VpbI77tnX22kju\nj/w6/MhavpgP1rd+GDvXLq0CckogKwIOiW7fio04I6ZylZwR5217126MQ8uKGGPs58ohugrGnBGH\n2GchhBBiX8gRIYQQQgghhBBCCCGEEEKIvSFpphBCCCEOnotyQtzEbIjz8t//6s8hhBBW9eW5VX74\n/vakcsiOgJaYnQl3Pn4dQgjh7Vf3QgghlMemFrbMCCjE6wdxW92L13M2BJwQTMq0oHnWqaNzZwT2\nSnNIpOwIUu8XvTX5cXx4v2dSo6wAr54S+QlV1/bsJG7hQGgn/ukitTXR+TBVsJ1cHaTu9+idd7Ic\nUjnHOeFRsPnljLATYrNfnCORXB6zrkxWR51vU11O+dpcHpwxsnwZL1g9MacD2p8POyG2ZWpWxFnZ\nlTPiMlBOxdVDzgchhBBCjgghhBBCCCGEEEIIIYQQQuwROSKEEEKIC0JZEdsjJ4SYb6pIodB2tDQt\nFNQ11vVnp0BUDz/+4IcQQgjrKs6vnqr4dpSKN3ACrGI5vLrIB6jvxALPnz3I6g2mNIczgrMi0nrs\nJd9Hkx1umlyej+yIdy+jA2T5/czu0+4OSnY4HlgJT5kRrHxH3gJU9+xagPuhnW38v9Uxfxe3Fcwp\n3ivlOB16qn0uR+p9ABU/zuMecJz3GZzvZUqQM6KXJWFTizMyUn8nCtU9x8SokwMxIYtTynB+he3j\nnksaU8z3VKfNkzQfkVGSLoib5bN4YvXULpjR4JyTqc6IbbMi0nWXkNtwKG6My86GkEtAbIKf95WT\npyGEEOLqoy+8EEIIIYQQQgghhBBCCCH2hiSZQggh9g47AI56C5QLkXPROQfXgbOOWbUjN8hyFuXV\nZ82KmJsy9yz9SWvRV8NZCS3WrJ/nimVWI0PRXC7ivdTL2JfWjsNhUa5C1h4U2y+fvxdCCOHB+6+z\nekedEV2HqOfD2RHzV7kTAsDpgE8sPr3YT/2mjIDZOj/PpHKr1urtxhlOA6joOTOCnQXoC+pMjoYR\nBwSfTw4Kp5wn9C6reA+Ld3Yvpt5vbQu3i9dfVFuQM4Lh4zyte/frOStOyYbA+Z7zgcaU8y3YRZHa\ndsY0zRs4JubD9S5fxBMn5owoFvGClu91z7kGl+mMuOjMhsvOivDycHbeziU7N8R+mcsZI4QQNwY5\nIoQQQgghhBBCCCGEEEIIsTfkiBBCCCHEpXNZDoh9ZkPMbQF2uTt2D9YVh7PAIzkhyny/q4f2TXUL\ntTsU3aGMzzCp46EMf1tm9X70ixdZ/5jaFNesIvYcEjObOjAqvP7zw3gc2RcjeQvcX4BsgJQNYQ0g\nOwDw6wEnRNG0oT4yJfZJS9egDC7K2+o5GJzshzEBNJwNaK+7Lh6fkbsD9zJbm+sl3au5XZAr4owZ\nXAbeJ4NzNVAOrz9nVKRyI84JDzxDdhsM1dWSK6W1PwHWyHxwTIrsnBlbtr0lZ8TqaRzkYj5Nrd9a\nA4Xz/kzNikjlL8kZIcZRNoQQQghxM5EjQgghhBBCCCGEEEIIIYQQe0OOCCGEEOKCQUYGZ2dcJ+QC\nEJdBUn6bYjppbqE6NwdEL5vBgFMBiuy2zt/RzXX5Q+gcB+xM8GAVsOeQ8Lj/s1chhBBe//ZR7M88\nz6wAvdtDf8nxkE7Tmv+9bIEenYy/c0kM15GuIBeGW65pnePDPamX+T6cEPNj1JPnWnCmRb1gR0Ve\nH7sK2pHPdirnZEekbIxzysGSw2Sg7YSVQW5Hcmkc5cXSPY3kayS3COYLZ0VsCdwcRcnHr64zYtvM\nhl25L86aFVG3Tl7NCBeVDSGuN5wNUY3ZroQQQlx59KUXQgghhBBCCCGEEEIIIcTeuL5STCGEEAfH\nUeksRC2uLFfV+bCLbIh93Xu1Zd+27ce29U9lVV/8XOgcDKZ+t0yHFlob++Qgw+HpR9FRwCpg1APH\nwoePvw8hhPDFFx+GEEJYvsrzA1L7b+1/7Bl89W8fhBBCePTLb0MIIRzNq6zes1LTMzv6Vezfye/v\nhxBCKFe5K2Gqm4DPs/rfq8fLdQhhIAvhjE4ILuc5FXAdZ0UgQ4LPo39wQlS38uP8eqT9ia/NmBPi\nrPTq3ehPGiOanyWNHe6ltv36tlPnyPzBj/I0NuyoSU6hIus851ngve0ft8sOxBkhdse238L5lk4N\ncTVgJwQflzNCCCGuL/rCCyGEEEIIIYQQQgghhBBib8gRIYQQYm/IATHMVcyGuKrOB+Y8ToixMdiX\n02DbfvTK77hfu3Y+jPVvijIyrTEPpTOU1su4LZe5HN1bDx2ZDVDtwkmxvnd6FkPnFCgG66+de6jq\n0+8NTgis/76u4tindeWr4XyD1Dw5GDA8vQwI6kbKwKBMieQamIUwOxl2IqQMBJomntPAz2SAy6Md\nPI/PaH0Uyy3exHJeNgQyISorX93O+z3mhOidL0b2d/TaefVsjkrqqv3IRSZE5wqJ+92PniKru5cZ\ngWfD8wbP1topcT13BK1gntl7UWD+eBkUW+YbgH07I3aV43ARbZw1K+LQ2NYJcV7XmbgYPCeEEEKI\nm4McEUIIIYQQQgghhBBCCCGE2BtXT5IphBDiYJED4vpxXZwQ58EbA1byH8pY7coBsa/Mh106NFgF\nmxTV8zw74sn7P4QQ+tkQY/z4s69DCCH89fdPQwghlMd2/bHJ31MGxbT+sgOCsx/S8SaX17MTYv35\nvdgPNzchbqGM94Tf7KDwXAqpvCniobSPZXNnRFLfL4avhVMi7achGA5X4NwKAKeDVx7n4ZhImRC3\nczeAi9Nu6q0NgZcJ0esX3Xcvl4POp3ZGym0+q/TcV7Fzi7fmCLLn1d27uUSWsXyDOtgFMuZggHOG\nMiOwXb6IL8bqCawaV1ulfx2Z+i0Ec/7oTG1nohJemRDXk22dEMqKEEKI64u+7EIIIYQQQgghhBBC\nCCGE2BtyRAghhDgzckBcPw5F1b9rzpoNsTkeYw6Iz797FELoOwl+8ejlYN3nVX4eevYD2FU/h3IW\nnn33XgghhMLUk+UcZaLy+vGD11l5Vv966l5u66NfvNi6vyH4GRCc/cDA+YDz2D9+cTsWuBP7PX8d\nn1kvG8LgTAjAzgfs45POqnwo45EZ0ZbFxjW5yh0OicK5Nw+eJpxvUC+Lwfa8673zPccBOTU4wmf0\nNYULYOR22cGQHBXczxFnxNAz7mV+oGtV7ohAholXp5eTAXdLb4w5I4XyO1qq1xsiOJcK5xm2Nv+L\nEVX1dciKOC9XPRtCXC/OmwkhZ4QQQlw/9EUXQgghhBBCCCGEEEIIIcTekCNCCCHEZOSAOHyuq6Ph\nkOAxhuL/6d2ovv/zq+iMOFnHX7N+89WHIYROdQs17d998Hz/nR3g0J0PqT5SQM7KJjkV/vZVHOPW\nHAfFLM+EaKp4/NmXD+Nxy4z46MNXO+2jx7ZOCM8B8e5ldEAkFTyus/3527idrUJWrpf1wCp6R6TK\na/xzuSFlvOd8cLMVavQ5V27D8QBHQD0ztfo6b7vLJbDr7cfS/CRX/2ObsisWw9elvARkWqDfnpB3\nJDti6msAB0W75evI4v2i6pwsnWMlbjGmKS9juZ1LBfTuaWyMRugcENup9/fljNgl53UkHKrrQtkQ\nYlvO64YYqkvOCCGEuProSy6EEEIIIYQQQgghhBBCiL0hR4QQQohR5ITYDSe8+LjYO2fNhtgESn/P\nCQGHQUVq93odjzdVrkKeLevsOrCc0aLwO+KqOiCYuinDn79+HEIIoX0b36ViFa9pl1Et2cxNaW1j\njm2Yn00JPobnfGC2dUJg7fpiZVus7U+3wbkD3hr/uAxTmB0PSWVf0XEnK6ItB7IKmtyB0OuDlWcn\nBIBToVoU2XX4bPbbi9vkhFjnx9GP3r3SWNVHtj/2eXacEKPZENRe2lIWBDsj0lhzZgQcIbZfViHM\nTvI8DbC+Uwz2YSzzwbuH1Df8D9/TiFukZ+cgkCnhZUVsy1RnxLZZEWKci3JCTG1HXBy7dEJ4dSsL\nRQghri5yRAghhBBCCCGEEEIIIYQQYm9ImimEEMLlpjghFiY1XW+7WLc4WM7rhDgta8NzQvzl25hH\nUDkOhBaqd8sv+P+efxBCCOHnT76JBfZjiDg3u3Y+pHrPsNZzYy6TQC6TlJ0QoMqPe7vyQUx1PjDb\nOiEqmxvVi1shhBBKc3wU9ilujk5XgfYyHZC3cCs/jmyJhJfnwEr6AXdCp4LPMx64jqT8t/LItfBy\nKbz9+ii2AxdA107+7Nd3ysH6kZfArgAvF6On+p/ogAA9B8WIE2IqBeVChNDdG7fZuUHarO2t26zz\nbRrDOdq3gjymizzDZSwbYswZMTUr4jLAO33oiu3ZRCfCttkQyoS4uezTCSGEEOL6IEeEEEIIIYQQ\nQgghhBBCCCH2hhwRQgghelx3J8TCkYPKGXH12UUmBAN3xOffPQohhHC8jjJgT+We1hlfxPlUQClO\nDgCsX56cAba5LKXoITkfPFqs+c6ZCY76uF3G4x9/+s1W7WzrgKgnjt1YJgQoaG6xdKhI48AN4PrB\nyzbqt+LLfB/K9oLdCgOfRM4b8Nrgzy1+vJR1fDa1vR/V7TzPYHaS14MsB6a2TIlmlo9ZdSvfRwZF\n5wqw9jBI+LGHe7axGHNAANdRgfPOj5UxZwTngKRsiJSJ0SY3ipf90Lkmhl0rY9N31DXilF9/ZBOJ\nshfgDhtzRowx1RlxE7IiLtuBcVlZDbXNAWVFXB5yQgghhNgGOSKEEEIIIYQQQgghhBBCCLE35IgQ\nQgiRcZ3cEJ7zQeyPfTgS9sm/vvgohNDlPYC/f/J1ryzKrKu4ZRU7A1VtOW+tvG3r3CHx+ddPQgid\nqveT91+FEEI4mh/Gu7hLR8MQ9Rb1H30RJd/4TGFN+uqeKcJvDSsz4XAYW+98104Idj4AdkJgTtTP\nbscCJm5OynPbLn4w140JzZEB0VgGRHIR4Hobp5ltofKHuwDlsWWXQ0EOi81PKlT1jfOnCdSFO0+5\nAnAalHmeAVT7tQ3B/E3eFx7qlPWA+ih7ITkrVm22z+V7YMwnugB6GRBe3gY5H3pZGvTjKo05zQU8\n+87h0XcstPRMetMazxljRvfGfRkzCfI8SfP1y2i5WT21hwtHguM4YIfEWFZEV+7wMiOuSlbERaNs\niOvDeZwQ3u8VclcIIcT152r9bYEQQgghhBBCCCGEEEIIIa4UckQIIYQIIVwvJwRA1oOcEYcPchh2\nXq9JhH//bXQd/OrRixBCCP/28mkIoXM3YMv9qdoyOSFev45S7XoF9W3ueGAaU/aNrUsO1W9jffjT\nl+9n53/6Ucw3wBrYUAye16mwjRNhiH1lSgAoZzfbqW/ZWB6bGt4yDj7+h6+ya7e9t22dELumehkt\nDRCKl2tz2ySl+enuGzgaODMiORtIVT+2BH7KjEBuyXHbO44MhoJU+DwtauROwMWyyLMgFm/sPaqw\ntXLkeEgOBbo37jO26DMcEehvqndJ1/OPQAhz4WAgZ4TrfNhS0Mtug/QsqT/sPIGjJITW7Qs+q62T\n4+G5MkpylbRwc2BajuSCJCcDjlfmfsH+jMrZt9TLjpjqjBDjzCZO0DH3WKpvooJ9X04IZUNcHIfg\nVqgoZ0kIIcTVQ7/OCSGEEEIIIYQQQgghhBBib8gRIYQQN5zr6IRg1iT79BwSXO4qMjcp674cBufF\n69dUdT2u/+2L6Gjgdbg/e/IsK49y4F+ffZRdV5nbAWv0w73wtlqkfv3BMhzqE+v7ia3vD4X2khdU\nN6WeyXfLea4ibB0lX7OK9UM9HKzcH/8U76G0dn7x4+d2D1uq/rd0MEytf1s3wVSl7ZBc5uk/Pusf\nDFfPAdEjZTDkuQkbmveweSTlHZgyfX4c8uvss57cClbR/F3ebMp5YHcBlPFV7oTIyiCDwRT8DWcw\nYGttQ5Wf1ParvL7F2za7h/Ud9C13RnRjQvfCf6qxMamX8Yr13dwJkfIRUBFnQSCToc7Lp0wL3Af/\nCPWyJWgMU38x1jYecIjgOjg58Ke2hpwnY26ZrM2RHws4j8gc5HZgjLwsCj7OeRyLl/F/Vk8QWsKO\nh9wJcVZnxCFlRZw1G6I8gL5P4bKdCJfd/k3iEJwQAH1R9ooQQlxdDuxPYUIIIYQQQgghhBBCCCGE\nuE7IESGEEDeUm+CE8Lis7AiM+Qkv9r0HDs0Zwf1gdf7YeQaZDo3JrZsmquP+y1fR8fDLJzEL4tOH\n34YQQvjDiyfZ9biOgTMC7Z9U81Adx+fVrk1tC7WuabLbnpPBKrPsiObIVO517nRIQNmH4xVU77Zv\na8H/9KcxK4LV/95YbetoePHNe4PnHz1+E0II4duXdwePT12rGQrGusr7NXXN8rOwL+dDfcZ8jLGx\nakkpjjlQ0tzCJ2Rmn/HaVP74pLHpBN2t7BHidZutqQPIV7Djs1V+/eani4fAcwDAbdHLI6A6+Xr8\niJq/s3nT5I4GdkAkFwgcDMlBkF9Xk/uDDBE9p0aqn/I14GAoaQyTuwT9o/tJ90nXI8uCy+F4Oxu+\nj6L1cyng2mjxibHjLbk7POoj5wQ7I3gu9PIsnHqSe2zYCXFomRH7dCucte5dK8MnO9am1rej77sc\nEBfPITkhhBBCXB/kiBBCCCGEEEIIIYQQQgghxN6QI0IIIcSN5TpkQhw6ntMBx3/3MjoVGlLvw8mA\n8n/59mEIIYTVytwJVL4h5d5xnf+KU1Wnay8aOv/7L2Muw0dPXoWPPnwVQgjhyy8foTHbmlp9lW9T\nnctc1ZtU7fly/6GdQ2Jt5e26n/38q/weJjof2AUwpt5/+eKedSTvPxwc33x1P3a3ys9/s7qf7d99\n/DaE0D1LVvg2pNzF+TrEudBT9l6sYWmvdGNh+SIkNEU2RLkmVw3OI49hle8jGyLlFyDzgfMNblF/\n7Di7A5AhkVT4cFJYTkFyN5RdH9L0wmtBanvOn2g2lPwhhFCFfF6xI6At6b2i+pPg2l55TH84CLz8\ngp6KP72PaMjKw2kRzkdyk3COBz1ruFtAcoisqR4861n3/wU993QPqAvTa5GXh4uEs0ca6gvoZUXQ\nHODjbMBM7jD0i34Ud46H050Ru6Yszl//tg6Fi3JC7Mp5NtWZICeEOA24LSr7fUHuCyGEuDnIESGE\nEEIIIYQQQgghhBBCiL0hR4QQQghxjTmUrIgxJwSvnY9Mh5QBYQra2vIYWipf0m80J+aI+OJZdFLA\n8QBFLbblvMnO85rlXzx7GB4+fJsdg4OhSCpf0kqnhdjtLDIfbIscgLbM6/v5z78OQ3z1XXQewNXx\nwcPX2Xk4INj5gDH9DtkOjuOhB9THTb7fV+kjwyL2/+0za4czLtCe3eedD19b/9gJ0WT7pzk59pkr\nsUvSvYxkQxT8LHDrlLfAav71PaddyjmYncQtMiKKN3l9AHkMXSZEMdhOWXfGHjgIWlLbs0oepKeN\nT5Kp7tdlPu+8x89OC1b1p6Gb5+f5+l5mBbVbVsPHe1t2gCDDAp8SbxrDwWG79RF909boR/5MUn83\nXhvuE27ZazvVDSeEExnVUn6H64RwSA4LehZtyL8d+BYVlJEy5oTwsiLgmCt2pLKe6lrYxqVwaE6I\nsWyIi3JCyAFx+VykO0FOCCGEuHnIESGEEEIIIYQQQgghhBBCiL0hR4QQQtwwjnixZnHh4BmcNNf3\nxzCcD5xrgP11ZefruG1IEV6aMhbH4VhojuOYpbwCU4c2piYuF1HiCydETeWTivsoV+G1cExQDkJz\nq1Oz37kfF+R/8+Jufi36YAptrPPP6/0ndbHd20e/epHV42VAJFeI9fGrZw9iPSMqe0iye84H57qe\nkHXMCQEpd4G1+HOlbucEsQOmeOc8kK4BtgFEhhTAY7kXU9nWWbGrdsH6izshhBDKnvOBXCk4TQr1\npNq3LZwPySWA8yhPTgleEn99l+a/zen6yK43JX3bhi4PoMjL9BwQPGSk2k99QUYDK/8594Dq8RwK\niJbAPXKORi8jAtkNlJeA/TT2UPdX+fnkRqB2MGboR++ZkoMC5QGcEqkcZV0UYcOExU4FZ3qna/m8\nc/3UfAzv9WB3VWFfYt8ZgQu3c0hcFtfZCTEVOSGuPnInCCGEuAjkiBBCCCGEEEIIIYQQQgghxN64\nvlJMIYQQQlwY6xGlOGdUQBVfnZgjosqvR3YDlNwp42FlitgaeQvxfDvHPlwAKO84HVBv0ebnk9PC\n6q3K5N5An5Pw08lCYFV7ckws44nHP3sVQgjhpIq/hrFylZWhcIVADdxYTkZyeUAhvWjs3uFkGMkl\n4AwI6v8YcHx0/TDFLvpjx1mFv/48Bg7MfhqDChq4Ypp4/aL32+l4h7ZVCwPP4bCvDAq4a06+iK4a\nzobo5WpgLEmpjgyI0hwQ83d5O1DPr+9buXW+Rfn6lpXHe0Sqe5YsbeYycFZD6/ypwv00WPnGU+/T\n8YadReycQHvUV7cfp9xbCJ0DIvWT6pvBhbJCi/Ye3Mvbw3XzYyv/Lj/eHA3XzzTk5ICDpKw7t0h6\npakubzrzc4Yrg/MzvHp6+RkjDorFq1jx+mFt5YadEV0H8e2zerfMitg3N8EJMeZUkBPi6iMnhBBC\niItEjgghhBBCCCGEEEIIIYQQQuwNOSKEEEKIS+ImZEUA5B+soH6n3IN2RY4JSHOxRvixuQBqckQs\nzNEwwxrjprE4yZ0QSbU/ozXH4bRgV8CGCrWXY0FOCM5C4LqaW7GuB59+F0Lo8jEWc170PlKbRPz5\nqyitrm1s2rfmoHgLR4TVf2SOi8HazpD9ABwhblLxY+3+FDtDDo3eebvMxotzQYJNAbgGoATezJTw\nVMU19f2sDolU346zIHpwXkBS++f9Llf53IITonCifpARAcwkE2pAp3z/AAAgAElEQVRSyKe8A1LA\ne64GkNwCiw3FP7soqCzg/ALOKeBci1Se+jTWTuGUS9CY907D4cEZEpz5QK/vmKOh+z7k27H2e+NI\nboVmo0/ospt/QXkbLTkhevfu5HCc2zCUrh/OjEgek5nngNhtVkTJYSkHwGU5IaY6FOSEuPrICSGE\nEOIykCNCCCGEEEIIIYQQQgghhBB74/pLMIUQQgixdxamjhzLivjzq0chhBDqNSTYw6uJI+egSNkQ\ntoUTwpTj2BZ8HRTyJiqFEwIOiuR4QLkS9cXd24/jQu5VVSblfvXyVla354RImRDmhLj9yQ8hhBBO\nVlFuDFV/aeVrswJAhQ9lK9wiyIQozeUxO8Y9hhxbIL1d0glyC3BeRg8S4vZzDLDNnRHpOJVL3YAK\nn57ZIYJncFZnBDs04Op4+0V0ufRU+47KH1s4ITrnhO0u8+u6DIg4xtVtmyuk5odzwqtnNFNgoM9c\nZmzoxrIjkuPijH9a4fyCRMowsd11Xj5lNyzy8uyggDNhfde+D7ft+Dwvz/Wn69EPjkXwxtXaYydG\n0fb7yPeSynIftnRCAH4mvT6jHW8upHZaaneaC4CdEPvKhvAcWPvMhtiXE2K83YtRx19FJ4TnHKia\n4YmH8t75y0IOCCGEEIeA/iFCCCGEEFvzX15+FEII4bNHX4cQun+IYP7t5dMQQvcXs1iWqOXiJS+x\nlC+DhCVq+C9i019k1fk/DqRyWNpjTn/h1eR/mY/jR49iomy1EZ6Nf4Do/0Wk9bkXSp3/IwmWdkrY\nX06ssdxO+osn+8tv+4eJBv9Yg/qtvcbqndmYlLhnC8Nt7GbSP7qkdvNdb+klwP8A4ZajJZh4TGv7\nS+76Vj4+JcK/d/iXI2ddksn7y7yp/yDB//AA8A8QP7y6PXielwpDADg/K4xh+gtm/ocDLGeEf3Cw\nuYB/kGjLwvatnPMPH6NL8HjB0gN1gd5yT/yX1Cg3tuyP9xf0flfyck79vFwRqPEPC7iO6sEzQIh0\n+kv/idO514/hldrcf4AAzawLrm4X+TUe/Jyn/gOE+xqMzAv3H4Uc0pJM9q802y7BxN+U0lniyVuS\n6azB0uepY9//AOEtybTtPwycdUmmQ/gHiF3/RfxYfdv+A8a+0D9ACCGEOCQO65/phRBCCCGEEEII\nIYQQQghxrZAjQgghhBBb89aWGfqnLz8NIYTwP3305xBCCH/8/v0QQggndfwVA0rxxlEA9pwM2Cb1\nMi2DBGUt1Pe2ZFNvqSV2QND5LiQ43189jzLo+ePj1Ef8f/XiVt55iNfhgEAbvIwJlnZ6dZTd4/xJ\nrHc+t6WaTLX+5uWd/J5REZTYt3InRrnCaSxbFfdZbc/LVaXjde64SIHgpGL2lOQcMgxSP2hbPI7r\nAsEVM3VZlV2olJnzLmsy1QmRQtERRk2h6x5p/mIM6TiPeUPLGiVsH8sHcYB4rz1im2HiupEfn54/\nt+k4JtLSSU54dW9ZK55/E4OVk8OB9vl86h65Drzltfh6rgdLNoECod8IkuZlk8jlsOmQGKs7XYPP\nIPqOOkecEB6dwyyvxyt33gz4sSWZprqrzuqEOKvjapd17iqUelu2dUJctgPikF0A3LddOyQO+d6F\nEEIIOSKEEEIIIYQQQgghhBBCCLE35IgQQgghxLmpTGILJwQcE8iE8BwRSfVvQcxYI99TM4+FovbC\nT6kdDm5O9ZpSHRkRzYZSHU6InnodBgPHCYG+sZMCzgQcr7ivgz3cuAcsn06OBNbVFnyvjhOiFz7d\nq8fpkFMuKbWhNJ94vadSnsJlBbwyvXne0ryrcicEsk+8OeSp8wua9zWZdUKZZ03UFFoMkqNi7D1D\n+VOU716+BBwNjaOKd+8RZqcVtY36+J4pJJrvycu7QBZE4VzX6ye5U8byD+BoqCm8uqws6+Uk9TCr\nh50f7Ig4LQcivYv0rWAnxNhzHnMyjN27d3z2Op6o7zvBFzvCy4bYln2EU1+2E2KqY+GqOCGusgvg\nKvddCCGE2BY5IoQQQgghhBBCCCGEEEIIsTfkiBBCCCEuiZPm6v4Y/l8+/PPg8c8efR1CCOE/ff1J\nCCGEqo7S23odt8gFaGwfCvHyxLZVrsrvFLm5YjzlH4wpxh2Vf6LMnRDoZ/3KFmxvNnIqnOyEdknK\nVquzgNUhSafzYsmBkOqje3JEknCNeJkNCUdwy/eR1uRvTlfnewpzdkKUEDmb0htr30N933xzlNVT\n2bOcf/h2sL+nKYwvywnhZUOAH76NOR/t2pw+x9hi7J25hPmLipycDs8p1NhYIyJgblEnrJ6v7pza\n/QQ+UexuGGo7OWDmedl07SrvI3/+WnqWnZoeOTGn19dzQaWb6Pd5s/2xLAkeg1S/4wZwnRc27TtH\nBK6nb56X8xGGjw/1m7Mb2AHhOct6dfWe0XCfuJ7Ut5HrOMOnmOhguOhsiKlcZo7NTXFCyD0ghBBC\nXG3kiBBCCCGEEEIIIYQQQgghxN64ulJMIYQQQhws//jBlyGEEP7vLz/JjlcnJM22bAaogBuW8ZNk\nollY+eSIGM6ECOQ26NVH6mtkQsAJUVR9xTtyJODiQB+Sk4CtD1ifvabyNSu8cydCUkTPaSyw6ymz\ne6rmkcwHckJwVkS3Vn3eD3Zy9JwR2Bb5/szyEGbmCsCzqO7sZh3309hXJgRANgScEi0yIMwJMXtr\nWzgUyEXQjenwWGzbfeQOYI7NKSuiMXV+vRy+vpfbkN7PCf1xsiImw++8zb8S85lcGSn/YJ5/CzCm\nqK71vgVezsHYeWp/zKmQytlY1kf5+5mcIiP5DV79U65pvKwQcjUlB43jvBl1Rkzsz/z72FD1kB+q\nfSvLfDsVzoY4a/7Mto6rXda5ayfEVA7NCSEHhBBCCHG9kCNCCCGEEEIIIYQQQgghhBB7Q44IIYS4\nIRwNLe4txI751xcfhRBCWNWz0wsWUP/HbX2vzk9DobuKmgk4FOAm6EwHrNZ32nOcEHBOVC9uZfVv\nuhi8PIqea8JREafy5IRAHkaRXs2433TxFOdiqrA1jTWLkrG2/YQ16UPo1M8oD/U1ys8RAQEFu93n\n8rPXcb/NFeynsWtVMQOVccU3PxHMjXKF7BMq4I1pRe6YMnfZuOp7qgc5HV0OgZ0fyQrw8g0A1Pyb\nrgRXJU/HOZOhh9cn2zZLqOPzMe05eeCMsG9DMx8Zu5Ep4uV1jMGOkDQO9gwKcqPguOdaSP2ZMKXH\nnAvuO+y4Mabec2Li9dvWO5YNwU6Ii2JKNsRlOyF27VzYlxNCDgghhBDieiNHhBBCCCGEEEIIIYQQ\nQggh9oYcEUIIIYTYOVgz3yOt+W2L12O/hap5ZdJcrBHOinCIS+Eq8BwPI06Is9Byn6DMhpqdMh44\nG6KHo9DuCWOhdocTI62Nj/aGy29LT0U/P32sPKV7UuevbLu28sgpMEX4IalitlUZY57/8Op2CCGE\nW/fsZukZJTX8Lezn+SL9PIFpzgiGVfettYcsCIz5aH7DiGOiLcP4gyMnhFdnAvM+5OV65e29aryx\nwH66Hg4JOp84PUsljZVzved8GHMdNE49HshvOC0bYsxt4n0TOLNkzDHD5Xv1OPses+/jJGkerSeV\nZyfEWfGcDFPdC1OcELtmX06IbbMhdo2cEEIIIcTN4JD+7CeEEEIIIYQQQgghhBBCiGuGHBFCCCHE\nBXPS7P/Hb9WMZDQY660X/z6dzx4/i+1bvcd1lL8jM+K3X3wYQgihZTWwrcXfrvNMiHR+BndBfl1y\nGaSICcqQICU5q6V74Lr5gCKW1qLvXQp1u4nicY/NYtilwarjIjkchtW4ZcodwD7K23ZE9XxWvLX1\nW6fdmfUPThHst3gEpsqvbc3/9d/uhhBCWHwcQySa3o0MPaxpSuaaOj22/vpYJkRtz/j7V3dCCJ2D\np4enMIdLBucrmp9ePekZnF4ec+HkUX4czwpzZ3ZstbBjA46JkB/v5ma+fxqeap7bbCeqz1NWC+4d\nY+nkWrj5CFxvL2cGbpTTr/d6PZZFkcZ6JAvCq7dgc9es+y56bXffLtvmkTzjjgqnLzwG7GaZXM+B\nSOP24YTYd56N3+5+HAa7rldOCCGEEOJmcSC/9gkhhBBCCCGEEEIIIYQQ4joiR4QQQtwQoMI/goxa\n3Eg8B8RUB0XvOqc+dkL821dPQwghNMhRgAMCjgZnrXxe552F8HBElOthx0NSHUMBDMluT2E+vJ+1\nhT5Ww33lbAesYY8R6jsK8nvyFOSsZi5TJkXc8pLhrWU6dNkNpytyeQ38s6qT+f6husbUao7iFnkF\nJble6j9FZ0T46Ru7IHYEa+nnKuS8kzhXk0PhvA6JUVrPyZA7H3r5AskhYfvViGradUZY/VDxO7/Z\n4zbhTlm8zc+v71n1lCEBeL+Zt26+BV/jne/mC+ZrPmbp/bAN3nXPHTVGup77NXFswbaZEYleO04/\ne9+4vHxyq8zy80N1cF9KimBgVwa+KS05X87smOjNI+s9O4Vm01wDu+Iish127YSYmg2xrWPhsrIh\n5IQQZwHzddcOIiGEEBeHHBFCCCGEEEIIIYQQQgghhNgbckQIIYQQF8RFZEN4sBOCHRCes8ErD377\nIjodWIlemRPiJ4+/DSGE0FTmgIByu0aeAmVCQAQ9y5WzCcctAJIy3CpiFTWYui79Zp29BdEdJXRP\nEY174+yHEcU0t5/WlrdplASyULvbuv9QtW893dz2Q9Y+3x87OVJmxZzOc3PIulgNq/krmzMlnl2z\nWZE3eLa3I4cEn2fSPKb5tXw/Poz1yrIk4GJBf5pYvssPgdsGN2D1e+p9MFVdjznyzqqxsa9vxW1l\nW+R2pDwSKOQHFPF4h7zMlK7c8PFkzoNa38aggZsk3YTVgzGm/Bf3/XSuH3VGgKlOhxG863h/WyfS\nphMiuSY4Q4fa8HIpevfkOCMYr8/uvYzcY/lt7GDzCNaN8zklSg7UOCe7dFLs2gmxLds6IXaVDSEn\nhDgLl+XcEUIIsXvkiBBCCCGEEEIIIYQQQgghxN6QI0IIIYS4RrBzwXNCsAPCczx4Tol/e/5BPF8P\nX1ev4/F//1ss1ybVcp4JkVTHtA58UjGHXPXcW2N8acr3ssiu768nTw6LEXHdaSprT0Hdy5ugNfBH\nGe0T2sm3vlr59LXuz0vPGWG/VdaeiroZPt45R+z6P1hgwc9iVgTmUlM2jjuiqxyOh8bOs4IZDglv\n/fYxBwRI8zk5cJxyab7SfPfYViKUcgNOtwOkaBRTxDemoIcTgt0oKb8BTo6Ql2vmRbq3sSHz3Uf2\nLMydURyjT/lYNR+exHqc+dt6OR04X9N5qofnY/l2ttG7/jco1cvZKk59br9G3ARj9QxlRHjOiPPm\nvoxlhrCDx62vyr/jGDse2+SMeBwnR7GFg+2ymZoNsft2Jzos5IQQVwg5IYQQ4vohR4QQQgghhBBC\nCCGEEEIIIfaGHBFCCHHDQE7BUVqgW+yby8yGAGNOCD6+cpwOKPeTR6/s+rj/p+ePQwghtLbfQPlK\nToh2bZkQnoofind2SBg9ZwSrlD0HBDiPE4KV1WiT2mDHRNfX09ssyDGQ1OiOg6CgVzg5Em4Nt+Mx\nXcE9ogz36kEGAOUNeP3Atnp2O+4/PrESZWisssa01H2HxLAzAkzNjmC4POZzl8EQ949/OAohhLC8\nZzL/eZ4FUUyUAHkK9MniUHZKmCOjumPdXebFvbyCgs63A+2zS4lV72O5Ftgiv2KRcjKoHoehPsUT\neX5H8QNCS6g7t2MFow6E1A969iP0yk10ZZ2F8Xtw+uDNS7yTXj1O+9587V3ntH+oImjPabUNu8qG\n2NapICeEuErICSGEENcXOSKEEEIIIYQQQgghhBBCCLE3Ll+iKYQQQogLh50Q7ID4w4snIYQQ1qv4\nq0JhqsaffvAyhBDC51/H821SSjsOCLgIoPqvhh0OKduhyo+DlpTlkxnJaeip/KdU72UcjLgvyorv\ncbgeOAd6jgdvTXlyQjTz4WwIT528tdqeGHNslM79MtyPchX73aS5FUJRmqPGysIhETB9HWcEOGt2\nBF8fCnLkpOri+ZN1fBgpPwDODQ4UIHpOCKj5kXky4r7pYcfrZaynLPP3gOdaMc/bwdwqVnl9bdn2\nVPX8bk8FhrH6ttXjOYd6GSzFqcfTM3o97/ocBvII3o3osnbtYMD8XtPhxXDx9D7BLDYwzN476M4X\n57wHfyt67eDAWEZE6qfNs3meM5Leky0pz3jdITLmhNi6PjkhxBVCTgghhLj+yBEhhBBCCCGEEEII\nIYQQQoi9IUeEEELcUJQVsX8uMxtibRJTOB884ISAM+KPL96Px1/HReQ50+H3rz8MIYRQLuusnrYu\nbZs7HwpSjBcNn4/HoX5nFT2Uss0ylwH3NHMwNJTDbgCPKU6I5Nage+BXx1MNo1y5yrc91TIrzPMh\n3hgL22LJ+3l+vmv3dCcEw/3oq6zZzULlaJ+dEOzwSPXYfSZFOCnsixcxd6F5vNq4Cg/MMknIGYFb\n8bIgpjojetkQxnsPY6DBD8f3rK/Wos3jsIz1JpcHZTb0HD+Oojs5Iei855DwqO/HQZ6/igOU3jee\nG6RQRz8bnmMb7U513HTl8+PNLRtryjZJbgz3Hic6JO7ZxHtrzgjnGfRq536nVls6wufpOno/ZhZ5\nMrdMDMx/OEKqW/nxElub/slBcuT3Pbkn/CI5jrOmnfgjjO8xON+kXnm4aMrhMb1qPofTHFVj2RBT\nnRBTHQuXpSyXE0KcBTkhhBDi5iBHhBBCCCGEEEIIIYQQQggh9oYcEUIIccO5Ds6IMefBVb63XVGR\nQyLtsxPiTZSlJycEZzqYkrpdmZaBHQj4H3ZCmIK7PMmdEl6eAD8ydiOgX6zg7pS4E7W0u5Dcom/O\naS8rgdXlvX3HGYHpDmdEaofHiAWGTj97WRHbrok/cX369FsnrZHvZgLQ3Jg96264fhrl4eW8yfuM\nJiY6HlLmg4HynhOC68H8SxkKmE9wcyCvgxTfmJ/l/XhhyjV4tczrwVXkjPAcEqDnsGCXANwI5Krh\nDBacLwOcEd37xo4Y7zn23R5WFxTgj+IYwMlQv43foPm9tR3PK26bPCdk40x2/WSBLVkZOJcj1V7m\nxcfeD699zPvFm9jObB23J3D3zPL2kpNqTf0YeM/ghEiuJOpqa3Xjm4JtO8sLDjlfNvGeecvfruHL\ne/W0ZZcDE/ftGUz9ju+IqVkxl8Gushsuq34hhpATQgghbh5yRAghhBBCCCGEEEIIIYQQYm/IESGE\nECKEkLsKLstBsK9MhcvMajhU/v3bxyGEEN6dRAV2ckJUuUahJUV3cWxK89VwuVR+IklBC7G7I5Fo\nWE1vU7TG9VByQxFMambOkEjrw49JdsOGQrrJ1ezNfPjipHavhhXVLWU8MJyPka7vZUKcvta9u8b9\ntjKUnuqZ2mU3Atb2H8umILm2tx49j3sIIcxeWIbJh8dx2+RZEeX89CyI8wLF9N3Hb0MIIbxp7sa+\nrk5/FouHeX83Smz8d+Ookx3B83asXGHn6w+irB4mm/nXcRybZZuVT/XCKYE5v9goB1eSkymSni/e\nvfsm6WcXVVK/W5/ICVFa3xtktdhxHkPXCTHibplKLwdhy+vcLTIgKhuHNneTdeNr35U13DjWQO33\nCW3AfNLW+fHkvuAoIcd14ea9OHkgPTiLgsciOXVG8juSKyZe2ND3Ps0ZG8uyyOfc2HfhIp0RU7Mh\nJte3pcJ8V04IZUOIIeR4EEIIwcgRIYQQQgghhBBCCCGEEEKIvSGJqBBCiB6eg+CsTgk5Eg4PKEKr\nE0hiocKFyt3UufMmO99lNIRsv57lzomuIXIFQGENFSivz36CteiHMyBKU5x3WRKknOW8g54qP19r\nf4ojors2r9PLoegU/FbO7rl25B89hTTuzQTkzcKuT06K4Xa7rAdSnI84JjxnxVmdE6Prx09UT6c5\ntqGS7mWCHAh3n7wJIYTw9ll0RgSal7cfvwshdArtMIvS9E7JbQrvJyfZdfXL+NDh8OGsiDR/nXmc\nnq2juq4/hkPDKngdJ2uawymTIuTtlqF7Z0nF3rJ74uEq72rPEYFt3seSXB5TnREJnveUoZJwXFRu\nxgrn2zR8frg7THoPbGxThgwcUYu8HMY35T8gl+QUOAMi/SjGmNu9z6wuzqdgZ4TrBiGnWjpMOTZj\nmROBnBHls6N4+CneC/725RWyQ2KqMyLVNpIpw9dNcVrNzqkIn+pYuKnKc9x3dWg/FPaA94z53m/q\nXBBCCHE1uP4/sYUQQgghhBBCCCGEEEIIcWlIoiqEEGIycjZcPealSWJ5bW6ihRqYnBBQLzfHUSo7\nW+XOiKRQX+QZEUVF5ZKyekNRvUFB657DMZGyH5JbAPLg3BmBdtIUHVHOu8py0PbLJmh9c4YdBvza\nuOusU/1wQkBV3KbsBbt3UzHPSBld3UG7GAPcDDse8mfRhuFy7pzhdeMnOiG89eQxHnw/m+VqquOy\ngVIayuk7T6Mz4s2Lu4PlWZEdsDZ/zyERmT+JjoXq5a14YMTB03PpYKxfL/LD9/NBTi4Fcy8038N+\nk9eD65rvl132Q8pvyZ0QeLdKxwHR7Z/uhBijeu1kQ4A9OSGmkt4T7MMhtTS1/Tp2ZH6cf7NW7+UZ\nLPgezE6w3WKc8J1M3728b9jO6B1m14b3Do+Nvfu+Ot/Szg1l/W3ybeeqGXZIbOuMSN11HBL7yIwY\ny4bYtxPiqmdD8H3z/iE4JC7KmSAHhBBCiKvE5f+EFkIIIYQQQgghhBBCCCHEtUXSViGEEOIasjCF\n3NpRBf7D0y9DCCFUT6LU9f/6/U/jCShG51h/PVcBQ+2MWutbduJ2VHQnbXGVOybc7AhWz7OKH+6C\nKr8PVt+3vJ9X23c+9Arku2lN/o3+sXo43avnjMC9987blUlJnnehZzyiR9jlY1ht5KxI+1if3cu0\ncFTNPQU3Z0+MOEK4Hq897q/nqNgHZ137HcdZOc31pMyI726HEEI4/iGudX/nwbu8HVJml/NcuQ2W\nH7zL9lffmEPCyf/wmD8+HjyOnAUoyQtzRlTspLDvQlO23XO0+Yo7KSnngpnqgOit59+Sup2dSqkg\nOxzO54Tw2DYbImU9mLugPrKtucwW5oxYvrb7s0yI6m5+PcD7P1u3oZmdPg+6a+376swbuC4CZUk0\nTX6e++I6z/h7TN+OsdipNKb0PrQ01nBIdMenOSMYL0PCc0YMnd+la+I0LtsJcVlMve950VyYK0KO\nhItjae7BxRWfx0IIcZORI0IIIYQQQgghhBBCCCGEEHtDjgghhBDiBsBZEVAK4vj//Ks/hRBC+Jcv\nP47FsJa4KT6redLrx/0o9A7FAnJgO29K024tcXY2kBMCKvk5qdug2EXWRIVMCM45wPV22Ug2xHnw\nsg/YGVFWuZsCDgc4Crot6j1dzczOB3YcIEMC7WC/3388G2eMHMeCm+1Q5sptzyjRPZu8/7xldTTf\nz6ZTpDXV/XZ+gN3hOSNAUkXTe9GppU9Xc6axdOq/9SQ6JN69jC9ip853RsTadfMXTGXK+7NH9UDh\nEJbvH4eT76KkH+r6xf3TnRCpKyMZEN76/YC/Td2J/NuytROCruOsl55jZ8QRNJqZQu9Fvx44qoaf\nKbIlynUIRW1ZJcu8bGnHm5Bn6jTz4TFG/gTGCA4I5HqUa7s+NUBboveNsQuRA8Nj2TpjsfgyfgRW\nT2MHCmRWuK9R8ufE8uSMYDynBObits6I87Avx8JNcUKAfboh5IC4OJb8s0kIIcSVR44IIYQQQggh\nhBBCCCGEEELsDTkihBBCiBsEOyMSJk34H3/8lxBCCBUWMidYZfj7b56EEEL41fsvQgghrOp43R9e\nxOM/fvQqhBDC51/H/cbcAk/e/yErD0Xp98/uhRA21P/IanDcAElVvDzdCcEqaA9kSXTbEMr1cJ4F\nOwTghJjZMvzlKu9jdafI+upmN6DPjhK6TQ4LqyblcISs3oKcGWMCUXY6jK377l3fLoePj9HwfVk9\nqyedVQIOnMtyQjBjCuj+2vWRqevIz8rT1aDvUL+NSBFOV9FD4c399pwXp3H74XDehLf+PvdhW9J1\nyFiYminiNJecOKv8G9O912POieH6xmjxaV3n7XQODOofZ8CYU6Kou/2ujkDb/DjcS00e/dHVXed9\nbNnFhHKYZrPh8kzKtSEnhPdt4Nya5BqBWwX9IGdEQe8VjncZKMPtNfRxZ4fEVGeEd2yXSI1/OnJC\nXG3kgBBCiOuPHBFCCCGEEEIIIYQQQgghhNgbckQIIYQQ15iFKfjWpBKEMwLOB1b6zWeWBcHXhfy6\nz548yxs0heovn7zIrsca3T/+8PsQQgi1KUvrlmT8RHIyJEVvfh4q4maRq09dFwPXT8rx5LDYqI/V\nuSzj8NaEdxXUW4oqvXvuyUlojfsx8aarSj6jTGWqELWXQeE4KKqHUUad+umsa3/I3H/4NoQQwg/f\nxyyHXawhn9X/5E2s/5WFthzbC8jKcLh2rH1Wa485L5j6lFwTL9/ivPfO9VWvo6x/qjsmuavYMQS1\nvpNRsu374GVDMMlNQPWXVXw2y9d5Pfwd6hwTbc+9AecDSI6IBbXpfEPS99b6CAdE7xvHMRxUL7Ig\nEnBqBOc4f1ur7HRYvIwPZf24yuoZz4xIPczrR7s0t+CQGMszOQv42cqMZTicVY1/07IhxNVCDggh\nhLh5yBEhhBBCCCGEEEIIIYQQQoi9IUeEEEIIcY1gpwNYkKoQDon5mBLaESN6qk44IKBifFtFCe4v\nfxQdEieV/eoBxayVe/nNgxBCX0XcJkWqSW/nw0pvkBwU5A7gHIbSyU8YUtSOOSF4jOplXieyDpq0\n3vqwY4EVz906744DAvTqoTHbEal/Ka/j9PrHlOH99exjv+v7yDGBG2Z7VXJV2VrumD+0tvu+YcdB\nY/357uXdEEIIDx6/GSy3PU1WH+pvV5TxYu2gvfOOQ1mO5/KrZ+cAACAASURBVEpMdVl47gr0lc+v\nXi+HiifSvISKv5czQ86IlBVhW8tuaI6sf/Q+e9kQ7nwfc0bApQAzizkiFq9jh+fmcqlulVl5ZEVs\nXjOn2A7OhuA+suvDy3hA7Ec7HB2USPdO7pKe48EZy1S8ovJ4drY/f2Vj8jDOsZ4zAj8HRvNIhh0S\nDGdFXAX27YSY77v+A3JCHFJfripyPgghhAByRAghhBBCCCGEEEIIIYQQYm/IESGEEEJcQzxnBGCH\nBDPZMWH0siZM6rCc2f9YNZWpKKs6Hn/+/H4IoVOwJu0zuwGgyCWVc+ut4U3Xe24Dj6H6vWvQ5wZu\nDfvtyhu5zqWR94nXqOcW2NUxxrblu+u2K+8pxNN+lR9nNXZ9K26hbsba/WD+ap6uS+6JeZ4NkhTR\nlAOwfnoSQghhtox1z2HI2ZGad7Kj4S39yv3YqW9L5e2MXu/HT15n+y9f3Mv2vYyIbambYnQMxxwT\nYNu+JLV7r0EUiJuCyzlxNHjvFm+tP+/itjZHRLAhhLPJczK5/QnD16XrbWqsb8f+zt/Zdh0vWJzg\nvYhWiPW9mbUbyzXz7v/B/JicY/Yda2Z5Obg9qttwnA3fU895NjJN2VXCePX530Arh/PWTzgj4KJq\nyUUFhwScEThflMP7Hts4Ic7vchLnAb+DcL7VlGsOHc9VsKpHrEqXhFwQQgghhpAjQgghhBBCCCGE\nEEIIIYQQe0OOCCGEEOIaM+aM8IBjYj2iKuR6oUL0jtemkv7bV4/iiTZX6HZriJuiFQ4G/MYC4SKJ\nnZPzYYbrhlXTcFSgmTap9ItsG6qNekjJ762jPhV2KuCey8pReo8xJiuhjIue2hlj5inNjV5WxkgG\nBJ/HGvxuBsZIu0UVOsU2q82dvo8pnS8M9GMkV+Os1M57yg6JQ8TLiABwVrx7ZdYZLs/vLsFZLtif\nWZ7CwoZo+Z3lLJzE7drex9byFaody7fS/LepAVfC6l5p/YuTffYu2gBm5oxAf+olHBFF+n9Q1nin\nbd8cCrOThsqRQ+LI6kQuBv3Y4H1+99EO58m05LTo5WnwN8Rzn/W+19ZvZP7Mcwdc1/E8M2KqE2KM\n09wP27qbRGRX7oSr4HLYlWPgJjkP8Fz1fgkhxNVFjgghhBBCCCGEEEIIIYQQQuwNOSKEEEKIGwBn\nPWzrkGDGnBDYx9rFlama//I8Lo7fknq5mLWDx5OCNuRWhqSIdxSpyenguAyQLZCU9E2blU85DqGr\nPymum1x12/URlQ82OYpnPuGci64/E+ul3/Y4V2PMCdH1Y6Rd5zjWfU9qaZs6yIbAM51/H0/gGeC6\nYnPdeByzNfFRp9tnPEebfw3k501u69j3uu6PP/lur/VfRTwnBGdLHL82eT6p4JMzwnHHuGp7I2VD\n/GDbt7kLq7B8g14955RxFSRehlgfLoSTBza37dt5y743pTki5m9ix5t5zIyol0XKW8E7WpNlDPc6\nP3b6hHueKKzGO8wOiWaR1+d9s4pq+Libw0Pn+Ru0tKyIFXJm0M+RzAg4IoqJz9TLRdlF9grcgjOn\nDfxMnar0H6vvrMx3XF+q9xoq3G+SU2FfXMd5IYQQNx05IoQQQgghhBBCCCGEEEIIsTfkiBBCCCFu\nIOyQAGNOiamZEMkJQRJXXtsfylTOimB6SllHecriObgJJqv+58P5DSGE5IQYa7OHJ/sYuS45Ic74\n25qXBbFveP13Vj/DCdHQWHuZE9l9eLkSnur9qyhrr5+u8hMLm/8X7IzwuEnrXbMTgh0QDefGWDiD\n54RITiV+dMiCWJkrht4HuAig4kfOAsohs6G28957uOtHh3ZWd03RfhK/pUdrynewHJvZqg3wAKDv\n6d6QJ2HujupWkV3rOheS88gO8DsJR0TK9MnLuc4G1Ou9416OzYgbDOcX5qpa36+z/qdnR9/AzoG3\nnTMC3wnPIRFCl9tyk97ts3AdFO9yPuyO6zAfhBBCnI4cEUIIIYQQQgghhBBCCCGE2BtyRAghhBBi\nlKmZEP3zcfsny4Zo1nk9bcpomOZY6Nb6bqddT0pdLzMi5T0MuAbgpuD1yV3h3lSZB/etV18/p2II\nT328a8bW3PfKJ5W2rYFf3bECvfsfzgfZHE8+xlkf3jPp1oKX2vI8eLkOuwBOiAbvG5wSzenveLka\ndkikbJJVvu0cOXF7/ATHbQ7ZdXBKcO5Bb/7z+7ejKYZ213diA2UVOzw7bqyfcU7Pj5ue6wNOiLW9\na8ndYc6I+ih/X5C10nMz4R12jHJj34Sz5tqgXOfsoH5VVD+1A2cEWD+OF7RrKwjn05w7tJ0zgqmb\nYtRVVdWW9TC7Wt+iXWdDXAflu5wQ5+c6zAMhhBDbIUeEEEIIIYQQQgghhBBCCCH2hhwRQgghhJgM\nOyF+8+xHIYQQ/u6D5yGEEP7fr+N+VdG679ifmAnB9NaHN2eDWwtUqRMV3F5+wtRsiUk4yv/Uh966\n6Ob6oL6xQyOVnw3nYXSCw+LUcmclOUVwgJ0QUKAvcdzar3JVtrfue7ftxqF+EuXtpSmam1dLqyPP\nC8A9ls+iFLz58DjuS8l64XjZEJ4TouV5MfL+9EBGRHzkYf42P13ds2L2pyE4dcoT29oUmR/n/cB8\nrJf5/nmdEOmTSA6i9R2bw3X+wsMZMTtuQ7O0sZwPv/sFpjv+5Id3Cl0n18doRkNN9XNkCzsoyHmR\nvg2LvD/cntePNCfK4eP8LVm8nGfHT57CUoE5Z9fhm4u5aR0vOc8Gt2XlTsuK2Jba6pw5deJn8FQl\n+Vh9YjpyQJwdOR+EEEIAOSKEEEIIIYQQQgghhBBCCLE35IgQQgghRGJuMmDOhGDghIAi9DdffRj3\neX13BgrUETW+K55zsyBOX5d7l0zNZOB7gJKbVcct991RA7v9IcfEVGfEVLx14Lt+U7bDMr+uW7ee\nnB2O4p17OWUcZo+jQ6L+3iTWyAJBJXBfUF1QMo+t6y4uDqjTW3I/lavhiYCMCOQGpOO2P7NsiPm7\nuIUKP2WVOMBJsXgT5wacEycP7RuGP0WNOHrOKgROzgvLdVjZNxVuhHJl73nTprIpC2JRZH0GvW8P\n3UM74jRI9bR0nM6n+IP18D3hx8uYA4PbR3+5fEnPPtB9pLlh+8sXc2qnzc635pzC9wKunQCnBBwe\nZd7QkDOitkZmUoSHEK6WMv5QHBBnHTPO7roo9vmM8TuqHD5CCHF1kSNCCCGEEEIIIYQQQgghhBB7\nQ44IIYQQV54x9f5FA8XWdQRjDScEsiB4XXeQsiEoE6LgTAbbb9ekkUg5Avna/yC5AVhagfKsUp6Y\nj3Ce/ITOoUB1YP1x9HVkHXYvt8I77vbHUzU7gsJtRZQt35fD5LX9e9el/0ttlS+j7aJ9Ehf0x9ru\n84dxv3oVZeQtlNBQYNtuyYvab8muHBTnVUnXF6R45XyH8zCWDQFac7fgXRxzHMD50GvPnn3KQcAn\ng1T0PJSzk1hw8TZuV/eKrJ6UQXGSb1HP+l5eXzHxxwI+lWmKkjMCmRGF3VjRtCkbgp0QON6SA6FY\nhaxcS9kRPbfS8GfczYdhRwI/gx5TX4ORb1jPYcFzhsoVyRFB+SVfxGAbjE9t39zyg/iQZ4v4MLd5\n+/hdrSlHaexbwArwbbMiDgVW6R9i//fthLioex5r5yo8C3Cdf68WQoibhhwRQgghhBBCCCGEEEII\nIYTYG3JECCHEFWZZVuGIZJ0nvCj0NWRXDoh/evaTEEIIr99FqSlU0kfLuLD1//Cjv4YQQlgcsEqM\nn/958ebPP3/94xBCCOsqjj2cEA1tWTILh0N5jPO2Wdj/3M5Vbt2a47kToqioXs8V4DkhkE/AClqo\nrR0HRNEMXEPqcF5fHPrgfnbDcJc9xbdXz1nx1mEfVRWDMfmK85rweI1lQ5zWTqqrMRfGt9EZ0TyK\nEu+Z5VP0lNrcVZunaAqKf2VFXDzspmrZfQEzFM0L77PMeQApE+K2nbdHjAyIhjJNZpZrwJ/W5C7o\nOSfidv6utXZO+ZYMXD+WJZHKz3EfcD3YHK7a3jsFd8hsledbtJ4DwHEusAgZY9dzRuDbNhsuV9+m\neiq6N6Jc5ffjjRGO87Mac7twBkXPCVLlOSCYI+1X5pT4+G3cmqVitjFQ+Bk5Ri9PotxvlkTdoK+H\n9fuMl2Nwker86+KA2JZD7VcIckAIIcR1Ro4IIYQQQgghhBBCCCGEEELsjesvmxVCiBvGmEL+Ojgm\nWCnFDonfffdBCCGE/+bB81PrOZrHsVov4vVQRUPR+E9ffpqVZ9U0FI1wToB1vd2/85/HcbHrfIy1\nqRP/5XnugGAnRPXOJLNvbJ3yetixkNbkP7axW9la5rfi+Rr5AvO8/Fi2Q2ovRUfkjgco59szKtxd\ntf5QGTgXAjsjqHw97AxIsDOB/m+qM2K0nYm4151TRNlTtk+st2j6a7/jXnlkjh5Fufvqm1vZ+ea5\nLbRva72D+Z4VsWDXamfUt++sCHzztsmKwPexaU7vW5EU2uauej3f3O3eKzuc3FHOO5pU7evT+4fM\nBqjzxxwJrMrH9eV6+HpW86eMijNmRaR6TJ2PH+VlVaQ+zI9jYTghNnoTQgihJsfQ2LRpKFsi1UaO\nh57j4PRqR90tbXrWdoB+bXHdXFa+dJ49nBvshPDGoen9PLHjf70Ttz99E0Lofj427TxlnpT0vcY3\nhl1X2D/rt2HbrIhDdUZcBvtyQhyy0+DQkRNCCCGuP3JECCGEEEIIIYQQQgghhBBib1x9WawQQoit\nuI6OCTggTurY98ocCf/ZVP3/8OTLwes+e/R1CKFT/4dq2BkBF0BLyl4oef+PP/4yOz9bREXX0SKO\nNZTB//hB7Mdvvvkwq+c/vP/VpPvcBd56zL//9kkIIYTj9SI7nsZiFce2PjFJqTkhZpb9wBkOzTJ3\nOqS1tmkN7uLExtZRZ8LR0GVFxONlhXXSrZ9Y85wV8w25FQAvQ+8JGMsBtSzyCXIhd9dncm949FS9\nnpOCnRektD0132ILxtaq9+AMCK/9yaL9kfHI2sSzGKnS6yNyYfbNvtZ95/r37Yw4D50S3MYC31Pk\nEniWGHJGFAHZCHBZWTXvbAuHgvOjLuUK2D4eTUkOiXqZz5myiu3P3+bH2X2A+ubIoKDcBajy2Rnh\n5iA4zgj0YvNHdk3firLOMyKQK5H64kwXzrPhfc9x0OQ/PjYyF2xbD9eXyuM8OyYo96N3XeNs6fVO\nOSTsUnGm3gzlrf1urM1h8qe7cZv6WYTqR5ZXc2QOiEX+oPH7gJdHM58Nd2bMycA/28dU+TX9PjPm\nkKis/PwaOCl25YSQ8+H8yAEhhBA3j8P904oQQgghhBBCCCGEEEIIIa48V0/2KoQQYq8cldWVdEWE\nEMJJZap9Uj//p68/CSGE8PdPogMC6v9VncsyS1qrvML68w0cEcG2drw2OeVJ/u/61Tz2Y72M29Jc\nAf/n619k5aCUPK5JRrpDPAcE88n977J9jM2xuUw+P45j1iKjwcql6ue5uhOq/eSIWDa2nzsooOYv\n39kYL7AevG2r09X2s2M4IeL19S1rp8xdCWfNjGjLvsOB1xfv9W3ECcE0C+7rtOt25YToXTe1/Wa4\nv177vGY+YIX3eTIokmsJild8C+yxF+/HbAhWKe+LfTshLpqzZEVsy/xhfEbVK8vzGHHcJJcUMhvM\nIQHVvvsJPBk+DJV8dRf75rw4MUfEu1O7n8qV69x9AFH/mDPCw3VGbEwxOB4w4ZumyI6zE4LHkP9k\n2HNO2LZ23Bs9dxjfw4ijoaVMCu9bO5bngX6n/A7O2cCPXHLQ9SCHBXd/yGk1ex4rLz6NjXvZEB5w\ndJ7VGZHq2VN2REVOiqvkkDivE0IOiLMj54MQQgggR4QQQgghhBBCCCGEEEIIIfbG1ZS8CiGE2CvI\nkbhqzoi0TrotOt6Ycu9X778IIXRqNqj9kQGRrh9R+3ZOCPt3/Hfx+mJN5bFu/Qz12b5lTRR2vDG1\n/3/6809CCCF89vF4VsQfXjyxJiiTwdTxv/zRi9E6ToPVjsjdePr4+xBCCF9VD2J7aBfKWMe5AKBE\nbc2xUKxMZWwZE+XKHBGmJm5ukaMiYN+eAdYc58wJdj7kAuFOwcriVF6X3aY+5zEM0RNJnpI3kbWB\nsSNng7dWfLf++Y7V6L129ly/bV2l+qnZEMPXtk3+oFdf345V2bzCdUkhDifOfPj54ttRXiG170Uy\nK9tzuyLS2FJWBOg5IygjpV7a0Vv2LKF+T3kIcYt3Gar/o+/M2XAct5Vdv74bt9Vtu/4ovw7ZEDNz\nUqQ5Nc/HobDvPTIluFxPlY/rzuiMKKvu3jmvovuWtNb33BmBNpPi3+ppkImAfStf25hgTLsfBNTH\nM7qcts6RwXXoDzkcUAz3mRwemBsj31omjZOND2dOtBu/MpX0s6N7X/JOpt87rBL8HgNnBGCHxLbO\niFTPgWdH7NJ1oCyIy2Ef7gd+BrPzWCeFEEJcKnJECCGEEEIIIYQQQgghhBBib1wtqasQQogLBc4I\ncKgOCSj2oBj87PGzuG+qrKqJUlsoA5tm+N/hoVjE+SZlRMTzyQlhmRBwQqTsACggUX5t5RanK7fg\njHizXmb3M8R6Nc/6xPzuLz8KIYTw4w+/DSGMK/m8DAmoIr96Fh0QT97/IbZbU8YDbUHKVWCnBK2T\njXyEmZUrUf7YzkOBa+UaZE3MrPxJXn9pmRFNLqLuOSNcBoZj2yyG2Qpjkh9vSamNHIzJKmCQR5lc\nOqwq5vvpqaIbOj6CV29Wl82r5kV88GVyyFDbE/GcEFPXeb8skits60l1hrYm5kVgLJuk4M6v6zkj\nUgNxw84I5AjAHVXdyd1SyIgAmAOL13adM1/rW3GbsiHsPU3qd6jo34bBdrp6yaHRDG97120pIt6s\nB04IdnkA3MNsbWr7ozw7Ao6JVF/KzyC3B6J7bKzYieDd41CfJzHRNTXmrsLYJscH+uPU4/2qM3Z/\noQqhLG1s/3AvhBDCyacxVKTLpcF22Bnh4WVHbO1gOKND4qKcEdtmW3jswg1x3Z0Qh5zbcN3HXggh\nbjIH8kdYIYQQQgghhBBCCCGEEEJcRw5T2iqEEOIgObTsiP/67YchhE5Bx2sqg998E8shE4KzIRbz\nOju+Oo7311RY+NkcD3A4NOSAcATBSXFrivoilad1vK0+5DHMBuwOXzx7ONinfqOx7r/+7f0QQggf\nPP0uhNCpKL/86uGp1z/+IDofXj5/Lzv+3JwR6V6GW+/lGMDxAGeEp1xtliiXXw8la3JYkPo6rf/O\nanu4VJAB0ObHezj92izPCuqe8r/KtzNSTEN7CBXumLa+t375WQWCWzookK+RnsFpjoQwQSXslE/O\nIe9zQv0umn4f6g+ixDmtvv4kSrnhjAhVPl/hnOD123fN7EDUnN++jPJ+fGPeexjV2XAh7MLhsXNn\nBKDsCLzLKU8GjqJ0Pm4Ly46AKwbOBaj4i/dyN0BLn1SeY/zjDvXMKKuBP6m9HIUR2tnw8W2mEpwQ\n9TLvDBwTcATMkaViY1AfOblIzjvPzgJ+h9kNdmb43ke+Qdw+nB0s/OZ9fvZelgT3K337Qlc+ZYLY\nPKz/GidM86lZaZznnN6fMs+K2JZ9OSSuijPiPE6I66bCP2THg8d1ewZCCCH6yBEhhBBCCCGEEEII\nIYQQQoi9cRiSViGEEFeKQ3FGQDH36wfPQwghLExJtYZDwrIhoDSE46Ex+WNZ5Ipc5C/U5ogoVvm/\n10NNnVT2qSN2nvMQRoBKGXJMODo2nR0vvonOhGZlMkpSHXvrjYNnXz7MDziqZdRzjAyK9bBWoVOz\n546DpKKHY6LGOuncnu2PiN5aUmq7TgZ0Z5k7H7qKpl1/2pL6ntqWKcn10ZCatqeu3TIroYfndOD+\nNvmYp/m7L+GhsyZ+ipyhfkOz6TojBkjr/n+5HDxffxyl0MXLeL55Gvdn83gh3n12BnjZEFcFfC+Q\n5dLSvO/cB/n+ITgj+n20Z4UTD+IzXMGSkHJApr3bUKiv79txm2/zN7Z9Z+3DTHMvr2csNyDt24Rm\ngx4cDzjP18FB5WVPQKXfdcjKrzdU+HOnbMi/Tdw2xsbLz2DHQHJvON9Nzw3iZkc4wm3PicD18QyA\nYwPbXnsjeR0Qkjf4sUvjma5nZ0UzMMY2P+EoqGoMXm3H4373HljOS8iPwyHBzk/OjGDYIQE8Z8OY\nE+GinBEXqYy/Lip8OSCEEEJcBeSIEEIIIYQQQgghhBBCCCHE3pAjQgghxJm5LGfE//Py49ju+vR2\nf/vyaQihU956QKHYQGFL2zY5IWL5YjG8ljkrul2VPTInSK0M98LDpz90VZsTIrktWP3LClFqqqB7\nT04DHMZ+A1dGbG9xP6qPq5e3snb6GQ2kpIZydpav356cEli7+9iU2KT+TS4C29Yhryc1gwyImeOE\noHJnpWhC7957CmhnHfPqjnWN1eYXJAPx+ssRJGOK8q7c1HbzbUnZGV3uh+3jmXNFE1wq/I719pEt\nckHZEJfF8+eQ+Q9/W+4+juvTIw8H7MIJwZzVGTHeJ+v7+8chhBBWz2/HfWdeJmU6ZbqkbwwyHqYK\nch2nUzmSh8AKec5IgWof78f8XX7/67v2Hrd5uZR/ULWhrG1+kyC6uo0MCPSFvt9OXkFDx1NmguOo\ncPNjehUPl5tKGiuMpZPPUYxNa8rp6T2bKivW+95w+6DZLIPniTr/Pc7X+hfv8q4UuRPCtbiV9J44\nDgkw1SlxqM6IbTMizpMNcdU5ZCeEHA9CCCEYOSKEEEIIIYQQQgghhBBCCLE35IgQQghxbo5IErpv\nhwSUeJ89fhZC6LIheuUcmWZJMsZ6HeWfraf2L2mL81Ads7sgDO+n8iNK4VdfRXVz0fTX9++p19mQ\nwM6DFecDxG19K17YLKG2jBtWGaf2vKwFKFIdpXnqj7e2N5SnULqachf6vrJEFkV+fYn7Smuf566V\nzpExLWtiSjnucw9WTKf8jPz6rZl6nbf++o4EiVNV0OlZVVTOcS94rgbP9XDaNUz5gWVDLOKM8rIh\nxphcbsvB9r5RU9t5+V0MMihNdo1vWGGK9pbU0nAfHFIWhteXMScb3i8vmwc/ljhzIV2PnAz75iAn\nAU4mvMe9bxeuD3k5fpRjOQSoB24DiJrhWuBsGbDphEC5emEOs3U8Nj/J52szpzrJ8YAx4ncyuQA9\nwfVUZ4TzLqfjjjODy7ETIY0tfw/gdqE8nl4ODZ1P13vfE8r34AyNrE+oC/+DHI80X+2mFwikyDuD\nb07TOJ0shzu5bZYEZ0iww+GynRH75Kqr9Q/VCXHVx1UIIcR+kSNCCCGEEEIIIYQQQgghhBB7Q44I\nIYQQO2ff2RFQ3t2eRRk9VGEVL2xNsPKWsyHS+urscGBnwzoWcBXiE9XTo6KxpujUvp4jgeuyLZwQ\n87d2Hur09EhQT+w0OyM8pSi3B8cE3B69zAgGrgFTH0O1jGY8lTwcEOxKmB+jn7lzorpja6PfGnZG\n9O6HMycG4DXneUnvThlN7oqdORJy90fax+Twnhn1k+vZGjwD2+VnVpD6GM0XfJ7V1852c/zOaCAY\nxVPl78sJcV7gpEDeB5wQPVfXOTNSDhG4WlKOTRVzbPCNQP5M922wjX1zUlaEOSHMEBfW0VySvhkd\nzjfXtqk0q/K5/IgzokIcD7sVTPQ8f2PbY1PK41t3u+vf3OIHUi5LymnBNfm9eNMWLpFAPxbSd5r7\nSO4nrrc8ya/z3B7sNkvfezgR2AmB/jjTnN1Ybjm6n5T/Uefbzilo++j/RubEWFZU+VUc3PbjOEEb\nvKPogzkHmp4BIu98Q9+m9A2bmCUxliHB7MoZIc7OZTsg8Owr5wexnBBCCCGmIEeEEEIIIYQQQggh\nhBBCCCH2hhwRQggh9sa+nREev/32RyGEEGpTB3dq5/jv7+sqSg85G4KdDmdWX3OOA5SLTa7qH8uK\nCE3oOSHY+cCU5qDAmt8zU8hCSVrReuY9B8CIoK0rTw4FbFfD98Rj2SxjPat53m8u762zDtgZ0W8H\n/abrRp4x7rNoig2167CqODkLzjxfwlbXFzyPAjkjUB2pos/of+hD8mh2RjApt4OeZU+tnLbk+Nih\nbIazIS4rK4GzIeqxbwHx/asYZIAMiJ5LC98Bq/ftKsr93/v4hzP19zLAsxnLikgkN5hdTyr8Lrsl\n34KeEyLN0xGHhJN7sKmSj/XQeTqenFXYXefl4WpI3dvMfUDGz4P824CfA3AUII/C+77iezliLuyr\n/GmsU7n58PHeN4CcD4z3/fccdAl2Xzn3VVD2Q8qC8PKAqP5sDpAjzM3IsXezqXJ3Ew9BYe9BOSNH\nRMPfsNxJwQ4Jdm3BIcHOiPM6G8auP4SsiKui3D8UB8TU40IIIcQU5IgQQgghhBBCCCGEEEIIIcTe\nkCNCCCHE3oEzApzXIfGPj78YPA71GJR4y1nc/9mDb0MIIfz2xdMQQgiNs1A08hhSLgOUsElBO5Iz\n4Cldx3BzGIqN/8/7SEPaU7jymt1QuFZRSB3q27EgOyIKVmZPldFTZkTqHylGa3NCtLS2dk1jWyQ1\nf5Htc4ZFT3UahvfLdf5sPRcDZ3C05YDCmTNDHKXzZJzMCT4PxwM/I87r4ByNlMMx536eMz/AU4KP\nwOvDsxOC73cKuPfqwyjNnlnmyWwRvwHzGb4NEzMfLigbwnNCjLkA4IQI7+KLjfldnlhOApxJUFmb\n2v+HL94LIWw6I3KHyC7Y1t1xVkp8u8hB01P5W95BUvsv2+x84G2voXwX87J779mhNMzYz4Ped8a+\n2UkTfXf4219WG26PGW1vnd4mHhU7BtiZ4DkauBw7L9iRMBXXAWH95RnG2RPJceFkP/RcXZRBkcp7\nv6qcdl8j7oz0M/JZnJjVw/hAC8xn60SBeV3mjgk4JDhTYh/v8hBjWRHi/Fy0E0LPUgghxEUiR4QQ\nQgghhBBCCCGEEEIIIfaGHBFCCCEunH1nR/z6wfMQAiht5QAAIABJREFUQgiV2QCO6yjPhFK3MdU7\nFIZJZpnW1YbeMm7rW3ZinitwOePBVcaPKGa7tfBJ8d72lfxp3X9euxpDSUrN+rZtTQ1c3YHq0jY1\nSUxJTMkOgTFYjY/12tN5uw/0o1tDnPINVuWp9fDa4Li/tE9K4Nlx/oxDac/WxrxZ2Nhveb8hbOGE\noHI99wmVG3Pg9NonJThnRPB2qkq5pxzfVsbitNvMh10p9ZP4sDtFMLYbZV7FBz5/HMNB5nNzQdG6\n45462MuG2LcTgrMhADsgPFfBm5fR0lS+jd+22dt4XcqEQT6CbTv3Sazvwaevsvr3rZ7eJehrQ0Pf\n3WM8Xy9zFxV+zDTJjYXAHHxThzNfWnYQkaI9lUtj6DjtRvINpuYfQN2P7IjZSb6NJ+makc/ZVCdE\nzxlAOQj8bePyU18XOCnSN2x9enl2ZvQccmf8ZnnjBscEHIbsoCiars1i81jYcKYlt6L9DLpjjgf+\n/WKeD1ox8i277Nwb5ipkRRwaF+WEkANCCCHEZSJHhBBCCCGEEEIIIYQQQggh9oYcEUIIIS4NzxnB\nmRJjjDkrfvfySQihrzqGwrqYWV5CmS8onTIjoIDFcVPvt86a5On6pKQleaWjbGQ1frkuek6IcsQZ\nkBShvP6+qS3hPOjuzbpIjoCeCj51ivZJJeyRnA2UT5CcEbPscHcd5x2YQpbXBMf9VuaEwJr43M8Z\nKcUbU4qXVG5TWXzuDAhibE351B7lWUxdi340q2RLZ0Tv/ieupc8ZFb32oAi2Z9U8jdLupCQmJ0Sx\noZxdfvDOqhhWAW/rhJjKVCfE3CZ6VW+n+UmuLfpWvXsZrU3lsamMzQkxfxvPcyZLmt/mFHrvH7/J\n6uf2wHkcEheWDYFnaGO0uB/nTbWKL3+5mll/rF/2TWjgbINCnd6n3jd0ljsdeo9+JDsiOS1YnY/y\nnGNjzxDPFO9PjYyLRXCBI2D2jtqw72Q/HyavOzki8G2wW4drsGanAf3cSf0YuUfvm5NmDj8LHiNH\nMM7fJs4g8spxfxsah/QsJwrVi7b/M4opqY/LF7Hg6klspF1SXhK9k8hGwbfPe2evktvpojhUJ4Cc\nEEIIIW4SckQIIYQQQgghhBBCCCGEEGJvyBEhhBBXmOuibvIcEP/b55+FEDpl3//6yR9CCCH873/8\nu6zcvdtREXs0j/VAhfzZ42dZuYYWfobCuihj+WYZ95s1qeQRq1DlzogeI2v+J2eF89N3yI3AmRBl\nWl/aivD656zS5eyFNWcl+G1v9jnV59w9q/Zxj9D5JcMDnBHHuM4U4EeQ4KJdK4c170fWCkdGBDsh\nproHipWjji67sd0VY2vKM5MdFI6amM+nevDa0Xwcc0h4WRMtncczxhr2SfSZXDhxu3pg7piP4TSy\n8rRGerkxl3fthBhTD581E8JzRoy5B45/MKn6CTIgbGtZJxjb/5+9d3uSJEmv+zwjM6uqe3rul50d\nLG4ECUEACQgPkpEmk0kPMtOb/mA9SUYTjWYyGQkYsMZdCCQI7HV2tme6p7u6KjMjUg/xHY/04/Gl\ne+SlLt3n9zA5EeHh7nHzyOo8xw8PnXjuMC7AcVTrVjgkO+LcTghcO3aJxO12Xyw+60/KxoJx4Hbq\nrtJsiNmK3gOs4ofjDe4xcqpt6dzE5wsrCqp/dhXk2TH958LcDXh+PEdEe5lnKsy61LXH42c2NtC4\nG8d/bL9O94dzoLPblDN6iq6sMF6OJXKov7lN13v14Tg7ej+67aG++f5l7+nnMS9py8a7eN2ccRks\nv+sb7Rb9hvYLZOX02+fLvkKMfctFqqLnrIi3hY2dqLfl++4uckIIIYR4F5EjQgghhBBCCCGEEEII\nIYQQZ0OOCCGEeOQsZ6dXVK29yY3vmNubXmYJJeD/+Y//PIQwKALhcLi2cjdN/1r7sy9+ndTzx598\nE0II4W+//kEIIYRVO/76m5ESNph7AIrYmAkBFfKGlbXYPkvqiZ8XkIKTgtgRMO66ETLlM80Bn6n2\nbZEdENGx4KkmHSfE0D72T/sBtXHsB+pZpOXhcJhRbgD6GefkhhOC1PRDBkTaPs5D7GWsP52nveVy\nhTnJd+uqlm94Kl9nTvkSwzUrFMRt7YkfvQwTNiTxfPBZf/pPviehPsa1W9yky4Na23Z4k7aztee8\ns+eN50LfpeSEqM2COLUTYjGfVh4qf86G2K77T89rgPuds2Didnsen/3Jt5P6A7zsiLvKgZhCfB/Y\n2eqeWOYP8g8w9jztb8BuhUEzpJ84h5WHGO/bOM5bNew6o/JZvoLrYLIMAHt+upgZYce5M5Zt+dlu\n97s+ANwWW6u742wF51nHueWsBS+uqTaPpvS4dZVfTeL4T84Idq/UegeKY+8uBedDtj720e5jcwfO\n7fsCOyFKY5v77FqDPKbBrTV17BL13JXzIbYnB4QQQogHjBwRQgghhBBCCCGEEEIIIYQ4G3JECCGE\nyGCXxX05JP73P/nrZPmW5Jb/9md/FEIIYb2xuZVNTczqM16G0rojp0N409czv4FEsf/Yktugue7L\nZWp6zCmO9ThtrHQNmKs81QMM++8oGh3FvueEgBujlPXA5HN6U+YD+k5Ohqiej/vZdiwHOCVIoYv9\nFmm/IfaEChjAybB5Gqy+tB9RsUsZFZ76lKlSvHpOhwciPvQyISLkmHCdDaiP5333GuZMCDgg6Bpi\nrnTulzf3PTshxpTAh86Hft9OiBLxOWZXFrJXFuTecsCYWOsQKbF73u7bHcHZERjXlx/0Mv/1qwtn\nx/4j5mlEdwlydrZpufn4WMjlGx7Pndwcrz9xPxgqFqlun50Q7c7zhCq8cYxf4XjFz1dpthAye7xc\nmVqXFDsrtlSPV1+Wq2HlLPYjvn+G9wbVWxCeZ/2vdGjUOiK2zYirpNCX0ruJGbJS9gds4Png3BfP\nGfFQOaW6/67zJuSEEEIIIXL0Q4QQQogi+GHivqds4lBr7x/C/sMvfhRCCOFfffnLEMLwxyD+EO9s\nyouN/YCxfdX/i878lf2D1gb/4GPlr9I/7mLg8ir9R5vNM/tD38oP0wLRPxZ6/4Bnq4d/mNiGgDBn\n+od7TOMw9Gn8BwiP2Lf4Dy11IdTD/vY/cQoMOxdOsDPqwQ8S+ZRI4//Yh7BS/sedLHg5pNu53uEf\n1sbPvTtV1Qh87jxinTz1S+W/FdT2tfQj07Af9YN/VMraT/eL9x6d44UF2fJUTLhmLabI4UBY275+\nZu2hexywPuEHiGNDqadyrulM8APE/E36j4txTMI0b84/Xj798lW/+cAfIGrO09RzeaofLrxpouIP\nV3afLZ/1N+KWQ64xJtgY2mZT6NnUOJjK6WX/L/9oDvuFp/0DtN3YdFpxOqTZzn+Hsdp7Tvk5ww9z\nm/ewncYbXHqEIc9D2OLQafoz1MX/6M0/XAwB2fSjB713Yp/j1H9p1/CXZfYP+GjfaTfW64VJox7q\nx5Z+CGd4Sib3R1qn/dIPEKM/pOyZ5m8f/GN8+EU//+AG9XzRD7DNoi/QzK18/ME2DavmHyHv+geJ\n+Yl+/HyM6AcIIYQQwkdTMwkhhBBCCCGEEEIIIYQQ4mzIESGEEKKah+KMAIMil8MX+/7Bhv+3v/pB\nsh50JjWcUej0oEw1JevSwk/XqZx0mCYpDaOOilnUu0kVssP+48r2XXEbh3/ytCHDvtghjOI5G7ht\n3xlh5UjVz1NylKdASh0K3D5AGHWJcvhpnQo7O/7deiuntxr6tD8IvNQH1+0R6+fA8PFrWzw3FOTK\nwNmAetqr9LgWHNZO9wCm0cIn94unxAlrmxoN4az34ISoVQefO9i1uTW1Mz9XRrxfoQW3c/j0i9ch\nhOluhVM7RWramOqQ4KmYePyPCnByRnS2HVM1xQmP+Jj5HNMUTOxsm2F5gQc2fb946nvP/ZU/F9as\nBUovLNwdAdNwGnVPhumc4qFs0NdZUjZwGzaWLF+nwdjwGGB6qJamT4PgGw4MwOaTzA3muMIyZ4Mz\nBpamhipN9ZS5FSY6IErDw6wbjmG4z8b39dpqnO8j21/1L8XWvm9s7L5cfNI7JRZxHE+dEbXcdWj1\n4i10TMgJIYQQQpSRI0IIIYQQQgghhBBCCCGEEGdDjgghhBCTeSjOiP/1934SQgjh//rFPw8hDKHV\nf/HlP4UQQvh/f/67IYQQVq9TOSeUrJjbG2ri9mmqIMRc4Y0pBDEHODIgkHcAJ0S4tE84JzDns4WY\nxjnIm1TFHCcfh5huR+TmOSDisVRmQtSSq+rTPkMh27BiNTojcI5M9Yt6M5XnfudFaS5vj0MVrVPn\n8z4FXu6Ge02dfAxck5LrhWEnRHS3mCo6qqOjqrmv9/bLXgbdfmAOjl/0FSEoNs5xb04Inl8+y+/4\n0uY+t5uKQ6rBmML3bXNC3Hzdn7Q5XCjIhLi044g5Mum4ACdEibtwPtRyrEMir2fcGbG6Scf/zN0F\nmjTsGmAZWRNbdwxJnRgIGM8ccIUxqKOcBXYaZZ/zIX8Fz14MjDdnRLe0zId4X9n2NT5nSXnsH10X\ny3QZ4BTWjtN8bO65WOzfPlRo5b0Qat5/f76z66iIziRbP+U9Ugq8zt+FuTMyhDC4ROhdunneOyWa\nz3vLzKleZW10Hr19qvu7Dq0+FY+tv0IIIcQYckQIIYQQQgghhBBCCCGEEOJsyBEhhBDiYB6KM+J/\n/ur/CyGE8O+//oMQQgjvL3oFK5wQcD7MzJEwM4XfDHN7z0mRaErahucEh6PhivabpQ6L5mY8e8Kl\noNLcW/auIFVvF9I57JnyXNr7z01pf88xcahgcJ9qdSy7wyu7W55xsyO8+jxXSOdtn6YoL54rOCRM\nNb247j+3X/fP1fqHvWz65g9XyW6LX1/srTY6Ib6yuc0vLRMiPndpxw5R8Z/aCXEqYntNf/E6W3z9\nvHdCRJcLcmcKjqinX75K629Odw7vGvSx5IzgrAhve1y2cT6OXav+fbUldf7Fpzd720XWRMyOiO3b\nuf2gf1C2L8mBQQ6JmB0QyMGEekPaL/Rz/az/hCsB9W4XO64JZC2we6odX45OhyfY3vdlvjJnxC1l\nRcAhgT5s0vq8d1nmgopOg3RsLWY8EHwNuZ7aMa7kOIz1oV1ne2gq+swOmSy3ws4R9W1wwNi1+aJ3\nQCzs/vbcYdEhZNvxfB06JkiVf/fonAshhHibkCNCCCGEEEIIIYQQQgghhBBnQ44IIYQQR/NQnBF/\n+dnPkuW5ZTdArAll7P/4R/85KYf5gjdd3/+//foHIYQQbm96ZXe7Sn+3n5GDIjoubuCISBWu3dIU\njvhcQAlr9UFx3+5RAmfK/2mq+1oHQlGtCSgzInMmFNwgXp5BrfCv7Ljw2t2/nLGnHU/Fm2U9xLZs\nnvZFqnbPczn2t5erjP0+TiHWZ98Ou4u03fkbKxjnKu8LrP+gV5Jv7ThaczqEb1NnRPN571SCI2lu\nz9GUTIhYl6P+PZcTYmo2xDyqj5tk/007frHYbXX1UX8Ob77ppeqs1udz5h33Y3BCMIc6I7zMiaun\nvWMH4/mTZ7215/plP7f+1bNVUh9gx0Vjz223teeY5PRedsSheJkR7Z6cBezTXppjbZ3WCedEg3L2\nyt5cpeWQKREzJGy/ucVnbN6z9jBGkDMujo2Ze8vybHAuF+Nj4HA8tfcvcnLS/ZqJLrESnjNiyr4l\nl9OuuyKE4Vzhc/6R3a/0PYSdD+LxIyeEEEKItxE5IoQQQgghhBBCCCGEEEIIcTbkiBBCCPHWcGmy\nzVuTkP5vf/zjEMLgdABrkpAvzDOxMfXZn33x637Zyl1v+om0//7Xn/Xrb/r64YQIpObsLskxMU8V\njRG4Bmh19fzWO3g5AbWK0lonRG0mg5enwP1hV0DRDXBi3GyIA5wQGeyMiEpmHOthavVBKU0ZJgUF\nuVePX3//2WDeeZpnHirpxc96OfX6Kzge7JqSA4IVvF4mxD6OVfvelROiFhzP+x/3Kn2o+DcmVV98\ndHtQfW8Dx2ZGlFwi73/0ZnT7UK5NlmM/0M4c6/vP7XX6ZxXG/fi8z8fV/6UxlsuN5hY4+yADonP+\n4mMX1BrPvN12DWdL4PVgsTCtOSm2+2Nhdtqj8R/96savVbXby9n/2DHSy56IVPSvNjMiW4/7Bd8T\nPkqzeDo4KO0+9JxlTLVrzBlL8L1Ian0hhBBCHIIcEUIIIYQQQgghhBBCCCGEOBtyRAghxCNmMese\nTD5DCA8nK4KdEQtStrpqd/t5HtNl37S9nJSVf1B8Z7pCmss5OiVIgQiFY0mdiWyJEIb8iKjMDPsz\nFvIsBE+NS0dRUudSrsVUPDfAodkWeXn7n0KegpfjMMw1PpyvrGy3W3JgqkOi5AJhTuWE8OtHvX19\n68/65+bW2rv4pn+e4nzw1J/515ap8nk/sXyzbNOC8/Q5ZPV+Sam7T+1f2ve+nBCcFVEL1M0d3WW1\nquepTD0/Y7SnCishpjojPJ492++AKIPjs3bsmq5u+/ddHFuoWMwrsKyhZmVjOT1HfPrmJoBHObgH\nRnNuUMcsLYN97FUWslezMz5GN5T1YU4OCWRHxPovxvuWHZtzrHjX4T0HN4Ab8cAuQjuuLGcpc5EU\nnp/C+42vWWx/5Nb33sH79knaond+F/t0njGAx7w2Zq8cNjYcu/+7jNwmQggh3mbkiBBCCCGEEEII\nIYQQQgghxNmQI0IIId4S4EYA9+1KuAv+/dd/kCz/6y/+IVmGMwK4DglAKvqFSRb/y/MfJMXiHPgL\nyCxNxclqzEwBT+2gPlI+gu08REVodFnATYEyIVWxl/CcEdzn2LeCCrk2zyKb17wyJ4FV+sVMisp+\nlNwNu+25ddP94rksDs3Z4P3ZCXFqcE1if2JeSP+5/iEk2nYPvehl1lGF/MVNCCGExSLNhGAV/9Qc\ng4fshDg3nKsR1xfOIZ+XUzgeStS2cahzonZu+9jOiZxCYDjnlh30koIRKF8Hjjg8H/Ob/nP5yoqZ\nywA5Dmy4gAsBj0/XpuUThwScCdTnmBFRyHCgV2V0RKCNjjIihoLpouvagFsQjgi+r/k9h/08x57j\nXIj71zomCH6vxvo5K4L6s3u87jutlBWBTAjq4+C0oXHUGV+zZs/kSHjMWRGPsc9CCCHE24IcEUII\nIYQQQgghhBBCCCGEOBtyRAghhDg5586K+A+//VGyvGn739Vfby5DCCG8t+ilpHBAeGy6tH9rU/hh\nPT47m+t4WzvPOyvLaX0ts3a2oyQ11WPsMjkjsL1S6FebycBOBt/BkB6zl1XhtVecuzuM11crbHTL\n1VzSgtNhRmpir+0tqXmztr1z5/TxVNkQQ3395+qzwgGBj3uHxMzu0VlzmBPCU7of44S4Kw6d/zy6\nBxrvpkhzCADOCR+/t/4unBBT4T6dO1uCOdQpgfdA3H9L9UT1fJoBNH9jc+X3hqHohJiv00/vNKCZ\nzDmxW4byKTbvjW8HcZy2Rx3ui1je9scrNOZUOBkUbt89J0SsZ+JznI3BdfvH94zjmIj12fu1Wffl\nY/6N54wYy4Fw8iNKbR/LuZwPj4VV21+8i7njeD0jrst2aj0PcLwWQgghTo0cEUIIIYQQQgghhBBC\nCCGEOBtyRAghxFvKuV0JU/oATtWXv/z0Zwft5zkgePt//PXv9Ns35ozYwBER7LM2j6H/LGYAzGk+\n6hG8bVkbJYnBsQ4CwELuO6bocOAMCHZ2FL4BjeU9nEm47WaJzMZF8aKCh5oNgXbg4gKDej99kOeO\n0pbVzyUnxF1mX/CxlfCu1bmdEkdnSJjjZ2vvh+wwOKcAWS+XthzV9/bpiKrbK9uvkPMQwo6DwVXs\np8szz8lA4yTars0CilimUTA3QHdBORolpr7XHIPRli5GyU3WLc3VtYZjL62vZnjJnBCVuPkZL80S\n88lq7/61brFTuaUOzYrY2IttcQ9OjsecbyGEEEI8dvSnrRBCCCGEEEIIIYQQQgghzoYcEUII8ZZz\nLlfCIRzr0uBjYVDvX3/7VQghhNu2f821pryDUhdKWzge2PnAcDZErSOiBOa3hjoTKsxZO9Q/24xn\nNpTrTpddx8Rkh4QpRAsTXZeUuOX2DsuO4PPjZVZ4mRcNne9tM+RwRPicecdWyslw+rrb9kNm9kBy\nGu6DqdkQKN/SWAJVMsak3Bnh1fdwHBBMqe1ax0StYvtczomuYEW6eK+3Mty+uBwvgFiBizQnobvo\nNzQmbJ/b55ZzGuw1uXlK1U5Q45eW8dk+2b8djojGsiKQGeG9VzJXFzKE2jRrIbbHroF4f8+c9QRf\nquzSpe2W3iv8TsZ+TVbP3mqsUNqF/IVS2L2Z6CKpJI45s9Slxc8vxqxD83AeIg/NCfHQ+iOEEEKc\nkwf+J64QQgghhBBCCCGEEEIIIR4zckQIIcQ7xkPMjjg1cEJcr3oZJxwPzJubfs7lzZv+c7u2ub6X\nvTptZsrAxtSa0QmxPY0jwiNmRuw6IzzBXKU6382roDm1t6RE9dqFYvRUKv7SnN1Mbf5GNlc6z9sd\nlbvpXOBj2RDVORyAVMVxf8cJUVIt8zmqdYswpbyRrDzmLLc58e/TCVE7/3kt9+UcyNrlIbEZvxie\nS8A7jnOqmNndUYvX16nZEuDQjAkvK8JzQqAcb4/PR+amsvXIgljZ+4XdBKifXAktZULAjTDUP9rN\nvfA+LWU/jGXjhDBkT8Cd0XBMAcYw+suS6+PMBR6HAzkjavMV4hjqlMc5H953zvuG3oPRGXFJrgR6\nbyR9xrFx1kNWbjy3wh3X71k0f1/OiNo8h4t5/ffKh5IRcd/tCyGEEPeBHBFCCCGEEEIIIYQQQggh\nhDgbckQIIcQ7ykNwRpwbKFhZ8bpp+2PuLBMiOiGgaDXl3/Zqa+X61V42xLY9kUMC1ZggcjvfRldE\ndeaDV/WBwjtW83uK2VzNP6729NT/fj9TlajnxGC3QfF4XadHvj87E7x9Su4Uz8Ux1d0xLJtadzGu\n1vXgc8ksn/dfD9efrfdXdCaaIxS3tbkCB9d/pBqY94fKuOiQMEoOjrtUK9e2Veuc4GM71CEB+F44\nNEsC74+b694+gPfA1vqH8d97C2AMh4tgcW39s0wIzmlor6iCKXkEDtm+XoYEuTXgJNiac6K1vItu\n4bmq9mc4sBPCyz9wnRAT3Vy8fXhvUv2o13uPdcnHTtZF3s/ohHBuiOwc4ZyEwjhu6zevegfn/OPx\nQQL3q+ce4+21WRGHcldOipV9r5vijDiWRXN3bQkhhBBvC3JECCGEEEIIIYQQQgghhBDibMgRIYQQ\n7zhvozMCCjyeyxtOiM2tOSLW6TFnKkxzTIRFquRDdgQUsZlKE4pzU0ZGxwW7GDyBIDkjxnAdEs5y\nETcrIu3UsYLzeidEHZ4TopSpMV/NRtePgsu8GHdluI4Gut6lY/TqKbtITKFdUtRm7e2fw3zxPP2a\n2H4Oh8T9ZEScMh/ivrIhmGqHhFO+xKnnIN8cIMv3+lxySpwrS6LWGYEMITjn8ImMoJITjseJLC/B\nxNQ4ym0cZ6gcuRTGHFpTs3qi0wFtIivCybOBm2NrTogOzoglPZOIUXJU/3g3uv2jd2cG3qmlc8/l\n0K5jU5jxmNal+0VnXiC3w8Z3+pXcIe56anPYjvePdRHHhq8x9jyxo6xz1ov7Q9kQQggh3mXkiBBC\nCCGEEEIIIYQQQgghxNmQI0IIIUQIYXBGhPD43RFQzN6u+9dca86HdmW/v9/Y8ZmasYGi/MJUapeQ\nrpKikZSw0elA6npkS2T7x/mmT5QpMYVCfgGr9939Kokq/YKKnVXCntPDVdBWZlZwfaX94v7NrlPB\nUdNWnqNYD1Y4+9XOd+7i5l/QfcoOoNI9wvfzA+KhZ0Ocqp37VtLWtF/rmvDcIMU+nNgpAdhBt0VW\nEJ6b7WHjNrsOWh7TyAnh7e8tzzp/DNpSnXBCwGXBbbLrC8ARMb9BPbb9af/RXsE64fUDbq2JY4gz\n5sxQn+NOibkdjjMi6x+5DzKHBDsjqJ7t7iLfJqXb0nVO0PcFzrr67jKEEMLiIwsbIWcEBvRTOskO\n4S5za4QQQgjx8JEjQgghhBBCCCGEEEIIIYQQZ0OOCCGEEBm77ogQHq9DYv2mf81tr+11h/mcTR3Z\nrKE07D/axpbN0dBYNkScG/w2zYTA/qgvqj5tv1mcD/twJ8SW5sqekfKzKFAuZCUcSslxwC6CbZMq\nbT0nRCkHgduBpMLTfHrnZ4qLoVqE7mQzFF0dvP+BlBwb2blmF0xltsSpuA+l7n1lQ5zayQCVMbsI\nzqU+rnUrhOAfa8kpcahDIrZL1/ZQh0RbGKezfAKnfBz37fUDR8TW3jOzKy6fLpdcXrvPNRwOoL0Y\n7VJWV8yhWKGP9C4zOsoIio6KmJGQZih4Y1FpjNvSeD7DVw9zRszYiYBzQA4JxsucyJwS8X013j92\nSsBxEh0i+4Y07nspxyOOz05+xYmdlchOgbsMz8995+nctwvslLxNxyKEEEIcihwRQgghhBBCCCGE\nEEIIIYQ4G3JECCGEuHP+9sUPQwi5QnZjCtg///gXIYTcmcGwU+PWJrz+y89/HkII4f/4zX9rFaeq\nzQx0Y5GqLqMTwhwV82s4ItLdoYpExgScFFtWLnapA8PtD5gFV2E5OROhFi8rwsHrh+eMKPWLnROl\n8hEn26Jyuvqd9vP6srYnnqN4vXm9N5/60c6I9DNzaMRyqYrZ49j+1NI4av59zonabIhDVb3HOgzO\nrYC96+yKqS6FXXAuzp0hEds70CGB+2078dQOGTCUJxDgjLMnz94z2V0d3QZ9OeQ38C2EnAa4Eprb\nYVv7xKryHBH4y4+yIuartK2NZT8M77ZtsgwHQJYzw66qLZd3+mXg3G3juRu29B118pdiMXJG8DIX\nr8yQ8JwSsb/sjNgHMqja/c4GfpdGqI+cFdHwNTGQgdLEZ7lv974zJE7FxXz/98VTsmjurq2HRGns\nluNDCCFEDXJECCGEEEIIIYQQQgghhBDibMjfQfA5AAAgAElEQVQRIYQQogicCafKivizD3+ZLHv1\n8vqfvvwihBDCH3/wdbIeTohNN7f9TPlnzoT2ihwKxvbSPqE8vOiPs7sxB8Srvr75jTkqTD26jXN+\nm3PC6p893STtbCkPYfLc+3uEitXzSx9KYf9S9oLvjKD6Jx7H0cfFWRSZ2nSkPXYYAHZdlOZ4d9rg\na3moA8FzQnj9GMqnz0XnKGofI2+bE+KuHBA17R/qjpjqjOC2j3FlhDC4aNqpD9osdTpMBe6BLUT9\nTt7PIe62btl/tldpHdn4SQ4IuCnwqsV+0ZVBiv9uaZ2HQyLLLwjj64GXdzBVlc+OB6Z2vdWTOSMY\nJ+eJXTB9ptB4bkZ896ydC+ycm9r7Ac6IYM6IRfwrvy8I50PJGVHKisDzd99jkbg7asdqlJMzQggh\nxD7kiBBCCCGEEEIIIYQQQgghxNmQI0IIIUQ1nNlwKodEbb03bf/a+g+//dHo9terfmLs17f958x+\nbl8+WyXl2lVf/9aUfjOaR7p5Y6quV/3ywubkbm3e7c2VKQef9f1urjbWXpotsbVPVkgew9aUm6ym\nBZOFaE62QtZuM00NXFL3sjOBsySqnRAFZwbIHBCOi6GGosvD6UPsC/7nRHKQaidEwbnB12DI67D1\nB84l7mU/lDhFNsT0NuWEqOVYhwKfq7vKjvDAffrmNg1YYCddfYXjWRHx+YLzKN7myAyA+p7WG8h/\nQIZEuNpxLFidzSYdRz0nBKa6b8kRgf3mK3NGdNT2Bd2PfAmawSGwuxyPaJ4ue06EolOBcbIgXIeE\n015WbSFDApkWsybUOx9L46njwohtOrf9+mXvjGg+7r+4oNjblglxHxyaDfFQHQIlB8NU11ptvUII\nId5t5IgQQgghhBBCCCGEEEIIIcTZkCNCCCHEwZzaIfF3r/oMiOtNP+H1xpSuULxCZcUK2OtVX/71\nda8E3GIO46U5FkwN2ZlacgNFKuZqJuVhE9WP/cfG5t1ef2hZEx9skvpBVM5i3ulN+jmZWXBzItgZ\n4eUXFDmxYK3WycBOiKn71zohXArZEPvw5l8/lFKuxmScc1u77M3vjvsb2StwHM0mqvXvQpE7NRtC\nTojDOZVD4dzZEbgnMOd9ieH+hip/Wr84P8BzRsDwEMf0ggJ+0umJzob+E06IOMZQUyi3RR4SjmGD\n/ewY5va5pGc5c5zZMcUxw84lHeMsukP2OyOGFYUMiFKGRAE4IDyHRAbGtCZ3lnllcU68Y652gRC4\ntp3t383STAjAWRHi3YHH2HM5GDbbRq4IIYQQGXJECCGEEEIIIYQQQgghhBDibMgRIYQQ4mSwQ6IW\nOClWNlH17aZ/PSHzoXUkoJjzGHN6b27stQZHgin9Li3D4fUrc0xYRkR0KlD1mP96DdUnVJHv9cc3\nM2UtsiDQXlRRol58sqhyitARZU8kIj80p2Ib5/xO1b3nErtNrffofrCSdxdcZmqr1hnhKpipzalO\nCDcDotAuOx44OyIqr9l1U3AyNFa+mahCnqLILWVDyAlx/xzrkDjWGTG1XTh08D6BknzLl8Du6xkZ\n//huR15AzEnAWFlyRjBx3Kl4nigDJ45NhTyY7jLdLzofcCwxeyLd3txCbY/6KLsIG5B3sUhPZuaM\n8BwMXuYD1cPg2sG9hXayvA92UGA1OSFqnRHbsHPuNlW7uK4N3GduywXnDLIiwge9DWYR/+rvzwk7\n0zhDAt+7zpXHs7jHMU4q/XHO4YxQXoQQQghGjgghhBBCCCGEEEIIIYQQQpwNOSKEEEI8GP7sw1+G\nEEL4q+9+J4Sw44iAUpUUrutNLxnc3PafW3MowJGwmfWvuTdWHmrIKMwiRfrW5qmOikZvvc0VviX1\n51Avlll9OXbUBbypsA+cPzoqVwvOiFy8NrU82ttf/tgchGHu8vH6Jte/zxnhUNvG1L6UzlGpPi/r\nwTs2dkLcF3eRISEnxN1Rm+HA8Dk+V3aEB9wEmTOiBMZYPEf2iUyi0q3TLcld4Dmv4GLoZnEcBKUx\no+tjlUJ7kW5HRgQ7K/IsH7j/sCM3RPWYC3HrZkaEZJkryjIlKseI6Fos4bm4Co6MrJr5lnwvO1U1\nQ5kQ9rk5+j0XT9YhhBA2b5bpdjjUlt3oenZKYP/mWX9x+YxwVgQ7I2I99r0HrjM8V3jOpHx/+NSO\noVPH2il16v4QQgghR4QQQgghhBBCCCGEEEIIIc6GHBFCCCHuHWRLICviTz/8VQghhL/uvgohhPDy\n5iqEEALEmnBCdFua9xmfyGZ41b/mNjT/NM+fzUrFuH9UnFq5Nf1+D3XnhWVjoD9cjgRg0Y1wAuH3\noeKykjOiNv+gtvzJRXCFdo4W9O1xRvCxHuvqKOG7PfZfQ6yPzoja9jBHPpTcpOCdxedn/0Xl7Idj\nnQ775iqfmg1xKHJCHM6xDoVjsyO4XdxPnEH05KpXor+56ZXks4bbS1X6s0WaM7Altxov472RzfFP\neQtYhsMvdyVYdasQmoIjgp0QMfvhgrZfpHX79XlWPdrOYyXyMeBUwDvXy0lo9rsHmHhNnPKcTZFl\nRhzJxbNV/P/Vq9RugpZmNFYtn+wPk0CdqM/Nq/De5Xb/3X7bf48KH9/Ylv57C8ZldkYAzopgZ8S7\nwKI5LP9M5MgZIYQQQo4IIYQQQgghhBBCCCGEEEKcDTkihBBCPFj+mw+/DiGE8OZZL+P8T9/+IIQQ\nwjWV6y6gRLXf120+aii7O3MozJ/2Slc4KWI5KE7t9/lMVwilIea1hlKcla6m6sTczbE+clgc4oQo\nZkKcSVx2rPq/pKw9lYOhlEUxtfzodqw7tM9HHuOhzoip9e+s6es/U2YEK2/vgqnOg3OrNt8FJwTD\nx3zX2RElcF/CGdHSc/Xm1WVVPUUnBBx5lS8E5DE0G8okCvmY0DjZOa2J4rf0FyDGkHy9ffIY4Dgf\nhg44jgRnDKt1PJRyO0q5CyUHRGPv+KE8KnD2IyfH6tVFdDDsuiNqgOMhZkDYOef62GkxdN5x2pzY\n9XEqNvbcLwpjoBT0x3OOzIdj0XUVQoh3l4f3VhJCCCGEEEIIIYQQQgghxFuDHBFCCCEePEtSTMW5\n5hfpvL1bU9hhLZwQzVUvD4Xasdukv8PP1uRYwJzfC8qUoPmsZ3M0fOCc4Aw2V4hD4yk5UEx2rHo+\nr49W8HKl9KEk3PMcFmcV1dXW/dDlHV7uhacutk+Lbgnb35oS/NPbfrvV05zIMeFlSJwiG0JOCAFw\nz2Cu+1purh0lOuHN4Y/sIG/M8lxvMQvCnBCNCe2jW2ERAkwlcEJAyZ+Nl4t0X2yf38yS7e1Vmg8z\ndAb7O898k74zPfhd6sGZD90mXfbK1WY/1Dox/A7mrsjV62Wy6uK93lkTnQx8zI7bAt8jsF/mjKB2\n4sXhY6JzgayI5pM3KBBCyMdfOIGOzfaB4+ldHvOk/hdCCCEGHvqfzEIIIYQQQgghhBBCCCGEeMTI\nESGEEOLB8HevvgghhHC96ZV+f/T+NyGEEH764vOkXFRIN/3v6Z0p9pYXvRx0BuXdEytm6svOFIbR\nGQHnA+ZkbqEKtfWXlgkBBaMpF7fkqPCcDiXxW2wP6tA9wkOUyVSzlFuQ92G/MvRUGQ3F+rECKt4T\nOxm87IlSubvk1G3j2rrqZKZw7vleidkT9FzAGdGZM6KZ9x6kxlE3H6uoPYaHpsJ9aP15zJx63nPc\np5wJcXsz7oTIVflUgJXuWUaEPV/2Pmlu7f2EzCJ6TmdO7kNyGjwnBL0n0LMhd6L/bGm9lxMzfewp\nOB/gToHb0HE8eFkPU50QWftehoSXDTGBLNMBdWK8pO8XmaPGGVevzBlxe23OCM+JQ8uo/+b7fhx/\n+uGbMAaeB3ak1brQ7ppV21v3LuZtoeR0Nl1f96I5fd3vKnKHCCHEu4scEUIIIYQQQgghhBBCCCGE\nOBtyRAghhHhwbGxO4Z+8+GJvOVauNqY2XtjbrePsho5+fzfHQ1RBktK0IeVfd9Or4mabVNEaHRQl\nNSapUjOV6cgc4VxmWDb1JM/7P1FkVisqrnUc1NbvOxbGj29qJsZJxdIHnttT459LUgdn/ZyoXgZ8\nb7FQ1+lPU1D9l7aDU2RDTOVcKk05IXJaHo/viNpsiDc3vdIcDrvs/UFUq/LZGWGLcELMb9LiHf21\nFseBne7A0TC3/Ajcxtg31lEYxzmPorPODQ4Iq++CdozjM7Ia0J6dE8ovQP5BzFmKBzdLtpcyJI6F\nsyV4PbsV3P5UOCc4Oyo79kJb6zf9RVw+2STrL5/2GRRwRqDvTz/ob6SlZWmxM+3lq6vR7bUOiFON\nafi+t9AY+U4gJ4QQQgg5IoQQQgghhBBCCCGEEEIIcTbkiBBCCPFg+BfPvk6W19teMvjX334VQghh\ns+p/P29JGg6F9Z981mdKYB7fv/n6hyGEEG7X46+7mSn9ugtTRW7IQWHK2aje3KSf21itqUaXppxd\n7/+dP869T4rYrH9dCN4c3a4zYiKeSr6ssi/ALgInl+Bg5wLvN7F/B52vB+KM8GD3SHZuaX54Pg7/\nXoj/15dD5spv+jnGZz/o5xhvFoepl8+RITFVrSsnxONhajbEoc4LzhYCrnqeKcz9H/fL8hdsOzkf\ncmfEduf/Z0kZdkS0V7Bd2Pb4LrPV9Im255kTou59MYRN2Hrky0RnRuqMiPXgXJAboPacT82K4Hr9\ngpXltrNyWcqjwPeGzFWROTr7z9Xr3vmAjAjw5Fmf2YPxFN+LvPH14/ev93bzoWVB4Ll/zIr6x3IM\n3L9T5PE89GMWQghxd8gRIYQQQgghhBBCCCGEEEKIsyFHhBBCiAfDctbLKOGEwPKff/yLEMLgjHh5\n089tPLc5lv/lZ79M6tl0/YY/+ax3WPzt1z9ItrdrTM5szoSFORno93k4G6JTIipVSWHoKFtLmRFc\nD5TmUV2649BAfgQ7I0pk7ou75kjJQ3U2RMGtMDnTosnXZXkcgPtI20tiwpPmWezguk+ic4IUtwV1\nsOeMaL9+0i9/1itylxe9rLo2C2IKD02l6yEnxMMF95CXFfHm9mJ0PXIPth2vT1X4vMyq/wiet5jj\nYA6MRVo+5jw4boTtfOu7JsghgWcfzofoosIy9osOiPGuxzyjuEO6nseejruOc0lZEkPH05wEPrfs\nZBjWxwqS/UrUlsvb850aWc4E5WQEuj/YMRPrifdPWu7mlV0cO1dX799auf3BUXBIwFm6L4tnDG9s\nO1bxfqqsiFXbf7+7mLdumcfiTLhrznE+dI6FEEIwckQIIYQQQgghhBBCCCGEEOJsyBEhhBDiwcPO\nCObWZKBwQuBn9p/+ps+I6Exph7m+Z5g7eWnKQCsf9XPbdA5nVpBH5Xh0G6AjTdJ+xBOEOXKALeSl\n8x3lqcHLtXjOiOyYnPKs2j+1yI3V9sX6K9vnejyXgDcf+6wbcWU8cBmH57Dga110yxScHezSQWbE\n6tN+++IST1T/WZsFMVWhm+x7T9kQckA8PLxsCHZC4H6DQrwxBftmYxlBVM/Mff5Jhc9z/nekiMde\nGHMsx6Ht0jGwfWKOvSWU9HnLWMXvh+iyQyYEjef8yTkznrjeew8Vx0onM8h1hcV8jb5gKQMCbpVT\nZUh4++frh+3dxml7TrkYleMh7zc4KKyapTlJVwvrQ38Snlytk3p4/PXG2cfiOnuM3LU74BTZDiEM\n/Z5an9wQQgghxnjgf0oLIYQQQgghhBBCCCGEEOIxI0eEEEKIBwdnRfD6DPtZ/ScvvgghDBkS0QlB\nytSFZUL8/qfPQwghvF6nE2H//FcfhxDyuZkzWFXJStcFOSYcpSTPGT0lz6GYocCq9lBQwVeS5Sac\niNr6ikraA+sdVQbzvqz29ZwtldkQp8rx8Fwr5X7A4eP0b2Iuia8YT6l1SIBTKnXlhLg/PKdCiVOp\nez1evbkcXQ+FOZwRnBHhYs4KKNcDK+KJ7YU5M3i9vUfi+4TfIzv9iQ4Ia6NZsxOCnG4xf8K6TMc2\nX9l2cgW2gcYMxzV4qnycbYtzP54NwXjrs3onjrmcVTGsR7t5H6pzKtj9SONu5qhw+oT79Pq6v5+f\nPk2zI0oZEXAMeeMtnl8e+0q5C95+TCkrQvkOOceOjac+l7o2Qggh9iFHhBBCCCGEEEIIIYQQQggh\nzoYcEUIIIR4srgOC+LtXXyTLUclnUyRjzu/GFKX/6stfhhBCeLnuFYM8/22mprT9worUpawKBVCd\nkoIR+0HRyrkMEVu9Lw/CE5yVFKmoMfa9gJ8tweX29yuvl/pVUu+T8vZYvGyIMbxjLeGdi1O7OLz6\nuZ3sfvPKsQOiIOidfdIrbpEJEZ+3JlXgNmd0D5zbmSDnw9sP7lt20DFQvXt5BG5GBBwSeJ8giwj7\n2YO2vSrca+SgS5rgd0YcV9NxvLM+NPYuaq9s/03/ubjpP+dv0uo276G+NGui2aTthSZ1TpC50c/L\nwLtygY7T8Wz5XKeb8Y4fcyiMlS85J/ja5k6IdLuXD9HvjH3rHJZb5xyxUwK0q/HvL54zItKMZ6Zw\nlgrA9yt2NmGMZHU+q+NrnRFvI4e6OU7tBjvUsXBoVoQQQgixi94iQgghhBBCCCGEEEIIIYQ4G3JE\nCCGEePSs2l5uCaUdK/kuL3prxJ9/3jshXm3ICWHKvJ/95qNkP6gyt/jd3uannjnzbkegZMR83ajH\nmSs6ugmiFcLqnW+jwtUVsNHc3FNxj6HgQDg2I4IdEEWBXa0rwel3bf0zWt5bB2dFFDi1E6LkSuEM\nislA2F3a/3n/PLWf9Yrb5mKzr7SbDcFzlYP7yIZ4F9W65+ausiEObacWzwlRAkp1qPajen6WOiiy\nDAlWxmfZRGFwPmRqeWRBDL6LEEJoblJnBJhb3Y2J5xfmiOBxGp8d/SXpOduiU6NyTNlu0grKDom0\nvia+c8fvhan5DbWZE3spOG1iW1kmUeo+ib6K+fgx8HJrTs5Xm6tk/ZNn/UXuyLHWhv77FI/TnlOC\nHRK12RHndkas2nm4mNe5amvZdP25WTSnrXcqU50JyoAQQgjxkJAjQgghhBBCCCGEEEIIIYQQZ0OO\nCCGEEI+e/+6jn4UQQljbRNT4vDW55pt2GUIY1Gz/9cXHIYQQblb99t/9+LsQQgjNnOeXNkUgVKYN\nORYclSQyIIIp/qBGnS3JYUFEcRvKb2Y7CteCM8KhXl1Px5Sp7cezIg6lJOQr9dvNlCg4IbjerJ5u\nvNw5GNo4UCl74n5ER0U7OHL67eP3+/yTVQghhKU5IA7NhDjGCVFS005Vbj5WJwSriz1XwDmP71RO\nhPuaf/zpVX8/v3pzObp9vuxV0O0agQd1qvrocMB7o3PU9YUMiVgfO+i6WT4+owpySMAJwc80lufI\nhrB8JcQ0sfOhWaWf28V4udifDeUjYBG344KObYmBmPNq0nPDzJz7m7MhhvJwpzj5UPcJ51Pgfgvp\nfTHD7cjnitiak2Fm4+qbV+l9DofE4IRIx/PWLg6P13BIlJwRU9lYPQunnkPzFk7Bsc6I0hjHx+Qd\n47HHfp/nUAghxLuLHBFCCCGEEEIIIYQQQgghhDgbckQIIYS4N5YHqsnW3Xx0/XKW1ndrr7kl1F7O\nz+9PF738c2HzCXfIZWjM0WAK1ihiu7D6eO5uAHGZbedZrKMzAkpZNztiOyhXyRkRaKk6B6CQKeFm\nRlA73v4lB8Kpxc6uM8LBy1Go2SdypmOYSn3mxP5rOhlrt31+0f/PJ/3HMmZDpOEZ7JQ4BXJC9LAL\ngZdZ8Xru/IRDOJUD4lTH1kBZPv6a2VHdoz1nrKSsB+QeYG+o71mFv22d49jnvIj5Aen4DHferCuU\nIycYLkn7xD6Xtt7OydwyJHCq8Erevof60/rim6pL32Ps2Ijtr029f9G/k/lcuXlLJ76/+dpMzZaY\nhJcBUgD3WdEZMRvvO44JDgk4Izhw6VhnxH1lRTxGvDHxXc56YPeJ55QRQgjx8Hl4f40IIYQQQggh\nhBBCCCGEEOKtQY4IIYR4xCxmhzkK7otDHRC19bBT4rLZpAVMQAUl33LR17Og5c4UeuuO5o1mJ0NI\nFYysOuW8ApTL9IqshGwG1WiW2cD2CidfIBO6OZkP+X7jc4h7x8QOA2aq2LlWoFesd2J2xNT2D6Hk\nwpjqJql3gdC1nipDKRgZoBD3MiI8J8TUbIgaxexDc0Icq9rcTFR4e0raWtfBXShkT50BcWqXR8w2\noXo7yyVAllCYp5kRWbZQtJ95eQapQj0q1SkHYYbsItofWQG7jrx4+VbjTrrtTv5QCCHMVmmdrZmc\neFyf2at0vkqXY7voCtd3FZJ2mZhHgxWUf7Fd2TvdnBE4R54zobXjbhZd8omKS1kRXH8tu+VdtwQy\nQOAeaQvOB+5r9k5Lr390RuAas9PGdZGMH3Mp4+fUzgiPc2ZFHJuRsKHvnIdmRlS3R2PnY3I0HMq5\nz6kQQoi7R44IIYQQQgghhBBCCCGEEEKcDTkihBDikQN3gJebcJ+cygFxLHBG/PjbL0MIueL677/7\ndO/+RYWrKVeHzIe6fs1ofuy4NKZcdOb2zpSmjlOiOOu0pxR1nBFZPxw1f20WQ1HYV3A4PCRpRcnZ\nUHtOSm6ToVylerfgqOD52nk58L1m+zdcrsDb4IS4q/mp0Q47I7wsCG87g+PPyjm77Tu/p3Y4lDjW\nAYH7bONkMVxZ1snNqv8zqXPa6wqK9mYBlf3pslF22c0agsMBsPMBzzJnRmDswCXs7C/DBuXM+XD5\nbf+5eJ32ARkSIGZH0Bi3bdL3CGdHxLEmc2+NZ0PEz016baJ7xLa3q/HvRV59nhOilBWx6zaodSJM\nxsmkitc4RpagT+P9KYGsiGBZEXP+/mNwZkRcP0ufL88ZwdxnVsSxzohYjxwSRyMHhBBCvP08oD/b\nhRBCCCGEEEIIIYQQQgjxtiFHhBBCvCWw++A+HRL37YRYUnbGetufi6i0s81/8snXIYRcgQVV23/8\n9e+EEEJ4c7MMIYTQbcZ/v4/KQ8z9zAVYEelkRXz1o+chhBCeLvuJtv/zzz8PnSk6B8Ur9t2f8RAV\nsO1+x4JHptrn/AtyRnD74Gxi6an1lhwVU+BzQeRq4HS9V87LhDj4HFZmSkQ18gzlbHmRqqUzJa7t\nt3rdPx+r0H9ePevv32X8lrk/K+IunBCHqmzvyvlwKr5+8X4IIYRP3++l657qn9ezahnwvO53yamz\nH2qBEwLOiOubi6r9PFX9jJ/DA2+pqPbHmL62DKPd+jD+8ljT0nuhUqWPuvGKxHLXP+qhXabLzTr9\nRKbE1saCbpE6+QYnRNpudHQs0gPBucV70ctJgGtxqhuAr2Fp/xr3g5dn4fYB7wMnlyl+F6Bsqfg+\nsZMZHTO1uUsFVwicQU10U/XlvXG9xKmzIk7BqZwRsT7ne7hU/zoHQgjxLiNHhBBCCCGEEEIIIYQQ\nQgghzoYcEUII8ZZyFw6J+3Y+1AKHxJ9//Itk/W2XvgahXluaOvjffPUPIYQQ3pjsE2q5G8hAiVW7\n/xx7qmJW3z1d9HLSq9/7ZfjJr74IIQxzXWcOCXYgeBkSEL2TQ+Jo4Z+j2pxa/1THxrk42XkZqWOq\nqLyUJVGbDVHthKAMiOyaWPkLczw0s3759jp9HqCYxedUJ0QN53JCPHQHxNff946H9SYdazqbF75d\n9+t//c0He+v56KPrEII/nzu4i3nb79r58Orm0trtzxlnQOC+hTOCQTYEslE6OH7MOAHnHCvR2RnB\nZMr5Gef/HJ41wA45tw+kroejIdBYhFcnnBDR8RDrScu7/VraMWPsgROB82rs/hxymNgF4OQ2wdGx\ngDtgNvoJDnVQHAI7XCK4D+ycxqGO3S5O2zEzgpwR2f3kgGNCVsQTyoooOSOQGeFlAZU45ZiD72QX\n82nfVU/tjMjqP5NT4tz9PgQ5H4QQQjByRAghhBBCCCGEEEIIIYQQ4mzIESGEEO8IcC8c44x46A4I\nPkbOisjKm0oOGRKXzWa0nKfogqptbSo0T+WW7VeQiULN9tPnnw2qWyhFMWf23JsnPM2QiHOBQ/Ve\nqYwd8gkqFaKUGREdGYV8BG4vE/IV8hgyeP8DJRd7L5G37URtM7Xq4kOpvcaLJ+vR9VDMLhf9c8IO\nCFbGlpwQ+5Sw76oTAkB9vImq+/SmgAI8y7PZjqvsWb1ccka8y8AB1Nlg2hXGUCZmCWWnspAh4Dkh\noJwPM9fBFbMWoJKPToPUUYDxGq/AxpwQzW1Iyg1tp59wSODYslcp7d8tOVvI+svnBs4GckIw3v4x\nU4KeBy8DgnMSvNwE71rNmm3mtqh1WWQuEPzPEa6LUZyx4FAOzYg4NfvcAOxWrXVI8He1czsN8B3y\nsTkj5HoQQggxBf1VIYQQQgghhBBCCCGEEEKIsyFHhBBCvGNMcUY8dAeEh3eMJYeEWx85J4DnoED2\nRMkhsS5I3P/0s1+Hv/rVVyGEENrGsiGiUtMK7ahiQxhzGux3RuCQokOiM3VjrbqfHQuVzghedp0Q\nh1LrWjglpTngPdVypSzkUGfE5P3iPZPuF9X3lWrSqXOEl1wMNerOh+aE4D6X3FBT4RyCTKntqOlb\nLtdQv+gSH5PncShwZzCH9gXZEMjX6DyngeP+8MqzK4UdD74T4jDcbIFdnOyeg9t0HGwxK+JivByP\n73hlwk0SMyJif+Hos/3g4MMxY/0mPXY3B2eqK8FxQtRmQYyVy9bBiUD3k3c9M7egd21L9xffv7Y8\no+fJy8/w4IwIxnNZHcrGnrdjxu5jsyOYUzsPTuWMODUPrT9CCCEeJ3JECCGEEEIIIYQQQgghhBDi\nbMgRIYQQ7yjsdoi5Co9Y8fRXL34UQgjherMMIQzKuX/96T/s3Q9OCXY8AG89gAOCgXrMc0YsSUXH\nWRObbh46b252T81bC3YnEeOWnA21Qr84BznPpZ2pJMedGy6nlkxMrW+3n49EvhFdKHRx4yXHtW3J\nJYPyUMKinK3fXPf3+exZf1KaxbS5wcrXIJ8AACAASURBVD1F7NvghKhVxE5Vzsa5vqm/yI5hRwQ/\n0Pn2npffPQ0hhPDBR9chhFzV3JLyt+XMiXuk1JeXr65CCCF88OwmWY8ME3yiHDsa8AacL/v/i+r9\nQk6G53zAMraz0rzkYsmU8t3IGIo6MkdCfLj7ZatrNm7my50Ol/Zp8TDYD6+0IUso/QTImpjbZwdn\nxMJyNuCkgMMB7wtkoCDjYkmqfT7HOLfOmBTPMeUjNJa5NNUFcCdkrqXx8Tlec3zSOUCOVHS6cRYF\nO48oO+LNq/4mQBbQsZkQyJuZOmbXMiUf4VBnxDFtTqr3SGfEXWdFnBP+vvw2HJMQQryrPJy/KoQQ\nQgghhBBCCCGEEEII8dYhR4QQQogQwuN2QoC/+PBnyfK//c0fhRBC+L9/88+S9f/DZ/81hJBnRpSc\nEQw7Idj58NMXn4cQQvjDD367t56/+eaHIYRBhQx18u1qGTZQhjpqWjBZHMZOiLmnckzbm7H6MuuH\nN8/1RBVlrVTi3KK4Pf0oTfdfuiZT4wLK7aXnvtl418I+TTm7JbV05pAIqdp688YcR6acjfOvm2I2\nmOIcuQNTsyLAQ3JC3LX68u+/+SyEEMLvf/o8WQ81cmdZLl0DRTfGiYKiewvld7/44rfPQgghvP/x\ntdWbFm8qzhe7tr7/7klf50dvivseA9rZ2hg55An0fUYWhMeTq17m/9oU3/m5S/f33CXD9nGbmau2\nr8wIiLATYscFkY27NNyyEwLlOeZoGBv6z9YcC6E30IS5PeKlV2R8NVI2RPxcpf2K47iXw9GgnHMO\n2C2A91nBOZhdG74m7DCpeI+dzFVBxxSdbI5zLYJzxPdhGM/fYAfEXeOp9o91Tmy2TfW4fWpnBDjV\ne+NUzgjwkN0E7HwQQgjx9iFHhBBCCCGEEEIIIYQQQgghzoYcEUIIId5a/qfP/z6EMORfwBnx737z\nhyGEEP77z/4xhBDCU8gzK4ETgh0QyHiAw+Fm3SvHX972c5H/2ae/Ssr/9W/6clAUt6Sk7Laz0G0q\nlc4OnkNhej3H1T8jJevZRW+o35m7vLb9qa6FsX25rVM5IfjcczvxXG/S5UH1TGpkOlczKGgx57gp\ndKHcni1SR8TUucPPNUf4GFOdEA9FMfpP334UQgjhdz/+LoSQjxGAldu1zghWQ6P+1897CTyrpp9+\nkOYujHH1rB9PX37zXrIedV29f1usY4yb7825YHPeD/c3xpY0p+blqh+fZ5YDgL67uTsGzl2H5wb3\nfSnbwalnWGH9iy6A8ee3OFZRHkQyDrC7ierIxmHOilikfYAzAp/smJnTqxOZD7w/HBJ8LM2a1P48\nRsHlwu6uLBPDlu1awxWT5R+A6HwojFnOc+I6E3fHQHZjnNp54I233fj95ZYvgGO9ftl/j8HzvbRr\nCteUlzNzqCOuBDLA9o3tUzMS4IwAp3ZIgEPfL8c6I2o5R/1yOgghhAByRAghhBBCCCGEEEIIIYQQ\n4mzIESGEEOKtB/kX/8sP/i6EMGRH/D/f/F5SDg4Jzo4AnAkB4IT4q9/8TgghhFdveuVuZ6rU9aJX\nsf3HX/fboSB8c3sR9tG1szAzyUBnKuBsHnFSP0blKClj/QwIg8Wbm1Q5OxWoklmBe+dA+VtZLnJG\nqUatQ+LQDArO8cBynA8eqmMoWlepKrkhB0V70W/fPDWl+JUpxG391Re90hz3NRSx51LC9m3U1f1Y\nnRB/9Nk3IYQQfvbywxBCCL948eGk/T0Vv1cuuhdY9W+fTz7qXQy7boKuMP8+q9jRlzff9crqyw+t\nTicbYf3yMq2PVf2mpoeqHu1h7Nmu+r52V/2OrzdPk3ZBe7Og/a2/KICxbOHcG3QeulXhwS2Mia4D\nIiuXjvUh7GRBZHWMt5FlRJAjoThWefWQswKvTi4Xx6Jlmkfjvq/4PRf7RY4FckLwexPuHM8xEV1f\npfcm79fNMufLjPMqyBnBbWTveD62UPlOJadb1tfYP9pwz5kRTCkr4hzOCHCq7IhT9SfufyZnxLH1\nyfUghBCiBjkihBBCCCGEEEIIIYQQQghxNuSIEEII8c6B7Ag4I17e9Ard15tegfvR8nrv/lCjsRPi\n5au+nnbFEkPbL+Y9mFKX5702msUeRaKXC4BPTOd/S+XQFuUAQHnKDoahvfFueNkQtU4IT3F7MI5S\nN1v2JBgNlTNm3XE5EaM4faltZ6oTguuHKnn+2qkHbhor11xif3NEXFoB5x6AE2IxH+9oyc2wTyX6\ntjshmB998CKEMDgjWsqlafD8xu6nTojMGeHNdU/PK9+LGLvG2Dy/sjrQKXzaWGAOARZar26epOUJ\nz2+BsYydEOyY2MZlZPD0y7dd39/Fs3X/+bS/0Tevlkl9keiQsOPwFOzemFeaq7+U55GNSRVOiIlj\nBJ714hjkZO7EzRtqHn9pkjNi6I/124TY0QkxS/dj2AERz2m89s455Zwb51pmTohtek/VMLguDnNX\nHA09hxEn62S2rDu42+v+OWmepQ98Q2PuVEfcsS6Bc7Zx6uyIh8ahTgg5IIQQQhyCHBFCCCGEEEII\nIYQQQgghhDgbckQIIYR4Z4Ez4rrtsxr+6rve2fBfw8chhBD+9MNf7d3/b775Yb//Tb9/Fx0PrEZO\nHRBRrUlzSQ/06rRdZ8TWm/sbauCWVMLWBs/JHRWpVvd23LyR4TkgYv9YdemWQ33jy56St0hpv4KS\nd8Zq7pFtJ3dGcNsEtzfVCcH9jucaTodeEB7mNmU+7oWOVMzxHim4TmJ+wD2qRd8WJ4THDz98GUII\n4efffpSsn9lxY6xhJ4SbGbHd71yCWwD1QhHefXcR5tf9uotVWuf6feuL3TfRwbBKx6T2wvqEPAFT\njHOuTTHfxiE+Bys4J/oOtU/6DW0HZfc6LZ/lD9h6LLuuNDgjsJ6cC1is/evLqwfV7bw3Dh4/rfx8\nldYD442dotBZnBGPSaV8m2wMo3Yz1xauNan5h2vguLGwXPkeKuFlSuQFyWk0kpviuStKbRb7mL07\nC/uTayQ79/iOsc+RGYaxBFlA7IQArXUQzoiN5XHAKVfKfsjqO0FWxKk4VXbEQ82KKCEnhBBCiGOQ\nI0IIIYQQQgghhBBCCCGEEGdDjgghhBDvPE9NDvpvPv0vIYTBIbE2Se+tScShPvvxdz8IIYRws+7l\nojx/OhSDgwo52GfqhIjsUXNuPbVjQf1YEqzlantSAc9JxrsZz5CITgjKWPDyCYo4WQ0luD1ehsrf\nUyPXug/Gykx2ShRcGbX5GSUnBMhUzOR4QPlmA/Vxf40tMiW0V9QvKHft4/oXz0IIITz96lVfHipP\nuojHZEPU7D+Fu3JCsGKW5xqv5cv3vw8hDKrfL80Z8YvvPty7X8kJ4e5nroD2eT8Wsip/fjsLi+t+\n3bLvWujsftk8Te8POHDmN7ZMYxrU9pwKEVX1C3NIQKm9TbcP46aTubNJ+4HMCBgfupfWAb4lEFfA\nGRCxf+l4zedo1o6PqTi3novMc0DE7WPuOCdHojQuckZEdNBhvKwci9gBsfX25zHKc3nFc2TLk8dY\nck44yyUXwlG5DhyKUnjmDqXoFgHZu9v24+8Y7PLgauz7ztocRsvsndpXCMdEyRlxH5wqj+KhOCPu\ninM6Iaa6OqZmkAghhHg4yBEhhBBCCCGEEEIIIYQQQoizIUeEEEI8Yh66euqxspz1yqy1E6Bwu+5f\nny1yGKJqMr0emFcdzEiFGdWYcFAseP9BfejNQ+6RKVTZuYA+OardOJ/5PHVCbMN4PSVOdauWchJY\n4RvV/vaNp7P17JDosnmx98+dH0J5fvSSY6KY+eDUV8Kbn53zQjbmdMC9tHiDnBErfxFGwVz/MS/A\n5pGfk8K7Vvl6inGsdl7wU4+ZU5WwteUPdU5AOb7le8hzYzGcc0DZM1h/+d0sXHwXbFv/efMp9QXZ\nEPHT1tNzgfX5fW73GZ5ZJ6cglnYcQV655sYcGebkyLIooqPDcTY4anpeP/QrVfnHv8LY0EGOipo8\nnVoHBGdCRBxHmOtkQDnbz3OcedcC1xyb43i94HNX+74jdwryRbKGaY3nHvCyJuj5ibkKu48rOQm2\neIdXuiti3z23hufuwDsLRx3v23Q9O4mwH7tE4jE5zoib7+3Beb8PGRqyIlJbI78XSu+DkjvgIWVF\nvC2UsiZO4YS46xwLIYQQDxc5IoQQQgghhBBCCCGEEEIIcTbkiBBCiEdOSb0vTgeyIeKcx1Hp11+D\nzuShnakmWxKRNaa67wIpB01x2GSOiNmgpuTOcJYCBM9RbbtffTkjdfwgTTDVrqfedNT2gwPhPPNg\newz5Bmk/oqrfRHjorwn/hvns4Qo44htRrTNi6nYw1QnB16hZ0eea9qP53JEVsXht9wLHk8BFAqUt\n9jMFKuZuhvL12GyHU2ZDHMuxc4EzhzofGHZfDevHFdyRqKTfrzyHKwAuiPd+uQ2XL/vCq2do23MC\nUJ8yt9Z4uajCpz5gbHOdEY7zgu9zALeHN3R5TomhwPh+GXQ8UKzH+gv1HCJK5nPrXQuMizwOQgzv\njmHcN3q1ee3zuD2HywrXqDabAScTrsJlX/GWcz2W4weQZafwdna3lPq16x7YwlnATgO+n6zv8/E2\nvbaLZ4ifDy+LynGTDA1RfxyHRKwO7wHPVVLgLrOCHhr3nRXhOSPW1q9aZ4TcD0IIIfahHyKEEOIt\nAT9IAP0wUU/tjzmYEiBOyWR/EDf4x5yu/8MbUzfN6A9m/GPhLP6B3tFyoPKznalS+B8QqSzqwD/k\nYAal9fg/MGY/GGA7/pGPTkUWeGz/WIH6T/YDROWUU95UTJhyxJsWqRSSWhOW7f/w4AV6U9tOfZP/\n7aF2Cho7NwsLC46hwfiRZk6f1P/4o84q7S/AvfDiH/vg5Pd/1KcXX4X0F49z/APSuadkOvUPD8D7\nAWLj3FybOPY0SblP33/dL9s0WM9fPBtvkP9RdM1TvgVbtnKb8ecZ98LyehsW1/1O3SIdA+LUXXEK\nGfvgvzpqfdn0rGf/+O3Uk01FRj9IZNMR4YeBwo+4/C/A7hRNzrnF9HbDXukPEtl0eXumZMq6VvvD\ng50DLwQ6/oDLP3A74yO3x2fQ+/Ep7o8fkFdpgLl7Tvgfy3Fu184Pcu4PcOP95H+0z34E4P7E5T3v\nQfQxq9vfZawN9CWbwsmboqnQH+8YM0gwgWkjwaE/PIi7o/QePPQHBP3wIIQQYgqamkkIIYQQQggh\nhBBCCCGEEGdDjgghhHhLkUOiDJ8jj5+8+CJZZpU0pmrqoEDH1AWYBshTzO5zQoQwHixL6tq4D9SQ\nVGcUDd8e6Fig0Oq4Ok59Mb7boQHLsZeV+/FUTOyEiFMvkZI3D1WlkN6K4NuSM4LrPtoZUQqi5X7x\nNCjmaFi+3ibrN1d9/zZPbDcKqY7TtngB6Aj1tXtivel3aBy3Qq0z4j6n4LgrJ4TngIjbnSmXPJ49\nexNCCOH7l09Gt18862+C22/twWAnE4auG7gbbBmf9py1F7PQXjXx/0PYcc7EQPh+/eLaqqZnbkvP\nKvBV6rYdzgXcf4VTxG6pOP0P1x8bdOqh8OhsjDpQ3sVh1hlF59Ow3zD2jE+TNZSjZUy3hr7w7DzY\nDicFua24nWxaLOtXdwEVvpWjoHLGDexuyb1Sq8Y/kXOvGCg90mbpGtS2WSR+F5hWf+2UUFPhqfri\n+jON7+9iSHUpbPpcyAkhhBDiEOSIEEIIIYQQQgghhBBCCCHE2ZAjQggh3hHeZYdErfOB+YfXnybL\nl/NevvknH/82hBDCf/rWwqs3aXZECW9e7g5KX5vvvUZtGedyj/P8IysC80UbTTr3eyl8NM7N7RzT\noQHLuVL2sPoaCqSFghfq/dYE4S3mGqd2eM5zVvbuUnYykDLbye+odUaU4P2yepxjKZ1zqJ75nOG4\nOmdufRBDf8nJA8dQoMfwGEXsubMh7ht2QnA2RFy2saIl+fN7z26tnF0TK//mu94JEccBjB8wt9CY\ngzyR5av+c/HG+nMRwvVn/cO2ea9f1y1tp/iM2Ri0SO971JnlDgDHaQC1/uBqsmOzsQ6fOCbcr7hV\nojPiBhWn+0eVPs7ZMs0nGPJoDnMuxFYpK6Ka0nnaqTtzdLlOBdtMToctBdp7mTulzJ74mNr7p73A\ntZqNHlIJrx+8PeNEY2/JNZBsd96d2TksZT9UUutcKNUb6yFbDByccGw+/aB/kJANcWxItRi479Bq\npjakWgghhKhBjgghhBBCCCGEEEIIIYQQQpwNOSKEEOIdBS6Bt9kZcagTAvyLZ1+HEEJ41V6GEIZ5\neNdbUiM7yscsK8KRzHYbOCDICdHNfGdBVDTbMlS8UeCcqnyhwmxYKYs+YA7wZUHNiEMtzWs+EU+p\nmiltSXGLS+w5IbJsiJIK21H8jvXJnYPbcUbUkp2LQl5Fdk6cHAwo1jGnf5y33dbDyBBV0o6bpJQL\n8hA4VEl6rmyIWmqdEFO5+b4fw6Kan9T9zcp5jskx1Gz6e6a9mIXVB9ZHZIsgeyGq600Fb3EUcCLg\nmUVmQ1f7CqLnIDokyNkQMyDYRUUq/+F+TdXy0cFUqUgv3WpZpkrsEBUs3bIVt3TWl8LYEccIOqcz\nGxO8V6jnrvKyI4byfK7H64/7RZX/jJZRwLau7N1ZOMczcqF4/Y/rT+QySPqANvAybeocEOyciPVN\nzHBwyzsOCObyaf8gsROCmUtFLw7gcp89dYeLynJCCCEeHg/oT0YhhBBCCCGEEEIIIYQQQrxtyBEh\nhBDvOGOugcfukjjWCVEiZkOYbLIjlTIUgps2PY8NKRE7VkDOMEd0v7gNYbA4kAOhWmwY52nH3Ny2\nepMqUzubu3sLRwTqLxkkJooeJ8/tXaoP5wqODs4xcOabz6jpF7kmZqTIzuZ8z9wY+3M3jgWK7wa3\nvynO4XTA+m106KRz6UeVMi3fNcdkR7wtsBOC4WwIzoLg5aim7ugzOh1CshyV7be2jHsHcR+XIXS9\nySJzQpQcNMWMEyK6udZpdQ1tnzlqe3ZIuKp9ykiZip8hQysq83G2lNvA74CYwdFsszGlOJ6W8mIq\nnGG7dM5flNm1tnO+5Rya2I4dU8wpsPWLOtU/nxuPqcfn0qWuhvE+HVg39j8wM+Lg9gpOiOVi/PuV\nlw2xmD+88fyhZC+UmJoVAdfuojnvd+BD6q91OgghhHj7kSNCCCGEEEIIIYQQQgghhBBnQ44IIYQQ\nGZ6j4KE7Jc7lhPjJiy9CCCFcr3sZ5+2mf32uN+n5gCKQMyMy5wMxmyILgGGBsxzi/P4oMN5mdA7E\nfIBUeRqrY4Vty4rbcWfGsUrTqNDFMq+HmtnZfnB7ztzqu9u47nxe8XSu+kjBGXGwGwRuFiinTaE+\ne2PNmoJ8Yap2zO+PfrZQtNt88JnCnc4NK8yjM8TcNVd/8LL/vOiVj5gjHIpYdjocokpdnMkt8dCy\nIdgJER0SBSdEEcqGGBTktt6KIc9h8SbdffVhX2JzNayD0LUlBT9/xmOx+4wzGwC/Zmbt+Pq43Znf\nn1X/nJnCzgh0c7ZwHE7oH+bG39Dz67ivPOdUXJ+NK3DB7Vf1T3FWeeeEt2fleH/Cc0IwfKzZmBKd\ne/vrKbnK4vsvHsf4OczvlfHcBS+XwXu/7q4/leienRBTnRFTMyS2dGxXz3prHWdBnDobovQ+OKVT\nbqrTQByGXBBCCCHGkCNCCCGEEEIIIYQQQgghhBBnQ44IIYQQ1bDj4KE7JE5NVCObYpCdD0xjmQ+Y\ni39LquWZKfyGT1M3ryB136k/KlQr58rewCJhy46KEuvjHNw8pzc7ITCn/IoqOpe0gVXNtjpTznrC\nRqdfniJ4yvz1pTne3b4UHBbusRCs6oVKHcsXvUEhzFdQcFM5zIlPTojYXcznTopyOD64/6tVWgHm\nEod6nx0SOB+nVKVOretcTgj0w814cNa3NEawE6IEOyQ6BIQ4GQJRsIp7ifMe7HNjc/qvPrDiixDm\nPAbAmUDLcRGOiZhLk/Zl5ohnS7ky7jObqe7Ht8d6SqfYy+mZ+Lyy/SyO6TTGxzG8UN/YLe85IEo5\nFtm52tuDvH43c4L75dXD6n0szlDvNCfE1EwiL4+h6EKocacU8iSmOheOZra/PXZCwPHJTggvG8Lj\nUGcDxsLS/nCX1bjneBx+KO+Ph8Ky8nzI+SCEEGIKckQIIYQQQgghhBBCCCGEEOJsyBEhhBCPmMWs\njS6F+3AnlDIZ7rpPp26P64OSe9X26zPVcSzXnxcoB5s5HBTT++DOYR3VuFA8Y/1+JwQUp/HQOFsC\n7bXUTmzXmxs7qc7NFyiRqYxLc5nzfs5y7X57+4S6eL03v/oBbe2jmFERHQtp+ah+b9OMiI7n4i/0\nz7uGuKe6XzwNIYSw+uo6LWDOiPm7ZaC6FwZ1cn8x3zx/OloOans4m3DtW3MpbGy33SyIXXYFsNmz\nXuijOzYsxtfXqvizcuRAYjijAg6NmFdg93WzhpKd6iNXSSmnxj8ezo44PDtmyI2h8ZzcGCxgZtcT\n3uxcLuZp0BiT5eKw64PfL4SXVZTt7+G4tCK0fzaWOk6IbPv6AA3fhCyPs1JwQIBDnRCcDRGdb5V4\nLjF2KbBr7BA8t0RtdsRDdUIsmsP6VbvfofULIYQQu8gRIYQQQgghhBBCCCGEEEKIsyFHhBBCvCU8\nxPyGkmMClPp6qnqK+5tEHPX8+MWXIYSyAq9jFSV1I2ZFmAqPsyKYqL4MIZ9X2lNX8vziVg6q3iwj\nAk6IqJwdrz8TKS7S3AtvauBaVbPLVAcF4WVAeOVq+ucda8xWOPBbVcnp4JXPyhXm2O/muCf2tzuo\nnG1DcY7+WbqfOSO633tlJaepKPfNBV6a/7tW0QrgbrovhevmSHUvOyDg0nptTohZzJvpP9jRxPcQ\nPjdXNgbBpWD31sXLfv/F653rfTneN1bNe04I975z+pjlGTh5Me5z5NQXseXohKBzh3HAc2HV5hLU\nusRqnSHJ/1dmQ8RcDsp/QV4R6pnRMeOzo2swuGJIfc/XbJ46IFzHg5P5EPMzOAuC64mOwbQ85zGw\nE6K0fR/u0HPX2RCVDojZ5IyH/U4If7/xct/fjA8gGMvYiVHqz26/kE/0weVN0gced2vyJEK4+/fE\nqfKTTpnDJIQQQhyLHBFCCCGEEEIIIYQQQgghhDgbckQIIcRbykN0SHjUOh6OZc2T4vN2O0d/++KH\nIYQQbjf9a9Kbu9ijI7UdZ0h4QJm47WIKgauijMrQbEZ2awsZD6bkhKK1MYU0nBDtgpWmVm6d9hlz\neEflazzEw+Yzz7IgACtnT5SzcKgauaauOEe8fbakSuY2Y44GuVeGCrF9Yn+oX5jvf/V+X//81orx\ntz9SorMTIle0p0pxqJlxFJf/7GUIIYSri/6EQDGLOcM9ZayoB+cUit+X3/UOiO11f/Ga2349nn9W\noHcX/fL6md0bN7aZ1P+dXTOMG1EZvx0yRvKxI13eepklt+kxwVmB+xZ4TolhR2e9s//Mcfp01C7g\nzAbkanD9Hq6To1KJXuO0iA4FLyuBHHMcxpC7m8z5tpjt7h73Htwo49kL/MYrOiG4v9mYZtcg4D3E\njgtyOsANYO9eOBrO5j7Yh5dPMdGJMLnZRfpgbB03Jb53XD3tb+xDMyEAZ0NgvH9x/SRZ31ZnZzRJ\n+eUi/b7o1dOGeezzy9vxsBt+NwE4KOAkeKxOiHNz6dlET0Dp74LHco6EEELkyBEhhBBCCCGEEEII\nIYQQQoizIUeEEEK8I0Bd9JCdEefCc0LwucAyHBBxPmGTuG/a9Pd7KAYX9jbdbJrk02NGSsMGmRBQ\npUJ5ON8W56Z25wDn2IoVqdhNRdiSUBAK6maVVuArYPcrbEv95vnW47zy8RPZFShXP1f3WH8OcULU\nuimiItzmV+94/vKpFPbjOfi9cw6lN5TcUKx75UoulHx7qqIGUKrO6fHjDAgv1+Fdyooo5dB4fPv8\nvRBCCN1NfzPEufONTIG+SBXlwzW3c23Pffacw0F1he352LGwSJAGp7BNPuJfHZw3wPfTnBwHfJ8z\nxbGG90cOBv0V1F5wvgC1A4dHIW+GnUSnYt8YVmorc2MV+srPNK517THBQVfEdQewS8y2X9pJcDId\nZnPkMNn2GdaHZJmdEkzmoDjwvXNW6Fg4E8JzQGA9vmdcXJ1H1c5OiPUmfRF4TlFkauH7FTseuB4v\nIyKEEDq4mJz3RGuBXi3d2M/b3mEGp8TlvD9HH1+9Ga2H3zNTXbQPTd2/vIf+3JUzWgghxP0jR4QQ\nQgghhBBCCCGEEEIIIc6GHBFCCPGO8ZiyI2rxjsFzQvz4+z4DAiroP/7g6xBCCLcmvYXamlXKUM2V\nMiC2tB0OiIbmp25IwdhZuZmpk7dtPqd1pv3zVI+kaI5zfZP4Mc5//sa23zoqRTgkVtOUobVOhGGO\n8v3lSur/0n5TVcpj7XhOhPgJxbc5IwLPZ16gdIyeutltheeoJ2V6Vs75dpjNbU/n9OL3ekk85vD2\nlKrvQkbEVEWstx+7sMDzF89CCCFsW2Q32N2QXSNnfnojOpGuzCFxaY4JKNpxT5hCfPN059rbtjmN\nCXzfekLXbml1e84erqcbLxezG3A/Yz8rF5un+9/NaiD31Yyfi8Lty1kWMUOlpKqnHIda99fuNR3y\nWyj7xzmnfA64zZgVAicNnAdtem6yvnAGBNXH92d2X4bx+3bmOByg7p/x/Y5ryE4J7M7vV3JIbNfT\nnuPkfFCWTojjth2znaToKTw0v2I2vl8pE+JQvGwID3Y04PtS57k9cW0LLjHPMZFU5Rxr9h2OnBPs\nlMA4fNv2D/eX732f7M8OvIfmcPBYNPfvQpATQggh3j3kiBBCCCGEEEIIIYQQQgghxNmQI0IIId5x\nxtRIj80lUZt/wduhYvvxiy/7ZXJCYHtp/vbobLDquwD15fh+C5qHHuo8OCY6KHt3lY7bdM7qbG7r\ngjNi86xvs1mZgjpTLdt8/pQlnx9c3gAAIABJREFUMXTaVvM86rF9XuGoMUvK3KhWHm+n1jGRr/fq\ng0KX1u+pO5tfnfpWnSmB7UMNyf6luehr3SG8PTo6Lvbv52LtYh543DvIRmkaexDMGREalqb3sDPi\nkKyIU3PfWREAx4z51aEexpjSruCasrvoIj03nro63nOm9I73mO2fK+it3Tkp5rv0/3eBEQ3DF2dG\nYHv7xJadv0a858BzQiyu0/XtEh2yejCGwQUS7//xjBPvOeZzxP0rugCYwnN+kLga54iWm824k4HP\ngdtnOB3m6ZjlZZEwXF88xmXaoXhf83uN8hA8B0TWLjmL+P3puhFQ/0PMiGCcrAiPy6d9WBBcAHAP\ncJ7CvgyGfcSxy8asohPCwPaGXZ2cKYHvTdTfzlk/hdIx/+ZNn82D7IgPLm4PbusxcA7nxKFOCOz3\nWFwnQgghcuSIEEIIIYQQQgghhBBCCCHE2ZAjQgghRMbbliOB/v/dqy9CCIPqGUx1QgxKO2zv0qUZ\nFIB1SsLPPniVLP/q+QchBFZ3bmkdqwO5k7YXqwrRdy4PlXKlSj6qg0lJOotKVvTX1nfjSlzUA3Uy\n5iKnU1s9XzqrlEtOiKEf6Vzlu/Pexz7iW5Oj0OZlKLW37CaBeNhT7zpK6JIy3MNzROCcV89hn1Vs\n+x86t/kJwLO7cNSvnsviruExp8Rvv+8Vt5vbfj8899kc9wtyQjQ8TvR88lk/xmB+d/QH6uHvv30a\nQghh+WRt+9sY+Ly/SZq1Pb87inp2MXU0dsxNJBzFtHgubNHTw3rPk5cNMX9jnzdOhWnzmQoL/eex\nz38OOX/Bc3/RehrTso45cD/iuHJg7s0uvuuDxuFYgBbZCcHjrTP+shOCMxxmeA+wCt5xQsR6sb+n\naOeMiRKFMTF73yy2Q15LgbivlxVR6WwYKtxfns/J6ib9Z4Crpyv7v9RRgAwGuASQmzA1K2IqmQPC\nK+d8T+vX23ezwjMSc42cY1rMx9cjM+KlnTo4I+76ffMQsh5qOdYJIYQQ4vEjR4QQQgghhBBCCCGE\nEEIIIc6GHBFCCPGIuSvV1WNzSKy78f5BBbw5RkI6Qj6fcH9dOlJBI0uiNP9wzIoYKRcVn1BNsvqS\nnRA0xzXWR2UzPuFk4FMD1TFnR0SXgDOXOOcokFIbvY55BTyneMFJUQurkllN7bkcQHKrOOeG6+S+\nui6Ticfkz0lP7Tr9GM41PrdJeXcOey/vw+6dxe++DiGEcLnspdo85zhUpqwqnZr90HbNneREhHB/\nWRHfvHwWQgihXZtjYYMHaL8yHJ+fftw7H3Cu+XzhnLfbVJ7/9MPeVvD6ee+MGJwP6TiBHIYQQmiv\nrAbcR1f0rGLadCdDopR1UuuEWJqhrNmYYvsyzX2JryuqZ24q5pgZ4Y1lBaaWd50RqA9jUrelcr5C\nvNiHOF5bDoCbFZGW3/L7xGuXx8aCE4LxHQ7j5UrZEHG5UlWf4bpcrFrPFdfNhn0f2BT2Xn4GuL1J\nX1SXV/0DArcAOyPOReM47BrH8YH3jeeM2N2GsvyOYjwHBI+n+A6OcfXl6jKEcH/OiGNZnrG/Ux0N\nckAIIcTbixwRQgghhBBCCCGEEEIIIYQ4G3JECCHEI+c+3AqPxSHx01c/CCGEcL1Zjm4vZUNsWlYP\np3hKPE+Zx2q8j5+8Sdp9/uppWn5n/nfM2Y51USkdC1s5OB6WqbJtW3AWYK7vGak5o3J2g2XaESLK\nJlVbstNhS4pc7vewo9M/RzVdInMpOBPFc/3xku+RbNT2JZ5TywyJ8+0XFKqeenhQP9e5RbK5/MkJ\n0Wz42m2T8lGZTfXBCbGodA3gefLUprHcA8l1uEu+/r53QqxX/UnvNqmie9uSs2eRKsN/8NnLEMKg\n2MW5mzepYhf3M1wqbUjH7icf9UELN9886eu3fjQjz/+GnhF2OGT3nTW1ec+W6a8Q73LzmNOY02KB\nbIjV+P0JMXytAc7Ll8ldWQUXmJMpESm4EGqf5311lMAz7o5BGDexbPcB3hNuLgw7FSqdEnifeRkn\n7IQoqfuHHTnUYppDwrsWiQPCY6rcD3Xh3KKvU7MiHNzcDDSPZ30x7twsOSFK4zqD7zQzchl0Nta5\nzgjHFcffx7xyIYwcm+PaYwfE5bwfjDg/jMvhOyfG4aeLtduXfRyb/eC9Q2vr9cpdZl8EfeSEEEII\nwcgRIYQQQgghhBBCCCGEEEKIsyFHhBBCvGW8yw6Jn5gDYmPSW3ZCQGkNJ8Qfvf/bEEIIP33xeQgh\nd0JMpXXUkVCMlxSFnzy7TvoRQgi//f49W5eWhbqR1e1RcTojVSU4cP5qbz53FsZhOwSCUK7yXOPd\n0hSubcGpkZ2zOheA64SgftbOrZ5QcG0M7o+0jZjD4SmmK90hwMukYIW6l8Mxv7E5v2/S3TdP+/Ut\nXau43w/e7O+YyEDWxIpyYxb2oGxaZELY89Klamg4IPA84/n/6ovv+nrIAcH1Ax7bMCYhj6Yr3Nvs\nYhgDZbpLW2EOhm45Xgffx41lNzQmIi4JwpHtEOu/SNvj5wHkz+vEXIOOnBH8XDv1eTKwqWNiyVG1\nj5gTE9I+cpuHxikNjjKqf07vJ96v5BJztrv7HZoRAbz3pZfzsdetMtEtMtEJUXKT1O/fL19f9w/w\n06dp2Iv3PebFdR8akzlE7Rp07Oo6MO+H62fnw+7yoXkWpSwItxyPt3aR8V30UGfEVI51QnjUOiFq\nXA1yPgghxLuLHBFCCCGEEEIIIYQQQgghhDgbckQIIcRbzn06JO7KGfGfXn0ZQvCdEB7rgtzTy4Yo\nEbMgbBmqvC/e/76vlzIp9vHh01R9/vxFP5c8VIuzBUtYU8U0zy1fIjoeaBnfGFr79DIjMiGebZ9Z\nPRBkQ/WbZVM48PbqzIiCW2DrfBPavTS1UQWssG6vTGV+OS0bgvvKYN5+1+3BWRDUr2Zlc3CvUF+6\nX6yH5uiO1wyq0MpsiMfIxZmPDYrVf/zu4xDCmFq4P/e//8PetfWrFx+EEELYcDaMUVLmZu3bXOjt\nZr+6GPcAXAzzHUcS7r+O1e9N4f4mNXl0RNh9uLhJt2OMiU4LG97Xz9JPEB0R0ZG0vztDNkPqcOBs\niEPdARmUU7OlMdF3cdHzHEbGTqduwFeGXRtbGBbofvTGAlQ4OCvsGEKaDZTBToUT5SBkOBkReC/O\nnByCye+VMRw1vueE8PpSgh0PngOi1hnBvLnpHzjObGjo3HYF9wn257HOy4pguH5uf5/7wcuLmNOF\n9pwQ2X4Tx1twbmfEoU6I5YyP5/S5DnJACCGEAHJECCGEEEIIIYQQQgghhBDibMgRIYQQ7xh36ZA4\nV1v/7rf/LIQwOAo8ddmfffjLpN1bk8jCOQFHxO8++y6p7x9efhJCyNXC4EcfvAghhPCzlx8m6z1F\nXskJ8c3LXtILtfN2Zx7iLatip84LzorQJlWusqo2OhRYdY/2obS9QH32yRkWpHaOrcY5t00xO0/r\njUd32PTRmVMCKn9P4cpq58EBYj3eDOcbim03mwF9WKSfmQz5THhOCMD9j3PjX6SfcHJ4HDq3dy21\nKtTHDMYO5Mr8/ufPk+08F/mXH74MIYTw9fc2VlimBM5VrSI3znVuNwcUwa25GL7/9tnofnhud/Mb\nBuePFSpknOD+jE4cckIgE4LHgo6ep/YybXfIIxjtehEvL4Zvw6IzwssNcMqdymGxbbZ5XgWXmafj\nrQscDl6+Rfr6iMuxfXZGzGvtZJSJwnkKcKccqOpnJ0RcHXN7jhykK65l9fWeeA7qMyAq24/l02vH\nTobg3EvsVMiYp/Ud+z5BZsScVPxjLgh8R8O4t3DuT34HTX33eO8wUHJGTHUknMoJ4eFlQ5RcDnJB\nCCGEGEOOCCGEEEIIIYQQQgghhBBCnA05IoQQ4h3nPhwSU9v4m++/CiEMTgawMSUc1GV/8dHPj2oH\n/OjZi7QdkzKu2vH6vvrwRdIfAFUzPjHPO+Y6ZoVh5zgwQsidEK4zwtSIcXth3uZacucA5o5PnRPs\ngDgVPG/7sN4rP74+czPA8bBI5zRPnBFwh8S52amOQttwl4R4DtmlMl5PCc65yFTM8VjH3S1wQAzO\nCKim96uqQVH5+g4DhSq7n5A9AVdVTU7MLj80ZwTDY4/nkEB7GwtrQQ4OVMVX79+GEEJ4s34SQhiE\n77tOiBDSeylz2MC5cJV+YvvylfXxOu0bsh/iMA+3FLmmslyXylNYOtX8nJSe7+w55vKFvIYildkW\nofP7Eo0Lhb/4PCdFzM/YpI454ObdoD4aI7Z4x2E/lMO5X/YdjxlIzbg74GicfmfXnHI5Svsn+xhe\nJkRsc56eq9qMByw3C5wzOCrHu+Wtx3sNzgQvy4EzIuJ66/dykX6XbL1zFDOGwmg7sZit99qN7dNY\nt7vMLtWSE4LxnBDe+MrOBIz33ndHjMeDsy09h976uD99Jz7WAVHrxJATQgghxDHIESGEEEIIIYQQ\nQgghhBBCiLMhR4QQQoiEXSXTudwRUx0L//L9X4yWX3d1+//4xZchhEE1DIcCZ0OAf3jxcV/O1q83\nfTu/99G3SfkvLfsB9f7824/6ZXI2sKIPSr9tt18PkM7pTE6HEjwntikDt5i7m+by5qyI7nJc+Trz\nVPbOfOqxO1Dtox9QYbbjan1vvnXPGcF487u7/WMFMYS7y+3ghLDPxtTBmbaQnRCVc8VndPu3w/mA\nK1Tt/uBmKEsic0LQPPDN57dOh4VHyRkR7IO312Y+HNwvUwRzDk5UE+M5o+cTroX2KoTNU1NOL9Oy\neEbbq3Tf+Y21bU6Iud1O7ZO0bpA5fug5HFxN4+WKmQ4hLec+r1OzIgD1N+bv0Dnlekr1Zs6k9czP\npYg5HORooOGzmDHhOANyd5eNTTa2xCHEcfvNNmlHtmsMslZvwS0wuALsnlvhZFe+Jx33AlwKnCnB\nTo59Y3y1EwKbF3XPPH8H6GK2VLq+5CLhzAh8H+noZsL3F88BhzEDn0Nmg12TA/M3Sg4Mrx9jWV2e\nE2Jy9oOVL2VAeMRxP6t3mhPC24+pzYDw6uFsiFM4IZYT8y88FnJdCCHEo0WOCCGEEEIIIYQQQggh\nhBBCnA05IoQQQricOz9iav1cfjkf3x9Oib979UUIYXAs3Lbpa++fXn2UrOd501nJ91+efxJCGBR/\ngDMf2OkAhaGn8PPmbp4C1I+ZKpLajCr6gjMCdDa/+8yEcc3K5m3epOtLRHUoqYObdZqfkFFwRjAl\npwarpovMdhwIuD3jnNrjyuYsqwFMlX+QMwLK89g1Z27tqLyGu2WRLnvq5qhohxq40n0ijudQhe3U\njAm009rFh1K4tXu5tbHz8sPerrC6sUyJp/b5rK+nfdplGSQRemYxRixep8XWqOsJ7W5OCeRR2LAc\n2ovCwRUyGdgpUX3KHadTnlczLcfAc0pwO9klJqfStskMDtm5GI41fcazMQR5HJfpyfEyJrI+ssNg\nVfmdwcmMiG8n9Av9JYcEZ0rEd+rWeR8Wcgdi/dR93iu+R/fdSwUnBOc6lZ0MdRkS/v68PN75zMlp\n5xKOBCwj82Ftzzl/PyrBmRTc7iEOCFDrhCiNv4c61JAN4TkhALIeni1vDmoH1DogQK0TotzueD2n\ncj8IIYR4u5AjQgghhBBCCCGEEEIIIYQQZ0OOCCGEENU8NIdECajR4Hi4XaevvXYORV3/ebvpt8MJ\n0XXN6DI7IAA7IQang613VHGsUKzBc0AMBSCXpawIbF526XpWCUdFqtUDJwEUsFHBSvsRMWsBrgJM\nAb7mdh11cWV2RMkJkc3X7s6Fziv2lKGsBq+OokiRzgHEiPHcXYzP6+7V7ynUOZ+jRDynn6TZEJ6z\n5yFyqNPg1Lj9OHYIrRWcwlVQUCtDXdx1/efis16hu/nuMoSQugpmmPffuR1wyIubdHn9gX0+S8sj\nOwK49yk7HgqXOMuUcLZPZWoGhfcAe04It519YxK1lLlCnGc3c0wgu8F1d00bSzzYSZFl96A/PL4b\nbdM/QHifNUt+IOrcBluz3QzvyTtwgTm5F3Ez7vMTDWGHfM/YhZ0QJacCOyMachXEx9fKNYtxB0ax\nXwe4FdqYY5F+B6wFTrTS+6XkhKhlqtPBo5Qp4TkhXMeDnBBCCCEOQI4IIYQQQgghhBBCCCGEEEKc\nDTkihBDiEbOcbcIlJtI2brvlHbZ/XofEqYjzojvzBEd1XJw/PT0OKPY2m/T3e98BEUbXt2tTb1q7\n+OR6psBqyqJTgubezvoK9bCTHYHbKzobVqlSNVO0eu6AgsAvyzOI/R8v58777jkhal0BIzkMMUOh\n8nZ3MxbYCbHql+f2OfTZ9r9IsxuG27TODcIZE3lHadGcEFDS8lziPEf3IcrUYzl07m6PYxWsd+3A\nQH/h/ioRFcA2KCEXZ3BC9OVwLZu5PYfIGVmZevmmCfMbu+9YRBvHhpBsb3tTRXRCxDGE9o9jjP2V\n0uGvlYJDgjnWCeG5p4rZEDTmTL0lODviENw66BnnPBn0tbHrvO0wXsIZBxdYmumQZRE5+TWRbFyv\ndCCgHMZOtGcnvTOVfrPAu7XghCi8J2vZvdbHukQYdjL8/+y92ZMkyXnt92VEZlZ1dc/0rD0bgAEJ\nkiAFXV7y6lKmq8VMetKfq1e9ySQzmR5osmukCVwuAQLgYDCDmcHM9FZdlUukHtyPR/qJ+NIjcqmu\n6j6/BwRic/eIjHCPnjrHz1CHxNAsCAaON9fxQNu53y85IdDXwGnKDoqSE8LLhhjTd6Mf9Prr87rf\nGTA2k+dQ9nVClJwPjJwQQgghbhI5IoQQQgghhBBCCCGEEEIIcTLkiBBCiFcMdkiAm3BKQCV1qDNi\n7Pl8PNahXoP67cxW2XZmHbdzNgQDB4PvgKB5raEadVSZuxg7P7Sr8GToOJ6HvOSMsFyYmlTPrvo/\n1du/eVPlyty2Xf3OiE4mRCon/p8DnRC9bSwplJ1LH6qMhhOifoH6wrKZh2VHazjJ2+Xd+9EiTvzE\n5IQoKWUBlKo1XfDUcSSl/ZPd+7NjC+resc6EfZ0QhzogSucPVeCOdUaMZRIzA+rLODf6s4nVMfsB\nQ04THQ/r+Lyi6evzuD9uR16M21ekXJlYDpbOrfCyGljky31bp1ocj5/Eqx8ugYHOI6+vYlL7Up4O\n3SfntdvZL5WGA/Tjsc1wgKV76hU9dCxLWT/UkCZ3VKTMCXJa2MB7l/Zv+sdewGOzFdT3Q3+zTvu3\ntrFbxMuGaL8TsN7vmPQcap6z0nNCcNYP+vXZNPQl3vcPYOdDKsepjx0MHiXHBZeDdixofz1pOmNO\nCR5XOuPCkQX/3P9frcN3+nkdOtVlfMCGOiOGOiE8BwQY6oSQA0IIIcQ+yBEhhBBCCCGEEEIIIYQQ\nQoiTIUeEEEK8JtykU8JTUx0rQ4LL+ednH5iZ2arJnRAlkA2BedJLCkCPBtkRrrrSm596t7MiP3a/\ntnXwlKa8PlC9mNS78SfBTz9aKD4wM6JTvzs/fL8Dws2soPW9poL2FMu4R3AasOoYatqq8BvjfL4X\njqo5nUbKV+x3ldTx9HqWv8dQqnrZEMdyQuziWE6Il+WA2Le+Uzkj8NusYx9WpdyccP7iD8HOUF2G\ndWTCbKZmq4tQRsd5QNkOnXfRuYVexon3zpYcDonUp1F7sHlF9i6vPQMzKjrvHzuiKBsmnYfj0/U5\n72lfN+GG/lhvGclpNu13d2ycfyGma0FODl8rZ0Ow88FzRjCl8Y6dCMnB109p/CxmWvD55ORIroed\n7pRhTojOaQOzHUrHsQMibad+HXCWAwPnRAnvfC8rwjvOy6Lwxp/tcWfomLOKZV7Ml9lxXv97aKYQ\ngAOCKTkhjuWAaOsblwmxD6WybmvWnBBCiOMhR4QQQgghhBBCCCGEEEIIIU6GHBFCCPGaw06JU2ZJ\nsBLKUz795sW7Zmb2Is6XW1IDe06IpGaLf3fH/utlPvx52RBYbwrzSIPNMhyfFLuYCx1uAZrzeUz+\ngzfX9eDzWcFKc1qnLAiU68zhzVkRSdWLKbdJ/d9pB7kB0vaB1+NlRQx1QHTLQznl41np3Lah/3ie\nKz6pklMx+b2Agnx9L1/HslriWp3y6DdglbTX/tI88kOdEMy+TggvG2KXG6LkVBirXL1p50OJsc6I\nQ7l8HB/CVf5wrO7HPqzepOepfpH3e808V6kD75ZOhol1E1xusZ6hzgk6n/NmElx+yTFheXnJ+UB9\naGr3ivpeekGbefdGpn7VcUO5/Ss7IWrqR51rw1iX1vtL950QA2HHHjv1OKOBXQetyyCsJ/citq5p\nvPPw6sVvN+/2L15ORYUx0nsfBjogPDznQ9rvZPy0mQ+7+/US7IAolVPa7zklvO152cPGHLhjcfzl\natZ7PPpfbIfz7Njjy9BMCI9DHRCl/WOyIca6KYb+O0EIIcTdRY4IIYQQQgghhBBCCCGEEEKcDDki\nhBBCZGw7JE7pjjDzlU/LqDp7tjozs+HznTOsHr5czM2sVewtV6FcnmsYwAnRYE59z8mwcfIWoiIc\nCsh0OBS3PEf4ANdDq/TcZMccLUOCSepc2g6VMTsjADeHRI+dedMd2AHRUeZ6+RvsGnBIeQmHSDPi\nNWCueZSFItfnTbYhtRlOGVKdr+c4L/7G8WtttEiylV6PAvd8+UUIAZh9dBmWIzMhXGfDQCfETTog\nhpZ7V+CsCHaHYX0d1+u0Ho5H37W5H8rDM5zmwr+ubLIgJ0Ts5+CISJkmhfn38b40A/9VMjTHhTMk\nPDcVyuOffk3OjnJftXt/B+4PStYkHEV97abalE+lrAjXIcEOg06/T50Jj2FT/DjURq9dTjvce+04\n9SbUz1dTLqDKtrMzolhvpPMbYxxe95RHzoN2zN5dB7Ovw+FQ8J001LHQJFeBk0VxoNOj6KzoyYYo\nl4lMnP7nwXOgHc0Z4TgLePtQh0OJoe6EsU6IY2ZIlMqWQ0IIIe4+ckQIIYQQQgghhBBCCCGEEOJk\nyBEhhBDChfMjwKmdEn9x/0szM1tewCERln/3+AdmVnZIQK22SpkPefYDnBCrWA4cD1Udl6Qw9JwQ\nyY2A+bmxg+arBlBheu4F3r7LEbEvnawI1JXmL6c5vY8lWUB+ARS57ESAa2Tk/O77+kBS/Y7atE/F\nzceWMiM2SWnt5GpE4KRo59iPyzPKgFjtbnPb0N27S5TyOtgJUcqCAOw2eJWyIDxlK4M8m73rGZkV\nwc6IobT9THRG4Pzn0WHxrDaIdFO2yUV0TYzMe2nm8bkm5w+yI6qFU8AU5XC5g6rfcrDl56Edxk4I\nep9Tfex4oz6OHRzIjkn9R8dd1n8d6b0nA0Xf/eb8mE4buZMgJ0TxHnKdWIcTAmMf3IJOn+WOhXzv\nCxkNVveP2al56DvRF8MRwfkcDp4TIu1fb12n41AoOSBLnM12q+I5mwE0jtq/5FDYN7uhtN8rd2i2\nBDvwwK5xaOjYMxZ2Rnh4+9N4UPVv98YVuATwbXyoI6GYFfESnBAeqPNVcS8KIcTriBwRQgghhBBC\nCCGEEEIIIYQ4GXJECCGEGA07JU7tkADndaiXVcFQ+3pOCGyHMpCdEADrjbFyMckyzazrjEguA5q/\nujhtt6cc3vRsn+R1sJqSMyPcOlnFj3IOzJjw5l93j3ecEZ1oCSfrYWi2Q2l++u4J3fYVHQhpjvuY\noRDVtykzAkJWfHVhevNlvxoX5aS24N7SV1snPwOXkNTJlq4hO4/U0h3i7tnHIRsCStzZNCgRhzoh\nhmZBpOMLKtVdCsihTohDVZRDnQ9Dzz/UIXEo+C1W63x+9wmeabh1rqLy91lob7UwW1+EY9cP4jXh\nueP+kRwFTPscc98UF6u8nNQn7Liu/vIdqA9BPdXCUcs7zjhkY3hTmLfl9DubSu/prutIrit2BnQy\ndvqzH7wcDF7fWP89Sb8JnAHswPD6YR5/HOcD5+gwrSMhrnuved/YuqvcUkZGJDk+ttwQQx0PeNc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mIRzodTYX0dz38RlvVlLnldX+TzNFtU2ibFoaNSRPkdB4SjvqyuscxVk3AHbEjhm1SlrFxl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\nOiPmUWG6iOdxhoRXztCMiLusaH8ZeRYi57b+Boc+17f1u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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112195588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import iris.quickplot as qplt\n",
    "\n",
    "# Set the size of the plot\n",
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "# Select the first forecast\n",
    "time_slice = surface_temp[0]\n",
    "\n",
    "# iris quickplot sets up our axes and labels so we don't have to\n",
    "qplt.pcolormesh(time_slice, cmap='viridis')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finally...\n",
    "\n",
    "We hope you found this page a useful introduction. Please have a look at the data page, where you can find other notebooks covering more advance uses. In particular the Met Office Informatics Lab are working on distributed cloud computing using Iris and Dask.\n",
    "\n",
    "You can find the Iris reference documentation, a user guide and examples at the [SciTools website]( http://scitools.org.uk/iris/docs/latest/index.html). There's also more information about the Met Office models at on the [Mogreps](http://www.metoffice.gov.uk/research/weather/ensemble-forecasting/mogreps) page.\n",
    "\n",
    "This data is free to use for research purposes only, and not for profit making applications. Any derived products should acknowledge the use of Met Office data. No ongoing level of support is implied and data may be changed or removed without notice."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
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