{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing an equation of state in python\n", "\n", "> In `FeOs`, you can implement your equation of state in python, register it to the Rust backend, and compute properties and phase equilbria as if you implemented it in Rust.\n", "> In this tutorial, we will implement the Peng-Robinson equation of state." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents \n", "\n", "- [Implementation](#Implementation)\n", "- [Computing properties](#Computing-properties)\n", "- [Critical point](#Critical-point)\n", "- [Phase equilibria and phase diagrams](#Phase-equilibria-and-phase-diagrams)\n", "- [Mixtures](#Mixtures)\n", "- [Comparison to Rust implementation](#Comparison-to-Rust-implementation)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import feos\n", "import si_units as si\n", "import numpy as np\n", "\n", "optional = True\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "if optional:\n", " import matplotlib.pyplot as plt\n", " import pandas as pd\n", " import seaborn as sns\n", "\n", " sns.set_style(\"ticks\")\n", " sns.set_palette(\"Dark2\")\n", " sns.set_context(\"talk\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation \n", "[↑ Back to top](#Table-of-contents)\n", "\n", "To implement an equation of state in python, we have to define a `class` which has to have the following methods:\n", "\n", "```python\n", "class MyEquationOfState:\n", " def helmholtz_energy(self, state: StateHD) -> D\n", " \n", " def components(self) -> int\n", " \n", " def subset(self, indices: List[int]) -> Self\n", " \n", " def molar_weight(self) -> SIArray1\n", " \n", " def max_density(self, molefracs: np.ndarray[float]) -> float\n", "``` \n", "\n", "- `components(self) -> int`: Returns the number of components (usually inferred from the shape of the input parameters).\n", "- `molar_weight(self) -> SIArray1`: Returns an `SIArray1` with size equal to the number of components containing the molar mass of each component.\n", "- `max_density(self, moles: np.ndarray[float]) -> float`: Returns the maximum allowed number density in units of `molecules/Angstrom³`.\n", "- `subset(self, indices: List[int]) -> self`: Returns a new equation of state with parameters defined in `indices`.\n", "- `helmholtz_energy(self, state: StateHD) -> Dual`: Returns the helmholtz energy as (hyper)-dual number given a `StateHD`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "SQRT2 = np.sqrt(2)\n", "\n", "class PyPengRobinson: \n", " def __init__(\n", " self, critical_temperature, critical_pressure, \n", " acentric_factor, molar_weight, delta_ij=None\n", " ):\n", " \"\"\"Peng-Robinson Equation of State\n", " \n", " Parameters\n", " ----------\n", " critical_temperature : SIArray1\n", " critical temperature of each component.\n", " critical_pressure : SIArray1\n", " critical pressure of each component.\n", " acentric_factor : np.array[float] \n", " acentric factor of each component (dimensionless).\n", " molar_weight: SIArray1\n", " molar weight of each component.\n", " delta_ij : np.array[[float]], optional\n", " binary parameters. Shape=[n, n], n = number of components.\n", " defaults to zero for all binary interactions.\n", " \n", " Raises\n", " ------\n", " ValueError: if the input values have incompatible sizes.\n", " \"\"\"\n", " self.n = len(critical_temperature)\n", " if len(set((\n", " len(critical_temperature), \n", " len(critical_pressure), \n", " len(acentric_factor)\n", " ))) != 1:\n", " raise ValueError(\"Input parameters must all have the same lenght.\")\n", " \n", " self.tc = critical_temperature / si.KELVIN\n", " self.pc = critical_pressure / si.PASCAL\n", " self.omega = acentric_factor\n", " self.mw = molar_weight / si.GRAM * si.MOL\n", "\n", " self.a_r = (0.45724 * critical_temperature**2 * si.RGAS \n", " / critical_pressure / si.ANGSTROM**3 / si.NAV / si.KELVIN)\n", " self.b = (0.07780 * critical_temperature * si.RGAS \n", " / critical_pressure / si.ANGSTROM**3 / si.NAV)\n", " self.kappa = (0.37464 \n", " + (1.54226 - 0.26992 * acentric_factor) * acentric_factor)\n", " self.delta_ij = (np.zeros((self.n, self.n)) \n", " if delta_ij is None else delta_ij)\n", " \n", " def helmholtz_energy(self, state):\n", " \"\"\"Return helmholtz energy.\n", " \n", " Parameters\n", " ----------\n", " state : StateHD\n", " The thermodynamic state.\n", " \n", " Returns\n", " -------\n", " helmholtz_energy: float | any dual number\n", " The return type depends on the input types.\n", " \"\"\" \n", " x = state.molefracs\n", " tr = 1.0 / self.tc * state.temperature\n", " ak = ((1.0 - np.sqrt(tr)) * self.kappa + 1.0)**2 * self.a_r\n", " ak_mix = 0.0\n", " if self.n > 1:\n", " for i in range(self.n):\n", " for j in range(self.n):\n", " ak_mix += (np.sqrt(ak[i] * ak[j]) \n", " * (x[i] * x[j] * (1.0 - self.delta_ij[i, j])))\n", " else:\n", " ak_mix = ak[0]\n", " b = np.sum(x * self.b)\n", " rho = np.sum(state.partial_density)\n", " a = rho * (-np.log(1.0 - b * rho) \n", " - ak_mix / (b * SQRT2 * 2.0 * state.temperature) \n", " * np.log((1.0 + (1.0 + SQRT2) * b * rho) / (1.0 + (1.0 - SQRT2) * b * rho)))\n", " return a\n", " \n", " def components(self) -> int: \n", " \"\"\"Number of components.\"\"\"\n", " return self.n\n", " \n", " def subset(self, i: list[int]):\n", " \"\"\"Return new equation of state containing a subset of all components.\"\"\"\n", " if self.n > 1:\n", " tc = self.tc[i] \n", " pc = self.pc[i]\n", " mw = self.mw[i]\n", " omega = self.omega[i]\n", " return PyPengRobinson(\n", " si.array(tc * si.KELVIN), \n", " si.array(pc * si.PASCAL), \n", " omega, \n", " si.array(mw * si.GRAM / si.MOL)\n", " )\n", " else:\n", " return self\n", " \n", " def molar_weight(self) -> si.SIObject:\n", " return si.array(self.mw * si.GRAM / si.MOL)\n", " \n", " def max_density(self, molefracs: list[float]) -> float:\n", " b = np.sum(molefracs * self.b)\n", " return 0.9 / b " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing properties \n", "[↑ Back to top](#Table-of-contents)\n", "\n", "Let's compute some properties. First, we have to instanciate the class and register it to Rust.\n", "This is done using the `EquationOfState.python` method." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "tc = si.array(369.96 * si.KELVIN)\n", "pc = si.array(4250000.0 * si.PASCAL)\n", "omega = np.array([0.153])\n", "molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n", "\n", "# create an instance of our python class and hand it over to rust\n", "pr = PyPengRobinson(tc, pc, omega, molar_weight)\n", "eos = feos.EquationOfState.python_residual(pr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Thermodynamic state: the `State` object\n", "\n", "Before we can compute a property, we create a `State` object. This can be done in several ways depending on what control variables we need.\n", "If no total amount of substance is defined, it is set to $n = \\frac{1}{N_{AV}}$.\n", "For possible input combinations, you can inspect the signature of the constructor using `State?`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[31mInit signature:\u001b[39m\n", "feos.State(\n", " eos,\n", " temperature=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " volume=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " density=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " partial_density=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " total_moles=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " moles=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " molefracs=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " pressure=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " molar_enthalpy=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " molar_entropy=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " molar_internal_energy=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " density_initialization=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " initial_temperature=\u001b[38;5;28;01mNone\u001b[39;00m,\n", ")\n", "\u001b[31mDocstring:\u001b[39m \n", "A thermodynamic state at given conditions.\n", "\n", "Parameters\n", "----------\n", "eos : Eos\n", " The equation of state to use.\n", "temperature : SINumber, optional\n", " Temperature.\n", "volume : SINumber, optional\n", " Volume.\n", "density : SINumber, optional\n", " Molar density.\n", "partial_density : SIArray1, optional\n", " Partial molar densities.\n", "total_moles : SINumber, optional\n", " Total amount of substance (of a mixture).\n", "moles : SIArray1, optional\n", " Amount of substance for each component.\n", "molefracs : numpy.ndarray[float]\n", " Molar fraction of each component.\n", "pressure : SINumber, optional\n", " Pressure.\n", "molar_enthalpy : SINumber, optional\n", " Molar enthalpy.\n", "molar_entropy : SINumber, optional\n", " Molar entropy.\n", "molar_internal_energy: SINumber, optional\n", " Molar internal energy\n", "density_initialization : {'vapor', 'liquid', SINumber, None}, optional\n", " Method used to initialize density for density iteration.\n", " 'vapor' and 'liquid' are inferred from the maximum density of the equation of state.\n", " If no density or keyword is provided, the vapor and liquid phase is tested and, if\n", " different, the result with the lower free energy is returned.\n", "initial_temperature : SINumber, optional\n", " Initial temperature for temperature iteration. Can improve convergence\n", " when the state is specified with pressure and molar entropy or enthalpy.\n", "\n", "Returns\n", "-------\n", "State : state at given conditions\n", "\n", "Raises\n", "------\n", "Error\n", " When the state cannot be created using the combination of input.\n", "\u001b[31mType:\u001b[39m type\n", "\u001b[31mSubclasses:\u001b[39m " ] } ], "source": [ "feos.State?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we use input variables other than $\\mathbf{N}, V, T$ (the natural variables of the Helmholtz energy), creating a state is an iterative procedure.\n", "For example, we can create a state for a give $T, p$, which will result in a iteration of the volume (density)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$1.6605\\times10^{-24}\\,\\mathrm{ mol}$" ], "text/plain": [ "1.6605390671738466e-24 mol" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# If no amount of substance is given, it is set to 1/NAV.\n", "s = feos.State(eos, temperature=300*si.KELVIN, pressure=1*si.BAR)\n", "s.total_moles" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$1\\,\\mathrm{ mol}$" ], "text/plain": [ "1 mol" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s_pt = feos.State(\n", " eos, \n", " temperature=300*si.KELVIN, \n", " pressure=1*si.BAR, \n", " total_moles=1*si.MOL\n", ")\n", "s_pt.total_moles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Critical point \n", "[↑ Back to top](#Table-of-contents)\n", "\n", "To generate a state at critical conditions, we can use the `critical_point` constructor." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Critical point\n", "temperature: 369.9506174234607 K\n", "density : 198.1862458057178 kg/m³\n", "pressure : 4.249677749116937 MPa\n" ] } ], "source": [ "s_cp = feos.State.critical_point(eos)\n", "print(\"Critical point\")\n", "print(\"temperature: \", s_cp.temperature)\n", "print(\"density : \", s_cp.mass_density())\n", "print(\"pressure : \", s_cp.pressure())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Phase equilibria and phase diagrams\n", "[↑ Back to top](#Table-of-contents)\n", "\n", "We can also create an object, `PhaseEquilibrium`, that contains states that are in equilibrium." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "||temperature|density|\n", "|-|-|-|\n", "|phase 1|300.00000 K|488.99014 mol/m³|\n", "|phase 2|300.00000 K|11.53399 kmol/m³|\n" ], "text/plain": [ "phase 0: T = 300.00000 K, ρ = 488.99014 mol/m³\n", "phase 1: T = 300.00000 K, ρ = 11.53399 kmol/m³" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vle = feos.PhaseEquilibrium.pure(eos, 300.0*si.KELVIN)\n", "vle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each phase is a `State` object. We can simply access these states and compute properties, just like before." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "|temperature|density|\n", "|-|-|\n", "|300.00000 K|11.53399 kmol/m³|" ], "text/plain": [ "T = 300.00000 K, ρ = 11.53399 kmol/m³" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vle.liquid # the high density phase `State`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "|temperature|density|\n", "|-|-|\n", "|300.00000 K|488.99014 mol/m³|" ], "text/plain": [ "T = 300.00000 K, ρ = 488.99014 mol/m³" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vle.vapor # the low density phase `State`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Heat of vaporization: 14.782343503305126 kJ/mol\n", "for T = 300 K\n", "and p = 9.95 bar\n" ] } ], "source": [ "# we can now easily compute any property:\n", "print(\"Heat of vaporization: \", vle.vapor.molar_enthalpy(feos.Contributions.Residual) - vle.liquid.molar_enthalpy(feos.Contributions.Residual))\n", "print(\"for T = {}\".format(vle.liquid.temperature))\n", "print(\"and p = {:.2f} bar\".format(vle.liquid.pressure() / si.BAR))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also easily compute **vapor pressures** and **boiling temperatures**:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vapor pressure (T = 300 K): 994.7761635610095 kPa\n", "boiling temperature (p = 3 bar): 247.84035574956758 K\n" ] } ], "source": [ "# This also works for mixtures, in which case the pure component properties are computed.\n", "# Hence, the result is a list - that is why we use an index [0] here.\n", "print(\"vapor pressure (T = 300 K):\", feos.PhaseEquilibrium.vapor_pressure(eos, 300*si.KELVIN)[0])\n", "print(\"boiling temperature (p = 3 bar):\", feos.PhaseEquilibrium.boiling_temperature(eos, 2*si.BAR)[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Phase Diagram\n", "\n", "We could repeatedly compute `PhaseEquilibrium` states for different temperatures / pressures to generate a phase diagram.\n", "Because this a common task, there is a object for that as well.\n", "\n", "The `PhaseDiagram` object creates multiple `PhaseEquilibrium` objects (`npoints` of those) between a given lower temperature and the critical point." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "dia = feos.PhaseDiagram.pure(eos, 230.0 * si.KELVIN, 500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can access each `PhaseEquilbrium` and then conveniently comput any property we like:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "enthalpy_of_vaporization = [\n", " (vle.vapor.molar_enthalpy(feos.Contributions.Residual) - vle.liquid.molar_enthalpy(feos.Contributions.Residual)) / (si.KILO * si.JOULE) * si.MOL \n", " for vle in dia.states\n", "]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(7, 4))\n", "sns.lineplot(x=dia.vapor.temperature / si.KELVIN, y=enthalpy_of_vaporization, ax=ax);\n", "ax.set_ylabel(r\"$\\Delta^{LV}h$ / kJ / mol\")\n", "ax.set_xlabel(r\"$T$ / K\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more convenient way is to create a dictionary. The dictionary can be used to build pandas `DataFrame` objects.\n", "This is a bit less flexible, because the units of the properties are rigid. You can inspect the method signature to check what units are used." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[31mSignature:\u001b[39m dia.to_dict(contributions)\n", "\u001b[31mDocstring:\u001b[39m\n", "Returns the phase diagram as dictionary.\n", "\n", "Parameters\n", "----------\n", "contributions : Contributions, optional\n", " The contributions to consider when calculating properties.\n", " Defaults to Contributions.Total.\n", "\n", "Returns\n", "-------\n", "Dict[str, List[float]]\n", " Keys: property names. Values: property for each state.\n", "\n", "Notes\n", "-----\n", "- temperature : K\n", "- pressure : Pa\n", "- densities : mol / m³\n", "- mass densities : kg / m³\n", "- molar enthalpies : kJ / mol\n", "- molar entropies : kJ / mol / K\n", "- specific enthalpies : kJ / kg\n", "- specific entropies : kJ / kg / K\n", "- xi: liquid molefraction of component i\n", "- yi: vapor molefraction of component i\n", "- component index `i` matches to order of components in parameters.\n", "\u001b[31mType:\u001b[39m builtin_function_or_method" ] } ], "source": [ "dia.to_dict?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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temperaturepressuredensity liquiddensity vapormolar enthalpy liquidmolar enthalpy vapormolar entropy liquidmolar entropy vapormass density liquidmass density vaporspecific enthalpy liquidspecific enthalpy vaporspecific entropy liquidspecific entropy vapor
0230.00000096625.27817414125.98894752.208491-18.921555-0.157448-0.035166-0.000148622.9024342.302196-429.097170-3.570554-0.797483-0.003363
1230.28046297830.13395614118.00685252.811929-18.911909-0.159193-0.035119-0.000150622.5504542.328805-428.878435-3.610123-0.796418-0.003399
2230.56092499046.72940014110.01022053.420767-18.902255-0.160952-0.035072-0.000152622.1978332.355653-428.659501-3.650021-0.795354-0.003436
3230.841386100275.14312014101.99901154.035036-18.892592-0.162726-0.035025-0.000153621.8445692.382740-428.440366-3.690251-0.794290-0.003473
4231.121849101515.45396414093.97318254.654773-18.882920-0.164515-0.034978-0.000155621.4906602.410068-428.221030-3.730814-0.793228-0.003510
\n", "
" ], "text/plain": [ " temperature pressure density liquid density vapor \\\n", "0 230.000000 96625.278174 14125.988947 52.208491 \n", "1 230.280462 97830.133956 14118.006852 52.811929 \n", "2 230.560924 99046.729400 14110.010220 53.420767 \n", "3 230.841386 100275.143120 14101.999011 54.035036 \n", "4 231.121849 101515.453964 14093.973182 54.654773 \n", "\n", " molar enthalpy liquid molar enthalpy vapor molar entropy liquid \\\n", "0 -18.921555 -0.157448 -0.035166 \n", "1 -18.911909 -0.159193 -0.035119 \n", "2 -18.902255 -0.160952 -0.035072 \n", "3 -18.892592 -0.162726 -0.035025 \n", "4 -18.882920 -0.164515 -0.034978 \n", "\n", " molar entropy vapor mass density liquid mass density vapor \\\n", "0 -0.000148 622.902434 2.302196 \n", "1 -0.000150 622.550454 2.328805 \n", "2 -0.000152 622.197833 2.355653 \n", "3 -0.000153 621.844569 2.382740 \n", "4 -0.000155 621.490660 2.410068 \n", "\n", " specific enthalpy liquid specific enthalpy vapor specific entropy liquid \\\n", "0 -429.097170 -3.570554 -0.797483 \n", "1 -428.878435 -3.610123 -0.796418 \n", "2 -428.659501 -3.650021 -0.795354 \n", "3 -428.440366 -3.690251 -0.794290 \n", "4 -428.221030 -3.730814 -0.793228 \n", "\n", " specific entropy vapor \n", "0 -0.003363 \n", "1 -0.003399 \n", "2 -0.003436 \n", "3 -0.003473 \n", "4 -0.003510 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_dia = pd.DataFrame(dia.to_dict(feos.Contributions.Residual))\n", "data_dia.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have a dataframe, we can store our results or create a nicely looking plot:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def phase_plot(data, x, y):\n", " fig, ax = plt.subplots(figsize=(12, 6))\n", " if x != \"pressure\" and x != \"temperature\":\n", " xl = f\"{x} liquid\"\n", " xv = f\"{x} vapor\"\n", " else:\n", " xl = x\n", " xv = x\n", " if y != \"pressure\" and y != \"temperature\":\n", " yl = f\"{y} liquid\"\n", " yv = f\"{y} vapor\"\n", " else:\n", " yv = y\n", " yl = y\n", " sns.lineplot(data=data, x=xv, y=yv, ax=ax, label=\"vapor\")\n", " sns.lineplot(data=data, x=xl, y=yl, ax=ax, label=\"liquid\")\n", " ax.set_xlabel(x)\n", " ax.set_ylabel(y)\n", " ax.legend(frameon=False)\n", " sns.despine();" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phase_plot(data_dia, \"density\", \"temperature\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phase_plot(data_dia, \"molar entropy\", \"temperature\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mixtures \n", "[↑ Back to top](#Table-of-contents)\n", "\n", "Fox mixtures, we have to add information about the composition, either as molar fraction, amount of substance per component, or as partial densities." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# propane, butane mixture\n", "tc = np.array([369.96, 425.2]) * si.KELVIN\n", "pc = np.array([4250000.0, 3800000.0]) * si.PASCAL\n", "omega = np.array([0.153, 0.199])\n", "molar_weight = np.array([44.0962, 58.123]) * si.GRAM / si.MOL\n", "\n", "pr = PyPengRobinson(tc, pc, omega, molar_weight)\n", "eos = feos.EquationOfState.python_residual(pr)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "|temperature|density|molefracs\n", "|-|-|-|\n", "|300.00000 K|40.96869 mol/m³|[0.50000, 0.50000]|" ], "text/plain": [ "T = 300.00000 K, ρ = 40.96869 mol/m³, x = [0.50000, 0.50000]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = feos.State(\n", " eos, \n", " temperature=300*si.KELVIN, \n", " pressure=1*si.BAR, \n", " molefracs=np.array([0.5, 0.5]), \n", " total_moles=si.MOL\n", ")\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, we can compute properties by calling methods on the `State` object. Some return vectors or matrices - for example the chemical potential and its derivative w.r.t amount of substance:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ -93.60749754, -120.5269973 ]) J/mol" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.chemical_potential(feos.Contributions.Residual)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 4.90721995, -0.10487987],\n", " [-0.10487987, 4.85361765]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.dmu_dni() / (si.KILO * si.JOULE / si.MOL**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Phase equilibria can be built from different constructors. E.g. at critical conditions given composition:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "|temperature|density|molefracs\n", "|-|-|-|\n", "|401.65562 K|3.99954 kmol/m³|[0.50000, 0.50000]|" ], "text/plain": [ "T = 401.65562 K, ρ = 3.99954 kmol/m³, x = [0.50000, 0.50000]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s_cp = feos.State.critical_point(eos, molefracs=np.array([0.5, 0.5]))\n", "s_cp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or at given temperature (or pressure) and composition for bubble and dew points." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "||temperature|density|molefracs|\n", "|-|-|-|-|\n", "|phase 1|350.00000 K|879.50373 mol/m³|[0.67631, 0.32369]|\n", "|phase 2|350.00000 K|8.96383 kmol/m³|[0.50000, 0.50000]|\n" ], "text/plain": [ "phase 0: T = 350.00000 K, ρ = 879.50373 mol/m³, x = [0.67631, 0.32369]\n", "phase 1: T = 350.00000 K, ρ = 8.96383 kmol/m³, x = [0.50000, 0.50000]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vle = feos.PhaseEquilibrium.bubble_point(\n", " eos, \n", " 350*si.KELVIN, \n", " liquid_molefracs=np.array([0.5, 0.5])\n", ")\n", "vle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar to pure substance phase diagrams, there is a constructor for binary systems." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "vle = feos.PhaseDiagram.binary_vle(eos, 350*si.KELVIN, npoints=50)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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buIe1Pf4SMgF3jR4Pd2u7K75Oc3MzEhIS0NzcbKpZWFhgzJgx8PX1veLzmhM26YmIiIiIiIiIiIgIgiDgUEUBNmUdxk+nsmAUhG7HTPAIxL1h8bhhRASUckWvrldcXIyMjAwYjUZTzdHRERqNBjY2Nr06tzlhk56IiIiIiIiIiIhoGGvRdeLL/ERszjqM/MaabuNWFkrMDYrFwrB4hDt79fp6Op0OycnJqKysFNVHjhyJ0NBQyOXyXl/DnLBJT0RERERERERERDQMFTTWYHPWEfw3PwEtus5u4yPsXLAobCJuHxUHR7V1n1yzvr4eiYmJaG9vN9XUajViYmLg7u7eJ9cwN2zSExEREREREREREQ0TBqMRe09nY1PWEfxantdtXAYZrvYZjXvDJ+Fqn2DIZX0zq10QBOTl5SE3NxfC75bRcXNzQ2xsLNRqdZ9cxxyxSU9EREREREREREQ0xJ3pbMPW3BP4d/ZRnGo5023cXmWJO4LH4p7QiQi0d+3Ta7e3tyMpKQl1dXWmmkwmQ1hYGIKCgiCTyfr0euaGTXoiIiIiIiIiIiKiISqjrhybsg7jm8JkdBr03cZDHD1wb/gkzA2KhbVS1efXr6ysRHJyMnQ6nalmbW2NuLg4ODo69vn1zBGb9ERERERERERERERDiM5owI/F6dicfQTHq4q7jStkcswcEYFFYfGY6BHYLzPZDQYDMjMzUVwsvr6Pjw+ioqJgYcHW9Dn8myAiIiIiIiIiIiIaAmram/FZznFsyTmGqrambuMuljZYMHo87g6ZAG9bx37L0dzcjMTERDQ1dWVQKBQYM2YM/Pz8+u265opNeiIiIiIiIiIiIiIzllRzCpuyDuP7olRojYZu49Guvrg3bBJmB0ZBrejflnBJSQkyMjJgMHTlcHBwgEajga2tbb9e21yxSU9ERERERERERERkZjoNenxfnIZNmYeRXHuq27hSrsDswCgsCouHxs2/3/PodDqkpqaivLxcVA8KCkJYWBjkcnm/ZzBXbNITERERERERERERmYnKtiZsyT6Kz3KOo7ajpdu4h7U9/hIyAQtCxsPNym5AMtXX1yMxMRHt7e2mmkqlQkxMDDw8PAYkgzljk56IiIiIiIiIiIhoEBMEASerS7Ap6zB+KE6HXjB2O2ac+wjcGzYJswIioZQrBixXfn4+cnJyIAiCqe7q6orY2FhYWloOSA5zxyY9ERERERERERER0SDUodfh26IUbMo6grS6sm7jaoUF5gRF496wSYh08RnYbB0dSEpKQm1trakmk8kQEhKCUaNGQSaTDWgec8YmPREREREREREREdEgUtHaiC3ZR/FpznHUd7Z2G/e2ccA9ofG4a/Q4OFvaDHi+qqoqJCcnQ6vVmmpWVlaIi4uDk5PTgOcxd2zSExEREREREREREUns3JI2H2cexg8l6TCcZ0mbeM8g3Bs2Cdf7h8FigJa0+T2j0YjMzEwUFRWJ6t7e3oiKioJSqRzwTEMBm/REREREREREREREEunQ6/BdUSo+zjp83iVtLBVKzB0Zi3vDJiHM2VOChGe1tLQgISEBTU1NpppCoUBkZCT8/f0lyzUUsElPRERERERERERENMAutaSNj40jFoXFY/7ocXBSW0uQsEtpaSnS09NhMBhMNXt7e8TFxcHW1lbCZEMDm/REREREREREREREA0AQBCRUl+LjrEP4oTgd+vMsaTPJMwj3hk/CDD9plrT5PZ1Oh7S0NJSViWf4BwYGIjw8HHK5XKJkQwub9ERERERERERERET9qNOgx3dFqdiUdRgptae7jQ+WJW1+78yZM0hMTERbW5upplQqERMTA0/PwZFxqGCTnoiIiIiIiIiIiKgf1LQ3Y0v2MWzJOYqa9pZu44NpSZtzBEFAQUEBsrOzIQiCqe7i4gKNRgNLS0sJ0w1NbNITERERERERERER9aHU2tP4KPMQvitKhdZo6DYe7xmEe8Mm4Xp/6Ze0+b2Ojg4kJyejpqbGVJPJZBg9ejSCg4Mhk8kkTDd0sUlPRERERERERERE1Et6owG7SjLwUeYhnKgu6TauVlhgTlAM7g+fhHBnbwkSXlx1dTWSkpKg1WpNNSsrK2g0Gjg7O0uYbOhjk56IiIiIiIiIiIjoCp3paMVnuSfwSdYRVLQ1dhv3sLbHotB43BUyDi6WthIkvDij0YisrCwUFhaK6l5eXoiOjoZSqZQo2fDBJj0RERERERERERHRZcptqMJHGYfwVUESOgy6buMaN3/cH34VbgyIhHIQLWnze62trUhISEBjY9eXC3K5HJGRkRgxYoSEyYYXNumJiIiIiIiIiIiIesAoGLG/LA8fZfyGA+V53caVcgX+FDAG94VfhVg3PwkS9tzp06eRmpoKg6FrzXw7OzvExcXBzs5OwmTDD5v0RERERERERERERBfRptPiy4JEfJR5CAWNNd3GXSxtcHfIBNwTOhEe1vYSJOw5vV6PtLQ0nD59WlQfMWIEIiIioFAMzln/Qxmb9ERERERERERERETnUdbSgM1ZR/B57jE0aju6jYc7e+GB8Ktwc2A0LC0G/9rtDQ0NSExMRGtrq6mmVCoRHR0NLy8vCZMNb2zSExEREREREREREf2PIAhIqC7Fh5m/4ceSDBgEo2hcBhmu9w/DAxGTMdEjEDKZTKKkPScIAgoLC5GVlQVBEEx1Z2dnaDQaWFlZSZiO2KQnIiIiIiIiIiKiYU9nNOD7ojR8mPkbUmpPdxu3VaoxP3gsFoVNQoC9iwQJr0xnZyeSkpJQU9O1TI9MJkNwcDBGjx5tFl8yDHVs0hMREREREREREdGwdaazDZ/lHMfmrMOobGvqNj7Czhn3hk3CHcFjYaeylCDhlaupqUFSUhI6OztNNUtLS2g0Gri4mM8XDUMdm/REREREREREREQ07BQ21uDDzEP4b34C2vW6buPxnkF4IPwqXOcXBoVcLkHCK2c0GpGdnY2CggJR3dPTE9HR0VCpVBIlo/Nhk56IiIiIiIiIiIiGBUEQcLiiAO9n/IZfTmd3G1fJFbglKBoPhE9GhIu3BAl7r7W1FYmJiWhoaDDV5HI5IiIiEBAQIFkuujA26YmIiIiIiIiIiGhI6zTosaMwGR9k/IasM5Xdxp3VNvhL6AQsDI2Hu7WdBAn7RllZGVJTU6HX6001W1tbxMXFwd7eXsJkdDFs0hMREREREREREdGQVNfRgi3Zx/BJ9hHUtLd0Gx/t6I4HIibj1qBYWFkoJUjYN/R6PdLT03Hq1ClR3d/fH5GRkVAoFBIlo55gk56IiIiIiIiIiIiGlNyGKnyQ8Ru+LkhCp0HfbXyaz2g8GDEZ07yDIZPJJEjYdxobG5GQkIDW1lZTTalUIioqCt7e5rlkz3DDJj0RERERERERERGZPUEQcKiiABvTD2JfWU63cbXCAnNHxuKB8MkIcfKQIGHfKywsRFZWFoxGo6nm5OQEjUYDa2trCZPR5WCTnoiIiIiIiIiIiMyW1qDHt0Wp+CDjIDLqK7qNu1nZYmFoPP4SOgEulrYSJOx7Wq0WSUlJqK6uFtWDg4MxevRoyOVyiZLRlWCTnoiIiIiIiIiIiMxOQ2cbPs05jk1Zh1HV1tRtPNTJEw9GTMacoBioFUOnDVpbW4ukpCR0dHSYapaWloiNjYWrq6uEyehKDZ3/OomIiIiIiIiIiGjIK2muw4cZh/BF3km06bXdxqf5jMZDEVMwxXuU2a83/3tGoxG5ubnIy8sT1T08PBATEwOVSiVRMuotNumJiIiIiIiIiIho0EuoLsHG9IPYVZoBoyCIxlRyBW4dGYMHI6Yg1MlTooT9p62tDYmJiThz5oypJpfLER4ejsDAQAmTUV9gk56IiIiIiIiIiIgGJYPRiN2lmdiY/isSakq7jTuqrXFPyAQsCpsEd2s7CRL2v/LycqSkpECv15tqNjY2iIuLg4ODg4TJqK+wSU9ERERERERERESDSrtei215CXg/4zeUNNd1Gw+0d8WDEZNx+ygNrCyG5jIvBoMB6enpKC0Vfznh5+eHyMhIWFiwtTtU8CdJREREREREREREg0JdRws2Zx3BJ1lHUd/Z2m18gkcAFkdMwQz/MMhlcgkSDoympiYkJCSgpaXFVLOwsEBUVBR8fHwkTEb9gU16IiIiIiIiIiIiklRhYw3ez/gN/81PQKdBLxqTy2S4acQYPBQ5BTFufhIlHDhFRUXIzMyE0Wg01RwdHREXFwdra2sJk1F/YZOeiIiIiIiIiIiIJHGyqgTvpR/AntIsCBBvBmtlocT84HF4MGIy/O2cJUo4cLRaLVJSUlBZWSmqjxo1CiEhIZDLh+6dA8Od2Tbpf/zxRxw+fBgZGRmorq5GQ0MDlEolAgICMG3aNCxcuBBOTk7nfW5rayvef/997N69G+Xl5bC2tkZ0dDTuu+8+TJgwYYBfCRERERERERER0fBhMBqx51QmNqSdfzNYNytb3Bd2Fe4OnQAn9fCYOV5XV4fExER0dHSYamq1GrGxsXBzc5MwGQ0Es23Sb9iwAdnZ2VCpVHBzc0NISAjq6+uRmZmJzMxMbNu2DR9//DFCQ0NFz6uvr8ddd92FoqIiqFQqjBo1CvX19di/fz8OHDiAl156CQsWLJDoVREREREREREREQ1NHXodvsxPxMaMgyhqqu02PsrBDQ9FTsWtQTGwtFBKkHDgCYKA3Nxc5Obmiuru7u6IiYmBWq2WKBkNJLNt0i9YsACBgYGIiYmBUtn1P21OTg6WLVuG3NxcPPnkk9i5c6foeS+88AKKiooQERGB9957Dx4eHhAEAdu2bcPy5cuxatUqaDQahIWFDfRLIiIiIiIiIiIiGnIaOtuwJfsYPso8hNqOlm7jEz0D8dfIqbjGN2RIbwb7R+3t7UhMTER9fb2pJpfLERYWhsDAQMhkMgnT0UAy2yb9vHnzzlsPCQnBqlWrcPvttyM/Px8FBQUYOXIkACAzMxN79+6FXC7H6tWr4eHhAQCQyWS44447kJCQgB07dmD9+vVYu3btgL0WIiIiIiIiIiKioaa8pQEfZv6Gz3KOo1WvFY2d2wx2ceQUxA6DzWD/qKKiAikpKdDpdKaajY0N4uLi4ODgIGEykoLZNukvJigoyPTn9vZ20593794NAJg4cSJGjBjR7Xl33HEHduzYgQMHDqCtrY27JRMREREREREREV2m7DOV2JD2K7YXJkMvGEVjaoUF5gePw+LIyRhh5yJRQukYDAZkZGSgpKREVPf19cWYMWNgYTEk27V0CUPyp56QkAAAsLa2RmBgoKmenJwMABg7dux5nxcVFQWVSoXOzk5kZWUhLi6u37MSERERERERERGZO0EQcLyqGOvTDuCX09ndxh3V1lgUFo97w+LhYmkrQULpNTc3IyEhAc3NzaaahYUFxowZA19fXwmTkdSGTJPeaDSipqYGhw4dwhtvvAEAWLZsGWxsbEzHFBcXAwD8/f3Pew6lUgkvLy+UlJSgqKjook36rVu3Ytu2bT3KVlBQ0MNXQUREREREREREZD6MghG7SzPxXtqvSKwp7TbuY+OIxZFTMD94LGyUw3cT1JKSEqSnp8No7LqzwMHBAXFxcaL+JQ1PZt+k37x5M1599VVRLSoqCq+99hqmTp0qqjc2NgLARdd1OjfW1NR00evW1NQgIyPjSiITERERERERERGZNa1Bj68LkvBe+q8oaKzpNh7m5IklY6ZhdmAUlHKFBAkHB51Oh5SUFFRUVIjqI0eORGhoKOTy4bNRLl2Y2TfpPTw8oNFoYDAYUF5ejtraWmRlZWHHjh2IiYmBvb296djOzk4AZ2fMX4hKpQIAdHR0XPS6bm5uiIiI6FHGgoKCS56PiIiIiIiIiIhosGvVdeLz3OPYmH4QlW3dJ7lO8gzCkjHTcLXPaMhkMgkSDh719fVITEwU7ZmpVqsRExMDd3d3CZPRYGP2TfpZs2Zh1qxZpsfZ2dlYsWIFvv/+exQUFOCrr76CQnH22zq1Wo329nbRrsl/pNWe3Wna0tLyotedP38+5s+f36OMc+fO5ax7IiIiIiIiIiIyW2c6WvFx1mFsyjqChs420ZgMMtwYEIklkVMR4+YnUcLBQxAE5OXlITc3F4IgmOpubm6IjY2FWj18l/2h8zP7Jv0fhYaGYuPGjbjuuuuQlZWFnTt34uabbwYA2Nvbo7293bTszfmcG/v9DHwiIiIiIiIiIqLhqLylAe9nHMRnucfRrhdPfFXJFbhtVBz+GjkVQQ6uEiUcXNrb25GUlIS6ujpTTSaTISwsDEFBQcP+7gI6vyHXpAcAW1tbjB8/Hrt370ZGRoapSR8QEICqqiqUlJSc93k6nQ7l5eWmY4mIiIiILqSxvQUN1UVSxyAiIiLqF/kN1Xgv/QC+LkiGzmgQjdlYqHB36EQ8GDEZntac6HpOZWUlkpOTRat4WFtbIy4uDo6OjtIFo0FvSDbpAUCv1wMADIauXyIxMTE4duwYEhISzvuc1NRU6HQ6qNVqhIWFDUhOIiIiIjI/v5RmIfeDRbBsqwdw8WUSiYiIiMxJcs0pvJu2H7tKMiFAEI05q21wf/gkLAyLh6PaWpqAg5DBYEBmZiaKi4tFdR8fH0RFRcHCYsi2YKmPDMn/QhoaGnD8+HEAEDXbb7jhBmzcuBHHjh1DSUkJRowYIXreF198AQCYOnUqbGxsBi4wEREREZmFM51t+Oex72G5913cd+okvoOv1JGIiIiIek0QBByuKMDa1P34rSK/27iPjSMeipyCO0ePg5WFSoKEg1dzczMSExPR1NS1ia5CocCYMWPg58f1+alnzLJJf/z4cZw8eRI333wzfH3FH4wyMjKwfPlyNDc3w8PDAzNnzjSNRUREYPr06di3bx8ef/xxbNiwAe7u7hAEAdu2bcOOHTsgl8uxZMmSgX5JRERERDTI7SrJwPNHtiO26AgeLv5N6jhEREREvSYIAn4+lYV3UvchqeZUt/HRju54eMw03BIUA6VcIUHCwa20tBTp6emilTwcHByg0Whga2srYTIyN2bZpG9qasKaNWuwZs0auLm5wd3dHQqFAhUVFaipqQEAeHh4YOPGjd1mxL/yyiu48847kZGRgWuvvRajRo3CmTNnUFFRAZlMhueffx4RERFSvCwiIiIiGoTqOlqw/Oh32FGUgnH1RViWs6tr0EItXTAzd/ToUWzatAkpKSloa2uDt7c3Zs6cicWLF8Pa+vJvny8vL8fHH3+M3377DRUVFTAajXBzc8OECROwaNEihISE9MOrICIiMk8GoxHfF6dhXeo+ZJ2p7DYe6+aHpWOuxgz/MMhlcgkSDm46nQ6pqammvS3PCQoKQlhYGORy/p3R5THLJn1sbCyee+45HDt2DPn5+SguLoZWq4W9vT0mTJiAa665Brfddtt5v7FydnbGV199hQ8++AC7du1Cfn4+rK2tMXXqVNx///2YOHGiBK+IiIiIiAYbQRDwfXEaXjy6A3UdrQhursQ/MnZA8b+1WRX2HlB7BAON3W8Jp4vbsmULVq1aBUEQ4OnpCS8vL+Tn5+O9997Dnj178Pnnn1/W5mpJSUm4//770draCqVSCV9fXyiVSpSWluLrr7/Gt99+izfeeAOzZs3qvxdFRERkBrQGPb4uSMK7aQdQ1FTbbXyK9ygsjZqOSZ5BkMlkEiQc/Orr65GYmIj29nZTTaVSISYmBh4eHhImI3Nmlk16FxcXLFq0CIsWLbqi59va2uLxxx/H448/3rfBiIiIiGhIqG5rxvNHtmNXaQYAwLO9Aa+mfQUrow4AILO0hc8T30P2xEopY5ql9PR0vPLKKwCAl19+GfPmzYNMJkNVVRWWLFmCjIwMvPTSS1i7dm2PzicIAp555hm0trYiNjYWb731Fry9vQGcXSP2H//4B77//nu8+OKLmDx5Muzs7PrttREREQ1W7Xod/pN7HBvSf0V5a2O38Rv8w7E0ajpi3biG+oUIgoD8/Hzk5ORAELo21HV1dUVsbCwsLS0lTEfmziyb9ERERERE/UEQBHxdmIz/O/YdGjrbAAD2ujb8K/0rOOvOPobCAt6PbINlgEbCpOZr/fr1MBqNmDNnDu644w5T3cPDA2+99RZmzZqFPXv2IDs7G6GhoZc8X35+PkpKSgAA//jHP0wNegCws7PDq6++ir1796KlpQUnT57E9OnT+/5FERERDVLN2g78O/soPsj4DbUdLaIxuUyG2YFRWDpmOsKcPSVKaB46OjqQlJSE2tquuw9kMhlCQkIwatQo3nVAvcYmPRERERERgIrWRjx35Bv8fCrbVFMbdHgn5wf4ttWbah73vg+bMTdIEdHstba24uDBgwCAefPmdRsPCAjAxIkTcfjwYezatatHTfqOjg7Tn/38us/+U6lU8PDwQFFREfR6fS/SExERmY8znW34KPMQNmUeQqO2QzSmlCtw2ygNHh4zDYH2rhIlNB9VVVVITk6GVqs11aysrBAXFwcnJycJk9FQwiY9EREREQ1rRsGIz3NPYNWJH9Cs6zTV7eQW2Fx2BE51Raaay9wVcJi8UIqYQ0JWVha0Wi1UKhWioqLOe0xcXBwOHz6MlJSUHp0zMDAQlpaWphlukydPFo1XV1fj9OnTUCgUCA8P7/VrICIiGsxq21vwfsZBfJJ1BK16rWjMUqHEgpDxeChiCrxtHaUJaEaMRiMyMzNRVFQkqnt7eyMqKgpKpVKiZDQUsUlPRERERMNWYWMtnj78FY5Wij98TfUahRWlB2HI/81Uc7h6MZxnPzfQEYeUcx9yvb29L/jB1t/fX3Tspdja2uLhhx/GW2+9heeeew4vvPACJkyYAKVSifT0dLz22mvQ6XRYsmQJfHx8LnqurVu3Ytu2bT26bkFBQY+OIyIiGghVbU3YkP4rtmQfQ4dBJxqzU6qxMCweD4RPhquVrUQJzUtLSwsSEhLQ1NRkqikUCkRGRpreqxD1JTbpiYiIiGjY0RkN2Jh+EKuTf0anoWsJFHuVJV4adxNmpH+H+sOfmuo2sbPh/pe1XG+0lxobz25U5+DgcMFjzo2dO7YnHnroIbi5ueGjjz7CY489JhoLCAjA6tWrceONN17yPDU1NcjIyOjxdYmIiKRW3tKAd9MOYGveCdF7GgBwVFvjwfCrsChsEhzUVhIlND+nTp1CWloaDAaDqWZvb4+4uDjY2vJLDuofbNITERER0bCSVluGZYe+REZ9hag+0z8CK+NvgeXRz1D97SpT3TL4Kngt+Q9kCr517q3OzrPLCV3s9nCVSiU6tid0Oh1OnTqFxsZGWFhYwNfXF0qlEiUlJSgpKcGXX34JjUYDT8+Lb4rn5uaGiIiIHl2zoKBAtB4+ERHRQCptrse7qfuxLT8BOqNBNOZiaYOHIqfintCJsFWqJUpofnQ6HdLS0lBWViaqBwYGIjw8HHK5XKJkNBzwkwYRERERDQvtei3eSvoF72cchEEwmuruVnZYOfEW3BgQieYTX6Fiy6OmMZV3OHwe2w65irPP+oJafbZRoNPpLnjMuU3Zzh3bE0uXLsX+/fsxdepUrFy5Eh4eHgDOzsZfuXIlvv32W9xxxx3YuXPnRWfAzZ8/H/Pnz+/RNefOnctZ90RENOAKG2uxLnUfvipIEr2fAQAPKzssGTMNC0LGw8pCJVFC83TmzBkkJiaira3NVFMqlYiJibnkl/xEfYFNeiIiIiIa8g5VFODpQ1+jpLlOVJ8fPBYvjrsRjmprtGXtR+XGuwFBAABYOPvC58kfoLB1liLykNSTpWx6siTO7+3duxf79++Hk5MT3nrrLdjZ2Ymu98orryA9PR2FhYX4/PPPsXjx4l68AiIiImnkNlThnZR9+LYoBcb/vVc5x9vGAQ+PuRrzg8fC0oKbmV4OQRBQUFCA7OxsCL/7e3VxcYFGo4GlpaWE6Wg4YZOeiIiIiIasxs52rDz5A/6Te0JUH2HnjH9NmourvEcBADpLU1D+zq0Q9GdnccttnODz5I9QuvgNeOahLCAgAABQXl4OnU533mVvSktLRcdeysmTJwEAUVFRogb9OUqlEhMmTEBhYSHS09OvLDgREZFEcs5UYU3KL/iuKA0CxM15f1tnPBJ1NW4fpYGKy/Jdto6ODiQnJ6OmpsZUk8lkGD16NIKDg7kXEQ0o/h9MREREREPSjyXpePHIDlS1N5tqcpkMD0ZMwbLY60y3getqinH6zRthbG8CAMiUlvD5+7dQ+4RLknsoCwsLg1KphFarRWpqKuLi4rodk5CQAACIiYnp0TlbW1t7fP3LWeeeiIhIShdrzgfau+JvUdMxZ2QMlHKFRAnNW3V1NZKSkkzL7AGAlZUVNBoNnJ15FyUNPDbpiYiIiGhIqWxrwktHd+DHEvF64eHOXnjjqj8jytXXVNM31eD0m7NgaKw8W5DJ4fXwf2AVPGkgIw8btra2mDx5Mvbt24dt27Z1a9IXFxfj6NGjAICZM2f26JyBgYEAgNTUVDQ3N3ebTa/T6XDs2DHRsURERINVzpkqvJ38C74v7t6cD3Zwx2Mx12B2QBQU3MT0ihiNRmRlZaGwsFBU9/LyQnR09EU3tyfqT/w/moiIiIiGBKNgxCdZRzD96zdFDXq1wgLPxt2AnbOXihr0xvZmlK3+E3SVuaaax6L3YBt784DmHm4efvhhyGQy7NixA1988YVp/dfq6mo88cQTMBqNuO666xAaGip63jXXXINrrrkGu3btEtVnzpwJlUqFM2fO4IknnkBVVZVprLGxEc8//zwKCwshk8lw88382RIR0eCUfaYSS/Z9juu2v43vilNFDfrRju5YP+1O/Dzn75gTFMMG/RVqbW3Fb7/9JmrQy+VyREVFYezYsWzQk6Q4k56IiIiIzF5WfSWeOfw1EmtKRfUJHgH411V/xkgHN1HdqOtE2Ttz0Vl00lRzmfsyHKY9MCB5h7OoqCg8++yzeO2117B8+XK89957cHJyQn5+PrRaLQIDA7FixYpuzysrKwMAtLW1ieqenp5YsWIFXnjhBfz666+45ppr4OvrC6VSiZKSEmi1WshkMixbtgzh4VzCiIiIBpfsM5WmmfN/NNrRHY/HXIebAiIhl7Ex3xunT59GWloa9Hq9qWZnZ4e4uLjz7mlDNNDYpCciIiIis9Wu12FNyi/YkPYr9ILRVHdQWeL5sTfiztFju32oFYwGVG68G+1Ze001x+uWwnn28wOWe7hbtGgRQkJC8PHHHyM1NRV1dXXw9vbGzJkzsXjxYtjY2FzW+ebMmYPQ0FB88sknOHnyJMrLyyEIAtzc3BAbG4sFCxacd/17IiIiqVysOR/i6IG/x1zL5nwf0Ov1SEtLw+nTp0X1gIAAhIeHQ6Hgmv40OLBJT0RERERm6WB5Hp49vB0lzXWi+s2BUfjH+Nlwt+4+K0oQBFT/+xG0nPzaVLObeCfc7loNmUzW75mpS3x8POLj43t8fE5OzkXHQ0ND8eqrr/Y2FhERUb/KOVOF1ck/X7A5/3jMtbiRzfk+0dDQgMTERNEm80qlEtHR0fDy8pIwGVF3bNITERERkVmp62jBy8d34quCJFHd19YRr8Tfimt8Qy783K+Xo3H/B6bH1lEz4fnAx5BxbVciIiLqRwWNNXgr+Wd8W5jabUPYEEcPPB57HW4cEcHmfB8QBAGFhYXIzs6G0dh1p6WzszM0Gg2srKwkTEd0fmzSExEREZFZEAQBX+Yn4uUTO3Gms2tdcoVMjgciJuPJmOtgrVRd8Pln9ryD+u9eMT22HBUP70e2QWZx4ecQERER9UZRUy3WJO/F14VJMApszve3zs5OJCcno7q62lSTyWQIDg7G6NGjeeckDVps0hMRERHRoFfYWIvnjnyDQxUFonq0qy/+36RbEenic9HnNx3+FDWfP256rPKJgM/fv4VcfXlrnxMRERH1xKnmeqxJ2Yv/5ifC8Lt9c4CzG8I+EXMdl7XpYzU1NUhKSkJnZ6epZmlpCY1GAxcXFwmTEV0am/RERERENGh1GvTYkHYA76TuQ6dBb6pbW6jwtOZ63Bs2CYpLLFXTkrwTlR/eZ3ps4RoAn2U/QmHr3G+5iYiIaHgqb2nA2tR92Jp3EjqjQTQWZO+Kx2Ouw82BUZd8/0I9ZzQakZOTg/z8fFHd09MT0dHRUKl41yQNfmzSExEREdGgdLiiAM8f2Y78xhpRfYZfGFZOvAU+to6XPEdbzq+oeHce8L8PyQp7d/gu2wWl08Vn3hMRERFdjqq2JqxL3Y/Pco5B+4fm/Ag7Z/w9+lrcOjIGFnKFRAmHpra2NiQkJKChocFUk8vliIiIQEBAgGS5iC4Xm/RERERENKjUtrdg5Ykf8GVBoqjuYWWHlyfejBtHRPZoPdGO4gSUr74Zgq4DACC3tIPPkz9A5RncL7mJiIho+Kltb8H6tP34JPuo6K4/APCxccRjMdfg9lFxULI53+fKysqQmpoKvb7r793W1hZxcXGwt7eXMBnR5WOTnoiIiIgGBaNgxH9yT+KVkz+iUdtuqssgw8KwiXhacwPsVZY9OldnWSZOvzELxo7ms+dQWsL78W9hOSK2X7ITERHR8NLQ2YaN6QfxYeZvaNfrRGOe1vb4W/Q1mB88FioFW299Ta/XIz09HadOnRLV/f39ERkZCYWCX4iQ+eFvCiIiIiKSXGZ9BZ47/A0SakpF9TEuPnht0q2IdvXt8bl0NUU4/foNMLbUnS0oLOC19L+wDpnal5GJiIhoGGrVdeLjzMPYkH4AjdoO0ZiblS2WRk3HgtHjYWmhlCjh0NbY2IiEhAS0traaakqlElFRUfD29pYwGVHvsElPRERERJJp1XXireRf8GHGbzAIRlPdVqnG05rrsTA0/rI2VtM3VOD06zfA0FB+tiCTwfPBT2AbfWNfRyciIqJhpEOvw6c5x7AudT9qO1pEY85qGzwSNQ33hE6ElQU3Ke0vhYWFyMrKgtHY9Z7RyckJGo0G1tbWEiYj6j026YmIiIhIErtLMvDSsW9R3tooqs8OiML/TfgTPK0vby1RQ0s9Tr8xE7rqAlPNfeF62E+c3yd5iYiIaPjRGw3Ylp+At5N/6faexU6pxl8jp+L+iMmwVaolSjj0abVaJCcno6qqSlQPDg7G6NGjIb+MCR1EgxWb9EREREQ0oE63nMHyo99iz6ksUX2EnTNWTrwF031DLvucxvZmlL11E7Sn000113n/D45XL+51XiIiIhp+jIIR3xWl4Y2kn1DUVCsas1QocX/4VfjrmKlwUnMGd3+qra1FUlISOjq6lhaytLREbGwsXF1dJUxG1LfYpCciIiKiAaEzGvBhxm94K/ln0QZrSrkCD4+ZhqVR02F1Beu3GrUdKHtnLjoKj5tqzn96Fs43LuuT3ERERDR8CIKAn09l4V+Je5B1plI0ppQrsCBkPB6Nmg6Py7zjjy6P0WhEbm4u8vLyRHUPDw/ExMRApeKyQjS0sElPRERERP3ucEUBXjy6A7kN1aJ6vGcQXo2fg1GO7ld0XkGvRcW789CetddUc7jmr3D588pe5SUiIqLh51BFAV5L2IWkmlOiulwmw+2jNPh79LXws3OWKN3w0dbWhsTERJw5c8ZUk8vlCAsLQ1BQkITJiPoPm/RERERE1G+q25qx4sROfFOYLKq7WNrgpXE34c8jYyGTya7o3ILRgIqNf0Fryk5TzW7inXC/e+0Vn5OIiIiGn7TaMryWsAsHyvO6jc0OiMKTsddd8YQCujzl5eVITU2FTtd116WNjQ3i4uLg4OAgYTKi/sUmPRERERH1Ob3RgE+yj+KNxD1o1nWa6jLIcHfIeDwdd0Ov1nAVjEZUffQAWk58aarZaG6B5wObIOPmYURERNQDRU21eD1xD74tSu02dq1vKJ7SzECki48EyYYfg8GA9PR0lJaWiup+fn6IjIyEhQVbmDS08b9wIiIiIupTCdUleP7IdmTUV4jqUS4+eCV+DmLc/Hp1fkEQUP3po2g69G9TzTryengt+Q9kV7CmPREREQ0vVW1NWJOyF5/nHIdeMIrGJngE4Nm4mRjnESBNuGGoqakJCQkJaGlpMdUsLCwQFRUFHx9+SULDA5v0RERERNQn6jtaserkj/gi76So7qCyxDNxM7Fg9HgoejnLXRAE1H7xDBr3bjDVrEKmwvvRryBXqnt1biIiIhramrQdeC/tAD7M/E20iT0AhDl54tm4mbjGN4TL5g2g4uJiZGRkwGjs+rLE0dERcXFxsLa+8rsuicwNm/RERERE1CtGwYjPc07g1YRdaNS2i8bmjYrD82NnwdXKtk+uVb9jBc7setP02DJoAnz+/i3kvVg6h4iIiIa2Dr0On2QfwdrU/WjobBON+dk6YZnmetwaFA25jEvmDRStVouUlBRUVlaK6qNGjUJISAjkXL6Qhhk26YmIiIjoiqXWnsZzR7Yjpfa0qB7m5IlV8XMwvg9vFa//8U3Ubf+n6bHaPwY+T+6E3Mquz65BREREQ4feaMCX+Yl4M+lnVLQ1isZcLG3wWPQ1uDtkAlQKtscGUl1dHRITE9HR0WGqqdVqxMbGws3NTcJkRNLhbyEiIiIiumxnOtvweuIebMk+BgGCqW6rVGNZ7AwsCouHhVzRZ9dr+OU91H7xtOmxyjsMPst2QWHj1GfXICIioqFBEATsKc3Eawm7kddYLRqzsVDhocipWBw5BbZcKm9ACYKA3Nxc5OXlQRC63j+6u7sjJiYGajV/HjR8sUlPRERERD1mMBqxNe8kXkvYhTN/uF38lqBovDTuJnha2/fpNRsPfIjqLUtNj5VuQfB9ag8s7DnTioiIiMQSqkux6uQPOF5VLKor5QrcEzoRf4ueDhfLvlmGj3quvb0diYmJqK+vN9XkcjnCwsIQGBjIfQBo2GOTnoiIiIh6JLGmFC8d/bbb0jYjHdzwysRbcJX3qD6/ZuNvn6Bq819Njy2c/eD79E+wcPLu82sRERGR+SpqqsVrCbuxszhNVJdBhrkjY7Asdgb87JwlSje8VVRUICUlBTpd12a9NjY20Gg0cHR0lC4Y0SDCJj0RERERXVRdRwteObkLX+SdFNWtLVR4POZa3B9+Vb+s5dp05HNUfXQ/8L/boRWO3vB95mco3QL6/FpERERknuo6WvB28l5syT4KvWAUjU33CcFzY2ci3NlLonTDm8FgQEZGBkpKSkR1X19fjBkzBhYWbEsSncP/G4iIiIjovPRGA7ZkH8MbSXvQqO0Qjd0SFI0Xx94ILxuHfrl28/H/ovKDRV0NensP+D3zM1QefT9bn4iIiMxPu16LjzIP4d3U/WjWdYrGxrj44IWxszC5H+7yo55pbm5GQkICmpubTTULCwuMGTMGvr6+EiYjGpzYpCciIiKibo5XFePFozuQWV8hqoc4emBl/C2I9wzqt2u3JGxHxca7AaMBAKCwc4PvMz9D5RXSb9ckIiIi82AwGvFlQSJeT9yDyrYm0ZivrSOe0czELUFRkMvkEiWkkpISpKenw2jsurPBwcEBcXFxsLGxkTAZ0eDFJj0RERERmVS1NWHVyR/xdUGSqG6nVGOZ5nosDJ0IC7mi367fkrwT5evnAwY9AEBu4wzfp/dA7RPeb9ckIiKiwU8QBOwvy8Wqkz8i+0ylaMxBZYm/RV+DhaHxsLRQSpSQdDodUlJSUFEhnuQxcuRIhIaGQi7nFydEF8ImPRERERFBZzRgU+ZhvJX8M1r+cMv4vFFxeG7sTLhZ2fVrhta03ahYdxtgOLupmNzaEb5P7YbaL6pfr0tERESDW3pdGVae+BG/VeSL6iq5AveGX4WlUVfDSW0tUToCgPr6eiQmJqK9vd1UU6lUiI2Nhbu7u4TJiMwDm/REREREw9yvZXn4v2PfIa+xWlQf4+KDlRNvRpz7iH7P0Jb5C8rfmQtBrwUAyK3s4btsFywDNP1+bSIiIhqcKlob8XriHvw3PxECBNHYrUExeFpzPfzsnCVKR8DZOxzy8vKQm5sLQej6Gbm5uSE2NhZqtVrCdETmg016IiIiomGqpLkOLx/fid2lmaK6o9oaz2puwJ2jx0ExALclt2X+grLVN0PQnd2cVmZpC58ndsIyaFy/X5uIiIgGnzadFhvSf8V76QfQrteJxq7yGokXx96IMa4+EqWjczo6OpCYmIi6ujpTTSaTITQ0FCNHjoRMJpMwHZF5YZOeiIiIaJhp02mxLnUfNmYcROf/1n4HABlkuGv0ODwbdwOcLAdmU69uDXqVNXwe/w5WwZMG5PpEREQ0eBgFI77KT8JribtR9YdNYUc7uuPFcTdhus9oNn8HgcrKSiQnJ0On6/oSxdraGhqNBk5OThImIzJPbNITERERDROCIODbolSsOLETlX/44DvOfQRWTLwZkS4DNyvtvA36J3fCOmTqgGUgIiKiweFwRQFWnPgBaXVlorqLpQ2Wxc7AnaPH9evm9dQzRqMRmZmZKCoqEtV9fHwwZswYKJXcuJfoSrBJT0RERDQMZNSVY/mxb3GsqlhU97S2xwtjb8ScoOgBnZXWlvkLyt6+hQ16IiKiYa6wsRarTv7Qbfk9tcICD4RPxtKoq2GnspQoHf1eS0sLEhIS0NTUNdlDoVBgzJgx8PPzkzAZkfljk56IiIhoCKvvaMXriXvwWe5xGH+3mZdKrsBDkVOxNOpq2CgHdkMvU4Ne2w6ADXoiIqLh6ExnG9Yk/4LNWUegF4yisVsCo/Fs3A3cFHYQKS0tRXp6OgwGg6lmb2+PuLg42NraSpiMaGhgk56IiIhoCNIbDdiSfQxvJO1Bo7ZDNHaDfzheGncTAuxdBjwXG/RERETDm9agx7+zj2J18i9o/N/7gXM0bv74v/E3Ic59hETp6I90Oh1SU1NRXl4uqgcFBSEsLAxyuVyiZERDC5v0REREREPMoYoCLD/6LXIaqkT1kQ5u+OeE2bjaZ7Qkudoy93Zv0D/xPRv0REREw4AgCNh7Ogf/PP49CptqRWN+tk54Lm4mZgdGcVPYQeTMmTNISEhAe3vXlykqlQoxMTHw8PCQMBnR0MMmPREREdEQUdJchxXHf8Cu0gxR3U6pxuMx12FRWDxUCmne/rWm70H5mlvFa9A/8T2sQ6dJkoeIiIgGTl5DNf55/HvsL8sV1W2Vavwt+hrcFzYJlhbccHSwEAQB+fn5yMnJgfC75RJdXV0RGxsLS0vuEUDU19ikJyIiIjJzLbpOrE3Zhw8yDkJrNIjG7ggei2fjboCblZ1E6YCWlB9QsfbPEPRaAGzQExERDRcNnW1YnfwLPvnDuvNymQwLRo/Hk7Ez4GrF9cwHk46ODiQlJaG2tutuB5lMhpCQEIwaNYp3OhD1EzbpiYiIiMyUUTDiv/mJ+H8Ju1Hd3iwai3Pzxz8mzEasm59E6c5qSdyB8nfvAAw6AIBMbcMlboiIiIY4g9GIz3OP41+Je3Cms000NskzCP+ccDPCnD0lSkcXUlVVheTkZGi1WlPNysoKGo0Gzs7cxJeoP7FJT0RERGSGTlQV4/+OfYfUujJR3cvaAc+PnYU5QdGSz3RqPvEVKjbcBRj0AAC5pR18nvwBVsGTJM1FRERE/edQRQH+cew7ZJ2pFNX9bZ3x0vgbMdM/QvL3KCRmNBqRlZWFwsJCUd3b2xtRUVFQKrkUEVF/Y5OeiIiIyIyUtTTglZM/YkdRiqiuVlhgyZhpeDhyGqyVKonSdWk6uhWV798D/G/5HbmVA3ye2gWroPESJyMiIqL+UNpcjxUnduLHEvHeONYWKvwtejoeCJ/MdecHoZaWFiQkJKCpqclUUygUiIyMhL+/v4TJiIYXNumJiIiIzEC7Xov1aQfwXtqv6Pjf0jHn3BIYjefHzoKPraM04f6g6dAWVH54H/C/tWflNs7wfWo3LAM0EicjIiKivtaq68S61P14P+MgOv9399w5t43U4NmxM+FpbS9ROrqYU6dOIS0tDQZD155G9vb20Gg0sLOTbj8jouGITXoiIiKiQUwQBOwoSsGqEz+ioq1RNBbl4oN/TJiN8R4B0oQ7j8ZfP0bVpsWAIAAAFHau8H1qD9T+0RInIyIior4kCAK+LkzGKyd+QNUf9saJdfPDPyfMhsaNM7EHI71ej9TUVJSViZdNDAgIQEREBORyuUTJiIYvNumJiIiIBqnEmlL889j3SKgpFdXdrGzxbNxM3D5KA7ls8HyIati7AdX/fsT0WGHvDt+nf4LaN1LCVERERNTXMurK8dKxb3G8qlhU97C2x/NjZ+HWoOhB9R6FujQ0NCAhIQFtbV0b+iqVSsTExMDTk5v5EkmFTXoiIiKiQaa8pQGvJOzC9sJkUV0lV+CBiMl4NGo67FSW0oS7gPof30TtF0+bHiscveD39M9QeYdKmIqIiIj6UkNnG15P/Albco7C+L+75oCze+M8FDEFj0RdDRulWsKEdCGCIKCgoADZ2dkQfvezc3FxQWxsLKysrCRMR0Rm2aQXBAFJSUnYu3cvEhISUFhYiJaWFtjZ2SE8PBxz5szB7Nmzz7tbeEhIyEXP7erqikOHDvVXdCIiIqILatV1Yn3aAWxI/7Xbmq43+IfjpXE3IcDeRaJ05ycIAuq2v4z6HS+bahbOvvB9+ieoPEdLmIyIiIj6ilEwYmveSbx2cjfqO1tFYzf4h2P5+Jswwm5wvUehLp2dnUhKSkJNTY2pJpPJMHr0aAQHB5+3f0ZEA8ssm/RHjx7FokWLTI/9/Pzg4+ODsrIyHDp0CIcOHcLOnTuxdu1aqFSq854jMjLyvGOOjo79lJqIiIjo/IyCEV/mJ+L/JezutqZrhLMXlo//E67yGilRugsTBAG1XzyDM7veNNWU7iPh+9QeKN0CpAtGREREfSap5hRePLoDKbWnRfVAe1e8PGE2pvtefDIkSau6uhpJSUnQarWmmpWVFTQaDZydnSVMRkS/Z5ZNekEQ4Ovri4ULF+Kmm26Ci0vXt7Xbt2/HSy+9hP3792PNmjV46qmnznuONWvWwNfXd6AiExEREZ3X0cpC/PP4TqTViTfucreyw9Oa63H7qDgoBuHmXYLRiOotS9G4b6OppvIOg+9Te2Dh5C1hMiIiIuoLdR0tePXkLmzNOymqW1ko8Vj0tXgwYjLUCrNsKw0LRqMR2dnZKCgoENU9PT0RExMDpVIpUTIiOh+z/G0aFRWFXbt2nfcXypw5c1BZWYnVq1fjyy+/xJNPPsldqYmIiGjQKW6qw6qTP+DHkgxRXa2wwOL/relqO0jXdBUMelR+dD+aD39qqqn9Y+CzbBcs7N0kTEZERES9pTca8GnOcbyeuBuN2g7R2M2BUXhx3E3wtnGQKB31RGtrKxISEtDY2GiqyeVyREZGYsSIERImI6ILMcsmva2t7UXHp06ditWrV6OhoQH19fVwdXUdoGREREREF9ek7cCalL3YlHkIWqNBNHZLYDSeGzsTvrZOEqW7NEGvRcWGu9Fy8itTzXLkRPg8sRMKG0fpghEREVGvHa8qxotHdyCzvkJUD3H0wMsTbx6Uy++R2OnTp5GWlga9vmt/Izs7O8TFxcHOzk7CZER0MWbZpL+Ujo6ub3otLS3Pe8z69etRXV0Ng8EADw8PTJw4ETfeeOMF17AnIiIi6g290YDPco7jreSfUdch3nAt1s0P/xj/J8S5D+6ZTUZtOyrW3Y7W1B9NNavQq+Hz2HbIrfihj4iIyFzVtDdj5Ykf8FVBkqhup1TjydgZWBgWD6VcIVE66gm9Xo+0tDScPi3eO2DEiBGIiIiAQsGfH9FgNiSb9Dt37gQAhIaGXnDW/VdffSV6/M033+Cdd97B2rVrERERcclrbN26Fdu2betRnj+u/0VERETDhyAI+OV0Nlae+AH5jTWiMW8bBzwXNwu3BEVBLhvcy/MZ2hpR/vYtaM89aKrZRM2C19L/Qq6ykjAZERERXSmD0YjPco/j/yXs6ra0ze2jNHgubhbcrflF/GDX2NiIhIQEtLZ2TQRRKpWIjo6Gl5eXhMmIqKeGXJM+PT0dW7duBQAsXry42/i1116LW265BaGhofD09ERrayuOHDmC1atX49SpU7jvvvuwffv2S/4Sq6mpQUZGxkWPISIiouEto64cL5/YiUMV4i/srS1UeGTMNCyOnAIri8F/F5++qRplb96IzpKu2XW2cbfCa8nnkJlBfiIiIuoutfY0njuyHSm14pnXkc7eWDnxFoz1GNx3+NHZySCFhYXIzs6G0Wg01Z2dnaHRaGBlxYkUROZiSDXpa2tr8eijj0Kv12PGjBm46aabuh2zfv160WO1Wo2bbroJ8fHx+POf/4zy8nKsW7cOq1atuui13NzcejTjHjg7k/73S/AQERHR0FbR2ojXE/fgv/mJECCY6jLIcEdwHJ7SXA8Pa3sJE/acrq4Up1+/AbrKXFPNfvJCeNz7PmSKIfVWkoiIaFho7GzH60l78O/sozAKXe9T7JRqPK25AfeEToRCPrjv8COgs7MTycnJqK6uFtWDg4MREhICmUwmUTIiuhJD5pNVc3MzHnzwQZSXlyMiIgKvvfbaZT3f2dkZixcvxj/+8Q/8/PPPWLly5UV/oc2fPx/z58/v0bnnzp3LWfdERETDQKuuE+vTDmBj+kF0GHSisanewXhx3I0IdzafW461FTk4/foN0NefMtWcbngcrnf8CzJ+eCciIjIrgiBge2EKXj7xPWraW0Rjc4Ji8NK4G81mEsFwV1NTg6SkJHR2dppqlpaW0Gg0cHFxkTAZEV2pIdGkb21txQMPPIDMzEwEBwfjo48+uuBa9BcTGxsLAGhoaEBDQwOcnJz6OioRERENQQajEV/kn8QbiT+hur1ZNDba0R0vjrsJ031Gm9WMpo7iRJS9OQuG5lpTzWXuCjjPfs6sXgcREREB+Q3VeOHojm5L8I10cMOqibdgsvcoiZLR5TAajcjJyUF+fr6o7uHhgZiYGKhUXIaQyFyZfZO+vb0dDz30EJKTkxEQEIBNmzZdcXNdqVSa/mwwGPoqIhEREQ1hB8pyseLED8g+Uymqu1raYlnsDMwfPRYWcoVE6a5MW86vKF99M4wd//vCQSaD+91r4XjtEmmDERER0WVp12vxTso+bEj/FTpjV59DrbDAY9HX4KHIqVBz+Tqz0NbWhoSEBDQ0NJhqcrkc4eHhCAwMlC4YEfUJs/5N3NnZiSVLluDEiRPw8fHB5s2b4ebmdsXny8vLA3B2nXpHR8c+SklERERDUfaZSqw88QP2l+WK6mqFBRZHTMHDY6bBTmUpUbor15K8ExXvzoOg+99+OgoLeD6wCfbxd0kbjIiIiC7LL6ey8eLRHTjVckZUv9Y3FCsm3gx/O2eJktHlKisrQ2pqKvR6valma2uLuLg42NtziSKiocBsm/Q6nQ6PPvoojhw5Ag8PD3zyySfw8rryNV71ej02bdoEAJg4cSIsLMz2r4aIiIj6UUVrI95I+gn/zU8QbbYGAHNHxuJZzQ3wtnWUJlwvNf72Cao+fhD430w7mdISXo9sg23MTRInIyIiop6qaG3E8mPf4scS8d543jYOeHnCzbjBP5xL15kJvV6P9PR0nDp1SlT39/dHZGQkFArzuluTiC7MLDvRBoMBTz75JA4cOAA3Nzd88skn8PPzu+Tz3njjDYwcORIzZswQrVlfUVGBFStWIDk5GRYWFnjkkUf6Mz4RERGZoRZdJ967wKawEzwCsXz8TYh29ZUoXe8IgoAzP76B2m3PmmpyK3t4/30HrEOmSpiMiIiIesooGLEl+xheTdiFFl3XhqIWMjkejJiCv8dcAxulWsKEdDkaGxuRkJCA1tZWU83CwgLR0dHw9vaWMBkR9QezbNL/+OOP2L17NwBApVLh+eefv+CxL730EsLDwwEAhYWF+OCDD/DCCy/Az88PDg4OaG5uRlFREQRBgFqtxsqVKxEdHT0gr4OIiIgGP53RgP/knsBbST+jtqNFNDbSwQ3Px83E9WY8I00wGlHzxVNo2P22qaaw94DPkzthOSJWumBERETUY1n1lXjm8NdIrCkV1Sd4BGBV/ByEOnlKlIyuRGFhIbKysmA0Gk01JycnaDQaWFtbS5iMiPqLWTbptVqt6c9lZWUoKyu74LHNzc2mP995551wdXVFeno6qqurUVZWBqVSieDgYMTHx+Puu++Gv79/v2YnIiIi8yAIAvaUZuKVhF0oaKwRjbla2uKJ2Otw5+hxUJrZprC/J+i1qPzwPjQf/Y+ppnQfCZ9lP0LlPlLCZERERNQT7Xod1qT8gg1pv0IvdDV0HVSWeHHcTbgjOA5ymVzChHQ5tFotkpOTUVVVJaoHBwdj9OjRkMv5syQaqsyyST937lzMnTv3sp83ZcoUTJkypR8SERER0VCSVHMKK0/sxLGqYlHdUqHEQ5FTsGTMNNia+e3ixo4WlK+7DW3pP5lq6hGx8HliJywcPCRMRkRERD1xsDwPzx7ejpLmOlH9lqBo/GP8n+BmZSdRMroStbW1SEpKQkdHh6lmaWmJ2NhYuLq6SpiMiAaCWTbpiYiIiPpDSXMd/l/CbnxblCqqyyDDvOA4LIudAS8bB4nS9R19Uw3KVs9GZ9EJU80q7Bp4/+0rKKzsJUxGREREl1LX0YJ/Ht+JrwuSRHU/Wye8Ej8H031DJEpGV8JoNCI3Nxd5eXmiuru7O2JjY6FSqSRKRkQDiU16IiIiGvbOdLTindR92Jx1BDqjQTR2tc9ovDD2RoQ5D421XHU1xTj95izoKnNNNdvxt8PzwU8gN/O7A4iIiIYyQRDw3/wEvHziBzR0tpnqCpkcD0RMxpMx18FayYauOWlra0NiYiLOnDljqsnlcoSFhSEoKEjCZEQ00NikJyIiomGrXa/Dx5mH8G7afjRpO0RjEc5eeHHcjZjiHSxNuH7QUZyIstWzYWisNNUcr30EbgvehoxrnBIREQ1ahY01ePbwNzhcWSiqR7v64l+T5iLCxVuiZHSlysvLkZqaCp1OZ6rZ2NggLi4ODg7mf+cmEV0eNumJiIho2DEYjfiyIBFvJP6EirZG0Zi3jQOe1lyPuSNjh9RGa62pu1D+7jwIna2mmsvcFXCe/RxkMpmEyYiIiOhCdEYDNqT9irdTfkGnQW+q21io8HTcDVgUGg8Fv2g3KwaDAenp6SgtLRXV/fz8EBkZCQsLtuqIhiP+n09ERETDhiAI2Hs6B6+c/BE5DVWiMXuVJR4ZczXuC78KVhZKiRL2j8ZfP0bV5r8C55bykSvgsfA9OEy7X9pgREREdEFptWVYduhLZNRXiOrX+4Vh5cRb4G3rKE0wumJNTU1ISEhAS0uLqWZhYYGoqCj4+PhImIyIpMYmPREREQ0LSTWnsOrkDzhaWSSqq+QKLAqLx6NR0+FkaSNRuv4hCALqd6xA3fZ/mmoytQ28H/kCNlGzJExGREREF9Ku1+Ht5F+wIf1XGASjqe5hZYcVE2/BrBERvAvODBUXFyMjIwNGY9fP1NHRERqNBjY2Q+s9KBFdPjbpiYiIaEgraqrFvxL24LviVFFdBhluHRmDp2JnwM/OWaJ0/UfQ61D1yRI0HdxkqinsPeDzxHewDIiTMBkRERFdyPGqYiz77UsUNtWK6neNHo8Xxs6Cg9pKomR0pbRaLVJSUlBZWSmqjxw5EqGhoZBzuSIiApv0RERENETVtrfg7ZRf8Gn2Meh/NwsNAKZ5B+P5sbOG7CZrxo4WlL87D21pu001pWcIfJ/cCaVboITJiIiI6HxadJ149eQufJJ9RFT3t3XG61fNxVXeoyRKRr1RV1eHxMREdHR0mGpqtRqxsbFwc3OTMBkRDTZs0hMREdGQ0qrrxAcZv+G9tANo1WtFY5HO3nh+7CxM9QmWKF3/0zdUomz1bHSWJJpqlsFXweexb6CwdZEwGREREZ3P/rJcPHPoa5S1NphqMsjwQMRVeCr2elgrVdKFoysiCAJyc3ORl5cHQRBMdTc3N8TGxkKtVkuYjogGIzbpiYiIaEjQGQ34POc43k75BTXtLaIxP1snPKW5HnOCoiGXDd1bijtPp6Ns9Wzo60pNNduxc+G5+N+Qq3h7PBER0WByprMN/zz2Pb4sSBTVRzu64/WrbkOcu79Eyag32tvbkZiYiPr6elNNLpcjNDQUQUFB3E+AiM6LTXoiIiIya0bBiO+L0vD/EvegpLlONOaotsZj0dNxT2g81Iqh/banNX0PKt69A8b2JlPNccbf4HbnG5DJFRImIyIioj/6vjgNLx7ZgdqOrokFFjI5lkZPx6NR04f8+5ahqqKiAikpKdDpdKaajY0NNBoNHB0dpQtGRIMef+sTERGR2fq1LA+vJuxCWl2ZqG6pUOL+8Kvw8Jhpw2KDtYb9H6D6348ARsPZgkwGt/lvwumGx6QNRkRERCLVbc144eh2/FiSIapHu/ri9av+jHBnL4mSUW8YDAZkZGSgpKREVPfx8UFUVBQsLNh+I6KL428JIiIiMjsptafx6sld+K0iX1RXyOSYHzwWj8deB09re4nSDRzBaETtl8/jzA+vm2oylTW8/vopbDW3SJiMiIiIfk8QBOwoSsGLR79FQ2ebqa5WWOCp2OvxQMRVsOCdb2apubkZCQkJaG5uNtUUCgWioqLg6+srYTIiMids0hMREZHZKGysxeuJe/BdcWq3sRtHROKZuBsw0sFNgmQDz6htR+X796Dl5NemmsLBEz6PfwvLgDgJkxH1zNGjR7Fp0yakpKSgra0N3t7emDlzJhYvXgxra+srOqcgCNi5cye++eYbZGVloampCY6Ojhg5ciSmTp2K+++/v49fBRHRpdW2t+C5I990mz0/0TMQr1/1ZwTau0qUjHqrpKQE6enpMBqNppqDgwPi4uJgY2MjYTIiMjds0hMREdGgV93WjLdTfsHnOcehF4yisXjPIDw3diY0bsNnczV9YxXK19yKjsJjpprKdwx8Hv8WSpfh8/dA5mvLli1YtWoVBEGAp6cnvLy8kJ+fj/feew979uzB559/ftlr97a2tmLp0qU4fPgwAMDPzw/e3t6oq6vDiRMnkJ2dzSY9EQ2474tS8fyRHajvbDXVrC1UeHHcjbg7ZPyQ3tB+KNPpdEhJSUFFRYWoHhQUhLCwMMjl/LkS0eVhk56IiIgGrSZtBzam/4r3Mw6iXa8TjYU7e+G5uJm42mc0ZDKZRAkHXmdZJspWz4a+tthUsx5zA7we3gqF1dBf4ofMX3p6Ol555RUAwMsvv4x58+ZBJpOhqqoKS5YsQUZGBl566SWsXbu2x+cUBAGPPvooDh8+jClTpmD58uXw9+/6wqqpqQknTpzo89dCRHQhdR0teOHIDnxfnCaqx3sG4c3Jt8HfzlmiZNRb9fX1SExMRHt7u6mmUqkQGxsLd3d3CZMRkTljk56IiIgGnXa9Dp9kHcG6tP2idVsBwN/WGcs0MzAnKHrYzT5rTf0RFe/dBWN7k6nmMP0huN/9DmQKvq0j87B+/XoYjUbMmTMHd9xxh6nu4eGBt956C7NmzcKePXuQnZ2N0NDQHp3z66+/xqFDhxAdHY0NGzZ026DP3t4e1157bZ++DiKiC9lZnIbnj2xHXUfX7HkrCyWej5uFhWETh937l6FCEATk5eUhNzcXgiCY6m5uboiJiYGlpaWE6YjI3PHTHBEREQ0aeqMBX+QlYHXyz6hsaxKNuVja4LHoa3B3yASohllDWhAENOx5BzVblwHnlvuRyeB2x+twvOHvw+pOAjJvra2tOHjwIABg3rx53cYDAgIwceJEHD58GLt27epxk37z5s0AgCVLlnRr0BMRDZQzHa148di32FGYIqpP8AjAm5NvR4C9i0TJqLc6OjqQmJiIuro6U00mkyE0NBQjR47kezEi6jW+gyUiIiLJGQUjdhan4/XEPShsqhWN2ViosDhyCh6KnApbpVqihNIR9FpUb3kUjQc+NNVkaht4PbQFtppbJExGdPmysrKg1WqhUqkQFRV13mPi4uJw+PBhpKSknHf8j0pLS5Gbmwu5XI4JEyYgJSUFX331FUpLS2FtbY2YmBjcdtttcHbm0hJE1H92l2Tg2SPfoKa9xVSzVCjx3NiZuDcsnrPnzVhlZSWSk5Oh03UtvWhtbQ2NRgMnJycJkxHRUMImPREREUlGEAQcKM/D/0vYjbS6MtGYSq7APaET8Wj0dLhY2kqUUFqGljqUr5uH9uz9ppqFsx98/r4Dav9o6YIRXaGioiIAgLe3N5RK5XmPObeW/LljLyU9PR0A4OjoiM8++wxvvvmmaBmCX375BR988AHWrl2LiRMnXvRcW7duxbZt23p03YKCgh4dR0RD25nONvzfse/wdUGSqD7OfQTenHw7ghxcJUpGvWU0GpGZmdnt3yMfHx+MGTPmgv+OERFdCTbpiYiISBIJ1aV4LWEXjlQWiupymQy3j9LgiZgZ8LF1lCbcINBZnoXyt2+BrrqrEWg5ciK8H/0KFo6eEiYjunKNjY0AAAcHhwsec27s3LGXUl1dDeDs5rBvvPEGrr76ajz11FPw9/dHUVERXnnlFRw9ehSPPvoovvvuO3h6Xvj/n5qaGmRkZPT05RDRMLf3dA6e+u1LVLU3m2pqhQWe0dyA+8OvgkLO2fPmqqWlBQkJCWhq6lp+UaFQIDIyUrQxORFRX2GTnoiIiAZUzpkq/CtxN3aXZnYbmzUiAk9rbkCwo7sEyQaP1rTdqFg/X7RBrF38Anjc+z7kKm5KRuars7MTAC46+1ClUomOvZS2trObS+v1evj7+2PdunWm84eEhGDDhg2YMWMGampq8Mknn+CZZ5654Lnc3NwQERHRo+sWFBSgo6OjR8cS0dDSptNixYmd2JJzTFTXuPlj9ZTbMdLBTaJk1BdKS0uRnp4Og8Fgqtnb2yMuLg62tsPz7k4i6n9s0hMREdGAKG2ux1tJP+OrgiQIEERjk71G4Zm4GxDr5idRusFBEAQ0/LwONZ8/0bVBLADX21bB6aZnuCkZmT21+uy+Er9f1/ePtFqt6NienhMAFixY0O0LACsrK8yfPx9r167FwYMHL9qknz9/PubPn9+j686dO5ez7omGoYTqUjz26xcobu7aQFStsMCy2BlYHDGFs+fNmE6nQ2pqKsrLy0X1wMBAhIeHQ86fLRH1IzbpiYiIqF9VtTXhnZS9+Dz3BHRGg2gs2tUXz8bdgCnewRKlGzyMuk5U//thNB3cbKrJVNbwfOjfsIu7VbpgRH2oJ0vZ9GRJnN+zt7c3/XnkyJHnPeZc/fTp0z06JxHRH+mMBryd/AvWpu6D8Xf7XkQ4e+GdqfMR4uQhYTrqrTNnziAxMdF0dxZw9s6umJgYeHjwZ0tE/Y9NeiIiIuoXZzpasT7tV2zKOowOg3jW7CgHNzytuQGzRkRwdjgAfUMFytfeho6Co6aahbMfvB/bDssRMdIFI+pjAQEBAIDy8nLodLrzLntTWloqOvZSgoKCTH++0DI652bbG43G844TEV1MXkM1Hvv1C6T+bpN7uUyGh8dMwxMx10GlYGvFXAmCgPz8fOTk5Ig2HXdxcYFGo4GlJZcZJKKBwX9JiIiIqE+16DrxYcZv2Jj+K5p14jWlfWwc8XjMtbhtlAYWcoVECQeX9oJjKF97GwwNXbdWWwZfBe9HtnGDWBpywsLCoFQqodVqkZqairi4uG7HJCQkAABiYmJ6dM7w8HBYWlqio6MDp06dwsSJE7sdc67xf7FNY4mI/sgoGLEp6wheOfkjOg16U32EnTPenjIP4zwCpAtHvdbR0YGkpCTU1taaajKZDCEhIRg1ahQnkhDRgGKTnoiIiPpEh16HLTlHsTZlP+o7W0Vjbla2+FvUNbgrZDzUnG1m0nhwM6o/WQJBrzXVHK5+EO53vwOZhUrCZET9w9bWFpMnT8a+ffuwbdu2bk364uJiHD169o6SmTNn9uicVlZWmD59On788Uds374dt99+u2hcEAR88803AHDeBj4R0fmUtzbiyd/+i4Pl+aL6XaPHY/n4m2Cr7Nm+GTQ4VVVVITk52bQPCnD23xONRgNnZ2cJkxHRcNXrT8knTpwAAISEhIjWgyQiIqLhQWc04Iu8k3g7+RdUtjWJxhxUllgy5mrcFzYJ1ko2nc8RDHrUbH0KDT+901VUWMB9wRo4XvNX6YIRDYCHH34Y+/fvx44dO6DRaDBv3jzIZDJUV1fjiSeegNFoxHXXXYfQ0FDR86655hoAwNNPP92tgb906VL89NNPOHnyJN5991389a9/hUKhgF6vx1tvvYXs7Gyo1WosWrRooF4mEZmx7YXJeOHIdjRqO0w1V0tbvH7VXMzwD5cwGfWW0WhEVlYWCgsLRXUvLy9ER0dfcNk0IqL+1usm/V/+8hcoFAocPny4L/IQERGRmTAKRuwoTMUbST+hpLlONGZlocSD4ZPxUORUOKitJEo4OBla6lD+7ny0Z+011RR2bvBaug3WIVMlTEY0MKKiovDss8/itddew/Lly/Hee+/ByckJ+fn50Gq1CAwMxIoVK7o9r6zs7FrQv9/U75xRo0Zh5cqVeOGFF/DOO+/g008/ha+vL0pLS9HQ0AClUolXXnlFtH49EdEfnelsw4tHdmBHUYqofoN/OP511Vy4WNpKlIz6QmtrKxISEkSblysUCkRGRsLf31/CZEREfdCkt7Ozg1wuh4ODQ1/kISIiokFOEATsKc3E60k/IftMpWhMJVfgL6ETsTTqarhZ2UmUcPDqLE1B+do/Q1dTZKqpR8TC+29fQ+nCD4c0fCxatAghISH4+OOPkZqairq6Onh7e2PmzJlYvHgxbGxsLvuct956K0aNGoUPP/wQJ0+eRFZWFhwdHfGnP/0JDz74YLeZ+UREv/dbeT7+fnCb6K5AW6Ua/5wwG/NGxXF9cjN36tQppKWlwWAwmGr29vbQaDSws+N7ViKSXq+b9P7+/sjJyYFWq4VKxdvYiYiIhipBEHCgPA+vJ+5BSu1p0ZhcJsO8UXF4POY6+Ng6ShNwkGs6/CmqNj0EQdd167zdhDvgcd+HkKutJUxGJI34+HjEx8f3+PicnJxLHjNmzBisWbOmN7GIaJjRGvR4PfEnbEj/FQIEU32CRwBWT5kHfzuuT27O9Ho9UlNTTXdjnRMQEIDw8HAoFAqJkhERifW6SX/TTTchIyMDP/zwA+bMmdMHkYiIiGiwOVpZiNcT9+BYVXG3sZsDo/Bk7AyMdHAb+GBmQNBrUfOfZWj45d2uokwO19tWwenGpzgzj4iISCKFjTVYemArUuu6GrhKuQJPaa7HQxFToJDLJUxHvdXQ0ICEhATRMmlKpRIxMTHw9PSUMBkRUXe9btLfc8892LNnD1asWAEnJydMmzatL3IRERHRIJBccwqvJ+7BgfK8bmMz/MKwLHYGIly8JUhmHvRnylH+7h3oyO/au0du4wyvJZ/DJnKGhMmIiIiGL0EQ8EXeSSw/9h3a9FpTfZSDG9ZNm49IFx8J01FvCYKAgoICZGdnQxC67o5wdnaGRqOBlRX3SyKiwafXTfoNGzZg3LhxyM3NxV//+leMGjUKGo0GLi4ukF/kW+elS5f29tJERETUTzLrK/Bm0k/YXZrZbWyqdzCWaWZA48Y11C+mLecgKtbPh6Gxa91+9QgNvJf+F0q3AOmCERERDWMNnW147vB2fFecKqovGD0e/zf+T7BWchlfc9bZ2YmkpCTU1NSYajKZDKNHj0ZwcDDvYCSiQavXTfp169ZBJpOZvp3My8tDfn7+JZ/HJj0REdHgU9BYg7eSfsa3RamidVkBYJz7CDwddwPiPYMkSmceBEFAw09rUfPFU4BBb6rbT1kE97+sg1zF2VtERERSOFZZhEd/3Yry1kZTzVFtjdevmotZIyIlTEZ9obq6GsnJyejs7DTVrKysoNFo4OzMvQWIaHDrdZN+3LhxfZGDiIiIJHSquR5vp/yC/+YnwiiIm/NRLj54SnM9rvYZzdlHl2DsbEXVpofQfPQ/XUWFEu53r4HD1Yv590dERCQBndGAt5N/wdrUfaL3OZM8g/D21DvgbeMgYTrqLaPRiOzsbBQUFIjqnp6eiImJgVKplCgZEVHP9bpJv2XLlr7IQURERBIob23E2pS92Jp3EjqjQTQW4uiBpzTX4wb/cDaXe0BbkYPyd+dBezrdVLNw8oHXI9tgNWqihMmIiIiGr5LmOjx64Ask1pSaahYyOZZprseSyKncHNbMtba2IiEhAY2NXXdHyOVyREREICAgQLpgRESXqddNeiIiIjI/VW1NWJe6H5/lHIP2D835QHtXPBl7HWYHRPGDaw81H9+Gyo8fhNDRYqpZhV4NryWfw8LBQ8JkREREw9fXBUl4/sh2tOi6lj8JsHPBumnzEePmJ2Ey6gunT59GWloa9Pqu5QXt7Oyg0Whgb28vYTIiosvHJj0REdEwUtfRgvWpB/BJ9lF0GHSiMR8bRzwecy1uG6WBhVwhUULzYtR1onbrU2j45V1R3WnmE3C9/VXIFHyrRURENNBadJ14/sh2fF2QJKrPGxWHlyfeDFulWqJk1Bf0ej3S0tJw+vRpUX3EiBGIiIiAQsH3sURkfvjJkYiIaBg409mGjem/4uPMw2jTa0Vjntb2+Fv0NZgfPBYqNpV7TFdbgvJ370Bn0QlTTW7lAM8HPoZt3BzpghEREQ1j6XVl+Ou+z1HcXGeq2ass8Wr8rbglKFrCZNQXGhsbkZCQgNbWVlNNqVQiOjoaXl5eEiYjIuqdPvsknp2djc8++wwJCQmorKxEe3v7BY+VyWTIzMzsq0sTERHRBTRpO/BBxkF8mPEbmn93qzcAuFnZ4pExV+PukAmwtOCGWpejJXknKj9YCGPrGVNNPSIWXo98AZX7SAmTERERDU+CIOCT7KN4+fj3oqX8xrmPwNpp8+Fr6yRhOuotQRBQVFSErKwsGI1GU93Z2RkajQZWVlYSpiMi6r0+adJ/+umneO2112AwGCD8bqd0IiIikkarrhMfZx7GhvRf0agVf3HupLbGw2OmYVFYPKwsVBIlNE+CQY+6b/4P9d+/Jqo7XP0g3O56G3KVpUTJiIiIhq/GznY8degr/FDStXm7DDL8LXo6Ho+5lsv4mbnOzk4kJyejurpaVA8ODkZISAhkMplEyYiI+k6vm/QpKSlYtWoVAOCuu+7CtGnTsHjxYjg4OODtt99GbW0tDh8+jO+//x62trZ48cUX4ebm1uvgRERE1F27XotPso5ifdoB1He2isYcVJZ4KHIq7gu/imuxXgF9QwUqNtyN9uz9pppMZQ2PRe/BftLd0gUjIiIaxpJqTuHh/Z/jVEvX3W3uVnZ4Z+odmOw9SsJk1BdqamqQlJSEzs6uO0ItLS2h0Wjg4uIiYTIior7V6yb9v//9bwiCgIULF+K5554z1ZVKJeLj4wEAs2fPxj333IP7778fa9aswddff93byxIREdHvtOt1+DTnKN5NPYDajhbRmK1SjQcjJuPBiCmw50zvK9KathuV7y+EobnGVFN5hcJr6TaofSIkTEZERDQ8CYKADzN/wysnd0H3u+VtpniPwjtT74CblZ2E6ai3jEYjcnJykJ+fL6p7eHggJiYGKhXvBiWioaXXTfqkpCTIZDLcc889Fz0uLCwML774Ip544gl89NFHeOyxx3p7aSIiomGvQ6/DZ7nH8W7qflS3N4vGrC1UuC98Eh6KnAontbVECc2boNeh9uvlOPPDv0R1u4nz4bFoI+SWthIlIyIiGr7OdLTi8d/+i59PZZtqcpkMy2JnYGnU1ZDL5BKmo95qa2tDQkICGhoaTDW5XI7w8HAEBgZKF4yIqB/1uklfW1sLlUoFHx8fU00ul4tuRTpnxowZsLCwwE8//cQmPRERUS90GvT4T+4JrE3dh6q2JtGYpUKJhaETsWTMNLhasYl8pXS1JajYsAAd+UdMNZnSEm53vQWHqxdz/VMiIiIJnKgqxiMH/oPy1kZTzdPaHu9OuxMTPNnANXdlZWVITU2FXq831WxtbREXFwd7e3sJkxER9a9eN+nPt4O2jY0NWlpaoNVqRbcgKZVKWFlZoaysrLeXJSIiGpa0Bj2+yDuJd1L2oaKtUTSmVljgntCJWBI5De7WvMW7N5pPfo2qjx+Esa3BVFN5hcLr4a1Q+42RLhgREdEwZRSMWJ/2K15P3AODYDTVr/ENwdtT5sHZ0kbCdNRbBoMBaWlpOHXqlKju7++PyMhIKBTc/JeIhrZeN+nd3d1RVFQEvV4PC4uzp/Pz80NWVhZSU1MxduxY07FVVVVobm4+b2OfiIiILkxnNOC/+Ql4J2UvTrc0iMbUCgssGD0ej0RdDQ9rzjDqDaO2AzVfPIXGX9aL6vZTFsH97ncgV7MBQERENNBq21vw2K9f4EB5nqlmIZPjubEz8WDEZC5vY+aampqQkJCAlpaufZUsLCwQHR0Nb29vCZMREQ2cXjfpR44cifz8fOTm5iI8PBwAMGHCBGRmZmL9+vV47733oFarodVqsWrVKgDA6NGje3tZIiKiYUFvNOCrgiSsSd6L0pZ60ZhKrsCdo8djadTV8LJxkCjh0KGtzEXF+jvRWZpsqsksbeFxz7uwn3S3dMGIiIiGsWOVRXh4/+eo+t3eO762jnh32l2Ic/eXMBn1haKiImRmZsJo7Lo7wsnJCRqNBtbW3FOJiIaPXjfpr7rqKuzatQt79+41NenvuusufPbZZzhy5AimTp2KwMBAFBcXo7GxETKZDAsWLOh1cCIioqFMbzTgm4JkvJ2yFyXNdaIxpVyB+cFj8WjUdHjbOkoTcAgRBAFNv32C6k//BqGz1VRX+8fA6+H/QOXJyQVEREQDTRAEbEw/iFcTdomWt7nBPxxvTr4Njmo2cM2ZVqtFcnIyqqqqRPXg4GCMHj0acjnvjiCi4aXXTfobbrgBVVVV8PDwMNX8/Pzw5ptv4rnnnkNjYyOSk5MBnN1Q9v7778fNN9/c28sSERENSRdrzlvI5JgXPBZ/i54OX1sniRIOLYbWM6j6ZAlajv9XVHe89hG43vEvyFWWEiUjIiIavpq0HXjyt//ix5IMU00pV+DFcTfivrBJ3LzdzNXW1iIpKQkdHR2mmlqthkajgaurq4TJiIik0+smvb29PZYuXdqtPmPGDIwbNw4HDhxAZWUlbG1tMXnyZIwYMaK3lyQiIhpyLtacV8jkuG1ULB6Lvhb+ds4SJRx62nJ+ReXGe6Cv79qgTG7jBI/7PoBd3K0SJiMiIhq+MusrsHjvpyj+3fshHxtHbJi+ALFufhImo94yGo3Izc1FXl6eqO7u7o7Y2FioVCqJkhERSa/XTfqLcXR0xC233NKflyAiIjJrPWnOPxp1DQLsXSRKOPQIeh3qdryM+u9fA353+7xV6NXwfHAzlC5sABAREUnhy/wEPHt4OzoMOlNtms9orJ16B5wtuXm7OWtra0NiYiLOnDljqsnlcoSFhSEoKEjCZEREg0O/NumJiIjo/Nicl4a2ugCVG/6CjsJjXUWFBVxvfRlONy6DTK6QLhwREdEw1aHX4f+OfYfPco+bajLI8HjMtXgs+hoouD65WSsvL0dqaip0uq4vX2xsbBAXFwcHBwcJkxERDR593qTPy8tDeno66urONhxcXFwQGRmJ4ODgvr4UERGR2blUc/7PI2PxaPR0BNpzPc6+JAgCmg9vQdWWRyF0tJjqSo9R8HroU1gGjZMwHRER0fB1qrkeD+37DKl1Zaaao9oa66bNx9U+3LzdnBkMBqSnp6O0tFRU9/PzQ2RkJCwsOG+UiOicPvuNuG/fPrz11lvIz88/7/ioUaPw97//Hddee21fXZKIiMhssDkvHUNrA6q3PILmo1tFdfsp98J9wduQW9pKlIzIvJ08eRJHjx497/5UREQ98cupbPzt1y/QqG031WJc/bBx+gL42DpKF4x6rampCQkJCWhp6ZocYWFhgaioKPj4+EiYjIhocOqTJv26devw7rvvQhCEsye1sICjoyMAoKGhAXq9Hnl5eVi6dCkefvhhPProo31xWSIiokFPZzTgq/xErE3dh5LmetEYm/P9ry3zF1R+eB/09adNNbm1IzwWbYDd+NslTEZk/k6cOIF3332XTXoiumwGoxFvJf+MNSl7RfWFoROxfPyfoFZwhrU5Ky4uRkZGBozGrr1/HB0dodFoYGPDvQWIiM6n1//y/frrr1i3bh0AYNy4cViyZAnGjh1r2pVbq9Xi5MmT2LBhA44fP47169cjJiYGU6ZM6e2liYiIBi2tQY8vCxKxLmU/Slu6N+fnjozB36KvYXO+nxi17aj973No+GmtqG4VMg2eiz/h5rBEREQSqetowdIDW3GwvOsufCsLJf416c+4dWSMdMGo17RaLVJSUlBZWSmqjxw5EqGhoZBzbwEiogvqdZN+8+bNAICZM2di9erVkMlkonGVSoVJkyYhPj4ejz/+OHbt2oXNmzezSU9EREOS1qDHtvwErEvdh9MtDaKxczPnl0ZNR5ADm/P9paPoJCrfXwhtRXZXUaGE663/5OawRJfw3HPP9fjYnJycfkxCRENRUs0pLN77KSraGk21kQ5ueH/63Qhx8pAwGfVWXV0dEhMT0dHRYaqp1WrExsbCzc1NwmREROah10369PR0yGQyPPfcc90a9L8nk8nw7LPPYteuXUhLS+vVNQVBQFJSEvbu3YuEhAQUFhaipaUFdnZ2CA8Px5w5czB79uwL5mltbcX777+P3bt3o7y8HNbW1oiOjsZ9992HCRMm9CobERENT50GPbblncS61P0oa20QjSlkctw2SoNHo6YjwN5FmoDDgGDQo/7711D37QrAoDfVVb6R8HzwE1iOiJEuHJGZ+OabbyCTyUzLWF7Kxd7/ExH93hd5J/H8ke3o/N2/0X8KGIM3Jt8GW6VawmTUG4IgIDc3F3l5eaJ/O9zc3BAbGwu1mj9bIqKe6HWTXqfTwd7eHh4el/7W29PTEw4ODtBqtb265tGjR7Fo0SLTYz8/P/j4+KCsrAyHDh3CoUOHsHPnTqxdu9a07M459fX1uOuuu1BUVASVSoVRo0ahvr4e+/fvx4EDB/DSSy9hwYIFvcpHRETDR4deh615J/Fu6n7RrDAAsJDJcXtwHB6Nmg5/O2eJEg4P2spcVL6/CB2Fx7qKMhmcbngCLnNfhlxlKV04IjPi6uqKyMhIvPPOO5c8duPGjVi/fv0ApCIic6YzGvCPY9/jk+wjpppCJsdL427E/eFX8cs+M9be3o7ExETU13ct7SiTyRAWFoagoCD+bImILkOvm/S+vr4oKiqCVqvt1hD/I61Wi9bWVgQFBfXqmoIgwNfXFwsXLsRNN90EF5euWYnbt2/HSy+9hP3792PNmjV46qmnRM994YUXUFRUhIiICLz33nvw8PCAIAjYtm0bli9fjlWrVkGj0SAsLKxXGYmIaGjr0Ovwee5xrE87gMq2JtGYUq7AHcFj8ciYafBjc75fCYKAxr0bUPPFUxC07aa6hcsIeD64Cdah0yRMR2R+IiMjkZmZecn39QBgYcGNHYno4mram/HXfZ/hWFWxqeZiaYMN0xcg3rN3fQGSVmVlJZKTk6HT6Uw1GxsbaDQaODo6SheMiMhM9XrXjtmzZ0Ov12PHjh2XPHbHjh3Q6/X405/+1KtrRkVFYdeuXbjnnntEDXoAmDNnDh555BEAwJdffinaTTwzMxN79+6FXC7H6tWrTbP/ZTIZ7rjjDtxyyy0wGAycEURERBfUrtfig4yDuOrLf2H5se9EDXqVXIF7Qifitz8/hdcm3coGfT/T1ZWi7I2ZqN6yVNSgt5+yCCNWJrNBT3QFIiIiUF1djerq6ksea2dnBy8vrwFIRUTmKKnmFGZ9u1bUoI929cWPsx9lg96MGQwGpKWl4cSJE6IGvY+PD6ZOncoGPRHRFer19Jd7770XBw4cwMqVK2FhYYFbb731vMdt374dK1euxNixY3Hffff16pq2trYXHZ86dSpWr16NhoYG1NfXw9X17OZ8u3fvBgBMnDgRI0aM6Pa8O+64Azt27MCBAwfQ1tYGa2vrXuUkIqKho1XXiX9nH8XG9IOo7WgRjankCtw5ejweGTMN3raO0gQcRgRBQNOvH6HmP8tg7Gg21RV2rvBYtBG2cXOkC0dk5u6//37MnTsXTk5Olzz27rvvxt133z0AqYjI3Jxv/fnbR2nwSvytsLJQSpiMeqO5uRkJCQlobv7d+y+FAlFRUfD19ZUwGRGR+busJv26devOWx87dixyc3Px/PPPY+3atRg/frxplnpVVRWOHz+OiooK2NnZIS4uDhs2bMDSpUt7n/4Cfr+buKVl1xq0ycnJprznExUVBZVKhc7OTmRlZSEuLq7fMhIRkXlo1nZgc9YRvJ9xEGc620RjaoUF7ho9Hg+PmQYvGweJEg4vurpTqNq0GG3pe0R1m5g/wePe92HhcOk9cojowqytrTlRhYiu2IXWn/+/8Tfh3rBJXKPcjJWUlCA9PV20WoGDgwPi4uJgY2MjYTIioqHhspv0F/tHVRAElJeXd1v65twO383NzXj//fcBoF+b9Dt37gQAhIaGimbdFxcXAwD8/f3P+zylUgkvLy+UlJSgqKjook36rVu3Ytu2bT3KU1BQ0MPkREQ0WDR0tuHjzMP4KPM3NGo7RGOWCiXuCZ2AhyKnwsPaXqKEw4sgCGg6uAk1/3kSxvauJYbk1o5wX/A27CbdzQ/+RJfpzjvvxPLly7kXExH1Ca4/PzTpdDqkpKSgoqJCVA8KCkJYWBjk8l6vokxERLjMJv24ceP6K0efSU9Px9atWwEAixcvFo01NjYCOPtt74WcG2tqarrgMQBQU1ODjIyM3kQlIqJB6ExHKz7I+A2bsg6jWdcpGrO2UGFRWDwWR0yBq9XFl16jvqOrP42qzQ+hLXWXqG4TfSM8Fm2EhZO3RMmIzFtSUhJuu+023H777fj73//OdYSJ6Iol1ZzCg3u3iPbqiXLxwQfX/AU+XArQbNXX1yMxMRHt7V17/6hUKsTGxsLd3V3CZEREQ89lNem3bNnSXzn6RG1tLR599FHo9XrMmDEDN910k2i8s/Nss0WpvPAaeCqVCoB4yZzzcXNzQ0RERI9yFRQUXPJ8REQkrdr2FmxMP4hPso+gTa8Vjdkp1bg3bBIeiJgMZ0vezjtQBEFA02+bUfP5kzC2N5rqcisHuC1YDfur7uHseaJeuOuuu/DFF1/giy++wI8//oi///3vmD9/Pv+/IqLLcr71528bqcGrk7j+vLkSBAF5eXnIzc01rYwAnO2DxMTEiJYVJiKivtHrjWMHi+bmZjz44IMoLy9HREQEXnvttW7HqNVqtLe3i3Yg/yOt9mxj5lL/6MyfPx/z58/vUba5c+dy1j0R0SBV2daEjem/Ykv2MXQYxP8+OKgscX/4ZNwXPgmOaq7RPJB0dadQ/ckStKb+KKpbR82Ex73vQ+nkI1EyoqFj+fLlmDdvHlasWIGEhAS8/PLL2LZtG1566SVoNBqp4xHRIKczGvDP499jc5Z4/fnl42/CfVx/3mx1dHQgMTERdXV1pppMJkNoaChGjhzJnysRUT8ZEk361tZWPPDAA8jMzERwcDA++ugj0Vr059jb26O9vd207M35nBuzt+caw0REQ9npljN4L+0AtuadFM38AgAntTUWR0zBorB42Kk4U2ggCUYjGvdvRO2252DsaDbV5Vb2cLvrLdhPXsQPh0R9KDQ0FJ999hl27NiBN954A1lZWViwYAFmz56Np556Cm5ublJHJKJB6ExnG5bs+xy/VeSbalx/3vxVVlYiOTlZNLHR2toaGo0GTk5OEiYjIhr6zL5J397ejoceegjJyckICAjApk2bLviPR0BAAKqqqlBSUnLecZ1Oh/LyctOxREQ09BQ11WJd6n58lZ8IvWAUjbla2uKhyCm4J3QibJRqiRIOX9qKHFRtegjtuQdFdevI68/OnnfxkygZ0dB3yy234LrrrsO6deuwZcsWfPfdd/jll1/w8MMPY+HChbCwMPuPDUTUR/IbqrHo509Q3Nw105rrz5s3o9GIzMxMFBUVieo+Pj4YM2bMRZcMJiKivmHW77Y7OzuxZMkSnDhxAj4+Pti8efNFZ/vExMTg2LFjSEhIOO94amoqdDod1Go1wsLC+is2ERFJILehCmtT9mFHUQqMv1tbEwA8rO2xJHIqFoSMh5WFSqKEw5eg1+HMrjdRt/1lCPquzXrlNk5wm/8G7Ccv5Ox5ogFgY2ODZ555BrfddhtWrlyJI0eO4I033sCXX36JF154AZMnT5Y6ItH/Z+++w6Oq0j+Af2cmM+mdkEJIg/Q+oXcCKrq2RQQsFBFxdXVXXdd1d9XfqmtbXXfXvqtSBQF7wYJUaQFM772QBNJ7Mv3+/ogMjAmQfjOZ7+d5fNb7nnPvvOPdJGfeOfccEtnhqgLcd2gHWjUX9ly7KTAWr8xZxvXnzVR7ezuSk5PR2nph01+ZTIaoqCj4+fmJmBkRkWUx2yK9VqvFgw8+iBMnTsDT0xNbtmyBt7f3Zc+55ppr8N///hcnT55EeXk5/P39Tdp37doFAJg3bx7s7bkxIBHRWJDdUI3XMg7im7IsCDAtzvs6uOC30Qtw6+QE2PCDpShUZSmo2XgP1BVpJnGHqcsw/o7/wMrFS5zEiCzYpEmTsGnTJnz//fd46aWXUFpainvuuQeJiYl4/PHHMXEin2ohsjSCIGBT7nE8fWoP9Bc9ifjH+Kvwu9hEfplupioqKpCVlQW9Xm+MOTk5ISEhodclhImIaPiYZZFer9fjD3/4Aw4fPgwPDw9s2bKlTx8WIiMjsXDhQhw8eBAPP/ww3nnnHYwfPx6CIGD37t344osvIJVKcd99943AuyAiouGUWncGr6UfwA9ncnu0BTi648HYhVg6KR5yqUyE7Mig6ULD50+j6btXAcOFD4YyF294rnoDDgk3i5ccEQEAEhMT4ezsjL/+9a+oqqrCgQMHcPToUaxfvx4bNmyAtTWXBSOyBFqDHk8mfYkP8k8aY7ZWcvx77nL8KiBaxMxooLRaLTIyMozL/Z4XGBiIiIgISKVSkTIjIrJcZlmk//bbb/H9998DABQKBf7yl79csu+TTz6JiIgI4/Hzzz+P2267DdnZ2Vi0aBEmT56MpqYmnD17FhKJBH/5y18QGRk57O+BiIiGx8lzpfhP+gH8WF3Yoy3EZTwejEnEDYHRsGJxXjSdeYdRs+leaGtM75HTvLvhseIfkNm7iJMYkYU7c+YM0tPTkZaWhoyMDOTl5ZlsHigIAtRqNd566y18+eWXePbZZzFjxgwRMyai4dak6sC9B7fj+LkSY8zbzhmbFq9GlPsEETOjgWpqakJKSgo6OzuNMYVCgbi4OHh6eoqYGRGRZTPLIr1GozH+e1VVFaqqqi7Zt62tzeTYzc0Nn3zyCd5991189913KCoqgp2dHebNm4e7776bHzSIiMyQIAg4VFWA1zMO4lRNWY/2KDcf/C42EUv8IyCVcGaQWPRt9ajb9Rhaj24xicvHT4LnXf+FXfhCkTIjskzHjh1Deno6MjIykJ6ejubmZmOb8PPeHTKZDCEhIVAqlYiPj4eNjQ1ee+01FBQUYN26dbj//vvxwAMPiPQOiGg4Ff68QWz5RRvExntMxPuJqzHezlHEzGggBEFAUVER8vPzjb/jAcDd3R1KpRI2NjYiZkdERGZZpF+6dCmWLl064PMdHBzw8MMP4+GHHx7CrIiIaKQZBAO+r8jB6+kHkdHQ8wvbeI+JeCh2ERJ9Q7lWqogEQUDrsa2o2/lHGNovfNCHRArXax6G+6//Bqm1nXgJElmou+++GxKJxKRY4+joiJiYGCiVSiiVSsTGxsLOzvTnMzExEVu3bsUrr7yCN998Ez4+PoMamxPR6HOwMh/3H9qBNu2FDd1/HRSHl2ffwn18zJBKpUJqairq6+uNMYlEgtDQUEyePJnjZCKiUaDfRfoTJ05g+vTpXKOMiIhEozPo8VVpJt7IOIj85poe7TO9gvC72IWY480PHWLTnM1HzZb70ZV3yCRu7R8Pz7XvwCZwijiJEREAwNfX1zhLXqlUIjg4+Iq/N6VSKdauXYtx48bh0Ucfxfbt21mkJxojBEHA+znH8MzpPTBc9AXe4wnX4LfRCziuMkO1tbVITU01WZHA1tYWSqUSbm5uImZGREQX63eR/q677oKzszPmz5+PxMREzJs3r8fsGiIiouGg0evwcXEK3sw4bPLo9XmJvqH4XUwipnj6i5AdXcygUaFxz0to2vMiBN2FD4USa3uMW/oMXBY/AInMLB/oIxozjh07Bnd39wGff/311+Nvf/sbSkpKrtyZiEY9jV6HJ5K+xI6CU8aYnZUCr81bgSX+3LfN3BgMBuTm5vb4He3t7Y3Y2FjI5XwigohoNOn3p+PQ0FDk5+fjyy+/xFdffQW5XI6ZM2ciMTERiYmJ8PDwGI48iYjIgnXpNNhRcBrvZP6Is50tJm0SSHCtfyQejFmI6HHcwGw06Mw5gJot9/fYGNY+/kaMv/M/kLv7iZQZEV1sMAX685ycnHD27NkhyIaIxNSk7sSGAx/gxEUbxPrYO2Pz4jWIcPMRMTMaiI6ODiQnJ6Ol5cK4WSaTISoqCn5+HIcREY1G/S7Sf/HFF6iursb+/fuxb98+JCcn4/Dhw/jxxx/x9NNPIyoqCosXL8aiRYswadKk4ciZiIgsRLtWjS25J/Bu9lHUq9pN2mQSKW4KisUDMQsQ4uIpUoZ0MV1rHep3/RGtx7aZxK3cfDH+jv/AIeFmcRIjomHzf//3f0hJSRE7DSIahPK2Bqz+YTOKW+qMsQQPP7y3aBU8bLlBrLk5c+YMMjMzodfrjTEnJycolUo4OvJ+EhGNVgN6ztzHxwerVq3CqlWr0NraikOHDmHfvn04evQoMjIykJmZiX/961/w8/PDokWLkJiYiISEBK5fR0REfdKo6sD7OcewOfc4WjQqkzaFVIZbJyfg/pj58Hcc/CxQGjzBoEfLof+h/pMnYehoutAgkcLlqgcx7tdPQ8oP+URj0vz58zF//nyx0yCiAUqurcC6/VvQoOowxm6ZFI+XZi3lBrFmRqfTISMjA1VVVSbxgIAAREREQCaTiZQZERH1xaAXg3VycsKNN96IG2+8ERqNBklJSdi3bx8OHjyI8vJybNy4EZs2bYKrqysWLlyIxMREzJkzB9bW1kORPxERjSFnO1rwv+wj+CD/JLp0WpM2G5kcd4ZOw71R8+Bt7yxShvRLXcUnUbv1AajLTWfSWgckdG8MG6AUKTMiIiK6nD1lmfjdj7ug1uuMsUfjr8LvYxM5wc7MNDc3Izk5GZ2dncaYXC5HXFwcvLy8RMyMiIj6akh3bFMoFJg3bx7mzZsHAMjIyMC+ffuwf/9+FBcX45NPPsGnn34KGxsbzJo1C4sWLcKiRYvg7MxiCxGRJSttrcfbmT/i46JkaAx6kzZHuTXWhM/E+og5GGfrIFKG9Ev6tnrUffQXtP74vklcauMI91uehcui+yGRcsYWERHRaCMIAv6XfQR/P/0tBAgAALlUhpdn34Jlk/nlujkRBAElJSXIzc2FIAjGuLu7O+Lj42FraytidkRE1B9DWqT/pZiYGMTExOCRRx5BeXm5cR37tLQ07N+/HwcOHEB1dTUeeOCB4UyDiIhGqdzGc3gz8xC+LE2H4aIPFgDgZm2PeyLnYHXYDDhb8wPGaCEY9Gg5/B7qP34Cho5GkzbHmXfAY8VLsHLxFik7IiIiuhydQY+nTn6FrXlJxpizwgbvJq7CLG/uKWdO1Go1UlNTUVd3YS8BiUSCkJAQBAcH82kIIiIzM6xF+ov5+/tj3bp1WLduHRobG3HgwAEcOHCA3+wSEVmg5NoKvJlxEHvP5PZo87Zzxm+i5+H2kKmwtVKIkB1diqrkNGq2PQB16U8mccWESIxf/QbsQueJlBkRERFdSYdWjfsO7cCBynxjbKKDK7ZedReCXcaLmBn1V11dHVJTU6FWq40xW1tbKJVKuLm5iZgZEREN1IgV6S/m5uaGZcuWYdmyZWK8PBERiUAQBBw7W4zXMw7i2NniHu2BTuPw2+j5WDopHgqZKH+e6BL07Q2o//ivaDn8HnDREw9SG0e4//pvcFn0W0i4uRwREdGoda6zFWt/2IysxmpjLG7cRGxavBoe3NzdbBgMBuTl5aG42HQs7eXlhbi4OMjlHI8REZkrVkGIiGhYGQQDvq/IwRsZh5BeX9mjPdzVCw/GLMSvAqIhk0pFyJAuRdDr0HLoXdR/+lTPpW1m3AaPFf+AlauPSNkRERFRX+Q2nsPqHzbhbGeLMbbELxKvz1/BpxbNSEdHB1JSUtDc3GyMSaVSREZGIiAgQLS8iIhoaLBIT0REw0Jr0OPz4jS8lXkYhS21PdoTPPzwu9hEJPqGcs3MUagz9yBqtz8MTWWmSVzhE4Hxq16HXfgCcRIjIiKiPjtcVYB7D25Hu/bCsigbIufgr1Ou4+QIM1JVVYWMjAzodDpjzNHREUqlEk5OTiJmRkREQ4VFeiIiGlJdOg0+LDiN/2YdQVVHc4/2eT7BeDB2IWZ4BrI4Pwpp60pRt/OPaE/+zCQusXGA+01PwfWq33FpGyIiIjOwo+AU/nz8c+gFAwBAKpHgmek3Ym34TJEzo77S6XTIzMxEZaXp06j+/v6IjIyETCYTKTMiIhpqLNITEdGQaFF3YUveCbyfcwwNqg6TNgkkuNY/Eg/ELEDMOF+RMqTLMag70Pj1i2j69p8QdGqTNqc5azBu2fOwcvESKTsiIiLqK0EQ8I+UvXg946AxZmslx9sLbsfiieEiZkb90dLSguTkZHR0XBhXy+VyxMbGwtvbW8TMiIhoOLBIT0REg1Lb2YZ3s49iW36SyaPUAGAlkeKWyfG4L2o+JruMFylDuhxBENB2YgfqP/ozdE1VJm02k2Zg/B3/hk3QVJGyIyIiov7QGvT449FP8HFxijE23tYRmxev4UQJMyEIAkpLS5GbmwuDwWCMu7m5QalUwtbWVsTsiIhouLBIT0REA1Le1oB3Mn/E7qJkqPU6kzZbKzluD5mGeyPnwsfBRZwE6YpUpT+hdvvDUBUdN4nLXHzgsfwFOM64HRKuV0tERGQWOrUa3HtwOw5W5RtjoS6e2HrVXZjA8ZhZUKvVSEtLQ22t6X5OISEhCAkJ4VKRRERjGIv0RETULzmNZ/FW5mF8VZphXOP0PGeFLe6KmIV14bPgZmMvUoZ0JdqmKjR8/ARaj28DBMEYl1hZw3XJI3C7/nFIbRxEzJCIiIj6o1HVgdU/bEZa/RljbIZXIN5PXA1na868Ngd1dXVITU2FWn3hyVQbGxsolUq4u7uLmBkREY2EISvSJycn48CBAzhzpntQMG7cOISHh2P+/PkYP55LHBARmTNBEHCqpgxvZh7Cgcr8Hu2eto7YEDUXd4ROh4PceuQTpD4xqNrR+M3LaPrunxA0XSZtDgk3Y9yKl6EYHyRSdkRERDQQZ9oaccfejShprTfGrvOPwmvzVsCGm72PegaDAfn5+SgqKjKJe3p6Ii4uDgqFQqTMiIhoJA26SG8wGPD444/jq6++MsYEQTA+hiWRSLBkyRI8+OCDCAwMHOzLERHRCDIIBuw/k4c3Mw/jp9ryHu3+ju64L3oebp2cAGsZH84arQSDHq1HNqH+0/+DvuWcSZtiQiTG3/Ev2EUsEik7IiIiGqicxrNYtXcjarrajLE1YTPwzPQbIeOSdaNeZ2cnUlJS0NTUZIxJpVJERESwfkJEZGEGXVF577338OWXXwIA7O3tER8fDw8PDzQ3NyMjIwMNDQ345ptvcODAATzzzDO48cYbB500ERENL61Bjy9K0vBW5mEUNNf2aI9y88FvYxbgOv8ofgAc5Tqy9qJu52PQVGaaxGVO4+G+9Gk4z10HCb9gISIiMjvHzxbj7v1b0aa9sDzKH+Ovwu9iE7l2uRmoqqpCRkYGdLoLezs5ODggISEBTk5OImZGRERiGPSn8s8++wwSiQTTp0/Hv//9b7i4uJi0nzx5Eu+++y6OHj2KP/3pT2htbcWdd9452JclIqJh0KnV4MPC0/hf1hFUdTT3aJ/lFYTfxizAPJ9gfvgb5dSVWajb9Rg6M783iUvkNt3rzl/3GKS2jiJlR0RERIPxdVkmfnd4JzQGPQBAKpHgxVm/xu0h00TOjK5Er9cjKysLFRUVJnE/Pz9ERUVBJpOJlBkREYlp0EX6yspKAMDf//73HgV6AJg+fTqmT5+OXbt24emnn8aLL76IadOmISQkZLAvTUREQ6RJ3YnNucexMec4mtSdPdqX+EXi/pj5UHr4iZAd9Yeu+RwaPvs/tPy4EfjFxr6Os+7EuFv+Drn7RJGyIyIiosHaknsCTyR9CQHdm79by6zw9oLbcbVfhMiZ0ZW0trYiOTkZ7e3txpiVlRViY2Ph4+MjYmZERCS2QRfpbW1todfr4evre9l+K1asQFlZGTZt2oRNmzbhhRdeGOxLExHRIFW3N+N/2Uewo+A0OnUakza5VIalk+Lwm6j5CHbhBuCjnUHVjqbv/4XGb1+BoGo3abMNWwCPlS/DJkApUnZEREQ0WIIg4OWUvXgt46Ax5qywxebFazDVM0C8xKhPSktLkZOTA4PhwiQKV1dXKJVK2NnZiZgZERGNBoMu0vv7+yM7OxtdXV2wtbW9bN9169Zh06ZNOHHixGBfloiIBiG/qQZvZx7G5yVp0P1itrWdlQJ3hE7DPRFz4OPgIk6C1GeCToOWQ++h4ctnoW813T9A7hUCjxUvwT7uBi5PREREZMZ0Bj0eP/4Zdhb+ZIx52zlj+zXrEOLiKWJmdCUajQZpaWmoqakxiQcHByMkJARS7u9EREQYgiJ9YmIiMjMz8emnn+KOO+64bF8PDw84ODigsbFxsC9LRET9JAgCTtWU4e2sw9h3Jq9Hu6u1HdZFzMLasJlwtbEXIUPqD8FgQNup3Wj45Elo60pM2qQO7nC/+f/gsmADJFZykTIkIiKiodCl0+C3hz7E3jO5xlioiye2Xb0OPvbOImZGV1JfX4/U1FSoVCpjzNraGkqlEuPGjRMxMyIiGm0GXaRfvXo1PvnkE/zrX/+CUqlEeHj4Jft2dHSgo6MD3t7eg31ZIiLqI4NgwA8VuXgr8zCS6yp6tPs6uGBD5FysDJ4KO7lChAypvzqyfkD9R3+GujzVJC5R2MH1mofgeu2jkNnxQzsREZG5a1F3Ye2+zThdW26MTfMMwMZFq+FizSVSRitBEJCfn4/CwkKT+Pjx4xEfHw+FgmNuIiIyNegi/f3334/IyEh8//33uPPOO/HQQw9hxYoVvf7Ree+99yAIAhITEwf7skREdAUavQ6flaThncwfUdhS26M9zNUL90fPxw2BMZBLZSJkSP2lKv0J9R/9BZ05+00bpDI4z18P95uehJULvwgnIiIaC+q72nH73veR03jWGLvGLwJvzL8NtnxSbtTq7OxESkoKmpqajDGpVIrw8HAEBQWJmBkREY1mgy7Snzx5EhKJBBKJBJ2dnXj++efxxhtvYOHChYiKioKrqysaGxvx448/4ujRowgJCcHvf//7ocidiIh60a5VY3v+SbybfRTnOlt7tM/wCsT90QuwcEII1yk3E5qaIjR88iTaTu3u0eYwdRnG3fIsFF4hImRGREREw6G6vRkrv38PJa31xtjtIdPw/MybYMXJFaPW2bNnkZ6eDq1Wa4zZ29sjISEBzs58ypGIiC5t0EX6Bx98ELm5ucjJyUF1dTUAoKWlBZ9//jm++OILk75OTk649dZbUVpairCwMD7iRUQ0hGo727Ax9xi25SWhRaMyaZNAgmv8InB/zHwoPfxEypD6S9tYicavnkfLj+8Dep1Jm234Qnjc+gJsgqaKlB0R0eAkJSVh06ZNSE9PR2dnJ3x8fLBkyRJs2LABdnaDX8Zj+/bteOaZZwAA06ZNw7Zt2wZ9TaKRUNJSj9u+fw9VHc3G2H1R8/GXKUs4wWKU0uv1yM7ORnl5uUl84sSJiIqKgpXVoEsvREQ0xg36L8Vvf/tb47+3tLQgJycH2dnZyM3NNf6REgTB2P78888DAGQyGQIDAxEWFoaIiAiEhYVh5syZg02HiMjiFLfU4b9ZR/BxUTI0Br1Jm0Iqw9JJ8bgvej4mOXuIlCH1l66lBo17XkLLgXcg6NQmbdZ+cRh36/Owi7qaH9SJyGxt27YNzz33HARBgJeXF7y9vVFUVIS3334be/fuxY4dO+Di4jLg69fU1ODVV18duoSJRkhO41ncsfd91HW1G2N/Ul6DB2IW8O/+KNXa2ork5GS0t1+4Z1ZWVoiJicGECRNEzIyIiMzJkH6d6+zsjJkzZ5oU2zs7O40z7c//U1xcDJ1Oh8LCQhQWFuLrr7+GRCJBTk7OUKZDRDSmJddW4O3Mw/i+IgcCBJM2B7k17gydjvWRc+Bl5yRShtRf+vZGNH77Cpp/eB2CptOkTe4RCPelz8Bx+kpIpFKRMiQiGrysrCzjxJ1nnnkGy5cvh0QiQU1NDe677z5kZ2fjySefxOuvvz7g1/jb3/6Grq4uLFy4EAcPHhyq1ImGVXJtBVb/sNHkichnp9+IuyJmiZgVXU5ZWRmys7NhMBiMMRcXFyiVStjb24uYGRERmZthf+bKzs4OCQkJSEhIMMY0Gg3y8/ONs+2zs7N77HpOREQ9GQQDDlTm4+3MwzhZU9aj3dPWEXdHzsGdodPhpLAZ+QRpQPRdrWje+x80ffcqDF2m+wjIXHzgfuNf4TxvHSRWXCaOiMzfW2+9BYPBgJtvvhkrVqwwxj09PfHqq6/i2muvxd69e5GXl4ewsLB+X/+bb77BgQMHsHr1ajg5ObFIT2bhaHUR1u3fik6dBgAglUjw6pxlWDY54Qpnkhi0Wi3S0tJw7tw5k/ikSZMQFhYGKSdUEBFRP4myMJpCoUB0dDSio6ONMb1ef5kziIgsm0avw+claXgn60cUNNf2aJ/s7IHfRM3DryfFw1rGNS/NhUHdgeb9b6Fxzz9g6Gg0aZM5esDt+sfhvPBeSBW2ImVIRDS0Ojo6cOTIEQDA8uXLe7QHBARgxowZOH78OL777rt+F+lbWlrw3HPPwcvLCw899BA2btw4JHkTDafvy7Nx36EdxmULFVIZ3lxwG671jxI5M+pNQ0MDUlJSoFJdeOLB2toa8fHx8PDg8pJERDQwo6aSI5Nxh3oiol9q06iwPf8U3ss5inOdrT3ap3kG4L6oeVg0MQxSCWfsmAuDRoWWw++i8asXoG+tMWmT2rnA7bo/wmXxA5DaOIiUIRHR8MjNzYVGo4FCoUBMTEyvfRISEnD8+HGkp6f3+/ovvvgi6uvr8eabb3KpCTILnxan4uEjH0EvdC+XYmslx/uJqzFvQrDImdEvCYKAgoICFBYWGvfdAwAPDw/Ex8fD2tpaxOyIiMjcjZoiPRERXXCusxXvZx/D9oKTaL1oXdLzrvGLwH1R8zHF01+E7GigDBoVWo9sQuOeF6FrrDRpk9g4wPXqh+B6zcOQ2buIkyAR0TArLS0FAPj4+EAul/fax8/Pz6RvX504cQKffvopEhMTsXjx4gHlt3PnTuzevbtPfYuLiwf0GkTnbc1Lwl9PfGHcW8hJYYOti+/i+G4U6urqQkpKChobLzz5KJFIEB4ejqCgIG7qS0REg8YiPRHRKJLfVIP/Zv2Iz0rSoDWYLgOmkMpwy2Ql7o2ci8ku40XKkAaie+b8e2j65h/QNVWZtEkUtnBZ9Fu4XfdHyBzHiZQhEdHIaGlpAQA4Oztfss/5tvN9+0KlUuGpp56CnZ0dnnrqqQHnV1dXh+zs7AGfT9RXb2QcwovJ3xmP3W3ssePquxHp7iNiVtSbc+fOIS0tDVqt1hizt7eHUqmEi4uLeIkREdGYwiI9EZHIBEHA8XMleCfzRxysyu/R7qSwwarQGVgXMQuedk4iZEgDZdB0oeXQu2j85h/QN581bZTJ4bJgA9xu+DOsXLzFSZCIaISp1WoAuOQseqB7/6qL+/bFa6+9hoqKCvz5z3+Gt/fAf6d6eHggMjKyT32Li4tN1qQm6gtBEPBi8vd4M/OQMeZt54ydS9ZjkjPXMx9N9Ho9cnJyUFZWZhKfMGECYmJiYGXFcgoREQ0d/lUhIhKJzqDHt+XZeCfrR6TXV/Zo97F3xvqIObg9dBoc5Fzj0pwY1J1oOfQ/NH7zMvQt50zaJFYKOM27G26/+hPk7hNFypCISBzn12y+eEbqL2k0GpO+V5KTk4MtW7YgIiICq1atGlR+K1euxMqVK/vUd+nSpZx1T/1iEAx4MukrbMk7YYwFOLpj55L18HVwFTEz+qW2tjYkJyejra3NGJPJZIiJiYGvr6+ImRER0VjFIj0R0Qjr1Gqws/A03ss+hor2xh7t4a5e+E30fNwYGAO5lJtqmxODuhMtB//bXZz/xYawEitrOM9fD9dfPQa5Gz/cEZFl6stSNn1ZEudif/3rX2EwGPDMM89AJuPfTRqdDIIBfzr+GT4sOG2Mhbl6YcfVd2O8naOImdEvlZeXIzs7G3r9haUnnZ2dkZCQwA2piYho2LBIT0Q0Quq72rEp9zi25CWhWd3Zo32uz2T8Jmoe5vkEc/MpM2NQd6D5wDto+vYV6FtrTdokchs4L7gHbtc9BitXrjNLRJYtICAAAFBdXQ2tVtvrsjcVFRUmfa8kJycHMpkMv/nNb3q0dXZ2/71NTU3F7NmzAQAff/zxoJbEIeovvcGAPx77BLuLko2xeI+J2HrVXXC1thMxM7qYVqtFeno6zp41XaIwKCgI4eHhkEqlImVGRESWgEV6IqJhVtxSh/9lHcHHxSlQ63UmbTKJFDcExuA3UXMR5T5BpAxpoPQdzWje/xaaf/gP9G31Jm0ShS2cF94Lt2sf5ZrzREQ/Cw8Ph1wuh0ajQUZGBhISEnr0SU7uLmTGxcX1+bp6vR719fWXbNdqtcb2i2fHEg03vcGAR45+hE+KU42xqeP9sfWqu+CosBExM7pYY2MjUlJS0NXVZYwpFArEx8dj/PjxImZGRESWgkV6IqJhIAgCTtWU4b9ZP2Lvmdwe7XZWCtweMhXrI+dwDVIzpGupQdPe/6Bl/1swqNpM2iQKW7gk3gfXJX+AlYuXSBkSEY1ODg4OmDNnDg4ePIjdu3f3KNKXlZUhKSkJALBkyZI+XTM/v+em6+e9/vrreOONNzBt2jRs27Zt4IkTDYDOoMfvj+zGFyXpxth0z0BsvWot7Lnf0KggCAKKioqQn58PQRCM8XHjxiE+Ph42NvwihYiIRgaL9EREQ0hn0OO78my8k3UEafVnerSPt3XEuohZuDN0Olz4eLPZ0daXo/HbV9D640YIWpVJm0RhB5dF98P12j/AyokzroiILuX+++/HoUOH8MUXX0CpVGL58uWQSCSora3FI488AoPBgMWLFyMsLMzkvMTERADAY4891ucCPpFYtAY9Hjy8E1+XZRpjs70nYdOiNbCTK0TMjM5TqVRISUlBQ0ODMSaRSBAWFoZJkyZx+UkiIhpRLNITEQ2BDq0auwp/uuRmsCEu43Fv1DzcHBQHaxl/9ZobdXUumvb8A61JO4BfLFkktXOBy1UPwnXxA5A5jhMpQyIi8xETE4PHH38cL774Ip566im8/fbbcHV1RVFRETQaDQIDA/Hss8/2OK+qqgrAhXXmiUYrjV6H+w99iO8qso2xeT7BeH/RKthasUA/Gpw7dw5paWnQarXGmJ2dHZRKJVxd+ZQrERGNPFaKiIgGoaazFZtzT2BrXhJaNF092md7T8K9UfOwcEIIZ+OYIVVZMhq/fhHtyZ8BFz0CDQAyJ0+4LnkELgvvhdTWUaQMiYjM09q1axEaGoqNGzciIyMDDQ0N8PHxwZIlS7BhwwbY29uLnSLRgKj1Otx3cLvJcocLJoTgvcRVsLHquVEyjSyDwYCcnByUlpaaxH18fBATE9PrZtZEREQjgUV6IqIBKGiuwX+zjuCz4lRoDKYb0MkkUlwfGI17I+ciZpyvSBnSQAmCgK78H9H49QvozPqhR7vVuAC4XfconObcBSk3fCMiGrCZM2di5syZfe5/ubXnL+XBBx/Egw8+2O/ziAZCpdNiw8EPcKDywv9XF/mG4X+Jd/JJylGgvb0dycnJaG1tNcZkMhmioqLg5+cnYmZEREQs0hMR9ZkgCDh2thj/zTqCg1U9CwX2VgrcHjoNd0fM5mawZkgw6NGe/Bmavv0nVCWnerQrfCLgdv2f4DhtBSScCUdEREQX6dJpsf7ANhyuKjDGrvGLwNsLboeCBXrRVVRUICsrC3r9hck1Tk5OSEhIgIODg4iZERERdeNogYjoCrQGPb4szcD/sn5EduPZHu2edk64O2I27giZBmdrWxEypMEwqDvRenQLmr7/F7S1xT3arQOnwP36P8M+/kZIpFIRMiQiIqLRrEunwbr9W3GkusgYu84/Cm8uuA1yqUzEzEir1SIjIwPV1dUm8cDAQEREREDKsR0REY0SLNITEV1Ci7oL2wtOYWPOMZzrbO3RHuriid9EzcNNQbGcIWWGdK11aN7/FloOvAV9W32PdtvwhXC7/nHYRSzifgJERETUq06tBmv3bcbxcyXG2A0BMXht/goW6EXW1NSElJQUk82mFQoF4uLi4OnpKWJmREREPbGqRET0C2faGvFezjHsLDiNDp2mR/t8n2BsiJqLeT7BLN6aIU1NEZq+/xdaj2yGoFWZNkqkcJy6DK7X/gE2gVPESZCIiIjMQrtWjTU/bMLJmjJj7OagOPx77q2wYoFeNIIgoLi4GHl5eRAEwRh3d3eHUqmEjQ33FCIiotGHRXoiop+l1p3B/7KOYE95JgwXDegBQC6V4ddBcbgnci7C3bxEypAGo6soCU3f/RPtyZ8Bv7i/EoUdnOffDderfw+5R6BIGRIREZG56NCqsfqHTTh1UYF+2SQl/jlnGWRcQkU0KpUKqampqK+/8JSkRCJBaGgoJk+ezAk2REQ0arFIT0QWTW8wYN+ZXPw3+4jJh6zznBW2WBU2HWvDZ8HLzmnkE6RBEQwGdKR/jaZv/4mugqM92mVO4+Gy+EG4JP4GMgc3ETIkIiIic9Ol02Dtvi0mY8cVwVPwj1lLWaAXUW1tLVJTU6HRXHgS1tbWFkqlEm5uHOcREdHoxiI9EVmkTq0Gu4uS8V72UZS1NfRo93d0w/qIOVgRPAV2coUIGdJgGLra0HJ0C5p/eK3XzWDlXqFwXfIwnGatglTBR56JiIiob1Q6Le7evw0nLlqD/raQqXhp1q8hlbBALwaDwYDc3FyUlJSYxL29vREbGwu5XC5SZkRERH3HIj0RWZRzna3YnHsc2/JOokXT1aN9ynh/bIici2v8IjgTygxp60rRvO9NtPz4PgxdPTf7tQ2ZA9dr/wD72Osh4f0lIiKiflDrddhw8AP8WF1ojN06WckCvYg6OjqQnJyMlpYWY0wqlSIqKgr+/v4iZkZERNQ/LNITkUXIbqjG/7KP4MvSDGgNepM2qUSCa/2jsCFyLhLG+4mUIQ2UIAjoKjiC5r3/QXvKl4BgMO0gkcIh4Wa4LvkDbCfPECdJIiIiMmtagx73H9qBA5X5xthNQbF4ZfYyFuhFcubMGWRmZkKvvzC2d3R0REJCAhwdHUXMjIiIqP9YpCeiMcsgGHCwsgD/yz6CY2d7Lnlib6XAbSFTsS5iNvwcuU6luTFo1Wg/tQtNe1+Dujy1R7vU1hnO89fDZfFvIR/HmVREREQ0MDqDHg8e3onvK3KMsev8o/Cfucv55KUIdDodMjIyUFVVZRIPCAhAREQEZDKZSJkRERENHIv0RDTmdOk0+KQoFe/mHEVxS12Pdh97Z6wLn43bQ6fBieuRmx1day1aDv4Xzfvfhr61pke73DMYrlf9Dk5zVkNq4yBChkRERDRW6A0GPHzkI3xdlmmMXT0xHG8uuA1WUhaDR1pzczNSUlLQ0dFhjMnlcsTFxcHLy0vEzIiIiAaHRXoiGjNqO9uwJe8EtuYloUnd2aM9dpwvNkTOxXUBUZDzQ5XZUZWloHn/m2g7sQOCTtOj3S5yEVyu+j3sY67levNEREQ0aAbBgMeOf4LPStKMsYUTQvH2wjs4lhxhgiCgpKQEubm5EATBGHdzc4NSqYStra2I2REREQ0ei/REZPZyGqvxbvZRfFGSDs0v1puXQIKr/cKxIXIupnkGQCKRiJQlDYRBq0b76Y/QvP9tqIqTerRLrKzhOOtOuF71IKwnRouQIREREY1FgiDgLye+wK7CZGNsrs9k/C/xTljL+DF6JKnVaqSmpqKu7sITshKJBMHBwQgJCeH4noiIxgSzHV3U1dXh2LFjyMrKQmZmJnJzc6FWqzFt2jRs27btkuclJib2WLvulzIyMmBtbT3UKRPRELrSevO2VnKsCJ6CuyNmI9BpnAgZ0mBoGyrQcvC/aDn8PvRtPZcskrl4wyXxfjgvuAdWTh4iZEhERERjlSAIeOrkV/gg/6QxNt0zEBsXrYatlVzEzCxPXV0dUlNToVarjTEbGxsolUq4u7uLmBkREdHQMtsi/Z49e/DCCy8M+PyQkBA4OPS+VjG/iScavbp0GnxclIL3co71ut68p50T7gqfhTtCp8HV2k6EDGmgBEFAZ85+NO9/Cx2pXwGCoUcfm0kz4LLofjhOuxUSK4UIWRIREdFYJggC/v7Tt9iUe9wYS/Dww5ar1sKWY48RYzAYkJeXh+Ji08k4Xl5eiI2NhULBe0FERGOL2RbpHRwcMGvWLERHRyM6Oho5OTl46623+nz+E088genTpw9jhkQ0lM51tmJz7nF8kH8Kzb2sNx/jPgHrI+fg+oBoKPgIslnRd7ag9dhWNO9/G9pz+T3aJXIbOM64DS6L7odNgFKEDImIiMhSvJyyF//N+tF4HDvOF9uuXgcHOZ+0HikdHR1ISUlBc3OzMSaVShEZGYmAgADR8iIiIhpOZlvJWrZsGZYtW2Y8rqmpETEbIhoumfVVeDfnKL4qzYCW682PKeozmWje/xZaT2yHoO7o0S73CIJz4n1wnrsWMgc3ETIkIiIiS/LvtP14LeOg8TjSzRvbr14HJ4WNiFlZlqqqKmRkZECn0xljDg4OSEhIgJOTk4iZERERDS+zLdIT0dilNxjww5lcvJdzFEnnSnu021kpjOvNBzhxLUpzYtCo0P7TJ2g59C66Co707CCRwD7mWrgsuh92UddAIpWOfJJERERkcd7OPIxXUn8wHoe5euHDa9bDhcsnjgidTofMzExUVlaaxP38/BAVFQWZTCZSZkRERCPDYov0O3fuxMaNG6FSqTBu3DhMmTIFN9xwwyXXqSei4deuVWNnwWlsyj2O8rbGHu0+9s5YFz4bt4VMhbO1rQgZ0kCpq3PRcvg9tB7dCkNHz3srtXeD89y74Jz4GyjGB4mQIREREVmqD/JP4rmfvjUeT3b2wM5r1sPNxl7ErCxHS0sLkpOT0dFx4clKuVyOmJgY+Pj4iJgZERHRyLHYIv0333xjcvz111/jP//5D/75z39i9uzZVzx/586d2L17d59e65eb3RCRqTNtjdiYexw7C06jTavu0R43biI2RM7BtQFRkEs5i8ZcGGfNH34PXfk/9trHOiABLovug+P0lZAq+MULERERjawvS9Lx5+OfG48DHN2xc8k9GGfLyVsjoaSkBLm5uTAYDMaYm5sblEolbG05NiQiIsthcUX6adOmYcaMGYiOjoaPjw+0Wi2Sk5Px2muvIScnB/fddx8+/PBDREZGXvY6dXV1yM7OHqGsicYeQRBwurYc72UfxXcV2TAIgkm7VCLBdf5RWB8xBwnj/bjevBnRVOeh+fC7l5w1L1HYwXHGSjgvuAc2gVN5b4mIiEgUByvz8fsjuyGgexzqZeeEnUvWw8uOa58PN7VajbS0NNTW1prEg4ODERoayvEhERFZHIsr0r/44osmx7a2tli4cCFmzpyJ22+/HdnZ2Xj55ZexefPmy17Hw8PjioX884qLi6FSqQaaMtGYotHr8HVZJt7POYb0+soe7U4KG9wWMg13hc+Er4OrCBnSQPRp1vzEWDgvvAeOM26HzM55hDMkIiIiuuCnmnLcc+ADaA16AICrtR0+vGY9x58joL6+HikpKVCrLzxBa2Njg/j4eIwbN07EzIiIiMRjcUX6S7GxscFDDz2Ee+65BydPnkRLSwucnS9dRFq5ciVWrlzZp2svXbqUs+7J4jWpOvBB/ilszjuBms7WHu0Bju64O2I2bg1OgIPcWoQMaSD6NGt++go4L9zAWfNEREQ0KuQ0nsWafZug0msBAPZWCmy76i4Eu4wXObOxzWAwID8/H0VFRSZxT09PxMXFQaFQiJQZERGR+Fikv4hSqQTQPXg4c+bMZYv0RNQ3+U012JhzDB8Xp0Ct1/Von+UVhPWRc7DINwwyqVSEDKm/DF1taDu1Gy1HNkNVdLzXPoqJMXBZsAGOMzlrnoiIiEaP0tZ63LH3fbRoup90tpZZYePiNYjzmChyZmNbZ2cnUlJS0NTUZIxJpVJEREQgMDBQxMyIiIhGBxbpLyKXy43/rtfrRcyEyLwZBAMOVhbg/Zxj+LG6sEe7QirDzUFxuDtiNiLdfUTIkPpLEAR0FRxB64+b0Hb6Ywiazh59jLPmF9wDm6BpnDVPREREo8rZjhbc/v37qOtqBwDIJFK8Nf82zPaeJHJmY1t1dTXS09Oh012YsOPg4ICEhAQ4OXH9fyIiIoBFehMFBQXGf/fy8hIxEyLz1KFV46OiFGzMOYaS1voe7e429lgdNgOrQmdgvJ2jCBlSf2mbqtB6dCtaj26Gtqao1z7WE2PhvOAezponIiKiUatJ1YE79r6PM+0XZnK/MvsWXOPft33GqP/0ej2ysrJQUVFhEvfz80NUVBRkMplImREREY0+LNJf5N133wUATJ48GZ6eniJnQ2Q+KtubsCn3BD4sOIVWTc9NkiPcvLE+YjZuDIyFjZW8lyvQaGLQqtGR9hVajmxCZ+ZeQDD06CO1d4XTjNvhNHctrP3jOWueiIiIRq0OrRqrftiMguZaY+xv067HrcEJImY1trW2tiI5ORnt7e3GmJWVFWJiYjBhwgQRMyMiIhqdLKpI//7770OhUOD666+Hq6urMd7U1IR//etf+P777wEAv/vd78RKkchsCIKA07XleC/7KL6ryIZBEEzaJZDgar9wrI+cgxmegSzimgF1RTpajmxC64kdMLQ39OwgkcAucjGc594F+/ibIFXYjHySRERERP2g1utw9/5tSKs/Y4w9FLcI6yPniJjV2FZaWoqcnBwYDBcmeri6ukKpVMLOzk7EzIiIiEYvsy3Snz17FjfffLPxWKPRAABSUlIwffp0Y3z9+vW45557AADnzp3D1q1b8dxzz2HChAlwc3ODSqVCSUkJdDodpFIpHnnkEVxzzTUj+l6IzIlar8NXpRnYmHMMGQ1VPdod5NZYGTwFd0XMgr+juwgZUn/o2+rRenIXWo9shro8pdc+co9AOM1ZC6c5qyF39xvhDImIiIgGRmfQ44HDH+Lo2QtL9q0Nn4k/xC0WMauxS6PRIC0tDTU1NSbxyZMnIzQ0FFKpVKTMiIiIRj+zLdLr9Xo0Nzf3iOt0OpO4SnVh6Y1f/epXAICMjAxUV1cjLy8PMpkMvr6+mDZtGm6//XaEh4cPd+pEZqmuqw3b8k5iW36ScbOti/k7umNdxCwsn5wAR86wHtUMGhU60r9G6/EP0JHxLaDX9egjUdjCYcotcJ67Frah8yHhhyoiIiIyI4Ig4E/HP8O35dnG2M1BcXhm+g18wnMY1NfXIzU11eTzt7W1NeLj4+Hh4SFiZkRERObBbIv0vr6+yM/P79c5cXFxiIuLG56EiMaozPoqvJ9zDF+WpkNj0Pdon+09CesjZiPRNwwyFnJHLUEQoCo8htbjH6Dt1EcwdDb32s8maDqc5q2F47QV3ASWiIiIzJIgCPj76W+wq/AnY2yRbxj+NfdWSCUcrw4lQRCQn5+PwsJCk/j48eMRFxcHa2trkTIjIiIyL2ZbpCei4aMz6PFdRQ425hzDqZqyHu3WMivcHBSHuyNmI8LNe+QTpD7T1BR1F+aPfwBtXWmvfWQu3j9vArsG1hMiRzhDIiIioqH1ZuZh/Df7iPF4umcg3ll4B+RSmYhZjT1dXV1ISUlBY2OjMSaVShEeHo7AQO5JRURE1B8s0hORUZO6Ex8WnMaW3BOo6mju0e5p54S1YTNxR+g0uNnYj3yC1Cf69ka0ndqF1uPboSo60WsficIODlOWwmnWnbCLSISEH1qJiIhoDPioMBkvJn9nPI5y88GmxWtgayUXMaux5+zZs0hPT4dWqzXG7O3tkZCQAGdnPo1JRETUXyzSExHym2qwMecYPilOhUqv7dGu9PDD3RGzcV1AFGcgjVIGrRodGd+g7fgHaE/bA/RyHyGRwC4iEU6zVsEh4deQ2jiMfKJEREREw+RwVQH+eOwT43Gg0zh8cPU6OHG/pCGj1+uRnZ2N8vJyk7ivry+io6NhZcUSAxER0UDwLyiRhTIIBhyozMf7OcdwpLqoR7uVRIrrA2Nwd8RsxHtMFCFDuhLBoEdX/o9oS9qJtp8+gaGjqdd+Ct8oOM26E44zboPczXeEsyQiIiIaflkNVdhw4APoBAMAYJyNAz64+i6Ms+WkhKHS2tqKlJQUtLW1GWNWVlaIjo6Gry/HmERERIPBIj2RhWnVqLC78Cdsyj2B8raGHu1u1vZYFTYdq8JmwMvOSYQM6XIEQYCq9HR3Yf7Ubuibz/baT+bkCceZt8Fp1ipY+8VyTVAiIiIas860NWL1D5vRodMAAOysFNh61Vr4O7qLnNnYUVZWhuzsbBgMBmPMxcUFSqUS9vZcBpOIiGiwWKQnshDFLXXYmHMcHxUlo/PnDzAXi3Dzxt0Rs3FTYCxsuGbnqKOuyu4uzJ/cBW1tca99JApbOChvgtOsVbCLXAyJjL/iiYiIaGxrUndi1Q+bUNvVPbtbJpHinYV3IGYcZ3YPBa1Wi7S0NJw7d84kPmnSJISFhUEqlYqUGRER0djCCg7RGGYQDDhUVYiNOcdwqKqgR7tUIsE1fhG4O2I2pnsGcrb1KKOtK0XbyV1oTdoJTWVm752kMthFXQWn6Sthr7wJMls+/UBERESWoUunxbp9W1DUUmeMvTTr10j0DRUxq7GjsbERycnJUKlUxpi1tTXi4uIwfvx4ETMjIiIae1ikJxqD2jQqfFSUjE25J1DaWt+j3Vlhi9tDpmFN+Az4OriKkCFdiq75HNpOf4S2pJ1QFSf13kkigW3IXDhOXwHHqcsgcxw3skkSERERiUxvMOB3P+7E6doLG5g+ErcYK0OmipjV2CAIAgoLC1FQUABBEIxxDw8PxMfHw9raWsTsiIiIxiYW6YnGkNLWemzKOY7dRclo16p7tIe6eGJdxGwsnRQHWyuFCBlSb/TtjWhP/gxtJ3ehM/cgIBh67WcdkADH6SvhOH05N4AlIiIiiyUIAv526mt8W55tjN0WMhUPxy0SMauxoaurCykpKWhsbDTGJBIJwsPDERQUxCdviYiIhgmL9ERmziAYcLiqEBtzjuNgVX6PdgkkuNovHOsiZmOWFwfWo4W+rR7tKZ+j7fQn6Mw9AOh1vfZTeIfBccZKOE5fAYVXyAhnSURERDT6vJP1IzblHjceJ/qG4oWZN3OcO0jnzp1DWloatFqtMWZnZ4eEhAS4uLiIlxgREZEFYJGeyExdaUkbJ4UNbgueijXhM+Hn6CZChvRLutY6tKd8hvbTn3TPmDfoe+1n5e7XvZTN9JWw9ovlB04iIiKin31ekobnfvrWeBw7zhdvL7gdVlKZiFmZN71ej5ycHJSVlZnEJ0yYgJiYGFhZsWxAREQ03PjXlsjMFLfUYVPucXxUmIwOnaZHe7DzeNwVMQu3TIqHvZzrRYpN11qL9uSfC/N5hy5ZmJc5e8FhylI4zVgJm0kzIZFKRzZRIiIiolHu2NliPHzkI+Oxv6MbNi9ewzHvILS1tSElJQWtra3GmEwmQ3R0NCZOnChiZkRERJaFRXoiM2AQDDhYWYCNucdxuKqgR7sEElw1MQzrImZjtvckzrwWma6lBu3Jn6Lt9Cfoyjt8yTXmZS7ecJxyCxym3gLb4NmQcAYYERERUa9yG89h/f6t0P484cHV2g7brroLHraOImdmvsrLy5GdnQ29/sIkEmdnZyiVSjg4OIiYGRERkeVhkZ5oFGvVqLC78Cdszj2BsraGHu3OChusDJ6K1eEz4O/oLkKGdJ6u+Rzaf/oUbac/RlfBj4Ag9NrPynUCHKYshePUZbCZPIsz5omIiIiuoLq9Gat+2Ig2rRoAYCOTY/PitQhy9hA5M/Ok1WqRnp6Os2fPmsSDgoIQHh4OKcenREREI45FeqJRqKC5BltyT+CjohR09rKkTaiLJ+4Kn4Wlk+JhJ1eIkCEBgLauFO0pX6A9+XN0FR69dGHezRcOU27pLsxPmsHCPBEREVEftai7sOqHTTjX2b0ci1QiwVsLbkPCeD+RMzNPjY2NSElJQVdXlzGmUCgQFxcHT09PETMjIiKybCzSE40SeoMB+87kYlPuCRw9W9SjXSqR4KqJ4VgXPguzuKSNKARBgOZMBtqSP0dHyhdQn0m/ZF8rdz/jUjY2QdNZmCciIiLqJ61Bjw0HP0B+c40x9tyMm3C1X4SIWZknQRBQVFSE/Px8CBdNLBk3bhzi4+NhY2MjYnZERETEIj2RyJpUHfiw8CdszTuByvbmHu3OClvcFjIVa8JmYKKj28gnaOEEgx5dhcfRntJdmNfWlV6yr5W7PxynLvu5MD+NX6QQERERDZAgCPjLic9x7GyxMfZAzAKsCpshYlbmSaVSISUlBQ0NF5bPlEgkCA0NxeTJkzlmJSIiGgVYpCcSSXZDNTbmHsfnJWlQ63U92sNcvX5e0iYOtlZc0mYkGTQqdObsQ3vKF+hI/Qr6trpL9lX4hMNBeTMclDfBOnAKP+QQERERDYF3s4/iw4LTxuObg+LwJ+U1ImZknmpqapCWlgaN5sISmra2tkhISICrq6uImREREdHFWKQnGkFagx7flmVhc94JnKop69Euk0ixxD8Sd4XPxHTPQBZ8R5C+oxkdGd+gPflzdGR+B0Hdccm+NpNmwEF5ExyUN0HhHTqCWRIRERGNfT9U5ODZ098YjxM8/PDK7Fs4Nu4Hg8GAnJwclJaaPgXq4+ODmJgYyOVykTIjIiKi3rBITzQC6rrasD3/FLbln0TNz5teXczdxh53hEzDnWEz4GPvLEKGlknbWImOtK/RnvI5OnMPAXpt7x1lVrALX9g9Yz7+Rli5+oxonkRERESWIqexGr89vBMCutdNn+jgivcXrYaNFYvKfdXe3o7k5GS0tl743CGTyRAVFQU/P264S0RENBqxSE80TARBQEpdBTblnsCeskxoDfoefWLH+eKu8Fm4ITAG1jL+OA43wWCAuiwZ7WlfoyN9D9TlqZfsK7G2h330Ejgk3Az7mOsgs3cZuUSJiIiILFBNZyvW7tuCTl330iwOcmtsWrwG42wdRM7MfFRUVCArKwt6/YXPHk5OTkhISICDA/87EhERjVasChINsS6dFl+WpmNz7glkNlT1aJdLZbghMAZ3hc9CvMdEETK0LAZ1BzqzfkB7+h50pH8Dfcu5S/aVOXrAPv4GOChvgl3EYkgVNiOYKREREZHl6tJpsW7/VlR3tAAApBIJ3lpwO8JcvUTOzDxotVpkZGSgurraJB4YGIiIiAhIpVKRMiMiIqK+YJGeaIicaWvE1ryT+LDwNJrVnT3aveyccGfodNwROg0eto4iZGg5tA0V6Ejbg/a0r9CVewiCTn3JvnKPoO7CfMLNsA2eDYlUNoKZEhEREZFBMODhI7uRXl9pjP1t2vVI9OXeP33R1NSElJQUdHZe+Awil8sRFxcHLy9+yUFERGQOWKQnGgRBEHCkugibc4/jhzN5xrUzLzbDKxBrw2fhGr8IyFkAHhaCQQ9Vyenu9eXT90BzJuPSnSVS2AbPhn3c9bCP+xUU3mHchIyIiIhIRP9M3YevyzKNx2vCZuCu8FkiZmQeBEFAcXEx8vLyIAgXPoe4u7tDqVTCxoZPhRIREZkLFumJBqBNo8JHRcnYkpeE4pa6Hu22VnLcMkmJNWEzEe7G2SvDwdDVho7sH9CR9nX3MjZtPe/DeVJbZ9hHX9NdmI+5FjIHtxHMlIiIiIgu5dPiVPwn/YDxeL5PMJ6efgMnUVyBSqVCamoq6uvrjTGJRIKQkBAEBwfzvx8REZGZYZGeqB/ym2qwNe8EPi5KQcfPG1pdLMDRHWvDZ+LWyQlwtrYVIcOxSxAEaKpz0ZHxHTqzvkNX/hEIvdyD8+SewXD4eba8bfAcSKzkI5gtEREREV3J6ZoyPHr0Y+NxsPN4vLXgdljx6dPLqq2tRWpqKjSaC2NhW1tbKJVKuLlxMgoREZE5YpGe6Aq0Bj2+r8jBltwTOHGupEe7BBIsmhiKNWEzMX9CMKQSbso0VPRdrejKOYCOzO/Qkfk9dA0Vl+4slcE2ZC7sY38Fh/jrofAKGblEiYiIiKhfKtoasf7ANmgMegCAq7UdNl+1hhNdLsNgMCA3NxclJaafSby9vREbGwu5nJNSiIiIzBWL9ESXUNvZhu0FJ/FB/inUdLb2aHdW2GJlyFSsDpsOf0d3ETIcewRBgKYyEx2Z36Mj4zt0FR4F9LpL9pfau8I+egns434F++glkNm7jmC2RERERDQQbRoV7tq3BQ2qDgCAQirD+4tWc0x9GR0dHUhOTkZLS4sxJpVKERUVBX9/fxEzIyIioqHAIj3RRQRBwOnacmzJPYFvyrOg/Xlmz8Wi3HywJnwmbg6Kha2VQoQsxxZ9RzM6c/ahI/N7dGZ+D11T1WX7W/vHwz76GthFL4Ht5JmQyPhrjIiIiMhc6Ax63H/oQ+Q31xhj/5i9FNM8A8RLapSrrKxERkYG9PoLn00cHR2RkJAAR0dHETMjIiKiocLqFhGATq0Gn5WkYXPuceQ2nevRLpfK8KuAaNwVPhNKDz9uxDQIgiBAXZHWvbZ85vfoKjoO9PJlyHlSe1fYR14Fu5glsI+6GlYu3iOYLRERERENpWdO7cHBqnzj8YMxC7FscoKIGY1eOp0OmZmZqKysNIkHBAQgIiICMhnX7iciIhorWKQni1bSUoeteUnYXZSMVo2qR7u3nTNWhU3HbSFT4WHLWSoDpWutQ2fOPnRm7kVH1l7oW3p+EWIkkcA6YArso6+BfcwS2ARNg4SbhxERERGZva15SdiYe9x4fJ1/FP6ovErEjEav5uZmpKSkoKOjwxiTy+WIjY2FtzcnrRAREY01LNKTxdEbDNh3Jhdb8pLwY3Vhr31me0/CmrCZuNovHFYsEPebQdOFroIj6Mzej87sfVBXpF22v8xxHOyiru5exibqGlg5eYxMokREREQ0IpLOleCppC+NxzHuE/CfecshlUhFzGr0EQQBJSUlyM3NhSAIxribmxuUSiVsbbmxLhER0VjEIj1ZjLquNuws+Akf5J9EVUdzj3YHuTWWTVZiTdhMBLuMH/kEzZhgMEBdkWosyncVHIWgU1/6BIkUNkHTYB+zBPbRS2AdoORseSIiIqIxqrq9Gb85uAM6wQAA8LRzwsbFa7i/0y+o1Wqkpqairq7OGJNIJAgODkZISAiX3CQiIhrDWKSnMU0QBPxUW44teUnYU5bZ60awoS6eWB02A7dMVsJBbi1CluZJW1fWveFr9j505hyAob3hsv1lLj6wj1wMu+hrYB91FWQO7iOUKRERERGJRaXT4p6DH6Be1Q4AUEhleDfxTnjZOYmc2ehSV1eH1NRUqNUXJrrY2NhAqVTC3Z3jZiIiorGORXoakzq1GnxakoqteUnIaTzbo91KIsW1/lFYHT4DMzwDOSulD/QdzejMO4jO7H3ozN4HbU3RZftLbBxgFzofdlFXwS5yMRTeYfzvTERERGRBBEHAn098hvT6CxufPj/zZig9/ETManQxGAzIy8tDcXGxSdzLywuxsbFQKPi0ARERkSVgkZ7GlOKfN4L96BIbwXraOeHO0Gm4LWQaZ+9cgaDToKs4CZ1Z3UV5Velp4OdHlHsllcEmaBrsIhfDPnIxbIKmQ2IlH7mEiYiIiGhU2ZR7HB8VpRiP14TNwMqQqSJmNLp0dHQgJSUFzc3NxphUKkVkZCQCAgJEy4uIiIhGHov0ZPZ0Bj32VuRiW34SjlT3Prt7llcQ1oTPxNV+EZBz7fNeCXodVKU/oSvvEDpzD6Kr8DgETedlz5F7hRiL8rZhCyCzcx6hbImIiIhoNDt+thhPn9pjPJ7mGYD/m3a9iBmNLlVVVcjIyIBOpzPGHBwckJCQACcnTiYiIiKyNCzSk9mq6WzFjoJT2J5/Cuc6W3u0n98IdnXYDIS4eIqQ4egmGPRQl6eiM/cQOvMOdm/2+vNaoZcicxwHu4hFsItcDLvIxZC781FlIiIiIjJV9fNGsfqfn8L0snPCfxfeAYWMHz91Oh2ysrJw5swZk7ifnx+ioqIgk3FCERERkSXiKInMiiAIOHGuBFvzkvBdeTZ0vSy/EuriibXhM/HrSfHcCPYigsEA9ZmMCzPl84/A0NVy2XMkchvYhsyBXeRVsItcBOuJsZBIpSOUMRERERGZmy6dFusPbEOjugMAYC2zwruJq+Bh6yhyZuJraWlBcnIyOjo6jDG5XI6YmBj4+PiImBkRERGJjUV6MgutGhU+LkrGtryTKGyp7dEul8pwXUAU1oTNxNTx/tygFN1faGiqc9CZewhduQfQmfcjDB2Nlz9JJoft5JmwDVsAu/AFsAmaDqnCZmQSJiIiIiKzJggCHj/+KTIbqoyx52fejHiPiSJmNTqUlJQgNzcXBsOFSUaurq5QKpWws7MTMTMiIiIaDVikp1Etp7EaW3KT8FlJGjp1mh7tPvbOWBU6AytDplj87BxBEKA9V4DO3IPozDuErrzD0Lf2/ELDhMwKNoFTYRe2ALbhC2A7eRak1vyQQERERET9937OMXxSnGo8Xhs+EyuCp4iYkfg0Gg1SU1NRW2s6Lg8ODkZISAikfEqViIiIwCI9jUIqnRZ7yrOwLS8JP9WW99pn/oQQrAmbgUW+YZBZ6MBWMBigOZuLrvwf0VVwFJ15h6FvPnv5kyRSWAckwC58QXdhPmQOpDYOI5MwEREREY1Zx84W49nT3xiPp3sGWvxGsfX19UhJSYFarTbGbGxsEB8fj3HjxomYGREREY02LNLTqFHe1oAP8k5hV+FPxjUsL+ZibYcVwVNwZ+g0BDpZ3qBW0Ou6N3rNP4KugiPoKjh65eVrJBJY+8VdmCkfMhcyO+eRSZiIiIiILEJlexPuu2ijWG87Z/x34R2QSy1zE1SDwYCCggIUFhaaxD09PREXFweFQiFSZkRERDRasUhPotIbDNhfmYeteUk4XFUIAUKPPnHjJmJN2AxcHxgDWyu5CFmKw6Dpgqr4JLoKjnYX5YtOQOjly4tfUvhGX5gpHzoPMge3EciWiIiIiCxRl06D9ftNN4p9b9EqjLO1zKc1Ozs7kZKSgqamJmNMKpUiIiICgYGBImZGREREoxmL9CSK2s427Cw8jQ/yT6K6o6VHu41MjpuDYrE6bAZixvmKkOHI03e2oKvwWHdRPv8IVKWnAb328idJJLCeGAvbkDmwDZ0L29D5sHLyGJmEiYiIiAYhKSkJmzZtQnp6Ojo7O+Hj44MlS5Zgw4YN/dpIU6/XIykpCYcOHUJqairKysqgUqng4uKC6OhorFixAgsWLBi+N2LBBEHAH499iqzGamPspVm/RqyFjN9/qbq6Gunp6dDpdMaYvb09EhIS4OzMp1mJiIjo0likpxEjCAJOnCvBtryT+LY8C7qfH4e92GRnD6wKm4Flk5RwtrYVIcuRo2upMS5b05V/BOoz6YDQ80kCEzI5bAKndhfkQ+bAdvIsyOxdRiRfIiIioqGybds2PPfccxAEAV5eXvD29kZRURHefvtt7N27Fzt27ICLi0ufrvXpp5/iiSeeANA9Y9nPzw/29vYoLy/HgQMHcODAAaxYsQJPP/00JBLJML4ry/Nu9lF8XpJmPF4XPgvLJieIl5BI9Ho9srKyUFFRYRL38/NDZGQkrKz4sZuIiIguj6MFGnYt6i58UpyCbXknUdhS26PdSiLFEv9IrAqbgVleQWPyw5MgCNDWFKGr6Di6Co6hq+AItOcKrniexNoetpNnwjakuyhvM2k6pIqx/eUFERERjW1ZWVl4/vnnAQDPPPMMli9fDolEgpqaGtx3333Izs7Gk08+iddff73P1wwNDcWqVauwZMkSODo6AgB0Oh22bNmCl19+Gbt27UJYWBhuv/32YXlPluhodRH+/tOFjWJneAXiyWm/EjEjcbS2tiI5ORnt7e3GmJWVFWJiYjBhwgQRMyMiIiJzwiI9DZv0+kpsy0vCF6Xp6NL1XLbF284Zd4ZOw8qQqfC0cxIhw+Fj0HRBVfoTVEUn0FV0HKqiE9C31V/xPKm9W/cM+ZC5sAudC2u/OEgsaB1+IiIiGvveeustGAwG3HzzzVixYoUx7unpiVdffRXXXnst9u7di7y8PISFhV3xeldddRWWLVvWY6KHlZUV7r77bpSVlWH37t3YtWsXi/RD5GxHC357+EMYfn4K1MfeGe8ssLyNYktLS5GTkwOD4cITwi4uLkhISOjXkk1ERERELNLTkOrUavBFaTo+yD+J9PrKXvvMnxCC1aHTsWhiGKzGyEBe21QFVeFxdBWdgKroBFTlqVdeTx6AleuEn5eumQvb0LlQeIdDIpWOQMZEREREI6+jowNHjhwBACxfvrxHe0BAAGbMmIHjx4/ju+++61OR/krL4sybNw+7d+9GaWnpgHImU1qDHvcf2oEG1UUbxSZa1kaxGo0G6enpOHfunEl88uTJCA0NhZTjeSIiIuonFulpSBQ012Bb3kl8UpyCVo2qR7urtR2WB0/BnaHTEOg0ToQMh46g00JdmYGuwhNQFXUX5nUNFVc+EYDCJxw2k2fC7ufCvNW4gDG5vA8RERFRb3Jzc6HRaKBQKBATE9Nrn4SEBBw/fhzp6elD8poqVffY1NaWSwYOhZeSv8fp2nLj8TPTb0SMBW0U29DQgJSUFOP/rwDA2toa8fHx8PDwEDEzIiIiMmcs0tOAqfU6fFeeja15SThZ0/vMpKnj/XFn2Az8yj8KNma6bIu+vdE4Q76r6ARUJacgaDqveJ5EYQebSdNhO3kmbCbPhO2kGZA5uI1AxkRERESj0/nZ7D4+PpDLex8b+vn5mfQdrD179gDoLv5fyc6dO7F79+4+Xbe4uHhQeZmjvRU5eCfrR+PxLZPicXvIVBEzGjmCICA/Px+FhYUm8fHjxyMuLg7W1tYiZUZERERjAYv01G8VbY3Ynn8KOwtPGx9zvZiD3Bq3TIrHHaHTEeHmLUKGAycYDNCcyzdZukZzNq9P51qNC+je5HXyLNhMngHriTGQyPgjRkRERHReS0sLAMDZ2fmSfc63ne87GPv27cPBgwchkUiwfv36K/avq6tDdnb2oF93LKpoa8TDRy58gRHq4okXZv7aIp4K7erqQkpKChobG40xqVSK8PBwBAYGWsR/AyIiIhperCBSn+gMeuw/k4dt+SdxuKoQAoQefSLdvLEqbAZuDoqDg9w8ZpLoms9BVXoKquJTUJWcgqr0NAxdrVc+USaHTUDChVnyk2fCytVn+BMmIiIiMmNqtRoALjmLHgAUCoVJ34EqLi7G448/DgBYs2YNlErlFc/x8PBAZGRkn69/8ZInY5lKp8VvDm5Hy8/LWtpZKfDOwjtgJ1eInNnwO3v2LNLT06HVXthvyt7eHgkJCZf9somIiIioP1ikp8s629GCDwtOY0fBKZzr7Fm8tpZZ4cbAGKwKm4H4cRNH9SwSg7oDqrJkqEpOQ1V8EqrS031eS17mNP7nYvws2AbPhLV/AqQKm2HOmIiIiGhsOb8kyMUFz1/SaDQmfQfi7NmzWL9+Pdra2jB//nw8+uijfTpv5cqVWLlyZZ/6Ll261GJm3T9zeg8yGqqMx/+YvRTBLuNFzGj46fV6ZGdno7y83CTu6+uL6OhoWFnxozQRERENHY4sqAeDYMCP1UXYlpeEfWfyoBcMPfoEOY3DqrDpWDY5Aa7WdiJkeXmCQQ9NVU737PiSU1CVnIa6Kgsw6K98skQCxYQo2AbP6p4pHzwLco+gUf0FBBEREZE56MtSNn1ZEudy6urqsHbtWlRXV2PatGl4/fXXLztzny7vs+I0bM1LMh6v/vnJ2bGsra0NycnJaGtrM8asrKwQHR0NX1/L2SSXiIiIRg6L9GRU39WOXYU/YXv+KVS0N/Zot5JIca1/FO4Mm45ZXqOraK1trDTOjlcVn4Kq7CcI6p7r5ffGynUCbIKmXfgnIAFSW8dhzpiIiIjI8gQEBAAAqqurodVqey2eV1RUmPTtj4aGBqxZswZlZWWIj4/HO++8ww09B6GouRZ/Ov6p8TjGfQL+b9r1ImY0/MrKypCdnQ2D4cJEJWdnZyQkJMDe3l7EzIiIiGgsY5HewgmCgBPnSvBB/kl8W54NbS8zzSc6uOKO0GlYPnkKxtuJX7zWd7VCXfoTVCWn0VVyEqqS09A3V/fpXImNA2wCp3b/M6m7KC93nTDMGRMRERERAISHh0Mul0Oj0SAjIwMJCQk9+iQnJwMA4uLi+nXt5uZm3HXXXSguLkZkZCTeffddFlUHoVOrwb0Ht6NT1738kLPCBu8svAPWsrH5EVKr1SItLQ3nzp0ziU+aNAlhYWGQSqUiZUZERESWYGyOsOiKmtSd+LgoGdvzT6Gopa5Hu1QiwWLfMNwZNgPzfYIhE2lQalB3QF2eClVZClRlyVCXpUBzNhcQem5c24NUBmvfaNgETe2eIT9pOhTeYZBIZcOfOBERERH14ODggDlz5uDgwYPYvXt3jyJ9WVkZkpK6l1ZZsmRJn6/b3t6OdevWIT8/HyEhIXj//ffh6Cj+5BJzJQgC/nLic+Q31xhj/5q7HH6ObiJmNXwaGxuRkpKCrq4uY8za2hpxcXEYP35sr71PREREowOL9BZEEAT8VFuOD/JP4uuyTKj1uh59PO2ccFvIVNwePBU+Di4jmp9B3QF1RVp3Qb70p58L8nlAL2vi98bK3f/ngvx02E6aBmv/eEitOXuKiIiIaDS5//77cejQIXzxxRdQKpVYvnw5JBIJamtr8cgjj8BgMGDx4sUICwszOS8xMREA8Nhjj5kU8Lu6urBhwwZkZ2cjKCgImzdvhqur64i+p7Hmw8LT+Lg4xXh8b9Q8XO0XIWJGw0MQBBQWFqKgoADCRZOAPDw8EB8fz6WSiIiIaMSYbZG+rq4Ox44dQ1ZWFjIzM5Gbmwu1Wo1p06Zh27Ztlz1Xq9Viy5Yt+PLLL1FRUQG5XI6wsDCsWrUKV1999Qi9g5HTou7CJ8Up2J5/ymQ2zMXmTwjBqtDpWDwxDFYjMNP84oK8uiwZqtLkfhXkpbbOsAmaApug6d2z5AOnwsrFa5izJiIiIqLBiomJweOPP44XX3wRTz31FN5++224urqiqKgIGo0GgYGBePbZZ3ucV1VVBQDo7Ow0iW/dutW4RA4APPDAA5d87ddeew0eHh5D9E7GpuyGajyZ9KXxeJpnAB5PuEbEjIZHV1cXUlNT0dDQYIxJJBKEh4cjKGh07b9FREREY5/ZFun37NmDF154od/nqdVq3HXXXUhOToZMJsPkyZPR1dWFU6dO4dSpU7jnnnvw6KOPDkPGI0sQBKTWn8H2/JP4oiQDKr22Rx93G3usCJ6C20OmIcDJfdhyMag7LyrI/wRVWQo01bl9LshLrO1h46+EdYASNgFK2ARMgdwrBBKuC0lERERkltauXYvQ0FBs3LgRGRkZaGhogI+PD5YsWYINGzb0ay15jUZj/PeSkpLL9lWr1QPO2RK0alTYcHC78Ylbdxt7vDn/NsjH2HKR586dQ1paGrTaC5+R7OzskJCQABcXF/ESIyIiIotltkV6BwcHzJo1C9HR0YiOjkZOTg7eeuutK5738ssvIzk5Gb6+vnj33XcRFBQEANi/fz8eeughvPvuu1AqlcbHac1Nu1aNz4pTsS3/JHIaz/baZ7b3JNwZOh3X+EVAMcQbP5kW5JOhKkseQEE+HtYBCbAJUMI6IAEKrxCuI09EREQ0xsycORMzZ87sc//8/Pxe4w8++CAefPDBoUrLYgmCgEePfozytu6Z5RJI8Mb8lfC2dxY5s6Gj1+uRk5ODsrIyk/iECRMQExMDKyuz/XhMREREZs5sRyHLli3DsmXLjMc1Nb0v43Kx+vp67Ny5EwDw3HPPGQv0ALBo0SKsX78eb731Ft544w2zK9Jn1Ffig/xT+LwkDZ06TY92V2s7LJ+cgDtCpyHIeWge8dV3NEFdkf7zP2lQladAU5XT94K8wq67EO/fPUPeOnAKC/JERERERCJ4P+cYvinPMh4/HLcIc32CRcxoaLW1tSElJQWtra3GmEwmQ3R0NCZOnChiZkRERERmXKQfiAMHDkCr1SIgIAAzZszo0b5y5Uq89dZbyM7ORkVFBfz8/ETIsu/atWp8XpKGHfmnkNFQ1Wuf6Z6BuDN0Oq71j4SNlXxAryMIAnQNFVBXpEFdkQ7Vz/+rqy/r8zUkCjtY+8fD5uIZ8t6hLMgTEREREYksubYCfz/9jfF4nk8wfh9rXpOWLqeiogJZWVnQ6/XGmLOzM5RKJRwcHETMjIiIiKibRRXp09LSAAAJCQm9tnt6esLX1xeVlZVIS0sbtUX6zPoqfJB/Ep+XpKGjl1nzzgobLPt51nyIi2e/ri3otNCcze2eGV+RDnV5GtQVaTB0Nvf5GhcK8j/PkA+YwoI8EREREdEo1KpR4YHDH0L389OwXnZOeH3+CsjGwP5PWq0WGRkZqK6uNokHBQUhPDwc0jHwHomIiGhssKgi/fm1By9XfPfz80NlZSVKS0sve62dO3di9+7dfXrd4uLiPud4KR1aNb4oTcf2/FNIr6/stc+U8f64I2Qarg+MgW0fZs3rO1ugPpNhXK5GXZEGTVU2hF4K/5citXOBtV8crP1iYeMf9/MM+TAW5ImIiIiIRjlBEPDn45/hTHsTAEAmkeLtBbfD3cb8Z5c3NjYiJSUFXV1dxphCoUBcXBw8Pfs3kYmIiIhouFlUkb6lpQVA96ONl3K+7eK1CntTV1eH7OzsoUvuErIbqvFB/kl8VpKGdq26R7uTwga3TFLijtBpCHP16vUagiBA11TVXYj/eWa8uiId2rqSfuVi5e4Pa/842PxclLf2i4OVux8kEsmA3hsREREREYnn46IUfFGabjx+JG4RpnoGiJfQEBAEAUVFRcjPz4cgCMb4uHHjEB8fDxsbGxGzIyIiIuqdRRXp1eruIrdcfulZ5gqFAgCgUqkuey0PDw9ERkb26XWLi4uveL2LdWjV+LI0Ax/kn7zkrPkEDz/cEToNNwTGwNZKYYwbNCpozuZCU5kJ9ZlM4xryhvaGPr8+ZFaw9ok0FuK7/zcWMnvXvl+DiIiIiIhGrdLWevw16Qvj8XTPQDwQs1DEjAZPpVIhNTUV9fX1xphEIkFoaCgmT57MyUVEREQ0allUkd7a2hpA99qEl6LRdC/1cqUZFitXrsTKlSv79LpLly7t06z7rIYqbM8/dclZ845yayydpMSdodMR5jIe2voyaDK+RcOZTKgrM6GpzIKmphAw6Hu5eu+ktk4mhXhrv3gofMIhlVv3+RpERERERGQ+NHodHji8E50/L3PprLDF6/PMex36mpoapKWlGT/PAYCtrS0SEhLg6srJRkRERDS6WVSR3snJCcCFZW96c77tfN/h1q5V44uSdOwouPRa83OcXLHKyRlT9F0QcvdA88NLKKrKgqDu6NdrWblNvDA7/udla6zGBXBGCRERERGRBXk55QeTzx7/mL0UPg4u4iU0CAaDATk5OT32FPPx8UFMTMxln6ImIiIiGi0sqkgfEBCAlJQUlJeXX7JPRUWFse9wyqivxPb8U/i8JA0dP89gUei18O9sRFBHHUK7GjHNoIJ3y1lI2moBAJf+asGUxEoBhU8EFL5RF60fHwuZg/swvRsiIiIiIjIHR6oL8XbWYePx7SHT8KuAaBEzGrj29nYkJyeb7Ccmk8kQFRUFPz8/ETMjIiIi6h+LKtLHxcXh008/RUpKSq/tNTU1qKysNPYdanqDAR/kncSO/CTUV+UgqKMOS9vrEdRRh8COekzoaoIMwpUvdBG5RyAUvlGw9o2G9cRoKHyjofAMhkRmUbeWiIiIiIiuoEHVjod+3G08nuzsgb9Nu17EjAbuzJkzyMzMhF5/YalPJycnJCQkwMHBQcTMiIiIiPrPoiq5ixYtwrPPPouysjIkJSVhxowZJu07d+4EAERERMDf33/IX7/hbD6c3lyKFzoaYGu49Lr4vZE6uHcX4n2jLhTlJ0RCaus45HkSEREREdHYIggCHj36CWq62gAACqkMb86/DXZyhciZ9Y9Wq0VmZiaqqqpM4oGBgYiIiIDUjNfVJyIiIstlUUX6cePGYcWKFfjggw/w17/+Fe+++y6CgoIAAAcOHMB7770HAPjtb387LK/vpO1EWNu5y/aRWFlDMSHi52J8d1HeemIMZM5eXDueiIiIiIgGZEteEn44k2s8/suUaxHp7iNiRv3X1NSElJQUdHZ2GmNyuRxxcXHw8vISMTMiIiKiwTHbIv3Zs2dx8803G481mu513VNSUjB9+nRjfP369bjnnnuMx3/84x+RnZ2N1NRUXH/99QgODkZnZ6dxLfp169Zh8eLFI/Ie5B5BUEyMNs6Qt/aNhtxzMpeqISIiIiKiIZPXdA7Pnt5jPF44IRR3R8wWMaP+EQQBxcXFyMvLgyBcWB7U3d0dSqUSNjY2ImZHRERENHhmWw3W6/Vobm7uEdfpdCZxlUpl0m5jY4OtW7di8+bN+Oqrr1BWVga5XI5p06bhzjvvxDXXXDNsOevs3OC6+i9w8I/rXqrGhmslEhERERHR8OnSafHbQx9CrdcBADxsHfDq3GVm85SuSqVCWloa6urqjDGJRIKQkBAEBwebzfsgIiIiuhyzLdL7+voiPz9/QOcqFAps2LABGzZsGOKsLs9hfCA8Eu8d0dckIiIiIiLL9ffTe5DfXGM8/tfc5fAwk32tamtrkZqaanxqGgBsbW2hVCrh5uYmYmZEREREQ8tsi/RERERERER0aXsrcrAlL8l4vCFyDhZMCBExo74xGAzIzc1FSUmJSdzb2xuxsbGQy+UiZUZEREQ0PFikJyIiIiIiGmPOdrTgD0c/Nh5HufngTwlLRMyobzo6OpCcnIyWlhZjTCqVIioqCv7+/iJmRkRERDR8WKQnIiIiIiIaQwyCAQ8d2Y0mdScAwNZKjjcX3AZr2ej++FdZWYnMzEzodDpjzNHREQkJCXB0NI8leoiIiIgGYnSP0oiIiIiIiKhf3s78EcfOFhuPn55+AyY5e4iY0eXpdDpkZmaisrLSJB4QEICIiAjIZDKRMiMiIiIaGSzSExERERERjRFpdWfwcspe4/GvAqJxW/BUETO6vObmZqSkpKCjo8MYk8vliI2Nhbe3t4iZEREREY0cFumJiIiIiIjGgC6dFr8/shs6wQAA8LF3xkuzfg2JRCJyZj0JgoCSkhLk5eXBYDAY425ublAqlbC1tRUxOyIiIqKRxSI9ERERERHRGPBS8ncobqkDAEggwWvzVsDF2k7krHpSq9VIS0tDbW2tMSaRSBAcHIyQkJBR+aUCERER0XBikZ6IiIiIiMjMHT9bjPdyjhmP742aixleQSJm1Lu6ujqkpqZCrVYbYzY2NlAqlXB3dxcxMyIiIiLxsEhPRERERERkxto0Kjxy9CPjcaiLJx6Nv0rEjHoyGAzIz89HUVGRSdzLywuxsbFQKBQiZUZEREQkPhbpiYiIiIiIzNgzp/egsr0ZAGAlkeJfc2+FjZVc3KQu0tHRgZSUFDQ3NxtjUqkUkZGRCAgIEC0vIiIiotGCRXoiIiIiIiIztf9MHj4sOG08/l1sImLG+YqYkamqqipkZGRAp9MZYw4ODkhISICTk5OImRERERGNHizSExERERERmaEmVQf+eOwT43GM+wQ8GLtQxIwu0Ol0yMrKwpkzZ0zifn5+iIqKgkwmEykzIiIiotGHRXoiIiIiIiIz9ETSl6jtagMAWMus8O95yyGXil/8bmlpQXJyMjo6OowxuVyOmJgY+Pj4iJgZERER0ejEIj0REREREZGZ+ao0A1+UphuPH1NejRAXTxEz6lZSUoLc3FwYDAZjzNXVFUqlEnZ2diJmRkRERDR6sUhPRERERERkRmo72/CXE58bj6d7BmB9xBzxEgKg0WiQlpaGmpoak3hwcDBCQ0MhkUhEyoyIiIho9GORnoiIiIiIyEwIgoDHjn+CJnUnAMDOSoFX594KmVQqWk719fVITU2FSqUyxmxsbBAfH49x48aJlhcRERGRuWCRnoiIiIiIyEzsLkrGvjN5xuOnpv4K/o7uouRiMBhQUFCAwsJCk7inpyfi4uKgUChEyYuIiIjI3LBIT0REREREZAYq25vwfye/Mh7PnxCCO0KniZJLZ2cnUlJS0NTUZIxJpVJEREQgMDBQlJyIiIiIzBWL9ERERERERKOcQTDgD0c/RrtWDQBwVtjgldm3iLLWe3V1NdLT06HT6Ywxe3t7JCQkwNnZecTzISIiIjJ3LNITERERERGNcptzT+DY2WLj8bMzboK3/cgWxPV6PbKyslBRUWES9/PzQ2RkJKys+PGSiIiIaCA4iiIiIiIiIhrFSlrq8PxP3xmPr/WPxK+D4kY0h9bWViQnJ6O9vd0Ys7KyQkxMDCZMmDCiuRARERGNNSzSExERERERjVI6gx4PHfkIKr0WAOBuY48XZ/16RJe5KS0tRU5ODgwGgzHm4uKChIQE2NnZjVgeRERERGMVi/RERERERESj1DtZR5BSd2F5mZdmLYW7jcOIvLZGo0F6ejrOnTtnEp88eTJCQ0MhlUpHJA8iIiKisY5FeiIiIiIiolEop/Es/pn6g/F42SQllvhHjshrNzQ0ICUlBSqVyhiztrZGfHw8PDw8RiQHIiIiIkvBIj0REREREdEoozPo8ejRj6E16AEA3nbOeHr6DcP+uoIgoKCgAAUFBSbx8ePHIy4uDtbW1sOeAxEREZGlYZGeiIiIiIholHkv+xgyGqqMx6/MuQXO1rbD+ppdXV1ISUlBY2OjMSaVShEeHo7AwMARXQefiIiIyJKwSE9ERERERDSKlLU24JWLlrlZPjkB8yeEDOtrnj17Funp6dBqtcaYvb09EhIS4OzsPKyvTURERGTpWKQnIiIiIiIaJQRBwJ+OfwqVvrtY7mHrgCen/WrYXk+v1yM7Oxvl5eUmcV9fX0RHR8PKih8ZiYiIiIYbR1xERERERESjxK7Cn3DsbLHx+NnpN8LV2m5YXqutrQ3Jycloa2szxqysrBAdHQ1fX99heU0iIiIi6olFeiIiIiIiolGgprMVz57eYzy+emI4fhUQPSyvVV5ejqysLBgMBmPM2dkZCQkJsLe3H5bXJCIiIqLesUhPREREREQ0CjyZ9CVaNCoAgKPcGs/NvHnIN2vVarVIT0/H2bNnTeKTJk1CWFgYpFLpkL4eEREREV0Zi/REREREREQi+7Y8C9+UZxmP/zrlOnjbD+2GrY2NjUhJSUFXV5cxZm1tjbi4OIwfP35IX4uIiIiI+o5FeiIiIiIiIhG1qLvwxIkvjMfTPQNxe+jUIbu+IAgoLCxEQUEBBEEwxj08PBAfHw9ra+shey0iIiIi6j8W6YmIiIiIiET03E/foqare/NWa5kVXp69FFLJ0Cw7o1KpkJKSgoaGBmNMIpEgPDwcQUFBQ76cDhERERH1H4v0REREREREIjl+thg7Ck4Zjx+OW4QgZ48hufa5c+eQlpYGrVZrjNnZ2SEhIQEuLi5D8hpERERENHgs0hMREREREYmgS6fFY8c+NR5Hunnj3qh5g76uwWBAdnY2ysrKTOITJkxATEwMrKz4MZCIiIhoNOHojIiIiIiISASvpu5DWVv3MjRSiQQvz74FcqlsUNdsb29HcnIyWltbjTGZTIbo6GhMnDhxUNcmIiIiouHBIj0REREREdEIy6yvwv+yjxiPN0TORcw430Fds6KiAllZWdDr9caYs7MzlEolHBwcBnVtIiIiIho+LNITERERERGNIJ1Bjz8e+wR6wQAA8Hd0xx/iFw/4elqtFhkZGaiurjaJBwUFITw8HFLp0GxCS0RERETDg0V6IiIiIiKiEfTfrCPIarxQUP/HrF/D1koxoGs1NTUhOTkZXV1dxphCoUBcXBw8PT0HnSsRERERDT8W6YmIiIiIiEZISUs9Xk3bZzxeGTwFs30m9/s6giCgqKgI+fn5EATBGB83bhzi4+NhY2MzJPkSERER0fBjkZ6IiIiIiGgEGAQDHjv+CdR6HQBgvK0jnph6Xb+vo1KpkJqaivr6emNMIpEgNDQUkydPhkQiGbKciYiIiGj4sUhPREREREQ0AnYUnEbSuVLj8d9n3AQXa7t+XaOmpgZpaWnQaDTGmK2tLRISEuDq6jpkuRIRERHRyGGRnoiIiIiIaJid62zFc6e/MR5f6x+J6wKi+ny+wWBAbm4uSkpKTOI+Pj6IiYmBXC4fslyJiIiIaGSxSE9ERERERDTM/nbyK7Rp1QAAJ4UNnp1xU5/PbW9vR3JyMlpbW40xmUyGqKgo+Pn5DXmuRERERDSyWKQnIiIiIiIaRoerCvB1Wabx+K9TroOXnVOfzj1z5gwyMzOh1+uNMScnJyQkJMDBwWHIcyUiIiKikcciPRERERER0TBR63V4IulL43GChx9uC5lyxfN0Oh0yMjJQVVVlEg8MDERERASkUumQ50pERERE4mCRnoiIiIiIaJi8k3kYpa31AACpRILnZ94MqeTyBfbm5mYkJyejs7PTGJPL5YiLi4OXl9ew5ktEREREI49FeiIiIiIiomFQ0daI1zIOGo/Xhs1EpLvPJfsLgoDi4mLk5eVBEARj3N3dHUqlEjY2NsOaLxERERGJg0V6IiIiIiKiYfB/J7+CWq8DAIy3dcSjyqsv2VetViM1NRV1dXXGmEQiQUhICIKDgyGRSIY9XyIiIiISB4v0REREREREQ2xvRQ5+OJNrPH5i6nVwUvQ+E762thapqanQaDTGmK2tLZRKJdzc3IY9VyIiIiISF4v0REREREREQ6hLp8FTJy9sFjvTKwi/Dorr0c9gMCAvLw/FxcUmcW9vb8TGxkIulw93qkREREQ0CrBIT0RERERENIReTz+IyvZmAICVRIrnZt7UY7majo4OJCcno6WlxRiTSqWIioqCv7//SKZLRERERCJjkZ6IiIiIiGiIFLfU4Z2sH43H90TORYiLp0mfyspKZGZmQqfTGWOOjo5ISEiAo6PjiOVKRERERKMDi/RERERERERDQBAEPJH0BTQGPQDA284ZD8UlGtt1Oh0yMzNRWVlpcp6/vz8iIyMhk8lGNF8iIiIiGh1YpCciIiIiIhoCX5dl4kh1kfH46ek3wF5uDQBoaWlBcnIyOjo6jO1yuRyxsbHw9vYe8VyJiIiIaPRgkZ6IiIiIiGiQ2rVq/O3U18bjBRNCcK1/JARBQElJCfLy8mAwGIztbm5uUCqVsLW1FSNdIiIiIhpFWKQnIiIiIiIapH+l7kNNZysAwFpmhWdn3AiNRoO0tDTU1tYa+0kkEgQHByMkJKTHZrJEREREZJlYpCciIiIiIhqE3MZzeC/nmPH4/uj5cFALOJx0GGq12hi3sbGBUqmEu7u7GGkSERER0ShlkUX6119/HW+88cZl+/ztb3/DbbfdNkIZERERERFZlqSkJGzatAnp6eno7OyEj48PlixZgg0bNsDOzm5A1/z+++/xwQcfIC8vD1qtFv7+/rjxxhuxevVqyOXyIX4H3bo3i/0ceqF7KRs/exckWnkiKSnJpJ+XlxdiY2OhUCiGJQ8iIiIiMl8WWaQ/z93dHf7+/r22eXh4jHA2RERERESWYdu2bXjuuecgCAK8vLzg7e2NoqIivP3229i7dy927NgBFxeXfl3zpZdewsaNGwEAfn5+sLW1RWFhIf7xj3/g4MGD2Lhx47AUyJvVnSisKQMAGNRaLHf0RWVZubFdKpUiMjISAQEBQ/7aRERERDQ2WHSRft68eXjxxRfFToOIiIiIyGJkZWXh+eefBwA888wzWL58OSQSCWpqanDfffchOzsbTz75JF5//fU+X/OHH34wFuH//e9/Y9GiRQCA4uJibNiwAadPn8arr76Kxx9/fMjfz9mf16HXN7UhWm2DSROcjW0ODg5ISEiAk5PTkL8uEREREY0dUrETICIiIiIiy/HWW2/BYDDgpptuwooVK4ybp3p6euLVV1+FVCrF3r17kZeX1+drnl/K8p577jEW6AFg0qRJ+Pvf/w4A2L59OxobG4fwnXTTGfTQVNRAUlmP2yZNMcb9/Pwwb948FuiJiIiI6IpYpCciIiIiohHR0dGBI0eOAACWL1/eoz0gIAAzZswAAHz33Xd9umZZWZmxoL9ixYoe7TNnzoS/vz80Gg32798/0NQvSVBpoG9sww0BsRhn6wC5XI6EhATExsZCJpMN+esRERER0dhj0cvd5OXl4Q9/+APq6upgb2+P0NBQ/OpXv0JwcLDYqRERERERjTm5ubnQaDRQKBSIiYnptU9CQgKOHz+O9PT0Pl0zLS0NADBx4kR4enpe8prl5eVIT0/HrbfeOqDcL0kQ4G3nhGv8IuDq6gqlUjngjW+JiIiIyDJZdJE+NzcXubm5xuMDBw7gnXfewerVq/GnP/3psjNfdu7cid27d/f5dYDuNTGXLl06uKSJiIiIaNQoLi4GAFRWVoqciXkoLS0FAPj4+EAul/fax8/Pz6TvlZSVlZmcN5hrDmSML2tXw+l4Id5L/x+sra2Ny/cQERERkXkSY4xvkUX68ePH43e/+x3mzp0LX19fODg4oLS0FDt27MDOnTuxZcsWWFlZ4bHHHrvkNerq6pCdnd2v11WpVP0+h4iIiIhGv46ODrFTMAstLS0AAGdn50v2Od92vu9QXrO1tfWy1xrIGF9iENBwrhYNqO3XeUREREQ0uo3kGN8ii/S9rVUZGhqKp59+Gr6+vnjllVewZcsW3H777fD19e31Gh4eHoiMjOzT650f6EulUoSHhw88cTI7xcXFUKlUsLGxwaRJk8ROh0YQ773l4r23TLzvlis3NxcGg4Gzp/tIrVYDwCVn0QOAQqEw6TuU11SpVJe9Fsf41Bf8nW+5eO8tF++9ZeJ9t1xijPEtskh/OevWrcPWrVtRW1uLAwcOYPXq1b32W7lyJVauXNmnay5duhTZ2dkIDw/Hp59+OpTp0ih3/t5PmjSJ997C8N5bLt57y8T7brnO3/uQkBCxUzEL1tbWAACtVnvJPhqNxqTvUF7TxsbmstfiGJ/6gr/zLRfvveXivbdMvO+WS4wxvnTEXslMyGQyxMbGAgDKy8tFzoaIiIiIaOzoy1I2fVm+5mJOTk59vub5vkREREREowmL9L04/6isTqcTORMiIiIiorEjICAAAFBdXX3Jme8VFRUmfa8kMDAQwOUn2PT3mkREREREI4lF+l4UFhYCALy8vETOhIiIiIho7AgPD4dcLodGo0FGRkavfZKTkwEAcXFxfbrm+adgKysrUVNTMyTXJCIiIiIaSSzS/8KhQ4eMRfrZs2eLnA0RERER0djh4OCAOXPmAAB2797do72srAxJSUkAgCVLlvTpmoGBgcb1Qnft2tWj/cSJEygvL4dcLseiRYsGmjoRERER0bCxuCJ9YWEhnnrqKeTl5ZnEDQYDvv76a/zhD38AACxcuBAxMTFipEhERERENGbdf//9kEgk+OKLL7Br1y4IggAAqK2txSOPPAKDwYDFixcjLCzM5LzExEQkJibiu+++63HNBx54AADw7rvv4sCBA8Z4SUkJnnjiCQDA7bffDjc3t+F6W0REREREA2YldgIjTafTYdeuXdi1axdcXFzg4+MDmUyGiooK44ZSU6ZMwT/+8Q+RMyUiIiIiGntiYmLw+OOP48UXX8RTTz2Ft99+G66urigqKoJGo0FgYCCeffbZHudVVVUBADo7O3u0XXPNNVizZg22bNmC++67D35+frCzs0NhYSH0ej0SEhKMk3GIiIiIiEYbiyvST5gwAQ899BDS0tJQXFyM8vJyaDQaODs7Y968ebj++utx/fXXQyaTiZ0qEREREdGYtHbtWoSGhmLjxo3IyMhAQ0MDfHx8sGTJEmzYsAH29vb9vuZf/vIXxMfHY8eOHcjNzUVtbS0mTZqEG2+8EWvXroVcLh+Gd0JERERENHgWV6R3cnLCfffdJ3YaREREREQWbebMmZg5c2af++fn51+xz7XXXotrr712MGkREREREY04i1uTnoiIiIiIiIiIiIhotGCRnoiIiIiIiIiIiIhIJBa33I0Yli9fjrq6Onh4eIidCo0w3nvLxXtvuXjvLRPvu+XivbdcvPeWi/fecvHeWy7ee8vE+265xLj3EkEQhBF7NSIiIiIiIiIiIiIiMuJyN0REREREREREREREImGRnoiIiIiIiIiIiIhIJCzSExERERERERERERGJhEV6IiIiIiIiIiIiIiKRWImdgLlJSkrCpk2bkJ6ejs7OTvj4+GDJkiXYsGED7OzsBnTN77//Hh988AHy8vKg1Wrh7++PG2+8EatXr4ZcLh/id0ADNVT3Xq/XIykpCYcOHUJqairKysqgUqng4uKC6OhorFixAgsWLBi+N0L9Nhw/9xfbvn07nnnmGQDAtGnTsG3btkFfk4bGcNx7QRCwZ88efPbZZ8jNzUVraytcXFwwadIkzJs3D3ffffcQvwsaiKG+99XV1di4cSOOHj2Ks2fPwmAwwMPDA9OnT8fatWsRGho6DO+C+qOurg7Hjh1DVlYWMjMzkZubC7VaPSS/l4f77wgNHsf4lotjfMvFMb7l4hjfcnGMb1nMbXwvEQRBGFRWFmTbtm147rnnIAgCvLy84ObmhqKiImg0GkyaNAk7duyAi4tLv6750ksvYePGjQAAPz8/2NraoqioCHq9HlOnTsXGjRuhUCiG4d1Qfwzlvf/oo4/wxBNPAACkUin8/Pxgb2+P8vJytLe3AwBWrFiBp59+GhKJZLjeEvXRcPzcX6ympgbXXXed8d5zAD96DMe97+jowAMPPIDjx48DACZOnAgXFxc0NDSgpqYGjo6OOHny5DC8G+qPob73qampuPvuu9HR0QG5XA5fX1/I5XJUVFRApVLBysoKr7zyCq699trhe1N0RZs3b8YLL7zQIz7Y38vD/XeEBo9jfMvFMb7l4hjfcnGMb7k4xrc8Zje+F6hPMjMzhbCwMCE0NFTYuXOnYDAYBEEQhHPnzgm//vWvhZCQEOGBBx7o1zX37t0rhISECFFRUcK+ffuM8aKiIiExMVEICQkRXnjhhSF9H9R/Q33vd+/eLdxwww3C7t27hdbWVmNcq9UK7733nhAaGiqEhIQI27dvH/L3Qv0zHD/3v/Sb3/xGCA8PF+69914hJCREuPPOO4cidRqk4bj3BoNBuOuuu4SQkBDh7rvvFsrLy03aW1paTP4WkDiG+t4bDAbhqquuEkJCQoQVK1YIVVVVxrbW1lbhkUceEUJCQgSlUmnyN4FG3kcffSSsXbtW+Oc//yns3btX+Pe//z3o38sj8XeEBodjfMvFMb7l4hjfcnGMb7k4xrdM5ja+Z5G+j+677z4hJCREeOyxx3q0lZaWCmFhYUJISIiQm5vb52veeOONQkhIiPCf//ynR9vx48eNg/uGhoZB5U6DM9T3vqmpyfhD3JsnnnhCCAkJEW688cYB50xDYzh+7i+2Z88eISQkRPj73/8uvPbaaxzAjyLDce8//vhjISQkRLj11lsFrVY7lOnSEBrqe19QUCCEhIRc8hy1Wi3ExcUJISEhwoEDBwadPw2dbdu2Dfr38nD/HaH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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(18, 6))\n", "# fig.title(\"T = 350 K, Propane (1), Butane (2)\")\n", "sns.lineplot(x=vle.liquid.molefracs[:,0], y=vle.liquid.pressure / si.BAR, ax=ax[0])\n", "sns.lineplot(x=vle.vapor.molefracs[:,0], y=vle.vapor.pressure / si.BAR, ax=ax[0])\n", "ax[0].set_xlabel(r\"$x_1$, $y_1$\")\n", "ax[0].set_ylabel(r\"$p$ / bar\")\n", "ax[0].set_xlim(0, 1)\n", "ax[0].set_ylim(5, 35)\n", "# ax[0].legend(frameon=False);\n", "\n", "sns.lineplot(x=vle.liquid.molefracs[:,0], y=vle.vapor.molefracs[:,0], ax=ax[1])\n", "sns.lineplot(x=np.linspace(0, 1, 10), y=np.linspace(0, 1, 10), color=\"black\", alpha=0.3, ax=ax[1])\n", "ax[1].set_xlabel(r\"$x_1$\")\n", "ax[1].set_ylabel(r\"$y_1$\")\n", "ax[1].set_xlim(0, 1)\n", "ax[1].set_ylim(0, 1);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison to Rust implementation \n", "[↑ Back to top](#Table-of-contents)\n", "\n", "Implementing an equation of state in Python is nice for quick prototyping and development but when it comes to performance, implementing the equation of state in Rust is the way to go.\n", "For each non-cached call to the Helmholtz energy, we have to transition between Rust and Python with our Python implementation which generates quite some overhead.\n", "\n", "Here are some comparisons between the Rust and our Python implemenation:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# rust\n", "eos_rust = feos.EquationOfState.peng_robinson(feos.Parameters.from_json([\"propane\"], \"../../../examples/data/peng-robinson.json\"))\n", "\n", "# python\n", "tc = si.array(369.96 * si.KELVIN)\n", "pc = si.array(4250000.0 * si.PASCAL)\n", "omega = np.array([0.153])\n", "molar_weight = si.array(44.0962 * si.GRAM / si.MOL)\n", "eos_python = feos.EquationOfState.python_residual(PyPengRobinson(tc, pc, omega, molar_weight))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# let's first test if both actually yield the same results ;)\n", "assert abs(feos.State.critical_point(eos_python).pressure() / si.BAR - feos.State.critical_point(eos_rust).pressure() / si.BAR) < 1e-12\n", "assert abs(feos.State.critical_point(eos_python).temperature / si.KELVIN - feos.State.critical_point(eos_rust).temperature / si.KELVIN) < 1e-12" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "import timeit\n", "\n", "time_python = timeit.timeit(lambda: feos.State.critical_point(eos_python), number=2_500)\n", "time_rust = timeit.timeit(lambda: feos.State.critical_point(eos_rust), number=2_500)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Critical point for pure substance\n", "Python implementation is slower by a factor of 24.\n" ] } ], "source": [ "rel_dev = (time_rust - time_python) / time_rust\n", "print(f\"Critical point for pure substance\")\n", "print(f\"Python implementation is {'slower' if rel_dev < 0 else 'faster'} by a factor of {abs(time_python / time_rust):.0f}.\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "time_python = timeit.timeit(lambda: feos.PhaseDiagram.pure(eos_python, 300*si.KELVIN, 100), number=100)\n", "time_rust = timeit.timeit(lambda: feos.PhaseDiagram.pure(eos_rust, 300*si.KELVIN, 100), number=100)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Phase diagram for pure substance\n", "Python implementation is slower by a factor of 80.\n" ] } ], "source": [ "rel_dev = (time_rust - time_python) / time_rust\n", "print(f\"Phase diagram for pure substance\")\n", "print(f\"Python implementation is {'slower' if rel_dev < 0 else 'faster'} by a factor of {abs(time_python / time_rust):.0f}.\")" ] } ], "metadata": { "kernelspec": { "display_name": "feos", "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 }