{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "*This notebook contains course material from [CBE30338](https://jckantor.github.io/CBE30338)\n", "by Jeffrey Kantor (jeff at nd.edu); the content is available [on Github](https://github.com/jckantor/CBE30338.git).\n", "The text is released under the [CC-BY-NC-ND-4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode),\n", "and code is released under the [MIT license](https://opensource.org/licenses/MIT).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Interacting Tanks](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/03.07-Interacting-Tanks.ipynb) | [Contents](toc.ipynb) | [Modeling and Control of a Campus Outbreak of Coronavirus COVID-19](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/03.09-COVID-19.ipynb) >

\"Open

\"Download\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Manometer Models and Dynamics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "This notebook demonstrates the modeling and interactive simulation of a u-tube manometer. This device demonstrates a variety of behaviors exhibited by a linear second order system. An interesting aspect of the problem is the opportunity for passive design of dynamics for a measurement device." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning Goals\n", "\n", "* Develop linear differential equations models for mechanical systems from momentum/force balances. \n", "* Describe role of position and velocity as state variables in a dynamic model.\n", "* Describe undamped, underdamped, overdamped, and critically damped responses.\n", "* Represent a second order system in standard form with natural frequency and damping factor.\n", "* Describe second order response to sinusoidal input, and resonance.\n", "* Construct a state space representation of a second order linear differential equation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initializations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.integrate import odeint\n", "from scipy import linalg as la\n", "from ipywidgets import interact,interactive\n", "from control.matlab import *\n", "\n", "# scales for all subsequent plots\n", "tmax = 20\n", "ymin = -0.02\n", "ymax = +0.02\n", "axis = [0.0,tmax,ymin,ymax]\n", "t = np.linspace(0.0,tmax,1000)\n", "\n", "# physical properties\n", "g = 9.8 # m/s\n", "rho = 1000.0 # density of water kg/m^3\n", "nu = 1.0e-6 # kinematic viscosity of water m/s^2\n", "\n", "# system dimensions\n", "L = 7 # meters\n", "d = 0.08 # meters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 1. Steady State Response to a Pressure Differential\n", "\n", "For this first model we will that the ends of the u-tube are exposed to a pressure differential $\\Delta P$. How does the level in the tubes change?\n", "\n", "The u-tube manometer of cross-sectional area $A$, filled with a liquid of density $\\rho$, the total length of the liquid column $L$. When the ends are open and exposed to the same environmental pressure $P$ the liquid levels in the two the legs of the device will reach the same level. We'll measure the levels in the tubes as a deviation $y$ from this equilibrium position.\n", "\n", "At steady state the difference in the levels of the tubes will be $h$. The static pressure difference \n", "\n", "$$\\Delta P = \\rho g h$$\n", "\n", "or \n", "\n", "$$y = \\frac{\\Delta P}{\\rho g}$$\n", "\n", "This is simple statics. Notice that neither the cross-sectional area or the length of the liquid column matter. This is the rationale behind the common water level.\n", "\n", "![https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Schlauchwaage_Schematik.svg/250px-Schlauchwaage_Schematik.svg.png](https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Schlauchwaage_Schematik.svg/250px-Schlauchwaage_Schematik.svg.png)\n", "\n", "(By [Bd](https://de.wikipedia.org/wiki/User:Bd) at the [German language Wikipedia](https://de.wikipedia.org/wiki/), [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/), [Link](https://commons.wikimedia.org/w/index.php?curid=46342405))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def model1(deltaP = 100.0):\n", " h = deltaP/(rho*g)\n", " plt.axis(axis)\n", " plt.plot(plt.xlim(),[h,h])\n", " plt.grid()\n", " plt.xlabel('Time [sec]')\n", " plt.ylabel('h [m]')\n", " plt.title('dP = {0:5.1f} Pascals'.format(deltaP))\n", "\n", "interact(model1,deltaP=(-200,200,20.0));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 2. Dynamic Response with Negligible Viscosity\n", "\n", "The second model for the manometer includes the dynamics associated with moving a mass $m$ of the liquid column held within the manometer. For this model we will chose a different measure of displacem\n", "\n", "\n", "The net force on the liquid column is due to the applied pressure differential, $A\\Delta P$, and the gravitational force due to the difference in liquid levels between the two arms of the manometer, $2 A \\rho g$. $A$ is the cross-sectional area. From Newton's law\n", "\n", "$$m \\frac{d^2y}{dt^2} = A \\Delta P - 2 A \\rho g y$$\n", "\n", "The mass of liquid is $m = \\rho L A$ where $L$ is the total length of the liquid column. After canceling a common factor $A$, the result is an inhomogeneous linear second order differential equation\n", "\n", "$$ \\frac{d^2y}{dt^2} + \\frac{2 g}{L} y = \\frac{1}{\\rho L} \\Delta P$$\n", "\n", "At steady state this model reduces to the static case outlined in model 1 above. The dynamic case corresponds to an undamped harmonic oscillator with an angular frequency\n", "\n", "$$\\omega = \\sqrt{\\frac{2 g}{L}}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For numerical solution using the scipy libraries, it is necessary to convert the second order differential equation to a system of first order differential equations. \n", "\n", "$$\\begin{align*}\n", "\\frac{dy}{dt} & = v \\\\\n", "\\frac{dv}{dt} & = -\\frac{2g}{L} y + \\frac{1}{\\rho L} \\Delta P \n", "\\end{align*}$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " natural frequency = 1.7 rad/sec\n", "period of oscillation = 3.8 seconds\n" ] }, { "data": { "image/png": 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FZrTXN59AEB6d0bpz/+lViZTX1PHGlkwjJDaOjA3warK+5i8OhG//1eSl/MqG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\nP2AtsM4rYr6qTstExkjp/wUomQX7t0O7Ho0vb9c6t5gz7q1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def model2(deltaP=100, L = 7.0):\n", "\n", " def deriv(X,t):\n", " x,v = X\n", " xdot = v\n", " vdot = -(2*g/L)*x + deltaP/rho/L\n", " return [xdot,vdot]\n", "\n", " IC = [0,0]\n", " \n", " w = np.sqrt(2*g/L)\n", " print(\" natural frequency = {0:0.1f} rad/sec\".format(w))\n", " print(\"period of oscillation = {0:0.1f} seconds\".format(2*np.pi/w))\n", " \n", " sol = odeint(deriv,IC,t)\n", " plt.axis(axis)\n", " plt.plot(t,sol)\n", " plt.grid()\n", " plt.xlabel('Time [sec]')\n", " plt.ylabel('y [m], v[m/s]')\n", " plt.title('dP = {0:5.1f} Pascals, L = {1:4.2f} meters'.format(deltaP,L))\n", " plt.legend(['Position','Velocity'])\n", " \n", "interact(model2, deltaP = (-200,200,1), L = (0.2,10,0.1));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 3. Dynamic Response with Viscous Dissipation\n", "\n", "This third model for manometer incorporates the energy loss due to viscous dissipation in fluid motion. The pressure drop due to the laminar flow of incompressible Newtonian fluid in a long pipe with circular cross-section is given by the Hagen-Poiseuille equation\n", "\n", "$$\\Delta P_{drag} = \\frac{32 \\mu L v}{d^2}$$\n", "\n", "where $\\mu$ is the dynamic viscosity and $d$ is pipe diameter. Doing a balance of forces acting on the fluid column\n", "\n", "$$\\rho AL\\frac{d^2y}{dt^2} + \\frac{32\\mu L A}{d^2}v + 2 A \\rho g y = A \\Delta P$$\n", "\n", "Denoting $\\nu = \\frac{\\mu}{\\rho}$ as the kinematic viscosity, substituting for velocity $\\frac{dy}{dt} = v$ leaves\n", "\n", "$$\\frac{d^2y}{dt^2} + \\frac{32 \\nu }{d^2}\\frac{dy}{dt} + \\frac{2g}{L} y = \\frac{1}{\\rho L} \\Delta P$$\n", "\n", "This can be recast as a pair of first-order linear differential equations\n", "\n", "$$\\begin{align*}\n", "\\frac{dy}{dt} & = v \\\\\n", "\\frac{dv}{dt} & = -\\frac{2g}{L} y - \\frac{32 \\nu }{d^2}v + \\frac{1}{\\rho L} \\Delta P \n", "\\end{align*}$$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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Cd/36o6FUoPn8lRSR7CqWFeCAMabGF8mIyDjgBcAFzDTGPF1uvjjzLwXygduM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\nsHsuUEqpaqqy1ZyIDBGRD0RknYhsFJH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def model3(dP = 100.0, L = 7.0, d = 0.008):\n", "\n", " def deriv(X,t):\n", " y,v = X\n", " ydot = v\n", " vdot = -(2*g/L)*y - (32*nu/d**2)*v + dP/rho/L\n", " return [ydot,vdot]\n", "\n", " IC = [0,0]\n", " sol = odeint(deriv,IC,t)\n", " plt.axis(axis)\n", " plt.plot(t,sol)\n", " plt.grid()\n", " plt.xlabel('Time [sec]')\n", " plt.ylabel('y [m], v[m/s]')\n", " plt.title('dP = {0:5.1f} bars, L = {1:4.2f} meters, d = {2:5.3f} meters'.format(dP,L,d))\n", " plt.legend(['Position','Velocity'])\n", "\n", "w = interactive(model3, dP=(-200,200,20), L = (0.2,30,0.1), d=(0.001,0.020,0.001));\n", "w.children[2].readout_format = '.3f'\n", "w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 4. Second Order System in Standard Form\n", "\n", "Standard form of a damped second order system is\n", "\n", "$$\\tau^2\\frac{d^2y}{dt^2} + 2\\zeta\\tau\\frac{dy}{dt} + y = K u(t)$$\n", "\n", "Examples include buildings, car suspensions, other structures. Starting with the model equation \n", "\n", "$$\\frac{d^2y}{dt^2} + \\frac{32 \\nu }{d^2}\\frac{dy}{dt} + \\frac{2g}{L} y = \\frac{1}{\\rho L} \\Delta P$$\n", "\n", "The first step is to normalize the zeroth order term in $y$ and compare to the second-order model in standard form\n", "\n", "$$\\underbrace{\\frac{L}{2g}}_{\\tau^2}\\frac{d^2y}{dt^2} + \\underbrace{\\frac{16 \\nu L}{g d^2}}_{2\\zeta\\tau}\\frac{dy}{dt} + y = \\underbrace{\\frac{1}{2\\rho g}}_K \\underbrace{\\Delta P}_{u(t)}$$\n", "\n", "Solving for the coefficients in standard form\n", "\n", "$$\\begin{align*}\n", "K & = \\frac{1}{2\\rho g}\\\\\n", "\\tau & = \\sqrt{\\frac{L}{2g}} \\\\\n", "\\zeta & = \\frac{8\\nu}{d^2}\\sqrt{\\frac{2L}{g}}\n", "\\end{align*}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Undamped ($\\zeta = 0$)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Underdamped ($\\zeta < 1$)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Critically damped ($\\zeta = 1$)\n", "\n", "$$d_\\text{critical damping} = \\left(\\frac{128 \\nu^2 L}{g}\\right)^\\frac{1}{4}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Overdamped ($\\zeta > 1$)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.10204081632653e-05 0.597614304667 0.00149403576167\n", "0.0030922207027757817\n" ] } ], "source": [ "K = 1/2/rho/g\n", "tau = np.sqrt(L/2/g)\n", "zeta = (8*nu/d**2)*np.sqrt(2*L/g)\n", "print(K,tau,zeta)\n", "\n", "\n", "dcritical = (128*nu*nu*L/g)**0.25\n", "print(dcritical)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 5. Dynamic Response to Sinusoidal Input\n", "\n", "$$\\frac{d^2y}{dt^2} + \\frac{32 \\nu }{d^2}\\frac{dy}{dt} + \\frac{2g}{L} y = \\frac{1}{\\rho L} \\Delta P$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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DAe8OrQlHUQeiwKDPKFqIH7fRtyF/sZBbh9bDQ0dkGStk54hGPEffzkc056lK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\nFkN5zdDqtL5C7KrzhZtYc17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kJ2K16eBmvXGwQX9IuwWk7qytKAu5xHNDI7FkeJ+9Zl+k\nbpKmHAu+IItla97OyK5Fc3tfZ3fl/NEm9NNPhH3rIZI7qngkoXtxHmtOqbTI8Jap9tQUw4SpSwBx\nTBBzt9PBfN37OkzVGwCrbHf9ItjziubaXCBfsbHss/oiZSMy3YwBsOdlV20Yb9gRon9RSu20+e0g\nRVT2doEWnyjq+zbA1ONGXzQn/iLZ7okjisaVM6fB/W8A0F83H4A46AVwynGwb132+9LgLtZcmi9K\nPAb7N+iFLhXWYN+33nE77BDOpRC26pmU9HkWw0dH5dy8/Eby4YgCRKkdeHMsJ2pN9hztGLSMYFJQ\nb0ZZ6B6MZL1PKcVgOJoMeLp3nVbIN81PFvKXweSFLGB7/n5EbvIRjQxA13b2VRw92l9m+okQj+o2\nOqzPTfRtSIvzuPd18FeM5ojLa6FpHux5ydFzc7czR8SPwU7o251YL4xUQhQddiVGB0ts7Ly81X/+\n/a9DeT3Updh3VU2EupmJTe3hiqy9q5RaletmJ2XeahgYiTKFLoIj3aN3WQCTFoLhZx47POmI4k4J\nw743oHoKqqJR32fN90nHwIFNjgwWlFKurOa0LDyFI+reAZGhUQQASC6CJrH0imguhfC+dZobLKtO\nnPL7DPaVzYb9G10bLOQj/jhadmOoqLaiTEXTMfrvfvsFZ2AkOia1iJUSwo4jCsf1t0+I7vat06LB\nVG4dYNICjqRtfEL87H8DUOyvnDM6usVkc5wc2JDzEW6DrGaM87j3db0xSPerm7xQb6AKgEiudOZ7\nTS2GuVmz+q+zxpoj7toRi+UnNg4cWK/bkC5anXpcwTaMhwpZVycRqRORW0Vko4h0iUiniGwwz9WP\nZyPHE/2hCIuMHfpgapqs3R+Ehtk0q/a8RXOuCMO+dTB5wdjo25MWQDSkiUQOWGuEU45IRAj6jCQh\nsgZwOhdQ2Qg1Uws32XOJ5vatH9MGQ4S9Zc3aWqk/lzHlaLjddfoMYb7s0geT0whRzRQor8u5+I6K\n2GGi3oFozpQWJ5Pi7V03tg0ATfOZoLoJhHtt25ENUTcckbn47q+aOzr0VP0s8JfrjVKu+hIRC5zq\niNLiPCql25HOrYPeKPXsgvCgo2fbtzNHhHCLE55ickQmIeirnAniy4sjcme+beAjRrBzw9g1C/Sm\npWsbxLJz3cWG3Qj4A9ANtCilGpVSE4AzzXN/GI/GFQP9oShzpV0fTDpmbIGmeRwRa8ubI7LmbE6F\ncCyqJ/OkBclYc6kcETja5Vi+IW5CCgV9RjKC8771IMZoMZCFSccUjCOK2AWWDA/piZS2+PoNoaNs\ntj5w2Y585PDHGLuIGkFtKpwKEc0V5eCI+kNRqtOC6VockRX+JxNCZt9UBf06xFH/nrFcGSS+0cQ8\nI364yke0dx2U1zFYPmV0AjjDpxc+B4uvNTYdi+as8FpWnMfedm1BmrEv5gEKDjqLEGHbzlx5svat\n06b0lVpyYZWN4IfGI+FgbqI8qr64W/NtoVn2YsRGsmxQ5mlxaVd+42I8YEeImpVS31ZK7bVOKKX2\nKqW+Dcw69E0rDgZGosyWDuLVU6CsZmyBiXOZHN1DNJI7UGUmOJaLd23XFjCTFyaISIIQNc0HxBE3\n4loBjfYlSoh39q/XkylYObZg0zE6llUB4s5F7QJLHtwEKp4U+5jwGcKeQLPZTpe7zrjCwN1knyvt\ndFfOHm20YWHSfM0R2YgIBzLoiOoqAohA91D23WrI9OmqKvMlOY10UanVBmBKnoTIlajs4BZomo/P\nZ4yNqdY03xFH5HZsjhHNdZpEZuK8sYWtjZODduSCFs3ZLJWdW2FiMpKFtXGMxZUmAi7jvcVdbpIC\nPmG2mMv0xAwRNSwLT5cEcTxhR4h2isiXRCQRq01EJovICkbHf3tbYSAUZbaxFyZkCd/TNA8/MSaE\ncwV4yIyY08meMsnGEKJgJTQ0HxI5PGhikOCIOrclB3I6Jh2jlbEORIS5YCua69ym/04YPcl8htBn\n1GorQpciwrhSOJQIJdrVLHvpLp+ZuUDTMdpqzibCwkBorGjOZwh1FQG6bTiiEZPOVwb9mjMEvTlI\nR90RDEsFU8I77F4lKxyPTdDtaDwqc/bgpnnQ25bTd8Xigt1mDx5JHZuQ2XS/8Uht0OFSLJYJWjSX\npY1KaUKUwiWPykfUNM+1WMx9QF49NoHM48KavwXoi0MFu6n4YWAC8LSIdItIN9AKNAIfGoe2FQUD\nI1GOlA6MiVkIkflRp0Z25fX8hKgsF+ttsdETjhxLiEAPOAesdsxN5GATAb+O5ks8ruvINLghSawL\nwPJH7NppPb+hedRpvyHEFEkZuAtEY8qVN7cRjzBDDtBVfkTmAlYsr4PZd7/aKXVssNqGHGF+hhMc\nkV8vvoZf62LSIcIe/wymhvMbm445lJEBrZObcCSGIegsECmD0+JGbPoitT6n6RUSOiJLNNe1HQKV\nWjjpi70AACAASURBVFeZDl9AE4dCcEQxld1qbqhLiwdTNq6jCdF8LRbrdDY+LfGoKx2RIRwpHUTK\nGhPiwVEoq9YO6IdxJG47q7lupdQKpdR8pVSD+TvGPJcp/cLbApGBLhqlPztHZBKiaXkSIitjQM5d\nYNd2bYpZ0ZCQF2ckRDmsxRIckYu82AljhYG9muNpnJ25oHW+2zshshTXmdKz07UdaqaNEQ8ahugM\nrQ2zXRNDt97r0tuGX+J0lc3IXKDB6osdGS8rpRiKxDJmWK2vDNBjI5qzOKKqMp/ui/qZmcWDwP7A\ndCZF3RluWEiYb+fcJJmRLBqPypyg0OJQcnwTt/rLsaK5rXoeZGvvhKPzirqRqZ1ZreY6t5p1ZeGI\nEps1Z4TImq8uVET4DC2aG65tzl5o4pyklOUwhO3qJCLzRWSFmSX1h+b/GTT4bx9UDuzQ/2QjRGXV\n9PsbmRzfl9fzHStou7YnOBGr7GhCNFtbi+VwosxHR5QQzSUWnCwcUfVkvSMtCEdk49yY0hep8Bui\nCVhDsyaaLgJMujVWsHa0B8umZ75eN0NbSGUhypGYzr2TKXpzLo4ooSOyRHPpxhKpzQxMoym+37UT\nJaSOlRyblq6kSCyx6KZuiCxuLYfI1lWeHzJYzXVus4+o0dCs2+AxQZxtZIVEX2TgiJRKcvEOAwRb\nlrHunK0NZhsdDNVk2TCCXi8KGKS40LAz316BTlYnwAvmT4DfZ0rr/XZB7eAO/Y/NZO8tm8bUPAmR\nq12nufhaZUdNdmsHXuBdJ+idZyTmgBCJ6IlWgF2nrY6oa3tGriyRprrRnhvJBNeEyHzHg4EsHJEv\noKNxZ2nDsClOKg+454hCFkcU9EHndtvFtys4HR9xraNxiaRDa46CnUk9lS8TRxSs1JuUHJxy0nDH\neYZWMB1aY1Ed8slmntLQrDl6B5HR7RCJ2Ti0dm7VG5D6pO5wVJ9UNOj4ew7HZtTp+pCCYGyYKdLN\nQFUW/SXozcGwKUY8DGE3Aq4ETlJK3aqU+o35uxWdWfVKm/ve0qgbMUPWNGQ3DOwrn8Z099klAIeh\n76NhvZCYC2xmHZEzsZhnjsgIaPlyNjTMLoxoLpu+INQHg/szEkOfIfq+Bmd9kQq3fkR0bWOACvoM\nGxe6huasG4PhsKYmmURzuTiiEZMjqox0QmTQdvHtLjNzSOVhQJJ0aM3FEb2pCU1ZzWgLsVQ0NOfc\ngbtOA2GJ5iJx6N2ldS/ZNklWG8CzMU3UzqE1ISpNmuWP6hMRvZY4bENyo+q8fRVDetMxUGlDiFxy\nZuMNuxEXB6ZlOD/VvPa2RH14Lz2+CWO91lMwUDmDqRwkFnXvIObIMqm3TZsrW6I5s+yoqW4NrBzc\nSD5Wc8FUjshGHwGYLP8Ox+mysyEhmkuf8NYEzkKIYvH8OSLHwT0But6kncnYpvppmJ2TI6rIxBFV\nBBgKx7I6SYdi+l3L+k29ZJrRRip6y81NQx6Lr+Ox0rMz0YaEs3X657fEYnb15ZEGAkzRXI/VFzae\nJIUiRHYOrT1to7ghyJBW3kFfWMiHEJUP6s3zYEWm5ZpkG0B/uyxYt7uXf/7lCzpB4jjDjhD9O/CE\niDwsIneYv0eAJ4DPjU/zxh8TovvoCWbMLp7AUOUM/BIn0pW/+MNWR2QNWlPWnhDNpU72QIW2Fsoh\nmnMs9099tMUR9eyyXfQAU/wR0joaD0jkfElvpyViqh+72/NbhKiiAcrqXOmqXIvmetvpkEmjw9mk\no6E5q/hjKKx1NhWZjBVMp9beLOK5UFRRFfQhvaajdX0Wyz1gqKyJMP68uNSYU8LQ25aIZ+ZPLLpp\nlKihWTucRrP72+WTBgJM0VyPOS7qsvdFYsx4JESRmA1H1Ns+5nuICIYkLeBoaNYEwIGuKh9CVDao\nXUn6y6dkL2QRbJu++M6jm3hq0wH+36Pjb+ZtZzX3CDAXuAmdM+hR4EZgnnntbYlJ8f30l9mnSQ5V\n611ntNO9bsSRDDhtwUmI5tLL1c/MqQuIuvTVAM0RhWNKt6PORiwHKTstb65liejb/rR2Wn2RYcEx\nxBTNuRR/gJ7wjhkipaC3jf1Gk32uHxvOLGTDETVYgU+zEKKRmGm6bX1rm2/iD/jZw6S8DEgsbs92\nrMTjOiWH2QYjVTGfioZmQNmOi6T+0p2OKByNm+NCdESDbAiUa2tLz6K5LBxRdERvwOoybZKM5Kal\nfpa5WcutV87HWKFsYDcjys+gP4PptoWKBh2GKktfhKNxnt+u8zc9u7XTUbzC7z5aOAdZ2xGglIoD\nb6b+lFLe3egPU6h4jMmqk+FKGxYXGKnWAy/WucN1HXEncvjedh1Wx/SPyKgjAr0YWAt1FuQVWcFn\n6ECngwfsd5xWGyAv5XgqItmib/fs0rHLqiaOucfvk+Sus+4IHQHZIVylgQj1QHiAA0bTWF1IKhJ9\nMfabDJk6okwcUUOOeHPDUaV1S73t2qQ/U8QPEwGfwW6aco6LTIg5MWwZ2KdTlFubJMkimrMs52xE\nQW6t5qzI8CNR0xijZoqO/2iHhlm2bXCCrCF+EpuksRsDw0iJDZnDtH9UXXmYb/v729mjJhDNxXDZ\niAi3HxwgHI1zzjGTGY7E2LJvIGe9O7vcp0HPBjuruSUi8jzaifXbwHfQzq3Pi8gJBWvBYYRQdwdl\nEmWk2maXBcSqpxJTkpRTu4AjDqW3XRMhUwFqS4j6dtvqZxK7ThdbrKDfoCF6MFmHHawdqQsikAlZ\n0zFbXFmGmZngiADqpuudukO4MlYwF5wDvhyiOcuoI0M7LGOFjDoikyPqyUKIRmJmVteetpwbg6DP\nYE+8Ma/vYW2CbQlDgiubOarsGNFcXe5x4TYNhN9n4DfEFM3tyr1JAkebtVyIxOOZrTkTYuOx7fAb\nRmKu221Q0pGXQ2t/O7vVRPuxCbq/srTBIjzvPlaL97YfzE2IhsPuXQSywY4juhP4nOnEeq5S6hyl\n1Hy07uiXBWvBYYThA1qcEa2xX3wDwSD7aEDymuwOjRVSCEAy6GnaQKudAbEwDB3MWZ87qzmhMWpa\nBeYiROW1Wj/jdbKbYVTGpGO2EQ8mdERWO0d6tZWdA7jSEZnvdtA3yZ4jqmrSVoZ9Y/siYaxgyxFl\nE82Z/kcZ9BHpCFiEaGCfrX4mEyyOyF5sPFo8mNVYoWYaILabg0gehjRlfsO0msvdF4DeKPXt8WRM\nk9WPyEZPZUjKfLWIsoM5ko/5tq+/nd2qKUn4sqFuRtbvsadHJzJcNkfnOt22P3fUcovLLwTsCFGV\nUmpN+kml1PNAVSEqF5HzRWSTiGzN5JskGj80r7+Wyollu1dEbhSR3SLyivl7t9P2jBzULLw02JhB\noif7XtWIMeA+O6mjnC9pi68vk0MrOBKL5RtrbmLMISEC19xIJkTjNuKPLG3wGSkBN11yZvkQok7f\npLE7/1QYhtkXGQhRDvNtsBPNmVEV0jYomeD3Ce1qgj5wmT3XEUfUM5oQWYzCGB2RPwjVkzIS5UR9\nuZIhZkBZwEc4EtXf2dHYnKFFiYMHHNeRjqwhfmz0VH6fkRwrZTV6s+ZgbLo2VoiEMAb3O+OIaqdD\nuD/jZq2jN0RNmZ+mmjKm11c444gi40OIHhaRv4rIh0XkNPP3YRH5K+DZWEFEfMCPgAuABcBlIpIe\nUvgCYI75uwr4icN7v6+UWmL+/ua0TdFuLWoLNNqYhKJ3ZXvUBPwD7kOp5JTDx+NjJtmYNBAWHLD8\n+VjNBf0GTfED6Elmry9LtKMAOqIx4o+EMjjzztdnpPivWGUccmYxN6H2e3aBr4wBX709RwSaS81A\nlIdsRHMVQR9lfiOrU2soqpjgD8FIX05xVMCnxybgWjyX4IhybZLK6zQnTNLQIJaJQNfab1CiLkVz\noOdeMHRASwKciOYSG5T8OXZtrJBpk9SmRegZ9FTatSDlhMPNmmtCZH7j3WqivSENJOdyhnGxtzfE\nlLpyAI5squLNg7k5ouHx4IiUUtcCt6NzEH3F/J0J/EgpdU0B6j4Z2KqU2q6UCqOjOFyUVuYi4FdK\n43mgXkSmOrzXPXra6FbV1NTa5/0L+Aw61AQCgx2uw4fk1BENjp1kFg3JTohyT3a3+YgmqwPaadHG\nnyqB2ukF0BFlIETWM7MsOH7DSO7EXYg/QItNHK9/ve1QNx2fz+dADp+5L+xEc2A6tWaJwD0cVcww\nnOnsgubY1O12902iTjIH97aNshLzZXItsJClL1LrA3di4zK/QVXI3ABmMOnP2AbwxLHHshkr9OzK\nKh7UUclTOqV2miNiGHOrIzL11LtxoiPKvl509CUJ0YyGioSozg6FFM3ZeCqCUuph4OGC1TYa0xmd\nTqIdWOqgzHQH935WRD4O/AP4glJqTEA2EbkKzWXR1NREa2sr0zs20qsmsvG1l+jamp2D2NQVY79q\nxBcLservfyEaqM31rgls6NQf7/XXXiXSPnZRqunbzInA67u66RxqBaAvrAfYcGiE1tbWZGGlWGaU\nsWf9araNZMhPA7yyXysUX33lJfrfzLwIpmPP7jDL1EH6pJaXUuvLgpldYY4c6uSZJx4l7itjYGBg\ndDsdYFf7CPFobNR99d2vsQR4ZUcnPb1jn9d5MET/QJzW1lYkHuMMDHa99ixvDowNB5SOaEwRjYQd\ntfP4tvXEjWoGh/qJDmN7z+yeGDN7d/P0U0/o0C8mNm4JI8BzK58ZqwcDAirM1rYOWltHD1UdLFUR\nMEP4r91+gP6D2evfsTOS4Ii2v7KSXd32PnGj7w0jKNv3e8fujYTKJ7HOLLNxnx5fa154gUZjeNS9\nR/fB1K5drHzqqYzGJlu2acL77KqVBBwSo+jIMPH9Oor0i5s7GNyTva0AgXAf7wS2vPQ0u/fX5jU2\nRyJROnbvprV1tHhv6d4t9NXOZUOG50XCI+zeszfxPecOGkw8uIPnctS9o1evDyOhkKN2Tun4O/OB\ndjWRrdu20yrZiV1Z6ACnAptefJKO3aOX/p37hzh2oo/W1lZCXWEODkR4/MmnbL9Lz0BuYuUUWQmR\niPjRoXzej174AXYDfwZ+rpQ6XPPO/gT4BjoQwTeA7wGfTC+klLoDuAP4/9h78yhJruu88/cit1qy\n9qququ6u3oBGg93YAQIgSAIFkZQIyTIoybak8Ui2zti0bGuOjs8ZjzVHsmX/o2Nr7DMzniNbkse2\nKFljDiVZFGVxAwEUCELY9wa6G713175XZVblGvHmjxcvMjIzlhfZBYqA+J3Tp7oyMyJeRb549917\nv/tdTpw4Iaenp1l75R/zmhzhh6Y/yUBPpvUQD4PXN/mtV54B4BO3HVE94Q2ROr8CL7/EfffczX1H\nAnj/72zCa3D7Jx7zWiBv7dbgqW+SyeaYnp5u+fxhpvpgqvV1F9V3FuG1V7n/vvu47cCA0Rjfss8z\neX2N/MEH2q8XhDcX4fLv8/CdN8HozczMzJgd58OfLr9Jb3Gt+bg35uFNuOvhHwnUV/uTpTeYq6w3\njnljP4eH0hyOubaUEvn1r5LLZs3G+WoBjnyU4cVBLAumpz8W/tnei3DtD5m+59bGbhx4tvguvXPX\nefTRRwMPO/jeC9Rsh+nph5pe363Wcb7xDU6NAutw76OPK9pyCOZevMrvnzmN0zXEsZEMxxJ8D98p\nvktm7lr0PXl+g/xNP+h9pvbuErz+Cnffcx9rF15vPjb7Nsz9KdMP3qXqWFrwZv08nH+PT01Pm7Un\nB4ZPf4ep+g6U4KOf/nEvRBgKKeGlLo6PdXF8erqjuel886scO3KI6Wlfl2LHgWfX6T5+D+MB5+t9\n6WnG9g0yPX23ekG8BE9/k+mPP6jqm0LwxvVNeP45enu6zMb59PNwDpYZZurQYaanA5oEatg1ePHz\nnJjMc8J37rrtsP2Nr3H3iSNMT59gufc6f3zhLW696wGmhgMaYrpwnv5G/PgMEZU4+D3gLlRB6w+7\n//4FcCfwX/bg2nOA36896L5m8pnQY6WUS1JK262B+g+oMJ4RusvLLDFIX1eko0gmJRrhj4QJYY8V\nE/bgBdQm6NBcICkmJiyWlCILkE0JDohV7KhiwdYxwA3liVRoLqSYNWQcKUs0k6FCiAKtaDAXDQZm\n11TvnYGDpFMBTeBaob+3lu9kt2oHCp5qDPVmAskK2yXlcQzXlxUjr3df5OV1eLOe3584HGVLGR3C\nrRQUM9H3fYSSFSA2dFx3HISIyUm1IJe26K+tQrYv3giB8sRuIHQspXSJNC2TpbSuQughOVSvV5aG\nvmeF6PXCy9OZDrCwAL1jSCvjsRBDkcqocHvL97FZquFIGO1TYXgdolvYKoeeSkrpqYXsBaL+3nul\nlH9fSvmClHLW/feClPLvA3fvwbVfBo4LIY4KIbLATwFfafnMV4CfddlzDwJbUsqFqGPdHJLGjwGn\njUZTr9BT32QzNRL7YGiyApA4CerExcW3ZiHTqwoXXejFQYY97BGLbydx+LyzTZeoUe81ICroMcAN\n5YnqToDC8fYc9IyG7iDTlmhmsRnWjHibAZOBFZcACf37GyKrUQghkJRrNt3Z8CsOdGcDyQrbZfVa\nf21VeUIxpJOs3xAlJivEGKKCqwzgW3yjyQrR88IoJ9WCXDpFf30V+qPVT5pwA6xOT4aodZwFN08V\n1JQPX68s/xggdhw612ac0y0sQt+kypfGzU1wjXLz3NT1awPdKgo06Rmi8NBbuea056xvAFGzel0I\n8deFEN5nhBCWEOIngegmOAaQUtaBX0BJB50BviSlfEcI8fNCiJ93P/ZV4BJwAeXd/IOoY91jfl0I\n8bYQ4i0UueIfGQ3Ild8oZsdiP5pJWawygCPSHSWEIWKiFRbUg+6LqYcWtIJa+IpLSrE7AI3dvzlr\nbsBWUh/V3gjtKj/0wnQDtURKz6tljO5DFgbLavFQ9M43pmZE13cYPesFV0Ovb6K5bikMITTy3Wqd\nnky4pz3Uk2GzVGvbbBRcQ5SvrkaG5DS051vt3Z/4+7DjDIO3+DbGEUtWgNBxqOsl6ZOrNoGD9prR\nvfDQf7DjTZKn/hA0NyF0frbNlRij7F3PnbvG5rmw4BoiYSTLowgkzV6Z3gDpMgLtES1GeEQ7e+gN\nQTRZ4adQigr/zm0TDjAIPO2+d8NwqdVfbXntN33/l8A/ND3Wff1nOhqMO7HKuejQByh6s4NFKTdG\nb+JajThDtNj2kHkPe9AaqB+E4lIgg6cTj6i/qpKy1e54o6xOnlOeS6GzzqAQEporLEQuOO0P+wEV\nLimtB0oCaRj3hNJjAOibIGXV4osGuwYgm2/boJRqDl0hjDlQi4DtSLbLdW9nCo3QXFdlGYbje1Jq\nY17pnVDSRJWiahVtgFhKe8Di64WNgwx0fly1NQ9ZfHURcxLkMhbD9hr0medlGTigvscOmgXWHN0n\nq2Wc+rkPmZ+WaJ2bZpu1RgdnwwEWFmD/3aRTIn5ugjKI57+lcmfud73RYoj6ujJ0Z1KsFMILoncq\ne2uIoujbV6SUPymlHAM+BnxMSrnPfS25ouL3OtwFp9IdzzLSD/tO13hH4Q+IMAzFAEMU5RHpRUEv\nEm3XS94Yr6+mqMKVrnij3DSOkDGYILB6PcAo+9Ek8QONz8aMI1Gthm/xNfKIhFDjaFEjL1Xr9ETk\niLTMT6sCtw7NZXeXI71DDR2aK2slZgOhTY3YUFmhffFNe6G5gPtipSA/AdvBGxTbkYmkpwByKYsR\nuZ7QI9qv2qp0oBAfqoen50U+eL1oyydme6A7XnqpniRHZLuFuv37SflFVqMwcED1tCpvei/p3OSg\nj6A1ks+yFlJOALBT2TvqNhj+vVLKNSnlmv5dCPGZPR3F9wLch8XOx0/wrCtHX8ju69gQBYbKpAxc\nfD1Z+aATeotv8MPeiUfU43pEpVy4VxE4jhvwiKqtdUR2XXXWjFh805ZP9BRijbJ36kSGaEHRsHtG\nSaVEtLKCfxwtYyjV7NAaIghXV9gu1+mmTKq6bbT4ZjxDNNoYvyGcOMNQWFTenk90NZKsANA3Hjk3\nk4bmhkSBDHUjo9wYg54Xybsq170+Wa2huQUVBQgRXW3bJOlxxIwhUdhYbzL6JsikRHxBqx4DNM1P\nvfnxG6LRfI7VYrhHtJdEBUhAzmjBf9zTUXwvoLBAjTSp3pHYj+pdZzE7mnhyezmioBBIeVPJxQc8\nZG0MMY1Yjyh5QWtPZZUNmadCjLJx0zgmbtAjajFEOyuAjFx8lWEI8oiiF187aY7IJQkYeUR6HC1j\n2K3GGKJe3QqixRCVauwT7u7VYPHVIaRdnetM8J3U45oFBoRKLU99O8wQTYZ6ZfUOQnMjcr1xXlPo\nMXfgEWkmWnvYeDGSMJHWbexbxxE3N91n3Oi2bDcIE0aMTvezQNM4NnarpC2hhHVdjOazrBYjPKI9\nLGaF6DqiVgab9xYQv1p/wCALCyzJQQZ64hdfvWBup0eVm1spRErz++Gp6wbtPH2J8VZYQgR7RD0j\nKg4ftuv0Qgvme47uygpLcoiqyQ5Lo29SeTAdxOEhQGsuhpUEunr9/Q7NNRbflCU8lfBIaKPsi8OX\nq3agvI/GoDvvWplzhXKd/TpFa+IRpXXY2A2rJjBEsfTtAPKInld1RxJYedc3AVefCzxdqL5gBIYd\nNzDTkUe0ANyc6Hr1MD28wnwskaYtZ9M3ActnIq/XEJ41GJzvGUlb6/H0bVAeKjTNi43dGoM9maZC\n69F8jrdm2xs8aux1jiiKrPBJ4H8EWtXvBAlqcz4ocLYXWJJDTe5pGFKWIGUJttKuPS4sGhuiyFBZ\nxOIbuMMClS3Oh3sjdpThC0FXaZllOUiunsQQTQCyY3HJar3FI4owyhqqVsN3T9I5FYeP3XUmkFEp\nLDa1bDfeddbLysN1Czl3a9GGKDw0V2MqrQ1RghyR6IV0d6LQnG3HGaIFONj86PvJCqGGqLQBtXIb\nDT+WpReAoboyRLJv3JxZ1jum+nsVFsFKZohqHmsuwCOavDP0uEAWW9+E28/JCaXhe/RtIyKNP3+5\nEUyhb0W+fbO2uVv1NkIaI/ks6ztVHEcGlrN818gKwAvArpTymZZ/M8Deteb7HoFniLrjDRGoB75h\niBI87FFS+xEJUMsS4bz9CJe/Iz2v0jJLcshs9++Nod3lT4K609KO2cQjsiykpD1P9D55REpR2dAj\ngqZxlGNyRAPdGYRobwWxXaoxmdpqPm8E9IJZc6Sbn0nqEYUsCSH5S+0pBG6SoLHwBYTF6nGGLwCD\nbmlBvcdcuggrpQqBOwgd1z3WXLL8ZWDNWd8kSDuybYtH3zaam/MqGtIzYu6tZ3uUErjvXmyXa/S3\nFPGP9OaoO5KtUrCAzl7qzEE0a+4xKeXTIe89vKej+B6AVVxiSQ4xmjcQ+UTFjDcsV6InQZ4o2iMK\n9wJSsYZoj1hzjkOmtMwSQ1TtBJPNMCwWhrYcUWFR7WJ7wynk+uNteSJDjyhI860JtbLazWtD1Fqk\nGAZv16nGIaWkUnfIpcP3fSlL0N+VaWuOVyjXmbQ2VJfarmgxXmgsmFXbiczPBEEVtIa8WdoIzF8G\nfgd+RBAF6mEN5yLQX19lTfZRCfa/wpHQKGsEsuZ0kXOMIWrLm+kNZsT8TNQqvLCo5pplkUkZFrRC\n2zNSLNfp62q+n1plYW0nmLCw13VEUR1avyGE+EdCiFvDPvNhgUCSqm4rQ9RnZoiy6RRrniFK4hHF\n5IhyA5Btb/eUjjREk7EekZGrD7C7ipC2yhHF9h5uGQN07BHV7BYGVWFB7WJT4dHjVNBu3IA0Yfyw\nFxuhD3U9A2UFPQbwxlGzJVISaYhAFbW2eUTlGuO4xtDgO9ShuZotEzMZlSEKGWPIJimerBBOIIlV\ncghAvrrKshyikrQXTt9kZ2QFO8AjiilmBfW8BXpEELlx1YbP2Fvvb8xNo4JWaIQI9WkqdfItHtFo\nrwrVhREWdip181YVBoh6Mv4WSkHhnwshXhNC/HshxONCiD1pive9BOEo674khxjpNWOKZVOCgtMN\nmZ5k4Y8owxBRwGkJEVzQCuqY8ibU2iU5dAM4Yz0vd8FYTkpW8MfhO0BbQWtMDRFEeETFJXDCF6q6\naWiuZfFNxJrzHV+pq7FEac2BIiwEeURjbBgn5zOeIXIS13ZF5mxCQqWRdUQQ6SnXwjqfRqC3qog0\nFcP85a98+W3+xm89T7Wn09BcQI4oQGGiFamguWLA6kxE395urBcZU9acHkeLR9QWmnMjQ2EU7p2K\nTW8uWpMzCaJCc4tSyt+RUv4UcB/wu8C9wDeFEN8SQvyvezaKv2AI6RoizENz2bTlxuHbixejECnx\nE7H4piwR3voogsKduFbDPceSHKKWhKyQSrtx+E49ooDQXMzimwpaBHUcfie+fbq5IdK7TsMcUba3\nKQ5frqn7GOcRDfa0C59u7FQZYSPWKGtoY163HRUKqhYVq9MAdccJ95zDPKIoZQVQ5BErE+oRBXY+\njUBPednYEF1b2+W/vHCNly6vc6bQAzsr3qbTFLUg1pxR/jLAMOTbGWutMN4k6fP4vXXTnG7fhPLK\n3AWlUK43UbdB0bcB1kI8ot1qnd7sd8EQ+SGldKSUz0sp/5mU8uMoiZ8b64T2PQRLqh1rIT0SmVD2\nI5OyqNadSMZaECJFTyMWX0vE5Ij08S1IHP7wPKLBZB6RHkfHHlGL1lxhoUE1DYG+h8EU7ohdp2mt\nRoshMvaI9Di0WofrEeXS0XNLNcdrhOYcR7KxW2XISeARpXWOyJfDMMxhOk6MBiK0GSL9+dCCVstq\nCwVp1B0n2dy063RV1lhkyLunUXjuYmMz8k5BtTPIVjfDPh4Ivbi3EWlEKiZ/KYLbp/eMRG5cHdNQ\nenVHKaG733HG35o8DvkJsCtQ2qBuO5RqNvlcc45osCeLJaI9op6c2VppglhDJIT4dSFEvxAiI4R4\nUgixAnxWSvn7ezaKv2DoXZKJqoJGNm254Y9ki2+oRySlK+8TIRkSWr0enp8JlM6Jgvu3rDBoiEsQ\n9gAAIABJREFUHnP2j6NDQ1S1HU+xArummEUxi68ONzY9gAbqCnXTWo3CAqSyHgVb73IDVdBb4ZsX\neveey8TliLJNHtFmqUa3LNEly8YeUbYpNGdW4KsRaRgKC+o+ZLqbXk4FbQZaEZKrSjw3d1YQOG6O\nKH5unl3Ypjeb4odOjXPaM0Tr5tejMVea7ouvyDkMgR4RxD4jycPGCfOX0LRx1VI9rTmilCUY7s2F\n54iq7V7UjcDEI/pBKeU28FeAK6iKsH+8ZyP4HoCQdcoiR1dvPCtJI5Oy1AKjJ5Zhy3Cds2ljbJU2\nlGBnyOKb6tgjcpLpeRUWcXpGqZFWHl8SdCjzI6WkZjtk9Th90iVR0ItY00YwSRw+bmB6wXG/q7Rn\n+AxribzQnJlHNNaXY7dqezUa6zsVxoV5DRH4ckR1J3JeBMGWcWK8AYofwsAQ5YMZa4F9fqLgfqem\nxdYXVorcvC/P8X19vLOtDGiuktAQeR5Rq7cel78MM0TRz4h52LjZQ01bVoLQnBZKXqRQUR54X4BR\nGc1nWQvxiHYrNj2G0SMTmMwC7bP9CPAHUsrwctsPKCynzgoj7B8K70bYioZHNN5QVzCALUNyNjEJ\n0Mg6ou4hSOWCd51JiwY1JRQ6CM1NKtadk6x5r/IyfA+7ASsJGotmk0dkEIc37vniSuxr6EUzkcyP\nS92GeI9ovF/lJ5dd1eO1YtVniMw8opQlEKLFIzLMYdpxHlFIWYE6NsojCvYCEhe0+gyRiUd0fqnI\nzfv6ODzSw7ytNpnZarIONsFkBYP8Zavqh0Z+IjJUmjh/6ap6t/XmioJPXaFQVpueoGagI/lseGju\nLyBH9BUhxFkUUeFJIcQYEN6o4gMIIW3m7QEOJjFEOkfkb8NggNCcTUwCNG0JQh91rfi8Rzki4VJC\nO/KISB6H14V4Or9hwkqCkN14KqNi9xG7TuOeLy3kkcQekVOD3XVv0YwjK+zrU8oDS9vq8VrfqbKP\nZB4RuPlLW0KuPxGr047MEYV4RKahuQBWZ81OmCNyv9NFGZ8j2i7XWC5UuHlfnsmBbtYYQAorcWgu\nkKywPR/vrYex2GJYncY6iK0ekWkbCGiqcyu63ndraA608Gk4fbvnuxWac5vi/SnwEHCflLIG7AKP\n79kIvgcgZJ1FOcTBoe74D7tQHpE0KlLzI7SaPEbSRvU3iThxSC1RJ6w50TeRrC7BPwaShz+0wUvq\nEeldaiBNNmJjkIis4BuDt+ia6s0BFBaM6dvaI9KGaHUnuUcEapNUsx3fBsVsbtphrDnHCWV0xpIV\nIDRvZ7eqacShsIgUFmsMxLLmZteV0Ts80sPEQBcOFpXcyA2E5txx1krKqMblL8M8or6JSFanbVpH\ntL2gNhk51S49nSRH5KkrLFF0PaKgfM9Ib471kFYQO1Wb/HeLrCCldIDfkFKuS6moZVLKHSll5zLL\n34MQjirgnBo294gyKdHsEZkyk8KEJfViEUKYSFkhoqcae+ER2XXYUdIlajFLUNCqx0DyhLAOAXo5\nIl/rhShYYfmJfEwc3qSgVbOSfHJL2vAZt4IAKCwa07e1R6Qbkq0Xq4yLTeqpLmMtQ6C5JUACVmc9\nrA3E7qpaPDv2iILDpZEFtEEoLGD37FNGJcYjWtxWhmhioMvrOLqdGe0gNNfSBsJ0kxTEmoPYcGki\nj6hvspG/TFlmbSD84ygsUKhEh+aKlbqX4/Rjt1KnO6LjcFKYzIInhRA/IWL1UD64EDgsycGEHlGq\nY2ZSKHW7a7BNGFIjUuIHQuPwiXJEOyuqgZjb3yR5aK4zj6itet2AlQQRobIYJqORwnHAgmO06Hpj\ncBff4qIxfbu/O00ubXke0VKhzFRmi2p2OP56PnihOUjE6nTC5kpEN1IjsoIvOe5HLexZCENhEcfd\nGMTliBbcNteTA13kc2n6cmk2rOQekRc21uM0EOMFt4190EYuhtXpOBIhDLoHt3jrmdaWKHFw54Um\nxvQE5Hu8WqIWr8h2JDtVO9B4dQoTQ/T3gD8AKkKIbSFEQQixvRcXF0J8VghxTghxQQjxSwHvCyHE\nv3Xff0sIcU/csUKIYSHEE0KI8+7PIZOxrInhRIYokxIqPJDrSxiHD1azjUuApsLUtzX6JqBaaCNN\nRCag28bQyFNl06nkZAW3JUW2uhb/WR+0Icr6c0QGoahQw+C1pAgmTRj1fAlYcBLliPL+0Jy6YFcM\nWUEIwcRAF/PuIjq3UeJAaotKLrkh8sKqCVidof2IvHuxv+0tY7KC/zwu7KT07e0GeSQuNLewWSZl\nCc/LnBjoYkkOJqdvtzbGMyhmBQOPKEKSy0x5u7nOLhF9W4+j2DBEQcSDkV5Xb66FsKDDef2GAtEm\niDVEUso+KaUlpcxKKfvd3/tv9MJCiBTwG8BjwEngp4UQJ1s+9hhw3P33eeDfGxz7S8CTUsrjwJPu\n77HIDR+I3bE2fT7dEoc3ZiaFPHwxkjaR9G0IDREmUjj2Lb7ZDjyiUl0i8+PkKsnCH8EeUXxyPtwQ\nTRDVksLMI2pfcAKVHMKQ6VJsxu0FTxfNZH4dHunl2touAHObJfaxntgj8hidkIjV6cQSadpr3Lzu\nwVGGrntI1WO1LL6J+xEVVf4SiA3NLWyV2deX8/6eff055u0BsrWt0A1KENpYcwk8okDD0Kv7RAWH\n8mN7QoFPCd3H6LQ6Cc0tUnINUVAh/0iIuoJuX98qC3QjMArQCiGGhBD3CyEe1v/24Nr3AxeklJek\nlFXgi7STIB4HflcqvAAMCiEmY459HPiC+/8vAJ8zGcyxY8cTDV6FP1p2nQaohz7sJh5RxIlDdlp2\nkoe9ySOyEpEVNnerfPxfPcV7u/nEHpEWV20LzcUgNFEeI8Da8Igi7kuER2QuLqnUr03p2wBHRnq4\nsraDlJK5jV0G7bXEHlFTL5wErM7QuVlcAkRgexIw2I0LEUhbDr1e4OAqsLuG5dKV40Jzi9slLzcE\nqlh4ru7WCSZQJG+E5nweUSrnFTmHoa2NvfdGVuU+w+amycaxvAX1UtvcTOQR5SfAruKUNkhbohGN\n8GE0RG9Ot4Y4Nfv/mV8vBrEmTQjxd4BfBA4CbwAPAs8DP3CD1z4AXPf9Pgs8YPCZAzHHjksp9be8\nCAQ+PUKIz6O8LG6fzLE/W2NmZsZ48EsLFcqVOjMzM5zcFeSLF3nJ4Pj5hTLVitN8LenwcGGB6xtV\nLoecY3urRK1uh46xZ+c69wPvvvQ0y1cbu8Wl1TK7NWn0tx25/AKHsfj2K+9SrVSYW6gY35NnZ2us\n71S5nOlnxFpIdC8vbanxnn33NN3Lb/NIaZ3LKyWuxpzjnVV13Cuvvsb2pcaOrm97jnuBt5//Fmuj\n7Z7A2wtqF1gu7YaO86YLL7PfyvLsC697CeH33OOef/ElruXjjcodtSzpuXOc2bkAwIt//p1GriEE\ntY0ahXKd//pnT5OpbZNJVSnIfKL7WSmVWFgqMTMzw+DGIncBbzz7NTaHolW5ypUKS4sLzMw0h69u\nOfcao5l+/vzZ4E6rSMmVq9c4daAaOs67ZTfOtXd50/d+uVJlaWGemZn4jUuuvMzHgPOL21gC3rt4\nmZlU+N9zZXGXiV7LG8/uZoVLu92Qglef+TMK/Sdirwlw/qLyBp77zrdJW4KPXHiT/swALz7zTORx\n169VqTvBz919oo/yldOcDnjv6vUK0rEpFovxz/r1DZbdz8zPValGrA+tGFte5xSwdvktMtb+wOMq\n7gbxpbfOMFa86L1+Zs1lgV56wuhaJjDxrX4R+CjwgpTyUbctxK/t2QjeR0gppRAicJsgpfxt4LcB\nbjlxQv7oDz+W6Nwvls/y1PVLTE9PQ/mb8OrrTD/ySKxU/x/Ov8ZyfVsdp1FcgWdsDp96gMMPTAce\n958uvURpaa35OD/KW/DyL3ByaoiTDzU+8x8uvEBXzWF6+qH4P2r7j2BtjEd+4FMMnX6WwYEupqc/\nGn8c8MQfvw1cY0kO0lN/N3ycAchfWYfnn+feu+/kk6O78G04esfHOHpP9DmyF1fhlRe5/Y67+NhN\nvu712yfgtf+F2w+PwEfbz7Hx+iy8+Sb53p7wca7+HuwcYPrRR72XyqcX4M3XuOfe+/jIpEF0evMU\nXJrhwNRhxIULfPrR6dgeSPaZJf7r2VeoDB1lXJwHQPSNJ7qfQ+88R393hunp+2H1ALz5T7nr2Djc\nGX0O69vfZOrgfqanb2t+Y/7fgzwUOobsU19n/4GD5PPL4eNcOgEr55reF099ncNTU0xPt0bkA3D9\nZXgBbr33EbrOOEwcOBh5XOU7T3DL4Qmmp28H4LXqOZ6ePQ8puPf4AfhIyDhb8FrtPTh/nk/p7+7q\nv4Hc0djv4836ebj4Hg8//Eh7Tnj2JvK7wc/yU1unya3Mk89nw69xaQZehpP3P8rJo58E4JXKOZwr\nF8znydUcvPu/M9UHA8Wu0OO6n/k6A/ua73XlnUV4+VUmupIVrkfBJDRXllKWAYQQOSnlWcBsOxGN\nOWDK9/tB2oVUwz4TdeySG77D/bkcN5BO6ICa3iylTBSHD8wRGRRwRvYjgtDixcQ5Ijf84kkYGeLN\n2U0+cfMopdwYPc5OYEuKMDTVERnG4CGiO2hMSwpjskKbwGeCHBF4cfhyrU4ubcU34gOO71M07T98\nddarIarkRqIOaUPGX2WfQF0hNCxUWAgtKwCXIRZ3TwLq3GphdPEg+J6RXDp6biqx2FpTS5fBniyL\nzlDzuQxQtxWzz/vujMPG7vFhtUTbYWFjAwKHDnE2qX6o9SG0L1TbGNRz3lVeiZTqGe3LtrHmtt3Q\nXLZkHuKMg4khmhVCDAJfBp4QQvwJcHUPrv0ycFwIcVQIkUUpen+l5TNfAX7WZc89CGy5YbeoY7+C\n6qWE+/NP9mCsbch6Ksf+WqL4h73uyPbchMHiG9mPCEKLFxPniNwYfDZlniOq2w5nFwrcdmAA6S18\n5pO06icrGLKSIOJhj2lJ0VA4jjh5i7wPJGTNAbolRbq0ZkyEmRruZjSf5Z35bY7lFDk1cY4o5Wsb\nneuDbN5obir5qSBDtGSwSYozRBNQ2Vb1Wfp6jmzkXuLgo9Nn01ZkjmirVMN2JMM+QzTcm2WNfhyS\n9cxqI1T4JLCiENi0UaNvUtXrBagr2EHrQysCyCOJ56b7N/RUViI7Doz05tpyREoWSJLajd3jG8OE\nNfdjUspNKeU/B/4p8B8xJADEnLcO/ALwDeAM8CUp5TtCiJ8XQvy8+7GvApeAC8B/AP5B1LHuMf8S\n+IwQ4jzwaff3PUdbJ0ww2mk5QYbBqL8J8arPAaSJepKiQd9uL5u2jFlzS4UKdUdyeKSH7OD+xrkM\noRfNXNpqWnDi0PBQAsYZUT8Tq3AcwEpS19MsvQSdMIGu8nIsdVtDCMFjt6nrPrRP7Tw7qSNqYlAZ\nqivUg0oLfEXOYTCiDrds1qSUyYqti4tgpaFnhFw6FcmaW3cVzDXrC1SvJweLUmYw4dx0GsayuqOM\n6V54RNIJZHWaeUSLanPhK3JOpIMISl2ha4B8NcYjymcDWXODFBF2sOpCJzDi37n1O58AJPCcy1S7\nYUgpv4oyNv7XftP3fwn8Q9Nj3dfXgE/txfiioCU/lLqC+eIbaBj0cSGsJDBgzYGa4POvN71kLCxp\n19SD4fU3EcbKCnMbKgx3YLCbhdEpuAKl9Tm6Dxkd3kzfLiyoRmo98Ytvox9RwJt9k7B1PeCNBssu\ndONZKahQa8uC4+06E6ocd5WXyaXNw2v/5LFbOT6e59GVp2BrACdl1qzRP87mrrVmrM7AglZfkXMY\nUmEMMT/8SuAjNzVo0UnDxpZFLm15ahVB0LI0Qz0NQ6T/X0gN01uYN7smbquKNup2kk1SRFFrgGZd\naJ2hH8X28KDH6HQcujEsQ+nbT//WKt0R4qUjvTnenmvWud4u1TmaMxN5NoVJP6J/hqJBjwCjwH8W\nQvzKno7iA4isG2rxFLjBaNdpO7I9JFRYUJTOdHib8pRlRYfmoCHn4vOcjCmyLa0XknhEc5uq7uXA\nUDfDE8r6bC1dMzoW/IZINDwRg3xKQ+InzCOKC81FCHxChEeUrO9LT2UlVt7Hj3wuzc9+7Ai50rJR\nGKgV6VZ5pgQeUds9MWmLLZJ4ROp8+h4at4HwFTn3ZFOUAmRnNPQO3h+a04ZoMzWcMDTnBMj7RDds\nhEbYN9IQBUlyhYVH/Qjw1r1NWRJZrr4JBu01eqNCc65H5I/GbJdrHM7ubRMGk1nwN4GPSil/VUr5\nqyj69s/s6Sg+gGjyiHJ9kO0zNkRtIqRGsvKYeUS13SbShG0qo9Ky+GYS5Ii0R7R/oJvh0XEqMk11\n07yBbzNZwUxVAfyipwFv9k3C7hrU25332NCcTuy37jpTetdpGodXC1a+tmpUQ9QGw8R4K5q05qCR\nHI8I7XrGuXVuGvSGSqWSeETqGenMI9KGKM1uNbzlt/aI/KG5gR6lArAhhhKRFWq2bFDuiwk8oqhQ\nWVQjSxOPqLDQFj3R16uZho3dcQzZ69E5onyOuiPZLjXu98ZOlUOZPRHX8WDydMwDfgG0HB+iNuGd\noomsANAfrH7disC4uMHiG9mPSCNgp2XsEbXsfLNpy1jiZ26zzEhvlu5sirG+bpblEHI7eY4oq3NE\nCXrvQIgIaQRpwokzRKEeUUROKnCAqiVFX22VrgSqHU3jSND+QSNtWe2hObc1dBganYNbx2CQvzTx\niLoGmlid2lAmYnS632lvLsVuNSJHtKOS636PSKtLr1tD6j7UzDrZ1O0gj8ggRxSlwRfB6nTiJH6k\nDCSPZJKGjQH6Jxlhg95M+PW03tyKj7CwtlPlYPq7ZIiEEP+3EOLfAlvAO0KI3xFC/GfgNJCs4cyH\nEJqs4IWvDMUlA9sxGyy+kf2INAJIE8Y5opbF1+u3ZICl7TLj/WqvMtqXZYkhUjvmhqjqJp49+rbh\n4hv5sMeEPyDKEAVL2iTOEQH0TTDQiUckZSLv0A/FmkvWPl2zu4Lzl6IhTROAVJiumh8trE5tuIza\nQNQrUFr3/o7ubDrSEK3tVMnn0k1MxZQl6M2mWCUZhbvmf34KC5DuUuLEMfBCZUH3xWN1tueqYjeO\nAaoK0EHYGKBvkgw2o1Z4vkc/14tbDcO9vlNlwtowug+miCIrvOL+fBX4Y9/rM3t29Q8wtEfUJKVy\n9fnY49o8IgNWEuiHPebkQR6Rbciaa2m9kETiZ61YYbRPJdR7smlWGeRwuQMZFXtXtV5I6BGF9n2B\nwAUntgtmACsJIvofRaFvksGVi4l0DAHYXVeN9fomE7ehzLS2jfaHgsaDi0BDQ2WFBbWDT4UvFakw\npelW+EgTdlgoMAgtuZnebMoT6wzC+k61yRvSyHelWWK4cc7ho7GXtlvJCvlxs/xlXM6mP5hAEqr3\np1FsryECX4v4BLJcdn6cFDDihHvKk65M0sJWoy5wfafKWGbDfcbMc8FRCJ1dUsovhL33fTS++IZH\n5IbmpIycqG3JSANWEqjEfHytRjtpIpFH5Gu9kEngEa0Wq9y0L+/9vmkNk6++E3FEM7x+RGWXzmro\nEUUahiiPSBuisBOHeCKJazUA+iYYcl42pm83jcE9PqkhSqda2kb7GWsh8BqytRmi6Boi0EQaE0PU\nYHU2Op8mIdKo77QnxiMKM0R9XRkWHHcXn6htSzIxXojxiECdZ7N9EY8lK4SQRzrZJFW6x+kBRpzg\nJn3Q8Ih0W41K3aZYqTPorHfkrYchKjT323EHm3zmwwovR+Q3RG5r6CjU7ZZkZAJZ+dg55pEmWnJE\nJuGPlsXX60AbAyklq8WKJ5AIUEwP0e3sNBUvRsFjze3EJ8b90KG5QMPgtqTo2CMKbAKXsFYDVEJY\nbtKdSnCMHoN7fFJkglhzELn46gUz0COK9dZNezQ1WlI0WHPJ85cqR1QPratbK1abVBU08rk0C47P\nIzJAzfZ1kU2Qv/Q8orB8YgiTMZa+7ZV6hNC3E4SNd3NjAAw54WtWVybFSG/W84g0EaS/ttrR3AxD\nVGjuc0KIqL2YAB6NeP9DDc8jsn05IlBx397wmpE2DyWBrLzR+tc33uIRJWDNDR/zftXq4lLKSGma\nYqVOpe54SU2A3cww1PFqRuJQc2VUrASsJGiE5gI9RctSYZQQj8gShP9dhQU42K6x16lHZCEZISHd\ntSlPdSXRoWmrhTWX6Vbx/AhDpD2odo9oEfbfHXm9lGUZGiKX1Vneou6k3WMT5C/dxbc7m8KRqidR\nUPv1jd0qp/a3awH2daWZ3+pR6tmGtURt9O2bzUoUI2vcwMfqrEC6sYmLjWCEUMjTHWySdtLDDEuh\njEoEJge7PI9IUeMl3ZXVyLrHpIgyRP/Y4Phn92ogHzTkWj2ifl9R68Ttoce19Rsx9Ihi+xFpuK0H\nNBKx5g43hFF12+6aLcmmw4/XNRu6iRZAKTMMJRIYIplYZw58rLmwXWDYrlMGUOg1PFWFgG6kSZUV\nwFs8RyJ2nYFoWnyvJDq0rY4IYota9Z/UtAjadbfIOSY0JxJ4RACFRerOAfd6hjkiV1UBGk3cdir1\nNkMkpWRtp8pwPig0l6ZkY0wsAjU305aASlE1njT1iDxvPcwj8oWOhw57LwdKgPkRkr9MeaUF5nNz\np26xygB91eC+XRoT/d1cX1e1gus7VYYoYMnad8cjklJ+QQhxF3Az8I6U8syeXfVDgIxf4gcaE3Q7\neqcV6BEJSyWEI5BKGZAV9DhmXw6/XhBqZUVpbQnNgfL4gnqVaGgdKk1WAKi4kjSysGAkKFutO24x\n6wKkuxXV1wCRHhGoB2XtYtvLKvwRctLyFtTLgQ9Zpx4RwLBMaIiKi6rnTUjr+ChkUqJ9QYopL9AL\nZhN1eGcZkLGLb9rYI2qQJurd6v9moTm3hsj90rQkzW7VpjX2sFO1qdad0NBcqY7aNJr2D7Md1UY7\nhCQQBj1XQu1CiCFyHBnNsAxQVYBGv6QkHlGpVmdJDrK/Gq0Zt3+wixcvq1Ydi9tlxoVLmv4u5Yj+\nKfAl4CeAPxNC/N09u+qHAI2F2k2aeq2hoyd4YI6od18kKwnUAmGSD/Z2e+6HjbTmAkJinqGNISys\neh5R48GvdSmKbG3TLPzhGTvtiZi0Sqaxmw41DBFx+HhVhXCPKBl9W93TwXp0+CNwHB3uONOWhZQt\ni5KhR9SJt24Z54gaz4iXIzL11n2hqB7XIwoiLKwX2+V9NPK5DKW6DJ0XQfBET/XnDcNRkTVu0BzK\nb7lerEcUmL/UEQxzj2i3arMkh+gqR3tEkwPdFMp1ipU6s+u7TLiq8N8VQ4RStL5LSvnTqH5En9+z\nq34IoBOYNbd5VFznRQ2nlRWToBupcWiuXoay2rUYiyhCqEcUha2SevAHexr961PZXkoya6yuUKs7\nvhoi88mt7WvoItg3oe5DS0uKSLHNiMVX5wqSeET17hFsKeivJ+ta22kNEfgUINqETxdDt+h6wWxT\nmtbHRl0vCWsOoLDgRRKM5ad830dPTnlEOwHqCmtuMetISGiubIOTj1ea0FChuWRivGDorUPb5qBt\nfWhFgKoCNNajJB6RMkTD5HajSy32DyqvfH6zxOxGiVt6iuqN75Ihqkgpd8ETEu1Ao+TDC71QV1oL\nB2MMUZuHYrjzTbkFrUnEJY0VjgMW37aC3RDotsED3Q1D1Ju1WJJD2FuGRYO2k1jeBwwStCEPe7Qh\nCl980x3kiKpSxeHjEsKB4+jQI8oGGUy3JQW7weNoMAkDPKIYvTvLtE11thdyA00eUcZEa65lXugc\n0W4lwCPa0Tpz7UKxfV3quGrPPuP+YXXbaWggwt7lL3uGIZVtWy8i+4eFqCoYXS8AJdcjSpeDpbA0\nbhpTpRnnl4rMbpQ42uUaog50EMMQNQuOCSG+4v77U+Am3++tfYP+0iEbFLoycPnVIuh7wXDx9VQE\n4nZx+cau0zj8EdBoSyeByxHikqCUeC3RkFAB6M0IlhhCJEgIN0Jz5ouv/rMiQ3PQbohaCSN+RCy+\nqQ5yRNW6w5IcIp/EEDlOYu/QD+3VtOnNQWgOM5C+XVgyy1+KJA3ZJqAw38hJmeYvfd9HI0cU5BG1\nh4q9S7uGaDen6+3M1PLTepOU7kqcvwx9Xj2liXaPKPSehKgqQMOgJ5mbpZrNklaaiOgfdvO+PJaA\ns4vbXN/YZSqz1XH+MgxRiYnHW37/13t21Q8BAkNX/ZOw+FbkcU2ip/Wq2qEaLL6NugRJAGO1Ad/i\n6+mHxSWECwtqd9Y95L2kWYFxXVq3SjX6uzNNVOieNCzLQW4zlPmp2g79Vjmw9UIUhBCqqj+WmdS8\nOYisXi8sql17tqftrU4UjrUhOlhNYIh2V5X30mmOqJVIA5EFvtDYSXeUv2zVtouCu/jq68VukgJE\nV/1khVY0PKLgHBFAMTOq9BUKCzB2S+TlVT8ikUhVAQwld/om2zYGkSzXiPBgwyMy99a1R+Sde3Aq\n8HNdmRRHR3t55coGi9tlJvMbe8qYg2jW3DN7eqUPGdqUFcClTi+r3j6pTOBxTR0YDZSNNdImE9t/\nrkQeUTtJwNQj2irVmsJy4HpEcohsKdooa9Rshwl0AjTZBFeGKORNfa6W3V5guwONCA+1E4+oUndY\nlkP0VK4YH5M0Md4KTwAzUF0hpDVGoEfUTBIIQ8pK4BH174cr3zFvA6EXat+86HW976Ac0fpOlVza\nCmz2lnc9oi3dG8qkNYaW+Nmeh4GDsZ/XMDNEE7D0btNLThS5aHtW/ew/0PZWJqkyPFCu+w1RNLHo\n5P4B/vRN9Zl9zioMtY/hRvD9vE+HSAftQPomAKmMUQjqjtNcqQ3GOSIwCM15cfgln6JyzNdcmG8L\nRTUMUfQOa7vcboh6XEOktOPi4/A122EcN5mfMByVEhEeUfdQYBzeiape355Xnm0AGh5YAo/IVh5R\nV3U9Mg7fhC2X5DHQ2cPukSr8HlF+HBDhHpET4BFtzUF//OJrTFYAn0dku8fGbJK29b3EJWIIAAAg\nAElEQVRojEMbmVKAR6RVFYKKlXVobiOVwBDpgtbt2UatoAEiBXm9AYV0VA67JRHzIrEyPOrZbvKI\nIvCpWxuit/nKUqJ7YYLvG6IOIYQgm7KadyAGnVqbEuXeDsfcEBntPN04vLFHtDXXttvT2mideETd\naVjWseft+Ie9WnfYpw1Rgl0nqL8t1CMKicNH6nltt98LP4zaYvtQrTtGcfi2MYCREQhCJog157ak\niGsW2HRftueMjKGVxDi7UlhWSdVVxeaIttxnxDeOHq+gtX1ubuwGF7MC9Lme1Jadg1y/UY6oZkuy\nQqp5HOCJhME4NFcttPQPi9okzQEissYticRPpWazkx5QHZFjjPJfuWOSv/vJo/zWT51C7K4mfk7j\n8BdiiIQQw0KIJ4QQ592fQyGf+6wQ4pwQ4oIQ4pfijhdCHBFClIQQb7j/fvP9/DsyKdFOVoBIN7fu\np1Nvte/2wpAoLDRwALbmzBLCUgYuOFotulyPN0T9Xc2GyBKC7Yy7g9qOp3BXbck+6dYyJHjYwS30\njdoFhsThAx/2elUtThEGoE0+JwY122FJJhPaZGtWLQ4xJIEwhNZXRZBpPO9ZexLlbahsG83NdCJD\npJ6RtEsZjm0DsT2nPHyfkkDKEuTSVihZIYgxB43QXKFcV+OIKT4HFfEYcDaVjuSeh+ba83aR5RZb\nc8qzDQj7dyJ6Wq7ZdGXS7jMSPTfTKYtf/pGT/NAhrSTzFxyaE0L8mhDinwghwgXV4vFLwJNSyuPA\nk+7vrddJAb8BPAacBH5aCHHS4PiLUsq73H8/fwNjjEW6tYtpTELYcSRS+kJlW7NKrsOgr4fOKxl5\nRAMHYWvWzCPaXVN1RwPNiUrtEVXiQnMuWaHttN2uUdY72gjU6g5j9opKjKeDF5EwxDZlGzgIW9eb\nXgptPlZYAGSkF5DuwCNakO6j0jKOUGy5YSAT+ZsABNYRgVo8wlhzXhhXe+tzjWNiYIkkhkhFDbI7\nC+71Yv7GrWCvrDcXrMC9vlMJZMzpY0BJA5kWtdYcyZCdfJMUq74NgUzGSEZnhIfqbT4SbJLKNUc9\n5wkKfL3N8/dAaO4llKTl/3ED130c+IL7/y8Anwv4zP3ABSnlJSllFfgiDSafyfHvO5QwqG+i9Y4p\nTawQL8Dr+aJ3gVvX1UJpwMQxmtgaA1Ows0y9ogo5Iz0ivTi2PGRejijCI5JStRBuDc0BVHsmcBBm\nhsh2lBR9BzkRVegbZYim1MPj85pC64gC8hGtSKcM5WxcVOsOc1L1eDK5F944BoIZTCbIePTtlnEO\nTsFmsDFsU8NO4K0n8ojc8+V25r1jI7E9G2gAerKpYLJCMbgFBEBPxse2G5gy+j7qtsNQzTVECean\nZeIR6XvrWy8iGZ3bc6HG0PvOE5IVujIpZVQMIhdNY93j0Fw0LzMAUsov78F1x6WU2gQvAkHUnAOA\n/6mZBR4wOP6oEOINVGfZX5FSBgqzCiE+j6sWMTY2xszMTOI/wqlXmZ2bZ2amUTX/QHaY7fde5Uy6\n/XwVd2G4evkSM2KWe2fPUM0O8LbBtc/PqsLR7zz3PGM90fuH8cUiHwHeevZrwCDn3zvHTLFdcw1g\ndOUFbgNeubBEcakxjp2aGuvpM+8xU7oceGzFllRth7WFa8zMNLzAYrFItZxijSHq517mnIj++7aL\nuwwwz0r1EO8k/B7qtSqzcwvMzARrue1fLnGLU+PPn/gy1ZzSwFtaKVOqSIpFu+l737c0w0ngpbPz\n7F4PHoddr3F9do6ZGTM69lsrdYr0ULF6WHn3RS7Ugs/rx4NLF9kcPMlZd2zFYjHR/Hx3VS3QL73y\nKhsXG+yxqdUKN1W2ePZbf4ad7m065s0Vdcwbr79O4XKKyfmnOAE8/+51KpeiGyItLVbYLdsUi078\nOKXDwyJNafZt4CQvv/QCF7vC5/NDq1dYZYL3Ws9bK3N1brHpelVbslO12VqaZWYmmDCUFpJzFy9z\nJWtzuLDIt596AmkFM1wdKXEk2EtKZvO5t69SOxfeRM6P5V218Tn9zrsMbJ4P/IxlV3kYuPzGt7m6\nqTyMcrXKwvw8xXSt+V5KySfXrzHfdYKLAfe45Cq8nH3vAjO1q0ZjnF0oY1ccrhXgwMY1nn366dhN\n8aGr3+EY8O03L+KkDI2XAUINkWHR6rqU8m+HHP8tIIgC9cv+X6SUUgiRsFlL6PELwCEp5ZoQ4l7g\ny0KIU1LKtgbrUsrfBn4b4MSJE3J6ejrxtftefpqRsUGmp30y+VdO0G1XGA84X6Fcgye+yfGbb2b6\n4WPw8jYc+QQm1157dRZOv8n9DzzA4ZHe6A9ftuDs/8VdR0fgZZvbTp1k+s4QV/qFs/AO3PcDj0Pv\nqPdyuWbDk1/n4OGjTE/fHHjo4lYZnniSu06dYPqBhnDjzMwMRw70s3xhjFPddSZj/r70808yWlun\n66a/anQv/Oh58SnG9g0zPX1X8AfOleH8b/HQqUNw8D4AfufyS8idKvl8vfl6z74GZ+D+z/yYYh8G\nXe/5J9k3Mcr09J1G46u8swivvoocPMTBXoeDcX+fY8O315k4fg8T7mdnZmYS3ZfsxVV45UVuv/Mu\nHjzmi6CfXodLX+CTtx+B8VNNx9TfXYJXX+H+++7j9oMD8NRzcN7iY5/5sdg6oic3T/PWxgL5fMZs\nnG9NcSCnpHg+8fGH2NcXUhhZK8PMFvtv/Sj7H2k+78SZPyeXsZieftB7bX6zBE88xb2338r0/YcC\nT5l78s8YGd/PkcOfhKtf5JG7bg7t1Fqp2/CNr3O0rw47XXz8Mz9qXEd0fX0Xvv00x0/cyvR9Ed7t\n6+McHUpx1L1v1tPf4PDUFPn8cvO9LG3AM2WmTj7I1EPTbacp12z41tc5cvQY09PxivcA/+nSS4xk\nqhy67eNw/ctMf/QU5MNbwgPw378CC4M8/KnPGl3DFFEz7CPA34l4X6ByOIGQUn469EAhloQQk1LK\nBSHEJBC0fZkD/N/gQfc1gMDjpZQVoOL+/1UhxEXgFhptz/cUaUu0s1QGpuDSTODnm+LwtZKS2DcM\nwaSTuN6u22xtzwET0eGP7VlVMd7TnPIzKWgNkvfR6O9KMydHOWUQ/sjZBbqcUkfuftoS0eFKXaS3\ndd0zRJGhua7BUCMEnbHmAOp9B81Cc8UlcOodU7fBV2UfNDdBjaPVEHn0bfeF7TlF6Y8xQuDekwS5\nCQYO0rOhQ3MR3r0XBmq/F/3dGVYKlabXdEsSf5PGVnSlhBuac+fa1vVQQ6Sf7f7qkgqJGRohaDyv\nsTndgeZwab1VeUUjhtLfSUGrIiukGs/I5vV4QxTDKu0UUTGeX5ZSPhPxbwb4Fx1e9yvA33L//7eA\nPwn4zMvAcSHEUSFEFiXC+pWo44UQYy7JASHEMeA4cKnDMcZCN49rwuCUSvwF1Iw05Yh0gtLwS01E\nVnDjyKmCWviic0SzgQ+ZEIqZVImgb2+Xww1RPpfmujOszh+T1xquJ4/Ba8TqnHkLTsMIhKpvb83G\nbgwS5UNoGCLZfwC22ltDt49BLzid54gaVN6AuQmBLaobBa0+Io3h92EsyOuN4xC9ZRVZj2wDEUGY\n6OtKe/NPYzVC8FQjl1LtD7z7G5Izg8ai3tdB3Uxk92A/BqeaSCyKrBCwLMeQRzppUVLRhsjboBjO\nzz1mzEGEIZJSfinuYJPPhOBfAp8RQpwHPu3+jhBivxDiq+6568AvAN8AzgBfklK+E3U88DDwlpsj\n+kPg56VM2gjGHNm01b4DGZgCZGDyr8kj8uojzAxRIrJCOgf5cdKFuaZjA7E1GzqGrkwqso5oazfc\nEPXm0lytD4NdUZ5fBMYc1yHuYPFNWyLaOHcNqJoR34ITWqsRwtDyI7FH5M4POTCltMLKbVHiZkRU\nz5ui0SurZW727lMFvgGeWaP42X1hK5gkEAR1T5J4RFP0VFbJUI+Zm+GJ8f6uDNulZkPkeUQh9G2A\nXEqo+qOADUor9HfXW1lK7AXEqm9ruAxXTaZp06LUiDFEutg6yffgseYG441y0zhuwFsPQ6zfLYS4\nD5XXOex+XqBSM3d0elFXzbut566Uch74Yd/vXwW+muD4PwL+qNNxJUUmqBPmoBub3rzW5vJ7HlGT\nITL7Ui3P9TZnJ6WLyuuK9ojm4KZHA9/Kpa1IZQUdmmutIwLlEZ322GLRLv8+6Sb+O1h8jXTOBg62\neUSBzf62Z2Hq/shTpa2AzUcEtEck9MO+PQdd7W2sPSScF0EIDeNalrrHAYuv4xkiq1FbduIxo+ul\nLBHeAC4IAwcRSCbEWvTc9IxyuzfS351mu1xvamW/VjTwiNKuIkM6p0KPEV5AzZaksFVb7KT1babP\n68AhsKuws4LM73MNUcDc3JpVjNwI5ZGkpQUea65rQNVqxZUXVIpQWn9fQnMmrLnfR7UNfxtIMt0+\n9EhbIjg0B4FfatPDvpVs56td/dgdlsbAQTKzb7vjDHF87ZoKI0Z4RJUI+nZUjiifSzPv1c/MwoF7\nA88hpWRcrmKLNKkOtNVSJk3ZBoLCHy0LYHVHJYQNPKJOQnOWf9e57yPhBySoLQuD/r4Dm6QF1FVB\nyyZpZ9WtLTNbcFQtV4Klwb0XB8Wq11k0EBtXlReX6W57q78rg+1Idqu2Vx+0WqzQlQnWmdPIpQS7\ntXpjHBFeQK3uMCnWsKTd1EXVBMYekW+9cHrVZi0wbLxxRc1jK/xvU8XWCQta3cL1uHsBwKbLxhs6\nYnwNU5jUEa1IKb8ipbwspbyq/+35SD6ACAzN9R8EROCX2uwRXVNV0oYFnKmkldMDU2R35oGIuoSt\nWVQBZ5ghivaIdIxea3j5kXfJCo3rBKNmS6bEMsXceEcFnCmTNtUtHlFgrcbGFfUz5iHLpDoLzaWH\nXU85bte5cUWNIUFivBWhdUSgPPaA70OrU1iW8N2L4CR+K3SOSBpvktTiu1+shcvZQONeBEAXUfvz\nRGvFKqP5XKDOnEYu5VPtHogmkNQdh0PCDRsnXHyNlVAGGnm7tlouPzauxBrDpDVuXmhOjyN2brrL\n/uAR42uYwuTJ/1UhxP8jhPhpIcSP6397PpIPIAJDc+mscp8DvlTbL7mzfiXR5DYSUfRjYIqUXWaU\n7fCE8IZbHxSy4HRlUpEFrVulGvlcOlBBuTeXZoteVa8SkBzXqNnqYS90d5acNyIPDBxUIYWKaugV\nqL69Hn0vNJJ6RJp1mB2YVKEVU0N0A2h0kg3xiAoLyhv2Qe+n0n5DFMIma0Vj9284QDcKcMiKqcXa\nuBpuiNxw8HapUdS6ulNlJIIxB65HpDXqdFFriDdXrcsbNkSxc8XnEQU2J9SIuBcaisXbAWtOjyPO\nIzLcrHUCE0P0c8BdwGeBH3X//ZU9H8kHEKFf/MBU4OLb5BFtXIbhY8bXMp7YGu65D4mlcI9IL74h\nC47KEUUboqCwHGiBSUEpf6hxnQBoQ1Ts7cwQGYWF9N/nPkiBZAXPKB+JPJXKESULzWVTFiKVVt6I\nfpiD4Dh7YogaoqdBHtFhkE6bQfQ8IiEa90LnO2PgGSLjAXZRyIxyUEQYonpV5YhC5mZ/t/LCmz2i\nCqMhqgoayiPSoblDLpkmuPhVz01HpJPniEw3jjo/s3k9uDkhKFHU3dX4uZkyD805jqRSdxqGaGAK\nKluKUBOGzasqbNwzbHSNJDDJEX1USnliz6/8IUAmHUDfBjXBr7/U9rKeJBlZVfRtw9AHdG6IjojF\ncGbSxmVI5Rqq4S3oyqQoVtplVDS2S/VAnTnwCUz2TpFfD1Z1AKjtbDAmilzq1BCZeCja4K9fhInb\ngoUlN66oRSHmIeskR+QRI4aPwVr4vaC4pHIzhp5IGHTeJZBUoe/F2qWmjVCTLuHGFaWbGJCbCUJD\nGd58jOu5AxypRqhfb11XBjPWI2oOzZ3aH0EEQXlEJZ0j0vd5/VIgCUAbokrvAbojcjNBSPS8Dh2G\njctew0XLEs1W3QuJxYTmEjQo1J66Z4h02G/9MuwPKQ7fg7BxGEw8oj/3iY1+Hz5kUyG745Gb1YNU\na5ZG0ZMyX3JzMwkWHON+RBqDh5BYHLZiPKKhI6G5mVw6FZ0jKtUY6A7ey/S6Uv2bXVPqQbKDDZqz\npsq8ynmz3XcrjCirniFS17JlgEe0ftloY5BOJUvMV23bZ4huUtcJ+w73KPQRWfw84lbdt2wOmvoR\nbVxJtkkSCT0iYDU7xWERIbQZcy/0BkgTZqSUrO1U4kNzaeUpVuuO+j4gdHNQsyUHxTKVvuRzUwiB\nJQwN0cjNsHbBe7bbIumG8yLJ3NSRDi9HNOKqp0RsGtm4GmsMO4WJIXoQeMNtx/CWEOJtIYRZ680P\nOTKpkNDcyM2AbIQ4XOiHPb/jhkXez9BcOkupZz9HxFI4a279cqQx7MpEF7QGtYDQ0ASG1exBJaG/\nHZwUFu5DVu7gYQdDDyXXp9hX7oLjBHpEl40MQEceUcrnEVUL4XVVMTk7U2SCWoVr9I5Btq9t8W3q\n0Lpudi80EueIgJXsAUaJqKuKCZVqirauHdou1anZMlR5WyPnrvIlLXxqZWDtQuBntUdU6+9sbho3\nDBy5GTavUXc3rqnWnKshWy1JjZvO/XoekecphxgiKdU4ErIHTWFiiD6LUij4QRr5oR99X0bzAUNb\nGwgNvetsmeB6AevRhqiDXWeSRbCYP8RhsRjsEUkZu/NVHlFnOSJNqV1Ou2G/9WCBC7F5BYBaf2cT\nPFbiR2P4mJeraiMrOHZg3VfY9ZJK/HgekeeNhIh9rF8GYd2QqoIeI4SE5oRQf2eIR5Syy6qfVgeG\nKEHqjOWMm3OJuhfprrbOwRp9uTTZlOWpKSwX1CI+1hdHVlA/d2t1JV8UcC805O46w6JIbaCzuWmZ\nlBaAmhfSQbghuDYizep5RefvDmzb5iGToMZNRzo8jyjTreZdiFFmaxZquw3PaY8Ra4j8lO3v07eb\nkU1ZXp1IE/SX1fKlare5p3hVVfsnSPol9oiAYs8Uh8VycI6ouAS1nXiPKEJrLqhNuEYmZZFLW8yn\n3B5NIQtOeuMSy3IQK5cP/0MiYJnWTozc5C04ba3CN64ofTcdqolAKmGtRtVuyRFB+K5z9T0V+khH\n7+pNxgg0dw/2Y+SmtjHo/ERqw3199Hji6xnXuAFLGbdkICwUtHJOjSHEmxdCMJrPslpQHtHCljJE\nkwPRea0u1yPyursO36TyZQHIbarntz7cWYo8bVJaAN56Ybn3ou15XTkHY7fG5mbSKXNv3QvNpX25\nr5Gbwg3R6jn1c+z9oQuEGiIhxGtxB5t85sOM0JqSrn5VIxTmEW1dgNFbEiX9OnnYt3sOMySKZMpr\n7W8uK2l7xm4NPT5K4qdmO+xW7VBDBKqodckZhHQ3rAZP8Oz6Od5zDpAJUjowQDquH5HG8FFFW64U\n21uF63sRVWiqr5eykuWI/KG5wUMgUhGL71mjMcRBCKHmZtjuePgm5QH6KNxefkIvOAnG0Ulobkl7\nyiFGwFt8IzCSz7HmekQLW6r31uRAiJK3C9dRV6E5aGxQAr7Tbrd9gzN6S+Q5w2CcI3I3KJa7WWvL\nX66eg7H4MQSKMIegkSPyGaJh1xAFPU8r76mfo99lQwR8xM0Jhf17GxiNOP5Dj0xYaA7cBGRw+KN7\n8z0YT8b/SNQq3MVGr9rhd22ca3/TW3zDx9GVsSiHeESevE+UIepKU6g6sO9WWH63/QOOQ9fmec7L\ng9EV9hEwjouPuQvryrl2+vaKNsrxD1lS0dOKPzSXyqiFb/ls+wfrVbUIxCy+pohkUI3eAtJump+2\nIxECrNWzqt7JwDvU6MQQlWSOJTGqvMBWVIqq4Dvm+xjNZ1l1ZX3mN8sIARNxhsj1iDwK98hNiqkY\nkMPs2b7Arsw15JkSwrjAtGcYuodJbajNWtMmaWdVdVE2MABJCloboTm/R3Szom/vBNDqV8+p0GDv\n+7PkR9G3TZ6I8ATCXwKk3YJWv96Vh9Fb4J3/pnYXOr9jS0bZUh5KhAEIgtVBjmgtr3ZR2fUzwGea\n31x+F3pGIT8WenwuncJ2JDXb8RLgGlHyPhq92TTFcl21HDj3taZ7oU5yjVR9l3NyitsynRsio3ui\n2x4sncZ2Jls8orNK8yvXZ3S9jnNEAOO3wVxAV5L1iyo8uAceEagwTegmyXcv2Kcec4/SvnJOGaEE\n4cGG/JT5+OqOw5XUEcaXTre/qY2TgUd0drEAqN5YY/lc2zxthZcj0h7R+G3q5+LptrqpfOESF+Uk\nI+nE/UMB9cwaz5V9J8muvQv8SMsmSYfE4pfjVIKC1gZZwXe/tOFffhfyjzQfsPKeUXiwU0Spbwfm\nhlr+GfY+/nAiG1U4OHmH2l34ClvrjuQWyyUqJFxw0h3kiHYyw6zKfrKrZ9rfXD4TOwY9SYPCc9sG\nhijflVZ1SOO3qV1dsaVw0PUM3nMOev2PksLYEA0eVsV4S6epO7K5en3lrLcgxyFxGwjbF5oDZQQ2\nr7UXDi6be2UmyISVFoDaJFkZWHzbe8nW92T5TOIxeL13Ehgi25FcTt+kFtqWMgfTxXc0n2O1WEFK\nyfxWKTYsB36PyJ3T+04CouleaPQXLnJeHoxuVRGBWGV4PybvILt2BguneZPk5WbiQ3OZJDmiakBo\nbtJt9rjYQoqWUj0jHYYoTdDZ0/99AL4GZEE5A/2lLrzpvWQ7khNCG6JT7cdEoBOyQt2RnHWmSLUa\nIsdxF99or0xP0iDCglFoLqcNkfu3Lr/T/AE3XHdeHgxWwzaAsWGwLPX3Lr3TrDVn19UO3DAkZqT2\n7UObRzRxu/q51BKqXDmrGHN79LBHyr2ks8rwthiiXqumaNMJN0met57AENVsydXMMRUiXGmZn8vv\nqnYVMazSyYEuarZkpVDh+vouB4d6Yq/b8Ijc0Fwur8JzrYtvaYPe8iLnnYPNG4kESOQ9T9yOVS9z\nVCw0b5IWTyu6fX+8AG3KssIJKi1oo2+DCrv17W9aswCVWy2tJ47iJMH3DdENwKvXqAd8+ftOqcS0\nb4LXHYePiGvY3SORIbEgdEJWsB2Hc/IQqZWziqKssXEZqsXYPJVm1HiJXR8aobnwsEU+l2anUm8Y\n3cWWMMziW+x2T1Kgh5yfvZMAyR7221RoTvp2nStnlAy/NhAx6KQxXrNH5IaCWkNS86/DyHFjNYM4\nBOog+jF+e9MY6o7kpHVFqRlMmrVB10h3kCOyHcm1rMsubZ0X86+r+xTTHfamMcW0fGdhm6vru9y8\nL555mUu7dUR+L3/i9naPaP51AN6SR2PDfWFImRJpACZUV51T4kpzucX8a0rpwCCHmrGEJ9UUhzb6\ntsbkHbDQYpTde8GBe4zO3Qli/zohxP8shIgmsP8lhdb0CpT5yXSpXXaLR3SPdZ7K+N2Jr9UJWaHu\nSN52jiLqpWaywOzL6ueB+yKP73Ll9ANDc2W1o4wjKxQrdegdUTu6+RaS5fWXWR1Ui96NhOaMwx8T\nt0N5iwPOYiMOr+/FwY8aXy9RPyK7xSPq3w/dwzD/RuM1KWH2FeMxmCC2yn7idkXhLyiZHduR3CVc\nZmPMvGiF1cEmqe44rGYmVbjUvwN3bLXwHYwfw7Ex1dL9iXeXkBJuGY/P8WmPyKNvg7oXm1ehtNl4\nbU7N1bedYzdkiIyf17ETOKksp6wrjU1SvaKM9H6z9SJJaUEgfRvUJmTtPFR3G6/Nv6421XoT9T7A\n5A6PAy8LIb4khPisiNJY/0uGyNAcqAk0+4pHDbXKG9xszVPbH918LQiJWoW7sG3JK1om8NoLjTeu\nv6Tc/ZgQTK9riHYDPKLtiKZ4Gl5oDuDQg3D1+QY1dHsetmdZHFA7wU5Dc6kkCeFDDwFwL2caD/vs\nK4q0YVjAeUMFraCSvYcehKvPNV5bv6RCHwaLrylie9McekD9vPrngKJv38F5VdTYl6wvVCceUd2W\npFIp9Te7YwBUjqpahP3xu++J/i66Mym+floZ01vG4z2irKW+glLVJzmlNwDXX2y8Nvsy692H2abX\n23AmRaJNUirD7vAp7rPea3hEC28qVZKQXl6tyKTMw8aBrDlQa5Z0mgk1115Q0ZNsfOizU5gUtP4K\nSlnhPwJ/GzgvhPg1IYQ5v/NDisjQHMDRh9UC44bnRlbVl1s/kNwQdUJWqDuSWTmK7NvfvPBdeRam\nPhrZZAugO8IQbZVq5NJW+0T2IZ9LU645Kldx+CEoLjYKWy8/C8Bc/kY9Isv8YR87gewZ4X7rrDLs\nUsKlZ5RhMNxfpZL2I2o1RACHP67Co9uqgy6XZtzXHzI+bxwiSwsAJu5Um5Er3wHAqde5X76txpYQ\n2iNKkiOqO1K1qzjySZU71JThS0+rn0c/aXTd4+N51neq9GRTHBntjT1GCEFPJtU8pw/er8R/L3/b\nHVwVrnyHq/33kbZEZH+jKCRtGLg18THuFBfJ1lW7Ei4+DQi1jphcL4G3rj2itufu8EPK+7n0jPq9\nuqsM9NEWFt0ew+jpl6rj1aL7rw4MAX8ohPj1Ti4qhBgWQjwhhDjv/gwM/bke2DkhxAUhxC/5Xv/r\nQoh3hBCO28rcf8z/5n7+nBDihzoZnyl0EWZgaA7gmPvlXfgWAJNLT7Mle3AOJA/BWB2E5nTbYXHz\np+D8t5Srv3ZRJeePx9+aHle41FMr9mFrN1xVQUPL/OxU6nDTD6gXz7md39/7GuTHme9RyflOc0Tp\nJIZBCOSRh5m23iAtbHp3rqr6kVvMp0nGtFreRVuOCBqt2c99Tf08/03F6ttDVlLsfUml4cjH1bUd\nh/27ZxmkAMc/E35M2LXcuZkgMtegix9tfkY4/4QKaRt2h/3MR5T39pmT48YhtO5smh2/Icp0KQ/x\n/DfVH3H9RagWudh/f8dhOdCMTvPPb4x/jLRwGFpyPcSLT6pQmaECi6Lsm5MVcmmrvXi2a0B5YPr7\nuPqcyqEee9T0z+gIJjmiXxRCvAr8OvAccLuU8u8D9wI/0eF1fwl4Ukp5HHjS/c0BaJAAACAASURB\nVL31uingN4DHgJPAT/tUwE8DPw58u+WYk8BPAadQGnn/zj3P+4KMllIJm219EzD1ILz5X6FSYGrp\naZ507iGdSS7h0glZoa7ZYSc/p8Q23/sGvP0H6s0Tj8Ue3xMVmouQ99Hocw1RsVJXygaTd6rr767D\ne9+EWz5LxfUmOw1/WCIZecA++TlGxTZHtl5mfGlGMdWO/6Dx8ZoubtqNtGo77bvOfSdVgeLbfwCF\nJbj4FJz44T2t0UhbMR4RwG0/oVTirz3PPZtfp0q6sWFIgFRHrDlHzc0D96r6nTe/qGjtV56FW3/E\n+Dyff+QYv/7X7uBf/FVzFmpPNtUcmgM49eNqgzb/Grzx/0I2z3s993Q8L0HPFXNLtDl2HytygH2X\n/pju3VllED9iLuuZhEhTqTnh0YxTn4OFNxSz8/X/ogpZj3zCeBydwMTcDwM/LqX8ISnlH0gpawBS\nSofOG+Q9DnzB/f8XgM8FfOZ+4IKU8pKUsgp80T0OKeUZKWWAXACPA1+UUlaklJeBC+553hd4OaKo\nJ/C+n1MV87/7OLn6Nr9X/4zX9jsJ0h15RC477Ngjigr7zV+G5/8d3PKYkYputztRdyvBoTlTj8jL\nE937cyru/buPK527B/4elbpaqDsNfxiLnrqoHfs0C3KYhy/8K/bPfw1O/VhgL5qo64FZiFRK2U5W\nAGVw7vlZuPY8/P5fU1I79/9d4zGYIGPSJO3EDyvixJ/+Ih/d/DpPph/pqOlZJ8oKtiPVIm9ZcPfP\nqpDcF/+mevPenzM+Ty6d4m/cN8Vgj/nmriebat9cnfoxRZz48j+A038Ed/wkO3TfuEeUJFxJmi/Z\njzB4/Vuceudfq1qvu3/G+Pgk8lOqO2vI33bHT6lQ5Vd+Ac7+d7jzf1Be4/uI2JJhKeWvRrwXUClp\nhHEppW5GsogiRLTiAOBvIzkLPBBz3gOALyvPrPtaG4QQnwc+DzA2NsbMzEz8qFtwZkUtsC++/Apr\nF4J3F8IZ447B2xmae5WXex/l9fJxnv/OdzwaqSm0Abpw8RIzmNURX7lWQTo2M88+x/DBn+G207+G\nncrx+sAPs2vw925X1TXffPcs+3aa5YrmVkoM5kTgfSsWi8zMzHDJvT/PPv8yi0MphHOIu/o/wsDi\nW8ztf4zzZ1a4dLVCCqej+w9w/VoV25E8/fTTRsasVJf8t9rf5jdL/yd2uocXez5NKcG1r15VIptP\nzTzjFTSHoe5IpIS561eZmWnuvSOcE9zdd5z+xbe4fvBzXHz7Os3TvQF9P5OguF3ClsQeN3b0f+Lk\nu/+Gkujjt53H6e7gezi/oRb1nd2S8TgLxV3WLPV5y76Te3qPkl98iyuHf5Irb1wE/v/2zj3Kruq+\n75/ffY1mNAIhgUEgHpIRCsKxqY0xGAO3AceYpn4kcWLXdchqvQhdJm3+8OrCiy4v/5O1nLRpmzRx\nHZq4JY0bQ22IaSIsXh1ThzdCAomXEDIgEAJJ6DGamXvn3vvrH+ecO3dG97H32fvec0fa37W0dB/7\nnLNn333O3r/f7/v7/rrUxXHA5OQktZk8b+49ekxfT1/7VS588T8zM7KSzcUreH3XWzRq9dRzc/LI\nNNWp3r9Bgi3v1Lit9jn+2fgznHJ0F6+u+QqvP/0CYPaY3bunwnSlZnS9n++eQWud77vV532Z83d+\nj+klp7M59zFmU46BKdJpVxhARB4A2m01b219o6oqIhb7Bj9Q1duA2wDWr1+v5XLZ+hylV/bB04/z\ngQ9dzGVrV3ZuWC7Dwdd5YksN7nuZcvkq65hIo6Fw30bOPXcN5bKZMvJDh7ax5N23iP62Mnzqt8gV\nSly65GSj46erdXjoJ5x17hrK5fny7/XHH2Lt6hWUy8dWc5yYmKBcLrPstQP80dOPsm7DL1Je/77o\ny6uvhsO7OWv5uZwlwqYDzzG2fy9pxh/g2foO2PkyV151dRT87oGDU1X+1QNT3HnlfazWd7ny2uut\nrvdybie8/CJXfOLKpsXXCUcrNbhvE+vPfz/lq9twe64uw+G3OPuUc+mmZpaMpw2+9+oTHJ6epVzu\nRT4ow5Gv8s27djB1QCmXzQLjrTjp9ffg8UcYWbLEuJ/FJx7izFUt8+fqMky+zXnLz+E86x6YY2Ji\ngjNOHWWqWmszNmU4fBNLlpzMx0tj/PDgFsanD6Sem9956VFyAuXy5Ubtq9vfZmrz07z1pQd58YWH\nufy6L2BesQwePvI8j+99w6i/f/PGU5zSmOrye5fh0NcZHVvBFZ5y27qhbwuRql7b6TsR2Ssiq1R1\nj4isAtoVjX8T5t2fq+PPuiHNMamRkBV6+uJzeVixhrpGar4dC9V1O0XTJWTuc67FZIUmLJNolxRz\nMdW1PX3bnKzQcny+MI8qXa21iaFYoLVyrclkTlxq1dHTqFePprheQtnvvXdKSoR0pKbni30rNFY0\nqVybYNkZTLGbfG461bVSJbTWFyigF0rHaL31C6OlPPuPVtt/edKq5stqG41FG9iy5pL4b640SmWJ\n3b0KSTUA84TWjq65BCe3dSb1BVkpK9wD3BC/vgH4cZs2TwLrRGSNiJSISAj3GJz3iyIyIiJriGjn\nT3jq8zEwihG1oFmOOWX8MycWpcJpc7NbQkQYXUh1JbLOjlRqnLSk+6M/KRd+tHIs6y5BJWbvpIWt\n9FGz3EHKceladG4BEjZl2hwpFxRMYkQtqDcaqcckjcRPRN/OJiWxLVmhDWbrjdSq8GBXHwhaihOm\n/B1sElortTojXVIvBo2sFqJvA58UkR3AtfF7RORMEdkIoKo14GZgE5GT9E5V3R63+7yI7AYuB/5e\nRDbFx2wH7gSeB34CfE1V+6YQ3lVZoQ0S8kDawHxU38S8fZM154B2gd0jMzVU4eQeAeLxhWSFNqi0\ny7OxgG1+Vd3DzW56vaZF5LCrTouO1YM7oK4Oi3MK0VMfczMt2m2u2mG2rhQtY7mtsGZ0Os7NQpzQ\nasLorNS6sOYyQN9cc92gqvuBa9p8/hZwfcv7jcDGNu3uBu7ucO7fB37fW2e7oJnQanjDu9580UJk\n8XBpNJx3nWOlwjG7x4PTkVtjuU0eUQdUaw2nnZm1RZTc7Ck3A8nmw8Q1V+nlmusjipYKEC4WUaoy\nEPVGKhe1D4yW8vO15jqgXfkTG9gyOl3nZuumrNd9PzPb4NTx4ZEaHZ6eLELYLkSurrIcw2ERHZyK\n5H2Wj3VfiEqFHKV8jskubpBKrc6II0UWzGntyTqe3iKK+mpjEbm4HtOi0K0MRBvUHeZKWvq2y73g\ngsg11/+FyLiMfQx3i8hmk+TmEveN4enJIkSxWz2iNnBdGPI5W4vI/WZvt3s8OG22EEEkfNo9RtRg\nJGVRPGh5CBo+BZNgrnOMyGQhyjBGVOxWGK8N6g1NvRNPk2w929BU+XQ+MFrMU2toc6PQCbN1dXKr\nGpexj+G8EFnMza4JrRkgLEQOKNlaRK6uOezICsew5lJgrJQ/ZiE5OBW55k4e7Z1EuHQkP581twCu\nrDnbRN+GI1lhzhVoQFZoxogGf8MbKSu0wMSd0wmpE1ozc80l0lXdraLZuptrO2frHo3nZtrNY+Lq\nrBtsjINFdByh0BQ9tYkRuZn6Nq45HxbR0lLhGNfcIQuLaGmpwJGZ/pEVbEuouzKTrCyiDGNE9qw5\nd9ec6eVU1XlT5oJEM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def model4(dP=100.0, L=1.0, d=0.10, freq=0.5):\n", "\n", " def deriv(X,t):\n", " x,v = X\n", " xdot = v\n", " vdot = -(2*g/L)*x - (32*nu/d**2)*v + dP*np.sin(2.0*np.pi*freq*t)/rho/L\n", " return [xdot,vdot]\n", "\n", " IC = [0,0]\n", " sol = odeint(deriv,IC,t)\n", " plt.axis(axis)\n", " plt.plot(t,sol[:,1])\n", " plt.plot(t,dP*np.sin(2.0*np.pi*freq*t)/10000)\n", " plt.grid()\n", " plt.xlabel('Time [sec]')\n", " plt.ylabel('y [m], P[bars/10000]')\n", " plt.title('dP = {0:5.1f} bars, L = {1:4.2f} meters, d = {2:5.3f} meters'.format(dP,L,d))\n", " plt.legend(['Position','Pressure/10000'])\n", "\n", "interact(model4, dP=(-200,200,20), L = (0.2,5,0.1), d=(0.01,0.20,0.002), freq=(0,4,0.01));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 6. State Space Representation\n", "\n", "State space models are widely used in textbooks, software, and the research literature to represent linear systems. It's a generic model that represents a system with inputs and outputs. Here's how to recast out manometer model is time-varying pressure as a state model where the liquid level is the measured output.\n", "\n", "Start with the model written as a differential equation\n", "\n", "$$\\frac{d^2y}{dt^2} + \\frac{32\\nu}{d^2}\\frac{dy}{dt} + \\frac{2g}{L} y = \\frac{1}{\\rho L} \\Delta P$$\n", "\n", "Assemble the dependent variables in a vector, and rewrite using matrix/vector operations.\n", "\n", "$$\\begin{align*}\n", "\\frac{d}{dt}\n", "\\left[\\begin{array}{c}y \\\\ v\\end{array}\\right]\n", "& =\n", "\\left[\\begin{array}{cc}0 & 1 \\\\ - \\frac{2g}{L} & -\\frac{32\\nu}{d^2} \\end{array}\\right]\n", "\\left[\\begin{array}{c}y \\\\ v\\end{array}\\right]\n", "+\n", "\\left[\\begin{array}{c}0 \\\\ \\frac{1}{\\rho L}\\end{array}\\right]\n", "\\left[\\Delta P\\right] \\\\\n", "\\left[y\\right]\n", "& =\n", "\\left[\\begin{array}{c} 1 & 0\\end{array}\\right]\n", "\\left[\\begin{array}{c}y \\\\ v\\end{array}\\right]\n", "+\n", "\\left[0\\right]\n", "\\left[\\Delta P\\right]\n", "\\end{align*}\n", "$$\n", "\n", "Use standard symbols to label the vectors and matrices.\n", "\n", "$$\\begin{align*}\n", "\\frac{d}{dt}\n", "\\underbrace{\\left[\\begin{array}{c}y \\\\ v\\end{array}\\right]}_{x}\n", "& =\n", "\\underbrace{\\left[\\begin{array}{cc}0 & 1 \\\\ - \\frac{2g}{L} & -\\frac{32\\nu}{d^2} \\end{array}\\right]}_{A}\n", "\\underbrace{\\left[\\begin{array}{c}y \\\\ v\\end{array}\\right]}_{x}\n", "+\n", "\\underbrace{\\left[\\begin{array}{c}0 \\\\ \\frac{1}{\\rho L}\\end{array}\\right]}_{B}\n", "\\underbrace{\\left[\\Delta P\\right]}_{u} \\\\\n", "\\underbrace{\\left[y\\right]}_{y} \n", "& =\n", "\\underbrace{\\left[\\begin{array}{c} 1 & 0\\end{array}\\right]}_{C}\n", "\\underbrace{\\left[\\begin{array}{c}y \\\\ v\\end{array}\\right]}_{x}\n", "+\n", "\\underbrace{\\left[0\\right]}_{D}\n", "\\underbrace{\\left[\\Delta P\\right]}_{u}\n", "\\end{align*}\n", "$$\n", "\n", "The result is a model of a linear system in a standard state space representation. \n", "\n", "$$\\begin{align*}\n", "\\frac{dx}{dt} & = Ax + Bu \\\\\n", "y & = Cx + Du\n", "\\end{align*}$$\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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tKGLQN83ZfncMnRWWdB2RlHKHlHKNlHKllPKr+ncPSikf1N9LKeWn9OObpJQH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\nH5r42Svn+Ist7rUpMz3LnigGwg22Ac1SyhYp5TT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def model6(dP=100, L=1.0, d=0.10):\n", "\n", " A = [[0,1],[-2*g/L, -32*nu/(d**2)]]\n", " B = [[0],[1/rho/L]]\n", " C = [[1,0]]\n", " D = [[0]]\n", " \n", " sys = ss(A,B,C,D)\n", " y,tout = step(sys,t);\n", "\n", " plt.axis(axis)\n", " plt.plot(t,dP*y)\n", " plt.grid()\n", " plt.xlabel('Time [sec]')\n", " plt.ylabel('y [m]')\n", " plt.title('dP = {0:5.1f} bars, L = {1:4.2f} meters, d = {2:5.3f} meters'.format(dP,L,d))\n", " plt.legend(['Position'])\n", " \n", "interact(model6, dP=(-200,200,1), L = (0.2,5,0.1), d=(0.01,0.20,0.002));" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w = np.logspace(0,1,200)\n", "\n", "def model6(L=1.0, d=0.10):\n", "\n", " A = [[0,1],[-2*g/L, -32*nu/(d**2)]]\n", " B = [[0],[1/rho/L]]\n", " C = [[1,0]]\n", " D = [[0]]\n", "\n", " mano = ss(A,B,C,D)\n", " bode(mano,w);\n", " \n", "interact(model6, L = (0.2,5,0.1), d=(0.01,0.20,0.002));" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w = np.logspace(0,1,200)\n", "\n", "def model6(L=1.0, d=0.10):\n", "\n", " A = [[0,1],[-2*g/L, -128*nu/(np.pi*d**4)]]\n", " B = [[0],[1/rho/L]]\n", " C = [[1,0]]\n", " D = [[0]]\n", "\n", " e_vals,e_vecs = la.eig(A)\n", " \n", " plt.axis([-5,5,-5,5])\n", " plt.axis('equal')\n", " plt.plot(e_vals.real,e_vals.imag,'o')\n", " \n", " \n", " \n", "interact(model6, L = (0.2,5,0.1), d=(0.01,0.20,0.002));" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Interacting Tanks](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/03.07-Interacting-Tanks.ipynb) | [Contents](toc.ipynb) | [Modeling and Control of a Campus Outbreak of Coronavirus COVID-19](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/03.09-COVID-19.ipynb) >

\"Open

\"Download\"" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "widgets": { "state": { "007355d1980343dbb76bde8d899a5771": { "views": [ { "cell_index": 16 } ] }, "2231f725990d48a299b436b50eb01364": { "views": [ { "cell_index": 14 } ] }, "25ab9be6097f4ea98940130d9c4762e7": { "views": [ { "cell_index": 15 } ] }, "299c003370c8484787fc850b3cf9578b": { "views": [ { "cell_index": 3 } ] }, "59c1a69ca2544c18b9623712889818cd": { "views": [ { "cell_index": 12 } ] }, "694968b4994e47a8b1b386bbf591b9fe": { "views": [ { "cell_index": 6 } ] }, "a89afad69df146a298330b5daf02cbc3": { "views": [ { "cell_index": 8 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 4 }