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    "The Community Aerosol and Radiation Model for Atmospheres, [CARMA](https://github.com/ESCOMP/CARMA), can be configured and run through musica. This tutorial walks through creating two small carma test..\n",
    "\n",
    "First, we need to import our packages"
   ]
  },
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   "execution_count": 1,
   "id": "8518f967-602a-450e-bf89-6f6b9037cc78",
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   "source": [
    "import musica\n",
    "import ussa1976\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "from musica.constants import GAS_CONSTANT\n",
    "import musica.mechanism_configuration as mc\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310d4404-a3a0-45fb-949a-535f40683dc0",
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   "source": [
    "# Aluminum\n",
    "\n",
    "The first test is a coagulation test for aluminum in 5 bins. Please note that all input data going into the musica carma package should be in SI units, so meters instead of centimeters, pascal instead of hectapascals, etc. Anytime CARMA would be expecting a different unit, the musica package will automatically make that conversion when configuring carma.\n",
    "\n",
    "First up, we create the groups and elements, and create our carma paramters class. This is then used to make an instance of CARMA."
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa06c252-1b3b-4945-abec-f0ff97c0a45e",
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   "source": [
    "group = musica.carma.CARMAGroupConfig(\n",
    "    name=\"aluminum\",\n",
    "    shortname=\"PRALUM\",\n",
    "    rmrat=2.0,\n",
    "    rmin=21.5e-6,\n",
    "    rmon=21.5e-6,\n",
    "    ishape=musica.carma.ParticleShape.SPHERE,\n",
    "    eshape=1.0,\n",
    "    mie_calculation_algorithm=musica.carma.MieCalculationAlgorithm.TOON_1981,\n",
    "    is_ice=False,\n",
    "    is_fractal=True,\n",
    "    do_wetdep=False,\n",
    "    do_drydep=True,\n",
    "    do_vtran=True,\n",
    "    solfac=0.0,\n",
    "    scavcoef=0.0,\n",
    "    df=[1.6] * 5,  # 5 bins with fractal dimension 1.6\n",
    "    falpha=1.0\n",
    ")\n",
    "\n",
    "# Create aluminum element\n",
    "element = musica.carma.CARMAElementConfig(\n",
    "    igroup=1,\n",
    "    isolute=0,\n",
    "    name=\"Aluminum\",\n",
    "    shortname=\"ALUM\",\n",
    "    itype=musica.carma.ParticleType.INVOLATILE,\n",
    "    icomposition=musica.carma.ParticleComposition.ALUMINUM,\n",
    "    rho=0.00395,  # kg/m3\n",
    "    arat=[1.0] * 5,  # 5 bins with area ratio 1.0\n",
    "    kappa=0.0,\n",
    ")\n",
    "\n",
    "# Create coagulation\n",
    "coagulation = musica.carma.CARMACoagulationConfig(\n",
    "    igroup1=1,\n",
    "    igroup2=1,\n",
    "    igroup3=1,\n",
    "    algorithm=musica.carma.ParticleCollectionAlgorithm.FUCHS)\n",
    "\n",
    "params = musica.carma.CARMAParameters(\n",
    "    nbin=5,\n",
    "    nz=1,\n",
    "    dtime=1800.0,\n",
    "    groups=[group],\n",
    "    elements=[element],\n",
    "    coagulations=[coagulation]\n",
    ")\n",
    "\n",
    "FIVE_DAYS_IN_SECONDS = 432000\n",
    "params.nstep = FIVE_DAYS_IN_SECONDS // params.dtime\n",
    "params.initialization.do_vtran = False\n",
    "\n",
    "carma = musica.CARMA(params)"
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   "id": "fd1fc77d-2bbd-4257-a480-6e7543f7bb69",
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   "source": [
    "Next, we make some envionrmental data which we will use to initialize our state."
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  },
  {
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   "execution_count": 3,
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   "source": [
    "n_levels = params.nz\n",
    "deltaz = 1000.0\n",
    "zmin = 16500.0\n",
    "\n",
    "vertical_center = zmin + (np.arange(n_levels) + 0.5) * deltaz\n",
    "vertical_levels = zmin + np.arange(n_levels + 1) * deltaz\n",
    "\n",
    "centered_variables = ussa1976.compute(z=vertical_center, variables=[\"t\", \"p\", \"rho\"])\n",
    "edge_variables = ussa1976.compute(z=vertical_levels, variables=[\"p\"])\n",
    "\n",
    "temperature = centered_variables.t.values\n",
    "pressure = centered_variables.p.values\n",
    "pressure_levels = edge_variables.p.values\n",
    "density = centered_variables.rho.values"
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  },
  {
   "cell_type": "markdown",
   "id": "e9bb3e37-78cb-4831-8d4e-b4d677c22ae9",
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   "source": [
    "Finally, we will run the carma box model. While the model runs, we will also collect the data. At the end we will combine it all into one xarray dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d77d442-68ed-4640-8992-8f96ab823975",
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   "outputs": [],
   "source": [
    "mmr_initial = 5e9 / (deltaz * 2.57474699e14) / density[0]\n",
    "\n",
    "state = carma.create_state(\n",
    "    time_step=params.dtime,\n",
    "    temperature=temperature,\n",
    "    pressure=pressure,\n",
    "    pressure_levels=pressure_levels,\n",
    "    vertical_center=vertical_center,\n",
    "    vertical_levels=vertical_levels,\n",
    "    longitude=0.0,\n",
    "    latitude=-105.0,\n",
    "    coordinates=musica.carma.CarmaCoordinates.CARTESIAN,\n",
    ")\n",
    "\n",
    "for i in range(params.nbin):\n",
    "    for j in range(len(params.elements)):\n",
    "        state.set_bin(i + 1, j + 1, mmr_initial)\n",
    "\n",
    "bin_data = state.get_bins()\n",
    "bin_data = bin_data.expand_dims({\"time\": [0]})\n",
    "env = state.get_environmental_values()\n",
    "env = env.expand_dims({\"time\": [0]})\n",
    "time_array = [0.0]  # Start with time 0\n",
    "\n",
    "# Run the simulation for the specified number of steps\n",
    "for step in range(1, int(params.nstep)):\n",
    "    state.step()\n",
    "    bin_data = xr.concat([bin_data, state.get_bins().expand_dims({\"time\": [step * params.dtime]})], dim=\"time\")\n",
    "    env = xr.concat([env, state.get_environmental_values().expand_dims(\n",
    "        {\"time\": [step * params.dtime]})], dim=\"time\")\n",
    "    time_array.append(step * params.dtime)\n",
    "\n",
    "ds = xr.merge([bin_data, env])"
   ]
  },
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   "execution_count": 5,
   "id": "3790977e-ac2c-4289-95e1-cdc5c13c834e",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 154kB\n",
       "Dimensions:                     (time: 240, bin: 5, element: 1,\n",
       "                                 vertical_center: 1, vertical_level: 2)\n",
       "Coordinates:\n",
       "  * time                        (time) float64 2kB 0.0 1.8e+03 ... 4.302e+05\n",
       "  * bin                         (bin) int64 40B 1 2 3 4 5\n",
       "  * element                     (element) int64 8B 1\n",
       "  * vertical_center             (vertical_center) float64 8B 1.7e+04\n",
       "  * vertical_level              (vertical_level) float64 16B 1.65e+04 1.75e+04\n",
       "Data variables: (12/18)\n",
       "    mass_mixing_ratio           (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_mixing_ratio         (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_density              (time, bin, element, vertical_center) float64 10kB ...\n",
       "    nucleation_rate             (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_radius         (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_density        (time, bin, element, vertical_center) float64 10kB ...\n",
       "    ...                          ...\n",
       "    sedimentation_flux          (time, bin, element) float64 10kB 0.0 ... 0.0\n",
       "    deposition_velocity         (time, bin, element) float64 10kB -9.99 ... -...\n",
       "    temperature                 (time, vertical_center) float64 2kB 216.6 ......\n",
       "    pressure                    (time, vertical_center) float64 2kB 8.85e+03 ...\n",
       "    air_density                 (time, vertical_center) float64 2kB 0.1417 .....\n",
       "    latent_heat                 (time, vertical_center) object 2kB None ... None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9e7ce58d-1558-41da-bc0b-81dd71aef5ef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9e7ce58d-1558-41da-bc0b-81dd71aef5ef' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 240</li><li><span class='xr-has-index'>bin</span>: 5</li><li><span class='xr-has-index'>element</span>: 1</li><li><span class='xr-has-index'>vertical_center</span>: 1</li><li><span class='xr-has-index'>vertical_level</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d6551334-b643-40cc-a286-3ba99c4ac500' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6551334-b643-40cc-a286-3ba99c4ac500' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.8e+03 ... 4.284e+05 4.302e+05</div><input id='attrs-f29d4ccd-9008-41ba-a396-68c42f478b0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f29d4ccd-9008-41ba-a396-68c42f478b0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4015c32-30df-4342-99e7-d6957ef77df5' class='xr-var-data-in' type='checkbox'><label for='data-d4015c32-30df-4342-99e7-d6957ef77df5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0.,   1800.,   3600., ..., 426600., 428400., 430200.], shape=(240,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bin</span></div><div class='xr-var-dims'>(bin)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-3e61d5b4-c529-424c-8c65-52ea6a1c106b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e61d5b4-c529-424c-8c65-52ea6a1c106b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb8667f2-a3d7-48db-a270-aa4fe95c350b' class='xr-var-data-in' type='checkbox'><label for='data-fb8667f2-a3d7-48db-a270-aa4fe95c350b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>element</span></div><div class='xr-var-dims'>(element)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-6535a559-6ab2-4056-ad9a-8093dd43cbf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6535a559-6ab2-4056-ad9a-8093dd43cbf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2e0afa4-fade-4da3-9fc3-1b5c51aa65a1' class='xr-var-data-in' type='checkbox'><label for='data-d2e0afa4-fade-4da3-9fc3-1b5c51aa65a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vertical_center</span></div><div class='xr-var-dims'>(vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.7e+04</div><input id='attrs-78882ea0-401a-4fcc-98af-e78af140959e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78882ea0-401a-4fcc-98af-e78af140959e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9fc96e8-1b19-403e-bf9a-207683ea567a' class='xr-var-data-in' type='checkbox'><label for='data-b9fc96e8-1b19-403e-bf9a-207683ea567a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([17000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vertical_level</span></div><div class='xr-var-dims'>(vertical_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.65e+04 1.75e+04</div><input id='attrs-89714c99-218e-45de-8530-6f7dd49f6a98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89714c99-218e-45de-8530-6f7dd49f6a98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39e1dcfb-dff8-442c-9aa4-651db43e413b' class='xr-var-data-in' type='checkbox'><label for='data-39e1dcfb-dff8-442c-9aa4-651db43e413b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([16500., 17500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b209a091-ba52-4906-a1c1-524e74d76278' class='xr-section-summary-in' type='checkbox'  ><label for='section-b209a091-ba52-4906-a1c1-524e74d76278' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mass_mixing_ratio</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.365e-07 1.365e-07 ... 1.453e-07</div><input id='attrs-df90c639-b9d6-42b9-a343-0acfaabb6985' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df90c639-b9d6-42b9-a343-0acfaabb6985' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd7ff2a6-c6c9-4f35-b771-2e093cb50476' class='xr-var-data-in' type='checkbox'><label for='data-bd7ff2a6-c6c9-4f35-b771-2e093cb50476' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>array([[[[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.36427335e-07]],\n",
       "\n",
       "        [[1.36453356e-07]],\n",
       "\n",
       "        [[1.36471940e-07]],\n",
       "\n",
       "        [[1.36477617e-07]],\n",
       "\n",
       "        [[1.36503414e-07]]],\n",
       "...\n",
       "       [[[1.27520200e-07]],\n",
       "\n",
       "        [[1.33176857e-07]],\n",
       "\n",
       "        [[1.37461746e-07]],\n",
       "\n",
       "        [[1.38929726e-07]],\n",
       "\n",
       "        [[1.45245134e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.27484358e-07]],\n",
       "\n",
       "        [[1.33162640e-07]],\n",
       "\n",
       "        [[1.37464927e-07]],\n",
       "\n",
       "        [[1.38939528e-07]],\n",
       "\n",
       "        [[1.45282210e-07]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_mixing_ratio</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.299e+08 4.149e+08 ... 5.522e+07</div><input id='attrs-5ac95a33-3a51-49f0-84df-1183dd150474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ac95a33-3a51-49f0-84df-1183dd150474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff7b88aa-a755-4749-aca9-8f01cc9212bb' class='xr-var-data-in' type='checkbox'><label for='data-ff7b88aa-a755-4749-aca9-8f01cc9212bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle number mixing ratio</dd></dl></div><div class='xr-var-data'><pre>array([[[[8.29899894e+08]],\n",
       "\n",
       "        [[4.14949947e+08]],\n",
       "\n",
       "        [[2.07474974e+08]],\n",
       "\n",
       "        [[1.03737487e+08]],\n",
       "\n",
       "        [[5.18687434e+07]]],\n",
       "\n",
       "\n",
       "       [[[8.29660306e+08]],\n",
       "\n",
       "        [[4.14909273e+08]],\n",
       "\n",
       "        [[2.07482891e+08]],\n",
       "\n",
       "        [[1.03745761e+08]],\n",
       "\n",
       "        [[5.18826855e+07]]],\n",
       "...\n",
       "       [[[7.75493033e+08]],\n",
       "\n",
       "        [[4.04946530e+08]],\n",
       "\n",
       "        [[2.08987726e+08]],\n",
       "\n",
       "        [[1.05609773e+08]],\n",
       "\n",
       "        [[5.52052684e+07]]],\n",
       "\n",
       "\n",
       "       [[[7.75275068e+08]],\n",
       "\n",
       "        [[4.04903301e+08]],\n",
       "\n",
       "        [[2.08992562e+08]],\n",
       "\n",
       "        [[1.05617224e+08]],\n",
       "\n",
       "        [[5.52193600e+07]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_density</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.176e+08 5.88e+07 ... 7.825e+06</div><input id='attrs-d15110d6-1bfb-4069-8fd6-3b35d8b55a42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d15110d6-1bfb-4069-8fd6-3b35d8b55a42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7efec7a4-f46d-4bf5-b9dd-0c3d67403391' class='xr-var-data-in' type='checkbox'><label for='data-7efec7a4-f46d-4bf5-b9dd-0c3d67403391' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m-3</dd><dt><span>long_name :</span></dt><dd>Aerosol particle number density</dd></dl></div><div class='xr-var-data'><pre>array([[[[1.17595615e+08]],\n",
       "\n",
       "        [[5.87978075e+07]],\n",
       "\n",
       "        [[2.93989038e+07]],\n",
       "\n",
       "        [[1.46994519e+07]],\n",
       "\n",
       "        [[7.34972594e+06]]],\n",
       "\n",
       "\n",
       "       [[[1.17561666e+08]],\n",
       "\n",
       "        [[5.87920441e+07]],\n",
       "\n",
       "        [[2.94000256e+07]],\n",
       "\n",
       "        [[1.47006243e+07]],\n",
       "\n",
       "        [[7.35170152e+06]]],\n",
       "...\n",
       "       [[[1.09886241e+08]],\n",
       "\n",
       "        [[5.73803377e+07]],\n",
       "\n",
       "        [[2.96132586e+07]],\n",
       "\n",
       "        [[1.49647521e+07]],\n",
       "\n",
       "        [[7.82250670e+06]]],\n",
       "\n",
       "\n",
       "       [[[1.09855356e+08]],\n",
       "\n",
       "        [[5.73742124e+07]],\n",
       "\n",
       "        [[2.96139439e+07]],\n",
       "\n",
       "        [[1.49658079e+07]],\n",
       "\n",
       "        [[7.82450347e+06]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nucleation_rate</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.99e+08 -9.99e+08 ... -9.99e+08</div><input id='attrs-cfb3c44c-6613-46b4-b107-a2fb9e2e385a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfb3c44c-6613-46b4-b107-a2fb9e2e385a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94bc7e23-d6d5-4ae7-9b22-70c13d4fc2a2' class='xr-var-data-in' type='checkbox'><label for='data-94bc7e23-d6d5-4ae7-9b22-70c13d4fc2a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m-3 s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle nucleation rate</dd></dl></div><div class='xr-var-data'><pre>array([[[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]],\n",
       "\n",
       "\n",
       "       [[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]],\n",
       "...\n",
       "       [[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]],\n",
       "\n",
       "\n",
       "       [[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wet_particle_radius</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 4.3e-05 5.418e-05</div><input id='attrs-d9782e38-f744-483b-bc2f-5220dd1ed7a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9782e38-f744-483b-bc2f-5220dd1ed7a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b27bdbe-b1e7-46b1-a354-4ce9bfd51ee1' class='xr-var-data-in' type='checkbox'><label for='data-3b27bdbe-b1e7-46b1-a354-4ce9bfd51ee1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Wet aerosol particle radius</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]]],\n",
       "\n",
       "\n",
       "       [[[2.14999995e-05]],\n",
       "\n",
       "        [[2.70883020e-05]],\n",
       "\n",
       "        [[3.41291219e-05]],\n",
       "\n",
       "        [[4.29999990e-05]],\n",
       "\n",
       "        [[5.41766039e-05]]],\n",
       "...\n",
       "       [[[2.14999995e-05]],\n",
       "\n",
       "        [[2.70883020e-05]],\n",
       "\n",
       "        [[3.41291219e-05]],\n",
       "\n",
       "        [[4.29999990e-05]],\n",
       "\n",
       "        [[5.41766039e-05]]],\n",
       "\n",
       "\n",
       "       [[[2.14999995e-05]],\n",
       "\n",
       "        [[2.70883020e-05]],\n",
       "\n",
       "        [[3.41291219e-05]],\n",
       "\n",
       "        [[4.29999990e-05]],\n",
       "\n",
       "        [[5.41766039e-05]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wet_particle_density</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 0.00395 0.00395</div><input id='attrs-a5545aeb-2f1f-4677-8518-c197c0fe501e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5545aeb-2f1f-4677-8518-c197c0fe501e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c68fc34d-20cf-4665-b1c2-aa6faf561f77' class='xr-var-data-in' type='checkbox'><label for='data-c68fc34d-20cf-4665-b1c2-aa6faf561f77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Wet aerosol particle density</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "...\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dry_particle_density</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 0.00395 0.00395</div><input id='attrs-abdc0197-532d-4aa7-bddd-0c6134c9eebc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abdc0197-532d-4aa7-bddd-0c6134c9eebc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36f99926-0b78-41c3-a29b-aa24f8d40426' class='xr-var-data-in' type='checkbox'><label for='data-36f99926-0b78-41c3-a29b-aa24f8d40426' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Dry aerosol particle density</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "...\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_particle_temperature</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-999.0 -999.0 ... -999.0 -999.0</div><input id='attrs-d9df7a01-2af1-4ec6-9923-02dc7753ea68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9df7a01-2af1-4ec6-9923-02dc7753ea68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3347ae4a-97f9-4336-81f6-4e75813e33d3' class='xr-var-data-in' type='checkbox'><label for='data-3347ae4a-97f9-4336-81f6-4e75813e33d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Aerosol particle temperature change</dd></dl></div><div class='xr-var-data'><pre>array([[[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]],\n",
       "\n",
       "\n",
       "       [[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]],\n",
       "...\n",
       "       [[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]],\n",
       "\n",
       "\n",
       "       [[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kappa</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -1.798e+308 -1.798e+308</div><input id='attrs-e5b22124-64c0-4245-ae6c-33d91712a1c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5b22124-64c0-4245-ae6c-33d91712a1c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1dbec4e-7629-47b1-a517-03414fc65a0f' class='xr-var-data-in' type='checkbox'><label for='data-e1dbec4e-7629-47b1-a517-03414fc65a0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>-</dd><dt><span>long_name :</span></dt><dd>Aerosol particle hygroscopicity parameter</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]]],\n",
       "\n",
       "\n",
       "       [[[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]]],\n",
       "...\n",
       "       [[[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]]],\n",
       "\n",
       "\n",
       "       [[[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_mass_mixing_ratio</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.365e-07 1.365e-07 ... 1.453e-07</div><input id='attrs-ff5e885e-f254-490f-8830-c8904d1c5c5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff5e885e-f254-490f-8830-c8904d1c5c5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcd86151-d4a5-4234-8def-4696cbb1193f' class='xr-var-data-in' type='checkbox'><label for='data-bcd86151-d4a5-4234-8def-4696cbb1193f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Total aerosol particle mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>array([[[[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.36427335e-07]],\n",
       "\n",
       "        [[1.36453356e-07]],\n",
       "\n",
       "        [[1.36471940e-07]],\n",
       "\n",
       "        [[1.36477617e-07]],\n",
       "\n",
       "        [[1.36503414e-07]]],\n",
       "...\n",
       "       [[[1.27520200e-07]],\n",
       "\n",
       "        [[1.33176857e-07]],\n",
       "\n",
       "        [[1.37461746e-07]],\n",
       "\n",
       "        [[1.38929726e-07]],\n",
       "\n",
       "        [[1.45245134e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.27484358e-07]],\n",
       "\n",
       "        [[1.33162640e-07]],\n",
       "\n",
       "        [[1.37464927e-07]],\n",
       "\n",
       "        [[1.38939528e-07]],\n",
       "\n",
       "        [[1.45282210e-07]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fall_velocity</span></div><div class='xr-var-dims'>(time, bin, element, vertical_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.99 -9.99 -9.99 ... -9.99 -9.99</div><input id='attrs-fad7532c-2448-413d-b76a-b0b8f45f3cf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fad7532c-2448-413d-b76a-b0b8f45f3cf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a5b3c78-f591-485c-acfa-5b3a1f083b98' class='xr-var-data-in' type='checkbox'><label for='data-6a5b3c78-f591-485c-acfa-5b3a1f083b98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle fall velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]],\n",
       "\n",
       "\n",
       "       [[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]],\n",
       "...\n",
       "       [[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]],\n",
       "\n",
       "\n",
       "       [[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]]], shape=(240, 5, 1, 2))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>particle_mass_on_surface</span></div><div class='xr-var-dims'>(time, bin, element)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-6523a4dc-ae2e-461e-a332-4e9ce09d7126' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6523a4dc-ae2e-461e-a332-4e9ce09d7126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b445cf1-7ba4-4ab8-a391-48d578233977' class='xr-var-data-in' type='checkbox'><label for='data-7b445cf1-7ba4-4ab8-a391-48d578233977' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>Aerosol particle mass on surface</dd></dl></div><div class='xr-var-data'><pre>array([[[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]]], shape=(240, 5, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sedimentation_flux</span></div><div class='xr-var-dims'>(time, bin, element)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-84b030ab-b915-4955-9d71-8629b8195577' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84b030ab-b915-4955-9d71-8629b8195577' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4eb087e0-79a9-4fc5-bd39-1c8e3a9c2519' class='xr-var-data-in' type='checkbox'><label for='data-4eb087e0-79a9-4fc5-bd39-1c8e3a9c2519' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle sedimentation flux</dd></dl></div><div class='xr-var-data'><pre>array([[[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]]], shape=(240, 5, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deposition_velocity</span></div><div class='xr-var-dims'>(time, bin, element)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.99 -9.99 -9.99 ... -9.99 -9.99</div><input id='attrs-6708e0f5-8bf0-406c-836a-13dbcc6d30e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6708e0f5-8bf0-406c-836a-13dbcc6d30e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c4c87f2-0166-4d56-9062-46e426d42751' class='xr-var-data-in' type='checkbox'><label for='data-9c4c87f2-0166-4d56-9062-46e426d42751' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle deposition velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]]], shape=(240, 5, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>216.6 216.6 216.6 ... 216.6 216.6</div><input id='attrs-b8b93918-9160-4c08-94d9-40e42868ca12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8b93918-9160-4c08-94d9-40e42868ca12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85be0c47-e7d8-4de1-ba82-63562694d77f' class='xr-var-data-in' type='checkbox'><label for='data-85be0c47-e7d8-4de1-ba82-63562694d77f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>array([[216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "...\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65],\n",
       "       [216.65]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.85e+03 8.85e+03 ... 8.85e+03</div><input id='attrs-01505757-c9a6-456d-af9b-daf218c0cbdc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01505757-c9a6-456d-af9b-daf218c0cbdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e38b9e37-0b86-449a-97b9-eaada360bcde' class='xr-var-data-in' type='checkbox'><label for='data-e38b9e37-0b86-449a-97b9-eaada360bcde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Pressure</dd></dl></div><div class='xr-var-data'><pre>array([[8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "...\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538],\n",
       "       [8849.7125538]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_density</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1417 0.1417 ... 0.1417 0.1417</div><input id='attrs-d4e4385e-c33c-453c-bd4b-453833b53490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4e4385e-c33c-453c-bd4b-453833b53490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0a823fb-6c79-4d0c-81b4-b8a4c19098c2' class='xr-var-data-in' type='checkbox'><label for='data-c0a823fb-6c79-4d0c-81b4-b8a4c19098c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Air density</dd></dl></div><div class='xr-var-data'><pre>array([[0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "...\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855],\n",
       "       [0.14169855]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latent_heat</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... None None None</div><input id='attrs-d690aab0-d28c-406d-9e77-3ee2cd79eb8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d690aab0-d28c-406d-9e77-3ee2cd79eb8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bd08357-60b0-44b8-80c5-8d259756378c' class='xr-var-data-in' type='checkbox'><label for='data-0bd08357-60b0-44b8-80c5-8d259756378c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>long_name :</span></dt><dd>Latent heat release rate</dd></dl></div><div class='xr-var-data'><pre>array([[None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "...\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None],\n",
       "       [None]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-980cdabf-8f1c-4a74-b1f5-33d5c10011be' class='xr-section-summary-in' type='checkbox'  ><label for='section-980cdabf-8f1c-4a74-b1f5-33d5c10011be' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b0b1c57f-09fc-4799-bb24-732047267cb6' class='xr-index-data-in' type='checkbox'/><label for='index-b0b1c57f-09fc-4799-bb24-732047267cb6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0.0,   1800.0,   3600.0,   5400.0,   7200.0,   9000.0,  10800.0,\n",
       "        12600.0,  14400.0,  16200.0,\n",
       "       ...\n",
       "       414000.0, 415800.0, 417600.0, 419400.0, 421200.0, 423000.0, 424800.0,\n",
       "       426600.0, 428400.0, 430200.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=240))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>bin</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cc01ec78-88e5-49ca-8930-958cce831df1' class='xr-index-data-in' type='checkbox'/><label for='index-cc01ec78-88e5-49ca-8930-958cce831df1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;bin&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>element</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-54fc9b7f-2ae9-4b27-9888-934f2a28be0f' class='xr-index-data-in' type='checkbox'/><label for='index-54fc9b7f-2ae9-4b27-9888-934f2a28be0f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1], dtype=&#x27;int64&#x27;, name=&#x27;element&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>vertical_center</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-778b9f48-496c-4751-b613-eb58d990fea5' class='xr-index-data-in' type='checkbox'/><label for='index-778b9f48-496c-4751-b613-eb58d990fea5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([17000.0], dtype=&#x27;float64&#x27;, name=&#x27;vertical_center&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>vertical_level</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8e90b00a-0e93-4aa1-b8aa-88370d2adcf4' class='xr-index-data-in' type='checkbox'/><label for='index-8e90b00a-0e93-4aa1-b8aa-88370d2adcf4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([16500.0, 17500.0], dtype=&#x27;float64&#x27;, name=&#x27;vertical_level&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa3474df-81bf-4755-b4c4-7b8e158b916d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa3474df-81bf-4755-b4c4-7b8e158b916d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 154kB\n",
       "Dimensions:                     (time: 240, bin: 5, element: 1,\n",
       "                                 vertical_center: 1, vertical_level: 2)\n",
       "Coordinates:\n",
       "  * time                        (time) float64 2kB 0.0 1.8e+03 ... 4.302e+05\n",
       "  * bin                         (bin) int64 40B 1 2 3 4 5\n",
       "  * element                     (element) int64 8B 1\n",
       "  * vertical_center             (vertical_center) float64 8B 1.7e+04\n",
       "  * vertical_level              (vertical_level) float64 16B 1.65e+04 1.75e+04\n",
       "Data variables: (12/18)\n",
       "    mass_mixing_ratio           (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_mixing_ratio         (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_density              (time, bin, element, vertical_center) float64 10kB ...\n",
       "    nucleation_rate             (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_radius         (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_density        (time, bin, element, vertical_center) float64 10kB ...\n",
       "    ...                          ...\n",
       "    sedimentation_flux          (time, bin, element) float64 10kB 0.0 ... 0.0\n",
       "    deposition_velocity         (time, bin, element) float64 10kB -9.99 ... -...\n",
       "    temperature                 (time, vertical_center) float64 2kB 216.6 ......\n",
       "    pressure                    (time, vertical_center) float64 2kB 8.85e+03 ...\n",
       "    air_density                 (time, vertical_center) float64 2kB 0.1417 .....\n",
       "    latent_heat                 (time, vertical_center) object 2kB None ... None"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc36c16-5f5b-482b-8c5e-eaa0be56aafc",
   "metadata": {},
   "source": [
    "Now we will make as serices of plots, but first we will transform the time from seconds to days to make the plotting labels nicer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef353950-cfe6-49db-acc7-3f3a37c614e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from seconds to days\n",
    "ds = ds.assign_coords(time=ds.time / 86400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a44a9ec8-e26c-4163-b727-2b6f0a0cf9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6rTn55JMNSUlJhri4OMPs2bMNL730kuGEE05Q2/7rX/8yr1dQUGBITExU+/b666+regwcONDg5+dn2LRpU6vvw4EDBwyPP/64euzGG280/Oc//1Fl7969artZs2YZ+vTpY7VvV199tVp/+vTphoULFxqef/55w7nnnmt4+eWXzevINrKts59Xc+T5pF5S10cffVTte3JysiE0NFR9Dr1791Z1khIREWFITU016HQ68/ayjyeeeKKq61tvvaVeUz6fcePGGfR6vcOfdU1NjSElJUV9Nk8++aRaR7438rnLe+nIZ9qrVy/DPffco94v+Tx8fHwMS5YsMSQkJKi6yXsqdZN6lJaWNvmOWX43b731VvUd3rBhg7p/5MgRQ1RUlGHq1KlW9bL9PEzPNXLkSMOUKVPUvvz9739X+3LxxRdb7fe9996r1pX34ZVXXjHccMMNhp49e6rfiuVz2tPS78+R7255ebl6TJ7jvPPOM38///rrr2Z/T7JPsuzCCy80vPrqq+p7JvdnzpzZ4r6S6/HYz2N/c8f++Ph4tb0cg0aNGqW237ZtW6vvr73jZnvbE6lfYGCgIS0tzXDllVeqfTr77LPV6zz88MNWr3/99der75ocN6Xu9913nyEkJER9X2pra632SV5HvoPyHZJ1f/3112bfF7YnbE+o/djmsM2xxTbHPvk+yutNnDhRfUfuvPNOQ2RkpPqM5TtkIvt9+umnG4KDg9U6b775pvpeyT7JZ2dJnm/48OHqfXniiSfUZyzPJ9sWFhY69buYZfH9aelYL+2g/F6kjbIl31lpwzwBg1JdYP369epLJAdwIX8IyR8stgcS+fLKj2z37t1Wy+WHJX8IZWVlWR0cw8PDDfn5+VbrjhgxQh2Ei4qKzMvkC6vVatUBzEQOEPLHWnPkRy7PM2TIEENVVZV5+TfffKNe+5FHHmlTIyHr/fOf/zQvk4CIaZ9NB5b6+nq13JI0SnIwvfbaa83LWnof1q1bpx6TA6kt20Zi2bJlat3bb7+9ybot/dHq6OfVHHk+ed2VK1eal/3www9qmRxcpNE3kQOgLLc8uFZWVjZ5zo8++kitJ3+IOPpZS8Mr23z66acGZ5k+08WLF5uXZWRkqGXynVu9enWTull+JvYaOml8pVE57rjjDNXV1YazzjpLfcaW70dLJxG2Jxt33XWX+jxKSkrU/dzcXNWQ2P4BLo2qbO/oSYS9752j3135Q0ieQ347tmx/T5s3b1b3pVG2dPfdd6vl8v0l98RjvxGP/faP/ZbHaalHQECA1R+Vzp4gtKc9MQUqbrvtNqv3QI6/8oezHLPE77//rtb78MMPrfbp+++/b7LctE/ymCPYnrA9ofZhm2PENsca25zmv3fynbD8DshFfnkOy6CUBH/key37YkmCXrLun3/+aV4m92X/9+zZY/W7kOWWgcfWfhf2vj8tHesvu+wyFYi1DPxt3Lix2e+mO+LwvS4gXWelq6F06RbSxe6SSy7BkiVLoNPprLquSrdR6XZbWFhoLlOnTlXrrVixwup5ZbiVqSufyMnJUYn2pLtfVFSUefmwYcNUl8DvvvvOvEy62a5Zs0Yl6LNHupHm5+fjlltuscpHIN2B09PT8e2337Y5v4F0uTTx9/dX9+W1pJutkG6GstzUZbO4uFh1N5eumLbdfe29D87673//qz4T6ZZpq6XcFM5+XvYMHjwYEyZMMN83zZQyZcoU9O7du8ly6eZqIl1NTaqrq9VrH3/88eq+5fvU2mctXZaFdHeVJHrOkildZaifiQyrkNccNGiQ1cwv9upgjwx/kK7C0gX8pJNOUt+1F154wer9aMmNN95o9bnJZySfx8GDB9X9X375RX2f5Ltt6bbbboMz7H3vnP3uOsL0u5UhJJb+/ve/q3/b+lukzsdjfyMe+5se++U5TKQecuxs7fjYWe2JiQw7sB2GIENcTEPlpO7SZsj3yrLuMjxD2gLbYS8yZEaGZjiK7UkjtifkLLY5jdjmWGObY/97d/PNN5u/A0K+06bzIhPZB2mD5PtouQ9ST2G7D/K5yJBAy99FeHi4Vf1b+10466qrrlLPZbkvcjyQc0X53noCBqU6mRwspDGQBkKSD8osGFLkB5qXl6f+oLHM6yNjTuVAYVnkyy3kx2P7w7Nk+iNJDjK25MckP6CKigp1/x//+Ae2bdumxsTKGFoZk2r5Y2npueRHaXrcWTJeWMYNWxowYID613Ks8r///W/1I5YGKjo6Wr0P0jDZy3Fk+z44a+/evWq/LBtWRzj7edlj+4ex6UAon4u95Zbjp6XxvOOOO9QfIHLQkdc2vReW71Nrn7VsI3+gvvPOO2ocvxzMX3311VbzSZlI/inbxlT215E6NGfSpEn429/+pmaOkf2RXFmOsn1PpRG3fF3Td1fGhVuSz9+0riOa+9458911hOyv5EWx3V/J/yWNWlt/i9S5eOy3xmO/NXtBETn+OHJ87Iz2RMhxRvKMtPQZSd3ls5D8F7b1Ly8vb/W72hq2J9bYnpCj2OZYY5tjjW2ONdP3Ki0trUlycdt9kn3Yvn17k9c37avtPjjyXrf2u3CWBO0SExPNOeQkyPrRRx+pPG9hYWHwBL6u3gFvJ0kA5YqCNBRSbMmX5/TTTzd/geRLJYnT7DF9+e31lHHWxRdfrCLmS5cuxY8//ojnnntOJcyTxHTTp0+HK33wwQcqUj1z5kzcc8896kAkVzPmz5+vDui22vM+tIezn5c9Ui9nllsm/5PPUGZYkfdoxIgR6oqB7JMkTLRMfOjIZ/3Pf/5TvedffvmlWuf2229X7/fq1avVSUJn1aE5kjjQlCxRPnPpwSVXvB3Rntd1hr3vnbPfXWe0ZUYpch0e+53HY7/1caq537xljwdHnrMjj4lSd/lcmkuebNuLwNnPiO1JI7Yn5Ay2Oc5jm8M2x9F9kKTtMqOlPbZBOEfP4Tryd+Hj44PLL78cb7/9tkrAL5N0SM8pmTDAUzAo1cnkRyQ/Jul5Yku+ePJllNkg5EckXf0k6muKfDurT58+6t9du3Y1eSwjI0P1grG8aiARVekuK0WivKNGjcJTTz2lfgyWz2Xqnmgiy0yPO0t+IHL1xHI/du/erf41zTAgM+1IlFreH8sDpL3urh3xB5e87zJ0TXoeOXP1or2fV3tItF2ueskMDJYzC0k0356WPmsTOeBKmTt3rgp2ydVl+W4++eST6GryWctwC5np5L777sP999+Pl156qUOe2/TdlSuIlldUZNbC9lwxcua768z3U/ZXGkT5bOUKpIlc+ZTZWNr6W6TOxWO/NR77nWfqaSO/c+nFYtJZvVnkOCNXai1PcGw/I6m7DKuQ9sFVJ2jOYntivb9sT7wT2xxrbHOc153aHNP3So6Flt+7uro61dNw+PDh5mWyD3/99RdOPfXUDg3oO3JuZqm115YhfNLJ4Ouvv8b//vc/FbBzZvi8q3H4XieqqqpSBzqZnlKmZbUtMm5WpoT86quvzFHTVatWqQOWLTlAyDjn1r7c0mNGuqLK+ibSPVCisGeeeaY54m3bLVUaMulSapraUsZTyzJpwCynu5QvufxxJ2O920LqYDk9p4wblvvyw5HxwZYRZsuIsoy7lffGUaZGyPJ9aI6MtZXXkgCPM1H99n5e7WHvPRIyZaolRz7r0tLSJvsqwSnpVuuKKaLls5aTB5kqVfJcyNWrV155RU0H3hGkUZFcA5bTgQt5jfZy9LtrukrvyPfT9Lu1/WxNV2za+lukzsNjf1M89jvPlJPCMl+InGTJ59xZLI+D8h7IfRnOIMdNU93le/TEE0802Vbq7cj73pXYnlhje+Kd2OY0xTbHed2pzZHvnXwX5Hsn3w0TyUFo+5yyD9nZ2aoXkr3fnmmoqqMc+V3Y09qxXoahSpF0LJK/TPIzSvvkKTxnTz2QHPylETjnnHPsPi5JqeUHIVc3JBGh/LEk20ijIt1J5aApX/StW7eqaL6Mr5WrDy2R7n8SYZXEc9ddd536sbz88stqPK+MVxWyTzIkSxoqiQTLsC+JQq9bt05FWIUcEKQb4TXXXIOTTz4Zl112mbqS9uKLL6ro9V133dWm90R+cPK8UheJjH/88ccqWeJbb72lXlNI/aVxPe+881RjJBFrOWhIQj25UuDogVWi/LKdjKWVRkPG1dsbayzj76+88kp15VQi5qbhb7///rt6zDIJn6WO+LzaShLmSdJWGZMsUf3k5GT1h4C8V5Yc+ayly7fU8aKLLlKfiRzk//Of/6jGuquT40nC9lmzZqkx3nK1QEjjLVF/+S7Ke2ubI8BZkoNLcnFJ/eW3KZ+3XAGRP4Dk82rPVRBHv7tytUeWyfdf3nO5YjZkyBBVbMnnJu+J/EakIZLfo+RGkT8SpMu5KaEpuQ8e+5visd95MtRGclPI5ymvKcfkd999V313srKy0NEkp4rkLZHjjbxnckyU3CoPPvigeYiEfCckWbAMb5HPT/ZRPj95/yQZrHxP5PvlDtiesD3pLtjmNMU2x3ndqc2R55CRIPLc0lNKfhfy+b/33ntNckrJZ/bJJ5+opOiSSFx6bUlgSXoFynIJFkqQy1GO/C7sceRYL72l7r77bnXbk4buKa6e/s+bzZgxwxAYGKimJG7O1VdfbfDz8zMUFhaq+2VlZYYHHnhATWEsU0rGxMQYJk6caHj++efNU5iapiZ97rnn7D7nzz//bJg0aZKaklOmL5X92LFjh/lxmfrynnvuMQwfPtwQFhamphmV26+99lqT5/r4448NI0eOVNOGRkVFGa644grD4cOHrdZxZopWmZZZpqydMGGCem9kqstXXnnFaj2ZEvTpp59Wj8nryuvL1LC2U2O29j58+eWXhsGDB6vpmi2nxLR9HtO0sPI86enp6n2PjY01TJ8+3bBhw4Zmp2h19PNqjjyfTH1qS/bVdppQe3WVz+G8884zREZGqqlFL7roIsORI0espgt15LPet2+fmvq2f//+6jORz3ny5Mnqe+ToZ9rWutlOM2uabnvNmjVW28l3Rj7Hv/3tb61O4S3T81qSKWhtp6KVz/vhhx82JCQkqN/JlClTDDt37jRER0cbbr755hbr3NL3ztHvrpBpdEePHq2+N5afmb3fU11dneGxxx4zpKSkqONFr1691PdOpjgn98NjvzUe+x07Psr7ZDkNtZD9GD9+vHqN3r17GxYsWNDs9NztaU+kfvJ92Lt3r+H00083BAcHq2nR5TO2nGLactpsOX7Jd02+S0OHDjXce++9qg1qbZ+aw/bEGtsTchTbHGtsc6yxzWmefBflWCif/5gxYwwrVqyw+77Ie/zss8+q75Ws26NHD7U/ciw9duxYi/W3/Uwd/V3McuJYb5KTk6PavQEDBhg8jUb+5+rAGHUPp5xyipqRQ7r3ErkbuWos4+nlyslDDz3k6t0h8ho89lN3w/aEyHXY5lB3VVhYqIbXSr7hhx9+GJ6EOaWIqNuRbua2TDk25I8ZIiIiR7A9ISIid7Bo0SI1tFCGHHoa5pQiom5HxmPLgVuSccpY7j/++AMfffSRGqsuY8WJiIgcwfaEiIhcadmyZdixY4fKnyj5AU2zF3oSBqWIqNuR2SlkRgpJFC+zD5qS1cpQCyIiIkexPSEiIld6/PHHsXLlSnUhRCYc8ETMKUVERERERERERF2OOaWIiIiIiIiIiKjLMShFRERERERERERdjkEpIiIiIiIiIiLqcgxKERERERERERFRl2NQqpOtWLECM2bMQFJSEjQaDb744otOfT2ZAlJex7bceuutnfq6RETkmjbihx9+wPHHH4+wsDDExsbiggsuwIEDBzplf4mIqGuxnSAib8egVCerqKjA8OHD8eqrr3bJ661btw45OTnm8tNPP6nlF110UZe8PhERdV0bsX//fpx77rmYMmUKNm/erE48CgsLcf7553f4vhIRUddjO0FE3k5jMBgMrt6J7kKubixduhQzZ840L6upqcFDDz2Ejz76CCUlJRgyZAieffZZnHLKKR3ymnfeeSe++eYbZGZmqtcnIiLvaSM+++wzXHbZZWo9rdZ4nenrr79WJyCyzM/Pz2X1ISKijsV2goi8EXtKudjs2bOxatUqLFmyBFu2bFE9ms444wwVRGqv2tpafPDBB7j22msZkCIi8sI2YvTo0eok47333oNOp8OxY8fwn//8B1OnTuWJBhFRN8B2gog8HXtKufDqRlZWFvr166f+lXHiJtJIjBs3Dk8//XS7Xu+TTz7B5Zdf3uT5iYjIe9qI3377DRdffDGKiorUCceECRPw3XffITIy0mV1ISKijsd2goi8EXtKudDWrVtVwzBgwACEhoaaizQce/fuVetkZGTYTVxuWe6//367z/+vf/0L06dPZ0CKiMhL24jc3FzccMMNmDVrlsopKI/5+/vjwgsvBK85ERF5N7YTROQNfF29A91ZeXk5fHx8sGHDBvWvJWlQhFz92LlzZ4vPEx0d3WTZwYMH8fPPP+Pzzz/v4L0mIiJ3aSMk8W1ERAT+8Y9/mB+TYdu9evXCmjVr1GxLRETkndhOEJE3YFDKhUaOHKmubuTn5+PEE0+0u45cyUhPT3f6uWXceFxcHM4666wO2FMiInLHNqKystKcuNbEdGKi1+u7ZD+JiMg12E4QkTfg8L0uuIIh069KMU3LKrdl7Ld0tb3iiitw1VVXqR5N8tjatWsxf/58fPvtt21+TWlgJCgl3XR9fRl3JCLy1jZCLjzIcIzHH39cJbXduHEjrrnmGvTp00edrBARkWdjO0FE3o6JzjvZ8uXLMXny5CbLJWC0aNEi1NXV4cknn8T777+P7OxsxMTEqG60jz32GIYOHdqm1/zxxx8xbdo07Nq1SzVWRETkvW2EzLgkwzJ2796N4OBglcBWpgNvSy9bIiJyL2wniMjbMShFRERERERERERdjsP3iIiIiIiIiIioyzEoRUREREREREREXY5BKSIiIiIiIiIi6nIMShERERERERERUZdjUMpN6HQ6ZGRkqH+9gbfVxxvrxPq4N2+rD7Wft30nWB/35m318cY6eVt9qH287fvgbfXxxjqxPu5N50H1YVCKiIiIiIjIjhUrVmDGjBlISkqCRqPBF1980eL6n3/+OU477TTExsYiPDwcEyZMwA8//NBl+0tE5GkYlCIiIiIiIrKjoqICw4cPx6uvvupwEEuCUt999x02bNiAyZMnq6DWpk2bOn1fiYg8ka+rd4CIiIiIiMgdTZ8+XRVHLVy40Or+008/jS+//BJff/01Ro4c2Ql7SETk2RiUIiIiIiIi6gR6vR5lZWWIiopyajtn88DI+vJanpA/pjvWxxvrxPp4X318fHzgCgxKERF5kPcfXAkfP60q+no9YnqFYfL/pcMvwAf7/ypA9q4SnHBxmlPPufbrfdj6WzZCIgPU/ajEEJx+3XGdVAMiIqLu4/nnn0d5eTkuvvhip7bLzMx0an05+SwuLsaePXug1Xp+hhZvq4831on18b76pKenwxUYlCIi8jCnX38cYnuFwaA34NvXtiBjVQ6GntITKcNjVWmLAePiceLFAzp8X4mIiLqrxYsX47HHHlPD9+Li4pzaNi3NuQtM0htCTj5TU1Nd1tuhI3lbfbyxTqyPe9N5UH0YlCIi8lA6nR71tToEBBsP5TtX5qjeUmf+bRiydx3Fio93Iyk1Ejl7j0GvN2Dq1YMQ1yfc1btNRETk9ZYsWYLrr78en376KaZOner09m05iZTeELKdu5+Adtf6eGOdWB/3pvWQ+nh0UEpmt3juuefUzBY5OTlYunQpZs6c6dC2f/75J04++WQMGTIEmzdvNi9/9NFH1RUNSwMHDkRGRkaH7z8RkS2DwYAju0uQsToHCaPsr/PjO9vV8L2yomrE9g5D6mj7V1+P5lZi8pXpOPnygdi2Ihurv9yHc24fYXfdvRvy1dC/wFBfjDkzBT0H9ujIalE7bcw6ij92F6C67BhSyrMRHuSPsEBfhAX4ITTQF6EBvup+gK9WTVlORESu89FHH+Haa69VgamzzjqrS17z3T8PIC+/BH2PHkJ4sKmN8FVtRFign2onpPho2UYQkXvx9YYpWuWgf/755zu8XUlJCa666iqceuqpyMvLa/L4cccdh59//tl839fXo98mIvIA5Udr1DC8natyUFpQpZZpwyKRPrj54Xt6nR7LP9yFlUv34oQLm3bzj4gNQkJKhLqd0C8cm37Ksvvax52UjNFn9oWPjxY5e0rwvze34sL7xyA8Oqijq0lttHZ/MRb83JBfZE1hs+v5+WgaAlQNJyCBvghvCFpZnpjIyYrleurkxWK9AF/3vqJGRNRVJB+UDIEx2b9/v7qgLYnLe/fujQceeADZ2dl4//33zUP2Zs2ahRdffBHjx49Hbm6uWh4UFISICGOb3BleW74XRyvrgPVFLa4X4u9jcTHDr/HYb2oTGoJZarmd9eTfEH9faBncIqIO4tudpmg1ufnmm3H55ZerbmxffPFFk8clCJWQkNBBe0lEZJ+uXo8DWwqx488cHNpRBIPBuNwv0Ef1fgqNrW9xe62PFv1GxmLl53uBC5s+Lr2pTKT3jEHX8AI2QiKMCc5FYmqkSp5ecLCMQSk3MjAhDBeP6YmcwqOAXxAqaupRXlOPsup6lEuprVffnzqdQZ2UqBOTdvD31VpcYTedsPg1CV6pExXzyUzDsob15Lafj+cnCiWi7m39+vWYPHmy+f6cOXPUvxJ4WrRokRqtkZXVeNHnrbfeQn19PW699VZVTEzrd5aZI5KQlVcEjX8wKmp0KKupM7YPNfUora5Hbb1erVdRq1MlDzXter3GQFZj8Crc4oKIZRDL8kJJmEXwK9jPh8EtIvLsoFRbvPfee9i3bx8++OADPPnkk83OdpGUlITAwEBMmDAB8+fPV1dCiIg6QtGRcuz8Mwe71uSiurwxeJCYGoHBk5LQf1QctL6OzbwjuaN6xAe3a3/Kj1YjtEegul2SV4nCQ2WITg5t13NSx5o8MA4npUar74Qkv7XNDSA5wypqjScfchIiJyCm22XVdeYAlgpi1djeb1inul6dqAg5eSmqr0VRRW279luGE1qdkFicwMjV+rrKUvTO8WkcjqhOYpr23vJlcIuIXOSUU05RQ+ubYxtoWr58OVxh7lmDmm0jRE29zhisqq6zOPY3tAkttBvW/9apix9CrVtTj9zStu+zjDZXx3vbYYZy7Pf3QX1VGXod0iDMPGS9aa9fKUF+Phy6TuTBulVQSg7U999/P37//fdmh+RJN1tpXCSPlFz5kPxSJ554IrZt24awsDCnst07Q9aXaRud3c5deVt9vLFOrE/Xqq2qx54N+chYmYv8g2Xm5cER/hg4Ph4Dj09ApEVwqbn6GGDAD29vg6+fVgUiwqICcdJlae2qt+SaKsgqg0arUVcsT7p0oNW+kPuTz804vMIPaMfoEF1DcMvcA6umzniiYhO8KrPspdWwvMx8MlOPqjrj97GmXo+a8loUlrcQ3NpS0up+yQlHc0NKmgxHbGE95lIhou5KhmVLiQrxb9fzVNfpLIJX0h40tAumoJbqmdXYS8vUVhjbjcZgl7Q3EuszXSDBsWZecHtzDzSSQ3uT4Yi2wauA5ocjmnr5BvoxLyORK3SboJScsMmQPQkyDRjQ/LTnlsMBhw0bpoJUffr0wSeffILrrrvO4ddzpIeDJTn5LC4uVmPWJUu+p/O2+nhjnVifzidXVkuya3FkeyXyM6uhrzdeXdRogZiUQCQdF4yovgHQanUoKM1GQWnr9Rl/VXST18kpPAQUAoMmpmPQxES1LHlgD1w6d5x5Hen5dNXTE+3u59Sr7SSuohbbE5kUQ3rcSq4Q6Vl79dVXY+7cuR7/x6wEbWT4hZT2qNfp1RV5dWLScJJivBLfcL+6HseqanE4rwi+gSEor2k4ybHp0VVdZxxuIkEuKQVl7RtuEuzvY3HCYhx6aHc4YgtDT0KZS4WIurFAPx9VYkIbh/635e8jOb5bDjG07b1bWlWHQ7kF8A0KRUWtHuU2PbxMAS69AarIBRQp7eGr1ZgDV+bhiE2CVzbDETnpCFG7dZugVFlZmRoTvmnTJsyePdt80icHRek19eOPP2LKlClNtouMjFRBLMsEh46QrrPOnuTIa6Smprr9lI3dsT7eWCfWp/NUlNRg15o8ZKzKNSctF9L7KH1iAgaMi0dwuL/H1IesPfvss3j99dfx73//W02MIW3LNddcoxLY3n777a7ePbcgw+0igqX4tfgdb2moiWkYYUUzV94be2Y1Brosr8Q3XsVvzKVSWatTpaNyqVjn3PKBrqYSybvrzT221FDEhpOYEMvhiw33JXcXEVF3IwGbIH8fVeLCWmoj9C22EXIeJxcs7A1Ht+y9axq23lzPX7kvvbbq9QaUVNap0h7NTToiF0Z01RVI3qtHeJCfuS0wD11X//qYbzPnFnUX3SYoFR4ejq1bt1ote+2117Bs2TJ89tlnSElJaXbGjb179+LKK6906vXachIpvSFkO285AfW2+nhjnVifjk1afnBrEXauPIKD2yySlgf4IHVMnMoVFZ8S7tSVM2/7fLzFypUrce6555qn+e7bt6+a/nvt2rWu3jWvI0Ebf19/9GjncBPJpWLvarycqNg7mWm6nv1cKrCXSyWzzKn6WQ4pUScnFvctg1/mWa+sglt+CAnw4UxYRNQtyd9Uwf4S7PFFfHjbn0fSIVTKsMSGCx22AS2r3Frm5PHWF0DM7QIcmHRkV6njObf8G477NrMfWvXgsgpu2bnPWXXJzfl2lyla5eRuyJAhVtvHxcWpZOaWy++++27MmDFDDdk7cuQI5s2bp04IL7vssi6tGxF5huIjFSoQJUnLq8oskpb3j8CgSYkqabl/oEcfasnGxIkT1exKu3fvVj1p//rrL/zxxx9YsGCBU8/D3INdVx9fDRAZ5KtKW8kVeelxZc6nZTPcsKy6FllH8hEQGoFKGWpi0XvL1NvLVEzDEjsqobwwXl1vPBmxDGZJ4MqcaL5heYjFSY1pHXlMAmVyoudt3zfhbXVqS314kYOoKQnqm46FCRHGiV/aGtyS2XCt8i1aXuioqsVB1U6Eo6JGb7Wu+d+GYs65Jds2dxHECf4+WmNb0HBBw9iDV+5b52a0vThiG9wK8Wd+Rup4vt1pilZHHD58WAWgioqKEBsbixNOOAGrV69Wt4mITEnLM9fnYefKHOTtb/wrISjcH+nHJ6i8Tj0SQly6j9R5ZMKM0tJSpKenqxM8OSF86qmncMUVVzj1PMw96Nn1CWwoMTJC0Q/Qh+gxUKNHVJRMGNCwsBlyslFZpzeXqjqDCmTJ7QrzsobHaxvXqbBYViVDG2v1KpeKMOblkuBE+4YmyojCYD+tKv4aPcKCjqghJ8ZlGvNjUoLkX//G+5bryGPuduLi6d+5jqiPHLeIqPOCWy3lZTQOSaxvcUiiZc4ty5yMpvxbaqZdi+GJctHDdFvWtb0vQ9ZFrU6P2kp9Q++txtQSbSFtgimI54t6RIcftZpoxNSzqzG4ZezVa5l7SwoTy5NXBKWcnaLVliSqlWJpyZIlHbZ/ROQ95FiTs6cEO/7Mwd4N+ahv6OkgM9b1HRqNQZOS0Pu4KPhw6nqvJxNffPjhh1i8eLHKKSU9dO+8806V8FwuijiKuQdZn444LskMh5YnLsbbxsTxph5apiv1xvuNM2eZTm7UYw0nLpJ+q7RGr4rSjuTycuISonpkNZ7A2L8Sb/G47dDFDjxx4XeOiDwt51ZsWNsTylvOqms5NN3ce9emN69VW2HOt9U4rN00hN2UnzHf1D4Utq2dkAsX5l68FsPTm8vFqHpq2RnmLsv8+Pe3R/PooBQRUWcrP1qDjNU5yFiZg2MWSct7JEjS8kQMHJ+AkIj2/cFAnuWee+5RvaUuvfRSdX/o0KE4ePAg5s+f71RQirkHWZ+O4OsLhAT6I76dz2M6cTGdjByrrMWufQcRGROvemg1dxW+ybCT6np1Rd7yxKWgvH37JicuIWrmRItZEO3k3LK6beekJtBX0y2+c5OeWaZm/wrw80FtvQ7HJUXgmQuGqrw7P+3Iw6q9RXhkhnOzrr7w0278Z/VBxIcbhzYNiA/Fi5eO7PD6EJF7zqprm59R5dWqrMXu/VkIj45TvXjtBr7sBLtk2KL0K5F251hVnSrtJRcv1LDEQMsLHZb3/VrMuSX/BvlqWuzw4i0muWEbwaAUEZGdpOUHthSq4XlZ262TlqeNiVO9opxNWk7eo7KysslQGTkhlKE0RF5x4hJh7IUTWlOAtLQEpwM4cuJSIT2yGoacqNsWyYKtrsI39NgqayaviunEpSOmexf+PhqEBR5qJq9W48xXaqhJw1V505V581V8D5g98eXLR6oTDclxc92/1+GzDYdx1YS+OG1wvCptce6IJMybcVyH7ysReQZJlh4Q6oPoUOPFWGknetQXIS0tyal2wpRYXl3ksHfsNyWRb2g7rIJdNr2+pLewkOGO1XU1KCxv3xB2GXUe7J9l3XPL3KNXLoz4WCWet8rjaBHskn/defbEl92sjWBQioioQdGRcuz8M0clLa8ut0hanhqBQROT0H9ULJOWk5oMQ3JIyYQaMnxv06ZNKsn5tdde6+pdI3KfExdfH0S1c9ZEuWItPa1se2TZvwpvDH5ZTv9ued+cXF5nUInlOyK5vASl7J6s2JygWA47sQp6mWZPDJCZsTont4r0Wquq0yEiyNhL4tP1h/Djjjy8fdUYdTV83lfbMC4lCusPHFXBv39ePBzDekZ2+H4QEdlLLN+eWRNFnU5vFdxq0pu3uV69zSSXlzyNljMptocc0kPU7Ik+zV7cMA9j9zclnW9sIyzbDnmezsjT6C5tBM+uiKhbq5Gk5euMScvzDzQmLQ+OkKTliSppeWR8sEv3kdzLyy+/jIcffhi33HIL8vPzVS6pm266CY888oird43Iq0iQxtRTqb3DE+t1epRW1WLLzkzEJfVSieKNwa2Gq/UNJzOWtxvzbZluG9eVP+BNsycW19eiuALtThzs59NQV39fh6/QRwT6IqGZ57tt8SY1NOPw0UoMTY7AWUMT7a63t6ACz14wDE/OHIoPVh/Ecz/swn+uG2933e+25qiTlB7B/rjt1FRM7B/TrjoTEbWX5JKKDPZXpb0XQSqq67Bl527EJfdWk4uYglOWF0FMATDVXjT08jW2HTq7syc2Brja14PLnKfRIrhl2TaYL3w09AA29tTSIqS2Dmke0EYwKEVE3Y40PEd2l6hA1N6NjUnL5cpN32ExKhAlScu1TJpIdoSFhWHhwoWqEJFn8PXRqivBCWF+SEsIa1dOKQlwSWJ4q5MVi1mwbINbph5bFXZOcEwzY0kC4ZLKOlUclRwZiH/NTG5xaIbs64NLt+KZ/2Vg7tlNc4T0iQ7GyN491O1RvXvg7d/32X2+K47vjdlTUtUJ4PoDxbjpPxvw5exJ6NmDF22IyHuSy0cF+yIlJqRdbYTVJCRWE41YB7fU8ESbnr8Vdm7bJpgvcGICklvGx+CkUe7fRjAoRUTdRvnRamSsysXOVTkotUlaLnmiJGl5cHj7rrQQEVF3CHAZg1ztJVfTK2ttr8Drmj9Bsei91SPYz6F9nT4kEU9/txNz7TwuwyxNZGhIfcPJj624MGPyWjGmbxQGJ4Vj6+FjDEoREdkJcAX6+agS05B/qyMSzFe00DbY670lj8WH+npEG8GgFBF5NV2dHvsbkpYf2mGRtDxQkpbHq15RTFpORESuIH/ky6yCUpwlSYYzMzNbXW/l3kL0iw1Be+Qcq0JiRJC6vb+wAjtzSjEwIaxdz0lERK1rTDDvvW0Eg1JE5JWKsi2Sllc0DodISotUgaj+o+LUbHpERETexpQvRKfXIzkyCE+dN7Rdzyd5RLZlH4OPVgsZ2f74uUPQL9bJMyQiInILt7lZG8GgFBF5jbpqPbb/fgS7VuUi/2CZddLyCYkYNIFJy4mIyLv9ef+UZh+7aEwvVcSE/tH43x0nmh+Tq9rNbbvg4hGdsKdERNTV3LGNYFCKiDyaQW9AdmYJdvyRrZKW63W5jUnLhzckLR/MpOVERERERETuhkEpIvJIZcXV2LU6R+WKKi2sNi/vkRiMwQ1Jy4PCmLSciIiIiIjIXTEoRUSel7T8zyPI2lkMNCQt9w/0QeqYOAT3rMPoEwbD15eHNiIiIiIiInfHMzcicnuFhyVp+RHsWpuLmop6q6Tlgyclot+oOGh9oGaY4Cx6REREREREnoFBKSJySzWVddi9Nk8NzyvIakxaHhIZgPQJCSpxeWRcsNW0p0REREREROQ5GJQiIrdKWn5491Hs/DMH+zYXqOF6QuujQYpKWp6EXpK0XMveUERERERERJ6OQSkicouk5RmrjEnLy4oak5ZHJYWopOUDxsUzaTkREREREZGXYVCKiFxCekHt+6tABaIO2SQtTxuXgEETExHXJ4w5ooiIiIiIiLwUg1JE1KUKDpWpQNRum6TlyQMj1fC8fiNj4efv49J9JCIiIiIios6n7YLXIKJurrqiDluXH8bHT63FJ0+tw9ZfD6uAVGiPAIw5sy/+74kJmHnXKAwcn8CAFBEREbmNFStWYMaMGUhKSlK9t7/44otWt1m+fDlGjRqFgIAApKamYtGiRV2yr0REnog9pYio85KW7zqqekXt21QAXb1l0vJYDJ6UiJ6DmLSciIiI3FdFRQWGDx+Oa6+9Fueff36r6+/fvx9nnXUWbr75Znz44Yf45ZdfcP311yMxMRHTpk3rkn0mIvIkDEoRUYcqLapCxqpcZEjS8uLGpOXRyaEYNCnRmLQ8lEnLiYiIyP1Nnz5dFUe98cYbSElJwT//+U91f9CgQfjjjz/wwgsvMChFRGQHg1JE1G71dTrs31yIHX8eUb2jzEnLg3wxYGy8CkbF9mbSciIiIvJuq1atwtSpU62WSTDqzjvvdNk+ERG5MwaliKjNCrLKsPPPI9i9Lg81lY1Jy3um91Cz5/UbEQtf5ogiIiKibiI3Nxfx8fFWy+R+aWkpqqqqEBQU5NDz6HQ6p15X1tfr9U5v5668rT7eWCfWx/vq4+PjmvM2BqWIyOmk5TJznuSKKjxUbl4uScvTJyZi0IREhMc49gcXERERETWVmZnp1Ppy8llcXIw9e/ZAq/X8uay8rT7eWCfWx/vqk56eDldgUIqIHEpafiij2Ji0fHMB9PXG8XlaX43qDSW9onqmM2k5ERERdW8JCQnIy8uzWib3w8PDHe4lJdLS0px6XekNISefMtufq3o7dCRvq4831on1cW86D6oPg1JE1KzSwirsXJWDjFU5KC+uMS+P7hmqZs8bMC4BgSF+Lt1HIiIiIncxYcIEfPfdd1bLfvrpJ7XcGW05iZTeELKdu5+Adtf6eGOdWB/3pvWQ+jAoRURW6mt1qjeU9Io6nHHUvDwg2JS0PEklLSciIiLyduXl5aq3gcn+/fuxefNmREVFoXfv3njggQeQnZ2N999/Xz1+880345VXXsG9996La6+9FsuWLcMnn3yCb7/91oW1ICJyXwxKEREMBoMxafnKHGTaS1o+qSFpuZ97R9mJiIiIOtL69esxefJk8/05c+aof2fNmoVFixYhJycHWVlZ5sdTUlJUAOquu+7Ciy++iJ49e+Kdd95RM/AREVFTDEoRdWN1VXps/fUwMlbnoeiwRdLyqACVsDydScuJiIioGzvllFPUxbvmSGDK3jabNm3q5D0jIvIODEoRdTN6vQGHdxZjx59HsO+vAhgaZgn18dWi34gYDJqYpHpHaZi0nIiIiIiIiDoRg1JE3cSxgiqVsFwlLT/amLQ8ppckLU9C2th4Ji0nIiIiIiKiLsOgFJGXJy3fu8mYtDx7l3XS8rSxcQhOrsOoSYPdfkYGIiIiIiIi8j4MShF5adLyHX8ak5bXVjUkLdcAvQZFYdDERKQMj4FGC2RmZrp6d4mIiIiIiKibYlCKyEtUlddi95o87Fx5BEXZFeblYdGBKhA18PgEhEc3Ji3X6RqSSRERERERERG5AINSRB6etPzQjmIViNr/VyH0OkNj0vKRsRg0KRE9BzBpOREREREREbkfBqWIPNCxgkqVJypjVS4qShqTlsf2DlO9opi0nIiIiIiIiNwdg1JEHqKuVod9G/ONSct3l5iXB4T4YuC4BNUrKqZnmEv3kYiIiIiIiMhRDEoRuXnS8vwDZWp4nkpaXt2QB0oD9B4UhfSJieg3PBY+flpX7yoRERERERGRUxiUInJDVWW12LUmV/WKKj7SmLQ8PCYQ6RMSVQmLCnTpPhIRERERERG1B4NSRG5Cr9Mja0cxMlbmYP8Wi6Tlflr0V0nLk5CcFsmk5UREREREROQVGJQicrGS/EoViMpYlYOKY7Xm5XF9wlQgKm1MHAKCmbSciIiIiIiIvAuDUkQuUFejw95N+dj5Zw6OZDYmLZcZ8waOT1C5omJ6hrp0H4mIiIiIiIg6E4NSRF2YtDzvQKnKEyVJy+ssk5YPjsKgiUlIGRbDpOVERERERETULTAoRdTJKktrsXttLnb8mYOjOdZJyyUQNfD4BCYtJyIiIiIiom6HQSmizkpavr1Y9Yo6IEnL9cak5b6StHxUHAZNTEQSk5YTERERERFRN8agFFEHOppboQJRu1bnqh5STZKWj41HQBB/dkREREREREQenbxmxYoVmDFjBpKSkqDRaPDFF184vO2ff/4JX19fjBgxosljr776Kvr27YvAwECMHz8ea9eu7eA9J29SW12PHX8ewX//sQGLH12DTT9mqYBUUJgfhk/thUsfHoeLHhiLISclMyBF5CWys7Pxf//3f4iOjkZQUBCGDh2K9evXu3q3iIiIiIg8ikefIVdUVGD48OG49tprcf755zu8XUlJCa666iqceuqpyMvLs3rs448/xpw5c/DGG2+ogNTChQsxbdo07Nq1C3FxcZ1QC/LUpOU5e45h58oj2LMhH/W1erVcowH6DIlWuaL6DI2Gj69Hx32JyI6jR49i0qRJmDx5Mv73v/8hNjYWmZmZ6NGjh6t3jYiIiIjIo3h0UGr69OmqOOvmm2/G5ZdfDh8fnya9qxYsWIAbbrgB11xzjbovwalvv/0W7777Lu6///4O23fyTNXlOmz8/iB2rc7DsYIq8/LI+GCVJ0qSlodEBLh0H4mocz377LPo1asX3nvvPfOylJQUl+4TEREREZEn8uigVFvIScS+ffvwwQcf4Mknn7R6rLa2Fhs2bMADDzxgXqbVajF16lSsWrXKqdfR6XROr6/X653ezl15U310dXoc2FqEjFU5OLTzKGDMWQ6/AB/0Hx2L9AkJiE8JV0NI1foeUmdv+owE6wMVaKfO99VXX6ketBdddBF+++03JCcn45ZbblEXNJzBdoL1cWfeVh9vrBPbCSIi8gbdKiglwyukt9Pvv/+u8knZKiwsVA17fHy81XK5n5GR4fRrOUP+qCguLsaePXtUIMzTeUN9ygrqcGR7JfIyqlBXbRyeJyKS/JA8JASxqYHw9deiTJePsj358DTe8BlZYn2A9PT0Tt8vgrqw8frrr6uh3g8++CDWrVuH22+/Hf7+/pg1a5bDz8N2gvVxZ95WH2+sE9sJIiLyBt0mKCXBJhmy99hjj2HAgAGd/nppaWlO75/8UZGamuoVV7E8tT7VFXXYsz4fGatyUXio3Lw8OMIfA8bHISChGsPGpHtUnbztM2oO60NdeSI4ZswYPP300+r+yJEjsW3bNjXc25mgFNsJ1sedeVt9vLFO3lYfIiLqnrpNUKqsrEzNjLRp0ybMnj3bfGIhCaul19SPP/6IE044QTXqtsnP5X5CQoJTr9eWPw7kKpds5y1/WHhKffR6A7Izjqqk5fs2F0JXb+wVpfXRIGV4jEpa3mtwFAwGverZ4Al18rbPyFGsD3WFxMREDB482GrZoEGD8N///tep52E7wfq4O2+rjzfWydvqQ0RE3U+3CUqFh4dj69atVstee+01LFu2DJ999plKUitDL0aPHo1ffvkFM2fONAeu5L4pkEXeQxKVS54oKeVHa8zLo5NDMWhSIgaMi0dQqL95uZekoCCidpKZ92RGVku7d+9Gnz59XLZPRERERESeyKODUuXl5arbssn+/fuxefNmREVFoXfv3ipheXZ2Nt5//311JWnIkCFW28fFxSEwMNBqueQIkeEXMjRj3LhxWLhwISoqKsyz8ZFnq6vVYd+mAtUrKntXiXl5QLAvBoyNx6BJSYjpFWpOWk5EZOuuu+7CxIkT1fC9iy++GGvXrsVbb72lChERERERdZOglAzHmzx5slVASUhQadGiRcjJyUFWVpZTz3nJJZegoKAAjzzyCHJzczFixAh8//33TZKfk+eQIZp5B0qxc2UO9qzLQ211Q5cnDdBrUBQGTUhEyogY+Pqx6zsRtW7s2LFYunSpuvDx+OOPq562cgHjiiuucPWuERFRJ3j11Vfx3HPPqXOD4cOH4+WXX1YXr5sjbYJMiCHnITExMbjwwgsxf/58dTGciIi8KCh1yimnqIBDcyQw1ZJHH31UFVsyVI/D9TxfZWktdq3JVcGoozkV5uXhMYFIn5CoSlgU/zggIuedffbZqhARkXf7+OOP1YVvmcxi/PjxKuA0bdo0NYxbRl3YWrx4sZrt+91331W9amV499VXX6164S9YsMAldSAicmceHZQisqXX6XFwezF2/nkEB7cWqSTmwsdPi/6jYlXS8uS0SGi0HJ5HnmnPlFOh8feHJjAQhtpaBA4ahMQnHoc2OBhly5ahcs0axD/wgFPPWfDyKzi6eDF8G3qEBqSmIvn55zqpBkRERJ5DAkk33HCDOZWHBKe+/fZbFXSS4JOtlStXqtyDMuu36Nu3Ly677DKsWbOmy/ediMgTMChFXqE4pwIZK3OQsSYXVaW15uXxKeEYNDERqWPiERDErzt5h+QXFqhglEGvx6G//Q0lS5ci6oorEDZliiptET7jbCQ8+GCH7ysREZGnqq2txYYNG9RwbRPJUzt16lSsWrXK7jbSO+qDDz5Q+QZliN++ffvw3Xff4corr3TqtXVOzrAj68sETc5u5668rT7eWCfWx/vq4+OimVx5lk4eq7aqHns25Kuk5bn7Ss3Lg8L8MHB8AtInJiI6KdSl+0jUmQx1dTBUVcMnPELdL/l8Kcp++QW9Xn0FFWvWIu/JJxA0ZgyqNm6CQVePpPnPIGio9YQPREREZF9hYaE6obPNLSv3MzIy7G4jPaRkuxNOOEGlGamvr8fNN9+MB5288JOZmenU+nLyWVxcrCaBksCZp/O2+nhjnVgf76tPeno6XIFBKfIo0rgfySxReaL2bsxHfa1eLZfheH2GRKteUX2GRsPHx/MPJETNyb5rjhq+V5edjcDjjkP49DPsrlezbz8Sn3wSifPm4eiSJShYuBC9//WO3XXLvv8BlWvWwicyEjF/+xtCjh/fybUgIiLyPsuXL1ezs7722msqB5WcEN5xxx144okn8PDDDzv8PGlpaU69rgTP5LVSU1Nd1tuhI3lbfbyxTqyPe9N5UH0YlCKPUH60GhmrcrFzVQ5KC6rMy3skBKseUdIzKiQiwKX7SOQsQ3096ouKUJ+fj/q8PNTJv+p2Pury8mA4/3z5q7T54Xv19ciZNw/5z/8T8fff12Q9/969ETR8uLodNGIEit59z+5+9Lj0EsTcfBM0fn6o3LgRh2ffhpRPP4FfcnIn1JqIiMgzyMx5cjKXl5dntVzuJyQk2N1GAk8yVO/6669X94cOHYqKigrceOONeOihhxzusdCWk0h5btnO3U9Au2t9vLFOrI9703pIfbokKBUVFeXU+jI7xcaNG9GnT59O2ydyf7o6PfZvKVTD87J2FAMNEy36BfggbUwcBk1KUjmj5PtC5G49+nQlJeZgk/yrAk55DUEndT8PusIiWbn5J5o4ocXX0fj6Ivz005H33HOIR9OglCbAIlArfwTX19t9Ht/YWPPt4FGjVMCratt2BqWIiKhb8/f3x+jRo/HLL79g5syZ5iExcr+5mborKyubBJ5MJ4QtzRpORNRddUlQqqSkRE2fGhFhzHvSEjlY33LLLV6TYIycV3CoTA3P2702FzUVjSfRSWmRGDQpEf1HxqnAFJEr6MorGgJLxmCTvYCTFMn35BAfHxUU8o2Pg19cHHxj49QseNqYGOTFxLS6ecXqNQhISWlXnepyc+HXcMW39sABVGdkIGCAc8MGiIiIvNGcOXMwa9YsjBkzRiUul3Ma6flkmo3vqquuQnJyMubPn6/uz5gxQ83YN3LkSPPwPek9JcvdvbcCEZFXD9+79NJLERcX59C6t912W6fvD7mX6oo67F6bp3pFFR4qNy8PiQxA+oQEpE9IRGRcsEv3kbybvrYW9fkFTQNOlsGmvDzoKysdfk6fqCj4SqDJJuDkGxerlvvFx6t1NHa68ktgPr+ZJKemnFLQ1cM3KQmJjz7arroXvLAQ1Tu2Az6+al8SHn643YEuIiIib3DJJZegoKAAjzzyCHJzczFixAh8//335uTnWVlZVj2j5s6dq3rxy7/Z2dmIjY1VAamnnnrKhbUgIurmQSnp5uqMsrKyTtsXch96vQGHdxarXlH7/iqAvt7YpVnrq0HKsFjVK6rXoChotRyeR21n0Oka8jYV2ASb8iyCUPnQHT3q8HNqQ0ONwSYVWDL+axlwMgagYqHx9+/w+qQu+6XZxyLPP08VETJ+HPp9sdT8WOCAAc1um/TsMx2+n0RERN5Chuo1N1xPEptb8vX1xbx581QhIqLWMdE5dbljBZUqaXnGqhyUH60xL4/uGYrBkxIxYGwCAkP9XLqP5Bn0FRXmnky1uTkw7NiBfJ0OuvwClbNJDakrKJBuRw49nwSRTMEmq4CTKo0BJ21ISKfXjYiIiIiIyNu5TVDq6NGj+Prrr9W4bPI+dTU67N2Uj4yVOcjeXWJeHhDsiwHjEjBoYiJie4e5dB/JDXs3qYBTnrFXk9xWvZvkfsNQuvLGoZ4mjd8uC1otfKOjG3oySYApVg2dsw04+URGMnE+ERERERFRdwtKyXhsSRjIoJT3kKT1eftLsWt1HjLX56GuuqG3igboPSgK6RMTkTI8Br5+TPrYnUhOJnOQySbg1JbeTdrgYBVs8omLQ1VgIHqkpsI/IcGYx0kCT1JiYtRMdUREREREROQ+uuwsrbS0tMXHmUfKe1SW1mLnqiPY8lsBKotzzMvDYwJVj6iBxyciLCrQpftIHc+g10NXVNQwnE6CS/YDTnpHf+sajQomqaBSfHzDUDqL2w3LfUJDzYnBMzMzEZuWxtltiIiIiIiIPECXBaUiWxkWI71qOGzGc+l0ehzcWqSSlh/cVgSD3pi03NdPi/6j4lQwKiktEhomLfdI+qqqhiCTZcCpYTidCjg19G6qr3fo+TTBwcZk4Cqw1NCjSQWc2LuJiIiIiIiou+iyM76wsDA89NBDGD9+vN3HpYfDTTfd1FW7Qx2k+EgFdq7Kwa7VOagqqzMvj08JQ49+WkyYPgTBoQEu3UdquXdTfWEhagsLW8zfpG+lp6OZRgOfmGj4NfRoai7gJLPXMQhNRERERETUvXVZUGrUqFHq35NPPrnZnlTSW4rcX01VPfasz1O9oiRnlElQmJ8amjdoQiIi4gNVoDEgiD1d3LJ3U36+Cj6hoAD7HO3dFBRk0bupmeF00rvJjzMnEhERERERUeu6LGJw+eWXo6qqqtnHExISMG/evK7aHXKSDMc7klmiAlF7N+ajvk6vlstwvD5DotXwvD5Do+HjozXn96HOIcFbXUmJMciUm9vQqykXdbl5be/dFB3dzHC6xoCTNiyMvZuIiIiIiIjI84JSN9xwQ4uPx8fHMyjlhsqKq9XQPAlGlRZWm5f3SAjGoIlJGDA+HiERHJ7XUQw6HeqLiuwHnHJzjcnCc/NgqKlpd+8mbWwMDlVUIG3sWPgGMvG8p5j22TT4+/gjwCcAtfpapEel49EJjyLYLxi/Zv2Ktblrcd+4+5x6ztc2v4YlGUsQFxyn7veP7I9nT3oW3mLOnDlObzN37lxERUV1yv4QEREREZGRS8dWHT58GElJSdBqjb1ryD3U1+mw/69CFYg6tLMYaBhV6Rfog7Qx8apXVHxKOHvNOMlQW6uSgRtzNuU1BpryLAJO+Y4nC/fp0QO+CQnmPE1+Cc71bpLebJrMTA6380DPnfycCkbpDXrM/mU2vtz7JS5LvwyTe09WpS3O6neW08EsT7Fw4UJMmDAB/v7+Dq3/xx9/YPbs2QxKERERERF5c1Bq8ODB2Lx5M/r16+fK3aAGBVllKhC1e20uaiobAyPJAyJVIKrfyDj4Bfi4dB/dPn9TbsPwOTsBJ11hkYy9a/3JtFr4xsY2BJfijYGnhoCT+jchAb5xcdAGsIdad1enr0O1rhrh/uHq/hd7vsCyrGV4acpLWJe7Dk+veRqj40djU/4m6PQ6PHXCUzgu5jh0R0uXLkVcnLEnmCMTcxARkedx9mKCXLjbuHEj+vTp02n7REREbhyUYmJz16sur8OutbkqGFV0uNy8PLRHANInJCJ9QgIiYoPRXcl3VF9ergJLNTk5MGzZgqKffoZOJQpvGF6XmwvdsWOOPaGfX2PPpuYCTpIs3JcJ4ru7en09dAb7udnu+e0eNXzvSPkRDI4ejGl9p9ldb/+x/Xhs4mOYe/xcfLLrE7y06SW8edqbdtf98cCPauhfZEAkbhp2E8YljoO3eO+99xAREeHw+m+++aYaUk5ERJ6lpKRE9Y515Jgvf+PdcsstzINKRORiPPPthvR6gxqWt/PPHOzfUgB9vTE4qPXVoN+IWDV7Xs9BUdBqNd6fMPzo0cYeTaY8Trl5VgEnfWWl1XZFLeVvkqCSbcDJPLwuQQ2503C4qkd+V2p0NapU11cb/9VVo6a+4V95rOF2ra62yWOmbext39xz1Rvqce+Ae5GO9GaH70ng6vFVj+OFDS/gnrH3NFmvV1gvDIsdpm4Pjx2ORdsX2a3fxQMvxg3DboCf1k/1qrrz1zvx0VkfISk0Cd5g1qxZTk/MQUREnunSSy91uGfsbbfd1un7Q0REbhyUevDBB5mzowuV5FciY1UOMlbloqKkMVF2TK9QY9LycfEIDPHznoThhYUWQ+gaAk0NM9SZglCS58kR2ogINStdbWgowlP6wT8xsUnAibPTdR3JpSTBHQnimP6tqq3CnrI9KM4pRp2hruWAkIOBIfNtnWOJ5TuaJDJvia/WF1P7TMWC9QvsBqWkN5WJVqNVQ/jsiQmKMd8eGTdSBby2F233mqAUERF1D3q9cXZoR5WVlXXavhARkQcEpR544AFXvny3UFejw96N+Wp43pHMEvPygBBfDBiXoHpFxfb2rPwpekkYnp/ftGeTZcCpoEAyeTv0fD4xMdbJwuNNPZyk11OcekwbHKy6d2dmZiIhLQ0+Psyt1WKeo/pqVNVXNf5rCh7JfZ1xud37usbtLINOts8ht5u1o3Pr56PxUcGeQN9A9a/l7UCfQAT4BljdVv/Keha3LdeXmfTsPZefxg85B3Ja3Z+1OWvRN6Jvu+qUW5GLhJAEdftg6UFkFGcgLTIN3clff/2FUaNGcRgHEREREZE3B6Wam5pbepgEBgYiNTUV5557LntQtXOoUe6+UmSsPILM9fkqMKVogN6Do1SvqJRhMfDx07pnwEl6N6lgk/wrvZxyrBKH64qaG0Bnw8dHJQS3G3BSycLj4RcXC42DM3J5yxA0U9CnorYC+yr2oSy/TPXIcSRAZL5vGSyy7LFUX62GnXUlf21jUEer1yIsKKzVgJAKBNkEiJoLGtkGmWSIW1eQ4EiBtqDFnFKScyoxJBGPTHikXa/18qaXsaNohwq4SY+qh8Y/1O5AlydinkMiIu9QVFSELVu2YPjw4eqcorCwEP/6179QU1ODiy66CIMGDXL1LhIRkauCUps2bVKzXMgJ18CBA9Wy3bt3q54n6enpeO211/D3v/9dTckts/OR4yqO1WDXamPS8pK8xjxI4TGBKhA18PgEhEUFur6HU44EmRoCTzkNw+tkmRMBJwkkqZxNNgEn3wTjUDoJOPnGREPjYT2aTIGjyvpKFfCpqqsy/mtTTI9X1lU2+5i97Q2wc9K9rXPqIsGNIN8gFcyRII/lbXv35bZaZue++V8f68clMOOjNX7Gpp5saV7ek+2HC39o9rGZqTNVEWMTxuKzcz4zP5bWI63ZbWVWPm93/vnnt/j4sWPHOPyWiMgLrF27FqeffjpKS0sRGRmJn376SQWifH191fC+Z555Rp1nSO9YIiLqhkEpUy8omQ0pPDzcfDJw/fXX44QTTsANN9ygkszedddd+OGH5k++yEin0+Pg1iIViDq4rQgGvTHo4OuvReqoOKRPTERSaiQ0nZy0XHIz1eUXmHs11R45AkNGBrJrqqFTw+vyoCssdOi5NAEBxsCS5G2SoFNiQmMC8cRE9a9PZKTLTiAlcGTqJaQCQG0IHFkFjxwJHHVC7yIJ9PjCF6EBoQjyswkYWQZ/7ASDHAkeSb4jnuSTu/j6669x2mmnNTurHoftERF5h4ceekgFoRYsWKBmU505cybOOOMMvP322+rxa6+9Fk888QSWLl3q6l0lIiJXBKWee+45dcXCFJASMm3ro48+qq5q3HHHHXjkkUfUbWpe0ZFyZKzMwa41uagqqzMvT+gXrnpFpY6Og39Qx3y8hro61cOpzt6wOtXTKRe6wiKJ1jTZtsJOwMnYm6khUbj534bAk8xQ14EBJ0nsbAoAVdRVqH9NQSL513JZa//KNjLkrWZNTZcEjqQXkAR5bEuwX7Dd5fYeC/YNbvKYBJGkd1F36VlEJGSoxgUXXIDrrrvO7uObN2/GN9980+X7RUREHWvDhg146aWXEBYWps4r7rvvPnXR22T27Nk455xzXLqPRETkwqCU9IrKz89vMjSvoKBAdbMV0tW21sFZ0bqTmqp6ZK7LU72i8g8Y3ysRFO6P9PEJqldUVGKIU89pqK83B5xUsKkhyGQ5rE5msbMXcGppSJ1PQjzK/AMQN2gQ/JOSjIGnxMQWA04yo5oKAlUVmINBEjQyBZBMwSHbQJIKFskyO4GkFhNid2LgyJngkWXgyPSYKXBERB1j9OjRauh4c0GpgIAA9O7du8v3i4iIOpacQwQFBanbfn5+CA4ORkxM4yyzcltyThERUTcevifdZv/5z39i7Nixatm6detw9913q+61prHgAwYM6Opdc0syHC9791HsXJWDvRsLoKszTnWr1WrQZ2g0Bk1MRO8h0fDx0doPOBUUWAWc6vOsA08q4OTA9LkaP7/GHE4yrC4hHpq4ONTHRqA2OgxVUSGoCvVFeV0FKuorUF5TjoM5BxESWYZq3V+oKK1AZXFjDyVzzyWLwJKUziIJnCX4IwEfVXxb/jfEN8R83xQokiFveYfzMKj/IOOQN98gBo6IPMQbb7zR4hA96Um1f//+Lt0nIiLqeL169cK+ffvQt69xwo4lS5YgMTHR/HhOTo5VkIqIiLpZUErGdku+qEsvvRT19cZZuiTx4KxZs9TYbyEJz9955x10Z2XF1chYlaN6RZUVNfb26ZEYogJRA8bEwr/mGOpzs1Hx04bGpOHm4XW5KiDlSMDJ4OuD+ujG4FJFj0CU9vBDSbgvisI1KAjVozCgFhU6CSQVoKLugAoo1enrgDwYS3MOdn4ASf4N8QsxBpD8ghoDSw3LTAGl9g4JlBNabaEWscGxHO5G5GGkJxQREXk/OceQURkmZ511ltXjX331FcaNG+eCPSMiIrcISoWGhqpEgy+88IK6iiH69eunlpuMGDEC3Y0kz9bVG7BnfT4yVuXi8K6jMKUt8vXRIzEwFwnV2xCydSfwcyEOFpVA40DASacFSsJ9VHCpMFSPgjCDul0UBvO/pSGAQSPDAUvt7JhEyBpKM0xD0CQIZCqyTF+tR3yPeIT4h3R5AImIqDVyoiIXQCyvoBMRkWebN29eq4nQeXGRiKgbB6V+/fVXTJ48WQWhhg0bZvXYq6++iltvvRXdzdHsAnxx/6uoDhwDvU+weXnk0V1Iyl2F2ILN8JFeSTZ0GqBYBZeAojCN3X+PqYCTKcDTOMRPAkChfqHo4ReMnn4h6rYpONRq8Q1BqL9xfXkemWWtyb4xiTYRubkVK1agqqrzhg0TEZF7+PPPPzFmzBjVa1ZyTBERUTcOSp1//vn4+eefVdJZSy+++CIefvjhbhmUyq+qRnXg8dD7+COguhiJuasRl7ca1X5FKAwD9g3UoDhMg7JwX1RE+qM2KgS1sT1giIlBSGCYOZgkgaUBFrctl5vyJEkwSXoxaTVNc1AREREREXmb6dOnq1lWZXQGERF186DUc889pxoGuUItuaOEJD1//PHH8e2336I7GtAnBuvC16CsNgdVQbtQ1LMCgf5HkaI5ikHQIVhvQJDBAPOANtNIu4OBQI++QFS/hhIHRDTcjugFMAk3EVGz+vTpo2ZmIiIi70+T0R4ymkPOYXJzczF8+HC8/PLLLealKikpUcMEP//8cxQXF6v2ZuHChTjzzDPbtR9ERN6oy4NS119/vTo4T506FX/88Qc+/vhjPP300/juu+8wadIkdEcavyBc9o+5arhbVFIfLN10BI+vzcKRolL01BSgjyYXJ0eX4eTYMvTR5MHn6H6g5CBQXw0UZBiLLa0f0KOPRcDKokT2Bnx4IkZE3du2bdtcvQtEROTm5Fxlzpw5ahbX8ePHq+DStGnTsGvXLsTFxTVZv7a2Fqeddpp67LPPPkNycjIOHjyIyMhIl+w/EZG76/KglLj33ntRVFSkxnZL7qEffvgBxx9/vCt2xe1Eh/jjppP744YT++HPvYX4cHUWftqZh+UFBqAAiArxx0Wje+KyMUno61sMFO9rKPsbb0vQSlcLFO0xFlsaHyCyVzMBqz6AX6Arqk5E1Om2bNlid7lMrhAYGIjevXtzpj4iIi8js3/Hx8e3aVuZHfyGG27ANddco+5LcEpGd7z77ru4//77m6wvy+UC/MqVK829cfv27dvOGhARea8uCUq99NJLTZbJVQNJNHjSSSdh7dq1qojbb7+9K3bJ7Wm1GpyYFqtKXmk1Pl53CB+tzULOsWq8uWKfKiemxeCK8YNx6ujJ8POxyBGl1wGlRywCVjaBq/oq4OgBY9m7zOaVNUBETyAqxRik6tHwryopgH9IV78VREQdRmZ3Nc3uKcM5bGf6lBOISy65RJ3ASJCKiIg83+WXX96m7aTX04YNG/DAAw+Yl2m1WjXiY9WqVXa3+eqrrzBhwgSVJ/fLL79EbGysev377ruPk/8QEbkqKPXCCy/YXS4HZpkNQ4qQkwMGpZqKDw/E7aem4ZZT+mP5rgJ8sOYgfttdgN8zC1WJCwvApWN74dJxvZEUGWTMJSU9oaT0O9n6yWRMfVmusTeVbdCqaB9QWwYcO2Qs+1c03ZnQBOsglWUvq8DwLntPiIjaYunSperE4J577jHnA5GLIpLbUKYRr6+vV1e+586di+eff97Vu0tERG1UXV2tcj/JzN/5+fnQ6/VWj2/cuLHV5ygsLFSjOmx7Wcn9jAw76TMA7Nu3D8uWLcMVV1yh0pPs2bMHt9xyC+rq6lQ74yh5XWfI+lJHZ7dzV95WH2+sE+vjffXxcVHgvEuCUvv37++Kl/F6vj5aTB0cr8qh4krVc+qT9YeQX1aDl5btwSu/7sGU9DhcMb4PThoQCx+tdQ8ARXoFhCcaS5+JTQNWlUV2elg1lKqjQHmusWStbPrcwTHmAJWmR1+E1wQDwaVATCoQHNV5bwwRkYOeeuopNdur5AMxGTp0KHr27KlmgJUAVUhICP7+97+7V1CqvgaorYJGerpKPkGDn/EChMykatPbi4i6uReGAr7+gG8QoKsBEoYB57xk7O2e8R1w4HfgjPnOPeev84F1bwNhScb7cenABe/AnV133XX48ccfceGFF6qLELY9YzuLnARKPqm33npLneDJjOPZ2dkqUbozQSnJNeuUmjKUHC3B3vpqaH18YVDtQ0PxQPI+yjBICepJ7zRv4G11Yn08sz79v5oJvY8fDD4B0OjrUB05ALnjHoTBNwjp2OeSNsIlOaWo/XpFBePeM9Jx59QB+HFHrso9tWpfEX7ema9KcmQQLh/fGxeN6Ym4MAeHoEhjHRJjLL3szChSWdzQw8pOL6uKAqCy0FgOr4V87dVXcnXDtoGR9nNYSZHX40kVEXWBrVu3qlmQbMkyecw0xC8nJ8fh53zmmWfU0I477rhDJcDtFGvegM9Pj2Cg3Qc1FgGqhn/VfY3NfdNt04mK5WMN95t9zGY928eavIa91ze9hvExDbSIOVoCzeEYwMfXZv/sbCfP1+R5mnnMan/a8lgb6iyTe7Vzhi+iDnPhe0DiMDkrAT66BNi8GBh3A5B+prG0xdCLgenPwFN888037Z5IKSYmRgWW8vLyrJbL/YSEBLvbJCYmqqHglj0OBg0apGbuk+GA/v7+Dr12WlqaU/uqfT4Nmqoiu48ZHD7G2bQBrbULrbUvFssNLW7f9LVk3vGjJcfQozIaGqs2oqXtbdoemzZJ7UNrdW7p/XG6vbN+bp0B2GMwIDU11SuGckoPHAl4sD6eVR+tny/0F/8HSBgKGPQIX3IZwsrXwjD2esAn3SVtBINSHs7fV4uzhyWpsie/XPWe+mzDYWSXVOG5H3bhhZ92Y9qQBFwxvjcm9Itu31Ui6e0kJXl008eqS62GBOqL9qI6eyeCqnOgKcsBqkuAIxuNpUklwhqGAtoMB5QiwwW9IFJNRO4hPT1dBZHkCrbpxECGVMgyeUzIFW1HE+KuW7dO5Z8aNmxYp+63/NHQwoOAvh6eRo7sMfAe8ueefIPkRKrVEyW7J0UtBeIcPRlr5rEWX8PmpKzhMQkbRhcfhSYvTvrzd2xAz+kAZ1vr3LCf3ZlMfFNXZbw4KDZ9CGR8C1y2GNj/O/DdPcae84fWGI8jM18HkkfBG0j+2rCwsHY9h7QT0tPpl19+wcyZM829D+T+7Nmz7W4jQbDFixer9Uy9E3bv3q2CVY4GpISzJ8UGNN9OaKQNMbUjLhqZ1JZfoje1EUI+0cENQUKNUxdfOua43tEXaqSdiC0+Ct/CONU7z/GLY44H8pxvLx2os+1jFu2E/Gblt+cNQanm66OBj7yPsqyuTvXA18g5vtx3URvBoJQXSY0LxcNnD8Y90wbi2y05+HDNQWzMKlG3pfSLCVG9py4c3RORwY43ig6RfFKJw41FDrY6HbIyM9VVHh/pOi5J1e0lXpfcVZLHKneLsdiSrufmYJVN0Co82XhQISJy0KuvvopzzjlHDdczBZKkh5RcTZKr6qZ8IJL/ozXl5eUqZ8jbb7+NJ598snN3fMJt0I29EXsydyO1Xwp85O8n00mGTG6hbuts7jf3mMHmfkuPNWzb0mPqvr6Vxyyfx3hbr9ehpLgIkRER0MJ2O0Pb69XS6zeps96511DdoVqmkXVkGw/PSSGn0rHwBo1BQq1GiwFyGiV/OzR7gtKWk7lWgmIO985oreeG9QmbJiAcCG1m9urPrjH+DVWSBSQNB447z/56hbuBc18Bzl4ArPsXsOwJ4Mql9tfd8YVxWEdQD+Dke4GUk+DOJFeg5BCU2fLs9ZB11Jw5czBr1iw1a7gMA5QesRUVFebZ+K666ioVAJs/3zjc5W9/+xteeeUV1Xv2tttuU8Pwnn766U7Pm6u/c4exjehv0UY0e4zTtfyY+RjszGN2jp0tPdbKMVevq0fJ0WJERoRBq47vnXRcb1N72cJ75wB3CBJ2BK9pJxqO4VqNDwaoier9Wjnmd+RxvaMv4misgoYBPinS79Lt2wgGpbxQoJ8PLhjdU5UdR0qxeO1BLN2YjX2FFXjy2534xw+7cPawRJV7alTvyM4fY+8fDMQPNhZ7eVKOHrSfw0p+JJI/JX+HsdjyCQB69AWi+zcGqtTt/g0BK/awIiJrEydOVHkOP/zwQ3XlWlx00UVqZiTTFfUrr7zSoeeSmZXOOussNQtTpwel5AqkhDvkD4iAMOPVLA8nFy/yMzMRIUNUPKU+5hOjpgE9na4e+/Zkol9K34YTQkdP2Do22Ne2E7amgUAJGpYeLUZ4eBi0qjdeCydzTgU423Ii3Mx750CQEBZBQvlYvKnflOTvxLSPWx6+p6sHvrkD+HkeMO2ppuvJ3049xxhvS+qGlS/bf74x1wIn3Q3IyVrWamDJFcCNvwKRveGuJIgkyc779eunZvyWIXWWJNeKI2RG1oKCAjzyyCNqCJ4M8f7+++/NPWqzsrKs8rX06tULP/zwA+666y518UMCVhKgkgBZp/INgME3EPAP9Zxjqre1EaKFY5yuvh779maiX98+xty/LV2MaetjnRaIa/qYaidKjiI8LEQFqNp+XO/E9tIRDa+nQb2xjZAOFV5ACyBojBx3znL7NoJBKS83OCkcT84civunD8JXm4/gg9UHsSOnFJ9vzFYlPSEMVxzfBzNHJCEs0Lqx7hK+AUDsAGOxpaszBqbs5bCSnldywCjcZSz2AlaqZ1V/INrUu0pu9zcmYGPAiqhbKi0tRXh4OG6++eYmj5nG3TtiyZIlauYmGb7XVpxZyZPr03DVE43tptSjzj8CuqBozzqBaobU58iePQh259walkFCB064dPV1OLh/P/r07mnR29DipMm2x57dk8umJ2yq14Ptui2eKDkQyGs1MGmAIbCH3d+QBBHVTHNquQYYOAPanx+BfurjaviQFQlimMjVdfU+2BFmMaS59/HGk5kjm9w6KHXZZZep4djSS0kCSO25CCtD9Zobrrd8+fImyyZMmIDVq02JValbUW2D1v5ptk4HXWAUEJbgNYHD3MxMhLlz4NCJgJ60EQf27UXfPr3tBA2bv4jjVCCwKwJxBuNjEjSsDevd+kXPQecCPz1sPyjVRW1ElweltmzZ0kyObQ0CAwPRu3dvBAQEOPRcK1asUDNZbNiwQSWllam+TeO97fnjjz/UVQqZwrWyslJ15b3pppvUlQyTRx99FI899pjVdgMHDmx22ldPERrgq4buXTauF/46fAwfrj6Ir/46gozcMjz8xTbM/24nzh2RrHJPDUmOgFuQSKsEkaTYkh+EDP0r2tsYqDLdNgWsCjKMxd6Pq0dKQ68qU+CqobcVA1ZEXk16Nv3000+qvbG0a9cunHrqqTh8+HCrz3Ho0CF11dve8zjD2ZmVususMJ6K9fGQOtUGoq7YlOvHVJxk6nKlddOZlerqcTgrCzVlxuNT7KYv4R+YiOzMTJX3rE2OZQMRycbb8vdW7lYg7ji4s5UrV2LVqlUYPtyYWoKIuiEJRqve5g6Qi0uhVcbzQ3cNsjkZNKx05G/N/b8B0Y5dlO2sNqLLg1LS5bWlKxXStVa6yUri2Nb+2Jfx3NLQXHvttTj//PNbfW2Z5luuckhXWrktQSoJSsntG2+80bzecccdh59//tl839fXezqUyXs/olekKnPPGozPNx3Gh2uyzEnSpchjEpyS5OlB/m76g5Qxs9JtXQpOtX5MuiFKwMo2WFW81xiwkunUC3YaS4s5rCyGA6pZAuO6rHpE1DlCQ0NVe/HVV1+Zj+07d+7ElClTcPHFFzv0HHIhJD8/H6NGNSZ6lJ4KcqFE8ojU1NQ41LPE2ZmVususMJ6K9XF/3Wlmpb7rHgP8Ao2JaSN6QX/+S0iL6Als2dC2F5M8Ikc2A1pf48W7M58HYtp5EtPJZPKKqqoqV+8GEZH7+awhp5S0EZG9gLNfaN/ztbON6PJoi/Rmkt5K99xzj0oWKNauXauSEc6bNw/19fW4//77MXfuXDz//PMtPtf06dNVcdTIkSNVMenbty8+//xz/P7771ZBKTlRaW6aV28SEeyHayal4OqJfbFmf7EKTn2/LQebD5Wo8sQ3O1ReKglQpca1b/aSLiXRcNNsfnYDVlkNwaqGQJUpcFVysMUcVlrfIPQNSYZ2UzoQ09+mh1UiZ/gh8gByzJccUJKgXIbgbd++XfWQkvsLFixw6DlkfUmObkkS3soJkLRvjp7stuWkuHvMCuO5WB/35211slufu7Y1Wc/86MgrjEWknAj87Y/GlST3513Wxzaz896Ap5FZVf/+97/jqaeewtChQ5vklJKh3ERE3c5dzRznXdhGdHlQShqGF198EdOmTTMvk4ZCZkJ6+OGHVYBKei5JI9JaUKq9Nm3apLr22iaolSEVSUlJqqeWjAmX2TRkWKG3kt5Tx/eLVqWwfDA+XX9YJUc/VFyF9/48oMr4lCg1/O+MIQkI8PXgP+RUwKqhF5Rt8NYUsLINVqkeVgehqa9C4LE9gBTbNFZ+wRYzBFoEq+S2jBtnwIrILQQFBeHbb7/FKaeconpGSe8mmTlJhoI7ShKiDxkyxGqZtFvR0dFNlhMRkWucccYZ5gsJlgwGg/rb1zPz2REReZ8uD0rJ1WV707LKMtOVZxniJzmiOosEwGQWDemVJTmkrr/+evNj48ePx6JFi1QeKdkHyS914oknYtu2beaZmbw5gW2PIF/ceGJfXD+pD/7YW4jFaw7hl4x81ZNKSlSwH84flYxLx/ZCSkyI29fHORogoo+x9Jts/ZCuDrriA8jd/ieSAqugLTkIjSlwVZIFTV0lkLfNWGwYJGAlOayi+sHQkHjd0KMhMBYa77KAlWd+Rs1jfdrW86a7JDe37Vnw8ccf47TTTsMFF1ygLoiY1uGVcyIi7/Drr7+6eheIiMgdg1IyvEG607711lvw9/dXy+rq6tQyeUzITBmmaVY7gwzXKy8vV7NiyFBBGYsvM3QIy+GAkntKglQSMPvkk09w3XXXdasEtokA/j4+DFcNCcL3u0vxQ2YpCivr8M4fB1QZnhCIMwdGYELvEPiraWzcuz7tpeoUMBDlPaKgjdYCpvzr+nr4VRyBf9kh+JcfVv/6Ndz2q8gxBqzyt6tiG37S+wahNrQnasN6oTa0F+rCeqp/ZaYENTtHJwasvO0zYn2Mx1dqKjIy0m4uQ7la/sYbb6gchu29cm5v9iUiIur6CZWkx6q0myeffHKr68sQbrkQ7U35Y4mIPE2XH4FfffVVnHPOOaq3kgR9hPSQkhOBb775Rt3ft28fbrnllk7bh5SUFPOwwby8PNVbyhSUsncyM2DAAHVi6AxvSmArNZk4AnhEp8dvuwvx0bpD+G13Af7KrVZFek9J7qlLxvQ0955y5/q0Vct1GmR3G72uVvWkQvF+c88qzdGGBOwlWdDKkMCSTFVsGfxDVQ8rg2kYYJTcNiVdj213wMrbPiPWh5rDq+VERN2D5I7Nzc1FbGysQ+tLmo7NmzejX79+nb5vRETkJkGpiRMnYv/+/fjwww+xe/duteyiiy7C5Zdfbh4ed+WVV3ZpbwSZKak50qNq7969Tu+TNyawlf06fUiiKtklVfh43SF8su4Qckur8fbv+1WZ2D8al43rjanpsW5fn7Zwuk4+QUDcQGOxVS8Bq4PWuatMt48dgqa2HMjbCk2enYRy/mHG/FWWswOabofEOByw8rbPiPUhe3r06GG+cu4IXjknIvJM0utVhmQHBwc7tH5tbW2n7xMREbWsy//ilrwdkrPj5ptvbvKYqVeAoyRgZNmDSYJdcrUjKipKJSZ/4IEH1FDA999/39xLS5abhrhIgltJpn777bebn+Puu+/GjBkz1JC9I0eOqBkB5YSwuZ5U3VVyZBDmnDYAt09Jxa+7CvDR2iz8uisfK/cWqRIV4o8pKcH4W1QS+scxR4tdvv5ATJqx2KqvMfawsg1Wye2SQ0BtGZC7xVhsBYQ3Jly3DFbJv8HRTLpO3Q6vnBMRdQ8nnXQSdu2ynY2m5eO9TIBBRETdKCh11lln4aefflIz21mSBkRmxzh8+LDDz7V+/XpMntyYkHrOnDnq31mzZqlk5ZKoPCsry6pXlASqJHglV8D79++PZ599FjfddJN5HXl9CUAVFRWpE5gTTjhB5Z5y9GSmu/H10eK0wfGqqN5Ta7Pw8fpDyCutwWfbavHZtt9V7ymZue/0wQnw9/X8XD9dwjeg5YDVUelhtbchUNUwW6DMGnjsEFBTCuT8ZSx2A1bGQJWmRwrCa0OAoIlA7AAgOKpLqkbU1XjlnIioe2B+PyIiz9PlQanQ0FCcf/75+Oqrr8xDI3bu3IkpU6ao6bmdIVN6y8lGcyQwZem2225TpSVLlixxah/IpvfU6QNx+6lp+HlHLv712y6sz640956KDvHHhWN64rKxvdG3hZn7yIGAlQSRpNgNWB2wCVbJ7f0WAavNqkh4MEm2WdOwbVAPIDq1sVeVZQ+rAMdnniRyN7xyTkRERETknro8KPX5559j6tSpuOKKK1QASHJ3SA8pub9gwYKu3h3qxN5Tff1KERzTE59tzDb3nnrzt32qTEqNxuXj+qj12HuqowNWA43FVl21MWDVEKzSF+5B1eGtCK7Ogab0CFB1FDi8zlhshcbbCVZJACsF8OPJO7k3XjknIiIiInJPXR6UkqvP3377rerlJD2jJK/TVVddheeee66rd4W6QHKPxt5Tv2Tkq9xTMnPfn3uKVIkJ9ceFo3vh0rG92Huqs/kFAnHpxiJDmnQ6HMrMVDNF+uhqLHpW7TEOBTTdrigAyvOMJWulzZNqgPDkxmCVZU+ryD7GvFlERERERERErgpKSXJzSzID0scff4zTTjsNF1xwgcr1YVpHkqCTd/aemnZcgiqHiivxyfpDava+/LIavPHbXlVOSI1RM/ex95QL+AcDCUOMxVb1scbhgEUNgSpTwEoeKz1sLPt/s95O4wNE9rYJVkk+q1Qgoheg5YxyRERERERE3VmXBKUiIyOhsTPjl+SDeuONN/Dmm2+q27KOTqfril0iF+oVFYy/N/SeWpaRj8VrsrAiswB/7ClUxdR76rJxvdAnmr2nXC4wAkgeZSyWJJ9bZbFF76q91j2t6iqAo/uNZc/P1tv6+AM9UhqGAjYEqkzDAsOTOEMgERERERFRN9AlQalff/21K16GPIyfTe8p6TkluacKbHpPycx9Uwex95TbkcBRSLSx9BrXNGAlw/1MwSrVu6qhp5X8K8MFC3cZiy2/YPMMgY15rBp6WoXEMGBFRERERETkJbokKHXyySd3xcuQh/eeunvaQNwxNQ2/7MzH4rVZ+N2q91QALmqYua93tGPTupMLSeAoLMFY+p5g/ZheB5Rm2wSrGoJXJQeBukogb5ux2AoItx+skmGB/hz6S0RERERE5Em6JCi1ZcsWDBkyROWScoTMyDdw4ED4+nZ5HnZyg95TZwxJUEV6Ty1Zl4VP1h9WvadeX75XlRPTYnD5uN6YOjherU8eRtuQa0pK/8nWj+nqgJIsm6GADbdLDgE1pcCRTcZi+7TB0egdlATNjiFAjOUMgf2AgNCuqx8RERERERE5pEuiPiNHjkRubi5iY2MdWn/ChAnYvHkz+vXr1+n7Ru7de+qeaem4c+oA/LIzD4vXHlK9p37PLFSFvae8kI9f40x+tuqqgaMHLBKtNxS5XZYDTWURgiuLgKKtTbcNS7ROtG7qaSV5rWRWQiIiIiIiIvLOoJQkMZcZ9oKDHQsc1NbWdvo+kaf1nkpUhb2nujEJHsWlG4utmnLoCvcgd/ufSPSvgFaSq5t6WkmgqizHWA7+YbOhxjgToCkQZupdJbelJ5cEyYiIiIiIiMhzg1InnXQSdu2yk9C4hZ5SQUFBnbpP5B29pz5ck2XuOWXqPXXh6J64dGwv9I3hzH3dhgzPSxiKsrJAJKSlAT4+jY9VlVj3rLLsaSXDAY9lGcs+mwkZtL5AZB+L3FX9Gm+H9wQcHI5MRERERERELgxKLV++vCtehrpp76msosbeU4XljTP3TegXjUvH9VKz+wX6WQQpqHsJigSSRxuL7QyBFYUWASuLYJUkX5eE63JfSuaP1tv6BABRKU2DVVJC4zt3hsAXhgK+/oBvkHEWw4RhwDkvAf4hQMZ3wIHfgTPmO/ecv84H1r0NhCUZ70tvtAve6ZTdJyIiIiIiMmEmcfJ4kk/q3jPScddp0nsqXwWofttdgFX7ilSJDPbD+SN74rJxvZAWH+bq3SV3IYGj0Fhj6X1804CVDPezTLRu6mklQwMlGFSQYSy2/EMbAlUNQSpzwKo/ENSjY/b9wveAxGGAXg98dAmweTEw7gYg/UxjaYuhFwPTn+mY/SMiIiIiInIAg1LklTP3HT5aqXpOfbr+EHKOVePdP/erMrpPDzW07+xhSQjyZ+8paiFgFZ5kLCknWT+m1wHHDlknWjf1tJKZA2vLgdwtxmIrONo60bopcCVBLP82JOvX1QJ1VUBgpPH+pg+BjG+ByxYD+38HvrsH6DMROLQG0NcDM18Hkke18U0hIiIiIiLqWAxKkVfq2SMYc04bgDtOTcNvu/Px0dpDWJaRjw0Hj6ry+Nc7cO7IJFw6tjeGJEe4enfJk2h9gB59jSX1VOvH6muBkoMNPawaelmZ/i07Yky6LkWCRLbCk62TrffoB5+ahmCTrc+uMQ7fkyBY0nDguPPsr1e4Gzj3FeDsBcC6fwHLngCuXGp/3R1fGIf+SW+uk+9tGowjIiIiIiLqYAxKkVfz0WowJT1elfzSany64TA+XncIWcWV+GB1lipDkyNU7qlzhichLJCzrVE7SK6nmDRjsVVbYcxVZRWwaghaVRUDpdnGsn+FWl368YWMnwcMHd/88D1dPfDNHcDP84BpTzVdT3pg9RxjvN1rHLDyZfv7PeZa4KS7jbMNZq0GllwB3PircQZCIiIiIiKiTsLpo6jbiAsPxK2TU7H87lPw4fXjcfawRPj5aLA1+xgeWroN45/+Bfd+9hc2Zh2FQXIKEXUkSUSeMNTYq+mke4Dz3gCu/wm4bz9w737gup+B8940PnbceTDED0VteN+Wn9PHFxh0LrDnZ/uP+wY23tb4GIce2hMWbwxICcmvJQGvI5vaWlMiIiKv8uqrr6Jv374IDAzE+PHjsXbtWoe2W7JkCTQaDWbOnNnp+0hE5KnYU4q6Ha1Wg0mpMaoUldfg843Z+GhdFvYVVKg8VFIGxoep3lOSID0imL2nqJMFRxlLr7HmRXqdDtWZma1vu/8343C/9jiWDUQkG29L763crUDcce17TiIiIi/w8ccfY86cOXjjjTdUQGrhwoWYNm0adu3ahbi4uGa3O3DgAO6++26ceOKJXbq/RESehkEp6taiQwNww0n9cP2JKVh34CiWrM3Ct1tzsCuvDI99vQPz/5eBM4ck4NJxvTGmN3NPkZsw5ZSS5OWRvYCzX2jf80muqSObAa2vRG2BM58HYtoZ6CIiIvICCxYswA033IBrrrlG3Zfg1Lfffot3330X999/v91tdDodrrjiCjz22GP4/fffUVJS0sV7TUTkORiUIlKTrWkwLiVKlXkzjsMXm7Px0dosZOSW4YvNR1RJiQnGlL6BuDGxBvERbZgpjagj3LW1+cdGXmEsIuVE4G9/ND4WP7j5bWUoIREREVmpra3Fhg0b8MADD5iXabVaTJ06FatWrWp2u8cff1z1orruuutUUKotJLDl7Pp6vd7p7dyVt9XHG+vE+nhffXx8XDM7PYNSRDZkuN6siX1x1YQ++OvwMdV76qu/jmB/YSX+VViJ9zctx2mD49XMfSekxqjhgERERETkXQoLC9UJXXx8vNVyuZ+RkWF3mz/++AP/+te/sHnz5na9dqYjQ/gtyMlncXEx9uzZowJnns7b6uONdWJ9vK8+6enpcAUGpYha6D01olekKnPPHowvNx3Gv//Yg92FNfhua64qPXsE4dKxvXDRmF6ID7dIKk1ERERE3UpZWRmuvPJKvP3224iJiWnXc6Wl2ZnJtwUSPJOTz9TUVJf1duhI3lYfb6wT6+PedB5UHwaliBwQGuCrgk+jI6tRHxqPTzZkY+mmbBw+WoXnf9yNF37OxOSBcbhsXC+cPCAWvj6eH10nIiIi6s4ksCQnc3l5eVbL5X5CQkKT9ffu3asSnM+YMcOqt4Lw9fVVydH79+/v0Gu35SRSekPIdu5+Atpd6+ONdWJ93JvWQ+rDoBSRkwYlhuPxc3vggemD8N3WHCxZl6WSpP+8M0+VhPBAXDymJy4e2ws9ezD3FBEREZEn8vf3x+jRo/HLL79g5syZ5iCT3J89e7bdoS9bt1rnb5w7d67qQfXiiy+iV69eXbbvRESegkEpojYK8vfBBaN7qrInvwxL1h7CfzceRm5pNV5atgcv/7oHJ6bF4rKxvTB1cDz82HuKiIiIyKPMmTMHs2bNwpgxYzBu3DgsXLgQFRUV5tn4rrrqKiQnJ2P+/PkIDAzEkCFDrLaPjIxU/9ouJyIiIwaliDpAalyYyjt1zxkD8eP2PNV76s89RVixu0CVmFB/FbyS5OgpMSGu3l0iIiIicsAll1yCgoICPPLII8jNzcWIESPw/fffm5OfZ2VleUVSZCIiV2FQiqgDBfj6YMbwJFUOFlXg43WH8OmGwygoq8Gbv+1TZVxKlMpPNX1IouptRURERETuS4bq2RuuJ5YvX97itosWLeqkvSIi8g4MShF1kj7RIbj3jHTcddoALMvIx5K1WfhtdwHW7i9WZd5X23HuiCTVe2pIcoSrd5eIiIiIiIioSzEoRdTJJJfUtOMSVMk5VoXP1h/GJxsO4VBxFT5YnaXK4MRwXDquF84dnoyIYD9X7zIRERERERFRp+MAaKIulBgRhNtOTcNvd0/Gh9ePxznDk+Dvo8WOnFI88uV2jHv6Z9y5ZBNW7i2EXm9w9e4SERERERERdRr2lCJyAa1Wg0mpMaqUVNbii03ZWLLuEDJyy/DF5iOq9IkOxsVjeuHC0T0RHx7o6l0mIiIiIiIi6lAMShG5WGSwP66elIJZE/tia/YxFZz6avMRHCyqxHM/7MI/f9yFyQPjcMnYXpicHqeGAxIRERERERF5OgaliNyERqPBsJ6Rqsw9axC+25qLj9dlYd2Bo/glI1+VmNAA1XNKAlQpMSGu3mUiIiIiIiKiNmNQisgNBfv7quCTlD355fh0/SH8d+NhFJbX4I3f9qoyLiUKl47thelDEhHk7+PqXSYiIiIiIiJyCoNSRG4uNS4UD5w5CHdPG4hfdubjk/WHsHxXPtbuL1Zl3pfbce7IJFwypjeGJIerHldERETUPU37bBr8ffwR4BOAWn0t0qPS8eiERxHsF4xfs37F2ty1uG/cfU4952ubX8OSjCWIC45T9/tH9sezJz3bSTUgIqLu1EYwKEXkISSX1BlDElTJOVaF/244jI/XH8Kh4ip8sDpLlcGJ4Wpo38wRyYgI9nP1LhMREZELPHfyc+pEQ2/QY/Yvs/Hl3i9xWfplmNx7siptcVa/s5w+USEiIvfznJu1EcyYTOSBEiOCMHtKGn67ezIWXz8e5wxPgr+vFjtySjHvq+0Y+/TPuGPJJqzcWwi93uDq3SUiIiIXqNPXoVpXjXD/cHX/iz1f4PZlt6vb63LX4bwvz8OTq5/EBV9dgJlfzMT2wu0u3mMiIupubQSDUkQeTKvVYGJqDF66bCTWPngqHp0xGOkJYait1+PLzUdw+dtrcMrzy/Hqr3uQV1rt6t0l8grz58/H2LFjERYWhri4OMycORO7du1y9W4REZnd89s9uPCrCzH548nQQotpfafZXW//sf04p/85+O85/8Xlgy7HS5teavY5fzzwozoxue6H67A2Z20n7j0REXWnNoLD94i8RGSwP66elIJZE/tia/YxfLzuEL7afARZxZV47odd+OePuzB5YJwa3jc5PY4RaaI2+u2333DrrbeqwFR9fT0efPBBnH766dixYwdCQjpnVsx9Jfuws2gncotyscd3D7RaLQxo7AVpum0w2O8Zae/xdm1j+dp2tndkGyn5+flYr1sPjVZjd/vmXsdyeYvb2Nm+pW0d2cZ82+JpZJler0dxcTF6VPYwfj6d8F43vnTL70urn09rdWz4fEpKShBxNKJJrsKW3pf2fgcd/Xxa/W7YfD5ChimUlZUhLDes2fejxTq25fNp4/fJkW1iAmNwddzVaGloRr2+Ho+vehwvbHgB94y9p8l6vcJ6YVjsMHV7eOxwLNq+yO7zXTzwYtww7Ab4af2wKX8T7vz1Tnx01kdICk2yuz51vWVZy5BVlGVuIzr6mOD0Ng62E81tI8fU1toIR/ajpW3a3cY4eUyQOhUVFyGqMsqqneiw97oNx5H2HLObayc6q12yt64jdWzucdvPR9YpLStVbYTUp7P/brC9bXDw++TwNgZgcvhkpCHN7dsIBqWIvIwcRIf1jFTlobMG4butufhk3SGsPVCMXzLyVYkJDcAFo5IwNrrezmGKiFry/fffW91ftGiR6jG1YcMGnHTSSZ3ymssPL1d/MCh74F0Owrtkw7vkw/sUwyv0DO0JGHPKNstX64upfaZiwfoFdk84JNGtiVajhU6vs/s8MUEx5tsj40aqk5ntRdsZlHIjj695HCU1JWwjPAHbCffmJW2EGB44HJ7QRjAoReTFgv19ceHonqrsLShXM/dJgvTC8hq8uWI/3gQwblM5LhnbG2cOTUSQv4+rd5nI4xw7dkz9GxUV1WmvkRiSiLHxY1FZVYngoGD1h4FicfFY03DH9K+6rWm6rC3bmG/b2dbqeTQtL7N8TK7ulZeXq2GQtq9pb38d3scWXtORfbR6bTvP19x7Jr1wSktKERFpvGLc6v7a+2xaeL1W99fBz7W11zQ9Jp+P9PyS77WPtrFtaPWzcfK70Oo2Dn6u9upgu41Bb0BBQYEKIpvXbeG7ZPf12vLZ2Nm+PZ+N6XaANgBwYGS+DKPoG9EX7ZFbkYuEkAR1+2DpQWQUZyAtkpe13MmI2BEoKC2wbiPa8DttyzbtbmPsHRsMUG1EaGioueeXo+2SM79Tu+t14O/U8rHm2onOapec+Vyb3aaF12u1neig74Kj7X+b2hgH24jW6mD1eg7+jeLw56pp23dBeuaFlId4RBvBoBRRN9E/NhQPTB+Eu08fiGUZ+ViyNgu/7S7A2gNHVXn0q+04Z0QSLh3bG0OSw5s9sBNRI2nw77zzTkyaNAlDhgxxaludzv7VJntO7306Tk0+FXv27EFqaip8fDw/gCz1Z33cl7fVx1wnjfd9RrbHEhm6cffyuxHgG6CuaktQe+74uU4dc2y9vOll7CjaAR+Njwp4PDT+oXafxFDHWnjKQmRmZiItLc1rvt/eVB9vrJNX1sfHy+qTmdlsTinpBaUzGNuIRyY80q7Xam8boTE0N5CUupRX/qi9qD7eWCepz6rNO7HxqD8+25itck+ZSLL0i8f0wnkjk9EjxB+ewBs/H2+qj7f629/+hv/973/4448/0LNnT6e2zcjIcGp9U84iuSJpumrsyVgf9+Zt9fHGOrWlPunp6Z2+X+Qa3vZ3g7fVxxvrxPq4N50H1Yc9pYi6sZgQX9w6oj9mT0nD6n1FWLLuEL7fnouM3DI8/s0OPPO/DJw2OB4XjemJE9Ni4WOTaJKoO5s9eza++eYbrFixwumAlJA/ErpzzxXWx715W328sU7eVh8iIuqeGJQiImi1GkxMjVHlWGUdvvorG5+sP6xm8ft2a44qCeGB5vxUfWM6Z4YxIk8gHYxvu+02LF26FMuXL0dKSkqbnqctJ5HSG0K285YTUNbHvXlbfbyxTt5WHyIi6n4YlCIiKxHBfrhyQl9VdhwpxacbDuGLTdnILa3GK7/uUWV8SpQa3jd9aIJKpk7Undx6661YvHgxvvzyS5WkOzc3Vy2PiIhAUFCQq3ePiIiIiMhj8GySiJo1OCkc85KOw/3T0/HLznw1e9+K3QVYs79YlXlfbceM4Ym4aEwvjOwVyeTo1C28/vrr6t9TTjnFavl7772Hq6++2kV7RURERETkeTw6y6Pk8ZgxYwaSkpLUyfAXX3zR4vqSiFZmSIqOjlZXsyXZ4wsvvNBkvVdffRV9+/ZFYGAgxo8fj7Vr13ZiLYjcX4CvD84cmohF14zDn/dPwT3TBqJPdDDKa+rx0dpDOP+1lTjthRV4a8VeFJTVuHp3iTp9+J69woAUEREREVE3CkpVVFRg+PDhKojkiJCQEJWYVoJZO3fuxNy5c1V56623zOt8/PHHmDNnDubNm4eNGzeq5582bRry8/M7sSZEniMxIgi3Tk7F8rtPwcc3Ho8LRvVEkJ8P9uSX4+nvMnD8/F9ww/vr8dOOPNTp9K7eXSIiIiIiInJTHj18b/r06ao4auTIkaqYSG+ozz//HL///jtuvPFGtWzBggW44YYbcM0116j7b7zxBr799lu8++67uP/++zuhFkSeSXonju8Xrcqj5wzGt1ty1PC+jVklKiAlJSY0ABeMSlaz96XGhbl6l4mIiIiIiMiNeHRPqfbatGkTVq5ciZNPPlndr62txYYNGzB16lSrWU3k/qpVq1y4p0TuLSzQD5eO643Pb5mEn+46CTee1A8xof4oLK/Bmyv2YeqCFTj/tT+xZG0WyqrrXL27RERERERE5AY8uqdUW/Xs2RMFBQWor6/Ho48+iuuvv14tLywshE6nQ3x8vNX6cj8jI8Op15DncXZ9vV7v9Hbuytvq44116qz69IsJxn3TBmDO1FQs31WAzzZk49fdBaoHlZTHvt6B6UPicdHonhjbt0eHJUfn5wNOCU5ERERERB6lWwalZLheeXk5Vq9erYbkpaam4rLLLuvQ18jMzHRqfTn5LC4uxp49e1TvLE/nbfXxxjp1RX36+gF3Hx+Ga4cF4Zd9ZfgxswyHjtXh801HVEkK88PpaWGY2j8MMSHtOxzx84GavIGIiIg6luSvfe6555Cbm6vyzb788ssYN26c3XXffvttvP/++9i2bZu6P3r0aDz99NPNrk9E1N11y6BUSkqK+nfo0KHIy8tTvaUkKBUTE6N6GsgyS3I/ISHBqddIS0tzan3pDSEnnxIg84beDt5WH2+sU1fXZ/xw4AGDAZsOleDTDdkqB9WRsjos2liM9zcV48S0GNV7akp6HAJ8nQ8q8fMhIiKijmaaBEnyzMqs3AsXLlSTIO3atQtxcXFN1l++fLk6r5g4caKayfvZZ5/F6aefju3btyM5OdkldSAicmfdMihl2xuhpsY4hb2/v7+6mvHLL79g5syZ5sflvsza54y2nERKbwjZzltOQL2tPt5YJ1fUZ2xKjCqPnnMcvtuaq5Kjr91fjN92F6rSI9gPM0cm4+IxvTAoMdyp5+bnQ0RERB3J2UmQPvzwQ6v777zzDv773/+q84mrrrqqy/abiMhTeHRQSobgSU8Ck/3792Pz5s2IiopC79698cADDyA7O1t1oTV1vZXlpiEuK1aswPPPP4/bb7/d/BxyJWTWrFkYM2aM6mYrV0MqKirMDRERdYxgf19cOLqnKvsLK/DZhkP4bMNh5JXW4L0/D6gyNDkCF4/piXNGJCMiyM/Vu0xERETdiGkSJDmnaOskSJWVlairq1PnJ0RE5GVBqfXr12Py5MlWASUhQaVFixYhJycHWVlZ5sel15M0KhK88vX1Rf/+/VWX2ptuusm8ziWXXKKSoD/yyCNq3PiIESPw/fffN0l+TkQdJyUmBPdMS8ec0wZiRWYBPl1/CD/tyMPW7GOqPPntTpwxJEH1nprQLxpabcckRyciIiJqTkdMgnTfffchKSnJanZvR3DSJO+qjzfWifVxbzoPmjTJo4NSp5xyCgwGQ7OPS2DK0m233aZKa2SonrPD9Yio/Xy0GkweGKdKcUUtvtiUrYb3ZeSW4cvNR1RJjgwy97DqFRXs6l0mIiIisuuZZ57BkiVLVJ4pyS/lDE6a5F318cY6sT7uTe9BkyZ5dFCKiLxXVIg/rj0hBddM6ott2aUqOPXF5mxkl1ThxV8yVZFeUxeN6al6UQX4sPcUERERdZz2TIIkKUIkKPXzzz9j2LBhTr82J03yrvp4Y51YH/em86D6MChFRG5No9FgaM8IVR46axB+2J6LT9cfxp97C7FqX5Eqj3y5HWcOScD4OD1SU5vvPUlERETkqLZOgvSPf/wDTz31FH744QeVp7YtOGmS99XHG+vE+rg3rYfUh0EpIvIYgX4+OHdEsiqHj1bi843ZKjl6VnElPtlwGJ8AeHltCS4a0wvnjUxGUmSQq3eZiIiIPFhrkyDJjHrJycmYP3++ui/5aiU37eLFi9G3b1+Vo1aEhoaqQkRE1hiUIiKP1LNHMG4/NQ23TUnF2v3FKjn6N1uO4EBRJZ77YRee/3EXTkiNUQGq0wfHq4AWERGRt9sz5VRo/P2hCQyEobYWgYMGIfGJx6ENDkbZsmWoXLMG8RazyTmi4OVXcHTxYvg2JPwOSE1F8vPPoTtobRIkmVTJMl/L66+/rmbtu/DCC62eZ968eXj00Ue7fP+JiNy9jWBQiog8fnjf+H7RGNMnElcM8sfu6hB8vvEI1uwvxu+ZhaqEBfrinOFJKjn6iF6RahsiIiJvlfzCAnWiYdDrcehvf0PJ0qWIuuIKhE2ZokpbhM84GwkPPojuqKVJkCSJuaUDBw500V4REXlHG8GgFBF5jSA/LS4c3BOXjO2Dg0UV+O/GbPx3w2GVHP3DNVmqpMaFquDU+SOTERfu3Ew4REREnsRQVwdDVTV8wiPU/ZLPl6Lsl1/Q69VXULFmLfKefAJBY8agauMmGHT1SJr/DIKGDnH1bhMRUTdqIzx/rkMiIjv6RIdgzmkD8Pu9k7H4+vEqx1SgnxZ78svxzP8yMOGZZbh20Tp8tzUHNfU6V+8uERFRh8m+aw72zTwPmSecKJluET79DLvr1ezbj8iZM9Hvyy8Q9X//h4KFC5t9zrLvf8C+c2fi4KyrUbF6TSfuPRERdac2gj2liMirabUaTEyNUeWxc4/Dd1ty8OmGw9hw8CiWZeSrEhnsh3OHJ6n8U8clhXN4H7mdyo2bUL5yJQxFhSiKim78jhoM1v8a7zQssnnMtEqTbazvN25n+ZSGFrdtsk0r6wu9Tg/DsWPIi2j4zbWyf47Xp/3715b3QGbkMpRXIDskpKE+zaxvsZ8Gm/e+zftnr+42/za+ViuvYfE9MFRXIyvQokdpa9s4+hrO1MnR13K0TvX12CezEDnwGZlfw4n9atzWiffA0W1s/vXr1RNYsAAtDs2or0fOvHnIf/6fiL//vibr+ffujaDhw9XtoBEjUPTue3afr8ellyDm5pug8fND5caNODz7NqR8+gn8kpPtrk9dr3jRIhgOZ6MoKgoabQccU51pJxw9ZjmxjeNthBN16sBjfpvaPSfbCYfbCFceU6WdCAhw6Jhq93Va3aaV+jhTJ0fbCF/fzm332njMN9iu78g2N1wPpKXB3dsIBqWIqNsID/TDpeN6q7KvoFzN3Ccz+OWWVuPfqw6qkp4Qpob3zRyZjJjQAFfvMpFStXEDil55Rd0ugnc5Bu9SAe9SDe9TD+8ZdtEaja8vwk8/HXnPPYd4ND3h0MjJpIkk6663/+74xsaabwePGqVOZqq2bWdQyo0Uv/0OUFLCNsIDsJ1wb97SRjQN3rpvG8GgFBF1S/1iQ3HvGen4++kD8ceeQjV734878pCRW4Ynv92phvhNTo/DRaN7qn/9fDjamVwnYGA6Ii66CMeOHUNEZAQ0Gi1g6tDX0GuqsYefRU8/07Im/8L+No6ub9WbsOVtmts/PQwoLipGdEx0K/WxfD1n968L6tPwr95gQH5+PuLi4o0zcXVGfZrdzmZ9J7Zptj56A47kHEFSUrLd+rRr/zqiPk22beU1NBrodTpkHT6M3r16QevT8Cew1UtoOuy17K3v0DY22zbetVMfAPtLStAaGUYRkJKC9qjLzYVfQoK6XXvgAKozMhAwoOnVd3Kd8HPOQUluLiIiIqAxzQbYWjvhbBvhzDYOHufsHiOhgd6gR3HxUURFR0Gr9XHouOXwMbUt2zhZn3a1E+2pTwcfh1usTyvthNOv1ZHtShuOw9KTLevQIfTu3dv4nWvra7Vj/zROthHWi6zX0en12J+fD09oIxiUIqJuzUerwckDYlU5VlmHr7YcUT2o/jpUgp925KkSHeKvek5dNKYn0hPCXb3L1A2FnngCgiZOQGlmJuLT0uAjw488nE6nw9HMTER7UX0KMjMR6UX10WRmItRL6mOuU2AgAr2kTlIf6RnTXL4QqSt09fBNSkLio4+267UKXliI6h3bAR9fFfBIePjhdp/EUMeKu+9eHPPCNiLGS+oj2E64N29rI7RSn7Iyj2gjGJQiImoQEeyHK4/vo8ruvDLz8L7C8hr864/9qgxJDsdFo3vh3BFJiAz2d/UuExERWUld9kuzj0Wef54qImT8OPT7Yqn5scABA5rdNunZZzphT4mIqKu5YxvBoBQRkR0D4sPw4JmDcO+0gfhtd4EKUP28Mw/bskuxLXs7nvp2J6YOluF9vXBiWgx8ObyPiIiIiIjIKQxKERG1QIJNpw6KV6W4ohZfbc5Ws/dtP1KK77bmqhIXFoDzRiWr/FOpcWGu3mUiIiIiIiKPwKAUEZGDokL8cfWkFFV2HClVvae+2JyN/LIavPnbPlVG9IpUuafOHpaEiCA/V+8yERERERGR22JQioioDQYnheORpMG4f3o6ft2Vj0/XH1b/bj5UosrjX+/AtOMScMHonjghNUYlVCciIiIiIqJGDEoREbWDv69WBZ+kFJTV4EsZ3rf+MHblleGrv46okhAeqIb3XTBKhveFunqXiYjIi73/4Er4+GlV0dfrEdMrDJP/Lx1+AT7Y/1cBsneV4ISLHZ+qW6z9eh+2/paNkMgAdT8qMQSnX3dcJ9WAiIi6UxvBoBQRUQeJDQvA9Sf2w3UnpGBr9jH8d8NhfPnXEeSWVuP15XtVkeF9F47uiRkyvC/Y+eF9k55ZhgBfLQL8fFBbr8NxSRF45oKhCPb3xU878rBqbxEemTHYqed84afd+M/qg4gPD1T3B8SH4sVLRzq9b0RE5B5Ov/44xPYKg0FvwLevbUHGqhwMPaUnUobHqtIWA8bF48SLB3T4vhIRUfduIxiUIiLqYBqNBsN6Rqry4FmD8GtGvso/9euugsbhfd/swGmD43HhqJ5Oz9738uUjVTBKrzfgun+vU8991YS+6vmktMW5I5IwbwavehMReROdTo/6Wh0Cgo1/8u9cmaOuhJ/5t2HI3nUUKz7ejaTUSOTsPabalKlXD0Jcn3BX7zYREXWjNoJBKSKiThTg64MzhiSqYhreJ0GkjNwyfLslRxXpYXX+yGSVf2pAvOOz99Xq9Kiq05kTqn+6/hB+3JGHt68ao3pMzftqG8alRGH9gaPQ6Q3458XDVaCMiIi824/vbFdDM8qKqhHbOwypo+Psrnc0txKTr0zHyZcPxLYV2Vj95T6cc/sIu+vu3ZCvhnUEhvpizJkp6DmwRyfXgoiIukMbwaAUEZELhvdtP1KK/248jC83H1HBqjdX7FNlWM8INbzvzCHN93i6bfEmNXzv8NFKDE2OwFlDE+2ut7egAs9eMAxPzhyKD1YfxHM/7MJ/rhtvd93vtuaoQFaPYH/cdmoqJvaP6bB6U/vp6vSoqa5HXbUeNZV10Gr1gEH+U/+DwdC4rsF0x7zceN+8WG6Yb5u3Mt62XMf8fLB6Lavlbd3OYFBX545m1+AISqDVNvQUNK1qUFs1bmdZV3PdLJ/UYjuLxQ69R3Yet3x9e++h7evLTb1ej/y8CtTkZEOr0bbzvbV4Pcv32eY9tHqPLD7bxuXWz+Poeyvr6A0GlJSUIGdDpur9ad7OYh3ze2T55lh+Rg69t40b2ftsG9/z1raz8x7ZvL48b1VVFbYHlkMDjfV32Oa9tXrtZj4Tq2q3tJ3te+TA78Pue2uzXXh0IIadH97i0Ay9To/lH+7CyqV7ccKFTXOERMQGISElQt1O6BeOTT9l2X2+405Kxugz+8LHR4ucPSX435tbceH9YxAeHWR3fep6tVXNtBHC5ndgZnGMa/K4VTthfRyzXM/q+GbxfW7y+zI4sZ20EXo9SrJrkKMxthFWx7GGFRuPWTbPYa6bTV1M29ke/5p7j2z2z/J4ZP06NnW1eX3Tcvk95ufbthNteY+atknWn6Gd98jieGS575afbYvvrfn9cqCdsDyOWW1ncTy0fP8deG+bbNfkPWh+O3ufvWXbblrfto2wXM/ee2t5317b7tR2BuvjfWu/D7vvrc13QtbqNzEUaWnu30YwKEVE1MWk4R6SHKHKA9MHYfku4/C+ZRn52HL4mCpPfLMDt4yPsduQmIbv1ev0eHDpVjzzvwzMPbtpHqk+0cEY2dt4lWJU7x54+/d9dvfniuN7Y/aUVPj5aLH+QDFu+s8GfDl7Enr2CO74ylOb/LXsEFYt3atur0AuvMlGFMGb7MIxeJNsVML71MIb1ATVt7qO1keLfiNjsfLzvcCFTR+XK+WWbZNBZ3lW3Sgkwpi8ViSmRqrEuAUHyxiUciOL561BdUW917URG7ysjRBsJ9ydd7QRQt/MMd3d2ggGpYiIXDx73+nHJahSVF6jZuuTAJX0pEqNajzA2yN5qKYPScTT3+3E3GaGDpr4aDWob6YhiQszJjgXY/pGYXBSOLYePsaglCfTqP/kL4iGfxuK+sf4YONy0/VAm+1MC223My833tA0t515uf3tRF1dLfz9/dUfOtbbNd7XWL1A4+bqqqz109nfznS7ue0a1rPazqZu5scbd6NxHYvXL68oR2hoKLSy0JHtLN4fq9ez/NzMyy0/N/ufmdXnavGZNbudxXtrtZ384WnQo7i4GFHR0aqXgmU9rbazWt64w1b1snnvrbez/9432c7muZq8t7bbNdkHjerNlpObg6TERPWHuOXjlq9v+V5Yvn5jtS22s/Nbs9xHZ34b5vfH5vNpbjtoDMgvOYzWSF6QHvHtO56XH61GaA9jW1GSV4nCQ2WITuZssm7F6ovS2rrWvxvLdsKqjTAvt/h+W7ye5XHU3m++PdvZthFNnteJY1Jr21m2EU22s/M7bHqssD2u228jTO1EWGgoNNrGF3X0WGZ1bLJ9by2Pk809bnXfuo2wu51NQ9S43LisuXaiyTGwuWNcG46BVm20nbaluc+1yWdi20429FoytxHSg9ve97PJ3wc2bYSd99beb61J+9vcb8N025G62mynN+iQU+AZbQSDUkREbiI6NADXTEpRJSPnGAwlR1rdZuXeQvSLDWnX6+Ycq0JihPFKxv7CCuzMKcXABMdzW1HnGzG1F4acnIQ9e/cgNS0VPj4+dv54dOKExA3odDpkZmYiLS1N1cfTeWd96pGW1tcr6mOqkz6zBP3SYr2iTlKf/JKW84XIzEphUYEqH0h7SB6RgqwydSKt1Wpw0qUDEdnOkxjqWFc+eTz27GloI3xt2ggPbCe87ZjqjXXytnbCG9uIwjKtR7QRDEoREbmhtLhQZB7TtJhTSvItJEcG4anzhrbrtSTX1LbsY/DRaiGTAD5+7hD0i+UVcHcivTp8YIDWR6PG60shIrLnqqcnNvvYoImJqojkgT1w6dxx5sfkqnZz2069uukQcXIvcoKp9dXAx5dtBBF5VhvBoBQRkQf58/4pzT520ZheqogJ/aPxvztOND8mPZ+a23bBxfZn0SAiIiIiIupMDKMTEREREREREVGXY1CKiIiIiIiIiIi6HINSRERERERERETU5RiUIiIiIiIiIiKiLsegFBERERERERERdTkGpYiIiIiIiIiIqMtpDAaDoetfloiIiIiIiIiIujP2lCIiIiIiIiIioi7HoBQREREREREREXU5BqWIiIiIiIiIiKjLMShFRERERERERERdjkEpIiIiIiIiIiLqcgxKERERERERERFRl2NQioiIiIiIiIiIuhyDUkRERERERERE1OUYlCIiIiIiIiIioi7HoBQREREREREREXU5BqXcxKuvvoq+ffsiMDAQ48ePx9q1a+GJVqxYgRkzZiApKQkajQZffPEFPNn8+fMxduxYhIWFIS4uDjNnzsSuXbvgyV5//XUMGzYM4eHhqkyYMAH/+9//4A2eeeYZ9b2788474akeffRRVQfLkp6e7urdIhfzljZCsJ1wb97cRgi2E+St2E64J29rI7y9nWAb4RoMSrmBjz/+GHPmzMG8efOwceNGDB8+HNOmTUN+fj48TUVFhdp/aRi9wW+//YZbb70Vq1evxk8//YS6ujqcfvrpqp6eqmfPnuqAu2HDBqxfvx5TpkzBueeei+3bt8OTrVu3Dm+++aZqJD3dcccdh5ycHHP5448/XL1L5ELe1EYIthPuzVvbCMF2grwV2wn35W1thDe3E2wjXMhALjdu3DjDrbfear6v0+kMSUlJhvnz5xs8mXy9li5davAm+fn5ql6//fabwZv06NHD8M477xg8VVlZmSEtLc3w008/GU4++WTDHXfcYfBU8+bNMwwfPtzVu0FuxFvbCMF2wjN4ehsh2E6QN2M74Tm8sY3whnaCbYRrsaeUi9XW1qoo89SpU83LtFqtur9q1SqX7hs1dezYMfVvVFQUvIFOp8OSJUvU1Rrpeuup5ArUWWedZfU78mSZmZmqy3q/fv1wxRVXICsry9W7RC7CNsLzeFM74S1thGA7Qd6K7YRn8aY2wpvaCbYRruXr4tfv9goLC9WPOT4+3mq53M/IyHDZflFTer1ejS+eNGkShgwZAk+2detW1XBUV1cjNDQUS5cuxeDBg+GJpCGUrurS5dYbSB6IRYsWYeDAgaq77WOPPYYTTzwR27ZtU/kIqHthG+FZvKWd8KY2QrCdIG/GdsJzeEsb4W3tBNsI12NQisiJCLr8mN1+TK4D5CC1efNmdbXms88+w6xZs9SYd09rTA4dOoQ77rhDjdGXxJ7eYPr06ebbMqZdGpY+ffrgk08+wXXXXefSfSOi7tFOeEsbIdhOEJG78JY2wpvaCbYR7oFBKReLiYmBj48P8vLyrJbL/YSEBJftF1mbPXs2vvnmGzUbiCT383T+/v5ITU1Vt0ePHq2uDLz44osquZ8nke7qksRz1KhR5mVytVA+p1deeQU1NTXq9+XJIiMjMWDAAOzZs8fVu0IuwDbCc3hTO+EtbYRgO0Heju2EZ/CmNsKb2gm2Ee6BOaXc4ActP+RffvnFqmun3PfkcbneQvIrSiMiXVKXLVuGlJQUeCP5zslB19OceuqpqvuwXKkxlTFjxqix03Lb0xsRUV5ejr179yIxMdHVu0IuwDbC/XWHdsJT2wjBdoK8HdsJ99Yd2ghPbifYRrgH9pRyAzKFq3R5lB/AuHHjsHDhQpUs7pprroEnfukto7D79+9XP2hJ5te7d294YjfbxYsX48svv1RjcHNzc9XyiIgIBAUFwRM98MADqlunfB5lZWWqfsuXL8cPP/wATyOfie2Y/JCQEERHR3vsWP27774bM2bMUN1sjxw5oqZ3lgbxsssuc/WukYt4Uxsh2E64N29qIwTbCeoO2E64L29rI7ytnWAb4SZcPPsfNXj55ZcNvXv3Nvj7+6tpXVevXm3wRL/++qua5tS2zJo1y+CJ7NVFynvvvWfwVNdee62hT58+6rsWGxtrOPXUUw0//vijwVt4+jSul1xyiSExMVF9PsnJyer+nj17XL1b5GLe0kYIthPuzdvbCMF2grwR2wn35G1tRHdoJ9hGdD2N/M/VgTEiIiIiIiIiIupemFOKiIiIiIiIiIi6HINSRERERERERETU5RiUIiIiIiIiIiKiLsegFBERERERERERdTkGpYiIiIiIiIiIqMsxKEVERERERERERF2OQSkiIiIiIiIiIupyDEoRNVi+fPn/s3cfYE5U6x/H392ld+lNmjSpIipSFBUUFQt6begVVK69YFdUQP8W7Irt2lGvesWGerEXrCAKogKK0gSV3ovSduf//A5MSLLJkmxNZr+f5wlsJpOZOTOTnMw757zHMjIybM2aNcW+bq1Xjxo1auQ534033mh77bVXsW2X1udv2/33319s6wWAVEQ9EXt91BMAQB0Rb33UEdgVglIolQ466CC79NJLI6b16NHDFi9ebNWrVy+RbRozZoz9+uuvlkquvPJKt08aN25c0psCAMWKeiIx1BMASiPqiMRQRyARZRKaCygFypUrZ/Xr1y+x9evORt26dS2VVKlSxT2ysrJKelMAoMRRT+RGPQEA21FH5EYdgUTQUgqlzhlnnGGfffaZjR49OtSc9LfffsvV5PaZZ55xX+7jx4+3Nm3aWKVKleyEE06wv/76y5599llr1qyZ7bbbbnbJJZdYdnZ2aPmbN292dwUaNWpklStXtm7durll58ftt99u9erVs6pVq9qQIUNs06ZNEa9/++23duihh1rt2rXdXZnevXvbd999F3r9rLPOsqOOOiriPVu3bnUV1lNPPeWev/rqq9axY0erWLGi1apVy/r27WsbN27M1/YCQBBQT1BPAEA81BHUEShcBKVQ6qgC6d69u5199tmuOakeu+++e8x5VWk88MAD9tJLL9l7773nKoTjjjvO3nnnHff4z3/+Y4899pj7MvZddNFFNmnSJPeeH3/80U488UQ7/PDDbfbs2Ult58svv+z6Yd922202ZcoUa9CggT3yyCMR86xfv94GDx5sX375pX399dfWqlUrO/LII910+de//uW2W2X0qWJUuU4++WQ3feDAga7C+fnnn135jj/+ePM8L8m9CgDBQT1BPQEA8VBHUEegkHlAKdS7d29v6NChEdMmTJigb09v9erV7vmYMWPc8zlz5oTmOffcc71KlSp569evD03r16+fmy4LFizwsrKyvD///DNi2X369PGGDRsWd3u0nnHjxkVM6969u3fBBRdETOvWrZvXuXPnuMvJzs72qlat6v3vf/8LTWvXrp13xx13hJ4fffTR3hlnnOH+njp1qlv3b7/95uWladOm3n333ZfnPAAQJNQT1BMAEA91BHUECg8tpYA8qJntHnvsEXqu5q9qaqu+0eHTli1b5v6ePn26a37bunXrUB9qPdTEd+7cuUmtW3cb1Fw3nO7KhFu6dKm7S6O7GmpyW61aNduwYYMtXLgwNI/ucCjxoT//u+++6+5mSOfOna1Pnz6uya3uwjzxxBO2evXqpLYTAEoz6gkAQDzUEcCukegcyEPZsmUjnqufeKxpOTk57m99iSuR39SpU3Ml9AuvfAqLmtuuXLnSNSNu2rSplS9f3lU2W7ZsCc0zaNAgu/baa10z4IkTJ1rz5s3tgAMOcK9pGz/88EM3/YMPPrAHH3zQrr/+eps8ebKbDwCQN+oJAEA81BHArtFSCqV2dIzwhIKFpUuXLm65utvRsmXLiEeyo3Hsueee7gs9nPp6h/vqq69cckT1/W7fvr2rSFasWBExjxIODhgwwN3hUMLFM888M1dF2LNnT7vpppts2rRpbt+MGzcu6bIDQJBQT+xEPQEAkagjdqKOQEHRUgqlkprN6ktaI2XorkPNmjULZblqanvaaae5Owr33HOPq1iWL19uH3/8sXXq1Mn69++f8LKGDh3qRvfYZ5993Bf9Cy+8YDNnzrQWLVqE5lFTWyVI1Dzr1q2zq666yo18EU3NbjVyhio53RHxaR9o2w477DA3ioaea3tViQFAaUY9sR31BADkRh2xHXUECgMtpVAqaZhVNTdt166d1alTJ6LfdEHpLoIqkiuuuMIN/6o7CxputUmTJkktRyNaDB8+3K6++mrr2rWrLViwwM4///yIeTQUq/pt77333nb66ae7Ox2qEKJpaFaNuNGvXz9r2LBhaLr6jX/++efu7ogqwRtuuMFVgEcccUQB9gAApD/qie2oJwAgN+qI7agjUBgylO28UJYEIN/U7FXNXFXpFAX1T2/UqJGr5DRMa37uBl166aXuAQAoftQTAIB4qCOQzmgpBaSIgQMHWuPGjQt1mUqaqD7pN998s9WoUcOOOeaYpN5/2223uSbJhXn3BwCQP9QTAIB4qCOQrmgpBaSAOXPmuP/VDLgwR6pQP3ctTxWUEhNqyNZkrFq1yj1ETZM1VCwAoPhRTwAA4qGOQDojKAUAAAAAAIBiR/c9AAAAAAAAFDuCUgAAAAAAACh2BKUAAAAAAABQ7AhKAQAAAAAAoNgRlAIAAAAAAECxIygFAAAAAACAYkdQCgAAAAAAAMWOoBQAAAAAAACKHUEpAAAAAAAAFDuCUgAAAAAAACh2BKUAAAAAAABQ7AhKAQAAAAAAoNgRlAIAAAAAAECxIygFAAAAAACAYkdQCihkGRkZduONN1o6+O2339z2PvPMM0m9T/PrfXp/qou1rQcddJB7AAAAAABKDkGpgHr55Zfdhfi4ceNyvda5c2f32oQJE3K91qRJE+vRo0dS63rxxRft/vvvL9D2omhxjAAAAAAAqYagVED16tXL/f/ll19GTF+3bp3NmDHDypQpY1999VXEa7///rt7+O9NFAGP1BfvGDVt2tT+/vtvO/300600+eCDD9wDAAAAAFByCEoFVMOGDa158+a5glKTJk0yz/PsxBNPzPWa/zzZoFQq2rhxY0lvQlrsB7WYq1ChgmVlZVlpOoblypVzDwAAAABAySEoFWAKLk2bNs21hPGpdVT79u3tiCOOsK+//tpycnIiXlOQomfPnqFpzz//vHXt2tUqVqxoNWvWtFNOOcW1pvIpL8/bb79tCxYscO/Vo1mzZnlu15gxY+yQQw6xunXrWvny5a1du3b273//O+a87777rh1wwAFWuXJlq1q1qvXv399mzpwZMc8ZZ5xhVapUsblz59qRRx7p5jvttNNCgY0rrrjCdt99d7euNm3a2N133+0Cc+E+/PBDt79q1KjhlqX5rrvuuoh5li1bZkOGDLF69eq5QI66QT777LOWH59++qnbV2PHjnXrqV+/vivjMcccE7F/5YsvvnBBRHWtVBlUlssuuyziuOa1H/I6RvFySs2aNctOOukkq1Onjjv22h/XX3/9LsuVyPHKK+/TZ599ZhdccIE7Nxo3buxe03ZrmrZB21KrVi23P2Lls9K6dG5pPr3/lltuiTjH4+WUipcjyz9O+t83e/Zs+8c//uGOmc4DrUefi7Vr1+6ynAAAAACAncqE/Y2AUZDlP//5j02ePDl0Aa7Ak3JG6aGLaHXl69SpU+i1tm3buot+ufXWW2348OEuOPGvf/3Lli9fbg8++KAdeOCBLtilAI4CFVrOH3/8Yffdd597nwIjeVEASoExBWDUjfB///ufCzooeHDhhReG5tO2Dx482Pr162d33HGH/fXXX+69frAtPPi1bds2N59eU9CpUqVKLvCkdSh3loJJe+21l73//vt21VVX2Z9//hnaXgUyjjrqKLcf/u///s8FfubMmRPRvVEBIO1DTb/oootcK7RXXnnFBYLWrFljQ4cOzdcx0j5W0OOaa65xQS91sevbt699//33LrAiWo/Kfv7557tj880337jjoH2u18LF2g8KniRzjH788UcXWCpbtqydc845bj8r0KXjpO2NJ5njFY/OAwXCRowYEWop9e2339rEiRNd4EcBIAWOtFwdj59++smVUZYsWWIHH3yw2wfXXnutC4w9/vjjof1YGLZs2eLKt3nzZrv44ovdvtW5NH78eHceVK9evdDWBQAAAACB5yGwZs6cqeZA3s033+yeb9261atcubL37LPPuuf16tXzHn74Yff3unXrvKysLO/ss892z3/77Tf3/NZbb41Y5vTp070yZcpETO/fv7/XtGnThLfrr7/+yjWtX79+XosWLULP169f79WoUSO0Pb4lS5Z41atXj5g+ePBgV85rr702Yt433njDTb/lllsipp9wwgleRkaGN2fOHPf8vvvuc/MtX7487jbff//9bp7nn38+NG3Lli1e9+7dvSpVqrj959N8I0eOzHMfTJgwwc3XqFGjiPe+/PLLbvro0aPz3F+jRo1yZViwYMEu90Nex2j+/PnuPWPGjAlNO/DAA72qVatGLFtycnJCf2t+vU/vT/Z4xeIvr1evXt62bdsiXotV/kmTJrn5n3vuudC0Sy+91E2bPHlyaNqyZcvc+sO3VXr37u0e8coTfZz0v0ybNs09f+WVV/IsDwAAAABg1+i+F2B77rmna1nj54r64YcfXOsTf3Q9/e+3BlKuqezs7FA+qddff921XFIrqRUrVoQeahnSqlWrmCP3JSq85Ypa8Gi5vXv3tnnz5oW6QKk7nVqeDBw4MGL9yn3UrVu3mOtXS6Jw77zzjpv/kksuiZiu7nyKHamrmajFl7z55psxu3r5y1LZtT0+tSTSsjds2OC6neXHoEGDXDc33wknnGANGjRw64u1v3T8tB907FQGtUDa1X5IhlrDff7553bWWWe57oLh1KIrnvwcr1jOPvvsXPmtwsu/detWW7lypbVs2dIdt++++y70mvbZ/vvvb/vtt19omlpd+V05C4PfEkot7tQSDAAAAACQf3TfCzAFERS8UJBBwRYFoJSrRxf0otceeugh97cfnPKDUsqbo6CHAlCxKCCTX1rXyJEjXSAs+sJeQSld+Gv9ovxAsVSrVi3iuboB+jmIfMpFpITv4UEfP1jnvy4nn3yyPfnkk66Lorp99enTx44//ngXIMrMzAzNq33hP4+3rGRF718dMx2f8NxGCxcudN3Z3nrrLVu9enXE/NF5jGLth2QoMCgdOnRI6n3JHq941C0ymrpOjho1yuUiU1e58Hxg4eXXMVAALJpyURUWbd/ll19u9957r73wwguum6O6iP7zn/+k6x4AAAAAJImgVMApyKRcQNOnTw/lk/Lpbz+/klpTKYDTokUL95qCWAqQqDVRrJHZdpU3Kh7lJlLQR7mrdGGvpN0aBU2tXJTvyG+p5P+vPEVqoRRNwZdwygMVHTBKlFriKHCn1jxKCP7ee++5BOQKsHzwwQclOjKdWq8deuihtmrVKpd3SvtNuZJ0zJTPKrplV0H2Q0Eke7ziiZX/SbmbFJC69NJLrXv37i74o3NTOabitWxLVrxWYNr/0e655x6379WyTueHWsspaKaBAwoSEAQAAACA0oagVMD5LZ8UdFJQShf2Po2qpyCGRhZTMnSN2ObbY489XIsUtQxp3bp1nuvIq1tXNAXIlCRarX7Cu4dFd+/S+kUtu5T4Oz+aNm1qH330ka1fvz6itZRGlvNf9ymQo2CZHgqW3XbbbS6Ju7ZL69e8SgCuIEh40CfWsvLTwsinfa5k6n7yeQUTf/31VzfKn7r6hXeXS0aix8gPSioBfjIK43jF8+qrr7oE6goG+TZt2uS6C4bTMYjen/LLL7/sch277bab+z96mfFawHXs2NE9brjhBpeEXSNWPvroo260PwAAAABAYsgpFXD77LOPG7ZeXY3Uuia8pZQCUnvvvbc9/PDDLleRH8ASdV9TC6GbbroporuU6Lny+vjUcie6G1k8fquj6C5YagkTTiOcqcuXgkPKIxQr99GuKMimli5+F0WfWmQpSHPEEUe452qFFE0j9YkCaP6yNLqbWlD5NMqbRsFTqzHlxMqP5557zgXNwgMwixcvDm1brP2lv0ePHp3UehI9RsrBpNEVn376addtMFz0eVDYxyse7YPodWu/R7di0jFSayWNThi+Xp37iQbV1GLOp+Vr9L5w69atc8c9nIJTClT65woAAAAAIDG0lAo4dY3bd9997YsvvnBBKLWOCqcgld8CJTwopYt0tfoYNmyYy280YMAA19po/vz5Nm7cODvnnHPsyiuvdPNqmQrWKNeO1qUgzdFHHx1zew477DC3TXr93HPPdUnCn3jiCdfCRsEYnwIc//73v+300093gTN11VLARIESdbFTy5ToYFM0rePggw92LZ5Uhs6dO7vuVup2pRZjfiDi//7v/1wwon///q61zbJly+yRRx5xXbH8faLyPvbYY67b1tSpU61Zs2YugKTWZ/fff3+uvFWJqlmzplvHmWeeaUuXLnXLUk4pJfwWddfTdmpfK6io/fLaa6/lyi21K8kcowceeMBtk/a7yq3Wctp/2u/ff/99zPcUxvGK56ijjnLdAtVtr127di4XmVrAKYl/uKuvvtrNd/jhh9vQoUNdIE5BJb+VW17at2/vkqTrfFeQUsflpZdeyhWA+uSTT+yiiy6yE0880bUg1OtapwJn//jHP/JVPgAAAAAotRIYoQ9pbtiwYW4Y+x49euR67fXXX3evVa1a1du2bVuu11977TWvV69eXuXKld2jbdu23oUXXuj98ssvoXk2bNjgnXrqqV6NGjXcspo2bZrn9rz11ltep06dvAoVKnjNmjXz7rjjDu/pp592750/f37EvBMmTPD69evnVa9e3c2/xx57eGeccYY3ZcqU0DyDBw922xbL+vXrvcsuu8xr2LChV7ZsWa9Vq1beXXfd5eXk5ITm+fjjj71jjz3WzVOuXDn3/8CBA71ff/01YllLly71zjzzTK927dpuvo4dO3pjxozJtU6VY+TIkXnuA5VL8/33v/91x6du3bpexYoVvf79+3sLFiyImPenn37y+vbt61WpUsWt++yzz/Z++OEH9/7w9ee1H+IdI+3v6OXIjBkzvOOOO87Nr/3epk0bb/jw4aHXNX9+j1cs/vK+/fbbXK+tXr06tN+1D7T8WbNmuTKozOF+/PFHr3fv3m7djRo18m6++WbvqaeeyrWtmkePcHPnznX7uXz58l69evW86667zvvwww/de1UumTdvnnfWWWe5cmkdNWvW9A4++GDvo48+yrN8AAAAAIDcMvRPSQfGgNJGebzUiuuVV15xo/wBAAAAAFDakFMKAACgiCiH4Y033mjpQF21tb3PPPNMUu/T/Hqf3l+Uims9idxY0nbofwDID6UEUTqQdHHQQQe5R6rWgalS1+qY6tgiOQSlAAAIuJdfftn9YFNOwGjKt6fXokdBFY2SGj5ARiJefPFFlx8PqYtjVPjYpwgCP/DrPzRYUsOGDd2ANso5Gj44D4Lnp59+coGdkg78Bwn7NDEEpQAACDh/0IYvv/wy14iSM2bMsDJlyriBG8L9/vvv7hE+CEYiuDhPffGOkQaG+Pvvv92gFYhPo9RqP+l/H+c9gkSDAGkgFw1ic/HFF7tpGiRIIw7vavAYpHcARSOvxwqgaLAoPZC3X375xQ3ilcg+xU6MvgeUADV/JZ0bgOKiO90aSTM6KKXRLPVdpBElo1/znycblEpFGzdudCNylna72g9+ywjkLTMzk/2EQDviiCNsn332CT3X6MQagVgjIh9zzDH2888/W8WKFdP+O3fTpk1uVHB9pkszfz/kZVevYzuNdo/kle5PIAAApYSCS9OmTXMtPHxqHdW+fXt3AfL1119bTk5OxGsKUvTs2TM07fnnn7euXbu6i5GaNWvaKaec4lpThQfc3377bVuwYEGo+8eucmaMGTPGDjnkEKtbt677MdeuXTt3dz6Wd9991w444AB3sVO1alXr37+/zZw5M2Ie5XKoUqWKzZ0714488kg332mnnRa6ULriiits9913d+tq06aN3X333bluEnz44Yduf9WoUcMtS/Ndd911EfMsW7bMhgwZYvXq1XMBCnWDfPbZZ60gOYrGjh3r1lO/fn1XRl38he9f+eKLL1wQUV0rVQaV5bLLLos4rnnth7yOUbycUrNmzbKTTjrJ6tSp44699sf111+/y3Ilcrzi0Xw6L7S+xo0b2y233BJxfia7Hn9//PnnnzZgwAD3t8pz5ZVXWnZ2dsS8L730kjvPtaxq1aq51iGjR4+Om1Mq3j7dsGGD26ahQ4fm2uY//vjDsrKybNSoUQntD6Ck6fM4fPhwd56rLtjVd83IkSOtbNmytnz58lzLOuecc9z3q4Ih8ahFlpbdokUL9x2r78WzzjrLVq5cmWtefa71mr6P9b2oeu3pp5+OmMf/3OrzfcMNN1ijRo2sUqVKrsWwaPAhv36rXbu2/fOf/3TLDbdkyRI788wz3XeS1tOgQQM79thjc7WCeeSRR9w2aB7dFLrwwgttzZo1lh/6LlEwUK2U9tprL7cvVE++/vrrEfOtWrXKfZ/p+0rHQ99dqtt/+OGHhPaDumeqbhENxuR/l4V/z0XnlNLxU9e01q1bu+3S/jj++OPduZCXRI5XPJs3b3Z1nr6/da6pntT3aX7X4+8PpTm49dZb3bFVWfr06WNz5syJmHf27Nn2j3/8w52Lmkfz6nfQ2rVrY+aUUl0ab58OHjzYnWdbt27Ntd2HHXaYq2dLE1pKAQBQCijIou4YkydPDv2wVOBJOaP00I8qdeXr1KlT6LW2bdtarVq13HP9WNMFiYIT//rXv9yFxoMPPui6MCnYpQsMBSq0HP1AvO+++9z79OM4LwpA6YeifliqG+H//vc/u+CCC1wAQj/kfdp2/YhTbpM77rjD/vrrL/deP9gWHvzatm2bm0+vKeikH9wKPGkdyp2lYJJ+3L///vt21VVXuR+u/vYqmKELAO0HdWHRD1n9MA3v3qgAkPahpl900UWuFZouaPRDVBcesYIQidA+1g/Wa665xgW91B2sb9++9v3334daJWg9Kvv555/vjs0333zjjoP2uV4LF2s/6Md0MsdIF4YK+OjiUheS2s+64NBx0vbGk8zxiqYLP/2A1/Zfe+21LrDz+OOPx2yZkcx6FHzSfN26dXP746OPPrJ77rnH9thjD7c//YDkwIED3QWJlidqFaLjH++4xjvv9TjuuONcsPHee+91QSjff//7X3dO+gFTIB2oa68C5wqQnH322Xl+13Tv3t19h+r81/ekb8uWLfbqq6+6i/u8Whzqszhv3jwXBNL3lr6b9T2g/3UTRd+VsnTpUtt///3dc61HwQoFqvU9r4CTuh2Gu/nmm12rHwVwFODQ3woeaD377ruvCxRrmQpE63Pv12+ibdb61aVR3y36ntZ2Lly4MPRdoyCNumvpu1vfK+rOpe+kb7/91i1P36XJUjDk5JNPtvPOO8993+lmjoId7733nh166KFuHu2rN954w01XnaQyPPbYY9a7d2/XhUzBsbz2gwIhl1xyiQtO6Rjvueeebj7//2j6PlVd+fHHH7vAjL4flXNM+0O/JfS9Gkuyxyuafn8oKHrqqae63y5qwacbEQVdz+233+5azGl/6Pv8zjvvdN/P+s3kn7c6x7WvdPx1Tuq3w/jx4129X7169VzboN9H8fapPkvPPfec+x1y1FFHRdR/KpOCuqWKBwAAAm/mzJlqDuTdfPPN7vnWrVu9ypUre88++6x7Xq9ePe/hhx92f69bt87Lysryzj77bPf8t99+c89vvfXWiGVOnz7dK1OmTMT0/v37e02bNk14u/76669c0/r16+e1aNEi9Hz9+vVejRo1QtvjW7JkiVe9evWI6YMHD3blvPbaayPmfeONN9z0W265JWL6CSec4GVkZHhz5sxxz++77z433/Lly+Nu8/333+/mef7550PTtmzZ4nXv3t2rUqWK238+zTdy5Mg898GECRPcfI0aNYp478svv+ymjx49Os/9NWrUKFeGBQsW7HI/5HWM5s+f794zZsyY0LQDDzzQq1q1asSyJScnJ/S35tf79P5kj1csl156qVve5MmTQ9OWLVvm3pvf9fj74//+7/8i5u3SpYvXtWvX0POhQ4d61apV87Zt27bL46X/d7VP33//fTfvu+++GzG9U6dOXu/evfPcD0Bx8z/L3377bdx59NnS5yaR7xp9J3br1i1i2uuvv57r8xNLrO+6//73v+69n3/+eWjakCFDvAYNGngrVqyImPeUU05x2+ovx//cqm4JX7a+u+vWret16NDB+/vvv0PTx48f7+YfMWKEe7569Wr3/K677oq7zfqeKleunHfYYYd52dnZoekPPfSQe+/TTz8dsd8SqSs1j9772muvhaatXbvWlTn8OGzatClinaLvyvLly0d878XbD/LKK6/EPTb6vgr/zlJZNO+9996ba97w+iG6Dkz0eMXy/fffu+VdcMEFEdNPPfXUfK/H3x977rmnt3nz5tB8qnc1Xb9zZNq0ae659tGujpeO7a72qY5V48aNvZNPPjliuvan6vN58+Z5pQnd9wAAKAV0Z04ta/xcUWrSr+5s/uh6+t9vDaRcU7oL6ueTUjcBtVxSK6kVK1aEHrpT2KpVq5gj9yUqvPWL7k5qubqzq7u+fpN43XnVnUi1YAlfv1qeqNVLrPX7LV9877zzjptfdy3DqTuffjfrDqr4d8TffPPNuN3FtCyVXdvj091vLVtdtj777LN87YtBgwa57gi+E044wXWH0Ppi7S8dP+0HHTuVQXf0d7UfkqHWcJ9//rnr/qDuguH8Vgqx5Od4hVN5dYd7v/32C03TXe7oVkX5WY9aGoRTKzCdaz4df+1XLbswqLWEWii88MILoWlqRaAWaOoeBKQbtQCMNQpfrO8afaeppUl4dy59FtTtWN/zeQn/rlM3MX229b0g3333nftf33uvvfaaHX300e7v8O8BtWpRHeLP61NLo/BlT5kyxbV4Ugvd8JZban2j1sLqmutvj1oWqevV6tWrY26zWl+qRY1a4YTnqVKrMnWn85eVLH2HqNWlT8vSvtV3vlrWiFr1+utU/a1ujn738+h9EGs/JEv7Xd3P/ET4idQP+Tle4fy6MLoej271lJ/1qKVceN4s1Q3i1w9+Syi1bFKL3ILSsVKd9tZbb0V8nvT5UJ2u1m6lCUEpAABKAf1I1A8dP3eUAlDK49SyZctcQSn/fz8opa4D+mGnAJSCA+EPdW3SD/r80rp04a4uWgoIaJl+/iY/KKX1+zlNotevbiTR61c3QOV6CKc8KPphHx70Eb85vV4XdZFQHi11EVAeCnVLUK6J8ACV5tW+iE6OG72sZGmZ0cdMxyc8X4m6iaiboHJ6+XmR/Iu78LwW8fZDMvwf4x06dEjqfcker2j+/o0WnWMj2fXoglOvhdttt90iLjB1Yar8KMrFon2ngJy6yBT0wkPdavwLGV10aFv8XCNAOlHgPfp7NN53jb5PFSzxg7L6jlJ3J30m8gps+zmS1CVM38MKnuiz61+o+991CpwrMK1ufdHfAQoySPT3QPTFvv99HSuHj4JS/usqh7r06gaGtklds9TFyw8K5bUsBTuUGyu/dYPqgej9pe8p8esH1VHqPqzvTm2rAkbaDwqAR9cNsfZDshRoVDl17BOVn+MVTvtP36nRXQOj93d+1hN940V1g/j1g/bX5Zdfbk8++aTbtwpuPfzwwzH3baIUWFQ6gHHjxrnn6uo5derUUjkCLjmlipjuMN51113uBFu8eLE76ZRgs6j4/ZijP6hKUgoAKN0UZFIuoOnTp4fySfn0t59fSa2pFMDRj2j/x65+EOvHeHheHN+u8kbl9aNWuXv0w185d3T3XD/edTdUP679QJD/v/IHqYVStOgfxeF3jJOlix/V3Wplo7vaCkgoJ4oCHwp0xCp/cdHdb+UP0cWa8k5pvymYp2OmQFV0y66C7IeCSPZ4Fdd6Ejl2CtQqh5fuhut810P5W3TxkN9E9nqvfgsqMKVWXS+++KLLIRIrBwmQypQ3TRfh/s2MXX3X6MJe57qCUiNGjHC5pJSTJ5FWgmqZO3HiRFcvKQeg6hl95g8//PBcdYOWp5Y/sfh5En0FaR2kFjlqfaPPsr4jlGdROaiUA6hLly5Wkm677Ta3PQqkK1+UblzomGibY7X6Lch+yK/8HK/iWk+8+iF8IBTlIFRdq5bU+j2gFls6/rrZl58bQEpYr+T6zz//vKsn9L9+A+ncL20IShUxNQHXiDz6gtBoBMVBCWPVfLSwf/wBANKb3/JJQScFpcKbvOuHkS4s1DVB3S00ipJPdyX1w0x3Cv27s/Hs6u53OAXIdIGi5uvhdymju135d0UVMFCrqvxo2rSpqxvVTD78Lr9/00av+/RDXsEyPRQs0499JbPWdmn9mld3n/XDN/xCLNaykuG3/PFpnyuZuv/jWcHEX3/91QVH9APWl2xXs0SPkR+UVHezZBT0eGn/Re8L/y5yYa4nHl0U6MJTDx1jtZ5SwmBd8EVfjCeyT9XSTBesujDXhYtauyk5PZBuFAAWtRJJlL6rNEKdEn3rM6DPgq5V8qLWKUqgrRvtCmb5or8X/BHYFLAvSN3gf7/o5kM4TYv+Ptf3jrp966HtUcBMwQoFFMKX5X9/irr0zZ8/P9/bqHpA9UH494zqAvETrCvgpwEinnrqqYj3qsWQWvYkIpn6W/tBvxU0elyiydsLery0f/Wd7LfSilc3FMZ5EY9GN9RDIxcqaKqW1Y8++qgbITY/+1Sfj8svv9w1XtENC3Ub9VtplSZ03ytiav6tkzS8H3A4/RhXln8Nx6m7ncqB4A+9mV8KQumOof9I9IsIABBs++yzj+s2pAsDta4JbymlgNTee+/tmqPrhoofwBLdVNFdRF0ghN81FD0PH6JbdVmizdn9O5Phy9R71TIlnC6AlENDwaFYwyfHGnI8moJs+oH60EMPRUxXiyz9aFR9LWqFFE0XHX6d7S9LXTbUgip89CkFGnQ3f1e5UuLRSDzhuSV0kaEfqv62xdpf+lujRCUj0WOkH/bqoqIhtBVICRd9HhTm8dL+1Z1njSwY/p7wvEyFsZ5YooebV9DRDwr6xz8/+1TdMXRnXSMqKrebf0yBdKHWQGqBo5sTyYwaqXNd1yLq+qZ8e4m0kor1XSf6/ETPpxHxlD8oVvA8ke8A1YsKbCuwEP4ZVytJdU/3R3ZT91vltooOzCj44b9PARAFtTXaWvi2K1Ck74dYo8QlYtGiRaEuXqLR41RfqG7yW4lqX0TvL43Iqro+Ufoe8wNZu6L9rhxN0XVqXvVDQY+X/72p/VvU50U07XPV8+EUnFIdsau6Ia99qtazGRkZrququsyX1lyDNKEpYRqiUsN0vvTSS66rhL5w1CxVd0Nj5VNIhKL2WpYuPDQcq5oVRveTBQCUPvqxrCGvv/jiCxeEUuuocApS6Y6vhAel9MNbN1iGDRvm8leoG7p+iOvOr+qtc845x91gES1TwRrd+dO6FKRRi5NYNAS13yrl3HPPdblKnnjiCXeBoGCMT4EHDamtC3sFzpTnSQETBUrUxU53KmP9MA6ndeguslo8qQxqxawggZrhq8WY3+pGQ5ir+54uHnRXVnknHnnkEdfCxd8nKq9azqgZv7rn6061AkhqfaYfx9H5VhKl7hZah3JeaDhrLUstc/yh19VdT9upfa0LDe0X/eiOl3Q3nmSOkX78a5u031VuXZBq/2m/q5tbLAU9XldffbVrkaHfQ/qhrh/1yg3it1ArrPXEolxiCkyqxYSOuXKYKNioi794Q6Mnsk81fLnKpc+LEkLnZ1h4oLgoIKOWn7oI13eRAlJqkanPoFq2hicE3xWd6/ps6rOoYEH4ABHx6LPt52xSwFk37/V9rTon2u233+5aserGvr4r1SVKn2Elslbr2Fg3GqK3TwEzfe/qhoK2T2VWsF/f7ZdddlmoZZJaz6prldahRgD6PGtelU/0/aN6Ujdw9P11zDHHuFY8qkP0vZDfgINaKA8ZMsS1NlM+K90o0HrDb+Com6TqL5VDdbmuJRXID2+xtSv6ntMx0v5QEE2/E/RdqDo5VgsfBcb0nacbCEoMrhta2udqXarWcbEU5Hhp+3R8tD+1fSqnWtSpJVlhricWfQZ03a5cgDoe+myonvIDYPndpzpnDj/8cBdAVF7N/AYu015JD/9Xmmh3jxs3LvRcwytriO0///wzYr4+ffp4w4YNy9c63nnnHTeE9A8//OC99957bijWJk2aRAwxDQAovVS/qD7q0aNHrtf8obqrVq3qbdu2LdfrGpK6V69eXuXKld2jbdu23oUXXuj98ssvoXk2bNjghmeuUaOGW9auhrx+6623vE6dOnkVKlTwmjVr5t1xxx2hoaY1nHU4Dancr18/N5yz5t9jjz28M844w5syZUpoHg3FrG2LZf369d5ll13mNWzY0CtbtqzXqlUrN7x3+PDVH3/8sXfssce6eTS0t/4fOHCg9+uvv0Ysa+nSpd6ZZ57p1a5d283XsWNHN5x6tOhhqmPxh6TWcOc6PhqevGLFil7//v3db4VwP/30k9e3b1+vSpUqbt1nn322q/P1/vD157Uf4h0j7e/o5ciMGTO84447zs2v/d6mTRtv+PDhuYaRz8/xiufHH390w4/rfY0aNfJuvvlm76mnnir080LHJvzn8KuvvuqGc9cx0HHVb6hzzz3XW7x4ccT6oof4TuS8P/LII91rEydO3GX5gZLgf5b9hz4D9evX9w499FBv9OjRMa8n8vqu8X3zzTduefpsJeqPP/4Ife/os33iiSd6ixYtivmdqu9j1UW77767+27XNut66vHHH8/1uX3llVdirm/s2LFely5dvPLly3s1a9b0TjvtNLcNvhUrVrh1qN5TebVN3bp1c9dd0R566CE3n7alXr163vnnn++tXr06137bVf0omkd1wfvvv+/qSm2flh1djk2bNnlXXHGF16BBA1d/9OzZ05s0aZL7HtUj0f3wxBNPeC1atHDXqOHfc9HLkb/++su7/vrrvebNm4f2+wknnODNnTs3NE9+j1c8f//9t3fJJZd4tWrVcsfh6KOP9n7//fdCPy+i68N58+Z5Z511lqtfVM/oHDn44IO9jz76KNfx0rFNZJ/6Xn75ZTf9nHPO8UqrDP1T0oGx0kJN88ITnesunqLafrM+n5oAqquE7rjpLkVed+ZEyU4VDY5FTQV1V0M5MRRhBwAAqUXd9tWKS3dKTzjhhJLeHBQRpXJQ64VYd/WBIPvhhx9cixG1rCmNI4sVhFprKS+dRi1EML355psuPqBW2mpxVhrRfa8EqZuCmvOp6X90xn9/JCM1uVR/5rwoN0E8agaoJob8AAIAACgZ6o6qm5HqPgqUNuqWrWub4hr0CUi3z0eLFi0i0iaUNgSlSpBGn1DSVeWriBcVVa4N5ZAoSOBLIxRwVwIAAKB4KQeOco09+eSTLneNcqcBpYVGWFXuXOWEUz6e6N4hQGmmnNLKk/j222+7HGbJjH4YNASlipiCQuGtlPTjRIlBlcxULZg0eoUSxSmxrIJUGg1ACds00kt+Ep0p+akSa6rLnkZqGDlyZMJJBQEAAFB4NNqYEg9rwJlnn302NFIWUBpcfPHFLiG3RtRU8m8AO+n6XC0IhwwZ4pLDl2bklCqmPBHRBg8ebM8884wbUUIjGqmPtUbS0ZCp+++/v/vi1jCTydLoD+qPqiGNlc1fzQBvvfXW0KhCAAAAAAAAqYCgFAAAAAAAAIpdZvGvEgAAAAAAAKUdQSkAAAAAAAAUO4JSAAAAAAAAKHYEpVJEdna2zZo1y/0fBEErTxDLRHlSW9DKg4IL2jlBeVJb0MoTxDIFrTwomKCdD0ErTxDLRHlSW3YalYegFAAAAAAAAIodQSkAAAAAAAAUO4JSAIC09fDDD1uzZs2sQoUK1q1bN/vmm2/ynP+VV16xtm3buvk7duxo77zzTsTrnufZiBEjrEGDBlaxYkXr27evzZ49O+ayNm/ebHvttZdlZGTY999/X6jlAgAUDuoJAEhtBKUAAGlp7Nixdvnll9vIkSPtu+++s86dO1u/fv1s2bJlMeefOHGiDRw40IYMGWLTpk2zAQMGuMeMGTNC89x55532wAMP2KOPPmqTJ0+2ypUru2Vu2rQp1/Kuvvpqa9iwYZGWEQCQf9QTAJD6ypT0BgAAEvfcdRMtq2yme+Rsy7Hau1e1g//Z1sqWz7L5Pyy3P39ZY71OapXUMr/53zyb/tmfVrlGefe8ZoPKdtiQ9pbq7r33Xjv77LPtzDPPdM91gfD222/b008/bddee22u+UePHm2HH364XXXVVe75zTffbB9++KE99NBD7r26+33//ffbDTfcYMcee6yb57nnnrN69erZG2+8YaecckpoWe+++6598MEH9tprr7m/ASBVPH7mK5bhbbMMb6t5GWUs0/vT+v/f8Va/6R723xE32t9/ZNhZT49MaplPnz3ctubsY1nb1rrnGbbUhjy7/bs0lVFPAEDq1xEEpQAgzRz2r/ZWZ/eq5uV49vYjP9qsSYut40GNrXnnOu6RH633q2cHnNTa0sWWLVts6tSpNmzYsNC0zMxM141i0qRJMd+j6bpjHk53t3UhIfPnz7clS5a4ZfiqV6/uunvovf7FxtKlS91Fjt5XqVKlfJch2dFQNH9OTk5ajKKSCMqT2oJWniCWKa/yNO61xg478xzbsmmTvXj+0/beXU/Z6aNvtoH/d2O+11c25wc769kRli7SvZ6gjghWeYJYJsqT2rLTqI4gKAUAaSo7O8e2bcm28pW2f5X/PHGxay115Pmd7M9fVtvnY3+1hi1r2OK5ay0nx7O+Z+xpdZtWsyBYsWKFq2R1dzqcnmv421h0IRFrfk33X/enxZtHd8nPOOMMO++882yfffax3377Ld9liJeDJB79sFi1apXNmTPHXVilO8qT2oJWniCWKa/yrF+33n3HrF+pbmplLadstns++bHnbNvKujbkuUvt1TtvtzU/NbZM+81yrImZZVn9/VfbUeddZEGQ7vUEdUSwyhPEMlGe1JaTRnUEQakUolYPa5f/bVlZmYG4WP5rzbbAlCeIZaI86RBwyon52gdPznTd99av3GR1mlS1ll3rxpxv9ZK/7ODT21rvU9vYjM//tK/fnGfHXLJXzHnnTl3muv5VqFLG9jmyuTVus1uhlicoHnzwQVu/fn3Enff8atUquW6Wurj64oN3bPnSPy0jM8OCUOetWb3asnfbjfKkoKCVJ4hlKlO2rNVs0cZatmxpWVlZoemf23Rb++Pu9uUP39i2sjWtrHl2wohrrHK16jY5ahmby9ez3TussGMuHmTPXDzMlk5sbHZe7PVty+hgT57xnGV4f1n1dqvthGsK/j0YRIVVT+Snjvjl559t5eyZ7lxPd0H7vAaxTJQn9cuTUbmKtdxvv5SvIwhKpZDv31xlqxYstiCZZLETSaazoJWJ8qSujv13M2sfv/teTnaOffrCLzZx3FzrdULuH6/V61S0+s2ru7/rt6hm0z5cGHM97Q9sZF2PbOaCeYvnrLF3H5tuJ1y7j1WrVdFSVe3atV0Fqy4S4fS8fv36Md+j6XnN7/+vaRpVKXwejZ4kn3zyieuiUb789vxbPt0NP+200+zZZ59NuAzhPxAS8e4T/7aF37UxLzMYrd22q2N/WJBQntQXnDKV27zcDrgm032XRH+fNO611o7413m2cd1ae+niR+yVKx60ITFyhGgZx1x8qfu7fpfm9vsX2+uMaPuccYg1b9/FqtaoYW/cd48tndHOvn73Ldv/iGMsVaV7PZFsHSHf3Puebaq4rwVHcD6vwS0T5UllFTLft56HHp7ydQRBqRSyfulW93+ZcpmBiM6qyWAQmj4GuUyUJ7Vl7KIomVmZ1qJLHZv4+lyzE3K/rtZUoWVlZJiXHfvOaeXqO384N2hZwyVPX75gfUoHpcqVK2ddu3a1jz/+2I2M5B9/Pb/ootjNirt37+5ev/TS7ZWrKIGtpkvz5s3dBYfm8S8u1q1b50ZXOv/8891zjbh0yy23hN6/aNEil29EIzwpp0hRWvnLIvMy25l52ZaVvaVI1wUg9WV4m3c5j+58V26x1TbMbxlnGdtCf2eVyTIvzsDcnXoeHPp7wGVX2FODHra5XyxJ6aBUaawncjIau/8zs7dYhheMvDgACqCClxZ1BEGpFOLZ9pPmxGH7utGv0pmaEKtfqpoe5+dOTyoKWpkoT3qUZ1eUO2q3evlPti0bVm+yKrtVcH+vWfqXrfh9vdVqVMVSnZLRDh482N193m+//dyISBs3bgyNsjRo0CBr1KiRjRo1yj0fOnSo9e7d2+655x7r37+/vfTSSzZlyhR7/PHHQ4E7XYjoYkLnkS4+hg8f7obz9i9omjRRn/qdqlTZvp/22GMPa9x4+8VAkdnxu6LC3z/bkOcusXQX1M8s5UldpbWe+GtBjmV4BWtFPPXjd61rnyPc35+//l/bVraRNd479bt5l7p6wrbf1K7b+kf7x1VXWzoL2uc1iGWiPOlQnj3Soo4gKJVKdlxwZKR/IykARcjPKaW+4lVrVnA5owpCuaaWL1zvWmhmZmbYgae0sRoFDHQVh5NPPtmWL19uI0aMcAlmddf6vffeCyWgXbhwYUTLuR49etiLL77ohvK+7rrr3I8OjYzUoUOH0DxXX321u2A555xzbM2aNdarVy+3zAoVtgftSlT6pwgBUEz++LK6PfnFc67JbUZOLet0+q4vTPLy/XM/2rT/rFSSErXHsSoNf7LeJ9xsqa7U1RM+riUApFEdkeFpiAikRCTzycs+t21bPDvtpv3T4oKwNEWag1gmypPaglYeFNwzQ6+zjZv7WoW/ptuQ54ZaugvaOU55Ul/QyhS08qDg58PT/3rZtpSvZw3bfGfHXXalpbMgnt9BKxPlSW3ZaVSe4CRfCQCigwCAXVcS1BYAgPhNpDJoKgUgjRCUSsXuexwVAEA0v2FzBkEpAEAsO4JRARr0BUDw8Y2Vkri7AQCIUzcQkwIA5NVSiis8AGmEr6xUvAlOTAoAEBdRKQBAfHTfA5BOCEqlZFSqpDcEAJBqdo5LQlAKAJCbt+PSLiPFkxoDQDiCUikog6ZSAIBofiyKKgIAEMuO+iEjk4oCQPogKJVCuPcNAIjPv8igtgAA5JFTihvcANIIQakYbrzxRvdlHv5o27Zt0a84lFOKigQAECUUiyIoBQDIK9E5l3gA0keZkt6AVNW+fXv76KOPQs/LlCmGXUWicwBAXASjAAC7xg1uAOmEoFQcCkLVr1+/WNcZymFLPQIAiOJ5O+6AE5wCAOSR6DyTROcA0ghBqThmz55tDRs2tAoVKlj37t1t1KhR1qRJk4Tfn52dndT6wufPyclJ+v2pRtsfhHIEuUyUJ3jlyeJHaCnJKEVQCgAQA4nOAaQhglIxdOvWzZ555hlr06aNLV682G666SY74IADbMaMGVa1atWEg1rJCL/wnD9/npWrlN4Xl7qYXrVqlc2ZM8cyA9KvPWhlojzBK0+x5L5DiQm1pgUAICYSnQNIPwSlYjjiiCNCf3fq1MkFqZo2bWovv/yyDRkyJKFltGrVKql1btu6zT61pe7vFnvsYRWrlLV0piCbLqZbtmwZmNYbQSsT5UltQSsPCvNig+gUACB+PUH3PQDphKBUAmrUqGGtW7d2F4iJSvYiMidn50VGmaysQFyEqnVHVkDKEtQyUZ7UFrTyoIBUTWSoxRRBKQBAbqH0tAFoMQ6g9OAbKwEbNmywuXPnWoMGDYpsHREXGbS4BQBEYzAMAEBeMkh0DiD9EJSK4corr7TPPvvMfvvtN5s4caIdd9xxrqXCwIEDi26l4TEpLjgAAHEw+h4AIC8kOgeQTui+F8Mff/zhAlArV660OnXqWK9evezrr792fxeZ8GsMolIAgDzG3wMAIG7uQYJSANIIQakYXnrppWJfZ3j3PWJSAIBcdlQThKQAALF4Oy4iMrO4xAOQPui+lyK4yAAAJILuewCAPFtKcYkHII3wjZUqInJK0VQKAAAAQDJ2tJRi9D0AaYRvrBRB9z0AQJ687ZWDl0FLKQBAfBllGH0PQPogKJUqIhKdl+B2AAAAAEjjnFJc4gFIH3xjpYiwhlJ03wMA5JErhJZSAID49URWFi2lAKQPglIpIzwqVZLbAQBIScSiAACJ3LzIICgFIH0QlErFllIluSEAgJQUqibIKQUAyKv7HonOAaQRvrFSBN33AAB5yfATndNkCgCQh8yytJQCkD4ISqUKmkoBABJAFQEAiCkjM/J/AEgDfGOlCFpKAQDy5tcNtJQCAETatnVr6O+yZcqU6LYAQDIISqUK/xqDeBQAIAZCUQCAeHKys0N/Z2QRlAKQPghKpQg/RwgxKQBAbDtqCCoKAECU7LCgVCa9LgCkEYJSKdZ9j657AIC8Ep3TZgoAEC07rPteZtmyJbotAJAMglKpgu57AIBEUE8AAKJk52wL/Z2Zyeh7ANIHHY5Tht9UqqS3A0Aqm3NIH8soV84yKlQwb8sWq7Dnntbg5v+zzEqVbP0nn9hfkydbvWHDklrm8gcfstUvvmhl6tVzz8u3bGmN7r6riEqAgqOlFAAgUvaWzaG/M7NodwAgfRCUShFezvb/6b4HYFca3XevC0Z5OTn2+/nn25px46zmaadZ1UMOcY/8qHb0UVb/uusKfVtReDzuWgAA4sjO2XnDIiuL7nsA0gdh9BRB7z0AyfK2bjXv702WVa26e77m9XH2+4UXub83Tv7G5h19tC2+6Sabd+wAm3vUUfb39BklvMUoED+nFBUFACCKl7Mz0XlWGS7xAKQPvrFSLtN5SW8IgFT352WX27wBx9nsXgcocYRVO+LwmPNtnjffagwYYC3efMNq/vOftvz+++Muc/1777vg1YLBZ9jGrycX4dYjv3ZWD3TfAwBE2rZ1S+jvMmXKlei2AEAy6L6XYjEp27rFtWxId16OZ97atba0enXLyAxGpC1oZaI8aVCebvuZtWoVv/vetm22eORIW3b3PVbv2mtyzVeuSROr2Lmz+7viXnvZyqfHxFzXbqecbLXPO9cyypa1v777zv646GJr/srLVrZRoyIoGfKL7nsAgHhywrrvZZBTCkAaISiVKryd3XHW/PclC4q1FjxBKxPlSWFNm+b5ckaZMlbtsMNs6V13WT3LHZTKKF9+55PMTLNtO0fmCVemTp3Q35X23tsFvP6eMZOgFAAAaSJ7286WUpmq8wEgTRCUShHejqZSGebZbqeealk1a1o687wcW7lyldWqVdMyMoJRMQatTJQnDcqze+NdzqeuduWbNy/QurYuWWJl69d3f2/57TfbNGuWlW+du4UWSlaG31Iqg+57AIBIXnbOzgu8smE3pQAgxRGUSjWe2W7/PM3Kt2hh6Sw7O9tWzZ5ttVq1sqysLAuCoJWJ8qRHeeLllMqoUEG3Ra1Mw4bW4MYbC7Su5ffdb5t+mqnMqJaRmWn1hw8vcKALhY/uewCAeHL8obzVUiqLSzwA6SP9mxMELaeU5ZhlcOEBILaWn3xse7z3rrV4Y5y1+N//rMljj1nZBg3cazWOP852f/gh93flbvu5eXwVWrd2742l4R23u2Vp/uavv2bVDu9n6eLhhx+2Zs2aWYUKFaxbt272zTff5Dn/K6+8Ym3btnXzd+zY0d55551crVZHjBhhDRo0sIoVK1rfvn1tdlRw8JhjjrEmTZq4ZWi+008/3RYtWlQk5YvcuB3/U0UAQMJKSz2xbcvOLvplypYt0nUBQGEiKJVy3ffMtVQAAORt7Nixdvnll9vIkSPtu+++s86dO1u/fv1s2bJlMeefOHGiDRw40IYMGWLTpk2zAQMGuMeMGTNC89x55532wAMP2KOPPmqTJ0+2ypUru2Vu2rQpNM/BBx9sL7/8sv3yyy/22muv2dy5c+2EE04o8vJm+DcsCEoBQEJKUz3hedmhv4PQYhxA6UH0I1X4d8AVnKKlFADs0r333mtnn322nXnmmdauXTt3gVCpUiV7+umnY84/evRoO/zww+2qq66yPffc026++Wbbe++97aGHHgrdHLj//vvthhtusGOPPdY6depkzz33nLu7/cYbb4SWc9lll9n+++9vTZs2tR49eti1115rX3/9tW3durWYWtSSUwoAElGa6omcnPDuewSlAKQPOhynWEspd7FBSykAyNOWLVts6tSpNmzYsIjRhtSNYtKkSTHfo+m6Yx5Od7f9C4n58+fbkiVL3DJ81atXd9099N5TTjkl1zJXrVplL7zwgrvoKJtkdwnlDUvOzpZSyb839agMuogKQlmE8qS+oJUpP+UpTS1o0r2eSPY8zd68c/Q99bpI9/M8aJ/XIJaJ8qS27DSqIwhK7cLtt9/uKrOhQ4e6OyNFzXXfo6UUAORpxYoVrpKtV69exHQ9nzVrVsz36EIi1vya7r/uT4s3j++aa65xd87/+usvdzd8/PjxSZchOgdJorycnHy/N5Xoh5Iu1ubMmROI4cspT+oLWpnyUx7lSiot0r2eSPZ7fvFSrb++KgnqiBQVtDJRntSWk0Z1BEGpPHz77bf22GOPuaa5RW1nQylaSgFAqlPXDuUcWbBggd100002aNAgd8GRzE2FVq1aJbXOr3a0lNId8GTfm4p0sagfSi1btgxE6w3Kk/qCVqaglSdoClpPJPs9v/6P+TY3n+9NRUE8v4NWJsqT2rLTqDwEpeLYsGGDnXbaafbEE0/YLbfcUvQrDO++R0spAMhT7dq1XQW7dOnSiOl6Xr9+/Zjv0fS85vf/1zSNlhQ+z1577ZVr/Xq0bt3a5R3ZfffdXb6Q7t27J1yG5H8g7AhKZQSnC47u3KkslCc1Ba08QSxT0MpTmNK9nkj2mPrBrgzPC8z5EMTzO2hlojypLTNNykOTnDguvPBC69+/f0Sf8eKISWW4oBSHBQDyUq5cOevatat9/PHHEc2U9TzeD35ND59fPvzww9D8zZs3dxcc4fOsW7fOja6U10WEn1x28+bNVpQyGHYPABJW2uoJL5Q3hsEwAKQXWkrF8NJLL7lhY9V9L7+STk7oz+95luN5aZ9gLWiJ4oJYJsqT2tIpOWFJUTLawYMH2z777GP77befy/u3ceNGN8qSqKtEo0aNbNSoUe65cgP27t3b7rnnHnfTQd/1U6ZMsccffzx0l/nSSy91rWPV9UEXH8OHD7eGDRu6IcFFFx6qG3r16mW77babG+Zb8+yxxx5JtZLKDy+U6JwLDgBIRGmqJ3JyCEoBSE8EpaL8/vvvrkLSXZEKFSrkeznJJhhcs3jnnZN5v823jDWrLZ0FLVFcEMtEeVJbOiUnLCknn3yyLV++3EaMGOESzKrrxHvvvRdKQLtw4cKIfaeRj1588UU3lPd1113nLig0olKHDh1C81x99dXuguWcc86xNWvWuIsKLdOvDzSU+Ouvv24jR45086n7hoYP1zLLly9ftAX2rzNoMAUACSlN9UROthfqvgcA6STD8/jmCqeK57jjjotocaCWCrozokpLzW4TaY2QbGuNRXNX21v3/mgVNq20f951kJWpWdPSWTolViutZaI8wStPEMqN+J4afI9tqtjFKpf5yM546DYLwjmuGzi66AvCuUt5Ul/QyhS08qBgvnrrNfv+nd0sM3uLnf/E4Zbugnh+B61MlCe1ZadReWgpFaVPnz42ffr0iGlq4qsWCBrataguDjPDRt/LKlMm5U+cICVWK81lojypLWjlQQF5O+7m01IKABAlx9uet4ruewDSDUGpKFWrVo1ooiuVK1e2WrVq5ZpemLycnaPvabhvAAAihIJRXHAAAOJfSwBAOiH6kSL8XpSuHzhBKQBANM9PdE5TKQBApJxt21OHkFMKQLqhpVQCPv3006JfyY6hYt3dDS44AAC5+EGpkt4OAEDKofsegDRFk5wUa3Kraw0lVQcAAACAROQkOcgSAKQKglIpwsvecXeD7nsAgJi237DgvgUAIJrnt5QKtZgCgPRA9CPFckq5JrcEpQAAudB9DwAQW05YrwsASCdEP1KFn+jc/UN1AgAAACDJXhfklAKQZghKpVz3vRxySgEA4nffy6SOAADE6b5HUApAmiEolSrCh2+l+x4AIJftwSguNwAA8QZNopIAkG6IfqTc6HvklAIAxEKicwBAbF7O9tH3MoxE5wDSC9GPFOHl7Bx9j+57AIDcSHQOAMg70TkApBuCUqnW5BYAgDwRlQIAxLnBTf89AGmGoFTKjb5HRQIAyCvReUlvBwAg1Xih/LRcSwBIL2VKegOwHU1uASSi36v9rFxWOSufVd625GyxtjXb2o3db7RKZSvZhIUT7Jsl39g1+12T1DIf+f4Re2nWS1a3Ul33fI8ae9gdB95RRCVAvoW6dtNSCgAQhUTnANIUQalUsWMYV1pKAdiVu3rf5YJROV6OXfTxRfbm3DdtYNuBdnCTg90jP/q36J90MAvFbEf1QEspAEC0nB3d90h0DiDdEJRKEeSUApCsrTlbbVP2JqtWrpp7/sacN+yThZ/YA4c8YN8u+dZum3ybda3X1aYtm2bZOdl2a69brX3t9iW92ShoSykaSgEAooW67wFAeiEolSpCQSkqFAB5u+qzq1z3vUUbFlm7Wu2sX7N+Meebv3a+3dTjJrth/xvs5V9etgemPWCPHfpYzHk/+O0D1/WvRvkadm6nc22/BvsV2va+9dZbSb/n0EMPtYoVKxbaNgQD3fcABA91ROEg0TmAdEVQKtVySnk5duPEGy3dqVvRurXrrNrKapYZkL4mQSsT5Un98nSr0M1aWau43fe25Wyz/5v0f3bf1Pvsqn2vyjXf7lV3t051Orm/O9fpbM/MfCbmuk5qc5Kd3elsK5tZ1rWqunTCpfbf/v+1hlUaFkpZBgwYkNT8GRkZNnv2bGvRokWhrD9wic6JSQEIEOqIwkGicwDpiqBUqthRkXjm2WuzX7PAWG7BE7QyUZ6U1aJV3j+4y2SWsb5N+9q9U+6NGZRSayqfAnXqwhdL7Yq1Q393qdvFBbxmrpxZaEEpWbJkidWtuz2R+q5UrVq10NYbJF4oKEVUCkCwUEcUZu89glIA0gtBqVQR1uT2n3v+02pWqGnp3spj5YqVVqt2rUC0WglimShP6pdn95zddznfN4u/sWbVmxVoXUs2LrH6leu7vxesW2CzVs2yVjVyt9DKr8GDByfVzeKf//ynVau2PU8WYiAoBSBAqCMKh+ffeCImBSDNEJRKuUTnnp3c5uQCX2SWtOzsbNe0ulWrVpaVlWVBELQyUZ70KE9eOaWyvWxrULmBjeg+okDrenDag/bTyp8sKyPLBfSu73Z9oX4HjRkzJqn5//3vfxfauoNlRzAq/WOuABBCHVFIaCkFIE0RlErBfuBBaOUBoGi8f8L7cV8b0HKAe8i+9fe1V495NfRaq91axX2vRuVDOqD7HgAgtpxsEp0DSE9EP1ItKOV5XHAACKQnn3zSddPw74qPHTvW9txzT5esduTIkSW9eekTlGL0PQABRB1RQDuuJTIISgFIM7SUShWh7nvbExIDQJDcf//9dsMNN1i/fv3s+uuvt0WLFtl9991nl112meuqeM8991ijRo3snHPOKelNTX1UEQAChjqi4Bh9D0C6IiiVIrwdic41+l4mVxwAAuaxxx6zxx9/3E499VSbNm2a7bfffvboo4/akCFD3Ou62FCeEC44Eum+V9LbAQCFizqiEBCLApCmiH6kCP/mhprc0lIKQNAsWLDAevXq5f7u0qWLS0a///77h17v3bu3zZ07twS3MA3siEZlZKZ/In8ACEcdUXCetyOnVKjFFACkB6IfqWJHBeJaShGUAhAwlSpVso0bN4ae16lTx6pUqRIxz7Zt20pgy9KxpRQXHACChTqiENB9D0CaIvqRIrwdOaXUUopE5wCCpm3btvbjjz+Gnv/+++/WtGnT0PNZs2ZZs2bNSmjr0gw3LgAEDHVEwXnZob9KdkMAIEnklEq5nFIkOgcQPHfccYdVrlw57usLFy60c889t1i3Kf3sqBu4cQEgYKgjCo5QFIB0RVAqBiVS1OO3335zz9u3b28jRoywI444osjWmbMjKKUqhUTnAIKmZ8+eeb5+wQUXFNu2pPsFR0YmQSkAwUIdUYg5pQhPAUgzRD9iaNy4sd1+++02depUmzJlih1yyCF27LHH2syZM4snKJXJYQEQfLrIWLFiRUlvRhrxE50TlAIQfNQRyckgpxSANEX0I4ajjz7ajjzySGvVqpW1bt3abr31Vpds8euvvy6ydXphFQktpQCUBs8//7ytW7eupDcjDROdU0cACD7qiOR4nn/DgqAUgPRC971dyM7OtldeecWNCNK9e/cizykl5JQCUBrsDMYjIf71BjmlAJQC1BH5v5YAgHRCUCqO6dOnuyDUpk2bXCupcePGWbt27ZIKZiVj5/w5ritfsu9PNdr+IJQjyGWiPMErT1ZWVpFuE0ra9hsWmXTfAwDEkWEEpwCkF4JScbRp08a+//57W7t2rb366qs2ePBg++yzzxIOTM2ePTup9a1evdrMdnN/z5s7z8pllrN0povpVatW2Zw5cwKTIytoZaI8wSuPhtROJ+vXry/pTUhPxKQAlALUEcmhZRmAdEVQKo5y5cpZy5Yt3d9du3a1b7/91kaPHm2PPfZYQu9XPqpkrKz2u/vfM89at2ptZTPLWjpT6w5dTGsfBqX1RtDKRHlSW9DKE23ZsmXusXOQh+06depUYtuUPonOg3c+AEA46oh8CMWkCE4BSC9pH5R66623kn7PoYceahUrVkzqPaoUN2/enPD8yV5E7ry54VnZrLKWFYCLDrXu0H4I0gV10MpEeVJb0MojGtVULU9//vnn0F3djIwM97f+D0r3y6LghRKd01QKQDBRRxRAaMwkglIA0kvaB6UGDBiQ1Pyq0NS1rkWLFnHnGTZsmB1xxBHWpEkT13T4xRdftE8//dTef/99KyqepztB2y88SXQOIKjOOussN6rpU089ZfXq1SPAkhSCUgCCjTqiELrvscsApJm0D0rJkiVLrG7dugnNW7Vq1V3Oo+bCgwYNssWLF1v16tVdU2EFpNTCqqjk5Ph3NXKogAEE1rx58+y1114LdY9GEnbUDRlZ3LgAEEzUEQVA9z0AaSrtf9mqiW8yXfH++c9/WrVq1fKcR3dnfvvtN9ddTwGqjz76qEgDUo7fZ554FIAA69Onj/3www+FtryHH37YmjVrZhUqVLBu3brZN998k+f8r7zyiksIr/k7duxo77zzTq47zSNGjLAGDRq4uqVv374RA1eobhgyZIg1b97cvb7HHnvYyJEjbcuWLVZcuG8BIKgKu44oTfVEqNce3fcApJm0byk1ZsyYpOb/97//bakoZ0cFokTnABDXfR3NypQzK1PRLHuzWf1OZsc8YFaustmsd8x++8Ls8FHJLXPCKLNvnzCr2nD787ptzf7xZJFs/pNPPuluJsyYMcM6dOhgZctGDupwzDHHJLyssWPH2uWXX26PPvqou9C4//77rV+/fvbLL7/EbD07ceJEGzhwoI0aNcqOOuoo1zVbXcC/++47ty1y55132gMPPGDPPvusu6AYPny4W+ZPP/3kLlBmzZrlcgxq0AvdyVc5zj77bNu4caPdfffdVrS2R6MyA5RjDACKqo4odfXEzqhU0a0DAIqCh5Tw8ej/eQ+d+7E3ZuDtXhBs27bN+/nnn93/QRG0MlGeNC3PvR08b9EP2//Ozva850/wvMmPF2xln9zmee9c4xWHt956y6tevbqXkZGR65GZmZnUsvbbbz/vwgsvDD3Pzs72GjZs6I0aNSrm/CeddJLXv3//iGndunXzzj33XPd3Tk6OV79+fe+uu+4Kvb5mzRqvfPny3n//+9+423HnnXd6zZs394rao2e+7uqJd5981AuCUvOZTVNBK08QyxS08hR2HVHa6olnr7jO1RFPnv6QFwRBPL+DVibKk9q2pVF50r6lVLhNmzbZgw8+aBMmTIg5jKzucqR6ckJaSgFIWPYWs61/m1Wosf35tBfMZr1tNvBFs/lfmL1zlVnTHma/TzbL2WY24N9mjfYu0U2++OKLXTdq3VlWEtv8UjcIjdKkgSnCRytUN4pJkybFfI+m6455ON3dfuONN9zf8+fPdzkKtQyf8grq7rree8opp8Rc7tq1a61mzZpJlyH5UaS2t5TyMvLz3tSjMqieDkJZhPKkvqCVKT/lSfXRXAurjghCPZHseeqF8tNSR6SqoJWJ8qS27DSqIwIVlFL/7Q8++MBOOOEE22+//dIqYbgXCqB5Zl8/aukuw8ux3ZYvt4zVdcwCMppg0MpEeVK/PGXLtDIzPaK8eub27ntrFpo17GzW/rjYC1nxq9mxD5kdda/Zt0+ZfXKz2enjYs/70xvbu/5V3M2s99VmzQ+0orBy5Uq77LLLCnyxsWLFClfJRi9Hz9V1IhZdSMSaX9P91/1p8eaJNmfOHHczJD9dMsJzkCRkR522YcPG5N+bgvRDadWqVW4f6kIx3VGe1Be0MuWnPMqVlMoKq44IQj2R7Pf8tm3+hadHHZGiglYmypPactKojghUUGr8+PEuGWHPnj0tbYdxVVDqvWss3em0L/jPidQStDJRntQvT4Weyg11SO4XTxhj1qCTWfY2s/FDzT4aadbv1tzz1Wxh1nif7X/vvp/ZxAdjr2yfs8wOvNIsq6zZwq/NXjrN7JwJZjWaFHKpzI4//njXmlWJX9Pdn3/+aYcffrideOKJLl9Islq1ihFwzMNn9ov7v3qNakm/NxXpYlE/lJRzJdVbbySC8qS+oJUpaOUJWh1R0Hoi2e/5yaFzwKOOSFFBKxPlSW3ZaVSeQAWlGjVqZFWrVrV0FBGUanmoWYW8RwhMdUrcvmH9eqtStaplplGLtdJUJsqT+uXZWil3EtYIWWXM9jzW7MPhsYNSZSrs/DsjyywnTvPdqmHhvCb7bw94LZpWJEGp1q1bu64UX375pRvVKDqJ7SWXXJLQcmrXru0q2KVLl0ZM1/P69evHfI+m5zW//7+maVSl8Hn22muviPctWrTIDj74YOvRo4c9/vjjlh/J/0DYfl5nZGal/I+LROnOncpCeVJT0MoTxDIFrTyFVUcEoZ5I+piGZQAJyvkQtPM7iGWiPKktM03KE6ig1D333GPXXHONG2GjadOmlk529gP3to+cVTu973B42dm2aPbs7XdqUvxDUFrLRHlSvzybEml+P/8zs1otC7aytX+aVW+0/e+Vc82WTDer296KamSlKlWq2GeffeYe4dTlOtELjnLlylnXrl3t448/diMj+c2U9fyiiy6K+Z7u3bu71y+99NLQtA8//NBNF42ipAsOzeNfXKxbt84mT55s559/fsSdb11oaP0aAba4mnh7O4Ktqf7DAgBKuo4olfVEKChFfloA6SVQQal99tnHJTtv0aKFVapUKdfdFfWpTIucUgHIhwOgCPk5pZS8vMbuZkfdV7DlKdfUou/NMsvolorZkXeb1S5goCsOJYktLEpGq6HD9d2vPIIa6ltDbp955pnu9UGDBrkWtBraW4YOHWq9e/d2NzD69+9vL730kk2ZMiV0B1sXPLoQueWWW1xw0x/qu2HDhqELGl1oHHTQQe7Gh/KDLF++PLQ98e68F54dLQAz078lIAAUdR1R2uqJnaEoglIA0kugglIDBw50FcFtt93mEg6mVaLzUAXimWVyFxxAHJdNj/9al9O2P6T5AWbnf7nztXrt4r/3uPQcXOHkk092P/ZHjBjhEszqrvV7770XSkC7cOHCiLvT6kLx4osv2g033GDXXXedu6DQiEodOnQIzXP11Ve7C5ZzzjnH1qxZY7169XLLrFChQuiOufrn69G4ceM43bCLVqaChwCAXSpV9QQtpQCkqUD9sp04caIbjrVz586Wbjy/oZSf+wUAsEvqghGvG8ann36aa5qSzeoRj25m/N///Z97xHLGGWe4R8nYkVOKKgIAElZ66gmCUQDSU6D6iWkIw7///tvSPtE5LaUAAHFySmUQlQIARPN29BDJIDgFIL0EKih1++232xVXXOHueqxcudIlHgx/pE2ic3JKAQByIdE5AGAXiqkrOQAUlkB13zv88MPd/3369MnVCklNbbOz4wyHnko5pXR3g7vgAIBcdrSUKqbR/gAAadjrgpZSANJMoIJSEyZMsHQVGn1PFQrd9wAE1I8//hhzum4cKElskyZNrHz58sW+Xekkqwx1BIBgoo4oAGJRANJUmSBUXhoRQyNnaAjXXZk5c6a1adPGypQpk5JBKc+1lOIuOIBg0shHeY2MWrZsWTda0mOPPRYayQiROaUC1vMeAEKoIwrC329EpwCkl7T/ZdulSxeXPypR3bt3d8O/pnT/b4JSAAJq3Lhxbojtxx9/3L7//nv30N+6WaBhuJ966in75JNP3HDciH3BkZkZ/4INANIZdUQB+L33Sno7ACBJqdVcKJ/9p4cPH26VKlVKaP4tW7ZYao++l0P3PQCBdeutt9ro0aOtX79+oWkdO3a0xo0bu+/yb775xipXruwGrbj77rtLdFtTtaVUZtmyJb0pAFAkqCMKwovMUwsAaSLtg1IHHnig/fLLL0m1lKpYsaKl7uh7usVBUApAME2fPt2aNm2aa7qm6TW/+8bixYtLYOvSpaUUrWkBBBN1RP55Ht33AKSntA9KffrppxYEnpezsyKhpRSAgGrbtq3dfvvtrjtGuXLl3LStW7e6aXpN/vzzT6tXr14Jb2nqyqCLN4CAoo7IPz8kRfc9AOkm7YNSgRzGlQsOAAH18MMP2zHHHOO6YnTq1MlN093v7OxsGz9+vHs+b948u+CCC0p4S1PQjrohsyxVN4Bgoo7IP/9Sgu57ANINv2xTRXj9QVAKQBw9b//EypfJtPJls2zLtmxr37C63f6PjlapXBn78KelNmnuShtxdLuklnnfh7/af75eYPWqbR/JqHW9Kjb6lC5Fsv09evSw+fPn2wsvvGC//vqrm3biiSfaqaeealWrVnXPTz/99CJZdzrbtnVr6G+67wEIKuqIgqD7HoD0RFAq5brv5ahvRglvDYBU9uCpXVwwKifHsyHPfnvBypcAALvNSURBVGuvTv3DBnVvZoe2q+ce+XHsXg1t5NHtraitW7fOqlWrZuedd16u1+bMmWMtW7Ys8m1IRznZ2aG/s7JIdA4gmKgjCkCxKHcJQVAKQHrhdmuKCMWkACBBW7Jz7O+t2Va94vYgxStTfrezn5vi/laLqcPu+8xueGO6HX7/53bovZ/Zj3+sKeEtNuvfv79t2rQp13QNWHHQQQeVyDalg61bNof+zszkxgWAYKKOyD9qBgDpiqBUqgjPKQUAebj4xWl2xOgvbN9bP7LMjAzr37FBzPnmLt9o/9i7sb136YE2uEczu+v9+COVvjN9sQteDXz8a5s4d0WRbXuVKlXs+OOPt23btoWm/fzzz+5i4x//+EeRrTfdqVWcL7MMg2EACCbqiIJfShCdApBu6L6XaonOPc8uevE7C0J5NqzfYFWmbrSMgHRHDFqZKE/ql+fQJlnWqlX87nvbsnPsunHT7fZ3Z9kNR+XOI9W0ViXr0mQ39/feTXazJ76YF3Ndp+3fxC46pKWVzcq0Kb+tsnP/M9XevKinNd6tUqGX6/XXX7e+ffvaaaedZi+99JLNnDnT+vTp457fe++9hb6+oNi2dWfLgawyVN0Agok6oiC2//bJUCoQAEgj/LJNFaG7G56N/3GxBccGC56glYnypKoutfLOD1UmK9OO6NDAbnvnZ7shxuvlw1rUZGVm2Lbs2C0x61bdnuBc9mlW09o1rGbT/1hbJEGpihUr2ttvv+3uep900kn2+eef26BBg+yuu+4q9HUFNadURgYtpQAEE3VEAYRG3wOA9EJQKoZRo0a5OzWzZs1ylaNGArnjjjusTZs2Rd9Syjy78OA9rHaV8pbOcnJybPny5VanTp3AjBQVtDJRntQvT/Pyf+1yPnW1a1GncoHWtXjt39agekX39/wVG+3nxeusTf3toxwVVuLacDo+Y8eOtUMPPdR1xxg+fHhoHiW4RezzwVemLFU3gOCgjihcGYSlAKQZftnG8Nlnn9mFF15o++67r+vTft1119lhhx1mP/30k1WuXLCLv3hCvfc09G3X3a1Z7aJZT3HJzs622bO3WqtWzSwrKxh39YNWJsqTDuWZHTenVPmyWZadk2ONalS0W4/rWKB1KdfUjD/XWlZmpmVlmv3fsR2sRZ0qVlhq1KgRs0ulgvGPPvqoPfbYY+5vzaNyI7fsrVtCfwch6AoAPuoIACjdCErF8N5770U8f+aZZ6xu3bo2depUO/DAA60oo1K6u6FuNgAQy1fXHhL3tRP32d09pPsetezdoQeEXlPLp3jvvfekvawoTZgwoUiXX9paSmVmUXUDCA7qiELibb9+oJ0UgHTDL9sErF271v1fs2bNIlvHzpZSBKUABEvv3r1LehPS3ratW0N/lyHROYAAoY4oZIzkDSDN8Ms2gbvTl156qfXs2dM6dOiQ8PuSbV4cyimleJSXk/bNk7X92nfpXo4gl4nyBK88qdpt8ccff3Tfn4l2O9NoS8rhR/AldkupDFpKAQgQ6ojCHn2PoBSA9MK3+S4ot9SMGTPsyy+/TOp98XLBxJO9bduOvzxb+Nt8W1exTNpfQK1atcrmzJkTmPwnQSsT5Qleedq2bWupqEuXLrZkyRKXhD4R3bt3t++//95atGhR5NuWLnKyw7vvpWbwEQDygzqikBCLApCm0jvyUcQuuugiGz9+vBuOtnHjxkm9t1WrVknNPzXrEzN3zeFZy5Z72G6Vylk6U+sOXUy3bNkyZVtvlPYyUZ7UFqTyqCWoRk+qVKlSQvNv2bIzqTe2y9m2c5+UKVO2RLcFAAoTdUQhx6TovgcgzRCUilM5XnzxxTZu3Dj79NNPrXnz5kkvI+mLyFD94Vm5smXS/iJU1LpD5QhCWYJaJsqT2oJSHg0Q8csvvyQ8v+6CV6xYsUi3Kb1bSlF1AwgO6ojCkUGicwBpil+2cbrsvfjii/bmm29a1apVXZNiqV69epFVgjtzSnmWFWNYXABIVwruo2By/DrCzMqWS++WtAAQjjqicJFTCkC6Sf/EK0Xg3//+txtx76CDDrIGDRqEHmPHji26lYbd3WD0PQBAuG1bwkbfK0v3PQBANK4fAKQnWkrl1WqphNZJUAoAEC7HC8aokgCAorHzSoKWUgDSCy2lUrAiofseACCcl70jKOXtzC0FAMBOO64fuIwAkGYISqVaVCrDs0xaSgEAwnihROfcAQcA5JXonHoCQHohKJUiaHILAIgnJ2d7UCqjBLqXAwDSBx0uAKQbglKpgusMAEAc2dnbdvxFZQEAyAv1BID0QlAqxeoPL4OKBAAQySOXFAAgDx7JpACkKYJSqYJYFAAgjpwdOaXovgcAiGlHTiliUwDSDUGplLu7wQUHACCSt23H6HvUEQCAGHbGoqgnAKQXglIpgpEyAAC7SnQOAEAsdN8DkK4ISqUKmtwCAOLIyd7eUiqD3FIAAAAIEIJSKYcWUwCASJ753fcAAMgtw7+zzQ1uAGmGoFSqxaIYfQ8AEMXL8esG6ggAQG7kpwWQrghKpQj6gQMA4skm0TkAIC87qocMLikApBmCUqmGmgQAEM3zIv8HACBMxo5rCGoJAOmGoFTK8CsSqhIASNTDDz9szZo1swoVKli3bt3sm2++yXP+V155xdq2bevm79ixo73zzjsRr3ueZyNGjLAGDRpYxYoVrW/fvjZ79uyIeW699Vbr0aOHVapUyWrUqGHFISd7e4LzDOoIAEhKaaknQvcsSAUCIM0QlEoV1B8AkJSxY8fa5ZdfbiNHjrTvvvvOOnfubP369bNly5bFnH/ixIk2cOBAGzJkiE2bNs0GDBjgHjNmzAjNc+edd9oDDzxgjz76qE2ePNkqV67slrlp06bQPFu2bLETTzzRzj//fCsuXg6JzgEgWaWpniDDOYB0RVAq1XB3AwAScu+999rZZ59tZ555prVr185dIOiu9NNPPx1z/tGjR9vhhx9uV111le25555288032957720PPfRQ6O73/fffbzfccIMde+yx1qlTJ3vuueds0aJF9sYbb4SWc9NNN9lll13m7qAXF23bjj+KbZ0AkO5KUz3B6HsA0lWZkt4ARCU6pyIBgF3SXeipU6fasGHDQtMyMzNdN4pJkybFfI+m6455ON3d9i8k5s+fb0uWLHHL8FWvXt1199B7TznllEItQ3Z24q2fcsISnSfzvlSmcuTk5FCeFBW08gSxTPkpT1ZWlpUW6V5PJHueht+yCMI5HrTPaxDLRHlSW3Ya1REEpQAAaWfFihWukq1Xr17EdD2fNWtWzPfoQiLW/Jruv+5PizdPYYrOQZKXdWvXhnJKzZkzx11YpTv9UFq1ahXlSVFBK08Qy5Sf8ihXUmmR7vVEMnVERH5aLycf7009Qfu8BrFMlCe15aRRHUFQKlWEkhOW8HYAAIpFq1atEp53bpUqttz95VnLli0D0dpBF4v6oUR5UlPQyhPEMgWtPMh/HSETd1xE6OIz2femoiCe30ErE+VJbdlpVB6CUimDaBQAJKp27dqugl26dGnEdD2vX79+zPdoel7z+/9rmkZVCp9nr732KvQyJPUDISyXlN6X6j8uEqWLJ8qTuoJWniCWKWjlKUzpXk8ke0zDU4EE5XwI4vkdtDJRntSWmSblSf92aYGxo8ktsSkA2KVy5cpZ165d7eOPP45opqzn3bt3j/keTQ+fXz788MPQ/M2bN3cXHOHzrFu3zo2uFG+ZxYVE5wCQnNJWT+zsdUE9ASC90FIqxRCTAoDEKBnt4MGDbZ999rH99tvPjYi0ceNGN8qSDBo0yBo1amSjRo1yz4cOHWq9e/e2e+65x/r3728vvfSSTZkyxR5//HH3ekZGhl166aV2yy23uK4PuvgYPny4NWzY0A0J7lu4cKHro6//1TT6+++/d9PVPLpKlSpFUlYvlKSSiw0ASFRpqid2NpTiagJAeiEolSIYfQ8AknPyySfb8uXLbcSIES7BrLpOvPfee6EEtLoYCE/s2KNHD3vxxRfdUN7XXXedu6DQiEodOnQIzXP11Ve7C5ZzzjnH1qxZY7169XLLrFChQmgere/ZZ58NPe/SpYv7f8KECXbQQQcVSVm9HC+U6BwAkJjSVE+Yt6McXEsASDMEpVINFQkAJOyiiy5yj1g+/fTTXNNOPPFE94hHd8H/7//+zz3ieeaZZ9yjOHmhYBRBKQBIRmmpJ3ZeQ1BPAEgv5JRKGUSjAACxedlcZAAA8uAnpuWSAkCaISiVajKoSQAAUUIJzglOAQBi8a8huJYAkF4ISqUMvx84FxwAgEg5Xs72Pxh9DwCQF2JSANIMQakYPv/8czv66KPdSBrqN64Eh8WGllIAgDiJzmkpBQCIbfs1BFcSANINQakYNKJG586d7eGHHy7+lVOTAACihWJRBKUAAHlcRHB1ByDNMPpeDEcccYR7FC8/OSFRKQBAJC8n2/1PDQEAAIAgIShVRLKzt19AJC6jAO9NPSpDTk5OIMoS1DJRnuCVJysrq0i3CSVnZyopWkoBAGJh9D0A6YmgVBGZPXt2cm/wdiazTfq9KUgX06tWrbI5c+ZYZmYw2hEHrUyUJ3jladu2bZFvF0oIo+8BAPJErwsA6YmgVBFp1apVUvN/kfGN+z+rTJmk35uK1LpDF9MtW7YMTOuNoJWJ8qS2oJUHBUOicwBAQonOiUkBSDMEpYpI8heROyuSoFyAqnWHyhKU8gSxTJQntQWtPCgIL7ofHwAAYei+ByA9pX8fl8BVJBwSAEAkL5uWUgCARBCVApBeaCkVw4YNG1y3Gd/8+fPt+++/t5o1a1qTJk2KduXUIwCAKB7BKABAnui+ByA9EZSKYcqUKXbwwQeHnl9++eXu/8GDB9szzzxTxBUJNQkAIAqj7wEA8uJfQ3AtASDNEJSK4aCDDjKv2PN2+EEpuu8BACKR6BwAkDdaSgFIT0RAUsaOGiSTmgQAAABAPnApASDNEJRKGYyYAQCIzcvJ8f8q4S0BAKQmuu8BSE8EpVJE6DKD7nsAgDiVRAZBKQBATHTfA5CeiICkWkVC9z0AQJSdaQ4JSgEAcvMYNAlAmiIolSp2VCAEpQAAuXg7uu8V+yAcAID0QPc9AOmJoFSKYfQ9AEAufiyKaw0AQB4yMrh5ASC9EAFJGX5LKQ4JACDSzksMLjYAAHmN5M21BID0wrdWyiAoBQCII2dHMIruewCAmPymtDSpBZBeiICkDIJSAIDYaCkFAEgEmUAApBu+tlJtxAyCUgCAKJ7fUgoAgJj80fdKejsAIDlEQFJtwAyCUgCAKDvz1hKcAgDE4EejiEoBSDNEQFLG9gokMyurpDcEAJBi6L4HAEjoWiKToBSA9EJQKuWa3FKRAACi0FIKAJCnjIiUIACQLghKpVpQipZSAIBojLoHAMgTN7gBpCeCUikmk5xSAIC4MSmCUwCA3PzaISOLawkA6YVvrRTh7birkZlVpqQ3BQCQsghKAQBioaUUgPREUCpl+EEpDgkAIAotpQAAiQSl6HUBIM3wrZVyFQk5pQAAUYhFAQDy4reQoqUUgDRDUCrlhnElKAUAiBeTIjoFAMir+15JbwcAJIegVIrwLzMyy5BTCgAQL9M5QSkAQHwZmUSlAKQXglKpImP7ociipRQAIJrnX2QQlAIA5NVSiss7AOmFb60UQ0spAEBcGQSlAAC5eSQ6B5Cm+NZKGdsrkqwsWkoBACJ5fvc9YlIAgFh2JJPKIKkUgDRDUCpFeDsqkMyytJQCAESj+x4AIIF6gpxSANIMQakUk5VJUAoAECUUiyIoBQCIL5OgFIA0Q1AqZexIdE5OKQBAND8WxbUGACCmHRUEic4BpBm+teJ4+OGHrVmzZlahQgXr1q2bffPNN8UysFJWmbJFuh4AKM3f1a+88oq1bdvWzd+xY0d75513cuVuGjFihDVo0MAqVqxoffv2tdmzZ0fMs2rVKjvttNOsWrVqVqNGDRsyZIht2LDBigctpQAgGaWlnvATndNSCkC6ISgVw9ixY+3yyy+3kSNH2nfffWedO3e2fv362bJly4pwrdsrkDJlSXQOAEXxXT1x4kQbOHCguziYNm2aDRgwwD1mzJgRmufOO++0Bx54wB599FGbPHmyVa5c2S1z06ZNoXl0oTFz5kz78MMPbfz48fb555/bOeecU0yhKIJSAJCo0lRP+NcSGZlcSwBILwSlYrj33nvt7LPPtjPPPNPatWvnKp1KlSrZ008/XXQr3dHUNjOLllIAUBTf1aNHj7bDDz/crrrqKttzzz3t5ptvtr333tseeuih0N3v+++/32644QY79thjrVOnTvbcc8/ZokWL7I033nDz/Pzzz/bee+/Zk08+6e649+rVyx588EF76aWX3HxFJYNYFAAkrTTVE6HR92gpBSDNkMAoypYtW2zq1Kk2bNiw0LTMzEzXNHfSpEkJLyc7OzvheXNyckJ/Z2VlJfXeVKUyqFxBKEtQy0R5glcefX+UFvn5rtZ03TEPp7vb/oXE/PnzbcmSJW4ZvurVq7uLCr33lFNOcf+rK8Y+++wTmkfza926Y37cccclXIZkjq0Xlui8NJ/jqYzypL6glYl6Itj1RH7PUwWlgnCOB+3zGsQyUZ7Ulp1GdQRBqSgrVqxwB65evXoR0/V81qxZCS8num95XrZu3Rz6e9mKVUm9N1XpA6D+9HPmzHGVcBAErUyUJ3jlUQ6M0iI/39W6kIg1v6b7r/vT8pqnbt26Ea+XKVPGatasGZonUcl813s53o62zV6pPsdTGeVJfUErE/VEsOuJZK8H/JxS69Zt4FoiRQWtTJQnteWkUR1BUKqItGrVKuF5t2zZbNOqvGxbt26zzvteZLXqN7R0px8B+gC0bNkyMHflglYmypPaglYeFKye+P2AVrbo+4+szO4VAnNOBO0cpzypL2hlClp5kP86Qr6t84Jt+Xuz7dV7oDVP8r2pKIjnd9DKRHlSW3YalYegVJTatWu7g7Z06dKI6Xpev379hJeTzIGvWLGSnXbH/7m7GgpIpfpJkyhFZFWWoJQniGWiPKktaOUp6e9qTc9rfv9/TdOoSuHz7LXXXqF5ohPkbtu2zd2JSqaOkGSO66Gnn2XZp2a7eiJI50TQznHKk/qCVqaglacwpXs9kewxPfXWG10doYBUUM6HIJ7fQSsT5UltmWlSnvRvl1bIypUrZ127drWPP/44oumbnnfv3r1Etw0AkP/vak0Pn180MpI/f/Pmzd0FQ/g869atczlA/Hn0/5o1a1yeEt8nn3zi1q2cIgCA1EA9AQDpgZZSMSjB4eDBg12Cwv3228+NsrFx40Y3cgcAID2+qwcNGmSNGjWyUaNGuedDhw613r172z333GP9+/d3IyFNmTLFHn/8cfd6RkaGXXrppXbLLbe4bhO6+Bg+fLg1bNjQDQkuGo1JIzNpNCeN4rR161a76KKLXHJ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",
      "text/plain": [
       "<Figure size 1200x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(12, 10), nrows=3, ncols=3)\n",
    "\n",
    "vars = [\n",
    "    'mass_mixing_ratio', 'number_mixing_ratio', 'number_density', \n",
    "    'wet_particle_radius', 'wet_particle_density', 'dry_particle_density',\n",
    "    'delta_particle_temperature', 'fall_velocity', 'total_mass_mixing_ratio'\n",
    "]\n",
    "\n",
    "for var, ax in zip(vars, axs.ravel()):\n",
    "    for bin in ds.bin.values:\n",
    "        y = ds[var].sel(bin=bin).isel(element=0)\n",
    "        if 'vertical_level' in y.coords:\n",
    "            y = y.isel(vertical_level=0)\n",
    "        else:\n",
    "            y = y.isel(vertical_center=0)\n",
    "        line, = ax.plot(ds.time, y, label=f'Bin {i+1}')\n",
    "        buffer = (ds.time.values[-1] - ds.time.values[-2]) * 4.50\n",
    "        ax.text(ds.time.values[-1] + buffer, y.values[-1], f'Bin {i+1}',\n",
    "                va='center', ha='left', fontsize=8, color=line.get_color())\n",
    "    \n",
    "    ax.spines[:].set_visible(False)\n",
    "    ax.grid(alpha=0.5)\n",
    "    ax.tick_params(width=0)\n",
    "    ax.set_xlabel('time [days]')\n",
    "    ax.set_ylabel(f'[{ds[var].units}]')\n",
    "    ax.set_title(f'{ds[var].long_name}')\n",
    "    \n",
    "fig.suptitle(\"Bin Properties\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f98eb86-f1ac-4107-9c54-61e77db9f696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9QmpnyOdgbW2NtGnTJluZKH6WlpawtbVNVNdv2YayLVOCj/n9s7KyUp8JmQce25MHj+0pn06nS9KACduLd7G90Ca2K8mD7UrKl9TtSlKzTmX7D4NUZkgamIwZM6JWrVpo1KhRvA3OgwcP0KNHD+TMmVPt1O7u7hg2bBjevn0btYzkY5AfaSNHjsTYsWORP39+tezZs2fV89u3b0f58uXh4OCADBkyoF69ejh37lyM93n8+LF6H7lKI+tmyZIFn376KY4dOxZjuWXLlqFEiRKws7NDpkyZ8L///Q/Xr19P1GcgY4O9vLxw9OhR+Pv7q9fMmzcvpk6dGmM5aTD69eun3tfJyUnVQ+qzY8eOGMvF9zlIF+lSpUqpZdq2bauWkZvks4hvfLB8vuPGjUPRokXVDz75UfDZZ5/hyJEjCdbJkO0VHylD7dq1VTS9ZMmS6vOQ99dH1//666+o8shncfz48Rjrnzx5UtUlX758apls2bKhXbt2uHfvntHbOjg4GJ9//rl6DXktNzc3NGvWDA8fPjRom0pZKlasCHt7e/UZLF++XD2/a9cu1VVd6lawYEFs3bo1wRwjsu9aWFio7R/dwoUL1XJTpkyJd7y//rX27t2Lnj17qm0o+06DBg1w9+7dGK8n20e62+bIkUOVuXLlyur7Y0gOgYS+f4bsu7K+lE3I1RT9/inliS9fg/yQ+u2336LeS8r5008/4eXLlwmWlZIej+08tsd3bA8MDETp0qXV+8pxeu7cuQblZokr99KHthd6ly9fRvXq1dVnL8e/gQMHqpOA2J+ZfO5FihRRr5c1a1Z06NAB9+/fj7OemzdvjirTtGnT4v1c2F6wvSDDsF1huxIb25X4TZ8+XW1PWVY+mz179sS5nBwD+/fvrz53+fxlO/Tu3fudY6N8Tl26dMGqVavUPijLSrk3bdpk9PeiTbT9J6Hj+ezZs9XfcX3GgwcPVhdpEvtdSlY6Mjuenp66L7/8Uv29e/du+ebqDh06FGOZp0+f6ooVK6ZzcXHR/fTTT7qpU6fqWrVqpUuTJo2ue/fuUcuFhoaq9QsXLqzLly+fbujQoboxY8borl69qtuyZYvOyspKV6BAAd3w4cN1v/76qy5Tpky6jBkzqvX0WrRoobOxsdH17NlTN3PmTN2wYcN0derU0c2fPz9qmdmzZ6v3KVWqlHr9H3/8UWdnZ6fLkyeP7v79+1HL9e/fXy33PhUrVtTlyJFDlyVLFl2XLl1048eP15UrV06tO2vWrKjl7t69q8uePbsq25QpU1Q9ChYsqLO2ttYdP378vZ/DlStXdAMHDlTPff3117p58+ap26VLl9R6rVu31uXOnTtG2dq0aaOWr1Gjhm7s2LG6kSNH6urVq6ebMGFC1DKyjqxr7PaKj7ye1EvqOmDAAFV2V1dXXbp06dR2yJUrl6qT3JycnHTu7u66yMjIqPWljOXLl1d1nT59unpP2T6lS5fWvX371uBt/fLlS13evHnVthk0aJBaRvYb2e7yWRqyTXPmzKn7/vvv1ecl28PS0lK3ePFiXbZs2VTd5DOVukk9Hj169M4+Fn3f7Ny5s9qHjx49qu7fuHFD5+zsrKtatWqMesXeHvrX8vX11X3yySeqLL169VJladKkSYxy9+7dWy0rn8PEiRN17du317m5uanvSvTXjEtC3z9D9t0nT56o5+Q1GjRoELV/njhxIt7vk5RJHmvUqJFu0qRJaj+T+/Xr10+wrJT0eGznsT2+Y3vWrFnV+nKMKV68uFr/9OnT7/184zoufmh7IfWztbXVeXh46L744gtVptq1a6v36du3b4z3/+qrr9S+JsdFqfsPP/ygc3BwUPvLq1evYpRJ3kf2QdmHZNkdO3bE+7mwvWB7QYZhu8J2JTa2K3GT/VHez9/fX+0jPXr00GXIkEFtY9mH9KTc1apV09nb26tlpk2bpvYrKZNsu+jk9by9vdXn8ttvv6ltLK8n64aHhxv1vWgdbf9J6HgubZ18X6Qdik32WWmnzAGDVGbmyJEjaoeUxkDIDyf5gRP7oCRfBPnCXrx4Mcbj8iWVH05hYWExDrTp06fX3blzJ8ayPj4+6oB+7969qMdk57ewsFAHQz052MiPu/jIAUNex8vLS/f8+fOox9etW6feu1+/folqcGS5UaNGRT0mARJ9mfUHqTdv3qjHo5MGTg7M7dq1i3osoc/h8OHD6jk5KMcWu8HZvn27WrZbt27vLJvQj1xDt1d85PXkffft2xf12ObNm9VjcqCSHxB6cjCVx6MfqJ89e/bOay5atEgtJz9qDN3W0ojLOsuWLdMZS79NFy5cGPXY+fPn1WOyzx04cOCdukXfJnE1mtKQSwNVpEgR3YsXL3S1atVS2zj655HQSUfsk5Nvv/1WbY8HDx6o+7du3VKNUuwf7NJAy/qGnnTEtd8Zuu/Kjyp5DfnuxBb7+xQUFKTuSwMf3Xfffacel/2XTIPH9n/x2B73sT36cVjqkTZt2hg/QI09mfiQ9kIfuOjatWuMz0COr/IjW45JYs+ePWq5BQsWxCjTpk2b3nlcXyZ5zhBsL9he0PuxXfkX25WY2K7Ev9/JPhF9H5AL9/Ia0YNUEgyS/VrKEp0EwWTZvXv3Rj0m96X8ISEhMb4X8nj0QOT7vhdx7T8JHc+bN2+uArPRA4HHjh2Ld99MiTjcz8xIN13p1ihdxIV052vatCkWL16MyMjIGN1kpYuqdPENDw+PulWtWlUtt3v37hivK8Oz9N0Gxc2bN1XSP+la6OzsHPV4sWLFVPfDDRs2RD0mXXoPHjyokgXGRbqs3rlzB506dYqR70C6Hnt6emL9+vWJzp8g3Tv1bGxs1H15L+nSK6RLozyu7x4aERGhuq9Lt8/YXYvj+hyMtWLFCrVNpAtobAnlvjB2e8WlcOHC8PPzi7qvn8Xlk08+Qa5cud55XLrU6km3Vr0XL16o9y5btqy6H/1zet+2lu7RQrrWSkI/Y8n0szI0UE+Gach7FipUKMasNHHVIS4ynEK6JUt38woVKqh9bcyYMTE+j4R8/fXXMbabbCPZHlevXlX3t23bpvYn2bej69q1K4wR135n7L5rCP33VoakRNerVy/1f2K/i/TheGz/fzy2v3tsl9fQk3rIsfF9x7+kai/0ZAhD7CENMlxGP7RO6i5tguxX0esuQz3kWB97CI0Mv5FhHoZie/H/2F5QXNiu/D+2KzGxXYl7v+vYsWPUPiBkn9af2+hJGaSdkf0xehmkniJ2GWS7yBDC6N+L9OnTx6j/+74XxmrVqpV6rehlkeOBnO/JfmsOGKQyI3LgkYZFGhtJhCgzdMhNvuy3b99WP4Ci5wWS8a5y0Il+ky+KkC9i7C9xdPofVXLAik2+mPJlfPr0qbo/fPhwnD59Wo3HlfG7Mh42+hcvodeSL7j+eWPJWGUZsxxdgQIF1P/Rx0n/+eef6oAgjZ2Li4v6HKSRiytHUuzPwViXLl1S5YreSBvC2O0Vl9g/pPUHVdkucT0efey2NMTdu3dXP2bkACbvrf8son9O79vWso78oJ05c6bKISANw6RJk96bj0pP8lfFbpilvIbUIT4BAQH45ptv1Kw2Uh7JtWWo2J+p/CCI/r76fVfGpEcn21+/rCHi2++M2XcNIeWVvCuxyyv5w6SBTOx3kT4Mj+0x8dgeU1xBEjm+GHL8S4r2QshxRHKYJLSNpO6yLSS3Ruz6P3ny5L376vuwvYiJ7QVFx3YlJrYrMbFdiUm/X3l4eLyTrDx2maQMZ86ceef99WWNXQZDPuv3fS+MJUG87NmzR+Wgk6DrokWLVJ44R0dHmAMrUxeADCcJCeVqhTQ6cotNdsRq1apF7Yyyg0oSt7jov0hx9aQxVpMmTVQ0fuXKlfj7778xYsQIlbxPkuTVqFEDpjR//nwVBa9fvz6+//57dVCTKyVDhgxRjUNsH/I5fAhjt1dcpF7GPB49EaFsQ5n9RT4jHx8fdTVCyiTJG6MnYTRkW48aNUp95qtXr1bLdOvWTX3eBw4cUCcVSVWH+EgSQ33iRtnm0sNLrpgb4kPe1xhx7XfG7rvGSMyMVpR0eGw3Ho/tMY9D8X2no/eWMOQ1P+YxT+ou2yW+RM2xeyAYu43YXvw/thcUG9sV47FdYbtiaBkkCbzMmBmX2EE5Q8/DPub3wtLSEi1atMCMGTNUQn+Z2EN6VskEBOaCQSozIl9I+WJKz5TYZCeWHVtmqpAvpHQrlIiyPqpurNy5c6v/L1y48M5z58+fV71kol+RkGitdM2Vm0SQixcvjt9//119saK/lr4rpJ48pn/eWPJlkysz0ctx8eJF9b9+9gOZ6Uci4PL5RD/YxtW19mP8QJPPXYa6Sc8kY66MfOj2+hASyZcrajI7RPSZjeRKQVwS2tZ6cvCW2y+//KKCX3J1WvbNQYMGIbnJtpbhGzILyw8//IAff/wR48eP/yivrd935epk9Ks1Mivih1yNMmbfNWb/lPJK4yrbVq5u6slVVZkpJrHfRfowPLbHxGO78fQ9ceR7LL1c9JKqt4scR+Qqb/STodjbSOouQzTk+G+qkzljsb2IWV62F+aL7UpMbFeMl5raFf1+Jce76Pvd69evVU9Eb2/vqMekDCdOnECVKlU+ahDfkPOr6N733jLkTzoOrF27Fhs3blQBPGOG1Jsah/uZiefPn6uDpkylKVPIxr7JmF2ZvnLNmjVREdn9+/erg19scrCRMdbv+6JIjxrp9irL60lXRInw1qxZMyqaHrsLrDSK0n1VPw2njOWWx6QxjD41p3xh5MegjDNPDKlD9KlEZcyy3JcvoYxNjh69jh6tljG/8tkYSt+gRf8c4iPjfOW9JOBjzBWDD91eHyKuz0jI9K7RGbKtHz169E5ZJVglXXhNMWW1bGs52ZBpXSWPhlwZmzhxopqe/GOQBkryHESfnlzIe3woQ/dd/VV+Q/ZP/fc29rbVXw1K7HeREo/H9nfx2G48fb6L6LlI5IRMtnNSiX6ck89A7svQCDku6usu+9Fvv/32zrpSb0M+9+TE9iImthfmi+3Ku9iuGC81tSuy38m+IPud7Bt6kqcw9mtKGa5fv656KcX13dMPbTWUId+LuLzveC7DVuUmKVgk/5nkcJQ2yFyYT0lTOWlIpEGpW7dunM9Lkmv5csmVE0mKKD+uZB1poKTrqhyA5Utz6tQpdaVAxvbKlY2ESFdDid5KErwvv/xSffEmTJigxhLLWFkhZZIhXNLoSZRZholJhPvw4cMqeivk4CJdFtu2bYuKFSuiefPm6krcuHHjVGT822+/TdRnIl9eeV2pi0TdlyxZohI3Tp8+Xb2nkPpLQ92gQQPVsEk0XA5AktxPrkIYepCWKwiynozjlQZIxvTHNc5Zxv5/8cUX6sqrROP1w+X27NmjnoueEDC6j7G9EkuS90mSWBkPLVcMXF1d1Y8K+ayiM2RbS/dyqWPjxo3VNpEGY968earhT+5EfZIAvnXr1mp8uVyJEPJDQK4oyL4on23s/ATGkhxekstL6i/fTdnecnVFfkzJ9vqQKyyG7rtyJUkek/1fPnO5Gufl5aVuscl2k89EviPSqMn3UXKvyA8O6d6uT65KyYfH9nfx2G48GbYjeS9ke8p7yjH3jz/+UPtOWFgYPjbJ1yI5UeR4Ip+ZHPMkb8tPP/0UNdxC9glJTCxDZWT7SRll+8nnJ4lnZT+R/SslYHvB9kJL2K68i+2K8VJTuyKvIaM95LWlJ5V8L2T7z549+52cVLLNli5dqpKsS2Jy6dUlgSbpNSiPS/BQgl6GMuR7ERdDjufSm+q7775Tf5vTUD/F1NMLkmHq1Kmjs7W1VVMkx6dNmzY6a2trXXh4uLr/+PFjXZ8+fdSUyjL9ZaZMmXT+/v66kSNHRk23qp9GdcSIEXG+5tatW3UBAQFq+lCZalXKcfbs2ajnZZrO77//Xuft7a1zdHRUU6LK35MnT37ntZYsWaLz9fVVU5w6OzvrWrZsqbt27VqMZYyZTlamiZbpdf38/NRnI9NyTpw4McZyMn3p4MGD1XPyvvL+Mo1t7Gk83/c5rF69Wle4cGE1fXT06Ttjv45+Clt5HU9PT/W5Z86cWVejRg3d0aNH451O1tDtFR95PZmmNTYpa+wpTeOqq2yHBg0a6DJkyKCmQW3cuLHuxo0bMaY2NWRbX758WU3Tmz9/frVNZDtXrlxZ7UeGbtPE1i32lLj66b8PHjwYYz3ZZ2Q7fvPNN++dUlymEo5OpsuNPW2ubO++ffvqsmXLpr4nn3zyie7cuXM6FxcXXceOHROsc0L7naH7rpApf0uUKKH2m+jbLK7v0+vXr3W//vqrLm/evOp4kTNnTrXfyZTrlPx4bI+Jx3bDjn/yOUWfEltIOcqUKaPeI1euXLrRo0fHO1X4h7QXUj/ZHy5duqSrVq2azt7eXk3RLts4+nTX0afwluOT7GuyLxUtWlTXu3dv1ca8r0zxYXsRE9sLio7tSkxsV2JiuxI/2RfleCfbv2TJkrrdu3fH+bnIZzxs2DC1X8myGTNmVOWR4+XDhw8TrH/sbWro96K1EcdzvZs3b6q2rUCBAjpzk0b+MXWgjMhYlSpVUrOFSFdiopRGrjrLWH65KvPzzz+bujhEZoPHdkpt2F4QJS22K5RahYeHq+G4knO4b9++MCfMSUVE9AGkS3ts+hwe8sOIiIhIsL0gIqLkMmfOHDUUUYYomhvmpCIi+gAyFlwaAUkMKuPIAwMDsWjRIjVOXsapExERCbYXRESU1LZv346zZ8+qHIuSQ1A/O6I5YZCKiOgDyMwZMluGJJ6X2Q31yXFl6AYREZEe2wsiIkpqAwcOxL59+9TFD5nAwBwxJxUREREREREREZkcc1IREREREREREZHJMUhFREREREREREQmxyAVERERERERERGZHINURERERERERERkcgxSpRC7d+9GnTp1kCNHDqRJkwarVq0y+fs9efIEXbp0gZubG+zs7FC4cGFMnTr1g95X8vSPHDkSBQoUQNq0aeHq6qqmx0zIxYsXUa9ePWTKlAnp06dHuXLlsGPHjqjnT5w4gebNmyNnzpyqnIUKFcK4ceNivIZM8ywzHLi4uKhlPD09MWbMmBjLTJkyRc28I+8hNz8/P2zcuDHGMh06dED+/PnVa2TOnFmV6/z58++UWaaYlteytbVFlixZ0LlzZ6M+B0PKGxkZib59+yJv3rxqGSnXb7/9pl47qcgUprK/xL7Frh8RmaYdWLp0KXx8fGBvb4/cuXNjxIgRSVJWIiJKme3A5s2bUbZsWTg6Oqrfqp9//jmuXLmSJOUlIkoKVknyqmS0p0+fwtvbG+3atUPDhg1TxPv17NkT27dvx/z581Vw4u+//0anTp1Uw1m3bt0415EGNTQ0VC0fF5lqWV5HAjRFixZFRESEuiWkdu3a8PDwUGWRYMzYsWPVY5cuXUK2bNlw9OhRFQiSckqgSqbc/Prrr2FpaamCbMLBwUH9LYEj+VuCQBJwkr9lWSHBuKFDh6r3kkDPn3/+qYJQx48fR5EiRdQyJUqUQMuWLZErVy5V7gEDBqBatWqqzvJ+YvTo0Rg1apQ6OSxTpoz6rGP/OHjf52BIeYcNG6YCa1JOKd+RI0fQtm1bODk5oVu3bkgKhw8fVsExvdOnT+PTTz9F48aNk+T9iFKTD20HJKguxyeZbliOS+fOnUP79u3VcVN/LCQiIu22A/J7VH67ym/4BQsW4OHDh/j222/Vax07dixJykxE9NHpKMWRzbJy5coYj7148ULXq1cvXY4cOXT29va60qVL63bs2JFk7yeKFCmiGzhwYIzHihcvrvv5558TfK3Q0NA4nzt79qzOyspKd/78eYPLdvfuXfWau3fvjnrs0aNH6rEtW7bEu16nTp10lStXTvC1GzRooPvf//6X4DIZM2bUzZw5M97nT5w4ocoSEhKi7kdEROjs7Ox0W7dujXedxHwOcZW3Vq1aunbt2sVYpmHDhrqWLVsmy34junfvrsufP7/u7du3H+01iShx7UDz5s11jRo1irHO+PHjdW5ubvyOEhGlgnZg2bJl6jdmZGRk1GNr1qzRpUmTRvfq1atkLT8RUWJxuJ+ZkKvg+/fvx+LFi3Hy5EnVc+Wzzz5DcHBwkr2nv78/1qxZg+vXr6ueRTLETobeyRX6xFi7di3y5cuHdevWqSFq0tvqq6++SrAnlQx3K1iwIObOnauuLr158wbTpk1TPaekV1N85MqRs7NzvM9L7yjpcVWxYsU4n5feQvJZy3vKsL+4yHOzZ89WdZEeXGLLli14+/at+sxk2KH0zmrSpAn++eefD/oc4iqvbJ9t27apbaIf9ig9rmrUqJEs+82rV69U7zW52ic96Igoab3v+/zy5Us1xDg66UV17do1XL161USlJiKi5GoH5LexhYWF+n0qv2Xl9/C8efNQtWpVWFtbm7r4RESGSXR4i5LtysnVq1d1lpaWuuvXr8dYrkqVKro+ffp89PeLfrWmVatW6nm5KmNjY6P7888/3/ta8fWk6tChgy5t2rS6MmXKqJ5RcuXHx8fnvT2e/vnnH12JEiXUVSD5HLJnz647duxYvMvv3btXlXfz5s3vPOfq6qrqYWFh8U4vMXHy5Emdg4ODeh8nJyfd+vXr31lm0qRJahmpa8GCBaN6UYkhQ4borK2t1eObNm3S7d+/X20nuf/y5UujP4eEyitXyX744Qf1uUh95f/Bgwcn236zZMmSOF+fiEzTDkybNk1dWZeenHJ8uHDhgs7T01O91r59+5K9DkRElPznAzt37tRlyZJFLSuv4efnp7t//36ylp2I6EMwJ5UZOHXqlLoaIkm2o5Or5tLTSEjybum5k5AffvhB5VwylOQ1OXDggOpNJQl4JZmjJMiWnFRyRUZIr509e/bEWE/yI+l71sh6Z86cUX9LDyMps/SK0tdl1qxZ6qrPhQsXVI+p2KSNlveUnlPyPtIrYObMmSqppORHyp49e4zlJUeSjMXv379/nD2+5DUkIbzU68cff4S7u7tKuq4nZQgKClJXnpYvX47WrVtj165dKmm8nuR8kTxMN2/eVDmlpKfU3r17VQ8GqePr168xfvz4qPdftGiRyp0lPdGqV69u1OeQUHklQbLkG1i4cKH6zKXcPXr0UNtHyp3U+42UWba/vB8RJS1Dvs+Sf0py9UnOPjkOyQQQkv9OcufJlXUiItJ2O3Dr1i3VFsjvQPm9+PjxY/Tr1w+NGjVSvf3Z852IzAGDVGZAghSSlFsShOuTc+ulS5dO/S/DxyRJbkL0DZghnj9/jp9++gkrV65ErVq11GOSxFsCIRKY0QepJGAky+pJ0vENGzao2epE9K7FElCysrKK0bjqAyRhYWFxBqkkWboMi7t//7464RKTJ09WDa0kDJfAjd7Zs2dRpUoVlVj8l19+ibNeMrxOSLLy27dvq5O36EEqGxsbFQgSEjSSQJjMFChDDPUkMbncpK4ye0rGjBnV5ySvow+aRQ9qycwqMjOh1NHYzyGh8n7//feq/s2aNYtaRob0DBkyRP04Scr9Rt5n69at+OuvvxJcl4g+DkO+z3LyIRMqDB48WJ2oyLFHhgTrv+tERKTtdmDSpEnqN+rw4cOjntNPLHTw4EH1u5WIKKVjkMoM+Pr6qisnd+7cQfny5eNcRoIrnp6eH+095Sq83GJffZdGUXoC6emDUdFJ76m4ZvcLCAhQOaXkSn/+/PnVY/p8SrJOXJ49e6b+j10OuR+9HNJb65NPPlHBmd9//92gOup7NH3IMtLTS276ZaSOQnpEST4qIbmmwsPDo+qYmM8hrrLIZ5PQ9knK/UZyHUjvNn0Ak4iSliHf5+jHAf2xWXpySl49CVgREZG224H4fhuK6L+biYhSMgapUtDVkZCQkBhTyEqvJUn+LT1uZIhZq1atMGrUKNVI3b17V10hl95NiQkUJPR+uXLlUr2WJEm39NaRIXYSPJFhbzJEbfTo0Ymqo/S+Kl68uEq0PXbsWNVYylA+GTqn71V06NAhVU+pm5xkycmV9FSS4JN0V5ayzJgxQ5VXX28Z4icBKhlKJ1PuSg8CfaOsPzGTK0tSL31ARoYuSo+wbt26RZWvT58+aviaLCfdo2UY3c6dO7F582b1/OXLl7FkyRI1jE9eV5IRyzA4KVPNmjXVMlIPGW4oQ2ymT5+uPkd5XXnfypUrG/w5GFJeGfIoATlZTob7SXJ12TbyuvqyfOz9Rkh5JUgl20R6hBFRymgHJBguw5QrVaqEFy9eqO/psmXL1LGbiIi03w7IbcyYMRg4cGDUcD8ZGSG/42V5IiKz8EEZreijkeTZsjli31q3bq2el2lj+/Xrp8uTJ49KzC3Jwxs0aKASfSfF+4mbN2/q2rRpo6a5tbW1Vcm/R40aleBU5gklTheS7LFhw4a6dOnS6bJmzape/969e++UK/prHD58WFetWjWds7OzztHRUVe2bFndhg0bop7v379/nHXJnTt3jGnYixQpopIKp0+fXufr66ubPHlyjCl627Vrp9aRROWZM2dWiSj//vvvGGWvUaOGSkYp20CmdW/RooXu/PnzMer48OFD9VoZMmRQZZbtFBYWZtTnYEh5Hz16pOvevbsuV65cavvky5dP9/PPP0claE+K/UZIQnr5fCUpMxGlnHbg7t276vgoEzvIsUOOYQcOHDBxrYiIKDnPBxYtWqR+N0pbIL9n69atqzt37pwJa0VEZJw08o+pA2VERERERERERJS6cbofIiIiIiIiIiIyOQap/iMzopUqVQqOjo4qIXT9+vVV8uv3kXwfkjfI1tZWza4mM9sREREREREREZFxGKT6jySWleTVBw4cwJYtW9TMdpIg++nTp/Gus2/fPpWU8Msvv1RJqyWwJTdJ5E1ERCmHTD4gkw3kyJEDadKkwapVq967jsym+fPPP6uEs2nTplWzlv7xxx8xZhX9/PPP1ePymjIRAhERERERJR6n5vrPpk2bYtyfM2eO6lF19OhRVKhQIc51xo0bh88++0zNgCd+++03FeCaOHEipk6dmizlJiKi95MLDt7e3mr2y4YNGxq0TpMmTXD79m3MmjUL7u7uuHnzZowpvGWq73z58qFx48b49ttvk7D0RERERESpA4NU8Xj48KH6X6Z8jc/+/fvRs2fPGI9Vr17doCv0epGRkUaXTdaR6WnlpMnS0hJawrqZLy3Xj3V7l7l9DjVq1FA3Yy5cSA/by5cvR7UD0mMqOhkiLjfx448/Irm3W3BwMDw8PMxuW6T2+rFu5ol1o5RIy9uOdTNPrJv5ikxB9WOQKg5ypbxHjx4ICAiAl5dXvMvdunULWbNmjfGY3JfHDSU7QmLKFxERoU4sLSy0NWKTdTNfWq4f6/YuycWnZWvWrEHJkiUxfPhwzJs3Dw4ODqhbt67qMWtnZ/fR38/YCxayvGy7xFzoMAdarh/rZp5Yt3eZ+iQmMfln//rrL5w/f14dx/39/TFs2DAULFgw3nVmzJiBuXPnRqXyKFGiBAYPHozSpUtHLfPkyRN1oUIuUt+7dw958+ZFt27d0LFjx2SpFxGR1jBIFQfJTSWNUWBgYJK/l0QqjcVeHeZJy3XTev1Yt9RHelBJGyCTYqxcuRLh4eHo1KmTOgGZPXv2R38/Yy9YaDlwqvX6sW7miXUz/4sV+vyz0gP2zZs3+Omnn1T+2bNnz6oLEXHZuXOnyj8rAS1pDySoJetITkJXV1e1jIyq2L59O+bPn6963P7999+qvZAciHJxg4iIjMMgVSxdunTBunXrVJJdNze3BJfNli2bylcSndyXxw2V2JNC+REh62rxpJJ1M19arh/rlrrISZskQ1+wYAGcnJzUY6NHj0ajRo0wefLkj96bytgLFloPLmq5fqybeWLdUmf+WWkDops5cyZWrFiBbdu2oVWrVlETKbVu3RqVKlVS97/++mtMmzYNhw4dYpCKiCgRGKT6j06nQ9euXdUVc7lqIl1138fPz081UjI0UE8Sp8vjRERkvrJnz66ukusDVKJQoUKqrbh27VqiesEmJDEnhloPLmq5fqybeWLdUl/+2dhkwgyZATz6OtLLSoaIy8Qc0ntKziMuXryIMWPGJEm5iYi0jkGq/0j334ULF2L16tVwdHSMyislJyj6K+ZyxUROWmRMu+jevTsqVqyIUaNGoVatWli8eDGOHDmC6dOnm7QuRET0YSQn4bJly1SukXTp0qnH5KRDTuTe18uWiIi0kX82th9++EEFoqpWrRr12IQJE1TvKWkbrKysVDshuazi6531sSdTYq4088O6mSct1y2x9UuqCxsMUv1nypQp6n99V109yT3Spk0b9XdYWFiMsfpy5UQCW7/88osa1y5X1iVpojGNHRERJT0JNslwFr3Q0FAEBQWpq+G5cuVCnz59cP36dZUgV7Ro0UIlSW/bti1+/fVXlZPq+++/V1fK9RcuXr16pXKZ6P+W9eU1Jaglw2aIiEg7+WeHDh2qLkhLTynJTxU9SHXgwAHVmyp37twqZYi8fuxg1vtwMqWYWDfzxLqlrvp5JlFuQgap/iNDON5HGqXYGjdurG5ERJRySS/XypUrR92XRLdC8ohIXpKbN2+qCxF6EmiS4dsyDFxm+XNxcUGTJk0waNCgqGVu3LgBX1/fqPsjR45UN+lhG1d7QURE5pV/Vk+O7RKk2rp1K4oVKxb1+PPnz9WFakkXIqMqhDwvFyxkHWOCVJxMKSbWzTyxbuYrMgXVj0EqIiLSPOklm9DFCAlUxXV1SAJV8ZFZnAy5wJEUDoVGYOmhcAzOmx92GvyhRESUEvLPiuHDh+P333/H5s2b1UWL6CQ/ldxi9zqQEzzplWAMY08K7z15iQFrzqC5p41m84lpOVca62aetFy3lFQ/BqmIiIjMyIvXkfh26QncevQSl6YfxKSWxZHT2d7UxSIi0lz+2WHDhqFfv35qPbkwoV9HetvKLX369Kr3rAwHl9eQ4X67du1SQ8dlRtik9PPK09h05hYCL1pifLosKF8gS5K+HxFRctHeYEoiIiINs7W2xMC6RZDOxgInrz9ErfF7sPnMvydOREQUf/5ZmdFPetbKDK7625IlS6KWkWHfMvw7+jqSc7BRo0Yx1pGhfHqSp6pUqVJo2bIlChcurIYFSs+rjh07Jml9vqteEAWypsP9F5FoNfswxm69iMi3pundS0T0MbEnFRERkZmpUigLJtV1w5iDDxH0z0N0mHcUX5bLix8+84SNFa8/ERF9jPyzV65cee862bJlUxMtJTf3LOnwV0c/9Fp4AJuDH2Ps1mAcvhKBsU19kdkxbbKXh4joY+EvWSIiIjOUNZ01Fn1VBl+V+zevyqzAUDSZth/X7j8zddGIiCgZ2NlY4tuALBjZqCjsrC2xN+Qeao7fg/2X7pm6aEREicYgFRERkZmSXlO/1C6M6V+UQHpbKwT98wC1xgdi69nbpi4aERElkwa+rljbNUAN/7v7+CVazjyACduC8ZbD/4jIDDFIRUREZOaqFcmG9d3KwztnBjx8/hpfzT2CwRvO4XWkcbNLERGReXLP4ohVnQPQqIQbJDY1astFtJ59COFPXpq6aERERmGQioiISANkhr9lHfzQLuDf4X/Td19G02n7cf3Bc1MXjYiIkoG9jRVGNvbGiEbFYGttgT3B4WpyjYOXOfyPiMwHg1REREQaGv7Xr05hTP1fCTjaWuFYmAz/24Pt5zn8j4gotWhcMifWdCmnkqvffvQSzWccwKQdIRz+R0RmgUEqIiIijfnMKxs2dCuPYm5OePDsNdrNOYIhGzn8j4gotSiQ1RFrugSgYXFXNfxvxOYLaDPnMO5x+B8RpXAMUhEREWl1+F9HP7Txz6PuT9t1Gc2nH8DNhxz+R0SUWob/jWrsjeGfF0NaKwvsvnhXTa5x+EqEqYtGRBQvBqmIiIg0Kq2VJQbULYLJLYvDMa0Vjly9j5rj9mDHhTumLhoRESWDNGnSoEmpnFjdJQD5Mjvg1qMXaDb9AKbsvMThf0SUIjFIRUREpHE1i2bHum7l4OWaHvefvUbb2YcxfNN5vOHwPyKiVMEzW3qs7VIO9X1yIPKtDsM2nceXfx5GxNNXpi4aEVEMDFIRERGlArldHLDiG3+08sut7k/eeQktZhzErYcvTF00IiJKBg5prTCmqQ+GNiyqhv/tuCDD//bgCIf/EVEKwiAVERFRKhr+N7CeFya28EW6tFY4dCUCNcfvwa6Ld01dNCIiSqbhf81K58KqzgHIl8kBNx++QNPpBzBtF4f/EVHKwCAVERFRKlO7WA6s7VoOhbOnV0M92sw+hJGbL3D4HxFRKlEoe3qs6VoOdbz/Hf43ZON5tJ97BA+ecfgfEZkWg1RERESpUN5MDvirkz/+VzYXdDpg4o4QtJx5EHcecfgfEVFqID1qxzfzweAGRWFjZYFt5++o2f+Ohd03ddGIKBVjkIqIiCiVsrW2xKD6RTG+uS8cbCxxMPTf4X97gjn8j4gotQz/a1EmF1Z28kceF3tcf/AcTabux8w9l6GTKxhERMmMQSoiIqJUrq73v8P/PLM5IvzJK7T64xBG/31BDQEhIiLtK5LDSbUDtYplx5u3Ogxafw7t5x7Fw2evTV00IkplGKQiIiIi5MucTiXSbV763+F/47eH4H8y/O8xh/8REaUGjrbWmNjcF7/V94KNpQW2nrutetcG/fPA1EUjolSEQSoiIiKKGv43pGFRjG3qA3sbS+y/fA81xwViX0i4qYtGRETJNPzvi7K5Vc7CXM7/Dv9rPHUfZgWGcvgfESULBqn+s3v3btSpUwc5cuRQB+dVq1a9d50FCxbA29sb9vb2yJ49O9q1a4d79+4lS3mJiIiSSn1fV6zpUg4Fs8rwv5doOesgxm69yOF/RESphJerE9Z1K4eaRbPhdaQOv607i47zj+Lhcw7/I6KkxSDVf54+faoCTpMmTTJo+b1796JVq1b48ssvcebMGSxbtgyHDh1C+/btk7ysRERESc09y7/D/5qWzKmG/43dGoxWfxzE3ccvTV00IiJKBultrTGpRXH8WreIGv63+cxt1J6wByc4/I+IkhCDVP+pUaMGBg0ahAYNGhi0/P79+5EnTx5069YNefPmRbly5dChQwcVqCIiItICOxtLDGtUDKObeMPO2hJ7Q+6p/CT7L7HXMBFRaiAjTFr758Hyb/yQ09kO/0Q8R6Op+zBnL4f/EVHSYJAqkfz8/PDPP/9gw4YN6gB9+/ZtLF++HDVr1jR10YiIiD6qhsXdsLZrAApkTad6UrWceQATtgXjLYf/ERGlCsXcMmBd1/KoXiSrGv43YO1ZdFpwDI9ecPgfEX1cVh/59VKNgIAAlZOqadOmePHiBd68eaNyWhk6XFAvMjLS6PeWdd6+fZuodVM61s18abl+rNu7LC0tYW55B0eMGIGjR4/i5s2bWLlyJerXr5/gOi9fvsTAgQMxf/583Lp1S+Ue7Nevn8o/qCdDvfv27YsrV67Aw8MDw4YN0+zFCvcsjmr4X//VZ7Ds6DWM2nIRh65EYExTH2RKl9bUxSMioiTmZGeNqf8rgTn7rmDwhnPYePoWztx4hMkti6scVkREHwODVIl09uxZdO/eXZ2wVK9eXZ30fP/99+jYsSNmzZpl8OsEBwcb/d5yQhkREYGQkBBYWGirMxzrZr60XD/W7V2enp4wx7yDEmBq2LChQes0adJE9ZKVY7q7u7s6zsvnpbdv3z40b94cQ4YMQe3atbFw4UIV+Dp27Bi8vLygRfY2VhjR2Btl8rngl1WnsCc4HLXG78H4Zr7qMSIi0v7wv7YBeeGbKyO6LDyGsIhnaDh5H/rWLoT/lc2tnici+hAMUiWSnJRIbyoJTIlixYrBwcEB5cuXV7mt5Iq7IeTKu7Gkx4OcUMpJk7n1Zngf1s18abl+rJs28g7KzVCbNm3Crl27cPnyZTg7O6vHJA9hdOPGjcNnn30W1Q789ttv2LJlCyZOnIipU6dCyxqVcEMxNyc11CPkzhM0n3EAvaoVxDcV88PCgicoRERa55MzA9Z3LY/vlp/AlrO30Xf1GRwIjcDQhkXhaGtt6uIRkRljkCqRnj17BiurmB+f/gTPmCSCiT0plB4Psq4WTypZN/Ol5fqxbqnLmjVrULJkSQwfPhzz5s1TFyHq1q2rAlF2dnZRE2j07NkzxnrSs3bVqlVGv5+xwy1TwhDU/JnssfKbsui35ixWHr+BEZsv4MDlexjVuBhcHGw+6LVTQv2SCutmnli3d7HNICd7a0z/ogRmBYZi6MbzWH/yJk5ff6hmBOTwPyJKLAap/vPkyRPVm0AvNDQUQUFB6gp6rly50KdPH1y/fh1z585Vz0v+qfbt22PKlClRw/169OiB0qVLI0eOHCasCRERfSjpQRUYGAhbW1uVvyo8PBydOnXCvXv3MHv2bLWM5KnKmjVrjPXkvjxuLGOHfqekIahfF7NFXvvMmHQgXA3/qzl2F36smBVeWf8N5iVGSqrfx8a6mSfWzfyHfVPSkOF9X5XPhxK5ZfjfcVy99wwNp+xDv9qF0bJMLg7/IyKjMUj1nyNHjqBy5cpR9/VXx1u3bo05c+aoIFRYWFjU823atMHjx4/VsI5evXohQ4YM+OSTT1TSXCIiMv+TNvlhLRNkODn9ezV49OjRaNSoESZPnhzVm+pjMXbod0obplmgAFC1xGN0XRSES3ef4ofNN9HrUw+0L5c3UcP/Ulr9PibWzTyxbkQJkxxV67uVw3fLTmDruTv4ZdVpHAyNwJCGRZEuLU85ichwPGL8p1KlSgkO05NAVWxdu3ZVNyIi0hbJK+jq6hoVoBKFChVS7cS1a9dUUClbtmwqsXp0cl8eN1ZiTgxT2jDNwjkyYE2Xcvh55SmsCrqB4Ztl9r/7GN3EB86JGP6X0ur3MbFu5ol1I0pYBnsbzGhVEjP3hGLYpvNYe+KGGv43sYUviuTg8D8iMoy2+isTERF9BDIxxo0bN9RQcL2LFy+qEzk3Nzd138/PD9u2bYuxniROl8dTK4e0VhjT1Eclzk1rZYGdF+6i5rg9OBQaYeqiERFRMpBeyO0r5MOSDn7I4WSL0PCnaDB5H+YduGpU3l4iSr0YpCIiIs2TYJPkGZRb9LyD+mHcknewVatWUcu3aNECLi4uaNu2Lc6ePYvdu3erWfzatWsXNdSve/fuahbAUaNG4fz58xgwYIAaOt6lSxek9hOUZqVzYVXnAOTL7IBbj16g2fT9mLg9GG/f8gSFiCg1kBxV67uVR9VCWfDqzVv0XXVa5ax69OK1qYtGRCkcg1RERKR5Ejzy9fVVN33eQfm7X79+6n7svIPp0qVTvaIePHigZvlr2bKlmjBj/PjxUcv4+/tj4cKFmD59Ory9vbF8+XI1s5+Xl5cJapjyFMqeHmu7lENDX1dIbGrk3xfRevYh3H380tRFIyKiZJDR4d/hf7/UKgQrizRYf+omao8PxMlrD0xdNCJKwZiTioiINC8xeQdl5ioJVCWkcePG6kbxD/8b3dQHfvld0Hf16X9n/xu/B+Oa+cA/fyZTF4+IiJJ59r+wiGf4fMo+/FSzENr45+Hsf0T0DvakIiIioiTVuGRO1auqQNZ0qifV/2YexNitFxHJ4X9ERKlm9r8N3cqjepGseB2pw69rz6Lj/KN4+IzD/4goJgapiIiIKMl5ZHXE6s7l0KSkmxr+N3ZrML6YdRB3Hr0wddGIiCgZONlbY+r/SmBAncKwsbTA5jO3UWvCHhwPu2/qohFRCsIgFRERESULOxtLDG/kjTFNvWFvY4l9l+6p4X97gu+aumhERJQMZHhfm4C8WPGNP3I52+Pa/edoPHU/Zu65zNn/iEhhkIqIiIiSVQNfN6zpUg6e2RwR/uQVWv1xCCM3X8CbyLemLhoRESWDom5OWNetHGoVzY43b3UYtP4c2s89ggfPXpm6aERkYgxSERERUbJzz5IOqzoHoEWZXJCL5xN3hKDFzIO49ZDD/4iIUoP0ttaY2MIXg+p7wcbKAlvP3UHNcXtw9GqEqYtGRCbEIBURERGZhK21JQY3KIrxzX2RLq0VDoVGqOF/uy5y+B8RUWoZ/ve/srmxspM/8mZywI2HL9Bk2gFM230Zbzn8jyhVYpCKiIiITKqudw6s7VoORXKkR8TTV2j351H8ceQeXnP4HxF9JEOGDEGpUqXg6OiILFmyoH79+rhw4UKC68yYMQPly5dHxowZ1a1q1ao4dOjQO8udO3cOdevWhZOTExwcHNT7hIWFJWFttKdIDifVDtTzyaFmfh2++SL6b72Fe085/I8otWGQioiIiExOrqBLIt1WfrnV/aWnH6DFzEO4/uC5qYtGRBqwa9cudO7cGQcOHMCWLVvw+vVrVKtWDU+fPo13nZ07d6J58+bYsWMH9u/fj5w5c6p1rl+/HrXMpUuXUK5cOXh6eqrlT548ib59+8LW1jaZaqYd0qN2bFMfDG1YFGmtLHD4+jPUmbAXBy/fM3XRiCgZWSXnmxERERElNPxvYD0vlM6TEb2Xn8SxsAeoNX4PRjbyRtXCWU1dPCIyY5s2bYpxf86cOapH1dGjR1GhQoU411mwYEGM+zNnzsSKFSuwbds2tGrVSj32888/o2bNmhg+fHjUcvnz50+SOqSW4X/NSudCMdf06DD3EP55+BLNZxxAz08LoFMld1hYpDF1EYkoibEnFREREaUoNbyyYVJdNxR1TY8Hz17jq7lHMGjdWbx6w+F/RPRxPHz4UP3v7Oxs8DrPnj1TPbD067x9+xbr169HgQIFUL16dRX0KlOmDFatWpVk5U4tCmZzxPjabmjgmwNvdcDIvy+i9exDuPv4pamLRkRJjD2piIiIKMXJ7miNpV+XxYi/g/HH3lDMDAzF4av3MbG5L3I625u6eERkxiS41KNHDwQEBMDLy8vg9X744QfkyJFD5aYSd+7cwZMnTzB06FAMGjQIw4YNUz22GjZsqIYIVqxY0eDXjoyMNLoeso7UJTHrpnRSp7SWwLAGRVA2nzMGrDmHPcHhava/0U2KwT+/C8yV1rcb65Z66mdpaZkkZWGQioiIiFIkmZK8X53C6gTlu2UncOKff4f/jWjsjepFspm6eERkpiQ31enTpxEYGGjwOhKIWrx4sco7pc83JSd0ol69evj222/V3z4+Pti3bx+mTp1qVJAqODjY6HrI+0dERCAkJAQWFtoaIBO9bt6OFhhbKwcG77yFqw9eotUfh9HCO6O6WZrh8L/Ust1YN+3Xz9PTM0nKwiAVERERpWjVimTDhhzp0XXRcRwPe4AO846ijX8e9KnpibRWSXMVj4i0qUuXLli3bh12794NNzc3g9YZOXKkClJt3boVxYoVi3o8U6ZMsLKyQuHChWMsX6hQIaMCYMLDwwPGkh4PckLp7u6eZD0aTCV23eTTCfD2xMB157D06DUsOHEflx6nwZjGxZAlvXklqU9N201LtFy3lFY/BqmIiIgoxXPLaI+lHfwwYvMFTN99GXP2XcFRGf7Xwhe5XRxMXTwiSuF0Oh26du2KlStXqt5QefPmNWg9SYj++++/Y/PmzShZsmSM52xsbFCqVClcuHAhxuMXL15E7tz/zlRqqMSeFEqPB1nX1CeVSSF23dLZWWJ4Y2/4u2fCTytP4cDlCNSZtA9jmvqgvEdmmJPUtN20RMt1S0n1014/NSIiItIka0sL/FSzEP5oUxIZ7a1x6vpD1B4fiPUnb5q6aERkBkP85s+fj4ULF8LR0RG3bt1St+fPn0ctIzP29enTJ+q+5Jjq27cv/vjjD+TJkydqHclDpff9999jyZIlmDFjhuqFMHHiRKxduxadOnVK9jqmFvV9XbG2azl4ZnNE+JNXaPXHIYzcfAFvIjm5BpEWMEhFREREZuUTz6zY0L08SubOiMcv36DzwmP4ZdUpvHitzWSmRPThpkyZomb0q1SpErJnzx51kwCTXlhYGG7evBljnVevXqFRo0Yx1pHhf3oNGjRQ+aekx1XRokUxc+ZMrFixAuXKlUv2OqYm+TOnw6rOAWhZJhd0OmDijhC0mHEQNx/+f9CRiMwTh/sRERGR2cnuZIfFX5fF6C0XMXnnJcw/EIZjVx9gUsviyJuJw/+I6N3hfu8jwwCju3LlikGv3a5dO3Wj5GVrbYnfGxRF2Xwu6PPXKRy6EvHv7H9NfVC5YBZTF4+IEok9qYiIiMgsWVlaoPdnnvizXWm4ONjg7M1HqD1+D1YHXTd10YiIKJnU8c6BdV3Lwcs1Pe4/e422sw9jyMZzeM3hf0RmiUGq/8gMH3Xq1EGOHDmQJk0arFq16r3rvHz5Ej///LNKjJg2bVo1Vl3GrBMREVHyqVggsxr+VyavM56+ikT3xUH4ccVJDv8jIkol8mRywIpv/NHa79+E9dN2XUbTaftx/QGH/xGZGwap/vP06VN4e3tj0qRJBq/TpEkTbNu2DbNmzVKzeixatAgFCxZM0nISERHRu7Kmt8WCr8qg2yfuSJMGWHz4H9SbuBchd/4/wTEREWlXWitL/FrPC1NaFoejrRWOhT1Qw/+2nr1t6qIRkRGYk+o/NWrUUDdDbdq0Cbt27cLly5fh7OysHpOeVERERGS64X89qxVE6bwu6LEkCBduP0adCYEYVN8Ln5dwM3XxiIgoGdQomh1Fcjih66JjOHHtIb6aewRflcurhofbWLGPBlFKxyBVIq1ZswYlS5ZUM3nMmzcPDg4OqFu3Ln777TfY2dkZ/DqRkcYPRZB13r59m6h1UzrWzXxpuX6s27ssLS2TrExEH6qcRyZs6F4O3y4Jwt6Qe+i17AT2X76HgfWKwN6GP32IiLQul4s9lnX0x9CN5/HH3lDMDAzF4av3MbG5L3I625u6eESUAP5SSyTpQRUYGAhbW1usXLkS4eHh6NSpE+7du4fZs2cb/DrBwcFGv7ecUEZERCAkJAQWFtq6GsC6mS8t1491e5enp2eSlovoQ2VxtMXcdmUwaUcIxm69iOVHr+HEP//O/lcgq6Opi0dERElMek31q1MYfvld8N2yE6oNqDl+D0Y0KobPvLKbunhEFA8GqT7gxE4SrC9YsABOTk7qsdGjR6NRo0aYPHmywb2pPDw8jH5v6fEgJ5Tu7u6a683AupkvLdePddPG5BgjRozA0aNHcfPmTXVxoX79+glOQ165cuV3Hpd1s2XLpv5+/Pgx+vbtq17rzp078PX1xbhx41CqVKkkrQsZztIiDbpV8UDpvM7otug4gu88Qd2Jgfi1bhE0KZlTteNERKRtnxbOivXdyqHrouM4HvYAHecfUwnWf6pVSOWxIqKUhUGqRMqePTtcXV2jAlSiUKFC0Ol0uHbtmsHBp8SeFEqPB1lXiyeVrJv50nL9WDdtTI7Rrl07NGzY0OD1ZFKM9OnTR93PkiVL1N9fffUVTp8+rYZ8y8yw8+fPR9WqVXH27FnVPlDKUTafi5r9T4b/7QkOxw8rTmH/pXv4vUFROKTlTyEiIq1zy2iPpR38MHLzBUzbfRl/7r+qEqtPbOGL3C4Opi4eEUWjrXErySggIAA3btzAkyf/P2vQxYsX1cmemxuTsxIRpSQyMcagQYPQoEEDo9aToJT0nNLf9EMinz9/jhUrVqi8hBUqVFA90QYMGKD+nzJlShLVgj5EpnRp8Wfb0uj9WUHVw2pV0A2VVP3MjYemLhoRESUDa0sL9KlZCH+0KYmM9tY4df0hao0PxJoTN0xdNCKKhkGq/0iwKSgoSN1EaGio+jssLEzd79OnD1q1ahW1fIsWLeDi4oK2bduqq+YylOT7779XV+mNSZxOREQpl4+Pj+o5++mnn2Lv3r1Rj79580YNlZS8hNHJ8V/yFVLKZGGRBp0quWPx12WR3ckWl8OfosHkfZi7/4rqCU1ERNr3iWdW1bu2VJ6MePLyjRoO3uevk3j+SnsT5BCZI/Zx/8+RI0di5B/p2bOn+r9169aYM2eOykOiD1iJdOnSYcuWLejataua5U8CVk2aNFFX6omIyLxJYGrq1Knq+P7y5UvMnDkTlSpVwsGDB1G8eHE4OjrCz89PzegqQ72zZs2KRYsWYf/+/ao3lbGMnV1RyzNOJkf9iud0wtou/mrY37bzd9Fv9RnsDQnHkAZecLKzRlLS8rZj3cwTZ3ml1Ci7kx0WtS+LcduCMXFHCBYd+gdHr97HpBbF4cHJNYhMikGq/8jJR0JXUSVQFdfsVhKoIiIibSlYsKC66fn7++PSpUsYM2aMykEl5H/pPSv5p+RkTYJXzZs3V8nZjWXsTK9annEyOev3XRlHuDu+xayj97D5zG0cv3IPP1bMisJZYvaQ+5i0vO1YN/PEWV4ptbKytECvagVV3sLui4Nw8fYT1OHkGkQmxyAVERGRAUqXLh1jKF/+/Pmxa9culZT90aNHqvdV06ZNkS9fPqNf29iZXrU+K2Ny1q93AeCzkg/RfckJhEU8w/ebbqDXpx5oXy6vGh74sWl527Fu5knLdSMyRIB7JmzsXh49l/7/5Bp7Q2RyDS842iZt71oieheDVERERAaQPIUSiIrNwcFB3e7fv4/NmzerZOrGSsyJodZnZUzO+vnmdlbTk/+08jTWnriB4Zsv4kDofYxu4q0Srn9sWt52rJt50nLdiAyR2fHfyTVk5r+Rf19QydRPXnuACc2Lo6jb/8/mTkRJT1v9lYmIiD7C5Bhjx47F6tWrVe+C06dPo0ePHti+fTs6d+4ctYwEpDZt2qReS4Z+S15DGf4iE2qQ+ZGr5eOb+WBow6KwtbbA7ot3UWPcHuwLCTd10YiIKBlI79lvKuXH0g5l4ZrBDlfuPUPDKXvxR2AoJ9cgSkYMUhERUaqYHMPX11fd9JNjyN/9+vVT92NPjvHq1Sv06tULRYsWRcWKFXHixAls3boVVapUiVrm4cOHKmglgSkJcJUrV04FrqytOTTAXEn+kWalc2FNl3LwyJIOdx+/RMtZBzH67wt4E/nW1MUjIqJkUOK/3rXVCmfF60gdBq47i/Zzj+LBs1emLhpRqsDhfkREpHnGTo7Ru3dvdUuIzOgqN9KeAlkdVaDq17VnsPjwPxi/PQQHLkdgXHMfNSMUERFpWwZ7G0z7ogTm7r+K39efw9Zzt1Fz3B6Ma+6LUnmcTV08Ik1jTyoiIiKiWOxsLDH082IY18wH6dJa4dCVCHWCsu3cbVMXjYiIkql3bWv/PPirkz/yZnLAjYcv0Gz6AUzaEYK3bzn8jyipMEhFREREFI96Pq5Y17Uciro64f6z1/jyzyP4bd1ZvHrD4X9ERKmBl6sT1nYthwa+roh8q8OIzRfQ6o9DuPP4hamLRqRJDFIRERERJSBPJgcs/8YP7QLyqvuzAkPRaOo+XL331NRFIyKiZCA9amXG1xGNisHO2hKBIeGqd+2e4LumLhqR5phdkMrZ2dmom4uLC65evWrqYhMREZEZS2tliX51CmNGq5LIYG+Nk9ceotb4QKw9ccPURSMiomQa/te4ZE6s7RoAz2yOCH/ySvWoGr7pPCfXIErNidMfPHigpgZ3cnJ677KSJLdTp06IjIxMlrIRERGRtn1aOCs2dCuP7ouP4/CV++i66Dj2XQpHv9pFVB4rIiLSNvcsjljVOUAN/V5wMAyTd17CwdAIjG/uC9cMnFyDKNUFqUSzZs2QJUsWg5bt2rVrkpeHiIiIUo8cGeywqH1ZjNsWjIk7QrDo0D84evU+JrUoDo+sjqYuHhERJTFba0v83qAo/PNnwo8rTqo2QIb/DW9UDNWLZDN18YjMmtkN93v79q3BASrx+PFj5MuXL0nLRERERKmLlaUFelUriHntyiBTurS4ePsJ6kwMxNLD/6ie3EREpH21imXH+m7l4e3mhIfPX6PDvKMYsOYMXr7hSB6iVBOkIiIiIkopynlkwsbu5VHeIxNevH6L3itOoseSIDx+8drURSMiomSQy8Ueyzr64+sK/3aMmLPvChpO3ofQcE6uQZQYmgxS3b9/H3PnzjV1MYiIiCgVyOyYFn+2LY3enxWEpUUarA66gToTAnH6+kNTF42IiJKBjZUFfqpZCLPblEJGe2ucufEItcfvweqg66YuGpHZ0WSQKiwsDG3btjV1MYiIiCiVsLBIg06V3LG0Q1mVOPfKvWfqSvrsvaEc/kdElEpU9syCjd0roExeZzx9FYnui4PQe/kJPHv1xtRFIzIbZhmkevToUYI3yUNFRERElNxK5HbG+m7lUK1wVryKfItf157F1/OO4sGzV6YuGhERJYNsTrZY2L4sulfxgEUaYOmRa6g7cS/O33pk6qIRmQWzDFJlyJABGTNmjPdWoUIFUxeRiIiIUqkM9jaY9kUJ/Fq3CGwsLbDl7G0169ORKxGmLhoRESUDGfr97acFsOCrssjimBYhd56g3sS9WHgwjL1rid7DCmbI0dERP//8M8qUKRPn88HBwejQoUOyl4uIiIhIpEmTBq3986BE7ozouui4SqDbdPoB9Kjijio5eIJCRJQa+OV3UZNr9Fp2Ajsv3MVPK08hMOQuvixqa+qiEaVYZhmkKl68uPq/YsWK8fa0YoSaiIiITM3L1Qlru5bDLytPYVXQDYzaEozt2e0wJXtuZMtgb+riERFREnNJlxZ/tC6FmYGXMXzTBWw4dQvHr1hhslN2+OZ2NnXxiFIcsxzu16JFC9jaxh99zpYtG/r375+sZSIiIiKKS7q0VhjT1AfDGxWDnbUljt98jloT9mJP8F1TF42IiJJpco2vK+THso5+cMtoh5uP36DJ9AOYuecyO1cQaaEnVfv27RN8PmvWrAxSERGZsZ49exq9zi+//AJnZ16RpJQ7/K9JyZzwdk2PDnMP4cr9V2j1xyF8UzE/en5aAFaWZnndkIiIjOCbKyPWdvZH13kHEHj1KQatP4d9l+5hZGNvODvYmLp4RCmCZn4RXbt2DW/fvk30+rt370adOnWQI0cO9UNy1apVBq+7d+9eWFlZwcfHJ9HvT0RE/2/s2LE4ePAgjh8/btBtwoQJePDggamLTfRe7lnSYVwtV7QonRNy8XzyzksqV9X1B89NXTQiIkoG6e2s8XOlrPitbmHYWFlg+/k7anKNg5fvmbpoRCmCWfakikvhwoURFBSEfPnyJWr9p0+fwtvbG+3atUPDhg0NXk9Oilq1aoUqVarg9u3biXpvIiJ618qVK5ElSxaDJ9QgMhdprSzwW70iCHDPjB9XnMTRq/fVCcqIRsVQrUg2UxePKEUwtmesXGQ+duwYcufOnWRlIvpYZH9tUSYXSuRxQZdFx3D57lM0n3EA3asUQJdP3NXsgESplWaCVB86lrdGjRrqZqyOHTuqHFmWlpZG9b4iIqL4zZ49G05OTgYvP23aNDXUm8ic1CqWHUVdndB10TGcuPYQX887ijb+edCnpifSWlmaunhEJiUXgqVXrSFtgZwHdOrUCZGRkclSNqKPpXCO9FjXtRz6rT6D5UevYczWizhw+R7GNvNB1vScAZBSJ80EqUx1EnX58mXMnz8fgwYNMnVxiIg0o3Xr1kYtLxcLiMxRLhd7LOvojxGbz2PGnlDM2XcFR65GYELz4sibycHUxSMyqWbNmhnco7Zr165JXh6ipGBvY6VyUvnnd8Evq05j/+V7qnftqCbeqFTQsP2fSEs0E6T66aefkjVhbnBwMH788Ufs2bNH5aNKrMRc8ZF1JP+WFq8WsW7mS8v1Y93eJb1HiejjkJwkP9cqDL/8Lui19AROX3+E2uP3YHDDoqjn42rq4hGZhLG5Zh8/fpxkZSFKDg2Lu8E7ZwZ0WXgc524+QpvZh9GhYj58V60grDm5BqUimglS9enTJ9neS07m5Kr9r7/+igIFCnxwsCsxjXZERARCQkJgYaGtAxbrZr60XD/W7V2enp4wtRMnTqB48eKaDB5S6vSJZ1Zs7F4B3Rcfx8HQCHRfHITA4HD8Wq+IutJORIk3ZMgQ/PXXXzh//jzs7Ozg7++PYcOGoWDBgvGuM2PGDMydOxenT59W90uUKIHBgwejdOnS8aYBkeHnY8aMQY8ePZKsLqRd+TOnw8pO/hiy4Rz+3H8V03ZdxsHL0rvWFzmd7U1dPKJkYaXFKcolEZ2trS3c3d1Rr169j97DSq7UHDlyRM0o1aVLl6gTPRkPL72q/v77b3zyyScGvZaHh4fR7y8nZHJCKfXTWm8G1s18abl+rJt28xESpTTZnGyxsH1ZjN8WjAnbg7Hs6DUcDbuvTlCK5DA8TxuRVty7dw8nT55UExzJb/rw8HDMmjULL1++ROPGjVGoUCGDXmfXrl3o3LkzSpUqhTdv3qhRGNWqVcPZs2fh4BD30NqdO3eiefPmKqAl5xYS1JJ1zpw5A1dX13cm+zhw4ICaKZzoQ9haW+LXel7wy58JvZefQNA/D9TwP+ldW8eb+xdpn1kHqSRIJLN4yEmW/irIxYsX1YmWXOWfPHkyevXqhcDAQDX738eSPn16nDp1KsZj8l7bt2/H8uXLkTdvXoNfK7EnhdLjQdY1x5PK92HdzJeW68e6Jb/3zbT68OFDdVGCSGtkVqdvPy2ghv/1WBykZn1qMGkffq5VCK38cnO/p1Tj0KFDKij06NEjZMiQAVu2bFGBKbkoLBeIhw4dqn7nS6/a99m0aVOM+3PmzFH5ro4ePYoKFSrEuc6CBQti3J85cyZWrFiBbdu2qdm99a5fv65yYm3evBm1atVKdH2JovvMKxu8XNOrduDI1fvouug49oaEo3+dIrCzSVm/2Yg+JrMetyK9pKpWrYobN26oBkZu165dw6effqquekiDIY3Ot99++97XevLkCYKCgtRNhIaGqr/DwsKihhPqGyM5ofPy8opxk0ZOrrDI3/FdjSEiIsOtXbsWL168UDM7xXVLly6dwa+1e/du1KlTR13hlhP8983GKlfPZbnYt1u3bkUtIxdI+vbtqy5MyNCR/Pnz47fffmPvLvpoyuZzwcbu5VG1UBa8inyL/mvOqBkA7z99ZeqiESWLn3/+WQWl5KKE9HyqX78+qlSpoi5KSy9gSawux93EkNcUxoy4ePbsGV6/fh1jHQmWffHFF/j+++9RpEiRRJWFKD5uGe2x+Ouy6PqJO+T6xOLD/6DOxECcv/XI1EUjSjJm3ZNqxIgR6oqK9GzSkxOXAQMGqKsu3bt3R79+/dTf7yPD9ypXrvzOUEKZYUqutNy8eTMqYEVERElPhnB8/vnn+PLLL+N8Xi4krFu3zqDXevr0qRoq0q5du/f20IruwoULMdqY6LNMybCPKVOm4M8//1QnJtKOtG3bVrVD3bp1M/g9iBKS0cEGM1qVxJ/7rmDwhvPYcvY2al7fg7FNfVAmn4upi0eUpOQC9Pjx4+Ho6Kh+1//www9o37591POSdqNu3bpGv64EliRnVEBAgLrAbCh5f7nYIRfJo7cF0rPrQ4/7nEwpJtbt/0nf2R5V3FEmb0b0XHoSIXeeoO7EvfilpidalM6ZonrXcrulrvpZJtEoDLMOUskVkDt37rwzlO/u3buqW7CQrsGvXr3/imOlSpUSvPotgaqESGBMbkRE9HFIgloZ0h1fkCpt2rTIlSuXQa9Vo0YNdTOWBKWkHYnLvn37VI9e/dCOPHnyYNGiRWp4CtHHJCcgbQLyomQeZ3RbdByXw5+i+YwD6FbFA10/8VDDA4m0SH7DS09VYW1tDXt7e2TKlCnqeflbclYZS3JTSTJ0GSpoKBlauHjxYtXTVkZP6INo48aNU23VhwYKOJlSTKzbu2TPH18rO0YF3sHha8/Qb81ZbAq6im8DMsMxbcoY/sftlrrq55lEEymZdZBKTg7kqvioUaNUEkRx+PBhfPfdd6o7sJCThQ+dgY+IiJLf1KlTE7yaIz2tZGh2UvLx8VHJeeVKu1yIkKvuepJId/r06WrYibQzMtugnPCMHj06SctEqZeXqxPWdi2nhv0tP3oNY7cGY9+lexjXzAfZnf49kSfSkpw5c+Ly5cvqIoCQIFH27NmjnpeRDtGDVoaQ3lfSC1eGgbu5uRm0zsiRI1WQauvWrShWrFjU43v27FEXzKNfMJF2S3Lijh07FleuXDG4XJxMKSbWLX6LvApi9r6rGL75AvaFPUXow0iMbeKNknkywtS43cxXZAqqn1kHqWSKV8k3JePRZZYOId1tZYie/iRBonuS5JCIiMyL9JQyFTkJkiBZyZIlVZBK2hHpcXvw4MGoBL0//vij6rUr7Yw05tK4//7772jZsqXR72ds13F2OU+9dbO1SoNhDb3gn88ZfVefwaHQCNQYuwfDPi+qcleZErdb6qpbcpzEyG98CQLpxU5KvmbNGpQuXdqg15IRE5LcXGbhk95Qhk50NHz4cHVsl6To0iZEJ7moog/9E9WrV1ePy/BvY3AypXexbvFrXyE/yubLhK6LjuHKvWdoPvMgvq1aAJ0qu5u8dy23m/mySCH1M+sglSTNnTFjBsaMGaOusoh8+fLFSKYrV8GJiEgb5ARFAkbRr6QnBZkxVj9rrL7X1KVLl1R7M2/ePPXY0qVL1cxPCxcuVDmpJEeW5DiRfCVysSQph3mwy7n5+lh1K+wATKjtiiG7biP43kt0mH8MdQs54asSzrCxMs1nxu2WuuqWVMM8ouvfv/97E6sbejIlQ/zkeL169WqV40o/EYbkEdQPKZRJklxdXTFkyJCofFOS31bWk95c+nXkXENuLi4u6hadDEvMli1bjDaEKCkUdXPCum7l0W/Vafx1/DpGbbmoeteObeaDrOn/HZJKZI7MOki1Y8cOlexcGonoXW/FpEmTVGNERETaIcMznj9/bpL3lqv10fOXyExO0ptKrvSLokWL4urVq+rkxtgglbHDPFJSl+ykoOX6fcy6yV7j5+2JkVsuYlbgFaw59xAhD95iXFNv5Mts+OyXHwu3m3kyt7rt3btX9WiS3raSo8pQMtGFkF6x0c2ePRtt2rRRf8skSdEDdbKO5MVq1KjRO8Ez5qKllCBdWiuMbuqDAPdM6Lv6NPZfvoca4/ZgZONi+MQzq6mLR5T6glQyQ5OMDZfkutFJAkOZFlyLQao3kW/ReOp+PH/xAna24ZJNFZqi07Fu5krL9dN43QZUNHz67dRMekpF78ElU5HH7nUgJ3jSK8FYiTkxTCldspOKluv3MetmZ2mJvrWLoJxHZvRaegJnbz5Gvcn78WvdImhUwi3ZZ33idjNP5lQ3mQRDjscyesIYCU2QpCfDAKMzJqfUh6xD9KE+L+EG31wZ0HXRcZy58Qjt5hxBu4C8+KFGQaS1SvnfayLNBKlGjBihGiq5sq7vcixJ1AcOHIj169dDq47/8/C/v15Cu1g386Xl+mmzbjqd+QSpcufOrYZSGOvJkyeqp4CeJFyXkxxnZ2eV8LZPnz64fv065s6dq56XhLeSr0SG8b148UINMdy+fTv+/vvvqNeoU6eOylMi68tyx48fV/kQZUIPouRWuWAWbOxeHt8uCVLDPb5ffhKBIeEYVN8LjrbGf2eIUipDgk1EqZH0oP2rkz+GbjyP2Xuv4I+9oTh05R4mNC+OvJkcTF08otQRpPrqq6/UGHpJWChDMJYsWYLBgwdjw4YNMWZg0hKLNGkwtaWvms1EruhrMS8C62aetFw/rdfN2lIf+E75ZMrwxDhy5IgaHq7Xs2dP9b8My5szZ47avjLMQ0+Gd8jsTBK4kuEkMqRceu5Gf40JEyaoXrudOnVSiX0lF1WHDh1U/hIiU5AcJPO+LIOpuy5h9JaLWB10A0H/PMCE5r4o5pbB1MUjIqIkJr2m+tcpgnLumfDdshM4ff0Rao/fg9/qe6FhccNmsyQyNbMOUonevXvj3r17amy6jKmXmTfKli0LrbKwSINPC2dFsPUjeHhkNYtu2caQbci6mSct10/zdQt+hJTs5MmTcT4uw5hsbW1VT6b3zQQoOUgSuvougarYbYvcEiKJd6XHldyIUgqZ1alzZXeUzeeMbouCcPXeM3w+ZR96V/fEl+Xyqt8RROY+u3fWrMy1Q5SQKoWyYmP3Cui++DgOhkag59ITCAwOx8D6XiqPFVFKZnZ76Pjx4995TGbhkCvdFSpUwKFDh9RNdOvWzQQlJCKij0lmadXn1ZFAU+wcOzL8r2nTpurERYJWRASUyO2MDd3K48e/TmLj6Vv4fcM57L0UjpGNvZEpXcJBXaKUrEWLFqYuApFZyOZki4Xty2LSjhCM3XpRzQB4/L/etV6uTqYuHpF2glQy/XdcpHeDzPYhNyEnMQxSERGZv5UrV+KHH35Qs+nJDHtCLkZIDkKZYenNmzdqlr1ffvkFI0eONHVxiVIMJ3trTG5ZHAsPhWHg2rPYeeGumvVp7H8zQRGZC8kNKEOsZWZvGV4de4KKY8eOmaxsRCm9d223Kh7wy++C7ouOIzT8KRpO3ocfa3iibUCeZJ9cg0iTQSpJdktERKmHJCeXWVurV68e9VjRokXh5uamckJJwMrBwUHlkGKQiigmOQFpWSY3SuZ2RpeFxxB85wn+N+sgOlXKjx5VC8DaUlt59kibvvzySzVxRaNGjdTFCp5YExmnVB5nbOheHr2Xn8TfZ29j4Lqz2BsSjhGNveHsYGPq4hGZd5CKiIhSl1OnTqlZ/WKTx+Q5/ZBASX5ORHErmM0Ra7qUUycmiw6FYdKOS9h/6R7GNfNFTmd7UxePKEHr1q3T9MRIRMkhg70Npn1RAvMOXMWg9eew7fwd1Bi3G2Ob+qqeVkQpBS+fERFRiubp6YmhQ4eqGff0Xr9+rR6T54TMwsdEukQJs7OxxJCGRTGpRXE42lrhWNgD1By/BxtOMcBLKZvkn5XJKojow0gvxFZ+ebCqUwDyZ3bA7Ucv0WLmAYz++wLeRMYcRktkKgxSERFRijZp0iR1FV2G91WtWlXd5G95bMqUKWqZy5cvo1OnTqYuKpFZqFUsu0qq7psrAx6/eINOC47hp5Wn8OJ1pKmLRhQnyUEouQmvXr1q6qIQaULhHOmxtms5NC2ZEzL58fjtIWg+4wCuP3hu6qIRcbgfERGlbP7+/iof4YIFC3Dx4kX1WOPGjdUMT/or61988YWJS0lkXmSI39IOfhiz5SKm7LqEhQfDcORKBCa2KI4CWdljhVKWkiVLquTp+fLlUzN6y6yu0UVERJisbETmyt7GCsMaFUOARyb89NcpHL5yHzXH7cGwz4vhM69spi4epWIMUhERUYr26NEjpE+fHh07dnznuZCQELi7u5ukXETmTpKm9/7ME/75M+HbpUG4ePsJ6k4MRL/aRdC8dE4mp6YUo3nz5mpY9+DBg9XQbu6bRB9PXe8c8HHLgK6Lj+PEPw/Qcf5RfFE2N36uVQi21pamLh6lQmYdpDp58mScj0vDZWtri1y5ciFt2rTJXi4iIvp4atWqhS1btqjjenQXLlxAlSpVcO3aNZOVjUgLynlkwsbu5dFz6QnsvnhXDf2TWZ8GNywKJ7uYPVaITGHfvn3Yv38/vL29TV0UIk3K5WKPZR38MOrvC5i2+7JKrn5Y9a71hXsW9q6l5GXWOalkNidfX993bvK4JNN1cnJC69atVfdgIiIyT+nSpUPDhg3x5s2bqMfOnTuHSpUq4fPPPzdp2Yi0IlO6tJjTphR+qukJK4s0WH/qphr2cfTqfVMXjUj9rn/+nLlyiJKSjZUF+tQshD/blUamdDY4f+sx6kzYiyWHw6CTxFVEycSsg1QrV66Eh4cHpk+fjqCgIHWTvwsWLIiFCxdi1qxZ2L59O3755RdTF5WIiBLpr7/+wsOHD9GyZUv1I+n06dMqQCXDP8aNG2fq4hFphoVFGnxdIT+Wf+OPXM72KoFuk2n7MWlHCN6+5QkKmY7M5tqrVy/s3LkT9+7dU8PAo9+I6OOpWCAzNnQvj3LumfD8dSR+WHEKXRcdx6MXr01dNEolzHq43++//65OUKpXrx71WNGiRdWsT3379sWhQ4fg4OCgGrWRI0eatKxERJQ4dnZ2WL9+vQpMNWnSBLt370arVq0wYsQIUxeNSJN8cmbA+m7l8PPK01hz4gZGbL6AfZfCMaaJD7Kkjznslig5fPbZZ+p/GeIdnVy4kDQfkZGcmZLoY8riaIu57UqroX8yBHDdyZs4ce0BJjQvrtoIoqRk1kGqU6dOIXfu3O88Lo/Jc0KG/t28edMEpSMiosSKfWXcwsICS5YswaeffqqG+MmFCP0yklSdiD4uR1trjGvmo/JV9V99BntD7qHGuD0Y1cQblQpmMXXxKJXZsWOHqYtAlCp7135TKT/K5HNGt0XH8U/EczSasg/fVS+Ir8vnU88TJQUrcx+fLt1/ZYifjY2Neuz169fqMXlOyEwgMgsIERGZjwwZMsQ5e5NcNZ86dSqmTZvGK+hESUy+X01K5kTxXBnRZeExlZ+kzezD+LpCPnxXraDKX0KUlBMkeXl5qYsUFStWfO/yZ86cUSk/rKzM+vSGKMWRNmB9t/JqUo31J29i6MbzanKN0U18kNmRk5TRx2fWR/FJkyahbt26anhfsWLF1GPSg0pOWNatW6fuX758GZ06dXrva8nwERk6cvToUdXzSvJd1a9fP8EcKVOmTFF5sF6+fIkiRYpgwIABMYYeEhFR4vCqOVHK4Z4lHVZ1DsCQDefw5/6rmL77Mg5evofxzX2R28XB1MUjjZLJkG7duoXMmTMbtLyfn5/6XZ4vX74kLxtRaiMzvU5s7ovy7pkwYO0Z7AkOV71rxzT1RnkPw76jRKkiSOXv74/Q0FAsWLAAFy9eVI81btwYLVq0gKPjv1NlfvHFFwa91tOnT9W0tu3atVOzSBkS1JJhJ4MHD1ZX/GfPno06derg4MGDqlElIqLEy5gxY9QVdEPwCjpR0rK1tsSv9bzg754JvZefxIlrD1FrfCAG1fdCfV9XUxePNEh6y8rQbnt7e4OWf/XqVZKXiSi1965tVjoXSuSW3rXHceH2Y3wx6xA6VMyHXp+ydy19PGb9a17ykUguko4dO77zXEhICNzd3Q1+rRo1aqibocaOHRvjvgSrVq9ejbVr1zJIRUT0gXgFnShlql4kG4q6OqHH4iAcuhKBHkuCsDv4LgbW84KdFfOT0MdToUIFXLhwweDlpR2QiTaIKGl5ZHXE6i4BGLT+LOYfCMO0XZdx4NK/vWvdMnByDUrlQapatWphy5YtsLWN+WWQBk1m/7h27VqyleXt27d4/PgxnJ2djVovMblUZB15Py3mYWHdzJeW68e6vcvS0hJJiVfQiVKuHBnssLB9GUzcEYLx24Lx17HrOHb1PsY08QZDBPSx7Ny509RFIKIEetcOql8U5dwz4YcVp1Tv2prj9mBgvSIowlHglJqDVOnSpVND89asWRM1xOPcuXP45JNP1DTlyWnkyJF48uSJ0e8bHBxs9HvJCWVERITqLWboUBhzwbqZLy3Xj3V7l35yiqTCK+hEKZuVpQV6VC0A//yZ0GPxcVy59wxNph9Aa19n/JBfhySOYxMRUQrwmVd2FHPLENW7tteyk6iSPx1G58oLJ3s2BJQKg1SSvLxq1apo2bIlFi9erHKSSA8quT969OhkK8fChQvx66+/quF+WbIYNy2zh4eH0e8nPR70wxmTujdDcmPdzJeW68e6JT9eQScyD6XzOmNj9wr48a+T2Hj6FmYeuYcLD45gVFMfZHHksA8iotTQu3bR12UxcXsIxm27iG2XnqDOxH2Y0NwX3jkzmLp4ZIbMOkglV83Xr1+PSpUqqR5Mksy8VatWapa+5CLBsa+++grLli1TATNjJfakUHo8yLop6aTyY2HdzJeW68e6ERHFzcneGpNbFsfCg1cxcO1Z7Am5p4Z9jGjsjcoFjbt4R0RE5sfSIg26V/VA2XwZ0XXBUYRFPMPnU/bh++oF0b58PlhYMGchGc7CHJOlR7/JydWSJUvUrHqff/65ymGify6pLVq0CG3btlX/S34sIiIiolQ761OpnBhfxw0Fs6ZD+JNXaDv7MAatO4uXb7SXz4+IiN5VMndGTKrrhppe2fDmrQ5DNp5H69mHcOfRC1MXjcyI2QWpMmTIoKYmj34rXLiwSpI+depUdV+/jDEkn5TMDCU3ERoaqv4OCwtT9/v06aN6aUUf4if3R40ahTJlyqhZqOT28OHDj1xjIiIiIvOQO4MNVn7jhzb+edT9mYGhaDh5Hy7ffWLqohERUTJwTGuJ8c28MbRhUdhaW2BPcDhqjNuDHefvmLpoZCbMbrjfjh07kuR1jxw5gsqVK0fd79mzp/q/devWmDNnDm7evBkVsBLTp0/Hmzdv0LlzZ3XT0y9PRERElBqltbbEgLpF1KxP3y8/gTM3HqH2hED1WOMSbqrXFRERabx3belcKJknI7ouCsK5m4/Qds5htAvIix9qFERaK6aYIA0FqSpWrJgkryt5rWTK8/jEDjwxqS8RkXmRvIWSs/Do0aPqwsPKlStRv379eJeX43z0ixd6sm62bNnU33ny5MHVq1ffWaZTp06YNGnSR64BkXmpWjirSqr+7ZIg7L98D72Xn1RX1H9v4IX0ttamLh4RESUx9yyOWNnJH0M3nsecfVfwx95QHLh8D+Ob+8I9SzpTF49SKLMb7nfy5Ek1ZbqhZMY/6fFERESp29OnT+Ht7W108OjChQsqMKW/RZ/F9fDhwzGe27Jli3q8cePGH738ROYom5Mt5n9VRiXPlcS6a0/cUEnVj169b+qiERFRMrD9r3ftrNYl4exgg7M3H6HOhEAsORyWYCcRSr3MLkjl6+uLe/fuGby8n59fjGF6RESUOtWoUQODBg1CgwYNjFpPglLSc0p/kwk79DJnzhzjuXXr1iF//vxJ1uuXyBxJcKpzZXcs6+iHnM52uHb/OZpM24+J24MR+ZYnKEREqUGVQtK7tjwC3F3w/HUkflhxCl0WHcfD569NXTRKYcxuuJ9EW2UGP3t7e4OWf/XqVZKXiYiItMvHxwcvX76El5cXBgwYgICAgHjbm/nz56uchsbm3ImMjDR6eelVbOx65kLL9UvNdfN2TY+1nf3Rd/VZrD15EyP/vqiG/41uXEz1uErJUvN2i4+lJXPKEJFxsqa3xbx2ZTBt92WM+vsC1p+8iaCwBxjf3AclcjubuniUQphdkKpChQpq6IUxPans7OyStExERKQ92bNnV7PGlixZUgWpZs6cqfIXHjx4EMWLF39n+VWrVuHBgwdo06aN0e8VHBxs1PJyQhkREYGQkJAYPbu0Qsv1Y92ATr52KJA+MyYdCMfB0Ah8Nm43egZkgV8uB6RU3G7v8vT0TNJyEZE2WVikwTeV8sMvvwu6LTqOsIhnaDLtAHpU8UCnyu6q9y2lbmYXpGLCciIiSg4FCxZUNz1/f39cunQJY8aMwbx5895ZftasWWpIYY4cOYx+Lw8PD6OWlx4PckLp7u6uyd4MWq4f6/avAgWAGqWeovuSf2f/+3X7LXxRJhd+rFFQ5S9JabjdiIg+Lp+cGbC+Wzn0XXUaq4JuYNSWiwgMCcfYZj7I7sROJqmZ2QWpiIiITKV06dIIDAx853GZ4W/r1q3466+/EvW6iTkxlB4Psp5WTyq1XD/W7V/uWdNjZacAjNh8HjP2hGLewTAcvnofE5r7wiOrI1Iabjcioo/L0dYaY5v5okKBzCpYJb1ra4zbg2GfF0P1Iv/OpEypj7b6KxMRESWhoKAgNQwwttmzZ6sE67Vq1TJJuYjMlY2VBX6uVRh/tiuNTOlscP7WY9SeEIgFB69y1iciolSiYXE3rOtWHkVdnfDg2Wt0mHdUBa1evNZeHkB6PwapiIgoVXjy5IkKMslNhIaGqr/1M8D26dMHrVq1ilp+7NixWL16tRoGc/r0afTo0QPbt29H586d38nnIkGq1q1bw8qKHZSJEqNigczY2L2Cupr+8s1b/LzyNL6ZfwwPnnECHCKi1CBvJges+MYfHSrkU/fnHbiKehP34sKtx6YuGiUzBqmIiChVOHLkCHx9fdVNyCx88ne/fv3U/Zs3b0YFrPSz9fXq1QtFixZFxYoVceLECTWkr0qVKjFeVx6T9dq1a5fMNSLSlsyOaTGnTSn8UqsQrC3TYNOZW2rYx8HL90xdNCIiSqbetX1qFsJc1bs2LS7cfoy6EwNVwIq9a1MPXvIlIqJUQWbmS+gHzpw5c2Lc7927t7q9T7Vq1fjDiegjzvr0Vfl8KJvPBV0XHUdo+FM0n3EAXT7xQLdP3GFlyeurRERaJ71qN/Uoj++WncDOC3fV0L89F++qXFUZHWxMXTxKYmzpiYiIiChF8XJ1wrqu5dC4hBve6oDx24LRbPoBXLv/zNRFIzM1ZMgQlCpVCo6OjiqHYP369XHhwoUE15kxYwbKly+PjBkzqlvVqlVx6NChqOdfv36NH374QfW4dXBwULO7yrDxGzduJEONiLRNelL90boU+tYurHrX/n32tupde4C9azWPQSoiIiIiSnEc0lphRGNvjGvmA8e0Vjhy9b46QVl/8qapi0ZmaNeuXSqn4IEDB7BlyxYVYJKesE+fPo13nZ07d6J58+bYsWMH9u/fj5w5c6p1rl+/rp5/9uwZjh07hr59+6r/ZYZXCXzVrVs3GWtGpO3etV+Wy6tmgs2XyQG3Hr1QvWtH/30BbyLfmrp4lEQ43I+IiIiIUqx6Pq4onisjui0+juNhD9B54TEEhuRUV9ftbfhTlgyzadOmd4Z4S4+qo0ePokKFCnGus2DBghj3Z86ciRUrVmDbtm2qx5STk5MKeEU3ceJElC5dWuUqzJUrVxLUhCh19q5d27Ucfl17BkuPXMP47SHYe+kexjb1QU5ne1MXjz4y9qQiIiIiohRNTkKWdvBD58r5kSYNsOjQP6gzIRBnbzwyddHITD18+FD97+zsbPA60nNKemAltI68bpo0aZAhQ4aPUk4i+v/etcMbeWNCc1/Vu/bo1fuoOX4P1p3k8Fqt4eUnIiIiIkrxrC0t8H11TwTkz4Rvlwbh0t2nqD9pL36q6YnW/nlUYIDIEG/fvkWPHj0QEBAALy8vg9eT/FOSd0pyU8XlxYsXahkZIpg+fXqjyhQZGWnU8vp1pC6JWTelY93MU3LUraZXVhTN4Yhvl57A8X8eosvC49gtydVreyZp71otb7fE1s/S0hJJgUEqIiIiIjIb/u6ZsLF7BfRefgJbz93BgLVnsSc4HMMbFYNLurSmLh6ZAclNdfr0aQQGBhq8ztChQ7F48WKVp8rW1vad56WHVZMmTdRsr1OmTDG6TMHBwUavIyeUERERCAkJgYWFtgbIsG7mKTnr9ltlF8wPSoMlJx9g6dFr2Bd8Gz9WzAp3l6RpB7S83RJbP09PTyQFBqmIiIiIyKw4O9hgRquSmHfgKgatP4dt5++opOpjmvogwD2TqYtHKViXLl2wbt067N69G25ubgatM3LkSBWk2rp1K4oVKxZvgOrq1avYvn270b2ohIeHh9HrSI8HOaF0d3dPsh4NpsK6mafkrtvvBYE6pe6h17KTuPboJXpuuI7e1QuijX/uj967VsvbLaXVj0EqIiIiIjI7cgLSyi8PSuVxRrdFxxF85wn+N+sgOlTIj17VCqjhgUR60sOpa9euWLlypeoNlTdvXoPWGz58OH7//Xds3rwZJUuWjDdAJT2hZBZAFxeXRJUvsSeF0uNB1jX1SWVSYN3MU3LXLcAjy7+9a1ecxJaztzFow3mVVF1mh830kXvXanm7paT6sfUmIiIiIrNVKHt6rOlSDi3K5IJOB0zddQmNpu7H1XtPTV00SmFD/ObPn4+FCxfC0dERt27dUrfnz59HLSMz9vXp0yfq/rBhw9C3b1/88ccfyJMnT9Q6T548iQpQNWrUCEeOHFEzAUpPBP0yr169Mkk9iVKjjA42mP5FCfxWrwhsrCyw48Jd1bt2T/BdUxeNEoFBKiIiIiIya3Y2lhjcoCim/q840tta4cQ/D1BrfCBWHb9u6qJRCiF5omTmvUqVKiF79uxRtyVLlkQtExYWhps3b8ZYR4JNEoiKvo4M/xPXr1/HmjVrcO3aNfj4+MRYZt++fSapJ1Fq7l37hV8erOkSgAJZ0+Hu45f4YtYhDNlwDq/evDV18cgIHO5HRERERJrwmVd2FHXLgG8XB+HQlQj0WBKEXRfvYmC9InC0tTZ18cjEw/3eR4YBRnflypUEl5feVYa8LhElH89s6bG6czkMWn8WCw6GYdruy9h36R7GNfNBvszpTF08MgB7Uv1HkifWqVNHTSsrUdhVq1YZ1JAVL14cadOmVQnG5syZkyxlJSIiIqK4uWaww8L2ZfBt1QKwtEiDlcevo+b4PTgWdt/URSMiomTqXft7g6KY9kUJZLC3xqnrD1F7QiCWHv6HgWUzwCDVf54+fQpvb29MmjTJoOVDQ0NRq1YtVK5cGUFBQejRowe++uorlVSRiIiIiEzHytIC3at6YGmHsnDLaId/Ip6j8dT9mLAtGJFveYJCRJQaVC+SDZu6V4BfPhc8exWpkqt3WXQcD5+9NnXRKAEMUv2nRo0aGDRoEBo0aGDQ8lOnTlWzgowaNQqFChVS09nKePUxY8YkeVmJiIiI6P1K5HbGhu7lUdc7hwpOjdpyEc1nHMCNB/+fLJuIiLQrm5Mt5n9VBj985gkrizRYf/Km6l17KDTC1EWjeDBIlUj79+9H1apVYzxWvXp19TgRERERpQzpba1VLpLRTbzhYGOpTkw+G7sbG079f4JsIiLSLhn6/U2l/FjxjT/yuNjj+oPnaDZ9P0b/fQFvIplUPaVh4vREkqlls2bNGuMxuf/o0SM1la2dnZ1BryNT1RpL1nn79m2i1k3pWDfzpeX6sW7vsrS0TLIyERF9bJJvtGFxN5TInRHdFgep2f86LTiGpiVzon/dwrC34U9iIiKt886ZAeu6lceANWew/Og1jN8egsCQcIxr5ouczvamLh79hy2yiQUHBxu9jpxQRkREICQkBBYW2uoMx7qZLy3Xj3V7l6enZ5KWi4goKeR2ccDyjn4Yu/UiJu+8hCVH/sHhKxHqBKWom5Opi0dEREksXVorjGzsjQoFMuPnladwLOwBao7bg0ENvFDPx9XUxSMGqRIvW7ZsuH37dozH5H769OkN7kUlPDw8jH5v6fEgJ5Qyo6DWejOwbuZLy/Vj3YiItMPa0gLfV/dEOffM+HZJEC6HP0XDKXvxffWC+KpcPlhYpDF1EYmIKIlJrkLfnBlUO3Dk6n10XxyEXRfu4td6ReBoa23q4qVqDFIlkp+fHzZs2BDjsS1btqjHjZHYk0Lp8SDravGkknUzX1quH+tGRKQtfvldsKlHefy44hQ2nbmFwRvOY/fFcJW7Kkt6W1MXj4iIkpgM8Vv8dVlM3BGC8duC8dfx6ypgJXkMfXNlNHXxUi1tjVv5AE+ePEFQUJC6idDQUPV3WFiYut+nTx+0atUqavmOHTvi8uXL6N27N86fP4/Jkydj6dKl+Pbbb01WByIiIiIyXAZ7G0z5X3EMbVgUdtaWKjfJZ+P2YOvZmL3liYhIm6wsLdCjagEs7eAH1wx2CIt4hkZT92Pi9mA1KywlPwap/nPkyBH4+vqqm+jZs6f6u1+/fur+zZs3owJWIm/evFi/fr3qPeXt7Y1Ro0Zh5syZaoY/IiIiIjKfpOrNSufC2q7lUCRHekQ8fYWv5h5B31Wn8eK19ibMICKid5XM44wN3cujjncOFZwa+fdFNJ9xADcePDd10VIdDvf7T6VKlaDTxR8pnTNnTpzrHD9+PIlLRkRERERJzT1LOvzVyR8jN1/AjD2hmHfgKg6G3lNJ1QtkcTB18YiIKIk52VljfDMfVCqQGf1Wn8ah0AjUGLcHQxoWRfXCWUxdvFSDPamIiIiIiACktbLEz7UKY2670siULi0u3n6CepP24s/9VxO8mElERNrpXft5CTes71Ye3m5OePj8NTotOIY+K6V37VtTFy9VYJCKiIiIiCgamZpckqp/4pkFr968xcB159Bv2y2EP3lp6qIREVEyyJPJAcu/8UenSvmRJg2w9Mg1dF57DaevPzR10TSPQSoiItK83bt3o06dOsiRI4e6QrZq1aoEl9+5c6daLvbt1q1bMZa7fv06/ve//8HFxQV2dnYoWrSoynFIROZPelLNal0SA+sVgY2VBQ5fe4ZaE/Zi18W7pi4aERElA2tLC/T+zBMLvyqLbOnT4vqj12g07QCm776Et0yqnmQYpCIiIs17+vSpmuRi0qRJRq134cIFNXGG/pYly//nI7h//z4CAgJgbW2NjRs34uzZs2oSjYwZOWUxkVZIcLqVXx6s+sYPuTNYI/zJK7T+4xAGrTuLl2+YVJ2IKDXwy++CdV0D4J/LAa8jdRi84Txazz6EO49emLpomsTE6UREpHk1atRQN2NJUCpDhgxxPjds2DDkzJkTs2fPjjHzKxFpT8Fsjhhf2w3LQyIx70AYZgaGYt+lexjf3AfuWRxNXTwiIkpiGe1t0LdyVhx9YIdBG85hT3A4Phu3B8M/L4aqhbOauniawiAVERFRPHx8fPDy5Ut4eXlhwIABqueU3po1a1C9enU0btwYu3btgqurKzp16oT27dsb/T6RkZFGL//27Vuj1zMXWq4f62aepE7WFkDfmgVRwSMTflhxCmdvPkLtCYH4pWYhNCvlpnpdpabtZmlpmWRlIiJKieQ437x0TpTNnwndFh1X7cBXc4+glV9u/FSzEGyteVz8GBikIiIiiiV79uyYOnUqSpYsqYJUM2fORKVKlXDw4EEUL15cLXP58mVMmTIFPXv2xE8//YTDhw+jW7dusLGxQevWrY16v+DgYKOWlxPKiIgIhISEwMJCeyP3tVw/1s3865bT0gITaufAqMA7OHbjOX5ZfQYbjl9BD//MSG9rmWq2m6enZ5KWi4gopXLPkg4rO/tjxKYLqmft3P1XceCy9K71hWe29KYuntljkIqIiCiWggULqpuev78/Ll26hDFjxmDevHlRJ3YSxBo8eLC67+vri9OnT6vglrFBKg8PD6OWlx4PckLp7u6uyd4MWq4f66aNusk3dklRT/yx7wpG/n0R+8Ke4vKDNxjZuBj88rnAnGh5uxERJZW0Vpb4pXZhlC+QGb2WnsDF209Qd+Je/FyzkOpZZa69a1MCBqmIiIgMULp0aQQGBsbobVW4cOEYyxQqVAgrVqww+rUTc2IoPR5kPa2eVGq5fqybNuom/3Wo6I4A98xq2Mfl8Kf44o/D+KZifnz7aQE1K5S50PJ2IyJKShULZMamHuXx/bIT2HHhLvqvOaNmgR3RqBhc0qU1dfHMkvm0nkRERCYUFBSkAlN6kp9KZv+L7uLFi8idO7cJSkdEpuLl6oR13cqhWamc0OmAyTsvodGUfbgS/tTURSMiomSQKV1a/NGmFAbUKQwbKwtsP39HJVXfffGuqYtmlhikIiIizXvy5IkKMslNhIaGqr/DwsLU/T59+qBVq1ZRy48dOxarV69WQ2BkCF+PHj2wfft2dO7cOWqZb7/9FgcOHFDD/WS5hQsXYvr06TGWIaLUwd7GCkM/L4YpLYvDyc4aJ649RK3xe7D86DXoJHJFRESaJsP72gTkxZouASiQNR3uPn6JVn8cwqB1Z/HyjfYmFElKDFIREZHmHTlyROWMkpuQZOfyd79+/dT9mzdvRgWsxKtXr9CrVy8ULVoUFStWxIkTJ7B161ZUqVIlaplSpUph5cqVWLRokZr977ffflPBrZYtW5qghkSUEtQomh0bu5dHmbzOePoqEt8tO4Fui4Pw8PlrUxeNiIiSgSROX9OlnMpLJSSxeoNJ+xBy54mpi2Y2mJOKiIg0T2bmS6g3w5w5c2Lc7927t7q9T+3atdWNiEgvRwY7LGxfFlN3XcLoLRex9sQNHLt6H+Oa+aBkHmdTF4+IiJKYrbUlBtbzQgWPzPh++QmcvfkItSfsQf86RdTQcCZVTxh7UhERERERfUSWFmnQubI7lnf0Qy5ne1x/8BxNpu3H2K0X8SbyramLR0REyaBq4azY1KMCyrlnwovXb9Hnr1P4Zv4xPHj2ytRFS9EYpCIiIiIiSgK+uTJifbdyaOjrirc6YOzWYDSbfgD/RDwzddGIiCgZZE1vi7ntSuOnmp6wtkyDTWdu4bOxe7D/0j1TFy3FYpCKiIiIiCiJONpaY3RTHzXczzGtFY5cvY+a4/ZgzYkbpi4aERElAwuLNPi6Qn6s7BSAfJkccOvRC7SYeQDDN53Ha/aufQeDVERERERESayejys2dC8P31wZ8PjlG3RbdBy9lp7Ak5dvTF00IiJKBl6uTljbtRyalswJSZU6eeclNJq6H1fCn5q6aCkKg1RERERERMkgp7M9lnXwQ7dP3GGRBlhx7JrqVXUs7L6pi0ZERMnAIa0VhjUqhsktiyO9rRVO/PMANcfvwdIj/yQ4yU9qwiAVEREREVEysbK0QM9qBbH4az+4ZrBDWMQzNJ66H+O3BSNSElcREZHm1SyaXSVVL5PXGc9eRaL38pPosvA4Hj57jdSOQSoiIiIiomRWOq+zGv5X1zuHCk6N3nIRzabvZ1J1IqJUIkcGOyxsXxa9PysIK4s0WH/qJj4btzvVJ1VnkIqIiIiIyASc7KxVQvUxTb2RLq0VDl/5N6n66qDrpi4aERElA0uLNOhUyR1/dfJH3kwOuPnw36TqQzeex6s3qTOpOoNUREREREQmkiZNGjTwdcPG7uVR/L+k6t0XB+HbJUF49ILDPoiIUoNibhmwrms5NCv1b1L1qbsu4fMp+3D57hOkNgxSRTNp0iTkyZMHtra2KFOmDA4dOpTg8mPHjkXBggVhZ2eHnDlz4ttvv8WLFy+SrbxEREREpJ2k6ks7+KFHVQ+VVH3l8euqV9WRKxGmLhoRESVTUvWhnxfD1P8VRwZ7a5y6/hC1xgdi8aGwVJVUnUGq/yxZsgQ9e/ZE//79cezYMXh7e6N69eq4c+dOnMsvXLgQP/74o1r+3LlzmDVrlnqNn376KdnLTkRERETaSKreo2oBLOvoh5zOdrh2/zmaTNuPMVsu4k1k6hz2QUSU2nzmlR2buleAf34XPH8diR//OoWO84/i/tNXSA0YpPrP6NGj0b59e7Rt2xaFCxfG1KlTYW9vjz/++CPO5fft24eAgAC0aNFC9b6qVq0amjdv/t7eV0RERERECSmR2xkbupVHQ19XyIR/47YFq2BV2D0mVSciSg2yOdli/pdl0KeGJ6wt02DzmdsqqfrekHBonZWpC5ASvHr1CkePHkWfPn2iHrOwsEDVqlWxf//+ONfx9/fH/PnzVVCqdOnSuHz5MjZs2IAvvvjCqPeOjIw0uryyztu3bxO1bkrHupkvLdePdXuXpaVlkpWJiIgAR1trjG7qg4oFM+OXVadxLOwBao7fg4H1iqCBr6vKZUVERNplYZEGHSrmR4B7JnRbfByX7z7F/2YdxNfl86FntQJIa6XN3+MMUgEIDw9XJ2hZs2aN8bjcP3/+fJzrSA8qWa9cuXJqfOibN2/QsWNHo4f7BQcHG11eOaGMiIhASEiICqZpCetmvrRcP9btXZ6enklaLiIi+lc9H1eUyJ1RJVKX2f96Lj2BHRfuYlB9LzU7IBERaZuXqxPWdy2P39afxcKDYZi2+zICQ8Ixrpkv3LOkg9YwSJVIO3fuxODBgzF58mSVZF1O8Lp3747ffvsNffv2Nfh1PDw8jH5vCajJ+7m7u2uuNwPrZr60XD/WjYiITMktoz0Wf+2HKTtDMGZrMNaeuIFjV+9jTFMflM7rbOrimYUhQ4bgr7/+UhegZdIjGRUxbNgwNQlSfGbMmIG5c+fi9OnT6n6JEiXU738ZRaEnF6slR60s++DBA5UOZMqUKYn6jU9EFB87G0sMblAUlQpkxg8rTuLMjUeoPWEP+tYujBalc2mqdy2DVAAyZcqkTs5u374d43G5ny1btjjXkUCUDO376quv1P2iRYvi6dOn+Prrr/Hzzz8b3CMhsSeF8vqyrhZPKlk386Xl+rFuRERkSpYWadDlEw817KPHkiBcvfcMzabvR+fK7uhWxQPWltrq6fux7dq1C507d0apUqXUCAgZ/SA5Zc+ePQsHB4d4L0pLzlkJaMns3xLUknXOnDkDV1dXtczw4cMxfvx4/Pnnn8ibN686R5DJl+R1ZR0ioo+pWpFs8MmZAb2WncCe4HD8vPI0dpy/i2GfF4VLurTQArZmAGxsbNSVkW3btsUYAiP3/fz84lzn2bNn7wSi9Cd4qWl6SCIiIiJKPr65MmJ9t/JoVMJNJVWfsD0Ejabux5Xwp6YuWoq2adMmtGnTBkWKFFGzeM+ZMwdhYWEqL218FixYgE6dOsHHx0cNc585c2bUOYL+N//YsWPxyy+/oF69eihWrJjqeXXjxg2sWrUqGWtHRKlJlvS2+LNtafxSqxBsLC2w9ZwkVd+D3RfvQgsYpPpPz549VTdduQpy7tw5fPPNN6pnlMz2J1q1ahUjsXqdOnVUV97FixcjNDQUW7ZsUVdO5HH2RiAiIiKipJIurRVGNvbGxBa+SG9rhRP//JtUfemRf3ix1EAPHz5U/zs7Gz5cUi5Sv379OmodOQe4deuWmmxJz8nJSaUCiW/yJSKij5VU/avy+bCqcwA8sqTD3ccv0eqPQ/ht3Vm8eG3ekz1xuN9/mjZtirt376Jfv36qsZErJnLFRZ9MXa60RO85JVdMZNyn/H/9+nVkzpxZBah+//13E9aCiIiIiFKL2sVyoHiuf5OqHwyNQO/lJ7Hrwl2Vt8TJnknV4yO9oXr06KHyR3l5eRm83g8//IAcOXJEBaXknEHENfmS/jlDccbvmFg388S6Jb+CWR2wqpMfhm68gHkHwzArMBR7Q8IxpkkxFMjqmKT1S6rOOQxSRdOlSxd1i29MenRWVlYqSaLciIiIiIhMIUcGOyxsXxbTdl/C6L8vYv2pmzgWdh+jm/jAL7+LqYuXIkluKkmGHhgYaPA6Q4cOVSMo5JwgKXJNccbvmFg388S6mU7LQtZwT5cNo/fewflbj1Fv0j58VdIFdTzTG5RUPTH1S6rZvhmkIiIiIiIy86TqnSq5o5x7JnRfHITQ8KdoMfMAOlbMj2+rFoCNVco7oTIVuSC9bt067N69G25ubgatM3LkSBWk2rp1q8o7paefYEkmW8qePXvU43JfRmUYgzN+x8S6mSfWzbQ8PIDPSr9E7xWnsOtiOCYfDMe5B2kwtKEXMr0nqXpKqh+DVEREREREGlDMLQPWdS2ncpIsPvwPpuy8hMDgcIxr5oN8mdMhNZNcXV27dsXKlStVbyiZic8QMnufpPPYvHkzSpYsGeM5eQ0JVEkidX1Q6tGjRzh48KDKb2sMzvj9LtbNPLFuppXVyR5z2pbGnH1XMGTjeey4cBe1JuzFiMbeqFwwi1nUj5dViIiIiIg0wiGtFYZ+XgxT/1ccGeytcer6Q9QaH4jFh8JSdVJ1GeI3f/58LFy4EI6OjipnlNyeP38etUzsiZKGDRumJkb6448/kCdPnqh1njx5op6XITSS22rQoEFYs2YNTp06pV5D8lbVr1/fJPUkIkqTJg3aBuTFmi4BKJjVEeFPXqHt7MMYsOaMWSRVZ5CKiIiIiEhjPvPKjk3dK8A/vwuev47Ej3+dwjfzj+H+01dIjWRWbpnRr1KlSmponv62ZMmSqGVkoqSbN2/GWOfVq1do1KhRjHVk+J9e7969VQ+tr7/+GqVKlVIBLJl8KSnyVhERGcMzW3qs7hKANv551H3pXVVv4l6cv/UIKRmDVERElCpI/hGZhVWucMsVplWrViW4vAwHkeVi36LP2DRgwIB3nk+qJJJERMbK5mSL+V+WwU81PWFtmQabztzCZ+N2q5mfUhvpRRbXrU2bNjGO+3PmzIm6f+XKlTjXkWO/nhz3Bw4cqNqGFy9eqLxVBQoUSPb6ERHFxdbaEgPqFsHstqVUXqoLtx+j7sS9+CMwNMX2rmWQioiIUoWnT5/C29sbkyZNMmq9CxcuqCvr+luWLDHH8xcpUiTG88bMFkVElNQsLNLg6wr5sbJTAPJldsDtRy/xv1kHMWTDObx689bUxSMiomRQuWAWbOpRHlU8s6hj/8B1Z9Fm9mHcefwCKQ0TpxMRUapQo0YNdTOWBKUyZMgQ7/NWVlZRMzwREaVUXq5OWN+1PH5bfxYLD4Zh2u7LCAwJx5gm/z9bHRERaVemdGkxs3VJzD9wFYPWn8Oui3dRY+weDG9UDJUKZEJKwSAVERFRAmTGppcvX8LLy0sN8QgICIjxfHBwsBpCKPlH/Pz8MGTIEOTKlcuo95Bpf41d/u3bt0avZy60XD/WzTxppW42lsBvdQujgrsL+qw8jTM3HqHupH3o4eeCfPmMq5upZ38iIiLjyRDlL/zyoGw+F3RbHIRzNx/hyz+P4MtyedDYPWUc1xmkIiIiioMkx506daqaclyCVDNnzlQJd2Vq8eLFi6tlypQpo/KXFCxYUA31+/XXX1G+fHmcPn1azR5lKAl0GUNOliMiIhASEqKmC9YaLdePdTNPWqtbHmtgYu0cGBV4BydvPUc63XOj68b8e0RE5ssjqyNWdfbHiE0XMDMwFLky2gN4iZSAQSoiIqI4SOBJbnr+/v64dOkSxowZg3nz5qnHog8fLFasmApa5c6dG0uXLsWXX35p8Ht5eHgYVTbpzSEnlO7u7prszaDl+rFu5kmLdZOjzpKinjh1/QFsn93RVN2IiOj90lpZ4pfahVHXJwcKZ0un2rmUgEEqIiIiA5UuXTrBxOiSu0pmdTK2kU/MiaH0eJD1tHpSqeX6sW7mSYt1k6p458yI4OBwzdWNiIgMU8wtQ4oazm7+/ZWJiIiSSVBQkBoGGJ8nT56o3lYJLUNERERERHFjTyoiIkoVJIAUvYdTaGioCjo5OzurROd9+vTB9evXMXfuXPX82LFjkTdvXhQpUgQvXrxQOam2b9+Ov//+O+o1vvvuO9SpU0cN8btx4wb69++veiI0b97cJHUkIiIiIjJnDFIREVGqcOTIEVSuXDnqfs+ePdX/rVu3VsnPJfF5WFhY1POvXr1Cr169VODK3t5e5ZzaunVrjNe4du2aCkjdu3cPmTNnRrly5XDgwAH1NxERERERGYdBKiIiShVkZj6dThfv8xKoiq53797qlpDFixd/tPIREREREaV2zElFREREREREREQmxyAVERERERERERGZHINURERERERERERkcml0CSXoICIiIiIiIiIiSgbsSUVERERERERERCbHIBUREREREREREZkcg1RERERERERERGRyDFIREREREREREZHJMUhFREREREREREQmxyAVERERERERERGZHINURERERERERERkcgxSERERERERERGRyTFIRUREREREREREJscgFRERERERERERmRyDVGZo0qRJyJMnD2xtbVGmTBkcOnQI5m737t2oU6cOcuTIgTRp0mDVqlXQiiFDhqBUqVJwdHRElixZUL9+fVy4cAFaMGXKFBQrVgzp06dXNz8/P2zcuBFaNHToULVv9ujRA1owYMAAVZ/oN09PT1MXi1JxO6DltkDL7UBqagvYDlBKwnbA/Gi5LUgt7YDW2oIBKbQdYJDKzCxZsgQ9e/ZE//79cezYMXh7e6N69eq4c+cOzNnTp09VXaTB1Zpdu3ahc+fOOHDgALZs2YLXr1+jWrVqqs7mzs3NTR2ojx49iiNHjuCTTz5BvXr1cObMGWjJ4cOHMW3aNNX4akmRIkVw8+bNqFtgYKCpi0SpuB3Qclug5XYgtbQFbAcoJWE7YJ603BakhnZAq21BkZTYDujIrJQuXVrXuXPnqPuRkZG6HDly6IYMGaLTCtktV65cqdOqO3fuqDru2rVLp0UZM2bUzZw5U6cVjx8/1nl4eOi2bNmiq1ixoq579+46Lejfv7/O29vb1MWgREgN7YDW2wKttwNaawvYDlBKw3ZAG7TeFmipHdBqW9A/hbYD7EllRl69eqWi01WrVo16zMLCQt3fv3+/SctGhnv48KH639nZGVoSGRmJxYsXq6tB0sVXK+SKV61atWJ877QiODhYdafPly8fWrZsibCwMFMXid6D7YA2aLUd0GpbwHaAUhK2A9qh1bZAi+2AltuC4BTYDliZugBkuPDwcPWlz5o1a4zH5f758+dNVi4y3Nu3b9X45YCAAHh5eUELTp06pRqgFy9eIF26dFi5ciUKFy4MLZAGVrrRS9derZH8FXPmzEHBggVV195ff/0V5cuXx+nTp1WuBEqZ2A6YPy22A1puC9gOUErDdkAbtNgWaLUd0HJbUCaFtgMMUhElcwRevvQpYqzvRyIHtaCgIHU1aPny5WjdurUac2/ujdI///yD7t27q5wBkpRUa2rUqBH1t4yrl0Yqd+7cWLp0Kb788kuTlo1Iy7TYDmi1LWA7QERJRYttgRbbAa23BTVSaDvAIJUZyZQpEywtLXH79u0Yj8v9bNmymaxcZJguXbpg3bp1atYSSS6oFTY2NnB3d1d/lyhRQl1hGDdunEoqaM6kK70kIC1evHjUY3LlUrbfxIkT8fLlS/V91IoMGTKgQIECCAkJMXVRKAFsB8ybVtsBrbYFbAcoJWI7YP602hZosR1IbW1BhhTSDjAnlZl98eULv23bthhdReW+lsb7ao3kfZTGSLq8bt++HXnz5oWWyT4pB2tzV6VKFdVtWa4I6W8lS5ZUY7Xlb600RnpPnjzBpUuXkD17dlMXhRLAdsA8pbZ2QCttAdsBSonYDpiv1NYWaKEdSG1twZMU0g6wJ5WZkelmpeukfDFKly6NsWPHqqR0bdu2hbl/IaJHbENDQ9WXXhIJ5sqVC+benXfhwoVYvXq1Gtt769Yt9biTkxPs7Oxgzvr06aO6ico2evz4sarnzp07sXnzZpg72VaxcwQ4ODjAxcVFE7kDvvvuO9SpU0d16b1x44aaxloa2ebNm5u6aJRK2wEttwVabge03BawHaCUiu2AedJyW6DVdkDrbcF3KbUdMPX0gmS8CRMm6HLlyqWzsbFRU9AeOHBAZ+527NihpmCNfWvdurXO3MVVL7nNnj1bZ+7atWuny507t9oXM2fOrKtSpYru77//1mmVVqabFU2bNtVlz55dbTtXV1d1PyQkxNTFolTcDmi5LdByO5Da2gK2A5RSsB0wP1puC1JTO6CltqBpCm0H0sg/pg2TERERERERERFRasecVEREREREREREZHIMUhERERERERERkckxSEVERERERERERCbHIBUREREREREREZkcg1RERERERERERGRyDFIREREREREREZHJMUhFREREREREREQmxyAVURLYuXMn0qRJgwcPHiT7e8v7yi1DhgwJLjdgwAD4+PgkW7nk/fRlGzt2bLK9LxGRKbAdiPv92A4QUWrBdiDu92M7QO/DIBXRB6pUqRJ69OgR4zF/f3/cvHkTTk5OJinT7NmzcfHiRaQk3333nfpM3NzcTF0UIqKPiu2AYdgOEJFWsR0wDNsBMoSVQUsRkVFsbGyQLVs2k72/XDXJkiULUpJ06dKpm6WlpamLQkSU5NgOvIvtABGlJmwH3sV2gAzBnlREH6BNmzbYtWsXxo0bF9V19cqVK+90750zZ45qKNatW4eCBQvC3t4ejRo1wrNnz/Dnn38iT548yJgxI7p164bIyMio13/58qW64uDq6goHBweUKVNGvXZiDB06FFmzZoWjoyO+/PJLvHjxIsbzhw8fxqeffopMmTKpKz4VK1bEsWPHop5v164dateuHWOd169fq8Zv1qxZ6v7y5ctRtGhR2NnZwcXF5f/YuxN4qeb/j+Pf9n2RtGtTVFrVv1QIRZHIEhKtZOsnshWpyE+2qJQlS9Z+kp/il0RahFJaSKsiRas2LVrvPf/H+8sZM3Pn3mZu996Zc+b1fDxutzn3zMz5njNzPud8vptp06aN2b9/f6a2FwC8gDhAHACQ3IgDxAFkLZJUwHFQMGrevLm56aabbNNV/Zx88skR11UAGjVqlHn33XfNtGnTbHC5/PLLzdSpU+3PW2+9ZV566SV7Ynf16dPHzJs3zz5n6dKlplOnTqZdu3ZmzZo1MW3ne++9Z/uAP/bYY2bhwoWmfPny5vnnnw9ZZ+/evaZbt27mq6++Mt98842pWbOmufjii+1yufHGG+12q4wuBVmV65prrrHLO3fubIPXypUrbfmuuOIK4zhOjHsVALyDOEAcAJDciAPEAWQxB8BxadWqldO3b9+QZbNmzdKZ2Nm1a5d9PG7cOPt47dq1gXVuvvlmp3Dhws7evXsDy9q2bWuXy/r16508efI4GzduDHnt1q1bOwMGDEh3e/Q+kyZNClnWvHlz57bbbgtZ1qxZM6dBgwbpvk5KSopTrFgx53//+19gWZ06dZwnnngi8LhDhw5O9+7d7f8XLVpk3/uXX35xMlKlShXn2WefzXAdAPAS4gBxAEByIw4QB5B1aEkF5BA16T3llFMCj9XUVs161S87eNm2bdvs/3/44Qfb1PfUU08N9N/Wj5oT//TTTzG9t2oy1DQ4mGp8gm3dutXWAKnGRM17ixcvbvbt22c2bNgQWEe1JxqE0V3/k08+sTUl0qBBA9O6dWvbvFc1PC+//LLZtWtXTNsJAH5GHACA5EYcAI6NgdOBHJIvX76Qx+qjHmlZamqq/b8CggYVXLRoUZrBBYMDWVZR094dO3bYJstVqlQxBQoUsIHr8OHDgXW6du1q+vfvb5scz50711SrVs2cffbZ9m/axunTp9vln332mXnuuefMgw8+aObPn2/XA4BkRxwAgORGHACOjZZUQBbM3BE8uGFWadSokX1d1aTUqFEj5CfWmUJq165tg0Mw9TMP9vXXX9uBGtXv/PTTT7dBafv27SHraPDDjh072toTDf7Yo0ePNEG1ZcuW5uGHHzZLliyx+2bSpEkxlx0AvIQ48A/iAIBkRBz4B3EAx4uWVMBxUhNdnfA1i4dqNEqVKpUlr6tmvV26dLG1FcOHD7dB6vfffzczZsww9evXN+3bt4/6tfr27WtnHmnSpIkNGu+8845Zvny5qV69emAdNevVYI1aZ8+ePebee++1s3KEUxNfzeqhgKnaFpf2gbbtwgsvtDN86LG2VwERAPyMOPAX4gCAZEUc+AtxAFmBllTAcdKUsGraWqdOHXPSSSeF9Nk+XqqhUFC6++677VS1qrXQ1LCVK1eO6XU028ZDDz1k7rvvPtO4cWOzfv16c+utt4aso2lj1Wf8jDPOMDfccIOtRVFwCadpZDUbSNu2bU2FChUCy9Vnfc6cObbmRQF14MCBNphedNFFx7EHACDxEQf+QhwAkKyIA38hDiAr5NLo6VnySgASgprYqkmtAlh2UN/4ihUr2oCpKWUzU9N055132h8AQNYjDgBAciMOwMtoSQX4UOfOnU2lSpWy9DU1gKP6ww8dOtSULFnSXHrppTE9/7HHHrPNn7OyZgkAEBlxAACSG3EAXkVLKsBn1q5da3+ryXFWzqKhPvZ6PQU7DZKo6WVjsXPnTvsjagataW0BAFmPOAAAyY04AC8jSQUAAAAAAIC4o7sfAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBWQoHLlymWGDBlivOCXX36x2/v666/H9Dytr+fp+Yku0raee+659gcAsgLn/ayTKPFl9uzZdjv0GwDS0717d1O1alXjFZm9Bs6pOJco8VTHVMcWsSFJhQy999579ks+adKkNH9r0KCB/dusWbPS/K1y5cqmRYsWMb3X+PHjzYgRI45re5G9OEaA/3HeRzCOUdZjn8Ir3GSv+1OwYEFToUIF07ZtWzNq1Cizd+/eeG8istGKFStsoifeyX4/YZ9GhyQVMnTWWWfZ31999VXI8j179phly5aZvHnzmq+//jrkb7/++qv9cZ8bLS7aEl96x6hKlSrmwIED5oYbbjDJ5LPPPrM/gJ9w3kcwzvvH55xzzrH7Sb9dfO7hNY888oh56623zAsvvGD+9a9/2WV33nmnqVevnlm6dGm8Nw/ZmFB5+OGHIyZUuAaOzurVq83LL78c1T7FP/IG/R9IQ7Ul1apVS3OzMm/ePOM4junUqVOav7mPY71ZSUT79+83RYoUMcnuWPvBrV1LtmOYP3/+bHldIJ4473Pe9/p5P5Hkzp2b/QTPu+iii0yTJk0CjwcMGGBmzpxpLrnkEnPppZealStXmkKFCnn+vHrw4EF7bafvbTJz90NGuAaOToECBeK9CZ6U3N9AREU3HUuWLLE1gS7Vop9++uk2aH3zzTcmNTU15G+6eG3ZsmVg2dtvv20aN25sA1ipUqXMtddea2vdXerT/PHHH5v169cHmhQfq1/2uHHjzPnnn2/KlCljTwB16tSxNTyRfPLJJ+bss8+2AbJYsWKmffv2Zvny5SHrqL9w0aJFzU8//WQuvvhiu16XLl0CwfXuu+82J598sn2v0047zTz99NP2hi3Y9OnT7f4qWbKkfS2t98ADD4Sss23bNtOrVy9TtmxZe+Gq7jNvvPGGOZ6xLiZMmGDfp1y5craMumAI3r/y5Zdf2ptLdclRGVSWu+66K+S4ZrQfMjpG6Y1NsmrVKnP11Vebk046yR577Y8HH3zwmOWK5nhl1Cz9iy++MLfddpv9bFSqVMn+TdutZdoGbcuJJ55o90ekmgy9lz5bWk/Pf/TRR0M+4+n1x09vDJRIY5KsWbPGXHnllfaY6XOg99H34o8//jhmOYHsxnmf875XzvuxnLOjfR93f2zcuNF07NjR/l/lueeee0xKSkrIuu+++679nOu1ihcvbluWjBw5Mt3zf3r7dN++fXab+vbtm2abf/vtN5MnTx4zbNiwqPYHkBP0nXvooYfsZ1nn+2OdTwYPHmzy5ctnfv/99zSv1bt3b3sOVXIkPWqxpdeuXr26PY/q3NezZ0+zY8eONOvqu6u/6Zyrc59i12uvvRayjvvd1Hd44MCBpmLFiqZw4cK21bBMnDgxEMNKly5trr/+evu6wbZs2WJ69Ohhzzt6n/Lly5vLLrsszXXg888/b7dB66gi6Pbbbze7d+82maHzhZKDasXUsGFDuy8UCz/44IOQ9Xbu3GnPWTon6Xjo/KT4/f3330e1H9SdU/FDzjvvvMD5KvhcFj4mlY6furKdeuqpdru0P6644gr7WchINMcrPYcOHbJxTedofdYUC3XOzOz7uPtDQx/8+9//tsdWZWndurVZu3ZtyLrRXMsHj0mleJnePu3WrZv9nB05ciTNdl944YU2liYTWlLhmHTxrSa+8+fPD5yMdEOisUf0oy+iuoDUr18/8LdatWrZJIDoC64gpovWG2+80Qan5557zjZ9102QgpIuYPU6Oqk8++yz9nk6oWZENyY6uehkpO4n//vf/2wSQhemOvm7tO364qv//BNPPGH+/PNP+1z3Jiz4pujo0aN2Pf1NNyM6SeuGRO+hMVh0k6GA8Omnn5p7773Xnuzc7dVFroKG9oOaRevkp5NZcLcY3RhoH2p5nz59bGsFBUGdvBSsIl2cRkP7WCe5+++/394MqRtBmzZtzHfffReo2dL7qOy33nqrPTYLFiywx0H7XH8LFmk/6AQcyzHSxYRuBHRBoosP7WcFKR0nbW96Yjle6dHnQMFq0KBB9kZTvv32WzN37lwbPBREdAGh19XxUNNbldG94FDg0D7o37+/vWkYO3ZshjWEsTp8+LAtnwKrms1r3+qzNGXKFPs5KFGiRJa9F5AZnPc573vlvB/LOTuW91EySus1a9bM7o/PP//cDB8+3Jxyyil2f7oJys6dO9ubF72eqEWJjn96xzW9z71+Lr/8cpt8fOaZZ2xSyvWf//zHfibdBCqQKNTdV8lyJUxuuummDM8nzZs3t+dJfcZ1Lgy+Jnr//fftzX5GrQ71ffv5559tUkjnJp1/9V3Xb1Wc6HwoW7duNWeeeaZ9rPfR9aCS0zqXKwGlborBhg4dalsFKaGj6zL9X8kEvc///d//2eSwXlPJZ3233Rgm2ma9v67ldP7QuVjbuWHDhsD5REkbde/S+VnnDnX/0nlH16V6PZ0vY6XkyDXXXGNuueUWe05TBY6SH9OmTTMXXHCBXUf7avLkyXa54o7K8NJLL5lWrVrZ614lyzLaD0qM3HHHHTZZpWNcu3Ztu577O5zOmYqHM2bMsNfaOgdqzDLtD10v6NwZSazHK5yuMZQkve666+z1iVr4qfLheN/n8ccfty3qtD90zn7yySftOVjXRZm9ltc1UHr7VN+lN998015rXHLJJSExTmVSkjepOMAxLF++XNXGztChQ+3jI0eOOEWKFHHeeOMN+7hs2bLOmDFj7P/37Nnj5MmTx7npppvs419++cU+/ve//x3ymj/88IOTN2/ekOXt27d3qlSpEvV2/fnnn2mWtW3b1qlevXrg8d69e52SJUsGtse1ZcsWp0SJEiHLu3XrZsvZv3//kHUnT55slz/66KMhy6+66ionV65cztq1a+3jZ5991q73+++/p7vNI0aMsOu8/fbbgWWHDx92mjdv7hQtWtTuP5fWGzx4cIb7YNasWXa9ihUrhjz3vffes8tHjhyZ4f4aNmyYLcP69euPuR8yOkbr1q2zzxk3blxg2TnnnOMUK1Ys5LUlNTU18H+tr+fp+bEer0jc1zvrrLOco0ePhvwtUvnnzZtn13/zzTcDy+688067bP78+YFl27Zts+8fvK3SqlUr+5NeecKPk37LkiVL7OOJEydmWB4gXjjvc973ynk/2nN2Zj4XjzzySMi6jRo1cho3bhx43LdvX6d48eJp4k1G5/+M9umnn35q1/3kk09CltevXz8k1gA5xf2+fvvtt+muo++PvhvRnE903mvWrFnIsg8++CDNdySSSOez//znP/a5c+bMCSzr1auXU758eWf79u0h61577bV2W93Xcb+bih/Br63zc5kyZZy6des6Bw4cCCyfMmWKXX/QoEH28a5du+zjp556Kt1t1rkof/78zoUXXuikpKQElo8ePdo+97XXXgvZb9HEQ62j5/73v/8NLPvjjz9smYOPw8GDB0PeU3Q+LFCgQMi5Lb39ILpOTe/YhF8Dqyxa95lnnkmzbnAMCI9z0R6vSL777jv7erfddlvI8uuuuy7T7+Puj9q1azuHDh0KrKfYquW6lonlWl7HS8f2WPtUx6pSpUrONddcE7Jc+1Mx++eff3aSCd39cEzK7qoG1h1zRM1E1TrFncVJv91aY41Zoky6Oy6Jmp6qhlu16du3bw/8KNtcs2bNiDNERSu4llQZbr2uagdUc+A2s1T2Xtls1XQGv79qKFU7Gun93RpS19SpU+36ynwHUzcQnWuVhRe3VuXDDz9Mt5uBXktl1/a4VIOi11ZTf3VTy4yuXbvaJq6uq666yjax1ftF2l86ftoPOnYqg2qFjrUfYqFWE3PmzLFNatXNJJhb0xVJZo5XJKrNC66FDi+/mtKqeXiNGjXscVu8eHHgb9pnqmlp2rRpYJlqW7KyBtutXVFtiWrygUTDeZ/zvlfO+9GeszPzPmqlEEytxPRZc+n4a7/qtbOCWlqodcM777wTWKYWCGqhpq5GQCJSK8BIs/xFOp/ovKWWKMHdv/R5V1dkncszEnw+U7cyfX/13Rf3Ok7ntv/+97+mQ4cO9v/B33W1elGcCL7mE7VECn7thQsX2hZRaqUb3LJLrXPUYljddd3tUcsjddXatWtXxG1WC0y1uFErneBxrnSdqu537mvFSucJtbx06bW0b3VeV8sbUcte9z0Vo3Xd63ZJD98HkfZDrLTf1V3NHVg/mhiQmeMVzI134bE6vFVUZt5HLemCx93S+V/cGJDV1/I6VopbH330Ucj3Sd8PxW21hksmJKlwTDqx6MvhjkGiGxONB6Ib/PCbFfe3e7Oi5qg6GejGRBeNwT9qEq8gkFl6L13QqWm/LhT1mu44IO7Nit7f7Tcf/v5qmhz+/uo+4o5h5FJfewWD4JsBcZto6u+iZrcaj0XNTtXXWU1d1Z85+MZF62pfhA/IGP5asdJrhh8zHZ/gPvFqeqzuJRobxh1fw70gCB8HKdJ+iIV7Aq9bt25Mz4v1eKUn0olcXW7U/c8dX0aBVK+rm5bg8rvHKFxW9gXX9vXr18+88sordjsUIMeMGcN4VEgYnPc573vlvB/tOTvW99HNqf4W7IQTTgi5GdVNrMZe0Tgv2ndK0Km7zfHepKiLjnvToxsUbYs7jgmQaJRsDz9Xpnc+0TlT12BuIlbnIXWP0uc+o2S2O8aSupDpXKtkir6f7vWeez5TslzXdeoGGP49V9JBwr/r4deM7jk50nWfklTu31UOdfNVpYW2SV251CXMTRJl9FpKfmhsrcye/3WuD99fOheJGwMUh9SlWOfH4OteJb0jXW8ebxJEiUeVU8c+Wpk5XsG0/3TeDO9KGL6/M/M+4ZUtOv+LGwOy41peiUbdr0yaNMk+VtfQRYsWJeUsuoxJhajo5kNjSvzwww+BcUlc+r87Todq3XVhrxOve4LUSVQn8PCWLdGMP5LRiVBjQChYaOwGJR50wldGXSdk9wbB/a1xKFSTHS78RBpc6xArBUzVIqs2VjUjulBVv3tdEOsCOFL5c4pqUNRHXQFe45dov+kmT8dMNzDhLQCOZz8cj1iPV3oi1QSpZkd99lW7onERVAOiz6ZuKtNrARGr9C6wwgfaFY1ton2vFhj6fKgWSOMeKClwPDeKQFbhvH9snPcT57yf1e8TzbFT4lZjgKkmXZ93/SjO6EYjswPj67lPPfWUTVSp1df48ePt+CSMVYhEpLHVdFPuVmAc63yiG319npWkUsWhxqLSmD7RtBRU61yNLarYo3ECFUv0vW7Xrl2a879eTy2DInHHUnQdT+shXVOqdY6+rzoPaCxGXctpDKFGjRqZeHrsscfs9ih5rvGmVFmhY6JtjnTdm5Vjr0YrM8crp94nvRgQPHlKVl/LawB8Ddb/9ttv21ig37rO0Wc/2ZCkQlTcGnLdjOhmJbgZpb5MCkZq7qomvJrJw6XMtr7Myja7Gf70HKsGJZhunBTU1CQyONMd3lzfzazrQlK175lRpUoV21xXTS+Da4o0g5H7d5dO/rqJ0o9uohQgNEiqtkvvr3VVg6GTZXDwjvRasXBriF3a5xqk1z3h6ibzxx9/tBfNOum5Yu2iEO0xcm9W1U0hFllxvNKjCyEFJgWU4Obi4bOr6BiE70+3NuNY3FqW8NdMr6ZMM67oR7Op6MJLLTJefPFFOzMVEG+c9znve+G8H+05O7vii24gdJOqHx1jta7S4MS6OQy/cY9mn6olmm5udROvmxy1htNg90AiUtJX1IokWjofaQY8DRyuz7k+75oQIyNqvaIBuTUAuZJbrvDvvjvDm5L0x3P+d88hqnAIpmXh52ydW9QVXD/aHiXQdK2pBEPwa7nnSFEXwHXr1mV6G3Wu1zk/+Fyi8724A7bruleTSrz66qshz9U1qlr+RCOWGK39oOsBDakR7WDwx3u8tH913nVbcaV3/s+Kz0V6Yr2WP9Y+1fejX79+ZvPmzbaSQt1M3fuLZEJ3P0SlSZMmtrm5golqYYNr1HWjcsYZZ9gmjhqbwb2xEU07qky0gkr4tN16HDxtrGp4o20i6Wa3g19Tz1UNZjAFTfXT1k1DpCk9I02DG043XzqpjR49OmS5au51olEzf1FtdTgFKtGNlftaagasmvbgGVB0AaoaoWP1x0+PZoMI7r+swKSTm7ttkfaX/h88TXY0oj1GCgZq9qxpXXWBHSz8c5DVxys92gfh7639Ht7KScdINSCaBSv4fYPHCEmPexOklhUuvb6aFwfTLCI67sEU4HQD635WgHjjvM953wvn/WjP2dkRX4I/y6JzuJskzOhcfqx9qq4dqpXXjI0aG849pkAiUWshtdBRhUQs43bq86wkibrKaUy+aFpRRTqfib4j4etpxj2NPxQpYR7N91yxT8lsJRqCv8dqKaku6+7MceqSq8rO8OtAJUPc5ykhokS2ZnML3nYljnQOiDQLXTQ2bdoU6BLmXlcqJij+uC1FI133alZXxfNo6VwVqfI1Eu13jfEUHjczigHHe7zcc6P2b3Z/LsJl9lr+WPtULWhz5cplu7aqG32yjkdISypERSdYTcP65Zdf2psT1aIH082L20Il+GZFJ2tlkgcMGGD7SHfs2NGevFV7oJOrpqjW1J6i19RFvLLHei9dvKtmMhJNi+rWXt588822P/zLL79sg4ou0l26INU0r7rg0w2VunbpQloX0OqaoWx3pJNpML2HaiJUM64yNGjQwF48qmmnWha4iQlNq6vkhAKOMvvq2/z888/bmlB3n6i8qmFV01D1MVZth24s1EpBJ9TwPv3RUhNevYf6VWuKVb2WanDd6YDVzUPbqX2t4KT9ohN1egM9pieWY6SAoW3Sfle5dRGj/af9ru4RkWTF8UqPmperxk9dJtScVoM9q6WEbgCC3XfffXY9NR9XgHCnM3dbQ2RENYEaxFOfd9286ri8++67aYKYLuw0/a3GGFFLE/1d7+kGUSARcN7nvO+F83605+zsiC8ai0znerW20DFXq1klH3WjmN407dHsU02lrnLp+6LBpzMzRT2QlZSgUetPXa/ofKPrGLXK1PdMrVuDBxg/Fn2e9f3T903XPcGTSqRH3193zCclmStWrGjPyYor4R5//HHbklUTIuh8qGs+fU81MLau+yJVLoRvnxJoOreqEkHbpzIrwa/z91133RVouaQWtOqKpfdQl2F9Z7Wuyic6xygWqtJG56hLL73UtvJRnNB3P7MJCF079urVy7ZG03hYqhzQ+wZX2ui6VzFK5VC8VutaJe+DW3Qdi85lOkbaH0qq6VpA5zvF3UgtgJQo03lNlQYaaFyVWNrnamGq1nORHM/x0vbp+Gh/avtUTrW4U0uzrHyfSDJ7LX+sfarPTLt27WxCUWNvZjaR6Xnxnl4Q3jFgwAA7ZWaLFi3S/M2dPlZTT0eailnTpJ511ll2CnP91KpVy7n99tud1atXB9bZt2+fnTJUU0TrtY41DetHH31kp2UuWLCgU7VqVeeJJ54ITH/qTjnt0jSfmqZcU4xq/VNOOcXp3r27s3DhwsA6mh5U2xaJpq6+6667nAoVKjj58uVzatasaaecDZ5SdcaMGc5ll11m19F0s/rduXNn58cffwx5ra1btzo9evRwSpcubderV69eyBTemZmKXFPw6vhoytxChQrZ6a3DpwBfsWKF06ZNGzvlud5b021///33aaYQz2g/pHeMIk1FLsuWLXMuv/xyu772+2mnneY89NBD6U5FHsvxinWqZE0V7O537QO9/qpVq9JMDStLly610+rqvTXN+9ChQ51XX301zbaGT78rP/30k93PmuK3bNmyzgMPPOBMnz49ZLpZTSPbs2dPWy69R6lSpZzzzjvP+fzzzzMsH5DTOO9z3k/0834s5+xo3ye9/aFjE3zp/P7779up5XUMdFwrV67s3Hzzzc7mzZtD3i98uvFoPvcXX3yx/dvcuXOPWX4gu7jfV/dHn/Ny5co5F1xwgTNy5Ehnz549aZ6T0fnEtWDBAvt6+v5E67fffgucW/T97dSpk7Np06aI502dcxVvTj75ZHv+1ja3bt3aGTt2bJrv5sSJEyO+34QJE5xGjRrZ6zldp3Xp0sVug2v79u32PRTbVF5tU7NmzZz33nsvzWuNHj3arqdt0bXhrbfeaq9Lw/fbsWKgaB2d7z/99FMbD7V9eu3wchw8eNC5++67nfLly9sY0bJlS2fevHlprl2PtR9efvllp3r16k6ePHlCzmWRroH//PNP58EHH3SqVasW2O9XXXWVvTZ2ZfZ4pefAgQPOHXfc4Zx44on2OHTo0MH59ddfs/xzER7zor2Wj3Sfkd4+db333nt2ee/evZ1klUv/xDtRBiBzNB6MavuVbdf04wAAf+O8nxw0vbxaPkRqEQB43ffff29blKjlTTLOXHY81JpLY9dpVkT404cffmhboaultlqkJSPGpAIAAAAShLqvqgsiN+/wK3XVVjdXjWEIIO33o3r16iFDKSQbxqQCAAAA4kzj62issldeecWOi6Ox1wA/0SytK1assOPGaTwfdxBpAMaOY6uxFD/++GM7Blossyv6DUkqAAAAIM4005kGOa5cubJ54403ArN0AX7xr3/9yw7wrVk5NZg4gH9oEHi1MOzVq5cdbD6ZMSYVAAAAAAAA4o4xqQAAAAAAABB3JKkAAAAAAAAQdySpAAAAAAAAEHckqQAAAAAAABB3JKk8KCUlxaxatcr+9hvK5l1+Lh9lQ6Lx+3Hzc/komzdRNiQiPx87yuZNlM27UhKofCSpAAAAAAAAEHckqQAAAAAAABB3JKkAAL4wZswYU7VqVVOwYEHTrFkzs2DBggzXnzhxoqlVq5Zdv169embq1KmBvx05csTcf//9dnmRIkVMhQoVTNeuXc2mTZtCXmPnzp2mS5cupnjx4qZkyZKmV69eZt++fdlWRgBAxogFAOBtJKkAAJ43YcIE069fPzN48GCzePFi06BBA9O2bVuzbdu2iOvPnTvXdO7c2d5ILFmyxHTs2NH+LFu2zP79zz//tK/z0EMP2d8ffPCBWb16tbn00ktDXkc3JcuXLzfTp083U6ZMMXPmzDG9e/fOkTIDAEIRCwDABxx4ztGjR52VK1fa335D2bzLz+WjbImvadOmzu233x54nJKS4lSoUMEZNmxYxPWvvvpqp3379iHLmjVr5tx8883pvseCBQschc3169fbxytWrLCPv/3228A6n3zyiZMrVy5n48aNTnbyy3FLxvJRNm+ibN5ALPAPyuZNlM27jiZQ+fLGO0kGAMDxOHz4sFm0aJEZMGBAYFnu3LlNmzZtzLx58yI+R8tV2x5Mte2TJ09O933++OMPkytXLtuVw30N/b9JkyaBdfSeeu/58+ebyy+/POoyxDqTitZPTU1NiBlYsoOfy0fZvImypZUnTx6TSLweCzLz2eJz6U2UzZv8XLbMli+74gBJKgCAp23fvt0G1LJly4Ys12NNpRvJli1bIq6v5ZEcPHjQjkuibiEac8R9jTJlyoSslzdvXlOqVKl0Xyc9a9asiWl9XURoDJS1a9faGyG/8XP5KJs3Uba0NI5TIvF6LIg1DgifS2+ibN7k57JltnzZFQdIUnlUquOYDTv/9N0XJCUl1Wzac8Tk3/GnyZOHsnmJn8vn97LpfIL0aeDcq6++Wt3jzQsvvJAt71GzZs2Y1teNmC4iipWpZI6k5jJ+k5qaYg7tPWLyl6pgcudOrNYax4uyeZPfy1b4SKqpUaNGwrWOSqZYEGsccGPBkuWrffu59PN3jrJ5j5/LJoXz/XU9mQixgCSVRz02e6v5av3Pxr82GP/yc9n8Xj5/lm1yl2rGy0qXLm2D6datW0OW63G5cuUiPkfLo1nfvSlZv369mTlzZqDm3H2N8MF4jx49amuh0nvf9GTmYmDK6r3m+Te/NP72m/EvyuZN/izbw63LmQZ188T9xiSZY0Fm9v2yjX+Y697bYFIcf16f+Pk79xfK5k3+LNuNZ1U1V9X4Kw7EOxaQpPKoNTsO2d+F8uUxeXL7qRbdsU0N/2oh5qdy+b1sfi+fv8vmdfnz5zeNGzc2M2bMsLMyiY6XHvfp0yfic5o3b27/fueddwaWaVYmLQ+/KVEXjFmzZpkTTzwxzWvs3r3bjoGi9xfdvOi9Ne15TsWB/Hlym/x5/dXCz+/fOcrmRf4umx+uJZMxFqzeus+kOMYeP90T+Iu/v3OUzYv8XDaTUNeSJKk8yu2d827vM02Dk/8auNEP1GxZFwFq8hzvDG5W83PZ/F6+ZCib12ng227dutmBa5s2bWpGjBhh9u/fb3r06GH/3rVrV1OxYkUzbNgw+7hv376mVatWZvjw4aZ9+/bm3XffNQsXLjRjx44N3JRcddVVdspxTSeu/eSOLaJxRnQzVLt2bdOuXTtz0003mRdffNE+RzdC1157ralQoUKOxYG7LzzV3NzqFOMnyfCdo2zekgxl84NkiwXO3xVNZ9cobV7v2dT4STJ85yibt/i5bIkWC0hSebztQy7/JXEBIGbXXHON+f33382gQYPsDUTDhg3NtGnTAgPibtiwIWQMvxYtWpjx48ebgQMHmgceeMBecGg2p7p169q/b9y40Xz00Uf2/3qtYKpJP/fcc+3/33nnHXsz0rp1a/v6V155pRk1alSOlNm9OSEOAEByxgIn9a/fxAEAfkKSyqM0aKPkJioBgKUbhPS6dMyePTvNsk6dOtmfSKpWrRo4z2ZENem6wYkHd/OIAwCQnLHA3TIf9NYEgIDE6XgIAACSaDQxAMDxcBNouXw4Pg6A5EWSyqNSqUEHgKRGSyoASG7u/QBhAICfkKTyKMakAoDkRhwAgOT2z9iEBAIA/kGSyqOoQQeA5MbYhACQ3P65H4j3lgBA1iFJ5VHM6gQAyY2WVACQ3NwkFXEAgJ+QpPIoak4AILn9c3NCIACAZJTqDpxOHADgIySpPD+rE0EJAJK6JVWctwMAEB/EAQB+RJLKo2hJBQDJjbEJASC5uS2piAMA/IQklUfRzQMAkps7cDphAACSFGNSAfAhklQeHzidllQAkJxS//5NHACA5ERLKgB+RJLK6y2p6IUOAMmJOAAASe2fMWoBwD9IUnm8Bp2KEwBI7jhAjgoAklMq3f0A+BBJKq8iKAFAcmPgdABIau7YhMQBAH5CksrzNScEJQBI5rFIiAIAkOzDfwCAf5Ck8iwGTgeAZOaORZKbSA4AST6REjcEAPyDS1uP35wwYC4AJDfiAAAkd0sqwgAAPyFJ5fHufrSkAoDkxIC5AJDcuB8A4EckqbyOoAQASY2xCQEguQdOp0UtAD8hSeX5mhOCEgAk88Dp1KADQHJ39yMOAPATklQerjURYhIAJPusTkQCAEjmgdNpUQvAT0hSeVBQjoqWVACQ7LP7EQYAICkxNiEAPyJJ5UFBOSqCEgAke0sq4gAAJCXiAAA/Iknl4XFIhOa9AJCcAjOPEwcAIMnHJiQOAPAPklQe7+5HTAKAZJ/VCQCQzIgDAPyEJJUHMXA6AOCfbh5EAgBI5pZUhAEAfhL3JNWYMWNM1apVTcGCBU2zZs3MggULMlx/4sSJplatWnb9evXqmalTp6ZJ4AwaNMiUL1/eFCpUyLRp08asWbMmZJ2dO3eaLl26mOLFi5uSJUuaXr16mX379gX+fvDgQdO9e3f7+nnz5jUdO3ZMsx2zZ8+2NwbhP1u2bDE5OSYVzXsBIDkxcDoAJDcqKwD4UVyTVBMmTDD9+vUzgwcPNosXLzYNGjQwbdu2Ndu2bYu4/ty5c03nzp1tUmnJkiU2eaSfZcuWBdZ58sknzahRo8yLL75o5s+fb4oUKWJfU4knlxJUy5cvN9OnTzdTpkwxc+bMMb179w78PSUlxSa47rjjDpvkysjq1avN5s2bAz9lypQxOTsmVba/HQAgATGrEwAkt0BLqnhvCAD4JUn1zDPPmJtuusn06NHD1KlTxyaWChcubF577bWI648cOdK0a9fO3HvvvaZ27dpm6NCh5owzzjCjR48OtKIaMWKEGThwoLnssstM/fr1zZtvvmk2bdpkJk+ebNdZuXKlmTZtmnnllVdsy62zzjrLPPfcc+bdd9+164kSWy+88ILdtnLlymVYBiWltI77kzt37hwdk4qWVACQ3KhBB4Bkb1FLHADgH3nj9caHDx82ixYtMgMGDAgsU4JHLZfmzZsX8TlarpZXwdRKyk1ArVu3zna3C279VKJECZuM0nOvvfZa+1td/Jo0aRJYR+vrvdXy6vLLL4+pHA0bNjSHDh0ydevWNUOGDDEtW7aM6flqtRWro0HPSU1JMSlx77SZdbQ/UlNTM7VfEp2fy+b38lG2tPLkyZNt24ToOH/fnnBrAgBJiha1AHwobkmq7du325uismXLhizX41WrVkV8jhJQkdZ3x4Fyfx9rnfAueRp3qlSpUjGNJ6Uxr9TyS8kuJanUMuvcc8+1iS617opW+HhZ0dh78Gjg/z/99JPJl8c/kUk3yxozbO3atTnSKi0n+blsfi8fZUtLYwMivtxWtdSgA0Byd/cjDgDwk7glqbzutNNOsz+uFi1a2ITRs88+a956662oX6dmzZoxv/eufRpfa/3fz69h8uXxz02zEpe6Wa5Ro4bvWmr4uWx+Lx9lQ2IPmBvvLQEAxEPwZEoA4BdxS1KVLl3a3hBt3bo1ZLkepzcOlJZntL77W8vU0il4HXXLc9cJH5j96NGjtiXBscafOpamTZuar776KqbnZOamMFdQa4d8efOaPD6b2kmtObRf/HjD7Oey+b18lA2JJvXv39SgA0ByoiUVAD+KWxOc/Pnzm8aNG5sZM2aEdDvR4+bNm0d8jpYHry+aoc9dv1q1ajbRFLzOnj17bBc8dx393r17tx0PyzVz5kz73hq76nh89913IcmxnKg1ISQBQJJyW1LFezsAAHFBi1oAfhTX7n4aBL1bt252XCe1QtLMfPv377ez/UnXrl1NxYoVzbBhw+zjvn37mlatWpnhw4eb9u3b2xn5Fi5caMaOHRuY4ejOO+80jz76qO1Gp6TVQw89ZCpUqGA6duxo19GsgJohUDP3aUypI0eOmD59+thB1bWea8WKFXZwd7Ww2rt3r01AidsiS9uq1z/99NPNwYMH7ZhUSnZ99tlnOTbt+F9lzva3AwAkoFR34HQCAQAk+diE8d4SAPBJkuqaa64xv//+uxk0aJAdtFwJoGnTpgUGPt+wYUPIQL4a92n8+PFm4MCB5oEHHrCJKM3sp5n1XPfdd59NdPXu3du2mDrrrLPsaxYsWDCwzjvvvGMTU61bt7avf+WVV5pRo0aFbNvFF19s1q//a9wnadSokf3t/B0NlMC6++67zcaNG03hwoVN/fr1zeeff27OO+88k93cbRBuTgAgSVGDDgBJzb0nyEWbWgA+EveB05Us0k8ks2fPTrOsU6dO9ic9Sto88sgj9ic9mslPya6M/PLLLxn+Xckw/cQDtSYAALdVLWORAEByxwHCAAA/8c+0cMlYa0JEAoCk5bapJRQAQHJy6PYNwIdIUnm51iTeGwIAiP+AufHeEABAXHBPAMCPSFJ5uPac7n4AkLyoQQeAJOd2++aODoCPcErzoMDA6dyYAEDSIhQAQHJLZeB0AD5EksqDGDgdANIaM2aMqVq1qp3NtVmzZmbBggUZrj9x4kRTq1Ytu369evXM1KlTQ/7+wQcfmAsvvNCceOKJtrXSd999l+Y1zj33XPu34J9bbrnF5GyrWoIBACRzHCAMAPATklRe7uJBrQkAWBMmTDD9+vUzgwcPNosXLzYNGjQwbdu2Ndu2bYu4/ty5c03nzp1Nr169zJIlS0zHjh3tz7JlywLr7N+/35x11lnmiSeeyPC9b7rpJrN58+bAz5NPPmlyAmNSAUCyxwG6fQPwH5JUnp52PN5bAgCJ4ZlnnrE3CT169DB16tQxL774oilcuLB57bXXIq4/cuRI065dO3Pvvfea2rVrm6FDh5ozzjjDjB49OrDODTfcYAYNGmTatGmT4XvrfcqVKxf4KV68uMkJtKQCgOSOA9wTAPCjvPHeAMSOcUgA4B+HDx82ixYtMgMGDAgsy507t72pmDdvXsTnaLlq3IOpxn3y5Mkxv/8777xj3n77bXtj0qFDB/PQQw/ZG5ZYpKSkxLy+W4OempoS8/MTncqTmprqu3IJZfMmypZWnjx5TKJIxjgQPCaVk+r47rPJd86bKFtylS9PNsUBklReHiSRLBUAmO3bt9uAWrZs2ZDlerxq1aqIz9myZUvE9bU8Ftddd52pUqWKqVChglm6dKm5//77zerVq+04JrFYs2ZNTOv/dRGRav//26+/mgJ/Ru7O4lUq386dO83atWvtjaafUDZvomxpaSynRJGMcUD27Nljf+/csd2sWeOvG2e+c95E2ZKrfLWyKQ6QpPIwUlQAEF+9e/cO/F+D7pYvX960bt3a/PTTT+aUU06J+nVq1qwZ0/vqZixX7g36n6lSpbKpWT5nupbkFJVPF0k1atRIqNYaWYGyeRNlQ6LFASm6aL9GzjInnXSSqVmzmvETP38uKZs3+blsiVY+klQebknFOCQAYEzp0qVtMN26dWvIcj1W14tItDyW9aOl2aREQT6Wm5PMXAy4Xb/z5s0T94uJ7KBaPJWLsnkLZfMmr5ctaeNA4Ll/HT+/8frnMiOUzZv8XLZEKp//2qklAcakAoB/5M+f3zRu3NjMmDEjpMmyHjdv3jzic7Q8eH2ZPn16uutHy52eXDXpOTb1OO1qASQ54gAA+Actqbw8JlW8NwQAEoQGv+3WrZtp0qSJadq0qRkxYoSdOlyzPEnXrl1NxYoVzbBhw+zjvn37mlatWpnhw4eb9u3bm3fffdcsXLjQjB07NvCa6pe/YcMGs2nTJvtYY4yIO3uTunKMHz/eXHzxxebEE0+0Y5Hcdddd5pxzzjH169fPwanHs/2tACDhEQcAwB9IUnlQoNaEiAQA1jXXXGN+//13O1W4Br1t2LChmTZtWmBQXN1kBA8C2aJFC3tjMXDgQPPAAw/YsUA0o1PdunUD63z00UeBmxu59tpr7e/BgwebIUOG2Jr7zz//PHAjdPLJJ5srr7zSvmZOxgKmHgeAJI0DfwcChgAB4CckqTyI7n4AkFafPn3sTySzZ89Os6xTp072Jz3du3e3P+nRzcgXX3xh4iXVzVLRrhYAkjoOEAUA+AljUnmQ27SXWhMAAC2pACA5/dPdj0AAwD9IUnkQtSYAgEAs4OYEAJISvSsA+BFJKi/XmlB9DgBJzG1VG+/tAADEgxOIAwQCAP5BksqDmG4WABCoQScaAECSxwEA8A+SVB70z0we8d4SAEC8pP79mwp0AEjybt/c0QHwEU5pHsQgiQAAt1ktoQAAkru7Hy1qAfgJSSoP15rQkgoAktc/sYBgAADJiHsCAH5EksrDtSb0QAeAZOa2qo33dgAA4oLeFQB8iCSVB1FrAgCgJRUAJDfuCQD4EUkqL2IcEgBIerSpBYDk5sYBAPATklQelOo27eXWBABMss/0SigAgCS/J6DmGoCPkKTyIPe+hKa9AJC8/okFBAMASObKCu4JAPgJSSoPotYEAJKbE2hGRUMqAEj2WEDvCgB+QpLKg9x7E3JUAJCcgnJUtKQCgCRF7woAfkSSytO1JgCAZG5RK+SoACDZe1fEe0sAIOuQpPIgxiEBgOQWPKMTXb8BINl7VxAHAPgHSSoPorsfACS34O5+xAIASE5M8grAj/LGewMQOwZOB+BVH330UczPueCCC0yhQoWyZXv8MHA6rWoBeAlxIOtjAXEAgJ+QpPJyS6p4bwgAxKhjx44xra9k/Jo1a0z16tWzbZs8390vjtsBALEiDmQdelcA8CO6+3kQtSYAvGzLli0mNTU1qp/ChQvHe3MTfuB0YgEAryEOZA16VwDwI5JUXu5/TjwC4DHdunWLqcvG9ddfb4oXL56t2+RFjEkFwKuIA1mH3hUA/Ijufh6U+ndAyk1EAuAx48aNi2n9F154Idu2xQ9xQEhSAfAS4kDWJ6ly0+wAgI9wSvPygLncmQBAkvonS5WLOnQASErO37GAOADAT0hSebgGnXAEwKteeeUV2+XDrVGfMGGCqV27th0Yd/DgwfHevIRHSyoAXkccyMJ7AuIAAB+hu58nMXA6AO8aMWKEGThwoGnbtq158MEHzaZNm8yzzz5r7rrrLpOSkmKGDx9uKlasaHr37h3vTU38FrXEAgAeRBzI2ljAwOkA/IQklQdRawLAy1566SUzduxYc91115klS5aYpk2bmhdffNH06tXL/l03JhqDhJuTKFtSxXNDACATiANZg94VAPyI7n5eHiSRiATAg9avX2/OOuss+/9GjRqZPHnymDPPPDPw91atWpmffvopjluY+IJyVFRYAPAc4kDW4p4AgJ+QpPKg1EA3DyISAO8pXLiw2b9/f+DxSSedZIoWLRqyztGjR+OwZd7s7kc3DwBeQxzI2nsC4gAAPyFJ5UHurQm1JgC8qFatWmbp0qWBx7/++qupUqVK4PGqVatM1apV47R13kCLWgBeRhzIGsQCAH7EmFQexCCJALzsiSeeMEWKFEn37xs2bDA333xzjm6T1xAHAHgZcSBrOH9XXeeidwUAH4l7S6oxY8bYmpKCBQuaZs2amQULFmS4/sSJE23ti9avV6+emTp1apoL90GDBpny5cubQoUKmTZt2pg1a9aErLNz507TpUsXU7x4cVOyZEk7SOO+ffsCfz948KDp3r27ff28efOajh07RtyW2bNnmzPOOMMUKFDA1KhRw7z++usmJ1BrAsDLWrZsaRo2bJju32+77TbTp0+fHN0mrw6WSxwA4EXEgazBZEoA/CiuSaoJEyaYfv36mcGDB5vFixebBg0a2Klot23bFnH9uXPnms6dO9ukkmYCUfJIP8uWLQus8+STT5pRo0bZGULmz59va2n0mko8uZSgWr58uZk+fbqZMmWKmTNnTsjsIZr6VgmuO+64wya5Ilm3bp1p3769Oe+888x3331n7rzzTnPjjTeaTz/91GQ3RqQC4De6Idm+fXu8N8MziAMA/IY4kPmKa5JUAPwkrkmqZ555xtx0002mR48epk6dOjaxpIEUX3vttYjrjxw50rRr187ce++9pnbt2mbo0KG2JdPo0aMDrahGjBhhBg4caC677DJTv3598+abb5pNmzaZyZMn23VWrlxppk2bZl555RXbckszizz33HPm3XffteuJElua9lbbVq5cuYjbom2tVq2aGT58uN0W1fZcddVV5tlnnzXZjUESAfjN22+/bfbs2RPvzfAMuvsB8BviQOZjQW5iAQAfiduYVIcPHzaLFi0yAwYMCCzLnTu3bbk0b968iM/RcrW8CqZWUm4CSq2btmzZEtL6qUSJEjYZpedee+219re6+DVp0iSwjtbXe6vl1eWXXx7V9ut1wltZaVvUoioWarUVq5SU1L//52Tq+YlM5UlNTfVdufxeNr+Xj7KlpenCs2u2OhwbtecA/IY4EDta1QLwo7glqdScVzdFZcuWDVmux5rRIxIloCKtr+Xu391lGa1TpkyZkL9r3KlSpUoF1olGetuiGqADBw7Y7oLRCB8vK6r33vqH/X3gzz8z9fxEpptljRm2du1amzj0Ez+Xze/lo2xpaWxAxE+gRS23JgCQtOhdAcCPmN0vzmrWrBnzcxbv3qA0nylapEimnp/IlLjUzbIGos/qlhrx5uey+b18lC377d27N27v7eXacwZOB+AXxIHY0aoWgB/FLUlVunRpe0O0devWkOV6nN44UFqe0fruby3T7H7B67gziGid8IHZjx49alsSpPe+sWyLZgyMthWVZO6m8K9IlDt3Lt/dMItac6hclM17/Fw+ypZ9dE7Wj1p1BdO4gjhW7Xm8twQAjh9x4DjHJ4z3hgBAPJJUH330UcwvfsEFF6SbsMmfP79p3LixmTFjhp2hTxSY9Di9KWebN29u/x487pNm6NNy0UDmSh5pHTcppe53Gmvq1ltvDbzG7t277XhYen+ZOXOmfW+NXRUtvc7UqVNDlgVvS470P+fuBICH6TzcrVs3O6FF8EDg+r9++3EMsKyvPScOAPAu4kDWxAIGTgeQlEkqN5EULQUWjZdUvXr1dNfRIOgKTBrEvGnTpnZmvv3799vZ/qRr166mYsWKZtiwYfZx3759TatWreyMeu3bt7cz8i1cuNCMHTs28J5KYD366KO2G5ySVg899JCpUKFCYPs1E59mCNTMfZqh78iRIzYppkHVtZ5rxYoVdnB3tbBS8+PvvvvOLneTX7fccoudVfC+++4zPXv2tImu9957z3z88ccm58YiAQDv0rnz1FNPNa+++qod04+ES/SoPQfgB8SB45NKdz8Ayd7dL9Kg4+kpVqzYMde55pprzO+//24GDRpkX1sJoGnTpgUGJN+wYUPIQL4tWrQw48ePNwMHDjQPPPCATURpZr+6desG1lHSSImu3r172xZTZ511ln3NggULBtZ55513bGKqdevW9vWvvPJKM2rUqJBtu/jii8369esDjxs1ahRyY6AEmBJSd911lxk5cqSpVKmSeeWVV+wMf9nOrTVhMBIAHvbzzz+b//73v3Y8LGR2TCriAADvIg4cH+fvaEAsAOAnUU/lpBZPsYy1dP3119vxmY5FySIlgw4dOmS75QV3uZs9e7Z5/fXXQ9bv1KmTWb16tV1/2bJlNpkUTDUwjzzyiE16HTx40Hz++ee2hiaYZvJTskstpP744w/z2muvmaJFi4as88svv9iEVPhPsHPPPdcsWbLEbstPP/1kunfvbnK01iRH3g0AsocqCr7//vsse70xY8aYqlWr2koJxZIFCxZkuP7EiRPtLIVav169emm6cH/wwQfmwgsvNCeeeKKNLW6L2mCKM7fffrtdR3FElR7h4xVmB2rPAfgBcSBrYgE3BQCSsiXVuHHjYnrhF154ITPbgyhQawLAD9T6VBUgqnBQi9h8+fKF/P3SSy+N+rUmTJhgu5CrG7duTNR9XC1bVakRqQXw3LlzTefOnW138ksuucRWXKhb+OLFiwOtc9UqV61xr776attFPBK1plWrWt3olChRwla8XHHFFebrr7822Sl47BYA8CriwHH6OxZwTwDAT+I2ux8yL9Cgi3gEwMPmzZtnL+I/+eSTNH+LdcDcZ555xt5AuGMa6iZFNw1qKdu/f/8066ubtsYnvPfee+3joUOH2skvNNagnis33HBDoGVtJGqJq3FUdGNz/vnnByp0NPbhN998Y84880yTXQgDAPyAOHB8iAUA/ChTSSo1a33uuefMrFmzIk4XqxoIZP/A6QxJBcDL/vWvf9mu4Zrgwh2LMDM0yYVmiBowYEBgmcYbbNOmjb0BikTLVeMeTDXuGucwWnpPTb6h93Gp20jlypXt68dycxLrDFZHj6YE4oAfZ79SmXRtQdm8hbIlV9ny5Mlz3O9NHPhHZj5b7j2B4/jvs8l3zpsoW3KVL08WxIEsS1L16tXLfPbZZ+aqq66ys/LR3SBn/VNrwn4H4F07duyw3SSO58ZEtm/fbgNq+Ovo8apVqyI+R+MWRlpfy6OldfPnz29Klix5XK8jmg03Fhu2H7C/Ve5Yn+sFukjS7Lpr164NmUDFDyibN1G2tJSMOV7EgX9k5lyekpIamGzK7Clg/ITvnDdRtuQqX60siANZlqSaMmWKHViwZcuWWb9FiLq7Hy2pAHiZxuxQi9xTTjnFJDvNVhuLg4V2GWM2mXz58sb8XC/QzaYukjTjV3bV0sULZfMmypY9iAP/yNS5PJdmIk811apUMdXLHHtmdS/hO+dNlM27UhKofJlKUlWsWNEUK+avE6GXMGAuAD/QzKvqmvHVV1/ZWZXCB8y94447onqd0qVL22AaPpuSHpcrVy7ic7Q8lvXTew11Mdm9e3dILXqsryOxXgzkypU7EAfifSGRXVSLp7L5sXyUzZsoW9YjDvwjc/v+r3sCPpfeQ9m8yc9lS6TyZSpJNXz4cHP//ffbQQWrVKmS9VuF6KabBQCPz+qk6bq/+OIL+xNMyZdob07U1aJx48ZmxowZdmYmt8myHmuWpUiaN29u/37nnXcGlmnAXC2Plt5TN1R6HU05LppFSt0uYnmd44kDVFUA8DLiQBbFAoIBAB/JVJKqSZMmdvD06tWrm8KFC6ep9VBfRmQfN0dFdz8AXrZu3bosey0NfqtpzBWfNFaiph7X1OHuLE9du3a1rYA11bj07dvXtGrVyla6tG/f3rz77rtm4cKFZuzYsSGxTDcamzZtCtx4iGrH9aOpxjVGo967VKlSpnjx4nYQYN2YZPeMTi7iAAAvIw4cH4YAAeBHmUpSde7c2WzcuNE89thjdmBAup3lLLr7AUCoa665xvz+++9m0KBBdrDahg0bmmnTpgUGxdVNRvAgkC1atLBThg8cONA88MADdiwQzehUt27dwDofffRR4OZGrr32Wvt78ODBZsiQIfb/zz77rH1d1aAfOnTIzgz1/PPPZ3t53RmdiAMAkJxxICQW0K4WgI9kKkk1d+5cO61qgwYNsn6LcEzUmgBAWurSkV63jtmzZ6dZ1qlTJ/uTnu7du9ufjBQsWNCMGTPG/uQk4gAAJHccCJnxm1gAwEcyNXeipho8cOCv6a+R86g1AYDk5sYBRqUCgORF7woAfpSpJNXjjz9u7r77blsjsWPHDrNnz56QH2Qv996EeAQAyYmxCQEAtKoF4EeZ6u7Xrl07+7t169ZpsvnK5KekpGTN1uEYY5HEe0sAAPGtPY/3lgAA4t+7AgCSPEk1a9asrN8SxCw3dycAPGzp0qURl6uyQ2N8VK5c2RQoUCDHt8tbtefEAQDeRRzIola1NKUCkIxJKgURzXah2Ss0XeuxLF++3Jx22mkmb95M5cEQTXe/eG8IABwHzbyU0Tga+fLls7M1vfTSS/ZmBf9gRCoAfkAcOD6B4QkBIBnHpGrUqJEdfypazZs3t1O9Ivua9lKDDsDLJk2aZKf8Hjt2rPnuu+/sj/6vCg5NC/7qq6+amTNn2unBkV63b+IAAO8iDhx/t2/hngCAn+SN5UT40EMPmcKFC0e1/uHDh49nu5CBQEgiHgHwsH//+99m5MiRpm3btoFl9erVM5UqVbLxZsGCBaZIkSJ2oo6nn346rtuaaJhAA4AfEAeyphUVsQBAUiapzjnnHLN69eqYWlIVKlQos9uFDNCSCoAf/PDDD6ZKlSpplmuZ/uZ2Bdm8eXMcts4bNejEAQBeRhw4/vsBIRYASMok1ezZs7N3SxA1atAB+EGtWrXM448/brt25M+f3y47cuSIXaa/ycaNG03ZsmXjvKWJhzGpAPgBcSDzgoejIhYA8BNGNffy1OPx3hAAOA5jxowxl156qe3WUb9+fbtMNecpKSlmypQp9vHPP/9sbrvttjhvaSKPSRXvLQGAzCMOZE1LKmIBAD8hSeXl6WaJSAA8rEWLFmbdunXmnXfeMT/++KNd1qlTJ3PdddeZYsWK2cc33HBDnLcy0VvUEgcAeBdxIKvGpCIWAPAPklQelEo/DwA+sGfPHlO8eHFzyy23pPnb2rVrTY0aNeKyXV6KA4QBAF5GHMiiJFU8NwQAsljurH5BZD8GzAXgB+3btzcHDx5Ms1yTdJx77rlx2SbPIA4A8AHiQOY5QaNSEQsA+AlJKi9384j3hgDAcShatKi54oorzNGjRwPLVq5caW9Mrrzyyrhum2daUhEIAHgYcSALelYQCwD4DEkqD9ec5CYgAfCwDz74wPzxxx+mS5cutoXosmXL7I1J586dzciRI+O9eR4ZmzDOGwIAx4E4cPw9K4QxqQD4CUkqT9egE5AAeFehQoXMxx9/bLt1XH311aZ169ama9eu5plnnon3pnlnVifiAAAPIw5kUUuqeG4IAGQxBk73IO5NAHh5kNxguXPnNhMmTDAXXHCB7drx0EMPBdbRYLrIOA7QkgqA1xAHskhQkopYAMBPSFJ5uLtfLupNAHhMyZIlI7YCVbeFF1980bz00kv2/1onJSUlLtvoDcQBAN5EHMj6gdPpXQHAT0hSeZCT+tdvak0AeM2sWbPivQm+6uZBHADgNcSBrO/uRywA4CckqTzIjUlUmgDwmlatWsV7E3yBbt8AvIo4kDUYOB2AXzFwuocHzKWbBwCvWbp0qUlN/bs5aBSWL18eMjU5wgZOJw4A8BjiQNa3pAIAPyFJ5UHUoAPwqkaNGpkdO3ZEvX7z5s3Nhg0bsnWbvMi9N6GLBwCvIQ5k7ZhUxAEAfkN3Py+3pCJLBcCD3RM0c1PhwoWjWv/w4cPZvk1e7uZBHADgNcSBrBHU2w8AfIUklQdRgw7Aq8455xyzevXqmGrQCxUqlK3b5OWbE+IAAK8hDmQN4gAAvyJJ5eka9HhvCQDEZvbs2fHeBJ+NTQgA3kIcyBrEAQB+xZhUnq45ISwBQHLP8kocAIBkRBwA4FckqTw9qxMAILnHJoz3lgAA4iH17+n9CAMA/IYklafHpCIsAUBSokUtAIDKCgA+RJLKg9yGVAQlAEhOf1egU4MOAEmKMakA+BVJKg8PnE4NOgAkJ+IAACS3fyqtiQMA/IUklZcHSozzdgAA4iMwMiGBAACSEvcDAPyKJJUHMWAuACQ3Nw7QkgoAkhP3AwD8iiSVB9G8FwCSHGMTAkBSY4xaAH5FksrLQSneGwIAiAsGTgeA5OaOTUgcAOA3JKk8yPm7Cp1uHgCQnIgDAJDcAmNSEQYA+ExCJKnGjBljqlatagoWLGiaNWtmFixYkOH6EydONLVq1bLr16tXz0ydOjVNzcKgQYNM+fLlTaFChUybNm3MmjVrQtbZuXOn6dKliylevLgpWbKk6dWrl9m3b1/IOkuXLjVnn322fZ+TTz7ZPPnkkyF/f/31122Xu+AfrZtjNegEJQBISm4coAodAJJ8TCoCAQCfiXuSasKECaZfv35m8ODBZvHixaZBgwambdu2Ztu2bRHXnzt3runcubNNKi1ZssR07NjR/ixbtiywjpJJo0aNMi+++KKZP3++KVKkiH3NgwcPBtZRgmr58uVm+vTpZsqUKWbOnDmmd+/egb/v2bPHXHjhhaZKlSpm0aJF5qmnnjJDhgwxY8eODdkeJbk2b94c+Fm/fr3JbvRBB4Dk5nbzyE0cAICkxP0AAL+Ke5LqmWeeMTfddJPp0aOHqVOnjk0sFS5c2Lz22msR1x85cqRp166duffee03t2rXN0KFDzRlnnGFGjx4duHAfMWKEGThwoLnssstM/fr1zZtvvmk2bdpkJk+ebNdZuXKlmTZtmnnllVdsy62zzjrLPPfcc+bdd9+168k777xjDh8+bLfj9NNPN9dee62544477PYGU+upcuXKBX7Kli2b7fuMmhMASG7/jE1IHACAZPTP/QAA+EveeL65kkBqpTRgwIDAsty5c9vuefPmzYv4HC1Xy6tgaiXlJqDWrVtntmzZYl/DVaJECZuM0nOVbNJvdfFr0qRJYB2tr/dWy6vLL7/crnPOOeeY/Pnzh7zPE088YXbt2mVOOOEEu0xdBNXaKjU11SbLHnvsMZvUilZKSorJbA26MamZen4iU3m0L/1WLr+Xze/lo2xp5cmTJ9u2CcfmRgFaUgFAcnJvB4gDAPwmrkmq7du32xuj8NZHerxq1aqIz1ECKtL6Wu7+3V2W0TplypQJ+XvevHlNqVKlQtapVq1amtdw/6Yk1WmnnWZbWqm11h9//GGefvpp06JFC9uNsFKlSlHtg/CxsqKx9++xs37f9rtZs+aw8RPdLGu8sLVr19qkoZ/4uWx+Lx9lS0vjAiIBatDp5wEASSlQZw0APhPXJJXXNW/e3P64lKBSF8SXXnrJdkOMRs2aNWN+38Jz/zDGHDDlypYxNWtWNn6ipKVulmvUqOG7lhp+Lpvfy0fZvEGTcGj8QFUkaHxDdeNu2rRphpNwPPTQQ+aXX36x52K1lL344otDWq1qvMSXX37Z7N6927Rs2dK88MILIedtTfoRPhbhsGHDTP/+/bOplO62/fWbHBUAJGcsYJZXAH4V1yRV6dKl7U3R1q1bQ5brscZ3ikTLM1rf/a1lmt0veJ2GDRsG1gkfmP3o0aO2NUHw60R6n+D3CJcvXz7TqFEje8MXrczcFAa6eeTJ7fmbykjUmkPlomze4+fyUbbE5k7CoXEN1b1bYxOqi/bq1avTtJwNnoRDNxGXXHKJGT9+vJ2EQxN41K1bN2QSjjfeeMO2rNVNjF5zxYoVITO5PvLII3ZsRVexYsWyvbyMRQIAyR0LArO8AoDPxLXfisZ7aty4sZkxY0ZI1xM9Dm6hFEzLg9cXzdDnrq/goSRS8DqaqU9jTbnr6LdqQjQelmvmzJn2vRXQ3HU049+RI0dC3kdd/NzxqCK1SPjhhx9CkmPZ2wed2xMAiNckHME3IsETaGhG2ZxCHACA5IwFzPIKwK/i3t1PtR3dunWzg5irKa4Cwf79+21wka5du5qKFSvaGg7p27evadWqlRk+fLhp3769nZFv4cKFZuzYsYHxOe68807z6KOP2ma4bo1HhQoVbM2IKAgpICmIKXgpEdWnTx87qLrWk+uuu848/PDDplevXub+++83y5Yts4Hs2WefDakxOfPMM203GSW91LRYTX1vvPHGbN1nbsUJMQkA4jcJh+vxxx+3NzaVK1e2seOuu+6y4xzGItaB61NSUv/+n8OA/h5D2byJsqWVaK1vvR4LMh8HMjcRU6LjO+dNlC25ypcnm+JA3JNU11xzjfn999/NoEGDbBBQl7xp06YFBinfsGFDyGC+GvdJTXFVo/HAAw/YRJQCidskV+677z6b6Ordu7dNHp111ln2NYOb5L7zzjs2MdW6dWv7+ldeeaVtyhscgD777DNz++2329Ze6pqobdRrujTLnxJd7kDqWk/NhlVzkzM1J6SpACBek3DIHXfcYWvdNfGGzv+6Odq8ebOtzY9FrJNobN+x0/7eu2dPpibgSHRMVuBNlM2b/DKBhtdjQazn8g3bDtrfTmoqn0uPoWze5OeyZbZ82RUH4p6kEiWL9BPJ7Nmz0yzr1KmT/UmPWlOplZN+0qMgomRXRtSk98svv0z372pVFdyyKqcwYC4AJIbgGnjFDHVjv/nmm23r3wIFCkT9OrFOolHqV93M7LIVKpmZgCPR+WlA/3CUzZsoG7I7FsR6Lv8j/y5jzEaTJ09uXx47P38uKZs3+blsiVa+hEhSIXMD5gIA4jcJRyTqAqKJODRLlMYwjFasFwPO3x2+c+fOFfcLiezihwH900PZvImyJTavx4JY932uXLkDlfNeP3Z+/lymh7J5k5/Llkjl8187tSQQmN2PplQAELdJOCL57rvvbICPNItUdiAOAEByxgIGTgfgV7Sk8iA3KHFvAgDxm4RDg+bqRuW8886zszrpsQbKvf7669OdBTarW9QSBgAgOWNBqjv8R7a9AwDEB0kqD3J7+1GDDgDxm4RD44zohmbIkCHm0KFD9uZFNybhM0VlB+IAACR3LHD+7ltBGADgNySpPIiaEwCI/yQcmsnpm2++MfGMAwQCAEjOWBCYSIlAAMBnGJPKg/6pOSEoAUAyxwFaUgFAcmK2bwB+RZLKyy2pCEoAkJS4OQGA5MbYhAD8iiSVFwVm8yAsAUAyYlYnAEhugV7fxAEAPkOSyoMYkwoAkts/cYBIAABJ3ZKKMADAZ0hSedA/szrFe0sAAPFADToAJDkqKwD4FEkqDw+Yy90JACR3dz8m0ACAZJ9IKd5bAgBZiySVB9HdDwCS2z9TjwMAklFq6l+/iQMA/IYklRe53f04egCQ1DXodPsGgOREt28AfkWaw9NTzhKVACCpW9RydwIASX4/AAD+QpLKwzUn1KADQJJ39yMOAECSxwECAQB/IUnl4ZoT7k4AIDnRohYAkltgAo14bwgAZDGSVB7k5qhoSQUASYo4AABJjZ4VAPyKJJUHUXMCAMkt0JKKQAAAyd2zAgB8hiSVp2tOuDsBgGREHACA5EbPCgB+RZLKg6hBB4DkRg06ACS3f+4HuCEA4C8kqTyI2TwAILn9U4NOHACAZEYUAOA3JKk8KNVNUsV7QwAAccEkrwCQ3OhZAcCvSFJ50l9BiRp0AEhODnEAAJJaoLIi3hsCAFmMJJWXW1IRlQAgKREHACC5EQcA+BVJKg9y3Oa98d4QAEBc4wAAINnvB7gjAOAvJKk8yL01YeB0AEjuOEB3PwBI9vuBOG8IAGQxklQeRPNeAEhuDJwOAMmNnhUA/IoklRf9HZSoQQeA5L45IQ4AQHKisgKAX5Gk8nJLqnhvCAAgLogDAJDciAMA/IoklYdrTnLnJiwBQDJy/h6NhBp0AEj2OEAgAOAvJKk8KJU+6ACQ1P4Zm5BIAADJiDFqAfgVSSoPYjYPAEhybota4gAAJCcqrQH4FEkqL8/mQZYKAJK8RS1xAACSES2pAPgVSSovj0lFUAKApG5RSxwAgCSf5ZXKCgA+Q5LKgxiTCgCSWyrTOgFAUnPDAHEAgN+QpPL0mFREJQBI7pZUxAEASEa0qAXgVySpPD0mVby3BAAQ1zgQ7w0BAMQ1DgCA35Ck8iA3JjFgLgAkeRygtgIAkvx+AAD8hSSVB9G8FwCSG3EAAJKb83ckIA4A8BuSVF4eOJ2gBABJHQeoQgeAJB84nUAAwGdIUnmQe2/CgLkAkJyIAwCQ3P6JA/HeEgDIWiSpPIiBEgEguTFwOgAkN1rUAvArklQexNTjAJDciAMAAOFmDoDfcF7zcB907k0AIDkRBwAguaX+HQiIAwD8hiSVh7t5UIMOAEne3Y84AABJ3aI2F/39APhMQiSpxowZY6pWrWoKFixomjVrZhYsWJDh+hMnTjS1atWy69erV89MnTo1zcX7oEGDTPny5U2hQoVMmzZtzJo1a0LW2blzp+nSpYspXry4KVmypOnVq5fZt29fyDpLly41Z599tn2fk08+2Tz55JMxb0t2oAYdALwTS7I1DmT7OwGAtyRLLGC2bwB+Ffck1YQJE0y/fv3M4MGDzeLFi02DBg1M27ZtzbZt2yKuP3fuXNO5c2d78l+yZInp2LGj/Vm2bFlgHSWTRo0aZV588UUzf/58U6RIEfuaBw8eDKyjQLJ8+XIzffp0M2XKFDNnzhzTu3fvwN/37NljLrzwQlOlShWzaNEi89RTT5khQ4aYsWPHxrQt2TloOjXoAJDYsST70KIWAJI5FgTGTScMAPAbJ86aNm3q3H777YHHKSkpToUKFZxhw4ZFXP/qq6922rdvH7KsWbNmzs0332z/n5qa6pQrV8556qmnAn/fvXu3U6BAAec///mPfbxixQqd1p1vv/02sM4nn3zi5MqVy9m4caN9/PzzzzsnnHCCc+jQocA6999/v3PaaadFvS3ZISUl1aly/xT7s+2PPx2/OXr0qLNy5Ur722/8XDa/l4+yJb5EjSXZ5aoXvrZx4H/f/eb4kV8+l5FQNm+ibN6QTLFg+KerbBzoM26OL46dnz+X4SibN/m5bIlWvrzxTJAdPnzYtlIaMGBAYFnu3LltM9p58+ZFfI6Wq4YkmGozJk+ebP+/bt06s2XLFvsarhIlStjmvnrutddea3+rKW6TJk0C62h9vbdqSC6//HK7zjnnnGPy588f8j5PPPGE2bVrlznhhBOOuS3RSElJiXpdu77bx0PRMDU15ucnOpUn1Yfl8nvZ/F4+ypZWnjx5TKJI5FgSrVj3vztgruPwufQayuZNlC2x44AfYkFm7wnUkIrPpbdQNm/yc9kyW77sigNxTVJt377d7oSyZcuGLNfjVatWRXyOAkWk9bXc/bu7LKN1ypQpE/L3vHnzmlKlSoWsU61atTSv4f5NSapjbUs0wvu0H8vRoCTVunU/m52F8hk/0RdD/frXrl1rg7uf+Llsfi8fZUtL43ckikSOJdkVC/48cMD+3rpli1lTcL/xG75z3kTZvMkPccAPsSDWOLBj5w77W90O+Vx6C2XzJj+XLbPly644ENckFYypWbNmzLXnT11Z2Aa900+raQoV8FeSShcX+mLUqFEj4Wrojpefy+b38lE2JFosuKddcbPi519NmzNONSefWNT4jZ8/l5TNmygbEi0OdClS1jSuucfkO7DTl8fOz59LyuZNfi5bopUvrkmq0qVL2x2wdevWkOV6XK5cuYjP0fKM1nd/a5lm4Qhep2HDhoF1wgdQPHr0qM0cBr9OpPcJfo9jbUs0Yv0AaPUrzqhk1qw5YBNU8f4AZQdlblUuyuY9fi4fZUtciRxLohXrvj+3VllTMc8em6Dy6nHz++cyI5TNmyhbYvN6LIh139c7+QRTp0Jxe0/g9WPn589leiibN/m5bIlUvri2U9N4T40bNzYzZswIaWamx82bN4/4HC0PXl80k4a7vrroKSAEr6OZ+tQn3F1Hv3fv3m37rbtmzpxp31t9zN11NDPHkSNHQt7ntNNOs139otkWAEByxxIAQM4gFgCAT8R75PZ3333XzpDx+uuv29kxevfu7ZQsWdLZsmWL/fsNN9zg9O/fP7D+119/7eTNm9d5+umn7ejzgwcPdvLly+f88MMPgXUef/xx+xoffvihs3TpUueyyy5zqlWr5hw4cCCwTrt27ZxGjRo58+fPd7766iunZs2aTufOnUNm7ihbtqx9/2XLltntLFy4sPPSSy/FtC1+H3k/q1E27/Jz+Shb4kvUWJJd/HLckrF8lM2bKJs3EAv8g7J5E2XzrqMJVL64J6nkueeecypXruzkz5/fTh37zTffBP7WqlUrp1u3biHrv/fee86pp55q1z/99NOdjz/+OOTvmi72oYceskkmBarWrVs7q1evDllnx44dNngULVrUKV68uNOjRw9n7969Iet8//33zllnnWVfo2LFijZIhTvWtvj9A5TVKJt3+bl8lM0bEjWWZAc/HbdkKx9l8ybK5h3EAn+gbN5E2bzraAKVL5f+iXdrLsQ+qJlmANEAi/HuL5rVKJt3+bl8lA2Jxu/Hzc/lo2zeRNmQiPx87CibN1E270qk8vlv7kQAAAAAAAB4DkkqAAAAAAAAxB1JKgAAAAAAAMQdY1IBAAAAAAAg7mhJBQAAAAAAgLgjSQUAAAAAAIC4I0kFAAAAAACAuCNJBQAAAAAAgLgjSQUAAAAAAIC4I0kFAAAAAACAuCNJBQAAAAAAgLgjSQUAAAAAAIC4I0kFAAAAAACAuCNJBQAAAAAAgLgjSeVBY8aMMVWrVjUFCxY0zZo1MwsWLDBeN2fOHNOhQwdToUIFkytXLjN58mTjF8OGDTP/93//Z4oVK2bKlCljOnbsaFavXm384IUXXjD169c3xYsXtz/Nmzc3n3zyifGjxx9/3H4277zzTuMHQ4YMseUJ/qlVq1a8NwtJHAf8HAv8HAeSKRYQB5BIiAPe4+dYkCxxwG+xYEiCxgGSVB4zYcIE069fPzN48GCzePFi06BBA9O2bVuzbds242X79++3ZVHA9ZsvvvjC3H777eabb74x06dPN0eOHDEXXnihLbPXVapUyZ6oFy1aZBYuXGjOP/98c9lll5nly5cbP/n222/NSy+9ZIOvn5x++ulm8+bNgZ+vvvoq3puEJI4Dfo4Ffo4DyRILiANIJMQBb/JzLEiGOODXWHB6IsYBB57StGlT5/bbbw88TklJcSpUqOAMGzbM8Qt9LCdNmuT41bZt22wZv/jiC8ePTjjhBOeVV15x/GLv3r1OzZo1nenTpzutWrVy+vbt6/jB4MGDnQYNGsR7M5AJyRAH/B4L/B4H/BYLiANINMQBf/B7LPBTHPBrLBicoHGAllQecvjwYZudbtOmTWBZ7ty57eN58+bFddsQvT/++MP+LlWqlPGTlJQU8+6779raIDXx9QvVeLVv3z7ke+cXa9assc3pq1evbrp06WI2bNgQ703CMRAH/MGvccCvsYA4gERCHPAPv8YCP8YBP8eCNQkYB/LGewMQve3bt9svfdmyZUOW6/GqVavitl2IXmpqqu2/3LJlS1O3bl3jBz/88IMNQAcPHjRFixY1kyZNMnXq1DF+oACrZvRq2us3Gr/i9ddfN6eddppt2vvwww+bs88+2yxbtsyOlYDERBzwPj/GAT/HAuIAEg1xwB/8GAv8Ggf8HAuaJWgcIEkF5HAGXl/6hOjrm0V0Uvvuu+9sbdD7779vunXrZvvcez0o/frrr6Zv3752zAANSuo3F110UeD/6levIFWlShXz3nvvmV69esV12wA/82Mc8GssIA4AyC5+jAV+jAN+jwUXJWgcIEnlIaVLlzZ58uQxW7duDVmux+XKlYvbdiE6ffr0MVOmTLGzlmhwQb/Inz+/qVGjhv1/48aNbQ3DyJEj7aCCXqam9BqA9IwzzggsU82ljt/o0aPNoUOH7PfRL0qWLGlOPfVUs3bt2nhvCjJAHPA2v8YBv8YC4gASEXHA+/waC/wYB5ItFpRMkDjAmFQe++LrCz9jxoyQpqJ67Kf+vn6jcR8VjNTkdebMmaZatWrGz/SZ1Mna61q3bm2bLatGyP1p0qSJ7aut//slGLn27dtnfvrpJ1O+fPl4bwoyQBzwpmSLA36JBcQBJCLigHclWyzwQxxItliwL0HiAC2pPEbTzarppL4YTZs2NSNGjLCD0vXo0cN4/QsRnLFdt26d/dJrIMHKlSsbrzfnHT9+vPnwww9t394tW7bY5SVKlDCFChUyXjZgwADbTFTHaO/evbacs2fPNsiYknYAAHCLSURBVJ9++qnxOh2r8DECihQpYk488URfjB1wzz33mA4dOtgmvZs2bbLTWCvIdu7cOd6bhiSNA36OBX6OA36OBcQBJCrigDf5ORb4NQ74PRbck6hxIN7TCyJ2zz33nFO5cmUnf/78dgrab775xvG6WbNm2SlYw3+6devmeF2kculn3Lhxjtf17NnTqVKliv0snnTSSU7r1q2dzz77zPErv0w3K9dcc41Tvnx5e+wqVqxoH69duzbem4UkjgN+jgV+jgPJFguIA0gUxAHv8XMsSKY44KdYcE2CxoFc+ie+aTIAAAAAAAAkO8akAgAAAAAAQNyRpAIAAAAAAEDckaQCAAAAAABA3JGkAgAAAAAAQNyRpAIAAAAAAEDckaQCAAAAAABA3JGkAgAAAAAAQNyRpAIAAAAAAEDckaQCssHs2bNNrly5zO7du3P8vfW++ilZsmSG6w0ZMsQ0bNgwx7ZL7+du24gRI3LsfQEgHogDkd+POAAgWRAHIr8fcQDHQpIKOE7nnnuuufPOO0OWtWjRwmzevNmUKFEiLts0btw48+OPP5pEcs8999h9UqlSpXhvCgBkKeJAdIgDAPyKOBAd4gCikTeqtQDEJH/+/KZcuXJxe3/VmpQpU8YkkqJFi9qfPHnyxHtTACDbEQfSIg4ASCbEgbSIA4gGLamA49C9e3fzxRdfmJEjRwaarv7yyy9pmve+/vrrNlBMmTLFnHbaaaZw4cLmqquuMn/++ad54403TNWqVc0JJ5xg7rjjDpOSkhJ4/UOHDtkah4oVK5oiRYqYZs2a2dfOjMcff9yULVvWFCtWzPTq1cscPHgw5O/ffvutueCCC0zp0qVtjU+rVq3M4sWLA3/v2bOnueSSS0Kec+TIERv8Xn31Vfv4/fffN/Xq1TOFChUyJ554omnTpo3Zv39/prYXALyAOEAcAJDciAPEAWQtklTAcVAwat68ubnpppts01X9nHzyyRHXVQAaNWqUeffdd820adNscLn88svN1KlT7c9bb71lXnrpJXtid/Xp08fMmzfPPmfp0qWmU6dOpl27dmbNmjUxbed7771n+4A/9thjZuHChaZ8+fLm+eefD1ln7969plu3buarr74y33zzjalZs6a5+OKL7XK58cYb7XarjC4FWZXrmmuuscs7d+5sg9fKlStt+a644grjOE6MexUAvIM4QBwAkNyIA8QBZDEHwHFp1aqV07dv35Bls2bN0pnY2bVrl308btw4+3jt2rWBdW6++WancOHCzt69ewPL2rZta5fL+vXrnTx58jgbN24Mee3WrVs7AwYMSHd79D6TJk0KWda8eXPntttuC1nWrFkzp0GDBum+TkpKilOsWDHnf//7X2BZnTp1nCeeeCLwuEOHDk737t3t/xctWmTf+5dffnEyUqVKFefZZ5/NcB0A8BLiAHEAQHIjDhAHkHVoSQXkEDXpPeWUUwKP1dRWzXrVLzt42bZt2+z/f/jhB9vU99RTTw3039aPmhP/9NNPMb23ajLUNDiYanyCbd261dYAqcZEzXuLFy9u9u3bZzZs2BBYR7UnGoTRXf+TTz6xNSXSoEED07p1a9u8VzU8L7/8stm1a1dM2wkAfkYcAIDkRhwAjo2B04Ecki9fvpDH6qMeaVlqaqr9vwKCBhVctGhRmsEFgwNZVlHT3h07dtgmy1WqVDEFChSwgevw4cOBdbp27Wr69+9vmxzPnTvXVKtWzZx99tn2b9rG6dOn2+WfffaZee6558yDDz5o5s+fb9cDgGRHHACA5EYcAI6NllRAFszcETy4YVZp1KiRfV3VpNSoUSPkJ9aZQmrXrm2DQzD1Mw/29ddf24Ea1e/89NNPt0Fp+/btIeto8MOOHTva2hMN/tijR480QbVly5bm4YcfNkuWLLH7ZtKkSTGXHQC8hDjwD+IAgGREHPgHcQDHi5ZUwHFSE12d8DWLh2o0SpUqlSWvq2a9Xbp0sbUVw4cPt0Hq999/NzNmzDD169c37du3j/q1+vbta2ceadKkiQ0a77zzjlm+fLmpXr16YB0169VgjVpnz5495t5777WzcoRTE1/N6qGAqdoWl/aBtu3CCy+0M3zosbZXAREA/Iw48BfiAIBkRRz4C3EAWYGWVMBx0pSwatpap04dc9JJJ4X02T5eqqFQULr77rvtVLWqtdDUsJUrV47pdTTbxkMPPWTuu+8+07hxY7N+/Xpz6623hqyjaWPVZ/yMM84wN9xwg61FUXAJp2lkNRtI27ZtTYUKFQLL1Wd9zpw5tuZFAXXgwIE2mF500UXHsQcAIPERB/5CHACQrIgDfyEOICvk0ujpWfJKABKCmtiqSa0CWHZQ3/iKFSvagKkpZTNT03TnnXfaHwBA1iMOAEByIw7Ay2hJBfhQ586dTaVKlbL0NTWAo/rDDx061JQsWdJceumlMT3/scces82fs7JmCQAQGXEAAJIbcQBeRUsqwGfWrl1rf6vJcVbOoqE+9no9BTsNkqjpZWOxc+dO+yNqBq1pbQEAWY84AADJjTgALyNJBQAAAAAAgLijux8AAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAAAAOKOJBUAAAAAAADijiQVAAAAAAAA4o4kFQAAAAD41OzZs02uXLns72Q2ZMgQux+8onv37qZq1aoxP0/P0XORdV5//XX72fnll19ifq5fj8eQbPw+kaQCAAAAgCykm7dofqJJHD322GNm8uTJObLdyFmbNm2yN/vfffddvDcFSOPPP/+0n8+cTnDnzdF3AwAAAACfe+utt0Iev/nmm2b69OlplteuXTuqJNVVV11lOnbsmOXbifgnqR5++GHb2qZhw4Yhf3v55ZdNampq3LYN/7jhhhvMtddeawoUKBDzc1evXm1y587t2STVww8/bP9/7rnnhvxt4MCBpn///tnyviSpAAAAACALXX/99SGPv/nmG5ukCl+ebI4ePWoTL/nz5zfJzN0PGcmXL1+ObQ8ylidPHvuTGZlJbHnh+5c3b177kx28mdIDAAAAAA/bv3+/ufvuu83JJ59sb2RPO+008/TTTxvHcQLrqEug1nvjjTcCXQTd8W3Wr19vbrvtNvu8QoUKmRNPPNF06tQpU+PmxPp6u3fvNnfeeWdg22vUqGGeeOKJkMSLnqftVZlGjBhhTjnlFLvuihUr7N9nzpxpzj77bFOkSBFTsmRJc9lll5mVK1eGvM/evXvt+6ilkZ5bpkwZc8EFF5jFixeHrDdx4kTTuHFju92lS5e2ycCNGzdmaj+oxUjdunXNokWLTIsWLexrVqtWzbz44osh6x0+fNgMGjTIvm+JEiVsOVSeWbNmhayX3n54/vnnzf/93//ZdXr06BE4vhr/KL0xqbR/R44caerVq2cKFixoTjrpJNOuXTuzcOHCDMsUzfFKj7bhkksusV2+mjRpYveH3t/tAvbBBx8Etkf7YsmSJSHPX7p0qS1L9erV7TrlypUzPXv2NDt27Ij5WK9Zs8ZceeWV9jX0WpUqVbItnP7444+ojqm2pVWrVqZw4cJ2H7z//vv271988YVp1qyZLZs+/59//nmGY1Lps6vWUTr+wcaPH2/Xe+GFF9Idk8p9ra+//tr069fPHkN9di6//HLz+++/h7yejo+621WoUMFu83nnnWe/P9GMc5XR9y+az66er20TtaZyP5/anvTGpFISbOjQoYH30nY+8MAD5tChQyYWtKQCAAAAgBykRNSll15qbwp79eplu3p9+umn5t5777XJlWeffdaup+6BN954o2natKnp3bu3XaYbQPn222/N3Llz7U26btZ1U6mbY92Q60ZUN7WxiPb11AVIN/razptvvtlUrlzZPm/AgAFm8+bN9oY42Lhx48zBgwft9uvGtVSpUjYJcNFFF9nEhW52Dxw4YJ577jnTsmVLm5RwkzO33HKLTST06dPH1KlTxyY2vvrqK5vMOuOMMwI3/UryKOEzbNgws3XrVpvIURJACRMlwGK1a9cuc/HFF5urr77adO7c2bz33nvm1ltvtS1QlGCRPXv2mFdeecX+/aabbrJJlldffdW0bdvWLFiwIE33vfD9oKSEnqNkgZYpSSBKjKVHnxWVV/tOnwslBb788kvbUk8JpEhiPV6RrF271lx33XX2+UoAKvHRoUMHm7hTEkLJTdH+1z4L7uKmFoQ///yzPUZKLi1fvtyMHTvW/tZ2u4mOYx1rJVa0b5Xw+Ne//mVfS2WaMmWKTcIp2XKsY6pkmz7fSr7qs63/v/POOzY5pvdXGZ966inbvfbXX381xYoVi/ha559/vi2zyqtuuNo+7UttV5s2bexrHYvWPeGEE8zgwYPtd03HQWWfMGFCYB0doyeffNLua5X9+++/t7/1OYpWpO9fNJ9dJai0j/S512f1iiuusK9Xv379dN9Ln0kl1LX/lICfP3++3Uc6hpMmTYrpBAkAAAAAyCa33367mkcFHk+ePNk+fvTRR0PWu+qqq5xcuXI5a9euDSwrUqSI061btzSv+eeff6ZZNm/ePPu6b775ZmDZrFmz7DL9zki0rzd06FC7TT/++GPIuv3793fy5MnjbNiwwT5et26dfW7x4sWdbdu2hazbsGFDp0yZMs6OHTsCy77//nsnd+7cTteuXQPLSpQoYfddeg4fPmxfp27dus6BAwcCy6dMmWLfe9CgQYFlgwcPDjkG6WnVqpVdb/jw4YFlhw4dCmyz3lOOHj1qlwfbtWuXU7ZsWadnz56BZRnth2+//db+bdy4cWm2Q8e8SpUqgcczZ860695xxx1p1k1NTQ38X88J/rxEe7zSo9fT+86dOzew7NNPP7XLChUq5Kxfvz6w/KWXXkrzWYv0ufrPf/5j15szZ07Ux3rJkiX2ORMnTnRi5R7T8ePHB5atWrXKLtNn7ptvvklTtuBjov9rmY6la//+/U6NGjWc008/3Tl48KDTvn17e4yD90ek4+G+Vps2bUKO21133WWPx+7du+3jLVu2OHnz5nU6duwY8npDhgyxz490TgiW0ecu2s/u77//bl9D351w4d+n7777zj6+8cYbQ9a755577HJ9fqNFdz8AAAAAyEFTp061Y9zccccdIcvV+kCtrD755JNjvoa6JrmOHDliW56oC5NaDoV3h4tGtK+nrnVq9aNWINu3bw/8qAVJSkqKmTNnTsjrqnuW221I1OJEs9mpu5JadbjUQkPdu7RvXHpvtcbQAOORqJvbtm3bbKsWdf9ytW/f3tSqVct8/PHHJjM01o5aDbnUgkqP9V7qBig6fu7YPuqWtXPnTtuySS2aIu3/8P0Qq//+97+21ZFa3oQL73YVLNbjFYlaNjVv3jzwWF3j3BZFapkVvlwtpyJ9rtSiR+995pln2sfB++lYx9ptKaUWh2odFquiRYvallMudevTe2ryAne70ytDJGpZqFZtaiV0zjnn2M+aWkAG74+MqGVT8HHTMdLxWL9+vX08Y8YM+3lyW6kFt8CKRaTPXayf3Wi431t1YQw/p0ks30WSVAAAAACQg3QjqnFmwrsTubP9uTeqGVEXOXUVc8cZ0lhMuhlV16djjdFzPK+ncYGmTZtm/xb8o6SHKJETTOM5hZfdTRKEU/mVxNA4XKKuTsuWLbPbpC6P6hoYnDzI6LWUpIpmP0aiY6NxeoKdeuqp9nfwGF3q2qTkmhJkGsNL+0E345H2f/h+iNVPP/1ktys4sReNWI9XJOGJFzdhpOMSabm61rmUAOnbt68pW7asTVjpvd19EbyfjnWs9RwlQNRNTZ9NdU0bM2ZM1J91dWENT+Zpe6MpQ3rUPVXd4dRFTtvjdgWNRvg+VRIx+H3X//3ZVaI4mI6/u2400vvcxfLZjYa2V108w7dX3TKVDIzlu8iYVAAAAADgMWpRofFmNJ6OWrno5lo34WotEs2A2Jl9Pf1fLZ7uu+++iK/jJnMitaSJlcY3UgsTjWfz2Wef2fGCNOC3BuvWuEzx9Pbbb9vWYBqTSGOJaaBvtVDRGDxKKIU7nv1wPGI9XpGkN7NdesuDB//XMdQYWNpHGutILZq0TRrwPfhzFc2xHj58uN3nH374oV1HLRG1vzW2lZJQ2VWG9Gh8LHcAeR1ztfCKdiy443nfWET63MX62Y1FRq36okWSCgAAAAByUJUqVezg4RqwOLg11apVqwJ/P9ZNnwaZ7tatm71xD+5OpZZPmRHt62ng9n379gVa4sTKLZsG1w6n8quVTHArpvLly9suT/pRqx8NUv3vf//bJi6CX0tdz4JpWfB+jIW6nKk1V/B2/Pjjj/a3O6i79pcGflcSJfgYReqOlxU39Nrv6uqmlkmxtKY63uN1PNQqSN3WNDtc8Ex4at0VSUbH2qWZBPUzcOBAm/xSayYN4P7oo4+anKZjre5+Gkj+/vvvN/379zejRo3Kkteu8vdnV4PWB7eGUjfcaFp5ZSTaz24sn09tr5KOOrZui1DRRAY6h8TyXaS7HwAAAADkIM0cp/FnRo8eHbJcY9roxjD4plyJkkiJJ7V8CG91oRny9LqZEe3rqcXLvHnzbMIknLZTY9tkRIkItahRd6Pgcqmrl1rHaN+I3je865FafKjLmzulvcbQ0TIlKYKnudeYXkoeaGyqzFAZXnrppcBjzSynx+oS1bhx45CWMMH7TGMqad9Ey02CRZNY1NhCei8lfGJpfXO8x+t4RNpHEj6jYDTHWjPShW+rklXqYhZ87HOKjrWSU2p5qHGX1CJJ3+cvvvgiS16/devWdmw0zbAXLPyckRnRfnbdVmHRfD7d7234sX3mmWfs71i+i7SkAgAAAIAcpCnlzzvvPPPggw/aMY4aNGhgEzTqxqSbXrV+cSkpolZXutnTTbtaVWhw50suucS89dZbtlueBrbWDabW0/gymRHt6+lm/KOPPrLrq8uQtk+tjn744QfbQkPlUWuojKgrlxJx6lbYq1cvOx6WEmJ6b41FJGplpi5cms5e+0fdxLQ93377baC1V758+WyXsB49ephWrVqZzp0725YbI0eOtC2e7rrrrkztC+1nva7Kou5wEyZMsIO9jx071r6nu7/UEuXyyy+3N+Dr1q2zyTLtO7VcioaOs8br0fPUok5JKx3bSOMI6fNyww032JY6aq3idpf78ssv7d/69OkT8T2y4nhlVvHixe2g4hpvSoPxV6xY0X7Ota+CRXOsZ86cacvYqVMne0yUsNLnVQkXJfBykloYqtVhzZo1bUsvUfLwf//7n/0sat+Gj2kWq7Jly9qxvFT+Sy+91B7v77//3iZgdbyOp1tdtJ9ddRXUMn3+tc/Vgq9u3br2J5yOm/aJviNKaun7qLG6lIxWt0J9RqMW9TyAAAAAAICY3X777SHTtcvevXvttPMVKlRw8uXL59SsWdN56qmnQqall1WrVjnnnHOOU6hQoZCp5zVlfI8ePZzSpUs7RYsWddq2bWvXDZ/yftasWfZ5+p2RaF/P3fYBAwY4NWrUcPLnz2+f06JFC+fpp592Dh8+bNdZt26dfV+VKZLPP//cadmypS1X8eLFnQ4dOjgrVqwI/P3QoUPOvffe6zRo0MApVqyYU6RIEfv/559/Ps1rTZgwwWnUqJFToEABp1SpUk6XLl2c3377LWSdwYMHpzkGkbRq1co5/fTTnYULFzrNmzd3ChYsaPfB6NGjQ9bTcXrsscfs3/S+ev8pU6bYfaVlrmPthw8//NCpU6eOkzdvXrveuHHj7PLw15GjR4/a16lVq5bd7yeddJJz0UUXOYsWLQqsk9njlR69Xvv27dMs17bqcx0sUll1HC6//HKnZMmSTokSJZxOnTo5mzZtsuvpmER7rH/++WenZ8+ezimnnGKPiY7zeeedZz9H0R7TzJZNx0TLVD7R9zZPnjzO/PnzQ56nz4yO46233pru8XBf69tvvw15bqTv6dGjR52HHnrIKVeunP2enH/++c7KlSudE0880bnlllsyLHNGn7toP7syd+5cp3HjxvZzE3zMIn2fjhw54jz88MNOtWrV7Dnt5JNPtp+7gwcPOrHIpX8ynYIDAAAAAMAnzj33XDvDoLofAolm9+7ddnY/jcGllph+xJhUyHZqspsVo/znFDWDdQdEjIWeo+cCx6LvQ3pNsgHkHOJT5qmrjgZdVTcLjS0T6w2gflzqaqLj8Prrr5uclN3vqxmf9PruzE8AAMTiwIEDaZa5Yz4Fx1G/IUmVQJ5//nl7MaN+yMhemrFDNyfqW57In4ecvmD3Ak3tqmPHRT+Qc4hPOccL8UnjiWgqc82opOnqH3vssXhvkmeMHz8+zaCyAABEMmHCBJuM0pheuha77rrr7NhXF154oY3BfsXA6QnknXfesbWdGmBMU03WqFEj3pvk65sAfcG1v8NrgF9++WU7CGG86USkQfFonZU2SeXOauLnGgQgkRCfco4X4pMGr9VsSq+++qrJnz9/vDcnYWmwYNWCB+8jJanUhUgDYwMAkJH69evbGf6UpNLshu5g6urq52e0pEoQGlF/7ty5dtYOTW2qG4KspNkPNHVqsotmP2jGjgIFCuTYNiW7RPlsJsp2AImG+JQzvBSftm3bZmf8IUGVMSXyChYsaH8D8A611mc8KiSCM844w85yqDHSdI3w66+/2ta4mv3Qz4iaCUIX/RoATVNAaurN9G4CNFCaat9OPvlke6Gq2mxNjxpcs+qOsfD000/bD7GmNtW6K1asCNSAnn322XZaTE15etlll5mVK1emmQZU76OaXD23TJky5oILLjCLFy8OWW/ixIl2GlNdrKrVz/XXX282btyYqX2gVjGaznLRokWmRYsW9jU1/aqmwwymL+igQYPs+2qaWpVD5Zk1a1bIeuntB7VQ+r//+z+7jqYI1TrBY1JEGvND+1dT2darV89ecOpGTdOALly4MMMyRXO8ItH7L1++3HzxxReB7QtuNRTr52DMmDF27JDChQvb5qE6wWnOhKFDh9rpXrWv9TnYuXNnmu3QFKXq2qEafZVd05BqytKs/mxGc1z1fO17UUsDd9+4UxWHj3PiCj+mx/qOrFq1yn4PNc2qytykSRM7dW80YvmsTJ482X7m9d6nn366mTZtWsjf169fb2677TZz2mmn2WOkKaA17a62P5g+uyrP119/bfr162ffU/tP08r+/vvvabZP+0tTK+vzoOlgVe5IY9Zk9vMLfyE+EZ+CaXvUxU/Tl4dvn5aff/759pjoNRUvXnjhBZMVVB69l6ayDvfpp5/av02ZMiWwTMe6Z8+ettbZPce+9tprUb1XNJ9D9z169eplz6d6D30mbr311kCyMXxMKn2OPv74Y3tud/edjqem+9Z7qXY83G+//WbH/Ro2bFhM+wsAAK+iu1+C0EX/FVdcYWslO3fubC/qvv3228DFqtvNqVWrVvai6OabbzaVK1e2tdsDBgwwmzdvTjPGgS4WDx48aHr37m0vnnTDrUzsRRddZBMWulFVM/TnnnvO9mnVBb578XvLLbeY999/3w7urIvMHTt2mK+++spepCmjK7oo1UW0tlEXT1u3brUXyrpRXrJkib2wi9WuXbvMxRdfbK6++mq7H9577z17waf9ootNUVPHV155xf79pptusjcs6nLQtm1b2xUlvHtE+H7QjbueoxsJLdOFqOjGIz26CFV5te9uvPFGW+P95Zdfmm+++cYmMCKJ9XgF09/+9a9/2Sy5O2uDLrQz87r6bOmCWa+nJJSai2r/6kZCF87333+/7b6jz8E999yT5iJ+zZo15pprrrGfiW7dutn9qUSJEiq6Mcyqz2Y0x1U3X/pu6DOh46jvjNsUNjMibYeSg/o+VKxY0fTv39/eOOhz2LFjR/Pf//7Xvm9Gov2s6PukZJ+SUMWKFTOjRo0yV155pdmwYYNNRonOAdqP1157rU0m6sZW5deNjm7qlWQKpmOsZMLgwYPtutrv+g6rP7tLx0SfgQ4dOth9+/3339vf2g9Z9fmFvxCf/kJ8+stbb71lxo4da8ujsgZvnz4bSgZdeumltnvC//73P3uOU+Lr9ttvN8dDZdFnQ/tdsSiYznE692k/i473mWeeGZikQrHjk08+sftKxyijrnbRfg7VLbNp06Y22adjVatWLbs/9dnU/o3Uykzx/I8//rCJp2effdYuU5zXj469yqEWi0pKuf7zn//YSqUuXboc1/4DAMAzHMTdwoULHR2K6dOn28epqalOpUqVnL59+4asN3ToUKdIkSLOjz/+GLK8f//+Tp48eZwNGzbYx+vWrbOvV7x4cWfbtm0h6zZs2NApU6aMs2PHjsCy77//3smdO7fTtWvXwLISJUo4t99+e7rbfPjwYfs6devWdQ4cOBBYPmXKFPvegwYNCiwbPHiwXXYsrVq1susNHz48sOzQoUOBbdZ7ytGjR+3yYLt27XLKli3r9OzZM7Aso/3w7bff2r+NGzcuzXZ069bNqVKlSuDxzJkz7bp33HFHmnV1rFx6jp4b6/FKz+mnn273SbhYPwcnnXSSs3v37sB6AwYMsMsbNGjgHDlyJLC8c+fOTv78+Z2DBw+GlEnr/ve//w0s++OPP5zy5cs7jRo1yvQ2RTom0R7X33//3b6GPlfhtL8i7bPwY5rRdrRu3dqpV69eyH7QcW7RooVTs2ZNJyPRfla0jvb12rVrQ76HWv7cc88Flv35559pXmfevHl2vTfffDOwTJ9jLWvTpk3I+9x11112/7vHf8uWLU7evHmdjh07hrzmkCFD7POz8vMLfyA+/YX4lHY79Pxwkc5Zbdu2dapXr57hudrdH5HKHEzxK1++fM7OnTsDy7S/S5YsGbJ/e/XqZePU9u3bQ55/7bXX2s+Pu52R3jfaz6H+r2U6Xunt+1mzZtnX129X+/btQ46h69NPP7XrfvLJJyHL69evHzGuAQDgV3T3S5BaarWSUbcbUc2fWq68++67JiUlJaTrgmpVVVuofqnuT5s2bex6c+bMCXldtcpwu0aJakc1W5C6C6jW2qVWKGoRM3Xq1MAy1TLPnz/f1hSm1+xeY1KohlTdC1zqDqLaRDVnzwzVvKpW16WaSD3We6mbhaiG0a2hVO2sWgep5li1rOHdPSLth1ip9YyOiVqnhMto6vJYj1e0Yn1dtXpStxOXOzuXur5ofwcvV4ur8O4w6sYQ3HqoePHipmvXrrY1wpYtWzK1TZGOSazHNSuEb4feU9081FJCrRnccqilhmro1aoso+5CsXxWtG/UxSf4e6h9+/PPPweWqUuR68iRI3Y71CVH389I+0S1+cHvo2Oi/a+uJTJjxgy7T/W9DW+BlVOfX3gL8ekfxKdjCz5nqcWQXlMttnRe0+Pjpc+ezoXBXc7VHV2tmfQ3UT2A9otai+r/weXTeVzbkV5MifZzqGOr7tp6j0it1TLa9+nRvle8De5OqzFxli5dauM1AADJgiRVnOliUBf7ugHQ4LTqdqUfJQzUXF03lS7dIKuLlS5og390YSO6UA6msRGCuTeqGt8mXO3ate0FnMaYEHUH0sWRxqpQc3Y1eQ++ec7otXQT4P49VrpAU/eqYKeeeqr9HTwOj8ak0EWjbkDUNUr7QTcekS6Cw/dDrH766Se7XcEXrNGI9Xhl1+uqG0cwN2GlYxtpubq0BFNSJPyCO/yYHO9nMzPHNSuEb4e+e7qpeeihh9KUxb0JzOi4xfJZCT8uohvG4P2vbibq9uOOGaNxdbQtuiGLtE/CX1OvJ+5rut/L8JnZtL3uutn9+YV3EJ9CEZ+OTd0p9RruWE56zQceeMD+LSvO4w0aNLDHMLgLs/6vc6O6sIvG4dM5Ul0Sw8unLqAZlS/az6HeQ90GNU5ZVtHg6urSp+SXuguKElb6HKmyCclBCWIlP/W91rWXPg/xfj+NmaZus+4YpupmHT4eX6x0raUx+XQO1fWNhlj497//neFzfvzxRzs+nL7vqtQ766yzQsb70/AF6mqt2KDt1HdW3byDqWu4uu7q3Kx1dD5xu9661G3ZrTjUT/PmzW134WCqoFBFo15D5xZtl8YzDaeu2G480Fh94d2ej7UfotlexWpdtyqeaB1tl8ac/avhfvZQt2d3XL3gn+Pt1g24GJMqztRqQzV3uhHQTzhdoGiga7fmTjV59913X8TXci+WI9VoxkotSVTLOmnSJFtL+dRTT9kBVVV7qbEa4untt9+2tZwaI+jee++1J313UFFdsIc7nv1wPGI9Xtn1usFjW0SzPDNBLSs+m7Ee10gUICNtf3CLj4y2wx0wWGNzuWObhAtP8GRWNPtfLZw0Zo3GT9FFkhKJKqPGqIo0uHE8jyn8h/gUu2SOTypf69at7U2UxlXSjaJalan1kW6qsmrCBbWY0k2cEkYaz0+TWujG1G0Z7L6PWh+Fj13lyuw4htlNrZT1eVaiQGUaP368nbwkuDU0/E1JUCVjNc6dO+5mvN9PE7IoHuj8puSEzrtqqarElsafi0TXKqrcCJ/owaVJAvQ6StBo0ge1Og2fvCecvgs1a9a026Jzp8bO0zKde8qVK2dbtOqcq+3U+Ufj7KmFuc7BSrKJEuj6v84B+r+SQEo46f9aV5SMe/zxx+176fpJFQ9KQqkHgcbcE02OoaSyKge13aosUTxUmd1rMZ0Hhw8fbr/TqtzRvg6f+OZY+yGa7VX8U2JN26ntU2tiJeR13rjjjjtMdtC4lMHX1qo4UkwhoY6sQpIqznSRrxOqZl8LpwtuXYSrtsLNjKs2w63pjFWVKlXs79WrV6f5m7L/qpkIriUuX768DUL6Ua2jBqTVhaFuAoJfy629dGmZ+/dYqfuGTuLB26GaE3EDnQYl1YCm2j/BLXwidXdITyxN8bXfNXOQgkYstdXHe7zS28bjfd1Yua2Lgrcn/JhkxTZFe1wzOnZqDRTcosIVbcsJvb87zXtmypLZz0pG+0Q3WbrIcWmQZbUSyAz3e6ljGtyCQ90Iw1vQ5fTnDImH+BSK+JQxDZJ+6NAhmzQKbtUZPrNhViSpNLuruvSpK6paNClx71KrBiWvdAMVa/mi/RzqM68WFpmZoj6j46uWWY0aNbLfPd0oayINDdqO5KFzWEbJdn3HNAC/BtTXtYA+M0pSRJrZOCveT5Ts0bWI+x5Kjrz00kt28oT0klQZ0SQXSqro++O2WjxWq1IlpdUCVBNRuElmJZI0I6peR0kqdwILl87F8+bNs+djN0ml75d+XDp36++abMJN+qhlWTDFFm2vJqNwk1Tuuu5rPProozbZpySUzq+6pho4cKA9Lyp5HylBHs1+iGZ7dXyURFOXdncdfT50fLLrcxPeTV3HQuVW924gK9DdL47UlUcnGtUCaFrv8B+dUDUuji743NpjnWx1QRpOJxyNe5ERXdRrZiFl2oNvcnVyVBZfsxaJLuzCm+XrRkU1JjrJicZg0DLdoLjLRM1hddJ1T5SxUhkU+FwaI0mPdTJUrYW4NRTBrUM0Pon2TbTcm4xobvY1ZojeSxfFsbRQOd7jpW2MtH3H+7qZuTHTzahLNwRvvvmm/SzpoiCrtina4+rOaBdp3yhA6kZCXTGCm3+rC0o09JlWwNZnTi1IwgW/blZ+VjLaJ+HP0w1Lei3DjkUXSmptED4l/OjRo+P+OUNiIT6lRXzKWKSy61ipNWhWUhcetThQNz/96LNzzjnnhGyH9ouSWJGSSBmdx6P9HKprnlrL6QZUrRZi2fc6vhl1fbzhhhvse6mViLr4xLt1IBKLzr367qp1q8YrU8uVdu3a2QROdtHsnTrXa0xOfbaVeFaC3m1JGyt9b5RAmjJlik3KKKmimUkzakml74ISObr+VGWBez7Wud49/0ai71pGCXy1jlKSJ73kitvtXe+pFu2R6G86z6ks7lAa06dPt606tc90zlLSWefdX3/99bj2Q6Tt1fFR93u30kTXvWpxFXzuyM7PjWKhWq8pSZiZ8fiASGhJFUc64esiP71aCE2frItf1aip5lBdB/Qc3TSoO4FOyjox/vDDD7b2Vtl71fJlRE1OddLSiVZTMbtTK6tJqJqqirZJJ1PdiKhWQFMja0pmNe10W3SopYky8GpOqhOlmqW7U3zrJHvXXXdlap/oRkOvq7Kou4EuQDWIqcaW0HuKyq+bJw3mrZsNNa3VzYj6yKtmOBpKZmi8DD1PNa66aFRT3Eg1ORqPRReNo0aNsidzndQVeFSLob+5tTPhjvd4aX0lE1Q7oy5mCsRqFZAVn4NY6Djos6Ljr1rr1157zR7r4BuPrNimaI+rOx6CPhvaNl18qEZIPwqQal6trnraZrWw0Guo5kvJtWio1YjGOdBNkKaQ1wWEyqvgrmnDFfzTk9nPSkb7RNO96/upMmsb9F3UxVpm6Pipabm+xzrvaPtUHt286/gEX1zk9OcMiYX4lBbxKWO6YVX3PrVCUHcUlffll1+2sStS0v946DOn8fo0zos+K0oahdfq60Za+03nce1/3fhpwHR9XjK6CYzmcyiPPfaYTSbpM6YWDboRVTk1KL1uEHUMI9G+1mdHXaj+7//+z36Gg1tuXHfddbYbpiqHbr311sBnC1DLOl176bfOR+7wBBpfTsv1mcwO+vzrM65zryq69H3Tdzs4ORwLtXhXC3d9V5R0UiJI52Wd19WVLxJdn+i7q+SwzovaBp1bVPbwMTVdSubouxZpsgyVRQlrJbv0vVZyKJjOhToHqPW6vqP6Puo8EkytuPRd1blTCTQlptyJM1RGnYt1TBR7dP5Qyyp1iVOSSOvFsh8y2t7+/fvba1x1tVaSXq+j1l/qjpgTnxt1T1ZSX7EEyDLxnl4wmXXo0MEpWLCgs3///nTX6d69u51u2Z1Gee/evXYK5ho1atgp7EuXLu20aNHCefrppwNTYLtTKj/11FMRX/Pzzz93WrZs6RQqVMhOf63tWLFiRch0zvfee6/ToEEDp1ixYnaaaf3/+eefT/NaEyZMcBo1auQUKFDAKVWqlNOlSxfnt99+C1knlim+Tz/9dDvlefPmze2+0TTNo0ePTjO182OPPWb/pvfV+2tq8fCpuY+1Hz788EOnTp06Tt68eUOmoA5/HXdacb1OrVq17H4/6aSTnIsuushZtGhRulN8R3u80rNlyxY7VbWOgbYveArq4/kcuFNiT5w4MWS5yq/lwdNpq0zaBk2NrWmwtb+1D8Kfe7zbFMtxlblz5zqNGze276PX02fM9fbbb9vpzvU3TSWubY/1s/HTTz/Z6cXLlStnv38VK1Z0LrnkEuf99993jiWaz4re+/bbb0/z3PDPkKau79Gjh92XRYsWtVO5r1q1Ks16kY5detOfa/seeughWzadA84//3xn5cqVzoknnujccsstWfb5hbcRn0IRn0LptbTvw3300Uc2Vmj/VK1a1XniiSec1157zZZBZQ7en8Exzd0fbjmPZc2aNXZ9/Xz11VcR19m6das9z5588sn2c6pzXuvWrZ2xY8ce832P9Tl0rV+/3sYK7XMdb8Uevac+p+mdg/ft2+dcd911TsmSJe3fwo+nXHzxxfZvinVIXvoMTJo0KfBY5xIt03cv+Efniauvvtquo3jufjfS+7n//vujej+Xzi+nnnqq/X5///33znPPPWevSaZPnx5Yp127diHbpNcqXLhw4LHOZ66bbrrJ/n316tWBZTpfaZmucSLRufXSSy+15zZ957X+rbfeaq/PNm3alGb9H374wZ7Thg4dGvH1fv75Z2fp0qX2fKD4MH78+JC/6zus84zO+f3797evtXz58pB1du/e7fz444/OF198Yc8RZ5xxhnPgwAH7t3//+9+2PLoGdW3bts3JnTu3M23atJj3Q0bb+5///MepVKmS/a113nzzTbvO66+/niOfmwsvvNBeIwNZKZf+ybqUF5B56malPueZGeMB2UOtDtRCSU2R4V+qAVNNpFrtacwCAKGIT8hJaomnlhwaPxDJS62H1IJHrYdErYLUOmb58uVpJkpRax8NwaCuV5HG5gzmzjp6rPcTtSRUKyAtD+4qrZY8al2u1jiibm1a16VBx2fPnm1nqxO1CHTHfNMYfWq9c+TIkZD30XAOap2o1kbh1J1NrTU11pPGgwt+H7V4VGsi14oVK2xLUm3jsWYMFF37qNV6pLHoXBrfTq1cg7t8B9N+13XUK6+8YlvvqoWSWvere59aQQW3aNf7qYVnZvZDpO1VF0OVP3hmPa2jLngaAiM7PzdqCeaOw6hxsYCsQnc/AEgiugAKn1FMY59IZgfQBABkDXUZVPckKgwQTgNoqyuXhjLQDKeRqBuZun1lFSVQ9BPepVbJjuAZO91kVDAlpSLN7teyZUvbbU2z8inxI+54SulNbPHnn3/a3+HbocfB26FEjIbG0EDv0SSoRM8PHr8wM+uozYd+3HVURlEiyU1SqZuxKjvcMmZmP0TaFu2bjI5Pdn5ulIxTt8vMjvUIpIckFQAkEdWovf7663YAYNWgaewUzfaiGkr3ogoAkLM0fpkm+VBLDLU60bheSD4ayy24BZ0+Fxr7TuNvaiw8tYjp2rWrHYNPyQeNU6RWRpo1LjOJgozeT7N0qtWSxl3TOHaq4FLy5IsvvrBjKGkM0MxQqyTNyKqWRqokUzJFrYDUckhlFM1Mp3KqbEqAaXwotVRS8knj0WlbNC6Wttctt1q6KkGlcUk15tuWLVsCCRu3BZDGHVW53ITMnDlzzNNPP23uuOOOwPYNGDDAjkun9TQO4vjx422rMHeiCbU40rWUrpv0umpRpnHwtE3u5Aoqh1oWaRxQjVuo/ajX1fuqlVe0+yGa7dWYdkrIaT2NwarB1XVs3NkOs+NzI9peJal0TDRWGZClsrTzIHAc3DE/kDjcMangHxrvQOOyaAwqjdOicQz69u1rx6cBEBnxCdnNHVuwcuXKEcd9RHJwxzEL/3HHlNN4cYMGDbJjvimGly9f3rn88svtWETZ8X6yefNmOwZhhQoV7Hhzp512mjN8+HA7TlR6wsehC7dx40bniiuusGNblS1b1r7+jh070mxX8Gto3E2Nf6TxljQm4ZlnnulMnTo1zRiD4T/B476NGjXKnss1XpbGm9O4gRrTMCUlJbBOz5497XPcMf50zfTZZ5+FbLvGxipTpkzgOkrjzIWPI/XHH3/Y19L4c9pmHacNGzbEtB+i2d49e/bY6zidO3R8NDbegw8+GBgbLzs+N6LxtsLH1AKyCmNSAQAAAAAAIO5CO7ACAAAAAAAAcUCSCgAAAEDSGzZsmPm///s/U6xYMTsgtGa6y2jWN9fEiRPtuEEFCxY09erVM1OnTs2R7QUAPyJJBQAAACDpaVBwDV79zTffmOnTp9uZ7TRA9v79+9N9zty5c03nzp1Nr1697KDVSmzpRwN5AwBiR5L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      "text/plain": [
       "<Figure size 1200x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(12, 10), nrows=3, ncols=3)\n",
    "\n",
    "vars = [\n",
    "    'mass_mixing_ratio', 'number_mixing_ratio', 'number_density', \n",
    "    'wet_particle_radius', 'wet_particle_density', 'dry_particle_density',\n",
    "    'delta_particle_temperature', 'fall_velocity', 'total_mass_mixing_ratio'\n",
    "]\n",
    "\n",
    "for var, ax in zip(vars, axs.ravel()):\n",
    "    ax.plot(ds.time, ds[var].sum(dim='bin')[:, 0, 0])\n",
    "    \n",
    "    ax.spines[:].set_visible(False)\n",
    "    ax.grid(alpha=0.5)\n",
    "    ax.tick_params(width=0)\n",
    "    ax.set_xlabel('time [days]')\n",
    "    ax.set_ylabel(f'[{ds[var].units}]')\n",
    "    ax.set_title(f'{ds[var].long_name}')\n",
    "    \n",
    "fig.suptitle(\"Bin Properties, summed\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64a3d71e-b6f6-4d94-b15f-aab772c43050",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_props, group_config = carma.get_group_properties()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2c86f7a5-2515-4a23-b967-d32c6d137057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vESNGqOAPAkLvvvuuvPnmm1me/80338izzz6rGpqPPvpImjdvLl27dpWzZ89KfsFFBxqzUqVKqYZg0qRJ6kLon3/+ybf3ICJyZvZsR65du6ZO+BF8QjuAE/cnnnhCfv75Z/U/UkZw3E5JSZFevXqp99HasWOHbN68WT3us88+k+eee07WrFmjjvH6Fza4UEJ50EZNnz5d3nrrLalYsaLs3r1b95iePXvK4sWL1cUTLnZeeukl9V64ICEiciX2aBMef/xxdZz/448/styPYznSBnH89/Dw0B3TEYRCMGrq1Knq+D1r1ixp27at6oTILwiC4ToHQSW0T+j0RvAMn6d9+/ZqNBmuLdDp3a9fPzl58mSWcqOz5YcfflB/QxuF65xRo0ap6yNyQRqiQmjOnDkabN6GP0WKFNHMnTs32+PxtzFjxuhu43fcN2jQoCyPe/TRRzUBAQFm379ly5bq+TNnzsz2t1u3bmW779lnn9UUK1ZMk5qaqm7fvXtXExgYqKlTp47mzp07usfNnj1bvS5eX+vkyZPqPnxm/ffXf4xW//79NWFhYbrbL7/8ssbPz0+TlpZm9jMREbkSR2lH5s2bp7vvyJEj6j53d3fN1q1bdfevXLkyWztgrK3ZsmWLetz333+vu6927dqaTp06mSzHtWvX1HMmT55stsxERIWVPduEjIwMTUhIiKZnz55Z7l+wYIF6zY0bN6rbly5d0nh7e2vatm2rSU9P1z1u+vTp6nHffvutyWuCdevWqcfgf33GrjPwXNw3YcKELG2Fj4+Pxs3NTTN//vxs7ZZ+XYwfP15TvHhxzbFjx7K815tvvqnx8PDQnDlzJsf6oMKHI6WoUMMwUowGwg+i8egJHjJkiPz6668WPR89y/oefPBBNQLqxo0bZp+LHm30KhvCEF8t9JSgtwWvi16DI0eOqPt37twply5dUu/v7e2dZbgshvrmFwwdRs+LfpoGERE5RjuCUbUY6aSFVDwct2vUqKFGT2lpfz9x4oTRtga943hPpEfg+fqjoHAbvf2xsbFGy4DXQTuElA6M3CIicmX2aBOQPof06z///FONytLCqFmkzSHlGpDGfffuXZUW5+7+32X+M888o6boMBxplVf43PptCdoopOxhBJhhu6XfPi1cuFB9bmRq4DpI+9OmTRs1Amvjxo35Wk5yfC4dlMIG36VLFzW8ETv7kiVLCvT9tLnE+j+RkZEF+p6uDvMw4QCHn759+6qDcVRUlBpeioO2OUhh0IeDJ1hyYo5GQj+gpIWT/0cffVQFl9BAlC1bVjfkF0OCtXOBAOb40IcV9ipXriz5BfONVKtWTQ2/xVwhgwYNkhUrVuTb6xMVNrZuNyx5PwSrDdsWDJ0n529HcFzG96kPbQfSJQzvM3xNrPyE1HDtfB1lypRR7Q3m8dC2NdqVpHAf2gLMKThy5Eg1j5UWnosUDCx5jsl8MU8iUjUwzxQROWe7gWsSXIMggIBjEo5v27ZtK9ByFRb2ahOQwofj+m+//aZuIziFIBWCVdp2Qnv9gECQPlyP4PpB+/f8gDkI0aYYtkWm2i39z4dOEFxv4Pn6P6hTQMc8uRaXDkphhEjt2rV1k7LZAiadi4+P1/1s2rTJZu9NonoN0KOBujfVK6xPm59tKHNUbs70e6m1cOKPHOp9+/apCwHkgaOnBSf8kF+T+xk2BlqGk6kHBgbK3r17VQOH+arWrVunAlT9+/fPl3IQFTa2bjcsfT8EofTbFqwCRM7fjph6riWv+eKLL8oHH3ygeqwXLFggf/31l2pvMJG5fluDINPx48fl22+/VROrf/3111KvXj31vxZ63Y8dO6bmQMGFyDvvvKNGa+3Zs8fsZyBydY7YbiAIjTnkMCE1rkUwHxDmHMI8SeSYbUKTJk3U94TjOeAaAkEqBKtsee2QH+0T2qBHHnlEN+LM8AfzYJFrybpMgIvBxTd+TLlz546a8BMn9wgm4GQNwQNjq5pZCiszYOJqsp+0tDT1v/7wV1tB+gOG6GKILy4EtPQn/wOsVAFo3LB6hX4KBh6Lk42coNdFf5islrEeEvSeoEcNP2gkMHoKEyLiogOpHkRkv3bD3Pvpj2Zh2+Ia7YilsEofOhgw0a1Wamqq0RWYSpcurdLN8YPPhPYJIyn0UzOwEuyrr76qftA2YREPvDbSV4jIudqNJ598Msvtjz/+WC2wg1GSDz/8cK7e15XZqk1AJwMWSUKqH1L3EKRCsMrw+uHo0aNZMiswggvXD9qRSMZoR2wZthH5ObpKvz1BXeVUHnItLj1SyhwMw9yyZYvMnz9fHaQxPBK90ZZEwU3BczGcFgcKDPnkyjW2haAOeosRiEEvr61pew/0ewvQUGA1I31YsQLDWGfOnJllKDBWtDC3pKv2YI/5qfR7vDA6y3BVPQTIDHt7YmJidCdJRGT/dsPSgDdGPmLIPlYCMty3qfC0I9a0N4Y9759//nm2Xm/DbQXzWKFDQtsGYL5DBLMM2xisqMR2gsh52w0tnGfOnj1bpViZ6/Qk+7YJGBWF4+53332n0t/0524CBHlQDqxmp3/8R8ARadudOnUy+doIaKHdMJzPyfAaJT+g3NjmV65cme1vuM7RBvnIdbj0SKmcIFg0Z84c9T+CSPDaa6+pAwDunzBhgtWviYlIEVTARQOGeGL5T0zydvDgQXVyR/kPc2BoJw9HfvK8efNUI//mm2+q+ZxsrVmzZqonAr3XWFIbQ2X/97//ZbtwwNxR77//vjz77LNqpBQaIfRwYNuzZE4pzA2FXq927drJ4MGD1WdHgAvpo/oTKaIXPDExUb0HcsDRG4KLFvSAO/LFFpGrtBuWwMVLjx49JDw8XKVhjR49WvWS44TP1DB6ct52xFKdO3dW7QsuNDHfCbYHTIKL9D19+BtGZNSvX1+NmMJCGxhlhQtlQNoeRk7gIgKPxYjvxYsXS0JCQpZJ2InIedoNWLZsmdqHEXgODg5WaVOYe44ct01AajU6DTCyDsEpw9Q9dGiPGjVKXWPi3ABTc2DUFAJLDRs21M1hawzaCgREcR2A6xN0PmAbKYj5nTB3IaYOQTuFeTHR/iDtFOmkaH9OnTrFbdHFMChlAnYK9CYi51ofDgDaEzockMxduL/xxhsyadIk9bv+UFqMRkGQClFp5AYjcED5D5O8amEeDEzq+OWXX6pgjz1g28EBHukPb7/9tgpQoYHACT8CSPr+7//+T22DkydPVgdvTECLAzjS6szBdvn999+rzz9ixAh1IYGLEzScGFGhhfdG7xgaK/RMIP0HDRzSNvRX7SAi+7QbltAPDOA4gfYFJ5PY15mGUfjaEUshxQNByR9//FGNdGrevLkKShm2NeggQduCnn5sqzgvQacI2h3AROl9+vSRNWvWqHYEQSnUAc5dOO8HkXO2G4B5kDCvKFY9++qrr1TgGZOdY9QtOW6bgPN0zBeI4BSCVIZwDo/gFOYMe+WVV1RnA64pEOBEp3dOEJDCyC90ZGNaAGwTuA5BSml+KlasmGzYsEGVCSvx4ZoFAT3sBwio5edK4+Qc3DSWzLTpAhARRs9f9+7d1W3k6SK9DiulGfY0Y2g7Lt4x3NXYvD360KAYrkygD1FrDLXE5KFEROQ8bN1uGL5fTvB87WhLIiJyDI7cbmDFZ4y0x0gbIiJb4kgpE+rWrat6LjBkESl2xiBnF9Hx3MIEb0i1ePrpp/NQUiIicpV2wxLnzp1T8wQhHYOIiByXo7QbgMVuOE8cEdmDSwelEBSKi4vT3cacPRjGimGOGD6Inot+/fqp1WXQaGDSaAxfR2pEThPFmYIccaxwhqHxFy5ckDFjxqheEQyLJyIix2frdiOn96tYsaL6O4a6I40KPero6Hj99dfVsH7DNC0iIrI9R2s3MHcP0r8w3xA6L5C+N2PGDDl//ryaU4iIyOY0LmzdunVIXcz2079/f/X3u3fvat59911NpUqVNF5eXprg4GDNo48+qtm/f3+u3u/xxx9Xr+Ht7a0JCQlRt+Pi4vL5UxERUWFpN8y9361btzRt27bVlC1bVr1fWFiY5plnntFcvHgxXz83EREVjnbj9u3b6vXLly+vrknwfl27dtVs3749Xz83EZGlOKcUERERERERERHZHJfXIiIiIiIiIiIim2NQioiIiIiIiIiIbI5BKSIiIiIiIiIisjkGpayAJVuPHDmi/ifjWEeWYT2Zxzoyj3Xk+Pgdmcc6sgzryTzWkXmsI8fH78g81pFlWE/msY4co47sGpQaO3asuLm5ZfmJjIw0+fi5c+dme3zRokVtWmYiIiIiIiIiIso7T3sXoGbNmrJ69WrdbU/PnIvk5+cnR48e1d1GYIqIiIiIiIiIiJyL3YNSCEIFBQVZ/HgEoax5PBEREREREREROR67B6ViY2OlfPnyKg2vadOmMnHiRKlYsaLJx9+8eVPCwsIkIyND6tWrJxMmTFCjrayR23xIPA/vy5xT01hHlmE9mcc6Kvg68vDwyPcyEREREREROUVQqnHjxmqeqOrVq0t8fLyMGzdOHnzwQTl48KD4+vpmezwe9+2330pMTIwkJSXJlClTpFmzZnLo0CEJDQ21KhCWG7j4S0xMlLi4OHF35xzxxrCOLMN6Mo91VPB1lNMcfkRERERERIU6KNWhQwfd7wg0IUiFUVALFiyQwYMHZ3s8RlLhRwsBqRo1asisWbNk/PjxFr9vRERErsqL0Qi4+KtatSpHGJjAOrIM68k81lHO0jM0su3EFTmQdFNqlS0tjSuXEQ93zrFHRESm242tJ67K/hPJctXjqjSpUpbtBhER2b3dsHv6nr6SJUtKtWrV1IWoJby8vKRu3boWP14rLxe4GI2A5/Mi2TTWkWVYT+axjoxbcTBexv1+WOKTUjPv+PuyBPsXlTFdoqR9dLC9i0dERI7ebmy8xHaDiIgcot1wqJwYzBd1/PhxCQ4OtngkxYEDByx+PBFRYWggnv9h938NxH0Xk1LV/fg7ERGRFtsNIiJy5HbDrkGp1157TTZs2CCnTp2SzZs3y6OPPqpGRPTp00f9vV+/fjJq1Cjd49977z3566+/5MSJE7J792556qmn5PTp0zJkyBA7fgoiItsNoUWPhcbI37T34e94HBEREdsNIiJy9HbDrkGpc+fOqQAUJjB/7LHHJCAgQLZu3Sply5ZVfz9z5oyaAF3r2rVr8swzz6h5pDp27Cg3btxQwayoqCg7fgoiItvYfjIxW4+FPjQN+DseR0RExHaDiIgcvd2w65xS8+fPz/Hv69evz3L7k08+UT9ERK7oUnJqvj6OiIgKN7YbRETk6O2GQ80pRUREpgX6Fs3XxxERUeHGdoOIiBy93WBQiojISfgW9ZScVmHFn7AqRqPw0rYsFhEROSi0B0F+pi8c2G4QEVH2dqOI2LLdYFCKiMgJ7Dt7Xfp+vU20cwoaxqa0t7FMq0dOkSsiInIZaA8aVzZ+4cB2g4iIDKE9qFuxlNiy3WBQiojIwe04lagCUkm370ndiiVl6mO1Jcg/a883bn/5VD1pHx1st3ISEZFjOXoxWf48kLlokL+PV5a/sd0gIiJD+89dl78OJ6jfSxazTbth14nOiYgoZ//EXZEh3+2U2/fSpXF4aflmQEMpUcRTutcJka3HL8v+2NMSExEmTaqUZU83ERHp3EvPkNcW7pN76Rp5ODJQZj1dX7aduMJ2g4iIjLqTlq7ajfQMjXSOCZZpT9S1yfUGR0oRETmotUcSZODcHSog9WBEGZk7sJEKSAEahCaVA6R1ZV/1v6tcWEycOFEaNmwovr6+EhgYKN27d5ejR4/m+Jy5c+eKm5tblp+iRTmpLxEVbrM2HJcD55PEr6inTOhRSzw93F2y3SAiIstMWx0rxxJuSpkS3vJet2ibXW8wKEVE5ICWH4iXZ/+3S+6mZcgjUeXk6/4NxMfbQ1zdhg0bZOjQobJ161ZZtWqV3Lt3T9q2bSspKSk5Ps/Pz0/i4+N1P6dPn7ZZmYmIbO3IxRsybU2s+n1s15pSLofJzomIiPadvS4zNxxXv7/fPVpKF/e22XszfY+IyMEs2XNeXtUbOvvJ43XEy4N9CLBixYpso6AwYmrXrl3SokULk8/D6KigoCAblJCIyHHS9trUCJRH64bYu0hEROTAUu9lpu1hQaUutcvbfK5BXuUQETmQ+dvPyCsL9qqAVK/6oSqXmwEp05KSktT/pUvnvCztzZs3JSwsTCpUqCDdunWTQ4cO2aiERES2NXP9cTl4/oaa2HzCo7VUUJ6IiMgUjKyNvZSZtjeua02xNY6UIiJyEHP+OSnjfj+sfn+qSUV5r2u0uHPOD5MyMjJk+PDh0rx5c4mOjjb5uOrVq8u3334rMTExKog1ZcoUadasmQpMhYaGWvRe6enpuSojnody5vb5roB1ZBnWk3msI6TtJctnazPT9t7tXEMCintlqY+81pGHB9PIiYgKk71nr6s5COH97rVsmranxaAUEZED+GJ9nHy0InPC7mceDJfRHWuwd9sMzC118OBB2bRpU46Pa9q0qfrRQkCqRo0aMmvWLBk/frxF7xUbm3mRZy1c/CUmJkpcXJy4u3PEmzGsI8uwnsxz9TpKy9DIy8vOqbS9JhWKSVSxm9mOXXmto8jIyHwsMREROUraXleVtmefqS4YlCIisiONRiOfrDomn62NU7dfejhCXmkTwYCUGcOGDZNly5bJxo0bLR7tpOXl5SV169ZVF2WWioiIyEUpM0cl4H2qVq3KEQYmsI4sw3oyz9Xr6PO1cXI88a5K2/vkyUYSaGRyc1evIyIi+s+nq2MlTqXtFbFL2p4Wg1JERHYMSE3481/56u+T6vbr7avLC62q2rtYDl9nL774oixevFjWr18v4eHhVr8GLsoOHDggHTt2tPg5ebl4w2gEPJ8XgKaxjizDejLPVevo3/gbMmN9ZvoFLiyCSxU3+VhXrSMiIvrPnjPXZPbGzHbjg0ejpZQd0va0GJQiIrKDjAyNvPvbQflh6xl1e0yXKBnY3PoAiyum7M2bN0+WLl0qvr6+cvHiRXW/v7+/+Pj4qN/79esnISEhMnHiRHX7vffekyZNmqiRAdevX5fJkyfL6dOnZciQIXb9LERE+b3a3iNR5aRbnfL2LhIRETlJ2l63OuWlXU37rlDNoBQRkY1hZb03ftkvi3adE2TpYXWkPo0q2rtYTuHLL79U/7dq1SrL/XPmzJEBAwao38+cOZNlrpRr167JM888owJYpUqVkvr168vmzZslKirKxqUnIsp/X6w7Locu3JCSxbxUbzfTv4mIKCefrD4mxy+nqLS9sV3sl7anxaAUEZGNe7Rf+XmvLNsfLx7ubjKld4w8Wte6OZFcPX3PHKT16fvkk0/UDxFRYXP4wg35/P5qe0jbC/TNPo8UERGR1u4z1+SrjSfU7xPsnLan5XpLkxAR2cmdtHR54cfdKiDl5eEm0/vUZUCKiIjylLaHVffaRpVTKydR/jh//rw89dRTEhAQoFLDa9WqJTt37rR3sYiI8py2N/J+2l73OuWlrZ3T9rQ4UoqIyAZu302X//vfTvk79op4e7rLzKfqyUOR5exdLCIiclIz1sXJ4fjMtL33mbaXb5Dy3bx5c2ndurUsX75cypYtK7GxsSr9m4jImX2yKjNtr6xvERlrx9X2DDEoRURUwG7eSZPBc3fItpOJ4uPlIV/3byDNq5axd7GIiMhJHbqQJNPXxqnfmbaXvz788EOpUKGCmqtQKzcrvRIROVza3t/atL1aUrKY/dP2tBiUIiIqQEm378mAOdtlz5nrUqKIp8wZ2FAaVipt72IREZGTupuGtL39Km2vXU2m7eW33377Tdq1aye9e/eWDRs2qNVcX3jhBbVghjXS09Nz9f54XkZGRq6f7wpYR5ZhPZnnKnWUitX2FvyXtvdQ9TIWf+a81pGHh4djB6XGjh0r48aNy3Jf9erV5ciRIyafs3DhQnnnnXfk1KlTEhERoXozOnbsaIPSEhFZJzHlrjz9zTa1KpK/j5d8P6iR1K5Q0t7FIiIiJ0/b+zf+hpRC2l73Wkzby2cnTpxQK72OGDFCRo8eLTt27JCXXnpJvL29pX///ha/DlL+cgMXf4mJiRIXF5dlJVn6D+vIMqwn81yljr7acUVOXEmRUj4e0reGt1XHp7zWUWRkpOOPlKpZs6asXr1ad9vT03SRsIR3nz59ZOLEidK5c2eZN2+edO/eXXbv3i3R0dE2KjERkXmXbqTKU99sk2MJNyWguLf8MKSx1Aj2s3exiIjIydP2EJSCcd2i1bwglL9wAdagQQOZMGGCul23bl05ePCgzJw506qgFDrPcwOjEXDxV7VqVYtGGLgi1pFlWE/muUId7T5zTX49fFz9/mHP2lKvRqDD1ZHdg1IIQgUFWTbr+7Rp06R9+/YycuRIdXv8+PGyatUqmT59umooiIgcwYXrt6Xv19vk5JUUKedXRH4c0kSqBpawd7GIiMjJ0/ZeXZC52l77mkHSJSbY3kUqlIKDgyUqKirLfTVq1JBffvnFqtfJy8UbRiPg+YX1Ijk/sI4sw3py7TpKvZcub/xyUDQakR51Q6RtdLBD1pHdx6hh6Fj58uWlcuXK0rdvXzlz5ozJx27ZskXatGmT5T7kfON+IiJHcObqLek9c4sKSIWU9JEFzzZlQIqIiPJs+ro4OXIxWaXtje/O1fYKClbeO3r0aJb7jh07JmFhYXYrExFRbkz966hK2wv0LSJjujjOansONVKqcePGMnfuXDWPVHx8vJpf6sEHH1RDZH19fbM9/uLFi1KuXNYl1HEb91uDEw8WHNaRZVhPhbOOjl++KU9/s0MSku9IWEAx+WFQQylfsmiBfQZbTDxIRET2d/B8knxxP23vPabtFahXXnlFmjVrptL3HnvsMdm+fbvMnj1b/RAROYtdpxPl600n1e8Te9QS/2Je4qjsGpTq0KGD7veYmBgVpEIvxIIFC2Tw4MEF9r6ceLDgsI4sw3oqfHV0IvGOjPrrgiSlZkjFkl4y4eGyknL5nMReLrj3tMXEg0RE5Air7WWm7XWIDpLOTNsrUA0bNpTFixfLqFGj5L333pPw8HD59NNPVUYHEZGzpO2NXLg/M22vXog8XCPrwB5HY/c5pfSVLFlSqlWrpi6wjMHcUwkJCVnuw21L56TS4sSDBYd1ZBnWU+Gqo/3nkmTUqp0qIBUV7CvfDWwopYt7F/j7OlMdERFR7kxfG6vS9tCuMG3PNrCgEn6IiJzRlJWZaXuY23ZMZ8dN23PIoNTNmzfl+PHj8vTTTxv9e9OmTWXNmjUyfPhw3X2Y6Bz3W4MTDxYs1pFlWE+Fo452nkqUgXN2SPKdNKlToaR8N7CRTYfHOkMdERFR7tP2ZqzPXDXpvW41pUwJpu0REVHO1ybf/OMcaXtads2Jee2112TDhg1y6tQp2bx5szz66KPqwqpPnz7q7/369VNDZ7VefvllWbFihUydOlWOHDkiY8eOlZ07d8qwYcPs+CmIyFVtjrsiT3+zXQWkGoeXlh+GNHaKAz8RETlP2l56hkY61kLaXnl7F4mIiBzY7bvpMnJRZtpez3qh8lCkY6ftOcRIqXPnzqkA1NWrV6Vs2bLywAMPyNatW9XvgJX49OdJwaSD8+bNk7fffltGjx6t0vCWLFki0dHRdvwUROSK1h25JM/+sEtdNDwYUUZmP91AfLw5WomIiPLH53ppe5jcnIiIKCdT/jqqVgBH2t67XaLEWdg1KDV//vwc/75+/fps9/Xu3Vv9EBHZy/ID8fLS/D1yL10jbWqUkxl960oRTwakiIgofxw4lyRf3E/bG98tmml7RESUox2nEuXb+2l7k3rEiL+P82RvONScUkREjm7JnvPy6v10ik4xwfLp43XEy8PxVwckIiLncCctXZe216lWsGpriIiIckzbW7hPpe31qh8qrSMDxZkwKEVEZKH528/IqMUHdHnaH/WKEQ93roJERET55/M1cXI0IVkCVNqe46+aRERE9jV55VE5dfWWBPkVlXc6O0/anhaDUkREFpj7z0kZ+/th9ftTTSrKe12jxZ0BKSIiykf7z12XLzfcT9vrHi0BTNsjIqIcbD+ZKHM2319tr2ctp0rb02JQiojIjC/XH5cPVxxRvw95IFze6lRD3NwYkCIiogJK24sJlo61mLZHREQ5p+29vigzba830vaqO1fanhaDUkREJmg0Gvlkdax8tiZW3X7poaryyiPVGJAiIqJ8h7bmWMLNzLS9rkzbIyKinH208ogube9tJ0zb02JQiojIREBq4vIjMnvjCXX79fbV5YVWVe1dLCIiKqRpezM3ZLY37zNtj4iILEjbm7v5lFOn7WkxKEVEZCAjQyNjfjsk/9t6Wt0e0yVKBjYPt3exiIiokKftdY4Jlg5M2yMiohzcupsmI++n7T3WwHnT9rQYlCIi0oOLgjd+2S+Ldp0TZOlNeLSW9GlU0d7FIiKiQmra6sy0vTIlsNpetL2LQ0REDu6jFUfl9NVbEuzv3Gl7WgxKERHddy89Q175ea8s2x8vHu5uMqV3jDxaN9TexSIiokJq31mk7R3Xpe2VLu5t7yIREZED23biqi5tb1LPGPEr6rxpe1oMShER3U+fGDZvj6w6nCCe7m7yeZ+6TKEgIqICk3ovM20vQyPSpXZ5aR/NNoeIiMyl7e1Xvz/eoIK0rFZWCgMGpYjI5WE51Wd/2CUbj10Wb093mflUPXkospy9i0VERIXYtDWxEnspM21vHFfbIyIiC9L2ziRmpu291bmGFBYMShGRS7t5J02GfLdDtp5IFB8vD/m6fwNpXrWMvYtFRESF2N6z12WWLm2vFtP2iIgoR1sLYdqeFoNSROSykm7fkwFztsueM9elRBFPmTOwoTSsVNrexSIiIhdJ2+uq0vaC7F0kIiJy8LS91++n7T3RsPCk7WkxKEVELikx5a48/c02OXThhvj7eMn3gxpJ7Qol7V0sIiIq5D5dHStxKm2vCNP2iIjIrA+XH1Fpe+WRttep8KTtaTEoRUQu51Jyqjz19Ta1BHdAcW/5YUhjqRHsZ+9iERFRIbfnzDWZvTEzbe+DR6OlFNP2iIgoB1uOX5XvtpzWpe35FqK0PS0GpYjIpVy4flv6fr1NTl5JkXJ+ReTHIU2kamAJexeLiIhcKG2vW53y0q4m0/aIiMi0lDtp8vov+9TvfRpVkBaFLG1Py93eBSAispUzV2/JY7O2qIBUSEkfWfBsUwaknMzEiROlYcOG4uvrK4GBgdK9e3c5evSo2ectXLhQIiMjpWjRolKrVi35888/bVJeIiKtT1Yfk+OXU1Ta3tguTNsjIqKcfbjiiJxNvK2uW0Z3LHxpe1oMShGRS8D8HQhInbt2WyoFFJMFzzWVsIDi9i4WWWnDhg0ydOhQ2bp1q6xatUru3bsnbdu2lZSUFJPP2bx5s/Tp00cGDx4se/bsUYEs/Bw8eNCmZSci17X7zDX5auMJ9fsEpu0REZEZm49fke91aXu1CmXanhaDUkRU6P0bf0OemL1FLt5IlYjAEmqEFHocyPmsWLFCBgwYIDVr1pTatWvL3Llz5cyZM7Jr1y6Tz5k2bZq0b99eRo4cKTVq1JDx48dLvXr1ZPr06TYtOxG5Jv20ve51yktbpu05vLFjx4qbm1uWH4y2JSKyWdreoszV9vo0qigPRhTOtD0tzilFRIXa/nPXpd+32+X6rXsSFewn/xvcSAJKFLF3sSifJCUlqf9Lly5t8jFbtmyRESNGZLmvXbt2smTJEovfJz09PVflw/MyMjJy/XxXwDqyDOvJeetoysojcuJyipQtUUTe6RRp1/LltY48PDzEVaDzY/Xq1brbnp68bCIi25i0/IjK7shM2yv8AXEeXYmo0Np5KlEGztkhyXfSpE6FkvLdwEbiX6zwDn11NbiwGj58uDRv3lyio6NNPu7ixYtSrly5LPfhNu63VGxsbK7LmJiYKHFxceLuzsHJxrCOLMN6cs46OnwpVb7ZdF79/kKjUnL5/Gm57MR15EqjhRCECgriqDYisq3NcVfkf1sz0/Y+LKSr7TlsUGrSpEkyatQoefnll+XTTz81+hikaQwcODDLfUWKFJHU1FQblZKInOmAPvi7nXL7Xro0Ci8t3w5oKCWKOMwhj/IB5pbCvFCbNm0q8PeKiIjI1fMwGgEXf1WrVnWpEQbWYB1ZhvXkfHWEtL3nl/0jGslM2+v3cIy9i+RwdeTI0BlRvnx5tUBG06ZN1UIbFStWtPj5HGFbcFhHlmE9OV8dpajV9u6n7TWsIE0rl7J72WwxwtYhrtB27Nghs2bNkpgY8421n59flpWWkONNRKRv3ZFL8uwPu+RuWoY8GFFGZj/dQHy8efJdmAwbNkyWLVsmGzdulNDQ0Bwfi57uhISELPfhtjU94Hm5eMNoBDyfF4CmsY4sw3pyrjr6dMVROXnllpT1LSJju9Z0iDI5Wh05qsaNG6vO8OrVq0t8fLyMGzdOHnzwQdURgtVfLcERtgWHdWQZ1pPz1dH0LZdV2l5gcU/pXc0z18cRZxtha/eg1M2bN6Vv377y1Vdfyfvvv2/28QhCcSgtEZmy4mC8vPjTHrmXrpE2NcrJjL51pYgnT7wLC41GIy+++KIsXrxY1q9fL+Hh4Wafgx7uNWvWqFQ/Lazch/uJiArCrtOJ8vWmk+r3iY/WkpLFuNqeM+nQoYPud3SaI0gVFhYmCxYsUCu5WoIjbAsO68gyrCfnqqPNx6/KsqPH1e9THq8rtasEiKvUkacjpF906tRJ2rRpY1FQCkEsNAqI2GH1pAkTJqiJCK3B4bQFh3VkGdZTwdTRb/suyGuLDkh6hkY61gqSj3vHiKdb7vd5R+eKE9aizZg3b54sXbpU9VZr54Xy9/cXH5/MFRX79esnISEhKtUCkBbesmVLmTp1qmpv5s+fLzt37pTZs2fb9bMQUeGEtL2RC/eLRiPSo26ItInKOqcdOZ+SJUtKtWrV1IWZpTjCtmCxjizDenKOOrp5J03e/PWg+r1v44rSolqguFId2TUohQuD3bt3q/Q9S2AI7bfffqt6LLDi0pQpU6RZs2Zy6NAhs+kb+jictuCwjizDesr/Olpx7IZM23xZzd3RpoqvDK1bTE6dyOxtKKxcccLaL7/8Uv3fqlWrLPfPmTNHBgwYoH4/c+ZMlvpAO4FA1ttvvy2jR49WvddYeS+nydGJiHJrysqjcuJKigT6FpExXazrOCXHhE7x48ePy9NPP23vohBRITTxz3/l/PXM1fZGdawhrsZuQamzZ8+q3mukUGACQUsg1UI/3QIXGjVq1FDzUY0fP97i9+Zw2oLDOrIM6yl/6+i7Lafl082Z6xn1bVRBxnaJEnf3wj/fnCtuR0jfMwdpfYZ69+6tfoiICnrV12/+uZ+216MWV3x1Uq+99pp06dJFZWdcuHBBxowZo9rZPn362LtoRFTI/BN3RX7cdkb9PrlXjEsuzGS3T7xr1y65dOmSSsHTv8DCpLXTp0+XO3fumL3I8vLykrp161o1lBY4nLZgsY4sw3rKnzqaueG4TFp+RP0+5IFweatTDZdaAIHbERGRY7h9N11GLrqftlcvRB6uwbQ9Z3Xu3DkVgLp69aqULVtWHnjgAdm6dav6nYgoP9P2Xl+UudreU00qSrOqZcQV2S0o9fDDD8uBAwey3Ddw4ECVTvLGG29YdIGFIBZeo2PHjgVYUiJy1BEzn66OlWlrMtNxX3qoqrzySDWXCkgREZHjmPIXVttLkXJ+RWRMZ6btOTNMMUJEVNAm3E/bCy3lI6M6uF7ant2DUpig1nA+j+LFi0tAQIDufsPJat977z1p0qSJSlW5fv26TJ48WU6fPi1Dhgyxy2cgIvsFpCYuPyKzN55Qt0e2qy5DW1e1d7GIiMhF7TiVKN8ybY+IiCy0KfaKzLuftvdRrxgp7oJpe1oO/ckNJ6u9du2aPPPMM2q1pVKlSkn9+vVl8+bNEhUVZddyEpHtZGRoZMxvh+R/W0+r2+92jpJBD4Tbu1hEROTKaXsL96m0vZ71QuWhSKbtERGRacmp9+SNXzLT9p5uEibNqrhm2p5DBqUMJ6c1vP3JJ5+oHyJyTekZGnnzl/2ycNc5QZbehEdrSZ9GFe1dLCIicmGTVx6VU1dvqbS9d7uwo5SIiHI24c8jKm2vQmkfebOD862GXaiDUkREptxLz5ARC/bJ7/suCBbWm/pYbXm0bqi9i0VERC5s+8lEmbM5M21vUo8Y8fdh2h4REZn2d+xl+Wn7/bS9nrVdOm1PizVARA45Imrriauy/0SyXPW4KnXDSsvw+Xvlr8MJ4unuJp/1qSsdawXbu5hEROTiaXuvL8pM2+tdP1RaRwbau0hEROToaXv3V9vr3zRMmlYJsHeRHAKDUkTkUFYcjJdxvx+W+KTUzDs2XhJvT3e5m5ah/p/5VD3O10FERHb30cojKm0vyK+ovN2ZaXtERGR+tb0LSalSsXQxeYNpezoMShGRQwWknv9ht2gM7kdACl5oWYUBKSIicoi0vbmbT6nfJ/asxbQ9IiLK0cZjSNs7q1ttr5g3QzFa/y1tR0Rk55Q9jJAyDEjp+3nnWfU4IiIie7l1N01G3k/be6xBqLSuzrQ9IiIy7UbqPbVYEwxoVkmaVGbanj4GpYjIYXqddSl7JuDveBwREZG9fLTiqJy+ekuC/Zm2R0RE5k34IzNtLyygmLzevrq9i+NwGJQiIodwKTk1Xx9HRESU37aduKpL25vUM0b8ijJtj4iITNtw7LLM33E/ba8n0/aMYVCKiBxCQHFvix4X6Fu0wMtCRERkPG0vM/3i8QYVpGW1svYuEhEROVHaXmOm7RnFMB0R2d3FpFT5dPWxHB/jJiJB/kWlUXhpm5WLiIhIP23vTGJm2t5bnWvYuzhEROTgPlj2r5p+hGl7OWNQiojsalPsFXl5/h65mnJXinq6S2pahgpA6U9njtswpkuUeLhrbxEREdnGVqbtERGRFdYfvaQWaXJzE5ncqzbT9nLA9D0isgusoofRUU9/u00FpGoE+8mK4S1k5lP11Igofbj95VP1pH10sN3KS0RErpu29/r9tL0nGjJtj4iIzKftjfr1gC5tj5keOWO4johs7urNOzL8573yd+wV3Un+2K41paiXh1QqU1weiQqSrccvy/7Y0xITESZNqpTlCCkiIrKLD5cfUWl75ZG214lpe0RElLP3lx1WaXuVkLbXLtLexXF4DEoRkU3tPJUow+btkYs3UqWol7u8372W9KofmuUxCEA1qRwgAemJElE5gAEpIiKyiy3Hr8p3W07r0vZ8mbZHREQ5WHf0kizYeS4zba93bfHx9rB3kRweg1JEZBMajUa+2XRSJi0/ImkZGqlctrh80beeRAb52btoRERE2aTcSZPXf9mnfu/TqIK0YNoeERHlIOn2PRn1S2ba3sBm4dKwEtP2LMGgFBHZ5AD9+qJ9svJQgrrdOSZY9TiXKMJDEBEROaYPVxyRs4m3JaSkj4zuyLQ9IiIyn7aHbJDwMsVlZDuutmcpXhESUYE6eD5JXvhxt5qPw8vDTd7pHCVPNwkTN4xpJSIickCbj1+R73Vpe7WYtkdERDlad+SSLNx1P22vVwzT9qzAoBQRFVi63k/bz8rY3w/J3bQM1dOMdL3aFUrau2hEREQ5p+3dX22vT6OK8mAE0/aIiCjnrJA3f81sNwY1D5cGTNuzCoNSRFQgy2e/tfigLN5zXt1+ODJQpj5WW0oW87Z30YiIiHKEuQ/PXdOm7XHVJCIiytn4ZYcl4cYdlbb3Wlum7VmLQSkiyldxl5Ll+R92S+ylm2rVPByYn21RWdy5gh4RETm4zXFX5H9bM9P2PuRqe0REZMbaIwmyiGl7eeKet6cTEf1n6d7z0nX6PyogFehbROYNaSzPt6rCgBQRETnJanuZ6RdPNq4oD0SUsXeRyAFMmjRJzYM5fPhwexeFiBxM0q17MurXzNX2BjNtL9c4UoqI8uxOWroatvrD1jPqdrMqATLtibpS1reIvYtGRERkkYnL/9VL2+NqeySyY8cOmTVrlsTExNi7KETkgN67n7ZXGWl7XG3P+UdKWdoLsXDhQomMjJSiRYtKrVq15M8//7RZGYkou7OJt6TXl1t0AakXH6oq/xvcmAEpIiJyqrQ9bTv2Ua8YKVGE/bau7ubNm9K3b1/56quvpFSpUvYuDhE5YNreL7vvp+31jpGiXkzbyy1PZ+qF2Lx5s/Tp00cmTpwonTt3lnnz5kn37t1l9+7dEh0dbbPyElGmVYcT5NUFe+VGapqUKuYlnzxeR1pVD7R3sYiIiCx2806ajLy/2l7fxhWleVWm7TmTHj16WP2cmTNnSmBgzucrQ4cOlU6dOkmbNm3k/ffft/o90tPTrX6O9nkZGRm5fr4rYB1ZhvVUcHWkVtv7JTNtb1DzSlIn1L/Q1nN6HrcjDw8Pxw9K6fdCmDvgT5s2Tdq3by8jR45Ut8ePHy+rVq2S6dOnq8aFiGzjXnqGTFl5VGZtPKFu161YUmY8WU/Kl/Sxd9GIiIisMvHPf+X89cy0vVFM23M6S5Yskccee0x8fCw7B0GnNq4/cgpKzZ8/X3V6o+M8t2JjY3P1PFz8JSYmSlxcnLi7O0xSi0NhHVmG9VRwdTTl7wS5lHxHQv28pGslt1zv766wHUVGRjp+UMqaXogtW7bIiBEjstzXrl071RhZgz0XBYd1VPjr6WJSqrz88z7Zefqauj2wWZi83q66eHu65+vnceY6Kkw9F0REhdk/cVfkx22ZaXtYNYlpe87ps88+MzvySWvRokU5/v3s2bPy8ssvq45vTBeSWxEREbl6Htp0XPxVrVqV7bQJrCPLsJ4Kpo7W/HtJVh8/LljH6ZM+9SS6YuFO7023wXZk15bX2l6IixcvSrly5bLch9u43xrsuSg4rKPCXU+7L9ySDzcmSFJqhhTzcpcRzcvKA5U85fTJ4/n+Xs5aR4Wt54KIqDCn7b1+P23vqSYVpRnT9pzSunXrpHRpy1e8Wr58uYSEhJj8+65du+TSpUtSr169LBdlGzduVNkZd+7csejCLC8Xb2jT8XwGEkxjHVmG9ZS/dYTV9t5eekj9PuTBytIw3DXaDfcC3o7sFpTKr16I3GDPRcFhHRXOesrI0MiM9cdl2tp40WhEagT5yvQn60ilgOIF9p7OVkf24Ip1hIuCyZMnq4uG+Ph4Wbx4sZpb0JT169dL69ats92P5wYFBRVwaYnIkU24n7YXWspHRnVg2p6zatmypVWPf+CBB3L8+8MPPywHDmTOFaM1cOBA1ZHzxhtvuEx7S0TZjfv9kErbq1y2uIx4pJq9i1NoWByUsqYHArCSHkZBhYWF5VsvBC4gEhISstyH29ZeWLDnomCxjgpXPV29eUeG/7xX/o69om4/0bCCjO1a0yYrTDhLHdmTq9VRSkqK1K5dWwYNGmTV5LZHjx4VPz8/3W1L0zyIqHDaFHtF5m37b7W94kzbKxRwLaHfHm7btk1dUzRt2lS8vLwseg1fX99sCygVL15cAgICuLASkQtbfThBft1zXqXtTeldm6vt5SOLW+Dr16/Lp59+Kv7+/mYfq9Fo5IUXXshxnpPc9EKgQVmzZo0MHz5cdx9GWuF+Isp/u04nytAf98jFG6lS1Mtd3u9eS3rVD7V3sciFdejQQf1YC0GokiVLFkiZiMi5JKfekzd+yUzbe7pJmDSr4hrpF4UZRr/27t1btm7dKs2bN1fzzT799NPy559/6rIkMHI2ODjY3kUlIid0/dZdGbU4M3bxzIOVpV4hn0fK1qzqFnriiScs7l1+8cUX89wL0a9fP5XzPXHiRHUb6X4Yojt16lQ1OTrmpNq5c6fMnj3bmo9BRBYElr/ZdFImLT8iaRkaNUT1i771JDLov5EmRM6kTp06qrcc7cvYsWPVRYs1uEBGwWEdWYb1lH919MEfh1XaXoVSPjKybYRL1WlhXSADHdo4d0FK948//iidO3dWZcV0IfisTz75pHzwwQcqGyM3ENAiItc17vfDcjn5jlQpW1xeYdqe/YJSaMCskZycLHl15syZLJP3NmvWTC3j+vbbb8vo0aNVrwd6QjiUlij/JN2+J68v2icrD2WmynaOCZZJPbkiETkn9IrPnDlTGjRooIJSX3/9tbRq1UqldOinj5vDBTIKDuvIMqyn/KkjLNgxf0e8+n1Yo1Jy4cxJcSWFdYGM1atXy6+//ipNmjRRnQ5lypRR2RTaCc3fe+89eeaZZ+xdTCJyQqsOJ8hipu0VKIe6yjTshTDWK4Ghufghovx38HySvPDjbjmTeEu8PNzknc5RKrUBc8QROaPq1aurH/3OjePHj8snn3wi//vf/yx+HS6QUXBYR5ZhPeW9jpJT0+TzxZvU7083rii9WkSJqyms29G1a9d0ASjMg1usWLEs89ri8yLFj4jI2rS90dq0vRaVpS7T9hwrKHXhwgXZtGmTmqzccBTVSy+9lB9lIyIbwZD3n7aflbG/H5K7aRkSUtJHpevVrsA5eKjwadSokWq/rMEFMgoW68gyrKe81dGHKw9JfFKqVCjtI292rOGy9VgYtyNML4KgU4UKFdTtYcOGZVmkCUErTBNCRGSNsb8dUml7VQNLyCttmLbnUEGpuXPnyrPPPive3t5qDij9URT4nUEpIudx626avL34oFpNAh6KDJSPH6stJYt527to5OSsWR1PC6l2Bb0y3t69eznZLZGL2Xjssup8gY961uZqe4Vw3sAtW7aoTgeYNGlSlr+jIyImJsZOpSMiZ/TXoYuyZO8Fpu3ZQK5a5HfeeUfeffddGTVqFOc1IHJicZeSVbresYSb6oD7Wrvq8lyLKuKOG0R5hDn/HnvsMfHx8bHo8Zgz8ObNmzkGpfB3pJ5onTx5UgWZ0CNesWJF1S6dP39evv/+e/V3rBobHh4uNWvWlNTUVDWn1Nq1a+Wvv/7Kh09IRM7gRuo9efP+anv9m4ZJ0yoB9i4S5bOlS5fm+PeGDRuqxZKIiCxxLQVpewfV7//XoorUYfaI4wWlbt26pVbiY0CKyHkt3XteRv16QG7dTZeyvkXk8z51pUllnqhT/vrss88sHvm0aNEis4/BiqutW7fW3R4xYoT6v3///moUL9I3sEiG1t27d+XVV19VgSrMMYKeckyIq/8aRFS4TfjjX7mQlCoVSxeTNzo45kTdVLC0I6iIiCyBKU2u3LwjEYElZHib3M0rSgUclBo8eLAsXLhQ3nzzzdw8nYjs6E5auoxfdlh+2Jp54d60coBM61NHAn2L2rtoVMisW7cuy5we5ixfvlw3Ua0pWDkPc6CZgsCUvtdff139EJFr2nDssszfcT9tr1eMFPNm2l5hx3lviSgvVh66KEvvp+1NZtqeTeSqZZ44caJ07txZVqxYIbVq1RIvL68sf//444/zq3xElI/OJt5S6XoHziep28NaV5VXHqkmHkzXowJgbarEAw88UGBlISLXTtsb0KwSRwO7AM57S0R5kZhyV966v9resy2ZtufwQamVK1fqltk2POATkeNZdThBXl2wV26kpknJYl7yyeN1pHX1gp1Qmkjrxo0bRu9Hm1GkSBF1AUFElJ8+WPavWm0vLKCYvN4+85yVCjfOe0tEeTHmN6Tt3WXanjMEpaZOnSrffvutDBgwIP9LRET56l56hkxZeVRmbTyhbiPiP6NvPQkpadnk00T5oWTJkjl2WoSGhqo2ZcyYMbyQIKI8W3/0kvy8U7vaHtP2XAXnvSWi3FpxMF5+33dBZZBgtb0inkzbs5VctdDo1W7evHn+l4aI8tXFpFR58afdsuPUNXV7YPNKMqpDDfH25Mka2T6l4q233lKBJ+2Es9u3b5fvvvtO3n77bbl8+bJMmTJFtS+jR4+2d3GJyIklp95TC3lo0/YaM23PZXDeWyLKbdre20syV9t7tkVlqc20PccPSr388svy+eefq1WViMgxbYq9Ii/P3yNXU+5KiSKeaoLXjrWC7V0sclEIPmGU7WOPPaa7r0uXLmpewlmzZsmaNWukYsWK8sEHHzAoRURWS8/QyNYTV2X/iWTZunkf0/ZcFOe9JaLctBsbNuxRaXvVypWQl5m25xxBKfRur127VpYtWyY1a9bMdsD/9ddf86t8RGSljAyNTF8XJ5+sPiZYpCwyyFe+fKq+hJcpbu+ikQvbvHmzzJw5M9v9devWlS1btugmOj9zJnNVSCIia1Iuxv1+WAWi9PWqF8q0PRfDeW+JKC/tRo96oUzbswPP3M4N0qNHj/wvDRHleejp8J/3ysZjl9XtxxtUkHHdanIpU7K7ChUqyDfffCOTJk3Kcj/uw9/g6tWrUqpUKTuVkIic9cLi+R92i8bI3z5edUwiypWQ9tEcJewqOO8tEeWl3fhw+RGpFFCM7YYzBKXmzJmT/yUhojzZdfqaDJu3W0X8i3q5y/vda0mv+qH2LhaRgvmievfuLcuXL5eGDRuq+3bu3ClHjhyRRYsWqds7duyQxx9/3M4lJSJnSr1AT7exCwst/P2RqCA1cS0Vfpz3lohywnbDMeVqtuOTJ09KbGxstvtx36lTp/KjXERkIY1GI1//fUIen7VFBaQqlykuS4Y2Z0CKHErXrl3l6NGj0rFjR0lMTFQ/HTp0UEEpzP8Bzz//POf7ICKLbT+ZmC31Qh8uOvB3PI5cg3beWyIiY9huFKKRUhgSO2jQIImIyDoJ2LZt2+Trr7+W9evX51f5iCgHN1LvyesL98uKQxfV7c4xwTKpZ4ya2JzI3vbv3y/R0dG6pbkrVaqk5vsw5dChQ2oeEE9Pbr9EZN6l5NR8fRw5P857S0Q5YbvhmHJ15r9nzx6jQ2ObNGkiw4YNy49yEZEZhy4kyQs/7pbTV2+Jl4ebvNM5Sp5uEsaJPMlhYBLzixcvStmyZS16fNOmTWXv3r1SuXLlAi8bETn/oh7bTly16LGBvkULvDzkGDjvLRHllLqH1cktwXbDCYJSuOhNTk7Odn9SUpKkp6fnR7mIKId0vfk7zsqY3w7J3bQMCSnpIzP61pM6FUrau2hE2bbVd955R4oVK2bR4+/evVvgZSIi53ct5a68unCfrD1yKcfHoYsmyL+oNAovbbOykX1x3lsiMuZy8h155ee9siku56AU2w0nCkq1aNFCpWD89NNP4uGRuaoXglG4D0t6E1HBuHU3Td5efFB+3XNe3X4oMlA+fqy2lCzmbe+iERltKzCPlKUwUsrHx6dAy0REzm3X6UQZNm+PmvPD29NdetULkZ+2n1V/05+4VjtmeEyXKE5W60Iw721aWlq2KUYw7y1S+ZBGTkSuZfPxK/Ly/L0qMOXj5SGPNQiV77ecVn9ju+HEQakPP/xQXWxg7o8HH3xQ3ff333/LjRs3VB43EeW/uEs35YUfd8mxhJuC4+Rr7arLcy2qiDsPmuSgOL8gEeVnut7sv0/I5JVHVQpGeJniMv3JulKzvL+0qFZWrZakP3kterpxYcFlvV0L570lIi20FdPXxsm0NcckQyMSEVhCvuhbTyLK+UrTKgFsN5w9KBUVFaUmsJ0+fbrs27dP9Wz369dPzSdVujSHuhHlt6V7z8uoXw/IrbvpUta3iHzep640qRxg72IREREVuMSUuzJiwV5Zf/Syut21dnmZ0KOWblEPXEBg+e6txy/L/tjTEhMRJk2qlGVPtwvivLdEpJ2ofPj8vbL5eObcg73rh8q4bjWlmDfbDUeUuSSSBRCEysjI0N0uX768TJgwQf744w9ZtGiRvPvuu1kCUlhFCcNnc/Lll19KTEyM+Pn5qR+kbixfvtzk4+fOnavms9L/KVqUk5BR4XUnLV3eXnJADTlFQKpp5QD546UHGJAiIiKXsONUonSc9rcKSCFdb2KPWjLtiTrZVpnFhQTaxtaVfdX/vLBwTfk176211yhE5Dg2x12RjtM2qYAU0vWm9q4tk3vX1gWktNhuOOFIqYJYRSk0NFQmTZqkhthiQtzvvvtOunXrpno5sIyrMWgY9Oco4UpjVFidTbwlQ+ftlv3nktTtYa2ryiuPVOMBk4iIXCJdb+bG4zL1r2MqBaOySterJ1Hl/exdNHJg+TXvbW6uUYjIvtBWfLYmVj5bGysajUi1cpnpelUDfe1dNMqvoFRBrKLUpUuXLLc/+OAD1TOxdetWkwd8BKGCgoIsLDWRc1p9OEGlKtxITZOSxbzkk8frSOvqgfYuFhERUYG7evOOjFiwTzYcy0zX61anvHzw6H/pekQFPe9tbq5RiMi+6Xov/7RXtpzITNd7vEEFGdu1pvh4ZwanybF5OsoqSujFWLhwoaSkpKjnmnLz5k0JCwtTqYT16tVTKYRsHKiwSEvPkMl/HZVZG06o23UqlJQZfetJSEmuSEZERIXf9pOJ8tJPe+TijVQp4uku47rWlMcbVuDIeLLbvLeWXqMYe15u3w/XObl9vitgHVnGVerpn+NXVUfGlZt3pZi3h4zvVlO61ymv/mbus7tKHeVFXutIO2o1X4JSBbVaxYEDB9QBPjU1VUqUKCGLFy9WDYox6PX49ttvVY43csOnTJkizZo1U/NXYZitpdhIFBzWUe7rKeFGqrz88z7Zceqauj2gWZi80a66mkPDFeuT25JjNBJERLZK1/tyw3H5eNX9dL2yxWXGk/WkRjDT9ShnCEJFR0eLu7t7lnlvTcF1A64pPD098+0axZjY2FjJDbTriYmJEhcXp/tMlBXryDKFvZ7QVszbd039aESkUklveatVOalQPMXi/a+w11F+yGsdRUZGmn2MmwZ5eXaENL8zZ86oIBMmTMdyrRs2bLDooH/v3j2pUaOG9OnTR8aPH2/xex45ciRPXwh6WrjRGsc6suwAeuDiLTlzOUkqlvWXWkHFZP/F2/LhxktyPTVdinm5ySvNA+XBSiXElXFbKvg6sqSRoLxBwBAnRpiXhEFA41hHlinM9YR0vVcW7JON99P1Hq0bIu93j5biVqbrFeY6yi+FsY7wOayZ9xbz05qb9zav1yh57QTHxV/VqlULzXeU31hHlinM9XTpRqq8snC/bD2RqG4/1iBU3u1Uw+p0vcJcR/klr3WUryOlCoq3t7f6gFC/fn3ZsWOHTJs2TWbNmmX2uV5eXmoCdlSSNdAQ5wY3WvNYRzlbeeiivLfsX7l44879e25LiSIecvNO5olLZJCvTO9TR8LLFBdXx23JPNYRETm7bSeuykvz90jCjTsqXW98t2jp3SCU6Xok9pz3Nq/XKJCXdhkdTXg+23bTWEeuW0+bYq/I8J/36NL1JjxaS7rXDcn16xXGOspvBV1Hdg9KGev5v3NHe8Fu/oIMQ2s7duxo1XuwkShYrCPjVhyMl6Hz9qrhpfq0AanmVQLkmwENpagX602L25J5rCMictZ0vS/Wx6l0vQyNSBWk6/WtJ5FBTNcj6xT0vLe5uUYhooLJNpm2+ph8vi5Ora6Hzny0G1XKunZ2SWFg16DUqFGjpEOHDlKxYkVJTk6WefPmqbmrVq5cqf6OiQlDQkLUMq7w3nvvSZMmTVSvxfXr12Xy5Mly+vRpGTJkiD0/BpFFB9Fxvx/OFpDSd+JKinh5ME2NiIgKtytI1/t5r/wde0Xd7lE3RMbnIl2PqKDmvTV3jUJEtoW5d7EIxraTmel6fRpVlDFdotiZX0jYtfW/dOmSCjzFx8eLv7+/msAcB/tHHnlE/R153PrzpFy7dk2eeeYZlTdeqlQpNZR28+bNVk06SGSv1YTik1JzfAz+jsc1rRJgs3IRERHZ0lak6/20Ry4l35GiXu7yHtL16jNdjxyLuWsUIrIdzDeIjoyrKXelONL1etSSbnVyn65HjseuQalvvvnGqp6PTz75RP0QOeNkfBY9LtmyxxERETnbiOEv1sXJJ6sz0/WqBpaQL/rWk2rlfO1dNCKrr1GIqOClpWfIp6tjZcb6zHQ9rMY648m6UpnpeoUOx0kTFbDTV1NkzuaTFj020LdogZeHiIjIli4nZ6brbYrLTNfrWS9UxnevKcW8eRpKRETG0/Ve/GmPyiKBJxtXlHc7M12vsOLZAFEBSb2XLl+uPy5fbjgud9MycnwskhaC/ItKo/DSNisfERFRQdt8/Iq8PH+vCkwhXS9zdb0K9i4WERE5qA3HLssIvXS9iT1jpGvt8vYuFhUgBqWICsC6I5dkzG+H5EziLXX7gaplpE1UoIz77bC6rT/huXYWDUzW5+HOOTWIiKhwpOtNXxsn09ZkputFBJZQqyQxXY+IiEyl6yHFe8a64+o20vWQ5h1epri9i0YFjEEponx07totee/3w/LX4QR1O8ivqLzTOUo61gpSk7jiNlbh05/0HCOkEJBqHx1sx5ITERHlD4yKGv7zHvkn7qq6jYnMx3Vjuh4RERl3MSlzdb3tpzLT9Z5qUlHe7sR0PVfBswOifID0vK/+PiGfr42V1HsZ4unuJoMeCJeXHo6QEnpLXCPw9EhUkGw9fln2x56WmIgwaVKlLEdIEVlo48aNMnnyZNm1a5daFWnx4sXSvXt3s4tmjBgxQg4dOiQVKlSQt99+WwYMGGCzMhO5ks1xV+TlnzPT9Xy8PGR892jpVT/U3sUiIiIHtf7oJRmxYJ8kptxV102TetaSzjFM13MlDEoR5dGm2Cvy7m8H5cTlFHUb80K93z3aZIoCAlBNKgdIQHqiRFQOYECKyAopKSlSu3ZtGTRokPTo0cPs40+ePCmdOnWS5557Tn788UdZs2aNDBkyRIKDg6Vdu3Y2KTORq6TroWNm2ppYtUpStXIlZMaT9SSC6XpERGQiXW/qqmNqDl6oWR6r69WTSkzXczkMShHlYZjp+D8Oyx/749XtMiWKyFudIqV7nRCVqkdE+a9Dhw7qx1IzZ86U8PBwmTp1qrpdo0YN2bRpk3zyyScMShHlk0vJqTJ8/l7ZfDwzXe+xBqEyrmu0+Hgz7YKIiLKLT7qt0vV2nLqmbj/dJEze6lSD6XouikEpIivdS8+Quf+ckk9XH5OUu+mCgU79mlaSEW2riV9RL3sXj4j0bNmyRdq0aZPlPgSjhg8fbrcyERUm/yBdb/5euXIzM13vg0ejpUc9pusREZFx65Cu9/NeuXbrnkrX+7BnjHSK4dy6roxBKSIrbDtxVd5ZelCOJdxUt+tVLKnmy6hZ3t/eRSMiIy5evCjlypXLch9u37hxQ27fvi0+Pj4WvU56enqu3h/Py8jIyPXzXQHryDnrKTNdL06mrz+uS9f7/Ik6UjWwhN3K6Gh15IjyWkceHhzFQES579if+tcxmbkhM10vOsRPpvdhuh4xKEVkcWrCxD+PyOI959Xt0sW95c32kWryVnfOCUVU6MXGxubqebj4S0xMlLi4OHF3d8/3chUGrCPnq6ert9Lko40Jsu9i5kqy7SN85bnGZUSTFC+xSfYrlyPVkaPKax1FRkYWSLmIqHC7cP22vPjTHtl1OjNdr3/TMBndqYYU8WSgmxiUIjI7Ad8PW0+rqH7ynTTBVFFPNqooI9tVl5LFvO1dPCIyIygoSBISErLch9t+fn4Wj5KCiIiIXL0/RiPg4q9q1aocYWAC68i56gnpeq/8sV+uptyVYt4eMr5bTelexzFWSXKUOnJkrCMisrW1RxLU6nrXb90TX6Tr9YqRjrWYrkf/YVCKyARE8t9ZclAOx99Qt2NC/WV8t2ipXaGkvYtGRBZq2rSp/Pnnn1nuW7VqlbrfGnm5eMNoBDyfF4CmsY4cv56Qrjdt9TH5fF2cSteLDPKV6U/WU+l6joTbknmsIyKyVbrelJVHZdbGE+p2rRB/mf5kXQkLYLoeZcWgFJGBxJS7Mmn5v7Jg5zl129/HS42M6tOoongwVY/Irm7evKl6+bVOnjwpe/fuldKlS0vFihVl1KhRcv78efn+++/V35977jmZPn26vP766zJo0CBZu3atLFiwQP744w87fgoi55JwI1WtkrTtZKK63adRBRnTpSZXSSIiIqPOI11v3m7Zfea6uj2gWSUZ1TGS6XpkFINSRHq9wPN3nJGPVhyVpNv31H2964fKmx0iJaBEEXsXj4hEZOfOndK6dWvd7REjRqj/+/fvL3PnzpX4+Hg5c+aM7u/h4eEqAPXKK6/ItGnTJDQ0VL7++mu1Ah8Rmbfx2GV55ee9Kl2vuLeHTOhRS7rVCbF3sYiIyEGt+TdBXl14P12vqKdM7hUj7aOZrkemMShFJCL7z11XqXr7zmXO0Foj2E/Nk9GgUml7F42I9LRq1Uo0yB0yAYEpY8/Zs2dPAZeMqPDNqfjp6liZsf6/dL0ZfetJlbKOla5HRESOk643eeVRmX0/XQ9Tn2B1vYoBxexdNHJwDEqRS0u6dU8m/3VEftx2Rp10Y/K9EW2rydNNwsTTgyv3EBGRa6brYZWk7ffT9Z5sXFHe7RzFdD0iIjLq3LVbqt3Ycz9db2DzSirbhOl6ZAkGpcglZWRoZNHuczJp+RE1hxRg9aDRHWtIoF9RexePiIjILjYcuywj9NL1JvaMka61HWN1PSIicjyrD2em62H6k8x0vdrSPjrI3sUiJ8KgFLmcwxduyLtLD8rO09fU7YjAEvJet2hpWiXA3kUjIiKyW7reJ6uPyYx1x3Vp7F/0rSfhZbhKEhERZXc3LUM+WnFEvt50Ut2ujXS9J+tJhdJM1yPrMChFLuNG6j35ZNUx+W7zKcnQiBTz9pCXH46QQQ+EixdT9YiIyEVdTMpcXW/7qcx0vaeaVJS3OzFdj4iITKfrDZu3R/aezUzXG9Q8XKXreXvymoqsx6AUFXqYFHnp3gvywZ//yuXkO+q+TrWC5e3ONSTY38fexSMiIrKb9UcvyYgF+1Qqe4kinjKpZy3pHMN0PSIiMu6vQxfltYX75EZqmvghXa93bWlXk+l6lHsMSlGhFpuQLO8sPShbT2T2/iINYVzXmtKiWll7F42IiMiu6XpTVx2TL9dnpuvVLO8nM56sJ5WYrkdERCbS9T5ccUS+0abrVSgp0/vUZboe5Zldx9d9+eWXEhMTI35+fuqnadOmsnz58hyfs3DhQomMjJSiRYtKrVq15M8//7RZecl5pNxJk4l//isdpv2tAlJFvdzltbbVZMXwBxmQIiIilxafdFv6fLVVF5DCirO/PN+MASkiIjLqbOIt6T1riy4gNeSBcFn4bFMGpMj5g1KhoaEyadIk2bVrl+zcuVMeeugh6datmxw6dMjo4zdv3ix9+vSRwYMHy549e6R79+7q5+DBgzYvOzluqt6fB+KlzccbZNbGE5KWoZE2NcrJqldayrCHIrgsKRERubR1Ry9Jx2l/y45T11S6HkZHje8ezfmjiPRMnDhRGjZsKL6+vhIYGKiuN44ePWrvYhHZxcpDF6XTZ3/LvrPXxd/HS77q10De7hzF+aOocKTvdenSJcvtDz74QI2e2rp1q9SsWTPb46dNmybt27eXkSNHqtvjx4+XVatWyfTp02XmzJk2Kzc5phOXb8qY3w7J37FX1O0KpX1kbJea8nCNcvYuGhERkV3dQ7reX8dk5obM0VHRIX4yvQ/T9YiM2bBhgwwdOlQFptLS0mT06NHStm1bOXz4sBQvzn2GXCddb+Lyf2XOP6fU7TpI13uyroSW4ugoKqRzSqWnp6vUvJSUFJXGZ8yWLVtkxIgRWe5r166dLFmyxOr3ym0ZMzIycv18V2CPOrp9N12+2HBcvv77pNxN16io/bMPhstzLSurnl9H/L64LZnHOir4OvLw4MgIIldw4fptefGnPbLr9DV1u3/TMBndqQZHDxOZsGLFiiy3586dq0ZMIbujRYsWdisXkS3T9YbN2y37ziWp2888GC4j23F1PSqkQakDBw6oIFRqaqqUKFFCFi9eLFFRUUYfe/HiRSlXLuuoF9zG/daIjY3NVVlx8ZeYmChxcXHi7s4d0hHqaMuZFJm5/Yok3ExTtxuE+MgLjctKeT+Rs6dOiKPitmQe66jg6wjz8xFR4bb2SIJaXe/6rXviW8RTPuwVIx1rBdu7WEROJSkp88K8dOnSFj+HneAFh3VUsPW08lCCvPHrAUlOTVPpepN71pKHawRiopRCV+fclhyjE9zuQanq1avL3r171cF+0aJF0r9/fzVk1lRgKj9ERETk6nn4InDxV7VqVY4wsHMdnUm8JeOX/Strj15Wt4P9i8o7nSKlbVQ5cXNzE0fHbck81pF5rCMiyildb8rKo2p+RagV4q/SLsICmHpEZA1cjA0fPlyaN28u0dHRFj+PneAFh3VUMPWEjJNvdl6Vpf9mBmEjyxaR0S3LSaBnksTGZt5X2HBbcoxOcLsHpby9vdUFFdSvX1927Nih5o6aNWtWtscGBQVJQkJClvtwG/dbIy8Xb/gi8HxeANqnjlLvpcusDSfki/VxcictQ7w83GTIg5XlxYeqSjFvu2/OVuG2ZB7ryDzWEREZOo90vXm7ZfeZ6+r2gGaVZFTHSKbrEeUC5pbCokqbNm2y6nnsBC84rKP8ryd0+L/20145eOGGLl3v1UcixMujcAdquC05Rh15OmIk7s6dO0b/hjS/NWvWqN4KLUx0bmoOKipc1h+9JGN/OySnrt5St5tVCZD3ukVL1cAS9i4aERGRQ1jzb4K8uvB+ul5RT5ncK0baRzNdjyg3hg0bJsuWLZONGzeqVcOtwU7wgsU6yr96Wn4gXl5ftF+S76RJyWJe8vFjteWhSNdZKIrbkv3ryK5BqVGjRkmHDh2kYsWKkpycLPPmzZP169fLypUr1d/79esnISEhallWePnll6Vly5YydepU6dSpk8yfP1927twps2fPtufHIBv0+I7//bCsOJQ5d1igbxG1DGmXmGCnSNUjIiKyRbre5JVHZfb9dL2YUH+1ul7FAK6SRGQtjUYjL774oprrFtcm4eHh9i4SUb67k5YuE/74V77bclrdrh9WSj7vU1fKl/Sxd9HIxdg1KHXp0iUVeIqPjxd/f3+JiYlRAalHHnlE/f3MmTNZ8habNWumAldvv/22WpoVw2Kx8p41+d3kXMuQfr3phHy+Jk5u30sXD3c3lYIwvE2E+Bb1snfxiIiIHMK5a7fU6np77qfrDWxeSd7swHQ9oryk7OGaY+nSpeLr66tbVAnXKz4+vGAn53f6aooMm7dHDpzPnCvq2ZaV5bW21Qt9uh45JrsGpb755psc/46eCUO9e/dWP1S4bY67Iu8sPSjHL6eo240qlZb3uteUyCA/exeNiIjIYaw+nJmul3Rbm65XW9pHWzfXJhFl9eWXX6r/W7VqleX+OXPmyIABA+xUKqL88eeBeHnjfrpeKZWuV0daR2J1PSL7cLg5pci1JdxIlff/+Fd+33dB3S5TwltGdaghPeqFMFWPiIhcUnqGRraeuCr7TyTLVY+r0qRKWXXfRyuOyNebTqrH1Ea63pP1pEJppusR5Uf6HlFhazeQ5j3hz3/l+/vpeg3CSslnTNcjB8CgFDkEHCS/23xKPl0dKzfvpIm7m8hTTcLk1bbVxd+HqXpEROSaVhyMl3G/H5b4pNTMOzZekrK+RaS4t4du4Y9BzcNVup63J9MuiIhcndF2o0QRKerlLmev3VZ3Pd+qiox4pBrT9cghMChFdrf9ZKK8u/SgHLmYrG7XqVBS3u8eLdEh/vYuGhERkV0vLJ7/YbcYjtm4nHxHLouIj5e7fPpEXWlXk+l6RESUQ7txM3N1e3RoTO9bT1pXZ7oeOQ4GpchucFI9cfm/8uvu8+o2cprfaB8pjzWoIO4YKkVEROTCqRfo6c4piQiLfrSp4TrLdhMRUd7ajeJFPKVFRFkblorIPAalyC4HzB+3nVZLVyenpgmminqiYQV5vV2klCrube/iEREROcQoYl3qhQmXku+oxzWtEmCzchERkWNiu0HOikEpsqk9Z66pVfUOnr+hbkeH+Mn4btFSt2IpexeNiIjIYRy8v0y3OZeSc74AISIi17D/3HWLHsd2gxwNg1JUoKs8eNxPw7uWclc+XHFE5u84q25j2erX21WXJxuH6R5DRETkypJu35M/9sfLol1nZfcZyy4uAn2LFni5iIjIMV2/dVetWr5o1znZd86yzgy2G+RoGJSiAlvlIdi/qLzTKUqSUu+pgNT1W/fUn3rWC5VRHSOlTIki9i00ERGRA3TobIq7Ir/sOicrD12UO2kZ6n7012BVJO1tQ+jOCfIvKo3CS9u4xEREZE9p6Rnyd+wVFYhadThB7qZnthMebiKebDfICTEoRQW2ygMCVC/M2627HRnkK+O7R0vDSjwQEhGRa4u7dFN+2X1OFu8+Lxdv/JdKUa1cCelVP1S61wmR3WeuqfYV9NtY7fjiMV2iONqYiMhFHEtIVoGoxXvOqwWjtKKC/aRn/VDpVqe87DyVyHaDnA6DUlTgqzzgsIeRUYOah6voPRERkStKunVPft9/QQWj9uil55Us5iXdapeXXvUrqLkW3bACiIi0jw6WL5+ql3Uk8v2eblxY4O9ERFR4YQqU3/Zlthv79dLzShf3Vp0XPeuHSM3y/rr72W6QM2JQigp8lQcErGqFlGRAioiIXLLz5u/Yy6p3+y+kWdxPq0BPdevqZVVK+0M1AqWIp4fR5+MC4pGoINl6/LLsjz0tMRFhWeZsJCKiwuVeeoZsPJbZbqz+N0HupWd2/3u6u8nDNQJVu9GqeqB4exq/tmK7Qc6GQSnKE0tXb+AqD0RE5EriLiHN4rws3nNOEm78l2ZRvZyv9G6ANIsQKetr2dyKuJBoUjlAAtITJaJyAC8siIgKoSMXb8iinedkyd4LcuXmf+1GzfJ+Kq27a+3yEmDhnLxsN8iZMChFuXYj9Z5sPn7VosdylQciInKF9Lzf9t9fBensf+l5pZCeVydEXVTg4kKbnkdERK4tEel5e8/Lot3n5OD5G7r7A5CeVzdEjYqKKu9n1zISFTQGpchqV2/ekW//OSnfbz4tyXfScnwsV3kgIiKXXAVJpecFqkDUQ5Gm0yyIiMj10vPWH0V63llZe+SSLj3Py8NNHo4sp9qNltXLqhVYiVwBg1JksYtJqTJ74wn5afsZuX0vXd0XEVhCHogoI3P/OaVuc5UHIipoM2bMkMmTJ8vFixeldu3a8vnnn0ujRo2MPnbu3LkycODALPcVKVJEUlOZUkx5XwXpl13n5FeDVZCw0iwuKKxJzyMiosLv3/gbqgNjyZ7zcjXlru5+LHDRq16odK0ToiYwJ3I1DEqRWWeu3pIvNxxXJ9/aHuBaIf4ytHVVaRtVTtzd3aRxeGmu8kBEBe7nn3+WESNGyMyZM6Vx48by6aefSrt27eTo0aMSGBho9Dl+fn7q71pMnaLcun4rcxUkXFQYroKEpbgz0/P+WwWJiIhcGzJMlu7NXD3v0IX/0vPKlPCWR5GeVz9UIoOYnkeujUEpMik2IVm+WH9cnYBj9SBoVKm0DH2oqrSIKJPlwo6rPBCRLXz88cfyzDPP6EY/ITj1xx9/yLfffitvvvmm0efgWBUUFGTjklJhSs/beH/1vNWHL+k6Z7AKUuvIzPQ8pOkxPY+IiLTpeeuOXFLtBtLz0jL+S89rUyMzPa9FNabnEWkxKEXZHDiXJDPWxcmKQxd197WsVlaNjMppbiiu8kBEBenu3buya9cuGTVqlO4+d3d3adOmjWzZssXk827evClhYWGSkZEh9erVkwkTJkjNmjVtVGpyVkcvYvW8s7J4T9ZVkKKCM1dBwsgoS1dBIiKiwu/QhSQViPpt74Us6Xkxof6q3egSU15KMT2PKBsGpUhn+8lEmb4uTjYeu6y7r33NIBWMqhXKdAQisq8rV65Ienq6lCtXLsv9uH3kyBGjz6levboaRRUTEyNJSUkyZcoUadasmRw6dEhCQ0Mtfm+8b27geQiG5fb5rsCR6ujarbvy+754+WX3eTmol2ah0vNqB0uPuiFZVkGyZZkdqZ4cFeuo4OvIw8Mj38tE5Myu3E/PQzAKc0ZplSlRRHrUy1w9r3qQr13LSOToGJRycRqNRjbGXpEZa+Nk+6lEdR9GOHWtXV5eaFVFIsrxIEpEzqtp06bqRwsBqRo1asisWbNk/PjxFr9ObGxsrt4fF3+JiYkSFxenRnWR49UR0ip2nr8lq+KSZdvZFEnLzM4TDzeRxhWKyyNVfaVhaDGVricpCRIbmyCuWE/OgHVU8HUUGRlZIOUiciZ30zJUWh7miVqnl57n7eEuj0Rlpuc9GFFGPJmeR+T4QamJEyfKr7/+qnq4fXx81MXChx9+qHq2TeFKSvkjI0Mjfx1OUGl6B84n6Q6kmGzv+ZZVpGJAMXsXkYgoizJlyqhe+oSErEEB3LZ0zigvLy+pW7euuiCzRkREhOQGRiPgvapWrcoRBg5WR0cuJsuvu8/LEoM0i5rl/aRH3fLSpXZ5CXCgNAtuS+axjsxjHRHlviMfE5VjRNTSvefl2q17ur/V1qbn1S4vJYs5TrtB5CzsGpTasGGDDB06VBo2bChpaWkyevRoadu2rRw+fFiKFy9u8nlcSSlvE7Yu2x8vX6yPk2MJN9V9Pl4e8mTjivLMg5XVinlERI7I29tb6tevL2vWrJHu3bvrev1xe9iwYRZfkB04cEA6duxo1Xvn5eINoxHwfF4A2r+OElPuqosJXFQYroLUvU7mKkg1gh13FSRuS+axjsxjHRFZ7nLyHV27gc4MrUDfIvJovRDpVS+UmSVEzhyUWrFiRbZRUFjSGxPZtmjRwuTzuJKS9e6kpcsvu87LzA3H5UziLXWfbxFP6d+skgxsXomTtRKRUxgxYoT0799fGjRoII0aNZJPP/1UUlJSdCNo+/XrJyEhIWokLrz33nvSpEkTNSrg+vXrMnnyZDl9+rQMGTLEzp+EbLkK0vqjWD3vrEq3uJf+3ypID0dmplm0rM5VkIiI6L/rprX/3k/PO3pZtwq5Ss+reT89ryrT84gK5ZxSmIQWSpc2vcIbcCUly926myY/bT8rX208IRdvpOombB38QLg83TRM/Ip62buIREQWe/zxx+Xy5cvy7rvvysWLF6VOnTqqg0M7+fmZM2eyzJNy7do1eeaZZ9RjS5UqpUZabd68WaKiouz4KcgWDuulWein59UKyUyzwNyJXAWJiIzZuHGj6sRAR3l8fLwsXrxYN0KXCm963sHzaDfOytJ9F+S6XnpenQoldavn+RfjtRNRoQ1KIcA0fPhwad68uURHR5t8XH6spOQKqyglp96T/209I3P+OSWJ9w+qQX5FZMgD4fJ4w1Ap5p351ef3Z3GmOrIn1pN5rCPzXHUVJaTqmUrXW79+fZbbn3zyifoh13BVbxWkwwarID1at7xKz4sMctz0PCJyDBiBW7t2bRk0aJD06NHD3sWhAnQpOVWW7slsN44m/JeeV84P7Uao9KofIlUDmZ5H5BJBKcwtdfDgQdm0aVOBr6RUmFdRup6aLksOX5ff/r0ht+5lLiEU7Ospj9UqJQ9X8RVvj3ty/vTJAnt/Z6gjR8B6Mo91ZB5XUSLKXAVp3dFL6oLCcBWkNlGBqne7RURZplkQkcU6dOigfqjwpuet+Tez3dhwTC89z9Nd2tUMUu3GA1XLqBXJichFglLo8V62bJkaKmvpaKe8rKRUGFdRupiUKl9vOinzd5yT2/cyR01EBJaQ51tWlk61gmx2Mu7IdeRIWE/msY7MYx2RKzt0Iel+et4FNYG5/ipIPe+nWTA9j4jsxRUyM+wlN3WE9LwD52+oVVd/2x8vSbf/S8+rW8FfetQLkc61gsXP5356ngavL06N25J5rCPHyMywa1AKB4cXX3xR5Wkj5SI8PNzq18jNSkqFaRWlM1dvyZcbjssvu87J3fQM3XwZQ1tXlbZR5cTdDhF+R6sjR8V6Mo91ZB7riFzJlZt3ZMme7KsglfUtIj3qZq6eV42rIBGRAyjMmRn2Zk0dXb2VJmtPJMuquGQ5c/2/QFSZYh4qi6RNVV+p4I8OjDuScO6UJEjhwW3JPNaRY2RmeNo7ZW/evHmydOlS8fX1VRPRgr+/v/j4+KjfuZKScbEJyfLF+uPy274LuiGnjSqVlqEPVZUWEWXUCoVERESODO3X1hNXZf+JZLnqcVWaVCmbLV0C6XlYNQ+BqPVHs6bnPRJ1fxWkCK6CRESOpTBmZjhKu7HtxBU5kHRTapUtLY0rZ0+zu3MvXVYfuSS/7r4gG2Mvy/1mQ4p4uqtO+571QqRZlYBCn57Hbck81pFj1JFdg1Jffvml+r9Vq1ZZ7p8zZ44MGDBA/c6VlLI6cC5JZqyLkxWHMgN40LJaWTUyqlF4zqsWEhEROYoVB+Nl3O+HJT4pc2VY2XhJgv2LypguUWpOj0N6q+dd01sFqbZuFaRgKVmM6XlE5JgKU2aGw7Ybf1/O0m7sO4e07rPy294LciM1Tfe8+mGlVLvRKSbY5VYe57ZkHuvI/nVk9/Q9c7iSUqbtJxNVMAqT8Wm1rxmkglG1Qv3tWjYiIiJrLyye/2G3GJ4F4ELjuR92S0jJonL++v2LDhEJ9C0ij9YLkV71QiWC6XlERC7HXLsR5FdULt74r91AsArzRPWsFyqVy5aweXmJyMkmOifTQbuNsVdkxto42X4qUd2HYaZda5eXF1pV4Yk5ERE5ZeoFerpz6pZCQMrLwy3LKkhMzyMiW7l582aWRZROnjwpe/fuldKlS0vFihXtWjZXZEm7gYCUt4ebdKwVLL3qV5CmLpCeR1RYMCjlgDIyNPLX4QQ1MurA+STd3BmYwPX5llWkYkAxexeRiIjIapeT78gvu8/9l3qRgy/71pc2UeVsUi4iIn07d+6U1q1b626PGDFC/d+/f3+ZO3euHUvmeh30VrUbT9WXh2uw3SByNgxKOZC09AxZtj9evlgfJ8cSbqr7inq5y5ONwuT/WlSWIP+i9i4iERGRRb3ap66myOELN+Rw/A3d/7i4sFTK3f/mAyEisiXMd2vJNCOUv+3GySs31XyC2nbj3/gbcuXmXYtf4+YdthtEzohBKQdwJy1dftl1XmZuOC5nEm+p+3yLeEq/ZmEyqHm4BJQoYu8iEhERGXXrbpocuZisCzzhIuJIfLLcvpee7bFYGBbzfljS4x3oy44YIqLCKOUO2o3/Oizw/9GEZEm9l5HtscjAQ8f8Bb15Bk1hu0HknBiUsvOJ/E/bz8pXG0/oJuYrXdxbBj8QLk83DXO51SGIiMixXUpOzTb66eSVFDE2oAAjfSOD/CSqvJ9EBWf+HxnkK0U8PeSBD9fKxaRUo/ODYAYQXIBwRVkiIueG0WaXku9kazcwktZYu1HM20O1E5nthr/6v3o5X/H2dGe7QVSIMShlBzdS78n/tpyWbzadlMSUzCGp5fyKyP+1qCJ9GlWQYt78WoiIyHHSKP6NzxwJdeWm8fS7sr5FdIEn7f+VAoqbnGQWy3djFSX8Vf8Cw03v75yglojIuaYhOXEla9o2Rs5evX+tYwjXPv+1G5kBqLDSxcSd7QaRy2H0w4YQgPp200n5bsspSU7NzHmuWLqYPNeyivSsH6J6j4mIiGyfRpGcpRf76MUbJtMowssUl6jy/rqLiRrBvlanTLSPDpYvn6qnVlPST+VDTzcuLPB3IiJyTJi76QjaDL12A+3I3TTj7UaVsiWydFrUCPaTMlZOT8J2g6jwYlDKBjDUdPbGE/LT9jO6OTYiAkvIC62rSJeY8lzmmoiI7JJGgV7skybSKHy8PFTAyTCNwsc7fzpQcAHxSFSQbD1+WfbHnpaYiDBpUqUse7qJiByo3cAUI6rd0Js38NTVzDlwDRX3RruRddRstXK+UtSL7QYRmcagVAE6c/WWfLnhuPyy65zcTc/sOagV4i9DW1eVtlHlTA5PJSIiymsaBeZ60u/Fxv/m0ij0LybCcki/yy94/SaVAyQgPVEiKgfwwoKIyE7uIf3uMtqNpCztxrVb94w+Pti/aJaRT/gdGSAFfX3DdoOo8GFQqgDEJiTLF+uPy2/7Lqh5OaBRpdIy9KGq0iKijLhh+SEiIqJ8TKNA77X2IgJpFHcKMI2CiIicV3LqvftzBSZl/o+07QTj6XcI+lQ10m5gcSYiovzAoFQ+OnAuSWasi5MVhy7q7mtRrawMa12Vq0EQEVGe0ygSbtzJ1ottLo1Cf/RT9aD8S6MgIiLHbzcw/5Lh6ndnEo23GyWKeGaZLxCp2xHlSrDdIKICxaCUhTDiaeuJq7L/RLJc9biaJX95+8lEFYzacOyy7vHtapZTaXoxoSXtWGoiInLEdsOS9Lvjl1OyjH7C/9oVWw0F+RXN0ottqzQKIiJyjHYD6Xdxl27q5gtUbUf8DbluIv2uvL9hu+EvoaV82G4Qkc0xKGWBFQfjs670sPGSWumhd/1Q2XYiUbafSlR3o9HoWru8PN+qiprUj4iIXJOxdiPYxApBSKNQq9/pTSRrSRqFthcb/wcw/Y6IyGXajRtIvzMY/RSbcFM3h60+T7QbgXrpd/dH0JZi+h0ROQgGpSxoIJ7/YbdojKyo9/naOPW7t4e79KwfKs+3rCIVA4rZpZxEROT47cZzP+xWbUUxbw9dL/bpq6bTKDIDT//1YjONgojI9dqN51pWFh8vz8z07fgbcjbxttHX8UW7YTBqFu1GEU+2G0TkuBiUMjOEFj0WRlbKzjJnx1+vtJSQUj42LBkRETlbu6G9D6uymkqj0K5ghN8rlGL6HRFRYWdJuzFzw4lsfwsp6aMLPKHtqFneT6XfcUElInI2DErlAHNF6YbQmpByN11NFsigFBERWdJuwINVy0jL6mWZRkFE5OIsbTewgnfL6oG6FDz/Yl42KR8RUUFjUCoHl5JT8/VxRERUuFnaHvRqECrd6oQUeHmIiKhwtBuYKoTtBhEVRu72LoAjC/Qtmq+PIyKiwo3tBhERWYPtBhG5OgalctAovLRa9cJUZjbux9/xOCIiIrYbRERkDbYbROTqGJTKAZbexjKsYNhQaG/j73gcERER2w0iIrIG2w0icnUMSpnRPjpYvnyqngT5Zx0yi9u4H38nIiLSYrtBRETWYLtBRK7MrhOdT5w4UX799Vc5cuSI+Pj4SLNmzeTDDz+U6tWr5/i8hQsXyjvvvCOnTp2SiIgI9ZyOHTsWWDnREDwSFSRbj1+W/bGnJSYiTJpUKcseCyIiMortBhERWYPtBhG5KruOlNqwYYMMHTpUtm7dKqtWrZJ79+5J27ZtJSUlxeRzNm/eLH369JHBgwfLnj17pHv37urn4MGDBVpWNAhNKgdI68q+6n82EERElBO2G0REZA22G0Tkiuw6UmrFihVZbs+dO1cCAwNl165d0qJFC6PPmTZtmrRv315Gjhypbo8fP14FtKZPny4zZ860SbmJiIiIiIiIiMiJg1KGkpKS1P+lS5teXWLLli0yYsSILPe1a9dOlixZYvH7pKen56p8eF5GRkaun+8KWEeWYT2Zxzoq+Dry8PDI9zIRERERERE5XVAKF1bDhw+X5s2bS3R0tMnHXbx4UcqVK5flPtzG/ZaKjY3NdRkTExMlLi5O3N05R7wxrCPLsJ7MYx0VfB1FRkYWSLmIiIiIiIicKiiFuaUwL9SmTZsK/L14IUZERLaA0Whsc3LGOrIM68k81pF5rCPHx+/IPNaRZVhP5rGOHKOOHCIoNWzYMFm2bJls3LhRQkNDc3xsUFCQJCQkZLkPt3E/ERERERERERE5B7vmxGg0GhWQWrx4saxdu1bCw8PNPqdp06ayZs2aLPdhonPcT0REREREREREzsHT3il78+bNk6VLl4qvr69uXih/f3/x8fFRv/fr109CQkJk4sSJ6vbLL78sLVu2lKlTp0qnTp1k/vz5snPnTpk9e7Y9PwoREREREREREVnBTYPhSnbi5uZm9P45c+bIgAED1O+tWrWSSpUqydy5c3V/X7hwobz99tty6tQpiYiIkI8++kg6duxos3ITEREREREREZETB6WIiIiIiIiIiMg1cZ11IiIiIiIiIiKyOQaliIiIiIiIiIjI5hiUIiIiIiIiIiIim2NQioiIiIiIiIiIbI5BKSIiIiIiIiIisjkGpSywceNG6dKli5QvX17c3NxkyZIl9i6Sw5k4caI0bNhQfH19JTAwULp37y5Hjx61d7EcypdffikxMTHi5+enfpo2bSrLly+3d7Ec2qRJk9Q+N3z4cHsXxaGMHTtW1Yv+T2RkpL2LRXrYbpjHdsM8thvWY7thHNsNx8d2wzy2G+ax3bAe2w37txsMSlkgJSVFateuLTNmzLB3URzWhg0bZOjQobJ161ZZtWqV3Lt3T9q2bavqjjKFhoaqg96uXbtk586d8tBDD0m3bt3k0KFD9i6aQ9qxY4fMmjVLNayUXc2aNSU+Pl73s2nTJnsXifSw3TCP7YZ5bDesw3YjZ2w3HBvbDfPYbpjHdsM6bDcco93wLJBXLWQ6dOigfsi0FStWZLk9d+5c1YOBA2KLFi3sVi5Hgt4vfR988IHqzUDDih2e/nPz5k3p27evfPXVV/L+++/buzgOydPTU4KCguxdDDKB7YZ5bDfMY7thObYb5rHdcGxsN8xju2Ee2w3Lsd1wnHaDI6WoQCQlJan/S5cube+iOKT09HSZP3++6tnBsFrKCr1gnTp1kjZt2ti7KA4rNjZWDfGvXLmyalDPnDlj7yIR5QnbjZyx3cgZ2w3z2G5QYcN2I2dsN3LGdsNx2g2OlKJ8l5GRoXJymzdvLtHR0fYujkM5cOCAahRSU1OlRIkSsnjxYomKirJ3sRwKGs/du3er4bRkXOPGjVXvYPXq1dVQ2nHjxsmDDz4oBw8eVPMsEDkbthumsd0wj+2GeWw3qLBhu2Ea2w3z2G44VrvBoBQVSNQZGyvnKsgOO/XevXtVz86iRYukf//+Kj+eDUWms2fPyssvv6zmCShatKi9i+Ow9If3IwcejUZYWJgsWLBABg8ebNeyEeUG2w3T2G7kjO2GZdhuUGHDdsM0ths5Y7vheO0Gg1KUr4YNGybLli1TK4hgoj3KytvbW6pWrap+r1+/vorOT5s2TU2wR6LmBLh06ZLUq1cvy9BjbE/Tp0+XO3fuiIeHh13L6IhKliwp1apVk7i4OHsXhchqbDdyxnYjZ2w3coftBjkzths5Y7uRM7YbjtduMChF+UKj0ciLL76ohoeuX79ewsPD7V0kpxl6jAMfZXr44YfVkGN9AwcOVMuPvvHGG2wgcpio8fjx4/L000/buyhEFmO7kTtsN7Jiu5E7bDfIGbHdyB22G1mx3XC8doNBKQu/AP2I4MmTJ9WQSEyqV7FiRbuWzZGG0M6bN0+WLl2qckwvXryo7vf39xcfHx97F88hjBo1Sg2DxDaTnJys6gsN6sqVK+1dNIeBbcdwXoDixYtLQEAA5wvQ89prr6nVVTCE9sKFCzJmzBjVgPbp08feRaP72G6Yx3bDPLYb5rHdsAzbDcfHdsM8thvmsd0wj+2G47UbDEpZYOfOndK6dWvd7REjRqj/kZ+Lyb9I1FKj0KpVqyz3z5kzRwYMGGCnUjkWDBPt16+fmigOjSdyc9FAPPLII/YuGjmZc+fOqQbh6tWrUrZsWXnggQfUUr/4nRwD2w3z2G6Yx3aD8gvbDcfHdsM8thvmsd0gZ2w33DQYB0lERERERERERGRD7rZ8MyIiIiIiIiIiImBQioiIiIiIiIiIbI5BKSIiIiIiIiIisjkGpYiIiIiIiIiIyOYYlCIiIiIiIiIiIptjUIqIiIiIiIiIiGyOQSkiIiIiIiIiIrI5BqWICtipU6fEzc1N9u7da++iEBGRE2C7QURE1mC7Qc6MQSmiPBowYIBqBLQ/AQEB0r59e9m/f7/6e4UKFSQ+Pl6io6PtXVQiInIAbDeIiMgabDeoMGNQiigfoFFAQ4CfNWvWiKenp3Tu3Fn9zcPDQ4KCgtR9REREwHaDiIiswXaDCisGpYjyQZEiRVRDgJ86derIm2++KWfPnpXLly9nG067fv16dRuNSYMGDaRYsWLSrFkzOXr0qO719u3bJ61btxZfX1/x8/OT+vXry86dO+34CYmIKD+x3SAiImuw3aDCikEponx28+ZN+eGHH6Rq1apqaK0pb731lkydOlUd/NGrMWjQIN3f+vbtK6GhobJjxw7ZtWuXanS8vLxs9AmIiMiW2G4QEZE12G5QYcLxfUT5YNmyZVKiRAn1e0pKigQHB6v73N1Nx30/+OADadmypfodjUCnTp0kNTVVihYtKmfOnJGRI0dKZGSk+ntERISNPgkREdkC2w0iIrIG2w0qrDhSiigfYOgrhsviZ/v27dKuXTvp0KGDnD592uRzYmJidL+jUYFLly6p/0eMGCFDhgyRNm3ayKRJk+T48eM2+BRERGQrbDeIiMgabDeosGJQiigfFC9eXA2fxU/Dhg3l66+/Vj0YX331lcnn6A+PRc43ZGRkqP/Hjh0rhw4dUr0Za9eulaioKFm8eLENPgkREdkC2w0iIrIG2w0qrBiUIioAOOhjKO3t27dz/RrVqlWTV155Rf766y/p0aOHzJkzJ1/LSEREjoPtBhERWYPtBhUWnFOKKB/cuXNHLl68qH6/du2aTJ8+XU1A2KVLF6tfCw0L8rt79eol4eHhcu7cOTUBYc+ePQug5EREZA9sN4iIyBpsN6iwYlCKKB+sWLFCl6eNZVUxYeDChQulVatWaolWa3h4eMjVq1elX79+kpCQIGXKlFE9F+PGjSug0hMRka2x3SAiImuw3aDCyk2j0WjsXQgiIiIiIiIiInItnFOKiIiIiIiIiIhsjkEpIiIiIiIiIiKyOQaliIiIiIiIiIjI5hiUIiIiIiIiIiIim2NQioiIiIiIiIiIbI5BKSIiIiIiIiIisjkGpYiIiIiIiIiIyOYYlCIiIiIiIiIiIptjUIqIiIiIiIiIiGyOQSkiIiIiIiIiIrI5BqWIiIiIiIiIiMjmGJQiIiIiIiIiIiKbY1CKiIiIiIiIiIhsjkEpIiIiIiIiIiKyOQaliIiIiIiIiIjI5hiUIiIiIiIiIiIim2NQioiIiIiIiIiIbI5BKSIiIiIiIiIisjkGpYiIiIiIiIiIyOYYlCIiIiIiIiIiIptjUIqIiIiIiIiIiGyOQSkiIiIiIiIiIrI5BqWIiIiIiIiIiMjmGJQiIiIiIiIiIiKbY1CKiIiIiIiIiIhsjkEpIiIiIiIiIiKyOQaliIiIiIiIiIjI5hiUIiIiIiIiIiIim2NQioiIiIiIiIiIbI5BKSIiIiIiIiIisjkGpYiIiIiIiIiIyOYYlCIiIiIiIiIiIptjUIocXqVKlWTAgAHibNavXy9ubm7qfzIP3zG+ayJ7wf46duxYKeyMHZsKy/7H42727Xju3LnqvlOnTokjSkhIkF69eklAQIAq56effiqOBPWGcqEeC0Jh2ffwOUqUKGHvYpAdtjdnPU93pXMDw++IbWXh+M5P3W+fpkyZIs6OQSnKFe1JrvanaNGiUq1aNRk2bJg6wXQmEyZMkCVLlti7GC7hwoUL6iC/d+9eexeFHPBYsmnTpmx/12g0UqFCBfX3zp0726WMRNb4/fffxd3dXS5evGjvojiFV155RVauXCmjRo2S//3vf9K+fXt7F4nIJg4cOKACsmFhYeo8OiQkRB555BH5/PPP7V00ysHmzZvVuez169ftXRSn1bNnT+nYsaO9i0EOxNPeBSDn9t5770l4eLikpqaqC8ovv/xS/vzzTzl48KAUK1YsX97j6NGj6gS/IINSOCno3r17gb0H/ReUGjdunOqxqVOnTpa/ffXVV5KRkWG3spF94YR83rx58sADD2S5f8OGDXLu3DkpUqRIgZfh9u3b4unpms1iYdn/WrRoob5Hb29vu5Xhjz/+kPr160tQUJA4gqefflqeeOIJm+xDubF27Vrp1q2bvPbaa+KKCsu+R9YHNlq3bi0VK1aUZ555Rh0vzp49K1u3bpVp06bJiy++aO8ikolzA3x3OJfFyKOSJUuKM7JnW3nv3j1ZtWqVTJw4URyVK58P2gtrm/KkQ4cO0qBBA/X7kCFD1PD7jz/+WJYuXSp9+vQx+pyUlBQpXry4xe/hqCfSjsDausxvCEaiQcuPoKGXl1e+lImcE3rMFi5cKJ999lmWEwEEqnCBf+XKFZsExlxVYdn/cCyy9/eIjplBgwZZ9RwEJe7evVsgZffw8FA/jurSpUtOe2GXHwrLvmcLGDmL8w4fHx9xdh988IH4+/vLjh07sm3/2CfIcc6L7dWmFOT2bs+28u+//5bk5GTp1KmTOCp7n0e44vUm0/coXz300EPq/5MnT2aZY+D48ePqotPX11f69u2r28BfffVVlZqDwFP16tVVTiwOwuZy1TFkdvjw4brnVq1aVT788MNsvY24jR6nWrVqqQNM2bJlVWrAzp071d+REoRyfPfdd7oUIv33On/+vLq4KFeunHqfmjVryrfffpvtc2MkB0ZaYYcNDAxU6Qh37tyxqM5Onz4tL7zwgvr8aHgQ2Ovdu3e2+T+0aU4YOYLH431CQ0N1f1++fLk8+OCDqgyoZxzsDx06lOU19u/frz5f5cqVVX2gZw6f7+rVq2bLqc0/nz9/vrz99ttqmDlGw924cUMSExNVLzfqGd+3n5+fClju27cvy/MbNmyofh84cKCuvrXzdBib58DSbYScH4LY2A7Re6aFi/RFixbJk08+afQ52BaaNWum9hnsOwhe4fH65syZo7Yzw/0WIyRxPwIIpuYQwO+479ixY/LUU0+pCwgcQ9555x21DaJXGyM8sL1jX5o6dWqW9zA1l4+xuRxatWol0dHRah9t2bKl2rdwXNN+Huz3jRs3Vp8T+8Hq1astqldLj02G+5/+PAUzZsxQxwyUqW3btupz4/OPHz9eHYNQJtQDjgOGLDkuadsJHG9RVvyOesYxJT09PctjcfzB94zXQr3jmINjfE51Cwh44nkoa5kyZdT3iffLbTlySsdB/Zg72UYZke7+448/qnYFx7cVK1ZYvF0Dvkd8nygj6qNr167q+zZkbDs0NV+GYXuLHm2MCIiIiFBtBsqE0Yz6+6kpJ06cUG1Z6dKl1bbTpEkTNYrMsFzYlrCNaduEnKBNx5xTqDOUB23zs88+K9euXcv2OZDuixHcjRo1Uo/FNvz9999ne02cT6Ae8Rx8D9im+/XrZzYQjhFe2m0bQQXsA//++2+Wx+DCC+cq2tfGPoj0rN27d1u0782ePVuqVKminov2EwEMQ9i2o6Ki1GfEMWTx4sUWzxuEc6F27dqpfQLbGka+6wdU9cvyySefqDQzPA7HKIyIN8aS/cfa7xHpnegAxXvPmjXLqvNAR4XzYnx+YwFZbCeGfvjhB7UtY18qVaqUGuny119/6f6OzmAcd8qXL6/qA9sNjtGWHLss/T6wr77//vtqH0E5MNLL8HhuirXbkiX7l7aNPnz4sDpPQL1oR1unpaWpz6/df7AtjR49Olv7Z24fMDxe4v+RI0eq3/FY7XELnw+fpXbt2kY/P9ptvE9OctrecS6DayxsG/g82OeRnWLI0u/IWFtpam4wnJ/gRx9STLG9aLdHlBediJZAO4DymztGWbqP43EoN87RsK30799fTRNiOBegsc8Bxo6X+t852l/t9ZchfD/4m6njoTGWtKsD7p+PoB3FdoP9APs2MpRMXQNZ0l4cOXJEZQehXcZ743v77bff8v16Mzc4UoryvZEF7GBaaBiwQ2GHQ2OEAxh2KJxAr1u3TgYPHqxSuXAQxoEeJzRosEy5deuWOvDjcWg0MfQZQ2kxH0V8fHyWSVLx2ti5ECDBSC6UBRF6DI/Gjoj5K3A/Gvr/+7//U8/BDg2YGwsn0dqLB5xcYUfEayIQgwOldojnww8/LGfOnJGXXnpJHTTwumhQLYGDBsqP9Ars9GjY0NDgwImG1jANEgcIlOXdd99VQRvA++EgjHrGARt1hNdAne/Zs0d3sMUBDwc4BIVwEY2DCA5i+B91Yu6CANDIY3QUTjTRuON3lBPzcuECBI006g4HanxP+BvqpEaNGupginKjrnFAA1x8GZOXbYScD7bRpk2byk8//aT2V8D+lpSUpPYNjKAyhGAEthEEuhHAQsAC2+CyZct0QQFs67/++quMGDFCXQzi5AaBA5wQYLuyZE6Dxx9/XG2/kyZNUidTOOFDg45tHCeJ2OcQXMA+gRMBXCzkBi4AcEKKz4vPgX0Yv+O1cbx57rnn1In35MmT1UkFgh84ITAlr8cmwHujbpFKgqDTRx99JI899pj63DiZfeONNyQuLk6doOLz6wf/LD0uAS6c8DgE3tBOIOiGIB+Ox88//7zu+IXgJT4TXg9wkfLPP//Iyy+/bPIzoA3AdoDvBukCOD5h28HzUA79i0JLypETBDlxAqcdQZwTfA8LFixQ7QsuiLT1Ycl2DWi7cKGKbQLHUbxefvc846QcdaZtJ9H24SIOQRXsT6agjlEmfOfY9nBOgM4ffC6c4D/66KNqP8E2gvRCvBYCQeagzdd+n3hddIBNnz5dfY/4PvVHHWG7xH6C/RzbIbZNnOgjyIeLKbh586Zqi7Ad4UK0Xr16KhiFk3QE+PC9GIPtAscpBLpQR9jXsA80b95c1Y32u8Q+i8+L7xgXYQi8I1CG98N75QQXeAhq4TOjbca+16NHD9WGaz8njkc4PiE4i+8JxxB8XnQamYPROAgy43zizTffVPsBzj9wvDSEYB7KMnToUDVyA9sojgE4liKAYe3+Y833iCkcsN/jOUhzw8W9NeeBjgpBmS1btqiLWQQTc4L2CtsZ9imcR+G8a9u2bWqfx3cIqE9cxKKtw//4G863sM+izciJpd8HXg/tH9pN/GBbx/vjOGUpS7YlS/cvLRwfcYGPzibtBTuOWTjm4BiAzk3UF/YR7HsI3Fq7D2hhH0RHFc5VcB6qPUbgNXAswzZq+J3iPB/PQYeuOca2d0DbieMWjqEYTY65C3E9gAAN6lIrP74jS1KOsZ2gbtH24ntEhxrq2FQnomE7aW6OUEv3cXzfCFjiuIrjLc7V8P3imJ9f0K5in0J7jTLp+/nnn9X3Ym4fzk27mp6ergZS4FoUx390XI0ZM0Zdy+I4YG17gWs97ENoH7C9I6iEz4ROhF9++UW1y/l1vZkrGqJcmDNnDo76mtWrV2suX76sOXv2rGb+/PmagIAAjY+Pj+bcuXPqcf3791ePe/PNN7M8f8mSJer+999/P8v9vXr10ri5uWni4uJ094WFhanX0Ro/frymePHimmPHjmV5Lt7Dw8NDc+bMGXV77dq16j1eeumlbOXPyMjQ/Y7X0n99rcGDB2uCg4M1V65cyXL/E088ofH399fcunVL3f7000/V+yxYsED3mJSUFE3VqlXV/evWrcuxLrWvo2/Lli3qud9//322On/ggQc0aWlpuvuTk5M1JUuW1DzzzDNZXuPixYuqnPr3G3uvn376Sb3uxo0bcywnPgceV7ly5Wyvk5qaqklPT89y38mTJzVFihTRvPfee7r7duzYoV4Dn8UQvgN817nZRsh5abdrbBvTp0/X+Pr66rav3r17a1q3bq1+x7bRqVOnLM813A7v3r2riY6O1jz00ENZ7o+Pj9eULl1a88gjj2ju3LmjqVu3rqZixYqapKSkLI9DOcaMGaO7jd9x3//93//p7sO+FxoaqrbBSZMm6e6/du2aOvbpH0u0nw37grF9Sf/Y0LJlS3XfvHnzdPcdOXJE3efu7q7ZunWr7v6VK1ea3I/0WXNsMtz/UGY8pmzZsprr16/r7h81apS6v3bt2pp79+7p7u/Tp4/G29tbHQusPS5p2wn9YwXge6pfv77u9ssvv6zx8/PLcvwzZFi32CYCAwPVdnH79m3d45YtW6Ye9+6771pdjpw8+OCDRtsTQ9rv9dChQ9n+Zsl2vXfvXvUaL7zwQpbHPvnkk9m2Y2PboeFjTLW3+J4N9ztLDB8+XL3H33//rbsP20R4eLimUqVKWdoLPG7o0KFmXxOvhcf++OOPWe5fsWJFtvvxOQzbtUuXLqk26dVXX9Xdh+8fj/v1119Nnido9wX9/a1OnTpqu7p69aruvn379qnvtF+/frr7sK2b+2ym9j2cTyUmJuruX7p0qbr/999/191Xq1YtdTxC3WqtX79ePU7/NY1ZvHix7thrirYs+ud1sG3bNnX/K6+8YvX+k5vvEX/TZ+l5oCP766+/VFnx07RpU83rr7+uju3Y3/XFxsaq7erRRx/Ndp6lfy5r7Pzu2Wef1RQrVkx3XDa2vVn6fWD/wTEexwP99x09erR6nLnjnjXbkqX7l7aNRvujT3t8HDJkSJb7X3vtNXU/rg8s3QeMHS8nT55stG1HW1m0aFHNG2+8keV+XIdge71582aO72Nqezf1/bZr106dk2tZ8x0ZOw8xPP7rn5/gR6tbt26amjVranLjxIkTFl0bWbqPa68VPvroI91jcI6AttjwuG34OUztE8a+c2xj2Cb1zz9wbolt0vCYZ44l7Wr/+8fTF198UXcfvlM8D98xrr2tbS8efvhh1WboHw/wms2aNdNERETk6/VmbjB9j/KkTZs2KoqK0Qfo0UckGRFqw146wx5mRMkxxwUi7frQm4FjAUZImIKh6ujZxHBR9Ghqf1AWRJU3btyoHoeoL6LFiCobMjciCGXA87t06aJ+138fRIcxekM7/B6fJTg4WPUYaGF0k3bklTn6ueIY0omeVAxRRY+N/hB/LfSc6M8PgtEDGLqKnhX9cuIx6K3ESCNj74WeDTwOEXgw9l7GIEJumN+OoaLaeaXwHeAzYFtAD4+lr2soL9sIOSeMwEGPKEaEoMcH/+fU66a/HWKEAPZLHBsMtzmMCkR6EPYV/B3DujFqAulflkBvlha2SYyCwTaIEQla2F+xvaNXKrewz+A4qoXXw+ui5w/7spb2d3Pvlddjk7YHGkPiDd8b6W/6c3/hfvTEalPirDkuaaGXUx++K/3PiLpAb50lqWNa6H1Ebzh6/PTniEDPZ2RkZJZ0MkvLYQo+L0Y+WDpaCT2uGD2Tm+1am3ZqeHzUjuDNL6hz9K7GxsZa9TyUDz3A+gsXYPvGtoeRCBhBay20/dgW0ZOsv01h5BNe23CbQt1qR+QCzlUM91G080i3Mewhzuk8Ab30OIZg1BVGTGrFxMSosumnBKP+MHoAi3xYCyOgcJ6jpf0s2vLjNTG6BCPM8Pn1tyuMnDJHO0IQx1mce+QEPen653X4brEf639WS/cfa79HjL42THuy9DzQkeHz43iBkS+Y6gAjG/A5Uc/66TQYhY7RMBitYDh/p/42qn/cQPuJ+kAdYSQDUnZMsfT7wOgl7ahZ/fe19phjbluyZv8ytc1pH4NRY4bnj6A97luzD1gC9YhROxhFpR2xhe0Ro2m0afTmGNveDb9ftAn4jrCvY9/C7fz8jsxBvWEkqbH0MHNQ96gnw0VtDFm6j+O7xrmI/nUmzjPye6EAHI9xLqGf7ohRsNg38beCaleHDRum+12buYPv2HAKB3PtBUa6Y/QkzrO1xwf84HoN2xvKYjilQV6uN3OD6XuUJ7jQq1atmjogYNgtTvgMG038TT8XVTuPElJJDFNPcPGl/bsp2HEwTBQnmMZoJ4hEKiHeQ79Rs9Tly5fVjofUNvzk9D4oK4JIhiew2iG35uAiHMM4kS+OA4J+rrC2oTFssPRpD2ra+bwM6V9446CEYeBIBzGcSNPYexlj+P76c3d98cUXati3/hwG+qmc1sjLNkLOCfs0TjgwDBkn0tiO9AMqhnAiiWHqOIHVnyfC2MUkgj1IdcIJES6MkQJmKQwb14cTKgQ4DFN7cL8l87OZguOkYdnxmgj6G94HhvN9GMrrscnUZwdzZbLmuATaOf/04QRL/zMisISh5kjrwEUNUhJwgoXh7aZojxPGPjOCUhjyb205TEF6MWjTaXJzLLV0u8bnQlurTTfPzXdrCaQI4CIL7TzSE1DXSFHBBWJOUD79QKqx47c16Q7abQrtlLH5dsCwTTPcdo19lzhPwNLk1shpm8Lnw3agnRQWgQZ05GB/wUU+0mkQREJakjmG5ddecGjLry0H9nFDuM9chxAuaPHZcU6ANCRMGYALZ3QEGC4wg9QoQ9gmsD9au/9Y+z0a208sPQ90dEgpRqoYLjIRmEKnLr4LtHvY/xFYxTaKfd1YAFsfLnKRHoYLT6QDWXp+Z+n3od3eDLcFfAf6F8PmmNuWrNm/TG0j2uOj4b6BDioEBLTvYc0+YCns3whCYaoQpCgjeIB0Zhw389IuII0SnewIZOL8SB++P7TB+fUdmYO0fXwuBBRRx2jzUGdIDTMH52B4vLmV7Szdx/GZ0fmmH5gviLYQbR/qGN+t9vwRv2NqEWy/BdGuuru7Z2srtO9lOFepufYC6ey4vsScqPgxVaf6AeO8XG/mBoNSlCc4IJmbO0N/FE1+QAAEvSWvv/660b9be3Aw9R7a0QCm8pLNnZRbCtF8BKTQk4E5dXDQw8UHLqKNTdhpOEpJ+xjk+Rpbglz/wI8LOORkY14mHEhxEMfzcUC0dHJQY6uAII8fBznMyYE5pxAIxHeOz+Qsk46SY8CJDXpnLl68qIIPplblwgkfephx0odgKE5KkDePfcnYZJsIFmkXOMAoDWyXlh6XjK1cZmo1M/2gsqmRFqYmnjX1mpa8V0HJbZmsOS7l9Hr6cNGECzVclGCkJH7wfeMiAHOH5Ie8rFKHHluclOuPLLP2WGrtdp3fDLdNlAMXxZhEGZMqf/311+ribebMmVlGENoCtilsA5jnzBjDixd77jf6bS56rBFsQP1hbh/Mw4FAhHbuPFMKuvw4PqGnH/NJYn4a7FdowzEHFO4zvMjLr/3H2u/R2H5ii/NAW8IcUQhQ4Qdlx9xOGClibKS/MehERYAFF4W44EWwGgFCBCYRQMjpPMza78MRmVqdzlxWREHsAxh1gk56dILh+In/0Qaiwy23nwXHYARC0JGCFc4R5MY2gzYHx+P8Os/O6ZxFf99GcBBzX6EDBfMcYcQp2iuM5kOAzxQE0zDSyNgE7bbYx7ULaxiyZDEAXMsiYIljOT4rAo0IFOL6x1oF0a56WHhOhrk/TU24bxjEzcv1Zm4wKEV2m+ARUXYMIdQfCaMdYoy/m4LGFpOTmjvA43FoYDA6KKfRUsYOwtrVjHCgMvc+KCsmNcSOr/9aOGBbAg0iAl/6K3chtQ4nGZbQ9pTjpCKnsiJavmbNGtVgoOHQsjYtw9RnwAof33zzTZb78Rn0R5NYMpF6fmwj5LyQRoOJGnFCiF4oU3AShJNu7OP6PZq4eDcGE4FiW8KoREyUiUkyDYf25zdtT5XhvmyrUX55PTblhaXHJWvhRBxp1fjBCRJGT2HCeQTFjY0Y0R4n8JkNe/dwX34dR1DHODnHCV9eWLpdo9z4/Dix1e8RtvS7xbZpuF1itAZSZwyh/cRFMn7Q9uKEGhO15nTyjPIZK0tejt/YptAmIPCXX0uk4zWtWTXJcJsy9vnQ5umP4kBgEdspftATjQnOP/jgA7NBKUvLgR5wQ8buMwUp/PhBmRD4xAT7GE2t//0aO0/AxM25mdQ2P75HS88DnZG2o1e7L+KzYl9HZwo6E43BhT46XhDs1F9oQ7sSdn58H9rtDduC/ugNZBZYMpLU0m3J2v3LVFlRZ3gv7ehMQCABxz3D448l+4C+nM5lERxA5xomjkcAGumXhmlQ1kLADKNmkdapPyLGMF0qr9+RsXZBe85iOGIH3wFSxvCDtgOTaqP+cH6lnyqvD6P48DksOfZZuo/jM+PaBo/VDyIa237w+Yyl4lt6TobPig4wvB8mzEe7b23qnjXtakZGhiqvfgAO+wpYe+zVfn/o5MrtcbOgzuu0OKcU2QWGsCPgg9U99CFSjIN9Tgcs9Dxi+Ko2VUIfDqZYlQAwJBcHDGNRe/1IOQ6shgdhNB54Pi4QjJ2w4gCv/1kwt4P+kt3oDTCV9mcI72UYuccqI5YuQY6IN3rHEK03lhOvLau2QTR8r/xYpcbYZ0Avn2F+svZEwpKAW162EXJeOKlALxoaZwQectrmsB3o7ycYzowTQEPYNxHgwup5WHEEoxCR5qBt3AuKtgHXn98E5bX02JBXeT025YWlxyVrGKZGYqSbdsSq4TLf+hd4OIFCD6T+YzDKCieV+bVaHebWQMAhr69n6XatPf4Zrkpp6fEc26bhvDvYLgzbHcM6x/6J4J+p+tbf9rZv367aai2k3OA9cDJtLhXJVNuP8mE0riG0+5Z25OhDO69Nm7J0RBKCTAgO4OJE/z1xroBeb+2KniirYdoUtkWkpZurP0vgdZD6gdXMcFGjhWW8MdeUObhINfyM2qCHYfmw/em35/huMVdWbtrh/PgeLT0PdGQIKBjbxrTzIWmDzRidgWMdRkAZjojRPt/Y+R0CBRjRkV/fBy5CcUGL81P997H2HNLctmTp/pUT7WMMy4ZRRqA9TluzD1hzLotULLw2OtiwbyLrIi+Mfb84thh2VuT1O0K7gA5B/ZX6MBoKK/3m1C6gswjHdLxnTnNzYdtGm6y/Ymde93F81/hdf/QVtmfUgbHPh8Cm/vkHjv8Y8WQJ1C+CSTifxA+yhfRT3BBIxuubm5/MmnZ1ut41EOoXt/EdWzMFhbbtQXoqOvGMdT5Zck5WEOd1+jhSiuwCF5sYWfPWW2+pE25MNIrGBkMZkfJlOE+GPqSeobcAy4lql3fGyS5OwnDxhddDTwpeHw0DTtrRa6BNUUN6BP6mnTwOz0cvERornOThAIO5MHABi5MG/I5eDhxwMeoKw6HxePwO+BsOEkgh2bVrl2pQMbQREwpbAp8Dj0fKB94DB2G8vqVzMeEAgYMxPit6YHHBjZFeWAYeudvo/UL58DhE4jHHBQ4myBtGnVvSk2bJZ8AJEyL+WLIY3wWGghv2rOB7RToWLhAx+gkNO+rXWA59XrYRcm6WLOWLk0rss9iv0SuJgADmuEPDjnkItHA/JsDU3+exP2DfxvEDcwrlZ3qxPiwTjN5X9BxqR2yi99VWF0x5PTblhaXHJWugBxH1iBFPmH8LvZs48cRFhH5vuD6cvKG3GscmpLdggk70lmMOPARHXnnllXz5vPhMuQ225Ga7xmfGZ8FFJy5OcNxF762lI2RQl5gcGEEZpEjgxBwn/4bzpOHz4EQW7SS2X6TAop3Vn3zVGAR/MdkvLjQxGTuei4tMtDfo7MnNPofvDxd5GO2INE7MS4LvF+07OkHwneY0B52p8wl8Hkzoj7QdfE5sYzjHQDuFdscYpOHhsyHlHgseaJesRzuOgDpgZCa2U5QJr4MLD7TtCGDqj4zOC1wcYG4S7E/YxnEhjP0KwSr9QJUx+D6w/WB0KtpTlBdLvWPfNbzwx/aHiYlxLMWFEy5ycY5iKrWmoL9HS88DHRmmbkAnAeofaVkIBGB6BVzs4liC71Nb9zgPQtAIqaAYkYJRlNiOcM6KesT+j1EgaDuxvyGwjWO9Jameln4fOH5jJCgeh3rHNoIl4BHgt6auLdmWLNm/coL9DXWBILg2tRHBL2zzCPLhfMDafUAftjfA94K2DfWFc1ZtsKpu3bpqH0T9oW1CG5gX+E60o4S1gS6UE4EG/QBDXr8jtAvYf9D+ICiEkbhIPzQ830Z5kL6F4w4CTOjgwXEH7ZfhPLCGQSntdm2Opfs46gTlQJuD+9BmYcSgsXnUcIxH+4rgCrYrtK84zuNczXAeNmPwPWP/w3kcyjJlypQsf8e5nrady2kkk6XtatGiRdUIbGzLuFbC94hzjdGjR+cqrRbnEtj3sBAGzg9xjYbzIVx3YuJ6nAfY+rwuizyt3UcuS38Zd3NLWmJJT2OwtCSWgC1fvrzGy8tLLUeJZVb1lzE1tUQpnoulybG0OZbGLFOmjFrScsqUKVmW08VSlnjNyMhI9Tgsb96hQwfNrl27siy73qJFC7VMreGSqQkJCWo55woVKqgyBgUFqSU1Z8+enaU8p0+f1nTt2lUtvYuyYOly7XK65pY9xVLyAwcOVM8rUaKEWuIVZTL83ObqHO+D52JZTixJW6VKFc2AAQM0O3fu1D0Gy/BiWWEs6YnH9e7dW3PhwgWTy4Mbvj4et3Dhwmx/w/KiWGY7ODhY1WPz5s01W7ZsMbr8KpYpjYqK0nh6emZZrtXYkqyWbiNU+I8l2DYMl9D95ptv1DaBZd6xj+O1tEtEa/Xo0UPj6+urOXXqVJbnapfL/fDDD3X3Ge4H2tfSLr1r7riGbd1wmeTjx49r2rRpo8pYrlw5tTTzqlWrsh0bjD3X1OfWltXcUvPWHJtMLUuP/c2S44Cp79GS45Kp+jT8LhctWqRp27atWpYZx/OKFSuqJc+xLLNh+QyPuz///LNamh7fQ+nSpTV9+/bNsiy5NeUwpkGDBpoXXnhBY6mcvj9Ltmu4ffu2WmocS0Gj3F26dNGcPXs223as/W70ly/H0vJYthzbBLYNfEdxcXHZ2p33339f06hRI9Vm4NiO8nzwwQfZlq03Btt+r1691HPx3eN1li1bZlVdGIP2t379+qo82LexxPXrr7+u2jJz+42xNgnLzg8bNkwTEhKitqvQ0FBVB1euXMmyL+gvLQ6rV69WbR3K4efnp+r/8OHDur/fuXNHM3LkSLX8N8qJ7wi/f/HFF1lex9J9T1tXhm31/Pnz1feC7SU6Olrz22+/aXr27Knuy8nu3bvVMufYj/Bc7FedO3fOsm/ql2Xq1KnqXAiPxXLr+/bty9P+k5fv0ZrzQEe1fPlyzaBBg9T3hHM/fAZ8FiwBj3NPQ99++63uGFaqVCm1HaMt0frnn380TZo0UfWJcybU5cqVK80e6635PnDcGDdunO5cr1WrVpqDBw8aPU83ZM22ZMn+lVMbDffu3VNlDQ8PV+ePeD9sLzhftWYfMLXfjR8/Xh0z3N3dsx1f4aOPPlL3T5gwQWOpnLZ37NcxMTHqWFqpUiV17oJtwtix3ZLvyFRbie8Gnwv1gfpHXRgeN2fNmqWum9D24HFo13GsS0pKMvnZUAa83/bt2y2uD0v3cRzDn376abWd4FwDv+/Zs8focfuHH37QVK5cWb1enTp11D5ibJ8wdV2kPX9zc3NT7a0+vI6xbcGQJe1q//vHU7SjOO9BO41zSJQJ33Fu2wu8Xr9+/dT1LPYLfNfY5nF+lZ/Xm7nhdr/QRA4LE/ohqo2J4IiIiBwJehoxCg1pDpaklhAVNIykQw82lvDOC4w8wEhmjFzJ63xp5NpcbVvCCDOMxMXnNrYSqKtBlgZGKWFklzXzy+Z1e0OKI0ZaOZsBAwao0VPmRrwWJpxTihwa0syQe+vow7CJiMg1IU0Ai0doU0KIbHmOZJgOjEmvkYaB9BAisj2M98DCP0gbZEAqE9LZtHPCEhnDOaXIYWF+C+TtIpfc2gndiIiIbAEr41gyzwlRfsOE0Zh8FxMpY34hTLKLOVIw3wvmDCMi28E8Q5gHCXNWYu4jzIFKmTBHFVFOGJQih4WJxjFpK5YYxUSsRERERJQJk1tjslxMb4CVjzDRMiYbxvmTpYulEFH+wD6IBSqwoA8mo+7atau9i0TkNDinFBERERERERER2RznlCIiIiIiIiIiIptjUIqIiIiIiIiIiGyOQSkiIiIiIiIiIrI5BqWIiIiIiIiIiMjmGJSyQnp6ulpuF/+Tcawjy7CezGMdmcc6cnz8jsxjHVmG9WQe68g81pHj43dkHuvIMqwn81hHjlFHDEoREREREREREZHNMShFRERERESF0saNG6VLly5Svnx5cXNzkyVLluT4+AEDBqjHGf7UrFnTZmUmInIlDEoREREREVGhlJKSIrVr15YZM2ZY9Php06ZJfHy87ufs2bNSunRp6d27d4GXlYjIFXnauwBEREREREQFoUOHDurHUv7+/upHCyOrrl27JgMHDiygEhIRuTYGpSyUnqGRrSeuyv4TyXLV46o0qVJWPNzd7F0sIiIiIiKzeC6bO9988420adNGwsLCrHpebicFxvMyMjI48XIOWEeWYT2Zxzoy325sO3FFDhy/IZfdLkvjymWsbjc8PDzMPoZBKQusOBgv434/LPFJ/8/efUBFdbRhAH6liQVBQAQLCoJi71iTaKJRY0yMscQSexITTdE0TTOmJ6ZoYkuxxhK7KSamaIy9gL2DoFgAKYoU6fznG11+QMrS9m55n3P2wF327s7OLnfu/Wbmm+Tbd+y4Bg9He0zv1wS9m3loXTwiIiIiogLxXLZkrl69ij/++AMrV64s9r5BQUElek25QI6NjUVwcDCsrJhpJT+sI/2wnorGOirYrosJWLA/GtFJdwJ2O6PgWtkaEzq4omu9qtCXn59fkY9hUEqPRvzZ5YeQlef+iLhkdf/8EW3YmBMRERGRUeK5bMktXboUTk5O6N+/f7H39fX1LdFryogNuUD28fHRa4SBJWId6Yf1VDTWUf7+PBmBD/89f1e7EZOUgQ//jcTcYR7o1dQdZYVBqSKGq0mvUt4PQ8h9MnBN/t6ziTuHPxMRERGRUeG5bMllZWVh0aJFePLJJ2FnZ1fs/UtzgSsjNmR/XiQXjHWkH9ZT0VhHd7cb728+U2i7IX/v1axWmbUbHKNWiAOhsf8f5lzAhyJ/l8cRERERERkTnsuW3H///adGUIwbN07rohARmXW7wZFShbgWn1ymjyMiIiIiMhSeywIJCQkquKQTGhqKI0eOwNnZGZ6enpg2bRquXLmCZcuW3ZXgvEOHDmjWrJkGpSYispx2g0GpQrg52Jfp44iIiIiIDIXnskBAQAC6d++evT1lyhT1c9SoUViyZAnCw8MRFhaWa5+4uDisX78es2fPNnh5iYgsrd1gUKoQ/l7OamUSSQSZ35xKIX+XxxERERERGZOGNavC1roC0jLyP5OVbCDuZn4u261bN5UfqiASmMrL0dERSUlJ5VwyIiLjI+1B9cq2uJ6UZrB2gzmlCiGJu2SpXFFQCq83+zZmYkgiIgP5+OOP0b59ezg4OMDNzU2tiHT27NlC9/n+++9xzz33oHr16urWo0cPHDhwwGBlJiLSQuTNZAz9fl+hASkh57o8lyUiInH08g0kpKTDkO0Gg1JFkCVyZalciQbmpPsIjl+J06RcRESWmnh24sSJ2LdvH/7++2+kpaXhwQcfRGJiYoH7bN++HUOHDsW///6LvXv3om7dumofySFCRGSOLkQn4vH5e3AuMgE1q1XEOw83UaP7c5JzWznHlXNdIiKikKgEjFtyUHVmNK1VDe7VDNNucPqeHqTSZancfeejcCzoIlr41sP1pHRMWnUY3/4Xgvb1nNGjSU2ti0lEZPa2bNly17QLGTEVGBiIe++9N999VqxYkWv7hx9+ULlCtm7dipEjR5ZreYmIDO3U1ZsYuegAohNSUN+lMn4c1wF1nStjVOf6uc5lOzaowRFSRESUnbh81OIDatpeizqOWPVUR9jbWhuk3WBQSk9S+R29XeCSEQtfbxdYW1sj4OJ1LNlzAS+vPYrfnu+qGnwiIjIcSUYrZBUlfUmeEBlhVZx9MjIySlQ+2S8zM7PE+1sC1pF+WE9FYx1BnZuOXxaI+OR0NHZ3wJIx7eBatWJ2nbSv54TqadHwqecEZEldFe/55fyXiIjMS0JKOsYuOYhLsbdQz6UyFo1ujyoVb4eKcsVAyqkjg0GpUnjjocY4fOkGjl66gUkrD2HNhE6oaMPGmojIEOTi86WXXkKXLl2KtWT366+/jlq1aqncUvoKCgoqcRljY2PVcuRWVpwxnx/WkX5YT0Wz9Do6eDkRH/wbiZSMLDRxs8d73V1wPTwM18uwjvz8/Mq0zEREpK20jEw8uzwQJ67chEsVOywd4686MwyJQalSsLOxwtxhrdH36104ejkOH/9+Bu8+0lTrYhERWQTJLXXixAns2rVL730++eQT/PTTTyrPlL29/kvZ+vr6lqiMMjpBLv58fHw4wqAArCP9sJ6KZsl19MvRq5ixLQTpmVno1rAG5gxthUp2d9eBJdcRERHlJiuTvr7+GHYGRaOSrTUWjm6P+q5VYGgMSpVSneqV8eXglhi3NEBN5Wtf3xl9WzBhJBFReZo0aRJ+++037NixA3Xq1NFrn88//1wFpf755x+0aNGiWK9Xmos3GY0g+/MCsGCsI/2wnopmiXX0494LeOeXk8jKAh5tVQufD2oJW+uCR0FZYh0REdHdPv/rLDYcuqKm5c0d3hqt6jpBC5Y3trkcPNC4Jibc10D9LpHG0OiCV4EiIqLS9ehIQGrjxo3Ytm0bvLy89Nrvs88+w/vvv68Spbdr167cy0lEZIjj4Tdbg/D2z7cDUiM71cNXg1sVGpAiIiLSdWjM/fe8+v2jx5rhfj/tFm5jq1VGXnmwIfzrO6skYTInMznNcpNsEhGV55S95cuXY+XKlXBwcEBERIS63bp1K/sxsqLetGnTsrc//fRTvP3221i0aBHq16+fvU9CQoJG74KIqHQyM7Pw/m+n8cXf59T2Cw/4YsYjTWHF1fSIiKgIW05EqBG2YnKPhhjS3hNaYlCqjNhYW+GbYa1VcrAzEfF4986HTEREZWf+/Plqxb1u3brBw8Mj+7Z69ersx4SFhSE8PDzXPqmpqRg4cGCufWQ6HxGRqUnPyMQr645i0e5Qtf3Ow00wpWdDVKjAgBQRERUu4EIsXvzpsBphO9S/Ll54wAdaY06pMlSzmj1mP9EaTy7aj58OXlL5pR5vq1+uEyIiKppMVymKJDHP6cKFC+VYIiIiw5GR+JNWHsY/pyNVDpDPHm/Bc00iItJL8LV4lQs7JT0TD/i54f1HmxlFhwZHSpWxrr6uePGB26s0vbXpBM5FxmtdJCIiIiIycfHJaRi9+IAKSMkK0AtGtGVAioiI9BJ5MxmjFh1E3K00ldBcZnnJbC9jYBylMDPP3++Le3xdcSstA8+tOITElHSti0REREREJiomIQXDvt+PfSGxqFrRBsvG+qNnE+2S0hIRkal1ahzElRu34OVaBQtHtUNlO+OZNKd5UGru3Lkq8ay9vT06dOiAAwcOFPjYtLQ0vPfee2jQoIF6fMuWLdVKSsZGhlN/NaQValariOBrCXhj43G9ppwQEREREeUkFxGDvt2L41fiVO7Sn57uiI7eLloXi4iITEBqeiYmLA/E6fCbcK1qh6Vj/OFStSKMiaZBKUlMO2XKFEyfPh2HDh1SQaZevXrh2rVr+T7+rbfewrfffotvvvkGp06dwoQJE/DYY4/h8OHDMDauVSvim6FtVIDq5yNXserAJa2LREREREQmRDo3B87fg5CoRNR2qoQ1EzqhWW1HrYtFREQmslLra+uOYndwDCrbWWPxaH94ulSGsdE0KPXll1/iqaeewpgxY9CkSRMsWLAAlStXVst25+fHH3/EG2+8gYceegje3t549tln1e9ffPEFjJG/lzNe7dVI/f7urydx4kqc1kUiIiIiIhNw7PINDP52L8LjktGgRhWsndAJDWpU1bpYRERkIj798ww2HbkKG6sKmDe8DZrXMc5ODc2CUrI8d2BgIHr06PH/wlhZqe29e/fmu09KSoqatpdTpUqVsGvXLhirp+/xRo/GbmrY3MSVh3AzOU3rIhERERGREdtzPhpDv9uH2MRUtKjjiLUTOqOWUyWti0VERCZi8e5QfPtfiPr9k8dboFsjNxgrzbJbRUdHIyMjAzVr5k7SKNtnzpzJdx+Z2iejq+69916VV2rr1q3YsGGDep7iKO7jc+6XmZlZ7P0/HdAMj8zdg4sxSXh17VHMHdrKKJZeLA8lrSNLw3oqGuuo/OvI2tq6zMtERESl89fJCExadVh1aHbydsH3o9qp5OZERET6+P14ON777ZT6XWZuDTTylVpNqoWbPXu2mu7n5+engjoSmJKpfwVN9ytIUFBQiV5fLv5iY2MRHBysRnUVx2tdXPDyH1fw58lIfP5LAPo3cYI5Kk0dWRLWU9FYR+VfR3IsJSIi47Eu8DJeX38MGZlZeLBJTXw9tDXsbdmBUBo7duzAzJkz1QyN8PBwbNy4Ef379y90H5mdIYsrLV++HBEREfDw8MA777yDsWPHGqzcREQlsT8kBi+tPgJZZ21ER088160BjJ1mQSlXV1fVSx8ZGZnrftl2d3fPd58aNWpg06ZNSE5ORkxMDGrVqoWpU6eq/FLF4evrW6Iyy2gEufjz8fEp9ggDecmYCtXw3m+n8UNALHq09kVrT/MLTJWmjiwJ66lorKOisY6IiMzHDztD8MHm0+p36dX+ZEBz2FizU6a0EhMT1WJKElAaMGCAXvsMHjxYXZMsXLhQtbESzJKOICIiY3YuMh5PLQtQI22lY2PGI81MYoaWZkEpOzs7tG3bVk3B0/VWyMFetidNmlTovpJXqnbt2khLS8P69etVw1Ecpbl4k9EIsn9JnmNMFy8EXryBzcfD8cJPR7D5hXtQvYodzE1p6siSsJ6KxjoqGuuIiMi0ZWVl4Yu/zmHOv8Fqe3xXL7zxUGNYWRn/hYQp6NOnj7rpa8uWLfjvv/8QEhICZ2dndV/9+vXLsYRERKUXHncLoxYdwM3kdLStV12NtLU2kXZE0+l7U6ZMwahRo9CuXTv4+/tj1qxZqjdDpuSJkSNHquDTxx9/rLb379+PK1euoFWrVurnu+++qwJZr732GkyBRCk/ebw5Tl6Nw4WYJExZcwQLR7XnSQcRERGRhS7X/c4vJ7B8X1h27g+ZamEKPdvm6pdfflHXJp999pla+btKlSp45JFH8P7776sFlow1h60lYR3ph/VkOXV081aaCkjJaq3erlXw7YjWsLUq+XHI0DlsNQ1KDRkyBFFRUWqOtszXlmCT9E7okp+HhYXlypMi0/beeust1XNRtWpVPPTQQ6qxcHIynWlwDva2mDe8LfrP241/z0ZhwY7zeK6bj9bFIiIiIiIDkukVL689il+PXoXEoN5/tBlGdKyndbEsnlxnyMreMjND8k/J4kzPPfecSh2yePFio85haylYR/phPVlGHaVmZOGtv6/iXGQyqleyxjv3uSD6ykVEm1AOW80TnctUvYKm623fvj3X9n333YdTp25nkTdlTWpVw3uPNMXUDcfx+Z9n0cazOjp6u2hdLCIiIiIygFupGZiwPBD/nYuCrXUFfDm4Ffq1rKV1sejOBZiMVFuxYgUcHR3VfbL698CBAzFv3jy9R0tpkcPWUrCO9MN6Mv86yszMwktrjuJYRDKqVrTGsrEdVKzB1OpI86CUpRrSvi4OXIjFhkNX8MKqwyq/VA2HiloXi4iIiIjKUVxSGsYuPYjAi9dRydYaC55si/sa1tC6WHSHrLQn6UN0ASnRuHFjlfvr8uXLegebtMphaylYR/phPZl3HX38xylsPh4BG6sKWDCiHZrXrW6SdWSaY9TMgPTAfNC/GXzdquJafApe/OmwWv6XiIiIiMzTtZvJGPLdXhWQqmZvg+Xj/RmQMjJdunTB1atXkZCQkH3fuXPn1EVZnTp1NC0bEVHOFVt/2BWqfp85qAW6+rrCVDEopaHKdjaYP6KN6iXbcz4Gs7eWbO45ERERERm3sJgkDFywF2ci4tXo+NXPdELberdXd6PyI8GlI0eOqJsIDQ1Vv0vuWjFt2jS1uJLOsGHD4OLiohZekrQhO3bswKuvvoqxY8cWK9E5EVF5+eXoVXyw+bT6fWofPzzW2rQD5gxKaczHzQEfD2iufv9mWxB2nIvSukhEREREVIbORsRj4II9CItNgqdzZayb0AmNPco27wflLyAgAK1bt1Y33erf8rsstCTCw8OzA1RCFlP6+++/cePGDbUK3/Dhw9GvXz98/fXXmr0HIiKdPeej8cqao+r30Z3r45l7vWHqmFPKCPRvXVvll1q5PwwvrT6CzS90hYcje2KIiIiITJ1M1Ru75CDibqWhUU0H/DjOH27V7LUulsXo1q2bygdVkCVLluS7WpQEpoiIjMmZiJt4ZlkgUjMy0aeZO95+uIlKC2TqOFLKSLzzcBM08aiG2MRUPL/yMNIyMrUuEhERERGVgoyAH/HDfhWQauPphDXPdGJAioiIiu3KjVsYtegA4lPS4V/fGV8NaQVrK9MPSAkGpYyEva015g1vA4eKNgi4eB2f/3lW6yIRERERUQltPhaOcUsP4lZaBu5tWAPLx3eAY2VbrYtFREQmuGrr6EUHEHkzRS2U9v3Idip+YC4YlDIi9V2rqMz54tsdIfj7VKTWRSIiIiKiYlp1IAyTVh1CWkYWHm7hgR9GtlML3BARERVHcloGnloWgKBrCahZrSKWjPU3uw4OBqWMTO9mHhjbxUv9/vKaI7gUm6R1kYiIiIhID5K7aN72YEzbcBySxmhYB0/MfqI17Gx4yk1ERMWTkZmFKWuOqPzTMqNqyRh/1HYyv9zTbCGNkCzr2KquE24mp2PiykNISc/QukhEREbh448/Rvv27eHg4AA3Nzf0798fZ88WPd157dq1KnGtvb09mjdvjt9//90g5SUiywpIffzHGXy25fYxaWL3BviwfzOzyflBRESGbVPe/+0Ufj8eAVvrCvh2ZFuzXbWVQSkjJL1pc4e3gVNlWxy7HIePNp/WukhEREbhv//+w8SJE7Fv3z61MlJaWhoefPBBJCYmFrjPnj17MHToUIwbNw6HDx9WgSy5nThxwqBlJyLzlZ6RidfXH8N3O0LU9lt9G+PVXn5msSoSEREZ3nc7QrBkzwX1+xeDW6FzA1eYKwaljJQMy/tycEv1+9K9F/HbsataF4mISHNbtmzB6NGj0bRpU7Rs2VIt5R0WFobAwMAC95k9ezZ69+6NV199FY0bN8b777+PNm3aYM6cOQYtOxGZb76PSSsPY03AZcigqM8GtsD4e7y1LhYREZmoTYevqJG3uk6OR1rWgjljUMqI3e9XE891a6B+n7r+OEKiErQuEhGRUYmLi1M/nZ2dC3zM3r170aNHj1z39erVS91PRFQaCSnpGLvkILacjICdtRXmDW+Lwe3qal0sIiIyUbuCovHquqPq93FdvSyik4PLgBi5KT0bIuDidRwIjcVzKw5h08QuZrX8IxFRSWVmZuKll15Cly5d0KxZswIfFxERgZo1a+a6T7blfn1lZJQst5/sJ+Us6f6WgHWkH9aT8dXR9aRUjF0SiGNX4lDFzhoLRrRB5wYuRv0ZlbaOrK15DkpEVF5OXo3DhOWBauXWvi088OZDjWEJGJQycjbWVvhmaGv0/XonzkTEY/rPJ/HpwBZaF4uISHOSW0ryQu3atavcXysoKKhE+8nFX2xsLIKDg2FlxcHJ+WEd6Yf1ZFx1FJWYjjf/uoqwuDRUq2iF93u6o0ZmLIKCYmHOdSQLRhARUdm7FJuE0YsPqhG4Hb2dVSofKwtZKINBKRNQs5q9Wk54xML9WB1wCe29nDGwbR2ti0VEpJlJkybht99+w44dO1CnTuHHQ3d3d0RGRua6T7blfn35+vqWqJwyGkEu/nx8fDjCoACsI/2wnoynjkKjEzF100FciUuDu6M9lo5uBx+3qjAF/B4RERmf64mpGLX4AKLiU9CopgO+fbIdKtpYzjGaQSkT0cXHFZN7NMSXf5/DW5uOo3ltRzRyd9C6WEREBl8e9/nnn8fGjRuxfft2eHl5FblPp06dsHXrVjXVT0dW7pP79VWaizcZjSD78wKwYKwj/bCetK+jE1fiMGrRAcQkpsLbtQp+HN9BLU5jSvg9IiIyrsUyxi8LQEhUIjwc7bFkbHs4VrKFJeH4bxMyqbsP7vF1RXJaJp5bEYjElHSti0REZPApe8uXL8fKlSvh4OCg8kLJ7datW9mPGTlyJKZNm5a9/eKLL6pV+7744gucOXMG7777LgICAtRoKyIifUl+z6Hf7VMBqaa1qmHNhE4mF5AiIiLjkZGZhedXHUbgxeuoZm+DpWP94eFoee0Kg1ImROaUzhrSCu7V7HE+KhFvbDyuRg0QEVmK+fPnqxX3unXrBg8Pj+zb6tWrsx8TFhaG8PDw7O3OnTurINZ3332Hli1bYt26ddi0aVOhydGJiHLaejoSTy7cj/iUdPh7OWPV0x3hWrWi1sUiIiITJdfx0385gb9PRarVW78f2Q4Na1rmTChO3zMxLlUrYs6w1hjy3T78fOSqOjEa3qGe1sUiIjIIfQLxMq0vr0GDBqkbEVFxbTp8BS+vPap6tHs0dsOcYW24EjIREZXKvO3nsXxfGCpUAL4a0godvF1gqThSygS1q++M13s3Ur/P+OWUym9ARERERGVrye5QvLT6iApIDWhdG/NHtGVAioiISmVtwCXM/POs+v2dh5ugbwsPWDIGpUzUU/d4q9661AzJL3UIcbfStC4SERERkdmMypz1zzm8++sptT2mS318PqglbK156kxERCW3/ew1TN1wXP3+zL3eGNOl6EV7zB1bVhNVoUIFfDGoFepUr4Sw2CS8tu4o80sRERERlVJmZhZm/HoKs/4JUttTejZUPdmS25NMz44dO9CvXz/UqlVLnT9LTsGipoDL4/LeZFENIqLSOH45Tg0okdG3j7aqhdd7+2ldJKPAoJQJc6xsi3nD26jEaH+ejMSi3Re0LhIRERGRyUrLyFT5o5bsuX1O9d6jTfHCA74qKEGmKTExUS1yMXfu3GLtd/bsWbVohu7m5uZWbmUkIvMXFpOEMUsOICk1A118XDBzYEt2dtzBROcmrkUdJ7z1cGO88/NJfPz7abT2dEIbz+paF4uIiIjIpCSnZWDiikPYeuYabKwq4IvBLfFoq9paF4tKqU+fPupWXBKEcnJyKpcyEZFliUlIwajFBxCdkIrGHtWwYERb2NlwfJAOa8IMPNmxnkqOlp6ZhUkrDuF6YqrWRSIiIiIyGTeT0zBy4QEVkKpoY4XvRrZlQMrCtWrVCh4eHujZsyd2796tdXGIyEQlpaZj7NIAhEYnorZTJSwZ0x4O9rZaF8uocKSUGZAh5Z8MaI5TV2+qL/vkNUewaFR7DgckIiIiKkJUfApGLTqAU+E34WBvg0Wj26N9fWeti0UakUDUggUL0K5dO6SkpOCHH35At27dsH//frRp00bv58nIyCjR68t+mZmZJd7fErCO9MN60r6O0jMyMWnFYRy9dAOOlWyxaFRbuFaxNanPJKOUdWRtXfSKtQxKmQmJtkp+qf5zd2P72SjM/+88Jnb30bpYREREREbr8vUkPLnwgOrUc61qh6Vj/dG0lqPWxSINNWrUSN10OnfujPPnz+Orr77Cjz/+qPfzBAXdTpRfXHLxFxsbi+DgYFhZcVJLflhH+mE9aVtHsgjZ13ujsO1cPOysK+CdbjWQFReOoDhYVB35+RWdzJ1BKTMi81Pff7QZXlt/DF/8dRZt61VHR28XrYtFREREZHSCIuNVQCriZrKaUrF8fAd4uVbRulhkhPz9/bFr165i7ePr61ui15LRCHLx5+Pjo9cIA0vEOtIP60nbOvpmWzD+OBcPWSdj1pBW6NW0JkxRhgG+RwxKmZlB7epgf2gs1h+6jOdXHcbmF7rCzcFe62IRERERGY0jl25g9OIDuJGUBl+3qvhxXAe4O/J8ifJ35MgRNa2vOEpz8SajEWR/BhIKxjrSD+tJmzpafTAMs7YGq99nPNIUD7WoBVNmVc7fIwalzDC/1Pv9m+L4lRs4F5mAF1cdUT1/1swvRURERITdwdF4almAWpa7ZV0nLBndHtWr2GldLConCQkJqpdfJzQ0VAWZnJ2d4enpiWnTpuHKlStYtmyZ+vusWbPg5eWFpk2bIjk5WeWU2rZtG/766y8N3wURmYptZyLxxsYT6vfnujXAyE71tS6S0ePkUjNU2c4G84a3RWU7a+wNicHsf85pXSQiIiIizW05EY4xiw+qgFRXH1esHN+BASkzFxAQgNatW6ubmDJlivr9nXfeUdvh4eEICwvLfnxqaipefvllNG/eHPfddx+OHj2Kf/75Bw888IBm74GITGcU7sQVh5GRmYUBbWrj1V7/z09HBeNIKTPl41YVHw9ojhd/OoJv/g1G2/rOuK9hDa2LRURERKSJNQcvYeqGY8jMAvo0c8esJ1qhog2ntJg7WTlPEg4XZMmSJbm2X3vtNXUjIiqOC9GJGLvkIG6lZeAeX1d8+ngLNYuJisaRUmbs0Va1MbyDJ6Qdnrz6CMLjbmldJCIiIiKD+27HebUQjASkhrSriznD2jAgRUREZSIqPgUjFx1AbGIqmtWuhvkj2sLWmqEWfbGmzNzbDzdR/xjyDzJp5WGkZWRqXSQiIiIig5ARMp9tOYOPfj+jtp+5zxufPN6cuTaJiKhMJKakY9zSgwiLTUJd50pYNLo9qlbkhLTiYFDKzNnbWmPesLZwsLdB4MXrmPnnWa2LRERERFTuJKeHJJudt/282p7axw/T+jTmdAoiIioTMuBj4spDOHY5DtUr22LpGH+ufF8CDEpZAE+Xypg5sKX6/bsdIfjrZITWRSIiIiIq0wDUvpAY/BsSr37eSs3AC6sOY9WBMMigqE8GNMeE+xpoXUwiIjKjkbhvbDiO7WejYG9rhYWj28O7RlWti2WSOK7MQvRu5o5xXb2wcFcoXll7FJs9qqGuc2Wti0VERERU6hX1Zvx6CuFxybfv2HENdjZWSE3PhJ21lUpo/lBzD62LSUREZuSrv89hbeBl1fExZ2gbtPGsrnWRTBZHSlmQ13v7obWnE24mp+O5FYeQkp6hdZGIiIiIShWQenb5of8HpO6QgJR4tlsDBqSIiKhMrdh/EV9vC1a/f9C/OXo0qal1kUwag1IWRHoN5w5ro+a7Hr8Shw83n9a6SEREREQlnrInI6SyCnnMmoBL6nFERERl4e9TkXh70wn1+wsP+GJYB0+ti2TyGJSyMLWcKuHLIa3U78v2XsSvR69qXSQiIiKiYjsQGnvXCKm85O/yOCIiotKShcOeX3UI0tcxuF0dTO7hq3WRzAKDUhaoeyM3TOx+O9nn1PXHcD4qQesiERERERXLtfjkMn0cERFRQeSaefzSg0hOy0T3RjXw4WPNuZprGWFQykJN7tEQHbyckZiagYkrDqlVaoiIjN2OHTvQr18/1KpVS50IbNq0qch9VqxYgZYtW6Jy5crw8PDA2LFjERMTY5DyElH5caio33o9XJ6biIhKQzo3Ri06gOtJaWhZxxFzh7eBrTVDKWWFNWmhbKyt8M3Q1nCtWhFnIuIx/Zfb82KJiIxZYmKiCjDNnTtXr8fv3r0bI0eOxLhx43Dy5EmsXbsWBw4cwFNPPVXuZSWi8nPq6k2899upQh8j/dcejvbw93I2WLmIiMi8JKSkY8zig7h8/RbquVTGwtHtUdlOv04R0g9r04K5VbPH10NbYcQP+7Em4DLa13fGoHZ1tS4WEVGB+vTpo2762rt3L+rXr48XXnhBbXt5eeGZZ57Bp59+Wo6lJKLykpWVhdUHL2H6LyeRkp6pFm+RnmsJQOVMZ66bUDG9XxNYy3rdRERExSQruT67PBAnr96ESxU7LB3jrwZ1kJmNlJLebrlgsLe3R4cOHVQPdmFmzZqFRo0aoVKlSqhbty4mT56M5GTmCiipzg1c1VQ+8fbPJ3Am4qbWRSIiKjOdOnXCpUuX8Pvvv6uL2cjISKxbtw4PPfSQ1kUjomJKSk3Hy2uOYuqG4yogdb+fG7a93A0LRrSBu2PuKXqyPX9EG/Ru5qFZeYmIyHTJeaPkX94ZFI1KttZYNLo96rtW0bpYZknTkVKrV6/GlClTsGDBAhWQkoBTr169cPbsWbi5ud31+JUrV2Lq1KlYtGgROnfujHPnzmH06NEqr8iXX36pyXswBxO7++DgxevYcS4Kz604hF8mdUVVPfM0EBEZsy5duqicUkOGDFEdGOnp6Sonlb7T/3QyMkqWd0/2y8zMLPH+loB1pB9Lr6egawmYtPIwgqMS1cinl3v64qmuXrCyqoCejd1wf6Ma2B8SjePBYWju44kO3q7qcZZaX+X1PbK2ti7zMhERGaOZf57FhsNXVFsyb3gbtKzrpHWRzJamkQcJJElejzFjxqhtCU5t3rxZBZ0k+JTXnj171AXGsGHD1LaMsBo6dCj2799v8LKbEzmhmzWkFR6avRMhUYmYtuE4vn6iFVcTICKTd+rUKbz44ot45513VKdHeHg4Xn31VUyYMAELFy7U+3mCgoJK9Ppy8RcbG4vg4GBYWWk+ONkosY70Y8n19M/5eHyzNwop6VlwqWyNaffVRLOaGTh/PjjX41wyM9HCMRXOGbEIOX9Ds/Ka8/fIz8+vXMpFRGRMlu29gHnbz6vfP36sObr73T1ghswgKJWamorAwEBMmzYt+z5pHHv06KFygORHRkctX75cTfHz9/dHSEiImpLx5JNPFuu12eN9N0d7a3z9REsM++EAfj16Ff71nDCsg2exn8ec66gssZ6KxjoqGnu8i/bxxx+rzgwJRIkWLVqgSpUquOeee/DBBx+o1fj04evrW6LXl89GLv58fHwsor5LgnWkH0usp+S0DMz47TTWBFxT210auODLwS0KzOdhiXVUXKwjIqLCbTkRofIWiik9G2Jwe+ZcNtugVHR0tGoYa9asmet+2T5z5ky++8gIKdmva9euao6nTMOQ3u433nijWK/NHu/8VQMwpo0zvg+IUSvaOGXehK9r8RK5mXsdlRXWU9FYR0Vjj3fRkpKSYGOTu6nTXYhJO6Kv0ly8yWcj+/MCsGCsI/1YUj2FRCWolAKyQrAM3H7pgYaYdL9PkUnLLamOSop1RESUv4MXYvHCT4chp4hD/T3x/P0+WhfJIphU4qDt27fjo48+wrx581QOKrkQk2kZ77//Pt5++229n4c93gV73ScLoYmH8c/pa/hsdwx+mdgZ1SrZ6r2/JdRRWWA9FY11VDRLrKOEhAT1nnVCQ0Nx5MgRODs7w9PTU42+vXLlCpYtW6b+LvmjZJr4/Pnzs6fvvfTSS2q0ba1atTR8J0RUEBmxLcllE1Mz4FrVDrOfaI0uPq5aF4uIiMxIRmYW9oXE4FhIPGKsY+DqYI/xSwPUins9Grvh/UebMp2NuQelXF1d1UWUrISUk2y7u7vnu48EnmSq3vjx49V28+bNkZiYiKeffhpvvvmm3iMF2ONduC8GtcLDc3biUuwtvL7hBL59sm2x/iEtoY7KAuupaKyjollaHQUEBKB79+7Z27JYhhg1ahSWLFmigk5hYWHZf5fFMOLj4zFnzhy8/PLLcHJywv33349PP/1Uk/ITUcFS0jPw4ebTWLb3otru4OWMb4a2hlu13CvrERXHjh07MHPmTJU2RNqIjRs3on///nrtu3v3btx3331o1qyZ6gAhIvOw5UQ4Zvx6CuFxybfv2HENMhA3MwtoVdcJ3wxtAxtrztQwFM1q2s7ODm3btsXWrVtzTUWRbVnCu6BpGHkDTyWZhkGFc6xsi7nD2sDO2gp/nYrEwl2hWheJiEjp1q2bOt7nvUlASshPGVWb0/PPP4+TJ0+qNuTq1asqN2Ht2rU1egdElJ9LsUkYOH9vdkBqYvcGWDG+AwNSVGrSgd2yZctir7p648YNjBw5Eg888EC5lY2ItAlIPbv80P8DUndIQEoM7+CJSnaW0dlrLDSdvic93NK73a5dOzWVYtasWarh0K3GJw2BXDhIolrdNAxZsa9169bZ0/dk9JTcbymjBAylRR0nvP1wY7z980l88scZtPasjrb1qmtdLCIiIjIzf52MwMtrjyI+OR1OlW3x1ZBW6N6IKx1R2ejTp4+6FZfkrZV8tnKNsWnTpnIpGxEZfsqejJAqbDjLl3+fw4A2dYrMYUhmEpQaMmQIoqKi1FLdERERaNWqFbZs2ZKd/FymYOQcGfXWW2+paWTyU3KG1KhRQwWkPvzwQw3fhfka0bEeDly4rnI7TFp5CJtfuAfOVey0LhYRERGZgbSMTHz6xxn8cGdEdhtPJ8wZ1ga1nCppXTSycIsXL1arfMvIWlmptSS42nf5YR3ph/V0N8khlXeEVF7y933no9DR28Vg5bL01b41T3Q+adIkdctP3ikYsoLS9OnT1Y3KnwQAPx7QHCevxCEkOhGTVx/B4tHtYcWoMREREZXClRu3VIfX4bAbavupe7zwWm8/2DKHB2lMVumeOnUqdu7cedfqrcV9npLg6sNFYx3ph/V0N0lqrtfjgi7CJSO23MtjCjINsNq35kEpMm5VK9pg3og2eHTObvx3Lgrz/zuPid25NCYRERGVzL9nrmHymiO4kZSGavY2+HxQSzzYNP9FbogMSUYCyJS9GTNmoGHDhqV6Lq72XX5YR/phPd1NVtmTpOZFaeFbD74cKWWw7xGDUlQkP/dqeL9/M7y27hi++Oss2nhWR6cG/CclIiIi/aVnZKpcHfO2n1fbLeo4qoVV6jpX1rpoRIqs1iqrvB4+fDh7JoeMEpAFNWTU1F9//aVWcNUHV/suX6wj/bCe/k/+j0+GFz5SSuYDuTvao2ODGswpZcDvEYNSpJfB7eriYGgs1gZexvOrDuP3F7vCzYEr4hAREVHRIm8mq/OHA6G3p0OM6lQPb/RtjIo2vFAi41GtWjUcP348133z5s3Dtm3bsG7dOnh5eWlWNiIqucSUdLy2/hg2HwvPvk9CTjkTnutCUNP7NWFAysAYlCK9vfdoMxy7HIezkfF4YdVhrBjfkf+wREREVKhdQdF48afDiElMVWkBPn28Bfq28NC6WGQhEhIS1NQTndDQUBw5cgTOzs7w9PTEtGnT1AJKy5YtU6MBmjVrlmt/Nzc32Nvb33U/EZmG81EJmPBjIIKuJcDGqgLefrgJ3Bwq4r3fTuVKei4jpCQg1bsZ2ydDY1CK9FbJzlrll3rkm13YFxKLWf+cw8sPNtK6WERERGSkS29/sy0Is7cGISsLaOxRDfOGt4GXaxWti0YWRKbjde/ePXt7ypQp6ueoUaOwZMkShIeHqxW/icj8bDkRjlfWHkNCSroKRM0f0QZt6zmrv0kuQ1llT5KaSw4pTtnTDoNSVCwNalTFx4+3UCOlvtkWjLb1qqNbIzeti0VERERGJDohBS/9dAS7gqPV9lD/upjerynsbTldjwyrW7duKpdMQSQwVZh3331X3YjItHIYfv7XOSz473YOQ38vZ8wZ1jpX+hkJQHX0dlGr7ElScwaktMO1IanYHmlZC092rKd+n7z6CK7euKV1kYiIiMhI7A+JwUOzd6qAVCVba3w1pCU+HtCCASkiIjJIp8jIRQeyA1Lju3phxfgOzIdsxDhSikrkrYcb48ilGzh+JQ6TVh7C6mc6wdaaMU4iIiJLlZmZhQU7zuPzP88iMwvwdauqpuv51nTQumhERGQBDoddx3MrDqlcUZXtrPHZwBZ4uEUtrYtFRWAUgUpEVsuRZZwd7G1wKOwGPttyRusiERERkUauJ6Zi3NKD+GzL7YDUgNa18fOkLgxIERFRuZMpusv3XcSQb/epgJR3jSr4eWIXBqRMBEdKUYl5ulTG54Na4pkfA/H9zlC09awOB3trHAuJR4x1DJPFERERWYBDYdcxacUhXI1LRkUbK7z3aFMMblcXFSrwHICIiMpXcloG3tx4AusPXVbbvZu6Y+agFnCwt9W6aKQnBqWoVHo1dcdT93ipoNSzKw4hO43kjmvw4LKaREREZt0zvXBXKD754wzSM7PUqnoyirpJrWpaF42IiCxAWEwSJiwPxKnwm5CxEK/39sPT93qzU8TEMChFpdayrpP6mXddk4i4ZDy7/JBaepOBKSIiIvMRdysNr607ij9PRqrth1t44OMBzdkzTUREBvHv2WtqlVdpj1yq2OGboa3R2cdV62JRCTAoRaWSkZmFDzefzvdvEqSSGPWMX0+hZxN3TuUjIiIyA8cvx+G5lYG4FHsLdtZWePvhxhjRsR57pomIyCCLany9LQiztwYhKwtoVddJLapRy6mS1kWjEmJQikrlQGisSiZXEAlMyd/lcZ0auBi0bERERFT2iWTf/+00UjMyUde5EuYNa4vmdRy1LhoREVmAuKQ0vLT6MP49G6W2R3T0xNsPN1GLcJHpYlCKSuVafHKZPo6IiIiMT0JKOqauP4bfjoWr7Qeb1MTMQS3hWInT9YiIqPydvBqn8kfJKF1ZVOPDx5pjYNs6WheLygCDUlQqbg72Zfo4IiIiMi6nw29i4opDCIlOhI1VBUzt44dxXb04XY+IiAxifeBlvLHxOFLSb4/SXTCiLZrW4ihdc8GgFJWKv5ezWmVPkprnTXSuI5HsZrW5Eg8REZGpTddbE3AJ7/x8Ul0I1HK0x5zhbdDGs7rWRSMiIguQkp6B9387heX7wtR290Y1MGtIazhW5ihdc2KldQHItEny8un9mqjfC+ovlRPZJxcewPXEVIOWjYiIiEomKTUdL689itfX3+6ZlguBzS/cw4AUEREZRHjcLQz5dp8KSMnA3Jd6+GLhqPYMSJkhBqWo1Ho388D8EW3g7ph7ip6MoHqtVyM4VbbFkUs3MOjbvergQkRERMYr+Fo8Hp2zGxsOXYEsnPta70bqQqB6FTuti0ZERBZgz/loPPz1LnUNWc3eBotGtcdLPRrCiqu5myUGpajMAlO7Xr8fK8a1x+v3uqmfsv1cdx+sfaaTClAFX0vAwPl7cT4qQeviEpGJ2rFjB/r164datWqpfDabNm0qcp+UlBS8+eabqFevHipWrIj69etj0aJFBikvkanZePgy+n2zG0HXEuDmUBGrnuqI57r58EKAiIgMMm382//OY8QP+xGTmIomHtXw2/P3oLufm9ZFo3LEnFJUplP5Onq7wCUjFr7eLmpb+NZ0wLpnO+PJhfsREpWIQQv2YsmY9mhRx0nrIhORiUlMTETLli0xduxYDBgwQK99Bg8ejMjISCxcuBA+Pj4IDw9HZmZmuZeVyJQkp2Vgxq8nserAJbXd1ccVs55oBdeqFbUuGhERWYD45DS8uvYYtpyMUNuPt6mDD/o3QyU7a62LRuWMQSkyiNpOldSIqTFLDuLY5TgM/W4fvn2yHbr6umpdNCIyIX369FE3fW3ZsgX//fcfQkJC4OzsrO6TkVJE9H+h0Yl4bsUhtcqe5O148QFfPH+/b3bnEhERUXkKiozHM8sD1QAGW2vJWdwUwzt4cpVXC8Hpe2QwLlUrYuVTHdHFxwWJqRkYu+Qgfj8ernWxiMiM/fLLL2jXrh0+++wz1K5dGw0bNsQrr7yCW7eY345IbD4Wjn7f7FIBKZcqdvhxbAeVt4MBKSIiMlQ79Ojc3SogJSlf1jzTCSM61mNAyoJwpBQZVNWKNlg0uj0mrz6C349HYOLKQ2pY5vAO9bQuGhGZIRkhtWvXLtjb22Pjxo2Ijo7Gc889h5iYGCxevFjv58nIyCjR68t+MlWwpPtbAtaRNvUkK+p9/McZ/HhnmW3/+tUxa0hL1Kxmb7KfBb9L5V9H1tacRkNEZSM9IxOfbjmD73eGqu1O3i74ZlhrThu3QAxKkcFVtLHGN0PbwKnyCazcH4Y3N55AbEIqJt3vw4g4EZUpufiS48qKFSvg6Oio7vvyyy8xcOBAzJs3D5UqVdLreYKCgkr8+rGxsQgODoaVFQcn54d1ZPh6iohPw4fbIxEUk6K2hzR3wsjW1XEz8hJuRsJk8btU/nXk5+cHU1wgY+bMmQgMDFQ5BaWDon///gU+XjoyXn/9dZw5cwZJSUlqkYxnnnkGkydPNmi5icxZVHwKJq08hP2hsWr7mfu88eqDjWBjzWO3JWJQijQh0wI+7N9MTRX4Zlswvvj7nFph4Z2Hm3CFHyIqMx4eHmrani4gJRo3bqxWd7l8+TJ8fX31eh59H5eXjEaQiz9JsM4RBvljHRm2nv4+FYnXNh/HzeR0OFWyxeeDWqB7oxowB/wuFc0S66i4C2RUqVIFkyZNQosWLdTvEqSSoJT8/vTTTxukzETmLPBirMpjGHkzRc2imTmwBfo099C6WKQhBqVIMzJ64eUHG8G5ih1m/HoKS/ZcwI2kVMwc1BK2jJITURno0qUL1q5di4SEBFStWlXdd+7cOTVCoE6dOno/T2ku3uS1ZH9LuQAsCdZR+ddTWkYmPssxTaK1pxPmDGujFiIxJ/wuFc3S6qi4C2S0bt1a3XRkcYwNGzZg586dDEoRlYJ0CC7bexHv/3YK6ZlZ8HGrigUj2qqfZNkYlCLNjeniheqV7fDK2qPYdOQqbtxKw7zhbVDZjl9PIspNgkvSy68TGhqKI0eOqJX1PD09MW3aNFy5cgXLli1Tfx82bBjef/99jBkzBjNmzFA5pV599VXVY67v1D0iU3f1xi01TeJQ2A21Pb6rF17r7Qc7G3YAERXl8OHD2LNnDz744INi7cdchOWHdWR69XQrNQNv/XxSXeuJh5q745PHmqFKRRtNy2dMdWTJuQh51U9GoX/r2nCsbItnlwdi+9kojPhhv0qI7lTZTuuiEZERCQgIQPfu3bO3p0yZon6OGjUKS5YsUflCwsJuJ24WMjrq77//xvPPP69W4XNxccHgwYOLfXFBZKr+PXsNU1YfwfWkNDjY2+DzQS3Rq6m71sUiMnoymjYqKgrp6el49913MX78+GLtz1yE5Yd1ZFr1dPVmGt7/NwKh11MhWVrGt3PBY00q42rY7ZG7WjKWOrL0XIQMSpHR6N7IDSvGd8CYxQdVb+7gb/di2dgOcHe017poRGQkunXrpoZ/F0QCU/k1hhKYIrK0VY2++ucc5v57Xm03r+2IucPawNOlstZFIzIJMl1PRufu27cPU6dOVXm4hg4dqvf+zEVYflhHplNPW09fw8u/H0N8cjpcq9rhmydawd/LGcbCGOrI2BmijhiUIqPStp4z1k7ojJGL9uNcZAIen78HP47zh3cNzjUmIiLSx7WbyXh+1eHsVY1GdqqHN/s2VqvfEpF+vLy81M/mzZsjMjJSjZYqTlCKuQjLF+vIuOspIzMLs/45pxa0Em3rVVfpWWpWM77BBvwuaV9HHKNGRqeRuwPWTegML9cquHLjFgYt2Ivjl+O0LhYREZHR2xMcjYe+3qkCUlXsrPHN0NZ479FmDEgRlXL6SkpKitbFIDIJ1xNTMXrxgeyA1OjO9bHqqY5GGZAi48CRUmSU6jpXxtoJndQB7cSVmxj6/T58N7ItOjdw1bpoRERERkd6pedsC8asrecgM1z93B1UrzRHGpOlK+4CGXPnzlX36/Kg7NixA59//jleeOEFzd4DkamQgQQTlgeqgQX2tlb4ZEALlTuYqDAMSpHRcq1aUUXVn14WiL0hMRi96CC+HtoKvZt5aF00IiIioxGdkILJq49gZ1C02n6ifV28+0hT2NtydBRRcRfIkFFREqiS4JWNjQ0aNGiATz/9FM8884wm5ScyFasPhuHtn08iNT0T9VwqY8GItmjsUU3rYpEJYFCKjJqDvS0Wj2mPl346gi0nI/DcikP48LHmGOrvqXXRiIiINHcgNBbPrzqEyJspqGRrjQ/6N8PjbetoXSwik10gQ1ZrlRsR6Sc5LQMzfj2JVQcuqe0ejd3wxeBWcKxkq3XRyEQwKEVGT3p65w5vgzc3HsdPBy9h2objiE1MxXPdGqBChQpaF4+IiMjgMjOz8O2OEHz+11k1dc/HraqartewpoPWRSMiIgtx+XqSGjRw7HIc5LLs5Z4N8Vw3H1hZ8RqN9MegFJkEa6sK+HhAczhXscO87ecx88+zKjD15kONedAjIiKzJkGnfSExOBYSjxjrGDRyd8Rr649h25lr6u+Pta6tRkhVqcjTOiIiMoydQVF4YdVhXE9Kg1NlW3z9RGvc27CG1sUiE8SzFzIZMirqtd5+KjD1webTWLgrVK3u8OnAFrC15kKSRERkfracCMeMX08hPC759h07rkH6YjKzADsbK7z3SFMMaV+XI4eJiMhgI3Xn/3ceX/x1VrVFzWs7qpG6slAVUUkwKEUmZ/w93qhe2U71Em84fAU3bqVh7rA2qGTHhK5ERGReAalnlx9C3mw4chEgXn2wEZ5gjkUiIjKQm8lpeHnNUfx9KlJtD2lXFzMe5cIaVDocXkImSZK4fvdkW1S0sVLTF55cuB9xSWlaF4uIiKjMpuzJCKmC0jPLuKhFu0PV44iIiMrb2Yh4PPLNLhWQkpG6nwxormasMCBFpcWgFJmsBxrXxPLxHVDN3gYBF69jyHd7EXnzzvQGIiIiE19VL3vKXj4kFCV/l8cRERGVp5+PXEH/ubtxISYJtZ0qYd2EThypS2WGQSkyae3rO2P1M53g5lARZyLi8fj8PbgQnah1sYiIiErl+JUbej3uWjw7Y4iIqHykZWTi3V9O4sWfjuBWWgbu8XXFr893RYs6TloXjcwIg1Jk8hp7VMP6ZzujnktlXL5+CwMX7MGJK3FaF4uIiKjYktMy8PmfZ/HpljN6Pd7Nwb7cy0RERJbn2s1kDP1uH5bsuaC2J3X3wZIx/mrRKaKyxKAUmQVZ7WHdhM5o4lEN0Qmp6gAqy2cTERGZiu1nr+HBr3Zgzr/ByMiEypuIQnJKeTjaw9/L2aBlJCIi8ydTw/t+s0ulSHGoaIPvR7bDK70awVqWfyUyt6DU3LlzUb9+fdjb26NDhw44cOBAgY/t1q2bWvI4761v374GLTMZpxoOFfHTMx3RwcsZ8SnpGLnoAP48GaF1sYiIiAol+RAnrjyE0YsPIiw2SQWbFoxoi1lDWqngU95LAN329H5NeIFARERlJisrCwt3hWLo9/sQFZ+CRjUd8MvzXdGzSU2ti0ZmTNOg1OrVqzFlyhRMnz4dhw4dQsuWLdGrVy9cu3Yt38dv2LAB4eHh2bcTJ07A2toagwYNMnjZyThVs7fF0rH+6sCZmp6JZ5cHYs3BS1oXi4iI6C6yct6S3aF44Iv/sPlYuAowje/qhb+n3IfezdzRp7kH5o9oA3fH3FP0ZFvu793MQ7OyExGReUlMScfzqw7j/d9Oqfbp0Va1sHFiZ3i5VtG6aGTmbLR88S+//BJPPfUUxowZo7YXLFiAzZs3Y9GiRZg6depdj3d2zj1E/aeffkLlypUZlKJcZFnS+cPb4I2Nx7Em4DJeW38MsUmpmHBfA62LRkREpBy/HKfaqeN3ciC2rOuEjx5rhqa1HHM9TgJPPZu4Y9/5KBwLuogWvvXQsUENjpAiIqIyExKVgAnLA3EuMgE2VhXwVt/GGNW5vpqVRGS2QanU1FQEBgZi2rRp2fdZWVmhR48e2Lt3r17PsXDhQjzxxBOoUqV40duMjIxil1e3X2ZmZon3twTGUkdy+Pyof1M4VbLFdztD8ckfZxAdn4ypvRsZxcHVWOrJmLGOyr+OZKQpERnWzeQ0fPnXOSzbewGZWYCDvQ1e7+2Hof6eBQaa5P6O3i5wyYiFr7cLA1JERFRmtpyIwCtrjyIhJV2taD5veBu0q898hWQBQano6Gh1IVWzZu75qbJ95kzRK85I7imZvieBqeIKCgpCScjFX2xsLIKDg1UAjYy/jgY0sELmLRf8EBCDH3ZdwIXwGEzuon0Ps7HVkzFiHZV/Hfn5+ZVLuYgo/zwdvx+PwIxfT+JafIq6T6ZGvNm3MVfQIyIig0vPyMQXf5/D/O3n1bZ/fWfMGd6abRJZ1vS90pBgVPPmzeHv71/sfX19fUv0mhJEk4s/Hx8fjjAwoTqa5gv41ruMNzaexD/n45Fla4+vn2ilpvlpxRjrydiwjorGOiIyDWExSXj75xP471yU2pb8HO8/2gxdfV21LhoREVmgmIQUvPDTYewOvr1a+biuXpjaxw+21uwIJgsKSrm6uqqLqMjIyFz3y7a7u3uh+yYmJqp8Uu+9916JXrs0F28yGkH25wWgadXRkPb14FzFHpNWHsLWM1EYsyQQ349qB8dKtpqVyRjrydiwjorGOiIyXrLgxvc7Q/D11iCkpGfCztoKz3ZroG5adowQEZHlOnLpBp5bHoirccmobGeNTx9vgX4ta2ldLLJgmoVC7ezs0LZtW2zdujXXVBTZ7tSpU6H7rl27FikpKRgxYoQBSkrmQlbkWzbWHw4VbXDgQiye+G4frsUna10sIiIyQ/tCYvDQ1zsx88+zKiDVxccFW166B5N7NmRAioiINJlGvnJ/GAYv2KsCUt6uVbBpYhcGpEhzmo7PmzJlCr7//nssXboUp0+fxrPPPqtGQelW4xs5cmSuROg5p+71798fLi4uGpSaTFkHbxesfqYTXKtWxOnwmxg4fy8uxiRqXSwi0tOOHTvQr18/1KpVSy1asGnTJr333b17N2xsbNCqVatyLSNZNpkS8fKao6rjI/haAlyr2mHWkFZYPq4DvGtU1bp4RBanuO3Ghg0b0LNnT9SoUQPVqlVTneV//vmnwcpLVBYyMrNU58i/IfHqZ2JKOl5bd0yt+pqakYleTWvi50ld0LCmg9ZFJdI2p9SQIUMQFRWFd955BxEREepCYcuWLdnJz8PCwu5K3nv27Fns2rULf/31l0alJlPXpFY1rH+2E55ceABhsUl4fP5eNYJK7ici4yYdFy1btsTYsWMxYMAAvfe7ceOG6uh44IEH7po2TlQWMjOzsDbwEj7+4wxuJKVBFnod5u+J13r5wbGydlPFiSxdcdsNCWJJUOqjjz6Ck5MTFi9erIJa+/fvR+vWrQ1SZqLS2HIiHDN+PYXwuDszQnZcg41VBaRnZkHWenq1lx8m3OdtFCuSExlFovNJkyapW362b99+132NGjVSQw+JSqOeSxWsm9AJIxcdwJmIeAz5bi8WjmoPfy8uf0pkzPr06aNuxTVhwgQMGzZM5d4qzugqIn2cjYjHW5uO4+CF62q7sUc1fPhYM7TxrK510YgsXnHbjVmzZuXaluDUzz//jF9//ZVBKTKJgNSzyw8h79WyBKTEC/f7qryGRMaE6fXJYrlVs1dT+WT50/jkdDy5cD/+OcURFETmRnq5Q0JCMH36dK2LQmYmKTUdn/xxBn2/3qkCUpIw9q2+jfHrpC4MSBGZCcl5Gx8fD2dndlyS8U/ZkxFShQ3fWB1wST2OyJhoPlKKSEuy+t6ycf5qVb5/Tl/DM8sD8cmA5hjUrq7WRSOiMhAUFISpU6di586dKp9USWVkZJR4P7mgKen+lsBU6+jfM9cw/ddTuHLj9vSIno3d8M7DjVHLqZKkky3z92Oq9WRIrKPyryNLXOn1888/R0JCAgYPHlys/dhulB/WUf4kd1T2lL0CyN/3nY9CR2/mZhb8LhlHu8GgFFk8WQVpwYi2eH39caw/dBmvrjuG60mpePpeDm0lMmXSeMqUvRkzZqBhw4alDm6VhDTisbGxCA4OvitHIplmHUUlpmPBgWjsvnh7kQy3KjZ4toMrOnlWQWLUZQRFlc/rmlo9aYF1VP515OfnB0uycuVK1YbI9D03N7di7ct2o/ywju4m6W3WH4zR67HHgi7CJSO23MtkCvhdMo52g0EpIvlHsLbCzIEt4FzFFt/vDMVHv59BTGIqpvb2YxJAIhMl0y0CAgJw+PDh7NyF0rDKiZuMmpIFM+6//369nsvX17fEgTFpxH18fCxyhIE51VF6RiaW7QvDrH8uIDE1A9ZWFTC2S328cH8DVLYr/9MpU6knLbGOisY60t9PP/2E8ePHY+3atejRo0ex92e7UX5YR/8n5zTbz0Vj1j9BOHH1pl77tPCtB1+OlFL4XTKOOmJQiugOK6sKeLNvE7hUrahyhHz7XwiuJ6bio8eaq6AVEZkWWcr7+PHjue6bN28etm3bhnXr1sHLy0vv5ypNIyy9SrI/T3ZMt46OXLqBNzYcx6nw2yf8betVV4nM/dwNu2qrsdeTMWAdFY11VLRVq1ap1fokMNW3b98SPQfbjfJl6XUkwaidQdH48u9zqo0SlWyt1PVMUkpGvnmlpJvd3dEeHRvUUB0rdJulf5eMoY4YlCLKY8J9DeBc2Q5TNxzDmoDLamnvr4e2VtP8iEhbktdDemt0QkNDceTIEZWA1tPTE9OmTcOVK1ewbNky1YA2a9Ys1/4y/cLe3v6u+4nyE3crDTP/PIMV+8MgC/9KHsJpffwwuF1ddeJPRObVbuim7I0aNQqzZ89Ghw4dEBERoe6vVKkSHB0dNXsfRDp7zkfjq7/PZa/4am9rhVGd6uPpe71x8EKsWn1PWqicgSldizW9XxMGpMjoMChFlI/B7evCsbItnl91GH+disSoRQfw/ah2qGZvq3XRiCyaTMfr3r179vaUKVPUT7mAWLJkCcLDwxEWFqZhCclceqB/OXoV7/92GtEJKeq+AW1q442HGsO1akWti0dE5dhufPfdd0hPT8fEiRPVTUf3eCKtSMDpy7/OYW/I7dxRdjZWGNGhHp7t1gA1HG63Tb2beWD+iDZqFb6cSc9lhJQEpOTvRMaGQSmiAvRq6o6lY/zx1LIA7A+NxRPf7sPSsf7ZB30iMrxu3bqpgEFBirpgePfdd9WNqCCh0Yl45+cTalqE8K5RBR/0b4bODVy1LhoRGaDd2L59uwFKRaS/Q2HX1cgoXbtkZ22Fof518Vx3H9SsZn/X4yXw1LOJu1plT5KaSw4pTtkjY8agFFEhOjVwwU9Pd8ToxQdULpFBC/bgx3EdUNe5stZFIyKiMpSSnoEF20Mwd3swUtMzVQ/089198PR93qhow+nbRERkWMcu31DBqH/P3l7W1caqgprNMbG7D2o7VSp0XwlAdfR2UavsSVJzBqTImDEoRVSEZrUdsXZCZzy5cD8uxCTh8fl7sGycv8ET3BIRUfnYExyNtzadQEh0otq+x9cV7z/aDPVdq2hdNCIisjCnrt7EV/+cw9+nItW2BJQeb1Mbz9/vy45xMksMShHpwcu1CtY/2xkjFx7A2ch4DF6wFwtHt0f7+s5aF42IiEooKj4FH/1+GhsPX1HbMj37nYeb4OEWHqhQgb3KRERkOOci4zHrn3P4/fjt5PoyuKl/69p44X5fdpKQWWNQikhPMmd7zTOdMG7pQQRcvI4RP+xXiQTv96upddGIiKgYMjOzsOpgGD794wxuJqdD4k8jO9bDy70acUELIiIyqOBrCZi9NQi/HbuqVnqVNqlfi1p44QFf+LhV1bp4ROWOQSmiYpAV+SSn1MSVh7DtzDU8tSwQMwe2wIA2dbQuGhER6Tkt4s1Nx3E47IbablqrGj56rDla1nXSumhERGRBLkQn4uutQdh05Aoy7+Tif6i5O158oCEauTtoXTwig2FQiqiYKtlZ49sn2+L1dcew4fAVTFlzFLGJqRh/j7fWRSMiogIkpqSraRGLdl9ARmYWqla0wcsPNsSTHevBxtpK6+IREZGFuBSbhG+2BWH9oSuqPRI9m9TE5B4N0aQWc9aS5WFQiqgEbK2t8PmglqhexQ4Ld4Xig82nVWDq1V6NmIeEiMjI/HUyAu/+chJX45Kze6Lfebgp3B3vXkqbiIioPFy9cQtz/g3GmoOXkH4nGNW9UQ1M6dkIzes4al08Is0wKEVUQlZWFfBW38ZwrmKHmX+exbzt53E9KRUf9G/OZVeJiIzA5etJePeXU/jn9O0VjOpUr6RW1evu56Z10YiIyEJE3kzGvH+DserAJaRmZGav8jq5Z0O08ayudfGINMegFFEpyKioid19VGDqzY3HVWNzPTENs55oBXtba62LR0RkkdIyMrF4dyi++jsIt9IyYGNVAU/f662W05Yp2ERERIZY4XX+9vNYsf8iUtJvB6M6ebuoYJS/F1fwJtJhUIqoDAz190T1yrZ4YdURbDkZgTGLD+K7kW3hwFWciIgMKvBiLN7ceAJnIuLVtn99Z3zwWDM0rMmksUREVP4kpce3/53H0r0XkJx2OxjVrl51THmwITo3cNW6eERGh0EpojLSu5kHloyxxVPLArA3JAZDv9+HJWP84Vq1otZFIyIyezeSUvHplrNYdSBMbUtHwbSHGmNQ2zrM9UdERAZph77fGYIluy8gMTVD3deqrpNaVKOrjyvbIqICMChFVIY6+7jip6c7YfTiAzhx5SYGLdiLZWP9Ude5stZFIyIyS1lZWdh4+Ao+3HwaMYmp6r7B7epgap/bOf+IiIjKU9ytNCzaFapu8Snp6r7mtR0xpWdDdGtUg8EooiIwKEVUxmT1jLUTOuHJhQcQGp2IgQv2YNnYDmjkzqkjRERl6XxUAt7aeEKNThW+blXxQf9m6ODtonXRiIjIzCWkpGPxrlA1Oupm8u1glJ+7gwpG9WxSk8EoIj0xKEVUDrxrVMX6Zztj5KL9OBeZgMHf7sWi0e3Qtp4zMjKzsC8kBsdC4hFjHYOODWpwtT4iomJITstQKxkt+C9ErWRkb2uFFx7wxfiu3rCzsdK6eEREZMaSUtOxdM9FfLdDVt5Oy+4UkQTmvZu6qxW6iUh/DEoRlRN3R3useaYTxi45iENhNzD8h/0Y19ULGw5dQXhc8u0H7bgGD0d7TO/XROWkIiKiwu04F4W3fz6BizFJalumRrz/aDNOkyYionLvEFm+76JaUU83XdzbtQpe7OGLh1vUYiczUQkxKEVUjpwq22H5+A54bsUhbD8bhbn/nr/rMRFxyXh2+SHMH9GGgSkiogJcu5mM9zefxq9Hr6rtmtUq4t1+TdG7mTunSBARUbkGo346EIa5288jKj5F3VfPpTJefMAXj7SsBRtrjtAlKg0GpYjKWWU7GywY0Rat3vsre1nYnLIAyOXUjF9PoWcTd/ayEBHlIFOeV+6/iM+2nFUJZOUQOapzfZWzw8HeVuviERGRmUpNz8SagEuY+29w9iyH2k6VVDDqsTa1YctgFFGZYFCKyAAOh93INyCVMzAljd2B0Fh0asAEvURE4sSVOLy58TiOXo5T2y3qOOKjx5qjWW1HrYtGRERmKi0jE+sDL+ObbcG4cuOWuk/SbUy63weD2tZl7kKiMsb/KCIDuBafXKaPIyIyB7qFH/4NiVc/ZVu3otF7v57CI3N2qYCUQ0UbvPdoU2x8rgsDUkRULDt27EC/fv1Qq1YtNdV306ZNhT4+PDwcw4YNQ8OGDWFlZYWXXnrJYGUlbaVnZGJd4GU88MV/mLrhuApIuTlUxIxHmmL7q90wvEM9BqSIygFHShEZgJuDvV6Pk4uybg3d4FiZU1KIyLxtORGupi3nXPhBFoh4pIUHfjkajoibt+/v17IW3u7bGG7V9DuOEhHllJiYiJYtW2Ls2LEYMGBAkY9PSUlBjRo18NZbb+Grr74ySBlJW9Ih8tuxq5j9TxBCohPVfa5V7fBsNx8M7+AJe1trrYtIZNYYlCIyAH8vZzXsV5Ka3x4HkL9VBy5h0+GrGNi2DsZ0qQ/vGlUNWEoiIsMFpGSBh7zHQzlGfrczNDuJ7HuPNsN9DWtoUkYiMg99+vRRN33Vr18fs2fPVr8vWrSoHEtGWsvMzMIfJyIw659zCLqWoO6rXtkWE+5rgCc71VN5YYmo/PE/jcgAJHn59H5N1EWYpDHPeSGmS2s+olM9HAyNxZmIePy476K63e/nhnFdvdC5gQtXlyIis+mRlhFShQXoq1a0we8v3IMqFXmaQkSmKSMjo8T7ZWZmlnh/S1DaOsrKysJfp65h9tYgnI28HYxyrGSL8V3rY2SneqoN0r2OKeN3qWiso/KvI2vrokca8myPyEB6N/PA/BFtck9XAdR0FQlYyd+lkdx7PgYLd4Vi65lr2Hbn5ufugLFdvPBIq1ocQkyw9NwgM2fORGBgoMr7sXHjRvTv37/Ax2/YsAHz58/HkSNH1JSMpk2b4t1330WvXr0MWm76P1nQIecxMD+SU+rY5Tgu/EBEJisoKKhE+8nFX2xsLIKDg1VOKyq7OpLz7AOXk7DscCzOx6aq+yrbWmFAU0c81sQRVewyER52e7SuOeB3qWiso/KvIz8/vyIfw6AUkQFJ4KlnE3fsOx+FY0EX0cK3Hjo2qKFGUgkZDdXZx1XdQqISsGTPBawNuKxGT722/hg+3XIGwzvWw4iOnnrnqSKy5NwgEsTq2bMnPvroIzg5OWHx4sUq4e3+/fvRunVrg5SZctPliioKF34gIlPm6+tbov1kNIJc/Pn4+Og1wsASFbeOJBi1Mzgas7YGZ6/mWsXOGqM718e4rvXVKClzxO9S0VhHxlFHDEoRGZgEoDp6u8AlIxa+3i7ZAam8JJ+U5FN5uWcj/HQwDEv3XMDVuGR8vTUIC7afV8l/x3atj6a1uBIVWY7i5gaZNWtWrm0JTv3888/49ddfGZTSYNre5uPhmLnljF6PZ+CdiExZaS7eZDSC7M+L5NLVkW4Gwpd/n0PAxevqvkq21hjVuT6evtcbzlXsYO74XSoa60j7OmJQisjIyUp8z9zXQOWW2nIyAot2heJQ2A2sP3RZ3Tp6O6upfQ80rllggIuI/j8EOT4+Hs7OzloXxaISyf5+IlytaqRLJJs3t15OFe5Ma5YFIoiIiEpCpop/8ddZ7A+NVdsVbazwZMd66py6hkNFrYtHRDkwKEVkImysrfBwi1rqdjjsOhbtvoDfj4djX0isuslKVTIMeVC7utkJGokot88//xwJCQkYPHhwsfZjwtqSBaP+OhWJ2duCce5OItlq9jYY26U+6lavhFfWHVf35bfww9t9/YAsqTcNCm6kLPm7pC/WkXEkrDU2csyXqSc6oaGhKs+gdE54enpi2rRpuHLlCpYtW5b9GPm7bt+oqCi1bWdnhyZNmmjyHij3qNt9ITE4FhKPGOuYXGkwRODF6/jq73PYFRyttu2srTCsgyee69YAbtU4ApfIGPHKlb6rDrIAAK0lSURBVMgEtfasjm88q2NaHz8s23sRqw6E4WJMkkqi/uVf5zCkfV01NLmuc2Wti0pkNFauXIkZM2ao6Xtubm7F2pcJa/WnpkuEJWH5kViEXP9/IllJIiu3qhUlDJWEN7vXxIL90YhO+v/FsWtlazzTwRXedvEICorX8F0YH0v8LhUX68g4EtYam4CAAHTv3j17e8qUKernqFGjsGTJErVoRlhYWK59ck7vloU1pP2oV68eLly4YMCSU15bToTnXjBoxzV43FkwyMOxEr765xy2n41Sf7K1roDB7epi0v0+6m9EZLwYlCIyYbWcKmFqHz+88IAP1h+6gsW7QhESnYgfdoVi0e5Q9Grqrqb9ta1XXSVRJ7JUP/30E8aPH4+1a9eiR48exd6fCWv1C0ZtPROFr7cG42T4TXVf1Yq3E8nK6Ki8iWSlSkfen4X9IdE4HhyG5j6e6ODtymnIBbCk71JJsY6KZol11K1bN3V8KogEpvIq7PGkXUDq2eWH7pr6LQGqCcsPZW9LGzKobR1M7O7DzlkiE8GgFJEZqGxno+bJD/f3xH/norBwV6gatvzHiQh1a1HHUQWnHmruAVtr9h6TZVm1apVarU8CU3379i3RczBhbcHk4u3fs9cw658gHMu5qlGX+njqHm84VS44kaxUSWefGqiRdQO+PjXMto7Kirl/l8oC66horCMyxSl7MkKqqFDhgNa18WIPX9RzqWKgkhFRWWBQisiMWFlVQHc/N3U7GxGvkqJvPHJFXSi++NMRfPT7aYzsVB/D/D1R3QJWHCHzU9zcIDLlQqZozJ49Gx06dEBERIS6v1KlSnB05MqVpQ1GSRD8q3+CcPTSDXVfZTtrdYyxlFWNiIjIMEnLs6fsFULyqjIgRWR6GJQiMlON3B3w6cAWeK13I6zYH6ZyT0XeTMHMP8/im21BGNCmjppS4+PmoHVRicotN8h3332H9PR0TJw4Ud10dI+nkgWjdgZFq9wdh8NuZC+xPbJTPRWMcqnKVY2IiKjsnIm4PSW8KNfiiw5cEZHxYVCKyMzJBeILD/jimfu88dvRcDW171T4TazcH6Zu9zWsgbFdvXCvryvzTpHZ5QbZvn27AUplGaTe95yPUasaBVy8ru7jEttERFQe0jIy8c+pSPx08JIalasPNweurkdkihiUIrIQFW2s8XjbOhjQpjb2h8aqqX1/n45UDb3cfN2qYkwXL/V3e1vmmSCi/9srwah/zqkpFMLOxgojOtTDhG7evAggIqIyExKVgNUBl7A+8DKiE26v4CrsrK2QmpGZ7z7SperuaA9/L2cDlpSIygqDUkQWRkZDdfR2UbeLMYlYvPsC1gZcQtC1BLyx8Thm/nkGwzvUw5Od6qFmNV5sElmy/SG3g1H7Qu4Eo6ytMKyDJ57t1oDHByIiKhPJaRn440Q4fjpwSXWc6sgIXFlJb0j7ujgdflOtvidyjpfWjfGf3q8JV28lMlEMShFZMEkG+e4jTTHlwYZYc/ASluy5gMvXb2HOv8FY8N95PNzCA+O6eqN5HSaEJrIkARdiVTBqd3BMdjBKLgqe694AHo6VtC4eERGZAQk0rT54CRsOXcbN5HR1n8SVujVywxPt6+J+PzfY3Fk1Ws5Z549oo1bhy5n0XEZISUCqdzMPzd4HEZl4UGru3LmYOXOmWhGpZcuW+Oabb+Dv71/g42/cuIE333wTGzZsQGxsLOrVq4dZs2bhoYceMmi5icxJNXtbjL/HG6M718c/pyNV3qmDF65j05Gr6uZf3xlju9ZHzybu7IUiMmOBF69j1j/nVCJzYWtdAYPb1cXE7j6o5cRgFBERlU5CSjp+PXpV5YrSrdwqajtVUp0fg9rVKbDzQwJPci6673wUjgVdRAvfeujYoAbPTYlMnKZBqdWrV6uVkxYsWKCW6pbgUq9evXD27Fm4ubnd9fjU1FT07NlT/W3dunWoXbs2Ll68CCcnJ03KT2RupDdKGny5Hbt8Q+Wd+u1YOA5ciFW3OtUrqcCVnDQ42NtqXVwiKiNHLt1QCcx1yWRtrCqoCwMJRtWpXlnr4hERkYkvlCHtjIyK+uXoVSSlZmR3fPRsUhNPtPdEVx9XWOkRXJIAlKSgcMmIha+3CwNSRGZA06DUl19+iaeeegpjxoxR2xKc2rx5MxYtWoSpU6fe9Xi5X0ZH7dmzB7a2ty+I69evb/ByE1mCFnWcMOuJ1pj2UGMs23sBK/aHqal9H2w+jVn/BKkL1jGdveDpwgtWIlN1/HKcmqa37cw1tS0n94+3qY3n7/dFXWf+bxMRUcndSErFxsNXVDDqTER89v3eNaqo6XkD2tSBa1Wu3Epk6TQLSsmop8DAQEybNi37PisrK/To0QN79+7Nd59ffvkFnTp1wsSJE/Hzzz+jRo0aGDZsGF5//XVYW3O1MKLyIMmMX+3lh0ndfdWJxaLdoQi+lqASpEsOqp6Na2JcVy+14okkUSci43fiSpyapvfP6dvBKOlolouD5+/3UXk7iIiISjoqShbHWH0wDL+fiEBq+u0V8yraWKFvcw884e+J9vWr85yRiLQPSkVHRyMjIwM1a9bMdb9snzlzJt99QkJCsG3bNgwfPhy///47goOD8dxzzyEtLQ3Tp0/X+7XldUtC9svMzCzx/paAdWS+9WRnDQxpVxuD29bCzuBoLN59ETuCovHXqUh1a+pRDWO61FMnHLJcvCXWkaGVto4YzLc8p67eVMEo+Z/VBaP6t6qN5x/whZcrg1FERFQyUfEpWBd4GWsCLiE0OjH7/sYe1TDUvy4ebVUbjpWY+oGIjDDReXHIxZfkk/ruu+/UxVTbtm1x5coVlSi9OEGpoKCgEr++TB+UYJiM6qK7sY4so55kfZM3ujhieNNK+PlUHLaej8fJ8Jt4Zd1xfLj5FPr5OeKhRtXgZG9tsXVkCKWtIz8/v3IpFxmfMxE3MfufIPxxIkJtSwf1Iy1r4YUHfNGgRlWti0dERCYoIzMLO4KisPrAJbVQTnpmlrq/ip01HmlVWwWjmtd25KgoIjLOoJSrq6sKLEVG3u6t1ZFtd3f3fPfx8PBQuaRy9u43btxYrdwn0wHt7Oz0em1fX98SlVlGI8jFn4+PD0cYFIB1ZFn1JP9JPdoD15NS1SoqP+4NQ2R8CpYdjsXq4zfwaKtaGN25HhrVdLDYOipPrCMqyrnIeBWM2nw8XG3LdYGMZnzxAV/4luD/koiI6MqNW1gbcAlrAy6r33XaeDqppOV9W3igSkWTGvtARBrS7GghASQZ6bR161b0798/u9dftidNmpTvPl26dMHKlSvV43SjAs6dO6eCVfoGpERpLt7kdWV/XgAWjHVkefXk6lAJk+5viKfv9cEfJ8KxcFcojl2Ow5oAGcZ9Gff4umJsFy/c17CGXiurmGMdlRfWEeUn+Fo8Zm8Nxm/HriLrdsf17WBUD180ZDCKiIiKKS0jE1tPR6pOSFmpVde2OFW2xWOta6tgVCN3ti9EVHyahrCnTJmCUaNGoV27dvD398esWbOQmJiYvRrfyJEjUbt2bXz88cdq+9lnn8WcOXPw4osv4vnnn1fT8D766CO88MILWr4NIrpDcklJzgCZFhR48boKTv15MgI7g6LVTVZbGdPFS63uVdmOPWhEZS0kKgFfbw3Cz0f/H4zq3dRdBaMkrwcREVFxSH6onw6GYX3gZUQnpGbf38nbBU/410Wvpu6wt2XHGBGVnKZXhUOGDEFUVBTeeecdNQWvVatW2LJlS3by87CwsFx5UurWrYs///wTkydPRosWLVTASgJUsvoeERkPyR3Qrr6zul2KTcLSPRfUcsAhUYl4e9MJfP7nWQz198SozvXg4Vgp3xwF+0JicCwkHjHWMejYoIZaqp6I8nchOhFfbwvCpsNXcCelBx5sUlMFo5rWctS6eEREZEKS0zJUp+KqA2FqJT0d16oVMahdHQxpVxf1uTgGEZURzYcqyFS9gqbrbd++/a77OnXqhH379hmgZERUFuo6V8ZbDzfBSz0bqvwDi3dfQFhsEhb8dx7f7wzBQ809MK6rF1rVdVKP33IiHDN+PYXwuOTbT7DjGjwc7TG9XxP0biYp1olIJywmSQWjNh6+ooK5okdjN7zUoyGa1WYwiohox44dalGkwMBAhIeHY+PGjdmpQwoi1yAyo+PkyZOqU/ytt97C6NGjYQmLYvx04JJqU+Jupan7pE9Q0i884e+J+/3cYGvNxWeIyMyCUkRkGapWtFFT90Z2qq9yEsjUvv2hsfj16FV1k+SYbetVxw87Q3FnoEe2iLhkPLv8EOaPaMPAFBGgRiDO2RaMdYcuZwejujeqoYJRLe8EeImICCo1SMuWLTF27FgMGDCgyMeHhoaib9++mDBhAlasWKHy3Y4fP17lsO3VqxfMTWJKujoPk1xRRy7dyL6/tlMlDG5XV42MquV096h2IqKywqAUERmUTMN7sKm7up24EqdGTv1y9AoOhd1Qt/zIJbdM3pMRVD2buHMqH1msy9eTMPffYLXikW7pbenBfqmHL1p7Vte6eERERqdPnz7qpq8FCxbAy8sLX3zxRfZK37t27cJXX31lNkGprKwsHL0ch9UHw/DLkatITM1Q99tYVUDPJjXVqKiuPq483yIig2BQiog0I9OLvhjcEq/3aYRPfj+NDYevFvhYufyWKX37Q2LQ2cfVoOUk0trVG7dUMGpNwCWkZdwORsmqljIySkYYEhFR2di7dy969OiR6z4JRr300kswdXFJadh4+LIaFXUmIj77fm/XKhjSvi4eb1tH5Y0iIjIkBqWISHNuDva4r5FboUEpnScXHkA9l8oqV5Wns/ysdOfn7W0He1uDlJnIEGTq6rztwSrHR2pGprqvcwMXTO7ZEO3rO2tdPCIisyOLL+kWXdKR7Zs3b+LWrVuoVEm/qWwZGbdHHxWX7JeZmVni/fMbFXXgwnWsCbiMP05EICX9dltS0cYKfZq5Y0i7Omhfv7papKY05Taksq4jc8V6KhrrqPzryNq66NU5GZQiIqMJTOkjIysLIdGJ6paf6pVtVYBKF6RSAavqt396ONkzQSeZhGs3JRh1HisPhCH1zgVER29nTO7REB28XbQuHhERFSEoKKhE+8nFX2xsLIKDg3OtQl5c12+l45/z8dhyLh5Xbt5OWi68qtuhT8Nq6O5dFQ4VrYH0GAQHx8CUlFUdmTvWU9FYR+VfR35+fkU+hkEpIjIK/l7OapU9GRmSN9G5kP47d0d7rHqqI67G3VKJnmUVv7DYW+rn5dgkxCSm4npSGq4nxeHY5bi7nkNyI9Ryss8OVOUKXDlXVgEtXU8hkRauxSdjwfYQrNh/Mbs327++M17q6YvODThtlYiovLm7uyMyMjLXfbJdrVo1vUdJCV9f3xK9voxGkIs/Hx8fvUYY5No3Mwu7gqPVqKh/Tl/Lzj1Yxc4aD7fwwBPt66J57Womf65TmjqyJKynorGOjKOOGJQiIqMgAaPp/ZqoVfbkVClnYEp36iR/r+9aRd3Q4O7nSEhJzw5Wyc//B66ScOn6LTXi5FKsBLRuAYjJd4XA24Gq/08J1AWuZBUae1s2VlQ+ohNS8O1/5/HjvotITrsdjJJcUVN6NlTT9Uz9AoKIyFR06tQJv//+e677/v77b3V/cZTm4k1GI8j++j6H5B2UBTAk7+CVG3KOc1uruk4Y6l8XD7eohSoVzeuyr7h1ZKlYT0VjHWlfR+Z1dCIik9a7mQfmj2ijVtmTpOY6MkJKAlLy98JIUKmxRzV1yyszMwtRCSm3g1QxOQJX12//HnkzRQW1ToffVLe8JCZQ0+HOKCvdCCuXStlTA2s4VGTggIotJiEF3+0IwbK9F3Er7fZc/daeTmqaniQy53eKiKh0EhISVC+/TmhoKI4cOQJnZ2d4enpi2rRpuHLlCpYtW6b+PmHCBMyZMwevvfYaxo4di23btmHNmjXYvHkzjElaRia2nr6mVtD771wU7gyKgmMlWzzWujae8K8LP/e7z4eIiIwNg1JEZFQk8NSziTv2nY/CsaCLaOFbDx0b1Cj1ssRWVhVQs5q9uuWXIDo5LQOXr0ug6vZ0wOwRVnduslxyxM1kdTtwIfau/e1trXJNCcw9NbASKtuV7eFWhujvC4nBsZB4xFjHlEkdEQz2GV1PTMV3O0OwdM8FJN1ZirtlHUe81LMhujWswWAUEVEZCQgIQPfu3bO3p0yZon6OGjUKS5YsQXh4OMLCwrL/7uXlpQJQkydPxuzZs1GnTh388MMPagW+8qZP234hOhGrAy5hXeBlRMWnZN8veQeH+nuiV1N3juwmIpPCoBQRGR05Aevo7QKXjFj4ersYJNgiJ3A+bg7qlt/KNbGJqdmBqsvXb2WPtpJbeNwtNeUq6FqCuuXHtard/wNWd0ZXqW2XynCvZl+s97jlRHju0WQ7rql8XPqMJjN1O3bswMyZMxEYGKguJDZu3Ij+/fsXus/27dvVRcjJkydRt25dvPXWWxg9enS5lrOgz+iVBxsiNDoJi3eHqkCnaF7bEZN7+qJ7IzcGo4iIyli3bt1UO14QCUzlt8/hw4dhSIW17d0aueHPkxFqJda9If9PP+BatSIGtq2DIe3rwktSGxARmSAGpYiIiiCBApeqFdWttWf1fIfQSz6HnCOsLucYcRV3Kw3RCanqdjjsxl3721pXUDmr8iZe1/2Uofg5T1ol71be02tJEC/3y/RHcw5MJSYmomXLlmpKxYABA4p8vEzT6Nu3r5qOsWLFCmzduhXjx4+Hh4dHufV6F/QZyYXGy2uPZW83rVUNL/VoiB6NGYwiIrJkhbUbE5YfQmU76+xRtdJc3NewBp5o74kHGrtxVWEiMnkMShERlZKcENZzqaJu+ZGgVN7E67oRVzJlMC0jCxdiktQtPxKUkgBVner22HEuOt/VCeU+CWtIL6tMfzTXqXx9+vRRN30tWLBATcX44osv1Hbjxo2xa9cufPXVV+USlJKpF/IZFNwnD9hYVcA3Q1ujdzN3BqOIiCycPu2GBKQ8qlXEEH9PDGpXV3VkERGZCwaliIjKmQSVHGs7olltx3xPRiVPlUwHzJl4XZfPSkZXSVDr+JU4dStM1p1e1QOhsejUwKUc35Hp2Lt3L3r06JHrPglGvfTSS8VeDlcfkgskZ5L+/MgS3dXsrZGZeXuVPUsndSt1oW8dWyrWU9FYR+VfR1ydquxJm11UuyE+H9wKXXxcDVImIiJDYlCKiEhDMqJJejzlll8gKTEl/XYOq9gk/H48HBsPXynyOa/FF31yaykiIiJQs2bNXPfJ9s2bN3Hr1i1UqqRfb3NQUJBej5PktHo9LuiiyplGsjJmJmJjY9XqWLLkMOWP9VQ01lH515Gfn1+5lMuS6dtmRyf8P6k5EZE5YVCKiMiIValog0buDupWtaKNXkEpNwd7g5TNkvj6+ur1OFktSZLTFkVWlZQk/nR75IZcIPv4+HAURiFYT0VjHRWNdWR89G2z2bYTkbliUIqIyET4ezmrlXgkqXl+uSckO5G7o716HN3m7u6OyMjIXPfJdrVq1fQeJSX0vXiT5bv1+YzyW+bbksmIDaljXiQXjvVUNNZR0VhHxoVtOxFZOo5tJiIyERLEkKWhRd5whm5b/s5gx/916tRJrbiX099//63uLw/8jIiIqDjYbhCRpWNQiojIhPRu5oH5I9qoXtOcZFvul7+bs4SEBBw5ckTdRGhoqPo9LCxMbU+bNg0jR47MfvyECRMQEhKC1157DWfOnMG8efOwZs0aTJ48udzKaOmfERERFQ/bDSKyZJy+R0RkYuTktGcTd+w7H6USZkt+IkuZDhYQEIDu3btnb0+ZMkX9HDVqFJYsWYLw8PDsAJXw8vLC5s2bVRBq9uzZqFOnDn744Qe1Al95suTPiIiIio/tBhFZKgaliIhMkJykdvR2USu4ScJsSzlp7datG7Ky8su6cZsEpvLb5/DhwzA0S/2MiIioZNhuEJEl4vQ9IiIiIiIiIiIyOAaliIiIiIiIiIjI4BiUIiIiIiIiIiIig6uQVVhyDiIiIiIiIiIionLAkVJERERERERERGRwDEoREREREREREZHBMShFREREREREREQGx6AUEREREREREREZHINSRERERERERERkcAxKERERERERERGRwTEoRUREREREREREBsegFBERERERERERGRyDUkREREREREREZHAMShERERERERERkcExKKWHHTt2oF+/fqhVqxYqVKiATZs2aV0ko/Pxxx+jffv2cHBwgJubG/r374+zZ89qXSyjMn/+fLRo0QLVqlVTt06dOuGPP/7QulhG7ZNPPlH/cy+99JLWRTEq7777rqqXnDc/Pz+ti0U5sN0oGtuNorHdKD62G/lju2H82G4Uje1G0dhuFB/bDe3bDQal9JCYmIiWLVti7ty5WhfFaP3333+YOHEi9u3bh7///htpaWl48MEHVd3RbXXq1FEHvcDAQAQEBOD+++/Ho48+ipMnT2pdNKN08OBBfPvtt6phpbs1bdoU4eHh2bddu3ZpXSTKge1G0dhuFI3tRvGw3Sgc2w3jxnajaGw3isZ2o3jYbhhHu2FTLs9qZvr06aNuVLAtW7bk2l6yZInqwZAD4r333qtZuYyJ9H7l9OGHH6reDGlY5R+e/i8hIQHDhw/H999/jw8++EDr4hglGxsbuLu7a10MKgDbjaKx3Sga2w39sd0oGtsN48Z2o2hsN4rGdkN/bDeMp93gSCkqF3Fxceqns7Oz1kUxShkZGfjpp59Uz44Mq6XcpBesb9++6NGjh9ZFMVpBQUFqiL+3t7dqUMPCwrQuElGpsN0oHNuNwrHdKBrbDTI3bDcKx3ajcGw3jKfd4EgpKnOZmZlqTm6XLl3QrFkzrYtjVI4fP64aheTkZFStWhUbN25EkyZNtC6WUZHG89ChQ2o4LeWvQ4cOqnewUaNGaijtjBkzcM899+DEiRMqzwKRqWG7UTC2G0Vju1E0thtkbthuFIztRtHYbhhXu8GgFJVL1Fm+rMxVcDf5pz5y5Ijq2Vm3bh1GjRql5sezobjt0qVLePHFF1WeAHt7e62LY7RyDu+XOfDSaNSrVw9r1qzBuHHjNC0bUUmw3SgY243Csd3QD9sNMjdsNwrGdqNwbDeMr91gUIrK1KRJk/Dbb7+pFUQk0R7lZmdnBx8fH/V727ZtVXR+9uzZKsEeQeUEuHbtGtq0aZNr6LF8n+bMmYOUlBRYW1trWkZj5OTkhIYNGyI4OFjrohAVG9uNwrHdKBzbjZJhu0GmjO1G4dhuFI7thvG1GwxKUZnIysrC888/r4aHbt++HV5eXloXyWSGHsuBj2574IEH1JDjnMaMGaOWH3399dfZQBSSqPH8+fN48skntS4Kkd7YbpQM243c2G6UDNsNMkVsN0qG7UZubDeMr91gUErPDyBnRDA0NFQNiZSkep6enpqWzZiG0K5cuRI///yzmmMaERGh7nd0dESlSpW0Lp5RmDZtmhoGKd+Z+Ph4VV/SoP75559aF81oyHcnb16AKlWqwMXFhfkCcnjllVfU6ioyhPbq1auYPn26akCHDh2qddHoDrYbRWO7UTS2G0Vju6EfthvGj+1G0dhuFI3tRtHYbhhfu8GglB4CAgLQvXv37O0pU6aonzI/V5J/EdRSo6Jbt2657l+8eDFGjx6tUamMiwwTHTlypEoUJ42nzM2VBqJnz55aF41MzOXLl1WDEBMTgxo1aqBr165qqV/5nYwD242isd0oGtsNKitsN4wf242isd0oGtsNMsV2o0KWjIMkIiIiIiIiIiIyICtDvhgREREREREREZFgUIqIiIiIiIiIiAyOQSkiIiIiIiIiIjI4BqWIiIiIiIiIiMjgGJQiIiIiIiIiIiKDY1CKiIiIiIiIiIgMjkEpIiIiIiIiIiIyOAaliIiIiIiIiIjI4BiUIipnFy5cQIUKFXDkyBGti0JERCaA7QYRERUH2w0yZQxKEZXS6NGjVSOgu7m4uKB37944duyY+nvdunURHh6OZs2aaV1UIiIyAmw3iIioONhukDljUIqoDEijIA2B3LZu3QobGxs8/PDD6m/W1tZwd3dX9xEREQm2G0REVBxsN8hcMShFVAYqVqyoGgK5tWrVClOnTsWlS5cQFRV113Da7du3q21pTNq1a4fKlSujc+fOOHv2bPbzHT16FN27d4eDgwOqVauGtm3bIiAgQMN3SEREZYntBhERFQfbDTJXDEoRlbGEhAQsX74cPj4+amhtQd5880188cUX6uAvvRpjx47N/tvw4cNRp04dHDx4EIGBgarRsbW1NdA7ICIiQ2K7QURExcF2g8wJx/cRlYHffvsNVatWVb8nJibCw8ND3WdlVXDc98MPP8R9992nfpdGoG/fvkhOToa9vT3CwsLw6quvws/PT/3d19fXQO+EiIgMge0GEREVB9sNMlccKUVUBmToqwyXlduBAwfQq1cv9OnTBxcvXixwnxYtWmT/Lo2KuHbtmvo5ZcoUjB8/Hj169MAnn3yC8+fPG+BdEBGRobDdICKi4mC7QeaKQSmiMlClShU1fFZu7du3xw8//KB6ML7//vsC98k5PFbmfIvMzEz1891338XJkydVb8a2bdvQpEkTbNy40QDvhIiIDIHtBhERFQfbDTJXDEoRlQM56MtQ2lu3bpX4ORo2bIjJkyfjr7/+woABA7B48eIyLSMRERkPthtERFQcbDfIXDCnFFEZSElJQUREhPr9+vXrmDNnjkpA2K9fv2I/lzQsMr974MCB8PLywuXLl1UCwscff7wcSk5ERFpgu0FERMXBdoPMFYNSRGVgy5Yt2fO0ZVlVSRi4du1adOvWTS3RWhzW1taIiYnByJEjERkZCVdXV9VzMWPGjHIqPRERGRrbDSIiKg62G2SuKmRlZWVpXQgiIiIiIiIiIrIszClFREREREREREQGx6AUEREREREREREZHINSRERERERERERkcAxKERERERERERGRwTEoRUREREREREREBsegFBERERERERERGRyDUkREREREREREZHAMShERERERERERkcExKEVERERERERERAbHoBQRERERERERERkcg1JERERERERERGRwDEoREREREREREZHBMShFREREREREREQGx6AUEREREREREREZHINSRERERERERERkcAxKERERERERERGRwTEoRUREREREREREBsegFBERERERERERGRyDUkREREREREREZHAMShERERERERERkcExKEVERERERERERAbHoBQRERERERERERkcg1JERERERERERGRwDEoREREREREREZHBMShFREREREREREQGx6AUEREREREREREZHINSRERERERERERkcAxKERERERERERGRwTEoRUREREREREREBsegFBERERERERERGRyDUkREREREREREZHAMShERERERERERkcExKEV62759OypUqIB169bBFERGRmLgwIFwcXFR5Z41a5bWRSIjVL9+fTz88MN6f//lJxHlT/5H3n33XZMphzxGHqsP3WOjo6NLVKbRo0er4w0RkSVjO2Ee7cSSJUvUe71w4UL2fd26dVM3ouJiUMpI/8Ht7e1x5cqVu/4u/+jNmjXTpGymZvLkyfjzzz8xbdo0/Pjjj+jdu7fWRSIiMsk2KefNzc0N3bt3xx9//AFz9NFHH2HTpk1aF4OIyCSwnSCi0rIp9TNQuUhJScEnn3yCb775RuuimKxt27bh0UcfxSuvvKJ1UcgM3Hvvvbh16xbs7Oy0LgqRwb333nvw8vJCVlaWGoUqFyEPPfQQfv3111wjDeV/xMZG+1OL0pRDLjZklG3//v3LtEzff/89MjMzy/Q5iYiMBduJ0jP1duKvv/7SughkorQ/IlC+WrVqpQ5MMsqnVq1asCSJiYmoUqVKqZ/n2rVrcHJyKpMyUfl+VuVJGvfU1FQ1+rA0rKysSv0cRKaqT58+aNeuXfb2uHHjULNmTaxatSrXxYax/I8YSzlysrW11boIRETlhu2EabQT5Xnuzo5bKilO3zNSb7zxBjIyMtRoqcLIPF4ZJiu9EUXNldbNcz537hxGjBgBR0dH1KhRA2+//bbq1bh06ZIaWVStWjW4u7vjiy++yPc1pVxSPnmMHNQeeeQRtW9e+/fvV1Pm5HUqV66M++67D7t37871GF2ZTp06hWHDhqF69ero2rVroe85JCQEgwYNgrOzs3rejh07YvPmzXcNI5b3NHfu3OyhxEXV4eeff64e7+3trZ73wQcfVO9Lnuf9999HnTp1UKlSJVVHsbGxdz3PvHnz0LRpU1SsWFEFEidOnIgbN27kO/1S3q8Ma5bXqV27Nj777LN8g2q6Bl0azpYtW2Lp0qXlUnYZXn3PPfeoz9PBwQF9+/bFyZMn75rnXrVqVZw/f171fMnjhg8frv4WFBSExx9/XH0npKzyek888QTi4uIK/Sx19REYGIjOnTurMkov24IFC/IdPTh9+nT4+PioOq5bty5ee+01dX9OUh+TJk3CihUrsj+PLVu2QJ/eHQkGS/mbNGmCDRs2FJlTqjifJ5E5kYC//L/m7WUuqN0JDg5WxxDZT9qEMWPGICkpqdDX+Prrr2FtbZ3rOCrtkjzflClTcrVJcjx6/fXXCyyH2LVrF9q3b6/+xxs0aIBvv/32rteU/eSEXY61urZDyp2TlKe47yW/XCE5j9/fffedKpMcr6SMBw8eLPL5dG2dvK8XXnhBtedSpmeeeUYF4qWcI0eOVO2q3OR4KW1CTvLacuyV3IvyebZt2zbfvJF///23apvl+aUdaNSokToPyElGdssxV46D8npycbpy5coi30dR++m+Q2fOnMHgwYPVOYqU98UXX0RycnKu51q8eDHuv/9+NXVI6lKO5fPnz8/3daXdk/MS+e7Ic0q95y2vPucxRJQ/thPG0078999/eO6559SxUc7RxcWLF9V9cjyXz0mOq3J9lTNHlI5cE8ixVR4n+3/wwQf5jujKm1Mqv7xTBZ1Tl/RaQp/9cl4byPuVx0l7t2PHjlzPVZw6kc9YUsXI5yWfibyutLk584npe+1CHClltOTCXL7YMlpq6tSpZTpaasiQIWjcuLEKeEkwRw4sEuCRA68ccD799FP1TyvT3uSgJ9OWcvrwww/VP7cc2CVwIgnEe/TogSNHjqh/YN3UOekxkX94+WeUUSa6k8WdO3fC398/13PKP7yvr68aDpv3pDknGQ4sJ9ByYJeTcDlYSKMggTE5kX7sscdUeSWH1JNPPomePXuqetSHvGc5kX/++edV4EYCC3ICLGWWg6a8X2kw5QRa6mbRokXZ+0qjNmPGDFUPzz77LM6ePatOhKXBkBPYnD0f169fVye5AwYMUM8v5Zbnbt68uaoz3ZBiOajL68lBVL4Pa9euVY2VHATlZLysyi51NWrUKPTq1Ut99lK3Una5ADl8+HCuxjE9PV09Tv4mDaScpMvryn1ygJXXl0ZB8qH99ttvqqzSGBdG6kOCXFLeoUOHYs2aNaoOpbdl7Nix6jHS8MlnLCcLTz/9tPr+Hj9+HF999ZUKsuad1y/fP3keqTtXV9cik0ZKgyb/FxMmTFB1Id9V+U5KMEu+Q0WVv6jPk8jUycmdnGjJ8VmO+3IsSUhIUB0c+pD/DTmOffzxxzh06BB++OEHdXIsx5yCSKBc/vfl/17Xyy7th7Qn8lNHjlNSlrxtVU5yvJBgvQRu5HgtxzJpmyTon5McD8ePH6/aKDnWCLkIKO17KYwEQuLj41UwSdpWOX7L8UQ6YPTpNdcdd6UN2rdvn7pwkQuhPXv2wNPTU7Wrv//+O2bOnKmC6DnbxNmzZ6tjq3QwyLH8p59+Usc+OX5L54TuYkTqv0WLFmp6jpxYS3uSMzgj5yrSJst0Fl2w6NixYyqoIx1OBSnOflLvciyXepf3KRejcvxdtmxZ9mOk7ZIAl7wnuRCWaUNygSHfI+koynmhJO2LPFZGpEt9yfdIjvm61y3ueQyRpWM7Ubr3Up7thBwH5X298847KqAm5BpF2gkJ4EhARQIvcgyV6w/pbJVzfBEREaE6XqU+5JpUOrClndFd85WFkl5LFGc/CcytXr1atTnSjslgAjl/P3DgQHa+Zn3rRL5L8t07ffq0akvatGmjvvu//PILLl++rK49invtYvGyyKgsXrxYIjJZBw8ezDp//nyWjY1N1gsvvJD99/vuuy+radOm2duhoaHq8bJfXnL/9OnTs7fld7nv6aefzr4vPT09q06dOlkVKlTI+uSTT7Lvv379elalSpWyRo0alX3fv//+q/avXbt21s2bN7PvX7Nmjbp/9uzZajszMzPL19c3q1evXup3naSkpCwvL6+snj173lWmoUOH6lU/L730knr8zp07s++Lj49Xz1u/fv2sjIyMXO9/4sSJRT6nrg5r1KiRdePGjez7p02bpu5v2bJlVlpaWvb9UlY7O7us5ORktX3t2jW1/eCDD+Z6/Tlz5qj9Fy1alOvzk/uWLVuWfV9KSkqWu7t71uOPP55936xZs9Tjli9fnn1fampqVqdOnbKqVq2aXf+lLbvUnZOTU9ZTTz2Vq04iIiKyHB0dc90v3wV5zqlTp+Z67OHDh9X9a9euzSouXX188cUXueqjVatWWW5ubuo9ix9//DHLysoq1+cuFixYoPbfvXt39n2yLY89efKkXmWoV6+e2mf9+vXZ98XFxWV5eHhktW7d+q7vv/ws7udJZOptUt5bxYoVs5YsWaJ3uzN27Nhcj3vssceyXFxcCn1tOZ5Wq1Yt67XXXlPb0p7IPoMGDcqytrZWxy/x5Zdfqv95abcKKkf//v2z7O3tsy5evJh936lTp9Tz5D0VqlKlSq62ryzei5DnlOONju74LfvGxsZm3//zzz+r+3/99Ve9Ppu8ba20E9KmT5gw4a62Xo5ZOUm7nJMcc5s1a5Z1//33Z9/31VdfqdeJiooqsCyPPvpornMTfemzn67eH3nkkVz3P/fcc+r+o0ePFvh+hNSPt7d39ra0lQ4ODlkdOnTIunXrVq7H6uqxOOcxRJaO7YTxtxNdu3ZV7UBO+R0v9+7de9d5re7aa//+/dn3ybWPXCfI/VJGHWljcrYzutfP+Zj8zqlLei2h736672RAQED2ffI5y+ctn01x6+Sdd95R923YsOGux+vajOJcu1BWFqfvGTGZiiWjfSQaHR4eXmbPK9F9HRnyKkPl5f9VporpSK+hDF2UCHxe0ssqQ2B1pIfTw8ND9cQKGTElI0+ktzEmJkZFjuUmkfkHHnhADZXMO+RTRqjoQ15DeiZyTvGTqQQSgZZotkSxS0p6h3NG1Dt06KB+Sg9PzqHHcr9E5nWrI/7zzz9q+6WXXlI9MzpPPfWUmhKQc2qhrrw5e41kRJC8p5x1Le9Tov0yckhHekIkui/ReYn2l0XZZUqG9CTI6+g+J7nJ90Ie+++//95VTzKKKSfd68pKh/oMTc5Lyic9PznrQ7all02m9QkZJSY9DH5+frnKKT3WIm85ZYqFTNvQl4xElFF2OvK5yfdcetakh6gw+nyeRKZOpgfL8UJuy5cvV72m0pbkneZakLzHeOlhlPbh5s2bBe4jx1MZGasbXi89krKP9NRKm7V37151v/SGSy9nQTkEZdqGHJ8kIa2MHNKRY4r0sBZXSd5LYWSUpkxby/l8Qt9jiLTdOaeoy7E7b5uua+vzPmfOnm4ZdSQjHeT1pWdfR1evP//8c4EJeOUx0jusz3SSku6Xc6STkF5xoTv3yPt+dKM2pD2Q962byiHfYRlxIN+jvDlldPVYkvMYIkvHdqJ076U82wm5JpF2IKecx8u0tDRVPplmJnWUsw2QY6ykSsk5OlRGXelSeJSFkl5LFGe/Tp06qZGvOvI5S1oT2Vc+/+LUyfr161ValZzXDnnbkeJeu1g6BqWM3FtvvaWGSxaVW6o4ch5sdf/QcmImQw3z3i8nqXnJNLu8/3zyD6ubbysnckKmQclBK+dNhq/KEMu884NliKs+ZK6vBMvykn963d/Lsl6EzP/N735d3eheM2+5JDghgcW8ZZLhoHlzXElDk7OuZR+p55xBrsLeZ0nLrvus5ACZ97OSHEsSGMobQNLNRc/52cm8ffls5TskjbecmBQ1BzxnQChvwsWGDRuqnzm/UzKFJG8ZdY/LW059v0868v3N+5nkLUNB9Pk8yXzIiW+/fv3U91Y+9/Iefq3P68m03rzLccuQ9LIkJ6MyPVluciIqwXYJ/MoUWQl0FyXvMUp3cl3U/4mceEtwWqY0y0WFdIDIMHk5GdRNzZCh8boT9PxERUWp/fO2XSK/9qS83kt5PV9xjv95n1OmOMjFhpwDyDR+Oa7KVIWcx2+5GOrSpYu6uJRpLDKtQaZH5wzKyJRlCdDL90TqWQJI+uReKs5+eT8/mS4jbWTOY7TsK99RaVPkIkLejy73le49SV5EoZuukZ+SnMcQGUu7ocvRlPMmF8blje1E6d9LeT1ffufF8n5lOp+0FTKdTc7h5RgnndU5j2+6a5KyqJfCyleSa4ni7Jffe5BzfQlmyedfnDqRdqSwNqQk1y6WjjmljJwENWQUhoyWkqh/XgUl8NZFfPOTN1Je0H2isPxOBdGdqEr+CkkcnR85Cc2pLOcll1RBdVCWdVMez1fYcxb1WrrPSubHy8isvPImp5QDdN5AmS6ppFwYS0+6BLNkRJcu70feIFZJSDklR9OXX36Z79/zXnwZ8vtUHp8nGS8ZKSEnupJDQPI5GMvrSRBK8t3k/F8tT3IckF5wyUkkJ16Sm6c8/k9kVKz0Vkpvt1xc6C4q5KdsS/JrOZks7GKjrBlbm1Cc43/O55T6k3wXkmNFcmvIhZyMyJXvUc6E33I8lYtq6dWVi0zJuyR5OaQzQ4738jrSYSK5FCXIJX+XXmR5Tjm5l1xXBSnpfvmd/8hFgoxikotvaSukXZDOIenllxwexRnZVJLzGCJjaTeEHJNlJH9B53OGwHbCeNqJ/M6LZbSpHO9lpoeMIpKOCzmuSsdDWY0ELc51akmvJcryGqQs66S41y6WjkEpExktJcNg80uOp4uU513lrTQjhoqi60HMeUCUpKeSBDVnsj+ZAiW9JWWpXr166gQ2L2lwdH83NN1rSrkkiKgjvUKhoaElqgN5Tkn2Kge0nEGgsn6fus9Kki+W9rOSA6/c5PsqSQKlZ11W0ZNE+oW5evXqXcvTSgJAoUtQLuU8evSouuAobCXFkpLvr3yPcz533jIQCUl8XFgCexlB8eabb6olsOW4LD1pcuzOuRpNWb5eziBUfoHl8iSjeIVMKS7PnncJLMiFhdxeffVVdb8EUiRJ9tatW7O3CyI9k3JCnrftEvm1J+VxjDFGEgCSEVIydSFnEDNncFNH2iE5/spNTrAlebp8zyVQpWs75Bguo6rkJu2fXHzLwiiSSLywpdf13U8+v5y9/XLcljZSd4yWpOby/yeJZnOOKsg7RULX7p04cUKNks1PeZ7HkOUxdLuhC0IZuk3ID9sJ4yUL88ho0JyrrctiE3mvKeWaQ996Ke11akmvJfTZL7/3IOf6krxcPv/i1Im0EdKGFKa8r13MDafvmQD5UstoKVkdL29+GzlhkqGFeZe0lJ7G8iIr3Ug+Bh35B5acV7oGV+brSplldbb8GiHdEMmSkFXaZJUE3RxxIQENGUkmJ6bFySNUVuSEVRpDWQkoZ4/FwoUL1VBP3QpGxX2f8llLb3TOhl1WMpHeWcmRURZkmKt8h+QCQ3qZSvJZyfx43UmHjjQMchGjz5Knsm/OJXflokS2pYHQzf2WVUwkD5acXOQlQ211K4mUlATGNm7cmOs9yfdcesiN4aSOTIdMU5Djk6xiJoFlyfcmo5jyOxkqS7LKpgSXZTi95H2TPAjlSY4X0iMpxz7dtOLyIEEJWQVWLtbCwsJy9YDL/74cd6W9kVE+hfUwy7FOpszIc+hI7hEJyOQXJMl7AmqOpF7kRDlnj7VMhcs7tUhWdM1LN3pId4zP+32T74W0x9Im5te26BRnP5mSkZO0h0J37qEbSZCzHZY2OG+QTVbXkryY0pMuFxs56fYtz/MYIkO0G7KvTBeUzlKZSpfz2GcobCeMm7znvCOt5LiadxSTXJPIqCO5/sp5DJSVv4uiC/DnvE6V55frtrK4lijOfvI/ljMv1KVLl9ToKmkTdO2HvnXy+OOPq4BTzmsHHd3+5X3tYm44UspESA+KTLGSqHTe4a+S50FyTslPSWQq//i6UR7lQfJOyFDZMWPGIDIyErNmzVK9jZJET8iBQOb2yomilFUeV7t2bfWPKT2WEgSRHs2SkCmM0ujIc8vwTCnL0qVL1Ygk6fXNb2pZeZPgifToylQDOYmQ6RDyOUlgUBpJfZfCzUkSt0tgRoajyjx5CbhJ8E/yZUh950w0XxryWUj+EEmoL/PvZXiqvB9pkGWahvQ0zJkzp9DnkGWz5YRKTqJknrQ0DvJdlQO7HLSLIidN0iMoF0OyvwTiJMmsNFi6ZW6lfJLDRBJHyndIyiUNhIwck/vlhEG++yUlrytJgSXZruRMWbRokfpu5zdigKgg8n8j3xn5Kd9r8corr6hpSXK/BH/Lgxx3ZHSJjCKRKUySQ0eOkXICVtCQ/+L6448/skdqSh4Emd4lFz1yTJbjSHmSCwtp42QYvZxsCl0ATo61cpwsihyf5XOQ55KlsXVBfmmj5CIwJwlIyLQXGREkn6PUq27xCHMiHSbyHuX7Iwm95XOVwI+05znr5L333lPnFfJ46TGXx0n7JtMidIuOyEm9BPDl2CzHULmQk7ZD9imsvSrOftLOS/sq5ZXvtowgl3LLtCjdc8nFr+TukcUyJJgkFwPyXcm5WIx8X2U6n5wzSRstzyG9+XKBIblF5JyiPM9jiMq73ZDj1ZIlS9QxUr77cvyTY5+M7Cir88f8sJ0wrXbi4YcfVufrUmfSGSDHVXlPLi4uuR732muvqcfJsffFF19UATk5R9fN6iiM1J3kLZTrJOngkOs2Cb7mDSSV9FqiOPvJCEQJPMr1o4wO1g3gyDlVXN86kdF4cl0mryvTcuX7IO9PRurKCC1pl8r72sXcMChlIuQkUYIbcrKUl+RekIi1/HPIl1xOoqRhkINxeZALHjkISS+jjJiSYYnyjy3DH3VkyLH8I7///vvqBFNODuXEUw7YOVdaKy45aZVhmZIcVRoK6eWUaYNycliSEUllmVRSgjnyXidPnqwOuhJYkpMJXWClOGQIsYx8kIZcPnPpCZCGVU5Q9GlYi0NOyKVBlcZc8mdIz4KcfEujLCfiRZEDrxzk5TOQE3b5Hsh98h2UhqgocjEg71HmccsFhHzGUo+6IKeQCwTpvZILCRnBJD0T8jrSAygNpC5pYElJ8kP5PkkjIycvcnIhwbGSrLhCluv48ePqhCPv91H+p3QnNHIyUlSPsRzfirO4hQSTdeRkXI6J0jspxxA5PpcFaWdy9kpL3h4JaJfmeF7ciw1ZYSlnx4PcL/+v+uQJkTqRE0BJiCrvRQIqciIqF2x5T6rlIkOO3zINQHozZSi/qV1s6ENyQsmIXqlbyZ8hxz1dB0HOOpFAkNwnwXpZOUhGZ8toXak/XVJ1+R5Ir7nUnbT3Ur9y4i91WJji7CfHZPnspF2UqUlyISJtlo60kXIeJPvKRb2cc8ioQWmb5aIhJ+mEkHMkee9yniLttHynpf0u7/MYovJuN3JOFZRjn3xnJYAg1wg5V+Usa2wnTKudkFxfEryRY7BcT0nQRAIwec99ZYSZBFXkPF3qWL6XEmiRawd9vk/y/PIdkH1lAQrZR3KN9ezZs9TXEsXZT9otyRMln6kEgSXoJMFbXfqZ4tSJzFqRqaLTp09X1yRyHSNtipxz6fJYlfe1i7mpkMVsvESkETnpl4ucouZlExkjmfokJxmyhLTuolmmSchqK3lHKMkJjFzQyvTUopZwlhM+XX6Dwl6vMLK/5FLgxTOZOun0kYsI6XzLu0owkakxdLuhI6MCJd2EdCgTWeL/nazuWtTsD9IOR0oRERGVgdatW6seb5m2UFCvrEwvKu+luS9fvqxy9RSWP4OIiCyj3ZBRfjK1W6YTEREZIwaliIiIinFyL6t+5cxzIznQZMquDMWWHu+RI0eqlVvkYkNGd8jKPzI8vCRTjAt7PVlhTP4uo0gkd4L0qMuFh+R/kCnfnH5KRGR57YZMX5XcajJlTxZykSlGMgpr6NChZfzOiIjKBoNSREREegoICFC5EHQk94SQfBKSm0Dyvsm0uZdfflnlN5DpRpLXQJJnlsfryYWG5LqQfAayEpDkeJCEz5IHRxJ5EhGRZbUbMlpWAlAyYlam9MmCBLJ6WmHT+4iItMScUkREREREREREZHD/X56AiIiIiIiIiIjIQBiUIiIiIiIiIiIig2NQioiIiIiIiIiIDI5BqWKQJVvPnDmjflL+WEf6YT0VjXVUNNaR8eNnVDTWkX5YT0VjHRWNdWT8+BkVjXWkH9ZT0VhHxlFHDEoREREREREREZHBMShFREREREREREQGx6AUEREREREREREZHINSRERERERERERkcAxKERGZoIzMLOwLicG/IfHqp2wTEREVhO0GEREZY7thUy7PSkRE5WbLiXDM+PUUwuOSb9+x4xo8HO0xvV8T9G7mAVO1Y8cOzJw5E4GBgQgPD8fGjRvRv3//Ah8/evRoLF269K77mzRpgpMnT6rf3333XcyYMSPX3xs1aqRWESEishTm2m4QEZHptxscKUVEZGINxLPLD/2/gbgjIi5Z3S9/N1WJiYlo2bIl5s6dq9fjZ8+erYJXutulS5fg7OyMQYMG5Xpc06ZNcz1u165d5fQOiIiMjzm3G0REZPrtBkdKERGZCBkyKz0W+Q2clfsqAOrvPZu4w9pKtkxLnz591E1fjo6O6qazadMmXL9+HWPGjMn1OBsbG7i7u5dpWYmITIG5txtERGT67QZHShERmYgDobF39VjkbSjk7/I4S7Rw4UL06NED9erVy3V/UFAQatWqBW9vbwwfPhxhYWGalZGIyJDYbhARkbG3GxwpRURkIq7FJ5fp48zJ1atX8ccff2DlypW57u/QoQOWLFmi8kjJ1D3JL3XPPffgxIkTcHBw0Pv5MzIySlQu2S8zM7PE+1sC1pF+WE9FYx3dLSIuSe/HZWQ4Ffk4a2vrMigVEREZq2saXG8wKEVEZCLcHOzL9HHmRBKeOzk53ZUYPed0wBYtWqgglYykWrNmDcaNG6f388toq5KQC+TY2FgEBwfDyoqDk/PDOtIP66lorKO7pcbd0vNx0QgKSizycX5+fmVQKiIiMlZuGlxvMChFRGQi/L2c1aoXBQ2plVnd7o726nGWJCsrC4sWLcKTTz4JOzu7Qh8rgauGDRuqi9bi8PX1LVHZZMSGvJaPjw9HGBSAdaQf1lPRWEd3826Qha/2xiDiZkqh7cZjXZszpxQREUGuIxwr2SLuVprBrjcYlCIiMhFywfB6bz+8tPrIXX/TXUrIMq2WdmHx33//qQtRfUY+JSQk4Pz58yqAVRylucCVERuyPy+SC8Y60g/rqWiso9ykGl7t5YeX1x4ttN2ws+UlARERASv3Xyw0IFUe1xsc20xEZEICL15XP/O2A9JjMX9EG/Ru5gFTJQGjI0eOqJsIDQ1Vv+sSk0+bNg0jR47MN8G5TMtr1qzZXX975ZVXVNDqwoUL2LNnDx577DF1sTp06FADvCMiIu3tOR+jfua9gDCHdkMfO3bsQL9+/dSCFxUqVFArtRZmw4YN6NmzJ2rUqIFq1aqhU6dO+PPPPw1WXiIirXy/IwRv/3xS/d7dr4ZqJwzRbrBbhIjIROwJjsaP+y6q35eO8YdVhSwcC7qIFr710LFBDZMfIRUQEIDu3btnb0+ZMkX9HDVqlEpWLonK866cFxcXh/Xr12P27Nn5Pufly5dVAComJkZdYHTt2hX79u1TvxMRmbttZyKx/tBlVKgArBzfAekZGWbVbugjMTERLVu2xNixYzFgwAC9glgSlProo4/UlO/FixeroNb+/fvRunVrg5SZiMjQqTC+2RaML/8+p7Yndm+AVx5shMwsYN/5qHJvNxiUIiIyAYkp6Xht/TH1+/AOnrinYQ2VP8UlIxa+3i5mcWHRrVs31SgWRAJTeTk6OiIpqeDVpX766acyKx8RkSmJS0rD1PXH1e/junihg7eL2bUb+pAFL3IuelGUWbNm5dqW4NTPP/+MX3/9lUEpIjI7WVlZ+OzPs5i//bzafuXBhph0/+1cqtYVgI7eLuXebjAoRURkAj7+4zQuX7+F2k6VMO2hxloXh4iIjNyM307iWnwKvF2r4JVejbQujkmv6hgfHw9nZ8taRISIzF9mZhbe++0Uluy5oLbf6tsY4+/xNng5GJQiIjKBaXvL992etjZzYAtUrchDNxERFeyfU5HYcOiKmrY3c1AL2Nsy8XtJff755yrn4eDBg4u1n4xKKwnZTwJhJd3fErCO9MN6Kpol11FGZhbe+vkk1gRcVtvvP9IEwzp43lUXpa0jfRYe4ZUNEZERS0hJx6vrbk/bG9HRE519XLUuEhERGfm0vTc23p62N76rF9rW4wifklq5ciVmzJihpu+5ubkVa9+goKASvaZc/MXGxqpVZWU1Sbob60g/rKeiWWodZWRm4fNd1/BvSIJaPGlylxpo75yS73GrtHXk5+enXVBKkgTOnDkTgYGBKjntxo0b0b9//+y/jx49GkuXLs21T69evbBly5ZCn3fu3LnqeSMiIlTSwm+++Qb+/v7l9TaIiDT18e+nceXGLdSpXgnT+nDaHhERFW7Gr3em7dWogpcf5LS9kpKchOPHj8fatWvRo0ePYu/v63s7J0txyWgEufjz8fHRa4SBJWId6Yf1VDRLrKPU9ExMXnNUBaRsrCrgy8Et0Le5h6Z1ZKPlShe9e/dWK1roVKxYsdDnXL16tVqNacGCBWr5b0lEKIGss2fPFrv3gojI2O0OjsaK/ben7X02sAWqcNoeEREVNW3v8BXV8z1zYEtO2yuhVatWqWsYCUz17du3RM9Rmos3GY0g+1vKRXJJsI70w3oqmiXVUXJaBp5beRj/no2CnbUV5g1vgx5NampeRzZarnQhQSh3d3e9n/PLL7/EU089hTFjxqhtCU5t3rwZixYtwtSpU0tdZiIiY5q299qdaXtPdqyHzg04bY+IiAp2IykV03TT9u7xRtt61bUuklGQfFDSy68TGhqKI0eOqMTlnp6emDZtGq5cuYJly5ZlT9kbNWoUZs+erTrBZXaGqFSpklrxlYjIFCWlpuOpZQHYHRwDe1srfD+yHe7xrQFjoGm3+/bt29UIp+rVq+P+++/HBx98ABcXl3wfm5qaqqYCSsORM2Inw2n37t1brNdl4sHywzrSD+upaJZeRx/+dkpN26tbvRJefdA333owROJBIiIyDTN+PYWoO9P2pvRsqHVxjEZAQAC6d++evS2zLoQEnpYsWaLSjISF3R6VLL777jukp6dj4sSJ6qajezwRkam5mZyGsYsPIuDidVSxs8ai0e3RwTv/uItFBaVk6p5M6/Py8sL58+fxxhtvqJFVEmDK70IpOjpaXXjVrJl7eJlsnzlzplivzcSD5Yd1pB/WU9EsuY4OXU3CqoPh6vdJ/tVxNSw038cZIvEgEREZv79PRWLjnWl7nw/itL2cunXrhqysrAL/njfQJJ3mRETmNIp25KIDOHY5DtXsbbB0rD9aexrXSFrNglJPPPFE9u/NmzdHixYt0KBBA9UQPPDAA+X62kw8WH5YR/phPRXNUusoPjkdczbtUr8/2cETA+9tUuBjLbWOiIgo9wWHbrW9p+7xRhsju9ggIiJtRCekYMQP+3EmIh7OVezw4zh/NK1lfNOQjSZrrre3N1xdXdUFVn5BKfmbXHRFRkbmul+2i5OXSjDxYPliHemH9VQ0S6yjT/88has3klHXuRKmPtS4yPduiXVERET/9+4vJ9W0vQY1qmAyp+0RERGAiLhkDPthH0KiElHDoSJWju8A35oOMEZGMyfm8uXLiImJgYdH/ssR2tnZoW3btti6dWuuqSuy3alTJwOWlIiofOwMisKqA3dW23u8JVfbIyKiQv11MgKbjlzltD0iIsp2KTYJg7/dqwJStRztseaZTkYbkCrXoJSsdCErW8gt50oXkkhQ/vbqq69i3759uHDhggosPfroo2oKSq9evbKfQ0ZMzZkzJ1diwu+//x5Lly7F6dOn8eyzzyIxMTF7NT4iIlMVn5yG1++stjeqUz10amA8yQeJiMj4XE+UaXsn1O9P3ettdDlCiIjI8EKjEzHk270Ii02Cp3NlrJnQCV6uVWDMbLRY6WL+/Pk4duyYCi7duHEDtWrVwoMPPoj3338fFStWzN5HEqBLgnOdIUOGICoqCu+8845anrVVq1bYsmXLXcnPiYhMzUe/n8bVuGTVeLzehwnIiYiocO/+elLlC/Fxq4rJPThtj4jI0p2LjMfwH/ZnT+leMb4j3B3tYexstFrp4s8//yzyOWQUVV6TJk1SNyIic7HjnEzbu6R+/2xgC1S247Q9IiIq2J8nI/Azp+0REdEdJ67E4cmF+3E9KQ1+7g5YPr4DXKv+f8CPMeOVDxGRhm4mp2Hq+tvT9kZ3ro+O3py2R0REhU/be/POtL2n722AVnWdtC4SERFp6FDYdYxadECt4t2yjiOWjvWHU2U7mAoGpYiINPTR5tvT9uq5VMZrvRtpXRwiIjJy03+5PW3P160qXurhq3VxiIhIQ/tCYjBuyUEkpmagff3qWDS6PRzsbWFKGJQiItLIf+ei8NPBO9P2Hue0PSIiKtyWExH45ejtaXszOW2PiAiWfi3x9LIApKRnoouPC74f2c4krydMr8RERGY4ba8Dp+0REVEhYhNT8dam4+r3Z+7jtD0iIkv218kITFp5GKkZmbjfzw3zhrcx2Y4KBqWIiDTw4W+nEc5pe0REVKxpe6mctkdEZOF+PXoVk1cfQXpmFh5q7o5ZQ1rDzsYKpopBKSIiA9t+9hpWB1xCBZl+MbClSQ6zJSIiw9lyIlxdhFhbVVCr7VW0Mc3ecCIiKp11gZfx2rqjyMwCHmtdGzMHtoCNtekGpASvhIiIDDxtb9qG49nT9vy9nLUuEhERGf20vdur7T1zrzdactoeEZFF+nHfRbx9pz0Y6l8XH/ZvDitJMmjiTDukRkRkYj747ZSatldfpu318tO6OEZlx44d6NevH2rVqoUKFSpg06ZNhT5++/bt6nF5bxEREbkeN3fuXNSvXx/29vbo0KEDDhw4UM7vhIio7Lzz8wk1ba9hzap4kdP2iIgs0g87Q7IDUmO61MdHj5lHQEowKEVEZCD/nr2GNQGXb0/bG9QSlew4/SKnxMREtGzZUgWRiuPs2bMIDw/Pvrm5uWX/bfXq1ZgyZQqmT5+OQ4cOqefv1asXrl27Vg7vgIiobP1xPBy/HQvntD0iIguVlZWFb7YG4YPNp9X2c90a4J2Hm6iOWHPB6XtERAYQdysN09bfnrY3prMX2tfntL28+vTpo27FJUEoJ6f8p7N8+eWXeOqppzBmzBi1vWDBAmzevBmLFi3C1KlTS11mIqLyEpOQkj1tb8J93mhRh9P2iIgsLSD12Z9nMX/7ebX9yoMNMel+8xsxy6AUEZGBpu1F3EyGl2sVvNqLq+2VpVatWiElJQXNmjXDu+++iy5duqj7U1NTERgYiGnTpmU/1srKCj169MDevXuL9RoZGRklKpvsl5mZWeL9LQHrSD+sJ8uro7d/PoGYxNvT9iZ2a1Am76u0dWRtzZFaRESGCkjN+PUUluy5oLbf6tsY4+/xhjliUIqIqJz9e+Ya1gbembY3sAWn7ZURDw8PNfKpXbt2Kij1ww8/oFu3bti/fz/atGmD6OhodeFVs2bNXPvJ9pkzZ4r1WkFBQSUqo1z8xcbGIjg4WAXE6G6sI/2wniyrjnb+r737gKu66v8A/pE9ZCigLEWWICgo7pWampla5sjM0oaWpWX59JiWMyst06w0bZn1L1Mz0xxpjtR83KIiTkAEBxvZIvP/OucCgjIuyN2f9+vFI78f98LhPLd7+H1/53PO1SxsP5sAsVzI6x3tERutuEuu6T7y9+daiEREqlZYVIyZm87i12PX5PEHQ1vj2S4e0FcsShERqTi2N31jmPz8xe6e6MDYXr3x8/OTH6W6deuGqKgofPbZZ/i///u/ev1Zvr51myotimLi4s/Hx4czDKrAPlIO+8lw+kjMjlrx28Gy2N5jXVvW2/fWlz4iItJXBYVFePu3M9h0+qa8MfHJiGCMaO8OfcaiFBGRCs3feh4JGXdkbO/tRxjbU7VOnTrh4EHFxZyjo6O86EpISKjwGHHs7Oxcq+/7IBdvYjaCeD4vAKvGPlIO+8kw+mjelgtIzc6DX1MbTOnXst5/F33oIyIifZRXUIQpa0/hr/B4mBg1wNKn22JwkCv0nW7PbSYi0mJ7LyZgA2N7anX69GkZ6xPMzMzQvn177Nmzp0J0RRx37dpVg60kIqrctrA4bDvL3faIiAxNbn4hJv58UhakzIyNsOLZ9gZRkBI4U4qISAXSc/IxY6Nit72XGNtTSlZWloyVlIqOjpZFpsaNG6N58+ZywfIbN27gp59+kl9funQpPD09ERgYiNzcXLmm1N69e/H333+XfY+pU6di3Lhxct0pMYtKPCc7O7tsNz4iIm2RnHVHLm5euuV3G3c7TTeJiIjUICevABN+OoH/RabAwtQI3zzXAQ+1dIKhYFGKiEgF3i+J7XmJ2B5321PKiRMn0KdPnwoFJUEUlVavXo24uDjExsaWfV3srvef//xHFqqsrKwQFBSE3bt3V/geo0aNQlJSEmbPno34+Hi5U9+OHTvuW/yciEjTZm8Ol7E9f2cbvK6HW34TEdH9MnPz8eLq4zh+9RaszYzx/fMd0cXLAYaERSkiIhXE9n4PLYntjQyChSnjF8oQO+eJ7W+rIgpT5U2bNk1+1GTy5Mnyg4hIW20Nu4ntZ+PLYntmJlxhg4hI36Xl5GHsqmMIu54OWwsTrH6xE0KaN4KhYVGKiKieY3vTf1fE9sb38ER7D8b2iIio+tje7M3n5OeTenujtRtje0REhvDe/+x3R3ExPhONrc3w04udDPb9n0UpIqJ6NG/rOSRm3oGXkzX+w932iIioGmJ26KxNd2N7kxnbIyLSe/HpuRjz3RFEJWXDycYcv4zvjJZNbWCoWJQiIqonu88nYGPoDRjJ3faCGdsjIqJqbQ2LK9v6m7E9IiL9dy01B2O+O4rY1By42lnglwld4OloDUPGohQRUT3F9t79oyS219ML7T0MLw9ORETKS8oUsb2S3fb6+BhsbIOIyFBEJ2djzLdHcDM9F80bW2HNhM5wb2QFQ8eiFBFRPZi35W5sb2r/lppuDhER6UBs71ZOPlq52GJyHx9NN4mIiFTockKmnCElbkh4O1njl/Fd4GxnoelmaQWVzRE+cOAAhgwZAldXVzRo0ACbNm0q+1p+fj7eeecdtGnTBtbW1vIxY8eOxc2bN6v9nnPnzpXfq/yHv7+/qn4FIiLlY3unFLE9Eb9gbI+IiKqzJSwOO86VxvaCGNsjItJj4TfSMerrw7IgJdYPXPdKVxakylHZCJidnY3g4GAsX778vq/l5OQgNDQUs2bNkv9u3LgRly5dwuOPP17j9w0MDERcXFzZx8GDB1X0GxARKbeV64yS2N6Enl4GuY0rEREpT1yUzCmJ7U3q44NAV8b2iIj0VWjsLYz+9oicGRvsboe1L3eBY0NzTTfLMOJ7AwcOlB+VsbOzw65duyqcW7ZsGTp16oTY2Fg0b968yu9rYmICZ2fnem8vEVFdzNtyvmwa7luM7RERUQ2xvZmbzpbF9kRRioiI9NORKyl4afVxZOcVomOLRlj1fEfYWJhqullaR2vWlEpPT5dxPHt7+2ofFxERIeN+FhYW6Nq1KxYsWFBtEasyhYWFdWqjeF5RUVGdn28I2EfKYT/pRx/tvpCIP0pie58MbwNTo7q/v2iij4yNGTMkIlKnP8/cxM5zCYztERHpuf2Xk/DK/51Abn4Ruvs44NuxHWBlpjXlF62iFb2Sm5sr15gaPXo0bG1tq3xc586dsXr1avj5+cno3rx589CzZ0+Eh4fDxsZG6Z8nClt1IS7+UlNTERkZCSMj/hFRGfaRcthPut9HmXcKMX3TNfn58EB7WOcmISIiSaf6iGvyERGpT2JmLub8eU5+PvlhxvaIiPTVrvMJmPRLKPIKi/CwfxN8NSaEa85qc1FKLHr+1FNPyenMK1asqPax5eOAQUFBskjl4eGB9evX46WXXlL6Z/r6+taprWI2grj48/Hx4QyDKrCPlMN+0v0+mrr+DG7dLoSPkzXmjegIcw0MNNreR0REVC6290c40nLyEcDYHhGR3tpy5ibeWncaBUXFGNjaGZ8/3Y6zYrW5KFVakIqJicHevXurnSVVGRH1a9mypbwoq40HuXgTsxHE83kBWDX2kXLYT7rbR3+fi8fmM3GK3faeagsrCzONtUVb+4iIiCrG9v4+XxrbC4apMS9QiIj0zYaT1zFtwxkUFQNPtnPDohFBMOH7fY2MNF2QElG63bt3w8HBodbfIysrC1FRUXBxcVFJG4mI7nUrOw/v/qHYNenlh7zRtln16+AREZFhKx/be/1hXwS41u4mLBERab+fj8Tg7d8UBanRnZph8chgFqSUpLJeEgWj06dPyw8hOjpafi521xMFqREjRuDEiRP45ZdfZAQlPj5efuTl5ZV9j759+8pd+Uq9/fbb2L9/P65evYpDhw7hySeflLMDxFpURETqMHfLOSRn3YFvk4Z4s1/dosBERGQ4sb33SmJ7ga62eK2Pt6abRERE9ey7f69g5ibFTevnu7XAR0+2gZGIVJBm43ui4NSnT5+y46lTp8p/x40bh7lz5+LPP/+Ux23btq3wvH/++Qe9e/eWn4tZUMnJyWVfu379uixApaSkwMnJCT169MCRI0fk50REqrZTxPZO35SxvUUjg7lgIRERVUuMGWLBW1NjxvaIiPTRl3sisHjXZfn5a7298d8BfmjQgAUprShKicKSuDtUleq+VkrMiCpv7dq19dI2IqK6xPbE3W7hlV6M7RERUfUSMyrG9lq5MLZHRKQvRD1j0c5L+GpflDz+T/+WeL0vUxQ6ufseEZEuEBcWjO0REZGyFyti/cH02/lo7WaLV3sztkdEpE/v8fO2nMfqQ4pJNDMHtcL4nl6abpbOYlGKiKgGO8Lj5c5JxiW7JpmbMLZHRERV23T6BnZfYGyPiEjfFBUV471NZ/HrsWvyeP7Q1niui4emm6XTWJQiIqpGanYeZm46Kz9/5SEvBDO2R0RENcT25v55Xn4+pa8v/J0Z2yMi0gcFhUX474Yw/HHqhlxj9pMRwRjR3l3TzdJ5LEoREdUY28tDy6YNMYWxPSIiqjG2d1bG9tq42WFiL8b2iIj0QV5BEaasPYW/wuNhYtQAn41qiyHBrppull7gXGIioirsCI/DFsb21ObAgQMYMmQIXF1d5a4lmzZtqvbxGzduRP/+/eUOrLa2tujatSt27txZ4TFit1fxvcp/+Pv7q/g3ISJDJe6e776QCDNjIzlumDC2R0Sk83LzCzHx55OyICXe31c8254FqXrEkZKIqMrYnmK3vYm9vBDkztieqmVnZyM4OBjLly9XuoglilLbt2/HyZMn0adPH1nUOnXqVIXHBQYGIi4uruzj4MGDKvoNiMiQJcjYnmK3PTGz1s/ZRtNNojrc8BD27duHkJAQmJubw8fHB6tXr1ZLW4lI++TkFWD8jyew92IiLEyN8N24Dugf0FTTzdIrjO8REVVi9uZwGdvza2qDN7i9q1oMHDhQfihr6dKlFY4/+ugjbN68GVu2bEG7du3KzpuYmMDZ2ble20pEdF9sb+NZZOQWIMjdTq5BSNp1w+PFF1/EsGHDanx8dHQ0Bg0ahIkTJ+KXX37Bnj17MH78eLi4uGDAgAFqaTMRaYfM3Hy8uPo4jl+9BWszY3z/fEd08XLQdLP0DotSRET3+OtsHLaGxTG2p2OKioqQmZmJxo0bVzgfEREh75BbWFjIiN+CBQvQvHlzjbWTiPTPxtAb2HORsT19uOGxcuVKeHp6YvHixfK4VatWcobtZ599xqIUkQFJy8nDuFXHcOZ6OmwtTLD6xU4Iad5I083SSyxKERGVk5J1pyy292ovb7Rxt9N0k0hJn376KbKysvDUU0+VnevcubOMXfj5+cno3rx589CzZ0+Eh4fDxkb5aE1hYWGd2iSeJ4pldX2+IWAfKYf9pL19JGJ787YoYntv9PWGt6OV1v7/9KB9ZGys/zdpDh8+jH79+lU4J4pRb775Zq2+D8cN1WEfKYf9VPc+Ss66g3E/nMDF+Ew0sjLFjy90RKCrrUH2ZaEaxg0WpYiIypn95zmkZCtie6/39dF0c0hJa9askQUnEd9r0qRJ2fnyd8eDgoJkkcrDwwPr16/HSy+9pPT3F7Ot6kIM4qmpqYiMjISREWdOVIZ9pBz2k3b2kYjtzdkTL2N7LR3N0ce5sM7vF7rQR4awUUR8fDyaNq24Xow4zsjIwO3bt2FpaanU9+G4oTrsI+Wwn+rWRyk5BZi+8yaupeejkaUxFvR3hll2AiIiEmCIitQwbrAoRURUYvvZOGxjbE/nrF27Vq738dtvv913d/te9vb2aNmypRxYa8PXt27riom7SuJniYVyDWGGQV2wj5TDftLOPvo99AaOXc+BmXEDfDGmI3ybNIQ24+tIfThuqA77SDnsp9r30Y1bt/HyquOyIOViZ4H/e7EjPB2tYcgK1fA6YlGKiKgktjerJLb3Wm/G9nTFr7/+KhevFYUpsTBtTUS8LyoqCs8991ytfs6DDMLirpJ4Pv8grBr7SDnsJ+3qo/j0XMzfdkF+/mb/lvB30Y1xg6+j6omNMRISKs6IEMe2trZKz5ISOG6oFvtIOeynqhUWFeN4TArCrmbjlmkamtpZYez3R3EzPRfNG1vhl/Gd0ayxlaabaRCvIxaliIjkbnuK2J6/sw1ef5i77WmCKBiVn8EkdkA6ffq0XLhcLEw+Y8YM3LhxAz/99FNZZG/cuHH4/PPPZSxPRC4EcdFgZ6e4OHz77bflVuAisnfz5k3MmTNHDqijR4/W0G9JRPpAxPZmbAxDZm4BgpvZ4+We3G1PX4gNMbZv317h3K5du+R5ItIPO8LjMG/LecSl5ypOHEiEUQOgqBjwdrLGL+O7wNnOQtPNNBgMlxKRwRORvW1n78b2zEz41qgJJ06cQLt27eSHMHXqVPn57Nmz5bFYqDw2Nrbs8d988w0KCgowadIkuVV36ceUKVPKHnP9+nVZgBILnYsF0B0cHHDkyBE4OTlp4DckIn2x4eR1/HMpSbHb3ogg7ran5Tc8xA0O8VH+hkfpeCJueIwdO7bs8RMnTsSVK1cwbdo0XLx4EV999ZVch/Ctt97S2O9ARPVbkHr159C7BakSoiAlvNLLmwUpNeNMKSIyaGJ3jVmbFbG9Sb290dpNN+IX+qh3795y9kFVxC565e3bt6/G7ylifURE9R3be3/refn5W/1bwrep8jt5kmZuePTp06fsWNzwEMRMWzGu3HvDw9PTE9u2bZNFKDET193dHd99953cgY+IdD+yJ2ZIVfXXZgMAn+26jOEh7vJmNakHi1JEZNBmbw5HaklsbzJje0REVA1ROJ9eLrY3oaenpptE9XzDo/Q5p06dUnHLiEjdjkWn3jdDqjzxTiG+Lh7X1dtBrW0zZJxrTEQGa2vYTWw/Gw8TxvaIiEgJv528jn0itmdihMUjGdsjItIliZm59fo4qh8cSYnIYGN7YnFz4bU+PoztERFRteLSb2P+FkVsb2r/lvBpwtgeEZFOqXrSZAVNbLimlDoxvkdEBkdM45+1SRHba+Vii8l9fDTdJCIi0vbY3u9nkXmnAG1lbI+77RER6ZK/z8XjvU1nq32MWEVKLHLeybOx2tpFLEoRkQHaGhaHv8JLY3tBjO0REVG1fjtxHfsvK2J7Iu7NBXCJiHRDfmERFu28hG8OXJHHno5WiE7OkQWo8hOnSt/V5wwJ4Hu8mvFKjIgMSlKmiO2V7LbXxweBroztERFR1W6m3cb8kt32/iNjew013SQiIlJyt9Rnvj1SVpB6qYcndr7ZCyufDZEzosoTxyueDcGjrV001FrDxZlSRGRwsb1bOfkytieKUkRERNXvtqeI7bVrbo/xjO0REemEgxHJmLL2FFKy82BjboJPRgRhYBtFwUkUnvoHOONIVBLCImIQ5OuBLt5OnCGlISxKEZHB2BIWhx3nGNsjIiLlrD9xDQdKYnuLRjC2R0Sk7QqLivHl3gh8vicCxcWQN6JXjAlBC0frCo8T7+ddvBzgUJgKXy8Hvr9rkMquyA4cOIAhQ4bA1dUVDRo0wKZNm+678zR79my4uLjA0tIS/fr1Q0RERI3fd/ny5WjRogUsLCzQuXNnHDt2TFW/AhHpWWxvTklsb/LDjO0REVHNsb0Ptl6Qn7/9CGN7RETaLiXrDp7/4RiW7lYUpJ7u2Ax/vNbtvoIUGUhRKjs7G8HBwbKIVJlPPvkEX3zxBVauXImjR4/C2toaAwYMQG5ubpXfc926dZg6dSrmzJmD0NBQ+f3FcxITE1X1axCRHhBF8JmbzsrYXgBje0REVIvYXkhze7zUg7E9IiJtduJqKgZ9cRD/RiTDwtQIi0cGY+HwIFiYGmu6aaSpotTAgQPxwQcf4Mknn6x0oF+6dClmzpyJJ554AkFBQfjpp59w8+bN+2ZUlbdkyRJMmDABL7zwAgICAmRBy8rKCqtWrVLVr0FEeuDPMzex81xCSWwvGKbGjO0REVHV1h1XxPbMRWyPu+0REWktUVv47t8rePqbI4jPyIWXkzU2T+qB4e3dNd00UpJGrsyio6MRHx8vI3ul7OzsZBzv8OHDlT4nLy8PJ0+erPAcIyMjeVzVc4iIEjNzMefPc/Lz1x/2RYCrraabREREWuyGiO1tK43t+cHbibE9IiJtlH47HxN/PinfswuKijEk2BV/Tu4BP2cbTTeNtH2hc1GQEpo2bVrhvDgu/dq9kpOTUVhYWOlzLl68WKufL75PXYjnFRUV1fn5hoB9pBz2k3r6SNw5eW/jWaTl5CPQxRavPNRCr/r8QfvI2JjTmYmI7ovt/R6GrDsFaO/RCC/28NR0k4iIqBLhN9Lx2i+hiE3NgZmxEWYNboVnu3jI9axJtxjk7nvKLKheGXHxl5qaisjISDlLi+7HPlIO+0k9ffTPlUzsupAIscnepI62uHolCvrkQfvI399fJe0iItJVa49fk+uRyNjeiCDG9oiItPDmwa/HrmHulnPIKyiCeyNLfDUmBEHu9ppuGtWRRopSzs7O8t+EhAS5+14pcdy2bdtKn+Po6Cjv6ovHlCeOS7+fsnx9fevUbjEbQVz8+fj4cIZBFdhHymE/qb6PxG57X687KD+f3McHAzrr3+LmfB0REdVvbO/Dktjefwf4wYuxPSIirZKTV4CZf4Rj46kb8rhfqyZYPLIt7KxMNd000rWilKenpywk7dmzp6wIlZGRIXfhe/XVVyt9jpmZGdq3by+fM3To0LJZAuJ48uTJtfr5D3LxJmYjiOfzArBq7CPlsJ9U10fiDsqsP88j7XY+WrvZYtLDvjDW08XN+ToiIqrf2F4Hj0Z4oTtje0RE2iQyMVPG9S4nZMlZrOLmwcs9vWDEGa06T2VFqaysLHkHv/zi5qdPn0bjxo3RvHlzvPnmm3J3PjFrSRSpZs2aBVdX17KCk9C3b1+5e19p0Wnq1KkYN24cOnTogE6dOskd/LKzs+VufEREpTafvold5xNgaszd9oiIqGYiClIa2/uEsT0iIq2y+fQNzNh4Fjl5hWhiY44vR7dDZy8HTTeLtL0odeLECfTp06fsWBSUBFFUWr16NaZNmyYLSi+//DLS0tLQo0cP7NixAxYWFmXPiYqKkguclxo1ahSSkpIwe/ZsuSC6mGUlnnPv4udEZLgSM+7utvfGw77wd+Zue0REVLXrt3Lw4bbz8nPG9oiItMedgkLM33oePx+JlcfdvB3w+dPt4GRjrummkS4UpXr37i2nQldFrIr//vvvy4+qXL169b5zYtZUbeN6RGQYxHvOu3+Ey+1hRWxvYm9vTTeJiIi0PrZ3Ftl5hYztERFpkWupOTKud/ZGujx+/WEfvNmvJWey6iGD3H2PiPTTptM3sPsCY3tERKScNcdicTAyGRamRlg0MpgXO0REWkAsw/Gf9aeRkVuARlam+GxUW/T2a6LpZpGK8IqNiPQmtjf3T0X8YkpfxvZ00YEDBzBkyBC5vqCYTbtp06Yan7Nv3z6EhITA3Nxc7kIo4uH3Wr58OVq0aCHj4Z07d8axY8dU9BsQka7dhf+obLc9f3g6Wmu6SUREBi2/sAgLtl/AhJ9OyIJUu+b22PZGTxak9ByLUkSkJ7G9szK218bNDhN7Mbani8Q6g8HBwbKIpAyxgcagQYPk+oViIw2xgcb48eOxc+fOssesW7dOrmk4Z84chIaGyu8/YMAAJCYmqvA3ISKdiO1tDJOxvY4tGuGFbi003SQC5IZItflwcHBATEyMpptNRPUgPj0Xz3x7BF8fuCKPX+zuiXUvd4WrvaWmm0YqxvgeEem8P06J2F4izIyNZGzPhLE9nTRw4ED5oayVK1fK3VsXL14sj1u1aoWDBw/is88+k4UnYcmSJZgwYULZLq3iOdu2bcOqVaswffp0Ff0mRKTtfjkai/9FpihieyOCuaW4lhCbH4ndte3s7JQqLL722msoLCxUS9uISHUORiRjytpTSMnOg425idwFdWAbF003i9SERSki0mkJMran2G1vSj9f+DnbaLpJpCaHDx9Gv379KpwTxSgxY0rIy8vDyZMnMWPGjLKvGxkZyeeI5xKR4cb2RDxEmDbAHy0Y29MqTz/9NJo0US6q8/rrr6u8PUSkOkVFxfhybySW7rkMsUdaKxdbrBgTwvdlA8OiFBHpdmxv41mZOQ9yt8MrD3lpukmkRvHx8WjatGmFc+I4IyMDt2/fxq1bt+Qd9Moec/HixVr9rLreiRfPKyoq4p38arCPlMN+qp8+EhdA0zacKYvtPde5mUH16YO+joyNjaFKom21kZmZqbK2EJFqpWTdwZvrTuPfiGR5/HTHZpj7eCAsTFX7PkPah0UpItJZG0NvYM9FxvZI9SIiIup8gZWamorIyEg5S4vuxz5SDvupfvpo68V0HL6SCnPjBng1xAZRUZEwJA/6OvL391dJu4jIsJyMScWkX04hPiNXxqg/GNoGI9q7a7pZpCEsShGRzsb25m25G9tr2ZSxPUPj7OyMhISECufEsa2tLSwtLeUdffFR2WPEc2vD19e3Tm0UsxHExZ/YGVDVMwx0FftIOeynB+8jEdtbFXpVfj7tUT/0am94i5vr2uvo5s2bcq1AsTnFvbOo3njjDY21i4jqnnL4/mA0Fv51EQVFxfByssZXY0K4a7aBY1GKiHRyQJtREtsLZmxPI4YNG1br54hFxpVdJ0QZXbt2xfbt2yuc27VrlzwvmJmZoX379tizZw+GDh0qz4mLGnE8efLkWv2sB7l4E7MRSgtkVDn2kXLYT3XvIxHbm/HHOeTkFaKTZ2O80N3LYBc315XX0erVq/HKK6/I93Kxy16DBnf//xKfsyhFpFvELtkiPr3znOJm4eAgFywcHoSG5ixJGDq+AohI5/weegN7GdvTqE2bNuGpp56SM5KUsWbNGmRlZVVblBJfF3fwS0VHR+P06dNy2+/mzZvLBctv3LiBn376SX594sSJWLZsGaZNm4YXX3wRe/fuxfr16+XueqWmTp2KcePGoUOHDujUqZPc1Sk7O7tsNz4iMgy/HI3B4SspsDQ1xqIRQQZbkNIls2bNwuzZs+V7PyOrRLot/EY6XvslFLGpOTA1boBZgwPwXBePCsVmMlwsShGRTolPvxvbe7O/L3wZ29OYL774QumZTxs2bKjxMSdOnECfPn0qFJQEUVQSd8zj4uIQGxtb9nVPT09ZgHrrrbfw+eefw93dHd99953cga/UqFGjkJSUJC9sxMLobdu2xY4dO+5b/JyI9Hy3vb8Umxu886gfPBy4q5MuyMnJkTvxsSBFpNvphl+PXcPcLeeQV1AEN3tLGdcLbmav6aaRFmFRioh0LLYXhkwR22tmj5d7MranKf/884+cwaSsv/76C25ubtU+pnfv3vL/46qIwlRlzzl16lS131dE9Wob1yMi/SBie//dcEbG9jp7NsbYroa3jpSueumll/Dbb79h+vTpmm4KEdVBTl4BZv4Rjo2nbsjjvv5NsPipYNhbmWm6aaRlWJQiIp2x4eR1/HMpSRHbGxHE2J4G9erVq1aP79Gjh8raQkRUlZ+PxuDIldSS2F4wY3s6ZMGCBRg8eLCc3dqmTRuYmppW+PqSJUs01jYiql5kYqaM611OyIKxUQO8/YifXAOW78FUGRaliEhnYnvvbz0vP3+rf0vG9rRIRkZGpefFOgHm5uZykVoiInWLTcnBgu2K2N70gf5o7mCl6SZRLYtSO3fuhJ+fnzy+d6FzItJOm0/fkBsSiRmqTWzM8eXodujs5aDpZpEWY1GKiLSeiHRNLxfbm9DTU9NNonLs7e2rvUAQaz09//zzmDNnDtcGISK1xvZu5xeii1djuaAu6ZbFixdj1apVcvwgIu13p6AQ87eex89HFOt/dvVywBej28HJxlzTTSMtx6IUEWm9305exz4R2zMxwuKRjO1pG7HW03vvvScvHMQOd8KxY8fw448/YubMmXKh8U8//VTOmnr33Xc13VwiMgD/dyQGR6NTYWVmjE+GM7ani8SY0b17d003g4iU3FBi0ppQhF1Pl8eT+/jIZIOI7hHVhEUpItI6hUXFOHIlBWFXMnE5Nw7v/6nYbW9q/5bwacLYnrYRxSdxR/upp54qOzdkyBC5BsjXX3+NPXv2oHnz5vjwww9ZlCIilY8b57NvYsH2C/I8Y3u6a8qUKfjyyy/lTq9EpL12n0/A1PWnkZFbAHsrU3w2qi36+Cm3OzORwKIUEWmVHeFxmLflPOLSc0vOJMr/beFghQncbU8rHTp0CCtXrrzvfLt27XD48OGyhc5jYxXTuYmI1DFutGzaEM92ZmxPV4kZt3v37sXWrVsRGBh430LnGzdu1FjbiAgoKCzCor8v4ev9V+Rx22b2WD4mBG72lppuGukYFqWISKsuLF79ORTFlXztakoOdp2Px6OtXTTQMqpOs2bN8P3332PhwoUVzotz4mtCSkoKGjVqpKEWEpEhjhti16e/OW7o9HqFw4YN03QziKgSCRm5eH3NKRy7miqPX+jeAjMGtpJLbRDVFotSRKQ10Qtxp7uyCwtBJNLF1/sHODOfrmXEelEjR47EX3/9hY4dO8pzJ06cwMWLF7FhwwZ5fPz4cYwaNUrDLSUifcJxQ7/98MMPmm4CEVXif5HJmLL2FJKz8tDQ3ASfjAjCY21Y/Ke6Y1GKiLTCsejUctGL+4mLDvF18biu3txWVps8/vjjuHTpklw/SvwrDBw4EJs2bUKLFi3k8auvvqrhVhKRvuG4od+io6NRUFAAX1/fCucjIiJklK90fCEi9e1quuyfSHy2+zKKiwF/ZxuseLY9PB2tNd000nEsShGRVkjMzK3Xx5FqhYWFoXXr1jAyUkzTFhcHCxYsqPLx586dg5+fH0xMOOwQUf3guKHfxI6uL7744n1FqaNHj+K7777Dvn37NNY2IkOTmp2HN9edxoHLSfJ4VIdmmPdEICxMjTXdNNIDDH0SkVZoYmOu5OMsVN4WqplYxFysE6Wsrl27cqFzIqpXHDf026lTp9C9e/f7znfp0gWnT5/WSJuIDNHJmFsY9MW/siBlYWqET0cG4+MRQSxIUb3hLWsi0risOwX49Vj1BQuxGoiznQU6eTZWW7uoasXFxZg1axasrJTbaj0vL0/lbSIiw5F+Ox+r/3e12sdw3NBtDRo0QGZm5n3n09PTUVhYqJE2ERna33rfH4zGwr8uoqCoGF6O1vjq2RD4O9tqummkZzRalBJxj5iYmPvOv/baa1i+fPl951evXo0XXnihwjlzc3Pk5nJaNpGuuhCXgUm/hOJKcjbEOrRFxYoLifIL15YuTztnSAAXq9USDz30UNn6UcrOlLK05BbBRPTgwq6nYdKaUFxLvQ1jI7HgOccNfR1nRCz8119/hbGxYkaGKEaJcz169NB084j0WkZuPqb9FoYd5+Ll8eAgFywcHiQXNieqbxp9VYndmMrf6QgPD0f//v3lLk5VsbW1rXAhJO6iEJFu3n359dg1zNtyDncKiuBiZ4EvR7dDctYduVtS+cVrxZ1ucWHBbb21B9fyICJNjBurD13FR9svIL+wGO6NLLH8mRDEpd/muKGHPv74Y1mYEusR9uzZU577999/kZGRgb1792q6eUR669zNdLz2SyhiUnJgatwAswYH4LkuHrzuJv0sSjk5OVU4XrhwIby9vdGrV68qnyP+Y3B2dlZD64hIlXG9dzeexZ9nbsrjPn5OWPxUWzS2NpPHYvvuI1FJCIuIQZCvB7p4O/FONxGRgcf1pm04g53nEuTxo4HOck0TO0tTBDez57ihhwICAuSmGsuWLcOZM2fkbNuxY8di8uTJaNy4dpFMkcBYtGgR4uPjERwcjC+//BKdOnWq8vFLly7FihUr5FqIjo6OGDFihJyhZWHB9clIvwv/a49fw5w/zyGvoAhu9pZYPiYEbZvZa7pppOe0Zv6dWG/k559/xtSpU6utwmZlZcHDwwNFRUUICQnBRx99hMDAwFr9rLrm0MXzxM9ljr1q7CPlGHI/ibje5F9P42pKjrxgePuRlhjfvQWMjBpU6I+OHvZolJ8MHw97oFj0lUabrZevo9I4BBGRNjt9LQ2T14Ti+q3b8q79e4+1wrhuLSr8vSjGky5eDnAoTIWvlwMLUnqys6urq6v8W/9BdnZdt26dvL5YuXIlOnfuLAtOAwYMkMmLJk2a3Pf4NWvWYPr06Vi1ahW6deuGy5cvy50AxettyZIl9fSbEmmXnLwCzPwjHBtP3ZDHD/s3wZKngmFvpbhhTGQQRalNmzYhLS1NvulXRQw6YoAICgqSixx++umncrAQA5K7u7vSPysiIqJObRQXf6mpqYiMjCwbLKki9pFyDLGfxN2X7ZczsPJoCvKLiuFkbYIZvZoioEkBoqIi73u8IfZRbT1oH/n7+6ukXURE9TVu/PC/q1jwlyKu16yxIq4X5M679vq8s6uYzXRvmqK69QrFTnxeXl5VPkYUkiZMmFC2Lq0oTm3btk1eU4ji070OHTokd/175plnytbAHT16NI4ePVrn34tIm0UmZuG1X07ickKWXN/17QF+mPiQt7xhTGRQRanvv/8eAwcOlHdEqht4xEcpUZBq1aoVvv76a8yfP1/pn+Xr61unNorZCOLiz8fHhzMMqsA+Uo6h9VNmbgHe2xSObWeTy+J6i0a0QaNq7r4YWh/VBfuIiPRVek4+/rvhDP4+r4jrDWztLBfZFXE90l/1vbOr+PrJkycxY8aMsnPiJk6/fv1w+PDhSp8jri9EeuPYsWMy4nflyhVs374dzz33XC1/GyLtt/n0DczYeBY5eYVwsjGX67uKWadEBleUEjvw7d69Gxs3bqzV80xNTeUdFXFRVhsPcvEmBjLxfF4AVo19pBxD6afwG+kydiHieiZGDTDtUT+M7+Gl1N0XQ+mjB8E+IiJ9jOuJXVlvpN2GmbERZg5uxUV2DUR97+yanJwsb+A0bdq0wnlxfPHixUqfI2ZIieeJHf5EkaygoAATJ07Eu+++W4vfhMuFqBL76MH7SWwy9OG2C/jl2DV53MWrMZY+FSwLU4bUr3wtacdyIVpRlPrhhx9kpnvQoEG1ep7omLNnz+Kxxx5TWduIqG7EH3I/H43F/K3n5WKJrmJ3vWdC0N6jkaabRkREWjpufH8wGgv/uoiComI0b2wl43pt3O003TQyoJ1dRRvEOlZfffWVXINK3PyeMmWKTGWIWVzK4nIhqsM+erB+is/Mx4f7EhCRckcePx1kj+fa2iMtPhZp8TAofC1px3IhJtrwS4qi1Lhx4+5bpFDssOHm5iZ3uxDef/99dOnSRUZVxPpTYhcNMctq/PjxGmo9EVUmMzcf0zeexbawOHncr1UTfDqSiyUSEVHl0nLy8PZvYdh9QRHXG9TGBQuGt4GtBeN6VHdi5zxxlz4hQfG6KiWOq9rNWxSeRFSv9PqiTZs2yM7Oxssvv4z33ntP6YsyLheiOuyjuvfTnguJeHtbGDJyC2BvaYrFI4PQ20+5Ndz0EV9L2tFHGi9Kidie2G71xRdfvO9r4nz5N/5bt27JhQrFAoiNGjVC+/bt5WKEYstYItKeuN6kNaGIKYnrTR/oj5d6eDJ2QSrZurt3797Yv3//fefFDFqxkK0gNtD48ccfK3xd7Ly0Y8cOFf0GRFQbobG38PqaU2VxvVmDW+FZxvWoHpiZmcnrhT179mDo0KFlN8TF8eTJkyt9Tk5Ozn2Fp9ILMTGbT1lcLkS12EfVKywqxvGYFIRdzcYt0zR0aOGAJbsv4+v9V+TX2zazx/IxIXCzrzr+aij4WtJ8H2m8KPXII49U+QZ/7xTezz77TH4QkZbG9Y7EYP7WC8grLJKD3LJn2qFdc8b1SHm13bpbrEVYfqHblJQUWcgaOXJkhcc9+uijclZuKXNzcxX/JkRU27heCwcrLHsmBK3dGNej+iPGFJHI6NChg7zBIcYVMfOpdDe+e5MZQ4YMkTv2iXVrS+N7YvaUOM+LVtIFO8LjMG/LecSl5ypOHEiEmXED5BUqrrmf79YC7z7WCmYmjKuRdtB4UYqIdF+GiOv9HobtZxVB9P4BTfHpiGDYWTF2QbVT2627GzduXOF47dq1cteme4tSoghVVVSDiDQV1zuD3RcS5fHgIBcsGNYGNozrUT0bNWoUkpKSMHv2bDkDt23btnKmbOni5/cmM2bOnCln6Yl/b9y4AScnJ1mQ+vDDDzX4WxApX5B69edQ3Dvlo7QgNaGnJ94bxJQRaRcWpYjogZy9rojrxabmwNRYxPVa4cXuLRi7oFqry9bd9/r+++/x9NNPw9ra+r6Zt2KmlYh+P/zww/jggw/g4KD8lsfcRUl12EeG108irjdl3RncTMuVd+pnPeaP0Z2ayXHjQX4/feojXd5FSRuJqF5Vcb17kxlijds5c+bIDyJdi+yJGVLVhUy3hsXJv9WNldgFm0hdWJQiojrHLn46HCO3kxVxPfdGIq4XIjPqRHVRl627yzt27BjCw8NlYere6N6wYcPg6emJqKgoua33wIEDZaFL2Qss7qKkOuwjw+mnouJibDyXjh9OpkDctHezNcW7vZrCu/Ed+Xs98PfXgz7Sh12UiEgzjkWn3o3sVUF8XTyuq7fyN+aIVI1FKSKqU1zvnQ1h+CtcEdd7JKApFjGuRxomilFil6R7F0UXM6dKia8HBQXB29tb3h3v27evUt+buyipDvvIMPrpVk4epm04i72XUuTx4CBnfPBEa9hY1N+forreR+rAPiLSXzEp2Uo9LjGz+sIVkbqxKEVEtRJ2PQ2T15wqi+uJhRLFgomM65Emtu4uJRatFetJvf/++zX+HC8vL/mzxIWZskUp7qKkWuwj/e6nkzGpcne9m+mKuN7cIYFlcb36pqt9pE7sIyL9UlRUjN9Dr+Oj7ReUenwTGwuVt4moNliUIiKl43o/HrqKD7dfQH5hsYzrLX8mBMGM65EGt+4u9dtvv+HOnTt49tlna/w5169fl7v0ubi41FvbiajyC6Vv/72CT3ZekmudeDlay5h3gKutpptGRKQXzt/MwOzN4TgRc0semxg1kLuZVkbcBnC2s0Anz4qbxBBpGotSRFSj9NuKuN6Oc4q43qOBzvh4RBDsLBnXI81u3V0+uicKWfcuXp6VlYV58+Zh+PDhcraVWFNq2rRpMroyYMAAtf5uRIYkNTsP/1l/Gv9cSpLHjwe74qNhbdDQnH96EhE9qMzcfCzZdVmu7yqK/lZmxpjS1xdujSzlzFShfGmqdF7qnCEBXOSctA7/MiCiap25lobJv4biWuptGdd777FWGMe4HmnJ1t3CpUuXcPDgQfz999/3fT8RTwkLC8OPP/6ItLQ0uLq64pFHHsH8+fNhbm6utt+LyJCcuJqK1389JRfUNRdxvccD8XRH1cT1iIgMLbnw55mb+GDbBSRl3pHnHmvjjFmDA+BiZ1k2W0rswld+0XMxQ0oUpB5tzVnipH1YlCKiKge9H/53FQv+UsT1mjVWxPWC3BnXI+3Zulvw8/OTr9fKWFpaYufOnfXeRiKqPK739YEr+PTvu3G95WNC0MqFcT0iogcVkZCJWZvDceRKqjz2dLTGvMcD8VBLpwqPE4Wn/gHOOBKVhLCIGAT5eqCLtxNnSJHWYlGKiO6TnpOP/244g7/PKxacHtjaGQuHM65HRERVx/Wmrj+NfSVxvaFtXfHBk4zrERE9qOw7BfhiTwS+Pxgt14uyMDXC5D4+mPCQF8xNKt+wQBSgung5wKEwFb5eDixIkVbjXwpEVMFpEddbE4rrt27DzNgIMwe3wnNdPBi7ICKiSh0Xcb01pxCfoYjrvf9EIJ7qwLgeEdGDELPA/wqPx/ytd6N4/Vo1lTG8Zo2tNN08onrDohQRlQ18q/53FQtL4nrNG1vJuF4bdztNN42IiLQ0rrfyQBQW/31ZxvW8nRRxPX9nxvWIiB5EdHK23FXv34hkeSyW0Zg7JBB9WynW2CTSJyxKEZGM67294Qx2lcT1BrVxwYLhbWBrwbgeERHdLyXrDqauP4P9lxVxvSfbueGDoa1hzbgeEVGd3c4rxPJ/IvHNgSvIKyyCmYkRJvbyxmu9vWFhWnlUj0jX8S8HIgN3KvYWJq85hRtpirjerMGt8CzjekREVIVj0WJ3vVAkZNyRa5u8/3hrjOzgznGDiOgBiJvD87ack0toCL1aOsmFzFs4Wmu6aUQqxaIUkQHH9cSCiQv/uigXTfRwUMT1WrsxrkdERJXH9VbsF3G9SygqhozrfTWmPfycbTTdNCIinXUtNQdz/zyHPRcT5bGrnQVmDwnAgEBnFvvJILAoRWSA0nLy8PZvYdh9oSSuF+SChcPawIZxPSIiqkRy1h28te502fomw0LcMP8JxvWIiOoqN79QxvREXO9OQRFMjBpgfE8vvNHXB1ZmfG8lw8FXO5GBCY29JXdJknE9EyPMHhyAMZ2b804MERFV6siVFLzx6ykkZirieqIYNbJDM003i4hIZ+27lChnR11NyZHH3bwd5M6lPk0485QMD4tSRAYU1/vu32h8vEMR12vhYIVljOsREVE1cb2v9kViya7LMq7n06QhvhoTgpZNedFERFQXN9Nu4/0t57HjXLw8bmJjjpmDAzAkyIU3iMlgsShFZABuZYu43pmyrPrgIBcsYFyPiIiUjOuNaO8u7+IzUkJEVHt5BUVyLdcv9kTgdn4hjI0a4PluLfBmP1/+PU4Gj39ZEOm5kzEirheKm+m5Mq43Z0gAnunEuB4REVXucFQKpqxVxPUsTY0xf2hrWZQiIqLaOxSZjFmbwxGVlC2PO7ZoJN9X/Z1tNd00Iq3AohSRHscuvv33ChbtvCTjep6O1lj2TDsEujKuR0RE9yssKpYL7i7drYjr+ZbE9XwZ1yMiqrWEjFx8sO0Ctpy5KY8dG5phxsBWcqMI3hwmuotFKSI9jev957cz2FsS13s82BUfDWuDhtwliYiIKpGUqYjrHYxUxPVGtnfHPMb1iIhqraCwCKsPXcXS3RHIulMAowbAs1088J9H/GBnyage0b34lwaRnjkZk4rJa04hriSuN+/xQDzdsRnvyBARUaUORSVjytrTsjAl4nofDG2N4YzrERHV2vGrqZi1KRwX4zPlcdtm9vI9lRsLEVXNCBo0d+5ceaFc/sPf37/a5/z222/yMRYWFmjTpg22b9+utvYSaXtcb8W+KDz19RFZkPJytMam17pjNNePIiKiKuJ6Iqr37HdHZUGqZdOG2PJ6dxakiIhqSbyHTl1/GiNXHpYFKXsrUywc1gYbX+3GghSRts+UCgwMxO7du8uOTUyqbtKhQ4cwevRoLFiwAIMHD8aaNWswdOhQhIaGonXr1mpqMZH2Sc3OkwPhvktJ8viJtq748EnG9YiIqHKJmbl4c+1pHIpKkcejOjTD3McDYWlmrOmmERHpVHH/l6Mxcg3XzNwCiPvAIqEwbYA/Glmbabp5RDpB41esogjl7Oys1GM///xzPProo/jvf/8rj+fPn49du3Zh2bJlWLlypYpbSqS904RfX3MK8Rm5MC+J641iXI+IiKrwv0hFXC856w6szIzx4ZOt8WQ7zo4iIqqNU7G35K564Tcy5HFrN1vMf6I12jVvpOmmEekUjRelIiIi4OrqKuN4Xbt2lbOgmjdvXuljDx8+jKlTp1Y4N2DAAGzatKlWP7OwsLBObRXPKyoqqvPzDQH7SH39JOJ63/wbjSW7I+RdGhHX+3J0W/g728jvrev4WlJ9Hxkbc0YEkSERY8XneyLw5d4IFBcDfk1tsHxMCHyaNNR004iIdGpDoU92XsTa49fke6mNhQmmDfDDM509YCxWNSci3SlKde7cGatXr4afnx/i4uIwb9489OzZE+Hh4bCxuX/74fj4eDRt2rTCOXEszte2EFYX4uIvNTUVkZGRMDLS6HJcWot9pJ5+SsstxKf/JuDEjdvy+GGvhni9qxOMM+MRkVm7/x60FV9Lqu+jmtbwIyL9kZiRK2dHHb6iiOuJeMmcIYzrERHV5obwuhPX8PGOi0jLyZfnhoe4Y8Zj/nBsaK7p5hHpLI0WpQYOHFj2eVBQkCxSeXh4YP369XjppZdU9nN9fX3r9DwxG0Fc/Pn4+HCGQRXYR6rvJxHXm/L7GSRk3lHE9YYEYER7N72L6/G1VDP2EREp42BEMt5cdwrJWXkyrvfRk20wtJ2bpptFRKQzwm+kY+amcJy+liaPRTLh/Sdao5NnY003jUjnaTy+V569vT1atmwpL7IqI9aeSkhIqHBOHCu7JlWpB7l4E7MRxPN5AVg19pFq+knurrc/Cov/voSiYsDbyVrGLvydbaGv+FoyzD5avnw5Fi1aJGfBBgcH48svv0SnTp0qfayYbfvCCy9UOGdubo7c3Nyy4+LiYsyZMwfffvst0tLS0L17d6xYsaLONyiIdCqut/syvvwnUkZMxEWUGDe8nRjXIyJSRvrtfPm3989HYuTf32IToTf7+eL5bi1gYsyZ/ET1Qav+S8rKykJUVBRcXFwq/bpYc2rPnj0VzomFzsV5In0mFqMd98MxubOHGBCHtXPDn5N76HVBigzTunXr5NqBoogkdlYVRSmxdmBiYmKVz7G1tZUR8NKPmJiYCl//5JNP8MUXX8gNMY4ePQpra2v5PcsXroj0TUJGLp759gi+2KsoSI3u1BybJnVnQYqISAnihtbvJ6+j7+J9+OmwoiD1eLAr9vynF8b39GJBikhfZkq9/fbbGDJkiIzs3bx5U16EiLv9o0ePll8fO3Ys3Nzc5OLnwpQpU9CrVy8sXrwYgwYNwtq1a3HixAl88803mvw1iFTqyJUUvPHrKSRm3oGFqZGcKjyyvbvexfWIhCVLlmDChAlls59EIWnbtm1YtWoVpk+fXulzxH8LVc2YFX9ULl26FDNnzsQTTzwhz/30009yPUKxScbTTz+twt+GSDMOXE7CW+tOIyU7D9YirjesDZ5oy7geEZEyLsZnYNamcBy/eksei3SC2FWvm4+jpptGpJc0WpS6fv26LEClpKTAyckJPXr0wJEjR+TnQmxsbIXFe7t164Y1a9bIi4t3331XRi/ERUXr1q01+FsQqYaI6321LxJLdl2Wd2fE7khfjQlBy6b3bwJApA/y8vJw8uRJzJgxo+ycGAP69esnd1+tbpatuLkhFn4PCQnBRx99hMDAQPm16OhoGQMU36OUnZ2dXMNQfE8WpUifFBQWYenuCCzfp5gd1crFFsufaQcvzo4iIqpRZm6+fA9dfeiqjD9bmhrjjb6+eKmHJ8xMODOKSC+LUmKmU3X27dt337mRI0fKDyJ9j+uJu9z/RiSX7ewxf2ggrMy0ahk4onqVnJwsF2+vbJfVixcvVvocsXurmEUlNstIT0/Hp59+Km9gnDt3Du7u7mW7sz7ozq2iXXUhnieKZXV9viFgH9VPP4m43pvrzuBYyZ390R2bYeYgf1iYGhtM3/K1pPo+0qf1C4nKz6reEhaHD7ael8kEYWBrZ8wcHAA3e0tNN49I7/EKl0jLHI5KwZS1d+N6YrrwyA7NNN0sIq0k1hQsv66gKEi1atUKX3/9NebPn19vPyciIqJOzxMXf6mpqXIDj/Izf+ku9tGD99OJGzlY9G8C0nOLYGnSAFO6OaG3lxmuXb0CQ8LXkur7yN/fXyXtItKUyMRMzN58DoeiUuRxCwcrzH08EL39mmi6aUQGg0UpIi0hpgkv/ycSS3cr4nq+TRrKXZIY1yND4ejoKO/CP8guq6ampmjXrl3ZLq6lzxPfo/wmGuK4bdu2Sretrjv1idkIoi0+Pj6cYVAF9lHd+0nG9fZEYsX+OHncytkGX45uC09HaxgivpZqxj4iUsjJK8AXeyLx/cEryC8shrmJESb18cHLD3nJGaZEpD4sShFpoPgkFi8Pu5KJFOMUdPF2Qmp2nozrHYxUxPXEQubznmBcjwyLmZkZ2rdvL3dZHTp0aNldfXE8efJkpS+4zp49i8cee0wee3p6ysKU+B6lRaiMjAy5C9+rr76qdNse5OJNzEYQz+cFYNXYRzWPG8djUhB2NRu3TNPkuJGUeUdugnHsaqp8zLNdmmPmoACDv5jia6lm7CMy9KjeznPxeH/LedxMV+zC29e/iZwd1ayxlaabR2SQeMVLpEY7wuMwb8t5xJUMgjiQiMbWZvJud0ZugVxQcf7Q1hjR3l3TTSXSiKlTp2LcuHHo0KEDOnXqJHfOy87OLtuN795dWd9//3106dJF3vVPS0vDokWLEBMTg/Hjx5ftzPfmm2/igw8+kLOdRJFq1qxZcHV1LSt8EenauNHIylTe2c+6U4CG5iZYOLwNBge5arqpRERaLTo5G3P/PIf9l5PksXsjS8wdEoh+ARXXnSQi9WJRikiNFxav/hyK4nvOi1lSgoudBX56sRN8GdcjAzZq1CgkJSVh9uzZciFyMbtpx44dZQuV37sr661btzBhwgT52EaNGsmZVocOHUJAQEDZY6ZNmyYLWy+//LIsXImdXsX3tLCw0MjvSPSg48atnHz5b7NGlvi/lzqjhYHG9YiIlJGbX4iv/onEyv1XkFdYBDNjI7zSywuv9faBpRlnDBJpGotSRGqKXog73fdeWJQntu/mtt1EkFG9quJ69+7K+tlnn8mP6ojZUmJGlfgg0qdxI7+omHETIqJq7LmQgLlbzuFa6m153NPXEfMeD+Tf3ERahEUpIjU4Fp16N3pRhfiMXPm4rt4OamsXERHp8LiRznGDiKgy11JzZGF/94WEskTC7MEBeLS1s7xZRUTag0UpIhW7nVeI7WcVOyPVJDGz+gsQIiLSf9l3CrA17KZSj+W4QUR0152CQnx74AqW/ROJ3PwimBg1wEs9PfHGw76wNuelL5E24n+ZRCra2eNkzC1sOHkdW8Pi5GK0ymhiwzVuiIgMUVFRsdxJT4wb4kZGTl6hUs/juEFEhr6LtbGRYubTgctJmPPnObmgudDFqzHmP9Ga67USaTkWpYjq0Y2029h48jp+D72Oqyk5ZefdG1kgLaegyuKUGEqd7SzQybOxGltLRESaFpuSI8cM8XH9lmLNE8GjsSVSsvM5bhARVbMbqYjlvdHXB/9GJGP72Xh52snGHDMHtcLjwa6M6hHpABaliB5QTl4Bdp6Ll3e3D0WlyAXLBSszYzzWxgUj2rujU4vG+Pt8vNxFSSi/cG3pUDlnSEDZnR4iItLveJ6YDSXGjaPRqWXnG5qbYHCQYtxo79FIji0cN4iIqt6NVBSoZmwMl5+L98NxXVvgrf6+sLEw1Ug7iaj2WJQiqmM87/hVEc+7Ju/KlL+T3dXLQV5QiIUUy2fXH23tghXPhlS8w1Nyp1tcWIivExGR/sbzjkSnyELUX2fjcTtfEc8TN/F7+DjKceORAOcK25Nz3CAiUm43UlPjBtj4ane0cbdTY8uIqD6wKEVUC9dv5WBj6A0Zs4gpF89r3tgKw0PcMSzErdrtucUFRP8AZxyJSkJYRAyCfD0qZOGJiEi/xKRk43cxbpy8LiPepbwcrTG8vTuebOcGV3vLKp/PcYOIDJ0yu5HmFxYrvYYrEWkXFqWIlIjnibva4u724SspZeetzYwxSMYsmqFji0ZKZ9bFhUQXLwc4FKbC18uBFxZERHpGXBhtD1PE88Ti5aVsRDwv2FXOigppbs9xg0hNli9fjkWLFiE+Ph7BwcH48ssv0alTpyofn5aWhvfeew8bN25EamoqPDw8sHTpUjz22GNqbTfVbpdR7kZKpJtYlCKqImZxvNwuSNnldkHq5n03nmdlxv+EiIioJJ53pSSeF14xntfT1wnDQ9wwINAZFqZ343lEpHrr1q3D1KlTsXLlSnTu3FkWlwYMGIBLly6hSZMm9z0+Ly8P/fv3l1/bsGED3NzcEBMTA3t7e42039CXyxDrtX69/4pSj+dupES6iVfUROVcS727C9K11HK7IDlYYUSIO54McYN7o6rjeUREZFiuJot43nUZ7a4Qz3OyljcwRDzPxa7qeB4RqdaSJUswYcIEvPDCC/JYFKe2bduGVatWYfr06fc9XpwXs6MOHToEU1PFYtktWrRQe7sNvRi150Iilv0TidPX0mp8PHcjJdJtLEqRwRO7IIm72mLR8iNXKu6CNEjsntfBHR08lI/nERGRfsvMzS/bPU9selHKxsIEQ0riee2aKR/PIyLVELOeTp48iRkzZpSdMzIyQr9+/XD48OFKn/Pnn3+ia9eumDRpEjZv3gwnJyc888wzeOedd2BsrPxMx8LCu7Psa0M8r6ioqM7P1/UFzcXf5Cv2X8HF+Ex5ztzECE91cIdf04aYtfl8lbuRzhrkDxSLftNAw7WUIb+WlMU+Un0fKfO+yaIUGWzMQmzDrYhZxCGnJJ4nrh+6eyt2QRIxi/K7IBERkeESF0uHo0Q87xp2nItHbn6RPG9UEs8T40b/gKaM5xFpkeTkZHkh1bRp0wrnxfHFixcrfc6VK1ewd+9ejBkzBtu3b0dkZCRee+015OfnY86cOUr/7IiIiDq1WVz8iZla4ueKApohEIuU772SifVn03AjI1+eszRpgCH+dngy0A6NLMUl6x2816cpVh5NRnLO3YtjRytjvNLZEV5mmYiIUBSyyHBfS7XFPlJ9H/n7+9f4GBalyKDEptyN512/dTdm0ULE80TMIsQdbtXsgkRERIYlWsTzTop43nXcLLf7k7eM5zWT8TwRGyEi/bkAE+tJffPNN/IOf/v27XHjxg25UHptilK+vr51+vmiiCYu/nx8fGo1M0sX5eYXYv2J6/j2YDRupineX+0tTfF8Nw+M7eoBO0tFfLKU6NKxDxfj6JVknI2MRRuf5ujs5cjNH6pgSK+lumIfaUcfsShFhrELUknMQmwpW3EXJJeSXZAYzyMiIoWM3HxsK9k972TM3XierYUJHm8r4nnNEOxux3GDSMs5OjrKi6iEhIQK58Wxs7Nzpc9xcXGRa0mVv/hq1aqV3LlPxAHNzMyU+tkPcvEmZiOI5+vrRbL42/znIzH47t9oJGfdkeecbMwxoacnnunsIZfQqIrokm4+TnAqToOvj5Pe9lF90ffXUn1gH2m+j1iUIv3dBSm6ZBeksxV3Qerho4jnPRLAeB4REd2N5x2KSpbjxo7weNwpuBvP69XSCcPbu6NfK8bziHSJKCCJmU579uzB0KFDy2ZCiePJkydX+pzu3btjzZo18nGlUZXLly/LYpWyBSmqXFpOHn7431WsPnQV6bcVMT2RUJjYywsjOzTj+yuRgWJRivRKTIoiZvH7vbsgOVrLCwoRs3BlPI+IiEpEJWWVxPNuID7jbjzPt0lDeQNjaDs3NLVlPI9IV02dOhXjxo1Dhw4d0KlTJyxduhTZ2dllu/GNHTsWbm5uWLBggTx+9dVXsWzZMkyZMgWvv/66XBvqo48+whtvvKHh30R3JWbm4vt/o+XsqOySdVzF3+av9vaW77GmxlzLh8iQsShF+hHPK4lZHLtaLp5nYYLBQYpdkEKacxckIiJSEHfoFfG8awiNvbvduFi/5PGS3fOCGM8j0gujRo1CUlISZs+eLSN4bdu2xY4dO8oWP4+Nja2weG+zZs2wc+dOvPXWWwgKCpIFK1GgErvvUe1cv5WDr/dfwboT15BXMvu0lYstJvfxwaOtnbkWFBFpvigl7khs3LhR7n5haWmJbt264eOPP4afn1+Vz1m9enXZnY1S5ubmyM29e3eTDCOed/hKSlnMonw8r3QXpEe4CxIREZWL5x2MVMTzdp6LL7tAEhdFIp4nxo2+rZrA3ITjBpG+EVG9quJ6+/btu+9c165dceTIETW0TH9noK7YF4VNp26goKhYnhM3iCc/7IM+fk1Y8Cci7SlK7d+/H5MmTULHjh1RUFCAd999F4888gjOnz8Pa2vrKp9na2uLS5culR3zjc1wXBW7IIUqYhYV4nlyFyRFPM/FjvE8IiJSiEzMKhk3riMhQ7GgrtCyaUk8r60bmjCeR0T0wM7fzMDyfZFyg6FiRS0K3X0cMKmPD7p6OfCajYi0ryglps7eOwtKbMF68uRJPPTQQ1U+T7yhVbVjBumfzHK7IJ0otwuSiOeVxizaNmM8j4iIFNJz8rEl7KYcN05fuxvPs7cyxRNy3GiG1m62HDeIiOpBaOwtLN8biT0XE8vO9WvVRBaj2jVvpNG2EZH206o1pdLT0+W/jRs3rvZxWVlZ8PDwkLtihISEyMUHAwMD1dRKUlfM4nCUiOddw45z8cjNv7sLUmk8rz/jeUR6afny5Vi0aJFc+yM4OBhffvmlXJy2Mt9++y1++uknhIeHy2Oxy5IYE8o//vnnn8ePP/5Y4XkDBgy478YI6f648W9EkixE/X0+oUI8r4+fE4aHuONhxvOIiOpFcbHYsTQFy/ZGyiU1BFHnH9TGRRajxNpRREQ6VZQSBaY333xTbsPaunXrKh8n1ptatWqVXHhQFLE+/fRTuRbVuXPn4O7urtTPKixUrD9UW+J5op11fb4heNA+ipbxvBv44/RNxKffXSfMx8kaw0LcMLSta4VdkHT1/wu+lmrGPlJ9Hxkba9/F+bp16+ROSStXrkTnzp3lLkmigCQi22ImbWVrgYwePVqOAxYWFnJdQhEDF2OCWJy21KOPPooffvihwlqEpB8iEjKxIfQ6/gi9gcTMu/E8v6Y2GNnBHU+0dYOTDf//JiKqr2LUnguJWPZPZNlMVBOjBvLv9Im9vOHl1FDTTSQiHaM1RSmxtpS4033w4MFqHycWHhQfpcSFSKtWrfD1119j/vz5Sv0ssbVrXYiLv9TUVERGRlbYpYMerI/E1rAHrmZjV2QmzifeLUQ1NDNCb8+G6O9jg5aO5mjQoBAZCdeQkQCdx9dSzdhHqu8jf39/aJslS5ZgwoQJZRtaiOLUtm3b5M2I6dOn3/f4X375pcLxd999h99//x179uyR23yXL0Ix9q1f8bw/S+J5Z8rF8xqJeF5bNzmbNtCV8TwiovqcjSrWilr+TyQuxmfKc+YmRni6YzO83MsbbvZc05WIdLgoJXbD2Lp1Kw4cOKD0bKdSpqamaNeunbwoU5avr2+dZyWIn+Pj46OVMwy0gbJ9JAY2MeVXzIoSMYs7BXfjeQ/5ipiFG/r6O8FcT+N5fC3VjH1keH2Ul5cn1xScMWNG2TlRbOvXrx8OHz6s1PfIyclBfn7+fTFwMaNKzLRq1KgRHn74YXzwwQdwcHCo99+BVKegsAj/Rih2z9sl4nmF5eN5TWQh6mH/JjAzYRGbiKi+iCi02EVvxf4omWgQrM2M8WxXD4zv4cWZqESk20UpMf3z9ddfxx9//CEvGDw9Pet0UXb27Fk89thjSj/nQS7exAWSeL4+XADWN1FoOh6TgrCr2bhlmoYu3k7yYuHeLWJ/P6nYPS8+4+6sKN8mDct2zzOUXZD4WqoZ+8iw+ig5OVm+pzdt2rTCeXF88eJFpb7HO++8A1dXV1nIKh/dGzZsmBxjoqKi5E6vAwcOlIUuZfuNsW/VjRtHryTjbFQGkhokobOX433jxuWETGw8dRObTt1EUtbdeJ6/s428gfF4sAscG5ZeFBXrbV/ztVQz9pFhxr5JNXLzC7Hu+DV8c+BK2Y7XdpameKF7CzzfrQXsrcw03UQi0hMmmo7srVmzBps3b4aNjY1c1Faws7ODpaViCqiIX4h1QRYsWCCP33//fXTp0kXODEhLS5OL4cbExGD8+PGa/FUM3o7wOMzbch5xpetAHUiEi50F5gwJQFdvR2wtiVmcir0bsxAD2xNtFbvntXGzY8yCiB7IwoULsXbtWnmTQ6wvVerpp58u+7xNmzZyTUJvb2/5uL59+yr1vRn7rn8HY7Kw8mgyknNKLo7/TYKjlTEmdnZEUFNL7IvOkrHuiJS7hSg7cyP08bKRsW5vB1GIysetuFjc3ZdVf/G1VDP2kWHGvql+Zd0pwM9HYvDdv9FILrkRIGZDTejpiWc6e6ChuVYEbYhIj2j0XWXFihXy3969e1c4LxajFbslCbGxsRUGzVu3bsn1RkQBS8QwxE5Lhw4dQkBAgJpbT+ULUq/+HIrie86LAtXEn0Pl4ocFRYqvijvgvVoqds/ry12QiKgcR0dHeRc+IaHiwnHiuKb1oMSmF6IotXv3bll0qo6Xl5f8WeKiTNmiFGPf9WvnuXh8+E/UfeOGKFB98E8CRAKvJNUtx5Decvc8N/Ru6WSw8Ty+lmrGPqoZ+4iqkpaThx/+dxWrD11F+u18eU6sEzWxlxdGdmjGHa+JSH/jezURd7LL++yzz+QHaU/0QsyQqu7/SVGQ8m1ijac6NMcT7VzRxMYw4nlEVDtmZmbyRoNYpHzo0KFld/XFsVh7sCqffPIJPvzwQ+zcuRMdOnSo8edcv34dKSkpcHFxUbptjH3X77gxf9vFGsYNoJWz2D2vmZxR61AWzzNsfC3VjH1UM/YRlZeYmYvv/42Ws6PE5kOCl6M1Xu3tjaHt3GBqbJg3AohIfTj/kupUTLyZnovzNzOwMzz+bmSvGu8/0VrG+IiIqjN16lSMGzdOFpc6deqEpUuXIjs7u2w3vnsj3R9//DFmz54to+AtWrQoi4E3bNhQfmRlZWHevHkYPny4nG0l1pSaNm2anCUwYMAAjf6uhjZuXL91G+fjMvD3OeXGjdlDAtHVm4vRExGpwvVbOXK9qLXHr8nFzIVWLraY1McbA1u73Le+HxGRqrAoRdUSg5RYnFwUoMTFROm/pdN6lZWYeXdNECKiqowaNQpJSUmy0CQKTG3btsWOHTvKFj+/N9ItYuBi174RI0ZU+D5z5szB3Llz5UyAsLAw/Pjjj3IdQrEI+iOPPIL58+fD3Jyzb1Q1bkQkZt43bmTmFtT67j0REdUv8Xf9in1Rcke90uU12jW3x+Q+PnIHU67xSkTqxqIUlRGFpgvlLiDEv5GJWWXbbpcn1vjwadJQLnwotuiuCSN7RKQsEdWrKq53b6T76tWr1X4vsWmGiPWR6tYgOV/JuFF6oVOeqXED+DaxgaONGQ5c5rhBRKRO4v15+b5IbD8bh9IVVLr7OGBSHx909XJgMYqINIZFKQONUYitXe+9iy2iFZWxsTBBgIutnNIb4GorP/dt2lAuUi7WBunx8V7Ep+dWuj6IGN6c7SzQybOxyn8vIiJSbfzuXLlxQ9zEKN0m/F5id9V7xw1xI0MsUs5xg4hIfUJjb2H53kjsuZhYdq5fqyZ4rY8PQpo30mjbiIgEFqUMLEZROhMqo4oYhdhlo/QCovRf90aWVd49EXnzOUMC5O574hHlLzBKnyG+zlw6EZFuuFNQiIiEcrFtMXbczEDmncrHjWaNLRVjhoudYtxwtYWrnQXHDSIiDd5IOByVgmX/ROJQVIo8J96SB7VxkTOjxA0DIiJtwaKUHknPyS+7gCi9mIhMzER+YdUxivJ3scWHnZVprX/uo61dsOLZELkLX/nFa8WdbnFhIb5ORETa51Z2nuJmRYVxo/L4nZmxEVo6N7w7A8rFFv5i3LDkuEFEpC3FqD0XEmVM71RsWtmSG0+2c5O76Xk5NdR0E4mI7sOilB7EKEpnP1UVo7AV8TvXcnexy8Uo6ou4gOgf4IwjUUkIi4hBkK8Hung78U43EZEWKCoqxrVbORVi22LsEDupVsbeShG/K5s162oLb6eG9bo1OMcNIqL6IWLRYq2o5f9E4mJ8pjxnbmKEpzs2w8u9vGUSgohIW7EopSsxinJ3sS9Us4uRiFG0ci43+8nVVg5E6li8UFxIdPFygENhKny9HHhhQUSkAbn5peNGerlxIxNZVcTvPBysKsx+EuOGSzXxu/rEcYOIqO7yC4vwx6kbWLkvCleSs+U5azNjPNvVAy/18OSGEUSkE1iU0tYYhRK7GLVsalNh7ae6xiiIiEg3pWbnlRSeFAUoUXyKTMqSd83vJWbH+pUfN1xt4e9sAxsLjhtERLp282H9iWv4ev+VsqSEuAZ4oXsLPN+tBeytzDTdRCIipbEopSTxB/6RKykIu5KJFOOUB4oYiBiFiN+Vv4st/q0qRiEGmUDXinexRYyiPuN3RESk3eNGbGpOhVmz4t/4jMrHjUZWYtywQysXm7L4tpeTdb3G74iISL3jhpjx+suRGHz7bzSSs+7Ic44NzTGhpyfGdPFAQ3Ne2hGR7uE7lxJ2hMdVXIz1QKKMNiizGOu9MQpxF1vG76qIUTRvrIhRlI/fqStGQURE2jFuXIrPrLD2k/jIzius9PEtRPyuwq6pdmhqa85xg4hIT8YNEXP+4X9XsfrQVaTfzpdfFstzvNLLC091aAYLU2PNNp6I6AGwKKXEACG2rb43CBGfnivPi92DSi8wRIyidNHxsvhdVTEKYyP4OYvd70qjFHbwd7GBLWMUREQGM26IO933jhtRSVmoZNiQi9aKuJ0oPJXf/Y53xomI9HPcEAWqiT+Hyvf/OwVF8pyXozUm9vaWO+px9isR6QP+JVsNUUwSdywquTYoO/f2b2FYf/waLsRnVtjW+t4Yxb13sRmjICIy1HHjDH49Fit3SErIUMQv7tXY2kzGtsvPnPV0tIYJxw0iIoMZN0qJgpS4KTH5YR8MbO3CTSGISK+wKFWNY9GpVRaayme7915KqhCjKL/2k/hwtmX8jojIECg3bhRi/+Vk+bkYGjwdrNGqtABVMnY0sWH8jojIECgzbggixtfV21EtbSIiUicWpaqRmFnzACGMaO+Opzs2k3E87mJERGS4lB03RnYQ40ZzeefbmvE7IiKDpey4kZhZ+cxaIiJdx7+Eq9HExkKpxw0PcUeHFo1V3h4iItKPcWNYO3e092ik8vYQEZF+jBvKPo6ISNdwcYpqdPJsrNj5roqvi/Pi6+JxREREHDeIiKg2OG4QkaFjUaoaYhFBkd8W7h0oSo/F17nYIBERCRw3iIioNjhuEJGhY1GqBmLbbrF9t7NdxSmz4rj8tt5EREQCxw0iIqoNjhtEZMi4ppQSxEDQP8AZR6KSEBYRgyBfD3TxduIdCyIiqhTHDSIiqg2OG0RkqFiUUpIYELp4OcChMBW+Xg4cIIiIqFocN4iIqDY4bhCRIWJ8j4iIiIiIiIiI1I5FKSIiIiIiIiIiUjsWpYiIiIiIiIiISO1YlCIiIiIiIiIiIrVrUFxcXKz+H0tERERERERERIaMM6WIiIiIiIiIiEjtWJQiIiIiIiIiIiK1Y1GKiIiIiIiIiIjUjkUpIiIiIiIiIiJSOxaliIiIiIiIiIhI7ViUIiIiIiIiIiIitWNRioiIiIiIiIiI1I5FKSIiIiIiIiIiUjsWpYiIiIiIiIiISO1YlFLCgQMHMGTIELi6uqJBgwbYtGmTppukdRYsWICOHTvCxsYGTZo0wdChQ3Hp0iVNN0urrFixAkFBQbC1tZUfXbt2xV9//aXpZmm1hQsXyv/m3nzzTU03RavMnTtX9kv5D39/f003i8rhuFEzjhs147hRexw3KsdxQ/tx3KgZx42acdyoPY4bmh83WJRSQnZ2NoKDg7F8+XJNN0Vr7d+/H5MmTcKRI0ewa9cu5Ofn45FHHpF9Rwru7u7yTe/kyZM4ceIEHn74YTzxxBM4d+6cppumlY4fP46vv/5aDqx0v8DAQMTFxZV9HDx4UNNNonI4btSM40bNOG7UDseN6nHc0G4cN2rGcaNmHDdqh+OGdowbJir5rnpm4MCB8oOqtmPHjgrHq1evlncwxBviQw89pLF2aRNx96u8Dz/8UN7NEAOr+A+e7srKysKYMWPw7bff4oMPPtB0c7SSiYkJnJ2dNd0MqgLHjZpx3KgZxw3lcdyoGccN7cZxo2YcN2rGcUN5HDe0Z9zgTClSifT0dPlv48aNNd0UrVRYWIi1a9fKOztiWi1VJO6CDRo0CP369dN0U7RWRESEnOLv5eUlB9TY2FhNN4nogXDcqB7Hjepx3KgZxw3SNxw3qsdxo3ocN7Rn3OBMKap3RUVFMpPbvXt3tG7dWtPN0Spnz56Vg0Jubi4aNmyIP/74AwEBAZpullYRg2doaKicTkuV69y5s7w76OfnJ6fSzps3Dz179kR4eLhcZ4FI13DcqBrHjZpx3KgZxw3SNxw3qsZxo2YcN7Rr3GBRilRSdRYvVq5VcD/xH/Xp06flnZ0NGzZg3LhxMh/PgULh2rVrmDJlilwnwMLCQtPN0Vrlp/eLDLwYNDw8PLB+/Xq89NJLGm0bUV1w3Kgax43qcdxQDscN0jccN6rGcaN6HDe0b9xgUYrq1eTJk7F161a5g4hYaI8qMjMzg4+Pj/y8ffv2sjr/+eefywX2CHJNgMTERISEhFSYeixeT8uWLcOdO3dgbGys0TZqI3t7e7Rs2RKRkZGabgpRrXHcqB7Hjepx3KgbjhukyzhuVI/jRvU4bmjfuMGiFNWL4uJivP7663J66L59++Dp6anpJunM1GPxxkcKffv2lVOOy3vhhRfk9qPvvPMOB4hqFmqMiorCc889p+mmECmN40bdcNyoiONG3XDcIF3EcaNuOG5UxHFD+8YNFqWU/D+gfEUwOjpaTokUi+o1b95co23Tpim0a9aswebNm2XGND4+Xp63s7ODpaWlppunFWbMmCGnQYrXTGZmpuwvMaDu3LlT003TGuK1c++6ANbW1nBwcOB6AeW8/fbbcncVMYX25s2bmDNnjhxAR48eremmUQmOGzXjuFEzjhs147ihHI4b2o/jRs04btSM40bNOG5o37jBopQSTpw4gT59+pQdT506Vf4r8rli8S+C3GpU6N27d4XzP/zwA55//nkNtUq7iGmiY8eOlQvFicFTZHPFANG/f39NN410zPXr1+WAkJKSAicnJ/To0UNu9Ss+J+3AcaNmHDdqxnGD6gvHDe3HcaNmHDdqxnGDdHHcaFAs5kESERERERERERGpkZE6fxgREREREREREZHAohQREREREREREakdi1JERERERERERKR2LEoREREREREREZHasShFRERERERERERqx6IUERERERERERGpHYtSRERERERERESkdixKERERERERERGR2rEoRaRiV69eRYMGDXD69GlNN4WIiHQAxw0iIqoNjhuky1iUInpAzz//vBwESj8cHBzw6KOPIiwsTH69WbNmiIuLQ+vWrTXdVCIi0gIcN4iIqDY4bpA+Y1GKqB6IQUEMBOJjz549MDExweDBg+XXjI2N4ezsLM8REREJHDeIiKg2OG6QvmJRiqgemJuby4FAfLRt2xbTp0/HtWvXkJSUdN902n379sljMZh06NABVlZW6NatGy5dulT2/c6cOYM+ffrAxsYGtra2aN++PU6cOKHB35CIiOoTxw0iIqoNjhukr1iUIqpnWVlZ+Pnnn+Hj4yOn1lblvffew+LFi+Wbv7ir8eKLL5Z9bcyYMXB3d8fx48dx8uRJOeiYmpqq6TcgIiJ14rhBRES1wXGD9Ann9xHVg61bt6Jhw4by8+zsbLi4uMhzRkZV130//PBD9OrVS34uBoFBgwYhNzcXFhYWiI2NxX//+1/4+/vLr/v6+qrpNyEiInXguEFERLXBcYP0FWdKEdUDMfVVTJcVH8eOHcOAAQMwcOBAxMTEVPmcoKCgss/FoCIkJibKf6dOnYrx48ejX79+WLhwIaKiotTwWxARkbpw3CAiotrguEH6ikUponpgbW0tp8+Kj44dO+K7776TdzC+/fbbKp9TfnqsyHwLRUVF8t+5c+fi3Llz8m7G3r17ERAQgD/++EMNvwkREakDxw0iIqoNjhukr1iUIlIB8aYvptLevn27zt+jZcuWeOutt/D3339j2LBh+OGHH+q1jUREpD04bhARUW1w3CB9wTWliOrBnTt3EB8fLz+/desWli1bJhcgHDJkSK2/lxhYRL57xIgR8PT0xPXr1+UChMOHD1dBy4mISBM4bhARUW1w3CB9xaIUUT3YsWNHWU5bbKsqFgz87bff0Lt3b7lFa20YGxsjJSUFY8eORUJCAhwdHeWdi3nz5qmo9UREpG4cN4iIqDY4bpC+alBcXFys6UYQEREREREREZFh4ZpSRERERERERESkdixKERERERERERGR2rEoRUREREREREREaseiFBERERERERERqR2LUkREREREREREpHYsShERERERERERkdqxKEVERERERERERGrHohQREREREREREakdi1JERERERERERKR2LEoREREREREREZHasShFRERERERERERqx6IUERERERERERFB3f4fhxLKd60K120AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(12, 10), nrows=3, ncols=3)\n",
    "\n",
    "vars = [\n",
    "    'bin_radius', 'bin_mass', 'bin_volume', \n",
    "    'projected_area_ratio', 'radius_ratio', 'porosity_ratio',\n",
    "    'number_of_monomers_per_bin', 'bin_width_mass', 'bin_width'\n",
    "]\n",
    "\n",
    "name_to_label = {\n",
    "    \"bin_radius\": \"Bin radius\",\n",
    "    \"bin_radius_lower_bound\": \"Lower bound of bin radius\",\n",
    "    \"bin_radius_upper_bound\": \"Upper bound of bin radius\",\n",
    "    \"bin_width\": \"Bin width in radius space\",\n",
    "    \"bin_mass\": \"Bin mass\",\n",
    "    \"bin_width_mass\": \"Bin width in mass space\",\n",
    "    \"bin_volume\": \"Bin volume\",\n",
    "    \"projected_area_ratio\": \"Projected area ratio\",\n",
    "    \"radius_ratio\": \"Maximum dimension / radius of enclosing sphere\",\n",
    "    \"porosity_ratio\": \"Scaled porosity radius / equiv. sphere\",\n",
    "    \"extinction_coefficient\": \"Extinction coefficient\",\n",
    "    \"single_scattering_albedo\": \"Single scattering albedo\",\n",
    "    \"asymmetry_factor\": \"Asymmetry factor\",\n",
    "    \"particle_number_element_for_group\": \"Index of element for particle number\",\n",
    "    \"number_of_core_mass_elements_for_group\": \"Number of core mass elements\",\n",
    "    \"element_index_of_core_mass_elements\": \"Indices of core mass elements\",\n",
    "    \"last_prognostic_bin\": \"Last prognostic bin\",\n",
    "    \"number_of_monomers_per_bin\": \"Number of monomers per bin\"\n",
    "}\n",
    "name_to_units = {\n",
    "    \"bin_radius\": \"cm\",\n",
    "    \"bin_radius_lower_bound\": \"cm\",\n",
    "    \"bin_radius_upper_bound\": \"cm\",\n",
    "    \"bin_width\": \"cm\",\n",
    "    \"bin_mass\": \"g\",\n",
    "    \"bin_width_mass\": \"g\",\n",
    "    \"bin_volume\": \"cm3\",\n",
    "    \"projected_area_ratio\": \"\",\n",
    "    \"radius_ratio\": \"\",\n",
    "    \"porosity_ratio\": \"\",\n",
    "    \"extinction_coefficient\": \"m-1\",\n",
    "    \"single_scattering_albedo\": \"\",\n",
    "    \"asymmetry_factor\": \"\",\n",
    "    \"particle_number_element_for_group\": \"\",\n",
    "    \"number_of_core_mass_elements_for_group\": \"\",\n",
    "    \"element_index_of_core_mass_elements\": \"\",\n",
    "    \"last_prognostic_bin\": \"\",\n",
    "    \"number_of_monomers_per_bin\": \"\"\n",
    "}\n",
    "\n",
    "for var, ax in zip(vars, axs.ravel()):\n",
    "    line, = ax.plot(group_props[var].sel(group=1), marker='o')\n",
    "    \n",
    "    ax.spines[:].set_visible(False)\n",
    "    ax.grid(alpha=0.5)\n",
    "    ax.tick_params(width=0)\n",
    "    ax.set_xlabel('Bins')\n",
    "    ax.set_xticks(range(5))\n",
    "    ax.set_xticklabels([1, 2, 3, 4, 5])\n",
    "    if name_to_units[var]:\n",
    "        ax.set_ylabel(f'[{name_to_units[var]}]')\n",
    "    ax.set_title(f'{name_to_label[var]}')\n",
    "\n",
    "fig.suptitle(\"Group Properties\")\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f06f3e2-fe10-4437-99a3-b4bc4d0c088b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UACDJ+Pfff+XLL780iadatWpJ0aJFTbJIf06cONHMkoqIiJC33npLmjdvLuXKlZNPP/1UTp48KV9//bU5x/79+2X58uXy8ccfS/Xq1eWRRx6RcePGydy5c007NWvWLLl586ZMmzZNSpcuLa1bt5aePXvKmDFjAvwbAAAAAJyDpBQAIMm4ffu23Llzxyyp86bL9DZs2CBHjx6V06dPm5lTbpkyZTLJp02bNpnn+lOX7FWpUsXTRtsnS5bMzKxyt9GkV6pUqTxtdLbVwYMH5e+//7YgUgBAfE2YMEHCwsJMn6H9wNatW2Nsq7NnW7ZsadqHhISYAY57GTFihGmny8mjo4MkjRs3Nm3cgyIAgNhLEYf3AACQKDJkyCA1atSQoUOHSsmSJSVXrlwyZ84ck0TS2VKakFJ63Js+d7+mP3PmzOnzeooUKSRr1qw+bQoVKhTlHO7XsmTJ4tf1agIttvQ9d+/ejdN7gwlx2ocTYnRKnPGJMXny5JKUzJs3T/r27WuWcmtCSpNM7sGFyH2AunbtmhQuXFiefvpp6dOnzz3PrXUMJ0+ebGbjxkQ/TxNSAIAgTkoNHz5cvvrqKzlw4ICnmO17770nxYsX9xSqDQ8Pl5UrV8rx48clR44c0qJFC3OzoiPjbvrayy+/LGvWrJH06dNL+/btzbn1JgQAEFy0llTHjh0lX7585iaoUqVK8uyzz5raT0lN5ALr/tAbQu3fDh8+bGZv2RVx2ocTYnRKnPGJsUSJEpKU6HLrzp07S4cOHcxzTU4tW7bMLMvu379/lPZVq1Y1DxXd6266KYbWJZw6daq888470bbZtWuXvP/++/LTTz+ZTTcAAHEX0KzNunXrpFu3bqaD0CUbb7zxhik8u2/fPlO4Vmt/6GP06NFSqlQpOXbsmLz00kvm2IIFC8w5dKSnadOmkjt3btm4caOcOnVK2rVrJylTppR33303kOEBAOKgSJEipn+4evWqKUiu/+Bv1aqVGeHW73p15swZnxsBfV6hQgXzZ21z9uxZn3NqH6M3Yu736099jzf3c3cbfxQrVizW8Wm/pTeEOvMrqc08SEjEaR9OiNEpcdolRq0JqAMVAwYM8BzTJJsu1XYv5Y4rvTfRews9V3RJKZ1x1aZNG7N0MDb9hbe4zsZjNp99EKd9OCHG+MTpT18T0KSUFqL1NmPGDDPdVjsZrfVRpkwZU/DW+0Zl2LBh8txzz5kbDJ0JpbOoNIn13XffmaUXelOiM6lef/11UxzXu14IACB46OCEPrTG04oVK0zxc11ypzcBq1ev9iShNHGltaJ0xqzS5X8XL140fUnlypXNse+//950pLrEw93mzTfflFu3bplBDKU79elMXX+X7qm43tTpzZO+N5hvCv1BnPbhhBidEqcdYjx//ry5MYpuKbeuwIgr3RBjx44dZvleTHTpn67u0M02rJxlq5jNZx/EaR9OiDE+cfozyzZJrW+7dOmS+al1P+7VJmPGjJ6leToaUrZsWZ9OSdeT682JFjSsWLGiBVcOAEgomoDSArKaINKOr1+/fqZD0yUa7qKzOnqts5Q0STVw4EDJmzevWd6ttBZVo0aNzLIOXc6hiafu3bubHfa0ndJR7rfffls6depkBjH27NkjY8eOlQ8++CDA0QMAAuGPP/6QXr16mQGKyJttuC1evNgMcuzcuTNenxWXWbZ2munm9BgVcdqHE2JM7DhTJKXMm95oPPzww2aGVEyjIjoLqkuXLp5jWpA2ulES92v+olits+N0QoxOidMJMSb2FNpA08EHXZLx559/mkEK3S1JZ8m6ZzS99tprZmmf9gU6I+qRRx4xM2+9byJmzZplElH16tUzozl6jg8//NDzutYl1Jm2ukxDZ1Nlz55dBg0a5NO/AACSJv3O1v4sumXYcV1Sp7Nrdem31jF00z52/fr1Mn78eLlx44ZJSB05csTs8OpN+5hHH31U1q5dm+h9sR1mut2PE2JUxGkfTogxMeNMMkkpvTHQkWrd8js6ujxD13drbSldlpfQKFbr7DidEKNT4nRCjIk9hTbQnnnmGfOIic6WGjJkiHnERJNZs2fPvufn6K5KP/zwQ7yuFQBgPS3PoQMKupTbPUtW+0V9rgMScaGDGLt37/Y5pjN0td/UGbV6E6YF0l988UWfNrpiQ2fZNmvWLB4RAYBzJYmklHYeS5cuNSMR+fPnj/L65cuXzVIM3Sp84cKFntFypaMhW7du9WlPsdqE5YQ4nRCjU+J0QoxOihMAgOj07dvX7LhdpUoVqVatmkRERJhZtO7d+HTjI93FVXfkdhdH1zq07j+fOHHC7KKnO3drX6r3GZFXa2hdw2zZsnmO671FdPcXBQoUMMvJAQBBlpTSmiE9evQwiSad7hrdl7nOkNIaUalTpzbruCOv8dZitbqsQ6fbapF0pWvBte6UzqryF8Vq780JcTohRqfE6YQYnRQnAACR6a6s586dM0uvtWSHbn6hS7ndZTyOHz/uM5NYd+/2rjWru3vro3bt2n4vuwMA2CwppUv2dHnFokWLzOiEuwaU1vpIkyaNSUg1aNDAbL36+eefm+f6UDly5DA3Yvq6Jp+ef/55szOTnuOtt94y59ZEFgAAAAD70dUWMS3Xi5xoCgsLMwPiseFPsiq25wQAJKGk1MSJE83POnXq+ByfPn26vPDCC2ZLVt3mW+m0Wm9Hjx41nYsmpnTpn+62p7OmdJqtTuW9V60RAAAAAAAAOHz53r1ossqf0YeCBQvKN998k4BXBgAAAAAAgMRk362pAAAAAAAAkGSRlAIAAAAAAIDlSEoBAAAAAADAciSlAAAAAAAAYDmSUgAAAAAAALAcSSkAAAAAAABYjqQUAAAAAAAALEdSCgAAAAAAAJYjKQUAAAAAAADLkZQCAAAAAACA5UhKAQAAAAAAwHIkpQAAAAAAAGA5klIAAAAAAACwHEkpAAAAAAAAWI6kFAAAAAAAACxHUgoAAAAAAACWIykFAAAAAAAAy5GUAgAAAAAAgOVSWP+RAAAAAJxg8eLFsX7PY489JmnSpEmU6wEAJC0kpQAAAAAkihYtWsSqfUhIiBw6dEgKFy6caNcEAEg6WL4HAAAAINGcPn1a7t6969cjbdq0gb5cAICFSEoBAAAASBTt27eP1VK85557TjJmzJio1wQASDpYvgcAAAAgUUyfPj1W7SdOnJho1wIASHqYKQUAAAAAAADLkZQCAAAAkKg+/vhjs5TPPXNq3rx5UrJkSVPQPDw8PNCXBwAIEJbvAQAAAEg0ERER8tZbb0nDhg3lzTfflJMnT8oHH3wgffr0kTt37sj7778v+fLlky5dugT6UgEAFiMpBQAAACDRTJ48WaZMmSJt2rSRnTt3SrVq1WTSpEnSqVMn87ompLSWFEkpAHAelu8BAAAASDTHjh2TRx55xPy5YsWKkjx5cnnooYc8r9euXVuOHDkSwCsEAAQKSSkAAAAAiSZt2rRy9epVz/McOXJI+vTpfdrcvn07AFcGAAg0klIAAAAAEk2JEiXkl19+8Tz/448/pGDBgp7nBw4ckLCwsABdHQAgkKgpBQAAACDRvPfee5IuXboYXz9+/Lh07drV0msCACQNJKUAAAAAJJqHH374nq+/8sorll0LACBpYfkeAAAAAEtpIur8+fOBvgwAQICRlAIAAABgqc8//1z++eefQF8GACDASEoBAAAAsJTL5Yr3OSZMmGAKpIeGhkr16tVl69atMbbdu3evtGzZ0rQPCQmRiIiIe557xIgRpl3v3r09xy5cuCA9evSQ4sWLS5o0aaRAgQLSs2dPuXTpUrxjAQCnIikFAAAAIKjMmzdP+vbtK+Hh4bJjxw4pX768NGzYUM6ePRtt+2vXrknhwoVNsil37tz3PPe2bdtk8uTJUq5cOZ/jJ0+eNI/Ro0fLnj17ZMaMGbJ8+XLp1KlTgsYGAE5CoXMAQJx5b/Htr1KlSkmKFHQ/AOBkly9fjtf7x4wZI507d5YOHTqY55MmTZJly5bJtGnTpH///lHaV61a1TxUdK+7XblyRdq2bStTp06Vd955x+e1MmXKyJdfful5XqRIERk2bJg899xzcvv2bfo2AIgDvjkBAHFWoUIFs7zB32UYyZIlk19//dWMVgMAnElnM+nj7t27Pscjz0yKyc2bN2X79u0yYMAAn/6lfv36smnTpnhdW7du3aRp06bmXJGTUtHRpXsZM2YkIQUAccS3JwAgXrZs2SI5cuS4bztNXOkoMwDAmTSR1L59e9m/f79nMMM9sKE/79y549d5dNc+bZsrVy6f4/r8wIEDcb6+uXPnmqWAunzP3+sYOnSodOnSJVaf42+c0b1PE3lxfX8wcEKMijjtwwkxxifO5MmT37cNSSkAQJzVrl1bihYtKpkzZ/arfa1atUxxWACA83Ts2FEefPBB+eSTT0wCSRNRScUff/whvXr1klWrVpnC6fejOwfqjCpdkj548OBYfdahQ4fidI16Q6jF1g8fPmxmhtmRE2JUxGkfTogxPnGWKFEi4ZJSlSpVktjQTmbx4sWSL1++WL0PABA81qxZE6v233zzTaJdCwAgafvtt99MTSYdzIiP7Nmzm9H3M2fO+BzX5/crYn6vWVy6pND7nkdnBKxfv17Gjx8vN27c8Iz4az2sRo0aSYYMGWThwoWSMmXKWH1WsWLF4nSNej16Q6i/P39mHwQjJ8SoiNM+nBBjYsfpd1Jq165d8r///U/Sp09/37Y6BVd3ttAvbwAAAACoV6+e/Pzzz/FOSqVKlUoqV64sq1evlhYtWnhG8fV59+7d43xtu3fv9jmmRdR1lP/111/33ITpDCnd5S916tRmAN6fWVWRxeeGTmco6PvtfPPrhBgVcdqHE2JMzDhjtXyvX79+kjNnTr/avv/++3G9JgBAkPrzzz/NP9KPHz9uCtFG3ikJAOBcH3/8sakptWfPHlNjMPIMoyeeeMLvc/Xt29ecq0qVKlKtWjWJiIiQq1evenbja9eunVmxMXz4cPNc+6R9+/Z5/nzixAkz6K4D7pok01lPkesepkuXTrJly+Y5rgmpBg0ayLVr1+Tzzz83z/WhtLai3W9IASAx+J2UOnr0qF+FbN30Sz9v3rxxvS4AQJDREWq9odCd9bTQrP4j/vfffzezZ2O7BBwAYD+6M96PP/4o3377bZTXYlPoXLVq1UrOnTsngwYNktOnT5vdYJcvX+4pfq6DI951T06ePCkVK1b0PB89erR5aG3EtWvX+vWZWgRdN/dQkWd76b1SWFiY39cPAIhlUqpgwYLmy/x+iSbdtaJ169bywAMPiJ3pTda1m7fl+q275mfy5P5thx6M9B8Ido/TCTE6JU4nxOiO071zUVKhW3O/+uqr8vbbb5sRZ60borNr27Zta2pvgL7DjpwQpxNidEqcge47evToIc8995wMHDgwys55caFL9WJarhc50aQJo9jGHvkcderUCdjvzyn9hxP+f6iI0z6cEGNi9x8hrlicWUe9N2zYEOMuS5qQ0qmykZds2JH+R1dq0IpAXwYAB/u6bSEpW6p4klkuoIkoXQpRpEgRyZIli+kvSpcubeqHNG/e3Myacjr6DgBO7ju8+wnEDv0HALv2H7Has1CX7zVu3Niso45s/vz58vzzz8uwYcMS8voAAEFCa2+4ByXy5MkjR44c8bx2/vz5AF4ZACApePLJJ2O9aysAwN5iVeh8yZIlZtqq7nKxbNkyT3HCL774wiSk3nnnHVMM3QnSpEwuu8Pry5HDR6RI0SJJZqZCYk3Vs3ucTojRKXE6IUZ3nH/+/pskJQ899JCZHVWyZElp0qSJ2bFVdzL66quvzGug77AjJ8TphBidEmeg+44HH3zQLPXWvqJs2bJRCp337NkzYNeW1Dml/3DC/w8VcdqHE2JM7P4jVkkp3Z1CCxPWqlVL2rRpY2ZHac0QrRcyePBgs12qU2gxxrSpUkhoymTmp73/AwyxfZxOiNEpcTohRnec+j2UlOjueleuXDF/1rpS+ud58+ZJsWLF2Hnv/6HvsB8nxOmEGJ0SZ6D7Dt19T+8n1q1bZx7e9LpISsXMKf2HE/5/qIjTPpwQY2L3H7FKSrmX8K1cuVIeeeQReeyxx+SHH34wu1688cYbiXKBAICkT7fE1uV6unxPl+5pXzFp0qRAXxYAIAnRHeoAAIhzUuqXX37x/HnUqFGmqLku5dMtwL1fK1euXGxOCwAIYlq0VpfrnTlzxuzKoYVsdSZtw4YNA31pAAAAAOySlKpQoYKZsqU3He6fWk9qwYIFnu0B9biuNwQAOIMu3S5UqJBZzh0aGipDhw41W3QfOnQo0JcGAAAAwC5JKabcAgAi2759u1nWXalSJfN82rRpkjVrVrOkL2PGjIG+PAAAAADBnpTS5XllypSRZMmS+dV+7969Urx4cUmRItZlqwAAQeTChQuSP39+z/PMmTNLunTp5K+//iIpBQAAACBGfmeMKlasKKdPnzbFa/1Ro0YNU2ekcOHC/n4EACBI7du3z/QRbrqke//+/XL58mXPMeoNAgAAAIhTUkpvMAYOHChp06b1q73uwAQAcIZ69ep5agu6Pf744z51CKk3CADO5r0xkjftI7QmYYECBSR16tSWXxcAIAiSUrVq1ZKDBw/6fWKdKZUmTZq4XhcAIEhQbxAAEJtNk2KSMmVKadWqlUyePNkkqQAA9ud3Umrt2rWJeyUAgKBDvUEAgL8WLlxodmzt16+fVKtWzRzbunWrvP/++xIeHi63b9+W/v37y1tvvSWjR48O9OUCACzAXQEAIM6oNwgA8NewYcNk7Nix0rBhQ8+xsmXLms0ytEyIJqh0o4z//e9/JKUAwCFISgEA4ox6gwAAf+3evVsKFiwY5bge09fcS/xOnToVgKsDAASCf+stAAC4R73BnTt3+vW4X71BLYauSa5ChQqZdkWKFJGhQ4f6FFG/cuWKdO/e3Yysa5tSpUrJpEmTfM5z/fp16datm2TLlk3Sp08vLVu2lDNnzvi0OX78uDRt2tQk1HLmzGmWk+jSEQBA4ihRooSMGDHCZ4Di1q1b5pi+pk6cOCG5cuUK4FUCAKzETCkAQJwldL3B9957TyZOnCgzZ86U0qVLy08//SQdOnSQTJkySc+ePU2bvn37yvfffy+ff/65hIWFycqVK+WVV16RvHnzyhNPPGHa9OnTR5YtWyZffPGFea8msZ588kn58ccfPckvTUjlzp1bNm7caEbl27VrZ4rsvvvuuwkaEwDg/5owYYL5ntZBhXLlypljOkNKv5OXLl1qnv/222/mOx0A4AwkpQAASYYmiJo3b24SRkqTTnPmzDF1RrzbtG/fXurUqWOed+nSxezUpG30ZufSpUvyySefyOzZs6Vu3bqmzfTp06VkyZKyefNmeeihh0wia9++ffLdd9+ZEXldLqIzsrQA7+DBgyVVqlQB+g0AgH3VrFnT7Ng6a9Ys+fXXX82xp59+Wtq0aSMZMmQwz59//vkAXyUAwEos3wMAJKkbltWrV3tuVn7++WfZsGGDNG7c2KfN4sWLzRIPXda3Zs0a075Bgwbm9e3bt5vlIPXr1/e8R5eFFChQQDZt2mSe608truu9REQL7/7zzz9mh0AAQMLT71hNPr300ksyZswY8+jatas5dvjw4UBfHgAgAJgpBQBIMnQrcL1p0SRS8uTJzZIO3a2pbdu2njbjxo0zs6N0+UeKFCkkWbJkMnXqVFPfSulugDrTKXPmzD7n1gSUvuZuE7lmifu5u40/9PpiS99z9+7dOL03mBCnfTghRqfEGZ8Y9Ts5vnQW7KpVqyQ0NNTnuNYmrFevnvz555/x/gwAQHAhKQUASDLmz59vlnXo0jutKbVr1y7p3bu3qRelS/bcSSldhqezpXTHpvXr15ui5trGe3aUFQ4dOhTr9+gN4YULF8ysAE2o2RVx2ocTYnRKnPGJ0V2IPD504wmt76ff3zqooPbv32+WWj/zzDPxPj8AIPiQlAIAJBm6A57OlmrdurV5rkvsjh07JsOHDzdJqX///VfeeOMNWbhwoafulBbL1eTV6NGjTVJKi5frzk4XL170mS2lu+/pa0p/etepcr/ufs1fxYoVi3WMOkNBbwiLFi2aIDMPkiritA8nxOiUOAMd41dffWW+p3X269y5c81yaZ0hpc91KR8AwHkCmpTSmwztnA4cOGC29dY6IbrzUvHixT1tpkyZYkbMd+zYIZcvX5a///47ypIMLYSrNy2Rz603NgCA4HHt2rUoo/d646Sj+0prRenjXm0qV65sdtHT2lQtW7b0LA05fvy41KhRwzzXn7os8OzZs5IzZ05zTJeUZMyYUUqVKuX39cb1pk6vX99r1xtfN+K0DyfE6JQ4Axmj/ntfd0bVjSp0ZpTOdNWdT0eNGmX5tQAAkoaAJqXWrVtnllxUrVpVbt++bUa/tVCt7oiULl06zw1Ko0aNzGPAgAExnmvIkCHSuXNnz3P3Dh4AgODRrFkzkyzSouS6fG/nzp1m9Lxjx47mdU0a1a5d28yo0psbXb6nfcmnn37qGWXPlCmTdOrUSfr27StZs2Y17+nRo4dJROnOe0r7Gk0+6S5PI0eONHWk3nrrLdMnpU6dOqC/AwCwE60TGDkpNm/ePHnsscfMwMHAgQM9bfT7GgDgLAFNSi1fvtzn+YwZM8yIte6c5C5Yq7VE1Nq1a+95Lk1CxWbJBQAg6dF6UXqD8sorr5hZTFonSndmGjRokKeNLvnQQQpd7qG1UTQxpYks3c3J7YMPPjA3PnrDc+PGDbOz3kcffeR5XWcILF26VF5++WWTrNKBEF0eqAMcAICEoyscQkJCohzX3VMnTZokkydPNn/WNnYuMg8ACIKaUpcuXTI/dWQ7tkaMGCFDhw41o+tt2rSRPn36eAooAgCCgw4wREREmEdMdABi+vTp9zyP7uw0YcIE84iJJrO++eabeF0vAODe1qxZE+hLAAAkYUkma6O1QHRW1MMPPyxlypSJ1Xt79uwplSpVMsmsjRs3mhH0U6dOxapgItt6OztOJ8TolDidEGN84rRznRQAQNKjS64BAEjySSmt47Fnzx7ZsGFDrN+rdUPcdBemVKlSmeUeWuzc39ogbOvt7DidEKNT4nRCjPGJMyG29AYAwF+//PKLGXD2t6/SHfl00yNWPACAMySJb/vu3bub2h66A0f+/Pnjfb7q1aubwum///67z05+98K23s6O0wkxOiVOJ8TopDgBAMGtYsWKZjOJHDly+NVe6/zt2rVLChcunOjXBgBweFJKixrqjkgLFy40hcwLFSqUIOfVjkxHY9zbfPuDbb3vzQlxOiFGp8TphBidFCcAIHjpv/d1A4u0adP61f7mzZuJfk0AgKQjRaCX7M2ePVsWLVpkitvqKIp7O2/d6lvpMX3ojAC1e/du01YLmmsNqU2bNsmWLVvkP//5jzmuz7XI+XPPPSdZsmQJZHgAAACAo+mO2gcPHvS7vc6Uct8HAADsL6BJqYkTJ5qfderU8Tmuuyq98MIL5s+6Vezbb7/t07F5t9GaUbo9+ODBg8223zrbSpNS3nWmAAAAAFhPV0MAAJBkl+/djyab9BET3XVv8+bNCXxlAAAAAAAASEz23ZoKAAAAAAAASRZJKQAAAAAAAFiOpBQAAAAAAAAsR1IKAAAAAAAAliMpBQAAACDoTJgwQcLCwiQ0NFSqV68uW7dujbHt3r17pWXLlqZ9SEiIRERE3PPcI0aMMO169+7tc/z69evSrVs3yZYtm6RPn96c88yZMwkWEwA4DUkpAAAAAEFl3rx50rdvXwkPD5cdO3ZI+fLlpWHDhnL27Nlo21+7dk0KFy5skk25c+e+57m3bdsmkydPlnLlykV5rU+fPrJkyRL54osvZN26dXLy5El58sknEywuAHAaklIAAAAAgsqYMWOkc+fO0qFDBylVqpRMmjRJ0qZNK9OmTYu2fdWqVWXUqFHSunVrSZ06dYznvXLlirRt21amTp0qWbJk8Xnt0qVL8sknn5jPrlu3rlSuXFmmT58uGzdulM2bNyd4jADgBCkCfQEAAAAA4K+bN2/K9u3bZcCAAZ5jyZIlk/r168umTZvidW5dmte0aVNzrnfeecfnNf3MW7dumdfcSpQoIQUKFDCf+9BDD/n1GXfu3InTten77t69G+f3BwMnxKiI0z6cEGN84kyePPl925CUAgAAABA0zp8/b26McuXK5XNcnx84cCDO5507d65ZCqjL96Jz+vRpSZUqlWTOnDnK5+pr/jp06FCcrk9vCC9cuCCHDx82STg7ckKMijjtwwkxxidOTdzfD0kpAAAAAI72xx9/SK9evWTVqlWmcHpiKlasWJzep4k4vSEsWrSoX7MPgpETYlTEaR9OiDGx4yQpBQAAACBoZM+e3dwURd71Tp/fr4h5THRpnhZJr1Spks9N2Pr162X8+PFy48YNc25dOnjx4kWf2VKx/dz43NDpDAV9v51vfp0QoyJO+3BCjIkZp33nlwEAAACwHV1Cp0XGV69e7bO0RJ/XqFEjTuesV6+e7N69W3bt2uV5VKlSxRQ91z/rTZh+ZsqUKX0+9+DBg3L8+PE4fy4AOB0zpQAAAAAElb59+0r79u1N4qhatWoSEREhV69eNbvxqXbt2km+fPlk+PDh5rnOcNq3b5/nzydOnDDJpvTp05vlKBkyZJAyZcr4fEa6dOkkW7ZsnuOZMmWSTp06mc/OmjWrZMyYUXr06GESUv4WOQcA+CIpBQAAACCotGrVSs6dOyeDBg0yRcYrVKggy5cv9xQ/19lL3sV4T548KRUrVvQ8Hz16tHnUrl1b1q5d6/fnfvDBB+a8LVu2NEv6GjZsKB999FECRwcAzkFSCgAAAEDQ6d69u3lEJ3KiKSwsTFwuV6zOH12ySougT5gwwTwAAPFHTSkAAAAAAABYjqQUAAAAAAAALEdSCgAAAAAAAJYjKQUAAAAAAADLkZQCAAAAAACA5UhKAQAAAAAAwHIkpQAAAAAAAGA5klIAAAAAAACwHEkpAAAAAAAAWI6kFAAAAAAAACxHUgoAAAAAAACWIykFAAAAAAAAy5GUAgAAAAAAgOVISgEAAAAAAMByJKUAAAAAAABgOZJSAAAAAAAAsBxJKQAAAAAAAFiOpBQAAAAAAAAsR1IKAAAAAAAAliMpBQAAAAAAAMuRlAIAAAAAAIDlSEoBAAAAAADAciSlAAAAAAAAYDmSUgAAAAAAALAcSSkAAAAAAABYjqQUAAAAAAAALEdSCgAAAAAAAJYjKQUAAAAAAADLkZQCAAAAAACA5UhKAQAAAAAAwHIkpQAAAAAAAGA5klIAAAAAAACwHEkpAAAAAAAAWI6kFAAAAAAAACxHUgoAAAAAAACWIykFAAAAAAAAy5GUAgAAAAAAgOVISgEAAAAIOhMmTJCwsDAJDQ2V6tWry9atW2Nsu3fvXmnZsqVpHxISIhEREVHaTJw4UcqVKycZM2Y0jxo1asi3337r0+b06dPy/PPPS+7cuSVdunRSqVIl+fLLLxMlPgBwApJSAAAAAILKvHnzpG/fvhIeHi47duyQ8uXLS8OGDeXs2bPRtr927ZoULlxYRowYYRJK0cmfP795ffv27fLTTz9J3bp1pXnz5iah5dauXTs5ePCgLF68WHbv3i1PPvmkPPPMM7Jz585EixUA7IykFAAAAICgMmbMGOncubN06NBBSpUqJZMmTZK0adPKtGnTom1ftWpVGTVqlLRu3VpSp04dbZtmzZpJkyZNpFixYvLggw/KsGHDJH369LJ582ZPm40bN0qPHj2kWrVqJsn11ltvSebMmU0iCwAQeyni8B4AAAAACIibN2+aJNCAAQM8x5IlSyb169eXTZs2Jchn3LlzR7744gu5evWqWcbnVrNmTTNLq2nTpiYZNX/+fLl+/brUqVMnVueO6zXdvXs3zu8PBk6IURGnfTghxvjEmTx58vu2ISkFAAAAIGicP3/e3BjlypXL57g+P3DgQLzOrUvyNAmliSadJbVw4UIzE8tNk1CtWrWSbNmySYoUKczsLG1TtGhRvz/j0KFDcbo2vSG8cOGCHD582CTh7MgJMSritA8nxBifOEuUKHHfNiSlAAAAAEBEihcvLrt27ZJLly7JggULpH379rJu3TpPYmrgwIFy8eJF+e677yR79uzy9ddfm5pSP/zwg5QtW9avz9DlgXGhiTi9IdQEmD+zD4KRE2JUxGkfTogxseMkKQUAAAAgaGgySG+Kzpw543Ncn8dUxNxfqVKl8sx6qly5smzbtk3Gjh0rkydPliNHjsj48eNlz549Urp0adNGC6xrQkp3AtS6Vv6Izw2dzlDQ99v55tcJMSritA8nxJiYcdp3fhkAIChHYXQUulChQpImTRopUqSIDB06VFwul0+7/fv3yxNPPCGZMmUyW3JrAdvjx497XtdlF926dTPLK3T5hW4DHvnmRdtrTRBdepEzZ07p16+f3L5927JYAQBxTxxpwmj16tU+S0v0uXf9p4Sg571x44ZnBz8VeemK3qBpOwBA7DFTCgCQZLz33nsyceJEmTlzphmF1i25dWclTT717NnTtNGR6kceeUQ6deokb7/9tmTMmNFs1x0aGuo5T58+fWTZsmWmSK2+t3v37mbb7h9//NGT/NKElI6o605Kp06dMtt8p0yZUt59992AxQ8A8E/fvn3N0roqVaqYnfAiIiJMUXLtM5R+p+fLl0+GDx/uKY6+b98+z59PnDhhlunpwIV7ZpQWTm/cuLEUKFBALl++LLNnz5a1a9fKihUrPLVRtG3Xrl1l9OjRZuBDl++tWrVKli5dGrDfBQAEM5JSAIAkQxNEzZs3NwkjFRYWJnPmzJGtW7d62rz55ptmy+6RI0d6jumMKjetA/LJJ5+Ym4m6deuaY9OnT5eSJUuabb0feughWblypbk50ZogWhi3QoUKZkbW66+/LoMHDzaj8ACApEuLjZ87d04GDRokp0+fNt/jy5cv9xQ/19mw3jOaTp48KRUrVvQ816SSPmrXrm0ST+rs2bMmmaUDFTqgUa5cOZOQeuyxx8zrOnDxzTffSP/+/aVZs2Zy5coVk6TSgRTtlwAAsUdSCgCQZOhW21OmTJFff/1VHnzwQfn5559lw4YNMmbMGPO6Lo/QGVCvvfaaNGzYUHbu3GmW+unodosWLUwb3Sb81q1bZmtwNx3d1pFv3Spck1L6UwvSeu/cpOd7+eWXzawr7xuXe4nL9r9sHWwvTojTCTE6Jc74xJgUa6XoLFh9RMedaHLTQY7IS8Ej0wENf4qUf/nll7G8UgBATEhKAQCSDB19/ueff0wSSW+A9MZp2LBh0rZtW88oto5MjxgxQt555x2z3E9HxnVp3po1a8yIt46Y60ynzJkz+5xbE1D6mtKf0W0l7n4tMbf1Zutge3FCnE6I0SlxxidGf7b1BgAgtkhKAQCSjPnz58usWbPM0jutKaX1Pnr37i158+Y1tUPchWR1iZ/WjVK6ZEOX/emuR5qUslJctvVm62B7cUKcTojRKXE6IUYAQHAhKQUASDJ0BzydLdW6dWvzXJfYHTt2zBSq1aSUbgOeIkUKKVWqlM/7tF6ULvNTWrxci9hevHjRZ7aU91bh+tO7TpX7dfdr/orrTR1bB9uLE+J0QoxOidMJMQIAgoc95yYDAIKSbrd9r622dVle1apV5eDBgz5ttAZVwYIFzZ91m3AtRuu9Vbi216K37q3C9efu3bvNckA33T1Jd/KLnPACAAAAYMOklI58681FhgwZJGfOnKZIbeQbDS14W6dOHXOjEBISYka+I9O18VpvRNvoqLhuE641RwAAwUV3M9IaUlrM/Pfff5eFCxeaIuf//e9/fWZTzZs3T6ZOnWqWoYwfP16WLFkir7zyinldd0zSfkC3C9c6U1r4XLcI10SUFjlXDRo0MMmn559/3hRT192V3nrrLenWrZukTp06YPEDAAAAThLQpNS6devMDYBu0a0j1Lpbkt4oXL161WfUvFGjRvLGG2/EeB5NSOluSXqOpUuXyvr166VLly4WRQEASCjjxo2Tp556yiSYdEneq6++Kl27dpWhQ4d62miCSutHjRw50izv+/jjj81OSI888oinzQcffCCPP/64tGzZUmrVqmWW5H311Vc+s6+0v9Cfmqx67rnnzDbgQ4YMsTxmAAAAwKkCWlNKd0zyNmPGDDNjSke19SZCaYHb6LZ1ddu/f785z7Zt26RKlSqem5omTZrI6NGjTXFcAEBw0JmzERER5nEvHTt2NI+YhIaGyoQJE8wjJrrc75tvvonX9QIAAACwSU2pS5cumZ9Zs2b1+z2bNm0yS/bcCSlVv359U5Nky5YtiXKdAAAAAAAAsMnue1rEVmdFPfzww1KmTBm/33f69Gkzu8qb7sykiS19LTZb5MaWvkevOy7vDSZOiNMJMTolTifEGJ842W0JAAAAQFKRZJJSWltqz549ni29rXbo0KFYv0dvCLXIuhbajbxblJ04IU4nxOiUOJ0QY3ziLFGiRKJeFwAAAAAEVVKqe/fungLl+fPnj9V7tXit95be6vbt2+ZmTV/zV7FixSS2dIaC3hAWLVrU1rMPnBCnE2J0SpxOiNFJcQIAAACwr4AmpVwul/To0cNs+a2FzAsVKhTrc+iuSRcvXjTF0StXrmyOff/992YWQfXq1f0+T1xv6nSGgr7X7jeFTojTCTE6JU4nxOikOAEAAADYU4pAL9mbPXu2LFq0yOy45K4BlSlTJkmTJo35sx7Th84IULt37zZtCxQoYOpG6ZbhjRo1ks6dO5stwm/dumVmXrVu3Zqd9wAAAAAAAJKogBZcmThxotlxr06dOpInTx7PY968eZ42mmiqWLGiSTqpWrVqmeeLFy/2tJk1a5apk1KvXj1p0qSJPPLIIzJlypSAxAQAAAAAAIAgWL53P4MHDzaPe9EZUzrjCgAAAAAAAMHBvltTAQAAAAAAIMkiKQUAAAAAAADLkZQCAAAAAACA5UhKAQA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      "text/plain": [
       "<Figure size 1200x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(12, 5), nrows=1, ncols=3)\n",
    "\n",
    "vars = [\n",
    "    'temperature', 'pressure', 'air_density'\n",
    "]\n",
    "\n",
    "for var, ax in zip(vars, axs.ravel()):\n",
    "    line, = ax.plot(ds.time, ds[var])\n",
    "    ax.spines[:].set_visible(False)\n",
    "    ax.grid(alpha=0.5)\n",
    "    ax.tick_params(width=0)\n",
    "    ax.set_xlabel('Time [days]')\n",
    "    ax.set_ylabel(f'[{ds[var].units}]')\n",
    "    ax.set_title(f'{ds[var].long_name}')\n",
    "\n",
    "fig.suptitle(\"Environmental data\")\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f9397a-1604-4bab-9b3e-fe7d08c2ece1",
   "metadata": {},
   "source": [
    "# Sulfate\n",
    "\n",
    "This next model is more complex and tests sulfate.\n",
    "\n",
    "First up we define the paramters to configure sulfate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ce4de30-b2e5-4dec-ab79-5a42630f616d",
   "metadata": {},
   "outputs": [],
   "source": [
    "GRAVITY = 9.806  # Acceleration due to gravity in m/s^2\n",
    "\n",
    "# Simplified constants for debugging\n",
    "NZ = 1\n",
    "NELEM = 1\n",
    "NBIN = 38\n",
    "\n",
    "dtime = 1800.0  # Time step in seconds\n",
    "deltaz = 10000.0  # Grid box height in meters\n",
    "nsteps = int(180000 / dtime)\n",
    "\n",
    "# Sulfate density\n",
    "rho_sulfate_kg_m3 = 1923.0  # kg/m³\n",
    "\n",
    "# Grid setup\n",
    "latitude = -40.0\n",
    "longitude = -105.0\n",
    "vertical_center = [17000.0]\n",
    "vertical_levels = [vertical_center[0] - deltaz, vertical_center[0] + deltaz]\n",
    "\n",
    "# Standard atmosphere\n",
    "pressure_centers = np.array([9000.0])\n",
    "temperature_centers = np.array([250.0])\n",
    "# this is directly from the original CARMA test, but it seems like there are some unit conversion issues\n",
    "air_mass_density_centers = pressure_centers * 10.0 / (8.31430e07 / 28.966 * temperature_centers) * (1.0e-3 * 1.0e6)\n",
    "pressure_levels = np.zeros(2)\n",
    "pressure_levels[0] = pressure_centers[0] - \\\n",
    "    (vertical_levels[0] - vertical_center[0]) * air_mass_density_centers[0] * GRAVITY\n",
    "pressure_levels[1] = pressure_centers[0] - \\\n",
    "    (vertical_levels[1] - vertical_center[0]) * air_mass_density_centers[0] * GRAVITY\n",
    "\n",
    "# Initial mass mixing ratios\n",
    "mass_mixing_ratios = {\n",
    "    \"H2O\": [1.0e-4],\n",
    "    \"H2SO4\": [0.1e-9 * (98.0 / 29.0)],\n",
    "    \"SULFATE\": [[0.0 for _ in range(NBIN)]]\n",
    "}\n",
    "satliq = {\n",
    "    \"H2O\": [-1.0],\n",
    "    \"H2SO4\": [-1.0]\n",
    "}\n",
    "satice = {\n",
    "    \"H2O\": [-1.0],\n",
    "    \"H2SO4\": [-1.0]\n",
    "}\n",
    "\n",
    "# Set up CARMA parameters\n",
    "params = musica.CARMAParameters()\n",
    "params.nz = NZ\n",
    "params.nbin = NBIN\n",
    "\n",
    "# Create sulfate group - simplified\n",
    "sulfate_group = musica.carma.CARMAGroupConfig(\n",
    "    name=\"sulfate\",\n",
    "    shortname=\"SULF\",\n",
    "    rmin=2.e-10,  # Minimum radius in m\n",
    "    rmrat=2.0,   # Mass ratio between bins\n",
    "    swelling_approach={\n",
    "        \"algorithm\": musica.carma.ParticleSwellingAlgorithm.WEIGHT_PERCENT_H2SO4,\n",
    "        \"composition\": musica.carma.ParticleSwellingComposition.NONE\n",
    "    },\n",
    "    do_drydep=True,\n",
    "    is_sulfate=True\n",
    ")\n",
    "params.add_group(sulfate_group)\n",
    "\n",
    "# Create sulfate element\n",
    "sulfate_element = musica.carma.CARMAElementConfig(\n",
    "    igroup=1,\n",
    "    name=\"Sulfate\",\n",
    "    shortname=\"SULF\",\n",
    "    rho=rho_sulfate_kg_m3,\n",
    "    itype=musica.carma.ParticleType.VOLATILE,\n",
    "    icomposition=musica.carma.ParticleComposition.SULFURIC_ACID\n",
    ")\n",
    "params.add_element(sulfate_element)\n",
    "\n",
    "# Create gases - simplified to match successful test_carma.py pattern\n",
    "water_gas = musica.carma.CARMAGasConfig(\n",
    "    name=\"Water Vapor\",\n",
    "    shortname=\"H2O\",\n",
    "    wtmol=0.018015,  # kg/mol\n",
    "    ivaprtn=musica.carma.VaporizationAlgorithm.H2O_MURPHY_2005,\n",
    "    icomposition=musica.carma.GasComposition.H2O,\n",
    "    dgc_threshold=0.1,\n",
    "    ds_threshold=0.1\n",
    ")\n",
    "params.add_gas(water_gas)\n",
    "\n",
    "# Create H2SO4 gas\n",
    "h2so4_gas = musica.carma.CARMAGasConfig(\n",
    "    name=\"Sulfuric Acid\",\n",
    "    shortname=\"H2SO4\",\n",
    "    wtmol=0.098079,  # kg/mol\n",
    "    ivaprtn=musica.carma.VaporizationAlgorithm.H2SO4_AYERS_1980,\n",
    "    icomposition=musica.carma.GasComposition.H2SO4,\n",
    "    dgc_threshold=0.1,\n",
    "    ds_threshold=0.1\n",
    ")\n",
    "params.add_gas(h2so4_gas)\n",
    "\n",
    "# Add growth process\n",
    "growth = musica.carma.CARMAGrowthConfig(\n",
    "    ielem=1,  # Sulfate element\n",
    "    igas=2    # H2SO4 gas\n",
    ")\n",
    "params.add_growth(growth)\n",
    "\n",
    "# Add nucleation process\n",
    "nucleation = musica.carma.CARMANucleationConfig(\n",
    "    ielemfrom=1,\n",
    "    ielemto=1,\n",
    "    algorithm=musica.carma.ParticleNucleationAlgorithm.HOMOGENEOUS_NUCLEATION,\n",
    "    rlh_nuc=0.0,\n",
    "    igas=2  # H2SO4 gas\n",
    ")\n",
    "params.add_nucleation(nucleation)\n",
    "\n",
    "# Add coagulation\n",
    "coagulation = musica.carma.CARMACoagulationConfig(\n",
    "    igroup1=1,\n",
    "    igroup2=1,\n",
    "    igroup3=1,\n",
    "    algorithm=musica.carma.ParticleCollectionAlgorithm.FUCHS\n",
    ")\n",
    "params.add_coagulation(coagulation)\n",
    "\n",
    "# Initialization\n",
    "params.initialization.do_substep = True\n",
    "params.initialization.do_thermo = True\n",
    "params.initialization.maxretries = 16\n",
    "params.initialization.maxsubsteps = 32\n",
    "params.initialization.dt_threshold = 1.0\n",
    "params.initialization.sulfnucl_method = musica.carma.SulfateNucleationMethod.ZHAO_TURCO.value\n",
    "\n",
    "# Create CARMA instance\n",
    "carma = musica.CARMA(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "064dab11-df84-47d3-aad4-af069d0086df",
   "metadata": {},
   "source": [
    "Next we print out some group properties for each bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84a9f407-1469-40eb-8eb3-2f4c9df5d5a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bin 1: bin radius = 1.9999999552965185e-10; bin mass = 6.444035157118609e-26\n",
      "Bin 2: bin radius = 2.519842043466889e-10; bin mass = 1.2888070314237217e-25\n",
      "Bin 3: bin radius = 3.1748020329740454e-10; bin mass = 2.5776140628474434e-25\n",
      "Bin 4: bin radius = 3.999999910593037e-10; bin mass = 5.155228125694887e-25\n",
      "Bin 5: bin radius = 5.039684086933778e-10; bin mass = 1.0310456251389774e-24\n",
      "Bin 6: bin radius = 6.349604065948091e-10; bin mass = 2.0620912502779548e-24\n",
      "Bin 7: bin radius = 7.999999821186074e-10; bin mass = 4.1241825005559095e-24\n",
      "Bin 8: bin radius = 1.0079368173867555e-09; bin mass = 8.248365001111819e-24\n",
      "Bin 9: bin radius = 1.2699208131896181e-09; bin mass = 1.6496730002223638e-23\n",
      "Bin 10: bin radius = 1.5999999642372146e-09; bin mass = 3.2993460004447276e-23\n",
      "Bin 11: bin radius = 2.015873634773511e-09; bin mass = 6.598692000889455e-23\n",
      "Bin 12: bin radius = 2.5398416263792355e-09; bin mass = 1.319738400177891e-22\n",
      "Bin 13: bin radius = 3.199999928474429e-09; bin mass = 2.639476800355782e-22\n",
      "Bin 14: bin radius = 4.031747269547022e-09; bin mass = 5.278953600711564e-22\n",
      "Bin 15: bin radius = 5.079683252758471e-09; bin mass = 1.0557907201423128e-21\n",
      "Bin 16: bin radius = 6.399999856948858e-09; bin mass = 2.1115814402846257e-21\n",
      "Bin 17: bin radius = 8.063494539094043e-09; bin mass = 4.223162880569251e-21\n",
      "Bin 18: bin radius = 1.0159366505516942e-08; bin mass = 8.446325761138503e-21\n",
      "Bin 19: bin radius = 1.2799999713897717e-08; bin mass = 1.6892651522277005e-20\n",
      "Bin 20: bin radius = 1.6126989078188085e-08; bin mass = 3.378530304455401e-20\n",
      "Bin 21: bin radius = 2.0318733011033884e-08; bin mass = 6.757060608910802e-20\n",
      "Bin 22: bin radius = 2.5599999427795427e-08; bin mass = 1.3514121217821604e-19\n",
      "Bin 23: bin radius = 3.225397815637617e-08; bin mass = 2.702824243564321e-19\n",
      "Bin 24: bin radius = 4.063746602206777e-08; bin mass = 5.405648487128642e-19\n",
      "Bin 25: bin radius = 5.119999885559085e-08; bin mass = 1.0811296974257283e-18\n",
      "Bin 26: bin radius = 6.450795631275234e-08; bin mass = 2.1622593948514567e-18\n",
      "Bin 27: bin radius = 8.127493204413553e-08; bin mass = 4.324518789702913e-18\n",
      "Bin 28: bin radius = 1.023999977111817e-07; bin mass = 8.649037579405827e-18\n",
      "Bin 29: bin radius = 1.2901591262550466e-07; bin mass = 1.7298075158811654e-17\n",
      "Bin 30: bin radius = 1.6254986408827104e-07; bin mass = 3.459615031762331e-17\n",
      "Bin 31: bin radius = 2.047999954223634e-07; bin mass = 6.919230063524661e-17\n",
      "Bin 32: bin radius = 2.580318252510093e-07; bin mass = 1.3838460127049323e-16\n",
      "Bin 33: bin radius = 3.250997281765421e-07; bin mass = 2.7676920254098646e-16\n",
      "Bin 34: bin radius = 4.0959999084472677e-07; bin mass = 5.535384050819729e-16\n",
      "Bin 35: bin radius = 5.160636505020186e-07; bin mass = 1.1070768101639458e-15\n",
      "Bin 36: bin radius = 6.501994563530842e-07; bin mass = 2.2141536203278916e-15\n",
      "Bin 37: bin radius = 8.191999816894535e-07; bin mass = 4.428307240655783e-15\n",
      "Bin 38: bin radius = 1.0321273010040372e-06; bin mass = 8.856614481311567e-15\n"
     ]
    }
   ],
   "source": [
    "# Output group properties\n",
    "group_props, _ = carma.get_group_properties()\n",
    "# Print bin radius and bin mass from group_props xarray Dataset\n",
    "for i_bin in range(NBIN):\n",
    "    print(\n",
    "        f\"Bin {i_bin + 1}: bin radius = {group_props.isel(bin=i_bin)['bin_radius'].values[0]}; bin mass = {group_props.isel(bin=i_bin)['bin_mass'].values[0]}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a3afd1-2540-46d6-87a8-3f880294807f",
   "metadata": {},
   "source": [
    "Finally, we will run the box model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca7caa52-e75d-4c84-be51-27f856f82e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_state = None\n",
    "gas_state = None\n",
    "env_state = None\n",
    "\n",
    "for i_step in range(nsteps):\n",
    "\n",
    "    # Create state\n",
    "    state = carma.create_state(\n",
    "        time_step=dtime,\n",
    "        temperature=temperature_centers,\n",
    "        original_temperature=temperature_centers,\n",
    "        pressure=pressure_centers,\n",
    "        pressure_levels=pressure_levels,\n",
    "        vertical_center=vertical_center,\n",
    "        vertical_levels=vertical_levels,\n",
    "        longitude=longitude,\n",
    "        latitude=latitude,\n",
    "        coordinates=musica.carma.CarmaCoordinates.CARTESIAN,\n",
    "        specific_humidity=mass_mixing_ratios[\"H2O\"],\n",
    "    )\n",
    "\n",
    "    # Initialize particle concentrations to zero\n",
    "    for ibin in range(1, NBIN + 1):\n",
    "        for ielem in range(1, NELEM + 1):\n",
    "            state.set_bin(ibin, ielem, mass_mixing_ratios[\"SULFATE\"][0][ibin - 1])\n",
    "\n",
    "    # Set H2O concentration\n",
    "    state.set_gas(\n",
    "        gas_index=1,\n",
    "        value=mass_mixing_ratios[\"H2O\"],\n",
    "        old_mmr=mass_mixing_ratios[\"H2O\"],\n",
    "        gas_saturation_wrt_ice=satice[\"H2O\"],\n",
    "        gas_saturation_wrt_liquid=satliq[\"H2O\"],\n",
    "    )\n",
    "\n",
    "    # Set H2SO4 concentration\n",
    "    state.set_gas(\n",
    "        gas_index=2,\n",
    "        value=[mmr * 1.05 for mmr in mass_mixing_ratios[\"H2SO4\"]],\n",
    "        old_mmr=mass_mixing_ratios[\"H2SO4\"],\n",
    "        gas_saturation_wrt_ice=satice[\"H2SO4\"],\n",
    "        gas_saturation_wrt_liquid=satliq[\"H2SO4\"],\n",
    "    )\n",
    "\n",
    "    # Perform model step\n",
    "    state.step()\n",
    "\n",
    "    # Get updated state values\n",
    "    if bin_state is None:\n",
    "        bin_state = state.get_bins()\n",
    "        bin_state = bin_state.expand_dims({\"time\": [i_step * dtime]})\n",
    "    else:\n",
    "        bin_state = xr.concat([bin_state, state.get_bins().expand_dims({\"time\": [i_step * dtime]})], dim=\"time\")\n",
    "\n",
    "    if gas_state is None:\n",
    "        gas_state = state.get_gases()[0]\n",
    "        gas_state = gas_state.expand_dims({\"time\": [i_step * dtime]})\n",
    "    else:\n",
    "        gas_state = xr.concat([gas_state, state.get_gases()[0].expand_dims({\"time\": [i_step * dtime]})], dim=\"time\")\n",
    "\n",
    "    if env_state is None:\n",
    "        env_state = state.get_environmental_values()\n",
    "        env_state = env_state.expand_dims({\"time\": [i_step * dtime]})\n",
    "    else:\n",
    "        env_state = xr.concat([env_state, state.get_environmental_values(\n",
    "        ).expand_dims({\"time\": [i_step * dtime]})], dim=\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f39f71c6-59fc-493a-87a5-7a6f7ea039a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.merge([bin_data, env_state, gas_state])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39b7ce46-30fe-4a5f-b27a-66a448e6036b",
   "metadata": {},
   "outputs": [
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       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 177kB\n",
       "Dimensions:                         (time: 240, bin: 5, element: 1,\n",
       "                                     vertical_center: 1, vertical_level: 2,\n",
       "                                     gas: 2)\n",
       "Coordinates:\n",
       "  * time                            (time) float64 2kB 0.0 1.8e+03 ... 4.302e+05\n",
       "  * bin                             (bin) int64 40B 1 2 3 4 5\n",
       "  * element                         (element) int64 8B 1\n",
       "  * vertical_center                 (vertical_center) float64 8B 1.7e+04\n",
       "  * vertical_level                  (vertical_level) float64 16B 1.65e+04 1.7...\n",
       "  * gas                             (gas) &lt;U5 40B &#x27;H2O&#x27; &#x27;H2SO4&#x27;\n",
       "Data variables: (12/24)\n",
       "    mass_mixing_ratio               (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_mixing_ratio             (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_density                  (time, bin, element, vertical_center) float64 10kB ...\n",
       "    nucleation_rate                 (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_radius             (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_density            (time, bin, element, vertical_center) float64 10kB ...\n",
       "    ...                              ...\n",
       "    gas_mass_mixing_ratio           (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_saturation_wrt_ice          (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_saturation_wrt_liquid       (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_vapor_pressure_wrt_ice      (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_vapor_pressure_wrt_liquid   (time, gas, vertical_center) float64 4kB ...\n",
       "    weight_pct_aerosol_composition  (time, gas, vertical_center) float64 4kB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-22d49233-c1bc-4132-b1c2-d69a97554d86' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22d49233-c1bc-4132-b1c2-d69a97554d86' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 240</li><li><span class='xr-has-index'>bin</span>: 5</li><li><span class='xr-has-index'>element</span>: 1</li><li><span class='xr-has-index'>vertical_center</span>: 1</li><li><span class='xr-has-index'>vertical_level</span>: 2</li><li><span class='xr-has-index'>gas</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7107da48-1a96-4341-bdd2-1eaeea189b36' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7107da48-1a96-4341-bdd2-1eaeea189b36' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.8e+03 ... 4.284e+05 4.302e+05</div><input id='attrs-f6f45b9f-2c05-4b29-97c9-62f17299fe5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6f45b9f-2c05-4b29-97c9-62f17299fe5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c7b6ce8-e8fd-4763-b4fa-f5ca17c6eb82' class='xr-var-data-in' type='checkbox'><label for='data-0c7b6ce8-e8fd-4763-b4fa-f5ca17c6eb82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0.,   1800.,   3600., ..., 426600., 428400., 430200.], shape=(240,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bin</span></div><div class='xr-var-dims'>(bin)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-d0a3555d-f87c-4a17-bcf1-ade6bfdc14dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0a3555d-f87c-4a17-bcf1-ade6bfdc14dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba9343e2-188c-4af9-9b30-fdb1cddfd454' class='xr-var-data-in' type='checkbox'><label for='data-ba9343e2-188c-4af9-9b30-fdb1cddfd454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>element</span></div><div class='xr-var-dims'>(element)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-cad25aa7-c73b-4032-aa62-4c375f5937f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cad25aa7-c73b-4032-aa62-4c375f5937f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b161ce30-50ab-4dee-8d8e-b3c439180822' class='xr-var-data-in' type='checkbox'><label for='data-b161ce30-50ab-4dee-8d8e-b3c439180822' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vertical_center</span></div><div class='xr-var-dims'>(vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.7e+04</div><input id='attrs-6f23b992-49c9-474d-8436-f4d3299b8009' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f23b992-49c9-474d-8436-f4d3299b8009' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9da3a8-9466-4fe4-bfd7-b7b412a98600' class='xr-var-data-in' type='checkbox'><label for='data-fd9da3a8-9466-4fe4-bfd7-b7b412a98600' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([17000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vertical_level</span></div><div class='xr-var-dims'>(vertical_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.65e+04 1.75e+04</div><input id='attrs-fa472105-ec4e-4e70-a9a5-3fe7653e13ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa472105-ec4e-4e70-a9a5-3fe7653e13ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c749fd9a-7dfd-438e-b04a-5a91fad9f815' class='xr-var-data-in' type='checkbox'><label for='data-c749fd9a-7dfd-438e-b04a-5a91fad9f815' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([16500., 17500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gas</span></div><div class='xr-var-dims'>(gas)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;H2O&#x27; &#x27;H2SO4&#x27;</div><input id='attrs-c3ba8acf-9c27-47f7-9a14-62b396a939c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3ba8acf-9c27-47f7-9a14-62b396a939c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b7af833-08ac-479d-919d-497834e90993' class='xr-var-data-in' type='checkbox'><label for='data-3b7af833-08ac-479d-919d-497834e90993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;H2O&#x27;, &#x27;H2SO4&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4c79a9e-57ed-40f8-a1b6-e17cd67e950d' class='xr-section-summary-in' type='checkbox'  ><label for='section-e4c79a9e-57ed-40f8-a1b6-e17cd67e950d' class='xr-section-summary' >Data variables: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mass_mixing_ratio</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.365e-07 1.365e-07 ... 1.453e-07</div><input id='attrs-0008acd3-290c-49d2-b83a-0af283b72ee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0008acd3-290c-49d2-b83a-0af283b72ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d67e7be9-39a6-4bbd-849c-2ca455349d7c' class='xr-var-data-in' type='checkbox'><label for='data-d67e7be9-39a6-4bbd-849c-2ca455349d7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>array([[[[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.36427335e-07]],\n",
       "\n",
       "        [[1.36453356e-07]],\n",
       "\n",
       "        [[1.36471940e-07]],\n",
       "\n",
       "        [[1.36477617e-07]],\n",
       "\n",
       "        [[1.36503414e-07]]],\n",
       "...\n",
       "       [[[1.27520200e-07]],\n",
       "\n",
       "        [[1.33176857e-07]],\n",
       "\n",
       "        [[1.37461746e-07]],\n",
       "\n",
       "        [[1.38929726e-07]],\n",
       "\n",
       "        [[1.45245134e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.27484358e-07]],\n",
       "\n",
       "        [[1.33162640e-07]],\n",
       "\n",
       "        [[1.37464927e-07]],\n",
       "\n",
       "        [[1.38939528e-07]],\n",
       "\n",
       "        [[1.45282210e-07]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_mixing_ratio</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.299e+08 4.149e+08 ... 5.522e+07</div><input id='attrs-3b1afc1a-1dc4-4011-8f79-657f5c809f56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b1afc1a-1dc4-4011-8f79-657f5c809f56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e794c93-cab7-498e-9b4a-dff8a8309533' class='xr-var-data-in' type='checkbox'><label for='data-3e794c93-cab7-498e-9b4a-dff8a8309533' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle number mixing ratio</dd></dl></div><div class='xr-var-data'><pre>array([[[[8.29899894e+08]],\n",
       "\n",
       "        [[4.14949947e+08]],\n",
       "\n",
       "        [[2.07474974e+08]],\n",
       "\n",
       "        [[1.03737487e+08]],\n",
       "\n",
       "        [[5.18687434e+07]]],\n",
       "\n",
       "\n",
       "       [[[8.29660306e+08]],\n",
       "\n",
       "        [[4.14909273e+08]],\n",
       "\n",
       "        [[2.07482891e+08]],\n",
       "\n",
       "        [[1.03745761e+08]],\n",
       "\n",
       "        [[5.18826855e+07]]],\n",
       "...\n",
       "       [[[7.75493033e+08]],\n",
       "\n",
       "        [[4.04946530e+08]],\n",
       "\n",
       "        [[2.08987726e+08]],\n",
       "\n",
       "        [[1.05609773e+08]],\n",
       "\n",
       "        [[5.52052684e+07]]],\n",
       "\n",
       "\n",
       "       [[[7.75275068e+08]],\n",
       "\n",
       "        [[4.04903301e+08]],\n",
       "\n",
       "        [[2.08992562e+08]],\n",
       "\n",
       "        [[1.05617224e+08]],\n",
       "\n",
       "        [[5.52193600e+07]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_density</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.176e+08 5.88e+07 ... 7.825e+06</div><input id='attrs-9d757bcf-fade-44db-91c9-ebe590941dfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d757bcf-fade-44db-91c9-ebe590941dfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed141ba7-d5e6-49ad-a84d-1e9f1fbb8bb3' class='xr-var-data-in' type='checkbox'><label for='data-ed141ba7-d5e6-49ad-a84d-1e9f1fbb8bb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m-3</dd><dt><span>long_name :</span></dt><dd>Aerosol particle number density</dd></dl></div><div class='xr-var-data'><pre>array([[[[1.17595615e+08]],\n",
       "\n",
       "        [[5.87978075e+07]],\n",
       "\n",
       "        [[2.93989038e+07]],\n",
       "\n",
       "        [[1.46994519e+07]],\n",
       "\n",
       "        [[7.34972594e+06]]],\n",
       "\n",
       "\n",
       "       [[[1.17561666e+08]],\n",
       "\n",
       "        [[5.87920441e+07]],\n",
       "\n",
       "        [[2.94000256e+07]],\n",
       "\n",
       "        [[1.47006243e+07]],\n",
       "\n",
       "        [[7.35170152e+06]]],\n",
       "...\n",
       "       [[[1.09886241e+08]],\n",
       "\n",
       "        [[5.73803377e+07]],\n",
       "\n",
       "        [[2.96132586e+07]],\n",
       "\n",
       "        [[1.49647521e+07]],\n",
       "\n",
       "        [[7.82250670e+06]]],\n",
       "\n",
       "\n",
       "       [[[1.09855356e+08]],\n",
       "\n",
       "        [[5.73742124e+07]],\n",
       "\n",
       "        [[2.96139439e+07]],\n",
       "\n",
       "        [[1.49658079e+07]],\n",
       "\n",
       "        [[7.82450347e+06]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nucleation_rate</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.99e+08 -9.99e+08 ... -9.99e+08</div><input id='attrs-7feb213f-30c2-410c-b1c2-c98ed5b6db41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7feb213f-30c2-410c-b1c2-c98ed5b6db41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cee2b13-98ac-44be-bcbb-aa42cc48188e' class='xr-var-data-in' type='checkbox'><label for='data-4cee2b13-98ac-44be-bcbb-aa42cc48188e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m-3 s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle nucleation rate</dd></dl></div><div class='xr-var-data'><pre>array([[[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]],\n",
       "\n",
       "\n",
       "       [[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]],\n",
       "...\n",
       "       [[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]],\n",
       "\n",
       "\n",
       "       [[[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]],\n",
       "\n",
       "        [[-9.99e+08]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wet_particle_radius</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 4.3e-05 5.418e-05</div><input id='attrs-46222b17-f83c-4c81-8d14-58d6f16f9a4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46222b17-f83c-4c81-8d14-58d6f16f9a4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d22ffea4-c8e1-4e10-ba7f-822e6b6b41dd' class='xr-var-data-in' type='checkbox'><label for='data-d22ffea4-c8e1-4e10-ba7f-822e6b6b41dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Wet aerosol particle radius</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]],\n",
       "\n",
       "        [[0.00000000e+00]]],\n",
       "\n",
       "\n",
       "       [[[2.14999995e-05]],\n",
       "\n",
       "        [[2.70883020e-05]],\n",
       "\n",
       "        [[3.41291219e-05]],\n",
       "\n",
       "        [[4.29999990e-05]],\n",
       "\n",
       "        [[5.41766039e-05]]],\n",
       "...\n",
       "       [[[2.14999995e-05]],\n",
       "\n",
       "        [[2.70883020e-05]],\n",
       "\n",
       "        [[3.41291219e-05]],\n",
       "\n",
       "        [[4.29999990e-05]],\n",
       "\n",
       "        [[5.41766039e-05]]],\n",
       "\n",
       "\n",
       "       [[[2.14999995e-05]],\n",
       "\n",
       "        [[2.70883020e-05]],\n",
       "\n",
       "        [[3.41291219e-05]],\n",
       "\n",
       "        [[4.29999990e-05]],\n",
       "\n",
       "        [[5.41766039e-05]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wet_particle_density</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 0.00395 0.00395</div><input id='attrs-90025cb0-e71c-4d03-8c4c-65863f75c908' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90025cb0-e71c-4d03-8c4c-65863f75c908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007aad59-9782-4097-89d9-0ce8a954467d' class='xr-var-data-in' type='checkbox'><label for='data-007aad59-9782-4097-89d9-0ce8a954467d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Wet aerosol particle density</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "...\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dry_particle_density</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 0.00395 0.00395</div><input id='attrs-61ca5609-3448-493d-a194-0f9e83b3a0ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61ca5609-3448-493d-a194-0f9e83b3a0ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a5ae22a-4347-4d80-ba50-d80b92243a3e' class='xr-var-data-in' type='checkbox'><label for='data-7a5ae22a-4347-4d80-ba50-d80b92243a3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Dry aerosol particle density</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]],\n",
       "\n",
       "        [[0.     ]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "...\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]],\n",
       "\n",
       "\n",
       "       [[[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]],\n",
       "\n",
       "        [[0.00395]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_particle_temperature</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-999.0 -999.0 ... -999.0 -999.0</div><input id='attrs-0be6473d-3c84-4850-91f4-fddb816ab075' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0be6473d-3c84-4850-91f4-fddb816ab075' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cfb2bac-8e56-4ea9-aea0-d6a75df5ef2e' class='xr-var-data-in' type='checkbox'><label for='data-4cfb2bac-8e56-4ea9-aea0-d6a75df5ef2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Aerosol particle temperature change</dd></dl></div><div class='xr-var-data'><pre>array([[[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]],\n",
       "\n",
       "\n",
       "       [[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]],\n",
       "...\n",
       "       [[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]],\n",
       "\n",
       "\n",
       "       [[[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]],\n",
       "\n",
       "        [[-999.]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kappa</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -1.798e+308 -1.798e+308</div><input id='attrs-4079db95-0aae-475b-ace4-1f5aa95181bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4079db95-0aae-475b-ace4-1f5aa95181bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08da9d21-fa89-4604-89d3-6608c58edea3' class='xr-var-data-in' type='checkbox'><label for='data-08da9d21-fa89-4604-89d3-6608c58edea3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>-</dd><dt><span>long_name :</span></dt><dd>Aerosol particle hygroscopicity parameter</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]],\n",
       "\n",
       "        [[ 0.00000000e+000]]],\n",
       "\n",
       "\n",
       "       [[[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]]],\n",
       "...\n",
       "       [[[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]]],\n",
       "\n",
       "\n",
       "       [[[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]],\n",
       "\n",
       "        [[-1.79769313e+308]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_mass_mixing_ratio</span></div><div class='xr-var-dims'>(time, bin, element, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.365e-07 1.365e-07 ... 1.453e-07</div><input id='attrs-8480c6d3-3e1a-4c7f-be89-e14b099532fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8480c6d3-3e1a-4c7f-be89-e14b099532fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-433d17a3-6360-4d6c-97e2-c51c9b158f53' class='xr-var-data-in' type='checkbox'><label for='data-433d17a3-6360-4d6c-97e2-c51c9b158f53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Total aerosol particle mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>array([[[[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]],\n",
       "\n",
       "        [[1.36466733e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.36427335e-07]],\n",
       "\n",
       "        [[1.36453356e-07]],\n",
       "\n",
       "        [[1.36471940e-07]],\n",
       "\n",
       "        [[1.36477617e-07]],\n",
       "\n",
       "        [[1.36503414e-07]]],\n",
       "...\n",
       "       [[[1.27520200e-07]],\n",
       "\n",
       "        [[1.33176857e-07]],\n",
       "\n",
       "        [[1.37461746e-07]],\n",
       "\n",
       "        [[1.38929726e-07]],\n",
       "\n",
       "        [[1.45245134e-07]]],\n",
       "\n",
       "\n",
       "       [[[1.27484358e-07]],\n",
       "\n",
       "        [[1.33162640e-07]],\n",
       "\n",
       "        [[1.37464927e-07]],\n",
       "\n",
       "        [[1.38939528e-07]],\n",
       "\n",
       "        [[1.45282210e-07]]]], shape=(240, 5, 1, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fall_velocity</span></div><div class='xr-var-dims'>(time, bin, element, vertical_level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.99 -9.99 -9.99 ... -9.99 -9.99</div><input id='attrs-90f9547e-e7a4-4537-b9c6-0d6de8156ee0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90f9547e-e7a4-4537-b9c6-0d6de8156ee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6885e011-f01d-4b61-bc0a-a643cb637eb9' class='xr-var-data-in' type='checkbox'><label for='data-6885e011-f01d-4b61-bc0a-a643cb637eb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle fall velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]],\n",
       "\n",
       "\n",
       "       [[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]],\n",
       "...\n",
       "       [[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]],\n",
       "\n",
       "\n",
       "       [[[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]],\n",
       "\n",
       "        [[-9.98999978, -9.98999978]]]], shape=(240, 5, 1, 2))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>particle_mass_on_surface</span></div><div class='xr-var-dims'>(time, bin, element)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f906fd17-18bd-49da-ab23-220b87bf0c97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f906fd17-18bd-49da-ab23-220b87bf0c97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd2ef4e9-f8a0-4124-aadd-c42537250362' class='xr-var-data-in' type='checkbox'><label for='data-dd2ef4e9-f8a0-4124-aadd-c42537250362' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>Aerosol particle mass on surface</dd></dl></div><div class='xr-var-data'><pre>array([[[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]]], shape=(240, 5, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sedimentation_flux</span></div><div class='xr-var-dims'>(time, bin, element)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-8a62acdb-eeb1-410f-b6fe-d5bc77a85f62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a62acdb-eeb1-410f-b6fe-d5bc77a85f62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e46ab00c-04b1-4fa8-9ae5-e1f7035363f7' class='xr-var-data-in' type='checkbox'><label for='data-e46ab00c-04b1-4fa8-9ae5-e1f7035363f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle sedimentation flux</dd></dl></div><div class='xr-var-data'><pre>array([[[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]],\n",
       "\n",
       "       [[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]]], shape=(240, 5, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deposition_velocity</span></div><div class='xr-var-dims'>(time, bin, element)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.99 -9.99 -9.99 ... -9.99 -9.99</div><input id='attrs-41ef6933-ca1a-469c-8fe8-cd9387999cad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41ef6933-ca1a-469c-8fe8-cd9387999cad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c32a46f-57d8-43ce-aa3b-a4d1f4af3bd1' class='xr-var-data-in' type='checkbox'><label for='data-1c32a46f-57d8-43ce-aa3b-a4d1f4af3bd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Aerosol particle deposition velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]],\n",
       "\n",
       "       [[-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978],\n",
       "        [-9.98999978]]], shape=(240, 5, 1))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>250.0 250.0 250.0 ... nan nan nan</div><input id='attrs-8b5e6722-cf3d-4d4e-a09b-7d39d1341715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b5e6722-cf3d-4d4e-a09b-7d39d1341715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3cf51bb-50a1-47bb-8aae-86f41c7151c7' class='xr-var-data-in' type='checkbox'><label for='data-a3cf51bb-50a1-47bb-8aae-86f41c7151c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>array([[250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "       [250.],\n",
       "...\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9e+03 9e+03 9e+03 ... nan nan nan</div><input id='attrs-0bb95f5a-8125-4f58-96a7-d42d75a8fb45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bb95f5a-8125-4f58-96a7-d42d75a8fb45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e3c8c5c-d3c9-44f8-9de5-e0df3fcdab8b' class='xr-var-data-in' type='checkbox'><label for='data-9e3c8c5c-d3c9-44f8-9de5-e0df3fcdab8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Pressure</dd></dl></div><div class='xr-var-data'><pre>array([[9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "       [9000.],\n",
       "...\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan],\n",
       "       [  nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_density</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1254 0.1254 0.1254 ... nan nan</div><input id='attrs-3b584344-730b-4576-b476-69ef4c73386c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b584344-730b-4576-b476-69ef4c73386c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b09aebbc-f600-4055-87ac-c1618e55261f' class='xr-var-data-in' type='checkbox'><label for='data-b09aebbc-f600-4055-87ac-c1618e55261f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Air density</dd></dl></div><div class='xr-var-data'><pre>array([[0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "       [0.12541958],\n",
       "...\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latent_heat</span></div><div class='xr-var-dims'>(time, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.949e-17 2.949e-17 ... nan nan</div><input id='attrs-813dbd35-2347-40c6-870c-8c3a84c68bd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-813dbd35-2347-40c6-870c-8c3a84c68bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d03d38c-2217-4cca-82d0-f14b156cb0c5' class='xr-var-data-in' type='checkbox'><label for='data-8d03d38c-2217-4cca-82d0-f14b156cb0c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>long_name :</span></dt><dd>Latent heat release rate</dd></dl></div><div class='xr-var-data'><pre>array([[2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "       [2.94915237e-17],\n",
       "...\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan],\n",
       "       [           nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gas_mass_mixing_ratio</span></div><div class='xr-var-dims'>(time, gas, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001 3.548e-10 0.0001 ... nan nan</div><input id='attrs-969e9361-0625-4152-a0a4-5ab9fcb280a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-969e9361-0625-4152-a0a4-5ab9fcb280a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20e09e4c-2f69-4b58-8d1b-ada25af779e5' class='xr-var-data-in' type='checkbox'><label for='data-20e09e4c-2f69-4b58-8d1b-ada25af779e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd></dl></div><div class='xr-var-data'><pre>array([[[1.00000000e-04],\n",
       "        [3.54827565e-10]],\n",
       "\n",
       "       [[1.00000000e-04],\n",
       "        [3.54827565e-10]],\n",
       "\n",
       "       [[1.00000000e-04],\n",
       "        [3.54827565e-10]],\n",
       "\n",
       "       [[1.00000000e-04],\n",
       "        [3.54827565e-10]],\n",
       "\n",
       "       [[1.00000000e-04],\n",
       "        [3.54827565e-10]],\n",
       "\n",
       "       [[1.00000000e-04],\n",
       "        [3.54827565e-10]],\n",
       "\n",
       "       [[1.00000000e-04],\n",
       "        [3.54827565e-10]],\n",
       "...\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gas_saturation_wrt_ice</span></div><div class='xr-var-dims'>(time, gas, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01903 2.305e+03 ... nan nan</div><input id='attrs-e752f51a-31fa-4ddf-a8f0-2ce5db2a6fb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e752f51a-31fa-4ddf-a8f0-2ce5db2a6fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fda6c68-458f-486d-83ab-07e061e6da9e' class='xr-var-data-in' type='checkbox'><label for='data-2fda6c68-458f-486d-83ab-07e061e6da9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[[1.90347283e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.90347283e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.90347283e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.90347283e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.90347283e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.90347283e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.90347283e-02],\n",
       "        [2.30484121e+03]],\n",
       "...\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gas_saturation_wrt_liquid</span></div><div class='xr-var-dims'>(time, gas, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01518 2.305e+03 ... nan nan</div><input id='attrs-4dbc8c85-d34a-4f89-b470-173e481633c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dbc8c85-d34a-4f89-b470-173e481633c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce0224f4-b027-4568-9aa5-2294b04477f0' class='xr-var-data-in' type='checkbox'><label for='data-ce0224f4-b027-4568-9aa5-2294b04477f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[[1.51844156e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.51844156e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.51844156e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.51844156e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.51844156e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.51844156e-02],\n",
       "        [2.30484121e+03]],\n",
       "\n",
       "       [[1.51844156e-02],\n",
       "        [2.30484121e+03]],\n",
       "...\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gas_vapor_pressure_wrt_ice</span></div><div class='xr-var-dims'>(time, gas, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.008447 4.547e-14 ... nan nan</div><input id='attrs-d81305b4-b96e-4530-93f6-46c4425afd9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d81305b4-b96e-4530-93f6-46c4425afd9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62ed7949-bc1f-417e-a28a-ea7bc302bf0b' class='xr-var-data-in' type='checkbox'><label for='data-62ed7949-bc1f-417e-a28a-ea7bc302bf0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[[8.44709889e-03],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[8.44709889e-03],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[8.44709889e-03],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[8.44709889e-03],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[8.44709889e-03],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[8.44709889e-03],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[8.44709889e-03],\n",
       "        [4.54662189e-14]],\n",
       "...\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gas_vapor_pressure_wrt_liquid</span></div><div class='xr-var-dims'>(time, gas, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01059 4.547e-14 ... nan nan</div><input id='attrs-8cb1a45d-4694-4890-b0e4-4a8a4113e96d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cb1a45d-4694-4890-b0e4-4a8a4113e96d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-616c49af-1718-419f-ae75-9764d852651c' class='xr-var-data-in' type='checkbox'><label for='data-616c49af-1718-419f-ae75-9764d852651c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[[1.05890300e-02],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[1.05890300e-02],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[1.05890300e-02],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[1.05890300e-02],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[1.05890300e-02],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[1.05890300e-02],\n",
       "        [4.54662189e-14]],\n",
       "\n",
       "       [[1.05890300e-02],\n",
       "        [4.54662189e-14]],\n",
       "...\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]],\n",
       "\n",
       "       [[           nan],\n",
       "        [           nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>weight_pct_aerosol_composition</span></div><div class='xr-var-dims'>(time, gas, vertical_center)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>72.52 72.52 72.52 ... nan nan nan</div><input id='attrs-cd80cb88-fb93-43c9-af18-ef2cf9a21ca4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd80cb88-fb93-43c9-af18-ef2cf9a21ca4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6e2fdc0-c792-466b-bb64-f37eaf272b83' class='xr-var-data-in' type='checkbox'><label for='data-b6e2fdc0-c792-466b-bb64-f37eaf272b83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[[72.52259169],\n",
       "        [72.52259169]],\n",
       "\n",
       "       [[72.52259169],\n",
       "        [72.52259169]],\n",
       "\n",
       "       [[72.52259169],\n",
       "        [72.52259169]],\n",
       "\n",
       "       [[72.52259169],\n",
       "        [72.52259169]],\n",
       "\n",
       "       [[72.52259169],\n",
       "        [72.52259169]],\n",
       "\n",
       "       [[72.52259169],\n",
       "        [72.52259169]],\n",
       "\n",
       "       [[72.52259169],\n",
       "        [72.52259169]],\n",
       "...\n",
       "       [[        nan],\n",
       "        [        nan]],\n",
       "\n",
       "       [[        nan],\n",
       "        [        nan]],\n",
       "\n",
       "       [[        nan],\n",
       "        [        nan]],\n",
       "\n",
       "       [[        nan],\n",
       "        [        nan]],\n",
       "\n",
       "       [[        nan],\n",
       "        [        nan]],\n",
       "\n",
       "       [[        nan],\n",
       "        [        nan]],\n",
       "\n",
       "       [[        nan],\n",
       "        [        nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bfb5a75-1076-4aba-b2c4-c8be7eaec31d' class='xr-section-summary-in' type='checkbox'  ><label for='section-1bfb5a75-1076-4aba-b2c4-c8be7eaec31d' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a2d51bdf-bf58-4aae-8308-7ef2de6e31fd' class='xr-index-data-in' type='checkbox'/><label for='index-a2d51bdf-bf58-4aae-8308-7ef2de6e31fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0.0,   1800.0,   3600.0,   5400.0,   7200.0,   9000.0,  10800.0,\n",
       "        12600.0,  14400.0,  16200.0,\n",
       "       ...\n",
       "       414000.0, 415800.0, 417600.0, 419400.0, 421200.0, 423000.0, 424800.0,\n",
       "       426600.0, 428400.0, 430200.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=240))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>bin</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-70c0691e-b7db-4651-9214-6a3d65ea0fec' class='xr-index-data-in' type='checkbox'/><label for='index-70c0691e-b7db-4651-9214-6a3d65ea0fec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;bin&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>element</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-52e82f83-c80f-4a78-a758-823e10af5e6e' class='xr-index-data-in' type='checkbox'/><label for='index-52e82f83-c80f-4a78-a758-823e10af5e6e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1], dtype=&#x27;int64&#x27;, name=&#x27;element&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>vertical_center</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b20d5e84-ee8d-431d-8183-4089acbc0c81' class='xr-index-data-in' type='checkbox'/><label for='index-b20d5e84-ee8d-431d-8183-4089acbc0c81' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([17000.0], dtype=&#x27;float64&#x27;, name=&#x27;vertical_center&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>vertical_level</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f8697ac4-7f21-496e-96b6-cef504aded27' class='xr-index-data-in' type='checkbox'/><label for='index-f8697ac4-7f21-496e-96b6-cef504aded27' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([16500.0, 17500.0], dtype=&#x27;float64&#x27;, name=&#x27;vertical_level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gas</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a975690c-ec6a-453a-abca-90c19ef860a8' class='xr-index-data-in' type='checkbox'/><label for='index-a975690c-ec6a-453a-abca-90c19ef860a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;H2O&#x27;, &#x27;H2SO4&#x27;], dtype=&#x27;object&#x27;, name=&#x27;gas&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-734c07b3-102a-4ba9-a4a4-b2c587fd43d5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-734c07b3-102a-4ba9-a4a4-b2c587fd43d5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 177kB\n",
       "Dimensions:                         (time: 240, bin: 5, element: 1,\n",
       "                                     vertical_center: 1, vertical_level: 2,\n",
       "                                     gas: 2)\n",
       "Coordinates:\n",
       "  * time                            (time) float64 2kB 0.0 1.8e+03 ... 4.302e+05\n",
       "  * bin                             (bin) int64 40B 1 2 3 4 5\n",
       "  * element                         (element) int64 8B 1\n",
       "  * vertical_center                 (vertical_center) float64 8B 1.7e+04\n",
       "  * vertical_level                  (vertical_level) float64 16B 1.65e+04 1.7...\n",
       "  * gas                             (gas) <U5 40B 'H2O' 'H2SO4'\n",
       "Data variables: (12/24)\n",
       "    mass_mixing_ratio               (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_mixing_ratio             (time, bin, element, vertical_center) float64 10kB ...\n",
       "    number_density                  (time, bin, element, vertical_center) float64 10kB ...\n",
       "    nucleation_rate                 (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_radius             (time, bin, element, vertical_center) float64 10kB ...\n",
       "    wet_particle_density            (time, bin, element, vertical_center) float64 10kB ...\n",
       "    ...                              ...\n",
       "    gas_mass_mixing_ratio           (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_saturation_wrt_ice          (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_saturation_wrt_liquid       (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_vapor_pressure_wrt_ice      (time, gas, vertical_center) float64 4kB ...\n",
       "    gas_vapor_pressure_wrt_liquid   (time, gas, vertical_center) float64 4kB ...\n",
       "    weight_pct_aerosol_composition  (time, gas, vertical_center) float64 4kB ..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d08babc-c219-46f9-ad15-5d986c2a582b",
   "metadata": {},
   "source": [
    "# Sulfates with gas-phase concentrations from a box model\n",
    "\n",
    "Now we are going to get really fancy and solve the gas phase with MICM and feed those values into CARMA. First up we will create a gas-phase mechanism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ee3d0c1-c805-4a07-8279-b5a74f728087",
   "metadata": {},
   "outputs": [],
   "source": [
    "MOLEC_CM3_TO_MOLE_M3 = np.float64(1.0e6) / np.float64(6.022e23)  # Convert from molecules/cm³ to moles/m³\n",
    "NUMBER_OF_GRID_CELLS = 120  # Number of grid cells for the simulation\n",
    "NUMBER_OF_AEROSOL_SECTIONS = 38  # Number of aerosol sections for the simulation\n",
    "DENSITY_SULFATE = np.float64(1923.0)  # Density of sulfate in kg/m³\n",
    "MOLECULAR_MASS_H2O = np.float64(0.01801528)  # kg/mol\n",
    "MOLECULAR_MASS_H2SO4 = np.float64(0.098078479)  # kg/mol\n",
    "MOLECULAR_MASS_AIR = np.float64(0.029)  # kg/mol (average molecular mass of air)\n",
    "\n",
    "\n",
    "def create_mechanism():\n",
    "    \"\"\"\n",
    "    Creates a chemical mechanism for the gas-phase reactions involved in sulfate formation.\n",
    "    \"\"\"\n",
    "    HO2 = mc.Species(name=\"HO2\")\n",
    "    H2O2 = mc.Species(name=\"H2O2\")\n",
    "    OH = mc.Species(name=\"OH\", constant_mixing_ratio_mol_mol=0.8e-11)\n",
    "    SO2 = mc.Species(name=\"SO2\")\n",
    "    SO3 = mc.Species(name=\"SO3\")\n",
    "    H2SO4 = mc.Species(name=\"H2SO4\")\n",
    "    H2O = mc.Species(name=\"H2O\")\n",
    "    M = mc.Species(name=\"M\", is_third_body=True)\n",
    "\n",
    "    gas = mc.Phase(name=\"gas\", species=[HO2, H2O2, OH, SO2, SO3, H2SO4, H2O, M])\n",
    "\n",
    "    rxn_2HO2_H2O2 = mc.Arrhenius(\n",
    "        name=\"2HO2 -> H2O2\",\n",
    "        reactants=[HO2, HO2],\n",
    "        products=[H2O2],\n",
    "        gas_phase=gas,\n",
    "        A=3.0e-13 / MOLEC_CM3_TO_MOLE_M3,\n",
    "        Ea=-6.35099e-21)\n",
    "\n",
    "    rxn_2HO2_M_H2O2 = mc.Arrhenius(\n",
    "        name=\"2HO2 + M -> H2O2\",\n",
    "        reactants=[HO2, HO2, M],\n",
    "        products=[H2O2],\n",
    "        gas_phase=gas,\n",
    "        A=2.1e-33 / (MOLEC_CM3_TO_MOLE_M3**2),\n",
    "        Ea=-1.2702e-20)\n",
    "\n",
    "    rxn_2HO2_H2O_H2O2 = mc.Arrhenius(\n",
    "        name=\"2H2O2 + H2O -> H2O2 + H2O\",\n",
    "        reactants=[HO2, HO2, H2O],\n",
    "        products=[H2O2, H2O],\n",
    "        gas_phase=gas,\n",
    "        A=4.2e-34 / (MOLEC_CM3_TO_MOLE_M3**2),\n",
    "        Ea=-3.67253e-20)\n",
    "\n",
    "    rxn_2HO2_H2O_M_H2O2 = mc.Arrhenius(\n",
    "        name=\"2HO2 + H2O + M -> H2O2 + H2O\",\n",
    "        reactants=[HO2, HO2, H2O, M],\n",
    "        products=[H2O2, H2O],\n",
    "        gas_phase=gas,\n",
    "        A=2.94e-54 / (MOLEC_CM3_TO_MOLE_M3**3),\n",
    "        Ea=-4.30762e-20)\n",
    "\n",
    "    rxn_H2O2_OH_HO2_H2O = mc.Arrhenius(\n",
    "        name=\"H2O2 + OH -> HO2 + H2O\",\n",
    "        reactants=[H2O2, OH],\n",
    "        products=[HO2, H2O],\n",
    "        gas_phase=gas,\n",
    "        A=1.8e-12 / MOLEC_CM3_TO_MOLE_M3)\n",
    "\n",
    "    rxn_SO2_OH_SO3_HO2 = mc.Troe(\n",
    "        name=\"SO2 + OH -> SO3 + HO2\",\n",
    "        reactants=[SO2, OH],\n",
    "        products=[SO3, HO2],\n",
    "        gas_phase=gas,\n",
    "        k0_A=2.9e-31 / MOLEC_CM3_TO_MOLE_M3,\n",
    "        k0_B=-4.1,\n",
    "        kinf_A=1.7e-12 / MOLEC_CM3_TO_MOLE_M3,\n",
    "        kinf_B=-0.2)\n",
    "\n",
    "    rxn_SO3_2H2O_H2SO4_H2O = mc.Arrhenius(\n",
    "        name=\"SO3 + 2H2O -> H2SO4 + H2O\",\n",
    "        reactants=[SO3, H2O, H2O],\n",
    "        products=[H2SO4, H2O],\n",
    "        gas_phase=gas,\n",
    "        A=8.5e-41 / (MOLEC_CM3_TO_MOLE_M3**2),\n",
    "        C=6540.0)\n",
    "\n",
    "    rxn_SO2 = mc.Emission(\n",
    "        name=\"SO2\",\n",
    "        products=[SO2],\n",
    "        gas_phase=gas\n",
    "    )\n",
    "\n",
    "    return mc.Mechanism(\n",
    "        name=\"Sulfate Box Model\",\n",
    "        phases=[gas],\n",
    "        reactions=[\n",
    "            rxn_2HO2_H2O2,\n",
    "            rxn_2HO2_M_H2O2,\n",
    "            rxn_2HO2_H2O_H2O2,\n",
    "            rxn_2HO2_H2O_M_H2O2,\n",
    "            rxn_H2O2_OH_HO2_H2O,\n",
    "            rxn_SO2_OH_SO3_HO2,\n",
    "            rxn_SO3_2H2O_H2SO4_H2O,\n",
    "            rxn_SO2\n",
    "        ],\n",
    "        species=[HO2, H2O2, OH, SO2, SO3, H2SO4, H2O, M]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c70497e-6621-48a5-b3d8-cd039acb68e1",
   "metadata": {},
   "source": [
    "Next we will setup the carma solver. This is very similar to before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e2c16b4-9f8d-4741-954f-bf65aacb419f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_carma_solver():\n",
    "    \"\"\"\n",
    "    Creates a CARMA solver with the sulfate box model configuration.\n",
    "    \"\"\"\n",
    "    params = musica.CARMAParameters()\n",
    "    params.nz = NUMBER_OF_GRID_CELLS\n",
    "    params.nbin = NUMBER_OF_AEROSOL_SECTIONS\n",
    "\n",
    "    # Set up a group for sulfate particles\n",
    "    sulfate_group = musica.carma.CARMAGroupConfig(\n",
    "        name=\"sulfate\",\n",
    "        shortname=\"SULF\",\n",
    "        rmin=2.0e-10,  # Minimum radius in meters\n",
    "        rmrat=2.0,  # Radius ratio for the group\n",
    "        swelling_approach={\n",
    "            \"algorithm\": musica.carma.ParticleSwellingAlgorithm.WEIGHT_PERCENT_H2SO4,\n",
    "            \"composition\": musica.carma.ParticleSwellingComposition.NONE\n",
    "        },\n",
    "        is_sulfate=True,\n",
    "        do_drydep=True,\n",
    "    )\n",
    "    params.groups.append(sulfate_group)\n",
    "\n",
    "    # Set up an element for sulfate\n",
    "    sulfate_element = musica.carma.CARMAElementConfig(\n",
    "        name=\"Sulfate\",\n",
    "        shortname=\"SULF\",\n",
    "        rho=DENSITY_SULFATE,  # Density in kg/m³\n",
    "        itype=musica.carma.ParticleType.VOLATILE,\n",
    "        icomposition=musica.carma.ParticleComposition.SULFURIC_ACID,\n",
    "        igroup=1,  # Group index for sulfate\n",
    "    )\n",
    "    params.elements.append(sulfate_element)\n",
    "\n",
    "    # Set up gases for water and sulfuric acid\n",
    "    water = musica.carma.CARMAGasConfig(\n",
    "        name=\"Water Vapor\",\n",
    "        shortname=\"H2O\",\n",
    "        wtmol=MOLECULAR_MASS_H2O,  # Molar mass of water in kg/mol\n",
    "        ivaprtn=musica.carma.VaporizationAlgorithm.H2O_MURPHY_2005,\n",
    "        icomposition=musica.carma.GasComposition.H2O,\n",
    "        dgc_threshold=0.1,\n",
    "        ds_threshold=0.1,\n",
    "    )\n",
    "    params.gases.append(water)\n",
    "\n",
    "    h2so4 = musica.carma.CARMAGasConfig(\n",
    "        name=\"Sulfuric Acid\",\n",
    "        shortname=\"H2SO4\",\n",
    "        wtmol=MOLECULAR_MASS_H2SO4,  # Molar mass of sulfuric acid in kg/mol\n",
    "        ivaprtn=musica.carma.VaporizationAlgorithm.H2SO4_AYERS_1980,\n",
    "        icomposition=musica.carma.GasComposition.H2SO4,\n",
    "        dgc_threshold=0.1,\n",
    "        ds_threshold=0.1,\n",
    "    )\n",
    "    params.gases.append(h2so4)\n",
    "\n",
    "    # Add microphysical processes\n",
    "    h2so4_uptake = musica.carma.CARMAGrowthConfig(\n",
    "        ielem=1,  # Element index for sulfate\n",
    "        igas=2,  # Gas index for sulfuric acid\n",
    "    )\n",
    "    params.growths.append(h2so4_uptake)\n",
    "\n",
    "    nucleation = musica.carma.CARMANucleationConfig(\n",
    "        ielemfrom=1,  # Element index for sulfate\n",
    "        ielemto=1,  # Element index for sulfuric acid\n",
    "        igas=2,  # Gas index for sulfuric acid\n",
    "        algorithm=musica.carma.ParticleNucleationAlgorithm.HOMOGENEOUS_NUCLEATION,\n",
    "    )\n",
    "    params.nucleations.append(nucleation)\n",
    "\n",
    "    coagulation = musica.carma.CARMACoagulationConfig(\n",
    "        igroup1=1,  # Group index for sulfate (from)\n",
    "        igroup2=1,  # Group index for sulfate (from)\n",
    "        igroup3=1,  # Group index for sulfate (to)\n",
    "        algorithm=musica.carma.ParticleCollectionAlgorithm.FUCHS,\n",
    "    )\n",
    "    params.coagulations.append(coagulation)\n",
    "\n",
    "    # Set initialization options\n",
    "    params.initialization.do_substep = True\n",
    "    params.initialization.do_thermo = True\n",
    "    params.initialization.maxretries = 16\n",
    "    params.initialization.maxsubsteps = 32\n",
    "    params.initialization.dt_threshold = 1.0\n",
    "    params.initialization.sulfnucl_method = musica.carma.SulfateNucleationMethod.ZHAO_TURCO.value\n",
    "\n",
    "    return musica.CARMA(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f33e07a-23fa-476b-99b1-17f93e44961c",
   "metadata": {},
   "source": [
    "Similarly, we'll get some conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "08fb4727-a030-4bd8-9715-feb7a6ff1a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_initial_conditions():\n",
    "    \"\"\"\n",
    "    Initializes and returns the environmental conditions and chemical species concentrations\n",
    "    for a box model simulation.\n",
    "    Returns:\n",
    "      tuple:\n",
    "        - environmental_conditions (dict): Dictionary containing arrays for temperature (K)\n",
    "          and pressure (Pa) for each grid cell.\n",
    "        - initial_concentrations (dict): Dictionary containing arrays for the initial\n",
    "          concentrations (in mol/m^3) of chemical species ('HO2', 'H2O2', 'OH', 'SO2', 'SO3',\n",
    "          'H2SO4', 'H2O', 'M') for each grid cell.\n",
    "    Notes:\n",
    "      - The number of grid cells is determined by the constant NUMBER_OF_GRID_CELLS.\n",
    "      - Concentrations are calculated using the conversion factor MOLEC_CM3_TO_MOLE_M3.\n",
    "      - Some species are initialized to zero concentration.\n",
    "    \"\"\"\n",
    "\n",
    "    # Set up the vertical grid in CARMA\n",
    "    zmin = np.float64(0.0)\n",
    "    deltaz = np.float64(100.0)\n",
    "    grid = {\n",
    "        \"vertical_center\": zmin + (np.arange(NUMBER_OF_GRID_CELLS, dtype=np.float64) + 0.5) * deltaz,\n",
    "        \"vertical_levels\": zmin + np.arange(NUMBER_OF_GRID_CELLS + 1, dtype=np.float64) * deltaz\n",
    "    }\n",
    "\n",
    "    centered_variables = ussa1976.compute(z=grid[\"vertical_center\"], variables=[\"t\", \"p\"])\n",
    "    edge_variables = ussa1976.compute(z=grid[\"vertical_levels\"], variables=[\"p\"])\n",
    "    centered_variables[\"rho\"] = centered_variables[\"p\"] / (GAS_CONSTANT * centered_variables[\"t\"])\n",
    "    environmental_conditions = {\n",
    "        \"temperature\": np.array(centered_variables[\"t\"], dtype=np.float64),\n",
    "        \"pressure\": np.array(centered_variables[\"p\"], dtype=np.float64),\n",
    "        \"pressure levels\": np.array(edge_variables[\"p\"], dtype=np.float64),\n",
    "        \"air density\": np.array(centered_variables[\"rho\"], dtype=np.float64)\n",
    "    }\n",
    "\n",
    "    initial_concentrations = {\n",
    "        \"HO2\": environmental_conditions[\"air density\"] * 80.0e-12,\n",
    "        \"H2O2\": environmental_conditions[\"air density\"] * 1.0e-9,\n",
    "        \"SO2\": environmental_conditions[\"air density\"] * 0.15e-9,\n",
    "        \"SO3\": np.zeros(NUMBER_OF_GRID_CELLS, dtype=np.float64),\n",
    "        \"H2SO4\": np.zeros(NUMBER_OF_GRID_CELLS, dtype=np.float64),\n",
    "        \"H2O\": environmental_conditions[\"air density\"] * 0.004,\n",
    "    }\n",
    "    return grid, environmental_conditions, initial_concentrations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fdaf3c4-af72-488a-9903-ed58c2c5865f",
   "metadata": {},
   "source": [
    "Finally, we'll run the box model and plot some results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f20fbfa3-249c-4c31-b7c8-1970213fbdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_box_model():\n",
    "    # Create the mechanism and solvers\n",
    "    mechanism = create_mechanism()\n",
    "    solver = musica.MICM(mechanism=mechanism, solver_type=musica.SolverType.rosenbrock_standard_order)\n",
    "    state = solver.create_state(number_of_grid_cells=NUMBER_OF_GRID_CELLS)\n",
    "\n",
    "    carma = create_carma_solver()\n",
    "\n",
    "    # Set initial conditions\n",
    "    grid, environmental_conditions, initial_concentrations = get_initial_conditions()\n",
    "    state.set_conditions(environmental_conditions[\"temperature\"], environmental_conditions[\"pressure\"])\n",
    "    state.set_concentrations(initial_concentrations)\n",
    "    state.set_user_defined_rate_parameters({\"EMIS.SO2\": np.full(NUMBER_OF_GRID_CELLS, 1.0e-8, dtype=np.float64)})\n",
    "\n",
    "    # Run the simulation for 6 hours with a timestep of 30 seconds\n",
    "    time_hours = 2.0\n",
    "    time_seconds = time_hours * np.float64(3600.0)\n",
    "    dt = np.float64(30.0)\n",
    "    num_steps = int(time_seconds / dt)\n",
    "\n",
    "    # Initialize combined state arrays\n",
    "    concentrations = [state.get_concentrations()]\n",
    "    current_temperature = environmental_conditions[\"temperature\"].copy()\n",
    "    bin_state = None\n",
    "\n",
    "    # Set up additional CARMA state arrays\n",
    "    satliq_h2o = np.full(NUMBER_OF_GRID_CELLS, -1.0, dtype=np.float64)\n",
    "    satice_h2o = np.full(NUMBER_OF_GRID_CELLS, -1.0, dtype=np.float64)\n",
    "    satliq_h2so4 = np.full(NUMBER_OF_GRID_CELLS, -1.0, dtype=np.float64)\n",
    "    satice_h2so4 = np.full(NUMBER_OF_GRID_CELLS, -1.0, dtype=np.float64)\n",
    "    last_h2so4_mmr = np.zeros(NUMBER_OF_GRID_CELLS, dtype=np.float64)\n",
    "\n",
    "    for i_time in range(num_steps):\n",
    "        state.set_conditions(temperatures=current_temperature, pressures=environmental_conditions[\"pressure\"])\n",
    "        solver.solve(state, dt)\n",
    "        micm_output = state.get_concentrations()\n",
    "\n",
    "        h2so4_mmr = np.array(micm_output[\"H2SO4\"], dtype=np.float64) * MOLECULAR_MASS_H2SO4 / \\\n",
    "            (environmental_conditions[\"air density\"] * MOLECULAR_MASS_AIR)\n",
    "        h2o_mmr = np.array(micm_output[\"H2O\"], dtype=np.float64) * MOLECULAR_MASS_H2O / \\\n",
    "            (environmental_conditions[\"air density\"] * MOLECULAR_MASS_AIR)\n",
    "\n",
    "        carma_state = carma.create_state(\n",
    "            vertical_center=grid[\"vertical_center\"],\n",
    "            vertical_levels=grid[\"vertical_levels\"],\n",
    "            temperature=current_temperature,\n",
    "            pressure=environmental_conditions[\"pressure\"],\n",
    "            pressure_levels=environmental_conditions[\"pressure levels\"],\n",
    "            time=i_time * dt,\n",
    "            time_step=dt,\n",
    "            latitude=-40.0,\n",
    "            longitude=-105.0,\n",
    "            specific_humidity=h2o_mmr,\n",
    "        )\n",
    "\n",
    "        if bin_state is None:\n",
    "            bin_state = carma_state.get_bins()\n",
    "            bin_state = bin_state.expand_dims({\"time\": [0.0]}).copy(deep=True)\n",
    "            bin_state[\"mass_mixing_ratio\"].values[0, :, :, :] = 0.0\n",
    "\n",
    "        for i_bin in range(NUMBER_OF_AEROSOL_SECTIONS):\n",
    "            carma_state.set_bin(\n",
    "                bin_index=i_bin + 1,\n",
    "                element_index=1,  # Sulfate element index\n",
    "                value=bin_state.isel(time=i_time, bin=i_bin, element=0)[\"mass_mixing_ratio\"].values,\n",
    "            )\n",
    "\n",
    "        carma_state.set_gas(\n",
    "            gas_index=1,  # Water vapor gas index\n",
    "            value=h2o_mmr,\n",
    "            old_mmr=h2o_mmr,\n",
    "            gas_saturation_wrt_liquid=satliq_h2o,\n",
    "            gas_saturation_wrt_ice=satice_h2o\n",
    "        )\n",
    "        carma_state.set_gas(\n",
    "            gas_index=2,  # Sulfuric acid gas index\n",
    "            value=h2so4_mmr,\n",
    "            old_mmr=last_h2so4_mmr,\n",
    "            gas_saturation_wrt_liquid=satliq_h2so4,\n",
    "            gas_saturation_wrt_ice=satice_h2so4\n",
    "        )\n",
    "        last_h2so4_mmr = h2so4_mmr\n",
    "\n",
    "        carma_state.step()\n",
    "\n",
    "        # Get updated state data from CARMA\n",
    "        bin_state = xr.concat([bin_state, carma_state.get_bins().expand_dims(\n",
    "            {\"time\": [(i_time + 1) * dt]})], dim=\"time\")\n",
    "\n",
    "        gas_state, gas_index = carma_state.get_gases()\n",
    "        micm_output[\"H2O\"] = np.array(\n",
    "            gas_state.isel(\n",
    "                gas=gas_index[\"H2O\"])[\"gas_mass_mixing_ratio\"],\n",
    "            dtype=np.float64) * environmental_conditions[\"air density\"] * MOLECULAR_MASS_AIR / MOLECULAR_MASS_H2O\n",
    "        micm_output[\"H2SO4\"] = np.array(\n",
    "            gas_state.isel(\n",
    "                gas=gas_index[\"H2SO4\"])[\"gas_mass_mixing_ratio\"],\n",
    "            dtype=np.float64) * environmental_conditions[\"air density\"] * MOLECULAR_MASS_AIR / MOLECULAR_MASS_H2SO4\n",
    "\n",
    "        # save concentrations for output\n",
    "        concentrations.append(micm_output)\n",
    "\n",
    "        # prepare for next time step\n",
    "        current_temperature = carma_state.get_environmental_values()[\"temperature\"]\n",
    "        state.set_concentrations({\n",
    "            \"H2O\": micm_output[\"H2O\"],\n",
    "            \"H2SO4\": micm_output[\"H2SO4\"]\n",
    "        })\n",
    "\n",
    "    # Collect output data\n",
    "    concentrations = pd.DataFrame(concentrations)\n",
    "    times = np.arange(num_steps + 1) * dt / 3600.0  # Time in hours\n",
    "\n",
    "    return concentrations, times, bin_state\n",
    "\n",
    "def plot_results(concentrations, times, sulfate_data=None):\n",
    "    \"\"\"Plots the results of the sulfate box model simulation.\n",
    "    Args:\n",
    "        concentrations (pd.DataFrame): DataFrame containing the concentrations of chemical species over time.\n",
    "        times (np.ndarray): Array of time values corresponding to the concentrations.\n",
    "        sulfate_data (xr.Dataset): xarray Dataset containing CARMA sulfate data over time.\n",
    "    \"\"\"\n",
    "\n",
    "    # Create a 4-panel plot for H2O, H2SO4, SO2, and HO2 concentrations over time at the first grid cell\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(14, 10), sharex=True)\n",
    "    species = [\"H2O\", \"H2SO4\", \"SO2\", \"HO2\"]\n",
    "    titles = [\n",
    "        \"H2O Concentration (mol/m³)\",\n",
    "        \"H2SO4 Concentration (mol/m³)\",\n",
    "        \"SO2 Concentration (mol/m³)\",\n",
    "        \"HO2 Concentration (mol/m³)\"\n",
    "    ]\n",
    "    for ax, specie, title in zip(axs.flat, species, titles):\n",
    "        conc = np.array([row[0] for row in [c[specie] for _, c in concentrations.iterrows()]])\n",
    "        ax.plot(times, conc, label=specie)\n",
    "        ax.set_title(title)\n",
    "        ax.set_ylabel(\"Concentration (mol/m³)\")\n",
    "        ax.legend()\n",
    "        ax.grid()\n",
    "    for ax in axs[1]:\n",
    "        ax.set_xlabel(\"Time (hours)\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Plot total sulfate mass mixing ratio (sum over all bins) over time at the first vertical level\n",
    "    if sulfate_data is not None and hasattr(sulfate_data, \"mass_mixing_ratio\"):\n",
    "        total_mmr = sulfate_data[\"mass_mixing_ratio\"].isel(vertical_center=0).sum(dim=\"bin\")\n",
    "        total_mmr.plot(x=\"time\")\n",
    "        plt.title(\"Total Sulfate Mass Mixing Ratio (First Vertical Level)\")\n",
    "        plt.ylabel(\"Mass Mixing Ratio (kg/kg)\")\n",
    "        plt.xlabel(\"Time (hours)\")\n",
    "        plt.grid()\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(\"SULFATE data structure:\", sulfate_data)\n",
    "        if sulfate_data is not None:\n",
    "            print(\n",
    "                \"Available variables:\",\n",
    "                list(\n",
    "                    sulfate_data.data_vars) if hasattr(\n",
    "                    sulfate_data,\n",
    "                    'data_vars') else \"No data_vars available\")\n",
    "            print(\n",
    "                \"Available dimensions:\",\n",
    "                list(\n",
    "                    sulfate_data.dims) if hasattr(\n",
    "                    sulfate_data,\n",
    "                    'dims') else \"No dims available\")\n",
    "        else:\n",
    "            print(\"No SULFATE data available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0b14cfe-c7a7-4929-9a00-75a426dc1028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "concs, plot_times, sulfate_data = run_box_model()\n",
    "plot_results(concs, plot_times, sulfate_data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "musica",
   "language": "python",
   "name": "musica"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}