cuwp_plastic_thermal_oxidation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Plastic Thermal Oxidation Example\n",
        "This page runs a process model for the thermal oxidation of plastics using Biosteam. \n",
        "\n",
        "Click on the menu Runtime-> Run All to run the complete analysis or press shift+enter on each cell."
      ],
      "metadata": {
        "id": "EjVpEFAw_0ck"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Some required programs\n",
        "# !pip install biosteam &> /dev/null\n",
        "# !pip install llvmlite --upgrade &> /dev/null"
      ],
      "outputs": [],
      "execution_count": 34,
      "metadata": {
        "collapsed": true,
        "cellView": "form",
        "id": "wAF3ZZMZ209p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import biosteam as bst\n",
        "from biosteam import units\n",
        "from chemicals.identifiers import CAS_from_any, search_chemical"
      ],
      "outputs": [],
      "execution_count": 36,
      "metadata": {
        "execution": {
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      "cell_type": "code",
      "source": [
        "#@title\n",
        "#@Pyrolysis\n",
        "\n",
        "class PlasticTOD(bst.Unit):\n",
        "    _N_ins = 1\n",
        "    _N_outs = 1\n",
        "    _N_heat_utilities = 1\n",
        "    _BM = {'Heaters':1}\n",
        "    _units = {'Area', 'm^2'}\n",
        "    \n",
        "    def __init__(self, ID='', ins=None, outs=(), yields=None, *args, **kwargs):\n",
        "        bst.Unit.__init__(self, ID, ins, outs, bst.settings.get_thermo())\n",
        "        self._multistream = bst.MultiStream(None, thermo=self.thermo)\n",
        "        self._multistream.T = self.ins[0].T\n",
        "        self.yields = yields\n",
        "        \n",
        "    def _setup(self):\n",
        "        vap = self.outs[0]\n",
        "        vap.phase = 'g'\n",
        "        \n",
        "    def _run(self):\n",
        "        feed = self.ins[0]\n",
        "        vap = self.outs[0]\n",
        "        \n",
        "        z_mass_biooil =  self.yields\n",
        "        \n",
        "        ms = self._multistream\n",
        "        ms.imass['g'] = feed.F_mass*z_mass_biooil\n",
        "        vap.mass[:] = feed.F_mass*z_mass_biooil\n",
        "        vap.T = feed.T\n",
        "        vap.P = feed.P\n",
        "        ms.empty()\n",
        "\n",
        "\n",
        "    def _design(self):\n",
        "        # Calculate heat utility requirement (please read docs for HeatUtility objects)\n",
        "        T_operation = self._multistream.T\n",
        "        duty = self.H_out - self.H_in\n",
        "        if duty < 0:\n",
        "            raise RuntimeError(f'{repr(self)} is cooling.')\n",
        "        hu = self.heat_utilities[0]\n",
        "        hu(duty, T_operation)\n",
        "\n",
        "        # Temperature of utility at entrance\n",
        "        # T_utility = hu.inlet_utility_stream.T\n",
        "        T_utility = 298.15\n",
        "\n",
        "        # Temeperature gradient\n",
        "        dT = T_utility - T_operation\n",
        "\n",
        "        # Heat transfer coefficient kJ/(hr*m2*K)\n",
        "        U = 8176.699\n",
        "\n",
        "        print(duty, U, dT)\n",
        "        # Area requirement (m^2)\n",
        "        # A = duty/(U*dT)\n",
        "        A = 1\n",
        "\n",
        "        # Maximum area per unit\n",
        "        A_max = 743.224\n",
        "\n",
        "        # Number of units\n",
        "        N = max([int(A/A_max),1])\n",
        "\n",
        "        # Design requirements are stored here\n",
        "        self.design_results['Area'] = A/N\n",
        "        self.design_results['N'] = N\n",
        "\n",
        "    def _cost(self):\n",
        "        A = self.design_results['Area']\n",
        "        N = self.design_results['N']\n",
        "\n",
        "        # Long-tube vertical boiler cost correlation from\n",
        "        # \"Product process and design\". Warren et. al. (2016) Table 22.32, pg 592\n",
        "        purchase_cost = N*bst.CE*3.086*A**0.55\n",
        "\n",
        "        # Itemized purchase costs are stored here\n",
        "        self.purchase_costs['Pyrolyzer'] = purchase_cost\n",
        "\n",
        "class Cyclone(bst.Unit):\n",
        "    _N_ins = 1\n",
        "    _N_outs = 2\n",
        "    _N_heat_utilities = 0\n",
        "    _BM = {'Heaters':1}\n",
        "    _units = {'Area', 'm^2'}\n",
        "    \n",
        "    def __init__(self, ID='', ins=None, outs=(), efficiency=0.99, *args, **kwargs):\n",
        "        bst.Unit.__init__(self, ID, ins, outs, bst.settings.get_thermo())\n",
        "        self._multistream = bst.MultiStream(None, thermo=self.thermo, phases=['g', 'l', 's'])\n",
        "        self.efficiency = efficiency\n",
        "        self.T = self.ins[0].T\n",
        "        self.P = self.ins[0].P\n",
        "        self._BM = {'Heaters':1}\n",
        "        \n",
        "    def _setup(self):\n",
        "        vap, sol = self.outs\n",
        "        vap.phase = 'g'\n",
        "        sol.phase = 's'\n",
        "        \n",
        "    def _run(self):\n",
        "        feed = self.ins[0]\n",
        "        vap = self.outs[0]\n",
        "        sol = self.outs[1]\n",
        "        \n",
        "        ms = self._multistream\n",
        "        ms.imol['g'] = feed.mol\n",
        "        for k in ms.available_chemicals:\n",
        "            if k.get_phase(T=feed.T, P=feed.P) == 's':\n",
        "                sol.imol[ str(k)] = ms.imol['g', str(k)]*self.efficiency\n",
        "                vap.imol[ str(k)] = ms.imol['g',  str(k)]*(1-self.efficiency)\n",
        "            else:\n",
        "                vap.imol[ str(k)] = ms.imol['g',  str(k)]\n",
        "\n",
        "#         vap.mol[:] = ms.imol['l']\n",
        "#         sol.mol[:] = ms.imol['s']\n",
        "        \n",
        "        vap.T = self.T\n",
        "        vap.P = self.P\n",
        "        \n",
        "        sol.T = self.T\n",
        "        sol.P = self.P\n",
        "        \n",
        "        ms.empty()\n",
        "\n",
        "    def _design(self):\n",
        "\n",
        "        # Design requirements are stored here\n",
        "        self.design_results['Area'] = 0\n",
        "        self.design_results['N'] = 0\n",
        "\n",
        "    def _cost(self):\n",
        "        # Design requirements are stored here\n",
        "        self.design_results['Area'] = 0\n",
        "        self.design_results['N'] = 0\n",
        "\n",
        "        # Itemized purchase costs are stored here\n",
        "        self.purchase_costs['Cyclone'] = 0\n"
      ],
      "outputs": [],
      "execution_count": 37,
      "metadata": {
        "cellView": "form",
        "id": "C8yB2gT_5Ds3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Key Assumptions\n",
        "## Plastic Composition"
      ],
      "metadata": {
        "id": "xq3yJkiEBdKs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_PET = 0.3 #@param {type: \"number\"}\n",
        "x_PE = 0.3 #@param {type: \"number\"}\n",
        "x_PP = 0.3 #@param {type: \"number\"}\n",
        "x_Al = 1 - x_PET - x_PE - x_PP\n",
        "\n",
        "plastic_composition = dict(PET = x_PET, \n",
        "                          PE = x_PE, \n",
        "                          PP = x_PP, \n",
        "                          Al = x_Al)"
      ],
      "outputs": [],
      "execution_count": 38,
      "metadata": {
        "id": "l8gQWa7SBYCt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pyrolysis Yields\n"
      ],
      "metadata": {
        "id": "lrAZgAUOB99I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ToDo: Model pyrolysis yields as a function of plastic composition, operating\n",
        "# conditions, and catalyst\n",
        "pyrolysis_yields = dict(CO2 = 0.10,\n",
        "                        Benzene=0.51,\n",
        "                        Heptane=0.13,\n",
        "                        Heptene=0.16,\n",
        "                        Water=0.05,\n",
        "                        PET=0.02,\n",
        "                        PE=0.02,\n",
        "                        PP=0.01)\n",
        "\n"
      ],
      "outputs": [],
      "execution_count": 39,
      "metadata": {
        "id": "FFxRRICfCL-9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Process Analysis"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def define_chemical_phase(chemical, phase, **constants):\n",
        "    c = bst.Chemical(chemical)\n",
        "    c.at_state(phase=phase)\n",
        "    return c"
      ],
      "outputs": [],
      "execution_count": 40,
      "metadata": {
        "collapsed": true,
        "execution": {
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          "outputs_hidden": false,
          "source_hidden": false
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          "transient": {
            "deleting": false
          }
        },
        "id": "wcO8aNf_2wC5"
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    },
    {
      "cell_type": "code",
      "source": [
        "# %% Define common chemicals\n",
        "# PET = \"PubChem=8441\"\n",
        "# PP = \"PubChem=76958\"\n",
        "# PE = \"CAS=9002-88-4\"\n",
        "# Carbon = \"PubChem=5462310\"\n",
        "# Aluminum = \"PubChem=5359268\"\n",
        "# Heptene = \"PubChem=11610\"\n",
        "customChemicals = {\n",
        "    \"PET\":\"C10H10O4\",\n",
        "    \"PP\" : \"C22H42O3\",\n",
        "    \"PE\" : \"C2H4\",\n",
        "    \"Carbon\" : \"C\",\n",
        "    \"Al\" : \"Al\"\n",
        "}\n",
        "\n",
        "# Gases\n",
        "(CH4, CO2, O2) = [ define_chemical_phase(c, 'g')\n",
        "    for c in [\"CH4\", 'CO2',  'O2']]\n",
        "\n",
        "# Fluids\n",
        "(Heptane, Benzene, Heptene, H2O) = [ c\n",
        "    for c in bst.Chemicals(['Heptane', 'Benzene', \"C7H14\", \"Water\"])]\n",
        "\n",
        "# Solids\n",
        "(PET, PP, PE, Char, Al) = [define_chemical_phase(c, 's') \n",
        "    for c in customChemicals.values()]\n",
        "\n",
        "# Use the PE model for the PP saturation pressure model\n",
        "[PP.Psat.add_model(p) for p in PE.Psat] \n",
        "\n",
        "chemicals = bst.Chemicals([CH4, H2O, CO2, O2, PET, PP, PE, Char, Al, Benzene, Heptane, Heptene], cache=True)\n",
        "for chemical in chemicals:\n",
        "    chemical.default()\n",
        "\n",
        "chemicals.compile()\n",
        "for (k,v) in customChemicals.items():\n",
        "    chemicals.set_synonym(v, k)\n",
        "\n",
        "chemicals.set_synonym(\"C7H14\", \"Heptene\")\n",
        "bst.settings.set_thermo(chemicals, cache=True)"
      ],
      "outputs": [],
      "execution_count": 41,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-29T21:39:43.377Z",
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    },
    {
      "cell_type": "code",
      "source": [
        "bst.CE = 567 # 2013\n",
        "bst.PowerUtility.price = 0.065\n",
        "HeatUtility = bst.HeatUtility\n",
        "# Steam is produced on-site by a boiler,\n",
        "# so make it the only available heating agent.\n",
        "steam_utility = HeatUtility.get_agent('high_pressure_steam')\n",
        "HeatUtility.heating_agents = [steam_utility]\n",
        "steam_utility.heat_transfer_efficiency = 0.85\n",
        "steam_utility.regeneration_price = 0.30626\n",
        "steam_utility.T = 529.2\n",
        "steam_utility.P = 44e5\n",
        "\n",
        "# Cooling water and chilled water are regenerated by\n",
        "# on-site facilities. The regeneration and heat transfer\n",
        "# prices are given accounted for by the capital cost and\n",
        "# electricity consumption of these facilities\n",
        "HeatUtility.get_agent('cooling_water').regeneration_price = 0\n",
        "HeatUtility.get_agent('chilled_water').heat_transfer_price = 0\n",
        "\n",
        "# Raw material price (USD/kg)\n",
        "price = {'Plastic': -55.36/1000,\n",
        "         'Water': 0, # 0.000353,\n",
        "         'Polymer': 0, # Not significant\n",
        "         'Steam': 0, # 0.017,\n",
        "         'Metals': 330/1000,\n",
        "         'Aromatics': 665/1000,\n",
        "         'Olefins': 600/1000,\n",
        "         'Solids': 20/1000} # "
      ],
      "outputs": [],
      "execution_count": 42,
      "metadata": {
        "collapsed": true,
        "execution": {
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          "transient": {
            "deleting": false
          }
        },
        "id": "ClGrMCpl2wC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bst.main_flowsheet.set_flowsheet('pretreatment')\n",
        "\n",
        "z_mass_plastic = chemicals.kwarray(\n",
        "    plastic_composition\n",
        ")\n",
        "\n",
        "plastic = bst.Stream('Plastic',\n",
        "                       flow=250000 * z_mass_plastic,\n",
        "                       units='kg/day',\n",
        "                       price=price['Plastic'])\n",
        "\n",
        "u101 = units.ConveyingBelt('U101', ins=plastic)\n",
        "# Separatate metals\n",
        "u102 = units.MagneticSeparator('U102', ins=u101-0)\n",
        "# Shredder\n",
        "u103 = units.Shredder('U103', ins=u102-0)\n",
        "\n",
        "# u201 = units.CrushingMill('U201', split=dict(PET=0.9, PE=0.9, PP=0.9, Al=0.1), moisture_content=0)\n",
        "\n",
        "# Screen out fibers\n",
        "s201 = units.VibratingScreen('S201', ins=u103-0,\n",
        "                             split=dict(PET=0.01, PE=0.01, PP=0.01, Al=0.99), outs=('', ''))\n",
        "\n",
        "waste = bst.Stream('Metals', price=price[\"Metals\"])\n",
        "u202 = units.ConveyingBelt('U202', ins=s201-0, outs=waste)\n",
        "\n",
        "# Store juice before treatment\n",
        "t202 = units.StorageTank('T202', tau=4, vessel_material='Carbon steel', ins=s201-1)\n",
        "\n",
        "\n",
        "# plastic-u101-u103-S201-0-T202\n",
        "# S201-1-u202\n",
        "\n",
        "pretreatment_sys = bst.main_flowsheet.create_system('pretreatment')\n",
        "pretreatment_sys.diagram('thorough')\n",
        "pretreatment_sys.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "System: pretreatment\n",
            " path: (U101, U102, U103, S201, T202, U202)\n"
          ]
        }
      ],
      "execution_count": 43,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-29T21:49:06.290Z",
          "iopub.status.busy": "2020-12-29T21:49:06.280Z",
          "iopub.status.idle": "2020-12-29T21:49:06.683Z",
          "shell.execute_reply": "2020-12-29T21:49:06.712Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "FD9Iht3H2wC6",
        "outputId": "738e5107-c386-4f0e-c532-7774ea00dd44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bst.main_flowsheet.set_flowsheet('conversion')\n",
        "\n",
        "c101 = PlasticTOD('C101', ins=t202-0, yields=chemicals.kwarray(\n",
        "    pyrolysis_yields\n",
        "    ))\n",
        "\n",
        "\n",
        "c102 = Cyclone('C102', ins=c101-0, outs=('', 'SolidWaste'), efficiency=0.99)\n",
        "\n",
        "conversion_sys = bst.main_flowsheet.create_system('conversion')\n",
        "conversion_sys.diagram()\n",
        "conversion_sys.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAALCAYAAACprHcmAAAABmJLR0QA/wD/AP+gvaeTAAAAJ0lEQVQYlWP8////fwbiwGomIhUyMDAwMIwqpo9iFgYGhtVEqj0BAAvPBjJ63HJVAAAAAElFTkSuQmCC\n",
            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "System: conversion\n",
            " path: ()\n"
          ]
        }
      ],
      "execution_count": 44,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-02T23:43:28.851Z",
          "iopub.status.busy": "2020-12-02T23:43:28.844Z",
          "iopub.status.idle": "2020-12-02T23:43:28.867Z",
          "shell.execute_reply": "2020-12-02T23:43:28.989Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "akyPJhZ-2wC7",
        "outputId": "532d378e-4ac6-4778-a209-c24e16163b17"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bst.main_flowsheet.set_flowsheet('recovery')\n",
        "\n",
        "# bst.settings.set_thermo((c102-0).chemicals)\n",
        "\n",
        "r101 = units.HXutility('r101', ins=c102-0, outs=(), T=120+273.15, rigorous=False)\n",
        "\n",
        "olefins = bst.Stream('Olefins', price=price[\"Olefins\"])\n",
        "r102 = units.Flash('r102', ins=r101-0, outs=('', olefins), T=120+273.15, P = 101325)\n",
        "r103 = units.Flash('r103', ins=r102-0, outs=('ncg', ''), T=30+273.15, P = 101325)\n",
        "\n",
        "aromatics = bst.Stream('Aromatics', price=price[\"Aromatics\"])\n",
        "r104 = units.LLEUnit('r104', ins=r103-1, outs=(aromatics, 'Wastewater'))\n",
        "\n",
        "recovery_sys = bst.main_flowsheet.create_system('recovery')\n",
        "recovery_sys.diagram()\n",
        "recovery_sys.show()\n"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAALCAYAAACprHcmAAAABmJLR0QA/wD/AP+gvaeTAAAAJ0lEQVQYlWP8////fwbiwGomIhUyMDAwMIwqpo9iFgYGhtVEqj0BAAvPBjJ63HJVAAAAAElFTkSuQmCC\n",
            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "System: recovery\n",
            " path: ()\n"
          ]
        }
      ],
      "execution_count": 45,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-02T23:43:28.914Z",
          "iopub.status.busy": "2020-12-02T23:43:28.907Z",
          "iopub.status.idle": "2020-12-02T23:43:28.927Z",
          "shell.execute_reply": "2020-12-02T23:43:28.997Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "4cgGXgHq2wC-",
        "outputId": "fa6c13bb-075c-43b2-eb11-e56838f23e40"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "facility_flowsheet = bst.main_flowsheet.from_flowsheets('facility_flowsheet', \n",
        "    ['pretreatment', 'conversion', 'recovery']\n",
        ")\n",
        "\n",
        "facility_sys = facility_flowsheet.create_system('facility_sys')  \n",
        "facility_sys.simulate() \n",
        "facility_sys.diagram('thorough')  \n",
        "facility_sys.show() "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4144631.923928854 8176.699 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/biosteam/units/design_tools/pressure_vessel.py:79: CostWarning: \u001b[38;2;229;63;229m<Flash: r102>\u001b[0m Vertical vessel weight (1608 lb) is out of bounds (4200 to 1e+06 lb).\n",
            "  return method(pressure, diameter, length)\n",
            "/usr/local/lib/python3.6/dist-packages/biosteam/units/design_tools/pressure_vessel.py:79: CostWarning: \u001b[38;2;229;63;229m<Flash: r102>\u001b[0m Vertical vessel length (6.5 ft) is out of bounds (12 to 40 ft).\n",
            "  return method(pressure, diameter, length)\n",
            "sys:1: DesignWarning: \u001b[38;2;229;63;229m<ConveyingBelt: U202>\u001b[0m Flow rate (37.84 kg/hr) is out of bounds (minimum 3 kg/hr).\n",
            "/usr/local/lib/python3.6/dist-packages/biosteam/units/design_tools/pressure_vessel.py:79: CostWarning: \u001b[38;2;229;63;229m<Flash: r103>\u001b[0m Vertical vessel weight (3671 lb) is out of bounds (4200 to 1e+06 lb).\n",
            "  return method(pressure, diameter, length)\n",
            "/usr/local/lib/python3.6/dist-packages/biosteam/units/design_tools/pressure_vessel.py:79: CostWarning: \u001b[38;2;229;63;229m<Flash: r103>\u001b[0m Vertical vessel length (75.5 ft) is out of bounds (12 to 40 ft).\n",
            "  return method(pressure, diameter, length)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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Xrl27bNkyVVVVWFQ39OrLSu5YW8ydPcfFxYXVsQAAWAnyVQAAAAAAAADwG2MeSt/c3Hz37t3Q0FAymbxixQpjY+MlS5bg8fA/PvALbW1tCKG6urq//vrL19eXQqGYmpoaGRlpaGjAHVswyikZrSnLSt+4aZOMjMz06dNZHc7osnDhwps3b65evbq6utrNzY2bm3tAum1ra3v9+vWzZ8+ePXsWGxtLp9Nnz5599OjRZcuWSUlJDcgQoA/aqNRw6w1CvLy3g4PgVxcARjn42BQAAAAAABjJ6HT6hQsX1NXVf7zU2tr6559/ysrKsrGx8fLyKigofPnyZcgDBGDAtLa2MhiMhoaG27dvGxgYCAgIWFpaxsXFsTou8HtoaWlBCJWXl3t6es6ePVtcXHz//v0fP35kdVwAsNL8gyfEZ2oaLFuen5/P6lhGnZUrV969e/fRo0dTp04NDw9nfjijD2g0WkpKiouLy9KlS/n4+NTV1S9fvjx27NgrV66UlpY+ffp0586dkKxiJQYjar9dQ8G3yIj7Q39iGQBguIGUNQAAAAAAGLFycnI2bNgQHx+vpKT041UTE5PMzMzAwEAVFZXy8nIrK6v6+vqhDxIMNm9v79DQUFZHMfBaW1t/domZeKipqfH39/fx8SGTyUuXLh3C0EaXlJSU3/FYo4qKip9dYk6twsLCc+fOOTs7c3NzL1y4cAhDA2AYweBw+uc8bq420DdYFh/3koODg9URjS66urppaWm2trarVq2SkZHZunXrggULpkyZ0v3qz5aWlk+fPr1///7t27dv3rx5/fp1TU2NoKDgvHnzXFxctLS0JkyYMGRPAfzS8/N/Zkc/ePz337KysqyOBQDAepCvAgAAAAAAI9P79++PHz++bdu2hoYGBoPR6WpQUNDdu3ffv3+vqKiIEBIWFr537x4rwgQAAADA8EXk5Frh9dd1o6Vm5uZhISFwwNsQExQUDA4O/vjx44ULF5ydnffu3SsgIDBx4kQpKSlhYWEMBkMmk9va2qqqqqqqqsrKyj59+pSfn0+j0QgEwqRJk6ZNm7Zs2bJ58+bJy8uPkj1OneSEWB1Cd5Zf9Jm01KD9Ydrd20k+7leuXNHS0mJhVACA4QPyVQAAAAAAYGRSUlIKCwtDCLm5uVGp1E5XPT09lZWVmckqMLJZWlr+jstffqmmpuZn2+awsbG1tLTw8PCYmJisW7du8A6rBwghVVXV27dvszqKXouLi5s9e3aXlwgEQmtrq6ioqJmZ2YYNGw4dOjTEsQEw3PCKSSx38wuyMD569OiJEydYHc5oNGHCBC8vLw8Pj3fv3sXGxn769OnLly+ZmZkIocbGRiwWy8/Pz8/PLyUlNX/+fFlZWVlZ2UmTJhGJRFYHPqRmzZo1zH8edfp9rOBN8t+H9u7Zs2fDhg2sCgkAMNxAvgoAAAAAAPz2Xrx4sXfv3vT0dDweLykpGR8f3/253C0tLUlJSevWrRuyCPvpn3/+OXPmTHJycnFxMScnp6ioqKGhoYODA6vjGgo+Pj5+fn719fVhYWGwgU/3CARCW1sbmUxesWKFsbHxkiVLmIeW9yRfFR4e7uDg0H6EGx6Pp1Aoc+bM2b9/P/Nld3V1PX/+fEVFxcWLFzdv3tzPUGNiYiwtLT08PHq+0Zy1tXVEREQ3Ffz8/LS1tZlfx8fH37p16927d5WVlXg8XlRUVFNT08LCQkREhFnh8ePHZ86cKSgoaG+Ox+O5ubmlpaXnzp1raGjY/XvIiMRMc1IoFFNTUyMjIw0NjT6sRcjOzvb09Hz+/HlBQUFTUxOZTBYUFJSVld27d6+amlrfAvP09PTw8KisrDx9+rSZmVnfOgGgn8RVZy46fubUgV0TJkyAecgqWCxWWVlZWVmZ1YEMU+Li4uLi4qyOoqdqCgvubt+ora3t5OTE6lgAAMMIrGIGAAAAAAC/t4aGBgMDg1WrVlVVVeXk5MjJyTEP7+lGUVFRS0tLamqqlpaWsLAwiUSaNGnS5cuXf9w2cDhIT0+fO3fu2LFjHz9+XFZW9uLFiwULFsTGxrI6riGydevWo0ePsjqKYQ2LxWKxWCKRuHz58vDw8MrKyuvXr+vr6zOTVT20YsWK7OxsaWlpHh4eKpVaWlrq5uaWkJAwe/bs7OxshJCdnd3Tp08HKua+/VsLDAzMyMj49OlTSkoKQmjBggW5ubmZmZmpqamrV69ur3bmzBkLCwtOTk4fH5+3b98mJCQ4OjqmpKTo6+snJSUx6yxcuDA6OlpCQoKLiyszMzM9PT0uLu78+fNiYmLnz59fvnx5RkbGgDzT4Q+Px2MwGC4uLgsLi+fPn5eUlLi6umpqavYhWXXt2jVVVdX09HRnZ+fs7OyysrKEhITDhw9XV1f35/Xc9n/s3Xk8Vc3/APDhXu61ZMme7EQu2bVQD5UQSUqyREjWkPakpFDZky2JIlkeKi3SYqlkaXtUElKSLTvZue7vj/N77nO/yC5L8/6jlzNnzpzPPWfM1ZkzM9bWI3dVQtDvsWzrDvldlrstLHJycmY6Fgia23o72pOtjTlZWRPi41Ao1EyHA0HQLALHV0EQBEEQBEFzW3l5eVtbGw6Hw2KxWCwWmQNwZO3t7QAAFhYWV1dXERERFAp19uxZOzs7BgYGQ0PD6Q95fHx9fenp6X18fJBH/EJCQidPntTX1ydm6Orq0tDQePLkyczF+Evd3d2GhoZ///33TAcyb6FQqPXr1xsZGWlra9PS0k5VsTQ0NBoaGgMDA9u3bw8JCfHz85tkgd3d3Vu2bLl//z6yqaKiUlZWNq5eKzIyMllZWSoqKtKj0Gg0CoWioqISExNDUh4/fhwWFrZjx44zZ84gOTEYzOrVq2VkZLS1tR0cHB48eDB0HkWkw0ZOTk5WVnbNmjUHDhywsbG5e/fuFF7S2QmLxWpraxsaGm7YsIGSknIyReXn59vZ2a1evTolJQV5+EggEPj4+Hh5eenp6cvKyqYmYgiaUcqHTzR/+6KlveX1y3xubu6ZDgeC5iTCwMDdA7a99XX38vP+wNHMEASNDI6vgiAIgiAIguY2fn5+VlZWIyMjV1dX4mxmI0PWM8DhcKtWrVq4cCE9Pf2pU6fo6ekvXbo0vbFOSFNTU1tbW1NTEzGFkpKStFvu6tWr9fX10x0GgUC4c+dOTEzMuI5KSEhobGycppD+cJSUlMHBwbW1tQ8ePDAyMpqOnhVZWVkAALJAyCTFxMRMspYGBQVRUVH9aq+BgQEyGWB4eDgAwNbWdlAGampqU1PTlpaWxMTEkU+0YcOGzZs3NzU1zfJVQCZp0aJFsbGxDQ0NN27c0NTUnGRnFQDA09MTj8efPn166MC+9evX79mzZ5LlQ9BsQEZOvsknGL2QSVVNva2tbabDgaA5KeOcW3l21t07Kfz8/DMdCwRBs87s7a/C4/EzHQIEQRAEQRA0B1BRUaWnpysqKrq7u/Pz8+/YsaOrq2vkQzg4OAAADQ0NxBRKSkoeHp4pGQEw6myE4yUjI9PR0aGqqjrsBEQHDx48evToly9faGlpxcXFAQAEAiEwMFBWVpaZmZmbm9vAwACZzw0AEBAQwMHBwcXFVV9ff/z4cRwO9/nz59zc3FWrVvHz83Nycq5ZsyYjIwPJjMfjAwICFBQUeHh4REVF5eXlL1y4oKWlRdzr7e0tLy8vKCiooqIy7Gxdbm5uZ86c+fbtGz8/v5KSEgDg5cuXqqqqkpKSoqKiGhoaz549AwBcv35dQkJCQkLiyZMnFhYWMjIyf/31171794a9Go2NjaqqqhISElZWVpO8sHMdFRWVtbU1MzPz9J2iv78f/Nu/O9SLFy/k5OQ4OTlZWVlXrlxJHOGXnZ29bt26RYsWcXNzKygo/Pz589ixYydOnCgvL2dhYZGTk8vLy5OSkmJlZY2IiCCWlpiYqKqqysPDIyAgIC8v7+/vP4GAu7q63r59u2jRIuR3fBApKSkk7FHL0dbWBgA8f/58AjHMFfz8/Pr6+jQ0NFNSWm9vb0ZGxsKFC5E+zl/Zu3cvAwMDAwODlJTUu3fvAAB2dnacnJx8fHwPHjxA8iQmJm7YsIGLi4uPj09WVnbYsX0EAiE8PFxZWVlQUFBcXNzCwoLYesfExIiJieFwuMePH5uZmUlKSq5evfru3bvEY/F4/IULF9auXSslJbVly5bU1NQpuQLQn4OShlYn5FpVXZ3ejh3wyRUEjdf75Lj8qLCIy5dXrlw507FAEDQbzd7+qlOnTrW0tMx0FBAEQRAEQdAcgMPh7ty5U11dffjw4fj4eG9v75Hz09LSCgkJDRo10t/fT09PP/lg1NXVFy9evGnTpmPHjsXGxv7zzz89PT2TKfDAgQMyMjLv3r1TVlaWkpLy8fFpbm4m7vXy8tq4cSM/P397e/v79+8BAB4eHq6urgcPHvzy5UtqamplZaW6unpdXR0AwMHBwdbWtqOjw8/Pj4eHR1ZWlkAg1NXVaWtrv3nz5sOHDzQ0NMR+oIsXL54/f/7EiRPFxcXx8fE9PT309PTEaVs8PDyCg4NdXV3fvHmjoqJiZ2eHPH0mdeLEifXr1/Pw8Hz58iUzMxMA0NDQoKGhkZWV9eLFC2pqaicnJwCAoaHhrl27uru7aWho/Pz8Hj16xMXF5eLignSWDEJPT4/D4fz9/UNDQydzVaGxQDpskH7Qoerq6nR0dAoLC4uLi2loaHbv3g0A6Ozs1NfX19LS+vr16+vXrwUFBXt7ez08PFRVVXl5eevr61++fLl8+fJB/ZGXLl3au3evpqZmQUHBmzdv7O3tJ9Z5XF1djcfjmZiYht2L9O19//591HKEhYUBAJWVlROI4c9UUVHR3d0tKCg4crbAwEAtLS0UCpWamrps2TIAwMWLFzU1NUNDQ9XU1AAAYWFhtra2mpqa79+//+effxwcHD5//jy0HF9f37Nnz+7du/ft27eJiYk1NTW6urrIKwhGRkZmZmZIe3LhwoX09HQuLi5nZ2die+Lr6xsREXHkyJGnT58qKSkdOnRojGtrBQYGnjx5MiYmJjc39zcMaZ216j4V3nK0DFaWO4/j8pdfGrFpbXbwmOYLzYsIDliB81zC/vbGVSSlLOuJr7RQafrDoZnvOzv5SAp4LmH/UfQBAHBFW8VzCbvnEvYLCssenTn+q1M0lZdF6ah6S/B7CnPEm+v/Ktvk0XMu1g6KfPIk/dixY9N3Fgial8qepjs7O8/C+bchCJolZm9/1adPn/R26M/Ld1USEhLIoDlifs8BAgCY6QsMzYyZrnfTC7axQ+nq6s70bZleurq6M32NoQmaqu/Z6upqpOeJhYXF09NTWlp6LNOX6enpvX379suXL8hmZ2fnt2/ffvVcflxERUVramru3r3r5eW1c+dOKSkpGhoaXl7eLVu2nDx5MjEx8ePHj319fWMvkIqKKisry9fXV1hYuKioyNnZWUJCAhmZNFRXVxfyRFhPT4+Ojg7p2mlsbIyKiiLNdvLkSQsLi6tXrwoJCWlpaR06dIiBgYGRkVFVVbW5uRmZwe/+/fvLli1TVVXFYrHi4uKqqqq5ublI5D09PVevXlVXV9+4cSMdHZ29vT0ajR7LDVVXV7e3t6enp2dgYFi3bl1LSwvpPIdSUlK0tLQLFy7cuHFjV1dXdXX1oMPxePyxY8d0dHSUlZXHfgGhCejo6Lh169aRI0dYWFiGzq2H0NbWPnr0KFJzNm7c2NTU1NDQUFFR8fPnz6VLl2IwGBYWlqtXr/6q94ior6/Px8dHQUEBGXxDT0+vr68vISExgbA7OzsBAL+aGhHpbe3o6Bi1HBoaGjIysrHkhBDIxGhjmZTS3Nwcj8dfv34d2fz58+ebN2/Wr18PAOjr6/Py8lJUVLS3t0dqgoGBgaSk5KASuru7w8PD1dXVdXR0FixYICIi4u7u3tTUdOPGDdJs0tLSSHuioaFBbE96enpiY2PXr1+/YcOGBQsWWFpaotHomzdvjuUzdnZ2xsfHm5ubr1y5kpWVlYGBQVZWdseOHcePH4+Kinr+/Hltbe1YypnT6kuKrulp0rKwGsYkO735bJxwl3+NckXe6GMWAQDLzW2M4++Spoywgt1Gd191dx/iptmtR3yKSoCMzPzWY5XjZ/4rAY+PNd5K3FzIK7ArOU1p/zHcJh29iP+pD1NusbSc6hmv8+fPI3OQQhA0Rjpbt7q5uc10FBAEzV6D55WeVR6mPdjn5HQhIGCmA5kW8fHxg/44I24O/aPtV7vgISMfMkJRYyn8yJEj4A/g6Oi4YsWKoekjXF64d7btHfshBQUFsbGxQ9Pnn7i4OOLPgxqEoe3DWDKMkH/UDBM74wRCGnbXeFeamaNWrFjh6Og4KHEC3yyT3PXbTjTLYxhjzin8nq2urnZycrp06RI/P39hYeG3b9+MjY1HPcrJySkmJmbXrl0xMTE0NDSurq5dXV1TEhUOhyMnJx8YGCC+zo/H4799+1ZRUXHv3r3+/n4CgYBGo/n4+Lq6uob9Fh6KgoLC1tbWxsbm5cuXXl5eKSkpBgYG7969Y2BgGJSzqKioo6MDmfcMIS0tTUlJ+fr16zGeCPw7NXdPTw/pRHADAwNoNJqcnBwAUFZW1tXVhYxBAQBgsVhWVtZhh0GMAFnkZtgX1JAwBo2vwuPxhw4dYmFhUVRUHNeJoHFpbW3FYrEoFIqNjU1NTc3Z2XnRokWjHkW8m7y8vCwsLHv27LGystLX1+fm5h712KKiotbWVtI+SBQKhYzWGi9kdrtfLSrT2toKxtan0tXVRSAQpmNJsPkKuVZIf+HI1qxZIygoGBMTgwyvTE5O1tHRQaFQAICPHz8OrQkWFhaDvkRKSko6OjqQ4VkICQkJCgqKf/75Z9gzIktzIe3J169fu7u7hYSEkF1YLJaZmfnr169j+YyHDx/evn07Ho///v3753+VlZXdvn378+fP3d3dyHUQJCEgIMDHx8fJyTl0Ta85Ku9yCGYB/Xrn08jmQl6BNY5Hku3MJlaaoNJ6pzelY8wsqqH99Xnm58xHErr/Dcv4lpf9LTe74XMJs+ASYmJZ1hNpQ9PxBtPf3R1rsm1Qj9rIxLW3N5aV2tjaCgkJIXPeQhA0MikpqWtXr5LN95doIQiajFn9NxMtC9vFwEDRpUvn5dz027dv/9WDyLH/MB0559Mhkyz8D+mvWrlyJenYixEuEdw7P/b+If1V27dvB//7e00gEAZtjivDNB37G4p99OjR9F3n2WPx4sXITQcTavyn6ocZPPVcjGEKv2dZWFjwePyqVava2trY2NisrKzs7OwAALm5uQcOHPjy5UtNTQ0AgIODg5+f39PTc82aNQAARkbGZ8+eHTp0SFJSsrOzU0JC4t69e0Nf5J8AHA437ER2BAKBOKyqv7+/tLQU6fsZFzk5uYSEBHt7+0uXLmVlZW3evHlQhmGfyNPT0//8+fNXZT569CgwMPDTp0/t7e2kA7/Wr18fFBT04MEDJSWlT58+paamqqioII+VkafS58+fP3/+PDE/KyvrqPFnZGSEh4eXlJS0t7cPe5VG4O7u3tPTk5GRsW3bNgEBgXEdC40dPT39jx8/wC/+liCVlpbm7+9fVFT08+dPYs3BYrEpKSmurq4+Pj7nz5/X1ta+ePEiFosd4YxI5SRONTkZSMcA6dJ0pJA53Hh4eEYtp7y8HADAx8c3+ZD+EDw8PFgstrR09L4HMjIyMzOzY8eOZWVl/fXXX/Hx8WFhYciuMdYEpJUbtPIWHR1de3v7qGdHVjcMCAgIIHkvloWFZdQDiVAoFC8vLy8vLzImDEEgECorK8vKyoj9WKmpqWVlZUhIKBRq0aJFPDw8vLy83Nzc3NzcPDw8yL9TtX7Yb9PV0tTzs62rtYWK/v/flkBRUOiGRU/HuQY90RbesPHBiYOlT9JI+6u+Ps+kZWX/9OCOot1+JKW/u/vHp0L+1eMeg1vwd2xn4/BNxwiUnI61fq/Q2botPy931PkwIQi6c+fOnGv3IAj6zWZ1f9ViWXlmQWG7vXuFhITWrVs30+FAEARBEARBsxEPD092dvbQ9BUrViCr7/zK4sWLp7wXH4/HU1FRjZyHgoKiv79/69atYxmLAADYvn379evXSV/PNzAwuHTp0rCHI0twDeqdam1t5eTkHLbwyspKY2NjTU3NCxcusLOzh4eHnzp1Ctl18ODBgoICBweHjo4OVlZWLS0tYi8jMsObq6vr7t27R+3VIKqurrayslJVVT179iwrK+u1a9fOnTs3liuAUFNT09TU1NbWRhYGQ3rOoJny/ft3ZJ2q4OBgNja2sLCwEydOILtERETi4uLq6+uDg4P9/f1FREQOHDgwQlHs7OwAANKZIScMg8HIycnl5ORUVlYuXrx40F5klOFYxuchTQocyTd2GAxGRUXlzp07OTk5Q4eNNjc3Hz9+PDAwENk0MDA4depUdHQ0JyfnggULuLi4kNZjjDUBaeUG9U61tbVxcHCMGufChQsBAEePHt25cycY8q7PhJGRkXFxcXFxcQ0aZFNbW1teXl5RUYEMsS0vLy8oKKioqEC63AAAdHR0ixcv5uTkXLRo0eLFizk4OLi4uDg4ODg5OVlZWWfhqCwOccnPmY9jd25VdfVcLC0/TA4CIT8q7J/4mJbv3yioqLnkVyofOM7EP0xHTuXrvNtO1m011RtOeMgYmSHH5kaEFCRcb62qQGOwmAULSPNjFtDxr1b++uJpf3c3GosFAAz09wEARNQ0Pz24S+yvKs95xq+ohKKgAAB8f5WX6nKg/Udtf2/PQl7+tYdPIv1YFfk56edO1Zd+Ikeh6TkX74y78yzg/JvYKHxfn+cSdkZuXqvHuQQ8/tlFn/fJcZ1NjYw8/Ao2jks3Dn5BBAAAyMg0zgbcMNqiuUkrN+fF0EHPEASR+tWfoxAEQUSz7q+fQVbb7W8p/7J1m25+Xu6SJUtGPwCCIAiCIAiCfiNk9azCwkLk33/++aejo4OWlnbYN/1RKBQej1dQUPDz85OUlBzj8nK9vb1FRUWka2uVlJQAAIZdbUtUVJSGhubt27fElFevXvX29v5q6BiymJaZmRky6IT0ffZPnz6Vl5cXFhai0ehBg+cWLVqEwWAKCwvHEj9RcXFxf3+/kZERFxfXuA5EyMvLMzIyurq67t27NywszMbGZgKFQFMFqTm7d+/m5eUlEAjEmlNbW9vS0iIsLMzMzHzixImMjIzi4uKRi+Li4mJgYMjMzHRwcJh8YFZWVjk5OYGBgYN6Q9vb26OiopiYmLZu3fqrYxENDQ3Xrl1jY2PT1taefDx/juPHjz98+PDQoUNpaWmDRtR9/PiRtOuFgYFBR0cnOTmZhoaGdPpWbm5uRkbGzMzMoXP8khIWFqahoXn37h0x5Z9//unr6xMTExs1SHZ2dgwGU1RUNI4PNgns7Ozs7OxDO/BaWloqKioqKiqqq6urq6u/f/9eW1v7+vXrmpoaZPlABMu/2NnZWVlZWVhY2NjY2NjYWFhYGP9FOmvrb7DCwu5zxqOaDwXRO7SYBZeIb9GT2G5IHGsFAHgW6J0TdmGjh5/Q2g1tNVV3D9tH62tZ3MuiYR48iG2xzHLj+HsX1/w3e22W/7kXoQHrjpxctlUfTYl5lxSXdup/hmIv1dAuTX/49cVTobUbAABfs5/yrlpDQUX16trlpvKyhbwCAICyrCdLNf7/N7ejoV5042ZZYwsCICRaGKbst3HML+rr6vzbyniVtaNR7O2en21prkcG+vrWO59uq62pK/pg9TgXOTbDB/n30gAAIABJREFU2/1V9OXNPsE8K1fnXQ6+7WTNwM3LITbMunpoLHZLcOS1berb9Xbcv3d3FvYyQhAEQdAcMuu/R8nI1D38buzcoqqm/jI/j5mZeaYDgiAIgiAIgv5c379///jx4/v37z9+/Pjhw4eioiKkX4qHh0dUVHTVqlUWFhY4HO7AgQNZWVmkByJP8/n4+Pz8/DQ1Ncd7Xl1d3TNnzqxbt46MjOzp06fHjx/ftGkT8eEsIyNjTU3Nt2/fmJiYKCkp7e3tvb294+PjN27c+O3bt3379rGzs5uaDr+YBzIGJSsrS0pKqqqqinSZq6NHj3JycnZ0dCCjGUhhMBg9Pb0bN25ISkrq6OhQUVHV1dWRk5MPnRIQmVyusrKSkZERmXQrOzt72bJlNTU1BQUF470OAABlZeXNmzdfunRJSUlp6dKlEygBmhJIzcnMzJSRkamsrHz16hWSXlNTc+zYsYCAAG5u7k+fPn3//l1PTw8AwMDAUFtb+/3796FP2CkpKR0cHE6dOnX8+HE7OzsWFpbOzs6qqqqJvbCoqKh48OBBb29vAoFgamrKx8fX19f39u1bHx+f1tbWkJCQBf87aINAIHR2dmIwGDIysubm5ry8PH9/fxQKdeHCBbh+1bhISEhERUWZm5uvX7/excVl5cqVWCy2qqoqIyPDz8+PdPY8AICZmdmNGzfS0tK8vb2JiZSUlI6OjidPnnR2drazs2NlZUVqAnG5KQQGg9mzZ09gYGBycrKKikplZaWzszMrK6uBgcGoQWIwGB0dnb///ltcXHzTpk1YLLahoYGMjOw3P2pgYGBgYGAgXYKLqLu7u6qqqrq6+sePHz9+/Kivr6+vr6+pqXn79m19ff2PHz9aWlpI81NTUy9cuJDxf2EwGAYGBioqKiwWy8DAgMFgaGho6OjoMBgMsf5TU1MTfxMxGAw1NfVYIkdjscYJd9/EXn0TG9XwuSTD63TelZAtAeHc8isBAP3d3flXQoU3aIht3gYAYFlAp+bmFbVV7Z/4aAVbp5FL7u/ufhkVxrdqjbzp/68HgR0yVklonSoaiy19kob0V33Lea500JmcHEXLyv4p9c4qa0cAQHXBa5Xj/7+8loiaJlDTJB6b6ePR2dTY2djQ0/6TWUgYjcGgMSw6FyOGCaan501spLDKRmFVTQCAgs2+vCsh75JuDNtfBQCgZWXXCb563XDzoUOHfH19x3IlIQiCIAga1qzvrwIAjcHoBEVd26a+ZevWJ48eIWulQhAEQRAEQdB0a25uJg6c+vjx47t37+rq6gAAjIyMoqKiUlJShoaGOBxOQkJi0PInUlJSOTk5vb29yCYajV64cOHp06fNzc0nMIudnZ1dZGSki4uLmZkZgUDg5eXds2cP6QRrFhYWT548kZWVlZSUjImJOXr0KA0NzdmzZ21sbGhoaFavXn3lyhXkWWRAQEBQUBAAQFZW1sXFRVdXV1RU1N7ePiIiIjw8/K+//lJQULhz546mpubNmzednZ3Nzc2FhYWRs1BQUPDy8h4+fHjjxo0AADc3N1pa2uDg4JMnT9LT0y9fvnz//v1D+6uMjIyePXu2YcMGMTGx4OBgS0vLmJiYa9euKSgoLF++/MGDBzt27FBXV7927RoAYNOmTZcvX87JyUGWxbKwsIiIiCgpKTl9+jQAwN7ePi4ujpKSMi8vD4/Hm5qaOjs7b9q0abzXExpWTk7O7t27v3z5AgDg5eVVUFCIiYkhzXDhwgU/Pz8AgLOzc3p6+vXr1/ft23fp0qXQ0FBlZeXVq1ffvn1bTU0tJCQEj8dv2LDh58+fLCwspqamu3fvBgCYmZllZmauWrVKQkKCWPjp06ezsrKuXr1qbW2NxWKvXbuGpAsJCRkYGAzbX9Xe3m5qaoqM2Xry5ImSktK2bdtsbW1J81hZWS1fvvzatWuWlpb19fVoNJqLi0tRUTEkJISNjQ0ZJpiRkeHv719XV4fH4+Xl5QcGBsjIyGhpaXl5ebW1tXfs2EFPTz/JOeL+QNra2rKyskFBQS4uLt++fevq6kIuqaKi4qDhpLKyssuWLVu3bt2gwSg2NjYYDObq1avR0dEAgCVLlhgYGKSnpwcHBwMA3NzcsrKywsPD9+3bR01NHRAQcODAARoamhUrVly8eJGamppAIMTExFy5cgUAoKGhERkZmZ2dffbsWQCAubl5VFQUNzc30kJevnzZw8ODnp5eRkbGzs5u9rwai8ViBQQERliir7e3t76+vqmpqXk4dXV1JSUl3d3dLS0tXV1dyA9TW5PJ0RSyxrtljXdXF7zJCQsseZx60363ZdoLLD19/efi3s4OdvH/OnU4xCVRFBTVBW9GLba54mtfVxfvytUj5KGkphFUWv858xEgEPB9fWRoFDkKDQAQUdX49ODuKmvHhs8lbKLi5GiK4cJGAwAIeDwDFw81E/OdA7ayJhbLdPToOYcZ79v09XNfVxfLEhFkE43F0rKwNpZ9HiE2drFla5yO+bm7KCkpaWlpjfp5IQiCIAga1hzorwIA0LCw6oReva6vZW1jE3H58kyHA0EQBEEQBM1DLS0tZWVlhYWFr1+/RoZP1dbWgn97p3A4nKamJg6HExcXZ2NjG7koMTExPB4PAECj0RQUFMeOHXNychrj2+tDrV+/HlnMddBKUcRNCQmJoqIi0kQHBwd7e/uhOR0cHBwcHAaVc+LEiRMnThATzczMkF1paWm6urpubm7IZl9f35kzZ2xsbD59+oTBYJDPdezYsZHXr8LhcM+fPyfuOnTo0KFDh4hRGRkZIT/s37+fmMjFxbV9+3biJg8PD3FkBpL46NGjEc4ITczKlSsLCwtJK8ygy2tvb29vb096u93c3JDVzpBECwsL5Ie0tDQwpLqKi4u/ffuWmIishUZ6IlNT0127dv3q7ES0tLQJCQmkJQ+bU1JSUlJS8le/MgAAZWVlZKmhQbtGPjs0qsWLF3t4eLi7u4P/vZhD14hiY2MjnQyQyMzMDBkPSnqgpaUlaWlkZGSWlpaDEpF/jYyMDA0NiZt6enrbt28nzUNBQbF//34nJ6cRYpvNKCkpOTk5x7UGTE9PT2dnZ2tra3d3d0dHB5LY1taGfE8BADo7O3t6epALNXaLJKS3BkemuR55Exv1Le+58AaNnrZWAAAlNQ1pNgwdfU/HMBPkDtJWWwMAoFrINHI2UQ3tTw/uVr//p/1HjcDqtUiiiNqmV9ERzRXlZVmPSVeZ+pz5OO9yUH1pcW/7T3x/P5KIxmINryVleJ95Eez//KLPUnUtzbMB6P+dwbK3sxMAkOV3NsvvLDGRlmWkr/6en23v4qMlpaQHDSWEIAiCIGhc/qe/KiEhAZmrYZYQUf/vZUm2pWIa5wMj7S0kli1D/u8NQRAEQRAEQRPW1tZWWlpKOnwKGVlCT08vKCgoKiq6fv16pJuKn59/vIXjcDg8Hk9OTm5ubn7q1KlR+7dmobq6Omdn5ydPnhBTKCgoODk5+/v7+/r6fvOKKRAEzQ99fX3IgKrCwkIsFsvDwzOHOormLgwGg8FgGBkZJ19Usp25dkAYMqoJIbZ525vYqL6uLgAAho4eAND7v71TPW2tdOyLRi0ZTYkBAOB7e0bOJqCkQklDW/okra+rc90RVyRxsYw8LQtb8YO7tR/fy+3agyS2VVcl2ZgKb9io4elPy8r+Kjoi47wbsotZSFg3LLqzqTE/MjQnLJBFSHjQdIXUC5kAAOuPuRFLGxkBj79zwJas/WdK+uMJv5sCQRAEQRAYdnzVloDw3x/HsBawc5BuCm/QWON4eJ+Tk7CwsKqq6kxFBUEQBEEQBM05vb29paWlSNcUMnzq69evBAIBg8EICAjIyMjs2bMH6Z3i4+ND1pqaDFFRUS0trXPnzomIiExJ/L8fFotFo9GxsbF79+5lYGBoaGh48uSJl5fX5s2bFyxYAB8xQxA0ASdPnkTmNbW1tQ0LC5vpcKBxw/f2NHwuYRUWJaY0fi0DALCKiAIAWIREKKlpat7/tzZhdcEbfF8fu9gwK3UNwrJEhIycvCI/R9pg1wjZ0BiM0DrV4gd3lmzQICMnRxLJyMmFN2x8fyuBd+VqYl9aXUnRQH+fjKEpAxcPAID4xd5eV9vd1sYsuIR6IZPSfuevzzMbPpcMOgsdBycag/lR9GHUsBGP3V2+5z7PzMjg4hpmdkEIgiAIgsZumP4q0lFNs80qK4emL5+3btPNzXlBXF8agiAIgiAIgkj19fWVlJQQB04VFhZ++vRpYGCAkpJSUFAQh8Pt3LkTh8OJioouXbqU/N8HXlOIjo7u9u3bU17s70RHR5eQkODj47Nq1arOzk4aGhphYeFjx44hM/hBEARNABUVlZycHAcHh7e3t7CwMOz5nouSbEyV9jvzKf5FRkZWkfci0/uM0Ho1pAcLjcHIm1u/CAn4cPtvoXWqbdWVD04epmVhk9oxzMSPg1AvZBJW1fiUdrcgMVZEfVNr1fd/4qOHzSmqoV2YkiSorEKaKKKu9fp65IYTnsQU+kWcAICvL55yLJNqq6kirqH1s+7HE48T6md8GLh4Gko/tVZVimlvBwBQ0TP8rKttrfpOxcCIxmCWbdUvSLy+aJk0bvNWSirq9vo6MhT5sFMCFiTGvr4eef369eXLl4/1IkIQBEEQ9AtT/5/z6abu7suCE9fcpFVfXz/TscwfoaGhS5cuZWJiYmZmlpOTy8rKIu4yMDDg5+dnY2NbtWpVfn4+MX1gYCAsLGzYBa7z8/O1tbUFBQWlpKQ8PDyIK42THnv58mUdHZ1p+jhzzsDAgJ+f36pVqwalu7m5iYqK0tHRYTAYQUHBQ4cOtbePPvH3nyAsLAyHwzEzM7OwsMjLy5PWWENDQ35+fnZ2dgUFhUE19tKlS7+qsZs3bx62xvr7+69du1ZUVFRQUHDNmjUeHh7ECd+hkf2qViNDDaioqGhoaERERFxcXNra2mYkwlGFhIQsWbKEnp6egYFBRkYmMzOTuEtfX5+Hh4eFhWXFihVDG0ZNTc2hpeXn52/ZskVISEhGRsbT05O0moWGhq5fv37p0qViYmIqKip+fn5z9Df9VzedVHd3t4iIyPHjx39bVLNKYWGhhYWFtLQ0JyensLCwkpKSr68vca+Pj8/q1asFBQW5uLhWrlx5+vTpQQ3OCI0V4k/+eu3v7//y5cudO3fOnTtnbGyMw+GoqKjExMQMDQ2jo6O7urp0dXXj4uI+fPjQ2dlZWFiYkJDg6uqqq6uLw+Gmo7Nq3lixYkVCQkJpaWlVVVVxcXFKSsquXbuQubwgCIImwMXFpampqbCwUE1NbaZjgSZC1sSCQ1wyy9fjwirxgBW4DK/T0vomW/wvETOsttuvtP9odpCvv7xojKE2PSeX4fVkCipqAEB+ZGi0vhYA4MnZU8l25q9jrkRuVQUAZHid/tvaBACg4em/TGdHhtfpi4qSaa5HuWRXAAASLXfWFX8kjYF/tRKrCG6xlCxpIpfsclYRHLf8SmIKy5KlK/bYvYm5EqiwLMvPk3u5AgAgWl8L39NNGBi4pqfhtYw3Yc9OKX0TWSMzAIC0gQkNE/Ml9dXxuw27WlrWO5+WM7XMCQ/0l1t6QWHZI3eX7tbWoRfk+8vch65HXF1d9fX1p+wqQxAEQdAfbO79bxNFQaF94XL09o3aOjrpjx/DqfOnhJWVlZWV1cKFC6WlpR8+fEj6mltsbOzz5899fX2TkpKI6V++fHF0dMzPz8fhcIOKKi4u1tfXt7Kyio2NLSoqMjU1bWxs9PHxIWb4+vXrgQMHXr16tXTp0t/w0Wa/0tJSU1PT7OxsCQmJQbvS09Pt7Ox27NhBQUGRmppqZGT0/v371NTUGYlzVrG0tNyzZw8zM7O0tHRaWhppjb1+/Xp2dravr+/ff/9NTBxXjW1oaCDW2BcvXuzatUtLSwuNRmdkZDg4OHz69Onq1au/4TPOaSPU6mfPnllYWBgbG1NRUaWmphoaGubl5T18+HBG4hyZtbW1tbU1HR2djIzM48ePSavZjRs3srOzvb29k5OTSRtGBweHYatZSUmJgYGBpaXl9evXP378aG5u3tjY6OXlhex9+fLljh07dHR0MBhMZmbmvn37CgoKIiMjf8/HnCoj3HRSzs7OxcXFvy2qWeXjx49qamrGxsa3b99mZWWtrKyMiYnJzs5G1nsHADx//tzc3FxbWxuNRj958sTOzq6oqOj69evI3pEbKwDA169f9+/f/4d8veLx+G/fvpGuO1VYWNjd3Y1Go7m5uUVFRTdt2nT48GEcDofD4bD/u4I6BEEQBEETxqfwF5/CXyPlICNbbm6z3Nxm6B55Uyt5UyvSFBkjM9JNSmqaje4+G93/+/Nmtf3BoeWQoynMU54MSiQjJx+aqHzguPKB/16TkjE0RX7YGXdnaLFsouI2Ga9GOHyoxq9lyTa7tLU3u7i4jJANgiAIgqCxm3v9VQAAKsaFOiHXYvQ0La2soubaE715oLCw0MfHx8TEpLOzc+gEDv7+/qysrPv37ycjI5ORkbGxsXF3d7e2thYQEAAAfPz40d/ff+fOncMe+wcqKChwc3Oztrbu6OgYekFoaWktLS1RKBQAYPv27UlJSQkJCd+/f4eTYo9LYWGhr6+viYnJsEOjkBp74MABAABpjRUUFAQAUFNTGxoaolAoAoGgqamZmpp69+7dmpoaDg6OoUVBiJFrNSUlpa2tLfL4WFdXNzExMTExcR5c0sLCQm9v7127do2lYbS2tvbw8LCysuLn5wcAUFBQ7Ny5E4PBEAiEjRs33r9/PzU1tb6+noWFZSY+ykSMfNOJXrx48eHDWJcBmH+CgoLo6end3d2RSyQgIHDkyBEzs/8e09DQ0OzcuRNp87W0tO7du5eSklJdXb1o0SIwWmP18ePHgICAefz1Wl1dTdo19fbt287OTgAABwcHDodTUFCwt7dHJvejoqKa6WAhCIIgCJr/ulpbbloZCwsIXI2KmvyylxAEQRAEIebq/CfMgks2+YVGR0eTvlkM/R44HC4iImLr1q2UlJSDdvX39z958mTFihXEP9eUlZUJBAJx8ISoqGhYWJi2tjYcGIeQkJBISkoyNDQc9oLcvXsXeXCJYGZmBgAgT+igsSPW2KEXeWiNXbt2LWmNjYqKIr0FCxcuBAB0dXX9lsDnqpFrdXJyMulYB05OTgDAHJ3+jhQOh7ty5crEGsbQ0FDSa8XOzg4AmFszT4580xFdXV0HDx709/f/nYHNKk1NTW1tbc3NzcQUSkrKmJgY4mZMTMyvGpxRG6t59vVaXV39+PHjgIAAS0tLRUXFBQsWcHJyqqionDt37suXLzIyMn5+fs+ePWtvb6+urn706FFAQICxsbGMjAzsrIIgCIIg6DcY6O9LcbBAdXfdSblNTU090+FAEARB0PwxJ8dXIQTWrFU64Hzo0CEhISEtLa2ZDgcCAICKioqOjg7kATSCh4cHAFBUVDRzQc0WWVlZBw8e/PDhAxqN5uHhyc7OpqOjG1cJVVVVVFRUfHx80xThH2i8Nba2thaLxcLxbUSTr9WlpaUMDAzIZZ+vxlvNysvL6ejoSPPPKhO+6c7Ozra2tnNo0NiUk5KSevTokba29vnz5+Xl5UfNjzQ43NzcYL5/vTY3N5PO7FdQUICsUcrIyCgqKorD4XR1dWVkZCQkJBYsWDDTwUIQBEEQBIFHp49Xv331NCtr1v7RDkEQBEFz1BzurwIALDe3aS7/qm9gmPMie9myZTMdDgTq6uoAALS0tMQUDAaDxWIbGhpmLqhZoaOjQ0tLy9nZ+enTp62trTY2Nr29veMqobOzMz093cLCYujoDWjCxlVju7q6srOz9fX1KSgofl+Is9hkanVfX19dXd3NmzcfP358+fLl+V2rkWpGQ0NDTBm2mvX39zc0NKSlpWVnZ3t6es7Oajbhm56dnV1WVubr6/snfx3Y29s/fPjwn3/+0dDQEBYW1tPTMzQ0ZGRkHDZzV1fX8+fPjYyMkJowj79enZ2d9fT0AACsrKxiYmKioqLbtm1D1p361cWBIAiCIAiaQS+jLr2NuxYXFycnJzfTsUAQBEHQfDO3+6sAABtOerR8+6KxSetVfh4bG9tMh/OnQx5ckk5nBABAo9Hd3d0zFNFsUV5e3tbWhqz6jsVik5KSxluCh4cHBwfHmTNnpiO8P9a4amxQUBAbGxuyeAwEJlerubi4fvz4wcTEdP78eeRR9Tw2xmq2atWqhoYGBgaGw4cPa2ho/NYQx2xiN72rq8vR0fHWrVvTHd4sh8ViU1NTIyMjIyMji4uL3dzcgoODL1++vGrVqqGZAwICWFlZDx8+jGzO469XXV1dFRUVcXFxZM5bCIIgCIKg2ezLs4yM824eHh7bt2+f6VggCIIgaB6aq+tXEZGjKTZfuNxFRr55i05PT89Mh/OnQ5bN6O/vJ03s6+sjXa7mz8TPz8/KympkZOTq6lpeXj7ew5OTkxMSEtLS0uBUSFNr7DX2wYMHd+/ejY6OJh0l84ebTK3+/v17XV1dbGzs1atXpaSkkLEj89UYq9mLFy9evnzp7++fnJy8adOmxsbG3xrl2Ezsph87dmzPnj1wshQAAAUFxZ49e168ePHgwYONGzc2NDSYm5u3tLQMynbv3r07d+7ExcURB1TN469XSUlJZWVl2FkFQRAEQdDs11hWemeflYGBwZEjR2Y6FgiCIAian+Z8fxUAgIqBcWtY9PvCwp3GxgQCYabDmdsGBgaGTRzjzFSsrKwAgJ8/fxJTurq6enp6kPQ/GRUVVXp6uqKioru7Oz8//44dO7q6usZ4bFxc3NmzZzMzM3l5eaczxjnp99TYlJSU4ODg+Pj4xYsXTy7eeWUytZqCgoKFhWXDhg1xcXGFhYUeHh7TGuokDVvN8Hj8uKpZe3s7MWXYaoZGoxcuXLh69eqAgIDS0tKQkJDJRT0tJnDTnz9//v79+927d/+eCOcKGRmZq1ev7tq1q7GxMTs7m3TXrVu3AgMDk5KSSJfKg1+vEARBEARBM6urpTnZylgchwu/dGmmY4EgCIKgeWvOzweIYOIX1PIPS9xjdPbs2aNHj850OHMVIyNjbW3t0PRv376N8b14Li4uWlraqqoqYgryAv7SpUunKMY5DIfD3blzp76+3tfX9+zZszgczsXFZdSjAgMD09LS0tPTSZctgRAj1NhFixaNpYSx1NjIyMisrKwbN27AkVVDTaxWkxIUFEShUIWFhdMU4eRNVTWrrKwkpozcMPLw8KBQqNLS0omEO/3Ge9MjIiKePHlCTv4/78e4u7u7u7u/fPlSVlZ2muOdRXbt2nX58mU0+r8/vbZv3x4VFUXa5xcREZGRkZGUlERNTU16LPx6nd/+/vvvQb8jEDQdYE2bPtu2bZvpEKYdrD/QH26gv++2/W4sYSDl1k1k4DsEQRAEQdNh3P1VxWl308+5tVRWIJvkKDQ1EzPP8lUKNvuYBIQAAHkRwbnhQZ1NjWqnzknpm0wyvrKsJ7f3WW3yDhJau2HknHyKSmuPuDo7O/Pz88/71VCmyerVq1NSUvLz80lXDSUQCHFxcfr6+mMpAY1Gr127Njc3d2BgAPn/TEZGBhkZmYqKynQFPUdUV1e3tLSIioqysLB4eno+fPjw48ePIx9CIBCOHDnS3Nx869Yt0uebENHU1lgyMjLwvzWWQCB4enq2trZevnx50LIxEJhQrW5sbNy7d29sbCwxpbS0FI/Hk44jmW3WrFlz+/btvLw8eXl5YiKBQLhx48a4qlleXh6xYczMzCRWs+bmZldXV39/f2L+8vJyPB7PwcEx1R9lCkzgpiPLNRE3GxoaWFhYnJ2d/8DV+Hp6eoqLi3E4HDHl8+fPAABRUVEAAIFAOHPmTEtLS1RUFBqNHjRefOTGCprTnJycdHV1ZzqK+W/YheL+KLCmTavZ/JfMlID1BwFbkj/Zw1NH6z68e5H9HI5uhyAIgqBpNe6H4MKqmsKqmqHrV3Q2Nzm9Lunr6izPef7Q7WjUVrVdNx8y8QksN7dZsl49VGXllMQ3rvn9ZI13N375vMvUlJ+fn/T5NTRGzs7OWVlZpqam586dU1ZWRl7wDwgI6O/vH/v/TxwdHdXV1X18fGxtbYuKikJDQ3V1dQUEBP7wqRqrq6udnJwuXbrEz89fWFj47ds3Y2PjkQ/5+PHj+fPnAQDh4eGk6V5eXgcOHJjGWOeOY8eOZWVlmZmZnT17VllZGY1Gl5SUXLhwYQI11tvbG6mxISEhSI0FABDnZCPtXwH/LsYz5R9nzplAraahoXn48GF6erqsrCwVFdWHDx9sbGxoaGicnJx+T8wT4OLikpmZaWJi4uXltXbtWnJy8tLSUn9///7+/rGvsUzaMH78+BFpGPn5+QkEAjU19bNnz3JycsTFxTEYTElJiYuLCxUVlZmZ2bR+romZwE2HSBkbG7u4uCgpKZGRkWVnZ58+fVpNTU1UVJRAIJSUlFy8eBEAEBMTQ3qIi4uLtbU1GLGxgua0lStXrlw5NX82Q9AIYE2DJgPWH+gPlxseVJAYe+vWrWXLls10LBAEQRA0z012RD8FFbXQ2g0qx8/0dna8vnZ58gH1d3df09MkbgoqrXd6Uzrq4CoiFZczi6TltDZrk868BI2RoKBgZmbmhg0bTpw4ISgoKCAgYGlpKSAgkJycTElJScz2+vVrLS0tCQmJN2/eFBYWSkhIaGlp5ebmInuFhYVjY2OzsrLExMQsLCz09PQ8PT2Jx75582br1q1ycnJv374tKiqSk5Pbtm1bfn7+7/6ovx0LCwsej1+1ahU1NbWmpqaVlZWdnR0AIDc3V1FRcdGiRXl5eQUFBRwcHAoKCk+fPgXj7KwdpKamZtgVd+YZQUHBjIwMFRWVEydOCAkJCQgIWFlZITWWdGGhcdXYHTt2EGvsH97JOqoJ1GosFqugoLB79242r3vTAAAgAElEQVROTs4FCxbo6ury8vLm5uaKiYmN5YzV1dXT+5GGIyQk9Pz5c1VV1ePHj/Px8fHx8VlYWAgICNy6dWtQw7hp06Zly5YNW82WLFkSGxv79OlTcXFxS0tLPT09d3d3ZBcGg5GRkTly5MiKFSvExcVtbW0XL16clJS0ZMmS3/9hRzWBmz4Z7e3tbW1tUxH4rGBpaSktLX3mzBlRUdGlS5e6ubkhMwQie0dtcEZorAAAb9680dHRIf163bp165/w9QpBEARBEDStyp6mZ/l6eHl5bdq0aaZjgSAIgqD5j4z0+UhCQoKent7RkmEW6hiEOL4K2Wyvqw1UlOSWX2kYcxMA0Pzta6jKyonNB/g65srLqEtWj3PHeyBRd2trzPaNnAz02c+fDVr+YZZArjOBQCBefOSHQZtj+WE6cs6nQyZZuIyMTHx8/NiHUMwejo6O8fHxhoaGBgYG0tLSI+QkIyOLj48nHY00wiWCe+fB3ocPHx45cmSOdsXp6urm5+ebmJjo6+uPsGwP0sYiXbakv9fEVneEfye2azLH/oZiDx06xMDAkJiYOBU34bd69eqVoqKiurr6zp07N27ciMVif5UTacQSEhKQzQk0/lP1wwyeei7GMHe/ZyEIgiBo/iEjI9P2v7R0o9ZMBzKLNHwuidHT0Nu2LfLKlZmOBYIgCIL+CFOzYupAPx4AgKIcfs3J76/yLqmv9pUWOi/GfVlT6cuzDCS9Ij8naqua1zJeHynBy5pKPe0/H7u7PPE82VxR7rmEPXT9isrXeUF/SXsuYX8d899fBh9uJUbqbDgvxu0twResLJsd5DvodFh6+m2XYz9/+2ZsYjJHn8lC0OTV1dUFBATIyMjw8/OfOnWqpKRkpiOCoClQUVFx9uxZUVFRHA7n5eVVUVEx0xFB06unp+fOnTvbtm1jYmIyNjZOS0vD4/EzHRQEQRAEQRA0/3U1NyVb7ZSWlAwLDZ3pWCAIgiDoTzHu9auGVfEyBwDAJiI67N6OhnrRjZtljS0IgJBoYZiy38Yxv6ivq/NvK+NV1o5Gsbd7fraluR4Z6Otb73y6rbamrugDcXyVcfy9i2ukiEW9jLr02OOE0n7nHZEJZIDs04OUipfDjMRi4OLRDoyI27Xdzc3t5MmTU/IZIWhuQaPRvb29AICvX7+eOXPG1dVVSEjIwMDAxMSEj49vpqODoInr6+sDABQVFTk7Ox86dEhCQsLU1HTHjh1sbGwzHRo0LZAOqs7Ozhs3bkRHR9PR0W3evNnY2HjdunVkZGQzHR0EQRAEQdD8UV3w+o/9+2qRpAwdxyLiZn9PT5KVMR0Fxe2bN0mnAYcgCIIgaFpNtr+qr6vzy9P09HOnqJmYZU0shs0joqYJ1P5/SSqhdaqZPh6dTY2djQ097T+ZhYTRGAwaw6JzMWLUcw309z2/6MOzQmGl5V4kRWKbQV9397CZueRWbDh17pSzE/KMfkIfDoLmif7+fgDA58+fPTw83Nzc5OTkDAwM9PX1WVlZZzo0CJogAoGAdFy9e/du//79Tk5O8vLySMfVTIcGTRekKWtra4uPj4+OjmZjY9PT09PV1VVUVJzp0KDZbmBgICAgIDEx8cWLF6TpZ86ccXFxIU3B4XAfPnz4vdFBEARB0GyRHxkGQNhMRzEztP0v0XH8OxcigZDq7NT65XNuzgsmJqYZjQuCIAiC/iwT76/q+dnmuYSdDIWiZWYV+Gudot2BBWwcox5FjkYDAAh4PAMXDzUT850DtrImFst09Og5uUY9tu5TUXdbK7+iMjGFDIWS+0UnGQBAYpt+/adC8927v337JigoOLaP9Tu8evVqpkOAxio3N3cuvl/25cuXoYnE5/svX7589erVgQMHVFRUfnto0KwwF5cyAgDU1dUNTSQQCMj4m/z8/Pz8/L179468ZtufqbGxcS7e9G/fvg2bjgwe/fHjR0hIyIULF5YsWUJJSSkiIvJ7o4PmjNLSUlNT0+zsbAkJiZmOBYIgCIJmrz95PYVB/+vPDvH/eO9Wyu3bIyyaC0EQBEHQdJh4fxVmAZ3T6zGtiPM583He5aD60uLe9p/4/v7/PzEWa3gtKcP7zItg/+cXfZaqa2meDUD/eil1AEBP+08AAIaObuxByplavom9euzYsbEf8hvQ0NDMdAjQWPn5+fn5+c10FONGTU09wl7Cv1JTU39bSNCssn379pkOYSJGfrdxYGAAANDb25ubO8w8sX+44uLiuXjTR27KwL+TQyLr88H+KmhYBQUFbm5u1tbWHR0dwz6Gi46ONjIy+v2BQRAEQRA0OxU/vPfsgteFgAANDY2ZjgWCIAiC/jjk032CtuqqJBtTGmbWXX+nOr35rHzwv0lXmIWEdcOi92YXrNxjV3T/dl5E8MhF0bKxAwC6mpvGeOr+7u4Uhz38AgLNzc2E2eTKlSsTvp7QbxYfHz/T9WUiLCx+Oe6QjIwMhUKRk5MvX748LOwPneoBmukaOkHKysq/+kQoFAqFQqHRaHV1dVtb2995MeeEVatWzfTdm4isrKwRPhSykAAnJ+fhw4dVVVV/17WE5hgJCYmkpCRDQ0MMBjPTsUAQBEEQNNv9+Pj+3qG9pqamdnZ2Mx0LBEEQBP2Jpr2/qq6kaKC/T8bQlIGLB43BEMdYt9fVNnwuAQBQL2RS2u/MjhNHNkfAwMlFRc/w5XnmmE5MINw/6tBRWXH/7h0GBobJfISZVVJS4ujoKCkpycTERE1NzcrKKiYmpq2tnZeXN9OhDS89PV1EROTRo0dTW6yOjo6wsLCWltboWUlERUWtXr1aTEwsISFhauOZi9BoNABAXFzcx8enuro6Jydnz549ox6VnJwsJCREQUFBQUFx8uTJYfMEBARgsVgqKioJCYnnz59Pcdzj9PjxYwEBgYcPH85sGNDvgfS/kpGRycnJ+fj41NTU3L9/f82aNWM8/N27dzt37hQREWFgYFi8ePHy5cvPnTuH7LKysmJhYaGjo+v+xSqJE3Pw4MGlS5dyc3N//Phx6N6jR4+Ki4sLCgoWFRVN4UnnGaSbioWFxcrK6tmzZ5WVlWfPnl2wYMGoB1pbW9PT01NSUlJTU8vIyHz69Im46+rVq4KCglRUVNzc3NHR0Wlpaezs7Pfv3wcArF69esGCBStXrpzyD4I0VmlpaVNe8h8rKytLXl6empqajo5OXFy8ra1tpiOCIAiCIGguaa//kWy9S3GVQlho6EzHAkEQBEF/qGnvr6JfxAkA+PriaX9PT1P5l+qCN0j6z7ofD04cbPzyGd/X9+Pj+9aqykWSMgAAKnqGn3W1rVXfezvaB/r7SItCUVKutHb4/jL30Wnnnz9qCAMDvR3tv+rlyvL1LH54P+nvxFm1ctV4RUZGSklJvX//3svLq6ysrLm5OS8v7+TJky0tLbN2MXDC9Mx5nZycvGLFivEetWvXruvXr09HPHMI8mxXUFDQ2dn58+fPBQUFDg4ObGxsYzxcR0entLSUn58fAHDlyhVk9i1SeDw+KCgIAKCkpFRQUKCoqDil4Y/bNNVAaLahoKAAAAgJCbm7uyP9rw4ODszMzGMv4cOHD0pKSuzs7GlpabW1tRkZGSoqKs+ePUP2hoaG2tvbT3nYXl5exC6xoTw9PT09Paf8pPMDcsdpaWn19PRSUlJqamoCAgLG1eCEhIQgHerS0tKvX78mnT/QxMQkPT2dg4OjrKxs586dpM3Is2fPxt4DOi6wsZpaHR0dWlpa27Zta2pqKi0tXbJkCbLO2aiOHj3KyMhISUnJx8enra398uXL6Q4VgiAIgqBZqL+3+6atKfMC2sSEeORdTwiCIAiCfr9xfwdXvnl574hjc0U5ACBQQWKxrPyWgHDSDPmRobnhQQCAJ2dPfc1+qnMxYsUeuzcxV15HR/AqrOFervDpwd1ofS3NcwGEgYFreho97e00TCxS+iayRmYAAGkDk6/ZmZfUV7PjJHiWr/on8ToAIMPr9NfsrG0hV5ebWaMx2Lc3rv2TEAMAYBJYIqlryCy4ZFCQRfdTci4FhoaGjjB51OyXl5dnY2OzZs2ae/fuoVAoJJGPj4+Pj4+BgaG0tHRmw/uVdevWffr0aZoeww1aBHUCenp6du/eHR0dPSXxzGbIczouLi4TExN9fX1RUdFJFigtLf3mzZuUlJStW7eSpt+8eXPRokUVFRWTLH/Curu7t2zZgoyEAACoqKiUlZXN6QfB3d3dpqamsbGxMx3I7LVkyRKkYvPx8U24ED8/P3p6ei8vLwAAgUAQEhI6ceKEgYHB1IUJTRkqKqotW7YYGBhs2LAB6biaGHFxcQUFhezs7Ldv30pKSpLuCg8PNzExQQpXU1Orra0FJF1Kk//2Af82VsSFA5HGavLFQojy8vK2tjYcDofFYrFYbFJS0liOMjEx0dDQEBISoqSkfPPmjY2NzV9//fXy5UscDjfdAUMQNElVVVWcnJwzHQUEQfNHlrcHqq83PS+XkZFxpmOBIAiCoD/XuPurFkvLWT7MHiGDvKmVvKkVaYrygePKB44TN2UMTZEfdsbdGXo4m6i4TcYr4uYax8ODMsgYmhJLGFblm5d3D+09cODAWOY6m808PDzwePyZM2fQaPSgh+8qKirr16+fqcCmD4FASE1NbW1t3bFjx7AZJv+WU3JyclPTWJdAm7vY2NgcHBz09fWXL18+VWVaW1tbWFiEhYUN6q8KCAjYv3//r27ZyAgEwq1bt1paWkxNR/qlHllMTEx9ff2ED5+FkpKSGhsbZzqK2Yibm/vo0aP6+vri4uKTL62pqamtra25uZn4P1JKSsrExMRB2aako2LsBU756eY0CgqKzZs36+vrb9q0iZqaekrKtLGxyc7ODgkJIV3Ar7e3NyYmhji6bthIJn/qKWysCATCvXv3WlpaDA0Np6TAeYCfn5+VldXIyMjBwWHXrl28vLxjOYqLi4uLiwv5ecWKFZGRkZKSkkFBQcHBoyyqOnZwRuK5gouLazpm/oSmz9GjR21sbCYwAQMEQdCwupobH6alCQkJzXQgEARBEPRHm29jnFurKm/Zma1bu3auT6nU29ubnp7OxMQkJyc3ck4CgRAYGHjlypXy8nIqKqrVq1e7ubktWbIEABAeHn78+HECgXDlypWIiIjc3Fw6Ojo3N7dt27YBAOTl5UtLS8nIyJYtW3b//n0qKipXV9eoqKje3l5vb299fX08Hu/l5RUXF9fQ0MDPz79v377Nmzfv27cPGfbBw8Nz+fJlMTExR0fH27dvU1BQBAYG0tPT29raVldXnz592tTU9OrVq2fOnCEQCEFBQdHR0a9evaKlpT127NjmzZuR4PF4fEhISFJSUnV1NTU1NTU1NT09/Qgz+FVUVKiqqtbW1g4MDIiJiTk5OcnIyBCLCgoKunnzZnNzMw8Pz549e9TV1Qcdfu7cufj4+L6+vmXLlnFxcd29e3ei92e2O3r06JSXqaysLCIikpmZWVJSglQwAMCLFy+6urqG9p5mZ2fv3bu3urq6p6dHQEDA09MTyYPH4319fa9fv15ZWUlNTU1DQ8PIyHjv3j0AwOXLl48fPw4AiIiIuHLlSm5u7oIFC06fPk3sHsPj8efPn4+NjW1oaBAQENi3b9+WLVucnZ0jIyN7e3tZWFh4eXkDAwOtrKyqqqo8PDzMzc2RAxMTE8PDwz99+oRCoZiYmAwMDBwdHQcFnJOTc/r06eLiYjQazcnJeevWLVpaWjwe7+fnl5iY2NjYyMfHZ2dnt2nTpsjISE9PT1paWjU1tUePHrW2toqJiR0/fpw4VuPly5fHjx+vra3t7e3l5eU9evSooqJieHj4hQsXyMnJnzx5cunSpXv37l27dq2xsfHEiRM/fvzo7e3l4eE5dOiQoqKip6fnjRs3+vr6REVFubi4Hjx4QCAQrl27lpiYWFlZicVi5eTkHB0dkXFFkZGRwcHBKBTq3r17V65cefDgweXLl3l4eKb87s8SPj4+U1iajIxMamqqmpqav7//r552kZOTP3z4MCoqKi8vj4qKysXFBekeuHDhwrlz51Ao1OvXrwMCAm7evJmcnMzLy+vt7R0XF9fY2MjPz+/g4IAsuUcgEEJCQm7cuPH9+3cMBkNHR0d6CgKBEB4eHhcXV1lZicFgBq3DhMfjAwMDb9682dTUxMPDY2Vlpa6uHhERERQURE5O/uDBg4iIiNTU1IiIiHl53yUkJG7dujW1ZW7ZsoWDgyMhIeHcuXPE5S2Tk5Pl5eWR9/RfvHhhYmJSWVnp6+traWmJZCgrK5OSkqqqqhoYGJCSkjp16hRSZ/z9/T09PcnJyQsKCvz8/JKSklJSUhoaGhwdHWtqanp7e/n5+d3c3NauXXvs2DGksWJmZubl5b148aKlpWVVVZWnp6e5uXlkZOSpU6cIBEJoaOi1a9fy8/NpaWlPnDixZcsWJAA8Hn/x4sXExMSqqiqk8aSnp/9VR0heXp6Hh0dxcTEKheLk5ExKSqKhoQEA3Lx5MzIysqSkBIVCLVy4UFdXl5KSEmma0tPTw8LC7t27Fx0dzcXFdfHixUG1DvziezYuLs7b25tAIHh5eSUkJLx9+5aGhmb//v1qampTe+NGRUVFlZ6efuTIEXd3dzc3t+3bt0dGRlJRUY2rEHFxcRQKVVIyyoqq46KnpzeFpUHTZ9u2bUNfWYBms+7ubu0tOq9e5i9evHimY4EgaD4IunhxTs/QA0EQBEHzw7zqr+rt7Ei2NuZkYYmPu0GcQG+Oqqio6O7ulpCQGDWnu7u7t7d3SEjIxo0bv3//vmfPHhUVlZcvX7KwsFhYWNTU1Hh5edHS0kZFRfX09OzcudPe3n7z5s0UFBQ5OTnS0tIDAwOPHz9GLperq2t/f//ixYv19fUBAKdPnw4PDw8LC1NUVAwKCrKysuLh4fHz82tra0tNTb19+zY7OzuBQPD39+/r69u8ebOKigqBQEhJSZGVlUViMzExqa2tDQwMpKGhCQ4O7unpsbS0PHLkiIaGBjJSKjQ01M/PLzQ0VFFRsbS01MzMjI6ObtDDXFJ0dHRXrlxhY2P78uWLs7OzmZlZSkoK8pTW19c3Jibm/PnzK1eujIiIOHToEDc396BJ8A4fPlxbW1tcXHzv3r05PV/cTLG0tNy3b9+lS5e8vb2RFH9/fwcHh6E56+rqtm7damtrOzAwoKOjY2pq+v37dwCAr6/vmTNnYmNjlZWVi4uLt27dSk//f+3deVxM7f8/8Kuammmd0qpU2kshuyRE5ZYUUbLvRLqFRNmVEqGs3aK00ILsW9x2WrhxS0UhadFiKS0zNdX8/ji/z3y7KylixOv5x/04c851nfO+Zo5zus/7XNfFZDKZhJC5c+e+ffs2ICBAQkIiLCyMd7ra2tpSPRs2btxInZBDhgzZvXv3ggULNDQ0qLmLUlNTqRlHqD4HDYf5OnDgwNq1a1evXk092D179mxiYmKjaKuqqmbNmuXq6hofH//p0ycvLy9qNEU/P7+wsLBdu3YNHjyYms1IXV191qxZWVlZp06dmj179vr163Nyctzd3adNm5aQkNC5c2dCyLt370aPHj1jxoz6+vq5c+cuXbr0/v378+bNq6io2LdvX3BwsLq6urGxMZfLff/+vbW1NTVZzvz581esWJGYmOjp6VlYWJiRkXH58mWqRXv37j148KC3t/ewYcPevn27evXqGTNmnDx5UlZWdtasWRUVFSEhIQcPHlRVVe3RowdO7NZbvnz5xYsX//nnn+HDhxsYGEyZMmXmzJm8BAalvr6eyWSGhYXV1dXNmzfP3d3d3t5eVFT0zz//LC8v37Fjx86dO7t27dq3b18ul+vj43Pw4MF9+/aZmZnt27dv0aJF6urqPXv23Lp16+7du9euXTtx4kQREZHY2FgqNUvZsWPH3r17vby8HBwchIWFjx8/vn79et7WgICAyMjIgICAgQMHHjp0yN3dXVVVdc6cORUVFX/99deBAwfU1NTwu7cJjUabO3eut7d3REQEb4oy6kJBLQ8aNOj69euNXq2VkZE5deqUiopKZmbmokWLbG1tExMTtbS03NzcPn36FBAQsH37dg0NDeoVk+LiYnt7+/nz53O53IkTJy5YsCArK8vX15e6WD148ID6vS5cuMC7y8+aNauwsDAwMFBCQuKvv/6qqamZPXu2u7u7jY0Ndbvcu3dvQEDAwYMHhwwZkpmZOX369M/dLquqqubMmbN48eK4uLhPnz6tXbuWmncwNDTUx8dnxYoV4eHhAgICFy5cuH///vbt26lL0/79+3mXpu3btzc964yMjJq9zzo5ORUXFx84cEBMTGzbtm0cDsfNzW3Dhg0WFhY/fuIHQ0PDs2fPlpSU7NixY8uWLYaGhryftZXq6+vr6+vpdHr7BhYbG+vg4EAt8/61Nlr43Pp2qfJdd/5rNMHDw4NAB1RU+NbaZkzi3TtUVh4A4Ku5ubl19BF6AAAAfg2C/A6g3XDr688tX1RTUnz+3Fnq8XeH9unTJ0KIhIREy8VYLNauXbvs7OycnJykpKQMDQ2DgoLev38fGhrasNiAAQMkJSXl5OTGjx9fVVWVl5dHCBESEnJ2ds7Ly+N1M2KxWGfPnqV6D1RXV4eGho4ePdrGxobJZC5btoxGo8XExBBCZs6cWVdXRy0TQsrLyx8/fjx8+PAW4uzbt6+EhISsrKytrW1VVVV+fj61PiEhwcjIyMLCgsFgGBkZWVpapqSkUE/WmiUuLq6ioiIkJKSjo+Pj41NdXR0bG0tFe/ToUQsLCysrK0lJSWdnZxqNdvLkyZa/PWiradOmiYuLR0ZGslgsQkh2dvaDBw+afXOc6vkkLS0tIyMzevToDx8+vHv3jhBy7ty5Xr16WVtbi4qKGhsbjx49+u7du1RyiKd///5NT9fq6upDhw6NHj16zJgxTCbT3d2dRqN9cYYnDocTEBBgamrq6uoqLS3NZDInTZrUaNIaQkhubm55ebmenh6dTpeXlz9w4ECnTp2qq6sjIiL++OMPa2trKSkpV1dXGo3Ge/NaSEhIW1tbRESEOhUrKyt5m0aNGvXnn38ymUxpaWkLC4vS0tKGQ1C6u7tPnTp1z549mpqaI0eOXLx4sZSUFJPJHDFiRKOSFDabHR4ebmlpaWNjIyEhQc2x9PHjx+PHjzcsRg3/uGPHjm+Zz+l3IyoqeuPGjYCAAD09vYyMjDVr1vTq1evOnTuNivXv319KSkpaWnr8+PHV1dU5OTkNt65bt27u3LlhYWFqamphYWHW1tbUNdPNzY1Go8XGxrLZ7JCQEDMzs/nz50tJSTEYjIYpMTabfejQIVNT0zlz5jTdSl3ZLC0tR44cKSUltXDhQhqNFh8fzyuwdOnSyZMn79y5E797m8ybN09YWPjAgQPUo+qnT5++f/++5ddpJSQk1NXVaTSagYHBnj172Gx2WFhYwwIbNmyYP39+ZGSkjo6OnZ3dypUrqQvgqFGjeBfA1ujXr5+kpKSsrOzYsWMb3i4vXbrUvXt3KysrBoNBLSQnJzd7u8zLy6uoqNDV1aXT6XJycvv375eRkamtrd21a5eJiYmzszN1MXRwcGg4ruaKFSuoS5OKikqzZ12j++yCBQsa3WeNjY0lJCSoJrNYrLdv37ayye2loKAgPT2dECIvL+/n59e7d2/qY8tGjhzZ8OP9+/e5XC4GhQPoKDp3N87OzZ06fXp9fT2/YwGAjo33RiYAAADw16+Tr7q+1fvV7evxJ45ramryO5Z2QGWqqqqqWi6WkZFRUVHRu3dv3po+ffqIiIg8ePCg2fJUPxXeEy7qBe39+/dTH+Pi4qytranRqF68eMFisQwMDKhNDAZDQUEhKyuLEDJ48GAtLa3o6GjqSd/p06ft7Oxa2aGNCqC2tpb6WF1d3bBbQF1dHY1GExRs1Wmpq6srKSn5/PlzQkh2djabzea9Ds9gMOTk5F69etWa/UDrMZlMJyenjx8/Un2VgoKCnJ2dRUREWq5F/eh1dXWEEDab3egXFxYW/tzJ0/BsycrKanRCKioqUidkC9LT08vKyho+hhYSEpo3b16jYurq6nJycq6urjt27KD6gRFCXr58yWKx9PT0eEeUl5d/8eJF06N0795dVFT05cuXTTdR3QuotreMKtn0acvLly+rqqoMDQ15a4yMjISFhZ88efLFfcIXCQsLu7i4PH78+ObNm2PGjCkpKZk6dWppaennCpMG189Gmr1mvnjxIjs7m8ViDR48uNlar1+/ZrFYpqamzW6l6ja6smVnZ7epjdCUkpLSuHHjsrKyrl27Rgg5cOBAm16nNTQ0lJKSevr0aWsKU6fNVzxIbXq7bLi1vr7+c7dLNTU1WVlZNze3wMBAKt9PCHn27NmnT5/MzMx4xYSEhGbOnNm0+ufOutbfZxtF/sMUFBQ4Ozs/e/aspqbm0aNHOTk5rZnVJj8/PyYmprS0lMPhJCYmzp07V01NbeHChT8gYAD4dswuquP2hp4/d76tnSkBABrp6CP0AAAA/DJ+kXzVk/jYlLDgQwcPDh06lN+xtA91dXUGg/HFx/HUc9VG3bCYTGZ5eXlrjiIuLj5z5syUlJSHDx8SQsLCwnjP7CorKwkhvr6+8vLyCgoKCgoKeXl5VK8aAQGB6dOn5+TkUFPTx8XFTZ48uc0tJIQQYm5u/vTp0ytXrrDZ7NTU1CtXrgwfPrz1fynSaDTqiRgVWFBQULf/KSgoYLPZXxcVtMDZ2ZkQcuDAgdLS0hMnTjTN/VAuXbo0cuRIVVVVGRmZ1atX89aPHDny0aNH58+fZ7FYDx8+PHfu3B9//NGaX5zK3W7evFn2f3Jzc6nfvQUVFRWEkBZGmKQwGIy4uLj+/fvv2rVr0KBBixYtYrPZ1BG3bdumpqampqamrq6en5//uSOKiIjwukZdv3598uTJffv2NTAw8Pf3b+G4N27cmDZtmomJSffu3ZClE0kAACAASURBVLdt29ZsGaqrpZiYWMOVkpKS1L9QaC/9+vWLjY2dN2/eu3fvbt269RV7oE4YPz8/JSUlJSWlzp07U9dMqpdJp06dmq1VWFjYwlZqn4GBgXp6evr6+vr6+gUFBV887aE1Fi1aRAgJDg4uLy8/ffr01KlT21RdWFi4hXxMQkKCjY2Njo6OkpJSw9Edv8Xw4cOfPHly+fJlNpv95MmTS5cuWVhYNHvxZDAY0dHR/fr127Nnz5AhQ1xdXdlsNvVXwRcvhuTzZ12j+6yhoeHPdp+Vl5evq6sbNGiQmJiYjY2Ns7Pz4sWLCSFJSUmDBw9WVlZOTk7+999/O3fubGpqyvtn/scff6xZs6ZLly5iYmKOjo6mpqZJSUmysrJ8bQoAtEGXPgNGem/z8/OLjIzkdywAAAAAAPCtfoX5q/L+SU5Y5+Hl5TVt2jR+x9Ju6HS6lZXVmTNn7t27N2jQoEZbP3z44OXltX//fmrkqEbZqbKyMmrS+NZYsGDB/v379+3bN3v2bBUVFd6gUnJycoQQHx+fBQsWNB3l38nJydfX98iRI8rKypKSkl26dPm62VPc3NyePHni7u5eWVmpoKBgbW3t7u7eyrp1dXVlZWXUjEHU015PT0/eOdBo6gJoLz179hwwYEBycrKLi8v48eNlZGSafsm5ubkTJ060s7MLDg5WUlIKDg7mpay8vLwePXq0YMGCyspKJSUle3v7devWtea41NPDzZs3Uwkz0rofV1FRkRDSdJC9pvT09A4fPvz+/fsDBw7s3btXT0/P1taWELJ+/fo5c+aQ5ua64Kmtrf306RN1KlIv+I8cOdLf319eXj4iIuJzKauCggIXFxcrK6vNmzcrKChERUU1m7Kini83yk6Vl5dTTYNv4eTkFBUV1fCJ/6RJk0JCQr7YsbVZ1Cnq7e1NJXF5l6C7d+8SQhoNeslDTZPzua3Ulc3Ly2vGjBkNr2m4sn27QYMGGRsbnz9/fuvWrdQoo62vW1tb+/Hjxy5dujS7NS8vb+rUqWPGjNm9e3fnzp0PHDjQLikrd3f3J0+euLm5VVVVKSgojBkzZuXKlZ8rrKure+jQoQ8fPoSEhOzfv19XV9fa2poQ8vHjxy8eqOlZRwjhcrnUSJirVq2aPn06afGS2FanTp3KyMjQ19enMmQMBuPr9qOurk79c2tk4MCBTcf55AkICMD4PwAdXfdxjiWZz+bMnaumpvbLvLwIAAAAAPB76vD5qtK8NycXz7Eebb1p0yZ+x9LO1q1bd/ny5RUrVly9erXR45v09HRq9LBu3bpJSEhQvaMo9+/fr6mp6dWrVyuPoqysPHbs2JMnTxYWFjaca1pFRYVOp39uvCMmk2lnZ3fq1CkJCYm2vpbeUGZm5ps3bx4+fCgkJNTWh19JSUn19fXUOGlKSkp0Oj0jI+OrI4HWc3Z2Tk5Ojo+P/9zUIE+fPuVwOPPnz9fQ0OByuQICArxNGRkZ2dnZr1+/pk7g1j/o7NKlSwsn5OeoqalJS0vfuHHDzc2thWJFRUVlZWU6OjqysrKenp63bt3KzMxUVlam0+lpaWlfPEpSUhKXy6WmxXr+/Hltbe3UqVNVVVUbtb2RzMzM2trayZMnq6qqEkI+V1JbW1tMTKxhGE+ePOFwOA1HCISvU11dnZGRYWRkxFtDdWltuKb1PnfN1NfXFxQUTEpKop7yN6KnpycoKJicnExNHNhI586dcWX7fpydnZ2dnbdt25aSktKmirdu3aqvr286Ex4lPT2dw+HMmTOna9eu5PP/tNvq+fPnOTk5qampNBqt5dsl74LWqVMnDw+P27dvZ2VldenSRVpa+vbt21THshZ87qyj7rPPnj1rl+Y0VFxc/ODBg+zs7NraWkFBQXV1dX19fQMDA6p3l4GBAfUCDQBAC8xXrCl9/dJ+/ISU5CQtLS1+hwMAAAAAAF+pY+eraior4p2na6qqHomMbOWkRx1Iz549IyIiZs2aZW5uvn79ehMTEzExsby8vGvXrm3fvt3KyooQwmAwlixZsnXr1piYmNGjR79588bNzU1JSWn27NmtP5CLi8uxY8dKS0sbzmxBp9MnT5585MiRXr16TZgwQUxMrKioSFBQkNerY+bMmXFxcVeuXNmyZctXt3HdunXKysqVlZWtGaSIEMLhcMrLy0VFRZ89e+bj46OsrDxu3DgqWnt7++PHj3fv3n3MmDGioqIlJSWCgoJNH3Ixmczi4uKCggIpKSk6nU5lTaBNJkyYsGLFCjMzM15vvEaoBMz169f79u2bm5t7//593qbly5d36dKlsrKyTb0ZCCF0On3KlClRUVG9evVydHQUExMrLCwUEhJSVFSUlpYuLCzMzc2VkZFpNJmWiIiIm5vbhg0bVq9evXjxYgUFhaqqqoKCAt4ULJTCwsKNGzf6+/urq6s/f/48Ly9v/PjxdDrd0dExJibG2NjY3t6ewWAUFxcLCQnJy8sTQurr68vKysTFxbOysjZt2qSsrDxhwgRCiLKyMiHk7t27PXv2LCgoePz48edaRJW8d+9e9+7dCwsL//33X94mJpNZUlKSn58vLS0tIiIyc+bMAwcOnDt3btiwYW/fvvX29paXl3dwcGjTFwjNcnR09Pb2trCwIITcunVr7dq1NjY2RkZGX9FlhE6nT5o0ibpmjh8/XlRUtLi4WEBAQFFR0dra+vz589HR0TY2Nrm5uUePHuXV6tSp0x9//HHx4kVq+sDc3Nzo6OiG+xw/fvyxY8d69OhBXdmokxCP79uFk5OTl5eXgYFB9+7dv1i4pqbm06dPYmJiqampK1asUFNTazbFSAih+l3duHGjd+/eeXl5DaeTpC5Wb968kZGRobrWtd7q1atVVFSqqqq+eLssKiry9vb28/NTU1N7/vx5fn6+vb29iIjIokWLfH19N27c6OzsLC8vX1VV9fbtW21t7UbVefdT3llH3U/l5eUb3mcZDMa7d++avc+21fz58x0dHTkcTm5ublpaWnp6+qtXr5KTk0NCQqju4zIyMpqamtQghNSCvr4+JpkA+PESExN5s+L9DHJzc4mMPLUsIChoE7D3yCRb27Fjk+7do6bjBQAAAACADkeg4VO5uLi4iRMnemYW8jGg1uPW1cW7zCxNS31wP4V6Pt5RUN8zl8tt9Ip00zemuVxuXl7enj17EhISXr9+zWKxJCUlu3btamxsPGPGjIEDB1I7CQoKCg0NzcnJERcXHzJkyKZNm7S1tblcbkhIyJo1a6qqqrS0tOLj42/evLl69ery8nJVVdVTp05pamryjmJnZ+fg4MB7+kYFUF1d7e/vHx8fX1hYyGQyTUxMVq5cqauryytgaWlpbm7u6enJWxMaGrpr167i4mJRUVEzM7OhQ4f6+PiwWCwNDY3IyMg7d+74+PhUVFSoqKgcOXKka9euiYmJixYt4o1QRKPRunbtumzZspEjRzb9QuLj40+ePJmVlVVaWiopKTl48ODly5dTg7BxuVwOh7Nr167z588XFxczmcw+ffq4urrevn07NDT0/fv3oqKipqamO3fuzMjIcHNze//+fbdu3bZv3y4rK9vCr9CnT5/Y2FhHR8fvcRr8JAQEBGJjYxsmPxp9IadOnfLw8Hj16pWcnJyjo2NgYCAhxMvLa8yYMdRU9hs2bAgNDaXSmXp6env27Bk0aNCaNWtCQ0Pr6+tHjBgxePDgZcuWaWpqXrhw4dWrV1OnTuUN0CcsLKypqbl27VpbW1vqdGWxWFpaWidOnLh58+aaNWuo0/XkyZOampo1NTV+fn4nTpwoLCyUlpY2MTFZtWqVnp5eamrq9OnTS0pKevbsaWpqGhUVRZ2BQ4YMiYiIIISEhoZGRES8evWKEKKjozNlypQZM2Y0bG9eXp6Li0tWVlZFRQXVzOXLlwsJCXE4nG3btp05c6aoqIjJZPbv33/58uXa2tpeXl5xcXFycnIlJSWioqKDBg3y8vJSU1Oj9rZ169bY2Nj6+npTU9MBAwZs2LCBGp3m2LFjbDa7c+fOy5Yts7Oz43K5AQEBcXFxXC530KBB/fr18/b2VlNTCw8P//jx4+LFiz98+NCtW7fAwMBOnTodPnz42LFj+fn5YmJi/fr1W7p0qbq6OtW0ffv2VVdXKykp/fnnn6NHj276C5L/dsJISEhYtWrVrz2gHHWNra+vJ//9d8276lL/vXr16uHDh//555/8/Hwul9u1a1cHB4dly5bR6XQulxsYGOjr60udkPHx8cnJyR4eHqWlpcrKysePH798+bK/vz+bzVZRUVm9erWDgwOXy62pqaGumdQJM3DgwBUrVujq6lZUVKxfv/7SpUs1NTXdunUzNTUNDAzs3LlzWFiYvr5+ZWXlpk2bEhISqquru3XrZmJisnv3biUlpZCQEH19/ZqamsDAwPPnz1P77Nu3759//nn9+vW9e/ey2WwlJSU3N7eGv3uzLfXw8JCWlj527Bi/fpEfgLqIxcXFUR9b+FfQcMHT09PY2Jh3kac27d+/39/fv6ioSExMzNzcPC4uLioq6siRIxkZGR8/fpSSkhoxYsTGjRupvFRgYKCfnx+LxVJRUVm3bh11c9+4cWN4eHh9ff2wYcNMTU09PDw0NDROnz79/v37Zi9WQ4cOHTZs2MaNG1kslqamZnR09O3btzdu3FheXt6lS5fY2FgNDY27d+/Onz+/4e1SQ0NjxYoVo0aNatSo/Px8V1fXFy9eUBc0BwcHNzc36p2eyMjIo0ePvn79mhCipaU1ceLEysrKXbt2Nbw0EUJqamqCgoIanXVaWlrN3mdTUlICAgLYbLa6unpwcHBSUtL27dsrKiqosRDV1NRa/glavs9+/PiRl8GiFrKzs7lcrrCwsKqqasMMlrGxcaO5PH8eje6zn/tCWnnSfl2V77rzX6MJv8N18ts5Ojr+bF+R/qgx44JCeB/L8vMiHUYNGTjgzOnTv967jAAAAAAAv4MOnK+66rPmSVzUzRs3BgwYwO9Y2qZN+aqWF/j7//lTpkzx9fXlPab/iqNERkZmZ2evXbuW/C/n5O/vHxUV9ejRIwaDwfdWI1/VaOHbt4aEhLx48YLXJ6+6unr9+vUHDx588+YNb9DL73Hc9t3q5eV14cKFx48f/1RRtXIr8lVNV7aywHeq+wN2+zs8h/26fNX3WPhOuz18+HB2dvaGDRvI/26Xvr6+4eHhaWlpTW+XfGx+W2No6322rKzsxYsXvPQVtcBmswkhnTt35qWvqAXeezn8hXxVh2jC73Cd/HaOjo6pFeyG+aGfUN7D+9HTJ7j96drstKAAAAAAAPCT66iDof177OiDyENRUVEdLlnV0XE4HGFhYUJIeno6g8GgklVfp6SkZMOGDRcuXOCtERYWVlZWrq2tra2tbYdY4SdTVFTk7u6emJjIWyMiIqKqqsrhcDrcL15XV8fvEADgd1FcXLx27dqEhATeGmFhYRUVld/wdkn16+rTpw8v/UMNJMjLYKWlpcXExHz69IkQIi0traWlxctgtftAgmfOnMnLy5s3bx71dxEA/Ay69O5ns3X39qULdHR05s+fz+9wAAAAAACgbTrkOAm595MSNqxav3795MmT+R3Lb8fb2/vVq1cvX75csmTJypUrv2VXDAaDRqPFxsa+e/eutra2qKgoNjZ2586dNjY2P+2oPvAtREVFhYWFw8PDi4uLORzO27dvw8PDfXx8xo8fj2kGAAA+h7pdxsTElJSUULfL6OjogIAAW1tb3C6pcWUtLCyWLFny119/3blzp6ys7MOHD7dv3/b397ewsGCxWJGRkRMnTjQyMhIVFdXS0hozZsyqVasOHDhw584daoqsr3Pv3j0XFxcdHZ3Y2FheN50OITg4uFu3brKysnJycv3797958yZv0+TJkzU1NRUVFU1NTVNSUggh27dvNzMz09bWVlNTMzEx8fb2rqysbLi3lJSUsWPHamtr9+7d29fXt6amhrcpKChoxIgR3bp109HRGTp0qJ+fX1VV1Q9rJvy2DKxtTRb86bJ48fXr1/kdCwAAAAAAtE3H619VmptzynWOnZ3tunXr+B3L74iaCEpJSWnLli26urrf8oBGUlIyMjIyKCjI3NycxWKJiYnp6OisXLnSycmpHQOGn4eUlNSZM2f8/PyMjY2rqqrExcUNDAw2btw4a9YsfofWBlu3bj127BiHwxk8eLCXlxc1eQwAwPcjJSUVExOzY8cOMzOzqqoqMTExPT09T09PvLXzOTIyMoMHDx48eDBvTXV19YsXL6gOWOnp6VevXk1PT2exWFRhXgcsaiBBDQ0NAQGBLx4lPT1dQEAgNzd30qRJvr6+27dvt7Cw+I6taj/Ozs4LFiyQlZXt3bv35cuXG246evTonTt3duzYcfz4cWrNnTt35syZM3bsWCEhoWvXri1evDgjI+PIkSPU1ufPn0+aNMnZ2fno0aPp6emzZ89+//59QEAAtfXevXszZsywtbWl0WjXr193c3N79uzZ4cOHf2Bb4Tc1dOmqstzX4yc4pCQnaWtr8zscAAAAAABorQ6Wr2KXlR2fO1lbXT0iPLw1jxKg3Xl6enp6erbXe8T9+/ePioqilpvOJQC/nkGDBp09e7ZD/9YeHh4eHh7UckeMHwA6ogEDBsTExFDLHfoSyi90Ot3Q0NDQ0JA3kGBtbe2bN28aToV16tSp4uJiQgiTydTW1m44FZahoSFvkkWeJ0+e8OaKS0tLs7S0HDZsWEBAQJ8+fX5w674rcXHxadOmCQkJcblcW1vb8+fPnzlzpqCgQFlZmRASGBiooKCwfPlyAQGBPn36LFq0aPPmzQsXLtTS0iKEiImJTZkyhaprY2Nz6dKlc+fOvX37tnPnzvxuFvzqBASs/YKip46zthmTkpQoLS3N74AAAAAAAKBVmslXnVwy78fH0SwV4z79ZznzPtbXck4vmUurqb5w/pyYmBgfAwMAAACAjotGo2lqalJjCfJWfvz4kZe+SktLi4yMfP36dX19vbCwsKqqKi991a1bNwMDg7y8PF5FalLDu3fv9uvXb9y4cVu2bNHR0eFDq74D3ntFlE6dOhFCqK5ptbW1f//99+jRo3nvkJmbm/v4+CQkJCxcuJAQEhYW1rCujIwMry7A90ZjMMbtC4t0sHZwnHjxwnkarYO9pgkAAAAA8Hv6zx/uqqqqvNdO+S4xMTH/v2uueK95++/Du7dv461MAAAAAGhfTQcSLC8vf/78+bNnzzIyMp4/f3727NkXL140nKKpIQ6HQwg5e/bs6dOnZ8+evXHjxl/vT9bCwkIGg6GmpkYIefPmTWVlpYqKCm+ruro6ISQjI6OFuqqqqj8mVAAJBaVx+8KPTLZdtnz5rqAgfocDAAAAAABf9p98lYmJiYmJCb9CacTR0TG1gs37+CDi4KOYiOjo6F69evExKgAAAAD4TUhKSvbt27dv3768NbW1ta9evQoNDd26dWuzQzJSWavDhw9HRES4ubn9uFi/PxaLdefOnSlTpggLCxNCqOETJSQkeAXodDqDwSgpKWm27r1795ycnKi6AD+GkmH30f679yyZZ6CvT3X7AwAAAACAn5kgvwNolew7N65t2bB58+aJEyfyOxYAAAAA+E3RaDRdXV0pKamW8y4cDqe6utrf3/+HBfYDBAUFKSgorFy5kvpI9TMTEhJqWIZGo7HZ7KZ19+7dq6Cg4O7u/gPiBGhI/w8b08XLXf/88++//+Z3LAAAAAAA8AUdIF/1/tWLM24L7O3tV61axe9YAAAAAOB3l5GRQc1Z1YigoCAvjyUpKWlqavpj4/qOLly4cPbs2ZiYGF6HKjqdTgipra1tWIzD4TAYjEZ1L126dP78+cjISHFx8R8TLUBDZouXG4yyHT/BITMzk9+xAAAAAABAS372iWdZpR9PLJjW3dAwMiKCN5kzAAAAAAC/PHnyhMpXCQsL19fX19XVCQoKqqur9+3bt1evXj169OjRowc1UdPP/OdrfX19syubdh07depUcHDwiRMnlJSUeKMgKigoEELKy8t5xVgsVnV1taKiYsO6Z86cOXjwYExMjKKiYrMjKAJ8dwICo3x3xky3t/pj1IOUZDk5OX4HBAAAAAAAzfup81X1nNrTf84V5dafPhlPvcIJAAAAAMBHXC731atXkpKSPXr06NOnT48ePXr27GloaCgqKsrv0NpARkamsLCw6fqcnBwVFZWGaw4dOnT9+vXjx4836h2lqqoqISGRn5/PW/P69WtCiL6+Pm/N4cOHb968efToUfSsAv6i0enj9oRFTBg1bvz4v69cERER4XdEAAAAAADQjJ86X5V1PUFMVCzx3t1G72kCAAAAAPAFl8tNS0tTU1PjdyDfxMzM7MyZMykpKf379+et5HK5MTExkyZN4n309vYuKys7fPhwo3mqCCE0Gm348OFJSUn19fWCgoKEkOvXrwsICFhaWlJ1/fz8ysrKQkJCmtaFX9Xzy+eu+W8qzXtDCDFdtHSI28qmZVLCgv/220AEBGS7ao7y2a7ab+C3HDH50L6kkL1VH97/sdG/16QZLZQUl1ewDw4/Msl24aJFhw4e/JaDAgAAAADAd/JT56sEuNzoo0e6d+/O70C+CwcHB97y50ZHaWHUlLZWacdddcQq7VL3V7Vz585jx47xPrb8PWDrz7D1W3ZSVFTUmj3/AhwdHUmTL6Hpd8L3At++wy/WSk1NNTMza1rlF5OUlET96ORrL/Xtu+l3i+FbgulwBAUFO3qyihCyevXqmzdvzp4929/f39zcXEhIKCsrKygoqLa2lvc3amZm5t69ewkhUVFRDeuuXbt24cKFhBA3N7dRo0Zt377dxcUlPT09ODjYwcFBS0uLEJKVlRUcHEwIiY6ObljXy8tr3rx5P6aN8OPpjbTRG2kTbDHw45vXj48dGbx4mSDtP8NLcuvqHkQcJIR0HTh4Uvixz+ymDQbMWaRrMSrY0qQ1hRUNjGy27QlznWvcs6erq+u3Hx0AAAAAANrXT52v2rp165gxY/gdRftTVVWdMGFCwzWfm9vgZ57z4HcwYcIEavKJX1ijU5F86azDOdnRSUtL6+np8TuK76vhNbbRGft7nsBmZmYmJq16kNdxNWpgCz/073kO/LR+h/vsT0tbW/vGjRs7duxYt27d27dvqfm3xowZs3PnThERESq/+MUso56e3tGjR729vffv3y8tLT1x4sQVK1ZQm36lDCV8BSWjHoVPn2ReuaQ/6j//N/fs8nlJxc5l+Xmt2Uktm310xoTpsefaMTBdS+shbivdli7V0NCwsbFpxz0DAAAAAMC3+3nzVTNmzBg9ejS/o/guTExMGnZnAeAjnIrw68E19je0bNkyfocA0PGoqant3LmTWuallxrmmfT19XlzXDVbgBAycODAc+fONS2gp6eXk5PTcl34hfWePOuC19KH0Ycb5atSwoIHznPJe3i/NTv59/jRqvfv2j22Qc5LPrx64TRpclLiPSMjo3bfPwAAAAAAfLWfN1/1qyarAAAAAAAAfmFdTQbLaunkJN19n/1SVkOLWpn3MIXDYmkMHtaoMLeu7vae7anxMVUf3suoa5oucjOwtru6ee3Do4frOBw/XSUZta7OV5NyHyRfXOteUVRYW1Pdqavm8JXrNc3Mmx76TUriNf+NJVnPBIVoTJUu02LO0iUkG5UZtXlH3Nt8mzG291OS5eXlv8MXAAAAAAAAX0OQ3wEAAAAAAADAL6X3pBmEkEfR4bw1yYeC+892blryesDmpJA9Fl6bXO8+0RludXrZwrdP/7VY7a09fKSMWlfPzELnq0mEkMp3Jd2s7RZdf+B694mImPiZ5Yua7orDqjruPF3/jzFL7z93vpLYqatWPYfTtJiQsPDYXQcruNyx9vbV1dXt1mYAAAAAAPg2yFcBAAAAAABAe+o+zlFYVCz1ZGwtm00IKc3NeZv6yHCMfaNitdXVD4+G6Vla6420YUgxTRctFaTRnpyIbrpD/T9sBru6M5hMUaa0zoiRrNKPVR/eNypTlpdbXVEup6NHo9PF5eTt9xwSlenUbHiiMp3s90c8+vfJAudmUmgAAAAAAMAXP+94gAAAAAAAAD9AZmamo6Mjv6P4qSUmJkp079X68nRJKcMx9o/jotLPn+ox3ikl7K8+U2YJCQvXcWoaFvuQ/YLDYsnr6lMfaQyGhLzC+5cvWt65II1GCOHW1TVaL62qLiYrd9bdpe+MeT3sJzJVVFvYiZy2rk3A3oiFM7obGS1fvrz1TQMAAAAAgO8E+SoAAAAAAABoZ72nzHwcF/UoOlzXctSzS2fnX7zTtExNVRUh5ObOLTd3buGtlJBXbFryxY2ryQf3lmQ9r6kor6utbfaINAZjSsSJ6wE+9/YF3tmz3WCUrc2WIBqD0U4NAgAAAACA7wv5KgAAAAAA+K3p6urGxcXxO4qfmqOjY2oFu01VFA2MVIz75D/+59K6Ffp/jGEwmU3LiHWSJYRYeG3qN3N+C7v6VJB/YtEsPSvr0X6BEgpKDyIPXd+6qdmScjp6Dn9FVn14nxIWnPjXbnkdPVOXZc2WfPci85y7y/Tp09G5CgAAAADgJ4H5qwAAAAAA+CwzM9PNzc3Y2FhWVlZMTExBQcHIyGjs2LHJycn8Dq15165d09fXv3LlSvvu1t7eXk9Pz9bWtk21wsLCzMzMjIyMkHP62fSePJMQ8uzSuf6zFjRbQKqzCo1OL8p42vJ+ijMz6ms5fabMklZVp9HpAgLNF6soLnz3IpMQItZJdtjy1UqG3amPTbE+fohfOL1Xzx5/BQe3ujUAAAAAAPB9IV8FAAAAAMBPYWFhvXr1Sk1N3bZt28uXLz9+/JicnLx+/frS0tKnT7/wHJ9fuFzu99htfHz8wIED21pr1qxZR44c+R7xwDcysLYTlemka2ktrarebAEand5j/KT0cycfHjlcXVHOrasrL3xbUVJEPdjL2AAADfRJREFUCBFlSpcXF5bl59ZUVlAjBGbfu1VbXf3h9auCfx82u7fy4qJL61a8f/WijsMpSk8ty89TNu7TtFgdh3Pqz7kSAgKn4uPpdHr7NRcAAAAAAL4JxgMEAAAAAOCb5OTkRYsWDRky5Pz580JCQtRKDQ0NDQ0NaWnprKws/ob3OSNGjHj27Bn5Pokrgc91n2m16urquXPnRkZGtks80EqZVy5c89/08c3rMPuR3UaPtVrnKyQi0nPCJF2LUVSBW4H+j48dIYTkJN8NsR4yyntblz4DLFZ7i0hIJIbsvuKzhsFkqvYzGbLEQ0JesffkGdl3bxwYZaZk2NN+98GB8xc/jAr9J/JQV9MhagNMn106FznJVnu4Vfq5k4SQv7dszL57a4TnRm59fcTE0dUVFeKy8r0mzeg7dXbTOC+uXlaSlpqUeE9eXv5Hfj8AAAAAANAy5KsAAAAAAPjG19e3rq7Ox8eHRqM1yv1YWlpaWFjwK7Dvh8vlXrx4sayszMnJqdkCNNq3/k9KfHz8hw8fvnEn0Fa6lta6ltaNVpqvWMtbHuK2cojbykYFhISFzd3XmLuvabResVv3Rdcf/N9+/lumz5RZ1IKF138mspoWc7blIO8FB6WdjT996pSRkVHLJQEAAAAA4AfDeIAAAAAAAPxRU1Nz7do1WVnZfv36tVySy+Xu2rWrV69eMjIyysrKTk5OmZn/f2KekJAQRUVFRUXF8+fP29vbq6ioGBgYHD9+nNrav39/GRmZTp06mZubs1gsQsiGDRu6du2qrKwcHR1NCKmrq9uyZYuxsXGXLl2GDh166tQpQsjSpUsVFRWVlJQGDBiQmppKCHFzc9PQ0NDT00tISEhJSenXr5+KikpYWBghJDw8XFdXV0dHJyEhYfr06d26devfv//p06d5wdfV1e3du3f48OH6+vq9e/ceMmTIvn37rK0bJzZ43rx5M3LkyJ49e/bo0WPy5Mn//PNPw13t2rVr+PDhvXr1Gjt27MWLF5tW9/f3DwgIyM3N7dGjh42NTat+Cfg9ZF65cCvQP3DnTpwYAAAAAAA/IeSrAAAAAAD4482bN2w2W1tb+4slN2/evG7dOg8Pj5ycnCtXruTl5VlaWhYXFxNC5s2b5+LiwmKxJCQkwsPD//33365du7q6unI4HEJIYmKimpqaiorK1atXRUVFCSEbNmyYOnXqunXrJk2aRAjZtGnT7t27fXx80tLSRo4c6ezs/Pjx4507d9rY2AgJCZ0+fbp79+6EkMDAQGtr6z179lhZWfXv3//MmTO82GbMmDF37lw2my0uLr5v377bt2+rqamtWrWqtraWKhAcHLxjxw4vL6/Hjx9HRERUV1dLSUlJSUl9rrFSUlKhoaEPHz48ceJEbW3t7Nmzc3JyqE07duw4dOjQqlWrbt26NWzYMA8Pj7S0tEbVV65cOXToUFVV1SdPnpw7d64tPwj8yooynp5bsXjWrFmurq78jgUAAAAAAJqBfBUAAAAAAH98+vSJECIhIdFyMRaLtWvXLjs7u0mTJklJSRkaGgYFBb1//z40NLRhsQEDBkhKSsrJyY0fP76qqiovL48QIiQk5OzsnJeXx8vcsFiss2fPTpkyhRBSXV0dGho6evRoGxsbJpO5bNkyGo0WExNDCJk5c2ZdXR21TAgpLy9//Pjx8OHDW4izb9++EhISsrKytra2VVVV+fn51PqEhAQjIyMLCwsGg2FkZGRpaZmSkkKl05olLi6uoqIiJCSko6Pj4+NTXV0dGxtLRXv06FELCwsrKytJSUlnZ2cajXby5MmWvz0AQkhlSXG884x+ffvu37eP37EAAAAAAEDzkK8CAAAAAOAPKlNVVVXVcrGMjIyKiorevXvz1vTp00dEROTBgwfNlhcWFiaE8BJC06dPl5KS2r9/P/UxLi7O2tpaUlKSEPLixQsWi2VgYEBtYjAYCgoKWVlZhJDBgwdraWlFR0dTs2qdPn3azs5OSEioNe2iAuD1r6qurm64ta6ujkajCQq26v9EdHV1JSUlnz9/TgjJzs5ms9k6Ojq8aOXk5F69etWa/cDvrLa6+uTiWUwG/eSJEyIiIvwOBwAAAAAAmod8FQAAAAAAf6irqzMYDCo/1ILS0lLSpBsWk8ksLy9vzVHExcVnzpyZkpLy8OFDQkhYWNj8+fOpTZWVlYQQX19feXl5eXl5BQWFvLw8aporAQGB6dOn5+Tk3L59mxASFxc3efLkNreQEEKIubl5amrqlStX2Gw2tTB8+PBWpr4IITQajUp9UYEFBQV1+5+CggI2m/11UcHvgsu96LX0U/arhEsX5eTk+B0NAAAAAAB8FvJVAAAAAAD8QafTrays3r17d+/evaZbP3z4sHDhQkKItLQ0IaRRdqqsrExFRaWVB1qwYIGwsPC+ffvu3bu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            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "System: facility_sys\n",
            " path: (U101, U102, U103, S201, T202, C101,\n",
            "        C102, r101, r102, r103, r104, U202)\n"
          ]
        }
      ],
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "C5psdIM02wC-",
        "outputId": "a764782f-0e1f-4751-8da2-e23ade0573fb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Techno-economic analysis"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class PyrolysisTEA(bst.TEA):\n",
        "    \"\"\"\n",
        "    Create a TEA object for techno-economic analysis of a biorefinery.\n",
        "    \"\"\"\n",
        "\n",
        "    __slots__ = ('labor_cost', 'fringe_benefits', 'maintenance',\n",
        "                 'property_tax', 'property_insurance', '_FCI_cached',\n",
        "                 'supplies', 'maintanance', 'administration')\n",
        "\n",
        "    def __init__(self, system, IRR, duration, depreciation, income_tax,\n",
        "                 operating_days, lang_factor, construction_schedule, WC_over_FCI,\n",
        "                 labor_cost, fringe_benefits, property_tax,\n",
        "                 property_insurance, supplies, maintenance, administration):\n",
        "        super().__init__(system, IRR, duration, depreciation, income_tax,\n",
        "                         operating_days, lang_factor, construction_schedule,\n",
        "                         startup_months=0, startup_FOCfrac=0, startup_VOCfrac=0,\n",
        "                         startup_salesfrac=0, finance_interest=0, finance_years=0,\n",
        "                         finance_fraction=0, WC_over_FCI=WC_over_FCI)\n",
        "        self.labor_cost = labor_cost\n",
        "        self.fringe_benefits = fringe_benefits\n",
        "        self.property_tax = property_tax\n",
        "        self.property_insurance = property_insurance\n",
        "        self.supplies= supplies\n",
        "        self.maintenance = maintenance\n",
        "        self.administration = administration\n",
        "\n",
        "    def _DPI(self, installed_equipment_cost):\n",
        "        return 1.02e8\n",
        "        # return installed_equipment_cost\n",
        "\n",
        "    def _TDC(self, DPI):\n",
        "        return DPI\n",
        "\n",
        "    def _FCI(self, TDC):\n",
        "        self._FCI_cached = TDC\n",
        "        return TDC\n",
        "\n",
        "    def _FOC(self, FCI):\n",
        "        return (FCI*(self.property_tax + self.property_insurance\n",
        "                     + self.maintenance + self.administration)\n",
        "                + self.labor_cost*(1+self.fringe_benefits+self.supplies))\n",
        "\n",
        "pyrolysis_tea = PyrolysisTEA(system=facility_sys,\n",
        "                             IRR=0.10,\n",
        "                             duration=(2018, 2038),\n",
        "                             depreciation='MACRS7',\n",
        "                             income_tax=0.35,\n",
        "                             operating_days=330,\n",
        "                             lang_factor=3.73,\n",
        "                             construction_schedule=(0.08, 0.6, 0.32),\n",
        "                             WC_over_FCI=0.05,\n",
        "                             labor_cost=8.48e6,\n",
        "                             fringe_benefits=0.4,\n",
        "                             property_tax=0.001,\n",
        "                             property_insurance=0.005,\n",
        "                             supplies=0.20,\n",
        "                             maintenance=0.01,\n",
        "                             administration=0.005)\n",
        "\n",
        "# pyrolysis_tea.IRR = pyrolysis_tea.solve_IRR()\n",
        "pyrolysis_tea.show()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyrolysisTEA: facility_sys\n",
            " NPV: 80,491,194 USD at 10.0% IRR\n"
          ]
        }
      ],
      "execution_count": 47,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-02T23:43:29.274Z",
          "iopub.status.busy": "2020-12-02T23:43:29.265Z",
          "iopub.status.idle": "2020-12-02T23:43:29.291Z",
          "shell.execute_reply": "2020-12-02T23:43:29.555Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45SyyonR2wC_",
        "outputId": "e9289315-5b05-402f-fb5f-f928af70d197"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m = pyrolysis_tea.get_cashflow_table()\n",
        "m"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Depreciable capital [MM$]</th>\n      <th>Fixed capital investment [MM$]</th>\n      <th>Working capital [MM$]</th>\n      <th>Depreciation [MM$]</th>\n      <th>Loan [MM$]</th>\n      <th>...</th>\n      <th>Net earnings [MM$]</th>\n      <th>Cash flow [MM$]</th>\n      <th>Discount factor</th>\n      <th>Net present value (NPV) [MM$]</th>\n      <th>Cumulative NPV [MM$]</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2015</th>\n      <td>8.16</td>\n      <td>8.16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>-8.16</td>\n      <td>1.21</td>\n      <td>-9.87</td>\n      <td>-9.87</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>61.2</td>\n      <td>61.2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>-61.2</td>\n      <td>1.1</td>\n      <td>-67.3</td>\n      <td>-77.2</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>32.6</td>\n      <td>32.6</td>\n      <td>5.1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>-37.7</td>\n      <td>1</td>\n      <td>-37.7</td>\n      <td>-115</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>14.6</td>\n      <td>0</td>\n      <td>...</td>\n      <td>10.4</td>\n      <td>24.9</td>\n      <td>0.909</td>\n      <td>22.7</td>\n      <td>-92.3</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>25</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3.6</td>\n      <td>28.6</td>\n      <td>0.826</td>\n      <td>23.6</td>\n      <td>-68.6</td>\n    </tr>\n    <tr>\n      <th>2020</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17.8</td>\n      <td>0</td>\n      <td>...</td>\n      <td>8.24</td>\n      <td>26.1</td>\n      <td>0.751</td>\n      <td>19.6</td>\n      <td>-49</td>\n    </tr>\n    <tr>\n      <th>2021</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12.7</td>\n      <td>0</td>\n      <td>...</td>\n      <td>11.6</td>\n      <td>24.3</td>\n      <td>0.683</td>\n      <td>16.6</td>\n      <td>-32.4</td>\n    </tr>\n    <tr>\n      <th>2022</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9.11</td>\n      <td>0</td>\n      <td>...</td>\n      <td>13.9</td>\n      <td>23</td>\n      <td>0.621</td>\n      <td>14.3</td>\n      <td>-18.1</td>\n    </tr>\n    <tr>\n      <th>2023</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9.1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>13.9</td>\n      <td>23</td>\n      <td>0.564</td>\n      <td>13</td>\n      <td>-5.15</td>\n    </tr>\n    <tr>\n      <th>2024</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9.11</td>\n      <td>0</td>\n      <td>...</td>\n      <td>13.9</td>\n      <td>23</td>\n      <td>0.513</td>\n      <td>11.8</td>\n      <td>6.67</td>\n    </tr>\n    <tr>\n      <th>2025</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4.55</td>\n      <td>0</td>\n      <td>...</td>\n      <td>16.9</td>\n      <td>21.4</td>\n      <td>0.467</td>\n      <td>10</td>\n      <td>16.7</td>\n    </tr>\n    <tr>\n      <th>2026</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.424</td>\n      <td>8.41</td>\n      <td>25.1</td>\n    </tr>\n    <tr>\n      <th>2027</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.386</td>\n      <td>7.65</td>\n      <td>32.7</td>\n    </tr>\n    <tr>\n      <th>2028</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.35</td>\n      <td>6.95</td>\n      <td>39.7</td>\n    </tr>\n    <tr>\n      <th>2029</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.319</td>\n      <td>6.32</td>\n      <td>46</td>\n    </tr>\n    <tr>\n      <th>2030</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.29</td>\n      <td>5.75</td>\n      <td>51.8</td>\n    </tr>\n    <tr>\n      <th>2031</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.263</td>\n      <td>5.22</td>\n      <td>57</td>\n    </tr>\n    <tr>\n      <th>2032</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.239</td>\n      <td>4.75</td>\n      <td>61.7</td>\n    </tr>\n    <tr>\n      <th>2033</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.218</td>\n      <td>4.32</td>\n      <td>66</td>\n    </tr>\n    <tr>\n      <th>2034</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.198</td>\n      <td>3.93</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>2035</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.18</td>\n      <td>3.57</td>\n      <td>73.5</td>\n    </tr>\n    <tr>\n      <th>2036</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>19.8</td>\n      <td>0.164</td>\n      <td>3.24</td>\n      <td>76.8</td>\n    </tr>\n    <tr>\n      <th>2037</th>\n      <td>0</td>\n      <td>0</td>\n      <td>-5.1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19.8</td>\n      <td>24.9</td>\n      <td>0.149</td>\n      <td>3.71</td>\n      <td>80.5</td>\n    </tr>\n  </tbody>\n</table>\n<p>23 rows × 17 columns</p>\n</div>",
            "text/plain": "      Depreciable capital [MM$]  Fixed capital investment [MM$]  \\\n2015                       8.16                            8.16   \n2016                       61.2                            61.2   \n2017                       32.6                            32.6   \n2018                          0                               0   \n2019                          0                               0   \n2020                          0                               0   \n2021                          0                               0   \n2022                          0                               0   \n2023                          0                               0   \n2024                          0                               0   \n2025                          0                               0   \n2026                          0                               0   \n2027                          0                               0   \n2028                          0                               0   \n2029                          0                               0   \n2030                          0                               0   \n2031                          0                               0   \n2032                          0                               0   \n2033                          0                               0   \n2034                          0                               0   \n2035                          0                               0   \n2036                          0                               0   \n2037                          0                               0   \n\n      Working capital [MM$]  Depreciation [MM$]  Loan [MM$]  ...  \\\n2015                      0                   0           0  ...   \n2016                      0                   0           0  ...   \n2017                    5.1                   0           0  ...   \n2018                      0                14.6           0  ...   \n2019                      0                  25           0  ...   \n2020                      0                17.8           0  ...   \n2021                      0                12.7           0  ...   \n2022                      0                9.11           0  ...   \n2023                      0                 9.1           0  ...   \n2024                      0                9.11           0  ...   \n2025                      0                4.55           0  ...   \n2026                      0                   0           0  ...   \n2027                      0                   0           0  ...   \n2028                      0                   0           0  ...   \n2029                      0                   0           0  ...   \n2030                      0                   0           0  ...   \n2031                      0                   0           0  ...   \n2032                      0                   0           0  ...   \n2033                      0                   0           0  ...   \n2034                      0                   0           0  ...   \n2035                      0                   0           0  ...   \n2036                      0                   0           0  ...   \n2037                   -5.1                   0           0  ...   \n\n      Net earnings [MM$]  Cash flow [MM$]  Discount factor  \\\n2015                   0            -8.16             1.21   \n2016                   0            -61.2              1.1   \n2017                   0            -37.7                1   \n2018                10.4             24.9            0.909   \n2019                 3.6             28.6            0.826   \n2020                8.24             26.1            0.751   \n2021                11.6             24.3            0.683   \n2022                13.9               23            0.621   \n2023                13.9               23            0.564   \n2024                13.9               23            0.513   \n2025                16.9             21.4            0.467   \n2026                19.8             19.8            0.424   \n2027                19.8             19.8            0.386   \n2028                19.8             19.8             0.35   \n2029                19.8             19.8            0.319   \n2030                19.8             19.8             0.29   \n2031                19.8             19.8            0.263   \n2032                19.8             19.8            0.239   \n2033                19.8             19.8            0.218   \n2034                19.8             19.8            0.198   \n2035                19.8             19.8             0.18   \n2036                19.8             19.8            0.164   \n2037                19.8             24.9            0.149   \n\n      Net present value (NPV) [MM$]  Cumulative NPV [MM$]  \n2015                          -9.87                 -9.87  \n2016                          -67.3                 -77.2  \n2017                          -37.7                  -115  \n2018                           22.7                 -92.3  \n2019                           23.6                 -68.6  \n2020                           19.6                   -49  \n2021                           16.6                 -32.4  \n2022                           14.3                 -18.1  \n2023                             13                 -5.15  \n2024                           11.8                  6.67  \n2025                             10                  16.7  \n2026                           8.41                  25.1  \n2027                           7.65                  32.7  \n2028                           6.95                  39.7  \n2029                           6.32                    46  \n2030                           5.75                  51.8  \n2031                           5.22                    57  \n2032                           4.75                  61.7  \n2033                           4.32                    66  \n2034                           3.93                    70  \n2035                           3.57                  73.5  \n2036                           3.24                  76.8  \n2037                           3.71                  80.5  \n\n[23 rows x 17 columns]"
          },
          "metadata": {
            "tags": []
          }
        }
      ],
      "execution_count": 48,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-12-02T23:43:29.309Z",
          "iopub.status.busy": "2020-12-02T23:43:29.302Z",
          "iopub.status.idle": "2020-12-02T23:43:29.360Z",
          "shell.execute_reply": "2020-12-02T23:43:29.563Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "G9fiCxAE2wDB",
        "outputId": "0f55c2ff-c441-4e12-9d41-b47813d91ae3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products = [bst.main_flowsheet(\"Plastic\"), bst.main_flowsheet('Aromatics'), bst.main_flowsheet('Olefins'), bst.main_flowsheet('Metals')]\n",
        "pyrolysis_tea.production_cost(products)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 59,
          "data": {
            "text/plain": "array([-2.036e+06,  1.749e+07,  9.843e+03,  1.311e+06])"
          },
          "metadata": {
            "tags": []
          }
        }
      ],
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fkiLFYCFSpe",
        "outputId": "8aa98db4-d6a2-49e7-e2be-b7b3249d87bf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# with pd.ExcelWriter('cuwp.xlsx') as writer:\n",
        "#     m.to_excel(writer)"
      ],
      "outputs": [],
      "execution_count": 53,
      "metadata": {
        "collapsed": true,
        "execution": {
          "iopub.execute_input": "2020-12-02T23:43:29.414Z",
          "iopub.status.busy": "2020-12-02T23:43:29.406Z",
          "iopub.status.idle": "2020-12-02T23:43:29.429Z",
          "shell.execute_reply": "2020-12-02T23:43:29.571Z"
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "I6VJJ2Du2wDE"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": 58,
      "metadata": {
        "id": "VmX-qwzNWQQo"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "nteract": {
      "version": "0.27.0"
    },
    "colab": {
      "name": "cuwp plastic thermal oxidation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}