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Heart Disease Prediction (UCI Data)
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| "( References : https://www.healthline.com ; https://www.mayoclinic.org)\n", | |
| "# Heart Disease\n", | |
| "Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease; heart rhythm problems (arrhythmias); and heart defects you're born with (congenital heart defects), among others.\n", | |
| "\n", | |
| "### Statistics :\n", | |
| "* As of 2016, 28.2 million U.S. adultsTrusted Source were diagnosed with heart disease. In 2015, nearly 634,000 Trusted Source people died of heart disease, making it the leading cause of death.\n", | |
| "\n", | |
| "* According to the American Heart Association, approximately every 40 seconds an American will have a heart attack. The estimated annual incidence of heart attacks in the United States is 720,000 new attacks and 335,000 recurrent attacks.\n", | |
| "\n", | |
| "### Symptoms can include:\n", | |
| "* Chest pain, chest tightness, chest pressure and chest discomfort (angina)\n", | |
| "* Shortness of breath\n", | |
| "* Pain, numbness, weakness or coldness in your legs or arms if the blood vessels in those parts of your body are narrowed\n", | |
| "* Pain in the neck, jaw, throat, upper abdomen or back\n", | |
| "\n", | |
| "### Risk factors :\n", | |
| "* Tobacco use. Chewing tobacco, smoking and long-term exposure to secondhand smoke damage the interior walls of arteries — including arteries to your heart — allowing deposits of cholesterol to collect and block blood flow.\n", | |
| "\n", | |
| "* Diabetes. Diabetes is the inability of your body to produce enough or respond to insulin properly. Insulin, a hormone secreted by your pancreas, allows your body to use glucose, which is a form of sugar from foods. Diabetes increases the risk of coronary artery disease, which leads to angina and heart attacks by speeding up atherosclerosis and increasing your cholesterol levels.\n", | |
| "\n", | |
| "* High blood pressure. Blood pressure is determined by the amount of blood your heart pumps and the amount of resistance to blood flow in your arteries. Over time, high blood pressure damages arteries by accelerating hardening of the arteries.\n", | |
| "\n", | |
| "* High blood cholesterol or triglyceride levels. Cholesterol is a major part of the deposits that can narrow arteries throughout your body, including those that supply your heart. A high level of the wrong kind of cholesterol, known as low-density lipoprotein (LDL) cholesterol (the \"bad\" cholesterol), increases your risk of angina and heart attacks. A high level of triglycerides, a type of blood fat related to your diet, also is undesirable.\n", | |
| "\n", | |
| "* Family history of heart disease. If a family member has coronary artery disease or has had a heart attack, you're at a greater risk of developing angina.\n", | |
| "\n", | |
| "* Older age. Men older than 45 and women older than 55 have a greater risk than do younger adults.\n", | |
| "\n", | |
| "* Lack of exercise. An inactive lifestyle contributes to high cholesterol, high blood pressure, type 2 diabetes and obesity. However, it is important to talk with your doctor before starting an exercise program.\n", | |
| "\n", | |
| "* Obesity. Obesity raises the risk of angina and heart disease because it's associated with high blood cholesterol levels, high blood pressure and diabetes. Also, your heart has to work harder to supply blood to the excess tissue.\n", | |
| "\n", | |
| "* Stress. Stress can increase your risk of angina and heart attacks. Too much stress, as well as anger, also can raise your blood pressure. Surges of hormones produced during stress can narrow your arteries and worsen angina." | |
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| "/kaggle/input/heart-disease-uci/heart.csv\n" | |
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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# This Python 3 environment comes with many helpful analytics libraries installed\n", | |
| "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", | |
| "# For example, here's several helpful packages to load in \n", | |
| "\n", | |
| "import numpy as np # linear algebra\n", | |
| "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", | |
| "\n", | |
| "# Input data files are available in the \"../input/\" directory.\n", | |
| "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", | |
| "\n", | |
| "import os\n", | |
| "for dirname, _, filenames in os.walk('/kaggle/input'):\n", | |
| " for filename in filenames:\n", | |
| " print(os.path.join(dirname, filename))\n", | |
| "\n", | |
| "# Any results you write to the current directory are saved as output.\n", | |
| "\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "import seaborn as sns\n", | |
| "sns.set(style=\"darkgrid\")\n", | |
| "\n", | |
| "#Ignoring the warnings\n", | |
| "import warnings\n", | |
| "warnings.filterwarnings('ignore')\n", | |
| "\n", | |
| "from sklearn.preprocessing import LabelEncoder # Encode Categorical Variable to Numerical Variable\n", | |
| "from sklearn.preprocessing import Imputer # Imputer Class to replace missing values\n", | |
| "from sklearn.metrics import confusion_matrix # Library for model evaluation\n", | |
| "from sklearn.metrics import accuracy_score # Library for model evaluation\n", | |
| "from sklearn.model_selection import train_test_split # Library to split datset into test and train\n", | |
| "\n", | |
| "from sklearn.dummy import DummyClassifier\n", | |
| "from sklearn.linear_model import LogisticRegression # Logistic Regression Classifier\n", | |
| "from sklearn.linear_model import SGDClassifier # Stochastic Gradient Descent Classifier\n", | |
| "from sklearn.tree import DecisionTreeClassifier # Decision Tree Classifier\n", | |
| "from sklearn.ensemble import RandomForestClassifier # Random Forest Classifier\n", | |
| "from sklearn.neighbors import KNeighborsClassifier # K Nearest neighbors Classifier\n", | |
| "from sklearn.naive_bayes import GaussianNB #Naive Bayes Classifier\n", | |
| "from sklearn.svm import SVC #Support vector Machine Classifier\n", | |
| "from sklearn.ensemble import AdaBoostClassifier # Ada Boost Classifier\n", | |
| "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix, roc_auc_score\n", | |
| "from sklearn.model_selection import cross_val_score\n", | |
| "from sklearn.metrics import precision_recall_curve\n", | |
| "from sklearn.metrics import average_precision_score\n", | |
| "from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, RandomizedSearchCV, train_test_split\n", | |
| "from sklearn.metrics import average_precision_score, precision_recall_curve\n", | |
| "from sklearn.model_selection import GridSearchCV\n", | |
| "\n", | |
| "from xgboost import XGBClassifier\n", | |
| "from lightgbm import LGBMClassifier\n", | |
| "from pdpbox import pdp, info_plots\n", | |
| "import shap\n", | |
| "shap.initjs()\n", | |
| "import lime\n", | |
| "import lime.lime_tabular" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "application/javascript": [ | |
| "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", | |
| " return false;\n", | |
| "}\n" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Javascript object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "%%javascript\n", | |
| "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", | |
| " return false;\n", | |
| "}" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", | |
| "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Rows - 303, Columns - 14\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>age</th>\n", | |
| " <th>sex</th>\n", | |
| " <th>cp</th>\n", | |
| " <th>trestbps</th>\n", | |
| " <th>chol</th>\n", | |
| " <th>fbs</th>\n", | |
| " <th>restecg</th>\n", | |
| " <th>thalach</th>\n", | |
| " <th>exang</th>\n", | |
| " <th>oldpeak</th>\n", | |
| " <th>slope</th>\n", | |
| " <th>ca</th>\n", | |
| " <th>thal</th>\n", | |
| " <th>target</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>63</td>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>145</td>\n", | |
| " <td>233</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>150</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2.3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>37</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2</td>\n", | |
| " <td>130</td>\n", | |
| " <td>250</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>187</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3.5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>41</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>130</td>\n", | |
| " <td>204</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>172</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.4</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>56</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>120</td>\n", | |
| " <td>236</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>178</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.8</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>57</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>120</td>\n", | |
| " <td>354</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>163</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", | |
| "0 63 1 3 145 233 1 0 150 0 2.3 0 \n", | |
| "1 37 1 2 130 250 0 1 187 0 3.5 0 \n", | |
| "2 41 0 1 130 204 0 0 172 0 1.4 2 \n", | |
| "3 56 1 1 120 236 0 1 178 0 0.8 2 \n", | |
| "4 57 0 0 120 354 0 1 163 1 0.6 2 \n", | |
| "\n", | |
| " ca thal target \n", | |
| "0 0 1 1 \n", | |
| "1 0 2 1 \n", | |
| "2 0 2 1 \n", | |
| "3 0 2 1 \n", | |
| "4 0 2 1 " | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# First Look into the data\n", | |
| "heart_df = pd.read_csv('/kaggle/input/heart-disease-uci/heart.csv')\n", | |
| "print(f'Rows - {heart_df.shape[0]}, Columns - {heart_df.shape[1]}')\n", | |
| "heart_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 303 entries, 0 to 302\n", | |
| "Data columns (total 14 columns):\n", | |
| "age 303 non-null int64\n", | |
| "sex 303 non-null int64\n", | |
| "cp 303 non-null int64\n", | |
| "trestbps 303 non-null int64\n", | |
| "chol 303 non-null int64\n", | |
| "fbs 303 non-null int64\n", | |
| "restecg 303 non-null int64\n", | |
| "thalach 303 non-null int64\n", | |
| "exang 303 non-null int64\n", | |
| "oldpeak 303 non-null float64\n", | |
| "slope 303 non-null int64\n", | |
| "ca 303 non-null int64\n", | |
| "thal 303 non-null int64\n", | |
| "target 303 non-null int64\n", | |
| "dtypes: float64(1), int64(13)\n", | |
| "memory usage: 33.3 KB\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Checking the datatype of the columns\n", | |
| "heart_df.info()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n", | |
| " 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "heart_df.columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "All Columns are numeric. Here are the column details\n", | |
| "* age - Person's Age in years\n", | |
| "\n", | |
| "* sex - Gender (1 = male; 0 = female)\n", | |
| "\n", | |
| "* cp - Chest Pain (1: typical angina ; 2: atypical angina ; 3: non-anginal pain ; 4: asymptomatic)\n", | |
| " * Angina is chest pain or discomfort caused when your heart muscle doesn't get enough oxygen-rich blood. It may feel like pressure or squeezing in your chest\n", | |
| " * Atypical angina implies that the complaint is actually angina pectoris, though not conforming in every way to the expected or\n", | |
| " classic description\n", | |
| " * Non-anginal pain - A chest pain is very likely nonanginal if its duration is over 30 minutes or less than 5 seconds.\n", | |
| " * means neither causing nor exhibiting symptoms of disease.\n", | |
| " \n", | |
| "\n", | |
| "* trestbps - Resting blood pressure (in mm Hg on admission to the hospital) . Optimal blood pressure typically is defined as 120 mm Hg systolic — which is the pressure as your heart beats — over 80 mm Hg diastolic — which is the pressure as your heart relaxes. For your resting heart rate, the target is between 60 and 100 beats per minute (BPM)\n", | |
| "\n", | |
| "* chol - Serum Cholestoral in mg/dl . Total cholesterol levels less than 200 milligrams per deciliter (mg/dL) are considered desirable for adults. A reading between 200 and 239 mg/dL is considered borderline high and a reading of 240 mg/dL and above is considered high\n", | |
| "\n", | |
| "* fbs - Fasting blood sugar > 120 (mg/dl) (1 = true; 0 = false) . A fasting blood sugar level less than 100 mg/dL (5.6 mmol/L) is normal. A fasting blood sugar level from 100 to 125 mg/dL (5.6 to 6.9 mmol/L) is considered prediabetes. If it's 126 mg/dL (7 mmol/L) or higher on two separate tests, you have diabetes\n", | |
| "\n", | |
| "* restecg - Resting electrocardiographic results (0: normal ; 1: having ST-T wave abnormality ; 2: showing probable or definite left ventricular hypertrophy by Estes' criteria).Resting echocardiography is a non-invasive test that can assess ventricular function, heart valve anatomy and function and regional wall motion abnormalities.\n", | |
| "\n", | |
| "* thalach - Maximum heart rate achieved\n", | |
| "\n", | |
| "* exang - Exercise induced angina (1 = yes; 0 = no). When you increase the demand for oxygen, such as when you exercise, this can cause angina\n", | |
| "\n", | |
| "* oldpeak - ST depression induced by exercise relative to rest . ST depression refers to a finding on an electrocardiogram wherein the trace in the ST segment is abnormally low below the baseline\n", | |
| "\n", | |
| "* slope - Slope of the peak exercise ST segment (1: upsloping ; 2: flat ; 3: downsloping)\n", | |
| "\n", | |
| "* ca - Number of major vessels (0-3) colored by flourosopy\n", | |
| "\n", | |
| "* thal - 3 = normal; 6 = fixed defect; 7 = reversable defect . Thalassemia is a blood disorder passed down through families (inherited) in which the body makes an abnormal form or inadequate amount of hemoglobin.\n", | |
| "\n", | |
| "* target - Heart Disease (1 - Yes ; 0 - No)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
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| " }\n", | |
| "\n", | |
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| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>age</th>\n", | |
| " <th>sex</th>\n", | |
| " <th>chest_pain</th>\n", | |
| " <th>rest_blood_pressure</th>\n", | |
| " <th>cholesterol</th>\n", | |
| " <th>fast_blood_sugar</th>\n", | |
| " <th>rest_ecg</th>\n", | |
| " <th>max_heart_rate</th>\n", | |
| " <th>exercise_ind_angina</th>\n", | |
| " <th>st_dep_ind_exc</th>\n", | |
| " <th>slope_peak_exer_st</th>\n", | |
| " <th>no_vessels_colored_fl</th>\n", | |
| " <th>thalassemia</th>\n", | |
| " <th>target</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>63</td>\n", | |
| " <td>1</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>2.3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>37</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2</td>\n", | |
| " <td>130</td>\n", | |
| " <td>250</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>187</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3.5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>41</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>130</td>\n", | |
| " <td>204</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>172</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.4</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>56</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>120</td>\n", | |
| " <td>236</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>178</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.8</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>57</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>120</td>\n", | |
| " <td>354</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>163</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " age sex chest_pain rest_blood_pressure cholesterol fast_blood_sugar \\\n", | |
| "0 63 1 3 145 233 1 \n", | |
| "1 37 1 2 130 250 0 \n", | |
| "2 41 0 1 130 204 0 \n", | |
| "3 56 1 1 120 236 0 \n", | |
| "4 57 0 0 120 354 0 \n", | |
| "\n", | |
| " rest_ecg max_heart_rate exercise_ind_angina st_dep_ind_exc \\\n", | |
| "0 0 150 0 2.3 \n", | |
| "1 1 187 0 3.5 \n", | |
| "2 0 172 0 1.4 \n", | |
| "3 1 178 0 0.8 \n", | |
| "4 1 163 1 0.6 \n", | |
| "\n", | |
| " slope_peak_exer_st no_vessels_colored_fl thalassemia target \n", | |
| "0 0 0 1 1 \n", | |
| "1 0 0 2 1 \n", | |
| "2 2 0 2 1 \n", | |
| "3 2 0 2 1 \n", | |
| "4 2 0 2 1 " | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Renaming the columns for better understanding\n", | |
| "heart_df = heart_df.rename(columns={'cp' : 'chest_pain', \n", | |
| " 'trestbps' : 'rest_blood_pressure', \n", | |
| " 'chol' : 'cholesterol', \n", | |
| " 'fbs' : 'fast_blood_sugar', \n", | |
| " 'restecg' : 'rest_ecg', \n", | |
| " 'thalach' : 'max_heart_rate',\n", | |
| " 'exang' : 'exercise_ind_angina', \n", | |
| " 'oldpeak' : 'st_dep_ind_exc', \n", | |
| " 'slope' : 'slope_peak_exer_st', \n", | |
| " 'ca' : 'no_vessels_colored_fl', \n", | |
| " 'thal' : 'thalassemia'})\n", | |
| "heart_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
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| "\n", | |
| " .dataframe tbody tr th {\n", | |
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| " .dataframe thead th {\n", | |
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| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>age</th>\n", | |
| " <th>sex</th>\n", | |
| " <th>chest_pain</th>\n", | |
| " <th>rest_blood_pressure</th>\n", | |
| " <th>cholesterol</th>\n", | |
| " <th>fast_blood_sugar</th>\n", | |
| " <th>rest_ecg</th>\n", | |
| " <th>max_heart_rate</th>\n", | |
| " <th>exercise_ind_angina</th>\n", | |
| " <th>st_dep_ind_exc</th>\n", | |
| " <th>slope_peak_exer_st</th>\n", | |
| " <th>no_vessels_colored_fl</th>\n", | |
| " <th>thalassemia</th>\n", | |
| " <th>target</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " <td>303.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>54.366337</td>\n", | |
| " <td>0.683168</td>\n", | |
| " <td>0.966997</td>\n", | |
| " <td>131.623762</td>\n", | |
| " <td>246.264026</td>\n", | |
| " <td>0.148515</td>\n", | |
| " <td>0.528053</td>\n", | |
| " <td>149.646865</td>\n", | |
| " <td>0.326733</td>\n", | |
| " <td>1.039604</td>\n", | |
| " <td>1.399340</td>\n", | |
| " <td>0.729373</td>\n", | |
| " <td>2.313531</td>\n", | |
| " <td>0.544554</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>9.082101</td>\n", | |
| " <td>0.466011</td>\n", | |
| " <td>1.032052</td>\n", | |
| " <td>17.538143</td>\n", | |
| " <td>51.830751</td>\n", | |
| " <td>0.356198</td>\n", | |
| " <td>0.525860</td>\n", | |
| " <td>22.905161</td>\n", | |
| " <td>0.469794</td>\n", | |
| " <td>1.161075</td>\n", | |
| " <td>0.616226</td>\n", | |
| " <td>1.022606</td>\n", | |
| " <td>0.612277</td>\n", | |
| " <td>0.498835</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>29.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>94.000000</td>\n", | |
| " <td>126.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>71.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>47.500000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>120.000000</td>\n", | |
| " <td>211.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>133.500000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>55.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>130.000000</td>\n", | |
| " <td>240.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>153.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.800000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>61.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>140.000000</td>\n", | |
| " <td>274.500000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>166.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.600000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>77.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>200.000000</td>\n", | |
| " <td>564.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>202.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>6.200000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " age sex chest_pain rest_blood_pressure cholesterol \\\n", | |
| "count 303.000000 303.000000 303.000000 303.000000 303.000000 \n", | |
| "mean 54.366337 0.683168 0.966997 131.623762 246.264026 \n", | |
| "std 9.082101 0.466011 1.032052 17.538143 51.830751 \n", | |
| "min 29.000000 0.000000 0.000000 94.000000 126.000000 \n", | |
| "25% 47.500000 0.000000 0.000000 120.000000 211.000000 \n", | |
| "50% 55.000000 1.000000 1.000000 130.000000 240.000000 \n", | |
| "75% 61.000000 1.000000 2.000000 140.000000 274.500000 \n", | |
| "max 77.000000 1.000000 3.000000 200.000000 564.000000 \n", | |
| "\n", | |
| " fast_blood_sugar rest_ecg max_heart_rate exercise_ind_angina \\\n", | |
| "count 303.000000 303.000000 303.000000 303.000000 \n", | |
| "mean 0.148515 0.528053 149.646865 0.326733 \n", | |
| "std 0.356198 0.525860 22.905161 0.469794 \n", | |
| "min 0.000000 0.000000 71.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 133.500000 0.000000 \n", | |
| "50% 0.000000 1.000000 153.000000 0.000000 \n", | |
| "75% 0.000000 1.000000 166.000000 1.000000 \n", | |
| "max 1.000000 2.000000 202.000000 1.000000 \n", | |
| "\n", | |
| " st_dep_ind_exc slope_peak_exer_st no_vessels_colored_fl thalassemia \\\n", | |
| "count 303.000000 303.000000 303.000000 303.000000 \n", | |
| "mean 1.039604 1.399340 0.729373 2.313531 \n", | |
| "std 1.161075 0.616226 1.022606 0.612277 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 1.000000 0.000000 2.000000 \n", | |
| "50% 0.800000 1.000000 0.000000 2.000000 \n", | |
| "75% 1.600000 2.000000 1.000000 3.000000 \n", | |
| "max 6.200000 2.000000 4.000000 3.000000 \n", | |
| "\n", | |
| " target \n", | |
| "count 303.000000 \n", | |
| "mean 0.544554 \n", | |
| "std 0.498835 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 1.000000 \n", | |
| "75% 1.000000 \n", | |
| "max 1.000000 " | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "heart_df.describe()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations :\n", | |
| "\n", | |
| "1. All rows have count=303. So no misssing data.\n", | |
| "2. sex/Gender - Max we have 1 . So Male is majority. We dont know the distribution yet.\n", | |
| "3. blood_pressure in the dataset varies from 94 to 200. Median is 130.\n", | |
| "4. cholestrol varies from 126 to 564 with median as 240 . \n", | |
| "5. Fasting blood sugar > 120 mg/dl is a categorical field having > 120 mg/dl as majority . \n", | |
| "6. rest_ecg is a categorical field with values ranging 0 to 2 with majority as 2.\n", | |
| "7. heart rate varies 71 to 202 with median as 153\n", | |
| "8. exercise_ind_angina is categorical field with 0 & 1 , 1 being the majority\n", | |
| "8. st_dep_ind_exc varies 0 to 6.2 with median depression at 0.8\n", | |
| "9. slope_peak_exer_st is a categorical field with value 0, 1 and 2\n", | |
| "10. no_vessels_colored_fl is a categorical field with value 0 to 4 with 4 being majority\n", | |
| "11. thalassemia is a categorical field with value 0 to 3 with 3 being majority\n", | |
| "12. Target - Has 2 values 0 & 1 with 1 being majority" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "1 54.0\n", | |
| "0 46.0\n", | |
| "Name: target, dtype: float64" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Distribution of the target variable\n", | |
| "round(heart_df['target'].value_counts(normalize=True), 2)*100" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* Almost balanced dataset with heart disease yes as 54% and non heart disease as 46%" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Univariate Analysis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "cat_cols = ['target', 'sex', 'chest_pain', 'rest_ecg', 'exercise_ind_angina', 'slope_peak_exer_st', 'no_vessels_colored_fl', 'thalassemia', 'fast_blood_sugar']\n", | |
| "cont_cols = ['age', 'rest_blood_pressure', 'cholesterol', 'max_heart_rate', 'st_dep_ind_exc', 'age']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "heart_df_uv = heart_df.copy()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "target - [1 0]\n", | |
| "sex - [1 0]\n", | |
| "chest_pain - [3 2 1 0]\n", | |
| "rest_ecg - [0 1 2]\n", | |
| "exercise_ind_angina - [0 1]\n", | |
| "slope_peak_exer_st - [0 2 1]\n", | |
| "no_vessels_colored_fl - [0 2 1 3 4]\n", | |
| "thalassemia - [1 2 3 0]\n", | |
| "fast_blood_sugar - [1 0]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Printing the unique values of Categorcal columns\n", | |
| "for cols in cat_cols:\n", | |
| " print(cols, '-', heart_df_uv[cols].unique())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>age</th>\n", | |
| " <th>sex</th>\n", | |
| " <th>chest_pain</th>\n", | |
| " <th>rest_blood_pressure</th>\n", | |
| " <th>cholesterol</th>\n", | |
| " <th>fast_blood_sugar</th>\n", | |
| " <th>rest_ecg</th>\n", | |
| " <th>max_heart_rate</th>\n", | |
| " <th>exercise_ind_angina</th>\n", | |
| " <th>st_dep_ind_exc</th>\n", | |
| " <th>slope_peak_exer_st</th>\n", | |
| " <th>no_vessels_colored_fl</th>\n", | |
| " <th>thalassemia</th>\n", | |
| " <th>target</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>63</td>\n", | |
| " <td>male</td>\n", | |
| " <td>non_ang</td>\n", | |
| " <td>145</td>\n", | |
| " <td>233</td>\n", | |
| " <td><=120</td>\n", | |
| " <td>normal</td>\n", | |
| " <td>150</td>\n", | |
| " <td>no</td>\n", | |
| " <td>2.3</td>\n", | |
| " <td>unknown</td>\n", | |
| " <td>0</td>\n", | |
| " <td>unknown1</td>\n", | |
| " <td>Yes</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>37</td>\n", | |
| " <td>male</td>\n", | |
| " <td>atyp_ang</td>\n", | |
| " <td>130</td>\n", | |
| " <td>250</td>\n", | |
| " <td>>120</td>\n", | |
| " <td>st-t abnorm</td>\n", | |
| " <td>187</td>\n", | |
| " <td>no</td>\n", | |
| " <td>3.5</td>\n", | |
| " <td>unknown</td>\n", | |
| " <td>0</td>\n", | |
| " <td>unknown2</td>\n", | |
| " <td>Yes</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>41</td>\n", | |
| " <td>female</td>\n", | |
| " <td>typ_ang</td>\n", | |
| " <td>130</td>\n", | |
| " <td>204</td>\n", | |
| " <td>>120</td>\n", | |
| " <td>normal</td>\n", | |
| " <td>172</td>\n", | |
| " <td>no</td>\n", | |
| " <td>1.4</td>\n", | |
| " <td>flat</td>\n", | |
| " <td>0</td>\n", | |
| " <td>unknown2</td>\n", | |
| " <td>Yes</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>56</td>\n", | |
| " <td>male</td>\n", | |
| " <td>typ_ang</td>\n", | |
| " <td>120</td>\n", | |
| " <td>236</td>\n", | |
| " <td>>120</td>\n", | |
| " <td>st-t abnorm</td>\n", | |
| " <td>178</td>\n", | |
| " <td>no</td>\n", | |
| " <td>0.8</td>\n", | |
| " <td>flat</td>\n", | |
| " <td>0</td>\n", | |
| " <td>unknown2</td>\n", | |
| " <td>Yes</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>57</td>\n", | |
| " <td>female</td>\n", | |
| " <td>none</td>\n", | |
| " <td>120</td>\n", | |
| " <td>354</td>\n", | |
| " <td>>120</td>\n", | |
| " <td>st-t abnorm</td>\n", | |
| " <td>163</td>\n", | |
| " <td>yes</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>flat</td>\n", | |
| " <td>0</td>\n", | |
| " <td>unknown2</td>\n", | |
| " <td>Yes</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " age sex chest_pain rest_blood_pressure cholesterol fast_blood_sugar \\\n", | |
| "0 63 male non_ang 145 233 <=120 \n", | |
| "1 37 male atyp_ang 130 250 >120 \n", | |
| "2 41 female typ_ang 130 204 >120 \n", | |
| "3 56 male typ_ang 120 236 >120 \n", | |
| "4 57 female none 120 354 >120 \n", | |
| "\n", | |
| " rest_ecg max_heart_rate exercise_ind_angina st_dep_ind_exc \\\n", | |
| "0 normal 150 no 2.3 \n", | |
| "1 st-t abnorm 187 no 3.5 \n", | |
| "2 normal 172 no 1.4 \n", | |
| "3 st-t abnorm 178 no 0.8 \n", | |
| "4 st-t abnorm 163 yes 0.6 \n", | |
| "\n", | |
| " slope_peak_exer_st no_vessels_colored_fl thalassemia target \n", | |
| "0 unknown 0 unknown1 Yes \n", | |
| "1 unknown 0 unknown2 Yes \n", | |
| "2 flat 0 unknown2 Yes \n", | |
| "3 flat 0 unknown2 Yes \n", | |
| "4 flat 0 unknown2 Yes " | |
| ] | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Create Numeric to Value mapping for better graphical representation\n", | |
| "map_sex = {0: 'female', 1: 'male'}\n", | |
| "map_chest_pain = {0:'none', 1:'typ_ang', 2:'atyp_ang', 3:'non_ang'}\n", | |
| "map_rest_ecg = {0:'normal', 1:'st-t abnorm', 2:'left_vent_hyptrophy'}\n", | |
| "map_exercise_ind_angina = {0:'no', 1:'yes'}\n", | |
| "map_slope_peak_exer_st = {0:'unknown', 1:'upsloping', 2:'flat'}\n", | |
| "map_thal = {0:'unknown0', 1:'unknown1', 2:'unknown2', 3:'normal'}\n", | |
| "map_fast_blood_sugar = {0:'>120',1:'<=120'}\n", | |
| "map_target={0:'No', 1:'Yes'}\n", | |
| "\n", | |
| "# Updating the mapping in dataframe\n", | |
| "heart_df_uv[\"sex\"] = heart_df_uv[\"sex\"].map(map_sex)\n", | |
| "heart_df_uv[\"chest_pain\"] = heart_df_uv[\"chest_pain\"].map(map_chest_pain)\n", | |
| "heart_df_uv[\"rest_ecg\"] = heart_df_uv[\"rest_ecg\"].map(map_rest_ecg)\n", | |
| "heart_df_uv[\"exercise_ind_angina\"] = heart_df_uv[\"exercise_ind_angina\"].map(map_exercise_ind_angina)\n", | |
| "heart_df_uv[\"slope_peak_exer_st\"] = heart_df_uv[\"slope_peak_exer_st\"].map(map_slope_peak_exer_st)\n", | |
| "heart_df_uv[\"thalassemia\"] = heart_df_uv[\"thalassemia\"].map(map_thal)\n", | |
| "heart_df_uv[\"fast_blood_sugar\"] = heart_df_uv[\"fast_blood_sugar\"].map(map_fast_blood_sugar)\n", | |
| "heart_df_uv[\"target\"] = heart_df_uv[\"target\"].map(map_target)\n", | |
| "heart_df_uv.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Univariate Analysis of Categorical Variables\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 9 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Univariate Analysis of Categorical Columns\n", | |
| "rows = 3 ; columns = 3\n", | |
| "cat_cols_np_arr = np.array(cat_cols).reshape(3, 3)\n", | |
| "\n", | |
| "f, axes = plt.subplots(rows, columns, figsize=(20, 20))\n", | |
| "print('Univariate Analysis of Categorical Variables')\n", | |
| "for row in range(rows):\n", | |
| " for column in range(columns):\n", | |
| " sns.countplot(heart_df_uv[cat_cols_np_arr[row][column]], \n", | |
| " palette = \"Set1\", \n", | |
| " ax = axes[row, column])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* Fairly balanced datset in terms of target variable.\n", | |
| "* Female sample size is almost half as male.\n", | |
| "* Chest pain none is majority. This filed has data issue and 0 is not defined in mapping. Assuming 0 means no pain\n", | |
| "* thalassemia also has data issues with mapping not provided for 0, 1 and 2 . Majority is 2.\n", | |
| "* Sample size for fasting blood sugar > 120mg/dl is more." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Univariate Analysis of Continuous Variables\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 5 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Univariate Analysis of Continuous Columns\n", | |
| "rows = 2 ; columns = 3\n", | |
| "cont_cols_np_arr = np.array(cont_cols).reshape(2,3)\n", | |
| "\n", | |
| "f, axes = plt.subplots(rows, columns, figsize=(20, 10))\n", | |
| "print('Univariate Analysis of Continuous Variables')\n", | |
| "\n", | |
| "for row in range(rows):\n", | |
| " for column in range(columns):\n", | |
| " sns.distplot(heart_df[cont_cols_np_arr[row][column]], \n", | |
| " ax = axes[row, column], \n", | |
| " hist=True, \n", | |
| " kde=True, \n", | |
| " color='b',\n", | |
| " hist_kws={\"linewidth\": 3,\n", | |
| " \"alpha\": 1, \"color\": \"b\"}\n", | |
| " )\n", | |
| "plt.tight_layout()\n", | |
| "f.delaxes(ax= axes[1,2])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* age field is normal distributed with majority band falling in the category of 50-65 years.\n", | |
| "* resting blood presssure follows normal distribution with majority b/w 120-140, median at 130\n", | |
| "* cholesterol follows normal distribution with 200-300 is majority. Under 200 is ideal.\n", | |
| "* max heart rate , slightly left skewed has heart rate from 50 to 225. Ideal is 60-100 depending on age.\n", | |
| "* ST depression induced by exercise relative to rest is right tailed varying b/w 0 to 6." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Univariate Analysis of Categorical Variables\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 1440x720 with 5 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Univariate Analysis of Continuous Columns\n", | |
| "rows = 2 ; columns = 3\n", | |
| "cont_cols_np_arr = np.array(cont_cols).reshape(2,3)\n", | |
| "\n", | |
| "f, axes = plt.subplots(rows, columns, figsize=(20, 10))\n", | |
| "print('Univariate Analysis of Categorical Variables')\n", | |
| "for row in range(rows):\n", | |
| " for column in range(columns):\n", | |
| " sns.boxplot(heart_df[cont_cols_np_arr[row][column]], ax = axes[row, column], orient=\"v\", color=\"skyblue\")\n", | |
| "f.delaxes(ax= axes[1,2])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* Resting blood pressure 170 an above stands outlier\n", | |
| "* Cholesterol above 400 is an outlier. Ideal is below 200. Above 400 brings big risk of heart attack.\n", | |
| "* ST depression induced by exercise relative to rest 4-6 seems above the IQR." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Bivariate Analysis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Categorical & Target" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Bivariate Analysis of Categorical Variables\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", 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| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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RKi0t1Z49e5ScnCxJSk5O1p49e1RSUmJnVAAAAOAMtl+pdjgcWrJkiaZMmaJ27dqpoqJCL730kvLz8xUVFSWn0ylJcjqdioyMVH5+vsLDw+t9/IiIDo0VPWB06dLR7ggAWonWMFMB4ELYXqpramr00ksv6cUXX1R8fLy++OILPfLII8rOzm6Q4xcXl8vnM7W2tbQSevx4md0RANisqeZaXTMVAFqaC5mpti//2Lt3rwoLCxUfHy9Jio+PV9u2bRUSEqKCggJ5vV5JktfrVWFhoaKjo+2MCwAAAJzB9lJ9ySWX6NixY/ruu+8kSfv371dRUZF+9atfKTY2Vrm5uZKk3NxcxcbGntfSDwAAAKAp2L78o0uXLnrmmWc0ffp0ORwOSdL8+fMVFhamZ555RrNmzdKLL76oTp06KSsry+a0AAAAwJlsL9WSlJKSopSUlDO2X3bZZXrjjTdsSAQAAADUn+3LPwAAAIDmjlINAAAAWESpBgAAACyiVAMAAAAW1btUr1q1qs7tq1evbrAwAAAAQHNU71K9fPnyOrevWLGiwcIAAAAAzdEvvqTexx9/LEny+Xz65JNPZMw/3p72yJEjat++feOlAwAAAJqBXyzVTz75pCTJ4/Fo9uzZ/u0Oh0NdunTRU0891XjpAAAAgGbgF0v1li1bJEkzZsxQdnZ2owcCAAAAmpt6v6Pizwu1z+ertS8oiBcRAQAAQOtV71L91Vdfac6cOfrmm2/k8XgkScYYORwO7d27t9ECAgAAAIGu3qV61qxZGjp0qJ599lmFhoY2ZiYAAACgWal3qT569KgeeeQRORyOxswDAAAANDv1Xgw9fPhwbd26tTGzAAAAAM1Sva9UezweTZ06VfHx8br44otr7eNVQQAAANCa1btU9+rVS7169WrMLAAAAECzVO9SPXXq1MbMAQAAADRb9S7VP71deV2uueaaBgkDAAAANEf1LtU/vV35T0pLS1VdXa2oqCi99957DR4MAAAAaC7qXap/ervyn3i9Xq1YsULt27dv8FAAAABAc3LB7y/udDo1efJkvfLKKw2ZBwAAAGh2LrhUS9KHH37Im8EAAACg1av38o8bbrihVoE+ffq0qqqqlJGR0SjBAAAAgOai3qV6wYIFtW63bdtWPXv2VIcOHRo8FAAAANCc1LtUDxo0SJLk8/lUVFSkiy++WEFBllaPAAAAAC1CvVtxeXm5ZsyYoX79+mnIkCHq16+fZs6cqbKyssbMBwAAAAS8epfqefPm6fTp08rJydHOnTuVk5Oj06dPa968eY2ZDwAAAAh49V7+8be//U3/8z//o7Zt20qSevbsqfnz52v48OGNFg4AAABoDup9pTokJEQlJSW1tpWWlsrlcjV4KAAAAKA5qfeV6ptvvlkTJkzQ3XffrZiYGOXl5enVV1/VLbfc0pj5AAAAgIBX71L9wAMPKCoqSjk5OSosLFRkZKQmTpxIqQYAAECrV+/lH5mZmerZs6deffVVbdy4Ua+++qouu+wyZWZmNmY+AAAAIODVu1Tn5ubqyiuvrLXtyiuvVG5uruUQHo9HGRkZ+v3vf69Ro0bp6aefliQdOHBAqampSkpKUmpqqg4ePGj5uQAAAICGVu/lHw6HQz6fr9Y2r9d7xrYLsWDBAoWEhGjTpk1yOBwqKiqSJGVkZCgtLU2jR4/Whg0blJ6erjVr1lh+PgAAAKAh1ftKtdvt1tKlS/0l2ufz6YUXXpDb7bYUoKKiQm+//bamT58uh8MhSbr44otVXFysPXv2KDk5WZKUnJysPXv2nPEKJAAAAIDd6n2l+sknn9T999+vhIQExcTEKD8/X126dNHKlSstBTh8+LDCwsK0bNkyffrpp2rfvr2mT5+u0NBQRUVFyel0SpKcTqciIyOVn5+v8PBwS88JAAAANKR6l+pLLrlEf/7zn7Vz507l5+crOjpa/fr1U1BQvS9216mmpkaHDx/WFVdcoZkzZ+rLL7/U5MmTtXTpUkvH/UlERIcGOU4g69Klo90RALQSrWGmAsCFqHeplqSgoCDFxcUpLi6uwQLExMQoODjYv8zj6quvVufOnRUaGqqCggJ5vV45nU55vV4VFhYqOjr6vI5fXFwun8/U2tbSSujx42V2RwBgs6aaa3XNVABoaS5kplq7zNwAwsPDNXjwYH344YeSfnzFj+LiYvXo0UOxsbH+VxfJzc1VbGwsSz8AAAAQcM7rSnVj+eMf/6jZs2crKytLwcHBys7OVqdOnfTMM89o1qxZevHFF9WpUydlZWXZHRUAAAA4Q0CU6u7du+vf//3fz9h+2WWX6Y033rAhEQAAAFB/ti//AAAAAJo7SjUAAABgEaUaAAAAsIhSDQAAAFhEqQYAAAAsolQDAAAAFlGqAQAAAIso1QAAAIBFlGoAAADAIko1AAAAYBGlGgAAALCIUg0AAABYRKkGAAAALKJUAwAAABZRqgEAAACLKNUAAACARZRqAAAAwCJKNQAAAGARpRoAAACwiFINAAAAWESpBgAAACyiVAMAAAAWUaoBAAAAiyjVAAAAgEWUagAAAMCiYLsDAACat46dQhUa0sbuGA2i0lOtspOVdscA0AxRqgEAloSGtFHajLV2x2gQr2XfrjJRqgGcP5Z/AAAAABZxpRoAAAs6X+RSsCvE7hiW1VR5VPpDld0xgGaLUg0AgAXBrhB9kT3R7hiWxc94RRKlGrhQLP8AAAAALKJUAwAAABYFVKletmyZ+vTpo3379kmSduzYoZSUFCUlJWnChAkqLi62OSEAAABwpoAp1V999ZV27NihmJgYSZIxRk888YTS09O1adMmud1uLVy40OaUAAAAwJkColRXVVVpzpw5ysjIkMPhkCTt2rVLISEhcrvdkqTx48fr3XfftTMmAAAAUKeAKNVLly5VSkqKunfv7t+Wn5/vv2otSeHh4fL5fDpx4oQdEQEAAICzsv0l9bZv365du3bp8ccfb5TjR0R0aJTjBpIuXTraHQFAK8FMbdla87kDVtleqj///HN99913GjZsmCTp2LFjuvfee3XnnXcqLy/Pf7+SkhI5HA6FhYWd1/GLi8vl85la21ra0Dh+vMzuCABs1lRzjZl6ppZ0/vw8AX50Id/Xti//mDRpkrZu3aotW7Zoy5YtuuSSS7Rq1SpNnDhRlZWV2rZtmyTp9ddf10033WRzWgAAAOBMtl+pPpugoCBlZ2crIyNDHo9HXbt21YIFC+yOBQAAAJwh4Er1li1b/B8PGDBAOTk5NqYBAAAAfpntyz8AAACA5o5SDQAAAFhEqQYAAAAsolQDAAAAFlGqAQAAAIso1QAAAIBFlGoAAADAIko1AAAAYBGlGgAAALCIUg0AAABYRKkGAAAALKJUAwAAABZRqgEAAACLgu0OAAAAmqdOF4UoxOWyO4ZlnqoqnfzBY3cMNHOUagAAcEFCXC7dvXq63TEse/WepZIo1bCG5R8AAACARZRqAAAAwCJKNQAAAGARpRoAAACwiFINAAAAWESpBgAAACyiVAMAAAAWUaoBAAAAiyjVAAAAgEWUagAAAMAiSjUAAABgEaUaAAAAsIhSDQAAAFhEqQYAAAAsolQDAAAAFlGqAQAAAIso1QAAAIBFlGoAAADAomC7A5SWlmrGjBn6/vvv5XK59Ktf/Upz5sxReHi4duzYofT0dHk8HnXt2lULFixQRESE3ZEBAACAWmwv1Q6HQxMnTtTgwYMlSVlZWVq4cKEyMzP1xBNPaP78+XK73XrxxRe1cOFCzZ8/3+bEzVvni1wKdoXYHaNB1FR5VPpDld0xAAAA7C/VYWFh/kItSXFxcVq3bp127dqlkJAQud1uSdL48eM1bNgwSrVFwa4QfZE90e4YDSJ+xiuSKNUAAMB+tpfqn/P5fFq3bp0SExOVn5+vmJgY/77w8HD5fD6dOHFCYWFh9T5mRESHxogaULp06Wh3BNu05nMH7MBMbdk4d+DCBVSpnjt3rtq1a6c77rhD//3f/90gxywuLpfPZ2pta2nfOMePl9X7vq353IGWrKm+t5mpZ2pJ58+5Az+6kK/tgCnVWVlZOnTokFauXKmgoCBFR0crLy/Pv7+kpEQOh+O8rlIDAAAATSEgXlJv8eLF2r17t5YvXy6XyyVJuvLKK1VZWalt27ZJkl5//XXddNNNdsYEAAAA6mT7lepvv/1WK1euVI8ePTR+/HhJUrdu3bR8+XJlZ2crIyOj1kvqAQAAAIHG9lJ9+eWX65tvvqlz34ABA5STk9PEiQAAAIDzY3upBgAAaG7COrrUJrRlvO9DdaVHJ8p4iVqrKNUAAADnqU1oiDbedY/dMRrEiDWrJUq1ZQHxh4oAAABAc0apBgAAACyiVAMAAAAWUaoBAAAAiyjVAAAAgEWUagAAAMAiSjUAAABgEaUaAAAAsIhSDQAAAFhEqQYAAAAsolQDAAAAFlGqAQAAAIso1QAAAIBFlGoAAADAIko1AAAAYBGlGgAAALCIUg0AAABYRKkGAAAALKJUAwAAABYF2x0AaEqdLgpRiMtldwzLPFVVOvmDx+4YAADg/6NUo1UJcbl09+rpdsew7NV7lkqiVAMAEChY/gEAAABYRKkGAAAALKJUAwAAABZRqgEAAACL+ENFAC3eRZ3ayhXSMsZdladGP5w8bXcMAMD/0TJ+ygDAObhCgvXsk2/aHaNBzM682e4IAIA6UKqBViKso0ttQkPsjmFZdaVHJ8qq7I4BAEAtlGqglWgTGqKNd91jdwzLRqxZLVGqAQABhj9UBAAAACwK+FJ94MABpaamKikpSampqTp48KDdkQAAAIBaAn75R0ZGhtLS0jR69Ght2LBB6enpWrNmjd2xAAAAWiVeUaluAf0ZKS4u1p49e7R69WpJUnJysubOnauSkhKFh4fX6xhBQY46t1/cuX2D5bTb2c7xbFydIhopSdM733OXpIs71O9rJ9BdyLm3vbhl/NtfyLlfFNauEZLY40LOv7GfuzXPVKnlzFVm6vlpKTNVOr/zd4UEa/mCjY2Ypuk8+MSIBpupDmOMaZAjNYLdu3dr5syZeuedd/zbRowYoQULFqhv3742JgMAAAD+IeDXVAMAAACBLqBLdXR0tAoKCuT1eiVJXq9XhYWFio6OtjkZAAAA8A8BXaojIiIUGxur3NxcSVJubq5iY2PrvZ4aAAAAaAoBvaZakvbv369Zs2bp5MmT6tSpk7KysnTppZfaHQsAAADwC/hSDQAAAAS6gF7+AQAAADQHlGoAAADAIko1AAAAYBGlGgAAALCIUt0IDhw4oNTUVCUlJSk1NVUHDx60O1KTyMrKUmJiovr06aN9+/bZHadJlZaW6r777lNSUpJGjRqlqVOnqqSkxO5YTWbKlClKSUnRmDFjlJaWpr1799odqcktW7asVX7tN4XWOlMl5ipzlbnanL72KdWNICMjQ2lpadq0aZPS0tKUnp5ud6QmMWzYMK1du1Zdu3a1O0qTczgcmjhxojZt2qScnBx1795dCxcutDtWk8nKytJf/vIXvf3225owYYJmz55td6Qm9dVXX2nHjh2KiYmxO0qL1FpnqsRcZa4yV5vTXKVUN7Di4mLt2bNHycnJkqTk5GTt2bOnVfy/a7fb3Wrf7TIsLEyDBw/2346Li1NeXp6NiZpWx44d/R+Xl5fL4XDYmKZpVVVVac6cOcrIyGhV591UWvNMlZirzNUfMVebh2C7A7Q0+fn5ioqKktPplCQ5nU5FRkYqPz+fd4JsJXw+n9atW6fExES7ozSpJ598Uh9++KGMMXrllVfsjtNkli5dqpSUFHXv3t3uKC0SMxUSc5W52jxwpRpoYHPnzlW7du10xx132B2lSWVmZur999/XI488ouzsbLvjNInt27dr165dSktLszsK0KIxV5mrzQGluoFFR0eroKBAXq9XkuT1elVYWNhqf33X2mRlZenQoUNasmSJgoJa57fXmDFj9Omnn6q0tNTuKI3u888/13fffadhw4YpMTFRx44d07333qutW7faHa3FYKaCucpcbS5ztXV+dTaiiIgIxcbGKjc3V5KUm5ur2NhYfk3ZCixevFi7d+/W8uXL5XK57I7TZCoqKpSfn++/vWXLFl100UUKCwuzMVXTmDT230qFAAAKeUlEQVRpkrZu3aotW7Zoy5YtuuSSS7Rq1SolJCTYHa3FYKa2bszVHzFXm8dcdRhjjN0hWpr9+/dr1qxZOnnypDp16qSsrCxdeumldsdqdPPmzdPmzZtVVFSkzp07KywsTO+8847dsZrEt99+q+TkZPXo0UOhoaGSpG7dumn58uU2J2t8RUVFmjJlik6fPq2goCBddNFFmjlzpvr27Wt3tCaXmJiolStXqnfv3nZHaVFa60yVmKvMVeZqc5qrlGoAAADAIpZ/AAAAABZRqgEAAACLKNUAAACARZRqAAAAwCJKNQAAAGARpRoAAACwiFIN1NNbb72l2267rcmfNzExUR999FGTPNesWbO0ePHiX7zfa6+9pmuvvVb9+/dXaWmp+vTpo0OHDjVBQgAtCXP1H5irzV+w3QEANC/V1dV67rnntH79ev3617+2Ow4ANHvM1ZaBK9VAC1VTU9Moxy0uLpbH41GvXr0a5fgAEKiYqzgXSjUaXGJiolatWqVRo0YpPj5eDz/8sDwejyRp/fr1Gj58uAYNGqTJkyeroKDgnMdKT09XVlZWrW0PPPCAVq9eLUkqKCjQtGnT9Jvf/EaJiYlas2aN/347d+7UP/3TP2nAgAG69tprNX/+fEmSx+PR448/rsGDB8vtdmvcuHEqKiqSJJWVlWn27NlKSEjQ9ddfr8WLF8vr9Z6RyxijZ599Vtdcc43i4+M1atQo7du375znUllZqeeee05Dhw5VfHy8brvtNlVWVkqS3nvvPY0cOVJut1t33nmn9u/fX+cxqqqqlJmZqYSEBCUkJCgzM1NVVVWSpE8//VRDhgzRyy+/rOuuu05/+MMfJEl//etfNXr0aLndbo0fP15ff/21/3h79uzR2LFj1b9//1r/Tmdz4MAB3XjjjZKkgQMH6q677jrn/QE0DOZq3ZirCCgGaGBDhw4148aNM8eOHTOlpaXmxhtvNK+99pr56KOPzKBBg8zu3buNx+Mxc+bMMWlpaec81meffWaGDBlifD6fMcaYEydOmKuuusocO3bMeL1eM3bsWPPCCy8Yj8djvv/+e5OYmGg++OADY4wxt956q/nzn/9sjDGmvLzcbN++3RhjzLp168z9999vTp06ZWpqasyuXbtMWVmZMcaYBx54wDz99NOmoqLCFBUVmXHjxpl169YZY4z505/+ZMaPH2+MMeaDDz4wY8eONT/88IPx+Xzm73//uykoKDjnuTzzzDPmjjvuMMeOHTM1NTXmiy++MB6Px3z33Xfm6quvNlu3bjVVVVXm5ZdfNr/73e+Mx+Pxfz4//PBDY4wxS5YsMbfccospKioyxcXFJjU11SxevNgYY8wnn3xiYmNjTXZ2tvF4POb06dNm9+7d5je/+Y3ZsWOHqampMW+99ZYZOnSo8Xg8xuPxmN/+9rdm9erVpqqqyvzXf/2XueKKK8yiRYvOeR6HDx82vXv3NtXV1f5tvXv3NgcPHjzn4wBcOOZq3ZirCCRcqUajuPPOOxUVFaWwsDANHTpUe/fuVU5OjsaNG6e+ffvK5XLp0Ucf1Y4dO3TkyJGzHsftdsvhcGjbtm2SpE2bNikuLk5RUVHatWuXSkpKNHXqVLlcLnXv3l233nqrNm7cKEkKDg7W999/r5KSErVv315xcXH+7SdOnNChQ4fkdDp15ZVXqkOHDioqKtIHH3yg2bNnq127doqIiNDdd9+td95554xcwcHBqqio0HfffSdjjC677DJFRkae9Tx8Pp/+9Kc/6cknn1RUVJScTqcGDBggl8uljRs36oYbbtB1112nNm3a6N5771VlZaW2b99+xnFycnL04IMPKiIiQuHh4XrwwQf1l7/8xb8/KChIDz30kFwul0JDQ7V+/Xqlpqbq6quvltPp1NixY9WmTRvt2LFDX375paqrq/XP//zPatOmjW688UZdddVV9fsHBtDkmKu1MVcRaPhDRTSKLl26+D9u27atCgsLdeLECfXt29e/vX379goLC1NBQYG6detW53EcDodGjBih3NxcDRw4UDk5OUpJSZEkHT16VIWFhXK73f77e71e/+3MzEw9//zzuummm9StWzdNnTpVQ4cO1ejRo3Xs2DE9+uijOnnypFJSUvTII48oLy9PNTU1SkhI8B/P5/MpOjr6jFzXXHONbr/9ds2ZM0d5eXkaPny4Zs6cqQ4dOtR5HqWlpfJ4POrevfsZ+woLCxUTE+O/HRQUpOjo6Dp/hft/7xsTE6PCwkL/7c6dOyskJMR/Oy8vT2+//bb+4z/+w7+turpahYWFcjgcioqKksPhqHU8AIGJuVobcxWBhlKNJhMZGamjR4/6b586dUonTpxQVFTUOR+XnJysCRMmaNKkSdq5c6eWL18uSYqOjla3bt20efPmOh/Xo0cPLVq0SD6fT5s3b9ZDDz2kTz/9VO3atdPUqVM1depUHTlyRJMmTVLPnj11ww03yOVy6ZNPPlFw8C9/a9x111266667VFxcrIcfflivvPKKHn744Trv+9NQPnz48Bl/2R0ZGVlr3aAxRvn5+XV+XiIjI5WXl6fLL79ckpSfn1/rSs7PB/lPn6PJkyfrgQceOONYn332mQoKCmSM8T8uLy+vzh9QAAITc5W5isDB8g80mVGjRumtt97S3r17VVVVpUWLFqlfv35nvZrykyuuuELh4eF66qmnlJCQoE6dOkmS+vXrpw4dOujll19WZWWlvF6v9u3bp507d0qSNmzYoJKSEgUFBfkf43Q69cknn+ibb76R1+tVhw4dFBwcLKfTqcjISF133XV67rnnVF5eLp/Pp++//16fffbZGZl27tzp/zVf27Zt5XK55HQ6z3oOQUFBGjdunObPn6+CggJ5vV5t375dVVVVuummm/S///u/+vjjj1VdXa1/+7d/k8vlUv/+/c84zsiRI7VixQqVlJSopKREy5cv16hRo876vLfccotef/11ffnllzLG6NSpU3r//fdVXl6uuLg4BQcHa82aNaqpqdHmzZu1a9euc/5bAAgszFXmKgIHV6rRZK655hpNnz5d06ZN08mTJ9W/f/96vSC+9OPQe/7557VkyRL/NqfTqRUrVigrK0vDhg1TVVWVevbs6b+q8be//U3PPfecKisrFRMTo8WLFyskJERFRUXKyMhQQUGB2rVrpxEjRvh/9Zmdna2FCxdqxIgRqqioUPfu3XXfffedkaeiokLPPvusjhw5IpfLpYSEBE2YMOGc5zBz5kz9y7/8i26++WadOnVKv/71r7Vq1SpdeumlWrBggebOnauCggLFxsZq5cqVcrlcZxxjypQpqqio8Oe98cYbNWXKlLM+51VXXaW5c+dqzpw5OnTokEJDQzVgwAC53W65XC698MILevrpp7VkyRLdcMMNGj58+C//YwAIGMxV5ioCh8MYY+wOAQAAADRnLP8AAAAALGL5B2y3bdu2On8VKKnOlz8KZCNHjlReXt4Z2//4xz/6f7XYHKxcuVIvvfTSGdvj4+P1yiuv2JAIwPlgrgYe5mrLx/IPAAAAwCKWfwAAAAAWUaoBAAAAiyjVAAAAgEWUagAAAMAiSjUAAABg0f8D8D+QTT7Al28AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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hmuipAHyFu3uqx0K1u+Tnn5PTaTxdBlBlvhCsUDNOny6+4WPddd3QU+Fr6Kl1l7t7qsdCtd1uV05OjioqKuTv76+Kigrl5ubKbrfLGPOTYwAAAIC38dhH6oWGhioqKkrp6emSpPT0dEVFRSkkJOSqYwAAAIC3ccud6unTp2vLli3Ky8vTyJEjFRwcrA0bNmjKlClKTEzU4sWL1aRJE6WkpLiOudoYAAAA4E3cEqpfffVVvfrqq5dtb926tdauXXvFY642BgAAAHgT/qIiAAAAYBGhGgAAALCIUA0AAABYRKgGAAAALCJUAwAAABYRqgEAAACLCNUAAACARYRqAAAAwCJCNQAAAGARoRoAAACwiFANAAAAWESoBgAAACwiVAMAAAAWEaoBAAAAiwjVAAAAgEWEagAAAMAiQjUAAABgEaEaAAAAsIhQDQAAAFhEqAYAAAAsIlQDAAAAFhGqAQAAAIsI1QAAAIBFhGoAAADAIkI1AAAAYBGhGgAAALCIUA0AAABYRKgGAAAALKpyqF62bNkVty9fvrzaigEAAAB8UZVD9aJFi664/fXXX7dcxAcffKCBAwdqwIAB6t+/v7Zs2SJJOnr0qIYMGaK4uDgNGTJEx44dszwXAAAAUN0CrrXDtm3bJElOp1Pbt2+XMcY19t1336lhw4aWCjDGaNKkSVq1apXatGmjr776So8//rh+9atfKTk5WUOHDtWAAQO0fv16JSUlaeXKlZbmAwAAAKrbNUP1K6+8IkkqLS3Vyy+/7Npus9kUFhamV1991XIRfn5+Ki4uliQVFxcrPDxchYWFOnjwoGt5Sb9+/TRt2jQVFBQoJCTE8pwAAABAdblmqN66daskadKkSZo1a1a1F2Cz2TRv3jz9+te/1k033aTz58/rjTfeUHZ2tiIiIuTv7y9J8vf3V3h4uLKzs68rVIeGNqr2mgGgJoSFNfZ0CddETwXgK9zdU68Zqn/w40DtdDorjfn53fiHiJSXl+uNN97Q4sWLFR0drS+++ELPP/98tQX4/PxzcjrNtXcEvIQvBCvUjNOni2/4WHddN/RU+Bp6at3l7p5a5VB94MABTZ06VV9//bVKS0slfb8e2maz6dChQ9c98Q8OHTqk3NxcRUdHS5Kio6PVoEEDBQUFKScnRxUVFfL391dFRYVyc3Nlt9tveC4AAACgJlQ5VCcmJurBBx/Ub3/7W9WvX7/aCrj55pt16tQpHTlyRLfffrsyMzOVl5enli1bKioqSunp6RowYIDS09MVFRXFemoAAAB4nSqH6pMnT+r555+XzWar1gLCwsI0ZcoUPfvss65zz5w5U8HBwZoyZYoSExO1ePFiNWnSRCkpKdU6NwAAAFAdqhyqe/XqpU8++UTdunWr9iLi4+MVHx9/2fbWrVtr7dq11T4fAAAAUJ2qHKpLS0s1fvx4RUdHq1mzZpXGauJTQQAAAABfUeVQfccdd+iOO+6oyVoAAAAAn1TlUD1+/PiarAMAAADwWVUO1T/8ufIrue+++6qlGAAAAMAXVTlU//Dnyn9QWFioS5cuKSIiQn/729+qvTAAAADAV1Q5VP/w58p/UFFRoddff10NGzas9qIAAAAAX3LDf1/c399f48aN09KlS6uzHgAAAMDn3HColqRPP/202v8YDAAAAOBrqrz844EHHqgUoC9evKiysjIlJyfXSGEAAACAr6hyqE5NTa30fYMGDXTbbbepUaNG1V4UAAAA4EuqHKq7dOkiSXI6ncrLy1OzZs3k52dp9QgAAABQK1Q5FZ87d06TJk1Shw4d1L17d3Xo0EGTJ09WcXFxTdYHAAAAeL0qh+rp06fr4sWLSktL0969e5WWlqaLFy9q+vTpNVkfAAAA4PWqvPzj448/1vvvv68GDRpIkm677TbNnDlTvXr1qrHiAAAAAF9Q5TvVQUFBKigoqLStsLBQgYGB1V4UAAAA4EuqfKf6scce06hRo/Qf//EfioyMVFZWllasWKHBgwfXZH0AAACA16tyqH766acVERGhtLQ05ebmKjw8XKNHjyZUAwAAoM6r8vKPGTNm6LbbbtOKFSu0ceNGrVixQq1bt9aMGTNqsj4AAADA61U5VKenp6t9+/aVtrVv317p6enVXhQAAADgS6ocqm02m5xOZ6VtFRUVl20DAAAA6poqh2qHw6H58+e7QrTT6dSCBQvkcDhqrDgAAADAF1T5jYqvvPKKxo4dq65duyoyMlLZ2dkKCwvTkiVLarI+AAAAwOtVOVTffPPNeu+997R3715lZ2fLbrerQ4cO8vOr8s1uAAAAoFaqcqiWJD8/P3Xs2FEdO3asqXoAAAAAn8NtZgAAAMAiQjUAAABgEaEaAAAAsIhQDQAAAFh0XW9UrCmlpaX67W9/q23btikoKEgdO3bUtGnTdPToUSUmJqqoqEjBwcFKSUlRq1atPF0uAAAAUIlXhOrU1FQFBQVp8+bNstlsysvLkyQlJydr6NChGjBggNavX6+kpCStXLnSw9UCAAAAlXl8+cf58+e1bt06Pfvss7LZbJKkZs2aKT8/XwcPHlS/fv0kSf369dPBgwdVUFDgyXIBAACAy3j8TvWJEycUHByshQsXaseOHWrYsKGeffZZ1a9fXxEREfL395ck+fv7Kzw8XNnZ2QoJCfFw1QAAAMA/eTxUl5eX68SJE/r5z3+uyZMna8+ePRo3bpzmz59fLecPDW1ULecBgJoWFtbY0yVcEz0VgK9wd0/1eKiOjIxUQECAa5nHL37xCzVt2lT169dXTk6OKioq5O/vr4qKCuXm5sput1/X+fPzz8npNDVROlAjfCFYoWacPl18w8e667qhp8LX0FPrLnf3VI+vqQ4JCVFMTIw+/fRTSdLRo0eVn5+vVq1aKSoqSunp6ZKk9PR0RUVFsfQDAAAAXsfjd6ol6b//+7/18ssvKyUlRQEBAZo1a5aaNGmiKVOmKDExUYsXL1aTJk2UkpLi6VIBAACAy3hFqG7RooXeeuuty7a3bt1aa9eu9UBFAAAAQNV5fPkHAAAA4OsI1QAAAIBFhGoAAADAIkI1AAAAYBGhGgAAALCIUA0AAABYRKgGAAAALCJUAwAAABYRqgEAAACLCNUAAACARYRqAAAAwCJCNQAAAGARoRoAAACwiFANAAAAWESoBgAAACwiVAMAAAAWEaoBAAAAiwjVAAAAgEWEagAAAMAiQjUAAABgEaEaAAAAsIhQDQAAAFhEqAYAAAAsIlQDAAAAFhGqAQAAAIsI1QAAAIBFhGoAAADAIkI1AAAAYBGhGgAAALDIq0L1woUL1bZtWx0+fFiStHv3bsXHxysuLk6jRo1Sfn6+hysEAAAALuc1ofrAgQPavXu3IiMjJUnGGE2cOFFJSUnavHmzHA6HZs+e7eEqAQAAgMt5RaguKyvT1KlTlZycLJvNJknat2+fgoKC5HA4JEkJCQnatGmTJ8sEAAAArsgrQvX8+fMVHx+vFi1auLZlZ2e77lpLUkhIiJxOp4qKijxRIgAAAPCTAjxdwK5du7Rv3z699NJLNXL+0NBGNXJeAKhuYWGNPV3CNdFTAfgKd/dUj4fqnTt36siRI4qNjZUknTp1Sk8++aSGDx+urKws134FBQWy2WwKDg6+rvPn55+T02mqtWagJvlCsELNOH26+IaPddd1Q0+Fr6Gn1l3u7qkeX/4xZswYffLJJ9q6dau2bt2qm2++WcuWLdPo0aNVUlKijIwMSdLq1avVu3dvD1cLAAAAXM7jd6p/ip+fn2bNmqXk5GSVlpaqefPmSk1N9XRZAAAAwGW8LlRv3brV9fU999yjtLQ0D1YDAAAAXJvHl38AAAAAvo5QDQAAAFhEqAYAAAAsIlQDAAAAFhGqAQAAAIsI1QAAAIBFhGoAAADAIkI1AAAAYBGhGgAAALCIUA0AAABYRKgGAAAALCJUAwAAABYRqgEAAACLCNUAAACARYRqAAAAwCJCNQAAAGARoRoAAACwiFANAAAAWESoBgAAACwiVAMAAAAWEaoBAAAAiwjVAAAAgEWEagAAAMAiQjUAAABgEaEaAAAAsIhQDQAAAFhEqAYAAAAsCvB0Ad6scZP6qh9Uz9NlwM1KSi+p+GyJp8sAAAA+hFB9FfWD6mnopFWeLgNu9v9mPaFiEaoBAEDVeTxUFxYWatKkSfrHP/6hwMBAtWzZUlOnTlVISIh2796tpKQklZaWqnnz5kpNTVVoaKinSwYA3ACe/tVNPP1DXeHxUG2z2TR69GjFxMRIklJSUjR79mzNmDFDEydO1MyZM+VwOLR48WLNnj1bM2fO9HDFAIAbwdO/uomnf6grPP5GxeDgYFeglqSOHTsqKytL+/btU1BQkBwOhyQpISFBmzZt8lSZAAAAwE/yeKj+MafTqbfffls9e/ZUdna2IiMjXWMhISFyOp0qKiryYIUAAADA5Ty+/OPHpk2bpptuuknDhg3TX//612o5Z2hoo2o5D+qWsLDGni4BdZAvXHf0VNwIX7i2Ufu4+7rzmlCdkpKi48ePa8mSJfLz85PdbldWVpZrvKCgQDabTcHBwdd13vz8c3I6zQ3VRBOou06fLvbY3Fx3dZeV685d1w09FTeCngpPcHdP9YrlH3PnztX+/fu1aNEiBQYGSpLat2+vkpISZWRkSJJWr16t3r17e7JMAAAA4Io8fqf6m2++0ZIlS9SqVSslJCRIkm655RYtWrRIs2bNUnJycqWP1AMAAAC8jcdD9Z133qmvv/76imP33HOP0tLS3FwRAAAAcH28YvkHAAAA4MsI1QAAAIBFhGoAAADAIkI1AAAAYBGhGgAAALCIUA0AAABYRKgGAAAALCJUAwAAABYRqgEAAACLCNUAAACARYRqAAAAwCJCNQAAAGARoRoAAACwiFANAAAAWESoBgAAACwiVAMAAAAWEaoBAAAAiwjVAAAAgEWEagAAAMAiQjUAAABgEaEaAAAAsIhQDQAAAFhEqAYAAAAsIlQDAAAAFhGqAQAAAIsI1QAAAIBFhGoAAADAIkI1AAAAYBGhGgAAALDI60P10aNHNWTIEMXFxWnIkCE6duyYp0sCAAAAKvH6UJ2cnKyhQ4dq8+bNGjp0qJKSkjxdEgAAAFBJgKcLuJr8/HwdPHhQy5cvlyT169dP06ZNU0FBgUJCQqp0Dj8/m6UamjVtaOl4+Car141VgU1CPTo/PMPT111V0FNxIzx9bdNT6yZ3X3c2Y4xx64zXYf/+/Zo8ebI2bNjg2tanTx+lpqaqXbt2HqwMAAAA+CevX/4BAAAAeDuvDtV2u105OTmqqKiQJFVUVCg3N1d2u93DlQEAAAD/5NWhOjQ0VFFRUUpPT5ckpaenKyoqqsrrqQEAAAB38Oo11ZKUmZmpxMREnT17Vk2aNFFKSopuv/12T5cFAAAAuHh9qAYAAAC8nVcv/wAAAAB8AaEaAAAAsIhQDQAAAFhEqAYAAAAsIlSjkk8++USPPvqo2rdvr5SUlEpjixYtUt++fRUfH69HH31UH3/8sWvs4sWLeu6559SrVy89/PDD+uCDD9xdOnzIsmXLFBcXp7vuuqvSteJ0OjVhwgTFxcUpPj5eI0eO1D/+8Q/X+NGjRzVkyBDFxcVpyJAhOnbsmAeqB64PfRU1jZ7qJQxqvYKCgirve+zYMXPgwAEzZ84c8z//8z+Vxv7+97+bCxcuGGOMOXTokImOjjYXL140xhizYMEC8/LLLxtjjDl69Kj55S9/ac6dO1dNrwC+5lrX3J49e8yxY8fMsGHDzNatW13bKyoqzPvvv28qKiqMMca89dZbZsSIEa7x4cOHm3Xr1hljjFm3bp0ZPnx4DVQPXBt9Fe5ET/UN3KmupS5evKgNGzZo3LhxGjx4cJWPa9mypX7+858rICDgsrFu3bqpQYMGkqS2bdvKGKOioiJJ0l/+8hclJCRIklq1aqX27dvr73//ezW8EviiZ555RiNGjNDatWt19uzZy8Y7dOigli1bXrbdz89PsbGx8vP7vjV17NhRWVlZkqT8/HwdPHhQ/fr1kyT169dPBw8eVEFBQQ2+EuCf6KvwFHqqb7j8v3D4rPLycn322WdKS0vTl19+qW7dumn06NGKjo6WJE2fPl07d+684rELFizQrbfJOgsMAAAKnUlEQVTeWuW51q1bp1tvvVU333yzJCkrK0vNmzd3jdvtdp06dcrCq4Eve+utt3TgwAGlp6frscceU5s2bdS/f3/16NFDQUFBVT7PqlWr1LNnT0lSdna2IiIi5O/vL0ny9/dXeHi4srOz+SurqDH0VXgDeqpvIFTXIo8++qgKCgr0X//1X5o5c+Zld0VeffXVapnn888/1/z58/WHP/yhWs6H2qldu3Zq166dJk2apO3bt2vKlCl65ZVXlJGRUaXjly5dqszMTL355ps1XCnw0+ir8Bb0VO9HqK5FZsyYofXr1ys1NVVbtmxR37591bVrVwUGBkqqnjsqu3bt0sSJE7V48eJKfy4+MjJSJ0+edP3fbXZ2tmJiYqrhVcFXOZ1O7dixQxs2bNC2bdt07733uh4zXssf//hHpaena8WKFa5H43a7XTk5OaqoqJC/v78qKiqUm5sru91eky8DdRx9Fd6Cnur9CNW1yN133627775bv/nNb7Rt2zalpaVp2rRpuv/++zV9+nTLd1T27t2r559/Xq+99pratWtXaezhhx/WO++8o7vvvlvHjh3Tvn379Lvf/c7SfPBd8+bN05///Ge1a9dO/fv3V1JSkiuEXMs777yjd955R2+++aaCg4Nd20NDQxUVFaX09HQNGDBA6enpioqK4jElahR9Fd6AnuobbMYY4+kiUHNKSkq0detW9enTp0r7Z2Rk6IUXXtC5c+dkjFHjxo01Y8YMdevWTYMGDdLJkycVERHh2n/WrFlq27atLly4oMTERB06dEh+fn6aOHGifvWrX9XUy4KXe//99xUTE6PGjRtfcXzp0qVauXKlCgoK1LBhQwUFBWnjxo2SJIfDocjISNexgYGBWrt2rSQpMzNTiYmJOnv2rJo0aaKUlJRKd/YAd6Cvwt3oqb6BUA0AAABYxEfqAQAAABYRqgEAAACLCNUAAACARYRqAAAAwCJCNQAAAGARoRpe48iRIxo4cKA6deqklStX1vh8bdu21fHjx6849qc//UmPP/54tc+5Y8cOde/evdrPCwD/ip4KuBehGl5j6dKl6tKli3bt2qURI0bc0DmGDx/u+vxNAKjL6KmAexGq4TWysrJ05513eroMSDLGyOl0eroMABbQU70HPbVuIFTDK4wYMUI7duzQ1KlT1alTJ7355psaOHCg7rnnHj3wwANasGCBa9/S0lK99NJLiomJkcPh0KBBg5SXl6e5c+cqIyPDdY6pU6dec96PPvpIsbGxiomJUUpKyk82vS+//FKDBg1SdHS0Bg0apC+//NI1lpOTo3HjxqlLly7q1auX1qxZ4xorKSlRYmKiOnfurD59+mjfvn1V+nn8/ve/V7du3dSpUyfFxcVp27ZtkqTExETNnTvXtd+/Pvo8cOCA63HvM888o+eee861/5kzZzR27Fjde++96ty5s8aOHatTp065jh0+fLjmzp2rhIQE/eIXv9CJEyeqVCsA70NPrYyeCrcwgJcYNmyYWbNmjTHGmO3bt5uvvvrKVFRUmEOHDpn77rvP/PWvfzXGGPP222+bsWPHmgsXLpjy8nKzb98+U1xcfNk5rqVNmzZm2LBhprCw0Jw8edI89NBDrmP/7//+zyQkJBhjjCksLDQOh8O899575tKlSyYtLc04HA5TUFBgjDHmiSeeMMnJyaakpMQcPHjQxMTEmM8++8wYY0xqaqp5/PHHTWFhocnKyjJ9+/Y13bp1u2pdmZmZpnv37ubUqVPGGGNOnDhhjh8/bowxZvLkyWbOnDmufbdv3+46X2lpqenRo4dZsWKFKSsrM5s3bzbt2rVz7V9QUGA2bdpkLly4YIqLi82ECRPM008/Xenn/8ADD5jDhw+bS5cumbKysir9HAF4J3rq9+ipcBfuVMMrxcTEqG3btvLz89Ndd92lvn376vPPP5ckBQQEqKioSMePH5e/v7/at2+vRo0a3dA8Tz31lIKDgxUZGakRI0YoPT39sn0+/PBDtWzZUgMHDlRAQID69eun22+/XR988IGys7P1xRdf6KWXXlJQUJCioqI0ePBgrV+/XpL0l7/8RePGjVNwcLDsdruGDx9+zZr8/f1VVlamzMxMXbp0SbfccotuvfXWax63Z88elZeXa8SIEapXr54eeugh3X333a7xpk2bKi4uTg0aNFCjRo309NNPa+fOnZXO8cgjj+jOO+9UQECA6tWrd805AfgGeio9FTUvwNMFAFeyZ88ezZ49W998840uXbqksrIyPfzww5KkAQMG6NSpU3rhhRd09uxZxcfH6/nnn7+hhmW3211fN2/eXLm5uZftk5ubq8jIyErbIiMjlZOTo9zcXP3sZz+r9AsoMjJS+/fvdx374zn+9TxX0rJlS7388stasGCBvv32W3Xt2lWJiYmKiIi46nG5ubmKiIiQzWa74uu7ePGiZs6cqY8//lhnzpyRJJ0/f14VFRXy9/e/bH8AtQc9lZ6KmsedanilF198UbGxsfroo4/0xRdfKCEhQcYYSVK9evU0fvx4bdy4UatXr9aHH36odevW3dA82dnZrq+zsrIUHh5+2T7h4eHKysq67LiIiAiFh4frzJkzOnfu3GVjkhQWFlZpjh9/fTX9+/fX22+/rQ8++EA2m02zZ8+WJDVo0EAlJSWu/fLy8lxfh4WFKScnx/Vz+tf5/vCHP+jo0aNas2aNvvzyS61atUqSKu3/418eAGoPeio9FTWPUA2vdP78ef3sZz9TUFCQ9u7dW+kR4vbt2/X111+roqJCjRo1UkBAgOuuQLNmza7rzSDLli3TmTNnlJ2drZUrV6pPnz6X7fPAAw/o2LFjSktLU3l5uTZu3Khvv/1WPXr0kN1uV6dOnTRnzhyVlpbqq6++0rvvvqv+/ftLknr37q3f//73OnPmjE6dOqW33nrrmjUdOXJE27ZtU1lZmQIDAxUUFOR6fVFRUfroo49UVFSk06dP680333Qd17FjR/n7++uPf/yjysvL9f7771d6E8/58+cVFBSkJk2aqKioSAsXLqzyzwmAb6On0lNR8wjV8ErJycl67bXX1KlTJy1atEi9e/d2jeXl5emZZ55RdHS0+vTpoy5duig+Pl7S9+9437x5szp37qzp06dfc57Y2Fg9+uijGjhwoHr06KHHHnvssn2aNm2qJUuWaPny5YqJidHSpUu1ZMkShYSESJLmzJmjkydPqlu3bho/frwmTJig+++/X5I0fvx4RUZGKjY2VqNGjdKAAQOuWVNZWZl+97vfKSYmRl27dlVBQYGef/55Sd8/pr3rrrvUs2dPjRo1qtIvrMDAQC1YsEDvvvuuOnfurD//+c/q0aOHAgMDJUn//u//rtLSUt17770aMmSIunXrds1aANQO9FR6Kmqezfz4OQWAWmXw4MFKSEjQoEGDPF0KAPg8eiquhjvVQC3y+eef6/Tp0yovL9d7772nr7/+mrsnAHCD6Km4Hnz6B2qtjIwMPfXUU1cc27Vrl5urqSwrK0t9+/a94tiGDRuq9I72Kzl69Kiee+45XbhwQS1atNBrr712xTcKAcD1oqfSU3F1LP8AAAAALGL5BwAAAGARoRoAAACwiFANAAAAWESoBgAAACwiVAMAAAAWEaoBAAAAi/4/RIDi43MeKo4AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Bivariate Analysis of Categorical Columns\n", | |
| "cat_cols = ['sex', 'chest_pain', 'rest_ecg', 'exercise_ind_angina', 'slope_peak_exer_st', 'no_vessels_colored_fl', 'thalassemia' ,'fast_blood_sugar']\n", | |
| "print('Bivariate Analysis of Categorical Variables')\n", | |
| "for row in cat_cols:\n", | |
| " sns.catplot(x=row, \n", | |
| " col=\"target\",\n", | |
| " data=heart_df_uv, \n", | |
| " kind=\"count\");" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* More males have heart disease\n", | |
| "* atypcal angina is contributing most to the heart disease. Chest pain 0 (assumed as None definitely means no heart diseases)\n", | |
| "* st-t abnormality is contributing most to the heart disease. if 'normal' , high chances there is no heart disease\n", | |
| "* Majority of the sample having heart disease is not exercise induced.\n", | |
| "* Slop_peak_exer_st 'yes' definitely indicating higher chances of heart disease\n", | |
| "* No of colored vessels 0 indicates heart disease\n", | |
| "* thal, value -2 indicates higher chances\n", | |
| "* high fasting blood sugar also indicates higher chances of heart disease" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Continuous & Target" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Bivariate Analysis of Continuous Variables\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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jdG3wQCW2tw0zdaA+i8dIyadwmidERMJpnuikOUJEpKsTmSfMHorluL3++utdttzNmzePv/71r8ycOZO//vWvPPDAAxGKrm8xTYMtxfXs2n8AgDaPnxeWbmfVlkreW1dKIGgTCNosX7+fjTtrvmQ0keNnOFxYmcPx712Pf/cawIagH//W5di+9rBrzX4ZWGmDlHgSERERERE5RZ0y+6aWLFnSpW/o0KG8/PLLEYhGSqubw9r+gM2O0obDXjdxRNrJCkv6mGBd6WH7rNSBEYhGRERERERETsQps/JJTi1jh6SEteNjnJw7NpND15YYh7lOpDs5cseHd5gOLJ1md1qaP38+06dPZ+TIkezYsSPUv3z5cr72ta8xe/ZsrrrqKpYuXRp6rbi4mDlz5jBz5kzmzJnDnj17IhC5iIiIiIh8mVOm5lNP0T7t41dV38q67dXsLm+ktLqZlIQoZp09kPK6NorLGtlb2YRpGMycmstZozMjHa6c5rxb3sNXuBwsB1baIKz0oTiGTMFwurH9HvxFa7C9rTiGTMGMTYp0uL3CqVjLY+3ateTk5HDDDTewYMECRowYgW3bTJkyhRdffJERI0awbds2rr/+etatW4dpmtx8881cc801obqAr776Ks8///xx/2zNE8enpKqZNVsrSYxzc+64TKJcp8wiaumDgo1V+HZ8hOF04xg5DTOq48RT//5C/CUFWCm5OIaehWHqeevxOBXniUjRHCEi0tWJzBP6F6OE2V3WyPy/rcfnDwIwZnAy/z57LPOeW0PNgY5aO0nxbubeMpmEGFckQ5U+wjXmYhz9x9Ly+gMEa/bg27oCc/O7RM/+GW0LHyFY23ECnmfdQmK/PjesGLn0Hvn5+YftN02TpqaOEwybmppIT0/HNE1qa2spLCzkueeeA2DWrFk89NBD1NXVkZycfNLi7mt2ljbw2N82EDj4RezjzRXcd/Mk1VyTiAgeqKDltXlwsBagt3A5sdc+jG/XJ3g+/DMAPsBZto2oC26NWJwiIiKibXfyBe+sLQklngC2FNfx9pq9ocQTQH2Th1VbKiMRnvRR3q3Lwdsaagdr9+Lb+K9Q4qnjotaO6+S0YRgGv/71r/n+97/PRRddxA9+8AMeffRRAMrLy8nIyMCyLAAsyyI9PZ3y8vJIhnzaW75hfyjxBFBc3khRWWMEI5K+zLd9ZSjxBGA3VePfuwHf5nfDr9uxEtvbdrLDExERkUNo5ZN8ucOsND7Nd2tKb6DP4GnP7/fzhz/8gaeffppJkyaxbt067rzzThYvXtytP0dbS45dfKy7S19aahxpafERiEb6uvr4WLxf6OuXlEC92x3Wb1gOUtMSMF1RJzM8EREROYSSTxLmkkn9Wbe9Gn+gY/VT3sAkLps6gE+2VFLb2PF0sV+ci7PHqNaTnDyuURfi2/Y+HHxybSbn4px4Bf69GwjWlRy8KBpX3oWRC1K63datW6mqqmLSpEkATJo0iejoaIqKisjJyaGyspJAIIBlWQQCAaqqqsjKyjrun6N6Hsdu2thMVn62nzZPAIDRg5LYvruG5sY2ctKUxJOTKzjgLIyYJditHafxmikDaKhvhAGToLIY7I5/yzjHXkrtAR8dm/DkWKjmk4iIdDcln4TmNh//WL6LnaUHGJaTwD03TKRwTz17K5sor2lhwcIt3HZlHvtrWthT0UhJZTNPvbKJy6YOYPKo9EiHL71QsLkWz6qXCNaWYOWOwz3lmwB41r5GYN9nmInZuM+ag5mQjnfrCnyFyzD7ZWKmDMRKG4QRm0z7v36FTRDH0KlYqQMxM0fg+fTVYxpTeofMzEwqKirYvXs3Q4YMoaioiJqaGgYMGEBiYiJ5eXksWrSI2bNns2jRIvLy8lTvqYf1T4/jF/92Fht21tDc5uOtj4sp3FMPwNenDeaqcwdHOELpS8zYJGK/+XP8xesIelvxrn8Lz7IFADgGT8bKGomZkosja2SEIxURERGddif8/rUC1u2oDrUnDEtl9KAk/vbuzlBfQoyTH107np+/sC6028kA/uu7+QzKTDjJEUtv1/LaPII1e0Jt55iLwXLi2/R2qM9M7o978rW0Lfl1542Wg5iv3U/rGw9DoHNTRdQlP8C7cfExjRl77cM98p56k1PxifbDDz/M0qVLqampISkpicTERBYvXsybb77Js88+Gypo/aMf/YhLLrkEgKKiIu655x4aGxtJSEhg/vz5DBky5Lh/tuaJE/PY39azbV9DqO2wTJ760XlEu/VcS06+thXP4t/xUVhf7JxHMftppfaJOBXniUjRHCEi0pVOu5MT8llRbVh7U1FtaNvd5xpbfXy4qTyszI4NFBTVKvkkxyXY1hiWJALw79uEYTnDr6srxbd7TfjNAT++rSvCEk8Avt1rj3nMYHMtZlzKV3kL0gPuu+8+7rvvvi79V199NVdfffVh7xk6dCgvv/xyT4cmR9Da7g9r+wNBvL6Akk8SGZ7WLl32YfpEREQkMnTanZCTGhvWzk6NJfsLfZZpMKx/vy73ZqfqqZgcH8MdixEd/lkyk7Ixk7LDr4uKx0wZ2OV+6zDbJ6y0Acc8phGtZKlIdzh/QvifrzOGptAvrmtBcpGTwTnqfDrWZHcwUwZgpmkbqIiIyKnCmjdv3rxIB9GT2tq8OhTrKDzeAC6Xyb7KJto8AVIS3Nw2K4/UftFU1LVS3+QhymVx/SXDGZadQHObj/LaVgzDYNoZ2Zw9JoOKulb6xbpC22JEjsYwTMykHAL7t4Dfg5mUTfSFt+PoP45A2VbstkaIisN1xpU4h03Fbq4l2FAOlgPHyGk4h07FiIonULkL7CBWzmicI8/H0X/sUcc0ouJxn38LVnL/SP8KIs4wDGJiXJEO45SheeLEDM5KICc1FrfT4qwxmXzzomE4LD3TksgwE7OwMkdgWBaOgWcSde5NGE4lQ0+U5olOmiNERLo6kXlCNZ/6sK176/n9awW0evy4nSY3XDqCvAHJPPmPjZTXtmIaBpfk92f2eYP5n8VbQ3WhRg9K4vZZo/lkSwWvvb+bQNAmPSmau+dMIDUxOsLvSnoLO+DHbjvQZQucr6SA9hV/hLYDYDmJmvYdzIzhtL23ALumGABn3oW4p86h/aO/4t/5MWBjpg8heuZPIODrMmawuRYjOqHLNry+SrU8wmmeEBEJp3mik+YIEZGuTmSe0MqnPuy3r26ittEDQCBoU1rdTFObj00Ha0DZwO7yRpLj3by9piR0X3VDOyn9onhleRGBg5NxS7ufNm+AicPTTvr7kN7JME0MV0yXfs9HL2DXHfy82UH8ZdvAFUVg1yeha4I1ezDi0/BteCvUZ7fUY0TF4hwwoevPcsVgmFb3v4leSk+0w2meEBEJp3mik+YIEZGuTmSe0Pr4PqzmQHtYu67RQ01DW1ifbcP+mpYu95bVtIQST5+r/cJ4IifCbqoJ7/C2Yh+o6HJdsK70y+8VERERERGRiFPyqQ+bPCo9rD1pZBpT8jLC+lIS3Ew/MyesjodlGlw4MYeM5PBVK18cT+REOIZOCWtbmSNwjpgWfpErBtfYGeAK3+bpGBJ+r4iI9A12wE/7yhdo+ssPaHn1v/DvL4x0SCIiInII1XzqY9o8ft7fWEbtgXYmjkhl+74GdpQ2EOWySIhxMXJAIoGgzZrCSizLJDbaSf/UWPqnx/H+xjLavX7iop2k9otm/NAUPt5cTnVDO5Pz0rlwQk6k3570cv59n+HbuxG7tQG8rZjJuTiG5OPfswG7pR67vQkjOgHHqAsJVuwgeKACu70ZDAPniPOwPc0E60px9B+HY9DESL+dU1ZP1vL46KOPWLx4MXV1dSxYsICCggKam5s5++yze+TndQfNEyK9n2fDW3g/fbWzwxlN3A1PYrhUi/JEqOZTJ80RIiJdncg84eihWOQU9eQ/NlK0vxGAZetL+eG14wkEbf65ai8AH24q57IpA5gxeQBPvfwZn0+1g7Pi+Y9vjOP+P62hpd0PwIoN+3no9qkkxes0GfnqvFtX4Pnwz6G2Y3A+zrwLaH39QQj4ADDiUoi5+Ae0vvEAdlNHAXxMBzGz78Nb8Db+XasA8BUuw33OjbjGXnKy30af9sILL/D888/zzW9+kyVLlgAQFRXFz3/+81M6+SQivV+gfHt4h6+NQO0+HFkjIxOQiIiIhNG2uz5kX2VTKPEEHQXFV2zYz4oN+8OuW75xP+9v3M+hz3iKy5tYsqYklHgCaPX4WV1Y2cNRS1/hK1we1vYXr8O75b1Q4gnAbq7Fu2lxZ+IJIOjHW/ge/qLV4eNtXdaj8UpXf/nLX3juuee44447MM2O6WXIkCEUFxdHODIROd1ZaYO/0OHCSu4fmWBERESkC6186kOiXF1P+4p2O4hyW7R6/GHXRbm6fjRio7v2Rbl1gph0D8MVFd5hWoc9Dc9wxx7m3hgwnRDwdnY6o7pcJz2rpaWFrKwsoOMEDAC/34/T6YxkWNLNPtlSwSebK+gX52LWOYPISOr651TkZHNNmEXwQCX+4rUYMYm4z7nhsPOFiIiIRIZWPvUh6UkxnDc+K9SOdltcPnUAX582BONgnwF8fdoQLp86gBh3Z7LpnLGZXJo/gP5pnfs6c1JjmfqFAuUiJ8p15mywOj9zrjMuxzXuUozY5FCflTUS57jLsXLGhPqMmERc4y7FNXFW52CmhfvMr52UuKXT5MmTeeaZZ8L6nn/+eaZOnRqhiKS7rdlaybNvFbK5uI6PCip47G8b8PmDkQ5LBMPpJvqS7xN327PEfvuXOAdPinRIIiIicggVHO8jtu2t52/v7qDmQDvDcxPJH5HGoMx4/rGiiJ0lDfRPi+XMkemMG5LMig1lrCqsICHGxcQRaZwxNIXS6hYWf7KHYNBm3JAUJgxPw+00+cfyXdQcaCd/VDo3zhhBc5uP5/61jZ0lDQzJTuC7V+SRnqhin3Jsgi31+Pesw1+8jkDVbsykHNxnX0+wuY7AnnX4SzdjuGNx5X8DMzoe/96N+PZuAF87rryLsAbnE9j3Gb7iddiNFThyxxM17bsYUSqaeqieKiRbVVXF9773PRoaGqisrKR///7ExcWxYMEC0tLSuv3ndRfNE8fu968VsG5HdVjfT6+bQN6g5CPcISK9kQqOd9IcISLSlQqOy2F5fAF+/3pBqF5TQVEtAzPiWLOtii3FdQDsLm/CtExs22b5wRpQbZ42lq/fz+iBSbz4zo7QeB9trmDSqHT+8OaW0JgrN5WTGOeiuLwpNOa2fQ38cVEh996op49ybMzYJALVewmUbQUgWL2b9hXP4pp4Nf7dawCwva14VjxL9JU/xVe4DOyOVRfejYtw98vEt/Mj7MYqAPzFa2l3RhN94W2ReUN9TGpqKq+++ioFBQXs37+frKwsxo8fH6r/JL1fSr/w7awGkNxPW1xFRERE5OiUfOoD9le3hBUKB9hRcoA95Y1hfbtKDxDlDK/h5PEFWLs9/Ck3wIYd1cc8pm3bofovIl8mULEjrG03VhEoLQi/yA7iL1oTSjyF7i0tCCWejjSe9IxAIMDEiRNZu3Yt48ePZ/z48ZEOSXrA5VMHsLm4jrKaFgwDzhqdwQcby8hOjeWsMRlYSjTKSeLbs55AxQ6sjOGhLXaBmr34d3+KEZuEc+R5GA43wZZ6fNs/BMA5chpmbFIkwxYREemzlHzqA7JTY4h2W7R5AqG+odkJmEbH6qTPDc5KYFhOPzYfXLkE4HKYTBieygeflYWNOW5oCmu3Vx3TmEo8yfGw0ofgb+w8RdGIS8HMHgVhp9kZOAZPwrd1BRxyLqOZnYdRuQu7uTZsPOl5lmUxaNAg6uvrychQLbjTVb84Nw/eNoV9lU3sKGngpfd2hV7bsqeOO64ac5S7RbqHZ91CvOteB8DH2wTPvBoraxRt/3wi9FDCX7SaqBn/Qetrc7HbOh6M+Ta/Q8y1D2HGJEYsdhERkb7Kmjdv3rxIB9GT2tq8nN5Vrb6cwzIZkBFP0f4DtHsCTM5LZ870YeQNSmZPRRN1jR5y0+M4e2wmE0ek0OYJUF7bQlK8m2njsxmSnUBGUjR7yptwWAaTR2UwOCueScPTKCo78piDsxK4fVYe8TGuSP8K5BQUbCjHX7YNIyoew+nbRK7mAAAgAElEQVTu6GtvgoAP29OK3VyHmZRD1AW3YBgWdjCA3VSDERWL+5xvY8QkYjjc2M01YAdxjp6Oc9AkDKebYEsDeFqwcsfhnnwNgfJtgIEZHR/ZN32KMAyDmB74c9nc3MyvfvUr3G43DQ0NlJaWhv6Xm5vb7T+vu2ieOD6GYZAY5+bFd3bQ0Nx5wuT+6hYuOjMHt1OnoErPanvntxDwhdqBmn3Q3kywofNBmd1cCwYESrd03uj3YsYkYWUMO5nh9ko9NU/0RpojRES6OpF5QgXH+5jDbYFbXVjBs29tJWjbmIbBbbPyGJgRx6MvbqC5reMfdxdOyOa6i4fz6Ivr2FPRDMDAjHjuueFMXE6zy5jaaidH4930Lzyr/l9Hw3IRfdlPwLRo+9eT4PcA4Mz/Bq68C2ld+HBoK53ZfyzRl92J553f4d+7AQAjPpXoq/8vgaJVYWNGzfwxhuUIG9M1+RrcE686uW/2FNRThWSnT59+2H7DMHjvvfe6/ed1F80TJ+YXf13HrtIDobZlGvz6R+cRG+WMYFTSFzT/9SfYrZ2rrI2oeKycMfiLVoVd55o0G++6hWF97nNvwjXm4pMSZ2+mguOdNEeIiHSlguPypQ6XEHr1/d0ED+Ygg7bNKyuKGD0oKZR4AlixsYzUxOhQ4glgb2UTq7dWcv4Z2cf0c0QAbL8Xz9o3OjsCXjxrX8OwnKEkEYBvw1vg94bVcAqWbsa3eWko8QRgN9XgK1jaUXz8kDG9617vMqZ3/Zu4xlyC4dIJjD1h2bJlX36RnDZmnT2Q375aQODgl7KLJ/VX4klOCteZs/Gs/Etne9JsrPRhHXPDwb/zHYPzcY27DP+u1QQPVABgJGTgHHZWRGIWERHp65R8ElrafV3aLW3+LtcdaPZ06Wtp83XpEzmqgA/83vA+Twu25ehy3ed1Og5ltx7o2tfedOxj+j1KPol0g/FDU3n436ayeXcdOamxjBqoQs5ycrhGX4SVPoRAxU6sjGFYaYMAiJ3zKP69GzDjkrFyx2MYJjHfeAD/nnVg2zgGT8Jw6nRGERGRSFDySZg2Ppuln5aEtUcPTGLjrppQX256HJdOzmVlQXmoyHiUy2JyXvpJj1d6N8Mdi2PwJPzFa0N9zpHng+XE8/FfQ33WgDNwjZmOf+dHEOz4zBkxibjGX46/aA12y8HC+IaFK+9C8LUf05gqNNtzLrjggiOuelyxYsXJDUZOioykGDImxUQ6DOmDrNSBWKkDw/rM2CRco8O3/xpON87h55zM0EREROQwVPOpD/MHgiz9tITt++qxTIMol4PhuYnERDlYU1iJbdu4HBaZKTEM75/Ix5vLaWn343Ja9It1kT8yjXU7qqlpaGfyqHTOHpsZ6bckp7BAQxm+z97G9rbiGH4udmMFgeo92H5PxxPp3PEYUfH4932G7W3DDvhwpA7oqOOxew22pwXb195RLHb42QRLCgg2VWP7fRiW47jGdJ1xBYY7NtK/kojpqVoea9asCWtXV1fz/PPPc8UVV/Cd73yn239ed9E8ISISTjWfOmmOEBHp6kTmCSWf+rAXlm5n+fr9ofY5YzMZPzSFBQs7T4ZJT4zmzjnjmfs/n+L1dRxf7LBM5t2Sz3+/sYX9NS2ha2+5YhTTxnet/yRie1tp+ft/Yns+rxlmEH3VPXjXv0lgf+fnzZX/DQC8a18L9Vk5Y3CdeTVtix7l8+NmDHccMdc8QOurc094zJgrf9oD77R3OJlfKqqrq7n99ttZuHDhl18cIZonRETCKfnUSXOEiEhXKjgux2XVloqw9urCyi41nKoa2njn09JQ4gk6Vky9t640LPHUMV6lkk9yWP6SzYckiQBsfNs/DEsSAfh3hZ9UBBDYvwVfbBKHnnNse5rxblrylcYMth7AjOl3Qu9Hjp3L5aK0tDTSYYiIiIiISAQp+dSHJca5afO0HtJ2kZwQXojTADKTu9bzSE+KxjSM0Cl5AEnx7h6LVXo3I7ZrnSUzLgWcUeBr73pdQ1nnhc4ojLiUrmP2y+jadzxjquh4t3vqqafC2u3t7bz//vucf/75EYpIRE5ndrDjcBTD1D9nRU5V/kCQqvo20hKjcTrMSIcjIhGk2boPu+7i4Tz9+mY8vgBOh8l1F49gUGY8BbtrqTnQjkHH0dnnjc9i0+5aNu/uKPA8MjeR6Wf2p90b5M2Vxdh0JJ6uOmdQJN+OnMKstME4hk7FX7QaADM5F+eYizFc0XhWvwx2ANyxuCdfi+1to61mL3hawLBwT52DI3ccgT3rCNZ1rKBxDDsb16jzCVbsCBvTNeZizGMc03C4Ivb7OF1VVISvpoyOjuaWW25h9uzZEYpIRE5XnjWv4C1YCga4xs3EPfmaSIckIl9QtP8Av3u9gAPNXuJjnPz77LE6GVWkD1PNpz6utd3H3oomcjPiiYt2Ah1PKAqL6/jn6n3sKGkgLtrJ9RcPJzs1lqBtMzgrIXR/TUMbtY3tDM3ph8PS0wzpyluwBM/a18Hvweo/DtcZl2PbNp4Vf8RuqcNIzsU94UrM1IG0r/gjwaoiiEnENe5SHEOm4F37Ov5dn4DpwDHiPFxjLyZQuvmEx3QOOxsztm//w0e1PMJpnhDpXfz7NtH29pNhfdFX3I2j/9gIRXT60TzRSXPEiXvguU/ZW9kUamckx/DIHWdFMCIR6S6q+STHLSbKSd6g5LA+h2WyvaSBHSUNADS3+XjuX9v45Q/OIT4mfLVIamI0qYnaviSHF2gow/PJ3zvbJZsI5IzBV/A2dks9AHZdCf7da2Dvho4kEUBrA75Nb4Plxr/zo4M3e/FvXYaj/5jDjDkaX8GSYxrTNe7Snn/jfdSqVavIyckhNzeX6upqnnjiCUzT5K677iItLS3S4YnIaSJQs6drX9Vu/Hs24CtahRmXjPus63HkjD75wYlISEVda1i7qr6VoG1jGkaEIhKRSNJSFTmskqrmsLY/EKS8tvUIV4scXrC2pGtf9e5QkuhzgdoSgrX7wvrstkaCVbu63P/FguIAgariYx7Tbj1wzPHL8XnggQewLAuARx99FL/fj2EY/Nd//VeEIxOR04mVndelL9hSh6/wPfC0EKwtoW3pb7G9bRGITkQ+N3FEalh7wrBUJZ7kpNI8cGrRyic5rNGDktlcXBdqx0Y5GJgZH8GIpDeyskaC5YCAP9TnGDiRYH0ZwbrOxJQjZwxYDoIN5aE+MzELa9CZHVvuQp0WjpHT8G3/IGxM56CJ2A3HNqYRG77ST7pPZWUl2dnZ+P1+Vq5cybJly3A6nUybNi3SoYnIacSRORz3ed/B+9k/wTBwnXEF/uK14Rf52gjU7sORNTIyQYoIN106krhoJztLDjAkO4FvXDAk0iFJHxGoK6X9vQUE60sxk3KIuvh7WMm5ANgBH4bljHCEfdMpkXzyeDz84he/4JNPPsHtdjNhwgQeeughiouLueeee2hoaCAxMZH58+czaNCgSId7Wtld1siOkgaG5iQwvH/HqWC1B9oxTDhzRCp7K5tJiXdz9XmDWVNYiQ3kj0wjJkp/YOXIgm2N+Hd/iuGKJuriH+Dd8Ca2pwUrbTC2pwX3tO/gXf8mwbpSzOQcjPgUrJwxEAzg3/cZRlwKVuYITFc07rOux7t1GYblwswYht1Q/pXGDOzforogPSQuLo6amhp27tzJ0KFDiY2Nxev14vf7v/xmEZHj4Bp9Ea7RF4XadnMtgdLNnRdYLqzk/hGITEQ+F+128O1LRkQ6DOmD2j/4H4L1HQcVBev30/7+/xA98ye0L/8Dgf2FGAkZRJ1/C47sURGOtG85JQqOP/zww5imyc9+9jMMw6CmpobU1FRuvvlmrrnmGmbPns3ChQt59dVXef75549rbBUJPLLl60t5YemOUPtbFw1j1MBEHn1xPV5fEIDxQ1P4X1eP4cE/f0plfceyxZSEKObeMjlUoFzkUMHGalpffwDb07F100wbTPRVP6PtrUcIVhcDYLjjiPn6/Xi3vIevYEnHjYZB1CX/AUD7u7+Dg381OcfNxDXmkm4fM+rs63v+l3GK6qlCss888wx/+9vf8Pl83HvvvVx55ZWsWrWKX/7yl7z88svd/vO6i+YJkd7P9nlof/9P+IvXYsQk4j7nBpyDJ0U6rF5LBcc7aY4Q6X2a/ngbBAOdHYaFY9hU/Ds/7uyKSST227/EMK0IRNj7ncg8EfHkU0tLCxdccAHvv/8+sbGxof7a2lpmzpzJ6tWrsSyLQCDA1KlTWbp0KcnJx75tRhPGkf3v339EfZMn1I6NcjBuaAqrtlSGXXfVuYN466M9YX3XXzycGZNzT0aY0st4Vv+jYyvEIVyTvo533ethfc6xM/AVLgubGMy0juXYwerdnReaFs7R0/Ftfqdbx4y7+bcYrpjjf4OngZ78UlFcXIxlWQwYMCDU9nq9jBx56m590TwhcvqwgwEwTAzVlflKlHzqpDlCpPdp/dcvCZQUhNpW/7HYzbVh5TgAYq9/AjM+9Yu3yzHolafdlZSUkJiYyO9+9ztWr15NbGwsP/7xj4mKiiIjIyNUvNayLNLT0ykvLz+u5JMmziP74jQatMHh6Jr5dbu7rnCKjnGRlqYaUNJVbZSF9wt90VFml74ol4nvC7lvh2mDYYRfa9tEu018X7g/5jBjRruMYx4zJTkWK1p/P3S3wYMHh/571apVWJbF5MmTIxiRiPQleoLdN/zud7/jt7/9LW+99RYjRoxg48aN3H///Xg8HnJycnj88cdJSUkBOOprInJ6irrgNjwrXyBQuRMrYzju827Cu25hWPLJSEjHiFMt2JMp4sknv99PSUkJo0eP5v/8n//DZ599xve+9z2eeuqpbhlfTyuObMak/ry8oqiznd+f0YOS+aSgnMDB39nI3ETOH5fJe5/uC62SSoh1MW5gItXVTRGJW05tgYFnw/p3wNcOgJmUjX/4DMzCTwjWl3Vc5IwiMOwCnK1t+LZ9ELrXHD2j4z8qngn1OUeeh3/oBbDp/bAxfYcZ0z/sQpyt7cc0Zl2zDc198zPcU0+0b7zxRu68804mTZrEM888w5///Gcsy+KGG27ge9/73lHvnT9/PkuWLGH//v2hLxNw5JqAgOoCioj0AiUlXU++PZzc3GNbUb9lyxY2btxIdnY2ALZt89Of/pRHHnmE/Px8nn76aZ544gkeeeSRo74mIqcvMyaR6Et/GNbnnvotbL8H/77PsJJycJ97E4ZhRijCvini2+7q6uqYNm0amzdvDi2RvuKKK3j00Ue59dZbte2um+2vaeG194uobWxnSl4G/dNi2bq3nrKaFuqbPAzL6cc54zLZvLuOsppWKutbSUmIYuaUXIrLm9hf3Ux5bQsOy2TmlAFMHJHWZczLpg7QMap9lG/HSryFy8CwOk6rSxmA2S8Db8ESgm3NWEmZmInZWNl5+AqXEagtwYxPxUzKwZE7lkDpFvwln2G4EzCTsrAyh2P7Pfi2ruiRMQ1nNK4zr+5zpyH1VPJp6tSpfPzxx1iWxYwZM1iwYAExMTFcf/31rFix4qj3rl27lpycHG644QYWLFgQSj4dqSYg0C11AUHzxBe1tvv4cFM5Le1+zh6TQVZKLMGgzerCSkqqmxk7OJnRgzrm4e376tlUVEt2aixnjcnAMk0q61v5ZHMFUS4H543PIi7aedgxRb4o2FKPb/uHADhHTsOMTcL2e/HtWIndXItj8GSstEEA+PasJ1CxAytjeKi2U6Bmb8dhF7FJOEeeh+FwH9eY0qk754lRo0ZhGAZH+8phGAZbt2790rG8Xi833XQTTzzxBN/5zndYsGAB7e3t3HvvvSxatAjo+G5x8cUXs2HDBjZt2nTE146V5ggRka565ba75ORkpk6dykcffcR5551HcXExtbW1DBo0iLy8PBYtWsTs2bNZtGgReXl5x5V4knD+QJAn/9/G0AqmfZXNXH/JcGobPRTsrgOgtLqFxlYfowcl8ebBOk8lVc0UVzTy42vG88qKIoIH//Gwc38B//fGSfz+jc1hYzodJjPyVQ+qr/Hv20T7ij+G2sGavTjHXUrbGw+Bv+Pz4a/dQ9SlP8Kz6u8EqzpqMAXqSzET0gmUFITVirI9TThyx9P+3n+Hjekadymt3ThmW8VOYq9/DDMmsQd+K31LMBjEMAz27duHbdsMHToUgAMHDnzpvfn5+V36WlpaeOONN3j//fdDDyc+TzzV1tZSWFjIc889B8CsWbN46KGHqKur0zzxFfgDQX7+wjrKa1sBWLpmH//35nzeXVvCh5s6lqq/vXof3718FA7L4I+LOr8sbtlTx1XnDOLBv6zF4+2ou7Zi437u/07+YcfMTde2V+kUbGuk9bW52G2NAPg2v0PMtQ/RvuwPBMo6Pmfez/5F9OV3EagsCtX88/E2wTOvxsoaRds/nwC748AUf9Fqomb8xzGPqVNQe862bdu6baynnnqKq6++OmyVVHl5eWgVFHR8twgGgzQ0NBz1tcTEY5v3VcLjqymvaWHrnlpGDEiif7pKdoj0ZRFPPgE88MAD3HvvvcyfPx+Hw8Fjjz1GQkIC8+bN45577uHpp58mISGB+fPnRzrUXm1vZVNYgXGAjTtr2LU//IvhZ7tq8PoDYX0Hmr188FlZKPEEHQeHfbip7LBjKvnU9/j3bQzvCHjxFy4PJYlC1xWtCSWJQn17N2BY4bXFgrX78BWt6jKmr3DZYcZc/ZXGDJRuwRxx7tHenhyDSZMm8eCDD1JdXc2MGR3bHfft20dSUtIJjXekmoD5+fmUl5d3S11ACbeluC6UJALw+oO8t66Ujwoqwq57Z20JTit8qfrqLZVEux2hxBNAVX0b/1y1r8uYH2ws44ZLdfy2dPIXrQ4liQDs9ia8m5aEkkQdnUF8W97DX7Ej7F5vwTs46vaHEk8AgYodeDf965jHVPLp5CorK6OyspLMzEyysrKO6Z4NGzZQUFDA3Xff3cPRhdPKpxO3aksFzy4qxLbBAG6+bCQXTMiJdFgi0g165con6Njj/cILL3TpHzp06Cl9PHdvk5YYjWUaoXpOAFkpMTS3+Sipag71ZSbHkJkcw+aDq6EALNNgSHYCKzaWhY05JLsfKwsquowpfY+Z2PUfj2bGMCh8L7wvpT9GWULYFwIzMQvDchJsOOTz5Y7FTBkAhxyJCmBlDO9IQB06ZnIuRtnWEx7zcLHL8XvkkUd47rnnSE5O5rbbbgNg9+7d3HzzzSc03pFqAr7zzjtffvNx0FPtTqn1bV36+sW7sSyDoL/z7/lotwPnFw6osCyDfvFRXe5PSozu0hcf79ahFRKmsV8cni/0xSbE0fCFPndMFLbTRcDTEuoznU7csdH4u9wf3+X+I42pz+PJUVVVxV133cXGjRtJTEykoaGBM844gyeffJKMjIyj3vvpp5+ye/duLr74YgAqKiq47bbbuOmmmygr65zr6+rqMAyDxMREsrKyjvia9LzXPtjN58+tbeD1D3Yr+STSh50SySfpfjUNbQSB9EP+0W8aBlecNZAln+7D6wsyOCuey6cOYHBmA698UMSBZi/94lzcNHMEtg07Sw6wt7IJl9PkmxcOY0RuIvmj0li3vRpsOGdsJmcMS+WKA+1dxtxb0URmSgxup06dOZ3Y3jaCjVWYyf1DJwrZwQDB+v04hkzGX7qZwL7PwLRwjDgXR/Yo7DOvxrtxMQQDWFmjsPqPxUrMoe39P4KnBSM+Deeo8zETswk2lHWcQuGKwTVxFs5hZxEo2xo2ppU9CtcXx8wdi5V04mMaseErc4It9di+NqzE7MP9GuQIkpKSuOuuu8L6LrzwwhMeLzs7G4fDwaxZswA444wzSEpKori4mOzsbCorKwkEAqG6gFVVVcf8BP1QeqrdKSsxihH9+7GjtGNFbFy0k3PHZhIMBFn8yV6gYy65bMoAHJbBjn31oYcP08/sz3ljMli2toTGlo7zJYdkJ3D+uAw+3VweNuZZeek6tELC2BkTMPtlEjzQscrOSMjAP/RCHOX78Bet7rjI4cIeOQNHyggCK/8Sutc58Wrs9GGw/dPQyljH4Hz8Qy/C3PTBMY2pz2O4nqoNOG/ePEaNGsUzzzxDTEwMra2tPPnkk8ydO5cFCxYc9d477riDO+64I9SePn06CxYsYNiwYfzjH/9g7dq15Ofn89JLL3H55ZcDMHbsWNrb2w/7mvS8dm+gS9u27dBWehHpWyJecLyn9bUvFcGgzbOLClldWAnAxOGp/PvXxrJ8/X5eXrELf8AmIzmaW6/Iw7bhd68V0NzmI8Ztcd3FIxg9MJGnXi2gpKoZyzSYkd+fWWcP4oV3doTGHDs4me9cNpL1O2qOOmZslIPvzR7LmMHaAnM68O1aRfsH/wN+L0ZsMtGXdyQZ2v71JHZLHThcRJ1/C2bKQNo/+B+ClbvAMHCOuQT3pK/R/slL+HeuBNvGyhpJ1MU/wLfrE7yfvgIBH0ZsMlGX3YndUkf7B3+G1vqTPmbUOTfQ/vGL+La8GxozeuZPMFxdV270Zj31pcLr9fL73/+eRYsW0dDQwLp161i5ciV79uzhxhtvPKYxPv8y8XnB8VtvvZVbb701VBPwuuuu45133iEhIYGbbrqJa6+9NlRw/JVXXjnsKtov09fmiS/jDwT5bFcNLe1+zhyRRlx0x/bVHSUNlFQ1M3pQUqhgeGV9K5t315GTGsuogR1J3OY2Hxt2VBPldjBhWCpOh3nEMUUOZfs8+PesA9vGMXgShjMK2w4SKNlMsLkGx4AJmAePxQ7U7CVQsRMrY1ioYHiwpR7/3g2YcclYueMxDPO4xpROPXkwxcqVK3E6O/8O8Hq9TJs2jdWrVx/XWIfOF+vXr2fu3Ll4PB5ycnJ4/PHHQzUCj/basdAcceJe+6CIRR/vDbUvye/Pty/RlmuR08GJzBNKPp1m1u+o5nevFYT13XjpCP7+7s6wrXHnn5FFcXlT2Ha79KRoxg9N4d21paE+w4CbZ47kL29vP+ExH/1fZ3fb+5PIsAM+mv/6Ezhkm4OVOx6AQMmmzgvdsTjHXYZv7ath97svuA3P+38K63NNvgbvpre7dUxn/jX4Co5tTNf4y/B+egxxTvkW7glXcDrpySfalZWV3HHHHfzbv/0ba9eupbKykltvvZXFixcf9d6HH36YpUuXUlNTQ1JSEomJiSxevJiSkhLuvfdeGhoacDgc/OQnP+GCCy4AoKioiHvuuYfGxsZQXcAhQ4Ycd9x9bZ4QEfkyPTVPXHrppfzmN79h1KhRob5t27bxwx/+sNu3VHcXzREnzrZtVm2pZEdpA0OyEjh3fJZOxBY5TfTamk/SfaoOU6+jpKo5LEn0+XVVDeHX1jS0U1XXGtZn27CvsutS9NLjGFPLa3s/29MSltABCDZW0eX/VU8LdkPZF3sJVu/p2ldf9tXGrNnbpc9uOPYxgw3lxzZmY1WXPjm8d999l6VLlxITE4NpdhSjzsjIoLKy8kvvve+++7jvvvu69B+pJiCoLqCISG9z++23893vfpdrr72W7OxsysrKeO211/jxj38c6dCkBxiGwdljMzl7bGakQxGRU4D55ZdIbzJheCqW2flV2zQMLpyQE1b7CWDSyHTyR6SF9Z05IpX8UeHFHpPi3Uw/s3+XMc8/I/uYx1TiqfczYxI7iocfwjk4H8fg/PDrMobh/OKpcc4oXGNngDO8ELFzxLlfbcwxl3y1MYefc0xjOoaE3ytH5nQ6CQTC6zvU1dWpsOtppL7JQ0u7L9JhiEgv9a1vfYtf/epX1NfXs3z5curr6/nlL3/JnDlzIh2aiIj0MG27Ow1t3VvPkjX78HgDJMW7SE+KIW9QEh9sLKeqoZXEWDcp/dxMGJZGwe5adpQ2EBvlJDnBzagBSbS0+1hdWIXTYZIU5yI3PZ70pGiWb9h/wmNOHpWuJFQvF2w9gHft6wTqS3Hkjsc14UoAvBsX4y/ZhJXUH8eIswns20SgsQa7tR4zKh7n6OkEavYQrN+P3XoA7CCOkedjBHz4q4qwW+qwvW04csdjpg7GX/IZdnMtdlsjVnJuRMd05l2Ic+jUCP/mu19PbaeYP38+e/fu5Wc/+xnXXHMNixYt4he/+AUDBw7kzjvv7Paf11364jxxvDy+AP/9xmY2FdVimQaXnzWAb5w/NNJhiUgP6Yl5IhAIcO+99/LQQw/hcrm6deyepDlCRKQr1Xw6jL46YRxo8XL/n1bT1NrxhDou2smDt01hwRubQycOGQb85JtnsKv0AG99vCd075VnD2TUgCR+9Y/PCB78eAzL6ccPvjHuK415zQX6onI6C9Ttp/WNB8DfccqVEZ9GzDfm0vrGw9gHTxrCchIz+z68m5fi3/FR6F73eTdjWE7aD6m35BgyBdeZsyM6ppU6sNt/T6eCniw4/vjjj/PKK6/Q1tZGdHQ03/zmN7n77rtP6S8afXWeOB5L1+zjpWW7wvrmfncyAzPj8fgCuBymHjCInEZ6ap4477zzWL58eVjB8VOd5ggRka5OZJ6w5s2bN69nwjk1tLV5Ob3Ta4f34aYy1m2vDrW9/iAAa7aG169p9wRYt70K38HXoaNGVEu7n7Lazto5dU2e/8/eeYfHUV59+56yVb13WbJkucm2XOSKcW8Ug8EQSgIBXjop5CMJCZ0kGAKhhMBL3pCQQAIh9GowtrHBvcndqrZ679JK22bm+2PlldcSsWyvLFue+7pyhTk7c54z69U+O+c5z+8gAAeLm07Z58XTUk77vnTOXpy7P0Otzj/G0IEAKCU53TZNBU3Dnb/B51qtrR6lvthTcdSF2lSJpqmotUU+PunFp6Zqnq53fvSJICEnj+vj3Z9bCIKA1erfZJCqqmzfvp3ly5dz9913c9111/HTn/6UCy+8EEmS/DqWvzlf54mTYcO+KkqqffX/kqICeWddIf/4Io/NB6pJigokMnRwdYbU0Tlf6Y95AjzVTwU4yBEAACAASURBVN9++y2TJk066+eGo+hzhI6Ojk5PTmWe0AXHBykmQ88J3WLs+c9tNIgYDRI2u/sYm4TR0FMOzGzq6dN8Ej51BjeC3MuXj7GXB1HZBKIMyjG6MbIRQTpuFVQUEWRTz3F682kw9t2noY8+e7sfne9EFEXuuusucnI8SbzwcL11+WBiXHok63d3C/8bDSK5pU3kljYDUNds588fH+Dpu6YjS7qcpI6OTu/885//pL6+ntdee43w8HCfisl169YNXGA6Ojo6Ov2OnnwapGSPiGbV9jIq6z3VS7HhVhZkJ1HT1MHmA57OUyajxOIpyYxKaecfK3M5uqhz2QWppMUHs7ugHrvTIx48eWQ0CyYlse1QrY/PhdlJ1PbRp87gxjBqLq68b9E6PA+jUswwDGMXo5QfQKnKBUCwBGMcswDBaMG56yPPhYKEacJSEGU6v3weVM9nzjhmEYbMBbiLtpzQp2nMQkSjte8+C0/s0zh6bv+/aYOM7Oxsdu/eTVZW1kCHouNnstIj+eGSEazfXYnVJHHpjFT+8UWuzzktNifN7Q4iQ/TqJ50zj6a4cWx+C1fRFsTAcExTr0VOGDXQYekcx9NPPz3QIejo6OjoDBC65tMgxulS2F1YD3j0md5dV0RdcyfpiSGMTYtkaFwwH204Qn5ZM/FRVsamRZKZEs6mA9VsPVhDsNXAmKERjE6NoK65g8+3lKKqKiNTwhkzNKJXnxOGRRIS6KksKa9rp7CihbT4EJKi/a8boHP2oTk7cRfvAqMFOXksgiijqQpK2V7cZftxl+8HtwPDiFlIiWNQyvfhLt2D2laHnDQG47iLUaoO4S7Zg1JfjBSWgHHK1agtNR4flbmIJiuGCZcjGox991lbhBSWgBQ77IRxao4O5CFZCKaAAX43+4/+0vJ49NFH+eyzz5g3bx6xsbE+K9pncxvt83meOB3e+DKPr3MqvMfRYRZW3DZV137SGRAcOZ/g3P5et8FgIfD6Z3uvbNU5If01T5yL6HOEjo6OTk9OZZ7QK58GMUaDxOSRMThdCve9vIn2Ts+WpD2FDQyJCeKDbw+zt6gBgMLyVkRBRBIFVu8oB6Ctw0VDq4MxaRG8/mW3ls/GfdVkj4jhlY8O9PB5NPEEkBgVSGKU/sPlfEIwWjBkzPC1iRJieBKuVX8CzVOB5Nz1EaaQGFxFW9BaPFVz7sIt3q17SsUBz/9X52Nf9xeM45fiLtoKgNrZguPr/8Ny6S9xHVrn6zM4ulef5gtv7lOc8pDxfnw3zj8cDgfz588HoKamZoCj0elvls9Ow+lW2FPYQEJkANcvyNATTzoDhlKV52twdaI0lCLHDR+YgHR6xel08tJLL/Hpp5/S3NzMzp072bBhA8XFxXz/+98f6PB0dHR0dPoRPfl0HlBRb/MmiY6SW9pMcXWrjy2/rBnTcVpPdqfSQ1AcYGdeba8+dXR6Q6nO9yaJvLbSvd4k0VHclcc9PABaV9WTr1HBXbC5p8+yvvnU6R9WrFgx0CHonEEsJplbLta3NemcHUhRqSjl+48xGJHCEwcuIJ1eeeKJJ6ipqeGZZ57h1ltvBWDYsGGsWLFCTz7p6OjoDHL05NN5QGy4FZNRwuHsflBPjQsCPAmnowyJDSI1Lph9hxu9NlkSGZMa7iM0CzA6NZxtubW9+tTROR4pKhUQgO6ydSk2A6U6H83WeMx5KYCAu7U7gSQEhCPGDYeuyqcuK9KQLE/l0zE+xdgMhOqCXnzqnCmKi4tZuXIltbW1REdHs2TJElJSUgY6LJ1+QNU0Vm8vY09RA/GRAVw6I4VgP3THKqluY+XWEhxOhTkTEhibFumHaHXOVpwHv8Z9ZAdiUBTGCUsRA0+uWYG7dA/Og2tBlJESR6NUHEKwhmKafr1ft09rqoJz75co5fsQw5MwTViKYNaru0+W1atXs2rVKqxWK6LoWfCMiYnRq2V1dHR0zgP0ljTnARaTzO2XjiY82IQgwISMKJbOSOWmJSO8CaOEyABmjIll2uhYJo+MRhQEQgKNLMxOJDrMyuUzUzEZPV3wpo2OISTAyK2XjurhU0enN8TQOEwX/ABMASDKGEbORkoYhWH0XISgKACk+JEYJ1yGnJSJGJ0OgBASg3nOrYiBkcgpEzwd7UwBmC74AaI5CMPoeT4+5YTRPXyapl07YPd9vvHJJ5+wbNky8vLysFgs5Ofns2zZMj755JOBDk2nH/h0UzH/XlvIoZIm1uws50/v7Tttny02J0++uYtth2rZU9TAC+/upbC8xQ/R6pyNOA+sxrHhHygVB3DlrqNz5TOcjBSpUl1A5xfPo5TuQSneiVJThPXaZwi47g8YUif6N9YdH+Dc9h+UykO49q+ic/VLfvV/vmAwGFAU36rlxsZGQkNDBygiHR0dHZ0zhV75dJ6QNSyScekRKKrmbYNtMck8dGM2Ww9U8+pnh3jzqwIksZBbLx3FpdNTePqtHD7fUsrnW0qZPzGR5++ZwTNv72bzgRo2H6ghLT6Y3/3PFCRJ1Ftr65wQ46i5GEbOBk3FdfBrOv7zK0AD2YT5op8jGkx0fPg4uOyAgDF7OYaRs+j8+AnU5ioApOQszAt/hGPtKzg2vO5xHBSN9crfoBTv7OHTkDh6gO72/OT555/n//7v/8jOzvbaduzYwS9+8QsuvfTSAYxMpz/Ynuu7JbuwooXGVjvhweZT9rm3qN6nolbTYEdeLemJIafsU+fsxX14u8+x2lSJ2liOFJHUp+tdh7dzbPUrLjtqxQGk4TP9GOXRsbb5HCuVh9Ds7Xr100myePFifvnLX/KrX/0KgNraWp544gkuvvjiAY5Mxx9U1Nv455d5lNe1M2ZoBN9fmIHVbBjosHR0dM4S9IzBeYQgCL0mid7/9jBKVxcPRdV45+tCvtxeRmtHt6bTmp3lfJ1TQVFFt05UUWUr23Jr9cSTTp8RBBFUFcf29/A+MLgduHZ9hGPH+12JJwAN566Pce77ypt4AlBKd+M6sMb3gaWtFteB1b361Dmz2Gw2srKyfGzjxo2jo6NjgCLS6U8ijksymY0SAZbTe8iI7CVxdfw4OoMHITDC1yDKCAF9r4ARgyJ62ISg/tmmKR4fqykADPpn82S59957SUhIYOnSpbS2trJo0SKio6O5++67Bzo0ndNE0zRe/mAfeWXN2Oxuthys4d9rCwc6LB0dnbMIv1Q+aZrGO++8w6effkpTUxOffPIJ27dvp66ujosuusgfQ+j0I20drh7H7cfZNKCxzXHCa3V0TojbCS7fz5LW2QrScQ+tihOto6eIvdbe0NPW2dK7T50zyk033cSzzz7LT3/6U0wmE3a7nT/+8Y/cdNNNAx3aeUtZbTvbDtUQGmhixphYzEaZFpuTDXsrUVSNGZlxRISYcbkVNu2vpra5kwkZUaTFeyqN9hTWk1fWTGpcMJOGRyEIgtdnYlQgJdVttNicyJLItNGxfPTtEYYlhjA+w7P1tbi6lR25dUSEmJmRGYvRINHYamfjvioQBC4YE0dYkAmHS2HjvioaW+2MS4tgT1cn1ozEEGaOixuw90+nfzFNvBylugCtrQ4ECXnYdJy7P0eKSkEeOhlBEFAay3EXbUWwBGPIuADBaEHtbMWV9y2a044YlYpadwTwVMe6y/ajNlViGH4BgmxCtTXhyvsWAMPwmYgBYWhuJ678DWjtDcip2V5tQFfxLpTqfKSYYd5te0p9Ce7D2z3jNJaj2dtAMmCefj2CpG8gOFmMRiMPPPAADzzwAI2NjYSFheldMgcJrTYnVQ2+i025JU0DFI2Ojs7ZiF9mzRdeeIFNmzZx44038sgjjwAQGxvLihUr9OTTOcCMMXGs2VnuczwqJYzdhfVeW0JkAAsmJfHt3irvlgijQSR7RPQZj1fn3EYwByIPycJdkuO1ycMvQJAMODa/5bVJiZkYR87Gnb/R29VOsARjHLsEd9HW7sSUIGIYMRscHT186pxZ3nzzTerr63njjTcIDg6mtbUVTdOIiorirbe6/23XrVs3cEGeRxSUN/P7N3O8la2b9ldz79Vjefzv22nqWkz4ansZj908mX98kce+w56EzxdbS/nRFWOparDxzroir7/Fk5MZnxHp4zMlNoh7rshke24dq7aXea7fBpfPTGVoXDDPv7MXtUvDZ0duLbdeOorH/r7du3CxZmc5j98ymZfe30dBl7aTJAr8zyWjSIoOJCla39I0mBGDown43pOo9SW4S/fi3PUhAC7AUFOIIX0qHR+vANXtsedvwHLxz+l4/9HuxhJGK5bF96La23Gs/ytK6W4A3EVbMS+4h473H/EuRrj2f4V1+W+wr/0zSuUhAJx7VmJZ8jOUmiKcOz/oGv8L1AlLkeJG0Pn5M6Cpnnhj0rEs+gliSKy+3e4UmTx5Mtu2ebYwhod3i8tPmzaNzZs3D1RYOn4gyGokIthEQ2v3YmBSdCB/+/wQBWXNDI0P4Xvz0v3SmEJH51RRO1tRqnIRQxOQwhMGOpzzDr8knz744AM++OADwsPDefTRRwFITEykrKzMH+51/gs5+XVsOVhDWJCJRZOTCQsyUVrTxuqd5WiaxtwJiaTGBdNqc/LFtlLqW+xkj4gme0Q0iqqyZkc5ja12stIjkWWBYQmhhAeZ2HaolvHDIhEEgdhwK6NSwvhkUzEjh4QiCiJBVgNTRsbwdU5Frz7zy1tIiw9mQXYSsiT2OU6d8wPz3Dtw7l+FWl+CpiqotUeQksdimn0rStleNLcT8HQxsiy5F1fhFjRnJ2gKzpyPMc+9A3fxLrT2BjRNxXVwDfLI2YjRQ318OnPXYxh+IQCuvG9QyvYhhiVgHLsYwWgZyLdgUPL0008PdAg6x/B1ToU3SQRwpKqVL7aWehNPADa7m1Xby7yJJ/DoLK3ZWUblcSvYa3eV09Ru9/FZXN2Gpgl8s8e3I+rqHeWkJ4R4E08Ah0qa+HJbqU/FbKvNyZdbS72JJ/Bs/84pqGN6Zuxp3L3OuYIgSkjRQ+lc87KP3XXoa1SHzZt4AlDrS3DtWenT0RRnB0rdEdSGMm+SCECpzse5d6VPFaxmb/N0rOtKPHmMqmc7d3W+z/jOfV8hN1b4+FRrCkEy6Imn08Dl6lkx73K5UFW1l7N1ziVEUeC2paP522eHqGnqZHhSKIqqsmGvRz6hpqmT9k4X9149boAj1TlfcVfl0bnyD55dGIBx0hWYJiwd4KjOL/ySfFIUhYAATzvbo6WzNpsNq9XqD/c638HOvFpe+mC/93hPUQP/7+pxrPjnLhwuT6XItkO1PHpTNq98dICy2nbAs/rsumQkxVWe5M9RLhgbR3iwycdnTLiVa+am89Bft3l9GmSxTz535ddR1djBuLSIPsX52M2TiQ3XPzPnA4LBhGn8pXSs/ANKmadDlrt4J8bJVyFGJOPc9g4ASkkOUtIYTOOX0vHJE6BpKID7yA6syx6l44NHvQ8W7qKtWC/9NY7qfB+fmt3zGT3qkyM7UOoOY13y/87sTZ8HTJ48+YTn3HbbbX06T+f06U2Pz2jozSYh4CPbjEGWkCXfrTDf1VzCIIsYZBH7MULhsiT0uB7AZJD6FJNB1xI87xBE2ecziCj3vq1N6qVqQjL03LoNCHIv5/Zmk2QEyeAzviDJvfpE32p3Slx33XUIgoDT6eT666/3ea26uprx48cPUGQ6/mRYYigrbp+Gy61gkCXu+MM6n9f3H25A1TREfaulzgDg3PmhN/EE4Mz5GGPmAn1B+gzil193s2bNYsWKFTidnn9MTdN44YUXmDNnjj/c63wHm/ZX+xzXNHbw5fYyb0IHwOVWWbOz3JskOvbajcddv+VAtUeHow8+V5+Mzz7GeXznJJ3BjdrZ6k0SHcVdsBF3wUYfm1K2D2fuek85RhdaZyvOfV/46jppGs7c9X32qeqaUAPCjh07BjqE84aFk5KwmLqTPWPTIlg0eQgJUQFeW2SImQWTEpkxpltXySCLLJ6SzKXTUzn28eDS6Sksyk728TkqJYyapg4uHBfvM/bSGaksnjLEJ7E0ZVQMC7OTiA7r/pEXG25lYXYyE4dHeW0mg8Siycmnde865x7GCUvhmE+cMesSjGMWwzEPBVJiJsZxSxCP6YYnBIQjWkOR4jJANnntcuokjGMWI4Z0V9AJwTGYxixETpvSPbBsxDh2CcYJl/nGM/EyjGMX9/AphenbNE6Fq666iiuvvBJJkli+fLn3f1dddRWPPvooL7744kCHqONHDLJnnkiI9K0SjIsM0BNPOgOG5rD5GhQ3mrunprFO/+GX5Ztf/epX/OIXv2DixIm43W7Gjx/PjBkzeOqpp/zhXuc7CAnouXoXFdKz80pkiAVB8Hl2JyTASEiAkU5Hdzl7kNVIaKCpx/VRob35NPfdZ29x9uKzt/vRGbwIBpOnU5C3wx0Ilq526k3HbOExmBECwnpeH9RTb0wICOu7T0PPz7qOzmAiMTqQJ26dSk5hPaEBJsamRSCKAg/dMIldBXUoisaEjCgsJpmbLhrB5FHR1DV1MiYtgsgQCxlJoQyJDSK/S3B8aLxna/RRn7YOFx9vOsLBYo+g7OyseBKiAklPCGFIbJD33D1FDUQEm8kcGo4oCDx6Uza78usQBIEJw6IwGSXuvDyTfUUNNLY5yEqPJCxI//s83zAMm44YMQSl8hBSVApSTDoAAVc/ibskB8ESjJw8DkGUsF72EO7iXaj2Npz7VmFf9xfAo8lkSJ+GGBSBlDQWQRCxXvEY7uKdoGnIqRMRDGbMc29HGTYDtb0eOTkLMTAcKXooUvRQlOoCpJh0rwh5wPc844uB4UhJYwfq7TnnWbZsGeDpgJqWljbA0eicKW5YNJyXP9xHXbOdiGATNy0ZMdAh6ZzHGEZciGPjP73HUvI4RGvfO6zqnD5+ST4FBgby8ssvU19fT2VlJXFxcURFRZ34Qp3TYsnUIewpavDqd8yflMjciYnsLqwnt9QjxpyWEMzcCQnY7C4+21wCeJI8C7OTaWpz8L8f7cflVpElgWUzhzI8OdTH55wJCczOSmB3QbfPofHBzBoXT4fdfUo+509KZO6ERB+faQnBTB0Vc+bePJ0BR5BNmLKvxLH5TU8W02DBlH0lAB0r/wDOThAETNlXIqdMwH1kB1qzpzJPHpqNcdQc1Kpc3Ec8lTRiaDzGMQsRLcF98inI+sOtzuAnJNDE7CzfSg2jQWLqKF89JUEQyEyNgFTf63sT/T7q8/dv7sLl7l6B2LCvmhd+fAEWU/dPi/BgM3PG+45vNspMz/TtYCcKAuPSI0/6/nQGF1J4TwFY0RqCceRsH5sgGzGkT8WR8wm01Xntak0h4vhLkJOzus81mDAMm+57vSAiJ/dMJEmRQ5Aih/iOHxCGcdTcU70lneM4dMijt5WWlsbhw4d5+OGHEUWRRx55RE9KDUKGxAax4vZpNLc5CA00IYp61ZPOwGEcPR/BEoK7dA9SWAKG0fp3+5lG0DRNO/Fp/53vEgkUxYHXbGhoaEdVT/sWz1pcbpX8smZCg0wkRHZvpSiqaEHVNNITQrw6XNWNHWzaX8U3uytp63AxLj2Sa+alc6C4idU7yqhq6CAxKoCbLx5JY6uD1TvKyCttJijAyHXzhxERbGbtrnJ25NYiCALzJiUyc2x8n33aOt19ilPn/EJtq0dtrkSKGebdc605O1FqChCCY3Dt+RxX/gYQDRgypmMYMQulphDH9vfA5UBKGoMhcz5y/EgEUTqhTzE0HjHo/H7IFUWBiIiBEcydMGECu3btGpCxv4vBPk/0F4/+bRulx22/fu6eGYT0UkGro9Mf2Le8jWvvSh+bec5tPZJNOidPf80T8+fP59///jeRkZHccccdpKamYrVa2b59O6+//rrfx/MH+hzRNzodbj7fUkJFnY3MoeHMGZ+g/7bXGXA0txO1pdrTpbQ3zT+dU+ZU5gm/VD6NGjWq1y8XSZKIjo5m4cKF/OhHP/KKkuv4D4MsMjo1vIc9LSGkhy3YamDV9jKcLk+ycHdhPbERVgrKm6nq6mpUXmfjjS/zGJUS7q1KarU5+etnh/ifS0ax+UBNlzeNlVtKGRIT1GefD92Y3ac4dc4vxKDIHskgwWhBThqL89A6XLnrPUZVwXVwLVLS2K6SWc8PQaV0N3JiJkJiZp986gwsfljv0BlAGlvtGA0SgRYDF2bF889V3R3CstIj9cSTzhnFkDEd14GvQPFs9xcsIYjhiWjOTl1A9iylsbGRyMhIHA4HO3fu5I9//COyLDN16tSBDk3nNPnfD/ez/4inE+XuwnraO11cPG0Ia3dWkF/eTFp8CPMnJfbauEJHpz9wVx7C/tVLaI52BFMg5gV3I8ePHOiwzmv8knx66KGHWL16NbfddhuxsbFUVVXx6quvMmvWLFJTU3nppZd44okn+N3vfueP4XROkcqGDm+S6Cgl1W2UVLf52Iqr27CafTu8uNwqB480cjwHixv77FNH52RR60t62JSyPfj25QKlvvjMBKRz2txxxx0DHYLOKeBwKfzvh/vZW9SAJAosnpLMlbPSCAkwsqewgfjIAOZM0IWYdc4sUngS1ssewpX7DQjgri6g472HQTZimnI1xtHzBzpEneMIDw+npKSE/Px8xowZg9FopLOzU1+YOMdp63B6E09H2XyghvYOl7cL9s68OqoabNx0kf7wr3NmcGx8A83hqdDWHO04NryBfPUTAxzV+Y1fkk+vvfYaH3zwAUFBHoHR1NRUMjMzueKKK1i9ejXDhw/niiuu8MdQOqdBcnQgAWYZm71bEHzEkDAkSWD/4e4JY+SQMEYOCePAMZOI1SQzZVQM6/dU+vicPDKGnXl1ffKpo3OySAkjcR36utsgSMjDZuDK/RbU7s+cnDBqAKLTAXjhhRf6dN5PfvITAG6//fb+DEenn1ifU8HeogYAFFXjs80lTBoezcTh0WQOjcAoi/r2Ch2/oykuEETvlmoATVVBUxAkzyKZFDkE6YIfYN/0JlpDqecktxPH5reQUyf5iMlqmgaKs4fmn+Z2gGTUP8NngLvuuosrrrgCSZJ47rnnANi8eTMjRuhC1OcyZqOExSTR6ejuZB0WaOzRmXvzgWp+uGSE/remc1poqorW2YJgDf2vnyW1tbbXY9XWhGAOQpD8kgrROQn88o63t7fT2dnpTT4BdHZ20tbmqXaJjIzEbrd/1+U6p0Fzu4PdBfWEBnV1MhIEHC6FXfl1aJqnk5HZKKNpGnllzVwwNp7ckkaabU4mj4ghMzUMAU9lU2W9jfSEEC6dkcLhilbGD4vkcFUr4UFmll2YSlO7g5lj48gr82zHWzw5GUXV+uxzXU6FT5w6Ot+F2lKNu/wAYlgCxsnLcR38GkE2IsaPRLM1YZ53J85dH6LZbUgx6WiKC83ejmD27Dt2V+aiNpUjJ2QihsaeYDSd06G6uvuHpcPhYNWqVWRmZpKQkEBlZSX79u1j4cKFAxihjj+oauzoYSuqaOHttQXkljYTFWrmpiUjGaEvNOj4AU1VcWx8HVfet54qponLMI5ZiPPgWo/en9OOPGw65gtvRBA9P2XVlipfJ6qC2lrrTT65q/Kwr3sVra0OKW445rmeKkz72ldQqvIQgqMxz7oFOW74Gb3X840rrriCJUuWAGCxeLZGjhs3jmeffXYgw9I5TQyyxFVz0vnXqnwUVcNqkrlyVhqvfnaIjuO6YOuJJ53TQakuoHPN/6LZGhGCY7AsuBspIrnXc+WUSbiLtniPpcRMbO89jNpQimAOwjzrZuQh489U6Dr4SXD8ySefZMOGDdxwww3ExsZSU1PD66+/zowZM7j//vtZv349L774Iu+++64/Yj4pBrNIYGlNGyv+tQuH07PKMCEjiv+5ZCS/+ccOr95SVKiZh3+YzZtf5Xv1moyyyM+vHU9FvY2/r8z1+rt23jCGJ4eeEZ/3XDGmn98dnXMVd0kOnav+BJrn82IcdxGGUXPp+PBxtM5WwNNO23Lx/XR+9iRqTSEAgiUY6+UP4zq4Fueezz3OBAnLwnv0ieU4+ktI9t5772Xx4sUsWrTIa1u1ahVffPHFWf1gMZjnCX+xu7CeP76713tsNIiMHRrBjrzuTmMhAUaevmu6ruehc9q4cr/B/s3ffGyWRT+l88sXOHbbtWn69RgzFwDgPLAGx8Y3vK8JlhACrnsGQTKgqQq2t+5DszV5X5fTpoCm4T68rfuagHACrn0G4SxomDPQ9GdjiqamJtavX09dXR233norNTU1aJpGbOzZuVikzxF9p6nNQVWDjaHxwZiNMrsL6r1dsCVR4NZLRzF5pN7dWufUsf3nV6jN3YsNYkw6AZc92Ou5msuOY+eHKNUFSDHpqG11KMXdTW8EcxAB1z/rraTVOTlOZZ6QHn300UdPd+Dp06cjiiIrV67kyy+/pKKigmXLlnH77bcjCAJxcXFcccUVyPKZL23r7HQyWLeR/+frIh8tpaqGDmRJ8HkY6LC7kSWRNTsrvDZF1eiwu9l6qManPPZIVSvtne4ePg2S2NOnKLBm16n7nJgRRXCA3nFApyed6/6CZuvesqnUHQFNQak85LVptkYEScJdsKn7QrcD0HAdWAPaUR0yDbWtDuOIWWcm+HMEQRCwWv3/9/fAAw+wYsUKn06nKSkpPPjgg2f1drvBPE/4i9hwK2FBJlpsThIirfxwyUi+3VtFe6fLe47DpTBzXFwPzUAdnZPFlbsete6Ir9FoRa33tQmWYNTGCuxr/hd33RHkpDEgyogRyQjWIByb30Qp3YsQGIX74BqfazXVjdbRDA7bMQN3Yhg5Wxcrp//miW3btnH99dfT3NzMxx9/zB133MHBgwd57rnnuOSSS/w+nj/Q54i+YzHJRIVavIsQsRFWZmXFMyI5lKvnpOuNhnROC01VcGz6l6/N2Ylh2DSc+79CqS5ADIlBMJgBECQZOTET44hZyEljcO7854YeCgAAIABJREFUCM1+jA6x29n1nW/tZSw37vyNuAq3gGQ47ztl98apzBN+yQaJosiCBQuIjo6mqal7Ven9999n+fLlmEx695v+wK2oPWzHi397bEoPm0tRcbt9z3WrGi53z3MdvVzvdPcc57t89hanqxebjg7g7VrkRVPQ3O4ep2luZy82t7di6jv96fQbQ4YM4V//+hc33HCD1/bmm2+SnNx7ObTOucWF4+K5cFy893hEcpi3IhYgOsxCRLB5IELTGWRI8SNwHVzbbRBEDMOm4c5bD2r3d7wgm3HueN977G6vJ+CqJ3DkfIq7YCMASnU+6sbXEUJi0Vq6twnL8SNBU3EdowkihsYhHKMRpeN/nnjiCZ5//nmmTZtGdranC/K4cePYu3fvCa7UOVcJthoZm6Y/uOucPoIoISWMRqk44LVJ8SPoeO8Rr7C468AaApb/FsEc6G1kcHSrp5Q0BrW5W79YDEtADIzodSz713/BXbTV43PvSsxzbsMwbHq/3Nf5hF+ST6tXr+bnP/85Q4YMobCwkPT0dAoKCpgwYQLLly/3xxA6vTB3QgK78utQukqB0xNCWDwlmU37q2mxeR7MAy0GFmYnU1bbzsFiT2JQEGDuhERKatr44JvDXn/zJyaSmRpOTkG9330eH2dqXHA/vzs65yrGzAXY1//VeyynT8eYOR934UboSjgJIbEYsy7GfWRn98OEbMSYOR9UJ+78jT7+dM4Mv/3tb7nnnnt49dVXiYmJobq6GoPBwIsvvjjQoen0A8tnp+F0K+wpbCAhMoDrF2ToWh46fsEwdDJqdi2ug2sRjGaME5chxw7DsuAeHNvfQ3PYMAy/ENXe7nuhpuGuzkepzvM1t1RjueR+nDs/RG2qQEoai2nK9zyvKW6Usn2I4YmYZnxf/wz3MxUVFUybNg3ofiA0GAwoSs+FTh0dHZ3jMc+5DceWf6PUHvZo9AWEo5TkeF/XOppxHd6G1tGCc9+XIIiYxl+CcdxFmLKvBMBduhspLAHT1Gu817mr81EqDiFFpSBGpuAu2uYzrnP/aj355Af8ovl0ySWXcPfdd7NkyRKys7PZvn077733HoWFhfzyl7/0R5ynzGDfp11a08b23FpqGjtoaLUTEWJh3sQECspaqG7ooK6lE4MsMndCIq02J8XVbdS3dOJwKmSPjCEqxMzBkibqmjtptTlJTwhh0oho9h1u6Hefy2amEhcRMNBvoc5Zgqa6ceZ8hrt0D5isSEFRHjFxBNx569EQEIMikcITEMOTce7/Cs3ehhgUiRgSgyF9OmJoLJqq4C7cgtJYjpw81rO6reNDf2p5uFwu9uzZQ21tLVFRUWRlZWEwnN3bsAb7PKGjM1hx5W/Evu4vPjbrlY/jzPkE9+HtXpsQFEnANb9HEHQtp77SX/PENddcw913383MmTOZPHky27ZtY8OGDfz5z3/mjTfeOLGDAUCfI76bI1Wt/GNlLpUNNsYMjeCmi0YSaOme8+tbOnnt81zyy5oZGh/MzReNJCa85xYnHZ1TxblvFY7Nb/rYDJkLcO3/ysdmWfoAcuyw3n0cXItjw+u+1x9Y67ObQoobjvXSX/kx8nOfU5kn/FL5VFlZ6e1ccZRly5YxY8aMAU8+DXaSY4I4XNXKZ5tLADhS1UZRRQs/vnIMH2044q02yi1p5sEbJvLZ5mIaWh0AFFW28v2FGbR3urwttIur22i2ORmVEtbD50+Wj+3p88bT8/nUHdN0cVodAJw7PsS5+9NuQ2QKcupE7J8/4zWpdSaM4y6i48PHweXpoKnWFmFZ9FNvVztBlDBkzODsTncMXgRBQNM0VFX1HusMTlRN46vtZewtaiA+MoBLZ6QQbDWyu6CedbsrMBslLpo6hOSYoBM709E5BeRh0zDUF+M6tA5kI3LSGBxb3kYwByBGp6HWFiGGxmOefYueeDpLuP/++7n99tuZPXs2drudhx9+mLVr1/Lyyy8PdGg6J4mqarz8wT7vM0BOQT0BXxdy80Xdi35/X5nLoRLPLomC8hZe/fQgD9wwaUDi1RmcGIZN9yxIt3n0icWweBB7pjjU2kLoSj6p7Q24i3MQAsKQh2Th3LPS51zXofUYRs3BdWC1xyBKGLMu7t8bOU/wS/IpIiKC+vp6IiMjSUhIICcnh7CwMO/Dh07/siu/zue4qc3B+j2V3iQReB4S1u+p9E4Qx15bUN7iY8vJr8Pu9NXJaWpzsG53RU+fu0/P55GqVoYl6voKOuAu2eVzrNYX4zpWUBzA7fAIinclnrzm4p3IQ7L6O0SdE1BUVMSdd96Jw+EgNjaWqqoqTCYTr7zyCmlpaQMdno6f+XRTMR9+6xGAPlTSREl1G1fOGsqL7+319iPbd7iBJ2+fRlA/CBfr6AiCiHn69ZimXourYCOOY7ZsC0FRBNz8Z0RZ1x09m8jKyuLjjz/m448/5sorryQuLo533333rO10p/PdNLU5ejwDFB73+7+wwve4qLIVVdMQj1mYUlWNd9cXsWl/NSEBRq6ek87o1PD+C1xnUCGYAwm48nHcxbtAFJFTJqBUF+Da65tQkmKHA6DUF9Px8YquRkUgJY+D4xcnBDBNuwY5dSJqUyVy0hjE4Ogzcj+DHb8sA1111VXs3LkTgB/+8IfccMMNXHbZZVx77bX+cK9zAmLCfMtXRUEgpZeV5iGxQUiibxVCTJiVmDDfri7RYZZefQ6J6anTNCTm9HxGhugdZXQ8iMHHtd41WhHDEnucJ0Wl9rAJIXrb3rOBxx57jKuvvpp169bx9ttv880333DNNdfgh6aqOmch23NrfY4LK1rYsK+KYzendDoU9h9uREenPxFE0WebHYDWVodWXzpAEen8N2JiYrj11lt55JFHuO222/TE0zlKWJCJiGDf5O7Q+GB25NayPbcWp0shLd63u11afLBP4glgza5yvthaSqvNSVltOy++vxeb3YWOTl8RjBbk9KmoDWXY3v4Vjp0fIo+ai2AJ9lQ3jbgQtb0BTXHh3PulN/EEoJTuwZA+1cefccwiBFFGjh+JcfQ8PfHkR/xS+XTbbbd5//vyyy9n8uTJdHZ29nmle+7cuRiNRm9XvPvuu4+ZM2eye/duHn74YRwOBwkJCTz99NNERPSuSH8+c8m0IRSUN1Na044siVw5aygXjI0nt6yZLQdqAJg8MpqZY+NwuVXe+boIt6KSFB3IkqnJpJWF8PaaAto6XQRaDNywaDiSKJBX2kR5nc3rMzM1nAnDItlVUO/1OXF4NA2tdr7cVub1ecn0FOqaO3npg320dXT7jA239ogzLEhfkdTxYJpyNUpjuads1mDCOP5SDBkXoFQc8HS1EETkYTOQksdiHHcRzr1fgKYixqQjJ40b6PB1gNzcXF577TWfrXY33ngjr7zyygmvfeqpp/jyyy+pqKjgk08+ISMjw+f1P/3pT7z44os+r+lzxMASEWymoq67Tb3ZKPVYZACICNE74On0P2JQJD6S1YKAEKhXT5wN/PznP+/TFuzf//73ZyAaHX8higJ3Xj6G17/IpaLeRmZqOMVVrWza72kEExdh5c7LMnlzdT75ZS0MTQjmlot66nDmlTb7HDtdKocrWxkzVJ/PdfqOa/8qnHs+B0CzNaI2VRJw1RN0fvYU7txvcOd+gxiW6JXpOBZ5SBZy8ljclYeQIlOQEzPPdPjnDX4RHD9d5s6dyyuvvOLzsKFpGgsXLmTFihVMmjSJl19+mbKyMlasWHFSvs8nkcCqBhtBVqOP0F9jq2d7Uvgx7a9tdhetNic2u5uX3t9Hi81JgFnmqjnpZKaG89IH+zhS1YYsicyfmMjiqcm883Uhm/ZVowFZ6RFcO38Y+w838vbaQpxulfiIAL6/KIMRyWHecdyKSnVjBzFhVgyy+F/j1NEB0DQVd+ke7Bv/Ce0NYLBgnnUTYmg89g2vo1bne1puj1mEcdwSHNvexZ2/ATQVKWE0loU/RjDoCc0T0V9CspdccgkPPPCAt5MRwJYtW/jNb37DZ5999l+v3bFjBwkJCVx//fU95oMDBw7w3HPPUVRUxJ///GcyMjL8NkfAqc0TTW0ONuyrAuCCMXGEBZlosTnZsNez5XlGZhwRIWZcboVN+6upbe5kQkaUdxV4T2E9eWXNpMYFM2l4FIIgUFbbzrZDNYQGmpgxJhazUT5tn/1JWW07z/1nN83tTmRJ5PsLM8geEc1z/9nj3Woxc2wcNx33sNHYamfjvioQBO9711fsTjcb91XT3O4ge0Q0yTFBaJrGzrw6Dle1kpEUSla6p6X34cpWdubXEhVqYUZmLAZZoqHFzsb9VUiiZ+yQQFO/+BzMaA4brrxv0ZwdJ2z04K48hLt0L1J4InL6VARRQm2pxlWwGcFoxpAx09MO+yR8Ho/aWosrfyOoiqfDUWtt12LFdARzEFJEEnLaVATRt9hfc9hw5W9As7cjp09DCotHU1XcRVtQGsqQE0d7H0DcVXkopXsQQ+OQh01DEGXvuILBhGH4hd33cRo+zyb8OU/86U9/6tN599xzj1/G8zfn07PE6fDtnkpeW5nrY/v+wgzmTvCtYne5VT745jC7C+uJDbcSHWZh1fYy7+uSKPD0XdMJHeTfpTr+pWPlH1DK9vnYDGMW4dr3pY/NOO4iTxc81bNcIUYNRQyOQqkvRo4fhWnK1QhGfWdOXziVeeKsTT7t3buXX//613z6qUeAuLGxkXnz5pGTk/NdbnpFnzC+m0df20ZpTXeb4phwK+PSInwmAFEQuHHx8B6TyQ2LMnhzdQFupfu9nZUVz42LR/R/4DqDmo4vnkMp3eM9FkyBGMYtxrntXZ/zzLNvw77u/3xspqnfwzjWt/mBTk/6K/m0Zs0a7rvvPmbPnk18fDyVlZWsW7eOp59+mvnz5/fJx/HzgdPp5Ac/+AHPPPOMt4oqIyPDb3MEnPw80dLu4OG/baOtw7MtIDjAyP3Xj+fpt3bT1OYp5Q4wyzx282T+8UUe+w57mi8IAvzoirFUNdh4Z12R19/iycmMz4jk92/meHX1UuOCuffqsTzyt+2n7PPquekn/V6cLG5FpaSmjehQi4+uU2lNG2aTTHSo7w+45nYHjxz33j1+y2SC+6AJpWkav3tjJ4crWwHPA8rPrx3PnsJ6Vm7t3l61fHYa8ZEBHu2prn/WzKHh/HDxCB752zZsdo/+YGigkcdunswL7+71q8/Hb5kyaBdXNMVFx7sPobZ4KhuQjVgvfxjn3i88CwFdmC+8GUQR+7pXvTZ52HSMWRfT8cHj3i0PQkgM1ssfofPDx/vk0zDiQp941JZqbO8/2q0DGBiJZc6tKDWFOLe94z3PMPxCzLNu7r4P1U3He4+gNlV4DJIB62UP4jr4Na7cdd7zTBfcgGC0YF/75+77SJuCaeIybB90jysERWG98jE6P/rdKfu0zLvzu9/4AaA/u6Kea+jPEn1j9Y4y3lxd4GO7/IJUWmxOb7e7q+ak88XWUj7fUuI9JybMQkpsENtz6wi0Gvje3HSmjda3YuqcHI5t7/o2LpJkjOMuwbnrQ5/zTNOvR4ofifvwdoSAMFz5G1BrCr2vyxkzscy+5UyFfU4zYN3u/MF9992HpmlMnDiRn/3sZ1RVVREfH+99PTw8HFVVaW5uJjS07wLV+sT53dQ1d/oeN3XQ2O77fqmaRnWLr7gzQG2LwyfxBNDY5iQqSu9qpHN6lLXX+Wyd0BztGDpqcR53nrG9lOM/mUZHo/4ZHEDmzZvH+++/z8qVK6mtrWXYsGH8+Mc/JjW1p05XX3nhhRdYunQpSUlJPnZ/zRGnwtaDNd7kCUCrzclH3x7xJokAbHY3q7aXeZNEAJoGa3aWUdnQ4eNv7a5ymtrtPg0djlS18sXW0tPyuXx2GqLYv9VPsiT20PQAvrPDXW/v3fZDtcyb2FPf7XgOV7Z6k0QAiqqxdlc5uwvrfc77akcZSVGBHLu0tv9wI1/tKPMmiQCa2518sa3U7z535NUyOyvhhPdzLqKU7+9OEgG4nbgOrMZdsNHnPOf+r+C4SiN34WaQTT5aG1pLDa49n/XZ5/HJJ1feBt8GFO31aLYm3AWbfc/L34Bp2rXe1Wyl4mB3kghAceE8uAZ3nu+Yrv2rwei7bdRdtA3BHOgzrtZWh3P356flU512LaL1/GjAsmXLFj766CNqa2uJjo5m6dKlPhWzOucm2SNj+GRTsfc7PsAsU1zdyu5Cz5xVUW+j1ebsIVJe09TJvVeP4+aLRyFJQg9NKB2dvmDMuhi1qQJ3yW4EcyCm6dchxY3AeeArcHRJBJgDUdvqca17FTEsAWPaFBzf/t3Hj1K2p6dzHb9xViSf/vWvfxEXF4fT6eR3v/sdjz/+OAsWLPCLb3214rsZlx7p1YQ6epyZEs6OQ90isiEBRqYMj2TVlhLvg5EgQHZGFNsP1tDQ2v3jKzMljLq6tjN3AzqDk8Rx0FjpPRSjhqImToT933SfI5twp86E3V/7PMi4YzL1z2Af6M8V7dTUVO644w5vB1RRPPW+Fjk5Oezbt4/77rvPjxH25GTfi7BedI0CA3vqGoWGWBAEfBIWAVYTpjbfH94GWSQooOf2gtCQnuOcjM+oqKB+Tz6dLGG9NJkIC7X0KWnc6lB62AIDjBhkCaeru7uuySgT0EslVUhwL2P3Es/p+gwPtQ7aJHhHWzCdx9ksgYG4RAmU7iScwWwGQcTnUylIBAQF0nLc9QEhwT0WF77L5/Hva1NQQI9rQ8KCaTKZfOyCJBMZFYzYlfTptPV2HwG0SxKau/tzJptMiCaz70KHKGIN7HkfgcGBNJ2Gz8ioUCTr4PzcHMs777zDs88+y1VXXcW4ceOoqqrivvvu4yc/+QlXX331QId33nKkqhVBgJTYns2F+kpIgJGHb8zmmz2VaGjMHBvPw3/d5nPO3qIGJo+Kobyue+dFgFkmNNDkI9HRF1RN43BlK2aDRGJ0oPc+Pt1UjN2pMCsrnskj9YY05wuC0YJl0U/QXHaQDAiiBEDAskdx5X0DgoBmt3m34an1xSiNZQhBkWht3QtOYngi7uIcnAdWe6qnsi5Gjs1AaSzDuetjtM5WDBkXYBg+c0Du81znrEg+xcXFAWA0Grnuuuu48847ueGGG6is7H4AbWxsRBCEfl/RPp+4YdFwgixG8subCbYaiAg2E2QxcN38YWw5WIPJIBEVaqaoso27lmWyZmc5TpdKVKiZA8WN3HrpKNbuKqeu2U72iGjmTzrxyrWOzokwZV+JIBtwl+1DDEvAMGIWSvl+5LQpqG31COZAjKPnd9kmo7XWgapgGDkbOVkXHh9I2tvbefzxx/n8889RFAVJkrj44ot58MEHCQo6+Yeq7du3c/jwYebNmwdAdXU1t9xyCytWrCAuLs5vc8TJLlKMTgolOsxCbZPn8TUm3MrFU5LILW7wCnBHhpiZMSqaypo2rzaUQRaZOz6e2qYwXvv8kLcr3MXTUshMDWfj3ko6uxIsY9MimJkZw/pdZafkc+bYeFZuKGJ4UqhXg6jV5iS3tIn4yAASozw/1F1uhf1HGjEbZUYkhyIIApqmkV/WjM3uJjM1HKPB8wOust5GeV37aflUXApRoRZv5W1suJWRiSHepLGmaeSVNtPp9IxtkD1jV9S1U1FvY3RqOAeOeLrnmY0Ss8bGExVs5u21npJ5Abh4ajKx4VZ25dXicnsSSDMyY7lgdAxrt5dS31XNGx8ZwMzRsRwsavBWOvnD5/D44EGbBNcCUpFihqHUeLbWCOYglPRZGJ2qV+gVQUTIXIIgiFBZCJrnM23InI87fTbCvvVonZ5qMzE6DWfaXKTcHSf0qSVmUbl1LXJipreCSU2eimD9Eq3DI1gsRiTT3NQKyROg5ghoatfYC2locQEulPoSlJYaxJh071YLwRSImj4Hg2rAuevjrjEFxDEXIRjMUJ7n1QcxjJrbfR9Hx41KxTlsLlJBDkpV3in5bLQBtrPnc9NfixSvvvoqr732GiNGdMs0LFmyhB//+Md68mkAcLkVnvvPHnK7RL9Hp4Txk6vGIUuntnAUGmRkTFoE4UEmwoPNxEVYKa7u/lzHRli5anYa1Q0dlNS0EWgxcOPiEd55pq902F08/dZuSmo8viePjOa6+cN4+q0c7E7P39WhkiYCzAZGp+qNBwY7Sn0xzt2fo7nsGEfNQR4y3vuaGByFKftKAGzv/NrnOq2hFPO8O3FsfgutoxkxJBZDxkw6V/0Run5RdVYc9Gyr/vT3aHbP502pygODCcPQyWfmBgcRA6751NHRgaIoBAV5BD6ff/55CgsLefHFF1m4cCFPPvmkLjjez3y88QgffnvEe7x0RgrDk8N49u3d3mqnjKRQ7lqWySN/3UaLzbOeGGw18NgtUwgJOLFWh47OqaA0V3r0QY7qahzVB/n4t6hNXYkH2Yj18oeQwpP+iyedY+mvh4r7778fm83Gz372MxISEqioqOC5557DYrHw1FNP9clHbxqAvb2mqqpf5gg4tXnC7nSzK78OAYEJGVGYjBJOl8KugjoURWNCRhQWk4ymaRwobqSuqZMxaRFEdlXalNW2k98lDj403rPS3NLuIKewntAAE2PTIhBF4ZR8Vjd2sGZnOeBJTv14+VhkUeC5d/Z4q3mWzUxlVlYCv3tjB3XNnr+v0anh/PSqsbz0/n5vMiYi2MSvfzCJrQdr+M/XhX7xGR5kZNGUIQRaDEwY5nnvwLOK/fw7e9h/2JNcigwx88ANk/h2TyXvf3PYM7YkcOmMVAItBrKGRXoFaQ9XtnKkSxw8qWsFvKHFzt6ieqJCLYxODUcQBDodnn83SRQYnxGFySChqhp7DzfQ3Obwm8/BjKa4cZfkeMTBUyZ6tqDhEdBWG8uQEzK93YTUlmrc5fsRwxKR4z3JBs3ejqt4J4LRijxkPIIkn9CnUnkI95GdgCc5ZV36a8RQz8Kl5rDhLt6F6rDh3PUJOD3JWjk1Gyl+OGJ4EnLccADsW97GtXel50ZkM8aJSxFMAcgpExDNngS5uzoftaEUKX4UUphna6/aWtu1IBLvFT4/Oi6yCTllPIJkQFPduEv2oDnaT8nn2UR/zRNTpkxhw4YNGAzdumhOp5OZM2eydetWv4/nDwbzs8TGfVX89bNDPrbbl44me2Q0ZTXthAQa+yz8XdfcydNv5VDfYkcQ4PKZQ72NjBpbHYQEGrnzskwykjyLRE1tDoKshlNKdK3cUuKjcwhw8dQhfHaMlhTA7Kx4btD1aAc1amcrtn//4pit0AKWS+9HjhuO0lThWcRwdGAYMQtX3jee7+2jGK0Efv95NLcDpaYIKX4krl0fdS98dCGPmof74Bpf29DJWObf1c93d3ZzTgqOl5WV8aMf/QhFUVBVlbS0NB588EGio6PZtWsXjzzyiE8b7cjIyJPyP5gnDH/xo+e/8dGssJgkRg0JZ2d+nc95i6ck88UxAqwA18xNZ+Hk5DMSp875h33Lv3Ht/cLHZsi6BNexgoKAYfR8zDO+fyZDO6fpr4eKGTNmsHr1aiyW7q1INpuNBQsWsGnTpv967W9/+1tWrVpFfX09YWFhhIaG9uiQd3xiyh9zBAyuecLpUvjJHzfgcHVv80lLCMYgid6VbfBoNS3KTurxQ/3qOWn852vfH/SLJyfxdU5l33xOTuKzzSf2efG0IVw5K83HdqC4kT/8e7eP7aIpyXy1s9xbbQQwPCmUX14/4b++DzqDB6W5ko7/+K5WHy8gDtC57i+48331lQK+9yRiSFcirKMZ279+5q2GApCHjMey6Cf9FPm5TX/NE3feeSfx8fHcd999WCwWOjo6ePbZZykvL+eVV17x+3j+YDDNEcfz6aZib3L/KEtnpLAjr47KehuiIHDpjBQuuyCVw5WtrNnpaUo0f1ISqXG+W/T+9tkhb1UueJoWPX3XdKwmmUMljQxPCsVi9iQdWzucfLGllJqmDsYPi+KCsXE+vlraHdjsbuIjAwDodLhZubWE8lpPBWxNYweruxZZjnLZjBQ+2ljsY7t6TjqLp+jPKYMZV/5G7Ov+4mMzjJ6PKfsK2t/6ebfeEwKm2f/j2TrXWgMGM+YLbwJRwv71X8DtQDAHYRg+s0fyyXThzTi++ZuPzZh1CabJy/vz1s56zknB8aSkJD788MNeX5swYQKffPLJGY7o/EM+bo+1LIk9bECvJbG9naej4y96az0tSL10kZIG/KtMBzCZTDQ2NpKQ0C223NTUhNF44urIBx98kAcffPC/nrN27VqfY32O6IlLUXG6fLWROuxuDMetLLsVlbZOF8dztLL1WNo7XX332dHz+t58HrvgcazP3sY+NvH0XdfqDGIcHT1Mmvdh4kTnHWNz2X0STwCas+c1Ov3LY489xs9+9jMmTZpESEgILS0tjB8/nj/84Q99uv6uu+6ivLwcURSxWq089NBDjBw5kiNHjnD//fd7m0489dRTpKSkAPzX1853Jo2I5uONxbgVz9+GURZpaLFTWe/5G1M1jY83HGF4cgjP/Wev9/t4Z14dj98ymehjNBAb23zbwKiaxr6iBt77poi2DhcBZpk7LstkdGo4L7yzhyNVni1MOQX1ON0Kcyd45DveW1/Eyi2lqJpGWnwwP716HP/38UFvs43dhfVcODYOURBQu2ooAswy8yYlYbO7WbOrHE3zLFTMHh+PzuBGCOq56CgGReIu339M4glAQ60uwHr171DLDyBGpiCYg7D9616vfqxmb8NVlYuUNBalbC8gYBju0XfS2utx5nwKmooYlYpx7OIzc4ODDP2JTYel01N4Y1V+9/GMVNISgtldUO9d6Z44PIpF2UlsPVhDTaPnx1p0qIWpo3QhP53+wzBqDq68b7r1QaKGYsi6CKV8v48+iHHU3IEMU6eL5cuXc/PNN/PDH/6Q+Ph4Kisr+fvf/67reJxBAswGJo6IZkdud+OIC8fFY5BF/nnM93xWeiRX3bBGAAAgAElEQVRzJySwaX+Vt3NpaKCRJVOGsCO31tuNSBIFZo1PwO5S++gzkU37q4/zmdzD54wxvm20Ox1u4iKshAWZvB3+JFFgzoRE2jpd5BR0i4HOytIfJs4nxOihiOFJqI1lXRYBaWg2qq0JMSDMe55hxIW4S3ZzVKdDjBiCGOXptKkpbkBAjB2OWp13zDWzztBd6BwlOjqa/8/eeYfXVZz5/3PKLbrqvVrNsizLsty7jbENGAjFdAcSkmw2dUuyyeaXbEgIkErYJMtmsyELaZQQOgbTMTa44G5ZtnrvvUu3nvL748j36lgCDNi4nc/z5Al37sycmeOrM2feed/v++ijj9LZ2RnMdpeSkvLBDce59957gxqCb775Jt///vd57rnn+NGPfsStt97Ktddey+bNm7nzzjt5+OGHAd73uwudlDgX371tPlsPtCIIhkfTiyd4D+nA3vJu00GAX9E4WN3DhsWZ9A57iY9ysGRWMuWNA6a+3zzYEsx+N+ZVeOT1Kr5xY3HQ8HScd8s6Wbcgg7beMZP3bF37MC/tbjRleQWoahnk27fMZXtJOw67xIYlmUSE2bj10nyuWJaF16+QGh9+iu6SxdmI2l1HoPJtkJ3IuUtQ6g1xezFpOlLWfPBOoaEn23A/dQf6UBfYwnCsvA3dc0L6iNF+XBvvJFC/H6VhP9ic6CM9OBZdj61wHbpvDCn2/Mxo+0lwxsPuTjfns6vsx2VozM9jr1dR3TpEapyLWdmxFGUb4XZ7yruIdNkoyIylICuWUbefV/Y2o6ga+dNiKMiMJS7KwXM7Gugd9LCoIImb1+Z9ZIFCC4vj6H4P3t1/Q20pRYzLwL7oOiN1als5alctosOFff61gI7aXonSUQGKH1vBxTjmX3Wmh39OcLrCKXRd55lnnmHLli3BTcWnPvUpbrzxRoSzOHXy+bZOBBSVbYfbae0epSg3Lpjt52BVN0dq+0hLCGftgnQcNonGzmF2lHbgtEusX5BBXJSTgREfbx1qZcyrsHJOCtPTok9Ln8fZXtLGE1tr8QVUMhLDmTktBk2HVcWp5KRG4Q+obDvcRlvPGMXT41lUkHRG7qvFmUP3juIv24o+1o/mG0NtPAS6hpy9EOf6ryGMe78qbeUodXsQwuOxz16P4IxAaa/Au/X3xiFGeBy27PmgBJBzFiBnzjvDMzt7OZ1ZUQH6+vpwu82eZ9OmfTjtxueff56HH36YBx98kA0bNrB3714kSUJVVZYuXcrrr7+Oruvv+V1c3MkJUZ9va8QH8e6xTh7cUh78HBvpYOPqHP78cqWp3rWrsnnnSAcDIz5iIx18bWMRnX1u9ld2kxDt5FPLs7jjob34/CHPWUGA3/zLKv79d7uChxQAi2Ym8vXr5lBS08t/P1Nqus6SWUkcre/H4wt5vVrh1xc2am8j7ud/HEzaIIRFEXblv+Mv22qEX2sKUuY8Yw2o3gkYBxKCK3rco2kcRzhSYg5q67Fgka3oUmwzL8L93N2gGb85wRlJ+M0/D+oRWhick2F3FmeOv75SGRSAHR7zI4oCDpvEK+O6TgMjPnoGvSzIT+C3T1cGsyj1DHayfHYKD2wuC55mvHmglYgwG9eszDkTU7E4j/DtfQKlegcAatsQvrF+bPOvQa0zhEi10T68b/2esKv/g0Dl9uDC49//NGJkAra8ZWdq6Bc8giBw4403cuONF3YM/JnGJktctnjyJm7hzCQWzgwZbnx+leyUKFNq7YCiEh1hN+kxKaqGIAgf2OdxT9kT+wRj8zKxT19AxWGTGHH7+dsbNcGQj9aeMWZkxPDZDTODde0241Tb4sJFcEbgWHgtSkcVnhdDSQWUxoMEqndin3UxuhpASi1ATi80tfW+8+eg9yxj/ai9TYRf+/4hvhanj3feeYc77riDnh6zrqggCFRUVLxHKzN33HEHu3btQtd1HnroITo6OkhOTkaSDHkISZJISkqio6MDXdff87uTNT6dTiPc2cg1ayNxhdvZfqiV+Ogwbrk0n8SYMA5W91I6vm8ozkugtK4/6Kk6MOLjb2/WcP+3LiY62klF4wBtAx5WzEll2wRtpuVzUsnKiOXGdTN48s1qNB1iIh0UTk/gie115KZFE+myBfcXAJcszWJJUSq/f6YUVdMJD7PxjxvnkJj44bPoWpwf9JYcCL7/A+ieYRy9lbgr3w6Wqc0lxF/6BVzrb0H1unGkTqf1/76JSUTAN0bqlV9kpGQrvs56HOn5xK3ZRP/2x4OGJzDC8cIGKoksXvsJzO78xjI+XcCUN/WbPlc0DSBLZu8Ej0/h3bIuTjzv2VveZVoYACoaByzjk8XHRmkzv3xqgx2ozWYRYjR1/GTDrEOjtpdbxqdPmKeffvqk6lkGqbOHzn43f9hcRlPXCOmJ4Xz56tkkxYTxx5crOFjVTaTLzqb1eSwrTGHzzgZe3duMruusW5jBTRdPn+TF1j3o4Q+by2joGCY13sWXri6cZHw6TlvvGA++UEZz9yjTkiK4cllm0PB0nI6+KfR8LCww1oNJZf1thth4zR6wO3EsuQn7rIsBI9xOH+7+wD4sPjnuuecevv71r3PdddfhdDo/Uh8//elPAcPz6Ze//CXf+MbpFY2/0DyfAObmxDE3Z9w4p+sMDrj55o3FNHQYhtyc1Ci+fN82U5uWrhF+/3RJMDnRq+82csmiDC5fmklNyyA5aVFkJ0dy+12vMuIOkJsaxeXLMqlvH+bhl0PvfstnJ+NXNEbcAVYXp5KXYhiZfvm1FXT0jZGbFoXTLtPTM0VYlcUFgU+brCU6Ojg4qWy4tQG3HIfSUooYWwHpxdAbMoaK8VkMEYuSVISv/F28zeWMVB9Cyiia3H9Axmv95kxYnk8WH4qs5EhqWkNxrplJEWSlRHG0PmSUkiWB2TlxvF3Sbmo7KyuWfRXdpuxHmcnWCYTFx0dKyEIZ7gp+FlwxiCkzoM6cglnKLCZQYX7xEROyP4khWkxg8+bNH1jnuEeUxdnBX1+ppKnLeIFq6xnjj1vKmTcjIajpNDzm508vVWCTRDbvbAi2e3VvMzPSo5mfn2jq75HXqoIbko4+Nw++WM5PvzS1Efgvr1TQ3D0KQEv3KK/tayEuykH/uB4UGNpRFhZTIWcU4RNl04k0uhrKcucbw7fzr8jphYhRSQiSjJRRZAqpsELtzizDw8Ns2rTplIRib9y4kTvvvJOUlBS6urpQVTUYWtfd3U1qaiq6rr/ndxYfnokZ7ubmJXCwqsf0efvhNlP9naUd/O+3DG01j0/hW7/bFQzDq+8YprxxgP0VXaY2B6t7eODbF0+6dmykg9hIx6maisU5jL1wHUrNbrShTgCkacXYii4hcOxNUENJTnRBwvNKKJmBlLsE+4JrUZpLEKNTcSy9CV3X8G5/EH3U0BXT+prBGYEYm4Y2YOx/pYwipIziT3CG5y+W8ekC5nOXF/DA5jJae0ZJjg3jonlpzMmNp61nlJKaXiLCbKyem0p6QjhXrcji9X0taLohPp4Q7eQfPlXA396oYWjMT/H0eK5dlX2mp2RxHuBY/mn0sQHUrhqEyAQcq78AgoCUOQ+15SjIdhyLrkOMSMBWuJ5A7W4I+JFnLMdWcNGZHv4FxyOPPHKmh2DxIWnsNJ/cNXePEh1hPkVUVJ1jDWbvWICGjmEqmwd5t6yT6Ag7t6zNo+mE/jr63PgDKoIg8PjWGvZXdBEf7eTT62fQeILIbHPXKHd/cQnPvl1H35CXxbOSuGSK8D6LCwt1oB3fzr+i9jUjp8/GufrzCM4IxMgEwq74Fv7DL6Irfuyz16O0HDU31nXUjmp8+55CaT2GGJOGlL0QfbgLKbUAx+IbTNU19xC+HX9Baa9ESszGufrziNFWMpXTxQ033MAzzzzzkQ4kxsbGGB4eDhqO3nrrLaKjo4mPj2fWrFls2bKFa6+9li1btjBr1qxgWN37fWfx0fn8FQVEhNmobR1ieno0N62dzp1/3Id3gsZTmMPYauq6Ts+gx6T/BNDSPYLTLpuymIbZre3p+YjudxOo3oXu92DLW44YlfjBjd4DwRmB66afoLZVgM2BnJIPYKwPhzajB7zYZl1MoOxNUzu1YT+OG3+C2lmNUrcHtbcBx/Lbgoan4FgHOwj/9H+itleA7EBOmfGRx3oiuq4hCCGNZKW9ArWtHDE+Ezln0VmtkXoqsATHLdhb3smfXq4koGjIkshXrikkOc7Fr58oYXDUsB5fviSTq1Zk89/PHKG6xfCWys+I5t9unosOOK2FwuIUo/s96KqC56V70foNF1kpaz7OS76O7+0/odS+C4AQnUrYld9GmiLVqsXUnE4h2eHhYbZv3x4UHL/44ouJipo6BOts4UJbJ+5/6ghH6kIvWjOnxTA3L4Ent9UGy5x2iX+9oZhfPn7Y1Hbt/DS2HQ55wtptIoXZcZRMyEaXmxbFD25fxOadDSbPqXCnTGZyJBVNoWxIs7Nj+fam+ad0fhbnPmNP/QBtIBQaIectI2zdV6esG6h8B+87fwoViDJS1nzUhv2hophUwm/++RStwfP6f6M0HgrVTcwh/LoffcwZnPucynXi1ltvDW6odF2ntLSU9PR0EhLM6/Zjjz32vv309vby9a9/HY/HgyiKREdH893vfpfZs2dTV1fH9773PYaHh4mKiuLee+8lNzcX4H2/OxkutDXi47CztIM/v1yBDggYiSNK6/oY8yqsmJNCaW1vcG8BcO2qHJJiwnjopXJ03WjzuSsKuGiuldX0fEJXA7ifuTMU9mxzEn7dXYgxJ5/p8kQCte/i2/MEuncUW/5KHKs+iyCa96Njm3+C1hV6t0GSjcPshgPBIiE8DiEsCq23MVhmm7ka55ovfuSxTYXaWYP3nT+jDXYgTZuDc+2XUOoP4Nv519B1iy7FueK2U3rd04kVdmfxkXhuR0MwfaqiajzxVi2F2bGmxeG1fc3ERNiDhieA6tYhDlT1sHKO5bpsceoR7GH4D78YNDwBqE2HCVRsDxqeAPShDpSqHUiLrjsTw7SYwLvvvsu//Mu/kJOTQ1paGh0dHdxzzz389re/Zfny5Wd6eBbjfP7KWTzyWhXVLYPkpkXx2ctmEhNpZ2jMx56yLmIiHdy8No+CrFi+cGUBL7/bhKrpbFiSSXmj2RvKH9BYVpiMLIlUNg2QnRIZFAuvah4w1R3zKly6KAOHTaK2bYi89GiTsLiFBRiZ7SYangDUjqr3rC/PXI19tJdA1Q4ERwSOJTfg3fWoqY422IHmHkJ0RU9qr5zQt9bTgK74EeTJmiIWH42bbrrpfT+fLAkJCTz55JNTfjd9+nSeeuqpD/2dxallVXEqeRnR1LUNERfl4Fd/P4I27ufwTkk7VyzLpKlzhO4BDwtnJvKp5VnIkkhuehS1rUPkpkWRGh9+hmdhcapRmkvNensBL/7K7Uix6fgOPIvu92IvXIt9yU0n5fmjjQ3g3fYQ6IYnXaDybcTYNOxzNpjqORZcg+e1+4MasfbiKwjU7TPV0cf6cV7yTwRKthjethmzcSz79Mebb1MJ3ncfRx8bwDZjOfbln8az9X/Rx4z3IrWlFN+ev6N21ZnaBcq34Vhy03m9/ljGJwuGxvyTPg+Nmst0oGfIO6nt4KhvUpmFxalCdw9NLhvuOal6Fp88P/7xj7nnnnu48sorg2WvvPIKd999N6+++uoZHJnFRKLD7fzz9XMmld+ybga3rDO7lq8uTmN1cegE2u1TODzBy0mWBAoyY1kya3KYUk5aFJXNIQFQh11iZmYs82Z8dFd7iwsARzhCVDL6BO0/KfG9k5kIgoBj0fU4Fl0fql+9C2Uk9DsVIhMQwqbWpTwxzbYYm3Fev/ifCa67LnQ49JOf/IQrr7ySBQsWBMsOHTrEK6+8Yqpnce6SEuciJc7FgcruoOHpOIMjPm67NJ9j9f2kxLuQRMPQkBzrIjnWdSaGa/EJIIjS5EK/F+/bf4LxtFb+Iy8jxqYjhMfie/dvaKN92KYvxbHiNgTJZmqq9jQEDU/Bsq46OOHVRp5WTPgtv0BpKkFzDyJGJyOl5Jm0ZcXoFKSkXOQNHy1xga6pKE0l6GMDyNnzEWQHnjf/N6g9Fah8G+yuoOHpOFp3PYIompN6TQjHO185/2do8YGsmG12eVw+O4XlReay1HgXlyzKwG4L/WTsssjigiQsLE4Xtrxlpgex4IjAXrwBIWxCGJcgIM+wvGrOBrq7u9mwwXzqdOmll9Lb2/seLSzOFY419PHfT5fS1DFMUW4ckigQE2FncUESf3ihjMfeqGbYbbxoldT08l9PHaG7301hdiyCAHFRDr58dWFQ/8PC4r0QBIGwdV9BjDWMnmLSdJBsuF+6D/94Gm1d8ePb/wzuLffi2/c0umIchAWqduB+6T4QBKMdIESnICXn43npPnwHN6OrRqZef/lbuF+6D8ERjjhu3BLjpuFc9+VPesoXFFu2bKGoyJxJqqioiC1btpyhEVmcLqanRweNS8dxOW3c+cd9PL61ht88eYRHX68+Q6Oz+CSRphUFn7MAgjMSIToZTsinrnRU4nn9t0bUg99DoGI7/iMvow604X7xF4z89Z/wvPWAEa53QoidEJnA2OafMPrwv+B950/BdQGbE3/pqwRKXsK7/SHUzhrk3CUw/uyXchebvGt1TcO75wlGH/lXxp75IcqEwwl/+TZGH/8Oo49/B3/5W4ARuu1947f4dj/K2JPfJ1C/zyR6DqAOtCGcIA8ipeRjn3cVRrCpgb14w3l/+GG9BV7AaJrO9pI2Rjz+YHahGRnRpMa72F/Zw4L8RFRNIznWxbwZCby+r4U5OXGomqHdsbwohZ1HO+kb8rCoIIn51mm2xSkiULN7PC1qOs4N30Sp2W0sIoKIv/Q1nOu/hlK/H220HySZQNVOEMRTKgho8eHZuHEjjz32GLfffnuw7PHHH2fjxo1ncFQWH5fGzmH+68nS4Am23Sbysy8vY/exDjbvbASgommAps4RbliTy2+fKQ2+ToY5JH71TyuJibAyFFmcPFJSLuE3/QxN8eN++gco3UZogtpWZvx/Zw1K9U7jv9sr0Eb7kDNm4337j8E+hMhEwv/hD/i2PYhSuztYV/cMIcak4dsdCs0TY9MJ/+KDiCecrlucegRBQNM0U5mqqpPKLM59YiMdfOWa2Tz9dh1jngCri9Oobx9CnaCf9XZJO9euziHKdX5vuC90BFHGdfV/oDQeRPd7kHMWgW8M/94nmWiAElwxEPCY2qod1Si1e9EGDb1JpXYPgmTDecnX8O19Ct0zjC1/JYHqXeAxIiECle+APRznsltQqnebBMX14W7k+Vdjy1+F57X70XoaCBx+EVvx5TiXbSJQvpVA6StGXc8wntf/m4jbfoM2ngjjOL6dD4NkR20+Ehqs4kNprwDJbjJAySkzkBdfj3fHw2gDbciZc3Esvdk4/IjLQG2rQEzIRE6bdcru+dmKZXy6gHlyWy2v728Jfl4zL42UeBf//Uwoc0xqvIvbLs3nR3/ah8c3Hi9rE7n7C4t54IXyYJajd8u6+PI1hSwr/OjCcRYWAP7SV/DteSL4Wcqci2PBNbg3/wx0FRVQ6vfhuv4u3M/ehe42wnqUml24rr0Dafy02+KTYaKQrKZpPP744zz00EMkJyfT1dVFX18fc+fOPcOjtPg4HKzqMYVO+AMax+r7OFBlDoGtbRti59EO0zmmx6dS0TgwyZvWwuJk0Ptb0Ye7TWVK3T7UrhpzWf1+dN+Yue1ID1p3A0rjwUntj3tVHUcbaEMf6oK4jFM4eoupWLRoEffffz/f+c53EEURTdP47W9/y6JFi8700Cw+gI6+MRo6hpmREUNiTNhJtVlUkERmcgS1bUPkpkVT22aWSdB1Hf09xNyrmgd45p16Rsb8rJyTyqeWZ533mcDOZwTZji1vQqRCWBTONf+Ab/8z49np1mIvvpzA0ddBCcm6iLFpBMYPHo6jtFfiXPNFbNkLAVD72wgce8NUR+2oBIywuBPRNZVAyRZT6F7g2Bs45l+N2l5prqz4UbvrUHubJvWjdtdNKhMQCLvkayHNp7zl2IsvR5BshF9356T6UnwmUnzmpPLzFcv4dAGzs7TD9HnX0U6GTtBw6uhz8/r+lqDhCYyNxxsHWiel195V2mEZnyw+NoGqnabPavMR/M4o0wKhuwfxH3klaHgyCjUC1bss49MnzInCsTfffPMZGonF6SI+yjm5LNpJfJSTtp7Qht9pl6bU7IiPntzewuJkEMJjjdBrPeQVI0YmoI8NBE/BAYTIeMTIBExbDEEwtJ5csehj/aa6QkS8+UKijOiKOU2zsJjIHXfcwVe+8hVWrVoVTEyRmJjIAw88cKaHZvE+bDvcxiOvGaFJoiDw5WsKp9T6O5E9ZZ08uCWUyW7NvDTq2oaChxRLCpOJHveMrW0dYtjtZ3Z2HIqm8V9Pl+LzG3/Vz75TT3S4ndVWFrzzCtvM1dhmrjaVha3/Kt5djxoaSrlLsC+6HqXxkMl7SYrLwPv2H1E6q5ESc7EvvgEc4TDhEEKMz0RpLkWMiANnBHhHASO7nS13saHFNBFNQ9dUxMRsmHhoIUqI8ZlT6jHZsuejD7ShdlYH69qKLkFOySciy8rkOxWW8ekCJtJlw+1Tgp8jwmSiwieHRky18YiLdCAIMFFHMNJymbU4BQjOE0RhZTvCFBmKhIi4D25rcdr5sAKxd911F3fdddfpGcwnSGPHME9uq2Vw1EdeejTrF04jK8X4/R2q7glmc1uQb4QjN3WOsL+ym/goByvmpOKwSQyM+Nh51DgEWDUnldhIB76Ayu6jHfSP+Fg0M+m09nmyrChKYX9lNxVNhlhmUU4c1S2DZCRG0NQ5wtCYH1kS2bR+BosLkiit6wuebq8uTiV/2gdv6n0BlV1HOxg4YYwHq3qoaxtiRkY088fn3dg5zIHKHuKjnawsSsFuk+gf9rLraAcIgmnen0Sfbm+AR9+opnvAw5p5ZoH2cxltuJtA9S4EmwN55mpEZyS6302gage6dxQ5bzlSbBq6rqHU7UXtNbIEyRmGno/SUYXafAQxJhV5xnIEUT7pPo8jhsdiX3Q9/gPPgq4hRCdjn3812lAXnjf+xwjPsIXhXHEbYmw6als52lCnEYY9YwWBsq3IOYuMTYbiA0c4juW3IrpiULtq0Ud6QJRwLLkJtasWpaPKEJ7NWYQgCKh9LSh1exFcMdhmrkKwOdHcg8Yhia5iy1+FGBGPrvgJ1Ow2wjlyFiEl5Rr3oKnkpPq8kEhJSeG5556jtLSUjo4OUlNTKS4uRhQtGdqzFU3Xee6devPnHQ0smZVMS/cofcNeZmXG4rBPFpV+9p364F5Bx1jLvv/ZhRyp6yU1LpwlhYZ27AObj7GvwvByjI6wc93q3KDh6TjHGvot49MFgBibbiSCsDkRoxIRZBvOdV/F+/ZD6ENdSKkzjXWnagcAylAXum+MsHVfxbvjL+ij/UjphUZ49riBSYjPwlZ0GdpQF+pgO57X7kdKnoHW0xC8rjx9KYLNiRiTipQxB7WtDMEZgZy3DO8bvwMw9KGaSwGwz7kMeVoxUmqB8fwfG0DOXYxkedC+L4Ku61P7Op4n9PWNor2HO+eFgKbroIN4guCfomocqe3jDy8cQ1F1JFHg81cUkJcRzS8ePRTMgLdmXhqfXj+DXz1RQk2rsZnITonkO5vm8+LuRl7d1wxARJiNf980j8xka/Nv8fFQOqrwvPobCHgBAceyW5CmL8Xz4i+C2Y/krPnYL/lnfG/+D2rTYQCEqGRc196BOFGM3GJKRFEgPj7ijFx7wYIFHDp06Ixc+734sOtES9cId/15Pye2+PamedS0DPLCrsZg2TUrs5kxLYbfPBFKNz1zWgxf3VjEj/64l2G3IX4c5bJx9xeX8sDzx6hqMTz6REHg326Ze1r6nJ092Xj7QbT2jNLWM8pDWyqCmh3ZKZHcdukMkmJdpgOI5q4RnA6ZpJMMzfj5oweDa4wkCnzrlnlUNA2wZXdo3htX55CbGsV/PRXSn5qVFcuXri7kR3/ax8jxeYfbueeLS/jds0c/kT5///wxRj2BYJ9Xr8jmuotyT/q+no1oQ52MPXvX+HPY0E9yXX83nhd+hjbQalSSbLiuvYNAxXYCFduDbR2rbkewu/C+FfJkkXOX4Fh8/Un3KSVkm8czNoA+NoCYkI0wbqTQA160/lYjO5Ld+J3puobW24TSVo5/31Oh6+etwD57HWL8NATZOGTTNRWttwkhIo5A9S5TfVvxFcjZ8/FsuTeYoltMzCXs8n/D/cwPQ163jnDCb7gH746/oLaMSxYIAmGXfQN1oO2k+nRt/OFZGUp0JteJs40LfS+hahpf+9U7KGrIAzEq3Eg4sfWg8bcb5bLx3dsWkBofTt+Ql7beMfLSo/neH941PR/tssjvv73G9Jtv7Bzmnr8cMF1zRVEK75Z1mg65r7sol6tXZH+suTR0DOMPqMyYFoN4Fv7dXejouo77qe+jDYaic+wLrsWxyDjo1BU/gmxn5E9fMYXmgUDEl/4ICKAGCFS9g2/Xo6a+7YtvGD/IGP9RCSKO1Z9HG2xHik1HTM7Ds+Xe4PNdnnkRttnr8Dx394Q2AmHX/hApIWvqDH4XGB9lnZDuOh+OoN8Hj8fP+W1ee29e2NnA/c+U8vKeJhRFoyArlsM1PfzqiRKe2l6Hpun803VFpMaH0z/sZXtJO7VtQ3zl2tnMzo7D7QtwsKqXnUc7uHZVDusWpIMOVS2DvH6ghZnTYrjtsnzCnTJtvaO8tq+Ftp5R5kyPR5asEyyLj4YYmYB91sWIyXnYF2xEbTuGb/uDoKnYCi7CueI2cITje/XXaANtSFnzcCzbhHP5JkT7yW10L3QEQcB1hjwVH3jgAb761a+ekWu/Fx92nXj41So6+t2Tyr0+hf0V3QQmvKC3dI8y4vbT0Req3zfsRQDKGsxguWoAACAASURBVENpd30BDQGdvRUhfRv9NPZZPD2ePeWdNHeOkBgThk3+4Gd2VLidl/c00dI9GiwbHPWzujiN1PhwU93oCAfhzpMTb27qHOHZCafqum54Qu0t70JRQ/8wrT1jDI35Tfe+d8gwZJSb5q0igHnep6nP7kE3nf1mcdS23lGuXJZ1UnM/W/GXvoraXj6hwA2CgNo4YYOoa+iahlK1k4mCsfpQF+pAqynUTRswQuS0zgmZrd6jTwA5a55pPII9DDE81rRhFSQZMSLOlIJbEATE8Fh8Ox5G9w5PuH4bjmWbTF5GgiAafdqceN/836BRDEDrbYaAF60/pIupuwcMz6XWkC4magAQUGp2m8ar+0ZRm0pOqk952hwjLOQs40yuE2cb5+pewh9QqW4dQhQEXM6PHuwiCgKDYz4aO0JyG6uKU3lr3PAExnrjC6gMjvr49ZMl7CnrYtvhVublxdPSHQqFWrsgnaLceF7d18xT2+uoaRkizCFzsNqsIZiRGM6aeenUtA6iqjrzZyRw89o8JFGgoWOEEbc/GK43FbquU98xzKg7QHSEA03Tuf/pUp7aVseuo52U1PaytDD5pNY+i08OfbgL/8HnzWV+L7aCNYbWn6YgOiNRmg6jj4XWaDE2DTl9NtpQB2JEAmpbGWp7hblz2Y4+1DmxZ6TkGTgXXYeUkIV//zNBnSgAra8JQXagddWauhEjE5DTCiaPXdcJlL2Jb88TKO0VSPHTEBzhk+qdT3yUdcIKuztPKW/s5/mdIVfCF3Y1kpEYwR9fqsAXME7cDlT1kBgbRm3rUHBhaOoc4dHXqynMjqOswfijHhjx8dCWcr58TSG7jnVO6vONA62T+rzp4rxPaqoW5yGCMwJb9gICle8QKNtqFKoKgbKtSJlz8b3zF45vdtTGQ8gZRQii9Tg7FzgbT/g/LDbb1C+rNllElkXwmcumerm1TxGeMFXIwunoUxQE7v7LfroHDKPJi7sb+dEXFp+UsWiqg4WP+/IuT9HeJhlz9E4Iu5AlAVma/Ptx2CbP0T7Fv9Hp6NMhTa4nnQ/hQ9Lk56kwRSY4QZJBlECdEB4j2SY/jwUB5KnaT/GbOxUZ504cvyhNqdfxnteU5CnvAfLkza4wVZlkO/k+rQx7FqeB1p5R/vPvJQyP+REEuOniPC5fenKixt0Dbl7c3cjAiI+ls5JZPTeN2y7JJys5koaOYWZOiyE5zhX0ejrO8Jifp7fXBQ11Hp9K76CXL11VSHXrILmpUawsTuWVPU0887Zx4FDbOkR9xxDJsWF0ja9JggCritOYmRlDRmIEDrtEVnIkvoDKzx89FAzrnjs9nn++Yc6kZ67Xr/Cffy+hvt0wQM+fkcCq4lRK60KaQc1do+wo7eCyxdM+/M21OG0IrliwhZky3gmRCbif/kFQ589WsAbnqs/heeN/0Ed6EMLjEKJTGXvyP4z60Sk4L/5HKHkp5B3lCEfOmm/OTAeIUYkonTWIUUnoHrOWMRj7kRMRY9LQNQV/ycuobeWICVk4FlxDoGY3vt2PGZU6q1G7agm/+RdBb10LA+tunKc0dAxPKjvW0B80Eh2nsWNkUt2GjuFJZX5F42hdPydyrKFvyj4tLE4F6oRY7GBZ02E4IeBJ7a6fVM/C4nRx08XG6etEbJLAhiWZXLMy21R+zcpsNizJNBkzFhckcdmiaSTHhjz1kuNcXLook8UFScEyh006LX0mx7uChicwPH32lHXh8SkcqOymbkI2Ik3TOdbQR2ldL4qqcdmiaYQ5QtednR1L14Cb1p6QN1RAUTlc00NF0wDHI/t1XaeqeYBD1T34J6wZ7b1jtPWMUjw9JADtsB+fd84J887h8qVZJiPQ0sJkLls8jaQJ806Jc3HZ4kwWzkw87X3euDbPVA/g2hP+vc5FbAVrjJTX44gJ2djmXomUOuG01xGOfc5l2OdeESoTBOwLrsY+/yrD4HO8v8J12IsuO6k+xbgMlJZSU4YitbueQP1+dH/IQ00b6SFQuwdtOOQxofs9BOr3I+cuNhmb7HM2oLsHCNTtRZtwWq57RwnU78OWvxJDDnl8GAuuxl68wdgEjSNlzsNefDlibHpoupEJhu5H/qrQHCQbtrlX4lhwzUn1KSWc215yFmcnz+9oYHhcQkPXDe2lMW8Aj0+hvn3Y9ByeiKJq3Pd4CbuOdlLeOMCfX6lk19EORFHgorlpfO7yApbNTiErJZKMRLNXx5JZySbjPsCwO0B4mI3hMT+VzYO094xx6AQvp/ZeN/9w5SyuWpHNRXPT+O6tC0hLCOfOP+7jF48d4p4/7+fvW2vYfazTlC3vSF0fpbWGQalvyBv0yt1Z2hE0PAEcruk1ebIGxzZ+fyzOHgSbA+fqz8F4JIMYl4HoijYlmDCEwnXCN91L+K2/xrnhG6gTBML1oU6UhoO4Nt6JbfYlSLlLkJJyUVrLkKYVG9ZNQUDKXoh358N4XvgpY3/7FpyQdEKMScVWfIUhiD7eRs5fjZw9H9++p/EfeBa1o5LA0dfwbP09SoM5s6o+3I3W33z6btY5iqX5dJ5S2zrEzx41/xF86+a5PLC5zCQyvnFVDjWtg6ZQjYLMGAqz40xhEGEOia9vLOJXT5gtxv92czF/2Fw+qc9rVplf8C0sPgqB2j0m3RAEkbCrvmdoZkzIfudc88VJ2TIs3pszqeUxf/58Dh8+fEau/V58lHVieMzPczvqGRj2kZ8Zw/LZKcRGGh4QTZ0jQXHw4yLXAyM+Smp7iY9yUpQbhygI+Pxq8CV8QX4iDruEpuscq++nb9jLvLyE09LnoeoeHnuj2jSfK5dlsaO0PahxtHJOCrdvmMm9fzscfInPSIzgPz6zAH9A5XBtL2PuAC/sbiCgGPfuutU5rJmXzk8fOUDPoBFuNDsnjm/eVMzvnj1GSW0vAPFRDr7/2UXsLe/iyW2GO7ssCVy9MofIMBtzJ8y7sXOYurZhZmREBzUF+4e9HKnrM83b61c4VN2DIAgsmDFx3n30D/tOa5+apvHy3mZau0e5ZOE08jImJ0g4F9F9YyiNh0B2IGfPR5Bs6JqK0lxiiINnL0AcT/KgdNag9TUhpRcixRiCwNpwN0rLUSMcIm3WB/c5Noi/Yjv6eFiaGJ+F65rv49v7BIHyt4xBOcJxXf0faP2teLf9nxGmJwg41/wjYmIOnhd+hu4zNqByzmKktJmI8Zlo/a34dj6CIYIpEXbJPyO4onG/9MtgaJycvwopIRspKTcoGK65B1EaDyOGxyBNm4sgiuiK35iDpiJnL0Cwh6HrOmpbGdpwN3LmXMTxbHpqfwtqe9UH9nk2Ymk+hTgX9xI/efiAyQAD8PkrZvL3rbV4/SrhTpl/vn4OMzNjUTWN9l43CdFOWntG+fmjZl3G4unxXLc6l9f3t6Cjs35BBtPToxl2+3nzQAt9Qz6WFiZRPD2B/3rqiMnD6KK5aew40h48MnQ5ZAqyYk0GKLtN5Df/vIoxbwBdh8SYMJ7aVssre80b9zXz0ni7pN1U9pnL8unsc7P1YCs6kJMaRV56FG8cMHtl3bx2Olt2NwX3K5IocOfnFzMtyfqNn43oig/dPYwYlYhn+4Mo1btM34dt+KZxECAIaAPteF79tel7OW85Yeu+gjrYjvvpO0Eb36dKNlxXfw8hMhHv9odQW0pDjWxOnGu/jNJwEDE8BlvRZYjjCY/8VTtQ6vcb0iBzr8T9ws9MoeUA8owV5hBsUSb8tl+f11q0lubTFJyrcdofh4Ci0dQ5gtMuMeoJEOaQuf6i6URHOHA5ZDx+BU3TWTUnlWWzk9HRCSgaHp9CQVYsN62djsdn6FuMuP2kxLm4/fICvH71pPssqe1FFkWiwo040GG3n/0V3Yy4AyTGOM+L0BuL04c20kOg4QBSdApCRBzaYAeCKxrb9KWIEbHIWfPHBWpF5My5CDGpiJEJU4dwWEzidGl5HDlyhJSUlEnlpaWlJCcbKZk7OjpYu3btKb/2x+GjrBMOu8S8vASWzU5hRkYMYY5QOE1MhIPctChiJuhRhDlkclKjSI5zBZ9/siQyLSmCaUkRwXA2QRBIjnORkxp12vpMjHGyp6wzeEIdFW4nIkymvj3ktdrSPYrTLrPraCjUetjtJz7KQUFWHNkpUTy/o57ugZCmTW3bMIIAJbWhjUfPoIcwu8TWQ22h++1TEQV4dV9LULhc0w2Pqds3FEw57+iTmnfklPPOfo97ear6FASB/GkxLCpIIm6K7LDnKoJsR0rIQopLDwqrCoKIFJOGlJBtCjcTI+KRknKDxigAwRFulEUmnlSf2kAbSuX2YF3dMwQ2J4EjL4cGpQYM76aqHYZm1PHingZ09yBad12wTBtsx7niVsS4aXhe+RWo414Ouo420Iba14I+ENqgav2tOC/+IlJM6Bkm2JxIiTmIManB34UgSkhxGUjxmcE1RxAExKgkpMQcBLsrdF/Coo17EB77vn2ejViaTyHOxb2EP6BytD60Oc5JjeJoXX/wgCGgaLR0jTI9PZof//UAr+xtZuuhVqYlRnC0vs/U1/T0KJ7cVktT5witPWO8W9bFogLj77qqeRB/QCUrNYqkmDDm5iUgCIZB6YplWYy6AzR0htaWgKqxem4aPQMexrwKNlnk0+tnsO1wG39+uZI3D7TS0j2K16/S3jtmGsdFxamUNfQH/y0cdok1c9P425s1wTqDoz4ykyNo6R4N1nPaJT5/heGxpWk6aQnhfObSmeSknr9GgXMdQZSDekmC7DAZdYSwaDT3ML5dDxM49rrhJet3mzT2HEtuQoxOIVCxHbWtLNSxriFGpyJnFOEvecmkDYim4Fi2CXv+SsTIRATZgSDbCTQewvfWA+jDXWg9DSiNBxEi4k3GJyEsCufaL6O2lqF7R0CScSy7BTm98PTdpLMAS/PJAp9f5aePHKC1x3hgJ8e5+OHtC/n71trgSbfDJvH/bp1Pe+8Yd0/I2PSZy/LJz4jh548dwjN+MrC4IIl/uHLWKe/zaxuLPpkbYnHOoTSX4nn9/mA2IPv8q3Fddyfu539MoHwrgfKtSKkzCbvxx3hfug+lfh9K/T78rhhcG38YPHG2+OT5whe+MGUmu3/8x39k3759ANx9992f9LAsTiDSZedHX1jC7mMd6LqRVehETyiYOiRhzBvycnVP+G8wwjVG3JPbDE3Rz4hHmRT2cWJ/FhcWum+yiL/uGeLEMGvdNzapru4bg6na+9wI4Qr4vZPL7SfU1zVj83Ien1JbXDhcsmgaDpvE4ZpeUuJcbFgyjW/9zuw90jPk5cm3ahgYMXRxfH6V53Y0cPWKbLbsbkLTdVLjXSREu/AHQkkvFFVjb3kXO0o7gm33lnfx75vm4fapvHWoDY9PoXvAw/wZiZxIlMvG7JxYMt0RrF+YgdursKesK/j9oeoeLl1k1mKKjrCzck4q6YkRvHWoFZskcuniaaYQ8tD4dP590zy2HW7DJotctjiT6AgH0REObr98slC0xdmNnFFE2OXfJFC1E8EZgRCdgn/P34Pfq/X7sK/6HHpfM9poH4JkI1C/DyQbYvgUyRzCY9GGe5BzFuKfcAAhpeQjSDbGNv/EEBkXZeyLNqL1Npma66N92OdeiX+o0zA0yXYcKz6DFJWE66afog20Irpip9SLsrCMT+cd+yu7g0YigK5+N28caGXn0VDKSl9A5ZU9TTR0jJhe6Z7f0cDc6fFBI9Hx/jISw095n9f0jJKeaP1RWkzGd/iFoOEJwF/6CroSCKW2BtSOKgJHXkXtqAqW6e5BAuXbcCy58RMdrwVomoau66b/Hae5uRlpClFmizNLdLidK5aGtGYumpvGoaqe4PM7IzGcDUumsetoR9Dg5LBLzMyMYdQTICLMxkXz0nj09ZDRal5eAusWZLD7WGcwo1xMhJ0rlmZyoLKbvmFjkyKJAhfPT8MXUDlQ2W0ag8X5gTbSg+CIQPgQGUjl3MX4Dj0P/vHNpC0M25wNqF21aBP0/2wFaxBj0wiUvmoqk9MLUZpKOG6sEuOzEBKy0Ed6kfOWmk7ObQVrECMTUCdk35PSZiFGhfTRLCzOdVbPTWP1hOfqwvxEDlSFwt0WFyRR0WQOHRr1BFi/MIMFMxIIqBo5adGm5/Rx3F4laHgC469u17FOjtb3Bd/5ewa9tPWMkpMaScO4HuyimYk89kZNsE5pfR+XLJos+u1yynxtYxG7jnYQ5bLzqeVZ2G0S+dNiyEuPZkdpO28caCErORKXUzYdXiwuSGJmZiwzM2Mn9Wtx9qB0VqNU70ZwRWGbfYkpPE3tbyVQsR1ECfvs9ciZ85AzjSyovhOy4QEIig/7ytsYe/IO9GHDkKlU78a54RtI6bOD3k9SagH+Q5vxDXaA3YU8czX62ABiTBr2BVfjL301lN1OU/DvewY5f+Wk60lpswifuRqtvxUxOjnkpSUISHGWiP37YRmfzjNOFP8GTIafUD1tUl2/ouL1T67r8X3MPqcYk2/CCYqFhQnFZ/6sqeiBySdbE4Vng2UntrX4RCgsLAyGjxQWml2MRVHkq1/96pkYlsWHYE5uPN/59Hz2lHcRG+lg/cIMIsJs/OBzi9h2qI2AotHWa2iBSKLA5UszuWHNdKLD7Ryp7SMtIZy1C9Jx2CS+/9mF7CjtwGmXWL8gg6hwQ+PprUOtjHkVVs5JYXpaNF+6KoK89Ghau0cpyo1jyazkM30bLD4muncUz2v3GymxJTuOJTdgn7PhpNqKEXGEb/wR/optoGvYCtciRSbguvLf8ZdtRR/tRc5dgpxRhJw9HzEmFbWzBik5D9vMixBEkbBPfQelbg9CeDxSeiHuJ/8Dfbgb7OHYZq1FVwPIabOQZ6xAEAQER7gRQhGdjL1w/Wm+OxYWZ5YvXDmLhJgw6tuNjHVXrcjiuXcaeHVfSFtpenoUv3vuGNUtg4Q5ZDatz2P57BRm58RR1mAYqvIzollUkMSbJ2S7c9rlYFjfcXoGvfz8K8soa+wnPtJJWWO/yQDmD2gEAiqSKATDsEVBYP6MBDKTI5mXF8/2knZe29/C3OnxzM1L4O9v1fDmuKbTrqOdrChKRlF1xrwKF81NY06u5QF/tqO0leN5+T6Ox0Yqdftw3fRTBFFCG+rC/fw9oBhe04HqnYTf/POgcUrOmo//0AuGtyqAKCFnzkPtqA4angx0lNp3cX3qO6h9zSAI+A9uRu+oNL72u1EaDxHxmf8KhlBrQ52Y0ZEzZqN2VhtrCWAruhQp1jDqGgkoQgeuut+Nv/wt9NF+5OlLkVNnmnpTe5sIVO9EsLuwFa4LakpdSFjGp/OMxQVJvLCrIfjwD3fKXLZ4Gs1dI1Q2G54jArB2fjo5qZG8sKsx2Hbd/AyKcuM4XNOHNv4wyEmNMk6/j3V8vD6re0195qSGdCEsLCZiK1yPb8dfgp/l6Uuxz77EOLVWjd+gEJmIfe6nUBoOBBcDJBu2mRedgRFbbN26FV3X+exnP8ujjz4aLBcEgbi4OJzO80cH53ymICuW3LQoZFlEHDcmJse62LR+Bq/va2bbYUO3SdV0Xnq3iUUzk1g4M4mi3Hjsshg0QGanRJGdYg5dio10cMOa6cHPPr+Kwy6Z0lz7A6rp2hbnHv4jLxuGJwDVj2/PE8g5ixAj4tEDPgSbw1RfV/wgykHRbTEmBcfSm0AHQRrX1HKEj2eOCyEIIvaCNVCwxlQuJechpRYgiCLul+4LrQ/+MZSmw4Tf+muTwLecNQ85a96kebznWCUZYUIWPV1VQDD0SSwsznbCHDI3r80zlV2/JheHXeJYfR/TkiLQdJ13jhiRDR6fwiOvVTMvL4FrVxkJIaJcdq67yGizfHYK75YZm/WkmDCuWpFFU+cwdROEzvMyovnBQ3vp6HOTFBvGyqLJupAp8eF86+a5vLqvBU3TWDMvPZgM4oHNZRyuMZJVbD/cxuevKOCdI2bR8cM1vfzu39ZM6tfi7CVQtYOJQmraUCdqRxVyeiGB2j1BwxMAvjGUhgNI8ZkE6vcjRsTjvOTrhmeUqiBExBKo3Y2UPGPSdQRnBLriQ4xNQxBltAHzbwffGNpAB0prKbp3DDEhCxoOhNo7IpCzFiBEJuE/+hpiRAKOBVcB4N3zdwLH3gR0bLMuxr78Njwv3Rf01A2UbyPs8n9DziwGQO1pxL35J0Hx80DNbsJv+imCfGFp61nZ7s5D+oe9vHOkHU2H1cWpJMaE4fOr7Chtp6lrhKFRPwFFY/GsJKJcdsobBxgY8TLqDTAjPYbivHhKanrpH/YyNOonIcbJRfPSqGgcoGfAw8CoD7tN4uL5afQOeqfss6plkOlpUSwtTEYQBBo6htlb3kVcpIPVc9NM4q8WFgC6puEvfRm1uRRsTgRXDFLydJAdKOOLlBAegxiTipQ0nUD5W2ieYcTwWMSIeOQZK5Hi0j/4QhafWBYjr9eLKIrY7Wf3wnohrhMn4vOr/PGlcg5W9xAZZmPTJTNYVhjaJPz11crJWYYuzedAVTeVzYMkRDv5wpWzmJX1/mEObb1j/N8LZbR0j5KRGMFXrikkLsrJQ1vKKanpJSrczq2X5rO4wAp/OhfxvHY/SpM5m6Xz4i/hL3sTracBMTYN59qvIMak4N3+R5SG/QiOCBzLNmHLX4nv0Av4j7wMmoqtcB2OZZtOSpRbVwN43/4TSt1eBLsLx9Kb8R18flI2oojP/S4YHjEVam8T3m1/QBtoR0zMIWzdVxEc4Xi2/R9qSylCeBzOVbcjZc7Ft+fvRhY+UcI+98pJBrJzHSvbXYgLaY34z78fpnxCBmyAm9dN56ltdUFbQX5GNN/7zEI6+928urcJAYGNq3OIjnAwMOLj2bfraO0Zoyg3joqmflMyi/QEFy6njZrWIQCykiP57m3zcdplyhr7+cvLFfQN+8jPiObWS/O568/7TWPJSolkaNTH4GjIOBEf5eS+r684TXfE4nTg3fkIgfKtpjLX9XcjJWThP/YGvt2Pmb6zzf0UgdKXgwYrKSUf5/qvMfb0D8BnSMMIzkiktFko9YbGqOCKRUzMRm0+AjYHjsU3oI/2G2vMOGJcBrqqoB/3eJJkbEWXoXZUIoZFY1+4ET3gxbPll8Es22J8JvYlN+J9xZxhz77kZvz7njSVyVnzCdvwjfE5PxzK3DpO2GXfQM6e/6Hv39mCle1uCs7FDBUfl7DxNKazsmIJdxpuhLIkkpUSyRNv1dLYOULfsJfSuj5mZcfSPeDhQFUPAyM+atuGUFSd6WlRvLynmf4RH609Y1Q2DXDdRbn87c0aega9dA14OFDZw/UX5fLKnuZJfV6zMoeMpIjgS2NspIOi3Himp0djk8/OtMIWZxb/wefxH3gOfbTPcJuVJGw5i/G+fj/6SA/6SA/aSA+O5bfhefmXaH3N6KN9aANt2BdcjZwy+cTDYmpOVxaje++9l6ioKJKTk9m+fTvXX389Dz74IDNnziQnJ+eUX+9UcSGuEyeyZXcj2w4bxiVfQONIbS9r5qXjsIf0uvaWh9zZ7TYRSRQoHc+m5PYplDX0c8miDETxvY0Fv32mlMbxzEfDbj+NnSMMjvqCJ+2+gMqR2l7Wzk/HbrO0ws41dMWH2lQS/Cw4I9HcA0ENDd07gtpZgx7wEih706ik+FGajyDGTTO8XjUFdA2tuw4xLgMp9oMPFQJHXydw9FVAB9XoT86ah9bfEqwjpeRjn/3+oXWel/4TbcDw8NPdg2iD7WgDrSi1e8Yv5EFpOYIYEY9/7xNG2IemoLZXIKUXnlcJL6xsdyHOtTWipXuUbYda6Rn0kJbgQhJP/r17xB0IhteBkQ01oGh0TRD27hv2kZEYzq+fLKG+fYSmrhFKavtYMzcVVdMZGvOTnhDORfPSePad+mA43fH+77h9IX6/xrTkCD572Uyiwu0oqsYvHjsUNCr1DfsY9QToHvQw0e6XGufi8qWZQW8oURD4zIZ8Miwd2XMKMSYVpW5fUGpDnr4Ue9Eloe+aSgwxb0BMzkP3e9CHQu8g+qiRkVFrrwh1qvixFazBsfgG5Ox5CJFJKOVbMdYFBbXlKI7Vn0OwOdH9bqS0Wch5y1Cqd4b60DWkuAxcG76BLW8ZoisG3/5n0PpDoanHM7FqvY3mOUWnmPQJAcSkXGw5C9F1Da2rBrWzxvS9bdbFpoyw5xpWtjuL96Wpc5TeIXPGl4OV3cHTh+McqOyelHWob9jH9sPtpgVE1XS2H26bss+L51keKBYfDqXBfLqlddcTmLggABzfsPjNGlBK/QHkacWne4gWH8CLL77Iv/7rvwLwu9/9jvvuu4/IyEh+/vOfs27dujM8Oov3o3FCKmwwsgVVtwyw+1gXVS0DZKdEccPF0zlU1YPLIXH1yhz++mqlqc3QmJ/BUR8J0YbIdEDReHxrDfsruoiPcvLpS2bQ2GG+TlPnCC6H2cgUUDTae8fInxZzGmZqcTqxF6wBv5dA7W4EVyyORdfh3nKvqY420IbaU29uqKmoLaWT+lN7GlC761CqdyGEReJYegty5lwCVTvwHXoB1AC2ossmbQLQNeTM+QhhUaitZYhRiWieYUb+/DXktAIcF30BQXbg3fUISuMhxOhkHMs2oQ20nnD9RvTACVqCfg9Ke/mksWo9jZCSf7K3ysLitFDR2M+vnzwSfF/fV9HFtzedvGfFpYum4fEp7K/sJj7KyY0XT+eld5sm1TtS22fKgNfV72Z/ZTfPvF0fFCJ/bX8LM6fFcLQ+ZMwqyIzhJ389GKxTUtPL3f+wBEXVJmVYbesd4/KlmWzZbVzfJotcszKHgqxYZmTE0NAxTG5aVHDNmYqyxn7qWofIy4imMHuKzGcWZwQxKonwTb9EaTlCoHo3SsNBRh/9Jo5lt2DLW07YVd/Fd+A5BF3HtvAa/HueYJJasN01uWObAykxG4BA3f4TvtRRu+tR+1uN8DtdR5rqmX1CyPVUYXFyaj5KxXYm6j3Za5NUuAAAIABJREFU8pahK16U6l3B8YnxmYw+9i10zxBS1gIEV0wwgZKUUYSUeuFlX7SMTxcQ8VEOk6AfQEJMGIOjftp6x0xliTFmjRZREMhImuyqnpEUMWWfFhYfFiEyESbGYtvDEGNTJ9WT4jMJnFAmRJ27pwbnEx6Ph7CwMAYGBmhpaWHDBkNouK2t7QyPzOKDKMiMpbSuL/jZaZfYUdoR3DRUNA3gD6j88HOLTG06+kLC/0mxYcRHhdaOl/c0sX1cJ2rMO8r/PHuU/GkxVDSFQjpmZcVQkBVL2YQwD5dDJivZ0gU8V7EXb8BeHBIZl9NmoTQeDH4Wk/OQ02ebPKSQHch5ywlUvM3El3kC3mCYgu4dwfPG/xB2xbfxvv2nYD3/viexFZ5g3JZsSGkzsU1fDMD/Z++84+Mo7/z/npntu+q9d8kqlmy5yhgbXDGG0EnAgSSQcMnlkl/ucinH5VJIDkJIQu5CEgKXhFQTCIRqwICNjXuRJbmpy+q9S6utM78/Rsx6LCcxYOPCvF8vv16eZ5959tnR7jzzfMvnO/nstzUDVaD1EOyQEMJiNY+33N+C581fIMbnIPc16eYuRibhO6lNsEdgypyvpoOHWpGSP3ybCIMLjzcOduieyY+eGKale4wtBzvYN21Qun11HiVZp4/SE0WB6y/P5vrLs7W2dYvTqWkexOtT046WlCQS7py5IW/uGtNVwBub9JGTHIHTbqahfYSs5AgyE8OofSv0exp3+9l9tIerFqaTEGXXRVgVZURz47IcyvPj6BqYpCgzmkiXahiIi7QTd9J+Q1EUqhsH6R6cZHZ2DKnxLl7efYJntoUM3bdcmaOr8mpwfhHMVpTJYYJt6lqguEfwbP0/xJhMpl79Mcq4KkwfaK3EesWnCbRVg18NeDDlLcE6ew3Bpj3II2rktBiRiOLz4Du2BXPOIrUCasPO0BtKJgIdRzRHhzzShbfqZaTkQoLTEVSCPRxL8Ur8jbvx7vsLincSU+Y8sDq19D5TZjnm3ApAwFf9MsgKltK1iHFZmCYGEUxWxIhEpLTZuJ/5LwiqAR3Blv2Yy69HikpGsDqQUorOKK38UsNIu7tEGXP7NEHXk1FkdXFQFEiKcXDnVQXEhFupaxvBH5Bx2kzctb6Q7KQIGjpGmJjyI4kCNyzLYkFhAn1DU/QMqZuN8vw4rqnIAIQZY05M+bFapL+bfmFgcDJidLq6sPimQLJgW3on5vylBHsbpsNrBUx5l2EpXYsSDCBPe87FhFxsSzZolSoM/jHnKp1i8+bN2O12duzYQVhYGGvXrmVoaIinnnqKu++++6y/39niw7pOnExWchh+v0z/6BSJ0Q7uurqQV/e1EwiGfI3D417WV2Ro9/X8tEhGJ72MTHjJTgrn7vVFRLhCHsMXdrToImP9AZnbV+XhC8hMTPkpyozmk+sKKcqMxuMLMjjqITnWwd3ri0iIPo1H0+CiREouVFOnPeNISbOwL78LKaUYgn7k8X7EiARsy+7ClFKIGBaneqQtdqzzb0Ae7kIeOckpocggmWamOyTmY0opQh7rQwyPQ0rIw1+9iWDnUdV4dPCvuv6KZ1xN45j2QAPg92BddjeKdwJ8bkxppVgv/wSm1GIU7yTKxCBiTBr2Kz6DKaUQwepEHulBsIdjq7gNU9rsc3gVP3iMtLsQF9MacbCuj65BfTVgUYC3a7qRZYWJKT9VjQOsKE/hxV0n+MVzR9lW1UWYw0JqnAufP8i+2j6aOkeJibBhNUtEuqxcXppMcqyD1fNSWbMgjYRoO3uP9WrVq3NSwinOitY5MUB1UvQMu+kZnMJlNxMfZZ+hKTU7O4YpXxCLSURWFGRZYVFRPB9dkYdJEol0WUmLD8NmUWMm9h3v5afPHuaFnSdwewMUZkTx+831/HlLI8dODPNWVSdp8S7+8lYT/kBoDWvvmzCMTxcYvsObtVRnFQUEgWDHkVBTwIsYlYJt+d1IUSmYS1Zjmb0WwaQWGhKj0zClzcbfeZRgy36CbdX4m/epmoFmm7ouhMVju/yTBBp2oXgmQmP7PdjW/huC2YoYEY9t+d0IosjUSw+Czw1yAHmoHcuCmzDnLsZceCWWudcgCAJSdCqWohVYilcgxWYw9cqP8Ne8gtzfQrCnDjE8Xu9kAUSLHeuCmxDD4y8Jw9N7WScMwfFLDEVR+P1rdWyr7gIFKkoSuevqQnYc7mbjmw14fUHS453cuiIPm0Xi588dYWjMS7jTzI3LcijJiubRF47S2DGK2SSwal4aaxak8fyOFm3Mufmx3LQ8h4aO0b87ZoTLwueuKzFSJwzOGEUOIg91IIbF6kRhgwOtePY8qeZ2T4u7motWqDnbZ6AHYqDnXAnJ1tTUcP/992Mymbj//vtJT0/nhRde4O233+ahhx466+93tviwrRNnyo+ePKSLSEqLd/Gduxae8flPv9XIK3tCOglWi8SPP3+ZUXDC4IzxVW/Cu/ckAVdBwrbmi3hee1jXz7bqnzFnq99N7/5n8B16MXRKRMJ0laPQBkdKK0UMi9WLv1qduDY8/KGrPPS3MATHQ1xMa0RT5yg/2HhIM7rMK4jD7QnoIk4Brr0sgxd3htLpREHgu59eyGMvHKO1V02PDneY+eYnFzDm9vGn1xvoGXIzNy+W21flY7VI1HeM8NahTlJinaxdmI4/IPOdJ/bTNx29FBthIzbCplXGBshJDmfCE6B32pEdE25lVkYUOw+rgs+SKPDFm0uZna1GZm2t7OCVveo6cnVFBiWZ0Xz9l3u0CtoAG1bnsfGNRl1bbkoE3YOTTJ4kIxLuMPOTL17+Pq+wwdnEd/RNvDt/H2oQTVgX34p31590/cxzr0UZ6SbQdRwpLgvb0jsRw0OFSXxH3sC76w+6c6xL78RySlSsZ/tv8Ndu044FVyw4IlDeiW61OrGWX4d3t/79TfmXYb/iM9pxoPMY3j1PorhHMOVWYMpewNTz39OdI2UvVI1PwVA6qXXxR7GUrjuDK3NxYAiOn4aLyVtxNqhpGuTPWxq14/a+CaLDbfxhc722EI1O+rGaJbZXd2kpE16/TO+wG7cnwL7jamlieTpKKiXOyV/fDgmodQ+6SU9w8YfNDX9/TF+Qpq5RVs5L/UA+u8HFjyCIiI7IGQ//gca9BI5vVQ8UhWBPPea8JUgxaacZxeAfca482gkJCdx8883ceOONREREAFBQUMCaNWvO+nudTT5s68SZkjutqTE87iUl1klqnIstlZ2MTfrITg4HYPP+dv66vZkTPeNkJoVhNUtUNQzw5JYGPN4AUWFWBkY9RIdb+dS6WaTFG+l0BmeOGJeJPDmEPNSlRhgtvQNz9gIEs5VgXwsgYMqcQ6D/BMHWQ4hhcfgOv4biPknL0juJ9fJPIo90oUyNqVWSlt+FKb2U4HAXymgvQlgc9is+jRQ5M9X7w4oR+RTiQlsjOgcmqWoYwCQJM9LfosNtVBQlEBtp58o5KVyzJJO+ETf17aHfhM0iEeWy0N4XktxQUA0/ldNC3qDuDSwmkb9sa6ZrYBJfQKatdwJ/QGZ00scjzxymo3+S463D2K0mCjOiKM6KxucPUpwVzT3XFvPkmw26NMDhcS8PfnYxidFOynJjuXZpFr99JaQfqCgwMu7lstlJ1LeP8PPnjuD2BnB7A1Q3DuKwmalt0xvSHDYznQMTuraoMCvL5iTroqxuuDybnJQIXT9FUahtHaa+fYRIl1XLGOnsn6CqcQCTSSTc+B2cM8TYTBS/B3mkG8EVg23ZJzBnLcDfsj9Uxc6u/s2CrYcg6EcZ6yPY24SlcLk2TrCviWD7Yd3YUkIu/unKecHeRqSkAkxppcjjA8jjA4gxGVhKVk5rN70zkB/BEYk81IFOz6lopaYlpfjcuJ+7T83KCHiR+5oQzFbkPr2WoRSTjnXBjcgDrShyEPOs5VjmXYcgXDqFtwzBcQOd/sY7NHeN6VIn1H6TM/r2DLmJjdDfvGVFoalLL0iujjl+xmMqinJJhBYanD90aRcntUmxRvj0hcbOnTt5+eWXGRoa4tFHH+Xw4cNMTExQUVFxvqdm8C6Jj7Tzn3fMJxCUefipavZMV7s73jqM2xvAbBJ5btoxcbx1mNaecW5ans1Pn6nRHtnsVokfff4yTafDwODdIEhm7Fd8BmXZXSCI2rOEpXQd5tlrCfY0MPXi9wEFGQi01yCllsDASQLJZjumtBK14pAcQBBDj76Oq740o83A4ELm7eounnilFgUQgDuvKmBpaRLPbm/mQG0fsRF2br0yl9XzQ8659RWZDI56VRHxCBsbVuUxNO5l55FQ9TABTivcPeb26XScAGrbhqlqHDhZnY0Xd50gKymMh5+qxjftmPYFZLKTw3WRT1lJYWyt7GRHTTdhDosaCXuKYS8QVLT3ORWPL4CA/pSC9EjMJpEdNd3aZ1m7MJ35s+LJTgqnsXOUvNTI02ZiPP7SMfYcVa+D3SrxtdvLaeke43ev1mnX+BPrZrGsLHnGuQbvH0EUsVXchq3iNl2784Zv42/aC8EAptxFuP/8H7rX5f5mlIAXwaQ+W5hyF+Or3oQyqX5nBGc0gc7jyNPFIQInDqIEfdhX/wuCLQzBHoZgc4Ews6quYLJgu+LTIc2nnEUE2mrwHXoRU0oRUsZcTXtKm89YH1JSAcHuOrVBMmMpWY0Un40pveysXKtLBWO1vcSYnR3NX94StNBTAVhamsiRlkGGxkKLR2lOLOFOixblBFCaHUNZbqyuKkW4w8yy0mS2V3W/5zENw5PB+8WUMUcXJotkQUouOn8TMjgtv//97/nd737HLbfcwmuvvQaAzWbjv//7vw3j00XMlHdmysb+432YzXrvXWPnKDsOd+s2BVPeIMdPDFNRkvgBzNTgUkUQT7NBEEQCLQc5VaDcFJ+DMtaPPNgKVie2pZ/QNiinMzIZhieDi4nndrRo33gFeO7tFiam/FqKc/+Ih588Xc1D/7wEk6Teo61mic9cW8Rnrg09N8myQnvfBNuru7BZJG5clk1FcSJbKjs00W+7VWLV/FQq6weYmAqVeslMDOdws17bKRiUeWn3Cc3wBLClsoN7P17On7c00dg5SmZiGOV5cZoIeO/wFG1945Tnx3GwXhWXFlA1ZffX9pEQNdMYVpIVQ1KMk2e3NeH2BllWlsSy0mSWlSVTmh1D1+AkpTkxZCaq0bkF6VEUpEdp5/cNuznRM05uSgRef1AzPIG6Xr26t43jrcOnXONmw/j0ASNY7FgKr9COxbhMnQ6UGJmMr/JFAi0HECISsC76KI4bv0OgcTeqRmwFk3/4km7MYMcxvPv+gv/Ym+rx+ICqBxWdOh3pBJgsmNJmowR8OK7/L0RnFJPPfw+5V80q8te9jezzgGgCOZTSKcVlYSm7mkDTXmT3COasBYiRxnPP6TBW3EuEzv4JDtb1ExVm5XPXF/P6/nb8QZmkaCeNHWN85tpiNu9ro39kivgoBx5fgFXzUnHazNR3jBDlspIQ7SAqzMqtV+ay52gPdquJ5BgH7X0T72vMmqZBSnNiZsxzcXECZtPMB0oDg5ORxwcIDnViylmEPNqDYHVhKf8IoiPiH59s8IHy29/+lieeeILU1FQef/xxALKzs2lpafkHZ8KDDz7Ia6+9RmdnJy+++CL5+fkMDw/z1a9+lba2NiwWCxkZGdx3331ER6vlkquqqvjmN7+J1+slJSWFhx56iJiY01fwMXjv2K0mnDaTTjsjJsKG2STS2R9K27BZJBKiZgqFx0TYZrQZGJwNxLCZv3cxLhNn2TrkiUHVw21oOBlcQrxTcU479gc5cpLTGGB00kdD+yj76/poaB8hJyWcm6/IxWU34/EFqG0bITbcxobV+dy2Kg+AurYRmrvGuPfOeew50ovHH9TS9/7pI8X89tVaBkc9FGdHc/MVOSRE2Xn6pKp1V8xN4UT3mG4eigJjbj92q4n0eBcVxYk0n9LH55dZXJRAeX4c3UOTjEx4eWqrutG3WiSWlCRyoK4PAYG1C9MozlLX/2VlyciKgigIKIpCQ8codpuJ9RUZSKJqdOsamKR32E1BWhQOm4nt1V389tVaFEVNMbxpec7M6+sP4vWfeo3lGf0M/jGKHCTYVQsmM1JC3vsKRrAt/QRTWx5F7mtCjEpGTMzHV/WS+uJoD1PDnTg/+gMss0OVVsWYDK04EYAYm06w46h+jqM92G/+HsHuWhTvJCgKU6/9D6CAaMJ25T2a4ekd5J46NTJq90YUzzimrPlYytYhmCyYC85cU0zxewl2HUcIi0GK/vDIiBiC45cAdW3D/PDJKi2nujAjis9dX8K3fr1PC5WNcFn4zl0L+b8Xj3GkRV2kJFHg3z46h+auUV0p0hsuzyI/LfIDGfMrt809x1fH4GJGHu1h8tnvgF/1womRyThu+o5R2e59cq6EZCsqKtixYweSJLFw4UL27duH1+tl5cqV7Nix4++ee+DAAVJSUtiwYQOPPvoo+fn5jIyMUFdXx6JFiwDVQDU6Osr999+PoiisWbOGBx54gPnz5/Pzn/+c9vZ2HnjggXc97/eyTqj3uCYEQeDm5TlkJoUzOuFlx+FugkGFJbMTiY2w4w8E2XWkh76RKcrz4jS9iarGAerbRshKDmd+QRyCINDeN8G+471EOC0sLU3CZjGd9zHfYfeRHp54tRZ/QCbcaeFLt5RiEkV+/FQVIxM+TJLIx9fks2BWPA8/VU1jp5qufXlpEp+6ulA31tCYh52Hu0EQWDo7iagwK15fkJ1Huhka87JgVjwZiao21MG6Ppo6x8hNjaA8Pw6Alu4xDtb1ExNhY0lJIlazdF7H9AeC7DzSQ//IFOX5ceQkX9qG8UBXLYG2aqSoZEx5FaqY92gv/oZdCGYr5oJlCDYXincSf93batpCXgVSZDKKLBNo2kNwsB1Tagmm1OL3NabojGbqlR8T7KkHQEovQ4xKQXTFYC5YqkU8gboRCjTuITjUgSm9FFOy+r0MdB4j0H4YKToVU+5iBFFCHunG37gbwWJX39vqRPFM4K9/G8U3hTl3CWJk4rsa82LDEBwPcSHtJZ7d3sRLu0JppesWp+P3y7xxsENrs5hEirOiOXSSftOc3FhuviKHB/9UybhbjWJasyCNG5Zl84M/HaJl2iiUmxLBV26bQ+/QFM/taGF0wsvi4kRWlKcwMuHjpV0naO4eoyAtktyUCJq6RkmNcxHuMNMz5Gbjm6GNenFWNC1dY7i9IcfF/FlxHKjt144F4IHPVhAfaWfS4+dff7pDS7t7Z4wv3VIKgCSKvF3dxds13bjsZq69LJP0BBcPP1WtaTulxjn5+oZyNu9v54WdJwBwWE18+WNlPPxUjS6CKynGgcNmoqlT/eyCAP96Sxm1bSNs2hO6xusrMk5rqDL42yg+N+4X7tciiqSUIuzrvowgSgT7mlGCfqTEvHetf6QEfAgmC5N/vU9nWAKwf+Q/1ftxXyNSYj6mnEV4t/8GebhTrZa38rNqtFTzPu0cwRaGc8PDCJIJRQ4y8bsvqFXu3nk9KhUCXpTx0HdWSinGsf4rKIoMcvA97UmCw11MvfgAikcV9zeXrMG25PZ3Pc755r2sE4bx6RLg5389zIG6fl3b1YszdDdOgKsXpbNpb5uubW5eLA0do7qbscNqoigz6jRjprNpj/789zvmtz+1gPQEQ4DW4PR49z6Fr3qTrs22+guYs+adpxldGpyrTcUXvvAFioqK+NznPqcZnx5//HFqa2v50Y9+dEZjrFixQjM+ncprr73Gxo0beeKJJ6ipqeHee+/lpZdUz9fQ0BArV67k0KFD73re73adaO8d59u/2a+F5QsC/MfHy/nFc0c147zTZuI7dy3kt6/WaekJggBfuLGU7sFJncf4qoXpzM2P5Qd/OqQZ57OSwvnXW0v51q/3n7cx5+TF6j73xJSf3iE36QlhmE3qA2MgKNPaO058pJ2wk0Qn23rHsVlNxEfq0yZGJrx869f7tM1PuNPCfXcv5GfPHqahQzVYiYLAlz82h+Otw7y064R27vVLs8hODucnT9doaeCFGVF85tqi8zrmK3tbtcgDQYAv3FTKnFz9tbtU8DfswrP1Me3YlLsYS/lHcP/1Pk0DQwiPx3HDt5l64XvIw9N6fZIFx/XfwH/0Dfy127XzrZd/EsFkeV9jSjHpBAfbCQ624d32K1DUKAUpMR/HR+7Vxp3a+jiBhp3asW353aAoeLb/OvTe+UuxlF6F+7n7IKBWKBIjErFf/01VYHa0Z7qjBccN38JXtemMxrRf8el3f7HPM4bxKcSFtJdQFIV9x/to7BglJzWcRYUJTHoC/OK5IxxvHcZlN3P76jyeeKUW30kRO4IAi4sS2X20J9QG3HxFjm7tAPjkulk8s61Ju/8B3HV1IQfq+qhpCqXbXVaSyOoFafzwySompvzTshxJWM2Slk3xyLN6EejyvFgQBA7V92O1SMzJjWVkwovLbmZZWTI/fqpa1z8rKYz/+sQCAA7W9fOzv4bGs1sl7lhTwGMvHtOdc8PSLF7YdUIndF6WE8PRE0M6w1a408ID9yzm7eouBse8LCiMJzclAkVR2Hu8l6YO1UmxsDDekBB5l/hqXsO7Z6Ouzbb6C/hrtxFsrwHUyCTHtV9HsNiRR3uQ3aNICblnZKz3bPsV/rq3Qw1mG1LSLIJtVVqTKXcx9hWfRfaM4z/+FoG2akRXDPJIN/JgG4IjEtuyuxCjkpHH+xFj0pn83RdBCUW+Cc5obCs/i2fLL1EmBhGjU7Gv+pe/m1KneCcJDrQixaSrulKA7+gbBJr2IbhisM67Hm/l8wQadp10loDztocQwy6uZ4f3sk4YaXeXAKI484Z42jbp9P1O7Xu6NkALY9WPeZq2dzHm6doMDDRO4xERTvM9NLgwuPfee/n85z/P008/zeTkJGvXrsXlcvHoo4++77FlWWbjxo2sWKGWze3u7iY5OaTBEB0djSzLjIyMEBk5U1T0bPLc2y06XSNFgT9urteJsk56Amze367TxVAUePNgO12nFGbYUtnB8IRH96Dc0j3Gq3vbzuuYpxqfXHYzrlMiokySeNpIn7/lVNh7rFe3oRmb9PHa3jbNoANqoYstlR0cbdGnkrxxsIPcnghdOe3jrcO8tq/tvI25ac8JjraE9LDUa9dxyRqffEde1x0HGveCxaETX1XG+vBXvxwyEgEEffiPvom/Th8B6T/yOpymuukZj3l8G9LSO5Bi0vAdfE4zPAEEe+rVDUBsBopngkDjyQ/6amlu0KfTBBp2ql7sQKg0tjzag796U8jwBBDw4TvyxhmPqVTchmB1YmDwfhEEgUVFCSwqStDaXHYzX7ltLhNTfmwWCZMk8tredlp7x7U+STFOxqd8urEUYOgUMXGAE91juvsfQGV9P4eb9DpPhxoGcHsDmrNZAXYf7eHH/7IUl91M38jUDHHwlDgXNyzLxu3xc7h5iF++EEqDOt46TGFGlE5jcH5BPA0dI2QmhlPVoHdgT3mDNHSMcCrDk17d2gcw4fGzpCSJ7dWhe8iysiTsVhNrFqbr+gqCwOKiRBYXGZo975V3InpOJth1XDM8AciDrfjrtiNPDOE/rOqECuHxOK75OoLFhvfg88gDrUgphVjK1oMo4T/6BoETlQiOSDWtblrbz3rZHXhPcmIABE5UAuo646t8QX3P3kbEuGycd/wvgtWJr3oTU689DIqC4IhESi8j2FqpjWGetRxTYj7O2x4i2NuIYLJqhid5chjfweeRx3oxZc3DXLSSYPthpt54RF1DJDO2Ff+EMjWOd+cfQtehpx5xRmVVBcU7AReZ8em9cEEZnx555BF++tOfanofhp7HmbF6QRpVDQOayN+c3FiuWpjG3mM99I+oD28x4TbWLEina8BN5bSon9kksnZBOvlpY2x8o0Ebb31FBrmpETPGXLswjT0zxkyja2DyPY+ZGmd41Qz+NubC5fiPv6XekFG9JFLa7PM8K4PTEQwGWbt2Lfv27aOuro6uri6SkpIoLS1FPAsGw+9+97s4HA4+/vGPn4XZ6nm3XhvXaSq32W0zdWUiI+wIAroS3U6HFespD/tmk0iYc+aYkREz9ZM+yDHj4s5+VGrUaaopRUXObHM51JLXnpP0TSxmCedpSvqe7zFntlnOybW7EPDbbOhq/IgijjAXY6f0c4aH4Tulze5yEJBMKCcZdkxWK6LZ+p7HdIQ5iJm+1r1OO4FTXo+Oi8QSG4bslZgQJFBCPcw2KygKul+OKGF3OfGfMo4z3DXjvZ1hTkbPcMzYuAhE68zvj4HB+6WjfwKbWSI20o7Lrqb/DI56WLswjaffamJ43EuEy8InripgeNyr04dKj3exen4qO2q6NZ0j27TO0rbqLt2akBTjoHPApu0BABKiHYxN6n8ZgaBCa+84+471MjbpY3FxAgfq+vFPV74bGJ3if56uZsnsJGqaBnTnTnoCLJ+TTElWNN2DbkQRnt3eTFBWiHBZmJ8fP+PzLyxMYN/xPk2T0CSJrChPpWvATX17yDC1dHYSs7NjcNnNjEx4KUiPZOnsJLy+IF2DkyTHOLFaLr702AsVU24FvsOvQnD6/mh1IoTHzegnD3fpCgopY334al5BHusj2KZGwQW7a1E8E4iuaLx7/qz1FWPS1JQ5mwtBMuM/+BzySU4CMUI1EgVaDujfs78ZggEU7+S000L9oivuEZAkrEs2EBw4gSm5CFPeEhRFwbPlMQJNewCQEvKwrfs3pjb9CHlYTSsMdh0HWcZ//K2Q8yLox7vrTzMMTcrEIFLhcr2Aekw6YsyHo4L3BWN8Onr0KFVVVZonW1EUvvKVr+j0PH74wx++Jz2PS52c5Ai+9+lFVDYMEB1mZW5+LJIo8q1PLuRAXR+KorBgVgIOm4l/vr6EQw0DDI5OMSc/jvhIO7mpEWQlhdPYMUpWUhjHTgzz2AtHSYl1kpMaQX76g/VBAAAgAElEQVRq5PsaMyclnLxUNRLh1HkaGPw9xLA4HLfeT6B5H8GeBgL9J3A//99Y592AKb2UQHsN3gPPofjcWAqvwFJ61fme8ocWSZLIzMxkdHSUsrIyysrOXmnZBx98kNbWVh599FHNkJWUlERXV8iDOTQ0hCAI7ynq6d2mVHykIoNdNV2aZ1USBe5cm8fP/npEE+COjbBxWVE8Xb3j7Disln82m0RWzE2mbziK32w6rnmD11dkUpIVzc6aLqa86gagLCeGy0sS2FbZfl7GzEsO47WdzZRkRWMxqw/kXQOTdPRPUJAWScS0AW5s0kdt2zDJsU7NmeAPBDnSMoTNYmJWeiTCtCBsffsIQX+QuEg7/SOqjltitIPLihKoOzHEwem0bKtF4oqyJFJjHfzx9Xrtul9TkUF6Qhj7j/do6SSLihJYWpzAlgPt9A1/8GNetSANkwg7D/fo/h79/TO9vpcCQvE66KwHWf1OmYtXEcy5AqFmG8rUdDpiXDa+vJVIDYe0stOCLYxg7pWYgyZ8h16cHkxEnL1ejTR6D2Nic+GxxtFduRMppRglfxXU7YeAavoxZc1nVIlAbjlBsKcBc/5loU2OICGWXK1uOrof0dIsLCVrCOYuRzi8TfPciwm5+HJXIdYd0IRnBXs4gZwrMHuC+Gte+YdjDo4FgIvrO2Gk3V3YTHkD/OTpaho6RhGAZXOS+cRVs/jj5nq2VHagoKasffGm2aTEubTKdxazxP7jvcRE2FmzIA2X3cy9d8xj66FOBGBFeQopcS5uWp7Dc283EwgqZCeHs25xBkVZ0fzy+aNMTPmJdFnYsDqf1t5xmrpCpuLs5HB+9dIxRiZCRql7ri0iKymMB/90iObpvtVNgyycNdOYlBLrZGFhAlPeAP/6yA5tnR2d8NE/4mZWeiS1bSNIosC6xekUpEfxjTvn88bBDvyBIMvnpJAa5+KLN5XyxoF2eobdlOfF0Tvs5muP7iYoKyRGO7hxWTbHW4f5+V+P4PYGsFvV/cw7guYG7w8pOgXHR/4T//GtIFmwlKwCiwPfwedDmkqihBifDSdXswbkiUGCbTW6tkDzfgSn/m8jD7arWnx1bxPsa0JKnoXinUTxjCPYwzHPXqNqRIXFw8lRsxY7gs2FPNavrTsa7lGk5EKCA20E2qoRXNEgBzXDE0CwtwFf1Uua4ekd/M37kCf1lYGVqVGE9DLoPEnoXJAwFyxDik7H37QXMSwW8+w1H5rUzgtC88nn83HHHXfwwx/+kE984hM8+uijeDyes6LncSHlaV8MvL6/nY1vhiKWnDYTP/znywxvgMF5xd+8H88bPws1iCbs1/0nU8//t67UqW3V5zFnLzgPM7y4OFebiscff5xNmzZx5513kpioD1evqKg4ozFO1Xx6+OGHqays5LHHHsNuD0UOyLLMmjVr+P73v39eBMfHJn0893YzgiBw3dIswp0WfP4glQ39BIMK5flx2K0mFEXh6Ikh+oenKM2J1Sq/dfRNUNc+QlZSONnJakno0QkvhxoHiHRaKc2JQRSFD3zMvuEpDtb2cbxN9RjHhFu594757D3Wq1UgMptEvnhzKSZR4OGnqzWjzQ2XZ7FsTgr3//6A5h0vzoziS7eW8cgzh6meTtmIDrNw1aIMnHYz5XlxWC0SsqJwpHmQoTEvZbmxRIWpxq0TPWM0dY6RlxqhpfINjXmobhokNsJGcVY0oiDg8QU4VD8AAh/4mNrfY8RDaXbMJV/dTx7rI9B+GDEqWRPYVryTBFoOgsWGKaN8Wrw1QKC1CsU7iTlznqZ9EeipRx5sw5RSrHmE3/WY7lF8x7aiTD/8i7GZOD7yHyheN4HWKkRXNFLabALNB/Bs+aVqCBIELKXrEFwxmFKLNa+4PNJDoPMoYnQqpqQC9b09E/hPHESwODBlzkUQTSjBAIHWQ+CbwpRZHvo83XXIQx3/cMyLDcP4FOJC3Eu8sreVp7eeqtVUwBOv1OnabliWzbVLMhka8yBJIhHOM6/+ODHlZ9Lj16qYtvdN8Pr+NtyeAOsq0slJVp0926o6OVjfT0ZCGOkJLn7xnL6i2MLCeJaWJvHjP+v1nMrzYxmd8NHUNYYoCMyfFYcoCLjsZubPiuP7f9Tv+dITXHz7UwvpG5nCbpF0OoN/j+FxL1/5+S5devXyOcnUt4/QfVLKekK0gwfuWXzG18fg3RMc7sJ/ZDNKwI+l8ArEuCwmn/o6yngoCs626vN4d/1RjUSaRkzIRXRG68TCkSxIGXMIntRmKlyBFJ+Fd/eT4JtUK2Qv/ii+g8+hTAyCyYI5twLFO4kYlULgxEFNFB3Asuij+A69AD7V8YQgYplzjdp2EqaiFQTqtociu1A1pgSLA/+xLaG2vMuwLryZqU0/RB7uBNGEdeFNWErXve9reSFw0QqOP/TQQyQnJ7NhwwZt49HS0sIzzzzDY4+F8jfLysrYtm3bu/JsX4gLxoXMT56u1okJAnz1trnMyog6TzMyMADP9t/ownIBzEUrdDd4APOsZdiW3fVBTu2i5FxtKt7RYzoVQRB48803/+653/ve99i8eTMDAwNERUURGRnJT37yE6655hoyMzOx2dQNfWpqKj/7mWqIrKys5Fvf+pYuNTs29t1HVBrrRIi6tmEe/JP+gf+qhWlsPdSlKz+dkxKOWRKpbQs9HJokkbUL03h5t77Yxa1X5vDUKZsko3qQwfvBV7sN7/bf6Npsy+/GlLcEua8ZwRmFGBbLxMav6KoUCfZwXHf87wc93YsSw/gU4kJcI36/uY6tlZ26tpXzUnnzoD4aY+nsJCY9fg41DCAIcOXcFD6+poChMQ+vH2hncirA0tIk8tPUvZU/EGRkwkdshE0XiTE05uE//28v3um0ZbNJ5L67FlLTPMhf3mrCH5DJSAzjYyvzePCPlbo5XLUwnaWlSXzj//bOaL91RS5dA5N09k/w6PNHtejdqDArkS4LLd2hiMFbrshhRXkq41M+YiNOdkYpDIx5iAm3avq0NU0D7D3WR0yElbzUSB4+Rci8OCua+vYR/IGQRptJEnjsK1f+w2tvcHaRJwbxVb2M4h7FlFeBOWs+/pYDeLY+DgEvgi0M+9r/h2B14d70kGpEEk2qUWn3kzqBcCwORGekTh9QjEzGcfN3kYc7CbRW4TvwbOi1xAKkuEyUsT5MWfNBkfFs+5VufqbCKwk07g7pEAoCjuv+i2BfE949T6oV78LicFz97whhMfhqNhPsqUOKz8FSehWCyYKiKMjDHapWle3SScu/KAXHDx06xOHDh/n3f//3czK+sXC+Owoyo3XGJ5MkUFIQT1TYpe3JNbiwGc3IY/AU41N04Xx6TzE+hafnEXGJaq1cDGzZsuUfd/obfOMb3+Ab3/jGjPa6urrT9FYpLy/nxRdffM/vaTATt+dU1RzV++3zB2f0M59ScCIQlBl3n6qMA6OTM9smT/M+BgZnjNc9o0ke62Pyz1+fNjYJWMqvRfHp+ym+KRRF+dCkNxhcuswviOetyk7NWGO3Sqyen8ruIz24vSdFhFslLaVaUWBLZSdlubE88UqtVnxi15EevrZhLhNuP7/edJxJT4CkGAdfvLlUi3o6WNevGZ4A/AGZt2u6eG1fu5Ya19ozzoHaPtYsSOP1/e0oQEKUHVmReeNAOwtmxXOgtk/X/ttXa6koTqSmaVAnTD487uWWK3No7hqje9DNnNxYzJLAlx7ZgdcXnE4pLGV4wsvPnj3M4JiqbfW560oYm/Tx8+dCejqpcQPERtgYGA3pVS2YFU+4w6Kr/jf/NGmABuce0RWDbemdujZz1nxMKUVq+ltPA/7abZgyynF+7AfIA60IYbGI9nD81a+gTIZ0zERnFPJYr24seawPQZSQYtJVg9bJr/XUYV/1OQRbGMrUGMHB9hnzkyISsVz7H/hqXoGAD3PRCqT4bPVfZjnycCemlGKtSp91ztXA1boxBEFAik57P5fpkuG8G5/2799Pc3MzK1euBKCnp4e7776bO+6446zoeVyI3ooLEbfHT2vvBAvyY6ltGeJw8yAOm4kr5qTQ3TNGwBOS32ztUb0QGYnGJt/g3KMoCkFrvFqBor1GzR0vvw53TBHWRbfiPfg8BH2YshfhTau4ZLVWziaGR9vgb1GcFU1MuJXBMXVTIokCy+em4PHLHKjt0/otK0vGbBL5w+aQftKc3FhWlKey60iPVs460mVh3aJ0DtT26ca8bLZRRcjgvWPKWYi38gXwT6dGWOzI4/0nRTkp+A69iLnwCvzHtmrnmQuWGYYng0uCwowoPn/jbN6q6sRmMbF+cQbxUQ6+evtcNu1pVcW7y5Jp6hqdcW5Vw4Cu6qmsKOyo6eZQw4DmGOgedPPUlka+cFMpAOGnS9dThBlV5XqH3PzbR+ewYl4qoxNefvXycTbvV6OxBAE+fU0hsZF2fn1S+/bqLhYUzDT8JEQ5KMqMJsxuZtIT4Ms/26lFKrV0j/P8zhO09oxpa8vohI/fvlpL3CnFITr6J/mXG2dT1TDA4JiHBYXxLCtLZlFRAtHhVho6RslNieDaJZlncukNPiAEiwNf1UuaMLe/7m2syz6FueByAvU78fU1Ycqap+pKBQNgsmBd/DH89Tt1Gk2mrHnIU2MItjAEe7j+TSQLwYFWvNt/g+IeQYhIREopJjit0STGZWGetQzBYse+4rO6U/2Ne/Ds/D14JxETcrGv/hdEx7mttnwpcN6NT/fccw/33HOPdvxO2l1ubi5PPfUUBw4cYP78+Tz55JOsW3dp5EdeaNQ0DfCL547i9QexmEU++5ESblqezU+fqWHTnlZe2dPK+iUZfOSyLP73LzUcmS5VXZgRxZduKcNsev+VrAwMTofincT9surlAJCyFmC/8jMI06W5LWVXYy5eBXIAwTKzipeBgcG7w2KWuPeO+Wyp7GDSE+Cy2YnkJEfwmWtc5KZE0NE3QUl2NAsL1TLfEU4L1Y2DJMc6ubI8BatZ4t475vF2TTc2i8TK8lTCndbTjmlg8F4RXTE4bvivacOSgKXoSjy7N+o7KQqm3CVIcdkEutUUCPOs5edlvgYG54Ly/DjK8+OYmhbMBkhPCOOz15VofcIcZl7bF4rmMEkihZlRbD2kT9mzmCQmpvR1HrsG3XT2T7DrSA9Ws0ReagQNHaoxKyc5nHUV6ew+1qMzZM3NU1Pf4yPtDI5MaUUbQI28OtwyxOWzk+g9pT0gy8RH2umbLkZRlhPDL184St/wFInRDq5bmqVLkQPoHpika1Af3dgz6CYzSW9gEIC0eBfl+fpqayZJYH1FBjbLed8OG5wGeWJQVxEOwF+7DXmoA/+R17U28+y1mNJKkeIyEaxOpMQ8vI4Igr2NiOHxBPqaCPz+iwgRCVjmfoRgf7Oq6SQIWObfiHfn7zV9KWW0B1zROG66DwI+xPic0zosFN8Unu2/0QpcyL2N+A78FduyT53DK3JpcMH+2kRR5Ac/+MEMPQ+Ds8/GNxs1LQ+fX2bjm/UUZUZrngQFeHl3K2EOi2Z4AjjeOsz+2l6WlCSdblgDg/eN7/hWzfAEEGzZT7BklU7AVTVEnbmApoGBwd8nKsw6Q4/JbFJTOnwBGas5VIBiXkE8JdkxWEyi9oCWmRhOSqwLUUTT3zjdmAYG7wcpMhlpyQbt2Jy7WI2OnUYIj0eKz0FIzMNccPn5mKKBwTmltWecx148Svegm7R4F5+9rpikGKeuT0F6FJ+9rpgtBzswmyXWL85gVkYU8writMqdsRE21i/JoKFzhLbeCe3cnORw7vvtAc3oExdp46u3z2FyKkjXwARbDnZw9/pCth7qZGDUQ2yEjf7RKVq6x8hKCsdhM8+Ys9NqPm17lMvGp9cX0dQ1SpjDwq9fPq4ZrnqG3Gze30Z0uJWhsZChqyw3lgiXhX3HQ1G5s3NiuKYigyPNg4y7VWPaynmpM6Kh9hzrYeMbDUy4/ZTlxvKZa4s0A57BhYFgsoIo6SrSCRYH/uNv6foFGnZhq7gNRQ6iyDKC2Yat4jYAJv/6HRhTvx/KaC/+I6/juv3HBHrqEcPjEZ1R+PY+qRtPHupAiklHCfpnGJ4URQZFRh4f0AxP7xAc1ht0DU7PBfcrO1kzxNDz+GAYGdf/eIbHfTovBqheid6hmRoLp/YzMDibKKeULFXbhk7T08DA4FzS0DHCr6Y3A3mpEfzTR4qRRIFfvnCU2rYRYiNsfOrqQvLTIvjD5np21HRjMUtcvzSL1QsMnQODc485bwmAWrraGY1l7jUIohGZbXDp8quXj2nV2tr7Jvjdq3V8bUM5hxr6aekeIz8tkpKsGBYWJpAQ5aCyvp/BMQ+BoMznb5hNU9cok1MBCjOiMJtE/uXG2fzlrSY6ByYpzY7B6w/qoo36RzwMjHp45q1mTcvPZTdz390LefzFY5ox67V97fzrLWWUZMewsDBeMw6FOy2EO81UNQ5QmhOjacyGO8x0Dkzw+Ye3Ex1u486rCmjvnzj5o9LRP8k3P7mAZ7c10T/iYcGsONYsSMPjC+KwmWloHyErOZxbr8zFZTfz/X9aTF3bKNHhVpJiHGyv7mJozMP8gngiXBZ+/XItgaD62aoaB9i0p9VwkFxgCDYXlrKr8R2atgWYbVjnXc/Ua/+DEjwpSs9ix7vvL/iObFar05Wtw1p+HQDyYJtuTHmwnWBPPd4dv0OZGEJKL0OMz0HuCxVFkRLzmXz+e8i9jYhRKdiu/AxSbCb+2u149z2N4nMj5VQgOKN1exJT2uxzdzEuIaRvf/vb3z7fkziXTE35OP/1/C5sBkY9tPaGdHKWlCQyNy+Wg/WhCjHxUXZuX5XP9pouTUPLJIlsWJ2Pyz7Tg2FgcFawOAjU7wgdW53YLvu4lnZn8N4QBAHHGZYo/jBgrBN/H1lRePCPlZpY69CYl8ExD7Vtw1Q1qpsHtzfA0ZYh7FYTL+w8oaZRBGWOtAwxryDu9HohBgZnGSkmDXNuBaaMOUYq9vvEWCdCXIhrRFCW+ePrDbq2SY8fjy/IHzbXU98+yu6jvdgsEm5PgB9srKSufYRDDQO0902wqCgBOahQ2zZM73RqW7jTwvxZ8awoT6U4K5qGjlEtze4dosKsuiqnvoCMAOw5phd5dnsCLC5OZP6seIozoynJjqGjb4I9x3qpaxthcMzLnVcVcNnsRCRR1PYcU94ANY2D5KVG0D8SEggvzY5h9fw0FhUlcGV5CgXpUfSNTLHrSDdJ0U42rM5jfkE8bxzs4H+ermHz/nZiI+wsKkrgx3+u5o0DHdS1j7C9uotIl5XqUyp7Wy0SFcWGFuGFhimlCFNmOVJyIdYltyNFJoHZTrBtOspVEDAXXI6/epMaISUHCHbVIiUVIIbFEexrRhkNfTel1GJ8h18HzxigptlJqSWIEYkowQDm7IXI7lHkbrXYjeIZJ9hdhym9jKlND6nRToqCMtSGuehKBIsdQRAwF16Bpfw6BOHD5fB4L+vEBRf5ZPDBs2F1PrERtmnBvXAyE8M4WN9PeX4sPr9MXJSdhbPi2Xqokzm5MXj9MjaLxKr5aSRGGw93BucOU2Ie9nVfxn/8LTX81WTFV/Uy5uJViK7o8z09A4MPBeNuv5aG/Q6tPeMz9P5GJ33Ut49wKq0946TGGQL3Bh8cimcCz47fEmg/ghSThnXpJ5CiU873tAwMzhqSKJKfFqm75xZmRPH6AX21rs3720mJdeqMZ1WNA6re6/NHtQp2Ww918l93zmd7dRfP72zB75dZUpJIhMvC6IQa5ZSfFkla/Mx7ufmkVOx3MJlEDjcP0t43QVFmFBFOi87RHQjKNHSMcPf6Il7Zq49OcXsDXL04A5fdTFPnKBmJYbi9AT7zg62kJ4Txqatn4Q/IPPjHSnzTkVk7arq4flk2f3krFMHy/I4WnHYzx1tDUfRBWeFY6zAOq0lXFbAow3imvFCRYtKRYtK1Y0vRlUhJBcj9LUiJefjrd844R+5vIYCAGJMGwQDyaA9SQi7mgmWqEenkviPdOK8LVVue+N0XZrwe7GnkVAu0MjmMY92/nY2P+KHCMD4ZYDaJXDNd4aGmaYCfPF2jlTtNjXPyyXWz+Oav9mk3aatF4r67Fs7InzYwOBeYUksQrE7cz39Py/v2N+7BeesDCGbreZ6dgcGlT4TTQnKsk66BSa1tVkYUZknUUj5AjZCdkxur84CLgkB+mlH9xeCDxbN7I4Hm/QAEe+rxvPEIzlsfOM+zMjA4u9xzbRF/2FxPc/cYBWmR3L46j3sf26PrI4kCojhTMHnfsT7N8ASqk2BbVSe/P6mC6RsHO/j0+kICskLXwCQJUXZKsqNJinFo9/74SDtrF6TRPzyl3fstZhGzSeThp6oBVfD72qWZM+bwjiZgQXokzV1jWnu400J+WiRFmapB6JFnD1PbqhrZWrrH+OULR8lMCNMMTwD1HaO6iqzv0DUwMaPNYhL54s2lPL21kaFxL4uKEli9IHVGP4Pzi+weIdByEMEehimzHEEMmS2kqGSkqGT1/8mzoPJ53bnB8UG8e5/Sjq1LNmApWa06su3hKFOh75sYnYr7xQcI9p/AlFKEmJBDsLUq9HpsJlJKIYgmkEMGSyl5ln6+4wN4tv+GYG8jUkIutuV3Ibpizs7FuIQwjE8GOrZXd3OyXbejf5LX9rXrvANeX5B9x3tZX5H5gc/P4MOJv36HTnBQmRwi0F6DOXvBeZyVgcGHh8/fUMIfNtfT3jfB7OxoPrYiF0EQ8AWC1DQNkhLr5PZV+aTGu+gfnWLroU7sFhPXLc0yHBUGHzjBnjrdsTzSjeweRXQYVRYNLm72Huvl+R0teP1BVpSn8MWbS3WvX704g2e2NWvH6ysySIx2cOzEEIGg+oS/uDgBu23mFrB7cHJG24mecWrbhunoV1+L3GXhPzaU09g1hqIohNnNPPRkFUOjU8zJjaU4K4rirBi++at92hgKUFnXT2FGlBaFpGZQpDIwMkWky0ppdgzN3WPERdqIj7Lz7z/fRaTTwi0rcmns1Kf+dfZPknaaaNq0hJlt8wviGZ3wcahhAFANT2sWpJOdHM5/3jkfAH9AZuMbDeyv7SM2wsbHVuaRl2o4Tc4nweEu3M9/V61KB0hJBdiv+TqBhl14K18AOYClZA2W0rWYkguxLtmAr+ZVVfNpznqd4QnQsiaCXbWY8i4j2HkUZWIIU9Z8Ar2NKEMdAARaDyGll6ntnceQ4jKxLtlAsLcJc+EV6nleN1JGGf66Hfj2Po2UXortsjvwbPsVwa7j6vw7j+J56/9wXPO1D/bCXQQYxicDHY7TLEYRrtNUqzhNpQoDg3OFYHWeUZuBgcG5ISnGyVdumzuj/e71RTPa1ldkGs4Jg/OKFJdNYHxAOxbC4hDsYedxRgZng+HhYb761a/S1taGxWIhIyOD++67j+joaKqqqvjmN7+pq5AdE6NGHfy91y4meobcPPbiUS3755ltzSRGOynLjeFw8yBef5CV81LJS42kpXuMgvRIMhPDAfjepxdR3ThIXJSd0pwYegbd7DrSzZRXdexlJYWzpCSJNw7qK3YJgqAZngBGJnzsPd7HNUsy8fqCfPlnOzUHdVXjADkp4US5rMApKUoK3L46jzcPdOCwmVk5L5WeITf3PXVAE/6+sjyFhCgHT76palmNTfp45JnDzEqP1Gk0pcQ6WbswncqGfnx+9dxZ6ZEsL0shEFDYtKcVWVZYuzCdosxoCtIjqW4cZHDMw9y8WCwmiR013cRE2JiVHskre1rZUql+7nG3n58+c5gf/vMSLKdJJzT4YPAffUMzPAEEu+vw1+/Au+3XvPPd8u7ZiBiVjCltNpaS1VhKVmv9vXv/rB9QkfG89TiBhl3qscmC45qvIUYm43/ic7qucn8Lrjv+Vz1NkZl66UGC0xpQgi0M+3XfYOqVH6FMV9ELNOzCa7JqfUJzrsdgJobguIGOxGgH+2v7tJv50tIkbrg8m6Mtw4xMqJofafEubluVh0n6cImqGZw/xMgkAi0HwKeGeUtppVjmXjujBKrBmWEIyeox1gk9Hf0TPPlmA9uru7GYRJJjnUxM+XlmWzOv7m1jbNJHdrK6odm8v52/bm/mRM84mUlhWM0SVQ0DPLmlgarGAeKj7ES43n967Oikj6ffamTzvnbcngBZyeHG7/8CxN+4B++eJwl0HEWKSkawnRuDjxLw4Tv4HL5DLyKP9CAl5CCIJvx1b+Pd+xSC2QJmG8rkMGJkMvYV93wg6Q++Y1vw7vsLwd4GxJh0BMvFG/V3Ia4TXq+XjIwMvv71r3P77bdz+PBhtm7dyooVK7jzzjv57ne/y9e+9jU6Ozt59dVXWbVqFYqi/M3XzpQLZY2orO+nqmFA1xbmMPPy7lZe3dfOwbp+9hzt4eqKTIqzoukbnqKxc5ToMCuRYTZyUiJQFIUjzYPERtpZNS+V6DAri4oSuPXKXGIi7IQ7LbT2jCOKAlctSic1zkllvf4989MimZUeRXPXGFsP6Y1VogCXlyUzPuXXpdKV58fxxCu1nOgZp6FjlJgIGzsOd+sqabf2jIMCfSMnGR1khesvz8brCzI45iEzMYxPX1NEekIYi4sSiAqzcdnsRG66IgdRFMhODmftwnSuWpRObmoEQ2MeahoHSY5xMjcvjr6RKb7zxH721/ax60gPg6Me2vsmtIIaoIqol+XFEh1mOyt/N4N3T+BEJfJAq65NdEUj9zfr25xRBAdO4Nn2a/xNexDDYhHD40CWtSgkAEvJKvzHtoROlIMoXjfm/MsINO9D8YT0yKTkQsy5iwEIdh3HV/nCSRPzoQQDyJ1H9RP2e1TR8pOq30kJuZhnLXuvl+CiwBAcN3hP9A27+b+Xj9PUMUp2SjhfvnUO3UOT7Dzcw+4jPdS2DvOxVXlYTCKKAkWZUVqetoHBB4HoiMR5y/0EO46CxY6UVGBsPA0MzgFuT4AH/1jJpEf1ZDv9aOUAACAASURBVB9uHuTfPlrGK3vatHSJ463DuL0BzCaR595u0dpae8a5aXk2P30mpBt4uHmQ7/9TBWHvcxP7v3+ppqV7XHsvXyBoRFddYPhbDuDZ8qh2HOw4gvO2h85JdVLPjt9plVCDXceRJwYxpRbj2fYrrY8QFofzrl8imj4YbUDfkTfw7vqDOqdOCPY04Lj5e8ZadRaJjIxk0aJF2vGcOXPYuHEjhw8fxmq1Mn++mkb1sY99jJUrV/LAAw/83dcuNjITZxpzRUGg6SQjz+CYl+1VnfQMudl9VNVgctpM/MfH59HQMcLvXq1Dgf/P3n2Hx1We+f9/n6nqXbLVi6tsueDeDTbYgA2mF5NAAsluwoYfKZsAKYYEst81kJDshkB62CRAgIRimukQDNjYxlWusrplq3dp6vn9MWbksWwwxtLI0ud1XbkuznPOnLlHkXXP3PM894NhwFcvGseSGTm0dLjZsr+erNQYzjkrk3POysTt8bGzrBGrxSAlPiJYnImOsDF3QmBXuMzUaBw2S0jvpbTEKB58ejuVte1MHJFMdloMk0ak8PsXikMKeM+9W0pmaugMdtOEjJRodpQetX291WBsbiIzxw0LubakuoXmdjdnn5VBhCPwcba2qZOyQ22MzIwnKS6CbSUN/Oqf24LLDa84ewTlh9roPqrX1bodh1gwMT1kBz+n3Up6kmbXh5N93Dl49r0HPg8AluRcbAUz8Ox8PeQ60+MK7HQHmC3QtfYXRK/8GY6JSzH9XsyOJuwjZ2NExePe8kLoY71u/M012EbNw7vvXfxNB7EOH40RGUf732/HmpiJNTt0WWsgGAMjKgGzs+d3xpKaj3PKCrre+i3+2gNY0gqIWHjTaf6pDA4qPgl/enE3+49spVpS3cpfX93LuLzE4B//+pZufvdcMT/7jzlEabmdhIlhc2DL673sR0ROn+KyxmDh6WPvbT8UslsQwIe7arHbQ7+E2F/dwrvbQ/sGdrl87DjQyOyiU9/Cuq65K1h4Cj7/7loVnwYYb8mGkGOzqyWwRXX2hNP/XAc2HHP8IaYrtF+N2VaHWV8Bw0ed9uc/mZj8TdX4m6qxJqmRcV/w+/089thjLFq0iJqaGjIyMoLnkpKS8Pv9NDc3f+K5hIQzq69PzrBYrlk0kmfXleHx+lkwKZ289N4FqfrW7mDhCaCj28vLGyrYur8++PfZNOHpdw4QF+Xgl09tw3OkgHTlOSOYPX44P/2/TTS0BgpO4/MSWTw1i+q6DmoaOnjomZ0snprJnKJ0vrJ8HI++tpeWDjdTRqeyr6o5uEyvtqmL1PhIRmbF4/L4QmJ0e/0smpLJvsqWYEyTRiQzMiueg/UdFJc1ERNlZ2bhMB58ejsAF8zM4axRqfzh+WLW7TgEQFyUnTu+MJW9lc38+eXdmGagyfrXVoznxQ/Kg4UngDXryhid3bvv29yJgZlaW/bXkxDj5LrzRh+3DYn0H2tKHtGX/wRPyXqMiFjso+ZgOCJxzLgC90fPg9+Hffxi/B3H7LDrdeMt3YR783M9s5DcXUSe9w2smePwVRcHxgwDIzqRjie+D5hgtRFx/rfwVe3Es+OVwK1aDuFrOYwlIR1/c82RwBw4ChdhFsyk+50/YrbWYs0oxDnrGixR8URfsgrT78fQJI0T0r8soeRgS69jpyN0nbPL46OqrkO7FomIDGLJ8b2XGQxLiiQ6whZSlEqOj8Bus1B9VC+QCIeVYYlRJ3XPzyIm0o7DbgkuBwdIjtNyiIHGEpvSa8yI7ZulbpaYFPzNB0OexxKbQsjHW8PAiOm/7dONY5f1WWxYovSeqa/cfffdREVF8YUvfIFXX321T58rObl3I+twuW7ZeK65YBymaWKzWujs9vDMu2XUH1mqFuGwMmtiBv/aWhPyOL9JyO52AN1uPy98UBEsPEGgQOM1CRaeAHaWNbFsfgH/ePtAsD/T759vJS8zkQvmj6AgJ5FOl5ectFi+dPcrIc+xs7yR1NRYLppfwF9f2h0cP392HssWjCIvK4kNOw8RHWljzb8OsPXpQG+npbNyWTorl//85Tv4j9SPHjy4g9uvnx4sPAG0dnp4a1sNH+yoCc6s8vlNnnm3rNfPzuP1ccHcAnaUNgavHZefxJyzsphzVhYerw+b1aLZigNFaiyMGh06dt61mIuvAtPEsNpofv8ZGkuO3uHRwN5ageuo5W/e0o3EumtIvPzbNK97CtP0Ezt+ATV//ynB3mQ+L+bW58DVFfJ0ZlMVmV99gM4DW/B3txNTtABHypEvFCZNx+91Y+mD2b2DmYpPwqishJBvtUdlJTAqK56dR017dTqsZKcNnOQrIiKhtuyvZ29FM/kZcUwbk4phGFTWtrNh12Hiox3Mm5hOhMNGS7uLd7fX4POZzJkwnJT4SDxeH+/tOERtcxdTx6SyeU8dJoFlHudNyyYtIYo/v7wbj9dPXLSDK88Zgc1ioeJwG83tbmxWC9csHsX0sWlsK2kI7k40eVQK20oaqK7vYE7RcJx2K42t3azbXgOGwbwJ6STGOnG5fazbUUNjq4vpY9PIPbK8ZNOeWkqqW5kzfjjvbq/B6zNJjHVy2cIRYfxJy/HYJ56Pt3I7/sZKMAxsBTPx7nkXf0I6tlGzMSw2/K21ePauA5sT+9j5WCJiMd2dePb8C7O7HdvIWVgTMzH9frwH1uOrr8CWNR5bVhEA3po9+Cq2Ys2ZiL+jCTxdYI8kYs51WBKz8FUX4285BIYF28jZuHe8hjU5G9uIWRgWC77mg3j3vY/hjMY+Zj6GMxqzuz3w/J5u7KNmB/p2+L14972Pr+kgtpxJ2I5sqe2t2om3asdx72mJScKIScZsbwCLFeeMKzEi9L6pL6xevZry8nIefvhhLBYL6enpHDzYU4xsbGzEMAwSEhI+8dzJamhox+8fAE2fTuAHX5jC21sP4vL4mDchnWFJUeSkxVBR2w4EltjNGpdGlMPKa5uqgo9bODmdTXvqQu7lcvuoa+jkWB/uPBQsPH3srY0VPPnanmAz8OzUaOKiHLR2uoPXpCVE8usnPmJvVTOTRiSTEOtkVFZ8oFn5L94mMdbJxXPzWLuhgub2nset/aAcv9fH0T92v99k3Zae+D9W39RJR5cnZKy1w8Wy2XnB5uUAM8YNY2xmHLdfN4WNu+tIjo9gwaR06urajr2lnCHMvPnY8nfhLd0EdifOaZfR3VDZ67qGXVsCu+R5usAwcNkTMT3dIde4O9qwJmVD41FfbETE0uyPxRhxDgAtJtDr98V1ul/WGcNiMT5zcV7FJ+HLF44NLL2rbmFERhxfvrCQhBgnja0u1u86TFKsk2vPHUWkU78uIiID0UsflPPkWyXB4/Nn5HDW6BTuffQjfEfevb+/8zDfumoiP3lkI01tgTdLr26s5Mc3zuCRl/ew/UDgA4RhwA3njyUvPZacYYEi0Oyi4UwYkczhxk5yhsVitwWmlN/79TmUH24jLSEy2Nfp+1+cSsXhNioOt/Hnl/YEG+Ru3F3LVy8ax4///CFtnYEPCq9vquInN83gwX9uZ9+R5d8vr6/gO9dMZld5E8+/VxZ8TRfMzGHKmFRyh8Vqw4sByBIZR9TlP8HfUI7v0P5g/yMAW+V2nNMvo+Ofd8GRN/yeXW8SddmP6Xruv/A3BT5Qure9TNSKH+DZ9RaeXW8Frtv2Es5512M4okJ6SlnzpuKcdAGWxMxgY++oq/4Lf3053upi3BueDDwesB/cjb3oPDqfuRt8gQ+4nt3vELnih3Q+czdma2CJknv7WqIuXYV783N4938QfP6Is7+K6fPg+tefP/GeRnw6kRd/H0tcmmY99ZEHHniAHTt28Nvf/haHI/A3p6ioiO7ubjZu3Mi0adN4/PHHueCCCz713GARH+Pk4rn5IWPfXXkWb26upqndxczCYYzOTmBcXhLZaTEcqGlldFYCs8YPIzrCzt/f2B983JQxqZx9Vibv7zwUzB1JcU6mjk7lzc2hzcX9JiG70FXWdbBgUjpb9tXT2ukhMzUam8XgpfUVwWumjknF74e/vNKzE9ieiqbgFw5HO16vwKKCJPZVtXCwPjDr1gAWTsogNtLOW1t6igYLJmWwZHo2yXFOdpQ2kp0Ww4JJgeWXgS/Z9e9zMDBsDiLP+0Zg2bXVjmFz4D20F+++dWAGiqVGbCqe8i2BwhOAaeLe/Cy2/Okhy6XtYxdgL5iJv7UWf2MlRmQcEQtuxLDq8+/pZJjmQNi/oe8M9G8rRET626l8UzGYDYY88Z0H1wULSgAOm4WzRqewvrg25LoLZ+Xw4gcVIWNLpmfzyoeh3xSOz0vkO9d8vh5r//PUNrbsD90laemMbNZuCH2uC2bmhHw4gcAHlJ2ljSGNYWMi7fzPrfM/V0zSPzqevQf/4f1HjRjYxy8ObJ99FPvk5Xi2PB8yZhuzAO/edWD2/H9viR8OETG97hn9hQeOW+TpePKHwYJW4FIL9jEL8Ox+K+Q6x+RlvZrQ2sYtwlv8JkdvFW9JzQev56TuGXn+N7HlTO4V05lmIOaJffv2sXz5cvLy8oiICCy9zcrK4sEHH2Tz5s3ceeeduFwuMjMzue+++0hJCSwF/aRzJ2Mw5IhPsmHXYbYfaCA7NYZzpmRit1kpOdjCuu2HiHLaWDw1i8RYJ39/Yx+vbazC5zeZOjqV8fmJ/N/a0O3kz5+Zw2ULCmjr9JAY6+TrP387ZLmfxTAoKkhi21FFK4DLFuTzz3dKg8cZKdHcdeN0HnlpN+8dWWY3f2I6158/lvYuD29sqqK53cWsccMZm5uIz+/n7S0HKT3YyuicBOZNSNfyuSHA9Lpxb3kBX20J1uGjcEy6EMNqx3toH9696zAiYrAXnUvX8/eGLNUGiFr5M3ylm/A1VGDNKMSamIElNhUjIgZ/RxO+2pJALnJG4Zi0DGtixgmiGLo080lERGQIsllD32RbrZbjzg5y2I83ZsWAkEbhdpu113WfNyYI7CJ0MjHZrRbsNktI8el495OBybAc8/bSMMDWe8MSw3q8MRtYrOA7qj+N1X78e1pO8Ht67DfVFisc57k4zk54htUOhiWk+GVY7fT6rvZE97Sq/0dfGTVqFHv27DnuuSlTprBmzZrPfE5gRuEwZhSG7iY3IiOeERmhzbmvXjSKZbPz8Pr8JMQ4aet084+3DwT7AVotBlERVh56ZgfJ8RFcOCuXtIRIKo8s/wNIiY8gLTEy5L6GAbPHp5M7PI4Pdh4mKc7JedOysVks3LRsHFeePRKAuOjAv624KAeXzC8IuYfVYmHRlCyYcnp+JnJm6P7XI4FZTgR2WDXbG4hYcCPWlFwMuxNLQjqG1Y5t9BzcG54KPs6aPRFrTDLWCUvwNVbS9dLPMTuawGonYt71GDHJdL/6q+D1voptRF9zb3CWrZw6zVsXERE5w100Jx8j5DiPpdNziHT2fDifNCKZpTNyQ7a3TomP4LxpWcydkB4cs9ssjM9LZPPeOtxH7VB0sL6DDbsO09LeM8OqtcPNhl2Hqarr+XDh8fr4aF8dY3MTQwpLM8cNY8n07JAPHsOTolgyPYepY1KDY06HlaUzcnotIzn2WAYux1nLQwpD9nGLcBQtwThqlpIlJQ/7pAuxpo/teaAzGseEJTgmHbUsyjBwTLmo1z1to2bjq9qJv7WnZ43p7sJz4ENsBdMDBaSP45mwFMeE8zAiepb2WIePxj7pAiypPR9ijch4HBOWYC86t+f5LVYck5fjOOuik7qnNeOo1yMyyMRE2kmICRRtY6McfP+LUzl7cgazxw/ngpk5/PPtUj7aV89rG6v42eNbuPbcUcREBoq00RE2vrh0DMtm5wX7yFotBpctKCA5PoIJBcl89aJxXL5wRLDQBIGi08fHLe0uGlu7EQHwhjQbB8/+9Xiri2n/27fo/McqOv72bbw1e3BMWoZzwZex5U/DlnsWWKy4Nj6N6e7Ctf7JQOEJwOeh+71H8ex7L+S+Zncb3qod/fWyBjUtuxMRGWIG4nKKcBoseaKqtp09lc3kp8dRkBEHBN6of7S/noRoJxNHJGOxGLg9Pjbvq8PnM5kyOpVIpw3TNNlZ1khtUxebdteyqyKwfXFynJPvf3Ea64sP88SbgSVPdpuF/++KidgsBg88uTW4C92l8/NZMDmT//rLRuqaAx8ORmfFM2PcMFITIhmfn4TFMOh2e/lobz0YMGVUKk6HFb9psuNAA42tLiaNTCExNvDhpuxQKyXVrYzKig/2n5Izg7+1Fm/ldiyJGdgyCgEwXR14yzaDzYkt76zAjCK/D2/FlkDD8bwpWI4Uc7yH9uFvKMeaOQ5rQkbIPf2dzXi2vBDo6WEYRCz8CpbUPLqe+3+YrkAh1JY/HWvGGCzJOdiGB3ZMMrvb8ZRtwnBGY8udjGGxYfq8eMs/AncXtvypGM5AcdZ7cBf+poPYsoqwxAdmhfiba/BW7/zUew4GyhM9BkuO6Gs/e/wjdpY1hYz96PpplBxsYduBBgpzEwMzmqwWTNOkur4jUFg6Tm+nY5mmyf+t3cM7Ww9imjBtTCr/dvF49f8b4tof+y5mW88XEJaEdEzTj9lyuGcsKZvoK+4GAn0FXR88HjxnzRyP2dmEvyl0SZ59wlI829eGjEVdsgprWuiMu6HuVPKEik/Sy7rtNawvPkxCrJPlc/JIS4hkf1ULaz+swO83OW9aNmNzE8MdpgxSpt+PZ8faIx9aMnFMuRhLRCzeso9w734LwxEZWHudnB3uUM9Y+lARSnmix56KJlY/+lHI2Pkzsnnzo8BOSh8bkRmH3Wph95EiFYDNamHpjGxeeL885PHfvnoSRfnHbEMv8jm0P/qdwK5yRxjRiVgzxwX6cxwl+ur/DvSLks9MeaLHUMsRfr/JO9sOUlLdwuisBOZOTMdyEv2T/vB8MeuO9GeCwHK6+RPTeWdrTXDs7MkZXH/+Z58duONAAz9/YmvI2E3LCkNm7crQ4y3fQtfrD4HXBfYIIhbfTPfaXwSbjQNgcxJ7428A6HjqR4EdWY9ybKHJMmwkUUu/SecL9+JvCPSjtI9dSMSCL/f9CzrDqOeTfG7v7zzEH17YFTzeVdbEt66axL2PfRTcYnVbSQN3fXk6mal6UyKnn3vzs7g3PwsQ2Da7oQLHlBV0vfI/fNyVxluxjZhr7wt+Qy0ip0fnkd4dR2vv8oQsv/v4Ovsx3zh7fX7ajtpi+5PuKfJ5mK7OY447wNV7e/hjrxORT/fY6/t4fVOguf667YeoaezkqnNGYpombV2eE85UWj43j+LyJpraXBjA8tl5vLG5KuSad7cf4otLx/RqBu7z++ly+YJL9AA+KD7E3orAbN4ud2gOAjjUqH/fQ501ZxJRl/8Ys6MFa0oOhiMSb94UvKUbg9fY8qcG/9uIjDvmBg4cU1ZgiYrHU7YZa1I2jmmXYkTEEHXZj/HXlWI4o/Qlxmmk4pOE+HBX6M5IDa3dvLG5Klh4AvD5TTbtrVPxSfrE0dueAvhq9uDZ+y4h7ZDdnXgrt2MfOat/gxMZ5MbnJ5Ec56ShNdDXyWoxWHhWJt0ePxt39+SHBZMysNss/PWo7bInj0xh0ZQs3ttxCK8v8O81IcbBxBHJdHZ76HL5SI6P6N8XJIOSvXAhnm0v9xyPXYgtcxze8i18nCssybmBXepE5DP519bQJUjvbDnIjMI0Hn52J7VNXaQnR/H1FUVkHenb1OXyEuGwMiwxitVfm83+qhZiouwMT4pi/a7DwYbkANGRNnx+E5/Pj9MR6OG2eW8d//fyblo7PYzKiufmSyfw5uYqnltXBsBbWw4ye9xwrBYD35EZaAYwedTJ71gog4+voYKu136N2XIII34YkYtvxpqSS8TCm3BFJ+GrLcGIiAF7BJ6S9dgKZuCcfhmddQfA3QWGgXP6ZeDpwnNgI/66A5ht9YHl39kTMAxDy+z6gPWuu+66K9xB9KWuLjeDe2Hh6bW/qoXSmtbgsQHMm5Dea0vUuRPS1X9D+oS3fAtm61FFUEck1pzJ+Gt2h1znmHg+lpikfo5ucDAMg6iT6LEwVChP9LBaLUwfOwy7zUJmagwrzxvFiIx4Jo9MJibSQUKMkwtn57Jwcib56XFkpUbjtFuZNX44V5wzguS4CCYc6S1VmJvIl84fy1tbDvI/T21j7YZK9lY2M2V0Knab+nTIqbNmjsOIScJwxgSamU9ejjUxA+vw0RhWK7bcKUTM/QKGvfdudnJylCd6DLUc8frmKlxHzTSKj3Gw/UAjNQ2BmUbtXR4qDrdRlJ/EA09s5a+v7OW9HYfIGRZDUlwEa9aV8ddX9vLqxkomFCRTXdeBaYLFMJg2NpU/v7SbZ94tpaahg8LcBO59bEuwQNXY6qKj28P6XbUhS70PNXZy6xUTaelwkxgbwTWLRzJey7mHtK61v8BsPDKzztWBr64UR+E5gd3tsidgttXj3fMO/rrSwEwovw/76HlYErMxfV7sY+bjmLAE178ewVd9pJm4pxtf9U7sRedhWPQ+5dOcSp7QzCcBwG+aNLe5OH9WDsXlgQRjGHDetGxmjR/OzrImNu8NNHQrKkjqtSWryOninHElXQ0VmF2tYLURMXsltvyp+Cq24q87AAS+5bYOGxnmSEUGp8RYJ5cvHIHL48NxpEhkt1lZMj0bj9fP0e/Hpo5JY+qYtJDH5w2PIz05UJSqaejg6XcOBM/tKm/i1Y2V2rlOPhfDsOAYuxDGLgwZt2WOw5Y5LkxRiQwOVywcwR9f3BUsGF22oIDfrSkOuaaytp3HX9/H/uoWAOpbuvntmmLOn5kT7PvU5fLx7rYablt5Fu1dXhJiHPy/v27Gf6SSt2FXLQkxTrpc3l73jnBYae3oGYtwWBmfn0RRgQpOEuBvqPzEY/fO1445fh1LQjrdb/0OAF/ZRvwNFcG+Th8zu1rxtxzGV7ML09WBfeRsLHGh73Pk1Kn4JJQdauXXT++gvqWb5LgIvrZiPN1uH0+/c4BXPqzkna0HuWbxKC5fWIDfb2q5nfQpa0ou0St/hr++HCN+WHDno+hLV+GrL8dwRCoJiPShpjYXv3luJ3srm0lNiODGCwsZmRXPX9buYd32QzjtVi5dUMDiqVm9Hru/uoU/PF/M4aYuRmbGM39i72awhxrUp0NEZKCaOyGdUdkJlB5sZURmHCnxkazbXhOyk934/CTKD7eFPK6pzUXJkWJUyHi7i1njhrNlX32w8NTzmMBnj4bW7uBYUX4S2Wkx/Pa54uD1l87P79UnSoY2a9Z4fBVbjzouCjlv2JyY7q6eY7uzV0HKu/8DbGPm4285qlF+Qjpdr/8as6kaAPeWF4m65EdYk3q/55HPTsvuhF8+tS04lbbL5aXsUBsmJhv3BGY6eX0mO0obWD47j9SEyHCGKkOEYbFiiUnCsIUumbBEJajJ+Gmg5RShlCdCPfLy7uBS685uL8VlTThsVta8V45pgsfnZ/uBBqaPTSP2qN8jv2my+m+bqWsOfIhobHPh8/tp6/SE9A1cNjuPLH2JITKgKU/0GIo5IjrCTlZqDFERgQbg4/OSaGx14XJ7mTQyhS8uGUNdc3dIASojJZoFkzLYdGSlBAT6Bs4eN5zNe+uIibSzZV99sG8TBFZYXDAzl9rmLkzTZO6EdC5dUEDOsFhmFQ0nd1gsly8cwcQR6u8koayZ4zE7mjA9XViHj8Z0deJ69xF81cVY08dgxKbgK/+4OGXgnHUNZmM1Zkdjz00sViKX3go+D2ZXK9Zho7CPWYB3zzs91/h9YFiw5Uzs19d3JtCyOzkl1XUdvY7jo0N/kbw+k0ONncRF642IiMhgVnao97fZ+6qbe123r6qZ598rY2tJAxkpUVy+YAT1Ld0h11TVdfCdqyfz7LultHW6mTcxnZnjtGxbRORMEh/j5OuXhM4suXrRSHx+PzsONJKVGs3K80aTnhxNfUs3b285SFSEjfz0WP73n9uDj5k3YTiVdR20dbqZW5TOgkkZGIbBd66e3Os50xIiSdOX3nIClsg4Ihd/DYDONf+Nv3Y/ENioqPut3xN18fcxbE68ZZuw5U0JLJ+LTqJr7S/BDPQTs48/F8PmwOxux3R1YLraA8WmXk9m7bfXNdip+CRMHJEc7Of08XFhXiI7Snsqw7FRdvLT1WBcRGSwK8xNpLapZ6r68KQoJo5IZn1xz0YAVovBnoomPjgyVlLdyh9f3EVWajRVR32hUZibSEFGHN+6alL/vQAREelzkU4bNy3r3WNt+Zw8ls/JA+Dbv3o35NymvfU8+K0F/RGeDCG+w/uPOd6He8eruN77GwDeAx9idrfjKDqPyAu/g2f3W1jSRuGccB7d7/wxuNO2v/YArs5WLGkj8NeWBG7mjMYxblG/vp7BTMUn4UsXjCXSaWV/VQsjMuO5etFIoiPtdLm8rC8+TFJcBFcsHIHdpqqviMhgd9U5I/F4A0vrMlOiuW7JGDJToqlv7ubtrQeJdNq4dH4B/3i7JORx9S3dfPfaybzwfjkVh9spyk/i2nNHhelViIhIuPVermhimqb6N8lpZR02Al/NnqOOR+H+aE3INe6P1mCJG0bXK78MzG4q2YDZ0YC3Zm/ozdrriVj2Xfx1pZiuDmz507BExffHyxgSDNMc3KuYGxra8fsH9UsUEflMLBaD5GT13PmY8sSp+f3zxby3o6dJZ0KMg/tunoNV2xOLnPGUJ3ooR5y82uYu9lc1k58e2PV07YYK/v5Gz6yUS+blc/E87XYqp5e/tY7ut3+P79A+rMNGEnH2V+h4+sfgOqq1jCMKa1IWvkNHFZssVmz50/CWrA8OGdFJRF97H4aW2n2qU8kTmvkkIiIivXi8fl78oJw9FU3kp8exfE4ekc6etw1XnTOS1g43O0sbSUuK4ssXjD1u4elgfQcvvF9GW6eHeRPTmVGonk8iIoPNB8WH+N2aYkwTDOD688ewdEYOOWkx7K1qoSAj6zv9cgAAIABJREFUjgkFyeEOUwYhS1wqURfdETLmmLAU98Z/hhx7K7aEPtDvxzHlYkx3F77K7VgS0olY8GUVnvqQik8iIiLSy+Ov7+PNjwJbDe+uaKa2uYv/uHRC8HxctINvXz0Zr8+PzXr82U4ut4/Vj26mrdMDwI7SRhw2K5NHaeciEZHB5Ol3DgSX2ZnAP985wPSxw9i4p469Vc00tXWTnx5HTKQ9rHHK0OCccjHWlBx8h/ZjHTYSW+5kLLEpdL/1u+A1thEzsSZmEnXBtzFNP4ahmdt9TcUnERER6WXDrsMhx5v31h230HT0scfr4/VN1VTUtjE+L4noSHuw8PSxD3cfVvFJRGSQ6XKF7hLW7fbxyMu7+HB3YFOj6roOWtrd3HqlNqCQ/mHLmYwtp2cnRfvouRgxSXgrtmJNzMQ2anbwnApP/UM/ZRERCbvVq1ezaNEixowZw969PevxS0tLufrqq1m6dClXX301ZWVlJ3VOPr/k+IiQ46RY5wlnOH3sd8/v4ok39/PBzsP84YVdFB+1a2rPfbV1tojIYHP2WZkhxwsnZbCtJDQHbCtpwD+42w3LAGfLKCRi1jXYx8zHsGgeTn9T8UlERMJu8eLF/O1vfyMzM/TN65133snKlStZu3YtK1euZNWqVSd1Tj6/axePIjoi8MbMabcydXQaT7y5n+Kyng8TeyqaeOLN/azbXkN7l5tNu2tD7rGtpIEl07P5eF+jnLQYzpuW1V8vQQYxT9kmuj94HE/ppuCYr74M14Ynce98HdPrCmN0IkPPpfPz+epF41g4OYMvXzCWa84dxfDkqJBrhidHYdFOdyJDlna7ExEZYgbyLkaLFi3i4YcfZvTo0TQ0NLB06VLWr1+P1WrF5/Mxc+ZMXnnlFUzTPOG5pKSkz/ScyhMn5vL4qKpt543NVby/s2cZ3g3nj8Fus/D753cFx2YUprH9QEPI0ouCjDh+eP006lu66OjykjMsRltsy+fm2vQs7k1PB48dUy7Gmj6WrhfvB9MPgHX4aKIu/n64QjzjDeQ80d+UIz4bj9fPy+vL2VvVQlKskx2ljTS1uYiPcfD1FUWMzk4Id4gyRJmmH8/O1/FWbMWSmInzrIswIvR37lRptzsRERk0ampqGDZsGFZrYNcRq9VKWloaNTU1mKZ5wnOftfgkJ+a0W0lPjmZ9ceiMptc2VWE/Zgneh7tquWxhAf98+wAm4LBZuGxBAQAp8ZGkxPdX1DLYuXe8Enq8/VVsjdXBwhOA79BefPXlWFNy+zs8kSHtsdf28taWg8HjmePSuHR+AUlxEZ+6dFukL7m3vID7w38A4Kvagb+uVF9S9DMVn0REZEjTt/ufrNvlxWo18Ht7vvmPdNqw20K3IrZaDa44dwxL5xRQVtPCuPxk4mOc/R2uDAGddgc+V0fw2GK344yOxHvMdUmpCThSYvs3OJEhbv0xm1Vs3F3Hv100XrNeJey8+9eHHPsO7cXf0YQlOjFMEQ09Kj6JiMiAlJ6ezuHDh/H5fMGldbW1taSnp2Oa5gnPfVZaUvHplkzP5oX3ywGwGAbnz8jBZrWwt6IJ35Gf3aIpWXR1uLABI4fH4u5yU9flDmPUMljZJl+M791Hgsf2sy7GTBsJez6EI72ebPnTaDHjoa4tXGGe0bTsTk5VYmwEXUcVhxNinCo8yYBgxCRBU1XPgD0CwxF14gfIaafik4iIDEjJyckUFhby/PPPs2LFCp5//nkKCwuDy+o+6ZycXpcvHMGEgmQqa9sZl5dIenI0APd8dSY7SxvJTIlmTI6+OZT+4Rh3Dta0AnyH92EdNhJrSh4A0Vf/N97yLVhikrBmTwhvkCJD1LWLR/Hg09vpdvtw2C1MHpXMH14oZkRGPAsmZWCxqBAl4eGcfjld9WWYXa1gseKceTWGXTO0+5MajouIDDED8Rvte+65h1deeYX6+noSExNJSEjghRdeoKSkhNtvv53W1lbi4uJYvXo1BQWBPkKfdO6zUJ4QEQk1EPNEuChHfHZdLi+Vte1s3FPLaxt7ZposmpLJF5aMCWNkMtSZXje++jIsccOwRKkZ5edxKnliQBSfbr75ZqqqqrBYLERFRfGjH/2IwsJCSktLuf3222lubiYhIYHVq1eTl5f3me6thCEiEkofKkIpT4iIhFKe6KEccepu+cU7dHT3dGOz2yw8/J2FWoYnMgicSp4YEFsOrF69mueee45nnnmGG2+8ke9/P9B1/s4772TlypWsXbuWlStXsmrVqjBHKiIiIiIiIp8mKiK0w0uk06bCk8gQNiCKT7GxPTuRtLe3YxgGDQ0NFBcXs3z5cgCWL19OcXExjY2N4QpTRERERERETsLlC0dgOVJsMgy4YuGIMEckIuE0YBqO/+AHP2DdunWYpsnvf/97ampqGDZsGFZrYCtnq9VKWloaNTU1aigrIiIiIiIygM0oHMaIjHj2V7eQnxFHWkJkuEOSIc5XV4q3chuWxExseVMwjAExF2fIGDDFp5/+9KcAPPPMM9x7773ceuutp+W+Wq8uIiIiIiLS/5LjI0iOjwh3GCJ4Dmyg+7WHgEAPN/vYBUQsuDG8QQ0xA6b49LFLLrmEVatWMXz4cA4fPozP58NqteLz+aitrSU9Pf0z3U9NAkVEQqmRrIiIiIgMJe5tL/Nx4QnAs+dfOGdchRGh98T9JezFp46ODlpbW4NFpTfeeIP4+HiSk5MpLCzk+eefZ8WKFTz//PMUFhZqyV0/8Xj9/O3VPawvriUpzsm1i0dRVJAc7rBERCQMquva+b+1e6iqa6coP5kvLh2DxTD46yt72FrSQEZKFF9cMoacYbGffjMRERkSmttd/GXtHvZWNlOQEc/1S8doFpSE0bHN7tX8vr8ZpmmGdVpQfX09N998M11dXVgsFuLj47ntttsYP348JSUl3H777bS2thIXF8fq1aspKCj4TPfXzKdT89y6Up75V2nw2Omw8vP/mEukM+z1ShH5nDTzKZTyxCczTZMf/n49NQ2dwbG5RcOx2yy8teVgcCwlPoL//trsYHNZETlzKU/0UI44db94civbShqCx2OyE7jtuilhjEiGMk/pJrpf+xUcKX/YC88hYv4NYY7qzHUqeSLslYSUlBSeeOKJ454bMWIETz75ZD9HJAD7qlpCjl1uH5W17YzOTghTRCIiEg6tnZ6QwhPAnspm7LbQJp31Ld00tnaTEq+GsiIiArsrmkKO91Q24zdNfUkhYWHPn4rlsh/jrdyONTETa86kcIc05Ki9uxzXiIy4kGOn3UpWanSYohERkXCJjbKTcswyifz0OPLTQ/NEQoyDxFhnf4YmIiIDWN7w0DyROyxWhScJK2tyDs7Jy7DlTsbQ72K/U/FJjuvCWbnMKRqO1WKQmhDBv68YT1SEPdxhiYhIP7MYBv++YjzpyVEAFOYmcu25o7jqnJEU5SdhAMOSovjaiiKsFr2tEBGRgC9dMJbc4YFegFmpMdy0vDDMEYlIOIW951Nf0zptEZFQ6uURSnni5Hl9fmxWy6eOiciZTXmih3LE5+f2+HDYreEOQ0ROo1PJE3q3KCIiIifleEUmFZ5EROSTqPAkIqDik4iIiIiIiIiI9CEVn0REREREREREpM+o+CQiIiIiIiIiIn1GxScREREREREREekzKj6JiIiIiIiIiEifUfFJRERERERERET6jC3cAfQ1i8UIdwgiIgOK/i6G0s9DRCSU/i720M9CRKS3U/nbaJimafZBLCIiIiIiIiIiIlp2JyIiIiIiIiIifUfFJxERERERERER6TMqPomIiIiIiIiISJ9R8UlERERERERERPqMik8iIiIiIiIiItJnVHwSEREREREREZE+o+KTiIiIiIiIiIj0GRWfRERERERERESkz6j4JCIiIiIiIiIifUbFpyHsjjvu4L777gsZ+9KXvsSjjz4apohEeixatIjly5fj9/tDxvbu3RvGqESGFuUJGaiUI0QGBuUJGaiUJwYeFZ+GsB/84Ae89NJLbN26FYDHH38cwzC49tprwxyZSEBnZyfPPvtsuMMQGbKUJ2QgU44QCT/lCRnIlCcGFhWfhrCYmBjuvvtu7rjjDkpLS3nooYf46U9/imEYPPXUU1xxxRVceuml3HDDDZSVlQGwceNGLrnkElasWMHy5ct58cUXw/siZFD7xje+wf/+7//idrtDxsvLy7nhhhu46KKLuPTSS3nnnXfCFKHI4KY8IQOZcoRI+ClPyECmPDGw2MIdgITX3LlzmT59OldccQV33HEHGRkZrF+/ntdee41HH30Uh8PBG2+8wQ9/+EP++te/8tvf/pavfOUrLF++HNM0aWtrC/dLkEGsqKiIoqIiHnvsMW644Ybg+H/+539y1VVXceWVV7J//36uu+46XnrpJZKSksIYrcjgpDwhA5VyhMjAoDwhA5XyxMCi4pNw00038dJLL3HFFVcA8MYbb1BcXMyVV14JgGmadHR0ADBz5kweeughKisrmTt3LhMnTgxb3DI0fPOb3+T6668P/n6apsmuXbu4/PLLARg5ciSFhYVs2bKFRYsWhTNUkUFLeUIGKuUIkYFBeUIGKuWJgUPFJ8FisWAYRvDYNE2uuuoqvvGNb/S69qabbuLcc8/lvffe46677uKcc87hlltu6c9wZYgpKChg4cKF/OlPfwICv5/Hc/TvsIicXsoTMlApR4gMDMoTMlApTwwc6vkkvSxatIhnnnmGw4cPA+Dz+dixYwcABw4cIDc3l2uvvZYvfvGLbNu2LZyhyhBxyy238Oijj9LR0YHFYqGwsJCnn34agJKSEnbv3s2kSZPCHKXI0KE8IQOJcoTIwKM8IQOJ8sTAoJlP0susWbP4xje+wb//+7/j9/vxer1ceOGFFBUV8cgjj7Bx40bsdjsOh4NVq1aFO1wZAoYPH86KFSv44x//CMD999/PqlWr+POf/4zNZuPee+/VGm2RfqQ8IQOJcoTIwKM8IQOJ8sTAYJgnmncmIiIiIiIiIiLyOWnZnYiIiIiIiIiI9BkVn0REREREREREpM+o+CQiIiIiIiIiIn1GxScREREREREREekzKj6JiIiIiIiIiEifUfFJRERERERERET6jIpPIidh0aJFvPfee2F57ttvv50HHnggLM8tIiInR3lCRERORDlCRMUnkT7n8/nCHYKIiAxgyhMiInIiyhEyWBimaZrhDkJkIPvud7/LmjVrcDgcWK1Wbr75ZrZv386mTZvo7u5m7Nix3HXXXYwaNQoIfLvgdDo5ePAgH374Ib/+9a8pLCzkjjvuYMOGDeTn5zNv3jw2bNjAY489BkBJSQn33HMPO3fuJDExkVtvvZULL7yQv//97/zkJz/BMAzsdjszZ87k4YcfDuePQ0REjqE8ISIiJ6IcIXKEKSKf6pxzzjHXrVsXPH7yySfNtrY20+Vymffcc4958cUXB8/ddttt5pQpU8yNGzeaPp/P7O7uNr/5zW+a3/zmN83Ozk5z37595oIFC8xrrrnGNE3T7OjoMBcsWGA+9dRTpsfjMXfs2GHOmDHD3Lt3b/B+P//5z/v3BYuIyGeiPCEiIieiHCFimlp2J3IKrrjiCmJiYnA4HNxyyy3s3r2btra24PnFixczdepULBYLNpuNV155hVtuuYXIyEhGjhzJJZdcErz2rbfeIjMzk8svvxybzcb48eNZunQpa9euDcdLExGR00B5QkRETkQ5QoYiW7gDEDnT+Hw+HnjgAV5++WUaGxuxWAI13KamJmJjYwFIT08PXt/Y2IjX6w0ZO/q/q6ur2bZtG9OmTQt5josvvrivX4qIiPQB5QkRETkR5QgZqlR8EvmM1qxZw+uvv86f/vQnsrKyaGtrY/r06ZgnaJ+WlJSEzWbj0KFD5OfnA1BTUxM8n56ezvTp0/nTn/503McbhnH6X4SIiPQZ5QkRETkR5QgZqrTsTuQkpKSkUFlZCUBHRwcOh4PExES6urr4+c9//omPtVqtnHfeefzqV7+iq6uLkpISnn322eD5s88+m7KyMp555hk8Hg8ej4dt27ZRUlICQHJyMlVVVX334kRE5HNTnhARkRNRjhBR8UnkpPzbv/0bDz30ENOmTaOlpYWMjAzmz5/PsmXLmDx58qc+ftWqVbS1tTF37ly+973vsWzZMhwOBwAxMTH84Q9/4MUXX2T+/PnMmzeP+++/H7fbDQTWhO/fv59p06Zx88039+nrFBGRU6M8ISIiJ6IcIQKGeaL5fSLSZ+677z7q6+tZvXp1uEMREZEBSHlCRERORDlCzkSa+STSD0pKSti9ezemabJt2zaeeuopzjvvvHCHJSIiA4TyhIiInIhyhAwGajgu0g86Ojr4zne+Q21tLcnJydx4440sXrw43GGJiMgAoTwhIiInohwhg4GW3YmIiIiIiIiISJ/RsjsREREREREREekzKj6JiIiIiIiIiEifUfFJRERERERERET6jIpPIiIiIiIiIiLSZ1R8EhERERERERGRPqPik4iIiIiIiIiI9BkVn0REREREREREpM+o+CQiIiIiIiIiIn1GxScREREREREREekzKj6JiIiIiIiIiEifUfFJRERERERERET6jIpPIiIiIiIiIiLSZ2zhDqCvNTV14Peb4Q5DRGTAsFgMEhOjwx3GgKE8ISISSnlCREROt0FffPL7TX2oEBGRE1KeEBERERHpW1p2JyIiIiIiIiIifUbFJxERERERERER6TMqPomIiIiIiIiISJ9R8UlERERERERERPqMik8iIiIiIiIiItJnVHyST+T1+TFN7QIlIiIiIiIiIqfGFu4AZGByuX388cVdbNpTR3yMg5XnjmLqmLRwhyUiIiIiIiIiZxjNfJLjeuGDcj7cXYvfNGlqc/G7NcV0dHvCHZaIiIjIcZnd7Xj2f4C3Zk+4QxEREZFj9MvMp6amJr73ve9RUVGBw+EgNzeXn/zkJyQlJbFlyxZWrVqFy+UiMzOT++67j+TkZIBPPCd9q7SmNeTY7fVTXdfB6OyEMEUkIiIicny+xio61/w/cHUAYBs9j8izvxLmqERERORj/TLzyTAMvvKVr7B27VrWrFlDdnY2999/P6Zp8t3vfpdVq1axdu1apk2bxv333w/wieek7405psgU6bSSMywmTNGIiIiInJh764vBwhOAd++7+JsPhTEiEREROVq/FJ8SEhKYOXNm8Hjy5MkcPHiQ7du343Q6mTZtGgDXXHMNL7/8MsAnnpO+d/7MHBZPySIm0k5OWgzfuGwiEQ61CBMREZEByN3Va8j09B4TERGR8Oj3aoLf7+exxx5j0aJF1NTUkJGRETyXlJSE3++nubn5E88lJGjpV1+zWS1ct2Q01y0ZHe5QRERERD6RvfBsvOVbgMAOvZbUfCwpeWGNSURERHr0e/Hp7rvvJioqii984Qu8+uqrff58yclaKiYiIiIymNlyJhF50e14S9ZjxCThGLcIwzDCHZaIiIgc0a/Fp9WrV1NeXs7DDz+MxWIhPT2dgwcPBs83NjZiGAYJCQmfeO6zaGhox+83T9trEBE501kshgrzIjLo2NLHYEsfE+4wRERE5Dj6pecTwAMPPMCOHTt48MEHcTgcABQVFdHd3c3GjRsBePzxx7ngggs+9ZyIiMjHXC4Xd955J0uWLOGiiy7iRz/6UbhDEhERERGRoximafb5tKB9+/axfPly8vLyiIiIACArK4sHH3yQzZs3c+edd+JyucjMzOS+++4jJSUF4BPPnSzNfBIRCTXYZj7dc889WCwW7rjjDgzDoL6+/jPlCuUJEZFQgy1PiIhI+PVL8Smc9KFCRCTUYPpQ0dHRwcKFC3n77beJjo4+pXsoT4iIhBpMeUJERAaGfm84LiJyqvydzRhWO4bz1IoMMvhUVlaSkJDAr371K9avX090dDS33nor06ZNO+l76AOWiIiIiEjfUvFJRAY80+um+/WH8JZ/BBYbjsnLcE67NNxhyQDg9XqprKxk3Lhx3HbbbWzdupWvfe1rvPrqq8TEnFxRSTOfRERCaeaTiIicbv3WcFzOPI2t3byxuYot++r1wUzCyrPrzUDhCcDvxb35WXz15eENSgaEjIwMbDYby5cvB2DSpEkkJiZSWloa5shERERERORjmvkkx1Va08q9j36Ey+MDYOroVP7jsglhjkqGKn/Twd5jzQexpuSGIRoZSJKSkpg5cybr1q1j3rx5lJaW0tDQQG6ufjdERERERAYKzXyS43rlw8pg4Qlg0946qus7whiRDGW2nMmhA1YH1oxx4QlGBpwf//jH/OY3v+Giiy7i29/+Nvfeey9xcXHhDktERERERI7QzCcJ4fX5sRgGXq+/97njjIn0B1veWTjnfwnPrrcwHJE4pqzAEhUf7rBkgMjOzuYvf/lLuMMQEREREZETUPFJAPCbJo+9uo+3tx7EYbMwa9wwrBYD35FeT2OyE8gdHhvmKGUocxSejaPw7HCHISIiIiIiIp+Rik8CwPs7DvH65iogMPvpjY+q+dqK8VTVtZMUG8GcouFhjlBEREREREREzkQqPgkAZTVtvca63T4uWzAiDNGIiIiIiIiIyGChhuMCwJichJBjwwj8748v7OKZfx2gvcsTpshERERERERE5ExmmKZphjuIvtTQ0I7fP6hf4mnzwvtlvLG5GofdyuQRyaz9sDJ4Ljsthru+PB3DMMIXoIicFhaLQXJyTLjDGDCUJ0REQilPiIjI6aZldxK0bHYey2bnAfA/T20LOVdZ205pTRsFGdq+XEREREREREROnopPgs/vp7SmjaRYJ0lxEQBER/b+1TjemIiIiEi4mT4vrvcfxbP/AyzRSTjnrMSWOS7cYYmIiMgRqiYMcXXNXdz32EfUt3RjMQxWzM/nojl5XDgrl20lDbR1Bno9nX1WJsMSo8IcrYiIiEhv7q0v4il+AwC/u5OuV/6XmOt+juGIDHNkIiIiAio+DXlr1pVR39INgN80efZfpcyfmE5Hl5fZ44fj85nMGj+MEZnxYY5URERE5Ph8h/aGDni68DVUYEsfE56AREREJISKT0NcY1t3yLHfNHlvxyH+8VYJH7ffbelwcfOlE/o/OBEREZGTYE0rwFe1o2fA5sCalBW+gERERCSEik9D3IzCYRSXNQWPhydFsWVfHUfv+7RxTx2HGzt586NqdpQ2kpUazVXnjCQpLoI3N1fx9taDREfYWTEvn9HZCf3/IkRERGRIc0xehr+lFu+BDzGiE3DOuQ7DGR3usEREROQIwzTNQb2/tLbQ/nT/2naQD3fXEhtp58JZuTz62j52lfcUpAwD5hal8+72muBYfnosF8zM5dfP9HzL6LBbuPfrc4iLcvRr/CLy2WgL7VDKEyKDh+n3Y1gs4Q7jjKc8ISIip5uyszBtTBo+n8n7Ow/zk0c2khIfgdViBM8vnJzJ7oqmkMeU1rSxcU9tyJjb42d3eeh1IiIiIv1FhScREZGBScvuhFc+rAzOdPJ4/by7rYb/vHYy+6paaO1wMy43kYaWrmBjcoD4aAc5aTFs2BVagMpI0RR3EREREREREemh4pNQ09ARcmwCFYfbefGDctweP29srmZcXiLpyVHUNHQSG2XnSxeMZWxuIvuqWtha0oDNamH5nFyyUjVFW0RERERERER6qPgkTBqZEjKDKdJpY39VM26PPzhWXNbET26cToTTRkKME5s1MK391isn0dzuwmm3EunUr5OIiIiIiIiIhFK1YIhyeXw47VYAZo8fTkeXh3e31xAX5eCS+QW8tL6812P8JqTER+L2+DAtJoYR6AuVEOPE6/Pj8/uxqteCnCamzwuGgWGxhjsUERERERER+RxUfBpi6pu7+M1zOyk52Ep6chRfWT6OrNQYSmtaqartICrCxcH6DhZPyWLLvnp8R3aAGpuTQFJcBA88sZUdBxpIjHNy/dIxTChI5u9v7OfNj6qxWgyWzc5l2ey88L5IOaOZponr/cfw7HoDLDYcZy3HOXl5uMMSERERERGRU2S966677gp3EH2pq8uNqR20g367ppjdFc0AtHd52FPZjM/vZ+2GSkzA7fWzdX8Dl8zPZ+roVLpcXs4ancoN54/lqbdLWF98GIAul49tJQ0kxkbw5Fsl+P0mXp/JrvImxucnkRQXEcZXKWcy74ENuNf/HUw/+L34qouxZU/AcMbgq9iGv7MFIyY5OPNOPjvDMIiKcoQ7jAFDeUJEJJTyhIiInG79MvNp9erVrF27lurqatasWcPo0aMBePPNN/nlL3+JaZr4/X5uueUWlixZAkBpaSm33347zc3NJCQksHr1avLy8voj3EGtrKY15PhwYyclVS0hY37TZMu+ep55t5T2Lg8QKFRVHG4Pua7T5WX3kV3yjn2OkZnxpzlyGSp8daW9xrzVxXhefxizrQ4Aa/ZEIs//lgpQIiIiIiIiZ4B+adCzePFi/va3v5GZmRkcM02T733ve9x77708++yz3Hfffdx22234/YEm13feeScrV65k7dq1rFy5klWrVvVHqGeExtZunnhjP39+aRf7jykcfZqxuYkhx7nDYxlfkBwyZrdZ2FvZHCw8Aby95SA5aaE72cXHOJgyJiVkzADG5oQ+x6n4oPgQv1uzkxc/KMfl9n3u+8mZw5ZReMyIgb+9IVh4AvBVbsN3cFf/BiYiIiIiIiKnpF9mPk2bNu244xaLhba2NgDa2tpIS0vDYvn/2bvzwKjKe+Hj33PObJmZTPZ9JQlZIKxhF0VBBFHBum8Vrbet9b5tbW/vvV7be7Uu7aX7rV6v2rrU1qV1ZUcQAZVN9hBISMi+JzNZJsvsc94/BiYMQxUskADP5x85zznPcg4xh/nN8/weGZvNxuHDh3nllVcAuP7663nyySfp6uoiNjb2fAx5xHK5fTz95z1097kA2HqwjUfunkzuac40uueaAlQVyuu7yUoyc8+CAhKiI+iyO4NFpMi/AAAgAElEQVQJx2+5MpdNe5vD6s4sTkaSYE9lJ4kxEdx1dT6jUizcMW80G3Y1otHI3DAri/STglRnasOuRt7cWHXsqJ2qxh6+f+uEf6hN4cKhyZyAfsYduMs2gKJFP3kxvo7qsOtU18AwjE4QBEEQBEEQBEE4U8OWcFySJH73u9/x0EMPYTQaGRgY4IUXXgCgtbWVpKQkFCWwy5WiKCQmJtLa2nrJB5/Kam3BwBOAz6+yrayNtq5BVm6tw+31Ma8knetmZlPbauf1DZW02QaZlB/PPfMLMEdo+c6NxWHt3jwnl5vn5AaPvT4/B45aOZ4GJTPRzOj0KPIzorl3YWFI3WumZnDN1IyQstJqG29vPkpvv5tZxcncdlUenb0OXlt3hLq2PgozA+0YdAqvb6hk3wkBrU9LW0LaOlBto3fATZRJ5B64VOjGL0Q3fmHwWI5OwVO+GfyBWXCSKRZNxvhhGp0gCIIgCIIgCIJwJoYt+OT1ennhhRd47rnnKCkpYc+ePfzgBz9g9erVZ7WfuLh/bBbOSJNmd4WV6fUaXl5THkyY++6WGvKz43jh/TK67E4gMEMqPsbEPy0pprPbgbXHQX5mNIoSWHk54PBQ29LLqNQoTBFarkmIJD7WxOqtdaTEG7l9fgGRxxJP1rYElvqNSh2abXVimwNOL899UIbbEwgUrN/VSFZaFJv3NFHVGEh2vq/KisFQTUqcic9KWwPttvbxvx+UkZ5gpqlzaFaLTquQlhJFhF5sznjJShiPM/pn9B3YiKw3EjVlERpL3JfXEwRBEARBEARBEIbdsH2aLy8vp6Ojg5KSEgBKSkqIiIigurqatLQ02tvb8fl8KIqCz+ejo6ODlJSUM+7HZuvH7794tjFKitQxaXQ8+6qsACREGzDrlbCdmj7Z0xgMPB23/0gHL71fyvKttagqxEcZ+Nc7J9HWNchzH5ThcvvQaxUeXDKWtAQT//v2ATp6HEgS+L1+brgsm2ffO0hptQ2AsaNi+f4t41mzvT6kzRsuyw4Gno7bXdYWDDwdV1rVSZs1dOlUT5+Lmy7PoaqxB9exNpZclk2/3UFounPhkqNNgil34Qe6XUBn33CP6IIly9JFF5gXBEEQBEEQBGHkGrbgU3JyMm1tbdTU1JCTk0N1dTVWq5XMzEyio6MpKipi1apVLFmyhFWrVlFUVHTJL7mDwHLF7948nqNNvQy6PBRlxdJs7YctNSHXFWXHcKDaFpI0PDXeGAwSAVh7nazaVkdVU28wqbfL4+PNj6oYkx1DR48DAFWFVdvrsBh1wcATwKHaLjbvbQ5r83BtF4os4Tsh6JeTZqGj10HzCTOaRqVYSIoxUnvCDnwmg4ZpRYlMHB3PkYZuUuNNpMSZzsqzEy5s/p42PEe3Ieki0OZfjmQQwRNBEARBEARBEIQLwXkJPj311FOsX78eq9XK/fffT3R0NKtXr+bxxx/n+9//fnC79J///OdER0cD8Pjjj/PII4/w3HPPYbFYWLZs2fkY6gUjL31oyVt2soXb5+axcmsdXp+fKyelMXNsMlFmPa+tq8Da42RcbhyXjUth+6H2kHa6+lx09TlPKnPS1Re6vE9VobUrPMFza9dA2KyrfqeXB64r4q8fH6Xf4WFaURLXTM1kXE4cf1xVTlNnP3lpUdy7oACjQYPN7uTAUStxUQbuXVCATqug0yqUFCT+g09JuFj4upoZ/OCn4HUD4C7fgumWJ5EU7TCPTBgJ5s6di06nQ6/XA/CjH/2Iyy+/fJhHJQiCIAiCIAjCcZKqnhw6uLhcbMvuvohfVUENLKk5kdfnR6PI+P0qP/7jTtq7BoPnvrGoiKqmHj49lncJ4LLiZMZkx/KHVYeDZYnREXz/1vE8/souPF4/ABpF4rH7pvLs+2Vhbc4en4KqqvhVFUWWTzmeLysThOOc297AU7Y+pMxwzffQZk8ephFd2C62ZXdz587l+eefJz8//yvVv5TeE4IgCKfjYntPCIIgCMNPZHC+iMiSBKFxJ1RVxWZ3EmPWo9Mq/OsdE1m9vR6b3cnUwkQuG5fC9DGJxEUZqGrqJS8timunZ6LTBnYa3FneTmyknutmZhN3LEfUR7sb8aswf0o6aQlmfnT7BN7ZUsOAw8P0MUlcNi6Qm6u7z4VOq2COCA0qnSrIJAJPwheRlPBfVacqEwRBEARBEARBEEYeMfPpItbeNcj/vFNKW9cgRr2G+xcVnvWlbKfqY1xOHM99UEZptQ1Fllg4PZOb5+Se1X6FS4u/z8rgB0+gOgL5weSkPIw3PIoki6DlV3GxfaM9d+5czGYzqqpSUlLCD3/4QywWy2nXv5TfE4IgCKdysb0nBEEQhOEngk8XsWfeLQ3uigdgjtDy63++jFbbABX13WQlR1KQGQMEZintrezEYgrspvf3ZiK1dw9SetRGYkwE43Lj+N/3Dob1ce2MTN7eVB1S7z+XTmFUyul/GBQuLarXjbd2N6rHhTZn6imTiavOfjx1e5B0RjRZk04588nf04q3sRQ5OgUlfVwwn5wQ6mL7UNHa2kpKSgput5unn36agYEBfvWrXw33sARBEARBEARBOEasW7mItZ2Qhwmg3+Fh874m3tp4lOPhuMWXZTO1KImf/Xk3Dldgx7vCzGj+9c5JYR/cy+u6+O3bB/D6ArUvK04+ZR9NHf3hY7ENiuCTcEqqz8Pg8ifx2xoBcO95H+PXHkc2h+5uKRnM6Arn/N12vI2lONb9D6iBn2Nt0ZUYLr/vnI1bGDlSUgJLfXU6HXfddRff+c53zqj+pfwlhSAIwqlcbF9SCIIgCMNPBJ+GmcvjY9W2Oqqbe8lLj+b6mVnBfEv/qAl58bTaGoLHmUlmNu9v4cSPWOs+b6Cn3x0MPAFUNPRwtLmXtq5BPj/cTkykgRsuy2bt5w3BwBPAtrI25kxMpdU2GNLH9DFJIbvq6TQyY7Jj2Hm4na0HW4k06rh+VhYpcSZqW+2s3dmAx+Njbkk643LiTnkvZbU2Pt7TjKIElvHlpkbR1jXIqm119A64mVWczMyxyWfhqQnnm7d+fzDwBKA67HgqtqCkF+MpXQeqH23xfDRpY0Lqucs3463ZhRwZh27yEtz71wQDTwCeii3optyEHCGCnhezwcFBfD4fkZGRqKrKmjVrKCoqGu5hCYIgCIIgCIJwAhF8GmavrasIBmoqGnrotjt54PrAh2yX24deFxqIcnl86DTyKZcTqaqK2+MP1rnpihxkSeLAUSvpiWZumZPLL9/cF1LH7wefzx/W1v4qK2t3DgWuKhq6SYg2hPYHXDM1A6NBG9JHXJSB+xcVsnlfMxF6DYsvG0V1i50XVhwK1j1Ua+M/7ilh2Rt7cXsC/ZfW2Pjx16eQk2rB6zu+o55MXZud3/2tNLCbH3Cw2sZPvzGVZa/vo3fAfay9LrSKzJTCs5vTSjgP1PCfP9XZh2P1MvB5AfA2lGK8+XGU2AwA3Ic/xvXZawD4AG9rZfhSPfXUbQsXF5vNxne/+118Ph9+v5/c3Fwee+yx4R6WIAiCIAiCIAgnEMGnYbaroiPseG5JOn9YeZi2rsBStQeXjMWgU3hxxSEO1XUTa9GzdGFhyCyhysYeXlp9mM4eJ/npUXx7STGSBNXNvTRbB3B5fHT2OLhmWgZ/WV8ZrDd3chozxibxeUUHHm/gg3p2ciSNJy2ds/Y6uWpSGhUNPRzPEjY5PwGDXhPWR3SkjurmXhra+9FpFerb+6hpsYe0Zx/08NGexmDgCUBVYfeRDvYf7WT9rkZQ4eopGUgSwcATgNvrZ8PupmDg6bjPKzpE8OkCpMmaiGRJQrUfmy2ni0DSGYOBJwBUH97avcHgk7dmV0gbam8b2sJbcbdXw7G5fZrc6cjG6PNxC8IwysjI4IMPPhjuYQiCIAiCIAiC8AVE8GmYxVkMtHc7ho6jDMHAE0Btq50/rz9CnMXAobpuALrsLl5ccYhf//Nl6LQKfr/KCysO0d3nAqCyqZc3N1ahyBJHGnuAQPDohZWH+NVDszBHaNl5uJ2irBjmlaQjSRKP3jOZtTsbiDHrWXL5qLCE4ZIE08ckkZUcyaa9zWQkmlk0M4uXVpeH9bF4VjafHGgFwOHy8uZHVVwxISXs3tMTwnMJuD0+1u1sDh6v2VHPvJK0sOsyEsPrxkcZwsqEkU/S6DHd+J94qrahel1o82bis9aFX2eMDiYcl8wnLc+UFLSjZ6NJKcRbvx85OgVN7vTzcwOCIAiCIAiCIAjCFxLBp2F29/x8/m95GQ6Xjwi9wi1X5vLMuwdDrqlv66Nv0BNSNuD00tnrJC3ehH3QHQw8DdWxI5+0DX1vv5ttB9t47cMj+Pwq+6usSJLEhNw4fvdOKb39gZlENruTO+aNpry+m7auQWRJYvHsbHoH3Dz73kGcbh97Kjtxun3UtfWF9VHV1Bt2nxmJZjISzTR29CNJsGBqJldMSKWysSe47LAwM5pIozasrlGvoSgrhvL6QPBtWlEil09IpbPHydqd9agqpCeYWDgt83QeuTACSQYzunHXDB2bYtFkl+Ct2wOAklqIa98K6LcBIKcUIFkSUe0dICnop92MbIwCYxRKYu6w3IMgCIIgCIIgCIJwaiL4NMyKc+L49T9fRnPnAGkJJgw6DaNSIqltHQrqFGXFEGsxUH9CoCcmUs+R+m5WbasjJ8VCSpwxJPF3UVYMsizTfsJudEmxRjbsbsR3bFcnFXj/kxpauwaCgSeA3Uc6uXZGFrfPzWPTvmYSog1cOSmNP62twOkeSui8flcjM8YmhfUxIS+OHYeHEo4rssSEvHjmTk5ne1k7pTVWvH4/nb1OvnnDWOZMTOXTA61IkoTFqDvlM/raFbk0d/ajUWSSYo0A3HJlLnMnp9E36CEzyXzKPFjCyOfrasJz+GMAtGPmosSmI8kyEdd8F39PK6rqx1u9E19LRbCOv/UIhkU/QtYZkcyxYnmdIAiCIAiCIAjCCCaCTyOAQachNy0qePztJcX85cMj1LX1MSY7hnuuKUCnkXG4vOyr7CQp1kh8VAR/2RDI3bTzcDtTCuKJNutp6uyneFQct101GkkCj8dHaY2NtHgTd8/P53dvHwjp2+XxMeDwcrIDR62s2FoXPD7S0IPREPrj4ldVrpmSgQQhfaQlmOnscbJlfzMGvYYbZ+cQHxVBfVsfr6wtDwa/dh5u56ffmMqLKw/TZQ/M3Npa1sq10zPZW9mJqsLCGZmMTg8EFtJOsUwv1mIg1iKW212o/H1WBpc/BR4nAJ6qbZhueQo5Mh4AOTqwXNPjGgiv7HagpBeft7EKgiAIgiAIgiAIX43y+OOPPz7cgziXHA43J+SqviCYDFpmFidz7YwsphQmotMqKIrMxLx4Fk7P4vLxqfxpXQVu71Cy7o4eBz+4dQJ1bX0cru+mvq2P4lFxTClMxNrroLy+h4qGbvIzoqlvH0omPqs4mSsmpLK9rJ3jjykpJgKfTw3JRWUf9DB7fCpHGnqCZYWZ0SyamU3xqNiQPlLjTcwsTkZVoby+m8N1Xeg0Ckcau0OW5Lk9flQVymq7Qu4/Jc7Ij+6cxNVTMshOtpzlpyuMJJ6KLfgaTgiI+r3Iphi8bUdwfvQc7kMfIeki0KSPw1P5Kcf/Z5ZMsRguuxtJDo+fe458imPDM7j3r0ZVfWiS88/X7VwwJEnCeIpZhpeqC/E9IQiCcC6J94QgCIJwtomZTxcoc4SWfsdQHiiTQctLa8o5eiy4U1pt40/rKshMMgeTfze099Nld/HAdYWU1/dgMmiYPyWD+OgIHrl7MtvKWvGpKrOKU9ha2hrW58yxSYxKiWTPkU4UReKqSekArNpeF9LHs+8d5J+uL+KvHx8N1n3twyPMnRyeODzWog+/t1PkfRIuTpIhfDabf6ALT9mG4LFzy8sYb36CiMU/xnNgDehN6EtuRNIEfnZ8PS3gdiAn5ODvasK55WWO73jn/vwdlJh0NFkTz8v9CIIgCIIgCIIgCOFE8OkCdcuVuTy/vAyvT0WWJG65MpeXVpeHXHOksQeHK3RJXb/Dg0ZRKKvtwj7g5uO9zdwxbzSzipOpb++nttXOpwdaGZcTi8Wkwz4QyAU1d3IaiTFGNIrMGx9V0dHtYMu+FhZOz+Roc29YH3uOdIaNOdKoC8lNVZwTy9VTMqhutrOrogMI7Fh3dUnGWXtOwsimyZ2OXL4Jf0cNAHJiDuETUFS8TQfxHt2J31YfKPJ7ibjymzg2/xFv5WeBunFZaPKmw0kteFsrRPBJEARBEARBEARhGIng0wVqcn4Cv/jOLKqb7WQnRxIXZeCjPU0hSclzUixkJplDlrpF6BW2lbUFg0o+v8rbm4/icHmpbbUHrztY08X3bhmHrddFfXsfCdERDDo9rN3ZQMex5XgqsG5nA7PHp4T1UZwTy6cnzZ4qyorhuplZVDR0o9cqwVxO37mxmAUtdvodHoqyYtBqQnfpEy5ekkaHcclP8LUeAUBJKcBbuwfvCTOfAPz2zqHAE+Ct3IorMS8YeALw2+rxJ44K60NJzDlHoxcEQRAEQRAEQRBOhwg+XcCizXpKChKCx/90XRF/XFVOfXsfuWkWll5bSGSEFmuvk72VncRZDNxzTQHvbakOacft8QcDSieqb+tnzY56PMdyS31W2kpCdETINSpQUpDAoMsb0sf43DgaZw2wYXcjsiSxaEYm+RmBYFPxqLiwvnJSRW6nS5UkyWhSi4LHmlFT0E1YhPvQRyBr0E9ejL+3Paye2tMS3hYS+hl34tq3AnwetGPmoRk19ZyOXxAEQRAEQRAEQfhiIvh0EUlLMPPY/VPx+f0oskxH9yAvrS6nxTrA1SXp3HJlLlqNQrO1n4aOoaTjeWlRzJmYytay1mDS3Qi9hs5eRzDwBNBsHWDi6Hj2H7UGy+KjDIzJjmV8bjxrdtSxvaydDz9vwGTQcNMVOdx4+SgkAokrBeHvUX1e3Lvfw1u3Fzk6Bf3029BNuwWQkCQJb0sFnvLNBJfU6U3oxi3AU7UNgjvhSWjyZqBJKUA77hpARZLELDpBEARBEARBEIThJna7uwjJxwI9//36Xiobe+h3eKhpseP1qowdFUtavAmdVkGnVZiQF8891+STHGciLy0Kt9dHWryJ26/Ko8vuoqbFHtL2TVfkMC4nDq9PJTs5kjvn5xMbaeDTAy288VEVfYMerL1Odh/pZO7kNHQaRQSehC/l3vN+YHc6Vz/+3ja8jWXoiq8GQO1tR45JRUkaDV43cmIuhjnfQIlKRpM1EdXrQTLHoRu/AE3WRCRJRpIk8XP3BcQuRqEuxfeEIAjCFxHvCUEQBOFsEzOfLlLWXgfN1oGQstIaGylxRt74qAqXx0dGopl7FxQQeewfF0VZMWwra2PHoTb2HOlkUn58yK56hZnR5GdE4/b4+ay0lR2Hreyq6GDh9EzaugZD+nK4vFQ19TI+N3yJnSCczNtYGnKs2tvxNpbi2vYmqr0ddEYMVz5AxDXfDblOiUlDW3g5zg3/i6t+H+49HxAx//+hJOWdz+ELgiAIgiAIgiAIX0CsSblIRZn0mCO0IWVJMRG8vqESl8cHQGNHPx98Vhs8v7eyk+2H2lAJLG7aW2nl9rl53Hn1aK6clMrMscm4PX427WumrLYLCCQsX729HrMhtC9JgtR44zm9R+HiIcekhxZoI3Af/CgQeAJwD+L65FVUvxdfZx3u0rV4WyoAcH36KqojkPBeHezB+dlr53PogiAIgiAIgiAIwpcQM58uUlqNzP2LCnllTQX9Dg8ZiWbmTExjX5U15LoTZyy1njR76fj5z0pb6R1ws5kW1u9qJDslMuy6nDQLVruTQ7Vd6LUKN12RQ3xURNh1gnAq+mm34O9pwd9ZC3oThsuX4vr8nZBrVGcfnrKNuHa8GSzTTfka/p7QZOT+ntBdFgVBEIRLh3+gG0lnRNLqh3sogiAIgiCcQASfzqNDtV18tLsRWZZYMC2w+1t71yArttbR0+9i5thkZo9POe32evtdLN9aR5ttgAl58cyfmhHM9wQwaXQC4/5fHPYBN7EWA36/SkK0gc4eZ/CaUckWnl9exoDTy5jsGCSJYO4TWZJwuH30DriD1zdbBxiXE7qUTn8sd9Tl41PpHXBj0CrodUrYeL0+P2t3NnC4touMRDOLZ48Km511urw+P2t21FNe101WciSLL8vGaNCy41AbWw+2EmnScf3MbFLjTV+pfeH8kk0xmL72WOBDg8GMpGjxtVfjOfjh0DWJObgPbwyp5z6wFiVrAr66vcEyTdakwLnyzXirdyKZ49FPXoxsScDbUo7n4HoAtOMWoEktPA93JwiCIJxrqrMfx/rf42urBK0B/fTb0Y25ariHJQiCIAjCMSLh+HnS0N7Hstf30dY1SFvXIDsOtzOlIIFfvrWfqqZerL1O9lVZSYqNID3BjM/vx+P1o1GGVkaqqorT7UOrCZT98s197K+yYu0NzDjSamTyM6JD+pVlCa1Gxuvzo9HIjM+Jo3fAjVaRuWJCKlv2N1PX1k9nj4PDdd0sviwbFYiL0nP73Dxcbh9HGnpC2rxqUhoT8uLoG3CTGmfk3oUFpMWbATDolJAxO91eZDmQ/PmdLdWs2laHze6kptVOQ3sfs4rDg20n3+dxDpcXjRJo662Pq1izowGb3Ul1i52mzgG0GoXnlx+is8dJU+cAuys6uGpyesh4hOGhqn7wupCUU8e7VZ8HVD+y3oQkBwKXSmohqH5UjwsltRD9FffjrfgE3I6hipKM8fp/A68L/F40o6ZimHU3nsqtgeV4fVb8tga8DQdQUgpxrPxZYIZVbxveozvQ5kxDMpjPxyMYUUQi2VAj5T0hCMJX59r1Lt6azwMHfi++pjK0hVciaQ3DO7ALlHhPCIIgCGebmPl0nuyt7MR/wqcbr8/Pxr1NdPe5Qq7bVd6Bw+nl3S01ON0+po9J5P5FRdS22nlpVTkdPQ5yUi3cdlUuta19YXWvm5kdUvbR7kbe/7QWt8fHjLFJLF1YyEM3FgOwu6KDQZcv5PquPhfjc+NYua2O55cfYlpRIiaDhgGnF4CEaAOT8uPRaxUaO/rZvK+FqrcPcO30LJbMHhVsp3fAzQvLy6ho6CHOoue+a4vYVd4R0tehum4GnR6MJ+SLOtLQzUury7H2OslLj+LBxWORJIkXlpdR2dRLfJSB+xcVsasitK2D1TZ0mtDZVvZBD0cauhmfG/93/16Ec89bvw/np39CHexBSS8mYu6DIQEf1+dv4z42G0lXPB/99NsAkBQtuvHX4rPW463eia/1CJrMCXgqtgTr6oqvRo6wYJh9b2ifNbtCjlV7B57DH4P/hJ93vxdP3V70Exed7VsWBEEQzjN/d/NJBT789nZkY9TwDEgQBEEQhBAi+HSexEWFf/OWlmBGIpDc+zijQcNf1lcGy7YfaiczKZKNe5qw9gaWy9W02FmzowG9VgkmDz/eR2VjD70DbopHxdLT7+KNj6qC57cebGNUioW5kwPJneOjw8ekkSXe2VwdPN5+qJ075+Xh8vqx9TopyIxGo8jsOdLJR7ubAhf5YPlntRRlxQRnXr2z6SgVx2ZM2ewuXlx5iJQ4Izb70JK/SKOWrj4npdU2RqdHExOp58WVh4MBuaNNvby1sQpFkalsCiSUtvY6eXHlIeItBnr7h5YDWsw6EmPC7ydO5J0aVqrHhWPTi8HZSr6mMly73wsGi7yNpbj3rw5e7z6wBiWlEE3meABcu9/D11QWaGuwB0/15xgWPIyv+TBIEpq8GUN9ed14Gw8iaQ1IkSftsigpyLEnJTUHZIsITAqCIFwMNJkTgu8LACnCghKfPXwDEgRBEAQhxHkJPi1btowPP/yQ5uZmVq5cSX5+PgAul4uf/exnbN++Hb1ez8SJE3nyyScBqK2t5ZFHHqGnp4fo6GiWLVtGdnb2+RjuOTFjTDK7Kzo5WGMDYGphInMmpNLb72LVtnr8qkpagonRaVFsPdgWUrempTcYeDquqbOfO+bl8fqGKrw+P7EWPR6vn/9+PZD7xmLSsWh6Ztg46tuGZktlJ1uYV5LOx3uaUIFRKRaS48J3qGvvdlDdYqe+rY8t+1tYk1DPmOzYsOvq2vqCwae69tBZWX2DHpYuzKCty4F9wI1OKzMxL57HXtqFCiiyxNcXFITNBKtv70OWQ5fN9fa7ufeaAl5dV0HfoAe9VuGe+fnkZ0RTVttFQ3s/kgQLp2WSJnI+DSu/vSN0mRzgs9YN/bmzjpP5rHXB4JPPWh960uNAdfTiKd8MPjeegx+iK7kRbdFVDC5/ErUvkFBfThqNFJWE2tsOkoJu6k1oi+bgayrDW78PAE12CZrskrN2r4IgCMLw0Y6dh+px4j26E8kci37qzUgasWxMEARBEEaKMw4+VVdXs27dOqxWK4899hjV1dV4PB4KC/9+4t558+Zx7733cvfdd4eU//KXv0Sv1/Phhx8iSRJW69BObI899hh33XUXS5YsYfny5fzXf/0Xr7124W6hrtXI/OC2CbTaBpBliaSYQJDnxstzmDMxDfuAm8wkM/YBNxolEFA6bnxuPK02B02d/cGyvLQorL1OJuTFkZcWRX56NE++tjt43j7gpqGjH0WW8PmH5lYlxkTwp3UVSJLEvMlp3D0/n9njktm4pxmf349eqwlJOg6BwNCJQaumzgGKsmJC7k8CdBqZP6w8TJRZR06KhebOgeD5hGgDXXYX+elRxFoMXDs9k5/8cWdwhpfPr7J+VyOp8SZarEP1irJiUWSJ9hN24kuONTJxdDy/yplFY8cAybFGjIbAj/Jj902lsaMfc4SWWIvI8zDc5JgUJGM06uBQ3jAlIQfn9jdRXf0oCTlhdSS9CcfmPyDpzSgJo/B3DM3Ek4zReCq3gW9o1pt7/yrweYKBJwB/exWGhT9E7bfibS5HdfahDtqJWPB9vI0H8VRtQ1K0+K31KInhYzujX30AACAASURBVBAEQRAuLJIko590A/pJNwz3UARBEARBOAVJVU8/zeratWt54oknmD9/PqtWrWLv3r0cPHiQX//617z66qtfWn/u3Lk8//zz5OfnMzAwwJw5c9iyZQsmU+jsFJvNxoIFC9i5cyeKouDz+Zg+fTrr168nNjZ8xs0Xsdn68fsvrEyypdVW3vukhgGHh8vHp3LDZdl0dDv4y4ZKGjv6GZMdQ0NbPy22oSDN4suyWbG1LqSdGWOTmFaYxPuf1jDo9DKlIIFN+5txewKBLYNO4YkHpvG7t0tDAj4Lp2VysMaGy+NjXkk6sizx5gnL9wBuuTIXvVZhw65GFEViQl48H+5sCAaTEqIMFGbFUFptIyXOSLRZz47D7cH6cyak8tnB1pDAWLRZx7/eOYnXN1TS1DnAuFGx3DU/MEvuzY+qKK2xkRZv4q75+WJG0wXE11mHa/sb+O0daLIm4qk/AIPdgZOShHb8tXiP7VanyZ6Mp3TtUPTTGI02ayLe+v3IlkT0M+/E+ckr+G0NIX1oCufgPSEXFIB20g149q3i+MJWyRyHcclPGHznP1FdxwK5sgbj1x5Dics4Z/c/EsmyRFzcyEm0/uKLLzJjxgzGjx8fLCstLWXnzp1885vfPOf9X4jvCUEQhHNppL0nBEEQhAvfGc18+v3vf8/LL79MUVERa9euBaCwsJCKiooz7rixsZHo6GieffZZdu7ciclk4vvf/z5TpkyhtbWVpKQkFOXYrleKQmJiIq2trWccfLoQjc+ND0uSnRRr5F9unwhAXZudJ17dHXK+psVOSpyRVltghpAkgcWo4y8bjuD1qSyYmoFfVYOBJwCn28f6XY0hgSeA9u5BvnZFDm9vDuxON7UwEaNBw+CxpOMROgWb3cmu8g6Meg1LZo9iX5U1JHdVZ6+TpWOSuH9REQAP/SY0MLD9UBuXjUvhkwMtwbIrJ6aREmfiR3dMCnsm37iu6HQenTACKQnZGBc/CoC3bl9gydxxqgqufsy3/zcAzk9eCZ12N9iDkj4WZAVP1Xacm/6AkloUEnzSZE1CN3Ye3qqt4Av8jEqmGPz2Tk7MqKb223AfWDsUeALwe/Ee3X7JBZ9Gmtdee4177rknpCw3N5eHHnrovASfhK+m3+HhlTXlx75kMLH02gJyU0VyZ2F4qF43zs9ew1u9A8kUi2HW3WgyJwz3sARBEARBOOaMgk9dXV3B5XWSJAX/e/zPZ8Lr9dLY2MiYMWP493//dw4cOMCDDz7Ihg0bzritL3IxfmvjV5SwpXGJcSbuWVTEx7sbUWSZ0RnR/P5v+4Pn395czQ2Xhy8vykqJAppCyiyRBp5ffii49G/z/hbuXlDAoMuHqqpYTDpeW1MOBD58vLjyMFeVhCdzTk6y0NbrIjXBRHSknjbb0NK5qEg9S28YQ0yUgb5BD5PyE7iyRAQALnZOVxKOk8pMsfGYvJ0AKLFx9Jx0Xttdg/PQRgD87kHUARsJi7+Lo/4QslZHZMm16OPTcd33c/oObMLvdmIeMwtHbSm91aFtRSan0lUWWmaOTyA6IfLs3aRwxjweDxpN6OtIq9Xidrv/Tg1hJPjbpqPsqwosd23q7Of/PijjFw/OQpbP/N8EgvCPch9Yg7fyMyCww6njo+cw3/NbJF14LktBEARBEM6/Mwo+jR07luXLl3PjjTcGy1avXh2yVOJ0paamotFouP766wGYMGECMTEx1NbWkpqaSnt7Oz6fL7jsrqOjg5SUlDPu52JcTiEDV5dksGF3IwAWoxZr9yCP/O9WALKSIjFq5bB6HpeHUSmR1LYG8jeNTo9iWkECNc1pbNob2KI4yqwjI94YknMK4GhjD99ePBaAl48Fno7z+1WSLAZiLXq67IGE4RNHx/PTP2ynb9CDLEnMKk6is9uBz6+iyBIFGdF862cb8flVzBFaLi9OprMzNEm5cBHSp6LJmYa35nMApMgE7DVl9Gx7DwA5OR8pMgG1LxCM0uRMw9EZmoBf9bjo6x3EdXQ/6kAX9j0fopt8I7oJ1+JorsHXWkF/6cfIKYVI5jjU/kCSf03eTNzZV6Ck7w7uiCTHpONOn3bJ/eyNtOUUY8eO5Y033uC+++4Llr311luMGTNm+AYlfKnq5t6Q4y67i64+J/Fil1FhGPjaj4YWeF34bI1oUgqGZ0CCIAiCIIQ4o+DTj3/8Yx544AHeeecdBgcHeeCBB6itreXll18+445jY2OZPn06W7duZfbs2dTW1mKz2cjKysJisVBUVMSqVatYsmQJq1atoqio6JJYcne67rx6NJePT8FmdyJLEr99+0DwXH17H7mplrA6cVEGiuQYMhLNTMiLZ2JePANOL/FRBmaPS2FUqoXLipPpd3h4Y0MV/hOmViXHRLDis1r8qkpSTOgHCwmwmHWU5Cfi9fmZOTaJ1dvr6Rv0AOBXVT6v6ODJB6bRahskNd7E46/sCuZ76nd4ePeTan5428Rz8KSEkSbi6ofwdSxEdQ3gH+zBteWl4Dl/WyX6OQ8A4GurRI5MQPW64NgOdQAoGjwNB1EHugLHqop773LQGfC1Di0B9rdWoJ/7bWS9CclgQUnIBsC46Ef42qpQfR6UlAIkWTnn9yx8sf/4j//g/vvvZ8WKFWRkZNDQ0IDVauWVV14Z7qEJXyAvLSq41BsgzqInNlJs9CAMDyVpdPCLBQC0BpS48F1/BUEQBEEYHmeUcBzA4XCwadMmWlpaSElJ4corrwxLGH6yp556ivXr12O1WomJiSE6OprVq1fT2NjIo48+Sk9PDxqNhocffpg5c+YAgV31HnnkEex2OxaLhWXLlpGTc+a7Ul2MM59Otv1QG39YeTikbF5JOnEWA6u31+H1q8wYk8T2Q23BnE8J0Qb+676pPP3aHtqO7SSn1cj8+OslZCZFsv1QG3/bdJQBh4ephYmU1djocwTy6Rj1CpPyE/m8vJ0IvYaphYl8vKcpmF1nTHYM/Q4PDe39IWP6xXdmEh8VQXefi385NkvruLQEE08+MP3sPxxhRHPtX4P787+FlGknXIvn8GbwHFugZ4xGk1KIt3YXUkQU+pl34infhK859GdeO2YunsMfh5TpZ9yJbvyCc3kLF6SRNvMJYGBggM2bN9Pa2nra75az5VJ4T5wL/Q4Pr66toLTaSmq8iXsXFJJzii8+BOF8UL1uXFv/jOfoTiRzLIZZd6HJOPOZ+ULASHxPCIIgCBe2Mwo+PfXUU/zkJz8JK3/66af58Y9/fFYHdrZcCh8q+h0e/uOF7QwcSwguSXDdzCxKj9qQZYnrZmZR3Wxn3eehO4QtmpHJmh2hZXMnpzE+N46VW+twefzMK0nD61N5fUNlyHW3XZXHNdMykCWJ3/x1P2W1XSHnF07PZN3OobZzUiykxBmpauolNy2Kjp5BqpvtwfM3z8nhupnZZ+NxCCOYt34frn0rwedBN3Y+SkoBA+/8BHyBWXIoWrQFV+A5vDGknmHOA/jdTrwVW5B0EciJuXgOrguel2PSMMx7iMH3HgN/4P8DNDpMtz6NZIrBves9PHV7AzvmzbgdJSbtfN3yiDTSPlR0d3cTExMTVt7Q0EBm5rmfuXApvCcEQRDOxEh7TwiCIAgXvvDEQF/gvffeO2X5ihUrzspghK/GHKHlkXtKmD0uhSkFCdx+VR6rttXT0NFPXVsf//fBIZweX1g9gy58uZHX5+eZdw9S3WKnqbOfP607QmfPYNh1Wo1MV6+TvkE3Om14O3MmpLBk9ijG5cSycFompggNW8va6OhxsP1QGwatwrXTMxmfG8fXFxSwaEbW2XkYwojl723Dsf5Z/B01+G2NOD95Gf9AF8YbHkHJnoySPRnjDY8gmePC6vq6mnBvfx1/dxO+9io8hzein3UPSuZEtMXzibjuX1Fi0zBe/+9ocqejZE0iYsHDyJEJuPeuxH1gDWpvG77GUhzrfofq959ihMJwueGGG9iyJXRHzDfeeINbb731jNp59tlnKSgooLKy8ssvFgRBEARBEAThvDmtnE/vvPMOAD6fL/jn4xobG4mOjj77IxPOSFq8iW9cVwTAu1tCt/jyqypRRh3RZh09/YHdo3LTLCyYnsnhum4qGgL7i1mMWuKjIoK5mI7zeFVS4020WAcASIyOYE9FO69vqESRJaYVJaHTysElfRNHx/Ort/Zjs7swGTTMK8ngoz2hO+qV1/fwL3dMOvsPQhixvE2HQA0NgnrrD6A67fjqAjmd3Bo9+um34zm8MZgoXI7LQnWdFAD1eZAMJowLHw4pliyJ+Hvb8VvrcDSVoZ9+G97G0pBr1L5O/L2tl/zsp5Hk6aef5ic/+Qnz5s3j/vvv58knn6Sjo4M//elPp93GoUOH2L9/P6mpqedwpIIgjASqsx/njr/ia69CScpFP+MOJL0J975VeKt3IJli0U+7FSU+C0/NLtwH1gIquvHXos2dNtzDFwRBEIRL0mkFn5YvXw4EtsM+/mcASZKIj49n2bJl52Z0wleSFh+eJyU5LoK5Jel0210UZkYzKT8Bj9fPtKIkkmONpCWYmTE2iVZr+CynpNgIkmIjaLUNkpNqobffzXuf1ADg86tsP9TGD26dQFefk4ToCJZ/Vovt2K53A04vr31YQVqCifq2oR3F0hLOTy4XYeSQY9PDC30evFXbgofeo9vRZE8m4oZHcO9ZjqRo0U29Ce/RHXhPmswix6Tj77fhrd2DFGFBM2oK7n0r8Fvrgm27dryFZlQJ/s7aoYpaA/IpZlcJw2fOnDmsXLmSr3/96/z1r3/luuuu47nnnkOn051WfbfbzRNPPMGvfvUrli5deo5HKwjCcHN+8greuj0AeHvbUJ0DaNLH4t59bIZ+dwsOWwOGa76Pc+NzcCzDhHPj/yFbElASRg3X0AVBEAThknVawac///nPAPz2t7/lBz/4wTkdkPCPm1aUxOH6brYdbEOWA8evrjuCyx2YddJqG2B8XjxPvbY7uFNRlFlHSUECeelRXD8ri3U7G/H5/UwenRCyc11VUy+jUiLD+hx0eZkzMTCT5OTk5112F9+6YQwvrS6ns8dJfJSB+64tPJePQBiBNCkF6CYswn1wPag+NHmzwBgVdp3PWodr619QHYFt3H2tR4hY/Ci+1iN4a/eAokU36XqQYODtH4PHCYCSUgDySb/S/D60eTPx91nxd9SA3oTh8qVIWrEj10gyMDDAsmXL6O/v57777uPdd9/l/fff5/bbbz+t+v/zP//D4sWLycjIOMcjFQRhJPA2Hgg59jWWDuX7O0Z12PFUfhoMPB0rxdt4UASfBEEQBGEYnFbw6bgTA0+qqnJirnJZPqP0UcI5JMsS31hUxO1z85Alibc2VgUDTwAVDT2s3VEfskV2b7+brQdbyUmx0NjeT2FmNFdOSqO5s589lZ3B61qsA4zPjQ3pT69VsPY6+cUbe4mPiqAwK4adh9uD54uyYsjPiOHn355Jb7+bKLMOWZLO4RMQRir99NvQTboB/D4kgxlfVyOePR+AeiwHkySjelzBwBOAv6cFX9NBNLnT8Tv6kPRGNJnj8ZR9FAw8QSBIpR23AF/zoWCZZIpBSR+HKWsS/sEeJL0ZSTmjX3vCebBkyRImTZrEihUriIyMZPHixfzbv/0bGzdu5MUXX/zCuvv27ePgwYP86Ec/+sr9i6S6gnBhcSVk4m6rCR7rEjKISBtFb1PZ0EWKhui8cVjLN4fUjcnKw5QQ/iWaIAiCIAjn1hl9Cmtvb+eJJ55g9+7d2O32kHPl5eVndWDCF3O5fSiKhEYZCvp5fX58fhX9sQTgJoMWOOlLv2NOzusE0NPv5jd/OxA8V17fzZWTwvOnZCREcu+CfLaWtWEyaEmONZ6QZ6qHxJgIFkzLoLy+m+zkSG6akwuALEnotXJY4Mnh8qLTyignBDA93kCwTKsJT2YuXNgkXQSqK5A/TInNIGLB93CXfgiAbvwCfNb6sDo+WyOeA2uCx4MtFWiyJoZdp2RNQjbF4KneiWyOQzfla0iKBtXvQ9IaROBphPrhD3/IokWLgsdFRUW88847/OY3v/nSurt27aKmpoZ58+YB0NbWxgMPPMDPf/5zZs+efVr9i93uBOHCopl5L56PnkXtsyKZ49DMWorPkojSUImv9QhoDehn3IEzeTLaMXPxHAtAaQuuYCC2kMHOvi/uQBC73QmCIAhnnaSqpwpNnNqDDz6IwWDg29/+Nvfccw+vv/46zzzzDHPmzOG22247l+P8yi62DxVen59X1pSz83AHep3CTVfkMK8knXU7G1ixtRaP18/M4mSWLiwIBnPq2uz8/C978XgDs0ty0yz84NYJ/PTVXXT2BGaOmCO0XD4hhbU7GkL6u25GJlsOtNLvCCy7S4g2kBJn4mC1DVOEltvn5vFZaStHGntC6j1+/1Qyk4a+WWzu7Of5FYdo7hwgNd7EtxePJT7KwB9WHubAUStmo5Y7rx7NjDHJvL35KB/tbkJV4eqSdG6bm3fOnqdwfvm6W3BufA5/VxNydAqGuQ+ixIfudOjvtzH43uOozsCHAykyASW1AO+Rz0Ku0029GffeleALJNGXE3MxLvkJ0knBTU/VNlzb30R19qPJmohh7rcv+WV3I/VDhd/vx2q1kpiY+JXbmDt3Ls8//zz5+fmnXedie08IwqVAVf2oA91IxhikE7688g/2IOkikDT6oWtdA6CqSIaR93tvpBqp7wlBEAThwnVG0wD27dvHpk2bMBqNSJJEYWEhTz/9NHfccceIDT5dbDbta2b7ocCSNofLyxsbKom16PnbpqPBaz4rbSU31RLMwZSdbOGJb0xj+6E27ANuxuXGYdBr+M+lU9le1kazdYDs5Eg0SvhSuJR4E1+7Iof6NjvpCWa6+pys29kIQL/Dw6trKygpSAipo1EkVBW2l7WRmWQmLcHMq2sraO4MzHZpsQ7w8ppyxmbHsv+oFYC+QQ8vr65AkeWQANi6zxvIz4xmYl78WXyKwnBxffoq/q7Azof+nlacW/6I6eYnQ66RzXEYb34C79HtoGjRjp6F+6RlEwCatDFoc6biqf4cKcKCdvTMsMCT32HHueXlYC4Qb/0+3PtXo59687m5QeErsdvt/PSnP+XDDz9Eo9Gwf/9+Nm7cSGlpqcgzKAjCKUmSjHSKzSNkY/gOzJJebHIiCIIgCMPtjIJPsiyj0QSqWCwWurq6MJvNtLe3f0lN4WxpaAudKq4CB2u6wq9r7w85Nug1bD3Yhs3uZPP+Fgozo/mX2ydSVtvFwRobnxyAaLOO4lGxlNUG2iseFcvbm47SOxCY9TQ+N46Tw1M+v8qE3Djq2/po73agyBIzxybz1Gu7g8v37pw3mvr20HE3tPdhjtCGlHl9fg7Xnupe+kTw6SJx8pI6v60RVfUjSaE542RTDLoJQ8uwdGPm4qvfj6+9CpDQjpmLkhhYzqmfvPjv9ufvbg5LQnuqZX3C8HrsscewWCx8/PHHXHfddQBMmjSJZcuWnXHw6eOPPz4XQxQE4QLgOboDb/VOJFMMuonXI5tjv7ySIAiCIAjnxRkFnyZMmMCWLVuYP38+s2fP5uGHH8ZgMFBcXHyuxiecZEx2LFvL2oLHGkVidnEynx5oCcnjFB9t4OU15WgUmXkl6eyu6MBmH0rOXNHQw/pdjRyssQXLevrdzCw2c/f8fJBgy76WYCAKoLTaxryS9JDxROgVnB4fGYlmirJjuHZ6Js+8WxYylg8+q6UwK4ayE4JkY7JjGZMdw6ET2jcZNMwYm8SWAy1h9yxcHDRpY/DW7wseKykFuPetxN/VjCZjHNqCywHwVH6Gt6EUOTYN3bgFSLoIdNNuwX1gHZLehG78AgD89k7cZetRXYNoC69Ak1KA6nHiPvgh/q5mlNRC0EWA23HCGMae35sWvtT27dv59NNP0Wq1wdlrsbGx2Gy2L6kpCIIQ4Dm6HefHLwSPvU2HMN3285AleYIgCIIgDJ8zCj794he/wO8P5A169NFHefnllxkYGGDp0qXnZHBCuJnFyVjtTj490ILRoOGmK3LISYviOzcWs/yzWhwuL1MKE3n/k1q8vsDf1c7D7UwrCs+h0jfoCStzuHwkxRoBGHR5w86PHRWDXquw43Ab0WY9mYmR/PnDyuB5a68Tx0n13B4fX7+mgL9tOsrRpl5y06K4e34+FpOWvkEPOw+3E2vRc9tVeYxOj+Ybi4pYu7MeVYWF0zPJS4v6h56ZMHLor7gftmrwtVaiJObgdztx734fAG/N58E8T66dfwtUqAFfezX6yYtxrFoW3BXP11SK8aafMrj8qeDOeN6j2zEufhTX3hWBbbePtaktugqfrQF1oBtt3gy0xfPP810LXyYyMpLu7u6QXE8tLS0kJCR8QS1BEIQhnqM7Qo5Vezv+zhqUJJE3UhAEQRBGgtMOPvl8Pp5++mmefDKQn8VgMPDQQw+ds4EJf98Ns7K5YVZ2SNnk/AQm5wc+qH3waU0w8ASB3FCREVo0ihwsj4nUc+2MTHYf6cDaG5gRpcgSWkXiB88EEjtPK0pEkaXgLKaEKAM7D7Wz/6iNxJgIbrsqj7+sPxIyjrKaLq6bmcXq7UNLm2YWJ5MQHcE/f21c2L3cdlUet10V+g/D2eNTmD0+5as8GmGEkyMsRFz9z0AgH9PAn78Xct5T+RmctLjT11iKOyIqGHgCUB123KXrgoGnQKEfd/nmYOApWL/tCKZbf3Z2b0Q4q2699Va+973v8fDDD+P3+9m3bx+/+c1vuOOOO4Z7aIIgXCBkYwy+EwskCekU+Z8EQRAEQRgepx18UhSFrVu3hiX0FUYei0kXVpaeaObr14ymoqGHuKgIrpqURqRRx9KFhewsb0evlUmLN/Pah0PBpA27m1i6sICmjgFMERrsg2427wssiWvs6Oe59w+SEh+axFOnlblyYhpI0G13kZsWxRUTRCBJCCdp9aDRg9c1VGawhF+o0SOZwme/nSrRrGSKPr02hRHlm9/8JjqdjieeeAKv18ujjz7K7bffLmbVCoJw2nSTrsPbXIbaZwUkdBOuQ44U+SIFQRAEYaQ4o2V3S5cu5ZlnnuG73/0uWq32yysIw2JWcTKfHmgNJvnOS4vi3S3VdPYEZjjNm5xOlEnH798pDe42l5tqwagP/zvtG/Rw9zWBLcuf/NPukHP2QQ+3jUuhvq0Pp9uHBEwtTOTRP+zA4/Wj08rHZk+JfAtCOEmjRz/1Jlzb3wJU0BrQHduFzrH21+BxAhL6qTehyZuJr2Y3/t5AvjNNdgm64vn4W4/grdsTaC8qGV3xNciGyFO2KYxckiRx3333cd999/3da1588UW+9a1vnb9BCYJwQZEjEzDd/t/42quRTbHIFrFsVxAEQRBGEklVVfXLLwuYM2cOVqsVWZaJjY0NmQW1efPmczG+f5jN1o/ff9q3eNHwqyqVDT0oisTuig427G4KOX/3/Hxe31AZUjavJI2Ne5pDyu5bWEh79yDmCC0dPQ627B9KBm6O0HL3/HwqGrrR6xSunJjGr9/aH5LYPDnWyM++NeMc3KFwofPU7MLXVokUmYAcmYAmJT+4HbbqGsDXVoUck4psCeQBUv1efC1HkHQRqO7BQELymFTkmDTwulBSC5HkQDzdb+/A392Ckjx6qE2/F0/lVvxdTWgyJ6BJv3Q3SpBlibg483AP44xMnjyZvXv3npO2L9X3hCAIwt9zIb4nBEEQhJHtjGY+/fKXvzxX4xDOMlmSKMyKAWDjnqaw821dg2FlBp2Gr12Rw7qdDQDMHJvEXzZUBvNEJcVEMDk/nv1VNhKiDeRnRPPCikPB+j6fn55+V0ibJx8LAoBr9/u49y4PHusmLELKnhQ8lvQmNFkTQ+pIsgZN+lg8Rz7FueWlYLkmZxoRV4fmn5MticGg1XHOTX/AW70TAE/ZBgxXfANt4RVn65aEc+wMvicRBOEionqceCq2/H/27jvMqupc/Ph3l9OmV6YyzDADSB9gqIJBkaYoiOUa682VaMzVmGLJjTfmxpgQS4zGaIj+YhKMMZrYNRQFCyJIHWboZWjDFKbXU/fevz8OnOEwKAwwBXk/z5PnYa+z19rvPhLOOe9e612YTTXY+o5GS+2HZVkEStZgVOxES+mHnjsWRVEwKnfjL1mLGhmP7YJvoNhdmE3V+Ld/AoDtgotQo2VGlBBCCNEdOpR8GjNmzEnPuf3223n++edPOyBx9o0bnMqabYdDxwkxDqaP6c3K4nI8vmB5Tk1VUBRYs62StMQIrrwwm8Jd1WGFyyvr3NwwtT//fdVQFEXhh7//LOw6n24qZ+ygFD7fXBF2bSGO59vy4XHHy7ANnYb3879jVO1FT78Ax7jrwebAt/YN/HvXo8Yk4xh3fbu+gZK1mO5GVFdbbSezuRbvqr9jVO9HzxiEPX8WgT1r2l9Tkk/nDKk3KMT5yb3oSYyK4Ext/5YPcE3/Pkblbnwb3z3Stgx7zQG0jEHBJdtHEtX+krW4pt1N65s/D+2k6tu6nMhrHkGNjO+emxFCCCHOYx1KPp2KdevWnfwk0aXy85K4e+5QPt9cQUyknZljs0iKdfHjG0fywdqD+A2TPinR/PPjPaE+z7xezIQh7RNHNl2loraVuCgHdpsW/pqmcvP0/qQkRFByqIF+veOYNrp3p9+f6DksbwuWuwk1ru3vjmUEMBsrUWNSULTgPzmK7sDytoTOUXQ7nuV/xCjbBoB/RxVWwI8al4Zv078BMBorcS96sn2hcVUFC4y6Q6FreJb9AaNyV3Cs7Z9g+r2gamAG2q5pc3TKeyCEEOLsMGoOhBJPAFgWvq3LMSp2hZ3n27ocvaEilHgCMA/vwbdpcSjxBIC3hcCe1diHzezs0IUQQghxnLOefBI904j+yYzoHz7VPCslmttmDQJoV//JMC1S4l1ER9hoavUD0C8zlpeW7KC8phW7TWXswBSq6t2h73pXXpiNw6ZzdRH1pgAAIABJREFUxYTsTr8f0fP4ipfgXfNPMAKoSdm4ZvwAs6ECzwe/x/I0obhicE69Gz21H45Rc/B8+mcg+JfHPvJKvJ+/HDZeoLQYtakqrM1qqcWePwtvVQmYwVl7es5oWv/1YOgajotvDyWejjLLtmIfNh1f4fvBBlXDPmJW57wRolPIsruey7IsmZkmOoWit39IoOgOFJsDy9d6TJs9uNPp8ec6XO0HPcF5QgghhOh8knwSAGQmR7ZrS0mIYMrITBpafAzOiWf9jip2lTYA4PObfLG1kgdvLuDA4SZyUmPokxr9peNX1raycVc1SbFORvRPkh3wvmbMljq8q18FK7hM06zeh6/wPYzSLaGnzpa7Ee9nC9Gv+QVKVCL6kKmoug09dxxaYm98W5ZhNbQt2dQSMlFjUzEPt83Iw+ZEjUvFNnQ6mAa2nAI8n/45/Bqr/oESnYx1TOJKjc9ETcrGNmgKiisWW7/xqDHJGBW7CFTsROvVFz19YNg9WWaAwN71WC216Nmj2tWQEl2roKCgu0MQx2lq9fGn97dRvKeGtKRI/nPGBeRlxnZ3WOJrRI1NQe93IYFdK4MNNif2/Mswqw+EP8AomIvWqy+B/YXgdwOg543DPnQ6gT1rMOuCm6mocenY8sZ3x60IIYQQ5z1JPgkALhyaxo6D9XyxtRJdUxk7KIXn392KPxBMJtQ0emhx+8P6+AImDpvK5PyMrxx758F6nvjHRgJG8EviyP7J3DV3aOfciOgWZlN1KPEUamuowGyobNfmXft6qFYHgBqbipbYG9c3bsO9fAFWcw1qbCqOC29GcUZj1pVhVO5CcUSh5YzC/f5job6K3dXuGlZjJa7L7sWz/I9YLbWo8emgqng+fPbIBXW0tH4EDhbhXflSqJ999DU4jpkN5V7yO4yDRQB4175JxBU/RuvV98zeKBGyatWqUzpv/PjgD8UXXnihM8MRp+G1j3ZTtKcGgLLqFv7w9mYeu3O8PFwQZ5Vz8jyMAROxmqrRsoajumLQkrJRU3IxKnej9cpDSwh+D4m8/lGMA5tQohLR0geiKAoRV/2MwIFNgIWelR+cJSWEEEKILnfWk0+yNKJnsSyLZetLWbejiuRYJ7Mn5pAU52JzSQ0frCtFVWDG2CwGZMVz+xWDuXFqfzRV4S+LtocSTwBFe2qYWpDJnrLGUFtKvIvVWyvYcbCBnNQYZk/MIcKps6KojFWbK4iNcnDFhGyWrDkQSjwBbNhZRXlNC2mJ7WdbiXOTlpyDEhGH1VofatP7jETRHQT2rjumLR9f0ZKwvr7C99F6D8O/ayVKZAJ67jjsBVehKEpwmZyioPefiL1gLu5354f3LVqMlp2PsXd9WyyZQwjs+jw4u6rfhdj6X0jraz9u62QG8BUtwaw5ED7Wpvex518e3DGp5kAo8QSA4cNXvBTXlO+cydskjvHggw+GHR8+HNwUIS4ujvr64N+jlJQUli1b1uWxiVNTcsznAUBdk5e6Ji9JsSdY6iTEaVIUpd3MVAAtPgMtPvzhl+qKQR0wKby/bsfWd3SnxiiEEEKIk+tQ8mnRokXMnNm+SOPixYuZMWMGAN/5jvw460mWbzjE3z8M1r/ZeRB2H2rgjtmDeeqfRZhHEoVb9tXyyLyx9IqPQFdVVFXBPEEOcfyQVFwOnaKSGlLiI7Asi/dXBX/A7y5toLrBzeiBvfjzv7eH+mzfX3fC5XiSo/x6UTQd18wf4l3/FlZrPbbccdgHXYyZOxrFEYlRvR8ttR/2EVcS2P/DsL4WFu6lvwstrzMrd6FoGphGW42mip1YjYc5usSirbOJY+LN+B1RoWsEyrZj7N8YGouA9wQRW+1mamFZWJYJPs+J/4Ief744I8uXLw/9ecGCBdTX13PPPffgcrlwu9387ne/Iy4urhsjFCfTLzOO8pq2ujtJsU4SYpzdGJE4nxhVezEqdqGl5IVmpZrNNQT2b0SNTETLGo6iqlh+T/AhiGWh5xSg2CU5KoQQQnQHxerAVKWRI0eyYcOGdu1jxoxhzZo1J+jR/WpqmjFPlEk5Tzz68gZ2HKwPa/vG8DQ+2VQe1vYfl+RSVt3K55sr0DWVcYNTWFlcHpqx1C8zloBhsbe8kcQYJ/NmDeT3bxTT4jlm9zAgv18SG3dVh4197cW5vPFJCcaR/w7DchP5/rXDO+FuRXfxbf4Q79rXIeBB7zsW5+TbMA6X4Pn4BaymatTkHFyXfhc1OhnP6lfxFy0K9bWPuRbfmn+GjafGpWOZBlZj+JK648/V88ZjVO4KXcMx7vp2s6PUxN6oUUkEjiSkUDRcM3+IWVeKd9UrbWP1uxCjfHtw2V+vvqComJW7jwyiEzHrAbTUfmfj7ep2qqqQmBjV3WGEjBs3jhUrVmCz2UJtfr+fSZMmsXr16k6//vn+OXG6Wj1+/rp4B5v2VJORFMnN0weQnRrT3WGJ84Bvy7KwZdOOCTeipeTR+u58CPgA0LNH4Zx8Gy1v/hzryPJsJTqZyLn/h+KQmdcn09M+J4QQQpz7Tmnm08GDB4HgEq6jfz72Nbtd1s/3VMlxrrDkk64pZCS3/zJRXe9hRVEwIWWYBp8UlvHfc4ZQVttKQrSD9TuqKNwdTCrVNHp4/t2tJMe5aKlo28I4IcZBr/jwJ4oKwRpPw/omsmFnFUmxLkYPlMLNXydmQ+WRneqCP94De1bjS+qDf8uHWM3BejBm1V48K18mYsb3seWOCc4iUhT07JFoSdn4Ct8Dnzs0phKTjGIaGMcmnxyRaL2HYve5sQJetIxBeFf+Lewa3o3vgc0Jfk+omxrdC+eldwaLzjZVB6+ZkIkREYdt+OVg+NAyBuNd8ZfQskHzcAla9kick+dhNtdi6zsaNS6tk9/J81dERARFRUWMGjUq1FZcXIzLJTMUerIIp4075wzp7jDEeejYuoEAvg3voGUMDiWeAAL71uMrygwlngCspir8u1djHzyly2IVQgghRNApJZ+mTp2KoihYlsXUqVPDXktKSuLuu+8+6RiPPvooS5Ys4dChQ7z77rv0798/7PXf//73PPPMM2GvFRYW8tBDD+H1esnIyODxxx8nMTHxVO9NAFdOzGbXoQYqa1vRNYVrJ+cxeUQGW/fVhZJJYwelnHCVUYs3wBUTsgF4+7O9Ya/VNXm5ZfoAXvz3Nppa/bgcGjdNG0BOegzb9tdxoLIZVVGYNaEPKfERACdMeolzn1FzgOOXw5lVe0NJoVBbzQG8a/4VTDQBaHb0PiNQdDvOCTfh+eyvEPChRCbgGHMdWCbuukNYLXWg29GzR+J+/aFgX0VBjc9odw2r7hDO8TfgWfkSGH6UqETsY65GUXVs/SaEzvNt/uBIwgxQ1OBYreEzBM3aUmzTvnfmb5A4qe9973vMmzePSy65hNTUVCoqKvjoo4946KGHujs0IURPZATCDi0zAGag3WmW4W/XdnxfIYQQQnSNU0o+bd8erOFz00038be//e20LjRlyhRuueUWbrzxxnavbdmyhcLCQtLT00NtlmVx3333MX/+fAoKCnjuued44oknmD9/frv+4sslxbr45bfHUnq4mfhoB9ERwVlqV07MRtMUVEXh8vF9qKp3s3zjoVA/TVUwTYsFb28mIcZJXkYs1Q1ts0kykyMZnpfEE9+dQFl1KykJLpz24F+n//vWGEoPNxMdYSM2ytG1Nyy6nJbaHzQbHPMlX88ajtlQiVmzP+w83zHL7TB8+Da8gzrlO5j1ZWgZQ9BS8rAPmw6Af+ty1OQc1JzR2PIvx/2v/23ra1n4Ni1CTexz3DX6YTZUoPUeFqwxNWQqYOErXnKkNkg/9EEX41335jFjmfg3L0WNzwhtxw2gZww+e2+S+Epz5sxhyJAhLFmyhMOHD5OTk8Odd95JXl5ed4cmhOiBbEOn4Vv3RujYPmQaWvoFBPZtBMsAQEsbgH3YDAI7V2K5GwBQXDHoeeO6JWYhhBDifHfKBccNw6CiogKfz3day+wKCgpO2O7z+Xj44Yd54oknuPXWW0PtxcXFOByOUL/rr7+eKVOmSPLpNKiKQlZKW9HvitpW5v9tQ2g3u+KSGn757XFcP6UfyzeU4rBpDM5OYOGSHaE+qQkRTByaxpZ9tWQmR3HDpcHaNzZdO2FB8cxeMsvpfKFGxOKa9j28697A8jRjGzAJW/8L0VL74Vn5N8yaA2iZQ7APv4zAnvD6PZbPjfvfT2BWBxNIxv4NqBGxGDUH8BcHd8UzAMvXinXMUjoA/G5cl/3omGsMxji8l8CeL4L99q1HjYzHKN+Bf2uwuHVg7zqM+rJ2Rcgtn4eIy+/Hs+plzNpS9MyhOMZe1wnvlvgyeXl5kmwSQpwSx8gr0RKzMCp3ofXKQ88eAUDE3J8RKFmLEpWIrd8EFN1OxNU/x79zJWBh6z8RNSK2e4MXQgghzlOnnHzSNA1N0/B4PGe1xtPTTz/NlVdeSe/evcPay8vLw2ZCJSQkYJom9fX1sgPSGVqzrTKUeALw+AzW7zjMtNG9mTY6+N/h6X9uCutTUdvKbbMG8l+Xh2933Oz289dF2ykqqSE9KZJbZ0jB2fOR3nsoeu+hYW1qTC8iZobvbKdl5WMcKGzr12c4vmNnIQH+nZ8dWcrXJrB7FbaBk/FvWdY2Vu9huD94BrO+Ej17BLb+kwjsXHncWCsxyneEj7VrFbb+k/Bv/zjUZhs4GTUulYiZPzr1mxZnTX19PS+++CLbtm2jtbU17LWXX365m6ISQvRkep989D75YW1aYhZaYlZYmxoRhyP/8q4MTQghhBAncMrJJ4BbbrmFH/zgB9xxxx2kpqaiKEroteOTR6di48aNFBcXc++993a476mSnTray0hpnxxK7xVNZaOX2CgHvVOi6ZUYCXva6ukoCmRnxhMT42T73lpSkyJJSYjglVc3sn5nFQD7K5r44ztbef5/LkVVlXbXEMK8/n6aCj/EV11KZL8C7Ck5HFj/drAA+RHO+ER83kb83pZQmxYRS+rka2mIisD0tODKGkTNspcwW4NLKQJ7vsAZGUGwxH1b/SlXfAKepjgC9W0FZ/WoWDKv+i5NmwbiLd+DK3sIUYMu7PR7F1/uRz/6ET6fj5kzZ0qRcSHEKTFbGzCr9qImZ6NGBB9KWgEfRvkOlMgEtISMbo5QCCGEEMfqUPLpF7/4BQArV4bPLlAUhW3btnX44mvXrqWkpIQpU4K7jlRUVHDbbbcxf/580tLSKCsrC51bW1uLoigdnvUkW2i3NyQrjpy0GPaWNwKQlxHLwn9v5XB9cFnT5Px0po/NYu3WCuqbgzvHXDqqNxWVjfzo6U9pbPGhAFdd1JeiI0XLj6qsbWXX3moSYpxdek/iHJJ9EWRDC9DiAfvwy0JFyBVXDNagmeiZVfg//H2wMKyqoWQM5uBz3w0mqewRBBL7hxJPR7WW7cU2bDr+osVHxorFumAGesoQAssWBIvRqjr66OuormmFzLGQORY34K5q4nzS07bQ3rhxI6tXr5adU4UQp8S/dz2eZX8I/bvuvOR2tMQ+tL47P7R5hG3INJwTbujmSIUQQghxVIeST0cLj58tt99+O7fffnvo+JJLLmHBggX0798f0zTxeDysW7eOgoIC/vGPfzBz5syzev3zlcOu8eAto9h1sB5NVdmwq4rFX7Qtc/q4sIyLR2Zyx5WD+WDdQVLiI5g1IZs/L9pOY0swGWUR3AFv1IBkKmvblskkxjiIkyLjogMcY65B7z8Bq6kaLW0Aiu6A+Awib3gSs6oEJS6d1tcfapsd5WvFt+0jFGc0lqctaaSl5OIcdz22ARdhNVejpV2AottR41LRbvgNZvVe1KS2J+Si5xgwYAAVFRVkZWWd/GQhxHnPu/ofbbvbmQG8q19FzxgUtmupf/MH2IdcihrTq5uiFEIIIcSxOpR8OhOPPPIIS5cupbq6mm9961vExcXx/vvvf+n5qqry2GOP8bOf/Qyv10tGRgaPP/54V4X7tacqCgOy4gFYvrG03euFu6t4a8VerCOTxnYerEc5bimdYVpcWpCJ12dQVFJDRlIkt868QJbciQ7T4tIhLj2sTXXFoGblY7ob4fhi4+5GnJf+N97PFmI2VKD3GYFj9NXBseLTIf64sSJiUbPCa4OInmPcuHHMmzePuXPnkpSUFPbaNddc001RCSF6qmMfPABY7sbgZ0V4a/A8ST4JIYQQPYJiWdYpr0kLBAL8/e9/Z+3atdTV1XFs155aFFaW3Z1ccUkNv32trcB4fLSDPinRFB63pO6ycVn8e3XbDKns1Gge+s/RXRanOH9YnmY8q17BqNiBltwXy90QVjjcNnAyZn0FZksdtrxx2EfNRlHUboz43NLTlt3dfPPNJ2xXFIWFCxd2+vXlc0KIns+/dx3+HStQnDFg+gnsbts91TZwMlrGYDwfPhtqU+PTibjmEflsOE097XNCCCHEua9DM5/mz5/P6tWrue6663jqqaf4/ve/zyuvvMLll8suIueyoX0T+d41w/i8uJzoSDszx2TxxoqSduddODSN9KRI1u+oIiU+ghnjZImM6ByeFX8hsHcdAIGmatS0C7CPuAKzthQtYyDedW+BL7jc07fhbRRnFPYhU7szZHEGXnrppe4OQQjRgwUOFOL54PdtDc5o7GOuw6wqQUvph23IFBRVR5l+D/7dX6BGJWAbOl0ST0IIIUQP0qHk09KlS3n11VdJT0/nmWee4dZbb2XixIn87Gc/4+677+6sGEUXyM9LIj+vbbnLjDFZbNpdjdtrADBuUAppiZGkJUYyYUhad4UpzhOB0s1hx2b5duyX34+iqgTKtocST6HzDxZL8ukcY1lWaMdU0zS/9DxVlR+PQpzv/Lu/CG/wNKHFp+PIvyysWe8zAr3PiC6MTAghhBCnqkPJJ4/HQ1paMPHgdDpxu93k5uaydevWTglOdJ+slGjm3zGeot01JMQ4GNgnvrtDEucRNSETs3J323F8BkZpEUZtKVpSNqgamEbodS0hsxuiFGdi1KhRbNiwAYBBgwaFElFHHU1OncpOqt/97ncpLS1FVVUiIiL46U9/ysCBAzslbhHOtCwqa1tJiHbisGvdHY74mlKjEts3umIx6spQY3qhaG1fZ82GShRnFIojsq2tpS44TmTbdxnL14rlbkSNTe28wIUQQggR0qHkU25uLsXFxQwbNowhQ4bwzDPPEBUVRUpKSmfFJ7pRTISdicNklpPoes5Jt+L54FnMhgqU6GSUuHTci5868qqCPnAygT1fgK8VLXMI9hGzujVe0XHHbjixbNmyMxrr0UcfJTo6GoAPP/yQn/zkJ7z55ptnNKY4ucraVp765yYq69y4HBq3zriAMQPl+4A4+2xDpxE4UIhZWwoo6Dmj8Cx5CsvdgOKKwXnpf6PGpeFe/FvMqr2g2XCMuRbbkEvxfPwnArs+B0DPG4dz8rfxb1uOd/VrYPhQk/rgmvED2QlVCCGE6GQdKjheVFSEpmkMHjyYffv28X//93+0tLTwwAMPUFBQ0JlxnjYpJHv6PtpQyhdbK0mIcXLlxBxSEyK6OyRxHrEsC8vdCJpGy8J7wGqb6aQmZeOccifedW9Ccw169ghsw2ZIfY9TdC4Wkr3iiit49913T3reW2+9xcKFC3njjTdOeWz5nDg9z7xexMZdbRtTRDp1nrzrQmy6zIASZ59lmZhV+1CcUbiXPoNZezD0mhqfjpYxGP/mD9o6KBqOSbfi/fTFsHEcF/0n3s9eCps9axt0Cc6Jt3T6PZxLzsXPCSGEED1bh2Y+DRs2LPTn7Oxs/vKXv5zteEQP8emmMl5auvPIUQM7S+v59R3j0bUT/7hvdvuJcOioqnLC18X5w/I0gyMybBmV5WsFzR62NOJkFEUBVcPiRH+nLDxLn8GsKwXAqNyFZVk48r9884MTxuVtAZsDRW2Lywp4g9fXHaccq+h8paWlX/n6gw8+yMqVK7Esi//3//5fh8aWH1inp7rRG3bc4glgczpIjnd1U0Tia6/XcAD2NlaGNZsNlTjiU/Af22gZ2JsPEf63FOxNh/Aek3gC0N3VJCdHn/14hRBCCBHSoeQTwMqVK3n//fepra1lwYIFFBcX09zczPjx4zsjPtFN1u+oCjuubfSyt7yRfplxx7V7eO6tzZSUNRIf7eC/LhvI4JyErgxV9BBmfTnuD5/FrC1FiU7GNeU7qPGZeD76I4F9G8HuwjH2OuwDJ590LKPuEJ4Pn8WsK0OJSUHPHklg79ojryrY8sbhXf1qWJ/A3nUnTD4ZNQfxLHsOs74cNTYV55Q7UWOScS/7A8bBYhRHFI4JN2DrNwHP6n/g3/whALbBU3CMu75dLSLRPU723+GXv/wlEJz59Nhjj/HCCy+c8tgy8+n0DOubwMHKptBxVkoUBAJUVTV9RS8hzpyWlU+gZE3oWO8zAjN9EJRsDLUpEXEE+kyAjR+AdWRTA0UlkD0BZed6rJba0LmBuD4cfPcFFM2GbeDFqFHyPUZmPgkhhDjbOpR8eumll1i4cCHXXnstS5YsAYKFx3/5y19K8ukc1+oJUFRSTXSEnUF94ukVF/7kWlUUkmJdNLT42FxSQ0p8BHmZsby6fDclZY0A1DV5eeG9rTzx3QlfOkNKfH15Plt4pB4HWE1VeD56Ab3feAL7gkWl8bXi/Wwheu9hoNuCiZ+oRPS0Ae3G8q74K2ZdWXCsxkoMVcc57XsYh7ai2CPQ0geBboeAL9RHjel14rg+fRGzvhwAs6ECz6d/RksfiHGwODi+txnPpy9iKSr+osWhfv7iJejpA9H75J/5myO6zJw5c3jooYeoq6sjPl42SuhMsyfmoKkKm3bXkJ4UydXf6NvdIYnzhPOib+F1xWBU7EJLycMx5mqwuSDgx79nNWpkAvbRc9HiM3BNvwdf0WKwLOzDZqAn5+C6/F58a9/AbK5BTx+Ir2gJGMHPE//2T4m87ldhBcuFEEIIceY6lHz661//yl/+8hcyMzNDT5X79u3L3r17OyU40TUO17Xyy5fW09QanLCen5fELTMGsONgHaVVLWiqwtyL+lLb6OGJfxTi9Qenq186KpODh5vDxmps8dHY4iMhxtnl9yG6l1FzIOzYbKjAOLwv/CTLJLBvA961r4PfDYBtwCSc37jtK8ey6g9httTi3xKcleQrfBe9/yQCu1eB4UeJ6YVj9NUnjMusOXjc8QEU53FPc40ARvn2E96TJJ96tpaWFhobG0M7sS5fvpzY2Fji4qR4cGfTNZU5k/oyZ5IknUTXUuwunBfe1K7dPmw69mHTw9r0rOHoWcPD2rS4dFxT7wLAu+6NUOIJwHI3ENi3AduASZ0QuRBCCHH+6lDyqaWlJfQF/+gSiEAggM1mO/uRiS7zwbrSUOIJoHB3NbMas5k9MYePC8voFedkwpBU/rp4RyjxBLBsQykXDkmjorY11JaWGEF8tNTKOR/pGYPDlkFoqf3Rs4ZiHGhbBoHNSaBsWyjxBODfsQL7iCswKncT2LcBNTYVLW0AxoFNbWOlD8S/7q22cSwLo3w7kf/xKN7C97BaGwiUbsE2MBlFUfDv/IzA/kLUuDS09AswSje3jZU5BD19YFib4ojCljeBwLZPgLblV3rm4LP07ogz9WV7Y7jdbu655x7cbjeqqhIbG8uCBQtkuaQQ5yjryBK54zeQsEwDRdWOawuE1ez70vO+akzbCR6WnahNCCGEEGekQ8mn0aNH8/zzz3PnnXeG2hYuXMjYsWPPemCi63h8gXZtm3ZX8+7n+wDYAmw/UE9MpD3sHMuCqaMz0TWFopLgsotvTuknP/rOU86Jt+DRdIyy7WjJ2Tgm3IgSmYDlbsS/cyVKRCyO0dfgK1rUrq9v+6f4C98LHSvJOeh54zDKd6L16ot9/DdpffXH4Z38Hryf/y20rM/Ytz647bY9Au+qv4dOU3vlofcdg1G5Gy0lF8eEm1CcUVjeZvx7vkCNTMAx9jq0lDyck+fh27QIsLAPn4nWK7dT3itxYpZlhZbLHf/vyMMPP3zCPklJSbz22mtdEZ4QopN5N7x95N9gsA+djqPgKgL7NuJZ+RJWax169iick+dhtdTj/uiPmFV7UZNzcF78bdTIRDyf/InA3nUoEXE4JtyILWfUScfUeg9HiU7GagrWulR79UXvM6Lb3gMhhBDi60qxvuxx8gkcPnyY73znO9TX11NZWUlmZiZRUVEsWLCA5OTkzozztEkh2ZPbVVrPY3/fiHHkfUpLjCA51klRSW3YeVdNyuHNFW1LLAdnx/Oj6+ULmuiYwP5C3Eue5ugMIzUlDywT83BJ2Hmuqx7Cv+FdAgeLURMyUWNTCOz5IvS6bfhl+I8kio5SopNR7BGYNfvDxoq84TeoUYmddk/nmp5WSLaxsZFf/OIXLF68GL/fj81mY8aMGTz44INdsnxOPieE6H6BQ1txv/9YWJtz6l14PnoBAm171tnzL8eo2IVRsTPUpvbKRc8YhG/ju22dNTvOKXfgWfpM+JiX3oXn4/AxbcNmoPXKRdFtaJlD282cOh/1tM8JIYQQ574OzXzq1asXr7/+OkVFRZSVlZGWlsawYcNQVSkufS7rlxnHT24exaotFcRE2Jk8IoN/frQ77BwFGDc4ldyMWDbsrKJXfATfyE/vnoDFOU3vk4/rih8TKFmDGp2E7YLJeD56HvPYk1Qd36ZFGPuDS/bM6n2Y3hYc37gNo2InijMG26CL8W/7CHxtS/iUiFgUewTUHDOWZgu2iR7rf/7nf9A0jbfeeov09HTKysr43e9+x09+8hOee+657g5PCNEFjOMeQADBjSGOSRIdPe/4c82qEgx7+EYpGD4CB4rajRkoLWo3plm9H+e4608zciGEEEKcig4lnyBY62n48OEMHTo01GaapiSgznE5aTHkpMWEji8b34fNe2upawp+QZs6ujfJcS6S41wMypYtiMWZ0dMGhO1yZx81B6OmLBDZAAAgAElEQVRiF5a3GVCwj5qNf/un4Z2aqlA0W3D2U8CHv3gxer8JBHauDG6jbXPiGH0Nis1B6+E94G0BRcFRMBfl+B8lokf54osv+Oyzz3A6g3VWcnNz+fWvf82kSVLwV4jzhZ42AN/xbTkF+EvWgq+ttqSWdgEoKsahLWFtWtqAsFp+2FzY+o4msP2T48YcTaBkXfsxhRBCCNGpOpR82rJlCw8//DA7duzA6w0mJSzLQlEUtm3b1ikBiu6REh/Br+8Yx86DDcRHO0hPki2HRefRkvoQecNv8G//GKP6AIqmoyXnEDhSgwOCS+q8he9B4MjPE9PAOLCJiOsfw6orQ0vJDW2NHfXNJzAqd6HGpqHG9MwlwaJNTk4Ohw4dIje3rcZWWVkZOTk53RiVEKIraan9cFx4M75N/wYrWHdP7z0U17Tv4V31CmZzNba+Y7DnX4blnoRnxV8wKnahpeTivOhbKK5YrJZ6/HtWo0Yl4Bj3TfSMQe3GtPUeinKCMYUQQgjRuTpU8+mKK67g4osvZvbs2aEn1EdlZGSc9eDOBqnlIcS5IVC6Gfei3wQr2QNKYhZqZDzGwc2oCZk4L/oW7sVPYrkb2zopKlH/9UcUTXbc7IieVsvjySef5O2332b27NmkpqZSUVHBO++8w+zZs+ndu3fovGuuuaZTri+fE0IIEa6nfU4IIYQ493Uo+TRy5EjWr19/Tu1mJj8qTs6yLN5ftZ+VxeVER9i5+ht9GZAV391hiXOUZRr41r6Ov2QtanQSjnH/gZaUfdJ+7qXPENi3PqzNdeWDBPatJ7B3PWpML5SIOAK7VoZe13PH4ZrynQ7F59+3Ad+GdyDgwzbkUuyDLulQ/6+Dnvaj4uabbz7pOYqisHDhwk65vnxOCCFEuJ72OSGEEOLc16Hk0wMPPMCsWbPOqToc8qPi5FZsKuPPi7aHjh02jce/O4FIp05ZTSuxkXaiXG0zS2oaPFhYJMVKHR3Rnnfju/jWvh46VlyxRN7wG1BVzLoy1KiEsALgZuNh0Gx41/yTwK7Pw8ayDbwE/7blbQ0R8diHzcAo344alYB9xJWoEbFtYzVUgs2BGtG2Q5rZWg8+D2pcKmZDJS2v/U+wRtQRrpk/RO897Gy+BT2e/KgIJ58Tp8frN3ht+W6K9lSTnhTF9VPySEuUJdri7AiUbsG77nUsbwu2ARfhyL889JrpbsT7+ctHlt3l4ZhwI4ozGt+6N/Dv+SK47G7sdWi92pbyBg4U4l3/NpbPjX3QxdiHTsesL8ez6hXMukPoWcNxjPsPFN3RHbfb48jnhBBCiLPtpDWf7rvvvtBMJ5/Px1133cWoUaNISkoKO++xxx47UXdxDtiyrzbs2Os32LS7mqVrD3LwcDO6pjD3olymje7NC+9t5YutlQCMvqAXt185CE2KzYtjhBV8BSx3A4H9G/Gu+SdW42HQ7TgvvBk9dyzupb87cr6CnlMAdldo9zo9dxxGzf7wwVvrUGOS8G8pxdi/Ef+Oz3BOuhU9exTupU9jHNoKioJt0BScF96EZ/Wr+IuXgGWipQ1Azx4VlngCCBzcfN4ln3qiuro6PvnkE6qrq5k3bx6VlZVYlkVqamp3hya+xOuf7OGjjYcAqGn0Ut3g5pF5Y8+p2dGiZzLdjbiXPA1GsMafb80/USPjsfWbAIDnkxcxDhQCEChZg+X3oGcMwlf4HgBGUxXuRb8l8sbfoOgOzOYa3EufAdMAwLvqFYhMxL/udcz6cgD8W5eDquGccGNX364QQghxXjhp8qlPnz5hx3l5eZ0WjOgemclRrNl2OHSsKLBtXx0HDzcDEDAs/vXxHlwOLZR4Ali7/TCjBiQzZmBKl8csei41oTdG+Y62Bs2Of+fKYOIJIODD8/nL2L3NxySqLAJ71+Kc9j3MxiqshnLUpBw4bMc8vKdtLJsT/7ZPsI4WIg948az8G/aWumDiCcCy8G/5EC0pG3/RolBXo3wHSlx6u3i1xN7t2kTXWrNmDXfffTdDhgxhw4YNzJs3j/379/Piiy+yYMGC7g5PfIkte8MfXJTXtFLT6JFZseKMGeU7QomnowKlm0PJJ+NQ+EMOo3RzuwcLlrcZo2ofetoAjLLtocRTqM++9aHEU9g4QgghhOgUJ00+3XXXXR0a8Pnnn+f2228/7YBE15s6ujd7yxsp3FWN06Ex96JcNu2pDjvHtCz2Vza161tZ5+6qMMU5wjFqDmbdIYyybSjOaBwTbgzuNHQsvwez5lC7vmZtKb5Ni8DvBj4OFh1PHYBZsSM41sRb8K1/M7yTrxWzrv1YRtXedm2KGcA+cnbwGmYAvd+F6Ed+zIju86tf/YqnnnqK8ePHM3r0aACGDx9OUVFRN0cmvkrvXlGU17RtVx8TYSMuSpYsiTMXfCigAG3LYdX4DAIHilBc0agJWZhVJW2vJfRGS8wKTx6pOoruwL9vPUQmtLuG2qsvyqEtYZtYqAnyMEIIIYToLCdNPnXUggULJPl0jnHYNO6+ehitHj82XcOmq+iawuaStqfa8dEOJudnsGJTOcaR2iiqojAsN4F3Vu6leE8N6UmRXHVRX/nxcZ5TnFFEzHoAy9MMdieKqmM2VuKrORA6R03sje2CSUeKhx/5caHZMZuqjiSegqyaAzgvuxejdgSBvesJlKxBTe2PWVfWNlZyDrZ+F4bXi9Id2AZPwb/r87Dx9JxR6Fn5wW21TRPFLjM0eoJDhw4xfvx4gNCSLZvNhmEYX9VNdLPrLs6jusFDSVkjcVF2vnXZQHRNlmGLM6fGpuIY/028696AgBet9zB8W5ZBS/B7iZY5FDU2FbOhAiU2Fec3/gs1Ogmj9iDGwWJwRKJnj6T1rZ8Hd1DVbOj9JxIoWQuGHz1vHPZBF6PFpuL55E9YrfWovfriGPcf3XznQgghxNfXWU8+daB+uehGja0+olw21C+pzfGN/AwChsUX2ypJiHYwe2IOaYmRfP/a4by/ej+qAjPGZLFuexXvrwrW5dlT1sih6hb+95aCrrwV0UMpzrZCpfb8K0DVCOzbiBqXhqNgLmpUAs5Lvxuss6HbceTPwl+ypt04gdItoeVzJoAzGvvI2QRKN6PG9MI++mq06CScU+7Ev/UjFLsL+4hZaPHpRMx6AN/Gd4IFay/4BnpWfjA2KSjbo+Tm5rJixYqwzSw+//xz+vfv341RiZNJiHHyv7cU0Oz2E+HQUVWp9STOHvvQadgGXQyGH++GdzAObAq9ZpQW45r9U9TYXiiOqFDSOmLmj7C8LViqjZa//yCYeAIw/Jg1B4i6JVj36eiDB733UCJveBJ8rWGfWUIIIYQ4+8568kkKjfZsFbWtPPdmMaVVLSTGOPn2FYPokxrNC+9uZePOKpwOjau/kcslIzOZMir4v6MaW3y8+/k+dh6sJybChtdvsGFnVdj4JWWN1DV5iY+WH/eijaKqOPJn4cifFdZu6zsaW9/RbQ2OSPw7VoDfA4CalB3cwe5YnibUxCyUip0Edq/CKN2MY8KN2PLGYcsdG3aqlpyNa9r3OuWexNnz4x//mDvuuIPJkyfj8Xh46KGHWL58Oc8991x3hyZOwbG7oQpxNimaDTQblqf9sn+8zajO3PZ9HJEQ8IU2rzjK8jSh6Pb256sqSOJJCCGE6HRnPfkkeraXP9hJaVULADWNHv70/lYmDUsLJZHcXoOXP9jJsL6J2O0axXtqSIhxckFWHG+uKGHnwXoAGlv9/On9beRlxobV/Ihw6ES55K+VOD1afDqR1/6SQMkaFHsket5YvOveJHzxlUJg/0aMsm1A8AeF59MX0XsPDf7oEOec/Px83nnnHd555x2uvvpq0tLS+Ne//iU73Z1j6pu9vLNyH5W1rYzol8SUUZnyQEqcFbb+E4NLq4/MZFKiEgmU78C3aRFaSh72kVcE6zvtWIF/92qUyAS0rHyM/RvCxhBCCCFE9+myZXePPvooS5Ys4dChQ7z77rv079+furo67r//fg4cOIDdbqdPnz48/PDDJCQEC0MWFhby0EMP4fV6ycjI4PHHHycxMfFsh3xeObqD3VFV9R72V4Q/UbQsKNxdzZsr9uL2BgAYPzilXXFxj89g8vAMKmpaqW7w4LBp3DStPzZd69ybEF9ralQi9mEzQ8f24ZdhlG3HrN4HqoZ95GyMQ1vCOwV8mA2VaL36dm2w4qxJSUnh29/+dneHIc7Ab1/bFPqM2ba/DsO0mD4mq5ujEl8HevpAXJffj3/nShRnFGZTDf4jG1kY5dsxm2vQMwfj+eRPbZ2ikrCPuQ6z5gBaxkBsAy7qpuiFEEIIAZ2QfCooOHG9nylTpnDLLbdw4403htoURWHevHmMHRtcKvPoo4/yxBNP8Ktf/QrLsrjvvvuYP38+BQUFPPfcczzxxBPMnz//bId8XhmcncCqLRWh49yMGIblJrF+Z9vudg6bxs6D9aHEE8CqLZVcPCKDkrK2XWESYhwM75dIfr8kyqpbSIx14nLIrCdxZizLwr/9E4yDxagJGdiHzSRy7v/h276CwIFCrJY61KTs4FbcRyiuWNTELPw7VhDYX4gal4Z9+EyZCdWD3Xfffac0K+axxx7rgmjEmSqvaWn3cGPNtsOSfBJnjZ4+ED19IABNfwrf2CZQsgbL2xLeobkaPbUfWv5lXRWiEEIIIb5Ch7alee6559rNbHK73Tz00EOh4xdeeOGEfQsKCkhLSwtri4uLCyWeILj0oqwsuItVcXExDocjlMy6/vrrWbx4cUfCFSdw49T+TByaRmKMgxH9kvjOlUOYOCyNqy7qS694F3kZsdxzzbDQjnbHmjAkhWmje5MU62RQdjz3XDMcTVVRVYXMXlGSeBJnhW/T+3hX/IXAvvX4NryD+8NnCVTswrviRYx96/Fv+wj/zpXYhkxDiU5GSx+Ia+YP8G/7CM8nfwr2K3wP95Knu/tWxFfo06cPWVlZZGVlER0dzYcffohhGKSmpmKaJsuWLSMmJqa7wxTH8PgClFY1Yx7z+WCaFqVVzdiP7JR6rKRYZ1eHKM4TanRS2LESndSuDUVBiUrowqiEEEII8VU6lC1YsWIFn376KY8//ji9e/dmw4YNPPDAAwwdOvSMAzFNk1deeYVLLrkEgPLyctLT00OvJyQkYJom9fX1xMXFnfH1zlcRTp3/unxgu/YrJmRzxYTs0LHfMCncXR3aKKZvegy5GXHkZsRx/ZR+XRStOB8Fdn0edmyUbsYfEde2axGAtxktJQ/nhBtCTZ4Vfw3vV7ETs6kKNTq5U+MVp+euu+4K/fm2227j+eefD5s5u27dOv7whz90R2jiBNZtP8yL/96Gx2eQGOMIPnzQFH772qbQsuvRF/Tii62VGKZFYoyDOZNyujts8TXlmHAj7g9/HywqbnPhnHATanwGxqGtmA0VoKjYR81GjZJSDUIIIURP0aHk08svv8wf//hHrrnmGi666CI+++wzHnzwQWbNmnXyzifxi1/8goiICG666aYzHutYiYmyg8npuCQ5mvSUGFYUHiIpzsX0cX2IcMqORqLz+WMT8dSVhY4Vu5Oo5FTqd4afF5+ehjPBhefgNrSIWIzYBFqr9rb10+0kp6egOmXpXU9XWFjI8OHDw9qGDx/Oxo0buykicayAYbJwyQ48vmDp/5pGL699tBubrlLdENyZ0us3KNxVzfw7xtHY4qdPahSa2qHJ1UKcMj1zMFE3/hazthQ1IRPFFpxlF3HdrzCr96NExKFGxndzlEIIIYQ4VoeST6qqMm3aNN555x2WLFnCxRdfzJQpU844iEcffZT9+/ezYMEC1CNfVtPS0kJL8ABqa2tRFKXDs55qasKXCIhTlxhpY86F2QC0NHloafJ0b0DivKAOnw1le8DXGnx6XXANgdxxqFtXYdYdAkDvO4ZGM4qKZ+/Cagru1Khnj0RxRGF5m0FRsI+aQ02TCU0n2KL7PKeqSo9KzA8aNIgnn3ySe+65B6fTicfj4Xe/+x0DB7afpSm6Xqs3QLPbH9ZWWdfabnOJVm8AVVHomy7LJUXnU2xOtJS88DZFRUuWGXdCCCFET9Sh5NPf/vY3nnnmGe68805mz57Nz3/+c2bPns1jjz1Gfn7+aQXw29/+ls2bN/P8889jt9tD7UOGDMHj8bBu3ToKCgr4xz/+wcyZM79iJCHE14GWkkfUDb/BOLwHNS4ttGzCcfHt+ArfQ3FE4xg1G9+mf4cSTwCBfRtwzf4p+N2osSmy3O4cMn/+fO69914KCgqIiYmhsbGRIUOG8MQTT3R3aAKIibCTlxnL7tKGUNvI/snYdJWy6rYiz9mp0STESJ0n0fksyySwcyVGxS60lDz0ARNRFJVA2TYCe9agRMZjHzwFxRGJ2VCJb9tHANgvmIwal4rla8W3ZTlWczV63zHoGYO6+Y6EEEKIrz/FOr6C+FeYM2cOjz/+OP36tdX8eeutt/j1r3/N6tWrv7LvI488wtKlS6muriY+Pp64uDieeuopZs2aRXZ2Nk5n8AtrZmYmzz77LAAbNmzgZz/7GV6vl4yMDB5//HGSkpK+6jLtyMwnIc59RvV+Wt/+BRjBHRiV6CS05L4EStaEnee67F70zCHdEeI5pafNfDqqvLycw4cPk5ycHFbzD2D9+vWMGjWqU64rnxMn19Di481PSzh4uJmhfROYNSEbRYH3V+2naE8N6UmRXDWpL/HRju4OVZwHPKtfxV+0KHRsGzodPWMg7sVPA8H/L6tJ2TinfY/Wf/1vcCYtgM1F5LWP4P7gWcyqkiO9FZzT7saWPbJrb6KH66mfE0IIIc5dHUo++f1+bLb2dX/Ky8vb7WTXU8iPis7l9Rm8+tFuivZUk5EUxTcv7UdqQkR3hyXOIZZp4lv/Jv7dq1Ej43GMuRYtNbyovefzl/Fv/iCszTbiCvwb3w0dK1GJRP7Hr1E0qU12Mufij4qRI0eyYcOGThlbPieEOLc0/eXOYLHxo3QHesYgAvvD68TZhk7HX7wkvG3INPybl4a1aVnDiZjxg06L91x0Ln5OCCGE6Nk6tOzuaOKpubmZurq6TglIdJ+y6hYiXTZiI+1feo5pWZRVtRAf4yDSaeNfH+/h443BOjy1jV6qX3fzyLyxKIrSVWGLc5x/y4f4jiSRjKYqWhf/lqgbnwwVkAVQ9PazKfS0Aegp/fBt+RAlIg7HqNmSePoa68BzEiHE15xij8A6Jvmk2F1gb//gS3FGn1qb3XXC61iWCaYhny1CCCHEWdCh5NPu3bu599572b59O4qiYFlWKMmwbdu2TglQdL5mt5+n/rmJkrJGVEXhsvF9mHtR33bnVdW7efK1TVTWtmLTVa6f0o8t+2rDzimvaaWuySt1P8QpCxzaEt7ga8Wo2oue3lZs2jZ4Cv5dK7FagklvLX0gamIW7sVPBZdOKBpqZByOgrldGbroQpLQFkIc5Rh9NZ6PXwDLAkXBMeYa1KQcAgcKwRusQ2YbMAn70KkEStZi1uwHQE3ojX3oVKzmGvzbPw4OZo/APvzydtfw7/wM7+pXsbwt6LljcV70LRT9yx/OCSGEEOKrdSj59POf/5yxY8eycOFCpkyZwvLly/nNb37DiBEjOis+0QWWrj1ISVkjEJzZ9N7n+xg/OAVFUVi/4zAJ0U5GD+zFWytKqKwN1k3wB0z+sWwXQ3MSqDjSBhATaSfmK2ZOCXE8LaE3xoFNbQ2qBkYA74Z3UBMy0PuMRI2Mx3XZfcEZUo5IHAVz8W/6d1vNDsvAt+EdbHkTUONSu+dGhBBCdAlbvwloKXkYlbvReuWixqYAEHX9YwRKN6NGJoSWb0dc9VOM0s1ggdZ7CIqq47zoP7ENmIjZVI3eeyiKIzJsfLO5Fs8nfwbLACCwexW+hEwc+e2TVEIIIYQ4NR1KPm3fvp0XX3wRm82GZVlER0dz//33M2vWLGbPnt1ZMYpOVlXvbtdWuKuaN1eUEDCCS11WbanA7QuEneMPmFwyMoO6Zh97yxuJj3bwrcsuQNfULolbfD3Y8y/HqD0YTEA5ItH7jsa96EmOFo21DboE+5BpwYLjR5ZZmOU7TphkMhsPS/JJCCHOA2pML9SYXmFtiiMSW+7Y8DZVR89qvyOzlpKHlpJ3wrHN2gOhxFOorWrvGUYshBBCnN86lHxyOBwEAgFsNhvx8fGUlZURExNDfX19Z8UnusCo/sl8sbUydBzlsrG7rCGUeALYvLeWaaN7s+dQY6gtLTGCgdkJ/DQnkWa3nwiHjqrK0hjRMYrdRcSMH2B5W0B30Pr2IxxNPAH4t32CpShhxWXN2oNovYeGj+OMRksbgG/zB/j3fIEamYCj4CrUuJ65GYLomC+r+VRXV8f999/PgQMHsNvt9OnTh4cffpiEhIQujvDrzbIsiktqKa1qZnB2An1Sg3VzDh5uprgkuNvd8NxEWR4pzojlbcG/5wsAbH3HoDijsEyDwL71WE016NkjUGODDxgC5TswKnehpfRDTxsAgNlQSWDfBpSoBPScUSiqflpjqnEZoNnB8IVi0zIGdeVbIYQQQnztdCj5NGrUKBYtWsTcuXOZPn068+bNw+FwMG7cuM6KT3SBggt68V+XDWRlcTnRkXaumJDNe5/va3fehCGpxEU52LCripQ4F7Mn5YR+aES5pBinOH2mpwnF7kJRdTjFH696Wn+0uDR82z9BdcViL5iDf9dKvJ+/HBwTMCp3E/nNx4Ljih5rzpw5vPXWW+3a586dyxtvvAHAxo0b270OwVpQ8+bNY+zY4GyHRx99lCeeeIJf/epXnRfweej 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